Aaron Hall Thesis 2015

Anthropogenic Impacts on Multihabitat Species and

Applications for Conservation

Aaron M. Hall

A thesis submitted in conformity with the requirements

for the degree of Doctorate of Philosophy

Ecology and Evolutionary Biology

University of Toronto

Anthropogenic Impacts on Multihabitat Species and

Applications for Conservation

Aaron M. Hall

Doctorate of Philosophy

Ecology and Evolutionary Biology

University of Toronto

2015

Abstract

Anthropogenic impacts are apparent in all ecosystems, threatening the persistence of biodiversity everywhere on Earth. Species’ responses to these impacts depend largely on their biology and ecology, and therefore all species are not equally impacted. Species with particular life histories, such as long generation times, specific habitat requirements, small geographic ranges, or poor dispersal potential, are more at risk because they are unable to respond positively in the face of anthropogenic threats. With respect to habitat preferences, the more specific a species’ needs, the higher the threat because even a small loss of habitat can lead to local extinctions, meaning that species requiring multiple habitats that are spatially adjacent might be even more at risk of extinction than those that do not. This need for multiple, adjacent habitats, is a common life history strategy, yet how anthropogenic impacts affect these species is currently understudied. Furthermore, the adjacency requirements of these species’ habitats are rarely incorporated into conservation tools intended to inform conservation practitioners.

To address these gaps in conservation science, in this thesis, I focus on a common group of species that require multiple habitats, Odonata, and investigate the influence of anthropogenic recreation, landuse change, and climate change on their diversity and community composition as larvae and as adults. I then incorporate adjacent habitat requirements into species distribution models and migration simulations.

Overall, I find that: (1) anthropogenic impacts from recreation, landuse change, and climate change do affect the diversity and community composition of odonata, generally resulting in reduced diversity of specialist species; (2) adults and larvae respond differently to anthropogenic pressures, likely because they utilize distinct habitats, or the same habitats in different manners; and (3) incorporating adjacent habitats into species distribution models and migration simulations leads to more restrictive, but potentially more realistic, range expansion predictions of one species of Odonata in a fragmented landscape in southern Ontario.

iii

Acknowledgments

I would like to thank, first and foremost, my advisor Dr. Marie-Josée Fortin, who has been an amazing and positive source of knowledge and guidance in all aspects of science, and in life in general. I would also like to thank the members who served on my advisory committee, Dr. Ben

Gilbert, Dr. Don Jackson, and Dr. Shannon McCauley, whose input was always supportive and helpful. I would like to thank my collaborators, Dr. Lenore Fahrig and Dan Bert of Carleton

University. I would also like to thank Colin Jones, curator of the Ontario Odonate Atlas (and those scientists, naturalists, and hobbyist who have contributed), and also The Royal Ontario

Museum Walker Collection, both which have been excellent sources of data. I am especially grateful for all those who have helped me in the field in the pursuit of, often hard to catch, dragonflies, and to those who helped me in the lab afterwards. Finally, to those sources which have provided funding, directly and indirectly, which has supported my research including the

Natural Science and Engineering Research Council of Canada, National Geographic, and the

Department of Ecology and Evolutionary Biology at The University of Toronto.

My sincerest thanks to my family for always being supportive, and to my friends and colleagues, both old and new, for constant entertainment and encouragement. I would like to especially thank my friend and confidant Pasan, who started this journey at the same time as me, and to

Molly the dog, my best friend and the best field assistant anyone could hope for. Finally, and most especially, I would like to thank Andrea for being the most amazing person I have ever known, and always being there whenever I needed someone.

This work was done in conjunction with the following co-authors, and permission to publish portions of this thesis has been obtained from the following publishers, where applicable.

Chapter 2:

Hall, A. M., S. J. McCauley, and M-J. Fortin. Recreational boating, landscape configuration, and local habitat structure as drivers of odonate community composition in an island setting.

2015. Insect Conservation and Diversity, 8: 31-42. License #3494340029662

Chapter 3:

Hall, A. M., D. Bert, L. Fahrig, and M-J Fortin. Odonate community responses to climate change and landuse change. In prep

Chapter 4:

Hall, A. M., and M-J Fortin. Habitat complementarity effects predicted range expansion of a temperate dragonfly. In prep

Table of Contents

Acknowledgments...... iv

Table of Contents ...... vi

List of Tables ...... viii

List of Figures ...... xii

Chapter 1 - Introduction ...... 1

1.1 Thesis Outline ...... 4

Chapter 2 - Recreational boating, landscape configuration, and local habitat structure as drivers of odonate community composition in an island setting ...... 7

2.1 Introduction ...... 7

2.2 Methods...... 10

2.3 Statistical analyses ...... 15

2.4 Results ...... 17

2.5 Discussion ...... 18

2.6 Conclusion ...... 27

2.7 Tables ...... 28

2.8 Figures...... 34

2.9 Supplemental material ...... 36

2.9.1 Tables ...... 36

Landscape Habitat Variable ...... 38

Description ...... 38

2.9.2 Figures...... 41

Chapter 3 - Odonate community responses to climate change and landuse change ...... 42

3.1 Introduction ...... 42

3.2 Methods...... 45

3.3 Results ...... 55

3.4 Discussion ...... 59

3.5 Conclusion ...... 64

3.6 Tables ...... 66

3.7 Figures...... 73

3.8 Supplemental material ...... 81

3.8.1 Tables ...... 81

3.8.2 Figures...... 100

Chapter 4 - Habitat complementarity affects predicted range expansion of a temperate dragonfly 103

4.1 Introduction ...... 103

4.2 Methods...... 107

4.3 Results ...... 112

4.4 Discussion ...... 115

4.5 Conclusion ...... 119

4.6 Tables ...... 121

4.7 Figures...... 124

4.8 Supplementary Material ...... 132

4.8.1 Tables ...... 132

Conclusions ...... 135

Future directions...... 137

Final remarks ...... 139

References ...... 140

vii

List of Tables

Chapter 2:

Table 2.1: Species included in the analyses and how many sites there were found at as adults and as exuviae.…………………………………………………………………………….. 28

Table 2.2: Description and type of each variable included in the linear multiple regression and

Redundancy Analyses as potential predictors…………………………..…………………. 30

Table 2.3: Results of the linear multiple regression, showing for adults and exuviae adjusted

R2 and predictors included in each model. Note that the best model for exuviae, containing only the shallow slope predictor, is not significant.…………..……………………………. 32

Table 2.4: Results of the Redundancy Analyses (RDA), showing for each set of community data the total adjusted R2 and partitioned variance of the model, predictors selected, and

Variance Inflation Factors (VIF). Significance is shown for the entire model, each predictor, and each axis………………………………………………………………………………….33

Table 2.S1: Local habitat variables which were recorded at each sampled island…………...36

Table 2.S2: Landscape scale habitat variables calculated for each island………………...….38

Table 2.S3: Description of anthropogenic proxies of recreational boating………………...... 39

Chapter 3:

Table 3.1: Climate data by season showing the change in climate in 2000 (2000-1990) and

2010 (2010-2000), standard deviation of climate change, range of values across our sampled sites, and standard deviation of the errors of the interpolated climate data across our sampled sites, all measurements in Celsius degrees. Positive values indicate an increase in temperatures across time and negative values a decrease. …………………………………. 66

viii

Table 3.2: Landcover data showing, for each landcover class, the percentage cover within 500 m of our sampled ponds in 2000 and 2010, and the change in mean percentage landcover in each class between 2000 and 2010. Positive values represent an increase in that landcover class, negative values a loss……………………………………………………………….….67

Table 3.3: Description of measured and derived variables included in the linear multiple regression and/or redundancy analyses………………………………………...……………. 68

(red), and interaction terms (blue). The direction of influence in the model for each predictor is indicated by either a lack of prefix (positive influence) or minus sign (negative influence)……………………………………………………………………………………..71

Table 3.5: Results of the redundancy analyses (RDA), showing for each set of community data the predictors selected, type of each predictor, and overall adjusted R2 of the model. For the entire model, and each predictor, significance is depicted by the following: ◦ = p < 0.10, *

= p < 0.05, ** = p < 0.01, *** = p < 0.001………………………………………….………..72

Table 3.S1: Description of measured and derived variables used as potential predictors of community composition at each site, including the scale (buffer of 50 m and 500 m) at which that predictor is expected to influence community dynamics. …………………………...... 81

Table 3.S2: For each sampled pond climate change in 2010 and 2000 and associated errors (in parenthesis) from interpolated values, and climate in 1990 with interpolated errors in parenthesis. ………...…………………………………………………………………………88

Table 3.S3. Shannon diversity, species richness, total number of individuals, and distance to the nearest weather station, for all sampled ponds in 2002 and

2012…………………………………………………………………………………………...93

Table 3.S4. Species abbreviations, scientific name, authority, and common name for species found in the multivariate analyses………………………………………..………………...... ……97

Chapter 4:

Table 4.1: Percent cover within our focal region of forests, waters, and complementary landcover types. The first line of each scenario group (disjunct and adjacent) represents the landcover before pond removal…………………………………………………………...…121

Table 4.2: Cross validation results of the SDMs utilizing the 20% of species occurrence data set aside for testing, showing AUC results for the disjunct and adjacent scenario groups.....122

Table 4.3: Variable contributions (averaged over all model replicates) for the disjunct and adjacent Maxent scenarios………………………………….…………………………..…...123

Table 4.S1. Amount of suitable habitat (ha) which is occupied at the end of the migration simulation with probability of long-distance dispersal events 0.01, for distances of 1 km, 2.5 km, 5 km, 7.5 km, and 10 km. Occupied habitat is shown as area, percentage of suitable habitat that is occupied, and percentage of the entire focal region that is occupied, for both the disjunct and adjacent scenario groups……………………………………………….………132

x habitat that is occupied, and percentage of the entire focal region that is occupied, for both the disjunct and adjacent scenario groups……………………………………………………….133

Table 4.S3. Amount of suitable habitat (ha) which is occupied at the end of the migration simulation with probability of long-distance dispersal events 0.10, for distances of 1 km, 2.5 km, 5 km, 7.5 km, and 10 km. Occupied habitat is shown as area, percentage of suitable habitat that is occupied, and percentage of the entire focal region that is occupied, for both the disjunct and adjacent scenario groups…………………………………………………….....134

List of Figures

Chapter 2:

Figure 2.1: The Waubaushene area of the Georgian Bay, Lake Huron, Ontario. Seventeen islands (black triangles) were sampled in an area of high boating pressure, showing marked boating channels (dotted lines). The four area marinas (black circles) are labeled by name, their circles are proportionally sized by the linear dock space of each marina. …………….. 34

Figure 2.2: Redundancy Analysis of adults (top panel) and exuviae (bottom panel) in the

Georgian Bay, Ontario, showing species (red text), and predictors (black text). For adults, species in the family Libellulidae are shown in the dashed rectangle. ……...……………… 35

Figure 2.S1: Wind rose plot for the Parry Sound weather station, located approximately 80km north of our study area. Data downloaded from the National Climate Weather Database, and are the daily maximum wind gust speed and direction for the years 2007-2011. …………. 41

Chapter 3:

Figure 3.1: Study region showing the sites sampled only in 2002 (open shapes) and sites resampled sites in 2012 (black shapes), as well as pond origin (anthropogenic = square; natural

= circle). The study region of approximately 4000 km2 spans from the St-Laurent Lowlands

Ecoregion in the south to the Southern Laurentian Ecoregion to the north.………………… 73

Figure 3.2: Figure 3.2: Maps showing the magnitude of climate change within our study region averaged for the entire year and by seasons for the years 2000 and 2010. All maps use the same scale (-1 ºC to +1 ºC) with blue colors representing decreases in climate temperatures and red colors increases…………………..………………………….………... 74

xii

Figure 3.3: Maps showing the errors associated with the interpolated climate data across our study region for each year, and each season. All maps use the same scale (0ºC to 1ºC) with dark colors representing small errors, and light colors larger errors…………………...……..75

Figure 3.4: Magnitude of landuse change (area in 2012 minus area in 2002) in a buffer size of

500 m around at each site for the eight landcover classes. Red circles represent loss of area (-1 standard deviation), white relatively no gain or loss, and blue circles represent increases in area (+1 standard deviation).…………………………………………………………...……. 76

Figure 3.5: Adult redundancy analyses (RDA) for 2002 (A1 and A2) and 2012 (B1 and B2) with corresponding site origins (A2 and B2) (natural sites = circles, anthropogenic sites = triangles). Predictor codes are: CCmam = climate change in spring between 2000 and 1990 or

2010 and 200, CCdjf = climate change in winter between 2000 and 1990 or 2010 and 2000,

Veg = vegetated cover within 500m, Wtld = wetland cover within 500m, Res = residential cover within 500m, LL = substrate that is leaf litter, WT = water temperature of the pond, FV

= percentage cover of floating vegetation, SV = percentage cover of submerged vegetation,

MEM = Moran’s eigenvector predictor of spatial scale, Wtld = wetland cover within 500m

.…...………………………………………………………………………………………….. 77

Figure 3.6: Larvae redundancy analyses (RDA) for 2002 (A1 and A2) and 2012 (B1 and B2) with corresponding site origins (A2 and B2) (natural sites = circles, anthropogenic sites = triangles). Predictor codes are CCmam = climate change in spring between 2000 and 1990 or

2010 and 2000, CCson = climate change in fall between 2000 and 1990 or 2010 and 2000, H

= herbaceous cover within 50m, PA = surface area of the pond, SV = submerged vegetation,

LL = substrate that is leaf litter, Dev = developed area within 500 m, Water = water area within 500m, Veg = vegetated area within 500 m…………………………………………... 78

xiii

Figure 3.7: Variance partitioning of the redundancy analyses (RDA) into three predictor categories: (1) pond abiotic variables (2) landuse predictors, and (3) climate change predictors. Numbers in the outer circles are the percentage of explained variance unique to each predictor category; numbers in the inner regions represent variation that is explained by more than one predictor category. Partitioning which adds up to more than 100% (Adults

2002, Adults 2012, Larvae 2012) means that there are likely non-linear relationships between the categories of predictors……………………………………………………………………………………79

Figure 3.8: MANOVA by RDA for adults (A1 and A2) and Larvae (B1 and B2) showing the full model (A1, B1) and the trajectory of change in community composition between 2002 and 2012 (A2, B2), separated by site origin: red=anthropogenic sites, black = natural sites.

Red and black arrows that are bold in A2 and B2 represent the average response of all sites by pond origin…………………………………………………………………………………....80

Figure 3.S1: Principal components analyses of pond substrate in 2002 (A) and 2012 (B).

Extracted scores from each first axis, which explain 45% of variation in 2002 and 39% of variation in 2012, were included as potential predictors in the linear and multivariate analyses.

. ……………………………………………………………...…………………………...…100

Figure 3.S2: Moran’s Eigenvector Maps (MEM) for 2002 showing the best three MEMs that explain the most variation in the community data for adults (row 1) and for larvae (row 2).

Eigenvectors denoted with an * are those included in the best MEM model as determined by

AICc, and those denoted by † are those included in the best RDA models for each community data set. Sites of the same color (black or white) are autocorrelated with variables at different scales, and the size of the box represents the magnitude of the correlation.….…..…………101

xiv

Figure 3.S3: Moran’s Eigenvector Maps (MEM) for 2012 showing the best three MEMs which explain the most variation in the community data for adults (row 1) and for larvae (row

2). Eigenvectors denoted with an * are those included in the best MEM model as determined by AICc, and those denoted by † are those included in the best RDA models for each community data set. Sites of the same color (black or white) are autocorrelated with variables at different scales, and the size of the box represents the magnitude of the correlation…….102

Chapter 4:

Figure 4.1: The study region in southern Ontario, Canada, where P. longipennis has expanded its range. Triangles are P. longipennis occurrence points in the Ontario Odonate Atlas recorded before 2000, and circles points recorded since 2000. The grey shaded region is our focal study region where P. longipennis has entered since 2000. …………………………..124

Figure 4.2: Analysis workflow. External data are displayed in white boxes, analyses in light gray boxes, and results in dark gray boxes. During calibration of SDMs species occurrence data were divided into 80% training and 20% for model testing/validation. Species occurrence and SOLRIS landcover data (unmodified for disjunct scenarios, modified for adjacent scenarios) were then used as inputs to Maxent (10 replicates) to develop a distribution model.

During prediction, ponds were randomly removed from the focal study region at rates of 5%,

25%, and 50% (10 replicates each), and used as input, along with the model formula from calibration, to Maxent to predict P. longipennis’ distribution in our focal region. Replicates were averaged within each pond removal level. The predicted distributions, in the three pond removal levels, were then used, along with species occurrence data from the focal region and model parameters, as input to the MigClim migration analysis (25 replicates). MigClim

xv replicates were averaged to produce a final migration result. This entire process was carried out for the disjunct and adjacent scenario groups. ………………………………………….125

Figure 4.3: Habitat complementarity of southern Ontario (a) and focal region (b). Pixels shown in black are the complementary habitat landcover type. ……………………………127

Figure 4.4: Maxent predictions into the focal study region for the disjunct scenarios (a,b,c) and adjacent scenarios (d,e,f), with climate induced pond loss of 5% (a,d), 25% (b,e) and 50%

(c,f). Darker areas represent the highest probability of occurrence for P. longipennis, and white areas the lowest. ……………………………………………………………………...128

Figure 4.5: MigClim Results for disjunct (a) and adjacent (b) groups at long-distance dispersal probability of 0.01 for distances of 1 km, 2.5 km, 5 km, 7.5 km, and 10 km, utilizing the pond loss scenarios in increasing order of 5%, 25% and 50% over 30 years. Black pixels represent areas where P. longipennis was found at the end of the simulation. Gray pixels represent suitable habitat which was not colonized during the simulation.………………………...…129

Figure 4.6: MigClim Results for disjunct (a) and adjacent (b) groups at long-distance dispersal probability of 0.05 for distances of 1 km, 2.5 km, 5 km, 7.5 km, and 10 km, utilizing the pond loss scenarios in increasing order of 5%, 25% and 50% over 30 years. Black pixels represent areas where P. longipennis was found at the end of the simulation. Gray pixels represent suitable habitat which was not colonized during the simulation.…………………………...130

Figure 4.7: MigClim Results for disjunct (a) and adjacent (b) groups at long-distance dispersal probability of 0.10 for distances of 1 km, 2.5 km, 5 km, 7.5 km, and 10 km, utilizing the pond loss scenarios in increasing order of 5%, 25% and 50% over 30 years. Black pixels represent areas where P. longipennis was found at the end of the simulation. Gray pixels represent suitable habitat which was not colonized during the simulation….………………………...131

xvi

Chapter 1 - Introduction

Most ecological studies aim to determine which biotic and abiotic interactions shape patterns of biodiversity. Yet, nowadays this endeavor requires ecologists to account for anthropogenic impacts as well. Hence, since the 1980s, the field of conservation biology investigates how to protect, restore and mitigate biodiversity loss on a human-dominated planet. Indeed, habitat loss and alteration, as a result of anthropogenic impacts, are a ubiquitous reality on most of

Earth’s landscapes. For example, globally only 25% of terrestrial lands remain wild (Ellis et al., 2010), and less than 50% of surface freshwater is unused by humans (Vitousek et al.,

1997). Consequently, major threats to biodiversity from anthropogenic sources are landuse change and climate change (Parmesan & Yohe, 2003; Foley et al., 2005; Mantyka-Pringle et al., 2012), which severely affect species abundances, distributions, and, ultimately, persistence in both aquatic (Reece & McIntyre, 2009; Harabiš & Dolný, 2012) and terrestrial

(Cardinale et al., 2012) ecosystems.

All species are not equally threatened, however. The more specific or rare a species’ required habitat (i.e. those necessary for survival and reproduction), the more those species are potentially impacted (Myers et al., 2000). This is true for all species, but species utilizing multiple habitat types during their life cycle (hereafter multihabitat species) are likely to be especially vulnerable. Multihabitat species require habitat from at least two distinct types, for example aquatic and terrestrial habitats, foraging and nesting habitats, or breeding and wintering habitats (e.g. migratory birds). While the loss or modification of one of these habitat types can reduce survival and reproduction, multihabitat species face increased threats because they need to regularly move between these habitats (Dunning et al., 1992; Pope et al.,

2000). These movements can happen on many spatial and temporal scales including

1 movements from nesting to foraging habitats within a single day or annual movements between summer breeding grounds and winter habitats.

Thus, for multihabitat species, it is not only the overall loss of each type of habitat that is important, but the breaking of the ability to move between these vital complementary habitats that can affect population dynamics and persistence (Dunning et al., 1992). For example, a loss of complementary habitat areas has been shown to reduce birds’ ability to reach foraging habitats that are not adjacent to their nests during their breeding period (Petit,

1989; Mueller et al., 2009). This exemplifies how multihabitat species are potentially more vulnerable to anthropogenic environmental changes than species requiring only one type of habitat.

Many multihabitat species are invertebrates where a switch of habitat use occurs during the transition between larval life stages and adult life stages. These ontological changes are usually dramatic enough that it results in a change in ecological niche between the different life stages. Utilizing this as a definition of a multihabitat species, a large proportion of species undergo large enough shifts in ecological niche to functionally be considered to utilize multiple habitats (Werner & Gilliam, 1984). Although this life history strategy is widespread, it presents a conservation challenge because more than one habitat, often in a different ecosystem (e.g. aquatic vs. terrestrial), must be considered for the persistence of a species.

The interfaces between ecosystems where multihabitat species live are often high in biodiversity (Gregory et al., 1991) and are critical in providing ecosystem goods and services

(Millenium Ecosystem Assessment, 2005). Despite this importance, and the apparent abundance of multihabitat species, multihabitat species are poorly represented in conservation

2 planning (Abell et al., 2007; Amis et al., 2009; Remsburg & Turner, 2009; Beger et al., 2010;

Hazlitt et al., 2010). With multihabitat species, it is expected that factors which affect one life stage might differ from those that affect another life stage (e.g. larvae vs. adults), especially if those life stages occupy different ecosystems (e.g. aquatic vs. terrestrial). Conservation efforts for multihabitat species, therefore, at the minimum need to account for multiple habitat types, but also for the possibility of one life stage to be more threatened than another. But, as a first step, we need to know whether or not multihabitat species do respond differently to anthropogenic threats across their ontogenetic development.

The loss of complementarity and adjacency between habitat types is an important problem facing multihabitat species, affecting their ability to persist in response to anthropogenic threats. With respect to landuse change, this means that some multihabitat species will have less habitat that is suitable (Dunning et al., 1992), depending upon the way in which habitat is lost or modified due to anthropogenic activities. Even a small amount of habitat loss, if it results in breaking the adjacency of required habitats, might then affect multihabitat species more than species requiring a single habitat. With respect to climate change, multihabitat species’ ability to move through a landscape may also be hampered. A common response to climate change is shifts in a species’ range as it tracks its climatic niche, usually resulting in expansions towards higher latitudes or elevations (Parmesan et al., 1999;

Parmesan & Yohe, 2003; Chen et al., 2011), and potentially corresponding contractions at trailing edges of range limits (Parmesan, 2006 and references therein). Given that multihabitat species have specific habitat requirements and need to move between these habitats, it is unknown if these requirements will be met in novel landscapes which now are part of their climatic niche as a result of climate change. This will impede movement of individuals and

3 tracking of their climate niche, which could ultimately lead to extirpations or extinctions if the climate changes too rapidly for multihabitat species to keep pace (Loarie et al., 2009;

Devictor et al., 2012). Combined effects between landuse change and climate change can also occur (Jetz et al., 2007; Mantyka-Pringle et al., 2012). Climate change forces a species to move to track its climactic niche, while landuse change simultaneously reduces the quantity and connectivity of complementary habitat patches in the landscape that a species is traversing. Understanding how the interactions between anthropogenic factors will affect multihabitat species represents a gap in conservation science.

1.1 Thesis Outline

The central thrust of my thesis is to investigate how anthropogenic activities affect multihabitat species, including their relative influence with respect to natural processes, directly and indirectly, at local and regional scales. For example, at local scales the direct loss of habitat due to human activities is important, while at regional scales human impacts might indirectly result in a decreased ability of species to move through the landscape. Because multihabitat species can occupy different ecological niches during their lives, I also aim to determine if these life stages are differentially impacted. Finally, because the habitat adjacency needs of multihabitat species are rarely explicitly included in conservation planning

(Amis et al., 2009; Beger et al., 2010). I will incorporate habitat complementarity into commonly used conservation tools such as species distribution modeling and simulations of migration to better reflect species needs and potential responses to global change. As a whole, my thesis contributes to a better understanding of multihabitat species responses to anthropogenic pressures, knowledge that can then be applied towards their conservation. In

4 the face of multiple anthropogenic impacts (i.e. recreation, climate change, landuse change), the insights gained from this thesis will also help to inform the effective design of conservation strategies to minimize biodiversity loss of multihabitat species.

The multihabitat species studied in this thesis are dragonflies and damselflies

(hereafter odonates). Odonates occupy lotic and lentic aquatic habitats as larvae and utilize terrestrial habitats as pre-reproductive adults and for foraging (Corbet, 1999). They are meso- predators in both realms, feeding on aquatic invertebrates, tadpoles, and small fishes as larvae, while being preyed upon by fishes or waterbirds. As adults, odonates feed on aerial invertebrates, and are fed upon by birds, spiders, and mantids. Due to their meso-trophic position in each realm, they are a critical link between aquatic and terrestrial ecosystems

(Knight et al., 2005). Because of their short generation times (from 0.25 to 6 years), high global diversity (over 5680 described species), and trophic position, odonates respond rapidly to habitat loss or alteration (Briers & Biggs, 2003; Dolný & Harabiš, 2012; Harabiš & Dolný,

2012). Odonates are excellent fliers and disperse locally, and some species undergo long migrations (Corbet, 1999). Consequently, they form a metacommunity through the movement of individuals between habitat patches. As such, odonates are commonly used as an indicator of ecosystem health and conservation status (Oertli et al., 2002; Briers & Biggs, 2003; Raebel et al., 2010), making them an ideal study system to investigate the conservation of multihabitat species.

In chapter 2, I assess the relative contributions of natural and anthropogenic factors on the structuring of odonate communities (18 species) in a natural metacommunity setting

(small islands within a very large lake), utilizing recreational boating as an anthropogenic impact that threatens both larval and adult odonates, directly and indirectly, at local scales. I

5 also investigate whether there are differences in the way that larvae and adults respond to recreational boating. The novelty of this chapter is in the quantification of the relative impact of an anthropogenic stressor and natural processes that act in tandem to structure these communities, all in the context of a study region where impacts from recreation are potentially very high due to large recreational use of the area and the sensitive nature of freshwater ecosystems.

In chapter 3, I use a quasi-experimental sampling design to investigate the direct and indirect effects of landuse and climate change over 10 years on odonate diversity and community composition (83 species) at the larval and adult stages. I also document the influx of new species into the regional species pool which is likely as a result of new ecological niches opened up due to climate change. The novelty of this chapter is the use of field data to address, at the landscape scale, how landuse and climate change affect multihabitat species.

