University of Southern California Dissertations and Theses

Optical properties of urban runoff and its effect on the coastal phytoplankton community

(USC Thesis Other)

Optical properties of urban runoff and its effect on the coastal phytoplankton community

PDF

Download

Open document

Flip pages

Download a page range

Download transcript

Copy asset link

Request this asset

Transcript (if available)

Content

OPTICAL PROPERTIES OF URBAN RUNOFF AND ITS EFFECT ON THE
COASTAL PHYTOPLANKTON COMMUNITY

by

Kristen Marie Reifel

A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(BIOLOGY)

December 2009

Copyright 2009 Kristen Marie Reifel
ii
ACKNOWLEDGEMENTS

So many people supported me and contributed to the projects that have become my
dissertation. I would like to start by thanking my committee (Doug Capone, David Caron,
Dale Kiefer, and Doug Hammond) and especially my advisor Burt Jones. I would like to
acknowledge those who supported me financially through grants, fellowships, and travel
grants: NASA, Sea Grant, the Wrigley Institute for Environmental Studies, Sigma Xi,
Women in Science and Engineering, and the American Society of Limnology and
Oceanography.
I would like to thank everyone involved in the Bight '03 project. I wish to thank the
members of the Bight '03 Steering Committee and all the organizations and individuals that
participated in planning, collecting samples, processing, and analysis of the collected data.
This study would not have been possible without the exceptional skill of the field sampling
personnel from the following organizations: City of San Diego, Orange County Sanitation
District, Los Angeles County Sanitation District, City of Los Angeles, Southern California
Coastal Water Research Project, Aquatic Bioassay and Consulting Laboratories, Weston
Solutions (formerly MEC Analytical Systems, Inc.), and Universidad Autónoma de Baja
California. I would also like to thank my co-authors for all their work and help: Scott
Johnson (Aquatic Bioassay and Consulting Laboratories), Paul DiGiacomo (NOAA-NESDIS
Center for Satellite Applications and Research), Michael Mengel (Orange County Sanitation
District), Nikolay Nezlin (Southern California Coastal Water Research Project), and Jonathan
A. Warrick (USGS Coastal and Marine Geology Program).
For the Hyperion project, I first have to thank Burt Jones and David Caron for getting
me involved in the project and for convincing the City of Los Angeles that it would benefit
iii
them to let a lowly graduate student join their cruises. Thanks to Curtis Cash and Bob
Brantley (City of LA, Environmental Monitoring Division) for taking me out in the field and
sharing their data, and to the captain and crew of the M/V Marine Surveyor. And thanks to
my dad for helping me in the field, and to Astrid Schnetzer and Adriane Jones (USC) for
giving me much-needed help in the lab. For the stormwater project, I owe so much to my
friend Alina Corcoran and her advisor Rebecca Shipe (UCLA) for letting me join up with
them on this project. I would also like to thank Ivona Cetini ć and especially Zhihong Zheng
for helping me in the field, and I would like to acknowledge the captains and crew of the R/V
Sea World (UCLA) and the M/V Yellowfin (Southern California Marine Institute).
Lastly, I would like to thank my family and friends for their much-needed support.
Thanks to my friends for understanding when I had to buckle down and work instead of
spending time with them. Thanks to my yoga students for their spirit in class and for always
making me feel better after a rough day at work. Thanks to my parents, my sister, and the
rest of my family for all of their support. And thanks to my boyfriend, Brandon Swan, for
teaching me statistics, for giving me great ideas and inspiration, for helping make all of these
papers better, and for keeping me going even when I was ready to quit. I couldn't have done
it without you.

iv
TABLE OF CONTENTS

Acknowledgements................................................................................................................... ii

List of Tables .......................................................................................................................... vii

List of Figures.......................................................................................................................... ix

Abstract.................................................................................................................................. xiv

Chapter 1: Linking Phytoplankton to Urban Runoff ................................................................1

Introduction....................................................................................................................1
Ecological theories of phytoplankton assembly ................................................1
Freshwater plumes in the coastal ocean.............................................................2
Using optical measurements to track and characterize
freshwater plumes ........................................................................................4
Study site – Ballona Creek and Santa Monica Bay ...........................................7
Past studies of stormwater in Southern California.............................................8
Research Questions......................................................................................................10
Optical characteristics of stormwater plumes..................................................10
Phytoplankton response to stormwater plumes................................................11
Significance of Research..............................................................................................12
Chapter 1 References ...................................................................................................16

Chapter 2: Impacts of Stormwater Runoff in the Southern California Bight:
Relationships among Plume Constituents..............................................................22

Chapter 2 Abstract .......................................................................................................22
Introduction..................................................................................................................24
Methods........................................................................................................................28
Field collections...............................................................................................28
Correlation analyses.........................................................................................31
Results..........................................................................................................................34
Spatial and temporal extents of impact............................................................35
Fecal indicator bacteria........................................................................35
Nutrients...............................................................................................38
Toxicity................................................................................................40
In situ relationships: CDOM vs. salinity and beam-c vs. TSS.........................40
In situ contaminant relationships with salinity and TSS..................................45
Discussion....................................................................................................................51
How problematic is stormwater? .....................................................................52
Feasibility of using CDOM and beam-c to map plumes..................................55
Conclusions..................................................................................................................56
Chapter 2 References ...................................................................................................59
v

Chapter 3: Tracking a Wastewater Plume and its Effects on Phytoplankton in the
Coastal Ocean ........................................................................................................65

Chapter 3 Abstract .......................................................................................................65
Introduction..................................................................................................................67
Methods........................................................................................................................69
Meteorological data.........................................................................................70
Field collections...............................................................................................70
Data processing................................................................................................72
Laboratory analyses.........................................................................................74
Statistical analyses...........................................................................................76
Results..........................................................................................................................79
Meteorological conditions and surface currents ..............................................79
Characteristics and movements of the plume ..................................................79
Nutrients...........................................................................................................86
Phytoplankton density and community structure.............................................87
Optical properties of the wastewater plume.....................................................94
Discussion....................................................................................................................98
Tracking a wastewater plume in the coastal ocean..........................................98
Optical properties of phytoplankton blooms....................................................99
Impacts on coastal phytoplankton..................................................................101
Conclusions................................................................................................................104
Chapter 3 References .................................................................................................107

Chapter 4: The Effects of Stormwater Runoff on Coastal Phytoplankton............................115

Chapter 4 Abstract .....................................................................................................115
Introduction................................................................................................................117
Methods......................................................................................................................118
Meteorological data.......................................................................................118
Field collections.............................................................................................120
Data processing..............................................................................................122
Laboratory analyses.......................................................................................125
Statistical analyses.........................................................................................126
Comparisons with other data sets ..................................................................128
Results........................................................................................................................129
Storm events...................................................................................................129
Phytoplankton density and community structure...........................................132
Comparisons to previous studies ...................................................................139
Discussion..................................................................................................................144
Chapter 4 References .................................................................................................150

vi
Chapter 5: Summary and Conclusions..................................................................................154

Introduction................................................................................................................154
Summary of results ....................................................................................................155
Chapter 2........................................................................................................155
Chapter 3........................................................................................................157
Chapter 4........................................................................................................159
Conclusions................................................................................................................160
Chapter 5 References .................................................................................................162

Bibliography ..........................................................................................................................166

Appendices.............................................................................................................................184

Appendix A................................................................................................................184
Appendix B................................................................................................................186
Appendix C................................................................................................................194
Appendix D................................................................................................................195
Appendix E................................................................................................................221

vii
LIST OF TABLES

Table 2-1. Summary of the number of single sample exceedances of FIBs for
each day for the first (2004) and second (2005) storm events. Numbers in
parentheses indicate total number of stations sampled. n.d. – no data .......................37

Table 2-2. Summary of toxicity evaluations (as percent fertilization in the sea
urchin assay) for each of the sampling regions for both storm events.
Significant toxicity was chosen to be those values where fertilization was
less than 84% (Bay et al., 2003). n.d. – no data.........................................................42

Table 2-3. Results of general linear models of CDOM vs. salinity and beam-c vs.
TSS with region and day after storm included as additional independent
variables. n/a – not applicable.....................................................................................44

Table 2-4. Results of general linear models of contaminants vs. salinity and
contaminants vs. TSS with region and day after storm included as
additional independent variables. n/a – not applicable ...............................................46

Table 3-1. Results from a 2-way crossed ANOSIM with location (before, outside,
and inside) and sample date (27 November, 29 November, 4 December,
and 7 December) as factors. Results are also presented from pairwise
comparisons between factor levels (note that the "before" and "27
November" factor levels could not be included in pairwise comparisons)..................91

Table 3-2. Results from a 2-way crossed SIMPER with location (before, outside,
and inside) and sample date (27 November, 29 November, 4 December,
and 7 December) as factors. The average percent contribution from each
taxon to the total similarity between communities inside and outside the
plume is shown. ...........................................................................................................92

Table 4-1. Results from 2-way crossed ANOSIM with location (inside and
outside) and sample date as factors for each of the three storms. Results
are also presented from pairwise comparisons between factor levels for
storm 3. ......................................................................................................................138

Table 4-2. Results from a 2-way crossed SIMPER for storm 1 with location
(inside and outside) and sample date (21 April and 24 April) as factors.
The average percent contribution from each taxon to the total similarity
between communities inside and outside the plume is shown...................................139

Table A-1. Station locations from the Hyperion Wastewater Treatment Plant
diversion event...........................................................................................................184

viii
Table B-1. Raw cell counts from Hyperion Wastewater Treatment Plant diversion
event...........................................................................................................................186

Table C-1. Station locations from stormwater sampling. .....................................................194

Table D-1. Raw cell counts from stormwater samples.........................................................195

ix
LIST OF FIGURES

Figure 1-1. Map of Santa Monica Bay. Bathymetry data from the USGS Pacific
Seafloor Mapping Project. .............................................................................................9

Figure 2-1. Map of the Southern California Bight indicating the regions sampled
during the Bight '03 study. Note that the San Pedro Shelf region was
broken into the Los Angeles/San Gabriel Rivers, the Santa Ana River, and
Newport Harbor for correlation analyses. Black dots indicate the locations
of stations sampled during the field sampling effort. ..................................................29

Figure 2-2. Spatial distributions of FIBs for each of the five major regions
following storm 1 (2004). Similar patterns were observed during storm 2
(2005). Areas in dark red represent areas where a California Ocean Plan
standard was exceeded. Note that the colorbars are plotted on log scales..................36

Figure 2-3. Surface distributions of nitrate for the Newport Harbor area
(including the Santa Ana River and Newport Harbor) for storm 2 (2005)
showing the dispersion and dilution of nitrate over time after the storm.
Note that the colorbars are plotted on log scales. ........................................................39

Figure 2-4. Relationships between CDOM and salinity separated by region for
the 2004 (A) and 2005 (B) storm events. Linear regressions are plotted for
the Santa Clara River (solid line), Ballona Creek (long dashed line), Los
Angeles/San Gabriel Rivers (short dashed line), Santa Ana River (dot-
dashed line), and Newport Harbor (grey line). These lines are plotted for
visual purposes and were not used for statistical analyses. CDOM data
were not available for the San Diego and Tijuana Rivers. The river mouth
subset in storm 1 (A, grey points) consists of three stations in the Los
Angeles/San Gabriel Rivers region and three stations near Newport
Harbor all located inside or just outside major river mouths (see section
3.2). ..............................................................................................................................41

Figure 2-5. Relationships between beam attenuation coefficient and total
suspended solids separated by region for the 2004 (A) and 2005 (B) storm
events. Linear regressions are plotted for the Santa Clara River (solid
line) Ballona Creek (long dashed line), Los Angeles/San Gabriel Rivers
(short dashed line), Santa Ana River (dot dashed line), Newport Harbor
(grey line), San Diego River (dotted line), and Tijuana River (dot dot
dashed line). These lines are plotted for visual purposes and were not used
for statistical analyses. No data were available for the San Diego River for
storm 2. ........................................................................................................................43

x
Figure 2-6. Box plots showing the medians and quartiles of nutrient
concentrations in five salinity ranges (A-D) and four ranges of total
suspended solids (E-H). Vertical lines in plots A-D indicate the 10%
stormwater salinity level. Whiskers on the box plots extend to the 10th
and 90th percentiles. Points outside this range are shown as open circles. ................49

Figure 2-7. Box plots showing the medians and quartiles of concentrations of
fecal indicator bacteria and toxicity in five salinity ranges (A-D) and four
ranges of total suspended solids (E-H). The solid horizontal lines indicate
the maximum detection limit. Below this level, the distributions are
estimated. The dashed lines indicate the single sample California Ocean
Plan standards. The dashed line in the toxicity plots indicates 84%
toxicity. Vertical lines in plots A-D indicate the 10% stormwater salinity
level. Whiskers on the box plots extend to four times the interquartile
range, except in plots in D and H where they extend to the 10th and 90th
percentiles. Points outside these ranges are shown as open circles. TC –
total coliforms, FC – fecal coliforms, Ent. – Enterococcus spp. .................................50

Figure 3-1. Map of Santa Monica Bay and stations sampled during the Hyperion
WWTP diversion event. Inset is a close-up showing stations near the 1-
mile outfall...................................................................................................................69

Figure 3-2. Property-property plots of temperature, salinity, and CDOM
concentration from vertical profiles. Stations considered inside the
wastewater plume are circled.......................................................................................77

Figure 3-3. Vertical profiles of salinity and CDOM (A) and beam attenuation (B)
collected over the 1-mile outfall on 28 and 29 November during the
diversion event. Note that CDOM concentration was not measured during
the 28 November cruise. ..............................................................................................80

Figure 3-4. Salinity and CDOM concentration (B), chlorophyll a concentration
(extracted and estimated from ac-s data; C) and the slope of the particle
size distribution (estimated from ac-s data; D) collected along the ship's
track (A) during the diversion event. Salinity and CDOM were not
collected continuously along the ship's track; measurements from the top
meter of the vertical profiles are plotted. In B-D, arrows indicate sampling
stations, and shaded areas indicate areas inside the plume..........................................81

xi
Figure 3-5. Salinity (C), and chlorophyll a fluorescence and concentration (D)
collected along the ship's track immediately after the diversion event. The
ship track is plotted in two parts. The portions of C and D to the left of the
vertical lines correspond to the dark portion of the track shown in A. The
portions to the right of the vertical lines correspond to the dark portion of
the track shown in B. The "+" symbols in A and B indicate the locations
of the Santa Monica (north) and Hermosa Beach (south) piers, and the "x"
symbol indicates the location of the 1-mile outfall. In C and D, arrows
indicate sampling stations, and shaded areas indicate areas inside the
plume............................................................................................................................83

Figure 3-6. Salinity and CDOM concentration (B), chlorophyll a fluorescence
and concentration (extracted and estimated from ac-s data; C), and the
slope of the particle size distribution (estimated from ac-s data; D)
collected along the ship's track (A) four days after the diversion event.
The "+" symbols in A indicate the locations of the Santa Monica (north)
and Hermosa Beach (south) piers, and the "x" symbol indicates the
location of the 1-mile outfall. In B-D, arrows indicate sampling stations,
and shaded areas indicate areas inside the plume. .......................................................84

Figure 3-7. Salinity and CDOM concentration (B), chlorophyll a fluorescence
and concentration (extracted and estimated from ac-s data; C), and the
slope of the particle size distribution (estimated from ac-s data; D)
collected along the ship's track (A) seven days after the diversion event.
The "+" symbols in A indicate the locations of the Santa Monica (north)
and Hermosa Beach (south) piers, and the "x" symbol indicates the
location of the 1-mile outfall. In B-D, arrows indicate sampling stations,
and shaded areas indicate areas inside the plume. Note that station 17 was
sampled after data collection from the surface sensors was stopped...........................85

Figure 3-8. Time series of salinity measurements from the Santa Monica Pier
showing freshwater runoff from the storm event on 27 November and
evidence of the wastewater plume after 30 November................................................87

Figure 3-9. Geometric means of extracted chlorophyll a concentration (A) and
total density of phytoplankton (from cell counts; B) before, during, and
after the diversion event. Only counts from samples preserved in lugols
were included in B. The shaded areas represent samples collected during
the diversion. Error bars indicate 1 standard deviation...............................................89

xii
Figure 3-10. NMDS of all stations (A), NMDS overlain with bubbles
representing the relative abundance of individual species (B-F), and
geometric means of absolute abundances (G-K) of individual species.
Shaded bubbles in B-F indicate stations inside the plume. Error bars in G-
K indicate standard deviation. Only species that most contributed to the
difference between communities inside and outside the plume are shown. ................90

Figure 3-11. Geometric means of the abundances of Synechococcus spp. (A) and
pico-eukaryotes (B) inside and outside the plume before, during, and after
the diversion event. Error bars indicate 1 standard deviation.....................................94

Figure 3-12. Particulate absorption spectra (A), normalized at 675 nm (B), and
spectra of the ratio of total scatter to total absorption (C) for six locations
inside high-chl a portions of the wastewater plume (solid lines) and two
low-chl a areas outside the plume (dot-dashed lines) on 4 December 2006.
In B, spectra from the low chl a areas are presented on a different scale.
Numbers indicate chl a concentrations (mg m-3) estimated from
particulate absorption spectra. .....................................................................................97

Figure 4-1. Daily precipitation and cumulative precipitation observed at the
Ballona Creek stream gauge, and average daily discharge from Ballona
Creek for storm 1 (A) and storms 2 and 3 (B). Cumulative precipitation is
the cumulative amount observed over the entire water year (starting
October 1). Vertical lines indicate sampling dates. Discharge data are
from LACDPW (2008, 2009). ...................................................................................119

Figure 4-2. Map of Santa Monica Bay showing the stations sampled during the
three storm events and the locations of the Ballona Creek precipitation
gauge and the NOAA National Buoy Data Center buoy used to collect
wave height. ...............................................................................................................121

Figure 4-3. Salinity and temperature collected along the ship track during the two
sampling dates, 21 April (A) and 24 April (B), after storm 1. Shaded areas
represent areas inside the plume, and arrows indicate sampling stations..................122

Figure 4-4. Salinity measured along the ship track during the two sampling dates,
6 January (A) and 8 January (B), after storm 2. Shaded areas represent
areas inside the plume, and arrows indicate sampling stations..................................123

Figure 4-5. Salinity measured along the ship track during the three sampling
dates, 26 January (A), 28 January (B), and 31 January (C), after storm 3.
Shaded areas represent areas inside the plume. Shaded areas represent
areas inside the plume, and arrows indicate sampling stations..................................124

xiii
Figure 4-6. Sea surface temperature (in °C) before (A) and during (B) storm 1
from MODIS, and temperature recorded at the Santa Monica Pier during
April 2007 (C)............................................................................................................131

Figure 4-7. Average chlorophyll a concentration (A) and total phytoplankton
abundance (from microscope counts; B) inside and outside the stormwater
plume after each storm. Error bars indicate 1 standard deviation.............................133

Figure 4-8. NMDS of all samples (A), overlain with bubbles representing the
ratio of dinoflagellates to diatoms (B), average abundance of
dinoflagellates (C) and diatoms (D), and average percent abundance of
dinoflagellates (E) and diatoms (F) inside and outside the plume. Error
bars indicate 1 standard deviation..............................................................................135

Figure 4-9. Average abundances of Synechococcus spp. (A) and pico-eukaryotes
(B) inside and outside the plume during the three storm events. Error bars
indicate 1 standard deviation. ....................................................................................136

Figure 4-10. Extracted chlorophyll a concentration versus salinity for samples
collected within plume waters during storm 2 and storm 3 (this project),
the Hyperion WWTP project, and the Bight '03 project. Dashed reference
lines are at 33.2 and 33.4 psu.....................................................................................141

Figure 4-11. Wave height measured at NBDC buoy 46221 during storm 1 in
April 2007 (A), storms 2 and 3 in January 2008 (B), and during the
Hyperion WWTP study in November-December 2006 (C). Vertical lines
indicate sampling dates..............................................................................................142

Figure 4-12. Vertical profiles of temperature and salinity during the Hyperion
WWTP diversion event (A), and during (B) and 1 day after (C) storm 3. ................143

Figure 4-13. Time series of surface chlorophyll a concentrations after storm 3. .................144

xiv
ABSTRACT

Although bacteria, human pathogens, and other public health concerns have been
investigated, few studies have examined the ecological effects of urban runoff. I examined
the optical characteristics of urban runoff plumes and explored phytoplankton community
changes within the plumes. Data collected after two storm events in 2004-2005 during the
Bight '03 program, after a planned release of wastewater, and after storm events in 2007-
2008 are presented. Plume waters were characterized by low salinity and high colored
dissolved organic matter (CDOM) concentration relative to ambient waters. Although
relationships between contaminants (nutrients, fecal indicator bacteria) and plume indicators
(salinity, total suspended solids) were not strong, California Ocean Plan standards were often
exceeded in waters containing >10% stormwater (<28-30 salinity range). Relationships
between CDOM and salinity and between TSS and beam attenuation indicate that readily-
measurable optically-active variables, that can be estimated from ocean color satellite
imagery, could be used as proxies to provide a qualitative, if not quantitative, evaluation of
the distribution of stormwater plumes. Localized blooms of Akashiwo sanguinea and
Cochlodinium sp. (chlorophyll a up to 100 mg m
-3
and densities between 100-2,000 cells mL
-
1
) occurred in plume waters 4-7 days after the wastewater release. Spectra of the ratio of
scatter to absorption were similar to reflectance spectra measured during blooms of
dinoflagellate species in other studies, especially when the blooms occurred in areas with
high CDOM concentration. Differences in the phytoplankton community inside and outside
stormwater plumes were only detected in dilute plume waters after a period of low wave
height. Several dinoflagellates (Akashiwo sanguinea, Cochlodinium sp., Prorocentrum spp.,
Ceratium spp., Protoperidinium spp., and Alexandrium sp.) comprised a greater percent
xv
abundance in plume waters when differences were apparent. These results are consistent
with previous models that predict the presence of functional dinoflagellate groups based on
environmental parameters such as stratification and nutrient loading. Future studies should
combine ship-based sampling with the use of remote platforms (gliders or drifters) and
satellite imagery to observe the phytoplankton community in older, dilute plume waters after
relaxation of sea state.

1
CHAPTER 1
Linking Phytoplankton to Urban Runoff

INTRODUCTION
Novel phytoplankton blooms, in particular harmful algal blooms (HABs), are
becoming more common in coastal areas and seem to be spreading throughout the world
(Smayda, 1990; Hallegraeff, 1993; Glibert et al., 2005). Many of these blooms result in
wildlife mortality events, which occur either directly through produced toxins or
indirectly as a result of oxygen depletion in the water column. Some toxins can also
move their way up the food chain and result in human sickness and mortality. Changes
in the phytoplankton community can alter foodweb dynamics with detrimental effects on
higher trophic levels such as fish and birds (Baduini et al., 2001). The recent spread of
harmful and nuisance algal species has been linked to various mechanisms including
ballast water discharge, climatological changes such as temperature shifts, and coastal
eutrophication (Smayda, 1990; Hallegraeff, 1993). Through my dissertation research, I
explored the specific effect of urban runoff in structuring the coastal phytoplankton
community and whether urban runoff is linked to blooms of particular phytoplankton
species.
Ecological Theories of Phytoplankton Assembly
The composition of phytoplankton assemblages and the seasonal succession of
phytoplankton have been well-described. One of the oldest models of phytoplankton
blooms is Sverdrup's critical depth hypothesis (Sverdrup, 1953). In this model, the spring
bloom is initiated when vertical mixing decreases until the mixed depth is at or above the
critical depth, determined in part by light intensity. Other models that describe
2
phytoplankton species structure and dynamics include Margalef's Mandala (Margalef,
1978; Margalef et al., 1979), Reynolds' C (colonist)-S (stress-tolerant)-R (ruderal) model
(Reynolds, 1988; Reynolds, 1997), and Smayda and Reynolds' update of the C-S-R
model to include marine species (Smayda and Reynolds, 2001; Smayda and Reynolds,
2003). In these models, phytoplankton species or functional groups based on growth
strategy (r- or K-selected) and adaptive strategies (e.g. nutrient stress tolerance,
disturbance tolerance) occur along axes of nutrient availability and turbulence or mixing
(Reynolds, and Smayda and Reynolds also incorporate light availability). Light, nutrient
availability, and turbulence are the major parameters that are important in structuring
phytoplankton communities and in initiating blooms according to these models.
Freshwater runoff into the coastal ocean can be a source of nutrients, can alter the
underwater light field through introduction of suspended sediments and colored dissolved
organic matter (CDOM), and can modify the vertical density structure of the water
column increasing stratification and reducing turbulent mixing. Therefore, runoff has the
potential to alter the phytoplankton species composition and may play a role in
phytoplankton blooms.
Freshwater Plumes in the Coastal Ocean
River plumes can both physically and biologically dominate the coastal ocean into
which they flow. Often they supply large amounts of “new” terrestrial nutrients to
coastal areas. The Mississippi River, for example, delivers approximately 1.82 Tg N per
year to the nitrogen-depleted northern Gulf of Mexico (Dagg and Breed, 2003). These
nutrients can be transported over large distances within the plumes. Voss et al. (2005)
observed an eddy originating from the Vistula River plume, carrying 293 tons of NO
3
-N,
3
up to 40 km offshore. River plumes also tend to carry high sediment loads. The Red
River transports 100 million tons of sediment annually into the Gulf of Tonkin (van
Maren and Hoekstra, 2005). In addition, river plumes can directly influence circulation
in coastal waters (Pullen and Allen, 2000).
Freshwater plumes, with their lower salinity, high nutrient concentrations, and
often warmer temperatures, represent water masses that are distinct from those typically
found in the coastal ocean. Although studies of river plumes often include measurements
of chlorophyll a (chl a), primary production, and sometimes pigment and/or molecular
data, only a few studies have directly analyzed phytoplankton communities within
plumes. Analysis of phytoplankton and zooplankton species within the Mississippi River
plume revealed that larger phytoplankton species were dominant at low to intermediate
salinities probably due to grazing by microzooplankton (Dagg and Breed, 2003).
Abundances of diatoms with endosymbiotic diazotrophs were positively correlated with
salinity in the Amazon River plume (Foster et al., 2007). Apart from these sparse studies,
little is known of the composition of phytoplankton within freshwater plumes and the
effects of the plumes on coastal phytoplankton.
Stormwater runoff creates freshwater plumes with characteristics similar to river
plumes, except stormwater plumes are episodic and only occur after significant rain
events (Schiff et al., 2000). As with river inflow, stormwater and other forms of urban
and agricultural runoff have the potential to alter phytoplankton species composition and
contribute to coastal blooms. Plumes contain water with high nutrient concentrations
creating ideal conditions for phytoplankton growth. Beman et al. (2005), for example,
were able to link large phytoplankton blooms in the Gulf of Mexico to runoff from
4
seasonal fertilization and irrigation events of nearby agricultural fields and to distinguish
them from upwelling-driven blooms. The conditions associated with stormwater plumes
(e.g. high nutrient concentrations, low salinity, and increased stability) have even been
correlated with HABs (Horner et al., 1997; Smayda, 1997). The causes of changes in the
phytoplankton community favoring bloom-forming species and the mechanisms of
selection of these species are major unresolved HAB issues (Smayda and Reynolds,
2001). Studies of phytoplankton community structure within stormwater plumes would
help answer these important questions.
Using Optical Measurements to Track and Characterize Freshwater Plumes
The best in situ tracer of freshwater plumes is salinity. Since evaporation will
have a minimal effect over the time spans of storm events, surface salinity acts as a
conservative tracer of freshwater runoff. Salinity, however, cannot currently be measured
from satellite imagery. Several optically-active characteristics of freshwater plumes can
be utilized to identify and track their location and movement in the coastal ocean.
Terrestrial runoff introduces components such as suspended sediments and CDOM which
affect both inherent optical properties (IOPs) and apparent optical properties (AOPs).
IOPs are defined as properties that depend only on the medium and are independent of
the ambient light field (e.g. absorption (a), scatter (b), and volume scattering function
( )). AOPs are properties that depend both on the medium and the structure of the
ambient light field (e.g. irradiance (E), radiance (L), and reflectance (R); Mobley, 1994).
Optical properties can be measured from multiple platforms (e.g. satellites, ships, and
moorings) at high spatial and temporal resolutions (Chang and Dickey, 2008; Schofield et
al., 2008). In addition to tracking plumes, changes in optical properties also reveal
5
information about the dissolved and particulate components of plumes (e.g. Twardowski
et al., 2001; Craig et al., 2006; Coble, 2007). They are therefore ideal tools to explore
dynamic features such as plumes, especially in conjunction with more traditional
sampling (e.g. Cullen et al., 1997).
Remote sensing studies of stormwater plumes have used remote sensing
reflectance (R
rs
) from the nearsurface layer, typically measured as normalized water-
leaving radiance in the range of 551-555 nm (nLw551 for MODIS and nLw555 for
SeaWiFS), as a tracer of plumes in the coastal area. By analyzing Sea-viewing Wide
Field-of-view Sensor (SeaWiFS) imagery, Nezlin and DiGiacomo (2005) concluded that
measurements of nLw555 greater than 1.3 mW cm
-2
µm
-1
sr
-1
distinguished stormwater
plumes from ambient water on the San Pedro shelf. R
rs
at these wavelengths is primarily
a function of backscatter from small particles and is therefore related to turbidity.
Turbidity can be measured in situ by measuring the concentration of total suspended
solids (TSS) or with a transmissometer that measures attenuation (c) at a wavelength of
660 nm (referred to as beam-c; Davies-Colley and Smith, 2001). Turbidity is associated
with suspended particles, the majority of which quickly sink from surface waters (Hill et
al., 2000; Warrick et al., 2004). Therefore, it can only be used as a short-term, non-
conservative tracer of stormwater plumes. CDOM, defined as the light-absorbing
fraction of dissolved organic matter, is a more conservative tracer and represents the
dissolved components of the plume. CDOM is not subject to sedimentation. Decreases
in its concentration occur through photodegradation, a process that takes weeks to months
to occur (Vodacek et al., 1997; Opsahl and Benner, 1998). Rivers constitute a major
source of CDOM in the coastal ocean (Siegel et al., 2002; Del Castillo, 2005). CDOM
concentration is therefore useful in tracking freshwater plumes and can be used to assess
the impact of river-borne components such as nutrients and pollutants in the coastal
ocean (Coble et al., 2004).
In addition to simply tracking freshwater plumes, the optical properties of the
plume also reflect the characteristics of its constituents. Both dissolved and particulate
components of ocean water have characteristic absorption spectra. Absorption by CDOM
(a
CDOM
) is generally described as an exponential decay function (Bricaud et al., 1981):
(1)
) (
) ( ) (
o
S
o g g
e a a
 
 
 

where 
o
is a reference wavelength, typically 440nm. Bricaud et al. (1981) found values
of the spectral slope of CDOM absorption (S) between 0.010 and 0.020 with a mean of
0.014 in samples taken from a variety of marine waters. Variability in S has been used as
a proxy for the composition of CDOM including the ratio of fulvic acids to humic acids,
molecular weight distribution, and aromaticity (Carder et al., 1989; Blough and Green,
1995; Twardowski et al., 2004). Phytoplankton also have characteristic absorption
spectra determined by the concentration and composition of pigments. Absorption by chl
a is characterized by strong absorption bands in the blue (~440 nm) and red (~675 nm),
but absorption can vary due to packaging effects and light history (Kirk, 1994). Some
accessory pigments, such as phycoerythrin, have absorption peaks that can be
distinguished from those of the chlorophylls and have been used to optically detect
specific phytoplankton species (e.g. Subramaniam and Carpenter, 1994).
Particulate attenuation and scattering spectra can be used to glean information
about the particles present. These spectra depend on the size, abundance, and
composition (e.g. organic versus inorganic) of particles. The slope of the particulate
6
7
attenuation spectrum ( ) can be used to derive the slope of the particle size distribution
( ; Boss et al., 2001). When backscatter (b
b
) data are available, the ratio of backscatter to
total scatter (b
b
/b) reveals several properties of the particles including the bulk index of
refraction (Twardowski et al., 2001) and the relative number of small versus large
particles (Sullivan et al., 2005). Suspended particles that do not strongly absorb, such as
suspended sediments, will also increase the ratio of backscatter to total absorption (b
b
/a)
(Kirk, 1994). Measurements of a relatively small number of IOPs can therefore not only
be used to track the location of stormwater plumes but can reveal the characteristics of
both the dissolved and particulate components of the plume.
Study Site – Ballona Creek and Santa Monica Bay
Santa Monica Bay is located in the northern portion of the Southern California
Bight and extends from Point Dume in the northwest to the Palos Verdes Peninsula in the
southeast (Figure 1). The mean depth of the Bay is 55 m, and its maximum depth is
450 m. Two canyons, Santa Monica Canyon and Redondo Canyon, cross the shelf within
the Bay. A shallow shelf (~50 m depth) known as “short bank” is located between these
two canyons (Terry et al., 1956). The continental shelf in Santa Monica Bay is wide
compared to other locations within the Southern California Bight (Hickey, 1993; Nezlin
et al., 2004). Circulation in this area of the Bight is complex. The poleward-flowing
Southern California Countercurrent dominates in the Southern California Bight and is
strong in winter (Hickey, 1993). Intense upwelling occurs in May and June, and can
occur as early as March (Jackson, 1986).
Ballona Creek is located in the western portion of the Los Angeles Basin. Its
watershed, the largest that enters into Santa Monica Bay, drains approximately 337 km
2

8
including all or parts of Beverly Hills, Culver City, Inglewood, Los Angeles, Santa
Monica, West Hollywood, and unincorporated Los Angeles County (Bay et al., 1999;
LADPW, 2004). Historically, Ballona Creek was a meandering stream lined with dense
vegetation. Transformation of the creek began in the 18th century when Spanish settlers
cleared large areas for cattle and farmland. After several major floods throughout Los
Angeles and Orange Counties in the 1930s resulted in significant property damage, the
federal and local governments began to channelize, straighten, and concrete-line the
creek and its tributaries (LADPW, 2004; SEMP, 2006a; SEMP, 2006b). Currently,
Ballona Creek consists of a 9-mile long, concrete-lined flood protection channel that runs
from Cochran Avenue near Venice Boulevard and enters Santa Monica Bay at Marina del
Rey. Most of its tributary streams have been converted into underground flood control
channels. Ballona Creek, the Ballona Wetlands, and Marina del Rey are all currently
listed on the federal 303(d) List of Impaired Water Bodies due to high levels of chlorides,
sulfates, heavy metals, and bacteria, and to high sediment toxicity (LARWQCB, 1994).
Past Studies of Stormwater in Southern California
The majority of studies of stormwater runoff conducted in the Los Angeles region
have focused on public health issues such as human pathogens and contaminants (Schiff
et al., 2002). However, stormwater runoff also has the potential to alter phytoplankton
species composition and foodweb dynamics due to its high nutrient and sediment loads.
Kleppel (1980) suggested that the availability and form of nutrients introduced into
coastal waters in the Los Angeles region can control the phytoplankton community
structure leading to a community dominated by either dinoflagellates or diatoms.
Phytoplankton are not only affected by nutrient loads, but also by the intensity and the
spectral composition of underwater light (Kirk, 1994). Sediment discharge from southern
California watersheds can be high and creates highly turbid conditions in the upper water
column (Mertes and Warrick, 2001; Warrick et al., 2004). In addition to suspended
sediments, high concentrations of CDOM in freshwater runoff have been shown to inhibit
phytoplankton growth by reducing light available for photosynthesis (McKee et al.,
2002). Both the high nutrient concentrations and changes in the underwater light field
caused by stormwater runoff could substantially affect the phytoplankton community
structure.

Figure 1-1. Map of Santa Monica Bay. Bathymetry data from the USGS Pacific
Seafloor Mapping Project.

Several in situ studies of stormwater runoff within Santa Monica Bay were
conducted in the mid- to late-1990s. Based on salinity measurements after relatively
large storm events (7.6-20.3 cm), McClure (2001) observed stormwater plumes up to 7.5
km offshore and 15 km alongshore. The majority of the plume was located in the top 3-
9
10
5 m of the water column. After an average-sized storm (~2 cm); the plume extended at
least 4 km offshore and 7 km alongshore and reached a depth of ~7 m. Ragan (2003)
collected optical measurements of these stormwater plumes. High absorption in the 412
nm range (indicative of CDOM) and high beam-c (indicative of suspended particulate
matter) were associated with low-salinity waters. Suspended particulate concentrations
of up to 49 and 67 mg L
-1
were measured offshore of Ballona and Malibu Creeks,
respectively. Changes in spectral absorption also indicated phytoplankton growth
occurred within the plume. The spatial and temporal dynamics of stormwater plumes
have only recently been examined through the analysis of satellite imagery (Nezlin and
DiGiacomo, 2005). Several in situ characteristics of these features, however, are not
currently able to be resolved using analysis of satellite imagery alone (i.e. phytoplankton
species composition).
RESEARCH QUESTIONS
In this section, I will describe the hypotheses and corresponding research
questions that were addressed through my dissertation research.
Optical Characteristics of Stormwater Plumes
Studies of river plumes have shown that they contain high concentrations of
suspended sediments and CDOM, both of which affect ocean color (Blough and Del
Vecchio, 2002; Coble et al., 2004; Warrick et al., 2004). These constituents have been
shown to be good tracers of river plumes, can be detected in situ with optical sensors
(spectral absorption and scatter, fluorescence), and can be measured using ocean color
satellite imagery (Blough et al., 1993; Johnson et al., 2003; D'Sa et al., 2007).
Stormwater also contains high concentrations of suspended sediments and CDOM;
11
however, few studies have examined the in situ optical properties of stormwater plumes.
Using only satellite imagery, several studies concluded that nLw551-nLw555, an
indicator of particulate backscatter, is the best remote sensing tracer of stormwater
plumes in the coastal ocean (Nezlin and DiGiacomo, 2005; Nezlin et al., 2005). CDOM,
however, should act as a more conservative tracer as suspended particles sink out of
surface waters over time. In addition, tracking other dissolved components of these
plumes, such as nitrate and ammonia, is relevant in determining the overall potential
effects these water masses may have on coastal phytoplankton communities.
Hypothesis 1: Stormwater runoff into the coastal ocean results in the formation of water
masses or plumes that possess optical signatures that can be used as tracers
to assess their temporal and spatial extent.

Specifically, I addressed the following research questions in the context of the above
hypothesis.
Question 1.1: What optical signatures of stormwater plumes can be used to track the
development (spatial) and maintenance (temporal) of these water masses?

Question 1.2: Can optical signals from suspended sediments and CDOM be used to
separate spatial and temporal effects of the particulate and dissolved
components, respectively, of the plume?

Phytoplankton Response to Stormwater Plumes
High levels of suspended sediment and/or CDOM may initially inhibit
phytoplankton growth (McKee et al., 2002) after which biomass and/or production are
predicted to increase. Several models of community assembly define nutrient
availability, turbulence/mixing depth, and light availability as the key environmental
factors governing species composition and species succession in phytoplankton
(Margalef, 1978; Margalef et al., 1979; Reynolds, 1988; Smayda and Reynolds, 2001).
12
According to Margalef's model, conditions within the initial stormwater plumes (low
turbulence, stratification, high nutrient concentrations, and ample light) would favor
rounded, chlorophyll-rich "red tide" dinoflagellates. Reynolds' model predicts that these
conditions would favor small, fast-growing, invasive "colonist" species. In marine
environments, these species would include small- to intermediate-sized gymnodiniod
dinoflagellates (Smayda and Reynolds' Type I life forms) and peridinioid/prorocentroid
dinoflagellates (Type II life forms). Older plumes might favor Smayda and Reynolds'
"mix-drift" dinoflagellates (Types IV-VI) through the formation of fronts and
entrainment in coastal currents.
Hypothesis 2: A shift from the main successional sequence (diatoms to flagellates) to a
parallel sequence favoring "red tide" dinoflagellates (Margalef's model)
and/or small, fast-growing "colonist" species (Smayda and Reynolds'
model) occurs within stormwater plumes due to increased availability of
limiting nutrients, decreased turbulence and/or vertical mixing, and ample
light as a result of decreased vertical mixing.

I specifically attempted to answer the following research questions in relation to the
above hypothesis.
Question 2.1: Does stormwater favor specific taxonomic or functional groups of
phytoplankton?

Question 2.2: Do community shifts within stormwater plumes occur as predicted by
previously described models of community assembly (e.g. Margalef,
Reynolds, Smayda and Reynolds)?

Question 2.3: How long do community shifts in phytoplankton persist while the
stormwater plume is present and after it decays?

SIGNIFICANCE OF RESEARCH
Data generated through this study provide important insights into stormwater
plumes and their corresponding phytoplankton communities in the coastal ocean. The
13
optical characteristics of plumes can be used both as tracers of their location and as
indicators of their dissolved and particulate components. Although a few studies of
stormwater have included optical measurements, none have attempted to link these
measurements to direct observations of CDOM, suspended sediments, and
phytoplankton. Chapter 2 describes a study of two winter storm events throughout the
entire Southern California Bight. Relationships between total suspended solids and
beam-c (particulate plume component) and salinity and CDOM (dissolved plume
component) were explored to determine whether optically-active variables (beam-c and
CDOM absorption) could be used to track the dissolved and particulate portions of the
plumes. These tracers of plume water were also related to concentrations of several
contaminants commonly present in stormwater (nutrients, fecal indicator bacteria, and
water column toxicity) to determine if rough approximations of the amount of
contaminants, and whether any California standards had been exceeded, could be inferred
from measurements of optical stormwater tracers. This study was done in collaboration
with the Southern California Coastal Water Research Project (SCCWRP), Orange County
Sanitation District, Aquatic Bioassay and Consulting Laboratories, USGS Coastal and
Marine Geology Program, and the NOAA-NESDIS Center for Satellite Applications and
Research, and has been published as a manuscript in the journal Continental Shelf
Research (Reifel et al., 2009).
Only a few studies of river plumes or other forms of urban runoff have attempted
to describe the phytoplankton associated with plumes. In chapter 3, the response of
coastal phytoplankton density and community composition to the planned release of
wastewater from the Hyperion Wastewater Treatment Plant is described. This study was
14
done in conjunction with the City of Los Angeles's Environmental Monitoring Division,
SCCWRP, Scripps Institution of Oceanography, and the University of California, Los
Angeles. Changes in the density and community structure within the wastewater plume
are documented. In addition, detailed optical properties of the plume and of the
dinoflagellate blooms encountered after the release are presented. Chapter 4 describes
the effects of three storm events on coastal phytoplankton in Santa Monica Bay. This
work was done in collaboration with Dr. Rebecca Shipe and Alina Corcoran at the
University of California, Los Angeles. The effects of stormwater runoff on
phytoplankton density and community structure are explored. Finally, all three data sets
are compared to reveal overall patterns in the response of the phytoplankton community
to urban runoff.
From both a public health and coastal management perspective, it is important to
determine what role stormwater discharge may play in the formation of algal blooms,
specifically HABs. Many algal blooms often consist of nuisance or harmful species, such
as Lingulodinium polyedrum and Pseudo-nitschia spp. which are problematic in this
region (Horner et al., 1997). Intuitively, increases in nutrient supplies to coastal areas
would be expected to result in increased phytoplankton growth, but this may not always
be the case. For example, Bay et al. (2003) found that runoff into Santa Monica Bay
tends to be toxic (as measured by the sea urchin fertilization test), most likely due to high
levels of zinc. Toxins such as zinc may initially prevent phytoplankton growth within
stormwater plumes, even in the presence of high concentrations of nutrients.
Additionally, suspended sediments and CDOM may inhibit phytoplankton growth by
reducing available light (McKee et al., 2002). Growth of certain phytoplankton groups or
15
species within plumes may be favored, at least initially, due to their tolerance of low
amounts of light and/or toxins present in stormwater. Thus, the interactions between
constituents within stormwater plumes and the phytoplankton community may be
complex and are worthy of detailed study.
16
CHAPTER 1 REFERENCES
Baduini, C. L., Hyrenbach, K. D., Coyle, K. O., Pinchuk, A., Mendenhall, V. and Hunt,
G. L., 2001. Mass mortality of short-tailed shearwaters in the south-eastern
Bering Sea during summer 1997. Fisheries Oceanography 10(1), 117-130.
Bay, S., Jones, B. H. and Schiff, K., 1999. Study of the impact of stormwater discharge
on Santa Monica Bay. Southern California Coastal Water Quality Research
Project, Westminster, California.
Bay, S., Jones, B. H., Schiff, K. and Washburn, L., 2003. Water quality impacts of
stormwater discharges to Santa Monica Bay. Marine Environmental Research
56(1-2), 205-223.
Beman, J. M., Arrigo, K. R. and Matson, P. A., 2005. Agricultural runoff fuels large
phytoplankton blooms in vulnerable areas of the ocean. Nature 434, 211-214.
Blough, N. V., Zafiriou, O. C. and Bonilla, J., 1993. Optical absorption spectra of waters
from the Orinoco River outflow: Terrestrial input of colored organic matter to the
Caribbean. Journal of Geophysical Research 98(C2), 2271-2278.
Blough, N. V. and Green, S. A., 1995. Spectroscopic characterization and remote sensing
of non-living organic matter. In: Zepp, R. G. and Sonntag, C., (Eds.), The Dahlem
workshop on the role of nonliving organic matter in the earth’s carbon cycle,
Berlin 1993. Wiley, New York, NY, pp. 23-45.
Blough, N. V. and Del Vecchio, R., 2002. Chromophoric DOM in the coastal
environment. In: Hansell, D. A. and Carlson, C. A., (Eds.), Biogeochemistry of
marine dissolved organic matter. Academic Press, San Diego, California, pp. 509-
546.
Boss, E., Twardowski, M. S. and Herring, S., 2001. Shape of the particulate beam
attenuation spectrum and its inversion to obtain the shape of the particulate size
distribution. Applied Optics 40(27), 4885-4893.
Bricaud, A., Morel, A. and Prieur, L., 1981. Absorption by dissolved organic matter of
the sea (yellow substance) in the UV and visible domains. Limnology &
Oceanography 26(1), 43-53.
Carder, K. L., Steward, R. G., Harvey, G. R. and Ortner, P. B., 1989. Marine humic and
fulvic acids: Their effects on remote sensing of ocean chlorophyll. Limnology &
Oceanography 34(1), 68-81.
Chang, G. C. and Dickey, T. D., 2008. Interdisciplinary sampling strategies for detection
and characterization of harmful algal blooms. In: Babin, M., Roesler, C. S. and
Cullen, J. J., (Eds.), Real-time coastal observing systems for marine ecosystem
dynamics and harmful algal blooms. UNESCO, Paris, France, pp. 43-84.
17
Coble, P., Hu, C., Gould, R. W. J., Chang, G. and Wood, M., 2004. Colored dissolved
organic matter in the coastal ocean. Oceanography 17(2), 50-59.
Coble, P. G., 2007. Marine optical biogeochemistry: The chemistry of ocean color.
Chemical Reviews 107(2), 402-418.
Craig, S. E., Lohrenz, S. E., Lee, Z., Mahoney, K. L., Kirkpatrick, G. J., Schofield, O. M.
and Steward, R. G., 2006. Use of hyperspectral remote sensing reflectance for
detection and assessment of the harmful alga, Karenia brevis. Applied Optics
45(21), 5414-5425.
Cullen, J. J., Ciotti, A. M., Davis, R. F. and Lewis, M. R., 1997. Optical detection and
assessment of algal blooms. Limnology & Oceanography 42(5 part 2), 1223-
1239.
D'Sa, E. J., Miller, R. L. and McKee, B. A., 2007. Suspended particulate matter dynamics
in coastal waters from ocean color: Application to the northern Gulf of Mexico.
Geophysical Research Letters 34(L23611), doi:10.1029/2007GL031192.
Dagg, M. J. and Breed, G. A., 2003. Biological effects of Mississippi River nitrogen on
the northern gulf of Mexico-a review and synthesis. Journal of Marine Systems
43(3-4), 133-152.
Davies-Colley, R. J. and Smith, D. G., 2001. Turbidity, suspended sediment, and water
clarity: a review. Journal of the American Water Resources Association 37(5),
1085-1101.
Del Castillo, C. E., 2005. Remote sensing of organic matter in coastal waters. In: Miller,
R. L., Del Castillo, C. E. and McKee, B. A., (Eds.), Remote sensing of coastal
aquatic environments. Technologies, techniques and applications. Springer,
Dordrecht, pp. 157-180.
Foster, R. A., Subramaniam, A., Mahaffey, C., Carpenter, E. J., Capone, D. G. and Zehr,
J. P., 2007. Influence of the Amazon River plume on distributions of free-living
and symbiotic cyanobacteria in the western tropical north Atlantic Ocean.
Limnology & Oceanography 52(2), 517–532.
Glibert, P. M., Anderson, D. M., Gentien, P., Graneli, E. and Sellner, K. G., 2005. The
global, complex phenomena of harmful algal blooms. Oceanography 18(2), 136-
147.
Hallegraeff, G. M., 1993. A review of harmful algal blooms and their apparent global
increase. Phycologia 32(2), 79-99.
Hickey, B. M., 1993. Physical oceanography. In: Dailey, M. D., Reish, D. J. and
Anderson, J. W., (Eds.), Ecology of the Southern California Bight. University of
California Press, Los Angeles, California, pp. 19-70.
18
Hill, P. S., Milligan, T. G. and Geyer, W. R., 2000. Controls on effective settling velocity
of suspended sediment in the Eel River flood plume. Continental Shelf Research
20(16), 2095-2111.
Horner, R. A., Garrison, D. L. and Plumley, F. G., 1997. Harmful algal blooms and red
tide problems on the U.S. west coast. Limnology & Oceanography 42(5, part 2),
1076-1088.
Jackson, G. A., 1986. Physical oceanography of the Southern California Bight. In:
Eppley, R. W., (Ed.), Lecture notes on coastal and estuarine studies vol. 15,
Plankton dynamics of the Southern California Bight. Springer-Verlag, New York,
NY, pp. 13-52.
Johnson, D. R., Miller, J. and Schofield, O., 2003. Dynamics and optics of the Hudson
River outflow plume. Journal of Geophysical Research 108(C10), 21-1 to 21-9.
Kirk, J. T. O., 1994. Light and photosynthesis in aquatic ecosystems. Cambridge
University Press, Cambridge.
Kleppel, G. S., 1980. Nutrients and phytoplankton community composition in southern
California coastal waters. CalCOFI Reports 21, 191-196.
LADPW, 2004. Ballona Creek watershed management plan. Los Angeles Department of
Public Works, Los Angeles.
LARWQCB, 1994. Water quality control plan Los Angeles region: basin plan for the
coastal watersheds of Los Angeles and Ventura Counties. California Water
Quality Control Board, Los Angeles Region, Los Angeles.
Margalef, R., 1978. Life-forms of phytoplankton as survival alternatives in an unstable
environment. Oceanologica Acta 1(4), 493-509.
Margalef, R., Estrada, M. and Blasco, D., 1979. Functional morphology of organisms
involved in red tides, as adapted to decaying turbulence. In: Taylor, D. L. and
Seliger, H. H., (Eds.), Toxic dinoflagellate blooms. Elsevier North Holland, Inc.,
New York, pp. 89-94.
McClure, K. A., 2001. Stormwater plumes in Santa Monica Bay. Thesis, Master of Arts,
University of California at Santa Barbara, Santa Barbara.
McKee, D., Cunningham, A. and Jones, K. J., 2002. Optical and hydrographic
consequences of freshwater run-off during spring phytoplankton growth in a
Scottish fjord. Journal of Plankton Research 24(11), 1163-1171.
Mertes, L. A. K. and Warrick, J. A., 2001. Measuring flood output from 110 coastal
watersheds in California with field measurements and SeaWiFS. Geology 29(7),
659-662.
19
Mobley, C. D., 1994. Light and water, radiative transfer in natural waters. Academic
Press, Inc., San Diego, CA.
Nezlin, N. P., Oram, J. J., DiGiacomo, P. M. and Gruber, N., 2004. Sub-seasonal to
interannual variations of sea surface temperature, salinity, oxygen anomaly, and
transmissivity in Santa Monica Bay, California from 1987 to 1997. Continental
Shelf Research 24, 1053-1082.
Nezlin, N. P. and DiGiacomo, P. M., 2005. Satellite ocean color observations of
stormwater runoff plumes along the San Pedro Shelf (southern California) during
1997–2003. Continental Shelf Research 25, 1692-1711.
Nezlin, N. P., DiGiacomo, P. M., Stein, E. D. and Ackerman, D., 2005. Stormwater
runoff plumes observed by SeaWiFS radiometer in the Southern California Bight.
Remote Sensing of Environment 98, 494-510.
Opsahl, S. and Benner, R., 1998. Photochemical reactivity of dissolved lignin in river and
ocean waters. Limnology & Oceanography 43(6), 1297-1304.
Pullen, J. D. and Allen, J. S., 2000. Modeling studies of the coastal circulation off
Northern California: shelf response to a major Eel River flood event. Continental
Shelf Research 20(16), 2213-2238.
Ragan, M. A., 2003. Response of inherent optical properties to stormwater runoff in
Santa Monica Bay, California. Master of Science, University of Southern
California, Los Angeles, CA.
Reifel, K. M., Johnson, S. C., DiGiacomo, P. M. P. M., Mengel, M. J., Nezlin, N. P.,
Warrick, J. A. and Jones, B. H., 2009. Impacts of stormwater runoff in the
Southern California Bight: Relationships among plume constituents. Continental
Shelf Research 29(15), 1821-1835.
Reynolds, C. S., 1988. Functional morphology and the adaptive strategies of freshwater
phytoplankton. In: Sandgren, C. D., (Ed.), Growth and reproductive strategies of
freshwater phytoplankton. Cambridge University Press, Cambridge, UK, pp. 388-
433.
Reynolds, C. S., 1997. Vegetative processes in the pelagic: A model for ecosystem
theory. Ecology Institute, Oldendorf/Luhe, Germany.
Schiff, K. C., Allen, M. J., Zeng, E. Y. and Bay, S. M., 2000. Southern California.
Marine Pollution Bulletin 41(1-6), 76-93.
Schiff, K. C., Weisberg, S. B. and Raco-Rands, V., 2002. Inventory of Ocean Monitoring
in the Southern California Bight. Environmental Management 29(6), 871-876.
20
Schofield, O. M., Bosch, J., Glenn, S., Kirkpatrick, G. J., Kerfoot, J., Lohrenz, S. E.,
Moline, M. A., Oliver, M. J. and Bissett, W. P., 2008. Bio-optics in integrated
ocean observing networks: potential for studying harmful algal blooms. In: Babin,
M., Roesler, C. S. and Cullen, J. J., (Eds.), Real-time coastal observing systems
for marine ecosystem dynamics and harmful algal blooms. UNESCO, Paris,
France, pp. 85-107.
SEMP, 2006a. Los Angeles Basin's 1938 catastrophic flood event. Biot Reports #0369
(June 7, 2006) http://www.semp.us/publications/biot_reader.php?BiotID=369.
SEMP, 2006b. Infamous New Year’s Day flood, Los Angeles Basin, 1934. Biot Reports
#0365 (May 28, 2006)
http://www.semp.us/publications/biot_reader.php?BiotID=365.
Siegel, D. A., Maritorena, S., Nelson, N. B., Hansell, D. A. and Lorenzi-Kayser, M.,
2002. Global distribution and dynamics of colored dissolved and detrital organic
materials. Journal of Geophysical Research 107(C12), 3228,
doi:10.1029/2001JC000965.
Smayda, T. J., 1990. Novel and nuisance phytoplankton blooms in the sea: evidence for a
global epidemic. In: Granéli, E., Sundström, B., Edler, L. and Anderson, D. M.,
(Eds.), Toxic Marine Phytoplankton. Elsevier Science Publishing Co., Inc., New
York, pp. 29-40.
Smayda, T. J., 1997. Harmful algal blooms: Their ecophysiology and general relevance
to phytoplankton blooms in the sea. Limnology & Oceanography 42(5), 1137-
1153.
Smayda, T. J. and Reynolds, C. S., 2001. Community assembly in marine phytoplankton:
Application of recent models to harmful dinoflagellate blooms. Journal of
Plankton Research 23(5), 447-461.
Smayda, T. J. and Reynolds, C. S., 2003. Strategies of marine dinoflagellate survival and
some rules of assembly. Journal of Sea Research 49(2), 95-106.
Subramaniam, A. and Carpenter, E. J., 1994. An empirically derived protocol for the
detection of blooms of the marine cyanobacterium Trichodesmium using CZCS
imagery. International Journal of Remote Sensing 15(8), 1559-1569.
Sullivan, J. M., Twardowski, M. S., Donaghay, P. L. and Freeman, S. A., 2005. Use of
optical scattering to discriminate particle types in coastal waters. Applied Optics
44(9), 1667-1680.
Sverdrup, H. U., 1953. On conditions for the vernal blooming of phytoplankton. Journal
du Conseil International pour l'Exploration de la Mer 18, 287-295.
21
Terry, R. D., Keesling, S. A. and Uchupi, E., 1956. Submarine geology of Santa Monica
Bay, California. Report to Hyperion Engineers, Inc., Geology Department,
University of Southern California, Los Angeles, California.
Twardowski, M. S., Boss, E., Macdonald, J. B., Pegau, W. S., Barnard, A. H. and
Zaneveld, J. R. V., 2001. A model for estimating bulk refractive index from the
optical backscattering ratio and the implications for understanding particle
composition in case I and case II waters. Journal of Geophysical Research
106(C7), 14,129-14,142.
Twardowski, M. S., Boss, E., Sullivan, J. M. and Donaghay, P. L., 2004. Modeling the
spectral shape of absorption by chromophoric dissolved organic matter. Marine
Chemistry 89(1-4), 69-88.
van Maren, D. S. and Hoekstra, P., 2005. Dispersal of suspended sediments in the turbid
and highly stratified Red River plume. Continental Shelf Research 25(4), 503-
519.
Vodacek, A., Blough, N. V., DeGrandpre, M. D., Peltzer, E. T. and Nelson, R. K., 1997.
Seasonal variation of CDOM and DOC in the Middle Atlantic Bight: Terrestrial
inputs and photooxidation. Limnology & Oceanography 42(4), 674-686.
Voss, M., Liskow, I., Pastuszak, M., Rüb, D., Schulte, U. and Dippner, J. W., 2005.
Riverine discharge into a coastal bay: A stable isotope study in the Gulf of
Gdansk, Baltic Sea. Journal of Marine Systems 57(1-2), 127-145.
Warrick, J. A., Mertes, L. A. K., Washburn, L. and Siegel, D. A., 2004. Dispersal forcing
of southern California river plumes, based on field and remote sensing
observations. Geo-Marine Letters 24(1), 46-52.

