The influence of diet on the gut microbial community in ring-tailed lemurs and Verreaux’s sifaka

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The
Influence
of
Diet
on
the
Gut
Microbial
Community

in
Ring-‐Tailed
Lemurs
and
Verreaux’s
Sifaka

by

Andrew
Fogel

A
Dissertation
Presented
in
Partial
Fulfillment

of
the
Requirements
for
the
Degree
of

Doctor
of
Philosophy

University
of
Southern
California

December
2014

Dissertation
Committee:

Craig
Stanford,
Chair

Nayuta
Yamashita

Steven
Finkel

Antoine
Bechara

1

ABSTRACT

Herbivores
encounter
plant
defenses
in
the
foods
they
consume.

If
ingested,

plant
defenses
can
have
toxic
effects
on
the
consumer.

Herbivores
who
are
unable

to
metabolize
a
plant
defense
can
avoid
ingestion
through
dietary
selection.

For

consumed
plant
defenses,
a
major
mechanism
for
detoxification
is
the
vast
microbial

community
within
the
herbivore’s
gastrointestinal
tract.

These
microbes
can
also

provide
additional
metabolic
pathways
allowing
for
the
fermentation
of
cellulose

into
the
more
flexible
form
of
fatty
acids
and
thereby
providing
nutrients
to
the

herbivore
from
otherwise
indigestible
fiber.

I
studied
two
sympatric
wild
lemurs,
the
ring-‐tailed
lemur
(Lemur
catta)
and

Verreaux’s
sifaka
(Propithecus
verreauxi),
to
investigate
the
impact
of
cellulose
and

phenolics
consumption
on
the
gut
microbes
that
metabolize
them.

The
cellulose
and

phenolics
levels
were
measured
in
their
diets
and
compared
with
the
gut
microbial

communities
recovered
from
feces.

The
consumption
of
cellulose
and
phenolics

varied
between
species
and
temporally,
with
higher
consumption
of
both
cellulose

and
phenolics
by
P.
verreauxi
during
the
wet
season.

The
microbial
communities

were
distinct
between
the
lemur
species
due
to
differences
in
both
abundance
and

diversity.

Changes
in
cellulose
and
phenolics
consumption
were
not
associated
with

significant
microbial
community
changes,
though
microbial
genera
associated
with

the
metabolism
of
these
plant
defenses
showed
some
correlations.

The
gut

microbial
community
provides
an
elastic
mechanism
for
the
host’s
metabolic
needs,

but
requires
further
investigation
to
comprehend
the
complex
community

dynamics.

2

ACKNOWLEDGEMENTS

Firstly,
I
would
like
to
thank
my
advisor,
Ny
Yamashita,
for
her
continued

support
and
guidance
through
this
long
journey.

Across
three
continents,
Ny
has

helped
me
to
shape
this
research
and
to
develop
myself
as
a
scientist
and
for
this
I

will
be
forever
grateful.

I
would
also
like
to
thank
my
doctoral
committee
(Craig

Stanford,
Steven
Finkel,
and
Antoine
Bechara)
for
their
critical
feedback
and
insight.

In
addition
to
my
current
committee,
I
would
not
be
here
today
without
the
advice

of
former
committee
members
Roberto
Delgado
and
Steve
Goodman.

This
research
could
not
have
happened
without
funding
from
an
NSF
Doctoral

Dissertation
Improvement
Grant
(BCS
1061309),
Sigma
Xi,
the
Jane
Goodall

Institute,
the
Department
of
Biological
Sciences
at
the
University
of
Southern

California,
and
a
fellowship
from
the
Austrian
Academy
of
Sciences
(ÖAW).

My
fieldwork
in
Madagascar
would
not
have
been
possible
without
the
tireless

help
of
my
field
assistants:
Joachim
Williams
and
Vole
Ramboazafy.

Without
them

the
long
days
following
lemurs
would
have
been
dramatically
less
exciting.

My
work

at
Beza
would
not
have
been
possible
without
mentorship
of
Teague
O’Mara
and
the

support
of
Jacky
Youssouf,
the
Beza
Mahafaly
Ecological
Monitoring
Team

(Elahavelo,
Enafa
Efitroaromy,
Efitira,
and
Edouard
Ramahatratra),
Marni
LaFleur,

Madagascar
National
Parks,
the
University
of
Antananarivo,
MICET,
and
especially

Vavy
who
makes
the
best
rice
and
beans
on
the
planet!

In
Vienna,
my
international
colleagues
were
always
available
to
brainstorm
with

me
and
always
enjoyed
hearing
about
research
on
something
other
than
fruit
flies.

Dankeschön
to
Julia
Hosp,
Daniel
Fabian,
Derek
Setter,
Ashley
Farlow,
Saad
Arif,

3

Nicola
Palmieri,
Andrea
Betancourt,
Robert
Kofler,
Viola
Nolte,
Petra
Prasser,
and

the
rest
of
the
Institut
für
Populationsgenetik
and
the
VetMedUni
Vienna.

To

Christian
Schlötterer,
it
was
an
enlightening
and
informative
three
years
in
your

department;
thank
you.

The
plant
labwork
would
not
have
been
possible
without

the
help
and
guidance
of
Becky
Hood-‐Nowotny.

Thank
you
for
taking
me
under

your
wing
and
allowing
me
to
use
your
lab.

My
graduate
career
was
bookended
in
Los
Angeles.

My
thanks
go
out
to
Laura

Loyola
for
keeping
me
sane
through
two
years
of
constant
grant
writing
and
to
Ben

Tully
for
your
friendship
and
microbial
expertise.

Along
the
way,
I
also
received

input
from
Maureen
McCarthy,
Jess
Hartel,
Brandon
Kayser,
Anh-‐Khoi
Nguyen,

Andrea
Currylow,
Lorraine
Turcotte,
Casey
Donovan,
and
Jill
McNitt-‐Gray.

I
would

never
have
made
it
through
the
university
red
tape
without
Linda
Bazilian,
Adolfo

de
la
Rosa,
Dawn
Burke,
Don
Bingham,
Elsie
Reyes,
and
Nancy
Levien.

Thank
you
to

Jamie
Waite
and
the
Katrina
Edwards
lab
for
allowing
me
to
use
your
lab
space
and

equipment.

Mucho
gracias
to
Chris
Schmitt
for
showing
me
the
joys
of
MCMCglmm.

To
my
family
and
friends,
I
would
not
be
where
I
am
today
without
your

unwavering
support.

Your
interest
(real
or
not)
in
hearing
me
talk
about
lemur
poo

is
much
appreciated.

Thank
you
to
Carol
and
Larry
for
letting
me
crash
on
your

couch
when
I
got
back
to
LA.

To
my
parents:
thank
you,
thank
you,
thank
you.

Despite
the
ups
and
downs,
I’m
here
at
the
end
of
the
road
because
of
you,
because

you
always
pushed
me
to
achieve
big
things,
and
because
you
were
always
there
and

always
supportive.

To
my
lovely
wife,
if
not
for
you
I
may
never
have
finished

4

writing
my
dissertation.

You
have
been
the
best
partner
imaginable
and
your

constant
encouragement
will
never
be
forgotten.

5

TABLE
OF
CONTENTS

CHAPTER

Page

1
INTRODUCTION
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14

Plant
Defenses

15

Mechanical
Defenses

16

Chemical
Defenses

18

Variability
in
Defenses

19

How
Herbivores
Cope
with
Plant
Defenses

22

Animal-‐Associated
Microbes

24

Development
of
the
Gut
Microbiome

27

Effects
of
an
Altered
Gut
Microbiome

28

Composition
of
the
Gut
Microbiome

29

Madagascar

31

Lemurs

32

Study
Species

34

Study
Site

36

Research
Plan

38

Figures

40

2
FEEDING
BEHAVIOR
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Introduction

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Methods

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Results

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Discussion

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6

CHAPTER

Page

Conclusions

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Figures

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Tables

79

3
CONSUMPTION
OF
PLANT
DEFENSES
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Introduction

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Methods

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Results

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Discussion

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Conclusions

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Figures

109

Tables

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4
GUT
MICROBIOME
COMPOSITION
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119

Introduction

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Methods

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Results

128

Discussion

133

Conclusions

137

Figures

139

Tables

150

7

CHAPTER

Page

5
INFLUENCE
OF
DIET
ON
THE
GUT
MICROBIOME
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153

Introduction

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Methods

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Results

161

Discussion

165

Conclusions

173

Figures

176

Tables

177

LITERATURE
CITED
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192

8

LIST
OF
FIGURES

FIGURE

Page

1-‐1
Comparison
of
primate
gut
microbial
abundances
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1-‐2
Range
map
of
Lemur
catta
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41

1-‐3
Range
map
of
Propithecus
verreauxi
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42

1-‐4
Relative
length
and
anatomy
of
the
gastrointestinal
tract

of
L.
catta
and
P.
verreauxi
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43

1-‐5
Detailed
map
of
Beza
Mahafaly
Special
Reserve,
Madagascar
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44

2-‐1
Map
of
Beza
Mahafaly
Special
Reserve
with
the
home
ranges

of
the
studied
lemur
groups
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2-‐2
Weather
conditions
during
the
study
period
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66

2-‐3
Monthly
activity
patterns
of
L.
catta
and
P.
verreauxi
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67

2-‐4
Monthly
activity
patterns
averaged
within
each
study
group
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68

2-‐5
Seasonal
consumption
of
leaves
by
all
L.
catta
individuals
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69

2-‐6
Seasonal
consumption
of
leaves
by
all
P.
verreauxi
individuals
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70

2-‐7
Seasonal
consumption
of
fruit
by
all
L.
catta
individuals
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71

2-‐8
Seasonal
consumption
of
fruit
by
all
P.
verreauxi
individuals
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72

2-‐9
Seasonal
consumption
of
flowers
by
all
L.
catta
individuals
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73

2-‐10
Seasonal
consumption
of
flowers
by
all
P.
verreauxi
individuals
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74

2-‐11
Seasonal
consumption
of
major
plant
species
by
all
L.
catta

individuals
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2-‐12
Seasonal
consumption
of
major
plant
species
by
all
P.
verreauxi

individuals
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9

FIGURE

Page

2-‐13
Lemur
species
comparisons
of
Shannon
dietary
diversity

within
each
season
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77

2-‐14
Seasonal
comparisons
of
Shannon
dietary
diversity
for

L.
catta
and
P.
verreauxi
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78

3-‐1
Standard
curve
for
the
tannic
acid
dilution
series
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109

3-‐2
Phenolics
consumption
rate
for
L.
catta
and
P.
verreauxi
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110

3-‐3
Cellulose
consumption
rate
for
L.
catta
and
P.
verreauxi
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111

3-‐4
ADF
consumption
rate
for
L.
catta
and
P.
verreauxi
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112

3-‐5
ADL
consumption
rate
for
L.
catta
and
P.
verreauxi
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113

4-‐1
Unique
and
overlapping
OTUs
for
each
preservation
method
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139

4-‐2
Taxonomic
tree
of
Vienna
zoo
samples
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140

4-‐3
Beta
diversity
of
gut
microbiomes
from
Vienna
zoo
samples
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141

4-‐4
Gut
microbial
abundances
of
Vienna
zoo
samples
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142

4-‐5
Variation
in
gut
microbial
abundances
for
Vienna
zoo
samples
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143

4-‐6
Unique
and
overlapping
OTUs
for
each
lemur
population
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144

4-‐7
Taxonomic
tree
of
beta
diversity
similarity
in
wild
and

captive
lemur
populations
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145

4-‐8
Beta
diversity
of
gut
microbiomes
from
wild
and
captive
lemurs
-‐

146

4-‐9
Gut
microbial
abundances
of
wild
and
captive
lemurs
-‐
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147

4-‐10
Variation
in
gut
microbial
abundances
for
wild

and
captive
lemurs
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148

10

FIGURE

Page

4-‐11
Gut
microbial
abundances
of
wild
and
captive
lemurs

by
sample
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-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
149

5-‐1
Average
abundance
of
cellulose-‐
and
phenolics-‐metabolizing

microbial
genera
by
lemur
species
and
season
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐

176

11

LIST
OF
TABLES

TABLE

Page

2-‐1
Major
plants
consumed
by
L.
catta
and
P.
verreauxi
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐

79

2-‐2
Home
range
size
of
each
study
group
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐

80

2-‐3

Major
plant
species
consumed
by
each
L.
catta
group
during
each

season
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐

81

2-‐4

Major
plant
species
consumed
by
each
P.
verreauxi
group
during

each
season
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐

82

3-‐1
Cross-‐seasonal
comparison
of
phenolics,
cellulose,
ADF,
and
ADL

consumption
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐

114

3-‐2
Lemur
species
comparison
of
phenolics,
cellulose,
ADF,
and
ADL

consumption
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
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-‐
-‐
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-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐

116

3-‐3
Plant
defense
consumption
rate
per
food
for
L.
catta

and
P.
verreauxi
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐

117

4-‐1
Fecal
sample
metadata
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
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-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
150

4-‐2
Results
tables
for
the
comparison
of
microbial
phyla
abundances

between
populations
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
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-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐

151

5-‐1
List
of
microbes
known
to
metabolize
cellulose
and
phenolics
-‐
-‐
-‐

177

5-‐2
MCMCglmm
parameters
for
each
microbial
metric
model
-‐
-‐
-‐
-‐
-‐
-‐
-‐
179

5-‐3
Model
comparison
of
the
Bayesian
mixed
models
for
the

Shannon
diversity
index
of
both
L.
catta
and
P.
verreauxi
samples
-‐
180

5-‐4
Full
model
optimization
for
the
Shannon
diversity
index
of
the

L.
catta
samples
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
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-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐

181

12

TABLE

Page

5-‐5
Model
comparison
of
the
Bayesian
mixed
models
for
the

Shannon
diversity
index
of
the
L.
catta
samples
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐

182

5-‐6
Summary
of
the
fixed
effects
in
the
optimal
model
of
the

Shannon
diversity
index
of
the
L.
catta
samples
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐

182

5-‐7
Model
comparison
of
the
Bayesian
mixed
models
for
the

Firmicutes/Bacteroidetes
ratio
of
the
L.
catta
samples
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
183

5-‐8
Summary
of
the
fixed
effects
in
the
optimal
model
of
the

Firmicutes/Bacteroidetes
ratio
of
the
L.
catta
samples
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
183

5-‐9
Model
comparison
of
the
Bayesian
mixed
models
for
the

Shannon
diversity
index
of
the
P.
verreauxi
samples
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐

184

5-‐10
Summary
of
the
fixed
effects
in
the
optimal
model
of
the

Shannon
diversity
index
of
the
P.
verreauxi
samples
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐

184

5-‐11
Model
comparison
of
the
Bayesian
mixed
models
for
the

Firmicutes/Bacteroidetes
ratio
of
the
P.
verreauxi
samples
-‐
-‐
-‐
-‐
-‐
-‐

185

5-‐12
Summary
of
the
optimal
fixed
effects
for
the
community-‐level

microbial
metrics
models
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
185

5-‐13
Summary
of
the
optimal
fixed
effects
for
the
genus-‐level

microbial
metrics
models
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
186

5-‐14
Summary
of
the
fixed
effects
in
the
optimal
model
of
the

Bacteroides
abundance
of
the
L.
catta
samples
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐

187

5-‐15
Summary
of
the
fixed
effects
in
the
optimal
model
of
the

Butyrivibrio
abundance
of
the
L.
catta
samples
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
187

13

TABLE

Page

5-‐16
Summary
of
the
fixed
effects
in
the
optimal
model
of
the

Fibrobacter
abundance
of
the
L.
catta
samples
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐

188

5-‐17
Summary
of
the
fixed
effects
in
the
optimal
model
of
the

Ruminococcus
abundance
of
the
L.
catta
samples
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐

188

5-‐18
Summary
of
the
fixed
effects
in
the
optimal
model
of
the

Streptococcus
abundance
of
the
L.
catta
samples
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
189

5-‐19
Summary
of
the
fixed
effects
in
the
optimal
model
of
the

Bacteroides
abundance
of
the
P.
verreauxi
samples
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
189

5-‐20
Summary
of
the
fixed
effects
in
the
optimal
model
of
the

Fibrobacter
abundance
of
the
P.
verreauxi
samples
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
190

5-‐21
Summary
of
the
fixed
effects
in
the
optimal
model
of
the

Ruminococcus
abundance
of
the
P.
verreauxi
samples
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
190

5-‐22
Summary
of
the
fixed
effects
in
the
optimal
model
of
the

Streptococcus
abundance
of
the
P.
verreauxi
samples
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐
-‐

191

14

CHAPTER
1
-‐
INTRODUCTION

Overview

Herbivores
consume
plant
tissues
that
contain
plant
defense
compounds,

including
structural
carbohydrates
(e.g.
cellulose)
and
secondary
metabolites
(e.g.

phenolics).

Cellulose
is
the
most
abundant
polysaccharide
on
Earth
and
is
found

ubiquitously
in
plant
tissues,
making
it
a
wholly
unavoidable
compound
for

herbivores
(Bennett
and
Wallsgrove
1994;
Bayer
et
al.
1998;
Faulkner
and
Lehman

2006).

While
cellulose
can
have
negative
effects
on
digestion,
it
also
contains
a
rich

source
of
energy
for
consumers
that
can
metabolize
it.

Phenolics
are
also
found
in

most
plants,
where
they
frequently
act
as
deterrents
to
herbivory
(Luckner
and

Nover
1977;
Zucker
1982;
Buchsbaum
et
al.
1984;
Bennett
and
Wallsgrove
1994;

Haslam
1995;
Pass
and
Foley
2000).

Gastrointestinal
microbes
play
an
important

role
in
the
breakdown
of
these
compounds
by
reducing
their
negative
impact
or

transforming
them
into
a
nutritive
source
(such
as
short-‐chain
fatty
acids
from

cellulose).

Descriptive
studies
have
cataloged
the
gastrointestinal
bacteria
found
in

ruminants
(Harborne
1990;
Herms
and
Mattson
1992;
Bennett
and
Wallsgrove

1994;
Devriese
et
al.
1998;
Koike
and
Kobayashi
2001)
and
humans
(Aspinall
1980;

Sharon
1980;
Wang
et
al.
1996;
Turnbaugh
et
al.
2007;
Costello
et
al.
2009;

Caporaso
et
al.
2011a),
while
other
work
has
looked
at
the
effects
of
fiber
on
these

microbes
in
captive
animals
(Kišidayová
et
al.
2009).

There
is
a
knowledge
gap
as

little
is
known
about
the
interplay
between
gastrointestinal
microbes
and
diet
in

wild
populations.

The
ring-‐tailed
lemur
(Lemur
catta)
and
Verreaux’s
sifaka

(Propithecus
verreauxi)
are
two
herbivorous
strepsirhines
that
consume
seasonally

varying
amounts
of
leaves
and
fruit
in
their
diet.

This
research
investigated
the

connection
between
consumption
of
plant
defenses
(cellulose
and
phenolics)
and

the
community
of
gut
microbes
that
are
vital
to
metabolizing
them
in
wild
sympatric

populations
of
L.
catta
and
P.
verreauxi.

15

Plant
Defenses

To
maximize
their
survival,
plants
must
find
a
way
to
reduce
herbivory
and

prevent
their
tissues
from
being
consumed.

Plants
can
deter
herbivory
through

mechanical
means,
chemical
defenses,
or
a
combination
of
these.

Mechanical

defenses
include
thorns,
spines,
and
tough
structural
carbohydrates
(Lucas
et
al.

2000).

While
thorns
and
spines
are
intended
to
prevent
an
herbivore
from

attacking
the
plant
at
all,
structural
carbohydrates
(including
cellulose,
lignin,
and

xylan)
reduce
the
efficiency
of
digesting
the
plant
tissues
consumed
(Van
Soest
et
al.

1966;
Milton
1979;
Zucker
1982;
Buchsbaum
et
al.
1984;
Bergman
1990;
Haslam

1995;
Lambert
1998;
Pass
and
Foley
2000).

Plants’
chemical
defenses
are
mostly

secondary
metabolites
(Bennett
and
Wallsgrove
1994;
Devriese
et
al.
1998;
Koike

and
Kobayashi
2001).

Primary
metabolites
are
involved
in
the
primary
metabolism

of
the
plant.

Secondary
metabolites,
however,
are
byproducts
of
primary

metabolism
that
are
not
involved
in
the
normal
growth
and
development
of
the

plant
(Luckner
and
Nover
1977;
Bennett
and
Wallsgrove
1994;
Wang
et
al.
1996;

Turnbaugh
et
al.
2007;
Costello
et
al.
2009;
Caporaso
et
al.
2011a).

While
primary

metabolites
are
common
to
all
plants,
the
profile
of
secondary
metabolites
varies

considerably
among
plants
and
within
their
different
tissues
(Harborne
1990;

Herms
and
Mattson
1992;
Bennett
and
Wallsgrove
1994;
Kišidayová
et
al.
2009).

As

their
name
suggests,
structural
carbohydrates
play
an
important
structural
role
in

plants,
acting
as
both
a
main
component
of
cell
walls
and
as
a
highly-‐concentrated

form
of
long-‐term
energy
storage
(Aspinall
1980;
Sharon
1980;
Lucas
et
al.
2000),

so
these
molecules
have
an
important
role
in
the
plant
aside
from
any
anti-‐
herbivory
effects.

According
to
the
resource
allocation
theory,
plants
are
limited
by
the
amount
of

resources
available
and
must
decide
whether
to
allocate
these
to
growth
and

development
(to
compete
with
other
organisms
for
access
to
further
resources)
or

to
defense
against
herbivores
(and
protecting
the
tissues
they
already
have)
(Lorio

1986;
Herms
and
Mattson
1992).

Synthesizing
secondary
metabolites
can
be
costly

to
the
plant
(Gulmon
and
Mooney
1986),
so
it
makes
sense
that
a
plant
will
only

invest
resources
in
their
creation
when
they
offer
a
direct
and
immediate
benefit.

16

Mechanical
defenses,
whose
primary
purpose
is
anti-‐herbivory,
are
thought
to
be

more
costly
to
the
plant
than
chemical
defenses.

This
is
because
mechanical

defenses
utilize
the
same
nutrients
and
directly
compete
for
resources
with
new

plant
growth.

Also,
the
resources
invested
into
mechanical
deterrents
cannot
be

easily
recycled
back
into
other
functions
within
the
plant,
whereas
chemical

deterrents
can
be
transformed
to
serve
other
purposes
(Skogsmyr
and
Fagerström

1992).

One
counterpoint
for
a
reduced
cost
to
external
mechanical
defenses
is
that

they
have
other
functions
within
the
plant
that
help
to
offset
their
cost
of
synthesis,

such
as
trapping
a
layer
of
air
adjacent
to
the
surface
of
the
plant
that
provides

thermal
insulation
against
extreme
temperatures
(Grubb
1992).

Mechanical
Defenses

Plant
mechanical
defenses
can
be
split
into
two
groups:
stress-‐limited
defenses

and
displacement-‐limited
defenses
(Lucas
et
al.
2000).

Stress-‐limited
defenses
are

hard,
dense
tissues
that
resist
cracks
from
developing
when
stressed
by
an

herbivore.

Due
to
their
density,
these
defenses
are
of
limited
usefulness
to
the
plant

in
normal
growth
and
maintenance,
so
their
positioning
on
the
plant
is
critical

(Lucas
et
al.
2000).

Thorns,
seed
shells,
spines,
and
hairs
located
on
the
outer

surface
of
plant
tissues
maximize
the
benefits
of
their
hardness
in
deterring

herbivores.

In
addition
to
their
more
primary
role
in
evapotranspiration
from
the

leaf
surface,
trichomes,
also
referred
to
as
leaf
hairs,
are
implicated
in
deterring

some
insect
herbivores
(Levin
1973;
Woodman
and
Fernandes
1991).

Due
to
their

larger
size,
mechanical
defenses
such
as
spines
and
thorns
are
thought
to
be
an

adaptation
developed
against
vertebrate
herbivory
pressure,
as
smaller
herbivorous

insects
would
be
able
to
avoid
these
defenses
and
access
the
plant
tissue

underneath
(Grubb
1992).

Displacement-‐limited
defenses
have
a
high
toughness
(or
toughness
to
Young’s

modulus
ratio
for
thicker
tissues)
and
are
designed
to
prevent
cracks
from

spreading
throughout
a
plant
tissue.

This
toughness,
rather
than
hardness,
causes

the
plant
tissue
to
flex
or
bend
when
pressure
is
applied.

The
cellulose
within
the

cell
walls
of
plant
tissues
is
where
this
toughness
is
derived,
though
additional

17

toughness
comes
from
the
organizational
matrix
of
cellulose
fibers
within
the
cell

wall
(Lucas
et
al.
2000).

As
opposed
to
the
dense
external
mechanical
defenses
that
resist
crack

initiation,
the
cellulose
located
within
a
tissue
has
additional
roles
in
the
plant.

The

cellulose-‐laden
cell
wall
also
provides
structural
strength
within
the
plant
tissue
and

acts
as
a
barrier
to
microbes
invading
the
plant
cells
(Albersheim
and
Anderson-‐
Prouty
1975).

Cellulose,
a
polymer
of
more
than
hundreds
of
glucose
molecules,
is

also
a
vast
repository
of
sugars
necessary
for
cellular
respiration
(Lambert
1998).

The
ubiquity
of
cellulose
in
plant
tissues
makes
it
the
most
abundant
polysaccharide

on
the
planet
(Bayer
et
al.
1998).

Cellulose’s
variety
of
purposes
as
sugar

warehouse,
cellular
structural
component,
and
anti-‐herbivory
compound
have
led
to

an
abundance
of
research
on
this
molecule
and
its
role
in
plant
biology
as
well
as
its

usefulness
in
human
applications
(e.g.,
Crampton
and
Maynard
1938;
Patton
and

Gieseker
1942;
Nagy
1977;
Costa
et
al.
1989;
Hon
1994;
Béguin
and
Aubert
1994;

Brown
et
al.
1996;
O’Sullivan
1997;
Bayer
et
al.
1998;
Whitney
et
al.
1999;

Watanabe
and
Tokuda
2001;
Pérez
et
al.
2002;
Lynd
et
al.
2002;
Ververis
et
al.
2004;

Bayer
et
al.
2004;
O’Sullivan
et
al.
2005;
O'Sullivan
et
al.
2007;
Flint
and
Bayer
2008;

Wilson
2011).

Other
structural
carbohydrates
play
similar
roles
in
the
plant,
but
not
to
the

same
degree
as
cellulose.

Lignin,
another
component
of
plant
cell
walls,
can
make

up
20-‐30%
of
wood
and
vascular
tissues
and
is
synthesized
from
the
precursor

alcohols:
coumaryl,
coniferyl,
and
sinapyl
(Kirk
and
Farrell
1987).

Lignin
is
also

classed
as
a
type
of
phenolic.

Lignin
can
have
some
beneficial
effects
within
the

digestive
tract
of
consumers,
possibly
being
able
to
scavenge
nitrates,
carcinogens,

and
other
harmful
compounds
(Jung
and
Fahey
1983).

Hemicellulose
is
another

structural
carbohydrate
present
in
plant
cell
walls,
where
it
is
cross-‐linked
with

lignin,
though
it
is
more
easily
hydrolyzed
than
cellulose
(Pérez
et
al.
2002).

Xylan,
a

type
of
hemicellulose,
is
composed
of
many
individual
xylan
units
and
is
also
found

commonly
in
plant
tissues.

18

Chemical
Defenses

Plant
chemical
defenses
are
a
diverse
group
of
secondary
metabolites
(see

review
by
Bennett
and
Wallsgrove
(1994)),
including
phenolics,
alkaloids,

cyanogenic
glucosides,
flavonoids,
glucosinolates,
and
steroids
(Bennett
and

Wallsgrove
1994;
Collin
2001).

The
chemical
structure
and
effect
on
herbivores

varies
between
these
different
classes
of
secondary
metabolites.

For
instance,
the

cyanogenic
glucosides
are
converted
into
cyanide,
a
respiratory
poison,
when
the

tissue
is
breached
and
they
are
released
from
their
storage
vacuoles
(Bennett
and

Wallsgrove
1994),
whereas
the
terpenes
have
an
anti-‐herbivory
function
by
being

either
toxic
or
having
an
anti-‐feedant
effect
when
ingested
(Harborne
1993).

Interestingly,
several
species,
including
some
lemurs,
that
consume
large
amounts

of
cyanide-‐rich
bamboo
in
their
diet
have
developed
strategies
to
deter
cyanide

poisoning
(Glander
et
al.
1989;
Ferreira
et
al.
1997;
Tan
1999;
Ballhorn
et
al.
2009).

Phenolics
are
found
in
all
higher
plants
and
are
one
of
the
most
studied
classes

of
secondary
metabolites
(Haslam
1995).

Phenolics
are
a
diverse
group
of

secondary
metabolites
including
terpenoids,
catechols,
tannins,
flavonoids,

isoflavonoids,
lignin
precursors,
and
many
others
(Wong
1973;
Bennett
and

Wallsgrove
1994).

All
of
these
chemicals
are
classified
as
phenolics
due
to
the

presence
of
a
phenol
ring,
an
aromatic
ring
with
at
least
one
hydroxyl
substituent

(Croft
1998).

Tannins
can
be
further
divided
into
hydrolysable
tannins
and

condensed
tannins
(proanthocyanidins)
based
on
their
chemical
structure.

Polyphenols
are
those
phenolic
chemicals
that
contain
multiple
phenol
structural

units.

There
is
an
abundance
of
literature
on
the
anti-‐herbivory
effects
of
phenolics
on

insects
(Feeny
1970;
Zucker
1982;
Larson
and
Berry
1984;
Larsson
et
al.
1986;

Langenheim
1994),
birds
(Buchsbaum
et
al.
1984;
Butler
1989),
and
mammalian

herbivores
(Berger
et
al.
1977;
Reichardt
et
al.
1990;
Foley
and
McArthur
1994;

Dearing
1996;
Foley
et
al.
1999;
Pass
and
Foley
2000;
Wiggins
et
al.
2003).

Phenolics
can
be
found
in
many
plant
tissues
(Gharras
2009),
including
leaves,
fruit,

and
flowers.

Just
as
the
chemical
classification
of
phenolics
is
diverse,
so
too
is
the

mechanism
that
makes
them
effective
anti-‐herbivory
molecules.

Tannins
are
anti-‐

19

herbivory
once
ingested
as
they
bind
to
dietary
proteins
(Swain
1979;
Cooper
et
al.

1988;
Robbins
et
al.
1991;
Hagerman
and
Robbins
1993).

These
tannin-‐protein

complexes
are
insoluble
and,
while
bound,
the
proteins
are
unavailable
for
digestion

by
the
herbivore
(Broderick
1978).

Tannins
can
also
have
a
toxic
effect
on

herbivores
(Lindroth
and
Batzli
1984;
Steinly
and
Berenbaum
1985;
Karowe
1989;

Mole
et
al.
1990).

The
precise
antiherbivory
mechanism
of
flavonoids
is
not
well

understood,
but
there
is
evidence
showing
their
effectiveness
in
deterring
some

insect
herbivores
(Harborne
2001).

Variability
in
Defenses

Depending
on
the
environmental
conditions,
nutrients
available,
and
the
most

likely
predator,
a
plant
can
shift
its
allocation
of
resources
towards
or
away
from
the

production
of
particular
plant
defenses.

In
environments
of
nutrient
stress,
the

production
of
phenolics
and
other
plant
chemical
defenses
usually
increase,

potentially
to
protect
the
plant
from
herbivory
in
a
setting
where
it
would
be

difficult
and
costly
to
regrow
destroyed
tissues
(Gershenzon
1984).

In
highly

favorable
environments,
however,
fewer
plant
resources
are
allocated
to
the

production
of
secondary
metabolites
leading
to
more
vegetative
growth
than

secondary
metabolite
synthesis
(Chung
and
Barnes
1980).

Moreover,
the

effectiveness
of
any
particular
defense
mechanism
is
complicated
by
the
quality
and

quantity
of
the
nutrients
in
the
tissue
being
protected
and
the
relative
defense
of

neighboring
plants
(Belovsky
and
Schmitz
1994).

In
a
habitat
where
all
of
the

surrounding
plants
have
high
levels
of
anti-‐herbivory
defenses,
a
plant
would
need

to
“keep
up
with
the
Joneses
next-‐door”
and
have
a
competitive
amount
of
defenses

itself
to
avoid
attack
by
herbivores.

Plants
can
choose
to
utilize
different
defensive

strategies
to
optimize
their
effect
based
on
the
predators
most
likely
to
consume

their
tissues.

If
a
plant
has
a
high
risk
of
predation
from
herbivorous
insects,
then

thorns
will
not
be
the
ideal
defense.

Similarly,
the
effectiveness
of
certain
chemical

defenses
can
vary
based
on
the
biology
and
coping
mechanisms
of
the
herbivore

(Thaler
et
al.
1999).

Some
defense
mechanisms
are
effective
against
a
broad
range

of
predators
while
others
are
useful
against
a
more
limited
group
of
herbivores

20

(Carroll
and
Hoffman
1980;
Krischik
et
al.
1991;
Linhart
1991;
Giamoustaris
and

Mithen
1995;
Stout
et
al.
1997).

Ripe
fruits
with
lower
levels
of
plant
defenses
and
higher
levels
of
sugars
are

often
more
palatable
to
consumers
than
their
unripe
forms
(Calvert
1985;
Conklin-‐
Brittain
et
al.
1998;
Schmidt
et
al.
2000).

This
benefits
the
plant
by
encouraging

herbivores
to
disperse
seeds
away
from
the
parent
plant.

In
some
cases,
seed

germination
can
be
improved
after
passage
through
an
herbivore’s
digestive
tract

(Krefting
and
Roe
1949;
Traveset
1998;
Traveset
et
al.
2001).

Many
factors
can
affect
the
concentration
of
a
plant
defense
in
any
particular

tissue,
particularly
the
season
and
the
plant
part
(Rice
1984).

As
the
availability
of

nutrients
and
the
intensity
of
herbivory
are
not
at
constant
levels
year-‐round,
there

are
seasonal
patterns
to
the
abundance
of
plant
defenses
(Dement
and
Mooney

1974).

In
addition
to
seasonal
differences
in
the
abundance
and
presence
of
plant

tissues
(leaves,
flowers,
and
fruit),
secondary
metabolite
concentrations
also
vary

within
plant
tissues
seasonally
(Feeny
1970;
Riipi
et
al.
2002;
Fischbach
et
al.
2002;

Witzell
et
al.
2003).

Mature
leaves
of
holm
oak
(Quercus
ilex)
show
increased
levels

of
monoterpene
(a
plant
chemical
defense
(Harborne
1991))
synthesis
in
the
spring

(Fischbach
et
al.
2002).

Seasonal
changes
in
tannin
concentration
in
the
leaves
of

pedunculate
oak
(Quercus
robur)
may
be
driving
the
timing
of
winter
moth

(Operophtera
brumata)
larval
development
(Feeny
1970).

The
moths
avoid

consuming
leaves
in
the
late
summer
when
there
is
a
rapid
increase
in
leaf
tannin

content,
since
the
growth
of
the
winter
moth
larvae
is
inhibited
by
tannin

consumption
(Feeny
1968).

Similar
to
the
pattern
seen
in
oak
leaves,
mountain

birch
(Betula
pubescens
czerepanovii)
leaves
also
showed
a
peak
in
secondary

metabolite
concentration
(in
this
case
proanthocyanidins)
during
midsummer
(Riipi

et
al.
2002).

Changing
environmental
conditions
can
also
alter
the
development
of
plant

chemical
defenses.

Temperature,
rainfall,
light
exposure,
and
even
air
pollution
can

all
affect
phenolic
levels.

A
rise
in
temperature
was
associated
with
an
increase
in

tannin
concentration
in
the
leaves
of
oak
(Quercus
robur)
trees
(Dury
et
al.
1998)

and
with
an
increase
in
total
phenolics
in
white
birch
(Betula
pendula)
(Kuokkanen

21

et
al.
2001).

When
facing
minimal
rainfall
consistent
with
drought
conditions,

phenolic
synthesis
was
reduced,
possibly
due
to
a
limited
carbon
supply
in
the
plant

(Shure
et
al.
1998).

When
a
wet
period
arrived,
the
plant
biochemistry
shifted
and

the
production
of
phenolics
increased
dramatically.

Norway
spruce
(Picea
abies)

trees
had
reduced
levels
of
phenolics
when
exposed
to
an
increase
in
atmospheric

carbon
dioxide
(Sallas
et
al.
2003).

When
a
variety
of
air
pollutants
were
tested,
the

verdict
was
not
clear,
with
some
pollutants
increasing
phenolic
levels
and
others

decreasing
them
(Pasqualini
et
al.
2003).

In
grand
fir
(Abies
grandis)
seedlings,
an

increase
in
nitrogen
availability
in
the
soil
was
associated
with
a
decrease
in
the

production
of
phenolics
(Muzika
1993).

Phenolic
levels
can
also
be
altered
by
toxic

metals
present
in
the
soil
(Karolewski
and
Giertych
2008).

Plants
in
a
low-‐light

setting,
with
reduced
photosynthesis
leading
to
lower
overall
carbon
levels,

produced
only
about
a
third
of
the
phenolics
produced
by
plants
exposed
to
an

abundance
of
light
(Larsson
et
al.
1986).

As
the
herbivory
pressure
is
not
identical
on
all
of
the
plant’s
tissues,
it
follows

that
there
are
higher
amounts
of
plant
defenses
in
those
tissues
at
highest
risk
of

predation.

The
phenolic
concentration
varies
between
different
plant
parts
within

an
environment
(Freeland
and
Janzen
1974).

A
study
of
two
sympatric
lemur

species,
L.
catta
and
P.
verreauxi,
found
seeds,
flowers,
and
fruit
to
have
higher
levels

of
phenolics
than
other
plant
parts
(Yamashita
2008).

Similarly,
Monsonia
burkeana

fruit
contained
higher
levels
of
phenolics
than
the
leaves
of
its
stems
(Mamphiswana

et
al.
2010)
and
Vaccinium
myrtillus
had
differing
levels
and
compositions
of

phenolics
between
stem
and
leaf
tissues
(Witzell
et
al.
2003).

In
creosotsebush

(Larrea
tridentata),
phenolics
were
most
abundant
in
leaf
and
green
stem
tissue

(Hyder
et
al.
2002).

Developing
plant
parts
have
been
shown
to
contain
higher

concentrations
of
plant
chemical
defenses
than
their
mature
counterparts
(Kimber

1973;
Waterman
and
McKey
1989;
Weston
et
al.
1989).

Despite
these
trends,
the

amount
and
type
of
chemical
defenses
in
a
plant
can
fluctuate
based
on
a
number
of

other
factors,
including
sun
exposure
(Ganzhorn
1995a)
and
even
diurnal
or
hourly

shifts
in
concentration
(Seigler
and
Price
1976).

Additionally,
herbivory
pressure

22

can
affect
both
the
concentration
and
types
of
plant
chemical
defenses
produced
in

plant
tissues
(Janzen
and
Waterman
1984).

How
Herbivores
Cope
with
Plant
Defenses

Herbivores
obtain
their
nutrition
from
the
plants
in
their
diet,
so
the
plant

defenses
employed
to
deter
their
consumption
pose
a
challenge
to
the
survival
of

herbivores.

If
plant
defenses
effectively
prevent
an
herbivore
from
consuming

enough
nutritionally
necessary
plant
tissues,
the
herbivore
may
suffer
from

malnutrition
or
death.

When
plants
utilize
defense
mechanisms
for
protection,
an

herbivore
has
two
choices:
(1)
avoid
these
defenses
and
refrain
from
digesting
their

protected
tissues
or
(2)
develop
mechanisms
to
detoxify
or
neutralize
the
negative

effects
of
any
ingested
plant
defenses
(Freeland
and
Janzen
1974;
Brattsten
1979;

Dowd
et
al.
1983;
Varga
and
Kolver
1997;
Bhat
et
al.
1998;
Provenza
et
al.
2003).

If
an
herbivore
lacks
mechanisms
to
deal
with
a
particular
plant
defense,
it
may

need
to
avoid
consuming
that
defense
altogether.

This
behavior
can
result
in
a

highly
selective
diet,
with
the
herbivore
ingesting
only
foods
that
contain
none
of

the
offending
plant
defense
(Freeland
and
Janzen
1974).

There
are
intermediate

cases
as
well,
where
an
herbivore
can
tolerate
small
amounts
of
a
plant
defense
with

minimal
negative
effects
(Iason
and
Villalba
2006).

The
effect
of
plant
defenses
on

an
herbivore
depends
on
the
amount
and
concentration
consumed
and
that

herbivore’s
ability
to
break
down
the
plant
defense.

This
means
that
each
plant

defense
affects
each
herbivore
differently
(Freeland
and
Janzen
1974;
Rhoades

1979;
Glander
1982).

Plant
defense
avoidance
is
a
more
effective
herbivore
strategy

for
variably
distributed
plant
defenses.

In
concert
with
the
defense
avoidance

strategy,
animals
may
choose
to
avoid
certain
plant
parts
that
contain
higher

concentrations
of
plant
defenses
and
to
focus
on
less
harmful
parts
of
the
same

plant.

In
general,
younger
tissues
contain
higher
concentrations
of
chemical
defenses,

as
mechanical
defenses
hinder
their
ability
to
grow
and
mature
(Feeny
1976;

Rhoades
and
Cates
1976;
Lucas
et
al.
2000).

Once
mature,
plant
tissues
can
then

change
focus
and
rely
on
mechanical
defenses
to
prevent
herbivory
(Coley
1988).

23

Additionally,
the
plant
defense
landscape
changes
over
time
within
a
plant,
with

seasonal
shifts
in
concentration
and
developing
tissues
containing
different

amounts
than
mature
tissues.

Cellulose
and
other
structural
carbohydrates
that
act

as
plant
mechanical
defenses
are
found
ubiquitously
in
the
plant
due
to
their

structural
role,
so
avoidance
is
more
difficult
(Bayer
et
al.
1998).

When
herbivores

are
unable
to
detoxify
phenolics,
avoiding
the
ingestion
of
these
plant
defenses
can

be
the
best
tactic
(Fashing
et
al.
2007).

To
avoid
consuming
plant
parts
containing
particular
defenses,
the
herbivore

must
be
able
to
identify
which
parts
actually
contain
that
defense.

