A Case Study of the Invasive Vine Vincetoxicum Rossicum

Consequences of Plant Invasion in a Peri-urban Ecosystem: A Case Study of the Invasive Vine Vincetoxicum rossicum

Stuart William Livingstone

A thesis submitted in conformity with the requirements

for the degree of Doctor of Philosophy

Department of Physical and Environmental Sciences

University of Toronto Scarborough

A Case Study of the Invasive Vine Vincetoxicum rossicum

Stuart William Livingstone

Doctor of Philosophy

Department of Physical and Environmental Sciences University of Toronto-Scarborough 2018

Abstract The impacts of non-indigenous invasive plant species on peri-urban ecosystems are known to be highly variable and are the subject of much debate within the discipline of invasion ecology. Yet, there is a desperate need to quantify those impacts, investigate the potential for control and engage with the stakeholders that are affected by invasion. Using the invasive species Vincetoxicum rossicum in and around Canada’s Rouge National Urban Park, I 1) quantify its impact on biodiversity and ecosystem functionality, 2) examine the effectiveness of a bio-control agent

(Hypena opulenta) in reducing the reproductive ability of V. rossicum in different light environments, 3) examine the ecological significance of rare and functionally unique species, and whether or not those species are disproportionately affected by V. rossicum invasion, and 4) examine stakeholder valuation of ecosystem services that are threatened by the spread of V. rossicum. I show that V. rossicum invasion is associated with significant declines in plant biodiversity and the impairment of several ecosystem functions including aboveground biomass production, floral resources and the diversity and abundance of local pollinators. I then show that, contrary to previous laboratory work, defoliation of V. rossicum by H. opulenta in shade conditions results in significantly greater seed production via a compensatory growth response. Then, through a combination of modelling simulations and empirical data collection I show that there are no ii general trends regarding the ecological significance of rare and/or functionally unique species, but that some rare plant species are ecologically important in the face of invasion. Then, by employing stakeholder analysis, I show that park-user knowledge of V. rossicum, operationalized as

“ecological engagement”, results in significantly greater valuation of ecosystem services, but also that there is substantial neutrality and disagreement about the ecological impact of V. rossicum among stakeholders. As V. rossicum invasion is undoubtedly having negative impacts on ecosystem functions and services, this work points to the need for continued research on the potential for control options. Furthermore, given that the spread of V. rossicum is prolific in urban environments, conservation practitioners would do well to develop more effective communication strategies that can engage local stakeholders.

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Acknowledgements I would first like to thank both of my supervisors, Dr. Marc Cadotte and Dr. Marney Isaac, for their support and insightful perspectives on this work. By culturing incredibly positive and engaging learning environments they allow their students to thrive academically, while also recognizing the importance of a work-life balance. I feel privileged to have had the opportunity to learn from, and collaborate with, such passionate and knowledgeable individuals and I strive to carry that passion forward in my own career. I would also like to thank my committee members, Dr. Sandy Smith and Dr. Robert Bourchier, for their constructive involvement with this dissertation, specifically on the practice and ecology of biological control. Thanks also to staff at Parks Canada, the Toronto and Region Conservation Authority, the Nature Conservancy of Canada and Silv-Econ for assisting with the logistics of my research.

Thanks as well to colleagues in both of my labs who provided an excellent support network during my time as a PhD student: Simone-Louise Yasui, Dr. Lanna Jin, Carlos Arnillas, Adriano Roberto, Alannah Biega, Jeya Venugopal, Dr. Kira Borden, Jesse Furze, Serra Buchanan, Caroline Mitchell, Keane Tirona, Rhokini Kunanesan, Darwin Sodhi, Alice Choi, Jacky Lee, Dr. Caroline Tucker, Dr. Scott MacIvor, Abdul Yossofazai, Jean-Yves Alfonso, Roland Law, Nicholas Sookhan, Garland Xie, Vivo Subhan, Shafak Joyia, Natasha Rashid, Sheema Everett, Ramanan Thanabalan, Mary-Louise Feliciano, Jissan Adam, Irfan Hakim and a small army of other volunteers that are too numerous to list here. I would also like to thank the many professors and administrative support staff in the Department of Physical and Environmental Science that were a part of my journey, specifically; Dr. Roberta Fulthorpe, Dr. Nicole Klenk, Dr. Adam Martin, Dr. Maria Dittrich, Dr. Jim MacLellan, Dr. Nick Mandrak, Dr. Andrew Drake, Elaine Pick, Julie Quennville, Gisela Bento, and most importantly Dr. Mart Gross for encouraging me to pursue the PhD.

Greatest thanks go to my wife Clarissa whose love, patience and support during my time in Graduate school made this possible. I am also thankful for my wonderful supportive parents, Glenn and Marge, my two children, Rowan and Ember, that inspire me to push myself. Thanks also to my extended family and friends for their encouragement.

This research was supported in part by the Department of Physical and Environmental Sciences, the University of Toronto, the Natural Sciences and Engineering Research Council of Canada grant to Dr. Marc Cadotte, and the TD graduate scholarship in Environmental Science. iv

Table of Contents

Acknowledgements ...... iv

Table of Contents ...... v

List of Tables ...... x

List of Figures ...... xi

List of Appendices ...... xv

List of Abbreviations ...... xviii

Chapter 1 Introduction ...... 1

1.1 Invasion ecology: Causes and consequences of biological invasions ...... 1

1.1.1 Mechanisms of plant invasion ...... 2

1.1.2 Management perspectives on invasions ...... 4

1.1.3 Susceptibility to invasions: urban beachheads ...... 5

1.1.4 Ecological impact of plant invasions: Theory and challenges in quantification ...... 6

1.1.5 Biodiversity-ecosystem function (BEF) relationships ...... 9

1.1.6 Functional ecology ...... 11

1.2 Biological control of plant invasions ...... 12

1.2.1 Selection of biocontrol agents: an exercise in ecological risk assessment ...... 13

1.2.2 Indirect effects associated with the application of bio-control ...... 15

1.3 Forms of ecological rarity and their association with invasive species ...... 16

1.4 Ecosystem services & invasive species: Role of stakeholder analysis ...... 18

1.5 Vincetoxicum rossicum ...... 21

1.6 Thesis statement and objectives ...... 25

Chapter 2 Effects of the invasive vine Vincetoxicum rossicum on biodiversity and ecosystem functionality ...... 27

2.1 Introduction ...... 27 v

2.2 Methods ...... 30

2.2.1 Study Sites ...... 30

2.2.2 Functional traits and diversity ...... 32

2.2.3 Functional traits and diversity ...... 34

2.2.4 Ecosystem functions ...... 35

2.2.5 Multi-functionality ...... 37

2.2.6 Statistical analysis ...... 38

2.3 Results ...... 40

2.3.1 V. rossicum abundance, taxonomic, functional and phylogenetic diversity ...... 40

2.3.2 The effect of V. rossicum invasion on community functional traits ...... 40

2.3.3 The effect of V. rossicum invasion on community diversity ...... 44

2.3.4 The effect of V. rossicum invasion on ecosystem functioning ...... 44

2.4 Discussion ...... 48

2.4.1 The effect of V. rossicum invasion on community functional structure and diversity ...... 48

2.4.2 The effect of V. rossicum invasion on ecosystem functioning ...... 51

2.4.3 Caveats ...... 55

2.5 Conclusion ...... 56

Chapter 3 Can a moth strangle the Dog-strangler? An experimental application of Hypena opulenta as a bio-control agent for the invasive Dog-strangling Vine (Vincetoxicum rossicum) ...... 57

3.1 Introduction ...... 57

3.2 Methods ...... 61

3.2.1 Experimental release of H. opulenta ...... 61

3.2.2 Artificial defoliation experiment ...... 65

3.2.3 Statistical analysis ...... 67

3.3 Results ...... 68

3.3.1 Defoliation by Hypena opulenta in shade and sun ...... 68

3.3.2 Effect of defoliation on V. rossicum seed production ...... 68

3.3.3 Distance and directionality of H. opulenta larval dispersal ...... 74

3.3.4 Effect of simulated herbivory of V. rossicum on belowground processes ...... 74

3.4 Discussion ...... 74

3.4.1 Defoliation and seed production of V. rossicum in shade and sun conditions following an application of H. opulenta ...... 74

3.4.2 Effect of simulated herbivory of V. rossicum on belowground processes ...... 78

3.4.3 Synthesis and study limitations ...... 80

3.5 Conclusion ...... 81

Chapter 4 On the distribution of different forms of ecological rarity in herbaceous plants and their importance for ecosystem functioning ...... 82

4.1 Introduction ...... 82

4.2 Methods ...... 85

4.2.1 Species relative abundance, functional traits and ecosystem functions ...... 85

4.2.2 Forms of rarity ...... 85

4.2.3 Statistical analysis ...... 86

4.3 Results ...... 88

4.3.1 Relationships between different forms of rarity ...... 88

4.3.2 Significance of relative abundance and functional uniqueness for ecosystem functionality ...... 90

4.3.3 Effect of V. rossicum invasion on the presence of functionally unique and geographically restricted species ...... 95

4.4 Discussion ...... 95

4.4.1 The relationship between different forms of rarity ...... 95

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4.4.2 The significance of different forms of rarity for ecosystem functionality ...... 99

4.4.3 The impact of invasion on functional uniqueness and geographical restrictedness ...... 102

4.5 Conclusion ...... 103

Chapter 5 Ecological engagement determines ecosystem service valuation: A case study from Rouge National Urban Park in Toronto, Canada ...... 104

5.1 Introduction ...... 104

5.1.1 Analytical framework ...... 105

5.1.2 Case study description: Rouge National Urban Park ...... 108

5.2 Methods ...... 109

5.2.1 Study design and survey protocols ...... 109

5.2.2 Ecological engagement classification ...... 111

5.2.3 ESs importance values ...... 112

5.2.4 Perceptions of cultural ESs and NIS impact on ESs ...... 112

5.2.5 Statistical analysis ...... 113

5.3 Results ...... 113

5.3.1 Park user attributes ...... 113

5.3.2 Ecosystem service valuation ...... 113

5.4.1 Ecosystem service ranking ...... 117

5.4.2 Provisioning of cultural ESs ...... 117

5.4.3 Impact of invasive species on ESs provisioning ...... 121

5.5 Discussion ...... 121

5.5.1 Variability of stakeholder ESs valuation ...... 121

5.5.2 Park user perception of invasive species impact ...... 126

5.5.3 Conclusion ...... 127

6 Synthesis and Conclusion ...... 129

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7 References ...... 133

8 Appendices ...... 183

List of Tables Table 2-1: linear models showing the effect of invasion by V. rossicum on multiple plant biodiversity measures (comparison between uninvaded and invaded sites; 2.06% ± 1.06 and 63.51% ±3.79 V. rossicum, respectively). Statistically significant models have bolded p values...... 43

Table 3-1: Results from linear mixed effects models for all variables showing the effects of experimental treatment on V. rossicum reproductive output, leaf count and leaf area ...... 71

Table 3-2: Mean (±SE) delta values for response variables following simulated herbivory treatments. For each variable, values denoted by the same letter are not significantly different (Tukey’s HSD, p<0.05) ...... 76

Table 4-1: z scores (standardized effect sizes) and p values showing plant species importance for ecosystem function, based on relative abundance. Pink and blue boxes indicate significantly positive and negative associations with ecosystem functions, respectively...... 94

Table 4-2: Results of mixed effects logistic regression models for species presence/absence in relation to V. rossicum abundance. Species’ presence/absence were fixed effects in the model while site was denoted as a random effect (see methods). Blue boxes indicate significantly negative species associations with V. rossicum abundance with bolded p values indicating the associated degree of statistical significance...... 96

Table 5-1: Relationship between ecological engagement and other attributes of Rouge National Urban Park users (n=178 for ecologically engaged, n=146 for non-ecologically engaged) ...... 114

Table 5-2: The effect of Park user attributes on ecosystem service (ES) valuation in Rouge National Urban Park. Note: Kruskal-Wallis tests were performed and Wilcoxon rank sum test for ecological engagement (due to low degrees of freedom) ...... 115

Table 5-3: Ranked importance of Rouge National Urban Park’s ecosystem services in relation to ecological engagement. n=178 for ecologically engaged, n=146 for non-ecologically engaged ...... 119

List of Figures

Figure 1-1: Vincetoxicum rossicum distribution in south-central Ontario (source: EDDmaps) ... 23

Figure 1-2: Vincetoxicum rossicum invasions in full sun (a) and forest understory conditions (b). Photos were taken in Kirkfield, Ontario. Photo credit: S. Livingstone ...... 24

Figure 2-1: Flow diagram showing the approach to assess the impact of V. rossicum invasion on plant biodiversity and ecosystem functioning. Impact is analyzed by considering A) the direct effect of V. rossicum on different components of plant biodiversity, B) the resultant change in the relationship between plant biodiversity and ecosystem function at various stages of invasion (See Figure 2-3 for detailed schematic of this approach) and C) a simple approach that examines the relationship between V. rossicum abundance and the listed ecosystem functions without analyzing the shift in plant biodiversity...... 31

Figure 2-2: Map of Rouge Park study sites ...... 33

Figure 2-3: Meta-analysis approach to assess impact of V. rossicum on biodiversity-ecosystem function relationships across Rouge NUP study sites. The coefficients from individual biodiversity-ecosystem function relationships (12 per function (9) at each of the 14 sites) (section a) are plotted in ranked order according to the relative abundance of V. rossicum at the site (section b). Then a second regression is conducted to examine how those relationships vary along the invasion gradient. .... 39

Figure 2-4: Ranked V. rossicum relative abundance (±SE) at Rouge National Urban Park study sites. (n=25 per site) ...... 41

Figure 2-5: Regression coefficients for the response of multiple biodiversity measures to invasion by V. rossicum (comparison between uninvaded and invaded sites; V. rossicum abundance: 2.06% ± 1.06 and 63.51% ±3.79, respectively). The bars around coefficient values denote 95% confidence intervals. A coefficient value is significantly positive or negative when its 95% confidence intervals do not bracket zero...... 42

Figure 2-6: Regression coefficients for the response of multiple ecosystem functions to invasion by V. rossicum (comparison between uninvaded and invaded sites; V. rossicum abundance: 2.06% ± 1.06 and 63.51% ±3.79, respectively) The bars around coefficient values denote 95% confidence intervals. A coefficient value is significantly positive or negative when its 95% confidence intervals do not bracket zero...... 45

Figure 2-7: Regression coefficients for biodiversity-ecosystem function relationships across all study sites (site was included as a random factor in the mixed effects regression model). The bars around coefficient values denote 95% confidence intervals. Model outputs are shown in Table 8-6. A coefficient value is significantly positive or negative when its 95% confidence intervals do not bracket zero...... 47

Figure 2-8: Regression coefficients measuring the effect of increasing V. rossicum abundance on biodiversity-ecosystem function relationships. These coefficient values are the result of a second regression from a 2 step regression procedure (ostensibly a meta- analysis). The first set of regressions was carried out on all biodiversity-ecosystem function relationships at each of the 14 study sites, then corresponding regression coefficients were arranged in ranked order of increasing V. rossicum abundance at each site. The above regression coefficients are the result of regression models run on the aforementioned ranked-order coefficients (see Figure 8-5 to Figure 8-12). The bars around coefficient values denote 95% confidence intervals. A coefficient value is significantly positive or negative when its 95% confidence intervals do not bracket zero. Models and outputs are shown in Table 8-8...... 49

Figure 3-1: Schematic showing the variables were assessed following simulated herbivory. Based on previous studies, it should be expected that both root exudation and CO2 efflux would increase following defoliation (indicated by the “+”). Studies have shown substantial variability with respect to the effect of plant defoliation on decomposition rate, microbial biomass and nutrient concentrations, so the uncertainty in response is indicated by “+/-“. Figure is modified from (Bardgett and Wardle, 2003) ...... 62

Figure 3-2: Experimental Hypena opulenta release site, Kirkfield, Ontario, Canada. Image from Google Earth...... 63

Figure 3-3: Percentage (±SE) of V. rossicum leaves from shade and sun treatments showing any defoliation at 12 and 24 days following the application of H. opulenta. Points at each time interval denoted with different letters are significantly different (Tukeys HSD) ...... 69

Figure 3-4: Mean (±SE) number of leaves on V. rossicum individuals in shade and sun treatments following three weeks of herbivory by H. opulenta. Bars denoted with different letters are significantly different (Tukey’s HSD) ...... 70

Figure 3-5: Mean leaf area (±SE) of bottom, middle and top leaves of V. rossicum individuals following the application of H. opulenta. Bars denoted with different letters in each leaf category are significantly different (Tukey’s HSD)...... 72

Figure 3-6: Mean (±SE) of seed mass, follicle length, follicle mass and seed count for V. rossicum a) shade treatment and b) sun treatment. “C” and “E” indicate control and experimental plots, respectively. Significance denoted as; **, p<0.01; *, p<0.05, n=100. (Wald’s Chi Square performed on linear mixed-effect model) Differences between treatments shown in Table 1-1...... 73

Figure 3-7: Mean (±SE) distance and directionality of H. opulenta larval dispersal for sun and shade treatments. Significance denoted as; *, p<0.01 (t-test), n=5...... 75

Figure 4-1: Relative abundance, functional uniqueness, geographical restrictedness and functional rarity of herbaceous plant species in Rouge National Urban Park study

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sites, placed in order of ranked abundance. Functional uniqueness, geographical restrictedness and functional rarity calculations are explained in section 4.2.3...... 89

Figure 4-2: The relationship between (a) functional uniqueness and relative abundance (n=49) and, (b) functional uniqueness and geographical restrictedness (n=49) of herbaceous plant species in Rouge National Urban Park study sites...... 91

Figure 4-3: The relationship between plant functional uniqueness and ecosystem function. Points represent mean z scores (standardized effect size). Species are categorized as having high, medium or low functional uniqueness (n=16 per category). Error bars indicate 95% CI...... 92

Figure 4-4: The relationship between plant relative abundance and ecosystem function. Points represent mean z scores (standardized effect size). Species are categorized as having high, medium or low relative abundance (high: n=4, medium: n=22, low: n=21). Error bars indicate 95% CI...... 93

Figure 4-5: Relationship between a) plant functional uniqueness (Ui) and species association with V. rossicum abundance, and b) Plant geographical restrictedness (Ri) and species association with V. rossicum abundance. A value of 1 for species association with V. rossicum indicates a significantly negative relationship between species presence and V. rossicum abundance (Table 4-2), values of 0 represent non- significant associations. The solid line in (b) indicates a statistically significant relationship (p=0.02). Shaded area indicates 95% confidence interval. (points are offset to make all points visible)...... 97

Figure 5-1: Analytical framework: Using the contextual boundaries of participatory governance in Rouge NUP, I identified the stakeholder group of Park Users, a priori, simply based on their presence in the Park. Within-group categorization of “ecological engagement” was stakeholder-defined (Prell et al. 2009) as knowledge of the NIS Vincetoxicum rossicum. To analyze within-group variability I examined stakeholder valuation and prioritization of ESs, and perception of NIS impact, with emphasis on cultural ESs - The “stake” here largely being access to, and protection of, ESs. .... 106

Figure 5-2: Map of survey sites in Rouge National Urban Park ...... 110

Figure 5-3: Effect of ecological engagement (EE) on ecosystem services valuation by Rouge National Urban Park users. Wilcoxon rank sum test performed to determine statistical significance...... 116

Figure 5-4: Effect of visitation frequency (a) and age group (b) on ecosystem service valuation by Rouge National Urban Park users ...... 118

Figure 5-5: Effect of Park user ecological engagement (EE) on perceptions of cultural ecosystem services (ESs) in Rouge National Urban Park (NUP), n=178 for EE, n=146 for non- EE...... 120

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Figure 5-6: Effect of Park user ecological engagement (EE) on perceptions of the impact of V. rossicum on ecosystem services in Rouge National Urban Park, n=178 for EE, n=146 for non-EE)...... 122

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List of Appendices

Table 8-1: Rouge National Urban Park site coordinates ...... 183

Table 8-2: Mean relative abundance (±SE) of all observed herbaceous plant species across 14 meadow communities in Rouge NUP...... 184

Table 8-3: mean values (±SE) for biodiversity metrics and ecosystem functions for all Rouge NUP study sites...... 186

Table 8-5: preliminary analysis of the relationship between V. rossicum abundance and ecosystem functionality using site means along the invasion gradient...... 189

Table 8-6: Mixed effects models for general biodiversity-ecosystem function relationships for Rouge National Urban Park (site coded as random factor) (±95% C.I.) ...... 190

Table 8-7: Mean functional trait values for herbaceous plant species observed at Rouge NUP study sites (n=20-40 for height & SLA, n=5 for LCC, LNC & LCN) ...... 193

Table 8-8: Linear and polynomial regression models showing the relationship between individual biodiversity-ecosystem function coefficients and V. rossicum invasion (statistically significant relationships have bolded p values) ...... 195

Table 8-9: Pearson correlations for response variables from simulated herbivory plots. (bolded p-values indicate statistically significant relationships) ...... 198

Table 8-10: Relative abundance, functional uniqueness, restrictedness and functional rarity or herbaceous plant species in Rouge National Urban Park ...... 199

Table 8-11: Mean z-score (±SE) for the effect of plant functional uniqueness (categorical) on ecosystem function. Functional uniqueness category calculation detailed in section 4.2.3 ...... 200

Table 8-12: mean z score (±SE) for the effect of plant relative abundance (categorical) on ecosystem function (mean z scores that differ significantly from zero are bolded) 201

Figure 8-1: Regression coefficients for the association of multiple biodiversity measures with V. rossicum along a gradient of increasing invasion. The bars around coefficient values denote 95% confidence intervals. A coefficient value is significantly positive or negative when its 95% confidence intervals do not bracket zero. Coefficients were generated using a mixed effects model with site as a random factor...... 202

Figure 8-2: Relationship between increasing V. rossicum abundance and plant community biodiversity metrics at the regional scale ...... 203

Figure 8-3: preliminary analysis showing the relationship between ecosystem functions and increasing V. rossicum abundance. Points represent site means, bars represent standard errors. n=25 for flower cover, pollinator abundance and pollinator richness, n=5 for biomass and litter, n=4 for soil N and C and decomposition rate. Solid lines indicate statistically significant relationship. Dashed line represents a near- statistically significant relationship. See Table 8-5 below for model output...... 204

Figure 8-4: Bray-Curtis dissimilarity values between Rouge NUP study sites a) calculated by species presence/absence, b) calculated by species relative abundance ...... 205

Figure 8-5: Regression coefficients (±S.E.) for the relationship between plant biodiversity measures (shown in individual panel headings) and aboveground biomass production, arranged in ranked order along a V. rossicum invasion gradient...... 206

Figure 8-6: Regression coefficients (±S.E.) for the relationship between plant biodiversity measures (shown in individual panel headings) and leaf decomposition rate, arranged in ranked order along a V. rossicum invasion gradient...... 207

Figure 8-7: Regression coefficients (±S.E.) for the relationship between plant biodiversity measures (shown in individual panel headings) and flower cover, arranged in ranked order along a V. rossicum invasion gradient...... 208

Figure 8-8: Regression coefficients (±S.E.) for the relationship between plant biodiversity measures (shown in individual panel headings) and inverse soil inorganic N, arranged in ranked order along a V. rossicum invasion gradient...... 209

Figure 8-9: Regression coefficients (±S.E.) for the relationship between plant biodiversity measures (shown in individual panel headings) and inverse soil total N, arranged in ranked order along a V. rossicum invasion gradient...... 210

Figure 8-10: Regression coefficients (±S.E.) for the relationship between plant biodiversity measures (shown in individual panel headings) and plant litter production, arranged in ranked order along a V. rossicum invasion gradient...... 211

Figure 8-11: Regression coefficients (±S.E.) for the relationship between plant biodiversity measures (shown in individual panel headings) and pollinator abundance, arranged in ranked order along a V. rossicum invasion gradient...... 212

Figure 8-12: Regression coefficients (±S.E.) for the relationship between plant biodiversity measures (shown in individual panel headings) and soil total C, arranged in ranked order along a V. rossicum invasion gradient...... 213

Figure 8-13: canopy photos from experimental Hypena opulenta release site in Kirkfield, Ontario...... 214

Figure 8-14: Initial plot conditions for simulated herbivory study. Error bars denote standard error of the mean, n values for each treatment are shown in figure legend...... 215

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Figure 8-15: Photos from control and experimental plots of shade and sun treatments approx. three weeks following the application of H. opulenta. a) shade plots, b) sun plots. 216

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List of Abbreviations

NIS Non-indigenous invasive species NIPS Non-indigenous invasive plant species ESs Ecosystem services NUP National Urban Park FD Functional diversity PD Phylogenetic diversity BEF Biodiversity-ecosystem functioning LNC Leaf nitrogen content LCC Leaf carbon content SLA Specific leaf area C:N leaf carbon to nitrogen ratio

CWMSLA Community weighted mean value for SLA

CWMLCC Community weighted mean value for leaf carbon content

CWMLNC Community weighted mean value for leaf nitrogen content

CWMVH Community weighted mean value for plant height (vegetation height)

CWMC:N Community weighted mean value for leaf carbon to nitrogen ratio

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"When we try to pick out anything by itself, we find it hitched to everything else in the Universe." (Muir, 1911)

1. Introduction

1.1 Invasion ecology: Causes and consequences of biological invasions

The conservation of biodiversity is now widely recognized as a central objective for sustainable development (Armsworth et al., 2007; Daily et al., 2000). Given that the contemporary rate of biodiversity loss is staggeringly high (Dirzo et al., 2014; Gonzalez et al., 2016; Johnson et al., 2017), there is a wealth of research concerned with how ecosystem functions and services will perform when diversity is reduced (Cardinale et al., 2012; O’Gorman et al., 2011). As a significant driver of biodiversity loss, non-indigenous invasive species (NIS) have been discussed and studied extensively over the past 60 years (Elton, 1958; Richardson, 2011); following early thoughts on the subject by Charles Darwin (Darwin, 1859). While there has been a wealth of research focused on the causal mechanisms of biological invasions (Baker, 1965; Brym et al., 2011; Callaway and Aschehoug, 2000; Callaway and Ridenour, 2004; Keane and Crawley, 2002; Levine et al., 2003; Rejmánek, 2000), there has also been a resurgence of literature investigating their impacts on ecosystem functioning and services (Hulme et al., 2013; Pyšek et al., 2012; Simberloff et al., 2013; van Hengstum et al., 2014; Vilà et al., 2011; Walsh et al., 2016). Broadly, invasive species are defined as “alien species that sustain self-replacing populations over several life cycles; produce reproductive offspring, often in very large numbers at considerable distances from the parent and/or site of introduction; and have the potential to spread over long distances” (Pyšek and Richardson, 2010: 29). Yet, throughout the field of invasion ecology there has been substantial variability and ambiguity in the terminology used to discuss the spread and impacts of non- indigenous species. Many terms are used to describe NIS: ‘invasive’, ‘introduced’, ‘exotic’, ‘weed’, ‘pest’, ‘naturalized’ and others (Blackburn et al., 2011). This inconsistent lexicon (Colautti and MacIsaac, 2004; Pereyra, 2016; Richardson et al., 2000), and the fact that invasion impacts tend to be quantified along either anthropocentric or ecological axes, each influenced by subjective values (Colautti and MacIsaac, 2004; Simberloff et al., 2013; Tassin et al., 2017), has led to confusion and debate about the appropriate degree of concern for the impacts of NIS (Crowley et

1 al., 2017b; Davis and Chew, 2017; Russell and Blackburn, 2017; Tassin et al., 2017; Vonesh et al., 2017).

There have been strong efforts to refine the frameworks by which invasion impact is assessed (Barney et al., 2013; Jeschke et al., 2014; Kumschick et al., 2015; Parker et al., 1999), but the variable scales and types of multiple response variables often confound synthetic analysis (Hulme et al., 2013; Mack and D’Antonio, 1998; Mooney and Cleland, 2001; van Hengstum et al., 2014). As such, quantifying the impact of NIS is often dependent on local ecological (Cameron et al., 2016; Pyšek et al., 2012; Thomsen et al., 2011) and political contexts (Head, 2017; Larson, 2007). Nevertheless, the field of invasion ecology has generated decades-worth of evidence detailing how NIS can alter the functioning of indigenous ecosystems (Vilà and Hulme, 2017a), sometimes causing significant economic impacts (Pejchar and Mooney, 2009) and reductions in indigenous biodiversity (Hejda et al., 2009; Pyšek et al., 2012). But even with such an abundance of evidence, the social and ecological complexity of the problem, both in general and with respect to individual invasive species, demand that we continue to investigate the causes and impacts of biological invasions.