22
CHAPTER 2
Impacts of Stormwater Runoff in the Southern California Bight:
Relationships among Plume Constituents

CHAPTER 2 ABSTRACT

The effects from two winter rain storms on the coastal ocean of the Southern
California Bight were examined as part of the Bight ’03 program during February 2004
and February-March 2005. The impacts of stormwater from fecal indicator bacteria,
water column toxicity, and nutrients were evaluated for five major river discharges: the
Santa Clara River, Ballona Creek, the San Pedro Shelf (including the Los Angeles, San
Gabriel, and Santa Ana Rivers), the San Diego River and the Tijuana River.
Exceedances of bacterial standards were observed in most of the systems. However, the
areas of impact were generally spatially limited, and contaminant concentrations
decreased below California Ocean Plan standards typically within 2-3 days. The largest
bacterial concentrations occurred in the Tijuana River system where exceedances of fecal
indicator bacteria were noted well away from the river mouth. Maximum nitrate
concentrations (~40 µM) occurred in the San Pedro Shelf region near the mouth of the
Los Angeles River. Based on the results of general linear models, individual sources of
stormwater differ in both nutrient concentrations and the concentration and composition
of fecal indicator bacteria. While nutrients appeared to decrease in plume waters due to
simple mixing and dilution, the concentration of fecal indicator bacteria in plumes
depends on more than loading and dilution rates. The relationships between
contaminants (nutrients and fecal indicator bacteria) and plume indicators (salinity and
total suspended solids) were not strong indicating the presence of other potentially-
important sources and/or sinks of both nutrients and fecal indicator bacteria. California
23
Ocean Plan standards were often exceeded in waters containing greater than 10%
stormwater (<28-30 salinity range). The median concentration dropped below the
standard in the 32-33 salinity range (1-4% stormwater) for total coliforms and
Enterococcus spp. and in the 28-30 salinity range (10-16% stormwater) for fecal
coliforms. Nutrients showed a similar pattern with the highest median concentrations in
water with greater than 10% stormwater. Relationships between colored dissolved
organic matter (CDOM) and salinity and between total suspended solids and beam
attenuation indicate that readily-measurable, optically-active variables can be used as
proxies to provide at least a qualitative, if not quantitative, evaluation of the distribution
of the dissolved, as well as the particulate, components of stormwater plumes. In this
context, both CDOM absorption and the beam attenuation coefficient can be derived
from satellite ocean color measurements of inherent optical properties suggesting that
remote sensing of ocean color should be useful in mapping the spatial areas and durations
of impacts from these contaminants.
24
INTRODUCTION
The monitoring and improvement of water quality is a major issue for local, state,
and federal agencies and organizations. Coastal waters provide numerous beneficial uses
including recreation, commercial and sport fisheries, marine habitat, commerce and
transportation, and aesthetic enjoyment. In southern California, approximately $9 billion
of the local economies of coastal communities comes from ocean-dependent activities
(Bay et al., 2003). A broad range of chemical and biological contaminants is discharged
into coastal waters of the Southern California Bight (SCB) including pesticides,
fertilizers, trace metals, synthetic organic compounds, suspended sediments, inorganic
nutrients, and human pathogens (National Research Council, 1990). Reductions in water
quality due to these discharges can adversely affect the beneficial uses of the receiving
waters and can affect the local coastal economies.
Flood events due to rainstorms contribute more than 95% of the total runoff
volume annually to the coastal zone (Schiff et al., 2000). Surface runoff, which receives
no treatment prior to discharge into ocean waters, is one of the largest sources of
contaminants to the SCB (Schiff et al., 2000). Many studies of stormwater runoff
conducted in southern California have focused on public health issues such as human
pathogens and contaminants (Schiff et al., 2002). Beach closures due to high levels of
fecal indicator bacteria (FIBs) and other indicators of human pathogens have been
common during and immediately following rain events (Geesey, 1993). Public health
officials currently advise the public to avoid any contact with stormwater runoff for at
least 72 hours following a significant storm event (CDPH, 2006). Evidence of high
levels of toxicity associated with urban runoff, especially stormwater runoff, has also
25
been noted in several southern California regions (Bay et al., 2003; Gersberg et al.,
2004). Even the high levels of sediment themselves can cause environmental damage
through several mechanisms such as smothering of benthic organisms, reduction of visual
clarity, irritation of fish gills, and reduction of light available for photosynthesis (Davies-
Colley and Smith, 2001). Proper management of these parameters is important for
restoring and maintaining healthy beaches, marinas, bays, and coastal areas.
Both in situ and satellite remote sensing studies of stormwater plumes in the SCB
have shown that plumes created from pulses of stormwater runoff can affect large areas,
can penetrate up to 10 m into the water column, and can persist for days to weeks
(Washburn et al., 2003; Nezlin et al., 2005). Although the spatial and temporal extents of
stormwater plumes have begun to be examined, the extent of impact from human
pathogens, nutrients, and toxicants is not well known (e.g. Nezlin et al., 2008). Runoff
plumes have the potential, however, to disperse these constituents over large distances
(Warrick et al., 2007), especially small particles and dissolved materials that remain in
the surface waters.
The California Ocean Plan (COP) and Assembly Bill 411 define the current
standards required by the state of California for beach monitoring (State Water Resources
Control Board, 2005). Beach posting is recommended, and in some cases required, when
single FIB samples exceed these standards. The accepted monitoring protocols involve
collection of water samples that are evaluated for FIBs using assays that require 24 to 48
hours to complete, thus limiting the number of samples that can be practically analyzed.
It is impossible to adequately and routinely sample plumes by collecting water samples
from a few locations limited by sampling capabilities and resources. Remotely sensed
26
ocean color could be used as a way to track stormwater plumes over large spatial scales
with high temporal frequency (e.g. Nezlin and DiGiacomo, 2005; e.g. Nezlin et al., 2005;
Nezlin et al., 2007b; Nezlin et al., 2008). Knowledge of the distribution and fate of
contaminants within the plumes is still limited and is a focus of this study.
Based on data collected by the Sea-viewing Wide Field-of-view Sensor
(SeaWiFS) and the Moderate Resolution Imaging Spectroradiometer (MODIS), remote
sensing studies of stormwater plumes have used reflectance from the nearsurface layer,
typically measured as normalized water-leaving radiance in the range of 551-555 nm
(nLw551 for MODIS and nLw555 for SeaWiFS), as a tracer of plumes in the southern
California coastal area. Remote sensing reflectance at these wavelengths is primarily a
function of light backscattering from small particles, and is therefore related to turbidity.
By analyzing SeaWiFS imagery, Nezlin and DiGiacomo (2005) concluded that
measurements of nLw555 greater than 1.3 mW cm
-2
µm
-1
sr
-1
distinguished stormwater
plumes from ambient water on the San Pedro shelf. As turbidity is associated with
sediment particles, the majority of which quickly sink from surface waters (Hill et al.,
2000; Warrick et al., 2004), turbidity can only be used as a short-term, non-conservative
tracer to follow the particulate components of stormwater plumes. The actual freshwater
plume could be much more extensive than the sediment plume (Geyer et al., 2000).
Colored or chromophoric dissolved organic matter (CDOM), defined as the light-
absorbing fraction of dissolved organic matter, is a more conservative tracer. CDOM is
not subject to sedimentation. Decreases in its concentration occur through the process of
photodegradation, which takes weeks to months to occur (Vodacek et al., 1997; Opsahl
and Benner, 1998). Rivers constitute a major source of CDOM in the coastal ocean
27
(Siegel et al., 2002; Del Castillo, 2005). CDOM concentration is therefore useful in
tracking freshwater plumes and can be used to assess the impact of river-borne
components such as nutrients and pollutants in the coastal ocean (Coble et al., 2004).
Like turbidity, CDOM can also be estimated from ocean color but is more likely to be
associated with the dissolved constituents of plumes rather than the particulate fractions.
The “Bight” projects, organized by the Southern California Coastal Water
Research Project (SCCWRP), coordinate regional monitoring efforts by local
municipalities that have agreed to work cooperatively toward a regional assessment of
coastal conditions. Bight '03, the most recent Bight project, focused on coastal ecology,
water quality, and shoreline microbiology and involved 65 federal, state, and local
agencies. Results from the water quality component of Bight '03 have been combined
into a synthesis report available through SCCWRP (Nezlin et al., 2007a). Also, several
studies utilizing data collected during this project have been published in the peer-
reviewed literature including an analysis of the dispersal patterns and dynamics of
stormwater plumes (Warrick et al., 2007) and the utility of satellite imagery to detect and
classify stormwater runoff plumes relative to in situ observations of surface salinity and
FIB concentrations (Nezlin et al., 2008). We analyzed data collected during the water
quality component of the Bight '03 Project to address two aspects of the impact of plumes
on the continental shelf of the SCB. First, we attempted to determine the magnitude and
area of impact of contaminants (nutrients and FIBs) in the coastal zone based on ship-
based sampling. Second, we evaluated the utility of variables that can be derived from
remotely sensed measurements of ocean color (e.g. CDOM and beam attenuation) to
estimate the distribution and impacts of runoff plumes in the coastal area. We examined
28
the correlation between known contaminants and components that can be readily
measured using ocean color data, to evaluate the extent to which remotely sensed ocean
color can be used to infer the magnitude and spatial extent of plume impacts.
METHODS
Field Collections
Seven agencies participated in field collections during Bight '03: City of
Oxnard/ABC Labs, City of Los Angeles, Los Angeles County Sanitation District, Weston
Solutions (formerly MEC Analytical Systems, Inc.), Orange County Sanitation District,
City of San Diego, and Universidad Autónoma de Baja California. Shipboard sampling
for this study occurred on grids offshore of five regions (including eight major river
systems) in the SCB (Figure 2-1). Storm 1 took place on 25 February, 2004, which is
considered Day 0 in the following analyses. For the San Diego and Tijuana Rivers, an
earlier storm that ended on 23 February was also sampled. Storm 2 occurred on 22
March, 2005 (Day 0). A separate storm that ended on 12 February, 2005 was sampled
offshore of the San Diego and Tijuana Rivers. Stations were scheduled to be sampled on
days 1, 3, and 5 after storm 1 and on days 1, 2, and 3 after storm 2. However, sampling
was sometimes shifted forward or back a day depending on sampling conditions and
vessel/crew availability. Not all sites were sampled on all days largely due to limitations
from weather and sea-state.
Vertical profiles of conductivity and temperature (Sea-Bird SBE 25 or SBE 9/11),
beam attenuation (WET Labs C-Star transmissometer), and CDOM fluorescence (WET
Labs WETStar) were collected at each station. The instrument and manufacturer are
given in parentheses; note that several different instruments were used among the seven

Santa Barbara
Oxnard
San Diego
Los Angeles
Santa Clara River
Ballona
Creek
Tijuana River
San Diego River
Newport Harbor
Santa Ana River
San Gabriel River
Los Angeles
River
34.0
34.1
34.2
34.3
34.4
-119.6 -119.4 -119.2
Ballona Creek
33.7
33.8
33.9
34.0
-118.9 -118.7 -118.5
34.1
San Pedro Shelf
-117.6 -117.4 -117.2 -117.0
32.5
32.6
32.7
32.8
32.9 San Diego River
33.4
33.5
33.6
33.7
33.8
-118.4 -118.2 -118.0
Santa Clara River
Tijuana River
Figure 2-1. Map of the Southern California Bight indicating the regions sampled during
the Bight '03 study. Note that the San Pedro Shelf region was broken into the Los
Angeles/San Gabriel Rivers, the Santa Ana River, and Newport Harbor for correlation
analyses. Black dots indicate the locations of stations sampled during the field sampling
effort.

participating agencies. Beam attenuation was computed from transmissometer
observations as the beam attenuation coefficient at 660 nm (hereafter referred to as beam-
c). CDOM fluorescence was converted to quinine sulfate dehydrate (QSD) concentration
(ppb) using linear calibrations provided by the manufacturer. Water samples for total
suspended solids (TSS), macronutrients (NO
2
, NO
3
, PO
4
, SiO
4
), FIBs and toxicity were
also collected using 5-liter Niskin bottles attached to the CTD carousel or by stringing
individual bottles on a line in lieu of using a rosette. These samples were collected at 1 m
depth at all stations. Multiple depths were sampled at three stations for each river
system. Sampling occurred on regularly spaced grids for each region (Figure 2-1). The
primary intent of the grids was to sample the nearshore discharge areas and assess water
quality there, not necessarily to fully encompass and track plumes as they advected away
29
30
from the river mouth regions. Some stations were positioned further offshore and were
intended to provide “non-plume” profiles for comparative purposes. Profiles were
obtained to within 2 m of the seabed or to a depth of 60 m for sites deeper than 60 m.
Figures of the spatial distributions of nutrients and FIBs were created using IGODS
(Ocean Software, 2009).
Samples for the measurement of macronutrients and TSS were analyzed at the
University of Southern California. NO
2
, NO
3
, PO
4
, and SiO
4
concentrations were
measured on an Alpkem RFA 300 Series nutrient analyzer (Sakamoto et al., 1990;
Gordon et al., 1993). The bulk concentration of TSS was determined using EPA method
160.2 (USEPA, 1983). Briefly, whole water samples were filtered through pre-weighed
Whatman GF/F (0.7 µm) filters. The filters were then dried at 100ºC for 2 h and re-
weighed. FIB concentrations were measured by six of the participating agencies
according to each agency's standard procedures. Prior to sampling, all laboratories
participated in an inter-calibration exercise to ensure comparability. The among-
laboratory variability was not significantly different from variability within each
laboratory (Griffith et al., 2006). Samples were collected in sterile 120 mL polystyrene
bottles and transported to local laboratories on ice. The concentration of total coliforms
and fecal coliforms were determined using standard methods for multiple-tube
fermentation (APHA methods 9221B and 9221E.1) or membrane filtration (APHA
methods 9222B and 9222E); (APHA, 1998d; APHA, 1998c; APHA, 1998b; APHA,
1998a). Enterococcus spp. were enumerated using Enterolert™ (IDEXX Westbrook,
ME) defined substrate kits following the manufacturer’s instructions, or using membrane
filtration and EPA Method 1600 (Messer and Dufour, 1998). Toxicity was measured as
31
percent fertilization in the sea urchin fertilization assay (USEPA, 1995). In this method,
sea urchin sperm are exposed to the sample, and the ability of the sperm to fertilize the
egg is evaluated. Significant toxicity was chosen to be those values where sea urchin
fertilization success was less than 84% (Bay et al., 2003).
Correlation Analyses
To examine the fate of various contaminants in relation to stormwater plumes,
relationships of contaminants to salinity and TSS were explored. The contaminants
measured include nitrate (NO
3
-
), nitrite (NO
2
-
), phosphate (PO
4
3-
), silicate (SiO
4
), FIBs
(total coliforms, fecal coliforms, and Enterococcus spp.), and toxicity (measured as
percent fertilization in the sea urchin assay). Stations were separated into regional
groupings based on their proximity to major sources of inflow. For the three regions in
the San Pedro Shelf area (Los Angeles/San Gabriel Rivers, Santa Ana River, and
Newport Harbor), stations were grouped by examining nearshore salinity data. Note that
these regions were not analyzed individually in the spatial analyses but were grouped as
the San Pedro Shelf. General linear models (GLMs) were constructed for each
contaminant for each storm. Salinity or TSS (continuous), region (categorical), and day
after storm (categorical) were included in the models as independent variables. Only data
from the top 5 m were included as the majority of stormwater is found within this depth
(Washburn et al., 2003). Because the number of samples was very high (250-376), P-
values were generally low and were not always useful in distinguishing model fit. We
therefore focused on improvements to other parameters such as the coefficient of
determination (R
2
) when selecting the best models. The adjusted R
2
was considered to
account for erroneous improvements in model fit due to the inclusion of additional
32
independent variables. Statistical analyses were done using SYSTAT™ v. 11.0 (SSI,
2004b).
Approximately 28%, 56%, and 59% of the total coliform, fecal coliform, and
Enterococcus spp. data, respectively, were recorded as being below one of 3 detection
limits (10 and 100 most probable number (MPN)/100 mL for total and fecal coliforms; 10
and 20 MPN/100 mL for Enterococcus spp.). An additional 7 total coliform values
(approximately 1%) were reported as >80,000 MPN/100 mL. A small number of nitrite
and nitrate samples were also recorded as being below a detection limit (0.1 and 0.05
M, respectively). Data whose values are known only to be above or below a threshold
value are referred to as censored data. Censored data cannot be analyzed using standard
statistical methods. Instead, the data were analyzed using the methods of Helsel (2005)
through the S-language software package NADA, an add-on package for the R
environment for statistical computing (R Development Core Team, 2006). These
methods can be used to analyze multiply-censored data sets (data sets with multiple
detection limits) with up to 80% censored data. The program, however, only supports
left-censored data. Therefore, the 7 total coliform data points reported as >80,000
MPN/100 mL were replaced with the value 80,000 MPN/100 mL. Though this will
introduce some error, these values represent such a small proportion of the data set that
this error is expected to be small. For data sets containing censored data, GLMs were
constructed using the cenreg function in NADA. This function computes GLM
parameters (e.g. slope and intercept) using maximum likelihood estimation (MLE). MLE
assumes that data above and below the detection limit follow a particular distribution.
Parameters are computed that best match a fitted distribution to the observed values
33
above each detection limit and to the percentage of data below each limit (Helsel, 2005).
The cenreg function also estimates the likelihood R
2
(similar to R
2
in linear regression),
the log-likelihood statistic, and the associated P-value.
In addition to GLM analyses, contaminant data were examined by grouping the
data into several salinity and TSS ranges and calculating summary statistics for each
group. Box plots were created showing the median and spread of data within each group.
For all uncensored data, box plots were created using SigmaPlot v. 9.01 (SSI, 2004a).
For groups containing censored data, summary statistics were calculated using the
censored regression on order statistics (ROS) method (Lee and Helsel, 2005). ROS is a
probability plotting and regression procedure that models censored distributions using a
linear regression model of observed concentrations vs. their normal quantiles. This
method has been evaluated as one of the most reliable procedures for developing
summary statistics of multiply-censored data (Shumway et al., 2002). Censored box
plots were created using the NADA package for R.
We also explored the relationships between in situ tracers of plume water and
variables that can be estimated using ocean color data from satellite imagery. The best in
situ tracer of freshwater plumes is salinity. Because evaporation will have a minimal
effect over the time spans of storm events, surface salinity acts as a conservative tracer of
freshwater runoff. Salinity is not currently measured using satellite imagery. Other
dissolved constituents with high concentrations in stormwater, such as CDOM, can be
estimated using satellite ocean color. We therefore explored the in situ relationship
between salinity and CDOM to determine whether salinity could ultimately be
approximated from CDOM via satellite ocean color observations (Monahan and Pybus,
34
1978; D'Sa et al., 2002; Busse et al., 2006). A second commonly used tracer of
stormwater plumes is turbidity. Turbidity can be measured in situ by measuring the
concentration of TSS in bulk water samples or optically with a transmissometer that
measures beam-c. Whereas salinity and CDOM represent the dissolved components of
the plume, TSS and beam-c represent the particulate components. Again, TSS cannot be
measured directly from satellites, but beam-c can be estimated from ocean color data.
GLMs were constructed with salinity or TSS, region, and day after storm as independent
variables and CDOM or beam-c as the dependent variable. The number of samples was
very high (276-1,030); therefore, we again focused on adjusted R
2
values when selecting
the best models.
RESULTS
This section will be presented in three parts. The first part evaluates the spatial
and temporal extent of contaminant impacts in the SCB, specifically FIBs, toxicity, and
nutrients. In the second part, the use of remotely sensed ocean color is considered for the
evaluation of plume impacts based on the ability to estimate water quality parameters of
interest from satellite ocean color observations using in situ-satellite proxy relationships
to infer spatial and temporal scales of stormwater impacts. The third part examines
relationships between the contaminants and readily measured, commonly used in situ
water quality parameters that are considered robust tracers of stormwater plumes
(salinity, TSS). It addresses the question of the extent to which these parameters can be
used as a proxy for contaminants of concern.
35
Spatial and Temporal Extents of Impact
The two sets of contaminants for which either a receiving water standard or
environmental impact threshold exists are FIBs and toxicity as measured by the sea
urchin fertilization test. Tables 2-1 and 2-2 summarize the number of samples and
exceedances of these thresholds that occurred for each of the five major river discharges
that were studied during the Bight’03 study (Santa Clara River, Ballona Creek, San Pedro
Shelf, San Diego River, and Tijuana River). Nutrient distributions were also examined,
but regulatory standards do not currently exist for these runoff constituents.
Fecal Indicator Bacteria
Over 2000 water samples were analyzed for FIBs from all surveys and river
systems combined. Elevated concentrations of FIBs were found offshore of every major
river system following both storm events, although the COP standards were not always
exceeded (Figure 2-2). Nearly all of the FIB exceedances occurred in the top 10 m of the
water column and in the very nearshore region of the discharge. In 2004, less than 10%
of the samples exceeded the COP standards offshore each of the river systems (Table 2-
1). Exceedances tended to be highest during the first day after the storm but were
sometimes higher on day 2, especially in the near the Tijuana River during storm 2
(2005). The extent of FIB impact was greatly reduced or absent by the third or fourth day
of sampling. Of the three FIBs, the Enterococcus spp. threshold was most often
exceeded. During 2005, the total number of exceedances across all river systems
increased (from 7.2% in 2004 to 13% in 2005). However, this was the result of a large
increase in exceedances of all FIBs offshore of the Tijuana River where the standards
were exceeded in 49% of the samples. Exceedances offshore the Santa Clara River,
Ballona Creek, and the San Pedro Shelf were similar or less than those in 2004.

Figure 2-2. Spatial distributions of FIBs for each of the five major regions following
storm 1 (2004). Similar patterns were observed during storm 2 (2005). Areas in dark red
represent areas where a California Ocean Plan standard was exceeded. Note that the
colorbars are plotted on log scales.

36
37
Table 2-1. Summary of the number of single sample exceedances of FIBs for each day
for the first (2004) and second (2005) storm events. Numbers in parentheses indicate
total number of stations sampled. n.d. – no data

Santa Clara Ballona San Pedro San Diego Tijuana
River Creek Shelf River River Total
Storm 1 – 2004
Total Coliforms
1

Day 1 n.d. 2 (8) 4 (38) 0 (18) 7 (18) 13 (82)
Day 2 n.d. 0 (23) 4 (74) n.d. 0 (16) 4 (113)
Day 3 0 (18) n.d. n.d. 0 (18) 1 (37) 1 (73)
Day 4 n.d. 0 (23) 0 (78) 0 (18) 2 (35) 2 (154)
Fecal Coliforms
2

Day 1 n.d. 2 (8) 1 (14) 0 (18) 8 (18) 11 (58)
Day 2 n.d. 0 (23) 1 (50) n.d. 0 (16) 1 (89)
Day 3 0 (18) n.d. n.d. 0 (18) 3 (37) 3 (73)
Day 4 n.d. 0 (23) 0 (54) 0 (18) 1 (35) 1 (130)
Enterococcus spp.
3

Day 1 n.d. 2 (8) 10 (38) 0 (18) 14 (18) 26 (82)
Day 2 n.d. 3 (23) 6 (74) n.d. 6 (16) 15 (113)
Day 3 0 (18) n.d. n.d. 0 (18) 4 (37) 7 (73)
Day 4 n.d. 0 (23) 2 (78) 1 (18) 2 (35) 2 (154)
TOTAL 0 (54) 9 (162) 28 (498) 1 (162) 48 (318) 86 (1194)
% of samples 0% 5.6% 5.6% 0.6% 15% 7.2%
Storm 2 – 2005
Total Coliforms
1

Day 1 n.d. 3 (10) 0 (26) n.d. 9 (18) 12 (54)
Day 2 0 (20) 0 (23) 1 (28) n.d. 11 (18) 12 (89)
Day 3 0 (20) 0 (23) 0 (80) n.d. 4 (18) 4 (141)
Day 4 0 (20) n.d. n.d. n.d. n.d. 0 (20)
Fecal Coliforms
2

Day 1 n.d. 3 (10) n.d. n.d. 14 (30) 17 (40)
Day 2 0 (20) 0 (23) 0 (28) n.d. 16 (30) 16 (101)
Day 3 0 (20) 0 (23) 0 (54) n.d. 1 (18) 1 (115)
Day 4 0 (20) n.d. n.d. n.d. n.d. 0 (20)
Enterococcus spp.
3

Day 1 n.d. 3 (10) 3 (26) n.d. 17 (30) 23 (66)
Day 2 0 (20) 0 (23) 0 (28) n.d. 23 (30) 23 (101)
Day 3 1 (20) 0 (23) 1 (80) n.d. 7 (18) 9 (141)
Day 4 1 (20) n.d. n.d. n.d. n.d. 1 (20)
TOTAL 2 (180) 9 (168) 5 (350) n.d. 102 (210) 118 (908)
% of samples 1.1% 5.4% 1.4% n.d. 49% 13%
1
Total coliform COP single sample standard = 10,000 MPN/100 ml
2
Fecal coliform COP single sample standard = 400 MPN/100 ml
3
Enterococcus spp. COP single sample standard = 104 MPN/100 ml
38
The extent of impacts due to FIBs varied among regions. The San Pedro Shelf
and the Tijuana River regions showed the largest areas of impact. During both storms, a
large proportion of stations sampled offshore of the Tijuana River (up to 78%) exceeded
the single sample standard on at least one day for each FIB group (Table 2-1; Figure 2-2).
This suggests that the sampling area may not have been large enough to encompass the
entire affected area. Whereas exceedances in all other regions were confined near the
major inflows, exceedances near the Tijuana River spanned a large area (Figure 2-2).
Coastal areas near the Santa Clara and San Diego Rivers appeared to be the least affected
during both storms (Table 2-1). It is conceivable, however, that by the time of sampling,
the plumes had advected away from the sampling areas, especially in the Santa Clara
River region (Warrick et al., 2007).
Nutrients
The distributions of nutrients offshore of the major regions after each storm
tended to mirror FIB distributions. High concentrations were typically found near the
river mouths, and nutrient concentrations tended to decrease and disperse over time
(Figure 2-3). Maximum nitrate concentrations (~40 µM) were found in the San Pedro
Shelf region at the mouth of the Los Angeles River. Concentrations of 10–15 µM were
also observed off the mouths of Ballona Creek and the Santa Clara River. In the San
Diego region, concentrations were elevated but were less than 10 µM. The maximum
near-surface nutrient concentrations were greater in 2004 than in 2005. Storm sampling
in 2004 occurred one month earlier than in 2005. It is possible that many regions had
been flushed out by earlier storm events prior to sampling in 2005 resulting in an overall
decrease in nutrient loading from that storm. Another apparent difference between the

Figure 2-3. Surface distributions of nitrate for the Newport Harbor area (including the
Santa Ana River and Newport Harbor) for storm 2 (2005) showing the dispersion and
dilution of nitrate over time after the storm. Note that the colorbars are plotted on log
scales.
39
40
two years is the higher concentrations of nutrients below 10 m in 2005. These higher
concentrations may be due to upwelling occurring along the coast, which usually begins
in mid to late March in this region.
Toxicity
Of the over 700 water samples that were analyzed for toxicity by the sea urchin
fertilization assay from all surveys and river systems combined, very few exhibited
toxicity (Table 2-2). Only 30 samples were considered toxic (<84% fertilization) and
even fewer (2) exhibited highly toxic effects (<50% fertilization). All of these were
located in the top 10 m of the water column. The greatest number of toxic samples was
observed in the Tijuana River plume on the first day of sampling during the February
2004 event, when the fertilization rate for 13 out of 18 samples (72%) was less than 84%.
In contrast, during February 2005 when high bacteria concentrations were observed in the
Tijuana River plume, no toxicity values less than 84% fertilization were observed in the
plume.
In Situ Relationships: CDOM vs. Salinity and Beam-c vs. TSS
CDOM concentration generally increased linearly with decreasing salinity, or
increased freshwater content (Figure 2-4). The opposite trend was observed in the beam-
c vs. TSS relationship. Beam-c generally increased with increasing TSS concentration,
with some scatter around the best-fit lines (Figure 2-5). The addition of region as an
independent variable greatly improved the CDOM/salinity models (increase in adjusted
R
2
of ~0.1), and the addition of day after storm resulted in a slight further improvement
(increase in adjusted R
2
of 0.01-0.04; Table 2-3). The addition of both region and day
after storm improved the beam-c vs. TSS relationships (increase in adjusted R
2
of 0.04-
0.06 and 0.03-0.05, respectively; Table 2-3). The CDOM vs. salinity relationship was
consistently strong (adjusted R
2
~0.6 for both storms; Table 2-3). The relationship
between beam-c and TSS, however, varied considerably between the two storm events
(adjusted R
2
ranged from 0.4 to 0.7; Table 2-3).
Storm 1 (2004)
Storm 2 (2005)
Salinity (psu)
26 28 30 32 34
0
10
20
30
40
0
10
20
30
40
CDOM (ppb of QSD)
Santa Clara River
Ballona Creek
LA/San Gabriel Rivers
Santa Ana River
Newport Harbor
A
B
river mouth
subset
Figure 2-4. Relationships between CDOM and salinity separated by region for the
2004 (A) and 2005 (B) storm events. Linear regressions are plotted for the Santa Clara
River (solid line), Ballona Creek (long dashed line), Los Angeles/San Gabriel Rivers
(short dashed line), Santa Ana River (dot-dashed line), and Newport Harbor (grey line).
These lines are plotted for visual purposes and were not used for statistical analyses.
CDOM data were not available for the San Diego and Tijuana Rivers. The river mouth
subset in storm 1 (A, grey points) consists of three stations in the Los Angeles/San
Gabriel Rivers region and three stations near Newport Harbor all located inside or just
outside major river mouths.

41

Santa Clara River Ballona Creek San Pedro Shelf San Diego Tijuana River Total
>84 84-50 <50 >84 84-50 <50 >84 84-50 <50 >84 84-50 <50 >84 84-50 <50 >84 84-50 <50
Storm 1 - 2004
Day 1 n.d. n.d. n.d. 8 0 0 37 1 0 15 2 1 5 13 0 65 16 1
Day 2 n.d. n.d. n.d. 22 1 0 72 2 1 n.d. n.d. n.d. n.d. n.d. n.d. 94 3 1
Day 3 18 0 0 n.d. n.d. n.d. n.d. n.d. n.d. 18 0 0 18 0 0 54 0 0
Day 4 n.d. n.d. n.d. 21 2 0 74 4 0 18 0 0 17 1 0 130 7 0
TOTAL 18 0 0 51 3 0 183 7 1 51 2 1 40 14 0 343 26 2
% of samples 100 0 0 94.5 5.5 0 96 3.5 0.5 94.4 3.6 2 74 26 0 92.5 7.0 0.5

Storm 2 - 2005
Day 1 n.d. n.d. n.d. 11 0 0 52 2 0 n.d. n.d. n.d. 18 0 0 81 2 0
Day 2 20 0 0 23 0 0 28 0 0 n.d. n.d. n.d. 18 0 0 89 0 0
Day 3 20 0 0 23 0 0 80 0 0 n.d. n.d. n.d. 18 0 0 141 0 0
Day 4 20 0 0 n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. 20 0 0
TOTAL
Table 2-2. Summary of toxicity evaluations (as percent fertilization in the sea urchin assay) for each of the sampling regions
for both storm events. Significant toxicity was chosen to be those values where fertilization was less than 84% (Bay et al.,
2003). n.d. – no data

42
Storm 1 (2004)
Storm 2 (2005)
Total suspended solids (mg L
-1
)
Beam attenuation (m
-1
)
010 20 30 40 80
0
5
10
15
20
25
30
0
5
10
15
20
25
30
Santa Clara River
Ballona Creek
LA/San Gabriel Rivers
Santa Ana River
Newport Harbor
San Diego River
Tijuana River
A
B
Figure 2-5. Relationships between beam attenuation coefficient and total suspended
solids separated by region for the 2004 (A) and 2005 (B) storm events. Linear
regressions are plotted for the Santa Clara River (solid line) Ballona Creek (long
dashed line), Los Angeles/San Gabriel Rivers (short dashed line), Santa Ana River
(dot dashed line), Newport Harbor (grey line), San Diego River (dotted line), and
Tijuana River (dot dot dashed line). These lines are plotted for visual purposes and
were not used for statistical analyses. No data were available for the San Diego River
for storm 2.

In analyzing the CDOM vs. salinity relationship, a subset of the samples
demonstrated increased CDOM fluorescence with little to no change in salinity (river
mouth subset, Figure 2-4). Samples within this subset were collected at three stations in
the Los Angeles/San Gabriel River region and three stations near Newport Harbor during
43
44
Table 2-3. Results of general linear models of CDOM vs. salinity and beam-c vs. TSS
with region and day after storm included as additional independent variables. n/a – not
applicable

Independent Independent Variable P-Value
variable(s) n Slope y-Intercept Adjusted R
2
Salinity/TSS Region Day
CDOM vs. Salinity
Storm 1 – 2004
salinity 816 -4.576 152.622 0.567 <0.0005 n/a n/a
salinity + region 816 -4.021 133.941 0.660 <0.0005 <0.0005 n/a
salinity + region + day 816 -3.984 132.082 0.664 <0.0005 <0.0005 0.004
Storm 2 – 2005
salinity 1030 -3.416 114.763 0.470 <0.0005 n/a n/a
salinity + region 1030 -3.198 107.596 0.569 <0.0005 <0.0005 n/a
salinity + region + day 1030 -3.169 106.633 0.608 <0.0005 <0.0005 <0.0005
Beam-c vs. TSS
Storm 1 – 2004
TSS 323 0.335 0.609 0.635 <0.0005 n/a n/a
TSS + region 323 0.339 0.552 0.675 <0.0005 <0.0005 n/a
TSS + region + day 323 0.329 0.545 0.702 <0.0005 <0.0005 <0.0005
Storm 2 – 2005
TSS 276 0.258 1.453 0.386 <0.0005 n/a n/a
TSS + region 276 0.263 1.460 0.451 <0.0005 <0.0005 n/a
TSS + region + day 276 0.237 1.590 0.499 <0.0005 <0.0005 <0.0005

the 2004 storm event. Four of the six stations were only sampled in 2004. These stations
were relatively shallow and were located either just inside or just outside major river
mouths. At these stations, CDOM fluorescence was generally high at all depths even
though low-salinity waters indicative of stormwater runoff were noted only in surface
waters (the top 1 or 2 meters). Twardowski and Donaghay (2001) mention that
deviations from the typical inverse linear relationship between CDOM and salinity can
arise even in coastal waters due to in situ CDOM production processes, including
sediment resuspension and reworking of the products of primary production. Chen &
Bada (1992) noticed that CDOM fluorescence decreased by about 20% after filtration in
nearshore samples. In a more recent study, however, Belzile et al. (2006) observed little
45
to no difference in CDOM fluorescence in filtered vs. unfiltered samples. The rapid
formation of CDOM from dissolved organic matter precursors exuded by phytoplankton
has been observed in several studies (reviewed in Twardowski and Donaghay 2001).
Localized phytoplankton blooms tend to persist near the mouth of the Los Angeles River
(Hardy, 1993; Gregorio and Pieper, 2000), and elevated concentrations of chlorophyll a
were observed at stations within the Los Angeles Harbor. Although chlorophyll a
concentrations were not high at stations near Newport Harbor at the time of sampling, we
cannot rule out the production of CDOM from a past localized bloom in that area.
Further research is needed to determine whether in situ production of CDOM occurs due
to processes such as sediment resuspension or production of algal exudates in the
Southern California Bight and whether these processes contribute to deviations in the
CDOM/salinity relationship.
In Situ Contaminant Relationships with Salinity and TSS
Relationships of nutrients and salinity were variable but were generally negative,
i.e. increasing nutrient concentrations with decreasing salinity (Table 2-4). The addition
of region as an independent variable again greatly improved most relationships. The
addition of day after storm also improved the models, but in many cases only slightly.
Even when both region and day after storm were included; however, the models only
explained up to half of the variation in the nutrient data (adjusted R
2
or likelihood R
2
=
0.26-0.55; Table 2-4). Although the data were variable, nutrient concentrations did
appear to decrease as the fraction of stormwater decreased (Figure 2-6 A–D). The largest
decrease in all nutrients occurred in the 32-33 psu (1-4% stormwater) salinity range
where median nutrient concentrations were 2-3 times lower than in the next lower
46
Table 2-4. Results of general linear models of contaminants vs. salinity and contaminants
vs. TSS with region and day after storm included as independent variables. n/a - not
applicable

Dependent Independent Variable P-Value
2

variable n Slope y-Intercept Adjusted R
2
Salinity/TSS Region Day
Storm 1 – 2004 Contaminants vs. Salinity
nitrite 339 -0.460 13.355 0.211
1
<0.0005 n/a n/a
nitrite+region 339 -0.356 10.391 0.423
1
<0.0005 n/a n/a
nitrite+region+day 339 -0.352 10.159 0.484
1
<0.0005 n/a n/a
nitrate 339 -2.663 90.201 0.281 <0.0005 n/a n/a
nitrate+region 339 -2.632 88.909 0.337 <0.0005 <0.0005 n/a
nitrate+region+day 339 -2.733 91.989 0.338 <0.0005 <0.0005 0.383
phosphate (PO
4
) 339 -0.374 13.005 0.327 <0.0005 n/a n/a
PO
4
+region 339 -0.369 12.765 0.435 <0.0005 <0.0005 n/a
PO
4
+region+day 339 -0.347 11.989 0.446 <0.0005 <0.0005 0.037
silicate 339 -3.931 133.361 0.305 <0.0005 n/a n/a
silicate+region 339 -3.771 127.709 0.345 <0.0005 <0.0005 n/a
silicate+region+day 339 -4.014 135.626 0.352 <0.0005 <0.0005 0.112
total coliforms (TC) 335 -1.542 54.404 0.255
1
<0.0005 n/a n/a
TC+region 335 -1.708 59.793 0.316
1
<0.0005 n/a n/a
TC+region+day 335 -1.507 54.710 0.394
1
<0.0005 n/a n/a
fecal coliforms (FC) 269 -0.980 33.762 0.092
1
<0.0005 n/a n/a
FC+region 269 -1.162 39.705 0.237
1
<0.0005 n/a n/a
FC+region+day 269 -0.744 28.244 0.355
1
<0.0005 n/a n/a
Enterococcus spp. (ent) 335 -1.260 42.321 0.195
1
<0.0005 n/a n/a
ent+region 335 -1.370 46.124 0.252
1
<0.0005 n/a n/a
ent+region+day 335 -1.071 38.513 0.346
1
<0.0005 n/a n/a
Storm 2 – 2005
nitrite 250 -0.038 1.632 0.003 0.176 n/a n/a
nitrate+region 250 -0.075 2.848 0.225 0.004 <0.0005 n/a
nitrate+region+day 250 -0.102 3.719 0.265 <0.0005 <0.0005 0.001
nitrate 250 -0.372 12.831 0.027
1
0.002 n/a n/a
nitrate+region 250 -0.505 15.225 0.526
1
<0.0005 n/a n/a
nitrate+region+day 250 -0.518 15.767 0.553
1
<0.0005 n/a n/a
phosphate (PO
4
) 250 -0.140 5.368 0.064 <0.0005 n/a n/a
PO
4
+region 250 -0.171 6.387 0.431 <0.0005 <0.0005 n/a
PO
4
+region+day 250 -0.159 5.975 0.448 <0.0005 <0.0005 0.015
silicate 250 -2.439 87.454 0.059 <0.0005 n/a n/a
silicate+region 250 -3.376 118.136 0.374 <0.0005 <0.0005 n/a
silicate+region+day 250 -3.590 124.777 0.387 <0.0005 <0.0005 0.040
total coliforms (TC) 252 -1.830 64.900 0.110
1
<0.0005 n/a n/a
TC+region 252 -1.824 64.513 0.301
1
<0.0005 n/a n/a
TC+region+day 252 -1.684 60.837 0.382
1
<0.0005 n/a n/a
fecal coliforms (FC) 232 -1.070 37.520 0.040
1
0.0002 n/a n/a
FC+region 232 -1.316 44.842 0.370
1
<0.0005 n/a n/a
FC+region+day 232 -1.208 42.244 0.434
1
<0.0005 n/a n/a

47
Table 2-4. (continued)

Dependent Independent Variable P-Value
2

variable n Slope y-Intercept Adjusted R
2
Salinity/TSS Region Day
Storm 2 – 2005 Contaminants vs. Salinity
Enterococcus spp. (ent) 276 -1.157 40.116 0.055
1
<0.0005 n/a n/a
ent+region 276 -1.403 47.873 0.421
1
<0.0005 n/a n/a
ent+region+day 276 -1.303 45.294 0.468
1
<0.0005 n/a n/a
Storm 1 – 2004 Contaminants vs. TSS
nitrite 376 0.012 -1.713 0.006
1
0.130 n/a n/a
nitrite+region 376 0.035 -1.385 0.383
1
<0.0005 n/a n/a
nitrite+region+day 376 0.033 -1.328 0.425
1
<0.0005 n/a n/a
nitrate 376 0.193 1.982 0.061 <0.0005 n/a n/a
nitrate+region 376 0.241 1.354 0.204 <0.0005 <0.0005 n/a
nitrate+region+day 376 0.240 1.241 0.201 <0.0005 <0.0005 0.652
phosphate (PO
4
) 376 0.034 0.582 0.112 <0.0005 n/a n/a
PO
4
+region 376 0.039 0.450 0.306 <0.0005 <0.0005 n/a
PO
4
+region+day 376 0.038 0.473 0.343 <0.0005 <0.0005 <0.0005
silicate 376 0.248 3.589 0.049 <0.0005 n/a n/a
silicate+region 376 0.352 2.299 0.196 <0.0005 <0.0005 n/a
silicate+region+day 376 0.358 2.252 0.188 <0.0005 <0.0005 0.636
total coliforms (TC) 372 0.138 3.124 0.085
1
<0.0005 n/a n/a
TC+region 372 0.166 3.289 0.156
1
<0.0005 n/a n/a
TC+region+day 372 0.147 5.740 0.296
1
<0.0005 n/a n/a
fecal coliforms (FC) 306 0.091 1.004 0.049
1
<0.0005 n/a n/a
FC+region 306 0.134 1.159 0.189
1
<0.0005 n/a n/a
FC+region+day 306 0.126 4.075 0.396
1
<0.0005 n/a n/a
Enterococcus spp. (ent) 372 0.108 0.303 0.050
1
<0.0005 n/a n/a
ent+region 372 0.127 0.767 0.123
1
<0.0005 n/a n/a
ent+region+day 372 0.108 3.550 0.275
1
<0.0005 n/a n/a
Storm 2 – 2005
nitrite 250 0.006 0.363 0.010 0.060 n/a n/a
nitrite+region 250 0.004 0.375 0.202 0.268 <0.0005 n/a
nitrite+region+day 250 0.006 0.357 0.227 0.093 <0.0005 0.012
nitrate 250 0.092 0.151 0.123
1
<0.0005 n/a n/a
nitrate+region 250 0.044 -1.401 0.494
1
<0.0005 n/a n/a
nitrate+region+day 250 0.041 -1.127 0.518
1
<0.0005 n/a n/a
phosphate (PO
4
) 250 0.029 0.620 0.208 <0.0005 n/a n/a
PO
4
+region 250 0.020 0.697 0.418 <0.0005 <0.0005 n/a
PO
4
+region+day 250 0.018 0.683 0.436 <0.0005 <0.0005 0.014
silicate 250 0.538 4.657 0.223 <0.0005 n/a n/a
silicate+region 250 0.369 5.885 0.345 <0.0005 <0.0005 n/a
silicate+region+day 250 0.390 5.425 0.358 <0.0005 <0.0005 0.049
total coliforms (TC) 252 0.134 4.532 0.062
1
<0.0005 n/a n/a
TC+region 252 0.144 4.530 0.241
1
<0.0005 n/a n/a
TC+region+day 252 0.118 5.895 0.317
1
<0.0005 n/a n/a
48
Table 2-4. (continued)

Dependent Independent Variable P-Value
2

variable n Slope y-Intercept Adjusted R
2
Salinity/TSS Region Day
Storm 2 – 2005 Contaminants vs. TSS
fecal coliforms (FC) 208 0.111 1.596 0.053
1
<0.0005 n/a n/a
FC+region 208 0.113 1.585 0.269
1
<0.0005 n/a n/a
FC+region+day 208 0.087 3.443 0.351
1
<0.0005 n/a n/a
Enterococcus spp. 252 0.115 1.282 0.060
1
<0.0005 n/a n/a
ent+region 252 0.121 1.724 0.306
1
<0.0005 n/a n/a
ent+region+day 252 0.098 3.192 0.366
1
<0.0005 n/a n/a
1
These are likelihood R
2
values estimated using the cenreg function in NADA.
2
Cenreg calculates one P-value for the entire model.

salinity range (30–32 psu; 4-10% stormwater). Not surprisingly, the relationships
between nutrients and TSS, an index of turbidity, were not as strong as the nutrient vs.
salinity relationships. GLMs that included region and day after storm as independent
variables again explained only up to half of the variation (adjusted R
2
or likelihood R
2
=
0.19-0.52; Table 2-4). When grouped into TSS ranges, higher nutrient concentrations
tended to occur at very high concentrations of TSS (>30 mg L
-1
; Figure 2-6 E-H).
Relationships between salinity and FIBs were generally negative (Table 2-4).
Unlike the nutrient relationships, the addition of day after storm as well as region
improved the models. When both variables were included, the models explained less
than half of the variation in the FIB data (likelihood R
2
= 0.35-0.48; Table 2-4). The
median FIB concentration dropped below COP standards in the 32-33 psu salinity range
(1-4% stormwater) for total coliforms and Enterococcus spp. and in the 28-30 psu salinity
range (10-16% stormwater) for fecal coliforms (Figure 2-7 A–D). When salinity values
indicated that greater than 10% stormwater was present (< 28 psu and 28-30 psu ranges),
the median FIB concentrations often exceeded COP standards. FIBs were generally at
very low concentrations, often below the maximum detection limit, in water where the
>30 10-30 3-10 <3 >30 10-30 3-10 <3
0
20
40
60
80
0
2
4
6
8
0
10
20
30
40
50
60
0
2
4
6
8
Silicate (µM) Nitrite (µM)
Nitrate (µM) Phosphate (µM)
TSS Range (mg L
-1
)
Salinity Range (psu)
<28 28-30 30-32 32-33 >33 <28 28-3030-3232-33 >33
0
20
40
60
80
0
2
4
6
8
Silicate (µM) Nitrite (µM)
0
10
20
30
40
50
60
0
2
4
6
8
Nitrate (µM) Phosphate (µM)
>10%
stormwater
>10%
stormwater
<10%
stormwater
<10%
stormwater
G H
F E
C D
AB
Figure 2-6. Box plots showing the medians and quartiles of nutrient concentrations in
five salinity ranges (A-D) and four ranges of total suspended solids (E-H). Vertical
lines in plots A-D indicate the 10% stormwater salinity level. Whiskers on the box
plots extend to the 10th and 90th percentiles. Points outside this range are shown as
open circles.

49
>30 10-30 3-10 <3 >30 10-30 3-10 <3
0
20
40
60
80
Toxicity (%fertile) FC (MPN/100 mL)
TC (MPN/100 mL) Ent. (MPN/100 mL)
TSS Range (mg L
-1
)
Salinity Range (psu)
<28 28-30 30-32 32-33 >33 <28 28-30 30-32 32-33 >33
0
20
40
60
80
Toxicity (%fertile) FC (MPN/100 mL)
TC (MPN/100 mL) Ent. (MPN/100 mL)
>10%
stormwater
>10%
stormwater
<10%
stormwater
<10%
stormwater
G H
F E
C D
AB
100
120
140
100
120
140
1
10
-4
10
-2
10
2
10
4
10
10
-3
0.1
10
3
10
-3
10
0.1
10
3
10
-5
10
-3
10
0.1
10
3
10
-3
0.1
10
10
3
10
5
10
-3
0.1
10
10
3
10
5
Figure 2-7. Box plots showing the medians and quartiles of concentrations of fecal
indicator bacteria and toxicity in five salinity ranges (A-D) and four ranges of total
suspended solids (E-H). The solid horizontal lines indicate the maximum detection
limit. Below this level, the distributions are estimated. The dashed lines indicate the
single sample California Ocean Plan standards. The dashed line in the toxicity plots
indicates 84% toxicity. Vertical lines in plots A-D indicate the 10% stormwater salinity
level. Whiskers on the box plots extend to four times the interquartile range, except in
plots in D and H where they extend to the 10th and 90th percentiles. Points outside these
ranges are shown as open circles. TC – total coliforms, FC – fecal coliforms,
Ent. – Enterococcus spp.

50
51
salinity was greater than 32-33 psu. Median FIB concentrations in the greater than 33
psu salinity range were 7-16 times lower than those in the next lower salinity range (32–
33 psu).
The relationships between FIBs and TSS were quite weak (Table 2-4). Similar to
the FIBs vs. salinity relationships, the strongest models included both region and day
after storm as independent variables. These models, however, explained only a small
amount of the variation in the FIB data (likelihood R
2
= 0.28-0.40; Table 2-4). Similar to
what was observed with nutrient concentrations, FIBs were characteristically higher in
waters with higher TSS loadings (> 30 mg L
-1
) (Figure 2-7 E–H). This result is
somewhat surprising as we generally find that human pathogenic bacteria are associated
with smallest size fractions, not with larger particulate size fractions (i.e. <1 m, J.
Fuhrman, personal communication; Ahn et al., 2005). At lower TSS concentrations,
FIBs were generally below COP standards, and for fecal coliforms and Enterococcus
spp., were often below detection limits.
Toxicity showed no patterns with salinity or with TSS (Figures 2-6 and 2-7). The
median percent fertilization was around 100% for all salinity and TSS ranges and never
fell below 84%.
DISCUSSION
The in-situ measurement of contaminants requires significant effort to both
acquire and analyze (especially in a timely manner) samples, limiting the ability to make
frequent offshore measurements for FIBs, water column toxicity, and nutrients. The
results presented above indicate that impacts from contaminants such as nutrients and
FIBs after storm events are generally brief and tend to occur near the major sources of
52
stormwater in the SCB. However, important exceptions to this, such as stormwater from
the Tijuana River, do occur.
How Problematic is Stormwater?
Relationships of nutrient concentrations with salinity and TSS varied among
regions but not between the days sampled. Individual sources of stormwater, therefore,
probably differ in nutrient composition, but nutrients seem to decrease in plume waters
due to simple mixing and dilution. The conservative mixing of nutrients associated with
stormwater runoff has also been observed in past studies (Warrick et al., 2005). The lack
of a strong linear relationship indicates that other sources of nutrients, such as upwelling,
were likely present in the coastal ocean creating variability in ambient nutrient
concentrations. Unlike nutrients, relationships of FIBs with salinity and TSS depended
on both region and the number of days after the storm. This indicates that the
composition and concentration of FIBs in stormwater runoff varies among regions, and
that their concentration within stormwater plumes is dependent on more than just loading
and dilution rates. FIBs cannot survive for long periods of time in the surface ocean, and
mortality is increased by exposure to ultraviolet radiation (Fujioka et al., 1981; Sinton et
al., 2002; Anderson et al., 2005). Therefore, they are likely lost from plume waters faster
than they would be through simple mixing with ambient coastal water. Some studies
have found relationships between salinity and various contaminants such as nutrients or
toxicity (Bay et al., 2003; Ragan, 2003). Others, however, have found no consistent
relationship between FIBs and plume tracers (Ahn et al., 2005). Concentrations of
contaminants seem to be highly variable, especially when the proportion of stormwater is
~1-4% (see Figures 2-6 and 2-7). We can, however, determine when and where
53
contaminants are likely to exceed COP standards through quick and easy measurements
such as salinity and TSS concentration by using the median values as a guideline.
In past studies of the SCB, high concentrations of FIBs and/or toxicity were found
in stormwater itself, near sewage outfalls and stormdrains/river outlets, and at sites very
nearshore (e.g. beaches and surfzone areas; Geesey, 1993; Bay et al., 2003; Gersberg et
al., 2004). The few studies that have attempted to examine FIBs in offshore waters have
found that they tend to occur in low concentrations but can exceed California standards
offshore of major rivers after storm events (ZoBell, 1941; Ahn et al., 2005). During the
Bight '03 project, exceedances of COP standards generally occurred near areas of
stormwater discharge during the first day or two after the storm event, similar to what
was found in these past studies. However, several major exceptions are worth noting.
Waters offshore of the Tijuana River consistently exceeded COP standards for
multiple FIBs, and in 2005 the area of exceedance was even larger than what could be
mapped based on the fixed sampling grid. Gersberg et al. (2004) also found marked
increases in toxicity in the Tijuana River during storm events. The Tijuana River, with a
discharge rate of 5-10 m
3
s
-1
, does not have a large flow volume compared to the Los
Angeles or San Gabriel River systems whose storm discharge rates often exceed
1,000 m
3
s
-1
(see Warrick et al., 2007). This implies that FIBs in the Tijuana River are
highly concentrated and are not always rapidly diluted or advected from the region in the
three to four days following the storm.
Contrary to prior studies, very few samples collected during the Bight '03 survey
showed high levels of toxicity, and toxicity was not related to the variables used to track
plume location (salinity and TSS). Bay et al. (2003) detected toxicity in samples
54
collected in the Ballona Creek discharge plume when the proportion of stormwater
exceeded 10%. Samples outside the Ballona Creek plume were not toxic. In their study,
the authors did not use a fixed grid of samples but adapted their stations based on salinity
levels always collecting samples both inside and outside the plume. The Bight '03 study
was designed to monitor specific locations around major river discharges and not, in most
cases, to adaptively track and sample plumes. This design likely missed much of the
plumes as evidenced by the small proportion of sites located in low salinity water.
Runoff plumes can be advected through the area of a given sampling grid in as little as a
day and are also often advected up or downcoast (Warrick et al., 2007; Nezlin et al.,
2008). Because the plume is moving and the sampling grid is stationary, it is likely that
even though the sampling was distributed over several days, the evolving discharge
plume was not adequately sampled. Relationships between toxicity and variables
indicative of plumes may have been detected under a different sampling scheme.
Differences between the results from Bight '03 and other past studies may also be
due to differences in the time spans over which sampling took place. In a project of this
type, it is difficult to obtain a good time series of observations that span the time from
initial discharge to thorough dilution and/or dispersion in the coastal receiving waters.
First, the exact timing of the storms is not known in advance, and it is difficult to
guarantee the availability of the boat and crew during the event. Second, even if
available, the sea state often prevents operations by the vessels typically used for this
type of sampling (Nezlin et al., 2007b). And third, it is hard to maintain a sufficiently
long time series to follow the evolution of these systems because of the commitments of
the technical and scientific crews and vessels to other projects. During Bight '03, no
55
sampling occurred during the initial portions of the runoff events, so the effects of the
initial mixing of the stormwater into the coastal ocean were missed. Bay et al. (2003)
were able to sample both during as well as immediately after storm events which may
also explain the differences in their findings.
Feasibility of Using CDOM and Beam-c to Map Plumes
Beam-c and TSS represent two different ways to examine the particulate
component of seawater. TSS is a measurement of the concentration, by weight, of
particles whereas beam-c is an optical measurement related to both the size and
concentration of particles. Because of the dependence of light attenuation on particle
size, beam-c depends on the geometrical cross-section of particles per unit volume, not
necessarily on TSS concentration alone (Davies-Colley and Smith, 2001). Therefore,
changes in particle size and composition (i.e. inorganic vs. organic) can result in a change
in beam-c without a corresponding change in TSS. We may expect the relationship
between beam-c and TSS to change over space and time as the particle composition could
be quite different just after a storm versus several days later due to the rapid sinking of
large particles and flocs (Hill et al., 2000; Warrick et al., 2004). Particle composition
might also vary among the various regions. Analysis of the Bight '03 data confirms that
the relationship between beam-c and TSS varies between regions and over time after a
storm event.
Salinity and CDOM both represent concentrations of dissolved constituents.
River and runoff systems typically have elevated levels of CDOM that can correlate with
salinity, such that CDOM concentration increases with decreasing salinity (Twardowski
and Donaghay, 2001; D'Sa et al., 2002). In theory, salinity and CDOM could be used
56
interchangeably as tracers of the dissolved portion of a runoff plume. CDOM has a
characteristic absorption spectrum and can therefore also be detected using existing and
future satellite-mounted ocean color sensors (e.g. Lee et al., 2002). In the SCB, the
composition and concentration of CDOM likely varies among sources of stormwater
runoff. It appears that over at least the first few days; however, CDOM concentration
decreases in plume waters through simple mixing processes.
CONCLUSIONS
As part of the Bight '03 program, the effects of runoff from two storms, one in
February 2004 and the other in February-March 2005, were detailed. The overall impact
of the plumes was, in most cases, not large. The areal impacts from bacterial
contamination and from water column toxicity were generally constrained to small areas
near the river mouths. The worst contamination occurred in the region off the Tijuana
River where exceedances of the COP standards persisted for at least 2 to 3 days following
the storm event and extended away from the river mouth. The effects from the rain
events were confined primarily to the upper 5–10 meters of the water column, and they
tended to decrease below threshold levels of concern within 2-3 days. In addition, we
found that the chance of exceeding the single COP standards was low in areas containing
less than 4-10% (>30-32 psu) stormwater. Dramatic decreases in all FIBs (often to
values below detection limits) and nutrients were observed in water with less than 1-4%
(>32-33 psu) stormwater.
Detection of stormwater plumes off southern California using ocean color has
generally relied on increases in nLw in the 531-551 nm range for MODIS (e.g. Nezlin et
al., 2008) and 555 nm for SeaWiFS (e.g. Otero and Siegel, 2004; Nezlin and DiGiacomo,
57
2005; Nezlin et al., 2005). The increase in nLw at these wavelengths is likely due to
increased concentrations of suspended sediments within plumes which increase
backscattering. This signal is an indication of only the particulate portion of the plume.
MODIS ocean color data analyzed as part of the Bight '03 project showed both an
increase in nLw within the plume waters at longer wavelengths (primarily 531-551 nm)
as well as a decrease in nLw at short wavelengths (primarily 412 nm), the latter
potentially explained by light absorption by CDOM (Nezlin et al., 2008). In this manner,
it should be possible to use satellite ocean color-derived estimates of CDOM absorption
and increased backscatter (increased nLw) at long wavelengths as fairly conservative
tracers of stormwater runoff plumes off southern California, representing the dissolved
and particulate constituents of the plumes, respectively, as described above.
Analogs for CDOM and beam-c can be derived from the inherent optical
properties of the water column, which in turn can be derived from the remote sensing
reflectance obtained by satellite ocean color sensors. In this context, methods such as the
Quasi-Analytical Algorithm (QAA) developed for deriving inherent optical properties
(IOPs) from remotely sensed ocean color measurements (Lee et al., 2002; Lee et al.,
2006) are available to do this in complex nearshore waters such as those off southern
California, deriving potentially informative properties such as a
dg
(412) and b
b
(551).
Strong correlations between CDOM and salinity, and beam-c and TSS indicate that
satellite ocean color data can potentially be used to infer and perhaps accurately assess
gradients in salinity and turbidity. The actual CDOM/salinity and beam-c/TSS
relationships differ among regions and between storm events. Therefore, quantitative
estimations of salinity or TSS via satellite ocean color measurements would require
58
building empirical relationships specific to each region. Regardless, ocean color imagery
can, and should, be built into regional monitoring programs to provide qualitative
information on the locations of plumes, locations of areas likely impacted by
contaminants, and to guide ship-based monitoring efforts.
59
CHAPTER 2 REFERENCES

Ahn, J. H., Grant, S. B., Surbeck, C. Q., DiGiacomo, P. M., Nezlin, N. P. and Jiang, S.,
2005. Coastal water quality impact of stormwater runoff from an urban watershed
in southern California. Environmental Science & Technology 39(16), 5940-5953.
Anderson, K. L., Whitlock, J. E. and Harwood, V. J., 2005. Persistence and differential
survival of fecal indicator bacteria in subtropical waters and sediments. Applied
Environmental Microbiology 71(6), 3041–3048.
APHA, 1998a. 9222D. Fecal coliform membrane filter procedure. In: Franson, M. A. H.,
Clesceri, L. S., Greenberg, A. E. and Eaton, A. D., (Eds.), Standard methods for
the examination of water and wastewater, 20th edition. American Public Health
Association, American Water Works Association, Water Environment Federation,
Washington, DC, pp. 63-65.
APHA, 1998b. 9221B. Standard total coliform fermentation technique. In: Franson, M.
A. H., Clesceri, L. S., Greenberg, A. E. and Eaton, A. D., (Eds.), Standard
methods for the examination of water and wastewater, 20th edition. American
Public Health Association, American Water Works Association, Water
Environment Federation, Washington, DC, pp. 48-51.
APHA, 1998c. 9221E. Fecal coliform procedure. In: Franson, M. A. H., Clesceri, L. S.,
Greenberg, A. E. and Eaton, A. D., (Eds.), Standard methods for the examination
of water and wastewater, 20th edition. American Public Health Association,
American Water Works Association, Water Environment Federation,
Washington, DC, pp. 54-55.
APHA, 1998d. 9222B. Standard total coliform membrane filter procedure. In: Franson,
M. A. H., Clesceri, L. S., Greenberg, A. E. and Eaton, A. D., (Eds.), Standard
methods for the examination of water and wastewater, 20th edition. American
Public Health Association, American Water Works Association, Water
Environment Federation, Washington, DC, pp. 57-62.
Bay, S., Jones, B. H., Schiff, K. and Washburn, L., 2003. Water quality impacts of
stormwater discharges to Santa Monica Bay. Marine Environmental Research
56(1-2), 205-223.
Belzile, C., Roesler, C. S., Christensen, J. P., Shakhova, N. and Semiletov, I., 2006.
Fluorescence measured using the WETStar DOM fluorometer as a proxy for
dissolved matter absorption. Estuarine, Coastal and Shelf Science 67(3), 441-449.
Busse, L. B., Venrick, E. L., Antrobus, R., Miller, P. E., Vigilant, V., Silver, M. W.,
Mengelt, C., Mydlarz, L. and Prezelin, B. B., 2006. Domoic acid in phytoplankton
and fish in San Diego, CA, USA. Harmful Algae 5(1), 91-101.
60
CDPH, 2006. Draft guidance for salt water beaches. California Department of Public
Health, Division of Drinking Water and Environmental Management,
Sacramento, CA.
Chen, R. F. and Bada, J. L., 1992. The fluorescence of dissolved organic matter. Marine
Chemistry 37(3-4), 191-221.
Coble, P., Hu, C., Gould, R. W. J., Chang, G. and Wood, M., 2004. Colored dissolved
organic matter in the coastal ocean. Oceanography 17(2), 50-59.
D'Sa, E. J., Hu, C., Muller-Karger, F. E. and Carder, K. L., 2002. Estimation of colored
dissolved organic matter and salinity fields in case 2 waters using SeaWiFS:
Examples from Florida Bay and Florida Shelf. Proceedings of the Indian
Academy of Sciences 111(3), 197-207.
Davies-Colley, R. J. and Smith, D. G., 2001. Turbidity, suspended sediment, and water
clarity: A review. Journal of the American Water Resources Association 37(5),
1085-1101.
Del Castillo, C. E., 2005. Remote sensing of organic matter in coastal waters. In: Miller,
R. L., Del Castillo, C. E. and McKee, B. A., (Eds.), Remote sensing of coastal
aquatic environments. Technologies, techniques and applications. Springer,
Dordrecht, pp. 157-180.
Fujioka, R. S., Hashimoto, H. H., Siwak, E. B. and Young, R. H. F., 1981. Effect of
sunlight on survival of indicator bacteria in seawater. Applied Environmental
Microbiology 41(3), 690-696.
Geesey, G. G., 1993. Microbiology. In: Dailey, M. D., Reish, D. J. and Anderson, J. W.,
(Eds.), Ecology of the Southern California Bight. University of California Press,
Los Angeles, California, pp. 190-232.
Gersberg, R. M., Daft, D. and Yorkey, D., 2004. Temporal pattern of toxicity in runoff
from the Tijuana River watershed. Water Research 38(3), 559-568.
Geyer, W. R., Hill, P., Milligan, T. and Traykovski, P., 2000. The structure of the Eel
River plume during floods. Continental Shelf Research 20(16), 2067-2093.
Gordon, L. I., Jennings, J. C. J., Ross, A. A. and Krest, J. M., 1993. A suggested protocol
for continuous flow automated analysis of seawater nutrients (phosphate, nitrate,
nitrite and silicic acid) in the WOCE Hydrographic Program and the Joint Global
Ocean Fluxes Study. WHP Operations and Methods. WOCE Hydrographic
Program Office, Methods Manual WHPO 91-1, Corvallis, Oregon.
Gregorio, D. E. and Pieper, R. E., 2000. Investigations of red tides along the Southern
California coast. Bulletin of the Southern California Academy of Sciences 99(3),
147-160.
61
Griffith, J., Aumand, L., Lee, I., McGee, C., Othman, L., Ritter, K., Walker, K. and
Weisberg, S., 2006. Comparison and verification of bacterial water quality
indicator measurement methods using ambient coastal water samples.
Environmental Monitoring and Assessment 116(1-3), 335-344.
Hardy, J. T., 1993. Phytoplankton. In: Dailey, M. D., Reish, D. J. and Anderson, J. W.,
(Eds.), Ecology of the Southern California Bight. University of California Press,
Los Angeles, California, pp. 233-265.
Helsel, D. R., 2005. Nondetects and data analysis. Wiley, New York, NY.
Hill, P. S., Milligan, T. G. and Geyer, W. R., 2000. Controls on effective settling velocity
of suspended sediment in the Eel River flood plume. Continental Shelf Research
20(16), 2095-2111.
Lee, L. and Helsel, D. R., 2005. Statistical analysis of water-quality data containing
multiple detection limits: S-language software for regression on order statistics.
Computers & Geosciences 31, 1241-1248.
Lee, Z., Carder, K. L. and Arnone, R. A., 2002. Deriving inherent optical properties from
water color: A multiband quasi-analytical algorithm for optically deep waters.
Applied Optics 41(27), 5755-5772.
Lee, Z., Carder, K. L. and Arnone, R. A., 2006. The quasi-analytical algorithm. In: Lee,
Z., (Ed.), Remote sensing of inherent optical properties: Fundamental tests of
algorithms and applications. IOCCG, Dartmouth, Canada, pp. 73-79.
Messer, J. W. and Dufour, A. P., 1998. A rapid, specific membrane filtration procedure
for enumeration of Enterococci in recreational water. Applied and Environmental
Microbiology 64(2), 678-680.
Monahan, E. C. and Pybus, M. J., 1978. Colour, ultraviolet absorbance and salinity of the
surface waters off the west coast of Ireland. Nature 274(5673), 782-784.
National Research Council, 1990. Monitoring southern California coastal waters.
National Academy Press, Washington, DC.
Nezlin, N. P. and DiGiacomo, P. M., 2005. Satellite ocean color observations of
stormwater runoff plumes along the San Pedro Shelf (southern California) during
1997–2003. Continental Shelf Research 25, 1692-1711.
Nezlin, N. P., DiGiacomo, P. M., Stein, E. D. and Ackerman, D., 2005. Stormwater
runoff plumes observed by SeaWiFS radiometer in the southern California bight.
Remote Sensing of Environment 98, 494-510.