External

mechanical
defenses
(e.g.
thorns
and
spines)
are
easily
visible
to
herbivores,
but

internal
defenses
can
be
difficult
to
detect.

In
many
plants,
the
color
of
young
leaves

is
more
yellowish
than
dark
green
and
sometimes
features
red
color
as
well
(Coley

and
Kursor
1996).

Most
insect
and
mammalian
herbivores
are
dichromatic
(unable

to
distinguish
between
red
and
green),
so
that
red
young
leaves
are
camouflaged

from
them
(Mollon
1991;
Briscoe
and
Chittka
2001).

Many
primates
are

trichromatic
allowing
them
to
exploit
the
plant’s
colorful
signal
and
providing
an

advantage
in
locating
young
leaves
that
likely
contain
less
cellulose
than
mature

leaves
(Coley
1988;
Lucas
et
al.
2003;
Dominy
2004).

Trichromatism
can
also
be

useful
in
identifying
ripe
fruits
that
are
redder
in
color
than
in
their
unripe
stage

(Mollon
1989),
though
olfactory
cues
can
also
be
useful
in
locating
ripe
fruit
(Lucas

et
al.
1998).

Specialized
morphological
features
in
the
jaw
can
overcome
mechanical

defenses,
such
as
the
hard
shells
of
nuts
and
fruits
and
tough
fibrous
leaves.

Folivores,
consuming
an
abundance
of
leaves,
have
sharp
crests
and
thin
enamel
on

their
molars
designed
to
shred
their
food
into
small
pieces
for
digestion
(Kay
and

Hylander
1978;
Fleagle
1988).

Herbivores
that
face
the
difficulty
of
hard
foods
tend

to
have
larger
chewing
muscles
and
molars
with
low,
rounded
cusps
and
thicker

enamel
(Kay
1975;
Fleagle
1988).

There
can
be
additional
morphological
adaptations
to
an
herbivorous
diet.

The

overall
size
and
shape
of
the
gut
can
affect
gut
passage
time.

For
folivores

consuming
large
amounts
of
structural
carbohydrates
and
plant
toxins,
a
longer
gut

24

passage
time
allows
more
time
to
detoxify
the
chemical
defenses
and
ferment
the

carbohydrates
to
access
their
nutrition
(Fleagle
1988).

Folivore
gastrointestinal

tracts
also
feature
enlarged
stomachs,
elongated
intestines,
and
a
complex
cecum.

The
mechanism
for
detoxifying
or
neutralizing
ingested
plant
chemical
defenses

can
be
provided
by
the
animal
host
or
by
their
symbiotic
gut
microbes.

Saliva,

produced
endogenously
by
mammals,
is
an
example
of
a
host-‐derived
neutralization

mechanism.

Upon
entering
the
mouth,
food
encounters
saliva,
which
contains

amylase
and
other
proteins.

Salivary
amylase
is
an
enzyme
that
catalyzes
the

hydrolysis
of
starch,
beginning
the
digestion
of
this
structural
carbohydrate

immediately
after
entering
the
digestive
system
(Lu
and
Bennick
1998).

Tannin-‐
binding
proteins
in
mammals’
saliva
are
used
to
neutralize
the
negative
effects
of

tannins
(Hagerman
and
Robbins
1993;
McArthur
et
al.
1995).

Since
tannins
bind
to

dietary
proteins
they
encounter
in
the
gut,
the
salivary
proteins
preferentially
bind

to
the
tannins,
prohibiting
them
from
attaching
to
dietary
proteins
(Mehansho
et
al.

1987;
Hagerman
and
Robbins
1993;
Bennick
2002).

The
vast
microbial
community

within
the
gastrointestinal
tract
of
the
herbivore
can
also
neutralize
plant
defenses.

Animal-‐Associated
Microbes

The
animal-‐associated
microbial
community
is
composed
mostly
of
bacteria,

with
small
numbers
of
archaea,
eukaryotes,
fungi,
and
viruses
also
present

(Breitbart
et
al.
2003;
Rajilić-‐Stojanović
2007).

Within
the
body
of
a
typical
animal,

there
are
more
microbial
cells
than
animal
cells
(Luckey
1972).

These
microbes
are

found
throughout
the
body,
but
particular
regions
are
characterized
by
discrete

community
assemblages,
including
the
mouth,
gastrointestinal
tract,
skin,
and

vagina
(Breitbart
et
al.
2003;
Turnbaugh
et
al.
2007;
Sauther
and
Cuozzo
2009;

Gilbert
et
al.
2010;
Stumpf
et
al.
2013).

Of
these
regions,
the
gastrointestinal
tract

has
the
largest
population
of
microbes
and
contains
the
majority
of
the
animal-‐
associated
microbial
cells
(Luckey
1972).

In
humans,
for
example,
there
are

approximately
320
billion
prokaryotes
per
gram
in
the
colon
(Whitman
et
al.
1998).

The
gut
microbial
community,
collectively
named
the
gut
‘microbiome,’
plays

many
roles
in
the
functioning
of
the
human-‐microbe
superorganism
(Sleator
2010).

25

The
gut
microbiome
has
an
influence
on
the
host’s
immune
system
(Braun-‐
Fahrländer
et
al.
2002;
Kelly
et
al.
2003;
Rakoff-‐Nahoum
et
al.
2004;
Mazmanian
et

al.
2005;
Guarner
et
al.
2006;
Cash
et
al.
2006;
Medzhitov
2007)
and
has
been

implicated
in
human
health
conditions,
including
obesity
(Ley
et
al.
2005;
Ley
et
al.

2006b;
Turnbaugh
et
al.
2006;
Duncan
et
al.
2008),
inflammatory
bowel
diseases

(IBD)
(Manichanh
2006;
Mazmanian
et
al.
2008;
Willing
et
al.
2009;
Mukhopadhya

et
al.
2012;
Manichanh
et
al.
2012),
and
cancer
(Gill
and
Rowland
2007).

In
terms
of

disease,
there
can
be
a
close
phylogenetic
relationship
between
strains
of

pathogenic
and
commensal
microbes,
particularly
in
the
Enterobacteriaceae
and

Spirochaetaceae
families
(Turner
and
Hollander
1957;
Lukehart
et
al.
1988;

Robinson
and
Enright
2004;
Robinson
et
al.
2006;
Leavis
et
al.
2006).

Importantly,
the
microbes
in
the
gut
play
a
key
role
in
digestion
(Bauchop
1971;

Breznak
and
Brune
1994;
Lambert
1998;
Turnbaugh
et
al.
2006;
Flint
and
Bayer

2008)
and
provide
additional
metabolic
pathways
that
the
host
lacks
endogenously,

such
as
the
metabolism
of
plant
structural
carbohydrates
(Stevens
and
Hume
1998;

Gibson
and
Roberfroid
1999;
Hooper
et
al.
2002;
DeSantis
et
al.
2006;
Ze
et
al.
2012;

Flint
et
al.
2012)
and
the
synthesis
of
some
vitamins
(Hill
1997).

In
hydrolyzing

plant
polysaccharides
down
to
their
simpler
components,
the
gut
microbial

community
provides
essential
nutrients
to
herbivores
(Breznak
and
Brune
1994;

Béguin
and
Aubert
1994).

In
some
mammals,
this
microbial
community
is
housed

within
the
foregut,
in
an
expanded,
often
multi-‐chambered
stomach.

These
foregut

fermenters
include
colobine
monkeys
and
ruminants
such
as
cows
(Fleagle
1988;

Schmidt-‐Nielsen
1997).

Other
mammals
maintain
their
gut
microbial
community
in

the
cecum,
a
pouch-‐like
offshoot
at
the
junction
of
the
small
and
large
intestines

(Schmidt-‐Nielsen
1997).

Hindgut
fermenters
include
humans
and
all
other
non-‐
colobine
primates.

Cellulose
is
a
particularly
difficult
material
to
digest
(Béguin
and
Aubert
1994).

The
chemical
structure
of
cellulose
is
multi-‐layered,
with
multiple
microfibrils
(each

composed
of
many
elementary
fibrils
that
in
turn
each
contain
about
30
cellulose

molecules
linked
by
β(1-‐4)-‐glycosidic
bonds)
assembled
into
the
characteristic

cellulose
chain
(Lynd
et
al.
2002).

The
cellulose
chains
are
bound
tightly
together

26

with
hydrogen
bonds,
resulting
in
a
tight
matrix
that
is
impregnable
to
cellulolytic

enzymes.

In
naturally
occurring
cellulose,
there
are
irregularities
in
this
structure,

with
bends
and
kinks,
leading
to
a
less
tightly
bound
chain,
where
enzymes
can

begin
to
break
down
the
cellulose.

Microbes
within
the
gastrointestinal
tract
use
cellulolytic
enzymes
to
separate

the
glucose
chains
and
anaerobically
ferment
cellulose
into
the
short-‐chain
fatty

acids
(SCFAs)
of
acetic
acid,
propionic
acid,
and
butyric
acid
(Miller
and
Wolin
1979;

Cummings
1981;
Cummings
1983;
Cummings
and
Macfarlane
1991).

These
SCFAs

are
then
available
for
the
host
animal
to
absorb
(Macfarlane
and
Gibson
1995).

The

energy
and
nutrients
from
microbial
fermentation
of
carbohydrates
to
SCFAs
can

contribute
between
10
to
30%
of
an
animal’s
basal
metabolic
needs
(Parker
1976;

Rérat
et
al.
1987).

Metabolism
of
phenolics
begins
upon
ingestion,
with
some
phenolic
compounds

broken
down
and
absorbed
in
the
mouth
and
stomach
(Scalbert
et
al.
2002;
Walle
et

al.
2005).

The
phenolics
that
continue
as
far
as
the
large
intestine
and
colon
must
be

deconjugated
before
being
further
metabolized
(Krishnamurty
et
al.
1970;

Schneider
and
Blaut
2000;
Rechner
2004).

Deconjugation
is
the
cleavage
from
the

phenolic
backbone
of
either
the
glucuronosyl
or
glycosyl
moiety
(Justesen
et
al.

2000;
Aura
et
al.
2002;
Rechner
2004).

Deconjugation
is
facilitated
by
the
α-‐
rhamnosidase,
β-‐glucosidase,
and
β-‐glucuronidase
fecal
microbial
enzymes
(Aura

2008).

Once
biotransformed,
these
phenolic
microbial
metabolites
can
remain

circulating
in
the
bloodstream
and
available
for
absorption
for
between
24
and
48

hours,
before
ultimately
being
excreted
in
the
urine
(Sawai
et
al.
1987;
Adlercreutz

et
al.
1995;
Gross
et
al.
1996;
Seeram
et
al.
2006).

Due
to
the
diversity
of

compounds
considered
to
be
phenolics,
the
specifics
of
their
metabolism
and

absorption
varies
(Osawa
et
al.
1993;
Bhat
et
al.
1998;
Scalbert
et
al.
2002;
Rechner

2004;
Aura
2008;
Selma
et
al.
2009;
Kemperman
et
al.
2010;
van
Duynhoven
et
al.

2011).

27

Development
of
the
Gut
Microbiome

The
gut
microbiome
is
an
incredibly
complex
ecosystem,
having
evolved
along

with
the
diets
of
mammals
for
millions
of
years
(Sussman
1991;
Mackie
2002;

Turnbaugh
et
al.
2007).

The
gut
microbiome
can
be
influenced
by
the
host’s

genotype
(Zoetendal
et
al.
2001),
the
host’s
development
(Hopkins
et
al.
2001;

Favier
et
al.
2002;
Edwards
and
Parrett
2007),
and
by
the
environment
(Sullivan
et

al.
2001).

There
can
be
a
large
amount
of
change
in
the
gut
microbial
community

over
the
life
of
an
individual.

There
are
changes
in
the
gut
microbiome
between

different
life
stages,
from
birth,
through
infancy
and
childhood,
and
ultimately
into

adulthood
and
senescence
(Yatsunenko
et
al.
2012).

At
birth,
mammals
have
a

sterile
gastrointestinal
tract,
which
is
rapidly
colonized
by
microbes
(Hentges

2010).

This
burgeoning
microbial
community
goes
through
many
permutations,

similar
to
the
ecological
succession
of
a
forest
ecosystem,
until
finally
stabilizing
at

around
2
years
of
age
with
a
community
similar
to
that
of
an
adult
(Hentges
et
al.

2006;
Stark
and
Lee
2008).

The
infant
gut
microbiome
is
highly
variable
between

individuals,
with
the
eventual
convergence
of
these
microbial
communities
overall,

though
still
maintaining
measurable
differences
based
on
the
diet
of
the
individual

(e.g.,
formula
or
breast-‐fed)
(Roger
et
al.
2010;
Roger
and
McCartney
2010).

Once
the
gut
microbiome
settles
into
a
more
stable
adult
configuration,
there
are

a
variety
of
factors
that
can
alter
this
healthy
and
functioning
microbial
ecosystem.

The
gut
microbiome
is
a
contained
community
that
receives
its
main
nutrient
and

energy
input
from
the
undigested
food
that
passes
through
the
gut
of
the
host

(Goldin
and
Gorbach
1977;
Reddy
et
al.
1992;
Blaut
and
Clavel
2007).

Shifts
in
the

chemical
makeup
of
this
input
can
cause
the
abundance
of
the
major
groups
of
gut

microbes
to
change
(Ley
et
al.
2006b;
Turnbaugh
et
al.
2006;
Ley
et
al.
2008;

Yildirim
et
al.
2010;
Wu
et
al.
2011).

Both
short-‐term
and
long-‐term
dietary
changes

can
have
dramatic
effects
on
the
gut
flora
(Duncan
et
al.
2007;
Walker
et
al.
2011;

Muegge
et
al.
2011;
Wu
et
al.
2011;
David
et
al.
2014).

Specific
changes
to
the
gut

microbial
composition
have
been
linked
to
a
reduction
in
the
amount
of

carbohydrates
consumed
(Duncan
et
al.
2007;
De
Filippo
et
al.
2010),
a
change
in

the
type
of
carbohydrates
ingested
(Walker
et
al.
2011),
a
high-‐fat/low-‐fiber
versus

28

a
low-‐fat/high-‐fiber
diet
(Wu
et
al.
2011),
and
whether
the
host
animal
is
a

carnivore,
herbivore,
or
omnivore
(Ley
et
al.
2008;
Muegge
et
al.
2011;
David
et
al.

2014).

Seasonal
changes
in
the
environment
of
the
microbial
community,
such
as
a

shifting
host
diet,
alter
the
nutritional
inputs
to
the
microbial
ecosystem
and
the

species
abundances
(Gilbert
et
al.
2012;
Sintes
et
al.
2012).

The
gut
microbial
community
can
also
be
dramatically
disturbed
by
the
use
of

antibiotics.

Many
clinically
used
antibiotics
target
a
broad
spectrum
of

microorganisms.

Treatment
with
antibiotics
can
thusly
act
as
a
very
strong
selective

force,
rapidly
shifting
the
abundance
and
composition
of
the
diverse
microflora
in

the
gut
(Dethlefsen
et
al.
2008).

The
microbes
that
survive
the
antibiotics
increase

in
abundance
and
the
community
profile
and
metabolic
landscape
within
the
gut
is

drastically
perturbed.

Differences
in
development,
host
genotype,
diet,
and
exposure
to
antibiotics
can

lead
conspecifics
to
have
distinct
microbial
communities.

Despite
this
inter-‐
individual
variation,
the
gut
microbial
profile
of
healthy
individuals
of
a
species

tends
to
be
distinct
from
that
of
other
species
(Yildirim
et
al.
2010;
Ochman
et
al.

2010).

Similar
diets,
however,
can
cause
a
strong
convergence
between
the
gut

microbiomes
of
otherwise
distantly-‐related
species
(Ley
et
al.
2008).

Effects
of
an
Altered
Gut
Microbiome

Whether
altered
by
a
change
in
diet
or
other
factors,
a
disturbed
gut
microbiota

can
lead
to
negative
health
consequences
for
the
host.

Among
humans
in
developed

countries,
obesity
and
metabolic
syndrome
have
become
rampant
(Zimmet
et
al.

2001).

Research
has
shown
a
strong
link
between
individuals
with
diet-‐induced

obesity
and
an
altered
gut
microbial
composition
when
compared
to
healthy

subjects
(Ley
et
al.
2005;
Turnbaugh
et
al.
2009a;
Turnbaugh
et
al.
2009b;
Schwiertz

et
al.
2009).

Along
with
obesity,
there
are
many
other
diseases
associated
with
changes
in
the

gut
microbiome.

Colorectal
cancer
has
been
linked
to
an
increase
in
species

diversity
and
a
decrease
in
temporal
stability
of
the
microbial
community
in
the

intestines
(Scanlan
et
al.
2008).

Inflammatory
Bowel
Disease
(IBD)
is
a
collection
of

29

chronic
conditions
involving
inflammation
and
an
immune
response
in
the
gut,
with

Crohn’s
disease
and
ulcerative
colitis
as
the
two
primary
diseases
in
IBD
(Centers

for
Disease
Control
and
Prevention).

Individuals
with
IBD
frequently
have
an

alteration
in
the
composition
of
their
gut
microbiome,
showing
a
reduction
in
the

abundance
of
Firmicutes
bacteria
and
an
increase
in
the
abundance
of
pathogenic

bacteria
(Sokol
et
al.
2006;
Takaishi
et
al.
2008;
Greenblum
et
al.
2012).

Since
IBD

involves
an
autoimmune
aspect
to
its
pathology,
some
research
has
investigated
the

link
between
IBD
in
individuals
and
an
increased
attention
to
hygiene.

This
link,

often
referred
to
as
the
Hygiene
Hypothesis,
is
supported
by
the
finding
that
an

increase
in
oral
hygiene
led
to
changes
in
the
oral
microbiota
which
could
also
cause

a
change
to
the
gut
microbiota
further
down
the
alimentary
canal
(Singhal
et
al.

2010).

Crohn’s
disease
in
particular
shows
an
opposite
change
to
that
seen
in

colorectal
cancer,
with
a
decrease
in
microbial
species
diversity,
specifically
from

the
Firmicutes
and
Bacteroidetes
phyla
(Seksik
et
al.
2003;
Manichanh
2006;
Frank

et
al.
2007).

The
abundance
of
Enterobacteria,
however,
increased
in
patients
with

Crohn’s
disease,
hinting
that
a
pathogenic
member
of
this
bacterial
phylum
may
be
a

causative
factor
in
the
disease.

Composition
of
the
Gut
Microbiome

Different
mammalian
species
harbor
unique
microbial
assemblages,
due
to
their

diets
and
phylogeny
(Ley
et
al.
2008;
Caporaso
et
al.
2010;
Muegge
et
al.
2011).

While
changing
diet
affects
the
gut
flora,
geography
also
has
as
influence,
with

distant
populations
having
distinct
gut
communities
(Lozupone
and
Knight
2005;

Yatsunenko
et
al.
2012;
David
et
al.
2014).

Within
a
population
there
can
be
large

inter-‐individual
gut
microbial
variation,
attributed
to
diet
or
environment
(Mann

and
Whitney
1947;
Yatsunenko
et
al.
2012),
as
well
as
intra-‐individual
temporal

variation
(Uenishi
et
al.
2007;
Costello
et
al.
2009;
Nakamura
et
al.
2011).

A
phylum-‐level
analysis
is
common
for
gut
microbiome
research
and
provides

sufficient
detail
to
characterize
the
overall
microbial
composition
and
to
compare

microbiomes
between
species
and
individuals
(Frey
et
al.
2006;
Ley
et
al.
2008;

Yildirim
et
al.
2010;
Ochman
et
al.
2010;
Xu
et
al.
2013).

In
gut
microbiome
research

30

most
studies
focus
on
humans
due
to
the
importance
of
the
gut
microbial

community
to
human
health,
yet
several
primate
gut
microbiomes
have
also
been

investigated
(Eckburg
et
al.
2005;
Frey
et
al.
2006;
Uenishi
et
al.
2007;
Ley
et
al.

2008;
Bo
et
al.
2010;
Yildirim
et
al.
2010;
David
et
al.
2014).

Many
primates
have

Firmicutes
and
Bacteroidetes
as
the
most
dominant
commensal
gut
phyla,
with

Actinobacteria
and
Proteobacteria
also
commonly
found
in
the
gastrointestinal

microbiome
(Fig.
1-‐1)
(Eckburg
et
al.
2005;
Frey
et
al.
2006;
Uenishi
et
al.
2007;
Ley

et
al.
2008;
Bo
et
al.
2010;
Yildirim
et
al.
2010).

Of
particular
interest
in
gut
microbial
communities
is
the
ratio
of
Firmicutes
and

Bacteroidetes
(F/B
ratio).

These
two
bacterial
groups
are
the
most
abundant
phyla

in
the
gut
microbiomes
of
many
primate
species
(Fig.
1-‐1).

An
increase
in
the
F/B

ratio
is
suggested
to
provide
an
increased
energy
harvesting
efficiency
in
the
gut,

explaining
why
obese
individuals
were
shown
to
have
a
significant
shift
in
their
F/B

ratio
(Bäckhed
et
al.
2004).

The
direction
of
this
shift
is
not
wholly
agreed
upon
as

several
studies
have
found
an
increase
in
the
proportion
of
Firmicutes
to

Bacteroidetes
(Ley
et
al.
2005;
Ley
et
al.
2006a;
Turnbaugh
et
al.
2006;
De
Filippo
et

al.
2010),
while
other
research
has
found
a
decreasing
F/B
ratio
(Schwiertz
et
al.

2009).

These
contradictory
findings
may
be
due
to
the
different
methodological

approaches
of
the
studies
(mouse
models
versus
human
subjects)
and
other
lifestyle

factors
of
the
humans
studied
that
complicated
the
microbial
shifts
among
obese

individuals.

Age-‐class
appears
to
also
modify
the
F/B
ratio
with
human
infants
and

the
elderly
having
low
F/B
ratios
and
adults
having
a
significantly
higher
ratio
at

nearly
double
that
of
the
other
age
groups
(Mariat
et
al.
2009).

Aside
from
the

human
health
implications
for
understanding
the
mechanisms
leading
to
obesity

and
possible
therapeutic
treatments,
a
gut
microbial
shift
that
increases
the

efficiency
of
extracting
energy
from
the
diet
would
be
an
important
adaptation
for

wild
animals
struggling
to
meet
their
caloric
demands
on
a
daily
or
annual
basis.

Associating
function
to
any
particular
phylum
can
be
difficult
as
there
are
a

variety
of
metabolic
regimes
among
their
members.

Several
gut
bacterial
genera

and
species
that
are
involved
in
the
metabolism
of
cellulose
and
phenolics
have

been
identified
(Lewis
and
Starkey
1969;
Krishnamurty
et
al.
1970;
Pettipher
and

31

Latham
1979;
Baldwin
and
Allison
1983;
Béguin
et
al.
1985;
Pavlostathis
et
al.
1988;

Winter
et
al.
1989;
Huang
and
Forsberg
1990;
Osawa
and
Sly
1992;
Osawa
1992;

Skene
and
Brooker
1995;
Schneider
et
al.
1999;
Schneider
and
Blaut
2000;
Clavel
et

al.
2006).

While
possibly
due
to
either
convergent
evolution
or
horizontal
gene

transfer,
this
overlap
in
functionality
between
widely
unrelated
bacterial
types
can

provide
a
functional
redundancy
within
a
microbial
community
(Gogarten
and

Townsend
2005;
McCutcheon
et
al.
2009).

Madagascar

Madagascar’s
separation
from
neighboring
continents
has
put
its
flora
and
fauna

on
a
unique
evolutionary
trajectory.

Madagascar
was
part
of
the
Gondwana

supercontinent
until
around
160
million
years
ago
(mya)
when
India
and

Madagascar
split
and
drifted
south
and
east
from
what
is
now
Africa
(Wells
2003).

Around
88
mya,
after
separating
from
India,
Madagascar
eventually
settled
from

350
to
750
miles
off
of
the
coast
of
southeastern
Africa
(Storey
1995;
Storey
et
al.

1997).

This
geographic
isolation
of
Madagascar
occurred
well
before
the

mammalian
radiation
at
the
time
of
the
Cretaceous-‐Tertiary
(K-‐T)
boundary
65
mya

(Benton
1999;
Foote
et
al.
1999;
Bromham
et
al.
1999),
so
the
accepted
theory
is

that
the
primate
order
evolved
on
the
continents
and
then
settled
the
island
of

Madagascar
at
a
later
date
(Tattersall
2006).

Early
primates
likely
reached

Madagascar
by
rafting
across
the
Mozambique
channel
between
50
and
60
mya
on

floating
assemblages
of
vegetation,
though
the
exact
date
of
their
arrival
is
unclear,

in
large
part
due
to
the
dearth
of
an
early
Malagasy
fossil
record
(Kappeler
2000;

Yoder
et
al.
2003;
Poux
et
al.
2005;
Ali
and
Huber
2010).

The
exceptionally
high
rates
of
endemism
among
the
plant
and
animal
life
in

Madagascar
are
due
to
both
the
long
isolation
from
the
mainland
and
an
abundance

of
diverse
forest
habitats
across
the
giant
600,000
km
2

island
(Tattersall
1982).

The

majority
of
Madagascar
lies
within
the
tropics
and
includes
mountainous
rainforest

along
the
eastern
edge
of
the
island
and
much
drier
habitats
in
the
northern
and

southern
tips.

The
southern
arid
region
contains
the
spiny
forest,
not
found

32

elsewhere
in
the
world,
with
gallery
forests
tracing
the
waterways
passing
through

the
region
(Tattersall
1982).

Lemurs

Lemurs
(superfamily
Lemuroidea)
are
a
diverse
group
of
strepsirhines
endemic

to
Madagascar.

Strepsirhines
are
one
of
the
two
suborders
of
primates
(the
other

being
the
haplorhines)
and
includes
the
lemurs,
lorises,
and
galagos.

Strepsirhine

species
have
a
rhinarium,
a
toothcomb
formed
out
of
the
front
teeth
on
the
lower

jaw,
a
lack
of
post-‐orbital
closure,
a
“grooming”
claw
on
the
second
digit
of
their
feet,

a
smaller
brain-‐to-‐body
size
ratio
than
haplorhines,
and
a
tapetum
lucidum
(among

other
traits).

Additional
traits
that
are
common
to
the
lemurs
are
female
dominance

(Jolly
1984;
Richard
1987;
Kappeler
1990;
Sauther
et
al.
1999;
Digby
and

Kahlenberg
2002),
a
lack
of
sexual
dimorphism
(Kappeler
1991;
Kappeler
1997),

and
strict
seasonal
breeding
triggered
by
photoperiodicity
(van
Horn
and
Resko

1977;
Petter-‐Rousseaux
1980;
Wright
1999).

Wright
(1999)
has
proposed
the

energy
frugality
hypothesis
to
explain
this
suite
of
unique
traits
among
the
lemurs,

with
the
goal
of
these
behaviors
to
conserve
energy
and
maximize
access
to
limited

resources.

There
are
over
100
lemur
species
(Mittermeier
et
al.
2010),
though
there
is
some

pushback
over
the
rapid
rise
in
new
species
identification
(Tattersall
2007;
2013;

2014).

What
is
agreed
upon
is
that
there
are
five
extant
families
of
lemurs:

Lemuridae
(the
‘true’
lemurs
and
bamboo
lemurs),
Cheirogaleidae
(the
mouse
and

dwarf
lemurs),
Lepilemuridae
(the
sportive
lemurs),
Indriidae
(the
indri,
wooly

lemurs,
and
sifakas),
and
Daubentoniidae
(the
aye-‐aye)
(Mittermeier
et
al.
2010).

Along
with
the
diverse
living
lemurs,
two
additional
families
(Archaeolemuridae
and

Paleopropithecidae)
are
completely
extinct
(Tattersall
1982).

Many
of
these

subfossil
species
were
folivorous,
arboreal,
slow-‐moving,
and
larger
than
any
extant

lemurs,
with
some
weighing
up
to
200
kg
(Godfrey
et
al.
1997).

There
is
some
fossil

evidence
supporting
the
presence
of
avian
predators
large
enough
to
prey
on
the

subfossil
lemurs
(Goodman
1994a;
Goodman
1994b;
Goodman
and
Rakotozafy

33

1995),
though
the
human
colonization
of
the
island
around
1600
years
ago
added

additional
predation
pressure
on
these
lemurs
(Dewar
1984;
Perez
et
al.
2005).

Amongst
this
speciose
group
of
primates,
there
are
lemurs
found
throughout
the

diverse
habitat
types
in
Madagascar.

These
primates
occupy
an
incredible
breadth

of
dietary
types,
including
species
that
are
omnivorous,
gumnivorous,
insectivorous,

frugivorous,
and
folivorous
(Petter
1962;
Martin
1972).

Some
lemur
species
are

specialized
in
their
feeding
behavior,
such
as
the
highly
insectivorous
aye-‐aye

(Daubentonia
madagascariensis),
while
other
species
(e.g.
Lemur
catta)
are

generalists,
consuming
a
highly
variable
range
of
foods.

This
study
focused
on
two
lemur
species:
the
ring-‐tailed
lemur
(Lemur
catta,

family
Lemuridae)
and
Verreaux’s
sifaka
(Propithecus
verreauxi,
family
Indriidae).

The
Lemuridae
family
contains
five
extant
genera
with
distinctive
dietary
strategies.

The
ruffed
lemurs
of
the
genus
Varecia
are
highly
frugivorous,
with
fruit
as
around

75%
of
their
diet
(Rigamonti
1993;
Ratsimbazafy
2007).

Species
in
the
Eulemur

genus
are
more
varied
in
their
diet,
with
a
range
of
diets
from
folivorous
to

frugivorous
and
several
species
located
along
this
gradient
(Donati
et
al.
2007).

Similar
to
the
diet
of
pandas,
the
bamboo
lemurs
(Prolemur
and
Hapalemur
genera)

are
highly
specialized
folivores
that
consume
cyanogenic
bamboo
(Glander
et
al.

1989;
Tan
1999).

A
single
bamboo
species
can
compose
between
72
and
95
percent

of
the
diet
of
these
lemurs
(Tan
1999).

With
such
a
variety
of
herbivorous
dietary

strategies
in
the
Lemuridae
family,
Lemur
catta
lands
in
the
middle
with
a
highly

flexible
diet.

L.
catta
is
a
generalist
herbivore
that
consumes
fruit
and
flowers

seasonally,
with
leaves
and
the
fruit
of
Tamarindus
indica
as
fallback
foods

(Sussman
1974;
Sauther
et
al.
1999;
Gould
et
al.
2003;
Yamashita
2008;
Sauther
and

Cuozzo
2009),
and
its
gut
morphology
matches
their
diet
with
a
simple
stomach

offset
by
an
expanded
cecum
and
colon
(Campbell
et
al.
2000).

The
genera
in
the
Indriidae
family
are
all
folivores.

The
Indri
sp.
and
Avahi
sp.

are
highly
folivorous
(Britt
et
al.
2002;
Powzyk
and
Mowry
2003),
with
the
eastern

wooly
lemur
(Avahi
laniger)
consuming
only
leaves
(Harcourt
1991;
Faulkner
and

Lehman
2006).

The
lemurs
in
the
Propithecus
genus
have
the
morphological

adaptations
for
a
highly
folivorous
diet,
including
a
long
gut
length,
a
sacculated

34

cecum,
and
a
long
gut
passage
time
(Campbell
et
al.
2000;
Campbell
et
al.
2004a).

Propithecus
spp.
have
a
more
varied
diet
than
the
Avahi
and
Indri
genera,
consuming

fruit
and
flowers
as
well
as
a
large
amount
of
leaves
(Richard
1977;
Richard
1978;

Meyers
1993;
Lehman
and
Mayor
2004;
Norscia
et
al.
2006).

So
while
Propithecus

spp.
are
morphological
folivores,
their
actual
diet
is
more
general
and
varied
than

their
anatomy
would
suggest.

An
in-‐depth
investigation
of
the
transit
time
through

the
upper
and
lower
gut
revealed
that
food
spent
more
time
in
the
upper
gut

(stomach)
in
P.
verreauxi
than
in
the
highly
folivorous
Hapalemur
griseus
(Campbell

et
al.
2004a).

These
species
have
a
similar
overall
transit
time,
so
the
longer
time
in

the
stomach
could
be
to
allow
P.
verreauxi
better
digestion
of
available
nutrients
in

their
non-‐leaf
food
(Campbell
et
al.
2004a).

Propithecus
spp.
have
seasonally

changing
diets,
consuming
fruit
and
flowers
when
abundant
(typically
in
the
wet

season)
and
relying
on
leaves
when
other
foods
are
less
available
(in
the
dry

season)
(Norscia
et
al.
2006;
Yamashita
2008).

Study
Species

Lemur
catta
are
found
across
southern
Madagascar,
with
the
most
northern
edge

of
their
range
at
Belo
sur
Mer
Marofihitsa
(20°44'S)
(Fig.
1-‐2)
(Sussman
1977;
Jolly

et
al.
2006;
Andriaholinirina
et
al.
2014a).

L.
catta
are
mainly
found
in
deciduous

dry
forests
and
spiny
bush,
though
some
populations
also
range
in
more

mountainous
areas
(Goodman
and
Langrand
1996;
Jolly
2006).

Propithecus
verreauxi
is
also
distributed
around
southern
Madagascar,
but

most
of
this
range
is
within
a
100km-‐wide
belt
along
the
southern
and

southwestern
coasts
of
the
island,
the
northernmost
edge
of
this
range
at
Belo

Tsiribihina
(19°41'S)
(Fig.
1-‐3)
(Andriaholinirina
et
al.
2014b).

P.
verreauxi
occupy

deciduous
dry
forests
and
spiny
bush
and
are
sympatric
with
L.
catta
at
several

sites,
including
Beza
Mahafaly
Special
Reserve
(BMSR),
Berenty
Reserve,
and

Kirindy
Reserve
(Simmen
et
al.
2003;
Loudon
et
al.
2006;
Norscia
et
al.
2006;
Axel

and
Maurer
2010).

The
average
adult
body
weight
is
2.2
kg
for
L.
catta
(Sussman

1991)
and
2.8
kg
for
P.
verreauxi
(Richard
et
al.
2002).

35

The
diets
of
L.
catta
and
P.
verreauxi
reflect
those
of
their
respective
families,

with
L.
catta
as
a
more
generalist
herbivore
and
P.
verreauxi
primarily
a
folivore

(though
also
consuming
fruit
at
times,
similar
to
other
Propithecus
spp.).

As
a

generalist
herbivore,
L.
catta
consumes
fruit
seasonally,
with
leaves
as
a
fallback

food
(Sussman
1974;
Sauther
et
al.
1999;
Gould
et
al.
2003;
Yamashita
2008),
and
its

gut
morphology
matches
its
diet
with
a
simple
stomach
offset
by
an
expanded

cecum
and
colon
(Campbell
et
al.
2000).

This
elaborated
hindgut
contains
a
large

microbial
community,
which
is
useful
for
digesting
complex
carbohydrates
in
their

diet
(Campbell
et
al.
2000).

Only
a
handful
of
foods
typically
make
up
the
majority

of
the
diet
of
L.
catta
at
any
given
time,
though
this
selection
of
foods
changes
from

month
to
month
based
on
availability
(Simmen
et
al.
2006b).

This
temporal

variability
is
seen
in
several
sites,
though
the
diet
is
composed
of
somewhat

different
foods
at
each
site
(Simmen
et
al.
2006b).

L.
catta
is
a
seed
disperser
for

Tamarindus
indica,
with
the
intact
seeds
present
in
the
animals’
feces.

It
has
not
yet

known,
however,
if
this
passage
through
the
L.
catta
gut
improves
the
germination

success
for
these
seeds
(Simmen
et
al.
2003).

P.
verreauxi
is
predominantly
a
folivore
(Richard
et
al.
2002),
with
the
majority

of
its
diet
consisting
of
mature
and
young
leaves,
though
also
consuming
unripe
fruit

and
flowers
when
abundant
(Simmen
et
al.
2003).

As
compared
with
L.
catta,
P.

verreauxi
has
a
much
longer
gut
length,
a
larger
stomach,
and
a
more
complex,

spiraled
cecum
that
houses
its
large
community
of
commensal
microbes
(Figs.
1-‐4a

&
1-‐4b)
(Campbell
et
al.
2000).

At
Berenty
Reserve,
P.
verreauxi
consumed
unripe

fruit
as
nearly
45%
of
its
diet,
but
only
consumed
fruit
as
12%
or
less
of
its
diet
in

the
seasons
before
and
afterwards
(Simmen
et
al.
2003).

When
fruit
was
not
a
large

portion
of
the
diet,
P.
verreauxi
consumed
more
leaves.

The
P.
verreauxi
population

in
Kirindy
Reserve
also
showed
seasonal
shifts
in
diet
composition,
consuming
fruits

in
abundance
during
the
wet
season
and
leaves
and
flowers
during
the
dry
season

(Norscia
et
al.
2006).

These
lemurs
also
have
dental
features
adapted
to
their
diets.

The
molar
shape

of
both
L.
catta
and
P.
verreauxi
reflect
the
expected
pattern
for
a
folivore,
with

sharp
shearing
crests,
though
this
form
is
more
pronounced
in
the
more
folivorous

36

P.
verreauxi
(Kay
and
Hylander
1978).

L.
catta
is
a
seed
disperser,
swallowing
many

seeds
whole
rather
than
cracking
them
open,
which
may
be
due
to
their
dentition

being
unable
to
tolerate
hard
foods
(Yamashita
2000).

P.
verreauxi
is
a
seed

predator,
with
an
overall
higher
hardness
threshold
of
its
diet
(Yamashita
2000).

At
Beza
Mahafaly
Special
Reserve
(BMSR),
the
diets
of
L.
catta
and
P.
verreauxi

have
been
thoroughly
studied,
with
investigations
into
the
development
of
feeding

behaviors,
the
seasonal
dietary
nutritional
composition,
the
impact
of
plant

mechanical
properties
on
feeding,
and
the
influence
of
some
secondary
compounds

on
diet
choice
(Sauther
1994;
Yamashita
2000;
Sauther
2002;
Yamashita
2002;

Simmen
et
al.
2006b;
Yamashita
2008;
Sauther
and
Cuozzo
2009;
O'Mara
2012).

The
diets
of
these
lemurs
vary
greatly
over
time,
both
annually
and
seasonally

(Sauther
1998;
Sauther
et
al.
1999;
Yamashita
2002).

L.
catta
and
P.
verreauxi

groups
both
occupy
a
range
different
microhabitats,
which
leads
to
only
partially-‐
overlapping
diets
between
L.
catta
groups
(Yamashita
2002).

Yamashita
(2002)

found
that
amongst
P.
verreauxi
groups,
animals
consumed
a
more
similar
menu
of

foods
despite
differences
in
their
availability.

Aside
from
a
handful
of
overlapping

plant
species
in
their
diets,
L.
catta
and
P.
verreauxi
each
have
a
unique
menu
of

plant
species
and
parts
that
they
consume.

For
instance,
the
succulent
branches
of

Euphorbia
tirucalli
are
a
staple
food
for
P.
verreauxi,
but
L.
catta
does
not
consume

its
latex-‐laden
branches
(Yamashita
2002).

While
both
of
these
lemur
species

consume
seasonally
variable
levels
of
phenolics,
P.
verreauxi
consumes
nearly
twice

the
amount
of
L.
catta
(Yamashita
2008).

These
lemurs’
dietary
cellulose
levels
have

yet
to
be
measured,
but
are
also
expected
to
vary
seasonally,
similar
to
phenolics.

Yamashita
(2008)
has
suggested
that
P.
verreauxi
has
an
increased
tolerance
for

plant
secondary
compounds,
similar
to
other
folivores,
due
to
their
specialized

digestive
tract.

The
microbial
metabolic
aspect
of
this
digestive
tract
adaptability
to

plant
defenses
is
currently
untested.

Study
Site

Beza
Mahafaly
Special
Reserve
(BMSR)
is
a
deciduous
tropical
dry
forest
in

southwest
Madagascar
(23.655°S,
44.63°E)
(Fig.
1-‐2).

Despite
the
habitat

37

degradation
outside
of
BMSR,
the
forests
inside
the
reserve
have
remained
mostly

intact
over
the
past
25
years
(Whitelaw
et
al.
2005).

BMSR
was
historically
divided

into
two
separate
patches
of
protected
forest,
Parcel
1
and
Parcel
2,
though
recently

the
land
between
these
parcels
was
also
given
protection,
creating
a
single

contiguous
reserve.

The
study
site
for
this
project
was
Parcel
1,
an
80-‐ha
forest

completely
enclosed
by
a
barbed-‐wire
fence
to
keep
livestock
from
grazing
inside

the
forest
(though
this
is
not
100%
effective).

This
area
is
highly
seasonal,
with
a

single
wet
season
dominated
by
increased
rainfall
and
higher
daily
temperatures

(ca.
November–March
(Yamashita
2008))
and
a
single
dry
season
(ca.
late
May–
October
(Rodriguez
et
al.
2012))
each
year.

The
annual
rainfall
can
vary
between

522
and
615
mm
(Wright
1999;
Lawler
et
al.
2009).

The
site
has
a
range
of

microhabitats
from
riverine
gallery
forest
bordering
the
Sakamena
River
on
the

eastern
edge
of
Parcel
1
to
xeric
habitat
to
the
west
and
there
is
a
gradient
of

increasing
plant
diversity,
less
canopy
cover,
and
a
lower
canopy
height
as
you
move

towards
the
west
(Sussman
and
Rakotozafy
1994).

Parcel
1
contains
multiple

groups
of
L.
catta
and
P.
verreauxi
at
high
densities
(Sussman
1991).

Facilitating

easy
identification,
both
L.
catta
and
P.
verreauxi
individuals
are
tagged
and
collared

as
part
of
long-‐term
demographic
studies
over
the
last
30+
years,
so
much
is
known

about
the
life
history
and
group
dynamics
of
these
populations
(Sussman
1991;

Sauther
et
al.
1999;
Richard
et
al.
2002;
Gould
et
al.
2003;
Lawler
et
al.
2009;

Sussman
et
al.
2011).

P.
verreauxi
and
L.
catta
live
sympatrically
in
BMSR,
with

overlapping
home
ranges.

This
provides
the
same
environmental
microbes

available
to
colonize
their
gastrointestinal
tracts.

Parcel
1
has
an
extensive
trail

system
(Fig.
1-‐5),
facilitating
finding
and
following
focal
animals.

