University of Toronto T-Space · ii Understanding how life-history traits and environmental gradients structure diversity Natalie T. Jones Doctor of Philosophy Department of Ecology

Understanding how life-history traits and environmental

gradients structure diversity

by

Natalie Tamara Jones

A thesis submitted in conformity with the requirements

for the degree of Doctor of Philosophy

Department of Ecology and Evolutionary Biology

University of Toronto

© Copyright by Natalie Jones 2016

ii

Understanding how life-history traits and environmental gradients

structure diversity

Natalie T. Jones

Doctor of Philosophy

Department of Ecology and Evolutionary Biology

University of Toronto

2016

Abstract

Determining how diversity is distributed through space and time is a fundamental goal of

ecology. My research tested how species’ life-history traits structure diversity at landscape and

broader scales and over time. I first asked how traits related to seed dispersal shape plant

diversity in a naturally fragmented landscape by testing the relationship between diversity and

patch characteristics (size and isolation) for species with different dispersal modes. Dispersal

mode altered outcomes predicted from theory ‒ while fragment isolation had a negative effect on

wind-dispersed species, it did not influence the diversity of animal-dispersed species. I then

examined how zooplankton traits (body size and dormancy) correlate with species distributions

at a large scale using lakes across an 1800 km north-south gradient in western Canada. Despite

predictions that body size should decrease with latitude and low temperatures, I found only weak

evidence for any effect of latitude on inter- and intraspecific body size. Zooplankton dormancy

dynamics are virtually impossible to test through sampling, yet dormancy underpins seasonal

fluctuations in abundance and long term persistence, and it is expected to vary with climate. I

therefore used an experimental approach to test how temperature and photoperiod affect hatching

rates of dormant eggs from lakes across the latitudinal gradient. My results suggest that

mismatches between temperature and photoperiod, as predicted to result from climate change,

could drive latitude-dependent shifts in zooplankton emergence. Finally, I examined the

temporal stability of diversity across the same latitudinal gradient by examining species

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colonization and extinction over 50 years. I found that low-latitude communities are increasingly

diverse and comprised of small-bodied species despite more rapid temperature change at higher

latitudes. Overall, my research has implications for how global changes, such as fragmentation

and climate change, alter diversity by changing the viability of specific life-history strategies.

iv

Acknowledgments

The completion of this thesis was accomplished with the assistance of many people.

First and foremost, I thank my supervisor, Benjamin Gilbert. Ben has been a wonderful mentor,

editor, constructive critic, sounding board all for which I am truly grateful. Ben was patient and

generous with his time and challenged me to mature as a researcher. His sage advice and

thoughtful perspective greatly improved this thesis. I continue to learn from Ben every day and

consider myself incredibly fortunate to have had the opportunity to be his first doctoral student. I

have no doubt that Ben will continue to find creative ways to tackle the big questions in ecology.

In the Gilbert lab I found a group of lifelong friends. I can’t imagine a more fun and supportive

group of people to work with; together we laughed and commiserated, sharing the highs and

lows of graduate school. Each member has a unique approach to science and life that has

influenced my perspective. I am particularly indebted to Rachel Germain, who I have worked

with for my entire tenure at UofT. Rachel continues to impress me every day with her ecological

knowledge, problem solving skills and perspective on academia and life outside it. Tess Grainger

is an excellent researcher and was an incredible addition to the lab. Tess taught me the

importance of preparation, realistic expectations and being direct. Rachel and Tess are truly

mentors to me. Kelly Carscadden became a wonderful friend and has taught me much about hard

work and perseverance. Finally, Denon Start brought a youthful exuberance to the lab; his

positive energy and cleverness is a pleasure to be around. All members have helped me to

become a better scientist.

The EEB department at large both past and present has had an incredible impact on me. I have

seen countless inspirational talks and had many discussions with the people working in EEB. I

learnt a great deal from discussions with the Jackson and Krkosek labs. As committee members,

Don Jackson and Megan Frederickson offered guidance that was very helpful over the years. I

have made many wonderful friends. In particular, Alex De Serrano, Nicholas Mirotchnick,

Frances Hauser and Jane Ogilvie have enriched my time at UofT.

This work could not have been completed without the tireless help of dedicated undergraduate

students at the University of Toronto and beyond. Alexandra Barany, Ewelina Chojecka, Nathan

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Lo, Patrick Beh, Jillian Moran and Veronica Jones were invaluable and approached the tedium of

ecological lab and field work with a sense of humor and tenacity that was much appreciated.

Many staff members in EEB and CSB provided technical support for my research. Donna

Wheeler, Jim Dix, Trung Luu, Bruce Hall and Andrew Petrie lent equipment, constructed

experimental gear and fixed growth chambers for my projects. Kitty Lam and Helen Rodd were

very helpful over the years. Helen in particular always put students first and does everything she

can to help us succeed.

Andrew MacDougall and Lyn Baldwin were early mentors to me. They both helped me cultivate

a love of plant ecology and natural history. There is no doubt that without their thoughtful

supervision I would not have been inspired to pursue a PhD.

I could not have completed this work without the unwavering support of my family, especially

my partner Scott Forster, who has been my best friend and cheerleader throughout the entire

process. My siblings are a constant source of inspiration for me. Gwyneth has attended talks I

have given and was my roommate for the first two years of my PhD. Her opinion was important

to me during the early years of my dissertation. Veronica never ceases to amaze me with her

cleverness and kind spirit. My brother Brendan has been in Toronto for the last year of my PhD

and being in the same city as him for the first time in 20 years was an amazing bonus.

Beyond EEB I have been lucky to become friends with an amazing cast of characters; we have

had many wonderful adventures over the years. Christina Doris, Elysse Schlein, Asher Miller

and Linda Naccarato are amazing friends and I can’t wait to see what lies ahead for all of them.

This research was generously supported by Ontario Graduate Scholarships and fellowships from

the University of Toronto and the Department of Ecology and Evolutionary Biology.

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Table of Contents

Acknowledgments.......................................................................................................................... iv

List of Tables ...................................................................................................................................x

List of Figures ................................................................................................................................ xi

List of Appendices ....................................................................................................................... xiii

....................................................................................................................................1 CHAPTER 1

GENERAL INTRODUCTION ........................................................................................................1

Spatially structured landscapes ...................................................................................................2

Traits that affect dispersal rates ..................................................................................................2

The effect of temperature on traits that influence dispersal ........................................................3

Dormancy, climate & dispersal through time .............................................................................4

Latitude & community stability ..................................................................................................5

Thesis overview ..........................................................................................................................6

Literature cited ............................................................................................................................7

..................................................................................................................................12 CHAPTER 2

DISPERSAL MODE MEDIATES THE EFFECT OF PATCH SIZE AND PATCH

CONNECTIVITY ON METACOMMUNITY DIVERSITY… ...............................................12

Abstract .....................................................................................................................................12

Introduction ...............................................................................................................................13

Materials & methods .................................................................................................................16

Study site & species sampling ...........................................................................................16

Data analyses .....................................................................................................................19

Results .......................................................................................................................................22

Discussion .................................................................................................................................29

vii

Acknowledgments .....................................................................................................................32

Literature cited ..........................................................................................................................33

..................................................................................................................................38 CHAPTER 3

ARE SPECIES LARGER AT HIGH LATITUDES? TESTING LATITUDE-BODY SIZE

RELATIONSHIPS IN ZOOPLANKTON ................................................................................38

Abstract .....................................................................................................................................38

Introduction ...............................................................................................................................39


Study species & species sampling .....................................................................................41

Environmental covariates...................................................................................................42

Body size measurements ....................................................................................................42

Statistical analysis ..............................................................................................................43

Results .......................................................................................................................................44

Discussion .................................................................................................................................50



..................................................................................................................................57 CHAPTER 4

CHANGING CLIMATE CUES DIFFERENTIALLY ALTER ZOOPLANKTON

DORMANCY DYNAMICS ACROSS LATITUDE ................................................................57

Abstract .....................................................................................................................................57

Introduction ...............................................................................................................................58


Sample collection & experimental design .........................................................................61

Data analyses .....................................................................................................................65

Results .......................................................................................................................................67

Zooplankton eggs ...............................................................................................................67

Phenology ..........................................................................................................................68

viii

Hatchling Diversity ............................................................................................................71

Discussion .................................................................................................................................73



..................................................................................................................................83 CHAPTER 5

GEOGRAPHIC SIGNATURES IN SPECIES TURNOVER: DECOUPLING

COLONIZATION AND EXTINCTION ACROSS A LATITUDINAL GRADIENT ............83

Abstract .....................................................................................................................................83

Introduction ...............................................................................................................................84


Study system & species sampling ......................................................................................87

Data analyses .....................................................................................................................88

Results .......................................................................................................................................90

Discussion .................................................................................................................................94


Literature Cited .........................................................................................................................98

................................................................................................................................103 CHAPTER 6

GENERAL CONCLUSION ........................................................................................................103

Chapter 2 .................................................................................................................................103

Significance......................................................................................................................103

Future directions ..............................................................................................................103

Chapter 3 .................................................................................................................................105

Significance......................................................................................................................105


Chapter 4 .................................................................................................................................106

Significance......................................................................................................................106

ix


Chapter 5 .................................................................................................................................107

Significance......................................................................................................................107


Conclusion ..............................................................................................................................107

Literature cited ........................................................................................................................108

Copyright Acknowledgements.....................................................................................................184

x

List of Tables

Table 2.1 Effects of stand size and connectivity on log-transformed species richness. ‘All

species’ includes all aspen-associated species from the three dispersal mode groups. ................ 27

Table 2.2 Effects of stand size and stand connectivity on species composition (axis 1 and 2

scores of PCoA using Jaccard dissimilarity coefficient) by dispersal mode. ............................... 28

Table 3.1 Summary of latitudinal extremes and the body size of each species given by the model

fit. Body size values were back transformed from the predicted values that were generated from

the linear model on log-transformed body size. P-values less than 0.05 are highlighted in bold,

and those less than 0.1 are highlighted with italics. ...................................................................... 46

Table 3.2 Results of log likelihood tests for the final model that includes latitude as well as

additional environmental variables that were significantly associated with zooplankton body size

in a separate linear mixed model. Table headings are: degrees of freedom (Df) and log-likelihood

ratio (LRT). ................................................................................................................................... 47

Table 3.3 The number of species that significantly increased or decreased in body size with

latitude, temperature or other environmental variables when tested in isolation. ........................ 48

xi

List of Figures

Figure 2.2 The effect of stand size and stand connectivity on species richness when all aspen-

associated species are grouped together (top panels) and for each dispersal group considered

separately. Species richness values were adjusted to account for the other factor in the model

(size or connectivity) whenever that factor was significant.. ........................................................ 24

Figure 2.3 The effect of (a) stand size and (b) connectivity on the relative representation of

species belonging to each dispersal mode group from a PCoA using the Bray-Curtis coefficient.

We only display the stand characteristic-axis score combinations that were significantly

correlated....................................................................................................................................... 25

Figure 2.4 Evidence for the role of competition but not herbivory in mediating relationships

between the stand characteristics and species richness. (a) The observed degree of negative

covariances in species richness between dispersal mode groups (solid line) compared to a null

distribution of random outcomes. ................................................................................................. 26

Figure 3.1 Plots of the slope (points) and 95% bootstrapped confidence intervals (lines). Lines

that do not overlap with zero are significantly associated with (a) latitude or (b) temperature.... 45

Figure 3.2 The association between latitude and the mean a) unweighted and b) weighted

zooplankton community body size. Community body size was weighted by the local abundance

of each species. ............................................................................................................................. 49

Figure 4.1 Map displaying the location of the 25 lakes in western Canada that were sampled for

sediment in July 2011. .................................................................................................................. 62

Figure 4.2 The relationship between latitude and the egg abundance of cladocerans (light grey),

copepods (dark grey) and rotifers (black). Eggs were isolated from 100 grams of lake sediment

using the sugar flotation method (see methods).. ......................................................................... 67

Figure 4.3 The effect of temperature (8°C; grey and 12°C; black) on the average number of days

until the first individual (‘First’; circles) and half (‘50%’; triangles) of all individuals from each

taxon hatch.. .................................................................................................................................. 68

Figure 4.4 The effect of temperature and photoperiod on the emergence of (a-b) cladocera

individuals, (c-d) copepods individuals, and (e-f) rotifera individuals that hatched from 25 lakes

across a 1800 km latitudinal gradient in western Canada. Emergence is summed by lake across

the 60 day sampling period. .......................................................................................................... 70

Figure 4.5 The effect of temperature and photoperiod on the proportion of the (a-b) total

crustacean diversity, (c-d) cladoceran diversity and (e-f) copepod diversity that hatched from 25

lakes across a 1800 km latitudinal gradient in western Canada. Diversity is summed by lake

across the 60 day sampling period. ............................................................................................... 72

Figure 5.1 Latitudinal patterns of diversity and temperature change. (a) Locations of the 43 lakes

in this study; (b) the change in temperature over 70 years, based on differences (present – past)

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of 30 years means: 1971 to 2000 – 1901 to 1930; (c) Species composition of zooplankton (first

axis from a Nonmetric Multidimensional Scaling with a 2D solution [stress = 0.19] based on

Sorensen dissimilarity), illustrating that closer sites are more compositionally similar; and (d)

Zooplankton species richness with latitude. ................................................................................. 91

Figure 5.2 The relationship between latitude and (a) the change in species richness, (b) species

turnover, measured using the Sorenson dissimilarity metric, (c) the proportion of new species per

lake, and (d) the proportion of species that went locally extinct. All graphs compare historic

zooplankton samples with contemporary samples (see methods). ............................................... 92

Figure 5.3 Species traits influence colonization and extinction rates. The relationship between

colonization and (a) zooplankton body size, (b) local abundance, and (c) occupancy. Bottom

panels: the relationship between extinction and (d) zooplankton body size, (e) local abundance,

and (f) occupancy. ......................................................................................................................... 93

xiii

List of Appendices

Appendix A: Supplementary information for Chapter 2 .............................................................111

Model Fitting ...........................................................................................................................121

Appendix B: Supplementary information for Chapter 3..............................................................123

Appendix C: Supplementary information for Chapter 4..............................................................132

Literature cited ........................................................................................................................148

Appendix D: Supplementary information for Chapter 5 .............................................................149

1

Chapter 1

General Introduction

“…the problem of pattern and scale is the central problem in ecology, unifying population

biology and ecosystem science, and marrying basic and applied ecology… there is no single

natural scale at which ecological phenomena should be studied; systems generally show

characteristic variability on a range of spatial, temporal, and organizational scales.” (Levin

1992).

In the 1989 MacArthur Award lecture for the Ecological Society of America, Simon Levin

argued that understanding ecological processes and patterns across spatial and temporal scales is

among the largest challenges facing ecologists (Levin 1992). At landscape scales, for example,

diversity patterns are hypothesized to be mainly influenced by environmental heterogeneity and

metapopulation processes, whereas climatic gradients and the history of speciation may largely

influence diversity at macro-ecological scales (Rosenzweig 1995). Across temporal scales,

similar shifts in the importance of different factors also likely occur as environmental

fluctuations over the short term give way to shifts in mean climatic conditions (Rosenzweig

1995; Wolkovich et al. 2014). Understanding spatial and temporal patterns of diversity, and their

reliance on environmental heterogeneity, is becoming increasingly important in the

Anthropocene, as environmental changes become increasingly common (Helmus, Mahler &

Losos 2014; Wolkovich et al. 2014).

In this thesis, I examine how species traits and species responses to environmental conditions

structure distributions at different spatial and temporal scales. At the landscape scale, I ask how

traits related to dispersal (dispersal mode of plants) and responses to the environment structure

diversity in natural habitat fragments. At a much larger spatial scale that spans 14° latitude

(approx. 1800 km), I test the hypothesis that zooplankton life history traits vary with temperature

and photoperiod, causing distinct ecological dynamics across this gradient. Finally, I use

historical data collected at this larger scale to test how temporal dynamics differ across latitudes.

Considering species diversity at these different spatial and temporal scales requires integration of

a number of concepts that are often considered separately in ecology, such as the links between

CHAPTER 1: GENERAL INTRODUCTION

2

dispersal-related traits, temperature, body size and dormancy. In what follows, I outline specific

links in chapters two through five, and provide a brief overview of some of the concepts that I

address in more detail in those chapters. I conclude the introduction with a concise overview of

the subsequent chapters.

Spatially structured landscapes

The spatial tapestry linking biological communities underlies the diversity we see in nature.

Many communities are not contiguous and instead rely on dispersal to connect local habitat

patches (Wilson 1992). At the regional scale, the diversity of these so called ‘metacommunities’,

is related to patch size and connectivity (Holyoak, Leibold & Holt 2005), which influences the

colonization and extinction dynamics of local patches. Larger sites are able to support more

species because higher colonization rates in combination with larger population sizes render

species less vulnerable to extinction (MacArthur & Wilson 1967; Holt 1993; Leibold et al.

2004). At the same time, the proximity of patches to each other influences the rate that

individuals colonize sites, thereby affecting diversity (Chisholm, Lindo & Gonzalez 2011;

Gilbert 2012). Higher connectivity of patches in close proximity may thus promote persistence of

many species, but can also limit the opportunity for species to find spatial refuge from predators

or superior competitors (Leibold et al. 2004). In the last twenty years, much effort has been

invested into clarifying how patch size and connectivity alter diversity, but more recently

researchers have begun to consider how the asymmetric dispersal ability of co-occurring species

can alter the magnitude of movement among patches, thereby influencing local and regional

species composition (Chisholm et al. 2011; Haegeman & Loreau 2014).

Traits that affect dispersal rates

Interspecific differences in dispersal ability affect coexistence and diversity in local communities

(Leibold et al. 2004; Holyoak et al. 2005). For any given assemblage of species, co-occurring

individuals at the same trophic level often show remarkable differences in traits that influence

dispersal (Howe & Smallwood 1982; De Bie et al. 2012). Adaptations that enhance movement

between patches cause differences in the probability that a species will colonize a new site.

Despite widespread recognition of the variation in dispersal ability, many experiments remove

dispersal differences by controlling movement of all species in an identical way (Cadotte 2006;


3

Howeth & Leibold 2010; Declerck et al. 2013), obscuring realistic tests of the effect of dispersal

on metacommunity dynamics.

Traits associated with dispersal may be especially important for passively dispersed species, as

they do not have behavioral adaptations that can influence colonization of new sites. For

example, morphological adaptations in seeds have long been recognized to increase how far and

how often plants colonize sites. Several classes of adaptations can be categorized into different

syndromes that reflect adaptations to a variety of dispersal vectors such as wind, water or

animals (Howe & Smallwood 1982). For passively dispersed animal species, adult body size

directly influences colonization dynamics (Vanschoenwinkel et al. 2008). However, unlike with

active dispersers, dispersal distance is negatively associated with body size, with smaller

individuals travelling further and more often (Soons et al. 2008; De Bie et al. 2012). The range

of dispersal propensities for species within a site suggests that the effect of patch size and

connectivity will differ among species; species with no dispersal aid are likely to be more

strongly associated with patch size and connectivity than species with adaptations to water, wind

or animal dispersal. Similarly, habitat selection by animals that move seeds could alter the

relationship between patch size and diversity if animal vectors prefer larger patches (Levey et al.

2005; Nathan et al. 2008; Evans et al. 2012). Focusing on patterns of diversity separately for

species with different dispersal traits may reveal unique relationships between patch size or

connectivity and diversity (Vanschoenwinkel, Buschke & Brendonck 2013).

The effect of temperature on traits that influence dispersal

The abiotic environment can affect traits that influence dispersal. The body size of organisms is

often associated with latitude and temperature (Gillooly & Dodson 2000), with larger bodied

species and individuals typically found at colder sites characteristic of more polar latitudes. This

pattern has been generalized with three ecological rules: “Bergmann’s rule”, “James’ rule” and

the “Temperature-size rule” (Mayr 1956; James 1970; Atkinson 1994). Recently, climate change

has reignited interest in body size-latitude relationships. To date, there is no universally agreed

upon mechanism for this pattern (Blackburn, Gaston & Loder 1999; Watt, Mitchell & Salewski

2010), however, scientists agree that temperature has a putative effect on body size.

Geographical clines in body size for some taxa, along with the observation that temperature

increases frequently cause a reduction in the average body size of many organisms (Atkinson


4

1994; Daufresne, Lengfellner & Sommer 2009), suggest that the direct effect of temperature on

body size could indirectly increase dispersal rates for passively dispersed species, and that these

effects will have a spatial component due to different rates of temperature change with latitude.

Dormancy, climate & dispersal through time

In addition to dispersing to new sites, organisms inhabiting variable environments often evolve

life-history strategies to persist in situ despite temporal fluctuations in habitat quality (Cohen

1968; Venable & Brown 1988). Many short-lived organisms produce dormant propagules such

as eggs, seeds and cysts, that are characterized by reduced metabolic rate and halted

development; these dormant propagules remain viable when active individuals would not survive

(Tauber, Tauber & Masaki 1986). Dormant life-stages act as a bet hedging strategy by enabling

persistence during unfavourable environmental conditions (Hairston, Hansen & Schaffner 2000;

Hairston & Kearns 2002; Brendonck & Meester 2003). This strategy is often referred to as

“temporal dispersal”. Prolonged dormant phases allow species to persist through unfavourable

years but only at the expense of decreased population growth in favourable years (Venable &

Brown 1988). The strategy of decreasing the mean and variance of population growth in order to

persist over the long-term is likely to be more important in cold, stressful environments

(Mousseau & Roff 1989; Molina-Montenegro & Naya 2012).

Many zooplankton species exhibit prolonged dormancy by forming an ‘egg bank’, or

accumulating resting eggs in lake sediment (Hairston & Cáceres 1996). Relatively little is known

about the dormancy dynamics of aquatic zooplankton (Hairston and Kearns 2012), despite a

well-developed literature on dormancy in other organisms (Cohen 1968; Venable & Lawlor

1980; Venable & Brown 1988). However, temperature and day length appear to be the most

important cue for the termination of dormancy in zooplankton (Gyllström & Hansson 2004;

Vandekerkhove, Declerck & Brendonck 2005; Davidson et al. 2006; Schalau et al. 2008; Dupuis

& Hann 2009; Angeler 2011).

Different environments favour distinct dormancy strategies, which results in variation in the

prevalence of strategies across environmental gradients (Cohen 1968; Stearns 1992). Dormancy

is predicted to be more important for population persistence in environments that have greater

seasonal variation and shorter growing seasons. For example, the prevalence of prolonged


5

dormancy has been found to increase at higher latitudes in some terrestrial invertebrates

(Mousseau & Roff 1989) and marine copepods (Marcus & Lutz 1998). Nonetheless, little is

known about the latitudinal distribution of zooplankton egg banks, and their dormancy dynamics,

in aquatic systems (Vandekerkhove et al. 2005). This is because prior studies have either

assessed emergence dynamics in a small number of lakes within a region (Cáceres 1997; Cáceres

& Tessier 2003; Dupuis & Hann 2009), or collected egg banks from across a latitudinal gradient,

but combined the samples into regional mixtures (Vandekerkhove et al. 2005), thus preventing

an analysis of how hatching dynamics vary across a latitudinal gradient. By collecting sediment

from across a latitudinal gradient, which naturally varies in temperature and growing season

length, it may be possible to determine how sensitivity to hatching cues differ across a latitudinal

gradient highlighting spatial variation in the contribution of the egg bank to community

composition.

Latitude & community stability

The links between temperature, dispersal and dormancy are particularly important in the

Anthropocene because they are expected to influence how latitudinal diversity patterns change

through time and how they are being altered by global climate change. Scientists have predicted

and observed differential warming across latitudes, with more warming occurring toward the

poles (IPCC 2013). This pattern suggests that temperature-related traits that differ among species

may cause shifts in the identity and abundances of species within communities, and that these

shifts may be larger towards the poles. Species diversity also has a consistent pattern with

latitude, with the vast majority of taxonomic groups having lower diversity at higher latitudes.

This gradient in diversity may also cause larger changes at higher latitudes, as predicted by two

hypotheses in community ecology. First, because species diversity is positively correlated with

phenotypic variation, elevated diversity could reduce the opportunities for new species to

establish even when they are no longer limited by climate (Elton 1958).

Second, diversity stabilizes food webs when they increase the number of weak interactions, as is

typical in most food webs (McCann et al., 1998). To date, support for the positive effects of

diversity on stability and resistance to new species has been mixed (May 1972; Levine &

D’Antonio 1999; McCann 2000; Gilbert & Lechowicz 2005; Belote et al. 2008; Adrian et al.

2010; Clark & Johnston 2011). Further research that relates the degree of diversity to the


6

magnitude of community change in natural systems is necessary to predict future shifts; currently

there is limited evidence documenting a greater vulnerability in northern regions despite

differences in diversity and temperature changes, but this may be due to a lack of long-term

studies across broad spatial gradients (Heino, Virkkala & Toivonen 2009).

Thesis overview

In my thesis, I sample natural communities and conduct experiments to assess the role of species

traits in structuring diversity at regional and broader spatial scales, and to determine whether

temporal turnover in species varies across large spatial scales. In chapter 2, I employ a

metacommunity framework to test how the dispersal mode of plants alters the effect of patch size

and connectivity on diversity. For the subsequent chapters, I focus on zooplankton from lakes

across a broad latitudinal gradient to address inter-related questions about temperature, species

traits and latitudinal distributions. In chapter 3, I determine if latitudinal patterns in zooplankton

body size, both within and among species, are consistent with macroecological hypotheses

(James’ rule, Bergmann’s rule). Chapter 4 focuses on zooplankton egg banks across latitudes and

experimentally isolates the effects of two climatic cues that break dormancy: temperature and

day length. These cues are being modified by climate change, however we lack studies that test

how the interactive effects of these cues will alter dormancy dynamics, and especially whether

their effects depend on the latitude of the zooplankton communities. Finally, in chapter 5 I take

advantage of the historical sampling of lakes in western Canada to compare contemporary and

historic (40+ years) samples. I use this comparison to determine how zooplankton communities

are changing, if these changes vary with latitude, the degree to which changes are driven by local

extinctions versus colonization of new lakes, and how species traits such as body size mediate

these changes. I conclude in chapter 6 with a summary of this work and highlight questions

raised by my findings.

All of these chapters are written as stand-alone research papers. As a result there is some

repetition in the Introductions and Methods sections. Benjamin Gilbert contributed substantially

to all of the research chapters presented in this thesis. Chapter 2 was a collaboration with Rachel

Germain, Tess Grainger, Aaron Hall and Lyn Baldwin. Chapter 3 was conducted in collaboration

with Jillian Moran, a fourth year undergraduate student in EEB that I mentored. Chapter 3 is in

preparation to be sent to Plos One and chapter 5 is currently in review at Global Ecology and


7

Biogeography. Chapters 2 and 4 are published and have been included in this thesis with

permission from the publishers, the citations are as follows:

Jones, N.T., Germain, R.M., Grainger, T.N., Hall, A., Baldwin, L. & Gilbert, B. (2015) Dispersal

mode mediates the effect of patch size and patch connectivity on metacommunity diversity.

Journal of Ecology, 103, 936–944.

Jones, N.T. & Gilbert, B. (2016) Changing climate cues differentially alter zooplankton

dormancy dynamics across latitudes. The Journal of Animal Ecology, 85, 559–569.

Literature cited

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stability and lake zooplankton diversity - contrasting effects of chemical and thermal

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12

Chapter 2

Dispersal mode mediates the effect of patch size and patch connectivity on metacommunity diversity

Published as Jones, N. T., R. M. Germain, T. N. Grainger, A. Hall, L. Baldwin, & B. Gilbert.

2015. Dispersal mode mediates the effect of patch size and patch connectivity on metacommunity

diversity. Journal of Ecology 103:934-943.

Abstract

Metacommunity

theory predicts that increasing patch size and patch connectivity can alter local

species diversity by affecting either colonization rates, extinction rates, or both. Although

species’ dispersal abilities or ‘dispersal mode’ (e.g., gravity, wind, or animal dispersed seeds) can

mediate the effects of patch size and connectivity on diversity, these important factors are

frequently overlooked in empirical metacommunity work. We use a natural metacommunity of

aspen stands within a grassland matrix to determine whether dispersal mode alters the influence

of stand size and connectivity on understorey plant diversity. We sampled the same area in each

patch, controlled for the presence of matrix species in aspen stands, and tested for the effects of

size, connectivity, and dispersal mode on metacommunity richness. Because dispersal groups

responded differently to patch size and connectivity, we created a null model and assessed

ungulate activity to explore whether competitive dynamics or herbivory were driving diversity

patterns. Animal-dispersed species and species with no dispersal aid had higher diversity per unit

area in larger stands, likely because large stands can both support larger populations that are less

prone to extinction and may also attract seed-dispersing animals such as birds and small

mammals that are sensitive to edge effects. Consistent with other empirical work, we found a

positive relationship between diversity and connectivity for wind-dispersed species. However,

we detected a negative effect of stand connectivity on the diversity of species with no dispersal

aid, possibly due to the presence of other highly competitive species groups dominating well-

connected patches, as our null model results suggest. We found no evidence for higher ungulate

activity in highly connected patches, suggesting that herbivory may not be driving the decline in

diversity of plants with no dispersal aid. Overall, we see a positive effect of stand area on

diversity for most groups despite sampling equal area in all stands, which is a prediction of

CHAPTER 2: DISPERSAL MODE & METACOMMUNITY DIVERSITY

13

metacommunity theory that is normally overlooked. Our results demonstrate the importance of

considering variation in the dispersal modes of focal species in explaining the diversity patterns

in natural metacommunities.

Introduction

Biological communities rarely occur in complete isolation, but instead often exist as part of a

‘metacommunity’ of local patches connected by dispersal (Wilson 1992). Island and pond

systems are classic examples of metacommunities (Simberloff & Wilson 1970), as are other

distinct assemblages of organisms that occur in patchily-distributed habitats. The

metacommunity paradigm, based on concepts from metapopulation and island biogeography

theories, was developed to understand the mechanisms that maintain species diversity in patchy

landscapes (Leibold et al. 2004). Several classes of metacommunity dynamics have been

identified, all of which recognize the importance of extinction and colonization dynamics of

species within and among patches for explaining local and regional diversity patterns (Leibold et

al. 2004). Recent theoretical and empirical work has focused on determining how factors that

alter the extinction and colonization rates of species within metacommunities scale up to alter

local and regional diversity (Altermatt, Schreiber & Holyoak 2011; Haegeman & Loreau 2014;

LeCraw, Srivastava & Romero 2014).

Patch size and connectivity (inter-patch distance) both affect colonization and extinction

dynamics and are predicted to be important drivers of diversity patterns in metacommunities

(Holyoak et al. 2005). Larger patches can support a greater number of species per unit area, as

higher colonization rates combined with larger population sizes that are less vulnerable to

extinction result in an increase in the ratio of colonization to extinction rate (MacArthur &

Wilson 1967; Holt 1993; Leibold et al. 2004). Similarly, by influencing the rate at which species

move between patches, patch connectivity can strongly affect local diversity; this phenomenon

has recently been demonstrated in both theoretical models (Pillai, Gonzalez & Loreau 2011;

Gilbert 2012; Haegeman & Loreau 2014) and empirical studies (Howeth & Leibold 2010;

Matthiessen, Mielke & Sommer 2010; Chisholm et al. 2011). In metacommunities with poorly-

connected patches, local diversity tends to be low because dispersal-limited species cannot reach

suitable patches (Cadotte 2006b) or priority effects exclude subsequent colonizers (Levins &


14

Culver 1971). As patch connectivity increases, local diversity increases as incoming colonists

rescue small populations from extinction (Brown & Kodric-Brown 1977). Finally, when

dispersal rates are high, diversity can decline if competitively dominant species or generalist

predators are able to reach all patches and drive other species locally extinct (Mouquet & Loreau

2003). Empirical studies have detected a variety of relationships between dispersal and diversity;

reported relationships are often positive (Warren 1996; Gilbert, Gonzalez & Evans-Freke 1998;

Cadotte 2006a; Chase, Burgett & Biro 2010) or hump-shaped (Kneitel & Miller 2003;

Matthiessen & Hillebrand 2006; Howeth & Leibold 2010; Vanschoenwinkel et al. 2013), but

negative relationships have also been detected (Matthiessen et al. 2010). However, it is difficult

to interpret these patterns and draw broader conclusions about the ecological processes shaping

natural systems, in part because of the difficulties associated with capturing a biologically

relevant range of dispersal rates when dispersal is manipulated experimentally.

