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Harmful algal blooms in the urbanized coastal ocean: an application of remote sensing for understanding, characterization and prediction
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Harmful algal blooms in the urbanized coastal ocean: an application of remote sensing for understanding, characterization and prediction
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Content
HARMFUL ALGAL BLOOMS IN THE URBANIZED COASTAL OCEAN
AN APPLICATION OF REMOTE SENSING FOR UNDERSTANDING,
CHARACTERIZATION AND PREDICTION
by
Ivona Cetinić
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)
August 2009
Copyright 2009 Ivona Cetinić
ii
EPIGRAPH
"He who is certain he knows the ending of things when he is only beginning them is
either extremely wise or extremely foolish; no matter which is true, he is certainly an
unhappy man, for he has put a knife in the heart of wonder." -- Tad Williams
“Osjećam da postoji nešto svježe i divlje u prirodi. Nisam siguran što, ali znam da je
tamo.” -- Henry David Thoreau
iii
DEDICATION
To my parents for making me who I am, to Marty for loving me for who I am, to
Burt for understanding who I am, and to all my beautiful friends that make this
world one big fluffy place.
iv
ACKNOWLEDGEMENTS
I would like to thank George Robertson from OCSD, OCSD commandos team and
M/V Nerissa captains Fred and Tom for their everlasting support; Wrigley Institute
for Environmental Sciences, Wrigley Island staff and Wrigley captains for their field
and financial support; Lee Karp-Boss and Emmanuel Boss for their help with culture
experiments and everything else; Grace Chang and Tommy Dickey for providing me
with hyperspectral data from SB CHARM mooring; Mark Moline and Oscar
Schofield for their help with glider dataset, captains of Sundiver for their help with
glider deployments. Also, I would like to thank Caron lab and Robotics (RESL) lab
(CINAPS) for their help and support; WetLabs and Teledyne Webb for answering
all my questions and returning my phone calls and College Machine Shop (Don) for
always fixing what ever I messed up. Big thank you to my committee, for dealing
with my ideas and tolerating my Slavic articles! Tons of love to my lab – you are the
best, and yes Matthew; photons rule (Table 2-3)! This work was supported by
NASA ESS fellowship NNX06AF88H, California Coastal Conservancy’s Coastal
Ocean Current Mapping Program, EPA, NOAA and ONR.
v
TABLE OF CONTENTS
EPIGRAPH ii
DEDICATION iii
ACKNOWLEDGEMENTS iv
LIST OF TABLES vii
LIST OF FIGURES viii
ABSTRACT xiv
CHAPTER I: Red Tides, History and Present 1
CHAPTER II ABSTRACT 1
INTRODUCTION 1
CHAPTER I REFERENCES 9
CHAPTER II: Seasonal evolution of the phytoplankton community
in San Pedro Bay 13
CHAPTER II ABSTRACT 13
INTRODUCTION 14
MATERIALS AND METHODS 16
RESULTS 22
DISCUSSION 98
CONCLUSION 102
CHAPTER II REFERENCES 104
CHAPTER III: Red tide optical index: in situ optics and
remote sensing models 110
CHAPTER III ABSTRACT 110
INTRODUCTION 111
MATERIALS AND METHODS 114
RESULTS AND DISCUSSION 121
CONCLUSION 138
CHAPTER III REFERENCES 140
vi
CHAPTER IV: Resolving urban plumes using autonomous gliders in the coastal
ocean 147
CHAPTER IV ABSTRACT 147
INTRODUCTION 148
MATERIALS AND METHODS 152
RESULTS 157
DISCUSSION 163
SUMMARY 172
CHAPTER IV REFERENCES 175
CHAPTER V: Calibration procedure for Slocum glider deployed
optical instruments 181
CHAPTER V ABSTRACT 181
INTRODUCTION 182
MATERIALS AND METHODS 185
RESULTS AND DISCUSSION 190
SUMMARY 200
CHAPTER V REFERENCES 202
BIBLIOGRAPHY 205
vii
LIST OF TABLES
Table 2- 1. Maximum, minimum, average and standard deviation 25
(st. dev.) for observed silicate (SiO
4
), phosphate (PO
4
), nitrate (NO
3
)
and nitrite (NO
2
).
Table 2-2. List of species/taxa encountered during the study with 27
respective species index number.
Table 2-3. Abundances of different taxa at different sampling stations, 29
depth and time. Species names are replaced with species index numbers,
as explained in Table 2-2. SCM – subsurface chl maximum.
Table 2-4. Species scores for first three axis of the CCA shown on 97
Figure 2-7B. Sp# - species index number as explained in Table 2-2.
viii
LIST OF FIGURES
Figure 1-1: Beach in San Pedro, California, covered with dead animals; 3
result of a red tide (L. polyedrum) bloom during July, 1902.
Image taken from Torrey (1902).
Figure 1-2: Lingulodinium polyedrum bloom in San Pedro Channel, 7
California, during August 2005 (photo taken by Astrid Schnetzer).
Figure 2-1. Map with position of the sampled stations (ST2…16) 18
in San Pedro Bay, sampled bimonthly in 2005. NBST presents the
position of the SCCOOS Newport Beach shore station where
automated datasets were acquired. Bathymetry contours (meters)
are represented by the gray lines, and the coastline by the black solid line.
Figure 2-2. Upper panel shows San Pedro Channel NOAA ERD 22
upwelling index (gray line) for the period from mid January to
September 2005. Central panel shows temperature (red) and salinity (blue)
at the Newport Beach pier as measured by the automated system,
with missing data replaced with manual measurements. Salinity minima
during February (below the graph axes) were 19-20 psu. Bottom panel
shows average mixed layer depth (MLD) during the sampling period.
Purple box highlights the sampling period.
Figure 2-3. Time series of normalized surface nutrient concentrations 24
at sampling stations; A) silicate B) phosphate C) nitrate and D) nitrite.
Blue line presents data from station 2, red line – station 5,
purple line- station 8, pink line – station 13 and black line for station 16.
For each station, data at the surface were normalized using the transformation
[X
n
]=(X - X
st
min)/(X
st
max –X
st
min), where [X
n
] is the normalized
values of nutrient X, and X
st
min and X
st
max are the minimum and maximum,
respectively, of nutrient concentration at this station.
Each normalized field of [X
n
] ranges between 0 and 1. Raw nutrient data
presented in Table 2-1.
ix
Figure 2-4. Time series of chlorophyll (extracted) at sampling stations; 26
A) surface B) subsurface chlorophyll maximum. Blue line presents data
from station 2, red line – station 5, purple line- station 8,
pink line – station 13 and black line for station 16. Lack of data in June
and September for subsurface chlorophyll maximum is due to the sampling
problems in that period.
Figure 2-5. Time series of Lingulodinium polyedrum abundance at 91
sampling stations – presented as log
10
cells L
-1
; top panel is surface and
bottom panel is from the subsurface chorophyll maximum. Blue line
presents data from station 2, red line – station 5, purple line- station 8,
pink line – station 13 and black line for station 16. Lack of data in
September for SCM is due to the sampling issues in that period.
Figure 2-6. Plot presenting the distribution of Multidimensional Scaling 93
(MDS) first axis scores for phytoplankton samples over time and depth.
MDS was performed on log transformed phytoplankton abundance,
with a stress value of 0.16 for 2-dimensional results. Color bar presents
results ranging from value of -1 to +1.
Figure 2-7. Biplots showing first and second axis of CCA. 96
Environmental variables with intra-set correlation r
2
≥0.2 are displayed
as red lines (T-temperature, NO2 – nitrates, NO3 nitrites,
Chl Flo – chlorophyll fluorescence). Length of the lines indicates
the importance of the variable, and lines are pointing to the direction
of the maximum change. Panel A) CCA with all the phytoplankton
samples collected during the investigated period, environmental
variables are measured at the same time/depth as the phytoplankton data.
Dots represent different samples, and color coding is based on the
respective MDS Axis 1 score (Fig 2-6). Black squares represent samples
in which L. polyedrum was dominant. Panel B) displays CCA with
all the phytoplankton samples collected, and the environmental variables
measured at the same time/depth as the phytoplankton data.
Blue dots represent different phytoplankton species (Axis scores for different
species in Table 2-4) red dot – L. polyedrum,
green dot – Prorocentrum micans.
x
Figure 2-8. Temperature anomalies for the specified years and their 101
standard deviation (±, dotted black lines). Boxes denote the period
when Lingulodinium polyedrum bloom was observed, as described
in the respective papers. Panel A shows bloom data from this effort,
historical mean and measurements from buoy 9410660, period 1993-2008,
panel B - L. polyedrum blooms off San Diego in 1938 (pink line) and
1942 (purple line) (Allen 1938; Allen 1942); temperature data from
Scripps pier, historical index 1919-2008; panel C - L. polyedrum bloom
of San Diego in 1965 (Holmes et al. 1967), temperature data same
source as B); D - L. polyedrum bloom of San Diego in 1995
(Kahru and Mitchell 1998), temperature data same source as B).
Figure 3-1. Inherent optical properties of Lingulodinium polyedrum. 123
Panel A is showing the particulate backscattering coefficients (b
bp
((λ))
measured in the algal culture, normalized with particulate backscattering
at 720 nm; B showing the particulate absorption coefficients (a
p
((λ))
normalized with particulate absorption at 675 nm.; C showing total
absorption coefficients (a
tot
((λ)) normalized with absorption at 675 nm
Figure 3-2. Absorption “red tide” ratios for different species of 124
phytoplankton. Red triangles represent value for the particulate absorption
ratio of 440nm/550 nm (RT1), and black squares represent particulate
absorption ratio of 440nm/675 nm (RT2). Additional absorption
measurements for L. polyedrum taken from Itturiaga, unpublished.
Abbreviations of species names are: , Syn- Synechococcus sp.,
Skel- Skeletonema costatum, Thal- Thalassiosira pseduonana ,
Aste- Asterionellopsis glacialis Gymn- Gymnodinium simplex ,
Cera- Ceratium longipes, Alex - Alexandrium catenella,
Ling- Lingulodinium polyedrum.
Figure 3-3. Chlorophyll, RT1 and RT2 as observed in the L. polyedrum 126
dominated patch (black box). Sample number is a proxy of distance/time.
Chlorophyll was calculated form a
tot
following Roesler method.
Figure 3-4. Red tide ratio (RT1 and RT2) for different log transformed 128
chlorophyll concentrations observed in the field during the
L. polyedrum (red dots), Ceratium spp. and Pseudonitzchia spp. (yellow dots),
Cochlodinium catenatum (blue dots) blooms, and non-bloom periods
(black dots). Black lines present RT to chlorophyll relationships specific
for L. polyedrum blooms.
xi
Figure 3-5. A) Remote sensing reflectance during the L. polyedrum 128
bloom. B) Primary peak (Rrs max) normalized remote sensing
reflectance during the L. polyedrum bloom. Red lines present values
measured during the L. polyedrum bloom and black lines values during
the non-bloom and diatom bloom period.
Figure 3-6. Log transformed Red tide ratio (RT1
Rrs
and RT2
Rrs
) vs. 133
log transformed chlorophyll concentrations observed on the field
during the L. polyedrum (red dots), Pseudo-nitzchia spp. bloom
(yellow dots), and non-bloom periods (black dots). Black dashed lines
present RT
Rrs
to chlorophyll relationship specific for L. polyedrum blooms,
black solid lines present the same relationship for non-bloom
and Pseudo-nitzchia spp. dominated bloom
Figure 3-7. Log transformed Red tide ratio (RT1
Rrs
and RT2
Rrs
) vs. 135
log transformed chlorophyll concentrations observed on the field
during the 2004 deployment (blue dots). Positive detects of L. polyedrum
using the RT
Rrs
and [chl] criteria are marked with red circles.
Black dashed lines present RT
Rrs
to chlorophyll relationship specific for L.
polyedrum blooms; black solid lines present the same relationship
for other surface phytoplankton population. Red vertical line presents
chlorophyll concentration of 7 μg L
-1
.
Figure 4-1. Glider tracks from the two deployments overlaid on the 153
coastal bathymetry (grey) of San Pedro Bay, California. The contour depths
are in meters. The coastline is denoted by the heavy black contour and
Orange County Sanitation District’s ocean outfall pipe and diffuser are
indicated by the orange line. Effluent is released from a series of diffuser
ports on either side of the along-isobath portion of the outfall pipe at a
depth of approximately 60 m. The lighter blue line indicates the Slocum
glider track from its first deployment, 17 to 26 September, and the purple
line is the glider track for the second deployment perioid from 26 September
to 2 October.
Figure 4-2. Depth-integrated currents in San Pedro Bay during the 157
Slocum glider deployment. Bathymetry (gray), coastline (heavy black),
and the OCSD outfall (blue) are indicated as in Figure 4-1.
Green vectors represent the depth-integrated currents measured by glider.
A vector scale representing a current of 25 cm s
-1
is shown in the
lower right of the figure.
xii
Figure 4-3. A cross-shelf section of (A) salinity, (B) temperature, 159
(C) CDOM, (D) chlorophyll, and (E) optical backscattering coefficient
at 880 nm obtained on September 17-18, 2006. The along-track
distances (km) are referenced to the coastline. The displayed transect
is highlighted in red in the inset map. Potential density anomaly is overlaid
on each panel with white contour lines with a contour interval of 0.2 kg m
-3
.
The σ
θ
= 25.0 kg m
-3
isopycnal is indicated by the heavier contour line.
The white triangular region in the lower right corner of each panel is the
region where the bottom is rising up over the shelf. Location of the
OCSD outfall pipe is noted on the inset map (blue).
Figure 4-4. Potential temperature versus salinity for downward portion 161
of each glider dive cycle. CDOM concentration is indicated by the color
of each dot and in panel B color is corresponding chlorophyll
concentrations. The region labeled with 1 is the low salinity anomaly
at potential temperatures less than 14 °C corresponding to the effluent
plume from the OCSD outfall. Region 2 is the minimum in salinity near
theta 16 °C indicative of subarctic waters from the California Current
apparent in the majority of profiles. Region 3 is lower salinity
nearsurface water encountered nearshore most likely due to the
Los Angeles/San Gabriel River plume.
Figure 4-5. Location of Slocum dives where the effluent plume 162
was indicated by low salinity anomalies (red dots) and by CDOM
anomalies (blue circles). Bathymetry (grey), coastline (heavy black),
the OCSD outfall (blue), and the track of the Slocum glider (black)
are as in Figure 4-1.
Figure 4-6. Scatter plots of optical backscattering vs. 166
CDOM (left) and b
bp
(550)/b
bp
(880) vs. CDOM (right). Black circles
represent all the data collected from the downward portion of the dive.
Figure 4-7. Relationship between optical backscattering and 171
chlorophyll a (top), and chl/b
bp
values versus depth along a segment of
the track across the San Pedro shelf (bottom). The details of the section
in the lower panel are the same as in Figure 4-3.
Figure 5-1. A) Slocum glider with science bay where optical instruments 186
are located, B) calibration chamber, C) calibration chamber with glider
mounted on it, ready for calibration.
xiii
Figure 5-2. Variability of Dark Counts as a function of temperature, 193
observed for A) CDOM fluorescence sensor and B) chlorophyll
fluorescence sensor deployed on the gliders, triangles -field deployment
(instruments on H glider), and circles lab measurements
(instruments on R glider). Dashed line represents in laboratory
predetermined mean values at 20°C, while dotted lines represent
standard deviation range for the same DC values.
Figure 5-3. Comparison of the factory and in house calibrations 195
for glider deployed fluorometers. X axis presents the instrument output
minus dark counts values, and y-axis presents concentration of the
standard. Dashed line with triangles represents in-house calibration
points for the H glider, while solid line with circles calibrations for
R glider. Dashed line (H) and solid line (R) with no markers are factory
calibration values.A) CDOM fluorescence sensor and B) chlorophyll
fluorescence sensor deployed on the glider.
Figure 5-4. Biological growth found on body of HeHaPe glider after 196
3 weeks of deployment in Southern California Bight. Black squares
highlight goose-neck barnacles growing on the sides one of the
ECO pucks, near the optical windows.
Figure 5-5. Comparison of pre, post A (without optical window cleaning) 198
and post B (with cleaning of the optical windows) scale factors
values for R 10 days deployment (empty circles) and H 10 day
deployment (black triangles), and H 3 week deployment (open triangles)
expressed as percentage difference from the initial pre calibration.
Panel A – CDOM fluorometer, Panel B Chl fluorometer.
Figure 5-6. Selected profile from the 7th day of the 10-day H glider 198
deployment. Panel A shows the chlorophyll data and panel B shows
CDOM data for the same profile. Black solid lines indicate values
calculated using factory calibration; black solid lines with circles
indicate the pre-deployment calibration; dashed lines – confidence
values; black solid line with triangles using a temporal derived
calibration, dotted lines – confidence values shown on the graph.
xiv
ABSTRACT
Harmful Algal Blooms (HABs) in Southern California have become recurring events
with impacts that surpass the realm of ocean ecosystems. Phytoplankton blooms are
natural phenomena, and the same environmental forcings that drive changes in
primary productivity and nutrient cycling in the coastal ocean will promote HABs
too, including human influences. Therefore, to predict the initiation of HABs, one
must define the specific environmental, chemical, and physical parameters that allow
the success of the specific species. Recently developed tools and techniques for real-
time coastal observing systems allow us to observe dynamics of the coastal ocean on
the appropriate spatial and temporal scales, to explore the dynamics of the coastal
ocean, to monitor the nutrient loadings, and to follow the development of the HABs.
Field studies conducted during 2005 confirmed that observed the transition from the
diatom dominated spring to the dinoflagellate dominated summer, both in surface
and subsurface waters, was dependent on natural processes affecting the coastal
ocean. Lingulodinium polyedrum, our model organism, was present with bloom
abundances (~10
5
cells L
-1
) found during the summer, concurrent with low
temperature episodes nearshore. Historical temperature record analysis supports our
findings on the occurrence of cool temperature anomalies during L. polyedrum
blooms in the Southern California Bight, and infer primary controls of temperature,
mixed layer depth, and nutrient availability for bloom formation. Using optical
xv
instruments deployed on Slocum gliders, we managed to follow the outfall plume, to
differentiate it from the natural occurring water masses in the coastal ocean, and to
calculate suspended particulate material concentration within the plume. No
interaction was found between the nutrient rich plume water and the phytoplankton
community. Optical tools were further used in development of red tide spectral
indices based on L. polyedrum inherent optical properties. These indices proved to
be a successful tool for detection of L. polyedrum blooms in this area, both for in situ
absorption and for mooring collected hyperspectral remote sensing reflectance
datasets.
1
CHAPTER I: Red Tides, History and Present
CHAPTER I ABSTRACT
Harmful Algal Blooms (HABs) are recurring events in the coastal ocean, and local
economies that depend on beach and coastal use are often adversely affected by these
events. This chapter outlines the rationale for the multidisciplinary approach that was
taken to investigate HABs in the coastal Southern California region using
Lingulodinium polyedrum as a model organism.
INTRODUCTION
In the summer of 1902, Harry Beal Torrey wrote following about an “unusual
occurrence of Dinoflagellata on the California Coast”(Fig. 1-1):
It was first noticed on July 7th as a red streak off the mouth of San Pedro
Harbor. During the next few days it approached the shore, changing its shape
and dividing into several patches, each many acres in extent...On the 20th,
four days after the red (characteristically a muddy vermilion) streak had
reached the shore, a most sickening odor arose from the water along the
beach. On the 21st it was almost unbearable. During the night, on a beach
2
about four hundred feet long, a large number of animals were left by the
tide...
Since then, Harmful Algal Blooms (HAB) in Southern California have become
recurring events (Allen 1938; Allen 1942; Anderson et al. 2008; Beers 1986; Holmes
et al. 1967; Kudela and Cochlan 2000), whose impacts are surpassing the realm of
the ocean ecosystems, and affect local economies that depend on beach use (e.g.,
http://www.swfwc.org/swffs_final_matrix.htm). Although HABs have been a
research focus for more than a century (Torrey 1902), the mechanisms that generate
HAB outbreaks along the southern California coasts remain poorly understood.
Large outbreaks can occur during the spring upwelling season, as happened in
March-April 1995 (Kahru and Mitchell 1998; Kudela and Cochlan 2000). Other
outbreaks frequently occur during the spring and summer months (Holmes et al.
1967; Shipe et al. 2008). Although strong internal tide events have been
hypothesized to contribute to these blooms, no specific effort has been mounted to
document and understand the physical/biological interaction associated with these
events.
Phytoplankton blooms are natural phenomena, and the same environmental forcing
(including human impact) that drive changes in primary productivity and nutrient
cycling in the coastal ocean will promote HABs too (Babin et al. 2008). The
3
abundance of HAB species at any point in time and space is dependent on the rates
of growth and mortality, physical processes, and movement due to the behavioral
aspect of the species (Malone 2008). Growth of these species is governed by genetics
and by environmental factors, especially nutrients (food) and light availability.
Therefore in order to predict the initiation of HABs, one must define the specific
environmental, chemical and physical parameters that allow the success of the
specific species.
Figure 1-1. Beach in San Pedro, California, covered with dead animals; result of a red tide (L.
polyedrum) bloom during July, 1902. Image taken from Torrey (1902).
4
Red tides occurring in the Southern California area are typically dominated by
dinoflagellate species Lingulodinium polyedrum or Prorocentrum micans (Beers
1986), and more recently Cochlodinium spp., and the raphidophyte Heterosigma
akashiwo (Caron et al. 2009; Kudela et al. 2008). L. polyedrum is a mixotrophic
dinoflagellate (Jeong et al. 2005; Seong et al. 2006), and it has similar affinity to
both organic and inorganic nitrogen sources (Kudela et al. 2008). L. polyedrum can
produce homoyessotoxin and yessotoxin that can cause diarrhetic shellfish poisoning
(DSP) (Draisci et al. 1999). A recent study by Armstrong and Kudela (2006)
demonstrated that Southern California isolates of L. polyedrum are capable of
producing yessotoxin. Hypoxia or anoxia may result from the decay of the large
amounts of organic material produced by the blooms. Hypoxia resulting from L.
polyedrum blooms has caused extensive vertebrate and invertebrate mortality,
especially in harbors and other areas of limited water exchange (Ventura Harbor
2005: Grace Chang, personal communication; Redondo Beach King’s Harbor 2005:
A. Dalkey, personal communication; Smayda and Reynolds 2001; Torrey 1902;).
Cases of skin irritation and respiratory irritation from aerosols have been reported in
association with red tide outbreaks (Chang et al. 2004). Very dense blooms may also
deter the utilization of beach areas by humans because of either real or perceived
effects – strong red color (Fig 1-1), reduced in situ visibility and surf-zone foam.
5
Many studies have focused on the physiological aspects of L. polyedrum including
its circadian rhythm and bioluminescence (Dassow et al. 2005; Hastings and
Sweeney 1960; Nicolas et al. 1987), life cycle and resting stages (Cullen et al. 1997;
Figueroa and Bravo 2005; Persson et al. 2000), and predation on L. polyedrum
(Cullen et al. 1997; Jeong et al. 2002), but rarely research has focused on finding a
specific optical signature of this cosmopolitan species (Kahru and Mitchell 1998).
Although L. polyedrum does not possess specific pigments that allow development
of a species-specific optical fingerprint, as has been done for Karenia brevis
(Cannizzaro et al. 2002; Kirkpatrick et al. 2000), Chang et al. (2004) found that L.
polyedrum, measured in situ, exhibits a different hyperspectral remote sensing
reflectance (remote sensing reflectance ratio of 580:400 nm was on average a factor
of four higher then in “normal” populations) than that found in presence of “normal
population”. Stramski (personal communication) noticed that absorbance ratios of
440nm to 550nm and 440 nm to 675 nm are much lower in red tides then in
“normal” phytoplankton populations. Laboratory backscattering measurements have
revealed that some phytoplankton species show highly specific backscattering
spectra (Bricaud et al. 1983). The relationship between spectral reflectance and the
inherent optical properties can be easily described as a ratio of backscattering to
absorbance (b
b
/a) at the specified wavelength (Morel and Prieur 1977), and therefore,
in situ inherent optical properties can be used to develop a functional remote sensing
model.
6
It is well known that phytoplankton blooms occur in the areas of upwelling (Jones
and Halpern 1981; Walsh et al. 1974), shelf sea fronts (Holligan et al. 1985),
estuarine/river plume fronts (Pearcy and Keene 1974) and other divergence and
frontal zones in the ocean. Shelf areas of Southern California are known to be
affected by upwelling, propagation of internal tides (Mcmanus et al. 2003; Pineda
1991; Pineda and Lopez 2002; Noble et al., 2009), as well as the strong
anthropogenic pressure, and all these could be the possible sources of the nutrients
that drive the initiation and pertinence of the L. polyedrum blooms in this area. The
interaction of nearshore processes with internal wave/internal tide processes may be
a mechanism for promoting germination of dinoflagellate cysts through
resuspension, possible physiological responses to turbulence, light and nutrients.
Internal tides also transport the subsurface chlorophyll maximum and nutrient rich
water into the nearshore and surf zone (Anderson et al. 2008).
7
Figure 1-2. Lingulodinium polyedrum bloom in San Pedro Channel, California, during August 2005
(photo taken by Astrid Schnetzer).
Recently developed tools and techniques for real-time coastal observing systems are
allowing us to observe dynamics of the coastal ocean on the special and temporal
scales that really matter (Babin et al. 2008). Using remote sensing instruments that
include buoys, moorings, autonomous underwater vehicles as well as satellite-borne
sensors, we can collect large interdisciplinary datasets that enable us to explore the
dynamics of the coastal ocean, monitor the nutrient loadings, and follow the
development of the HABs.
The objectives of this thesis are to: 1) characterize and define the phytoplankton
community through space and time in the San Pedro shelf area, 2) define the inherent
8
optical properties of the dinoflagellate L. polyedrum and combine in situ and
laboratory bio-optical measurements to develop a model that could provide early
prediction and detection of L. polyedrum HABs, and 3) evaluate possible usage of
the autonomous underwater vehicles in following anthropogenic nutrient loads in the
coastal ocean. Gliders and instruments deployed onboard the gliders are a relatively
new set of oceanographic tools; therefore an additional chapter is devoted to a novel
technique for calibration of instruments onboard the glider.
9
CHAPTER I REFERENCES
Allen, W. E. 1938. "Red Water" along the West Coast of the United States in 1938.
Science 88: 55-56.
---. 1942. Occurrences of ``Red Water'' Near San Diego. Science 96: 471.
Anderson, D. M., J. M. Burkholder, W. P. Cochlan, P. M. Glibert, C. J. Gobler, C. A.
Heil, R. M. Kudela, M. L. Parsons, J. E. J. Rensel, D. W. Townsend, V. L.
Trainer, and G. A. Vargo. 2008. Harmful algal blooms and eutrophication:
Examining linkages from selected coastal regions of the United States.
Harmful Algae 8: 39-53.
Armstrong, M., and R. Kudela. 2006. Evaluation of California isolates of
Lingulodinium polyedrum for the production of yessotoxin. African Journal
of Marine Science 28: 399-401.
Babin, M., C. Roesler, and J. J. Cullen [eds.]. 2008. Real-Time Coastal Observing
Systems for Ecosystem Dynamics and Harmful Algal Bloom; Theory,
Instrumentation and Modeling. UNESCO Publishing.
Beers, J. R. 1986. Organisms and the food web, p. 84-185. In R. W. Eppley [ed.],
Lecture Notes on Coastal and Estuarine Studies. Springer-Verlag.
Bricaud, A., A. Morel, and L. Prieur. 1983. Optical-Efficiency Factors of Some
Phytoplankters. Limnology and Oceanography: 816-832.
Cannizzaro, J. P., K. L. Carder, F. R. Chen, and C. A. Heil. 2002. Remote detection
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13
CHAPTER II: Seasonal evolution of the phytoplankton community in San Pedro Bay
CHAPTER II ABSTRACT
Coastal areas like the Southern California Bight are of special interest because the
coastal ecosystem is not only forced by natural physical and chemical processes, but
has significant anthropogenic input from the heavily populated coastal region. In
order to evaluate the specific factors contributing to the 2005 Lingulodinium
polyedrum bloom, we characterized the phytoplankton community succession in the
surface and subsurface waters. The observed transition from the diatom dominated
spring to the dinoflagellate dominated summer, both in surface and subsurface,
seems to be dependent on natural processes affecting the coastal ocean. L. polyedrum
was present in the phytoplankton community throughout the year, and multivariate
analysis confirms its wide ecological niche and capability of using different nutrient
sources. On the other hand, samples with bloom abundances (≥10
5
cells L
-1
) of L.
polyedrum were found during the summer when the mixed layer was relatively
shallow and nutrients were depleted in the surface layer. In the same period, low
temperature episodes were observed nearshore. Analysis of historical temperature
records supports our findings on the co-occurrence of cool temperature anomaly
when compared with previously published data on summer L. polyedrum blooms in
14
the Southern California Bight, and infer primary controls of temperature, mixed layer
depth and nutrient availability for bloom formation.
INTRODUCTION
Coastal areas like the Southern California Bight are of special interest because the
coastal ecosystem is not only forced by natural physical and chemical processes, but
additionally by significant anthropogenic inputs from the heavily populated coastal
region. Primary productivity in this area is high, and it is regulated by the nutrient
supply to the euphotic zone (Cullen et al. 1983), either through natural or
anthropogenic processes. Diverse nutrient delivery processes, combined with the
specific physical processes of the Bight, are driving force for phytoplankton
composition and abundance dynamics (Anderson et al. 2008a; Cullen et al. 1983;
Mantyla et al. 2008; Nezlin et al. 2004). Shoaling of the nutricline provides nutrient
pulses to the nutrient-depleted surface layer, either through upwelling (Eppley et al.
1979; Jones et al. 1983), internal waves and tides (Cullen et al. 1983; Evans et al.
2008; Gaxiola-Castro et al. 2002; Holligan et al. 1985) or other processes that
contribute to vertical transport and mixing. These processes not only transport
nutrients, but can also move the subsurface chlorophyll maximum (SCM) population
toward the surface (Gaxiola-Castro et al. 2002), change the available irradiance
(Evans et al. 2008) , induce significant levels of turbulence that have species specific
15
effects on phytoplankton physiology (Sullivan and Swift 2003), and can cause
significant phytoplankton aggregation (Lennert-Cody and Franks 1999; Scotti and
Pineda 2007).
Several studies of phytoplankton composition in this area confirm the large intra-
annual variability and changes (Anderson et al. 2008a; Shipe et al. 2008; Venrick
1998b). These papers, and several others, that focus on single or multiple species
evolution (Schnetzer et al. 2007; Shipe et al. 2008), and/or chlorophyll concentration
(Hayward et al. 1995; Hayward and Venrick 1998; Mantyla et al. 1995) demonstrate
that spring and summer are the most productive seasons and periods when harmful
algal blooms are most likely to occur.
Red tides, which can be harmful, are usually dinoflagellate-dominated accumulations
of phytoplankton, that can harm the local ecosystem either by production of toxins
and/or accumulation of large amount of biomass with subsequent depletion of
dissolved oxygen. In last couple of decades, red tides are becoming more abundant
and persistent in coastal areas (Anderson et al. 2008b; Kudela et al. 2008b; Schnetzer
et al. 2007; Shipe et al. 2008). In Southern California Bight, red tides are typically
dominated by such species as Prorocentrum micans, Lingulodinium polyedrum, and
more recently Cochlodinium sp (Kudela et al. 2008a; Shipe et al. 2008, Torrey
1902).
16
During the summer and fall of 2005, large bloom of L. polyedrum (>10
6
cells L
-1
)
occurred in Southern California Bight (Moorthi et al. 2006; Shipe et al. 2008),
following the pronounced spring bloom dominated by chain forming diatoms and
Pseudo-nitzchia sp (Schnetzer et al. 2007). Here, we analyze a 2005 spring-summer
time series of phytoplankton data from the San Pedro Channel in the central
Southern California Bight, looking at temporal changes of the phytoplankton
community in the surface layer and the subsurface chlorophyll maximum (SCM) and
examine the environmental factors that couple with the distribution and seasonality
of phytoplankton. Emphasis of our analysis will be on the evolution of HAB species,
L. polyedrum, and the factors regulating its appearance and persistence in the
bloom’s surface expression. By comparing our data with the previous blooms in this
region, we will try to derive the possible causes of the L. polyedrum bloom in San
Pedro Channel area.
MATERIALS AND METHODS
Study area: The San Pedro shelf is a region in Southern California Bight located
between Palos Verdes Peninsula on north and Newport Beach on the south. Both
sides of the San Pedro shelf are narrow, less then 3 km wide. The region is shallow,
with depth on the shelf edge averaging around 60 m. This area is affected by strong
17
anthropogenic influence, having Port of Los Angeles and Port of Long Beach within,
as well as input from 3 regional rivers (Los Angeles River, San Gabriel River and
Santa Ana River), and input from two major sewage outfalls, one located just north
on the Palos Verdes continental shelf, and other located offshore from Huntington
Beach.