A
consortium
of
local
villages
around
BMSR
have
agreed
to
refrain
from

harvesting
wood
or
other
materials
in
the
reserve
in
return
for
a
portion
of
the

revenue
from
scientists
and
the
handful
of
tourists
that
make
it
to
this
remote
site

each
year.

This
financial
infusion
into
the
local
community
is
used
largely
for

community
development,
including
the
construction
of
wells
and
schools
to
serve

the
villages.

These
efforts
in
combination
with
its
protected
reserve
status
have

reduced
keep
BMSR
forest
relatively
intact,
particularly
when
compared
to

38

disturbed
habitat
outside
of
the
parcel
(Axel
and
Maurer
2010).

Despite
the
success

of
local
efforts,
the
proximity
of
humans
and
these
lemur
species
in
BMSR
results
in

some
negative
effects.

The
lemurs
that
are
in
closest
proximity
to
humans
and

human
settlements
have
a
higher
risk
of
infection
and
a
higher
rate
of

endoparasites,
while
the
main
source
of
human-‐lemur
conflict
for
the
local
villagers

comes
from
some
groups
of
ring-‐tailed
lemurs
crop
raiding
villagers’
fields
along
the

Sakamena
river
(Loudon
et
al.
2006).

Research
Plan

The
majority
of
in-‐depth
studies
of
the
influence
of
diet
on
the
gut
microbiota

have
focused
on
humans
(Ley
et
al.
2006b;
Turnbaugh
et
al.
2006;
DiBaise
et
al.

2008;
Zhang
et
al.
2009;
Turnbaugh
et
al.
2009a;
Turnbaugh
et
al.
2009b;
Hosseini

et
al.
2011;
Greenblum
et
al.
2012).

The
limited
primate
gut
microbiome
research

has
been
on
captive
animals
or
has
only
sampled
wild
animals
at
one
or
two
time

points
(Campbell
et
al.
2002;
Uenishi
et
al.
2007;
McKenna
et
al.
2008;
Ley
et
al.

2008;
Rezzi
et
al.
2009;
Kišidayová
et
al.
2009;
Szekely
et
al.
2010;
Bo
et
al.
2010;

Yildirim
et
al.
2010;
Degnan
et
al.
2012;
Xu
et
al.
2013;
O'Sullivan
et
al.
2013).

What

is
lacking
is
a
long-‐term
study
of
the
gut
microbiome
and
its
interaction
with
a

changing
diet
in
wild
primates.

This
knowledge
gap
is
beginning
to
be
filled
by
the

recent
work
of
Amato,
but
more
research
on
a
variety
of
primates
is
necessary
to

better
understand
the
range
of
the
gut
microbiome’s
response
to
changes
in
diet

(Nakamura
et
al.
2011;
Amato
2013a;
Amato
et
al.
2013).

My
hypotheses
were
as
follows:

1. P.
verreauxi
consumes
a
greater
amount
of
phenolics
than
L.
catta
during

all
seasons,
as
previously
reported
(Yamashita
2008)
(Chapter
3);

2. Cellulose
consumption
is
also
higher
in
P.
verreauxi
than
L.
catta,
due
to

the
greater
percentage
of
leaves
in
its
folivorous
diet,
and
that
cellulose

consumption
changes
seasonally
for
both
lemur
species
as
their
diets

shift
(Chapter
3);

3. The
gut
microbial
communities
of
L.
catta
and
P.
verreauxi
would
be

distinct,
with
higher
interspecific
than
intraspecific
variation
(Chapter
4);

39

4. The
abundance
of
the
cellulose-‐
and
phenolics-‐metabolizing
microbes
in

the
guts
of
L.
catta
and
P.
verreauxi
directly
vary
with
the
consumption
of

cellulose
and
phenolics
respectively
(Chapter
5)

To
test
these
hypotheses,
I
recorded
the
feeding
behavior
of
wild
populations
of

L.
catta
and
P.
verreauxi
across
two
seasons
at
BMSR.

Samples
of
the
lemurs’
main

plant
foods
were
preserved
and
their
cellulose
and
phenolic
content
was

determined.

The
feeding
behavior
and
plant
chemical
analyses
were
used
to

estimate
the
lemurs’
intake
of
cellulose
and
phenolic
compounds.

Fecal
samples

were
collected
from
the
studied
lemurs
to
characterize
their
gastrointestinal

microbiota
via
DNA
sequencing.

Both
the
plant
defense
ingestion
and
gut
microbial

analyses
were
performed
at
several
time
points
to
investigate
seasonal
differences.

After
characterizing
the
seasonal
patterns
in
plant
defense
ingestion
and
gut

microbiome
composition,
these
measures
were
compared
to
identify
the
influence

of
diet
on
the
gut
microbial
community
in
the
lemurs.

Gastrointestinal
microbes
play
a
pivotal
role
in
digestion
and
toxin

neutralization
in
animals
(Bauchop
1971;
Lambert
1998;
Russell
et
al.
2007;
Flint

and
Bayer
2008;
Selma
et
al.
2009;
Flint
et
al.
2012).

Without
these
microbes,

animals
may
be
unable
to
extract
sufficient
nutrients
from
their
food.

Molecular
and

metagenomic
techniques
have
been
used
to
identify
the
gastrointestinal
microbial

community
in
a
variety
of
primates,
but
few
studies
have
gone
beyond
that

characterization
and
examined
the
functional
relationship
between
gastrointestinal

microbes
and
diet.
Also,
the
majority
of
these
comparative
studies
have
been
on

captive
animals,
which
may
not
be
representative
of
that
found
in
their
wild

counterparts.

A
knowledge
gap
currently
exists
because
little
work
has
been
done

to
quantify
the
relationship
between
gastrointestinal
microbes
and
diet
in
wild

primates.

This
project
helps
fill
this
knowledge
gap
by
analyzing
the
influence
of

ingested
plant
defenses,
specifically
cellulose
and
phenolics,
on
the
gastrointestinal

microbes
that
degrade
them
in
wild
strepsirhine
primates.

40

FIGURES

Figure
1-‐1:
Comparison
of
Primate
Gut
Microbial
Abundances.

The
gut
microbial

compositions
of
several
primate
species
are
compared,
with
each
bacterial
phylum

listed
as
a
percentage
of
the
total
microbiome.

The
data
for
the
three
leftmost
bars

are
from
this
research.

Data
come
from
this
study
as
well
as
Eckburg
et
al.
(2005),

Frey
et
al.
(2006),
Uenishi
et
al.
(2007),
Ley
et
al.
(2008),
Bo
et
al.
(2010),
and

Yildirim
et
al.
(2010).

41

Figure
1-‐2:
Range
map
of
Lemur
catta.

Modified
from
Andriaholinirina

et
al.

(2014a).

The
red
dot
marks
the
location
of
the
study
site
for
this
research,
Beza

Mahafaly
Special
Reserve.

42

Figure
1-‐3:
Range
map
of
Propithecus
verreauxi.

Modified
from
Andriaholinirina

et

al.
(2014b).

The
red
dot
marks
the
location
of
the
study
site
for
this
research,
Beza

Mahafaly
Special
Reserve.

43

Figure
1-‐4:
Relative
length
and
anatomy
of
the
gastrointestinal
tract
of
L.
catta
(a)

and
P.
verreauxi
(b)
from
Campbell
et
al.
(2000).

P.
verreauxi
has
a
much
longer

gastrointestinal
tract,
with
a
spiraled
cecum
(unspiraled
in
figure)
and
longer
small

and
large
intestines,
while
L.
catta
has
a
simple
stomach.

The
scale
bar
measures
1

cm.

44

Figure
1-‐5:
Detailed
map
of
Beza
Mahafaly
Special
Reserve
(Parcel
1),
Madagascar,

modified
from
Sussman
(1991).

The
map
shows
the
trail
system
through
Parcel
1
of

the
reserve,
where
every
vertical
and
horizontal
line
is
a
trail
at
approximately
100-‐
meter
intervals.

The
labels
on
the
edges
of
the
map
(Blue
1,
Red
West,
etc.)
are
the

names
of
the
individual
trails.

The
Sakamena
River
is
along
the
eastern
edge
of
the

parcel.

HIDDEN
CITATIONS
(from
abundance
of
research
on
cellulose
examples):

(Crampton
and
Maynard
1938;
Patton
and
Gieseker
1942;
Nagy
1977;
Costa
et
al.

1989;
Hon
1994;
Béguin
and
Aubert
1994;
Brown
et
al.
1996;
O'Sullivan
1997;

Bayer
et
al.
1998;
Whitney
et
al.
1999;
Watanabe
and
Tokuda
2001;
Pérez
et
al.

2002;
Lynd
et
al.
2002;
Ververis
et
al.
2004;
Bayer
et
al.
2004;
O'Sullivan
et
al.
2005;

Tattersall
2007;
O'Sullivan
et
al.
2007;
Flint
and
Bayer
2008;
Wilson
2011;
Tattersall

2013;
Tattersall
2014)

45

CHAPTER
2
-‐
FEEDING
BEHAVIOR

ABSTRACT

Varying
dietary
strategies
can
lead
to
large
differences
in
dietary
breadth
and

diversity,
as
well
as
home
range
size
and
activity
patterns.

Lemur
catta
is
a

generalist
herbivore,
while
Propithecus
verreauxi
is
a
folivore
that
consumes
fruit

and
flowers
when
available.

To
understand
the
influence
of
these
different
dietary

strategies
on
their
feeding
behavior,
sympatric
populations
of
L.
catta
and
P.

verreauxi
were
studied
at
Beza
Mahafaly
Special
Reserve
in
Madagascar.

Three

groups
of
each
species
were
observed
across
two
seasons
to
understand
how
these

different
dietary
strategies
were
affected
by
seasonal
changes
and
to
measure
the

intraspecific
variation
in
these
responses.

Both
species
spent
a
similar
amount
of

time
resting
and
eating,
but
L.
catta
spent
more
time
moving
around
its
larger
home

range.

The
diet
of
L.
catta
consisted
mainly
of
ripe
fruit,
unripe
fruit,
and
leaf
buds,

while
P.
verreauxi
ate
mostly
mature
leaves,
unripe
fruit,
and
occasionally
flowers.

P.
verreauxi
had
a
higher
dietary
diversity
than
L.
catta
and
this
diversity
was

highest
during
the
wet
season
for
both
species.

Along
with
seasonal
variation
in
the

foods
consumed,
there
were
local
variations
in
feeding
behavior
among
the
groups

of
each
lemur
species,
with
a
greater
variability
among
the
P.
verreauxi
groups.

These
findings
generally
agree
with
previous
research
and
support
the
concept
that

different
dietary
strategies
have
wide
reaching
effects
on
many
aspects
of
the

behavior
of
the
species.

INTRODUCTION

Feeding
Behavior

Feeding
can
consume
upwards
of
50%
of
an
primate’s
waking
hours
(Strier

2007).

The
large
amount
of
time
spent
feeding
influences
the
animal’s
overall

behavior
and
daily
schedule
as
time
spent
foraging
and
feeding
is
time
that
is
not

available
for
other
activities,
including
resting,
grooming,
and
other
social

interactions.

The
type
and
duration
of
feeding
activity
varies
with
different
dietary

strategies.

Despite
the
abundance
of
leaves
available
to
eat,
folivores
display
a

46

surprising
selectivity
in
choosing
which
leaves
to
consume
(Strier
2007;
Hampe

2008).

High-‐quality
foods
(e.g.
fruits)
are
typically
more
sporadically
distributed

than
low-‐quality
foods
(e.g.
leaves)
(Strier
2007).

To
find
enough
food
to
consume,

frugivores
(who
consume
a
large
amount
of
high-‐quality
food)
typically
have
larger

home
ranges
and
spend
more
time
moving
around
this
range
than
folivores
(who

consume
a
large
amount
of
low-‐quality
food)
(Milton
and
May
1976).

Not
only
must

frugivores
range
around
their
territory
to
consume
fruit,
but
they
also
spend
time

surveying
this
area
to
track
when
and
where
fruit
is
available
(Milton
and
May

1976).

Few
primates
consume
exclusively
fruit
and
so
most
frugivores
must

supplement
their
diet
with
leaves
and
other
plant
parts
when
fruit
availability

declines
and
to
provide
additional
nutrients
not
abundant
in
fruits.

These

supplementary
foods
are
called
fallback
foods
and
typically
include
foliage
with
low

nutritional
quality
(Marshall
and
Wrangham
2007).

Fallback
foods
provide
an

alternate,
less
ideal
food
source
to
allow
a
consumer
to
get
through
periods
of

primary
food
scarcity.

Lemurs

Beza
Mahafaly
Special
Reserve
(BMSR)
in
southwestern
Madagascar
contains

dense
populations
of
both
L.
catta
and
P.
verreauxi,
with
most
of
the
adult
animals

collared
and
tagged
for
identification
(Milton
and
May
1976;
Sussman
1991).

While

sympatric
populations
of
L.
catta
and
P.
verreauxi
have
the
same
potential
menu
of

foods
available,
they
display
distinct
dietary
strategies.

As
a
generalist
herbivore,
L.

catta
consumes
fruit
seasonally,
with
leaves
and
the
fruit
of
Tamarindus
indica
as

fallback
foods
(Sussman
1974;
Sauther
et
al.
1999;
Gould
et
al.
2003;
Yamashita

2008;
Sauther
and
Cuozzo
2009).

Only
a
handful
of
foods
typically
make
up
the

majority
of
the
diet
of
L.
catta
at
any
given
time,
though
this
selection
of
foods

changes
from
month
to
month
based
on
availability
(Simmen
et
al.
2006a).

P.
verreauxi
is
predominantly
a
folivore
(Richard
et
al.
2002),
with
the
majority

of
its
diet
consisting
of
mature
and
young
leaves,
though
also
consuming
ripe
fruit,

unripe
fruit,
seeds,
and
flowers
when
available
(Yamashita
2002;
Simmen
et
al.

2003).

When
fruit
is
not
a
large
portion
of
the
diet,
P.
verreauxi
spends
more
effort

47

consuming
leaves
and
these
large-‐scale
shifts
in
plant
parts
in
the
diet
occur

seasonally
(Simmen
et
al.
2003;
Norscia
et
al.
2006;
Yamashita
2008).

While
P.

verreauxi
has
the
morphological
traits
of
a
folivore,
its
actual
diet
is
more
general

and
varied
than
their
anatomy
would
suggest,
with
fruit
as
a
significant
portion
of

their
diet
at
times
(Simmen
et
al.
2003;
Norscia
et
al.
2006;
Yamashita
2008).

At
BMSR,
the
diets
of
L.
catta
and
P.
verreauxi
show
seasonal
and
annually

variation
(Sauther
1998;
Sauther
et
al.
1999;
Yamashita
2002).

The
populations
of

both
lemur
species
occur
across
a
habitat
gradient,
from
wetter
riverine
forest
in

the
east
to
xeric
spiny
forest
in
the
west
(Sussman
and
Rakotozafy
1994).

Aside

from
a
handful
of
overlapping
plant
species
in
their
diets,
L.
catta
and
P.
verreauxi

each
have
a
unique
menu
of
plant
species
and
parts
that
they
consume
(Yamashita

2002).

Both
lemur
species
consume
a
nutritionally-‐balanced
diet
year-‐round
(Strier

2007;
Yamashita
2008),
though
P.
verreauxi
has
a
more
diverse
diet
than
L.
catta

(Yamashita
2002;
Simmen
et
al.
2003).

Hypotheses

The
goal
of
this
research
was
to
record
the
feeding
behavior
of
L.
catta
and
P.

verreauxi,
with
special
focus
on
the
composition
of
their
diet,
and
to
compare
this
to

previous
feeding
studies
of
these
populations.

This
feeding
behavior
was
also

necessary
for
quantifying
the
consumption
of
plant
defenses
(Chapter
3).

Based
on

previous
studies,
I
hypothesized
that
L.
catta
would
have
larger
home
ranges
than
P.

verreauxi
and
would
spend
a
greater
portion
of
its
time
moving
around
this

territory.

I
also
hypothesized
that
the
diet
of
L.
catta
would
be
focused
on
fruit

much
more
than
P.
verreauxi,
while
P.
verreauxi
would
consume
larger
amounts
of

leaves
than
L.
catta.

Due
to
microhabitat
differences,
I
expected
to
find
inter-‐group

variation
in
both
lemur
species
in
terms
of
both
plant
species
consumed
and
the

dietary
composition
of
different
plant
parts.

I
hypothesized
the
dietary
diversity
of

P.
verreauxi
to
be
higher
than
that
of
L.
catta.

48

METHODS

Study
Site

Data
was
collected
for
this
study
in
and
around
Parcel
1
of
Beza
Mahafaly
Special

Reserve
(BMSR),
a
deciduous
tropical
dry
forest
in
southwestern
Madagascar

(23.655°S,
44.63°E)
(Fig.
2-‐1).

BMSR
is
highly
seasonal,
with
a
single
wet
season

dominated
by
increased
rainfall
and
higher
daily
temperatures
(ca.
November–
March
(Strier
2007;
Hampe
2008;
Yamashita
2008))
and
a
single
dry
season
(ca.
late

May–October
(Strier
2007;
Rodriguez
et
al.
2012))
each
year,
with
an
annual
rainfall

between
522
and
615
mm
(Milton
and
May
1976;
Wright
1999;
Lawler
et
al.
2009).

This
study
occurred
during
the
end
of
the
2011
dry
season,
across
the
transition

between
seasons
and
through
the
wet
season
of
2012
(Fig.
2-‐2).

The
start
of
the
wet

season
was
marked
by
the
first
large
(>10mm)
rainfall
on
December
8,
2011.

The

wet
and
late
dry
seasons
had
a
surprisingly
similar
average
maximum
daily

temperature
of
38.6°C
(±0.14).

This
contrasts
with
the
previously
published
pattern

of
higher
average
temperatures
during
the
wet
season
than
the
dry
season

(Sussman
1991;
Yamashita
2002;
O'Mara
and
Hickey
2014).

Interestingly,
the

transition
period
between
these
seasons
had
a
higher
average
maximum
daily

temperature
at
41.3°C.

During
the
wet
season,
there
was
a
total
rainfall
of

280.9mm,
which
is
nearly
half
of
the
rainfall
of
previous
studies
(Sussman
1991;

Yamashita
2002;
Lawler
et
al.
2009).

When
taken
together
with
reduced
rainfall
in

the
2009-‐2010
wet
season
at
BMSR,
this
decrease
in
precipitation
may
point

towards
a
climatic
shift
(O'Mara
and
Hickey
2014).

Subjects

Twenty
adult
lemurs
at
BMSR
were
studied:
ten
L.
catta
(4
male
and
6
female)

and
ten
P.
verreauxi
(5
male
and
5
female).

Due
to
the
amount
of
data
and
samples

being
collected
from
each
subject,
practical
considerations
necessitated
that
a

maximum
of
ten
individuals
from
each
species
could
be
studied.

Within
each
lemur

species,
individuals
were
studied
from
three
groups
across
the
east-‐west
gradient
of

microhabitats
in
Parcel
1,
with
3-‐4
individuals
studied
per
group.

The
majority
of

each
group's
home
range
was
within
Parcel
1,
with
the
exception
of
the
western
L.

49

catta
group
(Fig.
2-‐1),
which
ranged
extensively
to
the
west
and
south
of
the
parcel.

For
L.
catta,
the
three
groups
were
Blue
(in
the
west),
Yellow
(in
the
center
of
the

parcel),
and
Red
(in
the
east).

For
P.
verreauxi,
the
three
groups
were
Fanodrovery

(Fano;
west),
Felix
(center),
and
Vavymasiaka
(Vavy;
east).

The
focal
animals
in

each
group
were
a
mix
of
males
and
females,
with
the
exception
of
Red
group,
where

all
focal
animals
were
female
since
all
of
the
collared
males
emigrated
during
the

first
week
of
the
study
period.

All
focal
animals
were
collared
and
tagged,
providing

confident
identification
of
individuals.

Schedule

Each
group
was
followed
for
two
consecutive
days
every
two
weeks
from

October
3,
2011
until
February
9,
2012.

During
those
two
days,
the
group
was

located
in
the
early
morning,
shortly
after
sunrise
(typically
around
8:00am)
and

followed
until
around
5:00
or
6:00pm.

Two
observers
collected
data
on
the
focal

animals
in
the
group
continuously
throughout
the
day,
and
there
was
always
one

observer
with
the
main
group.

In
a
few
cases,
when
the
group
of
interest
was

unable
to
be
located,
that
group
would
not
be
observed
for
two
consecutive
days,

but
would
still
be
observed
for
a
total
of
two
mornings
and
two
afternoons
during

that
two-‐week
period.

There
were
a
total
of
eight
two-‐week
rotations
through
all
of

the
groups.

Observations

Focal
animal
behavior
was
observed
using
ten-‐minute
focal
sampling
with

continuous
recording
(Altmann
1974).

A
team
of
three
observers
recorded
a
total
of

1400
observation-‐hours
(700
observation-‐hours
for
each
lemur
species).

Focal

sampling
was
done
throughout
the
day
to
account
for
changes
in
activity
levels
and

type
across
the
circadian
rhythm.

The
main
behavior
of
each
focal
animal
was

categorized
as
one
of
the
following:
rest,
move,
forage,
eat,
groom,
mark,
sniff,
lick,

out
of
sight,
drink,
or
other.

For
certain
categories,
additional
information
was
recorded.

The
detailed

feeding
behavior
was
of
particular
interest
to
this
study,
so
whenever
the
main

50

behavior
was
classified
as
‘eat,’
the
following
were
also
noted:
plant
species,
plant

part,
bite
size,
and
bite
count.

The
plant
species
were
coded
to
a
four-‐letter

abbreviation
to
expedite
data
entry
(see
the
leftmost
column
in
Table
2-‐1
for

examples).

The
plant
part
was
classified
as
mature
leaf,
young
leaf,
leaf
bud,
ripe

fruit,
unripe
fruit,
flower,
flower
bud,
soil,
or
other.

Bite
counts
were
recorded
for

all
plant
species
and
plant
part
combinations
observed
being
eaten
by
that
focal

animal.

Bite
counts
were
only
recorded
if
the
observer
had
a
high
quality
view
of

the
animal
and
was
able
to
clearly
observe
each
bite
the
animal
took.

Observers

were
typically
between
three
and
fifteen
meters
from
the
subject
and
used

binoculars
when
necessary.

Inter-‐observer
reliability
tests
occurred
at
the
beginning
of
the
study
to
calibrate

all
observers
to
record
the
same
data
from
observing
the
same
event.

Scan
sampling

was
used
to
track
the
each
group’s
location,
with
GPS
coordinates
recorded
every
15

minutes
while
following
the
group.

GPS
locations
were
used
to
determine
the
home

range
of
each
group
by
calculating
the
convex
hull
of
their
recorded
locations
with

the
‘chull’
script
in
the
R
statistical
environment
(Marshall
and
Wrangham
2007;
R

Core
Development
Team
2010).

Data
Recording

Behavioral
data
was
recorded
using
the
Apple
iPad
2
tablet
computer.

Apple’s

Numbers
database
program
for
the
iPad
was
used
to
create
a
custom
spreadsheet

tailored
to
the
constraints
of
data
entry
in
the
field
and
for
the
specific
data
types
of

interest
in
this
study
(mentioned
above).

After
experimenting
with
many
database

applications
available
for
the
iPad
(as
of
August
2011),
Numbers
was
chosen
for
its

well-‐designed
layout,
appropriate-‐sized
buttons
for
finger
input,
and
the
ability
to

have
custom
input
fields.

These
custom
input
fields
(such
as
checkboxes
and

timestamp
options)
allowed
for
rapid
data
entry
in
real
time
with
the
focal
animal’s

behavior.

Specifically,
the
timestamp
feature
(wherein
a
single
button
records
the

current
time)
was
indispensable
to
rapid
data
entry.

Since
the
time
of
the
fieldwork

in
this
study,
Apple
has
released
an
updated
version
of
this
program,
which
includes

even
more
input
options
useful
to
the
field
biologist,
specifically
drop-‐down
menus.

51

The
battery
life
of
the
iPad
2
(with
all
wireless
services
turned
off
and
the
screen

brightness
reduced
to
the
lowest
level
that
still
provided
adequate
visibility)
was

more
than
enough
for
a
full
day
of
data
recording.

Putting
the
device
to
sleep

between
data
recording
sessions
also
helped
to
conserve
the
battery
life.

The

addition
of
a
small
rechargeable
battery
pack
extended
the
battery
life
of
the
iPad
on

heavy-‐usage
days.

The
iPad
2
suited
the
conditions
at
Beza
Mahafaly
well,
where
there
was
minimal

rain
during
the
dry
season
and
heavy,
yet
brief
rains
during
the
wet
season.

During

light
to
moderate
rains,
the
iPad
could
be
weatherproofed
in
a
transparent
plastic

bag
or
case,
allowing
data
collection
to
continue.

During
heavy
rains,
the
large

raindrops
hitting
the
touchscreen
(even
through
a
protective
layer
of
plastic)

registered
as
a
finger
input
and
rendered
accurate
data
recording
difficult
to

achieve.

Data
Analysis

The
focal
sampling
data
was
combined
into
one
datasheet,
adding
metadata
from

each
session
(date,
location
where
the
focal
session
began,
focal
animal
ID,
focal

animal
species,
focal
animal
group,
focal
animal
sex,
and
the
observer
ID)
to
every

line
of
data
from
that
session.

The
data
was
checked
and
corrected
for
data
entry

errors,
such
as
duplications.

For
each
group,
the
data
collected
over
each
two-‐day
period
with
that
group
was

treated
as
one
time
point.

There
were
a
total
of
eight
two-‐week
rotations
through

all
of
the
focal
lemur
groups,
so
there
were
eight
time
points
for
each
group.

Within

each
time
point,
the
general
activity
budget
was
calculated
(as
a
percentage
of
the

total
observation
time
for
that
time
point).

Additionally,
the
characteristics
of
the

group’s
diet
during
each
time
point
were
calculated,
including
the
percentage
of

time
spent
feeding
on
each
plant
part
and
the
percentage
of
time
spent
feeding
on

each
plant
species.

Due
to
the
large
number
of
plant
species
consumed,
only
plants

that
accounted
for
at
least
5
percent
of
the
total
time
spent
feeding
in
a
given
time

point
were
used
in
the
analysis.

The
dietary
diversity
was
calculated
using
the
Shannon
diversity
index,
H,
where

52

H
=
-‐
∑
pi
ln
pi
and
pi
is
the
proportion
of
the
i
th

species
to
the
total
number
of

species.

The
Shannon
diversity
index
takes
into
consideration
both
the
species

abundance
and
evenness.
The
dietary
diversity
of
L.
catta
and
P.
verreauxi
was

compared
in
each
season
using
the
Mann-‐Whitney
U-‐test
(also
known
as
the

Wilcoxon
Rank
Sum
test).

This
statistic
is
used
to
determine
if
there
is
a
difference

between
groups
and
tests
the
null
hypothesis
that
there
is
no
difference
between

the
two
groups.

The
Mann-‐Whitney
U-‐test
was
chosen
over
the
independent

samples
Student's
t-‐test
because
it
is
more
appropriate
for
datasets
with
non-‐
normal
distributions
and
the
sample
size
of
10
(the
number
of
focal
animals
per

species)
makes
it
unlikely
to
accurately
determine
if
these
data
are
normally

distributed.

RESULTS

General
Activity
Budget

I
compared
the
general
activity
budget
of
Lemur
catta
and
Propithecus
verreauxi

from
October
2011
through
February
2012
(Fig.
2-‐3).

Both
species
spent
roughly

half
(L.
catta:
mean=51%,
SD=10%;
P.
verreauxi:
mean=59%,
SD=8%)
of
their
time

resting,
with
eating
as
the
next
most
abundant
activity
(L.
catta:
mean=23%,

SD=9%;
P.
verreauxi:
mean=30%,
SD=7%).

L.
catta
spent
more
time
moving,

foraging,
and
grooming,
leading
to
a
lower
percentage
of
their
time
eating
and

resting
when
compared
to
that
of
P.
verreauxi.

The
time
spent
moving
and
grooming

were
stable
over
time
for
L.
catta,
but
foraging
time
increased
6-‐fold
during
the
wet

season.

Overall,
L.
catta
spent
twice
as
much
time
moving
as
P.
verreauxi.

When
looking
at
the
general
activity
budget
by
group,
similar
trends
are
seen

(Fig.
2-‐4).

Overall,
P.
verreauxi
groups
regularly
spent
more
time
feeding
than

sympatric
L.
catta
groups.

While
the
percentage
of
time
spent
feeding
is
relatively

consistent
in
most
groups,
the
geographically
central
group
of
each
species
had
a

large
increase
(17%)
in
time
spent
eating
at
the
onset
of
the
wet
season
(early

December).

An
interesting
trend
among
the
L.
catta
groups
is
in
the
timing
of
their

increase
in
foraging,
with
time
spent
foraging
peaking
first
in
the
eastern
group

53

(Red),
followed
by
an
increase
in
the
central
group
(Yellow),
and
lastly
by
an

increase
in
the
western
group
(Blue).

Home
Range

The
home
range
was
much
larger
in
L.
catta
than
P.
verreauxi
(Table
2-‐2).

On

average,
the
L.
catta
home
range
(0.225
km
2
)
was
over
five
times
as
large
as
that
of

P.
verreauxi
(0.043
km
2
).

The
P.
verreauxi
groups
studied
here
did
not
overlap
at
all

in
their
home
ranges,
while
the
L.
catta
groups
investigated
showed
a
thin
zone
of

home
range
overlap
along
their
shared
edges
(Fig.
2-‐1).

The
home
ranges
of
P.

verreauxi
and
L.
catta
groups
studied
largely
overlapped.

Diet
by
Plant
Part

Leaves:
The
feeding
pattern
for
each
plant
part
changed
dramatically
over
time

for
both
L.
catta
and
P.
verreauxi.

L.
catta
shows
sequential
peaks
in
consumption
of

leaf
buds,
young
leaves,
and
then
mature
leaves
(Fig.
2-‐5).

Notably,
the
plant
species

consumed
for
each
of
these
peaks
vary,
with
the
exception
of
Tamarindus
indica

consumed
subsequently
as
both
leaf
buds
and
young
leaves
and
Metaporana

parvifolia
consumed
first
as
young
leaves
and
then
as
mature
leaves
(data
for

individual
plant
parts
per
plant
species
is
not
shown).

For
P.
verreauxi,
the
pattern
of
leaf
consumption
is
less
sequential.

During
the

dry
season,
mature
leaves,
young
leaves,
and
leaf
buds
are
all
consumed
at
relatively

low
levels
(Fig.
2-‐6).
The
proportion
of
mature
leaves
in
the
diet
steadily
increased

throughout
the
wet
season,
peaking
near
50%.

At
the
same
time,
young
leaf
and
leaf

bud
consumption
fluctuates
at
low
consumption
amounts
(roughly
between
0%
and

20%),
with
a
notable
peak
in
young
leaf
consumption
at
the
onset
of
the
wet
season.

This
peak
in
consumption
of
young
leaves
also
coincides
with
the
similar
peak
in

young
leaf
consumption
by
L.
catta.

The
P.
verreauxi
young
leaf
consumption
peak

was
due
primarily
to
an
increase
in
Dichrostachys
humbertii
consumption,
as

opposed
to
Metaporana
parvifolia
causing
the
peak
in
L.
catta.

D.
humbertii
mature

leaves
were
also
consumed
greatly
by
P.
verreauxi,
though
the
young
leaves
of
this

plant
were
consumed
concurrently,
suggesting
no
developmental
sequence
in

54

consumption
for
this
plant
among
P.
verreauxi.

Leaf
buds
were
a
minor
portion
of

the
diet
of
P.
verreauxi,
with
the
only
large
amount
of
leaf
bud
consumption
from

Euphorbia
tirucalli
late
in
the
wet
season.

Fruits:
Ripe
fruit,
and
to
a
lesser
degree
unripe
fruit,
are
major
components
of

the
diet
of
L.
catta,
while
unripe
fruit
is
consumed
much
more
by
P.
verreauxi
(Figs.

2-‐7
&
2-‐8).

L.
catta
consumed
a
large
amount
of
unripe
fruit
during
the
transition

between
the
dry
and
wet
seasons,
which
consisted
exclusively
of
Azima
tetracantha

fruit.

As
the
unripe
A.
tetracantha
fruit
ripened,
L.
catta
shifted
its
diet
to
consuming

the
ripe
fruit
from
the
same
species,
with
a
period
of
overlap
(late
November)
where

both
ripe
and
unripe
fruit
were
eaten
abundantly.

Later,
as
the
wet
season
began

(December),
L.
catta
switched
to
consuming
ripe
fruit
from
Tamarindus
indica
and

Talinella
grevea.

In
January
2012,
Talinella
grevea
ripe
fruit
made
up
40%
of
the

diet
of
L.
catta.

P.
verreauxi’s
fruit
consumption
was
mostly
unripe
fruit,
with
a
low-‐level

consumption
of
ripe
fruit
during
the
wet
season
(Fig.
2-‐8).

The
seeds
from
unripe

Tamarindus
indica
fruit
were
consumed
steadily
from
November
through
January,

while
other
whole
fruits
were
consumed
in
more
discrete
pulses.

Euphorbia
tirucalli

unripe
fruit
was
consumed
during
the
dry
season,
followed
by
the
unripe
fruit
of

Acacia
bellula
and
Azima
tetracantha
between
the
wet
and
dry
seasons,
and
finally
a

pulse
of
Syregada
chauvetiae
during
the
wet
season.

Each
of
these
pulses
was
10-‐
20%
of
the
total
diet
of
P.
verreauxi
in
that
time
period.

Flowers:
Flowers
and
flower
buds
were
consumed
more
abundantly
in
the

earlier
seasons
of
this
study,
but
their
consumption
patterns
differed
between
L.

catta
and
P.
verreauxi
(Figs.
2-‐9
&
2-‐10).

While
L.
catta
only
consumed
flower
buds

and
flowers
in
the
dry
season,
P.
verreauxi
also
continued
eating
these
plant
parts
in

reduced
amounts
through
the
transition
and
early
wet
seasons
respectively.

L.
catta

only
spent
a
large
amount
of
time
eating
flowers
and
flower
buds
in
October,

spending
twice
as
much
time
feeding
on
flowers
than
flower
buds
(Fig.
2-‐9).

The

flowers
consumed
by
L.
catta
were
mostly
from
Quivisianthe
papinae,
which
were

mostly
dried
flowers
that
the
lemurs
foraged
among
the
leaf
litter,
having
fallen
off

of
their
trees.

The
picture
for
the
flower
bud
consumption
in
L.
catta
is
less
clear,

55

with
a
handful
of
plant
species
consumed
in
very
small
amounts
(below
3%
time

spent
feeding
on
each
species
of
flower
bud),
combining
to
constitute
around
10%

of
the
diet
in
the
dry
season.

While
P.
verreauxi
spent
more
time
consuming
flowers
and
flower
buds
than
L.

catta,
the
dry
season
decline
in
flower
consumption
was
similar
for
both
species.

Consumption
of
flowers
was
highest
in
the
dry
season
with
a
second
peak
at
the

onset
of
the
wet
season
(Fig.
2-‐10).

Flower
bud
consumption
fluctuated
between

around
10%
and
25%
of
time
spent
feeding
for
the
dry
season
and
the
transition

between
seasons,
before
dropping
to
a
trickle
of
consumption
during
the
wet

season.

During
the
dry
season,
P.
verreauxi
consumed
large
amounts
of
flowers

from
Combretum
sp.
and
Euphorbia
tirucalli,
while
the
early
wet
season
flower

consumption
was
from
Vitex
beravinensis
trees
located
along
the
riverbank
and

consumed
only
by
the
eastern
group.

P.
verreauxi
consumed
flower
buds
from

Ocotea
tricanta
during
the
dry
season
and
from
Dialium
madagascariensis
and

Tamarindus
indica
plants
during
the
transition
between
the
dry
and
wet
seasons.

Diet
by
Plant
Species

The
overall
picture
of
plant
species
consumed
between
the
two
lemur
species

through
time
is
one
of
variability.

While
there
are
some
small
patterns
of
plant

species
consumed
in
certain
seasons,
the
microhabitats
and
rapidly
changing

phenology
of
the
forest
at
Beza
Mahafaly
led
to
a
fluctuating
dietary
menu
for
both
L.

catta
and
P.
verreauxi
(Figs.
2-‐11
&
2-‐12).

For
L.
catta,
there
was
a
sequence
of
a

handful
of
plant
species
that
were
consumed
much
more
than
all
of
the
others
in

their
diet
(Fig.
2-‐11).

Beginning
in
the
dry
season,
the
major
foods
consumed
by
L.

catta
were
Quivisianthe
papinae
flowers
and
Tamarindus
indica
leaf
buds,
followed

by
just
T.
indica
leaf
buds,
then
Azima
tetracantha
ripe
fruit
then
Talinella
grevea

ripe
fruit,
and
ending
with
the
mature
leaves
of
vines
(Table
3-‐3a).

Below
these

peaks
of
one
or
two
major
foods
consumed
by
L.
catta
across
the
seasons,
the

remainder
of
their
diet
consisted
of
a
fluctuating
array
of
plant
species
at
up
to
20%

of
the
diet
at
a
particular
time
point.

56

For
P.
verreauxi,
the
general
pattern
is
consuming
several
plant
species
at

moderate
levels
(<
25%
of
time
spent
feeding),
with
the
menu
of
species
eaten

varying
greatly
over
a
short
time
scale
(Fig.
2-‐11).

Only
a
few
plant
species
are

consumed
across
multiple
sequential
time
points.

Some
plants
were
only
consumed
by
L.
catta
or
P.
verreauxi
during
particular

seasons,
most
notably
Azima
tetracantha
during
the
transition
and
early
wet

seasons
and
Metaporana
parvifolia
during
the
wet
season
(Figs.
2-‐11
&
2-‐12).

For
P.

verreauxi,
additional
seasonal
consumption
patterns
were
found
for
Dichrostachys

humbertii
during
the
transition
and
wet
seasons
and
for
Syregada
chauvetiae
during

the
wet
season.

Tamarindus
indica
was
an
important
component
of
the
diet
of
both

L.
catta
and
P.
verreauxi
during
all
seasons
of
this
study.

Euphorbia
tirucalli,
which

was
only
consumed
by
P.
verreauxi,
showed
a
bimodal
pattern
of
consumption

across
the
seasons
in
all
P.
verreauxi
groups,
with
one
peak
during
the
dry
and
into

the
transition
season
and
the
second
peak
months
later,
late
in
the
wet
season.

The

first
E.
tirucalli
peak
was
from
P.
verreauxi
eating
unripe
fruit
and
flower
buds

(along
with
smaller
amounts
of
flowers
and
ripe
fruit),
whereas
the
second
peak

was
exclusively
from
the
consumption
of
leaf
buds.

The
other
significant
foods

eaten
by
L.
catta
and
P.
verreauxi
lacked
seasonal
patterns
of
consumption.

Individual
groups
of
L.
catta
and
P.
verreauxi
echo
the
general
trends
of
their

respective
species,
though
there
is
further
variation
amongst
the
groups
within
each

lemur
species.

Even
when
individual
groups
are
considered,
the
diversity
of
plant

species
still
appears
to
be
lower
in
L.
catta
than
in
P.
verreauxi
(Tables
2-‐3
and
2-‐4).

The
major
plant
species
(plants
that
were
consumed
for
at
least
5%
of
the
time

spent
feeding
in
at
least
one
time
point
for
any
group)
consumed
in
each
season

were
similar
across
the
L.
catta
groups,
consuming
Quivisianthe
papinae
and

Tamarindus
indica
in
the
dry
season,
Azima
tetracantha
and
Tamarindus
indica
in

the
transition
between
seasons,
and
Metaporana
parvifolia,
Tamarindus
indica,

Talinella
grevea,
and
vines
in
the
wet
season
(Table
2-‐3).

In
P.
verreauxi,
the
major
plant
species
consumed
in
each
season
varied
by
group

(Table
2-‐4).

In
the
dry
season,
the
western,
central,
and
eastern
P.
verreauxi
groups

spent
the
most
time
feeding
on
Ocotea
tricanta,
Euphorbia
tirucalli,
and
Combretum

57

sp.,
respectively.

During
the
transition
between
the
seasons,
these
groups
spent
the

most
time
feeding
on
Dialium
madagascariensis,
Azima
tetracantha,
and
Tamarindus

indica.

In
the
wet
season,
the
western,
central,
and
eastern
P.
verreauxi
groups
spent

the
most
time
feeding
on
Dichrostachys
humbertii,
Dichrostachys
humbertii,
and

Tamarindus
indica
and
vines,
respectively.

While
the
major
foods
consumed
by
each
P.
verreauxi
group
were
not
the
same

within
each
season,
some
plant
species
were
important
foods
to
several
groups
but

at
different
times.

For
instance,
the
central
group
spent
about
20%
of
its
feeding

time
consuming
Dichrostachys
humbertii
during
the
transition
between
seasons
and

the
wet
season,
but
the
western
group
did
not
consume
any
material
from
this
plant

until
the
wet
season
(Table
2-‐4).

While
Acacia
rovumae
was
consumed
by
all
of
the

P.
verreauxi
groups
during
the
dry
season,
only
the
eastern
group
continued
to
eat

this
plant
into
the
following
seasons
and
actually
more
than
doubled
its

consumption
of
this
plant
during
the
transition
between
seasons.

More
time
was
spent
feeding
on
Tamarindus
indica
in
L.
catta
than
in
P.

verreauxi,
though
consumption
of
this
plant
varied
consistently
across
forest

regions.

T.
indica
consumption
was
highest
in
the
eastern
group
of
each
species

(Tables
2-‐3
and
2-‐4).

In
contrast
to
T.
indica
consumption,
A.
tetracantha
was

consumed
more
heavily
in
the
central
and
western
groups
of
both
L.
catta
and
P.

verreauxi.

In
the
eastern
groups,
P.
verreauxi
consumption
of
A.
tetracantha
was,
on

average,
about
one
third
of
that
found
in
the
other
P.
verreauxi
groups.

Other
regional
distinctions
in
diet
included
heavy
consumption
of
Combretum

species
in
the
eastern
P.
verreauxi
group
in
the
dry
season,
while
the
other
P.

verreauxi
groups
did
not
consume
this
plant
significantly
in
any
season.