One of the most fundamental predictions of metacommunity theory is that interspecific

differences in dispersal affect coexistence and diversity (Leibold et al. 2004; Holyoak, Leibold &

Holt 2005). Although co-occurring species often differ greatly in dispersal ability (Howe &

Smallwood 1982), these differences are often overlooked in experimental studies. For example,

most studies manipulate dispersal by transferring a set proportion of a community among

patches, thereby removing natural variation in species’ dispersal abilities (Kneitel & Miller 2003;

Cadotte, Fortner & Fukami 2006; Howeth & Leibold 2010; Declerck et al. 2013; but see Cadotte

2006a; Limberger & Wickham 2011; Vanschoenwinkel, Buschke & Brendonck 2013; Guelzow,

Dirks & Hillebrand 2014). Similarly, seed addition experiments used to test dispersal-diversity

relationships often remove dispersal differences among species (Cadotte 2006a). Although these

studies have made important advances in testing some aspects of metacommunity theory, the

higher tractability associated with homogenizing dispersal rates across species comes at the

expense of understanding how natural variation in dispersal abilities can affect the persistence of

coexisting species within a metacommunity. Studies that allow differential dispersal rates are

underrepresented in the literature (Logue et al. 2011), and are currently biased towards small

passively-dispersed organisms inhabiting freshwater ponds (e.g., protists, algae and zooplankton;

Louette & De Meester 2005; Vanschoenwinkel, Buschke & Brendonck 2013).


15

The manner in which patch size and patch connectivity affect the colonization rates of coexisting

species and subsequently shape diversity will depend on species’ traits that affect dispersal. In

plants, dispersal differences can manifest through morphological adaptations in seeds, with the

seed representing the primary dispersive stage of a plant’s life cycle. These adaptations can be

categorized into different dispersal modes or syndromes, reflecting how (and how far) seeds

move across the landscape. For example, common dispersal modes in plants include gravitropic

dispersal via passive release from the parent plant, dispersal via insects such as ants

(myrmecochory), wind dispersal via the presence of a feathery pappus, and vertebrate dispersal

via fleshy fruited seeds or burs that are carried by birds or mammals (Howe & Smallwood 1982).

Focusing on patterns of diversity separately for species with different dispersal modes can reveal

unique relationships between patch size or connectivity and diversity (Vanschoenwinkel,

Buschke & Brendonck 2013). For example, species that have no dispersal aid rarely disperse

long distances and may be more strongly affected by patch connectivity than animal or wind-

dispersed species that can easily reach all sites (Vanschoenwinkel, Buschke & Brendonck 2013).

Similarly, habitat selection by animals that move seeds could alter the relationship between patch

size and diversity if animal vectors prefer larger patches (Levey et al. 2005; Nathan et al. 2008;

Evans et al. 2012). Despite the recognized importance of dispersal for metacommunity dynamics

(Mouquet & Loreau 2003; Cadotte 2006b) and the ubiquity of variation in dispersal abilities

among co-occurring species (Nathan & Muller-Landau 2000; Muller-Landau 2003; Gilbert,

Turkington & Srivastava 2009), the implications of these dispersal differences on

metacommunity diversity is only beginning to be tested in natural systems (Löbel, Snäll &

Rydin 2009; Hájek et al. 2011; De Bie et al. 2012; Vanschoenwinkel, Buschke & Brendonck

2013).

In this paper, we investigate how patch size and connectivity affects understorey plant diversity

in a naturally-patchy landscape of aspen stands. Aspen (Populus spp.) are common tree species

in Northern climates, frequently occurring in grassland habitats where they form clonal forest

stands with clear boundaries. These stands are naturally-patchy, and support a distinct plant

community compared to the surrounding grassland matrix, indicating that aspen-associated

understorey species function as a metacommunity. However, unlike pond or island patches,

aspen stands have diffuse boundaries, meaning that important spatial dynamics may be swamped


16

out by the presence of species that are not constrained to habitat patches in the metacommunity

(e.g., generalists; Harrison 1999). How influential the presence of these species on our ability to

resolve the dynamics of diffuse metacommunities remains an open question (Leibold et al.

2004).

In Lac Du Bois Provincial Park, British Columbia, Canada, we sampled understorey plant

communities in aspen stands that varied in size and connectivity (Fig. 2.1). We sampled the same

total area in all stands to remove the confounding effects of species-area relationships in our

assessment of species diversity. Species were then categorized into three dispersal syndrome

groups based on fruit morphology: no dispersal-aid, wind dispersed, and animal dispersed. We

used these data to address four questions: (1) Does stand size and/or connectivity affect

understorey plant diversity and species composition? (2) Does dispersal mode mediate these

relationships? (3) Are the observed diversity patterns consistent with common ecological

processes such as competition or herbivory? (4) How sensitive are our results to the inclusion of

generalist and matrix-associated species, a common feature of metacommunities with diffuse

boundaries?

Materials & methods

Study site & species sampling

This study was conducted in the high elevation grasslands of Lac Du Bois Provincial Park in the

southern interior of British Columbia, Canada (latitude = 50.7007, longitude = -120.4603). The

region is semiarid, with hot dry summers and little annual precipitation (279 mm), 27% of which

falls as snow (Environment Canada 2014). Aspen (Populus tremuloides) cover ~100-ha of the

park and occur primarily on moist, north-facing slopes (Dickinson 1998). They form clonal

stands that support a unique flora of understorey plant species compared to the surrounding

grassland matrix (Fig. S2.1 in Appendix A: Supplementary information for chapter 2). These

stands are relatively undisturbed by humans, and range in age from approximately 24 to 148

years old.

In the summer of 2007, we randomly selected 24 of a total of 110 aspen stands in the park (Fig.

2.1), excluding any stands located within 50 meters of a road. The stands ranged from


17

approximately 658 m2 to 37,622 m

2 in size. We established a single 10 m × 25 m plot in the

center of each stand and recorded the occurrences of all identifiable understorey vascular plant

species. This large plot size was selected to capture species diversity at a scale that incorporates a

reasonable level of microsite heterogeneity. By using the same plot size in all stands, we

standardized sampling intensity and were thus able to assess species richness per unit area to

avoid the confounding influence of species-area relationships on our diversity measurements.

Although edge effects may be confounded with the size of the patch in our study (and in most

naturally patchy ecosystems), we believe that these effects are negligible for two reasons. First,

even the smallest stands were almost three times the size of the plot, and second, the abundance

of matrix species was generally low (Fig. S2.2).

To confirm that aspen stands support a unique flora and to identify aspen- and matrix-associated

species, we also sampled plant diversity in the adjacent grassland matrix. The grassland sampling

followed the same sampling protocol as in the aspen stands, with at least one plot placed 25 to 50

m outside of each of the sampled aspen stands (n = 24 total; May & Baldwin 2011). See Data

analyses for methods on statistically delineating grassland- and aspen-associated species.

Concurrent with the plant survey, we recorded the amount of ungulate scat within our plots,

ranked from 0 (none) to 3 (abundant), as a proxy for large herbivore activity (Bailey & Putman

1981; Heinze et al. 2011). Our surveys were conducted in a single year, and thus could not be

used to track colonization and extinction as they happened. However, island biogeography theory

predicts that the outcome of colonization/extinction dynamics can be inferred, rather than

observed directly, from the equilibrium species richness of habitat patches. There was no

relationship between species richness and the age of the stands (t1,23 = 0.31, P = 0.763)

suggesting that the patterns observed were not driven by differences in time to accumulate

species.

We classified all aspen-associated species into three dispersal mode categories based on seed

morphology: (1) no dispersal aid [gravitropic, ballistic, or ant dispersal; n = 32]; (2) wind-

dispersed [anemochorous; n = 17]; indicated by the presence of a pappus; and, (3) animal-

dispersed [bird or large mammal dispersal; n = 18], indicated by the presence of burs or fleshy

fruit. We grouped ant-dispersed species into the ‘no dispersal aid’ group because ants move


18

seeds at a spatial scale comparable to passive gravitropically- or ballistically-dispersed seeds

(Thomson et al. 2011). Only 2 of the 32 species in this dispersal mode category are known to be

dispersed by ants.

Figure 2.1 Map of sampled (black; n = 24) and unsampled (grey; n = 86) aspen stands at the Lac

Du Bois Provincial Park in the southern interior of British Columbia, Canada (latitude =

50.7007, longitude = -120.4603); both sampled and unsampled stands were included in our

calculations of stand connectivity. The matrix habitat was primarily grassland.


19

Data analyses

A presence-absence matrix was created for all taxa in the 24 sampled aspen stands and 24

grassland plots. We conducted a principal coordinates analysis (PCoA) using the Jaccard

dissimilarity coefficient to identify compositional differences between aspen stands and the

surrounding grassland matrix (Fig. S2.1). To identify and remove all species that were not

strongly aspen-associated, we calculated the proportion of aspen to grassland plots that each

species occurred in, and removed generalist and grassland specialist species that did not occur in

aspen stands at least 66% of the time (n = 103 species; Table S2.1). We removed generalists and

grassland specialists because only species that occur in favorable focal habitat patches imbedded

in a matrix of unfavorable habitat constitute a metacommunity (Cook et al. 2002). We repeated

our analyses using less stringent cut-offs (an analysis using all species and another requiring 50%

of occurrences to be in aspen stands), and a more stringent cut-off (requiring 75% of occurrences

to be in aspen stands). We found that including all species obscured patterns, but that our results

were qualitatively similar (Table S2.2) at all other indicator cutoff levels; we therefore report the

results generated using the 66% cutoff, which identified 67 aspen-associated species (Table 2.1)

but also discuss the sensitivity of our results to the cut-off level that was used.

To calculate stand size and distances among stands, all sampled and unsampled aspen stands

were digitized from online basemaps streamed through ArcGIS 10.1 (ESRI.com). The digitized

stand locations and shapes were compared with field notes to confirm accuracy. We calculated

the area of each stand and then created a matrix of pairwise Euclidian distances between all

stands based on edge-to-edge distances, which were then used in the connectivity function

described below.

Our model for incorporating stand size and connectivity came from a plant metapopulation

model where the expected occupancy per species increases monotonically with the ratio of

colonization (C) to extinction (E) rates. When summed across weakly interacting species, this

relationship predicts that species richness per unit area (S) in stand i increases with this ratio:

S~Ci/Ei. Using a logarithmic transformation, this equation becomes:


20

log(Si) ~ log(Ci) – log(Ei). The second of these terms, the probability of extinction (E) in stand i,

is a decreasing function of stand area, and is often modeled as inversely related to area. The other

term, colonization (C), is an increasing function of stand connectivity. We used a metapopulation

approach (Hanski 1994; Gilbert & Levine 2013) for calculating the connectivity of a stand that

incorporates the distance between stand i and all other j stands, and combined this with

extinction to predict species richness per unit area:

(1)

Where S is the species richness per unit area at site i, and ɛ is a normally distributed error term.

The variable d is the distance between any two sites; the summation incorporates distances from

all other sites. Our connectivity measure (the summation term in eqn. 1) uses the standard

assumption of an exponential dispersal curve with a mean dispersal distance, α. As a result,

connectivity between site i and j decreases at greater distances (dij) and increases with greater

dispersal ability (α). This model has a similar functional form as Hanski’s incidence function

(Equation 4 in Hanski 1994), but differs in that α represents the mean dispersal distance of seeds,

as it is commonly presented in plant dispersal literature (Hanski 1994; Muller-Landau et al.

2008). Here, we consider α identical for all species within a dispersal mode group, and fit eqn.

(1) separately for each group. To fit eqn. (1), we first used published estimates of mean dispersal

distance for our dispersal mode groups (Thomson et al. 2011) and fitted the other parameters

(intercept, b1, and b2) using linear regressions. We also fitted all parameters (α, intercept, b1, and

b2) using maximum likelihood; because the results predicted qualitatively similar effects of

connectivity on species richness, we report the second approach in the supplementary material

(Table S2.3). Specific details on the model fitting for both methods of estimating α are further

explained in the supplementary material.

𝑙𝑜𝑔(𝑆𝑖) = 𝑖𝑛𝑡𝑒𝑟𝑐𝑒𝑝𝑡 + 𝑏1 𝑙𝑜𝑔 ∑(𝑒−𝑑𝑖𝑗/𝛼) + 𝑏2 𝑙𝑜𝑔(𝐴𝑟𝑒𝑎𝑖) + 𝜀𝑖

𝑛

𝑗≠𝑖


21

When fitting eqn. (1), we noted that stand area and connectivity were often weakly correlated (r

= 0.36, P = 0.086 when all species are included in the analysis). To account for this, we report

our results for species richness from analyses with both stand size and connectivity included

(Table 2.1) and also analyses with each factor tested separately (Table S2.4). This is important in

interpreting our results because a significant effect of one stand characteristic could obscure

meaningful relationships of the other stand characteristic, purely because the stand characteristics

themselves are correlated.

Although our analyses of species richness allowed us to test if the dispersal mode groups

responded to stand size and connectivity, we could not conclude with certainty that differences in

responses between groups were statistically different because they were tested in separate linear

models. To confirm that they were statistically different, we used a multivariate approach to test

if the relative number of species belonging to any particular dispersal mode groups shifted with

stand size and connectivity. To do this, we created a distance matrix of Bray-Curtis dissimilarity

coefficients for all pairwise combinations of the 24 stands. Bray-Curtis dissimilarity is typically

used to compare sites based on the abundances of multiple species; our analysis is analogous, in

that we use ‘dispersal mode groups’ and ‘species richness’ rather than ‘species’ and

‘abundances’, respectively. We then ran a PCoA on the distance matrix, and used linear models

to test the effects of log(stand size) and log(connectivity) on the first and second axes scores of

the PCoA. Because sites with similar axis scores are compositionally similar in terms of

dispersal modes, the presence of significant relationships would indicate that the dispersal mode

groupings capture meaningful variation in how species are distributed across the landscape. For

this analysis, the α used to calculate connectivity was the average α value of the three groups.

Because we observed a negative relationship between species richness and connectivity for one

of the dispersal mode groups (Table 2.1; Fig. 2.2), we tested two additional hypotheses for the

negative species richness-connectivity relationship that can occur at intermediate to high

connectivity (i.e. the backend of a hump-shaped relationship). First, it is possible that increased

connectivity allows the establishment of highly competitive species that exclude inferior

competitors (Mouquet & Loreau 2003; Cadotte 2006a). We tested this hypothesis using a null

model designed to identify negative relationships among dispersal mode groups, after accounting


22

for environmental covariance. The model used was Schluter’s covariance test (Schluter 1984)

tested against a randomized null expectation calculated with row and column sums held constant

(the most conservative null model; Ulrich & Gotelli 2010). Second, the movement of large

herbivores might be restricted by stand connectedness. We tested this possibility using a linear

model looking at the effects of log(stand size) and log(connectivity) on the amount of ungulate

scat found per stand during the understorey sampling period.

We also used a multivariate approach to look at turnover in species composition across stands

within the dispersal mode groups to identify variation that is not accounted for by grouping by

dispersal mode. Specifically, we created three distance matrices, one for each dispersal mode

group, by calculating the Jaccard dissimilarity coefficient on the presence/absence data for all

pairwise combinations of the 24 stands. The Jaccard dissimilarity coefficient is a resemblance

measure that accounts for increased variation in species richness as species richness increases as

expected with random sampling (e.g., MacArthur & Wilson 1967). We then performed PCoAs

on the three distance matrices and used the first and second axis scores as response variables in

linear models testing the effects of log(stand size) and log(connectivity). The presence of

significant relationships would indicate that, within the dispersal mode groups, some species are

more likely than others to encounter and persist in stands of varying size and connectivity.

Results

We found a significant effect of stand size (P < 0.001) and a marginally non- significant effect of

connectivity (P = 0.078) on overall species richness, with higher richness observed in larger, less

connected stands (Table 2.1; Fig. 2.2). However, when species were broken down by dispersal

mode, the importance of these two stand characteristics varied markedly (Table 2.1).

Specifically, we found a positive effect of stand size (P < 0.001) and a negative effect of

connectivity (P = 0.041) on the species richness of the no-dispersal-aid group, whereas the

number of animal-dispersed species increased with increasing stand size (P < 0.001) but was

unaffected by stand connectivity (P = 0.768). The number of wind-dispersed species was not

affected by stand size (P = 0.507) or connectivity (P = 0.107) when both factors were included in

the model. However, when we considered each factor separately, species richness of wind-

dispersed species increased with greater connectivity (P = 0.025; Table S2.4). In comparing


23

among groups, there was a significant effect of stand size (axis 1; P < 0.001) and connectivity

(axis 2; P = 0.0122) on the relative number of species represented by each group (Fig. 2.3).

Together, these results support our hypothesis that both patch size and connectivity affect

metacommunity diversity, and that these effects vary with species’ dispersal mode.

We found additional variation within the dispersal mode groups in how species responded to the

stand size and stand connectivity (Table 2.2). Specifically, the composition of species with no

dispersal aid changed with stand size (axis 2, P = 0.024), and animal-dispersed species changed

with both stand size (axis 1; P = 0.002) and stand connectivity (axis 1; P = 0.012). We could not

calculate compositional turnover for species in the wind-dispersed group, as a low frequency of

joint presences precluded analysis with Jaccard similarity.

Because we found a negative relationship between species richness and stand connectivity for

plants that lack a dispersal aid, we tested the possibility that competition or herbivory could be

mediating this relationship (Fig. 2.4). Our null model revealed that, overall, the species

richnesses of the dispersal groups negatively covaried across stands (P = 0.033). This means that,

after accounting for and removing the common effects of stand size or connectivity among

dispersal mode groups, the diversity of the different groups were negatively associated. We

found no evidence to suggest that ungulates, common herbivores at the study site, were more

active in highly-connected stands (t1,20 = -0.803, P = 0.432).

Our results on the effects of stand size and connectivity on species richness were qualitatively

similar among analyses that used different cut-off values for identifying aspen-associated species

(i.e., species occurring in aspen stands 50, 66, and 75% of the time; Table S2.2). In all three

analyses, species with no dispersal aid were affected by stand size (all P < 0.001) and

connectivity (all P ≤ 0.002), animal-dispersed species were affected by stand size only (all P <

0.001), and wind-dispersed species were not affected by either stand characteristic (all P ≥

0.099). It was only when all species (i.e., generalists and grassland specialists) were included that

we failed to detect any trends, except for the effect of stand size on the species richness of

animal-dispersed species because animal-dispersed species did not occur in the grassland matrix.


24

Figure 2.1 The effect of stand size and stand connectivity on species richness when all aspen-

associated species are grouped together (top panels) and for each dispersal group considered

separately. Species richness values were adjusted to account for the other factor in the model

(size or connectivity) whenever that factor was significant. Fitted lines indicate when a factor

was significant at P < 0.05 in a model with both factors (solid line) or only the significant factor

(dashed line) included. All variables are log-transformed but shown on the original scale.


25

Figure 2.2 The effect of (a) stand size and (b) connectivity on the relative representation of

species belonging to each dispersal mode group from a PCoA using the Bray-Curtis coefficient.

We only display the stand characteristic-axis score combinations that were significantly

correlated. Each axis was delineated by changes in group representation: axis 1 primarily

summarized variation in wind and animal dispersed species richness (rwind = -0.35, ranimal = 0.35)

but not the richness of species with no dispersal aid (rno aid = -0.08). Axis 2 summarized variation

in the richness of species with no dispersal aid (rno aid = 0.91), as well as wind (rwind = -0.63) and

animal (ranimal = -0.51) dispersed species.


26

Figure 2.3 Evidence for the role of competition but not herbivory in mediating relationships

between the stand characteristics and species richness. (a) The observed degree of negative

covariances in species richness between dispersal mode groups (solid line) compared to a null

distribution of random outcomes. An observed value to the left of the distribution indicates that

covariances are less than expected by chance, a result interpreted as indicating that competition

among groups structures their distributions. (b) The effect of stand connectivity on ungulate

herbivore activity, as estimated from scat survey data.


27

Table 2.1 Effects of stand size and connectivity on log-transformed species richness. ‘All

species’ includes all aspen-associated species from the three dispersal mode groups.

Dispersal mode #

species α estimate

log Stand size log Stand connectivity

b t1,23 P b t1,23 P

All species 67 88.5 0.34 4.89 <0.001 -0.19 -1.85 0.078

No dispersal aid 32 5* 0.40 4.25 <0.001 -0.02 -2.18 0.0411

Wind-dispersed 17 8.5 0.07 0.68 0.507 0.03 1.68 0.1072

Animal-dispersed 18 254.5† 0.29 4.35 <0.001 -0.05 -0.30 0.768

Note: significant effects are bolded; all df = 24. b is the slope of the relationship.

1Was only significant when stand size was included in the model (t1,23 = -0.002, P = 0.998; Table

S2.4).

2Was significant when stand size was not included in the model (t1,23 = 2.41, P = 0.025; Table

S2.4).

*Using an α estimate of 2.43 m, the average for species with no dispersal aid from Thomson et

al. (2011), provided qualitatively equivalent model fit for this group (log (Stand size) P < 0.001;

log (Stand connectivity) P = 0.04; see supplementary material in Appendix A).

†Thomson et al. 2011 separated animal dispersal into ingestion (n = 116), attachment (n = 4) and

seed-caching (n = 26). We calculated a weighted mean based on the number of species in each

category.


28

Table 2.2 Effects of stand size and stand connectivity on species composition (axis 1 and 2

scores of PCoA using Jaccard dissimilarity coefficient) by dispersal mode.

Axis

#

Dispersal

mode

#

species

α

estimate


b t1,23 P b t1,23 P

1 All species 67 88.5 -0.11 -3.13 0.005 0.14 2.57 0.018

No dispersal

aid 32 5* -0.02 -0.34 0.736 0.01 0.84 0.411

Wind-

dispersed 17 8.5 NA NA NA NA NA NA

Animal-

dispersed 18 254.5 -0.15 -3.44 0.002 0.28 2.74 0.012

2 All species 67 88.5 0.12 3.32 0.003 -0.07 -1.38 0.183

No dispersal

aid 32 5* 0.12 2.44 0.024 -0.01 -0.98 0.341

Wind-

dispersed 17 8.5 NA NA NA NA NA NA

Animal-

dispersed 18 254.5 -0.0650 -1.25 0.224 0.05 0.40 0.694

Note: significant effects are bolded; all df = 24. b is the slope of the relationship. We could not

calculate Jaccard dissimilarity for plots in the wind-dispersed species group, because had many

species had single occurrences.

*α estimates ranging from 2-5 m provided qualitatively equivalent model fit for this group.


29

Discussion

Our study highlights the importance of considering variation in species’ dispersal modes in

metacommunity studies. When dispersal mode was ignored and all aspen-associated species

were grouped together, species richness per unit area was positivity associated with stand size

only (Table 2.1; Fig. 2.2), which has been observed in some (Holt, Robinson & Gaines 1995;

Harvey & MacDougall 2014) but not all (Holt, Robinson and Gaines 1995) metacommunities.

Our approach of separating species by fruit type, a life history characteristic that affects how

seeds are dispersed across landscapes, clarified how relationships between stand size,

connectivity, and diversity differ among species’ with different dispersal modes (Table 2.1; Fig.

2.2).

Larger stands contained more animal-dispersed species and species with no dispersal aid, a

pattern consistent with classic theory in which bigger patches support larger populations that are

less prone to extinction. However, animal-dispersed species’ responses to stand size might also

be explained by habitat selection by seed-dispersing animals. If animals preferentially select

larger patches, animal-dispersed species might be underrepresented in small patches simply

because their dispersal agents do not transport them there. Although we did not quantify animal

abundances within the aspen patches, previous work has documented that many bird and large

mammal species prefer larger stands (Johns 1993; Oaten & Larsen 2008), and are thus more

likely to deposit seeds in these stands. Interestingly, the diversity of wind-dispersed species was

unaffected by stand size. Although we can’t isolate the specific mechanism driving this pattern,

many of the wind-dispersed species found in our study, such as the Antennaria, Cirsium, and

Lactuca, are considered ruderal species, therefore their persistence should be more generally

limited by disturbance events (which in this system likely occur at low levels among all patches)

than factors such as stand size.

Although the range of relationships between connectivity and diversity presented by previous

empirical work precludes a single prediction, we expected to see a positive or hump-shaped

relationship, as these have most commonly been found in other studies (Kneitel & Miller 2003;

Matthiessen & Hillebrand 2006; Howeth & Leibold 2010; Chase et al. 2010; Vanschoenwinkel,

Buschke & Brendonck 2013). Consistent with these studies, we found evidence that connectivity


30

had a positive effect on the richness of wind-dispersed species (Fig. 2.2, Table S2.4), indicating

that colonization rates in this group are limited by connectivity; the most distant, least connected

site had only a single wind-dispersed species. Although wind-dispersed species may access all

stands via infrequent long-distance dispersal events (e.g., in wind storms; Soons, Nathan & Katul

2004), colonization events would be rare compared to extinctions, thus creating this gradient in

diversity.

We also detected a negative relationship between connectivity and richness for species with no

dispersal aid, which may be suggestive of the declining half of a hump-shaped curve. The most

widely accepted explanation for a decline in richness in highly connected patches is that

competitively dominant or generalist predator species that are poor dispersers can dominate

highly-connected patches and drive other species locally extinct (Forbes & Chase 2002; Kneitel

& Miller 2003; Cadotte 2006b; Chase et al. 2010; Matthiessen et al. 2010; Vanschoenwinkel,

Buschke & Brendonck 2013). If this were the case, we would expect to see either negative

relationships between dispersal mode groups (competitor hypothesis), increased herbivore

activity in highly connected patches (predator hypothesis), or both. Although our observational

dataset does not allow us to discriminate definitively among the mechanisms underlying

observed patterns, estimates of competition and herbivory were used to determine whether

observed patterns were consistent with either of these mechanisms. We found some evidence in

support of the competitor hypothesis only: the null model revealed negative covariance among

dispersal mode groups (P = 0.038; Fig. 2.4a). This suggests that species with no dispersal aid

might be competitively suppressed by the other dispersal mode groups in highly-connected

stands. We note, however, that theory predicts that the no dispersal aid group should be

competitively dominant, and our results suggest the opposite. Our results are nonetheless

consistent with experimental work in aspen stands in the boreal forest (Gilbert, Turkington &

Srivastava 2009), and raises questions about persistence of weak dispersers when they are also

weak competitors.

Our investigation of turnover in species composition among dispersal mode groups suggests that

these groupings capture meaningful variation in how species in this aspen metacommunity move

across the landscape (Fig. 2.3). Interestingly, our analyses also indicate that there is additional


31

variation within groups in how species are responding to stand size and connectivity (Table 2.2).

For example, both Rocky Mountain juniper (Juniperus scopulorum) and prickly wild rose (Rosa

acicularis) are animal dispersed, but differ in their association with large or small stands. This

variation would not likely be fully accounted for by incorporating information on species-

specific dispersal abilities, because species also varied in their responses to stand size. This result

could reflect interspecific variation in sensitivity to local extinction, or different animal vectors

(i.e., bird, rodent, deer) for animal-dispersed species. Overall, our findings indicate that while

including dispersal mode is important for understanding metacommunity dynamics, investigation

of interspecific differences in dispersal within modes may further clarify how spatial dynamics

structure diversity in this ecosystem.

Early metacommunity theory posited that species richness will increase per unit area in highly

connected patches when communities are comprised of weakly interacting species (Holt 1993).

This prediction is often overlooked, with many metacommunity studies estimating diversity

across a patch size gradient and confounding patch size with the area sampled by increasing

sampling effort proportionally with patch size. Other researchers have recognized this problem

and subsequently accounted for unequal sampling post hoc through rarefaction or by randomly

selecting a subset of patches (e.g., Meynard et al. 2013). Although our approach is unlikely to

capture the total diversity across all aspen stands, it is more consistent with metacommunity

predictions than approaches that attempt to standardize sampling effort post hoc. Standardizing

area in metacommunity sampling has long been advocated (Holt 1993) because this method

directly tests species’ responses to patch size by eliminating the confounding effects on species

richness of increased sampling effort and habitat heterogeneity in larger patches.

Unlike more classic examples of metacommunities, such as ponds or islands, the boundaries of

aspen stands are diffuse to some species that also occur in the surrounding grassland matrix. For

example, matrix-associated species may be present in the aspen stands if they are generalists that

persist in both habitat types, or if they are grassland specialists experiencing source-sink

dynamics whereby populations in aspen stands are supplemented with incoming colonists from

the matrix. In either case, these species’ pose a conceptual and methodological challenge for how

the metacommunity is defined, given that the matrix may be inhospitable to some species but not


32

others (Delong & Gibson 2012). In our study, we used a paired plot design consisting of one

grassland-matrix plot surveyed adjacent to each aspen stand. This method allowed us to identify

and exclude species that were highly associated with the grassland matrix, and that were

therefore not likely to be constrained by the boundaries of the aspen metacommunity. Of the 170

species observed in our paired plot surveys, 66 and 44 species were found primarily (≥ 66% of

the time) or exclusively in aspen stands, respectively, with 36 species occurring only in grassland

plots. This means that, of the 110 total species found in the aspen stand plots, 66 experience the

grassland matrix as inhospitable, and thus adhere to classic metacommunity definitions (Leibold

et al. 2004). Our data revealed effects of patch size and connectivity on diversity that were robust

to the choice of cut-off that was used (i.e., species occurring in aspen stands at least 50, 66, and

75% of the time; Table S2.2). It was only when all species (i.e., generalists and grassland

specialists) were included that we failed to detect these trends. The paired plot design used here

could be implemented in future work in similarly diffuse habitat-patch networks (e.g., coral

reefs, serpentine hummocks etc.), a recognized class of metacommunties that dominates many

landscapes (Leibold et al. 2004).

Our assessment of the effects of stand size and connectivity on diversity is one of the first to use

a naturally-patchy metacommunity to test how differences in species dispersal modes influence

local diversity. In doing so, we show that dispersal mode mediates the effects of stand size and

connectivity on metacommunity diversity in ways that would be obscured if all species were

grouped together. Our results also raise the intriguing possibility that life history traits that affect

dispersal may also alter distributions of these groups through differences in competitive ability,

habitat specific movement of animal vectors, and different local extinction rates. Our approach to

studying the effects of dispersal and patch characteristics on metacommunity diversity has

provided new insights into the complex relationship between patch characteristics and

metacommunity diversity.

Acknowledgments

We would like to thank Laura May for field assistance. N. T. J. received financial assistance

from the Thompson Rivers University Undergraduate Student Research Experience Award


33

(UREAP). We also thank two anonymous reviewers whose thoughtful comments on a previous

version of this manuscript improved it considerably.

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Chapter 3

Are species larger at high latitudes? Testing latitude-body size relationships in zooplankton

This paper in preparation to be published as: Jones, N.T., Moran J. & Gilbert, B. Are species

larger at high latitudes? Testing latitude-body size relationships in zooplankton.

Abstract

According to classic ecological rules, mean body size within and among species increases with

lower temperatures, creating a gradient of increasing body size with increasing latitude. Short-

term experimental evidence appears to support this prediction for zooplankton, as they

commonly decrease in size in warmer waters. However, latitudinal body size patterns remain

unclear for many ectothermic animals, but need to be clarified in order to understand long-term

effects of temperature on body size. We examined how body size within and among freshwater

aquatic zooplankton species changes with latitude by measuring the body size of crustacean

zooplankton communities (Cladocera and Copepoda) from 19 freshwater lakes that are spread

across an 1800 km gradient from southern British Columbia to the Yukon Territory. We found

weak evidence that body size is associated with latitude, and no evidence that suggests there is a

consistent trend in body size across species. When examining body size within species, we found

a significant effect of latitude for only three species, with two species showing significant

increases in size with latitude, and one species showing a marginal decrease. The overall null

result for within-species trends was not due to low power – species that occurred more frequently

had smaller confidence intervals but estimates that were much closer to zero. When examining

body size among species, we also found no trend for the mean community body size with respect

to latitude, regardless if body size was weighted by local abundance or simply averaged across

species. Additional environmental variables impacted the body size of a subset of species, but

similar to latitude, the overall effects were variable, and including these variables in our analysis

did not change the overall relationship between body size and latitude. Our results are consistent

with previous research investigating the body size patterns of insects, and indicate that latitude-

body size relationships cannot be consistently applied to ectothermic organisms. When

considered in relation to results of short-term experimental studies, our findings suggest that the

CHAPTER 3: BODY-SIZE LATITUDE RELATIONSHIPS

39

effects of climate change on the body size of zooplankton communities will be difficult to

predict and may show different short- and long-term trends.