62
Nezlin, N. P., DiGiacomo, P. M., Weisberg, S. B., Diehl, D. W., Warrick, J. A., Mengel,
M. J., Jones, B. H., Reifel, K. M., Johnson, S. C., Ohlmann, J. C., Washburn, L.
and Terrill, E. J., 2007a. Southern California Bight 2003 regional monitoring
program: V. Water quality. Southern California Coastal Water Research Project,
Costa Mesa, CA.
Nezlin, N. P., Weisberg, S. B. and Diehl, D. W., 2007b. Relative availability of satellite
imagery and ship-based sampling for assessment of stormwater runoff plumes in
coastal southern California. Estuarine, Coastal and Shelf Science 71(1-2), 250-
258.
Nezlin, N. P., DiGiacomo, P. M., Diehl, D. W., Jones, B. H., Johnson, S. C., Mengel, M.
J., Reifel, K. M., Warrick, J. A. and Wang, M., 2008. Stormwater plume detection
by MODIS imagery in the southern California coastal ocean. Estuarine, Coastal
and Shelf Science 80(1), 141-152.
Ocean Software, 2009. Interactive Graphical Ocean Database System (IGODS).
Opsahl, S. and Benner, R., 1998. Photochemical reactivity of dissolved lignin in river and
ocean waters. Limnology & Oceanography 43(6), 1297-1304.
Otero, M. P. and Siegel, D. A., 2004. Spatial and temporal characteristics of sediment
plumes and phytoplankton blooms in the Santa Barbara Channel. Deep-Sea
Research Part II 51(10-11), 1129-1149.
R Development Core Team, 2006. R: A Language and Environment for Statistical
Computing, v. 2.4.1. R Foundation for Statistical Computing, Vienna, Austria.
Ragan, M. A., 2003. Response of inherent optical properties to stormwater runoff in
Santa Monica Bay, California. Master of Science, University of Southern
California, Los Angeles, CA.
Sakamoto, C. M., Friederich, G. E. and Codispoti, L. A., 1990. MBARI procedures for
automated nutrient analyses using a modified Alpkem series 300 rapid flow
analyzer. MBARI Technical Report No. 90-2. Monterey Bay Aquarium Research
Institute, Moss Landing, CA.
Schiff, K. C., Allen, M. J., Zeng, E. Y. and Bay, S. M., 2000. Southern California.
Marine Pollution Bulletin 41(1-6), 76-93.
Schiff, K. C., Weisberg, S. B. and Raco-Rands, V., 2002. Inventory of ocean monitoring
in the southern California bight. Environmental Management 29(6), 871-876.
Shumway, R. H., Azari, R. S. and Kayhanian, M., 2002. Statistical approaches to
estimating mean water quality concentrations with detection limits.
Environmental Science & Technology 36(15), 3345-3353.
63
Siegel, D. A., Maritorena, S., Nelson, N. B., Hansell, D. A. and Lorenzi-Kayser, M.,
2002. Global distribution and dynamics of colored dissolved and detrital organic
materials. Journal of Geophysical Research 107(C12), 3228,
doi:10.1029/2001JC000965.
Sinton, L. W., Hall, C. H., Lynch, P. A. and Davies-Colley, R. J., 2002. Sunlight
inactivation of fecal indicator bacteria and bacteriophages from waste
stabilization pond effluent in fresh and saline waters. Applied Environmental
Microbiology 68(3), 1122–1131.
SSI, 2004a. SigmaPlot, v. 9.01. Systat Software, Inc., Richmond, California.
SSI, 2004b. SYSTAT For Windows, v. 11. Systat Software, Inc., Richmond, California.
State Water Resources Control Board, 2005. California Ocean Plan. Sacramento,
California.
Twardowski, M. J. and Donaghay, P. L., 2001. Separating in situ and terrigenous sources
of absorption by dissolved materials in coastal waters. Journal of Geophysical
Research 106(C2), 2545-2560.
USEPA, 1983. Method 160.2: Residue, non-filterable (gravimetric, dried at 103-105 ºC).
In: Methods for chemical analysis of water and wastes, EPA 600/4-79/020.
United States Environmental Protection Agency, Office of Research and
Development, Cincinnati, Ohio, pp. 160 2-1–160 2-3.
USEPA, 1995. Purple urchin, Strongylocentrotus purpuratus, and sand dollar,
Dendraster excentricus, fertilization test method. In: Chapman, G. A., Denton, D.
L. and Lazorchak, J. M., (Eds.), Short-term methods for estimating the chronic
toxicity of effluents and receiving water to West Coast marine and estuarine
organisms, EPA600/R-95/136. United States Environmental Protection Agency,
Office of Research and Development, Cincinnati, Ohio, pp. 389-465.
Vodacek, A., Blough, N. V., DeGrandpre, M. D., Peltzer, E. T. and Nelson, R. K., 1997.
Seasonal variation of CDOM and DOC in the Middle Atlantic Bight: Terrestrial
inputs and photooxidation. Limnology & Oceanography 42(4), 674-686.
Warrick, J. A., Mertes, L. A. K., Washburn, L. and Siegel, D. A., 2004. A conceptual
model for river water and sediment dispersal in the Santa Barbara Channel,
California. Continental Shelf Research 24(17), 2029-2043.
Warrick, J. A., Washburn, L., Brzezinski, M. A. and Siegel, D. A., 2005. Nutrient
contributions to the Santa Barbara Channel, California, from the ephemeral Santa
Clara River. Estuarine, Coastal and Shelf Science 62(4), 559-574.

64
Warrick, J. A., DiGiacomo, P. M., Weisberg, S. B., Nezlin, N. P., Mengel, M., Jones, B.
H., Ohlmann, J. C., Washburn, L., Terrill, E. J. and Farnsworth, K. L., 2007.
River plume patterns and dynamics within the Southern California Bight.
Continental Shelf Research 27(19), 2427-2448.
Washburn, L., McClure, K. A., Jones, B. H. and Bay, S. M., 2003. Spatial scales and
evolution of stormwater plumes in Santa Monica Bay. Marine Environmental
Research 56(1-2), 103-125.
ZoBell, C. E., 1941. The occurrence of coliform bacteria in oceanic water. Journal of
Bacteriology 42, 284.

65
CHAPTER 3
Tracking a Wastewater Plume and its Effects on Phytoplankton
in the Coastal Ocean
CHAPTER 3 ABSTRACT
Urban runoff from heavily-populated coastal areas can negatively impact water
quality, beneficial uses, and ecosystems of coastal areas. In southern California, the
majority of urban runoff to the coastal zone occurs as short, intense flood events after
storms which can be difficult to study. The planned release of wastewater from the
Hyperion Wastewater Treatment Plant provided an opportunity to study the effects of a
simulated urban runoff plume from its initial release until it could no longer be detected
in the coastal ocean. A grid of stations in the vicinity of the outfall was sampled prior to
and during the planned diversion, and sampling was conducted over a larger area after the
diversion. Continuous measurements of salinity and colored dissolved organic matter
(CDOM) collected along the ship track showed that the wastewater plume initially
streamed offshore but then turned onshore and upcoast in response to surface currents.
Non-metric multi-dimensional scaling analysis of phytoplankton community structure
revealed distinct differences between communities sampled within and outside plume
waters. This shift in phytoplankton community structure was due to increased percent
abundance of two dinoflagellates, Akashiwo sanguinea and Cochlodinium sp., and a
decrease in percent abundance of the dinoflagellate Lingulodinium polyedrum, the diatom
Cylindrotheca sp., and the silicoflagellate Dictyocha spp. within plume waters. Localized
blooms of Cochlodinium sp. and A. sanguinea (chlorophyll a up to 100 mg m
-3
and
densities between 100-2,000 cells mL
-1
) occurred 4-7 days after the diversion in plume
remnants. Spectra of the ratio of scatter to absorption measured during the blooms were

66
similar to reflectance spectra measured during blooms of dinoflagellate species in other
studies, especially when the blooms occurred in areas with high CDOM concentration.
Reflectance spectra measured from ocean color satellite imagery could, therefore, be used
to identify dinoflagellate blooms. Although both Cochlodinium sp. and A. sanguinea
have been occasionally reported from California waters, blooms of these species have
only recently been observed along the California coast. They appear to be favored during
strong stratification, low wind stress, and enhanced nutrients. Plumes from urban runoff
create similar conditions and could stimulate dinoflagellate blooms.

67
INTRODUCTION
The effects of urban runoff on coastal water quality are well known (Jickells,
1998; Ahn et al., 2005; Lotze et al., 2006). Coastal waters provide numerous beneficial
uses including recreation, commercial and sport fisheries, marine habitat, commerce and
transportation, and aesthetic enjoyment. With high levels of sediment, nutrients, and
even human pathogens, runoff from heavily-developed areas often negatively impacts
these uses (Geesey, 1993; Davies-Colley and Smith, 2001; Clarke et al., 2006). In
addition, urban runoff and the resulting nutrient load to the coastal ocean can stimulate
phytoplankton growth and production, sometimes resulting in nuisance or harmful algal
blooms (Riegman, 1995; Dagg and Breed, 2003; Hu et al., 2004; Glibert et al., 2005).
In southern California, more than 95% of runoff to the coastal zone comes from
storm events (Schiff et al., 2000). Unlike the major river systems of the world where
runoff into the coastal ocean is more or less continuous, storm events typically generate
substantial increases in runoff over short periods of time. In large, concrete-lined
channels, for example, flow can increase over three orders of magnitude in a matter of
hours (Schiff et al., 2000). These short but intense flood events create pulses of runoff
generating distinct low-salinity, high-nutrient plumes that can extend 4-7 km offshore and
10 km or more alongshore and can remain in the coastal ocean for days to weeks
(Washburn et al., 2003; Nezlin et al., 2005). Sampling stormwater plumes, however, can
be quite difficult because the exact timing of storm events is not known in advance, the
availability of boats and crew cannot always be guaranteed over adequate time spans, and
the sea state after storms often prevents operations by the vessels typically used for this
type of sampling (Nezlin et al., 2007). Planned releases of urban runoff can provide

68
unique opportunities to study the effects of runoff-generated plumes in the coastal ocean.
Other forms of urban runoff, such as wastewater, are similar in composition to
stormwater and would therefore be expected to have similar effects on the coastal
ecosystem.
The Hyperion Wastewater Treatment Plant (WWTP), located in Playa del Rey,
California, is one of the largest wastewater treatment plants on the west coast of the
United States and is one of four municipal wastewater treatment facilities that discharge
the majority of effluent into the Southern California Bight (National Research Council,
1990; Nezlin et al., 2004; Lyon et al., 2006). Hyperion began operation in 1894 and now
services about 4 million people in the city of Los Angeles and several other cities and
contract agencies. Currently, its average dry-weather capacity is 1.7 x 10
6
m
3
d
-1
, and its
average discharge into Santa Monica Bay (SMB) is about 1.14 x 10
6
m
3
d
-1
. Secondary-
treated wastewater is discharged through a 12-foot diameter submerged pipe (the 5-mile
outfall) that terminates about 8.1 km offshore in approximately 57 m of water (Figure 3-
1). Although the outfalls are externally inspected annually, the 5-mile outfall has never
been inspected internally during its near 50-year existence. From November 28-30,
2006, the City of Los Angeles conducted an inspection of the interior of the 5-mile
outfall. During this time (~50 hours), disinfected wastewater was discharged out of the
1-mile outfall, which terminates about 1.6 km offshore in approximately 15 m of water.
We tracked the resulting wastewater plume using both chemical (salinity) and optical
(colored dissolved organic matter, or CDOM; absorption and attenuation spectra)
signatures over approximately seven days. We also examined the phytoplankton

community within the plume to determine the overall effects of the plume on
phytoplankton density and specific effects on community structure.

−118.8 −118.75 −118.7 −118.65 −118.6 −118.55 −118.5 −118.45 −118.4 −118.35
33.7
33.75
33.8
33.85
33.9
33.95
34.0
34.05
Ballona Creek
Santa Monica
Pier
USC grid stations (before and during)
USC zig-zag stations (after)
UCLA stations (28 November)
UCLA stations (30 November)
UCLA stations (1 December)
x 1-mile outfall
5-mile
outfall
1-mile
outfall
Hermosa
Beach
Pier
Marina
del Rey
+
+

Figure 3-1. Map of Santa Monica Bay and stations sampled during the Hyperion WWTP
diversion event. Inset is a close-up showing stations near the 1-mile outfall.

METHODS
Wastewater was diverted from the 5-mile outfall to the 1-mile outfall starting at
03:30 PST on 28 November, 2006. The diversion event lasted for approximately 50
hours until 05:20 PST on 30 November. The surface wastewater plume that formed as a
result of the diversion was monitored and sampled during and after the event. Physical
and chemical measurements (e.g., salinity, temperature, CDOM, macronutrients) were
collected to characterize the plume and monitor its location. In addition, we monitored
69

70
the phytoplankton through both chlorophyll a (chl a) measurements and microscopic cell
counts.
Meteorological Data
Meteorological and oceanographic data were collected to help describe plume
dynamics. Observed precipitation measured in downtown Los Angeles at the University
of Southern California (34.016° N latitude , 118.283° W longitude) and wind data
collected at the Los Angeles International Airport (LAX; 33.938° N latitude, 118.388° W
longitude) were obtained from the NOAA’s National Climatic Data Center
(http://www.ncdc.noaa.gov). While the wind record from LAX does not provide the full
effect of the spatial wind patterns over SMB, it does indicate the general temporal
patterns that affected the bay. Surface salinity data from the Santa Monica Pier
(34.008° N latitude, 118.499° W longitude), and surface current data derived from high-
frequency radar were obtained from the Southern California Coastal Ocean Observing
System (SCCOOS; http://www.sccoos.org).
Field Collections
Sampling was conducted by the University of Southern California and the City of
Los Angeles before (27 November) and during (28-29 November) the diversion event
aboard the M/V Marine Surveyor along a grid of 30 stations in the vicinity of the 1-mile
outfall (Figure 3-1). Locations of all stations can be found in Appendix A. The stations
were set up along three lines approximately parallel to the shoreline at 0.8 km intervals.
Note that only a subset of stations was sampled on each date. Vertical profiles were
collected at each station using a package containing a Seabird SBE 25 conductivity-
temperature-depth (CTD) meter, a C-star transmissometer (WET Labs, Inc.), and a

71
WETStar CDOM fluorometer (WET Labs, Inc.). Continuous measurements of optical
properties were collected along the ship's track by pumping water from the surface pump
(~1.5 m depth) through an ac-s spectral absorption and attenuation meter with a 25-cm
pathlength (WET Labs, Inc.). The ac-s measures attenuation [c( λ)] and absorption [a( λ)]
in the visible region, from 400-760 nm, at approximately 4 nm intervals. To measure
dissolved a( λ) and c( λ), surface water was periodically pumped through a 0.2-µm
cartridge filter similar to the methods used in Slade et al. (2006). On 29 November,
continuous measurements of ammonium (NH
4
+
) were also collected by pumping water
from the surface pump to a Technicon Autoanalyzer. Water samples were collected at 1
meter depth for nitrate (NO
3
-
) using 5-L Niskin bottles. Additional nutrient samples
(nitrate, nitrite, phosphate, silicate) and samples for chl a concentration and
phytoplankton count analyses were collected at selected stations at the surface using a
bucket. Whole water samples for nutrients and chl a concentration were stored on ice in
the dark during transport back to the laboratory. For count analyses, whole-water
samples (250 mL) were preserved in the field using 1% acid Lugol's solution. Whole
water samples (3 mL) for flow cytometry were preserved in the field using buffered
formalin (10% final concentration), transported back to the laboratory on ice in the dark,
and stored at -80°C until analysis.
After the event (4 and 7 December), sampling was conducted over a larger portion
of SMB along a zig-zag track extending from Santa Monica Pier in the north to the
Hermosa Beach Pier in the south (Figure 3-1). In addition to the above measurements,
continuous surface measurements of temperature and salinity (SBE 9/11+), chl a
(WETStar chl a fluorometer), and CDOM concentration (WETStar CDOM fluorometer)

72
were collected along the ship's track using the surface pump. Whole water was again
collected at the surface at selected stations for inorganic nutrient concentrations, chl a
concentration, and phytoplankton count analyses.
Additional sampling was conducted by the University of California, Los Angeles
during (28 November) and after (30 November and 1 December) the diversion event
aboard the R/V Sea World. Continuous measurements of temperature, salinity, and chl a
were collected along the ship's track by pumping water from the surface pump (~1.5 m
depth) through an SBE 45 CTD (Seabird) and a WETStar chl a fluorometer (WET Labs,
Inc.). Stations were chosen adaptively based on the surface measurements (Figure 3-1).
At selected stations, vertical profiles of temperature and salinity were collected using a
Seabird SBE 9/11+ CTD. Water samples were collected using 5-L Niskin bottles or
directly from the surface pump. Whole water samples for inorganic nutrients
(nitrate+nitrite, phosphate, silicate), chl a concentration, and phytoplankton counts were
stored on ice in the dark during transport back to the laboratory.
Data Processing
All surface CTD data, and vertical profiles collected aboard the R/V Sea World,
were processed using SBE software versions 7.14c and 7.14e. Vertical profiles collected
aboard the M/V Marine Surveyor were processed using IGODS (Ocean Software, 2009).
Raw CDOM fluorescence was converted to quinine sulfate dihydrate (QSD) equivalent
concentration by subtracting the background fluorescence of water from the raw
fluorescence of the sample and multiplying by a scale factor (units of ppb/count) supplied
by the manufacturer. See Belzile et al. (2006) for a detailed description of this
instrument. The beam attenuation coefficient at 660 nm [c(660)] was calculated from

73
transmissometer measurements. Because absorption due to both phytoplankton and
CDOM is minimal at this wavelength, c(660) generally reflects attenuation due to
suspended sediments and other inorganic particles and is used as a measurement of
turbidity.
Measurements of optical properties were collected from both whole water and
filtered water. The filtered water represents the dissolved fraction of the samples. To
calculate spectra of the particulate fraction [c
p
( λ) and a
p
( λ)], the periodic measurements
of filtered water were subtracted from adjacent whole water measurements. The
scattering coefficient [b( λ)] can be calculated by subtracting a( λ) from c( λ) (Mobley,
1994; Roesler and Boss, 2008). Scatter due to dissolved components was assumed to be
negligible. Therefore, measurements of b( λ) calculated from whole water were
considered representative of particulate scatter [b
p
( λ)] once the effects of clean water
were removed. Raw data files were processed in Matlab using ACS Graphic User
Interface (GUI) v. 1.4.1 developed by WET Labs, Inc. The ACS GUI subtracts clean
water spectra from the measured spectra and performs temperature and salinity
corrections according to Sullivan et al. (2006). The absorption spectra were corrected for
scatter using the proportional subtraction method, also called the spectrally-dependent
method, in the ACS GUI. This method is described in detail by Roesler and Boss (2008).
During the post-diversion cruises, surface temperature and salinity collected along the
track were used to correct ac-s data. During the pre-diversion and diversion cruises,
temperature and salinity values from the 1 m depths of the vertical profiles collected at
each station were used for ac-s corrections.

74
The concentration of chl a was estimated from a
p
( ) spectra using the relationship
from Boss et al. (2007):
chl a (mg m
-3
) = a
p
(676) – [(39/65) · a
p
(650) + (26/65) · a
p
(715)] (1)
0.014
In this equation, chl a concentration is estimated using the line-height of the red chl a
absorption peak. The value 0.014 is the chlorophyll-specific absorption [a*(676)] taken
from literature values for oceanic phytoplankton assemblages (Boss et al., 2007).
Spectral slopes of the attenuation spectra ( ) and the slopes of the particle size
distributions (PSDs; ) were calculated following the relationships from Boss et al.
(2001)
c
p
( λ) = A λ
- 
(2)
  =  + 3 – 0.5e
-6 
(3) 
where A is the amplitude. A nonlinear least squares fitting approach (the fminsearch
routine in Matlab), which better approximates the spectral slopes, was used to estimate 
(Twardowski et al., 2004).
Laboratory Analyses
Samples collected from Niskin bottles were analyzed at the City of Los Angeles's
Environmental Monitoring Division laboratory. Nitrate concentrations were measured on
a Ion Chromatography Dionex DX500 (IC) using EPA method 300.0 (Pfaff, 1993).
Surface bucket samples and all samples collected aboard the R/V Sea World for inorganic
nutrients (nitrate, nitrite, phosphate, silicate) were analyzed with a Lachat QuikChem
8000 autoanalyzer at the Marine Science Institute Analytical Laboratory (University of

75
California, Santa Barbara). Whole water was filtered through a Whatman GF/F (0.7 m)
filter, and the filtrate was frozen at -20°C until analysis.
Phytoplankton density and community composition were determined through
measurements of chl a concentration and direct cell counts. For chl a concentration, each
sample was filtered through a Whatman GF/F (0.7 µm) filter within 12 h of collection
and was stored at -80°C until analysis. Filters were placed in 90% buffered acetone (v/v)
and stored at -20ºC for 24 h to allow pigment extraction to take place. Chl a
concentrations from samples collected aboard the M/V Marine Surveyor were measured
using standard fluorometric methods (APHA, 1998) on a Turner TD-700 fluorometer.
Chl a concentrations from samples collected aboard the R/V Sea World were determined
using spectrophotometric methods (Strickland and Parsons, 1968) on a Thermo-
Spectronic Genesys-20 spectrophotometer. Phytoplankton community composition was
determined using two methods. Subsamples (25-100 mL) of Lugol's-preserved samples
were settled for at least 48 h, and cells in two crossed diameters were enumerated with a
Leica DM Irbe inverted microscope using the Utermöhl method (Lund et al., 1958; Hasle,
1978). Cells were identified to species when possible. Whole-water samples collected
on board the R/V Sea World were filtered onto 8 µm polycarbonate filters, resuspended in
10 mL seawater and 1.1 mL buffered formalin, and enumerated to the genus level using a
Sedgwick-Rafter chamber. Fragile phytoplankton species do not preserve well in
formalin (Anderson et al., 2001; Turner et al., 2009). Therefore, formalin-preserved
samples were not included in analyses of the total phytoplankton community or of fragile
species, such as naked dinoflagellates. Because zero values cannot be plotted on a log
scale, the constant LLD = 0.01 was added to each datum for graphing purposes. This

76
constant represents the lower limit of detection (LLD), or the smallest non-zero value
possible given the sampling and counting protocols and reporting units (no. cells mL
-1
)
used. Raw counts, without addition of the constant, were used for all statistical analyses.
Picoplankton cell abundances were determined using a FACSCalibur (Becton Dickinson,
San Jose, CA) flow cytometer equipped with a 488 nm 15 mW laser and standard filter
setup by Dr. Lisa Campbell at Texas A&M. See Campbell (2001) for a detailed
description of this method.
Statistical Analyses
Stations were grouped into "before" (all stations sampled before the diversion
event), "inside" = plume (stations inside the wastewater plume, including stations within
the boils created by water upwelling from the outfall), and "outside" = non-plume
(stations outside the wastewater plume) classifications. To determine which stations
were inside and outside the plume, property-property plots of temperature, salinity, and
CDOM (when available) measurements from the vertical profiles (Figure 3-2) and
surface concentrations of CDOM and salinity along the ship's track (Figures 3-4 to 3-7)
were examined. Stations in which at least a portion of depths sampled showed decreased
salinity and/or elevated CDOM relative to all measurements collected during a particular
cruise were considered to be inside the plume. The "inside" stations all fell within the
low-salinity, high-CDOM patches observed in measurements along the ship's track
(shaded areas in Figures 3-4 to 3-7).
Differences in phytoplankton community structure were analyzed using non-
metric multi-dimensional scaling (NMDS). Because not all taxa could be identified to
species and only a subset of samples was analyzed to the species level, statistical analyses

33.32 33.36 33.4 33.44 33.48
15.0
15.2
15.4
15.6
15.8
16.0
16.2
16.4
16.6
16.8

1
1.5
2
2.5
3
3.5
4
CDOM concentration (ppb QSD)
33.32 33.36 33.4 33.44 33.48
15.2
15.4
15.6
15.8
16.0
16.2

Temperature (°C)
Salinity (psu)
Station 1
Station 8
Station 7
Station 9
Station 13
Station 1
4 December 2006 7 December 2006

Figure 3-2. Property-property plots of temperature, salinity, and CDOM concentration
from vertical profiles. Stations considered inside the wastewater plume are circled.

were performed on count data aggregated to the genus level. First, relative abundances
were calculated by normalizing raw counts by the total number of cells counted, and the
normalized values were transformed using an arcsine-square root transformation. A
species distance matrix was then generated using the Bray-Curtis similarity coefficient
(Bray and Curtis, 1957). This coefficient is commonly used in ecology because of its
robustness and reliability, and several desirable properties including invariance to
changes in scale and standardization to ensure that extreme values (100 and 0,
respectively) correspond to a complete match of the measurements and a complete lack
of species in common (Faith et al., 1987; Clarke, 1993). NMDS was used to construct a
map of the samples in low-dimensional space that attempts to maintain the relative
distances between points as close as possible to the actual rank order of similarities
between samples. Thus, samples with similar community structures are plotted as points
that are close together in ordination space. In contrast to other multi-dimensional
methods, NMDS does not assume that similarity has a linear relationship with ecological
77

78
distance but only assumes monotonicity (the rank order of distances between samples are
used). A stress factor was calculated for the NMDS ordination which indicates how well
the plotted configuration of sample distances agrees with the original rank orders
calculated from the similarity matrix. All NMDS calculations and subsequent analyses
(see next paragraph) were performed using PRIMER v6.0 (Primer-E Ltd., Plymouth).
To determine whether the phytoplankton community structure differed among
groups (before, inside, and outside), the ANOSIM test in PRIMER was used. ANOSIM
is a permutation procedure applied to the rank similarities of the samples (Clarke and
Warwick, 2001). An R value (ranging from 0 if similarities within and between groups
are the same, to 1 if all replicates within groups are more similar to each other than to any
replicates from other groups) was calculated indicating the similarity of sites within a
group compared to similarities of sites between groups. A corresponding p-value was
determined by referring the observed value of R to its permutation distribution. Note that
R is not heavily affected by the number of replicates within each group, but its statistical
significance (p-value) is dominated by the group sizes. To separate the effect of the
plume from general changes in the community over time, a 2-way crossed design was
employed using station location (inside or outside the plume) and sample date as factors.
In this case, a separate R statistic is calculated for locations within each date (and dates
within each location), and the reported R value represents the average of these values.
ANOSIM also conducts pairwise comparisons and calculates R and p-values for tests
between each pair of groups. The SIMPER routine in PRIMER was used to identify
which phytoplankton taxa contribute most to differences among groups. A 2-way
crossed design, with location and sample date as factors, was again employed. Briefly,

79
the average dissimilarity between all pairs of inter-group samples was calculated.
Separate contributions from each taxa (in this case genera) to the average dissimilarity
were then determined. These methods are described in detail in Clarke (1993) and Clarke
and Warwick (2001).
RESULTS
Meteorological Conditions and Surface Currents
A small rain event (~1 cm) occurred the morning of 27 November, prior to the
beginning of the diversion event. Immediately following the storm, winds increased to
more than 20 mph from the west to northwest, and were from the north to northeast late
on 28 November and early on 29 November. From 29 November onward, the measured
winds at LAX were less than 10 mph. On the afternoon of 29 November and overnight
into the 30 November, the winds were variable initially with westerly to southerly flow.
Over the next few days, flow patterns were variable but with continued onshore flow at
times. The surface current field appeared to respond quickly to the wind field.
Immediately after the rain event, strong offshore flow was observed in the surface
currents. The plume likely advected directly offshore on 28 November through the
afternoon of 29 November. Late on 29 November, the flow became onshore and upcoast.
Following this, the plume was predicted to advect upcoast and remain nearshore.
Characteristics and Movements of the Plume
Similar to rivers and other forms of runoff, the wastewater plume was
characterized by low salinity and high CDOM concentration. Vertical profiles collected
during the diversion event over the 1-mile outfall show that this low-salinity, high-
CDOM layer initially extended to 2-5 m depth (Figure 3-3). Although c(660) was

sometimes elevated in surface waters, values were not consistently high within the plume
boil (Figure 3-3). Ambient salinity ranged from 33.37-33.44 within SMB during the
diversion; therefore, surface waters consisted at most of about 5.8% wastewater (lowest
salinity measured = 31.5 psu) indicating a high level of mixing occurred as the newly-
discharged wastewater plume rose from the outfall to the surface. Nevertheless,
consistent differences in salinity and CDOM concentration were observed and could be
used to track the location of the plume.
Salinity (psu)
31 32 33 34
Depth (m)
CDOM (ppb of QSD)
0
2
4
6
8
10
12
14
16
02 4 6 8 10 12 14 16 18
salinity
CDOM
31.5 32.5 33.5 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2
c(660) (m
-1
)
AB

Figure 3-3. Vertical profiles of salinity and CDOM (A) and beam attenuation (B)
collected over the 1-mile outfall on 28 and 29 November during the diversion event.
Note that CDOM concentration was not measured during the 28 November cruise.

During the diversion, low-salinity, high-CDOM waters indicative of the
wastewater plume were observed in the vicinity of the 1-mile outfall. Sampling on the
first day of the diversion (28 November) was limited to the area immediately around the
outfall, and the spatial extent of the plume on that day is not known. On 29 November,
the wastewater plume appeared to be confined near the 1-mile outfall. Wastewater was
80

−118.5 −118.46 −118.42
33.88
33.90
33.92
33.94
33.96
1
2
3
4
5
6
7
20
9
10
19
12
13
21
15
16
Salinity (psu)
CDOM (ppb QSD)
12:15 8:15 10:45 11:15 11:45 14:45 15:15
32.2
32.4
32.6
32.8
33.0
5
7
9
11
13
15
salinity
CDOM
1 2 3 4 5 6 7 8a 9 10 12 14 16 18 19 18
Chlorophyll a (mg m
-3
)
Chlorophyll a (mg m
-3
)
0
2
4
6
8
10
0
2
4
6
12
14
During the Diversion (29 November)
18
extracted concentration
estimated concentration
A
B
C
3.0
3.5
4.0
4.5
5.0
PSD slope
D
22
23
24
Marina del Rey
33.2
33.4
1
3
38b8c 22
20 21 23
24
10
8
12
20
8:45 12:45 13:15 15:45
15 13 11 17

Figure 3-4. Salinity and CDOM concentration (B), chlorophyll a concentration
(extracted and estimated from ac-s data; C) and the slope of the particle size distribution
(estimated from ac-s data; D) collected along the ship's track (A) during the diversion
event. Salinity and CDOM were not collected continuously along the ship's track;
measurements from the top meter of the vertical profiles are plotted. In B-D, arrows
indicate sampling stations, and shaded areas indicate areas inside the plume. Stations
located within the plume are also shaded in A.
81

82
not observed north of the outfall and was observed at only a few stations to the south
(stations 8-14; Figure 3-4B). The plume was also observed offshore of the outfall
(stations 9 and 10). These observations are consistent the strong offshore flow in surface
currents throughout most of that day. The full offshore extent of the plume could not be
determined as sampling only extended to about 2.4 km offshore (~0.8 km offshore of the
1-mile outfall).
Remnants of the plume were detected in several locations within SMB for up to
seven days after the diversion event. Immediately after the diversion (30 November),
low salinity water was observed in the nearshore area of the northern portion of SMB
(stations 4, 7, and 9; Figure 3-5C). Low salinity water was also apparent within and just
outside the Marina del Rey harbor (before station 1 and after station 11). CDOM
measurements were not collected during this cruise. On this day, one continuous plume
remnant appeared to extend approximately 8.5 km alongshore from just south of the
harbor north to the Santa Monica Pier. Four days after the diversion event (4 December),
two low-salinity, high-CDOM plume remnants were evident. One remnant was in the
same nearshore area in northern SMB (station 1) and at least one was to the south in the
vicinity of the 1-mile outfall (stations 7-9, 13, and 14; Figure 3-6B). The southern
remnant may have been two separate patches, but was likely one continuous patch that
extended about 9.2 km alongshore and 6.5 km offshore. By 7 December, seven days
after the diversion event, much of the plume had mixed with ambient waters and/or
advected out of SMB. A remnant of the plume was still evident in the northern nearshore
portion of SMB and extended about 5.5 km alongshore from Marina del Rey to station 1
(Figure 3-7B). During each post-diversion cruise, a localized low-salinity, high-CDOM

2-8 Hours Post Diversion (30 November)
−118.60 −118.55 −118.50 −118.45 −118.40
33.86
33.90
33.94
33.98
34.02
1
2
3
4
5
6
7
8
9
10
11
Salinity (psu)
40 0 10 20 30 50 60
32.7
32.9
33.1
33.3
33.5
1 2 3 4 5 6 7 8 9 10 11
Chl fluorescence (relative units)
Chlorophyll a (mg m
-3
)
0
200
400
600
800
1200
0
4
12
20
fluorescence
extracted concentration
A B
C
D
+
+
+
+
6
−118.60 −118.55 −118.50 −118.45 −118.40
1000
8
16
1400
70 80 90
Distance along track (km)
1-mile
outfall

Figure 3-5. Salinity (C), and chlorophyll a fluorescence and concentration (D) collected
along the ship's track immediately after the diversion event. The ship track is plotted in
two parts. The portions of C and D to the left of the vertical lines correspond to the dark
portion of the track shown in A. The portions to the right of the vertical lines correspond
to the dark portion of the track shown in B. The "+" symbols in A and B indicate the
locations of the Santa Monica (north) and Hermosa Beach (south) piers, and the "x"
symbol indicates the location of the 1-mile outfall. In C and D, arrows indicate sampling
stations, and shaded areas and station markers indicate areas inside the plume.

83

−118.60 −118.55 −118.50 −118.45 −118.40
33.84
33.88
33.92
33.96
34.00
1
2
3
4
5
6
7
8
9
10 11
12 13
14
1-mile outfall
CTD
start
stop
acs start
Salinity (psu)
CDOM fluorescence (relative units)
40 0 10 20 30 50 60
33.2
33.3
33.4
33.5
0.10
0.14
0.18
0.22
0.26
0.30
salinity
CDOM
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Chl fluorescence (relative units)
Chlorophyll a (mg m
-3
)
0
0.2
0.4
0.6
0.8
1.0
0
10
20
25
30
60
Four Days Post Diversion (4 December)
fluorescence
extracted concentration
estimated concentration
A
B
C
3.0
3.5
4.0
4.5
5.0
PSD slope
D
+
+
70
Distance along track (km)
5 15 25 35 45 55
15
5
leaving
marina

Figure 3-6. Salinity and CDOM concentration (B), chlorophyll a fluorescence and
concentration (extracted and estimated from ac-s data; C), and the slope of the particle
size distribution (estimated from ac-s data; D) collected along the ship's track (A) four
days after the diversion event. The "+" symbols in A indicate the locations of the Santa
Monica (north) and Hermosa Beach (south) piers, and the "x" symbol indicates the
location of the 1-mile outfall. In B-D, arrows indicate sampling stations, and shaded
areas and station markers indicate areas inside the plume.
84

+
+
−118.60 −118.55 −118.50 −118.45 −118.40
33.84
33.88
33.92
33.96
34.00
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
1-mile outfall
CTD
start
acs stop
acs start
Salinity (psu)
CDOM fluorescence (relative units)
40 0 10 20 30 50 60
33.1
33.2
33.3
33.4
33.5
0.10
0.14
0.18
0.22
0.26
0.30
salinity
CDOM
1 2 3 4 5 6 7 8 9 10 11 121314 15 16
end
GPS
Chl fluorescence (relative units)
Chlorophyll a (mg m
-3
)
0
0.2
0.4
0.6
0.8
1.0
0
4
8
12
16
20
Seven Days Post Diversion (7 December)
17
fluorescence
extracted concentration
estimated concentration
A
B
C
3.0
3.5
4.0
4.5
5.0
PSD slope
D
Distance along track (km)
5 15 25 35 45 55 65
100
110
17
leaving
marina

Figure 3-7. Salinity and CDOM concentration (B), chlorophyll a fluorescence and
concentration (extracted and estimated from ac-s data; C), and the slope of the particle
size distribution (estimated from ac-s data; D) collected along the ship's track (A) seven
days after the diversion event. The "+" symbols in A indicate the locations of the Santa
Monica (north) and Hermosa Beach (south) piers, and the "x" symbol indicates the
location of the 1-mile outfall. In B-D, arrows indicate sampling stations, and shaded
areas and station markers indicate areas inside the plume. Note that station 17 was
sampled after data collection from the surface sensors was stopped.
85

86
(when measured) patch was detected in the immediate vicinity of the 1-mile outfall
(Figure 3-5C between stations 1 and 2; Figure 3-6B after station 14, and Figure 3-7B
after station 16). It is possible that the 1-mile outfall was still releasing a small amount of
wastewater throughout the seven days after the majority of flow was diverted back to the
5-mile outfall.
Salinity obtained at the Santa Monica Pier showed two major decreases (Figure 3-
8). The first occurred late on 27 November consistent with the small rain event that
occurred earlier in the day. Surface salinities returned to their pre-rain level on 28-29
November, and then dropped precipitously on the afternoon of 30 November. The 30
November decrease coincided with the period when surface currents became shoreward
and northward and with ship-based observations of plume waters in that area. Thus, it
appears that the second low salinity feature observed at the pier was the wastewater
plume. Salinity gradually returned to a stable, ambient level over the next five days. This
suggests that advection in SMB was relatively slow during the period from 30 November
to 5 December. Differences between the salinities measured during ship-based sampling
and the salinities shown for the Santa Monica Pier are probably due to calibration
differences between the sensors. Because of the long-term deployment of pier-based
sensors, some drift in the conductivity sensors is expected. Fouling of conductivity cells
tends to cause the measured salinity to be lower than actual salinity.
Nutrients
Prior to the diversion, nitrate, phosphate, and silicate ranged from 0.32-1.3, 0.17-
0.32, and 1.7-4.2 mol L
-1
, respectively. These concentrations are slightly higher than
typical surface concentrations in SMB (Shipe et al., 2008) probably reflecting runoff

from the small storm event. Concentrations of all three nutrients increased dramatically
within the plume boil reaching maximum values of 4.1, 3.2, and 39.8 mol L
-1
,
respectively. The highest ammonium concentrations (8.4 mol L
-1
) occurred
immediately around the 1-mile outfall and slightly offshore with a small concentration of
ammonium (~1 μmol L
-1
) detected southward from the discharge. After the diversion
event, concentrations of nitrate, phosphate, and silicate within plume waters quickly
returned to pre-diversion values and were similar to concentrations measured outside the
plume.
Salinity (psu)
33.2
25Nov 29Nov 27Nov 1Dec
33.1
33.0
32.9
32.8
3Dec 5Dec 7Dec 9Dec
stormwater
runoff
wastewater
plume

Figure 3-8. Time series of salinity measurements from the Santa Monica Pier showing
freshwater runoff from the storm event on 27 November and evidence of the wastewater
plume after 30 November.

Phytoplankton Density and Community Structure
Just before the diversion event, chl a concentrations at the surface were variable
and somewhat elevated with concentrations ranging from 1-10 mg m
-3
(geometric mean
3.5 mg m
-3
; Figure 3-9A). The concentration of phytoplankton cells mirrored chl a and
ranged from 17-248 cells mL
-1
(geometric mean 54 cells mL
-1
; Figure 3-9B). During the
diversion event, chl a concentration and the number of phytoplankton cells were quite
low within the newly-surfaced boil water (geometric mean 0.6 mg m
-3
and 18 cells mL
-1
,
87

88
respectively, Figure 3-9). A drop in the concentration of chl a estimated from a
p
( )
spectra was also apparent when the ship was over the 1-mile outfall (stations 8a and 8c,
Figure 3-4C). The presence of plume boils at these stations was noted during field
sampling. After the event, plume waters generally contained higher concentrations of chl
a. On 30 November, just after the wastewater was diverted back to the 5-mile outfall, chl
a fluorescence was elevated within the plume remnant that extended from Marina del
Rey to the Santa Monica Pier (Figure 3-5D), and extracted chl a concentrations within
the plume ranged from 2-15 mg m
-3
(geometric mean 7.7 mg m
-3
; Figures 3-5D and 3-
9A). Four days later on 4 December, chl a fluorescence and estimated concentration
were again elevated within the plume remnants. Extracted chl a concentrations in plume
waters ranged from 3-17 mg m
-3
and reached 67 mg m
-3
at station 8 (Figure 3-6C). By 7
December, chl a fluorescence and estimated concentration were low throughout much of
SMB (Figure 3-7C). Chl a concentration was again elevated within the plume remnant
that stretched from Marina del Rey to station 1 near the Santa Monica Pier, and extracted
chl a reached over 100 mg m
-3
at the mouth of the harbor (station 17; Figure 3-7C).
Unfortunately, data was no longer being collected along the ship's track at that station,
but this high-chl a patch was likely the same one that was apparent within the plume
remnant just outside the harbor at the start of the cruise. Overall, average chl a
concentration increased over 50-fold from a mean of 0.6 mg m
-3
in the initial plume boil
to 34 mg m
-3
seven days after the diversion (Figure 3-9A). Total phytoplankton
concentration showed a similar pattern increasing from an average of 18 cells mL
-1

within the boil to 605 cells mL
-1
after seven days (Figure 3-9B).

Chlorophyll a (mg m
-3
)
0.1
1
10
100
inside
outside 1000
100
10
1
Phytoplankton (no. cells mL
-1
+ LLD)
Before During After
before day 1 boil day 2 1 day 4
days
7
days
2-8
hours
A
B

Figure 3-9. Geometric means of extracted chlorophyll a concentration (A) and total
density of phytoplankton (from cell counts; B) before, during, and after the diversion
event. Only counts from samples preserved in Lugol's were included in B. LLD
indicates the lower limit of detection (0.01 cells mL
-1
). The shaded areas represent
samples collected during the diversion. Error bars indicate 1 standard deviation.

Based on results from multivariate analyses, a shift in the community structure
occurred within the wastewater plume in addition to a general increase in phytoplankton
density. The 2-dimensional NMDS plot generated using all samples shows that stations
inside the plume (and the plume boil) group together but overlap with other nearshore
stations (Figure 3-10A). Results from the 2-way ANOSIM confirm that the community
structure differed inside and outside the plume (Table 3-1). Differences in the
phytoplankton community were also apparent over time, with the largest difference
occurring between the 29 November and 7 December sampling dates. Out of the 51
89

Akashiwo sanguinea
before
outside
inside
boil
2D Stress: 0.12
1000
100
10
1
0.1
0.01
Abundance (no. cells mL
-1
+ LLD)
Before During After
inside
outside
Cochlodinium sp.
10
40
70
100
1000
100
10
1
0.1
0.01
Cylindrotheca sp.
Lingulodinium polyedrum
Dictyocha spp.
1000
100
10
1
0.1
0.01
1000
100
10
1
0.1
0.01
before boil day
1
day
2
2-8
hours
1
day
4
days
7
days
A
B
C
D
E
F
G
H
I
J
K
percent
abundance
1000
100
1
0.1
0.01
10
inside
outside

Figure 3-10. NMDS of all stations (A), NMDS overlain with bubbles representing the relative abundance
of individual species (B-F), and geometric means of absolute abundances (G-K) of individual species.
Shaded bubbles in B-F indicate stations inside the plume. LLD indicates the lower limit of detection (0.01
cells mL
-1
). Error bars in G-K indicate 1 standard deviation. Only species that most contributed to the
difference between communities inside and outside the plume are shown.
90

Table 3-1. Results from a 2-way crossed ANOSIM with location (before, outside, and
inside) and sample date (27 November, 29 November, 4 December, and 7 December) as
factors. Results are also presented from pairwise comparisons between factor levels
(note that the "before" and "27 November" factor levels could not be included in pairwise
comparisons).

Factors R p

location 0.357 0.03
sample date 0.455 0.01

Pairwise comparisons
outside and inside 0.346 0.02
29 Nov. and 4 Dec. 0.416 0.01
29 Nov. and 7 Dec. 0.520 0.003
4 Dec. and 7 Dec. 0.391 0.05

phytoplankton genera identified, 19 genera accounted for over 80% of the average
similarity between communities inside and outside the plume (Table 3-2; individual
species are listed when only one species from a particular genus was observed). Nearly
50% of the similarity was accounted for by just five genera. The dinoflagellates
Cochlodinium sp. and Akashiwo sanguinea tended to make up a greater percent of the
total number of cells inside plume waters while percent abundances of the dinoflagellate
Lingulodinium polyedrum, the diatom Cylindrotheca spp., and the silicoflagellate
Dictyocha spp. were higher outside the plume (Table 3-2, Figure 3-10B-F). Raw
abundances from all samples are given in Appendix B.
The average abundance of most phytoplankton taxa increased over time within
the wastewater plume. Cochlodinium sp. and A. sanguinea, however, increased by nearly
two orders of magnitude (Figures 3-10G and 3-10H). In contrast, L. polyedrum,
Cylindrotheca sp., and Dictyocha spp. increased by about 1 order of magnitude, and their
91

92
Table 3-2. Results from a 2-way crossed SIMPER with location (before, outside, and
inside) and sample date (27 November, 29 November, 4 December, and 7 December) as
factors. The average percent contribution from each taxon to the total similarity between
communities inside and outside the plume is shown.

Average percent abundance Percent Cumulative
Taxon outside inside contribution percent

Cochlodinium sp. 43.9 61.4 15.24 15.24
Akashiwo sanguinea 9.3 20.5 12.07 27.31
Cylindrotheca spp. 14.4 3.7 9.43 36.75
Dictyocha spp. 6.3 0.37 6.52 43.27
Lingulodinium polyedrum 5.7 3.8 4.97 48.24
Gyrodinium sp. 3.7 3.8 3.29 51.53
Navicula spp. 1.5 0.12 3.20 54.73
Protoperidinium spp. 0.50 0.33 2.96 57.69
Leptocylindrus spp. 1.4 0.77 2.87 60.57
Chaetoceros spp. 1.9 0.50 2.61 63.17
small dinoflagellates 1.7 0.33 2.60 65.78
Pseudo-nitzschia spp. 1.1 0.22 2.25 68.02
Guinardia spp. 0.70 0.57 2.01 70.03
Ceratium spp. 1.3 0.68 1.98 72.01
Eutreptiella spp. 0.48 0.13 1.94 73.95
Thalassiosira spp. 0.80 0.35 1.86 75.82
Thalassionema spp. 0.35 0.03 1.76 77.58
unknown pennate diatoms 0.31 0.22 1.58 79.16
Oxytoxum spp. 0.16 0.07 1.58 80.75

abundances tended to be similar inside and outside the plume (Figure 3-10I to 3-10K).
Patchy blooms of Cochlodinium sp. and A. sanguinea, singly and in combination, were
observed 4 days (Figure 3-6, stations 7-9 and 14) and 7 days (Figure 3-7, stations 1 and
17) after the diversion event. At station 8 on 4 December, red streaks were observed
moving past the boat while on station. The surface sample (extracted chl a and
phytoplankton count analysis) was collected within one of these dense streaks which may
explain the discrepancy between the extracted chl a concentration and the concentration

93
estimated using a
p
( ) spectra. The phytoplankton population within the red streak
consisted almost entirely of Cochlodinium sp. (98% of the total number of cells), which
reached over 2,200 cells mL
-1
. Nearby stations within the plume were also dominated by
Cochlodinium sp. (73% and 86% at stations 7 and 9, respectively), whose abundance
ranged from 180-260 cells mL
-1
. Further south, at station 14, A. sanguinea made up 55%
of the phytoplankton community (108 cells mL
-1
), while only 15% of the community (29
cells mL
-1
) consisted of Cochlodinium sp. Both dinoflagellates were abundant within
plume waters on 7 December. Cochlodinium sp. was dominant at station 1 making up
68% of the total number of cells. Just inside the Marina del Rey harbor, Cochlodinium
sp. and A. sanguinea reached over 800 cells mL
-1
each and made up 47% and 50% of the
phytoplankton, respectively.
Prior to the diversion event, Synechococcus spp. ranged from 37,800 to 51,200
cells mL
-1
at the stations sampled near the 1-mile outfall. Pico-eukaryotes were not as
dense, ranging from 11,000 to 29,900 cells mL
-1
. Similar to the larger phytoplankton
taxa, the abundance of both Synechococcus spp. and pico-eukaryotes increased in plume
waters over time (Figure 3-11). Pico-eukaryotes increased from an average of 9,300 cells
mL
-1
in the plume boil to 24,000 cells mL
-1
seven days after the diversion and tended to
be more abundant in plume waters. Although Synechococcus spp. were, on average,
more abundant outside the plume, they showed a 3.5-fold increase in abundance between
the boil (2,800 cells mL
-1
) and seven days post-diversion (10,200 cells mL
-1
).

1
10
100
inside
outside
100
10
1
Abundance (no. cells + mL
-1
+ LLD) x 10
3
Before During After
before day 1 boil day 2 1 day 4
days
7
days
2-8
hours
A
B
Synechococcus spp.
pico-eukaryotes

Figure 3-11. Geometric means of the abundances of Synechococcus spp. (A) and pico-
eukaryotes (B) inside and outside the plume before, during, and after the diversion event.
LLD indicates the lower limit of detection (0.01 cells mL
-1
). Error bars indicate 1
standard deviation.

Optical Properties of the Wastewater Plume
Within all of the high-chl a plume remnants, the slope of the particle size
distribution decreased indicating an increase in the average particle size (Figures 3-6D
and 3-7D). Cells of both Cochlodinium sp. and A. sanguinea are relatively large
averaging about 40 m and 75 m in length, respectively. The shift toward larger
particles likely reflects the shift in the phytoplankton community toward dominance by
one or both of these species. Interestingly, an increase in the slope of the particle size
distribution, and hence a decrease in average particle size, was observed within the
94

95
newly-surfaced plume waters (Figure 3-4D). This likely reflects the absence of high
abundances of large phytoplankton. It may also reflect the presence of small particles in
the effluent itself or that were entrained into the plume as it exited the outfall and
interacted with bottom sediments.
To explore the optical properties of Cochlodinium sp. and A. sanguinea,
particulate absorption and the ratios of scatter to absorption [b( ):a( )] were calculated at
several areas along the ship's track on 4 December. The b( ):a( ) ratio is proportional to
reflectance [R( )], the basic optical parameter measured by ocean color satellite sensors
(Kirk, 1994; Mobley, 1994; Davies-Colley et al., 2003). To better simulate R( ) spectra
that can be measured using ocean color, total absorption [a
tot
( ) = a
p
( ) + a
cdom
( ) +
a
water
( )] and total scatter [b
tot
( ) = b
p
( ) + b
water
( )] were used to calculate the b( ):a( )
ratio. Spectra from three high-chl a areas inside the plume remnant in the middle portion
of the track (average chl a concentration estimated from a
p
( ), 7.6, 6.9, and 4.9 mg m
-3
,
respectively), three high-chl a areas within the plume at the end of the track (average chl
a concentration 19.7, 15.9, and 22.4 mg m
-3
), and two low-chl a areas outside the plume
(average chl a concentration 1.2 and 0.63 mg m
-3
) were determined (Figure 3-6). At
about 50 km, flow from the surface pump was lost. The spectra collected after flow was
resumed (the first peak within the high-chl a patch at the end of the ship's track) appeared
to be affected by bubbles and were therefore not used. Spectra from the high chl a area at
the beginning of the ship's track were also not included because no samples for
phytoplankton community analysis were collected within that area. The high-chl a areas
in the middle plume remnant (stations 7, 8, and 9) were dominated by Cochlodinium sp.
(73-98% of total phytoplankton counted), and the plume remnant at the end of the track

96
(after station 14) was likely dominated by A. sanguinea with Cochlodinium sp. also
present (55% and 15% of total phytoplankton, respectively, at station 14). Within the
low chl a areas (near stations 5 and 10), the diatom Cylindrotheca sp. and silicoflagellate
Dictyocha spp. dominated the phytoplankton community (22-59% and 10-34%,
respectively).
A well-defined peak in all a
p
( ) spectra was apparent at 675 nm, corresponding to
the red chl a absorption peak (Figures 3-12A and 3-12B). A second maximum, likely the
blue chl a absorption peak, was apparent at about 434 nm in spectra with the highest
average chl a. The overall magnitude of the a
p
( ) spectra increased with increasing
average chl a concentration (Figure 3-12A). The absorption of non-algal particles is high
at low wavelengths and exponentially decreases toward longer wavelengths (Kishino et
al., 1985; Roesler and Boss, 2008). As the contribution by phytoplankton to a
p
( )
decreased (i.e. as average chl a concentration decreased), the shape of the spectra became
more exponential like that of non-algal particulate absorption (Figures 3-12A and 3-12B).
In general, the b( ):a( ) spectra increased with decreasing chl a concentration (Figure 3-
12C). In the high-chl a spectra, a strong minimum was present at 675 nm representing
the red chl a absorption peak. A narrow, high-magnitude peak was apparent at 571-575
nm. The minimum due to chl a absorption was absent in the low-chl a spectra (Figure 3-
12C). A broad peak was present in these spectra that was greatest in magnitude at about
567 nm.

a
p
(λ) (m
-1
)
0.1
0.5
0.6
0.8
5
2
1
0
b
tot
(λ):a
tot
(λ)
22.4
400 500 450 550 600 650 700
A
C
0.7
0.4
0.3
0.2
0
3
4
19.7
15.9
7.6
6.9
4.9
1.2
0.63
1.2
4.9 6.9
7.6
0.63
15.9
19.7
22.4
Wavelength (nm)
B
a
p
(λ)/a
p
(675)
0
1
2
3
4
5
6
750
6

Figure 3-12. Particulate absorption spectra (A), normalized at 675 nm (B), and spectra
of the ratio of total scatter to total absorption (C) for six locations inside high-chl a
portions of the wastewater plume (solid lines) and two low-chl a areas outside the plume
(dot-dashed lines) on 4 December 2006. Numbers indicate chl a concentrations (mg m
-3
)
estimated from particulate absorption spectra.
97

98
DISCUSSION
Tracking a Wastewater Plume in the Coastal Ocean
Natural tracers, such as salinity, CDOM, and turbidity, can be used to detect and
track wastewater and other forms of runoff in the coastal ocean. Both salinity and
CDOM concentration proved to be useful for tracking the wastewater plume, and distinct
low-salinity, high-CDOM plume remnants were apparent for at least seven days after the
discharge event. Salinity, CDOM, and turbidity have also been used in past studies as
tracers of river runoff (Siegel et al., 2002; Coble et al., 2004), stormwater (Nezlin et al.,
2008; Reifel et al., 2009), and wastewater (Petrenko et al., 1997; Jones et al., 2002).
Surfacing plumes, however, are often difficult to detect using these tracers due to high
dilution of the source water and low signal-to-noise ratios when large gradients in the
background values of these parameters were present. Differences in salinity between
wastewater plumes and surrounding surface waters can reach 0.4 psu but decrease to less
than 0.1 psu away from the source (Petrenko et al., 1997; Ramos et al., 2007). Larger
salinity differences were observed in this study between newly-surfaced wastewater
(minimum salinity of 31.5 psu) and ambient water (~33.4 psu). Older plume waters were
about 0.1-0.2 psu less than ambient waters similar to past plume studies.
Although elevated turbidity has been observed and used to detect wastewater
plumes in past studies (Petrenko et al., 1997; Jones et al., 2002), consistent increases in
c(660) were not observed within the surface wastewater plume in this study. These past
studies observed wastewater plumes in deep waters near their outfalls. Once the plume
reaches the surface, however, the majority of particles will likely sink from surface
waters as has been shown for river systems (Hill et al., 2000; Warrick et al., 2004). In

99
addition, the effluent itself is not expected to contain high concentrations of suspended
materials as one of the main goals of treatment is to remove such materials.
Improvements in waste treatment between past studies and our study could result in a
reduction in particulate loading over time. At the Hyperion WWTP, an average 94% of
suspended material was removed during the treatment process, and suspended solids in
effluent water averaged 22 mg L
-1
at the 5-mile outfall between January and December
2006 (Sidio, 2007). Initial dilution would greatly reduce this concentration resulting in a
surfacing wastewater plume with suspended solid concentrations at or near ambient
concentrations.
Optical Properties of Phytoplankton Blooms
During the post-diversion cruises, measurements were collected while passing
through several dinoflagellate blooms providing an opportunity to examine their optical
properties. Some studies suggest that differences in pigment composition could be used
to distinguish phytoplankton taxa (e.g. Johnsen et al., 1994; Kirkpatrick et al., 2000).
Ciotti et al. (2002), found that more than 80% of the variability in the spectral shape of
the phytoplankton absorption coefficient could be explained by the size of the dominant
organism. The effect of cell size is presumably due to factors such as pigment packaging
and the concentration and composition of accessory pigments which affect absorption
and co-vary with size. In our study, all high-chl a absorption spectra were measured in
water dominated by dinoflagellates. The major changes in both the magnitude and shape
of the a
p
( ) spectra reflected changes in chl a concentration and possibly the proportion
of detrital particles. The shapes of spectra measured from patches dominated by
Cochlodinium sp. and A. sanguinea appeared similar and were similar to absorption

100
spectra of phytoplankton in the microplankton size range and to normalized spectra
measured from dinoflagellates (Ciotti et al., 2002; Dierssen et al., 2006; Cullen, 2008).
Cells of the diatom and silicoflagellate that were dominant in low-chl a waters are
smaller, and these species differ from dinoflagellates in their suite of accessory pigments
(Jeffrey and Vesk, 1997). The majority of variability in phytoplankton absorption spectra
due to the chlorophylls and accessory pigments, however, occurs at short wavelengths
(~400-500 nm; Ciotti et al., 2002). High variability at these wavelengths was also
observed in this study (Figure 3-12B). At these wavelengths, particulate spectra are
heavily influenced by non-algal particles, such as detritus, especially when chl a
concentration is low. Thus, determination of the dominant taxonomic group through
changes in the shape of the a
p
( ) spectrum becomes difficult when absorption due to
phytoplankton is not separated from absorption due to non-algal particles.
Attempts have also been made to glean taxonomic information from reflectance
spectra. Inversion of R( ) to retrieve parameters such as b( ) and a( ) can be used to
discriminate phytoplankton from CDOM and non-algal particles, and hyperspectral
measurements may be able to identify taxonomic groups (Babin et al., 2005). The high-
chl a b( ):a( ) spectra measured in our study appear similar in shape to R( ) spectra
measured during blooms of other dinoflagellates including Lingulodinium polyedrum,
Karenia brevis, Ceratium sp., and Dinophysis sp. (Babin et al., 2005; Dierssen et al.,
2006; Schofield et al., 2006; Chang and Dickey, 2008; Cetini ć, 2009). This spectral
shape is a result of the specific absorption and scattering characteristics of dinoflagellate
cells and also of other attenuating components, primarily CDOM, which are present
during blooms (Carder and Steward, 1985; Craig et al., 2006). Further research is needed

101
to determine if this combination of high cell density and high CDOM concentration
occurs only during dinoflagellate blooms. If so, this spectral shape could be used to
identify dinoflagellate blooms when they occur in areas of high CDOM concentration
making possible the identification of dinoflagellate blooms through R( ) spectra
measured from satellite ocean color images (e.g. Cetini ć, 2009).
Impacts on Coastal Phytoplankton
Wastewater has been shown to stimulate increases in phytoplankton biomass (e.g.
chl a) and primary production in the coastal ocean presumably in response to loading of
nutrients such as phosphate, nitrate, and ammonium. Several studies have also included
more detailed analyses of the phytoplankton community response to wastewater or
sewage inputs. Some found an increase in phytoplankton standing crop or productivity
associated with sewage discharge, but no evidence of changes in community
composition. Thompson and Ho (1981) collected phytoplankton samples using nets with
62 or 94 m mesh sizes and preserved the samples in formalin. They may have missed
impacts on small and/or fragile phytoplankton species. Both Petrenko et al. (1997) and
Staehr et al. (2009) analyzed concentrations of chl a and accessory pigments to estimate
the phytoplankton community. Although most major phytoplankton groups can be
discerned using these methods, changes in abundance or dominance of species within
major groups would be missed. In contrast, Turner et al. (2009) noted that the highest
concentrations of dinoflagellates in their study site were located near a sewage outfall.
Phytoplankton in Turner et al.'s study were preserved in Lugol's solution which better
preserves cells without theca and other fragile flagellates.