Similarly,

Dialium
madagascariensis
was
only
consumed
in
a
large
amount
by
the
western
P.

verreauxi
group.

Dietary
Diversity

The
dietary
diversity
of
P.
verreauxi
was
consistently
higher
than
that
of
L.
catta.

When
analyzed
in
each
season,
this
difference
between
the
species
was
significant

during
the
transition
between
seasons
(p<0.001)
and
during
the
wet
season

58

(p<0.001)
(Fig.
2-‐13).

Both
L.
catta
and
P.
verreauxi
had
their
highest
average

dietary
diversity
during
the
wet
season
(2.38
and
2.86
respectively).

A
similar
pattern
emerged
in
each
species
when
comparing
the
dietary
diversity

across
seasons
(Fig.
2-‐14).

While
the
lowest
average
dietary
diversity
for
both
L.

catta
and
P.
verreauxi
was
during
the
transition
between
the
seasons,
this
diversity

was
not
significantly
lower
than
it
was
during
the
dry
season.

L.
catta
and
P.

verreauxi
did,
however,
have
significant
increases
in
their
dietary
diversity
during

the
wet
season.

DISCUSSION

General
Activity
Budget

Lemur
catta
were
found
to
spend
more
time
moving
than
Propithecus
verreauxi,

which
could
be
due
to
L.
catta
having
larger
home
ranges
than
P.
verreauxi
(Table
2-‐
1).

L.
catta
regularly
survey
the
phenological
status
of
the
plants
within
their

territory,
keeping
track
of
where
and
when
preferred
food
items
are
available

(Budnitz
1977;
Sauther
1994;
Sussman
1991),
which
would
also
contribute
to
their

increased
time
spent
moving.

P.
verreauxi,
consuming
large
amounts
of
highly

abundant
leaves,
need
to
spend
less
time
searching
for
food
and
their
much
more

confined
home
ranges
(Table
2-‐1)
allow
them
to
more
easily
track
the
food

availability
of
local
plants.

Despite
anecdotal
observations
in
the
field
that
the
lemurs
appeared
to
rest

more
on
very
hot
days,
there
was
no
consistent
change
in
time
spent
resting

between
the
seasons.

Of
note,
there
was
a
nonsignificant
trend
toward
an
increase

of
time
spent
resting
for
the
western-‐most
groups
of
both
lemur
species.

This
could

be
a
response
to
higher
temperatures
in
their
habitat
due
to
less
canopy
cover
in

their
spiny
bush
habitat
as
compared
to
their
conspecific
groups
to
the
east,
in
the

gallery
forest
(Sussman
1974;
Sussman
and
Rakotozafy
1994;
Sauther
et
al.
1999;

Gould
et
al.
2003;
Yamashita
2008).

Most
of
the
changes
in
time
spent
for
each
behavioral
category
changed

gradually
between
the
seasons,
with
the
main
exception
being
the
drastic
increase

59

in
time
spent
feeding
(at
the
expense
of
time
spent
resting)
at
the
onset
of
the
wet

season,
for
the
central
groups
of
L.
catta
and
P.
verreauxi
(Fig.
2-‐4).

The
increased

amount
of
time
spent
feeding
in
both
groups
was
due
to
a
concentrated
feeding

effort
on
a
small
number
of
foods.

Relative
to
previous
seasons,
the
central
P.

verreauxi
group
spent
an
additional
50%
of
time
consuming
the
young
leaves
of

Dichrostachys
humbertii
in
addition
to
an
approximately
33%
of
this
previous

feeding
time
eating
the
mature
leaves
of
D.
humbertii
(data
not
shown).

A
similar

pattern
was
seen
in
the
central
L.
catta
group,
expending
an
additional
50%
of
the

time
spent
feeding
in
prior
seasons
consuming
the
young
leaves
of
Metaporana

parvifolia
as
well
as
an
additional
40%
of
the
previous
seasons’
feeding
time
eating

the
ripe
fruit
of
Azima
tetracantha
(data
not
shown).

The
abundance
of
time
spent

feeding
on
these
few
foods
suggests
a
strong
preference
for
them.

These
foods
were

not
previously
important
in
the
diets
of
the
central
groups,
so
the
sudden
change
in

dietary
composition,
focusing
heavily
on
a
few
select
foods,
may
be
due
to
the

sudden
increase
in
availability
of
fruit
and
young
leaves
in
the
wet
season
(Sauther

1998;
O'Mara
2012).

Home
Range

The
home
ranges
for
groups
of
L.
catta
measured
here
fall
within
the
range
of

values
from
previous
studies
(0.16-‐0.35
km
2
)
of
these
species
at
BMSR
(Sussman

1991;
Campbell
et
al.
2000).

Similar
to
the
findings
of
Sussman
(1991),
the

westernmost
L.
catta
group
had
the
largest
home
range,
possibly
due
to
a
lower

quality
territory
than
groups
closer
to
the
river.

The
home
ranges
for
the
P.

verreauxi
groups
overlapped
with
that
of
previous
investigations
(0.04-‐0.06
km
2
)
of

the
BMSR
population
(Richard
et
al.
1991;
Simmen
et
al.
2006b).

The
western
group

in
this
study
had
a
home
range
lower
than
that
reported
by
Richard
et
al.
(1991),
at

only
0.024
km
2
.

The
territory
of
this
group
of
P.
verreauxi,
while
located
in
the
drier

western
portion
of
Parcel
1,
contained
a
particularly
lush
microhabitat
with
a

cluster
of
Acacia
bellula
trees
with
a
much
higher
canopy
than
is
typical
for
that
area

of
the
forest.

P.
verreauxi
consume
the
leaves
of
A.
bellula
trees,
so
this
assemblage

of
trees
provided
additional
food
to
the
central
group.

This
‘island’
of
dense,
tall

60

foliage
may
explain
how
this
group
was
able
to
find
enough
food
in
this
smaller

home
range.

The
home
ranges
of
L.
catta
and
P.
verreauxi
overlapped
amongst
the
study

groups,
though
there
was
minimal
overlap
between
groups
of
the
same
species.

Parcel
1
of
BMSR
contains
several
other
groups
of
both
L.
catta
and
P.
verreauxi
(not

studied
here)
though,
so
this
inter-‐group
home
range
overlap
reported
here
does

not
fully
show
the
degree
to
which
different
groups
of
the
same
species
overlap.

Diet
by
Plant
Part

The
plant
part
consumption
over
time
for
L.
catta
and
P.
verreauxi
was
similar
to

previous
findings
(Yamashita
2008),
with
the
consumption
of
flowers
in
the
late
dry

season
followed
by
an
increase
in
young
leaf
and
leaf
bud
consumption
during
the

transition
months,
and
finally
with
fruit
becoming
a
major
component
of
the
diet

during
the
wet
season.

A
similar
pattern
of
food
availability
has
been
measured,

with
flowers
abundant
at
the
end
of
the
dry
season
and
the
abundance
of
young

leaves
and
ripe
fruit
increasing
into
the
wet
season,
concomitant
an
increase
in

rainfall
(Sauther
1998).

Other
populations
of
both
of
these
lemur
species
also

showed
some
similar
dietary
patterns
at
Berenty
Reserve
in
southern
Madagascar

(Simmen
et
al.
2003).
The
current
study
observed
P.
verreauxi
consuming
large

amounts
of
flowers
and
leaf
buds
in
the
late
dry
season,
eating
unripe
fruit
and

seeds
during
the
transition
between
seasons,
and
then
consuming
significant

amounts
of
mature
leaves
during
the
late
wet
season
(Figs.
2-‐6,
2-‐8,
and
2-‐10).

The

Berenty
L.
catta
groups
studied
by
Simmen
et
al.
(2003)
consumed
large
amounts
of

young
leaves
(52%)
during
the
late
dry
season,
similar
to
the
53%
in
this
study

(when
young
leaves
and
leaf
buds
are
combined
as
in
the
Simmen
et
al.
study).

In

the
late
wet
season,
the
L.
catta
at
Berenty
consumed
ripe
fruit
as
92%
of
their
diet.

While
the
58%
ripe
fruit
consumption
I
observed
at
BMSR
is
not
as
high
a
value,
it

was
the
largest
percentage
of
time
spent
feeding
on
any
single
plant
part
category

during
my
study
period.

Regardless
of
these
differences
in
dietary
composition

between
groups
and
seasons,
L.
catta
and
P.
verreauxi
at
BMSR
appear
to
consume
a

nutritionally-‐balanced
diet
year-‐round
(Yamashita
2008).

61

One
notable
difference
from
Yamashita
(2008)
is
the
finding
in
this
study
that

the
P.
verreauxi
populations
consumed
much
more
fruit
during
the
transition
and

wet
seasons
as
well
as
less
flowers
during
the
transition.

During
the
2011-‐2012

season
of
this
study,
the
P.
verreauxi
groups
spent
a
large
percentage
of
time
spent

feeding
on
unripe
fruit
and
seeds,
reinforcing
the
concept
that
while
Propithecus

spp.
are
morphological
folivores,
their
diet
can
be
much
broader,
including
an

abundance
of
fruit
and
flowers
when
available
(Campbell
et
al.
2000;
Richard
et
al.

2002;
Simmen
et
al.
2003;
Norscia
et
al.
2006;
Yamashita
2008).

The
fruit
species

that
were
consumed
varied
by
group
though
all
groups
consumed
Azima

tetracantha
fruit
when
it
was
abundant
during
the
transition
season.

Additionally,

the
western
group
consumed
Acacia
bellula
fruit,
the
central
group
consumed

Syregada
chauvetiae
fruit,
and
the
eastern
group
spent
up
to
1/3
of
its
feeding
time

eating
the
seeds
of
unripe
Tamarindus
indica
fruit.

Yamashita
(2002)
also
found
T.

indica
fruit
(and
other
plant
parts)
to
be
in
the
top
five
foods
of
all
of
the
P.
verreauxi

groups
studied
during
the
1999-‐2000
season.

The
seeds
of
T.
indica
can
contribute

a
large
amount
of
protein
to
the
diet,
which
may
help
explain
why
the
eastern
P.

verreauxi
group
consumed
them
in
such
abundance
(Yamashita
2002;
Simmen
et
al.

2003;
Yamashita
2008).

T.
indica
seeds
were
not
consumed
by
the
other
P.

verreauxi
groups
in
any
significant
amounts.

Diet
by
Plant
Species

The
most
common
and
important
plant
species
consumed
by
L.
catta
and
P.

verreauxi
at
BMSR
changes
frequently.

The
phenology
of
this
forest
has
been
shown

to
change
dramatically
from
month
to
month,
so
the
lemurs
changing
diets
may
be

largely
a
response
to
a
changing
availability
of
acceptable
foods
(Sauther
1998;

O'Mara
2012).

The
consumption
levels
of
many
plant
species
changed
across
the

seasons,
but
a
few
remained
in
the
diet
in
significant
amounts
throughout
the
year.

Tamarindus
indica
was
an
important
food
for
both
lemur
species
throughout
the

year,
as
found
in
many
prior
studies
(Campbell
et
al.
2000;
Yamashita
2002;
Norscia

et
al.
2006;
Gould
et
al.
2011;
Ellwanger
and
Gould
2011).

Much
of
the
published

feeding
data
on
L.
catta
and
P.
verreauxi
does
not
show
intra-‐seasonal
variations
in

62

plant
species
consumed,
but
rather
tend
to
show
the
top
foods
consumed
over
an

annual
period.

For
L.
catta,
previous
studies
confirm
the
importance
of
Metaporana

parvifolia,
Gyrocarpus
americanus,
Salvadora
angustifolia,
Quivisianthe
papinae,

Marsdenia
cordifolia,
Azima
tetracantha,
Tamarindus
indica,
and
vines
(lianas)

during
the
annual
seasonal
cycle
(Yamashita
2002;
Simmen
et
al.
2003;
Norscia
et
al.

2006;
Yamashita
2008;
Gould
et
al.
2011;
Ellwanger
and
Gould
2011).

Other
studies

also
found
Terminalia
mantali,
Dichrostachys
humbertii,
Cedrelopsis
grevei,

Euphorbia
tirucalli,
Acacia
rovumae,
Acacia
bellula,
and
Tamarindus
indica
as

important
foods
in
the
diet
of
P.
verreauxi
(Yamashita
2002;
Simmen
et
al.
2003;

Norscia
et
al.
2006;
Yamashita
2008).

Of
note,
while
Yamashita
(2002;
2008)
recorded
the
P.
verreauxi
population
at

BMSR
consuming
the
latex-‐rich
stalks
of
Euphorbia
tirucalli
as
a
staple
food,
the

current
study
did
not
find
a
similar
reliance
on
this
food.

Similar
to
the
diet

recorded
in
1999
by
Yamashita
(2008),
P.
verreauxi
only
consumes
a
small
amount

of
stalks
in
its
diet
during
the
end
of
the
dry
season
(October
–
November).

During

the
wet
season
of
2011-‐2012,
however,
P.
verreauxi
did
not
consume
greater

amounts
of
stalk
(data
not
shown).

Only
one
group
consumed
E.
tirucalli
stalks
as

more
than
1%
of
its
diet
and
this
was
5.5%
of
the
eastern
group’s
diet
at
the
onset
of

the
wet
season
(December
2011).

The
diet
of
P.
verreauxi
in
December
was
also

different
between
years,
with
a
dramatic
change
in
flower
consumption.

In
1999

(Yamashita
2008),
P.
verreauxi
spent
half
of
its
feeding
time
eating
flowers,
while
the

diet
contained
no
flowers
in
December
2011
(Fig.
2-‐10).

So
the
change
in

consumption
of
E.
tirucalli
stalks
from
year
to
year
may
simply
be
a
part
of
a
diet

that
fluctuates
yearly.

Dietary
Diversity

This
study
found
that
the
dietary
diversity
of
P.
verreauxi
was
higher
that
that
of

L.
catta
during
all
seasons
of
the
study
(Fig.
2-‐13),
as
found
in
previous
studies

(Simmen
et
al.
2003;
Yamashita
2002).

I
was
able
to
confirm
Yamashita's
(2002)

finding
of
a
more
diverse
species
list
of
foods
consumed
by
P.
verreauxi
when

compared
to
that
of
L.
catta.

Simmen
et
al.
(2003)
had
much
higher
dietary
diversity

63

values
for
P.
verreauxi
than
in
this
study,
though
the
two
are
not
directly
comparable

as
each
used
different
categories
for
classifying
food
parts
and
different
diversity

indices.

The
more
folivorous
diet
of
P.
verreauxi
likely
led
to
this
species
having
a

larger
variety
of
plant
species
available
for
consumption,
while
L.
catta
spent
more

time
searching
for
preferred
species
of
fruit.

The
highest
dietary
diversity
was
during
the
wet
season
for
both
L.
catta
and
P.

verreauxi
(Fig.
2-‐14).

With
the
arrival
of
the
wet
season
rains,
the
entire
forest

rapidly
became
more
lush
and
dense
with
leaves
and
fruit.

An
increase
in
rainfall

has
been
shown
to
cause
a
rise
in
the
abundance
of
fruit
and
young
leaves
at
BMSR,

with
an
overall
increase
in
food
availability
in
the
forest,
rather
than
an
equal

replacement
of
some
plant
parts
with
others
(Sauther
1998;
O'Mara
2012).

With
an

increase
in
the
number
of
foods
available
during
the
wet
season,
L.
catta
and
P.

verreauxi
were
able
to
consume
a
greater
number
of
plant
species
than
in
previous

seasons.

One
complication
to
classifying
the
foods
in
this
study
was
the
numerous
species

of
vines
found
around
the
Beza
Mahafaly
forest.

These
vines
were
difficult
to

identify
accurately.

To
further
complicate
matters,
many
vine
species
were
often

overlapping
in
a
tangle;
making
it
difficult
to
discern
which
species
the
focal
animal

ate.

To
solve
this
problem,
all
of
these
plant
species
were
grouped
into
a
single

classification
of
'vines,'
though
the
plant
part
(usually
mature
leaves)
was
still

recorded
for
each
feeding
bout.
This
grouping
of
plant
species
had
the
effect
of

causing
the
dietary
diversity
to
be
underestimated.

The
eastern
edge
of
the
forest,

along
the
banks
of
the
Sakamena
River,
was
home
to
a
particularly
large
number

and
variety
of
these
vines
and
the
eastern
groups
of
L.
catta
and
P.
verreauxi
both

consumed
the
largest
number
of
vines
(particularly
during
the
wet
season)
(Tables

2-‐3
and
2-‐4).

CONCLUSIONS

The
above
analysis
of
the
diet
and
behavior
of
the
L.
catta
and
P.
verreauxi

populations
at
BMSR
helps
to
show
their
different
approaches
to
consuming
foods

64

in
this
highly
seasonal
forest.

The
different
feeding
strategies
of
L.
catta
and
P.

verreauxi
(generalist
and
flexible
folivore,
respectively)
influence
many
aspects
of

the
lives
of
these
animals.

These
feeding
strategies
affect
the
types
and
diversity
of

foods
they
consume,
the
distance
they
must
travel
to
find
their
food,
and
the
size
of

their
home
ranges.

P.
verreauxi
groups
have
a
smaller
territory
and
consume
a

much
wider
variety
of
plant
species.

Their
diet
focuses
on
unripe
fruit
and
a
variety

of
mature
leaves,
which
are
largely
ignored
by
L.
catta.

L.
catta
takes
a
broader
and

more
opportunistic
approach,
patrolling
a
large
home
range
and
taking
advantage
of

any
available
ripe
fruit,
flowers,
or
leaf
buds.

When
these
preferred
foods
are
not

available,
they
are
able
to
vary
and
expand
their
diet
to
include
fallback
foods
of

young
and
mature
leaves.

Their
expansive
territory
requires
lots
of
ranging
to
track

the
food
availability
therein,
leading
them
to
spend
more
time
moving
than
P.

verreauxi.

As
expected,
both
species
spent
the
majority
of
their
time
resting
and
eating,
and

while
there
were
no
significant
changes
in
the
time
spent
on
these
activities

between
seasons,
the
composition
of
their
diets
did
shift
drastically
from
season
to

season
(and
over
shorter
time
scales).

Furthermore,
there
were
strong
differences

in
the
diet
composition
(by
plant
part
and
plant
species)
between
conspecific
groups

of
both
lemur
species
in
different
microhabitat
patches
along
the
east-‐west
gradient

of
forest
in
Parcel
1.

This
study
confirmed
previous
observations
on
the
activity
and
diet
of
these

species
and
populations,
while
highlighting
the
incredibly
seasonal
diversity
in

dietary
composition
of
both
L.
catta
and
P.
verreauxi
in
response
to
shifting
food

availability.

These
differences
in
dietary
consumption
between
the
lemur
species

and
seasonally,
are
likely
to
lead
to
differences
in
consumption
of
cellulose
and

phenolics,
since
different
foods
contain
unique
levels
of
these
plant
defenses

(Freeland
and
Janzen
1974).

The
unique
dietary
strategies
between
L.
catta
and
P.

verreauxi
may
go
deeper
than
just
food
choices,
with
differences
in
consumption
of

plant
defenses
and
unique
morphological
and
microbiological
mechanisms
for

metabolizing
these
compounds.

65

FIGURES

Figure
2-‐1:
Map
of
Beza
Mahafaly
Special
Reserve
(23.655°S,
44.63°E).

The
home

ranges
of
each
study
group
are
plotted
on
the
map;
the
colored
dots
are
the

locations
of
the
groups
every
15
minutes
while
being
followed
by
observers.

The
L.

catta
Red
group
only
crossed
the
river
when
it
was
a
dry,
sandy
riverbed.

66

Figure
2-‐2:
Weather
conditions
during
the
study
period
(late
September
2011

through
February
2012).

67

Figure
2-‐3:
Monthly
activity
patterns
of
L.
catta
and
P.
verreauxi.

For
each
month,

the
activities
of
all
groups
of
that
species
were
averaged.

n=10

68

Figure
2-‐4:
Monthly
activity
patterns
averaged
within
each
study
group.

Observations
were
made
on
the
western
groups
of
both
species
from
early
October

through
late
January,
while
eastern
and
central
groups
were
observed
from
mid

October
through
early
February.

nwestern
groups
=
neastern
groups
=
3.

ncentral
groups
=
4.

69

Figure
2-‐5:
Seasonal
consumption
of
leaves
by
all
L.
catta
individuals

70

Figure
2-‐6:
Seasonal
consumption
of
leaves
by
all
P.
verreauxi
individuals

71

Figure
2-‐7:
Seasonal
consumption
of
fruit
by
all
L.
catta
individuals

72

Figure
2-‐8:
Seasonal
consumption
of
fruit
by
all
P.
verreauxi
individuals

73

Figure
2-‐9:
Seasonal
consumption
of
flowers
by
all
L.
catta
individuals

74

Figure
2-‐10:
Seasonal
consumption
of
flowers
by
all
P.
verreauxi
individuals

75

Figure
2-‐11:
Seasonal
consumption
of
major
plant
species
by
all
L.
catta
individuals.

Plants
were
only
included
if
they
were
consumed
for
at
least
5%
of
the
time
spent

feeding
in
at
least
one
time
point.

For
definitions
of
plant
species
abbreviations,
see

Table
2-‐1.

76

Figure
2-‐12:
Seasonal
consumption
of
major
plant
species
by
all
P.
verreauxi

individuals.
Plants
were
only
included
if
they
were
consumed
for
at
least
5%
of
the

time
spent
feeding
in
at
least
one
time
point.

For
definitions
of
plant
species

abbreviations,
see
Table
2-‐1.

77

Figure
2-‐13:
Lemur
species
comparisons
of
Shannon
dietary
diversity
within
each

season

78

Figure
2-‐14:
Seasonal
comparisons
of
Shannon
dietary
diversity
for
L.
catta
and
P.

verreauxi

79

TABLES

Table
2-‐1:
Major
plants
consumed
by
L.
catta
and
P.
verreauxi.

Plants
were
only

included
if
one
group
of
either
lemur
species
spent
at
least
5%
of
their
feeding
time

eating
that
plant.

Code Scientific+Name Family+Name Malagasy+Name Lc Pv
AC.BE Acacia%bellula Mimosaceae Tratriotse
AC.RO Acacia%rovumae Mimosaceae Robontsy
ANTI Antidesma%petiolare Euphorbiaceae Voafogne
AZIM Azima%tetracantha Salvadoraceae Filofilo
CATE*
CEDR Cedrelopsis%grevei Ptaeroxylaceae Katrafay
CO.SP Combretum%sp. Combretaceae Tamenake
CRAT Crateva%excelesa Capparidaceae Akaly
DIAL Dialium%madagascariensis Cesalpinaceae Karembolamitsy
DICH Dichrostachys%humbertii Fabaceae Avoha
EUPH Euphorbia%tirucalli Euphorbiaceae Famata
GR.GR Grewia%grevei Tiliaceae Kotipoke
GR.TU Grewia%tuleariensis Tiliaceae Maintifototse
GYRO Gyrocarpus%americanus Hernandiaceae Kapaipoty
IPOM Ipomae%majungansis Convovulaceae Velae
MAER Maerua%filiformis Capparidaceae Somangy
MANG
#
Mangifera%indica Anacardiaceae Manga
MARS Marsdenia%cordifolia Apocynaceae Bokabe
META Metaporana%parvifolia Convolvulaceae Kililo
NORO NoronhiaPsp. Oleaceae Tsilaitse
OCOT Ocotea%tricanta Lantaceae Maroanake
OLAX Olax%sp. Olacaceae Tanjaka
PLUC Pluchea%bojeri Asteraceae Sirasira
QUIV Quivisianthe%papinae Meliaceae Valiandro
RHOP Rhopalocarpus%lucidus Rhopalocarpaceae Tsiongake
SALV Salvadora%angustifolia Salvadoraceae Sasavy
SCUT Scutia%murtina Rhamnaceae Roiombilahy
SYRE Syregada%chauvetiae% Euphorbiaceae HazombalalaP
TALI Talinella%grevea Portulacaceae Dango
TAMA Tamarindus%indica Cesalpiniaceae Kily
TE.MA Terminalia%mantali Combretaceae
VINE
‡
VITE Vitex%beravinensis% Verbenaceae VoamaeP
ColoredPboxesPidentifyPplantPspeciesPconsumedPbyPeachPlemurPspecies
LcP=PLemur%catta%%%%%%PvP=P Propithecus%verreauxi
*PincludesPanyPcaterpillarPspeciesPconsumed
#
PincludesPmangoPfruitPfromPtreesPandPpeelsPfromPcampPtrash
‡
PincludesPanyPunidentifiedPvinePandPlianaPspeciesPconsumed

80

Table
2-‐2:
Home
range
size
of
each
study
group.

Species Group Home-Range-(km
2
)
Blue 0.257
Yellow 0.194
Red 0.222
Fano 0.024
Felix 0.061
Vavy 0.042
L.#catta
P.#verreauxi

81

Table
2-‐3:
Major
plant
species
consumed
by
each
L.
catta
group
during
each
season.

Values
are
the
percentage
of
the
total
time
spent
feeding
in
that
season.

Plants
were

only
included
if
they
were
consumed
for
at
least
5%
of
the
time
spent
feeding
in
at

least
one
time
point
for
any
L.
catta
group.

For
definitions
of
plant
species

abbreviations,
see
Table
2-‐1.

Group
Location
Season Dry Transition Wet Dry Transition Wet Dry Transition Wet
Plant5
Species
AZIM 0.00 50.51 26.52 6.83 47.09 7.53 0.00 35.81 7.11
CATE 0.00 0.00 4.55 0.00 0.00 0.00 0.00 0.00 5.02
CEDR 6.27 0.00 7.49 0.00 0.00 0.00 5.40 0.00 2.58
CRAT 0.00 0.00 1.86 0.00 0.00 3.22 0.00 0.00 1.51
GYRO 5.01 3.76 0.00 3.02 0.00 0.00 6.30 0.00 0.00
IPOM 3.85 0.00 0.00 3.27 0.00 0.00 0.00 0.00 0.00
MAER 0.00 4.40 0.00 0.00 4.09 0.00 0.00 0.00 0.00
MANG 0.00 0.00 0.00 3.37 3.55 0.00 0.00 0.00 0.00
MARS 0.00 0.00 2.98 0.00 0.00 0.00 0.00 0.00 9.02
META 0.00 0.00 8.55 0.00 0.00 23.66 6.86 0.00 8.92
NORO 0.00 0.00 0.00 0.00 0.00 0.00 0.00 8.82 1.56
OCOT 9.12 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
QUIV 21.60 0.00 0.00 22.26 9.34 0.00 13.08 0.00 0.00
SALV 7.38 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
TALI 0.00 0.00 8.88 0.00 0.00 21.78 4.30 0.00 7.00
TAMA 36.80 32.05 21.52 45.75 30.28 21.33 43.03 49.34 28.74
VINE 0.00 0.00 6.38 6.70 0.00 14.93 14.73 0.00 21.72
0 highestEvalue
West
Blue Yellow
Central East
Red

82

Table
2-‐4:
Major
plant
species
consumed
by
each
P.
verreauxi
group
during
each

season.

Values
are
the
percentage
of
the
total
time
spent
feeding
in
that
season.

Plants
were
only
included
if
they
were
consumed
for
at
least
5%
of
the
time
spent

feeding
in
at
least
one
time
point
for
any
P.
verreauxi
group.

For
definitions
of
plant

species
abbreviations,
see
Table
2-‐1.

Group
Location
Season Dry Transition Wet Dry Transition Wet Dry Transition Wet
Plant5
Species
AC.BE 11.47 26.22 0.00 13.00 7.27 0.00 0.00 0.00 0.00
AC.RO 5.09 0.00 0.00 9.04 0.00 1.36 7.40 17.34 1.39
ANTI 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.56 0.00
AZIM 0.00 10.31 13.54 2.79 45.84 0.00 0.00 12.28 0.00
CEDR 7.51 0.00 1.35 3.47 0.00 1.81 0.00 0.00 0.00
CO.SP 0.00 0.00 0.00 0.00 0.00 0.00 52.49 11.12 0.00
DIAL 18.71 29.01 4.99 3.46 0.00 0.00 0.00 0.00 0.00
DICH 0.00 0.00 20.49 6.77 19.45 18.64 0.00 0.00 0.00
EUPH 8.76 0.00 2.36 35.11 3.84 7.62 16.39 6.47 6.95
GR.GR 0.00 0.00 0.00 0.00 0.00 4.46 0.00 0.00 0.00
GR.TU 0.00 0.00 9.76 0.00 0.00 0.00 0.00 0.00 0.00
IPOM 0.00 0.00 0.00 4.62 0.00 0.00 0.00 0.00 0.00
META 0.00 0.00 2.80 0.00 0.00 13.11 0.00 0.00 4.93
OCOT 26.59 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
OLAX 4.02 0.00 2.64 0.00 0.00 0.00 0.00 0.00 0.00
PLUC 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.66
RHOP 0.00 12.32 2.82 0.00 0.00 0.00 0.00 0.00 0.00
SCUT 0.00 0.00 0.00 2.55 0.00 0.00 0.00 0.00 0.00
SYRE 0.00 0.00 15.70 0.00 0.00 10.66 0.00 0.00 7.97
TALI 0.00 0.00 2.70 0.00 6.22 0.00 0.00 0.00 0.00
TAMA 0.00 6.91 2.50 4.97 6.15 2.05 3.37 28.59 25.94
TE.MA 0.00 3.01 2.30 0.00 0.00 7.91 0.00 0.00 0.00
VINE 5.03 0.00 1.71 0.00 0.00 8.80 8.80 12.34 30.25
VITE 0.00 0.00 0.00 0.00 0.00 0.00 0.00 4.28 10.17
0 highestEvalue
Fano Felix Vavy
West Central East

83

CHAPTER
3
-‐
CONSUMPTION
OF
PLANT
DEFENSES

ABSTRACT

Herbivores
encounter
plant
defenses
when
consuming
the
nutrients
in
their
diet.

When
not
able
to
neutralize
these
defenses,
herbivores
can
avoid
their
consumption.

The
tolerance
of
plant
chemical
and
mechanical
defenses
can
vary
in
each

herbivorous
species.

This
study
investigated
the
tolerance
of
two
lemur
species

(Lemur
catta
and
Propithecus
verreauxi)
to
phenolics
(a
plant
chemical
defense)
and

to
fiber
(a
plant
mechanical
defense).

The
consumption
rate
of
phenolics
and
fiber

were
measured
in
wild
sympatric
populations
of
both
lemur
species
from
the
dry

season
to
the
wet
season.

L.
catta
and
P.
verreauxi
consumed
similar
amounts
of

fiber
in
the
dry
season,
but
P.
verreauxi
consumed
more
during
the
wet
season.

The

phenolics
consumption
rate
of
both
lemur
species
was
similar
in
the
dry
season.

L.

catta
consumed
less
phenolics
in
the
wet
season,
while
P.
verreauxi
continued

consuming
phenolics
at
the
same
rate
as
it
did
during
the
dry
season.

These

findings
suggest
that
there
is
no
avoidance
of
fiber
in
the
diets
of
both
L.
catta
and
P.

verreauxi
as
they
maintain
consistent
consumption
levels
across
seasons.

P.

verreauxi
appears
to
have
a
higher
tolerance
for
phenolics
as
they
consume
large

amounts
consistently.

L.
catta
may
also
be
able
to
detoxify
phenolics,
but
at
lower

levels
than
in
P.
verreauxi.

INTRODUCTION

Herbivores
obtain
the
majority
of
their
nutrition
from
the
plants
in
their
diet,
so

the
plant
defenses
employed
to
deter
their
consumption
pose
a
challenge
to
the

survival
of
herbivores.

If
plant
defenses
effectively
prevent
an
herbivore
from

consuming
enough
nutritionally
necessary
plant
tissues,
the
herbivore
may
suffer

from
malnutrition
or
death.

When
plants
utilize
defense
mechanisms
for
protection,

an
herbivore
has
two
choices:
(1)
avoid
these
defenses
and
refrain
from
digesting

their
protected
tissues
or
(2)
develop
mechanisms
to
detoxify
or
neutralize
the

negative
effects
of
any
ingested
plant
defenses
(Freeland
and
Janzen
1974;
Brattsten

84

1979;
Dowd
et
al.
1983;
Varga
and
Kolver
1997;
Bhat
et
al.
1998;
Provenza
et
al.

2003).

When
herbivores
are
unable
to
detoxify
plant
defenses,
avoiding
their
ingestion

can
be
the
best
tactic
(Lucas
et
al.
2000;
Fashing
et
al.
2007).

This
behavior
can

result
in
a
highly
selective
diet,
with
the
herbivore
ingesting
only
foods
that
contain

none
of
the
offending
plant
defense
(Freeland
and
Janzen
1974;
Brattsten
1979;

Dowd
et
al.
1983;
Varga
and
Kolver
1997;
Bhat
et
al.
1998;
Provenza
et
al.
2003;

Iason
and
Villalba
2006).

There
are
intermediate
cases
as
well,
where
an
herbivore

can
tolerate
small
amounts
of
a
plant
defense
with
minimal
negative
effects

(Freeland
and
Janzen
1974;
Rhoades
1979;
Glander
1982;
Iason
and
Villalba
2006).

The
impact
of
plant
defenses
on
an
herbivore
depends
on
the
amount
and

concentration
consumed
and
that
herbivore’s
ability
to
break
down
the
plant

defense.

This
means
that
each
plant
defense
affects
each
herbivore
differently

(Freeland
and
Janzen
1974;
Feeny
1976;
Rhoades
and
Cates
1976;
Rhoades
1979;

Glander
1982;
Lucas
et
al.
2000).

Cellulose
and
other
structural
carbohydrates
that
act
as
plant
mechanical

defenses
are
found
ubiquitously
in
the
plant
due
to
their
structural
role,
so

avoidance
is
more
difficult
(Bayer
et
al.
1998;
Fashing
et
al.
2007).

Despite
their

ubiquity,
leaves
typically
have
higher
concentrations
of
cellulose
than
other
plant

parts,
such
as
fruit
(Rothman
et
al.
2006;
Rothman
et
al.
2007a).

Folivores,
who

consume
mainly
leaves
in
their
diet,
are
likely
to
consume
greater
amounts
of

cellulose
than
herbivores
with
more
fruits
in
their
diets
(Rothman
et
al.
2007a).

Cellulose,
a
polymer
of
more
than
hundreds
of
glucose
molecules,
is
a
vast

repository
of
sugars
necessary
for
cellular
respiration
(Kirk
and
Farrell
1987;

Lambert
1998).

So
while
cellulose
can
have
negative
effects
on
digestion,
it
also

contains
a
rich
source
of
energy
for
consumers
that
can
metabolize
it.

Phenolics
are
also
found
in
most
plants,
where
they
frequently
act
as
chemical

deterrents
to
herbivory
by
mammalian
predators
(Wong
1973;
Berger
et
al.
1977;

Zucker
1982;
Buchsbaum
et
al.
1984;
Reichardt
et
al.
1990;
Bennett
and
Wallsgrove

1994;
Foley
and
McArthur
1994;
Haslam
1995;
Dearing
1996;
Foley
et
al.
1999;
Pass

and
Foley
2000;
Wiggins
et
al.
2003).

Phenolics
can
be
found
in
many
plant
tissues,

85

including
leaves,
fruit,
and
flowers
(Freeland
and
Janzen
1974;
Brattsten
1979;

Dowd
et
al.
1983;
Varga
and
Kolver
1997;
Bhat
et
al.
1998;
Provenza
et
al.
2003;

Gharras
2009).

In
concert
with
the
defense
avoidance
strategy,
animals
may
choose
to
avoid

certain
plant
parts
that
contain
higher
concentrations
of
plant
defenses
and
to
focus

on
less
harmful
parts
of
the
same
plant.

In
general,
younger
tissues
contain
higher

concentrations
of
chemical
defenses,
as
mechanical
defenses
hinder
their
ability
to

grow
and
mature
(Feeny
1976;
Rhoades
and
Cates
1976;
Coley
1988;
Lucas
et
al.

2000).

Once
mature,
plant
tissues
can
then
change
focus
and
rely
on
mechanical

defenses
to
prevent
herbivory
(Coley
1988;
Bayer
et
al.
1998).
With
ripe
fruits,
it

can
be
advantageous
to
the
plant
for
an
herbivore
to
consume
these
tissues
and

disperse
the
seeds
away
from
the
parent
plant.

To
this
end,
ripe
fruits
are
often

more
palatable
to
consumers
with
lower
levels
of
plant
defenses
and
higher
levels
of

sugars
when
compared
to
their
unripe
forms
(Calvert
1985;
Conklin-‐Brittain
et
al.

1998;
Schmidt
et
al.
2000).

The
seasonal
consumption
rate
for
different
plant
defenses
can
vary
among

species.

As
mentioned
above,
this
can
be
influenced
by
an
animal’s
dietary
breadth

and
by
their
physiological
capacity
to
metabolize
the
plant
defenses.

Amongst
the

sympatric
populations
of
Lemur
catta
and
Propithecus
verreauxi
at
Beza
Mahafaly

Special
Reserve
(BMSR),
Madagascar,
the
nutrient
composition
of
their
diets
has

been
characterized
(Yamashita
2008).

L.
catta
is
a
generalist
herbivore,
consuming

fruit
seasonally,
with
leaves
and
the
fruit
of
Tamarindus
indica
as
fallback
foods

(Sussman
1974;
Sauther
et
al.
1999;
Gould
et
al.
2003;
Yamashita
2008;
Sauther
and

Cuozzo
2009).

P.
verreauxi
is
predominantly
a
folivore
(Richard
et
al.
2002),
with

the
majority
of
its
diet
consisting
of
mature
and
young
leaves,
though
also

consuming
ripe
fruit,
unripe
fruit,
seeds,
and
flowers
when
available
(Yamashita

2002;
Simmen
et
al.
2003).

Yamashita
(2008)
began
to
look
at
the
consumption

patterns
of
plant
defenses,
but
only
investigated
the
chemical
defenses
of
phenolics

and
tannins.

To
broaden
our
understanding
of
the
role
of
both
mechanical
and

chemical
defenses
in
the
diet
of
these
lemur
populations,
this
study
measured
the

86

seasonal
changes
in
ingestion
of
cellulose
and
phenolics
among
sympatric

populations
of
L.
catta
and
P.
verreauxi.

Hypotheses

I
expected
P.
verreauxi
to
consume
a
greater
amount
of
phenolics
than
L.
catta
in

all
seasons,
similar
to
previous
findings
(Yamashita
2008).

I
hypothesized
that
P.

verreauxi,
as
a
folivore
with
a
diet
containing
more
cellulose-‐rich
leaves,
would

consume
more
cellulose
than
L.
catta
in
both
the
dry
and
wet
seasons.

I
also

hypothesized
that
both
lemur
species
would
consume
greater
amounts
of
cellulose

in
the
dry
season
than
in
the
wet
season.

During
the
wet
season,
both
species
shift

their
diet
to
consume
more
fruit
and
fewer
leaves,
which
should
reduce
the
overall

cellulose
in
their
diets
as
they
consume
fewer
leaves.

To
test
these
hypotheses,

behavioral
feeding
data
and
chemical
assays
of
the
foods
consumed
by
L.
catta
and

P.
verreauxi
were
used
to
compare
the
seasonal
consumption
of
plant
defenses.

METHODS

Sample
Collection
and
Preparation

Plant
samples
were
collected
from
October
2011
through
February
2012
at
Beza

Mahafaly
Special
Reserve
(BMSR)
in
southwestern
Madagascar.

Three
groups
of

were
observed
for
each
lemur
species,
spread
across
the
east-‐west
habitat
gradient

at
BMSR.

The
Lemur
catta
groups
were
Blue
(in
the
west),
Yellow
(in
the
center
of

the
parcel),
and
Red
(in
the
east).

For
Propithecus
verreauxi,
the
three
groups
were

Fanodrovery
(Fano;
west),
Felix
(center),
and
Vavymasiaka
(Vavy;
east).

For
each

group,
samples
were
collected
of
their
major
foods
every
two
weeks,
in
synchrony

with
the
recording
of
their
behavior
(see
Chapter
2).

A
food
item
was
considered
to

be
a
major
food
if
the
lemur
group
was
seen
consuming
that
plant
part
for
more

than
ten
minutes
over
a
two-‐day
observation
period.

A
small
number
of
foods
were

not
collected
either
due
to
a
difficulty
of
acquisition
(e.g.
flowers
only
present
25-‐
meters
up
a
tree)
or
due
to
a
lack
of
confident
identification
(e.g.
the
lemur
was

observed
consuming
the
leaves
of
a
vine
in
a
dense
patch
consisting
of
many

87

similar-‐looking
vine
species).

For
food
items
that
were
consumed
by
multiple

groups
or
across
multiple
time
points,
separate
samples
of
the
plant
were
collected

for
each
group
and
each
time
point.

This
helped
to
control
for
intraspecific
chemical

variation
between
plants
of
the
same
species
(Chapman
et
al.
2003).

Additionally,

whenever
possible,
plant
samples
were
collected
from
the
same
area
of
the
plant
the

lemur
was
consuming.

Plant
samples
were
dried
on
the
day
of
their
collection.

Plants
were
desiccated

with
color-‐changing
silica
beads
until
the
sample
no
longer
released
enough

moisture
to
alter
the
color
of
the
silica.

For
very
wet
samples
(particularly
fruit),
the

silica
was
changed
out
every
few
days.

Leaf
and
flowers
were
initially
air-‐dried
with

a
plant
press
before
being
transferred
to
silica
for
a
final
drying.

Once
dry,
all
of
the

plant
samples
were
stored
in
a
cool,
dry
place
in
plastic
bags
with
a
small
amount
of

silica
to
keep
the
samples
dry.

Samples
were
checked
frequently
to
make
sure
that

no
mold
had
appeared.

Dried
plant
samples
were
weighed
and
divided
by
the

number
of
food
items
in
that
sample
to
determine
the
plant
weight
per
part
(PWP)

for
each
sample.

The
dried
plant
samples
were
ground
to
a
fine
powder
using
a

rotating
ball
mill.

Tamarindus
indica
ripe
fruit
samples
were
not
tested
for
L.
catta
groups.

All
of

the
ground
T.
indica
ripe
fruit
samples
included
the
seeds,
which
are
excreted
intact

by
L.
catta.