Introduction

Differences in body size, both within and among species, have long fascinated biologists

(Schmidt-Nielsen 1984; Peters 1986). The body size of organisms is often associated with

latitude and temperature (Gillooly and Dodson 2012) and contemporary temperature increases

with climate change have renewed interest in temperature-body size relationships (Millien et al.

2006; Teplitsky et al. 2008). The effect of temperature on body size is particularly relevant for

ectotherms as temperature directly modifies development time and fitness for those organisms

(Atkinson 1994; Gillooly et al. 2001). Body size also has important implications for many

ecological processes, including energy requirements for population maintenance, competitive

asymmetries, and predator-prey dynamics (Yodzis & Innes 1992). Despite the increase in studies

examining latitude-body size relationships (e.g., Rypel 2014), high variation in the association

between body size and latitude for ectotherms indicates that additional studies are necessary to

clarify how the relationship manifests in diverse taxa (Shelomi 2012).

Three ecological rules attempt to explain geographic patterns of inter- and intra-specific body

size. Associations between geography and body size were first formalized by Bergmann (1847)

who observed the ecogeographical pattern of larger species being found in cooler regions,

whereas warm climates contain relatively small species, this pattern is now referred to as

“Bergmann’s rule” (translated in Mayr 1956 and James 1970). Over time, Bergmann’s rule has

been extended to explain interspecific (Blackburn, Gaston & Loder 1999) and intraspecific

(James’s rule; James 1970) differences in body size across a climatic gradient; with body size

generally increasing among species and populations at high latitudes where colder temperatures

prevail. Some scholars argue that Bergmann originally created the rule to explain size differences

in endotherms (although there is debate on this; Rensch 1938, Geist 1987, Watt et al. 2010), but

more recently additional efforts have been made to explain body size variation in ectotherms

across broad biogeographic gradients (e.g. Berke et al. 2013).


40

Experimental research has often supported the underlying link between higher temperatures and

smaller body size. For example, syntheses of experiments found empirical evidence that was

consistent with Bergmann’s hypothesis by demonstrating that individuals reared at higher

temperatures were usually smaller than individuals reared at low temperatures (Atkinson 1994;

Forster, Hirst & Atkinson 2012). This finding is more generally referred to as the “Temperature-

Size rule”, and appears to be particularly important in aquatic ecosystems (Forster et al. 2012).

The effect of temperature on body size may be direct or indirect. Temperature directly speeds up

metabolic rates (Gillooly et al. 2001; Brown et al. 2004), with higher temperatures causing

juveniles to reach adulthood faster, but this accelerated maturation comes with the cost of a

reduction in body size. This smaller body size has been hypothesized to be adaptive because it

may be thermally advantageous in a warmer climate, as smaller size provides a greater amount of

surface area relative to total body volume, which facilitates heat loss at high temperatures (Mayr

1956; Meiri 2011). Temperature may also have an indirect effect through oxygen availability in

aquatic ecosystems, where a greater surface area to volume ratio can aid uptake in warmer waters

that contain less available oxygen (Forster et al. 2012). Temperature can also indirectly affect

body size through its influence on food availability and predation risk (Gliwicz 1990; DeLong &

Hanson 2011; Gilbert et al. 2014; DeLong et al. 2015). The large array of temperature-dependent

factors that can influence body size has led to considerable debate about the mechanisms driving

the biogeographical patterns of body size (Blackburn et al. 1999; Watt et al. 2010). Regardless of

the underlying mechanism(s) driving body size-temperature relationships, theory and

experimental research suggest that temperature should have an important effect on body size.

The strength of support for Bergmann’s rule appears to depend on the life-history characteristics

of the organism. Many studies of homeotherms have documented a positive relationship between

body size and latitude. For example, the majority of studies on mammals (up to 65%) have

concluded that body size increases with latitude (Blackburn & Hawkins 2004) and meta-analyses

examining Bergmann’s rule in birds also tend to support the theory (Ashton 2002; Meiri &

Dayan 2003). On the other hand, studies of ectotherms provide less evidence for positive latitude

body-size clines (Blackburn et al. 1999). For example, poikilothermic groups such as turtles,

have been shown to increase in body size as latitude increases, while other groups such as fishes,


41

lizards and snakes exhibit reverse Bergmann clines (Ashton & Feldman 2003). Insects vary

strongly in their tendency to support Bergmann’s predictions, displaying a diversity of body-size

latitude relationships (Shelomi 2012). Research on latitudinal patterns in body size of freshwater

cladocerans has shown a different pattern altogether, with one study showing body size declining

north and south of 60° latitude (Gillooly & Dodson 2000), and another showing weak or non-

existent trends (Havens et al. 2014). However, in many instances this research has been

criticized for methodological concerns, such as examining mean interspecific trends (e.g.,

Gillooly & Dodson 2000; Havens et al. 2014), while failing to account for intraspecific

differences in body size (Shelomi 2012).

In this paper, we investigate latitudinal patterns in zooplankton body size across a latitudinal

gradient in western Canada. Latitudinal gradients offer a convenient proxy for temperature, as

temperature, in the lakes we sampled and more generally, is negatively correlated with

increasing latitude (Fig. S3.1). We collected freshwater crustacean zooplankton (Cladocera and

Copepoda) from 19 lakes that span an 1800 km latitudinal gradient to ask the following

questions: 1) what is the relationship between zooplankton body size and latitude? 2) Are these

patterns consistent among species? 3) do these relationships scale to the community level? And,

4) are other lake characteristics, besides latitude and temperature, associated with zooplankton

body size? Understanding how the body size of organisms is affected by different temperatures

over latitudinal gradients may provide important insight into how organisms will respond to

elevate temperatures associated with climate change.

Materials & methods

Study species & species sampling

The 19 lakes included in this study were part of a larger sampling effort in 2011 that collected

zooplankton communities from 43 freshwater lakes (Table S3.1 and Table S3.2 in Appendix B:

Supplementary information for chapter 3) across a ~1800 km latitudinal gradient in western

Canada, ranging from southern British Columbia to the middle of the Yukon Territory. We used

the contemporary zooplankton community data from chapter 5 to select the appropriate lakes to

include in this study. We had two main selection criteria. First, to isolate intra-specific body size

patterns, we chose lakes that contained species which occurred in at least three lakes. Second,


42

because the primary goal of this study was to determine the relationship between body size and

latitude, we selected lakes containing species that were distributed across a latitudinal range of at

least five degrees latitude.

To control for any confounding effects of the growing season on body size, we began

sampling lakes in the southern portion of the latitudinal gradient, sampling a subset of

lakes as we moved north along the transect, and others as we returned south. Plankton

communities were collected by hauling a Wisconsin net [mouth diameter 24 cm, net

mesh 76 µm] through the water column, beginning from near the lake bottom, at the

approximate center of each lake. Two vertical plankton tows per lake were taken and

zooplankton communities were immediately preserved in 70% ethanol.

Environmental covariates

Temperature is not the only factor that affects body size. To determine if local abiotic conditions

impact the body-size latitude relationships we observed, we quantified a subset of biotic and

abiotic characteristics that can also directly and indirectly affect the body size of zooplankton

(lake depth, chlorophyll a concentration (a measure of productivity), dissolved oxygen

concentration, fish predation; Table S3.1, Table S3.3). We used a YSI 6-series multiparameter

water quality sonde (Integrated Systems & Services, Yellow Spring, OH, USA) to determine the

water temperature, chlorophyll a concentration and dissolved oxygen concentration at the time

that we sampled the zooplankton communities. We used published estimates of lake depth and

the diversity of fish communities from the literature (Anderson 1974; Lindsey et al. 1981). Due

to the limited availability of data on fish density in these lakes, we simply used the number of

fish species in a lake as a proxy for zooplankton predation, as many species of fish eat

zooplankton at some developmental stage.

Body size measurements

We combined the two plankton tows and then randomly took subsamples, measuring the first 30

adults encountered for each species in a lake. Species names follow the taxonomy of Thorp and

Covich (2010) and Sandercock and Scudder (1994). For a subset of rare species we were unable

to find thirty individuals to measure. In these cases, we scanned samples and measured as many


43

individuals as possible. Next, we measured zooplankton body length under a dissecting

microscope using Olympus Stream software. The body size of Cladocera was measured from the

center of the eye, to the base of the tail spine (Gliwicz 1990; Yurista & Brien 2001). For

Copepoda, we measured the length of the prosome (Breteler & Gonzalez 1988; Ban 1994).

Statistical analysis

Prior to analysis of within-species trends, we removed all individuals that could not be identified

to the species level; Ceriodaphnia, Chydorus and Diaptomus species could not be identified to

species level and were removed. We also eliminated eight species that were so uncommon that

they occurred in less than three lakes, as we could not test the slope of the relationship between

latitude and body size for these species. We note that although these removed species could not

be used for within-species trends, they were incorporated into community-level mean body size

estimates that used each lake as the unit of measurement (below).

Following these removals, our within-species data set consisted of 10 species from a total pool of

21 species that have been documented in these lakes. Body size measurements were

logarithmically transformed to minimize heteroscedasticity and meet normality assumptions.

Before conducting any species-latitude analyses, we confirmed that latitude is a good proxy for

water temperature using linear regression. We used statistical software R for all analyses (R Core

Team 2014).

We used linear mixed models to conduct a cross-species analysis that tested if zooplankton body

size is associated with latitude (lmer function in lme4 package; Bates et al. 2014). The full model

included latitude, species and their interaction as fixed effects. The individual lake and species

were included in the model as random effects to account for correlated errors. The multispecies

model indicated that the effect of latitude on body size depended on the species considered,

therefore we ran a separate mixed model for each species to isolate the species-specific effect of

latitude on body size. To generate an average slope estimate across species, we reran the analysis

with all species and latitude as a fixed effect with a random slope and intercept, as well as a

random effect for lake.


44

Because we observed no significant effect of latitude on body size for the majority of the species

we considered (Table 3.1; Fig. 3.1), we tested how additional environmental factors (chlorophyll

a, dissolved oxygen and lake depth) influence the average adult body size of zooplankton. We

used a similar statistical approach as described above. First, we tested if the interaction between

the environmental variable and species improved model fit (using log ratio tests). If the

interaction did improve model fit, we added that variable to a final model that included latitude.

This resulted in a final model that included latitude, depth, chlorophyll a, dissolved oxygen, fish

predation (and their interaction with species).

To test if the body size of the entire crustacean zooplankton community shifted across a

latitudinal gradient, we calculated two measures: mean community body size and mean weighted

community body size. Mean weighted community body size was calculated by multiplying the

average body size of each species by its relative abundance within a lake (data from Jones and

Gilbert 2016 in review). For (unweighted) mean community body size, each species contributed

equally to the average, regardless of its local abundance in the lake. We used linear regression to

test if the mean weighted and unweighted community body size changed with latitude. Unlike

the previous analysis, all species present in each lake ‒ regardless of the number of times they

occurred in a lake ‒ were included in the calculation of community body size.

Results

Lake water temperature declined as latitude increased (F1,16 = 12.42, P <0.0001, r2 = 0.41; Fig.

S3.1), indicating that the latitudinal gradient we sampled represents a temperature cline. The

effect of latitude on body size depended on which zooplankton species was being considered

(significant latitude*species interaction; df = 6, χ2= -162.80, P < 0.0001), and the overall slope

for all species was not different from zero (P = 0.70). When we ran separate linear mixed models

for each species, we found weak evidence that adult body size is associated with latitude. In the

majority of cases (8/10 species), latitude had no effect on the average body size of zooplankton

(Table 3.1, Fig. 3.1a, Fig. S3.2). Importantly, the two species that do display a statistically

significant body size-latitude relationship occurred in few lakes. Specifically, the copepod

species, Acanthocyclops vernalis, increased in length by an average of 14%, while the cladoceran

species, Holopedium gibberum, increased by more than 50% (Table 3.1). However, sample sizes


45

for these species were small, with A. vernalis occurring in four lakes and H. gibberum occurring

in only three lakes. We saw weak evidence for reverse Bergmann clines (larger body size at

lower latitude sites), with only Leptodora kindii showing a marginally non-significant trend (P =

0.068). We re-ran the analysis with lake temperature as the independent variable and found

similar relationships (Table S3.3; Fig. 3.1b), except that the effect of temperature was weaker;

only one species showed a clear trend of increasing body size at lower temperatures.

Figure 3.1 Plots of the slope (points) and 95% bootstrapped confidence intervals (lines). Lines

that do not overlap with zero are significantly associated with (a) latitude or (b) temperature.

Numbers indicate the number of lakes that the species was present.


46

Table 3.1 Summary of latitudinal extremes and the body size of each species given by the model fit. Body size values were back

transformed from the predicted values that were generated from the linear model on log-transformed body size. P-values less than 0.05 are

highlighted in bold, and those less than 0.1 are highlighted with italics.

Minimum Maximum

Species

Latitude

(°)

Predicted body

size (µm)

Latitude

(°)

Predicted body

size (µm)

Number of

lakes

P-value % change in

body size

Acanthocyclops vernalis 49.38 202 54.25 234 4 0.025 14

Bosmina longirostris 49.38 185 62.30 232 16 0.318 20

Cyclops scutifer 54.02 304 62.30 296 8 0.362 -3

Daphnia longiremis 49.90 338 62.30 389 10 0.439 13

Daphnia longispina 50.88 447 62.30 386 6 0.880 -16

Daphnia pulex 49.38 521 58.45 415 8 0.412 -25

Diacyclops thomasi 49.38 271 58.45 314 12 0.907 14

Diaphanosoma luechtenb. 50.02 272 54.37 482 7 0.671 44

Holopedium gibberum 49.38 210 54.37 448 3 0.001 53

Leptodora kindii 50.08 1837 54.25 1123 3 0.069 -64


47

The four environmental variables we considered (chlorophyll a, dissolved oxygen, lake depth,

fish diversity) all significantly improved model fit (Table 3.2). Similar to the effect of latitude on

body size, each of these variables showed significant interactions with species, and in general the

species-specific effect of each variable was idiosyncratic, with species showing positive and

negative relationships (Table 3.3). However, including these factors as covariates in the latitude

versus body size analysis did not change the overall effect of latitude. There was still no

consistent effect of latitude among species (main latitude effect P = 0.38), and the slopes for

individual species were consistent with the first analysis (correlation of estimates, r = 0.86),

although some species showed slightly stronger positive trends with latitude (e.g., H. gibberum)

and some showed stronger negative trends (e.g., Daphnia longiremis; Table S3.4).

Table 3.2 Results of log likelihood tests for the final model that includes latitude as well as

additional environmental variables that were significantly associated with zooplankton body size

in a separate linear mixed model. Table headings are: degrees of freedom (Df) and log-likelihood

ratio (LRT).

Df LRT p-value

Latitude*species 6 162.8 <0.001

Depth*species 6 75.9 <0.001

Fish richness*species 6 77.5 <0.001

Chlorophyll a*species 6 41.1 <0.001

Dissolved oxygen*species 6 111.0 <0.001


48

Table 3.3 The number of species that significantly increased or decreased in body size with

latitude, temperature or other environmental variables when tested in isolation.

Variable Increase Decrease

Latitude (°) 2 0

Temperature (°C) 0 1

Depth (m) 2 2

Chlorophyll a (µg/L) 0 0

Dissolved oxygen (mg/L) 1 2

Fish (n) 1 2


49

We also tested for evidence of a community level shift in the average body size of zooplankton.

There was no relationship between latitude and the mean body size of the entire crustacean

community. This relationship was consistent whether we considered the raw average (t = -0.39,

P= 0.71; Fig 3.2a) or weighted body size by the relative abundance of species’ (t = -0.73, P =

0.48; Fig 3.2b).

Figure 3.2 The association between latitude and the mean a) unweighted and b) weighted

zooplankton community body size. Community body size was weighted by the local abundance

of each species. Neither relationship is significant (F1,17 = 0.15, P = 0.70 and F1,17 = 0.53, P =

0.47 respectively).


50

Discussion

The macroecological pattern of shifts in body size with latitude is one of the most widely

accepted ecological patterns in nature, and increasingly important to understand given global

shifts in climate. The results of our study highlight that the body size patterns of aquatic

zooplankton taken from an 1800 km north-south gradient do not conform to the predictions of

Bergmann (1847), James (1970) and Atkinson (1994). Below, we discuss how our results relate

to previous work on geographic patterns in body size as well as empirical studies on temperature

and body size.

Our results revealed that shifts in body size with latitude and temperature are largely absent from

zooplankton communities that we collected, despite having a samples from lakes that ranged by

1800 km and approximately 10° C (summer surface temperature) along a north-south gradient.

This result adds to the growing number of case studies where body-size latitude associations do

not display Bergmann clines. For example, in a recent meta-analysis on insects, Shelomi (2012)

revealed idiosyncratic body-size latitude relationships, with equal support for Bergmann and

non-Bergmann clines. Similarly, temperature can have inconsistent effects on the body size of

marine zooplankton, suggesting that zooplankton body size may be more strongly affected by

other factors (Sebastian et al. 2012). These studies are consistent with our work and suggest that

the body size of zooplankton and other ectotherms may respond heterogeneously to temperature

and latitudinal changes. The idiosyncratic response of body size to latitudinal increases that we

observed suggest that latitude- body size relationships that have been observed among birds and

mammals cannot be unanimously applied to ectothermic organisms such as zooplankton.

Although our results are consistent with some recent studies in other taxa, they differ from work

that has explicitly considered the relationship between body size and latitude in freshwater

zooplankton (e.g., Beaver et al. 2014). For example, Gillooly & Dodson (2000) amassed an

impressive cladoceran body-size dataset, from over 1100 western lakes that spanned Southern

and Northern hemispheres, and observed a striking increase in body size from tropical to

temperature regions. Differences in experimental methodology are a strong candidate for

explaining the discrepancy between their study and the results presented here. In this study we

sampled fewer lakes but more extensively, capturing accurate estimates of intraspecific size

differences. In contrast, Gillooly and Dodson (2000) used published species lists to indirectly


51

estimate body size for many lakes and did not incorporate intraspecific variation or species

abundance within a site. Indeed, a subsequent study that used more accurate body size estimates

showed weak or no support for Bergmann’s Rule, depending on the taxa considered (Havens et

al. 2014). The different results obtained by these studies and our own indicate that more detailed

measurements at the lake level can alter support for correlations between latitude and body size.

A common concern with analyses that fail to support a hypothesis is that the analysis is not

powerful enough, or that important covariates were not considered. The results from our initial

analysis, and follow-up analyses, suggest that these concerns are not likely to account for the null

results that we observed. For example, species that showed the largest (positive and negative)

changes in body size with latitude were those that had the smallest sample sizes (Fig. 3.1).

Indeed, the best-sampled species had very narrow confidence intervals, but slopes that were

extremely close to zero. Similarly, after accounting for potential covariates of body size, the

overall effect of latitude on body size was still not significant (P = 0.38), and we saw that

changes in the slope or standard error of the slope resulted in more positive and negative

relationships. In other words, accounting for covariates did not change support for a common

latitude-body size relationship within species.

Each of the environmental variables that we considered as covariates appears to be about as

important as latitude for zooplankton body size (Table 3.3), at least in our system. While it is

impossible to know with certainty what mechanism(s) drive body size in the lakes we

considered, factors beyond temperature have been hypothesized to be important (Atkinson 1995,

Gardner et al. 2011). For instance, it has been suggested that food availability and predation risk

may drive changes in zooplankton body size (Gliwicz 1990, Hart and Bychek 2011). However,

consistent with recent work testing the impact of biotic and biotic forces on the body length of

zooplankton in reservoirs across the western United States, the concentration of chlorophyll a, a

proxy for resource abundance, on zooplankton body size was weak (Beaver et al. 2014). Fish

predation is associated with smaller zooplankton because fish preferentially predate on larger

individuals (Dodson & Brooks 1965), which selects for rapid development (Allan 1976). We

found some evidence that supports this hypothesis, the number of fish species decreased the

average body size of two species, however, one species showed the opposite trend (Table 3.2).

The direct and indirect effects of environmental variables are complex and likely interact; future


52

work that disentangles the influence of these factors could help explain deviations from

ecogeographic rules.

Our results also highlight discordance between short-term experiments and observational

biogeographic studies. Ecologists have been attempting to generate clear predictions for how

organisms will react to climate-change driven temperature increases (Gilman et al. 2010). Recent

research suggests that ectotherms will undergo a reduction in body size as a response to global

warming (Daufresne et al. 2009), a phenomenon that has been referred to as the “third universal

response to global warming”. Controlled studies that manipulate temperature and subsequently

measure body size usually find a reduction in body size at higher temperatures. For instance, a

meta-analysis of the response of aquatic ectotherms to warmer temperatures found that 90 % of

aquatic ectotherm species decreased in size at higher temperatures; specifically, this study found

that 12 out of 13 species of Crustacea reached smaller body sizes at higher temperatures

(Atkinson 1995). However, these studies normally do not differentiate between the role of

phenotypic plasticity and microevolution in generating these patters (Kingsolver & Huey 2008;

Teplitsky et al. 2008). Within a given population, larger phenotypes are frequently more fit, and

thus selection within a specific temperature regime may lead to larger individuals with time,

counteracting plastic responses (Kingsolver & Huey 2008). These different responses could

explain why communities that have been collected from nature and evolved in situ under

different thermal regimes do not support the predictions of the Temperature-Size rule etc., while

results from controlled experiments often do support these rules.

Thermal stratification and habitat partitioning in lakes may contribute to the weak evidence for

geographic clines in body size that we documented. Many of the zooplankton species in this

study are found throughout the water column. In addition, some species such as H. gibberum

have been observed to migrate within the water column during the day (Balcer, Korda & Dodson

1984), while other species such as the copepod species D. thomasi and C. scutifer, tend to be

found deeper in the water column below the thermocline, where temperatures are low throughout

the growing season (Elgmork 1967). Because the zooplankton community integrates individuals

from throughout the water column, associations between temperature and body size may be

particularly weak in this these taxa. More generally, thermally stratified lakes are one example of

a habitat that allows species to maintain thermal conditions that are distinct from the surrounding


53

environment, and thus may provide opportunities for species to avoid typical temperature

constraints.

In conclusion, the lack of response of zooplankton body size to latitudinal changes that we

observed support a growing number of studies that show Bergmann’s rule cannot explain

zooplankton patterns of body size across latitudinal gradients. The different conclusions of

controlled experiments and observational studies of body size patterns in nature suggest that it

may be difficult to predict how body size of crustacean zooplankton will respond to global

warming.

Acknowledgments

We would like to thank Veronica Jones for field assistance and NSERC (B.G., Discovery Grant)

as well as Ontario Graduate Scholarships (N.T.J.) for funding.

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57

Chapter 4

Changing climate cues differentially alter zooplankton dormancy dynamics across latitude

This paper is published as: Jones, N.T. & Gilbert, B. (2016) Changing climate cues differentially

alter zooplankton dormancy dynamics across latitudes. The Journal of Animal Ecology, 85, 559–

569.

Abstract

In seasonal climates, dormancy is a common strategy that structures biodiversity and is necessary

for the persistence of many species. Climate change will likely alter dormancy dynamics in

zooplankton, the basis of aquatic food webs, by altering two important hatching cues: mean

temperatures during the ice-free season, and mean day length when lakes become ice free.

Theory suggests that these changes could alter diversity, hatchling abundances and phenology

within lakes, and that these responses may diverge across latitudes due to differences in optimal

hatching cues and strategies. To examine the role of temperature and day length on hatching

dynamics, we collected sediment from 25 lakes across a 1800 km latitudinal gradient and

exposed sediment samples to a factorial combination of two photoperiods (12 and 16 hours) and

two temperatures (8ºC and 12 ºC) representative of historical southern (short photoperiod, warm)

and northern (long photoperiod, cool) lake conditions. We tested whether sensitivity to these

hatching cues varies by latitudinal origin and differs among taxa. Higher temperatures advanced

phenology for all taxa, and these advances were greatest for cladocerans followed by copepods

and rotifers. Although phenology differed among taxa, the effect of temperature did not vary

with latitude. The latitudinal origin of the egg bank influenced egg abundance and hatchling

abundance and diversity, with these latter effects varying with taxon, temperature and

photoperiod. Copepod hatchling abundances peaked at mid latitudes in the high temperature and

long photoperiod treatments, whereas hatchling abundances of other zooplankton were greatest

at low latitudes and high temperature. The overall diversity of crustacean zooplankton (copepods

and cladocerans) also reflected distinct responses of each taxon to our treatments, with the

greatest diversity occurring at mid latitudes (~56° N) in the shorter photoperiod treatment. Our

results demonstrate that hatching cues differ for broad taxonomic groups that vary in

CHAPTER 4: DORMANCY & CLIMATE CUES

58

developmental and life-history strategies. These differences are predicted to drive latitude-

specific shifts in zooplankton emergence with climate change, and could alter the base of aquatic

food webs.

Introduction

Dormancy is a common strategy that is essential to the persistence of many species in seasonal

climates and has the potential to be strongly impacted by climate change (Hance et al. 2007;

Williams, Henry & Sinclair 2015). Both the onset and termination of dormancy depend on

environmental cues, with many species from diverse taxa responding to climatic conditions such

as temperature and precipitation (Vandekerkhove et al. 2005; Hance et al. 2007; Levine,

Mceachern & Cowan 2008). Despite the importance of dormancy for community assembly and

ecological dynamics generally (Hairston & Kearns 1995; Ellner et al. 1999; McNamara &

Houston 2008), there has been relatively little research on the impacts of climate on dormancy

dynamics for many taxa, and the work that has been done has often been too localized to allow

for the general predictions needed when planning for climate change (Hairston 1996; Dupuis &

Hann 2009; Angeler 2011).

Zooplankton are numerically and functionally dominant animals that form the basis of aquatic

food webs, with taxa performing different roles within lake ecosystems (Barnett, Finlay &

Beisner 2007). Although dormancy is a critical part of the annual life cycle of most zooplankton

species (Varpe 2012), biogeographic trends in zooplankton dormancy dynamics, and their

climatic underpinnings, are not understood. Nonetheless, most zooplankton species are sensitive

to environmental cues that alter their hatching dynamics, and as a result may be particularly

sensitive to climate change. Shifts in the timing, abundance or diversity of species that hatch as

climate cues shift could scale up to impact the functioning and trophic structure of aquatic

ecosystems (Winder & Schindler 2004; Woodward, Perkins & Brown 2010; Dossena et al.

2012).

In freshwater lakes, climate change is altering cues that terminate zooplankton dormancy by

changing the timing of ice-free conditions in spring and average spring temperatures (IPCC

2013). In temperate and polar aquatic ecosystems, water temperature and photoperiod are


59

considered the primary cues for terminating zooplankton dormancy (Stross 1966; Sorgeloos

1973; May 1987; De Stasio 2004). Many zooplankton have long-term dormancy strategies,

where some proportion of eggs hatch in a given year and the remainder lay dormant, potentially

hatching in subsequent years (Cáceres & Tessier 2003). Despite some overwintering under lake

ice (e.g., Vanderploeg et al. 1992), most species produce eggs in the fall that hatch somewhat

synchronously in the spring as day length and temperature increase (Hairston, Hansen &

Schaffner 2000; but see De Stasio, 1990), producing a seasonal succession in the taxa that appear

(Hutchinson 1967).

A major challenge to predicting the impact of climate change on dormancy dynamics within

ecological communities is that hatching rates, or termination of dormancy, are likely to differ

across latitudes, even for similar taxa (Posledovich et al. 2015). In regions where active

individuals fail to reproduce in some years, species are predicted to have lower average hatching

rates (Cohen 1966; Levins 1969; Ellner 1985). As a result, the prevalence of dormancy has been

shown to increase towards the poles in some taxa, such as plants and insects, because high

seasonal variation and short growing seasons have selected for dormancy (Mousseau & Roff

1989; Molina-Montenegro & Naya 2012). This “temporal dispersal” strategy, referred to as bet-

hedging in the evolutionary literature (e.g. Venable 2007), and storage in literature on species

coexistence (e.g., Chesson 1994), maintains long term persistence by decreasing the mean and

variability of population growth among years (Slatkin 1974; Ellner 1985).

In addition to a gradient in hatching rates across latitudes, strong selection for high latitude

species to emerge and reproduce in a short growing season may result in a gradient of sensitivity

to the cues that break dormancy (Conover, Duffy & Hice 2009); the importance of fast

emergence from dormancy may be reduced in lower latitude regions with longer growing

seasons (Masaki 1961). As a result, conditions that are typical of an ideal spring (warm

temperatures during a short photoperiod), may elicit higher and faster hatching rates in northern

compared with southern lakes. More generally, the interplay among latitude, long-term

dormancy and phenology is expected to lead to latitudinal differences in hatching rates and cues

that correspond to differences in species’ traits (species sorting, e.g., Whittaker 1975) as well as

differences among populations of widespread species (local adaptation; e.g., Kawecki & Ebert

2004).


60

A second challenge to predicting the effects of climate on dormancy lies in determining whether

co-occurring taxa have qualitatively similar responses to changing climatic cues. When most

species in a community are limited by similar environmental constraints, responses to climatic

cues should be similar, as has been seen for some annual plants (Elmendorf & Harrison 2009).

However, the major zooplankton taxa have very different rates of development (Gillooly 2000),

minimum sizes at which reproduction occurs (Geller 1987; Maier 1994), and reproductive modes

and, as a result, rates of reproduction (Allan 1976). These differences may lead to a systematic

divergence in responses to the climatic cues that terminate dormancy, with smaller taxa being

more responsive to temperature (Winder & Schindler 2004; Adrian, Wilhelm & Gerten 2006). In

addition, species that have dormant stages may coexist by specializing on environmental

conditions that occur in only some years (via the storage effect; Chesson 1994). Indeed, the

sensitivity of zooplankton to hatching cues can differ at several taxonomic levels, from broad

zooplankton taxa (copepods, cladocerans and rotifers), to co-occurring species within lakes (e.g.,

Dupuis & Hann 2009). However, much of the literature on hatching dynamics has focused on

subsets of the diversity within a lake by examining a single species or taxon at a time.

Despite the potential for different responses to cues that end dormancy across latitudes and

among taxa, aquatic studies have yet to incorporate this complexity into studies of plankton

dormancy dynamics to understand the effects of current and changing climatic conditions. Prior

research has mainly focused on assessing dormancy dynamics in a small number of lakes within

a region (e.g., Arnott & Yan 2002) or has combined lake samples into regional mixtures

(e.g.,Vandekerkhove et al. 2005), precluding an analysis of latitudinal variation in hatching

dynamics.

In this study, we determine how temperature and day length impact dormancy dynamics of

freshwater zooplankton that differ in latitudinal origin. We collected sediment containing ‘egg

banks’ from 25 lakes across an 1800 km latitudinal gradient and exposed a subsample of the

sediment from each lake to a factorial treatment of high and low temperature crossed with long

and short photoperiod. By assessing the effects of temperature and photoperiod on hatching

abundance, diversity and phenology within each lake, we were able to test how these climatic

cues drive biotic responses of taxa that co-occur across a latitudinal gradient. Based on the

ecological and evolutionary factors considered above, we predicted that: 1) at higher latitudes,


61

the density of eggs in the egg bank will increase, while under typical spring conditions the

abundance of hatchlings will decrease at higher latitudes because selection for dormancy is

higher in northern regions; 2) high temperatures will advance hatching phenology, and this

advance will be greatest for small-bodied taxa; 3) conditions suggestive of a good, early season

(warm temperature and short photoperiod) will generate the greatest abundance of hatchlings in

northern lakes, because the ability to capitalize on favorable conditions is essential for

persistence in those regions; and, 4) conditions typical of a late season (long days coupled with

high temperature) will decrease hatchling abundance and diversity, and this effect will be

strongest in northern lakes because they typically experience short growing seasons.