Finally, in chapter 4, I test whether a single species of odonate, which is known to be expanding its range in my study region, utilizes complementary habitats (those where aquatic and terrestrial habitats are adjacent) within the landscape. Then, based on species distribution modeling I investigate how predicted distributions and simulated migration changes when I incorporate complementary habitats as an entity rather than separately in a fragmented landscape. The novelty of this chapter is the explicit incorporation of complementary landcover types into species distribution models and simulations of dispersal, and the ability to compare results to models which do not explicitly incorporate complementary habitats.

Chapter 2 - Recreational boating, landscape

configuration, and local habitat structure as drivers of

odonate community composition in an island setting

2.1 Introduction

Habitat loss and alteration as a result of anthropogenic impacts are ubiquitous on most of the

Earth’s landscapes (Ellis & Ramankutty, 2008). Globally, only 25% of terrestrial lands remain wild (Ellis et al., 2010), and less than 50% of surface freshwater is unused by humans

(Vitousek et al., 1997). One of the most affected habitat types is the coastal zones of freshwater ecosystems, which are areas of high ecological productivity (Jenkins, 2003;

Strayer & Findlay, 2010). For species that utilize these habitats, anthropogenic impacts can have major effects on their persistence. Many species require coastal wetlands and terrestrial habitats to complete their life cycle (hereafter ‘multihabitat’ species). Even though these multihabitat species, typified by complex life cycles, are highly diverse (Werner & Gilliam,

1984; Werner, 1988), they are poorly represented in conservation planning (Abell et al., 2007;

Amis et al., 2009; Remsburg & Turner, 2009; Beger et al., 2010; Hazlitt et al., 2010). This mismatch between their abundance and low representation in conservation is, in part, due to the specialization of scientists (i.e. terrestrial or aquatic ecologists) and the mandates of managers (e.g. political boundaries not matching ecological boundaries) (Beger et al., 2010).

Furthermore, conservation planning for these systems is complicated by a lack of understanding of the extent to which the distinct life stages of these species are structured by forces acting within the aquatic or the terrestrial environment, and by natural or anthropogenic forces.

One major anthropogenic factor that causes impacts to freshwater coastal ecosystems is human recreation, particularly recreational boating (Mosisch & Arthington, 1998;

Gonzalez-Abraham et al., 2007). Impacts from recreational boating can be physical, chemical, and biotic. These impacts result in a decrease in water quality, damage or disturbance to plants and animals, and the transport of invasive species (Gergel et al., 2002; Burgin &

Hardiman, 2011). The physical impacts of recreational boating result in an increase in wave frequency and magnitude (increasing shear stress). Furthermore, boating can create waves with directions that differ from wind-generated waves. These impacts vary according to the size, shape, and speed of the boat generating the waves, and the distance between the boat and the shore (Rodriguez et al., 2002; Maynord, 2005; Gabel et al., 2012). Water depth, shoreline slope, and structural complexity of aquatic vegetation also determine the physical impacts of boat-generated waves (Gabel et al., 2008; Gabel et al., 2012). All of these factors will interact to determine the net effect of recreational boating on freshwater communities that occupy coastal environments.

One well-studied group that inhabits coastal wetlands and are subject to anthropogenic impacts are Odonata (dragonflies and damselflies; hereafter odonates). Odonates are a conspicuous group of insects commonly used as indicators of ecosystem health and conservation status (Oertli et al., 2002; Briers & Biggs, 2003; Raebel et al., 2010). An estimated 10-15% of odonates (5860 described species, 7000 species estimated: Kalkman et al., 2008) are at risk of extinction, a level of risk equivalent to that for bird species globally

(Clausnitzer et al., 2009). Larval odonates occupy a range of lotic and lentic aquatic habitats, while adults utilize a wide range of terrestrial and shoreline (primarily during breeding) habitats. Odonates play critical roles in ecosystems because they bridge multiple trophic

8 levels and can act as a critical linkage between freshwater and terrestrial food-webs (Knight et al., 2005; Burkle et al., 2012).

Depending upon the species, adult odonates may disperse locally or undergo long migrations, forming spatially dynamic metacommunities. These metacommunities are structured by local factors, such as vegetation and substrate structure (Buchwald, 1992;

Samways & Steytler, 1996; Corbet, 1999; Schindler et al., 2003; Clausnitzer et al., 2009;

Remsburg & Turner, 2009), and regional factors such as dispersal and habitat connectivity

(Angelibert & Giani, 2003; McCauley, 2006; 2007; Chin & Taylor, 2009). Odonate metacommunities respond rapidly to changes in the quantity, quality, and spatial arrangement of aquatic and terrestrial habitats (Clausnitzer et al., 2009), regardless of whether these changes are natural or anthropogenic in origin.

At a local scale, larval macroinvertebrates, including odonates, are impacted by waves generated by recreational boating. These changes in shear stress result in the direct dislodgement of individuals (Bishop, 2005; 2007; 2008; Gabel et al., 2008; Gabel et al., 2011;

Gabel et al., 2012) and an indirect reduction or simplification of aquatic vegetation (Liddle &

Scorgie, 1980; Ostendorp et al., 1995; Radomski & Goeman, 2001; Rodriguez et al., 2002;

Remsburg & Turner, 2009). As exemplified in these studies, high diversity, high trophic levels, and metacommunity dynamics make odonates an excellent study system in which to assess community responses to anthropogenic pressures.

Anthropogenic pressures act also in tandem with natural processes to structure odonate communities (Hamasaki et al., 2009). The interplay between natural and anthropogenic factors is especially important in localized areas where human impacts are high, such as freshwater ecosystems. Yet research quantifying human impacts on odonate communities in

9 freshwater ecosystems is lacking (Hamasaki et al., 2009). To address this gap of knowledge, we ask (1) what are the relative contributions of natural factors (local habitat structure and landscape configuration) and anthropogenic factors (pressure from recreational boating) that act together to determine the composition of odonate communities?; and (2) do different life stages (larvae vs. adults) respond differently to these natural and anthropogenic factors? We focus on common and abundant species in this study because they underpin ecosystem function and the delivery of ecosystem services (Kareiva & Marvier, 2003; Turner et al.,

2007; Gaston, 2010). We expect that boats passing close to a habitat, travelling at high speeds, or passing frequently would have the most impact. We hypothesize that boat-generated waves act to simplify aquatic communities through a reduction in the diversity of habitat types that, in turn, reduces species diversity and/or abundance of key species such as odonates. Given the different ecological niches occupied by larvae and adult odonates, we expect differences in the relative contributions of natural and anthropogenic factors structuring these communities.

Larvae should be structured predominantly by local habitat and more directly impacted by boat-generated waves. Adults should respond strongly to landscape configuration and indirect impacts of boat-generated waves.

2.2 Methods

The study system

Our study system is the Georgian Bay region of Lake Huron, Ontario, Canada (Figure 2.1).

The world’s largest freshwater archipelago consisting of approximately 30,000 islands is found in Georgian Bay, creating a model island biogeographic system. Georgian Bay’s proximity to Toronto (Ontario), North America’s 4th largest urban municipality (Viducis,

2013), places it under anthropogenic pressure. Approximately 10.3 million people visited the

Georgian Bay region annually from 2001 to 2006, with the largest proportion of domestic travelers originating in the greater Toronto area (TNS Canada, 2008). Of these domestic overnight travelers, 49% stayed in private accommodations such as cottages or houses (TNS

Canada, 2008), many of which are located on islands. Boating is, therefore, the primary mode of access to cottages located on Georgian Bay islands, and boating is also utilized by many of these travelers for recreational activities such as fishing, water sports, and sightseeing.

Odonate sampling

Surveys of the odonate communities were conducted once in August 2011, and monthly in

June, July, and August 2012. The abundance of adult odonates and final instar exuviae

(exoskeletons left behind during emergence from larvae into adults) were recorded/collected from coastal wetlands of 17 islands in the Waubaushene area of Georgian Bay (Figure 2.1).

For each sampling period all sites were surveyed within 3 days of each other, resulting in no change in flight season, and were carried out by the same observer. Surveys were conducted on sunny days between the hours of 9:00 and 18:00 during periods of typical wind speeds and directions for the region that did not inhibit adult flight. Adult surveys were conducted with close-focus binoculars from an anchored boat and from shore for a total duration of 20 minutes, recording only individuals identified to the species level (primarily mature males, or netted and released females and immatures). The small size of our surveyed islands (average perimeter 172 meters, average area 0.12 hectares) allowed us to survey the entire island, including terrestrial areas, the shoreline, and aquatic habitats to a distance of 10 meters from shore, minimizing the chance of non-detection of individuals.

Exuviae surveys were conducted on the same day as adult surveys using a standardized search protocol, but during a separate 20 minute period. Emergent vegetation at the entire shoreline and in the water was searched by wading or canoe within 10 meters of the island shore. Exuviae were removed intact, often by including the piece of vegetation to which they were attached. Rocky shoreline areas were also searched for exuviae, inland to approximately 5 meters. All exuviae were identified in the lab (Walker & Corbet, 1978;

Bright & O'Brien, 1999). For adults and exuviae, only individuals identified to the species level were included in the analyses. Rare species were excluded from analyses, i.e. when the species occurred only at one site (Table 2.1).

Environmental variables

At each sampled island, 24 local habitat variables known to be important to odonates were recorded (supplemental material, Table 2.S1). Aquatic vegetation variables were recorded as percentage cover within 10 meters of the shoreline. Terrestrial landcover variables were recorded as percentage cover of the entire island. All quantitative variables were measured with a Garmin Rhino650 GPS (garmin.com). Landscape-scale variables were calculated for each site at buffer distances of 1 km, 2 km, 4 km, and 8 km. These distances are typical movement distances for odonates (Conrad et al., 1999; Angelibert & Giani, 2003; Purse et al.,

2003; McCauley, 2006; 2007; McCauley et al., 2010) and likely represent non-migratory usage of the landscape for most (or at least for dragonflies) species in this region. For the statistical analyses we used only those predictors that, based on the literature, were expected to have the most influence on odonates, resulting in 11 variables (9 natural and 2 human;

Table 2.2). Islands and mainland were digitized from Bing Maps Aerial (online basemaps

12 accessed through ArcGIS). Potential habitat within each buffer distance was calculated with

ArcGIS 10.1 (hereafter GIS) and Geospatial Modelling Environment (spatialecology.com).

Boating pressure

Our study region allowed us to integrate the combined effects of four marinas, which vary in the amount of dock space available to recreational boats, proximity to a major highway

(Highway 400), and proximity to established boating channels. We proposed that this setting created a gradient of boating pressure on coastal habitats, where marinas represent a potential source of boats proportional to the size of the marina. The distance, speed, and frequency at which boats pass an aquatic habitat determine the magnitude and frequency of boat-generated waves (Bishop, 2005; 2007; 2008; Gabel et al., 2012), and the potential impact of these waves to aquatic habitats. For example, boats passing close to a habitat, travelling at high speeds, and passing by frequently would increase shear stress the most. Access to a marina will also determine the frequency of boat trips originating there. Marinas located on a marked boating channel were assumed to be more accessible to boaters, influencing the frequency of trips.

In our study region, four marinas represented a spectrum of these attributes: two adjacent to the highway (Waubaushene and Port Severn) and two further from the highway

(Victoria Harbor and Port McNicoll). Waubaushene was the second-largest marina (1338 meters, as determined by linear dock space measured from aerial imagery), located closest to the highway and adjacent to a marked boating channel. Port Severn was also located on the highway and is adjacent to a boating channel, but has less dock space (204 meters). Dock space varies at Victoria Harbor (2053 meters) and Port McNicoll (825 meters), and these two marinas were not located close to the highway or on a boating channel. Our sampled islands

13 were located at different distances relative to these marinas and at different distances from the established boating channels, and therefore, were exposed to a varying frequency and magnitude of boat-generated waves that potentially impact odonates directly (dislodgement of larvae and emerging adults) and indirectly (loss of aquatic vegetation).

We calculated several measurements of potential boating pressure (anthropogenic predictors). Boating pressure will be most strongly determined by the frequency, proximity, and speed of boats relative to odonates’ aquatic habitat, and our proxies of boating pressure were, therefore, calculated to represent these relationships (supplemental material, Table

2.S3). For each sampled island, we used GIS to calculate the on-water distance to each of the four marinas, the average on-water distance to all four marinas, distance-weighted averages, and distance-weighted and frequency-weighted averages utilizing dock space for potential boat origins. We also calculated the Euclidean distance to the nearest boating channel and estimates (distance-weighted, distance-un-weighted, distance and speed-weighted) of the portion of the shoreline of each island exposed to waves generated from boats in the boating channel. Speed of boats travelling in the boating channel was determined by posted signs in the field and nautical charts. Boating channels were digitized from TrakMaps Nautical

Chartbook (product number 644). Two proxies of boating pressure (average distance to the four marinas and speed-weighted exposure) were consistently important predictors of odonate diversity and community composition, and therefore they were included in the statistical analyses (Table 2.2).

Wind effects

In the Georgian Bay, wind is an important abiotic influence on ecosystems in the region. For flying insects such as adult odonates, winds will determine an individual’s ability to forage and move between habitat patches (Corbet, 1999). Wind speed and direction is also the primary driver of wave direction and height in Georgian Bay. Variations in these wave properties affect the ability of larval odonates to forage, move, and avoid predators (Gabel et al., 2008; Gabel et al., 2011). To assess wind directions in the region, data were downloaded from the National Climate Data and Information Archive (climate.weatheroffice.gc.ca) for the

Parry Sound weather station, the closest location for which these data were available, located approximately 80km north of our study area. Daily maximum wind gust speed and direction for the years 2007-2011 (2012 unavailable) were utilized. Fetch was calculated in GIS for each island as the longest unimpeded (i.e. no “barrier” islands which would intercept waves) on water distance within a 45 degree arc of the dominant wind direction determined from the

Parry Sound data.

2.3 Statistical analyses

We performed statistical analyses to determine the drivers of odonate diversity and community composition. For both sets of analyses adult and exuviae datasets were analyzed separately, as these different life stages were likely responding to different aspects of the local habitat, landscape configuration, and boating pressure. Prior to analyses, environmental variables were transformed. Proportions measured in the field were ln(p/1-p) transformed and areas calculated in GIS were ln(a+1) transformed, where p is percentage coverage, and a is area in square meters. All statistical analyses were performed using the packages vegan, ad4,

15 gclus, cluster, openair, leaps, MASS, and FD in R ver. 2.13.2 (R Development Core Team,

2011).

Odonate diversity

To determine which predictors were acting to determine the diversity of odonates at our islands multiple linear regression were performed using Shannon diversity as our response.

We used the nine natural and two boating pressure predictors (Table 2.2) in a stepwise AIC selection procedure using both forward and backward directions (“stepAIC” function from R package MASS) to determine the best model.

Odonate community composition

To determine which predictors were acting to structure odonate communities, redundancy analyses (RDA) were performed. A stepwise selection procedure (‘ordistep’ function from R package vegan) was used to determine the best predictors for RDA models. After the stepwise procedure, predictors were further evaluated based on Variance Inflation Factors (VIF)

(predictors excluded when VIF greater than 10; Borcard et al., 2011), and permutation tests

(n=10000 permutations, predictors excluded when p>0.05). This iterative process produced the most parsimonious models that attempted to simultaneously maximize the explained variance as measured by adjusted R2 values (Peres-Neto et al., 2006), while minimizing the number of predictors included in each model. To quantify the relative contributions of each group of predictors (local habitat structure and landscape configuration vs. boating pressure) included in the RDAs, a variance partitioning analysis was conducted (Borcard et al., 1992), a method applied in similar odonate studies (e.g. Hamasaki et al., 2009).

The community data were scaled from abundance to presence/absence, and then

Hellinger transformed prior to RDA (Legendre & Gallagher, 2001). Presence/absence at each site was used because abundances were highly skewed and therefore results skewed by a few very, highly abundant, species. This biased our analyses by weighting less abundant species equally to common species, but this bias will be consistent across sites, and we have incorporated abundances in our Shannon diversity response of the linear regression analyses.

RDA utilizes a Euclidean distance, but data Hellinger transformed prior to RDA circumvent the problems associated with calculating Euclidean distances on species matrices (for a more detailed description see Legendre & Gallagher, 2001). A Hellinger transformation of presence/absence data is equivalent to utilizing chord distance (Legendre & Legendre, 2012).

2.4 Results

A total of 23 species were recorded as adults or exuviae. Only one species (Epitheca princeps;

Hagen 1861) was found at two or more sites as both an adult and as exuviae, and 18 species were included in this study after removing rare species (Table 2.1). Mean species richness at each island was 7.11, with a standard deviation of 1.6.

Wind data from the Parry Sound weather station showed that, in this region, winds generally originate from the west (Figure 2.S1 in Supporting Information). Consequently, wind-generated waves will also generally flow in a west to east direction. Fetch distances for each island ranged from 14.9 meters to 18150 meters, with a mean of 9552 meters and standard deviation of 5944 meters.

The best model for each species from the multiple linear regression analyses had adjusted R2 of 0.53 for adults and 0.23 for larvae. The adult model included three predictors:

17 floating vegetation, island perimeter, and speed-weighted exposure. The best exuviae model included a single predictor: shallow shore slope (Table 2.3). Mean Shannon diversity for adults was 1.11 with a standard deviation of 0.28 and range of 0.49-1.51, and for exuviae 0.86 with a standard deviation of 0.53 and range of 0-1.73 (a Shannon diversity value of zero means that only a single species was found at that site). The final RDA models each included five predictors for adults and three for exuviae. Total variance explained was 35.0% for adults, and 15.3% for exuviae (Table 2.4, Figure 2.2). For adults, natural factors (local and landscape predictors) accounted for 69% of variance explained, proxies of boating pressure

31%, and no variance was shared between natural and boating pressure predictors. As no boating pressure predictors were included in the exuviae model, variance partitioning was not performed. The adult model included one local predictor (floating vegetation), two landscape structure predictors (island perimeter within 1km and mainland perimeter within 8km), and two proxies of boating pressure (average distance to marinas and speed weighted exposure).

The exuviae model included a single local predictor (shallow shore slope)

Within the adult RDA, the most prominent species specific responses were that species in the Libellulidae family were grouped together in ordination space, and associated primarily with increased average distance to the four marinas and increased cover of floating vegetation.Within the exuviae RDA, there were no prominent patterns in the species responses.

2.5 Discussion

This study sought to quantify the potential influence of recreational boating on odonate community composition. We found that the diversity and community composition of adult

18 odonates changed in response to a combination of local habitat structure, landscape configuration, and our proxies of boating pressure. The diversity and community composition of exuviae were only influenced by natural factors, and did not show a response to our proxies of recreational boating. We do find, however, that our proxies for boating pressure are significant predictors of both adult diversity and community composition. Based on these results, we suggest that a change in hydrodynamic regime (i.e. shear stress from boat generated waves) created by recreational boating likely acts directly and indirectly to influence adult odonates in our system. Our results support local conservation strategies focused on changes in recreational habits (e.g. boat speed or proximity to odonate habitats), which can be utilized in concert with habitat protection to lessen anthropogenic impacts on odonate communities to protect these vital components of freshwater and terrestrial ecosystems.

Influence of recreational boating on adult odonates

For adults, we found in the RDA that increases in the average on-water distance to all marinas correspond to sites with increased cover of floating vegetation. This supports the hypothesis that boat-generated waves can result in an overall reduction in macrophyte abundance and a decrease in macrophyte complexity or diversity (Liddle & Scorgie, 1980; Murphy & Eaton,

1983; Asplund & Cook, 1997). We also found that the distance and speed-weighted exposure of each island, a significant predictor of adult diversity and community composition, did not correlate with average marina distance. This predictor emphasized that it was not only the amount of boat traffic that was important, but also the distance and speed a boat travels with respect to coastal habitats that determines the level of boating impact (Maynord, 2005; Gabel

19 et al., 2012). The selection of our boating pressure proxies in the adult models of diversity and community composition indicated that our proxies were likely to represent the impacts of recreational boating shown empirically in other studies.

We also found that certain taxonomic groups of odonates responded to these gradients of boating pressure. The “perching” species found as adults in our study (family Libellulidae) were associated with increased average distance to the four marinas and increased coverage of floating vegetation. Adults in the Libellulidae family use vegetation for emergence, as foraging perches, oviposition site selection, and for territoriality (Corbet, 1999; Switzer &

Walters, 1999). Hence, these species are likely more susceptible to indirect wave action, which reduces habitat amount and quality through the reduction of aquatic vegetation. Indeed, these species were found at sites with ample vegetation and with low impacts from recreational boating.

Newly emerged adult odonates (hereafter tenerals) are a particularly vulnerable life stage because their bodies are still soft and wings fragile. During this period, before the maiden flight takes them away from their emergence site, they are especially susceptible to predation as well as to abiotic factors (Corbet, 1999; Jakob & Suhling, 1999; Worthen, 2010).

Wing damage to tenerals is especially traumatic and can happen by contact with water or with adjacent vegetation. Under natural conditions, emergence mortality can range between 8 and

17% for dragonflies (Anisoptera) (Corbet, 1957; Mathavan & Pandian, 1976), and as high as

28% for damselflies (Zygoptera) (Gribbin & Thompson, 1990). Mortality increases as wind speed increases (Jakob & Suhling, 1999) and with precipitation (Gribbin & Thompson, 1990) due to the increased likelihood that an individual will be dislodged and fall into the water or come in contact with adjacent vegetation or other surfaces and suffer wing damage (Corbet,

1957). We suggest that this is one mechanism potentially acting within our study system. An increase in wave frequency or magnitude from recreational boating could dislodge or splash tenerals or move the vegetation they are perched upon, leading to increased mortality during this vulnerable life stage. The general lack of species located in ordination space where distance and speed-weighted exposure was highest provided some evidence for this mechanism of impact.

Influence of recreational boating on exuvial odonates

We did not find evidence in our diversity or community analyses that our proxies of recreational boating are influencing exuviae. We would expect that our sites which are most exposed would result in larvae being dislodged from their preferred microhabitat, resulting in increased energetic costs and exposure to predators (Bishop, 2005; 2008; Gabel et al., 2008;

Gabel et al., 2011; Gabel et al., 2012), but we did not see evidence for this relationship in our exuviae data. This lack of a response could be because we sampled exuviae and not larvae.

Our data were therefore potentially biased towards individuals which have been least impacted because they were able to complete the larval stage of their life cycle and successfully emerge. The most impacted individuals might not have survived to emergence and were not represented in our study.

Our analyses suggest that adults and exuviae were responding differently to recreational boating. The taxonomic biases in our adult data towards species in the

Libellulidae family, and of our exuviae data towards species in the Gomphidae family, make it difficult to make generalities about how structuring forces change through ontogenetic life stages. We expect that these life stages should respond differently, and evidence that supports

21 this was found. The major differences we detected were changes in local habitat preference

(though this could be due to our taxonomic biases) and a lack of landscape-scale influence on exuviae community composition. If these differences in community response are real, successful conservation measures aimed at protecting odonates must account for factors affecting both life-history stages. Our results concur with recommendations in other studies

(e.g. Bried et al., 2012a), and suggest this will require surveys of both life stages as well as data on a wide range of environmental and anthropogenic factors affecting the distributions of these life history stages.

Local and landscape-scale influences

It is expected that if odonates exist as a metacommunity, both local and landscape factors would be important in determining community structure (Leibold et al., 2004). We saw evidence for this, and, additionally found that at local and regional scales adults and exuviae were influenced by different variables. This was expected because, during these life stages, odonates occupy different niches and are subject to different abiotic and biotic influences, or different magnitudes of the same influences. While other studies have looked at differences within a single life stage, either larvae (e.g. Wissinger, 1988) or adults (e.g. Kadoya et al.,

2008), our study is the first to our knowledge that specifically compares differences in the drivers of community composition between life stages.

At a local scale vegetation is a good predictor for both adults (floating vegetation) and for exuviae (emergent vegetation), as we would expect (Corbet, 1999). Differences occur when the specific ecology of each life stage is considered. For adults in the Libellulidae family floating vegetation likely represents perching sites, increasing overall habitat

22 availability. For exuviae in the Gomphidae family emergent vegetation represents potential emergence sites (Remsburg & Turner, 2009), however the inclusion of shallow shore slope might also be important for emergence of Gomphidae species because they are able to emerge on horizontal surfaces (Eda, 1963 in Corbet, 1999) such as rocky shores, which are typical in our study system. In the absence of vegetation, a shallow slope may be easier to transit than a steep slope when larvae emerge, perhaps because rocky surfaces are harder to grasp than vegetation. Furthermore, Gomphidae species are able to survive in deeper waters, up to 10 m, where floating and emergent vegetation is less likely to be available for emergence and where

Libellulidae are unlikely to be found (Corbet, 1999).

At a landscape scale, for adults the amount of mainland perimeter within 8km and island perimeter within 1km are important predictors. Species of Libellulidae were most associated with decreased areas of these predictors. However, both of these predictors were in the opposite direction of floating vegetation, which is likely the more important predictor. It is possible that these landscape scale predictors were important because adults from mainland habitats were dispersing to the island habitats, and that the island populations are maintained or supplemented through this process of mass effects (Shmida & Wilson, 1985). We did not record any Libellulidae exuviae from the sites where the Libellulidae adults were most abundant, suggesting that the presence of these adults at the islands was driven by dispersal from the mainland. However, we did observe a few Libellulidae (e.g. Libellula luctuosa;

Burmeister, 1839) individuals ovipositing around the sampled wetlands, suggesting that adults were at least attempting to colonize coastal environments.

For exuviae, no landscape-scale predictors were selected. Odonate larvae, especially in the suborder Anisoptera, do not generally disperse (Zahner, 1959; Rowe 1982 in Corbet,

1999), so their response at the landscape scale was a result of adult oviposition site selection.

Ovipositing adults select sites which should maximize the survival of their progeny, and exuviae represent the culmination of this habitat selection, definitively indicating whether or not larvae are surviving at a specific location (Raebel et al., 2010).

Caveats

Based on published species accumulation curves, we estimated that our survey effort captured between 55% and 65% of adult species richness at each island (Bried et al., 2012b). This estimate was supported by historical data collected by E. M. Walker and others in Go Home

Bay, Ontario, approximately 30 km north of our study site and also located on The Georgian

Bay. This sampling was carried out primarily during the summers of 1907, 1908, and 1912 but sporadically from 1902 to 1942 (Royal Ontario Museum, accessed 2013). Species richness in 1907, 1908, and 1912, was 32, 19, and 27, respectively. The cumulative richness for these three surveyed years was 41 species, and, for all years sampled, 43 species. The 23 species we detected represent 53% of this historic regional species pool, and 72%, >100%, and 85%, respectively, of the yearly species richness from the Walker collection. Given that the Walker surveys covered a larger variety of habitats (e.g. small lakes, swamps) and were focused on recording the maximum diversity of adult odonates, our survey was an adequate representation of the odonate communities at small island habitats in this region. This comparison does not, however, take into account that the regional species pool might have changed due to species range expansions and contractions since the Walker data were collected.