Since
these
large
seeds
pass
through
the
lemur
digestive
tract
intact,
it
is

not
likely
that
the
animals
absorb
much
cellulose
or
phenolics
from
these
seeds.

Since
the
seeds
and
fruit
pulp
could
not
be
separated
once
ground,
these
samples

were
excluded
from
the
analysis.

Phenolic
Measurement

Total
phenolic
content
of
the
plant
samples
was
determined
by
using
the

protocol
developed
by
the
Food
and
Agriculture
Organization
of
the
United
Nations

(FAO)
and
the
United
Nations’
International
Atomic
Energy
Agency
(IAEA)
(Makkar

2000).

This
protocol
measures
the
phenolic
content
of
a
sample
using
the
Folin-‐
Ciocalteu
method
(Folin
and
Ciocalteu
1927;
Singleton
and
Rossi
1965).

This
is
a

standard
method
for
measuring
the
phenolic
content
of
plants
and
other
materials

88

(e.g.,
Oates
et
al.
1980;
Box
1983;
Buchsbaum
et
al.
1984;
Coley
1988;
Singleton
et
al.

1999;
Moyer
et
al.
2002;
Asami
et
al.
2003;
Zainol
et
al.
2003;
Scalzo
et
al.
2005;

Meda
et
al.
2005;
Ainsworth
and
Gillespie
2007;
Mamphiswana
et
al.
2010;
Fu
et
al.

2011).

Briefly,
the
Folin-‐Ciocalteu
method
functions
as
a
colorimetric
assay,
with

increased
levels
of
phenolics
having
an
increased
effect
on
the
color
change
(Folin

and
Ciocalteu
1927;
Singleton
and
Rossi
1965).

A
micro-‐assay
of
the
procedure

(similar
to
Medina-‐Remón
et
al.
2009)
was
utilized
to
reduce
the
amount
of
plant

sample
necessary
for
the
phenolic
measurement.

By
reducing
the
amount
of
sample

needed
for
each
test,
more
chemical
analyses
would
be
possible
for
each
plant

sample.

This
was
particularly
important
for
rare
and
hard-‐to-‐collect
food
items,

where
large
samples
were
difficult
to
collect.

Finely
ground
black
tea
and
cactus
were
used
as
internal
standards
between

tests.

Final
sample
color
was
measured
using
a
spectrophotometer
with
a
96-‐well

plate
reader,
so
up
to
78
samples
were
analyzed
in
parallel.

A
new
tannic
acid

standard
solution
with
a
concentration
of
1
mg/mL
was
made
every
2-‐4
days
of

testing.

A
tannic
acid
dilution
series
was
created
at
the
following
dilutions:
1/2,
1/4,

1/8,
1/16,
1/32,
1/64,
and
1/128.

All
of
the
following
dilutions,
as
well
as
the

undiluted
tannic
acid
solution
and
a
tannic-‐acid-‐free
blank,
were
used
to
determine

the
calibration
curve
for
the
color
measurement
of
each
plant
sample.

Between
10
and
20
mg
of
each
plant
sample
was
combined
with
1.5
mL
of

methanol
in
a
2
mL
microcentrifuge
tube.

The
tubes
were
sealed
and
briefly

vortexed
before
being
incubated
in
an
80°C
water
bath
for
60
minutes
for
methanol

extraction
of
the
total
phenolics.

The
tubes
were
vortexed
briefly
and
then

centrifuged
at
10,000
RPM
for
10
minutes.

The
samples
were
then
diluted
at
a
ratio

of
4:1
to
bring
their
phenolic
values
into
the
range
of
the
tannic
acid
dilution
series.

For
this
dilution,
100
µL
of
each
methanol-‐extracted
sample
was
combined
with
400

µL
of
MilliQ
(deionized
and
0.22
μm
pore
size
filtered)
water
in
a
new
2
mL

microcentrifuge
tube.

The
dilution
tubes
were
vortexed
briefly
to
mix
the
contents.

Next,
100
µL
of
each
diluted
sample
was
combined
with
60
µL
of
Folin-‐Ciocalteu

reagent,
250
µL
of
17%
sodium
carbonate,
and
840
µL
of
MilliQ
water.

100
µL
of

each
of
the
tannic
acid
dilution
series
solution
was
similarly
combined
with
60
µL
of

89

Folin-‐Ciocalteu
reagent,
250
µL
of
17%
sodium
carbonate,
and
840
µL
of
MilliQ

water,
with
the
exception
of
the
tannic-‐acid-‐free
blank.

This
blank
was
created
by

combining
60
µL
of
Folin-‐Ciocalteu
reagent,
250
µL
of
17%
sodium
carbonate,
and

940
µL
of
MilliQ
water
(no
sample
or
tannic
acid
standard
added).

These
tubes
were

allowed
to
sit
for
30
minutes,
followed
by
transferring
300
µL
of
each
sample
or

standard
into
the
wells
of
a
96-‐well
plate.

This
plate
was
then
read
on
a

spectrophotometer
at
a
wavelength
of
760
nm.

The
measured
absorbance
and
the
known
tannic
acid
concentration
of
each

solution
in
the
tannic
acid
dilution
series
were
used
to
create
a
linear
regression.

A

best-‐fit
line
was
calculated
using
the
dilution
series
data.

The
total
phenolic
content

of
the
plant
samples
was
determined
based
on
where
their
absorbance
values
fell
on

this
best-‐fit
line
(Fig.
3-‐1).

The
quality
of
the
best-‐fit
line
was
determined
by

calculating
how
well
the
line
explained
the
variance
in
the
tannic
acid
dilution
series

data
(R
2

value).

The
best-‐fit
line
provided
a
measurement
of
the
tannic
acid
equivalent
(TAE)

amount
of
phenolics
in
each
sample
tube,
but
the
original
sample
weight
varied

slightly
(from
10
to
20
mg).

To
standardize
the
phenolic
measurement
across

samples,
the
TAE
values
were
divided
by
the
sample
weight
at
the
beginning
of
the

protocol,
yielding
a
measurement
of
total
phenolics
in
TAE
per
gram
of
dry
weight

of
the
plant
sample.

Fiber
Measurement

While
the
goal
of
this
research
was
to
investigate
the
temporal
consumption
of

cellulose,
the
standard
methods
implemented
here
involve
a
three-‐step
analysis
of

different
fiber
fractions
in
the
plant
samples
(Van
Soest
and
Wine
1968;
Anderson

and
Ingram
1993;
Rowland
and
Roberts
1994).

Three
fiber
fractions
were

quantified:
acid
detergent
fiber
(ADF),
cellulose,
and
acid
detergent
lignin
(ADL).

ADF
does
not
correspond
to
a
specific
chemical
class
of
fiber,
but
rather
to
the

more
indigestible
classes
of
fiber
as
a
whole.

ADF
is
all
of
the
fiber
that
remains

after
a
sample
is
boiled
in
an
acid
detergent
solution
(mostly
cellulose
and
lignin)

(Campbell
et
al.
2004b),
with
the
more
digestible
types
of
fiber
(such
as

90

hemicellulose)
removed
(Schmidt
et
al.
2000).

Mammals
endogenously
lack
the

enzymes
necessary
to
digest
the
types
of
fiber
within
the
ADF
portion
of
plants
(Van

Soest
and
McQueen
1973;
Campbell
et
al.
2004b).

ADL,
however,
corresponds

mostly
to
the
lignin
fraction
of
the
total
fiber
in
a
sample.

First,
the
samples
were
digested
in
a
boiling
sulfuric
acid
solution
containing
the

detergent
cetyltrimethylammonium
bromide
(CTAB),
leaving
behind
the
ADF

portion
of
the
sample.

The
ADF
content
was
calculated
by
comparing
the
dry
weight

of
the
plant
sample
before
and
after
this
digestion.

Second,
cellulose
was
removed

from
the
samples
in
a
cold,
strong
sulfuric
acid
solution.

Again,
comparing
the

weight
change
before
and
after
this
digestion
revealed
the
cellulose
content
in
each

sample.

Lastly,
ADL
was
measured
by
ashing
the
samples
in
a
500°C
oven.

As

before,
the
weight
difference
before
and
after
ashing
was
used
to
show
the
ADL

content
in
the
plant
samples.

Finely
ground
dried
barley
was
used
as
an
internal
standard
between
fiber
tests

and
all
tests
also
included
a
blank
sample.

ANKOM
F57
filter
bags
were
used
to

contain
the
ground
plant
samples
during
the
multiple
extractions
and
washes.

Each

filter
bag,
containing
0.75
g
±
0.37
g
(W1)
of
each
ground
plant
sample,
was
heat-‐
sealed
and
labeled
on
both
sides
in
pencil
with
the
sample
number.

Dried
filter
bags

were
weighed
before
being
filled
(W2,
0.50
g
±
0.05
g)
and
stored
with
silica

desiccant
before
and
between
analyses.

These
methods
allow
for
a
large
capacity
(up
to
75
filter
bags)
to
be
processed
at

a
time,
greatly
reducing
the
time
needed
for
this
analysis.

ADF
Measurement

To
measure
ADF,
filter
bags
containing
the
plant
samples
were
placed
in
a

beaker
with
enough
CTAB/Sulfuric
Acid
(0.5
M)
solution
to
cover
the
bags.

A
few

anti-‐bumping
granules
(3mm
glass
beads)
and
a
few
drops
of
antifoaming
agent
(1-‐
octanol)
were
added
to
the
beaker.

The
beaker
was
placed
on
a
hotplate
with
a

round-‐bottomed
flask
on
top
as
a
refluxing
condenser.

The
hotplate
was
turned
on

to
300°C
and
the
filter
bags
were
boiled,
with
refluxing,
for
90
minutes.

The
system

was
allowed
to
cool
down,
and
then
the
filter
bags
were
removed
and
run
under
hot

91

tap
water
until
most
of
the
CTAB
was
washed
off.

The
filter
bags
were
repeatedly

washed
with
demineralized
water
until
the
pH
was
neutral
(about
4
washes,
10-‐20

minutes
each),
with
water
squeezed
out
of
the
filter
bags
after
each
wash.

The
filter

bags
were
washed
in
acetone
until
no
more
color
was
removed
(about
3
washes,
10-‐
15
minutes
each),
by
placing
the
filter
bags
in
a
large
beaker,
covering
them
with

acetone,
and
agitating
the
bags
by
raising
and
lowering
a
smaller
beaker
inside
the

larger
one,
with
acetone
squeezed
out
of
the
filter
bags
after
each
wash.

Excess

acetone
was
allowed
to
evaporate
from
the
filter
bags
before
they
were
placed
in
a

60°C
oven
overnight
to
dry.

Once
dry,
the
filter
bags
were
cooled
to
room

temperature
in
a
desiccator
and
then
weighed
(W3).

An
empty
filter
bag
(blank)

was
included
in
each
run
to
determine
the
moisture
factor
(mf)
in
the
laboratory.

To
calculate
the
ash
containing
ADF
as
a
percentage
of
the
total
weight
of
the

plant
sample,
the
following
formula
was
used:

ash
containing
ADF
(%)
=
((W3
-‐
W2)
x
mf
/
W1)
x
100

(F1)

mf
=
moisture
factor
=
W3
blank
/
W2
blank

W1
=
initial
sample
weight

W2
=
filter
bag
weight

W3
=
weight
after
ADF
extraction
(dried
filter
bag
plus
fiber
post-‐
extraction)

Cellulose
Measurement

To
measure
cellulose,
the
filter
bags
were
then
placed
in
a
large
beaker
and

covered
with
cold
sulfuric
acid
(72%
w/w)
solution.

A
smaller
beaker
was
placed

inside
the
larger
one
and
used
to
agitate
the
filter
bags
every
30
minutes
for
3
hours.

The
filter
bags
were
removed
from
the
sulfuric
acid
solution
and
run
under
hot
tap

water.

The
filter
bags
were
repeatedly
washed
with
demineralized
water
until
the

pH
was
neutral
(about
4
washes,
10-‐20
minutes
each),
with
water
squeezed
out
of

the
filter
bags
after
each
wash.

The
filter
bags
were
washed
in
acetone
until
no

more
color
was
removed
(about
3
washes,
10-‐15
minutes
each),
by
placing
the
filter

bags
in
a
large
beaker,
covering
them
with
acetone,
and
agitating
the
bags
by
raising

and
lowering
a
smaller
beaker
inside
the
larger
one,
with
acetone
squeezed
out
of

92

the
filter
bags
after
each
wash.

Any
remaining
acetone
was
allowed
to
evaporate

from
the
filter
bags
before
they
were
placed
in
a
60°C
oven
overnight
to
dry.

Once

dry,
the
filter
bags
were
cooled
to
room
temperature
in
a
desiccator
and
then

weighed
(W4).

To
calculate
the
cellulose
as
a
percentage
of
the
total
weight
of
the
plant
sample,

the
following
formula
was
used:

cellulose
(%)
=
((W3
-‐
W4)
/
W1)
x
100

(F2)

W1
=
initial
sample
weight

W3
=
weight
after
ADF
extraction
(dried
filter
bag
plus
fiber
post-‐ADF

extraction)

W4
=
weight
after
cellulose
extraction
(dried
filter
bag
plus
fiber
post-‐
cellulose
extraction)

ADL
Measurement

To
measure
ADL,
labeled
porcelain
crucibles
were
prepared
for
the
samples
by

drying
in
a
500
°C
oven
for
1
hour.

Once
cooled,
these
crucibles
were
weighed
(W5)

and
stored
in
a
desiccator.

Each
filter
bag
was
placed
in
a
crucible
and
ashed
for
3

hours
once
the
oven
reached
500
°C.

Once
cooled,
the
crucibles
were
weighed
(W6).

To
calculate
the
lignin
as
a
percentage
of
the
total
weight
of
the
plant
sample,
the

following
formula
was
used:

lignin
(%)
=
([(W4
-‐
W2)
-‐
(W6
-‐
W5)]
/
W1)
x
100

(F3)

W1
=
initial
sample
weight

W2
=
filter
bag
weight

W4
=
weight
after
cellulose
extraction
(dried
filter
bag
plus
fiber
post-‐
cellulose
extraction)

W5
=
dried
crucible
weight

W6
=
crucible
with
ash
weight

Food
Consumption
Calculations

To
calculate
the
amount
of
phenolics
and
fiber
consumed
by
the
lemurs
over

time,
first,
the
amount
of
each
food
consumed
was
calculated
using
the
behavioral

data
and
the
following
formula:

93

NPP
=
TFD
X
ABR
X
ABS

(F4)

NPP
=
Number
of
Plant
Parts
Consumed
(e.g.
4
leaves,
3
fruits,
etc.)

TFD
=
Total
Feeding
Duration
(min)

ABR
=
Average
Bite
Rate
(bites/min)

ABS
=
Average
Bite
Size
(food/bite,
e.g.
3
leaves/bite)

Several
foods
that
were
consumed
in
large
amounts
were
missing
bite
rate
or

bite
size
data
for
a
lemur
at
one
time
point.

So
that
these
important
foods
could
be

included
in
the
analysis,
the
average
bite
rate
and
bite
size
were
estimated
using
the

feeding
data
for
that
food
and
individual
from
a
nearby
time
point.

This
formula

(F4)
was
applied
for
each
individual
lemur
at
each
time
point
and
with
each
food

consumed
by
that
animal.

Phenolics
Consumption
Rate
Calculations

The
amount
of
phenolics
consumed
(g)
was
calculated
using
the
formula:

Phenolics
Consumed
=
NPP
X
PWP
X
PPW

(F5)

NPP
=
Number
of
Plant
Parts
Consumed
(e.g.
4
leaves,
3
fruits,
etc.)

PWP
=
Plant
Weight
per
Part
(g/part)

PPW
=
Phenolics
per
Plant
Weight
(g/g)

This
formula
(F5)
was
applied
for
each
lemur
at
each
time
point
and
with
each

food
consumed
by
that
animal.

Several
foods
were
collected
for
multiple
groups
and

time
points.

When
there
was
no
plant
sample
available
for
a
group,
plant
weight
per

part
(PWP)
and
phenolics
per
plant
weight
(PPW)
values
were
substituted
from

neighboring
groups
or
adjacent
time
points.

The
total
phenolics
consumed
for
each
lemur
at
each
time
point
was
calculated

by
summing
the
phenolics
consumed
in
its
foods
during
that
time
period.

The
phenolics
consumption
rate
(g/hr)
was
calculated
using
the
following

formula:

94

Phenolics
Consumption
Rate
=
Total
Phenolics
Consumed
(g)

(F6)

Observation
Time
(hr)

This
formula
(F6)
was
applied
for
each
lemur
at
each
time
point.

The

observation
time
was
the
total
amount
of
time
spent
recording
the
behavior
of
that

lemur
during
that
time
point.

A
phenolic
consumption
rate
was
calculated
to
correct

for
different
total
observation
times
between
lemurs
and
time
periods.

Fiber
Consumption
Rate
Calculations

The
amount
of
fiber
consumed
(g)
was
calculated
using
the
formula:

Fiber
Consumed
=
NPP
X
PWP
X
FPW

(F7)

NPP
=
Number
of
Plant
Parts
Consumed
(e.g.
4
leaves,
3
fruits,
etc.)

PWP
=
Plant
Weight
per
Part
(g/part)

FPW
=
Fiber
per
Plant
Weight
(g/g)

This
formula
(F7)
was
applied
for
each
lemur
at
each
time
point
and
with
each

food
consumed
by
that
animal.

The
consumption
of
each
fiber
fraction
(ADF,

Cellulose,
and
ADL)
was
calculated
separately
using
this
formula.

Several
foods

were
collected
for
multiple
groups
and
time
points.

When
there
was
no
plant

sample
available
for
a
group,
plant
weight
per
part
(PWP)
and
fiber
per
plant
weight

(FPW)
values
were
substituted
from
neighboring
groups
or
adjacent
time
points.

The
total
fiber
consumed
for
each
lemur
at
each
time
point
was
calculated
by

summing
the
fiber
consumed
in
its
foods
during
that
time
period.

The
total
amount

consumed
was
calculated
separately
for
ADF,
Cellulose,
and
ADL.

The
fiber
consumption
rate
(g/hr)
was
calculated
using
the
following
formula:

Fiber
Consumption
Rate
=
Total
Fiber
Consumed
(g)

(F8)

Observation
Time
(hr)

This
formula
(F8)
was
applied
for
each
lemur
at
each
time
point.

The

observation
time
was
the
total
amount
of
time
spent
recording
the
behavior
of
that

lemur
during
that
time
point.

A
fiber
consumption
rate
was
calculated
to
correct
for

95

different
total
observation
times
between
lemurs
and
time
periods.

The

consumption
rate
was
calculated
separately
for
ADF,
Cellulose,
and
ADL.

Analysis

The
consumption
rate
of
each
plant
defense
(phenolics,
cellulose,
ADF,
and
ADL)

was
compared
between
lemur
species
within
each
season
using
the
Mann-‐Whitney

U
test
with
a
Bonferroni
correction
for
multiple
comparisons
(Remis
et
al.
2001).

The
Bonferroni
correction
adjusts
the
p-‐value
threshold
to
account
for
the

increasing
likelihood
of
a
Type
I
error
due
to
multiple
comparisons
(Bland
and

Altman
1995).

To
compare
the
plant
defense
consumption
rates
across
seasons
within
each

lemur
species,
a
Friedman
test
was
used
with
a
post-‐hoc
Wilcoxon
signed
rank
test

with
Bonferroni
correction
(Doyle
et
al.
2005;
Finlayson
et
al.
2007).

The
Friedman

test
indicated
whether
there
was
an
overall
significant
difference
between
the

temporal
consumption
rates
within
a
lemur
species.

The
post-‐hoc
Wilcoxon
signed

rank
test
performs
pairwise
comparisons
of
each
time
point
to
determine
which
are

significantly
different
from
one
another.

RESULTS

Phenolics
Consumption

Within
Species:
Seasonally,
there
were
unique
patterns
of
phenolics

consumption
between
Lemur
catta
and
Propithecus
verreauxi
(Fig.
3-‐2a).

The

seasonal
pattern
within
L.
catta
is
a
steady
decline
in
the
consumption
rate
of

phenolics
from
the
dry
season
to
the
wet
season.

A
Friedman
test
revealed
a

significant
effect
of
Season
on
Phenolics
Consumption
Rate
in
L.
catta
(X
2
(2)
=
15.2,

p
<
0.01)
(Table
3-‐1a).

A
post-‐hoc
test
using
Wilcoxon
signed
rank
tests
with

Bonferroni
correction
showed
significant
differences
between
the
dry
season
and

the
wet
season
(r
=
0.63,
p
<
0.01)
and
between
the
transition
between
seasons
and

the
wet
season
(r
=
0.63,
p
<
0.01).

There
was
no
significant
difference
in
L.
catta

phenolics
consumption
rate
between
the
dry
season
and
the
transition
between

96

seasons
(r
=
0.17,
p
=
1.00).

P.
verreauxi
had
a
near
constant
phenolics
consumption

rate
across
the
seasons,
with
a
small
increase
after
the
dry
season,
though
a

Friedman
test
revealed
no
significant
effect
of
season
on
phenolics
consumption

rate
(X
2
(2)
=
0.2,
p
=
0.91)
(Table
3-‐1a).

Between
Species:
L.
catta
and
P.
verreauxi
had
similar
phenolics
consumption

rates
in
the
dry
season
(z
=
-‐0.23,
nL.
catta
=
nP.
verreauxi
=
10,
p
=
0.85)
(Table
3-‐2a).

During
the
transition
between
seasons,
the
consumption
rates
diverged,
with
the

consumption
rate
of
L.
catta
decreasing
relative
to
P.
verreauxi
(z
=
-‐3.40,
nL.
catta
=
nP.

verreauxi
=
10,
p
<
0.01).

Similarly,
L.
catta
had
a
significantly
lower
phenolics

consumption
rate
than
P.
verreauxi
in
the
wet
season
(z
=
-‐3.78,
nL.
catta
=
nP.
verreauxi
=

10,
p
<
0.01).

Looking
at
the
consumption
rates
of
L.
catta
and
P.
verreauxi
at
finer

time
scales
reveal
a
rising
rate
in
P.
verreauxi
through
the
transition
between

seasons
and
into
the
early
wet
season
(Figs.
3-‐2b
and
3-‐2c).

This
steady
increase
is

followed
by
a
declining
phenolics
consumption
rate
through
the
wet
season.

Between
Groups:
At
the
group
level,
L.
catta
shows
a
consistent
downward

trend
among
all
three
groups,
despite
the
variation
in
their
initial
phenolics

consumption
rates
(Figs.
3-‐2d
and
3-‐2e).

Among
the
P.
verreauxi
groups,
however,

there
was
a
wide
variety
in
phenolics
consumption
rates.

The
eastern
P.
verreauxi

group
had
the
highest
consumption
rates,
with
double
the
rate
of
the
other
P.

verreauxi
groups
during
the
transition
between
seasons
and
throughout
the
wet

season.

The
higher
rates
in
the
eastern
P.
verreauxi
group
were
principally
due
to

the
consumption
of
the
seeds
from
unripe
fruit
from
Tamarindus
indica.

This
group

spent
a
large
portion
of
their
feeding
time
on
this
one
food
during
the
transition

between
seasons
and
the
wet
season
(around
20%
for
each
season)
(Table
3-‐3b).

At

one
time
point
during
the
transition
between
seasons,
the
eastern
P.
verreauxi
group

spend
45%
of
its
feeding
time
consuming
these
T.
indica
seeds
(data
not
shown).

This
group
consumed
the
majority
of
their
phenolics
from
this
one
food
due
to
the

moderately
high
concentration
of
phenolics
in
this
food,
combined
with
the
large

amount
of
time
spent
feeding
on
it.

The
peaks
in
consumption
rate
for
the
central
P.

verreauxi
group
in
the
dry
season
and
at
the
beginning
of
the
wet
season
were
due

to
different
foods.

In
the
dry
season,
this
group
consumed
large
amounts
of

97

phenolics
from
the
unripe
fruit
of
Acacia
bellula
and
from
the
unripe
fruit
and

flowers
of
Euphorbia
tirucalli
(Table
3-‐3b).

The
more
modest
increase
in
phenolic

consumption
rate
of
the
western
P.
verreauxi
group
during
the
transition
between

seasons
was
due
to
consuming
large
amounts
of
A.
bellula
unripe
fruit
and

Rhopalocarpus
lucidus
mature
leaves,
the
former
of
which
composed
28%
of
the
diet

during
that
period
(Table
3-‐3b).

For
the
western
and
central
L.
catta
groups,
the
highest
phenolic
consumption

rate
was
the
first
time
point
in
the
dry
season
(Fig.
3-‐2f).

For
all
three
of
the
L.
catta

groups,
the
majority
of
the
phenolics
consumed
during
the
dry
season
were
from

Tamarindus
indica
leaf
buds
(Table
3-‐3a).

In
the
eastern
L.
catta
group
there
was
a

noticeable
spike
in
phenolics
consumption
early
into
the
transition
between
seasons

(Fig.
3-‐2f).

Other
than
this
temporary
rise,
this
group
consumed
the
least
phenolics

of
any
of
the
six
groups.

The
foods
responsible
for
this
phenolics
consumption
rate

increase
are
the
unripe
fruit
of
Noronhia
sp.
and
the
leaf
buds
of
T.
indica
(Table
3-‐
3a).

Interestingly,
while
both
of
these
foods
contributed
large
amounts
of
phenolics

to
the
diet
the
eastern
L.
catta
group
during
the
transition
between
seasons,
the

phenolics
concentration
of
T.
indica
leaf
buds
was
nearly
four
times
that
of
Noronhia

sp.
unripe
fruit.

While
these
lemurs
spent
47%
of
their
feeding
time
during
this

period
consuming
T.
indica
leaf
buds,
it
took
only
10%
of
their
feeding
time
to

consume
a
similar
amount
of
phenolics
from
Noronhia
sp.
unripe
fruit,
highlighting

the
disparate
levels
of
phenolics
in
different
foods.

Cellulose
Consumption

Between
Species:
L.
catta
and
P.
verreauxi
had
similar
cellulose
consumption

rates
in
the
dry
season
and
the
transition
between
the
seasons,
but
P.
verreauxi
had

a
somewhat
higher
consumption
rate
in
the
wet
season
(Figs.
3-‐3a
and
3-‐3b).

The

consumption
rates
of
L.
catta
and
P.
verreauxi
were
significantly
different
in
the
wet

season
(z
=
-‐2.72,
nL.
catta
=
nP.
verreauxi
=
10,
p
<
0.01),
but
not
in
the
dry
season
(z
=

0.91,
nL.
catta
=
nP.
verreauxi
=
10,
p
=
0.39)
nor
in
the
transition
between
seasons
(z
=

1.59,
nL.
catta
=
nP.
verreauxi
=
10,
p
=
0.12)
(Table
3-‐2b).

98

Within
Species:
When
investigating
the
temporal
changes
in
cellulose

consumption
rate
in
each
lemur
species,
a
Friedman
test
revealed
a
significant
effect

in
both
L.
catta
(X
2
(2)
=
16.2,
p
<
0.01)
and
P.
verreauxi
(X
2
(2)
=
16.8,
p
<
0.01)

(Table
3-‐1b).

A
post-‐hoc
test
using
Wilcoxon
signed
rank
tests
with
Bonferroni

correction
showed
significant
differences
in
L.
catta
between
the
dry
season
and
the

transition
between
seasons
(r
=
0.58,
p
<
0.05),
between
the
dry
and
wet
seasons
(r

=
0.63,
p
<
0.01),
and
between
the
wet
season
and
the
transition
between
seasons
(r

=
0.60,
p
<
0.05).

For
P.
verreauxi,
the
post-‐hoc
test
revealed
significant
differences

only
in
the
cellulose
consumption
rate
between
the
dry
season
and
the
transition

between
seasons
(p
<0.05,
r
=
0.63)
and
between
the
dry
and
wet
seasons
(r
=
0.63,

p
<0.05)
(Table
3-‐1b).

At
a
finer
time
scale,
the
similar
cellulose
consumption
rates
in
L.
catta
and
P.

verreauxi
during
the
transition
between
the
seasons
is
revealed
to
actually
be
more

dynamic
(Fig.
3-‐3c).

In
each
species,
the
consumption
rate
increase
during
the

transition
between
the
seasons
was
due
to
a
single
spike
and
occurred
at
different

time
points
for
each
species.

Between
Groups:
The
western
and
central
groups
of
both
lemur
species
had
a

steady
cellulose
consumption
rate
across
the
seasons
(Fig.
3-‐3d).

The
cellulose

consumption
in
the
eastern
groups
greatly
diverged
from
the
other
groups
during

the
transition
between
the
seasons,
with
both
the
eastern
L.
catta
and
P.
verreauxi

groups
having
a
more
than
3-‐fold
increase
in
from
the
dry
season
to
the
transition

between
seasons
followed
by
a
large
rate
drop
in
the
wet
season
(Figs.
3-‐3d,
3-‐3e,

and
3-‐3f).

Investigating
these
differences
between
groups
at
a
finer
time
scale

reveals
a
variety
of
consumption
rates
for
each
group
at
any
single
time
point
(with

the
exception
of
the
time
point
at
the
very
end
of
the
dry
season)
(Fig.
3-‐3f).

Amongst
the
L.
catta
groups,
there
was
an
increase
in
the
cellulose
consumption

rate
during
the
transition
between
the
seasons,
with
each
group
consuming
higher

rates
than
at
the
end
of
the
dry
season
or
the
beginning
of
the
wet
season.

The

degree
of
this
increase
varied,
with
the
eastern
group
having
a
much
greater

increase
than
the
other
L.
catta
groups.

In
all
three
groups,
consuming
a
large

amount
of
ripe
and
unripe
Azima
tetracantha
fruit
contributed
to
the
rises
in

99

cellulose
consumption
(Table
3-‐3a).

The
higher
consumption
rate
in
the
eastern

group
was
due
to
consumption
of
both
A.
tetracantha
fruit
and
the
cellulose-‐rich

unripe
fruit
of
Noronhia
sp.

The
cellulose
consumption
rate
showed
a
much
different
pattern
in
the
P.

verreauxi
groups
(Fig.
3-‐3f).

The
western
and
central
groups
consumed
steady,

small
amounts
of
cellulose
across
the
seasons,
but
the
eastern
group
consumed

cellulose
at
much
higher
rates
during
the
transition
between
seasons
and
the
wet

season.

For
both
of
these
time
periods,
the
eastern
P.
verreauxi
group’s
cellulose

consumption
was
driven
by
a
large
amount
of
seeds
from
the
unripe
fruit
of

Tamarindus
indica
in
its
diet
(Table
3-‐3b).

ADF
Consumption

Between
Species:
Overall,
the
seasonal
ADF
consumption
rates
of
L.
catta
and
P.

verreauxi
were
strikingly
similar
to
the
consumption
pattern
seen
for
cellulose,

though
the
consumption
rate
of
ADF
was
roughly
double
that
of
cellulose
(Fig.
3-‐4,

Tables
3-‐3a
and
3-‐3b).

The
consumption
rate
of
ADF
was
only
significantly
different

between
L.
catta
and
P.
verreauxi
during
the
wet
season
(z
=
-‐2.80,
nL.
catta
=
nP.
verreauxi

=
10,
p
<
0.01)
(Table
3-‐2c),
revealing
a
similar
pattern
to
that
reported
above
for

the
consumption
of
cellulose.

Within
Species:
As
opposed
to
cellulose
consumption,
a
Friedman
test
revealed

that
there
was
only
a
significant
effect
of
season
on
the
consumption
rate
of
ADF
in

L.
catta
(X
2
(2)
=
16.8,
p
<
0.01),
while
the
effect
only
approached
significance
in
P.

verreauxi
(X
2
(2)
=
5.6,
p
=
0.06)
(Table
3-‐1c).

There
were
also
fewer
significant

seasonal
comparisons
for
ADF
consumption
in
both
lemur
species.

A
post-‐hoc
test

using
Wilcoxon
signed
rank
tests
with
Bonferroni
correction
showed
significant

differences
in
L.
catta
only
between
the
dry
and
wet
seasons
(r
=
0.63,
p
<
0.01),
and

between
the
wet
season
and
the
transition
between
seasons
(r
=
0.63,
p
<
0.01).

For

P.
verreauxi,
the
post-‐hoc
test
only
revealed
a
significant
difference
in
the
cellulose

consumption
rate
between
the
wet
season
and
the
transition
between
seasons
(r
=

0.63,
p
<0.05).

100

ADL
Consumption

Between
Species:
Overall
the
consumption
rate
patterns
for
ADL
in
L.
catta
and

P.
verreauxi
resembled
the
patterns
of
consumption
of
both
cellulose
and
ADF,

though
at
rates
more
similar
to
cellulose.

Both
species
had
similar
ADL

consumption
rates
during
the
dry
season,
with
parallel
reductions
in
this
rate

between
the
transition
between
the
seasons
and
the
wet
season
(Figs.
3-‐5a
and
3-‐
5b).

During
the
transition
between
the
seasons,
P.
verreauxi
had
a
nonsignificantly

higher
rate
of
ADL
consumption
(Figs.
3-‐5a
and
3-‐5b,
Table
3-‐1d).

The
ADL

consumption
rates
were
only
significantly
different
between
L.
catta
and
P.

verreauxi
during
the
wet
season
(z
=
-‐2.72,
nL.
catta
=
nP.
verreauxi
=
10,
p
<
0.01)
(Table

3-‐2d).

Within
Species:
A
Friedman
test
revealed
a
significant
effect
of
season
on
ADL

consumption
rate
in
both
L.
catta
(X
2
(2)
=
16.8,
p
<
0.01)
and
P.
verreauxi
(X
2
(2)
=

7.8,
p
<
0.05)
(Table
3-‐1d).

A
post-‐hoc
test
using
Wilcoxon
signed
rank
tests
with

Bonferroni
correction
showed
significant
differences
in
ADL
consumption
of
L.
catta

between
the
dry
and
wet
seasons
(r
=
0.63,
p
<
0.01),
and
between
the
wet
season

and
the
transition
between
seasons
(r
=
0.63,
p
<
0.01)
(Table
3-‐1d).

For
P.

verreauxi,
the
post-‐hoc
test
only
revealed
a
significant
difference
in
the
ADL

consumption
rate
between
the
wet
season
and
the
transition
between
seasons
(r
=

0.63,
p
<0.05).

Between
Groups:
While
the
ADL
consumption
rate
increase
during
the

transition
between
the
seasons
was
due
to
the
eastern
group
in
both
species
(Fig.
3-‐
5f),
this
increase
was
lower
in
the
eastern
L.
catta
group
than
were
the
concurrent

increases
in
consumption
of
cellulose
or
ADF.

The
sudden
increases
in
consumption

of
all
three
fiber
fractions
for
the
eastern
L.
catta
group
were
all
due
to
the

consumption
of
the
unripe
fruit
of
Noronhia
sp.
(Table
3-‐3a).

The
ADL
consumption

rate
increase
was
lower
than
that
for
cellulose
and
ADF
due
to
a
lower
relative
ADL

concentration
in
Noronhia
sp.

101

DISCUSSION

Phenolics

Similar
to
previous
research
(Yamashita
2008),
this
study
found
that
the
Lemur

catta
and
Propithecus
verreauxi
at
BMSR
consumed
different
amounts
of
phenolics.

These
differences
in
consumption
applied
during
both
the
transition
between

seasons
and
the
wet
season,
where
L.
catta
reduced
its
phenolic
intake
compared
to

the
dry
season.

The
consumption
of
phenolics
in
P.
verreauxi
remained
constant

across
seasons.

The
majority
of
foods
consumed
by
L.
catta
had
low
phenolics

concentrations.
Of
the
foods
tested,
the
14
foods
with
the
highest
concentration
of

phenolics
were
all
consumed
by
P.
verreauxi.

The
different
consumption
patterns
between
the
lemur
species
may
be
due
to

unique
tolerances
to
phenolics.

For
many
vertebrate
herbivores,
the
presence
of

phenolics
in
a
plant
can
act
as
an
effective
feeding
deterrent,
leading
the
consumer

to
prefer
foods
with
lower
phenolic
contents
(Buchsbaum
et
al.
1984;
Roy
and

Bergeron
1990;
Müller-‐Schwarze
et
al.
2001;
Dearing
et
al.
2005).

It
is
likely
that

the
physiological
capacity
to
detoxify
phenolics
is
higher
in
P.
verreauxi
as
they

ingest
high
levels
of
this
chemical
plant
defense.

L.
catta,
however,
seems
to

minimize
consumption
of
foods
with
a
high
density
of
phenolics
when
possible.

The
large
amount
of
phenolics
consumed
by
L.
catta
in
the
dry
season
was

mostly
due
to
consuming
an
abundance
of
Tamarindus
indica
leaf
buds.

This
food

had
a
relatively
low
concentration
of
phenolics,
but
was
a
significant
proportion

(between
28%
and
40%)
of
the
diet
of
the
L.
catta
groups
during
the
dry
season

(Table
3-‐3a).

T.
indica
leaf
buds
were
not
consumed
in
any
significant
quantity
by
P.

verreauxi,
and
were
the
main
species
of
leaf
bud
consumed
by
L.
catta.

The

explanation
for
such
a
high
consumption
of
this
food
by
L.
catta
is
unclear.

It
is

possible
that
these
leaf
buds
contained
a
high
concentration
of
another
nutrient
and

that
in
order
to
access
the
nutrients
within
the
T.
indica
leaf
buds,
L.
catta
must
also

consume
the
phenolics
in
this
plant
tissue.

The
water
content
of
T.
indica
leaf
buds

is
82%
(by
weight),
and
may
have
provided
a
critical
source
of
water
to
the
lemurs

during
the
dry
months
prior
to
the
wet
season
(Simmen
et
al.
2013).

Even
with
the

102

phenolics
ingested
from
T.
indica
leaf
buds,
the
consumption
rate
of
L.
catta
was
low

compared
to
that
of
P.
verreauxi
groups
(Fig.
3-‐2f).

The
seasonal
phenolics
consumption
rate
varied
between
groups
in
both
L.
catta

and
P.
verreauxi.

This
inter-‐group
variation
follows
expectation,
as
the
differences

in
diet
between
groups
in
different
microhabitats
in
BMSR
has
already
been

characterized
(Yamashita
2002).

The
P.
verreauxi
groups
generally
had
higher

phenolics
consumption
rates
than
the
L.
catta
groups,
though
each
P.
verreauxi

group
obtained
their
phenolics
from
different
foods
(Table
3-‐3b).

During
the
transition
between
seasons
and
in
the
wet
season,
the
food

contributing
the
most
to
driving
up
the
phenolics
consumption
rate
in
P.
verreauxi

was
the
seeds
of
unripe
T.
indica
fruit
consumed
by
the
eastern
group.

Across

several
weeks
of
observation,
this
group
spent
a
large
portion
of
their
feeding
time

eating
these
seeds
from
a
cluster
of
large
T.
indica
trees
near
the
Sakamena
River.

These
trees
all
fruited
around
the
same
time
and
had
an
abundance
of
unripe
fruit

available.

While
this
study
did
not
conduct
phenological
studies
of
food
availability,

the
other
P.
verreauxi
groups
did
not
appear
to
have
the
same
abundance
of
fruiting

T.
indica
trees
in
their
territory
as
the
eastern
group
did.

Similarly,
the
eastern
L.

catta
group
did
not
overlap
with
the
part
of
the
eastern
P.
verreauxi
range

containing
the
large
T.
indica
trees.

These
findings
match
my
hypotheses
that
P.
verreauxi
consumes
more
phenolics

than
L.
catta,
though
the
consumption
rates
were
surprisingly
similar
during
the

wet
season
due
to
the
consumption
of
an
abundance
of
Tamarindus
indica
leaf
buds

in
the
diet
of
L.
catta.

This
suggests
that
while
both
lemur
species
can
tolerate

certain
quantities
of
phenolics,
this
amount
may
be
lower
in
L.
catta,
possibly
due
to

the
morphological
adaptations
to
folivore
of
P.
verreauxi
providing
a
more
robust

pathway
for
the
detoxification
of
this
plant
chemical
defense.

Fiber

Confirming
my
hypothesis,
P.
verreauxi
consumed
more
cellulose
than
L.
catta,

though
this
effect
was
only
significant
during
the
wet
season
(Table
3-‐2b).

While
I

hypothesized
that
cellulose
consumption
would
be
highest
for
both
lemur
species

103

during
the
dry
season,
this
was
not
found
to
be
the
case.

Instead,
L.
catta
consumed

the
most
cellulose
during
the
transition
between
the
seasons,
while
P.
verreauxi

consumed
high
levels
of
cellulose
during
the
transition
between
the
seasons
and

during
the
wet
season
(Fig.
3-‐3a).

Generally
similar
consumption
patterns
were

found
for
ADF
and
ADL.

While
fiber
is
treated
as
a
sign
of
low
food
quality
for
most
animals,
many

herbivores
have
the
ability
to
ferment
structural
carbohydrates
into
short-‐chain

fatty
acids,
removing
the
main
negative
effects
of
consuming
fiber.

The

consumption
of
fiber
by
both
lemur
species
varied
both
temporally
and
between

groups.

The
peak
consumption
rate
of
both
L.
catta
and
P.
verreauxi
reached
similar

levels
for
cellulose
(Fig.
3-‐3f),
though
P.
verreauxi
consumed
ADF
and
ADL
in
higher

maximal
rates
(Figs.
3-‐4f
and
3-‐5f).

Additionally,
one
P.
verreauxi
group
showed
a

prolonged
increase
in
fiber
consumption.

This
sustained
high
fiber
consumption

could
be
due
to
dietary
choices,
with
the
eastern
P.
verreauxi
group
obtaining
high

fiber
levels
in
its
diet
from
a
single
food
throughout
this
period
of
elevated

consumption.

It
is
also
possible
that
P.
verreauxi
was
physiologically
able
to

continue
consuming
fiber
in
such
high
amounts
due
to
its
morphological

adaptations
to
a
folivorous
diet,
particularly
its
long
gut
length,
long
gut
passage

time,
and
a
large
sacculated
cecum
containing
the
microbial
community
responsible

for
fiber
fermentation
(Campbell
et
al.
2000;
Campbell
et
al.
2004a)

The
relative
amounts
of
each
fiber
fraction
(cellulose,
ADF,
and
ADL)
consumed

were
constant
over
time.