1.1.1 Mechanisms of plant invasion

The relative invasiveness and subsequent ecological impact of a given introduced plant species can be the result of a number of different ecological mechanisms. According to one hypothesis, dubbed the “enemy release hypothesis” (Keane and Crawley, 2002), it may be the case that in its native range, a plant species’ abundance is largely regulated by the presence of a natural enemy (e.g. insect or mammal herbivory, soil pathogen, competing plant species). Then, when introduced into a location outside of its native range these “natural controls” are no longer in place, allowing the introduced species to realize its full physiological potential (i.e. greater photosynthetic potential due to undamaged leaves, greater nutrient uptake capacity due to lack of root herbivory or the presence of soil pathogens). Researchers have largely concluded that this is by no means a generalized phenomenon (Colautti et al., 2004; Liu and Stiling, 2006), but it has been shown to be important for some cases (Colautti et al., 2004; DeWalt et al., 2004; Vilà et al., 2005). Closely related to this hypothesis is the notion that plants can evolve an increased competitive ability in their introduced ranges due to the lack of natural enemies (Blossey and Notzold, 1995). In their native range, plant species are involved in an evolutionary “arms race” with their natural enemies where their evolutionary response to herbivory can involve the development of defensive 2 secondary metabolites to curb the extent of attack. Then, when introduced to a non-native range without the presence of natural enemies, these plants may no longer need to devote energy to the development of such bio-chemical compounds. Instead, plants are able to direct their resources towards their reproductive output, which results in the evolution of greater fitness in non-native ranges. Yet, there has been little evidence to support this mechanism of invasion (Felker-Quinn et al., 2013).

Another mechanism that is thought to enhance plant invasiveness is the release of allelopathic biochemical compounds by exotic plant species (i.e. the “novel weapons hypothesis”(NWH); Callaway & Aschehoug, 2000). Allelopathy refers to “the suppression of the growth and/or establishment of neighbouring plants through direct or indirect (litter decay) chemical release from a plant” (Inderjit et al. 2011, pg. 1). According to the hypothesis, allelopathy can be amplified in non-native ranges where allelochemicals (secondary metabolic products: tannins, phenolic compounds, fatty acids) produced by an exotic species are novel to native plant and microbial communities. The novelty of these allelochemicals, produced as root exudates and leaf leachates, can enhance the competitive ability of the exotic species as natives lack the co- evolutionary history that would otherwise limit the success of the exotic (Inderjit et al., 2011). Considering the NWH alongside the many comparative studies that find the decomposition rates of invasive species to be far greater than native species in their invaded range (Allison and Vitousek, 2004; Ashton et al., 2005; Liao et al., 2008), it is a logical assumption that the rapid decomposition of leaf litter from invasive species, coupled with enhanced allelopathic potential, may very well lead to a positive feedback further facilitating plant invasion (Smith and Reynolds, 2012). The assertion here is that these exotic allelochemicals may accumulate in invaded systems thereby inhibiting the growth and regenerative capacity of the native community. And in fact, a meta-analysis by Lamarque et al., (2011) has shown that evidence supporting the NWH has proven to be more powerful than evidence for several other dominant theories for the success of species invasions. Yet, as previously noted, any efforts to make generalizations or unified theories of invasiveness or invasibility are often confounded by variability in localized biophysical interactions, environmental conditions, and variable species assemblages.

The above hypotheses, and others focused on the mechanisms driving invasion, have been eloquently synthesized by MacDougal and colleagues (2009) where the authors employ recent advances in species coexistence theory to understand the invasion process. Specifically, they assert 3 that invasions, and species’ invasiveness, need to be understood by considering both the ecological niche and fitness of an introduced species in contrast to those of the “recipient plant community”. According to their framework, species with significant ecological impact are likely to have a large degree of niche overlap with native species, but also have a fitness advantage (i.e. through the physical structure of the plant, greater reproductive output, biochemical makeup, etc.). Certainly, hypotheses focused on relatively simple ecological processes are unlikely to explain the invasiveness of a given species (at least in most cases)(Gallien and Carboni, 2017; MacDougall et al., 2009). Due to the multi-dimensional nature of species’ co-existence or antagonistic interactions, these kind of nuanced approaches are required to understand the mechanisms driving species invasion.

The variability of different ecosystems, with respect to both their abiotic and biotic structure and composition, can lead to different susceptibilities to invasion by NIS. The so called diversity-resistance (or diversity-invasibility) hypothesis posits that ecosystems with high biodiversity are more resistant to invasion due to strong competition dynamics and high degree of resource use (Elton, 1958; Kennedy et al., 2002). Effectively, it is believed that diverse ecosystems have a high degree of niche occupancy which limits the available niche space that NIS would use to establish (MacDougall et al., 2009). And while there is evidence for this at relatively small scales (Kennedy et al., 2002; Lyons and Schwartz, 2001), the counterpoint is that many NIS exhibit high niche breadth and thus have a greater probability of establishing in unoccupied niche space (Higgins and Richardson, 2014). It has also been shown that NIS can possess extremely powerful dispersal abilities, highly efficient resource use strategies and accelerated rates of evolution that can facilitate their establishment and spread in both distant and low resource environments (Blossey and Notzold, 1995; Funk and Vitousek, 2007; Whitney and Gabler, 2008).

1.1.2 Management perspectives on invasions

In general, biological invasions are now conceptualized as a stage-based process (Blackburn et al., 2011; van Wilgen et al., 2014). The first two stages are simply the transport and introduction of a non-indigenous species, intentional or not, which are then followed by stages of establishment and spreading to the point of dominance. Of course, not all non-indigenous species reach the dominance stage. In fact, a very small minority of non-indigenous species that pass through the transport and introduction stages actually spread to the point of becoming invasive (Williamson and Fitter, 1996). Species that are introduced into a new ecosystem are often hindered 4 by competition from indigenous species and/or mis-matches between the climate of their indigenous and introduced range. However, researchers are now well aware that many non- indigenous species that go on to become invasive do so after a ‘lag phase’ that follows establishment. This knowledge, paired with our increasingly refined understanding of “invasion pathways”, promotes the need for preventative legislation and highlights the value of predictive modelling (Hulme, 2009; Leung et al., 2002; Turbelin et al., 2017). Yet, due to insufficient or ineffective policy with respect to the management and control of invasive species, researchers continue to produce evidence of the impacts of NIS in hopes of spurring legislative and regulatory action (Andersen et al., 2004; Mackay et al., 2017; Walsh et al., 2016).

Knowledge of the potential risks associated with the establishment and spread of NIS can inform environmental management decisions. Some NIS are known to be extremely problematic, in which case managers can devote significant efforts to their eradication in the early establishment phase within uninvaded systems. But the general uncertainty regarding which non-indigenous species have the potential to become invasive requires the development of preventative policy measures. For example, to limit the introduction of many aquatic non-indigenous species, which have caused significant ecological and economic impacts around the globe (Bax et al., 2003; Pimentel et al., 2005), the Government of Canada has put regulations in place which mandate the flushing of ballast water from transoceanic vessels that may harbour potentially invasive exotic species. Such measures have been shown to very effective (Bailey et al., 2011). With respect to preventative measures focused on non-indigenous invasive plant species (NIPS), international regulatory agreements such as the International Plant Protection Convention (IPCC), or regulatory bodies such as the North American Plant Protection Organization (NAPPO), utilize risk assessment methodologies to regulate the international trade of plants (Campbell, 2001; Meyerson and Mooney, 2007; Schrader and Unger, 2003). However, when NIPS pass through such “regulatory filters” and become highly dominant, management efforts are often limited to marginally effective control techniques, monitoring, and public outreach to limit further spread (Kubeck, 2008; Mashiloane, 2011; van Wilgen and Richardson, 2014; Wallace and Bargeron, 2014).

1.1.3 Susceptibility to invasions: urban beachheads

Many authors have shown and theorized both how disturbance can facilitate invasions (Hierro et al., 2006; Shea and Chesson, 2002), and how invasions can alter disturbance regimes (Gaertner et 5 al., 2017; Mack and D’Antonio, 1998). Studies investigating the link between disturbance and the establishment and impact of NIPS are particularly numerous in the field of urban ecology. Urban and peri-urban ecosystems are highly fragmented mosaics containing semi-natural ecosystems, transportation corridors, large ranges of human population density and an abundance of introduced species. Continuous anthropogenic disturbance in urban and peri-urban ecosystems results in extremely heterogeneous environments, in both biotic and abiotic characteristics and across space and time (Cadotte et al., in press; Grimm et al., 2000; Pickett and Cadenasso, 2009). Such heterogeneity presents an ideal environment for the establishment and spread of species with ruderal life history syndromes, which tends to be the case with many NIPS (Daehler, 2003). Of course, many NIPS are able to tolerate a broad array of environmental conditions (including Vincetoxicum rossicum, discussed below), which often allows them to establish and dominate in both the highly disturbed environments of urban and peri-urban ecosystems as well as nearby natural areas (Hobbs et al., 2009; Mcdonald et al., 2009).

1.1.4 Ecological impact of plant invasions: Theory and challenges in quantification

The establishment and spread of NIPS can alter the biotic composition of their invaded systems by outcompeting indigenous species, sometimes causing local extirpations (Doody et al., 2017; Farris et al., 2017), and/or disrupting trophic interactions (de Groot et al., 2007; van Hengstum et al., 2014). The change in biotic structure can then alter abiotic characteristics, such as altering water quality (Norkko et al., 2012; Walsh et al., 2016), nutrient concentrations (Liao et al., 2008), light availability and/or microclimatic conditions (Charles and Dukes, 2007; Watling et al., 2011). Many studies have examined these impacts of NIPS in specific ecological contexts, often finding alterations to nutrient pools and cycling rates, productivity and other ecosystem properties and functions (eg. soil moisture, carbon (C) storage, disruption of trophic interactions) (van Hengstum et al., 2014; Vilà et al., 2011; Walsh et al., 2016). Yet, it has been shown that the degree and directionality of these impacts tends to be dependent on local ecological context (Cameron et al., 2016; Cook-Patton and Agrawal, 2013; Hulme et al., 2013; Liao et al., 2008). Examining the impact of a single invasive grass species (Bromus tectorum) on aboveground C stocks in shrubland habitat of the western United States, Bradley et al. (2006) found that by converting the habitat to grassland, the invasion reduced biomass production and C stocks and had increased the probability and severity of wildfires. Further, Dassonville et al. (2007) examined the impact of invasion by Fallopia japonica in multiple habitat types in Belgium and found that in addition to causing drastic 6 reductions in plant diversity F. japonica has increased aboveground biomass and soil nutrient concentrations. Given such variability in ecological context, as well as subjective and scale- dependent interpretations of impact (Barney et al., 2013; Novoa et al., 2016), researchers and policy makers have struggled to develop a unifying framework that allows impact analysis to be comparable across different contexts. Nevertheless, recently, many authors have developed fairly comprehensive frameworks to evaluate impacts of NIS in hopes of encouraging cohesiveness in the field (Blackburn et al., 2014; Kumschick et al., 2015).

A small number of meta-analyses and reviews of the invasion impact literature for NIPS have been undertaken in hopes of finding dominant trends that could serve to guide conservation and/or restoration initiatives. Both Liao et al., (2008) and Ehrenfeld (2010) did identify some dominant trends within the invasion impact literature where accelerated decomposition and nutrient cycling rates, increased biomass and greater nutrient concentrations were commonly observed in invaded systems. However, a meta-analysis by Vila et al., (2011) examined over 1000 invasion impact studies and while some similar trends to those identified by Liao et al., (2008) and Ehrenfeld (2010) were seen, there were also many contrasting results. This observed variability in the ecological impact of NIPS highlights the challenges faced by ecosystem managers with respect to the quantification of invasion impact as well as communicating those impacts to the general public. Indeed, many field studies on invasion impact have shown how local differences in environmental and ecological contexts can alter experimental outcomes (Cipollini and Greenawalt Bohrer, 2016; Gornish et al., 2016). Invaded ecosystems often display novel ecological dynamics where ecological processes (i.e. dispersal, competition, natural selection, biotic controls on nutrient cycling, trophic interactions, etc.) often operate at different rates or magnitudes than in uninvaded ecosystems due to the presence of dominant non-indigenous species (Lodge, 1993; Ochocki and Miller, 2017; Vilà et al., 2011). Undoubtedly, such novel ecological conditions can lead to context-dependent assessments of the impact of NIPS.

One of the challenges associated with the quantification of invasion impact is that some of these “alterations” or “impacts” can be perceived as a positive change from the perspective of the science of biodiversity and ecosystem functioning (further discussed below). For example, Allison and Vitousek (2004) found that the leaf litter of several invasive plants in Hawaii exhibited rapid decomposition rates relative to indigenous species with similar life forms. Similarly, Ashton et al., (2005) observed that leaf litter decomposition rates in invaded hardwood forests of the north- 7 eastern United States were greater than uninvaded systems for paired invasive and indigenous species of similar growth forms. This trend of increased decomposition rates following invasion is extremely common (Ehrenfeld, 2003; Liao et al., 2008; Standish et al., 2004; but see Vilà et al., 2011). The challenge here, with respect to identifying invasion impact is that an increased decomposition rate is typically seen as a positive ecological property and as evidence of an efficiently functioning ecosystem (Bardgett and Wardle, 2010; Belovsky, 1986; Cardinale et al., 2011). Though interestingly, invasion by NIPS can also result in increased nutrient concentration, which is often perceived as an undesirable ecological property, indicating inefficient uptake of resources and increased chance of nutrient leaching into adjacent systems (Jones et al., 2014; Knops et al., 2001; Tilman et al., 1997b). Of course, in general care must be taken when evaluating changes in soil nutrient concentrations, especially soil nitrogen (N) where temporal and spatial dynamics can determine both the concentration and form of N. For example, soil samples that are taken before dominant plants emerge and begin utilizing “bio-available” soil N might lead one to conclude that N concentrations are relatively high, where samples taken during seasonal senescence would be more of an indication of residual soil N concentrations (Bardgett et al., 2005; Hawkes et al., 2005). Similarly, soil N goes through the microbially mediated process of - + mineralization where organic N is converted into the “bio-available” forms NO3 and NH4 , and quantifying total soil N does not elucidate the amount of available N (Schimel and Bennett, 2004). Moreover, as “bio-available” soil N can be transported from system to system, sometimes causing deleterious impacts, it is useful to consider whether or not soil N is a limiting resource in a given ecosystem, or whether it is likely to be transported to a different system via water flow (Zeng et al., 2016).

The presence of NIPS has also been shown to decrease nutrient concentrations (Ehrenfeld, 2003), which can indicate more efficient use of available resources than was achieved by the system in its uninvaded state. The prevailing wisdom here with respect to the effect of NIPS on decomposition and nutrient pools is that the majority of NIPS produce relatively high quality litter, which makes organic matter more readily available to decomposers, which in turn stimulates nitrification and subsequent nutrient re-uptake by the invasive plant (Allison and Vitousek, 2004; Wardle et al., 2004). Of course, there are often confounding factors in these aboveground- belowground interactions (i.e. allelopathic compounds in leaf leachates and/or root exudates of NIPS; Inderjit et al., 2011) that again highlight the context dependency of plant invasions.

Regarding the perception that increased rates of decomposition and reduced nutrient concentrations can be indicative of efficient ecological functioning (Cardinale et al., 2011; Tang et al., 2013), invasion impact studies would do well to explicitly compare any alterations in functionality to a “reference state” of the indigenous ecosystem (Parker et al., 1999). Of course, these alterations to ecosystem functionality as a result of invasion by NIPS, whether “positive” or “negative”, typically occur in the wake of local extirpations of indigenous species and communities. Given the variability in the impact of NIPS with respect to ecosystem function, the quantification of impact should also put strong emphasis on the manner in which indigenous plant communities, and biodiversity in general, are altered following invasion (Ricciardi and Cohen, 2007; Strayer et al., 2006).

In urban environments, the quantification of invasion impact is particularly challenging. This is in part due to the fact that urban regions present environmental conditions that can be an impediment to the establishment and/or persistence of native plant species; meaning that non- native species have often been utilized to provide ecosystem services that would otherwise be absent. An abundance of non-native plant species have been introduced to urban regions, typically for their aesthetic qualities or broad environmental tolerance. These species often provide ecosystem services (ESs) at the local scale where native species fail to establish. For example, the broad range of both temperature and moisture that is observed in highly urbanized environments requires that street trees be extremely stress tolerant, a trait which may not be found in the native species pool (Kowarik, 2011; McKinney, 2006). Similarly, non-native species planted in urban environments can offer an enhanced resource supply for native birds and/or arthropods that might otherwise suffer from the lack of native species (Hobbs et al., 2009; Salisbury et al., 2015). Of course, the counter-point to these positive effects is that some of these non-native species can go on to become highly invasive and spread throughout urban and peri-urban regions, further threatening the persistence of native species. As such, ecologists and governments are working to improve our predictive abilities in order to inform the development of regulations focused on the introduction and transport of non-native species (Early et al., 2016; Hulme, 2015).

1.1.5 Biodiversity-ecosystem function (BEF) relationships

Outside of the invasion impact literature, yet highly related, are studies examining the relationship between biodiversity and ecosystem functioning (BEF). Similar to the invasion impact literature, BEF studies are concerned with how biodiversity loss will affect ecosystem functioning and the 9 provisioning of ESs. The basic hypothesis for most BEF studies is that increasing biodiversity (taxonomic, functional traits, phylogenetic distances) can lead to greater ecosystem functionality (i.e. productivity, efficient nutrient cycling, support for other trophic levels, etc.) (Cadotte et al., 2008, 2011a; Cardinale et al., 2012; Kennedy et al., 2002; Schulze and Mooney, 2012; Tilman, 1999; Yachi and Loreau, 1999). Yet, research has shown that species richness, evenness, dominance, phylogenetic relatedness and different components of functional diversity (richness, divergence, community weighted means) can produce highly variable results as predictors of ecosystem functionality (Cadotte et al., 2008, 2009; Dormann et al., 2017; Gross et al., 2017a; Hillebrand et al., 2008; Maestre et al., 2012; Orwin et al., 2013). And although many studies provide evidence of a positive relationship between biodiversity and ecosystem functioning (Cadotte et al., 2009; Cardinale et al., 2011; Gamfeldt et al., 2013; Lefcheck et al., 2015; Tilman et al., 1997a), there continues to be debate about whether or not these kind of experimental results can be extrapolated to natural systems (Snelgrove et al., 2014; Wardle, 2016 but see Eisenhauer et al., 2016; Mirotchnick, 2017; Flombaum and Sala, 2008). Studies have found that BEF relationships vary significantly in natural systems (Brose and Hillebrand, 2016; Mora et al., 2014, 2011) and also that these relationships can be confounded by scale of measurement (i.e. temporal, spatial) (Ricketts et al., 2016). Other researchers note the challenges and shortcomings of considering ecosystem functions as solitary response variables, which can obscure complex interactions and feedbacks (Byrnes et al., 2014; Grace et al., 2010; Shipley, 2016).

There is increasing interest in identifying the best methods to quantify BEF relationships and the distribution of ecosystem functions in different ecological contexts (Lavorel and Grigulis, 2012; Mouchet et al., 2014). An emerging trend in this regard is the use of “multi-functionality” indices to quantify the contributions of different components of biodiversity to the simultaneous production of multiple ecosystem functions and services (Gamfeldt et al., 2008; Maestre et al., 2012; Pendleton et al., 2014). Multi-functionality indices can be calculated using a few different methods, each with their own advantages and disadvantages (Byrnes et al., 2014; Lefcheck et al., 2015). The most commonly used method to assess multi-functionality involves integrating the average standardized values for multiple ecosystem functions and examining those values in relation to some aspect of changing biodiversity (see Byrnes et al., 2014 for detailed discussion of multi-functionality). Researchers stress that analyses of the relationship between BEF that consider multiple functions and services offer the greatest insight for conservation and sustainability goals

(Bennett et al., 2009; Zavaleta et al., 2010). Yet, examining multi-functionality indices to assess the effect of biodiversity on ecosystem functionality can be limited by potentially divergent responses of ecosystem functions to different aspects of biodiversity. Further, integrating positive and negative relationships for different ecosystem functions into a single index can obscure variability in the response of different functions, and other ecological nuances (Bradford et al., 2014, 2014; Byrnes et al., 2014; Lefcheck et al., 2015). Though, if these kinds of dynamics are minimal multi-functionality indices can be a powerful communication tool that simplify complex multi-variate relationships.

Invasion impacts are particularly interesting in the context of BEF research and ecosystem multi-functionality as invasions can alter ecosystem functions in a number of ways and with variable magnitudes (i.e. increasing biomass production but decreasing nutrient availability, homogenizing functional diversity but increasing single ecosystem functions/services; Norkko et al., 2012; Novoa et al., 2016; or multi-functionality; Ramus et al., 2017). The positive or negative impacts caused by invasion may be dependent on local factors like habitat type, moisture regime, natural or anthropogenic disturbances, functional traits, etc. Moreover, impacts on BEF may occur either directly due to the presence of a new species, or indirectly through the extirpation of native species. As such, studies examining the local impacts of invasion on ecosystem functions and services are vital. Future work seeking to identify trends and expectations regarding invasion impacts will need to consider such local scale factors. This kind of nuanced examination may be necessary to predict long-term impact of invasion, identify high risk ecosystems, negotiate social conflicts and develop effective restoration programs at the local scale.

1.1.6 Functional ecology

Ecologists are increasingly recognizing the strength of trait-based approaches to analyze patterns of biodiversity and their associations with ecological functionality (Cadotte et al., 2009, 2011b; Cadotte, 2017; Gross et al., 2017a; Martin and Isaac, 2015; Tilman et al., 1997a; Villéger et al., 2008). Traits are characteristics of an organism that impact performance and/or fitness and “can be physical (e.g. plant branching pattern, predator tooth morphology), biochemical (e.g. plant photosynthetic pathway, presence of secondary metabolites), behavioural (e.g. nocturnal vs. diurnal foraging, females cannibalizing males) or temporal or phenological (e.g. flowering time, pelagic duration of larval stage)” (Cadotte et al., 2011b :1080). By measuring multiple traits and integrating those values into multivariate metrics researchers are now better able to characterize 11 biodiversity, both within and between species and communities of species (Mason et al., 2005; Petchey et al., 2004; Petchey and Gaston, 2002a; Villéger et al., 2008), as well as the effects of biodiversity on ecosystem functioning (Cadotte et al., 2011b, 2009). The products of these multivariate metrics are broadly known as components of ‘functional diversity’ (FD); the diversity of functional traits. Specifically, these are functional divergence, functional evenness, functional richness (Mason et al., 2005; Villéger et al., 2008), functional dispersion (Laliberté and Legendre, 2010) and more recently, functional rarity (Violle et al., 2017). Each of these metrics quantifies different aspects of the functional diversity of a community; namely i) functional richness (FRic) which captures the total amount of “trait space” taken up by a species or a community, ii) functional evenness (FEve), which is a measure of the relative evenness or skewness of a communities functional trait distribution within “trait space”, iii) functional divergence (FDiv) is an abundance-weighted measure of the range of functional trait variation (Mason et al., 2005; Villéger et al., 2008), and iv) functional dispersion (FDis) which is the “mean distance in multidimensional trait space of individual species to the centroid of all species” (Laliberté and Legendre, 2010)(Functional rarity is discussed in detail in section 161.3 below).

In addition to spurring a broad scale paradigm shift in ecological analysis, these measures offer a powerful new perspective on both the composition and functionality of species and ecosystems. Indeed, it is often the case that the diversity of species functional traits, as well as their interactions in a community, can be more informative for analysing ecosystem functioning (Cadotte et al., 2011b; Castro-Díez et al., 2016; Gross et al., 2017b; Hooper et al., 2005; Petchey and Gaston, 2006) and community assembly (Cadotte et al., 2015; Mouchet et al., 2010) than taxonomically-based measures of diversity. Indeed, trait-based approaches are now guiding ecological restoration efforts (Laughlin, 2014; Ostertag et al., 2015), the identification of conservation priorities (Devictor et al., 2010; Mayfield et al., 2010), and the study of biological invasions (Drenovsky et al., 2012; Gallagher et al., 2015; Rejmanek and Richardson, 1996).

1.2 Biological control of plant invasions

Invasions by NIPS are often so severe that controlling their spread by physical or chemical means is not feasible, either due to associated costs or the spatial extent of the invasion (Pimentel et al., 2005). As a result, evidence-based classical biological control (hereafter “bio-control”) has come to be seen as a vital component of ecological restoration practices (Clewley et al., 2012; Coombs, 2004; Fiedler et al., 2008; Simberloff, 2012). The application of classical bio-control for NIPS 12 involves the identification of a “natural enemy” of the invasive plant, rigorous lab testing to evaluate the potential risk and effectiveness of the agent, evaluation by regulatory bodies and eventual field release. Despite the successful application of bio-control for plant invasion (Embree, 1971; Hinz et al., 2014; Phillips et al., 2008), wariness of bio-control still exists due to several well documented cases where control agents ran amok in their introduced ranges. Though, these problem cases resulted from the introduction of generalist mammals and amphibians to control insect pests in the absence of scientific oversight (Kopf et al., 2017; Simberloff, 2012; Zimmermann et al., 2000). Such introductions no longer take place, and current regulatory practices for bio-control of invasive plants mandate a detailed assessment of the ecological risks associated with the release of a bio-control agent (Simberloff, 2012; Van Driesche and Simberloff, 2016). In short, control agents only pass through “regulatory filters” after a undergoing a slew of laboratory experiments that quantify both the relative ecological risk and benefit of release (Marohasy, 1998; Paynter et al., 2015). It is also important to note that current scientific evaluations of bio-control agents now place equal emphasis on the potential risks posed to economically important species as well as threatened and endangered species (Sheppard and Warner, 2016). Yet, several researchers believe that the current conservative regulatory climate with respect to bio-control research is hindering the potential for control efforts (Hinz et al., 2014; Messing and Wright, 2006; Sheppard et al., 2006).

1.2.1 Selection of biocontrol agents: an exercise in ecological risk assessment

Research has shown that even highly promising bio-control agents often fall short of expectations when subjected to the complex ecological interactions, and sometimes highly variable environments, of the “real world” (Myers and Bazely, 2003; van Driesche et al., 2002). In fact, most introductions of species to new regions, either intentional or accidental, fail to establish self- sustaining populations (Williamson and Fitter, 1996). For classical bio-control to be effective, in the case of widely spread dominate NIPS, control agents need to establish themselves in their recipient ecosystem and reduce the density, spread and fitness of their target plant. A critical determinant of both the selection of bio-control agents and their potential effectiveness in reducing the abundance of target NIPS is the degree of host specificity between the agent and the target species. By offering potential agents the opportunity to consume and reproduce on non-target organisms, in both choice and no-choice scenarios, researchers can determine the host range of a given arthropod (Schaffner, 2001). Regulatory bodies assess the results of these host range tests to 13 evaluate both the potential effectiveness of the agent and the potential risk to non-target organisms. Though, it is important to note that a high degree of host-specificity between an agent and a target organism does not necessarily equate to a strong potential for that agent to reduce the abundance of the invasive plant. The potential effectiveness of a bio-control agent is a product of several variables (e.g. climatic suitability, synchrony with the target organism’s phenology, dispersal ability, the length of the control agent’s own life cycle) (Harris, 1973; Morin et al., 2009; Thomas and Reid, 2007; Trethowan et al., 2011). Essentially, by evaluating potential agents under choice and no-choice scenarios (for feeding and oviposition) researchers are quantifying the fundamental and realized niches of an organism’s diet and reproductive requirements (Van Driesche and Sands, 2000). A gamut of no-choice tests, where an agent is only provided with a single host species upon which to feed and oviposit, quantifies the agent’s fundamental (i.e. physiological) host range (with the successful growth of the next generation as the major consideration). Multiple choice tests, where an agent is given a smörgåsbord of potential hosts for feeding and reproduction, provide an estimate of the agent’s realized (i.e. “ecological”) host range – what plant(s) the agent is likely to use as a feeding source and means of reproduction under more “realistic conditions” (Cullen, 1988). This approach is considered to be a more liberal assessment of the potential risk to non- target species (Zwölfer and Harris, 1971). In fact, it is generally accepted that measuring fundamental host range via no-choice tests is extremely conservative from a risk perspective and is not indicative of an agent’s realized host range (Blossey, 2016). In other words, if a bio-control agent exhibits minimal consumption of a non-target plant, when given no alternative, this does not necessarily mean that it will choose to consume that same plant if given other more desirable choices. In essence, identifying an organism’s realized and fundamental host range is an exercise in probability where realized and fundamental range are end points of a continuum where many forms of testing (no-choice, multiple-choice, field trials, etc.) are used to evaluate where an organism falls along a probability distribution.

In the lead up to testing and release of bio-control agents, research is often also carried out that investigates the potential of control agents by mimicking their actions. With respect to arthropod agents for the control of NIPS, many preliminary studies employ experimental simulations to evaluate plant response to herbivory, typically of roots and foliage (Lehtilä and Boalt, 2008). While there are noted shortcomings of this approach (lack of soil inputs, absence of biochemical interactions, difficulty in mimicking typical agent densities), it is usually an

14 informative first step in the process of agent selection and the evaluation of ecological impacts. A second precursory approach in bio-control is the use of ecological modelling (i.e. computer simulations) to evaluate the response of target organisms to the introduction of control agents (Shea and Kelly, 1998; Sheley and Rinella, 2001). Parameterization for bio-control models considers organismal densities, feeding preferences, life stages, climatic tolerances and the interactions therein to evaluate how these variables will affect the growth and reproduction of target organisms (de Souza et al., 2009; Trethowan et al., 2011), occasionally including the response of neighbouring species to the projected decline of NIPS (Sheley and Rinella, 2001). And although these models are able to integrate much of the ecological interactions between an agent and a target organism, there are many confounding factors that are difficult to account for (e.g. habitat heterogeneity, stochastic disturbances, predation, compensatory growth and indirect effects) (Buckley, 2008; Louda et al., 2003; Pearson and Callaway, 2003). Due to these confounding factors, it is likely the case that computer simulations for bio-control overestimate the potential effectiveness of control agents (Morin et al., 2009). Yet, the organismal responses observed in these kinds of models can still be a useful indication of the potential effectiveness of a control agent (Buckley, 2008).