Our exuviae response might have resulted from a sampling or taxonomic bias. An increase in boat-generated waves was also likely to remove exuviae from substrates and make it more likely to find exuviae at islands that were less exposed. Yet, even at less exposed sites, we did not find exuviae of Libellulidae. We expected to find Libellulidae exuviae if they were present because exuviae persistence is similar between substrates (rock vs. vegetation)

(Lubertazzi & Ginsberg, 2009) and we did find Gomphidae exuviae on rock substrates.

Nonetheless, one explanation is that Libellulidae exuviae were present in small numbers and were missed in our monthly visits. Another possibility is that near the mainland, exuviae of

Libellulidae are absent because these are sink habitats for mainland populations with little successful reproduction. Farther from the mainland, adult Libellulidae were absent due to either dispersal limitation, lack of suitable habitat, or competitive exclusion. We hypothesize that many species of Gomphidae exuviae are absent near the mainland due to a lack of suitable habitat. Farther from the mainland, adult Gomphidae might have been observed too far away to be identified to the species level, or that, after emergence, adults of these

Gomphidae species are rather disassociated from the islands, spending more time over open water (e.g. Macromia illinoiensis often takes long patrol flights). Neurocordulia yamaskanensis is crepuscular so unlikely to have been flying during our survey period.

Even though our sample size was low, we were still able to identify anthropogenic effects. For adults several of our proxies for recreational boating pressure were significant predictors in the multiple linear regression and RDA analyses. Our island locations might be confounded by large-scale changes in lake morphology as distance from the mainland increases. We tested for the influence of distance to the mainland on our models by conducting a partial redundancy analysis (pRDA) using the predictors from the original RDA

25 models, while adding distance to the mainland as a confounding factor and thus removing its influence. The pRDA models for adults and exuviae were both significant (F=1.70 p=0.023;

F=1.86 p=0.016, respectively). This indicates that our models explained variation in the adult and exuviae data that are not a result of a simple gradient of isolation from the mainland.

Conservation implications for odonates in Georgian Bay

The traditional approach to protecting biodiversity focuses on conserving existing habitat, or restoring lost or degraded habitat (Pimm et al., 1995; Rands et al., 2010). However, for species that persist as a metacommunity, the connectivity of a designed reserve network between different habitat patches is a critical component (Rayfield et al., 2011). In the case of this study, the recommendation would be to focus on maintaining a high amount of floating vegetation for adult Libellulidae species. For exuviae/larvae, the recommendation would be to focus on protecting islands with shallow shore slopes and minimal shoreline vegetation which can be used for emergence.

While this habitat approach is likely to be the best long-term solution, additional approaches that are more management-based should also be considered. The selection and importance of anthropogenic boating proxies in our study offers an additional conservation option. The most likely mechanism explaining these anthropogenic impacts from recreational boating are the direct and indirect impacts of an increase in magnitude and frequency of waves, causing direct dislodgement of larvae and emerging adults and altering the abundance and cover of vegetation. A reduction in these anthropogenic impacts, therefore, could have positive effects on odonate abundance and diversity. This could be achieved through relatively simple means. For example, boating channels could be relocated farther away from

26 important odonate habitats, reducing the chances of direct dislodgement of adults and indirect impacts to aquatic vegetation. If relocating boating channels is not an option, simply changing or establishing boating speed limits could have similar results (Maynord, 2005). These alternative approaches could potentially be implemented in a shorter time frame than habitat protection or restoration, and offer viable conservation benefits that should be considered in a holistic conservation program.

2.6 Conclusion

Knowing that terrestrial and aquatic habitats are at risk from local anthropogenic pressures, practical conservation solutions that provide alternative methods of protection are vital. This study suggests that odonate communities are influenced by a combination of habitat structure and anthropogenic pressure from recreational boating. Depending on the focal odonate taxonomic group and life stage, different conservation actions would be recommended. Due to their critical trophic positions as larvae and adults, even common odonates are key components of the ecosystems in which they are found. Odonate responses to anthropogenic pressure can cascade through aquatic and terrestrial foodwebs, and affect the abundance and persistence of many additional species. This study supports assertions from other studies

(Ostendorp et al., 1995; Gabel et al., 2012) that human actions at highly local scales can be important for the diversity and composition of biodiversity.

2.7 Tables

Table 2.1: Species included in the analyses and how many sites there were found at as adults and as exuviae.

Code Species Family Number of

Sites

(adult/

exuviae)

Ana.jun Anax junius (Drury, 1773) Aeshnidae 5/0

Arg.moe Argia moesta (Hagen, 1861) Coenagrionidae 6/0

Cel.epo Celithemis eponina (Drury, Libellulidae 4/0

1773)

Dro.spi Dromogomphus spinosus Gomphidae 0/8

(Selys, 1854)

Ena.sig Enallagma signatum (Hagen, Coenagrionidae 16/0

1861)

Epi.pri Epitheca princeps (Hagen, Corduliidae 17/17

1861)

Gom.exi Gomphus exilis (Selys, 1854) Gomphidae 0/6

Gom.fra Gomphus fraternus (Say, Gomphidae 0/8

1839)

Gom.spi Gomphus spicatus (Hagen in Gomphidae 0/3

Selys, 1854)

Hag.brev Hagenius brevistylus (Selys, Gomphidae 0/11

1854)

Hyl.ade Hylogomphus adelphus Gomphidae 0/5

(Selys, 1854)

Isc.ver Ischnura verticalis (Say, Coenagrionidae 15/0

1839)

Lib.luc Libellula luctuosa Libellulidae 6/0

(Burmeister, 1839)

Lib.pul Libellula pulchella (Drury, Libellulidae 6/0

1770)

Mac.ill Macromia illinoiensis Macromiidae 0/5

(Walsh, 1862)

Neu.yam Neurocordulia Corduliidae 0/2

yamaskanensis (Provancher,

1875)

Pac.lon Pachydiplax longipennis Libellulidae 2/0

(Burmeister, 1839)

Sym.vic Sympetrum vicinum (Hagen, Libellulidae 2/0

1861)

Table 2.2: Description and type of each variable included in the linear multiple regression and

Redundancy Analyses as potential predictors.

asub_veg Local Within 10 m of shore, percent

coverage of submerged aquatic

vegetation

afloat_veg Local Within 10 m of shore, percent

coverage of floating aquatic vegetation

aemerg_veg Local Within 10 m of shore, percent

coverage of emergent aquatic

vegetation

perc_shore_veg Local Percentage of shoreline which was

vegetated

tslope_1 Local Percentage of shoreline which has a

shallow slope at water interface (<

25°)

Island_Perimeter Local Perimeter of sampled island

perc_shore_veg Local Percentage of shoreline which is

vegetated

Isle_Perim_1k Landscape Total perimeter of all islands within

1km of sampled island

Main_Perim_8k Landscape Total perimeter of the mainland within

8 km of sampled island

5) marinaavg Anthropogenic Average on water distance to all four

marinas

17) Exp_weighted_s Anthropogenic Same as Exp_perc, but weighted by

distance to the boating channel and

estimated travel speed of boats in the

boating channel (determined from

posted signs and nautical charts)

Table 2.3: Results of the linear multiple regression, showing for adults and exuviae adjusted

R2 and predictors included in each model. Note that the best model for exuviae, containing only the shallow slope predictor, is not significant.

Adjusted Predictors

Adults 0.53* Floating vegetation

Island perimeter

Speed weighted

exposure

Exuviae 0.23 Shallow shore slope

Table 2.4: Results of the redundancy analyses (RDA), showing for each set of community data the total adjusted R2 and partitioned variance of the model, predictors selected, and

Variance Inflation Factors (VIF). Significance is shown for the entire model, each predictor, and each axis.

Variance Adj R2 Predictors VIF Model

Partition Axes

Adults Floating vegetation* 2.19 RDA1**

Natural 0.24 Island perimeter 1 km** 6.70 RDA2***

Anthropogenic 0.11 Mainland perimeter 8 km* 12.6 RDA3*

Shared 0.00 Average distance to marinas** 3.99

Total 0.350** Speed weighted exposure* 1.17

Larvae Shallow island shore slope ° 1.06 RDA1**

Natural 0.153 Emergent vegetation° 1.05

Anthropogenic Na Island perimeter 1km° 1.01

Shared Na

Total 0.153**

Significance: ° = p < 0.10, * = p < 0.05, ** = p < 0.01, *** = p < 0.001

2.8 Figures

Figure 2.2: Redundancy Analysis of adults (top panel) and exuviae (bottom panel) in the

Georgian Bay, Ontario, showing species (red text), and predictors (black text). For adults, species in the family Libellulidae are shown in the dashed rectangle. Species acronyms can be found in Table 2.1.

2.9 Supplemental material

2.9.1 Tables

Table 2.S1: Local habitat variables which were recorded at each sampled island.

Local Habitat Description

Variable

asub_veg Within 10 m of shore, percent coverage of submerged aquatic

vegetation

afloat_veg Within 10 m of shore, percent coverage of floating aquatic

vegetation

aemerg_veg Within 10 m of shore, percent coverage of emergent aquatic

vegetation

aheight0.5 Percentage of emergent vegetation less than 0.5 m in height

aheight.5_1 Percentage of emergent vegetation between 0.5 m and 1 m in

height

aheightg1 Percentage of emergent vegetation greater than 1 m in height

abranch_no Percentage of emergent vegetation with no branching

abranch_sim Percentage of emergent vegetation with simple branching

abranch_com Percentage of emergent vegetation with complex branching

apatchS_num Number of patches of submerged aquatic vegetation

apatchE_num Number of patches of emergent aquatic vegetation

apatchF_num Number of patches of floating aquatic vegetation

Ashade Within 10 m of shore, percentage of water shaded

perc_shore_veg Percentage of shoreline which is vegetated veg_max_dist Maximum distance from shore at which aquatic vegetation was

present

Tmeadow For the island, percent coverage of meadow

Tscrub/shrub For the island, percent coverage of scrub/shrub ttree_con For the island, percent coverage of coniferous trees ttree_dec For the island, percent coverage of deciduous trees tslope_1 Percentage of shoreline which had a shallow slope at water

interface (< 25°) tslope_2 Percentage of shoreline which had a moderate slope at the

water interface (25°-75°) tslope_3 Percentage of shoreline which had a steep slope at the water

interface (> 75°) t_num_pools Number of small aquatic pools located on the island exp_wind Percentage of the island shore exposed to predominant wind

direction

Table 2.S2: Landscape scale habitat variables calculated for each island.

Landscape Description

Habitat Variable

Island_Perimeter Perimeter of sampled island

Island_Area Area of sampled island

Nearest_Island Euclidean distance to nearest island

Nearest_Mainland Euclidean distance to nearest point on the mainland

Isle_Perim_1k Total perimeter of all islands within 1 km of sampled island

Isle_Num_1k Total number of islands within 1 km of sampled island

Main_Perim_1k Total perimeter of the mainland within 1 km of sampled island

Isle_Perim_2k Total perimeter of all islands within 2 km of sampled island

Isle_Num_2k Total number of islands within 2 km of sampled island

Main_Perim_2k Total perimeter of the mainland within 2 km of sampled island

Isle_Perim_4k Total perimeter of all islands within 4 km of sampled island

Isle_Num_4k Total number of islands within 4 km of sampled island

Main_Perim_4k Total perimeter of the mainland within 4 km of sampled island

Isle_Perim_8k Total perimeter of all islands within 8 km of sampled island

Isle_Num_8k Total number of islands within 8 km of sampled island

Main_Perim_8k Total perimeter of the mainland within 8 km of sampled island

Table 2.S3: Description of anthropogenic proxies of recreational boating.

Anthropogenic Proxy Description

On water distance from each island to Waubaushene

1) Waubaushene marina

On water distance from each island to Port Severn

2) PortSevern marina

On water distance from each island to Victoria

3) VictoriaHarbour Harbour marina

On water distance from each island to Port McNicoll

4) PortMcNicoll marina

5) marinaavg Average on water distance to all four marinas

Average on water distance to all four marinas, with

6) marinawavgh marinas weighted by their proximity to highway 400

Average on water distance to all four marinas,

7) marinawavgd weighted by distance

8) Shortest Euclidean distance from each island to a

Nearest_Boat_Channel marked boating channel

Weighted distance proxy of boating pressure,

prioritizing the number of boats found in each marina

9) BP1 and highway 400 proximity

Weighted distance proxy of boating pressure,

10) BP2 prioritizing the number of boats found in each marina

Weighted distance proxy of boating pressure,

prioritizing dock length found in each marina and

11) BP3 highway 400 proximity

Weighted distance proxy of boating pressure,

12) BP4 prioritizing dock length found in each marina

Weighted distance proxy of boating pressure, dock

13) BP5 length and highway 400 proximity not prioritized

Weighted distance proxy of boating pressure, number

14) BP6 of boats and highway 400 proximity not prioritized

Length of the shoreline which is exposed to waves

(i.e. perpendicular) originating from the boating

15) Exp_len_perp channel

Percentage of the shore of the island exposed to waves

(i.e. perpendicular) originating from the boating

16) Exp_perc channel

Same as Exp_perc, but weighted by distance to the

17) Exp_weighted boating channel

Same as Exp_perc, but weighted by distance to the

boating channel and estimated travel speed of boats in

the boating channel (determined from posted signs and

17) Exp_weighted_s nautical charts)

2.9.1.1

2.9.2 Figures

Figure 2.S1: Wind rose plot for the Parry Sound weather station, located approximately 80 km

north of our study area. Data downloaded from the National Climate Weather Database, and

are the daily maximum wind gust speed and direction for the years 2007-2011.

Chapter 3 - Odonate community responses to climate

change and landuse change

3.1 Introduction

Climate change and landuse change are now acknowledged as anthropogenic drivers affecting ecological systems (Parmesan, 2006; de Chazal & Rounsevell, 2009 and references within) and causing changes in the abiotic and biotic variables underpinning freshwater, marine, and terrestrial realms (Root & Schneider, 2006). The pace at which these changes occur, and their potential interaction, significantly reduces species’ ability to persist, leading to local extirpations of populations and potentially global extinctions of species (McLaughlin et al.,

2002). These threats act simultaneously along with natural processes both positively and negatively structuring ecological communities and affecting their long-term persistence

(Thomas et al., 2004; Root & Schneider, 2006; Brook et al., 2008).

One way for a species avoid local extinction is to track the geographic distribution of its climatic niche, but this is only possible if it possesses sufficient dispersal ability relative to the rate of climate change (Parmesan et al., 1999; Parmesan & Yohe, 2003). For example,

Parmesan et al. (1999) found that 35 species of non-migratory butterfly had expanded their ranges poleward 35-240 km, and Hickling et al. (2005) documented northward shifts for 37 species of non-migratory dragonflies and damselflies. Yet, for some species, landuse change can affect their dispersal ability by reducing the total amount of habitat, decreasing habitat patch size, and increasing isolation among habitat patches (Fahrig, 2003). Hence, climate change forces species to move and follow their climatic niche, while landuse change simultaneously alters the configuration of the landscape they are moving through, potentially

42 reducing movement success (Walther et al., 2002). Thus, these stressors are expected to act together and potentially affect species’ persistence (Folt et al., 1999; Opdam & Wascher,

2004; Vinebrooke et al., 2004).

Climate and landuse affect aquatic and terrestrial environments differently, however, making species which require multiple habitats to complete their life cycle (hereafter multihabitat) especially vulnerable. The conservation of many of these multihabitat species, relative to single habitat species, is understudied, and invertebrates are especially understudied relative to vertebrates (Clausnitzer et al., 2009). One such order of multihabitat invertebrate species are the Odonata (hereafter odonates), commonly known as dragonflies and damselflies. Odonates play critical roles in ecosystems because they bridge multiple trophic levels, often acting as a critical linkage in freshwater and terrestrial food-webs (Knight et al., 2005; Burkle et al., 2012). Larval odonates occupy a range of lotic and lentic aquatic habitats, while adults use a wide range of terrestrial habitats (Clausnitzer et al., 2009). As larvae, odonates can be top predators in fishless ecosystems feeding on invertebrates and tadpoles (McPeek, 1990). Larval odonates are meso-predators in waters occupied by fishes, feeding on invertebrates and small fishes, as well as being preyed upon by larger fishes and waterbirds. As adults, odonates play a similar meso-predator role, feeding on invertebrates and being preyed on by many species such as amphibians, birds, and other invertebrates such as spiders and mantids (Corbet, 1999). Fluctuations in odonate abundances can, therefore, affect foodwebs through top-down and bottom-up cascades (e.g. Knight et al., 2005; Burkle et al., 2012), making them key components in many ecosystems.

Odonates have been shown to respond to anthropogenic climate change (for reviews see Hassall & Thompson, 2008; Ott, 2010) by shifting poleward their geographic ranges

(Hickling et al., 2005; Flenner & Sahlén, 2008; Goffart, 2010), changing their phenology towards earlier flight seasons (Hassall et al., 2007; Dingemanse & Kalkman, 2008), or even increasing the number of generations completed per year (Braune et al., 2008). For odonates, human-modified landscapes are dominated by fewer, highly abundant generalist species, and lower species diversity (Samways & Steytler, 1996; Reece & McIntyre, 2009; Aliberti et al.,

2010). Thus, anthropogenic climate and landuse change can influence the composition of odonate communities by creating more habitats for generalist species, removing habitat for specialists, and by modifying the matrix between habitat patches which individuals must navigate through in response to climate change.

We used odonate community data to test whether, and to what extent, odonate diversity, evenness, and community composition have changed due to climate change and landuse change across the 10-year time period of our study. Specifically, we investigated the relative contribution between landuse change, climate change and abiotic variables (local pond attributes) on odonate diversity, community, and composition structure. We expected that landuse change will result in the loss of habitats for specialist species and gains in habitat generalists. We also expected that climate change will affect species diversity and community composition by shifting the relative abundance and/or dominance resulting in an overall change in regional species pool. Furthermore, to determine if landuse change and climate change are simultaneously influencing odonates, we looked for evidence of interactions between these factors. Given the bipartite life cycle of odonates (larvae inhabit aquatic systems and adults are terrestrial), we also determined if adult and larval odonates respond differently to landuse change and climate change. Finally, we looked for shifts in diversity

44 and community composition during our study period, and for the influx of new species expanding their ranges northward, or the disappearance of species due to range retraction.

3.2 Methods

Study region

Our study region spans over two ecoregions and two ecozones near Ottawa, Ontario, Canada

(Figure 3.1). The southern portion is located in the St-Laurent Lowlands ecoregion, part of the

Mixwood Plains ecozone. The portion north of the Ottawa River falls in the Southern

Laurentians ecoregion, part of the Boreal Shield ecozone. The St-Lawrence lowlands are characterized by warm summers and cold, snowy winters. Sixty percent of the landuse is agriculture, with areas of natural mixed woods forests. Mean annual temperature is 5ºC, mean summer temperature 16.5ºC, and mean winter temperature -7ºC. Approximately one million people reside in the Ottawa/Gatineau portion of this ecoregion. The Southern Laurentians are characterized by warm summers and cold snowy winters, with a more boreal climate which influences the vegetation of this region. The Southern Laurentians are underlain by

Precambrian granites and gneisses, creating a mixture of till and fluvioglacial soils, in contrast to the poorly drained gleysolic soils of the St-Lawrence Lowlands. In the Southern

Laurentians, mean annual temperature is 1.5ºC, mean summer temperature 14ºC, and mean winter temperature -11ºC. The major anthropogenic landuses in this ecoregion are forestry and recreation/tourism, with only 2% of the ecoregion used for agriculture (Ecological

Stratification Working Group, 1995).

By spanning the boundary of these two ecoregions, our study encompassed a gradient of ecological communities and anthropogenic landuses. Because of these ecozone and

45 ecoregion transitions our study was situated in an area expected to be impacted by climate change, and where species’ ranges might be more in flux than more stable non-boundary regions (Beatty et al., 2010). Indeed, many odonates in this region are at the northern or southern edge of their distributions (Jones et al., 2008), restricted by habitat availability, climate, or a combination of both.

In 2002 all potential ponds/wetlands ranging between 1 and 5 hectares in the study region were identified from aerial photos. From these, 61 ponds were selected for this study.

The selected ponds vary in the amount of forest cover within 500 meters and pond origin

(natural or anthropogenic) (see Figure 3.1), covering a geographic range of approximately 50 km north-south, and 80 km east-west. These variations in forest cover, pond origin, and spatial location ensured that the sites cover the ecological, anthropogenic, and climactic gradients in the region.

Odonate data

In 2002 the 61 ponds were sampled once in June and once in July. At each pond adults were surveyed through close focus binoculars by walking the perimeter of the pond and visually searching over the surface of the pond and the immediately adjacent land, for a total of 40 minutes. Species were identified on the wing and recorded to species level, or caught with a net if possible, identified, and then released. In general adult odonates are not as active at low temperatures or in high winds, so surveys were conducted on calm, sunny days between 9am and 6pm. Ponds were sampled for larvae by standard dipnet sampling, with 50 dips per pond covering all unique microhabitats in each pond/wetland (e.g. floating vegetation, submerged

46 vegetation, organic bottom etc.). Larvae were preserved in 70% ethanol and identified in the lab under a dissecting microscope (Walker & Corbet, 1978; Bright & O'Brien, 1999).

In 2012, 47 of the 61 original ponds were re-visited, once in June and once in July. At each pond the same protocols as 2002 were followed for both adults and larvae sampling. 14 sites were not sampled in 2012 because the ponds were: (1) destroyed/modified (6 sites); (2) no longer accessible (4 sites); or (3) not located due to poor GPS accuracy in 2002 (4 sites).

One additional site was excluded from analysis because it was only sampled once in 2012

(due to accessibility), leaving a total of 46 sites with full data sets for both sampling years. To ensure consistency between sampling years the primary researcher from the 2002 sampling crew (D. Bert) accompanied the 2012 crew (A. Hall) to a sub-set of the sites. Our sampling effort captures approximately 70% of adult species richness at our sites (Bried et al., 2012b), across years and sites.

Climate data

We generated climate data for our study region based on Environment Canada climate data, which are compiled every 10 years from the previous 30 years of data from local weather stations (http://climate.weather.gc.ca). We used data from 11 weather stations within and surrounding our study region. To get climate estimated values and their associated errors at each sampling site, we spatially interpolated the climate data for years 1990 (i.e. 1960-1990),

2000 (i.e. 1970-2000), and 2010 (i.e. 1980-2010) using kriging (Cressie, 1993). Based on these spatially interpolated climate data, we calculated mean yearly temperature and mean temperature for each season: spring (March, April, May), summer (June, July, August), fall

(September, October, November) and winter (December, January, February). Then we

47 calculated “climate change” data for 2000 and 2010 as the absolute difference between values in 2000 and 1990, and in 2010 and 2000, respectively, for each season and year (2000 and

2010).

During the 10 years (2002 to 2012) of our study, climate at each of our sampling sites changed in magnitude and spatially (Table 3.1, Figure 3.2). Overall there was an increase in climate temperature of 0.084ºC (± 0.034ºC std. dev.) between 1990 and 2000 with larger increases in the northeastern portion of our study region (mostly in the Southern Laurentians ecozone), and 0.27ºC (± 0.22ºC std. dev.) between 2000 and 2010 with larger increases in the

Ottawa/Gatineau urban areas.

Seasonal changes were also found: between 1990 and 2000 spring temperatures increased 0.15ºC (± 0.026ºC std. dev.) and were mostly uniform across our study region, summer temperatures increased 0.12ºC (± 0.034ºC std. dev.) with larger increases in the east, and winter temperatures increased 0.28ºC (± 0.057ºC std. dev.) with greater changes in the

Ottawa/Gatineau urban areas and lands east of Ottawa. Fall temperatures decreased by 0.22ºC

(± 0.024ºC std. dev.) with the largest decreases outside of the Ottawa/Gatineau urban areas.

Between 2000 and 2010 spring temperatures increased 0.094ºC (± 0.10ºC std. dev.) with the largest increases in the north, summer temperatures increased by 0.040ºC (± 0.293ºC std. dev.) with the largest increases in the northeast, fall temperatures increased 0.33ºC (± 0.072ºC std. dev.) and were mostly uniform across our study region, and winter temperatures increased

0.55ºC (± 0.12ºC std. dev.) with larger increases in the north.

Landcover data

Landscape scale environmental variables for each site were calculated within a 500 meter buffer of each pond margin, an estimated scale of influence for daily (non-migratory) movements of species found in our study (Conrad et al., 1999; Angelibert & Giani, 2003;

McCauley, 2006). We calculated the percentage cover within the 500m buffer for each of the following landcover classes (extracted from CanMap Route Logistics 2002 and 2012 data;

(DMTI, 2012)) which have been shown to influence odonate diversity and composition: vegetated area, water area, wetland area commercial area, industrial area, open area, residential area, and road area (Corbet, 1999; Hamasaki et al., 2009; Reece & McIntyre, 2009;

Aliberti et al., 2010). Road data included all paved roads. Open area was a combination of

Open Area (un-vegetated) and Parks and Recreational categories extracted from the landuse layer. Commercial area was a combination of Commercial and Government and Institutional layers, also extracted from the landuse layer. Vegetated area was defined as non forest vegetation, usually representing fields (agricultural and other). Landuse changes between

2002 and 2012 were calculated as the difference between the areas covered by each landcover class in 2012 minus the area covered in 2002.We were unable to find equivalent landuse data for 1992, and were therefore unable to calculate landuse change data for 2002, so we used our

2002 landuse variables instead in our analyses. All landscape analyses were performed with

ArcGIS 10.1 (esri.com) and/or Geospatial Modelling Environment (spatialecology.com).

Between 2002 and 2012, within 500 m buffers of our study sites, there was an increase in mean commercial area (0.73%), mean industrial area (0.78%), mean residential area

(2.47%), and mean road area (0.55%) (Table 3.2, Figure 3.4). Commercial and industrial areas increased mainly around the perimeters of Ottawa/Gatineau urban areas, with industrial

49 also showing some decreases south of Ottawa. Residential area increased mostly within and south of Ottawa, and northwest of Gatineau, with corresponding increases in road area at these areas as well.

There was also a decrease in mean open area (-3.6%), mean vegetated area (-0.89%), and mean wetland area (-0.04%), with no obvious spatial patterns at the landscape scale for vegetated, water, and wetland landcover (Table 3.2, Figure 3.4). Open areas, however, did show a pattern, and were lost mostly south of Ottawa, at sites where increases of residential area were also found. There was no mean gain or loss of water area within 500m of our ponds between 2002 and 2012.