Both
species
consumed
similar
amounts
of
cellulose
and

ADL
in
their
diets,
but
ADF
was
consumed
at
around
twice
the
level
of
the
other

fiber
fractions.

Other
herbivores
have
also
been
found
to
consume
higher
levels
of

ADF
than
ADL,
though
the
ratio
of
these
fiber
types
varies
(Baker
and
Hobbs
1982;

Yokoyama
et
al.
2000).

Feral
pigs
(Sus
scrofa)
and
mule
deer
(Odocoileus
hemionus

hemionus)
had
a
3:1
ratio
of
ADF
to
ADL
in
their
diet
(Baber
and
Coblentz
1987;

Hodgman
et
al.
1996),
though
this
ratio
can
vary
between
different
types
of
plants

(graminoids,
shrubs,
and
forbs)
(Baker
and
Hobbs
1982).

Among
herbivorous
primates,
similar
patterns
emerge.

Gorillas
(Gorilla

beringei)
in
Bwindi
Impenetrable
Forest
consume
foods
that
have
stable

104

percentages
of
cellulose,
ADF,
and
ADL
(Rothman
et
al.
2006).

These
foods
also
all

had
higher
percentages
of
ADF
than
cellulose
or
ADL.

The
mature
and
young
leaves

consumed
by
the
folivorous
south
Indian
leaf-‐monkey
(Presbytis
johnii)
contained

more
than
double
the
amount
of
ADF
than
cellulose
(Oates
et
al.
1980).

Similarly,
in

the
folivorous
red
colobus
monkey
(Procolobus
rufomitratus),
all
of
the
seven

different
groups
investigated
consumed
nearly
double
the
amount
of
ADF
than

cellulose
or
ADL,
despite
small
consumption
differences
between
groups
(Ryan
et
al.

2012).

Since
ADF
includes
both
the
cellulose
and
lignin
fiber
fractions
(Campbell
et
al.

2004b),
it
follows
that
ADF
consumption
should
be
approximately
the
sum
of

cellulose
and
ADL.

For
studies
that
only
measured
ADF
and
ADL,
it
is
possible
to

determine
the
cellulose
content
of
a
plant
with
the
following
formula:
Cellulose
=

ADF
–
ADL.

Similar
to
the
consumption
of
phenolics,
there
were
variations
in
the
seasonal

consumption
of
fiber
between
groups
of
each
lemur
species.

In
general,
all
of
the

groups
had
a
similar
consumption
rate
of
fiber,
but
the
eastern
group
of
each

species
consumed
fiber
at
an
increased
rate
during
the
transition
between
the

seasons
(and
into
the
wet
season
for
the
eastern
P.
verreauxi
group)
(Figs.
3-‐3d,
3-‐
4d,
and
3-‐5d).

The
increase
can
be
attributed
primarily
to
a
single
food
in
each

group,
with
the
eastern
L.
catta
group’s
increased
fiber
coming
from
the
unripe
fruit

of
Noronhia
sp.
and
the
eastern
P.
verreauxi
group’s
increased
fiber
coming
from
the

seeds
of
unripe
T.
indica
fruit
(Tables
3-‐3a
and
3-‐3b).

These
are
the
same
foods

responsible
for
the
concurrent
rise
in
phenolics
consumption
in
these
groups,

though
the
Noronhia
sp.
caused
a
larger
increase
in
fiber
consumption
than
it
did
in

phenolics
for
the
eastern
L.
catta
group.

The
fiber
consumption
by
both
L.
catta
and
P.
verreauxi
at
BMSR
is
similar
to
that

of
other
herbivores.

The
tolerance
for
high
levels
of
fiber
consumption
suggests
that

fiber
is
not
a
feeding
deterrent,
but
rather
can
be
utilized
as
a
nutrient
when

seasonally
present
in
the
diet.

It
is
likely
that
these
lemur
species
have
a

mechanism,
most
likely
relying
on
a
gut
microbial
community,
to
convert
these

fibrous
plant
mechanical
defenses
into
useful
nutrients.

105

Fiber
consumption
varied
seasonally,
but
was
consumed
in
equivalent
amounts

by
both
lemur
species,
suggesting
that
both
L.
catta
and
P.
verreauxi
have
digestive

mechanisms
for
metabolizing
fiber
fractions
such
as
cellulose.

Phenolics

consumption,
however,
was
significantly
higher
in
P.
verreauxi
than
in
L.
catta.

The

consumption
of
phenolics
by
P.
verreauxi
was
stable
across
seasons,
whereas
L.

catta
reduced
its
phenolic
intake
seasonally
suggesting
that
it
may
have
a
lower

capacity
to
detoxify
phenolics
in
its
diet.

CONCLUSIONS

Phenolics

The
consumption
of
phenolics
differed
between
L.
catta
and
P.
verreauxi.

While

both
species
had
similar
phenolic
consumption
rates
during
the
dry
season,
L.
catta

significantly
reduced
its
intake
during
the
wet
season.

These
findings
are
similar
to

previous
work
investigating
the
total
phenolics
consumed
by
these
lemurs
in
BMSR

(Yamashita
2008)
and
among
sympatric
populations
at
Berenty
(Simmen
et
al.

1999).

Yamashita
also
found
a
higher
amount
of
phenolics
consumed
by
P.

verreauxi
than
L.
catta,
though
this
held
for
all
seasons
investigated.

While
this
study
found
similar
interspecific
phenolic
intake
during
the
dry

season,
Yamashita
reported
higher
consumption
in
P.
verreauxi
during
this
season

(Yamashita
2008).

There
are
two
likely,
not
mutually
exclusive,
explanations
for

this
discrepancy.

The
first
possibility
is
that
the
differences
in
phenolic

consumption
are
due
to
methodological
gaps.

Both
studies
used
a
version
of
the

Prussian
blue
colorimetric
test
with
one
main
distinction.

While
Yamashita

measured
the
phenolic
content
of
fresh
plant
material,
the
current
study
dried
the

plant
samples
before
testing
for
phenolic
content.

Measuring
the
phenolic
content

of
fresh
material
yields
a
similar
concentration
to
that
experienced
by
the
lemur,

while
testing
dried
samples
standardized
the
phenolic
content
of
different
foods,
by

eliminating
the
dilution
effect
of
water
in
the
plant
tissues.

Each
approach
has
its

merits
and
the
overall
similar
findings
of
higher
phenolic
consumption
in
P.

verreauxi
than
L.
catta
seem
to
hold
true
regardless
of
the
testing
method
used.

106

The
second
possibility
is
that
the
current
study
and
that
of
Yamashita

documented
the
inter-‐annual
variation
in
diet
of
these
populations
(Yamashita

2008).

Madagascar
has
a
highly
variable
environment
that
can
lead
to
striking

differences
in
the
forest
ecology
and
foods
available
to
the
lemurs
(Dewar
and

Richard
2007).

Multiple
environmental
effects
combine
to
create
serious
changes
to

the
environmental
stability
in
any
given
year.

Cyclones
are
a
common
occurrence
in

Madagascar,
with
an
average
of
seven
storms
battering
the
island
nation
every
year

during
the
middle
half
of
the
20
th

century
(Donque
1975;
Ganzhorn
1995b).

Regular

El
Niño
events
also
affect
Madagascar,
alternatively
causing
drought
and
flooding

due
to
heavy
rainfall
(Diaz
and
Kiladis
1992;
Grant
and
Grant
1996).

Madagascar

has
also
experienced
a
number
of
extreme
droughts
aside
from
those
caused
by
El

Niño/La
Niña
cycles
(Sauther
1991;
Gould
1992;
Jolly
1998;
Sauther
1998;
Gould
et

al.
1999).

Droughts
often
have
severe
effects
in
the
drier
southwest
of
Madagascar,

with
the
drought
of
1991-‐1992
causing
a
complete
lack
of
rain
for
months
on
end

(Gould
et
al.
1999).

When
averaging
the
rainfall
over
nearly
50
years,
southwestern

Madagascar
in
and
around
BMSR
showed
an
inter-‐annual
variation
in
rainfall
well

above
the
global
average
(Dewar
and
Richard
2007).

These
stochastic
changes
in

the
environmental
conditions
in
southwestern
Madagascar
can
lead
to
shifting
food

availability
for
the
animals
inhabiting
those
environments
(Wright
1999).

Phenolics
are
typically
treated
as
a
feeding
deterrent
in
herbivores,
and
avoided

when
possible
(Berger
et
al.
1977;
McKey
et
al.
1981;
Glander
1982;
Buchsbaum
et

al.
1984;
Iason
and
Waterman
1988;
Moore
and
Foley
2005).

When
phenolic-‐rich

foods
are
consumed,
it
is
due
to
the
presence
of
other,
beneficial
nutrients
(Glander

1982;
Whiten
et
al.
1991).

Animals
that
consume
large
amounts
of
phenolics
must

find
ways
to
detoxify
these
compounds
and
neutralize
their
negative
effects.

The

greater
phenolics
consumption
in
P.
verreauxi
than
L.
catta
is
likely
due
to
a

combination
of
phenolics
detoxifying
mechanisms.

P.
verreauxi
has
more
developed

anatomical
and
physiological
adaptations
associated
with
herbivory
that
may

contribute
to
a
greater
phenolics
detoxifying
capacity.

P.
verreauxi
has
a
much

longer
gut
length,
a
larger
stomach,
and
a
more
complex,
spiraled
cecum
(that

contains
a
symbiotic
microbial
community)
than
L.
catta
(Campbell
et
al.
2000).

107

These
adaptations
allow
for
more
thorough
digestion
of
both
cellulose
and

phenolics
(Lambert
2002).

While
the
morphology
and
gut
physiology
of
L.
catta

may
allow
them
to
detoxify
a
small
amount
of
phenolics,
their
feeding
behavior

suggests
that,
when
possible,
they
avoid
consuming
large
amounts.

The
feeding

behavior
of
P.
verreauxi,
however,
suggests
a
tolerance
for
the
levels
of
phenolics

consumed,
which
may
be
facilitated
by
its
more
elaborate
digestive
adaptations
or

by
its
gut
microbiome.

Cellulose

The
consumption
of
cellulose
was
markedly
distinct
from
that
of
phenolics.

While
there
was
significant
interspecific
and
temporal
variability
in
phenolics

consumption,
cellulose
was
consumed
at
a
relatively
consistent
level
in
both
L.
catta

and
P.
verreauxi.

This
pattern
reflects
those
of
the
macronutrients
(protein,
free

amino
acids,
and
sugars)
consumed
by
these
populations
(Yamashita
2008).

Other

herbivorous
primates
have
similarly
consistent
cellulose
consumption
year-‐round.

A
comparison
of
several
mainly
frugivorous
primates
revealed
near-‐constant

cellulose
consumption
in
the
blue
monkey
(Cercopithecus
mitis
stuhlmanni),
the
red-‐
tailed
monkey,
(C.
ascanius
schmidti)
and
the
gray-‐cheeked
mangabey
(Lophocebus

albigena
johnstoni)
(Conklin-‐Brittain
et
al.
1998).

The
comparison
of
two
Mountain

gorilla
(Gorilla
beringei
beringei)
populations
yielded
similar
results
with
the

animals
in
both
populations
consuming
similar
amounts
of
fiber
despite
the
Bwindi

Impenetrable
Forest
population
being
more
frugivorous
than
that
near
the
Virunga

Volcanoes
(Stanford
and
Nkurunungi
2003;
Rothman
et
al.
2007b).

A
study
of
two

colobine
monkeys
in
Asia
(Presbytis
rubicunda
and
Presbytis
melalophos)
whose
diet

consisted
largely
of
fruits,
seeds,
and
leaves,
revealed
similar
annual
consumption
of

fiber
in
these
species
despite
different
foods
comprising
their
diets
(Davies
et
al.

1988).

The
variation
in
cellulose
consumption
was
highest
when
comparing
the
groups

within
each
species.

This
variability
may
be
due
to
microhabitat
differences
within

BMSR.

The
forest
in
Parcel
1
of
BMSR
has
a
strong
habitat
gradient
from
gallery

forest
to
xeric
spiny
forest
(Sussman
and
Rakotozafy
1994).

The
groups
studied

108

here
were
intentionally
chosen
to
cover
this
range
of
habitats
to
discern
any
habitat-‐
based
effects
on
diet.

Cellulose
consumption
was
highest
in
the
eastern
group
of

each
species.

The
foods
contributing
to
this
higher
consumption
were
different

species
of
unripe
fruit
for
each
lemur
species.

In
both
lemur
species,
this
unripe

fruit
was
not
an
important
food
in
the
central
and
western
groups.

Both
of
these

unripe
fruits
(Noronhia
sp.
for
L.
catta
and
Tamarindus
indica
for
P.
verreauxi)

contained
high
concentrations
of
cellulose
and
made
up
at
least
10%
of
the
diet
of

these
eastern
lemur
groups.

The
lack
of
these
foods
in
the
diets
of
the
other
lemur

groups
is
most
likely
due
to
their
absence
in
large
amounts
in
the
drier
habitat
of
the

central
and
western
groups.

Without
the
contribution
of
these
two
unripe
fruits,
the

cellulose
consumption
of
all
of
the
lemur
groups
was
more
similar
across
seasons.

Despite
differences
in
diet
among
the
L.
catta
and
P.
verreauxi
populations
at
BMSR,

their
similar
consumption
of
cellulose
suggests
a
physiological
limit
to
how
much

cellulose
can
be
fermented
by
these
primates.

The
role
of
the
gut
microbial

community
in
fermenting
ingested
cellulose
may
shine
further
light
onto
the

digestive
capacity
of
L.
catta
and
P.
verreauxi.

109

FIGURES

Figure
3-‐1:
An
example
of
the
construction
of
the
standard
curve
using
the
tannic

acid
dilution
series
standards
for
the
measurement
of
phenolics.

y
=
0.17x
-‐
0.016

R²
=
0.99

0

0.1

0.2

0.3

0.4

0.5

0
0.5
1
1.5
2
2.5
3

Tannic
acid
concentration
(mg/ml)

Absorbance
@
760nm

110

Figure
3-‐2:
Phenolics
consumption
rate
for
L.
catta
and
P.
verreauxi.

Subdivided
at

multiple
levels
of
time
and
lemur
grouping:
(a)
lemur
species
and
season,
(b)
lemur

species
and
monthly
time
intervals,
(c)
lemur
species
and
twice
a
month
time

intervals,
(d)
lemur
groups
and
season,
(e)
lemur
groups
and
monthly
time

intervals,
(f)
lemur
groups
and
twice
a
month
time
intervals.

111

Figure
3-‐3:
Cellulose
consumption
rate
for
L.
catta
and
P.
verreauxi.

Subdivided
at

multiple
levels
of
time
and
lemur
grouping:
(a)
lemur
species
and
season,
(b)
lemur

species
and
monthly
time
intervals,
(c)
lemur
species
and
twice
a
month
time

intervals,
(d)
lemur
groups
and
season,
(e)
lemur
groups
and
monthly
time

intervals,
(f)
lemur
groups
and
twice
a
month
time
intervals.

112

Figure
3-‐4:
ADF
consumption
rate
for
L.
catta
and
P.
verreauxi.

Subdivided
at

multiple
levels
of
time
and
lemur
grouping:
(a)
lemur
species
and
season,
(b)
lemur

species
and
monthly
time
intervals,
(c)
lemur
species
and
twice
a
month
time

intervals,
(d)
lemur
groups
and
season,
(e)
lemur
groups
and
monthly
time

intervals,
(f)
lemur
groups
and
twice
a
month
time
intervals.

113

Figure
3-‐5:
ADL
consumption
rate
for
L.
catta
and
P.
verreauxi.

Subdivided
at

multiple
levels
of
time
and
lemur
grouping:
(a)
lemur
species
and
season,
(b)
lemur

species
and
monthly
time
intervals,
(c)
lemur
species
and
twice
a
month
time

intervals,
(d)
lemur
groups
and
season,
(e)
lemur
groups
and
monthly
time

intervals,
(f)
lemur
groups
and
twice
a
month
time
intervals.

114

TABLES

Table
3-‐1:
Cross-‐seasonal
comparison
of
phenolics
(a),
cellulose
(b),
ADF
(c),
and

ADL
(d)
consumption

A
Phenolics
X
2
df p0value
Seasonal5
Comparison
Effect5
size5(r) p0value
Sample5
Size
L.#catta 15.2 2 0.001** Dry*~*Transition 0.17 1.000 20
Dry*~*Wet 0.63 0.006** 20
Transition*~*Wet 0.63 0.006** 20
P.#verreauxi 0.2 2 0.905 Dry*~*Transition 0.22 1.000 20
Dry*~*Wet 0.08 1.000 20
Transition*~*Wet 0.08 1.000 20
****p*<*0.05*************p*<*0.01**********‡*with*Bonferroni*correction
Friedman5Test Wilcoxon5Signed5Rank5Test
‡
B
Cellulose
X
2
df p-value
Seasonal2
Comparison
Effect2
size2(r) p-value
Sample2
Size
L.#catta 16.2 2 0.001** Dry*~*Transition 0.58 0.018* 20
Dry*~*Wet 0.63 0.006** 20
Transition*~*Wet 0.60 0.012* 20
P.#verreauxi 16.8 2 0.001** Dry*~*Transition 0.63 0.018* 20
Dry*~*Wet 0.63 0.018* 20
Transition*~*Wet 0.33 0.461 20
****p*<*0.05*************p*<*0.01**********‡*with*Bonferroni*correction
Friedman2Test Wilcoxon2Signed2Rank2Test
‡

115

C
ADF
X
2
df p*value
Seasonal4
Comparison
Effect4
size4(r) p*value
Sample4
Size
L.#catta 16.8 2 0.001** Dry+~+Transition 0.44 0.147 20
Dry+~+Wet 0.63 0.006** 20
Transition+~+Wet 0.63 0.006** 20
P.#verreauxi 5.6 2 0.061 Dry+~+Transition 0.40 0.249 20
Dry+~+Wet 0.13 1.000 20
Transition+~+Wet 0.63 0.018* 20
++*+p+<+0.05++++++++++**+p+<+0.01++++++++++‡+with+Bonferroni+correction
Friedman4Test Wilcoxon4Signed4Rank4Test
‡
D
ADL
X
2
df p)value
Seasonal3
Comparison
Effect3
size3(r) p)value
Sample3
Size
L.#catta 16.8 2 0.001** Dry+~+Transition 0.35 0.393 20
Dry+~+Wet 0.63 0.006** 20
Transition+~+Wet 0.63 0.006** 20
P.#verreauxi 7.8 2 0.020* Dry+~+Transition 0.49 0.097 20
Dry+~+Wet 0.08 1.000 20
Transition+~+Wet 0.63 0.018* 20
++*+p+<+0.05++++++++++**+p+<+0.01++++++++++‡+with+Bonferroni+correction
Friedman3Test Wilcoxon3Signed3Rank3Test
‡

116

Table
3-‐2:
Lemur
species
comparison
of
phenolics
(a),
cellulose
(b),
ADF
(c),
and

ADL
(d)
consumption
using
the
Mann-‐Whitney
U
test
with
Bonferroni
correction.

Season Z(score
Dry $0.23 0.853 10 10
Transition $3.40 0.001** 10 10
Wet $3.78 0.001** 10 10
88*8p8<80.058888888888**8p8<80.01
(((((A(((((Phenolics
p1value
L.#catta(
Sample(Size
#P.#verreauxi(
Sample(Size
Season Z(score
Dry 0.91 0.393 10 10
Transition 1.59 0.123 10 10
Wet 42.72 0.005** 10 10
77*7p7<70.057777777777**7p7<70.01
(((((B(((((Cellulose
p0value
L.#catta(
Sample(Size
#P.#verreauxi(
Sample(Size
Season Z(score
Dry 1.13 0.280 10 10
Transition 0.45 0.684 10 10
Wet 62.80 0.004** 10 10
88*8p8<80.058888888888**8p8<80.01
(((((C(((((ADF
p0value
L.#catta(
Sample(Size
#P.#verreauxi(
Sample(Size
Season Z(score
Dry 1.29 0.218 10 10
Transition 11.13 0.280 10 10
Wet 12.72 0.005** 10 10
88*8p8<80.058888888888**8p8<80.01
(((((D(((((ADL
p/value
L.#catta(
Sample(Size
#P.#verreauxi(
Sample(Size

117

Table
3-‐3:
Plant
defense
consumption
rate
per
food
for
L.
catta
(a)
and
P.
verreauxi

(b).

For
definitions
of
plant
species
abbreviations,
see
Table
2-‐1.

Season Dry Trans
‡
Wet Dry Trans Wet Dry Trans Wet Dry Trans Wet Dry Trans Wet
Species Part
AZIM Ripe)Fruit 0.0537 0.1486 23.95 43.17 45.26 77.75 19.98 31.67 17% 27%
Unripe)Fruit 0.0051 0.0917 0.0075 1.28 48.58 2.87 2.12 87.82 4.49 0.81 37.68 1.55 1% 33% 2%
CEDR Young)Leaf 0.1541 15.28 27.53 11.68 9%
Flower)Bud 0.1280 7%
GYRO Flower 0.1716 23.67 35.77 10.83 6%
MAER Unripe)Fruit 0.0462 42.24 76.38 32.56 6%
META Mature)Leaf 0.0155 3.59 5.29 1.58 6%
Young)Leaf 0.0013 0.0095 0.18 2.26 0.30 3.44 0.11 1.10 0% 5%
OCOT Flower)Bud 0.0471 12.39 32.75 19.86 10%
QUIV Flower 0.0371 3.43 8.35 4.79 14%
SALV Young)Leaf 0.0029 0.0006 0.0001 1.49 0.36 0.09 2.04 0.52 0.11 0.50 0.14 0.02 6% 1% 0%
TALI Ripe)Fruit 0.0120 4.16 11.27 6.97 12%
TAMA Young)Leaf 0.1769 0.1834 0.0063 12.89 11.96 1.86 48.09 34.10 4.22 34.40 21.41 2.31 5% 6% 1%
Leaf)Bud 0.3519 0.2568 0.0802 35.69 21.83 10.58 140.89 63.82 25.38 102.81 40.64 14.49 29% 26% 6%
TOTAL 0.9205 0.6338 0.4338 90.84 149.09 83.86 270.00 308.19 159.48 173.99 152.52 71.35
AZIM Ripe)Fruit 0.0817 0.0554 25.19 15.54 43.05 22.18 16.52 5.77 18% 14%
Unripe)Fruit 0.0180 0.0877 0.0020 3.95 37.74 0.77 6.12 60.35 1.20 2.00 20.20 0.41 6% 22% 1%
META Mature)Leaf 0.0036 1.16 1.77 0.57 8%
Young)Leaf 0.0024 0.0454 0.39 8.04 0.66 13.71 0.25 5.17 1% 17%
QUIV Flower 0.0819 0.0498 13.73 6.48 35.16 26.35 20.97 19.04 21% 10%
TALI Ripe)Fruit 0.0038 1.19 2.92 1.68 24%
TAMA Young)Leaf 0.0639 0.1525 0.0483 5.59 10.65 6.69 13.18 21.89 14.31 7.27 10.63 7.47 4% 10% 2%
Leaf)Bud 0.5304 0.1793 0.0038 56.74 14.15 0.90 134.25 29.47 2.71 74.05 14.39 1.78 40% 17% 1%
TOTAL 0.6967 0.5510 0.1622 80.40 94.22 34.30 189.36 181.11 58.80 104.54 80.78 22.84
AZIM Ripe)Fruit 0.0338 0.0209 14.27 9.23 31.05 17.87 11.39 5.22 5% 4%
Unripe)Fruit 0.0011 0.1139 0.0085 0.23 36.86 3.24 0.37 60.18 5.06 0.13 21.70 1.74 0% 26% 2%
CEDR Leaf)Bud 0.0567 3.93 8.30 4.17 5%
MARS Young)Leaf 0.0017 0.91 1.37 0.44 9%
META Mature)Leaf 0.0069 2.26 3.27 0.95 11%
Young)Leaf 0.0204 3.09 5.27 1.99 7%
NORO Unripe)Fruit 0.4963 0.0276 38.32 460.36 25.62 61.72 741.41 41.26 21.95 263.62 14.67 1% 10% 1%
QUIV Flower 0.0323 2.91 7.02 3.99 8%
TALI Ripe)Fruit 0.0019 0.91 2.08 1.14 9%
TAMA Young)Leaf 0.0922 0.0533 0.0895 13.07 3.51 13.36 29.95 9.14 32.47 16.16 5.32 18.62 9% 3% 9%
Leaf)Bud 0.3648 0.3811 0.0089 63.06 41.66 0.89 192.87 128.74 2.36 126.47 85.13 1.42 28% 47% 1%
TOTAL 0.5675 1.0785 0.1659 124.61 556.67 56.40 305.50 970.52 105.73 174.85 387.16 44.20
0 Highest)Value
*)TAE)=)Tannic)Acid)Equivalent))))))))))))))‡)Trans)=)Transition)between)the)dry)and)wet)seasons)))))))))))))))Δ)Percent)of)Diet)=)%)of)Time)Spent)Feeding
A
L.#catta (mg8/8hr) (mg8/8hr) (mg8/8hr) (mg8TAE8/8hr)*
ADL Percent8of8Diet8
Δ
Western8group8(Blue)
Central8group8(Yellow)
Eastern8group8(Red)
Phenolics Cellulose ADF

118

Hidden
References:
studies
using
Folin-‐Ciocalteu
method
for
measuring
phenolics

(Oates
et
al.
1980;
Box
1983;
Buchsbaum
et
al.
1984;
Coley
1988;
Singleton
et
al.

1999;
Moyer
et
al.
2002;
Asami
et
al.
2003;
Zainol
et
al.
2003;
Scalzo
et
al.
2005;

Meda
et
al.
2005;
Ainsworth
and
Gillespie
2007;
Mamphiswana
et
al.
2010;
Fu
et
al.

2011).

Phenolics
micro
assay
(Medina-‐Remón
et
al.
2009)

Season Dry Trans
‡
Wet Dry Trans Wet Dry Trans Wet Dry Trans Wet Dry Trans Wet
Species Part
AC.BE Unripe,Fruit 0.3452 25.29 0.16 53.29 0.33 26.55 0.17 28% 0%
Flower 0.0940 5.03 9.67 4.40 11%
AC.RO Young,Leaf 0.0084 0.0005 0.62 0.04 1.58 0.11 0.93 0.06 5% 0%
AZIM Unripe,Fruit 0.0281 0.0149 7.72 5.14 14.27 8.97 6.19 3.63 9% 7%
DIAL Mature,Leaf 0.0264 0.0009 1.49 0.22 4.49 0.59 2.49 0.35 7% 0%
Young,Leaf 0.0174 0.0000 0.0004 1.92 0.00 0.03 4.45 0.01 0.11 2.43 0.01 0.07 11% 0% 0%
Flower 0.0547 5.37 18.74 12.65 10%
Flower,Bud 0.0002 0.0492 0.01 8.98 0.06 32.57 0.05 22.79 0% 17%
DICH Mature,Leaf 0.1136 27.82 51.70 23.06 17%
GR.TU Mature,Leaf 0.0978 10.32 25.36 14.72 9%
OCOT Flower,Bud 0.1406 32.52 73.79 40.00 25%
RHOP Mature,Leaf 0.0084 0.3322 0.0034 2.52 41.74 5.15 4.74 166.33 10.04 2.19 120.76 4.81 0% 13% 1%
SYRE Ripe,Fruit 0.0007 2.16 3.40 1.17 10%
Unripe,Fruit 0.0074 12.73 21.70 8.64 6%
TAMA Flower,Bud 0.0815 14.41 0.60 37.97 1.53 22.73 0.91 5% 0%
TOTAL 0.2953 0.8910 0.2396 44.13 103.54 64.37 98.79 323.19 123.83 52.48 211.68 57.58
AC.BE Unripe,Fruit 0.3314 0.0999 32.01 16.25 49.52 49.83 17.02 32.18 11% 8%
AZIM Ripe,Fruit 0.0121 0.0017 3.54 1.18 6.24 1.65 2.29 0.43 9% 1%
Unripe,Fruit 0.0076 0.1114 0.0005 1.80 42.84 0.19 2.67 72.21 0.33 0.81 27.25 0.13 3% 36% 0%
DICH Mature,Leaf 0.4513 0.4695 22.46 26.93 57.06 47.02 33.52 19.35 10% 9%
Young,Leaf 0.0384 0.1561 0.1791 2.43 5.30 12.63 6.95 14.87 21.69 4.39 9.05 8.68 4% 7% 16%
Leaf,Bud 0.0480 0.0290 0.0019 1.85 1.12 0.11 2.92 1.77 0.17 1.01 0.61 0.06 5% 3% 1%
EUPH Unripe,Fruit 0.2977 0.0896 32.48 60.79 46.97 95.55 13.82 32.11 13% 3%
Flower 0.5966 0.0025 47.32 0.33 109.13 0.67 54.84 0.33 16% 1%
META Mature,Leaf 0.0037 0.91 1.21 0.29 9%
SYRE Unripe,Fruit 0.0005 4.44 7.47 2.69 8%
TAMA Flower,Bud 0.0943 0.1168 7.31 13.38 0.09 20.15 42.89 0.23 12.48 28.81 0.14 5% 6% 0%
TOTAL 1.4141 1.0661 0.6595 125.20 165.69 46.80 238.31 340.43 80.44 104.37 165.83 32.09
AC.RO Mature,Leaf 0.2394 0.0249 75.19 5.45 154.12 10.90 76.24 5.26 6% 1%
Flower 0.0498 9.58 28.27 18.32 7%
AZIM Unripe,Fruit 0.0371 0.0009 12.36 0.31 18.71 0.54 5.97 0.22 12% 0%
CO.SP Mature,Leaf 0.2508 38.65 133.36 91.21 6%
Flower 0.2653 0.0160 47% 3%
EUPH Unripe,Fruit 0.3403 0.1195 23.23 88.74 0.57 34.19 139.55 0.89 10.15 47.70 0.31 16% 6% 0%
SYRE Unripe,Fruit 0.0079 19.92 31.46 10.62 10%
TAMA Ripe,Fruit 0.0117 0.1664 1.84 20.12 3.85 54.92 1.91 31.97 1% 6%
Unripe,Fruit 1.9872 2.3479 312.19 262.81 677.81 607.84 357.93 338.71 23% 19%
VITE Flower 0.0428 13.54 31.61 17.57 11%
TOTAL 0.6554 2.6617 2.5908 32.81 528.98 322.72 62.46 1127.41 738.16 28.47 580.97 404.64
0 Highest,Value
Central7group7(Felix)
*,TAE,=,Tannic,Acid,Equivalent,,,,,,,,,,,,,,‡,Trans,=,Transition,between,the,dry,and,wet,seasons,,,,,,,,,,,,,,,Δ,Percent,of,Diet,=,%,of,Time,Spent,Feeding
Western7group7(Fano)
Eastern7group7(Vavy)
P.#verreauxi (mg7TAE7/7hr)* (mg7/7hr) (mg7/7hr) (mg7/7hr)
B Phenolics Cellulose ADF ADL Percent7of7Diet7
Δ

119

CHAPTER
4
-‐
GUT
MICROBIOME
COMPOSITION

ABSTRACT

Mammalian
gut
microbes
are
invaluable
to
the
host’s
metabolism,
but
little

research
has
examined
gut
microbial
dynamics
under
natural
conditions
in
wild

mammals.

This
study
aims
to
help
fill
this
knowledge
gap
with
a
survey
of
the

variation
of
the
gut
microbiome
in
two
wild
lemur
species,
Lemur
catta
and

Propithecus
verreauxi.

The
wild
L.
catta
were
also
compared
to
a
captive
population

to
discern
the
effect
of
habitat
within
a
species.

Gut
microbial
DNA
was
extracted

from
fecal
samples
collected
in
Madagascar
and
the
Vienna
Zoo
and
sequenced.

The

wild
and
captive
L.
catta
had
distinct
microbial
communities,
likely
due
to

differences
in
diet
and
development
between
their
populations.

The
wild
L.
catta

and
P.
verreauxi
also
had
distinct
gut
microbiomes,
due
to
a
change
in
microbial

abundance,
not
composition.

Within
each
lemur
species,
there
was
abundant

variation
between
individuals
and
seasons.

This
intraspecific
microbial
variation

requires
more
investigation,
with
changes
in
diet
a
likely
contributor.

INTRODUCTION

The
gut
microbial
community,
collectively
named
the
gut
‘microbiome,’
plays
a

key
role
in
digestion
(Bauchop
1971;
Lambert
1998;
Turnbaugh
et
al.
2006;
Flint

and
Bayer
2008;
Sleator
2010)
and
provide
additional
metabolic
pathways
that
the

host
lacks
endogenously,
such
as
the
metabolism
of
plant
structural
carbohydrates

(Stevens
and
Hume
1998;
Gibson
and
Roberfroid
1999;
Hooper
et
al.
2002;
DeSantis

et
al.
2006;
Ze
et
al.
2012;
Flint
et
al.
2012).

In
hydrolyzing
plant
polysaccharides

down
to
their
simpler
components,
the
gut
microbial
community
provides
essential

nutrients
to
herbivores
(Béguin
and
Aubert
1994).

The
mammalian
gut
microbiome
is
composed
mostly
of
bacteria,
with
small

numbers
of
archaea,
as
well
as
eukaryotes,
fungi,
and
viruses
(Breitbart
et
al.
2003;

Rajilić-‐Stojanović
2007).

Different
mammalian
species
harbor
unique
microbial

assemblages,
due
to
their
diets
and
phylogeny
(Ley
et
al.
2008;
Caporaso
et
al.
2010;

120

Muegge
et
al.
2011).

While
changing
diet
affects
the
gut
flora,
geography
also
has
as

influence,
with
distant
populations
having
distinct
gut
communities
(Lozupone
and

Knight
2005;
Yatsunenko
et
al.
2012;
David
et
al.
2014).

Within
a
population
there

can
be
large
inter-‐individual
gut
microbial
variation,
attributed
to
diet
or

environment
(Mann
and
Whitney
1947;
Yatsunenko
et
al.
2012),
as
well
as
intra-‐
individual
temporal
variation
(Uenishi
et
al.
2007;
Costello
et
al.
2009;
Nakamura
et

al.
2011).

While
inter-‐individual
variation
exists
in
the
gut
microbial
community,

the
gut
microbial
profile
of
healthy
individuals
of
a
species
tends
to
be
distinct
from

that
of
other
species
(Yildirim
et
al.
2010;
Ochman
et
al.
2010).

Seasonal
changes
in
the
environment
of
the
microbial
community,
such
as
a

shifting
host
diet,
alter
the
nutritional
inputs
to
the
microbial
ecosystem
and
the

species
abundances
(Gilbert
et
al.
2012;
Sintes
et
al.
2012).

Both
short-‐term
and

long-‐term
dietary
changes
can
have
dramatic
effects
on
the
gut
flora
(Duncan
et
al.

2007;
Walker
et
al.
2011;
Muegge
et
al.
2011;
Wu
et
al.
2011;
David
et
al.
2014).

A
phylum-‐level
analysis
is
common
for
gut
microbiome
research
and
provides

sufficient
detail
to
characterize
the
overall
microbial
composition
and
to
compare

microbiomes
between
species
and
individuals
(Frey
et
al.
2006;
Ley
et
al.
2008;

Yildirim
et
al.
2010;
Ochman
et
al.
2010;
Xu
et
al.
2013).

Many
primates
have

Firmicutes
and
Bacteroidetes
as
the
most
dominant
commensal
gut
phyla,
with

Actinobacteria
and
Proteobacteria
also
commonly
found
in
the
gastrointestinal

microbiome
(Fig.
1-‐1)
(Eckburg
et
al.
2005;
Frey
et
al.
2006;
Uenishi
et
al.
2007;
Ley

et
al.
2008;
Bo
et
al.
2010;
Yildirim
et
al.
2010).

In
humans,
Bacteroidetes
showed

the
largest
variation
between
individuals
(Eckburg
et
al.
2005;
Arumugam
et
al.

2011).

When
present,
Fibrobacteres,
Spirochaetes,
and
Euryarchaeota
only

represented
a
few
percent
of
the
total
gut
microbiome
(Frey
et
al.
2006;
Ley
et
al.

2008;
Yildirim
et
al.
2010;
Ochman
et
al.
2010).

Currently,
there
is
no
research
that
has
investigated
the
gut
microbiome
of
wild

lemurs.

The
only
published
study
to
analyze
the
gastrointestinal
flora
of
a
lemur

characterized
the
microbiome
of
the
ring-‐tailed
lemur
(Lemur
catta)
to
be

approximately
60%
Firmicutes,
20%
Bacteroidetes,
and
20%
Verrucomicrobia,
with

smaller
amounts
of
Spirochaetes
and
Gammaproteobacteria
(Ley
et
al.
2008).

The

121

goal
of
the
Ley
et
al.
study
was
to
compare
the
gut
microbes
across
a
wide
range
of

60
mammals,
so
only
a
single
captive
L.
catta
was
sampled.

The
gut
microbiome

among
this
captive
L.
catta
may
not
be
representative
of
their
wild
siblings
(Uenishi

et
al.
2007;
Nakamura
et
al.
2011).

The
majority
of
research
into
the
gut
microbiome
of
mammals
is
done
in
a

laboratory
or
clinical
setting,
with
the
standard
protocol
to
freeze
the
fecal
samples

immediately
after
collection
(Ley
et
al.
2008;
Yildirim
et
al.
2010;
Xu
et
al.
2013).

Investigations
into
the
gut
microbiome
of
wild
animals
usually
collect
samples
from

their
study
populations
over
a
short
time
period,
allowing
the
use
of
liquid
nitrogen

(Ley
et
al.
2008).

Liquid
nitrogen
only
works
for
sampling
trips
of
up
to
a
few-‐
weeks
in
length,
but
the
current
study
collected
lemur
feces
over
a
five-‐month

period.

The
lack
of
adequate
infrastructure,
such
as
reliable
and
available

electricity,
meant
a
freezer
could
not
be
utilized
at
the
remote
size
of
Beza
Mahafaly

Special
Reserve
(BMSR).

Preservation
in
ethanol
is
commonly
used
to
preserve
DNA
in
fecal
samples
of

wild
animals
(Murphy
et
al.
2002;
Frantz
et
al.
2003;
Uenishi
et
al.
2007;
Amato
et
al.

2013)
and
was
determined
to
be
the
best
method
for
this
study
to
preserve
the
fecal

samples
collected
in
Madagascar.

The
equivalency
of
different
fecal
DNA

preservation
methods
can
vary
by
species
[REF],
but
Frantzen
et
al.
(1998)
found

freezing
feces
and
preserving
them
in
ethanol
to
be
equally
effective.

To
test
for
an

influence
of
preservation
method
on
the
gut
microbial
composition
in
lemurs,
the

efficacy
of
freezing
and
ethanol-‐preservation
were
compared
on
feces
collected

from
a
captive
population
of
L.
catta.

To
better
comprehend
the
natural
variation
of
the
gut
microbiome
and

ultimately
understand
the
impact
of
diet
on
this
community,
long-‐term
studies
of

wild
populations
are
necessary.

I
compared
wild
and
captive
populations
of
Lemur

catta
to
understand
if
captive
gut
microbiomes
are
representative
of
those
in
their

wild
counterparts.

I
then
catalogued
the
seasonal
variability
in
two
wild
lemur

species,
L.
catta
and
Propithecus
verreauxi.

These
sympatric
species
overlap
in

environmental
exposure
to
microbes,
yet
their
distinct
diets
may
cause
their
gut

microbiomes
to
be
distinct.

These
lemurs’
gut
microbiota
will
shed
light
on
the

122

natural
variation
of
this
microbial
community
and
lay
the
groundwork
for

elucidating
the
influence
of
a
seasonally
changing
diet.

Hypotheses

I
hypothesized
that
there
would
be
only
a
small
effect
of
preservation
method
on

the
gut
microbial
composition
as
found
by
Frantzen
et
al.
(1998).

I
expected
to
find

distinct
gut
microbial
communities
between
populations
of
wild
L.
catta
and
P.

verreauxi,
with
smaller
amounts
of
intraspecific
variation
within
each
species,
since

species-‐specific
communities
tend
to
be
more
similar
to
one
another
than
those
of

related
species
(Yildirim
et
al.
2010;
Ochman
et
al.
2010).

I
also
hypothesized
that
a

comparison
of
wild
and
captive
populations
of
L.
catta
would
reveal
population-‐
specific
differences
in
their
gut
microbial
communities,
since
geography
has
been

shown
to
influence
the
gut
microbial
community
(Yatsunenko
et
al.
2012).

METHODS

Sample
Collection

Feces
were
collected
from
wild
L.
catta
and
P.
verreauxi
at
Beza
Mahafaly
Special

Reserve
(BMSR)
over
a
5-‐month
period
from
October
3,
2011
through
February
9,

2012.

Feces
contain
microbial
cells
from
the
intestinal
community
and
can
be
an

effective,
non-‐invasive
tool
to
study
this
microbial
community
(Nechvatal
et
al.

2008).

Focal
animals
from
L.
catta
groups
Blue,
Yellow,
and
Red
and
P.
verreauxi

groups
Fano,
Felix,
and
Vavy
were
identified
and
followed
until
observed
defecating

(Table
4-‐1).

Fecal
samples
were
collected
promptly
after
defecation
using
sterilized

forceps
to
prevent
sample
contamination.

Fecal
samples
were
stored
temporarily

(up
to
a
few
hours)
in
aluminum
foil
pouches
until
returning
to
base
camp
where

1.25
±
0.8
g
of
feces
was
immersed
in
90%
ethanol,
fixing
the
gastrointestinal

microbes
present
in
the
feces
[Frantzen
et
al.
1998].

A
total
of
eight
fecal
samples

were
collected
for
each
focal
animal,
one
sample
every
two
weeks
for
the
5-‐month

sampling
period.

123

In
order
to
test
for
a
preservation
method
bias,
several
additional
fecal
samples

were
collected
from
a
captive
population
of
L.
catta
at
the
Vienna
Zoo
(Vienna,

Austria)
on
November
14,
2012.

These
fecal
samples
were
collected
ad
libitum

among
the
samples
deposited
freshly
that
day,
though
identification
of
which
lemur

provided
each
fecal
sample
was
not
possible.

Each
sample
was
divided
in
half
to

test
two
preservation
methods.

One
half
of
each
sample
was
frozen
in
liquid

nitrogen
followed
by
storage
at
-‐80
°C
and
the
other
half
preserved
in
ethanol.