1.2.2 Indirect effects associated with the application of bio-control

For many years now there have been calls for bio-control research to study the potential indirect effects associated with the release of classical bio-control agents (Denslow and D’Antonio, 2005; Louda et al., 2003; Pearson and Callaway, 2003; Simberloff and Stiling, 1996). For invasive species in general, modelling of the risks associated with establishment and spread of non-native species is widely employed (Duncan et al., 2014; Evangelista et al., 2008; Thuiller et al., 2005; Václavík and Meentemeyer, 2012), but models rarely integrate potential indirect effects associated with invasion impact (White et al., 2006). This is also the case for the majority of studies that employ modelling techniques to investigate the potential effectiveness and risk associated with classical bio-control agents (Pearson and Callaway, 2003; Simberloff and Stiling, 1996). In both cases, this is due to the challenges of developing predictive models that accurately reflect the interactions within complex and dynamic ecosystems (e.g. integrating inter-trophic interactions; Wootton, 2002; effective parameterization; Grace et al., 2010; rapid evolution of both invasive species; Müller-Schärer et al., 2004; and bio-control agents themselves; Simberloff and Stiling, 1996). Due to these challenges, and the fact that control agents may not yet have been approved 15 for release by regulatory bodies, prediction of potential risk and effectiveness of bio-control agents is often limited to experimental lab work and physical simulations in the field. Of course, these kinds of experiments are not without their own set of complexities.

Many NIPS have been shown to exert substantial allelopathic capabilities, independent of aboveground tissue loss (Hierro and Callaway, 2003). Sometimes, this is by virtue of the fact that they have colonized a new environment where soil microbial communities are lacking the ability to assimilate secondary metabolites that may have been mediated by microbes in the plant’s native range (Callaway and Aschehoug, 2000; Willis, 1985; Yuan et al., 2013). Some studies have observed that herbivory of NIPS stimulates the release of allelochemicals (Thelen et al., 2005), sometimes resulting in significant declines in the fitness of neighbouring species (e.g. Centaurea maculosa; Callaway et al., 1999). Yet others have measured no stimulation of allelopathic response following herbivory (Norton et al., 2008). To further complicate the matter, recent work has shown that microbial communities have the ability to both alleviate (Kaur et al., 2009; Li et al., 2017) and enhance (Cipollini et al., 2012) the effects of allelopathic compounds released by NIPS. Secondary metabolites isolated from Vincetoxicum rossicum have been shown to inhibit the germination and growth of other plant species (Douglass et al., 2011; Gibson et al., 2011), but this effect has not been consistent across studies (Gibson et al., 2015). Further, it is unknown whether or not natural or simulated herbivory of V. rossicum would stimulate the exudation of allelopathic compounds. Moreover, comparisons of plant response to defoliation have shown significant differences in biochemical response between plants subjected to simulated and true herbivory (Baldwin, 1990). Clearly then, there is significant variability in how plants respond to defoliation, invasive or not, and much uncertainty in how those responses affect soil processes.

1.3 Forms of ecological rarity and their association with invasive species

Another major concern associated with biodiversity loss and the invasion of NIS is the protection of rare and unique species. Rare species have fascinated ecologists and the general public for ages (Angulo and Courchamp, 2009; Preston, 1948; Richardson et al., 2012; Wagner and Driesche, 2010a). Presumably facing the highest probabilities of local extirpation and extinction from anthropogenic activities, including human-caused species invasions (Bracken and Low, 2012; Doherty et al., 2016; Farnsworth, 2004; Magurran and Henderson, 2003; Sikkema and Boyd, 2015; Wagner and Driesche, 2010a), rare species dominate conservation priorities around the globe (Czech and Krausman, 1997; Walsh et al., 2013). Most ecological communities contain an 16 abundance of rare species (Gaston, 2012a; Hessen and Walseng, 2008; Magurran and Henderson, 2003); that is, rare in relation to the abundance of neighbouring species and not in an absolute sense (Orians, 1997; Verdade et al., 2014). Of course, in terms of their abundance, the commonness or rarity of species actually exists along a continuum. From a conservation perspective we typically perceive of rarity along axes of local/regional abundance, geographic range and habitat specificity, as identified by Rabinowitz (1981). Currently with over 1200 citations, Rabinowitz’ framework (1981) has been applied by several authors exploring extinction vulnerability and associated threats to different taxa across these various forms of rarity (Broennimann et al., 2005; Harnik et al., 2012; Hartley and Kunin, 2003; Yu and Dobson, 2000). Yet, recent advances in the conceptualization of biodiversity have increased our perceived dimensionality of ecological commonness and/or rarity (Violle et al., 2017).

As the field of functional ecology has emerged as vital for the study of general ecological dynamics (Cernansky, 2017), researchers concerned with the conservation of rare and unique species are now investigating rarity using a functional-trait perspective (Jain et al., 2014; Jousset et al., 2017; Lyons et al., 2005; Violle et al., 2017). Conservationists are now not only interested in quantifying rarity in terms of abundance, phylogenetic distinctiveness, niche breadth, or range size but are also examining species and/or community uniqueness along a functional trait axis (Leitão et al., 2016; Mouillot et al., 2013; Umaña et al., 2017). When characterizing ecological communities using functional traits, measurements for multiple traits across multiple species are integrated into the single metric, FD (Petchey and Gaston, 2002a). But many recent studies have found that species that are rare in terms of their abundance often occupy unique regions of multidimensional ‘trait space’ (Jain et al., 2014; Leitão et al., 2016; Mouillot et al., 2013; Richardson et al., 2012), indicating potential importance for ecosystem functionality. This integrated functional and abundance-based perspective on rarity has been conceptualized by Violle et al. (2017) as ‘functional rarity’. And while the application of this metric easily illustrates patterns of rarity across different axes, a major goal is to understand how species that occupy different “forms of rarity” contribute to ecosystem functionality (Jousset et al., 2017; Violle et al., 2017).

Species that share similar trait values, or are in the same functional group, are typically classified as ‘functionally redundant’ (Cadotte et al., 2011b; Naeem, 1998). Of course, the measurement of intra-specific trait variability can reveal the degree to which a given species overlaps (i.e. is redundant) with another in functional ‘trait space’ (de Bello et al., 2013; Macarthur 17 and Levins, 1967). Through the lens of biodiversity conservation, functional redundancy and uniqueness are both valuable ecological properties (Scheffer et al., 2015). Functional redundancy across species serves to minimize the effects of biodiversity loss on ecosystem functioning (Cardinale et al., 2011; Ellingsen et al., 2007), while functional rarity (i.e. uniqueness) diversifies the functional composition of a community and may be important for functionality under either current environmental conditions (Jain et al., 2014; Violle et al., 2017) or as a response to changing environmental regimes. Functionally rare species can contribute to the “portfolio effect” (i.e. the insurance hypothesis) (Yachi and Loreau, 1999) where species that are currently rare, either in terms of their abundance or their functional composition, and may not be significant contributors to functionality, may become functionally significant under future environmental conditions or as redundancy is eroded (Reich et al., 2012; Violle et al., 2017).

1.4 Ecosystem services & invasive species: Role of stakeholder analysis

Widespread loss of biodiversity is occurring due to a complex interplay between multiple anthropogenic drivers: Habitat loss/modification (Thompson et al., 2017), invasive species (Vilà and Hulme, 2017a), pollution (Tsvetkov et al., 2017), human population growth and over- harvesting (Liu et al., 2003), and market failure−a failure to assign and integrate a proper value of ecosystem functionality into economic practices (Liu et al., 2010). In recognition of the extent of biodiversity loss in recent years conservationists, researchers, policy makers and NGO’s have embraced the concept of ecosystem services (ESs) as a means of capturing the value, economically and/or conceptually, of biodiversity and ecosystem functioning (Bryan et al., 2010; Gamfeldt et al., 2013; Hauck et al., 2013; Paudyal et al., 2016). The ESs framework conceptualizes biodiversity and the functioning of ecosystems as “services” upon which we depend (e.g. the capacity of a forest ecosystem to purify fresh water and regulate its flow, the ability of mangrove forests to buffer storm surges, the pollination of agricultural crops, etc.). Essentially, the ESs framework aims to internalize what was previously external to our valuations of the non-human world. Some valuations of ESs assign a monetary value, which can be calculated in number of ways (e.g. contingent valuation, willingness-to-pay) (Turner et al., 2016). Yet, recognizing the public nature of ESs and the variable importance of different ESs to different groups of stakeholders, there have recently been calls for alternative valuation approaches for ESs that are more inclusive and holistic such as social valuation (De Vreese et al., 2016; Hicks et al., 2013; Sagoff, 1986), relational value (Chan et al., 2016) and approaches that delve further into the political ecology of environmental 18 valuation (Jacobs et al., 2016; Wilson and Howarth, 2002). In fact, many authors feel the ESs concept itself oversimplifies human-environment interactions by obscuring complex political perspectives and perceptions of nature (Gómez-Baggethun et al., 2010; Muradian and Rival, 2012; Norgaard, 2010; Schröter et al., 2014). As a result of such divergent discourse, more recent perspectives on the valuation of ESs put greater emphasis on the variable ways in which humans relate to the non-human world (Chan et al., 2016; Gunton et al., 2017). It is often contended that assessments of ESs are best situated under the broad umbrella of sustainable environmental governance, which embraces multiple perspectives of environmental valuation (Andersson et al., 2015; Cavender-Bares et al., 2015; Elmqvist et al., 2015; Jacobs et al., 2013; Schröter et al., 2017)

Of the studies examining the relationship between NIS and ESs, the majority focus on how NIS impact the biophysical interactions that drive the delivery of ESs (Vilà and Hulme, 2017b), with much less attention devoted to socio-cultural aspects (Chan et al., 2012; Crowley et al., 2017b; Estévez et al., 2015; McNeely, 2001; Seppelt et al., 2011). Nevertheless, interest in public awareness of NIS and ESs is growing with many recent studies examining the role of stakeholders in both the valuation of ESs (Bryan et al., 2010; Martín-López et al., 2012) and the management of NIS (Crowley et al., 2017a; Estévez et al., 2015; Novoa et al., 2016; Sharp et al., 2011). In particular, stakeholder analysis is increasingly recognized as a vital tool for the assessment of ESs and perceptions of NIS in urban and peri-urban regions (Felipe-Lucia et al., 2015; Gaertner et al., 2016; Graham and Ernstson, 2012; Haase et al., 2014). “Stakeholder analysis” is a broad term that is used when researchers aim to investigate the social dynamics surrounding social, economic or natural phenomena (Prell et al., 2009). In basic terms, it is a process that involves the identification of individuals, groups and/or organizations that are affected by and/or can affect some phenomenon (Reed et al., 2009). It can entail the application of a wide range of methodologies, which has led to some confusion about exactly what it means and how it’s accomplished (Crosby, 1992). For example, stakeholder analysis can involve an assessment of the needs of stakeholders for the development of different kinds of management programs or structural designs (i.e. technical assistance, physical accessibility, etc.) (Brinkerhoff, 1991). It could also aim to identify how knowledge is transferred to stakeholders (Isaac et al., 2009), sometimes focusing on disparate power dynamics and/or conflicts of opinion between stakeholder groups (Lindenberg and Crosby, 1981). Broadly though, it is typically conducted as an inclusionary process where the opinions, perspectives and experiences of various stakeholders are evaluated in order to mediate conflicts

19 and inform optimal management action (Felipe-Lucia et al., 2015; King et al., 2015; Reed et al., 2009). Certainly, it has often been shown that including stakeholders in environmental management activities leads to the development of effective policy (Guénette and Alder, 2007; Ostrom et al., 1999; Reed, 2008; Turnhout et al., 2010).

Various forms of stakeholder analysis are now employed to investigate social preferences and motivations for the conservation and prioritization of different ESs. Indeed, it is increasingly recognized that studies on the social dimensions of biodiversity conservation (i.e. ecosystem services and NIS management) are needed to complement the wealth of biophysical research (Cebrián-Piqueras et al., 2017; Menzel and Teng, 2010; Palacios-Agundez et al., 2014). For example, by conducting a large-scale questionnaire-based study (interviews with 3,379 individuals across four years) in multiple socio-ecological contexts in Spain, Martin Lopez et al. (2012) were able to identify consistent trends with respect to the socio-economic factors that affect preferences for different ESs. They found that the ranked importance placed on different ESs by stakeholders was consistently correlated with place of residence in either urban or rural regions. Specifically, rural stakeholders tended to place higher importance on provisioning ESs (cattle, agriculture, hunting) where urban residents placed greater importance on cultural ESs (tourism, aesthetics, existence value). This dynamic also points to the issue of the spatial scale at which ESs can be accessed by different stakeholders. Indeed, other studies have identified how ES valuation can be dependent on accessibility to the services in question (Gómez-Baggethun et al., 2013; Wyborn and Bixler, 2013; Young et al., 2013). By identifying conflicting preferences for ESs between different stakeholder groups, these kinds of studies create the groundwork for conflict mediation in environmental management. Certainly, the challenge of conflict mediation has been discussed at length in the environmental management literature (Ostrom, 2009; Young et al., 2016), with many recent studies focusing on how to best navigate conflicting values of ESs between stakeholder groups (Iniesta-Arandia et al., 2014; Ives and Kendal, 2014; Van Riper and Kyle, 2014). Certainly, conflict mediation in environmental management is a thoroughly political endeavour with many authors approaching the subject through the lens of environmental justice (Felipe-Lucia et al., 2015; Schröter et al., 2014).

Investigations focused on stakeholder perceptions of NIS are also on the rise where managers are realizing that public inclusion in invasive species management can be a vital component of integrated management plans (Dickinson et al., 2012; Maistrello et al., 2016). 20

Though, with high variability in both public awareness of NIS (Colton and Alpert, 1998; Connelly et al., 2015) and the manner in which they are perceived (García-Llorente et al., 2008; Lindemann- Matthies, 2016; Russell and Blackburn, 2017), managers face an uphill battle in communicating the importance of NIS management. It is commonly noted that differential valuation of NIS can lead to disagreement with respect to management objectives (Estévez et al., 2015; Novoa et al., 2016). For example, many aquarium fish species are known to be invasive, but consumers continue to place high value on their aesthetic qualities despite the risks associated with their potential invasiveness (Padilla and Williams, 2004). Similarly, in urban and peri-urban regions around the world, local managers are engaged in restoration efforts involving the removal of non-native and invasive tree species in order to restore native habitat (Bertin et al., 2005; Perring et al., 2013), but as many of these trees provide ESs, conflicts arise between management objectives and the values of local stakeholders (Dickie et al., 2014). Although, managers have been able to sway public opinions on NIS through the use of awareness campaigns (Courchamp et al., 2017; García-Llorente et al., 2008) and are also developing conflict resolution methods for stakeholder engagement in the management of NIS (Dickie et al., 2014; Estévez et al., 2015). In general, the sociological factors that affect the management of NIS and ESs need to be investigated with the same rigour as the wealth of biophysical analyses on the subjects (Cebrián-Piqueras et al., 2017). In order for conservation science to effectively inform management practices, scientists and policy makers need to hone their communication skills. And although conflicting perspectives and valuations of the environment are likely inevitable, clear articulation of the risks and benefits associated with NIS and ESs as well as inclusion of the public in the development of management programs will increase the potential for success.

1.5 Vincetoxicum rossicum

The highly invasive exotic vine, Vincetoxicum rossicum, (commonly known as “Dog-strangling vine” or Pale swallow-wort) has become extremely abundant in both urban environments and remnants of indigenous ecosystems within urban centres, throughout southern Ontario, Canada and northeastern United States (Figure 1-1; DiTommaso et al., 2005). Originally introduced in the late 1800’s, there was a significant lag period before V. rossicum became a species of significant concern (Kricsfalusy and Miller, 2010). It is now clear that invasion by V. rossicum is having a significant impact on local and regional biodiversity by suppressing the growth of indigenous and non-invasive exotic plants. As one would expect, this has been shown to have negative impacts on 21 the diversity of other trophic levels (Ernst and Cappuccino, 2005). Several studies have examined potential mechanisms driving invasion by V. rossicum throughout these regions (e.g. Enemy release (Milbrath, 2008), propagule pressure & fitness (Ladd and Cappuccino, 2005), broad environmental niche breadth (DiTommaso et al., 2005; Yasui, 2016), associations with fungal generalists (Bongard et al., 2013), novel allelopathic compounds (Douglass et al., 2009), strong allee effect (Cappuccino, 2004), a high degree of phenotypic plasticity (Yasui 2016), toleration of low resource availability (Sanderson and Antunes, 2013). Despite research showing that the vast majority of V. rossicum seeds fall relatively close to the parent plant (Ladd and Cappuccino, 2005), occasional strong wind events can carry V. rossicum’s feathery pappus-covered seeds significant distances leading to widespread invasion. Even with a low mean dispersal distance, the few individuals that disperse long distances have the strongest effect on the spread of a non-indigenous (Caswell et al., 2003). Similarly, seed dispersal in urban environments is facilitated by colonization along highway corridors where vehicles have been shown to enhance dispersal of invasives (von der Lippe and Kowarik, 2007). V. rossicum has recently been added to the list of Noxious Weeds in Ontario, but land managers are struggling to control its spread due to its resilience and remarkable ability to colonize a wide range of habitats (including both the forest understory and full sun conditions; Figure 1-2), both in the urban matrix and natural spaces (DiTommaso et al., 2005). Due to the remarkable breadth of environmental conditions where V. rossicum is able to thrive, potential management solutions need to be assessed for their feasibility across these different environmental conditions, most notably in different light conditions.

Figure 1-1: Vincetoxicum rossicum distribution in south-central Ontario (source: EDDmaps)

Figure 1-2: Vincetoxicum rossicum invasions in full sun (a) and forest understory conditions (b). Photos were taken in Kirkfield, Ontario. Photo credit: S. Livingstone

1.6 Thesis statement and objectives

This thesis investigates the ecology and management of plant invasions, with specific focus on invasion by the non-indigenous invasive species “dog-strangling vine” (Vincetoxicum rossicum (Kleopow) Borhidi) in southern Ontario. In Chapter 2, I assess the impact of V. rossicum invasion on the biodiversity and ecological functionality of meadow ecosystems in Rouge National Urban Park (NUP). Chapter 3 investigates the potential effectiveness of a bio-control agent, Hypena opulenta Christoph (Lepidoptera: Erebidae), for V. rossicum as well as its implications for belowground processes in V. rossicum dominated environments. In Chapter 4, I examine the relationship between different forms of ecological rarity, their importance for ecosystem function and how they are impacted by V. rossicum invasion. Chapter 5 details how the ecosystem services concept can be utilized to examine both stakeholder valuation of the ESs within Rouge NUP and perceptions of the impact of V. rossicum. Finally, Chapter 6 presents a synthesis of the thesis chapters and general conclusions.

The main hypotheses of this dissertation are: 1) V. rossicum invasion will be associated with significant reductions in biodiversity and impairment of ecosystem functionality of meadow plant communities; 2a) Defoliation of V. rossicum by H. opulenta will be significant in both the sun and the shade, but to a greater degree in the shade given that the forest edge/understory is H. opulenta’s preferred natural habitat, 2b) that this defoliation will then result in a significant reduction in seed production, and 2c) defoliation of V. rossicum will result in alterations to belowground processes; 3a) Species that are rare and unique, in terms of their abundance and functional traits, will make significant contributions to ecosystem functioning, and 3b) V. rossicum invasion will be negatively associated with the presence of rare and unique species; 4a) Stakeholder “ecological engagement” will result in greater valuation of the ecosystem services that are threatened by V. rossicum invasion in Rouge National Urban Park, and 4b) “Ecological engagement” will result in a greater degree of negative perception of the impact of V. rossicum.

While there has been a wealth of research exploring the ecological impact and management of plant invasions, and associated social dynamics, this research is the first to 1) examine how biodiversity-ecosystem function (BEF) relationships vary along an invasion gradient; 2) conduct an experimental open-field release of the bio-control agent H. opulenta in established V. rossicum population in both the shade and sun conditions; 3) utilize recently developed metrics that integrate taxonomic and functional trait uniqueness to explore the relationship between “functional rarity” 25 and ecosystem functioning in communities with varying abundance of V. rossicum, and 4) to examine how public awareness of invasive species (here, termed “ecological engagement”) affects valuation of ecosystem service and perceptions of the impact of plant invasion.

The significance of this work is that it presents a multi-disciplinary perspective on the consequences of plant invasion in a peri-urban ecosystem. By drawing from both theoretical and applied ecology I quantify the impact of reduced biodiversity via plant invasion on multiple ecosystem functions. In addition to making a contribution to the wealth of research on the impact of V. rossicum, this work details, for the first time, the effects of Hypena opulenta on the reproductive capacity of V. rossicum in an open-field release. Additionally, by applying recently developed trait-based methods I characterize herbaceous plants according to different forms of ecological rarity and quantify their importance for ecosystem functioning. Lastly, this dissertation advances the social dimension of invasion ecology by employing the ecosystem services concept to assess the relationship between invasive species awareness and differential valuation of ESs.

This thesis is comprised of four studies that are either in review or in preparation for peer- reviewed journals (Chapters 2, 3, 4, and 5). Chapter 1 provides a brief introduction and the motivation for the study, and Chapter 6 summarizes the major findings of this work and suggests key research questions to investigate in future work. Chapter 5 contains the same content as was submitted in manuscript form. The similarity between the other chapters and submitted versions is yet to be determined. The four individual studies are listed below

Livingstone, SW; Cadotte, MC; Isaac, ME. Effects of the invasive vine Vincetoxicum rossicum on biodiversity and ecosystem functionality [Chapter 2, page 27] (In preparation for the Journal of Applied Ecology) Livingstone, SW; Cadotte, MC; Isaac, ME; Smith, S; Bourchier, R. Can a moth strangle the Dog-strangler? An experimental application of Hypena opulenta as a biological control agent for the invasive Dog-strangling Vine (Vincetoxicum rossicum) [Chapter 3, page 57] (In preparation for Biological Invasions) Livingstone, SW; Cadotte, MC. On the distribution of different forms of rarity in herbaceous plants and their importance for ecosystem functioning. [Chapter 4, page 82] (In preparation for Functional Ecology) Livingstone, SW; Cadotte, MC; Isaac, ME (accepted) Ecological engagement determines ecosystem service valuation: A case study from Rouge National Urban Park in Toronto, Canada. Ecosystem Services [Chapter 5, page 104]

2 Effects of the invasive vine Vincetoxicum rossicum on biodiversity and ecosystem functionality

2.1 Introduction

The establishment and spread of non-indigenous invasive plant species (NIPS) has been shown to cause significant changes to the way that ecosystems function (Vilà and Hulme, 2017b). Though, unsurprisingly the manner and degree of change is not consistent for different invasive species and/or invaded systems (Davis, 2009; Vilà et al., 2011). This variability has led to contentious debate about how to best quantify the ecological impact of NIPS (Crowley et al., 2017b; Davis and Chew, 2017; Russell and Blackburn, 2017; Tassin et al., 2017; Vonesh et al., 2017). Many studies have focused on how the spread of NIPS affects the biodiversity of their invaded ranges (Cadotte et al., 2010a; Hejda et al., 2009; Hejda and de Bello, 2013; Powell et al., 2013) as well as trophic interactions; both aboveground (Clusella-Trullas and Garcia, 2017; Schirmel et al., 2015) and belowground (Stinson et al., 2006; Zhang et al., 2017). Many other studies focus on how NIPS alter ecosystem functions (i.e. productivity, nutrient pools, carbon storage; Bradley et al., 2006; Ehrenfeld, 2010; Vilà et al., 2011; Weidenhamer and Callaway, 2010). And while some global analyses have identified some trends with respect to the impact of NIPS on recipient communities and ecosystem functioning, the same studies are quick to point out that the high degree of variability confounds generalizable conclusions (Pyšek et al., 2012; Vilà et al., 2011).

Despite the variability in how invasion by NIPS affects biodiversity and ecosystem functions (Ehrenfeld, 2010; Jeschke et al., 2014; Liao et al., 2008; Vilà et al., 2011), a common observation is that they can cause significant reductions in the biodiversity (taxonomic, functional and phylogenetic) of recipient plant communities (Castro-Díez et al., 2016; Hejda and de Bello, 2013; Vilà et al., 2011; Winter et al., 2009). Furthermore, the reduction in plant diversity that can often follow invasion has also been shown to cascade upwards through higher trophic levels (Clusella-Trullas and Garcia, 2017, 2017; van Hengstum et al., 2014). In an effort to better capture the cumulative impact of NIPS on both biodiversity and ecosystem functionality, many researchers are now measuring the changes in functional and phylogenetic diversity that follow invasion by NIPS (Cadotte et al., 2010a; Castro-Díez et al., 2016; Drenovsky et al., 2012). Functional and phylogenetic diversity (FD and PD, respectively) have proven to be more informative than taxonomic richness for explaining the role of biodiversity in ecosystem functioning (Cadotte et al., 27

2009; Dı́az and Cabido, 2001; Lefcheck and Duffy, 2015). As species-to-function relationships are often highly context-dependent (Cardinale et al., 2011; Mace et al., 2012; Wardle, 2016; Wardle et al., 2011), examining shifts in these biodiversity metrics has become a useful method for assessing both the impact of species invasions on biodiversity and an indirect way to assess the potential effect on overall ecosystem functionality (Castro-Díez et al., 2016). Of course, this isn’t to say that directly measuring the effect of invasion on ecosystem functions cannot be informative. For instance, knowing that a species invasion is generating greater aboveground biomass stocks in an ecosystem with strong fire dynamics is extremely valuable information (Brooks et al., 2004). Similarly, measuring the effect of plant invasions on decomposition rates and/or nutrient concentrations may indicate some of the mechanisms driving the invasion (e.g. a positive feedback in plant-soil interactions; Didham et al., 2005; Levine et al., 2003; Simberloff and Von Holle, 1999).

One of the major challenges faced by invasion ecologists, with respect to the quantification of invasion impact, is the difficulty in disentangling the relationship between invasion, biodiversity loss and ecosystem functionality. At first glance, it may seem that assessing invasion impact is simply an inverse approach to that of biodiversity-ecosystem function (BEF) research (i.e. where BEF studies tend to focus on how the addition of biodiversity affects functionality, invasion impact studies consider the loss of biodiversity). Though, certainly studies have shown that the “functional composition” of many invasive species (e.g. highly productive, high plasticity, strong dispersal ability, high resource use efficiency, etc.; Rejmanek and Richardson, 1996; Van Kleunen et al., 2010; Whitney and Gabler, 2008; Yasui, 2016) and the ecological context into which they’re introduced (e.g. absence of co-evolved enemies (Blossey and Notzold, 1995; Keane and Crawley, 2002), low diversity (Davis et al., 2005), high anthropogenic disturbance (Cadotte et al., 2017; Seabloom et al., 2006) may mean that the “virtual monocultures” often created by invasive species are not the analogue of the low diversity system theorized in biodiversity loss scenarios. For example, where many BEF studies show that increasing biodiversity leads to greater productivity, species invasions are also known to increase the productivity of their invaded ecosystems (Liao et al., 2008; Macdougall and Wilson, 2011), increased carbon pools (Wolkovich et al., 2010) and/or accelerated decomposition rates (Ashton et al., 2005; Liao et al., 2008), despite decreases in biodiversity; generally indicating “positive” impacts on ecosystem functionality. These “positive” impacts often come at the expense of biodiversity.

It is often difficult to assess the general impact of an invasive species on ecosystem functioning. This is because measurements of different ecosystem functions do not represent isolated ecological processes. Indeed, the manner in which ecosystems function is the product of a myriad of inter-dependent interactions (e.g. aboveground productivity is partially controlled by nutrient availability, decomposition rates are strongly affected by soil moisture retention; Fridley, 2002; Tylianakis et al., 2008). Furthermore, as species invasions often involve novel population growth dynamics (e.g. lag times, allee effects, exponential growth) it can be difficult to predict long-term trends with respect to their ecological impact (Jäger et al., 2009; Strayer et al., 2006; Vilà and Hulme, 2017b). Similarly, as a species invasiveness results from a combination of the functional traits of the invasive plant (i.e. woodiness, climbing capability, nitrogen fixation, height, leaf carbon-to-nitrogen ratio (C:N), dispersal, seed production, mode of reproduction, etc.) and/or the competitive dynamics at play (i.e. allelopathy, allee effect, phenology, phenotypic plasticity, association with soil mutualists or pathogens or lack thereof, resource use efficiency), it is extremely difficult to disentangle how shifts in community composition and inter-specific interactions will affect a given ecosystem function.