Local abiotic data

In addition to our focus on climate change and landuse change, we expect that local factors will also influence on odonates within our study region. Odonates have been shown to exist as metacommunities (McCauley, 2007; McCauley et al., 2008) which are structured by local

(pond level) and landscape scale conditions. To account for influences from localized pond characteristics we measured 6 local habitat variables at each site by visual estimation to the nearest 10 percent cover (Tables 3.3 and 3.S1). Water temperature was measured with an

Oakton PC 10 Series multi-meter. To summarize pond substrate components (e.g., sand, clay, silt, organic, and rock) that are important determinants of odonate diversity and composition

(Corbet, 1999), we used a principal components analysis (PCA) (Figure 3.S1).

Landscape and regional levels data

In addition to local pond characteristics we computed Moran’s Eigenvector Maps (MEM) as a surrogate for processes acting at different spatial scales, but which we did not directly measure, such as connectivity among ponds (Dray et al., 2006). To produce MEMs we first constructed a neighborhood network (Delaunay network; Fortin & Dale, 2005) representing binary connections between our sites, with a distance decay relationship (R spdep package).

We then constructed MEMs based on this neighborhood, and determined which eigenvectors had significant positive and negative contributions to explaining community composition.

Finally, we selected the combination of significant eigenvectors which produced the best model for explaining each of our four sets of community data, as determined by AICc (MEMs constructed and evaluated with R spacemakeR package).

Statistical analyses

Four different data analyses were performed: by year (2002, 2012) and species life-stage

(larvae, adults. Only individuals identified to species were retained in the analyses. Less than one percent of adult individuals (17,564 total over the two sampled years) and less than two percent of larval individuals (8,492 total over the two sampled years) were excluded. To relate odonate diversity to environmental factors, linear analyses were performed using the full dataset of species found at each pond. To relate odonate community structure multivariate analyses were performed (Redundancy Analysis; RDA). Although our sampling effort was sufficient to characterize the common species in this region, it was not thorough enough to be fully representative of the abundance and occurrences of rare species (especially of larvae).

Therefore we excluded species found at 5% or fewer sites, and two additional species

(Enallagma ebrium and Ishnura verticalis) which were present and abundant at almost all of our sampling sites, and showed no variation in response to any of our predictors (Poos &

Jackson, 2012).

For theses statistical analyses (univariate and multivariate) we used only those predictors that, based on the literature, were expected to have the most influence on odonates, resulting in 19 variables (6 local, 7 landcover, 5 climate, 1 connectivity; see Table 3.3). We included one additional predictor, elevation, in all analyses because elevation is a known correlate of climate (e.g. Alvarez et al., 2014). Prior to analyses, environmental variables were transformed: proportions were ln(p/1-p) transformed; areas calculated in GIS were ln(a+1) transformed, where p is percentage coverage, and a is area in square meters. Statistical analyses were performed using the vegan, spacemakeR, MASS, maptools, lme4, AICCmodavg,

MuMIn, and raster packages in R ver. 2.13.2 (R Development Core Team, 2011).

Odonate diversity

To determine which factors and possible interactions affect odonate diversity, here Shannon diversity index, we used linear multiple regression. To determine the best subset of 19 potential predictors we performed a dredge analysis (“dredge” function from R package

MuMln) which utilizes Aikakes Information Criteria (Burnham & Anderson, 2004) corrected for small sample sizes (hereafter AICc). We included all two-way interaction terms as potential predictors and we interpreted all models with ∆AICc less than two as equivalent

(Burnham & Anderson, 2004). Then, to find the best model(s) for each year/life stage we retained all predictors from the best models in the dredge analysis and used a stepwise AICc

52 selection procedure using both forward and backward directions (“stepAIC” function from R package MASS) for each analysis group. Through this process we were able to determine (1) the important drivers of species diversity and (2) if interaction terms (specifically between climate change and landuse change) were included in these best models.

Odonate community response

To determine which drivers affect community composition, redundancy analysis (RDA) was performed. RDA requires non-zero species data at each site, so sites where no individuals were recorded within a particular year and life stage were removed: for adults this resulted in three ponds in 2002, and one in 2012; for larvae, it was three sites in 2002 and three in 2012.

Species abundance data were Hellinger transformed to account for combined absences between sampling locations and to minimize the effects of overly abundant species (Legendre

& Gallagher, 2001).

A stepwise selection procedure (‘ordistep’ function from R package vegan) was used to determine the best predictors for RDA models. After the stepwise procedure, predictors were further evaluated based on Variance Inflation Factors (VIF) (predictors excluded when

VIF greater than 10 (Borcard et al., 2011)), and permutation tests (n=10000 permutations, predictors excluded when p>0.05). This iterative process produced the most parsimonious models that attempt to simultaneously maximize the explained variance as measured by adjusted R2 values (Peres-Neto et al., 2006), while minimizing the number of predictors included in each model. We then partitioned the variance in each RDA model to assess the portion explained solely by climate change, landuse change, and abiotic variables (Borcard et al., 1992; Peres-Neto et al., 2006). This also allowed us to determine if any variance in our

53 community datasets was shared between landuse and climate change, which would indicate that these predictors are acting together.

Odonate diversity and community change between 2002 and 2012

To determine if the diversity of odonates changed between 2002 and 2012 we first created

PCAs of all species in 2002 and 2012. We then extracted the first principal component scores from each PCA and used these PCA scores as a response variable representing odonate diversity at each site, and then performed an ANOVA using year as an explanatory variable.

To determine if the community composition of odonates changed between 2002 and

2012 we performed a multivariate analysis of variance by redundancy analysis (hereafter

MANOVA-RDA) (Borcard et al., 2011; Legendre & Legendre, 2012). MANOVA-RDA compares community composition across a categorical variable, in our case sampling year.

Furthermore, within the MANOVA-RDA it is possible to plot the site scores of community composition for each year in ordination space. By tracking their location in 2002 relative to

2012, we assessed the trajectory of community composition change, if any, with respect to our predictors. If the community was not changing between years, we would expect to have small trajectories in varying directions within the ordination space. Alternatively, if there are changes in the community we would expect to see large shifts of site scores in ordination space, potentially uniform in their direction if the pattern of change is consistent across sites.

3.3 Results

Odonate community change

A total of 83 species were found at the 46 sites sampled in both 2002 and in 2012, 58 species as adults and 49 species as larvae (24 species found as both adults and larvae). In 2002, 63 species were found (20 only as adults, 21 only as larvae, 22 as both adults and larvae) and in

2012, 66 species were found (26 only as adults, 21 only as larvae, 19 as both adults and larvae). For adults, site occupancy of each species ranged from 1% to 85% in 2002, and from

1% to 87% in 2012. For larvae, site occupancy ranged from 1% to 54% in 2002, and from 1% to 57% in 2012. The trimmed dataset used in the multivariate analyses had a total of 28 species from 2002 (19 adult species and 22 larvae species) and 30 species from 2012 (20 adult species and 18 larvae species).

Between 2002 and 2012 we documented range expansion into our region for two species, Pachydiplax longipennis and Perithemis tenera. These species were undocumented in the study region in our 2002 survey and in the Ontario Odonate Atlas (Ontario Odonata Atlas,

2013), a government-organized repository for sightings from scientists and citizen-scientists.

P. longipennis was first documented in our study region in 2011, and P. tenera in 2012. We did not find either species as larvae and therefore their breeding cannot be confirmed, though larvae of P. longipennis have been collected approximately 85 km south of our study region

(Rayfield et al., in prep.).

Shannon diversity

Climate and landuse change predictors were included in many of the best models where overall the selected predictors explained 31% of the variance for adults and 22% for larvae in

2002 while in 2012, 45% was explained for adults and 41% for larvae (Table 3.3). Several interaction terms were included in our best models, however, no interactions between landuse change and climate change were found to be significant.

Community composition response

The two first axes of RDA explained 14.2% (2002) and 19.9% (2012) of the variance in adult community abundance, and explained 14.3% (2002) and 6.69% (2012) for larvae community abundance (Figures 3.5 and 3.6, Table 3.5). All RDAs were statistically significant and contained between three and six predictors, including landcover/landcover change, climate change, and local abiotic variables. Adult and larvae RDAs included abiotic, landcover/landcover change, and climate change predictors, whereas adult RDAs included

MEMs as well (Figure 3.S2 and 3.S3).

With respect to landcover/landcover change the RDAs showed that in 2002 vegetated area was an important predictor for adults and in 2012 vegetated area, wetland area, and residential area were important predictors (Figures 3.5 and 3.6). For larvae, in 2002 developed area and water area were important predictors, and in 2012 vegetated area was the best predictor. Landuse or landuse change uniquely explained 19.2% of variance in the 2002 adult model, and 46.6% in the 2012 adult model. With respect to climate change the RDAs showed that in 2002 climate change in the spring was an important predictor for adults and for larvae

(Figure 3.5). In 2012 climate change in the winter was important for adults and climate change in the fall for larvae (Figure 3.6). When partitioning the variance between climate change and other predictors in the RDAs, climate change was solely responsible for 21.2%

(2002) and 9.65% (2012) of explained variance in the models for adults, and 18.0% (2002) and 14.1% (2012) for larvae (Figure 3.7).

We found evidence that certain groups of adult species responded differently to changes in landcover. For example, for adults in 2002 and in 2012 decreased percentage of vegetated area (which includes forests) was associated with three generalist species; Libellula pulchella, Libellula luctuosa, and Plathymis lydia. In 2012 these species were also associated with increased percent coverage of residential area. Additionally, we found that in both 2002 and 2012 these generalist adult species were most associated with sites of anthropogenic origin. With respect to our climate change predictors, we found that in 2002 increases in spring climate change influenced mostly three species (Ladona Julia, Libellula quadrimaculata, and Sympetrum obtrusum), all in the family Libellulidae. In 2012 we found that greater changes in winter climate influenced mostly six species in the family Libellulidae.

For both 2002 and 2012, increases in our climate change variables were also associated with increases in floating vegetation. For our larvae RDAs in 2002 and 2012, there were no distinct patterns which occurred in the distribution of species within ordination space with respect to our predictors.

Community change between 2002 and 2012

The ANOVA analyses of PCA scores were not significant (F<0.001, p=0.98), meaning that when considering all sites at once, there was no change in the diversity of our communities.

The MANOVA-RDA models for adults and for larvae were also not significant (F=2.39, p=0.001; F=1.67, p=0.001, respectively), meaning that when the entire community was considered at a regional scale, we did not detect a change in species composition between

2002 and 2012. However, when we looked at the trajectory of species composition at our sites through time in the MANOVA-RDA, we did see patterns and shifts between 2002 and 2012.

For adults, we found that in general community compositions shifted in ordination space in the direction of increased winter climate change, increased pond water temperatures, decreased leaf litter substrate, and decreased floating vegetation (Figure 3.8). Ponds of anthropogenic and natural origin appeared to exhibit a similar magnitude and direction of change (Figure 3.8). For larvae, we found that species compositions were shifting towards increased fall climate change and decreased leaf litter substrate, with ponds of anthropogenic and natural origins showing similar direction and magnitude of change (Figure 3.8).

Local abiotic variables

Local pond attributes were important predictors for adults in both 2002 and 2012. In 2002 the cover of floating vegetation, cover of leaf litter on the substrate, and water temperature were good predictors for adults. In 2012 the amount of submerged vegetation was important. For larvae in 2002 cover of herbaceous vegetation at the pond margin, cover of submerged vegetation, and cover of leaf litter on the substrate were important, and in 2012 the area of the pond was important.

Influence of elevation

Shannon diversity index was not influence by elevation. For adults 2002, adults 2012, larvae

2002, and larvae 2012, when including elevation in the stepAIC analysis, elevation was not selected as a predictor. When manually adding elevation to our best models, no model was improved nor was elevation a significant predictor. Furthermore, when comparing any best

58 model with the corresponding best model plus elevation, no model was significantly different when elevation was included (ANOVA: adults 2002 F=0.003, p=0.92; adults 2012 F=0.328, p=0.15; larvae 2002 F=0.109, p=0.74; larvae 2012 F=3.90, p=0.056).

In the RDAs, we found that elevation may have some influence on our models. For all model groups elevation was not significant when added as an additional predictor to the best model, nor were any interaction terms between elevation and climate change significant.

However, when we conditioned out the influence of elevation in our RDA models, the variance explained (i.e. adjusted R2) decreased for each model (adults 2002 14.2% to 10.7%; adults 2012 19.9% to 14.8%; larvae 2002 14.3% to 10.0%; larvae 2012 6.69% to 2.95%).

These results indicate that despite not being an important predictor by itself, elevation did appear to explain some variance in our models, likely that variation which is already explained by the climate change variables.

3.4 Discussion

Both climate change and landcover change influenced odonate diversity and community composition in our study region. Increases in residential areas represent residential development in our study region, and these changes appeared to benefit generalist species at man-made ponds the most. Climate change was detected in the period preceding both sampling years, and, shared variance between climate and landcover change was found in the

RDAs, indicating the potential for combined impacts from multiple anthropogenic stressors.

We found evidence that the community composition at our sites shifted between 2002 and

2012, and we documented the influx of two new species into the region.

Landcover change

In the adult RDAs, we found that three generalist species are associated with decreases in vegetated landcover and increases in residential landcover. In our study region between 2001 and 2011, the population of the Ottawa/Gatineau region grew 15%, with the highest annual growth rates in Quebec, and specifically municipalities adjacent to, but not part of, Gatineau

(City of Ottawa, 2013), a pattern that we also saw in our landcover change results (though also with residential development south of Ottawa). We believe that generalist species in our study were taking advantage of these anthropogenically influenced habitats, relative to specialist species whose habitat was being impacted and lost as a result of residential development creating new human made pond habitats, and decreasing naturally vegetated areas. One of these generalists, L. luctuosa, has been previously documented to be positively correlated with sites of anthropogenic origin (Aliberti et al., 2010), and we found a similar pattern of distribution for this species in our study, along with other generalists which appear to be responding similarly.

We also detected patterns of landuse change in the MEMs which were good predictors of community composition. For adults the MEMs included in the RDAs clearly differentiate natural sites from anthropogenic sites within the landscape, indicating that our species are responding to the differences in site origin. On a large time scale anthropogenic sites represent novel habitat within the landscape (Simberloff, 1997; Ehrenfeld, 2000; Faeth et al., 2005), and these anthropogenic ponds might be driving the composition of our communities towards generalists species, a trend documented elsewhere (Samways & Steytler, 1996; Reece &

McIntyre, 2009; Aliberti et al., 2010), but not always (Flenner & Sahlén, 2008). Generalist species have been shown to disperse more than specialists (McCauley, 2007) and might,

60 therefore, be better at finding habitats located in highly disturbed landscapes such as those in newly developed residential areas. Indeed, the two species which have entered our study system are generalists. In addition these habitats are likely to have a less developed and more homogenized aquatic vegetation profile, as evidenced in our study by lower floating vegetation and leaf litter covers, creating a scenario for generalist species to increase in abundance and occupancy in these landscapes.

Another possible explanation for the influence of anthropogenic sites is a change in microclimate due to a “heat island” or other change associated with urban and suburban development, for which we did see evidence in the spatial distribution of our climate change predictors, most pronounced in year 2010. Other studies have seen a contrast between rural and urban sites, mostly noting changes in phenology between the two, with urban sites having advanced phenology relative to rural sites (Roetzer et al., 2000; White et al., 2002; Jochner et al., 2012). This relationship could lead to a further dominance of generalist species, due to more homogenized local habitat at anthropogenic sites, and microclimate which might allow these species to gain a developmental advantage relative to species inhabiting natural sites.

Climate change

In both the linear models and RDAs, climate changes were important predictors of diversity and community composition. Though we have measured climate change using air temperatures, we expect that water temperatures closely track air temperatures (Hassall et al.,

2007), a possible reason why we found climate change to be a good predictor in all analyses.

Underlying mechanisms for these observations included earlier and/or longer flight seasons giving individuals a longer flight season and therefore time to disperse, which in turn might

61 be driven by earlier emergence of larvae due to increased water temperatures, or due to increased larval survival over winter or larvae overwintering at later instars (resulting in increased overall body size) during the winter (for a review see Hassall & Thompson, 2008).

We suspect, based on the literature, that the range shifts documented for two species,

P. longipennis and P. tenera, were driven by climate change, and possibly due to the observed increases in winter temperature between 1990 and 2000 (0.28ºC increase), and 2000 to 2010

(0.55ºC increase). Several studies have shown a direct link between winter climate and range shifts for invertebrates and plants (Crozier, 2004; Berger et al., 2007; Cavanaugh et al., 2014), especially for butterflies (Parmesan et al., 1999; Hill et al., 2002; Chen et al., 2011).

Adequate habitat existed in the study region prior to 2012, so climate was likely limiting these species’ northern ranges, and has now changed to allow their survival and persistence. Indeed, our 2012 adult RDA shows that increased changes in winter climate are associated with species in the family Libellulidae, of which both new species are members. P. longipennis has been shown to increase its phenology (earlier emergence and flight season) by nearly 2 weeks under experimental induced warming of aquatic habitats (McCauley et al., 2015) one possible mechanism which could lead to range expansion. Based on the Ontario Odonate Atlas, P. longipennis has expanded its range approximately 65 kilometers between 2001 and 2011, and

P. tenera approximately 200 km between 2001 and 2012, resulting in rates of 6.5 km/year and

18 km/year, respectively. Hickling et al. (2005) has shown that 23 British odonates have expanded their northern ranges on average 6.8 km per year, while some Scandinavian odonates have expanded at rates of 88, 29, and 15 km/year (Nielsen 1998, 1999, 2001;

Lejfelt-Sahlén 2007; Stenholm Jakobsen 2007 in Flenner & Sahlén, 2008). The influx of two new species into our study region is indirect evidence supporting other studies which have

62 shown directly that odonates possess the ability to expand their ranges rapidly and take advantage of an increasing ecological niche provided by climate change.

Community change between 2002 and 2012

While we did not see an overall shift in diversity or community composition when looking across our entire study region, we did find that in the MANOVA-RDA the trajectory of community composition at each site shifts through time, and that these shifts were uniform for most sites, regardless of their origin (i.e., natural or man-made). The predictors which drove these shifts are those which homogenized habitat due to development over the 10 years of our study. Increased water temperatures, decreased cover of leaf litter and decreased floating vegetation could all be the result of development and/or eutrophication around our ponds

(Egertson et al., 2004). For example, the loss of forest cover could decrease the total amount of leaf matter available and reduce shading of ponds leading to higher water temperatures.

These higher temperatures could change phenology of odonates, and accelerate phenological responses when combined with climate induced temperature changes. The specific species which appear to be increasing with the change in our communities were generalists in the family Libellulidae, which, as generalists are good dispersers (McCauley, 2007) and inhabit a large breadth of habitats, are well adapted to take advantage of these documented human changes in landcover and climate.

Combined impacts of landuse change and climate change

We found some evidence for combined impacts between climate change and landcover change on our communities. In our linear models we did not discover any interaction terms in

63 our best models, however, our RDA variance partitioning analyses show for adults 9.2% variance is shared in 2002, and 18.4% variance shared in 2012, and for larvae 1.3% in 2002 and 25.3% shared in 2012. The changes in climate included in many of our models may dramatically affect phenological cues (winter survival, development rates, emergence times), while residential development homogenizes habitats and species pools. Indeed, we expected that these interactions should exist (Brook et al., 2008; Darling & Côté, 2008; de Chazal &

Rounsevell, 2009). In general increasing air temperatures (and corresponding increased water temperatures) will change almost all aspects of odonate ecology (Hassall et al., 2007;

Dingemanse & Kalkman, 2008), while landuse change will continue to both destroy/modify existing habitats and create new habitats. Climate and landuse change are currently prominent anthropogenic forces acting on communities and the influence of their interactions could possibly be even more pronounced in the future (Ott, 2010).

3.5 Conclusion

We found that, in addition to natural local and landscape factors, diversity and community composition of odonates were influenced by both climate change and landuse change

(especially increases in residential development). We found that landuse change was possibly driving the prevalence of generalist species in our study region, by disproportionately affecting odonates’ habitats. Likely in direct response to climate change, we documented two species which have expanded their northern range boundary during our study period. We expect that other species of invertebrates in this region are experiencing similar anthropogenic pressures and will respond in a similar fashion (Flenner & Sahlén, 2008). In general we found that in our study region landuse change reduced the amount of specialist habitats and the

64 connectivity between habitat patches, negatively impacting specialist species and positively influencing generalists species, while our documented climate changes is likely forcing species to move to new habitats.

To respond to climate change species will have to move through an increasingly anthropogenic landscape resulting from landuse change. Our findings highlight the potential importance of considering not only the stressors independently, but also together to explain the distribution of species. In order to be successful, conservation practitioners need to understand these complex relationships between anthropogenic stressors and biodiversity.

From an applied perspective, in the short term, conservation practitioners have the most control over landuse change relative to climate change. So even while both stressors are expected to act together, the best opportunity to mediate biodiversity losses in the immediate future, at least in our study region, remains with protecting high quality specialist habitats, while focusing on mediating the impacts of climate change into the future.

3.6 Tables

Table 3.1: Climate data by season showing the change in climate in 2000 (2000-1990) and 2010 (2010-2000), standard deviation of climate change, range of values across our sampled sites, and standard deviation of the errors of the interpolated climate data across our sampled sites, all measurements in Celsius degrees. Positive values indicate an increase in temperatures across time and negative values a decrease.

2000 2010

climate Std Range Error climate Std Range Error

(◦C) Dev Std (◦C) Dev Std

Dev Dev

Year 0.084 .034 -0.0037 to 0.19 0.022 0.27 0.22 -0.072 to 0.76 0.095

Spring 0.15 0.026 0.12 to 0.22 0.011 0.094 0.10 -0.056 to 0.34 0.005

Summer 0.12 0.034 0.050 to 0.25 0.032 0.040 0.047 -0.010 to 0.15 0.017

Fall -0.22 0.24 -0.62 to 0.23 0.13 0.33 0.072 0.11 to 0.44 0.090

Winter 0.28 0.057 0.11 to 0.34 0.047 0.55 0.12 0.43 to 0.80 0.024

Table 3.2: Landcover data showing, for each landcover class, the percentage cover within 500 m of our sampled ponds in 2000 and

2010, and the change in mean percentage landcover in each class between 2000 and 2010. Positive values represent an increase in that landcover class, negative values a loss.

Commercial Industrial Open Residential Road Vegetated Water Wetland

Area % Area % Area % Area % Area % Area % Area % Area %

2000 1.52 4.76 34.87 13.19 6.19 34.19 1.86 3.42

2010 2.24 5.54 31.27 15.67 6.74 33.30 1.85 3.38

Change 0.73 0.78 -3.60 2.47 0.55 -0.89 0.00 -0.04

Table 3.3: Description of measured and derived variables included in the linear multiple regression and/or redundancy analyses.

Scale Variable Description

Local Pond Herb Percentage of pond margin covered by

herbaceous vegetation

Float_veg Percentage of the pond covered by floating

vegetation

subVeg Percentage of the pond which supported

submerged vegetation

substrate PC Principal Component representing pond substrate

which includes sand, silt, clay, rock, and organic

variables

leafLitter Percentage of the pond substrate which was

primarily leaf litter

pondArea Surface area of the pond in square meters

Landcover Inds_Area Percentage cover of industrial area within 500 m

500 m of the pond margin

Open_Area Percentage cover of open area within 500 m of

the pond margin

Res_Area Percentage cover of residential area within 500 m

of the pond margin

Road_Area Percentage cover of road area within 500 m of the

pond margin

Veg_Area Percentage cover of vegetated area within 500 m

of the pond margin

Water_Area Percentage cover of water within 500 m of the

pond margin

Com_Area Percentage cover of commercial area within 500

m of the pond margin

Wtld_Area Percentage cover of wetland area within 500 m of

the pond margin

Climate cc2000_djf Difference in mean temperature of Environment

Change Canada climate normals between 2000 and 1990

for the months December, January, and February

cc2000_mam Difference in mean temperature of Environment

Canada climate normals between 2000 and 1990

for the months March, April, and May

cc2000_jja Difference in mean temperature of Environment

Canada climate normals between 2000 and 1990

for the months June, July, and August

cc2000_son Difference in mean temperature of Environment

Canada climate normals between 2000 and 1990

for the months September, October, November

cc2000_year Difference in mean temperature of Environment

Canada climate normals between 2000 and 1990

for the year 2000

cc2010_djf Difference in mean temperature of Environment

Canada climate normals between 2010 and 2000

for the months December, January, and February

cc2010_mam Difference in mean temperature of Environment

Canada climate normals between 2010 and 2000

for the months March, April, and May

cc2010_jja Difference in mean temperature of Environment

Canada climate normals between 2010 and 2000

for the months June, July, and August

cc2010_son Difference in mean temperature of Environment

Canada climate normals between 2010 and 2000

for the months September, October, November

cc2010_year Difference in mean temperature of Environment

Canada climate normals for the year 2010

Connectivity MEM Moran’s Eigenvector Maps representing

unmeasured spatial processes within our study

region

Table 3.4: Results of linear multiple regression using Shannon diversity as a response showing the best model for each year and life stage (adults and larvae). Predictors are color coded by abiotoc variables (green), landcover and landcover change (black), climate change (red), and interaction terms (blue). The direction of influence in the model for each predictor is indicated by either a lack of prefix (positive influence) or minus sign (negative influence).

Model Adj-R2 Terms

Adults 0.31 submerged vegetation + emergent vegetation -winter climate change

2002

Adults 0.45 -submerged vegetation + water area + residential area + residential area:submerged vegetation

2012

Larvae 0.22 pond substrate PCA + spring climate change + -fall climate change + fall climate change:spring

2002 climate change

Larvae 0.41 submerged vegetation + pond water temperature + water area -residential area + developed area +

2012 submerged vegetation:water area + submerged vegetation:residential area -submerged

vegetation:developed area -submerged vegetation:pond water temperature

◦ = p < 0.10, * = p < 0.05, ** = p < 0.01, *** = p < 0.001

Adj R2 Predictors Scale Adj R2 Predictors Scale

0.142 ** floatVeg ◦ Local 0.199 ** subVeg ** Local

leafLitter ** Local Veg_Area *** Landscape

waterTemp * Local Wtld_Area ◦ Landscape

Veg_Area ** Landscape Res_Area ◦ Landscape

2002 2002 2012 Adults Cc2000_mam ** Climate Cc2010_djf * Climate

MEM12 ◦ Connectivity MEM10 ** Connectivity

0.143 ** Herb ** Local 0.0669* pondArea ◦ Local

subVeg * Local Veg_Area * Landscape

leafLitter * Local Cc2010_son * Climate

Water_Area * Landscape

2002 2012 Larvae Dev_Area * Landscape

Cc2000_mam * Climate

3.7 Figures

Figure 3.1: Study region showing the sites sampled only in 2002 (open shapes) and sites re-sampled sites in 2012 (black shapes), as well as pond origin (anthropogenic = square; natural = circle). The study region of approximately 4000 km2 spans from the St-

Laurent Lowlands Ecoregion in the south to the Southern Laurentian Ecoregion to the north.

