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THE SPATIAL DISTRIBUTION OF DAPHNIA RESOURCES IN
SHALLOW LAKES: IS FOOD FOR ZOOPLANKTON DETERMINED
BY MACROPHTYES?

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Joshua Booker

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THE SPATIAL DISTRIBUTION OF DAPHNIA RESOURCES IN SHALLOW
LAKES: IS FOOD FOR ZOOPLANKTON DETERMINED BY MACROPHTYES?

By

Joshua Booker

A THESIS
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
MASTER OF SCIENCE
Fisheries and Wildlife

2010

ABSTRACT

THE SPATIAL DISTRIBUTION OF DAPHNIA RESOURCES IN SHALLOW
LAKES: IS FOOD FOR ZOOPLANKTON DETERMINED BY MACROPHYTES?

By

Joshua Booker

Zooplankton species in shallow lakes are known to segregate themselves based on
macrophyte density and move between the nearshore and offshore habitats. However, the
distribution of their resources are unclear. A common assumption in limnology is that
food quantity and quality for pelagic zooplankton is poor in the littoral zone, due to the
deleterious influence of macrophytes on phytoplankton. I tested this assumption with a
combination of a field survey and lab experiments. I collected lake seston samples from
the littoral and pelagic zones within four shallow Michigan lakes, and related seston
quality to macrophyte abundance. Lake seston was analyzed for food quality using three
different measures: stoichiometric ON and C:P ratios, polyunsaturated fatty acid
content, and the community composition of the phytoplankton community. In the lab, I
also fed seston from both habitats to Daphnia pulicaria in somatic growth and
reproduction experiments. There was heterogeneity in food quality within lakes, but this
heterogeneity usually did not correspond to macrophyte abundance. In addition, the lab
experiment showed higher D. pulicaria growth when fed littoral resources as compared
to pelagic resources. These results suggest that there is no nutritional cost to pelagic
zooplankton in the littoral zone, and that other factors are involved in determining

zooplankton habitat use.

ACKNOWLEDGEMENTS

Financial support was provided by the US. EPA STAR Fellowship Program (F P-
91695501-1), Gates Millennium Scholars Program, Michigan State University College of
Agriculture and Natural Resources, and MSU Center for Water Sciences.

I would like to thank Eric Moellering and the Benning Lab for helping with the
fatty acid analysis. Numerous people at Kellogg Biological Station contributed their time
and advice to my project, including Pam Woodruff, Steve Hamilton, and Stacey
VanderWulp. Alex Migda, Katie Droscha, Emily Norton, Emi Fergus, Emily Jacobson,
and Emily Chavez all provided me with field and/or lab assistance, and for that I will
always be grateful. I also could not have produced a quality thesis without the support
and feedback of the Limnology Lab, all the way from the early stages of finding a topic
to the late stages of choosing a defense title. Thank you to Stacie Auvenshine, Dianna
Dziekan, Kevin Pangle, Geoff Horst, Jeff White, Patricia Soranno, Kim Peters, Mary
Bremigan, Brett Alger, Tom Alwin, Scott Peacor, and the rest of the lab for all of the
helpful proposal revisions, practice presentations, and general advice. I also want to
thank my committee members Elena Litchman and Ace Samelle for their useful
comments and sharing of lab space/supplies throughout the project. Finally, much
appreciation goes to my advisor Kendra Cheruvelil, who took a chance on a naive young

man and a project peripheral to her own expertise.

iii

TABLE OF CONTENTS

List of Tables .......................................................................................... v
List of Figures ........................................................................................ vi
Chapter 1: The spatial distribution of Daphnia resources in shallow lakes: Is food for
zooplankton determined by aquatic plants? ....................................................... 1
Introduction .................................................................................. 1
Methods ...................................................................................... 5
Study Lakes ......................................................................... 5
Lake Field Survey .................................................................. 6
Quantifying Lake Conditions ............................................ 6
Quantifying Zooplankton Food Quality ................................ 8
Quantifying Zooplankton Communities .............................. 10
Daphnia Lab Experiments ....................................................... 10
Growth Experiment ...................................................... 10
Reproduction Experiment ............................................... 11
Statistical Analysis ................................................................ 12
Results ......................................................................................... 1 3
Lake Field Survey ................................................................. 13
Macrophytes ............................................................... 1 3
Zooplankton Food Quality and Quantity .............................. 14
Zooplankton ............................................................... l7
Daphnia Lab Experiments ....................................................... 17
Discussion ................................................................................... 19
Conclusions .................................................................................. 26
Appendix: Tables and Figures .................................................................... 29
Literature Cited ....................................................................................... 65

iv

LIST OF TABLES

Table 1: Study lake summary. Latitude and longitude use the NAD83 coordinate
system ................................................................................................. 29

Table 2: Competitive macrophyte table. The following macrophyte species have been
documented to have a negative impact on phytoplankton biomass either through
allelopathy, nutrient uptake, shading, or sediment resuspension .............................. 30

Table 3: Relative macrophyte species frequencies by lake. We calculated the fi'equency
of macrophyte species within each lake with the following formula: (sampling points
with a species / total sampling points) X 100. These frequencies were then converted to
relative frequencies by dividing by the sum of frequencies for all species (Nichols, Weber
and Shaw, 2000). Bolded species are “competitive macrophytes” (see Table 2). Species
with an E superscript are exotic invaders .......................................................... 31

Table 4: Correlation tables by lake for temperature, water depth, macrophyte abundance
(PVI), and dissolved oxygen (DO). Values above 0.65 are bolded .......................... 32

Table 5.1: Coefficients of linear regressions for food quality variables and PVI by lake.
Significant slopes are bolded (p<0.05). Muskrat Lake is not represented because all
phytoplankton taxa samples from Muskrat Lake were preserved incorrectly and damaged
beyond recovery ..................................................................................... 33

Table 5.2: Summary of linear regressions for food quality variables and PVI, with lake
as a covariate. Significant p-values are bolded (p<0.05). DF = degrees of
freedom ............................................................................................... 36

Table 6.1: Coefficients of linear regressions for food quality variables and CMI by lake.
Significant slopes are bolded (p<0.05). Muskrat Lake is not represented because all
phytoplankton taxa samples from Muskrat Lake were preserved incorrectly and damaged
beyond recovery ..................................................................................... 38

Table 6.2: Summary of linear regressions for food quality variables and CMI, with lake
as a covariate. Significant p-values are bolded (p<0.05). DF = Degrees of
freedom ............................................................................................... 41

Table 7: Unconditional mixed models for food quality variables grouped by lake. . . . . . ...43