Most studies investigating the ecological impacts of NIPS do so by comparing the biodiversity and/or ecosystem properties and functions between invaded and uninvaded ecosystems (Hejda and de Bello, 2013; Liao et al., 2008; Vilà et al., 2011). It is rare to have the opportunity to examine how these variables respond at different stages of invasion (but see Blank, 2008; Robertson and Hickman, 2012; Taylor et al., 2016). Certainly, biodiversity and ecosystem functions may not respond in a linear fashion to increasing abundance of an invasive species (Elgersma and Ehrenfeld, 2011; Parker et al., 1999). Moreover, given that there is considerable variability in the relationship between biodiversity and ecosystem function (Cardinale et al., 2011; Gamfeldt and Roger, 2017; Šímová et al., 2013), independent of the presence of an invasive species, there is considerable value in not only examining how biodiversity and/or ecosystem functions are affected by NIPS, but also how BEF relationships might be affected at various stages of invasion.

In the current study, the overall objective is to assess the ecological impact of an invasive plant. I do this by using all of the aforementioned approaches. Specifically, I examine: 1) how biodiversity and ecosystem functions differ between an invaded and uninvaded meadow

29 community (sections A and C in Figure 2-1); 2) how biodiversity metrics vary along an invasion gradient (section A in Figure 2-1), and 3) how biodiversity-ecosystem function relationships vary along the same invasion gradient (section B in Figure 2-1). To achieve this objective I: 1) quantify the taxonomic, functional and phylogenetic diversity of 14 plant communities in Rouge National Urban Park (NUP) in southern Ontario, containing variable abundance of the invasive vine Vincetoxicum rossicum; 2) measure multiple ecosystem functions across the sites (aboveground plant biomass, soil carbon (C) and nitrogen (N), decomposition rate, flower production), as well as the biodiversity of a higher trophic level (pollinator richness and abundance); and 3) using both comparative analysis between invaded and uninvaded sites, as well as a meta-analysis approach to quantify the variability of BEF relationships across an invasion gradient, I illustrate the effect of plant invasion on community biodiversity, multiple ecosystem functions and biodiversity- ecosystem function relationships. I hypothesize that 1) increasing V. rossicum abundance will be associated with a significant shift in community functional trait structure, 2) increasing V. rossicum abundance will be associated with reduced taxonomic, functional and phylogenetic diversity of meadow communities in Rouge NUP, and 3) that general BEF relationships will be positive for the study region, and that increasing V. rossicum abundance will be negatively associated with those positive relationships.

2.2 Methods

2.2.1 Study Sites

Study sites were located throughout Rouge NUP in Scarborough, Ontario. 14 Study sites were selected from a list of potential sites provided by Rouge NUP staff (Site coordinates are shown in Table 8-1). Mean annual precipitation for the region is 831 mm/year (714 mm rainfall, 122 mmsnowfall). Annual temperature ranges from an average of 22.3 in July to an average of -3.7 in January (Canada, 2013).

Each site was 50m x 50m and consisted of twenty-five 1m2 plots that were evenly spread across the site. Plant species were identified by prior knowledge or by field guide (Peterson, 1996) and their relative abundances were estimated as percent coverage of the plot area. Using

31 two observers, plant coverage was visually estimated to the nearest 5%, unless abundance was less than 5% in which case plant abundances of less than 2.5% were assigned a value of 1%. Percent site invasion by V. rossicum was calculated as the average abundance of V. rossicum across plots (±SE). All sites (Figure 2-2) can be characterized as “old field habitat” with 8 of the 14 sites located in hydro power transmission corridors.

2.2.2 Functional traits and diversity

Functional trait sampling was carried out in Rouge NUP in the summers of 2013 and 2014. Species averages for plant height, specific leaf area (SLA) and leaf C & N content were quantified for the majority of the species observed at the study sites (species without trait values are shown in Table 8-7). These traits were selected due to their previously shown significance for ecosystem functionality (Laughlin, 2011; Lavorel and Garnier, 2002; Roscher et al., 2012) and the fact that they represent relatively independent components of plant functional composition (Lavorel et al., 2011). Leaves for elemental analysis, and vegetation height, were collected and measured as per the methods described Pérez-Harguindeguy et al. (2013). Height was measured on mature flowering individuals in order to represent an average maximum height for each species. Specific leaf area is the ratio of leaf area to dry weight expressed as cm2/g. SLA was measured on twenty to forty individuals when the species was flowering. Two healthy leaves from each individual were collected for this measurement and scanned within 6 hours of sampling. Leaf area was quantified for scanned leaf images using the ImageJ computer program. The leaves were then dried for a minimum of 48 hours, in a standing oven at 60°C and then weighed in order to determine the dry mass.

Leaf nitrogen content (LNC) and leaf carbon content (LCC) (mass of nitrogen and carbon per unit dry leaf mass) were quantified as averages for single species based on 5 replicates per species (occasionally from sub-samples of composites from multiple leaves where the mass of single leaves did not meet the requirements for elemental analysis). LNC and LCC was determined using the Thremo-Fischer EA 2000 elemental analyzer in the in TRACES (Teaching and Research in Analytical Chemical and Environmental Science) lab at UTSC. The carbon nitrogen ratio (C:N) for each plot was determined by dividing LCC by LNC.

Functional diversity was calculated using the function dbFD of the R package ‘FD’ in the software R (Laliberté and Legendre, 2010; “R: The R Project for Statistical Computing,” n.d.).

Figure 2-2: Map of Rouge Park study sites

(±SE). All sites (Figure 2-2) can be characterized as “old field habitat” with 8 of the 14 sites located in hydro power transmission corridors.

2.2.3 Functional traits and diversity

Functional diversity was calculated using the function dbFD of the R package ‘FD’ in the software R (Laliberté and Legendre, 2010; “R: The R Project for Statistical Computing,” n.d.).

Community weighted means (CWM) for plant height, SLA, LNC, LCC and C:N were calculated, denoted as CWMVH (“vegetation height”), CWMSLA, CWMLNC, CWMLCC and CWMC:N, respectively. Functional dispersion (FDis), divergence (FDiv), evenness (FEve) and richness (FRic) were also calculated using the ‘FD’ package. Species Richness, Shannon’s H, Simpson’s D and evenness were calculated using the ‘Vegan’ package in R (Oksanen et al., 2017; “R: The R Project for Statistical Computing,” n.d.).

To calculate phylogenetic diversity (PD), a maximum likelihood ultrametric tree for the 912 angiosperm species found in the Rouge Park was generated using four sequences (ITS1, MatK, 5.8s and ITS1) from the GenBank database (Benson et al., 2011; details of estimation of model of nucleotide substitution model and ML parameters found in Jin, 2015). The tree was loaded into R statistical software (“R: The R Project for Statistical Computing,” n.d.) using the ape package (Paradis et al., 2017) and scaled such that the root to tip distance was equal to 1. Faith's phylogenetic diversity (FPD), which sums the branch lengths that separate species in an assemblage, was used to estimate PD while retaining the tree root. FPD was calculated at both the site and plot level using the function "pd" in the picante package (Kembel et al., 2010).

2.2.4 Ecosystem functions

I use the term “ecosystem function” to refer to 1) the processes of leaf decomposition and aboveground biomass production (including annual growth, remnant litter and flower cover), 2) the properties of soil carbon and nitrogen, and 3) the diversity of the pollinator community. Mean values for all the below functions (per site) are shown in Table 8-3.

2.2.4.1 Pollinator diversity and flower cover

To assess diversity and abundance of pollinator visits I conducted timed observations for 9 minutes at each of the 25 plots at each of the 14 sites throughout the summer. Observations were conducted at estimated peak flowering time for each study site. All observations were undertaken between 1030 and 1630 hours and only on days with “partly-cloudy” conditions or no clouds. Temperature was noted at the mid-point of the sampling time. For observations conducted in September after 5 plots had been cleared for biomass analysis timed observations were extended to 11.25 minutes per plot to compensate for the reduction in plot number.

All observations were conducted using two observers. Pollinator species were given working monikers after early-season species collection and then later taxonomically identified. As new species appeared throughout the season, they were assigned a new moniker, collected and later taxonomically identified. Before each observation period open flowers were counted for all species in each plot. I defined a flower visit as either an insect entering an open tubular flower, presence on a cluster, spike, head or umbel of flowers. Flower visitation was then assessed for all insects on all flower forms for each plot. Individual flowers were counted for V. rossicum (Kleopow) Barbar, Rudbeckia Hirta L., Hypericum perforatum L., Erigeron annuus L., Silene vulgaris (Moench) Garcke, Dianthus armeria L., Asclepias syriaca L., Vicia cracca L., Monarda fistulosa L., Inula helenium L., Echium vulgare L., linaria vulgaris Mill., Leucanthemum vulgare Lam., Symphyotrichum novae-angliae L. and Ranunculus acris L.. Clusters of flowers for Solidago spp L., Symphyotricum ericoides (L.) G.L. Nesom, Symphyotrichum lanceolatum (Wild.) G.L. Nesom and Symphyotrichum cordifolium (L.) G.L. Nesom, umbels for Dacaus carota L. and Achillea millefolium L., spikes of flowers for Melilotus albus Medik., flower heads of Trifolium pretense L., Lotus corniculatus L., and Cirsium arvense L.. Flower cover was quantified as flower count (log transformed) divided by total plant coverage in a single plot.

2.2.4.2 Soil and decomposition rate

At each of the 14 sites four plots were chosen at random for soil sampling and placement of decomposition bags. Samples were composites from 4 cores per plot each taken to a depth of 10 cm. Soil total C and N was determined on air dried samples using elemental analysis (Leco CS444 analyzer; Leco Corp., St. Joseph, MI) at the University of Guelph soil laboratory. Fresh soil samples were used to determine soil moisture as percent water by mass. Fresh soil samples were extracted with 2M KCl (2.0 g: 20 mL), shaken for 30 minutes, and filtered through Fisherbrand + - P8 filter paper. Ammonium (NH4 ) and nitrate (NO3 ) concentrations were determined colourimetrically, using flow injection analysis (Lachat QuikChem). Soil pH was measured in a 1: 5 soil to water solution with a pH meter (Mettler Toledo FiveEasy pH meter, Mississauga, Ontario). Soil characteristics for each site are summarized in Table 8-3.

The rate of decomposition for V. rossicum leaf litter was measured across the 14 study sites. Litter bags were constructed from polyethylene mesh with a size of 1 mm2 with dimensions of approximately 15 x 18 cm. Each litter bag was filled with 4-6 g of pooled V. rossicum leaves

36 that were dried in a VWR drying oven (VWR International) at 35°C for 48 hours. Subsamples of this air-dried litter were weighed, dried at 60°C, and reweighed to determine air-to-oven-dry conversion ratios, therefore initial dry mass in all bags was known. Four litter bags per species were placed in close proximity directly on the soil surface by clearing a small amount of existing plant litter and secured using metal stakes. Litter bags were deployed on June 17th and collected approximately 4 weeks apart during the following four months. At time of collection litter bags were randomly selected from each set of 4, placed in paper envelopes to catch any litter that would fall out during transport, dried at 60°C and weighed to calculate mass loss. Decomposition was assessed by single exponential decay function: At=A0e−kt; where At is the amount remaining (g) after time t, A0 the initial amount (g), t the time (day), and k is the decay rate constant (day−1). The specific decomposition constant, k, was estimated from the slope of the line of the linear regression between ln(At/A0) and time (Paul, 2014).

2.2.4.3 Aboveground biomass

Aboveground live biomass was clipped at soil surface from 5 randomly selected plots at each of the 14 sites at mid-August and separated by species. Sampling was conducted in August in order to minimize variation between peak growth for early-season (June) and late-season (October) species. Dead biomass was collected in each of the five plots and pooled together. Collected plant material was dried in a VWR drying oven (VWR International) at 60oC for 48 h and weighed using a Mettler Toledo ML Series precision balance.

2.2.5 Multi-functionality

Multi-functionality was quantified for the least invaded and most invaded study sites using the R package ‘multifunc’ (Byrnes et al., 2014; “R: The R Project for Statistical Computing,” n.d.). To my knowledge, all quantitative assessments of ecosystem multi-functionality have been conducted using constructed experimental communities. As outlined in Byrnes et al., (2014), there are a number of methods to assess ecosystem multi-functionality each with their own set of limitations and caveats. The majority of these methods rely on detailed information about the contributions of individual species to multiple ecosystem functions. These assessments are possible as most experimental systems are constructed in way that a given species can be assessed for its performance, with respect to multiple functions, in monoculture and in assemblages of variable species richness. Given that the current study was conducted in semi-natural novel communities

37 that have not undergone experimental manipulation, I employ the “averaging approach” as outlined in Byrnes et al. (2014). The “averaging approach” combines averaged standardized values of multiple ecosystem functions into a single index. The averaged multi-functionality of a plot can be calculated using the following equation (Byrnes et al., 2014)

Where F is the number of functions being measured, fi are the measures of function i, ri is a function that reflects fi to be positive (if necessary for standardization), and g is a transformation to standardize all functions to the same scale. Standardization was achieved by assessing the multifunctional performance of each plot at an invaded and uninvaded site (Byrnes et al., 2014).

2.2.6 Statistical analysis

To assess the effect of V. rossicum invasion on plant community biodiversity (functional traits, FD, PD, species richness, evenness and diversity; section A in Figure 2-1), I: 1) used simple linear models to examine how these biodiversity values differed between the community that was most invaded by V. rossicum and the community that was least invaded (V. rossicum relative abundance; 63.51 ± 3.79% and 2.06 ± 1.06%, respectively; hereafter “the dichotomous analysis”, and 2) used linear and polynomial models to examine how those same community metrics varied along a regional gradient of increasing V. rossicum abundance, including site as a random factor in a mixed effects regression model (Bates et al., 2014; Bolker et al., 2009). For this second component, models with the lowest Akaike Information Criterion (AIC) were considered to best represent the relationship.

To assess the impact of V. rossicum on ecosystem functionality I: 1) used simple linear models to examine how ecosystem functions (including an integrated multi-functionality measurement) varied between the most invaded and least invaded community (section C in Figure 2-1); 2) used a linear mixed effects model, including site as a random factor (Bates et al., 2014; Bolker et al., 2009), to calculate regression coefficients for general BEF relationships for the study region, followed by a meta-analysis approach to contrast the effect of increasing V. rossicum abundance on BEF relationships across the 14 study sites with the general BEF relationships for the study region (section B in Figure 2-1; Figure 2-3). Specifically,

I arranged the regression coefficients for site-level BEF relationships in ranked order of increasing V. rossicum abundance for each sites (Table 8-3) and then used linear and polynomial regression models to examine how those coefficients vary along a gradient of increasing V. rossicum abundance (section B in Figure 2-1; Figure 2-3). Of the two types of models, the model with the lowest AIC value was selected and then regression coefficients were visually compared to general biodiversity-ecosystem function relationships across the full study region. This second approach was used in order to resolve variability across sites with respect to species composition (independent of increasing V. rossicum abundance) (Figure 8-4) and land use histories (i.e. agricultural fields undergoing restoration, forest clearing for hydro development).

Data normality was assessed using visual inspection of normal probability plots and the Shapiro-Wilk W-test (Shapiro and Wilk, 1965). All variables conformed to a normal distribution with the exception of aboveground biomass (both living and dead) and flower count (a component of the flower cover metric). Log transformations were applied to resolve issues of non-normality in the data for flower count, aboveground biomass and litter.

2.3 Results

2.3.1 V. rossicum abundance, taxonomic, functional and phylogenetic diversity

After quantifying the relative abundance of all species across the 14 study sites (Table 8-2), I observed an “invasion gradient” with V. rossicum’s relative abundance ranging from 2.06 ± 1.06% to 63.51 ±3.79% across the study sites (Figure 2-4). Mean site values (±S.E.) for all biodiversity metrics and ecosystem functions are shown in Table 8-3, and mean functional trait values for all species are shown in Table 8-7.

2.3.2 The effect of V. rossicum invasion on community functional traits

I found V. rossicum invasion to be associated with a significant increase in CWMSLA, both in the dichotomous invaded/uninvaded analysis and along the invasion gradient (Figure 2-5; Figure 8-1; Table 2-1). However, the significance of V. rossicum abundance on other CWM trait values differs between the two analyses. Specifically, I observed a significant increase in CWMVH (plant height) and a significant decrease in CWMLCC (leaf carbon content) in the dichotomous invaded/uninvaded analysis but not across the invasion gradient (Figure 2-5; Figure 8-1; Table 2-1). V. rossicum invasion was also associated with a near-significant decrease in CWMLNC (leaf

Figure 2-4: Ranked V. rossicum relative abundance (±SE) at Rouge National Urban Park study sites. (n=25 per site)

Table 2-1: linear models showing the effect of invasion by V. rossicum on multiple plant biodiversity measures (comparison between uninvaded and invaded sites; 2.06% ± 1.06 and 63.51% ±3.79 V. rossicum, respectively). Statistically significant models have bolded p values.

43 nitrogen content) values for both analyses. Initial follow-up analyses indicate that there may be non-linear trends for four of the five CWM trait values (Figure 8-2 & Table 8-4).

2.3.3 The effect of V. rossicum invasion on community diversity

I found V. rossicum invasion to be associated with significant or near-significant declines in the majority of community biodiversity measures. Significant declines in species richness, PD, functional richness, Shannon’s H, and Simpson’s D were observed both along the invasion gradient and in the dichotomous analysis (Figure 2-5; Figure 8-1; Table 2-1). Other measures of functional diversity (evenness, divergence and dispersion) showed variable response to V. rossicum invasion and were not significant, with the exception of functional divergence which declined significantly in the dichotomous analysis (Figure 2-5; Figure 8-1; Table 2-1). Again, it is likely here that non-linear dynamics are at play (Figure 8-2 & Table 8-4). I also observed that V. rossicum invasion was associated with significantly lower species richness in the dichotomous analysis, and a near-significant negative trend along the invasion gradient (Figure 2-5; Figure 8-1; Table 2-1). I also observed a significantly negative relationship between V. rossicum abundance and the proportion of native species in both analyses (Figure 2-5; Figure 8-1; Table 2-1).

2.3.4 The effect of V. rossicum invasion on ecosystem functioning

2.3.4.1 Dichotomous invaded/uninvaded analysis

Using dichotomous analysis I observed V. rossicum invasion to be associated with significantly less richness and abundance of pollinators, flower cover, aboveground biomass and plant litter (Figure 2-6; Table 2-2). However, I also observed V. rossicum invasion to be associated with significantly lower concentrations of total soil N (Figure 2-6; Table 2-2).

2.3.4.2 The effect of V. rossicum on BEF relationships along the invasion gradient

In general, without considering the relative abundance of V. rossicum, I observed no positive relationships between measures of biodiversity and plant biomass production (“green” or “litter”), with the exception of the significantly positive effect of CWMVH (plant height) on “green” biomass (Figure 2-7). In fact, for some biodiversity measures (functional richness, functional dispersion, species richness, functional divergence, Simpson and Shannon diversity) I observed a significant or near significant negative effect on “green” biomass, with this effect being more pronounced for the production of plant litter (Figure 2-7). When examining how BEF relationships varied along

Table 2-2: linear models showing the effect of invasion by V. rossicum on multiple ecosystem functions (comparison between uninvaded and invaded sites; 2.06% ± 1.06 and 63.51% ±3.79, respectively). Statistically significant models have bolded p values. Reponse variable r^2 adj r^2 sigma df Estimate SE t statistic p value pollinator richness 0.41 0.39 1.23 2 -0.29 0.06 -5.1 <0.0001 pollinator abundance 0.38 0.36 7.07 2 -1.56 0.32 -4.83 <0.0001 flower cover 0.11 0.08 1.96 2 -0.19 0.09 -2.15 0.038 inverse inorganic N 0.11 -0.03 5.88 2 0.52 0.59 0.88 0.414 k constant 0.24 0.11 0.01 2 -0.001 0.001 -1.38 0.217 inverse total soil N 0.97 0.97 0.05 2 0.07 0.004 15.01 <0.0001 soil total C 0.22 0.08 0.6 2 0.08 0.06 1.28 0.247 litter (log) 0.53 0.47 0.27 2 -0.07 0.02 -2.97 0.018 biomass (log) 0.49 0.43 0.18 2 -0.04 0.02 -2.78 0.024 multifunctionality 0.11 0.06 0.71 2 -0.07 0.05 -1.41 0.18

47 the V. rossicum invasion gradient, I did observe that the relationship between Simpson’s D and biomass (“green” and plant litter) is positively affected by V. rossicum abundance (Figure 2-8). This same trend was found for the effect of both Simpson’s D and Shannon’s H on flower cover where again, I observed that a significantly negative effect of both biodiversity measures on flower cover (Figure 2-7) was positively affected by increasing V. rossicum abundance (Figure 2-8). I observed several significantly positive effects of biodiversity on soil total C (Figure 2-7), with increasing V. rossicum invasion tending to decrease the magnitude of these positive relationships (Figure 2-6 & Figure 2-8). In general, I observed biodiversity to have no significant effect on soil N (Figure 2-7), which also seems to be the case for the effect of V. rossicum invasion on most of these relationships (Figure 2-8). Although I did observe that the relationship between PD and soil inorganic N flips from being significantly negative as a general relationship (Figure 2-7), to being near significantly positive with increasing invasion (Figure 2-8) (These N dynamics are discussed in detail below). I also observed that, in general, PD to have a significantly positive effect on litter decomposition rate (k constant) (Figure 2-7), but also that this positive relationship is negatively affected by increasing V. rossicum invasion (Figure 2-8).

General BEF relationships for the richness and abundance of pollinators and flower cover were found to be highly variable, though some biodiversity measures showed consistent trends across the three functions. For example, I observed Simpson’s D, Shannon’s H and evenness to have significantly negative effects on pollinator richness, abundance and flower cover, but PD was observed to have a near-significant positive effect on both pollinator richness and abundance (Figure 2-7). When examining how these same BEF relationships respond to increasing V. rossicum invasion, I observed that the relationship between flower cover and both Simpson’s D and Shannon’s H turns positive, while the pollinator diversity measures appear to be unaffected (Figure 2-8).

2.4 Discussion

2.4.1 The effect of V. rossicum invasion on community functional structure and diversity

My results show fairly strong support for my first hypothesis; that increasing V. rossicum abundance will be associated with significant alterations to the composition of community functional traits. Specifically, I show that V. rossicum invasion is associated with significant

Figure 2-8: Regression coefficients measuring the effect of increasing V. rossicum abundance on biodiversity- ecosystem function relationships. These coefficient values are the result of a second regression from a 2 step regression procedure (ostensibly a meta-analysis). The first set of regressions was carried out on all biodiversity- ecosystem function relationships at each of the 14 study sites, then corresponding regression coefficients were arranged in ranked order of increasing V. rossicum abundance at each site. The above regression coefficients are the result of regression models run on the aforementioned ranked-order coefficients (see Figure 8-5 to Figure 8-12). The bars around coefficient values denote 95% confidence intervals. A coefficient value is significantly positive or negative when its 95% confidence intervals do not bracket zero. Models and outputs are shown in Table 8-8.

increase in CWMSLA (specific leaf area) (Figure 2-5; Figure 8-1). Indeed, it has been shown in many cases that NIPS exhibit high SLA values (Feng et al., 2008; Grotkopp and Rejmánek, 2007), with their increasing abundance then largely controlling the CWM value. It is likely that the measured increase in CWMSLA with V. rossicum invasion is an indication of V. rossicum having a particularly strong nutrient acquisition capability, which is known to occur in many invasive species (Funk and Vitousek, 2007; Grassein et al., 2015). Though where I observe CWMSLA values increasing with V. rossicum invasion I do not observe a corresponding increase in CWMLNC values (Figure 2-5; Figure 8-1), as might be expected given previous work on the traits of invasive plants (Leishman et al., 2007).

Finding that V. rossicum invasion is associated with significantly greater CWMVH (plant height) values is not surprising. This is simply due to the increasing abundance of V. rossicum and the fact that it is taller than the majority of the resident plant community (Table 8-7). It should be noted here though that although V. rossicum does have comparatively greater values for height, there is strong seasonal variability in how its height is exhibited. Specifically, in highly invaded systems, as V. rossicum reaches peak height individuals tend to twine amongst themselves and then keel over causing a smothering effect. So while CWMVH values tend to increase with invasion, this doesn’t actually result in a “taller community”, but likely an increase in the overall density in late season; a dynamic that has strong implications as a potential mechanism driving V. rossicum invasion.

I also found strong support for my second hypothesis; that increasing V. rossicum abundance will be associated with reduced taxonomic, functional and phylogenetic diversity of meadow communities in Rouge NUP (Figure 2-5; Figure 8-1). The reduced biodiversity that is associated with V. rossicum invasion has very strong implications for the stability and functionality of these ecosystems (King and Sargent, 2012; Pfisterer et al., 2004; Simberloff et al., 2013; van Hengstum et al., 2014). And though I did observe V. rossicum invasion to be associated with significantly lower values for functional richness (Figure 2-5; Figure 8-1), it was surprising not to see corresponding negative effects on other functional diversity measures, with the exception of functional divergence in the dichotomous analysis (Figure 2-5). Observing that V. rossicum invasion is associated with significantly lower functional richness is not surprising given that the functional richness metric is highly affected by species richness (Villéger et al., 2008) (I did

50 observe substantially lower species richness both along the invasion gradient and in the dichotomous analysis). The significantly lower functional divergence that I observed in a highly invaded site indicates that the species present are less dispersed in “trait space” (Mason et al., 2005). And given that the traits included in the FD measures have been shown to strongly influence ecosystem functioning (Cadotte, 2017; Garnier et al., 2007; Laughlin, 2011), this reduced value points to substantial shifts in ecosystem functionality throughout the region as V. rossicum continues to spread. And while this analysis is focused on the potential for invasion to influence ecosystem function, a similar approach could be used to test hypotheses concerned with the competitiveness and/or coexistence of native, non-native and invasive species, likely via an analysis involving a different suite of functional traits (Cadotte et al., 2015; Laughlin, 2014; MacDougall et al., 2009; Van Kleunen et al., 2010).

Observing that V. rossicum invasion is associated with a significantly lower proportion of native species (Figure 2-5; Figure 8-1) is interesting from an invasion ecology perspective, but troubling from a conservation point of view. It is unclear how this change in native/exotic community composition relates to the measured shifts in ecosystem functionality, but it certainly has strong implications for the persistence of specialized native plant-herbivore interactions that are of conservation interest (i.e. dependence of the monarch butterfly on native milkweed). Certainly, this finding seems to support the invasional meltdown hypothesis (invader begets invader) (Simberloff and Von Holle, 1999), though, due to the non-experimental nature of this study and a lack of long-term trends in species composition among the sites it cannot be said with certainty that this is occurring. An alternative hypothesis is that sites that are highly invaded by V. rossicum have also been colonized by other highly competitive non-native species (Funk and Vitousek, 2007). Certainly, these types of disturbed peri-urban systems can be more conducive to the establishment of non-natives over native plant species (Cadotte et al, in press). In either case though, noting this observed decline in native plant species that is associated with V. rossicum invasion can hopefully serve to spur further interest in the conservation of native plant species.

2.4.2 The effect of V. rossicum invasion on ecosystem functioning

By using a simple dichotomous approach to examine how ecosystem functions varied between an invaded and uninvaded community I found partial support for my third hypothesis; that increasing V. rossicum abundance will be negatively correlated with generally positive BEF relationships.

Specifically, in addition to previously noted reductions in biodiversity, I also observed significantly reduced flower cover, pollinator richness and abundance, and aboveground biomass (“green” and plant litter). Though, as V. rossicum invasion is also associated with significant declines in the biodiversity that governs those ecosystem functions, I also examined how those BEF relationships vary with increasing V. rossicum abundance. In general, the relationship between biodiversity and ecosystem functioning is hypothesized to be a positive interaction where greater biodiversity enhances function (Cadotte et al., 2009; Hooper et al., 2005; Tilman et al., 1997a). Though, as substantial variability in this relationship has been observed both in non- experimental systems (Cadotte et al., 2011b; Mora et al., 2014; Wardle, 2016) and following invasion (Kull et al., 2007; Powell et al., 2013; Ramus et al., 2017), it is difficult to predict how invasion may impact a given function. In the current study, the majority of BEF relationships examined are either significantly negative or non-significant, with the exception of the significantly positive effect of PD on decomposition rate and some diversity measures on total soil C (Figure 2-7). This overall non-significance or negative effect of different facets of biodiversity on ecosystem function is surprising given the number of studies that have shown positive relationships (Cadotte, 2013; Grace et al., 2016; Tilman et al., 1997a). Though, similar non- significant and negative relationships have also been observed in other “natural experiments” (Šímová et al., 2013). In these cases, it is believed that the positive relationship that is often observed between biodiversity and ecosystem functioning is likely obscured by a high degree of environmental heterogeneity (Fridley, 2002). Interestingly though, some of the negative and neutral BEF relationships in the current study seem to be positively affected by V. rossicum invasion.