Station Sampling: During the period from March to September 2005, phytoplankton
abundance, physical, and chemical parameters were measured on a biweekly /
monthly basis at 5 stations in San Pedro basin (Fig 2-1), over the San Pedro shelf.
All stations were visited on the same day. Vertical profiles of conductivity,
temperature, depth, chlorophyll fluorescence and oxygen concentration were
obtained using a Conductivity Temperature Depth (SeaBird 9/11) sensor package.
The surface mixed layer depth (MLD) was determined as depth at which the density
more than 0.125 kg m
-3
greater than the near-surface densities.
Discrete water samples were collected from the surface and subsurface chlorophyll
maximum (SCM) for further laboratory analysis. Subsamples for macronutrient
(nitrate, nitrite, silicate and phosphate) analysis were processed following protocols
established for the Joint Global Ocean Flux Study (Gordon et al. 1992).
18
Seawater samples for chlorophyll a (50 ml - triplicates) analyses were filtered
through GF/F filters and the filters were immediately stored at -20°C until
measurement. Pigments were extracted in 90% acetone at -20°C. Chlorophyll and
phaeopigment concentrations were measured using a Turner Design fluorometer,
both before and after the addition of two drops of 10% HCl (Parsons et al. 1984).
Figure 2-1. Map with position of the sampled stations (ST2…16) in San Pedro Bay, sampled
bimonthly in 2005. NBST presents the position of the SCCOOS Newport Beach shore station where
automated datasets were acquired. Bathymetry contours (meters) are represented by the gray lines,
and the coastline by the black solid line.
Phytoplankton composition analysis samples (250 ml each sample) were preserved
with acid Lugol's iodine solution (5% final solution). After sedimentation for 24 h,
19
50 ml subsamples were analysed, and cell counts obtained using the inverted
microscope method (Utermöhl 1958). Microphytoplankton cells (longer then 20 μm)
were counted at magnifications of 200x and 400x. Nanophytoplankton cells (2-20
μm) were counted in 20 randomly selected fields of vision, under magnifications of
400 and 1000x.
Automated datasets - SCCOOS temperature and salinity datasets: Conductivity and
temperature were measured every 4 minutes by an automated shore station operated
by the Southern California Coastal Ocean Observatory System (SCCOOS) at
Newport Beach Pier (Fig 2-1). Data was downloaded from SCCOOS’ website
(www.sccoos.org). Data gaps in the automated dataset were filled with manually
collected data from the same sampling site, also available for download from
SCCOOS website. Data from SCRIPPS pier historical data set (1919-2008), were
also downloaded from SCCOOS website. Gaps in this data set were filled by linear
interpolation between neighboring data.
Automated datasets – Buoy historical dataset: Hourly temperature measurements
(1993 – 2008) dataset from NOAA buoy 9410660 (33° 43.2' N, 118° 16.3' W) were
obtained from the National Oceanic and Atmospheric Administration National Data
Buoy Center (NDBC) (http://tidesandcurrents.noaa.gov/ ). Data for 2000 and 2001
were excluded from the calculation of the historical mean.
20
Automated datasets - Upwelling index: Upwelling indices were obtained from
Pacific Fisheries Environmental Laboratory website
(http://www.pfel.noaa.gov/products/PFEL/modeled/indices/upwelling/upwelling.htm
l). Upwelling indices (Bakun 1973; Schwing et al. 1996) were computed from the 6-
hourly, 1°-resolution sea level pressure fields obtained from the U.S. Navy Fleet
Numerical Meteorology and Oceanography Center (FNMOC). Upwelling indices are
derived from the estimates of the offshore Ekman transport driven by geostrophic
wind stress. Positive values result when the wind stress is equatorward, and the
magnitude is a proxy for the amount of water upwelled from the base of the Ekman
layer. Negative values indicate shoreward advection of surface waters accompanied
by a downward displacement of water, i.e. downwelling.
Data analysis: Phytoplankton community composition was analyzed statistically
using non-metric multidimensional scaling - NMDS (Clarke 1993), a statistical
technique that, similar to principal component analysis (PCA), can represent the
spatial and temporal variability in dataset as statistical axes. Where PCA is limited
only to the correlation and covariance coefficients, NMDS is capable of measuring
any statistical means of association (Zuur et al. 2007). In NMDS, the dissimilarity
matrix of data is not involved in the calculations directly but dissimilarities are used
to define the ranking order of ordination distances. NMDS is not based on
21
eigenvalue solutions, but on numerical optimization methods to access the quality of
the final results. Therefore, we describe the axes as stress – a measure of error of the
fitted curve (Legendre and Legendre 1998). Phytoplankton taxa abundance per
sample is often assumed to have log-normal distribution, therefore log
10
(x+1)
transformation was applied to dataset prior to using the Primer NMDS analysis
(Clarke and Gorley 2006). Additionally, all taxa not appearing in more than 2% of
the dataset were removed from further analysis, in order to remove outliers that
could lead to spurious results. Two of the resulting axes are used in this analysis, as
defined by minimal level of stress (Clarke and Gorley 2006).
In order to access the relationship between the environmental parameters and the
phytoplankton distribution in our samples, we used canonical correspondence
analysis (CCA) (Ter Braak 1986), using PC-Ord v. 5 program (Mccune et al. 2002).
CCA has been used in ecology for several decades, and it has proven to be a good
method for establishing environmental-species relationships (Bertics and Ziebis
2009; Cetinic et al. 2006). Environmental data used in this analysis include salinity,
temperature, depth, mixed layer depth, density, oxygen and chlorophyll fluorescence
provided by the CTD profiler; and nutrients measured in the laboratory. The same
transformation and outlier removal as for NMDS analysis was applied to the
phytoplankton taxa dataset, while environmental variables were mean-centered
standardized.
22
RESULTS
Figure 2-2. Upper panel shows San Pedro Channel NOAA ERD upwelling index (gray line) for the
period from mid January to September 2005. Central panel shows temperature (red) and salinity
(blue) at the Newport Beach pier as measured by the automated system, with missing data replaced
with manual measurements. Salinity minima during February (below the graph axes) were 19-20 psu.
Bottom panel shows average mixed layer depth (MLD) during the sampling period. Purple box
highlights the sampling period.
Physical and chemical properties: During the investigated time period, surface water
temperature, both on the sampled stations and Newport Beach Pier, ranged from 12°
C to 20°C (Fig 2-2). Temperatures during the winter period were 14-16 °C followed
by an intense wind driven upwelling/cooling event in late March-early April when
temperatures dropped to less than 12°C. After the cooling episode, temperature rose
steadily through early June, consistent with increasing insolation. During July,
August and September sea surface temperature at Newport Pier dropped several
23
times to14º C, but no corresponding increases in the upwelling index were observed,
unlike April upwelling episode. Salinity followed a pattern similar to temperature,
exhibiting significant variations; with lower values in the storm predominating
period and higher values during upwelling and internal bore intrusion period. Mixed
layer depth from the sampling stations changed with observed patterns in the
temperature. Maximum mixed layer depth depths encountered in March toward the
end of the winter period were about 10 meters. During the subsequent spring
upwelling/mixing period, mean mixed layer depth was about 12m, the deepest
observed for the entire observational period. Following the upwelling, mixed layer
depth shallowed to 5 m, with deeper mixed layers (> 8 m) occurring during the
summer upwelling/internal tide period. For the remainder of the observational
period, mixed layer depth stabilized at average depth of 5-6 m.
Nutrient variability (Fig 2-3) reflects some of the variability observed in temperature
and salinity. The highest surface nutrient concentrations were observed during the
April upwelling episode, when highest concentrations for all the nutrients were
found at all stations except station 16. The highest surface nitrate concentration
observed was 17.1 μM during the April upwelling, although values higher than the
data set mean (1.41 μM) occurred sporadically during the summer at some of the
stations (Fig 2-3).
24
Figure 2-3. Time series of normalized surface nutrient concentrations at sampling stations; A) silicate
B) phosphate C) nitrate and D) nitrite. Blue line presents data from station 2, red line – station 5,
purple line- station 8, pink line – station 13 and black line for station 16. For each station, data at the
surface were normalized using the transformation [X
n
]=(X - X
st
min)/(X
st
max –X
st
min), where [X
n
] is
the normalized values of nutrient X, and X
st
min and X
st
max are the minimum and maximum,
respectively, of nutrient concentration at this station. Each normalized field of [X
n
] ranges between 0
and 1. Raw nutrient data presented in Table 2-1.
Silicate and phosphate concentrations in the surface waters were high at the
beginning of our sampling period, possibly due to the prior input from stormwater
and/or low uptake in that period due to the lack of diatoms in the surface
phytoplankton community (Table 2-1). The highest concentrations were measured in
the surface waters (silicates 20.61μM and phosphates 1.63 μM) during April. Higher
values of both of these nutrients, especially silica, were also encountered during the
summer months (Fig 2-3). Nitrites, were low in the surface waters (mean of
25
0.31μM, max 0.6 μM), and with two maxima observed, first one, as expected during
the upwelling period, and second one in July (Table 2-1).
Table 2- 1. Maximum, minimum, average and standard deviation (st. dev.) for observed silicate
(SiO
4
), phosphate (PO
4
), nitrate (NO
3
) and nitrite (NO
2
).
Station SiO
4
(μM) PO
4
(μM) NO
3
(μM) NO
2
(μM)
2 minimum
(date collected)
0.474
(8/19/2005)
0.11
(8/19/2005)
0.06
(3/22/2005)
0.11
(9/20/2005)
maximum
(date collected)
20.60
(4/12/2005)
1.63
(4/12/2005)
17.15
(4/12/2005)
0.57
(4/12/2005)
Average ± st.dev. 5.34± 5.99 0.48±0.38 1.97±4.8 0.281±0.13
5 minimum
(date collected)
0.11
(7/6/2005)
0.22
(5/25/2005)
0.01
(8/19/2005)
0.10
(9/20/2005)
maximum
(date collected)
11.542
(4/12/2005)
1.26
(4/12/2005)
11.42
(4/12/2005)
0.60
(4/12/2005)
Average ± st.dev. 4.13±3.47 0.46±0.28 1.27±3.22 0.34±0.14
8 minimum
(date collected)
0.69
(5/13/2005)
0.20
(6/7/2005)
0.05
(5/25/2005)
0.12
(8/19/2005)
maximum
(date collected)
13.31
(6/22/2005)
0.72
(4/12/2005)
9.37
(4/12/2005)
0.54
(4/12/2005)
Average ± st.dev. 4.93±4.03 0.43±0.183 1.30±2.57 0.309±0.112
13 minimum
(date collected)
0.286666
(4/21/2005)
0.225707
(3/22/2005)
0
(3/22/2005)
0.095556
(5/13/2005)
maximum
(date collected)
11.46
(4/12/2005)
1.13
(4/12/2005)
14.03
(4/12/2005)
0.41
(4/12/2005)
Average ± st.dev. 3.95±3.477 0.48±0.26 1.856±3.91 0.277±0.089
16 minimum
(date collected)
0.190
(8/19/2005)
0.13
(5/25/2005)
0.01
(5/25/2005)
0.097
(3/15/2005)
minimum
(date collected)
5.23
(6/7/2005)
0.56
(3/15/2005)
2.18
(3/22/2005)
0.58
(7/6/2005)
Average ± st.dev. 2.63±1.44 0.37±0.129 0.63±0.68 0.336±0.158
26
Figure 2-4. Time series of chlorophyll (extracted) at sampling stations; A) surface B) subsurface
chlorophyll maximum. Blue line presents data from station 2, red line – station 5, purple line- station
8, pink line – station 13 and black line for station 16. Lack of data in June and September for
subsurface chlorophyll maximum is due to the sampling problems in that period.
Phytoplankton community: The highest observed surface chlorophyll concentrations
occurred at the end of March (13.34 μg L
-1
), preceding the observed surface
maximum in nutrient concentrations. Secondary surface chlorophyll maxima were
observed in June and near the end of the observational period in September (Fig 2-4).
The subsurface chlorophyll time series was similar to the surface pattern with higher
chlorophyll values in March, April and June. Station 16 exhibited an earlier spring
chlorophyll peak then remainder of the sites (Fig 2-4), following the high nitrate
observed at that station 2 weeks prior (Fig. 2-3). Elevated subsurface chlorophyll
concentrations in mid March preceded the surface maxima at the end of March.
Subsurface chlorophyll maximum had high chlorophyll concentrations again at the
end of April and in June concurrent with surface chlorophyll maximum.
27
Table 2-2. List of species/taxa encountered during the study with respective species index number.
Species index number Species name
1 Alexandrium sp.
2 Asterionellopsis glacialis (Castr.) Round
3 Asterolampra
4 Asterolampra marylandica Ehrenb.
5 Bacteriastrum sp.
6 Cerataulina pelagica (Cleve) Hendey
7 Chaetoceros compressus Laud.
8 Chaetoceros curvisetus Cleve
9 Chaetoceros dadayi Pav.
10 Chaetoceros danicus Cleve
11 Chaetoceros didymus Ehrenb.
12 Chaetoceros sp.
13 Chaetoceros socialis
14 Chaetoceros tenuissimus
15 Corethron hystrix Cleve
16 Coscinodiscus sp.
17 Dactyliosolen fragilissimus (Bergon) Hasle
18 Detonula pumila (Castr.) Schuett
19 Diploneis bombus Ehrenb.
20 Ditylum brightwellii (West) Grun.
21 Eucampia cornuta (Cleve) Grun.
22 Eucampia zoodiacus Ehrenb.
23 Guinardia flaccida (Castr.) Perag.
24 Guinardia striata (Stolter.) Hasle
25 Hemiaulus hauckii Grun.
26 Hemiaulus sinensis Grev.
27 Leptocylindrus mediterraneus (Perag.) Hasle
28 Leptocylindrus minimus Gran
29 Licmophora sp.
30 Melosira sp.
31 Nitzschia longissima (Breb.) Ralfs./ Cylindrotheca closterium
32 Nitzschia sp.
33 Odontella sp.
34 Pleurosygma sp.
35 Proboscia alata (Brightw.) Sund.
36 Pseudonitzschia spp.
37 Pseudosolenia calcar-avis (Schultze) Sund.
38 Rhizosolenia sp.
39 Skeletonema costatum
40 Thalassionema nitzschioides Grun.
41 Thalassiosira sp
42 Penatae
28
Table 2-2. Continued.
Species index number Species name
43 Bacteriastrum sp.
44 Chaetoceros constrictus
45 Ceratium furca (Efrenb.) Clap. et Lachm.
46 Ceratium fusus (Ehrenb.) Dujardin.
47 Ceratium hexacanthum Gourr.
48 Ceratium longirostrum Gourr.
49 Ceratium pentagonum Gourr.
50 Ceratium sp.
51 Dinophysis acuminata Clap. et Lachm.
52 Dinophysis acuta Ehrenb.
53 Dinophysis caudata Seville-Kent
54 Dinophysis rotundata (Clap. et Lachm.) Abe
55 Dinophysis sp.
56 Gymnodinium sanguineum.
57 Cochlodinium catenatum
58 Gymnodinium sp.
59 Gyrodinium sp.
60 Lingulodinium polyedra (Stein) Dodge
61 Oxytoxum sp.
62 Prorocentrum micans Ehrenb.
63 Prorocentrum sp.
64 Prorocentrum gracillis
78 Pyrocystis lunula Schuett
66 Pyrophacus horologicum Stein
67 Scrippsiella sp.
68 Protoperidinium conicum (Gran) Bal.
79 Protoperidinium crassipes (Kof.) Bal.
70 Protoperidinium globulus (Stein) Bal.
80 Protoperidinium pellucidum Bergh
72 Protoperidinium sp.
73 Protoperidinium steinii (Joerg.) Bal.
81 Protoperidinium tubum (Schiller) Bal.
75 Pronoctyluca sp.
76 Dictyocha fibula Ehrenb.
77 Dictyocha speculum Ehrenb. (= D. octonaria)
82 Nano flagellata >2 μm <10μm
83 nano dinoflagellata >10μm <20μm
84 nano other >10μm <20μm
29
Table 2-3. Abundances of different taxa at different sampling stations, depth and time. Species names
are replaced with species index numbers, as explained in Table 2-2. SCM – subsurface chl maximum.
Species index number
Sample # Date Station Depth 1 2 3 4
1 3/15/2005 2 0 11817 0 0 0
2 3/15/2005 2 SCM 0 0 0 0
3 3/22/2005 2 0 0 0 0 0
4 3/22/2005 2 SCM 0 13506 0 0
5 4/5/2005 2 0 0 39392 0 0
6 4/5/2005 2 SCM 0 0 977 0
7 4/12/2005 2 SCM 489 11817 0 0
8 4/21/2005 2 0 977 1954 0 0
9 4/21/2005 2 SCM 0 4000 0 0
10 5/13/2005 2 0 33764 0 0 0
11 5/13/2005 2 SCM 0 1000 0 2000
12 5/25/2005 2 0 0 0 0 0
13 5/25/2005 2 SCM 0 0 0 0
14 6/7/2005 2 0 0 0 0 0
15 6/22/2005 2 0 0 0 0 0
16 6/22/2005 2 SCM 7878 0 0 0
17 7/6/2005 2 0 0 108045 0 0
18 7/6/2005 2 SCM 0 323011 0 0
19 8/19/2005 2 0 0 0 0 0
20 8/19/2005 2 SCM 0 0 0 0
21 9/20/2005 2 0 0 0 0 0
22 3/15/2005 5 SCM 0 0 0 0
23 5/13/2005 5 0 5909 0 489 0
24 5/13/2005 5 SCM 0 4000 0 0
25 5/25/2005 5 0 0 0 0 0
26 3/15/2005 16 SCM 0 0 0 0
27 5/13/2005 16 0 977 0 0 0
28 3/15/2005 13 0 0 0 0 0
29 3/15/2005 13 SCM 0 1477 0 0
30 3/22/2005 13 0 0 0 0 0
31 4/21/2005 13 SCM 9848 0 0 0
32 6/7/2005 13 0 1466 0 0 0
33 3/15/2005 16 0 0 0 0 0
34 3/15/2005 16 SCM 0 489 0 0
35 3/22/2005 16 0 0 0 0 0
36 6/7/2005 16 0 0 0 0 0
37 4/12/2005 13 SCM 0 27563 0 0
38 9/20/2005 13 0 0 0 0 0
39 9/20/2005 16 0 0 0 0 0
40 7/6/2005 16 SCM 0 0 0 0
30
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 1 2 3 4
41 4/21/2005 16 0 0 0 0 0
42 4/12/2005 16 0 0 39392 0 0
43 4/5/2005 16 0 0 18908 0 0
44 5/25/2005 16 0 0 0 0 0
45 5/25/2005 13 SCM 0 0 0 0
46 5/25/2005 16 SCM 0 0 0 0
47 5/25/2005 13 0 0 0 0 0
48 5/25/2005 16 SCM 0 0 0 0
49 5/13/2005 16 SCM 0 0 0 0
50 3/15/2005 13 0 0 0 0 0
51 3/15/2005 13 SCM 0 1477 0 0
52 3/15/2005 16 0 0 0 0 0
53 7/6/2005 16 0 0 0 0 0
54 3/22/2005 5 0 0 0 0 0
55 4/5/2005 13 SCM 0 47270 0 0
56 7/6/2005 13 0 0 0 0 0
57 6/22/2005 16 SCM 0 0 0 0
58 8/19/2005 16 SCM 0 0 0 0
59 8/19/2005 16 0 0 0 0 0
60 5/25/2005 5 SCM 0 0 0 0
61 5/13/2005 16 0 0 0 0 0
62 3/22/2005 16 SCM 0 3909 0 0
63 4/12/2005 16 SCM 0 17726 0 0
64 4/5/2005 16 SCM 0 3908856 0 0
65 6/22/2005 16 0 0 0 0 0
66 8/19/2005 16 0 0 0 0 0
67 7/6/2005 13 0 0 0 0 0
68 3/22/2005 13 SCM 0 0 0 0
69 4/5/2005 5 0 0 13566 0 0
70 4/21/2005 5 0 0 0 0 0
71 4/5/2005 5 SCM 0 15757 0 0
72 4/12/2005 5 0 0 3909 0 0
73 6/7/2005 5 0 0 0 0 0
74 9/20/2005 5 0 0 5909 0 0
75 3/22/2005 5 SCM 0 0 0 0
76 5/25/2005 5 0 0 0 0 0
77 4/5/2005 5 SCM 0 0 0 0
78 8/19/2005 5 SCM 0 0 0 0
79 8/19/2005 5 0 0 815406 0 0
80 6/22/2005 5 SCM 0 0 0 0
81 4/5/2005 16 0 0 8381 0 0
31
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 1 2 3 4
82 6/22/2005 16 0 0 0 0 0
83 4/12/2005 16 0 0 56724 0 0
84 4/21/2005 16 SCM 0 0 0 0
85 4/12/2005 16 SCM 0 94540 0 0
86 6/22/2005 13 0 0 0 0 0
87 8/19/2005 13 SCM 0 0 0 0
88 4/12/2005 5 SCM 0 67528 0 0
89 7/6/2005 5 0 0 236349 0 0
90 4/12/2005 13 0 0 16078 0 0
32
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 5 6 7 8
1 3/15/2005 2 0 0 0 0 0
2 3/15/2005 2 SCM 11817 0 0 0
3 3/22/2005 2 0 6753 20259 0 0
4 3/22/2005 2 SCM 489 0 0 0
5 4/5/2005 2 0 0 0 0 15757
6 4/5/2005 2 SCM 0 0 0 0
7 4/12/2005 2 SCM 0 0 0 17726
8 4/21/2005 2 0 0 0 0 0
9 4/21/2005 2 SCM 0 0 22000 34000
10 5/13/2005 2 0 0 0 0 0
11 5/13/2005 2 SCM 0 0 0 0
12 5/25/2005 2 0 0 0 0 0
13 5/25/2005 2 SCM 0 0 0 0
14 6/7/2005 2 0 489 0 0 0
15 6/22/2005 2 0 0 0 0 0
16 6/22/2005 2 SCM 0 0 0 0
17 7/6/2005 2 0 0 0 0 1954
18 7/6/2005 2 SCM 0 0 0 0
19 8/19/2005 2 0 977 0 0 0
20 8/19/2005 2 SCM 0 0 0 0
21 9/20/2005 2 0 6753 21665 0 0
22 3/15/2005 5 SCM 0 0 0 1954
23 5/13/2005 5 0 0 0 0 17726
24 5/13/2005 5 SCM 0 0 22000 34000
25 5/25/2005 5 0 0 0 0 0
26 3/15/2005 16 SCM 0 0 0 0
27 5/13/2005 16 0 0 0 0 0
28 3/15/2005 13 0 0 0 0 0
29 3/15/2005 13 SCM 1477 0 0 0
30 3/22/2005 13 0 0 0 0 0
31 4/21/2005 13 SCM 0 0 0 7878
32 6/7/2005 13 0 489 977 0 0
33 3/15/2005 16 0 0 0 0 0
34 3/15/2005 16 SCM 0 0 0 0
35 3/22/2005 16 0 0 0 0 977
36 6/7/2005 16 0 0 0 0 0
37 4/12/2005 13 SCM 0 1994 0 0
38 9/20/2005 13 0 0 0 0 0
39 9/20/2005 16 0 0 489 0 0
40 7/6/2005 16 SCM 0 0 0 0
33
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 5 6 7 8
41 4/21/2005 16 0 0 0 0 0
42 4/12/2005 16 0 0 0 0 7818
43 4/5/2005 16 0 0 21271 0 0
44 5/25/2005 16 0 0 0 0 0
45 5/25/2005 13 SCM 0 0 0 0
46 5/25/2005 16 SCM 0 0 0 0
47 5/25/2005 13 0 0 0 0 0
48 5/25/2005 16 SCM 0 1954 0 0
49 5/13/2005 16 SCM 0 29316 0 11727
50 3/15/2005 13 0 0 0 0 0
51 3/15/2005 13 SCM 1000 0 0 0
52 3/15/2005 16 0 0 0 0 0
53 7/6/2005 16 0 0 0 0 0
54 3/22/2005 5 0 0 3939 0 0
55 4/5/2005 13 SCM 3000 13787 0 11817
56 7/6/2005 13 0 0 0 0 0
57 6/22/2005 16 SCM 0 0 0 0
58 8/19/2005 16 SCM 0 0 0 0
59 8/19/2005 16 0 0 0 0 0
60 5/25/2005 5 SCM 0 0 0 0
61 5/13/2005 16 0 0 1954 0 0
62 3/22/2005 16 SCM 0 0 0 0
63 4/12/2005 16 SCM 0 8795 0 0
64 4/5/2005 16 SCM 0 8803721 0 0
65 6/22/2005 16 0 0 0 0 0
66 8/19/2005 16 0 0 0 0 0
67 7/6/2005 13 0 0 0 0 0
68 3/22/2005 13 SCM 0 0 0 0
69 4/5/2005 5 0 0 9044 0 0
70 4/21/2005 5 0 0 4886 0 0
71 4/5/2005 5 SCM 0 19696 0 19696
72 4/12/2005 5 0 0 0 0 0
73 6/7/2005 5 0 0 0 0 0
74 9/20/2005 5 0 0 0 0 0
75 3/22/2005 5 SCM 0 0 0 0
76 5/25/2005 5 0 0 0 0 0
77 4/5/2005 5 SCM 0 0 0 0
78 8/19/2005 5 SCM 0 0 0 0
79 8/19/2005 5 0 0 0 0 0
80 6/22/2005 5 SCM 0 0 0 0
34
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 5 6 7 8
81 4/5/2005 16 0 0 24950 0 8317
82 6/22/2005 16 0 0 0 0 0
83 4/12/2005 16 0 0 2932 0 0
84 4/21/2005 16 SCM 0 11727 0 0
85 4/12/2005 16 SCM 0 10749 0 0
86 6/22/2005 13 0 0 0 0 0
87 8/19/2005 13 SCM 0 0 0 0
88 4/12/2005 5 SCM 0 3909 0 0
89 7/6/2005 5 0 0 0 0 0