LIST OF FIGURES

Figure 1.1: Comparison of mean surface temperature and mean surface dissolved oxygen
by lake with standard error bars. All comparisons with surface temperature were
significantly different using Bonferoni corrections (d=0.05). Only Dagget Lake had a
significantly different mean DO from the other three lakes (p<0.05) ........................ 44

Figure 1.2: Daily maximum air temperatures from the LTER site at Kellogg Biological
Station (Hickory Comers, MI). Sampling dates for our four study lakes are noted with
arrows. P=Potter Lake, M=Muskrat Lake, H=Hall Lake, D=Dagget Lake ................ 45

Figure 2: Scatterplots of C:N and C:P by macrophyte PVI (percent volume infested) with
fitted linear regression lines. C:N and C:P ratios are atomic. All lines are insignificant,
except the C:P in Hall Lake (p = 0.02) ............................................................ 46

Figure 3: Scatterplots of C:N and C:P by Competitive Macrophyte Index with fitted
linear regression lines. All lines are insignificant (p > 0.05) ................................... 47

Figure 4: Scatterplots of PUFA, C20:5n-3, C1823n-3, and C20:4n—6 by PVI with fitted
linear regression lines. All lines are insignificant (p > 0.05) .................................. 48

Figure 5: Scatterplots of PUFA, C20:5n—3, C1823n-3, and C2014n-6 by Competitive
Macrophyte Index with fitted linear regression lines. All lines are insignificant (p >
0.05) ................................................................................................... 50

Figure 6: Relative biovolume of phytoplankton divisions, grouped by lake. Means :t
standard error bars are shown. Muskrat Lake is not represented because all
phytoplankton taxa samples from Muskrat Lake were preserved incorrectly and damaged
beyond recovery. Phytoplankton taxonomic groups are presented in the same order from
left to right as the legend is ordered from top to bottom ....................................... 52

Figure 7: Scatterplots of phytoplankton divisions by PVI (% volume infested) with fitted
linear regression lines. All lines are insignificant, except the cyanobacteria biomass in
Potter Lake (p = 0.01) and Hall Lake (p = 0.04), and the low quality chlorophyte biomass
in Hall Lake (p = 0.02). Muskrat Lake is not represented because all phytoplankton taxa
samples from Muskrat Lake were preserved incorrectly and damaged beyond
recovery......... ............................................................................................................... .53

vi

Figure 8: Scatterplots of phytoplankton divisions by Competitive Macrophyte Index with
fitted linear regression lines. All lines are insignificant, except the diatom biomass in
Dagget Lake (p < 0.01) and Hall Lake (p = 0.05). Muskrat Lake is not represented
because all phytoplankton taxa samples from Muskrat Lake were preserved incorrectly
and damaged beyond recovery ..................................................................... 56

Figure 9: Mean carbon concentrations by lake, with standard error bars. Carbon
concentrations were obtained from 35 um filtered lake seston. All comparisons were
significantly different using Bonferoni corrections (p < 0.05) ................................ 59

Figure 10: Cladoceran abundance by lake and littoral/pelagic zone. Cladoceran
taxonomic groups are presented in the same order from left to right as the legend is
ordered from top to bottom ......................................................................... 60

Figure 11: Mean lengths of cladocerans by lake. Missing lengths indicate that the
zooplankton genus was not detected in that lake. A single Daphnia was found in Dagget
and Potter Lake samples, and a single member of the Macrothricidae family was found in
Hall and Potter Lakes ................................................................................ 61

Figure 12: Boxplots of lab growth and reproduction experimental results. Treatments
with the same letter are not significantly different from each other using Bonferoni-
corrections (p < 0.05). Error bars on mean chlorophyll a graph represent standard
deviation .............................................................................................. 62

vii

Introduction

Limnologists have known for some time that macrophytes have negative effects
on phytoplankton. Hasler and Jones (1949) were among the first to demonstrate this
interaction by conducting experiments that showed the decline of cyanobacteria biomass
under high densities of Potamogetonfolisus and Elodea canadensis. Today, it is
generally accepted that there is a negative relationship between phytoplankton biomass
and submerged macrophyte density due to three main factors. First, it is well-known that
macrophytes have the ability to outcompete phytoplankton by reducing nutrient
(Carpenter and Lodge, 1986) and light availability (Mulderij, Mauvan, van Donk et al. ,
2007). Second, many studies have shown that allelopathic chemicals exuded by Chara
and other macrophytes can directly reduce grth and biomass of phytoplankton (Gross,
Hilt, Lombardo et al. , 2007). Third, we know that the physical structure of submerged
macrophytes such as Ceratophyllum demersum reduces water movement and leads to the
net sinking of non-buoyant phytoplankton and eventual burial of phytoplankton cells in
the sediments (Horppila and Nurminen, 2005). Competition between phytoplankton and
macrophytes is especially evident in shallow lakes, as these systems are dominated by
either one or the other (Scheffer, Hosper, Meijer et al., 1993). It is less well documented
what this competition between primary producers means for primary consumers that
graze on phytoplankton, such as filter-feeding zooplankton.

Macrophyte density changes from nearshore to offshore in many shallow lakes,
forming different habitats for zooplankton. Zooplankton species of the order Cladocera
typically segregate themselves based on this horizontal vegetative gradient (Smiley and

Tessier, 1998), but the mechanisms behind that pattern are not well studied. Although we

know that many habitually pelagic cladocerans such as Daphnia migrate horizontally in
and out of macrophyte-dense littoral areas to avoid predators (Timms and Moss, 1984),
we know less about the distribution of food resources along that gradient and how food
influences zooplankton habitat selection. Horizontal migration behavior exposes
daphniids to two distinct habitats, with potentially very different food resources that
could impact population dynamics. Based on the known antagonistic relationship
between macrophytes and phytoplankton, conventional wisdom has been that food
quantity for daphniids within the vegetated littoral zone is poor (Burks, Lodge, Jeppesen
et al., 2002). This hypothesis appears to explain the distribution of Daphnia within
shallow lakes (higher densities in the pelagic zone) and shed light on the costs of
horizontal migration behavior, but no studies have tested this hypothesis.