Materials & methods

Sample collection & experimental design

We collected sediment samples from 25 lakes along an 1800 km latitudinal gradient that ranged

from southern British Columbia to the mid-latitude Yukon Territory in Canada in July 2011 (Fig.

4.1). The pelagic zooplankton community of these lakes had been previously characterized in the

1960s and 1970s (Lindsey et al. 1981; Patalas, Patalas & Salki 1994) and were again

characterized in 2011 (Jones unpublished). Chemical and physical properties of lakes were also

characterized in earlier studies, and were used to select lakes that showed no latitudinal patterns

in these properties (Table S3.1 and Table S3.2 in Appendix B: Supplementary information for

chapter 3; Fig. S4.1 in Appendix C: Supplementary information for chapter 4).

We used a 15” x 15” x 15”cm Eckman dredge to collect the top 5 centimeters of the sediment

from nearshore areas, from a maximum depth of 20 m if shallower samples could not be

collected (2 samples per lake which were combined). Most hatching occurs in nearshore

environments (De Stasio, 1989; but see Hairston et al., 2000), which are characterized by higher

temperatures and light levels as well as more substantial mixing events (Hairston & Kearns

2002). Eggs that settle in the deeper parts of the lake require mixing events to re-suspend and

transport them to the sediment surface in shallower nearshore waters (Kerfoot et al. 2004).

Previous work investigating egg viability through time has provided mixed results; some studies

show that egg quality declines with age (e.g.,Weider et al., 1997), while others have found that


62

viability is maintained through time for decades to hundreds of years (e.g., Cousyn & Meester

1998). Our collection was designed to maximize egg collection by targeting depths where eggs

settle but would still be likely to re-suspend through mixing events. Because our sampling was

consistent across lakes, and eggs from deeper waters can hatch when incubated under nearshore

conditions (Cáceres & Tessier 2003), we expect our results to reflect general trends.

Figure 4.1 Map displaying the location of the 25 lakes in western Canada that were sampled for

sediment in July 2011.


63

The collected sediment was packaged in black whirlpak bags to eliminate light then stored in the

dark at 4º C in a refrigerator (to simulate the conditions of a lake bottom to maintain dormancy)

until the experiments were initiated in 2012. We determined the density of copepod, cladoceran

and rotifer eggs using the sugar floatation method on a 100g subsample of sediment (Marcus

1990). We identified different types of eggs by morphology and counted the eggs of cladocerans

(eggs within undamaged, unopened ephippia), rotifers and copepods. Cyclopoid copepods

diapause as juveniles (Ferrari & Dahms 2007), therefore our egg counts of copepods refer to the

density of calanoid eggs only.

To mimic spring conditions when the bulk of hatching occurs (Hutchinson 1967; Hairston et al.

2000), we compiled thaw dates for our focal lakes using national (Polar Data Catalogue) and

international (The National Snow and Ice Data Center) databases. We determined average ice

thaw dates by converting annual thaw dates into Julian dates and taking the average from 1971-

2000. The average day length and air temperature for that month was recorded to determine

appropriate treatments. We were unable to collect this information for all lakes, but had data for

lakes across our entire gradient and used typical conditions from northern and southern lakes for

our treatments (Table S4.1).

Our experimental treatments were designed to simulate a nearshore aquatic environment, as the

majority of spring hatching occurs in the littoral zone for freshwater crustaceans (Cáceres &

Schwalbach 2001; Cáceres & Tessier 2003). Our experiment crossed temperature (two levels; 8

ºC and 12ºC) and light (16 hours and 12 hours), which represent the approximate mean spring

temperatures and photoperiods at the northern limit and southern limit respectively. We

conducted the experiment in a growth chamber, by setting up 10 racks that each had a single

photoperiod treatment. Within these treatments, we randomized the placement of 20 water baths

(each bath was 76” x 56” x 27”cm), with water baths randomly assigned to racks and equally

divided between two temperatures. We then placed egg banks from five lakes inside each bath,

with the egg banks housed in their own 7-litre aquaria (Fig. S4.2; Table S4.2). Thus, the

treatment combination was nested in bath and rack, which we account for using a mixed model

(below). We used submersible heaters to increase water temperature and water pumps to

circulate water within each bath. We enclosed each rack with shade cloth to eliminate


64

surrounding light. After 12 hours, lights in the short-day treatment would turn off, while the 16

hour photoperiod treatment would receive light for an additional four hours. For each lake, we

divided the sediment into four samples of 75 grams, and randomly assigned each sample to a

temperature by light treatment (temperature x light x 25 lakes = 100 experimental units; Fig.

S4.2; Table S4.2). By incorporating the lake as a random effect we were able to account for the

lack of independence among treatments (see data analyses section). However, because each lake

was exposed to each treatment combination only once, we do not have an estimate of error for

each lake within a treatment. The sediment layer was spread evenly to < 1 cm thick across the

aquarium, such that all eggs were close enough to the sediment surface to experience treatment

conditions.

To create a suitable environment for zooplankton, aquaria were filled with four litres of fresh

artificial Daphnia medium (ADaM) and four phytoplankton species (Ankistrodesmus sp.,

Chlorella vulgaris, Scenedesmus obliqous, Pseudokirchneriella subcapitata; approximately 30 x

106 cells of each species). We replenished the ADaM and phytoplankton every 6-10 days, and

topped up the mesocosms with dechlorinated water as needed every three days.

The experiment ran for sixty days, with zooplankton collected from each aquarium every three

days. To collect zooplankton, we created polycarbonate (lexan) inserts that were the length of the

aquaria and 1 cm deep. The inserts were placed in the bottom of the aquaria prior to the initiation

of the experiment. We also created 20µm mesh filters that were designed to exactly fit the

dimensions of the aquaria. To collect the plankton that hatched, we conducted a single “sweep”

by anchoring the filter on the lexan guide rails at one end of the aquarium, then gently pushing

the filter through the water, along the length of the aquarium. This method moderately disturbed

the sediment layer, but this layer remained < 1 cm deep.

On sampling days we counted all copepods, cladocerans and rotifers. At the same time, we

identified juvenile crustaceans to the family level (cladocerans) or order level (copepods) using a

dissecting microscope. We did not identify rotifers, therefore the analysis and discussion of

zooplankton diversity refers to the crustacean community only. Crustacean zooplankton

juveniles were individually reared in 50 ml centrifuge tubes. We maintained individuals by


65

transferring them every 3-7 days into fresh ADaM and feeding them approximately 30 x 106

cells of a mixture of the four phytoplankton species previously described every three or four

days. Following the rearing process we identified the individuals that survived to maturity

(65%). The individuals that did not survive to maturity could only be included in the abundance

analysis as they were not identified to species or genus.

Data analyses

We used R statistical software for all analyses (R Core Team, 2014). We tested for a relationship

between latitude and the density of dormant eggs (log transformed to account for

heteroscedasticity of residuals) using a linear mixed model (lmer function in lme4 package;

Bates et al., 2014). Taxon, latitude and their interaction were included as fixed factors and lake

as a random factor.

In all analyses (egg density, phenology abundance, and diversity), we began with the most

complex model for fixed effects and dropped higher order terms if they did not significantly

improve model fit (using log likelihood ratios based on maximum likelihood) until we arrived at

a best-fit model. All random effects were kept in models, as these reflected known constraints on

sampling (i.e., a random effect for lake as each lake was used in all four treatment combinations)

and on experimental design (a random effect for bath nested within rack to account for the

nesting structure). We initially explored the effect of number of eggs on emergence dynamics.

However, egg number was not a significant predictor of abundance or diversity in any group (all

P values > 0.20), and we therefore discuss the difference in these responses qualitatively instead

of including egg number as a covariate in abundance and diversity analyses.

To test for changes in phenology, we calculated the first hatching day and the time for 50% of

individuals to hatch for each taxon (cladocerans, copepods, rotifers) in each lake over the 60 day

experiment. We fit linear mixed models with a Gaussian distribution and the same fixed factors

and random factors as described previously. A single lake, Watson, was an outlier and drove a 3

way interaction (Table S4.3 and S4.4) so we removed it from all subsequent phenology analyses.


66

We determined how our treatments affected the number of hatchlings of copepods, cladocerans

and rotifers using generalized linear mixed models with a Poisson distribution and a log link

function. To account for a non-linear trend in the hatching rates across the latitudinal gradient,

we created a second order polynomial latitude variable (after centering latitude), which

accounted for the curvature in the data relationship. Initial models included latitude, latitude2,

temperature (8 or 12 °C), day length (16 h or 12 h), and taxon (copepod, cladocerans, rotifer),

and their interactions as fixed effects. The individual lake was included as a random effect and

the experimental water bath nested within the shelf rack was added as an additional random

effect. Zooplankton hatching was calculated as the total number of individuals that emerged over

the 60 day duration of the experiment. If we detected a significant 4th

order interaction

(temperature x photoperiod x latitude x taxon interaction), we fit the models separately for each

taxon as the scale differed among taxa by orders of magnitude.

To test how seasonal cues impact crustacean hatchling diversity across latitudes, we used a

similar statistical approach. The predictor variables for the initial full model contained latitude,

latitude2, temperature, day length, and taxon (copepod or cladoceran), and their interactions as

fixed effects, and the same random effects as in the abundance analysis. For our response

variable, we developed a ‘proportional diversity’ measure that counted the number of species

that emerged relative to the total number of species found in the lake [based on previous

standardized sampling from the lakes (Lindsey et al. 1981; Patalas et al. 1994) and samples that

we collected following the same methods in 2011 (Jones unpublished)]. The proportional

diversity approach allowed us to account for differences in species richness among the lakes,

which also shows a latitudinal trend (Fig. S4.3). Our resulting data were binomial (hatching

species/total species), and analyzed using a generalized linear mixed model with a logit link

function. As with the hatchling abundance analysis, we fit models separately for each taxon after

detecting a significant 4th

-order interaction.


67

Results

Zooplankton eggs

We found a significant effect of taxon (F = 11.5, P < 0.0001) and latitude (F = 7.6, P = 0.010) on

the density of zooplankton resting eggs. Contrary to our prediction, the density of eggs declined

latitudinally for all taxa (no latitude x taxon interaction; F = 0.71, P = 0.50), but each taxon

differed in their average egg density at a given latitude (Fig. 4.2).

Figure 4.2 The relationship between latitude and the egg abundance of cladocerans (light grey),

copepods (dark grey) and rotifers (black). Eggs were isolated from 100 grams of lake sediment

using the sugar flotation method (see methods). Fitted lines indicate when latitude was

significant at P < 0.05.


68

Phenology

Phenology was affected by temperature and latitude. Higher temperatures advanced the first and

median hatching day (Fig. 4.3; Table S4.3 and S4.4) for all taxa. The magnitude of the

temperature effect depended on taxon, but not in the way that we expected; the hatching of the

smallest taxon, rotifers, advanced the least (Fig. 4.3). The days to hatch was advanced the most

for cladocerans, by approximately 10 days, followed by copepods ~ 5 days and rotifers by ~ 2

days. The patterns were qualitatively similar for the first and median days to hatch, except that

rotifers took the longest to reach 50% hatching, likely due to their higher abundances. For the

first hatching day, temperature caused the order of emergence among taxa to reverse at 12°C

relative to 8°C. The time for 50% of individuals to hatch was slightly affected by latitude for

copepods (t = 2.92, P = 0.004) and rotifers (t = 2.34, P = 0.02) (Table S4.3). Day length did not

impact phenology (all P values > 0.20).

Figure 4.3 The effect of temperature (8°C; grey and 12°C; black) on the average number of days

until the first individual (‘First’; circles) and half (‘50%’; triangles) of all individuals from each

taxon hatch. Error bars represent one standard error of the mean. See Table S4.3 and S4.4 for the

model summary.


69

Hatchling Abundances

The abundance of hatchlings of each taxon was affected differently by latitude, temperature and

photoperiod (temperature x photoperiod x latitude x taxon interaction; F = 34.30, P < 0.001). The

abundance of cladocerans that hatched varied with temperature and day length, but the effects of

these cues depended on the latitude of the lake (significant temperature x photoperiod x latitude

interaction, χ2

= 33.6, P < 0.0001; Fig. 4.4a,b; Table S4.5). Warmer conditions and long days

caused a greater number of individuals to hatch in low latitude lakes, but not in high latitude

lakes (Fig. 4.4b). In contrast, in the low temperature treatment, latitude and photoperiod had no

effect on the abundance of cladocerans that hatched (Fig. 4.4a).

For copepods, the number of individuals hatching peaked at mid-latitudes (~ 56 °N), with the

height of this peak differing by treatment (Fig. 4.4c,d); higher temperatures and longer days led

to more copepods hatching (significant third order interactions; linear = χ2

= 8.6, P = 0.0034,

non-linear = χ2

= 7.9, P= 0.005; Table S4.5).

The abundance of rotifers that hatched was greatest at low latitudes (Fig. 4.4e,f). However,

unlike the crustaceans, the greatest abundance of rotifer hatchlings occurred at the higher

temperature and shorter photoperiod treatment (Fig. 4.4f). In particular, the higher temperature

caused a large increase in rotifer hatching in the 12 hour photoperiod treatment (solid lines in

Figs. 4.4e,f), and caused a more modest increase in the 16 hour photoperiod treatment (dashed

lines in Fig. 4.4e,f; significant third order interaction, χ2

= 131.7, P < 0.0001; Table S4.5).

Together these results provide mixed support for our hypotheses that hatching will be higher at

low latitudes and that early spring conditions will increase hatching in northern lakes. Under

simulated spring conditions, hatching declined at higher latitudes for copepods and cladocerans,

however these conditions caused higher hatching for rotifers.


70

Figure 4.4 The effect of temperature and photoperiod on the emergence of (a-b) cladocera

individuals, (c-d) copepods individuals, and (e-f) rotifera individuals that hatched from 25 lakes

across a 1800 km latitudinal gradient in western Canada. Emergence is summed by lake across

the 60 day sampling period. Data points are the abundance of hatchlings + 1. Lines are the fitted

curves for a general linear mixed model for Poisson distributed data using a log link function.

Note that the y-axes are presented on a logarithmic scale.


71

Hatchling Diversity

In total, 406 individual crustaceans hatched from the egg banks (9 cladoceran species and 9

copepod species, Table S4.6) which represent 41% of the species documented in these lakes.

Thirty-five percent of the hatchlings did not survive to adulthood, but survival did not differ

between copepods and cladocerans (χ2 = 2.0, P = 0.16). Due to potentially confounding patterns

in crustacean diversity with latitude (Fig. S4.3), we tested for trends in relative diversity by

examining the proportion of species present in each lake that emerged – our relative diversity

measure therefore calculates the fraction of species in each lake that were both present in the egg

bank and responded to our experimental treatments.

The relative diversity of species that emerged differed between cladocerans and copepods, and

these diversity responses were distinct from abundance responses for both taxa (temperature x

photoperiod x latitude x taxon interaction; χ2

= 5.14, P = 0.0233). When all crustaceans species

were considered together, diversity showed a unimodal trend with latitude (χ2

= 5.5, P = 0.002),

with the location of the peak in diversity depending on photoperiod (significant photoperiod x

latitude interaction, χ2

= 9.4, P = 0.002; Table S4.7; Fig 4.5a,b).

Cladoceran diversity responded strongly to photoperiod and latitude, but not temperature

(photoperiod x latitude interaction, χ2

= 5.5, P = 0.02; temperature, χ2

=2.3, P = 0.13; Fig.

4.5c,d). In the longer (16 hr) photoperiod, the relative diversity of cladoceran species that

hatched was the highest at low latitudes, whereas in the shorter photoperiod treatment relative

diversity was greatest at high latitudes (Fig. 4.5 c,d).

Copepod diversity varied with temperature and day length, but the effects of these cues depended

on the latitude of the lake (significant third-order interactions; linear χ2 = 8.8, P = 0.003,

quadratic χ2 = 4.00, P = 0.046; Fig. 4.5e,f; Table S4.7). We predicted that conditions typical of a

late season (long days coupled with high temperature) would decrease the diversity of the

hatching community; however, diversity was highest at mid latitudes, with 30% or more of

species emerging when day length was short and temperatures were low. Copepod diversity was

lower in the warmer temperature treatment, with the maximum peak of approximately 20% of

species hatching (Fig. 4.5f). Interestingly, the treatment that combined long day length and low

temperatures had higher copepod diversity in lower latitudes (Fig. 4.5e).


72

Figure 4.5 The effect of temperature and photoperiod on the proportion of the (a-b) total

crustacean diversity, (c-d) cladoceran diversity and (e-f) copepod diversity that hatched from 25

lakes across a 1800 km latitudinal gradient in western Canada. Diversity is summed by lake

across the 60 day sampling period. Data points are the proportion of species that hatched. Lines

are the fitted curves for a general linear mixed model using a logit link function.


73

Discussion

Our study demonstrates that several responses of zooplankton resting eggs to hatching cues

change with latitude, and that the pattern of this change differs among taxa. Our assessment of

the effects of day length and temperature on the phenology, abundance and diversity of

zooplankton communities is the first to systematically collect egg banks from across a latitudinal

gradient. In doing so, we have shown that cues associated with changing climate can have

consistent (phenology) or distinct (abundance, diversity) effects at different latitudes, indicating

that we cannot accurately predict responses to climate change without considering how these

factors interact across biologically diverse landscapes.

Contrary to our hypothesis, phenological shifts in response to temperature caused the relative

order of the first hatching to reverse for the three taxa (Fig. 4.3). At 8°C, phenological patterns

were consistent with previous research, with rotifers hatching first and cladocerans hatching last,

but rotifers showed a surprising lack of phenological response to temperature, thereby reversing

the relative order of first appearance. Our phenology results are partially consistent with field

research, which has shown that crustacean zooplankton dominating the water column in early

spring (i.e., cladocerans) are more sensitive to temperature than later successional taxa (i.e.,

copepods) (Adrian et al. 2006). It is, however, inconsistent with a meta-analysis that spanned

many species and showed that high temperatures advance phenology, but that species with the

smallest egg sizes always tend to emerge first (Gillooly & Dodson 2000b). For rotifers, our taxa

with the smallest eggs, we clearly did not see this pattern. Similarly, Winder and Schindler

(2004) used long-term monitoring of a single lake to show that rotifer populations advanced their

phenology over a 40 year period of warmer springs whereas cladocerans did not. Although we do

not have an explanation for the reversal of hatching times observed, our results suggest that

elevated spring temperatures have the potential to alter the order that zooplankton hatch in lakes.

Given the importance of phenological differences for competitive and successional dynamics,

further verification of this trend and its causes are important for aquatic ecology.

Beyond phenological changes, the effect of climate on community composition can be quantified

through two general responses: changes in abundance of specific taxa and changes in diversity.

Numeric and diversity effects are frequently considered inter-dependent as they are often


74

correlated in nature (via the species-accumulation curve; e.g., Ugland et al., 2003), suggesting

that higher hatching rates should translate into a greater proportion of the community emerging.

However, abundance responses appear to have been influenced by high hatching rates from a

subset of species, as our results show that copepods and cladocerans have qualitatively distinct

abundance and diversity responses (compare Fig. 4.4 and Fig. 4.5). For example, the higher

temperature increased the abundance of cladocerans in the long photoperiod treatment, but did

not impact cladoceran diversity. A similar result was found by Preston & Rusak (2010), who

showed that temperature effects manifested as a numerical response, have little impact on

diversity. However, those authors linked ice-off date with community composition and found

that spring warming reduced zooplankton density, while in our study the higher temperature

treatment generally increased hatching. Overall, this difference between numeric and diversity

responses suggests that the effects of climate change can manifest by favouring a small subset of

species and by simultaneously altering the diversity of communities.

The effects of climate cues on zooplankton hatchling diversity offers new insights into how

climate can differentially structure community dynamics across latitudinal gradients. We

expected that hatching rates would be greatest in southern latitudes, where growing seasons are

longer and climatic conditions are milder. Surprisingly, the dynamics we observed were more

complex, and could not have been predicted from a geographically and taxonomically restricted

study. In particular, cladoceran diversity was only influenced by photoperiod, with a longer

photoperiod increasing diversity at low latitudes, but decreasing diversity at high latitudes (Fig.

4.5). The reversal of the day length effect at high latitudes is consistent with our hypothesis that

northern zooplankton may experience strong selection to emerge and reproduce in a short

growing season, causing northern populations to be locally adapted to those conditions (Kawecki

& Ebert 2004; McNamara & Houston 2008). However, when we investigated the hatching

dynamics of three common species (copepods: Diaptomus sicilis and Hetercope sepentrionalis;

cladoceran: Ceriodaphnia lacustris), we did not detect evidence for local adaptation. Instead,

differences among populations of widespread species were idiosyncratic (Fig. S4.4), suggesting

that the apparent consistency with our hypothesis was due to species sorting effects. We note,

however, that our experiment is not well-suited to testing local adaptation because we have no

measure of individual fitness and no control of maternal effects; more targeted tests of local


75

adaptation in plankton from across latitudes would be valuable. Our results, which effectively

average the effects of climate cues over species that change along the latitudinal gradient (Patalas

et al. 1994), indicate that northern cladoceran communities respond positively to shorter days, as

is predicted when growing season is limited (Conover et al. 2009) or shorter day length

corresponds with increases in food availability (Cáceres & Schwalbach 2001).

Differential responses of taxa to climate cues also challenge simple models for community

change across latitudinal gradients. Space-for-time substitutions, which are often employed

where temporal replication is difficult, can be a powerful tool to predict community or

population level responses to climate change (Pickett 1989). If experimental temperature

responses are correlated with the temperature response across latitude in nature, then a space-for-

time substitution would capture how the community will respond to climate change (Dunne et al.

2004). Our experiment revealed that numeric and diversity responses of zooplankton to

temperature and photoperiod can differ across latitude, suggesting that we may be unable to

construct predictions for how temperature will alter community composition based on spatial

patterns of temperature responses.

An important question that arises from our study is how our findings can be generalized to

different habitats and organisms. Marine plankton, for example, differ from freshwater plankton

in that long term dormancy is less prevalent overall (Hairston & Cáceres 1996), potentially

because of the more continuous nature of the marine realm. However, the hatching of marine

zooplankton is also influenced by temperature and photoperiod (Uye, Kasahara & Onb 1979;

Preziosi & Runge 2014), but more work is needed to understand how these dynamics vary

latitudinally. In addition, extrapolating our results to latitudes beyond our sampling sites is

challenging due to the complexity of the responses we observed. Future work should extend

sampling to determine whether egg banks continue to increase at low latitudes, and assess

patterns in hatching dynamics. In most cases we see declines in hatching at the northern extreme

of the latitudinal gradient that we sampled, but species persist at (and beyond) these latitudes

(Patalas, Patalas & Salki 1994), raising questions about the nature of this latitudinal variation that

should be addressed with more detailed studies within and across species.


76

Life-history differences among zooplankton are likely candidates for the different responses to

light and temperature that we observed, and may be generally useful for predicting responses to

changing climate cues. Cladocerans are born as miniature adults and are facultatively sexual,

reaching reproductive maturity in 5-10 days at 20°C (Geller 1987). In contrast, copepods are

obligately sexual and have a development time of 20 - 42 days at 20°C depending on species

(Maier 1994). Fast generation times and parthenogenesis cause cladocerans to have higher

growth rates than copepods (Allan 1976), and may structure differences in the successional

niches of these taxa (Adrian et al. 2006). Growing season lengths declines latitudinally

(Environment Canada 2014), and lower temperatures that are characteristic of northern lakes

slow development of all zooplankton (Gillooly 2000). This time constraint could be especially

acute for copepods because of their comparatively long development times. Moreover, when

cladocerans undergo sexual reproduction they have large egg size to adult body size ratios

compared to copepods, leading to relatively long development times for the egg stage (Gillooly

2000). This systematic difference between taxa may impose different selection pressures on the

initiation of egg development in response to environmental cues. For example, the relatively

slow development rates of sexual cladoceran eggs into juveniles may cause cladocerans to be

more strongly impacted by longer-term environmental conditions, as may be signaled by day

length.

Although we were unable to quantify rotifer diversity in our lakes, we saw that rotifer abundance

responded to temperature and that the size of this response depended on day length (Fig. 4.4).

Our results for rotifers supports previous work that used a 40 year time series to show that the

abundance of rotifers can increase in response to spring warming (Winder & Schindler 2004).

Rotifers play a critical role in lakes by acting as a food source for crustacean zooplankton

(copepods and cladocerans) and by facilitating nutrient cycling by consuming bacteria, detritus

and algae (Hutchinson 1967; Bogdan & Gilbert 1982; Arndt 1993). The strong, positive effect of

short days and high temperature on the abundance of rotifers that hatch raises the possibility that

changing climate cues could greatly increase rotifer abundances, and thus alter nutrient cycling

and the supply of food to higher trophic levels.


77

Interestingly, the abundance and diversity responses of zooplankton were not related to the

density of eggs in lake sediment, which declined latitudinally for all taxa (Fig. 4.2). This pattern

of declining egg density may be due to the negative correlation between season length and

latitude, reducing the number of generations per growing season in the north (Corbet, Suhling &

Soendgerath 2006). However, in addition to lower voltinism in higher latitudes, the density of

eggs in lake sediment is also a consequence of the accumulation of eggs that are produced but do

not hatch the following year. Because of short growing seasons and lower temperatures, we

predicted this unhatched fraction to represent a greater proportion of eggs produced at higher

latitudes. Egg density is ultimately the product of both processes; the reservoir of eggs declines

with latitude because of lower voltinism, but the fraction of these eggs that hatch determine the

number that remain in the sediment. Our study suggests that the latter of these two processes is

unlikely to account for differences in egg densities, as hatchling densities were not universally

higher in northern lakes for any of the taxa studied (Fig. 4.4).

Our investigation of the effects of temperature and day length on the termination of dormancy is

one of the first to choose communities that differ in latitudinal origin. In doing so, we have

demonstrated that the sensitivity of zooplankton to temperature and day length can differ across

latitude and between co-occurring taxa, a result that would be obscured if we selected

communities from the same region. By considering how climatic cues may shift dynamics across

latitudes, we were able to provide new insights that suggest changes in dormancy dynamics with

spring warming may be an under-appreciated but important consequence of climate change, and

could lead to zooplankton community shifts that will depend on latitudinal origin.

Acknowledgments

We thank A. Barany, E. Chojecka, V. Jones and N. Lo for sampling assistance and members of

the Gilbert lab for helpful comments on a previous version of this manuscript. We also thank the

Editor and two anonymous referees whose comments improved this manuscript. This work was

supported by NSERC (B.G., Discovery Grant) as well as Ontario Graduate Scholarships (N.T.J.).


78

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Chapter 5

Geographic signatures in species turnover: decoupling colonization and extinction across a latitudinal gradient

In review as Jones, N.T., & Gilbert B. Geographic signatures in species turnover: decoupling

colonization and extinction across a latitudinal gradient. Global Ecology & Biogeography

Abstract

High latitude areas are characterized by low species richness and rapid warming with climate

change. As a result, temporal turnover of communities is expected to be greatest at high latitudes.

We assess colonization and extinction rates of zooplankton through time across a latitudinal

gradient to test this prediction, and further test whether species-specific rates are predicted by

body size local abundance, or regional occupancy of lakes. Lakes across an 1800 km latitudinal

gradient in western Canada (49°N - 64°N).We resampled zooplankton communities from 43

lakes that had been sampled 25-75 years previously. We evaluated temporal turnover of copepod

and cladoceran species using Sorensen dissimilarity, colonization and extinction. We tested

whether lake-level turnover, colonization or extinction changed with latitude. We also tested

whether species-level differences in colonization and extinction were explained by body size,

local abundance (abundance when present in a lake), and regional occupancy. Lake-level

temporal turnover was highest at low latitudes due to by higher colonization rates at lower

latitudes, and consistent extinction rates across the latitudinal gradient. At the species level,

colonization increased with regional occupancy, and tended to increase for abundant species with

small body sizes. Local extinction rates decreased with local abundance and regional occupancy,

but were not influenced by body size. Contrary to expectations, low-latitude zooplankton

communities are changing faster than high-latitude communities and becoming more species

rich. Moreover, colonization and extinction trends suggest that lakes have become increasingly

dominated by species with smaller body sizes and that are already common locally and

regionally. Together, these findings indicate that rates of species turnover in freshwater lakes

across the latitudinal gradient are not predicted by rates of temperature change, but that species

CHAPTER 5: TEMPORAL TURNOVER IN ZOOPLANKTON COMMUNITIES

84

turnover is nonetheless resulting in trait-shifts that are consistent with predictions for temperature

change.

Introduction

Species turnover through time has long fascinated ecologists, with classic theories positing that

that local and biogeographical properties of communities determine rates of turnover (Elton

1958; MacArthur & Wilson 1967; May 1973). Recently, there has been renewed interest in the

factors that promote temporal turnover. Classic work on species-time relationships suggest that

turnover dynamics are partially the consequence of island biogeography or metapopulation

processes (Rosenzweig 1998; Nuvoloni, Feres & Gilbert 2016) whereas more recent research has

focused on how global climate change is modifying the latitudinal range of many species,

thereby altering the composition of communities (Chen et al. 2011; Burrows et al. 2014).

Species dynamics in the anthropocene are increasingly influenced by a mixture of

metapopulation and anthropogenic processes (Helmus, Mahler & Losos 2014). As a result,

determining patterns of species turnover through time and across broad spatial scales is

increasingly important for conservation and basic ecology (Wolkovich et al. 2014).

Temporal turnover is the outcome of a variety of dynamic processes that result in two changes to

local diversity: gain of species through new colonization events and loss of species through local

extinction events (Anderson 2007). Island biogeography and metacommunity theory highlight

how the relative importance of colonization and extinction may differ across regional gradients

(MacArthur & Wilson 1967; Leibold et al. 2004; Viana et al. 2015). Although these theories

make general predictions about characteristics of patches and species that may lead to

qualitatively different patterns of diversity and turnover, empirical patterns are often far more

complex than suggested from these models (Matthews & Pomati, 2012; Jones et al. 2015). The

observed complexity is due to an incredible variation in the importance of dispersal limitation,

local interactions and species-environment relationships among ecosystems that make

predictions of turnover for any particular community difficult (Shurin et al. 2007; Bennett et al.

2010; Matthews & Pomati 2012; Jones et al. 2015). This challenge is particularly difficult for

studies across latitudinal gradients, because there is a simultaneous change in three determinants


85

of turnover: environmental conditions, species traits, and the composition of communities

(James, 1970; Parmesan, 2006; Jones & Gilbert 2016).

Latitudinal gradients in environmental conditions and anthropogenic change set the stage for

spatial directionality to community change over time. In North America, mean temperatures

decline northward along a latitudinal transect and long-term data indicates that during the last

100 years, temperatures have increased more in northern regions (IPCC, 2013; Environment

Canada, 2014). As a result, mean temperature and temperature increases are negatively

correlated across a latitudinal gradient, rendering high latitude sites more vulnerable to the

effects of climate change (e.g., Smol et al., 2005). However, geographic gradients in other

aspects of global change suggest that the opposite pattern could occur. Specifically, larger human

populations and urbanization characterize lower latitude regions. This increased anthropogenic

pressure has led to land-use changes at lower latitudes, such as higher road density and increased

recreational use of natural areas (Gayton, 2007; Ministry of Forests, Mines and Lands, 2010),

which have increased connectivity among discrete habitats such as lakes (Kelly et al. 2012).

Given the more pronounced temperature changes at high latitudes and the increased

anthropogenic pressure at southern latitudes, the resulting effect of these global changes on

colonization-extinction dynamics across latitudinal gradients remains unknown.

In aquatic communities, two life-history characteristics are hypothesized to drive colonization

and extinction dynamics of zooplankton: body size, and the local abundances of species. Local

abundances reflect a suite of traits that determine the impacts of intra- and inter-specific

interactions and resource specialization, and are often broadly defined as species carrying

capacities (Levin 2009). From a metapopulation or meta-community perspective, high local

abundance buffers against local extinction, and also provides more propagules that can disperse

to other lakes (Hanski 1994), and thus is commonly related to occupancy, or the proportion of

lakes where a species is found (Hanski, Kouki & Halkka 1993). Likewise, body size affects both

local and regional distributions. Locally, body size may structure competitive asymmetries

among species (Gliwicz 1990) and also increase trophic position (Woodward et al. 2005). Body

size can also indirectly impact local success if larger body sizes are associated with smaller

population sizes and/or slower growth rate (Savage et al. 2004). Apart from these local effects,

body size also directly influences colonization dynamics in passively dispersed organisms such


86

as aquatic zooplankton, which are dispersed via wind, water or animals (Vanschoenwinkel et al.