Figure 3.2: Maps showing the magnitude of climate change within our study region averaged for the entire year and by seasons for the years 2000 and 2010. All maps use the same scale (-1ºC to +1ºC) with blue colors representing decreases in climate temperatures and red colors increases.

Figure 3.4: Magnitude of landuse change (area in 2012 minus area in 2002) in a buffer size of 500 m around at each site for the eight landcover classes. Red circles represent loss of area (-1 standard deviation), white relatively no gain or loss, and blue circles represent increases in area (+1 standard deviation).

2010 and 200, CCdjf = climate change in winter between 2000 and 1990 or 2010 and 2000,

Veg = vegetated cover within 500m, Wtld = wetland cover within 500m, Res = residential cover within 500m, LL = substrate that is leaf litter, WT = water temperature of the pond, FV

= percentage cover of floating vegetation, SV = percentage cover of submerged vegetation,

MEM = Moran’s eigenvector predictor of spatial scale, Wtld = wetland cover within 500 m.

Species acronyms can be found in Table 3.S4.

2010 and 2000, CCson = climate change in fall between 2000 and 1990 or 2010 and 2000, H

= herbaceous cover within 50m, PA = surface area of the pond, SV = submerged vegetation,

LL = substrate that is leaf litter, Dev = developed area within 500 m, Water = water area within 500m, Veg = vegetated area within 500 m. Species acronyms can be found in Table

3.S4.

2002, Adults 2012, Larvae 2012) means that there are likely non-linear relationships between the categories of predictors.

Red and black arrows that are bold in A2 and B2 represent the average response of all sites by pond origin. Species acronyms can be found in Table 3.S4

3.8 Supplemental material

3.8.1 Tables

Table 3.S1: Description of measured and derived variables at each site, including the scale

(buffer of 50 m and 500 m) that each predictor was expected to influence community dynamics. A subset of these variables were used in the analyses (Table 3.3).

Scale Variable Description

Pond Herb Percentage of pond margin covered by herbaceous

vegetation

sunShore Percentage of pond shoreline exposed to sunlight

vegg5 Percentage of pond margin covered by vegetation

greater than 0.5 meters

vegl5 Percentage of pond margin covered by vegetation less

than 0.5 meters

floatVeg Percentage of the pond surface covered by floating

vegetation

subVeg Percentage of the pond which supported submerged

vegetation

bareShore Percentage of pond shoreline with no vegetation

sedge Percentage of the pond margin covered by Sedge spp

pondSun Percentage of the pond surface exposed to sunlight

duckweed Percentage of the pond surface covered by duckweed

sandBottom Percentage of the pond substrate which was primarily

sand

clayBottom Percentage of the pond substrate which was primarily

clay

siltBottom Percentage of the pond substrate which was primarily

silt

orgBottom Percentage of the pond substrate which was primarily

organic

rockBottom Percentage of the pond substrate which was primarily

rock

leafLitter Percentage of the pond substrate which was primarily

leaf litter

waterTemp Temperature of the water, measured in two locations

per visit and averaged

pondArea Surface area of the pond in square meters

woodyTree Percentage of the pond margin covered by woody trees

woodyBush Percentage of the pond margin covered by woody

shrubs

fishPA The presence or absence of fish, as opportunistically

observed during other sampling and measuring times

Pond standTrees Percentage cover of trees within 50 m of the pond

50m margin

Meadow Percentage cover of herbaceous meadow within 50 m

of the pond margin

Scrub Percentage cover of scrub/shrub within 50 m of the

pond margin

bareGround Percentage cover of bare ground within 50 m of the

pond margin

Landcov Com_Area Percentage cover of commercial area within 500 m of er 500 m the pond margin

Inds_Area Percentage cover of industrial area within 500 m of the

pond margin

Open_Area Percentage cover of open area within 500 m of the

pond margin

Res_Area Percentage cover of residential area within 500 m of

the pond margin

Road_Area Percentage cover of road area within 500 m of the pond

margin

Veg_Area Percentage cover of vegetated area within 500 m of the

pond margin

Water_Area Percentage cover of water within 500 m of the pond

margin

Wtld_Area Percentage cover of wetland area within 500 m of the

pond margin

Weather may02_totp Total accumulated precipitation in 2002 for the month

of May

jun02_totp Total accumulated precipitation in 2002 for the month

of June

jul02_totp Total accumulated precipitation in 2002 for the month

of July

may02_norain Total days without rain in 2002 for the month of May

jun02_norain Total days without rain in 2002 for the month of June

jul02_norain Total days without rain in 2002 for the month of July

may12_totp Total accumulated precipitation in 2012 for the month

of May

jun12_totp Total accumulated precipitation in 2012 for the month

of June

jul12_totp Total accumulated precipitation in 2012 for the month

of July

may12_norain Total days without rain in 2012 for the month of May

jun12_norain Total days without rain in 2012 for the month of June

jul12_norain Total days without rain in 2012 for the month of July

Climate djf_1990 Average mean temperature of Environment Canada

climate normals in 1990 for the months December,

January, and February

jja_1990 Average mean temperature of Environment Canada

climate normals in 1990 for the months June, July, and

August

mam_1990 Average mean temperature of Environment Canada

climate normals in 1990 for the months March, April,

May son_1990 Average mean temperature of Environment Canada

climate normals in 1990 for the months September,

October, November year_1990 Average mean temperature of Environment Canada

climate normals for the year 1990 djf_2000 Average mean temperature of Environment Canada

climate normals in 2000 for the months December,

January, and February jja_2000 Average mean temperature of Environment Canada

climate normals in 2000 for the months June, July, and

August mam_2000 Average mean temperature of Environment Canada

climate normals in 2000 for the months March, April,

May son_2000 Average mean temperature of Environment Canada

climate normals in 2000 for the months September,

October, November year_2000 Average mean temperature of Environment Canada

climate normals for the year 2000 djf_2010 Average mean temperature of Environment Canada

climate normals in 2010 for the months December,

January, and February

jja_2010 Average mean temperature of Environment Canada

climate normals in 2010 for the months June, July, and

August

mam_2010 Average mean temperature of Environment Canada

climate normals in 2010 for the months March, April,

May

son_2010 Average mean temperature of Environment Canada

climate normals in 2010 for the months September,

October, November

year_2010 Average mean temperature of Environment Canada

climate normals for the year 2010

Climate cc2000_djf Difference in mean temperature of Environment

Change Canada climate normals between 2000 and 1990 for the

months December, January, and February

cc2000_jja Difference in mean temperature of Environment

Canada climate normals between 2000 and 1990 for the

months June, July, and August

cc2000_mam Difference in mean temperature of Environment

Canada climate normals between 2000 and 1990 for the

months March, April, May

cc2000_son Difference in mean temperature of Environment

Canada climate normals between 2000 and 1990 for the

months September, October, November

cc2000_yea Difference in mean temperature of Environment

Canada climate normals for the years 2000 and 1990 cc2010_djf Difference in mean temperature of Environment

Canada climate normals between 2010 and 2000 for the

months December, January, and February cc2010_jja Difference in mean temperature of Environment

Canada climate normals between 2010 and 2000 for the

months June, July, and August cc2010_mam Difference in mean temperature of Environment

Canada climate normals between 2010 and 2000 for the

months March, April, May cc2010_son Difference in mean temperature of Environment

Canada climate normals between 2010 and 2000 for the

months September, October, November cc2010_year Difference in mean temperature of Environment

Canada climate normals for the years 2010 and 2000

Table 3.S2: For each sampled pond climate change in 2010 and 2000 and associated errors (in parenthesis) from interpolated values, and climate in 1990 with interpolated errors in parenthesis.

2010 Pond 2010 year 2010 spring 2010 summer 2010 fall 2010 winter 2 0.442 (0.214) 0.145 (0.262) 0.059 (0.400) 0.333 (0.222) 0.611 (0.357) 5 0.282 (0.165) 0.000 (0.263) 0.020 (0.409) 0.411 (0.167) 0.465 (0.368) 8 0.187 (0.373) 0.113 (0.273) 0.114 (0.478) 0.345 (0.352) 0.617 (0.465) 10 0.413 (0.235) 0.131 (0.263) 0.061 (0.404) 0.361 (0.242) 0.603 (0.362) 13 0.015 (0.068) 0.271 (0.262) 0.144 (0.412) 0.221 (0.069) 0.755 (0.369) 20 0.291 (0.292) 0.111 (0.266) 0.070 (0.416) 0.376 (0.293) 0.611 (0.378) 21 0.286 (0.297) 0.102 (0.267) 0.069 (0.418) 0.381 (0.297) 0.602 (0.381) 24 0.613 (0.094) 0.063 (0.258) 0.019 (0.396) 0.429 (0.096) 0.454 (0.350) 29 -0.004 (0.325) -0.056 (0.270) 0.008 (0.427) 0.337 (0.318) 0.434 (0.395) 30 0.157 (0.293) -0.015 (0.267) 0.005 (0.418) 0.355 (0.291) 0.437 (0.384) 32 0.201 (0.290) -0.001 (0.267) 0.005 (0.416) 0.351 (0.288) 0.438 (0.381) 34 0.326 (0.255) 0.025 (0.265) 0.007 (0.410) 0.352 (0.256) 0.441 (0.372) 35 -0.072 (0.368) -0.030 (0.272) -0.005 (0.436) 0.314 (0.351) 0.439 (0.410) 36 0.097 (0.127) 0.222 (0.261) 0.101 (0.402) 0.200 (0.132) 0.700 (0.358) 37 0.152 (0.133) 0.216 (0.261) 0.091 (0.401) 0.203 (0.137) 0.691 (0.356) 41 0.149 (0.206) 0.307 (0.266) 0.151 (0.429) 0.291 (0.208) 0.780 (0.393) 43 0.299 (0.310) 0.339 (0.270) 0.155 (0.445) 0.365 (0.307) 0.798 (0.416) 48 0.603 (0.157) 0.132 (0.260) 0.032 (0.395) 0.334 (0.163) 0.533 (0.351) 53 0.423 (0.231) 0.121 (0.264) 0.025 (0.401) 0.265 (0.234) 0.530 (0.360) 60 0.371 (0.257) 0.046 (0.265) 0.007 (0.408) 0.325 (0.257) 0.449 (0.370) 61 0.359 (0.250) 0.035 (0.265) 0.007 (0.408) 0.344 (0.251) 0.443 (0.370) 62 0.491 (0.182) 0.048 (0.262) 0.011 (0.402) 0.379 (0.185) 0.443 (0.360) 63 0.085 (0.344) 0.006 (0.270) -0.001 (0.422) 0.303 (0.334) 0.446 (0.391) 67 0.050 (0.356) 0.014 (0.271) -0.002 (0.423) 0.292 (0.343) 0.453 (0.393)

68 0.228 (0.218) 0.172 (0.263) 0.081 (0.404) 0.285 (0.227) 0.700 (0.361) 70 0.711 (0.080) 0.098 (0.258) 0.030 (0.394) 0.432 (0.082) 0.479 (0.346) 71 0.757 (0.043) 0.099 (0.257) 0.027 (0.392) 0.430 (0.044) 0.461 (0.344) 72 0.079 (0.269) -0.032 (0.268) 0.032 (0.422) 0.362 (0.268) 0.476 (0.386) 74 -0.033 (0.371) 0.024 (0.273) -0.010 (0.440) 0.316 (0.352) 0.465 (0.415) 76 0.027 (0.365) 0.051 (0.272) -0.010 (0.442) 0.288 (0.349) 0.478 (0.417) 80 0.177 (0.315) 0.108 (0.269) -0.009 (0.434) 0.215 (0.311) 0.510 (0.402) 81 0.436 (0.131) 0.196 (0.261) 0.004 (0.413) 0.114 (0.135) 0.570 (0.367) 91 0.032 (0.342) -0.028 (0.270) 0.000 (0.427) 0.323 (0.332) 0.434 (0.396) 92 0.084 (0.338) -0.006 (0.270) 0.000 (0.423) 0.318 (0.329) 0.440 (0.392) 104 0.201 (0.228) 0.250 (0.264) 0.111 (0.411) 0.297 (0.234) 0.776 (0.370) 110 0.335 (0.158) 0.010 (0.262) 0.017 (0.407) 0.419 (0.160) 0.459 (0.365) 111 0.107 (0.250) -0.035 (0.267) 0.022 (0.419) 0.370 (0.249) 0.460 (0.382) 112 0.146 (0.204) 0.183 (0.263) 0.090 (0.405) 0.273 (0.212) 0.734 (0.362) 113 0.419 (0.134) 0.175 (0.260) 0.054 (0.396) 0.275 (0.139) 0.639 (0.350) 114 0.250 (0.174) 0.208 (0.262) 0.083 (0.402) 0.269 (0.180) 0.724 (0.358) 116 0.211 (0.231) 0.257 (0.264) 0.114 (0.412) 0.298 (0.236) 0.774 (0.371) 119 0.008 (0.357) -0.013 (0.271) -0.003 (0.428) 0.309 (0.343) 0.441 (0.399) 120 0.027 (0.317) -0.049 (0.269) 0.008 (0.425) 0.341 (0.311) 0.435 (0.392) 121 0.504 (0.083) 0.061 (0.259) 0.033 (0.400) 0.443 (0.085) 0.503 (0.355) 123 0.552 (0.116) 0.076 (0.259) 0.036 (0.400) 0.439 (0.119) 0.507 (0.354) 124 0.495 (0.195) 0.167 (0.265) -0.008 (0.414) 0.195 (0.196) 0.517 (0.373) 2000 Pond 2000 year 2000 spring 2000 summer 2000 fall 2000 winter 2 0.094 (0.286) 0.151 (0.220) 0.118 (0.441) 0.071 (0.268) 0.289 (0.208) 5 0.074 (0.295) 0.124 (0.226) 0.113 (0.455) -0.313 (0.209) 0.307 (0.216) 8 0.191 (0.384) 0.201 (0.272) 0.252 (0.584) -0.034 (0.495) 0.317 (0.398) 10 0.113 (0.290) 0.157 (0.223) 0.133 (0.447) 0.023 (0.296) 0.310 (0.216) 13 0.149 (0.295) 0.222 (0.227) 0.189 (0.455) -0.458 (0.086) 0.164 (0.198) 20 0.143 (0.305) 0.170 (0.230) 0.165 (0.469) -0.090 (0.371) 0.325 (0.245)

21 0.146 (0.308) 0.171 (0.232) 0.171 (0.473) -0.102 (0.379) 0.329 (0.250) 24 0.066 (0.278) 0.131 (0.217) 0.110 (0.430) 0.013 (0.118) 0.316 (0.183) 29 0.059 (0.320) 0.117 (0.238) 0.096 (0.491) -0.559 (0.422) 0.289 (0.275) 30 0.066 (0.310) 0.133 (0.233) 0.109 (0.476) -0.424 (0.378) 0.308 (0.256) 32 0.069 (0.308) 0.139 (0.231) 0.115 (0.472) -0.376 (0.373) 0.315 (0.252) 34 0.073 (0.299) 0.146 (0.227) 0.120 (0.461) -0.244 (0.326) 0.326 (0.236) 35 0.068 (0.335) 0.144 (0.245) 0.108 (0.512) -0.623 (0.485) 0.305 (0.312) 36 0.125 (0.285) 0.208 (0.221) 0.171 (0.441) -0.308 (0.160) 0.223 (0.195) 37 0.114 (0.284) 0.203 (0.220) 0.159 (0.439) -0.237 (0.167) 0.238 (0.195) 41 0.131 (0.318) 0.211 (0.239) 0.169 (0.488) -0.366 (0.262) 0.138 (0.250) 43 0.104 (0.339) 0.195 (0.249) 0.140 (0.519) -0.256 (0.399) 0.109 (0.298) 48 0.065 (0.280) 0.153 (0.217) 0.110 (0.433) 0.167 (0.198) 0.306 (0.198) 53 0.087 (0.289) 0.176 (0.221) 0.142 (0.444) 0.006 (0.293) 0.322 (0.218) 60 0.079 (0.297) 0.155 (0.226) 0.128 (0.457) -0.163 (0.328) 0.334 (0.234) 61 0.075 (0.298) 0.149 (0.226) 0.123 (0.458) -0.198 (0.320) 0.329 (0.233) 62 0.069 (0.288) 0.141 (0.222) 0.117 (0.445) -0.083 (0.231) 0.325 (0.210) 63 0.078 (0.318) 0.155 (0.236) 0.127 (0.486) -0.457 (0.448) 0.321 (0.278) 67 0.079 (0.320) 0.159 (0.237) 0.130 (0.489) -0.485 (0.465) 0.317 (0.283) 68 0.099 (0.289) 0.155 (0.222) 0.112 (0.446) -0.063 (0.273) 0.249 (0.212) 70 0.076 (0.275) 0.138 (0.216) 0.114 (0.426) 0.181 (0.101) 0.327 (0.178) 71 0.068 (0.273) 0.135 (0.214) 0.110 (0.422) 0.227 (0.054) 0.325 (0.172) 72 0.089 (0.312) 0.125 (0.234) 0.133 (0.479) -0.416 (0.346) 0.302 (0.253) 74 0.053 (0.339) 0.146 (0.247) 0.093 (0.518) -0.623 (0.491) 0.272 (0.319) 76 0.041 (0.341) 0.140 (0.249) 0.075 (0.521) -0.571 (0.481) 0.252 (0.317) 80 0.026 (0.326) 0.133 (0.242) 0.056 (0.500) -0.356 (0.407) 0.220 (0.278) 81 0.033 (0.293) 0.135 (0.227) 0.063 (0.452) 0.054 (0.165) 0.203 (0.199) 91 0.068 (0.322) 0.139 (0.238) 0.110 (0.493) -0.526 (0.445) 0.310 (0.284) 92 0.074 (0.318) 0.148 (0.236) 0.120 (0.487) -0.470 (0.440) 0.318 (0.277) 104 0.089 (0.297) 0.177 (0.227) 0.100 (0.457) -0.162 (0.288) 0.203 (0.222) 110 0.070 (0.292) 0.126 (0.224) 0.111 (0.451) -0.276 (0.200) 0.307 (0.210)

111 0.073 (0.308) 0.117 (0.232) 0.112 (0.473) -0.436 (0.320) 0.296 (0.244) 112 0.108 (0.290) 0.159 (0.223) 0.115 (0.447) -0.129 (0.255) 0.236 (0.211) 113 0.069 (0.279) 0.162 (0.217) 0.107 (0.431) 0.082 (0.168) 0.273 (0.192) 114 0.079 (0.286) 0.164 (0.221) 0.101 (0.441) -0.063 (0.219) 0.227 (0.203) 116 0.091 (0.298) 0.183 (0.227) 0.104 (0.459) -0.176 (0.291) 0.204 (0.224) 119 0.074 (0.325) 0.150 (0.239) 0.119 (0.497) -0.541 (0.467) 0.315 (0.292) 120 0.060 (0.318) 0.119 (0.237) 0.098 (0.487) -0.534 (0.411) 0.292 (0.270) 121 0.098 (0.282) 0.143 (0.220) 0.132 (0.437) -0.074 (0.105) 0.334 (0.187) 123 0.099 (0.282) 0.145 (0.219) 0.131 (0.436) -0.002 (0.147) 0.337 (0.190) 124 -0.004 (0.300) 0.129 (0.229) 0.051 (0.461) -0.037 (0.248) 0.181 (0.224) 1990 Pond 1990 Year 1990 Spring 1990 Summer 1990 Fall 1990 Winter 2 5.463 (0.136) 4.898 (0.133) 19.098 (0.250) 7.577 (0.243) -9.522 (0.124) 5 5.772 (0.136) 5.184 (0.135) 19.340 (0.253) 7.877 (0.245) -9.081 (0.119) 8 5.598 (0.250) 5.031 (0.200) 18.737 (0.416) 7.672 (0.418) -9.360 (0.303) 10 5.490 (0.141) 4.932 (0.136) 19.063 (0.257) 7.577 (0.250) -9.502 (0.131) 13 5.205 (0.133) 4.622 (0.137) 18.266 (0.253) 7.351 (0.243) -9.928 (0.101) 20 5.491 (0.158) 4.937 (0.146) 18.951 (0.283) 7.559 (0.277) -9.538 (0.158) 21 5.501 (0.162) 4.949 (0.148) 18.946 (0.288) 7.567 (0.283) -9.515 (0.164) 24 5.715 (0.121) 5.136 (0.126) 19.413 (0.231) 7.839 (0.221) -9.055 (0.095) 29 5.807 (0.173) 5.194 (0.154) 19.406 (0.303) 7.949 (0.299) -8.934 (0.182) 30 5.764 (0.165) 5.157 (0.149) 19.425 (0.291) 7.911 (0.286) -8.986 (0.170) 32 5.739 (0.165) 5.132 (0.149) 19.428 (0.291) 7.892 (0.287) -9.016 (0.171) 34 5.715 (0.155) 5.110 (0.143) 19.420 (0.277) 7.869 (0.272) -9.054 (0.154) 35 5.738 (0.203) 5.115 (0.170) 19.459 (0.345) 7.929 (0.344) -8.973 (0.233) 36 5.280 (0.132) 4.689 (0.134) 18.628 (0.248) 7.447 (0.239) -9.837 (0.110) 37 5.308 (0.132) 4.713 (0.133) 18.737 (0.247) 7.480 (0.239) -9.815 (0.111) 41 5.157 (0.160) 4.584 (0.151) 18.284 (0.290) 7.306 (0.284) -10.009 (0.148) 43 5.105 (0.190) 4.544 (0.167) 18.324 (0.332) 7.259 (0.328) -10.092 (0.200) 48 5.560 (0.128) 4.968 (0.129) 19.293 (0.239) 7.718 (0.231) -9.368 (0.110)

53 5.532 (0.143) 4.932 (0.137) 19.235 (0.261) 7.709 (0.254) -9.406 (0.136) 60 5.650 (0.156) 5.069 (0.144) 19.401 (0.278) 7.814 (0.273) -9.132 (0.156) 61 5.702 (0.155) 5.097 (0.143) 19.414 (0.277) 7.858 (0.272) -9.075 (0.155) 62 5.714 (0.140) 5.119 (0.136) 19.424 (0.256) 7.857 (0.249) -9.052 (0.128) 63 5.704 (0.183) 5.088 (0.158) 19.433 (0.317) 7.888 (0.314) -9.047 (0.202) 67 5.683 (0.187) 5.066 (0.161) 19.421 (0.322) 7.874 (0.320) -9.076 (0.208) 68 5.366 (0.140) 4.805 (0.136) 18.913 (0.257) 7.459 (0.250) -9.739 (0.129) 70 5.611 (0.119) 5.055 (0.125) 19.320 (0.228) 7.730 (0.219) -9.194 (0.094) 71 5.656 (0.115) 5.092 (0.123) 19.396 (0.222) 7.786 (0.212) -9.092 (0.084) 72 5.744 (0.155) 5.167 (0.145) 19.185 (0.278) 7.823 (0.272) -9.180 (0.151) 74 5.614 (0.207) 5.037 (0.173) 19.489 (0.352) 7.837 (0.351) -9.142 (0.238) 76 5.591 (0.203) 5.017 (0.172) 19.524 (0.348) 7.828 (0.346) -9.179 (0.229) 80 5.546 (0.176) 4.977 (0.158) 19.556 (0.311) 7.800 (0.306) -9.274 (0.181) 81 5.458 (0.135) 4.895 (0.137) 19.474 (0.254) 7.717 (0.245) -9.478 (0.113) 91 5.751 (0.184) 5.131 (0.159) 19.438 (0.319) 7.918 (0.316) -8.982 (0.203) 92 5.723 (0.181) 5.104 (0.157) 19.427 (0.314) 7.895 (0.311) -9.028 (0.198) 104 5.236 (0.148) 4.652 (0.141) 18.739 (0.270) 7.375 (0.263) -9.984 (0.140) 110 5.764 (0.135) 5.176 (0.134) 19.363 (0.251) 7.873 (0.243) -9.071 (0.118) 111 5.787 (0.149) 5.197 (0.141) 19.281 (0.270) 7.886 (0.263) -9.080 (0.140) 112 5.328 (0.140) 4.769 (0.137) 18.840 (0.258) 7.413 (0.251) -9.813 (0.128) 113 5.429 (0.129) 4.840 (0.130) 19.132 (0.241) 7.585 (0.233) -9.631 (0.111) 114 5.308 (0.138) 4.724 (0.135) 18.892 (0.255) 7.448 (0.247) -9.860 (0.126) 116 5.221 (0.148) 4.634 (0.142) 18.690 (0.270) 7.366 (0.263) -10.007 (0.140) 119 5.716 (0.192) 5.097 (0.163) 19.446 (0.329) 7.902 (0.327) -9.023 (0.216) 120 5.797 (0.170) 5.186 (0.152) 19.410 (0.299) 7.939 (0.295) -8.948 (0.178) 121 5.660 (0.125) 5.095 (0.129) 19.245 (0.237) 7.748 (0.228) -9.255 (0.100) 123 5.642 (0.126) 5.078 (0.129) 19.240 (0.238) 7.733 (0.230) -9.268 (0.104) 124 5.497 (0.154) 4.920 (0.146) 19.533 (0.280) 7.751 (0.273) -9.280 (0.144)

Table 3.S3. Shannon diversity (SD), species richness (SR), total number of individuals (TI), and distance to the nearest weather station (NWS), for all sampled ponds in 2002 and 2012. In column headings A = adults and L = larvae, 02 = year 2002 and 12 = year 2012.