Food quality is probably even more important to Daphnia fecundity and growth
rates than food quantity (Sterner, 1993, Kilham, Kreeger, Goulden et al. , 1997), but it
remains unclear whether macrophytes alter the nutritional quality of phytoplankton as
food for daphniids. Very few studies have directly tested the effect of macrophytes on
phytoplankton quality, but the results gathered so far suggest that food quality for
Daphnia is poor in the presence of macrophytes. Lab experiments have shown that
macrophyte exudates can directly increase phytoplankton cell volume and colony
formation in Scenedesmus obliquus (Mulderij, Mooij and Van Donk, 2005), rendering the
cells less edible for Daphnia. In field observations of single European lakes, Vuille
(1991) and Sendergaard and Moss (1998) observed a higher proportion of large and
inedible algae in the presence of macrophytes compared to their absence. In addition,

Smiley and Tessier (1998) used a reciprocal transplant experiment with pelagic and

littoral zooplankton species in one North American shallow lake to show that littoral
resources were of poor quality for pelagic herbivores. These studies provide some
evidence supporting the hypothesis that macrophytes may lower phytoplankton quality,
but research to date has been restricted to the lab or a single lake. Therefore, we are
unsure how the results will extrapolate to additional shallow lakes. More recent studies
on zooplankton food quality have focused on examining differences between lakes, both
tropical (Ferrao, Demott and Tessier, 2005) and temperate (Tessier and Woodruff, 2002b,
Dobberfuhl and Elser, 2000). Yet these studies generalize results based on strictly
pelagic samples, not taking into account possible within-lake heterogeneity due to
macrophytes in the littoral zone. Hence, although the literature suggests that
macrophytes may have a large impact on Daphnia resource quality through their
competition with phytoplankton and their structuring role of the aquatic environment, a
clear link between macrophytes, phytoplankton, and Daphnia nutrition has not yet been
made.

In order to determine how macrophytes affect Daphnia nutrition, we must first
examine the different methods previously used to measure food quality. The elemental
content of phytoplankton, specifically the stoichiometric ratio of carbon (C) to nitrogen
(N) and phosphorus (P), is the most widespread measurement. In many limnological
studies, high food quality is often equated with low C:P or low C:N of lake seston (e. g.,
Sterner, 1993, Elser, Hayakawa and Urabe, 2001, Kilham et al., 1997). More recently,
researchers have put a biochemical focus on resources for zooplankton. For example, the
amount of polyunsaturated fatty acids (PUFAs) in algae has been shown to have

significant impacts on zooplankton growth (Getseit et al. 2007) and egg production

(Muller-Navarra, Brett, Liston et al., 2000). In particular, d—linolenic acid (C18z3n-3) is
strongly correlated with Daphnia growth (Wacker and von Elert, 2001), and
eicosapentaenoic acid (C20:5n-3) and arachidonic acid (C2024n-6) are correlated with
egg production (Martin-Creuzburg and von Elert, 2009, Muller-Navarra et al. , 2000).
However, no studies have looked for differences in phytoplankton stoichiometry or fatty
acids between the pelagic and littoral zones (Burks, Lodge, Jeppesen et al., 2002).

A third commonly used way to categorize Daphnia food quality is through
enumeration of phytoplankton taxa (DeMott and Gulati, 1999). In fact, Ravet and Brett
(2006) found that Daphnia food quality was largely determined by phytoplankton species
composition. This result makes sense because a given phytoplankton taxon encompasses
the biochemical properties described above, as well as important digestibility traits (e.g.,
cell wall thickness, the presence of spines or mucilage). In terms of general
phytoplankton divisions, limnologists consider cyanobacteria poor quality food for
Daphnia, and diatoms and cryptophytes are considered high food quality (Brett, Muller-
Navarra and Park, 2000). Although chlorophytes are described as an adequate food
source (Brett, Muller-Navarra and Park, 2000), certain taxa have been shown to be poorly
digested by Daphnia due to their cell structure [Sphaerocystis in a study by Porter (1976),
and Cosmarium in a study by Coesel (1997)]. Due to the variability in the quality of
phytoplankton species within the generalized divisions, measuring the phytoplankton
elemental stoichiometry and PUFAs in conjunction with the species community
composition may be necessary to fully understand Daphnia food quality.

The aim of our study was to test the hypothesis that Daphnia food quantity and

quality is negatively influenced by macrophyte density. We investigated the spatial

distribution of Daphnia food quantity and quality by sampling the littoral and pelagic
seston of four shallow lakes. Daphnia food quality was quantified multiple ways:
elemental content (C, N, P), fatty acid content, phytoplankton taxa, and non-algal
biomass. We also measured food quality directly by conducting Daphnia growth and
reproduction experiments with lake seston in the lab. Results of such experiments can
more easily elucidate the ultimate nutritional value of a resource for Daphnia, which is
difficult to determine in situ due to the complexity of the interacting parameters involved.
We predicted that: l) lake seston samples would show a negative relationship between
macrophyte abundance and food quantity (measured as total phytoplankton cell
biovolume), and high food quality parameters (total polyunsaturated fatty acids, C20:5n-
3, C18z3n-3, C20z4n-6, diatom biovolume, cryptophyte biovolume, naked Chlorophyta
biovolume), 2) lake seston samples would demonstrate a positive relationship between
macrophyte abundance and low food quality parameters (C:P, C:N, cyanobacteria
biomass, and poorly digested Chlorophyta), and 3) experimental lab results would
support field findings by showing higher Daphnia growth and reproduction when feeding

on pelagic resources as compared to resources from macrophyte-dense littoral areas.

Methods

Study Lakes

Four shallow lakes in south central Michigan, USA were sampled for lake seston

during June 2009. These lakes were chosen based on maximum depth (less than 5

meters), thermal stratification (not consistently stratified throughout the summer based on
summer 2008 observations), and management (no herbicide or algaecide applications
within the past five years). Each lake also had undeveloped and forested shoreline, was
well connected to streams, and was located in Michigan State Game Areas. The lakes
varied in size from 7.2 - 23.1 hectares (Table 1). All lakes had fish present, but detailed
fish community composition information was only available for Hall Lake via a
Michigan Department of Natural Resources Status of the Fishery Resource Report
(Dexter Jr., 1996).

L_a_ke Field Survey

Quantifizing Lake Conditions

Secchi depth was taken at the deepest point in each lake and within each lake, 14-
18 sample sites were determined using a stratified random sampling protocol. The strata
consisted of a littoral zone, defined as the near-shore area of the lake where the depth was
less than 1.5 meters or macrophytes were visible from the surface, and a pelagic zone,
defined as the open-water area of the lake where depth was greater than 1.5 meters or
macrophytes were not visible. At each sample point, lake seston, temperature, dissolved
oxygen, and macrophytes were sampled. Surface temperature and dissolved oxygen were
measured with an YSI 550A at a depth of 0.5 meters.

To sample lake seston at each sample point, two liters of water were collected
from the entire water column, stored in dark coolers with ice, transported back to the lab,
and then filtered. At macrophyte-dense sample points, water was collected using a
manual hand pump and narrow tubing to minimize macrophyte disturbance and

collection of periphyton. The tubing was vertically moved to ensure that the entire water

column was sampled during collection. In areas with low macrophyte density, an
integrated tube sampler was used to sample seston from the entire water column.