90 4/12/2005 13 0 0 2493 0 1994
35
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 9 10 11 12
1 3/15/2005 2 0 0 0 0 0
2 3/15/2005 2 SCM 0 0 0 5909
3 3/22/2005 2 0 0 0 0 13506
4 3/22/2005 2 SCM 0 0 0 0
5 4/5/2005 2 0 0 0 15757 165445
6 4/5/2005 2 SCM 0 0 0 3909
7 4/12/2005 2 SCM 5863 0 0 11817
8 4/21/2005 2 0 3909 0 0 1954
9 4/21/2005 2 SCM 0 0 0 8363
10 5/13/2005 2 0 0 0 0 11000
11 5/13/2005 2 SCM 0 0 2000 4000
12 5/25/2005 2 0 0 0 0 611
13 5/25/2005 2 SCM 0 0 0 0
14 6/7/2005 2 0 0 0 0 0
15 6/22/2005 2 0 0 0 0 0
16 6/22/2005 2 SCM 0 0 0 977
17 7/6/2005 2 0 0 0 0 13506
18 7/6/2005 2 SCM 0 1466 0 3420
19 8/19/2005 2 0 0 0 0 0
20 8/19/2005 2 SCM 0 0 0 977
21 9/20/2005 2 0 0 0 0 13506
22 3/15/2005 5 SCM 0 0 0 0
23 5/13/2005 5 0 0 0 0 82722
24 5/13/2005 5 SCM 0 0 0 6000
25 5/25/2005 5 0 0 0 0 0
26 3/15/2005 16 SCM 0 0 0 5909
27 5/13/2005 16 0 0 0 0 9454
28 3/15/2005 13 0 0 0 0 0
29 3/15/2005 13 SCM 0 0 0 2954
30 3/22/2005 13 0 0 0 0 0
31 4/21/2005 13 SCM 0 0 0 31513
32 6/7/2005 13 0 0 0 0 0
33 3/15/2005 16 0 0 0 0 0
34 3/15/2005 16 SCM 0 0 0 3420
35 3/22/2005 16 0 0 0 0 29089
36 6/7/2005 16 0 0 0 0 0
37 4/12/2005 13 SCM 0 0 0 15955
38 9/20/2005 13 0 0 0 0 0
39 9/20/2005 16 0 0 0 0 0
40 7/6/2005 16 SCM 0 0 0 0
41 4/21/2005 16 0 0 0 0 0
36
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 9 10 11 12
42 4/12/2005 16 0 0 0 0 173323
43 4/5/2005 16 0 0 0 0 9454
44 5/25/2005 16 0 0 0 0 0
45 5/25/2005 13 SCM 0 0 0 0
46 5/25/2005 16 SCM 0 0 0 0
47 5/25/2005 13 0 0 0 0 0
48 5/25/2005 16 SCM 0 0 0 0
49 5/13/2005 16 SCM 0 0 0 35452
50 3/15/2005 13 0 0 0 0 0
51 3/15/2005 13 SCM 0 0 0 2954
52 3/15/2005 16 0 0 0 0 0
53 7/6/2005 16 0 0 0 0 0
54 3/22/2005 5 0 0 0 0 0
55 4/5/2005 13 SCM 0 0 0 163081
56 7/6/2005 13 0 0 0 0 249
57 6/22/2005 16 SCM 0 0 0 0
58 8/19/2005 16 SCM 0 0 0 0
59 8/19/2005 16 0 0 0 0 0
60 5/25/2005 5 SCM 0 0 0 1954
61 5/13/2005 16 0 0 0 0 3909
62 3/22/2005 16 SCM 0 0 0 0
63 4/12/2005 16 SCM 0 0 0 13192
64 4/5/2005 16 SCM 0 0 0 13205581
65 6/22/2005 16 0 0 0 0 0
66 8/19/2005 16 0 0 0 0 0
67 7/6/2005 13 0 0 0 0 244
68 3/22/2005 13 SCM 0 0 0 489
69 4/5/2005 5 0 0 0 0 6029
70 4/21/2005 5 0 0 0 0 2932
71 4/5/2005 5 SCM 0 0 0 7878
72 4/12/2005 5 0 0 0 0 7818
73 6/7/2005 5 0 0 0 0 0
74 9/20/2005 5 0 0 0 0 0
75 3/22/2005 5 SCM 0 0 0 4886
76 5/25/2005 5 0 0 0 0 0
77 4/5/2005 5 SCM 0 0 0 4886070
78 8/19/2005 5 SCM 0 0 0 0
79 8/19/2005 5 0 0 0 0 0
80 6/22/2005 5 SCM 0 0 0 6753
81 4/5/2005 16 0 0 0 0 14554
82 6/22/2005 16 0 0 0 0 0
37
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 9 10 11 12
83 4/12/2005 16 0 0 0 0 8795
84 4/21/2005 16 SCM 0 0 0 9772
85 4/12/2005 16 SCM 0 977 0 10749
86 6/22/2005 13 0 0 0 0 0
87 8/19/2005 13 SCM 0 0 0 0
88 4/12/2005 5 SCM 0 0 0 13681
89 7/6/2005 5 0 0 0 0 141810
90 4/12/2005 13 0 0 0 0 297447
38
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 13 14 15 16
1 3/15/2005 2 0 0 0 0 0
2 3/15/2005 2 SCM 0 0 0 0
3 3/22/2005 2 0 27011 0 0 0
4 3/22/2005 2 SCM 0 0 0 0
5 4/5/2005 2 0 86661 0 0 0
6 4/5/2005 2 SCM 7818 0 0 0
7 4/12/2005 2 SCM 0 0 0 0
8 4/21/2005 2 0 0 0 0 0
9 4/21/2005 2 SCM 0 0 0 0
10 5/13/2005 2 0 0 0 0 0
11 5/13/2005 2 SCM 0 0 0 0
12 5/25/2005 2 0 0 0 0 0
13 5/25/2005 2 SCM 2932 0 0 0
14 6/7/2005 2 0 0 0 0 0
15 6/22/2005 2 0 0 0 0 0
16 6/22/2005 2 SCM 0 0 0 0
17 7/6/2005 2 0 0 0 0 0
18 7/6/2005 2 SCM 0 0 0 0
19 8/19/2005 2 0 0 0 0 0
20 8/19/2005 2 SCM 0 0 0 0
21 9/20/2005 2 0 0 0 0 0
22 3/15/2005 5 SCM 0 0 0 0
23 5/13/2005 5 0 0 0 0 0
24 5/13/2005 5 SCM 0 0 0 0
25 5/25/2005 5 0 0 4727 0 0
26 3/15/2005 16 SCM 0 0 0 0
27 5/13/2005 16 0 66178 0 0 0
28 3/15/2005 13 0 0 0 0 0
29 3/15/2005 13 SCM 2954 0 0 0
30 3/22/2005 13 0 0 0 0 0
31 4/21/2005 13 SCM 27011 0 0 0
32 6/7/2005 13 0 0 0 0 0
33 3/15/2005 16 0 0 0 0 0
34 3/15/2005 16 SCM 2932 0 0 0
35 3/22/2005 16 0 0 0 0 0
36 6/7/2005 16 0 0 0 0 0
37 4/12/2005 13 SCM 0 0 0 997
38 9/20/2005 13 0 0 0 0 0
39 9/20/2005 16 0 0 0 0 0
40 7/6/2005 16 SCM 0 0 0 1954
41 4/21/2005 16 0 0 0 0 0
39
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 13 14 15 16
42 4/12/2005 16 0 0 0 0 9772
43 4/5/2005 16 0 170172 0 0 7090
44 5/25/2005 16 0 0 0 0 0
45 5/25/2005 13 SCM 0 0 0 0
46 5/25/2005 16 SCM 0 0 0 0
47 5/25/2005 13 0 0 0 0 977
48 5/25/2005 16 SCM 0 0 0 0
49 5/13/2005 16 SCM 0 0 0 0
50 3/15/2005 13 0 0 0 0 0
51 3/15/2005 13 SCM 2954 0 0 0
52 3/15/2005 16 0 0 0 0 0
53 7/6/2005 16 0 0 0 0 0
54 3/22/2005 5 0 0 0 0 0
55 4/5/2005 13 SCM 0 0 1970 3939
56 7/6/2005 13 0 0 0 0 0
57 6/22/2005 16 SCM 0 0 0 489
58 8/19/2005 16 SCM 0 0 0 0
59 8/19/2005 16 0 0 0 0 489
60 5/25/2005 5 SCM 0 0 0 1954
61 5/13/2005 16 0 0 0 0 0
62 3/22/2005 16 SCM 0 0 0 977
63 4/12/2005 16 SCM 1954 0 0 1466
64 4/5/2005 16 SCM 1956382 0 0 2444501
65 6/22/2005 16 0 0 0 0 0
66 8/19/2005 16 0 0 0 0 0
67 7/6/2005 13 0 0 0 0 0
68 3/22/2005 13 SCM 0 0 0 0
69 4/5/2005 5 0 0 0 0 0
70 4/21/2005 5 0 0 0 0 0
71 4/5/2005 5 SCM 0 0 1970 0
72 4/12/2005 5 0 0 0 0 0
73 6/7/2005 5 0 0 0 0 0
74 9/20/2005 5 0 0 0 0 0
75 3/22/2005 5 SCM 0 0 0 0
76 5/25/2005 5 0 0 4727 0 0
77 4/5/2005 5 SCM 0 4731716 0 0
78 8/19/2005 5 SCM 0 0 0 0
79 8/19/2005 5 0 0 0 0 0
80 6/22/2005 5 SCM 0 0 0 0
81 4/5/2005 16 0 58668 0 0 0
82 6/22/2005 16 0 0 0 0 0
40
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 13 14 15 16
83 4/12/2005 16 0 0 0 0 2932
84 4/21/2005 16 SCM 0 0 1954 0
85 4/12/2005 16 SCM 0 0 977 0
86 6/22/2005 13 0 0 0 0 0
87 8/19/2005 13 SCM 0 0 0 0
88 4/12/2005 5 SCM 0 0 0 0
89 7/6/2005 5 0 0 0 0 0
90 4/12/2005 13 0 0 0 0 0
41
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 17 18 19 20
1 3/15/2005 2 0 0 0 0 1954
2 3/15/2005 2 SCM 25408 0 0 0
3 3/22/2005 2 0 0 0 0 0
4 3/22/2005 2 SCM 489 0 0 0
5 4/5/2005 2 0 55148 0 0 0
6 4/5/2005 2 SCM 1954 0 0 0
7 4/12/2005 2 SCM 489 0 0 0
8 4/21/2005 2 0 7818 0 0 0
9 4/21/2005 2 SCM 3000 0 1000 0
10 5/13/2005 2 0 101293 0 0 0
11 5/13/2005 2 SCM 2000 0 0 0
12 5/25/2005 2 0 0 0 0 0
13 5/25/2005 2 SCM 977 6840 0 0
14 6/7/2005 2 0 0 0 0 0
15 6/22/2005 2 0 0 0 0 0
16 6/22/2005 2 SCM 1954 0 0 0
17 7/6/2005 2 0 977 0 0 0
18 7/6/2005 2 SCM 1466 0 0 0
19 8/19/2005 2 0 0 0 0 0
20 8/19/2005 2 SCM 11817 0 0 0
21 9/20/2005 2 0 7878 0 0 0
22 3/15/2005 5 SCM 977 0 0 0
23 5/13/2005 5 0 189080 0 0 0
24 5/13/2005 5 SCM 299376 0 0 0
25 5/25/2005 5 0 0 0 0 0
26 3/15/2005 16 SCM 0 0 0 0
27 5/13/2005 16 0 122902 0 0 0
28 3/15/2005 13 0 3636 0 0 0
29 3/15/2005 13 SCM 0 0 0 0
30 3/22/2005 13 0 0 0 0 0
31 4/21/2005 13 SCM 19696 0 0 0
32 6/7/2005 13 0 0 0 0 0
33 3/15/2005 16 0 0 0 0 0
34 3/15/2005 16 SCM 489 0 0 489
35 3/22/2005 16 0 3636 0 0 0
36 6/7/2005 16 0 0 0 0 0
37 4/12/2005 13 SCM 0 0 0 0
38 9/20/2005 13 0 977 0 0 0
39 9/20/2005 16 0 0 0 0 0
42
Table 2-3. Continued
Species index number
Sample # Date Station Depth 17 18 19 20
40 7/6/2005 16 SCM 0 0 0 0
41 4/21/2005 16 0 0 0 0 0
42 4/12/2005 16 0 1954 0 0 1954
43 4/5/2005 16 0 7090 0 0 2363
44 5/25/2005 16 0 0 0 0 0
45 5/25/2005 13 SCM 0 0 0 0
46 5/25/2005 16 SCM 0 0 0 0
47 5/25/2005 13 0 0 0 0 0
48 5/25/2005 16 SCM 0 0 0 0
49 5/13/2005 16 SCM 0 0 0 0
50 3/15/2005 13 0 3636 0 0 0
51 3/15/2005 13 SCM 0 0 0 0
52 3/15/2005 16 0 0 0 0 0
53 7/6/2005 16 0 0 0 0 0
54 3/22/2005 5 0 0 0 0 0
55 4/5/2005 13 SCM 11817 0 0 0
56 7/6/2005 13 0 0 0 0 0
57 6/22/2005 16 SCM 0 0 0 0
58 8/19/2005 16 SCM 0 0 0 0
59 8/19/2005 16 0 0 0 0 0
60 5/25/2005 5 SCM 0 0 0 0
61 5/13/2005 16 0 0 0 0 0
62 3/22/2005 16 SCM 2932 0 0 0
63 4/12/2005 16 SCM 0 0 0 0
64 4/5/2005 16 SCM 2931642 0 0 0
65 6/22/2005 16 0 0 0 0 0
66 8/19/2005 16 0 977 0 0 0
67 7/6/2005 13 0 0 0 0 0
68 3/22/2005 13 SCM 4886 0 0 0
69 4/5/2005 5 0 1507 0 0 0
70 4/21/2005 5 0 4886 0 0 0
71 4/5/2005 5 SCM 3939 0 0 3939
72 4/12/2005 5 0 7818 0 0 0
73 6/7/2005 5 0 0 0 0 0
74 9/20/2005 5 0 3909 0 0 0
75 3/22/2005 5 SCM 1954 0 0 0
76 5/25/2005 5 0 0 0 0 0
77 4/5/2005 5 SCM 1954428 0 0 0
78 8/19/2005 5 SCM 3909 0 0 0
79 8/19/2005 5 0 977 0 0 0
80 6/22/2005 5 SCM 0 0 0 0
43
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 17 18 19 20
81 4/5/2005 16 0 4158 0 0 0
82 6/22/2005 16 0 1629 0 0 0
83 4/12/2005 16 0 977 0 0 0
84 4/21/2005 16 SCM 0 0 0 0
85 4/12/2005 16 SCM 977 0 0 977
86 6/22/2005 13 0 0 0 0 0
87 8/19/2005 13 SCM 0 0 0 0
88 4/12/2005 5 SCM 0 0 0 0
89 7/6/2005 5 0 0 0 0 0
90 4/12/2005 13 0 0 0 0 499
44
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 21 22 23 24
1 3/15/2005 2 0 0 0 0 0
2 3/15/2005 2 SCM 0 17726 0 0
3 3/22/2005 2 0 0 0 0 0
4 3/22/2005 2 SCM 0 0 0 0
5 4/5/2005 2 0 0 23635 0 23635
6 4/5/2005 2 SCM 0 0 0 0
7 4/12/2005 2 SCM 0 0 0 0
8 4/21/2005 2 0 0 0 0 0
9 4/21/2005 2 SCM 0 0 0 0
10 5/13/2005 2 0 0 20259 0 0
11 5/13/2005 2 SCM 7000 5000 0 7000
12 5/25/2005 2 0 0 0 0 0
13 5/25/2005 2 SCM 0 8795 0 0
14 6/7/2005 2 0 0 977 0 0
15 6/22/2005 2 0 0 0 0 0
16 6/22/2005 2 SCM 0 0 0 0
17 7/6/2005 2 0 0 0 0 3909
18 7/6/2005 2 SCM 0 0 0 0
19 8/19/2005 2 0 0 0 0 0
20 8/19/2005 2 SCM 0 0 0 0
21 9/20/2005 2 0 0 0 0 0
22 3/15/2005 5 SCM 0 3909 0 0
23 5/13/2005 5 0 5909 2443 0 2932
24 5/13/2005 5 SCM 0 0 0 2932
25 5/25/2005 5 0 0 0 0 0
26 3/15/2005 16 SCM 0 7818 7818 0
27 5/13/2005 16 0 0 14181 0 18908
28 3/15/2005 13 0 0 0 0 0
29 3/15/2005 13 SCM 0 8863 2954 0
30 3/22/2005 13 0 0 0 0 0
31 4/21/2005 13 SCM 0 0 977 2443
32 6/7/2005 13 0 0 0 0 0
33 3/15/2005 16 0 0 0 0 0
34 3/15/2005 16 SCM 0 0 0 0
35 3/22/2005 16 0 0 2443 0 489
36 6/7/2005 16 0 0 0 0 0
37 4/12/2005 13 SCM 0 499 997 499
38 9/20/2005 13 0 0 0 0 0
39 9/20/2005 16 0 0 0 0 0
40 7/6/2005 16 SCM 0 0 0 0
45
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 21 22 23 24
41 4/21/2005 16 0 0 0 0 0
42 4/12/2005 16 0 0 0 0 0
43 4/5/2005 16 0 0 0 0 2363
44 5/25/2005 16 0 0 0 0 0
45 5/25/2005 13 SCM 0 0 0 0
46 5/25/2005 16 SCM 0 0 0 0
47 5/25/2005 13 0 0 0 0 0
48 5/25/2005 16 SCM 0 0 0 0
49 5/13/2005 16 SCM 0 15635 0 0
50 3/15/2005 13 0 0 0 0 0
51 3/15/2005 13 SCM 0 8863 2954 0
52 3/15/2005 16 0 0 0 0 0
53 7/6/2005 16 0 0 0 0 0
54 3/22/2005 5 0 0 3939 1970 0
55 4/5/2005 13 SCM 0 0 15757 7878
56 7/6/2005 13 0 0 0 0 0
57 6/22/2005 16 SCM 0 0 0 0
58 8/19/2005 16 SCM 0 0 0 0
59 8/19/2005 16 0 0 0 0 0
60 5/25/2005 5 SCM 0 0 0 0
61 5/13/2005 16 0 0 0 0 0
62 3/22/2005 16 SCM 0 0 0 0
63 4/12/2005 16 SCM 0 2932 2443 489
64 4/5/2005 16 SCM 0 2934574 2445478 489096
65 6/22/2005 16 0 0 0 489 0
66 8/19/2005 16 0 0 0 0 0
67 7/6/2005 13 0 0 0 0 0
68 3/22/2005 13 SCM 0 0 0 0
69 4/5/2005 5 0 0 0 3015 9044
70 4/21/2005 5 0 0 0 0 0
71 4/5/2005 5 SCM 0 0 5909 3939
72 4/12/2005 5 0 0 3909 1954 1954
73 6/7/2005 5 0 0 0 0 0
74 9/20/2005 5 0 0 0 0 0
75 3/22/2005 5 SCM 0 0 977 0
76 5/25/2005 5 0 0 0 0 0
77 4/5/2005 5 SCM 0 0 977214 0
78 8/19/2005 5 SCM 0 0 0 0
79 8/19/2005 5 0 0 1954 1954 0
80 6/22/2005 5 SCM 0 0 0 0
46
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 21 22 23 24
81 4/5/2005 16 0 0 0 2079 8317
82 6/22/2005 16 0 0 0 0 0
83 4/12/2005 16 0 0 17590 0 977
84 4/21/2005 16 SCM 0 0 1954 0
85 4/12/2005 16 SCM 0 0 977 3909
86 6/22/2005 13 0 0 0 0 0
87 8/19/2005 13 SCM 0 0 0 0
88 4/12/2005 5 SCM 0 25408 15635 0
89 7/6/2005 5 0 0 0 0 0
90 4/12/2005 13 0 0 0 499 0
47
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 25 26 27 28
1 3/15/2005 2 0 0 0 0 0
2 3/15/2005 2 SCM 0 0 0 0
3 3/22/2005 2 0 0 0 0 0
4 3/22/2005 2 SCM 0 0 0 1954
5 4/5/2005 2 0 0 0 0 0
6 4/5/2005 2 SCM 1954 0 0 0
7 4/12/2005 2 SCM 0 0 0 0
8 4/21/2005 2 0 1954 0 0 2932
9 4/21/2005 2 SCM 0 0 0 0
10 5/13/2005 2 0 0 0 0 0
11 5/13/2005 2 SCM 2000 0 0 4000
12 5/25/2005 2 0 0 0 0 0
13 5/25/2005 2 SCM 3909 0 0 0
14 6/7/2005 2 0 0 0 0 0
15 6/22/2005 2 0 0 0 0 0
16 6/22/2005 2 SCM 0 0 0 2932
17 7/6/2005 2 0 0 0 0 3909
18 7/6/2005 2 SCM 0 0 0 0
19 8/19/2005 2 0 0 0 0 0
20 8/19/2005 2 SCM 0 0 0 0
21 9/20/2005 2 0 0 0 0 0
22 3/15/2005 5 SCM 0 0 0 0
23 5/13/2005 5 0 0 0 0 0
24 5/13/2005 5 SCM 0 0 0 0
25 5/25/2005 5 0 0 0 0 0
26 3/15/2005 16 SCM 2932 0 0 0
27 5/13/2005 16 0 1954 0 0 56724
28 3/15/2005 13 0 0 0 0 0
29 3/15/2005 13 SCM 2954 0 0 0
30 3/22/2005 13 0 0 0 0 0
31 4/21/2005 13 SCM 0 0 0 0
32 6/7/2005 13 0 0 0 0 2443
33 3/15/2005 16 0 0 0 0 0
34 3/15/2005 16 SCM 0 0 0 0
35 3/22/2005 16 0 0 0 0 0
36 6/7/2005 16 0 489 0 0 0
37 4/12/2005 13 SCM 0 0 0 499
38 9/20/2005 13 0 0 0 0 0
39 9/20/2005 16 0 0 0 0 0
40 7/6/2005 16 SCM 0 0 0 0
48
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 25 26 27 28
41 4/21/2005 16 0 0 0 0 977
42 4/12/2005 16 0 1954 0 0 7818
43 4/5/2005 16 0 0 0 0 0
44 5/25/2005 16 0 0 0 0 0
45 5/25/2005 13 SCM 0 0 0 0
46 5/25/2005 16 SCM 0 0 0 1954
47 5/25/2005 13 0 0 0 0 0
48 5/25/2005 16 SCM 0 0 0 1466
49 5/13/2005 16 SCM 0 0 0 5863
50 3/15/2005 13 0 0 0 0 0
51 3/15/2005 13 SCM 2954 0 0 0
52 3/15/2005 16 0 0 0 0 0
53 7/6/2005 16 0 0 0 0 0
54 3/22/2005 5 0 0 0 0 3939
55 4/5/2005 13 SCM 0 9848 0 13787
56 7/6/2005 13 0 0 0 0 0
57 6/22/2005 16 SCM 0 0 0 0
58 8/19/2005 16 SCM 0 0 0 0
59 8/19/2005 16 0 0 0 0 0
60 5/25/2005 5 SCM 0 0 0 0
61 5/13/2005 16 0 0 0 0 0
62 3/22/2005 16 SCM 3909 0 0 0
63 4/12/2005 16 SCM 977 0 0 0
64 4/5/2005 16 SCM 4887047 0 0 0
65 6/22/2005 16 0 0 0 0 977
66 8/19/2005 16 0 0 0 0 0
67 7/6/2005 13 0 0 0 0 0
68 3/22/2005 13 SCM 0 0 0 0
69 4/5/2005 5 0 1507 0 0 0
70 4/21/2005 5 0 0 0 0 1954
71 4/5/2005 5 SCM 0 0 0 0
72 4/12/2005 5 0 0 0 0 0
73 6/7/2005 5 0 0 0 0 0
74 9/20/2005 5 0 0 0 0 489
75 3/22/2005 5 SCM 0 0 0 0
76 5/25/2005 5 0 0 0 0 0
77 4/5/2005 5 SCM 0 0 0 0
78 8/19/2005 5 SCM 0 0 0 0
79 8/19/2005 5 0 0 0 0 0
80 6/22/2005 5 SCM 0 0 0 0
49
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 25 26 27 28
81 4/5/2005 16 0 0 0 0 10396
82 6/22/2005 16 0 0 0 0 0
83 4/12/2005 16 0 3909 0 0 20521
84 4/21/2005 16 SCM 0 0 0 0
85 4/12/2005 16 SCM 1954 0 0 3909
86 6/22/2005 13 0 0 0 0 0
87 8/19/2005 13 SCM 0 0 0 0
88 4/12/2005 5 SCM 1954 0 7818 19544
89 7/6/2005 5 0 0 0 0 0
90 4/12/2005 13 0 997 0 0 1496
50
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 29 30 31 32
1 3/15/2005 2 0 0 0 112266 5909
2 3/15/2005 2 SCM 0 0 5909 47270
3 3/22/2005 2 0 0 0 13506 0
4 3/22/2005 2 SCM 0 977 6753 0
5 4/5/2005 2 0 0 15757 70905 0
6 4/5/2005 2 SCM 0 1954 9772 0
7 4/12/2005 2 SCM 0 0 2443 0
8 4/21/2005 2 0 0 10749 10749 0
9 4/21/2005 2 SCM 0 0 26726 1000
10 5/13/2005 2 0 0 1466 74281 1466
11 5/13/2005 2 SCM 0 2000 1000 0
12 5/25/2005 2 0 0 0 36930 0
13 5/25/2005 2 SCM 0 977 0 0
14 6/7/2005 2 0 0 0 10261 0
15 6/22/2005 2 0 0 977 114798 0
16 6/22/2005 2 SCM 0 0 15635 0
17 7/6/2005 2 0 0 20259 13506 0
18 7/6/2005 2 SCM 0 0 55148 0
19 8/19/2005 2 0 0 0 55542 0
20 8/19/2005 2 SCM 0 0 602691 0
21 9/20/2005 2 0 0 0 70905 0
22 3/15/2005 5 SCM 0 0 13681 0
23 5/13/2005 5 0 0 47270 106357 0
24 5/13/2005 5 SCM 977 15757 9000 1000
25 5/25/2005 5 0 0 0 20033 0
26 3/15/2005 16 SCM 0 0 59087 0
27 5/13/2005 16 0 0 3909 33089 0
28 3/15/2005 13 0 0 0 65451 0
29 3/15/2005 13 SCM 0 0 11817 0
30 3/22/2005 13 0 0 0 0 0
31 4/21/2005 13 SCM 0 0 7878 5909
32 6/7/2005 13 0 0 0 7818 1466
33 3/15/2005 16 0 0 0 1954 0
34 3/15/2005 16 SCM 0 0 0 0
35 3/22/2005 16 0 29089 0 3636 0
36 6/7/2005 16 0 489 0 489 0
37 4/12/2005 13 SCM 0 1496 0 0
38 9/20/2005 13 0 0 0 977 977
39 9/20/2005 16 0 0 0 47270 0
40 7/6/2005 16 SCM 0 0 1466 0
51
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 29 30 31 32
41 4/21/2005 16 0 0 0 37816 0
42 4/12/2005 16 0 0 0 7878 0
43 4/5/2005 16 0 0 7090 18908 0
44 5/25/2005 16 0 0 0 75632 0
45 5/25/2005 13 SCM 0 0 0 0
46 5/25/2005 16 SCM 0 0 27011 0
47 5/25/2005 13 0 0 0 4886 0
48 5/25/2005 16 SCM 0 0 0 0
49 5/13/2005 16 SCM 0 21499 23635 0
50 3/15/2005 13 0 0 0 65451 0
51 3/15/2005 13 SCM 0 0 11817 0
52 3/15/2005 16 0 0 0 1954 0
53 7/6/2005 16 0 0 0 977 0
54 3/22/2005 5 0 0 0 15757 0
55 4/5/2005 13 SCM 0 5909 18908 0
56 7/6/2005 13 0 0 0 16078 0
57 6/22/2005 16 SCM 0 0 7878 0
58 8/19/2005 16 SCM 0 2932 0 0
59 8/19/2005 16 0 0 0 122902 0
60 5/25/2005 5 SCM 0 0 0 0
61 5/13/2005 16 0 0 3909 23635 0
62 3/22/2005 16 SCM 0 0 141810 0
63 4/12/2005 16 SCM 0 0 11817 0
64 4/5/2005 16 SCM 0 0 1536390 0
65 6/22/2005 16 0 0 0 33764 0
66 8/19/2005 16 0 0 0 425429 0
67 7/6/2005 13 0 0 0 15757 0
68 3/22/2005 13 SCM 0 0 35452 0
69 4/5/2005 5 0 0 0 7537 0
70 4/21/2005 5 0 0 1954 20259 0
71 4/5/2005 5 SCM 0 9848 31513 0
72 4/12/2005 5 0 0 0 17590 0
73 6/7/2005 5 0 0 0 56724 0
74 9/20/2005 5 0 0 0 1954 0
75 3/22/2005 5 SCM 0 0 5863 0
76 5/25/2005 5 0 0 0 2443 0
77 4/5/2005 5 SCM 0 0 8308762 0
78 8/19/2005 5 SCM 0 0 63027 0
79 8/19/2005 5 0 0 23453 0 977
80 6/22/2005 5 SCM 0 0 47270 13506
52
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 29 30 31 32
81 4/5/2005 16 0 0 0 8381 0
82 6/22/2005 16 0 0 0 126053 3257
83 4/12/2005 16 0 0 11727 0 0
84 4/21/2005 16 SCM 0 0 9772 0
85 4/12/2005 16 SCM 0 6840 28362 0
86 6/22/2005 13 0 0 0 52522 0
87 8/19/2005 13 SCM 0 0 11817 0
88 4/12/2005 5 SCM 0 3909 9772 0
89 7/6/2005 5 0 0 7818 0 0
90 4/12/2005 13 0 0 0 4487 0
53
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 33 34 35 36
1 3/15/2005 2 0 0 0 0 0
2 3/15/2005 2 SCM 0 0 0 11817
3 3/22/2005 2 0 0 0 0 977
4 3/22/2005 2 SCM 0 0 489 0
5 4/5/2005 2 0 977 0 977 31513
6 4/5/2005 2 SCM 0 0 0 46906
7 4/12/2005 2 SCM 0 0 0 977
8 4/21/2005 2 0 0 0 977 17590
9 4/21/2005 2 SCM 0 5000 0 42998
10 5/13/2005 2 0 977 0 6753 607756
11 5/13/2005 2 SCM 0 1000 2000 26000
12 5/25/2005 2 0 0 0 1222 7386
13 5/25/2005 2 SCM 0 0 0 81109
14 6/7/2005 2 0 0 0 0 2443
15 6/22/2005 2 0 0 0 0 94540
16 6/22/2005 2 SCM 0 0 0 26385
17 7/6/2005 2 0 0 977 977 573991
18 7/6/2005 2 SCM 0 0 0 1536271
19 8/19/2005 2 0 0 0 0 0
20 8/19/2005 2 SCM 0 0 0 47270
21 9/20/2005 2 0 0 0 5909 7878
22 3/15/2005 5 SCM 0 0 0 12704
23 5/13/2005 5 0 0 0 489 277711
24 5/13/2005 5 SCM 15757 5000 977 208836
25 5/25/2005 5 0 0 0 1466 18908
26 3/15/2005 16 SCM 0 0 0 59087
27 5/13/2005 16 0 0 0 0 198534
28 3/15/2005 13 0 0 0 0 0
29 3/15/2005 13 SCM 0 0 0 20681
30 3/22/2005 13 0 0 0 0 0
31 4/21/2005 13 SCM 0 0 977 102418
32 6/7/2005 13 0 0 0 0 15635
33 3/15/2005 16 0 0 0 0 977
34 3/15/2005 16 SCM 0 0 489 10504
35 3/22/2005 16 0 0 0 0 43634
36 6/7/2005 16 0 0 0 489 2443
37 4/12/2005 13 SCM 499 0 0 20672
38 9/20/2005 13 0 0 0 0 4886
39 9/20/2005 16 0 0 0 0 3909
40 7/6/2005 16 SCM 0 0 0 3909
54
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 33 34 35 36
41 4/21/2005 16 0 0 0 0 18908
42 4/12/2005 16 0 0 0 0 15757
43 4/5/2005 16 0 0 0 0 94540
44 5/25/2005 16 0 0 0 0 85086
45 5/25/2005 13 SCM 0 0 0 5375
46 5/25/2005 16 SCM 0 0 0 195832
47 5/25/2005 13 0 0 0 0 10504
48 5/25/2005 16 SCM 0 0 977 15757
49 5/13/2005 16 SCM 0 0 1954 106357
50 3/15/2005 13 0 0 0 0 10908
51 3/15/2005 13 SCM 0 0 0 20681
52 3/15/2005 16 0 0 0 0 977
53 7/6/2005 16 0 0 0 0 489
54 3/22/2005 5 0 0 0 0 23635
55 4/5/2005 13 SCM 0 1970 0 56724
56 7/6/2005 13 0 0 0 0 48235
57 6/22/2005 16 SCM 0 0 0 23635
58 8/19/2005 16 SCM 0 0 0 0
59 8/19/2005 16 0 0 0 0 1954
60 5/25/2005 5 SCM 0 977 977 132356
61 5/13/2005 16 0 0 0 0 78783
62 3/22/2005 16 SCM 0 0 977 0
63 4/12/2005 16 SCM 0 489 489 12704
64 4/5/2005 16 SCM 0 489096 1466310 12716486
65 6/22/2005 16 0 0 0 0 489
66 8/19/2005 16 0 0 0 489 47270
67 7/6/2005 13 0 0 0 0 47270
68 3/22/2005 13 SCM 0 0 977 35452
69 4/5/2005 5 0 0 0 1507 6029
70 4/21/2005 5 0 0 0 977 27011
71 4/5/2005 5 SCM 0 0 0 110296
72 4/12/2005 5 0 0 0 0 23635
73 6/7/2005 5 0 0 0 0 3909
74 9/20/2005 5 0 0 489 0 5909
75 3/22/2005 5 SCM 0 0 0 4886
76 5/25/2005 5 0 0 0 977 18908
77 4/5/2005 5 SCM 0 0 978191 23812933
78 8/19/2005 5 SCM 0 0 0 15757
79 8/19/2005 5 0 0 0 0 3190717
80 6/22/2005 5 SCM 0 0 0 20259
55
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 33 34 35 36
81 4/5/2005 16 0 0 0 0 16762
82 6/22/2005 16 0 0 0 0 173323
83 4/12/2005 16 0 0 0 0 47270
84 4/21/2005 16 SCM 0 0 0 55148
85 4/12/2005 16 SCM 0 0 0 6840
86 6/22/2005 13 0 0 0 0 0
87 8/19/2005 13 SCM 0 0 0 2954
88 4/12/2005 5 SCM 0 0 0 67528
89 7/6/2005 5 0 0 0 0 1389735
90 4/12/2005 13 0 0 499 0 56274
56
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 37 38 39 40
1 3/15/2005 2 0 0 0 0 0
2 3/15/2005 2 SCM 0 0 76814 0
3 3/22/2005 2 0 0 977 0 0
4 3/22/2005 2 SCM 0 0 13506 0
5 4/5/2005 2 0 0 0 78783 0
6 4/5/2005 2 SCM 0 0 0 0
7 4/12/2005 2 SCM 0 0 35452 0
8 4/21/2005 2 0 0 489 0 0
9 4/21/2005 2 SCM 0 0 8727 2000
10 5/13/2005 2 0 0 0 13506 0
11 5/13/2005 2 SCM 0 0 0 1000
12 5/25/2005 2 0 611 0 0 0
13 5/25/2005 2 SCM 3909 0 3909 0
14 6/7/2005 2 0 0 0 0 0
15 6/22/2005 2 0 0 0 0 0
16 6/22/2005 2 SCM 0 0 0 0
17 7/6/2005 2 0 0 0 0 0
18 7/6/2005 2 SCM 0 0 20521 0
19 8/19/2005 2 0 0 0 4727 0
20 8/19/2005 2 SCM 0 0 0 0
21 9/20/2005 2 0 0 0 0 0
22 3/15/2005 5 SCM 0 0 28339 0
23 5/13/2005 5 0 0 489 82722 0
24 5/13/2005 5 SCM 0 2932 94540 2000
25 5/25/2005 5 0 0 0 0 0
26 3/15/2005 16 SCM 0 0 58633 977
27 5/13/2005 16 0 0 2932 47270 2932
28 3/15/2005 13 0 0 489 10749 0
29 3/15/2005 13 SCM 1477 0 20681 4432
30 3/22/2005 13 0 0 0 0 0
31 4/21/2005 13 SCM 0 489 0 0
32 6/7/2005 13 0 0 489 0 0
33 3/15/2005 16 0 0 0 0 0
34 3/15/2005 16 SCM 0 0 4886 0
35 3/22/2005 16 0 0 0 36361 3636
36 6/7/2005 16 0 0 0 0 0
37 4/12/2005 13 SCM 0 0 9972 0
38 9/20/2005 13 0 0 0 0 0
39 9/20/2005 16 0 0 0 0 0
40 7/6/2005 16 SCM 0 0 0 0
57
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 37 38 39 40
41 4/21/2005 16 0 0 0 0 0
42 4/12/2005 16 0 0 0 0 0
43 4/5/2005 16 0 0 0 0 0
44 5/25/2005 16 0 1954 0 0 0