2008). For these species, dispersal distance is negatively associated with body size (Soons et al.

2008; De Bie et al. 2012), with smaller individuals travelling further and more often. Together,

the associations between body size and dispersal for passive dispersers suggest that traits

conferring competitive dominance locally may come at the expense of dispersal among lakes,

making the overall impact of body size difficult to predict in a regional context. Moreover, body

size is a trait that often changes with latitude. Bergmann’s rule and James’ rule, for example,

describe how body size within and among species increases in cooler regions, and thus increases

with latitude (James 1970). Because body size-latitude relationships in ectotherms can be highly

variable (Shelomi 2012b), it is important to quantify the joint and independent effects of latitude

and body size on species turnover to understand proximate and ultimate causes of diversity

patterns with latitude.

Differences in species diversity across latitudes are also predicted to impact patterns of turnover.

Higher latitude regions contain fewer species on average (Shurin et al., 2007; Jones & Gilbert,

2016) and low diversity is expected to be associated with high rates of temporal turnover. This

prediction arises from two hypotheses in community ecology. First, because species diversity is

positively correlated with phenotypic variation, elevated diversity could reduce opportunities for

new species to establish even when they are no longer limited by climate (Elton 1958). Second,

diversity is predicted to stabilize community composition by increasing the number of weak

interactions in food-webs, which reduce large fluctuations in predators and their prey (McCann

et al. 1998). When considered in terms of latitudinal patterns of diversity, these competitive and

food-web models predict higher turnover (lower stability) in high latitude communities.

In this paper, we investigate temporal turnover in freshwater zooplankton communities from

across an 1800 km latitudinal gradient in western Canada that has shown typical shifts in

temperature over the past 70 years (Fig. 5.1a,b). Zooplankton are ubiquitous ectothermic animals

in freshwater lakes that form the basis of lake food-webs, and display a latitudinal gradient in

species diversity and composition typical of many organisms (Fig. 5.1c,d). We resampled

zooplankton communities that were originally sampled fifty years ago on average, and asked the

following four questions: (1) Is there evidence for a latitudinal trend in species turnover? (2)

How do colonization and local extinction events within lakes structure this temporal turnover?


87

(3) Are body size, local abundance and occupancy predictive of species’ colonization and local

extinction rates? And, if so, (4) do body size and local abundance change predictably with

latitude?

Materials & methods

Study system & species sampling

This study was conducted in freshwater lakes across a ~1800 km latitudinal gradient in Canada,

ranging from southern British Columbia to the middle of the Yukon Territory (Fig. 5.1a). Long-

term temperature averages indicate there is a positive relationship between latitude and the

magnitude of temperature warming across our study sites during the last 100 years (Fig. 5.1b).

Lakes that are closer together have more species in common (Fig. 5.1c) and species richness

declines at higher latitudes (Fig. 5.1d).

Using data from Patalas et al. (1994), we systematically selected 43 lakes that were originally

sampled between 1939-1986 (details on how we accounted for differences in time between

historic and contemporary samples are given in the data analyses section below). We chose lakes

that spanned the latitudinal gradient and had similar levels of known environmental variables

(phosphorus, nitrogen, turbidity, etc.). We followed the original collection methods of Anderson

(1974), Lindsey et al. (1981) and Patalas et al. (1994), and sampled in the same season (July 4 -

July 28, 2011). To minimize any confounding effects of species succession through the growing

season, we began sampling in the southern portion of the latitudinal gradient, sampling some

lakes as we moved north along the transect, and others as we returned south. Plankton

communities were collected by hauling a Wisconsin net [mouth diameter 24 cm, net mesh 76

µm] through the water column, beginning from near the lake bottom, at the approximate center

of each lake. Two vertical tows per lake were taken. Zooplankton were immediately preserved in

70% ethanol.

We combined the two replicate tows and randomly identified zooplankton, following the

taxonomy of Thorp and Covich (2010) and Sandercock and Scudder (1994) and additional keys

as needed, until we had identified at least 500 adult individuals. Some morphologically similar

species could not be differentiated. In those cases, we decreased the taxonomic resolution to the


88

generic or family level (e.g. Bosmina). This resulted in a consolidated species list, with 25

species/genera/families, making our estimate of turnover relatively conservative. For simplicity

we refer to each grouping as a “species” throughout the manuscript.

We measured adult body size for 30 randomly selected individuals of each species for a subset of

19 lakes across the latitudinal gradient (Table S3.2). Body size of cladocera was measured from

the centre of the eye to the base of the tail spine (Gliwicz 1990, Yurista and O’Brien 2001),

while we measured the length of the prosome for copepods (Klein Breteler and Gonzalez 1988,

Ban 1994). The historical relative abundance of zooplankton species was available for 31 lakes.

We estimated local abundance by averaging the abundance of each species (or genus or family)

across all the lakes they were present in the historical sampling dataset (Anderson 1974; Lindsey

et al. 1981; Patalas 1990). In total, we had both body size and local abundance estimates for 14

species (Table S5.2). Finally, we determined historic occupancy for each species (hereafter

simply ‘occupancy’) as the proportion of lakes historically occupied relative to all the lakes that

fall within the latitudinal range of that species (see explanation of colonization, below).

We selected lakes to minimize differences in local abiotic factors, which can also impact

diversity patterns (Dodson 1992; Hessen et al. 2006), so that differences in turnover could be

attributed primarily to latitude. We verified this by characterizing a subset of physical and

chemical characteristics of our focal lakes (Table S3.1 and Table S3.2 in Appendix B:

Supplementary information for chapter 3). We quantified chlorophyll a (a measure of

productivity) measurements mid-lake using a YSI 6-series multiparameter water quality sonde

(Integrated Systems & Services, Yellow Spring, OH, USA). We used published estimates of lake

size and depth from the literature (Anderson 1974; Lindsey et al. 1981).

Data analyses

To test how zooplankton communities have changed since the historical survey, we transformed

the zooplankton species abundance matrix into a presence-absence data matrix for both time

periods, then calculated two measure of community change: the total change in species richness

and the Sorenson dissimilarity. Because we observed heterogeneity in variance, we used

Generalized Least Squares (GLS) in the lme4 package in R (Bates et al., 2014; R Core Team,

2014) to determine how the total change in species richness and Sorenson dissimilarity changed


89

across a latitudinal gradient. We accounted for differences in time between historic and

contemporary samples by including historic sampling date as a covariate in community-level

models; it did not improve model fit (Fig. S5.1 in Appendix D: Supplementary information for

chapter 5; Table S5.3) and there is no correlation between sampling date and latitude (Fig. S5.2),

so we do not report it further below.

We next determined how colonization and local extinction contributed to turnover (Table S5.1).

We define colonization in a lake as the proportion of species in the contemporary survey that

were not present in the historic survey, and extinction as the proportion of species present in the

historic survey that were absent in the contemporary survey. This approach allowed us to

account for differences in the species richness among the lakes, which showed a latitudinal trend

(Fig. 5.1d). We analyzed latitudinal variation in colonization and extinction with a generalized

linear model (GLM) using a quasibinomial error distribution and a logit link function.

We determined how body size, local abundance and occupancy influence colonization and

extinction. In this case, the denominator for colonization and extinction were calculated for each

species. For colonization, we extracted the latitudinal range of each species from Patalas (1994)

and created a potential colonization data-frame by summing the number of times a species was

not historically present in a lake that occurs within the maximum and minimum latitudinal

distribution of that species – colonization was defined as the proportion of these potential lakes

that were colonized in the contemporary sample. Occupancy was defined as the proportion of

lakes within a species’ latitudinal distribution where it was historically found. Extinction was

calculated as the proportion of lakes where a species was present in the historical sample and

absent in the contemporary sample. Because colonization and extinction were binomial

responses, they were analysed using GLMs with a quasibinomial error distribution to account for

overdispersion. We conducted separate analyses to test whether body size and local abundance

changed predictably with latitude and to test for a correlation between occupancy and local

abundance.

We used linear mixed models to confirm that patterns in temporal turnover were not driven by

three environmental factors that can also impact diversity patterns: lake size, lake depth and a

measure of productivity (chlorophyll a) (Hessen et al. 2006).


90

Results

We found a significant effect of latitude on temporal turnover; both the change in species

richness (Fig. 5.2a; t = -3.19, P = 0.003) and species turnover declined with latitude (Fig. 5.2b; t

= -2.96, P = 0.005; Table S5.4). The patterns of species gains and losses differed across the

latitudinal gradient, with turnover primarily driven by colonization in the southern portion of the

latitudinal gradient (Fig. 5.2c, z = -4.28, P < 0.0001) and local extinction rates showing no trend

with latitude (Fig. 5.2d, z = -0.83, P = 0.41).

Body size, local abundance and regional occupancy influenced the frequency of colonization and

local extinction among species, but in different ways (Table S5.5; Fig. 5.3). Colonization rates

were highest for species that had high occupancy historically (Fig. 5.3c; t = 2.5, P = 0.03), and

tended to be higher for small-bodied zooplankton (Fig. 5.3a; t = -1.02, P = 0.051), and species

with high local abundance (Fig. 5.3b; t = 0.29, P = 0.061). Body size had no relationship with

local extinction rates (Fig. 5.3d; t = 0.58, P = 0.38). However, locally abundant species and those

with high historical occupancy were extirpated less often (Fig. 5.3e, t = -0.59, P = 0.003; and

Fig. 5.3f, t = -3.18, P = 0.008).

To test whether body size and local abundance were independently predictive of colonization

and extinction rates, we tested for the correlation between these traits and latitude. We detected

no correlation between body size and local abundance (Pearson correlation test, r = 0.17, P =

0.52; Fig. S5.3), suggesting that, for this group of aquatic zooplankton, smaller bodied species do

not have larger population sizes on average. Similarly, the average body size of crustacean

zooplankton communities did not change across a latitudinal gradient (Fig.S5.4a; t = -0.53, P =

0.60), and there was no relationship between average local abundance and latitude (Fig. S5.4b; t

= 0.53, P = 0.60). There was, however, a strong relationship between occupancy and local

abundance of species (r = 0.70, P = 0.008; Fig. S5.5). Overall, these relationships show that

species traits influence colonization and extinction rates independent of latitude, and suggest that


91

Figure 5.1 Latitudinal patterns of diversity and temperature change. (a) Locations of the 43 lakes

in this study; (b) the change in air temperature over 70 years, based on differences (present –

past) of 30 years means: 1971 to 2000 – 1901 to 1930; (c) Species composition of zooplankton

(first axis from a Nonmetric Multidimensional Scaling with a 2D solution [stress = 0.19] based

on Sorensen dissimilarity), illustrating that closer sites are more compositionally similar; and (d)

Zooplankton species richness with latitude. Data used in (c) and (d) pooled species in historic

and current samples for each lake. Lines display the model fit for significant relationships at α =

0.05. Data for (b) was extracted from the Canadian Center for Climate Normals

(URL:http://climate.weather.gc.ca/climate_normals/index_e.html)


92

Figure 5.2 The relationship between latitude and (a) the change in species richness, (b) species

turnover, measured using the Sorenson dissimilarity metric, (c) the proportion of new species per

lake, and (d) the proportion of species that went locally extinct. All graphs compare historic

zooplankton samples with contemporary samples (see methods). In (c) colonization = the

number of species that colonized the lake / the contemporary species richness. In (d), extinction

= the number of species that were locally extirpated / the historical species richness. Lines

display the model fit if the relationship was significant at α < 0.05. We report correlation

coefficients here, but the statistical tests for panels a and b were done using generalised least

squares to account for error heteroscedasticity, while a generalised linear model with a

quasibinomial error distribution was used for panels c and d (see data analyses section).


93

Figure 5.3 Species traits influence colonization and extinction rates. The relationship between

colonization and (a) zooplankton body size, (b) local abundance, and (c) occupancy. Bottom

panels: the relationship between extinction and (d) zooplankton body size, (e) local abundance,

and (f) occupancy. Local abundance is a species’ mean abundance when present, and occupancy

is the proportion of lakes that a species historically occupied within its latitudinal range. Lines

display the model fit if the relationship was significant (solid lines; P < 0.05) or marginal

(hashed lines; 0.05 < P< 0.10).


94

local abundance or regional occupancy reflect a common suite of species traits that influence

colonization and extinction rates.

To ensure that our standardization of lake environments (other than temperature) was successful,

we tested the relationship between turnover and lake size, maximum lake depth and productivity.

None of these were significant (all P > 0.14, Table S5.6).

Discussion

Our study highlights how considering colonization and extinction leads to a richer understanding

of the causes of latitudinal gradients in species turnover. We found that higher latitudes had

lower turnover, a trend that has been observed in several latitudinal studies (e.g., Shurin et al.,

2007; Korhonen et al., 2010; but see Soininen et al., 2004). However, by decomposing turnover

into local losses and gains, we were able to attribute the spatial signature in compositional

change to elevated colonization events in the southern portion of the latitudinal gradient.

Moreover, our results suggest that species-specific patterns of turnover can be partially explained

by commonly measured traits: body size, local abundance and regional occupancy. Together,

these biogeographical and species-specific perspectives on colonization and extinction provide

insights into the directions and rates of change in ecological communities across latitudes.

The latitudinal patterns of colonization and extinction that we document in this study may be

important for understanding how climate influences community changes more generally.

Although arctic and subarctic lakes are often considered to be “sentinels of climate change”

(Adrian 2009), our results did not support the hypothesis that communities in high latitude lakes

are more likely to show compositional shifts (Fig. 5.2). This result was surprising, given that

these lakes have experienced larger changes in temperature (Fig. 5.1b), and also support less

diverse assemblages of species (Fig. 5.1d). The apparent compositional stability of subarctic

zooplankton communities through time may arise from several factors that together slow change

in high latitude communities. First, relatively extreme seasonal fluctuations in environmental

conditions may prevent new species from colonizing those sites directly by causing a higher

variation in population growth rates (e.g., Lande, 1988) and by creating a shorter seasonal

window in which colonization is possible. Second, shorter growing seasons may slow absolute


95

population growth over the season, again increasing chances of stochastic extinction in newly

establishing species or generating longer lags between colonization of sites and detection of

species in high latitude lakes. Third, many zooplankton have long-lived dormant stages, which

may buffer species from extinction across the latitudinal gradient (Jones & Gilbert 2016).

Finally, these lakes thermally stratify in the summer months, but zooplankton species’ are

distributed throughout the water column in the pelagic zone. Zooplankton may avoid extreme

fluctuations by capitalizing on refugia below the thermocline, where cool waters persist

throughout the summer months.

Instead of greater species turnover in northern lakes, our study shows a clear pattern of higher

colonization in southern lakes. Interestingly, this increase arose without the addition of new

species to the study, and thus suggests that lakes were either in disequilibrium in early surveys,

or that the non-equilibrium dynamics observed have resulted from increased rates of colonization

between surveys (~70 years). Although we cannot isolate the causal mechanism driving this

pattern, the association between latitude and colonization events may reflect an increase in

connectivity in lower latitude lakes. The southern portion of the latitudinal gradient in our study

occurs in regions with relatively high anthropogenic influences, such as higher road density

(Ministry of Forests, Mines and Lands, 2010) and larger human populations (Gayton 2007). As a

result, dispersal limitation may be relaxed in those areas due to inadvertent movement via boats

and bait fish with water (Kelly et al. 2012), facilitating the introduction of zooplankton into

lakes. An increase in diversity is predicted by metapopulation and island biogeography theory in

such cases, so long as local abiotic and biotic conditions do not prevent recruitment (Shurin

2000). The geographic distribution of many passively dispersed aquatic invertebrates is limited

by dispersal (Bohonak & Jenkins 2003), therefore increased connectivity among lakes may be an

important component of the anthropocene, altering the diversity of local communities.

Through linking body size, local abundance and regional occupancy to colonization and

extinction dynamics, our results illustrate how trait-based approaches are useful for predicting

turnover and metacommunity dynamics (De Bie et al. 2012; Jones et al. 2015). Body size is

important for passively dispersed organisms, and our results support previous work which has

shown that for passive organisms, larger individuals tend to colonize fewer sites. This is in

contrast to active dispersers, where larger individuals normally sequester more resources and


96

disperse farther (Shurin, Cottenie & Hillebrand 2009). The observation that temperature

increases during the last 100 years has caused a reduction in the average body size of many

organisms (Daufresne et al. 2009) raises the intriguing possibly that the direct effect of

temperature on body size could indirectly increase dispersal rates for passively dispersed species.

Our results also support a growing number of studies that show a positive association between

local abundance and occupancy (e.g., Soininen & Heino, 2005; but see Thompson et al., 1998).

A number of mechanisms have been put forth to explain why this pattern persists across distantly

related species (reviewed in Gaston et al., 2000). Although no single mechanism has emerged as

the sole explanation for this pattern, the association between regional occupancy and

colonization-extinction dynamics has important implications for predicting species range

expansion and extinction. Specifically, if the proportion of inhabitable lakes a species occurs in

is known, detailed abundance data may be unnecessary to estimate how vulnerable that species is

to local extinction or the likelihood that it will colonize new lakes (Gaston et al. 2000).

An important question for communities facing global changes is whether patterns of diversity are

at an equilibrium or, alternately, if they are shifting over time (Nuvoloni et al. 2016). When

communities and species’ are at equilibrium in a landscape, both are expected to show equal

rates of colonization and extinction on average. Our results highlight two important non-

equilibrium trends that are shifting lake communities. First, non-equilibrium dynamics in

southern communities are causing an increase in diversity, whereas more northern communities

appear to be in equilibrium (Fig. 5.2a). Interestingly, many studies of turnover do not explicitly

consider non-equilibrium colonization-extinction dynamics as drivers of turnover (but see

Matthews & Pomati, 2012; Nuvoloni et al., 2016). Second, we observed different non-

equilibrium dynamics among species, with these dynamics predicted by species traits (Fig. 5.3).

These trends suggest that relatively small, and locally abundant species that are widespread are

becoming even more common, while species with low local abundances are disappearing from

lakes at a greater rate than they are establishing elsewhere. In other words, these species-level

non-equilibrium dynamics indicate that conditions over the past several decades are causing

directional shifts in the traits of species by favouring small, common species to a greater degree

than they were historically favoured.


97

The results from our study also suggest that space-for-time substitutions are inappropriate for

predicting changes with climate in temperate and northern aquatic communities. Space-for-time

substitution is often used to replace temporal replication and can be a powerful tool for

predicting community and population level responses to climate change (Dunne et al. 2004;

Blois et al. 2013). However, smaller changes in northern lakes relative to southern lakes, despite

greater temperature change in the north, indicates that temperature alone is a poor predictor of

changes to community composition, at least over the timescale of this study (approx. 50 years).

Regardless of whether these differences are mainly due to larger anthropogenic influences in

temperate lakes, higher seasonal variation in northern lakes, or other mechanisms, failing to

incorporate this greater complexity into climate change studies will lead to erroneous predictions

for biological communities (Jones & Gilbert 2016).

In conclusion, by decoupling community responses across a latitudinal gradient, we were able to

demonstrate colonization and extinction dynamics that depend on geographic location and

species traits. Lakes are particularly vulnerable to the effects of global change because they are

naturally fragmented and often heavily exploited (Woodward 2009). By resampling

communities, we were able to refute a common hypothesis, that high latitude communities

exposed to greater temperature increases will change at a faster rate, and provide evidence that

intraspecific traits such as body size and local abundance predict colonization and extinction

rates. These results provide a first step towards informing ecologists about species turnover

across latitudes, and offer new insights into the proximate drivers of this turnover.

Acknowledgments

We would like to thank Veronica Jones for field assistance and Kazimierz Patalas for generously

sharing his historical sampling data with us. We also thank NSERC for funding (B.G., Discovery

Grant) as well as Ontario Graduate Scholarships (N.T.J.).


98

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103

Chapter 6

General Conclusion

This thesis was motivated by two main questions: how does species diversity change over space

and time? And, are these changes structured by differences in traits among species and across

environmental gradients? I used historical data, observational surveys and manipulative

experiments to address these questions. I found that traits modulate diversity patterns both at

regional scales where metacommunity processes predominate (chapter 2) and across latitudinal

gradients where climate and historical factors are also important for structuring diversity

(chapters 3-5). In what follows, I summarize the main findings and significance of the previous

chapters, while considering some remaining uncertainties. Addressing these uncertainties will

help inform how species traits will structure species diversity under future climatic conditions.

Chapter 2

Significance

The ability of species to disperse through patchy landscapes drive the diversity patterns we see in

nature (Wilson 1992; Holyoak, Leibold & Holt 2005), however metacommunity studies often

ignore or remove dispersal differences among species (Cadotte, Fortner & Fukami 2006; Howeth

& Leibold 2010; Declerck et al. 2013). Chapter 2 considered the dispersal mode of plants and

demonstrated the importance of considering dispersal modes of focal species for explaining the

diversity patterns of heterogeneous metacommunities. I found that patch isolation and patch area

have surprisingly variable effects on plant diversity that depend on dispersal mode. Wind-

dispersed plants, for example, show no increase in diversity with patch size but a strong response

to patch connectivity, whereas animal dispersed species show the opposite patterns.

Future directions

Although grouping plants by their dispersal mode clarified the influence of patch size and

connectivity on diversity, I found that not all species within a dispersal group responded

similarly. For example, both Rocky Mountain juniper (Juniperus scopulorum) and prickly wild

rose (Rosa acicularis) are animal dispersed, but differ in their association with large or small

CHAPTER 6: GENERAL CONCLUSION

104

stands. I suspect that this within group variation for plants dispersed by animal vectors reflect

species-specific habitat constraints. If species are selecting habitat using different criteria or are

being excluded by competitors (Chase, Burgett & Biro 2010; Vanschoenwinkel, Buschke &

Brendonck 2013), the influence of size and connectivity will be obscured (Resetarits 2005;

Matthiessen, Mielke & Sommer 2010). Future work that explicitly considers interspecific

differences in dispersal and species interactions within the broader “mode” classification will

help isolate how spatial dynamics structure diversity in this ecosystem.

The empirical sampling I used was powerful in that it used a naturally patchy metacommunity of

understory plants. This allowed me to avoid confounding effects that occur with anthropogenic

fragmentation, which can cause extinction debts that alter diversity estimates (Vellend et al.

2006; Krauss et al. 2010), or create artificial communities, that do not function as a

metacommunity under natural conditions (Cadotte 2006). However, this observational approach

necessitated that I make some assumptions regarding the status of the understory plant

community. Specifically, I assumed that communities were at equilibrium (sensu MacArthur &

Wilson 1967), which, as I observed in chapter 5, is not necessarily a safe assumption. Moreover,

my approach precluded me from discriminating definitively among the mechanisms underlying

the patterns I observed. Recently, ecologists have articulated an experimental metacommunity

“best practices” to serve a guide for researchers to craft experiments that can differentiate

between alternative hypotheses (Grainger & Gilbert 2016). Chief among their recommendations

is the selection of species that reflect real differences in dispersal and experimental designs that

allow species to colonize patches naturally. By demonstrating that traits associated with dispersal

alter the association between patch size and connectivity, this work represents an important first

step that should be followed by a more general shift to experimentally testing how interspecific

differences in dispersal maintain diversity in natural systems.


105

Chapter 3

Significance

An increase in organism body size at higher latitudes is a widely accepted ecogeographic rule

(e.g., Ashton 2002). However, in chapter 3 of this thesis I document body size-latitude

associations that do not conform to expected patterns. Unlike previous research that tests only

interspecific body size trends and shows a decrease in body size at lower latitudes (e.g., Gillooly

& Dodson 2000; Beaver et al. 2014), I tested inter- and intra-specific trends and detected weak

and variable relationships between zooplankton body size and latitude. Moreover, other

environmental factors were just as likely as latitude to affect the body size of zooplankton. These

results appear to be in opposition to many recent experimental studies, which have documented a

reduction in the body size of ectotherms at higher temperatures (Daufresne, Lengfellner &

Sommer 2009).

Future directions

Overall, the intraspecific results of this study suggest that experiments demonstrating a reduction

in body size at high temperatures may represent plastic changes (Teplitsky et al. 2008), as these

patterns are not consistent with samples collected in nature that have adapted to their temperature

regime over long time periods. Determining the effects of temperature on the fitness,

development time and body size of ectotherms represent a considerable challenge (Ohlberger

2013). This is largely because dynamic plastic responses to temperature can obscure long-term

responses to temperature and lead to trade-offs in development time and fitness (Savage et al.

2004; Gotthard, Berger & Walters 2007). In future work, efforts should focus on disentangling

plastic from genetic changes to isolate directional and stabilizing selection on body size with

increasing temperature. Future research will also need to address how plastic and genetic

changes in body size alter population and community dynamics (Kingsolver & Pfenning 2007;

Kingsolver & Huey 2008). Although these are formidable challenges, they are necessary to

address in order to understand and predict short- and long-term responses of communities to

global climate change.


106

Chapter 4

Significance

The importance of dormancy for community dynamics in both aquatic and terrestrial ecosystems

is often overlooked, especially in climate change research. In chapter 4, I tested the effects of day

length and temperature on the hatching dynamics of zooplankton that originate from lakes

spanning an 1800 km latitudinal gradient. These cues consistently terminate dormancy in

zooplankton (Stross 1966; May 1987; De Stasio 2004), and are being modified by climate

change; however, we lack studies that test how the interactive effects of these cues will alter

dormancy dynamics, and especially whether their effects depend on the latitude of the

zooplankton communities. Moreover, the majority of egg bank studies to date have considered

the responses of a single taxon at a time (e.g., Vandekerkhove, Declerck & Brendonck 2005),

preventing an analysis of the generality of species’ responses. Indeed, my results are the first to

reveal that copepods and cladocerans from a common set of lakes show systematically different

responses to day length and temperature.

Future directions

This project documents interesting latitudinal patterns in zooplankton hatching, however I was

unable to determine if the differences in hatching that I observed represent an evolutionary stable

bet hedging strategy. Bet hedging should cause a reduction in hatching rates as temporal

variation increases (Cohen 1968; Ellner 1985). The role of dormancy for optimizing

reproduction in heterogeneous environments has been well explored in plant communities. For

example, Venable (2007) used a long-term dataset of desert annual plants to demonstrate that the

relationship between environmental conditions, reproductive variability, and germination

fractions are consistent with theory on bet hedging. However, despite the influence of

environmental conditions on the fitness of freshwater zooplankton, we lack evidence that

differences in environmental responses and reproductive variability correspond to dormancy in

zooplankton. Future work should focus on predictions for bet hedging both within communities

and across a latitudinal gradient, to test whether higher rates of dormancy are indeed adaptive at

higher latitudes. This knowledge is critical to understanding the effects of climate change and

climate variability on species persistence.


107

Chapter 5

Significance

The effect of climate change on species persistence depends on the biotic and abiotic attributes

of those communities. In chapter 5, I resampled zooplankton communities and provide evidence

that changes to community composition depend on geographic location and are associated with

differences in colonization-extinction dynamics and species traits. By resampling communities

across a broad north-south latitudinal gradient, I test and subsequently reject a common

hypothesis that higher latitude communities change faster than lower latitude sites (e.g., Smol et

al. 2005). Determining if rates of community change depend on geographic location, and the

traits associated with this change, is an important first step to predicting the vulnerability of

communities to anthropogenic change.

Future directions

The association between species traits and colonization success that I document suggest that

current environmental conditions are favoring smaller, locally abundant species. Although, the

generality of this compositional shift should be investigated in additional taxa, this observation

has the potential to have large consequences. For aquatic zooplankton, body size affects

ecological processes including population maintenance, competitive asymmetries and predator-

prey dynamics (Gliwicz 1990; Yodzis & Innes 1992; Woodward et al. 2005). Together, this

suggests that changes to community composition could scale up to alter ecosystem dynamics,

and based on my results, these effects will be greater in temperate lakes.

Conclusion

The findings presented in this thesis attempt to connect the characteristics of species and their

environment to make inferences about the forces that structure diversity. I demonstrate the

importance of traits for diversity patterns locally and across broad latitudinal gradients. My

results caution against the use of space-for-time substitutions, as latitudinal differences in

hatching dynamics (chapter 4) and community shifts (chapter 5) would not have been predicted

based on environmental conditions alone. The geographic signature of community shifts, as well


108

as persistence strategies such as dormancy that are influenced by climate, should be incorporated

into future work that tests how global changes will alter community dynamics.

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111

Appendix A: Supplementary information to Chapter 2

Table S2.1. Species list from aspen stands and grassland plots in Lac Du Bois Provincial Park,

British Columbia, Canada (see main text for sampling protocol). Aspen-associated species

occurred in aspen stands at least 66% of the time. Unknown species were identified to the

generic or family level, we selected a seed type based on the predominate characteristics of that

genus or family. Species were identified using the Illustrated Flora of British Columbia and

follow the nomenclature of (Douglas et al. 1998).