102
Wastewater from the Hyperion WWTP also appeared to stimulate dinoflagellates,
specifically two species of athecate dinoflagellates: Cochlodinium sp. and Akashiwo
sanguinea. Several genera of harmful or nuisance algae have been reported in past
studies of the western Unites States including the dinoflagellates Alexandrium, Ceratium,
Lingulodinium, and Dinophysis, and the diatoms Pseudo-nitzschia and Chaetoceros
(Horner et al., 1997). Many recent studies in southern California have focused on the
recurring nuisance or harmful blooms of both domoic acid-producing Pseudo-nitzschia
spp. and the red-tide former Lingulodinium polyedrum (Kudela and Cochlan, 2000;
Schnetzer et al., 2007; Shipe et al., 2008). Blooms of species in the genus Cochlodinium,
however, appear to be increasing in coastal areas worldwide, and its apparently rapid
emergence along the California coast was recently documented (Curtiss et al., 2008;
Caron et al., 2009). Before 2004, Cochlodinium sp. was only occasionally reported in
California. It reached concentrations of at least 63 cells mL
-1
and was regularly observed
co-occurring with several other dinoflagellates including A. sanguinea during the 2004
bloom in Monterey Bay. Both species have since been observed within blooms in
Monterey Bay (Kudela et al., 2008; Ryan et al., 2008; Jessup et al., 2009). These species
are also becoming problematic in other areas of the California coast. Cochlodinium sp.
was recently listed among previously-rare dinoflagellate species now commonly
observed at Scripps Pier in San Diego (McGowan and Carter, 2008). And dense blooms
of A. sanguinea were documented in San Francisco Bay reaching 400 cells mL
-1
(Cloern
et al., 2005). Although blooms of Cochlodinium sp. (notably C. polykrikoides) and of A.
sanguinea have resulted in mortalities of fish and shellfish in other areas due both to
deoxygenation of the water column and production of toxins (Faust and Gulledge, 2002;

103
Kahru et al., 2004; Iwataki et al., 2008), no deleterious effects from blooms of these
species were documented in our study.
Concentrated patches of phytoplankton (up to 10 times the ambient surface
concentration) can occur due to accumulation of cells along water mass or buoyant plume
fronts and along the convergence zones of internal waves (Franks, 1992; Franks, 1997).
These physical accumulation processes have been proposed as mechanisms for enhanced
phytoplankton biomass without an increase in growth rates (Watanabe and Harashima,
1986; Franks, 1992). However, high growth rates have been measured during
dinoflagellate blooms indicating that high cell concentrations can be due to more than
aggregation or physical accumulation (Fauchot et al., 2005). The concentration of
ammonium in the wastewater reached a maximum of 38.1 mg L
-1
on 29 November
(Sidio, 2007). Based on a dilution of 418:1 within plume waters 4 and 7 days after the
diversion, and a chl a to cellular nitrogen ratio between 3 and 9.4 for natural
phytoplankton populations (Yentsch and Vaccaro, 1958), the ammonium present in the
source wastewater could support 10-30 mg m
-3
of chl a. Except for the dense patches of
dinoflagellates (station 8 on 4 December, and station 17 on 7 December), chl a
concentrations measured within the plume remnants were within this range (Figures 3-6
and 3-7). This indicates that the extremely dense concentrations of dinoflagellate cells
were due, at least in part, to aggregation and/or physical accumulation, but the majority
of phytoplankton biomass within plume waters could have been supported by ammonium
supplied by the initial wastewater.
Cochlodinium sp. and A. sanguinea have similar ecophysiological characteristics
(e.g. Smayda, 2002). Both belong to Smayda's Type IV to VI (frontal zone, upwelling-

104
relaxation, and coastal current entrained taxa) dinoflagellate functional groups. These
three groups consist of "mixing-drift" species that can withstand both weak vertical
mixing and increased horizontal velocities associated with frontal zones. Blooms of
these species documented in the above-mentioned studies occurred during conditions that
typically favor dinoflagellate blooms, including strong thermal stratification, low wind
stress, and locally enhanced nutrient supply (Smayda and Reynolds, 2001; Smayda,
2002). Similar conditions (stratification, enhanced nutrients, low winds) were apparent
within the wastewater plumes where blooms of Cochlodinium sp. and A. sanguinea
occurred. Both the stratified, nutrient-laden wastewater plume and, after a brief period of
high winds, calm coastal conditions with persistent onshore flow appeared to provide the
environmental conditions necessary for development of blooms of these species.
CONCLUSIONS
The planned release of wastewater from the 1-mile outfall of the Hyperion
WWTP provided a unique opportunity to follow the evolution of a simulated urban
runoff plume from the time of its release into the coastal ocean until it was no longer able
to be detected within the study area. Peak flow rates after typical storm events in large
southern California river systems (e.g. the Los Angeles or San Gabriel Rivers) can
exceed 1,000 m
3
s
-1
(Warrick et al., 2007). In Ballona Creek which discharges into SMB,
flow rates during an average storm range from <20 to >120 m
3
s
-1
, and the total volume
of stormwater discharged is on the order of 5-10 x 10
6
m
3
(Washburn et al., 2003).
Discharge from the 1-mile outfall averaged ~18.4 m
3
s
-1
with a total of approximately
3.31 x 10
6
m
3
of wastewater released during the diversion. Although the flow rate was
considerably less than peak flows of major river systems and of peak flows within the

105
smaller Ballona Creek, the total volume of discharged water was comparable to the
discharge from Ballona Creek during a small storm event.
Even though the wastewater plume quickly mixed with ambient water, plume
waters could be detected by their decreased salinity and elevated CDOM concentration
for up to seven days after the diversion event. Newly-surfaced plume waters contained
high concentrations of nutrients (nitrate, ammonium, phosphate, and silicate), and
phytoplankton density quickly increased within the plume. Plume waters favored two
dinoflagellate species (Cochlodinium sp. and Akashiwo sanguinea), which formed patchy
blooms either singly or together four to seven days after the discharge reaching densities
of >2,200 cells mL
-1
and 850 cells mL
-1
, respectively. More typical southern California
bloom species (Lingulodinium polyedrum and Pseudo-nitzschia spp.) were not affected or
even decreased in percent abundance within the plume. Although these blooms were
relatively short-lived, they may foretell the establishment of these species as regular
bloom-formers in southern California.
Because urban runoff typically occurs nearshore, its effects are a primary concern
for managers and regulators. Even though phytoplankton blooms can be relatively short-
lived and small in area, they can disproportionately affect aesthetics, local water quality,
and public perception when they occur in highly visible areas such as beaches and
marinas. In King Harbor in Redondo Beach, for example, fish kills were noted when a
bloom of L. polyedrum became established within the harbor, even after major efforts to
improve water quality (Cagle, 2006). We documented a dense bloom co-dominated by
Cochlodinium sp. and A. sanguinea that occurred nearshore and within the Marina del
Rey harbor as a result of wastewater discharge. Other forms of urban runoff, such as

106
stormwater, create plumes with similar characteristics and could also stimulate
dinoflagellate blooms. Relative nutrient ratios and the dominant forms of nutrients
(nitrate vs. ammonia, for example) differ among forms of urban runoff such as
wastewater and stormwater. Therefore, stormwater plumes could also promote growth of
other species or phytoplankton groups.

107
CHAPTER 3 REFERENCES
Ahn, J. H., Grant, S. B., Surbeck, C. Q., DiGiacomo, P. M., Nezlin, N. P. and Jiang, S.,
2005. Coastal water quality impact of stormwater runoff from an urban watershed
in southern California. Environmental Science & Technology 39(16), 5940-5953.
Anderson, D. M., Andersen, P., Bricelj, V. M., Cullen, J. J. and Rensel, J. E. J., 2001.
Monitoring and management strategies for harmful algal blooms in coastal
waters, APEC #201-MR-01.1. Asia Pacific Economic Program, Singapore, and
Intergovernmental Oceanographic Commission Technical Series No. 59, Paris.
APHA, 1998. Method 10200H. Chlorophyll. In: Clesceri, L. S., Eaton, A. D. and
Greenberg, A. E., (Eds.), Standard methods for the examination of water and
wastewater, 20
th
ed. American Public Health Association, American Water Works
Association and Water Pollution Control Federation, Washington D. C.
Babin, M., Cullen, J. J., Roesler, C. S., Donaghay, P. L., Doucette, G. J., Kahru, M.,
Lewis, M. R., Scholin, C. A., Sieracki, M. E. and Sosik, H. M., 2005. New
approaches and technologies for observing harmful algal blooms. Oceanography
18(2), 210-227.
Belzile, C., Roesler, C. S., Christensen, J. P., Shakhova, N. and Semiletov, I., 2006.
Fluorescence measured using the WETStar DOM fluorometer as a proxy for
dissolved matter absorption. Estuarine, Coastal and Shelf Science 67(3), 441-449.
Boss, E., Twardowski, M. S. and Herring, S., 2001. Shape of the particulate beam
attenuation spectrum and its inversion to obtain the shape of the particulate size
distribution. Applied Optics 40(27), 4885-4893.
Boss, E., Collier, R., Larson, G., Fennel, K. and Pegau, W. S., 2007. Measurements of
spectral optical properties and their relation to biogeochemical variables and
processes in Crater Lake, Crater Lake National Park, OR. Hydrobiologia 574,
149-159.
Bray, J. R. and Curtis, J. T., 1957. An ordination of the upland forest communities of
southern Wisconsin. Ecological Monographs 27(4), 325-349.
Cagle, C., 2006. The Water Quality Task Force in Redondo Beach; how a small coastal
city is dealing with red tides and fish kills. Southern California Academy of
Sciences and Society for Environmental Toxicology and Chemistry, Malibu,
California.
Campbell, L., 2001. Flow cytometric analysis of autotrophic picoplankton. In: Paul, J. H.,
(Ed.), Marine Microbiology, Methods in Microbiology vol. 30. Academic Press,
San Diego, California, pp. 317-343.

108
Carder, K. L. and Steward, R. G., 1985. A remote-sensing reflectance model of a red-tide
dinoflagellate off west Florida. Limnology & Oceanography 30(2), 286-298.
Caron, D. A., Garneau, M.-È., Seubert, E., Howard, M. D. A., Darjany, L., Schnetzer, A.,
Cetinic, I., Filteau, G., Lauri, P., Jones, B. and Trussell, S., 2009. Harmful algae
and their potential impacts on desalination operations off southern California.
Water Research doi:10.1016/j.watres.2009.06.051.
Cetini ć, I., 2009. Harmful algal blooms in the urbanized coastal ocean an application of
remote sensing for understanding, characterization and prediction. PhD
dissertation, University of Southern California, Los Angeles, California.
Chang, G. C. and Dickey, T. D., 2008. Interdisciplinary sampling strategies for detection
and characterization of harmful algal blooms. In: Babin, M., Roesler, C. S. and
Cullen, J. J., (Eds.), Real-time coastal observing systems for marine ecosystem
dynamics and harmful algal blooms. UNESCO, Paris, France, pp. 43-84.
Ciotti, A. M., Lewis, M. and Cullen, J. J., 2002. Assessment of the relationships between
dominant cell size in natural phytoplankton communities and the spectral shape of
the absorption coefficient. Limnology & Oceanography 47(2), 404-417.
Clarke, A. L., Weckström, K., Conley, D. J., Anderson, N. J., Adser, F., Andrén, E., de
Jonge, V. N., Ellegaard, M., Juggins, S., Kauppila, P., Korhola, A., Reuss, N.,
Telford, R. J. and Vaalgamaa, S., 2006. Long-term trends in eutrophication and
nutrients in the coastal zone Limnology & Oceanography 51(1 part 2), 385-397.
Clarke, K. R., 1993. Non-parametric multivariate analyses of changes in community
structure. Australian Journal of Ecology 18(1), 117-143.
Clarke, K. R. and Warwick, R. M., 2001. Change in marine communities: An approach to
statistical analysis and interpretation, 2nd ed. PRIMER-E, Plymouth, UK.
Cloern, J. E., Schraga, T. S., Lopez, C. B., Knowles, N., Labiosa, R. G. and Dugdale, R.,
2005. Climate anomalies generate an exceptional dinoflagellate bloom in San
Francisco Bay. Geophysical Research Letters 32(L14608),
doi:10.1029/2005GL023321.
Coble, P., Hu, C., Gould, R. W. J., Chang, G. and Wood, M., 2004. Colored dissolved
organic matter in the coastal ocean: An optical tool for coastal zone
environmental assessment and management. Oceanography 17(2), 50-59.
Craig, S. E., Lohrenz, S. E., Lee, Z., Mahoney, K. L., Kirkpatrick, G. J., Schofield, O. M.
and Steward, R. G., 2006. Use of hyperspectral remote sensing reflectance for
detection and assessment of the harmful alga, Karenia brevis. Applied Optics
45(21), 5414-5425.

109
Cullen, J. J., 2008. Observation and prediction of harmful algal blooms. In: Babin, M.,
Roesler, C. S. and Cullen, J. J., (Eds.), Real-time coastal observing systems for
marine ecosystem dynamics and harmful algal blooms. UNESCO, Paris, France,
pp. 1-41.
Curtiss, C. C., Langlois, G. W., Busse, L. B., Mazzillo, F. and Silver, M. W., 2008. The
emergence of Cochlodinium along the California Coast (USA). Harmful Algae
7(3), 337-346.
Dagg, M. J. and Breed, G. A., 2003. Biological effects of Mississippi River nitrogen on
the northern gulf of Mexico--a review and synthesis. Journal of Marine Systems
43(3-4), 133-152.
Davies-Colley, R. J. and Smith, D. G., 2001. Turbidity, suspended sediment, and water
clarity: a review. Journal of the American Water Resources Association 37(5),
1085-1101.
Davies-Colley, R. J., Vant, W. N. and Smith, D. G., 2003. Colour and clarity of natural
waters: Science and management of optical water quality. The Blackburn Press,
Caldwell, New Jersey.
Dierssen, H. M., Kudela, R. M., Ryan, J. P. and Zimmerman, R. C., 2006. Red and black
tides: Quantitative analysis of water-leaving radiance and perceived color for
phytoplankton, colored dissolved organic matter, and suspended sediments.
Limnology & Oceanography 51(6), 2646–2659.
Faith, D. P., Minchin, P. R. and Belbin, L., 1987. Compositional dissimilarity as a robust
measure of ecological distance. Vegetatio 69(1), 57-68.
Fauchot, J., Levasseur, M., Roy, S., Gagnon, R. and Weise, A. M., 2005. Environmental
factors controlling Alexandrium tamarense (Dinophyceae) growth rate during a
red tide event in the St. Lawrence estuary (Canada). Journal of Phycology 41(2),
263-272.
Faust, M. A. and Gulledge, R. A., 2002. Identifying harmful marine dinoflagellates.
Smithsonian Institution, Washington, DC.
Franks, P. J. S., 1992. Sink or swim: accumulation of biomass at fronts. Marine Ecology
Progress Series 82, 1-12.
Franks, P. J. S., 1997. Spatial patterns in dense algal blooms. Limnology &
Oceanography 42(5, part 2), 1297-1305.
Geesey, G. G., 1993. Microbiology. In: Dailey, M. D., Reish, D. J. and Anderson, J. W.,
(Eds.), Ecology of the Southern California Bight. University of California Press,
Los Angeles, California, pp. 190-232.

110
Glibert, P. M., Anderson, D. M., Gentien, P., Graneli, E. and Sellner, K. G., 2005. The
global, complex phenomena of harmful algal blooms. Oceanography 18(2), 136-
147.
Hasle, G. R., 1978. Settling: the inverted-microscope method. In: Sournia, A., (Ed.),
Phytoplankton manual. UNESCO, Paris.
Hill, P. S., Milligan, T. G. and Geyer, W. R., 2000. Controls on effective settling velocity
of suspended sediment in the Eel River flood plume. Continental Shelf Research
20(16), 2095-2111.
Horner, R. A., Garrison, D. L. and Plumley, F. G., 1997. Harmful algal blooms and red
tide problems on the U.S. west coast. Limnology & Oceanography 42(5, part 2),
1076-1088.
Hu, C., Muller-Karger, F. E., Vargo, G. A., Neely, M. B. and Johns, E., 2004. Linkages
between coastal runoff and the Florida Keys ecosystem: A study of a dark plume
event. Geophysical Research Letters 31(L15307), doi:10.1029/2004GL020382.
Iwataki, M., Kawami, H., Mizushima, K., Mikulski, C. M., Doucette, G. J., Relox, J. R.
J., Anton, A., Fukuyo, Y. and Matsuoka, K., 2008. Phylogenetic relationships in
the harmful dinoflagellate Cochlodinium polykrikoides (Gymnodiniales,
Dinophyceae) inferred from LSU rDNA sequences. Harmful Algae 7(3), 271-277.
Jeffrey, S. W. and Vesk, M., 1997. Introduction to marine phytoplankton and their
pigment signatures. In: Jeffrey, S. W., Mantoura, R. F. C. and Wright, S. W.,
(Eds.), Phytoplankton pigments in oceanography: Guidelines to modern methods.
UNESCO Publishing, Paris, pp. 37-85.
Jessup, D. A., Miller, M. A., Ryan, J. P., Nevins, H. M., Kerkering, H. A., Mekebri, A.,
Crane, D. B., Johnson, T. A. and Kudela, R. M., 2009. Mass stranding of marine
birds caused by a surfactant-producing red tide. PLoS ONE 4(2), e4550.
Jickells, T. D., 1998. Nutrient biogeochemistry of the coastal zone. Science 281(5374),
217-222.
Johnsen, G., Samset, O., Granskog, L. and Sakshaug, E., 1994. In vivo absorption
characteristics in 10 classes of bloom-forming phytoplankton: taxonomic
characteristics and responses to photoadaptation by means of discriminant and
HPLC analysis. Marine Ecology Progress Series 105, 149-157.
Jones, B. H., Noble, M. A. and Dickey, T. D., 2002. Hydrographic and particle
distributions over the Palos Verdes Continental Shelf: spatial, seasonal and daily
variability. Continental Shelf Research 22(6-7), 945-965.
Kahru, M., Mitchell, B. G., Diaz, A. and Miura, M., 2004. MODIS detects a devastating
algal bloom in Paracas Bay, Peru. EOS 85(45), 465-472.

111
Kirk, J. T. O., 1994. Light and photosynthesis in aquatic ecosystems. Cambridge
University Press, Cambridge.
Kirkpatrick, G. J., Millie, D. F., Moline, M. A. and Schofield, O., 2000. Optical
discrimination of a phytoplankton species in natural mixed populations.
Limnology & Oceanography 45(2), 467-471.
Kishino, M., Takahashi, M., Okami, N. and Ichimura, S., 1985. Estimation of the spectral
absorption coefficients of phytoplankton in the sea. Bulletin of Marine Science
37(2), 634-642.
Kudela, R. M. and Cochlan, W. P., 2000. Nitrogen and carbon uptake kinetics and the
influence of irradiance for a red tide bloom off southern California. Aquatic
Microbial Ecology 21(1), 31-47.
Kudela, R. M., Ryan, J. P., Blakely, M. D., Lane, J. Q. and Peterson, T. D., 2008. Linking
the physiology and ecology of Cochlodinium to better understand harmful algal
bloom events: A comparative approach. Harmful Algae 7(3), 278-292.
Lotze, H. K., Lenihan, H. S., Bourque, B. J., Bradbury, R. H., Cooke, R. G., Kay, M. C.,
Kidwell, S. M., Kirby, M. X., Peterson, C. H. and Jackson, J. B. C., 2006.
Depletion, degradation, and recovery potential of estuaries and coastal seas.
Science 312(5781), 1806-1809.
Lund, J. W. G., Kipling, C. and Le Cren, E. D., 1958. The inverted microscope method of
estimating algal numbers and the statistical basis of estimations by counting.
Hydrobiologia 11(2), 143-170.
Lyon, G. S., Petschauer, D. and Stein, E. D., 2006. Effluent discharges to the Southern
California Bight from large municipal wastewater treatment facilities in 2003 and
2004. In: Weisberg, S. B. and Miller, K., (Eds.), Southern California Coastal
Water Research Project 2005-06 biennial report. Southern California Coastal
Water Research Project, Westminster, CA, pp. 1-15.
McGowan, J. and Carter, M., 2008. Scripps Pier chlorophyll and HAB time series. The
Regional Workshop for Harmful Algal Blooms (HABs) in California Coastal
Waters. Report #565. Southern California Coastal Water Research Program,
Costa Mesa, California, pp. B-4.
Mobley, C. D., 1994. Light and water, radiative transfer in natural waters. Academic
Press, Inc., San Diego, CA.
National Research Council, 1990. Monitoring southern California's coastal Waters.
National Academy Press, Washington, DC.

112
Nezlin, N. P., Oram, J. J., DiGiacomo, P. M. and Gruber, N., 2004. Sub-seasonal to
interannual variations of sea surface temperature, salinity, oxygen anomaly, and
transmissivity in Santa Monica Bay, California from 1987 to 1997. Continental
Shelf Research 24(10), 1053-1082.
Nezlin, N. P., DiGiacomo, P. M., Stein, E. D. and Ackerman, D., 2005. Stormwater
runoff plumes observed by SeaWiFS radiometer in the Southern California Bight.
Remote Sensing of Environment 98, 494-510.
Nezlin, N. P., Weisberg, S. B. and Diehl, D. W., 2007. Relative availability of satellite
imagery and ship-based sampling for assessment of stormwater runoff plumes in
coastal southern California. Estuarine, Coastal and Shelf Science 71(1-2), 250-
258.
Nezlin, N. P., DiGiacomo, P. M., Diehl, D. W., Jones, B. H., Johnson, S. C., Mengel, M.
J., Reifel, K. M., Warrick, J. A. and Wang, M., 2008. Stormwater plume detection
by MODIS imagery in the southern California coastal ocean. Estuarine, Coastal
and Shelf Science 80(1), 141-152.
Ocean Software, 2009. Interactive Graphical Ocean Database System (IGODS).
Petrenko, A. A., Jones, B. H., Dickey, T. D., LeHaitre, M. and Moore, C., 1997. Effects
of a sewage plume on the biology, optical characteristics, and particle size
distributions of coastal waters. Journal of Geophysical Research 102(C11),
25,061-25,071.
Pfaff, J. D., 1993. Method 300.0: Determination of inorganic anions by ion
chromatography, revision 2.1. In: Methods for the determination of inorganic
substances in environmental samples (EPA/600/R-93/100). Environmental
Monitoring Systems Laboratory, Office of Research and Development, U.S.
Environmental Protection Agency, Office of Water, Cincinnati, Ohio, pp. 300.0-
1–300.0-30.
Ramos, P. A., Neves, M. V. and Pereira, F. L., 2007. Mapping and initial dilution
estimation of an ocean outfall plume using an autonomous underwater vehicle.
Continental Shelf Research 27(5), 583-593.
Reifel, K. M., Johnson, S. C., DiGiacomo, P. M. P. M., Mengel, M. J., Nezlin, N. P.,
Warrick, J. A. and Jones, B. H., 2009. Impacts of stormwater runoff in the
Southern California Bight: Relationships among plume constituents. Continental
Shelf Research 29(15), 1821-1835.
Riegman, R., 1995. Nutrient-related selection mechanisms in marine phytoplankton
communities and the impact of eutrophication on the planktonic food web. Water
Science and Technology 32(4), 63-75.

113
Roesler, C. S. and Boss, E., 2008. In situ measurement of inherent optical properties and
potential for harmful algal bloom detection and coastal ecosystem observations.
In: Babin, M., Roesler, C. S. and Cullen, J. J., (Eds.), Real-time coastal observing
systems for marine ecosystem dynamics and harmful algal blooms. UNESCO,
Paris, France, pp. 153-206.
Ryan, J. P., Gower, J. F. R., King, S. A., Bissett, W. P., Fischer, A. M., Kudela, R. M.,
Kolber, Z., Mazzillo, F., Rienecker, E. V. and Chavez, F. P., 2008. A coastal
ocean extreme bloom incubator. Geophysical Research Letters 35(L12602),
doi:10.1029/2008GL034081.
Schiff, K. C., Allen, M. J., Zeng, E. Y. and Bay, S. M., 2000. Southern California.
Marine Pollution Bulletin 41(1-6), 76-93.
Schnetzer, A., Miller, P. E., Schaffner, R. A., Stauffer, B. A., Jones, B. H., Weisberg, S.
B., DiGiacomo, P. M., Berelson, W. M. and Caron, D. A., 2007. Blooms of
Pseudo-nitzschia and domoic acid in the San Pedro Channel and Los Angeles
harbor areas of the Southern California Bight, 2003–2004. Harmful Algae 6(3),
372-387.
Schofield, O., Kerfoot, J., Mahoney, K., Moline, M., Oliver, M., Lohrenz, S. E. and
Kirkpatrick, G. J., 2006. Vertical migration of the toxic dinoflagellate Karenia
brevis and the impact on ocean optical properties. Journal of Geophysical
Research 111(C06009), doi:10.1029/2005JC003115.
Shipe, R. F., Leinweber, A. and Gruber, N., 2008. Abiotic controls of potentially harmful
algal blooms in Santa Monica Bay, California. Continental Shelf Research 28(18),
2584-2593.
Sidio, L. A., 2007. Effluent quality. In: Santa Monica Bay biennial assessment report:
2005-06. City of Los Angeles, Department of Public Works, Environmental
Monitoring Division, Los Angeles, California, pp. 1-5.
Siegel, D. A., Maritorena, S., Nelson, N. B., Hansell, D. A. and Lorenzi-Kayser, M.,
2002. Global distribution and dynamics of colored dissolved and detrital organic
materials. Journal of Geophysical Research 107(C12), 3228,
doi:10.1029/2001JC000965.
Slade, W., Langner, M. R., Boss, E. and Roesler, C. S., 2006. Retrieving in situ
particulate absorption spectra in optically clear waters: An example from the
Equatorial Pacific. Ocean Optics XVIII.
Smayda, T. J. and Reynolds, C. S., 2001. Community assembly in marine phytoplankton:
Application of recent models to harmful dinoflagellate blooms. Journal of
Plankton Research 23(5), 447-461.

114
Smayda, T. J., 2002. Adaptive ecology, growth strategies and the global bloom expansion
of dinoflagellates. Journal of Oceanography 58(2), 281-294.
Staehr, P., Waite, A. and Markager, S., 2009. Effects of sewage water on bio-optical
properties and primary production of coastal systems in West Australia.
Hydrobiologia 620(1), 191-205.
Strickland, J. D. H. and Parsons, T. R., 1968. A practical handbook of seawater analysis.
Fisheries Research Board of Canada, Ottawa.
Sullivan, J. M., Twardowski, M. S., Zaneveld, J. R. V., Moore, C. C., Barnard, A. H.,
Donaghay, P. L. and Rhoades, B., 2006. The hyperspectral temperature and salt
dependencies of absorption by water and heavy water in the 400-750 nm spectral
range. Applied Optics 45(21), 5294-5309.
Thompson, G. B. and Ho, J., 1981. Some effects of sewage discharge upon
phytoplankton in Hong Kong. Marine Pollution Bulletin 12(5), 168-173.
Turner, J. T., Borkman, D. G., Lincoln, J. A., Gauthier, D. A. and Petitpas, C. M., 2009.
Plankton studies in Buzzards Bay, Massachusetts, USA. VI. Phytoplankton and
water quality, 1987 to 1998. Marine Ecology Progress Series 376, 103-122.
Twardowski, M. S., Boss, E., Sullivan, J. M. and Donaghay, P. L., 2004. Modeling the
spectral shape of absorption by chromophoric dissolved organic matter. Marine
Chemistry 89(1-4), 69-88.
Warrick, J. A., Mertes, L. A. K., Washburn, L. and Siegel, D. A., 2004. A conceptual
model for river water and sediment dispersal in the Santa Barbara Channel,
California. Continental Shelf Research 24(17), 2029-2043.
Warrick, J. A., DiGiacomo, P. M., Weisberg, S. B., Nezlin, N. P., Mengel, M., Jones, B.
H., Ohlmann, J. C., Washburn, L., Terrill, E. J. and Farnsworth, K. L., 2007.
River plume patterns and dynamics within the Southern California Bight.
Continental Shelf Research 27(19), 2427-2448.
Washburn, L., McClure, K. A., Jones, B. H. and Bay, S. M., 2003. Spatial scales and
evolution of stormwater plumes in Santa Monica Bay. Marine Environmental
Research 56(1-2), 103-125.
Watanabe, M. and Harashima, A., 1986. Interaction between motile phytoplankton and
langmuir circulation. Ecological Modeling 31(1-4), 175-183.
Yentsch, C. S. and Vaccaro, R. F., 1958. Phytoplankton nitrogen in the oceans.
Limnology & Oceanography 3(4), 443-448.

115
CHAPTER 4
The Effects of Stormwater Runoff on Coastal Phytoplankton
CHAPTER 4 ABSTRACT
Recently, blooms of potentially harmful phytoplankton species have been linked
to episodic events such as stormwater runoff. Urban runoff, including stormwater, has the
potential to alter phytoplankton community structure due to its effects on light, water
column stability, and nutrient availability. We sampled plumes from three storm events
during 2007 and 2008 in Santa Monica Bay, California. The first was relatively small
(0.8 cm precipitation) and occurred during an upwelling event and concurrent Pseudo-
nitzschia spp. bloom in April 2007. The last two storms occurred in January 2008 (3.96
and 9.55 cm precipitation). Non-metric multidimensional scaling analysis revealed large
differences among the phytoplankton communities present during each storm event.
These differences were generally related to the proportion of dinoflagellates to diatoms,
although differences in the dominant diatom species were also apparent. Differences in
the phytoplankton community inside and outside the plume were only detected after the
April 2007 storm event. The percent abundances of diatom species were generally lower
inside the plume even though diatoms were numerically dominant. Several
dinoflagellates (Prorocentrum spp., Ceratium spp., Protoperidinium spp., and
Alexandrium sp.) comprised a greater percent of the phytoplankton inside the plume.
Comparisons with two other data sets collected after storm events (Bight '03) and a
planned release of wastewater (Hyperion WWTP) revealed that dramatic increases in
phytoplankton density only occurred in dilute plume waters (>33.2 psu), and shifts in
community structure were only observed after a period of low wave height. These results
116
are consistent with previous models that predict the presence of various functional
dinoflagellate groups based on environmental parameters such as stratification and
nutrient loading. Future studies should combine ship-based sampling with the use of
remote platforms (e.g. gliders or drifters) and satellite imagery to observe the
phytoplankton community in older, dilute plume waters after relaxation of sea state.

117
INTRODUCTION
Understanding land-sea interactions is vital to improving water quality, a high-
level goal set by agencies such as the California Ocean Protection Council (OPC, 2006).
As of 2003, 53% of the U.S. population lives in coastal counties, only about 17% of the
total land area (Crossett et al., 2005). Equitable use of beaches and coasts and access to
clean water are therefore problematic, especially in large coastal cities such as Los
Angeles. Even coastal aesthetics are at risk by the negative impact of phytoplankton
blooms, which affect recreation and ultimately California’s economy. Bloom initiation
and propagation have been linked to oceanographic conditions including frontal zones,
internal waves, and the intrusion of California Current waters (Ryan et al., 2005).
Anthropogenically-derived nutrients, such as urea, may also be associated with blooms
(Kudela et al., 2008).
Coastal California is generally viewed as an upwelling-dominated system, and
upwelling tends to drive phytoplankton dynamics (Otero and Siegel, 2004). Blooms of
Pseudo-nitzschia spp. and other diatoms have been linked to periods of upwelling along
California’s coast (Trainer et al., 2000; Ramos et al., 2007; Anderson et al., 2008). Red
tides along coastal California tend to occur most often during mid to late summer when
coastal upwelling has ceased and the water column becomes stratified (Horner et al.,
1997). However, episodic high-impact events such as precipitation-driven coastal runoff
may also be important to the biota over large spatial and temporal scales. Kleppel (1980)
suggested that the availability and form of nutrients introduced into coastal waters in the
Los Angeles region can control the phytoplankton community structure leading to a
community dominated by either dinoflagellates or diatoms. Blooms of potentially
118
harmful phytoplankton species in California are beginning to be linked to coastal runoff,
particularly agricultural and urban runoff. For example, a massive bloom of
Lingulodinium polyedrum (= Gonyaulax polyedra) was recorded from Santa Barbara to
San Diego in January-March 1995, and the initiation and maintenance of this bloom was
linked to unusually large storm events resulting in massive stormwater runoff into coastal
areas (Hayward et al., 1995; Gregorio and Pieper, 2000). Runoff events are thought to
“fertilize” the coastal ocean with anthropogenically derived nutrients. However, their
role in the stimulation of particular algal species is not known. Through the work
described here, we will examine the effects of stormwater on the coastal phytoplankton
community in southern California.
METHODS
Sampling was conducted after three storm events. Storm 1 occurred in April
2007, and storms 2 and 3 occurred in January 2008 (Figure 4-1). Boat-based sampling
was conducted to characterize and monitor the resulting stormwater plumes. In this
paper, we will focus on measurements of phytoplankton density (chlorophyll a) and
community structure (microscopic and flow cytometry-based cell counts).
Meteorological Data
Observed precipitation was obtained from the Los Angeles County Department of
Public Works water resources website (http://ladpw.org/wrd/Precip/index.cfm).
Precipitation and cumulative precipitation over the entire water year (October-
September) from the Ballona Creek rain gauge (gauge 370; 33° 59' 55" N latitude,
118° 24' 05" W longitude) were recorded. Wind data were collected at the Los Angeles
International Airport (33.938° N latitude, 118.388° W longitude) from NOAA's National
Climatic Data Center (http://www.ncdc.noaa.gov), and wave height was collected at buoy
number 46221 (33.8545° N latitude, 118.6328° W longitude) from NOAA's National
Buoy Data Center (http://www.ndbc.noaa.gov/). Satellite imagery was downloaded from
the Southern California Coastal Ocean Observing System (SCCOOS) website
(www.sccoos.org). Images were collected from the Moderate Resolution Imaging
Spectroradiometer (MODIS) satellite and processed by SCCOOS for sea surface
temperature and chlorophyll a (chl a) concentration.

Precipitation (cm)
0
0.2
0.4
1.0
April 2007
A
B
0.6
0.8
5.0
5.2
5.4
5.6
5.8
6.0
6.2
6.4
Cumulative precipitation (cm)
18 Apr 20 Apr 22 Apr 24 Apr
January 2008
Precipitation (cm)
Cumulative precipitation (cm)
0
0.5
1.0
1.5
2.0
2.5
3.0
8
10
12
14
16
18
20
22
24
31 Dec 5 Jan 10 Jan 15 Jan 20 Jan 25 Jan 30 Jan 4 Feb 9 Feb 14 Feb
0
2
4
6
8
10
12
14
16
18
20
Average daily discharge (m
3
s
-1
)
0
10
20
30
40
50
60
70
80
Average daily discharge (m
3
s
-1
)
precipitation
cumulative
discharge
Time (PST)
Time (PDT)

Figure 4-1. Daily precipitation and cumulative precipitation observed at the Ballona
Creek stream gauge, and average daily discharge from Ballona Creek for storm 1 (A) and
storms 2 and 3 (B). Cumulative precipitation is the cumulative amount observed over the
entire water year (starting October 1). Vertical lines indicate sampling dates. Discharge
data are from LACDPW (2008, 2009).
119
120
Field Collections
Sampling was conducted one day (21 April) and four days (24 April) after storm
1 on board the R/V Sea World (University of California Los Angeles; Figure 4-1). This
boat is equipped with a conductivity-temperature-depth (CTD) sensor (Seabird SBE 45)
and a chl a fluorometer (WET Labs WETStar) integrated into the surface pump (~1.5 m
depth) that continuously measure temperature, salinity, and chl a fluorescence along the
ship's track. Using surface salinity measurements, several stations both inside and
outside the plume were chosen (Figures 4-2 and 4-3; see Appendix C for locations of all
stations sampled during storms 1, 2, and 3). At each station, vertical profiles of
temperature and salinity (Seabird SBE 9/11+), beam attenuation (WET Labs C-Star
transmissometer), colored dissolved organic matter (CDOM) fluorescence (WET Labs
WETStar CDOM fluorometer), and chl a fluorescence (WET Labs WETStar chl a
fluorometer) were collected. Water samples for analysis of nutrient concentrations,
extracted chl a, and phytoplankton counts were collected at 2-3 depths within the top 15
m of the water column with 5-liter Niskin bottles. Whole water samples for nutrients and
chl a were stored in acid-washed polycarbonate bottles on ice in the dark during transport
back to the laboratory. For count analyses, whole water samples (250 L) were preserved
in the field using 1% acid Lugol's solution. Whole water samples for flow cytometry (3
mL) were preserved in the field using buffered formalin (10% final concentration),
transported back to the laboratory on ice in the dark, and stored at -80°C until analysis.
During storm 2, sampling was conducted between the two waves of the storm (6
January) and two days after the storm (8 January). During storm 3, sampling was
conducted between two waves of the event (26 January), on the first day after the event
*
−118.8 −118.75 −118.7 −118.65 −118.6 −118.55 −118.5 −118.45 −118.4 −118.85
33.75
33.8
33.85
33.9
33.95
34.0
34.05
Ballona Creek
Storm 1 (April 2007)
Storm 2 (January 2008)
Storm 3 (January 2008)
Marina
del Rey
precipitation
gauge
NBDC buoy
#46221

Figure 4-2. Map of Santa Monica Bay showing the stations sampled during the three
storm events and the locations of the Ballona Creek precipitation gauge and the NOAA
National Buoy Data Center buoy used to collect wave height.

(28 January), and four days after the storm (31 January; Figure 4-1). Sampling was
conducted on board the M/V Yellowfin (Southern California Marine Institute).
Continuous measurements of temperature, salinity, and chl a were collected along the
ship's track by pumping water from the surface pump (~1.5 m depth) through an SBE
9/11+ CTD (Seabird) and a chl a fluorometer (Seapoint Sensors, Inc.). Vertical profiles
were collected at stations inside and outside the plume (Figures 4-2, 4-4, and 4-5) using a
package containing an SBE 49 FastCAT CTD (Seabird), a C-star transmissometer (WET
Labs), and an ECO FL3 (WET Labs) which measures chl a and CDOM fluorescence.
Water samples were again collected at 2-3 depths for concentrations of nutrients,
extracted chl a, and phytoplankton cells (microscopic cell counts and flow cytometry).
121
Salinity (psu)
20 0 510 15 25 30
33.3
33.4
33.5
33.6
33.7
leaving
marina
1 2 3 4 5
entering
marina
Salinity (psu)
33.3
A
B
35 40
Distance along track (km)
Temperature (°C)
15.0
14.5
14.0
13.5
13.0
12.5
salinity
temperature
20 0 510 15 25 30 35 40
Temperature (°C)
33.4
33.5
33.6
33.7
15.0
14.5
14.0
13.5
13.0
12.5
15.5
12.0
21 April 2007
1 2
entering
marina
24 April 2007
45

Figure 4-3. Salinity and temperature collected along the ship track during the two
sampling dates, 21 April (A) and 24 April (B), after storm 1. Shaded areas represent
areas inside the plume, and arrows indicate sampling stations.

Data Processing
CTD data was processed using SBE software versions 7.14c and 7.14e to derive
salinity (psu), temperature (°C), the beam attenuation coefficient (m
-1
), CDOM (relative
fluorescence), and chl a (relative fluorescence). Raw fluorescence of CDOM was
converted to quinine sulfate dihydrate (QSD) equivalent concentration by subtracting the
background fluorescence of water from the raw fluorescence of the sample and
multiplying by a scale factor supplied by the manufacturer. See Belzile et al. (2006) for a
detailed description of this instrument. Chl a concentration was similarly converted from
raw fluorescence using a scale factor supplied during factory calibration.
122
Salinity (psu)
40 0 10 20 30 50 60
32.2
32.4
32.6
32.8
33.0
1 2 3 4 5
Salinity (psu)
33.20
A
B
70 80
Distance along track (km)
25 5 10 15 20 30 35 40 45
33.25
33.30
33.35
33.40
6 January 2008
leaving
marina
1 2 3
8 January 2008
6
33.2
33.4
50 55 60 65 70
45
33.45

Figure 4-4. Salinity measured along the ship track during the two sampling dates, 6
January (A) and 8 January (B), after storm 2. Shaded areas represent areas inside the
plume, and arrows indicate sampling stations.

Vertical profile data collected during storms 2 and 3 were processed using the
WET Labs Archive File Processing program to derive salinity (psu), temperature (°C),
chl a concentration (mg m
-3
), CDOM concentration (ppb), and transmittance.
Transmittance was converted to beam attenuation (m
-1
) using the relationship:
beam c = -ln(Tr)/0.25 (1)
where beam c is beam attenuation, Tr is transmittance, and 0.25 is the pathlength in
meters.
123
Salinity (psu)
40 0 10 20 30 50 60
32.2
32.4
32.6
32.8
33.0
1 2 3 4 5
Salinity (psu)
32.0
A
B
70
Distance along track (km)
20 0 510 15 25 30 35 40
32.2
32.4
32.6
33.0
26 January 2008
leaving
marina
1 2 3
28 January 2008
6
33.2
33.4
45 50 55 60
45
33.2
entering
marina
33.4
32.8
5 15 25 35 45 55 65 75
6
entering
marina
31 January 2008
leaving
marina
1 2 345
Salinity (psu)
33.0
33.1
33.2
33.4
33.5
33.3
50 0 10 20 40 80 90 30 60 70
C

Figure 4-5. Salinity measured along the ship track during the three sampling dates, 26
January (A), 28 January (B), and 31 January (C), after storm 3. Shaded areas represent
areas inside the plume. Shaded areas represent areas inside the plume, and arrows
indicate sampling stations.
124
125
Laboratory Analyses
To determine nutrient (nitrate, nitrite, phosphate) concentrations, whole water
samples were filtered through Whatman GF/F (0.7 m) glass fiber filters, and the filtrate
was frozen (-20°C) until analysis. Samples for ammonia concentration were not filtered
but were frozen immediately after returning to the lab. Nutrient concentrations were
determined with a Lachat QuickChem 8000 autoanalyzer at the Marine Science Institute
Analytical Laboratory (University of California, Santa Barbara) following the methods of
Strickland and Parsons (1968). Ammonia samples collected during storm 1 were
measured with an Akpkem RFA 300 Series autoanalyzer at the University of Southern
California (Sakamoto et al., 1990; Gordon et al., 1993).
Phytoplankton density and community composition were determined through
measurements of chl a concentration and direct cell counts. For chl a concentration, each
sample was filtered through a Whatman GF/F (0.7 µm) filter within 12 h of collection,
and the filters were stored at -80°C until analysis. Filters were placed in 90% buffered
acetone (v/v) and stored at -20ºC for 24 h to allow pigment extraction to take place. Chl
a concentrations were measured using standard fluorometric methods (APHA, 1998) on a
Turner TD-700 fluorometer. To determine community composition, subsamples (25-100
mL) of Lugol's-preserved samples were settled for at least 24 h (25 mL) or at least 48 h
(50 and 100 mL), and cells in two crossed diameters were enumerated at a magnification
of 200X with a Leica DM Irbe inverted microscope using the Utermöhl method (Lund et
al., 1958; Hasle, 1978). Cells were identified to species when possible. Picoplankton
cell abundances were determined using a FACSCalibur (Becton Dickinson, San Jose,
CA) flow cytometer equipped with a 488 nm 15 mW laser and standard filter setup by
126
Dr. Lisa Campbell at Texas A&M. See Campbell (2001) for a detailed description of this
method.
Statistical Analyses
Stations were grouped into "inside" = plume (stations inside the stormwater
plume), and "outside" = non-plume (stations outside the stormwater plume)
classifications. To determine which stations were inside and outside the plume, surface
concentrations of salinity along the ship's track and property-property plots of
temperature, salinity, and CDOM measurements from the vertical profiles were
examined. Samples collected within areas or at depths with decreased salinity and/or
elevated CDOM concentration relative to all measurements collected during a particular
cruise were considered to be inside the plume. The "inside" stations all fell within the
low-salinity, high-CDOM patches observed in measurements along the ship's track
(shaded areas in Figures 4-3 to 4-5).
Differences in phytoplankton community structure were analyzed using non-
metric multi-dimensional scaling (NMDS). Because not all taxa could be identified to
species, statistical analyses were performed on count data aggregated to the genus level.
First, relative abundances were calculated by normalizing raw counts by the total number
of cells counted, and the normalized values were transformed using an arcsine-square
root transformation. A species distance matrix was then generated using the Bray-Curtis
similarity coefficient (Bray and Curtis, 1957). This coefficient is commonly used in
ecology because of its robustness and reliability, and several desirable properties
including invariance to changes in scale and standardization to ensure that extreme values
(100 and 0, respectively) correspond to a complete match of the measurements and a
127
complete lack of species in common (Faith et al., 1987; Clarke, 1993). NMDS was used
to construct a map of the samples in low-dimensional space that attempts to maintain the
relative distances between points as close as possible to the actual rank order of
similarities between samples. Thus, samples with similar community structures are
plotted as points that are close together in ordination space. In contrast to other multi-
dimensional methods, NMDS does not assume that similarity has a linear relationship
with ecological distance but only assumes monotonicity (the rank order of distances
between samples are used). A stress factor was calculated for the NMDS ordination
which indicates how well the plotted configuration of sample distances agrees with the
original rank orders calculated from the similarity matrix. All NMDS calculations and
subsequent analyses (see next paragraph) were performed using PRIMER v6.0 (Primer-E
Ltd., Plymouth).
To determine whether the phytoplankton community structure differed among
groups (inside and outside), the ANOSIM test in PRIMER was used. ANOSIM is a
permutation procedure applied to the rank similarities of the samples (Clarke and
Warwick, 2001). An R value (ranging from 0 if similarities within and between groups
are the same, to 1 if all replicates within groups are more similar to each other than to any
replicates from other groups) was calculated indicating the similarity of sites within a
group compared to similarities of sites between groups. A corresponding p-value was
determined by referring the observed value of R to its permutation distribution. Note that
although R is not heavily affected by the number of replicates within each group, but its
statistical significance (p-value) is dominated by the group sizes. To separate the effect
of the plume from general changes in the community over time, a 2-way crossed design
128
was employed using station location (inside or outside the plume) and sample date as
factors. In this case, a separate R statistic is calculated for locations within each date (and
dates within each location), and the reported R value represents the average of these
values. ANOSIM also conducts pairwise comparisons and calculates R and p-values for
tests between each pair of groups. The SIMPER routine in PRIMER was used to identify
which phytoplankton taxa contribute most to differences among groups when group
differences were found. A 2-way crossed design, with location and sample date as
factors, was again employed. Briefly, the average dissimilarity between all pairs of inter-
group samples was calculated. Separate contributions from each taxa (in this case
genera) to the average dissimilarity were then determined. These methods are described
in detail in Clarke (1993) and Clarke and Warwick (2001).
Comparisons with Other Data Sets
Two additional data sets were incorporated into the analyses for comparison with
data collected during the three storm events sampled in this study. The "Bight" projects,
organized by the Southern California Coastal Water Research Project (SCCWRP),
coordinate regional monitoring efforts by local municipalities that have agreed to work
cooperatively toward a regional assessment of coastal conditions. Data collected during
Bight '03, the most recent Bight project, was used. Vertical profiles of temperature,
salinity, CDOM concentration, and chl a fluorescence, and water samples for
measurement of extracted chl a, were collected at grids of stations offshore of the major
rivers in the Southern California Bight after two storm events (February 2004 and
February-March 2005). Only vertical profiles and extracted chl a concentrations
measured at stations offshore of Ballona Creek were used in this study. For a complete
129
description of sample collection and methodologies used during Bight '03, see Warrick et
al. (2007), Nezlin et al. (2008), and Reifel et al. (2009).
Data was also used from a planned release of wastewater from the Hyperion
Wastewater Treatment Plant (WWTP). Secondary-treated wastewater was discharged
through the 1-mile outfall (terminates ~1.6 km offshore at 15 m depth) into Santa Monica
Bay from 28-30 November 2006. The resulting surface wastewater plume was tracked
and monitored for seven days. Extracted chl a concentrations and phytoplankton count
data from surface samples collected inside and outside the wastewater plume were
included in our analyses. See Reifel et al. (in prep.) for a complete description of this
project.
RESULTS
The spatial and temporal extents, movements, and bulk characteristics of the
stormwater plumes are described in Corcoran et al. (in review). In this paper, we will
focus on a more detailed analysis of the impacts of stormwater plumes on the coastal
phytoplankton community. We will also compare changes in density (chl a) and
community structure to those observed after storms in 2003-2004 (the Bight '03 data set)
and after the planned discharge of treated wastewater (the Hyperion WWTP data set).
Storm Events
Storm 1 (April 2007) was relatively small totaling about 0.8 cm (Figure 4-1A).
Measurable precipitation was recorded at the Ballona Creek rain gauge from 09:00-11:15
Pacific Daylight Time (PDT) on 20 April. This event followed a dry period of
approximately two months during an overall dry year. From 1 October 2006 to 30
September 2007, rainfall totals across Los Angeles County were 17-36% of the seasonal
130
normal (LACDPW, 2008). The average daily discharge measured in Ballona Creek
reached 11.44 m
3
s
-1
on 20 April (average daily discharge over the entire month of April
was 1.5 m
3
s
-1
; LACDPW, 2008). A small, ephemeral plume was produced that was
detected within 2 km of the mouth of Ballona Creek one day after, and was only observed
in the immediate vicinity of the creek mouth four days after the storm. An upwelling
event occurred prior to this storm event. The upwelling index calculated by the NOAA's
Environmental Research Division reached a daily average of 431 m
3
s
-1
100 m coastline
-1

(measured at 33° N latitude, 119° W longitude) on 12 April, the highest daily average at
that location for 2007. Sea surface temperature shows patches of cooler temperatures
(12-14°C) in Santa Monica Bay prior to the storm on 18 April and between sampling
dates on 23 April (Figures 4-6A and 4-6B). Water temperature decreased at the Santa
Monica Pier starting on 13 April, remained low through 25 April, and then gradually
increased through the rest of the month of April (Figure 4-6C). Stations located outside
the plume (stations 4 and 5, 21 April; station 1, 24 April) were within the high-salinity,
low-temperature upwelled water (Figure 4-3).
In early January 2008, measurable precipitation was recorded at the Ballona
Creek gauge starting at 16:30 Pacific Standard Time (PST) on 4 January lasting through
12:30 PST on 5 January. A second wave of rainfall occurred just after our sampling trip
on 6 January between 16:30-21:45 PST. The entire storm event (storm 2) resulted in 3.96
cm of precipitation. Average daily discharge in Ballona Creek peaked on 4 January at
57.8 m
3
s
-1
and remained elevated (7-27 m
3
s
-1
) through 7 January (Figure 4-1B;
LACDPW, 2009). Storm 3 occurred over several days in late January (Figure 4-1B).
After a small amount of precipitation on 22 January, the storm occurred in two main
18 April 2007 23 April 2007
24
22
20
18
16
14
12
12
13
14
15
16
Temperature (°C)
1-Apr 5-Apr 9-Apr 13-Apr 17-Apr 21-Apr 25-Apr 29-Apr
A B
C

Figure 4-6. Sea surface temperature (in °C) before (A) and during (B) storm 1 from
MODIS, and temperature recorded at the Santa Monica Pier during April 2007 (C).

waves, the first beginning the afternoon of 23 January and ending the morning of 25
January, and the second beginning the evening of 26 January and lasting through early
morning on 28 January. A total of 9.55 cm of precipitation was recorded over the
duration of the storm. High winds were associated with this storm preventing us from
sampling any earlier than 26 January. Discharge from Ballona Creek was high
(>10 m
3
s
-1
) from 23 January to 28 January, reaching a maximum of 72.8 m
3
s
-1
on 25
January, and remained elevated through 7 February (Figure 4-1B; LACDPW, 2009).
These storms created plumes in the coastal ocean that were apparent at least 15 km
offshore and 15 km alongshore from the mouth of Ballona Creek (Corcoran et al., in
131
132
review). Strong low-salinity signals were observed, especially on 6 January during storm
2 and on 28 January, after the second wave of storm 3 (Figures 4-4 and 4-5).
Phytoplankton Density and Community Structure
Total phytoplankton density was elevated after the April 2007 storm event. Chl a
concentrations inside the plume ranged from 1.0-6.0 mg m
-3
(average of 4.4 and 3.0 mg
m
-3
on 21 April and 24 April, respectively), and the concentration of total phytoplankton
ranged from 375-940 cells mL
-1
(average of 576 and 749 cells mL
-1
on 21 April and 24
April, respectively; Figure 4-7). Outside the plume within upwelled waters, chl a ranged
from 1.0 to 18.0 mg m
-3
, and total phytoplankton ranged from 40-840 cells mL
-1
with the
highest concentrations generally occurring at the middle depth that was sampled (3-7 m).
On 21 April, chl a concentration and the concentration of cells were actually higher
outside the plume within upwelled waters (average concentration of chl a and
phytoplankton cells 7.4 mg m
-3
and 640 cells mL
-1
, respectively). By 24 April,
phytoplankton biomass had dropped considerably outside the plume (average
concentration of chl a and total phytoplankton 1.8 mg m
-3
and 88 cells mL
-1
, respectively)
but remained elevated inside plume waters (3.0 mg m
-3
and 750 cells mL
-1
, respectively).
During and after storm 2, phytoplankton density was quite low with a maximum
chl a concentration of 3.2 mg m
-3
, and a maximum total phytoplankton density of 28 cells
mL
-1
. Concentrations inside the plume were slightly higher on 6 January (average chl a
concentration 1.6 and 1.2 mg m
-3
, and average concentration of cells 20 and 11 cells mL
-1

inside and outside the plume, respectively; Figure 4-7). On 8 January, concentrations
inside and outside the plume were nearly identical (chl a concentration 2.02 and 1.98 mg
m
-3
, and concentration of cells 21 and 20 cells mL
-1
inside and outside the plume,
respectively). Approximately 20 days later during and after storm 3, phytoplankton
density had increased slightly to a maximum chl a concentration of 4.4 mg m
-3
and a
maximum phytoplankton density of 150 cells mL
-1
. The highest phytoplankton densities
occurred on 26 January between the two main waves of the storm (Figure 4-7). Both chl
a concentration and total number of cells dropped over the next few days. Even though
overall density was relatively low, average chl a concentration and total number of cells
were 2.4 and 3.8 times higher in plume waters on 31 January, four days after the storm.
Chlorophyll a (mg m
-3
)
0
1
8
12
inside
outside
1000
400
50
0
Abundance (no. cells mL
-1
)
Storm 1 Storm 2 Storm 3
21 Apr 6 Jan 24 Apr 8 Jan 28 Jan 31 Jan 26 Jan
A
B
2
3
4
100
150
200
600
800
5
6
7
13

Figure 4-7. Average chlorophyll a concentration (A) and total phytoplankton abundance
(from microscope counts; B) inside and outside the stormwater plume after each storm.
Error bars indicate 1 standard deviation.

133
134
A total of 85 species comprising 66 genera were identified from all three storms
(see Appendix D for raw abundance data). In all samples, dinoflagellates and diatoms
made up 94-99.9% of the total number of cells counted. The 2-dimensional NMDS plot
generated using all samples shows that the phytoplankton community was very different
among the three storms (Figure 4-8A). This difference was largely due to shifts in the
proportion of dinoflagellates to diatoms (Figure 4-8B). During storm 1, diatoms
dominated the community making up 93-99% of the total phytoplankton in all samples
(Figures 4-8D and F). This was due to high concentrations of the diatoms Pseudo-
nitzschia spp. (130-660 cells mL
-1
) and Chaetoceros spp. (50-330 cells mL
-1
). These
genera alone constituted 30-80% and 20-70% of the phytoplankton, respectively, during
storm 1. The diatoms Astrionellopsis sp. (1-46 cells mL
-1
), Eucampia spp. (2-26 cells
mL
-1
), and Thalassiosira spp. (1-9 cells mL
-1
) were also abundant. The abundance of
diatoms was slightly higher outside the plume on 21 April (average of 557 and 636 cells
mL
-1
inside and outside the plume, respectively). Three days later on 24 April, diatom
abundance was still high within plume waters but had considerably decreased outside the
plume. On this date, high numbers of empty Pseudo-nitzschia spp. frustules were present
in all samples collected outside the plume and in the deep sample collected inside the
plume. Although only a small proportion of the community consisted of dinoflagellates,
they were consistently more abundant within plume waters (Figures 4-8C and E).
The abundance of Synechococcus spp. and pico-eukaryotes followed a similar
pattern to the diatoms with high abundances on 21 April (average abundance 31 and 24.5
x 10
3
cells mL
-1
, respectively) and a large decrease in abundance outside the plume on 24
April (average of 2.1 and 4.1 x 10
3
cells mL
-1
outside the plume, respectively; Figure 4-
9).

inside
outside
2D Stress: 0.11
Storm 1
Storm 3
Storm 2
25
15
5
0
Abundance (no. cells mL
-1
)
10
20
21
Apr
6
Jan
24
Apr
8
Jan
28
Jan
31
Jan
26
Jan
inside
outside
Dinoflagellates
Storm 1 Storm 2 Storm 3
Diatoms
1000
150
50
0
100
200
400
600
800
50
30
10
0
20
40
60
70
Percent abundance
AB
C D
E
21
Apr
6
Jan
24
Apr
8
Jan
28
Jan
31
Jan
26
Jan
80
60
40
30
50
70
90
100
F
dinoflagellates:diatoms
0.1 0.4 0.7 1.0
Storm 1
Storm 3
Storm 2

Figure 4-8. NMDS of all samples (A), overlain with bubbles representing the ratio of
dinoflagellates to diatoms (B), average abundance of dinoflagellates (C) and diatoms (D),
and average percent abundance of dinoflagellates (E) and diatoms (F) inside and outside
the plume. Error bars indicate 1 standard deviation.

135
Abundance (no. cells mL
-1
) x 10
3
0
5
40
inside
outside
Storm 1 Storm 2 Storm 3
21 Apr 6 Jan 24 Apr 8 Jan 28 Jan 31 Jan 26 Jan
A
B
10
15
20
25
30
35
45
50
Synechococcus spp.
0
5
40
10
15
20
25
30
35
45
50
pico-eukaryotes

Figure 4-9. Average abundances of Synechococcus spp. (A) and pico-eukaryotes (B)
inside and outside the plume during the three storm events. Error bars indicate 1 standard
deviation.

During storm 2, a much higher proportion of the total phytoplankton consisted of
dinoflagellates. The abundance of dinoflagellates was similar to storm 1, but the
abundance of diatoms was up to 2 orders of magnitude lower (Figures 4-8C and D).
Ceratium spp. and Prorocentrum spp. were the dominant dinoflagellates reaching 9.5 and
6.1 cells mL
-1
, and making up 1.2-34% and 1.9-25% of the total phytoplankton,
respectively. Chaetoceros spp. were also abundant reaching 6.1 cells mL
-1
and making
up 7-40% of the total number of cells. Pseudo-nitzschia spp., however, were generally
<1 cell mL
-1
and only made up a few percent of the total phytoplankton. The absolute
136
137
abundance and percent abundance of dinoflagellates was higher in plume waters (Figure
4-7). The abundance of Synechococcus spp. was initially higher in plume waters (42.5
and 23.1 x 10
3
cells mL
-1
inside and outside the plume, respectively on 21 April). Pico-
eukaryote abundance was similar inside and outside the plume on both dates (Figure 4-9).
At the end of the month during storm 3, the community had shifted to one
dominated by diatoms. Again, the abundance of dinoflagellates was similar to the
previous two storms, but the abundance of diatoms had increased from those measured
during storm 2 (Figures 4-8C and D). Unlike storm 1, Chaetoceros spp. and Skeletonema
spp. dominated the phytoplankton with abundances of 2-77 and 0.5-54 cells mL
-1
, and
consisting of 20-63% and 1-44% of the total number of cells, respectively. Of the
dinoflagellates, Ceratium spp. and Prorocentrum spp. were again dominant reaching a
maximum of 3% and 6% of the total phytoplankton, respectively. The percent abundance
of diatoms and dinoflagellates were nearly identical inside and outside the plume (Figures
4-8E and F). Synechococcus spp. were slightly more abundant outside the plume,
especially on 26 January (average abundance 14.7 and 21.8 x 10
3
cells mL
-1
inside and
outside the plume, respectively; Figure 4-9). Initially, the abundance of pico-eukaryotes
was higher inside the plume (average abundance 19.1 and 11.8 x 10
3
cells mL
-1
inside
and outside the plume, respectively, on 26 January), but by 28 January, abundances
inside and outside the plume were nearly identical.
Because of the large differences in phytoplankton communities between storm
events, separate ANOSIM analyses were done for each storm. The community structure
differed inside and outside the plume only during storm 1 (Table 4-1). Differences in
Table 4-1. Results from 2-way crossed ANOSIM with location (inside and outside) and
sample date as factors for each of the three storms. Results are also presented from
pairwise comparisons between factor levels for storm 3.

Factors R p

Storm 1
location 0.520 0.01
sample date (21 Apr. and 24 Apr.) 0.880 0.005

Storm 2
location 0.186 0.02
sample date (6 Jan. and 8 Jan.) 0.328 0.01

Storm 3
location 0.093 0.077
sample date 0.345 0.01

Pairwise comparisons – Storm 3
26 Jan. and 28 Jan. 0. 298 0.001
26 Jan. and 31 Jan. 0.603 0.001
28 Jan. and 31 Jan. 0.203 0.01

community structure were apparent between sampling dates for all storms. Results from
the SIMPER analysis of storm 1 show that 17 of the 66 genera present accounted for over
80% of the average similarity between communities inside and outside the plume (Table
4-2). Nearly 30% of the similarity was accounted for by Pseudo-nitzschia spp. and
Chaetoceros spp., the most abundant diatoms. In general, the average percent abundance
of diatoms was higher outside the plume, and dinoflagellates were more abundant inside
the plume (Table 4-2). These differences were most pronounced for Prorocentrum spp.,
Alexandrium sp., Protoperidnium spp., and Ceratium spp., whose average percent
abundances were 8.8, 5.0, 3.7, and 3.5 times higher inside the plume, respectively. The
average percent abundance of Dictyocha spp., a silicoflagellate, was 3 times higher
138
139
outside the plume, and Eutreptiella sp., a euglenoid, was 8.5 times more abundant inside
the plume.
Table 4-2. Results from a 2-way crossed SIMPER for storm 1 with location (inside and
outside) and sample date (21 April and 24 April) as factors. The average percent
contribution from each taxon to the total similarity between communities inside and
outside the plume is shown.