Pond SD SD SD SD SR SR SR SR TI TI TI TI NWS A 02 L 02 A 12 L 12 A L A L A L A A 02 02 12 12 02 02 12 12 2 1.77 2.22 1.59 0.95 9 11 8 6 125 22 59 49 8579 5 1.94 1.14 1.96 1.30 11 4 11 5 80 16 108 13 4970 8 1.81 1.41 1.80 1.89 10 7 10 8 120 19 78 17 31229 10 1.68 1.63 1.27 0.00 8 6 7 1 166 13 85 2 8954 13 2.19 0.00 1.49 1.91 15 1 14 8 301 1 424 25 1922 20 1.54 2.26 1.23 0.64 11 16 9 2 232 52 97 3 12346 21 2.02 1.10 1.23 0.00 10 3 10 0 38 3 133 0 11887 24 1.64 0.00 2.05 1.43 7 1 9 5 45 1 48 9 4076 29 1.45 2.24 0.87 1.78 11 13 11 11 295 35 326 87 13301 30 1.88 1.23 1.89 1.92 9 5 11 9 47 12 54 54 11044

32 1.62 0.00 2.15 1.69 9 1 15 10 215 1 259 49 11397 34 1.76 2.06 2.22 1.90 14 9 16 9 473 19 141 52 9210 35 2.31 1.32 1.49 1.74 17 7 10 10 357 29 109 83 20854 36 1.62 0.56 2.26 1.75 7 2 15 11 43 4 107 53 5159 37 1.43 1.82 0.00 1.94 9 8 1 8 309 33 2 12 6520 41 0.00 0.64 2.19 1.56 0 2 12 5 0 3 94 8 6470 43 1.39 1.26 0.00 0.00 4 5 0 0 4 17 0 0 12124 48 0.00 0.64 1.33 0.00 0 2 5 1 0 3 14 1 6749 53 1.44 0.68 2.03 0.00 8 3 11 1 185 9 145 1 9298 60 0.00 1.56 2.14 1.39 0 11 13 5 0 101 133 52 8794 61 1.67 0.69 1.85 1.40 13 2 9 5 85 2 79 16 8777 62 2.04 0.49 2.06 1.99 10 3 12 8 124 22 136 20 6118 63 2.24 2.06 2.20 0.80 15 15 12 3 284 113 77 7 15291 67 2.33 1.63 1.92 1.68 16 7 11 6 202 41 67 9 16918

68 1.50 1.86 1.52 1.31 8 10 10 4 181 40 160 9 9928 70 2.06 1.71 1.88 1.35 14 12 9 14 169 101 37 197 2361 71 0.67 1.50 1.41 1.43 4 6 7 6 79 17 42 17 1117 72 2.02 1.28 0.58 0.86 9 7 5 3 60 37 121 13 9516 74 0.94 1.78 1.83 1.60 5 10 8 8 61 67 90 33 22405 76 1.76 2.27 0.89 1.01 7 14 6 3 41 66 149 6 19399 80 1.59 2.11 2.06 1.74 10 16 10 7 430 94 70 18 13163 81 2.03 0.00 1.97 0.00 13 0 10 1 163 0 31 2 5815 91 2.05 1.68 1.28 0.23 12 6 6 2 130 9 24 16 15505 92 1.61 0.00 1.57 0.00 8 0 7 0 43 0 33 0 14943 104 2.22 0.00 2.01 1.54 15 1 13 7 142 2 88 27 8693 110 2.16 1.24 1.90 0.00 12 4 10 1 207 6 33 1 4577 111 1.86 0.00 1.81 1.92 18 1 10 10 905 1 122 34 8584 112 1.30 0.00 1.69 1.57 6 1 12 9 18 1 107 60 8094

113 2.09 0.66 1.78 1.64 15 2 11 9 241 11 114 37 4606 114 2.30 1.76 1.85 1.39 15 10 10 5 158 77 39 8 6343 116 0.74 1.38 1.51 1.59 3 6 10 6 8 30 70 15 8725 119 1.32 0.00 2.19 1.66 4 0 11 7 8 0 77 16 17544 120 0.61 1.04 1.33 0.00 7 3 7 1 235 4 23 1 12602 121 1.93 1.67 1.44 1.23 13 10 6 4 135 42 19 14 2360 123 1.61 1.72 2.16 1.68 7 6 11 9 35 11 64 44 3708 124 0.96 1.36 1.17 0.69 3 7 7 2 9 41 33 4 6045

Table 3.S4. Species abbreviations, scientific name, authority, and common name for species found in the multivariate analyses.

Abbreviation Name Authority

Common Name

A.can Aeshna canadensis Walker, 1908 Canada Darner

A.cor Arigomphus cornutus Tough, 1900 Horned Clubtail

Hagen in Selys,

A.fur Arigomphus furcifer 1878 Lilypad Clubtail

Common Green

A.jun Anax junius Drury, 1773 Darner

A.umb Aeshna umbrosa Walker, 1908 Shadow Darner

C.eli Celithemis elisa Hagen, 1861 Calico Pennant

C.shu Codulua shurtleffi Scudder, 1866 American Emerald

D.lib Dorocordulia libera Selys, 1871 Raquet-tailed Emerald

McLachlan,

E.can Epitheca canis 1886 Beaverpond Baskettail

E.cyn Epitheca cynosura Say, 1839 Common Baskettail

E.ebr Enallagma ebrium Hagen, 1861 Marsh Bluet

E.pri Epitheca princeps Hagen, 1861 Prince Baskettail

Erythemis

E.sim simplicicollis Say, 1839 Eastern Pondhawk

E.sp Enallagma spp NA Bluets

I.pos Ischnura posita Hagen, 1861 Fragile Forktail

I.ver Ischnura verticalis Say, 1839 Eastern Forktail

L.con Lestes congener Hagen, 1861 Spotted Spreadwing

L.dis Lestes disjunctus Selys, 1862 Northern Spreadwing

Amber-winged

L.eur Lestes eurinus Say, 1839 Spreadwing

L.for Lestes forcipatus Rambur, 1842 Sweetflag Spreadwing

L.fri Leucorrhinia frigida Hagen, 1890 Frosted Whiteface

L.ine Lestes inaqualis Walsh, 1862 Elegant Spreadwing

L.int Leucorrhinia intacta Hagen, 1861 Dot-tailed Whiteface

Chalk-fronted

L.jul Ladona julia Uhler, 1857 Corporal

Burmeister,

L.luc Libellula luctuosa 1839 Widow Skimmer

Leucorrhinia

L.pro proxima Calvert, 1890 Belted Whiteface

Twelve-spotted

L.pul Libellula pulchella Drury, 1773 Skimmer

Libellula

L.qua quadrimaculata Linnaeus, 1758 Four-spotted Skimmer

Lyre-tipped

L.ung Lestes unguiculatus Hagen, 1861 Spreadwing

L.vig Lestes vigilax Selys, 1862 Swamp Spreadwing

N.ire Nehalennia irene Hagen, 1861 Sedge Sprite

Pachydiplax Burmeister,

P.lon longipennis 1839 Blue Dasher

P.lyd Plathemis lydia Drury, 1773 Common Whitetail

Somatochlora Burmeister,

S.alb albicincta 1839 Ringed Emerald

Sympetrum Saffron-winged

S.cos costiferum Hagen, 1861 Meadowhawk

White-faced

S.obt Sympetrum obtrusum Hagen, 1867 Meadowhawk

Sympetrum

S.rub rubicundulum Say, 1839 Ruby Meadowhawk

Sympetrum Band-winged

S.sem semicinctum Say, 1839 Meadowhawk

Yellow-legged

S.vic Sympetrum vicinum Hagen, 1861 Meadowhawk

3.8.2 Figures

Figure 3.S1: Principal components analyses of pond substrate in 2002 (A) and 2012 (B).

Extracted scores from each first axis, which explained 45% of variation in 2002 and 39% of variation in 2012, were included as potential predictors in the linear and multivariate analyses.

100

Figure 3.S2: Moran’s Eigenvector Maps (MEM) for 2002 showing the best three MEMs that explain the most variation in the community data for adults (row 1) and for larvae (row 2).

Eigenvectors denoted with an * are those included in the best MEM model as determined by

101

Figure S3.3: Moran’s Eigenvector Maps (MEM) for 2012 showing the best three MEMs which explain the most variation in the community data for adults (row 1) and for larvae (row

102

Chapter 4 - Habitat complementarity affects predicted

range expansion of a temperate dragonfly

4.1 Introduction

In human-modified landscapes, one of the most important species’ responses to climate change is the ability to follow their climatic niche (Loarie et al., 2009; Schloss et al., 2012;

Saura et al., 2014). Such ability depends primarily on the species’ biology (i.e. dispersal ability) and the permeability (i.e. connectivity) of the landscape that it has to traverse.

Anthropogenic landuse affects the diversity of habitats (compositional heterogeneity) and pattern of those habitats within a landscape (spatial heterogeneity or configuration) (Fahrig et al., 2011), while climate change simultaneously affects the geographic location of suitable climatic conditions. The combined effects of landuse and climate change, therefore, play an important role in determining species’ persistence in an anthropogenic landscape (Ordonez et al., 2014).

Anthropogenically disturbed landscapes typically contain less total habitat area of any particular type than undisturbed landscapes and such habitat exists in smaller patches with greater distances between those patches, a phenomenon known as habitat fragmentation

(Fahrig, 2003). Fragmentation affects species’ ability to travel through a landscape differently depending on their dispersal ability and habitat preferences, affecting persistence and/or population dynamics (Vos et al., 2001; Cascante et al., 2002; Levey et al., 2005). The less dispersive a species, or the more specific or rare their habitat requirements, the more their movement and persistence will be impacted by fragmentation.

103

For this reason, the species that require adjacent and low substitutable habitats of different types, a concept known as habitat complementarity (Dunning et al., 1992), are especially susceptible to landscape fragmentation. As landuse/landcover changes within a landscape, it is more likely that the probability of dispersal between the required habitats for these species will be low or nonexistent (Gibbs, 1998; Chan-McLeod, 2003; Rothermel, 2004;

Rittenhouse & Semlitsch, 2006). For example, a loss of habitat area of a single required habitat type is detrimental, but changes in the spatial configuration of habitats, resulting in a decrease in the connectivity between required habitats, is equally problematic. Furthermore, many species require multiple habitat types during their life cycle, be it at different stages of ontogenetic development or seasonally (Werner & Gilliam, 1984), and are therefore impacted by changes in either habitat type. Indeed most amphibians, many insects, and even mammals and birds exhibit these complex life cycles which are challenged by anthropogenic activities that directly and indirectly impact all ecosystems. Freshwater ecosystems are particularly affected by landuse change. For example, climate change can affect the availability of runoff from snowmelt in lotic waters of mountainous regions (Barnett et al., 2005) and the persistence of lentic habitats (Chapter 3). These changes in water availability act simultaneously with climate and direct landcover change to impact species persistence and movement through a landscape.

One group of semi-aquatic species currently shown to be responding to climate and landuse change are Odonata (damselflies and dragonflies, hereafter odonates) (Samways &

Steytler, 1996; Hickling et al., 2005; Hassall & Thompson, 2008; Aliberti et al., 2010;

Harabiš & Dolný, 2012). In general odonates spend the larval portion of their lives in lotic and lentic waters, and the adult portion of their lives in terrestrial ecosystems before returning

104 to aquatic habitats to reproduce (Corbet, 1999). They are meso-predators in both systems, and, therefore, act as a bridge between aquatic and terrestrial realms (Knight et al., 2005).

Odonates are generally thought to be good fliers as adults. They are a tropically centered order which have expanded and radiated into higher latitudes, so their potential to move through a landscape is high. Contemporary responses of odonates moving in response to climate change have been documented (for reviews see Hassall & Thompson, 2008; Ott,

2010). Hickling et al. (2005) showed that 37 species of non-migratory odonates have expanded their ranges in Britain. Flenner & Sahlen (2008) observed changes in species community composition and species abundance in Sweden in response to climate change, and

Goffart (2010) found that, in Belgium, southern species have expanded their distributions into the north. While odonates have been shown to respond to climate change, our ability to understand and predict how odonates move through a human-modified landscape in response to climate change remains limited and they are poorly represented in conservation planning

(Abell et al., 2007; Amis et al., 2009; Remsburg & Turner, 2009; Beger et al., 2010; Hazlitt et al., 2010).

A common technique to assess species’ responses to climate change are species distribution models (hereafter SDMs), which correlate species known presences (and ideally absences) with spatial predictors such as environmental, landcover, and/or climate variables.

This correlative model can then be used to predict a species’ distribution into a different landscape (in the case of range expansion or invasive species), or into the future (when dealing with climate and landcover change). Recent advances in SDMs have allowed the use of “presence only” data, an increasingly common resource available through museum collections or citizen science projects (Phillips & Dudik, 2008; Ashcroft et al., 2012). SDMs

105 do have limitations, however, including issues associated with model fitting, small sample sizes, sampling biases, etc. (for a review see Elith & Leathwick, 2009). When applying SDMs for species that require multiple habitats it is especially important to explicitly include habitat complementarity because specific and complex habitat requirements can potentially reduce connectivity of a landscape. Failing to include this restriction in SDMs could overestimate a species’ distribution, producing less accurate predictions for conservation scientists and wildlife managers. This is a critical oversight given the number of species thought to utilize complementary habitats. Furthermore, the distribution of a species is limited by its ability to disperse, yet this component is often ignored in SDMs (Pearson, 2006).

In this study we utilized citizen-science data to develop SDMs for a single species,

Pachydiplax longipennis (Odonata:Libellulidae), which is known to be expanding its range in

Ontario (Canada). This is a region under anthropogenic development pressure (City of

Ottawa, 2013) and which is undergoing climate change (Chapter 3), both factors which are challenging P. longipennis’ ability to persist and move through the landscape. To assess P. longipennis’ future expansion in this region, we developed two groups of models: one that views aquatic and terrestrial habitats as independent and disjunct landcover types, and one that explicitly included adjacent aquatic and terrestrial habitats as a unique landcover representing habitat complementarity. We first determined if P. longipennis uses adjacent habitats within our focal landscape. Second, we asked if including habitat complementarity in

SDMs and migration simulations changes the predicted distributions and estimated movements through a landscape generated from these models, a very important conservation concern in the context of P. longipennis’ ability to move through the landscape in response to climate change and climate-induced landcover change. We expected that P. longipennis will

106 be found to utilize complementary habitats (here defined as water and forests that are spatially adjacent), and that because of this models that incorporate habitat complementarity would find a more restricted predicted distribution, and lowered estimated movement through the landscape when compared to models that only utilized disjunct habitats.

4.2 Methods

Study region

We focused on a single species of Odonata, Pachydiplax longipennis, whose range is known to be expanding in southern Ontario, Canada (Ontario Odonate Atlas; Chapter 3). P. longipennis is known historically (1893 earliest record) from southern Ontario, but since 2000 has been expanding northward and eastward, most notably into the St-Laurent Lowlands ecoregion (Figure 4.1) and likely as a result of climate change. In the Ottawa/Gatineau area of this ecoregion, climate (mean yearly temperature) has increased by 0.27ºC between the periods 1970-2000 and 1980-2010 (Chapter 3). P. longipennis is known from 961 occurrences in Ontario, of which only 56 occur in the St-Laurent ecoregion (Ontario Odonata Atlas, 2013).

We therefore chose the St-Laurent ecoregion as our focal region because P. longipennis was not likely to have colonized all sites and/or be at equilibrium, providing us with the opportunity to investigate its continued range expansion. The St-Laurent ecoregion also provided the opportunity to look at habitat complementarity because it is, in certain areas, abundant in diverse aquatic and terrestrial habitats, which may be ideal for P. longipennis.

Furthermore, we have observed climate induced landcover change (loss of small ponds) in this region, another complicating factor that determines P. longipennis’ ability to move through the landscape.

107

Quantifying habitat complementarity

Odonates require both aquatic and terrestrial habitats during their life cycle, and P. longipennis specifically inhabits lentic waters during larval development and breeding, as well as terrestrial uplands during pre-reproductive maturation, foraging, and nighttime roosting (Corbet, 1999). In this study, we defined open waters and marshes as suitable aquatic habitats, and forests (of all types) as suitable terrestrial habitats.

We quantified habitat complementarity utilizing the Southern Ontario Land Resource

Information System (SOLRIS) landcover data at 15 m resolution, which we resampled to 100 m resolution (The Ontario Ministry of Natural Resources, 2008). We calculated a new landcover class, “complementary habitat” (defined as water and forests that are adjacent) within our study region utilizing a focal neighborhood approach (Fortin & Dale, 2005). For each pixel in our study region, we looked at the landcover of the adjacent eight neighbor pixels for complementary landcover types. For example, if the focal pixel had as landcover either marsh or open water, with neighborhood pixels of forest type, then we reclassified the focal pixel as complementary habitat. We did the same for the opposite case, where the focal pixel is of a forest landcover, and any neighbor of wetland or open water landcover, producing a spatial distribution of complementary habitats.

To determine if P. longipennis was utilizing these complementary habitats we extracted the landcover associated with each of the 961 occurrence points of P. longipennis across all of Ontario. We then calculated the proportion of these occurrences that were in our habitat complementarity landcover type. To determine if P. longipennis is a unique case among odonates in its habitat use, we took occurrence data for an additional four species

(Perithemis tenera, Tramea carolina, Enallagma basidens, Tramea lacerata) which are

108 known or suspected to be expanding their northern range boundary in this region (Jones, personal communication), determined the number of unique sites these occurrences were from, and then calculated the proportion of these sites which were found in our habitat complementarity landcover type.

In this region, the summer of 2012 was exceptionally hot and dry. While doing fieldwork in the Ottawa area we observed that several small ponds and wetlands dried up completely during the course of the summer (Chapter 3). This represented a reduction in breeding/larval habitat for odonates, potentially affecting habitat amount, connectivity, and complementarity, therefore affecting odonates’ ability to move through the landscape and follow their climatic niche. We would expect that these drying events will become more common with increasing temperatures resulting from climate change (Ott, 2010), producing a scenario where climate-induced landcover change is an important factor for species persistence and movement.

As a result of this observed reduction of pond numbers in our study region, we attempted to incorporate into our SDMs landcover change scenarios that could result from climate warming in this region. We did this by randomly removing small ponds/wetlands of less than 1 hectare; those that we observed to be most susceptible to desiccation in the field, at rates of 5% (what we observed in the field), 25% (an estimated high proportion) and 50% (an extreme proportion). We then used these landcover data as input to our SDMs, giving us a range of P. longipennis’ distributions under different climate-induced landcover change scenarios.

109

Species distribution modeling

We used maximum entropy modeling (hereafter Maxent) to model P. longipennis’ distribution, implemented with the R package ‘Maxent’ (Phillips & Dudik, 2008; Elith et al.,

2011). Maxent is well suited to utilize presence only data obtained from citizen science programs, which is the case for our study. Utilizing species occurrence points and spatial predictors, Maxent attempts to maximize the percentage of known occurrences within suitable regions of the model, while simultaneously attempting to minimize the amount of habitat deemed suitable. The quality of the model is quantified by the receiver-operator curve, or area under the curve (hereafter AUC). AUC values of 0.5 are equivalent to random prediction, and

AUC values close to one indicate good models (Elith et al., 2011). As input to Maxent, we utilized the SOLRIS landcover data as well as climate variables for climate (years 1950 to

2000) (bio10: mean temperature of the warmest quarter and bio12: annual precipitation from bioclim downloaded from worldclim.org). We calibrated our model using the entire range of

P. longipennis in Ontario, and used that model to predict its distribution into our focal study region. We ran two separate groups of SDM analyses for our focal region. The first group utilized the original landcover data and viewed aquatic and terrestrial habitats as single disjunct landcover types (hereafter: disjunct group). The second group utilized our reclassified habitat complementarity landcover class, which explicitly included adjacent aquatic and terrestrial habitats (hereafter: adjacent group). Within each group, we ran SDMs for the three levels of pond loss, producing a total of six scenarios (three under the disjunct group, and three under the adjacent group).

We performed model cross validation by splitting our species occurrence data into training (80%) and testing (20%) sets. The training data was utilized as input for the SDM

110 calibration, and the testing data to assess the models by AUC. We ran 10 replicates for each group of scenarios.

Migration

Species distribution models assume unlimited dispersal while in reality changes in the landscape due to fragmentation can dramatically affect a species’ dispersal ability (Pearson,

2006). To account for this, we simulated migration in our focal landscape through the

‘MigClim’ R package (Engler et al., 2012). MigClim acts as a supplemental analysis performed after an SDM and constrains the predicted distribution based on the species’ ability to disperse and reach suitable habitat, utilizing discrete time steps to simulate migration. A further advantage of MigClim is that it allows the user to incorporate changes in the landscape, such as pond loss, during the time of simulated dispersal.

We parameterized dispersal within the MigClim simulation in two ways. First, we simulated dispersal with an exponential decay dispersal kernel with a maximum dispersal of

500m, a fairly conservative yet realistic estimate of P. longipennis’ movement (McCauley,

2010). Second, we incorporated long-distance dispersal events at three probabilities: 0.01 that is documented in the literature for odonates (Angelibert & Giani, 2003; Purse et al., 2003;

Rouquette & Thompson, 2007; Ward & Mill, 2007), 0.05 which is more frequent than documented, and 0.10 which is likely unrealistic but we included for demonstration purposes.

Finally, we varied the maximal distance of these long-distance dispersal events between 1 km,

2.5 km, 5 km, 7.5 km, and 10 km, which represent conservative and maximal documented dispersal distances for odonates (Michiels & Dhondt, 1991; Conrad et al., 1999; Angelibert &

Giani, 2003; Purse et al., 2003; McCauley, 2007; McCauley, 2010). These dispersal

111 parameters, daily movements and long-distance dispersal events, likely capture how odonates move through a landscape, as a combination of frequent but short movements and rare but long-distance movements. During our simulation, occupied cells produced propagules the year after occupancy and became fully productive after 3 years. We simulated dispersal for a total of 30 years, updating the landcover data every 10 years. For both the disjunct and adjacent groups, we used each scenario from the SDMs as changes in landcover during simulation, increasing the percentage of ponds removed with time: 5% ponds removed years

1-10, 25% ponds removed years 11-20, and 50% ponds removed years 21-30. We created 25 replicates of each migration scenario, long-distance dispersal probability and long-distance dispersal distance, and report results averaged over all 25 replicates. A summary of workflow can be seen in Figure 4.2.

4.3 Results

Under the disjunct group of scenarios, within our focal region, there was 17.48% forest cover and 6.75% water before random pond removal. Water cover values dropped to 6.71%, 6.65%, and 6.54% under the 5%, 25%, and 50% pond removal scenarios (Table 4.1). By definition, there was no complementary habitat cover type under the disjunct scenarios. Under the adjacent group of scenarios, within our focal region, there was 15.33% forest cover, 5.31% water cover, and 5.53% cover of our calculated complementary class before random pond removal. After random pond removal of 5%, 25% and 50%, forest cover increased to 16.58%,

16.65%, and 16.76%, while water cover dropped to 5.27%, 5.20%, and 5.10%, respectively.

Cover of complementary habitat also dropped to 4.28%, 4.21%, and 4.11% (Table 4.1). The increases in forest cover come from pixels that were classified as complementary habitat, but

112 after random pond removal the adjacency relationship was broken, so these pixels reverted to forest cover. Through this process, complementary habitat was lost as more and more ponds were removed from the landscape.

Habitat complementarity

Throughout the entire Ontario range of P. longipennis, there was 2.9% habitat complementarity landcover type, and 5.5% in our focal region (Figure 4.3). Across all of

Ontario P. longipennis was found to occupy complementary habitats 35% of the time. When an additional four odonate species were included (987 unique sites), we found that 23.4% of these sites were located in complementary habitats. These findings indicate that P. longipennis, and other species of odonate, were utilizing complementary habitats within our landscape

Maxent models

The fits of the Maxent models were very good with average AUC values of 0.845 +/- 0.005 for disjunct scenarios and 0.864 +/- 0.003 for adjacent scenarios (Table 4.2). Mean temperature of the warmest quarter was the best predictor in the disjunct scenarios (51.49% contribution), followed by landcover (40.19% contribution), and finally annual precipitation

(7.62% contribution). In the adjacent scenarios, however, landcover was the best predictor

(52.17% contribution), followed by mean temperature of the warmest month (43.57% contribution) and annual precipitation (4.24% contribution) (Table 4.3).

Predictions in the focal region indicated suitable habitat for P. longipennis in the central portions of our study region under disjunct scenarios, and similar range but smaller

113 and patchier distributions under the adjacent scenarios (Figure 4.4). Within each group, the pond removal scenarios had minimal impact on the predicted distributions, resulting in small and localized changes associated with the areas where ponds were randomly removed.

However, the differences between the two groups were readily apparent at the landscape scale for all levels of pond removal. The inclusion of habitat complementarity reduces the predicted amount of suitable habitat in the focal landscape, and the amount and adjacency of high- quality habitat (Figure 4.4).

MigClim predictions

The migration results found that, for both disjunct and adjacent groups, P. longipennis is likely to be dispersal limited over the next 30 years at all dispersal probabilities and distances

(Figures 4.5, 4.6, and 4.7). At a dispersal probability of 0.01 in the disjunct scenario between

7.22% and 21.21% of suitable habitat becomes occupied across our range of dispersal distances, and in the adjacent scenario between 5.95% and 9.74% (Table 4.S1). At a dispersal probability of 0.05 in the disjunct scenario between 6.60% and 34.78% of suitable habitat becomes occupied across our range of dispersal distances, and in the adjacent scenario between 6.55% and 19.63% (Table 4.S2). At a dispersal probability of 0.10 in the disjunct scenario between 7.03% and 48.16% of suitable habitat becomes occupied across our range of dispersal distances, and in the adjacent scenario between 7.25% and 23.83% (Table 4.S3).

Under both scenarios a large proportion of the landscape that contains suitable habitat does not become occupied during the simulation, and this proportion unoccupied is higher under the adjacent scenarios. This dispersal limitation is likely a direct result of an increasingly fragmented landscape. Indeed, we found when comparing our landscapes between disjunct

114 and adjacent scenarios that the suitable habitat (from the SDM predictions) is patchier and less continuous in the adjacent scenarios (Figure 4.4).

4.4 Discussion

P. longipennis is an insect with a complex life cycle requiring multiple habitats. In this study, we found evidence that P. longipennis utilized complementary habitats within our landscape.

We also found that, when small ponds are removed from the landscape, there was localized variability in P. longipennis’ predicted distribution. When considering migration, the explicit inclusion of complementary habitats limits P. longipennis’ predicted dispersal at all dispersal probabilities and dispersal distances. Complementary habitats are likely of higher quality for

P. longipennis, but they are less common within the landscape, and more susceptible to pond removal due to their complementary nature. These combined factors potentially limit P. longipennis’ ability to track its climatic niche, an important insight for species distribution modeling.

Habitat complementarity

For species that have complex life cycles and need multiple habitats the spatial configuration of the landscape is very important, not just the total area of habitat (i.e. compositional heterogeneity vs. spatial configuration) (Hocking & Semlitsch, 2007; Fahrig et al., 2011). In our study P. longipennis utilized complementary habitats in our study region, likely because survival and reproduction are higher in these areas because resources for larvae and for adults are spatially adjacent. For example, teneral (recently emerged) adults are very susceptible to predation (Corbet, 1999; Jakob & Suhling, 1999; Worthen, 2010), so the shorter the distance

115 they have to travel to find terrestrial habitat increases their survival (Dunning et al., 1992).

This is similar to the “matrix” concept of a metapopulation, where time spent in the less than ideal matrix habitats leads to reduced survival and reproduction (Hanski, 1998).

Complementarity requirements are even more important for species as the frequency of trips between habitats increases. For example, birds moving between nesting and foraging sites many times per day (Petit, 1989; Mueller et al., 2009), or, for odonates, reproductively active individuals moving from aquatic breeding grounds to terrestrial roosting sites at night.