Scientific name Status

Growth

form Seed type Association

Agropyron repens exotic grass no mechanism aspen

Agrostis gigantea exotic grass no mechanism aspen

Amelanchier alnifolia native shrub animal aid aspen

Antennaria pulcherrima native forb wind aid aspen

Aquilegia formosa native forb no mechanism aspen

Arabis hirsuta native forb wind aid aspen

Argentinia anserina native forb no mechanism aspen

Aster unknown forb wind aid aspen



Aster conspicuus native forb wind aid aspen

Betula occidentalis native tree wind aid aspen

Betula papyifera native tree wind aid aspen

Big aster unknown forb wind aid aspen

Bromis anomalus native grass no mechanism aspen

Calamagrostis canadensis native grass no mechanism aspen

Calamagrostis rubescens native grass no mechanism aspen

Carex unknown grass no mechanism aspen

Carex unknown grass no mechanism aspen

Carex bebbii native grass no mechanism aspen

Carex deweyana native grass no mechanism aspen

Circium sp. unknown forb wind aid aspen

Cirsium arvense exotic forb wind aid aspen

Cirsium vulgare exotic forb wind aid aspen

Cornus canadensis native forb animal aid aspen

Cynoglossum officinale exotic forb animal aid aspen

Dactylis glomerata exotic grass no mechanism aspen

Elymus glaucus native grass no mechanism aspen

Fragaria vesca native forb animal aid aspen

Fragaria virginiana native forb animal aid aspen

Fritillaria lanceolata native forb no mechanism aspen

Heuchera cylindrica native forb no mechanism aspen

APPENDIX A: SUPPLEMENTARY INFORMATION FOR CHAPTER 2

112

Juncus effusus native forb no mechanism aspen

Juniperus communis native shrub animal aid aspen

Juniperus scopulorum native shrub animal aid aspen

Lactuca serriola exotic forb wind aid aspen

Lathyrus ochroleucus native forb no mechanism aspen

Lilium columbianum native forb no mechanism aspen

Linnaea borealis native forb no mechanism aspen

Lonicera lanceolata exotic forb animal aid aspen

Mahonia aquifolium native forb animal aid aspen

Mentha arvense native forb no mechanism aspen

Moeringia lateriflora native forb no mechanism aspen

Mohonia aguifolia native forb no mechanism aspen

Osmorhiza chilensis native forb animal aid aspen

Petasites sagitattus native forb wind aid aspen

Phleum pratense exotic grass no mechanism aspen

Polygonum convolvulus exotic forb no mechanism aspen

Potentilla gracilis native forb no mechanism aspen

Prosartes trachycarpa native forb animal aid aspen

Prunus virginiana native shrub animal aid aspen

Pseudotsuga menziesii native tree wind aid aspen

Ribed cereum native shrub animal aid aspen

Ribes lacustre native shrub animal aid aspen

Rosa acicularis native shrub animal aid aspen

Salix sp. native shrub wind aid aspen

Scolochloa festucacea native grass no mechanism aspen

Silene menziesii native forb no mechanism aspen

Smilacina racemosa native forb animal aid aspen

Smilacina stellata native forb animal aid aspen

Sonchus arvensis exotic forb wind aid aspen

Symphoricarpos albus native shrub animal aid aspen

Viola adunca native forb no mechanism aspen

Viola canadensis native forb no mechanism aspen

Viola sp. unknown forb no mechanism aspen

Viola sp. unknown forb no mechanism aspen

Cichorium intybus exotic shrub wind aid grassland/generalist

Achillea millefolium native forb no mechanism grassland/generalist

Achnatherum nelsonii native grass no mechanism grassland/generalist

Achnatherum richardsonii native grass no mechanism grassland/generalist

Agoseris glauca native forb wind aid grassland/generalist

Allium cernuum native forb no mechanism grassland/generalist

Anemone multifida native forb no mechanism grassland/generalist

Antennaria rosea native forb wind aid grassland/generalist

Antennaria sp. native forb no mechanism grassland/generalist

Antennaria umbrinella native forb wind aid grassland/generalist


113

Arabis drumondii native forb wind aid grassland/generalist

Arabis holboellii exotic forb wind aid grassland/generalist

Arenaria capillaris native forb no mechanism grassland/generalist

Arnica fulgens native forb wind aid grassland/generalist

Artemisia tridentata native shrub no mechanism grassland/generalist

Aster unknown forb wind aid grassland/generalist

Aster unknown forb wind aid grassland/generalist

Aster campestris native forb wind aid grassland/generalist

Astragalus collinus native forb no mechanism grassland/generalist

Astragalus miser native forb no mechanism grassland/generalist

Balsamorhiza sagittata native forb no mechanism grassland/generalist

Bromus unknown forb no mechanism grassland/generalist



Bromus inermis native grass no mechanism grassland/generalist

Bromus japonicus exotic grass no mechanism grassland/generalist

Bromus tetorum exotic grass no mechanism grassland/generalist

Calachortus macrocarpus native forb no mechanism grassland/generalist

Camelina microcarpa exotic forb no mechanism grassland/generalist

Campanula rotundifolia native forb no mechanism grassland/generalist

Carex adusta native grass no mechanism grassland/generalist

Carex petasata native grass no mechanism grassland/generalist

Castilleja thompsonii native forb no mechanism grassland/generalist

Centauria diffusa exotic forb no mechanism grassland/generalist

Centauria maculosa exotic forb wind aid grassland/generalist

Cerastium arvense native forb no mechanism grassland/generalist

Chenopodium album exotic forb no mechanism grassland/generalist

Chysothamnus naueosus native shrub wind aid grassland/generalist

Clover unknown unknown forb no mechanism grassland/generalist

Collinsia parviflora native forb no mechanism grassland/generalist

Collomia linearis native forb no mechanism grassland/generalist

Comandra umbellata native forb animal aid grassland/generalist

Crepis atrabarba native forb wind aid grassland/generalist

Delphinium nuttallianum native forb no mechanism grassland/generalist

Descurania sophia exotic forb no mechanism grassland/generalist

Draba nemorosa native forb no mechanism grassland/generalist

Elymus repens exotic grass no mechanism grassland/generalist

Elymus trachycaulus native grass no mechanism grassland/generalist

Erigeron corymbosus native forb wind aid grassland/generalist

Erigeron flaellaris native forb wind aid grassland/generalist

Erigeron speciosus native forb wind aid grassland/generalist

Eriogonum heracleoides native forb no mechanism grassland/generalist

Festuca campestris native grass no mechanism grassland/generalist

Fritillaria pudica native forb no mechanism grassland/generalist


114

Gaillardia aristata native forb wind aid grassland/generalist

Galium boreale native forb animal aid grassland/generalist

Gentianella amarella native forb no mechanism grassland/generalist

Geranium viscossissimum native forb no mechanism grassland/generalist

Geum triflorum native forb wind aid grassland/generalist

Hesperostipa comada native grass no mechanism grassland/generalist

Hieracium sp. unknown forb wind aid grassland/generalist

Ionactis stenomeres unknown forb wind aid grassland/generalist

Juncus balticus native grass no mechanism grassland/generalist

Lappula occidentalis native forb animal aid grassland/generalist

Lithophragma parviflora native forb no mechanism grassland/generalist

Lithospermum ruderale native forb no mechanism grassland/generalist

Lomatium dissectum native forb wind aid grassland/generalist

Lomatium macrocarpum native forb wind aid grassland/generalist

Lotus denticlulatus native forb no mechanism grassland/generalist

Medicago lupulina exotic forb no mechanism grassland/generalist

Medicago sativa exotic forb no mechanism grassland/generalist

Muhlenbergia richardsonis native grass no mechanism grassland/generalist

Myosotis verna exotic forb no mechanism grassland/generalist

Penstamon procerus native forb no mechanism grassland/generalist

Plantago major native forb no mechanism grassland/generalist

Poa fendleriana ssp.

Fendleriana native grass no mechanism grassland/generalist

Poa marstida native grass no mechanism grassland/generalist

Poa pratensis exotic grass no mechanism grassland/generalist

Poa secunda native grass no mechanism grassland/generalist

Poaceae unknown unknown forb no mechanism grassland/generalist

Polygonum douglasii native forb no mechanism grassland/generalist

Potentilla anserina native forb no mechanism grassland/generalist

Potentilla diversifolia native forb no mechanism grassland/generalist

Potentilla glandulosa native forb no mechanism grassland/generalist

Pseudoroegneria spicata native grass no mechanism grassland/generalist

Rhinanthus minor native forb no mechanism grassland/generalist

Rosa nutkana native shrub animal aid grassland/generalist

Senecio pseudaureus native forb wind aid grassland/generalist

Silene alba native forb no mechanism grassland/generalist

Silene noctiflora exotic forb no mechanism grassland/generalist

Sisymbrium altissimum exotic forb no mechanism grassland/generalist

Sisymbrium loeselii exotic forb no mechanism grassland/generalist

Sisyrinchium idahoense native forb no mechanism grassland/generalist

Spartina gracilis native grass wind aid grassland/generalist

Taraxacum officinale exotic forb wind aid grassland/generalist

Tragopogon dubius exotic forb wind aid grassland/generalist

Trifolium pratense native forb no mechanism grassland/generalist


115

Unknown unknown forb unknown grassland/generalist

Unknown Aster unknown forb wind aid grassland/generalist

Unkown unknown forb unknown grassland/generalist




Verbascum thapsus exotic forb no mechanism grassland/generalist

Vicia Americana native forb no mechanism grassland/generalist

Viola glabella unknown forb no mechanism grassland/generalist

Viola orbicular native forb no mechanism grassland/generalist

Zigadenus venenosus native forb no mechanism grassland/generalist


116

Table S2.2.The sensitivity of the effects of log stand size and log stand connectivity on log

species richness by dispersal mode to three alternative criteria to determine aspen-association,

with percentages specifying the percent of occurrences that had to be within aspen stands in

order for a species to be included in the analysis. The analysis in the main text was for a 66%

cut-off, and here we present: 75% (more strict), 50% (less strict), and all species (least restrictive

possible).

75% occurrence in aspen stands

Dispersal mode #

species α estimate

log Stand size log Stand

connectivity

b t1,23 P b t1,23 P

All species 59 88.5 0.38 5.51 <0.001 -0.21 -2.11 0.047

No dispersal aid 28 5* 0.45 4.83 <0.001 -0.03 -2.37 0.028

Wind-dispersed 14 8.5 0.03 0.28 0.780 0.03 1.45 0.162

Animal-dispersed 17 254.5 0.33 4.79 <0.001 -0.15 -0.94 0.358

50 % occurrence in aspen stands

Dispersal mode #

species α estimate


connectivity

b t1,23 P b t1,23 P

All species 75 88.5 0.27 5.26 <0.001 -0.16 -2.13 0.045

No dispersal aid 37 5.0 0.32 4.41 <0.001 -0.02 -2.42 0.025

Wind-dispersed 18 8.5 0.10 1.03 0.313 0.03 1.58 0.128


All species occurring in aspen stands (no species excluded from analysis)

Dispersal mode #

species α estimate


connectivity

b t1,23 P b t1,23 P

All species 136 88.5 0.01 0.33 0.742 -0.03 -0.51 0.616

No dispersal aid 79 5.0 -0.04 -0.76 0.456 0.00 -0.48 0.635

Wind-dispersed 35 8.5 -0.08 -1.05 0.308 0.01 0.91 0.373



117

Table S2.3. The results of the effects of log stand size and log stand connectivity on log species

richness by dispersal mode when estimating the average dispersal distances (α) using our

maximum likelihood function. We varied the criteria to determine aspen-association from all

species, 75% (more conservative), 66% (intermediate; used in Table 2.1) and 50% (less

conservative). The results using the α estimates that we calculated using the maximum

likelihood function are qualitatively similar to those using published values from the literature

(compare to Table 2.1 and Table S2.2).

All species occurring in aspen stands

Dispersal mode #

species

α

estimate


b t1,23 P b t1,23 P

All species 136 61 0.14 0.37 0.715 -0.02 -0.55 0.586

No dispersal aid 79 65 -0.03 -0.57 0.574 -0.06 -1.01 0.322

Wind-dispersed 35 10* -0.08 -1.04 0.310 0.02 0.90 0.377

Animal-dispersed 21 280 0.19 4.32 <0.001 -0.03 -0.24 0.810

75% occurrence in aspen stands

Dispersal mode

#

species

α

estimate


b t1,23 P b t1,23 P

All species 59 123 0.37 5.57 <0.001 -0.24 -2.2 0.042

No dispersal aid 28 78 0.48 6.14 <0.001 -0.41 -3.9 0.001

Wind-dispersed 14 9 0.03 0.28 0.780 0.03 1.4 0.163



Dispersal mode

#

species

α

estimate


b t1,23 P b t1,23 P

All species 67 102 0.34 24.11 <0.001 -0.2 3.47 0.076

No dispersal aid 32 73* 0.42 5.26 <0.001 -0.36 11.91 0.002

Wind-dispersed 17 5 -0.07 0.45 0.508 0.02 2.97 0.099



Dispersal mode

#

species

α

estimate


b t1,23 P b t1,23 P

All species 67 106 0.34 4.91 <0.001 -0.20 -1.864 0.076

No dispersal aid 32 77 0.42 5.25 <0.001 -0.37 -3.448 0.002

Wind-dispersed 17 9 0.07 0.68 0.506 0.04 1.676 0.109


*Alpha estimates ranging from 5m-78m provided qualitatively equivalent model fit for this

group.


118

Table S2.4. Effects of stand size and stand connectivity on log-transformed species richness by

dispersal mode, with each factor analyzed in separate linear models. The average dispersal

distances are taken from Thomson et al. 2011.

Dispersal mode log Stand size log Stand connectivity

b t1,23 P b t1,23 P

All species 0.29 4.31 <0.001 0.03 0.26 0.798

No dispersal aid 0.29 3.37 0.003 <0.001 0.002 0.998

Wind-dispersed 0.15 1.74 0.096 0.04 2.41 0.025

Animal-dispersed 0.28 4.55 <0.001 0.15 0.76 0.458


Table S2.5: Effects of stand size and stand connectivity on log-transformed species richness by

dispersal mode with a connectivity function that incorporates the size of the surrounding sites.

Dispersal mode #

species

α

estimate


b t1,23 P b t1,23 P

All species 67 249 0.32 4.67 <0.001 -0.16 -1.48 0.153

No dispersal aid 32 109 0.37 4.44 <0.001 -0.25 -2.60 0.017

Wind-dispersed 17 47 0.09 0.96 0.350 0.12 1.70 0.104




119

Figure S2.1. Principal coordinates analysis (PCoA) with Jaccard’s coefficient confirming that

aspen stands differ in species composition from the surrounding grassland matrix. Species that

were highly associated with aspen stands included Rosa nutkana, Symphocarpus albus,

Taraxicum officinale, Osmorhiza chilensis, and Lathyrus ochroleucus. In contrast, species that

were highly associated with the surrounding grassland matrix included Poa secunda, Astralagus

miser, Tragapogon dubius, and Festuca campestris. Smaller aspen stands contained more

grassland-associated species, a commonly observed indicator of edge effects.


120

Figure S2.2. Rank abundance curve comparing the abundance of matrix-associated plants to

aspen–associated and generalist species’ in the understory of 24 aspen stands. Abundance was

averaged from cover data that was estimated in each stand from ten 1 m x 0.25 m subplots that

were placed within the single large plot.


121

Model Fitting

The fitted parameters in eqn. (1) are the intercept, b1, and b2. The α parameter was estimated with

two approaches: using estimates from the literature and through maximum likelihood fitting.

For the estimates from the literature, we started with the mean dispersal distances provided in

Thomson et al. (2011). There was a computational issue that arose for species with no dispersal

aid using the mean dispersal distance given (2.43 m). When we used the estimated 2.43 m

distance in eqn. (1), the estimated connectivity values were so small that they were calculated as

zero for four stands (stands 18, 47, 62 and 65). Because of an implicit assumption in

metapopulations that there is some probability for species to reach every stand, and because our

zero values resulted from rounding errors for very low connectivity measures, we took two

approaches to correct this. First, we added a small constant (8.0 x 10E-36) to the connectivity

measurement for all stands. This value is two orders of magnitude less than the connectivity

value of the least connected stand that still had a non-zero connectivity. Second, to ensure that

our results were not being driven by a statistical artifact from the addition of that constant, we

also calculated connectivity for species with no dispersal aid using an alpha of 5 m, which is well

within the natural limits of dispersal for unassisted species (range = 0.03-18.37 m; Thomson et

al. 2011). The results were qualitatively the same. When alpha is set to 2.43 m, the connectivity

for those four stands is simply equal to the constant we added, and so we therefore report the

model using a mean dispersal distance of 5 m in Table 2.1 and Figure 2.2. We use a footnote in

Table 2.1 to indicate that running the models using a dispersal distance of 2.43 m did not

qualitatively change the results.

For the maximum likelihood approach, we iteratively tested the parameter space of all plausible

α values (1 to 2000 m), and simultaneously fit the other parameters. We selected the combination

of parameter values that minimized the AIC value of eqn (1) (i.e., that minimized the residual

sum of squares). We also used a connectivity measure that incorporates the size of all donor

stands, with the assumption that stand size affects the potential number of colonizing species, or

the number of colonists per species. To do this, the contribution of stand j to stand i is the

connectivity of two sites (e-dij/αk) × the area of the donor stand (Aj). Our results were

qualitatively similar using connectivity functions that were or were not weighted by the size of


122

donor stands (Table S2.5); we therefore use the unweighted connectivity function from equation

1 in all reported analysis.

Literature Cited

Douglas, G.W., G.B. Straley, D.V. Meidinger, and J. Pojar (Editors). 1998. Illustrated Flora of

British Columbia, Volume 1: Gymnosperms and Dicotyledons (Aceraceae through

Asteraceae). B.C. Min. Environ., Lands and Parks, and B.C. Min. For., Victoria, B.C. 436

pp.

Douglas, G.W., G.B. Straley, D.V. Meidinger, and J. Pojar (Editors). 1998. Illustrated Flora of

British Columbia, Volume 2: Dicotyledons (Balsaminaceae through Cuscutaceae). B.C.

Min. Environ., Lands and Parks, and B.C. Min. For., Victoria, B.C. 401 pp.

Douglas, G.W., D.V. Meidinger, and J. Pojar (Editors). 1999. Illustrated Flora of British

Columbia, Volume 3: Dicotyledons (Diapensiaceae through Onagraceae). B.C. Min.

Environ., Lands and Parks, and B.C. Min. For., Victoria, B.C. 423 pp.


Columbia, Volume 4: Dicotyledons (Orobanchaceae through Rubiaceae). B.C. Min.



Columbia, Volume 5: Dicotyledons (Salicaceae through Zygophyllaceae) and

Pteridophytes. B.C. Min. Environ., Lands and Parks, and B.C. Min. For., Victoria, B.C.

389 pp.


Columbia, Volume 6: Monocotyledons (Acoraceae through Najadaceae). B.C. Min.



Columbia, Volume 7: Monocotyledons (Orchidaceae through Zosteraceae). B.C. Min.

Sustain. Res. Manage., and B.C. Min. For., Victoria, B.C. 379 pp.


Columbia, Volume 8: General Summary, Maps and Keys. B.C. Min. Sustain. Res.

Manage., and B.C. Min. For., Victoria, B.C. 457 pp.

123

Appendix B: Supplementary information to Chapter 3

Figure S3.1. The correlation between surface temperature and latitude in the 19 lakes that we

collected zooplankton from in western Canada in the summer of 2011.

APPENDIX B: SUPPLEMENTARY INFORMATION FOR CHAPTER 3

124

Figure S3.2. The relationship between latitude and mean body size for the 10 species that

occurred in a minimum of three lakes. The species names are as follows: a) Daphnia pulex, b)

Diaphanosoma luechtenb., c) Bosmina longirostris, d) Diacyclops thomasi, e) Acanthocyclops

vernalis, f) Daphnia longiremis, g) Cyclops scutifer, h) Daphnia longispina, i) Holopedium

gibberum, j) Leptodora kindii. Error bars represent one standard error of the mean. See Table 3.1

linear model summaries.


125

Table S3.1. Lakes and their associated physical and environmental characteristics. See Table S3.2 for a summary of which lakes were

included in each chapter of this thesis.

Lake name Historical

sampling

date*

2011

sampling

date

Latitude

(°)

Longitude

(°)

Lake

size

(km2)

Max

depth

(m)

Elevation

(m)

TDS

(mg/L)

DO

(µmg/L)

pH Chlor

a

(ug/L)

Adams 1986 29-Jul-11 51.3 -119.5 137.00 464 404 0.02 9.6 8.2

0

.5

Alleyne 1-Aug-51 29-Jul-11 49.9 -120.6 0.55 36 1016 0.22 8.1 8.9

0

.8

Beaver 15-Jul-29 09-Jul-11 52.5 -121.9 2.55 23 1201 0.12 0.1 8.3

6

.0

Becker 15-Jul-57 08-Jul-11 51.8 -121.1 0.10 11 879 0.17 0.8 8.6

1

7.7

Braeburn 28-Jul-70 15-Jul-11 61.5 -135.8 6.00 37 760 0.15 2.5 8.6

2

.2

Cobb 1-Aug-58 10-Jul-11 54.0 -123.5 2.00 10 783 0.04 7.1 8.2

2

.1

Corbett 15-Jul-67 29-Jul-11 50.0 -120.6 2.90 20 1065 0.35 8.3 8.5

1

.6

Dease 15-Jul-47 11-Jul-11 58.5 -130.0 16.22 142 753 0.08 11.2 8.4

1

.3

Dezadeash 8-Nov-70 14-Jul-11 60.5 -137.0 77.20 8 702 0.29 1.5 8.6

1

.1

Fox 22-Jun-75 15-Jul-11 60.5 -137.0 15.90 75 835 0.15 18.3 8.7

-

0.1

Frenchman 21-Aug-75 17-Jul-11 62.2 -135.8 14.10 39 535 0.12 3.4 8.7

0

.6

Harrison 1999 29-Jul-11 50.1 -121.5 218.00 270 41 0.02 9.2 8.4

0

.8


126

Heffley 1-Aug-50 28-Jul-11 50.8 -120.1 2.23 27 993 0.10 2.7 9.0

1

.6

Hicks 15-Jul-25 05-Jul-11 49.3 -121.7 1.04 55 230 0.01 9.1 8.7

-

0.2

Kathlyn 1-Aug-58 10-Jul-11 54.8 -127.2 1.70 10 506 0.02 0.9 8.3

3

.0

Kawkawa 17-Jul-40 29-Jul-11 49.4 -121.4 0.77 14 76 0.05 9.6 8.4

1

.1

Kentucky 1-Aug-51 29-Jul-11 49.9 -120.6 0.36 40 1029 0.19 8.5 8.7

1

.3

Kluane 12-Aug-70 18-Jul-11 61.3 -138.7 409.50 82 781 0.11 2.5 8.8

0

.4

Lakelse 1-Aug-75 21-Jul-11 54.4 -128.6 20.00 20 72 0.02 5.5 8.1

1

.5

Little Atlin 30-Jul-70 19-Jul-11 60.3 -134.0 39.80 14 686 0.11 5.4 8.9

0

.9

Little Salmon 22-Aug-75 16-Jul-11 62.2 -134.7 62.60 96 608 0.10 12.2 8.7

1

.4

Maxan 1-Jul-61 23-Jul-11 54.3 -126.1 24.00 24 762 0.02 7.5 8.0

7

.4

McConnel 1-Aug-50 29-Jul-11 50.5 -120.5 0.39 24 1313 0.15 7.8 8.3

1

.5

Meziadin 1-Jul-74 11-Jul-11 56.1 -129.3 31.10 133 246 0.03 5.7 8.5

1

.0

Minto 21-Jul-70 17-Jul-11 63.7 -136.2 4.30 33 685 0.09 1.2 8.5

1

.9

Ness 1-Aug-52 09-Jul-11 54.0 -123.1 2.10 18 781 0.07 20.9 8.6

1

.3

Nicola 15-Jul-66 29-Jul-11 50.2 -120.5 62.15 55 630 0.08 9.0 8.8

4

.2

Paul 1-Aug-50 27-Jul-11 50.7 -120.1 3.90 55 769 0.14 0.9 8.8

1

.4


127

Pemberton 1-Aug-50 27-Jul-11 50.8 -119.9 0.12 14 1239 0.11 0.0 8.8

1

.7

Pillar 1-Aug-48 06-Jul-11 50.6 -119.6 0.43 16 953 0.08 2.6 8.3

1

.7

Pinantin 1-Aug-50 27-Jul-11 50.7 -122.6 0.68 19 878 0.14 3.2 8.8

3

.9

Pine 31-Aug-75 18-Jul-11 60.1 -130.9 4.30 27 685 0.12 2.8 8.7

0

.6

Quiet 2-Aug-70 19-Jul-11 61.1 -133.1 53.00 100 802 0.04 4.0 8.8

1

.4

Seymour 1-Aug-58 22-Jul-11 54.7 -127.2 0.70 9 523 190.00 3.9 8.3

1

.1

Shuswap 15-Jul-57 29-Jul-11 50.9 -119.3 309.60 267 348 0.03 9.8 8.8

0

.9

Sullivan 1-Aug-50 28-Jul-11 51.0 -120.1 86.71 24 1166 0.13 3.0 8.8

1

.7

Summit 1964 27-Jul-11 54.3 -122.6 13.84 16 721 0.02 5.4 7.7

3

.7

Tatchun 25-Jul-70 16-Jul-11 62.3 -136.2 6.60 53 535 0.12 4.6 8.4

4

.5

Walloper 1-Aug-50 26-Jul-11 50.5 -120.5 0.36 8 1324 -0.01 1.6 8.5

2

2.3

Watson 7-Aug-70 12-Jul-11 60.1 -128.8 14.30 20 680 0.08 0.7 8.7

1

.4

Wheeler 19-Aug-75 12-Jul-11 59.7 -129.2 2.80 30 663 0.18 8.2 8.6

2

.7

White 15-Jul-69 07-Jul-11 50.9 -119.3 5.61 40 470 0.11 5.9 8.7

0

.3

Wood 1-Aug-71 06-Jul-11 50.1 -119.4 0.27 10 391 0.13 26.5 8.9

2

.6

*For three lakes only the sampling year was available.


128

Table S3.2. Summary of each lake and the chapters of this thesis that the lake was included in.

Lake name Latitude

(°)

Longitude

(°)

Included

in Ch. 3

Included

in Ch. 4

Included

in Ch. 5

Adams 51.3 -119.5 yes no yes

Alleyne 49.9 -120.6 no no yes

Beaver 52.5 -121.9 yes yes yes

Becker 51.8 -121.1 yes no yes

Braeburn 61.5 -135.8 no no yes

Cobb 54.0 -123.5 no yes yes

Corbett 50.0 -120.6 yes no yes

Dease 58.5 -130.0 yes no yes

Dezadeash 60.5 -137.0 no yes yes

Fox 60.5 -137.0 yes no yes

Frenchman 62.2 -135.8 no yes yes

Harrison 50.1 -121.5 no no yes

Heffley 50.8 -120.1 no yes yes

Hicks 49.3 -121.7 no no yes

Kathlyn 54.8 -127.2 no no yes

Kawkawa 49.4 -121.4 yes no yes

Kentucky 49.9 -120.6 yes yes yes

Kluane 61.3 -138.7 no yes yes

Lakelse 54.4 -128.6 yes yes yes

Little Atlin 60.3 -134.0 no yes yes

Little Salmon 62.2 -134.7 yes no yes

Maxan 54.3 -126.1 no yes yes

McConnel 50.5 -120.5 no yes yes


129

Meziadin 56.1 -129.3 yes yes yes

Minto 63.7 -136.2 no yes yes

Ness 54.0 -123.1 yes yes yes

Nicola 50.2 -120.5 no no yes

Paul 50.7 -120.1 yes no yes

Pemberton 50.8 -119.9 no yes yes

Pillar 50.6 -119.6 no yes yes

Pinantin 50.7 -122.6 no yes yes

Pine 60.1 -130.9 yes yes yes

Quiet 61.1 -133.1 yes no yes

Seymour 54.7 -127.2 no yes yes

Shuswap 50.9 -119.3 no no yes

Sullivan 51.0 -120.1 no yes yes

Summit 54.3 -122.6 yes yes yes

Tatchun 62.3 -136.2 yes no yes

Walloper 50.5 -120.5 no yes yes

Watson 60.1 -128.8 no yes yes

Wheeler 59.7 -129.2 no yes yes

White 50.9 -119.3 yes yes yes

Wood 50.1 -119.4 yes no yes


130

Table S3.3. Model summaries for the effect of temperature on the average body size of 10 zooplankton species. The maximum and

minimum body sizes were back transformed from the predicted values generated by the linear model. Species names follow the

taxonomy of Thorp and Covich (2010) and Sandercock and Scudder (1994).

Minimum Maximum

Species Temp

(°C)

Predicted

body size

(µm)

Temp

(°C)

Predicted

body size

(µm)

Number of

lakes

% change in

body size

P-value

Acanthocyclops vernalis 15.5 207 20.5 221 4 7 0.2632

Bosmina longirostris 7.2 220 20.8 193 16 -14 0.8311

Cyclops scutifer 11.9 223 16.2 331 8 33 0.5017

Daphnia longiremis 11.9 345 19.8 374 10 8 0.4040

Daphnia longispina 13.3 390 20.8 403 6 3 0.3779

Daphnia pulex 7.2 516 20.5 548 8 6 0.5949

Diacyclops thomasi 7.2 302 20.8 292 12 -3 0.6119

Diaphanosoma luechtenb. 15.3 376 20.8 351 7 -7 0.7081

Holopedium gibberum 15.3 502 20.5 190 3 -164 <0.0001

Leptodora kindii 16.5 1167 20.8 1936 3 40 0.3244


131

Table S3.4. Summary of species level estimates from linear mixed model with latitude only

(without covariates) and with latitude and with covariates (lake depth, [chlorophyll a], [dissolved

oxygen], fish richness). Species names follow the taxonomy of Thorp and Covich (2010) and

Sandercock and Scudder (1994).”

Species

Estimate (without

covariates)

Estimate (with

covariates)

Acanthocyclops vernalis 0.06 ± 0.11 0.17 ± 0.20

Bosmina longirostris 0.01 ± 0.11 -0.14 ± 0.19

Cyclops scutifer -0.09 ± 0.11 0.14 ± 0.22

Daphnia longiremis 0.00 ± 0.11 -0.18 ± 0.20

Daphnia longispina -0.10 ± 0.12 -0.92 ± 0.26

Daphnia pulex -0.20 ± 0.12 -0.04 ± 0.20

Diacyclops thomasi 0.00 ± 0.11 -0.12 ± 0.20

Diaphanosoma leuchtenb. 0.52 ± 0.13 0.84 ± 0.21

Holopedium gibberum 0.51 ± 0.26 1.35 ± 0.37

Leptodora kindii -1.09 ± 0.28 -1.23 ± 0.62

132

Appendix C: Supplementary information to Chapter 4

Figure S4.1. Latitudinal changes in six physical and chemical characteristics from the 25 lakes

that we collected sediment containing zooplankton egg banks from in July 2011. All chemical

characteristics were quantified at the same time as sediment collection. The measurements were

taken mid-lake using a YSI 6-series multiparameter water quality sonde (Integrated Systems &

Services, Yellow Spring, OH, USA). We used published estimates of lake size and depth from

the literature (Anderson 1974; Lindsey et al. 1981). No correlations were significant (all P values

> 0.15).

APPENDIX C: SUPPLEMENTARY INFORMATION FOR CHAPTER 4

133

Figure S4.2. Schematic of experimental design. Sediment from 25 lakes was collected from

across a latitudinal gradient and exposed to four treatment combinations. Numbers indicate the

degrees latitude of each lake. See Table S4.1 for the corresponding lake names and the methods

section in the main text for a detailed description of the experimental approach.


134

Figure S4.3. Relationship between the crustacean zooplankton species richness of our 25

experimental lakes and latitude. Species richness was calculated by summing the unique species

identified in historical samples and the samples we collected in 2011.

R2

= 0.20, p=0.002


135

Figure S4.4. The effect of temperature and day length on the hatching of three zooplankton

species that occurred across the latitudinal gradient (2 copepods, a & c, and 1 cladoceran, b). If

local adaptation causes greater relative hatching rates in ‘home’ conditions, we would expect to

see red points above blue points at low latitudes and blue points above red points at high

latitudes (i.e., there would be higher hatching rates for low latitude populations under warm,

short days or and relatively high hatching for northern populations under cool and long days).

For each of these species there was little evidence of higher hatching rates in typical ‘home’

conditions relative to away conditions. Instead, at the species level the abundance of hatchlings

shows idiosyncratic patterns with respect across latitude. Points are jittered to reduce overlap.

136

Table S4.1. Summary of temperature and day length data during ice-off. If ice off occurred at the beginning of a month, an average

was taken between that month and the previous month. If ice off occurred at the end of a month, and average was taken between the

current month and the following month. If ice off happened during the middle of the month, the average of that month was calculated.

Ice off dates differ in precision and were obtained from the Polar Data Catalogue (polardata.ca) and The National Snow and Ice Data

Center (nsidc.org). Average temperatures were obtained from the Canadian Center for Climate Normals (climate.weatheroffice.gc.ca).

Photoperiod data was obtained from (ou.edu/research/electron/internet/solarjav.html).

Lake Latitude

Ice-off

interval

Ice-off

average

temperature

interval

Mean air

temperature (°C)

photoperiod

(hrs)

Beaver 52.250 No data Late April 1971-2000 11 No data

Cobb 54.817 No data mid-April 1971-2000 4.4 14.01

Dezadeash 61.467 1966-1985 May 15th 1971-2000 6.1 17:10

Frenchman 61.250 1947-1966 May 31st 1971-2000 9 19:00

Heffley 50.967 1973-2011 April 22nd 1971-2000 9.7 13.48

Kentucky 49.917 No data April 15th 1971-2000 9.7 13.45

Kluane 60.126 1966-1985 May 15th 1971-2000 1.7 17:30

Lakelse 54.366 2008-2011 April 9th 1971-2000 6.2 13.37

Little.Atlin 61.094 No data Late May 1971-2000 10.25 17:55

Maxan 54.300 No data mid-April 1971-2000 3.5 14:03

McConnel 50.167 No data May 1971-2000 No data 14.5

Meziadin 58.450 No data No data No data No data No data

Minto 63.683 No data No data No data No data No data

Ness 54.017 2005 April 20th 1971-2000 3.9 14.23

Pemberton 50.733 No data early April 1971-2000 7.3 13:10

Pillar 50.583 No data early April 1971-2000 7.25 13.13

Pinantin 50.740 No data mid-April No data No data 13.48

Pine 60.254 1966-1985 May 15th 1971-2000 6.1 17:10

Seymour 54.750 2003 April 17th 1971-2000 4.8 14.14

Sullivan 50.967 No data early May 1971-2000 12.05 15

http://climate.weatheroffice.gc.ca/climate_normals/index_e.html

http://www.ou.edu/research/electron/internet/solarjav.html


137

Summit 50.833 No data mid-April 1971-2000 4.4 14.03

Walloper 50.483 No data mid May 1971-2000 14.4 15.27

Watson 60.117 1957-1991 May 1st 1971-2000 3.7 16:01

Wheeler 60.117 1948-1988 May 12th 1971-2000 6.4 16:50

White 50.883 2009-2011 early April 1971-2000 7.8 12.55

138

Table S4.2. The treatment allocation and corresponding latitude of the 25 lakes that we collected

sediment from. See Figure S4.3 for a schematic of the experimental design and the methods

section in the main text for a detailed description of the experimental approach.