45 5/25/2005 13 SCM 0 0 0 0
46 5/25/2005 16 SCM 0 0 0 0
47 5/25/2005 13 0 0 0 0 0
48 5/25/2005 16 SCM 0 0 0 0
49 5/13/2005 16 SCM 5863 1954 7818 0
50 3/15/2005 13 0 0 489 10749 0
51 3/15/2005 13 SCM 1477 0 20681 0
52 3/15/2005 16 0 0 0 0 0
53 7/6/2005 16 0 0 0 0 0
54 3/22/2005 5 0 0 985 0 0
55 4/5/2005 13 SCM 0 1970 37816 1970
56 7/6/2005 13 0 249 0 0 249
57 6/22/2005 16 SCM 0 0 0 0
58 8/19/2005 16 SCM 0 0 0 0
59 8/19/2005 16 0 0 0 0 0
60 5/25/2005 5 SCM 977 0 0 0
61 5/13/2005 16 0 3909 0 0 0
62 3/22/2005 16 SCM 0 977 94540 0
63 4/12/2005 16 SCM 0 0 6352 0
64 4/5/2005 16 SCM 0 977214 100898016 0
65 6/22/2005 16 0 0 0 0 0
66 8/19/2005 16 0 0 0 0 0
67 7/6/2005 13 0 244 0 0 244
68 3/22/2005 13 SCM 0 0 9772 0
69 4/5/2005 5 0 0 1507 13566 0
70 4/21/2005 5 0 0 0 0 0
71 4/5/2005 5 SCM 0 3939 51209 3939
72 4/12/2005 5 0 0 0 0 0
73 6/7/2005 5 0 0 0 0 0
74 9/20/2005 5 0 0 0 0 0
75 3/22/2005 5 SCM 0 0 0 0
76 5/25/2005 5 0 0 0 0 0
77 4/5/2005 5 SCM 0 0 0 0
78 8/19/2005 5 SCM 0 0 0 0
79 8/19/2005 5 0 0 0 307254 0
80 6/22/2005 5 SCM 0 0 0 0
58
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 37 38 39 40
81 4/5/2005 16 0 2079 0 0 0
82 6/22/2005 16 0 0 0 0 0
83 4/12/2005 16 0 0 0 0 0
84 4/21/2005 16 SCM 0 0 0 0
85 4/12/2005 16 SCM 0 0 0 4886
86 6/22/2005 13 0 0 0 0 0
87 8/19/2005 13 SCM 0 0 0 0
88 4/12/2005 5 SCM 0 0 27011 0
89 7/6/2005 5 0 0 0 113448 0
90 4/12/2005 13 0 0 0 80391 0
59
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 41 42 43 44
1 3/15/2005 2 0 0 0 0 0
2 3/15/2005 2 SCM 0 11817 0 0
3 3/22/2005 2 0 0 0 0 0
4 3/22/2005 2 SCM 0 1954 0 0
5 4/5/2005 2 0 0 0 0 23635
6 4/5/2005 2 SCM 0 0 0 0
7 4/12/2005 2 SCM 0 23635 0 0
8 4/21/2005 2 0 0 977 0 0
9 4/21/2005 2 SCM 0 2000 0 0
10 5/13/2005 2 0 0 1000 0 0
11 5/13/2005 2 SCM 0 0 0 0
12 5/25/2005 2 0 0 0 0 0
13 5/25/2005 2 SCM 0 9454 0 0
14 6/7/2005 2 0 0 0 0 0
15 6/22/2005 2 0 0 0 0 0
16 6/22/2005 2 SCM 0 0 0 0
17 7/6/2005 2 0 0 977 0 0
18 7/6/2005 2 SCM 0 0 0 0
19 8/19/2005 2 0 0 11727 0 0
20 8/19/2005 2 SCM 0 82722 0 0
21 9/20/2005 2 0 0 31513 0 0
22 3/15/2005 5 SCM 0 0 0 0
23 5/13/2005 5 0 0 0 0 11817
24 5/13/2005 5 SCM 0 0 0 0
25 5/25/2005 5 0 0 977 0 0
26 3/15/2005 16 SCM 1954 0 0 0
27 5/13/2005 16 0 0 0 0 0
28 3/15/2005 13 0 0 0 0 0
29 3/15/2005 13 SCM 0 0 0 0
30 3/22/2005 13 0 0 0 0 0
31 4/21/2005 13 SCM 0 0 0 15757
32 6/7/2005 13 0 0 0 0 0
33 3/15/2005 16 0 0 489 0 0
34 3/15/2005 16 SCM 0 0 0 0
35 3/22/2005 16 0 0 7272 0 0
36 6/7/2005 16 0 0 0 0 0
37 4/12/2005 13 SCM 0 0 0 0
38 9/20/2005 13 0 0 0 0 0
39 9/20/2005 16 0 0 0 0 0
40 7/6/2005 16 SCM 0 0 0 0
60
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 41 42 43 44
41 4/21/2005 16 0 0 0 0 0
42 4/12/2005 16 0 0 0 0 0
43 4/5/2005 16 0 0 0 0 30725
44 5/25/2005 16 0 0 0 0 0
45 5/25/2005 13 SCM 0 0 0 0
46 5/25/2005 16 SCM 0 0 0 0
47 5/25/2005 13 0 0 0 0 0
48 5/25/2005 16 SCM 0 0 0 0
49 5/13/2005 16 SCM 0 0 0 0
50 3/15/2005 13 0 0 0 0 0
51 3/15/2005 13 SCM 4432 0 0 0
52 3/15/2005 16 0 0 489 0 0
53 7/6/2005 16 0 0 0 0 0
54 3/22/2005 5 0 0 0 0 0
55 4/5/2005 13 SCM 3939 0 0 11817
56 7/6/2005 13 0 0 0 0 0
57 6/22/2005 16 SCM 0 0 1970 0
58 8/19/2005 16 SCM 0 0 0 0
59 8/19/2005 16 0 0 0 0 0
60 5/25/2005 5 SCM 0 0 0 0
61 5/13/2005 16 0 0 2932 0 0
62 3/22/2005 16 SCM 0 0 0 0
63 4/12/2005 16 SCM 0 0 0 3909
64 4/5/2005 16 SCM 0 0 0 3912765
65 6/22/2005 16 0 0 489 2000 0
66 8/19/2005 16 0 0 0 0 0
67 7/6/2005 13 0 0 0 0 0
68 3/22/2005 13 SCM 0 0 489 0
69 4/5/2005 5 0 0 0 0 0
70 4/21/2005 5 0 0 0 1954 0
71 4/5/2005 5 SCM 0 0 0 3939
72 4/12/2005 5 0 0 0 0 0
73 6/7/2005 5 0 0 1954 0 0
74 9/20/2005 5 0 0 0 0 0
75 3/22/2005 5 SCM 0 977 0 0
76 5/25/2005 5 0 0 977 0 0
77 4/5/2005 5 SCM 0 1955405 0 0
78 8/19/2005 5 SCM 0 15757 2000 0
79 8/19/2005 5 0 3909 0 0 0
80 6/22/2005 5 SCM 0 0 0 0
61
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 41 42 43 44
81 4/5/2005 16 0 0 0 0 0
82 6/22/2005 16 0 814 0 0 0
83 4/12/2005 16 0 6840 1954 0 0
84 4/21/2005 16 SCM 23453 0 0 0
85 4/12/2005 16 SCM 1954 0 0 0
86 6/22/2005 13 0 0 0 3257 1629
87 8/19/2005 13 SCM 0 10340 0 0
88 4/12/2005 5 SCM 1954 0 1477 0
89 7/6/2005 5 0 0 0 0 0
90 4/12/2005 13 0 0 1496 0 1496
62
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 45 46 47 48
1 3/15/2005 2 0 1954 0 0 0
2 3/15/2005 2 SCM 7818 977 0 0
3 3/22/2005 2 0 0 0 0 0
4 3/22/2005 2 SCM 0 489 0 0
5 4/5/2005 2 0 15757 0 0 0
6 4/5/2005 2 SCM 0 0 0 0
7 4/12/2005 2 SCM 0 0 0 0
8 4/21/2005 2 0 977 0 0 0
9 4/21/2005 2 SCM 2182 0 0 0
10 5/13/2005 2 0 5000 4000 0 0
11 5/13/2005 2 SCM 3000 0 0 0
12 5/25/2005 2 0 0 0 0 0
13 5/25/2005 2 SCM 0 0 0 0
14 6/7/2005 2 0 2443 0 0 0
15 6/22/2005 2 0 0 0 0 0
16 6/22/2005 2 SCM 1954 0 0 489
17 7/6/2005 2 0 3909 0 0 0
18 7/6/2005 2 SCM 0 0 0 0
19 8/19/2005 2 0 5909 0 0 0
20 8/19/2005 2 SCM 2443 11817 0 0
21 9/20/2005 2 0 3939 0 0 0
22 3/15/2005 5 SCM 977 0 0 0
23 5/13/2005 5 0 0 0 0 0
24 5/13/2005 5 SCM 1000 0 0 0
25 5/25/2005 5 0 0 489 0 0
26 3/15/2005 16 SCM 2932 0 0 0
27 5/13/2005 16 0 0 0 0 0
28 3/15/2005 13 0 7272 977 0 0
29 3/15/2005 13 SCM 4432 0 0 0
30 3/22/2005 13 0 0 0 0 0
31 4/21/2005 13 SCM 5000 0 0 0
32 6/7/2005 13 0 977 489 0 0
33 3/15/2005 16 0 3420 489 0 0
34 3/15/2005 16 SCM 0 0 0 0
35 3/22/2005 16 0 3420 0 0 0
36 6/7/2005 16 0 0 977 0 0
37 4/12/2005 13 SCM 0 0 0 0
38 9/20/2005 13 0 13787 4886 0 0
39 9/20/2005 16 0 23453 1954 0 0
40 7/6/2005 16 SCM 489 0 0 0
63
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 45 46 47 48
41 4/21/2005 16 0 2932 0 0 0
42 4/12/2005 16 0 39089 0 0 0
43 4/5/2005 16 0 49633 0 0 0
44 5/25/2005 16 0 0 0 0 0
45 5/25/2005 13 SCM 0 0 489 0
46 5/25/2005 16 SCM 0 0 0 0
47 5/25/2005 13 0 977 0 0 0
48 5/25/2005 16 SCM 0 0 0 0
49 5/13/2005 16 SCM 0 0 0 0
50 3/15/2005 13 0 7272 0 0 0
51 3/15/2005 13 SCM 4432 0 0 0
52 3/15/2005 16 0 3420 489 0 0
53 7/6/2005 16 0 0 0 0 0
54 3/22/2005 5 0 12802 2954 0 0
55 4/5/2005 13 SCM 0 0 0 0
56 7/6/2005 13 0 0 0 0 0
57 6/22/2005 16 SCM 489 0 0 0
58 8/19/2005 16 SCM 3909 2932 0 0
59 8/19/2005 16 0 0 0 0 0
60 5/25/2005 5 SCM 977 0 0 0
61 5/13/2005 16 0 977 0 0 0
62 3/22/2005 16 SCM 977 0 0 0
63 4/12/2005 16 SCM 489 0 0 0
64 4/5/2005 16 SCM 1466310 0 0 0
65 6/22/2005 16 0 489 0 0 0
66 8/19/2005 16 0 0 0 0 0
67 7/6/2005 13 0 0 0 0 0
68 3/22/2005 13 SCM 977 11817 0 0
69 4/5/2005 5 0 0 0 0 0
70 4/21/2005 5 0 0 0 0 0
71 4/5/2005 5 SCM 9848 0 0 0
72 4/12/2005 5 0 11727 0 0 0
73 6/7/2005 5 0 1954 1954 0 0
74 9/20/2005 5 0 23635 0 0 0
75 3/22/2005 5 SCM 11727 0 0 0
76 5/25/2005 5 0 0 489 0 0
77 4/5/2005 5 SCM 11726568 489096 0 0
78 8/19/2005 5 SCM 2443 0 0 0
79 8/19/2005 5 0 977 0 0 0
80 6/22/2005 5 SCM 1954 0 0 0
64
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 45 46 47 48
81 4/5/2005 16 0 0 0 0 0
82 6/22/2005 16 0 2443 0 0 0
83 4/12/2005 16 0 9772 0 0 0
84 4/21/2005 16 SCM 0 0 0 0
85 4/12/2005 16 SCM 0 0 0 0
86 6/22/2005 13 0 0 0 0 0
87 8/19/2005 13 SCM 1477 0 0 0
88 4/12/2005 5 SCM 13681 0 0 0
89 7/6/2005 5 0 977 0 0 0
90 4/12/2005 13 0 997 0 0 0
65
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 49 50 51 52
1 3/15/2005 2 0 0 0 0 0
2 3/15/2005 2 SCM 0 0 0 0
3 3/22/2005 2 0 0 1954 0 0
4 3/22/2005 2 SCM 0 0 0 0
5 4/5/2005 2 0 0 977 0 977
6 4/5/2005 2 SCM 0 977 0 0
7 4/12/2005 2 SCM 0 0 0 0
8 4/21/2005 2 0 0 977 0 0
9 4/21/2005 2 SCM 0 0 1000 0
10 5/13/2005 2 0 0 7420 1000 0
11 5/13/2005 2 SCM 0 2000 0 1000
12 5/25/2005 2 0 0 1222 0 0
13 5/25/2005 2 SCM 0 0 0 0
14 6/7/2005 2 0 0 489 0 0
15 6/22/2005 2 0 489 0 0 0
16 6/22/2005 2 SCM 0 0 0 0
17 7/6/2005 2 0 0 2932 0 0
18 7/6/2005 2 SCM 0 489 0 0
19 8/19/2005 2 0 0 0 0 0
20 8/19/2005 2 SCM 0 11817 0 0
21 9/20/2005 2 0 0 0 0 0
22 3/15/2005 5 SCM 0 0 0 0
23 5/13/2005 5 0 0 0 0 0
24 5/13/2005 5 SCM 0 0 1000 0
25 5/25/2005 5 0 0 0 977 0
26 3/15/2005 16 SCM 0 0 0 0
27 5/13/2005 16 0 0 0 0 0
28 3/15/2005 13 0 0 0 0 0
29 3/15/2005 13 SCM 0 0 0 0
30 3/22/2005 13 0 0 0 0 0
31 4/21/2005 13 SCM 0 977 0 0
32 6/7/2005 13 0 0 0 0 0
33 3/15/2005 16 0 0 1466 0 0
34 3/15/2005 16 SCM 0 0 0 0
35 3/22/2005 16 0 0 0 0 0
36 6/7/2005 16 0 0 0 0 0
37 4/12/2005 13 SCM 0 0 0 0
38 9/20/2005 13 0 0 0 0 0
39 9/20/2005 16 0 0 0 0 0
40 7/6/2005 16 SCM 0 0 0 0
66
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 49 50 51 52
41 4/21/2005 16 0 0 0 0 0
42 4/12/2005 16 0 0 9772 0 0
43 4/5/2005 16 0 0 7090 0 0
44 5/25/2005 16 0 0 0 0 0
45 5/25/2005 13 SCM 0 0 0 0
46 5/25/2005 16 SCM 0 0 0 0
47 5/25/2005 13 0 0 0 0 0
48 5/25/2005 16 SCM 0 0 0 0
49 5/13/2005 16 SCM 0 0 0 0
50 3/15/2005 13 0 0 977 0 0
51 3/15/2005 13 SCM 0 0 0 0
52 3/15/2005 16 0 0 1466 0 0
53 7/6/2005 16 0 0 0 0 0
54 3/22/2005 5 0 0 4924 0 985
55 4/5/2005 13 SCM 0 1970 0 0
56 7/6/2005 13 0 0 0 0 0
57 6/22/2005 16 SCM 0 0 0 0
58 8/19/2005 16 SCM 0 0 0 977
59 8/19/2005 16 0 0 0 0 0
60 5/25/2005 5 SCM 0 0 0 977
61 5/13/2005 16 0 0 977 0 3909
62 3/22/2005 16 SCM 0 0 0 0
63 4/12/2005 16 SCM 0 0 0 0
64 4/5/2005 16 SCM 0 0 0 0
65 6/22/2005 16 0 0 0 0 0
66 8/19/2005 16 0 0 0 0 0
67 7/6/2005 13 0 0 0 0 0
68 3/22/2005 13 SCM 0 0 0 0
69 4/5/2005 5 0 0 3015 0 0
70 4/21/2005 5 0 0 0 0 0
71 4/5/2005 5 SCM 0 0 0 0
72 4/12/2005 5 0 0 3909 0 0
73 6/7/2005 5 0 0 0 0 0
74 9/20/2005 5 0 0 0 0 0
75 3/22/2005 5 SCM 0 0 0 0
76 5/25/2005 5 0 0 977 977 0
77 4/5/2005 5 SCM 0 978191 978191 0
78 8/19/2005 5 SCM 0 0 0 0
79 8/19/2005 5 0 0 0 0 0
80 6/22/2005 5 SCM 0 0 0 0
67
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 49 50 51 52
81 4/5/2005 16 0 0 0 0 0
82 6/22/2005 16 0 0 4072 0 0
83 4/12/2005 16 0 0 2932 0 0
84 4/21/2005 16 SCM 0 1954 0 0
85 4/12/2005 16 SCM 0 0 0 0
86 6/22/2005 13 0 0 0 0 0
87 8/19/2005 13 SCM 0 0 0 0
88 4/12/2005 5 SCM 0 1954 0 0
89 7/6/2005 5 0 0 1954 0 0
90 4/12/2005 13 0 0 0 0 0
68
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 53 54 55 56
1 3/15/2005 2 0 0 0 0 0
2 3/15/2005 2 SCM 0 0 0 0
3 3/22/2005 2 0 0 0 0 0
4 3/22/2005 2 SCM 0 0 0 0
5 4/5/2005 2 0 0 0 0 0
6 4/5/2005 2 SCM 0 0 0 0
7 4/12/2005 2 SCM 0 0 0 0
8 4/21/2005 2 0 0 0 0 0
9 4/21/2005 2 SCM 0 0 0 0
10 5/13/2005 2 0 0 0 0 5000
11 5/13/2005 2 SCM 0 0 0 0
12 5/25/2005 2 0 0 0 0 0
13 5/25/2005 2 SCM 0 0 0 0
14 6/7/2005 2 0 0 0 0 0
15 6/22/2005 2 0 0 0 0 0
16 6/22/2005 2 SCM 0 0 0 0
17 7/6/2005 2 0 0 0 0 0
18 7/6/2005 2 SCM 0 0 0 0
19 8/19/2005 2 0 0 0 0 0
20 8/19/2005 2 SCM 0 0 0 0
21 9/20/2005 2 0 0 0 0 0
22 3/15/2005 5 SCM 0 0 0 1954
23 5/13/2005 5 0 0 0 0 0
24 5/13/2005 5 SCM 0 0 4886 0
25 5/25/2005 5 0 0 0 0 0
26 3/15/2005 16 SCM 0 0 0 977
27 5/13/2005 16 0 0 0 0 0
28 3/15/2005 13 0 0 0 0 0
29 3/15/2005 13 SCM 0 0 0 1477
30 3/22/2005 13 0 0 0 0 0
31 4/21/2005 13 SCM 0 0 0 0
32 6/7/2005 13 0 0 0 0 0
33 3/15/2005 16 0 0 0 489 489
34 3/15/2005 16 SCM 0 0 0 0
35 3/22/2005 16 0 0 0 0 0
36 6/7/2005 16 0 0 0 0 0
37 4/12/2005 13 SCM 0 0 0 0
38 9/20/2005 13 0 0 0 0 0
39 9/20/2005 16 0 0 0 0 0
40 7/6/2005 16 SCM 0 0 489 0
69
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 53 54 55 56
41 4/21/2005 16 0 0 0 0 0
42 4/12/2005 16 0 0 0 0 0
43 4/5/2005 16 0 0 0 0 0
44 5/25/2005 16 0 0 1954 0 0
45 5/25/2005 13 SCM 0 0 0 0
46 5/25/2005 16 SCM 0 489 0 0
47 5/25/2005 13 0 0 0 0 0
48 5/25/2005 16 SCM 0 0 0 0
49 5/13/2005 16 SCM 0 0 0 0
50 3/15/2005 13 0 0 0 0 0
51 3/15/2005 13 SCM 0 0 0 1477
52 3/15/2005 16 0 0 0 489 489
53 7/6/2005 16 0 0 0 0 0
54 3/22/2005 5 0 0 0 0 0
55 4/5/2005 13 SCM 0 0 0 0
56 7/6/2005 13 0 0 0 0 0
57 6/22/2005 16 SCM 0 0 0 0
58 8/19/2005 16 SCM 0 0 0 0
59 8/19/2005 16 0 0 0 0 0
60 5/25/2005 5 SCM 0 0 0 0
61 5/13/2005 16 0 0 0 0 0
62 3/22/2005 16 SCM 0 0 0 0
63 4/12/2005 16 SCM 0 0 0 0
64 4/5/2005 16 SCM 0 0 0 0
65 6/22/2005 16 0 0 0 0 0
66 8/19/2005 16 0 0 0 0 0
67 7/6/2005 13 0 0 0 0 0
68 3/22/2005 13 SCM 0 0 0 0
69 4/5/2005 5 0 0 0 0 0
70 4/21/2005 5 0 0 0 0 0
71 4/5/2005 5 SCM 1970 0 0 0
72 4/12/2005 5 0 0 0 0 0
73 6/7/2005 5 0 0 0 0 0
74 9/20/2005 5 0 0 0 0 0
75 3/22/2005 5 SCM 0 0 0 0
76 5/25/2005 5 0 0 0 0 0
77 4/5/2005 5 SCM 0 0 0 0
78 8/19/2005 5 SCM 0 0 0 0
79 8/19/2005 5 0 0 0 0 0
80 6/22/2005 5 SCM 0 0 0 13681
70
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 53 54 55 56
81 4/5/2005 16 0 0 0 0 0
82 6/22/2005 16 0 0 0 0 814
83 4/12/2005 16 0 0 0 0 0
84 4/21/2005 16 SCM 0 0 0 0
85 4/12/2005 16 SCM 0 0 0 0
86 6/22/2005 13 0 0 0 0 814
87 8/19/2005 13 SCM 0 0 0 0
88 4/12/2005 5 SCM 0 0 0 0
89 7/6/2005 5 0 0 0 977 0
90 4/12/2005 13 0 0 0 0 0
71
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 57 58 59 60
1 3/15/2005 2 0 0 0 0 0
2 3/15/2005 2 SCM 35452 2932 29544 0
3 3/22/2005 2 0 0 977 0 0
4 3/22/2005 2 SCM 7329 0 489 3420
5 4/5/2005 2 0 0 0 0 1954
6 4/5/2005 2 SCM 0 0 0 0
7 4/12/2005 2 SCM 0 0 0 0
8 4/21/2005 2 0 0 0 0 0
9 4/21/2005 2 SCM 0 0 0 0
10 5/13/2005 2 0 8000 53000 0 3000
11 5/13/2005 2 SCM 0 0 0 0
12 5/25/2005 2 0 0 0 0 611
13 5/25/2005 2 SCM 977 0 0 0
14 6/7/2005 2 0 2932 0 0 0
15 6/22/2005 2 0 977 0 0 0
16 6/22/2005 2 SCM 4886 1954 0 977
17 7/6/2005 2 0 0 0 6753 9772
18 7/6/2005 2 SCM 0 0 0 489
19 8/19/2005 2 0 0 0 0 54360
20 8/19/2005 2 SCM 23635 0 489 3474337
21 9/20/2005 2 0 0 0 0 19696
22 3/15/2005 5 SCM 7818 0 0 0
23 5/13/2005 5 0 0 0 0 0
24 5/13/2005 5 SCM 0 0 0 0
25 5/25/2005 5 0 0 0 0 0
26 3/15/2005 16 SCM 19544 0 0 1954
27 5/13/2005 16 0 0 0 1954 977
28 3/15/2005 13 0 0 0 0 0
29 3/15/2005 13 SCM 14772 0 0 0
30 3/22/2005 13 0 0 0 0 0
31 4/21/2005 13 SCM 0 0 977 0
32 6/7/2005 13 0 0 0 0 0
33 3/15/2005 16 0 489 0 0 2443
34 3/15/2005 16 SCM 2932 8306 0 489
35 3/22/2005 16 0 0 489 0 0
36 6/7/2005 16 0 0 54023 0 6753
37 4/12/2005 13 SCM 0 0 0 499
38 9/20/2005 13 0 5863 0 0 145749
39 9/20/2005 16 0 1466 1466 0 87949
40 7/6/2005 16 SCM 977 0 0 489
72
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 57 58 59 60
41 4/21/2005 16 0 0 0 0 977
42 4/12/2005 16 0 0 0 0 11727
43 4/5/2005 16 0 0 0 0 0
44 5/25/2005 16 0 0 0 0 0
45 5/25/2005 13 SCM 2443 0 0 0
46 5/25/2005 16 SCM 81034 0 0 489
47 5/25/2005 13 0 3909 977 0 977
48 5/25/2005 16 SCM 63027 0 0 0
49 5/13/2005 16 SCM 0 0 0 3909
50 3/15/2005 13 0 0 0 0 0
51 3/15/2005 13 SCM 0 14772 0 0
52 3/15/2005 16 0 489 0 0 2443
53 7/6/2005 16 0 0 0 0 0
54 3/22/2005 5 0 0 0 0 0
55 4/5/2005 13 SCM 0 0 0 1970
56 7/6/2005 13 0 0 0 0 249
57 6/22/2005 16 SCM 0 0 0 0
58 8/19/2005 16 SCM 2932 0 0 30294
59 8/19/2005 16 0 0 0 0 2443
60 5/25/2005 5 SCM 0 0 0 0
61 5/13/2005 16 0 9772 0 0 4886
62 3/22/2005 16 SCM 11727 0 0 977
63 4/12/2005 16 SCM 0 0 0 1466
64 4/5/2005 16 SCM 11726568 0 0 2444501
65 6/22/2005 16 0 2932 0 0 0
66 8/19/2005 16 0 1466 0 0 489
67 7/6/2005 13 0 0 0 0 244
68 3/22/2005 13 SCM 8795 0 0 0
69 4/5/2005 5 0 0 0 0 0
70 4/21/2005 5 0 0 0 0 0
71 4/5/2005 5 SCM 0 0 0 1970
72 4/12/2005 5 0 0 0 0 0
73 6/7/2005 5 0 4886 0 0 1954
74 9/20/2005 5 0 0 0 0 9772
75 3/22/2005 5 SCM 9772 0 0 2932
76 5/25/2005 5 0 0 0 0 0
77 4/5/2005 5 SCM 9772140 0 0 2931642
78 8/19/2005 5 SCM 0 0 0 17590
79 8/19/2005 5 0 0 0 0 977
80 6/22/2005 5 SCM 0 11727 0 0
73
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 57 58 59 60
81 4/5/2005 16 0 8317 0 0 0
82 6/22/2005 16 0 12215 0 814 4072
83 4/12/2005 16 0 0 0 0 0
84 4/21/2005 16 SCM 0 0 0 0
85 4/12/2005 16 SCM 0 0 0 0
86 6/22/2005 13 0 4886 0 0 0
87 8/19/2005 13 SCM 0 0 0 45793
88 4/12/2005 5 SCM 0 0 0 0
89 7/6/2005 5 0 0 3909 0 0
90 4/12/2005 13 0 0 0 0 997
74
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 61 62 63 64
1 3/15/2005 2 0 5909 5909 0 1954
2 3/15/2005 2 SCM 0 0 0 977
3 3/22/2005 2 0 0 14658 0 2932
4 3/22/2005 2 SCM 0 489 0 6753
5 4/5/2005 2 0 0 31513 0 1954
6 4/5/2005 2 SCM 0 977 0 0
7 4/12/2005 2 SCM 0 1954 0 0
8 4/21/2005 2 0 0 0 1954 1954
9 4/21/2005 2 SCM 0 1000 0 0
10 5/13/2005 2 0 5489 20443 0 8000
11 5/13/2005 2 SCM 1000 0 0 2000
12 5/25/2005 2 0 0 295437 0 51701
13 5/25/2005 2 SCM 0 2932 0 4727
14 6/7/2005 2 0 977 2443 0 0
15 6/22/2005 2 0 0 0 0 4397
16 6/22/2005 2 SCM 489 0 489 0
17 7/6/2005 2 0 0 1954 0 0
18 7/6/2005 2 SCM 0 0 0 7878
19 8/19/2005 2 0 0 2363 2363 0
20 8/19/2005 2 SCM 489 0 0 1466
21 9/20/2005 2 0 489 0 0 6753
22 3/15/2005 5 SCM 1954 3909 0 977
23 5/13/2005 5 0 0 5909 0 5909
24 5/13/2005 5 SCM 0 1000 0 0
25 5/25/2005 5 0 0 170172 0 23635
26 3/15/2005 16 SCM 0 977 0 0
27 5/13/2005 16 0 0 6681 0 2932
28 3/15/2005 13 0 0 7272 0 489
29 3/15/2005 13 SCM 0 0 0 1477
30 3/22/2005 13 0 0 0 0 0
31 4/21/2005 13 SCM 0 0 0 8000
32 6/7/2005 13 0 0 1466 0 2932
33 3/15/2005 16 0 0 14658 0 2932
34 3/15/2005 16 SCM 0 977 0 977
35 3/22/2005 16 0 0 977 0 3636
36 6/7/2005 16 0 977 20259 0 0
37 4/12/2005 13 SCM 0 0 0 0
38 9/20/2005 13 0 0 7818 0 2932
39 9/20/2005 16 0 0 2443 0 3420
40 7/6/2005 16 SCM 489 0 0 0
75
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 61 62 63 64
41 4/21/2005 16 0 0 0 0 977
42 4/12/2005 16 0 0 3909 0 11727
43 4/5/2005 16 0 0 0 0 4727
44 5/25/2005 16 0 0 61057 0 33225
45 5/25/2005 13 SCM 0 47270 0 283619
46 5/25/2005 16 SCM 489 1954 0 7329
47 5/25/2005 13 0 0 63027 0 57774
48 5/25/2005 16 SCM 0 977 0 2932
49 5/13/2005 16 SCM 1954 0 0 3909
50 3/15/2005 13 0 0 7272 0 489
51 3/15/2005 13 SCM 0 0 0 1477
52 3/15/2005 16 0 0 14658 0 2932
53 7/6/2005 16 0 0 0 0 0
54 3/22/2005 5 0 0 26589 0 6894
55 4/5/2005 13 SCM 0 1970 0 0
56 7/6/2005 13 0 0 249 0 249
57 6/22/2005 16 SCM 0 0 0 0
58 8/19/2005 16 SCM 0 977 0 977
59 8/19/2005 16 0 0 0 0 2932
60 5/25/2005 5 SCM 1954 13681 0 0
61 5/13/2005 16 0 2932 33225 0 25408
62 3/22/2005 16 SCM 0 0 0 0
63 4/12/2005 16 SCM 0 0 0 0
64 4/5/2005 16 SCM 0 0 0 0
65 6/22/2005 16 0 0 7818 0 1954
66 8/19/2005 16 0 0 0 0 0
67 7/6/2005 13 0 0 244 0 244
68 3/22/2005 13 SCM 0 3909 0 0
69 4/5/2005 5 0 0 0 0 0
70 4/21/2005 5 0 0 0 0 977
71 4/5/2005 5 SCM 0 9848 0 0
72 4/12/2005 5 0 1954 3909 0 3909
73 6/7/2005 5 0 977 2932 0 1954
74 9/20/2005 5 0 489 0 0 977
75 3/22/2005 5 SCM 0 2932 0 2932
76 5/25/2005 5 0 0 170172 0 23635
77 4/5/2005 5 SCM 0 173273405 0 26590220
78 8/19/2005 5 SCM 489 2932 0 1466
79 8/19/2005 5 0 0 0 0 0
80 6/22/2005 5 SCM 6753 0 0 1954
76
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 61 62 63 64
81 4/5/2005 16 0 0 2079 0 4158
82 6/22/2005 16 0 0 0 0 11401
83 4/12/2005 16 0 0 0 0 1954
84 4/21/2005 16 SCM 0 0 0 0
85 4/12/2005 16 SCM 0 977 0 0
86 6/22/2005 13 0 0 0 0 814
87 8/19/2005 13 SCM 1477 4432 0 0
88 4/12/2005 5 SCM 0 1954 0 5863
89 7/6/2005 5 0 0 4886 0 2932
90 4/12/2005 13 0 0 0 0 1496
77
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 78 66 67 68
1 3/15/2005 2 0 0 0 0 0
2 3/15/2005 2 SCM 0 0 0 0
3 3/22/2005 2 0 0 0 0 0
4 3/22/2005 2 SCM 0 489 489 0
5 4/5/2005 2 0 0 0 0 0
6 4/5/2005 2 SCM 0 0 0 0
7 4/12/2005 2 SCM 0 0 0 0
8 4/21/2005 2 0 0 0 489 0
9 4/21/2005 2 SCM 0 0 0 0
10 5/13/2005 2 0 0 0 6753 0
11 5/13/2005 2 SCM 0 0 0 0
12 5/25/2005 2 0 0 0 0 0
13 5/25/2005 2 SCM 0 0 977 0
14 6/7/2005 2 0 0 0 489 0
15 6/22/2005 2 0 0 0 977 0
16 6/22/2005 2 SCM 0 0 0 0
17 7/6/2005 2 0 0 0 12704 0
18 7/6/2005 2 SCM 0 0 7878 0
19 8/19/2005 2 0 0 0 0 0
20 8/19/2005 2 SCM 489 489 11817 0
21 9/20/2005 2 0 0 0 0 0
22 3/15/2005 5 SCM 0 977 977 0
23 5/13/2005 5 0 0 0 0 0
24 5/13/2005 5 SCM 0 0 0 0
25 5/25/2005 5 0 0 0 0 0
26 3/15/2005 16 SCM 0 0 0 0
27 5/13/2005 16 0 0 0 0 0
28 3/15/2005 13 0 0 0 0 0
29 3/15/2005 13 SCM 0 0 0 0
30 3/22/2005 13 0 0 0 0 0
31 4/21/2005 13 SCM 0 0 0 0
32 6/7/2005 13 0 0 0 0 0
33 3/15/2005 16 0 0 0 1466 0
34 3/15/2005 16 SCM 0 0 0 0
35 3/22/2005 16 0 0 0 0 0
36 6/7/2005 16 0 0 0 0 0
37 4/12/2005 13 SCM 0 0 499 0
38 9/20/2005 13 0 0 0 1954 0
39 9/20/2005 16 0 0 0 3420 0
40 7/6/2005 16 SCM 0 0 0 0
78
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 78 66 67 68
41 4/21/2005 16 0 0 0 5863 0
42 4/12/2005 16 0 0 0 53133 0
43 4/5/2005 16 0 0 0 0 0
44 5/25/2005 16 0 0 0 0 0
45 5/25/2005 13 SCM 0 0 0 0
46 5/25/2005 16 SCM 0 0 489 0
47 5/25/2005 13 0 0 0 0 0
48 5/25/2005 16 SCM 0 0 0 0
49 5/13/2005 16 SCM 0 0 11817 1954
50 3/15/2005 13 0 0 0 0 0
51 3/15/2005 13 SCM 0 0 0 0
52 3/15/2005 16 0 0 0 1466 0
53 7/6/2005 16 0 0 0 0 0
54 3/22/2005 5 0 0 0 985 0
55 4/5/2005 13 SCM 0 0 1970 0
56 7/6/2005 13 0 0 0 0 0
57 6/22/2005 16 SCM 0 0 1466 0
58 8/19/2005 16 SCM 0 0 0 0
59 8/19/2005 16 0 0 0 977 0
60 5/25/2005 5 SCM 0 0 1954 0
61 5/13/2005 16 0 0 0 977 0
62 3/22/2005 16 SCM 0 0 0 0
63 4/12/2005 16 SCM 0 0 0 0
64 4/5/2005 16 SCM 0 0 0 0
65 6/22/2005 16 0 0 0 0 0
66 8/19/2005 16 0 0 0 0 0
67 7/6/2005 13 0 0 0 0 0
68 3/22/2005 13 SCM 0 0 0 0
69 4/5/2005 5 0 0 0 1507 0
70 4/21/2005 5 0 0 0 0 0
71 4/5/2005 5 SCM 0 0 0 0
72 4/12/2005 5 0 0 0 1954 0
73 6/7/2005 5 0 0 0 977 0
74 9/20/2005 5 0 0 0 0 0
75 3/22/2005 5 SCM 0 0 1954 0
76 5/25/2005 5 0 0 0 0 0
77 4/5/2005 5 SCM 0 0 1954428 0
78 8/19/2005 5 SCM 0 0 489 0
79 8/19/2005 5 0 0 0 1954 0
80 6/22/2005 5 SCM 0 1954 20259 0
79
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 78 66 67 68
81 4/5/2005 16 0 0 0 2079 0
82 6/22/2005 16 0 0 0 3257 0
83 4/12/2005 16 0 0 0 0 0
84 4/21/2005 16 SCM 0 0 0 0
85 4/12/2005 16 SCM 0 0 0 0
86 6/22/2005 13 0 0 0 1629 0
87 8/19/2005 13 SCM 0 0 2954 0
88 4/12/2005 5 SCM 0 0 0 1954
89 7/6/2005 5 0 0 0 0 0
90 4/12/2005 13 0 0 0 0 0
80
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 78 79 70 80
1 3/15/2005 2 0 0 0 0 0
2 3/15/2005 2 SCM 0 0 0 0
3 3/22/2005 2 0 0 0 0 0
4 3/22/2005 2 SCM 0 0 0 0
5 4/5/2005 2 0 0 0 0 0
6 4/5/2005 2 SCM 0 0 0 0
7 4/12/2005 2 SCM 0 0 0 0
8 4/21/2005 2 0 0 0 0 0
9 4/21/2005 2 SCM 0 0 0 0
10 5/13/2005 2 0 0 0 0 0