Species distribution models

The SDMs’ results indicate that the probability of occurrence was high in some areas of our focal region for P. longipennis at all levels of pond loss, but we found the largest differences at the landscape scale between the disjunct and adjacent scenarios. We expect that climate will induce pond loss which will have an impact on odonate habitats by breaking the adjacency between aquatic and terrestrial complementary habitats, as seen in the reduction of complementary habitat cover in our SDMs as pond removal increases. The inclusion of complementarity, therefore, adds an extra layer of refinement to the Maxent models giving a better prediction of P. longipennis’ distribution. While climate variables associated with increases in temperature were good predictors in our scenarios, we found that landcover was potentially more limiting of species’ distributions than climate for P. longipennis, due to its obligate complementary habitat requirements.

While we incorporated the pond-removal scenarios into our SDMs, we saw little impact at the landscape scale in terms of the predicted distribution of P. longipennis. This is likely because we only modeled the loss of very small ponds, but not the potential shrinking

116 of larger waterbodies, which likely support more individuals. Over time, however, we saw that the loss of these small ponds does have a cumulative effect in reducing complementary habitats, which may impact movement through the landscape.

Migration

A species’ ability to move through a landscape is critical in its ability to follow its climatic niche in response to climate change (Loarie et al., 2009; Schloss et al., 2012; Saura et al.,

2014). For species with complex life cycles, movement limits are exacerbated by additional complicated anthropogenic landuse change (Pope et al., 2000). Anthropogenic landuse change can break the critical links between complementary habitats required and we saw the results of this in our simulation of migration for P. longipennis. At all levels of pond removal, long- distance dispersal probability, and long-distance dispersal distance, the adjacent scenarios found that P. longipennis were more dispersal limited than disjunct scenarios. We argue that this response is because the suitable habitats we have defined as complementary are critical in supporting P. longipennis populations, likely because they have better access to their required resources and are at less risk when moving between aquatic and terrestrial habitats (Debinski et al., 2001; Ries & Sisk, 2008). Complementary habitats represent the highest quality habitat, yet they are not common within our landscape (5.5%). As landcover type was the most important predictor in the adjacent SDM, landcover may be a limiting factor on P. longipennis’ potential ability to move within our landscape.

In addition to supporting healthy populations and facilitating movement through our landscape we also suggest that complementary habitats may act as stepping stones allowing P. longipennis to follow its climatic niche through areas of the landscape where the overall

117 amount of habitat is low. An example of this is the middle of our study region. We did not have species occurrence points from this region, yet P. longipennis must have traversed this landscape to reach the Ottawa region of our study area because the region farther east is even less suitable, as indicated from our SDMs. The lack of occurrence points in this region supports the supposition that this species made short term use of stepping stone habitats between higher quality areas. Stepping stones are most effective when of sufficient size and quality (Kramer-Schadt et al., 2011; Saura et al., 2014), such as complementary habitats in the case of P. longipennis. These patches should play an important role in successive dispersal events, either supporting a single successful generation which then disperses, or acting as temporary refuges for individuals that are dispersing but choose not to reproduce. For species such as P. longipennis which can produce a large number of offspring (r strategists), stepping stones may facilitate rare dispersal events suitable for establishing populations (Saura et al.,

2014).

Our study was conservative with respect to complementarity and migration, defining complementary habitats only within eight neighbor pixels, a maximum distance of 141 m

(diagonal of 100 m pixel). Our occurrence data from the Ontario Odonate Atlas indicates that

P. longipennis has expanded its range approximately 65 kilometers between 2001 and 2011, a rate of 6.5 km/year, likely as a result of relatively rare long-distance dispersal events, potentially utilizing stepping stone habitats. Studies that quantify the dispersal distance of odonates are few; Conrad et al (1999) documented dispersal distances between 730 m and 1.2 km, Angelibert & Giani (2003) found that 29.5% of movements occurred over distances >

725 m, and other studies found maximal distances of between 1km and 1.75 km (1.75 km

Michiels & Dhondt, 1991; 1.06 km Purse et al., 2003; 1.2 km McCauley, 2007; 1.0 km

118

McCauley et al., 2010). In general, the larger area surveyed for dispersal, the larger the maximal dispersal found (Conrad et al., 1999). Even at the most extreme dispersal probabilities and distances (e.g. p = 0.10 and distance = 10 km) modeled in our study dispersal limitation was still evident, especially in our adjacent scenarios.

4.5 Conclusion

Our study highlights the complexity of predicting species movement and distributions through an anthropogenic landscape. Suitable habitat and climatic conditions must be present but, more importantly, individuals need to be able to move through the landscape to reach those suitable habitats. For P. longipennis, we propose that the most suitable habitats are complementary habitats, yet we find that they are rare within the landscape. In an increasingly fragmented landscape the links between complementary habitats are more likely to be lost

(Kadoya & Washitani, 2012) affecting population persistence and, more importantly, P. longipennis’ ability to move through the landscape and find high quality habitat in response to climate change. Our study shows that incorporating habitat complementarity gives a more restricted and possibly more realistic prediction of species distributions and movement through a landscape, an important insight given that dispersal ability is a primary driver in determining species ranges (McCauley et al., 2014), and these findings should be used as novel hypotheses requiring further investigation. Many species in varied taxonomic groups utilize complementary habitats and, therefore, our conclusions apply broadly to modeling the distribution and movement of these species in response to climate change and landuse change.

Furthermore, because individual species experience the anthropogenic impacts on a landscape at different scales (Fischer et al., 2005), our species-specific approach can be incorporated

119 into a larger framework towards a more general understanding of species responses to anthropogenic climate and landuse change (Betts et al., 2014).

120

4.6 Tables

Scenario %forest %water %comp

Disjunct 17.48 6.75 na

Disjunct 5% ponds removed 17.48 6.71 na

Disjunct 25% ponds removed 17.48 6.65 na

Disjunct 50% ponds removed 17.48 6.54 na

Adjacent 15.33 5.31 5.53

Adjacent 5% ponds removed 16.58 5.27 4.28

Adjacent 25% ponds removed 16.65 5.20 4.21

Adjacent 50% ponds removed 16.76 5.10 4.11

121

Table 4.2: Cross validation results of the SDMs utilizing the 20% of species occurrence data set aside for testing, showing AUC results for the disjunct and adjacent scenario groups.

Replicate Disjunct AUC Adjacent AUC

1 0.85 0.867

2 0.85 0.863

3 0.847 0.866

4 0.848 0.861

5 0.836 0.865

6 0.845 0.862

7 0.838 0.864

8 0.845 0.862

9 0.844 0.858

10 0.849 0.871

Mean (std. dev.) 0.845 (0.005) 0.864 (0.004)

122

Table 4.3: Variable contributions (averaged over all model replicates) for the disjunct and adjacent Maxent scenarios.

Disjunct Adjacent

Bio10: Mean temp of warmest 51.49 Landcover 52.17

quarter

Landcover 40.91 Bio10: Mean temp of warmest 43.57

quarter

Bio12: Annual precipitation 7.62 Bio12: Annual precipitation 4.24

123

4.7 Figures $

! ! ! !! !

!!! !

! ! ! ! ! # ! !! !! ! !!! ! ! !#! ! ! ! ! # ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! # # ! !! # ! !#!! #!! ! ! ! #! ! !! ! !!##!# # !#! !! ! ## ! #! # !!#! !! !# ! !!! ! !!!# ! !!!!! ! ! !!# ! #!! ! !! ! !!! ! ! !!! !! ! !!#! # ! !! ! !!# ! !! ! ! !! ! ! ! ! #!# # ! ! !! # P. longipennis ! ! # !!! # ! ! ! # # # # ! # # # ! !# # # !! #! # #!!# Year ! ! # ! ## # # #!### # ## #### #! #!# #! # #!#! ###!## pre 2000 (223) #!! # # ## #!# # # ##! ##! # !!!##! #! ! ! ### !#!# 2000 - 2012 (738) !#!! #!! ! # ##! #! Focal Study Region #!

Kilometers 0 25 50 100 150 200

124

During prediction, ponds were randomly removed from the focal study region at rates of 5%,

125 replicates were averaged to produce a final migration result. This entire process was carried out for the disjunct and adjacent scenario groups.

126

Figure 4.3: Habitat complementarity of southern Ontario (a) and focal region (b). Pixels shown in black are the complementary habitat landcover type.

127

Figure 4.4: Maxent predictions into the focal study region for the disjunct scenarios (a,b,c) and adjacent scenarios (d,e,f), with climate induced pond loss of 5% (a,d), 25% (b,e) and 50% (c,f). Darker areas represent the highest probability of occurrence for P. longipennis, and white areas the lowest.

128

129

130

Black pixels represent areas where P. longipennis was found at the end of the simulation. Gray pixels represent suitable habitat which was not colonized during the simulation.

131

4.8 Supplementary Material

4.8.1 Tables

Table 4.S1. Amount of suitable habitat (ha) which is occupied at the end of the migration simulation with probability of long- distance dispersal events 0.01, for distances of 1 km, 2.5 km, 5 km, 7.5 km, and 10 km. Occupied habitat is shown as area, percentage of suitable habitat that is occupied, and percentage of the entire focal region that is occupied, for both the disjunct and adjacent scenario groups.

Disjunct Adjacent Disjunct % Adjacent % Disjunct % (of Adjacent % (of (ha) (ha) (of suitable) (of suitable) focal region) focal region)

1 km 15646 4815 7.22 5.95 1.07 0.33

2.5 km 19861 5748 9.17 7.11 1.36 0.39

5 km 31811 7074 14.68 8.74 2.18 0.49

7.5 km 45075 6741 20.81 8.33 3.10 0.46

10 km 45943 7880 21.21 9.74 3.15 0.54

132

Table 4.S2. Amount of suitable habitat (ha) which is occupied at the end of the migration simulation with probability of long- distance dispersal events 0.05, for distances of 1 km, 2.5 km, 5 km, 7.5 km, and 10 km. Occupied habitat is shown as area, percentage of suitable habitat that is occupied, and percentage of the entire focal region that is occupied, for both the disjunct and adjacent scenario groups.

Disjunct Adjacent Disjunct % Adjacent % Disjunct % (of Adjacent % (of

(ha) (ha) (of suitable) (of suitable) focal region) focal region)

1 km 16261 4863 6.60 6.55 1.12 0.33

2.5 km 30126 6962 12.23 9.38 2.07 0.48

5 km 58987 10298 23.95 13.88 4.05 0.71

7.5 km 85120 12984 34.55 17.50 5.84 0.89

10 km 85665 14564 34.78 19.63 5.88 1.00

133

Table 4.S3. Amount of suitable habitat (ha) which is occupied at the end of the migration simulation with probability of long- distance dispersal events 0.10, for distances of 1 km, 2.5 km, 5 km, 7.5 km, and 10 km. Occupied habitat is shown as area, percentage of suitable habitat that is occupied, and percentage of the entire focal region that is occupied, for both the disjunct and adjacent scenario groups.

Disjunct Adjacent Disjunct % Adjacent % Disjunct (% of Adjacent (% of

(ha) (ha) (of suitable) (of suitable) focal region) focal region)

1 km 17259 5200 7.03 7.25 1.19 0.36

2.5 km 33952 9547 13.84 13.32 2.33 0.66

5 km 70785 14189 28.85 19.79 4.86 0.97

7.5 km 97124 17061 39.59 23.80 6.67 1.17

10 km 118147 17081 48.16 23.83 8.11 1.17

134

Conclusions

Despite being a common and abundant life history strategy, multihabitat species are under- represented in conservation science. The goal of this thesis was, therefore, to determine how the diversity and community composition of species with complex life cycles are impacted by anthropogenic pressures and to stress the importance of considering complementarity habitats into common conservation modeling tools. I have shown that anthropogenic pressures including recreation (Chapter 2), landuse change and climate change (Chapter 3) affect odonate diversity and community composition, and that different life stages (larvae and adults) are differentially impacted by these anthropogenic pressures (Chapters 2 and 3). I have also found that when complementary habitats are incorporated into species distribution and migration models, new insights are gained, which showed that predicted distributions and migration are more restricted, due to the preferential use of these habitats (Chapter 4).

In Chapter 2, I quantified the relative influences of recreational boating and natural factors on the diversity and community composition of adult and larval odonates in an island setting where anthropogenic pressures are high. I found evidence that recreational boating did have an influence on community composition of adults, but not larvae, showing that in this system larvae and adults responded differently to this anthropogenic pressure. My results showed that even small-scale anthropogenic pressures, such as recreational boating, might have important influence on diversity and community composition. Specific to my study system, my findings suggested that simple conservation measures such as changes in boating speeds and boat channel locations could help to mediate impacts on the adult life stage of odonates.

135

In chapter 3, I investigated the influence of landuse change and climate change on adult and larval odonate diversity and community composition over a 10-year period. I found that there were strong signals of landuse change (characterized by increased residential development) and climate change (characterized by increased mean yearly temperatures and winter temperatures) in the study region that affected adult and larval life stages differently.

Then, likely as a response to climate change, I documented the influx of two species from southern regions (centers of distribution in U.S.A.) into the sampled region around Ottawa.

Based on the findings of this chapter, I suggest that conservation should account for species’ differential responses according to their life stages. Furthermore, this chapter also showed that odonates, and likely other dispersive multihabitat species, might be able to follow their climatic niche, which should be considered in designing future protected areas.

In chapter 4, I focused on a single species of odonate, P. longipennis, and investigated how the explicit inclusion of complementary habitats influenced the predicted distribution of this species in a newly available landscape and how complementary habitats change the simulation of this species’ movement through this landscape. I found that my focal species utilized complementary habitats within this new landscape, and because of this, I found that the factors most influencing the species distribution models including habitat complementarity were landcover types rather than climatic variables. Similarly, I found that my focal species was dispersal limited in migration simulations, and most dispersal limited when less high-quality complementary habitat was available. This chapter showed that one multihabitat species utilized habitats within a landscape in a specific way based on their adjacency and spatial configuration. Most importantly, this chapter revealed that modeling the distribution and movement of this multihabitat species was highly influenced by how

136 landcover is incorporated in these analyses and that current methods might overestimate predicted distributions and migration potential.

In this thesis, I have improved our overall understanding of how one group of multihabitat species respond to anthropogenic threats and provided a case study of how conservation tools can incorporate their habitat requirements. This new knowledge could lead to improved conservation recommendations for the protection and persistence of all multihabitat species, not just odonates. One way this could be accomplished is by designing protected areas that explicitly include more than one habitat type (e.g. forest and wetlands).

Hence, by delineating reserves across habitat types, it is more likely that a protected habitat patch will be suitable for multihabitat species, even if specific species are not targeted. This is a relatively simple way in which conservation practitioners could pro-actively protect multihabitat species.

Future directions

Conservation threat assessment of multihabitat species

Complex life cycles are a common life history strategy (Werner, 1988) that span numerous taxonomic groups from invertebrates to birds to mammals. The habitat requirements of these species can be more restrictive than other species groups (Dunning et al., 1992; Pope et al.,

2000). I have shown evidence in this thesis that multihabitat species are potentially more impacted by anthropogenic threats such as recreation, landuse change, and climate change.

Furthermore, I have shown that different life stages are differentially impacted by anthropogenic threats, leading to the potential for one life stage to be more vulnerable (i.e. have a different conservation status) than another within the same species.

137

A recent review specific to odonates (Clausnitzer et al., 2009) showed that 10% of odonate species are threatened (IUCN Red List criteria of critically endangered, endangered, or vulnerable). This is despite odonates being relatively abundant, generalist meso-predators, and potentially dispersive as a taxa; hence having all the attributes that should help to minimize their risk of extinction (Corbet, 1999; Clausnitzer et al., 2009). This level of threat is lower than that for amphibians (31%) and mammals (20%), but close to that of birds (12%)

(IUCN, 2007). Given that almost all amphibians would be considered multihabitat species, some mammals, and many birds, the expected threat level of multihabitat species as a group is seemingly alarming. Furthermore, the threat status of distinct life stages could be assessed to further determine how anthropogenic impacts are affecting species persistence by specifically asking if anthropogenic threats are biased towards particular life stages, a question which might be crucial for the effective conservation of multihabitat species.

Inclusion of complementary habitats into conservation tools

I have shown in this thesis that one focal species preferentially utilizes complementary habitats and that the inclusion of a new landcover type representing complementary habitats changes predicted distributions. An important question, then, is to determine how generalized is this finding. In other words, do other multihabitat species show a similar preference for utilizing their respective complementary habitats? An underlying aspect of species distribution modeling is that the more accurate the inputs represent a species’ ecological niche, the better the model should predict that species’ distributions. As habitat loss through landuse change is a pervasive anthropogenic impact that threatens species persistence (Pimm

138

& Raven, 2000; Sala et al., 2000; Foley et al., 2005), incorporating more accurately the way in which species utilize the remaining habitat should be a vital goal of conservation scientists.

Final remarks

This thesis determined how anthropogenic pressures (recreation, landuse change, climate change) affect odonates. Thus, a central theme in this thesis is that the more specific habitat requirements of multihabitat species, and especially the need for adjacency of those habitats, make them a unique and understudied conservation challenge. Considering that habitat loss and alteration are the primary driver of biodiversity loss (Foley et al., 2005; Jetz et al., 2007;

Butchart et al., 2010), species that have more restrictive habitat requirements, such as those focused on in this thesis, are at increased risk. To mediate such risk, I suggest that accounting for the complementary needs of multihabitat species needs to be incorporated into conservation tools such as the modeling of species distributions, the simulations of movements through anthropogenic landscapes, and, ultimately, the systematic design of protected areas. Anthropogenic impacts are not likely to decline in scope and magnitude (Sala et al., 2000; Butchart et al., 2010), so our ability to understand how these impacts affect ecological communities and how we can incorporate this knowledge into more effective conservation tools are critical advancements to the field of conservation science.

139

References

Abell, R., Allan, J. D. & Lehner, B. (2007). Unlocking the potential of protected areas for freshwaters. Biological Conservation, 134, 48-63.

Aliberti, L., Maria, A. & Ginsberg, H. S. (2010). Emerging dragonfly diversity at small Rhode Island (U.S.A.) wetlands along an urbanization gradient. Urban Ecosystems, 13, 517- 533.

Alvarez, O., Guo, Q., Klinger, R. C., Li, W. & Doherty, P. (2014). Comparison of elevation and remote sensing derived products as auxiliary data for climate surface interpolation. International Journal of Climatology, 34, 2258-2268.

Amis, M. A., Rouget, M., Lotter, M. & Day, J. (2009). Integrating freshwater and terrestrial priorities in conservation planning. Biological Conservation, 142, 2217-2226.

Angelibert, S. & Giani, N. (2003). Dispersal characteristics of three odonate species in a patchy habitat. Ecography, 26, 13-20.

Ashcroft, M. B., Gollan, J. R. & Batley, M. (2012). Combining citizen science, bioclimatic envelope models and observed habitat preferences to determine the distribution of an inconspicuous, recently detected introduced bee (Halictus smaragdulus Vachal Hymenoptera: Halictidae) in Australia. Biological Invasions, 14, 515-527.

Asplund, T. R. & Cook, C. M. (1997). Effects of motor boats on submerged aquatic macrophytes. Lake and Reservoir Management, 13, 1-12.

Barnett, T. P., Adam, J. C. & Lettenmaier, D. P. (2005). Potential impacts of a warming climate on water availability in snow-dominated regions. Nature, 438, 303-309.

Beatty, C., Fraser, S., Pérez-Jvostov, F. & Sherratt, T. (2010). Dragonfly and Damselfly (Insecta, Odonata) Distributions in Ontario, Canada: Investigating the Influence of Climate Change. BIORISK – Biodiversity and Ecosystem Risk Assessment, 5.

Beger, M., Grantham, H. S., Pressey, R. L., Wilson, K. A., Peterson, E. L., Dorfman, D., Mumby, P. J., Lourival, R., Brumbaugh, D. R. & Possingham, H. P. (2010). Conservation planning for connectivity across marine, freshwater, and terrestrial realms. Biological Conservation, 143, 565-575.

Berger, S., Sohlke, G., Walther, G. R. & Pott, R. (2007). Bioclimatic limits and range shifts of cold-hardy evergreen broad-leaved species at their northern distributional limit in Europe. Phytocoenologia, 37, 523-539.

Betts, M. G., Fahrig, L., Hadley, A. S., Halstead, K. E., Bowman, J., Robinson, W. D., Wiens, J. A. & Lindenmayer, D. B. (2014). A species-centered approach for uncovering

140

generalities in organism responses to habitat loss and fragmentation. Ecography, 37, 517-527.

Bishop, M. J. (2005). Compensatory effects of boat wake and dredge spoil disposal on assemblages of macroinvertebrates. Estuaries, 28, 510-518.

Bishop, M. J. (2007). Impacts of boat-generated waves on macroinfauna: Towards a mechanistic understanding. Journal of Experimental Marine Biology and Ecology, 343, 187-196.

Bishop, M. J. (2008). Displacement of epifauna from seagrass blades by boat wake. Journal of Experimental Marine Biology and Ecology, 354, 111-118.

Borcard, D., Legendre, P. & Drapeau, P. (1992). Partialling out the spatial component of ecological variation. Ecology, 73, 1045-1055.

Borcard, D., Gillet, F. & Legendre, P. (2011). Numerical Ecology with R. New York, Springer.

Braune, E., Richter, O., Sondgerath, D. & Suhling, F. (2008). Voltinism flexibility of a riverine dragonfly along thermal gradients. Global Change Biology, 14, 470-482.

Bried, J. T., D'Amico, F. & Samways, M. J. (2012a). A critique of the dragonfly delusion hypothesis: why sampling exuviae does not avoid bias. Insect Conservation and Diversity, 5, 398-402.

Bried, J. T., Hager, B. J., Hunt, P. D., Fox, J. N., Jensen, H. J. & Vowels, K. M. (2012b). Bias of reduced-effort community surveys for adult Odonata of lentic waters. Insect Conservation and Diversity, 5, 213-222.

Briers, R. A. & Biggs, J. (2003). Indicator taxa for the conservation of pond invertebrate diversity. Aquatic Conservation-Marine and Freshwater Ecosystems, 13, 323-330.

Bright, E. & O'Brien, M. F. (1999). "Michigan Odonate Survey: Odonata larvae of Michigan." from http://insects.ummz.lsa.umich.edu/MICHODO/test/HOME.HTM.

Brook, B. W., Sodhi, N. S. & Bradshaw, C. J. (2008). Synergies among extinction drivers under global change. Trends in Ecology & Evolution, 23, 453-460.

Buchwald, R. (1992). Vegetation and dragonfly fauna - Characteristics and examples of biocenological field studies. Vegetatio, 101, 99-107.

Burgin, S. & Hardiman, N. (2011). The direct physical, chemical and biotic impacts on Australian coastal waters due to recreational boating. Biodiversity and Conservation, 20, 683-701.

141

Burkle, L. A., Mihaljevic, J. R. & Smith, K. G. (2012). Effects of an invasive plant transcend ecosystem boundaries through a dragonfly-mediated trophic pathway. Oecologia, 170, 1045-1052.

Burnham, K. P. & Anderson, D. R. (2004). Multimodel inference - understanding AIC and BIC in model selection. Sociological Methods & Research, 33, 261-304.

Butchart, S. H., Walpole, M., Collen, B., van Strien, A., Scharlemann, J. P., Almond, R. E., Baillie, J. E., Bomhard, B., Brown, C., Bruno, J., Carpenter, K. E., Carr, G. M., Chanson, J., Chenery, A. M., Csirke, J., Davidson, N. C., Dentener, F., Foster, M., Galli, A., Galloway, J. N., Genovesi, P., Gregory, R. D., Hockings, M., Kapos, V., Lamarque, J. F., Leverington, F., Loh, J., McGeoch, M. A., McRae, L., Minasyan, A., Hernandez Morcillo, M., Oldfield, T. E., Pauly, D., Quader, S., Revenga, C., Sauer, J. R., Skolnik, B., Spear, D., Stanwell-Smith, D., Stuart, S. N., Symes, A., Tierney, M., Tyrrell, T. D., Vie, J. C. & Watson, R. (2010). Global biodiversity: indicators of recent declines. Science, 328, 1164-1168.

Cardinale, B. J., Duffy, J. E., Gonzalez, A., Hooper, D. U., Perrings, C., Venail, P., Narwani, A., Mace, G. M., Tilman, D., Wardle, D. A., Kinzig, A. P., Daily, G. C., Loreau, M., Grace, J. B., Larigauderie, A., Srivastava, D. S. & Naeem, S. (2012). Biodiversity loss and its impact on humanity. Nature, 486, 59-67.

Cascante, A., Quesada, M., Lobo, J. J. & Fuchs, E. A. (2002). Effects of dry tropical forest fragmentation on the reproductive success and genetic structure of the tree Samanea saman. Conservation Biology, 16, 137-147.

Cavanaugh, K. C., Kellner, J. R., Forde, A. J., Gruner, D. S., Parker, J. D., Rodriguez, W. & Feller, I. C. (2014). Poleward expansion of mangroves is a threshold response to decreased frequency of extreme cold events. Proceedings of the National Academy of Sciences of the United States of America, 111, 723-727.

Chan-McLeod, A. C. A. (2003). Factors affecting the permeability of clearcuts to red legged frogs. The Journal of Wildlife Management, 67, 663-671.

Chen, I. C., Hill, J. K., Shiu, H.-J., Holloway, J. D., Benedick, S., Chey, V. K., Barlow, H. S. & Thomas, C. D. (2011). Asymmetric boundary shifts of tropical montane Lepidoptera over four decades of climate warming. Global Ecology and Biogeography, 20, 34-45.

Chin, K. S. & Taylor, P. D. (2009). Interactive effects of distance and matrix on the movements of a peatland dragonfly. Ecography, 32, 715-722.

City of Ottawa (2013). Ottawa Annual Developmental Report 2012. P. a. G. M. R. a. F. Unit. Ottawa.

Clausnitzer, V., Kalkman, V. J., Ram, M., Collen, B., Baillie, J. E. M., Bedjanic, M., Darwall, W. R. T., Dijkstra, K. D. B., Dow, R., Hawking, J., Karube, H., Malikova, E., Paulson, D., Schutte, K., Suhling, F., Villanueva, R. J., von Ellenrieder, N. & Wilson, K.

142

(2009). Odonata enter the biodiversity crisis debate: The first global assessment of an insect group. Biological Conservation, 142, 1864-1869.

Conrad, K. F., Willson, K. H., Harvey, I. F., Thomas, C. J. & Sherratt, T. N. (1999). Dispersal characteristics of seven odonate species in an agricultural landscape. Ecography, 22, 524-531.

Corbet, P. S. (1957). The life-history of the Emperor Dragonfly Anax imperator Leach (Odonata: Aeshnidae). Journal of Animal Ecology, 26, 1-69.

Corbet, P. S. (1999). Dragonflies: Behaviour and Ecology of Odonata, Comstock Publishing Associates.

Cressie, N. (1993). Statistics for spatial data, revised edition. New York, Wiley.

Crozier, L. (2004). Warmer winters drive butterfly range expansion by increasing survivorship. Ecology, 85, 231-241.