Macrophytes were quantified at each sample point. Water depth, macrophyte
height and macrophyte percent cover were measured to obtain a percent volume infested
(PVI) metric of macrophyte abundance (calculated as the product of the percentage cover
and plant height divided by the water depth; Canfield, Shireman, Colle et al. , 1984). PVI
of free-floating macrophytes were measured using root length in lieu of macrophyte

height (Meerhoff, Mazzeo, Moss et al., 2003). Percent cover was determined by floating
a 0.1m2 PVC quadrat on the water and visually estimating the percentage area occupied

by macrophytes. All macrophyte species were recorded at each point.

To quantitatively compare macrophyte taxa between lakes, we calculated the
frequency of macrophyte species within each lake with the following formula: (sampling
points with a species / total sampling points) X 100 (Nichols, Weber and Shaw, 2000).
These frequencies were then converted to relative frequencies by dividing by the sum of
frequencies for all species. We also calculated species richness and Simpson’s (1949)
diversity index for each lake. To test the impact of macrophyte taxa on zooplankton food
quality parameters, we created a “competitive macrophyte” index (CMI). This index
measures the proportion of macrophyte species observed at each sample point that have
been shown in the literature to have negative impacts on phytoplankton biomass (number
of competitive macrophyte species / total number of macrophyte species). A complete

list of competitive species can be found in Table 2.

Quantijjzing Zooplankton Food Quality

We used lake seston samples to determine food quality for zooplankton in three
ways: elemental content, fatty acid content, and phytoplankton taxa. All collected water
was prefiltered three times through a 35pm Nitex sieve to remove zooplankton and large,
inedible algae. A vacuum pump was used to filter a known volume of water and the
seston through precombusted (500C for 2 hours) Whatrnan GF/F filters. For each sample
point, three filters were obtained, one for each of three different chemical analyses
(particulate carbon and nitrogen, particulate phosphorus, and fatty acid), within 7 hours
of collection.

Filters for particulate carbon and nitrogen were dried and stored in a dessicator
until analysis. The filters were packed into tin capsules and processed on a combustion
analyzer. Filters for particulate phosphorus were stored at 0 °C until analysis during
October 2009. Particulate phosphorus was determined using a persulfate digestion
(Menzel and Corwin, 1965), followed by a measurement of the soluble reactive
phosphorus (Murphy and Riley, 1962) with a Perkin Elmer Lambda 20 spectrometer. We
then calculated the molecular C:N and C:P using atomic weights.

Filters for fatty acid analysis were immediately placed in glass vials that had been
washed with hi gh-performance liquid chromatography grade chloroform. The filters
were immersed in chloroform and the air space above the chloroform was flushed with
nitrogen before replacing the Teflon-coated cap. The vials were stored at -20 °C until
analysis during October 2009. Fatty acids filters were transesterified in 1 ml 1N
methanolic HCL (80C, 30 min) with 5 ug C15:0 fatty acid added for quantification.

Subsequently, fatty acid methyl esters (FAMEs) were extracted by the addition of 1 ml

9% NaCl (w/v) and 1 ml hexanes, followed by 30 seconds of vortexing and
centrifirgation at 2,000 x g for 5 minutes. The hexane layer was transferred to a new
glass tube and dried under a nitrogen gas stream. 100 pl hexane was added and the re-
dissolved FAMEs were transferred to GC vials. FAMES were analyzed by gas
chromatography on a HP 6890 GC equipped with a flame ionization detector and a DB-
23 (J&W Scientific) capillary column as previously described (Xu et al., 2005). The
detected mass of each fatty acid was converted to relative mol % values for statistical
analysis.

To quantify phytoplankton communities, 250mL of filtered water from each
sample point were reserved in glass bottles and preserved with 1% Lugol’s solution.
Phytoplankton cells were identified and enumerated in lOmL sedimentation chambers
using the methods described in Wetzel and Likens (2000) at 400x and 200x on an
inverted microscope. At least 400 natural units (single cells, colonies, or filaments) per
sample were identified, and cells with dimensions greater than 35pm were not counted.
Cells were identified down to genus if possible and grouped into the following divisions:
Diatoms, Cyanobacteria, Cryptomonads, High-Quality Chlorophyta, Low-Quality
Chlorophyta. Chlorophytes were separated into high and low food quality groups based
on published data on Daphnia growth and reproduction experiments (e.g., Porter, 1976,
Coesel, 1997), with low-quality chlorophytes consisting of the genera Cosmarium, and
Sphaerocystis. Biovolume estimates were calculated from published equations

(Hillebrand, Durselen, Kirschtel et al., 1999).

Quantifying Zooplankton Communities

To assess the population of cladoceran consumers, we collected a zooplankton
sample at each sample point in the lake with either a tube sampler (depth under 1.5
meters) or zooplankton net (depth over 1.5 meters). Specimens were preserved in
ethanol. Two composite samples per lake were formed: one by pooling all littoral
samples and one by pooling all pelagic samples. At least five l-mL subsamples were
counted in Sedgwick-Rafter cells under a dissecting scope at 10X (Wetzel and Likens
2000). We identified cladocerans to genus level and measured body lengths with a

digitizer.

Daphnia Lab Experiments
Growth Experiment

A five day Daphnia growth experiment was conducted in the lab from July 19 to
July 23, 2009. Female clones were used from an established lab culture of Daphnia
pulicaria, originally derived from Michigan State University Inland Lake #1. This
Daphnia strain is not resistant to cyanobacteria (Samelle, pers.comm.). The clones were
kept in a Daphnia media (Samelle and Wilson, 2005) and fed a high-quality alga culture
(Ankistrodesmus; for culture information see Samelle and Wilson, 2008) until enough
neonates were produced within 24' hours of each other to use in the experiments (n~100).
Thirteen or fourteen replicate neonates were randomly assigned to one of four treatments:
littoral water, pelagic water, littoral water supplemented with Ankistrodesmus, and

pelagic water supplemented with Ankistrodesmus. Each neonate was placed in a 30mL

10

vial with treatment water and attached to a plankton wheel that rotated at 1 revolution per
minute (RPM) in a 18:6 light-dark cycle at 23 °C.

Treatment water was obtained from Muskrat Lake each day of the experiment.
Littoral water was taken from a nearshore point with 100% macrophyte PVI that was
dominated by anhar spp., but also had Polygonum amphibium and Chara spp. present.
Pelagic water was taken from the deepest area of the lake with 0% macrophyte PVI using
an integrated tube sampler. The collected water was filtered through 3 35pm Nitex sieve
and was allowed to reach the temperature of the experiment (23C) before replacing the
older water. Fresh Ankistrodesmus was added to the appropriate treatments every day.