These
paired
samples
were
then
compared
to
determine
the
effect
of
each

preservation
method.

Understanding
the
influence
of
preservation
method
on
the

microbial
community
is
an
important
when
comparing
gut
microbiomes
between

studies
using
different
methods.

DNA
Extraction

DNA
extraction,
PCR,
and
genetic
sequencing
followed
the
protocols
of
the
Earth

Microbiome
Project
(http://www.earthmicrobiome.org/emp-‐standard-‐protocols/)

with
minor
modifications
(Gilbert
et
al.
2010).

Due
to
budgetary
limitations,

sequencing
was
only
available
for
a
subset
of
the
fecal
samples
collected.

The

easternmost
(L.
catta
Red
and
P.
verreauxi
Vavy)
and
westernmost
(L.
catta
Blue
and

P.
verreauxi
Fano)
groups
of
each
lemur
species
were
sequenced
at
three
time
points

across
the
sampling
period:
during
the
dry
season,
at
the
immediate
onset
of
the
wet

season,
and
late
into
the
wet
season.

Several
samples
from
the
Vienna
Zoo

population
of
L.
catta
(both
preservation
methods)
were
also
sequenced.

In
total,
46

samples
(18
from
L.
catta
in
Madagascar,
18
from
P.
verreauxi
in
Madagascar,
and
5

pairs
of
preserved
samples
from
L.
catta
in
the
Vienna
Zoo)
were
sequenced.

First,
the
ethanol
was
removed
from
the
fecal
sample
by
centrifuging
the
tubes

containing
the
feces
for
5
minutes
at
10,000
RCF,
the
ethanol
supernatant
removed

by
micropipette,
and
the
sample
fully
desiccated
in
a
speed
vacuum
for
40
minutes

at
30
°C.

DNA
was
purified
from
the
frozen
fecal
samples
and
the
desiccated

ethanol-‐preserved
fecal
samples
using
the
MoBio
Powersoil
DNA
Isolation
Kit.

Before
beginning,
Solution
C1
was
checked
to
ensure
that
it
had
not
precipitated.

Approximately
0.5
g
of
dried
feces
were
added
to
a
MoBio
Powerbead
tube,
making

124

sure
to
not
overfill
the
tube
with
sample.

The
Powerbead
tube
was
vortexed
briefly,

then
60
μL
of
solution
C1
was
added
to
the
tube,
followed
by
being
vortexed
again

briefly.

The
tube
was
then
vortexed
in
a
horizontal
position
for
10
minutes
at

approximately
65%
power.

The
tube
was
centrifuged
for
30
seconds
at
10,000
RCF

then
up
to
500
μL
of
the
supernatant
was
transferred
to
a
new
2
mL
microcentrifuge

tube,
making
sure
to
avoid
removing
the
pellet.

250
μL
of
Solution
C2
was
added
to

this
new
tube,
vortexed
for
5
seconds,
then
incubated
for
5
minutes
at
4
°C.

The

tube
was
centrifuged
for
1
minute
at
10,000
RCF
and
then
up
to
600
μL
of

supernatant
was
transferred
to
a
new
2
mL
microcentrifuge
tube,
making
sure
to

avoid
removing
the
pellet.

200
μL
of
Solution
C3
was
added
to
this
new
tube,

vortexed
for
5
seconds,
and
incubated
for
5
minutes
at
4
°C.

The
tube
was

centrifuged
for
1
minute
at
10,000
RCF
and
up
to
750
μL
of
supernatant
was

transferred
to
a
new
2
ml
micrcentrifuge
tube.

Solution
C4
was
mixed
by
shaking,

then
1200
μL
of
Solution
C4
was
added
to
this
new
tube
and
vortexed
for
5
seconds.

675
μL
of
this
solution
was
loaded
onto
a
spin
filter
and
centrifuged
for
1
minute
at

10,000
RCF.

The
flow
through
liquid
was
discarded
and
another
675
μL
of
the

sample
solution
was
loaded
on
the
same
spin
filter
and
centrifuged
another
1

minute
at
10,000
RCF.

The
flow
through
liquid
was
discarded
and
any
remaining

sample
solution
(up
to
600
μL)
was
loaded
onto
the
same
spin
filter
and
centrifuged

for
1
minute
at
10,000
RCF.

The
flow
through
liquid
was
discarded
and
500
μL
of

Solution
C5
was
loaded
on
the
same
spin
filter
and
centrifuged
for
30
seconds
at

10,000
RCF.

The
flow
through
liquid
was
discarded
and
the
spin
filter
was

centrifuged
for
another
1
minute
at
10,000
RCF.

The
spin
filter
was
transferred
to
a

new
2
mL
microcentrifuge
tube
labeled
for
DNA
storage,
100
μL
of
Solution
C6
was

added
to
the
center
of
the
spin
filter’s
filter
membrane,
and
centrifuged
for
30

seconds
at
10,000
RCF.

The
spin
filter
was
discarded
and
the
remaining

microcentrifuge
tube
containing
the
eluted
DNA
was
stored
at
-‐20
°C
until
needed.

Using
the
above
methods,
up
to
seven
samples
were
extracted
in
parallel.

DNA
extraction
yield
was
quantified
using
a
Life
Technologies
Qubit

Fluorometer
measuring
double
stranded
DNA
using
a
High
Sensitivity
(HS)
kit.

In
a

0.5
mL
microcentrifuge
tube,
196
μL
of
HS
buffer
was
combined
with
1
μL
HS
dye

125

and
1
μL
sample
DNA.

The
tube
was
vortexed
for
5
seconds
and
then
incubated
for

5
minutes
in
the
dark
at
room
temperature.

The
sample
absorbance
was
then

measured
in
a
Qubit
fluorometer
with
the
DNA
concentration
calculated
in
the

machine.

The
Qubit
machine
was
calibrated
regularly
using
two
standards.

PCR
and
Sample
Barcoding

PCR
primers
(515F/806R)
targeted
the
V4
region
of
the
microbial
16S
rDNA.

The
V4
region
was
chosen
due
to
its
low
error
rate
and
assignment
as
the
preferred

region
for
the
Earth
Microbiome
Project
(Wang
et
al.
2007;
Liu
et
al.
2008;
Gilbert
et

al.
2010).

The
PCR
primers
also
included
the
Illumina
adapter
needed
for

sequencing
on
the
Illumina
HiSeq
platform.

The
reverse
primer
(806R)
additionally

included
a
12-‐base
GoLay
barcode
so
that
multiple
samples
could
be
pooled
for

sequencing
and
later
separated.

Primers
were
ordered
from
Invitrogen
and
resuspended
to
a
stock
solution
of

100
μM
using
MilliQ
(deionized
and
0.22μm
pore
size
filtered)
water.

An
aliquot
of

each
barcoded
reverse
primer
was
combined
with
an
aliquot
of
the
forward
primer

to
create
a
primer
mix
for
each
sample
at
200
nM
concentrations
for
each
primer.

In
a
PCR
tube,
13
μL
of
MilliQ
water,
10
μL
of
5
PRIME
HotMasterMix
hot-‐start

PCR
reagent
mix
(at
2.5x
concentration),
1
μL
of
DNA
template
(from
sample
DNA),

and
1
μL
of
the
primer
mix
(with
the
reverse
primer
barcode
matching
the
DNA

sample
added)
were
combined,
mixed,
and
all
liquid
collected
at
the
bottom
of
the

tube
(using
brief
centrifugation
when
necessary).

All
samples
were
amplified
in

triplicate
and
later
combined
before
checking
the
PCR
products
on
an
agarose
gel

and
quantifying
the
DNA
yield.

An
Applied
Biosystems
2720
Thermocycler
was
preheated
to
90
°C.

Once
at
90

°C,
the
PCR
tubes
were
placed
in
the
cycler
block
of
the
thermocycler,
their
lids

sealed
tightly,
and
the
following
PCR
program
was
begun.

After
an
initialization

phase
(3
minutes
at
94
°C),
the
PCR
program
ran
through
35
cycles
of
denaturation

(45
seconds
at
94
°C),
annealing
(1
minute
at
50
°C),
and
elongation
(90
seconds
at

72
°C),
ending
with
a
single
final
elongation
phase
(10
minutes
at
72
°C)
and
a
final

126

hold
at
94
°C
until
the
samples
were
removed
from
the
thermocycler.

The
three

replicate
PCR
products
were
pooled
for
each
sample.

The
PCR
products
were
visualized
using
electrophoresis
on
a
1%
agarose
gel

against
a
1-‐kb
ladder.

The
expected
PCR
product
was
419
bp
long.

PCR
amplicons
were
then
quantified
using
a
Life
Technologies
Qubit

Fluorometer
measuring
double
stranded
DNA
using
a
High
Sensitivity
(HS)
kit
and

the
same
protocol
as
listed
above
for
DNA
extraction
yield
quantification.

Pooling
PCR
Amplicons

The
PCR
amplicons
from
each
sample
were
pooled
into
a
single
microcentrifuge

tube
in
equimolar
amounts.

The
amplicon
with
the
lowest
DNA
yield
contained
88.6

ng
DNA,
so
88.6
ng
DNA
from
each
sample
amplicon
was
combined
in
a
new
1.7
mL

microcentrifuge
tube
and
vortexed
for
5
seconds
to
mix.

The
pooled
amplicons
were
cleaned
up
with
the
Qiagen
QIAquick
PCR

Purification
Kit,
making
sure
to
adjust
the
sample
to
≤7.5
pH.

Purified
amplicon

DNA
was
stored
at
-‐20
°C
until
used.

The
purified
pooled
amplicons
were
visualized
using
electrophoresis
on
a
1%

agarose
gel
against
a
1-‐kb
ladder.

The
darkest
bar
was
at
the
expected
length
of
419

bp
long.

The
purified
pooled
amplicons
were
then
quantified
using
a
Life
Technologies

Qubit
Fluorometer
measuring
double
stranded
DNA
using
a
High
Sensitivity
(HS)
kit

and
the
same
protocol
as
listed
above
for
DNA
extraction
yield
quantification.

The

final
DNA
content
of
the
purified
and
pooled
PCR
amplicons
was
29.0
μg/mL.

DNA
Sequencing

The
pooled
16S
PCR
amplicons
were
sequenced
in
a
single
lane
of
an
Illumina

HiSeq
2500
platform
at
the
University
of
Delaware
Sequencing
&
Genotyping
Center.

Demultiplexing
and
Quality
Filtering

The
sequence
data
was
analyzed
using
the
Quantitative
Insights
Into
Microbial

Ecology
(QIIME,
http://qiime.org)
pipeline
(Caporaso
et
al.
2011b),
version
1.7.0.

127

The
raw
sequence
data
was
demultiplexed
and
quality
filtered
using
the
QIIME

script
split_libraries_fastq.py
with
the
default
parameters.

The
demultiplexed

samples
were
then
checked
for
chimeric
sequences
with
USEARCH
6
.1
using
the

QIIME
script
identify_chimeric_seqs.py
and
the
Greengenes
database
(DeSantis
et

al.
2006)
at
a
97%
match
for
operational
taxonomic
units
(OTUs).

Identified

chimeric
sequences
were
removed
using
the
QIIME
script
filter_fasta.py.

OTU
Picking

The
quality-‐filtered
sequences
were
then
concatenated
back
into
a
single
file
for

OTU
picking.

All
of
the
sequences
from
the
ethanol-‐preserved
Madagascar
and

Vienna
Zoo
samples
were
concatenated
into
one
file
and
all
of
the
sequences
from

both
the
frozen
and
ethanol-‐preserved
Vienna
Zoo
samples
were
concatenated
into

another
file,
for
separate
but
parallel
analysis
of
these
two
datasets.

OTUs
were
picked
using
the
QIIME
script
pick_open_reference_otus.py,
which

takes
a
combined
reference-‐based
and
de
novo-‐based
OTU-‐picking
approach,

wherein
the
sequences
are
matched
against
the
reference
database
and
any

sequences
that
fail
to
match
that
database
are
then
clustered
using
a
de
novo

approach.

OTU
picking
was
done
with
parallel
processing
when
possible
and
using

the
Greengenes
97%
OTU
database
as
the
reference
database.

Diversity
Analysis

The
beta
diversity
of
the
samples
was
calculated
using
the
QIIME
script

core_diversity_analyses.py,
trimmed
to
a
sequence
depth
of
15,824
sequences
for

the
ethanol-‐preserved
Madagascar
and
Vienna
dataset
and
to
a
depth
of
634,026
for

the
dataset
including
both
preservations
of
the
Vienna
Zoo
samples.

Beta
diversity

is
a
measure
of
the
species
diversity
between
different
habitats
(in
this
case,

between
microbiome
samples).

This
script
also
summarized
the
taxonomy
based
on

several
metadata
criteria
(sex,
species,
date,
etc.),
creating
taxonomy
summary
plots

at
varying
levels
of
microbial
taxonomy
and
grouping
the
OTUs
based
on
the

different
metadata
categories
provided.

128

Taxonomic
Tree

The
samples
within
each
dataset
were
arranged
into
a
taxonomic
tree
using
the

script
jackknifed_beta_diversity.py,
which
uses
the
distance
matrices
from
the

beta-‐diversity
above
to
cluster
samples
together
into
Unweighted
Pair
Group

Method
with
Arithmetic
Mean
(UPGMA)
trees.

The
rarefied
consensus
UPGMA
tree

was
colored
and
visualized
using
FigTree
v1.3.1.

Microbial
Abundance
Comparisons
between
Populations

The
abundance
of
each
microbial
phylum
was
compared
between
lemur

populations
using
the
Mann-‐Whitney
U
test
with
a
Bonferroni
correction
for

multiple
comparisons
(Mann
and
Whitney
1947;
Remis
et
al.
2001).

The
Bonferroni

correction
adjusts
the
p-‐value
threshold
to
account
for
the
increasing
likelihood
of
a

Type
I
error
due
to
multiple
comparisons
(Bland
and
Altman
1995).

The
Mann-‐
Whitney
U
test
was
chosen
over
a
parametric
t-‐test
due
to
the
small
sample
size
of

microbiomes
being
compared
and
the
non-‐normal
distribution
of
the
data.

RESULTS

Preservation
Method
Influence
on
Microbial
Composition

The
fecal
samples
from
the
Vienna
Zoo
population
of
Lemur
catta,
with
half
of

each
sample
preserved
in
ethanol
and
half
frozen
at
-‐80°C,
were
compared
in
order

to
discern
what
changes
or
bias
was
introduced
due
to
preservation
method.

18,177,132
microbial
16S
rDNA
sequences
were
recovered
from
the
10
(5
ethanol-‐
preserved,
5
frozen)
captive
L.
catta
samples,
with
11,439,171
of
these
from
the

ethanol
samples
and
6,737,961
sequences
from
the
frozen
samples.

There
were
916
OTUs
found
in
both
the
frozen
and
ethanol-‐preserved
samples,

with
46
OTUs
unique
to
the
ethanol-‐preserved
samples
and
28
OTUs
exclusive
to

the
frozen
samples
(Fig.
4-‐1).

Relative
to
the
total
number
of
OTUs
detected
in
these

samples,
4.6%
were
unique
to
the
ethanol-‐preserved
samples
and
2.8%
were
only

found
in
the
frozen
samples,
while
92.5%
of
the
OTUs
were
found
in
the
samples

regardless
of
preservation
method.

129

The
rarefied
OTU
table
for
the
frozen
and
ethanol-‐preserved
captive
L.
catta

samples
was
used
to
make
Unweighted
Pair
Group
Method
with
Arithmetic
Mean

(UPGMA)
trees.

The
consensus
arrangement
from
these
trees
was
used
to
create
a

taxonomic
tree
of
this
cluster
analysis
(Fig.
4-‐2).

The
samples
did
not
wholly
cluster

according
to
preservation
method.

Instead,
the
five
gut
samples
clustered
into
two

groups,
with
samples
1,
2,
and
4
grouped
together
and
samples
3
and
5
grouped

together.

Within
each
of
these
groups,
the
samples
then
separated
based
on

preservation
method.

A
Principle
Component
Analysis
(PCA)
using
unweighted
UniFrac
distances

(Lozupone
and
Knight
2005)
of
the
beta
diversity
of
the
gut
microbial
samples
from

the
captive
L.
catta
showed
separate
clustering
of
the
ethanol-‐preserved
samples

and
the
frozen
samples,
though
there
was
some
overlap
between
their
95%

confidence
intervals
(Fig.
4-‐3).

This
analysis
also
shows
the
separation
between

samples
3
and
5
from
the
other
zoo
samples
for
each
preservation
method.

The

separation
between
the
preservation
methods
is
due
to
principle
component
2,

which
explains
13.97%
of
the
sample
variation.

The
separation
between
samples
3

and
5
and
the
other
zoo
samples
is
mostly
due
to
principle
component
1,
which

explains
20.26%
of
the
sample
variation.

The
microbial
composition
of
the
frozen
and
ethanol-‐preserved
captive
L.
catta

samples
shows
the
source
of
the
separation
by
preservation
method
in
the
PCA
and

taxonomic
tree
analyses.

While
there
are
some
noticeable
differences
among
the

samples
within
each
preservation
method,
there
are
broader
differences
based
on

preservation
method
(Fig.
4-‐4).

To
test
the
statistical
significance
of
these

differences
in
microbial
proportions,
the
abundance
of
each
microbial
phylum
was

compared
between
all
of
the
frozen
and
all
of
the
ethanol-‐preserved
captive
L.
catta

samples
using
a
Mann-‐Whitney
U-‐test
(Fig.
4-‐5)
(Mann
and
Whitney
1947).

The

only
statistically
significant
difference
between
the
microbial
abundances
based
on

preservation
method
was
the
greater
abundance
of
Firmicutes
in
the
frozen
samples

(Fig.
4-‐5
and
Table
4-‐2a).

There
was
a
greater
abundance
of
unclassified
bacteria
in

the
ethanol-‐preserved
samples
(Fig.
4-‐4).

130

The
variation
shown
within
each
group
also
helps
explain
the
separation
of

samples
3
and
5
from
the
rest.

In
the
ethanol-‐preserved
samples,
samples
3
and
5

have
tiny
amounts
(0.46%
and
0.40%
respectively)
of
Proteobacteria
compared
to

samples
1,
2,
and
4
(17%,
13%,
and
17%
respectively)
(Fig.
4-‐4).

Samples
3
and
5
in

the
frozen
samples
also
had
lower
abundances
of
Proteobacteria
(4.9%
and
2.7%

respectively)
than
samples
1,
2,
and
4
(22%,
18%,
and
37%
respectively).

Furthermore,
samples
3
and
5
had
higher
abundances
of
Euryarchaeota
and

Spirochaetes
than
the
other
frozen
samples.

Variation
Between
Wild
L.
catta
and
P.
verreauxi
Populations

The
sequencing
effort
resulted
in
a
total
of
43,729,047
microbial
16S
rDNA

sequences
from
36
samples.

There
were
a
total
of
1,973
unique
OTUs
found
in
the

Madagascar
samples.

1569
of
these
OTUs
were
found
in
both
L.
catta
and
P.

verreauxi,
while
there
were
252
unique
OTUs
in
L.
catta
and
152
OTUs
only
found
in

P.
verreauxi
(Fig.
4-‐6).

Relative
to
the
total
number
of
OTUs
detected
in
these

samples,
12.8%
of
the
OTUs
were
unique
to
the
L.
catta
samples
and
7.7%
of
the

OTUs
were
only
found
in
the
P.
verreauxi
samples,
while
79.5%
of
the
OTUs
were

found
in
the
samples
of
both
lemur
species.

A
taxonomic
tree
showing
the
clustering
of
the
gut
microbial
samples
from
the

wild
L.
catta
and
P.
verreauxi
(Fig.
4-‐7)
was
created
using
the
same
methods
as
for

the
above
comparison
of
preservation
methods.

Samples
from
L.
catta
and
P.

verreauxi
could
be
unambiguously
differentiated.

Within
P.
verreauxi
there
was

abundant
intermixing
between
the
Fano
and
Vavy
groups.

Similarly,
there
was
no

distinct
separation
between
the
L.
catta
groups
Blue
and
Red.

When
investigating
the
P.
verreauxi
samples
at
a
finer
scale,
there
are
some

parallels
between
animals
within
each
group.

Animals
720
and
747
in
Vavy
group

clustered
together
at
each
time
point.
However,
their
samples
from
each
time
point

did
not
cluster
closely
with
their
samples
from
the
other
time
points.

Fano
group

showed
a
different
pattern,
with
animals
359
and
608
clustering
together
in
the
dry

season,
all
focal
animals
(359,
608,
and
663)
from
this
group
clustering
together
at

131

the
onset
of
the
wet
season,
and
a
different
pair
(608
and
663)
clustering
together

late
in
the
wet
season.

There
was
less
clear
of
a
pattern
within
the
L.
catta
groups.

In
Blue
group,
all

of
the
animals
at
the
first
time
point
clustered
together
and
separately
from
the
later

samples
from
these
animals.

The
separation
between
the
gut
microbial
communities
in
L.
catta
and
P.

verreauxi
seen
in
Fig.
4-‐7
is
supported
by
the
beta
diversity
analysis.

Principal

Component
Analysis
(PCA)
was
used
to
show
the
beta
diversity
between
the
L.
catta

and
P.
verreauxi
samples
from
Madagascar
(Fig.
4-‐8).

The
wild
P.
verreauxi
(purple

and
orange)
and
wild
L.
catta
(blue
and
red)
samples
clearly
separated
from
one

another,
with
no
overlap
between
the
95%
confidence
intervals
of
each
species.

This
separation
between
lemur
species
was
mainly
due
to
principal
component
(PC)

1,
which
explained
15.55%
of
the
variation
between
samples.

There
is
a
large

amount
of
overlap
in
P.
verreauxi
between
Fano
and
Vavy
groups
and
in
L.
catta

between
Blue
and
Red
groups.

The
PCA
separation
between
the
lemur
species
is
also
borne
out
in
the
microbial

composition
of
these
samples.

At
the
phylum
level,
there
are
clear
microbial

abundance
differences
between
the
wild
L.
catta
and
P.
verreauxi
populations
(Fig.

4-‐9).

There
were
significantly
greater
abundances
of
Bacteroidetes,
Firmicutes,
and

Actinobacteria
in
P.
verreauxi,
while
L.
catta
had
a
statistically
larger
proportion
of

Proteobacteria
and
Euryarchaeota
(Fig.
4-‐10
and
Table
4-‐2b).

Variation
Between
Groups,
Animals,
and
Across
Time

Despite
the
broad
differences
in
microbial
abundance
between
L.
catta
and
P.

verreauxi,
there
was
microbial
variability
within
each
lemur
population.

Of

particular
note
are
the
wide
range
of
Firmicutes
abundances
among
the
wild
L.
catta

and
among
the
wild
P.
verreauxi.

To
investigate
the
source
of
this
intraspecific

variation,
the
microbial
abundance
of
each
sample
was
plotted
side-‐by-‐side,

organized
by
lemur
species,
group,
animal,
and
time
point
(Fig.
4-‐11).

Taken
all

together,
a
picture
appears
of
extreme
inter-‐sample
variation.

Across
the
P.

132

verreauxi
samples
there
are
some
fluctuations
in
the
microbial
phyla
abundances,

though
the
variation
is
much
larger
among
the
L.
catta
samples.

At
the
group
level
in
P.
verreauxi,
there
was
less
inter-‐sample
variation
in

Vavy
than
in
Fano.

Amongst
the
animals
in
Vavy,
720
shows
the
least
amount
of

variation
over
time.
Animal
747
showed
an
increase
in
Proteobacteria
and
a
large

decrease
in
Bacteroidetes
late
in
the
wet
season.

Animal
617
showed
a
large
change

at
in
the
early
wet
season,
with
a
large
increase
in
Firmicutes
and
a
compensating

decrease
in
Bacteroidetes,
while
the
abundances
in
the
dry
and
late
wet
seasons
are

notably
similar
despite
the
different
composition
at
the
time
point
between.

The
animals
in
Fano
group
had
a
general
pattern
of
minimal
change
from
dry

to
early
wet
season,
followed
by
a
large
shift
in
the
late
wet
season
with
an
increase

in
Bacteroidetes,
a
reduction
in
Firmicutes,
and
a
greater
than
10%
abundance
of

Proteobacteria.

Note
that
late
wet
season
sample
for
animal
359
was
excluded
due

to
a
low
OTU
count.

Within
the
L.
catta
Blue
group
there
is
substantial
variation
within
the

samples
for
each
animal
and
when
comparing
all
three
animals
at
each
time
point.

Red
group
shows
a
similarly
high
level
of
variation
within
the
three
time
points
of

each
animal
and
when
comparing
each
time
point
across
all
animals.

Note
that
the

late
wet
season
sample
for
animal
231
was
excluded
due
to
a
low
OTU
count.

Comparing
the
Gut
Microbiomes
Between
Wild
and
Captive
L.
catta

There
were
11,439,171
microbial
16S
rDNA
sequences
from
the
five
ethanol-‐
preserved
fecal
samples
from
the
Vienna
Zoo
population
of
L.
catta.

The
18
samples

from
the
wild
L.
catta
living
in
Madagascar
yielded
22,804,661
16S
rDNA
sequences.

There
were
a
total
of
1,933
unique
OTUs
found
in
all
of
the
L.
catta
samples
(Fig.
4-‐
6).

There
were
1,196
OTUs
found
in
both
the
wild
and
captive
populations
of
L.

catta,
with
625
OTUs
identified
only
in
the
wild
population
and
112
unique
to
the

captive
L.
catta.

Relative
to
the
total
number
of
OTUs
detected
in
these
samples,

32.3%
were
unique
to
the
wild
L.
catta
and
5.8%
were
only
found
in
the
captive
L.

catta,
while
61.9%
of
the
OTUs
were
found
regardless
of
whether
the
animals
were

in
captivity
or
the
wild.

133

The
relationship
between
each
microbial
gut
sample
based
on
the
consensus

arrangement
between
UPGMA
trees
from
the
rarefied
OTU
table
shows
a
strong

separation
between
the
wild
and
captive
L.
catta
samples
(Fig.
4-‐7).

This
separation

is
greater
than
the
distance
between
samples
from
the
sympatric
wild
L.
catta
and
P.

verreauxi
populations.

When
comparing
the
beta
diversity
of
the
wild
and
captive
L.
catta
samples
(Fig.

4-‐8),
the
samples
clearly
separate
by
location,
with
the
wild
L.
catta
samples
(blue

and
red)
clustering
together
but
separately
from
the
tightly
clustered
captive
L.

catta
samples
(green).

There
is
no
overlap
between
the
95%
confidence
intervals
of

each
population.

The
majority
of
this
separation
between
L.
catta
populations

comes
from
principal
component
2
which
explained
9.11%
of
the
variation
between

samples,
though
there
is
a
slight
separation
between
these
groups
due
to
principal

component
1.

The
abundance
of
microbes
in
the
wild
and
captive
L.
catta
populations
showed

striking
differences
in
several
phyla
(Fig.
4-‐9).

While
the
wild
L.
catta
had
a

statistically
greater
abundance
of
Firmicutes,
Euryarchaeota,
and
Actinobacteria,
the

captive
L.
catta
had
significantly
larger
populations
of
Bacteroidetes
and

Spirochaetes
(Fig.
4-‐10
and
Table
4-‐2c).

DISCUSSION

Preservation
Method
Influence
on
Microbial
Composition

When
comparing
the
Vienna
Zoo
L.
catta
fecal
samples
preserved
in
ethanol
or

frozen
at
-‐80°C,
there
was
an
extremely
high
overlap
in
gut
microbial
composition,

with
93%
of
the
OTUs
identified
with
both
methods.

Samples
from
the
two
methods

clustered
very
closely
together
and
were
separately
from
the
wild
L.
catta
samples,

showing
that
any
variation
between
preservation
methods
was
tiny
compared
to
the

differences
seen
between
populations.

There
were
some
small
differences
in

microbial
abundances
between
the
preservation
methods,
though
the
only

significant
difference
was
a
higher
abundance
of
Firmicutes
in
the
frozen
samples.

134

Overall,
there
was
minimal
bias
due
to
the
preservation
method
used
for
the
captive

L.
catta
feces.

In
a
comparison
of
methods,
Frantzen
et
al.
(Frantzen
et
al.
1998)
found
that

ethanol
and
freezing
at
-‐20°C
were
equally
effective
at
preserving
DNA.

While
their

results
show
that
the
two
methods
yield
similar
percentages
of
successful
DNA

extractions,
the
results
in
the
current
study
indicate
that
there
still
may
be
small

shifts
in
the
microbial
abundance
of
a
fecal
sample
based
on
preservation
method.

All
preservation
methods,
including
freezing
(Bahl
et
al.
2012),
will
introduce
some

bias,
so
each
research
project
must
individually
weigh
the
tradeoffs
between

logistical
concerns
and
sampling
bias
when
choosing
a
DNA
preservation
method.

Variation
Between
Wild
L.
catta
and
P.
verreauxi
Populations

The
distinct
gut
microbiome
profiles
found
between
the
wild
Lemur
catta
and

Propithecus
verreauxi
populations
echo
other
research
showing
that
different

mammalian
species
have
unique
microbial
communities
(Ley
et
al.
2008;
Caporaso

et
al.
2010;
Muegge
et
al.
2011),
even
between
closely
related
species
(Goldberg
et

al.
2007;
Moeller
et
al.
2013).

The
most
abundant
microbial
phyla
match
that
of

many
other
primate
species,
with
Firmicutes,
followed
by
Bacteroidetes,
as
the
most

abundant
phyla
(Fig.
1-‐1)
(Frey
et
al.
2006;
Ley
et
al.
2008;
Bo
et
al.
2010;
Yildirim

et
al.
2010;
Ochman
et
al.
2010;
Moeller
et
al.
2013).

Microbial
phyla
feature
species

with
many
metabolic
functions,
so
it
is
difficult
to
connect
abundance
changes
at
the

phylum
level
with
specific
functional
regimes.

The
abundances
of
both
Firmicutes
and
Bacteroidetes
were
significantly
greater

in
P.
verreauxi
than
L.
catta,
though
the
greater
percentage
of
unclassified
bacteria
in

L.
catta
contributed
to
the
lower
relative
percentages
of
these
phyla
(Figs.
4-‐9
and
4-‐
10).

Several
studies
have
utilized
the
ratio
of
Firmicutes
to
Bacteroidetes
(F/B
ratio)

as
a
diagnostic
measure
of
the
human
gut
microbiome
(Ley
et
al.
2005;
Ley
et
al.

2006a;
Turnbaugh
et
al.
2006;
Schwiertz
et
al.
2009;
De
Filippo
et
al.
2010).

An

increase
in
the
ratio
of
Firmicutes
to
Bacteroidetes
is
associated
with
an
increase
in

the
gut’s
efficiency
of
harvesting
energy
(Bäckhed
et
al.
2004).

The
F/B
ratio
was

higher
in
L.
catta
than
in
P.
verreauxi
(1.85
and
1.26
respectively),
suggesting
that
L.

135

catta
may
have
a
gut
microbiome
with
an
increased
capacity
for
extracting
energy

from
its
food.

This
could
help
explain
the
results
of
the
previous
chapter,
where
the

maximum
cellulose
consumption
was
at
a
similar
level
for
both
lemur
species.

While
P.
verreauxi
has
the
morphological
advantages
of
a
folivore
(much
longer
gut

length
and
longer
gut
transit
time
(Campbell
et
al.
2000;
Campbell
et
al.
2004a)),
the

increased
F/B
ratio
in
L.
catta
might
be
responsible
for
helping
to
equalize
the

digestive
capabilities
of
these
lemurs
when
it
comes
to
dietary
fiber.

The
F/B
ratio

had
a
large
amount
of
variance
between
samples,
particularly
in
L.
catta,
so
a
further

analysis
of
this
ratio
and
dietary
consumption
of
fiber
may
help
to
elucidate
a

deeper
pattern
(see
Chapter
5).

The
amount
of
unclassified
bacteria
was
twice
as
large
in
the
L.
catta,
with
about

50%
of
the
OTUs
only
identified
as
‘other’
bacteria.

On
a
per-‐sample
basis,
there

was
no
discernible
pattern
(i.e.
seasonal
or
between
groups)
to
the
unclassified

percentage
of
the
gut
microbiome
in
L.
catta
(Fig.
4-‐11).

The
abundance
of

unclassified
OTUs
could
indicate
that
there
are
more
bacterial
phylotypes
in
the
L.

catta
gut
that
have
yet
to
be
identified,
or
it
might
point
to
a
methodological
bias

between
the
L.
catta
and
P.
verreauxi
samples.

The
latter
is
unlikely
as
every
effort

was
taken
to
treat
all
of
the
samples
in
an
identical
fashion.

There
was
more
variation
between
samples
in
L.
catta,
though
the
reason
for
this

is
unclear.

A
fluctuation
in
plant
species
consumed
does
not
explain
the
microbial

variation,
since
P.
verreauxi
had
a
higher
dietary
diversity
than
L.
catta
(Fig.
2-‐13).

However,
L.
catta
did
show
significant
changes
in
consumption
of
phenolics,

cellulose,
ADF,
and
ADL
between
seasons
(Figs.
3-‐1a,
3-‐1b,
3-‐1c,
and
3-‐1d),
so
it
is

possible
that
one
of
these
plant
defenses
had
a
strong
effect
on
the
gut
microbial

composition.

The
relationship
between
diet
and
the
gut
microbiome
is
analyzed
in

Chapter
5.

Variation
Between
Wild
and
Captive
L.
catta
Populations

The
large
separation
between
the
gut
microbiomes
of
wild
and
captive
L.
catta

was
reflected
in
their
microbial
abundances.

This
suggests
that
the
environment

(including
diet)
has
a
strong
influence
on
the
composition
of
the
gut
microbial

136

community.

Similar
population-‐specific
differences
have
been
observed
between

live
and
captive
populations
in
black
howler
monkeys
(Nakamura
et
al.
2011)
and

chimpanzees
(Uenishi
et
al.
2007).

Diet
has
also
been
shown
to
cause
broad
shifts
in

the
gut
flora
(Turnbaugh
et
al.
2009b;
David
et
al.
2014).

While
phylogeny

significantly
influences
the
gut
microbiome
(Ochman
et
al.
2010),
diet
(David
et
al.

2014)
and
development
(Dominguez-‐Bello
et
al.
2011)
also
play
strong
roles
in

determining
the
community
composition
and
structure
within
the
gut.

So,
it
is
not

surprising
that
geographically
and
environmentally
separate
populations
of
L.
catta

would
contain
highly
distinct
microbiomes.

The
captive
L.
catta
at
the
Vienna
Zoo
show
a
distinctive
microbial
profile
to
the

captive
population
at
the
St.
Louis
Zoo
(the
only
other
sequenced
L.
catta
gut

microbiome)
(Ley
et
al.
2008).

The
disparity
between
these
two
captive
populations

is
similar
in
scale
to
the
difference
between
each
of
these
populations
to
the
wild
L.

catta
analyzed
here.

The
captive
L.
catta
in
Vienna
had
a
significant
abundance
of
Spirochaetes
(26%

of
unique
OTUs)
while
both
the
wild
Madagascar
and
captive
St.
Louis
populations

had
very
little
Spirochaetes
present
(1-‐2%).

It
is
possible
that
a
shift
in
the
gut

microbial
structure
in
the
Vienna
animals
allowed
the
Spirochaetes
that
are

otherwise
present
in
low
abundance
to
expand
and
occupy
one
quarter
of
the
gut

community.

In
a
survey
of
60
mammals
species,
none
had
such
a
high
abundance
of

Spirochaetes
except
the
omnivorous
Hamadryas
baboon
(about
23%
of
the
gut
flora)

(Ley
et
al.
2008).

This
Hamadryas
baboon
analysis
contained
fecal
samples
from

one
wild
and
one
captive
animal,
though
whether
these
bacteria
were
abundant
in

both
the
wild
and
captive
individuals
is
unclear
and
unpublished.

At
the
genus
level,
the
Spirochaetes
bacteria
in
the
Vienna
lemurs
belonged
to
the

Treponema
genus,
famous
for
containing
the
pathogens
responsible
for
the
syphilis,

pinta,
and
yaws
diseases.

In
a
comparison
of
human
gut
microbiomes
between
rural

Africans
and
developed
Europeans,
Treponema
was
only
found
in
the
African

population.

It
has
been
hypothesized
that
this
difference
is
due
to
the
higher
fiber

content
in
the
African
diet,
though
we
are
unable
to
support
this
idea
with
L.
catta.

137

The
captive
lemurs
would
have
a
lower
fiber
intake
in
their
refined
zoo
feed,
so
the

increase
in
Treponema
is
unlikely
fiber-‐related.

We
found
the
least
intragroup
variation
among
the
captive
L.
catta.

This
could

be
due
to
a
similar
environmental
exposure
within
the
controlled
confines
of
their

enclosure,
where
all
of
the
individuals
receive
a
stable
diet.

It
is
also
possible
that

the
sample
similarity
could
be
due,
in
part,
to
multiple
samples
being
deposited
by

the
same
animal.

CONCLUSIONS

Studies
comparing
the
gut
microbiomes
of
a
variety
of
mammalian
species
have

discovered
that
both
host
phylogeny
and
diet
are
strong
forces
that
shape
the
gut

microbial
community
(Ley
et
al.
2008;
Ochman
et
al.
2010;
Muegge
et
al.
2011;

Amato
2013a;
Amato
et
al.
2013).

The
relative
influence
of
diet
versus
phylogeny
is

still
under
debate,
but
the
effect
of
each
factor
is
clearly
evident.

Within
primates
in

particular,
phylogeny
seems
to
be
the
distinguishing
trait
separating
the
gut

microbial
composition
(Ochman
et
al.
2010).

In
line
with
these
previous
findings,

the
populations
of
L.
catta
and
P.
verreauxi
at
BMSR
have
distinct
gut
microbiomes.

This
held
true
despite
both
species
featuring
a
large
amount
of
inter-‐individual
and

temporal
variation
in
the
gut
microbial
composition.

The
differences
between
these

sympatric
lemur
species
are
clear
when
comparing
the
abundances
of
the
various

microbial
phyla.

P.
verreauxi
had
significantly
higher
percentages
of
Bacteroidetes,

Firmicutes,
Actinobacteria,
and
Fibrobacteres,
while
L.
catta
had
a
significantly

higher
percentage
of
Euryarchaeota.

Similar
to
several
hominids
and
the
pygmy

loris,
Bacteroidetes,
Firmicutes,
and
Proteobacteria
were
the
most
abundant
phyla
in

the
gut
microbiome
of
the
lemurs
(Bo
et
al.
2010;
Ochman
et
al.
2010).

The
gut

microbiota
of
P.
verreauxi
has
not
been
studied
before,
but
Ley
et
al.
sequenced
the

gut
community
in
a
single
captive
L.
catta
as
part
of
a
mammal-‐wide
gut
microbial

survey
(Ley
et
al.
2008).

The
wild
L.
catta
had
a
similar
Firmicutes/Bacteroidetes

ratio
to
the
captive
sample,
along
with
a
small
percentage
of
Spirochaetes.

The

similar
F/B
ratio
in
both
populations,
suggests
that
their
guts
have
a
commensurate

138

energy
harvesting
efficiency
(Bäckhed
et
al.
2004).

The
striking
difference
between

the
wild
and
captive
L.
catta
gut
microbiomes
was
the
large
abundance
of

Proteobacteria
in
the
wild
animals
and
Verrucomicrobia
in
the
captive
animal.

Other

captive
L.
catta
gut
samples
from
the
Vienna
Zoo
had
a
unique
microbial
community

composition
to
the
other
populations,
with
an
F/B
ratio
well
below
1
and
with
the

Spirochaetes
phylum
accounting
for
nearly
one
quarter
of
the
sequences.

These

differences
may
be
partly
due
to
the
differences
between
a
wild
diet
and
the
captive

diets
that
include
a
large
amount
of
pelleted
dry
food.

Comparisons
of
gut
microbes

between
wild
and
captive
populations
in
chimpanzees
(Uenishi
et
al.
2007;
Szekely

et
al.
2010),
gorillas
(Ley
et
al.
2008),
and
black
howler
monkeys
(Nakamura
et
al.

2011)
have
also
found
large
differences
between
these
microbial
communities
(see

review
by
Amato
2013b).

The
lemurs
in
captivity
are
more
likely
to
be

administered
antibiotics
during
their
tenure
(Wierup
2000),
which
could
help

explain
these
drastically
different
gut
communities
to
those
found
in
their
wild

conspecifics
(Dethlefsen
et
al.
2008;
Dethlefsen
and
Relman
2011).

Additionally,
the

proximity
and
interaction
with
humans
that
frequently
occurs
in
captive

environments
can
lead
to
convergence
of
their
gut
microbial
profiles
(Goldberg
et
al.

2007).

139

FIGURES

Figure
4-‐1:
Unique
and
overlapping
OTUs
for
each
preservation
method.

140

Figure
4-‐2:
Taxonomic
tree
of
frozen
and
ethanol-‐preserved
Vienna
zoo
samples.

0.04
Sample 2 (Ethanol)
Sample 5 (-80°C)
Sample 1 (-80°C)
Sample 5 (Ethanol)
Sample 4 (-80°C)
Sample 3 (-80°C)
Sample 1 (Ethanol)
Sample 4 (Ethanol)
Sample 3 (Ethanol)
Sample 2 (-80°C)

141

Figure
4-‐3:
Beta
diversity
of
gut
microbiomes
from
frozen
and
ethanol-‐preserved

Vienna
zoo
samples.

Ellipses
are
95%
confidence
intervals.

142

Figure
4-‐4:
Gut
microbial
abundances
of
frozen
and
ethanol-‐preserved
Vienna
zoo

samples.

143

Figure
4-‐5:
Variation
in
gut
microbial
abundances
for
each
preservation
method.

Bold
lines
are
average
abundances.

Open
circles
are
outliers.

144

Figure
4-‐6:
Unique
and
overlapping
OTUs
for
each
lemur
population.

145

Figure
4-‐7:
Taxonomic
tree
of
beta
diversity
similarity
in
wild
and
captive
lemur

populations.