My observation of greater PD to be generally associated with higher concentrations of soil inorganic N appears to be significantly affected by V. rossicum invasion (Figure 2-8). This is an interesting dynamic where I found PD to be significantly reduced in a highly invaded system (Figure 2-5). And where PD is often thought of as a proxy for functional diversity (FD), which is known to enhance resource capture within an ecosystem (Diaz et al., 2007; Flynn et al., 2011; Kraft et al., 2015), it was surprising to measure this positive association with invasion. To clarify, in the majority of BEF studies, higher concentrations of soil N are perceived to be an undesirable ecosystem property representing inefficient resource use by the plant community. Yet, as discussed below, we must also consider whether or not these “high” concentrations of soil N are likely to

52 result in transport to adjacent ecosystems (i.e. through water flow), and whether or not they are likely to have deleterious effects. The observed relationship in this study is indicative of two potential mechanisms: 1) it may be the case that the positive effect of PD (i.e. greater functional diversity) on resource usage (i.e. reduced soil inorganic N) is revealed under conditions of reduced competition, or 2) that sites with greater soil N concentrations are more conducive to colonization by a phylogenetically diverse species pool (Hobbie et al., 1994). There is a stronger case to be made for this second mechanism given that “conditions of reduced competition” occur in the presence of an abundance V. rossicum, which is surely the most competitive herbaceous plant species in the regional pool. Contrastingly, as a general relationship, I found decomposition rate (k constant) to be positively associated with PD (Figure 2-7), but also that this positive relationship appears to decline along the V. rossicum invasion gradient (Figure 2-8). Considering the above complexities, it may be the case that in assessing the impact of plant invasions on soil N and decomposition rate, we need to consider not only whether invasions are causing an increase or decrease in these functions/properties, but rather how those changes relate to absolute concentrations or regional values, respectively. In other words, an observed increase or decrease in soil N concentrations or decomposition rate following invasion might be best examined in relation to how “closed” or “open” the system is with respect to its nutrient cycling dynamics (i.e. is N still being retained despite the shifts caused by invasion?; Oelmann et al., 2011; is an increase in decomposition rate providing a competitive advantage to a non-native species?; Allison and Vitousek, 2004). In the current study, where I observed V. rossicum invasion to be associated with decreased soil N concentrations (Figure 2-6), this broader perspective elucidates how that decrease may in fact be a negative impact indicating impaired soil fertility and a lower probability that native species will be able to persist in those conditions. Certainly, these findings set the stage for future analyses on regional soil N dynamics.

In general, I did not find CWMVH (plant height) values to affect the majority of ecosystem functions across the study sites (Figure 2-6), though I did find that V. rossicum invasion was associated with a significant increase in CWMVH and a significant decline in the relationship between CWMVH and flower cover (Figure 2-8; Table 8-8; Figure 8-7). In other words, although I do see an increase in mean plant height with invasion, this trend is accompanied by a reduction in flower cover. As the flower cover measure is meant to capture the potential support for higher trophic levels, this indicates that V. rossicum invasion has strong implications for the conservation

53 of regional pollinator biodiversity. Indeed, this is a common observation for studies examining the impact of plant invasion (Montero-Castaño and Vilà, 2012; Vilà et al., 2011). Of course, it should also be noted that invasive plants can often increase floral resources, which can then inhibit the pollination of native plants by providing more easily accessible resources for pollinators (Campbell et al., 2015; King and Sargent, 2012). So with respect to V. rossicum invasion, while the observed reduction in floral resources could potentially promote the pollination of native species, the fact that V. rossicum has a general fitness advantage allowing it to rapidly spread throughout the region (Averill et al., 2011; DiTommaso et al., 2005; Sanderson and Antunes, 2013) still threatens the persistence of native plant species and regional pollinator biodiversity. And while I did observe highly variable relationships between plant and pollinator biodiversity, both in general and along the invasion gradient, the dichotomous analysis shows significant declines for both pollinator biodiversity measures in a highly invaded system (Figure 2-6). This observed reduction in pollinator diversity corroborates work by Ernst and Cappuccino (2005) who found V. rossicum invasion to be associated with a significant decline in arthropods.

Many studies have found NIPS to have high leaf N concentrations that promote greater soil N concentrations and accelerated nutrient cycling rates in invaded systems (Allison and Vitousek, 2004; Funk, 2013; Kuglerová et al., 2017). In the current study, I did not find V. rossicum invasion to be associated with significant differences for any of these variables (Figure 2-6; Figure 8-1). In fact, I observed greater inorganic soil N concentrations at sites with low levels of V. rossicum abundance (Figure 2-6; Figure 8-3). In general, it could be expected to observe a significantly positive relationship between leaf N and soil N (Ordoñez et al., 2009), but instead I observed high variability in this relationship, and no significant trend along the V. rossicum invasion gradient (Figure 2-7 Figure 2-8). It could be the case that the (potential) allelopathic properties of leaf leachates and/or root exudates from V. rossicum are resulting in variable response of soil microbial communities, which could then have subsequent effects on soil N concentrations (Douglass et al., 2011; Inderjit et al., 2011). Alternatively, it could be that the different histories of each study site are largely determining soil N concentrations (i.e. “priority effects”: longer duration of vegetative growth, presence of well-established N fixing species, etc.; Kardol et al., 2013; Zirbel et al., 2017).

It is also useful to think about how change in a given ecosystem function, as a result of changes in biodiversity, might affect other functions. Many studies have used structural equation

54 modelling (SEM) to try to disentangle some of these BEF relationships by exploring “environment-trait-function” and/or “environment-species-function” dynamics, but they do so without considering the relationships between different ecosystem functions (i.e. how aboveground biomass relates to decomposition rate; I was unable to use an SEM approach to analyze the dataset due to the nature of the data sampling methods; see section 2.2.6). Certainly though, it is likely impossible to model all of the dynamic interactions between, and within, environmental parameters, biodiversity, and ecosystem functions. Many studies, including this one, are forced to take an “acyclic” approach and forego analysis of the interactions between individual ecosystem functions (Shipley, 2016; Zirbel et al., 2017). In reality, it is likely that “inter- function” dynamics need to be assessed along a temporal axis in order to understand how one might affect another and/or how an uncoupling of those interactions may lead to “alternate stable states” (i.e. “novel ecosystems”; Beisner et al., 2003; Hobbs et al., 2009; Kowarik, 2011) or the spread and impact of an invasive species (Barney et al., 2013; Strayer et al., 2006; Zhang et al., 2017).

2.4.3 Caveats

Although I observed that V. rossicum abundance is negatively associated with species richness, both in the dichotomous analysis and along the invasion gradient, I cannot say with certainty that V. rossicum has caused this decline. This is due to potential differences in site history (i.e. It may be the case that the invaded site existed in a low diversity state prior to the arrival and spread of V. rossicum)(Foster et al., 2003; MacDougall and Turkington, 2005). Of course, it is still possible to comment on the effect of that reduced species diversity and high V. rossicum abundance on other biodiversity metrics. Similarly, in my examination of soil functions across the study sites, these measures could also be strongly influenced by site history. For example, where I found V. rossicum invasion to be associated with lower concentrations of soil N (see methods for explanation of inverse value), it is unclear whether V. rossicum abundance is causing this change or if the measured soil N values existed prior to invasion. Moreover, this may in fact suggest that there is a mechanistic interaction taking place where V. rossicum is more readily invading systems with lower levels of N. This dynamic could be further explored using germination trials in a laboratory setting, potentially contributing to the many studies that have focused on the mechanisms driving V. rossicum’s invasiveness (Cappuccino, 2004; DiTommaso et al., 2005; Gibson et al., 2015; Yasui, 2016).

It also needs to be noted that while the current study’s analysis was limited to meadow ecosystems, V. rossicum is also highly invasive in forest understory conditions, where invasion threatens forest regeneration (DiTommaso et al., 2005). In the long term, this impact can drastically reduce not only biomass production, but also other ecosystem services such as carbon sequestration, soil retention, water purification, micro-climatic regulation, and habitat for forest dependent species.

2.5 Conclusion

This study highlights the variable ways in which an invasive plant species can alter biodiversity and how such alterations can affect ecosystem functionality. By employing a multitude of biodiversity measures, I show that invasion impact is by no means uniform. I also show that different analytical approaches (dichotomous, examining BEF relationships along an invasion gradient) can lead to different insights about the manner in which invasion affects ecological functionality. And while the challenges of disentangling these relationships in a non-experimental system are evident, my analysis provides insight into not only the ecological impact of V. rossicum, but also some of the potential mechanisms driving its invasion. This kind of empirical analysis is needed to inform management interventions and has the potential to galvanize public support for the control of NIPS (Crowley et al., 2017a; Head, 2017).

3 Can a moth strangle the Dog-strangler? An experimental application of Hypena opulenta as a bio-control agent for the invasive Dog-strangling Vine (Vincetoxicum rossicum)

3.1 Introduction

Non-indigenous invasive plant species (NIPS) can spread rapidly through native ecosystems, sometimes creating “virtual monocultures”. Such invasions can lead to local extirpations of native species and disruption of ecological functioning (Vilà and Hulme, 2017a). The degree of some of these invasions is to the extent that chemical or physical control are essentially unviable management options (Culliney, 2005; Rejmánek and Pitcairn, 2002). As a result, many conservationists are calling for the advancement of classical biological control (hereafter “bio- control) programs for extremely problematic NIPS (Clewley et al., 2012; Hinz et al., 2014). As a corollary, the need for effective methodologies to assess both the potential efficacy and associated risk of bio-control cannot be understated (Kopf et al., 2017; Simberloff, 2012). Although bio- control can be an effective means of reducing the survival, growth, reproduction and spread of NIPS (Clewley et al., 2012; Hinz et al., 2014; Thomas and Reid, 2007), research has also shown that the majority of bio-control agents fail to reduce the invasiveness of NIPS, either by failing to establish or by having an insignificant effect on fitness (Dray et al., 2001; Mason et al., 2013). Further, steadfast concern by some regulatory bodies about the potential direct and indirect effects on non-target species, and across trophic levels, is limiting bio-control research and the development of bio-control programs (Hajek et al., 2016; van Lenteren et al., 2006; Rob Bourchier, pers. comm.). Still, petitions for the release of new bio-control agents are granted approval when researchers are able to present evidence of extremely low-risk and potentially effective agents (Casagrande et al., 2012; Hunt et al., 2008). This evidence is typically gathered through extensive laboratory work examining both the efficacy of the control agent, either through direct application or simulation (Baldwin, 1990; Lehtilä and Boalt, 2008), and their specificity to their target organisms (Hazlehurst et al., 2012). The challenge that follows is then to examine how the results of laboratory assays, which are conducted in highly controlled conditions, as well as simulation experiments, translate to “real-world” conditions where ecological complexity and environmental stochasticity can present a substantially different experimental context (Diamond, 1983; Lehtilä and Boalt, 2008). 57

Just as we need to understand the direct and immediate effects of bio-control agents on their target organisms, there is also a need to assess the indirect ecological interactions that follow the application of bio-control agents (Blossey, 1999; Clewley et al., 2012), even in the case of highly host-specific agents (Louda et al., 2003; Pearson and Callaway, 2003; Simberloff, 2012). A significant hurdle for bio-control efforts is the challenge of predicting the novel ecological interactions and indirect effects that follow the release of non-native organisms as control agents (Simberloff and Stiling, 1996). Indirect effects in ecology are broadly defined as “how ‘one species alters the effect that another species has on a third’ or, ‘how and to what degree pairwise species interactions are influenced by the presence and density of other species in the community’” (Abrams, 1987; Miller and Kerfoot, 1987; cited in Strauss, 1991). Many authors have extended the concept of indirect effects to include both individual species and communities of species - typically in the form of herbivore and plant interactions with microbial communities (Bardgett and van der Putten, 2014; Bennett et al., 2016; Biere and Bennett, 2013). Specifically, for the bio- control of NIPS, there is a need to understand how aboveground herbivory affects belowground processes (e.g. ammonification, nitrification, soil and root respiration) (Van der Putten et al., 2001). Examination of the belowground dynamics that follow aboveground herbivory by bio- control agents can improve our understanding of the relative efficacy of the agents as well as the effect of their activity on the biochemistry of NIPS (Thelen et al., 2005). Indeed, such effects have been understudied in bio-control releases (Simberloff, 2012).

As a precursor to the selection and testing of many bio-control agents, researchers often engage in laboratory and/or field studies employing simulated herbivory to investigate the potential for an agent to reduce the fitness of a target organism. And while there is empirical evidence documenting some differences between the effects of simulated vs. natural herbivory (Baldwin, 1990; Bardgett et al., 1998), the practice of simulated herbivory is still widely employed in the field of bio-control out of strict necessity (Lehtilä and Boalt, 2008). There are several differences between simulated and natural herbivory. First, simulated herbivory lacks the “biochemical signature” associated with the feeding of the natural enemy on a target organism, and any organismal responses that are specific to that interaction (Bennett and Wallsgrove, 1994); second, it is difficult to mimic the timing and intensity of natural herbivory; and third, there is no accurate way to mimic the soil inputs that occur during natural herbivory (frass & insect cadavers), and the associated effect on soil processes (Hjältén, 2008; Hunter, 2001; Lehtilä and Boalt, 2008).

Regardless of the means of defoliation, laboratory experiments testing the efficacy of potential control agents often lack the ecological complexity of the systems where they would be released. Furthermore, laboratory tests for the efficacy of control agents are often conducted on plants that are either grown in laboratories or that have been excavated and transplanted into pots. It is uncertain how tests conducted on these types of plants may differ as compared to well-established mature individuals and populations (Chapin, 1991; Mathers et al., 2007).

As an extremely problematic NIPS, Vincetoxicum rossicum (Kleopow) Borhidi (AKA pale swallow-wort or “dog-strangling vine”) has spread aggressively through its invaded range of eastern North America since its initial introduction in the late 1800’s. Given the extent of its spread a wealth of research has been conducted on its distribution and ecology (Averill et al., 2011; DiTommaso et al., 2005; Miller & Kricsfalusy, 2007), the mechanisms driving its invasion (Bongard et al., 2013; Cappuccino, 2004), the manner in which it’s altering ecosystems (Ernst and Cappuccino, 2005) and the effectiveness of physical and chemical control (Averill et al., 2008). V. rossicum is particularly problematic in that it has been able to colonize and become dominant in both forest edges and understory, similar to its native habitat in Eurasia, as well as old-fields and horticultural nurseries (Miller and Kricsfalusy, 2007). As noted by Averill et al., (2008) in a 16 month study, attempts to control the spread of V. rossicum through clipping have largely proven ineffective, but when paired with the application of chemical herbicides reduced density and seed production have been observed. Yet the development of a control program using herbicides and clipping has remained untenable due to the environmental impact of long-term herbicide use and the fact that clipping or mowing of V. rossicum infestations would require significant labour investment (Smith et al., 2006). Because of these issues, preliminary work on the potential for bio- control of V. rossicum was conducted by Milbrath (2008) who simulated herbivory in different light environments to examine the effect on growth and fitness. This study found that defoliation significantly reduced the growth and seed production of V. rossicum in high and low-light conditions, but with a much greater negative effect on seed production in low-light conditions. However, field studies examining defoliation of V. rossicum, both artificially and with the application of a bio-control agent, are necessary to assess the effect of defoliation on V. rossicum’s fitness in established populations, as well as the indirect effects of defoliation on other ecological processes.

Building on field and laboratory work by Hazlehurst et al., (2012) and Weed and Casagrande (2010), Casagrande et al., (2012) assembled a petition for the field release of the leaf- feeding moth Hypena opulenta (Christoph)(Lepidoptera: Noctuidae: Hypeninae) as a control agent for the invasive vine V. rossicum. H. opulenta is a co-evolved natural enemy of V. rossicum and was found in its native range of eastern Europe (Weed and Casagrande, 2010). Hazlehurst et al., (2012) were able to show extremely high degree of host specificity through no-choice tests of feeding and reproduction of H. opulenta on 82 potential host plants. Specifically, they found that successful development of H. opulenta larvae only occurred on species of the genus Vincetoxicum. While they did measure minor feeding by H. opulenta on a few other species, they determined that release of the control agent would pose no risk to native plant species or species of economic importance in North America (Hazlehurst et al., 2012). In light of this research, the petition for the release of H. opulenta was approved by the Canadian Food Inspection Agency (Casagrande et al., 2012).

To investigate the effect of bio-control on the fitness of V. rossicum in “real world” conditions, as well as its potential indirect effects on belowground processes, the current study employs both an experimental application of H. opulenta and simulated herbivory treatments. The aim of this study was to: 1) assess the effectiveness of H. opulenta as a control agent for V. rossicum in shade and sun conditions; 2) determine patterns of H. opulenta larval dispersal; and 3) investigate the implications of V. rossicum defoliation for belowground processes. To address these objectives, I: 1) compare the defoliation of V. rossicum by H. opulenta by measuring V. rossicum leaf area, total number of leaves, and number of defoliated leaves from control and experimental plots from sun and shade treatments; 2) examine the quantity and mass of seeds produced by V. rossicum following defoliation by H. opulenta, again in both control and experimental plots from sun and shade conditions; 3) measure the distance and directionality of larval dispersal by H. opulenta in both light conditions; and 4) conduct a secondary, controlled simulated herbivory experiment at another established study site to measure the effect of defoliation on soil N and C processes. I hypothesized that: 1) Defoliation of V. rossicum by H. opulenta would be significant in both the sun and the shade, but to a greater degree in the shade given that the forest edge/understory is H. opulenta’s preferred natural habitat (Weed and Casagrande, 2010); 2) V. rossicum seed production would be significantly reduced in both the sun and the shade following defoliation by H. opulenta, but to a greater degree in the shade given the

60 expected defoliation differences; 3) H. opulenta larval dispersal would be significantly greater in the shade condition due to its preference for those conditions; and 4)Defoliation of V. rossicum would result in alterations to belowground processes (ammonification, nitrification, respiration, decomposition rate). (Note: The directionality of “belowground responses” to defoliation is unknown given the ambiguousness of the literature focused on the bio-chemistry of bio-control for species invasions; Figure 3-1).

3.2 Methods

3.2.1 Experimental release of H. opulenta

3.2.1.1 Study site

The study site for the experimental release of H. opulenta was located near Kirkfield, Ontario and consisted of both deciduous forest and a meadow, both highly invaded by V. rossicum (44°35'44.86"N, 78°59'58.33"W). Mean annual precipitation for the region is 932.9 mm/year (709.9 mm rainfall, 223 mm snowfall). Annual temperature in the region ranges from an average of -7.7 oC in January to an average of 20.8oC in July (Environment Canada, 2013). This study site was located with the assistance of the Nature Conservancy of Canada.

Twenty 1m2 plots were divided equally between understory and open-field treatments within a total area of approximately 100 x 130m for the study site. Understory plots were selected using the criteria that plots have a minimum of 75% coverage by V. rossicum and a minimum 75% canopy coverage by overstory trees. Open-field plots were selected using the criteria of a minimum 75% coverage by V. rossicum and no overstory shrubs or trees within approximately 10m of the plot. Using GPS, plots were located with a between-plot minimum distance of 12.5 m (Figure 3- 2). Within each treatment, five plots were randomly selected as control plots and 5 plots as experimental for H. opulenta release.

Figure 3-2: Experimental Hypena opulenta release site, Kirkfield, Ontario, Canada. Image from Google Earth.

3.2.1.2 H. opulenta release

Hypena opulenta larvae were released into each of the ten experimental plots on July 24th and August 1st 2014. The total number of larvae released was approximately 710 per plot, which was approximately divided equally across the two dates. H. opulenta larvae were sourced from laboratory-reared populations from the University of Rhode Island, Agriculture Canada’s Lethbridge Research Centre and the University of Toronto-Scarborough. All lab populations were from the same initial population collected in Donetsk, Ukraine in 2006, which had been screened for the release petition (Weed and Casagrande, 2010). To control for the variation in in larval source, larvae from all sources were pooled, then divided into ten equal batches in the lab. For transport to the field, each treatment of caterpillars was placed in a lidded 4 litre plastic container. A square of stucco wire, covered with sheets of paper towel, was placed at the bottom of the container to allow caterpillars to be easily transferred to release points with minimal disturbance. Stems of V. rossicum were added to each container as a food source and to provide habitat during transfer to the field. Bins were placed in a cooler for transport. For field release each batch of H. opulenta was placed in the quarter of the plot near the primary stake. Wire grids were left in each experimental plot in order to minimize disturbance, ensure release of all larvae and allow larvae to move into the plot on their own.

3.2.1.3 Defoliation assessment

To assess defoliation of V. rossicum by H. opulenta I employed three different techniques; 1) counting leaves with or without defoliation, 2) counting total number of leaves, and 3) collection and scanning of leaves. For leaf counting two 50 cm transects were inserted into each plot 50 cm from the primary stake at an angle of 90o. Along each of these transects the ten individual plants whose stems were in closest proximity to each 10 cm interval were selected for sampling. For these ten individuals, the total number of leaves were counted as well as the number showing any defoliation. Leaf counting was done on 12 August and 22 August 2014. Collection of defoliated leaves for scanning was done on 22 August and used the same 50 cm transect as previously mentioned with stems identified that day. Bottom, middle and top leaves were taken from these ten individuals and placed in a notebook. For leaf selection, the bottom leaf was chosen at random then middle and top leaves from alternating sides of the plant were taken. Collected leaves were immediately placed between pages of a notebook book to maintain their form, then scanned within

3 hours. Leaf area was determined by scanning bottom, middle and top leaves and analyzed with ImageJ software.

3.2.1.4 Reproduction

To examine the effects of defoliation by H. opulenta on the reproductive output of V. rossicum, follicles were collected on 9 September, 2014 for laboratory analysis. Twenty follicles were randomly sampled from each plot. Fresh follicles were measured for mass, length, and diameter on the day following collection. Following these measurements follicles were opened, pappus was removed and individual seeds were counted and weighed. For individual seed counts and seed mass, seeds were required to meet a minimum threshold of 1 mg to be included.

3.2.1.5 Canopy cover

Consistency of canopy coverage across treatments was assessed using hemispheric photos taken on 9 September 2014. Proportion of canopy cover was analyzed using ImageJ software. Hemispheric canopy photos were converted to black and white binary images, and percentage of black and white pixels were calculated to determine percent coverage. Analysis of variance on the canopy coverage values between sun and shade plots indicated significant differences (F(1,17) = 129.89, p<0.001), but with no significant differences between control and experimental plots in each light treatment (F(1,17) = 0.02 , p=0.88).

3.2.1.6 Larvae Dispersal

Larvae dispersal distances were estimated for each release plot on 22 August 2014 by measuring the distance between the primary stake and the furthest leaf showing any defoliation (along cardinal directions).

3.2.2 Artificial defoliation experiment

3.2.2.1 Study site

The simulated herbivory experiment was conducted in an old field, heavily invaded by V. rossicum, in Rouge National Urban Park (43°48'38.71"N, 79°10'15.24"W). I conducted the simulated herbivory study at a separate location from the biocontrol release site in order to not disturb H. opulenta pupae, which could have been located throughout the site following dispersal and subsequent egg laying by adult moths. Regional weather, soils and neighbouring vegetation

65 are similar between the two sites (Canada, 2013). Mean annual precipitation for this secondary study region is 831mm/year (714 rainfall, 122 snowfall). Annual temperature ranges from an average of 22.3 in July to an average of -3.7 in January (Canada, 2013).

3.2.2.2 Description of site and simulated herbivory treatments

In July 2015, 40 1m2 study plots were established along four transects, separated by a distance of 5m. To mimic expected variability in the degree of herbivory by H. opulenta, in addition to control plots, I used scissors to apply simulated herbivory treatments of 25%, 50%, 75%, and 100%, which were randomly assigned to plots across the four transects. Treatments were applied to all leaves on V. rossicum individuals with a minimum height of 10 cm in a given plot. Using scissors, leaves were cut in early July with a clean cut across the leaf blade, perpendicular to the mid-rib. The percentage of simulated herbivory for each treatment was confirmed after weighing the total mass of clipped foliage from each plot. Total plant coverage for each plot was assessed using a 1m2 quadrat and summing the estimated area covered by each species. All plots had a minimum relative V. rossicum abundance of 60 percent. Initial plot conditions, and clipping mass for each simulated herbivory treatment are summarized in Figure 8-14. I found no significant differences between the initial conditions of the study plots with respect to soil moisture, pH, total plant coverage, or relative V. rossicum coverage (Figure 8-14).

3.2.2.3 CO2 efflux

2 To measure soil CO2 efflux, PVC collars (11.7cm in diameter and 5 cm in height) were inserted 2–3 cm into the ground at the center of each plot one day prior to defoliation treatments. Blades of grass inside the soil collars were cut at the soil surface to eliminate aboveground plant respiration.

Using a LI-COR 6400 portable photosynthesis system attached to a 6400-09 soil CO2 flux chamber

(LI-COR. Inc., Lincoln, Nebraska) soil CO2 efflux measurements were taken prior to the application of defoliation treatments and again after 6 days, between 10am and 3pm. Otherwise, standard procedures recommended by LI-COR were applied to measure soil CO2 efflux. Data were recorded at a 5-s interval by the data logger in LI-COR 6400 console. Each of the measurements usually took 1–3 min after placing the chamber over the collar. The CO2 efflux value at each measurement point was the mean of three sequential flux estimates at each sampling interval; –2 –1 values are expressed as µmol CO2m s .

3.2.2.4 Decomposition

To measure V. rossicum decomposition rates, I collected leaves from a neighbouring invaded site, dried the leaves at 35°C for 48 hours and filled polyethylene mesh bags (1 mm2 mesh, 15 x 18cm bag) with 4-6 g of V. rossicum leaves. Wet/dry ratios were calculated for the initial leaf material by drying subsamples at 60°C for 48 h to correct for moisture. Litter bags were deployed at treatment application and collected every three weeks for a total of four collection dates. The material remaining in each collected bag was cleaned, dried, weighed and recorded.

Decomposition rate was assessed by single exponential decay function: At=A0e−kt; where At is the amount remaining (g) after time t, A0 the initial amount (g), t the time (day), and k is the decay rate constant (day−1). The specific decomposition constant, k, was estimated from the slope of the line of the linear regression between ln(At/A0) and time (Paul, 2014).

3.2.2.5 Soil properties & plant coverage

Soil was sampled from all plots prior to the application of simulated herbivory treatments as well as 6 days following. Samples were composites from 4 cores per plot each taken to a depth of 10 cm. Fresh soil samples were used to determine soil moisture as % water by mass. Fresh soil samples were extracted with 2M KCl (2.0 g: 20 mL), shaken for 30 minutes, and filtered through + - Fisherbrand P8 filter paper. NH4 and NO3 concentrations were determined colourimetrically, using flow injection analysis (Lachat QuikChem). Soil pH was measured in a 1: 5 soil to water solution with a pH meter (Mettler Toledo FiveEasy pH meter, Mississauga, Ontario).

3.2.3 Statistical analysis

Using R statistical software (R core team, 2017), I used the “lme4” package (Bates et al. 2017) to create linear mixed-effects models to analyze differences in leaf area, number of leaves, and seed characteristics of V. rossicum at the H. opulenta release site. As measurements were taken from multiple individuals both within and across plots, ‘plot’ was designated as a random effect to resolve the non-independence of sampling (Winter, 2013). Five different types of univariate models were used, the model with the lowest Akaike Information criterion (AIC) value was considered used for further analysis. Following model selection, I used the “car” package in R (Fox et al., 2017) to assess significance across treatments using Wald chi-square tests, which were followed with post-hoc Tukey HSD test to determine specific differences between treatments.

Student’s t-tests were used to examine differences in larval dispersal distance across treatments. Analysis of Variance tests were performed to assess differences in treatment groups the response of soil processes to simulated herbivory, with a post-hoc Tukey HSD test to specify significant differences across treatment groups for a single response variable. I used the “Hmisc” R package (Harrell, 2017) to conduct correlation analysis (Pearson’s r) for variables in the simulated herbivory study.

3.3 Results

3.3.1 Defoliation by Hypena opulenta in shade and sun

As expected, I found highly significant differences in the number of leaves showing defoliation on V. rossicum individuals in the experimental plots in both light conditions (Figure 3-3; Table 3-1, see photos in Figure 8-15). Also as expected, the number of leaves showing defoliation was significantly greater in shade experimental plots as compared to experimental plots in the sun (Table 3-1). Regarding the total number of leaves on V. rossicum individuals from experimental plots, I found significant decreases in both light conditions as compared to control plots (Figure 3-4). Yet, the proportion in the number of leaves was not significant across light treatments (Table 3-1). Also as expected, I observed significant differences in leaf area for the experimental plots for bottom, middle and top leaves from both shade and sun treatments (Table 3-1; Figure 3-5). Though interestingly, in the top leaves of the sun experimental plots I observed a significant increase in leaf size (Figure 3-5).

3.3.2 Effect of defoliation on V. rossicum seed production

Regarding the effect of defoliation by H. opulenta on the reproductive output of V. rossicum, contrary to expectations, intense defoliation of V. rossicum did not reduce any of the measured seed parameters. In fact, I observed a significant increase for all seed parameters following defoliation of V. rossicum in the experimental plots in the shade, but no significant differences in the sun plots (Figure 3-6).