11 5/13/2005 2 SCM 0 0 0 0
12 5/25/2005 2 0 0 0 0 0
13 5/25/2005 2 SCM 0 0 0 0
14 6/7/2005 2 0 0 0 0 0
15 6/22/2005 2 0 0 0 0 0
16 6/22/2005 2 SCM 0 0 0 0
17 7/6/2005 2 0 0 0 0 0
18 7/6/2005 2 SCM 0 0 0 0
19 8/19/2005 2 0 0 0 0 0
20 8/19/2005 2 SCM 489 11817 0 0
21 9/20/2005 2 0 0 0 0 0
22 3/15/2005 5 SCM 0 0 0 0
23 5/13/2005 5 0 0 0 0 0
24 5/13/2005 5 SCM 0 0 0 0
25 5/25/2005 5 0 0 0 0 0
26 3/15/2005 16 SCM 0 0 0 0
27 5/13/2005 16 0 0 0 0 0
28 3/15/2005 13 0 0 0 0 0
29 3/15/2005 13 SCM 0 0 0 0
30 3/22/2005 13 0 0 0 0 0
31 4/21/2005 13 SCM 0 0 489 0
32 6/7/2005 13 0 0 0 0 0
33 3/15/2005 16 0 0 0 0 0
34 3/15/2005 16 SCM 0 0 0 0
35 3/22/2005 16 0 0 0 0 0
36 6/7/2005 16 0 0 0 0 0
37 4/12/2005 13 SCM 0 0 0 0
38 9/20/2005 13 0 0 0 0 0
39 9/20/2005 16 0 0 0 0 0
40 7/6/2005 16 SCM 0 0 0 0
81
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 78 79 70 80
41 4/21/2005 16 0 0 0 0 0
42 4/12/2005 16 0 0 0 0 0
43 4/5/2005 16 0 0 0 0 0
44 5/25/2005 16 0 0 0 0 0
45 5/25/2005 13 SCM 0 0 0 0
46 5/25/2005 16 SCM 0 0 1466 0
47 5/25/2005 13 0 0 0 0 0
48 5/25/2005 16 SCM 0 0 489 0
49 5/13/2005 16 SCM 0 0 0 0
50 3/15/2005 13 0 0 0 0 0
51 3/15/2005 13 SCM 0 0 0 0
52 3/15/2005 16 0 0 0 0 0
53 7/6/2005 16 0 0 0 0 0
54 3/22/2005 5 0 0 0 0 0
55 4/5/2005 13 SCM 0 0 0 0
56 7/6/2005 13 0 0 0 249 0
57 6/22/2005 16 SCM 0 0 0 0
58 8/19/2005 16 SCM 0 0 0 0
59 8/19/2005 16 0 0 0 0 2443
60 5/25/2005 5 SCM 0 0 0 0
61 5/13/2005 16 0 0 0 0 0
62 3/22/2005 16 SCM 0 0 0 0
63 4/12/2005 16 SCM 0 0 0 0
64 4/5/2005 16 SCM 0 0 0 0
65 6/22/2005 16 0 0 0 0 0
66 8/19/2005 16 0 0 0 0 0
67 7/6/2005 13 0 0 0 244 0
68 3/22/2005 13 SCM 0 0 0 0
69 4/5/2005 5 0 0 0 0 0
70 4/21/2005 5 0 0 0 0 0
71 4/5/2005 5 SCM 0 0 0 0
72 4/12/2005 5 0 0 0 1954 0
73 6/7/2005 5 0 0 0 0 0
74 9/20/2005 5 0 0 0 0 0
75 3/22/2005 5 SCM 0 0 0 0
76 5/25/2005 5 0 0 0 0 0
77 4/5/2005 5 SCM 0 0 0 0
78 8/19/2005 5 SCM 0 0 489 0
79 8/19/2005 5 0 0 0 0 0
80 6/22/2005 5 SCM 0 0 0 0
82
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 78 79 70 80
81 4/5/2005 16 0 0 0 0 0
82 6/22/2005 16 0 0 0 0 0
83 4/12/2005 16 0 0 0 0 0
84 4/21/2005 16 SCM 0 0 0 0
85 4/12/2005 16 SCM 0 0 0 0
86 6/22/2005 13 0 0 0 0 0
87 8/19/2005 13 SCM 0 0 0 0
88 4/12/2005 5 SCM 0 0 0 0
89 7/6/2005 5 0 0 0 0 0
90 4/12/2005 13 0 0 0 0 0
83
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 72 73 81 75
1 3/15/2005 2 0 0 0 0 0
2 3/15/2005 2 SCM 977 0 0 0
3 3/22/2005 2 0 0 0 0 0
4 3/22/2005 2 SCM 0 0 0 0
5 4/5/2005 2 0 977 0 0 0
6 4/5/2005 2 SCM 0 0 0 0
7 4/12/2005 2 SCM 0 0 0 0
8 4/21/2005 2 0 0 0 0 0
9 4/21/2005 2 SCM 1000 0 0 0
10 5/13/2005 2 0 0 0 0 0
11 5/13/2005 2 SCM 2000 0 0 0
12 5/25/2005 2 0 0 0 0 0
13 5/25/2005 2 SCM 977 977 0 0
14 6/7/2005 2 0 0 0 0 0
15 6/22/2005 2 0 0 0 0 0
16 6/22/2005 2 SCM 977 0 0 0
17 7/6/2005 2 0 977 0 0 0
18 7/6/2005 2 SCM 0 0 0 0
19 8/19/2005 2 0 0 0 0 0
20 8/19/2005 2 SCM 0 0 0 0
21 9/20/2005 2 0 0 0 0 0
22 3/15/2005 5 SCM 0 0 0 0
23 5/13/2005 5 0 0 0 0 0
24 5/13/2005 5 SCM 1000 0 0 0
25 5/25/2005 5 0 489 0 0 0
26 3/15/2005 16 SCM 977 0 0 0
27 5/13/2005 16 0 0 0 0 0
28 3/15/2005 13 0 3636 0 0 0
29 3/15/2005 13 SCM 0 0 0 0
30 3/22/2005 13 0 0 0 0 0
31 4/21/2005 13 SCM 489 0 0 0
32 6/7/2005 13 0 0 0 0 0
33 3/15/2005 16 0 0 0 0 0
34 3/15/2005 16 SCM 0 0 0 0
35 3/22/2005 16 0 0 0 0 0
36 6/7/2005 16 0 0 0 0 0
37 4/12/2005 13 SCM 499 0 0 0
38 9/20/2005 13 0 0 3909 0 0
39 9/20/2005 16 0 0 1466 0 0
40 7/6/2005 16 SCM 0 0 0 0
84
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 72 73 81 75
41 4/21/2005 16 0 0 0 0 0
42 4/12/2005 16 0 0 0 0 0
43 4/5/2005 16 0 0 2363 0 0
44 5/25/2005 16 0 0 0 0 0
45 5/25/2005 13 SCM 0 0 0 0
46 5/25/2005 16 SCM 0 489 0 2443
47 5/25/2005 13 0 0 0 0 0
48 5/25/2005 16 SCM 0 977 0 0
49 5/13/2005 16 SCM 0 0 0 1954
50 3/15/2005 13 0 0 0 0 0
51 3/15/2005 13 SCM 0 0 0 0
52 3/15/2005 16 0 0 0 0 0
53 7/6/2005 16 0 0 0 0 0
54 3/22/2005 5 0 0 0 0 0
55 4/5/2005 13 SCM 1970 0 0 0
56 7/6/2005 13 0 0 0 0 0
57 6/22/2005 16 SCM 0 0 0 0
58 8/19/2005 16 SCM 0 977 977 0
59 8/19/2005 16 0 0 0 0 0
60 5/25/2005 5 SCM 0 0 0 1954
61 5/13/2005 16 0 0 977 0 0
62 3/22/2005 16 SCM 0 0 0 0
63 4/12/2005 16 SCM 0 0 0 0
64 4/5/2005 16 SCM 0 0 0 0
65 6/22/2005 16 0 0 0 0 0
66 8/19/2005 16 0 0 0 0 0
67 7/6/2005 13 0 0 0 0 0
68 3/22/2005 13 SCM 0 0 0 0
69 4/5/2005 5 0 0 0 0 0
70 4/21/2005 5 0 0 0 0 0
71 4/5/2005 5 SCM 1970 0 0 0
72 4/12/2005 5 0 0 0 0 0
73 6/7/2005 5 0 0 0 0 0
74 9/20/2005 5 0 0 489 0 0
75 3/22/2005 5 SCM 0 0 0 0
76 5/25/2005 5 0 489 0 0 0
77 4/5/2005 5 SCM 489096 0 0 0
78 8/19/2005 5 SCM 0 1954 0 0
79 8/19/2005 5 0 0 0 0 0
80 6/22/2005 5 SCM 0 0 0 0
85
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 72 73 81 75
81 4/5/2005 16 0 0 0 0 0
82 6/22/2005 16 0 4072 0 0 0
83 4/12/2005 16 0 0 0 0 0
84 4/21/2005 16 SCM 1954 0 0 0
85 4/12/2005 16 SCM 0 0 0 0
86 6/22/2005 13 0 0 0 0 0
87 8/19/2005 13 SCM 0 0 0 0
88 4/12/2005 5 SCM 0 0 0 0
89 7/6/2005 5 0 0 977 0 0
90 4/12/2005 13 0 499 0 0 0
86
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 76 78 77 82
1 3/15/2005 2 0 0 0 0 271802
2 3/15/2005 2 SCM 11817 0 0 0
3 3/22/2005 2 0 0 0 0 6753
4 3/22/2005 2 SCM 489 0 0 222844
5 4/5/2005 2 0 0 0 0 401794
6 4/5/2005 2 SCM 0 0 0 401794
7 4/12/2005 2 SCM 0 0 0 431338
8 4/21/2005 2 0 0 0 0 418676
9 4/21/2005 2 SCM 0 0 0 195832
10 5/13/2005 2 0 0 0 489 594250
11 5/13/2005 2 SCM 0 0 0 78000
12 5/25/2005 2 0 0 0 0 228964
13 5/25/2005 2 SCM 0 0 0 311981
14 6/7/2005 2 0 0 0 1466 738592
15 6/22/2005 2 0 0 0 0 634767
16 6/22/2005 2 SCM 0 0 0 811466
17 7/6/2005 2 0 977 0 977 729307
18 7/6/2005 2 SCM 0 0 0 480577
19 8/19/2005 2 0 0 0 0 409672
20 8/19/2005 2 SCM 11817 489 11817 673596
21 9/20/2005 2 0 0 0 0 965656
22 3/15/2005 5 SCM 977 0 977 0
23 5/13/2005 5 0 0 0 489 531786
24 5/13/2005 5 SCM 0 0 0 441186
25 5/25/2005 5 0 0 0 0 255257
26 3/15/2005 16 SCM 0 0 0 0
27 5/13/2005 16 0 0 0 0 89813
28 3/15/2005 13 0 3636 0 489 0
29 3/15/2005 13 SCM 0 0 0 0
30 3/22/2005 13 0 0 0 0 0
31 4/21/2005 13 SCM 0 0 0 195832
32 6/7/2005 13 0 0 0 489 435933
33 3/15/2005 16 0 0 0 0 60776
34 3/15/2005 16 SCM 0 0 489 47270
35 3/22/2005 16 0 489 0 3636 69087
36 6/7/2005 16 0 0 0 6753 290372
37 4/12/2005 13 SCM 0 0 0 620159
38 9/20/2005 13 0 977 0 0 212714
39 9/20/2005 16 0 2443 0 0 417551
40 7/6/2005 16 SCM 0 0 0 397067
87
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 76 78 77 82
41 4/21/2005 16 0 0 0 0 1314103
42 4/12/2005 16 0 0 0 0 464821
43 4/5/2005 16 0 0 0 0 576693
44 5/25/2005 16 0 0 0 0 1219563
45 5/25/2005 13 SCM 0 0 0 29307330
46 5/25/2005 16 SCM 0 0 0 722554
47 5/25/2005 13 0 0 0 977 824597
48 5/25/2005 16 SCM 0 0 0 307254
49 5/13/2005 16 SCM 0 0 0 460881
50 3/15/2005 13 0 3636 0 489 145446
51 3/15/2005 13 SCM 0 0 0 0
52 3/15/2005 16 0 0 0 0 60776
53 7/6/2005 16 0 0 0 0 0
54 3/22/2005 5 0 0 0 0 598752
55 4/5/2005 13 SCM 0 0 0 1616630
56 7/6/2005 13 0 0 0 0 200977
57 6/22/2005 16 SCM 0 0 0 898128
58 8/19/2005 16 SCM 0 0 0 397067
59 8/19/2005 16 0 3420 0 0 1654446
60 5/25/2005 5 SCM 0 0 0 62542
61 5/13/2005 16 0 0 0 0 811466
62 3/22/2005 16 SCM 0 0 0 605055
63 4/12/2005 16 SCM 0 0 489 1069481
64 4/5/2005 16 SCM 0 0 489096 16756053
65 6/22/2005 16 0 489 0 0 1114219
66 8/19/2005 16 0 0 0 0 7043213
67 7/6/2005 13 0 0 0 0 196958
68 3/22/2005 13 SCM 0 0 489 330889
69 4/5/2005 5 0 0 0 0 1591741
70 4/21/2005 5 0 0 0 0 573991
71 4/5/2005 5 SCM 0 0 0 1567785
72 4/12/2005 5 0 0 0 0 1938065
73 6/7/2005 5 0 0 0 4886 1484274
74 9/20/2005 5 0 0 0 0 336798
75 3/22/2005 5 SCM 1954 0 2932 702295
76 5/25/2005 5 0 0 0 0 255257
77 4/5/2005 5 SCM 1954428 0 2931642 957808102
78 8/19/2005 5 SCM 0 0 0 535725
79 8/19/2005 5 0 0 0 0 1359009
80 6/22/2005 5 SCM 1954 0 0 1039938
88
Table 2-3. Continued.
Species index number
Sample # Date Station Depth 76 78 77 82
81 4/5/2005 16 0 2079 0 0 284960
82 6/22/2005 16 0 0 0 814 1433853
83 4/12/2005 16 0 0 0 0 727956
84 4/21/2005 16 SCM 0 0 0 425429
85 4/12/2005 16 SCM 977 0 0 690140
86 6/22/2005 13 0 814 0 0 472699
87 8/19/2005 13 SCM 0 0 0 319072
88 4/12/2005 5 SCM 0 0 0 1093960
89 7/6/2005 5 0 0 0 0 1342465
90 4/12/2005 13 0 0 0 0 932535
89
Table 2-3. Continued.
Species index number
Sample # Date Station 83 84
1 3/15/2005 2 0 0
2 3/15/2005 2 0 0
3 3/22/2005 2 0 0
4 3/22/2005 2 2443 0
5 4/5/2005 2 0 0
6 4/5/2005 2 8874921 0
7 4/12/2005 2 1466 0
8 4/21/2005 2 0 0
9 4/21/2005 2 9000 0
10 5/13/2005 2 30000 0
11 5/13/2005 2 1000 0
12 5/25/2005 2 611 0
13 5/25/2005 2 2932 0
14 6/7/2005 2 7818 0
15 6/22/2005 2 0 0
16 6/22/2005 2 2932 0
17 7/6/2005 2 10749 0
18 7/6/2005 2 0 0
19 8/19/2005 2 0 0
20 8/19/2005 2 11817 0
21 9/20/2005 2 0 0
22 3/15/2005 5 0 0
23 5/13/2005 5 0 0
24 5/13/2005 5 977 0
25 5/25/2005 5 977 0
26 3/15/2005 16 0 0
27 5/13/2005 16 1954 0
28 3/15/2005 13 0 0
29 3/15/2005 13 0 0
30 3/22/2005 13 0 0
31 4/21/2005 13 0 0
32 6/7/2005 13 0 0
33 3/15/2005 16 977 0
34 3/15/2005 16 489 0
35 3/22/2005 16 0 0
36 6/7/2005 16 13506 0
37 4/12/2005 13 0 0
38 9/20/2005 13 3909 0
39 9/20/2005 16 977 0
40 7/6/2005 16 0 0
90
Table 2-3. Continued.
Species index number
Sample # Date Station 83 84
41 4/21/2005 16 1954 0
42 4/12/2005 16 5863 0
43 4/5/2005 16 0 0
44 5/25/2005 16 9772 0
45 5/25/2005 13 0 0
46 5/25/2005 16 1954 0
47 5/25/2005 13 7818 0
48 5/25/2005 16 0 0
49 5/13/2005 16 0 0
50 3/15/2005 13 0 0
51 3/15/2005 13 0 0
52 3/15/2005 16 977 0
53 7/6/2005 16 977 0
54 3/22/2005 5 0 0
55 4/5/2005 13 0 0
56 7/6/2005 13 249 0
57 6/22/2005 16 2443 0
58 8/19/2005 16 0 0
59 8/19/2005 16 3909 0
60 5/25/2005 5 0 10749
61 5/13/2005 16 0 0
62 3/22/2005 16 0 0
63 4/12/2005 16 0 0
64 4/5/2005 16 0 0
65 6/22/2005 16 977 0
66 8/19/2005 16 0 0
67 7/6/2005 13 244 0
68 3/22/2005 13 489 0
69 4/5/2005 5 0 0
70 4/21/2005 5 0 0
71 4/5/2005 5 0 0
72 4/12/2005 5 1954 0
73 6/7/2005 5 9772 0
74 9/20/2005 5 0 0
75 3/22/2005 5 11727 0
76 5/25/2005 5 0 977
77 4/5/2005 5 11726568 978191
78 8/19/2005 5 5375 0
79 8/19/2005 5 1954 0
80 6/22/2005 5 11727 0
91
Table 2-3. Continued.
Species index number
Sample # Date Station 83 84
81 4/5/2005 16 4158 0
82 6/22/2005 16 4072 0
83 4/12/2005 16 0 0
84 4/21/2005 16 0 0
85 4/12/2005 16 1954 0
86 6/22/2005 13 6515 0
87 8/19/2005 13 0 0
88 4/12/2005 5 0 0
89 7/6/2005 5 7818 0
90 4/12/2005 13 1496 0
Figure 2-5. Time series of Lingulodinium polyedrum abundance at sampling stations – presented as
log
10
cells L
-1
; top panel is surface and bottom panel is from the subsurface chorophyll maximum.
Blue line presents data from station 2, red line – station 5, purple line- station 8, pink line – station 13
and black line for station 16. Lack of data in September for SCM is due to the sampling issues in that
period.
Eighty-four microphytoplankton taxa were encountered during our study. These taxa
were comprised mostly of diatom, dinoflagellate and silicoflagellate species (Table
2-2 and 2-3). The first part of the year (Feb-May) was dominated by diatom species.
Following the upwelling period until the end of the sampling period dinoflagellate
populations numerically dominated the surface and subsurface samples. Occasional
92
high diatom numbers (>10
5
cells L
-1
), including Pseudo-nitzschia sp, were seen
during the post-upwelling period, at stations closer to the LA Harbor (stations 13 and
16).
The abundance of L polyedrum in the first part of the observed period was variable
between stations (Fig 2-5). Higher abundances were found in the subsurface (station
16, late March, > 10
5
cells L
-1
) than in the surface samples (~ 10
4
cells L
-1
) prior to
and during the spring upwelling. Following the spring upwelling period, higher
abundances were found in the surface only. During May and June, L. polyedrum
accounted for less than 30 % of phytoplankton community abundance, while
Prorocentrum micans, another HAB species, was dominant numerically in the
phytoplankton population with cell counts higher than 10
5
cells L
-1
. In the beginning
of June L. polyedrum abundance increased both in the surface and subsurface
samples, starting at station 2 and moving north, and remained high (> 10
5
cells L
-1
)
and dominant (> 70% of total microphytoplankton abundance) until the rest of the
sampling period (Fig 2-5).
93
Figure 2-6. Plot presenting the distribution of Multidimensional Scaling (MDS) first axis scores for
phytoplankton samples over time and depth. MDS was performed on log transformed phytoplankton
abundance, with a stress value of 0.16 for 2-dimensional results. Color bar presents results ranging
from value of -1 to +1.
Statistical analysis: NMDS analysis of the phytoplankton assemblages yielded two
factors, Axis 1 and Axis 2 that allow us to observe the changes in the phytoplankton
community in more comprehensive way. The first two axes explained most of the
variance encountered in the samples with good confidence levels (stress=0.16). The
first axis had significant negative correlation with 7 taxa of chain forming diatoms
species - Chaetoceros spp, Dactyliosolen spp. and Thalassionema spp (r
2
=0.15-0.3,
p<0.05). These species were found in higher abundances during the April upwelling
event; therefore we assumed reciprocal relationship between Axis 1 and upwelling
conditions within the sampling region. The spatial and temporal distributions of the
Axis 1 values (Fig. 2-6) are consistent with this interpretation; the lowest (negative)
values are found at all the stations during the upwelling period in April. Negative
values of Axis 1 are encountered again later in the season, at surface of stations 13
94
and 16, where presence of the diatom blooming species was observed. Positive
values of Axis 1 characterized the surface and subsurface communities during the
remainder of the sampling period.
Canonical correspondent analysis of phytoplankton assembly and measured water
properties further confirms the importance of upwelling in regulation of the
community structure within San Pedro Bay (Fig. 2-7). Total variance explained by
the first two axes was 15%, with eigenvalues of 0.109 and 0.087, respectively.
Samples with negative MDS values (light and dark blue dots, panel B Fig 2-7) were
positioned in the high chlorophyll fluorescence, high nitrate and low temperature
portion of the biplot. Nitrate is significantly correlated with chlorophyll fluorescence,
and inversely correlated with temperature. Perpendicular position of the nitrite vector
in relation to temperature could allude to the possible role of nutrient regeneration
and release in nitrite distribution (Fig 2-7). Silicate is found to have low but positive
correlation (r
2
= 0.159) with second axis, and perpendicular position to temperature
(not shown) that could allude both regenerative processes and fast uptake rates that
govern the distribution of silica in San Pedro bay. Mixed layer depth was negatively
correlated with Axis 1 (r
2
=-0.350) and inversely correlated with temperature as
explained in physical and chemical properties subchapter.
95
Possible relationships between the L. polyedrum abundance and environmental
parameters were explored by identifying several surface samples on CCA biplot
where cell abundance of L. polyedrum was higher than 10
5
cells L
-1
(black squares,
Fig 2-7A). Three samples with high abundances of L. polyedrum were found along
the temperature vector, in the area of high temperature and low nutrient availability,
within the MDS group that was present during the stratified, non-upwelling part of
the season. In order to further elucidate ecological optima of taxa encountered during
this study, taxa scores were plotted in the same CCA space (Fig 2-7B). L. polyedrum
ecological optimum seems to be in the area of slightly higher temperatures and
nitrates, and lower nitrites compared with the position of P. micans. P. micans, the
species that preceded the L. polyedrum bloom in 2005, was positioned higher on
Axis 2 and on the temperature vector, consistent with the higher abundance of P.
micans encountered during the warm stratified periods of early summer.
96
Figure 2-7. Biplots showing first and second axis of CCA. Environmental variables with intra-set
correlation r
2
≥0.2 are displayed as red lines (T-temperature, NO2 – nitrates, NO3 nitrites, Chl Flo –
chlorophyll fluorescence). Length of the lines indicates the importance of the variable, and lines are
pointing to the direction of the maximum change. Panel A) CCA with all the phytoplankton samples
collected during the investigated period, environmental variables are measured at the same time/depth
as the phytoplankton data. Dots represent different samples, and color coding is based on the
respective MDS Axis 1 score (Fig 2-6). Black squares represent samples in which L. polyedrum was
dominant. Panel B) displays CCA with all the phytoplankton samples collected, and the
environmental variables measured at the same time/depth as the phytoplankton data. Blue dots
represent different phytoplankton species (Axis scores for different species in Table 2-4) Red dot – L.
polyedrum, green dot – Prorocentrum micans.
97
Table 2-4. Species scores for first three axis of the CCA shown on Figure 2-7B.
Sp# - species index number as explained in Table 2-2.
Sp # Axis 1 Axis 2 Axis 3 Sp # Axis 1 Axis 2 Axis 3
Sp1 0.457 0.279 0.035 Sp37 -0.149 0.995 0.193
Sp2 -0.498 -0.205 0.004 Sp38 -0.291 -0.074 -0.215
Sp3 -0.808 0.610 -0.054 Sp39 -0.187 -0.335 0.285
Sp4 0.990 0.966 0.594 Sp40 0.011 -0.338 0.394
Sp5 0.467 -0.865 -0.152 Sp41 -0.220 -0.770 0.561
Sp6 -0.592 -0.043 -0.093 Sp42 0.158 0.008 0.346
Sp7 -0.144 0.415 0.174 Sp43 0.531 -0.138 0.260
Sp8 -0.494 0.085 0.014 Sp44 -0.568 -0.082 -0.609
Sp9 -0.144 -0.380 0.879 Sp45 0.144 -0.084 -0.098
Sp10 -1.541 -1.069 1.414 Sp46 0.852 -0.452 -0.155
Sp11 -0.018 0.511 -0.271 Sp47 1.325 0.113 0.932
Sp12 -0.278 -0.135 -0.029 Sp48 1.912 -0.322 0.562
Sp13 -0.338 0.066 -0.532 Sp49 3.043 0.734 -1.419
Sp14 1.142 0.680 0.388 Sp50 -0.126 0.092 -0.495
Sp15 -0.745 -1.197 0.568 Sp51 0.506 0.544 -0.036
Sp16 -0.376 -0.005 -0.273 Sp52 0.058 0.795 -0.153
Sp17 -0.059 0.045 -0.049 Sp53 -0.495 -0.646 0.630
Sp18 -1.178 2.363 1.539 Sp54 0.447 1.279 -0.372
Sp19 0.109 0.067 0.991 Sp55 0.157 -0.288 -0.766
Sp20 -0.389 -0.121 -0.156 Sp56 1.205 -0.773 -0.176
Sp21 0.337 1.074 0.328 Sp57 0.753 -0.007 0.150
Sp22 -0.056 -0.038 0.427 Sp58 0.961 -0.445 -0.342
Sp23 -0.384 -0.581 0.091 Sp59 0.957 -0.161 -0.169
Sp24 -0.467 0.119 -0.328 Sp60 0.092 -0.283 -0.058
Sp25 -0.417 -0.196 0.454 Sp61 0.442 0.695 0.523
Sp26 -1.312 -0.531 -1.113 Sp62 0.248 0.187 0.022
Sp27 -0.420 1.035 2.018 Sp63 0.992 -0.266 0.231
Sp28 -0.079 0.180 0.266 Sp64 0.323 0.236 -0.033
Sp29 -0.333 0.249 -0.272 Sp66 1.242 -0.841 1.076
Sp30 -0.215 0.363 -0.086 Sp67 0.121 0.116 -0.191
Sp31 0.190 -0.081 -0.005 Sp68 -0.670 1.068 1.893
Sp32 0.686 0.103 -0.355 Sp70 -0.031 1.169 0.319
Sp33 -0.342 0.223 -0.731 Sp72 0.132 -0.276 0.035
Sp34 -0.485 -0.123 -0.117 Sp73 -0.238 1.372 -0.363
Sp35 -0.020 0.263 0.052 Sp75 -0.285 1.318 0.806
Sp36 0.057 0.064 0.032 Sp76 0.553 -0.591 0.060
Sp77 0.374 -0.364 -0.296
98
DISCUSSION
Our data collected during 2005 confirm previously observed Southern California
Bight seasonality of the phytoplankton community (Anderson et al. 2008a; Venrick
1998a). Spring of 2005 was marked by strong upwelling in April that resulted with
high nutrient availability, lower temperatures and domination of a chain-forming
diatom community in the surface waters (Fig.2-6). The first surface chlorophyll
maximum of the year contained Pseudo-nitzschia spp, a HAB species previously
observed in San Pedro Bay (Schnetzer et al. 2007) and more widely in the Southern
California Bight (Anderson et al. 2009; Busse et al. 2006; Shipe et al. 2008).
Negative NMDS scores, correlated with chain forming diatom species, were
observed in the subsurface chlorophyll maximum prior to their dominance in the
surface during and following the April upwelling period. It is likely that the April
upwelling episode not only transported nutrients into the nutrient depleted surface
layer (Fig 2-3) but also transported the diatom-dominated subsurface chlorophyll
maximum. Negative NDMS scores were observed again later in the summer at the
stations neighboring LA harbor (Fig 2-1 and Fig 2-6). This summer diatom
community could have been one of the later
generations of the initial upwelling
associated diatom bloom (Smayda and Reynolds 2003), that was advected from
nutrient rich waters within Los Angeles/Long Beach harbor. Pseudo-nitzschia spp,
one of the species found to be correlated with the negative NDMS scores, has been
99
known to cause toxin related events during the spring and summer time in the harbor
area (Schnetzer et al. 2007; Schnetzer et al. 2006).
After the upwelling, our data showed the development of a secondary, non-diatom
community in the subsurface chlorophyll maximum (Fig. 2-6), and the spread of that
community into the surface layer. As expected, coincided with the depletion of the
surface nitrate and silicate (Fig. 2-3). During this period, first dominant species
observed in the surface was P. micans, a HAB species known to precede L.
polyedrum blooms in this area (Allen 1942; Holmes et al. 1967; Shipe et al. 2008).