0.05
44 - Early Wet
663 - Late Wet
265 - Late Wet
44 - Dry
137 - Early Wet
3
231 - Dry
347 - Early Wet
218 - Early Wet
720 - Early Wet
608 - Dry
1
347 - Late Wet
747 - Dry
359 - Early Wet
747 - Late Wet
265 - Dry
617 - Early Wet
137 - Dry
747 - Early Wet
720 - Dry
359 - Dry
137 - Late Wet
231 - Early Wet
720 - Late Wet
617 - Dry
5
663 - Dry
218 - Dry
617 - Late Wet
608 - Early Wet
218 - Late Wet
347 - Dry
663 - Early Wet
44 - Late Wet
608 - Late Wet
265 - Early Wet
2
4

146

Figure
4-‐8:
Beta
diversity
of
gut
microbiomes
from
wild
and
captive
lemur

populations
by
group.

Ellipses
are
95%
confidence
intervals.

147

Figure
4-‐9:
Gut
microbial
abundances
of
wild
and
captive
lemur
populations.

148

Figure
4-‐10:
Variation
in
gut
microbial
abundances
for
wild
and
captive
lemur

populations.

149

Figure
4-‐11:
Gut
microbial
abundances
of
wild
and
captive
lemur
populations
by

sample.

Season
labels
are:
dry
(D),
early
wet
(EW),
and
late
wet
(LW).

150

TABLES

Table
4-‐1:
Fecal
sample
metadata.

Sample'ID Location Species Group
Forest'
Region
Focal'
ID
Sex
Collection'
Date
Season
Zoo1EtOH Vienna Lc ViennaZoo NA NA NA 11/14/2012 NA
Zoo2EtOH Vienna Lc ViennaZoo NA NA NA 11/14/2012 NA
Zoo3EtOH Vienna Lc ViennaZoo NA NA NA 11/14/2012 NA
Zoo4EtOH Vienna Lc ViennaZoo NA NA NA 11/14/2012 NA
Zoo5EtOH Vienna Lc ViennaZoo NA NA NA 11/14/2012 NA
Mad21 Madagascar Lc Blue West 218 M 10/19/2011 Dry
Mad22 Madagascar Lc Blue West 137 F 10/19/2011 Dry
Mad23 Madagascar Lc Blue West 265 M 10/19/2011 Dry
Mad24 Madagascar Pv Fano West 359 M 10/21/2011 Dry
Mad25 Madagascar Pv Fano West 663 M 10/21/2011 Dry
Mad26 Madagascar Pv Fano West 608 F 10/21/2011 Dry
Mad27 Madagascar Lc Red East 347 F 10/24/2011 Dry
Mad28 Madagascar Lc Red East 44 F 10/24/2011 Dry
Mad29 Madagascar Lc Red East 231 F 10/24/2011 Dry
Mad30 Madagascar Pv Vavy East 720 F 10/26/2011 Dry
Mad31 Madagascar Pv Vavy East 617 M 10/26/2011 Dry
Mad32 Madagascar Pv Vavy East 747 F 10/26/2011 Dry
Mad80 Madagascar Lc Blue West 137 F 11/30/2011 EarlyJWet
Mad81 Madagascar Lc Blue West 218 M 11/30/2011 EarlyJWet
Mad82 Madagascar Lc Blue West 265 M 11/30/2011 EarlyJWet
Mad83 Madagascar Pv Fano West 608 F 12/2/2011 EarlyJWet
Mad84 Madagascar Pv Fano West 359 M 12/2/2011 EarlyJWet
Mad85 Madagascar Pv Fano West 663 M 12/2/2011 EarlyJWet
Mad86 Madagascar Lc Red East 231 F 12/5/2011 EarlyJWet
Mad87 Madagascar Lc Red East 44 F 12/5/2011 EarlyJWet
Mad88 Madagascar Lc Red East 347 F 12/5/2011 EarlyJWet
Mad89 Madagascar Pv Vavy East 720 F 12/7/2011 EarlyJWet
Mad90 Madagascar Pv Vavy East 747 F 12/7/2011 EarlyJWet
Mad91 Madagascar Pv Vavy East 617 M 12/7/2011 EarlyJWet
Mad120 Madagascar Lc Blue West 218 M 1/10/2012 LateJWet
Mad121 Madagascar Lc Blue West 137 F 1/10/2012 LateJWet
Mad122 Madagascar Lc Blue West 265 M 1/11/2012 LateJWet
Mad123 Madagascar Pv Fano West 608 F 1/12/2012 LateJWet
Mad124 Madagascar Pv Fano West 663 M 1/12/2012 LateJWet
Mad125 Madagascar Pv Fano West 359 M 1/12/2012 LateJWet
Mad126 Madagascar Lc Red East 231 F 1/14/2012 LateJWet
Mad127 Madagascar Lc Red East 44 F 1/14/2012 LateJWet
Mad128 Madagascar Lc Red East 347 F 1/14/2012 LateJWet
Mad129 Madagascar Pv Vavy East 720 F 1/18/2012 LateJWet
Mad130 Madagascar Pv Vavy East 747 F 1/21/2012 LateJWet
Mad131 Madagascar Pv Vavy East 617 M 1/22/2012 LateJWet

151

Table
4-‐2:
Results
tables
for
the
comparison
of
microbial
phyla
abundances
using

the
Mann-‐Whitney
U
test
with
Bonferroni
correction.

Comparisons
include
(a)
the

ethanol-‐preserved
and
frozen
samples
from
the
L.
catta
population
in
Vienna,
(b)

the
populations
of
L.
catta
and
P.
verreauxi
in
Madagascar,
and
(c)
the
populations
of

L.
catta
in
Madagascar
and
Vienna.

The
U-‐value
is
a
measure
of
the
difference

between
the
ranks
of
the
two
groups.

The
lower
the
U-‐value
relative
to
the
product

of
the
sample
sizes,
the
less
likely
any
difference
in
medians
occurred
by
chance.

Phyla n
L.#catta'Ethanol
n
L.#catta'+80°C
U p+value
Bacteroidetes# 37.0% 44.0% 5 5 7 1.000
Firmicutes# 6.2% 19.0% 5 5 0 0.040*
Proteobacteria 13.0% 18.0% 5 5 6 1.000
Spirochaetes 28.0% 4.8% 5 5 2 0.159
Euryarchaeota 0.0% 0.0% 5 5 10 1.000
*.p.<.0.05..........**.p.<.0.01
Median
L.#catta L.#catta
Ethanol +80°C
A
Median
Phylum n
L.#catta
n
P.#verreauxi
U p*value
Bacteroidetes# 11.0% 29.0% 17 17 16 0.001**
Firmicutes# 24.0% 37.0% 17 17 54 0.001**
Proteobacteria 3.6% 3.3% 17 17 135 1.000
Spirochaetes 0.3% 0.1% 17 17 102 1.000
Actinobacteria 0.4% 2.1% 17 17 45 0.001**
Euryarchaeota 0.7% 0.0% 17 17 11 0.001**
Fibrobacteres 0.0% 0.3% 17 17 0 0.001**
*-p-<-0.05----------**-p-<-0.01
Madagascar Madagascar
B
Median Median
L.#catta P.#verreauxi

152

Phyla n
L.#catta'Madagascar
n
L.#catta'Vienna
U p3value
Bacteroidetes# 11.0% 37.0% 17 5 0 0.001**
Firmicutes# 24.0% 6.2% 17 5 6 0.015*
Proteobacteria 3.6% 13.0% 17 5 41 1.000
Spirochaetes 0.3% 28.0% 17 5 0 0.001**
Actinobacteria 0.4% 0.0% 17 5 0 0.001**
Euryarchaeota 0.7% 0.0% 17 5 0 0.001**
Fibrobacteres 0.0% 0.0% 17 5 42 1.000
*-p-<-0.05----------**-p-<-0.01
Madagascar Vienna
C
Median Median
L.#catta L.#catta

153

CHAPTER
5
-‐
INFLUENCE
OF
DIET
ON
THE
GUT
MICROBIOME

ABSTRACT:

The
gut
microbiome
is
a
complex
community
that
varies
based
on
host

phylogeny,
diet,
sex,
and
temporally.

To
determine
whether
these
factors
were

associated
with
changes
in
the
gut
microbiome
in
wild
populations
of
Lemur
catta

and
Propithecus
verreauxi,
several
microbial
metrics
were
modeled
using
Markov

chain
Monte
Carlo
Generalized
Linear
Mixed
Models
(MCMCglmm).

To
look
for

microbes
that
metabolize
cellulose
and
phenolics,
the
abundances
of
six
genera

were
modeled
with
cellulose
and
phenolics
consumption
as
effects.

The
microbial

community
diversity
was
associated
with
the
sex
of
the
individual
for
both
species

and
with
the
season
for
L.
catta.

At
the
genus
level,
the
abundance
of
the
cellulolytic

Fibrobacter
genus
increased
with
increasing
cellulose
consumption
in
P.
verreauxi.

In
L.
catta,
a
rising
consumption
of
phenolics
was
associated
with
an
increasing

abundance
of
the
Streptococcus
genus,
which
contains
several
phenolics-‐
metabolizing
species.

While
small
dietary
effects
on
the
gut
microbiome
were

detected,
the
broader
inter-‐individual
differences
were
based
on
phylogeny,
sex,

and
seasonal
shifts.

INTRODUCTION:

The
gut
microbiome
is
an
important
adaptation
that
allows
herbivores
to

neutralize
and
metabolize
the
plant
defenses
in
their
diet.

The
vast
microbial

community
housed
within
the
gut
provide
additional
metabolic
pathways
that
the

host
lacks
endogenously,
such
as
the
metabolism
of
plant
structural
carbohydrates

(Stevens
and
Hume
1998;
Gibson
and
Roberfroid
1999;
Hooper
et
al.
2002;
DeSantis

et
al.
2006;
Ze
et
al.
2012;
Flint
et
al.
2012).

The
gut
microbiome
provides
essential

nutrients
to
herbivores
by
anaerobically
fermenting
cellulose
into
short-‐chain
fatty

acids
(SCFAs),
which
can
provide
up
to
30%
of
an
animal’s
basal
metabolic
needs

(Parker
1976;
Miller
and
Wolin
1979;
Cummings
1981;
Cummings
1983;
Rérat
et
al.

1987;
Cummings
and
Macfarlane
1991;
Béguin
and
Aubert
1994).

Within
the

hindgut,
phenolics
are
deconjugated
by
microbial
enzymes
before
being
further

154

metabolized
(Krishnamurty
et
al.
1970;
Schneider
and
Blaut
2000;
Rechner
2004;

Aura
2008).

Among
adult
animals,
there
is
no
consensus
on
the
temporal
stability
of
the
gut

microbiome.

Some
research
has
found
little
temporal
variation
in
the
gut

microbiome
within
individuals,
particularly
when
compared
to
the
inter-‐individual

variation
(Costello
et
al.
2009).

Another
study
found
a
surprisingly
small
‘core

microbiome’
(those
microbial
species
present
in
all
samples
from
an
environment)

among
longitudinal
gut
samples
from
the
same
individual
(Caporaso
et
al.
2011a).

These
conflicting
results
highlight
the
currently
sparse
understanding
of
the

temporal
variation
in
the
gut
microbiome,
with
only
a
handful
of
studies
having

investigated
the
temporal
stability
of
the
adult
microbiome.

Seasonal
changes
to
the

environment
of
the
microbial
community,
such
as
a
shifting
host
diet,
alters
the

nutritional
inputs
to
the
microbial
ecosystem
and
the
species
abundances
(Gilbert
et

al.
2012;
Sintes
et
al.
2012).

In
addition
to
temporal
changes
in
the
gut
microbiota,
individuals
often
retain

unique
community
profiles
that
distinguish
them
from
conspecifics,
even
as
these

individual’s
microbiomes
change
over
time
(Eckburg
et
al.
2005;
Frank
et
al.
2007;

Ley
et
al.
2008;
Turnbaugh
et
al.
2009a).

The
variation
between
members
of

separate
species
tends
to
be
greater
than
this
inter-‐individual
variation
(Ley
et
al.

2008;
Yildirim
et
al.
2010).

Microbial
composition
also
varies
by
sex
in
both
the
gut

(Mueller
et
al.
2006)
and
on
the
skin
of
the
palms
(Fierer
et
al.
2008).

This
latter

study
found
a
higher
diversity
of
microbes
on
the
palms
of
women
than
men.

The
gut
microbiome
is
a
contained
community
that
receives
its
main
nutrient

and
energy
input
from
the
undigested
food
that
passes
through
the
gut
of
the
host

(Goldin
and
Gorbach
1977;
Reddy
et
al.
1992;
Blaut
and
Clavel
2007).

Shifts
in
the

chemical
makeup
of
this
input
can
cause
the
abundance
of
the
major
groups
of
gut

microbes
to
change
(Ley
et
al.
2006b;
Turnbaugh
et
al.
2006;
Ley
et
al.
2008;

Yildirim
et
al.
2010;
Wu
et
al.
2011).

Both
short-‐term
and
long-‐term
dietary
changes

can
have
dramatic
effects
on
the
gut
flora
(Duncan
et
al.
2007;
Walker
et
al.
2011;

Muegge
et
al.
2011;
Wu
et
al.
2011;
David
et
al.
2014).

Specific
changes
to
the
gut

microbial
composition
have
been
linked
to
a
reduction
in
the
amount
of

155

carbohydrates
consumed
(Duncan
et
al.
2007;
De
Filippo
et
al.
2010),
a
change
in

the
type
of
carbohydrates
ingested
(Walker
et
al.
2011),
a
high-‐fat/low-‐fiber
versus

a
low-‐fat/high-‐fiber
diet
(Wu
et
al.
2011),
and
whether
the
host
animal
is
a

carnivore,
herbivore,
or
omnivore
(Ley
et
al.
2008;
Muegge
et
al.
2011;
David
et
al.

2014).

Methodological
Approaches
to
Investigating
Microbiomes

There
are
three
main
approaches
to
determining
a
changing
functional
profile
of

a
microbial
community:
categorize
the
taxonomy
of
the
microbes
using
their

conserved
16S
rDNA
sequences
and
identify
functions
associated
with
those
species,

reconstruct
the
entire
community
genome
using
metagenomic
approaches
and

investigate
the
functional
genes
of
the
community,
and
transcriptomics
and

proteomics
which
catalogue
the
rRNA
and
proteins
(respectively)
actually
present

within
a
community.

The
16S
rDNA
approach
is
the
oldest
of
these
approaches.

While
it
provides
a
solid
phylogenetic
classification
of
the
microbes
in
a
community,

the
functional
information
is
less
thorough.

Metagenomic
techniques
are
much

costlier
than
those
of
16S
rDNA
as
the
entire
genome
must
be
sequenced
rather
than

a
short
diagnostic
stretch
of
DNA.

Microbial
communities
are
complex
ecosystems
and
there
is
currently
no
single

metric
used
to
analyze
their
composition
and
diversity.

Despite
this
lack
of

standardization,
researchers
have
used
a
variety
of
different
measurements
to

quantify
microbial
communities.

To
analyze
the
diversity
of
microbial
types,
a

simple
count
of
unique
OTUs
can
be
informative,
but
can
easily
be
confounded
if
the

number
of
total
OTUs
is
not
equal
among
the
samples.

For
a
more
comprehensive

analysis
of
the
community
(β)
diversity,
the
Shannon
diversity
index
was
used

(Shannon
and
Weaver
2002).

The
Shannon
index
is
a
combined
measure
of
the

richness
and
evenness
of
the
species
present
within
the
microbiome
(Magurran

1988).

Of
particular
interest
in
gut
microbial
communities
is
the
ratio
of
the

dominant
bacterial
phyla
Firmicutes
and
Bacteroidetes
(F/B
ratio).

An
increase
in

the
F/B
ratio
has
been
suggested
to
provide
an
increased
energy
harvesting

efficiency
in
the
gut,
explaining
why
obese
individuals
were
shown
to
have
a

156

significant
shift
in
their
F/B
ratio
(Bäckhed
et
al.
2004;
Ley
et
al.
2005;
Turnbaugh
et

al.
2006;
Mariat
et
al.
2009).

An
increasing
F/B
ratio
in
wild
animals
may
be

indicative
of
a
change
in
the
nutritional
quality
of
the
animal’s
diet,
such
as
an

increase
in
fat
intake.

Cellulose-‐
and
Phenolics-‐Metabolizing
Microbes

Several
microbial
species
found
in
the
gastrointestinal
tract
of
animals
are

known
to
metabolize
cellulose
and
phenolics
(Tables
5-‐1a
and
5-‐1b).

The
genus

Streptococcus
contains
several
species
(S.
gallolyticus
and
S.
bovis
Biotype
I)
that
can

metabolize
phenolics
such
as
tannins
(Osawa
and
Sly
1992;
Osawa
1992;
O'Donovan

and
Brooker
2001).

The
genera
Fibrobacter
and
Ruminococcus
contain
species
that
ferment
cellulose.

The
genus
Ruminococcus
contains
two
species
known
to
metabolize
cellulose.

R.

flavefaciens
was
isolated
from
the
rumen
of
cows
and
metabolized
the
cellulose

present
in
a
cellulose-‐based
culture
medium
(Hungate
1947).

Similarly,
R.
albus
was

shown
to
ferment
cellulose
in
culture
(Pavlostathis
et
al.
1988).

The
bacterium

Fibrobacter
succinogenes,
commonly
found
in
the
rumen,
was
shown
to
be
able
to

metabolize
both
cellulose
and
its
monomers
of
glucose
(Huang
and
Forsberg
1990).

The
genera
Butyrivibrio,
Bacteroides,
and
Clostridium
all
contain
some
species

that
ferment
cellulose
and
other
species
that
metabolize
phenolics.

Butyrivibrio

fibrisolvens
primarily
ferments
soluble
sugars,
but
several
strains
are
also

cellulolytic
(Baldwin
and
Allison
1983).

B.
sp.
C3
metabolizes
flavonoids,
including

rutin
(Krishnamurty
et
al.
1970).

Bacteroides
ruminicola
is
commonly
found
in
a
variety
of
ruminant
animals,

where
it
metabolizes
native
cellulose
(Russell
and
Wilson
1996).

B.
succinogenes
is

one
of
the
primary
cellulolytic
bacteria
in
the
rumen
and
is
specialized
at
breaking

down
crystalline
cellulose
(Baldwin
and
Allison
1983).

At
least
three
Bacteroides

species
are
known
to
metabolize
phenolics.

B.
distasonis,
B.
fragilis,
and
B.
ovatus
all

deglycosylate
lignan,
the
first
step
in
the
metabolism
of
this
phenolic
(Clavel
et
al.

2006).

Clostridium
termitidis
is
a
cellulolytic
bacterium
isolated
from
the
gut
of
a
wood-‐

157

feeding
termite
(Hethener
et
al.
1992).

This
genus
also
contains
C.
thermocellum,
a

cellulolytic
bacteria
found
in
the
rumen
(Mackie
and
White
1990)
and
in
soil
in

Yellowstone
National
Park
(Bayer
et
al.
1983).

C.
thermocellum
is
being
thoroughly

studied
due
to
the
potential
industrial
and
energy
applications
of
its
ability
to

convert
cellulose
to
ethanol
(Lamed
and
Zeikus
1980;
Ng
et
al.
1981).

Similar
to

Bacteroides
distasonis,
B.
fragilis,
and
B.
ovatus,
C.
cocleatum
deglycosylates
the

phenolic
lignan
(Clavel
et
al.
2006).

All
four
of
these
lignan-‐metabolizing
bacteria

are
found
in
the
healthy
human
gut.

Also
found
in
the
human
gut,
C.
orbiscindens
is

able
to
metabolize
flavonoids
by
cleaving
the
C-‐ring
bonds
of
quercetin
(Winter
et

al.
1989).

This
study
utilized
a
16S
rDNA
approach
in
order
to
maximize
the
number
of

microbiomes
compared
(to
both
increase
the
number
of
lemurs
and
the
number
of

time
points
included).

A
total
of
eight
measures
of
the
microbiomes
were
combined

to
provide
a
multifarious
complement
of
diagnostic
metrics
of
the
gut
microbiome.

Two
of
these
microbial
measures
looked
at
community-‐wide
patterns:
the
Shannon

diversity
index
and
the
F/B
ratio.

The
remaining
microbial
measures
were
the

abundances
of
six
microbial
genera
that
contain
species
known
to
metabolize

cellulose,
phenolics,
or
both
(Tables
5-‐1a
and
5-‐1b).

Hypotheses

When
comparing
the
gut
microbiomes
of
Lemur
catta
and
Propithecus
verreauxi,

I
hypothesized
that
lemur
species
would
have
a
strong
impact
on
the
microbial

community
composition
(as
revealed
in
Chapter
4).

Based
on
the
data
on

consumption
rates
of
cellulose
and
phenolics
(Chapter
3),
I
expected
the
significant

differences
in
phenolics
consumption
between
the
species
(Table
3-‐2a)
to
lead
to
a

strong
influence
of
phenolics
consumption
on
the
gut
microbiome.

In
the
phylum-‐
level
microbiome
analysis
there
was
a
much
greater
abundance
of
Fibrobacteres
in

P.
verreauxi
(Fig.
4-‐10),
which
leads
to
the
expectation
that
the
abundance
of
the

cellulolytic
genus
of
Fibrobacter
would
be
more
influenced
by
cellulose

consumption
in
P.
verreauxi
than
in
L.
catta.

I
hypothesized
that
the
community-‐wide
microbial
metrics
of
the
Shannon

158

diversity
index
and
the
F/B
ratio
would
remain
relatively
stable
seasonally
in
both

L.
catta
and
P.
verreauxi.

For
both
lemur
species
I
expected
that
the
F/B
ratio
would

be
higher
in
females,
similar
to
the
higher
percentage
of
Bacteroidetes
found
in

males
than
females
in
the
human
gut
(Mueller
et
al.
2006).

Conversely,
I
expected

the
overall
community
diversity
to
be
greater
in
females
than
in
males.

I
hypothesized
that
for
both
lemurs
as
the
consumption
of
cellulose
and

phenolics
increased,
the
Shannon
diversity
index
of
the
microbiome
would
decrease,

but
that
the
F/B
ratio
would
remain
relatively
stable.

An
increase
of
either
cellulose

or
phenolic
molecules
in
the
gut
would
cause
a
rise
in
the
abundance
of
those

microbes
that
metabolize
these
plant
compounds.

The
increase
of
these
microbes

would
then
cause
a
compensatory
decrease
in
other
microbes,
which
could
reduce

the
overall
community
diversity
within
the
gut.

I
hypothesized
that
the
abundances
of
the
microbes
that
metabolize
cellulose
or

phenolics
would
increase
as
the
amount
of
cellulose
or
phenolics
(respectively)

consumed
increased.

I
expected
this
relationship
to
hold
in
both
L.
catta
and
P.

verreauxi.

I
did
not
expect
these
groups
of
microbes
to
vary
by
sex
or
season.

METHODS:

Plant
Defense
Consumption

Feeding
observations
for
the
amount
of
each
food
consumed
and
laboratory

measurement
of
the
amount
of
cellulose
and
phenolics
in
each
food
were
used
to

determine
the
approximate
amount
of
these
plant
defense
chemicals
consumed
by

each
individual
lemur
at
the
time
of
fecal
sample
collection
(see
Methods
section
in

Chapter
3
for
more
details).

For
each
lemur
in
each
season,
the
consumption
of

cellulose
and
phenolics
was
calculated
using
data
from
the
same
time
period
that

the
fecal
sample
was
collected.

Microbiome
Composition

The
fecal
microbial
data
was
collected
and
analyzed
using
the
methods
listed
in

Chapter
4.

The
Shannon
diversity
index
was
calculated
for
each
fecal
microbiome

159

based
on
the
presence
and
abundance
of
each
OTU
within
that
sample.

The

Firmicutes
to
Bacteroidetes
(F/B)
ratio
was
calculated
by
dividing
the
percentage
of

OTUs
assigned
to
the
Firmicutes
phylum
by
the
percentage
of
OTUs
assigned
to
the

Bacteroidetes
phylum.

The
microbiomes
were
surveyed
for
the
presence
of
microbial
genera
containing

species
that
metabolize
either
cellulose
or
phenolics

(see
Tables
5-‐1a
and
5-‐1b
for

the
full
list
of
target
species).

This
survey
resulted
in
six
genera
with
members

present
in
at
least
one
microbiome
sample:
Bacteroides,
Butyrivibrio,
Clostridium,

Fibrobacter,
Ruminococcus,
and
Streptococcus.

Due
to
the
OTU
classification
method

for
the
16S
rRNA
sequences,
OTUs
could
only
be
classified
down
to
the
genus
level.

Modeling
the
Association
Between
Microbiome
and
Lemur
and
Environment

Markov
chain
Monte
Carlo
Generalized
Linear
Mixed
Models
(MCMCglmm)
can

be
used
to
determine
which
environmental
variables
best
explain
the
variance
in
a

community
(Hadfield
2010).

The
MCMCglmm
package
was
used
in
the
R
statistical
environment
(R
Core

Development
Team
2010)
to
separately
model
eight
microbial
metrics
as
the

independent
microbial
variable:
the
Shannon
diversity
index
values,
the
F/B
ratio

values,
and
the
abundances
of
the
genera
Bacteroides,
Butyrivibrio,
Clostridium,

Fibrobacter,
Ruminococcus,
and
Streptococcus
for
each
microbiome
sample.

MCMCglmm
used
a
combination
of
random
and
fixed
effects
to
explain
the
variance

in
the
microbiome
data.

For
random
effects,
there
is
the
assumption
that
the

independent
microbial
variable
is
uncorrelated
to
each
random
effect.

By
treating

variables
as
random
effects,
the
model
controls
for
unobserved
heterogeneity
in

these
variables.

For
fixed
effects,
there
is
the
assumption
that
the
independent

microbial
variable
is
correlated
to
these
effects.

To
determine
the
best
model
with
MCMCglmm,
first
the
null
model
was
tested.

The
null
model
contains
only
the
random
effect
(individual)
and
no
fixed
effects.

The
output
from
each
model
is
a
deviance
information
criterion
(DIC)
value.

The

DIC
is
a
measure
used
to
compare
models
and
combines
measures
of
the
fit
and
the

complexity
of
a
model(Spiegelhalter
et
al.
2002).

Using
this
method,
a
higher
degree

160

of
fit
and
a
lower
complexity
lead
to
a
lower
DIC
value.

DIC
values
can
be
either

positive
or
negative,
depending
on
the
mechanics
of
the
model,
and
in
either
case
a

model
with
a
lower
DIC
value
is
interpreted
to
better
explain
the
variance
in
the

data
than
an
alternative
model
with
a
higher
DIC
value
(Zuur
et
al.
2009).

For
each

fixed
effect
(or
each
class
if
the
fixed
effect
is
categorical,
e.g.
male
and
female
for
the

fixed
effect
of
sex),
the
model
provides
the
posterior
mean
(β),
the
upper
(u-‐CI)
and

lower
(l-‐CI)
95%
confidence
intervals,
and
the
p-‐value
(pMCMC).

The
posterior

mean
is
the
mean
of
the
posterior
distribution
and
the
95%
confidence
intervals
are

the
range
that
would
contain
95%
of
the
population
parameters
if
the
experiment

was
to
be
repeated
many
times
(Zuur
et
al.
2009).

The
default
prior
was
used
for
all
models.

Each
model
was
tested
for

autocorrelation
between
successive
values
and
optimized
until
any
autocorrelation

was
reduced
to
less
than
0.1
(Hadfield
2009).

To
achieve
this
low
level
of

autocorrelation,
most
models
used
the
same
parameters.

In
several
models,
the

data
had
a
large
amount
of
autocorrelation,
so
the
model
parameters
were

expanded
until
the
autocorrelation
was
reduced
to
below
0.1
(Table
5-‐2).

The

parameters
for
each
model
resulted
in
at
least
1300
independent
samples
from
the

posterior,
well
above
the
minimum
of
1000
independent
samples
suggested
by
the

developer
of
MCMCglmm
(Hadfield
2014).

Individual
(Focal
ID)
and
group
were
treated
as
nested
random
effects,
with
the

assumption
that
the
independent
microbial
variable
is
uncorrelated
to
these

random
effects.

This
assumption
is
supported
by
the
lack
of
a
clear
influence
of

individual
or
group
on
the
microbiome
composition
(see
Figs.
4-‐7
and
4-‐11).

The

fixed
effects,
where
the
assumption
is
that
the
independent
microbial
variable
is

correlated
to
these
effects,
were
the
sex
of
the
lemur,
the
season,
the
amount
of

cellulose
consumed,
and
the
amount
of
polyphenol
consumed.

The
data
were
split
into
separate
analyses
for
each
lemur
species
after
an
initial

test
with
the
combined
data
of
both
species.

In
the
combined
analysis,
the
model

was
optimized
with
species
as
an
additional
fixed
effect.

With
the
Lemur
catta
and

Propithecus
verreauxi
data
pooled,
the
best
model
included
species
as
a
fixed
effect

(Table
5-‐3).

Species
had
an
influence
on
the
Shannon
diversity
index,
with
both
L.

161

catta
(β
=
0.906,
l-‐CI
=
0.599,
u-‐CI
=
1.226,
pMCMC
<
0.01)
and
P.
verreauxi
(β
=

1.835,
l-‐CI
=
1.511,
u-‐CI
=
2.172,
pMCMC
<
0.01)
associated
with
a
significant

increase
in
microbial
diversity.

This
finding
of
the
large
effect
of
species
matches

expectations
as
prior
analyses
(Figs.
4-‐8
and
4-‐9)
found
consistent
and
large

differences
between
the
gut
microbiomes
of
L.
catta
and
P.
verreauxi.

While
there
is

technically
no
problem
with
keeping
species
in
the
model,
the
strong
effect
of
lemur

species
in
this
model
masks
the
influence
of
the
other
fixed
effects
on
the
variance
in

the
data.

A
primary
goal
of
this
research
was
to
elucidate
the
effect
of
diet
on
the
gut

microbiome,
so
clarity
into
the
influence
of
the
cellulose
and
phenolic
consumption

fixed
effects
was
paramount.

To
better
understand
the
role
of
these
dietary
effects,

the
large
influence
of
lemur
species
was
removed
and
the
data
were
separated
into

two
separate
analyses,
one
for
each
lemur
species.

Next,
the
full
model,
with
all
fixed
effects
was
run,
yielding
its
own
DIC
value.

This
full
model
was
then
incrementally
reduced,
removing
one
fixed
effect
at
a
time

(Table
5-‐4).

With
all
fixed
effects
less
one,
the
model
with
the
lowest
DIC
was
kept

and
further
reduced
by
one
fixed
effect.

The
model
lacking
two
fixed
effects
with
the

lowest
DIC
value
was
kept,
and
so
on,
removing
fixed
effects
and
keeping
the
best

model,
until
only
one
fixed
effect
remained.

The
overall
model
with
the
lowest
DIC

value
is
the
best
model,
with
the
fixed
effects
remaining
in
that
model
best

explaining
the
variance
in
the
independent
microbial
variable.

If
the
null
model
has

the
lowest
overall
DIC,
then
the
fixed
effects
do
not
help
to
explain
the
variance
in

the
microbial
data.

RESULTS:

Shannon
Diversity
Index
for
Lemur
catta

The
best
model
to
explain
the
variance
in
the
microbial
Shannon
diversity
index

values
for
L.
catta
included
sex,
season,
and
cellulose
consumption
as
fixed
effects

(Table
5-‐5).

Seasonality
had
an
influence
on
the
Shannon
diversity
index,
with
both

the
dry
season
and
the
early
wet
season
associated
with
a
significant
increase
in

microbial
diversity
(Table
5-‐6).

The
microbial
diversity
also
varied
by
sex,
with

162

female
lemurs
associated
with
a
significant
increase
in
diversity.

There
was
a

negative
relationship
between
cellulose
consumption
and
Shannon
diversity,
though

this
was
not
a
significant
association.

Firmicutes/Bacteroidetes
Ratio
for
L.
catta

The
best
model
to
explain
the
variance
in
the
microbial
Firmicutes/Bacteroidetes

ratio
for
L.
catta
included
sex
and
phenolics
consumption
as
fixed
effects
(Table
5-‐
7).

Sex
had
a
strong
influence,
with
female
lemurs
associated
with
a
significant

increase
in
the
F/B
ratio
(Table
5-‐8).

There
was
a
negative
relationship
between

phenolics
consumption
and
the
F/B
ratio,
though
this
was
not
a
significant

association.

Shannon
Diversity
Index
for
Propithecus
verreauxi

The
best
model
to
explain
the
variance
in
the
microbial
Shannon
diversity
index

values
for
P.
verreauxi
included
sex
as
a
fixed
effect
(Table
5-‐9).

Sex
had
a
strong

influence
on
the
Shannon
diversity
index,
with
female
lemurs
associated
with
a

significant
increase
in
microbial
diversity
(Table
5-‐10).

Firmicutes/Bacteroidetes
Ratio
for
P.
verreauxi

The
best
model
to
explain
the
variance
in
the
microbial
Firmicutes/Bacteroidetes

ratio
for
P.
verreauxi
included
no
fixed
effects
(Table
5-‐11).

This
suggests
that
sex,

season,
cellulose
consumption,
and
phenolics
consumption
did
not
help
to
explain

the
variance
seen
in
the
F/B
ratio
of
P.
verreauxi.

A
summary
of
the
fixed
effects
in
the
best
model
for
both
community-‐wide

microbial
metrics
for
L.
catta
and
P.
verreauxi
can
be
found
in
Table
5-‐12.

Cellulose-‐
and
Phenolics-‐Metabolizing
Microbes

Bacterial
genera
with
members
known
to
metabolize
cellulose
were
present
in

both
L.
catta
and
P.
verreauxi.

The
most
abundant
of
these
genera
differed
between

the
lemur
species,
with
L.
catta
having
an
abundance
of
Ruminococcus
spp.
and
P.

verreauxi
instead
having
Fibrobacter
spp.
(Fig.
5-‐1).

During
the
late
wet
season,

163

both
L.
catta
and
P.
verreauxi
had
increased
percentages
of
Bacteroides
spp.,
which

includes
several
species
that
metabolize
cellulose
and
others
that
metabolize

phenolics
(Baldwin
and
Allison
1983;
Xu
et
al.
2003;
Clavel
et
al.
2006).

The

abundance
of
the
Streptococcus
genus,
with
phenolics-‐metabolizing
members,
was

higher
in
P.
verreauxi
than
in
L.
catta.

The
abundance
of
Streptococcus
spp.
in
P.

verreauxi
increased
noticeably
from
the
dry
season
to
the
late
wet
season.

Cellulose-‐
and
Phenolics-‐Metabolizing
Microbial
Abundances
for
L.
catta

The
best
model
to
explain
the
variance
in
the
percent
abundance
of
the

Bacteroides
genus
for
L.
catta
included
cellulose
consumption
as
a
fixed
effect
(Table

5-‐13).

There
was
a
significant
negative
relationship
between
cellulose
consumption

and
Bacteroides
abundance
(Table
5-‐14).

For
the
Butyrivibrio
genus
in
L.
catta,
the
best
model
to
explain
the
variance
in

abundance
included
sex
as
a
fixed
effect
(Table
5-‐13).

Sex
had
a
strong
influence
on

the
Butyrivibrio
abundance,
with
female
lemurs
associated
with
a
significant

increase
in
abundance
(Table
5-‐15).

The
best
model
to
explain
the
variance
in
the
percent
abundance
of
the

Clostridium
genus
for
L.
catta
included
no
fixed
effects
(Table
5-‐13).

This
suggests

that
sex,
season,
cellulose
consumption,
and
phenolics
consumption
did
not
help
to

explain
the
variance
seen
in
the
Clostridium
abundance
in
L.
catta.

Sex,
season,
and
phenolics
consumption
were
the
fixed
effects
in
the
best
model

to
explain
the
variance
in
the
percent
abundance
of
the
Fibrobacter
genus
for
L.

catta
(Table
5-‐13).

The
sex
of
the
animal
had
a
strong
influence
on
the
Fibrobacter

abundance,
with
female
lemurs
associated
with
a
significant
increase
in
abundance

(Table
5-‐16).

Seasonality
had
an
influence
on
the
Fibrobacter
abundance,
with
the

dry
season
associated
with
a
significant
increase
in
abundance
and
the
early
and

late
wet
season
associated
with
a
decrease
in
abundance.

There
was
a
negative

relationship
between
phenolics
consumption
and
Fibrobacter
abundance,
though

this
was
not
a
significant
association.

The
best
model
to
explain
the
variance
in
the
percent
abundance
of
the

Ruminococcus
genus
for
L.
catta
included
season
as
a
fixed
effect
(Table
5-‐13).

164

Seasonality
had
an
influence
on
the
Ruminococcus
abundance,
with
the
early
wet

season
associated
with
a
significant
increase
in
abundance
(Table
5-‐17).

The
variance
in
the
percent
abundance
of
the
Streptococcus
genus
for
L.
catta

was
best
explained
by
the
model
that
included
sex,
cellulose
consumption,
and

phenolics
consumption
as
fixed
effects
(Table
5-‐13).

Sex
had
a
slight
influence
on

the
Streptococcus
abundance
(Table
5-‐18).

There
was
a
positive
relationship

between
female
lemurs
and
an
increase
in
Streptococcus
abundance,
though
this

was
not
quite
a
significant
association.

Male
lemurs
had
the
opposite
trend,
with
a

nearly
significant
association
with
a
decrease
in
Streptococcus
abundance.

There

was
a
negative
relationship
between
cellulose
consumption
and
Streptococcus

abundance,
though
this
was
not
a
significant
association.

There
was
a
significant

positive
relationship
between
phenolics
consumption
and
Streptococcus
abundance.

Cellulose-‐
and
Phenolics-‐Metabolizing
Microbial
Abundances
for
P.
verreauxi

The
best
model
to
explain
the
variance
in
the
percent
abundance
of
the

Bacteroides
genus
for
P.
verreauxi
included
season
as
a
fixed
effect
(Table
5-‐13).

Seasonality
had
an
influence
on
the
Bacteroides
abundance,
with
the
late
wet
season

associated
with
a
significant
increase
in
abundance
(Table
5-‐19).

For
the
Butyrivibrio
genus
in
P.
verreauxi,
the
best
model
to
explain
the
variance

in
abundance
included
no
fixed
effects
(Table
5-‐13).

This
suggests
that
sex,
season,

cellulose
consumption,
and
phenolics
consumption
did
not
help
to
explain
the

variance
seen
in
the
Butyrivibrio
abundance
in
P.
verreauxi.

There
were
no
members
of
the
Clostridium
genus
detected
in
any
of
the
P.

verreauxi
gut
microbiomes,
so
there
was
no
variance
to
associate
with
fixed
effects

in
a
modeling
approach.

Cellulose
consumption
and
phenolics
consumption
were
the
fixed
effects
in
the

best
model
to
explain
the
variance
in
the
percent
abundance
of
the
Fibrobacter

genus
for
P.
verreauxi
(Table
5-‐13).

There
was
a
significant
positive
relationship

between
cellulose
consumption
and
Fibrobacter
abundance
(Table
5-‐20).

There

was
a
significant
negative
relationship
between
phenolics
consumption
and

Fibrobacter
abundance.

165

The
best
model
to
explain
the
variance
in
the
percent
abundance
of
the

Ruminococcus
genus
for
P.
verreauxi
included
season
as
a
fixed
effect
(Table
5-‐13).

Seasonality
had
an
influence
on
the
Ruminococcus
abundance,
with
the
late
wet

season
associated
with
a
significant
increase
in
abundance
(Table
5-‐21).

The
variance
in
the
percent
abundance
of
the
Streptococcus
genus
for
P.

verreauxi
was
best
explained
by
the
model
that
included
season
as
a
fixed
effect

(Table
5-‐13).

Seasonality
had
an
influence
on
the
Streptococcus
abundance,
with
the

late
wet
season
associated
with
a
significant
increase
in
abundance
(Table
5-‐22).

DISCUSSION:

Microbiome
Community-‐Level
Variability

Using
gut
microbiomes
from
both
Lemur
catta
and
Propithecus
verreauxi
in
the

same
analysis
of
community
diversity,
revealed
a
strong
influence
of
lemur
species

on
the
microbial
composition.

This
matched
expectations
as
the
QIIME
analysis
in

Chapter
4
found
a
similar
species
distinction
in
the
gut
microbiome.

In
addition
to

species,
sex
had
an
impact
on
the
microbiome
and
sex
continued
to
have
an
effect

when
the
lemur
species
were
analyzed
separately.

The
factors
affecting
the
gut
microbial
communities
were
different
between
L.

catta
and
P.
verreauxi
when
using
both
the
Shannon
diversity
index
and
the
F/B

ratio
as
metrics
of
the
community
composition.

The
variation
in
these
community-‐
level
metrics
modeled
to
different
fixed
effects.

The
community
composition
was

stably
different
between
the
lemur
species,
with
the
L.
catta
microbiomes
clustering

together
and
the
P.
verreauxi
microbiomes
grouped
into
a
separate
cluster
from
the

L.
catta
samples
(Fig.
4-‐8).

Within
each
of
these
clusters,
there
was
further
variation

among
the
gut
microbiome
samples
from
each
lemur
species.

The
MCMCglmm
revealed
the
source
of
this
variation
within
each
lemur
species.

In
both
L.
catta
and
P.
verreauxi,
the
sex
of
the
lemur
had
a
strong
impact
on
the

Shannon
diversity
of
their
gut
microbiome.

The
female
lemurs
in
both
species
had
a

significantly
more
diverse
microbiota
than
the
males.

This
result
was
similar
to
a

study
of
the
skin
microbiome
on
the
human
palm,
where
females
had
a
greater

166

microbial
diversity
(Fierer
et
al.
2008).

It
is
unclear
why
there
is
more
microbial

diversity
in
females.

If
this
was
a
unique
finding
among
the
lemurs
in
this
study,

then
the
difference
could
be
due
to
the
female
dominant
social
structure
among
L.

catta
and
P.
verreauxi
(Taylor
and
Sussman
1985).

Since
humans
have
shown
a

similar
pattern
in
microbiome
diversity
between
the
sexes,
it
is
possible
that
there
is

another
sex-‐specific
cause
for
this
difference.

On
a
methodological
note,
the
sample

size
of
microbiomes
in
male
L.
catta
was
only
6,
half
of
that
of
the
female
L.
catta.

It

is
worth
noting
that
a
larger
sample
size
may
yield
a
more
statistically
reliable
test

of
sex-‐based
differences
in
this
species.

Seasonal
diff