Average percent abundance Percent Cumulative
Taxon outside inside contribution percent

Pseudo-nitzschia sp. 59.7 59.3 15.63 15.63
Chaetoceros spp. 33.5 32.2 13.01 28.64
Eutreptiella spp. 0.10 0.85 6.56 35.19
Asterionellopsis spp. 2.19 2.59 5.80 41.00
Scrippsiella sp. 0.18 0.47 5.34 46.34
Ceratium spp. 0.11 0.38 5.27 51.61
Prorocentrum spp. 0.04 0.35 5.09 56.70
Eucampia spp. 0.41 1.25 4.03 60.73
Protoperidinium spp. 0.06 0.22 3.86 64.59
Thalassiosira spp. 1.86 1.02 3.56 68.15
diatom 5 0.45 0.23 2.49 70.64
Thalassionema spp. 0.07 0.05 2.09 72.73
Leptocylindrus spp. 0.11 0.08 2.08 74.80
Gyrodinium sp. 0.06 0.10 1.73 76.54
Dictyocha spp. 0.22 0.07 1.72 78.26
unknown pennate diatoms 0.08 0.04 1.40 79.66
Alexandrium sp. 0.01 0.05 1.32 80.98

Comparisons to Previous Studies
All stations sampled in Santa Monica Bay during Bight '03 were located within
stormwater plumes. The samples were collected 1-4 days after each storm event.
Salinity at 1 m ranged from 29.7-33.1 and 27.6-33.0 psu during the first and second
storms, respectively. Extracted chl a concentration (at 1 m depth) ranged from 0.1-5.7
and 0.9-5.3 mg m
-3
, respectively. The taxonomic composition of the phytoplankton in
these samples was not determined. Samples were collected before, during, and up to 7
140
days after the planned release of wastewater during the Hyperion WWTP study. Prior to
the release, phytoplankton density was elevated (1-10 mg m
-3
chl a), and the community
was dominated by dinoflagellates (35-90% of the total number of cells). Within the
wastewater plume, chl a concentration increased from a geometric mean of 0.6 in newly-
surfaced wastewater to 34 mg m
-3
after 7 days (Reifel et al., in prep.). Patchy blooms of
the dinoflagellates Akashiwo sanguinea and Cochlodinium sp. were observed 4 and 7
days after the release.
Because salinity is a conservative tracer of plume waters, it can be used as an
approximation of the age of the plume and the amount of mixing with ambient waters
that has occurred. Figure 4-10 shows the relationship between salinity and chl a
concentration for all samples. Results from storm 1 were not included because salinity
was elevated due to the upwelling event. Chl a concentration was generally low (<2 mg
m
-3
) at salinities <32.0 psu. Between 32.0 and 33.0 psu, chl a concentration was variable
ranging from 1.0-5.7 mg m
-3
. Between 33.2 and ambient salinity (~33.4 psu), chl a
concentration increased dramatically reaching a maximum of 67 mg m
-3
. Chl a
concentration was not always high at salinities >33.2 psu, however, indicating that
dilution, and ultimately time, was not the only important factor.
Turbulence can also structure phytoplankton communities. In Figure 4-11, wave
height was used as a proxy for the level of turbulence in Santa Monica Bay. All storm
events (including the storm just before the start of the Hyperion project) resulted in an
increase in wave height from 0.5-1 m to 2.5-3.0 m. Only after storm 1 and the Hyperion
wastewater release did sampling occur after wave height had decreased to ~1 m. During
the Hyperion wastewater release, the water column was stratified to the surface whereas a
Chlorophyll a (mg m
-3
)
20
28.0 29.0 28.5 29.5
12
8
4
0
30.0 30.5 31.0 31.5 32.0 32.5 33.0
Salinity (psu)
16
60
Storm 2
Storm 3
Hyperion
Bight ‘03

Figure 4-10. Extracted chlorophyll a concentration versus salinity for samples collected
within plume waters during storm 2 and storm 3 (this project), the Hyperion WWTP
project, and the Bight '03 project. Dashed reference lines are at 33.2 and 33.4 psu.

mixed layer between 3 and 6 m in depth was apparent after storm 3 (Figure 4-12).
Statistical analyses indicated differences in the phytoplankton community structure inside
and outside plume waters only during the Hyperion wastewater release and storm 1.
Sampling after storm 1 took place 2 and 5 days after the decrease in wave height. During
the Hyperion WWTP project, sampling occurred up to 8 days after wave height returned
to ambient levels. After storms 2 and 3, all samples were collected during periods of high
waves (1.5-3.0 m) and presumably elevated turbulence. Data from Bight '03 are not
included because no taxonomic data were available from that project.
Cloud cover prevented acquisition of good satellite imagery during and
immediately after the storm events as commonly occurs after storm events in Southern
California (Nezlin et al., 2007). However, MODIS images were available soon after
storm 3. A time series of chl a concentration calculated from MODIS shows the
development of patches of elevated chl a in Santa Monica Bay and around San Pedro Bay
to the south starting 4 days after our last sampling cruise, 8-13 days after storm 3
141
Wave height (m)
0
0.5
1.0
1.5
2.0
A
B
Time (UTC)
1 Apr 8 Apr 15 Apr 22 Apr
Storm 1
Storm 2
2.5
3.0
29 Apr
Hyperion
3.5
Storm 3
0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
1 Jan 8 Jan 15 Jan 22 Jan 29 Jan 5 Feb 12 Feb 19 Feb
0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
15 Nov 22 Nov 29 Nov 6 Dec 13 Dec 20 Dec
C

Figure 4-11. Wave height measured at NBDC buoy 46221 during storm 1 in April 2007
(A), storms 2 and 3 in January 2008 (B), and during the Hyperion WWTP study in
November-December 2006 (C). Vertical lines indicate sampling dates.

142
Temperature (ºC)
Salinity (psu)
Depth (m)
salinity
temperature
0
2
4
6
8
10
12
14
31.0 31.5 32.0 32.5 33.0 33.5 34.0
15.6 16.0 16.4 16.8 17.2
28 November
A
0
1
2
3
4
5
8
10
6
7
9
13.10 13.12 13.14 13.16 13.18
32.8 32.9 33.0 33.1 33.2 33.3
26 January 2008
Temperature (ºC)
Depth (m)
Salinity (psu)
Hyperion WWTP
Storm 3
B
28 January 2008
0
2
4
6
8
10
12
14
16
18
20
13.22 13.26 13.30 13.34
32.0 32.2 32.4 32.6 32.8 33.0 33.2 33.4
C

Figure 4-12. Vertical profiles of temperature and salinity during the Hyperion WWTP
diversion event (A), and during (B) and 1 day after (C) storm 3.
143
4 February 2008 7 February 2008 9 February 2008

Figure 4-13. Time series of surface chlorophyll a concentrations after storm 3.

(Figure 4-13). On 4 February, wave height was still elevated within Santa Monica Bay,
but by 8 February, wave height had decreased considerably (Figure 4-11). Patches of
elevated chl a concentration were apparent on 7 February but especially on 9 February.
DISCUSSION
The results presented here indicate that the phytoplankton community structure
within coastal stormwater plumes is dependent, at least in part, on the ambient
community composition at the time of the storm event. Corcoran et al. (in review) also
noted that bulk phytoplankton responses (chl a concentration and biogenic silica) were
dependent on the ambient conditions within Santa Monica Bay prior to each storm event.
Before the planned discharge from the Hyperion WWTP, abundant species included the
dinoflagellates Akashiwo sanguinea (10-20, up to 105 cells mL
-1
), Cochlodinium sp. (1-
10, up to 45 cells mL
-1
), and Lingulodinium polyedrum (2-20, up to 41 cells mL
-1
), and
the diatom Cylindrotheca sp. (2-20 cells mL
-1
). These species were also the most
abundant within the wastewater plume. The composition of the phytoplankton
community before storms 2 and 3 is not known. Shipe et al. (2008) showed that overall
biomass at the Santa Monica Bay Observatory tended to be lowest in winter, coinciding
144
145
with low concentrations of nutrients, but that the phytoplankton exhibited high
interannual variability. Analysis of phytoplankton pigment concentrations in the Santa
Barbara Channel also revealed that biomass is typically low during winter months and is
dominated by a mixed-assemblage "pre-bloom" community (Anderson et al., 2008).
From these past studies, phytoplankton biomass was likely low prior to storms 2 and 3,
but the community composition could have consisted of any number of mixtures of
phytoplankton groups or species.
Prior to storm 1, a large and dense Pseudo-nitzschia spp. bloom was already in
progress along the California coast presumably in response to the upwelling event (Figure
4-6). High abundances of Pseudo-nitzschia spp. were first detected in March 2007 off
the coast of San Luis Obispo and then appeared to propagate downcoast. Pseudo-
nitzschia spp. abundances began increasing in Los Angeles County toward the end of
April (Langolis, 2007). High densities of Pseudo-nitzschia spp. were detected in San
Pedro Bay (Caron and Schnetzer, USC), Ventura, Santa Barbara, San Luis Obispo,
Monterey Bay and possibly north to San Francisco from early-mid April through the
beginning of May 2007 (California Program for Regional Enhanced Monitoring for
PhycoToxins (Cal-PReEMPT), 2007 Harmful Algal Bloom Event,
http://calpreempt.ucsc.edu/2007HABevent.htm). Densities of Pseudo-nitzschia spp.
recorded at the Santa Cruz wharf during the bloom (100-400 cells mL
-1
) were similar in
magnitude to those observed during storm 1. In Santa Monica Bay, runoff from storm 1
appeared to entrain diatoms that were already present in coastal waters. The plume also
appeared to have sustained the Pseudo-nitzschia spp. bloom after it had crashed outside
plume waters.
146
Though the overall phytoplankton community tended to reflect the ambient
community, shifts in the community structure within plume waters were detected. Clear
increases in phytoplankton density (above ambient density) and shifts in community
structure only occurred after periods of decreased wave activity and were most apparent
once considerable mixing of plume waters with ambient waters had occurred. After the
release of wastewater from the Hyperion WWTP, increases in the percent abundance of
the dinoflagellates Akashiwo sanguinea and Cochlodinium sp. and decreases in the
percent abundance of the dinoflagellate Lingulodinium polyedrum, the diatom
Cylindrotheca spp., and the silicoflagellate Dictyocha spp. were observed within the
plume relative to non-plume waters (Reifel et al., in prep.). Even though diatoms
dominated the phytoplankton after storm 1, their percent abundances were lower within
plume waters while the percent abundance of the dinoflagellates Prorocentrum spp.,
Ceratium spp., Alexandrium sp., and Protoperidinium spp. increased within plume
waters. Bulk phytoplankton measurements (chl a and biogenic silica) also indicated that
the phytoplankton community changed over time favoring larger, non-diatom species in
older plumes (Corcoran et al., in review).
High turbulence has been shown to inhibit cell division and cause morphological
and behavioral changes in dinoflagellates (Thomas and Gibson, 1990; Estrada and
Berdalet, 1997; Fauchot et al., 2005). Community shifts toward dinoflagellates and
dinoflagellate blooms have been observed during periods of low turbulence or low wind
(Estrada et al., 1987; Fauchot et al., 2005). Conditions within plumes (e.g. stratification
and reduced turbulence) would be expected to favor dinoflagellates over diatoms
according to several ecological models of phytoplankton community structure based in
147
part on the effects of turbulence (Margalef, 1978; Margalef et al., 1979; Smayda and
Reynolds, 2001). The species that increased in percent abundance within plumes belong
to Smayda and Reynold's (2001) Type II (Prorocentrum spp. and Protoperidinium spp.)
and Types IV-VI, the "mix-drift" group (A. sanguinea, Cochlodinium sp., Alexandrium
sp., and Ceratium spp.) dinoflagellate functional groups. These functional groups are
based on an ecological model originally developed by Margalef (Margalef, 1978;
Margalef et al., 1979) and updated by Smayda and Reynolds (Smayda and Reynolds,
2001) relating phytoplankton taxonomic composition to axes of nutrient availability and
turbulence or mixing. Types II and IV-VI dinoflagellates tend to occur relatively
nearshore often in physically- or chemically-disturbed water masses, can be favored by
stratification, and are usually tolerant of entrainment within mixing or circulating layers.
It therefore appears that environmental factors (e.g. turbulence and nutrient availability)
were important in structuring the phytoplankton communities within stormwater and
wastewater plumes, and that models based on these factors are able to predict at least
which functional groups are favored by runoff. Dictyocha spp. appears to be functionally
similar to diatoms in that its percent abundance was consistently higher within deeply-
mixed more turbulent water outside the plume. The opposite is true for Eutreptiella sp.
Species within this genus can bloom in response to nutrient inputs especially under
reduced turbulence (Olli et al., 1996).
Based on the results present here, urban runoff, in the form of stormwater or
wastewater, can affect the structure of coastal phytoplankton communities and can
sometimes even stimulate phytoplankton blooms. Both stormwater and wastewater
appear to stimulate dinoflagellates, specifically those adapted to high nutrients and
148
stratification but with some tolerance of turbulence. This only occurs, however, in
extremely dilute plume waters after relaxation of the high turbulence initially generated
by storm events. During the two largest storms sampled in this study (storms 2 and 3),
we were only able to conduct short-term sampling and were not able to follow the
evolution of the phytoplankton through this dilution and relaxation period. For future
studies, we recommend combining ship-based sampling with the use of various remote
sensing platforms such as gliders or drifters to explore the full progression of the
phytoplankton community within stormwater plumes (e.g. Cullen et al., 1997). Mapping
of easy-to-measure chemical and optical parameters such as salinity and CDOM
concentration using gliders could be used to both track the location of the plume and
provide a measure of the dilution of plume waters. Gliders can be outfitted with other
optical sensors to help track and characterize plumes (Dickey et al., 2006). Drifters,
which can be deployed to follow a particular water mass, are useful for determining
variability on the smaller scales to which phytoplankton respond (Abbott et al., 1995;
Abbott and Letelier, 1998). They could be used to determine the approximate location of
the plume, and can be outfitted with various chemical and optical sensors or even
automated samplers. Both drifters and gliders can be deployed for weeks, or longer,
making it possible to follow newly-formed plume waters through their evolution to old,
dilute plumes. Satellite imagery could also be used to observe the large-scale distribution
of plumes through estimates of turbidity and CDOM concentration, and patterns in chl a
concentration (e.g. Warrick et al., 2007; Nezlin et al., 2008). Higher-resolution satellites,
such as the MEdium Resolution Imaging Spectrometer (MERIS; 350 m), would allow
mapping of finer-scale plume features. Future satellites, such as NASA's Hyperspectral
149
Imager for the Coastal Ocean (HREP-HICO), will even have hyperspectral capabilities.
Ship-based sampling could then easily be targeted to determine the phytoplankton
structure in older, more dilute, plume waters or at any spatial/temporal scale deemed
appropriate. In future projects, we plan to combine glider mapping and satellite imagery
with ship-based sampling to better track the evolution of coastal features such as plumes.
150
CHAPTER 4 REFERENCES
Abbott, M. R., Brink, K. H., Booth, C. R., Blasco, D., Swenson, M. S., Davis, C. O. and
Codispoti, L. A., 1995. Scales of variability of bio-optical properties as observed
from near-surface drifters. Journal of Geophysical Research 100(C7), 13345-
13367.
Abbott, M. R. and Letelier, R. M., 1998. Decorrelation scales of chlorophyll as observed
from bio-optical drifters in the California Current. Deep Sea Research Part II:
Topical Studies in Oceanography 45(8-9), 1639-1667.
Anderson, C. R., Siegel, D. A., Brzezinski, M. A. and Guillocheau, N., 2008. Controls on
temporal patterns in phytoplankton community structure in the Santa Barbara
Channel, California. Journal of Geophysical Research 113(C04038),
doi:10.1029/2007JC004321.
APHA, 1998. Method 10200H. Chlorophyll. In: Clesceri, L. S., Eaton, A. D. and
Greenberg, A. E., (Eds.), Standard methods for the examination of water and
wastewater, 20
th
ed. American Public Health Association, American Water Works
Association and Water Pollution Control Federation, Washington D. C.
Belzile, C., Roesler, C. S., Christensen, J. P., Shakhova, N. and Semiletov, I., 2006.
Fluorescence measured using the WETStar DOM fluorometer as a proxy for
dissolved matter absorption. Estuarine, Coastal and Shelf Science 67(3), 441-449.
Bray, J. R. and Curtis, J. T., 1957. An ordination of the upland forest communities of
southern Wisconsin. Ecological Monographs 27(4), 325-349.
Campbell, L., 2001. Flow cytometric analysis of autotrophic picoplankton. In: Paul, J. H.,
(Ed.), Marine Microbiology, Methods in Microbiology vol. 30. Academic Press,
San Diego, California, pp. 317-343.
Clarke, K. R., 1993. Non-parametric multivariate analyses of changes in community
structure. Australian Journal of Ecology 18(1), 117-143.
Clarke, K. R. and Warwick, R. M., 2001. Change in marine communities: An approach to
statistical analysis and interpretation, 2nd ed. PRIMER-E, Plymouth, UK.
Corcoran, A. A., Reifel, K. M., Jones, B. H. and Shipe, R. F., in review. Spatiotemporal
development of physical, chemical, and biological characteristics of stormwater
plumes in Santa Monica Bay, California (USA). Journal of Sea Research.
Crossett, K. M., Culliton, T. J., Wiley, P. C. and Goodspeed, T. R., 2005. Population
trends along the coastal United States: 1980-2008. National Oceanic and
Atmospheric Administration, National Ocean Service, Special Projects Office.
151
Cullen, J. J., Ciotti, A. M., Davis, R. F. and Lewis, M. R., 1997. Optical detection and
assessment of algal blooms. Limnology & Oceanography 42(5 part 2), 1223-
1239.
Dickey, T. D., Lewis, M. and Chang, G., 2006. Optical oceanography: recent advances
and future directions using global remote sensing and in situ observations.
Reviews of Geophysics 44(RG1001), 2003RG000148.
Estrada, M., Alcaraz, M. and Marrasé, C., 1987. Effects of turbulence on the composition
of phytoplankton assemblages in marine microcosms. Marine Ecology Progress
Series 38, 267-281.
Estrada, M. and Berdalet, E., 1997. Phytoplankton in a turbulent world. Scientia Marina
61(suppl. 1), 125-140.
Faith, D. P., Minchin, P. R. and Belbin, L., 1987. Compositional dissimilarity as a robust
measure of ecological distance. Vegetatio 69(1), 57-68.
Fauchot, J., Levasseur, M., Roy, S., Gagnon, R. and Weise, A. M., 2005. Environmental
factors controlling Alexandrium tamarense (Dinophyceae) growth rate during a
red tide event in the St. Lawrence estuary (Canada). Journal of Phycology 41(2),
263-272.
Gordon, L. I., Jennings, J. C. J., Ross, A. A. and Krest, J. M., 1993. A suggested protocol
for continuous flow automated analysis of seawater nutrients (phosphate, nitrate,
nitrite and silicic acid) in the WOCE Hydrographic Program and the Joint Global
Ocean Fluxes Study. WHP Operations and Methods. WOCE Hydrographic
Program Office, Methods Manual WHPO 91-1, Corvallis, Oregon.
Gregorio, D. E. and Pieper, R. E., 2000. Investigations of red tides along the Southern
California coast. Bulletin of the Southern California Academy of Sciences 99(3),
147-160.
Hasle, G. R., 1978. Settling: the inverted-microscope method. In: Sournia, A., (Ed.),
Phytoplankton manual. UNESCO, Paris.
Hayward, T. L., Lynn, R. J., Mantyla, A. W., McGowan, J. A., Smith, P. E., Schwing, F.
B. and Venrick, E. L., 1995. The state of the California Current in 1994-1995: A
period of transition. CalCOFI Reports 36, 19-39.
Horner, R. A., Garrison, D. L. and Plumley, F. G., 1997. Harmful algal blooms and red
tide problems on the U.S. west coast. Limnology & Oceanography 42(5, part 2),
1076-1088.
Kleppel, G. S., 1980. Nutrients and phytoplankton community composition in southern
California coastal waters. CalCOFI Reports 21, 191-196.
152
Kudela, R. M., Lane, J. Q. and Cochlan, W. P., 2008. The potential role of
anthropogenically derived nitrogen in the growth of harmful algae in California,
USA. Harmful Algae 8(1), 103-110.
LACDPW, 2008. Hydrologic report 2006-2007. Los Angeles County Department of
Public Works, Water Resources Division, Alhambra, California.
LACDPW, 2009. Hydrologic report 2007-2008. Los Angeles County Department of
Public Works, Water Resources Division, Alhambra, California.
Langolis, G. W., 2007. Marine Biotoxin Monitoring Program Annual Report 2007.
California Department of Public Health, Division of Drinking Water and
Environmental Management.
Lund, J. W. G., Kipling, C. and Le Cren, E. D., 1958. The inverted microscope method of
estimating algal numbers and the statistical basis of estimations by counting.
Hydrobiologia 11(2), 143-170.
Margalef, R., 1978. Life-forms of phytoplankton as survival alternatives in an unstable
environment. Oceanologica Acta 1(4), 493-509.
Margalef, R., Estrada, M. and Blasco, D., 1979. Functional morphology of organisms
involved in red tides, as adapted to decaying turbulence. In: Taylor, D. L. and
Seliger, H. H., (Eds.), Toxic dinoflagellate blooms. Elsevier North Holland, Inc.,
New York, pp. 89-94.
Nezlin, N. P., DiGiacomo, P. M., Diehl, D. W., Jones, B. H., Johnson, S. C., Mengel, M.
J., Reifel, K. M., Warrick, J. A. and Wang, M., 2008. Stormwater plume detection
by MODIS imagery in the southern California coastal ocean. Estuarine, Coastal
and Shelf Science 80(1), 141-152.
Olli, K., Heiskanen, A.-S. and Seppälä, J., 1996. Development and fate of Eutreptiella
gymnastica bloom in nutrient-enriched enclosures in the coastal Baltic Sea.
Journal of Plankton Research 18(9), 1587-1604.
OPC, 2006. A vision for our ocean and coast: Five-year strategic plan. California Ocean
Protection Council, Sacramento, CA.
Otero, M. P. and Siegel, D. A., 2004. Spatial and temporal characteristics of sediment
plumes and phytoplankton blooms in the Santa Barbara Channel. Deep-Sea
Research Part II 51(10-11), 1129-1149.
Ramos, P. A., Neves, M. V. and Pereira, F. L., 2007. Mapping and initial dilution
estimation of an ocean outfall plume using an autonomous underwater vehicle.
Continental Shelf Research 27(5), 583-593.
153
Reifel, K. M., Johnson, S. C., DiGiacomo, P. M. P. M., Mengel, M. J., Nezlin, N. P.,
Warrick, J. A. and Jones, B. H., 2009. Impacts of stormwater runoff in the
Southern California Bight: Relationships among plume constituents. Continental
Shelf Research 29(15), 1821-1835.
Reifel, K. M., Corcoran, A. A., Dojiri, M., Shipe, R. F. and Jones, B. H., in prep.
Tracking a wastewater plume and its effects on phytoplankton in the coastal
ocean.
Ryan, J. P., Dierssen, H. M., Kudela, R. M., Scholin, C. A., Johnson, K. S., Sullivan, J.
M., Fischer, A. M., Rienecker, E. V., McEnaney, P. R. and Chavez, F. P., 2005.
Coastal ocean physics and red tides: An example from Monterey Bay, California.
Oceanography 18(2), 246-255.
Sakamoto, C. M., Friederich, G. E. and Codispoti, L. A., 1990. MBARI procedures for
automated nutrient analyses using a modified Alpkem series 300 rapid flow
analyzer. MBARI Technical Report No. 90-2. Monterey Bay Aquarium Research
Institute, Moss Landing, CA.
Shipe, R. F., Leinweber, A. and Gruber, N., 2008. Abiotic controls of potentially harmful
algal blooms in Santa Monica Bay, California. Continental Shelf Research 28(18),
2584-2593.
Smayda, T. J. and Reynolds, C. S., 2001. Community assembly in marine phytoplankton:
Application of recent models to harmful dinoflagellate blooms. Journal of
Plankton Research 23(5), 447-461.
Strickland, J. D. H. and Parsons, T. R., 1968. A practical handbook of seawater analysis.
Fisheries Research Board of Canada, Ottawa.
Thomas, W. H. and Gibson, C. H., 1990. Effects of small-scale turbulence on microalgae.
Journal of Applied Phycology 2(1), 71-77.
Trainer, V. L., Adams, N. G., Bill, B. D., Stehr, C. M., Wekell, J. C., Moeller, P.,
Busman, M. and Woodruff, D., 2000. Domoic acid production near California
coastal upwelling zones, June 1998. Limnology & Oceanography 45(8), 1818-
1833.
Warrick, J. A., DiGiacomo, P. M., Weisberg, S. B., Nezlin, N. P., Mengel, M., Jones, B.
H., Ohlmann, J. C., Washburn, L., Terrill, E. J. and Farnsworth, K. L., 2007.
River plume patterns and dynamics within the Southern California Bight.
Continental Shelf Research 27(19), 2427-2448.

154
CHAPTER 5
Summary and Conclusions

INTRODUCTION
Urban runoff from heavily-populated coastal areas can negatively impact water
quality, beneficial uses, and ecosystems of coastal areas (Jickells, 1998; Ahn et al., 2005;
Clarke et al., 2006). In southern California, the majority of urban runoff to the coastal
zone occurs as short, intense flood events after storms which can be difficult to study
(Schiff et al., 2000; Nezlin et al., 2007). Although bacteria, human pathogens, and other
public health concerns have been investigated, few studies have examined the ecological
effects of urban runoff (Schiff et al., 2002). Recently, blooms of potentially harmful
phytoplankton species have been linked to episodic events such as stormwater runoff
(Riegman, 1995; Dagg and Breed, 2003; Hu et al., 2004; Glibert et al., 2005). Urban
runoff, including stormwater, has the potential to alter phytoplankton community
structure due to its effects on light, water column stability, and nutrient availability.
Through my dissertation research, I participated in three projects which examined the
optical characteristics of urban runoff plumes and explored phytoplankton community
changes within the plumes. In the first project, the effects from two winter rain storms on
the coastal ocean of the Southern California Bight were examined during February 2004
and February-March 2005 (Chapter 2). In the second project, a planned release of
wastewater from the Hyperion Wastewater Treatment Plant provided an opportunity to
study the effects of a simulated urban runoff plume from its initial release until it could
no longer be detected in the coastal ocean (Chapter 3). And in the third project, we
155
sampled plumes from three storm events during 2007 and 2008 in Santa Monica Bay,
California (Chapter 4).
SUMMARY OF RESULTS
Chapter 2
Plume waters were characterized by low salinity and high colored dissolved
organic matter (CDOM) concentration relative to ambient waters. High concentrations of
contaminants (nutrients, fecal indicator bacteria) were observed offshore of most of the
major river systems, and bacterial concentrations sometimes exceeded California Ocean
Plan standards (State Water Resources Control Board, 2005). However, the areas of
impact were generally spatially limited, and contaminant concentrations decreased below
the standards typically within 2-3 days. Unlike other river systems, waters offshore of
the Tijuana River consistently exceeded California Ocean Plan standards for multiple
fecal indicator bacteria, and in 2005 the area of exceedence was larger than what could be
mapped based on the sampling grid. This implies that fecal indicator bacteria in the
Tijuana River are concentrated and are not always rapidly diluted or advected from the
region in the first 3-4 days after a storm event. Maximum nitrate concentrations (~40
µM) occurred in the San Pedro Shelf region near the mouth of the Los Angeles River.
Based on the results of general linear models, individual sources of stormwater
differed in both nutrient concentrations and the concentration and composition of fecal
indicator bacteria. While nutrients appeared to decrease in plume waters due to simple
mixing and dilution, the concentration of fecal indicator bacteria in plumes was likely
affected by other factors such as exposure to ultraviolet radiation (Fujioka et al., 1981;
Sinton et al., 2002; Anderson et al., 2005). The relationships between contaminants
156
(nutrients and fecal indicator bacteria) and plume indicators (salinity and total suspended
solids) were not strong indicating the presence of other potentially-important sources
and/or sinks of both nutrients and fecal indicator bacteria. However, California Ocean
Plan standards were often exceeded in waters containing >10% stormwater (<28-30
salinity range). The median concentration dropped below the standard in the 32-33
salinity range (1-4% stormwater) for total coliforms and Enterococcus spp. and in the 28-
30 salinity range (10-16% stormwater) for fecal coliforms. Nutrients showed a similar
pattern with the highest median concentrations in water with > 10% stormwater.
General linear model results of the relationship between salinity and CDOM
indicated that CDOM concentration and composition differed among sources of
stormwater, but CDOM concentration decreased in plume waters through simple mixing
processes. Strong linear relationships between salinity and CDOM, indicating
conservative mixing, have also been observed in other systems (Twardowski and
Donaghay, 2001; Coble et al., 2004). In contrast, relationships between total suspended
solids and beam attenuation varied both among stormwater sources and over time after
each storm event. This implies that particle size and composition changed over time
which would more strongly affect the beam attenuation coefficient (Davies-Colley and
Smith, 2001). Relationships between CDOM and salinity and between total suspended
solids and beam attenuation indicate that readily-measurable optically-active variables,
that can be estimated from ocean color satellite imagery, could be used as proxies to
provide a qualitative, if not quantitative, evaluation of the dissolved, as well as the
particulate, components of stormwater plumes. In this context, both CDOM absorption
and the beam attenuation coefficient can be derived from satellite ocean color
157
measurements of inherent optical properties suggesting that remote sensing of ocean
color should be useful in mapping the spatial areas and durations of impacts from these
contaminants.
Chapter 3
Distinct low-salinity, high-CDOM plume remnants were apparent for at least
seven days after the discharge event from the Hyperion Wastewater Treatment Plant.
Differences in salinity between older plume waters and surrounding waters were small
(0.1-0.2 psu) similar to past plume studies (Petrenko et al., 1997; Ramos et al., 2007).
Similar to rivers (Coble et al., 2004) and to results from Chapter 2, salinity and CDOM
concentration together proved to be useful for tracking the wastewater plume.
Phytoplankton density (chlorophyll a concentration and total number of cells) was
low in initial plume waters and increased over 50-fold by the seventh day after the
diversion. In addition to the overall increase in density, non-metric multi-dimensional
scaling analysis of phytoplankton community structure revealed distinct differences
between communities sampled within and outside of the wastewater plume. This shift in
phytoplankton community structure was due to increased percent abundance of two
dinoflagellates, Akashiwo sanguinea and Cochlodinium sp., and a decrease in percent
abundance of the dinoflagellate Lingulodinium polyedrum, the diatom Cylindrotheca sp.,
and the silicoflagellate Dictyocha spp. within plume waters. Localized blooms of
Cochlodinium sp. and A. sanguinea (chlorophyll a up to 100 mg m
-3
and densities
between 100-2,000 cells mL
-1
) occurred 4-7 days after the diversion in plume remnants.
Although both Cochlodinium sp. and A. sanguinea have been occasionally reported from
California waters, blooms of these species have only recently been observed along the
158
California coast (Curtiss et al., 2008; Caron et al., 2009). They appear to be favored
during strong stratification, low wind stress, and enhanced nutrients. Plumes from other
forms of urban runoff, such as stormwater, create similar conditions and could stimulate
dinoflagellate blooms.
During the post-diversion cruises, measurements of absorption, attenuation, and
scatter were collected while passing through several dinoflagellate blooms providing a
unique opportunity to examine their optical properties. Other studies have suggested that
differences in pigment composition could be used to distinguish absorption by different
phytoplankton taxa (Johnsen et al., 1994; Kirkpatrick et al., 2000). The shapes of
particulate absorption spectra measured within dinoflagellate blooms in this study were
similar to spectra of microphytoplankton and to spectra measured from dinoflagellates in
other studies (Ciotti et al., 2002; Dierssen et al., 2006; Cullen, 2008). However, the
majority of variability due to accessory pigments occurs at short wavelengths (Ciotti et
al., 2002). Particulate absorption spectra are heavily influenced by non-algal particles at
these wavelengths making it difficult to determine phytoplankton taxa without first
separating phytoplankton absorption from absorption of non-algal particles. Attempts
have also been made to glean taxonomic information from reflectance spectra, and
hyperspectral measurements may be able to identify taxonomic groups (Babin et al.,
2005). Spectra of the ratio of scatter to absorption measured during the dinoflagellate
blooms were similar to reflectance spectra measured during blooms of dinoflagellate
species in other studies, especially when the blooms occurred in areas with high CDOM
concentration (Babin et al., 2005; Dierssen et al., 2006; Schofield et al., 2006; Chang and
Dickey, 2008; Cetini ć, 2009). Further research is needed to determine if this combination
159
of high cell density and high CDOM concentration is specific to dinoflagellate blooms.
If so, this spectral shape could be used to identify dinoflagellate blooms through
reflectance spectra measured from satellite ocean color images (e.g. Cetini ć, 2009).
Chapter 4
We sampled plumes from three storm events during 2007 and 2008 in Santa
Monica Bay, California. The first (storm 1) was relatively small (0.8 cm precipitation)
and occurred during an upwelling event and concurrent Pseudo-nitzschia spp. bloom in
April 2007 (Langolis, 2007). The last two storms occurred in January 2008 (3.96 and
9.55 cm precipitation for storms 2 and 3, respectively). Total phytoplankton density was
variable among the three storm events. Chlorophyll a concentrations were elevated after
storm 1 (up to 18 mg m
-3
), were quite low after storm 2 (maximum of 3.2 mg m
-3
), and
were relatively low after storm 3 (maximum of 4.4 mg m
-3
). Non-metric
multidimensional scaling analysis revealed large differences among the phytoplankton
communities present during each storm event. These differences were generally related
to the proportion of dinoflagellates to diatoms, although differences in the dominant
diatom species were also apparent. Differences in the phytoplankton community inside
and outside the plume were only detected after the April 2007 storm event. The percent
abundances of diatom species were generally lower inside the plume even though
diatoms were numerically dominant. Several dinoflagellates (Prorocentrum spp.,
Ceratium spp., Protoperidinium spp., and Alexandrium sp.) comprised a greater percent
of the phytoplankton inside plume waters.
160
CONCLUSIONS
Comparisons between all three projects revealed that dramatic increases in
phytoplankton density only occurred in dilute plume waters (>33.2 psu), and shifts in
community structure were only observed after a period of low wave height. Conditions
within plumes (e.g. stratification and reduced turbulence) would be expected to favor
dinoflagellates according to several ecological models of phytoplankton community
structure (Margalef, 1978; Margalef et al., 1979; Reynolds, 1988). The species that
increased in percent abundance within stormwater and wastewater plumes belong to
Smayda and Reynolds (2001) Type II and Types IV-VI dinoflagellate functional groups.
These functional groups are based on ecological models relating phytoplankton
taxonomic composition to axes of nutrient availability and turbulence or mixing
(Margalef, 1978; Margalef et al., 1979; Reynolds, 1988). Types II and IV-VI
dinoflagellates are predicted to occur relatively nearshore often in physically- or
chemically-disturbed water masses, can be favored by stratification, and are usually
tolerant of entrainment within mixing or circulating layers. Both stormwater and
wastewater appear to stimulate dinoflagellates within these functional groups but only in
extremely dilute plume waters after relaxation of the high turbulence initially generated
by storm events.
It is difficult to track urban runoff plumes at the spatial and temporal scales
needed to observe shifts in the phytoplankton community structure by ship-based
sampling alone. Future studies should combine ship-based sampling with the use of
remote platforms (e.g. gliders or drifters) and satellite imagery to observe the full
progression of the phytoplankton community within urban runoff plumes (e.g.Cullen et
161
al., 1997). Mapping of easy-to-measure chemical and optical parameters such as salinity
and CDOM concentration could be used to both track the location of the plume and
provide a measure of the dilution of plume waters. Both gliders and drifters can also be
outfitted with sensors to measure chlorophyll a concentration, and can be deployed up to
a period of several weeks making it possible to follow newly-formed plume waters
through their evolution to old, dilute plumes. Satellite imagery could be used to observe
the large-scale distribution of plumes through estimates of turbidity and CDOM
concentration, and patters in chlorophyll a concentration (e.g. Warrick et al., 2007;
Nezlin et al., 2008). Higher-resolution satellites (e.g. MERIS) would allow mapping of
finer-scale plume features, and future satellites (e.g. HREP-HICO) will even have
hyperspectral capabilities. Ship-based sampling could then easily be targeted to
determine the phytoplankton community structure in older, more dilute, plume waters or
at any spatial/temporal scale deemed appropriate.

162
CHAPTER 5 REFERENCES
Ahn, J. H., Grant, S. B., Surbeck, C. Q., DiGiacomo, P. M., Nezlin, N. P. and Jiang, S.,
2005. Coastal water quality impact of stormwater runoff from an urban watershed
in southern California. Environmental Science & Technology 39(16), 5940-5953.
Anderson, K. L., Whitlock, J. E. and Harwood, V. J., 2005. Persistence and differential
survival of fecal indicator bacteria in subtropical waters and sediments. Applied
Environmental Microbiology 71(6), 3041–3048.
Babin, M., Cullen, J. J., Roesler, C. S., Donaghay, P. L., Doucette, G. J., Kahru, M.,
Lewis, M. R., Scholin, C. A., Sieracki, M. E. and Sosik, H. M., 2005. New
approaches and technologies for observing harmful algal blooms. Oceanography
18(2), 210-227.
Caron, D. A., Garneau, M.-È., Seubert, E., Howard, M. D. A., Darjany, L., Schnetzer, A.,
Cetinic, I., Filteau, G., Lauri, P., Jones, B. and Trussell, S., 2009. Harmful algae
and their potential impacts on desalination operations off southern California.
Water Research doi:10.1016/j.watres.2009.06.051.
Cetini ć, I., 2009. Harmful algal blooms in the urbanized coastal ocean an application of
remote sensing for understanding, characterization and prediction. PhD
dissertation, University of Southern California, Los Angeles, California.
Chang, G. C. and Dickey, T. D., 2008. Interdisciplinary sampling strategies for detection
and characterization of harmful algal blooms. In: Babin, M., Roesler, C. S. and
Cullen, J. J., (Eds.), Real-time coastal observing systems for marine ecosystem
dynamics and harmful algal blooms. UNESCO, Paris, France, pp. 43-84.
Ciotti, A. M., Lewis, M. and Cullen, J. J., 2002. Assessment of the relationships between
dominant cell size in natural phytoplankton communities and the spectral shape of
the absorption coefficient. Limnology & Oceanography 47(2), 404-417.
Clarke, A. L., Weckström, K., Conley, D. J., Anderson, N. J., Adser, F., Andrén, E., de
Jonge, V. N., Ellegaard, M., Juggins, S., Kauppila, P., Korhola, A., Reuss, N.,
Telford, R. J. and Vaalgamaa, S., 2006. Long-term trends in eutrophication and
nutrients in the coastal zone Limnology & Oceanography 51(1 part 2), 385-397.
Coble, P., Hu, C., Gould, R. W. J., Chang, G. and Wood, M., 2004. Colored dissolved
organic matter in the coastal ocean: An optical tool for coastal zone
environmental assessment and management. Oceanography 17(2), 50-59.
Cullen, J. J., Ciotti, A. M., Davis, R. F. and Lewis, M. R., 1997. Optical detection and
assessment of algal blooms. Limnology & Oceanography 42(5 part 2), 1223-
1239.
163
Cullen, J. J., 2008. Observation and prediction of harmful algal blooms. In: Babin, M.,
Roesler, C. S. and Cullen, J. J., (Eds.), Real-time coastal observing systems for
marine ecosystem dynamics and harmful algal blooms. UNESCO, Paris, France,
pp. 1-41.
Curtiss, C. C., Langlois, G. W., Busse, L. B., Mazzillo, F. and Silver, M. W., 2008. The
emergence of Cochlodinium along the California Coast (USA). Harmful Algae
7(3), 337-346.
Dagg, M. J. and Breed, G. A., 2003. Biological effects of Mississippi River nitrogen on
the northern gulf of Mexico--a review and synthesis. Journal of Marine Systems
43(3-4), 133-152.
Davies-Colley, R. J. and Smith, D. G., 2001. Turbidity, suspended sediment, and water
clarity: A review. Journal of the American Water Resources Association 37(5),
1085-1101.
Dierssen, H. M., Kudela, R. M., Ryan, J. P. and Zimmerman, R. C., 2006. Red and black
tides: Quantitative analysis of water-leaving radiance and perceived color for
phytoplankton, colored dissolved organic matter, and suspended sediments.
Limnology & Oceanography 51(6), 2646–2659.
Fujioka, R. S., Hashimoto, H. H., Siwak, E. B. and Young, R. H. F., 1981. Effect of
sunlight on survival of indicator bacteria in seawater. Applied Environmental
Microbiology 41(3), 690-696.
Glibert, P. M., Anderson, D. M., Gentien, P., Graneli, E. and Sellner, K. G., 2005. The
global, complex phenomena of harmful algal blooms. Oceanography 18(2), 136-
147.
Hu, C., Muller-Karger, F. E., Vargo, G. A., Neely, M. B. and Johns, E., 2004. Linkages
between coastal runoff and the Florida Keys ecosystem: A study of a dark plume
event. Geophysical Research Letters 31(L15307), doi:10.1029/2004GL020382.
Jickells, T. D., 1998. Nutrient biogeochemistry of the coastal zone. Science 281(5374),
217-222.
Johnsen, G., Samset, O., Granskog, L. and Sakshaug, E., 1994. In vivo absorption
characteristics in 10 classes of bloom-forming phytoplankton: taxonomic
characteristics and responses to photoadaptation by means of discriminant and
HPLC analysis. Marine Ecology Progress Series 105, 149-157.
Kirkpatrick, G. J., Millie, D. F., Moline, M. A. and Schofield, O., 2000. Optical
discrimination of a phytoplankton species in natural mixed populations.
Limnology & Oceanography 45(2), 467-471.
164
Langolis, G. W., 2007. Marine Biotoxin Monitoring Program Annual Report 2007.
California Department of Public Health, Division of Drinking Water and
Environmental Management.
Margalef, R., 1978. Life-forms of phytoplankton as survival alternatives in an unstable
environment. Oceanologica Acta 1(4), 493-509.
Margalef, R., Estrada, M. and Blasco, D., 1979. Functional morphology of organisms
involved in red tides, as adapted to decaying turbulence. In: Taylor, D. L. and
Seliger, H. H., (Eds.), Toxic dinoflagellate blooms. Elsevier North Holland, Inc.,
New York, pp. 89-94.
Nezlin, N. P., Weisberg, S. B. and Diehl, D. W., 2007. Relative availability of satellite
imagery and ship-based sampling for assessment of stormwater runoff plumes in
coastal southern California. Estuarine, Coastal and Shelf Science 71(1-2), 250-
258.
Nezlin, N. P., DiGiacomo, P. M., Diehl, D. W., Jones, B. H., Johnson, S. C., Mengel, M.
J., Reifel, K. M., Warrick, J. A. and Wang, M., 2008. Stormwater plume detection
by MODIS imagery in the southern California coastal ocean. Estuarine, Coastal
and Shelf Science 80(1), 141-152.
Petrenko, A. A., Jones, B. H., Dickey, T. D., LeHaitre, M. and Moore, C., 1997. Effects
of a sewage plume on the biology, optical characteristics, and particle size
distributions of coastal waters. Journal of Geophysical Research 102(C11),
25,061-25,071.
Ramos, P. A., Neves, M. V. and Pereira, F. L., 2007. Mapping and initial dilution
estimation of an ocean outfall plume using an autonomous underwater vehicle.
Continental Shelf Research 27(5), 583-593.
Reynolds, C. S., 1988. Functional morphology and the adaptive strategies of freshwater
phytoplankton. In: Sandgren, C. D., (Ed.), Growth and reproductive strategies of
freshwater phytoplankton. Cambridge University Press, New York, pp. 388-433.
Riegman, R., 1995. Nutrient-related selection mechanisms in marine phytoplankton
communities and the impact of eutrophication on the planktonic food web. Water
Science and Technology 32(4), 63-75.
Schiff, K. C., Allen, M. J., Zeng, E. Y. and Bay, S. M., 2000. Southern California.
Marine Pollution Bulletin 41(1-6), 76-93.
Schiff, K. C., Weisberg, S. B. and Raco-Rands, V., 2002. Inventory of ocean monitoring
in the southern California bight. Environmental Management 29(6), 871-876.

165
Schofield, O., Kerfoot, J., Mahoney, K., Moline, M., Oliver, M., Lohrenz, S. E. and
Kirkpatrick, G. J., 2006. Vertical migration of the toxic dinoflagellate Karenia
brevis and the impact on ocean optical properties. Journal of Geophysical
Research 111(C06009), doi:10.1029/2005JC003115.
Sinton, L. W., Hall, C. H., Lynch, P. A. and Davies-Colley, R. J., 2002. Sunlight
inactivation of fecal indicator bacteria and bacteriophages from waste
stabilization pond effluent in fresh and saline waters. Applied Environmental
Microbiology 68(3), 1122–1131.
Smayda, T. J. and Reynolds, C. S., 2001. Community assembly in marine phytoplankton:
Application of recent models to harmful dinoflagellate blooms. Journal of
Plankton Research 23(5), 447-461.
State Water Resources Control Board, 2005. California Ocean Plan. Sacramento,
California.
Twardowski, M. J. and Donaghay, P. L., 2001. Separating in situ and terrigenous sources
of absorption by dissolved materials in coastal waters. Journal of Geophysical
Research 106(C2), 2545-2560.
Warrick, J. A., DiGiacomo, P. M., Weisberg, S. B., Nezlin, N. P., Mengel, M., Jones, B.
H., Ohlmann, J. C., Washburn, L., Terrill, E. J. and Farnsworth, K. L., 2007.
River plume patterns and dynamics within the Southern California Bight.
Continental Shelf Research 27(19), 2427-2448.

166
BIBLIOGRAPHY
Abbott, M. R. and Letelier, R. M., 1998. Decorrelation scales of chlorophyll as observed
from bio-optical drifters in the California Current. Deep Sea Research Part II:
Topical Studies in Oceanography 45(8-9), 1639-1667.
Abbott, M. R., Brink, K. H., Booth, C. R., Blasco, D., Swenson, M. S., Davis, C. O. and
Codispoti, L. A., 1995. Scales of variability of bio-optical properties as observed
from near-surface drifters. Journal of Geophysical Research 100(C7), 13345-
13367.
Ahn, J. H., Grant, S. B., Surbeck, C. Q., DiGiacomo, P. M., Nezlin, N. P. and Jiang, S.,
2005. Coastal water quality impact of stormwater runoff from an urban watershed
in southern California. Environmental Science & Technology 39(16), 5940-5953.
Anderson, C. R., Siegel, D. A., Brzezinski, M. A. and Guillocheau, N., 2008. Controls on
temporal patterns in phytoplankton community structure in the Santa Barbara
Channel, California. Journal of Geophysical Research 113(C04038),
doi:10.1029/2007JC004321.
Anderson, D. M., Andersen, P., Bricelj, V. M., Cullen, J. J. and Rensel, J. E. J., 2001.
Monitoring and management strategies for harmful algal blooms in coastal
waters, APEC #201-MR-01.1. Asia Pacific Economic Program, Singapore, and
Intergovernmental Oceanographic Commission Technical Series No. 59, Paris.
Anderson, K. L., Whitlock, J. E. and Harwood, V. J., 2005. Persistence and differential
survival of fecal indicator bacteria in subtropical waters and sediments. Applied
Environmental Microbiology 71(6), 3041–3048.
APHA, 1998. Method 10200H. Chlorophyll. In: Clesceri, L. S., Eaton, A. D. and
Greenberg, A. E., (Eds.), Standard methods for the examination of water and
wastewater, 20th ed. American Public Health Association, American Water
Works Association and Water Pollution Control Federation, Washington D. C.
APHA, 1998a. 9222D. Fecal coliform membrane filter procedure. In: Franson, M. A. H.,
Clesceri, L. S., Greenberg, A. E. and Eaton, A. D., (Eds.), Standard methods for
the examination of water and wastewater, 20th edition. American Public Health
Association, American Water Works Association, Water Environment Federation,
Washington, DC, pp. 63-65.
APHA, 1998b. 9221B. Standard total coliform fermentation technique. In: Franson, M.
A. H., Clesceri, L. S., Greenberg, A. E. and Eaton, A. D., (Eds.), Standard
methods for the examination of water and wastewater, 20th edition. American
Public Health Association, American Water Works Association, Water
Environment Federation, Washington, DC, pp. 48-51.

167
APHA, 1998c. 9221E. Fecal coliform procedure. In: Franson, M. A. H., Clesceri, L. S.,
Greenberg, A. E. and Eaton, A. D., (Eds.), Standard methods for the examination
of water and wastewater, 20th edition. American Public Health Association,
American Water Works Association, Water Environment Federation,
Washington, DC, pp. 54-55.
APHA, 1998d. 9222B. Standard total coliform membrane filter procedure. In: Franson,
M. A. H., Clesceri, L. S., Greenberg, A. E. and Eaton, A. D., (Eds.), Standard
methods for the examination of water and wastewater, 20th edition. American
Public Health Association, American Water Works Association, Water
Environment Federation, Washington, DC, pp. 57-62.
Babin, M., Cullen, J. J., Roesler, C. S., Donaghay, P. L., Doucette, G. J., Kahru, M.,
Lewis, M. R., Scholin, C. A., Sieracki, M. E. and Sosik, H. M., 2005. New
approaches and technologies for observing harmful algal blooms. Oceanography
18(2), 210-227.
Baduini, C. L., Hyrenbach, K. D., Coyle, K. O., Pinchuk, A., Mendenhall, V. and Hunt,
G. L., 2001. Mass mortality of short-tailed shearwaters in the south-eastern
Bering Sea during summer 1997. Fisheries Oceanography 10(1), 117-130.
Bay, S., Jones, B. H. and Schiff, K., 1999. Study of the impact of stormwater discharge
on Santa Monica Bay. Southern California Coastal Water Quality Research
Project, Westminster, California.
Bay, S., Jones, B. H., Schiff, K. and Washburn, L., 2003. Water quality impacts of
stormwater discharges to Santa Monica Bay. Marine Environmental Research
56(1-2), 205-223.
Belzile, C., Roesler, C. S., Christensen, J. P., Shakhova, N. and Semiletov, I., 2006.
Fluorescence measured using the WETStar DOM fluorometer as a proxy for
dissolved matter absorption. Estuarine, Coastal and Shelf Science 67(3), 441-449.
Beman, J. M., Arrigo, K. R. and Matson, P. A., 2005. Agricultural runoff fuels large
phytoplankton blooms in vulnerable areas of the ocean. Nature 434, 211-214.
Blough, N. V. and Del Vecchio, R., 2002. Chromophoric DOM in the coastal
environment. In: Hansell, D. A. and Carlson, C. A., (Eds.), Biogeochemistry of
marine dissolved organic matter. Academic Press, San Diego, California, pp. 509-
546.
Blough, N. V. and Green, S. A., 1995. Spectroscopic characterization and remote sensing
of non-living organic matter. In: Zepp, R. G. and Sonntag, C., (Eds.), The Dahlem
workshop on the role of nonliving organic matter in the earth’s carbon cycle,
Berlin 1993. Wiley, New York, NY, pp. 23-45.
168
Blough, N. V., Zafiriou, O. C. and Bonilla, J., 1993. Optical absorption spectra of waters
from the Orinoco River outflow: Terrestrial input of colored organic matter to the
Caribbean. Journal of Geophysical Research 98(C2), 2271-2278.
Boss, E., Collier, R., Larson, G., Fennel, K. and Pegau, W. S., 2007. Measurements of
spectral optical properties and their relation to biogeochemical variables and
processes in Crater Lake, Crater Lake National Park, OR. Hydrobiologia 574,
149-159.
Boss, E., Twardowski, M. S. and Herring, S., 2001. Shape of the particulate beam
attenuation spectrum and its inversion to obtain the shape of the particulate size
distribution. Applied Optics 40(27), 4885-4893.
Bray, J. R. and Curtis, J. T., 1957. An ordination of the upland forest communities of
southern Wisconsin. Ecological Monographs 27(4), 325-349.
Bricaud, A., Morel, A. and Prieur, L., 1981. Absorption by dissolved organic matter of
the sea (yellow substance) in the UV and visible domains. Limnology &
Oceanography 26(1), 43-53.
Busse, L. B., Venrick, E. L., Antrobus, R., Miller, P. E., Vigilant, V., Silver, M. W.,
Mengelt, C., Mydlarz, L. and Prezelin, B. B., 2006. Domoic acid in phytoplankton
and fish in San Diego, CA, USA. Harmful Algae 5(1), 91-101.
Cagle, C., 2006. The Water Quality Task Force in Redondo Beach; how a small coastal
city is dealing with red tides and fish kills. Southern California Academy of
Sciences and Society for Environmental Toxicology and Chemistry, Malibu,
California.
Campbell, L., 2001. Flow cytometric analysis of autotrophic picoplankton. In: Paul, J. H.,
(Ed.), Marine Microbiology, Methods in Microbiology vol. 30. Academic Press,
San Diego, California, pp. 317-343.
Carder, K. L. and Steward, R. G., 1985. A remote-sensing reflectance model of a red-tide
dinoflagellate off west Florida. Limnology & Oceanography 30(2), 286-298.
Carder, K. L., Steward, R. G., Harvey, G. R. and Ortner, P. B., 1989. Marine humic and
fulvic acids: Their effects on remote sensing of ocean chlorophyll. Limnology &
Oceanography 34(1), 68-81.
Caron, D. A., Garneau, M.-È., Seubert, E., Howard, M. D. A., Darjany, L., Schnetzer, A.,
Cetinic, I., Filteau, G., Lauri, P., Jones, B. and Trussell, S., 2009. Harmful algae
and their potential impacts on desalination operations off southern California.
Water Research doi:10.1016/j.watres.2009.06.051.
169
CDPH, 2006. Draft guidance for salt water beaches. California Department of Public
Health, Division of Drinking Water and Environmental Management,
Sacramento, CA.
Cetini ć, I., 2009. Harmful algal blooms in the urbanized coastal ocean an application of
remote sensing for understanding, characterization and prediction. PhD
dissertation, University of Southern California, Los Angeles, California.
Chang, G. C. and Dickey, T. D., 2008. Interdisciplinary sampling strategies for detection
and characterization of harmful algal blooms. In: Babin, M., Roesler, C. S. and
Cullen, J. J., (Eds.), Real-time coastal observing systems for marine ecosystem
dynamics and harmful algal blooms. UNESCO, Paris, France, pp. 43-84.
Chen, R. F. and Bada, J. L., 1992. The fluorescence of dissolved organic matter. Marine
Chemistry 37(3-4), 191-221.
Ciotti, A. M., Lewis, M. and Cullen, J. J., 2002. Assessment of the relationships between
dominant cell size in natural phytoplankton communities and the spectral shape of
the absorption coefficient. Limnology & Oceanography 47(2), 404-417.
Clarke, A. L., Weckström, K., Conley, D. J., Anderson, N. J., Adser, F., Andrén, E., de
Jonge, V. N., Ellegaard, M., Juggins, S., Kauppila, P., Korhola, A., Reuss, N.,
Telford, R. J. and Vaalgamaa, S., 2006. Long-term trends in eutrophication and
nutrients in the coastal zone Limnology & Oceanography 51(1 part 2), 385-397.
Clarke, K. R. and Warwick, R. M., 2001. Change in marine communities: An approach to
statistical analysis and interpretation, 2nd ed. PRIMER-E, Plymouth, UK.
Clarke, K. R., 1993. Non-parametric multivariate analyses of changes in community
structure. Australian Journal of Ecology 18(1), 117-143.
Cloern, J. E., Schraga, T. S., Lopez, C. B., Knowles, N., Labiosa, R. G. and Dugdale, R.,
2005. Climate anomalies generate an exceptional dinoflagellate bloom in San
Francisco Bay. Geophysical Research Letters 32(L14608),
doi:10.1029/2005GL023321.
Coble, P. G., 2007. Marine optical biogeochemistry: The chemistry of ocean color.
Chemical Reviews 107(2), 402-418.
Coble, P., Hu, C., Gould, R. W. J., Chang, G. and Wood, M., 2004. Colored dissolved
organic matter in the coastal ocean. Oceanography 17(2), 50-59.
Corcoran, A. A., Reifel, K. M., Jones, B. H. and Shipe, R. F., in review. Spatiotemporal
development of physical, chemical, and biological characteristics of stormwater
plumes in Santa Monica Bay, California (USA). Journal of Sea Research.
170
Craig, S. E., Lohrenz, S. E., Lee, Z., Mahoney, K. L., Kirkpatrick, G. J., Schofield, O. M.
and Steward, R. G., 2006. Use of hyperspectral remote sensing reflectance for
detection and assessment of the harmful alga, Karenia brevis. Applied Optics
45(21), 5414-5425.
Crossett, K. M., Culliton, T. J., Wiley, P. C. and Goodspeed, T. R., 2005. Population
trends along the coastal United States: 1980-2008. National Oceanic and
Atmospheric Administration, National Ocean Service, Special Projects Office.
Cullen, J. J., 2008. Observation and prediction of harmful algal blooms. In: Babin, M.,
Roesler, C. S. and Cullen, J. J., (Eds.), Real-time coastal observing systems for
marine ecosystem dynamics and harmful algal blooms. UNESCO, Paris, France,
pp. 1-41.
Cullen, J. J., Ciotti, A. M., Davis, R. F. and Lewis, M. R., 1997. Optical detection and
assessment of algal blooms. Limnology & Oceanography 42(5 part 2), 1223-
1239.
Curtiss, C. C., Langlois, G. W., Busse, L. B., Mazzillo, F. and Silver, M. W., 2008. The
emergence of Cochlodinium along the California Coast (USA). Harmful Algae
7(3), 337-346.
Dagg, M. J. and Breed, G. A., 2003. Biological effects of Mississippi River nitrogen on
the northern gulf of Mexico-a review and synthesis. Journal of Marine Systems
43(3-4), 133-152.
Davies-Colley, R. J. and Smith, D. G., 2001. Turbidity, suspended sediment, and water
clarity: a review. Journal of the American Water Resources Association 37(5),
1085-1101.
Davies-Colley, R. J., Vant, W. N. and Smith, D. G., 2003. Colour and clarity of natural
waters: Science and management of optical water quality. The Blackburn Press,
Caldwell, New Jersey.
Del Castillo, C. E., 2005. Remote sensing of organic matter in coastal waters. In: Miller,
R. L., Del Castillo, C. E. and McKee, B. A., (Eds.), Remote sensing of coastal
aquatic environments. Technologies, techniques and applications. Springer,
Dordrecht, pp. 157-180.
Dickey, T. D., Lewis, M. and Chang, G., 2006. Optical oceanography: recent advances
and future directions using global remote sensing and in situ observations.
Reviews of Geophysics 44(RG1001), 2003RG000148.
Dierssen, H. M., Kudela, R. M., Ryan, J. P. and Zimmerman, R. C., 2006. Red and black
tides: Quantitative analysis of water-leaving radiance and perceived color for
phytoplankton, colored dissolved organic matter, and suspended sediments.
Limnology & Oceanography 51(6), 2646–2659.
171
D'Sa, E. J., Hu, C., Muller-Karger, F. E. and Carder, K. L., 2002. Estimation of colored
dissolved organic matter and salinity fields in case 2 waters using SeaWiFS:
Examples from Florida Bay and Florida Shelf. Proceedings of the Indian
Academy of Sciences 111(3), 197-207.
D'Sa, E. J., Miller, R. L. and McKee, B. A., 2007. Suspended particulate matter dynamics
in coastal waters from ocean color: Application to the northern Gulf of Mexico.
Geophysical Research Letters 34(L23611), doi:10.1029/2007GL031192.
Estrada, M. and Berdalet, E., 1997. Phytoplankton in a turbulent world. Scientia Marina
61(suppl. 1), 125-140.
Estrada, M., Alcaraz, M. and Marrasé, C., 1987. Effects of turbulence on the composition
of phytoplankton assemblages in marine microcosms. Marine Ecology Progress
Series 38, 267-281.
Faith, D. P., Minchin, P. R. and Belbin, L., 1987. Compositional dissimilarity as a robust
measure of ecological distance. Vegetatio 69(1), 57-68.
Fauchot, J., Levasseur, M., Roy, S., Gagnon, R. and Weise, A. M., 2005. Environmental
factors controlling Alexandrium tamarense (Dinophyceae) growth rate during a
red tide event in the St. Lawrence estuary (Canada). Journal of Phycology 41(2),
263-272.
Faust, M. A. and Gulledge, R. A., 2002. Identifying harmful marine dinoflagellates.
Smithsonian Institution, Washington, DC.
Foster, R. A., Subramaniam, A., Mahaffey, C., Carpenter, E. J., Capone, D. G. and Zehr,
J. P., 2007. Influence of the Amazon River plume on distributions of free-living
and symbiotic cyanobacteria in the western tropical north Atlantic Ocean.
Limnology & Oceanography 52(2), 517–532.
Franks, P. J. S., 1992. Sink or swim: accumulation of biomass at fronts. Marine Ecology
Progress Series 82, 1-12.
Franks, P. J. S., 1997. Spatial patterns in dense algal blooms. Limnology &
Oceanography 42(5, part 2), 1297-1305.
Fujioka, R. S., Hashimoto, H. H., Siwak, E. B. and Young, R. H. F., 1981. Effect of
sunlight on survival of indicator bacteria in seawater. Applied Environmental
Microbiology 41(3), 690-696.
Geesey, G. G., 1993. Microbiology. In: Dailey, M. D., Reish, D. J. and Anderson, J. W.,
(Eds.), Ecology of the Southern California Bight. University of California Press,
Los Angeles, California, pp. 190-232.
172
Gersberg, R. M., Daft, D. and Yorkey, D., 2004. Temporal pattern of toxicity in runoff
from the Tijuana River watershed. Water Research 38(3), 559-568.
Geyer, W. R., Hill, P., Milligan, T. and Traykovski, P., 2000. The structure of the Eel
River plume during floods. Continental Shelf Research 20(16), 2067-2093.
Glibert, P. M., Anderson, D. M., Gentien, P., Graneli, E. and Sellner, K. G., 2005. The
global, complex phenomena of harmful algal blooms. Oceanography 18(2), 136-
147.
Gordon, L. I., Jennings, J. C. J., Ross, A. A. and Krest, J. M., 1993. A suggested protocol
for continuous flow automated analysis of seawater nutrients (phosphate, nitrate,
nitrite and silicic acid) in the WOCE Hydrographic Program and the Joint Global
Ocean Fluxes Study. WHP Operations and Methods. WOCE Hydrographic
Program Office, Methods Manual WHPO 91-1, Corvallis, Oregon.
Gregorio, D. E. and Pieper, R. E., 2000. Investigations of red tides along the Southern
California coast. Bulletin of the Southern California Academy of Sciences 99(3),
147-160.
Griffith, J., Aumand, L., Lee, I., McGee, C., Othman, L., Ritter, K., Walker, K. and
Weisberg, S., 2006. Comparison and verification of bacterial water quality
indicator measurement methods using ambient coastal water samples.
Environmental Monitoring and Assessment 116(1-3), 335-344.
Hallegraeff, G. M., 1993. A review of harmful algal blooms and their apparent global
increase. Phycologia 32(2), 79-99.
Hardy, J. T., 1993. Phytoplankton. In: Dailey, M. D., Reish, D. J. and Anderson, J. W.,
(Eds.), Ecology of the Southern California Bight. University of California Press,
Los Angeles, California, pp. 233-265.
Hasle, G. R., 1978. Settling: the inverted-microscope method. In: Sournia, A., (Ed.),
Phytoplankton manual. UNESCO, Paris.
Hayward, T. L., Lynn, R. J., Mantyla, A. W., McGowan, J. A., Smith, P. E., Schwing, F.
B. and Venrick, E. L., 1995. The state of the California Current in 1994-1995: A
period of transition. CalCOFI Reports 36, 19-39.
Helsel, D. R., 2005. Nondetects and data analysis. Wiley, New York, NY.
Hickey, B. M., 1993. Physical oceanography. In: Dailey, M. D., Reish, D. J. and
Anderson, J. W., (Eds.), Ecology of the Southern California Bight. University of
California Press, Los Angeles, California, pp. 19-70.
173
Hill, P. S., Milligan, T. G. and Geyer, W. R., 2000. Controls on effective settling velocity
of suspended sediment in the Eel River flood plume. Continental Shelf Research
20(16), 2095-2111.
Horner, R. A., 2002. A taxonomic guide to some common marine phytoplankton.
Biopress Limited.
Horner, R. A., Garrison, D. L. and Plumley, F. G., 1997. Harmful algal blooms and red
tide problems on the U.S. west coast. Limnology & Oceanography 42(5, part 2),
1076-1088.
Hu, C., Muller-Karger, F. E., Vargo, G. A., Neely, M. B. and Johns, E., 2004. Linkages
between coastal runoff and the Florida Keys ecosystem: A study of a dark plume
event. Geophysical Research Letters 31(L15307), doi:10.1029/2004GL020382.
Iwataki, M., Kawami, H., Mizushima, K., Mikulski, C. M., Doucette, G. J., Relox, J. R.
J., Anton, A., Fukuyo, Y. and Matsuoka, K., 2008. Phylogenetic relationships in
the harmful dinoflagellate Cochlodinium polykrikoides (Gymnodiniales,
Dinophyceae) inferred from LSU rDNA sequences. Harmful Algae 7(3), 271-277.
Jackson, G. A., 1986. Physical oceanography of the Southern California Bight. In:
Eppley, R. W., (Ed.), Lecture notes on coastal and estuarine studies vol. 15,
Plankton dynamics of the Southern California Bight. Springer-Verlag, New York,
NY, pp. 13-52.
Jeffrey, S. W. and Vesk, M., 1997. Introduction to marine phytoplankton and their
pigment signatures. In: Jeffrey, S. W., Mantoura, R. F. C. and Wright, S. W.,
(Eds.), Phytoplankton pigments in oceanography: Guidelines to modern methods.
UNESCO Publishing, Paris, pp. 37-85.
Jessup, D. A., Miller, M. A., Ryan, J. P., Nevins, H. M., Kerkering, H. A., Mekebri, A.,
Crane, D. B., Johnson, T. A. and Kudela, R. M., 2009. Mass stranding of marine
birds caused by a surfactant-producing red tide. PLoS ONE 4(2), e4550.
Jickells, T. D., 1998. Nutrient biogeochemistry of the coastal zone. Science 281(5374),
217-222.
Johnsen, G., Samset, O., Granskog, L. and Sakshaug, E., 1994. In vivo absorption
characteristics in 10 classes of bloom-forming phytoplankton: taxonomic
characteristics and responses to photoadaptation by means of discriminant and
HPLC analysis. Marine Ecology Progress Series 105, 149-157.
Johnson, D. R., Miller, J. and Schofield, O., 2003. Dynamics and optics of the Hudson
River outflow plume. Journal of Geophysical Research 108(C10), 21-1 to 21-9.
174
Jones, B. H., Noble, M. A. and Dickey, T. D., 2002. Hydrographic and particle
distributions over the Palos Verdes Continental Shelf: spatial, seasonal and daily
variability. Continental Shelf Research 22(6-7), 945-965.
Kahru, M., Mitchell, B. G., Diaz, A. and Miura, M., 2004. MODIS detects a devastating
algal bloom in Paracas Bay, Peru. EOS 85(45), 465-472.
Kirk, J. T. O., 1994. Light and photosynthesis in aquatic ecosystems. Cambridge
University Press, Cambridge.
Kirkpatrick, G. J., Millie, D. F., Moline, M. A. and Schofield, O., 2000. Optical
discrimination of a phytoplankton species in natural mixed populations.
Limnology & Oceanography 45(2), 467-471.
Kishino, M., Takahashi, M., Okami, N. and Ichimura, S., 1985. Estimation of the spectral
absorption coefficients of phytoplankton in the sea. Bulletin of Marine Science
37(2), 634-642.
Kleppel, G. S., 1980. Nutrients and phytoplankton community composition in southern
California coastal waters. CalCOFI Reports 21, 191-196.
Kudela, R. M. and Cochlan, W. P., 2000. Nitrogen and carbon uptake kinetics and the
influence of irradiance for a red tide bloom off southern California. Aquatic
Microbial Ecology 21(1), 31-47.
Kudela, R. M., Lane, J. Q. and Cochlan, W. P., 2008. The potential role of
anthropogenically derived nitrogen in the growth of harmful algae in California,
USA. Harmful Algae 8(1), 103-110.
Kudela, R. M., Ryan, J. P., Blakely, M. D., Lane, J. Q. and Peterson, T. D., 2008. Linking
the physiology and ecology of Cochlodinium to better understand harmful algal
bloom events: A comparative approach. Harmful Algae 7(3), 278-292.
LACDPW, 2008. Hydrologic report 2006-2007. Los Angeles County Department of
Public Works, Water Resources Division, Alhambra, California.
LACDPW, 2009. Hydrologic report 2007-2008. Los Angeles County Department of
Public Works, Water Resources Division, Alhambra, California.
LADPW, 2004. Ballona Creek watershed management plan. Los Angeles Department of
Public Works, Los Angeles.
Langolis, G. W., 2007. Marine Biotoxin Monitoring Program Annual Report 2007.
California Department of Public Health, Division of Drinking Water and
Environmental Management.
175
LARWQCB, 1994. Water quality control plan Los Angeles region: basin plan for the
coastal watersheds of Los Angeles and Ventura Counties. California Water
Quality Control Board, Los Angeles Region, Los Angeles.
Lee, L. and Helsel, D. R., 2005. Statistical analysis of water-quality data containing
multiple detection limits: S-language software for regression on order statistics.
Computers & Geosciences 31, 1241-1248.
Lee, Z., Carder, K. L. and Arnone, R. A., 2002. Deriving inherent optical properties from
water color: A multiband quasi-analytical algorithm for optically deep waters.
Applied Optics 41(27), 5755-5772.
Lee, Z., Carder, K. L. and Arnone, R. A., 2006. The quasi-analytical algorithm. In: Lee,
Z., (Ed.), Remote sensing of inherent optical properties: Fundamental tests of
algorithms and applications. IOCCG, Dartmouth, Canada, pp. 73-79.
Lotze, H. K., Lenihan, H. S., Bourque, B. J., Bradbury, R. H., Cooke, R. G., Kay, M. C.,
Kidwell, S. M., Kirby, M. X., Peterson, C. H. and Jackson, J. B. C., 2006.
Depletion, degradation, and recovery potential of estuaries and coastal seas.
Science 312(5781), 1806-1809.
Lund, J. W. G., Kipling, C. and Le Cren, E. D., 1958. The inverted microscope method of
estimating algal numbers and the statistical basis of estimations by counting.
Hydrobiologia 11(2), 143-170.
Lyon, G. S., Petschauer, D. and Stein, E. D., 2006. Effluent discharges to the Southern
California Bight from large municipal wastewater treatment facilities in 2003 and
2004. In: Weisberg, S. B. and Miller, K., (Eds.), Southern California Coastal
Water Research Project 2005-06 biennial report. Southern California Coastal
Water Research Project, Westminster, CA, pp. 1-15.
Margalef, R., 1978. Life-forms of phytoplankton as survival alternatives in an unstable
environment. Oceanologica Acta 1(4), 493-509.
Margalef, R., Estrada, M. and Blasco, D., 1979. Functional morphology of organisms
involved in red tides, as adapted to decaying turbulence. In: Taylor, D. L. and
Seliger, H. H., (Eds.), Toxic dinoflagellate blooms. Elsevier North Holland, Inc.,
New York, pp. 89-94.
McClure, K. A., 2001. Stormwater plumes in Santa Monica Bay. Thesis, Master of Arts,
University of California at Santa Barbara, Santa Barbara.
McGowan, J. and Carter, M., 2008. Scripps Pier chlorophyll and HAB time series. The
Regional Workshop for Harmful Algal Blooms (HABs) in California Coastal
Waters. Report #565. Southern California Coastal Water Research Program,
Costa Mesa, California, pp. B-4.
176
McKee, D., Cunningham, A. and Jones, K. J., 2002. Optical and hydrographic
consequences of freshwater run-off during spring phytoplankton growth in a
Scottish fjord. Journal of Plankton Research 24(11), 1163-1171.
Mertes, L. A. K. and Warrick, J. A., 2001. Measuring flood output from 110 coastal
watersheds in California with field measurements and SeaWiFS. Geology 29(7),
659-662.
Messer, J. W. and Dufour, A. P., 1998. A rapid, specific membrane filtration procedure
for enumeration of Enterococci in recreational water. Applied and Environmental
Microbiology 64(2), 678-680.
Mobley, C. D., 1994. Light and water, radiative transfer in natural waters. Academic
Press, Inc., San Diego, CA.
Monahan, E. C. and Pybus, M. J., 1978. Colour, ultraviolet absorbance and salinity of the
surface waters off the west coast of Ireland. Nature 274(5673), 782-784.
National Research Council, 1990. Monitoring southern California coastal waters.
National Academy Press, Washington, DC.
Nezlin, N. P. and DiGiacomo, P. M., 2005. Satellite ocean color observations of
stormwater runoff plumes along the San Pedro Shelf (southern California) during
1997–2003. Continental Shelf Research 25, 1692-1711.
Nezlin, N. P., DiGiacomo, P. M., Diehl, D. W., Jones, B. H., Johnson, S. C., Mengel, M.
J., Reifel, K. M., Warrick, J. A. and Wang, M., 2008. Stormwater plume detection
by MODIS imagery in the southern California coastal ocean. Estuarine, Coastal
and Shelf Science 80(1), 141-152.
Nezlin, N. P., DiGiacomo, P. M., Stein, E. D. and Ackerman, D., 2005. Stormwater
runoff plumes observed by SeaWiFS radiometer in the Southern California Bight.
Remote Sensing of Environment 98, 494-510.
Nezlin, N. P., DiGiacomo, P. M., Weisberg, S. B., Diehl, D. W., Warrick, J. A., Mengel,
M. J., Jones, B. H., Reifel, K. M., Johnson, S. C., Ohlmann, J. C., Washburn, L.
and Terrill, E. J., 2007a. Southern California Bight 2003 regional monitoring
program: V. Water quality. Southern California Coastal Water Research Project,
Costa Mesa, CA.
Nezlin, N. P., Oram, J. J., DiGiacomo, P. M. and Gruber, N., 2004. Sub-seasonal to
interannual variations of sea surface temperature, salinity, oxygen anomaly, and
transmissivity in Santa Monica Bay, California from 1987 to 1997. Continental
Shelf Research 24, 1053-1082.