Before the experiment began, initial neonate length was obtained from the mean
length of 40 randomly selected neonates. The length of each neonate (top of head to base
of tail spine) was measured with a compound microscope (40x) and digitizer after four
days. Growth was measured as daily increase in body length (final length -— initial length
/ 4 days).

Reproduction Experiment

A nine day Daphnia reproduction experiment was conducted in the lab from July
23 to July 31, 2009. Female clones were used from the same established lab culture of
Daphnia pulicaria described above. Fourteen adult replicates were each placed in
separate 60mL glass vials, assigned randomly to the four treatments described above and
kept under the same conditions as in the above growth experiment. A11 adult replicates
used in the experiment were seven days old, fed on a diet of Ankistrodesmus until the
start of the experiment, and had not previously reproduced. The first clutches occurred

on day five of the experiment. Offspring were counted and removed every day. At the

11

end of the experiment, total offspring produced and average clutch size were calculated
for each individual.

To estimate the amount of food present in each treatment, we filtered unused
treatment water onto Whatrnan GF/F filters on four days of the reproduction experiment
(7/26, 7/28, 7/29, and 7/30/09). Algal biomass was determined by fluorometer analysis
of chlorophyll a on a Turner 10-AU-005 Flourometer using the methods of Welschmeyer

(1994)

Statistical Analyses

We analyzed the data using linear regression models, with lake as a factor and
macrophyte PVI or CMI as a covariate. If the full model was not significant, we ran an
analysis of covariance without the Lake x PVI/CMI interaction term. Data within each
lake were also analyzed separately in simple linear regression models with macrophyte
PVI or CMI as the predictor variable. We log-transformed any non-normal data to fit the
assumptions of the linear regression. Data were analyzed using R 2.8.0 (R Foundation
for Statistical Computing, Vienna, Austria) software and a was set at 0.05. We also fit
the data to a linear mixed mode] with lake as the grouping variable using SAS 9.2
software. Mixed models take into account the non-independence of hierarchical samples
(e.g., samples taken within the same lake) and provide two separate variance estimates,
one for within-lake and one for among-lakes variance (e. g., Singer 1998)

Somatic growth rates, total offspring, average clutch sizes, and chlorophyll a
concentrations from the lab experiments were analyzed by one-way analysis of variance

(ANOVA). Post-hoe comparisons were made using the Bonferroni adjustment. The data

12

met the assumption of homogeneity of variance, so were untransformed. u. was set at
0.05 and data were analyzed using R 2.8.0 (R Foundation for Statistical Computing,

Vienna, Austria) software.

Results
1:1ng ield Survey

Although the four study lakes had much in common, they differed in size, depth,
clarity, temperature, and dissolved oxygen (DO; Table 1, Figure 1.1). Differences in
water temperature between lakes were attributed to differences in air temperature among
sampling dates (Figure 1.2). Muskrat Lake had a much shallower Secchi depth than the
other lakes, and Potter Lake’s maximum depth is less than half of the next shallowest
lake. Surface water temperature varied a great deal between lakes, ranging from 18 — 29
degrees Celsius. All lakes maintained oxic conditions, with mean DO concentrations of
approximately 5 mg/L in Dagget Lake DO and greater than 7 mg/L in all other lakes.
Macrophytes

A total of 20 macrophyte species were found across the study lakes (Table 3).
Lakes were generally dominated by native floating-leaf and submerged species. Muskrat
Lake had mostly floating-leaf macrophyte species, probably due to low water clarity
(Table 1), and had the only exotic macrophytes found, albeit infrequently (Table 3).
Macrophyte diversity ranged from 4.48 to 7.38 across lakes, with the highest in Dagget
Lake. Six species previously found to be “competitive macrophyte” species (negative
effects on phytoplankton biomass) were found in our study lakes (Table 3). Hall Lake

had the highest frequency of competitive macrophytes.

l3

Abiotic factors (temperature, water depth, dissolved oxygen) were measured to
test their correlation with macrophyte abundance. As expected, PVI was negatively
correlated with water depth (r < -0.60 within each lake; Table 4). PVI also showed a
strong negative correlation (r = -0.81) with dissolved oxygen within Dagget Lake.
Zooplankton Food Quality and Quantity

We analyzed our samples of lake seston in the lab for food quantity and four
different measurements of food quality (elemental ratios, fatty acid, phytoplankton taxa
and non-algal carbon), and examined their relationships with our macrophyte metrics.
Because increasing C:N and C:P ratios are associated with low food quality, we predicted
a positive relationship between these ratios and either macrophyte density (measured as
PVI) or the proportion of macrophyte taxa that have been shown to have negative impacts
on phytoplankton biomass (CMI). Among lakes, the mean C:N of lake seston ranged
between 7.6 - 9.4, and the mean C:P ranged between 59.0 — 85.3 (Table 5.1, Figure 2).
These values are considered indicative of high food quality, as they are near the atomic
ratios found within natural Daphnia populations (Andersen and Hessen, 1991) and well
below the P-limitation threshold of 300 C:P for Daphnia resources (Sterner, 1993). Both
C:N and C:P varied significantly among lakes (p<0.05, Table 5.2). We found no
significant relationship across lakes between the atomic ratios and PVI (p>0.10, Table
5.2) or CMI (p>0.35, Table 6.2), and trends between both C:N and C:P and PVI within
lakes were not consistent (Figure 2, Table 5.1). Hall Lake in particular showed a
significant negative relationship with C:P (p<0.02), while other lakes displayed positive
trends. Similarly, the slopes of C:N/C:P and CMI varied in direction and strength among

lakes (Figure 3, Table 6.1). Dagget Lake displayed positive trends between both C:N/C:P