Table 3-1: Results from linear mixed effects models for all variables showing the effects of experimental treatment on V. rossicum reproductive output, leaf count and leaf area Variables Models Factors df χ2 p Seed mass Sun/Shade 1 57.837 <0.0001 Control/Experimental 1 5.746 0.017 S/S:C/E 1 38.466 <0.0001 Follicle length Sun/Shade 1 67.289 <0.0001 Control/Experimental 1 9.691 0.002 Seeds S/S:C/E 1 11.197 <0.0001 Follicle mass Sun/Shade 1 21.98 <0.0001 Control/Experimental 1 6.54 0.011 Seed count Sun/Shade 1 13.465 <0.0001 Control/Experimental 1 4.002 0.045 S/S:C/E 1 6.72 <0.01 Bottom leaves Control/Experimental 1 4.913 0.0267 Middle leaves Sun/Shade 1 91.976 <0.001 Control/Experimental 1 34.665 <0.001 Leaf Area S/S:C/E 1 18.369 <0.001 Top leaves Sun/Shade 1 51.693 <0.001 Control/Experimental 1 0.627 0.428 S/S:C/E 1 11.843 <0.001 Total num. leaves Control/Experimental 1 29.774 <0.0001 Leaf Percent leaves defoliated Sun/Shade 1 3.839 0.05 counts Control/Experimental 1 1140.203 <0.0001 S/S:C/E 1 19.431 <0.0001

3.3.3 Distance and directionality of H. opulenta larval dispersal

I observed significantly greater larval dispersal distance in the shade experimental plots as compared to sun conditions for South and East directions; t(5)=5.1, p <0.01 and t(5)=6.37, p<0.01, respectively; Figure 3-7). Interestingly, in the sun treatment, the directionality and distance of larval dispersal was skewed towards the North and West directions (Figure 3-7), which correspond to the positioning of the forest cover at the site (Figure 3-2).

3.3.4 Effect of simulated herbivory of V. rossicum on belowground processes

Overall, I found minimal effects of simulated herbivory of V. rossicum on belowground processes. + Though, the change in NH4 values following simulated herbivory yielded significant variation + among treatments, (F(4,35) = 3.92, p=0.05). Soil NH4 across all simulated herbivory treatments was significantly different from the control (p=0.05)(Table 3-2). This increase was significantly negatively correlated with leaf decomposition rate for two of the treatments (Table 8-9), and when all simulated herbivory treatment plots are pooled together, this relationship holds as a marginally significant negative correlation (Table 8-8).

3.4 Discussion

3.4.1 Defoliation and seed production of V. rossicum in shade and sun conditions following an application of H. opulenta

Results of the current study show that herbivory of H. opulenta led to significant defoliation of V. rossicum in both shade and sun conditions, measured by leaf area, number of leaves defoliated and total number of leaves (Figure 3-3 to Figure 3-5). The potential impact of H. opulenta as a defoliating agent is perhaps best reflected by the total number of leaves on V. rossicum stems following release because we observed abscission of leaves by V. rossicum after feeding on leaves began. This appears consistent with an adaptive response to drop damaged leaves as plants reallocate resources and exhibit compensatory growth (Karban and Baldwin, 2007; Zvereva and Kozlov, 2014). This inference is supported by the fact that even undamaged leaves were observed to be undergoing abscission. Had only damaged leaves been abscised, the cause may have been simply due to physical strain (Stiling and Simberloff, 1989).

Figure 3-7: Mean (±SE) distance and directionality of H. opulenta larval dispersal for sun and shade treatments. Significance denoted as; *, p<0.01 (t-test), n=5.

Soil metrics Treatment application Ti control 25% 50% 75% 100% p -value NH4(µg/g) 8.64 (1.1) a 5.75 (1.09) a 6.8 (0.99) a 8.31 (0.75) a 7.19 (1.96) a 0.36 NO3(µg/g) 3.11 (1.13) a 1.61 (0.67) a 6.03 (1.82) a 2.996 (0.93) a 3.5 (0.95) a 0.13 CO2 efflux (µmol CO2m–2s –1) 6.06 (0.19) a 5.58 (0.36) a 5.4 (0.63) a 5.34 (0.21) a 5.32 (0.43) a 0.36 Tf NH4(µg/g) 5.99 (1.15) a 8.21 (0.77) a 7.59 (1.7) a 9.35 (0.72) a 9.6 (1.14) a 0.15 NO3(µg/g) 2.21 (0.62) a 1.94 (0.61) a 2.5 (0.9) a 1.01 (0.21) a 3.02 (1.17) a 0.41 CO2 efflux (µmol CO2m–2s –1) 5.71 (0.39) a 4.72 (0.35) a 5.1 (0.45) a 5.17 (0.33) a 4.76 (0.32) a 0.32 Delta (Tf -Ti ) NH4(µg/g) -2.65 (1.16) b 2.46 (0.86) a 0.79 (1.56) a 1.05 (1.16) a 2.41 (2.43) a 0.05 NO3(µg/g) -0.9 (1.4) a 0.33 (0.75) a -3.53 (1.82) a -1.99 (1.01) a -0.47 (0.66) a 0.26 CO2 efflux (µmol CO2m–2s –1) -0.35 (0.3) a -0.86 (0.27) a -0.3 (0.31) a -0.17 (0.36) a -0.56 (0.25) a 0.54

a a a a a Decomposition (k constant) 0.0239 (-0.003) 0.025 (-0.002) 0.019 (-0.003) 0.0174 (-0.002) 0.023 (0.006) 0.39

Counter to expectations, significant herbivory by H. opulenta did not lead to reductions in seed production in either light condition. Furthermore, observing significantly increased seed production by V. rossicum following herbivory in shade conditions was unexpected given the results of previous studies (Doubleday and Cappuccino, 2011; Milbrath, 2008; Milbrath and Biazzo, 2016; Weed and Casagrande, 2010). The conflicting results here could be related to the fact that my study was conducted in a well-established population of V. rossicum. Milbrath’s (2008) study involved the excavation of V. rossicum root stalks with subsequent transplant into pots, after which simulated herbivory treatments were applied in a greenhouse setting. It is possible that the added stress from root excavation skewed the response to simulated herbivory, causing declines in seed production that would not be observed in well-established mature individuals. Similarly, where Weed and Casagrande (2010) observed decreases in V. rossicum’s seed production following herbivory by H. opulenta, their experiment was conducted on young V. rossicum individuals that were grown from seed. While those findings are informative for the potential effect of herbivory on young V. rossicum plants, my findings indicate that the response of mature well-established individuals is considerably different, at least after a single season of intense herbivory. Doubleday and Cappuccino (2011) observed significant declines in V. rossicum seed production following simulated leaf and root herbivory in a field study, although, the vast majority of the reduction was due to root damage, further supporting the possibility that root excavation was a confounding variable in previous studies. Furthermore, Doubleday and Cappuccino’s experiment was conducted on “sparser, recently established populations that have been expanding over the past several years” (Doubleday and Cappuccino, 2011:236). Again, the conflicting results between my findings and these other studies may be due to differences in root mass, added stress from excavation, the phenological stage at which defoliation took place (populations in high-light conditions tend to flower and set seed before those in lower light conditions (Livingstone, pers. observation)) and/or differential resource re-allocation strategies (compensatory growth) as a response to herbivory by individual plants at different life stages (McNaughton, 1983; Orcutt and Nilsen, 2000). Although, as noted by Milbrath (2008), V. rossicum individuals tend to have extremely high root:shoot ratios, at all life stages, with greater investment in root mass under high-light conditions. In order to better understand how populations in these two light conditions may or may not exhibit compensatory growth, further experimental work is needed that accounts for the phenological stage at which herbivory takes place.

Greater investment in the number and mass of seeds following herbivory has been observed in many cases (e.g. Jurinea mollis (Asteraceae) (Inouye, 1982 cited in Trumble et al., 1993); Arabidopsis thaliana (Damgaard et al., 2005); (Hawkes and Sullivan, 2001)). Yet, many other studies have shown negative (Clewley et al., 2012; van Driesche et al., 2002; Hawkes and Sullivan, 2001) and no response (Hawkes and Sullivan, 2001) of leaf herbivory on seed production. The current study’s finding that V. rossicum seed production was stimulated following defoliation by H. opulenta (for a mature stand in shade conditions) has significant implications for large scale bio-control and restoration efforts. The greater numbers and sizes of V. rossicum seeds that are produced following defoliation may enhance germination and seedling recruitment in subsequent years (Cappuccino et al., 2002; Eriksson, 1999; Stanton, 1984), with V. rossicum seeds already exhibiting extremely high germination probabilities (Ladd and Cappuccino, 2005). Of course, it is difficult to predict the long term population dynamics since we are not considering the case where H. opulenta has established a self-sustaining population that repeatedly feeds on V. rossicum in subsequent years (Casagrande et al., 2012). If this is the case, then herbivory of newly recruited seedlings and juvenile plants may in fact inhibit seed production (Doubleday and Cappuccino, 2011; Weed and Casagrande, 2010). Yet, it is unclear how long-term exposure to herbivory by H. opulenta will affect mature V. rossicum stands. In addition, recent work by Milbrath et al. (2016) found that an annual application of simulated herbivory and mowing of mature V. rossicum stands resulted in no significant reduction in plant biomass or seed production in following years. In fact, they observed an increased root mass in mature V. rossicum individuals following clipping treatments (Milbrath et al., 2016). Yet, they did find that multiple applications of simulated herbivory in a single season sometimes inhibited seed production (Milbrath et al., 2016). This is promising given that in laboratory settings, H. opulenta has been identified as a multi-voltine species with overlapping generations. If this multi-voltinism is maintained outside of laboratory settings it would provide relatively continuous herbivory across a single growing season if (Weed and Casagrande, 2010). Of course, when discussing studies that employ simulated herbivory, it is necessary to acknowledge that there can be considerable variability in the manner in which plants respond to simulated vs. natural herbivory. The specific biochemical signatures provided by natural herbivory can cause plants to respond differently than they would to simulated herbivory (Baldwin, 1990; Lehtilä and Boalt, 2008). Yet, no consistent trends have been shown with respect to the directionality of those differences with respect to effects on investment in reproduction

(Lehtilä and Boalt, 2008). Though, in the long-term, it might be expected that populations of V. rossicum would invest in defenses to herbivory, possibly at the expense of reproductive output as an evolution of reduced competitive ability (Johnson et al., 2015).

3.4.2 Effect of simulated herbivory of V. rossicum on belowground processes

Studies have shown that plant response to natural and simulated herbivory can be highly variable with respect to reproduction and allocation of resources (Hawkes and Sullivan, 2001), and also how that response may differ across life stages (Doubleday and Cappuccino, 2011; Weed and Casagrande, 2010). The same is true for the study of the indirect effects of bio-control of NIPS on belowground processes. In general, with respect to plant-herbivore interactions, regardless of a plant’s invasive status, there has long been a debate about whether or not insect herbivory creates a mutualism with its host plant (Dyer and Bokhari, 1976; but see Belsky, 1986). The bio-physical mechanism underlying this hypothesis is that following herbivory, plants exude carbon into the rhizosphere (rhizodeposition), which then stimulates microbial activity. Root exudates are broken down by heterotrophic microbes during the process of N mineralization (ammonification and nitrification) (Bengtson et al., 2012; Tracy and Frank, 1998), which, in turn, leads to increases in soil inorganic N pools and subsequent N uptake and regrowth of the defoliated plant (Hamilton and Frank, 2001). This process is also known to occur in the absence of herbivory (Haichar et al., 2014). However, there is added complexity to the conceptualization of this positive feedback mechanism when considering the role of plant secondary metabolism. As a well studied characteristic of many NIPS, secondary metabolites can affect the “quality” of carbon transferred from plants to soil microbial communities during rhizodeposition (Meier and Bowman, 2008).

+ I observed a significant increase in soil NH4 following defoliation (Table 3-2). This increase represents the ammonification of the carbon inputs to the soil from defoliated plants via root exudation and rhizodeposition (Doornbos et al., 2012). This finding agrees with much of the literature that has examined the response of the rhizosphere to natural herbivory in aboveground plant tissue (Hamilton and Frank, 2001; Tracy and Frank, 1998). Of course, in the study of invasion ecology, there are added complexities to an already complex interaction. With respect to the possibility that V. rossicum exuded allelochemicals in response to simulated herbivory, this + measured increase in soil NH4 is an indication of one of three things; 1) root exudates were “benign” and “high quality” carbon-based metabolites were deposited into the rhizosphere, which

79 then stimulated ammonification in the soil microbial community, or 2) that root exudates contained phytotoxic and/or anti-microbial compounds which were either at low enough concentration to have no effect, or 3) that the microbial community was able to degrade the compounds, possibly due to local adaptations that could have developed over time given that the study site is highly invaded by well established mature V. rossicum plants (Li et al., 2015).

Other studies have shown that the effect of herbivory (simulated and natural) on root respiration is dependent on soil nutrient availability. In nutrient rich conditions, plants tend to respond to herbivory by increasing shoot growth via re-allocation of carbohydrate reserves often found in robust root masses, with little effect on root respiration (Chapin and Slack, 1979; Trumble et al., 1993). Plants subjected to herbivory in nutrient poor conditions have been shown to increase nutrient absorption and root growth, resulting in subsequent increases in root respiration (Chapin and Slack, 1979; Trumble et al., 1993). The current study site has been shown to be relatively nutrient poor (see Chapter 2), which further supports the expectation that an increase in root, and cumulative, soil respiration should have been measured for simulated herbivory treatments. Of course, as CO2 efflux measurements were taken in the field, these values represent a combination of both microbial and root respiration, which complicates analysis. Nevertheless, a measured increase in total CO2 efflux was a likely expectation. It could be the case that any changes to total

CO2 efflux following simulated herbivory were obscured by the growth and/or senescence of neighbouring plant species, which occupy an average relative abundance of 21% (±2.87) per plot, though the vast majority of plots are dominated by V. rossicum (Table 8-2).

3.4.3 Synthesis and study limitations

This study revealed surprising differences in V. rossicum’s response to herbivory by H. opulenta as compared to previous laboratory work employing both natural (Weed and Casagrande, 2010), and simulated herbivory (Milbrath, 2008), as well as a field study that used simulated herbivory (Doubleday and Cappuccino, 2011). I also show that simulated herbivory of V. rossicum results in + little-to-no effect on the soil processes, with the exception of an increase in NH4 soon after defoliation takes place. Taken together, these findings suggest that, 1) in shaded conditions, established populations of V. rossicum are likely to increase seed production following herbivory by H. opulenta, and 2) that defoliation of V. rossicum appears not to stimulate or significantly affect soil properties, which challenges previously hypothesized inhibitory effects of V. rossicum

80 on soil microbial properties (Douglass et al., 2011). Of course, study limitations must also be considered. Specifically, the natural herbivory study is based on data from a single year where an established population of V. rossicum had likely persisted unscathed for multiple decades. It could be the case that consistent herbivory by H. opulenta across multiple years would negatively affect the plant’s stored resources and its ability/strategy to re-allocate to reproductive output. Moreover, as H. opulenta field releases occurred relatively late in the season (24 July and 1 August), it is difficult to make robust conclusions about the effect of defoliation on seed production. Results may have varied had the release coincided with seasonal emergence of V. rossicum. Indeed, we may have observed a different response by V. rossicum had the H. opulenta population been able to carry out its multi-voltine life cycle (multiple generations) across the full growing season, as is expected once populations are able to establish (Casagrande et al., 2012). In regards to the simulated herbivory experiment, interpretation of findings is limited by the fact that the treatments did not contain the insect frass and abscised leaves that would be present had natural herbivory taken place. These aboveground inputs can also affect belowground processes, and it is unclear whether the concentration of any allelochemical properties of V. rossicum’s root exudates would differ significantly from leaf leachates. Further, it is unclear whether the specific bio-chemical dynamics associated with H. opulenta’s herbivory of V. rossicum could significantly alter the chemistry of root exudation.

3.5 Conclusion

The implementation of the bio-control of invasive plant species is increasingly relevant for biodiversity conservation efforts. Host range testing studies required for the release of biological control agents have a primary focus on agent specificity and safety (Paynter et al., 2015; Schaffner, 2001). Impact studies on laboratory plants may not reflect what the agents encounter in field release situation with high densities of well establish plants, which makes predicting efficacy and impact difficult. Even impact studies in countries of origin under field conditions to assess agent effectiveness (Hinz and Schwarzlaender, 2004) may not accurately reflect density or state of the plants in the invaded habitat. Pre-release efficacy testing of biocontrol agents will be enhanced by examining agent responses across different life stages of the target organism (Doubleday and Cappuccino, 2011) as well as variable environmental conditions. Post-release bio-control agent

81 impacts must be followed by detailed monitoring to assess impacts on fitness and ecological interactions under the invaded habitat conditions (Shea et al., 2002).

4 On the distribution of different forms of ecological rarity in herbaceous plants and their importance for ecosystem functioning

4.1 Introduction

The characterization of biodiversity has undergone substantial evolution in recent years. Trait- based analyses are now at the forefront of biodiversity and conservation science (Cadotte et al., 2015; Drenovsky et al., 2012; Laliberté and Legendre, 2010; Laughlin, 2014; Martin and Isaac, 2015; Mason et al., 2005; Violle et al., 2017, 2007). By examining the distribution of, and differences between, species functional trait values researchers have been able to gain insights into community assembly (Laughlin et al., 2012), invasion dynamics (Drenovsky et al., 2012; Gallagher et al., 2015) and ecosystem functionality (Cadotte et al., 2009; Laughlin, 2011). Functional traits can be defined as “morphological, biochemical, physiological, structural, phenological, or behavioral characteristics that are expressed in phenotypes of individual organisms and are considered relevant to the response of such organisms to the environment and/or their effects on ecosystem properties” (Díaz et al., 2013). The surge of trait-based approaches has spurred interest in the potential for functional trait analysis to inform the characterization of ecological rarity (Jain et al., 2014; Violle et al., 2017). Historically, biodiversity conservation efforts tended to focus strictly on the preservation of species that were rare in terms of their abundance (Mace et al., 2003; Myers, 1979; Talbot, 1960). Then in the 1980’s, Rabinowitz (1981) further refined the concept of ecological rarity by also considering the geographical distribution and/or habitat specialization of rare species; creating seven “forms of rarity” (i.e. species can be geographically widespread but maintain low density at the local scale, or might exist in high density at the local scale but be restricted to a very specific habitat, etc.). More recently, advancements in trait-based research have yielded even more axes of ecological rarity. In addition to characterizing ecological rarity along Rabinowitzian axes, researchers are now broadening the concept by considering the relative uniqueness of species functional traits (hereafter “functional uniqueness”) (Ames et al., 2017; Jain et al., 2014; Leitão et al., 2016; Mouillot et al., 2013; Violle et al., 2017). As one can imagine, this new axis of commonness and rarity creates substantially more “forms of rarity”. Specifically, Violle et al (2017) identify fifteen forms of “functional rarity” by considering species geographic distribution, regional and local abundances and the uniqueness 83 or redundancy (i.e. overlap in trait values) of their functional traits. Obviously, these more complex notions of commonness and rarity have the potential to further refine our characterization of ecological rarity, with strong implications for biodiversity conservation goals.

It's often noted that species’ abundance distributions for many ecosystems exhibit a negative exponential trend when species are placed in ranked order from high to low abundance. That is, ecosystems contain a small number of highly abundant species with the majority being comparatively rare (Gaston, 2012a; Magurran and Henderson, 2003; Mariotte, 2014). In other words, rarity is actually a common phenomenon (Hessen and Walseng, 2008; Pimm, 2013). And while this is easily observed by plotting species abundance data, elucidating the significance of those rare species for ecosystem functionality (i.e. productivity, trophic interactions, nutrient cycling, etc.) presents a much greater challenge (Gaston, 2012b). A dominant perspective, with some convincing evidence, is that due to their abundance, common species are likely the most significant “drivers” of ecosystem functionality (i.e. the “mass ratio hypothesis”; (Grime, 1998; Laughlin, 2011; Lavorel, 2013). Paired with the fact that many conservation efforts are focusing on ecosystem services, which might be largely supported by common species, rare species may be left in limbo given that there is scant evidence of their contributions to ecosystem functioning. Due to this impasse, there has been a recent surge in studies striving to clarify not only the role of rare species in ecosystem functioning, but also their contribution to community diversity (Jain et al., 2014; Leitão et al., 2016; Mouillot et al., 2013). Researchers are exploring these dynamics by quantifying the positioning of rare and common species, in terms of the relative redundancy or uniqueness of their functional traits, within the functional diversity (FD) of the community at large (Cadotte et al., 2010; Jain et al., 2014). Indeed, it has been shown that rare species sometimes exhibit functional uniqueness, making a significant contribution to functional diversity (Leitão et al., 2016). A large study by Mouillot et al., (2013) examined the abundances and functional characteristics of coral reef fishes, alpine plants, and tropical trees and found that rare species also possess distinct combinations of traits (i.e. functional uniqueness). Though, perhaps unsurprisingly rare species have also been shown to be redundant in their functional trait composition (Ellingsen et al., 2007).

Despite their low abundance, and evidence to the contrary (Lavorel, 2013; Winfree et al., 2015 but see Lyons et al., 2005 & Mouillot et al., 2013), it’s often noted that rare species may still

84 make significant contributions to ecosystem functionality (Jain et al., 2014; Lyons and Schwartz, 2001; Soliveres et al., 2016). A commonly cited phenomenon in this regard is the “keystone species” effect, where a given species contributes disproportionately to the stability and/or functionality of an ecosystem, based on its relative abundance (Libralato et al., 2006; Paine, 1966). There are multiple examples of this phenomenon (Lyons et al., 2005), though it is typically identified in higher trophic levels as a “top-down” process that stabilizes taxonomic diversity (Estes et al., 1998; Heithaus et al., 2008; but see Libralato et al., 2006), as of yet with little-to-no consideration of species functional traits (but see Gravel et al., 2016). Functional uniqueness, regardless of trophic level, may in fact contribute to ecosystem functionality in other ways. For instance, species that are rare today may be important for long-term ecological stability. The “portfolio effect” or “insurance hypothesis” (Tilman, 1999; Yachi and Loreau, 1999) predicts that the traits of subdominant or rare species, if distinct from those of dominant species, can act to enhance an ecosystem’s “adaptive capacity” under environmental variability (Bussotti et al., 2015). Of course, redundancy in the functional traits of rare species can also act as a stabilizing force if common species experience reduced abundance (Ellingsen et al., 2007). Though, it could also be the case that rare and unique species face the greatest threat from environmental change (Boulangeat et al., 2012; Harnik et al., 2012) and/or anthropogenic pressures (Courchamp et al., 2006), such as human-caused species invasions (Mooney and Cleland, 2001; Sodhi et al., 2009; Wagner and Driesche, 2010a). Invasive species have been shown to cause significant reductions in community functional diversity (Castro-Díez et al., 2016; Olden et al., 2004), and their impact on functional uniqueness is only now being explored (Matsuzaki et al., 2016). Indeed, it is often hypothesized that the success of some invasive species may be due to the fact that they themselves possess unique traits that either gives them access to unused niche space or engenders them with greater competitive ability (Drenovsky et al., 2012; Higgins and Richardson, 2014; Sanderson and Antunes, 2013).

In this study, I examine the distribution and significance of ecological rarity in herbaceous plant species in Rouge National Urban Park (NUP), in Toronto Canada. Specifically, the objectives of this study were to: 1) examine the relationships between different facets of ecological rarity and commonness in herbaceous plants; focusing specifically on species relative abundance, functional uniqueness and geographical restrictedness; 2) determine the importance of plant species’ relative abundance and functional uniqueness for ecosystem functionality; and 3) determine the effect of

V. rossicum invasion on the presence of functionally unique and geographically restricted plant species. To address these objectives I: 1) quantify the relative abundance, functional uniqueness and geographical restrictedness of herbaceous plant species across 14 sites in Rouge NUP; 2) use a null model approach to test for non-random associations between species relative abundance and ecosystem functioning, with post-hoc tests examining how those associations differ in relation to plant functional uniqueness; and 3) examine how the presence or absence of functionally unique and geographically restricted plant species relates to increasing V. rossicum abundance. I hypothesized that: 1) functional uniqueness will be negatively associated with relative abundance (rare plants will not be functionally redundant); 2) rare and functionally unique plants will make significantly positive contributions to ecosystem functionality; and 3) the presence of functionally unique species will be negatively correlated with V. rossicum invasion.

4.2 Methods

4.2.1 Species relative abundance, functional traits and ecosystem functions

Herbaceous plant species were identified and their relative abundance was estimated across14 meadow community sites in Rouge National Urban Park with 25 replicate plots per site. Relative abundance was also calculated at the regional scale by pooling all site abundances and dividing species total abundance by the regional sum. This was done in order to contrast regional relative abundance with other regional metrics for functional uniqueness and geographic restrictedness (see below section). For detailed site and sampling information see section 2.2.1. Trait measurements were taken for plant height (cm, n=20-40 per species), leaf carbon and nitrogen content (% mass, n=5 per species) and specific leaf area (SLA) (cm2/g, n=20-40 per species). These traits were selected due to their previously shown significance for ecosystem functionality and the fact that they represent relatively independent components of plant functional composition. Sampling protocols detailed in Pérez-Harguindeguy et al. (2013) were followed. See section 2.2 for details on the measurement of ecosystem functions (biomass production - living and dead, decomposition rate, soil C and N content, pollinator richness and abundance and flower coverage).

4.2.2 Forms of rarity

Species functional uniqueness and geographical restrictedness were calculated using the package ‘funrar’ for R statistical software (Grenié et al., 2017; “R: The R Project for Statistical Computing,”

86 n.d.). Functional uniqueness (Ui) is an index that represents the relative “isolation” of a species in the functional trait space relative to the global species pool; values closer to 1 indicate a greater degree of functional uniqueness. Geographical restrictedness (Ri) is an index that integrates the frequency of species’ presence/absence as well as their relative abundance in different communities to establish a regional estimate of restrictedness; values closer to 1 indicate a greater degree of geographical restrictedness.

At the regional scale I measure species restrictedness using the extent of occurrence or the area of occupancy, the most geographically restricted species receiving a value of 1 while widespread species will tend to values close to 0. The geographic extent of the most widespread species is used to standardize restrictedness, which ranges from 0 to 1 (Dapporto & Dennis 2008)

Ge R 1 i i Ge max

Functional uniqueness (Ui) is measured by the functional distance to the nearest neighbor (or to the k nearest neighbors) within the regional species pool as:

U i  min( d ij ) j  i*

Ui is high when species i has a unique combination of traits compared to other species and more particularly has a high functional distance even with its closest species. At the opposite Ui is 0 when species i shares exactly the same traits as another species in the pool, i.e. is perfectly redundant. U scales between 0 and 1 since d scales between 0 and 1 (Violle et al. 2017). i ij 4.2.3 Statistical analysis

To calculate the importance of individual species for ecosystem functionality I followed a similar approach to that developed by Gotelli et al. (2011). This technique involves the application of randomization tests to quantify the average effect of a given species on a given ecosystem function. This model generated 999 random permutations of species abundance-ecosystem function relationships to establish a null distribution of regression coefficients. The identification of significant species-function relationships was achieved by assessing the divergence of observed

87 species-to-function coefficients from the mean of the null distribution. This divergence was also used to calculate standardized effect scores (SES, z scores).

SES = (Xobs -Xnull)/SD(Xnull)

I used species relative abundance data, as opposed to presence or absence, in hopes of capturing a more accurate representation of species relative importance to function. The calculation of regression coefficients required that the species be present at two or more sites. This meant that some species had to be excluded from the analysis; specifically, ANMA, CLVIR, GEUR, LOCO, OXST, PLLA (See Table 8-7 for species names and codes).

To examine the importance of relative abundance and functional uniqueness for ecosystem functionality, I categorized species as either of high, medium, or low uniqueness or relative abundance. Each uniqueness category was comprised of a third of the species pool, where relative abundance categories were delineated as low:0-0.5 %, medium:0.5-5.2% and high: 14.23-33.48% to account for the negative exponential trend in values. To assess significantly positive or negative effects of the different categories on ecosystem function, mean standardized effect size (z scores) and 95% confidence intervals were calculated. Mean values were considered significant if the 95% confidence interval did not overlap zero.

To determine if V. rossicum invasion may be causing a reduction in the abundance of functionally unique or geographically restricted species I first used a logistic mixed effects model to regress species presence/absence against V. rossicum abundance, including site a lme3 to account for spatial autocorrelation of species presence/absence (Bolker et al., 2009; Dormann et al., 2007). Then, using these models, I coded species associations with V. rossicum as either significantly negative or neutral (one and zero, respectively), and then applied a second logistic regression to examine how those significantly negative associations relate to species’ functional uniqueness or restrictedness.

To calculate “functional rarity” (a combined metric that captures both species functional uniqueness and geographic restrictedness) I first scaled the functional uniqueness and restrictedness values for individual species by dividing the observed values by the maximum value from the species pool for each metric. This was done to compensate for the substantially greater range of observed values for species restrictedness as opposed to functional uniqueness.

Otherwise, a simple addition of the two metrics would result in the functional rarity value being heavily weighted by restrictedness.