Its appearance following upwelling, prior to nutrient depletion (Fig 2-3), and when
surface layer warming is occurring (Fig 2-7), is consistent with its assignment to the
Smayda-Reynolds Type II group (Smayda and Reynolds 2001).
L. polyedrum, although present in the water column throughout the sampling period,
formed a large bloom in midsummer (> 10
5
cells L
-1
) that persisted in the San Pedro
Channel until early October, and resulted in chlorophyll concentrations as high as
120 μg L
-1
chlorophyll (Schnetzer, personal communication). L. polyedrum’s central
position in the CCA biplot (Fig 2-7A and B), suggests it is ubiquitous in this dataset,
its presence independent of the specific conditions in the coastal ocean. No affinity
toward the measured sources of the nitrogen was observed, consistent with the
species’ capability of utilizing different forms of the available nutrients in the system
100
(Kudela and Cochlan 2000; Kudela et al. 2008a). L. polyedrum did not show an
affinity towards any mixed layer depth range, but that may result from the fact that
dataset was comprised of data prior to and during the bloom. During July and
August, highest L. polyedrum cell counts (>10
4
cells L
-1
) were seen both in the SCM
and surface layer. Surface nitrate was low (<0.02 μM) during this period at all
stations. Samples with highest L. polyedrum abundance in the (Fig 2-7 A) were
found in the area of the CCA where low mixed layer depth and high temperature
were dominant, similar to findings of Shipe et al. (2008) in Santa Monica Bay.
Shallowest mixed layer depths were observed during the period of secondary
temperature minima measured on the Newport Beach Pier (Fig 2-2). These intrusions
of cold water measured in the surface waters could be either summer upwelling
episodes – due to the favorable upwelling winds (Fig 2-2), or internal bores that are
known to occur in this area (Noble et al. 2009). Upwelling and internal bores result
with the shoaling of the thermocline, nutricline and mixed layer depth over the whole
shelf area (Fig 2-2 this chapter, Noble et al. 2009). High surface abundances of L.
polyedrum during this nutrient depleted period could infer that cells were either
obtaining nutrients from other (organic) sources or migrating through the water
column to obtain nutrients from the shallow nutricline. L. polyedrum is a relatively
fast swimmer capable of velocities between 0.9 and 1.4 m · h
−1
(Lewis and Hallett
1997), and migrat through 2 °C temperature gradient (Blasco 1978; Walsh et al.
101
1974). Migration combined with phototaxy and ability of this species to temporally
and spatially separate growth from photosynthesis (Heaney and Eppley 1981;
Moorthi et al. 2006), could allow it to thrive in conditions that are unfriendly for
other non motile phytoplankton organisms.
Figure 2-8. Temperature anomalies for the specified years and their standard deviation (±, dotted
black lines). Boxes denote the period when Lingulodinium polyedrum bloom was observed, as
described in the respective papers. Panel A shows bloom data from this effort, historical mean and
measurements from buoy 9410660, period 1993-2008 , panel B - L. polyedrum blooms off San Diego
in 1938 (pink line) and 1942 (purple line) (Allen 1938; Allen 1942) ; temperature data from Scripps
pier, historical index 1919-2008; panel C - L. polyedrum bloom of San Diego in 1965 (Holmes et al.
1967), temperature data same source as B); D - L. polyedrum bloom of San Diego in 1995 (Kahru and
Mitchell 1998), temperature data same source as B).
102
Secondary temperature minima observed during the summer of 2005 (Fig 2-2 A)
were below the historical average for this area (Fig 2-8A). Cool temperature
anomalies were also observed off Northern California and Oregon during the same
period of the year (Barth et al. 2007). From the set of temperature anomaly time
series presented in Figure 2-8, including the 2005 study (Allen 1938; Allen 1942;
Holmes et al. 1967; Kahru and Mitchell 1998) it is clear that cool temperature
anomalies were associated with 4 of the 5 observed L. polyedrum blooms, further
emphasizing our conclusion.
Cool anomalies were not present during the large bloom in 1995 (Kahru and Mitchell
1998, Fig 2-8 D) that occurred earlier in the season (April), prior to the spring
upwelling, when positive temperature anomalies were observed. It may have been
triggered by unusually heavy rainfall that resulted with heavy coastal runoff
throughout California (Hayward et al. 1995; Kudela and Cochlan 2000). That would
provide L. polyedrum population with organic sources of nutrients, and allow it to
overpower other species present in the community and form a bloom.
CONCLUSION
This study has shown that temporal changes of the phytoplankton community in San
Pedro Shelf are correlated with local and wider regional environmental factors.
103
Diatom domination in the spring was concurrent with high nitrate and silica
availability. The transition to a dinoflagellate dominated community followed
nutrient depletion in upper layer. We have shown that the SCM and surface
community are coupled and interchange, but develop on different temporal scales.
L. polyedrum was present in the water column throughout the year, but blooms
occurred later in the season during the low nutrient concentrations in the surface and
shallow mixed layer, co-occurring with low temperature episodes observed near the
shore. L. polyedrum’s migrational capabilities of could enable it to benefit from
nutricline shoaling driven by summer internal bores and/or upwelling episodes.
Furthermore, low temperature episodes were found co-occur with several summer L.
polyedrum blooms previously observed in Southern California Bight, supporting
primary correlations with temperature, mixed layer depth and nutrient availability in
bloom formation.
104
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110
CHAPTER III: Red tide optical index: in situ optics and remote sensing models
CHAPTER III ABSTRACT
Harmful Algal Blooms (HABs) are recurring events in the coastal ocean, and local
economies that depend on beach and coastal use are often adversely affected by these
events. Inherent optical properties (absorption and backscattering coefficients) of the
HAB dinoflagellate Lingulodinium polyedrum were measured in order to develop
specific wavelength index that would provide easier detection of this organism in the
field. Two red tide indices (RT1 = a(440) / a(550), RT2 = a(440) / a(675)) were
measured using laboratory cultures of local algal isolates, and it has been noticed that
for L. polyedrum are significantly lower then for other measured species. Further
field testing of red tide indices was done during multiple coastal ocean monitoring
campaigns in the Los Angeles region (period of 2006-2008). RTI proved to be a
good detection tool for L. polyedrum blooms (> 5μg chl L
-1
) in coastal Southern
California. Application of the red tide indices in hyperspectral remote sensing
reflectance was tested on datasets collected in Santa Barbara/Ventura area. Results
confirm the success of this technique for detection of L. polyedrum blooms in this
area, and support its applicability for satellite ocean color remote sensing.
111
INTRODUCTION
Large algal blooms of Lingulodinium polyedrum have been observed in Southern
California Bight since the beginning of 20th century (Torrey 1902). These high
biomass, L. polyedrum-dominated (~90% of the population) blooms mostly occur
during the summer–fall period along Southern California coast (Allen 1938; Moorthi
et al. 2006; Shipe et al. 2008). The negative impacts of these blooms may include
possible toxicity (Howard et al. 2008), water discoloration, respiratory and skin
irritation, and most importantly hypoxia and anoxia resulting from decay of large
amounts of organic material. These anoxic and hypoxic events can cause extensive
benthic animal mortality (Chang et al. 2004b) that is sometimes accompanied with a
specific odor (Torrey 1902). As a result, local tourism and other ocean related
industries have incurred millions of dollars in lost revenue due to red tide events.
Measurements of the in situ spectral inherent optical properties (IOP’s) have been an
important tool for understanding biological processes in the ocean in recent years
(Babin et al. 2008; Chang et al. 2004a; Dickey et al. 2006; Schofield et al. 1999).
Highly precise in situ instrumentation has made it possible to obtain specific optical
fingerprints for organic matter (CDOM), phytoplankton, detritus, sediment, etc.
(Boss et al. 2001; Lee et al. 2002; Roesler and Perry 1995; Twardowski et al. 2004).
112
Those optical fingerprints allow us to define optical “weights” proportional to
concentration of each of these parameters.
Although L. polyedrum does not possess specific pigments that allow development
of a pigment based optical fingerprint, as has been done for Karenia brevis (Craig et
al. 2006; Kirkpatrick et al. 2000), Chang et al. (2008; 2004b) found that L.
polyedrum, measured in situ, exhibits a different spectral shape for remote sensing
reflectance (remote sensing reflectance ratio of 580:400 nm was on average a factor
of four higher) than spectral shape found in “normal populations”. Stramski
(personal communication) noticed that absorbance ratios of 440nm to 550nm and
440 nm to 675 nm are much lower in red tides then in “normal” phytoplankton
populations. Laboratory and field backscattering measurements have revealed that
certain phytoplankton species show highly specific backscattering spectra too
(Bricaud et al. 1983; Cannizzaro et al. 2008). The relationship between spectral
reflectance and the inherent optical properties can be easily described as a ratio of
backscattering and absorbance (b
b
/a) at the specified wavelength (Morel and Prieur
1977), and therefore, in situ measured optical properties can be used to develop a
functional remote sensing model.
Monitoring strategies for tracking phytoplankton communities in Southern California
bight are primarily based on the collection of discrete water samples and labor
113
intensive laboratory analysis (microscopic cell counts, DNA and toxin analyses)
(Schnetzer et al. 2007; Shipe et al. 2008). Recently, novel remote sensing
instruments have been deployed in the Southern California Bight (Caron et al. 2009;
SCCOOS 2007) which enable us to follow changes in the phytoplankton community
(chlorophyll and Colored Dissolved Organic Matter fluorescence, backscattering),
although not to the phytoplankton species level. This approach leads to certain bias
due to the inappropriate spatio-temporal resolution and inefficiency when it comes to
timely response to harmful algal blooms (Babin et al. 2008). Improvement of
detection tools for specific species, in this case L. polyedrum, could help mitigate
potential risks associated with bloom formation.
Discoloration of the surface waters associated with L. polyedrum blooms has been
described as red water (Holmes et al. 1967), muddy vermilion (Torrey 1902), and
nearly fresh blood (Allen 1938). If such species-specific discoloration could be
traced by satellite-based ocean color sensors, that would provide us with a tool for
detection and monitoring of L. polyedrum blooms on appropriate spatio-temporal
scales (Ahn and Shanmugam 2006; Cannizzaro et al. 2008). The objective of this
study is to develop techniques based on laboratory and field measurements of
inherent optical properties that would enable us to classify and quantify blooms of L.
polyedrum in the Southern California area. A large, multi-year/multi-season,
comprehensive database of phytoplankton community data, absorption coefficients
114
and chlorophyll concentrations collected during MERHAB and other local
monitoring studies in Los Angeles area are used for this purpose. Further application
of the chlorophyll/absorption based red tide index algorithm is tested with
hyperspectral remote sensing data collected in Santa Barbara/Ventura area during
two bloom seasons in order to investigate possible applications of red tide indices in
the ocean color sensor domain.
MATERIALS AND METHODS
Laboratory experiments: Inherent optical properties measurements were made with
Lingulodinium polyedra and seven additional species, including the dinoflagellates
Alexandrium tamarense, Gymnodinium simplex, and Ceratium longipes, the diatoms
Skeletonema costatum, Thalassiosira pseduonana, and Asterionellopsis glacialisa
and the cyanobacterium Synechococcus sp. Most of these species were local isolates,
HAB species, and usually found in high abundance in coastal waters. All cultures
were grown in f/2 enriched sterile seawater media. Cultures were maintained at
17°C with a 10:14-h light:dark cycle.
In preparation for the spectral measurements, cultures were mixed with filtered sea
water (0.2 μm) collected locally. A WetLabs ac-s absorption and attenuation meter
was used to measure spectral coefficients at 82 wavelengths. Total absorption a
tot
(λ)
115
and attenuation coefficients c
tot
(λ) measurements were followed by measurements of
a
<5
(λ) and c
<5
(λ) for 5 μm filtered samples. This filtering step allows us to separate
smaller detrital matter from the phytoplankton cells associated signal. This was done
for all the species except for Synechococcus spp, due to its size. Sample was
additionally filtered on GF/F filter, so absorption, a
diss
(λ), and attenuation, c
diss
(λ), of
the dissolved constituents can be measured. Absorption and attenuation
measurements were processed following the manufacturer directions, allowing us to
calculate particulate absorption a
p
(λ) and attenuation c
p
(λ) coefficient following:
a,c
p
(λ) = a,c
tot
(λ) – a,c
<5
(λ)
In case of Synechococcus spp. we used a,c
diss
(λ) instead of a,c
<5
(λ) to calculate a
p
(λ).
Absorption of the dissolved part of the water constituents can be described with
following formula:
a
diss
(λ) = a
diss
(λ
0
) exp
(-γ (λ- λ0))
where a
diss
(λ) and a
diss
(λ
0
) are the absorption coefficients at wavelength λ and
reference wavelength λ
0
(532 nm), and γ is the slope of the absorption spectra in 400-
532nm area.
Measurements of backscattering coefficient for b
btot
(λ), b
b<5
(λ)
and b
bdiss
(λ) were
made using bb9 – a multispectral (9 wavelengths) scattering meter measuring optical
scattering at 117° in the visual spectra. Backscattering was calculated according to
116
the manufacturer’s protocol with corrections for temperature, salinity and absorption.
Particulate backscattering coefficient of specific size filtrate, b
b*
(λ) was subsequently
calculated following;
b
b*
(λ)=b
b
(λ)-b
bw
(λ)
where estimate of pure seawater backscattering coefficient , b
bw
(λ), was based on
Morel (1974). Calculation for the b
bp
(λ) of the phytoplankton cells was done by
subtracting b
b<5
(λ) from b
btot
(λ), except in the case of Synechococcus spp. where
b
b<5
(λ) was not measured due to small cell size.
For the chlorophyll and phaeopigment analysis, seawater samples for chlorophyll a
(50 ml - triplicates) analysis were filtered on GF/F filters, and the filters were
immediately stored on -20°C until measurement. Pigments were extracted in the 90%
acetone at -20°C for 4 hours. Chlorophyll and pheaeopigment concentration was
measured using a Turner Design fluorometer, before and after the addition of two
drops of 10% HCl respectively (Parsons et al. 1984).
Underway Inherent Optical Properties: Underway data were collected during the
MERHAB cruises, and SCCOOS Hyperion diversion experiment
(http://www.sccoos.org/projects/hyperion/). Underway measurements of salinity and
temperature (CTD), absorption and attenuation spectral coefficients (acS – WetLabs)
117
were measured during 2005-2008 in San Pedro channel and Santa Monica Bay.
Instruments were either part of the shipboard instrumentation, or additionally
connected to the ships sea-water flowthrough system, that was pumping water from
2-3 m depth. Total absorption a
tot
(λ) and attenuation coefficients c
tot
(λ) were
measured continuously, while filtered (0.2 μm) measurements a
diss
(λ) and c
diss
(λ)
were done for 10 minutes every hour, or in some cases were measured continuously
using ac9 instrument. In cases when the dissolved measurements were made using
ac9, desired wavelengths were calculated by interpolation of the measured data (9
wavelengths to 84 wavelengths). Absorption and attenuation data were processed
following the methodology explained in previous subchapter.
Chlorophyll a concentration was calculated from the total absorption following the
method described in Boss et al (2004) developed by C. Roesler and verified with
discrete chlorophyll measurements.
In addition to these measurements, discrete water samples were collected for analysis
of chlorophyll concentration and phytoplankton. Subsamples for phytoplankton
enumeration (250 mL) were preserved in 5% acidified lugol’s iodine. After
sedimentation for 24 h, 50ml subsamples were analyzed, and cell counts obtained by
the inverted microscope method (Uthermöhl 1958.). Microplankton cells were
118
counted at magnifications of 200 and 400 x. Chlorophyll and pheaopigment analysis
was performed following the method explained in previous subchapter.
Surface hyperspectral radiometry data: Surface radiometric data was collected on
the SB CHARM mooring located ~1 1/2 miles offshore of La Conchita, California,
during the period of May 19 - October 2, 2003 (Chang et al. 2004b; Chang et al.
2006) and May 14 - September 21, 2004. The mooring was deployed by Ocean
Physics Laboratory, UCSB, and equipped with array of instruments measuring
temperature and salinity on 4m, surface downwelling irradiance (E
d
(λ)) and
upwelling radiance (L
u
(λ)) on 2 and 4 m depth. Both irradiance and radiance
measurements were hyperspectral, on 124 wavelengths in visual spectra, ranging
from 400-800 nm and 3 nm resolution (Satlantic, Inc.). Only measurements done at
the solar noon were used in further analysis.
Remote sensing reflectance was calculated by normalizing the water-leaving
radiance L
w
(λ) to downwelling irradiance E
d
(λ) following the formula
Rrs (λ) = Lw(0+,λ)/Ed(0+,λ )
We assumed that the water column was homogeneous up to 4 m depth, and
calculated attenuation coefficient of irradiance (K
u
) from
Ku = (ln L
u2
– ln L
u4
) / 2
119
Where L
u2
is upwelling irradiance measured at depth, z, of 2 meters, L
u4
is upwelling
irradiance measured at depth of 4 meters, and 2 is the difference in depths of the
measured irradiance.
Using calculated K
u
we propagated the radiance to just beneath the surface and
calculated upwelling irradiance, L
u
(0
-
):
L
u
(0
-
) = L
u2
exp (K
u
z)
We calculated water leaving radiance (L
w
):
L
w
= 0.543 L
u
(0
-
)
where 0.543 is the coefficient that corrects for the internal reflection of the upwelling
radiance through the surface (Ahn and Shanmugam 2006; Morel and Antoine 1994).
Chlorophyll concentration was calculated from R
rs
(λ) following Carder et al. (1999)
semi-analytical algorithm. Determination of chlorophyll via semi-analytical
algorithms have proved to be better in retrieving chlorophyll in optically complex
waters such as Southern California Bight (Cannizzaro et al. 2008; Carder et al. 1999;
Craig et al. 2006).
Theoretical background: Light leaving the surface of the ocean is not only depended
on the absorption, a(λ), but the backscattering coefficient, b
b
(λ), of the different
120
constituent of the water (Gordon et al. 1975). Remote sensing reflectance can be
explained by simple ratio;
R
rs
=k*( b
b
/ a+b
b
)
Where k is a coefficient depended on the transmittance across the air–sea interface,
refraction of seawater, solar zenith angle, and upwelling irradiance-to-radiance ratio
(Lee et al. 1994).
Remote sensing reflectance below the surface, r
rs
, is related to R
rs
via:
r
rs
= R
rs
/(0.52+ 1.7 R
rs
)
Based on predetermined coefficients, g
0
and g
1
, specific for type of optical
complexity of the water (Lee et al. 2002) r
rs
can be defined with:
r
rs
(λ)
=
g
0
u(λ) + g
1
[u(λ)]
2
where u(λ) is a ratio of backscattering coefficient to sum of the absorption and
backscattering coefficients:
u(λ)=b
b
(λ ) / [a(λ)+b
b
(λ)]
The absorption coefficient, a(λ), and backscattering coefficient, b
b
(λ), are the sum of
contributions by water and its constituents:
a(λ)=a
phi
(λ) + a
det
(λ)+ a
diss
(λ) + a
w
(λ)
b
b
(λ)=b
bp
(λ)+b
bw
(λ)
121
where the subscripts phi, det, p, diss, and w refer to phytoplankton, detritus, total
particles (phytoplankton and detritus), dissolved organic material (gelbstoff), and
water, respectively.
Red tide indices for absorption coefficients are defined as:
RT1 = a
p,tot
(440) / a
p,tot
(550)
RT2 = a
p,tot
(440) / a
p,tot
(675)
Following the relationship where R
rs
~ f (a,b
b
) we define red tide indices for remote
sensing:
RT1
Rrs
= R
rs
(440) / R
rs
(550)
RT2
Rrs
= R
rs
(440) / R
rs
(675)
RESULTS AND DISCUSSION
Laboratory absorption and backscattering measurements: Particulate absorption
spectra (a
p
(λ)) of L. polyedrum (Fig 3-1 B) showed expected two peeks in the area of
chlorophyll a (437 and 676 nm) and chlorophyll b and c2 absorption
(a
phi
*(437)=0.0167 m
2
mg chl a-
1
, a
phi
*(675)=0.0114 m
2
mg chl a
-1
, [chl a] =1-10
mg m
3
) and broad shoulder in the area of absorption of peridinin-chorophyll-protein
complex (a
phi
*(550)=0.0074 m
2
mg chl a
-1
, [chl a] =1-10 mg m
3
). The observed flat
122
shape spectra is probably due to the combination of the large cell size (~45 μm) and
strong pigment packaging (Ciotti et al. 2002; Kahru and Mitchell 1998). High
absorption in the low blue region (<440nm) is due to the presence of micosporine
like amino acids (MAA) (Kahru and Mitchell 1998; Vernet and Whitehead 1996),
UV-absorbing compounds which are used as photoprotectants in L. polyedrum and
other species (Vernet et al. 1989). These MAAare released in the surrounding waters
and are known to be a strong component of the dissolved organic material pool in the
surface waters (Kahru and Mitchell 1998; Vernet and Whitehead 1996). High
CDOM concentrations can be observed in our data (Fig 3-1. C), where spectral shape
of absorption (a
tot
(λ)) in the first part of the spectra (400-600 nm) is dominated by
absorption of colored dissolved organic matter. Spectral slope of the dissolved
absorption coefficients (a
diss
) was low, γ=0.011, therefore influencing the absorption
coefficients both in blue and green portions of the spectra.
123
Figure 3-1. Inherent optical properties of Lingulodinium polyedrum. Panel A is showing the
particulate backscattering coefficients (b
bp
(λ)) measured in the algal culture, normalized with
particulate backscattering at 720 nm; B showing the particulate absorption coefficients (a
p
(λ))
normalized with particulate absorption at 675 nm.; C showing total absorption coefficients (a
tot
(λ))
normalized with absorption at 675 nm
Particulate backscattering spectra of L. polyedrum follows the reversed spectral
shape observed in absorption. Low relative particulate backscattering
(b
bphi
*(550)=0.0025 m
2
mg chl a
-1
, for [chl a] =1-10 mg m
3
) observed in cultures is
due to the low refractive index of this species, combined with the high absorption,
pigment packaging and large cell size (Bricaud et al. 1983). Similar low
backscattering was observed for other dinoflagellate species of the similar cell size
124
(Cannizzaro et al. 2008), and predicted from modeling (Dierssen et al. 2006;
Stramski et al. 2001).
Figure 3-2. Absorption “red tide” ratios for different species of phytoplankton. Red triangles
represent value for the particulate absorption ratio of 440nm/550 nm (RT1), and black squares
represent particulate absorption ratio of 440nm/675 nm (RT2). Additional absorption measurements
for L. polyedrum taken from Itturiaga, unpublished. Abbreviations of species names are: , Syn-
Synechococcus sp., Skel- Skeletonema costatum, Thal- Thalassiosira pseduonana , Aste-
Asterionellopsis glacialis Gymn- Gymnodinium simplex , Cera- Ceratium longipes, Alex -
Alexandrium catenella, Ling- Lingulodinium polyedrum.
The pigment structure of the cell, as well as the cell packaging had a big influence on
the observed differences in absorption red tide ratios (Fig 3-2). Although similar size
and same functional group as L. polyedrum , A. tamarense and G. simplex has shown
differences in both blue to green ratio (RT1 = a
p
(440):a
p
(550)) and blue to red
(RT2=a
p
(440):a
p
(650)) ratio. Highest observed difference was in case of
Synecococcus sp, which could arise from the completely different pigment structure
(presence of phycobiliproteins and lack of Chlorophyll b and c) and specific pigment
125
packaging in the small cell. L. polyedrum had lowest observed values for both RT1
and RT2 (RT≤2).
Development of absorption red tide index algorithm: In summer of 2006, a red tide
patch observed offshore Newport Beach, California, was dominated by L. polyedrum
(>80% of the microphytoplankton community), with the values of chlorophyll
ranging from 5 μg L
-1
in the middle part of the patch to > 15 μg L
-1
in the edge of the
patch (Fig 3-3).
Values of RT indices found in L. polyedrum bloom were lower than 2 in all samples
where chlorophyll concentrations was higher than 5 μg L
-1
. That part of the dataset
seems to be following a power-relationship (black line, Fig 3-4 A and B):
RT1 = a
tot
(440):a
tot
(550)= 2.2813 [Chl]
-0.1429
(r
2
= 0.89)
RT2 = a
tot
(440):a
tot
(650) = 2.5555 [Chl]
-0.2122
(r
2
=0.76)
126
Figure 3-3. Chlorophyll, RT1 and RT2 as observed in the L. polyedrum dominated patch (black box).
Sample number is a proxy of distance/time. Chlorophyll was calculated form a
tot
following Roesler
method.
Species specific chlorophyll absorption coefficient a
phi
* is known to have negative
correlation with chlorophyll concentration, due to the increase of accessory pigments
relative to chlorophyll concentration and species specific packaging effect (Babin et
al. 1996; Babin et al. 1995; Bricaud et al. 1995). This would be more pronounced in
the part of the spectra where accessory pigments dominate (480-500nm), which
could explain the better performance of RT1 than RT2. Absorption spectra could be
expected to vary, depending on health of the population and evolution of the bloom.
However, studies looking at the changes of the absorption spectra of several HAB
127
species during the different growth phases have not found significant differences
(Dierssen et al. 2006; Harding 1988). Average spectral slope of the dissolved
absorption coefficient, γ, measured during all the field studies was around 0.017,
while during L. polyedrum dominated tides, slope seems to shift to the lower values
(γ<0.015). Similar negative trend was previously observed for (Breves and Reuter
2000; Zhao et al. 2009). As observed in L. polyedrum cultures, the lower slope (γ) of
a
diss
could cause the higher absorption in the green region too, therefore impacting
the stability of the RT1. Although some authors have found a positive correlation
between the CDOM concentration and the chlorophyll concentration during the
bloom episodes (Carder et al. 1989; Zhao et al. 2009), release of the MAA from the
cells of L. polyedrum in the surrounding waters is a function of the UV damage,
stress, and possibly associated with the cell death (Whitehead and Vernet 2000).
Such non-linear relationship could influence the spectral shape of the total absorption
coefficient, and possibly accuracy of RT indices in different stages of the bloom.
Testing of absorption red tide index algorithm: In order to further test the efficiency
of RT1 and RT2 for detection of L. polyedrum in coastal waters of Southern
California, we compared the values of the RT indices for multiple cruises during the
period of 2005-2008 (Fig 3-4 A, B). During this period we measured absorption
coefficients for several L. polyedrum, Ceratium spp and Pseudo-nitzschia spp (Fig 3-
4 A and B yellow dots), and Cochlodinium cochlodinium blooms (Fig 3-4A and B
128
blue dots). For non-L. polyedrum blooms, similar to the values observed in the
laboratory, values of the RT indices were higher than the ones for L. polyedrum
bloom.
Figure 3-4. Red tide ratio (RT1 and RT2) for different log transformed chlorophyll concentrations
observed in the field during the L. polyedrum (red dots), Ceratium spp. and Pseudonitzchia spp.
(yellow dots), Cochlodinium catenatum (blue dots) blooms, and non-bloom periods (black dots).
Black lines present RT to chlorophyll relationships specific for L. polyedrum blooms.
Some of the RT values associated with Cochlodinium catenatum bloom (blue dots
Fig 3- 4 A, B) and Ceratium spp and Pseudo-nitzchia spp. bloom (yellow dots Fig 3-
4 A, B) were found to be below the cutoff line of 2 (RT1) or 1.8 (RT2) with
chlorophyll concentration lower than 5 μg L
-1
. Cocchlodinium catenatum bloom was
observed during and post the SCCOOS Hyperion diversion experiment, when a
129
sewer discharge plume was released in shallow waters which eventually reached the
surface (http://www.sccoos.org/projects/hyperion/ , Reifel, personal
communication). Spectral characteristics of the sewage, due to its high organic
content (Petrenko et al. 1997, Chapter 4 of this thesis), could produce false positives
in RT indices.
Observed correlation between the RT indices and chlorophyll seem to be distinctive
enough to detect L. polyedrum bloom when all three following criteria are satisfied:
1) chlorophyll concentrations higher than 5 μg L
-1
2) RT1 lower than 2
3) RT2 lower than 1.8
Remote sensing reflectance: During the late summer and fall of 2003, a L.
polyedrum dominated red tide (>90% of the phytoplankton population) occurred in
wider Southern California area (California Department of Public Health's Marine
Biotoxin Monitoring Program 2003a; 2003b; 2003c). The red tide was observed at
the SB CHARM location between August 30 and September 7, 2003 and again from
September 17 until the mooring recovery on October 3 2003(Chang et al. 2004b).
Prior to the L. polyedrum bloom, in the spring of 2003, a Pseudo-nitzschia australis
bloom occurred along the southern California coast (Langlois 2003). Domoic acid
130
levels at the SB CHARM location peaked during the end of May, just at the
beginning of the mooring deployment (Chang et al. 2004b).
Due to the high absorption and low backscattering in the blue portion of the spectra
of L. polyedrum, as observed in the laboratory and field experiments, the red portion
of the remote sensing reflectance spectra is low, up to 8 times lower when compared
to the non-bloom situations (Fig 3-5). This part of the spectra is additionally
influenced by the strong absorption of CDOM, which is abundant during the L.
polyedrum red tides (Kahru and Mitchell 1998). The primary peak, situated around
570-580 nm, is the portion of the light spectra where absorption is the lowest and
backscattering is the highest. Compared to non-bloom conditions, the peak
associated with L. polyedrum was 2-3 times lower confirming low backscattering
efficiency of this species. This peak has previously been documented in the red tides
and other bloom types (Carder and Steward 1985; Chang et al. 2004b; Craig et al.
2006; Dierssen et al. 2006). The position and the width of this peak is specific to the
taxonomic pigment composition (Dierssen et al. 2006), as well as the level of CDOM
present in the water. Previous studies shown that shift of this peak towards higher
(red) wavelength is a function of a chlorophyll concentration/cell density (Dierssen
et al. 2006; Roesler and Boss 2003), while shift towards the blue wavelengths is a
function of higher CDOM concentration (Craig et al. 2006) and change in CDOM
spectral slope (γ ). In our samples observed shift was towards higher wavelengths,
131
therefore mostly controlled by high cell density. It is quite possible that the low
impact of the CDOM on the shift of the R
rs
spectral peak is due to the different
spectral slope of the CDOM in this area compared to the one used in the models by
Craig et al (2006). As mentioned before, the CDOM concentration (as a function of
MAA production) is not linearly correlated with chlorophyll in L. polyedrum, but
depends upon stress and death of the population, so it is possible that shift of the
peak towards the lower wavelengths could be a signal for the decline and end of the
bloom.
Figure 3-5. A) Remote sensing reflectance during the L. polyedrum bloom. B) Primary peak (Rrs
max) normalized remote sensing reflectance during the L. polyedrum bloom. Red lines present values
measured during the L. polyedrum bloom and black lines values during the non-bloom and diatom
bloom period.
132
Secondary minimum (675 nm) is due to the strong absorption of the chlorophyll in
that spectral region, which is not pronounced in non-bloom conditions, due to the
water absorption dominating that part of the spectra (Pope and Fry 1997). The depth
of the trough on 675 nm is highly depended on the natural chlorophyll fluorescence
peak, which can be seen as secondary peak in Rrs spectra, usually centered at 685
nm, and has width of 25 nm (black lines Fig 3-5). However, in the case of L.
polyedrum bloom, the secondary minimum seems to shift to the near-infrared part of
the spectra (>700 nm). This shift is driven by the combination of the high absorption
of the chlorophyll in the 685 region, the lack of absorption in NIR region (Dierssen
et al. 2006), absorption of the water (Gower et al. 2005), and fluorescence of the
chlorophyll. The height of the peak (Gower et al. 2005) and the shift in wavelengths
has been known to correlate with chlorophyll concentration and density of the bloom
(Gitelson 1992; Gower et al. 2005; Ryan et al. 2009).
Development of remote sensing red tide index: Prior modeling efforts of L.
polyedrum dominated blooms (Cetinic et al. 2007) and red tide blooms of other
species (Ahn and Shanmugam 2006; Cannizzaro et al. 2008) from the in situ
measured inherent optical properties (a(λ ), b
b
(λ)) showed distinctive differences
which allow the detection of certain species using remote sensing reflectance.