14

and CMI (p<0.05, Table 6.1), while other lakes had no significant relationships. Results
of mixed models indicate that over 85% of the total variation for C:N and C:P was within
lakes (Table 7). However, the grouping variable (Lake) was not significant in either
model, indicating that a mixed model analysis is not necessary for this data (Table 7).
We also expected to find negative relationships between nutritionally important
polyunsaturated fatty acids (PUFAs) and our macrophyte metrics. PUFAs comprised
between 7.3 — 19.5% of all fatty acid among lakes (Table 5.1). One of the fatty acids
particularly important for Daphnia growth, u-linolenic acid (Cl8z3n-3), made up the
highest proportion of total PUFAs, ranging from 4.8 — 14.7% of all fatty acid. Using
mixed models, we found that over 70% of the total variation for total PUFA and C18:3n-
3 was found among lakes (Table 7). Accordingly, the mol % of all PUFAs we measured
differed significantly among lakes (p<0.001, Table 5.2), except for eicosapentaenoic acid
(p=0.88; C20:5n-3). Arachidonic acid (C20:4n-6) was the only fatty acid measured that
had a significant relationship with PVI across lakes (p<0.01), although this relationship
was not the negative one predicted (Figure 3, Table 4). Within lakes, total PUFAs
showed virtually no trends with PVI, while the individual PUF As we measured changed
with PVI in different ways (Figure 4, Table 5.1). There was no significant interaction
between any of the PUFA variables and PVI (Table 5.2). As with PVI, C20:4n-6 was the
only individual fatty acid measured that had a significant, positive relationship with CMI
across lakes (p=0.02; Table 6.2). As predicted, total PUFAs decreased significantly with
increases in CMI across lakes (p<0.001; Table 6.2), but not within any individual lakes
(Table 6.1). C18:3n—3 had consistent, slightly positive slopes with CMI within lakes,

while total PUFAs, C20:5n—3, and C20:4n-6 all had inconsistent slopes across lakes

15

(Figure 5, Table 6.1). There was no significant interaction between CMI and any fatty
acid variable (Table 6.2).

Third, we examined food quality for zooplankton by identifying and counting the
phytoplankton community. A total of 27 distinct taxa were found in Hall Lake, 28 taxa in
Potter Lake, and 29 in Dagget Lake. Muskrat Lake is not represented because all
phytoplankton taxa samples from Muskrat Lake were preserved incorrectly and damaged
beyond recovery. Dagget Lake was dominated by high quality chlorophytes, Potter Lake
by cryptophytes, and Hall Lake by cyanobacteria and high quality chlorophytes (Figure
6). The biovolume of nearly every division varied significantly across lakes (p<0.001;
Table 5.2), except for diatoms, which were nearly significant (p=0.10). Only low-quality
chlorophytes had a significant relationship with PVI across lakes (p=0.02), and this was a
positive relationship, as predicted by the hypothesis. Within lakes, the only significant
relationships we found with PVI were negative and positive relationships with
cyanobacteria biomass in Hall Lake (p=0.04, Table 5.1) and Potter Lake (p=0.01)
respectfully, as well as a slightly positive relationship with low quality chlorophytes in
Hall Lake (p=0.02). We also unexpectedly found significant positive trends in diatom
biovolume with increasing CMI in Hall and Dagget Lakes (p<0.05; Table 6.1, Figure 8).
In all, these results disagree with previous expectations and indicate that food quality
trends across macrophyte abundance gradients are mostly non-existent.

Finally, we quantified food quantity. Algal food quantity, measured as total algal
biovolume, was significantly different among lakes (p<0.001, Table 5.2). Dagget Lake
had five times the algal biovolume of Hall Lake, and Hall Lake had almost three times

the biovolume of Potter Lake (Table 5.1). However, Potter Lake had significantly more

16

edible carbon biomass than Hall Lake (Figure 9), indicating a higher amount of non-algal
food. Algal food quantity was not significantly related to PVI either across lakes or
within any individual lakes (Table 5.1 , 5.2). These results contradict the conventional

wisdom that less zooplankton resources are available in macrophyte-dense areas.

Zooplankton

Total cladoceran zooplankton abundances ranged from 150 (Potter Lake) to less
than 15 organisms/L (Dagget Lake; Figure 10). We found similar abundances of
Daphnia in the littoral zone of Muskrat Lake compared to the pelagic zone, but Daphnia
was restricted to the pelagic zone in the other lakes. Daphnia were very rare in Potter
and Dagget Lake, with abundances less than 1 organism/L. The mean length of common

cladoceran taxa did not differ significantly between lakes (p<0.05; Figure 11).

Dapfihnia Lab Experiments

The chlorophyll a analysis associated with our lab experiments revealed a
significantly higher algal biomass in the littoral zone as compared to the pelagic zone of
Muskrat Lake (p=0.002; Figure 12). The added Ankistrodesmus treatments more than
doubled the amount of food available to Daphnia, raising the chlorophyll a concentration
to over 120 ug/L. Our experimental additions of food could have made it difficult to
disentangle the effects of food quantity and food quality. However, chlorophyll (1
concentrations we found in the natural Muskrat Lake seston (both pelagic and littoral)
were 3-4 times greater than the chlorophyll a concentration needed to maximize Daphnia

feeding, assimilation, and growth (6-15 ug/L; Sterner and Schulz, 1998). Therefore,

17

because Daphnia in the littoral and pelagic treatments were saturated in terms of food
quantity, the food limitation effects seen in our experiments likely correspond to
differences in food quality.

To directly test the quality of littoral vs. pelagic Daphnia food resources, we
conducted Daphnia grth and reproduction experiments in the lab. We expected
juvenile somatic growth, total number of offspring, and clutch size to be significantly
lower in the treatments using vegetated littoral water than those using unvegetated
pelagic water. However, we found a trend towards higher juvenile Daphnia growth rate
in the littoral zone as compared to the pelagic zone, although the means were marginally
significantly different (p=0.07; Figure 12). Juvenile Daphnia in the pelagic treatment
saw a significant increase in growth rate when Ankistrodesmus was added to the
treatments (p=0.03), indicating food limitation in terms of somatic growth. Conversely,
food was not limiting growthin the littoral treatment (p=0.11).

In the reproduction experiment, food was limiting to the total number of offspring
within both the littoral and pelagic water treatments (p<0.03; Figure 12). The total
reproductive output and average clutch size were not statistically different in each
treatment without added Ankistrodesmus (p=1.00; Figure 12). However, adding
Ankistrodesmus increased the clutch size only in the littoral treatment (p=0.01),
suggesting that the food quality in the pelagic was already high. The results of these
experiments generally suggest very little difference between the littoral and pelagic

habitats in terms of food quality, confirming our lake survey results.

18

Discussion

It is generally assumed that resources for pelagic zooplankton are poor in quantity
and quality in dense macrophyte beds. However, in this study, we found that
measurements of Daphnia food quality did not vary consistently with macrophyte
abundance (measured as percent volume infested, PVI). Along a gradient of increasing
macrophyte PVI, low food quality indicators such as C:P increased in some lakes (Figure
2), while beneficial fatty acids (e. g. C20:4n—6) also increased (Figure 4). However, the
majority of the variables we measured showed no significant relationship with
macrophyte PVI. Examining food quality patterns in relation to specific macrophyte taxa
that have documented negative impacts on phytoplankton (i.e., CMI; Table 2) also
produced very few significant trends. In addition, our experimental results suggest that
overall food quantity and quality may actually be higher in the littoral zone as compared
to the pelagic zone. These results do not support the conventional wisdom that
macrophytes negatively impact resources for Daphnia through allelopathy, competition
for nutrients and light, and sedimentation.