4.3 Results

4.3.1 Relationships between different forms of rarity

In terms of species relative abundances, the regional species pool was dominated by four species; Vincetoxicum rossicum (Kleopow) Barbar., Solidago sp. L., Poa pratensis L. and Bromus inermis Leyss., with 33.48%, 20.2%, 16.28% and 14.23% relative abundance, respectively (Figure 4-1;Table 8-10). Interestingly, some of the least abundant species include Allaria petiolata (M. Bieb.) Cavara & Grande, Centaurea maculosa, and Euphorbia cyparissias L. (Figure 4-1); species that are known to be extremely invasive in other systems. Overall, species’ relative abundances exhibit a log-normal distribution (Figure 4-1).

In addition to the distribution of species’ relative abundances, Figure 4-1 also shows species’ functional uniqueness, restrictedness and functional rarity. Inula helenium L. had the highest functional uniqueness value from the regional species pool, and also exhibited a relatively high degree of restrictedness, resulting in it also having the highest ‘functional rarity’ value (Figure 4-1). Equisetum arvense L., Sonchus arvensis L. and Erigeron annuus L. (pers.) also exhibited relatively high functional uniqueness values and relatively low abundance. While the most dominant species, V. rossicum, exhibited low functional uniqueness, other dominant species (Solidago sp, Poa pratensis) show variable degrees of functional uniqueness (Figure 4-1). Interestingly, one of the most common species, Bromus inermis, exhibited relatively high functional uniqueness (0.096) (Figure 4-1; Table 8-10). Symphyotrichum cordifolium was the most geographically restricted of the species pool, being present at only site (Table 8-2), but was only moderately unique in its functional trait composition. V. rossicum, P. pratensis and Linaria vulgaris had zero values for restrictedness, being present at each of the 14 sites, though L. vulgaris maintains relatively low densities at the local scale (Figure 4-1; Table 8-2).

Overall, plant functional uniqueness (Ui) was not significantly related to either relative abundance or restrictedness (Ri), β=-3.86, t(45)=-0.11, p=0.91 and β=-1.59, t(45)=-0.99, p=0.33,

Figure 4-1: Relative abundance, functional uniqueness, geographical restrictedness and functional rarity of herbaceous plant species in Rouge National Urban Park study sites, placed in order of ranked abundance. Functional uniqueness, geographical restrictedness and functional rarity calculations are explained in section 4.2.3.

90 respectively (Figure 4-2). Further, the vast majority of rare species appear to be functionally redundant with more common species (Figure 4-2a). Though these plots do reveal where species fall in terms of different “forms of rarity” (i.e. functionally redundant but geographically restricted, functionally unique, but relatively abundant, etc.), keeping in mind though that these distinctions are a part of a continuous measurement.

4.3.2 Significance of relative abundance and functional uniqueness for ecosystem functionality

Overall, I did not find a significant positive or negative trend between plant functional uniqueness and ecosystem functionality (Figure 4-3; Table 8-11). Considering only species abundance, I did find that more dominant species tended to have a significantly positive effect on biomass production (mean z score: 0.004, 95% CI [0.0022, 0.0058], litter production (mean z score: 0.0078, 95% CI [0.0061, 0.0094] and inverse inorganic nitrogen (mean z score: 0.0045, 95% CI [0.00008, 0.0089] (Figure 4-4; Table 8-12). However, dominant species tended to have a significantly negative effect on pollinator richness (mean z score: -0.0155, 95% CI [-0.017, -0.013] (Figure 4-4; Table 8-12). Interestingly, the most abundant species, the invasive Vincetoxicum rossicum, was only found to significantly affect decomposition rate (k constant), and inverse inorganic nitrogen, in negative and positive manners, respectively (Table 4-1). Considering the effect of other individual species, Anemone Canadensis L. (the least abundant species with moderate functional uniqueness), and Ranunculus acris L. (a rare species with relatively redundant functional trait composition), were found to have significantly positive effects on pollinator richness (Table 4-1). At moderate abundance and low functional uniqueness (i.e. redundant), Symphyotrichum ericoides (L.) G.L. Nesom, Symphyotrichum lanceolatum (Wild.) G.L. Nesom were found to have significantly positive effects on both pollinator richness and abundance (Table 4-1). The second most abundant species, Solidago sp, had the greatest number of significantly positively effects on ecosystem function - specifically for flower cover, biomass and litter production and decomposition rate (k constant) (Table 4-1). As one of the rarest species, Centaurea maculosa, a species known to be invasive in other systems, was found to have a significantly positive effect on inverse total soil nitrogen (Table 4-1). At moderate abundance, Erigeron annuus was found to make a significantly positive contribution to biomass production (Table 4-1). Interestingly, several species that exhibit both moderate abundance and

95 moderate functional uniqueness (Fragaria virginiana Duchesne, Daucus carota L., Medicago lupulina L. and Agrostis gigantean Roth) had significantly positive effects on pollinator richness and abundance, but these same species had significantly negative effects on litter production.

4.3.3 Effect of V. rossicum invasion on the presence of functionally unique and geographically restricted species

I found that while increasing V. rossicum abundance had a significantly negative effect on the presence of many species (Table 4-2), there was no observed trend regarding the functional uniqueness of the significantly affected species (Figure 4-5). Unsurprisingly, I found that the species that were negatively affected by V. rossicum abundance tended to be more geographically widespread, β= -2.42, t(51)= -2.27, p=0.02 (Figure 4-5).

4.4 Discussion

4.4.1 The relationship between different forms of rarity

It is commonly noted that most ecosystems contain a few common species, while the majority of species are comparatively rare in terms of their abundance (Ellingsen et al., 2007; Gaston, 2012a; Mariotte, 2014). My results also support this observation (Figure 4-1). Though, as noted in the seminal work by Rabinowitz (1981), species rarity and commonness can also be quantified along several other ecologically relevant axes (i.e. habitat specificity, geographical distribution and population density). The current analysis adds to these axes of rarity by also considering the relative uniqueness of species functional traits and their geographic restrictedness (Mouillot et al., 2013; Violle et al., 2017). I show that while individual species can exhibit different “forms of rarity” (i.e. widespread but functionally unique (e.g. Carex sp, Figure 4-1); widespread and relatively redundant in functional trait composition, but occurring in relatively low abundances (eg., Linaria vulgaris, Figure 4-1); functionally unique, geographically restricted and occurring at relatively low abundances; e.g. Inula helenium), there are no clear trends regarding the relationship between these axes (species that are functionally unique are not necessarily geographically restricted; the functional traits of species that are relatively rare in terms of their abundance tend not to be significantly different from more common species; Figure 4-2). Though, it is important to note here that the quantification of geographical restrictedness presented in this analysis is a measurement of the study region only and does not consider the

Figure 4-5: Relationship between a) plant functional uniqueness (Ui) and species association with V. rossicum abundance, and b) Plant geographical restrictedness (Ri) and species association with V. rossicum abundance. A value of 1 for species association with V. rossicum indicates a significantly negative relationship between species presence and V. rossicum abundance (Table 4-2), values of 0 represent non-significant associations. The solid line in (b) indicates a statistically significant relationship (p=0.02). Shaded area indicates 95% confidence interval. (points are offset to make all points visible).

98 absolute geographical distribution of the species’. Certainly a more comprehensive assessment of species distributions could drastically alter the relative rarity or commonness of a given species. For example, my identification of Symphyotrichum cordifolium (Linnaeus) G. L. Nesom as the most geographically restricted species in the regional species pool is extremely interesting given that the species’ full distribution spans the entire eastern coast of north America (eFloras.org, N.D.). Following Rabinowitz’ (1981) classification of different forms of rarity then, this is an example of a species with extremely widespread distribution that is sparsely distributed at the local scale (at least for the local scale measured in this study).

My finding of no significant difference between the functional trait composition of rare and abundant species indicates that rare species exhibit functional redundancy (at least for the traits measured in this study). This does not support my first hypothesis and is also contrary to recent studies that have found relatively rare species to be significantly different in terms of their functional trait composition (i.e. functionally unique) (Leitão et al., 2016; Mouillot et al., 2013; Richardson et al., 2012). Yet, other studies have found rare species to be functionally redundant (Ames et al., 2017; Ellingsen et al., 2007) or that the relative contribution of rare species’ to functional diversity can be dependent on the ‘axis of rarity’ that is considered (Jain et al., 2014). Jain et al. (2014) found rare species to be functionally redundant when species’ mean abundance is used to identify relative commonness or rarity, but that rare species make significant contributions to functional diversity (i.e. they are functionally unique) when maximum abundance, geographic range and habitat specificity are used to delineate their commonness or rarity.

In general, it is likely the case that the number and type of functional traits, as well as the range of trait values, used to quantify species functional composition will significantly affect which species are identified as functionally unique or functionally redundant. By integrating more traits into a multi-dimensional comparative index, species differences can be more easily determined given the increase in total trait space (Cadotte et al., 2011b; Petchey and Gaston, 2002b). Furthermore, similar to studies examining the effect of functional traits on ecosystem functionality, researchers need to be cognizant of correlations between different traits when quantifying functional uniqueness. The identification of functional uniqueness may be confounded by indices containing highly correlated trait values (i.e. the overall trait dissimilarity will be more heavily weighted towards the correlated traits, obscuring potentially significant differences in

99 other traits) (Violle et al., 2017). Furthermore, it may be of interest to identify and focus on certain plant traits that could be more significant for conservation purposes (e.g. unique floral structures, unique bio-chemical profiles that may support specialized trophic interactions, unique phenological dynamics (e.g. early, ephemeral or late emergence) that may enhance invasion resistance or support diversity of other trophic levels, etc.).

4.4.2 The significance of different forms of rarity for ecosystem functionality

Considering the mean values from the entire species pool examined here, I found neither functional uniqueness or functional redundancy to be associated with positive or negative effects on ecosystem functionality (Figure 4-3). And while this indicates little support for my second hypothesis (that rare and functionally unique species would make significantly positive contributions to ecosystem function), a very small minority of rare and moderately unique species were found to make significantly positive contributions to certain ecosystem functions (see section 4.3.2). Keeping in mind that these results were obtained using “non-experimental” methods, these findings do set the stage for more controlled examinations of the functionality of these individual species. In contrast, I did find more convincing support for the “mass ratio hypothesis” with dominant species tending to make significantly positive contributions to ecosystem function (Figure 4-4). Though, I also see that the positive effect of abundant species is dependent on the ecosystem function considered - some functions are fact being negatively affected by abundant species (pollinator species richness and soil total nitrogen). Clearly then, it is difficult to comment on the overall effect of either functional uniqueness or relative abundance on ecosystem functionality given that I observed substantial variability at the species level along each of these axes.

My observation that Anemone canadensis and Ranunculus acris are making significantly positive contributions to pollinator species richness is quite interesting. Both of these species are rare with moderate and redundant functional trait uniqueness, respectively. Both A. Canadensis and R. acris are members of the Ranunculaceae family, with the latter being an exotic species that is native to Europe. And although I found R. acris to be relatively rare in my study sites, it is known to be invasive in some conditions (typically roadsides and agricultural fields; Wu and Kalma, 2009). This is an extremely interesting dynamic where both a non-native (R. acris) and native species (A. canadensis) are positively affecting the diversity of the pollinator community despite

100 their low abundance. R. acris has been shown to be an important pollen resource for bees (Somme et al., 2015), and in a study conducted in Sweden, which is in the plant’s native range, Jakobsen et al. (2009) observed the floral productivity (number of flowers) of R. acris to be highly abundant compared to the regional species pool. However, it has also been shown to make insignificant contributions to regional pollen and nectar resources in plant communities in the United Kingdom (Hicks et al., 2016). Certainly, the relative importance of any plant’s floral production as a resource for pollinators is dependent on the characteristics of the regional species pool in question. In that regard, given that I did not observe R. acris to make a significant contribution to flower cover, it could be that, in the meadow communities studied here, it is providing resources at a time in the season when dominant plants have yet to bloom and/or its floral resources are more attractive than other species blooming in synchrony. Certainly, in the Toronto region R. acris blooms early in the season along with A. canadensis (Peterson, 1996) and V. rossicum (DiTommaso et al., 2005), and I have also shown that V. rossicum abundance is negatively associated with pollinator diversity and abundance (Figure 2-6). Pollination occurs in all three species via visitation by generalist arthropods (Douglass et al., 2009; Kipling and Warren, 2014; Molano‐Flores and Hendrix, 1999). Though, given that A. canadensis is the sole native species of the three, it could be the case that the diversity of pollinator species visiting A. canadensis is skewed towards native-native interactions. Certainly, this provides an interesting grounding for future analysis. In fact, studies have shown that A. canadensis exhibits a strong attractiveness for natural enemies (Fiedler and Landis, 2007), though it is unknown whether its pollen (the flowers contain no nectar; Douglas and Cruden, 1994) is more attractive to native or exotic pollinators. Furthermore, it could be the case that both A. canadensis and R. acris are actually rather unique species compared to the regional species pool. It needs to be noted though that the traits included in the functional uniqueness value for the current study (height, SLA and leaf C:N) are not ideal for capturing the mechanism behind the “trait-to-function” relationship in this case. It could be that by integrating floral traits or flowering phenology into the functional uniqueness measure A. canadensis and R. acris might exhibit a greater degree of uniqueness, which could then be more closely examined for its contribution to pollinator diversity. Generally, this points to the need for more focused trait- to-function analysis.

I also observed the presence of R. acris to be negatively associated with V. rossicum abundance (Table 4-2), likely indicating that it is a relatively weak competitor in these meadow

101 ecosystems. But this also indicates that the spread of V. rossicum is likely further reducing the diversity of local pollinators by extinguishing an important floral resource, albeit from another non-native “quasi-invasive” species. Certainly there are cases where the floral resources of non- native invasive plants have been shown to provide resources for pollinators, but often this dynamic can impede pollination success in native plants (King and Sargent, 2012). In the current study, given that V. rossicum is negatively impacting pollinator diversity, R. acris could be perceived as a vital resource. Identifying the appropriate “conservation perspective” for these type of novel dynamics is extremely challenging. Clearly there are cases where non-native species, and those species with context-dependent invasion potential (i.e. only invasive in highly disturbed habitats), can act as a “safety net” for pollinator communities in the face of invasion by more aggressive and resource limiting invasives. Given that the native A. canadensis is a co-blooming species with R. acris, with both occurring in low abundance, this is a case where a non-native species is actually providing a vital resource for local pollinators and is indirectly benefiting a co-blooming native species. In general though, as invaded systems are characterized by dynamic population trajectories (i.e. lag effect, allee effects), there is a need to continually examine how invasions might be disrupting the diversity of pollinator communities and the pollination of native species (Kearns et al., 1998; Traveset and Richardson, 2006).

Regardless of the minor contributions of rare and functionally unique species to “current” ecosystem functionality (Figure 4-3; Figure 4-4), there remains substantial interest in their importance for biodiversity conservation (Crain and White, 2010; Espeland and Emam, 2011; P. G. Harnik et al., 2012; Lawler, 2003). Many authors note the potential future significance of rare and/or unique species for ecosystem functioning and services (Ellingsen et al., 2007; Jain et al., 2014; Yachi and Loreau, 1999). For example, rare and/or functionally unique species may become more “ecologically significant” under emerging climate regimes, or by enhancing resistance to future invaders (Lyons and Schwartz, 2001; Zavaleta and Hulvey, 2004). Furthermore, as rare species face the threat of extirpation due to the spread of invasive species (van Kleunen and Richardson, 2007; Wagner and Driesche, 2010b), there is substantial value in assessing their contribution to functional diversity (i.e. their functional uniqueness). Interestingly, in this study I observed that the presence of geographically restricted species tends not to be negatively associated with V. rossicum invasion (Figure 4-5; Table 4-2). This trend is in fact supported by Powell et al. (2013), who noted that at the regional scale, rare plant species tend to be “buffered”

102 against extirpation from invasive species, at least in moderate stages of invasion. This is thought to be due to either common species being outcompeted by invaders (perhaps due to greater niche overlap and competitive edge of the invader; MacDougall et al., 2009) or due to significant differences in the environmental tolerances or functional traits of rare species that allows them to occupy unique niche space (Markham, 2014). Certainly, in addition to shedding light on the mechanisms of invasion, examining the differences in the functional trait composition between invasive species and resident natives, both common and rare, may yield insights into both the persistence of rare species in the face of invasion (Powell et al., 2013) and potential restoration strategies following the removal of invasive species (Laughlin, 2014).

Due to the nature of this study system, with variable site histories and pre-existing differences in soil conditions, it is difficult to say whether C. maculosa’s or V. rossicum’s presence is resulting in a reduction of soil nitrogen, or if their presence/abundance is due to the pre-existing nutrient poor conditions at a given site (Table 4-1). Certainly, many studies have shown that invasive plants can often colonize and thrive in nutrient poor conditions (Funk, 2013; Funk and Vitousek, 2007). Further, while the null model approach used to determine species-specific effects on ecosystem functioning is useful for examining these relationships in non-experimental systems, it also needs to be noted that species interactions (e.g. competition and facilitation) and unmeasured environmental variables (e.g. pre-existing differences in soil chemistry between sites, differences in light availability, differences in micro-climate, etc.) may confound species-level estimates (Gotelli et al., 2011).

4.4.3 The impact of invasion on functional uniqueness and geographical restrictedness

In the current study, I observed that the invasive V. rossicum is functionally redundant with the majority of the resident community (Figure 4-2a). Of course, this assertion is based only on the traits included in the study. It may be the case that by including different traits, or those that are known to be significant predictors of invasiveness (i.e. seed set, phenology, secondary metabolites, etc.), significant differences in functional uniqueness might be observed (Van Kleunen et al., 2010; Whitney and Gabler, 2008). In the current study, my assessments of plant species’ relative abundance, restrictedness and functional uniqueness include many exotic species, some of which are here classified as relatively rare species, but are known to be invasive in other systems within the region (i.e. Allaria petiolata and Hieracium vulgatum; Ontario Invasive Plants Council, 2013).

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With this in mind, it is essential that researchers investigate species’ origins and the broader regional context to their distribution when assessing their relative rarity and/or conservation importance. Similarly, it needs to be acknowledged that the functional uniqueness values presented here are constrained by the inclusion of a relatively small set of traits. A more extensive functional trait “profile” will likely expand the distribution of functional uniqueness values, potentially revealing a more accurate portrayal of the differences in species functional composition.

4.5 Conclusion

New perspectives on ecological rarity are an essential step forward for conservation science. Recent studies reveal that the erosion of biodiversity can be characterized, without hyperbole, as “biological annihilation” (Ceballos et al., 2017; Dirzo et al., 2014). Given that the identification of different forms of ecological rarity can stimulate both conservation goals and public interest (Angulo and Courchamp, 2009; Broennimann et al., 2005; but see Courchamp et al., 2006), a more nuanced understanding of how species contribute to biodiversity via their functional composition may spur conservation action. Furthermore, given the recent trend in conservation science to focus on ecosystem services, as opposed to species-based conservation goals, there is a desperate need identify the contribution of different forms of ecological rarity to the provisioning of ecosystem services (Bryan et al., 2010). Currently, ecosystem service assessments seem to only consider either the abundance of rare species as indicators of habitat quality or how the presence of rare species contributes to human appreciation of nature (i.e. cultural services) (Lamy et al., 2016; Lawler, 2003; Richardson and Loomis, 2009). In this regard, the identification of other forms of rarity, and their contribution to ecosystem functionality, could serve to broaden the ecological foundations of ecosystem service assessments.

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5 Ecological engagement determines ecosystem service valuation: A case study from Rouge National Urban Park in Toronto, Canada

5.1 Introduction

Contemporary human activities are drastically altering the earth’s ecosystems (Kareiva et al., 2007). A troubling consequence of this alteration is the staggering decline in biodiversity around the globe (Ceballos et al., 2015; Dirzo et al., 2014). It is increasingly recognized that humans depend on biodiversity in a number of ways for our well-being (Daily, 1997; Haines-Young and Potschin, 2010; Liu and Opdam, 2014). This recognition has spurred the popularization of the concept of ecosystem services (ESs) as a means of quantifying, communicating and integrating that dependence into conservation policy and environmental governance (Cimon-Morin et al., 2013; Costanza et al., 1997; Daily, 1997; Martinez-Harms et al., 2015; Snäll et al., 2015). The Millennium Ecosystem Assessment broadly defined ESs as “the benefits that people obtain from ecosystems” (Millennium Ecosystem Assessment (Program), 2005) and introduced a four- category classification system to differentiate different kinds of ESs. These categories are; provisioning services (e.g. food, water, timber), regulating services (e.g. natural water filtration, climate regulation via evapotranspiration), supporting services (e.g. soil formation, nutrient cycling), and cultural services (e.g. recreation in nature, aesthetics of natural systems)(Millennium Ecosystem Assessment (Program), 2005). By conceptualizing the functioning of ecosystems as “services” upon which we depend, and communicating that these ESs are provided by nature at no cost to society, the ESs concept has become “a powerful discursive tool for conservation practitioners and policy-makers” (Muradian and Rival, 2012). Essentially, the ESs concept is a tool to internalize a positive environmental externality (Gómez-Baggethun et al., 2010) either in an economic sense (Bellver-Domingo et al., 2016) or as a heuristic for public appreciation of the value of nature (Potschin and Haines-Young, 2016).

In addition to communicating the value of preserved indigenous ecosystems, the ESs concept is increasingly used in the context of urban biodiversity conservation (Ahern et al., 2014; Haase et al., 2014). Urban regions provide a diverse ecological, socio-economic and governance context to examine the importance of different ESs (Ahern et al., 2014; Kroll et al., 2012), and

105 threats to their conservation (Marvier et al., 2004; Mcdonald et al., 2009). “Natural ecosystems” and protected areas provide a rich array of ESs (Foley et al., 2005; Gamfeldt et al., 2013) but they are extremely scarce in most urbanized regions (Mcdonald et al., 2009; Scolozzi and Geneletti, 2012). Moreover, those located in urban regions are often relatively small and contain an abundance of non-indigenous invasive species (NIS) which threaten the continued provisioning of ESs (Pejchar and Mooney, 2009; Trentanovi et al., 2013). Because of these factors, valuation of ESs in urban regions is highly dependent on the priorities of local stakeholders that benefit from those ESs (Hein et al., 2006; Menzel and Teng, 2010) and their perception of threats to ESs, such as NIS (García-Llorente et al., 2008). Furthermore, given the high degree of anthropogenic land conversion, high human population density and relatively small size of protected areas in urban regions, it has been often noted that the cultural ESs provided by urban protected areas are likely of greater relative importance than supporting, regulating and provisioning ESs and can serve as a conduit for the recognition of other ESs (Andersson et al., 2015; Chan et al., 2012; Lin et al., 2014).

From a conservation management perspective, there are now multiple empirical studies that highlight the importance of analyzing stakeholder priorities with respect to ESs (Castro et al., 2011; Koschke et al., 2012; P. Lamarque et al., 2011; Martín-López et al., 2012; Orenstein and Groner, 2014; Palacios-Agundez et al., 2014) as well as perceptions of the impact of NIS on ESs (Bardsley and Edwards-Jones, 2006; García-Llorente et al., 2008; Humair et al., 2014; Lohr and Lepczyk, 2014). By highlighting synergies and conflicts with respect to prioritization of ESs and perceptions of NIS (Hicks et al., 2013), and shedding light on attributes that can influence those priorities and perceptions, stakeholder analysis can inform both management and communication actions by conservation practitioners (Bardsley and Edwards-Jones, 2006; Bryan et al., 2010; Hein et al., 2006; Sherrouse et al., 2014).

5.1.1 Analytical framework

I developed a stakeholder analysis framework following the key methodological steps formalized by Reed et al (2009). These are 1) identifying the context and system boundaries; 2) applying stakeholder analysis methods to identify stakeholders and the stake; 3) differentiate between stakeholders; 4) examine relationships between stakeholders; and 5) utilize analysis to make recommendations for stakeholder engagement. Often, in studies employing stakeholder analysis,

106 categorization of stakeholder groups is based on a priori knowledge of significant differences in interests and/or worldview between groups of people, sometimes referred to as a “top-down”

107 process (Prell et al., 2009). Yet, the categorization of stakeholder groups and differentiating attributes can also arise as a “bottom-up” process through self-identification and emergent grouping based on survey responses (Figure 5-1)(Prell et al. 2009). Using the contextual boundaries of participatory governance in Rouge NUP, I identified the stakeholder group of Park Users, a priori, simply based on their presence in the Park. Within-group categorization of “ecological engagement” was stakeholder-defined (Prell et al. 2009) as knowledge of the NIS Vincetoxicum rossicum. To analyze within-group variability I examined stakeholder valuation and prioritization of ESs, and perception of NIS impact, with emphasis on cultural ESs - The “stake” here largely being access to, and protection of, ESs.

Broadly, with respect to the ESs provided by urban protected areas, stakeholders are individuals or groups whose well-being is affected by the presence and governance of those protected areas (Ostrom, 2009; Palomo et al., 2014), which, in essence includes all residents in and around these protected areas. Yet, this large stakeholder group can be subdivided to offer greater insight into ESs valuation and perceptions of local ecological threats. For example, all local residents that reside in close proximity to a protected area and benefit from its capacity to regulate the micro-climate or buffer storm surges (regulating ES) hold an inherent interest in the governance of that protected area. But there can be significant variability within a stakeholder group where individual attributes (e.g. consumer behavior, age, level of education, etc.) might be correlated with certain opinions and perceptions about the relative importance of the issue at hand. This within-group variability is often assessed a posteriori following a “bottom up” process of consultation with stakeholders (Reed et al 2009). Using the same protected area example, if we were interested in examining how “local residents” value the relative importance of different ESs provided by the protected area, we would likely be interested in whether or not these individuals visit the park. Here, a posteriori knowledge of their visitation rate could inform analysis within the stakeholder group.

In this case, I introduce the concept of “ecological engagement” as a dichotomous independent variable to examine variability within a stakeholder group (Figure 5-1). “Ecological engagement” or the near-analogous designations “environmental engagement” (Vitali, 2014), “environmental attitude” (Castro et al., 2011; Opdam et al., 2015) or “nature orientation” (Gunnarsson et al., 2016; Lin et al., 2014) have been consistently shown to be an important

108 attribute for the examination of social attitudes towards conservation issues and one that transcends typical stakeholder categorization (Imran et al., 2014; Ray and Bhattacharya, 2013). I define “ecological engagement” as an individual’s awareness of local ecological issues, operationalized in my study as awareness of the highly invasive non-indigenous vine, Vincetoxicum rossicum, commonly known as “Dog-strangling vine”.

The aim of this study was to: 1) assess the distribution, within a stakeholder group, of ESs valuations of an urban protected area; 2) assess the proportion of ecologically engaged individuals within that group; and 3) determine whether ecological engagement affects ESs valuation and perceptions of NIS impact. To do this, I surveyed park users in Rouge National Urban Park, Canada’s newest National Park, and first National Urban Park, which is highly invaded by the non-indigenous invasive vine V. rossicum. To address these objectives I: 1) rank the importance placed by Park users on ESs provided by the Park; 2) examine the relative ability of Park user’s ecological engagement and their Park visitation rate to predict ESs valuation, and 3) examine Park user perception of; i) the potential of the park to provide cultural services, and ii) the impact of V. rossicum on ESs provisioning.

5.1.2 Case study description: Rouge National Urban Park

Rouge National Urban Park (NUP) is located within the Greater Toronto Area in southern Ontario, Canada and is governed by Parks Canada. The Park is within one hour’s drive for 20% of the Canadian population (Parks Canada Agency, 2015). When fully established the Park will be 79.1 km2 and will consist of several land cover types (crop/grazing lands: 54.4%, forests/wetlands/plantations: 21.2%, developed areas: 20.4%, urban green space: 4%) (Parks Canada Agency, 2015). Parks Canada is currently in the process of developing an official management plan for the Park. In the interim, they have released a draft management plan (DMP) that was developed following a round of community engagement activities (Parks Canada Agency, 2015). Though, the DMP does not use the concept of ecosystem services to articulate how the Park’s ecological functioning benefits stakeholders. Given the clear challenges associated with policy development for a peri-urban protected area, we feel that an application of the ecosystem services concept will provide keen insight into the synergies and conflicts that may exist among Park users. Prior to Parks Canada’s involvement, Rouge Park was governed by the Rouge Park Alliance; a multi-lateral governing body consisting of over 100 organizations (ENGO’s,

109 agricultural groups, government agencies and residents). For a detailed account of Rouge Park’s governance prior to federal involvement, see Macaraig (2011). A “National Urban Park” is an entirely new category of federal protected area in Canada and one that reflects a recognition that much of our urban populations are disconnected from, or lacking access to, biodiversity (Government of Canada, 2015), and the fact that traditional conservation management and enforcement does not translate to urban areas very well. The Park contains some of the last remnants of the Carolinian forest in Canada and is home to over 762 plant species, 225 bird species, 55 fish species, 27 mammal species, 19 reptile and amphibian species, and hundreds of invertebrate species (Parks Canada Agency, 2015). There are also many exotic and NIS species present in the Park, including an abundance of the problematic V. rossicum (Parks Canada Agency, 2015). Park users can access the Park at several trailheads which link together over 12km of hiking trails (Ontario Parks, n.d.).