177
Nezlin, N. P., Weisberg, S. B. and Diehl, D. W., 2007. Relative availability of satellite
imagery and ship-based sampling for assessment of stormwater runoff plumes in
coastal southern California. Estuarine, Coastal and Shelf Science 71(1-2), 250-
258.
Ocean Software, 2009. Interactive Graphical Ocean Database System (IGODS).
Olli, K., Heiskanen, A.-S. and Seppälä, J., 1996. Development and fate of Eutreptiella
gymnastica bloom in nutrient-enriched enclosures in the coastal Baltic Sea.
Journal of Plankton Research 18(9), 1587-1604.
OPC, 2006. A vision for our ocean and coast: Five-year strategic plan. California Ocean
Protection Council, Sacramento, CA.
Opsahl, S. and Benner, R., 1998. Photochemical reactivity of dissolved lignin in river and
ocean waters. Limnology & Oceanography 43(6), 1297-1304.
Otero, M. P. and Siegel, D. A., 2004. Spatial and temporal characteristics of sediment
plumes and phytoplankton blooms in the Santa Barbara Channel. Deep-Sea
Research Part II 51(10-11), 1129-1149.
Petrenko, A. A., Jones, B. H., Dickey, T. D., LeHaitre, M. and Moore, C., 1997. Effects
of a sewage plume on the biology, optical characteristics, and particle size
distributions of coastal waters. Journal of Geophysical Research 102(C11),
25,061-25,071.
Pfaff, J. D., 1993. Method 300.0: Determination of inorganic anions by ion
chromatography, revision 2.1. In: Methods for the determination of inorganic
substances in environmental samples (EPA/600/R-93/100). Environmental
Monitoring Systems Laboratory, Office of Research and Development, U.S.
Environmental Protection Agency, Office of Water, Cincinnati, Ohio, pp. 300.0-
1–300.0-30.
Pullen, J. D. and Allen, J. S., 2000. Modeling studies of the coastal circulation off
Northern California: shelf response to a major Eel River flood event. Continental
Shelf Research 20(16), 2213-2238.
R Development Core Team, 2006. R: A Language and Environment for Statistical
Computing, v. 2.4.1. R Foundation for Statistical Computing, Vienna, Austria.
Ragan, M. A., 2003. Response of inherent optical properties to stormwater runoff in
Santa Monica Bay, California. Master of Science, University of Southern
California, Los Angeles, CA.

178
Ramos, P. A., Neves, M. V. and Pereira, F. L., 2007. Mapping and initial dilution
estimation of an ocean outfall plume using an autonomous underwater vehicle.
Continental Shelf Research 27(5), 583-593.
Reifel, K. M., Corcoran, A. A., Dojiri, M., Shipe, R. F. and Jones, B. H., in prep.
Tracking a wastewater plume and its effects on phytoplankton in the coastal
ocean.
Reifel, K. M., Johnson, S. C., DiGiacomo, P. M. P. M., Mengel, M. J., Nezlin, N. P.,
Warrick, J. A. and Jones, B. H., 2009. Impacts of stormwater runoff in the
Southern California Bight: Relationships among plume constituents. Continental
Shelf Research 29(15), 1821-1835.
Reynolds, C. S., 1988. Functional morphology and the adaptive strategies of freshwater
phytoplankton. In: Sandgren, C. D., (Ed.), Growth and reproductive strategies of
freshwater phytoplankton. Cambridge University Press, Cambridge, UK, pp. 388-
433.
Reynolds, C. S., 1997. Vegetative processes in the pelagic: A model for ecosystem
theory. Ecology Institute, Oldendorf/Luhe, Germany.
Riegman, R., 1995. Nutrient-related selection mechanisms in marine phytoplankton
communities and the impact of eutrophication on the planktonic food web. Water
Science and Technology 32(4), 63-75.
Roesler, C. S. and Boss, E., 2008. In situ measurement of inherent optical properties and
potential for harmful algal bloom detection and coastal ecosystem observations.
In: Babin, M., Roesler, C. S. and Cullen, J. J., (Eds.), Real-time coastal observing
systems for marine ecosystem dynamics and harmful algal blooms. UNESCO,
Paris, France, pp. 153-206.
Round, F. E., Crawford, C. M. and Mann, D. G., 1990. The diatoms: Biology and
morphology of the genera. Cambridge University Press, New York.
Ryan, J. P., Dierssen, H. M., Kudela, R. M., Scholin, C. A., Johnson, K. S., Sullivan, J.
M., Fischer, A. M., Rienecker, E. V., McEnaney, P. R. and Chavez, F. P., 2005.
Coastal ocean physics and red tides: An example from Monterey Bay, California.
Oceanography 18(2), 246-255.
Ryan, J. P., Gower, J. F. R., King, S. A., Bissett, W. P., Fischer, A. M., Kudela, R. M.,
Kolber, Z., Mazzillo, F., Rienecker, E. V. and Chavez, F. P., 2008. A coastal
ocean extreme bloom incubator. Geophysical Research Letters 35(L12602),
doi:10.1029/2008GL034081.

179
Sakamoto, C. M., Friederich, G. E. and Codispoti, L. A., 1990. MBARI procedures for
automated nutrient analyses using a modified Alpkem series 300 rapid flow
analyzer. MBARI Technical Report No. 90-2. Monterey Bay Aquarium Research
Institute, Moss Landing, CA.
Schiff, K. C., Allen, M. J., Zeng, E. Y. and Bay, S. M., 2000. Southern California.
Marine Pollution Bulletin 41(1-6), 76-93.
Schiff, K. C., Weisberg, S. B. and Raco-Rands, V., 2002. Inventory of Ocean Monitoring
in the Southern California Bight. Environmental Management 29(6), 871-876.
Schnetzer, A., Miller, P. E., Schaffner, R. A., Stauffer, B. A., Jones, B. H., Weisberg, S.
B., DiGiacomo, P. M., Berelson, W. M. and Caron, D. A., 2007. Blooms of
Pseudo-nitzschia and domoic acid in the San Pedro Channel and Los Angeles
harbor areas of the Southern California Bight, 2003–2004. Harmful Algae 6(3),
372-387.
Schofield, O. M., Bosch, J., Glenn, S., Kirkpatrick, G. J., Kerfoot, J., Lohrenz, S. E.,
Moline, M. A., Oliver, M. J. and Bissett, W. P., 2008. Bio-optics in integrated
ocean observing networks: potential for studying harmful algal blooms. In: Babin,
M., Roesler, C. S. and Cullen, J. J., (Eds.), Real-time coastal observing systems
for marine ecosystem dynamics and harmful algal blooms. UNESCO, Paris,
France, pp. 85-107.
Schofield, O., Kerfoot, J., Mahoney, K., Moline, M., Oliver, M., Lohrenz, S. E. and
Kirkpatrick, G. J., 2006. Vertical migration of the toxic dinoflagellate Karenia
brevis and the impact on ocean optical properties. Journal of Geophysical
Research 111(C06009), doi:10.1029/2005JC003115.
SEMP, 2006a. Los Angeles Basin's 1938 catastrophic flood event. Biot Reports #0369
(June 7, 2006) http://www.semp.us/publications/biot_reader.php?BiotID=369.
SEMP, 2006b. Infamous New Year’s Day flood, Los Angeles Basin, 1934. Biot Reports
#0365 (May 28, 2006)
http://www.semp.us/publications/biot_reader.php?BiotID=365.
Shipe, R. F., Leinweber, A. and Gruber, N., 2008. Abiotic controls of potentially harmful
algal blooms in Santa Monica Bay, California. Continental Shelf Research 28(18),
2584-2593.
Shumway, R. H., Azari, R. S. and Kayhanian, M., 2002. Statistical approaches to
estimating mean water quality concentrations with detection limits.
Environmental Science & Technology 36(15), 3345-3353.
Sidio, L. A., 2007. Effluent quality. In: Santa Monica Bay biennial assessment report:
2005-06. City of Los Angeles, Department of Public Works, Environmental
Monitoring Division, Los Angeles, California, pp. 1-5.
180
Siegel, D. A., Maritorena, S., Nelson, N. B., Hansell, D. A. and Lorenzi-Kayser, M.,
2002. Global distribution and dynamics of colored dissolved and detrital organic
materials. Journal of Geophysical Research 107(C12), 3228,
doi:10.1029/2001JC000965.
Sinton, L. W., Hall, C. H., Lynch, P. A. and Davies-Colley, R. J., 2002. Sunlight
inactivation of fecal indicator bacteria and bacteriophages from waste
stabilization pond effluent in fresh and saline waters. Applied Environmental
Microbiology 68(3), 1122–1131.
Slade, W., Langner, M. R., Boss, E. and Roesler, C. S., 2006. Retrieving in situ
particulate absorption spectra in optically clear waters: An example from the
Equatorial Pacific. Ocean Optics XVIII.
Smayda, T. J. and Reynolds, C. S., 2001. Community assembly in marine phytoplankton:
Application of recent models to harmful dinoflagellate blooms. Journal of
Plankton Research 23(5), 447-461.
Smayda, T. J. and Reynolds, C. S., 2003. Strategies of marine dinoflagellate survival and
some rules of assembly. Journal of Sea Research 49(2), 95-106.
Smayda, T. J., 1990. Novel and nuisance phytoplankton blooms in the sea: evidence for a
global epidemic. In: Granéli, E., Sundström, B., Edler, L. and Anderson, D. M.,
(Eds.), Toxic Marine Phytoplankton. Elsevier Science Publishing Co., Inc., New
York, pp. 29-40.
Smayda, T. J., 1997. Harmful algal blooms: Their ecophysiology and general relevance
to phytoplankton blooms in the sea. Limnology & Oceanography 42(5), 1137-
1153.
Smayda, T. J., 2002. Adaptive ecology, growth strategies and the global bloom expansion
of dinoflagellates. Journal of Oceanography 58(2), 281-294.
SSI, 2004a. SigmaPlot, v. 9.01. Systat Software, Inc., Richmond, California.
SSI, 2004b. SYSTAT For Windows, v. 11. Systat Software, Inc., Richmond, California.
Staehr, P., Waite, A. and Markager, S., 2009. Effects of sewage water on bio-optical
properties and primary production of coastal systems in West Australia.
Hydrobiologia 620(1), 191-205.
State Water Resources Control Board, 2005. California Ocean Plan. Sacramento,
California.
Strickland, J. D. H. and Parsons, T. R., 1968. A practical handbook of seawater analysis.
Fisheries Research Board of Canada, Ottawa.
181
Subramaniam, A. and Carpenter, E. J., 1994. An empirically derived protocol for the
detection of blooms of the marine cyanobacterium Trichodesmium using CZCS
imagery. International Journal of Remote Sensing 15(8), 1559-1569.
Sullivan, J. M., Twardowski, M. S., Donaghay, P. L. and Freeman, S. A., 2005. Use of
optical scattering to discriminate particle types in coastal waters. Applied Optics
44(9), 1667-1680.
Sullivan, J. M., Twardowski, M. S., Zaneveld, J. R. V., Moore, C. C., Barnard, A. H.,
Donaghay, P. L. and Rhoades, B., 2006. The hyperspectral temperature and salt
dependencies of absorption by water and heavy water in the 400-750 nm spectral
range. Applied Optics 45(21), 5294-5309.
Sverdrup, H. U., 1953. On conditions for the vernal blooming of phytoplankton. Journal
du Conseil International pour l'Exploration de la Mer 18, 287-295.
Terry, R. D., Keesling, S. A. and Uchupi, E., 1956. Submarine geology of Santa Monica
Bay, California. Report to Hyperion Engineers, Inc., Geology Department,
University of Southern California, Los Angeles, California.
Thomas, W. H. and Gibson, C. H., 1990. Effects of small-scale turbulence on microalgae.
Journal of Applied Phycology 2(1), 71-77.
Thompson, G. B. and Ho, J., 1981. Some effects of sewage discharge upon
phytoplankton in Hong Kong. Marine Pollution Bulletin 12(5), 168-173.
Tomas, C. R. (ed.), 1997. Identifying marine phytoplankton. Academic Press, London.
Trainer, V. L., Adams, N. G., Bill, B. D., Stehr, C. M., Wekell, J. C., Moeller, P.,
Busman, M. and Woodruff, D., 2000. Domoic acid production near California
coastal upwelling zones, June 1998. Limnology & Oceanography 45(8), 1818-
1833.
Turner, J. T., Borkman, D. G., Lincoln, J. A., Gauthier, D. A. and Petitpas, C. M., 2009.
Plankton studies in Buzzards Bay, Massachusetts, USA. VI. Phytoplankton and
water quality, 1987 to 1998. Marine Ecology Progress Series 376, 103-122.
Twardowski, M. J. and Donaghay, P. L., 2001. Separating in situ and terrigenous sources
of absorption by dissolved materials in coastal waters. Journal of Geophysical
Research 106(C2), 2545-2560.
Twardowski, M. S., Boss, E., Macdonald, J. B., Pegau, W. S., Barnard, A. H. and
Zaneveld, J. R. V., 2001. A model for estimating bulk refractive index from the
optical backscattering ratio and the implications for understanding particle
composition in case I and case II waters. Journal of Geophysical Research
106(C7), 14,129-14,142.
182
Twardowski, M. S., Boss, E., Sullivan, J. M. and Donaghay, P. L., 2004. Modeling the
spectral shape of absorption by chromophoric dissolved organic matter. Marine
Chemistry 89(1-4), 69-88.
USEPA, 1983. Method 160.2: Residue, non-filterable (gravimetric, dried at 103-105 ºC).
In: Methods for chemical analysis of water and wastes, EPA 600/4-79/020.
United States Environmental Protection Agency, Office of Research and
Development, Cincinnati, Ohio, pp. 160 2-1–160 2-3.
USEPA, 1995. Purple urchin, Strongylocentrotus purpuratus, and sand dollar,
Dendraster excentricus, fertilization test method. In: Chapman, G. A., Denton, D.
L. and Lazorchak, J. M., (Eds.), Short-term methods for estimating the chronic
toxicity of effluents and receiving water to West Coast marine and estuarine
organisms, EPA600/R-95/136. United States Environmental Protection Agency,
Office of Research and Development, Cincinnati, Ohio, pp. 389-465.
van Maren, D. S. and Hoekstra, P., 2005. Dispersal of suspended sediments in the turbid
and highly stratified Red River plume. Continental Shelf Research 25(4), 503-
519.
Vodacek, A., Blough, N. V., DeGrandpre, M. D., Peltzer, E. T. and Nelson, R. K., 1997.
Seasonal variation of CDOM and DOC in the Middle Atlantic Bight: Terrestrial
inputs and photooxidation. Limnology & Oceanography 42(4), 674-686.
Voss, M., Liskow, I., Pastuszak, M., Rüb, D., Schulte, U. and Dippner, J. W., 2005.
Riverine discharge into a coastal bay: A stable isotope study in the Gulf of
Gdansk, Baltic Sea. Journal of Marine Systems 57(1-2), 127-145.
Warrick, J. A., DiGiacomo, P. M., Weisberg, S. B., Nezlin, N. P., Mengel, M., Jones, B.
H., Ohlmann, J. C., Washburn, L., Terrill, E. J. and Farnsworth, K. L., 2007.
River plume patterns and dynamics within the Southern California Bight.
Continental Shelf Research 27(19), 2427-2448.
Warrick, J. A., Mertes, L. A. K., Washburn, L. and Siegel, D. A., 2004. Dispersal forcing
of southern California river plumes, based on field and remote sensing
observations. Geo-Marine Letters 24(1), 46-52.
Warrick, J. A., Washburn, L., Brzezinski, M. A. and Siegel, D. A., 2005. Nutrient
contributions to the Santa Barbara Channel, California, from the ephemeral Santa
Clara River. Estuarine, Coastal and Shelf Science 62(4), 559-574.
Washburn, L., McClure, K. A., Jones, B. H. and Bay, S. M., 2003. Spatial scales and
evolution of stormwater plumes in Santa Monica Bay. Marine Environmental
Research 56(1-2), 103-125.
Watanabe, M. and Harashima, A., 1986. Interaction between motile phytoplankton and
langmuir circulation. Ecological Modeling 31(1-4), 175-183.
183
Yentsch, C. S. and Vaccaro, R. F., 1958. Phytoplankton nitrogen in the oceans.
Limnology & Oceanography 3(4), 443-448.
ZoBell, C. E., 1941. The occurrence of coliform bacteria in oceanic water. Journal of
Bacteriology 42, 284.
184
APPENDIX A

Table A-1. Station locations from the Hyperion Wastewater Treatment Plant diversion
event.

Station Latitude (°N) Longitude (°W) Station Latitude (°N) Longitude (°W)

28 and 29 November 30 November
1 33.9472 118.4558 1 33.9175 118.4487
2 33.9401 118.4517 2 33.9046 118.4430
3 33.9333 118.4477 3 33.8830 118.4475
4 33.9309 118.4557 4 33.9412 118.4743
5 33.9248 118.4529 5 33.9257 118.5071
6 33.9271 118.4443 6 34.0312 118.5503
7 33.9210 118.4407 7 33.9993 118.5126
8a 33.9186 118.4481 8 33.9681 118.5235
8b 33.9190 118.4482 9 33.9781 118.4862
8c 33.9182 118.4478 10 33.9265 118.5047
9 33.9158 118.4568 11 33.9179 118.4476
10 33.9097 118.4530
11 33.9124 118.4451 4 December
12 33.9147 118.4373 1 34.0046 118.5043
13 33.9086 118.4341 2 33.9771 118.5209
14 33.9061 118.4424 3 33.9496 118.5376
15 33.9026 118.4508 4 33.9221 118.5542
16 33.8966 118.4470 5 33.9256 118.5216
17 33.8988 118.4395 6 33.9290 118.4891
18 33.9015 118.4315 7 33.9325 118.4565
19 33.8860 118.4324 8 33.9058 118.4728
20 33.8925 118.4357 9 33.8792 118.4890
21 33.8948 118.4279 10 33.8525 118.5053
22 33.8886 118.4240 11 33.8551 118.4731
23 33.8823 118.4194 12 33.8578 118.4409
24 33.8750 118.4161 13 33.8604 118.4087
14 33.8834 118.4243

185
Table A-1 (continued)

Station Latitude (°N) Longitude (°W)

7 December
1 34.0046 118.5043
2 33.9771 118.5209
3 33.9496 118.5376
4 33.9221 118.5542
5 33.9256 118.5216
6 33.9290 118.4891
7 33.9325 118.4565
8 33.9058 118.4728
9 33.8792 118.4890
10 33.8525 118.5053
11 33.8551 118.4731
12 33.8578 118.4409
13 33.8584 118.4298
14 33.8604 118.4087
15 33.8815 118.4195
16 33.8952 118.4274
17 33.9617 118.4550

186
APPENDIX B

Table B-1. Raw cell counts from Hyperion Wastewater Treatment Plant diversion event.

27 November 2006
Taxon 3 4 7 8a 9 13 14 15

Dinoflagellates no. cells mL
-1

Akashiwo sanguinea 10.07 47.06 105.55 8.61 1.63 7.98 19.86 0.73
Alexandrium sp. 0.09 0 0 0 0 0 0 0
Ceratium azoricum 0 0 0 0 0 0 0 0
Ceratium balechii 0 0.18 0 0 0 0 0 0
Ceratium furca 0.09 0.36 0.04 0.63 0.18 0.18 0.09 0.45
Ceratium fusis 0.09 0.01 0.04 0 0.01 0.01 0.09 0
Ceratium lineatum 0.09 0.18 0 0.01 0.09 0 0.09 0.09
Ceratium macroceros 0 0.01 0 0 0 0.01 0 0
Ceratium tripos 0 0 0 0 0 0 0 0
Cochlodinium sp. 8.43 31.28 44.61 1.54 0.18 3.72 18.41 0.09
Dinophysis acuminata/fortii 0.73 0.73 1.09 0.36 0.09 0.27 0.45 0.09
Dinophysis caudata 0.09 0 0 0.09 0.01 0.01 0.09 0.09
Dinophysis rotundata 0.01 0 0 0 0 0 0 0
Dissodinium pseudolunula 0 0 0 0 0 0 0 0
Gonyaulax sp. 0.27 0.91 1.09 0.18 0.09 0 0.27 0.18
gymnodinioid 0.27 0.45 0.73 0.27 0.09 0.09 0.09 0.09
Lingulodinium polyedrum 19.22 40.99 32.28 17.95 2.36 6.71 22.49 1.45
Oxytoxum/Oxyphysis spp. 0.18 0.18 0.36 0 0.27 0 0.18 0.09
Polykrikos sp. 0.01 1.81 2.18 0 0 0 0.27 0
Prorocentrum micans/small 1.45 3.17 3.63 2.45 0.63 0.27 1.90 1.36
Protoperidinium spp. 0.09 0.63 0 0.73 0.18 0.09 0.91 0.45
Protoperidinium steinii 0.01 0 0 0 0 0.01 0.09 0.01
Scrippsiella sp. 0 0.63 1.09 0.18 0.09 0.18 0.73 0.18
long pointed dino 1.72 15.23 27.93 0.91 1.00 0.36 6.89 1.63
Podolampas sp? 0 0 0 0 0 0 0 0
big Oxytoxum/Oxyphysis? 0 0 0 0 0 0 0 0
spiral dino 0 0 0 0 0 0 0 0
big dino 0 0 0 0 0 0 0 0
small unknown dinos 0.36 0.45 1.81 0.18 0.73 0.18 0.54 0.28

187
Table B-1 (continued)

27 November 2006
Taxon 3 4 7 8a 9 13 14 15

Diatoms no. cells mL
-1

Asterionellopsis sp. 0 0 0 0.09 0.27 0 0 0.36
Asteromphalus sp. 0 0 0 0 0 0 0 0
Bacillaria sp.? 0 0 0 0 0 0 0 0
Bacteriastrum sp. 0 0 0 0 0.09 0 0 0
Bacterosira spp. 0 0 0 0 0 0 0 0
Cerataulina sp. 0.09 0.09 0 0 0.18 0 0.27 0.27
Chaetoceros spp. 0 0.01 0.73 0 0 0.18 0.01 0.63
Coscinodiscus spp. 0 0 0 0 0 0 0 0
Cylindrotheca sp. 1.99 12.51 19.59 2.90 4.72 3.90 10.07 7.89
Entomoneis sp. 0 0 0 0 0 0 0 0
Fragilariopsis sp. 0 0.01 0 0 0 0.09 0.01 0.01
Grammatophora sp. 0 0 0 0 0 0 0 0
Guinardia spp. 0.36 0.37 0 0.36 0.63 0.73 0.55 1.09
Hemiaulus spp. 0 0.18 0 0 0.18 0 0.01 0.18
Eucampia/Hemialus sp. 0 0 0 0 0 0 0 0
Leptocylindrus sp. 2 0 0.18 0 0.45 0.54 0 0 0.18
Navicula sp. 0 0 0.36 0 0.36 0 0.27 0.36
Odontella aurita 0 0 0 0 0 0 0 0
Pleurosigma sp. 0.09 0 0 0 0 0 0 0
Pseudonitzschia spp. 0 0 0.73 0 0.18 0 0.09 0
Rhizosolenia spp. 0 0.01 0 0.01 0.01 0.18 0.27 0.09
Skeletonema sp. 0 0 0 0 0 0 0 0
Suriella sp. 0 0 0 0 0 0 0 0
Thalassionema nitzschioides 0.18 0 0.04 0 0.09 0 0.27 0
Thalassionema spp. 0 0 0 0 0 0.01 0.01 0
lg Thalassiosira spp. 0.09 0 0 0.01 0 0 0 0
sm Thalassiosira spp. 0 0.18 0 0 0.36 0.01 0.27 0.09
unk pennates 0.91 0.37 0.73 0 0.28 0.54 0.02 0.37
unk centrics 0 0 0 0 0 0.09 0 0
unk diatom 5 0 0 0 0 0 0 0 0
unk diatom 8 0.09 0.01 0 0 0 0 0 0

Other groups
Dictyocha spp. 2.45 2.18 1.81 1.36 1.00 1.00 1.27 1.81
Eutreptiella sp. 0.36 0.18 0.36 0.27 0.09 0 0.09 0.01
Total number of cells 50 161 247 40 17 27 87 21

Synechococcus sp. 48,976 51,185 50,945 39,631 39,440 40,838 43,497 37,827
pico-eukaryotes 15,724 28,846 29,912 12,830 13,433 11,025 13,556 12,850

tintinnids 0.09 0.36 0.36 0.27 0 0.18 0.45 0.18
other ciliates 4.53 8.71 22.49 2.54 5.26 1.72 7.16 6.26

Total number of cells does not include Synechococcus spp. or pico-eukaryotes; ciliates (including
tintinnids) were not included in analyses.

188
Table B-1 (continued)

29 November 2006
Taxon 3 4 7 8a 8c 9 13 14 15 21 20

Dinoflagellates no. cells mL
-1

Akashiwo sanguinea 4.35 3.08 15.51 0 0.27 1.54 11.43 15.87 22.49 25.84 58.76
Alexandrium sp. 0 0 0 0 0 0 0 0 0 0.09 0
Ceratium azoricum 0 0 0 0 0 0 0 0 0 0 0
Ceratium balechii 0.04 0 0.27 0 0 0 0 0.01 0.01 0 0.73
Ceratium furca 1.81 0.54 3.81 0.09 0.73 0.27 1.00 0.63 1.36 2.27 3.26
Ceratium fusis 0 0.01 0.01 0 0 0 0.09 0 0.09 0.09 0
Ceratium lineatum 0.36 0.18 0.27 0 0 0.01 0 0.36 0.09 0.18 0
Ceratium macroceros 0 0 0.09 0 0 0.01 0 0 0 0 0
Ceratium tripos 0 0 0 0 0 0 0 0.09 0 0 0
Cochlodinium sp. 413.13 79.43 218.53 4.26 39.17 98.29 58.85 67.19 19.31 39.90 46.43
Dinophysis acuminata/fortii 0.36 0.27 0.82 0 0.18 0.27 0.27 0.36 1.09 0.09 0.73
Dinophysis caudata 0 0.18 0.18 0 0 0.01 0.01 0 0.01 0.01 0.73
Dinophysis rotundata 0.04 0 0 0 0.09 0 0 0 0 0.09 0
Dissodinium pseudolunula 0 0 0 0 0 0 0 0 0 0 0
Gonyaulax sp. 0.36 0.45 0.36 0 0 0.27 0.09 0.27 0.18 0.09 0
gymnodinioid 0 0.36 0.09 0 0 0 0 0.63 0.63 0.09 1.09
Lingulodinium polyedrum 19.95 18.23 25.30 0.18 0.54 12.06 1.99 7.07 22.85 14.15 58.76
Oxytoxum/Oxyphysis spp. 0.04 0.18 0.27 0 0 0.01 0.09 0.01 0.01 0.09 0
Polykrikos sp. 0.73 0.09 0.82 0 0.01 0.36 0.09 0.36 0.91 1.18 1.45
Prorocentrum micans/small 1.09 0 0.45 0.09 0.01 0 0.09 0.36 0.36 0.09 1.09
Protoperidinium spp. 0.73 1.27 1.45 0.01 0.18 0.45 0.45 0.73 1.18 1.00 0.73
Protoperidinium steinii 0 0.36 0.09 0.27 0.09 0.18 0.36 0.18 0.36 0.36 0
Scrippsiella sp. 0 0.09 0.36 0 0.18 0.18 0.09 0.27 0.63 0.18 0.36
long pointed dino 4.72 4.08 6.35 0.54 1.18 3.72 6.17 7.80 9.34 11.88 19.22
Podolampas sp? 0 0 0 0 0 0 0 0 0 0 0
big Oxytoxum/Oxyphysis? 0 0 0 0 0 0 0 0 0 0 0
spiral dino 0 0 0 0 0.09 0 0 0 0 0 0
big dino 0 0 0 0 0 0 0 0 0 0.18 0
small unknown dinos 0.73 0.54 0.54 0 0.01 0.27 0.45 0.36 0.91 0.27 2.18

189
Table B-1 (continued)

29 November 2006
Taxon 3 4 7 8a 8c 9 13 14 15 21 20

Diatoms no. cells mL
-1

Asterionellopsis sp. 0 0 0 0 0 0 0 0 0 0 0
Asteromphalus sp. 0 0 0 0 0 0 0 0 0 0 0
Bacillaria sp.? 0 0 0.09 0 0 0.01 0 0 0 0 0
Bacteriastrum sp. 0 0.01 0.09 0 0 0 0 0 0 0 0
Bacterosira spp. 0 0 0 0 0 0.09 0 0.01 0 0 0.36
Cerataulina sp. 0 0 0 0.01 0 0.09 0 0.18 0 0 0.01
Chaetoceros spp. 0.01 0.18 0.09 0 0 0.09 0 0.09 0.09 0.01 0
Coscinodiscus spp. 0 0 0 0 0 0 0 0 0 0 0
Cylindrotheca sp. 7.62 8.16 4.53 0.36 0.18 1.63 0.54 0.82 9.25 8.61 8.34
Entomoneis sp. 0 0 0 0 0 0 0 0 0 0 0
Fragilariopsis sp. 0 0.01 0.01 0 0 0 0 0.09 0 0 0
Grammatophora sp. 0 0 0 0 0 0 0 0 0 0 0
Guinardia spp. 0 0.09 0.18 0.09 0 0 0.27 0.09 0.01 0.27 0.73
Hemiaulus spp. 0 0 0 0 0 0 0 0 0.01 0 0
Eucampia/Hemialus sp. 0 0 0.01 0 0 0 0 0 0 0 0
Leptocylindrus sp. 2 0 0.01 0 0 0 0.36 0.36 0.01 0.54 0 0
Navicula sp. 0 0.45 0 0 0 0.09 0.09 0 0 0 0
Odontella aurita 0 0 0 0 0 0 0 0 0.18 0.36 0
Pleurosigma sp. 0.36 0 0.01 0.09 0.18 0.01 0.01 0.01 0.01 0.01 0
Pseudonitzschia spp. 0 0 0 0 0 0 0.09 0 0.18 0.09 0
Rhizosolenia spp. 0.36 0.09 0.09 0 0.01 0.09 0.01 0.09 0.01 0.09 0
Skeletonema sp. 0 0 0 0 0 0 0 0 0 0 0
Suriella sp. 0 0 0 0 0 0 0 0 0 0 0
Thalassionema nitzschioides 0.36 0 0.27 0 0 0 0.09 0 0 0 0.01
Thalassionema spp. 0 0.01 0 0.09 0.09 0 0 0 0 0 0
lg Thalassiosira spp. 0 0 0 0 0 0 0 0.09 0 0 0
sm Thalassiosira spp. 0 0 0.18 0 0.18 0 0.09 0 0.09 0.36 0
unk pennates 0.36 0.09 0.18 0.27 0.10 0.10 0.10 0.09 0.18 0.18 0.40
unk centrics 0.36 0 0.18 0 0 0 0 0 0 0 0
unk diatom 5 0 0 0 0 0.09 0.18 0 0 0 0.36 0
unk diatom 8 0.01 0.09 0.09 0 0 0.18 0 0 0.09 0 0

Other groups
Dictyocha spp. 1.81 0.36 0.27 0.09 0 0.27 0 0.09 0.27 0.09 0.36
Eutreptiella sp. 1.09 0.27 0.18 0 0 0 0.18 0.18 0.27 0.09 0.36
Total number of cells 461 119 282 7 44 121 84 104 93 109 206

Synechococcus sp. 30,439 15,865 25,931 5,830 14,784 20,863 22,083 16,243 9,434 13,067 13,166
pico-eukaryotes 7,312 11,404 9,703 2,447 3,298 3,287 5,143 11,166 12,535 15,923 10,004

tintinnids 0.36 0.27 0.09 0 0 0.09 0.09 0.09 0.82 0.27 1.09
other ciliates 7.62 8.16 3.45 1.99 3.54 2.18 3.72 4.26 5.17 8.61 5.08

Total number of cells does not include Synechococcus spp. or pico-eukaryotes; ciliates (including
tintinnids) were not included in analyses.

190
Table B-1 (continued)

4 December 2006
Taxon 3 5 7 8 9 10 11 12 13 14

Dinoflagellates no. cells mL
-1

Akashiwo sanguinea 0 0 33.91 17.05 1.09 0 0 0.45 17.77 108.45
Alexandrium sp. 0.09 0 0 0 0 0 0 0 0 0
Ceratium azoricum 0 0 0 0 0 0 0.09 0 0 0
Ceratium balechii 0 0 0.02 0.36 0 0 0 0 0 0
Ceratium furca 0.18 0 1.09 1.09 5.80 0 0.01 0.45 0.45 0.36
Ceratium fusis 0.01 0.01 0.18 0 0.04 0.01 0.09 0.01 0 0
Ceratium lineatum 0 0.27 0 0 0 0 0 0.09 0.01 0
Ceratium macroceros 0 0 0 0 0 0.01 0 0 0 0
Ceratium tripos 0 0 0 0 0 0 0 0 0 0
Cochlodinium sp. 2.18 0.63 180.09 2287.98 263.33 0 2.36 29.20 4.53 29.38
Dinophysis acuminata/fortii 0.01 0.01 0.18 0.73 0.36 0.01 0 0.01 0.36 0.04
Dinophysis caudata 0 0 0.36 0.04 0.36 0 0 0.01 0.01 0
Dinophysis rotundata 0 0 0 0 0 0 0 0 0 0
Dissodinium pseudolunula 0 0 0 0 0 0 0 0 0 0
Gonyaulax sp. 0 0 0.36 0.73 0.04 0 0 0 0.09 0
gymnodinioid 0 0 0.36 0 0.36 0 0 0.18 0.36 1.45
Lingulodinium polyedrum 0.18 0.01 3.26 3.99 4.35 0.01 0.09 1.45 5.08 6.17
Oxytoxum/Oxyphysis spp. 0 0 0 1.81 0.36 0.01 0.09 0.09 0.18 0
Polykrikos sp. 0 0.01 0.54 1.09 0.04 0.01 0.01 0 0.09 0
Prorocentrum micans/small 0 0 0 0.36 0.36 0 0.09 0.09 0 0
Protoperidinium spp. 0.09 0.01 0.36 0.73 0.73 0 0.09 0.27 0.27 0.04
Protoperidinium steinii 0.18 0 0.36 0.73 0.36 0 0.09 0.27 0 0.04
Scrippsiella sp. 0.18 0 0.36 0.36 1.45 0 0.09 0.18 0.27 1.45
long pointed dino 0.27 0.09 8.71 1.45 7.25 0.09 0.01 1.63 3.99 8.34
Podolampas sp? 0 0 0 0 0 0 0.09 0 0 0
big Oxytoxum/Oxyphysis? 0 0 0 0 0 0.09 0 0 0 0
spiral dino 0 0 0 0 0 0 0 0 0 0
big dino 0 0 0 0 0 0 0 0 0 0
small unknown dinos 0.18 0 1.63 1.09 0.36 0 0.18 0.27 0.27 1.45

191
Table B-1 (continued)

4 December 2006
Taxon 3 5 7 8 9 10 11 12 13 14

Diatoms no. cells mL
-1

Asterionellopsis sp. 0.36 0.01 0.02 0 0 0 0.18 0.27 0.09 2.90
Asteromphalus sp. 0 0 0 0 0.04 0 0 0 0 0
Bacillaria sp.? 0 0 0 0 0 0 0 0 0 0
Bacteriastrum sp. 0 0 0.18 0.36 0 0 0.01 0 0 0
Bacterosira spp. 0 0 0.91 0 0.36 0 0 0.01 0 0
Cerataulina sp. 0 0 0 0.36 0 0 0 0 0 0
Chaetoceros spp. 0 0 0.36 0.73 1.45 0 0.18 0 0.63 5.08
Coscinodiscus spp. 0 0 0 0 0 0 0 0 0 0
Cylindrotheca sp. 5.98 6.17 5.44 8.34 9.43 0.63 2.99 1.99 7.07 16.32
Entomoneis sp. 0 0 0 0 0 0 0 0 0 0
Fragilariopsis sp. 0 0.09 0.36 0 0.04 0 0 0 0 0
Grammatophora sp. 0 0 0 0 0 0 0 0 0 0
Guinardia spp. 0.10 0.18 0.38 0.36 1.09 0 0 0.10 0.63 4.35
Hemiaulus spp. 0 0.18 0.18 0.36 0 0 0 0.18 0.09 0
Eucampia/Hemialus sp. 0 0 0 0 0 0 0.09 0 0 0
Leptocylindrus sp. 2 0 0 0 1.81 0 0 0 0.01 2.36 3.99
Navicula sp. 0.18 0.54 0.18 0.36 1.09 0.45 0.09 0 0.27 0
Odontella aurita 0 0 0 0 0 0 0 0 0 0
Pleurosigma sp. 0 0 0.73 0.04 0.04 0 0 0 0.01 0.36
Pseudonitzschia spp. 0 0 0.54 2.54 1.09 0 0 0.73 0.63 0
Rhizosolenia spp. 0 0.18 0 0.04 0.73 0 0.09 0.27 0.18 0.36
Skeletonema sp. 0 0 0 0 0 0 0 0 0 0
Suriella sp. 0 0 0 0 0 0 0 0 0 0
Thalassionema nitzschioides 0 0.01 0.36 0.04 0 0.01 0 0 0 0
Thalassionema spp. 0.02 0 0.01 0.01 0 0 0.09 0 0 0
lg Thalassiosira spp. 0 0 0 0 0 0 0 0 0 0
sm Thalassiosira spp. 0 0.27 0.36 0.36 1.09 0 0.09 0.09 0.45 2.18
unk pennates 0 0.09 1.27 0 0 0 0 0 0.27 0.36
unk centrics 0 0 0.18 0 0 0 0 0 0 0
unk diatom 5 0 0 0.36 0.36 0 0 0 0 1.18 0.36
unk diatom 8 0 0 0 0 0 0 0 0 0 0

Other groups
Dictyocha spp. 1.18 1.09 0.73 2.54 1.45 1.00 1.36 0.73 0.63 1.45
Eutreptiella sp. 0.18 0.09 0 0 0.73 0.09 0.01 0.09 0 0.73
Total number of cells 12 10 245 2,338 305 2 9 39 48 196

Synechococcus sp. 25,333 22,775 8,973 3,586 6,479 23,273 21,734 24,548 11,095 20,400
pico-eukaryotes 10,109 7,925 19,040 11,666 26,520 9,830 7,223 13,097 8,234 25,548

tintinnids 0.45 0.09 0.36 0.36 0.04 0.27 0.09 0.45 0.18 0
other ciliates 8.71 3.90 16.32 14.87 11.24 2.54 5.71 5.98 3.81 5.44

Total number of cells does not include Synechococcus spp. or pico-eukaryotes; ciliates (including
tintinnids) were not included in analyses.
192
Table B-1 (continued)

7 December 2006
Taxon 1 3 5 7 8 9 10 11 12 14 13 17

Dinoflagellates no. cells mL
-1

Akashiwo sanguinea 37.36 0.01 0.36 45.70 7.89 19.04 0.45 0.09 0.27 14.87 3.99 852.37
Alexandrium sp. 0 0 0 0 0 0 0 0 0 0 0 0
Ceratium azoricum 0 0.09 0 0 0 0 0 0 0 0 0 0
Ceratium balechii 0.04 0 0 0 0 0.18 0 0 0 0.09 0.01 0.04
Ceratium furca 1.09 0.36 0.01 2.72 0.45 0.82 0.01 0.01 0 0.45 0.36 0.36
Ceratium fusis 0 0 0 0.02 0.01 0.09 0 0.09 0.01 0.01 0.01 0
Ceratium lineatum 0.04 0 0.01 0.36 0.09 0.01 0 0 0.01 0.01 0.09 0
Ceratium macroceros 0 0 0 0 0 0 0 0 0 0 0 0
Ceratium tripos 0 0 0 0 0 0 0 0 0 0 0 0
Cochlodinium sp. 145.81 37.54 6.62 59.67 30.29 44.34 2.18 4.72 4.08 1.18 15.14 805.94
Dinophysis acuminata/fortii 0.36 0 0 0.02 0.09 0.18 0.01 0 0 0.18 0 1.81
Dinophysis caudata 0.73 0 0.09 0.02 0.01 0 0.09 0 0 0.01 0 0.04
Dinophysis rotundata 0 0 0 0 0 0 0 0 0 0 0 0.36
Dissodinium pseudolunula 0 0 0.09 0 0 0 0 0 0 0 0 0
Gonyaulax sp. 0.36 0 0 0 0 0.09 0 0 0 0 0 0.36
gymnodinioid 0.36 0 0 0.54 0.09 0 0 0 0 0.09 0 1.09
Lingulodinium polyedrum 4.35 0.63 0.27 7.44 1.63 1.00 0.18 0.36 0.18 1.54 1.18 9.07
Oxytoxum/Oxyphysis spp. 0 0.18 0.09 0.18 0.18 0 0 0.01 0.01 0.01 0.01 0.36
Polykrikos sp. 0.36 0.01 0 0.73 0.09 0 0 0 0.01 0.01 0.01 0.36
Prorocentrum micans/small 0 0 0 0 0 0 0 0.09 0 0.27 0.01 0.73
Protoperidinium spp. 1.45 0.01 0.09 0.18 0.09 0.36 0.09 0.01 0.09 0.09 0.18 0.36
Protoperidinium steinii 0 0.09 0.09 0.54 0.01 0.27 0.27 0 0 0.36 0.01 1.45
Scrippsiella sp. 0 0.18 0.09 0.36 0.18 0.45 0.01 0.09 0.09 1.09 0.18 3.99
long pointed dino 2.54 0.63 0.36 3.81 2.90 3.26 0.09 0.01 0.09 1.72 1.18 14.15
Podolampas sp? 0 0 0 0 0 0 0 0 0 0 0 0
big Oxytoxum/Oxyphysis? 0 0 0 0 0 0 0 0 0 0 0 0
spiral dino 0 0 0 0 0 0 0 0 0 0 0 0
big dino 0 0 0 0 0 0 0 0 0 0 0 0
small unknown dinos 0 0.18 0.09 0.18 0 0.27 1.00 1.54 0.82 0.36 0.09 1.13

193
Table B-1 (continued)

7 December 2006
Taxon 1 3 5 7 8 9 10 11 12 14 13 17

Diatoms no. cells mL
-1

Asterionellopsis sp. 0.36 0.36 0 0.18 0 0.36 0 0.18 0.09 0.36 0.36 0
Asteromphalus sp. 0 0 0 0 0 0 0 0 0 0.09 0 0
Bacillaria sp.? 0 0 0 0 0 0 0 0 0 0 0 0
Bacteriastrum sp. 0 0.01 0 0.02 0 0 0 0 0 0.73 0 0
Bacterosira spp. 0 0 0 0 0 0 0 0 0 0 0 0
Cerataulina sp. 0.36 0 0.18 0.36 0 0.27 0 0 0 0.36 0.01 0
Chaetoceros spp. 0.36 0.09 0.82 4.90 0.82 1.00 0 0.27 0.73 8.16 1.18 2.18
Coscinodiscus spp. 0 0 0 0 0 0 0 0 0 0 0.01 0
Cylindrotheca sp. 10.16 4.99 4.35 6.17 1.54 3.36 1.63 2.36 2.27 2.45 1.72 6.89
Entomoneis sp. 0 0.09 0 0 0 0 0 0 0 0 0 0
Fragilariopsis sp. 0 0 0 0.02 0.01 0.09 0 0 0.36 0.18 0.09 0.04
Grammatophora sp. 0 0 0 0.18 0 0 0 0 0 0 0 0
Guinardia spp. 2.54 0.36 0.18 1.09 0.54 0.18 0 0.27 0.18 0.54 0.82 0.36
Hemiaulus spp. 0 0.09 0.18 0.91 0.09 0 0.09 0 0 0.54 0.27 0.73
Eucampia/Hemialus sp. 0 0 0 0 0 0 0 0 0 0 0 0
Leptocylindrus sp. 2 0 0 0 1.63 3.99 0.36 0.18 1.00 0.18 2.81 1.90 0.04
Navicula sp. 0 0.36 0.27 0.18 0 0 0.09 0.27 0.18 0.09 0.18 0
Odontella aurita 0 0 0 0 0 0 0 0 0 0 0 0
Pleurosigma sp. 0 0 0 0.18 0.01 0 0 0 0.09 0 0.09 0
Pseudonitzschia spp. 0 0.63 0.54 3.08 0.73 0.73 0.09 0.27 0.27 3.63 0.54 1.81
Rhizosolenia spp. 0.36 0.09 0.09 0.91 0.09 0.01 0 0 0.01 0.09 0.09 0.36
Skeletonema sp. 0 0 0 0 0 0 0 0 0 0 0 0
Suriella sp. 0 0 0 0 0 0 0 0 0 0 0 0
Thalassionema nitzschioides 0 0.27 0.09 0 0.09 0.27 0 0.01 0 0 0.09 0.73
Thalassionema spp. 0 0.09 0.19 0.18 0.09 0.09 0 0 0.01 0.36 0 0
lg Thalassiosira spp. 0 0 0 0 0 0 0 0 0 0 0 0
sm Thalassiosira spp. 1.45 0.36 0.73 0.54 0.27 0.18 0.09 0.27 0.18 0.63 0.45 0.36
unk pennates 1.45 0 0.09 0.18 0 0.09 0 0 0 0.09 0 0
unk centrics 0 0 0 0.18 0 0 0 0 0.27 0 0 0
unk diatom 5 0 0.45 0.09 0.54 0.09 0 0 0 0.01 0.54 0.18 0.36
unk diatom 8 0 0 0 0 0 0 0 0 0 0 0 0

Other groups
Dictyocha spp. 0.73 0.45 0.54 1.99 1.81 1.09 1.27 2.45 1.90 0.82 0.82 1.45
Eutreptiella sp. 0.36 0.18 0.09 0.18 0 0.36 0 0.27 0 0 0 1.45
Total number of cells 213 49 17 146 54 79 8 15 12 45 31 1,711
Synechococcus sp. 19,530 17,010 23,711 13,952 12,629 17,847 31,902 25,847 31,799 11,222 17,269 5,364
pico-eukaryotes 19,942 10,815 10,670 16,332 12,166 16,187 12,124 12,924 12,540 7,900 12,460 29,216

tintinnids 0 0.27 0.09 0.36 0.18 0.36 0.09 0.18 0.01 0.45 0.27 0
other ciliates 11.97 9.61 7.62 26.30 9.52 18.32 4.53 6.80 8.80 6.08 22.58 8.34

Total number of cells does not include Synechococcus spp. or pico-eukaryotes; ciliates (including
tintinnids) were not included in analyses.
APPENDIX C

Table C-1. Station locations from stormwater sampling.

Station Latitude (°N) Longitude (°W)

21 April 2007
1 33.9608 118.4678
2 33.9585 118.4766
3 33.9821 118.4890
4 33.9846 118.5217
5 33.9480 118.4982
24 April 2007
1 33.9426 118.6079
2 33.9569 118.4662
6 January 2008
1 33.9955 118.5009
2 33.9788 118.5088
3 33.9671 118.4703
4 34.0182 118.5173
5 33.9239 118.5891
6 33.9066 118.5727
8 January
1 33.9998 118.5923
2 33.9396 118.5248
3 33.9537 118.4672
4 33.8639 118.4100
5 33.9672 118.4670
26 January
1 33.9641 118.4683
2 33.9413 118.5043
3 33.9895 118.5096
4 33.9899 118.5787
5 34.0352 118.5920
6 33.9114 118.4742
28 January
1 33.9729 118.5323
2 34.0066 118.5740
3 33.9879 118.6338
4 34.0085 118.6059
5 34.0297 118.6327
6 33.9486 118.5636
31 January
1 33.9248 118.5556
2 33.9076 118.6839
3 33.9231 118.6252
4 33.9223 118.5071
5 33.9390 118.4640
194
195
APPENDIX D

Table D-1. Raw cell counts from stormwater samples.