Lake

name

Bath

tank Rack Position Temperature(°C) Photoperiod

(hours) Latitude

Cobb 1 1 1 8 16 53.95

Maxan 1 1 2 8 16 54.30

Pemberton 1 1 3 8 16 50.78

Walloper 1 1 4 8 16 50.48

Summit 1 1 5 8 16 54.25

McConnel 2 1 1 12 16 50.52

Pinantin 2 1 2 12 16 50.74

Heffley 2 1 3 12 16 50.83

Wheeler 2 1 4 12 16 59.69

Kentucky 2 1 5 12 16 49.90

Summit 3 2 1 8 12 54.25

Seymour 3 2 2 8 12 54.75

Lakelse 3 2 3 8 12 54.37

Minto 3 2 4 8 12 63.68

Heffley 3 2 5 8 12 50.83

Maxan 4 2 1 8 12 54.30

Walloper 4 2 2 8 12 50.48

Pinantin 4 2 3 8 12 50.74

Ness 4 2 4 8 12 54.02

Pine 4 2 5 8 12 60.13

Pemberton 5 3 1 12 12 50.78

Sullivan 5 3 2 12 12 50.97

Meziadin 5 3 3 12 12 56.07

Beaver 5 3 4 12 12 52.25

Frenchman 5 3 5 12 12 62.17

Lakelse 6 3 1 12 12 54.37

Minto 6 3 2 12 12 63.68

Maxan 6 3 3 12 12 54.30

Little Atlin 6 3 4 12 12 60.25

Seymour 6 3 5 12 12 54.75

Sullivan 7 4 1 12 16 50.97

Minto 7 4 2 12 16 63.68

Lakelse 7 4 3 12 16 54.37

Watson 7 4 4 12 16 60.12

White 7 4 5 12 16 50.88


139

Lake

name

Bath


(hours) Latitude

Cobb 8 4 1 12 16 53.95

Frenchman 8 4 2 12 16 62.17

Pillar 8 4 3 12 16 50.58

Pemberton 8 4 4 12 16 50.78

Ness 8 4 5 12 16 54.02

Watson 9 5 1 8 16 60.12

Pinantin 9 5 2 8 16 50.74

White 9 5 3 8 16 50.88

Kentucky 9 5 4 8 16 49.90

Kluane 9 5 5 8 16 61.25

McConnel 10 5 1 8 16 50.52

Little Atlin 10 5 2 8 16 60.25

Heffley 10 5 3 8 16 50.83

Wheeler 10 5 4 8 16 59.69

Dezadeash 10 5 5 8 16 60.50

Heffley 11 6 1 12 12 50.83

Dezadeash 11 6 2 12 12 60.50

White 11 6 3 12 12 50.88

Wheeler 11 6 4 12 12 59.69

Watson 11 6 5 12 12 60.12

Ness 12 6 1 12 12 54.02

Summit 12 6 2 12 12 54.25

Pine 12 6 3 12 12 60.13

Walloper 12 6 4 12 12 50.48

Pillar 12 6 5 12 12 50.58

Meziadin 13 7 1 8 12 56.07

Sullivan 13 7 2 8 12 50.97

Watson 13 7 3 8 12 60.12

Wheeler 13 7 4 8 12 59.69

Kentucky 13 7 5 8 12 49.90

McConnel 14 7 1 8 12 50.52

Pillar 14 7 2 8 12 50.58

Little Atlin 14 7 3 8 12 60.25

Dezadeash 14 7 4 8 12 60.50

Kluane 14 7 5 8 12 61.25

Pine 15 8 1 8 16 60.13

Frenchman 15 8 2 8 16 62.17

Beaver 15 8 3 8 16 52.25


140

Lake

name

Bath


(hours) Latitude

Minto 15 8 4 8 16 63.68

Pillar 15 8 5 8 16 50.58

Beaver 16 8 1 12 16 52.25

Summit 16 8 2 12 16 54.25

Maxan 16 8 3 12 16 54.30

Dezadeash 16 8 4 12 16 60.50

Little Atlin 16 8 5 12 16 60.25

Pemberton 17 9 1 8 12 50.78

Cobb 17 9 2 8 12 53.95

Beaver 17 9 3 8 12 52.25

Frenchman 17 9 4 8 12 62.17

White 17 9 5 8 12 50.88

McConnel 18 9 1 12 12 50.52

Kentucky 18 9 2 12 12 49.90

Cobb 18 9 3 12 12 53.95

Kluane 18 9 4 12 12 61.25

Pinantin 18 9 5 12 12 50.74

Walloper 19 10 1 12 16 50.48

Seymour 19 10 2 12 16 54.75

Pine 19 10 3 12 16 60.13

Kluane 19 10 4 12 16 61.25

Meziadin 19 10 5 12 16 56.07

Meziadin 20 10 1 8 16 56.07

Sullivan 20 10 2 8 16 50.97

Lakelse 20 10 3 8 16 54.37

Seymour 20 10 4 8 16 54.75

Ness 20 10 5 8 16 54.02


141

Table S4.3. Summary of final models, determining the effects of latitude, temperature and

photoperiod on zooplankton median hatching day, after removing non-significant higher-order

terms. Watson Lake, removed from the first analysis, had a single, large outlier that drove a 3-

way interaction.

Median hatching day

Not including Watson Lake

Estimate Std. Error df t value Pr(>|t|)

(Intercept) 62.5 15.95 121 3.92 0.0001

taxon (copepod) -55.3 18.60 143 -2.97 0.0035

taxon (rotifer) -21.5 16.22 149 -1.33 0.1866

temperature (high) -7.3 2.01 138 -3.64 0.0004

latitude -0.7 0.29 122 -2.31 0.0224

copepod:temperature -1.9 2.65 138 -0.70 0.4867

rotifer:temperature 5.2 2.30 137 2.28 0.0243

copepod:latitude 1.0 0.34 143 2.92 0.0041

rotifer:latitude 0.7 0.30 149 2.34 0.0207

Including Watson Lake

df Log Ratio Test Pr(Chi)

taxon:temperature:latitude 2 10.71 0.005


(Intercept) 45.7 24.8 154 1.84 0.068

taxon (copepod) -21.8 28.6 146 -0.76 0.447

taxon (rotifer) -6.2 26.3 149 -0.24 0.813

temperature (high) 20.6 32.3 142 0.64 0.524

latitude -0.4 0.5 154 -0.79 0.430


rotifer:temperature -21.4 35.5 141 -0.60 0.548

copepod:latitude 0.4 0.5 146 0.70 0.486

rotifer:latitude 0.4 0.5 149 0.84 0.400

temperature:latitude -0.5 0.6 142 -0.87 0.386

copepod:temperature:latitude 2.1 0.8 143 2.76 0.007

rotifer:temperature:latitude 0.5 0.6 141 0.76 0.451

Note: significant effects are bolded


142

Table S4.4. Summary of final models, determining the effects of latitude, temperature and

photoperiod on the number of days until the first individual of each taxon hatched per lake, after

removing non-significant higher-order terms. Watson Lake, removed from the first analysis, had

a single, large outlier that drove a 3-way interaction.

First day hatching was observed

Not including Watson Lake


(Intercept) 22.5 1.41 114 15.96 <0.001

taxon (copepod) -1.9 1.58 131 -1.20 0.233

taxon (rotifer) -4.8 1.45 134 -3.34 0.001

temperature (high) -10.0 1.91 100 -5.21 <0.001

copepod:temperature 2.2 2.37 132 0.92 0.3616

rotifer:temperature 7.8 2.04 130 3.80 0.0002

Including Watson Lake

df Log Ratio Test Pr(Chi)

taxon:temperature:latitude 2 12.98 0.0015


(Intercept) -5.3 22.63 163 -0.23 0.816

taxon (copepod) 41.5 26.12 155 1.59 0.114

taxon(rotifer) 24.9 24.03 158 1.04 0.302

temperature (high) 11.2 29.51 152 0.38 0.704

latitude 0.5 0.42 163 1.23 0.219


rotifer:temperature -17.6 32.36 150 -0.54 0.587

copepod:latitude -0.8 0.48 156 -1.67 0.096

rotifer:latitude -0.5 0.44 158 -1.25 0.214

temperature:latitude -0.4 0.54 152 -0.72 0.471

copepod:temperature:latitude 2.2 0.70 150 3.13 0.002

rotifer:temperature:latitude 0.5 0.59 150 0.79 0.430

Note: significant effects are bolded


143

Table S4.5. Summary of full models determining the effects of latitude, temperature and

photoperiod on zooplankton hatchling abundance. Higher-order terms were tested for

significance and subsequently removed if they did not improve model fit.

Cladocera hatching*

Estimate Std. Error z value Pr(>|z|)

Latitude -0.01 0.052 -0.14 0.893

Temperature -0.83 2.597 -0.32 0.749

Photoperiod 0.18 3.000 0.06 0.951

Latitude*Temperature 0.02 0.047 0.49 0.623

Latitude*Photoperiod -0.01 0.054 -0.13 0.901

Photoperiod*Temperature 11.58 3.917 2.96 0.003

Latitude*Photoperiod*Temperature -0.21 0.072 -2.86 <0.0001

Copepoda hatching


Latitude 0.20 0.067 2.98 0.003

Photoperiod -0.06 0.329 -0.18 0.854

Temperature -0.07 0.326 -0.20 0.841

Latitude2 -0.04 0.016 -2.59 0.010


Latitude*Temperature -0.14 0.064 -2.28 0.023


Photoperiod*Latitude2 0.03 0.014 1.97 0.049

Temperature*Latitude2 0.01 0.015 0.50 0.617

Latitude*Photoperiod*Temperature 0.23 0.079 2.88 0.003

Photoperiod*Temperature*Latitude2 -0.06 0.020 -2.84 0.005

Rotifera hatching*


Latitude -0.08 0.026 -2.90 3.7E-03

Photoperiod -1.54 0.280 -5.49 4.1E-08

Temperature -1.29 0.120 -10.74 < 2e-16

Latitude*Photoperiod 0.04 0.002 17.25 < 2e-16

Latitude*Temperature 0.05 0.002 23.12 < 2e-16

Photoperiod*Temperature 0.64 0.302 2.13 3.3E-02

Latitude*Photoperiod:*Temperature -0.03 0.003 -11.47 <0.0001

*We detected no evidence for non-linear patterns in Cladoceran or Rotifer hatching patterns

therefore we did not fit the Latitude2 term for those analyses.


144

Table S4.6. The crustacean zooplankton species that hatched from the sediment of 25 lakes in

western Canada . See Table S3.1 for lake characteristics and Figure 4.1 for a map of lake

locations. Species names follow the taxonomy of Thorp and Covich (2010) and Sandercock and

Scudder (1994).

Species Lake

Bosmina longirostris Cobb

Bosmina longirostris Ness

Ceriodaphnia lacustri Beaver

Ceriodaphnia lacustri Kentucky

Ceriodaphnia lacustri Kentucky

Ceriodaphnia lacustri Seymour

Ceriodaphnia lacustri Walloper

Ceriodaphnia lacustri Wheeler

Ceriodaphnia quadrangula Beaver

Ceriodaphnia quadrangula Lakelse

Ceriodaphnia quadrangula Meziadin

Ceriodaphnia quadrangula Walloper

Ceriodaphnia reticulata Walloper

Ceriodaphnia sp. Cobb

Ceriodaphnia sp. Ness

Chydoris sp. Cobb

Chydoris sp. Dezadeash

Chydoris sp. Meziadin

Chydoris sp. Ness

Chydoris sp. Seymour

Chydoris sp. Sullivan

Cyclops phaleratus Beaver

Cyclops scutifer Maxan

Cyclops scutifer Seymour

Cyclops scutifer Wheeler

Daphnia galeata complex Cobb

Daphnia galeata complex Frenchman

Daphnia longiremus Cobb

Daphnia longispina Beaver

Daphnia longispina Frenchman

Daphnia longispina Maxan

Daphnia pulex complex Cobb

Daphnia pulex complex Frenchman

Daphnia pulex complex Seymour

Daphnia pulex complex Wheeler


145

Species Lake

Daphnia sp. Lakelse

Daphnia sp. Little.Atlin

Daphnia sp. Pillar

Diacyclops thomasi Cobb

Diacyclops thomasi Heffley

Diacyclops thomasi Lakelse

Diacyclops thomasi Lakelse

Diacyclops thomasi Summit

Diaphanosoma leuchtenbergianum Beaver

Diaphanosoma leuchtenbergianum Frenchman

Diaptomus dentricornis Seymour

Diaptomus nudus Cobb

Diaptomus pribliofensis Beaver

Diaptomus pribliofensis Cobb

Diaptomus pribliofensis Lakelse

Diaptomus pribliofensis Maxan

Diaptomus pribliofensis Seymour

Diaptomus pribliofensis Sullivan

Diaptomus pribliofensis Summit

Diaptomus pribliofensis Watson

Diaptomus pribliofensis Wheeler

Diaptomus.sicilis Beaver

Diaptomus.sicilis Dezadeash

Diaptomus.sicilis Lakelse

Diaptomus.sicilis Ness

Diaptomus.sicilis Wheeler

Epischura nevadensis Ness

Heterocope septentrionalis Dezadeash

Heterocope septentrionalis Frenchman

Heterocope septentrionalis Kentucky

Heterocope septentrionalis Little.Atlin

Heterocope septentrionalis Meziadin

Heterocope septentrionalis Minto

Heterocope septentrionalis Pemberton

Heterocope septentrionalis Sullivan

Heterocope septentrionalis Walloper

Heterocope septentrionalis White

Unknown Calanoid Beaver

Unknown Calanoid Cobb


146

Species Lake

Unknown Calanoid Dezadeash

Unknown Calanoid Frenchman

Unknown Calanoid Lakelse

Unknown Calanoid Ness

Unknown Cyclopoid Dezadeash

Unknown Cyclopoid Ness

Unknown Cyclopoid Pillar

Unknown Cyclopoid Sullivan

Unknown Cyclopoid Watson


147

Table S4.7. Summary of full models, determining the effects of latitude, temperature and

photoperiod on zooplankton diversity. Higher-order terms were tested for significance and

subsequently removed if they did not improve model fit.

Copepods and Cladocerans

Estimate Std. Error z-value Pr(>|z|)

Latitude 0.25 0.101 2.50 0.013

Photoperiod -0.37 0.457 -0.80 0.424

Temperature -0.11 0.425 -0.26 0.798

Latitude2 -0.05 0.024 -2.32 0.019






Latitude*Photoperiod*Temperature 0.19 0.145 1.29 0.199

Photoperiod*Temperature*Latitude2 -0.05 0.036 -1.46 0.145

Cladocera diversity*


Latitude -0.03 0.104 -0.24 0.807

Photoperiod 0.37 0.500 0.74 0.459

Temperature 0.65 0.474 1.38 0.167


Latitude*Temperature 0.16 0.120 1.30 0.193

Photoperiod*Temperature -0.47 0.662 -0.71 0.478

Latitude*Photoperiod*Temperature -0.20 0.175 -1.14 0.253

Copepod diversity


Latitude 0.51 0.182 2.79 0.005

Photoperiod -1.09 0.586 -1.87 0.062

Temperature -0.39 0.568 -0.68 0.494

Latitude2 -0.10 0.040 -2.47 0.013






Latitude*Photoperiod *Temperature 0.70 0.261 2.68 0.003

Photoperiod *Temperature*Latitude2 -0.12 0.062 -1.89 0.046


148

*We detected no evidence for non-linear patterns in Cladoceran diversity patterns therefore we

did not fit the Latitude2 term for that analysis.

Literature cited



Canada, 31, 855–869.








149

Appendix D: Supplementary information to Chapter 5

Figure S5.1. The relationship between the number of years between the historical and

contemporary samples and (a) the change in species richness, (b) species turnover, measured

using the Sorenson dissimilarity metric, (c) the proportion of new species per lake, and (d) the

proportion of species that went locally extinct. All graphs compare historic zooplankton samples

with contemporary samples (see methods). In (c) colonization = the number of species that

colonized the lake / the contemporary species richness. In (d), extinction = the number of species

that were locally extirpated / the historical species richness. See Table S5.3 for model summary.

APPENDIX D: SUPPLEMENTARY INFORMATION FOR CHAPTER 5

150

Figure S5.2. The relationship between latitude and the number of years since the crustacean

zooplankton communities were originally sampled and our sampling in 2011. Note that the y-

axis is presented on a logarithmic scale. We included years since historical sample as a covariate

in community change analyses, but this term was not significant in any analysis (See Fig. S5.1

and Table S5.3).


151

Figure S5.3. The non-significant relationship between the average body size of a species and its

average local abundance (r = - 0.17, P = 0.52).


152

Figure S5.4. The relationship between latitude and crustacean zooplankton (a) body size and (b)

mean local abundance. Neither relationship is significant (P = 0.60 and P = 0.60 respectively).


153

Figure S5.5. The relationship between % occupancy (i.e., the number of lakes that fall within the

latitudinal range of a zooplankton species and a species is present) and the average local

abundance (r = 0.70, P = 0.002).


154

Table S5.1. The status of each species (genus or family) in the historical and contemporary

samples (see Table S3.1 for sampling dates and site locations). Species names follow the

taxonomy of Thorp and Covich (2010) and Sandercock and Scudder (1994).