Phillips (1978) hypothesized that phytoplankton can overcome the allelopathy of
macrophytes when excess nutrients are available. In fact, some studies show that
macrophytes may only have a growth-inhibiting effect on algae when nutrients are
limiting (Fitzgerald, 1969; Lurling, 2006). Because many shallow lakes, including our
study lakes, are typically nutrient-rich (Scheffer, 2004), allelopathy may not have much
impact on phytoplankton biomass. In addition, above limiting nutrient thresholds,
competition between macrophytes and phytoplankton for nutrients might not be strong

enough to impact food quality for zooplankton. Therefore, other mechanisms, such as

19

shading and reduced sediment resuspension, may be more important in macrophyte-
phytoplankton interactions within shallow lakes.

However, even these physical factors may be inconsequential to phytoplankton in
shallow turbid lakes. In such lakes, including Muskrat Lake with its low water clarity,
reduced light in macrophyte beds may not create such a drastically different environment
for phytoplankton as what they experience in the pelagic zone. In addition, the
dominance of floating-leafed plants in many similarly turbid lakes may not increase net
sinking rates of phytoplankton as drastically as other macrophyte growth forms (Horppila
and Nurminen, 2005). Therefore, the fact that our lake seston survey showed no decrease
in food quality or quantity for Daphnia among the macrophytes in Muskrat Lake makes
sense, and perhaps we would not expect to see this pattern in other turbid, shallow lakes.

It is more difficult to explain the macrophyte-phytoplankton quality patterns (or
lack thereof) in our other, less-turbid lakes. Hall Lake, for example, was our clearest
lake, and was also the only lake to show a significant negative relationship between C:P
and macrophyte abundance (the opposite direction of our prediction). However, this
result makes sense in the context of the light:nutrient hypothesis (Sterner, Elser, Fee et
al., 1997). This hypothesis states that the increased light availability in clear-water lakes
such as Hall Lake leads to increased carbon fixation in phytoplankton, which decreases
the amount of relative phosphorus in the cell and the overall food quality for Daphnia. In
contrast, within the shaded environment of dense macrophyte beds, carbon fixation is
lower, and C:P and food quality are relatively high. Yet we did not see this same
negative pattern with C:P in Dagget and Potter Lakes, despite their relatively clear water.

We also did not see this pattern with C:N in any of the lakes, as would be predicted by

20

the light:nutrient hypothesis. Indeed, we found significant positive trends between both
C:N and C:P and our competitive macrophyte index (CMI) in Dagget Lake. These
findings further reinforce the inconsistency of food quality measurements among lakes
and place doubt on the conventional wisdom of decreased Daphnia food quantity and
quality in littoral as compared to pelagic zones of shallow lakes.

If significant and consistent gradients of resource quantity and quality do not exist
within shallow lakes along macrophyte metrics, littoral and pelagic grazer species may
segregate themselves based on differences in predation or abiotic factors between
habitats. The lack of a consistent food quality pattern in our lake survey results suggest
that Daphnia shallow lake habitat use may represent adaptation to patchy environments
that vary in mortality risk instead of resources. For example, chemical cues from
invertebrate predators cause Daphnia to avoid densely vegetated areas (Lauridsen and
Lodge, 1996). Alternatively, abiotic factors such as dissolved oxygen are often lower
among macrophytes (Table 4), and this may play a role in the distribution of pelagic
zooplankton. Even in lakes with food quality gradients in existence, the distribution of
Daphnia may not reflect those gradients. In Muskrat Lake for example, even though
Daphnia was just as abundant in the pelagic as the littoral zone (Figure 10), our growth
experiment showed significantly higher food quality in the vegetated littoral habitat.
Therefore, other factors may be preventing Daphnia from preferentially inhabiting an
area with higher quality food. In deep, stratified lakes, much research has shown that
Daphnia allocates time to different vertical habitats to optimize fitness based on abiotic
and biotic factors (Larnpert, McCauley and Manly, 2003). Similar research on Daphnia

horizontal habitat choice in shallow lakes is lacking.

21

Can the results of Daphnia-focused food quality studies be extrapolated to other
herbivorous zooplankton? This is an important question, because many zooplankton taxa
besides Daphnia have been shown to move in and out of macrophytes daily (e. g.,
Scapholeberis, Ceriodaphnia, and Bosmina; Burks, Lodge, Jeppesen et al., 2002), which
exposes them to different habitats. Indeed, our sampling also showed significant
abundances of macrophyte-associated grazers such as Bosmina present in the pelagic
zone (Figure 10). The variation seen in stoichiometric ratios, PUFAs, and phytoplankton
taxa likely does not reflect the nutritional requirements of all zooplankton taxa in the
same way. Therefore, more studies need to determine what constitutes high-quality food
for other zooplankton species and how spatial patterns in quality may affect these species
differently than daphniids.

For nearly every measurement of food quality we tested, we found marked
differences in food quality among the four shallow study lakes (Table 5.2, 6.2). One
previous study has shown that shallow lakes vary in food quality based on a single
measure (e. g., zooplankton bioassays in Tessier and Woodruff, 2002b), but our study is
the first to show food quality differing among shallow lakes by use of multiple
measurements. These measurements were consistent in determining the general quality
of the whole-lake seston. For example, Muskrat Lake had the lowest C:P ratio and the
highest percentage of C18:3n-3, C20:4n—6, and total PUFAs (Table 5.1), which all
indicate high food quality. At the other end of the food quality spectrum, Hall Lake had a
high C:P and cyanobacteria biomass, and the lowest percentage of C20:5n—3, C18:3n-3,
and total PUFAs. Despite this agreement across lakes, our food quality measures did not

show consistent patterns within lakes. Dagget Lake, for example, displayed a negative

22

relationship between cyanobacteria and PVI, but other measures of poor food quality
(C:N, C:P, and low-quality chlorophytes) actually had positive slopes with PVI (Table
5.1). Previous studies of zooplankton food quality in multiple lakes have examined only
one measure of food quality (e. g., C:P ratio, phytoplankton taxa) and taken'samples
solely from the pelagic zone (e.g., Dobberfuhl and Elser, 2000). Although our study
suggests that these different measures agree on the general quality of zooplankton
resources in whole lakes, more systems need to be sampled for within-lake heterogeneity
to determine why food quality measures do not agree at this scale.