21 APRIL 2007
station 1 station 2 station 3
Taxon 1 m 3 m 7 m 1 m 7 m 12 m 1 m 5 m 12 m

Dinoflagellates no. cells mL
-1

Akashiwo sanguinea 0.18 0.01 0.01 0.18 0 0 0.01 0.01 0
Alexandrium sp. 0 1.81 0.18 0 0 0 0 0.54 0.36
Amylax tricantha 0 0 0 0 0 0 0 0.09 0
Ceratium azoricum 0 0 0 0 0 0 0 0 0
Ceratium balechii 0.54 0.91 0.09 0.54 0.09 0.09 0.27 0.45 0.01
Ceratium furca 2.27 1.09 0 1.00 0.45 0.01 1.90 1.99 0.36
Ceratium fusis 0 0 0 0 0 0 0.36 0 0.09
Ceratium lineatum 0 1.45 0.01 0 0.36 0.09 0 1.63 0
Cochlodinium sp. 0.36 0.01 0 0 0 0 0.18 0 0
Dinophysis acuminata 0.18 0.36 0 0.09 0.01 0.09 0.27 0.27 0
Dinophysis fortii 0 0.09 0.01 0 0.01 0.09 0 0 0.01
Dinophysis caudata 0 0 0 0 0 0 0 0 0
Dinophysis rotundata 0 0 0 0 0.01 0 0 0 0
Dinophysis tripos (?) 0 0 0 0 0 0 0 0 0
Dissodinium pseudolunula 0 0.09 0.01 0 0.09 0.01 0 0.09 0.09
Gonyaulax sp. 0 0 0 0 0.09 0 0 1.09 0.27
gymnodinioid 0 0 0 0 0 0 0 0 0
Lingulodinium polyedrum 0 0.01 0 0 0.01 0 0.09 0.09 0
long pointed dinoflagellate 1.09 0.63 0.54 0.54 0.09 0.01 0 0.27 0.18
Noctiluca sp. 0 0 0 0 0 0 0 0 0
Oxytoxum/Oxyphysis sp. 0 0 0 0.09 0 0 0 0 0
Polykrikos sp. 0 0 0.01 0 0 0 0 0 0
Prorocentrum (gracile?) -- 2.81 0 -- 0.18 0 -- 1.63 0
Prorocentrum micans -- 2.27 0.01 -- 0.18 0.01 -- 1.54 0
Prorocentrum (gracile/micans) 4.35 -- 0 1.36 -- 0 2.18 -- 0
Protoperidinium spp. 1.99 0.54 0.09 1.09 0.09 0.01 0.63 0.36 0.01
Protoperidinium steinii 0.36 0.63 0.09 0.82 0.09 0.01 0.63 0.36 0
Scrippsiella sp. 7.07 4.72 0 2.63 0 0 3.99 2.36 0.09
unknown dino (Protoperidinium?) 0 0 0 0 0 0 0 0 0
round dino 0 0 0 0 0 0 0 0 0
small dino chain 0 0 0 0 0 0 0 0 0
tiny dino 0.63 0 0 0 0 0 0 0 0
unk dino 3 0 0 0 0 0 0 0 0.09 0

Other groups
Phaeocystis sp. 0.54 0.63 1.18 1.09 0.82 0.82 0.45 1.45 0.91
Dictyocha spp. 0.09 0.91 0.54 0.09 0.18 1.00 0.73 0.45 0.45
Eutreptiella sp. 5.80 0.73 0 12.88 0.09 0 13.33 1.54 0
radiolarian 0 0.09 0.09 0 0 0.09 0 0.09 0.09
Rhizomonas setigera 0 0 0 0 0 0 0 0 0
Prasinophyte (Pyramimonas sp?) 0 0 0 0 0 0 0 0 0
Chrysophyte (Synura sp?) 0 0 0 0 0 0 0 0 0

tintinnids 1.09 1.63 3.17 0.82 0.27 0.54 0.27 2.18 2.36
other ciliates 23.03 36.45 6.53 7.34 9.52 5.35 12.97 36.00 6.62

Synechococcus spp. (x 10
3
) 28.21 34.39 30.37 34.98 38.06 35.68 26.99 26.52 24.31
pico-eukaryotes 21.05 23.21 22.74 25.46 28.84 24.20 21.34 24.27 24.07
196
Table D-1 (continued)

21 APRIL 2007
station 1 station 2 station 3
Taxon 1 m 3 m 7 m 1 m 7 m 12 m 1 m 5 m 12 m

Diatoms no. cells mL
-1

Asterionellopsis sp. 19.31 14.51 18.32 18.50 46.25 26.39 16.87 10.16 18.32
Asteromphalus sp. 0.09 0.09 0.01 0 0.01 0 0.27 0.01 0.09
Bacteriastrum sp. 0 0 0 0 0 0 0 0 0
Bacterosira sp.? 0 0.45 0.01 0 0 0.27 0 0.01 0.36
Ceratulina sp.? 0 0 0 0 0 0 0 0 0
Chaetoceros spp. 179.95 229.23 150.16 128.40 140.73 159.96 143.43 306.85 310.85
Corethron sp. 0 0 0 0 0 0 0 0 0
Coscinodiscus spp. 0.09 0.09 0.01 0.01 0 0.27 0 0.09 0
Cylindrotheca sp. 0.54 0.63 1.00 0.18 0.82 0.82 0.73 0.36 1.09
Dactyliosolen sp.? 0 0 0 0 0 0 0 0 0
Detonula sp. 0 0.27 0.54 0 0.54 0.27 0 0.45 0.63
Ditylum brightwellii 0.09 0 0 0 0 0 0 0 0
Entomoneis sp. 0.18 0.09 1.27 0.09 0.73 0.73 0.36 0.18 1.18
Eucampia sp. 6.89 6.08 6.80 5.26 3.72 2.09 3.99 5.26 5.89
Fragilariopsis sp. 0 0 0 0 0 0 0 0 0
Guinardia spp. 0 0 0 0 0 0 0 0 0
Helicotheca sp. 0 0 0 0 0 0 0 0 0
Hemialus sp. 0 0 0 0 0 0 0 0 0
Eucampia/Hemialus 0 0 0 0 0 0 0 0 0
Leptocylindrus sp. 1 0 0.63 0 0 0 0 0 0 0
Leptocylindrus sp. 2 0 0.54 1.00 0 0 0 0 1.63 0.01
Melosira sp. 0 0 0 0 0 0 0 0 0.18
Navicula sp. 0.09 0 0 0 0 0.09 0 0 0
Odontella aurita 0.63 0.45 2.54 1.00 1.99 1.72 1.18 1.45 1.09
Odontella sp. 0 0 0 0 0 0 0 0 0
Odontella longicruis (?) 0 0 0.09 0 0 0 0 0 0.01
Odontella mobiliensis (?) 0 0 0 0 0 0 0 0 0
Planktoniella sol 0 0 0 0 0 0 0 0 0
Pleurosigma sp. 0 0.01 0.18 0.09 0.01 0.18 0.09 0.09 0.36
Pseudonitzschia spp. 227.67 407.51 534.08 329.51 467.43 575.85 179.51 383.36 482.54
Rhizoselenia spp. 0 0 0 0 0 0 0 0 0
Skeletonema sp. 0 0 0 0 0 0 0 0 0
Surirella sp. 0 0 0 0 0 0 0 0 0
Thalassionema nitzschioides 0 0.09 0 0.82 0 0 0.18 0.01 0.18
large Thalassionema spp. 0.09 0.18 0.18 0.18 0.01 0.18 0.36 0.01 0.27
large Thalassiosira spp. 1.27 2.72 3.63 3.08 2.27 3.45 2.36 3.63 3.17
small Thalassiosira spp. 2.27 1.45 4.90 2.09 5.62 4.53 1.54 5.53 8.98
Tropidoneis sp. ? 0 0 0 0 0 0 0 0 0
all unknown centrics 0 0.09 0 0.09 0 0.09 0.09 0 0
diatom 1 0 0 0 0 0 0 0 0 0
diatom 4 0 0 0 0 0 0 0 0 0
diatom 5 0.27 0.73 2.54 2.81 1.09 2.72 0 1.54 2.36
diatom 7 0 0 0 0 0 0 0 0 0
diatom 10 0 0 0 0 0 0 0 0 0
diatom 12 0 0 0 0 0 0 0 0 0
all unknown pennates 0 0.45 1.99 0.45 0.27 0.18 0 0.36 0.73

Total number of cells 464.93 686.11 732.12 514.96 674.34 782.11 375.99 737.42 841.22
chlorophyll a (mg m
-3
) 4.01 4.46 4.01 2.67 5.35 4.01 4.01 6.01 18.04
Total number of cells does not include Synechococcus spp. or pico-eukaryotes; ciliates (including
tintinnids) were not included in analyses.
197
Table D-1 (continued)

21 APRIL 2007
station 4 station 5
Taxon 1 m 5 m 12 m 1 m 8 m 18 m

Dinoflagellates no. cells mL
-1

Akashiwo sanguinea 0 0 0 0 0 0
Alexandrium sp. 0 0 0.09 0 0 0.09
Amylax tricantha 0 0 0 0 0 0
Ceratium azoricum 0 0.01 0.01 0.01 0 0
Ceratium balechii 0.27 0.09 0.09 0.27 0.36 0
Ceratium furca 1.90 0.27 0.01 0.01 0.18 0
Ceratium fusis 0.18 0.01 0 0.01 0 0
Ceratium lineatum 0 0.18 0.09 1.09 0.09 0
Cochlodinium sp. 0 0 0 0.01 0.01 0
Dinophysis acuminata 0.18 0.01 0.01 0.09 0 0
Dinophysis fortii 0 0 0.01 0 0.01 0
Dinophysis caudata 0 0 0 0 0 0
Dinophysis rotundata 0 0 0.01 0 0 0.09
Dinophysis tripos (?) 0 0 0 0 0 0
Dissodinium pseudolunula 0.09 0.09 0.01 0.09 0.18 0.01
Gonyaulax sp. 0 0 0.01 1.18 0 0
gymnodinioid 0 0 0 0 0 0
Lingulodinium polyedrum 0 0 0 0 0 0
long pointed dinoflagellate 0 0 0.27 1.09 0.27 0.01
Noctiluca sp. 0 0 0 0 0 0
Oxytoxum/Oxyphysis sp. 0 0 0 0 0 0
Polykrikos sp. 0 0 0 0 0 0.01
Prorocentrum (gracile?) -- 0 0.09 -- 0 0
Prorocentrum micans -- 0.09 0.09 -- 0.09 0
Prorocentrum (gracile/micans) 0.63 -- 0 0.73 -- --
Protoperidinium spp. 0.82 0.09 0.18 0.27 0.09 0
Protoperidinium steinii 0.09 0.01 0.01 0.09 0 0
Scrippsiella sp. 1.00 0 0 5.71 0 0
unknown dino (Protoperidinium?) 0 0 0 0 0 0
round dino 0 0 0 0 0 0
small dino chain 0 0 0 0 0 0
tiny dino 0.09 0 0 0.18 0 0
unk dino 3 0 0 0 0 0 0

Other groups
Phaeocystis sp. 1.00 0.91 0.73 1.00 0.27 0.45
Dictyocha spp. 0.54 0.36 0.73 0.36 0.82 0.73
Eutreptiella sp. 0.36 0.09 0 3.54 0 0
radiolarian 0 0.09 0.18 0 0.27 0.09
Rhizomonas setigera 0 0 0 0 0 0
Prasinophyte (Pyramimonas sp?) 0 0 0 0 0 0
Chrysophyte (Synura sp?) 0 0 0 0 0 0

tintinnids 0.36 0.09 0.82 0.63 4.72 0.18
other ciliates 14.42 9.61 6.98 12.97 11.70 5.08

Synechococcus spp. (x 10
3
) 37.26 38.34 32.59 33.84 27.80 16.15
pico-eukaryotes 29.99 33.50 27.60 23.32 23.40 13.26

198
Table D-1 (continued)

21 APRIL 2007
station 4 station 5
Taxon 1 m 5 m 12 m 1 m 8 m 18 m

Diatoms no. cells mL
-1

Asterionellopsis sp. 16.32 11.52 8.25 24.30 1.09 13.60
Asteromphalus sp. 0 0.09 0 0 0 0
Bacteriastrum sp. 0 0 0 0 0 0
Bacterosira sp.? 0 0.18 0.54 0 0.01 0.01
Ceratulina sp.? 0 0 0 0 0 0
Chaetoceros spp. 184.35 182.81 164.67 95.09 114.62 46.97
Corethron sp. 0 0 0 0 0 0
Coscinodiscus spp. 0.09 0.09 0.01 0.18 0.01 0
Cylindrotheca sp. 0.18 0.54 0.36 0.54 0.27 0.54
Dactyliosolen sp.? 0 0 0 0 0 0
Detonula sp. 0 0.27 0.09 0.82 0.01 0
Ditylum brightwellii 0 0 0 0 0 0
Entomoneis sp. 0.18 0.45 0.73 0.18 0.18 0.45
Eucampia sp. 2.81 1.99 2.09 3.45 1.63 1.81
Fragilariopsis sp. 0 0.01 0 0 0 0
Guinardia spp. 0 0 0 0 0 0
Helicotheca sp. 0 0 0 0 0 0
Hemialus sp. 0 0 0 0 0 0
Eucampia/Hemialus 0 0 0 0 0 0
Leptocylindrus sp. 1 0 0 0 0 0 0
Leptocylindrus sp. 2 0 0 0 0 0 0.36
Melosira sp. 0 0 0 0 0 0
Navicula sp. 0 0 0 0 0 0.01
Odontella aurita 1.54 1.09 0.63 0.82 0.45 0.54
Odontella sp. 0 0 0 0 0 0
Odontella longicruis (?) 0 0 0 0 0 0
Odontella mobiliensis (?) 0 0 0 0 0 0
Planktoniella sol 0 0 0 0 0 0
Pleurosigma sp. 0.01 0.09 0.01 0.27 0.09 0.01
Pseudonitzschia spp. 519.33 520.53 534.08 231.40 509.58 132.03
Rhizoselenia spp. 0 0 0 0 0 0
Skeletonema sp. 0 0 0 0 0 0
Surirella sp. 0.09 0 0 0 0 0
Thalassionema nitzschioides 0.09 0.09 0.09 0.09 0 0.09
large Thalassionema spp. 0.18 0.01 0.01 0 0 0.27
large Thalassiosira spp. 2.81 2.18 2.81 1.54 2.63 1.27
small Thalassiosira spp. 3.63 4.81 3.90 4.35 2.45 2.09
Tropidoneis sp. ? 0 0 0 0 0 0.09
all unknown centrics 0 0.09 0 0 0 0
diatom 1 0 0 0 0 0 0
diatom 4 0 0 0 0 0 0
diatom 5 1.90 0.82 0.27 0.91 1.18 0.09
diatom 7 0 0 0 0 0 0
diatom 10 0 0 0 0 0 0
diatom 12 0 0 0 0 0 0
all unknown pennates 0.27 0.82 0.54 0 0.36 0.18

Total number of cells 740.95 730.78 721.71 379.67 637.21 201.91
chlorophyll a (mg m
-3
) 7.02 5.01 2.00 14.03 5.01 7.02
Total number of cells does not include Synechococcus spp. or pico-eukaryotes; ciliates (including
tintinnids) were not included in analyses.
199
Table D-1 (continued)

24 APRIL 2007
station 1 station 2
Taxon 1 m 9 m 12 m 1 m 4 m 7 m

Dinoflagellates no. cells mL
-1

Akashiwo sanguinea 0 0 0 0.09 0 0
Alexandrium sp. 0 0 0 0 1.09 0
Amylax tricantha 0 0 0 0 0.09 0
Ceratium azoricum 0 0 0 0 0 0
Ceratium balechii 0.45 0 0.01 0.36 0.18 0
Ceratium furca 0 0 0 2.45 0.73 0.18
Ceratium fusis 0 0 0 0 0 0
Ceratium lineatum 0 0 0 0 1.27 0.18
Cochlodinium sp. 0 0 0 0 0 0
Dinophysis acuminata 0 0 0 0.27 0.27 0.18
Dinophysis fortii 0.09 0 0 0 0 0
Dinophysis caudata 0 0 0 0 0 0
Dinophysis rotundata 0 0 0 0 0 0.01
Dinophysis tripos (?) 0 0 0 0 0 0
Dissodinium pseudolunula 0.09 0 0.01 0.09 0.09 0.09
Gonyaulax sp. 0 0 0 0 0.09 0
gymnodinioid 0 0 0 0 0 0
Lingulodinium polyedrum 0 0 0 0 0 0
long pointed dinoflagellate 0 0.01 0.09 0.82 2.18 0.45
Noctiluca sp. 0 0 0 0 0 0
Oxytoxum/Oxyphysis sp. 0 0 0 0 0 0
Polykrikos sp. 0 0 0 0 0 0
Prorocentrum (gracile?) -- 0 0 -- 0 0
Prorocentrum micans -- 0 0 -- 0.27 0
Prorocentrum (gracile/micans) 0.18 -- -- 1.09 -- --
Protoperidinium spp. 0.36 0.01 0.01 1.36 0.91 0.18
Protoperidinium steinii 0 0.09 0 0.73 0.36 0.01
Scrippsiella sp. 0.82 0 0 0.82 0 0
unknown dino (Protoperidinium?) 0 0 0 0 0 0
round dino 0 0 0 0 0 0
small dino chain 0 0 0 0 0 0
tiny dino 0 0 0 0 0 0
unk dino 3 0 0 0 0 0 0

Other groups
Phaeocystis sp. 0.36 0.01 0.01 0.91 1.09 1.36
Dictyocha spp. 0.82 0.18 0.27 0.18 0.45 0.54
Eutreptiella sp. 0.45 0 0 0.01 0 0
radiolarian 0 0.09 0.09 0 0.09 0.18
Rhizomonas setigera 0 0 0 0 0 0
Prasinophyte (Pyramimonas sp?) 0 0 0.09 0 0 0
Chrysophyte (Synura sp?) 0 0 0 0 0 0

tintinnids 0 0.01 0.18 0.63 0.63 0.54
other ciliates 0.82 1.00 1.36 1.27 9.34 4.81

Synechococcus spp. (x 10
3
) 4.59 0.89 0.77 21.98 38.77 15.28
pico-eukaryotes 7.88 2.60 1.93 16.72 33.34 14.10

200
Table D-1 (continued)

24 APRIL 2007
station 1 station 2
Taxon 1 m 9 m 12 m 1 m 4 m 7 m

Diatoms no. cells mL
-1

Asterionellopsis sp. 0 0 0 0.09 0.01 3.54
Asteromphalus sp. 0 0 0 0 0.09 0.01
Bacteriastrum sp. 0 0 0 0 0 0
Bacterosira sp.? 0 0 0 4.17 0.09 0
Ceratulina sp.? 0 0 0 0 0 0
Chaetoceros spp. 125.68 25.21 23.76 333.88 231.94 131.66
Corethron sp. 0 0 0 0 0 0
Coscinodiscus spp. 0.01 0 0 0.63 0.01 0.09
Cylindrotheca sp. 0.18 0.09 0 0.18 0.18 0.73
Dactyliosolen sp.? 0 0 0 0 0 0
Detonula sp. 0 0.01 0 0 0.82 0
Ditylum brightwellii 0.18 0 0 0 0 0
Entomoneis sp. 0 0.01 0 0.18 0.27 0.73
Eucampia sp. 0 0 0 13.42 26.30 5.35
Fragilariopsis sp. 0 0 0 0 0 0
Guinardia spp. 0.09 0 0 0 0 0
Helicotheca sp. 0 0 0 0 0 0
Hemialus sp. 0 0.09 0 0 0.01 0
Eucampia/Hemialus 0 0 0 0 0 0
Leptocylindrus sp. 1 0 0 0 0 0 0
Leptocylindrus sp. 2 0.63 0.27 0 0 0 1.36
Melosira sp. 0 0 0 0 0 0
Navicula sp. 0 0 0.82 0 0 0.18
Odontella aurita 0 0 0 0 0.18 0
Odontella sp. 0 0 0 0 0 0
Odontella longicruis (?) 0 0 0 0 0.01 0
Odontella mobiliensis (?) 0 0 0 0 0 0
Planktoniella sol 0 0 0 0 0 0
Pleurosigma sp. 0 0.01 0.01 0.27 0.18 0.63
Pseudonitzschia spp. 44.43 14.33 13.87 460.68 661.16 317.25
Rhizoselenia spp. 0 0.01 0.01 0 0 0
Skeletonema sp. 0 0 0 0 0 0
Surirella sp. 0 0 0 0 0 0
Thalassionema nitzschioides 0.18 0.09 0.18 0 0 0.18
large Thalassionema spp. 0 0.01 0 0.18 0.01 0.09
large Thalassiosira spp. 1.45 1.45 0.54 1.54 1.99 4.81
small Thalassiosira spp. 0.54 1.81 0.91 0.54 5.44 6.26
Tropidoneis sp. ? 0 0 0.01 0 0 0
all unknown centrics 0 0.09 0 0 0.36 0.09
diatom 1 0 0 0 0 0 0
diatom 4 0 0 0 0 0 0
diatom 5 1.09 0.45 0.82 0 3.72 2.90
diatom 7 0 0 0 0 0 0
diatom 10 0 0 0 0 0 0
diatom 12 0 0 0 0.73 0 0
all unknown pennates 0 0.09 0 0 0 0.63

Total number of cells 176.65 42.97 40.97 824.13 939.93 475.06
chlorophyll a (mg m
-3
) 2.67 1.00 0 1.00 6.01 2.00
Total number of cells does not include Synechococcus spp. or pico-eukaryotes; ciliates (including
tintinnids) were not included in analyses.
201
Table D-1 (continued)

6 JANUARY 2008
station 1 station 2 station 3
Taxon 1 m 3 m 7 m 1 m 3 m 7 m 1 m 3 m 5 m

Dinoflagellates no. cells mL
-1

Akashiwo sanguinea 0.54 0.45 0.27 0.18 0.09 0.45 0.09 0.18 0.09
Alexandrium sp. 0.54 0.45 0 0 0.09 0.09 0.18 0 0
Amylax tricantha 0.01 0.09 0.09 0 0 0 0.09 0 0
Ceratium azoricum 0 0 0 0 0 0 0 0 0
Ceratium balechii 0.01 0.01 0 0 0.01 0 0.01 0 0
Ceratium furca 0.18 0.54 0.01 0 0.01 0.01 0 0 0.09
Ceratium fusis 2.72 3.99 2.18 1.09 1.72 0.91 2.36 1.81 1.00
Ceratium lineatum 0.18 0.18 0.01 0.01 0.18 0.01 0.36 0 0.01
Cochlodinium sp. 0.36 0.01 0 0.01 0 0.01 0.36 0.01 0
Dinophysis acuminata 0.09 0.01 0.01 0.18 0.01 0.09 0 0 0
Dinophysis fortii 0 0.27 0 0.01 0 0 0 0.01 0
Dinophysis caudata 0 0 0 0 0 0 0 0 0.09
Dinophysis rotundata 0 0.01 0 0 0 0 0.09 0 0
Dinophysis tripos (?) 0 0 0 0 0 0 0 0 0
Dissodinium pseudolunula 0 0 0 0 0 0.09 0 0 0
Gonyaulax sp. 0.01 0.09 0 0 0 0 0 0 0
gymnodinioid 0 0.09 0 0 0 0 0 0 0
Lingulodinium polyedrum 0.36 0.91 0.45 0.09 0.45 0.45 0.18 0.09 0.27
long pointed dinoflagellate 0.82 0.73 0.36 0.01 0.01 0.18 0.27 0.36 0.63
Noctiluca sp. 0 0 0 0 0 0 0 0 0
Oxytoxum/Oxyphysis sp. 0 0.09 0 0 0 0 0 0 0
Polykrikos sp. 0.09 0.09 0.01 0 0.01 0 0 0.18 0
Prorocentrum (gracile?) 0.63 0.63 0.27 0.09 0.36 0.09 0.54 0.18 0.18
Prorocentrum micans 1.54 1.81 0.54 0.27 0.63 0.18 0.73 0.27 0.91
Protoperidinium spp. 0.18 0.27 0.18 0.09 0.01 0.01 0.09 0.09 0.01
Protoperidinium steinii 0.18 0.09 0.18 0.09 0.01 0.09 0.18 0 0.27
Scrippsiella sp. 0.45 0.18 0 0.45 0.18 0.18 0.36 0 0
unknown dino (Protoperidinium?) 0 0 0 0 0 0 0 0 0
round dino 0 0 0 0 0 0 0 0 0
small dino chain 0 0 0 0 0 0 0 0 0
tiny dino 0 0 0 0 0 0 0 0 0
unk dino 3 0 0 0 0 0 0 0 0 0

Other groups
Phaeocystis sp. 0 0.09 0 0 0.09 0 0 0 0
Dictyocha spp. 0.27 0.27 0.27 0.01 0.27 0.01 0.27 0.63 0.54
Eutreptiella sp. 0.73 0.54 0 0 0.18 0.01 0.27 0.09 0
radiolarian 0 0 0.01 0 0 0 0 0 0
Rhizomonas setigera 0.45 0.82 0.09 0.01 0.01 0.01 0 0 0
Prasinophyte (Pyramimonas sp?) 0.36 0.18 0 0 0.09 0.09 0.18 0.09 0.09
Chrysophyte (Synura sp?) 0.09 0.09 0 0 0 0 0.09 0.27 0

tintinnids 0.09 0.09 0.01 0.18 0.09 0.18 0.27 0.36 0.18
other ciliates 14.33 9.88 5.89 3.81 3.45 3.72 5.80 6.26 5.80

Synechococcus spp. (x 10
3
) 48.98 51.19 50.95 48.70 39.63 36.98 39.44 34.23 40.84
pico-eukaryotes (x 10
3
) 15.72 28.85 29.91 11.97 12.83 15.01 13.43 16.57 11.02

202
Table D-1 (continued)

6 JANUARY 2008
station 1 station 2 station 3
Taxon 1 m 3 m 7 m 1 m 3 m 7 m 1 m 3 m 5 m

Diatoms no. cells mL
-1

Asterionellopsis sp. 0.01 0.09 0 0 0 0 0 0 0
Asteromphalus sp. 0.01 0 0 0 0 0 0 0 0
Bacteriastrum sp. 0 0 0 0.09 0 0 0 0 0
Bacterosira sp.? 0 0 0 0 0 0 0 0 0
Ceratulina sp.? 0 0 0 0 0 0 0 0 0
Chaetoceros spp. 2.90 6.08 3.90 1.72 1.99 2.36 2.09 3.63 2.99
Corethron sp. 0 0.09 0 0 0.01 0 0.01 0 0
Coscinodiscus spp. 0 0 0 0 0 0 0 0 0
Cylindrotheca sp. 1.09 0.63 1.27 0.36 0.27 0.18 0.73 0.82 1.63
Dactyliosolen sp.? 0 0 0 0 0.09 0 0 0.01 0
Detonula sp. 0 0 0 0 0 0 0 0 0
Ditylum brightwellii 0.01 0.01 0 0.09 0 0 0.01 0 0
Entomoneis sp. 0.09 0.18 0.09 0.18 0.09 0.09 0 0 0.18
Eucampia sp. 0 0 0 0 0 0 0 0 0
Fragilariopsis sp. 0 0 0 0 0 0 0 0 0
Guinardia spp. 0 0.18 0 0 0 0.01 0.09 0.09 0.01
Helicotheca sp. 0 0 0 0 0 0 0 0 0.01
Hemialus sp. 0 0.01 0 0 0 0 0 0 0
Eucampia/Hemialus 0 0 0 0 0.01 0 0 0 0
Leptocylindrus sp. 1 1.27 3.90 0.27 0.91 0.01 1.18 0.91 1.00 0.27
Leptocylindrus sp. 2 3.45 0.36 0.45 1.99 0.01 0.01 0.01 0.01 0.73
Melosira sp. 1.00 0.82 0.54 0 0 0 0.82 1.45 0.73
Navicula sp. 0 0 0.09 0 0 0 0.18 0 0
Odontella aurita 0.45 0.27 0.27 0 0 0 0.45 0.36 0.18
Odontella sp. 0 0 0 0 0 0 0 0 0
Odontella longicruis (?) 0 0.01 0 0.01 0 0 0 0.01 0
Odontella mobiliensis (?) 0.01 0 0.09 0 0 0 0 0 0
Planktoniella sol 0 0 0 0.01 0 0 0 0 0
Pleurosigma sp. 0 0.09 0.09 0 0.09 0 0.18 0.09 0
Pseudonitzschia spp. 1.18 1.99 0.82 0.36 0.91 0.91 0.45 0.73 0.91
Rhizoselenia spp. 0 0 0 0.09 0.01 0 0 0 0
Skeletonema sp. 1.36 0.73 0.36 0.36 0.01 1.09 1.81 2.45 0.82
Surirella sp. 0 0 0 0 0 0 0 0 0
Thalassionema nitzschioides 0.73 0.18 0.01 0.09 0.01 0.27 0.01 0.27 0.27
large Thalassionema spp. 0 0 0 0 0 0 0 0 0
large Thalassiosira spp. 0.54 0.10 0.27 0.09 0.01 0 0.09 0.09 0.18
small Thalassiosira spp. 0.82 0.45 0.45 0.18 0.01 0.09 0.45 0.63 0.36
Tropidoneis sp. ? 0 0 0 0 0 0 0 0 0
all unknown centrics 0 0.18 0 0 0 0 0 0.09 0
diatom 1 0 0 0 0 0 0 0 0 0
diatom 4 0 0 0 0 0 0 0.09 0.09 0
diatom 5 0 0 0 0 0 0 0 0 0
diatom 7 0.01 0 0.09 0.01 0.18 0.09 0 0.01 0.01
diatom 10 0 0 0 0 0 0 0 0 0
diatom 12 0 0 0 0 0 0 0 0 0
all unknown pennates 0 0 0 0 0 0 0 0 0

Total number of cells 25.29 28.55 13.93 9.15 8.14 9.24 15.10 16.11 13.47
chlorophyll a (mg m
-3
) 2.23 1.79 1.07 1.48 1.30 1.59 1.65 1.75 1.20
Total number of cells does not include Synechococcus spp. or pico-eukaryotes; ciliates (including
tintinnids) were not included in analyses.
203
Table D-1 (continued)

6 JANUARY 2008
station 4 station 5 station 6
Taxon 1 m 3 m 1 m 3 m 7 m 1 m 3 m 7 m

Dinoflagellates no. cells mL
-1

Akashiwo sanguinea 0.09 0.18 0.09 0.18 0.09 0.01 0.27 0.09
Alexandrium sp. 0.18 0.27 0.09 0.09 0.09 0.18 0 0.09
Amylax tricantha 0 0 0 0 0 0 0 0
Ceratium azoricum 0 0 0 0 0 0 0 0
Ceratium balechii 0 0 0 0 0 0 0.01 0.01
Ceratium furca 0.01 0 0.09 0.01 0.01 0 0.09 0.01
Ceratium fusis 0.63 0.54 0.18 0.27 1.18 0.73 0.54 0.54
Ceratium lineatum 0.09 0 0.09 0.01 0 0 0.09 0.09
Cochlodinium sp. 0.18 0.01 0 0 0 0.18 0 0.01
Dinophysis acuminata 0 0.01 0.18 0.01 0.01 0 0.01 0.01
Dinophysis fortii 0.01 0 0 0.01 0.18 0.18 0 0.09
Dinophysis caudata 0 0.01 0 0 0 0 0.01 0
Dinophysis rotundata 0.01 0 0 0 0.09 0 0 0
Dinophysis tripos (?) 0 0 0 0 0 0 0 0
Dissodinium pseudolunula 0 0 0 0 0.09 0 0 0
Gonyaulax sp. 0 0 0 0 0.01 0 0.01 0
gymnodinioid 0 0 0 0 0 0 0 0.01
Lingulodinium polyedrum 0.09 0.18 0.09 0.18 0.45 0.27 0.36 0.18
long pointed dinoflagellate 0.63 0.36 0.01 0.09 0.45 0.27 0.09 0.09
Noctiluca sp. 0 0 0 0 0 0 0 0
Oxytoxum/Oxyphysis sp. 0 0.09 0 0 0 0 0 0
Polykrikos sp. 0.01 0.01 0 0 0 0.01 0 0
Prorocentrum (gracile?) 0.54 0.54 0.09 0.09 0 0.27 0.09 0.09
Prorocentrum micans 1.18 0.09 0.09 0.18 0.27 0.18 0.54 0.18
Protoperidinium spp. 0.18 0.09 0.01 0 0.01 0.09 0 0
Protoperidinium steinii 0.09 0.18 0.01 0.01 0.09 0.27 0 0.09
Scrippsiella sp. 0.18 0 0 0.45 0.45 0.18 0.01 0.36
unknown dino (Protoperidinium?) 0 0 0 0 0 0 0 0
round dino 0 0 0.09 0 0 0.18 0 0
small dino chain 0 0 0 0 0 0 0 0
tiny dino 0 0 0 0 0 0 0 0
unk dino 3 0 0 0 0 0 0 0 0

Other groups
Phaeocystis sp. 0.09 0.09 0.09 0 0.01 0 0.01 0
Dictyocha spp. 0.01 0.09 0.18 0.18 0.27 0.18 0.18 0.18
Eutreptiella sp. 0.18 0 0.18 0.09 0.09 0.36 0.73 0.45
radiolarian 0.09 0 0 0 0.01 0 0 0
Rhizomonas setigera 1.00 0 1.90 0 1.63 1.09 0.73 1.00
Prasinophyte (Pyramimonas sp?) 0 0 0 0.09 0.09 0.09 0 0
Chrysophyte (Synura sp?) 0.09 0 0 0 0 0 0 0

tintinnids 0.45 0.27 0.01 0.27 0.36 0.36 0.36 0.18
other ciliates 5.98 7.44 6.71 5.08 10.70 7.34 5.35 5.62

Synechococcus spp. (x 10
3
) 43.50 37.83 7.31 11.40 9.70 2.45 3.30 3.29
pico-eukaryotes (x 10
3
) 13.56 12.85 30.44 15.86 25.93 5.83 14.78 20.86

204
Table D-1 (continued)

6 JANUARY 2008
station 4 station 5 station 6
Taxon 1 m 3 m 1 m 3 m 7 m 1 m 3 m 7 m

Diatoms no. cells mL
-1

Asterionellopsis sp. 0.09 0.09 0 0 0.18 0 0.54 0
Asteromphalus sp. 0.09 0 0 0 0 0.09 0 0
Bacteriastrum sp. 0.18 0 0 0 0 0 0.01 0.01
Bacterosira sp.? 0 0 0 0 0 0 0 0
Ceratulina sp.? 0 0 0 0 0 0 0 0
Chaetoceros spp. 4.26 4.26 3.08 2.63 3.54 5.71 3.26 2.63
Corethron sp. 0.09 0.09 0.01 0.01 0.01 0.09 0 0.01
Coscinodiscus spp. 0.01 0 0 0 0 0 0 0
Cylindrotheca sp. 1.36 1.45 0.27 0.01 0.54 0.45 0.09 0.18
Dactyliosolen sp.? 0 0 0 0 0 0 0 0
Detonula sp. 0 0 0 0 0 0 0 0
Ditylum brightwellii 0 0.01 0 0 0 0.09 0.01 0.36
Entomoneis sp. 0.09 0 0 0 0.01 0 0 0
Eucampia sp. 0 0 0 0 0.09 0 0 0
Fragilariopsis sp. 0 0 0 0 0 0 0 0
Guinardia spp. 0 0.01 0.01 0.36 0 0.00 0.09 0
Helicotheca sp. 0 0 0 0 0 0 0 0
Hemialus sp. 0 0 0 0 0.18 0.01 0.01 0
Eucampia/Hemialus 0 0 0.09 0 0 0 0.09 0.09
Leptocylindrus sp. 1 0.73 1.54 1.09 1.45 1.27 1.18 1.09 1.00
Leptocylindrus sp. 2 0.27 0.45 0 0.01 0 0.01 0 0.01
Melosira sp. 0.54 0.18 0 0 0 0 0 0
Navicula sp. 0 0 0 0 0 0 0 0
Odontella aurita 0.82 0.36 0 0 0 0 0 0
Odontella sp. 0 0 0 0 0 0 0 0
Odontella longicruis (?) 0 0 0 0 0 0 0 0
Odontella mobiliensis (?) 0 0 0 0 0 0 0 0
Planktoniella sol 0 0 0 0 0 0 0 0
Pleurosigma sp. 0.27 0.09 0 0 0 0 0.09 0
Pseudonitzschia spp. 0.45 1.00 1.72 2.27 0.91 0.36 1.81 1.00
Rhizoselenia spp. 0 0 0 0 0 0 0 0
Skeletonema sp. 2.63 1.27 0.45 1.45 1.00 1.00 0.01 0
Surirella sp. 0 0 0 0 0 0 0 0
Thalassionema nitzschioides 0.36 0.27 0.36 0.09 0.01 0 0.18 0.01
large Thalassionema spp. 0 0 0.36 0 0 0 0 0
large Thalassiosira spp. 0.27 0.01 0.36 0.27 0.01 0.27 0.18 0.01
small Thalassiosira spp. 1.00 0.37 0.18 0.27 0.36 0.45 0.09 0.28
Tropidoneis sp. ? 0 0 0 0 0 0 0 0
all unknown centrics 0 0.09 0.09 0.01 0 0.09 0 0.09
diatom 1 0.09 0 0.01 0 0 0.36 0.01 0
diatom 4 0 0 0.01 0 0 0.54 0.18 0
diatom 5 0 0 0 0 0 0 0 0
diatom 7 0.01 0.01 0 0 0 0 0 0
diatom 10 0 0 0 0 0 0 0 0
diatom 12 0 0 0 0 0 0 0 0
all unknown pennates 0 0 0 0 0 0 0 0

Total number of cells 18.21 14.33 9.68 10.79 12.07 14.37 10.81 8.27
chlorophyll a (mg m
-3
) 0.86 1.15 1.05 1.17 0.84 1.05 1.12 1.25
Total number of cells does not include Synechococcus spp. or pico-eukaryotes; ciliates (including
tintinnids) were not included in analyses.
205
Table D-1 (continued)

8 JANUARY 2008
station 1 station 2 station 3
Taxon 1 m 3 m 7 m 1 m 5 m 10 m 1 m 3 m 7 m

Dinoflagellates no. cells mL
-1

Akashiwo sanguinea 0.27 0.36 0.36 1.90 0.36 0.45 1.00 0.63 0.09
Alexandrium sp. 1.81 1.72 0.82 0.45 0 0 0 0.91 0.01
Amylax tricantha 0.09 0.27 0.27 0.01 0 0 0.09 0.01 0.01
Ceratium azoricum 0 0 0 0 0 0 0 0 0
Ceratium balechii 0.01 0 0.09 0.01 0 0 0.01 0 0
Ceratium furca 0.09 0.01 0.18 0.09 0.01 0.01 0 0.18 0.01
Ceratium fusis 1.45 4.35 9.16 1.00 2.72 2.27 0.18 0.73 3.17
Ceratium lineatum 0.18 0.45 0.09 0.27 0.01 0.09 0.01 0.01 0.09
Cochlodinium sp. 0.36 0.54 0.45 0 0.91 0.09 0.01 0.01 0.36
Dinophysis acuminata 0.36 0.18 0.18 1.00 0.18 0.18 0 0.09 0.01
Dinophysis fortii 0.09 0.18 0.27 0.09 0 0 0 0 0
Dinophysis caudata 0.01 0.09 0.18 0 0 0 0.01 0.01 0
Dinophysis rotundata 0 0.09 0 0 0 0.09 0 0 0
Dinophysis tripos (?) 0 0 0 0 0 0 0 0 0
Dissodinium pseudolunula 0.01 0 0 0 0 0.09 0 0 0
Gonyaulax sp. 0 0.09 0.09 0 0 0.09 0 0 0.09
gymnodinioid 0 0 0.09 0 0 0 0 0 0
Lingulodinium polyedrum 0.18 1.09 1.90 2.36 0.63 0.45 0.45 1.27 0.91
long pointed dinoflagellate 0.01 0.45 0.45 0.01 0.54 0.18 0.09 0.18 0.27
Noctiluca sp. 0 0 0 0 0 0 0 0 0
Oxytoxum/Oxyphysis sp. 0 0.01 0 0 0 0 0.01 0 0
Polykrikos sp. 0.36 0.36 0.27 0.09 0.01 0 0 0.09 0.09
Prorocentrum (gracile?) 2.99 1.99 0.82 2.36 0.18 0.63 2.36 1.99 0.45
Prorocentrum micans 2.09 3.63 5.17 0.73 0.45 0.18 1.00 4.08 1.99
Protoperidinium spp. 0.09 0.01 0.27 0 0 0.01 0 0.01 0.09
Protoperidinium steinii 0.18 0.54 0.18 0.01 0.18 0 0.18 0.09 0.27
Scrippsiella sp. 0.54 0.91 0.36 0.91 0.91 0.82 1.63 1.36 0
unknown dino (Protoperidinium?) 0 0 0 0 0 0 0 0 0
round dino 0 0 0 0.63 0 0 0 0 0
small dino chain 0.73 0 0 0 0 0 0 0 0
tiny dino 0 0 0 0 0 0 0 0 0
unk dino 3 0 0 0 0 0 0 0 0 0

Other groups
Phaeocystis sp. 0.01 0 0 0.01 0 0 0 0 0
Dictyocha spp. 0.36 0.18 0.27 0.09 0.27 0.18 0.27 0.27 0.01
Eutreptiella sp. 0.54 0.73 0.36 0.54 0.18 0.73 1.00 0.36 0
radiolarian 0 0.09 0 0 0 0 0 0 0
Rhizomonas setigera 3.90 5.35 6.53 0.36 0.01 0.01 0.18 0 0.01
Prasinophyte (Pyramimonas sp?) 0 0.09 0.09 0 0.91 2.27 0 0 0.09
Chrysophyte (Synura sp?) 0.01 0.36 0.09 0 0 0.09 0.01 0.09 0.01

tintinnids 0.18 0.27 0.45 0.27 0.27 0.09 0.45 0.27 0.18
other ciliates 6.71 13.60 8.34 6.62 7.44 6.89 6.80 5.35 4.26

Synechococcus spp. (x 10
3
) 5.14 11.17 12.54 15.92 10.00 25.33 22.78 8.97 3.59
pico-eukaryotes (x 10
3
) 22.08 16.24 9.43 13.07 13.17 10.11 7.93 19.04 11.67

206
Table D-1 (continued)

8 JANUARY 2008
station 1 station 2 station 3
Taxon 1 m 3 m 7 m 1 m 5 m 10 m 1 m 3 m 7 m

Diatoms no. cells mL
-1

Asterionellopsis sp. 0 0 0 0 0 0.45 0 0.09 0
Asteromphalus sp. 0 0 0 0 0 0 0 0 0
Bacteriastrum sp. 0 0 0 0 0 0 0 0 0.09
Bacterosira sp.? 0 0 0 0 0 0 0 0 0
Ceratulina sp.? 0 0 0 0 0 0 0 0 0
Chaetoceros spp. 4.62 3.08 1.99 2.45 2.36 2.09 3.36 4.81 1.27
Corethron sp. 0 0 0 0 0 0.01 0 0.01 0
Coscinodiscus spp. 0 0 0.09 0 0 0 0 0 0
Cylindrotheca sp. 0.27 1.27 0.54 0.27 0.73 0.36 0.91 0.91 1.54
Dactyliosolen sp.? 0.18 0 0 0 0 0 0 0 0
Detonula sp. 0 0 0 0 0 0 0 0 0
Ditylum brightwellii 0.01 0 0 0 0.09 0 0 0 0.01
Entomoneis sp. 0.09 0.09 0 0 0 0.09 0 0.18 0.27
Eucampia sp. 0 0 0 0 0 0 0.18 0 0
Fragilariopsis sp. 0 0 0 0 0 0 0 0 0
Guinardia spp. 0 0.09 0.00 0 0.54 0.01 0.09 0.18 0.09
Helicotheca sp. 0 0 0 0 0 0 0 0 0.01
Hemialus sp. 0 0 0 0 0.36 0 0.09 0 0
Eucampia/Hemialus 0 0.09 0 0 0 0 0 0 0
Leptocylindrus sp. 1 0.45 0.54 0.54 0.54 0.63 1.63 0.36 0.82 0.54
Leptocylindrus sp. 2 0 0 0 0.36 0 1.99 0.01 0.36 0
Melosira sp. 0 0 0 0 0 0 0.18 0.45 0.01
Navicula sp. 0 0.18 0 0 0 0 0 0 0
Odontella aurita 0 0.01 0 0 0 0 0.09 0.73 0.63
Odontella sp. 0 0 0 0 0 0 0 0 0
Odontella longicruis (?) 0 0 0 0 0 0 0 0 0
Odontella mobiliensis (?) 0 0 0 0 0 0 0 0 0.09
Planktoniella sol 0 0 0 0 0 0 0 0 0
Pleurosigma sp. 0 0 0 0 0.01 0.01 0.27 0.18 0.01
Pseudonitzschia spp. 0.27 1.18 0.45 0.36 0.09 0.45 0.27 0.45 0.45
Rhizoselenia spp. 0 0 0 0 0 0 0 0 0
Skeletonema sp. 0.91 0.73 1.09 0.63 0.01 0.82 2.18 0.01 0.01
Surirella sp. 0 0 0 0 0 0 0 0 0
Thalassionema nitzschioides 0.18 0.18 0.54 0 0.27 0.09 0.27 0.09 0.18
large Thalassionema spp. 0 0 0 0 0 0 0 0 0
large Thalassiosira spp. 0.09 0 0.18 0.09 0.09 0.27 0 0.18 0.09
small Thalassiosira spp. 0.27 0.18 0.18 0.73 0.18 0.00 0.45 0.74 0.45
Tropidoneis sp. ? 0 0 0 0 0 0 0 0 0
all unknown centrics 0 0.18 0 0 0 0 0 0 0.09
diatom 1 0 0.01 0 0 0 0 0 0 0
diatom 4 0 0 0.18 0 0 0.01 0.01 0 0
diatom 5 0 0 0 0 0 0 0 0 0
diatom 7 0 0 0 0 0 0 0.09 0.27 0.27
diatom 10 0 0 0 0 0 0 0 0 0
diatom 12 0 0 0 0 0 0 0 0 0
all unknown pennates 0 0 0 0 0 0 0 0 0

Total number of cells 20.20 26.62 28.29 18.00 13.83 17.20 17.13 22.84 14.17
chlorophyll a (mg m
-3
) 22.08 16.24 9.43 13.07 13.17 10.11 7.93 19.04 11.67
Total number of cells does not include Synechococcus spp. or pico-eukaryotes; ciliates (including
tintinnids) were not included in analyses.
207
Table D-1 (continued)

8 JANUARY 2008
station 4 station 5
Taxon 1 m 3 m 5 m 1 m 4 m

Dinoflagellates no. cells mL
-1

Akashiwo sanguinea 0.54 0.27 0.18 0.09 0.36
Alexandrium sp. 0.45 0.73 0.09 0.18 0.63
Amylax tricantha 0.09 0 0 0 0
Ceratium azoricum 0 0 0 0 0
Ceratium balechii 0 0 0 0 0
Ceratium furca 0.01 0.01 0 0 0.09
Ceratium fusis 2.90 1.27 1.54 1.18 0.82
Ceratium lineatum 0.09 0 0.01 0 0
Cochlodinium sp. 0.01 0 0 0.01 0
Dinophysis acuminata 0.01 0 0.18 0 0
Dinophysis fortii 0 0.09 0 0.01 0
Dinophysis caudata 0 0 0 0 0
Dinophysis rotundata 0 0 0 0 0
Dinophysis tripos (?) 0 0 0 0 0
Dissodinium pseudolunula 0.01 0.18 0.09 0 0
Gonyaulax sp. 0.09 0.01 0 0 0
gymnodinioid 0.09 0.01 0 0 0
Lingulodinium polyedrum 0.91 0.36 0.63 0.18 0.27
long pointed dinoflagellate 0.73 0.09 0.27 0.45 0.36
Noctiluca sp. 0 0.09 0 0 0
Oxytoxum/Oxyphysis sp. 0 0 0 0 0
Polykrikos sp. 0.01 0.01 0.01 0.01 0.01
Prorocentrum (gracile?) 0.18 0.36 0.01 0.36 0.82
Prorocentrum micans 2.99 2.99 3.45 2.90 2.63
Prorocentrum (gracile/micans)
Protoperidinium spp. 0.09 0 0.09 0.01 0.01
Protoperidinium steinii 0.01 0.09 0.01 0.01 0.09
Scrippsiella sp. 2.09 0.45 0.36 0.73 0.09
unknown dino (Protoperidinium?) 0 0 0 0 0
round dino 0 0 0 0 0
small dino chain 0 0 0 0 0
tiny dino 0 0 0 0 0
unk dino 3 0 0 0 0 0

Other groups
Phaeocystis sp. 0 0.01 0.01 0 0
Dictyocha spp. 0.27 0.36 0.27 0.01 0.54
Eutreptiella sp. 0.63 0.45 0.01 0.01 0.18
radiolarian 0 0 0 0 0
Rhizomonas setigera 0 0 0 0 0
Prasinophyte (Pyramimonas sp?) 1.18 1.00 1.36 0 0
Chrysophyte (Synura sp?) 0 0 0 0 0

tintinnids 0.45 0.27 0.09 0.01 0.18
other ciliates 8.34 6.62 5.26 3.17 3.08

Synechococcus spp. (x 10
3
) 6.48 23.27 21.73 24.55 11.10
pico-eukaryotes 26.52 9.83 7.22 13.10 8.23

208
Table D-1 (continued)

8 JANUARY 2008
station 4 station 5
Taxon 1 m 3 m 5 m 1 m 4 m

Diatoms no. cells mL
-1

Asterionellopsis sp. 0.27 0 0.09 0 0.09
Asteromphalus sp. 0 0 0.09 0 0
Bacteriastrum sp. 0.01 0 0 0 0
Bacterosira sp.? 0 0 0.27 0 0
Ceratulina sp.? 0 0 0 0 0
Chaetoceros spp. 4.08 3.08 5.35 2.90 2.09
Corethron sp. 0.01 0 0 0.01 0
Coscinodiscus spp. 0 0 0 0 0
Cylindrotheca sp. 1.00 1.00 0.91 2.09 1.36
Dactyliosolen sp.? 0 0 0.01 0 0
Detonula sp. 0 0 0 0 0
Ditylum brightwellii 0.01 0 0.01 0.09 0.01
Entomoneis sp. 0.54 0.36 0.36 0.09 0.45
Eucampia sp. 0.18 0.01 0 0 0
Fragilariopsis sp. 0 0 0 0 0
Guinardia spp. 0.18 0.01 0.09 0 0.09
Helicotheca sp. 0.09 0 0 0.01 0
Hemialus sp. 0 0 0 0 0
Eucampia/Hemialus 0 0.01 0 0 0
Leptocylindrus sp. 1 1.09 3.17 0.27 0.36 0.54
Leptocylindrus sp. 2 0.27 0.73 1.90 0.01 0
Melosira sp. 0 0 0 0.45 0.91
Navicula sp. 0 0.36 0 0 0
Odontella aurita 0.36 0.54 0.45 0.36 0.54
Odontella sp. 0 0 0 0 0
Odontella longicruis (?) 0 0.09 0.01 0.09 0.09
Odontella mobiliensis (?) 0.01 0.01 0 0.18 0
Planktoniella sol 0 0 0 0 0
Pleurosigma sp. 0.09 0 0.27 0.01 0
Pseudonitzschia spp. 0.91 0.45 0.82 0.36 0.01
Rhizoselenia spp. 0 0.01 0 0 0
Skeletonema sp. 3.45 5.35 5.44 1.45 0.73
Surirella sp. 0 0 0 0 0
Thalassionema nitzschioides 0.09 0.18 0.09 0.54 0.45
large Thalassionema spp. 0 0 0 0 0
large Thalassiosira spp. 0.09 0.01 0.01 0.01 0.19
small Thalassiosira spp. 1.00 0.45 0.82 0.91 1.00
Tropidoneis sp. ? 0 0 0 0 0
all unknown centrics 0 0.09 0 0 0
diatom 1 0 0 0 0 0
diatom 4 0 0.09 0.01 0 0
diatom 5 0 0 0 0 0
diatom 7 0.01 0.01 0.01 0.27 0.18
diatom 10 0 0 0 0 0
diatom 12 0 0 0 0 0
all unknown pennates 0 0 0 0 0

Total number of cells 27.13 24.88 25.87 16.35 15.65
chlorophyll a (mg m
-3
) 2.13 2.13 1.67 1.73 1.42
Total number of cells does not include Synechococcus spp. or pico-eukaryotes; ciliates (including
tintinnids) were not included in analyses.
209
Table D-1 (continued)

26 JANUARY 2008
station 1 station 2 station 3
Taxon 1 m 5 m 1 m 5 m 10 m 1 m 5 m 10 m

Dinoflagellates no. cells mL
-1

Akashiwo sanguinea 0 0.01 0.01 0.01 0 0 0 0
Alexandrium sp. 0.01 0.01 0 0.36 0.09 0 0.63 0
Amylax tricantha 0.18 0.09 0.01 0 0 0.09 0.09 0
Ceratium azoricum 0 0 0 0 0 0 0 0
Ceratium balechii 0.01 0.01 0.09 0 0 0.01 0.01 0
Ceratium furca 0.54 0 0.01 0.27 0.09 0.18 0.91 0.18
Ceratium fusis 0.36 0.73 0.18 0.18 0.09 0.63 0.54 0.01
Ceratium lineatum 0.45 0.73 0.18 0.27 0.09 0.63 0.63 0.18
Cochlodinium sp. 0.01 0.01 0 0.01 0.01 0.82 0.18 0.01
Dinophysis acuminata 0.01 0.09 0.01 0 0 0.01 0.09 0.01
Dinophysis fortii 0 0.01 0.01 0 0.01 0.09 0.01 0.18
Dinophysis caudata 0.01 0.09 0 0 0 0.01 0.18 0
Dinophysis rotundata 0 0.01 0 0.18 0 0 0 0.18
Dinophysis tripos (?) 0 0 0 0 0 0 0 0
Dissodinium pseudolunula 0 0 0.01 0 0 0.01 0 0
Gonyaulax sp. 0 0.09 0 0 0.01 0 0 0
gymnodinioid 0.45 0.01 0.09 0 0 0.36 0.36 0
Lingulodinium polyedrum 0.73 0.82 0.01 0.01 0.09 1.09 0.82 0.36
long pointed dinoflagellate 0.36 0.63 0.09 0.54 0.09 1.27 1.45 0.27
Noctiluca sp. 0 0 0 0 0 0 0 0
Oxytoxum/Oxyphysis sp. 0 0 0 0 0 0 0 0
Polykrikos sp. 0 0 0 0.01 0 0 0 0
Prorocentrum (gracile?) 2.36 1.54 0.27 0.36 0.09 1.90 1.90 0.54
Prorocentrum micans 1.00 1.36 0.18 0.01 0.18 0.91 0.82 0.54
Protoperidinium spp. 0.18 0.09 0 0.01 0.01 0.01 0.18 0.01
Protoperidinium steinii 0.09 0.73 0.01 0.09 0.18 0.18 0.45 0.09
Scrippsiella sp. 2.36 4.53 0.73 0.36 0 3.45 4.99 0.54
unknown dino (Protoperidinium?) 0.01 0.01 0 0 0 0 0 0
round dino 0 0 0 0 0 0 0 0
small dino chain 0 0 0 0 0 0 0 0
tiny dino 0 0 0 0 0 0 0 0
unk dino 3 0 0 0 0 0 0 0 0

Other groups
Phaeocystis sp. 0 0.27 0.09 0.09 0.01 0.18 0.01 0.01
Dictyocha spp. 0.54 0.18 0.45 0.09 0.27 0.18 0.27 0.09
Eutreptiella sp. 0.27 0.18 0.09 0.09 0.18 0.18 0.09 0
radiolarian 0 0 0 0.09 0 0 0 0.09
Rhizomonas setigera 0 0 0 0 0 0.63 0 0
Prasinophyte (Pyramimonas sp?) 1.18 2.09 0.63 0.73 0.27 0.91 0.91 0.63
Chrysophyte (Synura sp?) 0.01 0 0.01 0 0.09 0.18 0.09 0

tintinnids 0.36 0.18 0.01 0.01 0.01 0.36 0.27 0.09
other ciliates 7.98 10.61 16.14 12.24 5.71 12.15 14.06 3.99

Synechococcus spp. (x 10
3
) 20.40 19.53 17.01 23.71 13.95 12.63 17.85 31.90
pico-eukaryotes (x 10
3
) 25.55 19.94 10.81 10.67 16.33 12.17 16.19 12.12

210
Table D-1 (continued)

26 JANUARY 2008
station 1 station 2 station 3
Taxon 1 m 5 m 1 m 5 m 10 m 1 m 5 m 10m

Diatoms no. cells mL
-1

Asterionellopsis sp. 1.00 1.27 0.27 0.54 0.18 0.27 0.36 1.81
Asteromphalus sp. 0 0.09 0 0 0 0 0 0
Bacteriastrum sp. 0 0.18 0 0 0 0 0.18 0
Bacterosira sp.? 0 0 0 0 0 0 0.18 0
Ceratulina sp.? 0 0.09 0 0 0 0 0 0
Chaetoceros spp. 37.63 37.90 12.60 15.60 17.86 39.17 45.16 31.19
Corethron sp. 0.01 0.01 0.09 0.09 0.01 0.01 0 0
Coscinodiscus spp. 0.01 0 0.01 0.09 0 0 0 0
Cylindrotheca sp. 2.27 1.99 1.18 0.82 0.91 2.09 3.08 2.99
Dactyliosolen sp.? 0 0 0 0 0 0 0 0
Detonula sp. 0.54 0.45 0.63 0.09 0.01 0.63 0.54 0.18
Ditylum brightwellii 0 0 0.01 0 0.01 0.45 0.01 0.01
Entomoneis sp. 0.36 1.09 0.09 0.54 0.27 0.91 0.36 0.73
Eucampia sp. 0.01 0.54 0.54 0.63 0.27 0.09 0.01 0
Fragilariopsis sp. 0 0 0 0 0 0 0 0
Guinardia spp. 0.54 0.27 0.01 0.73 0.18 0.54 1.00 0.27
Helicotheca sp. 0 0 0 0 0 0 0 0
Hemialus sp. 0.36 0.09 0.01 0.01 0.18 0.18 0.27 0
Eucampia/Hemialus 0 0 0 0 0 0 0 0
Leptocylindrus sp. 1 6.08 7.44 1.00 1.36 0.18 6.98 7.98 3.36
Leptocylindrus sp. 2 0.73 0.45 0.45 0.45 0 0.36 0.01 1.09
Melosira sp. 0 0 0 0 0 0 0.27 0
Navicula sp. 0.18 0 0.09 0 0 0 0 0
Odontella aurita 0.01 0.09 0.01 0 0 0.36 0.09 0.18
Odontella sp. 0.09 0 0 0 0 0 0 0
Odontella longicruis (?) 0.09 0.01 0 0 0.18 0.09 0.09 0
Odontella mobiliensis (?) 0 0 0 0 0 0.18 0 0
Planktoniella sol 0 0 0 0 0 0 0 0
Pleurosigma sp. 0.01 0.01 0 0.09 0.01 0.09 0.01 0.01
Pseudonitzschia spp. 5.53 6.53 1.27 3.17 1.90 6.71 5.44 3.54
Rhizoselenia spp. 0 0 0 0 0 0.09 0 0
Skeletonema sp. 39.35 21.49 4.99 11.24 9.97 32.64 41.17 41.26
Surirella sp. 0 0 0 0 0 0 0 0
Thalassionema nitzschioides 0.36 0.91 0.45 0.36 0.09 0.45 0.73 0.82
large Thalassionema spp. 0 0.09 0 0.01 0 0.09 0.01 0
large Thalassiosira spp. 4.17 1.45 1.55 0.36 2.27 3.72 1.81 1.10
small Thalassiosira spp. 1.36 2.36 0.18 2.90 1.09 3.63 2.72 1.81
Tropidoneis sp. ? 0 0 0 0 0 0 0 0
all unknown centrics 0 0.19 0 0 0 0 0.27 0.27
diatom 1 0 0.01 0 0.09 0 0.36 0.18 0.01
diatom 4 0 0 0 0 0 0 0 0
diatom 5 0 0 0 0 0 0 0 0
diatom 7 0 0 0 0 0 0 0
diatom 10 0.09 0.09 0 0 0.18 0 0 0.01
diatom 12 0 0 0 0 0 0 0 0
all unknown pennates 0 0 0 0 0 0 0 0

Total number of cells 111.93 99.42 28.62 42.97 37.63 113.41 127.57 94.59
chlorophyll a (mg m
-3
) 3.15 4.35 1.25 1.44 1.21 3.27 2.68 2.34
Total number of cells does not include Synechococcus spp. or pico-eukaryotes; ciliates (including
tintinnids) were not included in analyses.
211
Table D-1 (continued)

26 JANUARY 2008
station 4 station 5 station 6
Taxon 1 m 5 m 10m 1 m 3 m 5 m 1 m 5 m 12 m

Dinoflagellates no. cells mL
-1

Akashiwo sanguinea 0 0 0 0.01 0.01 0 0.01 0.01 0.01
Alexandrium sp. 0 0.27 0.27 0 0 0 0.01 0 0.63
Amylax tricantha 0.01 0.09 0.18 0 0 0 0.01 0.01 0
Ceratium azoricum 0 0 0 0 0 0 0 0.01 0.01
Ceratium balechii 0.01 0 0 0 0 0 0 0.01 0
Ceratium furca 0.01 0.09 0.09 0 0 0 0.09 0.01 0
Ceratium fusis 0.27 0.09 0.36 0.09 0.01 0.18 0.36 0.27 0.09
Ceratium lineatum 0.36 0.27 0.18 0.09 0 0.01 0.27 0.36 0.54
Cochlodinium sp. 0.01 0 0.01 0 0 0 0.18 0.01 0.01
Dinophysis acuminata 0.09 0.09 0 0 0 0 0 0.01 0.01
Dinophysis fortii 0.01 0.09 0 0 0 0 0.01 0.09 0.18
Dinophysis caudata 0 0 0.01 0 0 0 0 0 0.01
Dinophysis rotundata 0 0 0 0 0 0.18 0 0 0
Dinophysis tripos (?) 0 0 0 0 0 0 0 0 0
Dissodinium pseudolunula 0.01 0.01 0.18 0 0 0.09 0 0 0.01
Gonyaulax sp. 0 0 0 0 0 0 0 0 0.01
gymnodinioid 0.27 0 0 0.01 0 0.09 0.01 0.36 0.18
Lingulodinium polyedrum 0.54 0.45 0.18 0.09 0.27 0.09 0.09 0.73 0.36
long pointed dinoflagellate 0.54 0.27 0.63 0.09 0.01 0.54 0.63 0.36 0.45
Noctiluca sp. 0 0 0 0 0 0 0 0 0
Oxytoxum/Oxyphysis sp. 0 0 0 0 0 0 0 0 0
Polykrikos sp. 0 0 0 0 0 0 0 0 0
Prorocentrum (gracile?) 0.91 0.91 0.82 0 0.09 0.09 0.45 0.27 0.63
Prorocentrum micans 1.27 0.82 0.91 1.72 1.27 3.17 0.45 0.45 0.45
Protoperidinium spp. 0.01 0.09 0.01 0.01 0.01 0 0 0.18 0.09
Protoperidinium steinii 0.09 0.36 0.18 0.27 0.09 0.09 0.18 0.09 0.09
Scrippsiella sp. 1.90 1.27 0.63 0.27 0.18 0.27 1.45 1.90 0.54
unknown dino (Protoperidinium?) 0 0.01 0 0 0 0 0.18 0 0
round dino 0.45 0 0 0 0 0 0.09 0 0
small dino chain 0 0 0 0 0 0 0 0 0
tiny dino 0 0 0 0 0 0 0 0 0
unk dino 3 0 0 0 0 0 0 0 0 0

Other groups
Phaeocystis sp. 0.01 0.18 0.18 0.09 0.18 0.09 0 0 0.09
Dictyocha spp. 0.18 0.27 0.18 0.36 0.45 0.73 0.09 0.36 0.27
Eutreptiella sp. 0.09 0 0 0.01 0 0 0 0 0
radiolarian 0 0.01 0.01 0.01 0.01 0 0.01 0 0
Rhizomonas setigera 0.82 0.01 0 0 0.91 0.01 0 0 0
Prasinophyte (Pyramimonas sp?) 0.36 1.00 0.09 0.09 0 0.09 0.27 0.45 0.27
Chrysophyte (Synura sp?) 0 0 0.01 0.09 0 0 0.09 0.09 0.18

tintinnids 0.09 0.36 0.45 0.27 0.01 0.09 0.09 0.27 0.09
other ciliates 6.44 8.16 6.80 8.07 6.71 7.44 12.42 14.06 6.53

Synechococcus spp. (x 10
3
) 25.85 31.80 11.22 17.27 5.36 25.19 23.79 21.01 16.46
pico-eukaryotes (x 10
3
) 12.92 12.54 7.90 12.46 29.22 9.70 9.30 11.47 8.18

212
Table D-1 (continued)

26 JANUARY 2008
station 4 station 5 station 6
Taxon 1 m 5 m 10 m 1 m 3 m 5 m 1 m 5 m 12m

Diatoms no. cells mL
-1

Asterionellopsis sp. 0 0.82 0.82 0.01 0.36 0.63 0.27 0.63 0.54
Asteromphalus sp. 0 0.01 0.01 0 0 0 0 0 0
Bacteriastrum sp. 0 0 0.18 0 0 0 0 0 0
Bacterosira sp.? 0 0 0 0 0 0 0 0 0
Ceratulina sp.? 0 0 0 0 0 0 0 0 0
Chaetoceros spp. 47.06 55.40 61.21 75.17 61.75 76.53 20.04 22.13 34.09
Corethron sp. 0.01 0 0.01 0.09 0.09 0.18 0 0.01 0.01
Coscinodiscus spp. 0 0.01 0.01 0 0 0 0 0 0
Cylindrotheca sp. 3.36 3.63 3.26 2.36 3.26 3.63 0.82 0.73 1.63
Dactyliosolen sp.? 0 0 0 0 0 0 0 0 0
Detonula sp. 0.18 0 0.63 0.27 0.36 0.54 0 0 0.63
Ditylum brightwellii 0.01 0.01 0 0.27 0.09 0.18 0 0.01 0.01
Entomoneis sp. 0.45 0.54 0.54 0.36 0.36 0.36 0.36 0.36 0.18
Eucampia sp. 0 0 0.27 0 0.73 0.91 0.82 0.45 0.01
Fragilariopsis sp. 0 0 0 0 0 0 0 0 0
Guinardia spp. 0.36 0.74 0.82 0.82 0.63 0.18 0.36 0.36 0.09
Helicotheca sp. 0 0 0 0.09 0.01 0.01 0 0 0
Hemialus sp. 0 0.01 0.09 0.09 0.18 0 0.01 0.27 0
Eucampia/Hemialus 0 0 0 0 0 0 0 0 0
Leptocylindrus sp. 1 6.35 2.54 1.72 4.62 3.08 3.99 3.54 2.63 3.26
Leptocylindrus sp. 2 2.18 0 0.63 0.18 0.63 0.91 0.18 0.18 0.09
Melosira sp. 0 0 0 0.91 0 0.45 0 0 0
Navicula sp. 0.09 0 0 0 0 0 0.09 0 0.09
Odontella aurita 0.18 0.09 0.45 0.18 0.18 0.45 0.18 0 0.01
Odontella sp. 0.01 0.18 0 0 0 0 0 0 0
Odontella longicruis (?) 0.01 0 0.01 0.18 0 0.09 0.01 0 0
Odontella mobiliensis (?) 0 0.01 0 0 0 0 0 0 0
Planktoniella sol 0 0 0 0 0 0 0 0 0
Pleurosigma sp. 0.09 0.01 0 0.18 0 0.09 0.01 0.09 0.01
Pseudonitzschia spp. 3.36 6.62 3.72 7.53 7.80 9.34 1.27 1.81 3.54
Rhizoselenia spp. 0.01 0.01 0.18 0 0.09 0 0 0.01 0.01
Skeletonema sp. 44.25 53.68 48.88 37.45 29.92 36.63 10.52 11.06 30.38
Surirella sp. 0 0 0 0 0 0 0 0 0
Thalassionema nitzschioides 0.01 0.27 0.54 0.18 0.27 0.09 0.18 0.27 0.54
large Thalassionema spp. 0.01 0.01 0 0 0.01 0.09 0.09 0 0
large Thalassiosira spp. 0.54 3.17 2.18 6.44 9.79 5.35 0.55 0.91 1.45
small Thalassiosira spp. 1.00 1.99 3.17 1.54 1.90 3.17 0.45 1.54 0.91
Tropidoneis sp. ? 0 0 0 0 0 0 0 0 0
all unknown centrics 0 0.18 0.09 0.18 0.09 0 0 0 0.36
diatom 1 0.01 0.01 0.01 0 0.63 0.01 0 0.09 0
diatom 4 0 0 0 0 0 0 0 0 0
diatom 5 0 0 0 0 0 0 0 0 0
diatom 7 0 0 0 0 0 0 0 0 0
diatom 10 0 0 0 0 0 0 0 0 0.01
diatom 12 0 0 0 0 0 0 0 0 0
all unknown pennates 0 0 0 0 0 0 0 0 0