Species Lake name Status

Alona sp. Adams Never present

Alona sp. Alleyne Never present

Alona sp. Beaver Never present

Alona sp. Becker Never present

Alona sp. Braeburn Never present

Alona sp. Cobb Never present

Alona sp. Corbett Never present

Alona sp. Dease Lost

Alona sp. Dezadeash Never present

Alona sp. Fox Never present

Alona sp. Frenchman Never present

Alona sp. Harrison New

Alona sp. Heffley Never present

Alona sp. Hicks Never present

Alona sp. Kathlyn Never present

Alona sp. Kawkawa Never present

Alona sp. Kentucky Never present

Alona sp. Kluane Never present

Alona sp. Lakelse New

Alona sp. Little Atlin Never present

Alona sp. Little Salmon Never present

Alona sp. Maxan Never present

Alona sp. McConnel New

Alona sp. Meziadin New

Alona sp. Minto Never present

Alona sp. Ness Never present

Alona sp. Nicola Never present

Alona sp. Paul Never present

Alona sp. Pemberton Never present

Alona sp. Pillar Never present

Alona sp. Pinantin Never present

Alona sp. Pine Never present

Alona sp. Quiet Never present

Alona sp. Seymour Never present

Alona sp. Shuswap Never present

Alona sp. Sullivan Never present

Alona sp. Summit Never present


155

Alona sp. Tatchun Never present

Alona sp. Walloper Never present

Alona sp. Watson Never present

Alona sp. Wheeler Never present

Alona sp. White Never present

Alona sp. Wood Never present

Bosmina sp. Adams Always present

Bosmina sp. Alleyne Always present

Bosmina sp. Beaver New

Bosmina sp. Becker New

Bosmina sp. Braeburn Lost

Bosmina sp. Cobb New

Bosmina sp. Corbett New

Bosmina sp. Dease Always present

Bosmina sp. Dezadeash Always present

Bosmina sp. Fox Always present

Bosmina sp. Frenchman New

Bosmina sp. Harrison New

Bosmina sp. Heffley Never present

Bosmina sp. Hicks New

Bosmina sp. Kathlyn Never present

Bosmina sp. Kawkawa New

Bosmina sp. Kentucky New

Bosmina sp. Kluane Never present

Bosmina sp. Lakelse Always present

Bosmina sp. Little Atlin Always present

Bosmina sp. Little Salmon Lost

Bosmina sp. Maxan Never present

Bosmina sp. McConnel New

Bosmina sp. Meziadin New

Bosmina sp. Minto New

Bosmina sp. Ness Always present

Bosmina sp. Nicola Never present

Bosmina sp. Paul New

Bosmina sp. Pemberton Lost

Bosmina sp. Pillar Never present

Bosmina sp. Pinantin Lost

Bosmina sp. Pine Always present

Bosmina sp. Quiet New

Bosmina sp. Seymour Never present

Bosmina sp. Shuswap Always present

Bosmina sp. Sullivan Never present

Bosmina sp. Summit Always present


156

Bosmina sp. Tatchun Always present

Bosmina sp. Walloper Never present

Bosmina sp. Watson Always present

Bosmina sp. Wheeler Lost

Bosmina sp. White New

Bosmina sp. Wood Never present

Ceriodaphnia sp. Adams Never present

Ceriodaphnia sp. Alleyne Always present

Ceriodaphnia sp. Beaver New

Ceriodaphnia sp. Becker New

Ceriodaphnia sp. Braeburn Never present

Ceriodaphnia sp. Cobb Never present

Ceriodaphnia sp. Corbett New

Ceriodaphnia sp. Dease New

Ceriodaphnia sp. Dezadeash Never present

Ceriodaphnia sp. Fox Never present

Ceriodaphnia sp. Frenchman Never present

Ceriodaphnia sp. Harrison Never present

Ceriodaphnia sp. Heffley Lost

Ceriodaphnia sp. Hicks Never present

Ceriodaphnia sp. Kathlyn Never present

Ceriodaphnia sp. Kawkawa Never present

Ceriodaphnia sp. Kentucky Always present

Ceriodaphnia sp. Kluane Never present

Ceriodaphnia sp. Lakelse Never present

Ceriodaphnia sp. Little Atlin Never present

Ceriodaphnia sp. Little Salmon Never present

Ceriodaphnia sp. Maxan Never present

Ceriodaphnia sp. McConnel Never present

Ceriodaphnia sp. Meziadin Never present

Ceriodaphnia sp. Minto Never present

Ceriodaphnia sp. Ness New

Ceriodaphnia sp. Nicola Never present

Ceriodaphnia sp. Paul Never present

Ceriodaphnia sp. Pemberton Always present

Ceriodaphnia sp. Pillar Always present

Ceriodaphnia sp. Pinantin Always present

Ceriodaphnia sp. Pine Never present

Ceriodaphnia sp. Quiet Never present

Ceriodaphnia sp. Seymour New

Ceriodaphnia sp. Shuswap Lost

Ceriodaphnia sp. Sullivan Lost

Ceriodaphnia sp. Summit Never present


157

Ceriodaphnia sp. Tatchun Never present

Ceriodaphnia sp. Walloper Never present

Ceriodaphnia sp. Watson Never present

Ceriodaphnia sp. Wheeler Never present

Ceriodaphnia sp. White New

Ceriodaphnia sp. Wood New

Cyclops scutifer Adams Never present

Cyclops scutifer Alleyne Never present

Cyclops scutifer Beaver Never present

Cyclops scutifer Becker Never present

Cyclops scutifer Braeburn Always present

Cyclops scutifer Cobb Never present

Cyclops scutifer Corbett Never present

Cyclops scutifer Dease Never present

Cyclops scutifer Dezadeash Always present

Cyclops scutifer Fox Always present

Cyclops scutifer Frenchman Always present

Cyclops scutifer Harrison Never present

Cyclops scutifer Heffley Never present

Cyclops scutifer Hicks Never present

Cyclops scutifer Kathlyn Never present

Cyclops scutifer Kawkawa Never present

Cyclops scutifer Kentucky Never present

Cyclops scutifer Kluane Always present

Cyclops scutifer Lakelse New

Cyclops scutifer Little Atlin Always present

Cyclops scutifer Little Salmon Always present

Cyclops scutifer Maxan Always present

Cyclops scutifer McConnel Never present

Cyclops scutifer Meziadin Always present

Cyclops scutifer Minto Always present

Cyclops scutifer Ness Always present

Cyclops scutifer Nicola Never present

Cyclops scutifer Paul Never present

Cyclops scutifer Pemberton Never present

Cyclops scutifer Pillar Never present

Cyclops scutifer Pinantin Never present

Cyclops scutifer Pine Always present

Cyclops scutifer Quiet Always present

Cyclops scutifer Seymour Always present

Cyclops scutifer Shuswap Never present

Cyclops scutifer Sullivan Never present

Cyclops scutifer Summit Lost


158

Cyclops scutifer Tatchun Always present

Cyclops scutifer Walloper Never present

Cyclops scutifer Watson Always present

Cyclops scutifer Wheeler Always present

Cyclops scutifer White Never present

Cyclops scutifer Wood Never present

Daphnia ambigua Adams Never present

Daphnia ambigua Alleyne Never present

Daphnia ambigua Beaver Never present

Daphnia ambigua Becker Never present

Daphnia ambigua Braeburn Never present

Daphnia ambigua Cobb Never present

Daphnia ambigua Corbett Never present

Daphnia ambigua Dease Never present

Daphnia ambigua Dezadeash Never present

Daphnia ambigua Fox Never present

Daphnia ambigua Frenchman Never present

Daphnia ambigua Harrison Never present

Daphnia ambigua Heffley Never present

Daphnia ambigua Hicks Never present

Daphnia ambigua Kathlyn Never present

Daphnia ambigua Kawkawa Never present

Daphnia ambigua Kentucky Never present

Daphnia ambigua Kluane Never present

Daphnia ambigua Lakelse New

Daphnia ambigua Little Atlin Never present

Daphnia ambigua Little Salmon Never present

Daphnia ambigua Maxan Never present

Daphnia ambigua McConnel Never present

Daphnia ambigua Meziadin Never present

Daphnia ambigua Minto Never present

Daphnia ambigua Ness Never present

Daphnia ambigua Nicola Never present

Daphnia ambigua Paul Never present

Daphnia ambigua Pemberton Never present

Daphnia ambigua Pillar Never present

Daphnia ambigua Pinantin Never present

Daphnia ambigua Pine Never present

Daphnia ambigua Quiet Never present

Daphnia ambigua Seymour Never present

Daphnia ambigua Shuswap Never present

Daphnia ambigua Sullivan Never present

Daphnia ambigua Summit Never present


159

Daphnia ambigua Tatchun Never present

Daphnia ambigua Walloper Never present

Daphnia ambigua Watson Never present

Daphnia ambigua Wheeler Never present

Daphnia ambigua White Never present

Daphnia ambigua Wood Never present

Daphnia galeata Adams Never present

Daphnia galeata Alleyne Never present

Daphnia galeata Beaver Never present

Daphnia galeata Becker Never present

Daphnia galeata Braeburn Never present

Daphnia galeata Cobb Never present

Daphnia galeata Corbett Never present

Daphnia galeata Dease Never present

Daphnia galeata Dezadeash Never present

Daphnia galeata Fox Never present

Daphnia galeata Frenchman Lost

Daphnia galeata Harrison Never present

Daphnia galeata Heffley Never present

Daphnia galeata Hicks Never present

Daphnia galeata Kathlyn Never present

Daphnia galeata Kawkawa Never present

Daphnia galeata Kentucky Never present

Daphnia galeata Kluane Never present

Daphnia galeata Lakelse Never present

Daphnia galeata Little Atlin Always present

Daphnia galeata Little Salmon Always present

Daphnia galeata Maxan Never present

Daphnia galeata McConnel Never present

Daphnia galeata Meziadin Never present

Daphnia galeata Minto Never present

Daphnia galeata Ness Never present

Daphnia galeata Nicola Never present

Daphnia galeata Paul Never present

Daphnia galeata Pemberton Never present

Daphnia galeata Pillar Never present

Daphnia galeata Pinantin Never present

Daphnia galeata Pine Never present

Daphnia galeata Quiet Never present

Daphnia galeata Seymour Never present

Daphnia galeata Shuswap Never present

Daphnia galeata Sullivan Never present

Daphnia galeata Summit Never present


160

Daphnia galeata Tatchun Never present

Daphnia galeata Walloper Never present

Daphnia galeata Watson Lost

Daphnia galeata Wheeler Never present

Daphnia galeata White Never present

Daphnia galeata Wood Never present

Daphnia longiremis Adams Always present

Daphnia longiremis Alleyne Never present

Daphnia longiremis Beaver Never present

Daphnia longiremis Becker Never present

Daphnia longiremis Braeburn Lost

Daphnia longiremis Cobb Never present

Daphnia longiremis Corbett Never present

Daphnia longiremis Dease Never present

Daphnia longiremis Dezadeash Never present

Daphnia longiremis Fox Always present

Daphnia longiremis Frenchman Always present

Daphnia longiremis Harrison Never present

Daphnia longiremis Heffley New

Daphnia longiremis Hicks Never present

Daphnia longiremis Kathlyn Always present

Daphnia longiremis Kawkawa Never present

Daphnia longiremis Kentucky Always present

Daphnia longiremis Kluane Never present

Daphnia longiremis Lakelse Never present

Daphnia longiremis Little Atlin Always present

Daphnia longiremis Little Salmon Always present

Daphnia longiremis Maxan Always present

Daphnia longiremis McConnel Never present

Daphnia longiremis Meziadin Never present

Daphnia longiremis Minto Never present

Daphnia longiremis Ness Always present

Daphnia longiremis Nicola Lost

Daphnia longiremis Paul New

Daphnia longiremis Pemberton Never present

Daphnia longiremis Pillar Never present

Daphnia longiremis Pinantin Never present

Daphnia longiremis Pine Always present

Daphnia longiremis Quiet Always present

Daphnia longiremis Seymour Never present

Daphnia longiremis Shuswap Never present

Daphnia longiremis Sullivan Never present

Daphnia longiremis Summit Never present


161

Daphnia longiremis Tatchun Always present

Daphnia longiremis Walloper Never present

Daphnia longiremis Watson Lost

Daphnia longiremis Wheeler Never present

Daphnia longiremis White Never present

Daphnia longiremis Wood Always present

Daphnia longispina Adams Never present

Daphnia longispina Alleyne Never present

Daphnia longispina Beaver Always present

Daphnia longispina Becker Never present

Daphnia longispina Braeburn Never present

Daphnia longispina Cobb Never present

Daphnia longispina Corbett Never present

Daphnia longispina Dease Never present

Daphnia longispina Dezadeash Lost

Daphnia longispina Fox Always present

Daphnia longispina Frenchman Never present

Daphnia longispina Harrison Never present

Daphnia longispina Heffley Never present

Daphnia longispina Hicks Never present

Daphnia longispina Kathlyn Never present

Daphnia longispina Kawkawa Never present

Daphnia longispina Kentucky Never present

Daphnia longispina Kluane Never present

Daphnia longispina Lakelse Never present

Daphnia longispina Little Atlin New

Daphnia longispina Little Salmon Never present

Daphnia longispina Maxan Never present

Daphnia longispina McConnel Never present

Daphnia longispina Meziadin Always present

Daphnia longispina Minto Never present

Daphnia longispina Ness Never present

Daphnia longispina Nicola Never present

Daphnia longispina Paul Never present

Daphnia longispina Pemberton Never present

Daphnia longispina Pillar Never present

Daphnia longispina Pinantin New

Daphnia longispina Pine Always present

Daphnia longispina Quiet Always present

Daphnia longispina Seymour Never present

Daphnia longispina Shuswap Always present

Daphnia longispina Sullivan Never present

Daphnia longispina Summit New


162

Daphnia longispina Tatchun Never present

Daphnia longispina Walloper Never present

Daphnia longispina Watson Never present

Daphnia longispina Wheeler Never present

Daphnia longispina White New

Daphnia longispina Wood Never present

Daphnia magna Adams Never present

Daphnia magna Alleyne Never present

Daphnia magna Beaver Never present

Daphnia magna Becker Lost

Daphnia magna Braeburn Never present

Daphnia magna Cobb Never present

Daphnia magna Corbett Never present

Daphnia magna Dease Never present

Daphnia magna Dezadeash Never present

Daphnia magna Fox Never present

Daphnia magna Frenchman Never present

Daphnia magna Harrison Never present

Daphnia magna Heffley Never present

Daphnia magna Hicks Never present

Daphnia magna Kathlyn Never present

Daphnia magna Kawkawa Never present

Daphnia magna Kentucky Never present

Daphnia magna Kluane Never present

Daphnia magna Lakelse Never present

Daphnia magna Little Atlin Never present

Daphnia magna Little Salmon Never present

Daphnia magna Maxan Never present

Daphnia magna McConnel Never present

Daphnia magna Meziadin Never present

Daphnia magna Minto Never present

Daphnia magna Ness Never present

Daphnia magna Nicola Never present

Daphnia magna Paul Never present

Daphnia magna Pemberton Never present

Daphnia magna Pillar Never present

Daphnia magna Pinantin Never present

Daphnia magna Pine Never present

Daphnia magna Quiet Never present

Daphnia magna Seymour Never present

Daphnia magna Shuswap Never present

Daphnia magna Sullivan Never present

Daphnia magna Summit Never present


163

Daphnia magna Tatchun Never present

Daphnia magna Walloper Never present

Daphnia magna Watson Never present

Daphnia magna Wheeler Never present

Daphnia magna White Never present

Daphnia magna Wood Never present

Daphnia midderdorffiana sp. Adams Never present

Daphnia midderdorffiana sp. Alleyne Never present

Daphnia midderdorffiana sp. Beaver Never present

Daphnia midderdorffiana sp. Becker Never present

Daphnia midderdorffiana sp. Braeburn Always present

Daphnia midderdorffiana sp. Cobb Never present

Daphnia midderdorffiana sp. Corbett Never present

Daphnia midderdorffiana sp. Dease Never present

Daphnia midderdorffiana sp. Dezadeash Never present

Daphnia midderdorffiana sp. Fox New

Daphnia midderdorffiana sp. Frenchman Always present

Daphnia midderdorffiana sp. Harrison Never present

Daphnia midderdorffiana sp. Heffley Never present

Daphnia midderdorffiana sp. Hicks Never present

Daphnia midderdorffiana sp. Kathlyn Never present

Daphnia midderdorffiana sp. Kawkawa Never present

Daphnia midderdorffiana sp. Kentucky Never present

Daphnia midderdorffiana sp. Kluane Never present

Daphnia midderdorffiana sp. Lakelse Never present

Daphnia midderdorffiana sp. Little Atlin Never present

Daphnia midderdorffiana sp. Little Salmon Never present

Daphnia midderdorffiana sp. Maxan Never present

Daphnia midderdorffiana sp. McConnel Never present

Daphnia midderdorffiana sp. Meziadin Never present

Daphnia midderdorffiana sp. Minto Never present

Daphnia midderdorffiana sp. Ness Never present

Daphnia midderdorffiana sp. Nicola Never present

Daphnia midderdorffiana sp. Paul Never present

Daphnia midderdorffiana sp. Pemberton Never present

Daphnia midderdorffiana sp. Pillar Never present

Daphnia midderdorffiana sp. Pinantin Never present

Daphnia midderdorffiana sp. Pine Always present

Daphnia midderdorffiana sp. Quiet Never present

Daphnia midderdorffiana sp. Seymour Never present

Daphnia midderdorffiana sp. Shuswap Never present

Daphnia midderdorffiana sp. Sullivan Never present

Daphnia midderdorffiana sp. Summit Lost


164

Daphnia midderdorffiana sp. Tatchun Never present

Daphnia midderdorffiana sp. Walloper Never present

Daphnia midderdorffiana sp. Watson Always present

Daphnia midderdorffiana sp. Wheeler Never present

Daphnia midderdorffiana sp. White Never present

Daphnia midderdorffiana sp. Wood Never present

Daphnia pulex Adams New

Daphnia pulex Alleyne Always present

Daphnia pulex Beaver Never present

Daphnia pulex Becker Never present

Daphnia pulex Braeburn Never present

Daphnia pulex Cobb Never present

Daphnia pulex Corbett New

Daphnia pulex Dease New

Daphnia pulex Dezadeash Never present

Daphnia pulex Fox Never present

Daphnia pulex Frenchman Never present

Daphnia pulex Harrison Always present

Daphnia pulex Heffley Always present

Daphnia pulex Hicks New

Daphnia pulex Kathlyn Never present

Daphnia pulex Kawkawa New

Daphnia pulex Kentucky Always present

Daphnia pulex Kluane Never present

Daphnia pulex Lakelse Never present

Daphnia pulex Little Atlin Never present

Daphnia pulex Little Salmon Never present

Daphnia pulex Maxan Always present

Daphnia pulex McConnel Always present

Daphnia pulex Meziadin Never present

Daphnia pulex Minto Never present

Daphnia pulex Ness Always present

Daphnia pulex Nicola Never present

Daphnia pulex Paul New

Daphnia pulex Pemberton Always present

Daphnia pulex Pillar Always present

Daphnia pulex Pinantin Always present

Daphnia pulex Pine Never present

Daphnia pulex Quiet Never present

Daphnia pulex Seymour Never present

Daphnia pulex Shuswap Never present

Daphnia pulex Sullivan Lost

Daphnia pulex Summit New


165

Daphnia pulex Tatchun Never present

Daphnia pulex Walloper Never present

Daphnia pulex Watson Lost

Daphnia pulex Wheeler Never present

Daphnia pulex White Never present

Daphnia pulex Wood New

Daphnia retrocurva Adams Never present

Daphnia retrocurva Alleyne Never present

Daphnia retrocurva Beaver Never present

Daphnia retrocurva Becker Never present

Daphnia retrocurva Braeburn Never present

Daphnia retrocurva Cobb Always present

Daphnia retrocurva Corbett Never present

Daphnia retrocurva Dease Never present

Daphnia retrocurva Dezadeash Never present

Daphnia retrocurva Fox Never present

Daphnia retrocurva Frenchman Never present

Daphnia retrocurva Harrison Never present

Daphnia retrocurva Heffley Never present

Daphnia retrocurva Hicks Never present

Daphnia retrocurva Kathlyn Never present

Daphnia retrocurva Kawkawa Never present

Daphnia retrocurva Kentucky Never present

Daphnia retrocurva Kluane Never present

Daphnia retrocurva Lakelse Never present

Daphnia retrocurva Little Atlin Never present

Daphnia retrocurva Little Salmon Never present

Daphnia retrocurva Maxan Never present

Daphnia retrocurva McConnel Never present

Daphnia retrocurva Meziadin Never present

Daphnia retrocurva Minto Never present

Daphnia retrocurva Ness Never present

Daphnia retrocurva Nicola Never present

Daphnia retrocurva Paul Never present

Daphnia retrocurva Pemberton Never present

Daphnia retrocurva Pillar Never present

Daphnia retrocurva Pinantin Never present

Daphnia retrocurva Pine Never present

Daphnia retrocurva Quiet Never present

Daphnia retrocurva Seymour Never present

Daphnia retrocurva Shuswap Never present

Daphnia retrocurva Sullivan New

Daphnia retrocurva Summit Never present


166

Daphnia retrocurva Tatchun Never present

Daphnia retrocurva Walloper Never present

Daphnia retrocurva Watson Never present

Daphnia retrocurva Wheeler Never present

Daphnia retrocurva White Never present

Daphnia retrocurva Wood Never present

Daphnia rosea Adams Never present

Daphnia rosea Alleyne Lost

Daphnia rosea Beaver Never present

Daphnia rosea Becker Never present

Daphnia rosea Braeburn Never present

Daphnia rosea Cobb Never present

Daphnia rosea Corbett Never present

Daphnia rosea Dease Never present

Daphnia rosea Dezadeash Never present

Daphnia rosea Fox Never present

Daphnia rosea Frenchman Never present

Daphnia rosea Harrison Never present

Daphnia rosea Heffley Never present

Daphnia rosea Hicks Never present

Daphnia rosea Kathlyn Never present

Daphnia rosea Kawkawa Never present

Daphnia rosea Kentucky Never present

Daphnia rosea Kluane Never present

Daphnia rosea Lakelse Never present

Daphnia rosea Little Atlin Never present

Daphnia rosea Little Salmon Never present

Daphnia rosea Maxan Never present

Daphnia rosea McConnel Never present

Daphnia rosea Meziadin Never present

Daphnia rosea Minto Never present

Daphnia rosea Ness Never present

Daphnia rosea Nicola Never present

Daphnia rosea Paul Never present

Daphnia rosea Pemberton Always present

Daphnia rosea Pillar Never present

Daphnia rosea Pinantin Never present

Daphnia rosea Pine Never present

Daphnia rosea Quiet Never present

Daphnia rosea Seymour Never present

Daphnia rosea Shuswap Never present

Daphnia rosea Sullivan Always present

Daphnia rosea Summit Never present


167

Daphnia rosea Tatchun Never present

Daphnia rosea Walloper Never present

Daphnia rosea Watson Never present

Daphnia rosea Wheeler Never present

Daphnia rosea White Never present

Daphnia rosea Wood Never present

Daphnia schoedleri Adams Never present

Daphnia schoedleri Alleyne Never present

Daphnia schoedleri Beaver Never present

Daphnia schoedleri Becker Never present

Daphnia schoedleri Braeburn Never present

Daphnia schoedleri Cobb Never present

Daphnia schoedleri Corbett Never present

Daphnia schoedleri Dease Never present

Daphnia schoedleri Dezadeash Never present

Daphnia schoedleri Fox Never present

Daphnia schoedleri Frenchman Never present

Daphnia schoedleri Harrison Never present

Daphnia schoedleri Heffley Never present

Daphnia schoedleri Hicks Never present

Daphnia schoedleri Kathlyn Never present

Daphnia schoedleri Kawkawa Never present

Daphnia schoedleri Kentucky Never present

Daphnia schoedleri Kluane Never present

Daphnia schoedleri Lakelse Never present

Daphnia schoedleri Little Atlin Never present

Daphnia schoedleri Little Salmon Never present

Daphnia schoedleri Maxan Never present

Daphnia schoedleri McConnel Never present

Daphnia schoedleri Meziadin Never present

Daphnia schoedleri Minto Never present

Daphnia schoedleri Ness Never present

Daphnia schoedleri Nicola Lost

Daphnia schoedleri Paul Never present

Daphnia schoedleri Pemberton Never present

Daphnia schoedleri Pillar Never present

Daphnia schoedleri Pinantin Never present

Daphnia schoedleri Pine Never present

Daphnia schoedleri Quiet Never present

Daphnia schoedleri Seymour Never present

Daphnia schoedleri Shuswap Never present

Daphnia schoedleri Sullivan Never present

Daphnia schoedleri Summit Never present


168

Daphnia schoedleri Tatchun Never present

Daphnia schoedleri Walloper Always present

Daphnia schoedleri Watson Never present

Daphnia schoedleri Wheeler Never present

Daphnia schoedleri White Lost

Daphnia schoedleri Wood Never present

Daphnia thorata Adams Lost

Daphnia thorata Alleyne Never present

Daphnia thorata Beaver Never present

Daphnia thorata Becker Never present

Daphnia thorata Braeburn Never present

Daphnia thorata Cobb Never present

Daphnia thorata Corbett Never present

Daphnia thorata Dease Never present

Daphnia thorata Dezadeash Never present

Daphnia thorata Fox Never present

Daphnia thorata Frenchman Never present

Daphnia thorata Harrison Never present

Daphnia thorata Heffley Always present

Daphnia thorata Hicks Never present

Daphnia thorata Kathlyn Always present

Daphnia thorata Kawkawa Never present

Daphnia thorata Kentucky New

Daphnia thorata Kluane Never present

Daphnia thorata Lakelse Never present

Daphnia thorata Little Atlin Never present

Daphnia thorata Little Salmon Never present

Daphnia thorata Maxan Never present

Daphnia thorata McConnel Never present

Daphnia thorata Meziadin Never present

Daphnia thorata Minto Never present

Daphnia thorata Ness Never present

Daphnia thorata Nicola Always present

Daphnia thorata Paul Never present

Daphnia thorata Pemberton Never present

Daphnia thorata Pillar Never present

Daphnia thorata Pinantin Never present

Daphnia thorata Pine Never present

Daphnia thorata Quiet Never present

Daphnia thorata Seymour Always present

Daphnia thorata Shuswap Never present

Daphnia thorata Sullivan Never present

Daphnia thorata Summit Never present


169

Daphnia thorata Tatchun Never present

Daphnia thorata Walloper Never present

Daphnia thorata Watson Never present

Daphnia thorata Wheeler Never present

Daphnia thorata White New

Daphnia thorata Wood Never present

Diacyclops thomasi Adams Always present

Diacyclops thomasi Alleyne New

Diacyclops thomasi Beaver Never present

Diacyclops thomasi Becker New

Diacyclops thomasi Braeburn Never present

Diacyclops thomasi Cobb Always present

Diacyclops thomasi Corbett New

Diacyclops thomasi Dease New

Diacyclops thomasi Dezadeash Never present

Diacyclops thomasi Fox Never present

Diacyclops thomasi Frenchman Never present

Diacyclops thomasi Harrison Always present

Diacyclops thomasi Heffley Always present

Diacyclops thomasi Hicks New

Diacyclops thomasi Kathlyn Always present

Diacyclops thomasi Kawkawa New

Diacyclops thomasi Kentucky Always present

Diacyclops thomasi Kluane Never present

Diacyclops thomasi Lakelse Always present

Diacyclops thomasi Little Atlin Never present

Diacyclops thomasi Little Salmon Never present

Diacyclops thomasi Maxan Never present

Diacyclops thomasi McConnel Always present

Diacyclops thomasi Meziadin New

Diacyclops thomasi Minto Never present

Diacyclops thomasi Ness Never present

Diacyclops thomasi Nicola Never present

Diacyclops thomasi Paul New

Diacyclops thomasi Pemberton New

Diacyclops thomasi Pillar New

Diacyclops thomasi Pinantin Never present

Diacyclops thomasi Pine Never present

Diacyclops thomasi Quiet Never present

Diacyclops thomasi Seymour Never present

Diacyclops thomasi Shuswap Never present

Diacyclops thomasi Sullivan Always present

Diacyclops thomasi Summit Always present


170

Diacyclops thomasi Tatchun Never present

Diacyclops thomasi Walloper Always present

Diacyclops thomasi Watson Never present

Diacyclops thomasi Wheeler Never present

Diacyclops thomasi White New

Diacyclops thomasi Wood Always present

Diaphanosoma sp. Adams New

Diaphanosoma sp. Alleyne Never present

Diaphanosoma sp. Beaver New

Diaphanosoma sp. Becker New

Diaphanosoma sp. Braeburn Never present

Diaphanosoma sp. Cobb Lost

Diaphanosoma sp. Corbett New

Diaphanosoma sp. Dease Never present

Diaphanosoma sp. Dezadeash Never present

Diaphanosoma sp. Fox Never present

Diaphanosoma sp. Frenchman Never present

Diaphanosoma sp. Harrison Never present

Diaphanosoma sp. Heffley Lost

Diaphanosoma sp. Hicks Never present

Diaphanosoma sp. Kathlyn Never present

Diaphanosoma sp. Kawkawa Never present

Diaphanosoma sp. Kentucky Lost

Diaphanosoma sp. Kluane Never present

Diaphanosoma sp. Lakelse Always present

Diaphanosoma sp. Little Atlin Never present

Diaphanosoma sp. Little Salmon Never present

Diaphanosoma sp. Maxan Never present

Diaphanosoma sp. McConnel Never present

Diaphanosoma sp. Meziadin Never present

Diaphanosoma sp. Minto Never present

Diaphanosoma sp. Ness Never present

Diaphanosoma sp. Nicola New

Diaphanosoma sp. Paul Never present

Diaphanosoma sp. Pemberton Never present

Diaphanosoma sp. Pillar Never present

Diaphanosoma sp. Pinantin Never present

Diaphanosoma sp. Pine Never present

Diaphanosoma sp. Quiet Never present

Diaphanosoma sp. Seymour Never present

Diaphanosoma sp. Shuswap Always present

Diaphanosoma sp. Sullivan Never present

Diaphanosoma sp. Summit Never present


171

Diaphanosoma sp. Tatchun Never present

Diaphanosoma sp. Walloper Never present

Diaphanosoma sp. Watson Never present

Diaphanosoma sp. Wheeler Never present

Diaphanosoma sp. White New

Diaphanosoma sp. Wood Always present

Diaptomus sp. Adams Always present

Diaptomus sp. Alleyne Never present

Diaptomus sp. Beaver Always present

Diaptomus sp. Becker Never present

Diaptomus sp. Braeburn Always present

Diaptomus sp. Cobb Always present

Diaptomus sp. Corbett Always present

Diaptomus sp. Dease New

Diaptomus sp. Dezadeash Always present

Diaptomus sp. Fox Always present

Diaptomus sp. Frenchman Always present

Diaptomus sp. Harrison Always present

Diaptomus sp. Heffley Always present

Diaptomus sp. Hicks Lost

Diaptomus sp. Kathlyn Always present

Diaptomus sp. Kawkawa New

Diaptomus sp. Kentucky Always present

Diaptomus sp. Kluane Always present

Diaptomus sp. Lakelse Always present

Diaptomus sp. Little Atlin Always present

Diaptomus sp. Little Salmon Always present

Diaptomus sp. Maxan Always present

Diaptomus sp. McConnel New

Diaptomus sp. Meziadin Always present

Diaptomus sp. Minto Always present

Diaptomus sp. Ness New

Diaptomus sp. Nicola Always present

Diaptomus sp. Paul Always present

Diaptomus sp. Pemberton Always present

Diaptomus sp. Pillar Always present

Diaptomus sp. Pinantin Always present

Diaptomus sp. Pine Always present

Diaptomus sp. Quiet Always present

Diaptomus sp. Seymour Always present

Diaptomus sp. Shuswap Always present

Diaptomus sp. Sullivan Always present

Diaptomus sp. Summit Always present


172

Diaptomus sp. Tatchun Always present

Diaptomus sp. Walloper Always present

Diaptomus sp. Watson Always present

Diaptomus sp. Wheeler Always present

Diaptomus sp. White Always present

Diaptomus sp. Wood Always present

Epischura nevadensis Adams Lost

Epischura nevadensis Alleyne Never present

Epischura nevadensis Beaver Never present

Epischura nevadensis Becker Never present

Epischura nevadensis Braeburn Never present

Epischura nevadensis Cobb Never present

Epischura nevadensis Corbett Never present

Epischura nevadensis Dease Never present

Epischura nevadensis Dezadeash Never present

Epischura nevadensis Fox Never present

Epischura nevadensis Frenchman Never present

Epischura nevadensis Harrison Lost

Epischura nevadensis Heffley Lost

Epischura nevadensis Hicks Never present

Epischura nevadensis Kathlyn Always present

Epischura nevadensis Kawkawa Always present

Epischura nevadensis Kentucky Never present

Epischura nevadensis Kluane Never present

Epischura nevadensis Lakelse Always present

Epischura nevadensis Little Atlin Never present

Epischura nevadensis Little Salmon Never present

Epischura nevadensis Maxan Always present

Epischura nevadensis McConnel New

Epischura nevadensis Meziadin Never present

Epischura nevadensis Minto Never present

Epischura nevadensis Ness Always present

Epischura nevadensis Nicola Lost

Epischura nevadensis Paul Lost

Epischura nevadensis Pemberton Never present

Epischura nevadensis Pillar New

Epischura nevadensis Pinantin Never present

Epischura nevadensis Pine Never present

Epischura nevadensis Quiet Never present

Epischura nevadensis Seymour Never present

Epischura nevadensis Shuswap Always present

Epischura nevadensis Sullivan Never present

Epischura nevadensis Summit Never present


173

Epischura nevadensis Tatchun Never present

Epischura nevadensis Walloper Never present

Epischura nevadensis Watson Never present

Epischura nevadensis Wheeler Never present

Epischura nevadensis White Never present

Epischura nevadensis Wood Never present

Heterocope septentrionalis Adams Never present

Heterocope septentrionalis Alleyne Never present

Heterocope septentrionalis Beaver Never present

Heterocope septentrionalis Becker Never present

Heterocope septentrionalis Braeburn New

Heterocope septentrionalis Cobb Never present

Heterocope septentrionalis Corbett Never present

Heterocope septentrionalis Dease Never present

Heterocope septentrionalis Dezadeash Never present

Heterocope septentrionalis Fox Never present

Heterocope septentrionalis Frenchman Lost

Heterocope septentrionalis Harrison Never present

Heterocope septentrionalis Heffley Never present

Heterocope septentrionalis Hicks Never present

Heterocope septentrionalis Kathlyn Never present

Heterocope septentrionalis Kawkawa Never present

Heterocope septentrionalis Kentucky Never present

Heterocope septentrionalis Kluane Never present

Heterocope septentrionalis Lakelse Never present

Heterocope septentrionalis Little Atlin Lost

Heterocope septentrionalis Little Salmon Never present

Heterocope septentrionalis Maxan Never present

Heterocope septentrionalis McConnel Never present

Heterocope septentrionalis Meziadin Never present

Heterocope septentrionalis Minto Never present

Heterocope septentrionalis Ness Never present

Heterocope septentrionalis Nicola Never present

Heterocope septentrionalis Paul Never present

Heterocope septentrionalis Pemberton Never present

Heterocope septentrionalis Pillar Never present

Heterocope septentrionalis Pinantin Never present

Heterocope septentrionalis Pine Never present

Heterocope septentrionalis Quiet Never present

Heterocope septentrionalis Seymour Never present

Heterocope septentrionalis Shuswap Never present

Heterocope septentrionalis Sullivan Never present

Heterocope septentrionalis Summit Lost


174

Heterocope septentrionalis Tatchun Never present

Heterocope septentrionalis Walloper Never present

Heterocope septentrionalis Watson Never present

Heterocope septentrionalis Wheeler Lost

Heterocope septentrionalis White Never present

Heterocope septentrionalis Wood Never present

Holopedium gibberum Adams Never present

Holopedium gibberum Alleyne Never present

Holopedium gibberum Beaver Lost

Holopedium gibberum Becker Never present

Holopedium gibberum Braeburn Never present

Holopedium gibberum Cobb Never present

Holopedium gibberum Corbett Never present

Holopedium gibberum Dease Never present

Holopedium gibberum Dezadeash New

Holopedium gibberum Fox Never present

Holopedium gibberum Frenchman Never present

Holopedium gibberum Harrison New

Holopedium gibberum Heffley Never present

Holopedium gibberum Hicks New

Holopedium gibberum Kathlyn Never present

Holopedium gibberum Kawkawa New

Holopedium gibberum Kentucky Never present

Holopedium gibberum Kluane Never present

Holopedium gibberum Lakelse New

Holopedium gibberum Little Atlin Never present

Holopedium gibberum Little Salmon Never present

Holopedium gibberum Maxan Never present

Holopedium gibberum McConnel Never present

Holopedium gibberum Meziadin Never present

Holopedium gibberum Minto Never present

Holopedium gibberum Ness Never present

Holopedium gibberum Nicola Never present

Holopedium gibberum Paul Never present

Holopedium gibberum Pemberton Never present

Holopedium gibberum Pillar Never present

Holopedium gibberum Pinantin Never present

Holopedium gibberum Pine Never present

Holopedium gibberum Quiet Never present

Holopedium gibberum Seymour Never present

Holopedium gibberum Shuswap Never present

Holopedium gibberum Sullivan Never present

Holopedium gibberum Summit New


175

Holopedium gibberum Tatchun Never present

Holopedium gibberum Walloper Never present

Holopedium gibberum Watson Never present

Holopedium gibberum Wheeler Never present

Holopedium gibberum White Never present

Holopedium gibberum Wood Never present

Leptodora kindii Adams New

Leptodora kindii Alleyne Never present

Leptodora kindii Beaver Never present

Leptodora kindii Becker Never present

Leptodora kindii Braeburn Never present

Leptodora kindii Cobb Lost

Leptodora kindii Corbett Never present

Leptodora kindii Dease Never present

Leptodora kindii Dezadeash Never present

Leptodora kindii Fox Never present

Leptodora kindii Frenchman Never present

Leptodora kindii Harrison Never present

Leptodora kindii Heffley Lost

Leptodora kindii Hicks Never present

Leptodora kindii Kathlyn Never present

Leptodora kindii Kawkawa Never present

Leptodora kindii Kentucky Never present

Leptodora kindii Kluane Never present

Leptodora kindii Lakelse Never present

Leptodora kindii Little Atlin Never present

Leptodora kindii Little Salmon Never present

Leptodora kindii Maxan Lost

Leptodora kindii McConnel Never present

Leptodora kindii Meziadin Never present

Leptodora kindii Minto Lost

Leptodora kindii Ness Never present

Leptodora kindii Nicola Always present

Leptodora kindii Paul Never present

Leptodora kindii Pemberton Never present

Leptodora kindii Pillar Never present

Leptodora kindii Pinantin Never present

Leptodora kindii Pine Never present

Leptodora kindii Quiet Never present

Leptodora kindii Seymour Never present

Leptodora kindii Shuswap Lost

Leptodora kindii Sullivan New

Leptodora kindii Summit Never present


176

Leptodora kindii Tatchun Never present

Leptodora kindii Walloper Never present

Leptodora kindii Watson Never present

Leptodora kindii Wheeler Never present

Leptodora kindii White New

Leptodora kindii Wood Always present

Polyphemus pediculus Adams Never present

Polyphemus pediculus Alleyne Never present

Polyphemus pediculus Beaver Never present

Polyphemus pediculus Becker Never present

Polyphemus pediculus Braeburn Never present

Polyphemus pediculus Cobb Never present

Polyphemus pediculus Corbett New

Polyphemus pediculus Dease Never present

Polyphemus pediculus Dezadeash Never present

Polyphemus pediculus Fox Never present

Polyphemus pediculus Frenchman Never present

Polyphemus pediculus Harrison Never present

Polyphemus pediculus Heffley New

Polyphemus pediculus Hicks Never present

Polyphemus pediculus Kathlyn Never present

Polyphemus pediculus Kawkawa Never present

Polyphemus pediculus Kentucky Never present

Polyphemus pediculus Kluane Never present

Polyphemus pediculus Lakelse Never present

Polyphemus pediculus Little Atlin Never present

Polyphemus pediculus Little Salmon Never present

Polyphemus pediculus Maxan Never present

Polyphemus pediculus McConnel New

Polyphemus pediculus Meziadin Never present

Polyphemus pediculus Minto Never present

Polyphemus pediculus Ness Never present

Polyphemus pediculus Nicola Never present

Polyphemus pediculus Paul Never present

Polyphemus pediculus Pemberton Never present

Polyphemus pediculus Pillar Never present

Polyphemus pediculus Pinantin Never present

Polyphemus pediculus Pine Always present

Polyphemus pediculus Quiet Never present

Polyphemus pediculus Seymour Never present

Polyphemus pediculus Shuswap Never present

Polyphemus pediculus Sullivan Never present

Polyphemus pediculus Summit Never present


177

Polyphemus pediculus Tatchun Never present

Polyphemus pediculus Walloper Never present

Polyphemus pediculus Watson Never present

Polyphemus pediculus Wheeler Never present

Polyphemus pediculus White Never present

Polyphemus pediculus Wood Never present

Senecella calanoides Adams Never present

Senecella calanoides Alleyne Never present

Senecella calanoides Beaver Never present

Senecella calanoides Becker Never present

Senecella calanoides Braeburn Never present

Senecella calanoides Cobb Never present

Senecella calanoides Corbett Never present

Senecella calanoides Dease Never present

Senecella calanoides Dezadeash Never present

Senecella calanoides Fox Never present

Senecella calanoides Frenchman Never present

Senecella calanoides Harrison Never present

Senecella calanoides Heffley Never present

Senecella calanoides Hicks Never present

Senecella calanoides Kathlyn Never present

Senecella calanoides Kawkawa Never present

Senecella calanoides Kentucky Never present

Senecella calanoides Kluane New

Senecella calanoides Lakelse Never present

Senecella calanoides Little Atlin Never present

Senecella calanoides Little Salmon Never present

Senecella calanoides Maxan Never present

Senecella calanoides McConnel Never present

Senecella calanoides Meziadin Never present

Senecella calanoides Minto Never present

Senecella calanoides Ness Never present

Senecella calanoides Nicola Never present

Senecella calanoides Paul Never present

Senecella calanoides Pemberton Never present

Senecella calanoides Pillar Never present

Senecella calanoides Pinantin Never present

Senecella calanoides Pine Always present

Senecella calanoides Quiet Never present

Senecella calanoides Seymour Never present

Senecella calanoides Shuswap Never present

Senecella calanoides Sullivan Never present

Senecella calanoides Summit Never present


178

Senecella calanoides Tatchun Never present

Senecella calanoides Walloper Never present

Senecella calanoides Watson Never present

Senecella calanoides Wheeler Never present

Senecella calanoides White Never present

Senecella calanoides Wood Never present


179

Table S5.2. A summary of body size and regional abundance, as well as the associated

colonization and local extinction events, for the subset of species for which data was available.

Species Average body

size (µm)

Local

abundance

Colonization

events

Extinction

events

Epischura nevadensis 191.52 9.6 2 5

Bosmina sp. 195.88 3.6 15 5

Ceriodaphnia sp. 276.74 22.5 8 3

Diacyclops thomasi 288.53 42.9 11 0

Cyclops scutifer 316.27 65.7 1 1

Polyphemus pediculus 357.81 0.1 3 0

Diaphanosoma sp. 369.63 1.5 6 3

Diaptomus sp. 375.82 20.5 4 1

Daphnia longiremis 407.54 8.9 2 3

Daphnia galeata sp. 496.04 5.4 0 2

Daphnia pulex 537.63 18.9 8 2

Daphnia thorata 564.57 6.3 2 1

Daphnia

midderdorffiana sp.

645.89 1.5 1 1

Leptodora kindii 1829.31 0.4 3 5


180

Table S5.3. Results of log likelihood tests that include time between the historical and

contemporary zooplankton samples.

Df LRT p-value

Change in species richness

Latitude 1,40 7.34 0.0099

Time 1,40 0.01 0.9242

Sorenson dissimilarity

Latitude 1,40 11.80 0.0014

Time 1,40 0.16 0.6956

Colonization

Latitude 1,40 15.64 0.0001

Time 1,40 0.53 0.4661

Extinction

Latitude 1,40 0.25 0.6205

Time 1,40 0.23 0.6285


181

Table S5.4. Results of linear model testing the association between latitude and four estimates of

community change. Values in bold font are significant at α = 0.05.

Estimate Std. Error t values Pr(>|t|) r

Change in species richness

Latitude -0.18 0.057 -3.19 0.003 -0.40

Sorenson dissimilarity

Latitude -0.02 0.006 -2.96 0.005 -0.40

Estimate Std. Error z values Pr(>|z|) r

Colonization

Latitude -0.16 0.036 -4.28 1.89E-05 -0.47

Extinction

Latitude -0.03 0.038 -0.83 0.405 -0.15


182

Table S5.5. Results of linear models testing the influence of body size and abundance on the

number of times a species colonized or went locally extinct in as lake. Values in bold font are

significant at α < 0.10.

Colonization

DF Deviance Scaled deviance Pr(>Chi)

log (length) 1 62.24 3.82 0.051

log (abundance) 1 62.24 3.52 0.061

Local extinction

DF Deviance Scaled deviance Pr(>Chi)

log (length) 1 37.69 0.78 0.377

log (abundance) 1 37.69 8.83 0.003


183

Table S5.6. The influence of lake characteristics on three estimates of community change.

Values in bold font are significant at α = 0.05.

Estimate Std. Error z-values Pr (>|z|)

Maximum depth (m)

Sorenson Dissimilarity 0.0286 0.0386 0.74 0.462

Colonization 0.0715 0.1293 0.55 0.580

Extinction 0.2257 0.1581 1.43 0.153

Lake size (ha)

Sorenson Dissimilarity -0.0150 0.0175 -0.86 0.397

Colonization -0.0913 0.0622 -1.47 0.142

Extinction 0.0927 0.0785 1.18 0.238

Productivity (chlorophyll A)

Sorenson Dissimilarity -0.0335 0.0595 -0.56 0.577

Colonization -0.2059 0.2297 -0.90 0.370

Extinction -0.0431 0.2981 -0.14 0.885

Literature Cited





184

Copyright Acknowledgements

The published chapters of this thesis are included with permission from the publishers.

Documents

University of Toronto T-Space · ii Understanding how life-history traits and environmental gradients structure diversity Natalie T. Jones Doctor of Philosophy Department of Ecology