It is unclear what caused our lakes to vary in food quality at the whole-lake scale.
Some research suggests that heterogeneity in phytoplankton communities among
temperate lakes is created by a multitude of interacting abiotic factors, such as turbidity,
terrestrial subsidies, and lake morphometry (Pinel-Alloul, Methot, Verrault et a1. , 1990).
We indeed saw differences among our study lakes in some of these factors (Table 1), in
addition to surface temperature and dissolved oxygen (Figure 1). However, it is not
known how this heterogeneity translates into resource quality for grazers. The lakes we
sampled also differed in macrophyte richness, diversity, and in the relative frequency of
competitive macrophytes (Table 3). Although macrophytes may not have an impact on
phytoplankton quality within lakes, the macrophyte composition of the lake as a whole
may play a role in shaping the lake’s phytoplankton community. Finally, zooplankton
predators can also have indirect effects on their prey’s resources through trophic cascades
(Pace, Cole, Carpenter et al. , 1999). For example, lakes with high planktivore
abundances may preferentially graze on larger zooplankton and thus alter the

phytoplankton community. However, we did not find evidence for differences in the

23

relative strength of such predation on zooplankton in our study lakes; mean length of
cladocerans was similar across our lakes (Figure 11). We also cannot rule out the
possibility of temporal changes driving the food quality differences among lakes since
seston samples were collected over a three week period (Table 1). Still, our study shows
that food quality for zooplankton can vary greatly among shallow lakes with many
similar properties (e. g., watershed, management practice, hydrology, and predation
pressure).

Food quality for grazers has been found to be much richer in shallow lakes as
compared to deep lakes (Tessier and Woodruff, 2002b, Ferrao et al., 2005), and both top-
down and bottom-up effects may contribute to this pattern. Tessier and Woodruff
(2002a) proposed that the high-quality resources in shallow systems are the result of
strong trophic cascades caused by high levels of predation on grazers. It is also known
that shallow lakes have greater terrestrial inputs and internal nutrient recycling as
compared to deep lakes (Jeppesen, Jensen, Sondergaard et al. , 1997). Despite these
generalities of high food quality in shallow systems, our experiments show that
zooplankton may be resource limited in terms of food quality. However, the mean C:P
ratios in our study lakes were well below the threshold level of 300 that is hypothesized
to restrict growth in Daphnia (Table 5.1). For lake seston with less than 300 molar C:P,
phosphorus limitation of Daphnia grth is replaced by the limitation of polyunsaturated
fatty acids (PUFAs) (Wacker and von Elert, 2001). Therefore, it is possible that
zooplankton production is PUFA-limited in shallow lakes, and future research should

explicitly test this idea.

24

The results of our Daphnia growth experiment were inconsistent with those of the
reproduction experiment. Daphnia neonates tended to grow better on littoral resources as
compared to pelagic resources, but Daphnia adults had the same reproductive output on
pelagic and littoral resources. . One explanation for this inconsistency relies on the
theory of age-dependent measures of food quality. A study by Vanni and Lampert (1992)
found that food quality depends on the age structure of Daphnia, with some species of
algae being low quality food for Daphnia juveniles but not adults (e. g., Oocystis). Future
work should test for significant differences among particular phytoplankton taxa that
might explain different responses of adults and juveniles such as we saw in our
experiments.

A second potential reason for conflicting results between our reproduction and
growth experiments is that the littoral zone contains alternative, non-algal resources that
may be more beneficial to small neonate Daphnia as compared to adults. For example, a
high quantity of periphyton, fungi, bacteria, and detritus is assumed to be present among
macrophytes (Burks et al. 2002). Daphnia are able to utilize all of these resources
(Siehoff, Hammers-Wirtz, Strauss et al., 2009, Ojala, Kankaala, Kairesalo et al., 1995,
Pilati, Wurtsbaugh and Brindza, 2004), and some research suggests that daphniid
juveniles are able to utilize bacterial resources more efficiently than adults (Pace, Porter
and F eig, 1983). Therefore, the juveniles in our study may have been able grown more
than adults on these supplemental littoral resources. However, contrary to past studies
showing increased reproductive success of Daphnia adults on non-algal resources
(Sanders, Williamson, Stutzman et al. , 1996), our reproduction experiments suggested no

difference between littoral and pelagic resources. Our findings question the common, but

25

rarely tested, assumption of macrophyte-dependent distribution of detritus, bacteria, and

fungi within lakes.

Conclusions

Phytoplankton nutritional content is an integral part of the aquatic foodweb
paradigm. High food quality phytoplankton are able to produce strong trophic cascades
(Hall, Leibold, Lytle et al., 2007, Danielsdottir, Brett and Arhonditsis, 2007) because
zooplankton are able to grow and reproduce quickly enough to withstand intense
predation. This increased zooplankton production efficiently transfers energy from
primary producers up the aquatic food web. There is also evidence showing that low
food quality for zooplankton results in weak or no cascades, and represents a barrier to
energy transfer (Elser, Chrzanowski, Sterner et al., 1998, Danielsdottir et al. , 2007). We
saw evidence of this in our own study, as the clear-water Dagget Lake had a high amount
of edible food but the lowest abundance of grazers. Moreover, Daphnia population
oscillations and the co-occurring “clear water phases” in lakes are dependent on
phytoplankton food quality (Kerfoot, Levitan and Demott, 1988). Therefore, measuring
the natural heterogeneity of Daphnia food quality in addition to traditional measures of
food abundance (chlorophyll a, algal biomass, algal biovolume, primary productivity)
provides a more accurate picture of zooplankton population dynamics.

Although there is an abundance of zooplankton food quality research, only a
Small portion of those studies examine measurements of such quality in situ (Scheuerell,
Schindler, Litt et al. , 2002). Of these field studies, most have focused on food quality

Variation among the pelagic zones of lakes (Dobberfuhl and Elser, 2000, Tessier and

26

Woodruff, 2002a). Our observations shed light on the horizontal distribution of
phytoplankton quality within multiple lakes, a topic that has received little attention in the
literature, even though it has important implications for zooplankton productivity and
foraging behavior. Zooplankton productivity and foraging behavior have a direct impact
on phytoplankton populations, lake water clarity, and fish population dynamics. Despite
our finding of no relationship with macrophyte metrics, many of the food quality
parameters we measured had high variation within shallow lakes. Therefore, we point
out the inherent patchiness of shallow systems that warrants further studies on the
distribution of zooplankton resources to identify possible mechanisms behind this
variability. Future studies of zooplankton nutrition, as well as theoretical models of
aquatic foodwebs, should also take into account this spatial heterogeneity of resources

and how it affects zooplankton habitat selection.

27

APPENDIX

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