Darling, E. S. & Côté, I. M. (2008). Quantifying the evidence for ecological synergies. Ecology Letters, 11, 1278-1286. de Chazal, J. & Rounsevell, M. D. A. (2009). Land-use and climate change within assessments of biodiversity change: A review. Global Environmental Change-Human and Policy Dimensions, 19, 306-315.

Debinski, D. M., Ray, C. & Saveraid, E. H. (2001). Species diversity and the scale of the landscape mosaic: do scales of movement and patch size affect diversity? Biological Conservation, 98, 179-190.

Devictor, V., van Swaay, C., Brereton, T., Brotons, L., Chamberlain, D., Heliola, J., Herrando, S., Julliard, R., Kuussaari, M., Lindstrom, A., Reif, J., Roy, D. B., Schweiger, O., Settele, J., Stefanescu, C., Van Strien, A., Van Turnhout, C., Vermouzek, Z., WallisDeVries, M., Wynhoff, I. & Jiguet, F. (2012). Differences in the climatic debts of birds and butterflies at a continental scale. Nature Climate Change, 2, 121-124.

Dingemanse, N. J. & Kalkman, V. J. (2008). Changing temperature regimes have advanced the phenology of Odonata in the Netherlands. Ecological Entomology, 33, 394-402.

DMTI (2012). CanMap Route Logistics [computer file] Markham, DMTI Spatial Inc.

Dolný, A. & Harabiš, F. (2012). Underground mining can contribute to freshwater biodiversity conservation: Allogenic succession forms suitable habitats for dragonflies. Biological Conservation, 145, 109-117.

Dray, S., Legendre, P. & Peres-Neto, P. R. (2006). Spatial modelling: a comprehensive framework for principal coordinate analysis of neighbour matrices (PCNM). Ecological Modelling, 196, 483-493.

143

Dunning, J. B., Danielson, B. J. & Pulliam, H. R. (1992). Ecological processes that affect populations in complex landscapes. Oikos, 65, 169-175.

Ecological Stratification Working Group (1995). A national ecological framework for Canada. Agriculture and Agri-Food Canada Research Branch Centre for Land and Biological Resources Research and Environment Canada State of the Environment Directorat Ecozone Analysis Branch. Ottawa/Hull, Report and national map at 1:7500 000 scale.

Egertson, C. J., Kopaska, J. A. & Downing, J. A. (2004). A century of change in macrophyte abundance and composition in response to agricultural eutrophication. Hydrobiologia, 524, 145-156.

Ehrenfeld, J. G. (2000). Evaluating wetlands within an urban context. Ecological Engineering, 15, 253-265.

Elith, J. & Leathwick, J. R. (2009). Species Distribution Models: Ecological Explanation and Prediction Across Space and Time. Annual Review of Ecology, Evolution, and Systematics, 40, 677-697.

Elith, J., Phillips, S. J., Hastie, T., Dudík, M., Chee, Y. E. & Yates, C. J. (2011). A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 17, 43-57.

Ellis, E. C. & Ramankutty, N. (2008). Putting people in the map: anthropogenic biomes of the world. Frontiers in Ecology and the Environment, 6, 439-447.

Ellis, E. C., Goldewijk, K. K., Siebert, S., Lightman, D. & Ramankutty, N. (2010). Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography, 19, 589-606.

Engler, R., Hordijk, W. & Guisan, A. (2012). The MIGCLIM R package - seamless integration of dispersal constraints into projections of species distribution models. Ecography, 35, 872-878.

Faeth, S. H., Warren, P. S., Shochat, E. & Marussich, W. A. (2005). Trophic dynamics in urban communities. Bioscience, 55, 399-407.

Fahrig, L. (2003). Effects of habitat fragmentation on biodiversity. Annual Review of Ecology Evolution and Systematics, 34, 487-515.

Fahrig, L., Baudry, J., Brotons, L., Burel, F. G., Crist, T. O., Fuller, R. J., Sirami, C., Siriwardena, G. M. & Martin, J. L. (2011). Functional landscape heterogeneity and animal biodiversity in agricultural landscapes. Ecology Letters, 14, 101-112.

Fischer, J., Lindenmayer, D. B., Barry, S. & Flowers, E. (2005). Lizard distribution patterns in the Tumut fragmentation “Natural Experiment” in south-eastern Australia. Biological Conservation, 123, 301-315.

144

Flenner, I. & Sahlén, G. (2008). Dragonfly community re-organisation in boreal forest lakes: rapid species turnover driven by climate change? Insect Conservation and Diversity, 1, 169-179.

Foley, J. A., Defries, R., Asner, G. P., Barford, C., Bonan, G., Carpenter, S. R., Chapin, F. S., Coe, M. T., Daily, G. C., Gibbs, H. K., Helkowski, J. H., Holloway, T., Howard, E. A., Kucharik, C. J., Monfreda, C., Patz, J. A., Prentice, I. C., Ramankutty, N. & Snyder, P. K. (2005). Global consequences of land use. Science, 309, 570-574.

Folt, C. L., Chen, C. Y., Moore, M. V. & Burnaford, J. (1999). Synergism and antagonism among multiple stressors. Limnology and Oceanography, 44, 864-877.

Fortin, M. & Dale, M. R. T. (2005). Spatial analysis: A guide for ecologists. New York, Cambridge University Press.

Gabel, F., Garcia, X. F., Brauns, M., Sukhodolov, A., Leszinski, M. & Pusch, M. T. (2008). Resistance to ship-induced waves of benthic invertebrates in various littoral habitats. Freshwater Biology, 53, 1567-1578.

Gabel, F., Stoll, S., Fischer, P., Pusch, M. T. & Garcia, X. F. (2011). Waves affect predator- prey interactions between fish and benthic invertebrates. Oecologia, 165, 101-109.

Gabel, F., Garcia, X. F., Schnauder, I. & Pusch, M. T. (2012). Effects of ship-induced waves on littoral benthic invertebrates. Freshwater Biology, 57, 2425-2435.

Gaston, K. J. (2010). Valuing Common Species. Science, 327, 154-155.

Gergel, S. E., Turner, M. G., Miller, J. R., Melack, J. M. & Stanley, E. H. (2002). Landscape indicators of human impacts to riverine systems. Aquatic Sciences, 64, 118-128.

Gibbs, J. P. (1998). Distribution of woodland amphibians along a forest fragmentation gradient. Landscape Ecology, 13.

Goffart, P. (2010). Southern dragonflies expanding in Wallonia (south Belgium): a consequence of global warming? BIORISK – Biodiversity and Ecosystem Risk Assessment, 5.

Gonzalez-Abraham, C. E., Radeloff, V. C., Hawbaker, T. J., Hammer, R. B., Stewart, S. I. & Clayton, M. K. (2007). Patterns of houses and habitat loss from 1937 to 1999 in northern Wisconsin, USA. Ecological Applications, 17, 2011-2023.

Gregory, S. V., Swanson, F. J., Mckee, W. A. & Cummins, K. W. (1991). An Ecosystem Perspective of Riparian Zones. Bioscience, 41, 540-551.

Gribbin, S. D. & Thompson, D. J. (1990). A quantitative study of mortality at emergence in the damselfly Pyrrhosoma nymphula (Sulzer)(Zygoptera: Coenagrionidae). Freshwater Biology, 24, 295-302.

145

Hamasaki, K., Yamanaka, T., Tanaka, K., Nakatani, Y., Iwasaki, N. & Sprague, D. S. (2009). Relative importance of within-habitat environment, land use and spatial autocorrelations for determining odonate assemblages in rural reservoir ponds in Japan. Ecological Research, 24, 597-605.

Hanski, I. (1998). Metapopulation Dynamics. Nature, 396, 41-50.

Harabiš, F. & Dolný, A. (2012). Human altered ecosystems: suitable habitats as well as ecological traps for dragonflies (Odonata): the matter of scale. Journal of Insect Conservation, 16, 121-130.

Hassall, C., Thompson, D. J., French, G. C. & Harvey, I. F. (2007). Historical changes in the phenology of British Odonata are related to climate. Global Change Biology, 13, 933- 941.

Hassall, C. & Thompson, D. J. (2008). The effects of environmental warming on Odonata: a review. International Journal of Odonatology, 11, 131-153.

Hazlitt, S. L., Martin, T. G., Sampson, L. & Arcese, P. (2010). The effects of including marine ecological values in terrestrial reserve planning for a forest-nesting seabird. Biological Conservation, 143, 1299-1303.

Hickling, R., Roy, D. B., Hill, J. K. & Thomas, C. D. (2005). A northward shift of range margins in British Odonata. Global Change Biology, 11, 502-506.

Hill, J. K., Thomas, C. D., Fox, R., Telfer, M. G., Willis, S. G., Asher, J. & Huntley, B. (2002). Responses of butterflies to twentieth century climate warming: implications for future ranges. Proceedings. Biological sciences / The Royal Society, 269, 2163- 2171.

Hocking, D. J. & Semlitsch, R. D. (2007). Effects of timber harvest on breeding-site selection by gray treefrogs (Hyla versicolor). Biological Conservation, 138, 506-513.

IUCN. (2007). "IUCN Red List of threatened species www.iucnredlist.org."

Jakob, C. & Suhling, F. (1999). Risky times? Mortality during emergence in two species of dragonflies (Odonata: Gomphidae, Libellulidae). Aquatic Insects, 21, 1-10.

Jenkins, M. (2003). Prospects for biodiversity. Science, 302, 1175-1177.

Jetz, W., Wilcove, D. S. & Dobson, A. P. (2007). Projected impacts of climate and landuse change on the global diversity of birds. PLOS Biology, 5, 1211-1219.

Jochner, S. C., Sparks, T. H., Estrella, N. & Menzel, A. (2012). The influence of altitude and urbanisation on trends and mean dates in phenology (1980-2009). International Journal of Biometeorology, 56, 387-394.

146

Jones, C. D., Kingsley, A., Burke, P. & Holder, M. (2008). Field guide to the dragonflies and damselflies of Algonquin Provincial Park and the surrounding area, The Friends of the Algonquin Park.

Kadoya, T., Suda, S. I., Tsubaki, Y. & Washitani, I. (2008). The sensitivity of dragonflies to landscape structure differs between life-history groups. Landscape Ecology, 23, 149- 158.

Kadoya, T. & Washitani, I. (2012). Use of multiple habitat types with asymmetric dispersal affects patch occupancy of the damselfly Indolestes peregrinus in a fragmented landscape. Basic and Applied Ecology, 13, 178-187.

Kalkman, V. J., Clausnitzer, V., Dijkstra, K. D. B., Orr, A. G., Paulson, D. R. & van Tol, J. (2008). Global diversity of dragonflies (Odonata) in freshwater. Hydrobiologia, 595, 351-363.

Kareiva, P. & Marvier, M. (2003). Conserving biodiversity coldspots. American Scientist, 91, 344-351.

Knight, T. M., McCoy, M. W., Chase, J. M., McCoy, K. A. & Holt, R. D. (2005). Trophic cascades across ecosystems. Nature, 437, 880-883.

Kramer-Schadt, S., S. Kaiser, T., Frank, K. & Wiegand, T. (2011). Analyzing the effect of stepping stones on target patch colonisation in structured landscapes for Eurasian lynx. Landscape Ecology, 26, 501-513.

Legendre, P. & Gallagher, E. D. (2001). Ecologically meaningful transformations for ordination of species data. Oecologia, 129, 271-280.

Legendre, P. & Legendre, L. (2012). Numerical Ecology. Amsterdam, Elsevier.

Leibold, M. A., Holyoak, M., Mouquet, N., Amarasekare, P., Chase, J. M., Hoopes, M. F., Holt, R. D., Shurin, J. B., Law, R., Tilman, D., Loreau, M. & Gonzalez, A. (2004). The metacommunity concept: a framework for multi-scale community ecology. Ecology Letters, 7, 601-613.

Levey, D. J., Bolker, B. M., Tewksbury, J. J., Sargant, S. & Haddad, N. M. (2005). Effects of landscape corridors on seed dispersal by birds. Science, 309, 146-148.

Liddle, M. J. & Scorgie, H. R. A. (1980). The effects of recreation on freshwater plants and animals: A review. Biological Conservation, 17, 183-206.

Loarie, S. R., Duffy, P. B., Hamilton, H., Asner, G. P., Field, C. B. & Ackerly, D. D. (2009). The velocity of climate change. Nature, 462, 1052-1055.

Lubertazzi, M. A. A. & Ginsberg, H. S. (2009). Persistence of dragonfly exuviae on vegetation and rock substrates. Northeastern Naturalist, 16, 141-147.

147

Mantyka-Pringle, C. S., Martin, T. G. & Rhodes, J. R. (2012). Interactions between climate and habitat loss effects on biodiversity: a systematic review and meta-analysis. Global Change Biology, 18, 1239-1252.

Mathavan, S. & Pandian, T. J. (1976). Patterns of emergence, import of egg energy and energy export via emerging dragonfly populations in a tropical pond. Hydrobiologia, 54, 257-272.

Maynord, S. T. (2005). Wave height from planing and semi-planing small boats. River Research and Applications, 21, 1-17.

McCauley, S. J. (2006). The effects of dispersal and recruitment limitation on community structure of odonates in artificial ponds. Ecography, 29, 585-595.

McCauley, S. J. (2007). The role of local and regional processes in structuring larval dragonfly distributions across habitat gradients. Oikos, 116, 121-133.

McCauley, S. J., Davis, C. J., Relyea, R. A., Yurewicz, K. L., Skelly, D. K. & Werner, E. E. (2008). Metacommunity patterns in larval odonates. Oecologia, 158, 329-342.

McCauley, S. J. (2010). Body size and social dominance influence breeding dispersal in male Pachydiplax longipennis (Odonata). Ecological Entomology, 35, 377-385.

McCauley, S. J., Brodin, T. & Hammond, J. (2010). Foraging rates of larval dragonfly colonists are positively related to habitat isolation: Results from a landscape-level experiment. The American naturalist, 175, E66-73.

McCauley, S. J., Davis, C. J., Werner, E. E. & Robeson II, M. S. (2014). Dispersal, niche breadth and population extinction: colonization ratios predict range size in North American dragonflies. Journal of animal ecology, 83, 858-865.

McCauley, S. J., Hammond, J. I., Frances, D. N. & Mabry, K. E. (2015). Effects of experimental warming on survival, phenology and morphology of an aquatic insect (Odonata) Ecological Entomology, in press.

McLaughlin, J. F., Hellmann, J. J., Boggs, C. L. & Ehrlich, P. R. (2002). Climate change hastens population extinctions. Proceedings of the National Academy of Sciences of the United States of America, 99, 6070-6074.

McPeek, M. A. (1990). Determination of species composition in the Enallagma damselfly assemblages of permanent lakes. Ecology, 71, 83-98.

Michiels, N. K. & Dhondt, A. A. (1991). Characteristics of Dispersal in Sexually Mature Dragonflies. Ecological Entomology, 16, 449-459.

Millenium Ecosystem Assessment (2005). Ecosystems and Well Being: Biodiversity Synthesis, World Resources Institute.

148

Mosisch, T. D. & Arthington, A. H. (1998). The impacts of power boating and water skiing on lakes and reservoirs. Lakes and Reservoirs: Research and Management, 3, 1-17.

Mueller, T., Selva, N., Pugacewicz, E. & Prins, E. (2009). Scale-sensitive landscape complementation determines habitat suitability for a territorial generalist. Ecography, 32, 345-353.

Murphy, K. J. & Eaton, J. W. (1983). Effects of pleasure-boat traffic on macrophyte growth in canals. Journal of Applied Ecology, 20, 713-729.

Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B. & Kent, J. (2000). Biodiversity hotspots for conservation priorities. Nature, 403, 853-858.

Oertli, B., Auderset Joye, D., Castella, E., Juge, R., Cambin, D. & Lachavanne, J. B. (2002). Does size matter? The relationship between pond area and biodiversity. Biological Conservation, 104, 59-70.

Ontario Odonata Atlas. (2013). 2013, from http://www.mnr.gov.on.ca/MNR/nhic/odonates/ohs.html.

Opdam, P. & Wascher, D. (2004). Climate change meets habitat fragmentation: linking landscape and biogeographical scale levels in research and conservation. Biological Conservation, 117, 285-297.

Ordonez, A., Martinuzzi, S., Radeloff, V. C. & Williams, J. W. (2014). Combined speeds of climate and land-use change of the conterminous US until 2050. Nature Climate Change, 4, 811-816.

Ostendorp, W., Iseli, C., Krauss, M., Krumscheidplankert, P., Moret, J. L., Rollier, M. & Schanz, F. (1995). Lake shore deterioration, reed management and bank restoration in some Central-European lakes. Ecological Engineering, 5, 51-75.

Ott, J. (2010). Dragonflies and climatic change - recent trends in Germany and Europe. BIORISK – Biodiversity and Ecosystem Risk Assessment, 5.

Parmesan, C., Ryrholm, N., Stefanescu, C., Hill, J. K., Thomas, C. D., Descimon, H., Huntley, B., Kaila, L., Kullberg, J., Tammaru, T., Tennent, W. J., Thomas, J. A. & Warren, M. (1999). Poleward shifts in geographical ranges of butterfly species associated with regional warming. Nature, 399, 579-583.

Parmesan, C. & Yohe, G. (2003). A globally coherent fingerprint of climate change impacts across natural systems. Nature, 421, 37-42.

Parmesan, C. (2006). Ecological and Evolutionary Responses to Recent Climate Change. Annual Review of Ecology, Evolution, and Systematics, 37, 637-669.

Pearson, R. G. (2006). Climate change and the migration capacity of species. Trends in Ecology & Evolution, 21, 111-113.

149

Peres-Neto, P. R., Legendre, P., Dray, S. & Borcard, D. (2006). Variation partitioning of species data matrices: Estimation and comparison of fractions. Ecology, 87, 2614- 2625.

Petit, D. R. (1989). Weather-dependent use of habitat patches by wintering woodland birds. Journal of Field Ornithology, 60, 241-247.

Phillips, S. J. & Dudik, M. (2008). Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography, 31, 161-175.

Pimm, S. L., Russell, G. J., Gittleman, J. L. & Brooks, T. M. (1995). The future of biodiversity. Science, 269, 347-350.

Pimm, S. L. & Raven, P. (2000). Extinction by numbers. Nature, 403, 843-845.

Poos, M. S. & Jackson, D. A. (2012). Addressing the removal of rare species in multivariate bioassessments: The impact of methodological choices. Ecological Indicators, 18, 82- 90.

Pope, S. E., Fahrig, L. & Merriam, H. G. (2000). Landscape complementation and metapopulation effects on leopard frog populations. Ecology, 81, 2498-2508.

Purse, B. V., Hopkins, G. W., Day, K. J. & Thompson, D. J. (2003). Dispersal characteristics and management of a rare damselfly. Journal of Applied Ecology, 40, 716-728.

R Development Core Team (2011). R: a language and environment for statistical computing. R. F. f. S. Computing. Vienna.

Radomski, P. & Goeman, T. J. (2001). Consequences of human lakeshore development on emergent and floating-leaf vegetation abundance. North American Journal of Fisheries Management, 21, 46-61.

Raebel, E. M., Merckx, T., Riordan, P., Macdonald, D. W. & Thompson, D. J. (2010). The dragonfly delusion: why it is essential to sample exuviae to avoid biased surveys. Journal of Insect Conservation, 14, 523-533.

Rands, M. R. W., Adams, W. M., Bennun, L., Butchart, S. H. M., Clements, A., Coomes, D., Entwistle, A., Hodge, I., Kapos, V., Scharlemann, J. P. W., Sutherland, W. J. & Vira, B. (2010). Biodiversity conservation: Challenges beyond 2010. Science, 329, 1298- 1303.

Rayfield, B., Fortin, M. J. & Fall, A. (2011). Connectivity for conservation: a framework to classify network measures. Ecology, 92, 847-858.

Reece, B. A. & McIntyre, N. E. (2009). Community assemblage patterns of odonates inhabiting a wetland complex influenced by anthropogenic disturbance. Insect Conservation and Diversity, 2, 73-80.

150

Remsburg, A. J. & Turner, M. G. (2009). Aquatic and terrestrial drivers of dragonfly (Odonata) assemblages within and among north-temperate lakes. Journal of the North American Benthological Society, 28, 44-56.

Ries, L. & Sisk, T. D. (2008). Butterfly edge effects are predicted by a simple model in a complex landscape. Oecologia, 156, 75-86.

Rittenhouse, T. A. G. & Semlitsch, R. D. (2006). Grasslands as movement barriers for a forest-associated salamander: Migration behavior of adult and juvenile salamanders at a distinct habitat edge. Biological Conservation, 131, 14-22.

Rodriguez, J. F., Admiraal, D. M., Lopez, F. & Garcia, M. H. (2002). Unsteady bed shear stresses induced by navigation: Laboratory observations. Journal of Hydraulic Engineering-Asce, 128, 515-526.

Roetzer, T., Wittenzeller, M., Haekel, H. & Nekovar, J. (2000). Phenology in central europe - differences and treds of spring phenophases in urban and rural areas. International Journal of Biometereology, 44, 60-66.

Root, T. L. & Schneider, S. H. (2006). Conservation and Climate Change: the Challenges Ahead. Conservation Biology, 20, 706-708.

Rothermel, B. B. (2004). Migratory success of juveniles: A potential constraint on connectivity for pond-breeding amphibians. Ecological Applications, 14, 1535-1546.

Rouquette, J. R. & Thompson, D. J. (2007). Patterns of movement and dispersal in an endangered damselfly and the consequences for its management. Journal of Applied Ecology, 44, 692-701.

Royal Ontario Museum (accessed 2013). Entomology collection, Odonata database. Toronto, ON.

Sala, O. E., Chapin, F. S., Armesto, J. J., Berlow, E., Bloomfield, J., Dirzo, R., Huber- Sanwald, E., Huenneke, L. F., Jackson, R. B., Kinzig, A. P., Leemans, R., Lodge, D. M., Mooney, H. A., Oesterheld, M., Poff, N. L., Sykes, M. T., Walker, B. H., Walker, M. & Wall, D. H. (2000). Global biodiversity scenarios for the year 2100. Science, 287, 1770-1774.

Samways, M. J. & Steytler, N. S. (1996). Dragonfly (Odonata) distribution patterns in urban and forest landscapes, and recommendations for riparian management. Biological Conservation, 78, 279-288.

Saura, S., Bodin, Ö. & Fortin, M.-J. (2014). Stepping stones are crucial for species' long- distance dispersal and range expansion through habitat networks. Journal of Applied Ecology, 51, 171-182.

Schindler, M., Fesl, C. & Chovanec, A. (2003). Dragonfly associations (Insecta : Odonata) in relation to habitat variables: a multivariate approach. Hydrobiologia, 497, 169-180.

151

Schloss, C. A., Nunez, T. A. & Lawler, J. J. (2012). Dispersal will limit the ability of mammals to track climate change in the Western Hemisphere. Proceedings of the National Academy of Sciences of the United States of America, 109, 8606-8611.

Shmida, A. & Wilson, M. V. (1985). Biological Determinants of Species-Diversity. Journal of Biogeography, 12, 1-20.

Simberloff, D. (1997). The biology of invasions. Strangers in Paradise: Impact And Management Of Nonindigenous Species In Florida. D. Simberloff, D. C. Schmtz and T. C. Brown. Washington, D.C., Island: 3-19.

Strayer, D. L. & Findlay, S. E. G. (2010). Ecology of freshwater shore zones. Aquatic Sciences, 72, 127-163.

Switzer, P. V. & Walters, W. (1999). Choice of lookout posts by territorial amberwing dragonflies, Perithemis tenera (Anisoptera : Libellulidae). Journal of Insect Behavior, 12, 385-398.

The Ontario Ministry of Natural Resources (2008). Southern Ontario Land Resource Information System (SOLRIS) Land Classification Data,, Version 1.2. Peterborough, Ontario.

Thomas, C. D., Cameron, A., Green, R. E., Bakkenes, M., Beaumont, L. J., Collingham, Y. C., Erasmus, B. F. N., de Siqueira, M. F., Grainger, A., Hannah, L., Hughes, L., Huntley, B., van Jaarsveld, A. S., Midgley, G. F., Miles, L., Ortega-Huerta, M. A., Peterson, A. T., Phillips, O. L. & Williams, S. E. (2004). Extinction risk from climate change. Nature, 427, 145-148.

TNS Canada (2008). Georgian Bay Positioning and Product Analysis. Toronto, ON.

Turner, W. R., Brandon, K., Brooks, T. M., Costanza, R., da Fonseca, G. A. B. & Portela, R. (2007). Global conservation of biodiversity and ecosystem services. Bioscience, 57, 868-873.

Viducis, P. (2013). Toronto Economic Dashboard - Annual Summary 2013. Toronto, Ontario, Strategic Growth and Sector Development.

Vinebrooke, R. D., Cottingham, K. L., Norberg, J., Scheffer, M., Dobson, S. I., Maberly, S. C. & Sommer, U. (2004). Impacts of multiple stressors on biodiversity and ecosystem functioning, the role of species co-tolerance. Oikos, 104, 451-457.

Vitousek, P. M., Mooney, H. A., Lubchenco, J. & Melillo, J. M. (1997). Human domination of the earth's ecosystem. Science, 277, 494-499.

Vos, C. C., Verboom, J., Opdam, P. F. M. & Ter Braak, C. J. F. (2001). Toward ecologically scaled landscape indices. The American naturalist, 157, 24-41.

152

Walker, E. M. & Corbet, P. S. (1978). The Odonata of Canada and Alaska, University of Toronto Press.

Walther, G. R., Post, E., Convey, P., Menzel, A., Parmesan, C., Beebee, T. J. C., Fromentin, J. M., Hoegh-Guldberg, O. & Bairlein, F. (2002). Ecological responses to recent climate change. Nature, 416, 389-395.

Ward, L. & Mill, P. (2007). Long range movements by individuals as a vehicle for range expansion in Calopteryx splendens (Odonata: Zygoptera). European Journal of Entomology, 104, 195-198.

Werner, E. E. & Gilliam, J. F. (1984). The ontogenetic niche and species interactions in size- structures populations. Annual Review of Ecology Evolution and Systematics, 15, 393- 425.

Werner, E. E. (1988). Size, scaling, and the evolution of complex life cycles. Size-structured populations: ecology and evolution. B. Ebenmann and L. Persson. Berlin, Springer.

White, M. A., Nemani, R. R., Thornton, P. E. & Running, S. W. (2002). Satellite evidence of phenological differences between urbanized and rural areas of the eastern United States deciduous broadleaf forest. Ecosystems, 5, 260-273.

Wissinger, S. A. (1988). Life history and size structure of larval dragonfly populations. Journal of the North American Benthological Society, 7, 13-28.

Worthen, W. B. (2010). Emergence-Site selection by the dragonfly Epitheca spinosa (Hagen). Southeastern Naturalist, 9, 251-258.

153