Values of RT
Rrs
indices found in L. polyedrum bloom seem to be following the
relationship (Fig. 3-6);
133
RT1
Rrs
= 0.3544 [Chl]
-0.25
(r
2
= 0.7602, n=20)
RT2
Rrs
= 2.7705 [Chl]
-0.36
(r
2
=0.8568, n=20)
While this relationship for period when surface population was not dominated by L.
polyedrum can be described as (Fig. 3-6):
RT1
Rrs
= 1.3 [Chl]
-1.09
(r
2
= 0.9203, n=20)
RT2
Rrs
= 7.12 [Chl]
-0.8509
(r
2
=0.669, n=20)
Figure 3-6. Log transformed Red tide ratio (RT1
Rrs
and RT2
Rrs
) vs. log transformed chlorophyll
concentrations observed on the field during the L. polyedrum (red dots), Pseudo-nitzchia spp. bloom
(yellow dots), and non-bloom periods (black dots). Black dashed lines present RT
Rrs
to chlorophyll
relationship specific for L. polyedrum blooms, black solid lines present the same relationship for non-
bloom and Pseudo-nitzchia spp. dominated bloom
Observed correlation between the RT indices and chlorophyll seem to be distinctive
enough to detect L. polyedrum bloom when all three following criteria are satisfied:
1) chlorophyll concentrations higher than 7 μg L
-1
134
2) RT1
Rrs
higher than RT1
Rrs
= 1.3 [Chl]
-1.09
3) RT2
Rrs
higher than RT2
Rrs
= 7.12 [Chl]
-0.8509
Chang et al. (2008; 2004b) found that spectral remote sensing ratio R
rs
(580):R
rs
(400)
is up to 4 times higher for the L. polyedrum blooms than in other phytoplankton
populations, which is, due to the reciprocity of Rrs and species specific
backscattering/absorption coefficient relationship, in agreement with our field and
laboratory absorption coefficient measurements. R
rs
(580) is highly impacted by
species specific backscattering, while R
rs
(400) strongly dominated by not only
species absorption, but absorption of CDOM which is high in L. polyedrum
dominated red tides (Kahru and Mitchell 1998). When we compare the values of
RT1
Rrs
and RT2
Rrs
with R
rs
(580):R
rs
(400) for L. polyedrum bloom we find that RT1
Rrs
has a strong negative correlation with R
rs
(580):R
rs
(400) (r
2
= 0.8538, n=20),
while RT2
Rrs
has a less significant negative correlation (r
2
= 0.4639, n=20) with
higher spread of the RT2
Rrs
values. Strong negative correlation between Chang’s and
RT1
Rrs
occurs because of the inverse relationship of R
rs
(580) and R
rs
(550) (Fig 3-5.
and Craig et al. 2006). The lower correlation observed in the case of the RT2
Rrs
could be due to the combination of different factors controlling the values of
R
rs
(675); intensity of fluorescence peak at 673 nm, NIR peak height and position,
chlorophyll and water absorption.
135
Figure 3-7. Log transformed Red tide ratio (RT1
Rrs
and RT2
Rrs
) vs. log transformed chlorophyll
concentrations observed on the field during the 2004 deployment (blue dots). Positive detects of L.
polyedrum using the RT
Rrs
and [chl] criteria are marked with red circles. Black dashed lines present
RT
Rrs
to chlorophyll relationship specific for L. polyedrum blooms; black solid lines present the same
relationship for other surface phytoplankton population. Red vertical line presents chlorophyll
concentration of 7 μg L
-1
.
Testing of remote sensing red tide index: Based on the criteria defined in the
previous subchapter, we can easily separate low chlorophyll or diatom dominated
surface water from the L. polyedrum dominated bloom. During the period of second
mooring deployment (2004), L. polyedrum was abundant in the phytoplankton
population south of Point Conception during June and August, and was mixed with
Ceratium spp, Prorocentrum spp., and Protoperidinium spp and in much lower
concentrations than observed during September 2003 (California Department of
Public Health's Marine Biotoxin Monitoring Program 2004a; 2004c). During the rest
of the deployment period, surface phytoplankton population was a mixture of
136
different diatom and dinoflagellate species, including L. polyedrum but in very low
abundances (California Department of Public Health's Marine Biotoxin Monitoring
Program 2004b; 2004d). When we calculate RT
Rrs
indices for this deployment and
apply the criteria for detection of L. polyedrum dominated red tide, we find that only
10 days, 9 of them during the June and August, satisfy all three criteria (Fig 3-7).
Reflectance red tide to chlorophyll ratio measured for positive detects seems to be
lower than the one calculated for 90% L. polyedrum dominated samples during the
2003 bloom. This could be due to the lower abundance and proportion of L.
polyedrum in the phytoplankton population during the 2004 period, as well as the
influence of the other species. Therefore we can use the position of the positive
detects as a measurement of the presence or dominance of L. polyedrum in the
observed portion of the water. If positive detects are positioned more towards the
function that explains the RT and chl relashionship of “normal population”, L.
polyedrum is not a dominant species in the sample, but present with < 90% of the
population.
Although known to produce toxins in some areas of the world (Paz et al. 2008), L.
polyedrum hasn’t been found to be toxic in this area (Armstrong and Kudela 2006;
Howard et al. 2008). The large biomass associated with these blooms is known to
cause post-bloom hypoxia/anoxia in coastal systems, killing benthic organisms and
fish (Chang et al. 2004b; Torrey 1902). Phytoplankton population in these harmful L.
137
polyedrum blooms is usually dominated (~90%) by L. polyedrum (Lewis and Hallett
1997) with chlorophyll concentrations higher than 20 μg L
-1
. This algorithm seem to
yield good performance in higher chlorophyll (>7 μm) blooms of L. polyedrum, and
L. polyedrum dominated and mixed dinoflagellate blooms. Therefore it can be used
as a “warning” algorithm, where local economies that are depended on the beach and
coastal ocean usage can be warned of possible incoming problems.
Species discrimination via hyperspectral remote sensing data collected aboard a boat,
moorings or from aircraft have been used with success before (Craig et al. 2006;
Kutser et al. 2006; Lubac et al. 2008), while the translation of those algorithms to
currently operating ocean color sensors (e.g. SeaWIFS and MODIS) has been
troublesome (Cannizzaro et al. 2008). Launch of a space station hyperspectral sensor
in the near future (Gould and Jones, personal communications) could allow further
applications of a variety of recently developed hyperspectral algorithms in coastal
ocean. The wavebands used in this algorithm do overlap with available channels on
some ocean color sensors, but differences observed could be too fine for derivation
from water-leaving radiances measured from satellites, due to the atmospheric
correction impacts and relatively big pixel size (Ahn and Shanmugam 2006). Upon
its validation, this L. polyedrum detection algorithm would allow us to go back in
historical datasets of ocean color data, and look for the specific environmental
factors governing these blooms. For instance, the previous chapter discusses an
138
interesting correlation of the lower temperature water during the summer months and
appearance of L. polyedrum blooms. Surface low temperature-high nutrients
episodes were observed during 2003 L. polyedrum bloom in Ventura county
(Mcphee-Shaw et al. 2007). Therefore future developments of L. polyedrum
detection algorithms based upon remotely sensed or locally measured sea surface
temperature should also be taken into consideration. Combining the knowledge about
the specific algal ecology and its response to variation in the environment could be
introduced into this remote sensed model to improve the detection success. A similar
approach was taken in development of the prediction model for Pseudo-nitzschia
spp. in Santa Barabara Channel, which proved to increase model success (Anderson
et al. 2009).
CONCLUSION
Optical classification of L. polyedrum blooms developed here is based on the
absorption ratios that are found in algal cultures and field population. Due to the
species-specific high absorption/low backscattering coefficients, these classification
techniques allow us the detection of L. polyedrum blooms from hyperspectral remote
sensing instruments. Combination of two red tide ratios is discriminating enough to
separate other dinoflagellate blooms from L. polyedrum dominated blooms using
remote sensing reflectance, but only in chlorophyll concentrations higher than 7 μg
139
L
-1
. Upon validation in satellite ocean color domain, historical analysis of the
specific environmental factors governing the bloom initiation would be possible,
which would improve our knowledge about mechanisms governing the evolution of
L. polyedrum dominated red tides. Implementation of this “warning” algorithm in
current and future monitoring efforts may allow coastal managers to better mitigate
the harmful effects of L. polyedrum dominated blooms in Southern California.
140
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CHAPTER IV: Resolving urban plumes using autonomous gliders in the coastal
ocean
CHAPTER IV ABSTRACT
A Slocum autonomous underwater glider was deployed off Southern California
during the Huntington Beach 2006 experiment, in September and October of 2006.
The glider measured temperature, salinity, density, colored dissolved organic matter
(CDOM), chlorophyll fluorescence and optical backscatter. Deployment was done
with idea to test the applicability of gliders and optical instruments deployed on
glider in detecting and following the outfall plume, and monitoring its impact and
interaction with the natural processes in coastal ocean. The effluent plume from the
Orange County Sanitation District’s ocean outfall was identified using conservative
(salinity and temperature) and “nonconservative” (CDOM) water tracers. Both
methods agreed well with the observed currents, but method that used CDOM
measurements proved to be more robust in cases when plume water overlapped with
California Coastal Current in temperature-salinity space. A subsurface chlorophyll
maximum was found in proximity of the California Coastal Current salinity
minimum throughout the region, and no interaction was found between the nutrient
rich plume water and phytoplankton community. Specific optical backscattering
coefficients associated within the effluent plume allowed us to calculate suspended
148
particulate material concentration within the plume. Cross-shelf transport, driven by
internal wave propagation, was present, but no plume water was detected in the
nearshore surface area. The gliders provided observations that improve our
understanding of coastal dynamics, and its suite of sensors enabled detection and
discrimination of natural and human derived water masses in coastal waters. In
addition to the gliders’ scientific merit, they provide an invaluable resource for
interpreting the processes of the coastal ocean and management tools for sustained
ecosystem and water quality monitoring of urbanized coastal ocean environments.
INTRODUCTION
Coastal oceans adjacent to major urban areas are affected by variety of natural and
anthropogenic processes. Southern California is a highly urbanized, densely
populated coastal region with more than 20 million people living near the coast
between Santa Barbara and San Diego, CA, representing approximately 25% of the
United States’ coastal population (Culliton et al. 1990). Several rivers and numerous
submerged ocean outfalls discharge into the Pacific Ocean within the Southern
California Bight. Four major sewage outfalls discharge nearly 4 × 10
6
m
3
d
-1
of
treated sewage directly into the coastal ocean at depths ranging from 60 to 100 m
(Lyon and Stein 2008). Increasing urbanization of the Southern California coasts has
raised many concerns related to both environmental and public health. These
149
concerns include toxicity to the ecosystem (Bay et al. 2003; Lee and Wiberg 2002),
public health risks due to microbial inputs from either runoff or treated sewage
discharges (Hatcher et al. 2001; Jiang 2001), and both public and environmental
health issues arising from the occurrence of nuisance or harmful algal blooms
(HABs) (Cetinic et al. 2006; Schnetzer et al. 2007). The dynamics of the coastal
zone have been demonstrated to be important in dispersal of microbial contaminants
affecting public contact in Southern California (Jeong et al. 2006; Noble et al. 2009),
and to play an important role in harmful algal blooms (Anderson et al. 2008).
Understanding of these processes with the expectation of evaluating effects and
implementing mitigating management practices requires understanding of the
oceanographic processes that contribute to dispersion, fate and effects of
anthropogenic modifications. Unfortunately, the usage of traditional sampling
approaches doesn’t allow us to appropriately monitor temporal and spatial variability
of anthropogenic inputs (Rudnick and Perry 2003), or even more, try to part the
human from the natural driven processes in the coastal ocean.
The ongoing evolution of sensor and vehicle technologies provides increasing ability
to detect, differentiate, and map the distribution of anthropogenic inputs into the
coastal ocean. Early work used relatively simple towed vehicles equipped with
conductivity-temperature-depth (CTD) packages, fluorometers, and
transmissometers (Jones et al. 2002; Washburn et al. 1992; Wu et al. 1994). This
150
work enabled discrimination of effluent and runoff plumes from ambient water and
three-dimensional mapping of those plumes (Washburn et al. 2003). A major step
forward in the ability to map in situ constituents came with the development of
sophisticated optical sensors. Petrenko et al. (1997) used in situ spectrophotometric
and spectral fluorometric devices to discern components associated with an effluent
plume off Sand Island, Hawaii. Other techniques have been used in order to address
the sewage plumes (Carvalho et al. 2002; Petrenko et al. 2000; Ramos et al. 2007
and previously mentioned studies), and most underscore the inadequacies for
resolving appropriate temporal and spatial scales (Ramos et al. 2007), and the
operational costs of coastal research vessels. The development of a new class of
autonomous underwater vehicles (AUVs) holds promise for observing the coastal
ocean with the spatial and temporal coverage necessary for understanding the
transport, dispersal and effects of contaminants. AUVs first appeared in 1970 (Chang
et al. 2006), and Stommel (1989) proposed ‘a fleet of small neutrally-buoyant floats
called ‘Slocums’ that ‘migrate vertically through the ocean by changing ballast, and
can be steered horizontally by gliding on wings’. Only recently have advances in
marine technology allowed the production of autonomous underwater gliders that
match Stommel’s vision. Modern underwater gliders are relatively small (about 2 m
in length), buoyancy-driven vehicles that complete dives to depths of a few hundred
meters in the span of a few hours while covering approximately 3 km horizontally.
The gliders are capable of carrying a variety of oceanographic instruments tailored to
151
the particular mission. Each variety of glider is capable of two-way satellite
communications for transmitting profile data in near real-time and for receiving
instructions from shore. Davis et al.(2002) and Rudnick et al.(2004) discuss the
development of these gliders, which can be deployed for periods of up to several
months and provide measurements with high spatial resolution along virtually any
specified course. These capabilities make them well suited for observations of the
coastal ocean (Davis et al. 2002).
During the summer and fall of 2006, the Southern California Coastal Ocean
Observing System (SCCOOS) conducted the Huntington Beach 2006 (HB06)
experiment in collaboration with Orange County Sanitation District (OCSD), and the
United States Geological Survey (USGS). HB06 consisted of an intensive in situ
observational campaign focused on the nearshore region of the San Pedro Bay coast
northward from the Santa Ana River for a one month intensive period in September-
October 2006. These observations are being used to validate and calibrate nearshore
models that allow greater spatial coverage over longer times (SCCOOS 2007), and
ultimately improve our understanding of the processes that transport and disperse
sediment, biota, and contaminants in the nearshore ocean. The observational portion
of HB06 included mooring arrays, shore-based measurements, surfzone surveys,
ocean drifters, propelled AUVs and two types of underwater gliders. In the study
area, OCSD, third largest publicly owned treatment works in Southern California,
152
discharges on the order of 10
6
m
3
d
-1
of treated wastewater through an outfall
diffuser off Huntington Beach, California (Grant et al. 2000; OCSD 2007). The 3 m
diameter pipe extends 7.5 km offshore and discharges the wastewater through a
series of diffuser ports along the 60 m isobath. The OCSD outfall has not been
shown to be a source of contamination to Orange County beaches (SCCOOS 2007),
but tidal or diurnal processes may lead to cross-shelf transport of diluted effluent that
could bring it to the surface near shore (Hamilton et al. 2004). On he other hand,
Santa Ana River, which discharges near Huntington Beach, is a known source of
surface contamination in the area (SCCOOS 2007).
This paper focuses on the observations from the Webb Slocum underwater gliders
during HB06, with the intent to evaluate the applicability of gliders and optical
instruments deployed on the glider for detecting and following an outfall plume, as
well as monitoring its effects and interaction with the natural processes in the coastal
ocean.
MATERIALS AND METHODS
Gliders: The Slocum glider was deployed twice in the San Pedro Channel during the
HB06 experiment that took place in September-October 2006 (SCCOOS 2007). Both
deployments, one immediately following the other, were over the San Pedro Shelf,
153
near Huntington Beach, California, in close proximity to the OCSD outfall diffuser.
The track of the Slocum glider was generally 14-15 km cross-shelf from near the 25
m isobath to beyond the shelf break where water depths were 300-400 m (Fig. 4-1).
Figure 4-1. Glider tracks from the two deployments overlaid on the coastal bathymetry (grey) of San
Pedro Bay, California. The contour depths are in meters. The coastline is denoted by the heavy black
contour and Orange County Sanitation District’s ocean outfall pipe and diffuser are indicated by the
orange line. Effluent is released from a series of diffuser ports on either side of the along-isobath
portion of the outfall pipe at a depth of approximately 60 m. The lighter blue line indicates the Slocum
glider track from its first deployment, 17 to 26 September, and the purple line is the glider track for
the second deployment perioid from 26 September to 2 October.
The Webb Slocum glider is a buoyancy-driven underwater vehicle that generates
horizontal motion by ascending and descending with pitched wings (Schofield et al.
2007). Slocum uses a rudder to control heading and controls its buoyancy by
pumping seawater into and out of the nose of the vehicle. During our deployment,
the glider achieved horizontal velocities of approximately 25-30 cm s
-1
with vertical
154
velocities of 10-15 cm s
-1
while sampling at a surface horizontal resolution of less
than 0.5 km with dives to 60 m.
During the first deployment (17-26 September) the Slocum glider completed 988
dives and covered 140 km. The second deployment (26 September-2
October)
consisted of 569 dives and covered 116 km. The Slocum deployments were intended
to focus on the upper water column, so all dives were to maximum depths of 60 m,
bottom depth permitting.
Instruments deployed on the glider: The Slocum glider was equipped with a SeaBird
non-pumped, low-drag conductivity, temperature, depth (CTD 41CP) recorder
optimized for lower power operation, an Airmar altimeter, and two WetLabs
Environmental Characterization Optics (ECO) Pucks. This CTD configuration,
combined with the slow horizontal and vertical speeds (10 - 20 cm/s) inherent to
gliders, resulted in significant spiking in recorded salinity profiles. Spiking of
salinity profiles is primarily due to several factors: 1) short-term
conductivity/temperature measurement misalignment, 2) heat/cold stored in the wall
of the conductivity cell which warms or cools the water parcel to a temperature
different from the "free-stream" temperature measured by the temperature probe
(Morison et al. 1994), and additionally 3) insufficient flow over the sensors that
accounts for the increased salinity variation near the thermocline, as observed in our
155
data set. Salinity spiking effects are noticeably more severe in ocean regions that
have sharp thermal gradients. Profiles that showed strong spiking effects (outliers
that had greater value than 3 standard deviations above running mean) removed from
further data analysis.
One of the ECO Pucks housed a triplet of fluorometers that measure chlorophyll a,
colored dissolved organic matter (CDOM) and phycoerythrin/rhodamine. The
excitation/emission wavelengths were 470/695 nm, 370/460 nm and 540/570 nm,
respectively. The values reported for the chlorophyll and CDOM fluorometers use
factory calibrations. The second ECO Puck housed a scattering meter that measured
optical scattering at 117° for wavelengths of 532, 660, and 880 nm. The spectral
shape of backscattering is highly dependent on particle absorption in the ocean, so
wavelengths for our backscattering sensors were chosen to minimize interference
from chlorophyll and CDOM absorption (Bricaud et al. 1983; Roesler and E. Boss
2008). Backscattering was calculated according to the manufacturer’s calibration and
corrected for temperature and salinity. Particulate backscattering coefficient,
b
bp
(λ)was subsequently calculated following b
bp
(λ)=b
b
(λ)-b
bw
(λ), where estimate of
pure seawater backscattering coefficient was based on Morel (1974). Only data from
the descending portion of the Slocum dives are used in this paper, and the
phycoerythrin/rhodamine fluorescence data are not discussed.
156
Chlorophyll values observed during this study were high compared to the previously
observed values (Jones et al. 2002; Schnetzer, pers. comm). Comparison with
concurrent chlorophyll samples that were obtained in the area during the deployment
period (Schnetzer, pers. comm), indicates that the factory-calibrated chlorophyll
concentration from glider chlorophyll fluorescence was approximately 3 times the
concentration found in direct water samples that were analyzed fluorometrically
following acetone extraction. For this reason, we regard chlorophyll a concentrations
presented here as relative values only.
Depth integrated current calculation: The glider obtains a GPS fix each time it
surfaces. These GPS fixes provide a measure of the total glider displacement
between surfacings. Measurements of pressure, pitch and heading enable the glider
to dead reckon its position during a dive. The difference between measured and
reckoned displacement relative to the water divided by the duration of the dive is an
estimate of the depth-integrated current during set dives between surfacings.
Although these estimates contain a slight error due to the slower ascent and decent
rates of the glider near the top and bottom of profiles, the overall estimates are a
robust estimate of depth-integrated currents (Fig. 4-2).
157
RESULTS
Background Conditions: Throughout both deployments estimated depth-integrated
currents were predominantly northwestward, with spatial and temporal variations
apparent in the data set (Fig. 4-2). Current speeds were typically less than 0.25 m s
-1
on the continental shelf, and over the slope speeds were sometimes more than 0.5 m
s
-1
(Fig. 4-2).
Figure 4-2. Depth-integrated currents in San Pedro Bay during the Slocum glider deployment.
Bathymetry (gray), coastline (heavy black), and the OCSD outfall (blue) are indicated as in Figure 4-
1. Green vectors represent the depth-integrated currents measured by glider. A vector scale
representing a current of 25 cm s
-1
is shown in the lower right of the figure.
158
Transects of the San Pedro Shelf revealed three distinct hydrographic regions:
offshore waters, plume dominated waters, and a coastal, stratified region. Offshore
waters are defined by the dominating presence of subsurface low salinity (33.2)
water mass, centered around σ
θ
= 24.4 kg m
-3
isopycnal (Fig 4-3A). This salinity
minimum layer had an average thickness of 15-20 m, and was centered between 20
and 40 m depth. It was sometimes detected on the shelf at the nearshore end of the
transects where the water depth was ~20 m. This low salinity water mass derives
from California Coastal Current (CCC), that transports low salinity subartic water
southward into the Southern California Bight (Hickey 1979). A distinct subsurface
chlorophyll maxima (SCM) was found, with chlorophyll of ~10 mg m
-3
, was
centered on the 24.8 kg m
-3
isopycnal on the deeper side of the CCC layer (Fig. 4-
3D). The SCM coincided with a backscattering layer centered along the same
isopycnal (Figure 4-3E).
A second cool, low salinity feature was observed near the shelf edge. Salinity in this
feature ranged from 33.1-33.3 and temperatures were less than 14 °C (Fig 4-3A and
4-3B). In contrast to the offshore feature, this feature appears to be the result of the
discharge from OCSD’s sewage outfall diffuser located 5-6 km east of this transect.
Although no chlorophyll signature was found associated with outfall plume, the
discharge plume appears to lift the isopycnal surfaces and the SCM, but with no
obvious mixing observed (Fig. 4-3D). This low salinity region is has a large CDOM
159
signal, usually >4 - 4.5 ppb, and high backscattering associated with the plume (Fig.
4-3C and 4-3E).
Figure 4-3. A cross-shelf section of (A) salinity, (B) temperature, (C) CDOM, (D) chlorophyll, and
(E) optical backscattering coefficient at 880 nm obtained on September 17-18, 2006. The along-track
distances (km) are referenced to the coastline. The displayed transect is highlighted in red in the inset
map. Potential density anomaly is overlaid on each panel with white contour lines with a contour
interval of 0.2 kg m
-3
. The σ
θ
= 25.0 kg m
-3
isopycnal is indicated by the heavier contour line. The
white triangular region in the lower right corner of each panel is the region where the bottom is rising
up over the shelf. Location of the OCSD outfall pipe is noted on the inset map (blue).
A region of high backscattering with intermediate CDOM concentrations (3.5-4 ppb)
was found in the bottom boundary layer of shoreward of the outfall plume. Higher
CDOM concentrations near the seafloor may indicate suspended particulate organic
material rather than dissolved organic matter since a near-bottom nepheloid layer is
common in the region (Jones et al. 2002). Experience from other regions suggests
160
that near-bottom particulates may contribute to organic absorption and fluorescence
(Jones et al., unpubl.). The highest backscattering coefficients (b
b
) observed in the
nearbottom area occurred on September 22-23, toward the end of first Slocum
deployment. Maximum values of b
b
were 0.05 m
-1
at 532 nm, 0.04 m
-1
at 660 nm,
and 0.03 m
-1
at 880 nm
indicative of strong resuspension of the bottom sediment.
This resuspension b
b
signature was typically observed up to 10 m above the seafloor
(Fig. 4-3E), and on September 22
-
23, high backscattering was detected up to 20 m
above the seafloor. In this region located over the shelf, stratification was obvious in
temperature and in salinity, the SCM was positioned closer to the surface, and
chlorophyll values were lower then found off shore. Chlorophyll fluorescence was
low near the surface, except during the last few days of the study after September 26
when higher chlorophyll associated with lower salinity was found in the nearshore
surface waters.
Effluent plume: The glider tracks were within 15 km of the OCSD ocean outfall in an
effort to identify and monitor variability effluent plume. Salinity profiles in vicinity
of the OCSD outfall show the presence of the second salinity minimum, spatially
offset from the subarctic waters of the previously mentioned California Coastal
Current during this period (Fig. 4-3B). The effluent plume, characterized by the
salinity anomaly, centered at σ
θ
= ~25.0 kg m
-3
, spreading in the westward and west-
northwest direction from the outfall, is consistent with the depth integrated currents.
161
The proximity of the effluent plume to the σ
θ
= 25.0 kg m
-3
isopycnal suggests that it
spread along this density surface after its initial buoyant rise and mixing phase after
discharge (Roberts et al. 1989).
Figure 4-4. Potential temperature versus salinity for downward portion of each glider dive cycle.
CDOM concentration is indicated by the color of each dot and in panel B color is corresponding
chlorophyll concentrations. The region labeled with 1 is the low salinity anomaly at potential
temperatures less than 14 °C corresponding to the effluent plume from the OCSD outfall. Region 2 is
the minimum in salinity near theta 16 °C indicative of subarctic waters from the California Current
apparent in the majority of profiles. Region 3 is lower salinity nearsurface water encountered
nearshore most likely due to the Los Angeles/San Gabriel River plume.
Nearly all potential temperature-salinity (θ-S) profiles from the 1557 dives of the
Slocum glider showed a minimum in salinity near 16 °C that was representative of
California Coastal Current (Fig. 4-4). As explained previously, the larger salinity
162
variation in the Slocum data (Fig. 4-4A), especially near the thermocline, was due to
the configuration of the CTD.
Figure 4-5. Location of Slocum dives where the effluent plume was indicated by low salinity
anomalies (red dots) and by CDOM anomalies (blue circles). Bathymetry (grey), coastline (heavy
black), the OCSD outfall (blue), and the track of the Slocum glider (black) are as in Figure 4-1.
In a subset of the θ-S profiles (Fig. 4-4) a second salinity minimum below 14 °C
was observed. We identified any portion of a θ-S profile with 1) a local minimum in
salinity, 2) potential temperature less than 14 °C, 3) potential density anomaly, σ
θ
,
greater than or equal to 24.6 kg m
-3
, and 4) a deviation from the background θ-S
profile that was greater in magnitude than the small-scale variability in the remainder
163
of the profile as indicative of the effluent plume from the OCSD outfall (Todd,
personal communication). These criteria were satisfied by portions of 194 dives.
Although dive location was not a criterion for identifying the effluent plume, nearly
all dives that showed evidence of the plume were located directly downcurrent from
the OCSD outfall (Fig. 4-5 blue circles).
In order to evaluate CDOM concentration as an additional variable for discerning the
effluent plume, we examine the relationship between CDOM and T-S variability.
Within the T-S region where the plume is observed, CDOM and salinity are
negatively correlated (Figure 4-4). Although values from 4-4.5 ppb are correlated
with the plume, we disregard them since similar values were also observed within
the bottom boundary layer and therefore may not provide an unambiguous
delineation of the plume. When applied to our dataset, CDOM criterion was satisfied
by portions of 140 glider dives, out of which 122 were also selected using θ-S
criteria (Fig. 4-5). All but one of these selected dives were located northwest of the
outfall.
DISCUSSION
Plume detection using multiple water properties: Hamilton et al. (2004) showed that
the effluent plume from the OCSD outfall was characterized by a salinity minimum
164
at temperatures below 14 °C that stood out from the background. Jones et al. (2002)
and Hamilton et al. (2004) used salinities below an empirical threshold combined
with detectible levels of fecal indicator bacteria to identify the effluent plume from
the OCSD outfall. Here we compare two methods of tracking the plume in the
coastal ocean: θ-S criteria and θ-S criteria combined with CDOM fluorescence.
Regardless of the data set or criteria used for identification, the core of the effluent
plume was detected primarily west or northwest of the OCSD outfall (Fig. 4-5)
consistent with the observed depth-integrated currents (Fig. 4-2). Although our
observations to the south and east of the outfall were sparse, some of the glider dives
in that area detected water that met the θ-S criteria (Fig. 4-5). In our dataset, only 60
% of the dives selected by the θ-S criteria were also selected with the CDOM
criterion. When CDOM-θ-S criterion is applied, all selected dives are positioned
west or northwest from the diffuser, as expected from the trend observed in
integrated currents. It is likely that the θ-S criterion included some observations from
the California Coastal Current to be included due to the CCC’s proximity in θ-S
space.
CDOM has previously been proven to be a useful supplement to traditional
temperature and salinity techniques for characterizing water masses (Belzile et al.
2006; Hojerslev et al. 1996; Oliver et al. 2004), such as sewage plumes in coastal
165
oceans (Petrenko et al. 1997) and river plumes (Mcmanus et al. 2003). Although
CDOM is considered to be nonconservative property of water masses in the large
scale (Tomczak 1999), the temporal and spatial scales of interest for this study are
sufficient that CDOM fluorescence can be considered a conservative property. For
the analysis of the Huntington Beach glider dataset CDOM fluorescence clearly
provides improved discrimination of plume waters in the coastal ocean. It enabled
better distinction between water masses of similar θ-S characteristics, and provides
for real time discrimination without sample pretreatment and time consuming
analyses (Belzile et al. 2006). This method has direct utility in the coastal ocean
where real time and correct plume detection could help coastal managers grappling
with beach closures and sewage spill responses.
Along shelf transport: Previous research in nearshore regions of Southern California
has defined three (Washburn et al. 1992; Wu et al. 1994) or four groups (Jones et al.
2002) of suspended particles using beam attenuation coefficient and chlorophyll
fluorescence that include phytoplankton, near-bottom suspended particles, particles
from treated sewage effluent, and suspended particles from terrigenous runoff.
Metals, bacteria and other organic constituents are often associated with the small
particles present in effluent plumes (e.g. Steinberger and Schiff 2003). Particles with
density greater than that of seawater, sink near the point of discharge, but this
represents only about 10% of the total particles originating from a sewage discharge
166
(SAIC et al.,2001). The sinking speeds and settling locations of other particles are
dependent upon their size, density, local currents, and turbulence.
Figure 4-6. Scatter plots of optical backscattering vs. CDOM (left) and b
bp
(550)/b
bp
(880) vs. CDOM
(right). Black circles represent all the data collected from the downward portion of the dive.
CDOM has been shown to be effective for differentiation of turbid buoyant layers
from turbid benthic nepheloid layers (Schofield et al. 2004), so we use CDOM to
distinguish and trace particles from the sewage effluent. The bulk backscattering
coefficient is dependent on particle concentration, particle size distribution (PSD),
refractive index (material composition) and particle shape. The wavelength-
dependent spectral slope of backscattering correlates with the PSD, where increase in
slope indicates a shift toward smaller particles in the total particle concentration
(Loisel et al. 2006; Reynolds et al. 2001). Therefore, the particulate backscattering
spectral ratios can be used as a proxy for PSD. The backscattering coefficients and
spectral particulate backscattering ratios (b
bp
(532)/b
bp
(880)) associated with the
plume have a distinct pattern (Fig. 4-6). Above CDOM concentrations of ~4 ppb,
b
bp
880 tends to center around the 0.003 m
-1
, while b
bp
(532)/b
bp
(880) appears to
167
narrow to a value of about 3.5 (Fig. 4-6). The combined primary and secondary
treatments of the sewage remove 85% of solids prior to discharge into the ocean
(OCSD 2007). The remaining solids are likely the source of this highly specific
signal observed in backscattering. Thus, the effluent plume contains both suspended
particulate matter and CDOM from the effluent. In fact, a portion of the CDOM
signal may be organic particulate matter that fluoresces similarly to the dissolved
organic matter (Jones, unpublished). The mean trend of the particulate backscattering
signal is following the decrease in CDOM fluorescence, although the range of the
observed backscattering values is increasing (Fig 4-6). In addition, the mean of the
spectral backscattering ratio remains approximately constant, and the variance
increases, indicative of the broader particulate size distribution. These observations
are likely due to the processes such as aggregation and sinking of particulate material
that modify the particle field within the plume.