Total number of cells 116.95 136.60 134.58 142.42 124.84 149.56 44.71 49.61 83.02
chlorophyll a (mg m
-3
) 1.91 2.10 2.09 2.82 2.84 3.21 1.84 1.53 1.96
Total number of cells does not include Synechococcus spp. or pico-eukaryotes; ciliates (including
tintinnids) were not included in analyses.
213
Table D-1 (continued)

28 JANUARY 2008
station 1 station 2 station 3
Taxon 1 m 7 m 12 m 1 m 5 m 10 m 1 m 5 m 10 m

Dinoflagellates no. cells mL
-1

Akashiwo sanguinea 0 0.01 0 0.01 0.01 0 0 0 0
Alexandrium sp. 0 0.01 0.09 0 0 0 0 0 0
Amylax tricantha 0 0 0.01 0.01 0 0 0 0 0
Ceratium azoricum 0 0 0 0 0 0.09 0 0 0
Ceratium balechii 0.09 0 0 0 0.01 0 0 0 0
Ceratium furca 0.09 0.09 0 0.09 0.18 0 0 0 0
Ceratium fusis 0.01 0.18 0.01 0.45 0.18 0 0.01 0 0
Ceratium lineatum 0 0 0.01 0.27 0.09 0.18 0.01 0 0
Cochlodinium sp. 0 0 0.01 0.54 0.36 0 0 0 0.09
Dinophysis acuminata 0 0 0 0.18 0.01 0 0.09 0 0
Dinophysis fortii 0 0 0 0 0 0 0 0 0
Dinophysis caudata 0 0 0 0.01 0 0 0 0 0
Dinophysis rotundata 0 0 0 0 0 0 0 0 0
Dinophysis tripos (?) 0 0 0 0 0 0 0 0 0
Dissodinium pseudolunula 0.01 0.09 0 0 0.18 0 0 0 0
Gonyaulax sp. 0 0 0 0 0 0 0 0 0
gymnodinioid 0 0 0 0.01 0.09 0 0 0 0
Lingulodinium polyedrum 0.01 0.01 0.09 0.18 0.27 0 0 0.01 0
long pointed dinoflagellate 0.18 0.36 0.01 0.09 0.45 0.45 0.01 0.09 0.09
Noctiluca sp. 0 0 0 0 0 0 0 0 0
Oxytoxum/Oxyphysis sp. 0 0 0 0 0 0 0 0 0
Polykrikos sp. 0 0 0 0 0 0 0 0.01 0
Prorocentrum (gracile?) 0 0.09 0.27 0.63 1.27 0 0 0 0
Prorocentrum micans 0 0.01 0.01 1.09 0.82 0.01 0 0.09 0.09
Protoperidinium spp. 0.09 0.01 0.09 0.01 0.18 0 0 0.01 0
Protoperidinium steinii 0 0.09 0.01 0 0.27 0 0 0.01 0.18
Scrippsiella sp. 0 0.09 0 0.91 0.73 0.09 0.09 0.01 0
unknown dino (Protoperidinium?) 0 0 0 0 0.01 0 0 0 0
round dino 0 0 0 0 0 0 0 0.01 0
small dino chain 0 0 0 0 0 0 0 0 0
tiny dino 0 0 0 0 0 0 0 0 0
unk dino 3 0 0 0 0 0 0 0 0 0

Other groups
Phaeocystis sp. 0 0 0 0 0 0 0 0 0.09
Dictyocha spp. 0.09 0.18 0.01 0.18 0.27 0.01 0 0.27 0.09
Eutreptiella sp. 0 0 0 0 0 0 0 0 0
radiolarian 0 0.09 0 0 0.09 0 0 0 0
Rhizomonas setigera 0.01 0.01 0 0 0 0.01 0 0 0
Prasinophyte (Pyramimonas sp?) 0 0 0 0 0.18 0 0 0 0
Chrysophyte (Synura sp?) 0 0 0 0 0 0.09 0 0 0

tintinnids 0.01 0.01 0.09 0.09 0.09 0.09 0.01 0.01 0.01
other ciliates 5.98 5.89 5.53 12.33 9.88 3.36 5.62 6.62 6.89

Synechococcus spp. (x 10
3
) 18.02 17.99 25.94 25.96 26.44 21.80 14.57 10.28 10.02
pico-eukaryotes (x 10
3
) 9.08 9.73 12.00 11.80 12.43 9.71 6.67 5.15 4.97

214
Table D-1 (continued)

28 JANUARY 2008
station 1 station 2 station 3
Taxon 1 m 7 m 12 m 1 m 5 m 10 m 1 m 5 m 10 m

Diatoms no. cells mL
-1

Asterionellopsis sp. 0 0.01 0 0.54 1.18 0 0 0.36 0
Asteromphalus sp. 0 0 0 0.09 0 0 0 0 0
Bacteriastrum sp. 0 0 0 0 0 0 0 0 0
Bacterosira sp.? 0 0 0 0 0.01 0 0 0 0
Ceratulina sp.? 0.01 0 0 0 0.18 0 0 0 0
Chaetoceros spp. 6.89 23.03 7.34 12.79 29.20 11.33 2.36 3.45 3.36
Corethron sp. 0 0 0.01 0.09 0.18 0.09 0 0.01 0
Coscinodiscus spp. 0 0.01 0.01 0.09 0 0 0 0 0
Cylindrotheca sp. 1.36 1.63 0.73 0.45 1.63 0.82 0.18 0.54 0.18
Dactyliosolen sp.? 0 0 0 0 0 0 0 0 0
Detonula sp. 0 1.27 0 0 0.36 0 1.18 0.01 0
Ditylum brightwellii 0 0.01 0.01 0.09 0.01 0.01 0 0 0
Entomoneis sp. 0.09 0.45 0.27 0 1.09 0.18 0 0.01 0
Eucampia sp. 0 0 0.27 1.18 0.01 0 0 0 0.36
Fragilariopsis sp. 0 0 0 0 0 0 0 0 0
Guinardia spp. 0.09 0.01 0.18 0.27 1.19 0.18 0 0.18 0
Helicotheca sp. 0 0 0 0 0 0 0 0 0
Hemialus sp. 0.01 0 0.18 0.18 0 0.18 0 0 0
Eucampia/Hemialus 0 0 0.01 0 0 0 0 0 0
Leptocylindrus sp. 1 1.00 2.27 0.45 3.17 4.26 0.91 0 0.91 0.36
Leptocylindrus sp. 2 0 0.01 0.01 1.09 2.45 2.63 0 0 0
Melosira sp. 0 0 0 0 0.09 0 0 0 0
Navicula sp. 0.01 0.18 0.09 0 0 0.09 0 0 0
Odontella aurita 0.09 0 0 0.27 0.27 0.18 0 0 0.09
Odontella sp. 0 0 0 0 0 0 0 0 0
Odontella longicruis (?) 0 0 0 0.09 0.09 0 0 0 0
Odontella mobiliensis (?) 0 0 0 0 0.09 0 0 0 0
Planktoniella sol 0 0 0 0 0 0 0 0 0
Pleurosigma sp. 0.18 0 0.09 0.18 0.09 0 0.09 0 0
Pseudonitzschia spp. 0.73 0.63 0.82 5.26 5.08 1.09 0.09 1.45 0.27
Rhizoselenia spp. 0 0 0.01 0 0 0.09 0 0 0.09
Skeletonema sp. 2.63 4.26 0.63 27.02 48.06 5.26 0.73 0.18 0.36
Surirella sp. 0 0 0 0 0 0 0 0 0
Thalassionema nitzschioides 0.18 0.09 0.09 0.27 0.45 0.27 0.09 0 0
large Thalassionema spp. 0 0.18 0 0.01 0 0.09 0 0.01 0.09
large Thalassiosira spp. 0.19 1.46 1.63 2.54 4.72 1.81 0.09 0 0.54
small Thalassiosira spp. 1.91 0.91 0.45 1.90 4.72 0.63 0.01 0.01 0.10
Tropidoneis sp. ? 2.11 2.37 2.09 4.44 9.43 2.45 0.10 0.01 0.64
all unknown centrics 0 0.01 0 0 0.18 0 0 0.01 0
diatom 1 0.09 0.09 0 0.36 0.09 0 0 0 0
diatom 4 0 0 0 0 0 0 0 0 0
diatom 5 0 0 0 0 0 0 0.27 0 0
diatom 7 0 0 0 0 0 0 0 0 0
diatom 10 0 0 0 0.27 0 0.18 0 0 0
diatom 12 0 0 0 0 0 0 0 0 0
all unknown pennates 0 0 0 0 0 0 0 0 0

Total number of cells 16.04 37.84 13.92 62.99 111.34 26.96 5.30 7.65 6.45
chlorophyll a (mg m
-3
) 1.04 1.04 1.00 3.06 2.85 0.95 0.60 0.68 0.69
Total number of cells does not include Synechococcus spp. or pico-eukaryotes; ciliates (including
tintinnids) were not included in analyses.
215
Table D-1 (continued)

28 JANUARY 2008
station 4 station 5 station 6
Taxon 1 m 5 m 10 m 1 m 5 m 10 m 1 m 5 m 10 m

Dinoflagellates no. cells mL
-1

Akashiwo sanguinea 0 0 0 0 0 0 0 0 0
Alexandrium sp. 0 0.27 0.09 0 0 0 0 0 0
Amylax tricantha 0 0 0 0 0 0 0 0 0
Ceratium azoricum 0 0 0 0 0 0 0 0 0
Ceratium balechii 0.01 0 0 0 0 0 0 0 0
Ceratium furca 0.01 0.01 0 0.09 0 0 0 0.09 0.01
Ceratium fusis 0.01 0.01 0.01 0.18 0 0 0.01 0.09 0.01
Ceratium lineatum 0.01 0.01 0 0.01 0 0.01 0.01 0 0.01
Cochlodinium sp. 0.01 0 0 0 0.01 0 0 0 0
Dinophysis acuminata 0 0 0.01 0 0 0 0.01 0.01 0
Dinophysis fortii 0 0 0.09 0 0 0.01 0.01 0.01 0.01
Dinophysis caudata 0 0 0 0 0 0 0 0 0
Dinophysis rotundata 0 0 0 0 0 0 0 0 0
Dinophysis tripos (?) 0 0 0 0 0 0 0 0 0
Dissodinium pseudolunula 0 0.01 0.01 0.09 0.09 0 0.01 0 0
Gonyaulax sp. 0 0 0 0 0 0 0 0 0
gymnodinioid 0.01 0.09 0 0 0 0 0.01 0 0
Lingulodinium polyedrum 0.09 0.18 0.01 0.09 0.09 0.01 0.09 0.01 0.01
long pointed dinoflagellate 0.27 0.01 0.27 0.27 0.36 0.01 0.01 0.18 0.09
Noctiluca sp. 0 0 0 0 0 0 0 0 0
Oxytoxum/Oxyphysis sp. 0 0 0 0 0.01 0 0 0 0
Polykrikos sp. 0 0 0 0 0 0 0 0 0
Prorocentrum (gracile?) 0.18 0.27 0 0.27 0 0 0.01 0.09 0.09
Prorocentrum micans 0.09 0.36 0.01 0.01 0.18 0.09 0.18 0.01 0.01
Protoperidinium spp. 0 0.01 0.01 0.01 0.09 0 0 0.01 0.27
Protoperidinium steinii 0.09 0.01 0.01 0.01 0.09 0.01 0.01 0.18 0.01
Scrippsiella sp. 0.09 0.27 0 0 0.18 0.09 0.09 0 0.09
unknown dino (Protoperidinium?) 0 0.01 0 0 0 0 0.01 0 0
round dino 0 0 0 0.09 0 0 0 0 0
small dino chain 0 0 0 0 0 0 0 0 0
tiny dino 0 0 0 0 0 0 0 0 0
unk dino 3 0 0 0 0 0 0 0 0 0

Other groups
Phaeocystis sp. 0.01 0.18 0 0.01 0 0 0 0 0
Dictyocha spp. 0.09 0.09 0.27 0.27 0.18 0.01 0.09 0.18 0.36
Eutreptiella sp. 0 0 0 0 0 0.09 0 0 0
radiolarian 0.01 0 0.01 0 0 0 0 0.01 0
Rhizomonas setigera 0 0 0 0 0 0 0 0.36 0
Prasinophyte (Pyramimonas sp?) 0 0.01 0.09 0 0 0 0 0 0
Chrysophyte (Synura sp?) 0 0 0 0 0 0 0 0 0

tintinnids 0.01 0 0 0.01 0.18 0.18 0 0.01 0.01
other ciliates 5.80 9.97 4.44 5.35 4.99 3.54 5.26 6.08 5.44

Synechococcus spp. (x 10
3
) 11.01 13.23 12.97 13.77 6.96 7.82 7.46 33.22 35.73
pico-eukaryotes (x 10
3
) 5.75 6.23 5.31 6.27 4.31 6.49 4.61 12.82 11.70

216
Table D-1 (continued)

28 JANUARY 2008
station 4 station 5 station 6
Taxon 1 m 5 m 10 m 1 m 5 m 10 m 1 m 5 m 10 m

Diatoms no. cells mL
-1

Asterionellopsis sp. 0.73 0.36 0.09 0.54 0 0 0 0.18 0
Asteromphalus sp. 0 0 0 0 0 0 0 0 0
Bacteriastrum sp. 0 0 0 0 0 0 0.18 0 0
Bacterosira sp.? 0 0 0 0 0 0 0 0 0
Ceratulina sp.? 0 0 0 0 0 0 0 0 0
Chaetoceros spp. 13.15 19.41 7.89 17.14 12.60 7.89 5.44 4.62 4.99
Corethron sp. 0.09 0.01 0 0.01 0 0.01 0 0 0
Coscinodiscus spp. 0 0 0 0 0 0 0 0 0
Cylindrotheca sp. 0.63 1.27 0.18 1.18 1.72 0.27 0.18 0.18 0.45
Dactyliosolen sp.? 0 0 0 0 0 0 0 0 0
Detonula sp. 0.18 0.54 0.09 0.01 0.01 0.27 0.01 0.01 0
Ditylum brightwellii 0 0.01 0.01 0 0 0 0 0 0.09
Entomoneis sp. 0.01 0.18 0.18 0.36 0.27 0 0.09 0.18 0.01
Eucampia sp. 0.27 1.45 0.27 0 1.63 0 0 0.01 0.01
Fragilariopsis sp. 0 0 0 0 0 0 0 0 0.27
Guinardia spp. 0.19 0.09 0.18 0.54 0.19 0 0.27 0.36 0.18
Helicotheca sp. 0 0 0 0 0.01 0 0 0 0
Hemialus sp. 0 0.18 0 0 0.09 0.09 0 0.01 0
Eucampia/Hemialus 0 0 0 0 0.18 0 0 0 0
Leptocylindrus sp. 1 1.81 0.18 0.18 0.91 0.63 0 0.01 0 1.18
Leptocylindrus sp. 2 0 0.01 0 1.09 0 0 0.01 0 0
Melosira sp. 0 0 0 0 0 0 0 0 0
Navicula sp. 0 0 0.09 0 0 0.09 0.09 0 0.09
Odontella aurita 0 0 0 0.09 0.01 0 0 0.01 0.09
Odontella sp. 0 0 0 0 0 0 0 0 0
Odontella longicruis (?) 0 0.09 0.01 0.01 0.18 0 0 0 0
Odontella mobiliensis (?) 0 0 0 0 0 0 0 0 0
Planktoniella sol 0 0 0 0 0 0 0.01 0 0
Pleurosigma sp. 0 0 0.09 0.01 0.09 0 0 0.01 0
Pseudonitzschia spp. 0.91 1.09 1.00 0.91 1.54 2.99 0.18 1.54 0.27
Rhizoselenia spp. 0 0 0 0 0 0 0.01 0 0
Skeletonema sp. 7.16 7.98 6.44 14.15 9.79 2.99 0.91 3.54 4.99
Surirella sp. 0 0 0 0 0 0 0 0 0
Thalassionema nitzschioides 0.09 0.09 0.01 0.09 0.09 0.01 0.18 0.09 0.01
large Thalassionema spp. 0.09 0 0.01 0 0.01 0 0 0 0
large Thalassiosira spp. 3.26 0.09 0.37 0.91 0.46 0.01 0.02 0.19 0.02
small Thalassiosira spp. 0.54 0.82 0.55 1.45 1.28 0.36 0.54 0.36 0.73
Tropidoneis sp. ? 0 0 0 0 0 0 0 0 0
all unknown centrics 0 0.09 0.36 0 0.01 0.01 0.01 0 0
diatom 1 0 0.09 0.01 0.09 0.01 0 0 0 0
diatom 4 0 0 0 0 0 0 0 0 0
diatom 5 0 0 0 0 0 0 0 0 0
diatom 7 0 0 0 0 0 0 0 0 0
diatom 10 0 0.01 0.09 0 0 0 0 0 0
diatom 12 0 0 0 0 0 0 0 0 0
all unknown pennates 0 0 0 0 0 0 0 0 0

Total number of cells 30.11 35.86 19.01 40.90 32.12 15.33 8.70 12.18 14.36
chlorophyll a (mg m
-3
) 1.30 1.28 1.17 1.40 1.07 0.88 0.84 0.84 0.93
Total number of cells does not include Synechococcus spp. or pico-eukaryotes; ciliates (including
tintinnids) were not included in analyses.
217
Table D-1 (continued)

31 JANUARY 2008
station 1 station 2 station 3
Taxon 1 m 5 m 10 m 1 m 5 m 15 m 1 m 5 m 15 m

Dinoflagellates no. cells mL
-1

Akashiwo sanguinea 0.01 0 0 0 0 0 0 0 0
Alexandrium sp. 0 0 0 0.01 0 0.18 0 0 0
Amylax tricantha 0 0 0 0 0 0 0 0 0
Ceratium azoricum 0.01 0 0.09 0 0 0 0 0 0
Ceratium balechii 0.18 0.01 0 0 0 0.09 0 0 0
Ceratium furca 0.01 0.01 0.01 0.09 0.18 0.09 0.09 0 0
Ceratium fusis 0.09 0.01 0.01 0.18 0.27 0.27 0.01 0.01 0.01
Ceratium lineatum 0.18 0.45 0.01 0.36 0.27 0.09 0.01 0.01 0
Cochlodinium sp. 0 0.36 0 0 0 0 0 0 0
Dinophysis acuminata 0 0 0 0.01 0 0.01 0.01 0 0
Dinophysis fortii 0.01 0 0.01 0 0.01 0 0 0.01 0
Dinophysis caudata 0.01 0.01 0 0 0 0 0 0 0
Dinophysis rotundata 0 0 0 0.01 0 0 0 0 0
Dinophysis tripos (?) 0 0 0 0 0 0 0 0 0
Dissodinium pseudolunula 0.01 0.01 0 0 0.09 0.09 0 0.09 0.01
Gonyaulax sp. 0 0 0 0 0 0 0 0 0
gymnodinioid 0 0 0 0.01 0 0 0 0 0
Lingulodinium polyedrum 0.09 0.01 0.01 0 0.01 0.36 0.01 0.09 0.09
long pointed dinoflagellate 0.09 0.18 0.18 0.36 0.18 0.01 0.09 0 0.01
Noctiluca sp. 0 0 0 0 0 0 0 0 0
Oxytoxum/Oxyphysis sp. 0 0 0 0 0 0 0 0 0
Polykrikos sp. 0 0 0 0 0 0 0 0 0
Prorocentrum (gracile?) 0.91 0.45 0 0.18 0.09 0.01 0.09 0 0
Prorocentrum micans 0 0.09 0.09 0.45 0.27 0.18 0.18 0.09 0.09
Protoperidinium spp. 0.09 0 0.01 0.09 0.01 0.18 0 0.01 0.09
Protoperidinium steinii 0 0.27 0.09 0.09 0.09 0.09 0 0.01 0.18
Scrippsiella sp. 0.36 0.36 0 0 0.09 0 0 0 0
unknown dino (Protoperidinium?) 0 0 0 0.09 0.18 0 0 0.01 0
round dino 0 0 0 0 0 0 0.36 0 0
small dino chain 0 0 0 0 0 0 0 0 0
tiny dino 0 0 0 0 0 0 0 0 0
unk dino 3 0 0 0 0 0 0 0 0 0

Other groups
Phaeocystis sp. 0.27 0 0.09 0 0.01 0 0 0 0.01
Dictyocha spp. 0.45 0.18 0.09 0.01 0.09 0.09 0.27 0.27 0.18
Eutreptiella sp. 0.09 0 0 0 0 0 0 0 0
radiolarian 0.09 0 0 0 0 0.01 0.01 0 0.01
Rhizomonas setigera 0 0 0 0 0.01 0 0 0 0
Prasinophyte (Pyramimonas sp?) 0 0.18 0 0 0 0 0 0 0
Chrysophyte (Synura sp?) 0 0 0 0 0 0 0 0 0

tintinnids 0.01 0.01 0.01 0.09 0 0.01 0.01 0.09 0.01
other ciliates 6.35 5.62 5.26 3.36 3.26 2.27 2.36 4.26 2.54

Synechococcus spp. (x 10
3
) 37.21 15.25 13.98 14.75 19.85 2.60 19.63 15.36 15.75
pico-eukaryotes (x 10
3
) 10.85 7.68 6.74 8.81 9.67 1.29 9.17 8.76 9.57

218
Table D-1 (continued)

31 JANUARY 2008
station 1 station 2 station 3
Taxon 1 m 5 m 10 m 1 m 5 m 15 m 1 m 5 m 15 m

Diatoms no. cells mL
-1

Asterionellopsis sp. 1.27 1.09 0 0 0.01 1.36 0.09 0 0.01
Asteromphalus sp. 0 0 0.09 0 0 0 0 0 0
Bacteriastrum sp. 0 0 0 0 0 0 0 0 0
Bacterosira sp.? 0.18 0 0 0 0.18 0 0 0 0
Ceratulina sp.? 0 0 0 0 0 0 0 0 0
Chaetoceros spp. 13.96 17.14 9.43 12.97 13.24 12.33 2.36 2.27 2.99
Corethron sp. 0 0.09 0.01 0 0.01 0.01 0 0.01 0.01
Coscinodiscus spp. 0.01 0.01 0 0.09 0.09 0 0 0 0
Cylindrotheca sp. 1.18 1.99 0.36 1.00 0.54 1.81 0.36 0.45 0.18
Dactyliosolen sp.? 0 0 0 0 0 0 0 0 0
Detonula sp. 1.45 0.01 0.01 0.82 0.27 0.36 0.45 0 0.01
Ditylum brightwellii 0.09 0.01 0 0 0.01 0.01 0 0 0
Entomoneis sp. 0.18 0.45 0.09 0.09 0.01 0.27 0 0.18 0.09
Eucampia sp. 1.09 6.44 0.36 1.36 0.18 1.18 0 0 0.01
Fragilariopsis sp. 0 0 0 0 0 0 0 0 0
Guinardia spp. 0.09 0.36 0.36 0.09 0.54 0.27 0.09 0 0.27
Helicotheca sp. 0 0 0 0 0 0 0 0 0
Hemialus sp. 0 0.36 0.01 0.45 0.09 0.54 0 0 0.01
Eucampia/Hemialus 0 0.01 0 0 0 0 0 0 0
Leptocylindrus sp. 1 1.81 2.54 0.91 0.09 0.01 0.18 0.54 0 0.45
Leptocylindrus sp. 2 0 0.01 0.63 0 0 0.01 0 0 0.45
Melosira sp. 0 0 0 0 0 0 0 0 0
Navicula sp. 0.09 0.09 0 0 0 0 0.09 0.18 0.09
Odontella aurita 0.09 0.18 0 0.18 0 0.09 0 0 0
Odontella sp. 0 0 0 0 0 0 0 0 0
Odontella longicruis (?) 0 0.01 0.09 0.01 0 0.01 0 0 0
Odontella mobiliensis (?) 0 0 0 0 0 0 0 0 0
Planktoniella sol 0 0 0 0 0 0 0.09 0 0
Pleurosigma sp. 0.09 0.09 0.09 0.27 0 0.09 0 0 0
Pseudonitzschia spp. 3.63 3.26 0.27 0.63 0.54 1.09 0.54 0 0.27
Rhizoselenia spp. 0 0 0 0 0 0.09 0 0 0.09
Skeletonema sp. 7.44 8.80 4.90 0.18 2.36 0.18 0.82 0.09 0.63
Surirella sp. 0 0.01 0 0 0 0 0 0.09 0
Thalassionema nitzschioides 0.45 0.18 0.09 0.27 0.09 0.09 0.01 0.09 0.01
large Thalassionema spp. 0.54 0 0 0.01 0 0 0.01 0 0
large Thalassiosira spp. 1.72 2.36 1.81 1.54 0.92 0.37 0.01 0.02 0.91
small Thalassiosira spp. 1.63 4.35 1.81 0.54 0.83 1.09 0.36 0.18 0.27
Tropidoneis sp. ? 0 0 0 0 0 0 0 0 0
all unknown centrics 0.09 0 0 0 0.01 0.18 0 0 0.09
diatom 1 0.09 0.01 0 0 0 0 0.18 0 0.09
diatom 4 0 0 0 0 0 0 0 0 0
diatom 5 0 0 0 0 0 0 0 0 0
diatom 7 0 0 0 0 0 0 0 0 0
diatom 10 0 0.09 0.09 0 0 0 0 0 0
diatom 12 0 0 0 0 0 0 0 0 0
all unknown pennates 0 0 0 0 0 0 0 0 0

Total number of cells 40.15 52.55 22.12 22.56 21.79 23.39 7.15 4.17 7.64
chlorophyll a (mg m
-3
) 1.95 1.59 1.25 1.15 1.03 1.23 0.61 0.64 0.87
Total number of cells does not include Synechococcus spp. or pico-eukaryotes; ciliates (including
tintinnids) were not included in analyses.
219
Table D-1 (continued)

31 JANUARY 2008
station 4 station 5
Taxon 1 m 5 m 10 m 1 m 5 m 10 m

Dinoflagellates no. cells mL
-1

Akashiwo sanguinea 0 0 0 0 0.09 0
Alexandrium sp. 0.01 0 0 0.01 0.82 0.01
Amylax tricantha 0.09 0.27 0 0.36 0.18 0.01
Ceratium azoricum 0 0 0.01 0 0 0.01
Ceratium balechii 0.18 0.18 0 0.09 0.01 0
Ceratium furca 0.45 0.54 0.01 0.18 1.18 0.09
Ceratium fusis 0.91 0.73 0.27 0.27 0.45 0.01
Ceratium lineatum 0.36 0.36 0.27 0.27 0.27 0.09
Cochlodinium sp. 0.01 0.27 0.01 1.09 0.01 0.01
Dinophysis acuminata 0 0.18 0 0.09 0.09 0.18
Dinophysis fortii 0 0.01 0.01 0.01 0.09 0
Dinophysis caudata 0.01 0.01 0 0 0 0.01
Dinophysis rotundata 0 0 0 0 0 0
Dinophysis tripos (?) 0 0 0.09 0 0 0
Dissodinium pseudolunula 0.01 0 0 0 0.18 0
Gonyaulax sp. 0 0 0.01 0 0 0
gymnodinioid 0 0.09 0 0.73 0.27 0
Lingulodinium polyedrum 0.01 0.63 0.27 0.45 0.73 0.09
long pointed dinoflagellate 0.09 0.36 0.01 0.63 0.45 0.63
Noctiluca sp. 0 0 0 0 0 0
Oxytoxum/Oxyphysis sp. 0 0 0 0 0 0
Polykrikos sp. 0 0 0.01 0.01 0.01 0
Prorocentrum (gracile?) 1.99 3.17 0.36 2.09 2.27 0.18
Prorocentrum micans 0.54 0.45 0.09 0.73 1.54 0.18
Protoperidinium spp. 0.09 0.09 0.09 0.63 0.27 0.18
Protoperidinium steinii 0.27 0.09 0.18 0.54 0.63 0.18
Scrippsiella sp. 1.36 1.99 0.63 2.09 1.45 0.09
unknown dino (Protoperidinium?) 0.36 0.27 0 0.54 0.27 0.01
round dino 0.09 0 0 0 0 0
small dino chain 0 0 0 0 0 0
tiny dino 0 0 0 0 0 0
unk dino 3 0 0 0 0 0 0

Other groups
Phaeocystis sp. 0.09 0.01 0 0 0 0
Dictyocha spp. 0.45 0.91 0.27 0.18 0.18 0.54
Eutreptiella sp. 0.18 0.27 0 0.09 0.01 0
radiolarian 0 0 0 0 0 0
Rhizomonas setigera 0 0 0 0 0 0.01
Prasinophyte (Pyramimonas sp?) 0 0 0.45 0 0.36 0.18
Chrysophyte (Synura sp?) 0 0 0 0 0 0

tintinnids 0.18 0.36 0.01 0.01 0.09 0
other ciliates 8.71 15.32 11.88 9.70 11.52 3.99

Synechococcus spp. (x 10
3
) 9.35 9.37 8.27 7.34 8.03 7.92
pico-eukaryotes 6.45 4.46 10.32 8.52 9.11 6.03

220
Table D-1 (continued)

31 JANUARY 2008
station 4 station 5
Taxon 1 m 5 m 10 m 1 m 5 m 10 m

Diatoms no. cells mL
-1

Asterionellopsis sp. 0.36 0.27 0.82 1.63 1.00 0.54
Asteromphalus sp. 0.09 0 0 0.09 0 0
Bacteriastrum sp. 0 0 0.09 0 0 0
Bacterosira sp.? 0 0.01 0 0 0.63 0
Ceratulina sp.? 0 0 0 0 0 0
Chaetoceros spp. 20.31 33.19 33.28 17.59 25.12 27.02
Corethron sp. 0 0.18 0.09 0.18 0.01 0.01
Coscinodiscus spp. 0 0 0.01 0 0 0
Cylindrotheca sp. 0.73 2.18 1.81 1.45 3.36 2.81
Dactyliosolen sp.? 0 0 0 0 0 0
Detonula sp. 0.27 1.09 1.54 1.09 0.01 0.91
Ditylum brightwellii 0.01 0 0.01 0.01 0.01 0.01
Entomoneis sp. 0.54 0.63 0.54 0.18 0.91 1.27
Eucampia sp. 2.36 3.17 0.91 0.91 0.36 1.54
Fragilariopsis sp. 0 0 0 0 0 0
Guinardia spp. 1.01 1.09 0.91 0.54 0.54 0.55
Helicotheca sp. 0 0 0 0 0 0
Hemialus sp. 0 0.18 0.01 0.09 0.36 0.09
Eucampia/Hemialus 0 0 0 0 0.09 0
Leptocylindrus sp. 1 4.26 2.72 1.72 3.36 3.63 2.54
Leptocylindrus sp. 2 0.54 0.45 0 0 0.54 0.91
Melosira sp. 0 0 0 0 0.01 0
Navicula sp. 0 0 0 0.18 0.09 0
Odontella aurita 0 0.09 0 0 0 0.18
Odontella sp. 0 0 0 0 0 0.01
Odontella longicruis (?) 0.09 0 0.01 0 0 0.09
Odontella mobiliensis (?) 0 0 0 0 0 0
Planktoniella sol 0 0 0 0 0 0
Pleurosigma sp. 0.01 0 0.01 0.18 0.01 0.18
Pseudonitzschia spp. 3.36 7.44 6.89 3.90 2.45 5.71
Rhizoselenia spp. 0 0 0.27 0.09 0 0.01
Skeletonema sp. 0.73 7.34 3.99 4.62 4.62 18.23
Surirella sp. 0 0 0 0 0 0
Thalassionema nitzschioides 0.27 0.45 0.27 0.45 0.63 1.27
large Thalassionema spp. 0.01 0.09 0.01 0 0.09 0
large Thalassiosira spp. 0.37 1.10 2.90 0.54 4.17 1.45
small Thalassiosira spp. 3.26 4.17 3.54 4.62 4.90 4.08
Tropidoneis sp. ? 0 0 0 0 0 0
all unknown centrics 0.10 0 0.27 0.27 0.18 0.91
diatom 1 0.27 0.09 0 0.36 0 0.01
diatom 4 0 0 0 0 0 0
diatom 5 0 0 0 0 0 0
diatom 7 0 0 0 0 0 0
diatom 10 0.36 0.01 0.01 0.18 0.01 0.18
diatom 12 0 0 0 0 0 0
all unknown pennates 0 0 0 0 0 0

Total number of cells 46.90 76.86 62.98 53.63 65.57 73.22
chlorophyll a (mg m
-3
) 2.37 2.42 2.82 2.30 2.59 2.46
Total number of cells does not include Synechococcus spp. or pico-eukaryotes; ciliates (including
tintinnids) were not included in analyses.
221
APPENDIX E
Phytoplankton Guide

The following represents phytoplankton species observed in samples collected in
Santa Monica Bay during cruises between 2006 and 2008. Samples were preserved in
acid Lugol’s solution (unless otherwise specified) and counted with a Leica inverted
microscope (Caron lab). Images were collected under DIC (differential interference
contrast) using Openlab software.
Species are separated by major groups (i.e. dinoflagellates and diatoms), and are
listed alphabetically within each group.
Identifications and taxonomy from Tomas (1997), Horner (2002), Round et al.
(1990), the National Center for Biotechnology Information (NCBI) Taxonomy Browser
(http://www.ncbi.nlm.nih.gov/Taxonomy/), the Global Biodiversity Information Facility
(GBIF) Data Portal (www.gbif.net), the World Register of Marine Species
(http://www.marinespecies.org), and the Encyclopedia of Life (www.eol.org).

DINOFLAGELLATES (class Dinophyceae)
Domain: Eukaryota; Superphylum: Alveolata; Phylum: Dinophyta

Akashiwo sanguinea
Describer: (Hirasaka, 1924)
Hansen et Moestrup, 2000
Taxonomy: Order: Gymnodiniales
Family: Gymnodiniaceae
Synonyms: Gymnodinium sanguineum
Gymnodinium nelsonii
Size range: large
Abundance: common, forms blooms

Alexandrium sp.
Describer: Halim
Taxonomy: Order: Gonyaulacales
Family: Gonyaulacaceae
Synonyms: Gonyaulax, Protogonyaulax
Size range: small
Abundance: occasional, can form long chains

Amylax tricantha
Describer: (Jørgensen, 1899) Sournia, 1984
Taxonomy: Order: Gonyaulacales
Family: Gonyaulacaceae
Synonyms: Gonyaulax triacantha
Size range: small
Abundance: rare-occassional

Ceratium azoricum
Describer: Cleve, 1900
Taxonomy: Order: Gonyaulacales
Family: Ceratiaceae
Synonyms:
Size range: large
Abundance: rare

Ceratium balechii
Describer: del Castillo, Okolodkov et Zamudio, 2003
Taxonomy: Order: Gonyaulacales
Family: Ceratiaceae
Listed as Ceratium divaricatum in Horner (2002)
Size range: large
Abundance: occasional

222
DINOFLAGELLATES (class Dinophyceae)
Domain: Eukaryota; Superphylum: Alveolata; Phylum: Dinophyta

Ceratium candelabrum
Describer: (Ehrenberg, 1860) Stein, 1883
Taxonomy: Order: Gonyaulacales
Family: Ceratiaceae
Synonyms:
Size range: very large
Abundance: very rare

Ceratium furca
Describer: (Ehrenberg, 1834)
Claparède et Lachmann, 1859
Taxonomy: Order: Gonyaulacales
Family: Ceratiaceae
Synonyms:
Size range: large
Abundance: common

Ceratium fusis
Describer: (Ehrenberg, 1834) Dujardin, 1841
Taxonomy: Order: Gonyaulacales
Family: Ceratiaceae
Synonyms:
Size range: large
Abundance: common
no preservative

Ceratium lineatum
Describer: (Ehrenberg, 1854) Cleve, 1899
Taxonomy: Order: Gonyaulacales
Family: Ceratiaceae
Synonyms:
Size range: large
Abundance: common

Ceratium macroceros
Describer: (Ehrenberg, 1840)
Vanhöffen, 1897
Taxonomy: Order: Gonyaulacales
Family: Ceratiaceae
Synonyms:
Size range: very large
Abundance: very rare

223
DINOFLAGELLATES (class Dinophyceae)
Domain: Eukaryota; Superphylum: Alveolata; Phylum: Dinophyta

Ceratium tripos (?)
Describer: (Ehrenberg, 1840)
Vanhöffen, 1897
Taxonomy: Order: Gonyaulacales
Family: Ceratiaceae
Synonyms:
Size range: large
Abundance: very rare

Cochlodinium sp.
Describer: Schütt, 1896
Taxonomy: Order: Gymnodiniales
Family: Gymnodiniaceae
Synonyms:
Size range: large
Abundance: common, forms blooms
*cells often explode in preservatives live (by Alina Corcoran)

Dinophysis acuminata
Describer: Claparède & Lachmann, 1859
Taxonomy: Order: Dinophysiales
Family: Dinophysiaceae
Synonyms:
Size range: mediuim-large
Abundance: common

Dinophysis caudata
Describer: Saville-Kent, 1881
Taxonomy: Order: Dinophysiales
Family: Dinophysiaceae
Synonyms:
Size range: large
Abundance: common

Dinophysis fortii
Describer: Pavil1lard, 1923
Taxonomy: Order: Dinophysiales
Family: Dinophysiaceae
Synonyms:
Size range: large
Abundance: common

224
DINOFLAGELLATES (class Dinophyceae)
Domain: Eukaryota; Superphylum: Alveolata; Phylum: Dinophyta

Dinophysis rotundata
Describer: Clarepède & Lachmann, 1859
Taxonomy: Order: Dinophysiales
Family: Dinophysiaceae
Synonyms: Phalacroma rotundatum
Size range: large
Abundance: rare

Dinophysis sp. (odiosa?)
Describer: Clarepède & Lachmann, 1859
Taxonomy: Order: Dinophysiales
Family: Dinophysiaceae
Synonyms:
Size range: large
Abundance: very rare

Dissodinium pseudolunula
Describer: Swift, 1973
ex Elbrächter et Drebes, 1978
Taxonomy: Order: Pyrocystales
Family: Pyrocystaceae
Synonyms: Pyrocystis lunula
Size range: large
Abundance: rare
no preservative

Gonyaulax sp.
Describer: Diesing, 1866
Taxonomy: Order: Gonyaulacales
Family: Gonyaulacaceae
Synonyms:
Size range: large
Abundance: rare

Gymnodinium sp. (?)
Describer: (Stein, 1878)
emend. G. Hansen et Moestrup, 2000
Taxonomy: Order: Gymnodiniales
Family: Gymnodiniaceae
Synonyms:
Size range: small
Abundance: common

225
DINOFLAGELLATES (class Dinophyceae)
Domain: Eukaryota; Superphylum: Alveolata; Phylum: Dinophyta

Lingulodinium polyedrum
Describer: (Stein, 1883) Dodge, 1989
Taxonomy: Order: Gonyaulacales
Family: Gonyaulacaceae
Synonyms: Gonyaulax polyedra
Size range: large
Abundance: abundant, forms blooms

no preservative
Oxytoxum/Oxyphysis
Describer: Stein, 1883/Kofoid, 1926
Taxonomy: Order: Gonyaulacales/Dinophysiales
Family: Oxytoxaceae/Oxyphysaceae
Synonyms:
Size range: small-medium
Abundance: rare
*didn't separate genera in counts

big Oxytoxum/Oxyphysis (?)
Describer: Stein, 1883/Kofoid, 1926
Taxonomy: Order: Gonyaulacales/Dinophysiales
Family: Oxytoxaceae/Oxyphysaceae
Synonyms:
Size range: large
Abundance: very rare

Podolampas sp.
Describer: Stein, 1883
Taxonomy: Order: Peridiniales
Family: Podolampadaceae
Synonyms:
Size range: medium
Abundance: very rare

no preservative

Polykrikos sp.
Describer: Bütschli, 1873
Taxonomy: Order: Gymnodiniales
Family: Polykridaceae
Synonyms:
Size range: large
Abundance: occassional-common

226
DINOFLAGELLATES (class Dinophyceae)
Domain: Eukaryota; Superphylum: Alveolata; Phylum: Dinophyta

Prorocentrum micans
Describer: Ehrenberg, 1834
Taxonomy: Order: Prorocentrales
Family: Prorocentraceae
Synonyms:
Size range: medium
Abundance: abundant

Prorocentrum gracile (?)
Describer: Schütt, 1895
Taxonomy: Order: Prorocentrales
Family: Prorocentraceae
Synonyms:
Size range: small
Abundance: abundant

Scrippsiella sp.
Describer: Balech ex Loeblich III, 1965
Taxonomy: Order: Peridiniales
Family: Peridiniaceae
Synonyms: Glenodinium, Peridinium
Size range: small
Abundance: occasional

Protoperidinium steinii (?)
Describer: (Jørgensen, 1899) Balech, 1974
Taxonomy: Order: Peridiniales
Family: Protoperidiniaceae
Synonyms: Peridinium steinii
Peridinium michaelis
Size range: small
Abundance: common

Protoperidinium oceanicum (?)
Describer: (VanHöffen, 1897) Balech, 1974
Taxonomy: Order: Peridiniales
Family: Protoperidiniaceae
Synonyms:
Size range: large
Abundance: common

no preservative

227
DINOFLAGELLATES (class Dinophyceae)
Domain: Eukaryota; Superphylum: Alveolata; Phylum: Dinophyta

Protoperidinium conicum (?)
Describer: (Gran, 1900) Balech, 1974
Taxonomy: Order: Peridiniales
Family: Protoperidiniaceae
Synonyms:
Size range: large
Abundance: common

Protoperidinium leonis (?)
Describer: (Pavillard, 1916) Balech, 1974
Taxonomy: Order: Peridiniales
Family: Protoperidiniaceae
Synonyms:
Size range: large
Abundance: rare

unknown dinoflagellate
Possible IDs: Heterocapsa sp.
Size range: very small
Abundance: rare

unknown dinoflagellate (round dino)
Possible IDs: ??
Size range: small
Abundance: rare

unknown dinoflagellate
(small dino chain)
Possible IDs: ??
Size range: small
Abundance: rare

228
DINOFLAGELLATES (class Dinophyceae)
Domain: Eukaryota; Superphylum: Alveolata; Phylum: Dinophyta

unknown dinoflagellate (big dino)
Possible IDs: ??
Size range: big
Abundance: very rare

unknown dinoflagellate (small dino)
Possible IDs: ??
Size range: small
Abundance: rare

unknown dinoflagellate
Possible IDs: Protoperidinium sp.
Size range: large
Abundance: rare

unknown dinoflagellate (tiny dino)
Possible IDs: ??
Size range: very small
Abundance: rare

unknown dinoflagellate (spiral dino)
Possible IDs: ??
Size range: large
Abundance: very rare

229
DINOFLAGELLATES (class Dinophyceae)
Domain: Eukaryota; Superphylum: Alveolata; Phylum: Dinophyta

unknown dinoflagellate (unk dino 1)
Possible IDs: ??
Size range: small
Abundance: very rare

unknown dinoflagellate (unk dino 2)
Possible IDs: ??
Size range: medium
Abundance: very rare

unknown dinoflagellate (unk dino 3)
Possible IDs: ??
Size range: medium
Abundance: very rare

unknown dinoflagellate (unk naked dino)
Possible IDs: ??
Size range: small
Abundance: rare

unknown dinoflagellate
(long pointed dino)
Possible IDs: Gymnodinium spirale
Size range: medium
Abundance: common

230

DIATOMS (phylum/division Bacillariophyta)
Domain: Eukaryota; Superphylum: stramenopiles

Asterionellopsis sp.
Describer: Round
Taxonomy: Class: Fragilariophyceae
Order: Fragilariales
Family: Fragilariaceae
Synonyms:
Size range: small
Abundance: rare-occasional
Note: usually found in short chains

Asteromphalus sp.
Describer: Ehrenberg, 1844
Taxonomy: Class: Coscinodiscophyceae
Order: Asterolamprales
Family: Asterolampraceae
Synonyms:
Size range: medium
Abundance: rare

Bacillaria sp. (?)
Describer: J.F. Gmelin, 1791
Taxonomy: Class: Bacillariophyceae
Order: Bacillariales
Family: Bacillariaceae
Synonyms:
Size range: medium
Abundance: very rare

Bacteriastrum sp.
Describer: Shadbolt, 1854
Taxonomy: Class: Coscinodiscophyceae
Order: Chaetocerotales
Family: Chaetocerotaceae
Synonyms:
Size range: small
Abundance: rare-occasional
empty frustule

231
DIATOMS (phylum/division Bacillariophyta)
Domain: Eukaryota; Superphylum: stramenopiles

Bacterosira sp. (?)
Describer: Gran, 1900
Taxonomy: Class:
Order: Biddulphiales
Family: Thalassiosiraceae
Synonyms: Lauderia
Size range: medium
Abundance: rare-occasional

Cerataulina sp. (?)
Describer: H. Peragallo ex Schütt, 1892
Taxonomy: Class: Coscinodiscophyceae
Order: Hemiaulales
Family: Hemiaulaceae
Synonyms:
Size range: medium
Abundance: rare-occasional

Chaetoceros spp.
Describer: Ehrenberg, 1844
Taxonomy: Class: Coscinodiscophyceae
Order: Chaetocerotales
Family: Chaetocerotaceae
Synonyms:
Size range: small
Abundance: abundant
Note: did not try to separate species

232

Corethron sp.
Describer: Castracane, 1886
Taxonomy: Class: Coscinodiscophyceae
Order: Corethrales
Family: Corethraceae
Synonyms:
Size range: very large
Abundance: rare-occasional
DIATOMS (phylum/division Bacillariophyta)
Domain: Eukaryota; Superphylum: stramenopiles

Coscinodiscus spp.
Describer: Ehrenberg, 1839
emend. Rattray, 1890,
Hasle & Sims, 1986
Taxonomy: Class: Coscinodiscophyceae
Order: Coscinodiscales
Family: Coscinodiscaceae
Synonyms:
Size range: very large
Abundance: rare

Cylindrotheca sp.
Describer: Rabenhorst, 1859
Taxonomy: Class: Bacillariophyceae
Order: Bacillariales
Family: Bacillariaceae
Synonyms: Nitzschia
Size range: small but large size range
Abundance: common-abundant

Dactyliosolen sp. (?)
Describer: Castracane, 1886
Taxonomy: Class: Coscinodiscophyceae
Order: Rhizosoleniales
Family: Rhizosoleniaceae
Synonyms:
Size range: medium
Abundance: rare

Detonula pumila
Describer: (Castracane) Schütt, 1896
Taxonomy: Class: Coscinodiscophyceae
Order: Thalassiosirales
Family: Skeletonemaceae
Synonyms: Lauderia pumila
Size range: medium
Abundance: rare-occasional

Ditylum brightwellii
Describer: (T. West) Grunow in Van Heurck, 1883
Taxonomy: Class: Coscinodiscophyceae
Order: Lithodesmiales
Family: Lithodesmiaceae
Synonyms:
Size range: large
Abundance: rare-occasional

233
DIATOMS (phylum/division Bacillariophyta)
Domain: Eukaryota; Superphylum: stramenopiles

Entomoneis sp.
Describer:
Taxonomy: Class: Bacillariophyceae
Order: Surirellales
Family: Entomoneidaceae
Synonyms:
Size range: small-large (big size range)
Abundance: rare-common

Eucampia spp.
Describer: Ehrenberg, 1839
Taxonomy: Class: Coscinodiscophyceae
Order: Hemiaulales
Family: Hemiaulaceae
Synonyms:
Size range: small-medium
Abundance: occasional-common
can be found in long chains

Eucampia / Hemialus sp.
Describer:
Taxonomy: Class: Coscinodiscophyceae
Order: Hemiaulales
Family: Hemiaulaceae
Synonyms:
Size range: small-medium
Abundance: occasional

Fragilariopsis sp.
Describer: Hustedt in A. Schmidt, 1913 emend. Hasle, 1993
Taxonomy: Class: Bacillariophyceae
Order: Bacillariales
Family: Bacillariaceae
Synonyms:
Size range: medium
Abundance: rare

Grammatophora sp.
Describer: Ehrenberg, 1840
Taxonomy: Class: Fragilariophyceae
Order: Striatellales
Family: Striatellaceae
Synonyms:
Size range: small-medium
no preservative
Abundance: rare
234
DIATOMS (phylum/division Bacillariophyta)
Domain: Eukaryota; Superphylum: stramenopiles

Guinardia spp.
Describer: H. Peragallo, 1892
Taxonomy: Class: Coscinodiscophyceae
Order: Rhizosoleniales
Family: Rhizosoleniaceae
Synonyms:
Size range: medium
Abundance: occasional-common

Helicotheca sp. (?)
Describer: Ricard, 1987
Taxonomy: Class: Coscinodiscophyceae
Order: Lithodesmiales
Family: Lithodesmiaceae
Synonyms:
Size range: large
Abundance: rare

Hemialus spp.
Describer: Heiberg, 1863
Taxonomy: Class: Coscinodiscophyceae
Order: Hemiaulales
Family: Hemiaulaceae
Synonyms:
Size range: medium
Abundance: occasional-common

Leptocylindrus sp. 1 (minimus?)
Describer: P.T. Cleve, 1889
Taxonomy: Class: Coscinodiscophyceae
Order: Leptocylindrales
Family: Leptocylindraceae
Synonyms:
Size range: medium
Abundance: occasional-common
usually found in chains

235
DIATOMS (phylum/division Bacillariophyta)
Domain: Eukaryota; Superphylum: stramenopiles

Leptocylindrus sp. 2
Describer: P.T. Cleve, 1889
Taxonomy: Class: Coscinodiscophyceae
Order: Leptocylindrales
Family: Leptocylindraceae
Synonyms:
Size range: medium
Abundance: occasional-common
usually found in chains

Melosira sp.
Describer: Agardh, 1824
Taxonomy: Class: Coscinodiscophyceae
Order: Melosirales
Family: Melosiraceae
Synonyms:
Size range: small
Abundance: rare-occasional

Navicula sp. (?)
Describer: Bory, 1822
Taxonomy: Class: Bacillariophyceae
Order: Naviculales
Family: Naviculaceae
Synonyms:
Size range: small
Abundance: rare

Odontella aurita
Describer: (Lyngbye) C. A. Agardh, 1832
Taxonomy: Class: Coscinodiscophyceae
Order: Triceratiaceae
Family: Triceratiales
Synonyms: Biddulphia, Diatoma auritum
Size range: small-medium
Abundance: occasional-common

236
DIATOMS (phylum/division Bacillariophyta)
Domain: Eukaryota; Superphylum: stramenopiles

Odontella longicruis
Describer: (Greville) Hoban
Taxonomy: Class: Coscinodiscophyceae
Order: Triceratiaceae
Family: Triceratiales
Synonyms: Biddulphia longicruris
Size range: medium-large
Abundance: rare-occasional

Odontella mobiliensis
Describer: (Bailey) Grunow, 1884
Taxonomy: Class: Coscinodiscophyceae
Order: Triceratiaceae
Family: Triceratiales
Synonyms: Biddulphia mobiliensis
Size range: large
Abundance: rare

Odontella sp.
Describer: Agardh, 1832
Taxonomy: Class: Coscinodiscophyceae
Order: Triceratiaceae
Family: Triceratiales
Synonyms: Biddulphia
Size range: small
Abundance: very rare

Planktoniella sol
Describer: Schütt, 1892
Taxonomy: Class: Coscinodiscophyceae
Order: Thalassiosirales
Family: Thalassiosiraceae
Synonyms: Coscinodiscus sol
Size range: medium-large
Abundance: very rare

Pleurosigma sp.
Describer: W. Smith, 1852
Taxonomy: Class: Bacillariophyceae
Order: Naviculales
Family: Pleurosigmataceae
Synonyms:
Size range: large
Abundance: rare-occasional
*probably included both Pleurosigma and Gyrosigma
237
DIATOMS (phylum/division Bacillariophyta)
Domain: Eukaryota; Superphylum: stramenopiles

Pseudo-nitzschia spp.
Describer: Peragallo, 1900
Taxonomy: Class: Bacillariophyceae
Order: Bacillariales
Family: Bacillariaceae
Synonyms: Nitzschia
Size range: small-large
Abundance: occasional-abundant
can form blooms; sometimes in chains

Rhizosolenia spp.
Describer: Brightwell, 1858
Taxonomy: Class: Coscinodiscophyceae
Order: Rhizosoleniales
Family: Rhizosoleniaceae
Synonyms:
Size range: large
Abundance: rare-occasional

Skeletonema sp.
Describer: Greville, 1865
Taxonomy: Class: Coscinodiscophyceae
Order: Thalassiosirales
Family: Skeletonemaceae
Synonyms:
Size range: small
Abundance: occasional-abundant
found in long chains

Surirella sp. (?)
Describer: Turpin, 1828
Taxonomy: Class: Bacillariophyceae
Order: Surirellales
Family: Surirellaceae
Synonyms:
Size range: small
Abundance: occasional-abundant
found in long chains

238
DIATOMS (phylum/division Bacillariophyta)
Domain: Eukaryota; Superphylum: stramenopiles

Thalassionema nitzschioides
Describer: (Grunow, 1862) Van Heurck, 1896
Taxonomy: Class: Fragilariophyceae
Order: Thalassionematales
Family: Thalassionemataceae
Synonyms: Thalassiothrix nitzschioides
Size range: medium
Abundance: occasional-common

large Thalassionema spp.
Describer: (Grunow, 1862) Van Heurck, 1896
Taxonomy: Class: Fragilariophyceae
Order: Thalassionematales
Family: Thalassionemataceae
Synonyms: Thalassiothrix spp.
Size range: medium
Abundance: rare-occasional

T. frauenfeldii (?)

T. bacillare (?)

Tropidoneis sp.
Describer: Cleve, 1891
Taxonomy: Class: Bacillariophyceae
Order: Naviculales
Family: Naviculaceae
Synonyms:
Size range: large
Abundance: very rare

239
DIATOMS (phylum/division Bacillariophyta)
Domain: Eukaryota; Superphylum: stramenopiles

small Thalassiosira spp.
Describer: Cleve, 1873 emend. Hasle, 1973
Taxonomy: Class: Coscinodiscophyceae
Order: Thalassiosirales
Family: Thalassiosiraceae
Synonyms:
Size range: small-large
Abundance: occasional-abundant

large Thalassiosira spp.
Describer: Cleve, 1873 emend. Hasle, 1973
Taxonomy: Class: Coscinodiscophyceae
Order: Thalassiosirales
Family: Thalassiosiraceae
Synonyms:
Size range: small-large
Abundance: occasional-abundant

240

DIATOMS (phylum/division Bacillariophyta)
Domain: Eukaryota; Superphylum: stramenopiles

unknown diatom (unk diatom 1)
Possible IDs:
Size range: medium
Abundance: occasional

unknown diatom (unk diatom 4)
Possible IDs:
Size range: medium
Abundance: occasional-rare

unknown diatom (unk diatom 5)
Possible IDs:
Size range: medium
Abundance: rare-occasional

unknown diatom (unk diatom 7)
Possible IDs:
Size range: large
Abundance: occasional

unknown diatom (unk diatom 8)
Possible IDs:
Size range: small
Abundance: rare

241
DIATOMS (phylum/division Bacillariophyta)
Domain: Eukaryota; Superphylum: stramenopiles

unknown diatom (unk diatom 10)
Possible IDs:
Size range: medium
Abundance: rare

unknown diatom (unk diatom 12)
Possible IDs:
Size range: medium
Abundance: rare

unknown centric diatoms

242

DIATOMS (phylum/division Bacillariophyta)
Domain: Eukaryota; Superphylum: stramenopiles

unknown pennate diatoms

243
OTHER GROUPS

Dictyocha spp.
Describer: Ehrenberg, 1839
Taxonomy: Phylum: Ochrophyta
Class: Dictyochophyceae
Order: Dictyochales
Family: Dictyochaceae
Synonyms:
Size range: small
Abundance: occasional-abundant

Eutreptiella sp.
Describer: de Cunha, 1914
Taxonomy: Phylum: Discomitochondria
Class: Euglenida
(was Euglenophyceae)
Order: Eutreptiales
Family: Eutreptiaceae
Synonyms:
Size range: small
Abundance: occasional-common

Rhizomonas setigera
Describer: (Pavillard, 1916)
Patterson, Nygaard, Steinberg & Turley, 1993
Taxonomy: Phylum: Protoctista
Class:
Order:
Family:
Synonyms: Solenicola setigera
Size range: small
a flagellate that colonizes Leptocylindrus spp.

Phaeocystis spp.
Describer: Lagerheim, 1893
Taxonomy: Phylum: Haptomonada
Class: Prymnesiophyceae
Order: Prymnesiales
Family: Phaeocystaceae
Synonyms:
Size range: small
Abundance: rare-occasional

244
OTHER GROUPS

Synura sp. (?)
Describer: Ehrenberg, 1834
Taxonomy: Phylum: Ochrophyta
Class: Synurophyceae
Order: Synurales
Family: Synuraceae
Size range: small
Abundance: rare

Pyramimonas sp. (?)
Taxonomy: Division: Chlorophyta
Class: Prasinophyceae
Order: Pyramimonadales
Family: Pyramimonadaceae
Size range: very small
Abundance: rare-common

unknown raphidophyte
Taxonomy: Phylum: Ochrophyta
Class: Raphidophyceae
Size range: medium
Abundance: very rare

unknown radiolarian
Possible IDs:
Taxonomy: Kingdom: Protoctista
Phylum: Sarcomastigophora
Size range: small
Abundance: rare-occasional

245
CILIATES
Domain: Eukaryota; Superphylum: Alveolata; Phylum: Ciliophora

tintinnids
Taxonomy: Class: Spirotrichea
Order: Tintinnida
Size range: small-large
Abundance: abundant

246

other ciliates
Size range: small-large
Abundance: abundant

247
REFERENCES

Horner, R. A., 2002. A taxonomic guide to some common marine phytoplankton.
Biopress Limited.

Round, F. E., Crawford, C. M. and Mann, D. G., 1990. The diatoms: Biology and
morphology of the genera. Cambridge University Press, New York.

Tomas, C. R. (ed.), 1997. Identifying marine phytoplankton. Academic Press, London.

Abstract (if available)

Abstract Although bacteria, human pathogens, and other public health concerns have been investigated, few studies have examined the ecological effects of urban runoff. I examined the optical characteristics of urban runoff plumes and explored phytoplankton community changes within the plumes. Data collected after two storm events in 2004-2005 during the Bight '03 program, after a planned release of wastewater, and after storm events in 2007-2008 are presented. Plume waters were characterized by low salinity and high colored dissolved organic matter (CDOM) concentration relative to ambient waters. Although relationships between contaminants (nutrients, fecal indicator bacteria) and plume indicators (salinity, total suspended solids) were not strong, California Ocean Plan standards were often exceeded in waters containing >10% stormwater (<28-30 salinity range). Relationships between CDOM and salinity and between TSS and beam attenuation indicate that readily-measurable optically-active variables, that can be estimated from ocean color satellite imagery, could be used as proxies to provide a qualitative, if not quantitative, evaluation of the distribution of stormwater plumes. Localized blooms of Akashiwo sanguinea and Cochlodinium sp. (chlorophyll a up to 100 mg/m3 and densities between 100-2,000 cells/mL) occurred in plume waters 4-7 days after the wastewater release. Spectra of the ratio of scatter to absorption were similar to reflectance spectra measured during blooms of dinoflagellate species in other studies, especially when the blooms occurred in areas with high CDOM concentration. Differences in the phytoplankton community inside and outside stormwater plumes were only detected in dilute plume waters after a period of low wave height. Several dinoflagellates (Akashiwo sanguinea, Cochlodinium sp., Prorocentrum spp., Ceratium spp., Protoperidinium spp., and Alexandrium sp.) comprised a greater percent abundance in plume waters when differences were apparent.

Linked assets

University of Southern California Dissertations and Theses

Conceptually similar

PDF

Harmful algal blooms in the urbanized coastal ocean: an application of remote sensing for understanding, characterization and prediction

PDF

Phytoplankton bloom initiation in the Southern California Bight: a multi-year local and regional analysis

PDF

Future impacts of warming and other global change variables on phytoplankton communities of coastal Antarctica and California

Asset Metadata

Creator Reifel, Kristen Marie (author)

Core Title Optical properties of urban runoff and its effect on the coastal phytoplankton community

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Biology

Publication Date 09/26/2011

Defense Date 08/17/2009

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag Akashiwo sanguinea,Ballona Creek,Cochlodinium,diatom,dinoflagellate,OAI-PMH Harvest,Santa Monica Bay,Southern California Bight,stormwater,wastewater

Place Name bays: Santa Monica Bay (geographic subject), bights: Southern California Bight (geographic subject), California (states), creeks: Ballona Creek (geographic subject)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Capone, Douglas (committee chair), Jones, Burton H. (committee chair), Caron, David (committee member), Hammond, Douglas E. (committee member), Kiefer, Dale A. (committee member)

Creator Email kmreifel@gmail.com,kreifel@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-m2618

Unique identifier UC1130933

Identifier etd-Reifel-3238 (filename),usctheses-m40 (legacy collection record id),usctheses-c127-257436 (legacy record id),usctheses-m2618 (legacy record id)

Legacy Identifier etd-Reifel-3238.pdf

Dmrecord 257436

Document Type Dissertation

Rights Reifel, Kristen Marie

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Repository Name Libraries, University of Southern California

Repository Location Los Angeles, California

Repository Email cisadmin@lib.usc.edu