Babin et al. (2003) looked at the variation of the mass-specific backscattering
coefficient of particles (b
m
p
(555)) in different coastal environments, where b
m
p
(555)=b(555)/SPM. They found that for coastal waters, regardless of the composition
(organic vs. mineral), the average value was ~0.5 m
2
g
-1
. If we assume that the
backscattering coefficient at CDOM values above 7 ppb is most representative of the
core of the effluent plume prior to significant ambient mixing processes,
flocculation, degradation processes, and values of CDOM around 4.5 ppb are
168
representative of a more mixed and diluted plume, we can define the effluent plume
specific backscattering ratio. For our data set the average b
bp
(555) value is
0.0103±0.0013 m
-1
(median= 0.0106 m
-1
) for CDOM concentrations >7 ppb, and for
more dilute plume water where CDOM concentrations were ~4.5ppb, b
bp
(555)
ranged from 0.0030 m
-1
to maximum values of 0.0169 m
-1
. Assuming that most of
the particulate material found within the plume is detrital, and that chlorophyll
concentration is low within the effluent plume, we can use previously published
values of the backscattering ratio (b
bp
(λ)/b
p
(λ)) to calculate scattering coefficients for
the effluent plume. Whitmire et al. (2007) found that non-algal, organic particles
have a backscattering ratio of 0.016. Loisel et al. (2007) found a ratio of 0.022±0.007
for post-bloom, high detrital water. Using the values from Loisel et al. (2007), our
scattering coefficient at 532 nm would be 0.47 m
-1
(range: 0.31-0.77 m
-1
) for high
CDOM values, and in case of mixed plume 0.19 m
-1
for minimum and 1.05 m
-1
for
the highest backscattering values observed within the plume. For the b
m
p
(555)
calculation, we assumed the differences in b
m
p
at 555 nm and 532 nm are negligible.
SPM concentration for samples where highest CDOM values are encountered would
be 0.94 mg L
-1
(range: 0.62 – 1.55 mg L
-1
), and for mixed plume SPM value would
range from 0.38 mg L
-1
to 2.11 mg L
-1
, respectively. The larger range of SPM
concentration found in the diluted plume (CDOM ~4.5 ppb), compared with values
encountered in the “core” of the plume, are consistent with expected modification of
the particle field associated with the plume due to the physical and chemical
169
processes associated with plume mixing. The dilution factor of “core” of the plume
was 1:104, (calculation based on Petrenko, Jones et al (1998) and Xu (2004)), and
from there we can calculate that in the plume when released from diffuser, the
concentrations of the SPM were >90 mg L
-1
. This finding is higher than 35 mg L
-1
reported for the SPM within the released effluent, but still well within the expected
limits. The results shown here confirm that the effluent plume produces a significant
flux of suspended particulate material (SPM) into the coastal ocean.
In our study, chlorophyll a was present throughout the upper 60 meters of the water
column with highest concentrations, as expected, in the SCM, and scarce chlorophyll
a signal in surface (Fig 4-3). The low chlorophyll a concentrations observed in the
offshore surface layer are characteristic of the stratification typical for late summer-
fall period, when nutrients are depleted following summer stratification (Cullen and
Eppley 1981). The formation of the SCM in the proximity of the
pycnocline/nutricline that coincided with 14-15 °C water was observed before in this
region (Schofield et al. 2007). Various authors have shown that nitrate generally
increases linearly with decreasing temperature, starting at ~14 °C off southern
California (Dugdale et al. 1990; Jones et al. 1983; Noble et al. 2009). Sewage
effluent is another source of (new) nitrogen in the coastal ocean, with ammonia
concentrations in outfall plumes ranging from about 0.5 to more than 20 μmol L
-1
(OCSD 2007). In this nutrient depleted system as ours, such a high nutrient input
170
could have a large impact on the phytoplankton abundance as well as the community
structure. On the Figure 4-4B, we can see that most of the high chlorophyll is
associated with the California Coastal Current water mass. For the glider profiles
obtained from this study, high chlorophyll was never associated with plume water. In
most of the cases, SCM was either lifted higher in the water column by the plume.
Advection rates of the plume may have been too high for phytoplankton to benefit
from the nutrients associated with the plume between the time of discharge and the
time of crossing the glider track. In her study of the Sand Island outfall, Petrenko et
al. (1997) found higher chlorophyll fluorescence associated with sewage plume
characterized by low Froude number, i.e. comparatively low advection rates, but
found no community response in phytoplankton populations associated with the
plume. Additionally, the position of the plume in the water column changes, based
on its volume, density, coastal currents, and other processes such as internal tides
(Petrenko et al., 2000). Such spatial variability occurs on smaller time scales than the
average doubling rate of phytoplankton (nominally, once per day), and may not yield
a measurable response from the phytoplankton population.
171
Figure 4-7. Relationship between optical backscattering and chlorophyll a (top), and chl/b
bp
values
versus depth along a segment of the track across the San Pedro shelf (bottom). The details of the
section in the lower panel are the same as in Figure 4-3.
Cross shelf transport: The possibility of cross-shelf transport of the plume is a great
concern for this region, not only because of the potential for transporting microbial
contamination into the nearshore zone, but also because of the transport of nutrients
into the nearshore region. Pulses of nutrient-rich water into the nearshore euphotic
zone could contribute to the phytoplankton population and possibly stimulate a
harmful algal bloom (HAB) response. During our study, we observed the rise of
deeper low salinity water up to a depth of 15 m (Fig 4-3). The chlorophyll signal
associated with that water and lack of CDOM signal, confirms that it is CCC, not
plume water being transferred to the inner-shelf region. Previous studies (Boehm et
172
al. 2002; Rosenfeld et al. 2006) found that shoreward internal tide driven
propagation lifts the pycnocline along the shelf bottom, and may bring deeper water
to the surface. Although, some hypothesized that internal tide-driven pulses could
transport plume water into the surf zone (Grant et al. 2000; Rosenfeld et al. 2006),
we did not encounter plume water nearshore. Lower temperature and salinity water
with chlorophyll >3 mg m
-3
was encountered within 4 km of the shore. Coincidence
of nearsurface chlorophyll with lower salinity nearsurface water such as the Los
Angeles and San Gabriel Rivers suggest an origin from these runoff systems.
SUMMARY
We have presented the use of autonomous gliders for mapping and characterization
of coastal variability and the influence of anthropogenic inputs on the urban coastal
ocean. During the one-month deployment, the glider provided resolution of small
spatial and temporal scales. Optical and physical sensors deployed on the glider
enabled discrimination of effluent from the OCSD outfall using temperature, salinity
and CDOM concentration. Low salinity anomaly and elevated CDOM
concentrations characterized plume water and indicated that it spread primarily west
to northwest of the outfall diffuser consistent with the direction of observed currents.
The good agreement between the two methods suggests that both are reliable, and
highlights measurements of CDOM co-ncentration as powerful tool for detecting and
173
characterizing water types in the coastal ocean. Including CDOM as an additional
variable is particularly useful when traditional water mass tracers are insufficient
such as when there is overlap between the position of the plume and other “natural”
water masses in θ-S space. The specific backscattering signal associated with the
high CDOM concentrations (above 4.5 ppb), which we interpreted as representing
the core of the plume, enabled us to calculate the concentration of suspended
particulate material in the effluent plume, and subsequently estimate the particulate
load of the effluent plume in the coastal ocean. From previous work such as
Petrenko et al. (1997), we expected some effect of the effluent plume on the
subsurface chlorophyll maximum. Although the plume appeared to displace the
subsurface chlorophyll maximum upward, it did not appear to have a direct effect on
biomass possibly due to the discrepancy of the plume spatial and temporal presence
and timescales and position of phytoplankton population. Cross-shelf transport of the
plume-associated water was not observed, although the presence of possible
mechanism, internal tide driven water upwelling, was.
Because gliders can be deployed in an area for extended periods, they are excellent
tools for sustained monitoring of the coastal ocean environment. In the Southern
California Bight where urban influences are of significant concern, the combination
of physical measurements (T, S, density, and integrated currents) with bio-optical
fluorescence and backscattering sensors provides a powerful capability for
174
characterizing particle fields and/or dissolved organic material that are often
indicative of contaminant sources. Thus, gliders are an important resource for
sustained monitoring of the natural coastal oceanographic processes, monitoring the
input and dispersion of a variety of anthropogenic inputs that includes sewage
outfalls and stormwater runoff, and monitoring and detecting changes in the
phytoplankton population in response to both natural and anthropogenic processes.
Gliders are fundamental tools that can be integrated with other observational
resources including satellite remote sensing, traditional boat-based observations,
telemetering moorings, and HF radar. By assimilating the more traditional and newer
glider bio-optical observations into coupled physical-biogeochemical models, new
insights into the biogeochemical dynamics of the coastal ocean will emerge (Boss et
al. 2008 ). In addition to their merit for fundamental scientific research, gliders
provide an invaluable resource for understanding coastal ocean processes and the
influence of anthropogenic inputs on coastal processes, providing a resource for
regional managers who require near real time data make appropriately informed
management decisions and enacting new policy that will better protect our ocean
resources.
175
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CHAPTER V: Calibration procedure for Slocum glider deployed optical instruments
CHAPTER V ABSTRACT
Recent developments in the field of the autonomous underwater vehicles allow the
wide usage of these platforms as part of scientific experiments, monitoring
campaigns and more. The vehicles are often equipped with sensors measuring
temperature, conductivity, chlorophyll, CDOM, phycoerithrin fluorescence and
spectral particulate backscattering, providing users with high resolution, real time
data. However, calibration of these instruments can be problematic. Most in situ
calibrations are performed by deploying complementary instrument packages or
water samplers in the proximity of the glider. Laboratory calibrations of the mounted
sensors are difficult due to the placement of the instruments within the body of the
vehicle. For the laboratory calibrations of the Slocum glider instruments we
developed a small calibration chamber where we can perform precise calibrations of
the optical instruments aboard our glider, as well as sensors from other deployment
platforms. These procedures enable us to obtain pre- and post-deployment
calibrations for optical fluorescence instruments, which may differ due to the
biofouling and other physical damage that can occur during long-term glider
deployments. We found that biofouling caused significant change of the calibration
182
of fluorescent sensors, and the quality of the data without the consistent and
repetitive calibrations is questionable.
INTRODUCTION
The development of the new class of autonomous underwater vehicles (AUVs)
allows observations of the ocean with better spatial and temporal coverage necessary
for understanding more complex ocean processes. Modern underwater gliders are
relatively small (about 2 m in length), buoyancy-driven vehicles that complete dives
to depths up to a few hundred meters in the span of a few hours while covering
approximately 3 km horizontally. Gliders can be deployed for several months
carrying a variety of oceanographic instruments tailored to a particular mission.
Combination of optical measurements from gliders can provide proxies for
phytoplankton biomass (Cullen et al. 1982), dissolved organic material absorption
(Belzile et al. 2006), dissolved organic carbon (Amon et al. 2003), total particulate
mass and particulate organic carbon(Stramski et al. 2008), evaluation of light
availability for photosynthesis, optical tracking of the water masses and much more.
Gliders can acquire large optical datasets during a single deployment, for example a
three-week glider deployment in a coastal area can complete ~4,000 profiles up to 60
m depth, with most of the instruments sampling at ~ 2 Hz, resulting in more than
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240,000 data points per instrument per deployment. Data received can be processed
in almost real time and assimilated into physical and biogeochemical models (Dickey
2003).
Many issues arise due to the nature of the instrument and long time deployments.
Factory calibrations usually differ from calibrations performed by the user after
receiving the instrument for many reasons: calibration standards used by the factory,
factory calibration performed prior the insertion of the optical instrument into the
glider, or offsets that can occur on the instruments during shipping. Instruments
signals can fluctuate and drift due to power and temperature oscillations (Roesler and
E. Boss 2008; Twardowski et al. 2007). Biofouling and physical damage to the
sensors can occur during long-term deployments, especially in coastal areas. Optical
instruments deployed in the ocean are strongly affected by continuous biofilm
growth on the optical windows (Lehaitre et al. 2008). Only 10% coverage of the
optical path can have a strong effect on the quality of the data, and based on previous
experiments, that amount of biofouling can be acquired on an instrument after one to
three weeks deployment (Lehaitre et al. 2008), which is within the timescales of a
slocum glider deployment.
In situ calibrations of instruments deployed onboard gliders are done by deploying
calibrated complementary instrument packages in the proximity of the glider
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(Niewiadomska et al. 2008; Perry et al. 2008). Vicarious calibrations using remote
sensing data from satellites can be performed (Boss et al. 2008; Jones et al. 2008),
but retrievals of the inherent optical properties in the coastal areas are not so reliable.
In order to use these datasets for future biogeochemical models, or to constrain
uncertainties related to the ocean color remote sensing measurements (Boss and
Maritorena 2006) we must be able to distinguish natural from instrumental
variability, and ensure that we are not trading quantity for quality.
Laboratory calibrations are complicated due to the placement of the instruments
within the body of the vehicle. Removal of these sensors is generally not practical,
since sensors become an integrated with the glider. Therefore, in order to calibrate
these instruments with some standard, the whole vehicle, or a section of it has to be
shipped to the manufacturer for an extended period of time. Additionally,
instruments should be calibrated in the mounted position, so the reference calibration
pertains to the actual deployment calibration as much as possible.
Here we present a calibration procedure using a small chamber that enables easier
laboratory calibrations of Slocum glider optical sensors. Additionally, we evaluate
several different calibration standards and describe the calibration chamber that
allows easy pre- and post-deployment calibrations, providing correction of the long
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deployments datasets. We also look at the impact of biofouling on the optical
fluorescence instruments onboard gliders during long term deployments.
MATERIALS AND METHODS
Instrumentation testing: Two Slocum gliders were tested during the laboratory and
field experiments, each one equipped with a SeaBird non-pumped, low-drag
conductivity, temperature, depth (CTD 41CP) recorder optimized for lower power
operation, an Airmar altimeter, and two WETLabs Inc Environmental
Characterization Optics (ECO) Pucks. These ECO Pucks house a triplet of
fluorometers to measure chlorophyll a, colored dissolved organic matter (CDOM)
and phycoerythrin/rhodamine. The excitation/emission wavelengths were 470/695
nm, 370/460 nm and 540/570 nm, respectively. The second ECO Pucks is a
backscattering sensor that will not be used in this experiment. The ECO Pucks are
mounted in the bottom part of the science bay, facing downwards (Fig 5-1A).
Calibration: Raw voltages from the instrument are converted to concentration of the
targeted constituent using the following linear relationship:
[constituent] = scale factor * (output – dark counts) (1)
186
where dark counts (DC) is the signal measured in the absence of targeted constituent
and scale factor is the linear fit between the instrument output and the real
constituent concentration. Initial calibration of the tested fluorescence sensors was
performed by the manufacturer, using a suspension of chlorophyll standard for
chlorophyll fluorescence, and a quinine sulfate standard for CDOM fluorescence.
Figure 5-1. A) Slocum glider with science bay where optical instruments are located, B) calibration
chamber, C) calibration chamber with glider mounted on it, ready for calibration.
Dark counts measurements: The most accurate way of measuring the dark counts in
these instruments is to cover all the detector windows with black electrical tape, but
187
leaving the emitter windows uncovered (Twardowski et al. 2007). While measuring
dark counts, we repeated the measurements several times, and readjusted the dark
tape, so we were sure that the measurements were consistent and with minimal or no
light penetration possible. In order to test the temperature dependency of the dark
counts, both laboratory room temperature and Milli-Q water within the chamber
were set at 3 different temperatures (16, 20, and 25 °C). Additionally, the effect of
environmental and platform dependent (power) parameters on the dark count values
was tested in the field, with gliders deployed with covered pre cleaned sensors (as
explained above) on several dives prior to a normal glider deployment.
Chlorophyll standard: For the chlorophyll calibration procedure we used a mixture
of local phytoplankton isolates from Southern California Bight. Cultures of
Lingulodinium polyedra, Prorocentrum gracile, Thalassionema sp, Pseudo-nitzchia
spp. and unidentified nano protists were grown in F/2 media, on 20° C and 12:12
light/dark regime. The primary standard was made by mixing cultures with locally
collected, filtered (0.2 μm pore size) sea water bringing the total volume to ~20 L
(chamber volume). Lower concentrations were made by diluting the primary
standard with the filtered sea water, and the 0 mg chl L
-1
by using Milli-Q water.
This non-standard serial dilution approach ensured that the standards are comparable,
and there are minimal species composition differences between the standards.
188
Number of the calibration points throughout our experiment differed, due to the
chlorophyll standard (healthy culture) or time availability (glider turnover).
For each of the calibration dilutions, 50 mL of the standard was filtered onto GF/F
filters, and after 24 hours extraction in 90% acetone, chlorophyll concentration of the
sample was determined in vitro using a Turner bench top fluorometer (Parsons et al.
1984).
Due to the previously reported differences between the Mili-Q water and filtered sea
water (FSW), and the importance of using FSW as a blank for the correct calibration
(Cullen and Davis 2003 ), we ran a series of tests on both of the gliders and
measured differences between FSW (0.2 μm pore size filtered calibration standard)
and Mili-Q water. For our sensors, we found that values for FSW were 0.05±0.02,
which is well within the confidence range of our instruments, and therefore used
Mili-Q water as our “blank” calibration standard.
Colored Dissolved Organic Matter (CDOM) standard: The standard calibration
procedure for WETLabs CDOM flourometers is performed using a solution of
quinine sulfate in 0.5 M H
2
SO
4
(Belzile et al. 2006). Because the solution’s low pH
could potentially damage the instrument and the glider body itself, quinine sulfate
standard was not used for calibration of the glider deployed instruments. Instead, we
used several dilutions of Sprite Zero
®
(The Coca-Cola Company) and MilliQ water
189
to make a calibration curve for the glider CDOM fluorometer (Walsh, personal
communication).). Sprite Zero
®
was chosen because its optical characteristics are
similar to dissolved organic matter, possibly due to the presence of the aspartame
(Hayakawa et al. 1990; Wróbel and Wróbel 1997), easy availability and because it is
made to a consistent standard (Walsh, personal communication).). Walsh (personal
communication) found Sprite Zero/quinine sulfate scale factors ratio to be 0.73,
when tested on a series of fluorometric measurements. To remove the carbonation of
the Sprite Zero, ~ 20 L of the solution was de-bubbled overnight using a slow
turning magnetic stirrer.
Calibration chamber: The Calibration chamber is a box shaped container built out of
1.9 cm thick black acrylic, and shaped so the length and the width (35.5 cm) of the
box is 7 cm longer and wider than the slocum glider science bay (Fig 5-1B). The side
plates of the chamber are 32.5 cm high, while two opposite side plates of the
calibration chamber are shaped such that the optical sensors mounted in the glider
science bay are immersed in the calibration standard (Fig 5-1C), and the optical
windows of the instruments are 20 cm above the bottom of the chamber but still
submerged into the standard. The inside of the chamber is black and matted to
minimize the possibility of light contamination. The chamber and glider are draped
with an opaque black blanket to prevent external light form entering the chamber.
190
Glider deployments: During the fall and winter of 2008 and the spring of 2009 two
slocum gliders (named HeHaPe (H) and Rusalka (R)) were deployed a total of 3
times in a coastal area of the Southern California Bight. Deployments lengths varied,
from 7 to 10 days for glider R (fall) and a 21 day deployment for glider H (early
spring). Prior to the deployments, gliders were calibrated in the lab, using the above
mentioned standard and calibration procedure (pre calibration). Upon recovery, the
science bay was wrapped in wet cloth to prevent the drying of the possible
biofouling growth, and post-calibration was done in the lab within 24 hours of
recovery. First post calibration (post A) was done without cleaning the sensor
windows. Therefore those measurements were expected to be affected by biofouling
and instrumental drift. After the first calibration, all sensor windows were cleaned
using isopropyl alcohol, and another calibration was performed to identify non-
fouling instrument drift (post B). Time dependent linear interpolation between pre
and post A calibration was used to calculate new scale factors that were used to
process the data into concentration units (Roesler and E. Boss 2008).
RESULTS AND DISCUSSION
Dark counts (DC): Measured dark counts for the glider (R) mounted chlorophyll
fluorometer (immersed in the calibration chamber filled with Milli-Q water) ranged
from 50-53, with a mean value of 51 counts compared to the factory values of 50
191
counts. Dark counts measured for the glider mounted CDOM fluorometer varied
between values of 35-37, with a mean value of 36 (compared to dark counts of 34
from factory calibrations). Average DC for the chlorophyll fluorometer deployed on
glider H was 56 (range 53-58)compared to the 60 from the factory calibration, while
mean DC for the CDOM fluorometer was 50 (range 47-52), compared to the value of
62 from the factory calibration. For all our DC measurements, uncertainties were
defined as the DC range and propagated in further calculations Repetitive
measurements of the dark counts for both of the gliders (performed during the pre
and post deployment calibrations) for 80 % of the points varied in the predetermined
uncertainties range (Fig 5-2 dashed lines). During the field measurements of the dark
counts performed on the instruments deployed on glider H, we again found
discrepancies from the factory observed values (10-20% lower), but still within the
values observed pre and post deployment in the lab (Fig 5-2, dashed lines). Observed
offsets from the factory calibration dark scale numbers have been reported (Boss et
al. 2008 ; Twardowski et al. 2007), and may result from the different procedure in
dark count measurements, as well as the fact that the instruments were not mounted
within the glider bodies when the factory calibrations were performed.
It has been previously noted that fluctuations of the dark counts are dependent on
temperature(Cullen and Davis 2003 ; Roesler 2008) , other environmental parameters
(Twardowski et al. 2007), and power oscillations induced by other instruments and
192
motors on the platform (glider). Temperature dependency of dark counts was tested
in the lab using three different temperatures (16, 20, and 25 °C). The mean values of
the previously established dark counts did not vary much outside of the
predetermined standard deviation range (Fig 5-2). Dark counts measured in the field
to a depth of 40 m showed no significant temperature or pressure dependency
(temperature having inverse relationship to pressure; 11°C ~ 40 m depth). As can be
seen in Figure 5-2, highest outliers for both CDOM and CHL DC are found at the top
or the bottom of the dive (minimum and maximum temperature for the field dataset).
These outliers could result from a power oscillation caused by the ballast pump and
the battery positioning motor turning on a the top and bottom of the diver profiles.
Additionally, these outliers fall outside of 2 standard deviation range, and should not
be considered for the final calculation of the DC that will be used in the data
calculation and analysis.
Both laboratory and field measurements of the DC confirm the importance repetitive
laboratory measurements of DC, since differences in DC are significant enough
(~20% difference) to affect the final measurements. Additionally, these differences
are not comparable between the 2 instruments, and do not follow changing trends in
the environment. At the top and bottom of the dive, results must be used with caution
193
because platform dependent variability cannot be compensated by measurable
environmental effects.
Figure 5-2. Variability of Dark Counts as a function of temperature, observed for A) CDOM
fluorescence sensor and B) chlorophyll fluorescence sensor deployed on the gliders, triangles -field
deployment (instruments on H glider), and circles lab measurements (instruments on R glider).
Dashed line represents in laboratory predetermined mean values at 20°C, while dotted lines represent
standard deviation range for the same DC values.
Scale factors: Factory calibration scale factors differed from the slope obtained from
our experiments (Fig. 5-3). For both of the glider deployed chlorophyll fluorimeters
our scale factor (R=0.0305 μg chl L
-1
count
-1
, H=0.0293 μg chl L
-1
count
-1
) was
higher than the factory provided scale factors (R=0.0131 μg chl L
-1
count
-1
,
H=0.0121 μg chl L
-1
count
-1
). Uncertainties for our measurements were R=± 0.09 μg
chl L
-1
and H=± 0.14 μg chl L
-1
. As suggested by the manufacturer, and other authors
(Boss et al. 2008), our calibration was performed using a local species assemblage.
Experimental scale factors were up to 3 times higher than factory values, possibly
194
because of the differences between the pigment composition within the local
community and the chlorophyll standards used by the factory. As noticed before, the
excitation light used by this chlorophyll fluorometer (470 nm) is not directly
absorbed by chlorophyll, but rather by the photosynthetic accessory pigments (Perry
et al. 2008), which makes the fluorescence response dependent on the phytoplankton
community composition.
In situ calibrations from a parallel CTD casts or in situ water samples could be
affected by the variations in quantum yield of fluorescence and/or by the community
composition, especially in cases where the environment is highly stratified. Cultures
grown under constant conditions (light, nutrients, and temperature) provide
repeatable calibrations that facilitate accumulation of long term intercomparable
datasets.
CDOM fluorometer calibrations showed similar offsets where the scale factor
(H=0.1561 ppb/count, R=0.1027 ppb/count) differed up to 50% from the initial
factory calibrations (H=0.0939 ppb/count, R=0.0908 ppb/count), as it can be
observed on Figure 5-3B. Uncertainties for our measurements were R=±0.21 ppb and
H=±0.62 ppb. Large differences in scale factors could be attributed to the calibration
standard, but when the same standard is applied to the stand-alone ECO triplet (data
not shown) difference on scale factors (factory vs. in laboratory) is only 5%. This
195
suggests that large differences between scale factors are due to the placement of the
instrument in the body of the glider, i.e. the factory calibration is not performed
while the instrument is mounted on the vehicle or platform.
Figure 5-3. Comparison of the factory and in house calibrations for glider deployed fluorometers. X
axis presents the instrument output minus dark counts values, and y-axis presents concentration of the
standard. Dashed line with triangles represents in-house calibration points for the H glider, while solid
line with circles calibrations for R glider. Dashed line (H) and solid line (R) with no markers are
factory calibration values. A) CDOM fluorescence sensor and B) chlorophyll fluorescence sensor
deployed on the glider.
Assessment - Glider deployment: Our gliders were deployed within a productive
coastal area, and spent ~ 50% of their time in the euphotic zone, with most dives
going to 60 m, and deepest dives to 80 m (along San Pedro shelf). Extensive
biological growth was found on the glider bodies after each deployment (Fig 5-4),
even after a short 7-day deployment. Following the pre and post calibration
procedure for the several glider deployments, we observed differences (up to 120%)
between pre, post A and post B calibration values (Figure 5-5 A and B).
196
Figure 5-4. Biological growth found on body of HeHaPe glider after 3 weeks of deployment in
Southern California Bight. Black squares highlight goose-neck barnacles growing on the sides one of
the ECO pucks, near the optical windows.
Instrumental drift, defined as the difference between the pre and post B calibration,
could be due to the changes at both the detector, as seen in the pre and post dark
counts differences, and the emitter. Instrumental drift did not seem to have a large
effect on our calibrations, 5% for the CDOM fluorometer and <1% for the
chlorophyll fluorometer, which falls within the error previously determined for the
DC (Fig 5-2).
Biofouling caused drift, defined as the difference between post A and post B
calibrations, and was large for both the CDOM and chlorophyll scale factors (Fig 5-
197
5). Biofouling caused large drift, but no trend based on the deployments length was
obvious.
The formation of biofilms, although highly dependent on environmental factors, is
thought to have 5 successional stages (Lehaitre et al. 2008) starting with adhesion of
inorganic and organic molecules, progressing through bacterial and algal
communities, and maturing into a macroorganismal community. It is possible that
differences between the biofouling related drifts are due to the different stages in the
succession of the biofilm, as well as the ambient community and other environmental
conditions present during the deployments. The glider deployments used in our
analysis are from different periods of the year (7 days – summer/fall, 10 and 21 days
– winter/early spring) perhaps resulting in seasonal effects on the community
composition of the biofilm layer. Organic molecules (predominantly
exopolysaccharides), chlorophyll, and other pigments assumed to be part of the
biofilm deposited on the optical windows. The pigments absorb light in the portion
of the spectra where the fluorometer excitation for CDOM and chlorophyll are
located (370 and 470 nm respectively)(Decho et al. 2003). It is possible that growth
on the optical windows blocks or absorbs some of the light coming from the emitter,
therefore lowering the expected (calibrated) amount of light reaching the instrument
detector. If the biofilms fluoresce at the detector wavelengths, a cumulative effect
that includes both concentration in the measurement volume and the biofilm on the
instrument windows will result in higher that expected measurements.
198
Figure 5-5. Comparison of pre, post A (without optical window cleaning) and post B (with cleaning
of the optical windows) scale factors values for R 10 days deployment (empty circles) and H 10 day
deployment (black triangles), and H 3 week deployment (open triangles) expressed as percentage
difference from the initial pre calibration. Panel A – CDOM fluorometer, Panel B Chl fluorometer.
In order to correct our dataset, we assumed that the scale-factor drifts linearly with
time between the pre and post A calibrations. Biofilm growth is exponential for up to
3 weeks of deployment (Lehaitre et al. 2008), but biofilm dispersal processes and
shedding due to the vehicle movement could change the spatial coverage and/or
thickness of biofilm and make the growth curve somewhat non-linear. Such
oscillations from the assumed linear biofilm growth trend could affect our data, but
in absence of a continuous calibration reference (such as internal dark counts), we
find this approach is the most appropriate one.
199
Figure 5-6. Selected profile from the 7th day of the 10-day H glider deployment. Panel A shows the
chlorophyll data and panel B shows CDOM data for the same profile. Black solid lines indicate values
calculated using factory calibration; black solid lines with circles indicate the pre-deployment
calibration; dashed lines – confidence values; black solid line with triangles using a temporal derived
calibration, dotted lines – confidence values shown on the graph.
When Eq. (1) with temporally corrected scale factors is applied to our dataset (5-6),
both chlorophyll and CDOM fluorescence values fall outside of the measurement
uncertainties established in pre-deployment calibration after only one week of
deployment. For longer deployments, where we observed higher differences in scale
factors, the uncertainty in the corrected values is even greater. If the biofilm on the
optical window is sufficiently developed that it totally masks the ambient
concentrations, even the best calibration procedures will not be able to reconstruct
the data.
200
SUMMARY
In this study we reaffirm the importance of proper calibrations when dealing with
datasets acquired from long term deployments of optical sensors. The differences
between factory established calibrations and pre- and post-deployment calibrations,
emphasize the importance of the consistent and repetitive sensor calibration
procedures.
The calibration chamber allowed easier, lab-controlled, repetitive calibrations that
can be used for both fluorescence sensors and backscattering sensors on the gliders,
due to the low reflective nature of the chamber. It can be also used for
intercalibration of multiple instruments deployed within an observational network, to
ensure consistent, interchangeable datasets. Repetitive calibrations and dark count
measurements enable us to track instrumental drift and constrain uncertainties within
our measurements. These large datasets then become valuable inputs to
biogeochemical models and for calibration and validation of remote sensing
observations.
Biofouling proved to be a significant concern for retrieving calibrated data.
Connection between the length of the deployment and effect of the biofouling was
not straightforward, due to the variability of the scale factors from deployment to
201
deployment. These results point to the need for effective antifouling strategies
optical instruments deployed autonomous platforms. Additionally, macrobiofouling
observed on gliders raises the question about its effect on platform drag, velocity and
power consumption of the glider, a well known problem for all aspects of the marine
industry (Townsin 2003).
202
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Abstract (if available)
Abstract
Harmful Algal Blooms (HABs) in Southern California have become recurring events with impacts that surpass the realm of ocean ecosystems. Phytoplankton blooms are natural phenomena, and the same environmental forcings that drive changes in primary productivity and nutrient cycling in the coastal ocean will promote HABs too, including human influences. Therefore, to predict the initiation of HABs, one must define the specific environmental, chemical, and physical parameters that allow the success of the specific species. Recently developed tools and techniques for realtime coastal observing systems allow us to observe dynamics of the coastal ocean on the appropriate spatial and temporal scales, to explore the dynamics of the coastal ocean, to monitor the nutrient loadings, and to follow the development of the HABs. Field studies conducted during 2005 confirmed that observed the transition from the diatom dominated spring to the dinoflagellate dominated summer, both in surface and subsurface waters, was dependent on natural processes affecting the coastal ocean. Lingulodinium polyedrum, our model organism, was present with bloom abundances (~10^5 cells L^-1) found during the summer, concurrent with low temperature episodes nearshore. Historical temperature record analysis supports our findings on the occurrence of cool temperature anomalies during L. polyedrum blooms in the Southern California Bight, and infer primary controls of temperature,mixed layer depth, and nutrient availability for bloom formation.
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Cetinić, Ivona
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Core Title
Harmful algal blooms in the urbanized coastal ocean: an application of remote sensing for understanding, characterization and prediction
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College of Letters, Arts and Sciences
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Doctor of Philosophy
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Biology
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08/08/2009
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autonomous underwater vehicles,gliders,harmful algal blooms,Lingulodinium polyedrum,OAI-PMH Harvest,optics,phytoplankton,plume,red tides,remote sensing
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bays: San Pedro
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), Jones, Burton H. (
committee chair
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), Fuhrman, Jed Alan (
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), Kiefer, Dale A. (
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)
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icetinic@gmail.com,icetinic@usc.edu
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Tags
autonomous underwater vehicles
gliders
harmful algal blooms
Lingulodinium polyedrum
optics
phytoplankton
plume
red tides
remote sensing