5.2 Methods

5.2.1 Study design and survey protocols

I conducted in-person surveys with Rouge NUP users at five locations along hiking trails in Rouge NUP in August and October of 2014 and 2015 (Figure 5-2). A total of 324 of the 476 individuals (Park users) approached for the study participated in the survey. Here I identified a single stakeholder group a priori as Park users, simply by their presence in the Park. This research received ethics approval from the Social Sciences, Humanities and Education Research Ethics Board, University of Toronto for research involving human participants (Protocol #30191). Informed consent was secured in advance of every interview and respondents were restricted to individuals over 18 years old.

The survey was structured in five sections to examine the following topics: 1) general Park user attributes, including: frequency of visitation, age group, education level, 2) valuation of different ESs provided by the Park, 2) perceptions of the cultural ESs provided by the Park, and 4) awareness of, and perceptions of the impact of, V. rossicum on the Park’s ESs. Park user visitation frequency were assessed using a 5-point ordinal scale. To describe visitation frequency, respondents selected from “very rarely”, “rarely”, “occasionally”, “frequently”, or “very frequently”. The age of Park users was assessed using a 6-point interval scale of age brackets, including:18-25, 26-35, 36-45, 46-55, 55-65, and 65+. Education level was assessed

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Figure 5-2: Map of survey sites in Rouge National Urban Park

111 using a 5-point ordinal scale, choices were: “elementary”, ”high school”, ”college”, ”university” and ”Graduate”. To examine the relationship between ecological engagement (see section 2.2) and these other Park user attributes ordinal and interval scale data was converted to a numeric scale for statistical analysis (e.g. age brackets 18-25, 26-35, and 36-45 were converted to 1, 2, 3, respectively).

I minimized the influence of certain survey components on response data by placing questions about specific ESs before asking about the perceived effect of V. rossicum. When conducting surveys, the term “ecosystem services” was not explicitly used until after the respondents had completed the questions on valuation of different ESs, which were simply called “aspects” of Rouge Park. This was important to not exclude respondents who are unfamiliar with the concept of ESs. Similarly, surveyors did not mention V. rossicum until respondents indicated whether or not they had knowledge of the plant.

5.2.2 Ecological engagement classification

I gathered information about Park user’s awareness, or lack thereof, of V. rossicum to define ecological engagement. Other studies have presented more nuanced assessments of ecological engagement (Imran et al., 2014), but we believe that awareness of a regionally problematic and widely publicized NIS (Allemang, 2015; Day, 2013; Dillon, 2015) appropriately captured a Park user’s ecological engagement. This was also done to minimize the survey length and increase the potential for participation (see Koschke et al. 2014). While other metrics (e.g. “environmental attitude”, etc.) often provide a more nuanced psychometric profile by considering a variety of personality traits (i.e. purchasing habits, degree of knowledge about ecology, etc.; Castro et al., 2011), they are typically used in electronic correspondence surveys as opposed to in-person surveys due to the time it takes to complete a survey (see Koschke et al. 2014). We queried Park users regarding their awareness of V. rossicum and its status as an invasive plant to define an independent variable, a posteriori, to examine within-group variability. Our 324 surveyed park users divided nearly in half by ecological engagement; 178 Park users were aware of V. rossicum and 146 were unaware of V. rossicum.

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5.2.3 ESs importance values

To determine valuations of Rouge Park’s ESs survey respondents were asked to assign importance values (see section 2.4) to a suite of ESs provided by Rouge NUP, spanning the four categories of ESs: Cultural - recreation, environmental education; Provisioning - agricultural production; Supporting - habitat conservation, nutrient cycling, carbon storage; and Regulating - pollination, air purification, and climate regulation. Ecosystem services listed in the survey were chosen based on common usage in stakeholder analysis of ESs valuation (Darvill and Lindo, 2015; Ekins et al., 2003; Hein et al., 2006; Koschke et al., 2014). To assign importance values to ESs, respondents selected from an eleven point Likert scale for each of the ESs listed; 0=not important at all…5=moderately important…10=extremely important (Schaberg et al., 1999).

5.2.4 Perceptions of cultural ESs and NIS impact on ESs

I used a Likert scale approach to assess perceptions of both Rouge NUP’s cultural ESs and the impact of V. Rossicum on ESs. This was a seven-point Likert scale where respondents were asked to note their degree of agreement, neutrality or disagreement with a series of statements about Rouge NUP’s cultural ESs and the impact of V. rossicum on ESs. Specifically, I assessed Park user perception of 3 cultural ESs within Rouge NUP: educational potential, spiritual connection and aesthetic value. Historically, these cultural ESs have been associated with “pristine wilderness” areas (Gunderson et al., 2000; Muir, 1997; Nash, 2001). But there is now an emerging trend to assess the perception of these cultural ESs within urban regions, particularly in urban protected areas and green space (Andersson et al., 2015; Hansen and Pauleit, 2014; Plieninger et al., 2015). Regarding perceptions of the impact of V. rossicum on Rouge NUP’s ESs respondents were presented with two statements; one regarding the impact of V. rossicum on the park’s “ecological functioning” (a term used to capture both supporting and regulating ESs) and one regarding its impact on the Parks aesthetic quality (a cultural ES). In the cases where individuals had noted that they not aware of the invasive V. rossicum they were informed of its invasive status at this point in the survey and shown photographs of forests and open-fields that had become dominated by the invasive vine.

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5.2.5 Statistical analysis

All statistical analyses were conducted using R statistical software (“R: The R Project for Statistical Computing,” n.d.). Student’s t-tests were used to determine the relationship between ecological engagement and other Park user attributes. After finding that ecological engagement significantly affected Park user visitation frequency and age group (Table 5-1), Kruskal-Wallis tests were performed to examine the effect of these other attributes on the valuation of ESs (Table 5-2). Due to low degrees of freedom, Wilcoxon rank-sum tests were used to determine whether ecological engagement predicted significant differences in ESs valuation among Park users. Kruskal-Wallis and Wilcoxon rank sum tests were applied due to the non-normal distribution of the data, with Wilcoxon tests used for the variables with low degrees of freedom. A Likert scale approach was used to assess Park user perceptions of both the importance of the Park’s cultural ESs as well as the impact of V. rossicum on ESs within Rouge NUP.

5.3 Results

5.3.1 Park user attributes

After converting ordinal and interval survey data to a numeric scale (see methods) I found that the visitation frequency and age group of Park users were significantly affected by ecological engagement (p<0.001 and p<0.01, respectively, by t-test, Table 5-1). Mean visitation frequency for ecologically engaged Park users fell directly between “occasionally” and “frequently” (3.53±0.09). In contrast, mean visitation frequency for non-ecologically engaged Park users fell between “rarely” and “occasionally” (2.61±0.1). Mean age of ecologically engaged Park users fell close to the 46 to 55-year-old bracket (3.65 ± 0.10) while mean age of non-ecologically engaged Park users fell closer to the 36 to 45-year-old age bracket (3.21 ± 0.11). Ecological engagement did not significantly predict education level of Park users (Table 5-1), thus education level was excluded from further analysis.

5.3.2 Ecosystem service valuation

Ecological engagement was the most informative variable for assessing differences in the valuation of ESs (Table 5-2). The valuations of all ESs, with the exception of agricultural production and recreation, were significantly different between ecologically engaged and non- ecologically engaged Park users (Table 5-2, Figure 5-3). Park user visitation frequency did

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Table 5-1: Relationship between ecological engagement and other attributes of Rouge National Urban Park users (n=178 for ecologically engaged, n=146 for non-ecologically engaged)

Ecologically Non-ecologically Attribute engaged engaged p-value Visitation frequency 3.53 ± 0.09 2.61 ± 0.10 <0.001 Age group 3.65 ± 0.10 3.21 ± 0.11 <0.01 Education level 3.75 ± 0.07 3.87 ± 0.07 NS

Note: ordinal survey data was converted to numeric values to generate mean values (See methods). Bolded values indicate significant differences.

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Ecological Visitation engagement frequency Education level Age group

Ecosystem service W df p-value H df p-value H df p-value H df p-value Agriculture 13424 1 N.S. 8.52 4 N.S. 1.54 4 N.S. 8.93 5 N.S. Recreation 12716 1 N.S. 15.2 4 <0.01 3.04 4 N.S. 12.8 5 <0.05 Education 11202 1 <0.05 5.69 4 N.S. 4.27 4 N.S. 4.14 5 N.S. Conservation 11066 1 <0.05 8.84 4 N.S. 5.91 4 N.S. 13.3 5 <0.05 Nutrient cycling 10429 1 <0.01 6.85 4 N.S. 1.44 4 N.S. 5.41 5 N.S. Carbon storage 5163 1 <0.05 5.71 4 N.S. 2.01 4 N.S. 4.77 5 N.S. Air purification 4708.5 1 <0.01 8.79 4 N.S. 3.01 4 N.S. 5.5 5 N.S. Pollination 3508.5 1 <0.01 18.3 4 <0.01 6.86 4 N.S. 7.35 5 N.S. Climate regulation 4667.5 1 <0.01 6.54 4 N.S. 2.94 4 N.S. 6.26 5 N.S.

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Figure 5-3: Effect of ecological engagement (EE) on ecosystem services valuation by Rouge National Urban Park users. Wilcoxon rank sum test performed to determine statistical significance.

117 explain select differences in ESs valuation, specifically for recreation (Kruskal Wallis test χ2:15.2; p<0.01) and pollination (χ2: 18.3; p<0.01, Table 5-2, Figure 5-4). Similarly, Park user age group explained differences in valuation of recreation (χ2: 12.8; p<0.05) and habitat conservation (χ2: 13.3; p<0.05; Table 5-2, Figure 5-4). Mean importance values for all ESs ranged from 5.16 (± 0.24) to 9.3 (± 0.11) for ecologically engaged Park users and from 5.42 (± 0.25) to 8.81 (± 0.13) for non-ecologically engaged Park users (Table 5-3).

5.4.1 Ecosystem service ranking

All Park users placed relatively high importance on the listed ESs, with the exception of agricultural production which was valued as moderately important by both groups (5.16 ± 0.24 by ecologically engaged Park users and 5.42 ± 0.25 by non-ecologically engaged Park users (Figure 5-3). But ecological engagement resulted in significantly higher importance values for all ESs, with the exception of agricultural production and recreation (Table 5-3). After ranking mean importance values for ESs, I found that both ecologically engaged and non-ecologically engaged Park users ranked climate regulation, carbon storage and agricultural production as 7th, 8th and 9th in importance, respectively. Interestingly, non-ecologically engaged Park users tended to give recreation (a ‘cultural’ ES) the highest importance value (8.81 ± 0.13). Conversely, ecologically engaged Park users tended to assign pollination (a ‘supporting’ ES), the highest importance (9.30 ± 0.11, Table 5-3).

5.4.2 Provisioning of cultural ESs

The majority of surveyed Park users either strongly or moderately agree that Rouge NUP provides cultural ESs of great value (Figure 5-5). A strong majority of both ecologically engaged and non- ecologically engaged Park users agreed that Rouge NUP has a strong aesthetic value (93.3% and 72.1%, respectively). Similarly, the majority of Park users agreed that the Park was of spiritual importance (80.9% for ecologically engaged and 78.1% for non-ecologically engaged; Figure 5-5). Regarding the potential for Rouge NUP to provide environmental education, 93.3% of ecologically engaged and 82.2% of non-ecologically engaged Park users agreed that there is a strong potential (Figure 5-5). The remaining percentages represent Park users that were either neutral or disagreed about the potential for Rouge NUP to provide cultural ESs.

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Figure 5-4: Effect of visitation frequency (a) and age group (b) on ecosystem service valuation by Rouge National Urban Park users

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Table 5-3: Ranked importance of Rouge National Urban Park’s ecosystem services in relation to ecological engagement. n=178 for ecologically engaged, n=146 for non-ecologically engaged Ecologically engaged Non-ecologically engaged Rank Ecosystem service Mean ±S.E. Rank Ecosystem service Mean ± S.E. 1 Pollination 9.30 ± 0.11 1 Recreation 8.81 ± 0.13 2 Enviro. education 9.08 ± 0.12 2 Nutrient cycling 8.45 ± 0.16 3 Air purification 8.98 ± 0.13 3 Habitat conservation 8.36 ± 0.14 4 Nutrient cycling 8.90 ± 0.12 4 Air purification 8.23 ± 0.19 5 Habitat conservation 8.83 ± 0.11 5 Enviro. education 7.97 ± 0.21 6 Recreation 8.77 ± 0.13 6 Pollination 7.95 ± 0.19 7 Climate regulation 8.61 ± 0.15 7 Climate regulation 7.61 ± 0.24 8 Carbon storage 8.11 ± 0.18 8 Carbon storage 7.48 ± 0.22 9 Agri. production 5.16 ± 0.24 9 Agri. production 5.42 ± 0.25

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Figure 5-5: Effect of Park user ecological engagement (EE) on perceptions of cultural ecosystem services (ESs) in Rouge National Urban Park (NUP), n=178 for EE, n=146 for non-EE.

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5.4.3 Impact of invasive species on ESs provisioning

The percentage of Park users that consider V. rossicum as having a negative ecological impact (sum of all degrees of agreement) is 84.6% for ecologically engaged Park users. This percentage is considerably less for non-ecologically engaged Park users (61.6%) (Figure 5-6). Showing a similar trend, 68% of ecologically engaged Park users and 45.9% of non-ecologically engaged Park users believe V. rossicum is having a negative impact on the Park’s aesthetics (Figure 5-6).

5.5 Discussion

The Rouge NUP Draft Management Plan (DMP) was developed after significant public consultation giving local stakeholders an opportunity to voice their concerns for the Park’s future management (Parks Canada Agency, 2015). Prior to NUP designation, the Rouge Valley system had a history of multi-stakeholder and community led governance. Various actors including ENGO’s, multiple levels of government, residents and agricultural associations were involved over different phases of management during the introduction of the ‘national urban park’ idea. During this transition in governance, ESs and the ESs concept were not explicitly cited in the DMP or the Rouge NUP Act, but the goals and language of both documents evoke a desire to maximize the protection of all ESs provided by the Park, i.e., “protect the cultural landscapes”, “protect natural ecosystems and maintain wildlife in the Rouge Valley”, “encourage sustainable farming practices to support the preservation of agricultural lands in the park” (Government of Canada, 2015) and engage Park users in conservation activities (i.e. NIS management, ecological restoration) (Parks Canada Agency, 2015). The value of the ESs provided by the Park have been previously quantified, using a “natural capital” approach, by a Canadian non-governmental organization – The David Suzuki Foundation (Wilson, 2012). Yet, these valuations were quantified using cost-based (de Groot et al., 2010) and revealed preference methodologies (DuWors et al., 1999), neither of which integrated specific perceptions and/or valuations of Rouge NUP’s ESs by Park users.

5.5.1 Variability of stakeholder ESs valuation

Rouge NUP provides a unique socio-ecological context to examine stakeholder valuation of ESs and perceptions of NIS impact. There are a multitude of ESs provided by this urban protected area (Wilson, 2012), yet many of these are threatened by the prolific advancement of V. rossicum

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Figure 5-6: Effect of Park user ecological engagement (EE) on perceptions of the impact of V. rossicum on ecosystem services in Rouge National Urban Park, n=178 for EE, n=146 for non-EE).

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(Sanderson and Antunes, 2013; Yasui, 2016). Park managers seek to strengthen existing ties to the Park’s ESs, increase awareness of lesser known ESs (i.e. agricultural production) and engage the public in the Park’s ecology through restoration projects and NIS removal (Parks Canada Agency, 2015). Understanding how stakeholder attributes and/or ecological engagement influences perceptions of both the Park’s ESs and the impact of NIS can highlight conflicts and synergies in ESs valuation between and within stakeholder groups.

This research shows that while all Rouge NUP users place relatively high importance on the Park’s ESs, with the exception of agricultural production, within-group variability is evident where those individuals that are ecologically engaged place significantly higher importance on the majority of the listed ESs (Figure 5-3). Many studies have shown that valuation of ESs can differ between (or among) stakeholder groups (Cavender-Bares et al., 2015; Martín-López et al., 2012; Orenstein and Groner, 2014) and others have shown significant variability within stakeholder groups with respect to ESs valuation and conservation management practices (de Nooy, 2013; Hicks et al., 2013; Lohr and Lepczyk, 2014). In fact, examination of within-group variability can lead to the identification of important general attributes that transcend the boundaries between stakeholder groups.

I highlight synergies as well as conflicts in the ranking of ES importance between the two group. The most obvious similarity between the two groups is the low ranking of climate regulation, carbon storage and agricultural production (Table 3). The relatively low rank of climate regulation can likely be attributed to the general population’s perception of climate as a large scale phenomenon (Hein et al. 2006). Yet in fact, there are multiple studies detailing the ability of natural areas to regulate local climate in urban and peri-urban areas, providing an important ES (Haase et al., 2014). Low ranking of the importance of carbon storage within Rouge NUP by both groups is also not unexpected (Table 3). Carbon dynamics are fairly well understood by the general public (Dowd et al., 2014), but are often perceived of as a large-scale global phenomenon (Hein et al., 2006). Because of this, it is likely that Park users see the carbon being sequestered in Rouge NUP as relatively insignificant compared to that of an expansive forest ecosystem. And while this may be true, carbon accumulation in forest and wetland ecosystems provides habitat for functionally important local biodiversity (Seibold et al., 2015). Given that Park users place greater importance

124 on other ESs (Table 3), it may be useful for stakeholder engagement strategies to communicate the functionality of Rouge NUP’s ecosystems as a localized phenomenon.

The low importance placed on Rouge NUP’s agricultural production by both groups is very interesting given that more than half of the land area of Rouge NUP is devoted to crop production (Parks Canada Agency, 2015). This perceived low importance by Park users of agricultural production within Rouge NUP is likely caused by two main factors; 1) the relatively new integration of these croplands into Rouge NUP; and 2) the fact that the Canadian National Park system has historically only prioritized the protection of ecological integrity and “nationally significant examples of Canada’s natural and cultural heritage” (Government of Canada, 1998). The inclusion of croplands in a National Park may seem counter-intuitive to Park users who clearly place the highest importance on the protection of biodiversity and the associated cultural, supporting and regulating ESs. Yet, it is a primary goal of Parks Canada to have Park users recognize the importance of protecting the agricultural production in the Park (Parks Canada Agency, 2015). In the case of agricultural production ecological engagement was not a significant factor in differentiating valuation within the stakeholder group. This means that stakeholder engagement strategies aimed at improving the perceived value of this ES will be tasked with communicating its role within the new designation of a “National Urban Park”. This may be aided by communicating the synergies with other highly valued ESs (e.g. aesthetically valuable rural viewscapes, educational programs focusing on sustainable farming practices).

The majority of Park users either strongly or moderately agreed that the Park offers valuable cultural ESs (Environmental education, spiritual and aesthetic value). Ecologically engaged Park users tended to agree more strongly about the positive value of the park’s cultural ESs (Figure 5-5). This extremely positive valuation of Rouge NUP’s cultural ESs is representative of an emerging paradigm shift in biodiversity conservation where increasingly, urban protected areas and green space are being recognized as important spaces for the provisioning of cultural ESs (Andersson et al., 2015; Elmqvist et al., 2015; Riechers et al., 2016). Furthermore, it is expected that this perceived value will only increase in the coming years given the relative scarcity of protected areas in urban regions coupled with increasing population density (Cheesbrough, 2015; Sadler et al., 2010; Tzoulas et al., 2007). It was surprising to see that some Park users were either neutral or disagreed that the Park offered valuable cultural ESs, especially given that these

125 individuals completed the survey while hiking in the Park. I believe that this neutrality and disagreement is likely a reflection of a “traditional” public imaginary surrounding the concept of wilderness preservation (Gómez-Pompa and Kaus, 1992; Wuerthner, 2014). It may be the case that the expressed neutrality and disagreement about the value of the cultural ESs in Rouge NUP is driven by a sense that the composition of Rouge NUP does not reflect a typical conception of a wilderness area or National Park. Even though the Rouge NUP does contain many intact “natural” ecosystems, in reality, Rouge NUP is also crisscrossed with roads, train tracks, hydro corridors and contains a decommissioned landfill, an auto wrecker, and many non-indigenous and NIS. Here, it is important to recognize that some conceptions of wilderness are reflective of historical Western ideals, where “pristine nature” was often seen as a refuge from urbanism (Cronon, 1996), though as previously mentioned, alternative perceptions of nature and wilderness are emerging. Given that urban populations are increasingly diverse, both culturally and economically, managers of urban protected areas will have to consider multiple conceptions of nature and ESs as they strive to engage with the public to foster appreciation for the cultural ESs of urban protected areas (Satz et al., 2013). Furthermore, encouraging appreciation of the range of ESs offered by urban protected areas has the potential to foster a culture of stewardship and social cohesion (Chan et al., 2016). These findings suggest that while a strong majority of Park users have positive perceptions of cultural ESs, this positivity is enhanced by ecological engagement. Considering the analytical framework, I feel that this knowledge is actionable in that Rouge NUP managers should strive to improve ecological engagement among Park users, which may serve to enhance positive perceptions of the Park’s ESs.

Variability in stakeholder perception of the relative importance of different ESs is prominent in the literature. Some studies have shown that stakeholders assign the greatest importance to regulating ESs (Martín-López et al., 2012), where others have shown preferences for cultural ESs (Darvill and Lindo, 2015; Raymond et al., 2009), and others have shown provisional ESs to be of greatest importance (Hartter, 2010). This variability points to a context dependency of ESs valuation where the relative importance of a given ES is dependent on specific social and ecological conditions. This variability in the relative importance of different ESs categories is also reflected in the current study where ecologically engaged Park users placed the highest importance on pollination services (a supporting ES; possibly a product of ecologically engaged individuals being aware of regional Monarch butterfly and pollinator conservation

126 campaigns; Suzuki and Roberts, 2016; TRCA, 2017), and non-ecologically engaged Park users placed the highest importance on recreation services (a cultural ES). This type of conflict in ESs prioritization is common and can be caused by differences in worldview and/or relative dependency on, or access to, different ESs (Imran et al., 2014; King et al., 2015). From a participatory governance standpoint, the challenges of mediating stakeholder conflict with respect to conservation management and ESs valuation is well documented (Bouamrane et al., 2016; Felipe-Lucia et al., 2015). Yet, recent studies have shown strong promise for inclusionary adaptive management approaches as a means of mediating such conflict (Ahern et al., 2014; Young et al., 2016).

5.5.2 Park user perception of invasive species impact

Currently, the biodiversity within Rouge NUP, and its role in the delivery of multiple ecosystem services, is threatened by the spread of V. rossicum (Kricsfalusy and Miller, 2010; Miller and Kricsfalusy, 2007). This highly invasive vine has been shown to significantly reduce arthropod diversity (Ernst and Cappuccino, 2005), is known to be invasive in both forest and meadow ecosystems (Smith et al., 2006), and perhaps is of greatest concern for the continued regeneration of forest ecosystems (Kricsfalusy and Miller, 2010). Quantifying the degree of indifference with respect to public perceptions of NIS impact, as well as general awareness of their presence, has the potential to guide efforts for participatory governance and stakeholder engagement. I found variability within the stakeholder group with respect to perception of the impact of V. rossicum on ESs. Specifically, ecological engagement results in stronger agreement that V. rossicum is negatively impacting ESs (Figure 5-6). Of course, this was expected given that awareness of V. rossicum defines ecological engagement in this study. Yet, a substantial proportion of Park users were neutral or disagreed with the notion that V. rossicum is having a negative effect on the Park’s ESs, even among those that were aware of its presence (ecologically engaged) (Figure 5-6). With respect to the perception of V. rossicum’s effect on ecological functioning, this was surprising as survey respondents were informed about its invasive status and shown images of the near- monocultures that it forms both in full sun in forest understory. Generally, public awareness of NIS, perception of their impacts on ESs and preferred management practices have been shown to be highly variable (García-Llorente et al., 2008; Lindemann-Matthies, 2016; Sharp et al., 2011). It could be that many people are not convinced of its ability to outcompete indigenous species and

127 disrupt trophic interactions, or that some people have adopted a position of denialism with respect to the significance of NIS (Russell and Blackburn, 2017). Another possibility is that some people simply perceive of V. rossicum’s undamaged and deep green foliage (due to lack of natural enemies) as a sign of a healthy organism, which is in fact the case, but this health and rapid spread comes at the cost of the biodiversity of the indigenous ecosystem. In any case, Parks Canada would do well to integrate creative communication strategies that capture the public’s attention regarding the risks associated with the spread of V. rossicum (Estévez et al., 2015; Gozlan et al., 2013). Following the study’s analytical framework (Figure 5-1), I feel that Parks Canada should take action to increase ecological engagement among Park users in order to improve understanding of the ecological impact of NIS (Crowley et al., 2017a).

Clearly, increasing public awareness of the presence and impact of local NIS (presented here as ecological engagement) can lead to greater public valuation of ESs which will aid in future restoration efforts (Bossenbroek et al., 2005; Dickinson et al., 2012). Given the extreme invasiveness of V. rossicum and the value of early detection and eradication of such a highly invasive species, prioritizing this type of public engagement is certainly recommended in the context of Rouge NUP (Douglass et al., 2009). Due to the lack of understanding among the general public about the deleterious effects of NIS (Simberloff et al., 2013), communicating the risks associated with the spread of NIS may prove to be as challenging as the communication of climate change (Ross et al., 2016) (Pearce, 2015; Russell and Blackburn, 2017). Just as associating the implications of climate change with human health has shown promise for the development of effective public engagement strategies (Myers et al., 2012), drawing the linkages between the spread of NIS as a threat to ecological integrity and the continued provisioning of multiple ESs can lead to the galvanization of public interest concerning the importance of biodiversity conservation (Novacek, 2008).

5.5.3 Conclusion

Rouge NUP managers are well positioned to engage with local stakeholders using a participatory governance approach. Of course, it is unreasonable to expect that conservation managers can engage with the public in a manner that aligns the importance placed on all aspects biodiversity conservation and the ESs provided by a protected area. But understanding variability in ESs valuation and misperceptions of NIS impact can aid in the mediation of conflicts and capitalize on

128 synergies both within and between stakeholder groups (Bryan et al., 2010). This work contributes to both stakeholder analysis and ESs literature by presenting the significance of within stakeholder group variability. Moreover, the analysis strongly conveys the value of assessing stakeholder priorities and attributes for the development of participatory governance of an urban protected area.

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6 Synthesis and Conclusion This thesis advances the discipline of invasion ecology by presenting a comprehensive analysis of the ecological impact, potential for control and the utility of social engagement with a single invasive plant; Vincetoxicum rossicum. Though it is likely that this plant will forever be a part of southern Ontario’s terrestrial ecosystems, ideally it will exist in lower abundances so as not to further diminish the proportion of native species in the region. V. rossicum’s exponential population trajectory in many southern Ontario ecosystems is a fascinating phenomenon, but I show here that its dominance results in drastic reductions to many aspects of biodiversity. And in spite of observing a strong degree of variability in the general contribution of biodiversity to ecosystem functionality, I show that V. rossicum’s dominance is associated with significant reductions in the production of aboveground biomass and the diversity and abundance of pollinator communities; likely as result of an overall decline in the diversity and abundance of floral resources. And after showing that V. rossicum invasion is correlated with low soil nitrogen concentrations (which is often perceived to be an indication of a well-functioning ecosystem, but see section 1.1.4), I highlight the need for multi-scale perspectives in ecosystem functionality assessments. Such analysis will lead to more nuanced understandings of both invasion impact and the mechanisms driving invasion.

As there is much interest in the potential for control of V. rossicum invasion, I show here that while Hypena opulenta serves an exceptional defoliating bio-control agent for V. rossicum, in both forest understory and meadow environments, that in forest understory conditions V. rossicum exhibits significant compensatory growth in the form of increased reproductive output. This finding has strong implications for the widespread release of Hypena opulenta, where at least in the short-term this would likely result in a more substantial and vigorous V. rossicum seed bank in understory conditions (as well as no reduction in seed output in sun conditions) (But see the caveats discussed in Section 3.4). Of course, it is difficult to predict the long-term dynamics here especially given that the experimental bio-control population did not overwinter. It may take several years of V. rossicum defoliation by a self-sustaining population of H. opulenta to understand the potential effectiveness of a single agent control program. As has been the case with other invasives, it might be the case that introduction of other bio-control agents targeted at different structural components of the plant are necessary to see significant declines in V. rossicum abundance (Beirne, 1975). This

130 bio-control work also highlights how experiments conducted in the “real-world” may lead to substantially different conclusions than those conducted in laboratory conditions. It is a truism in most disciplines that experimental research conducted in either highly controlled laboratory conditions (i.e. growth chambers, microcosms, computational modelling, etc.) or “the field” (natural/urban/invaded systems) each come with their own set of advantages and disadvantages (Cadotte et al., 2005; Diamond, 1983). For ecological investigations, laboratory work has the advantage of controlling environmental variables, species interactions, organismal densities, etc. Yet, this high degree of control can sometimes obscure how experimental findings will translate to “real world” conditions. This is most certainly the case for the study of many biological invasions (Brown et al., 2011; Jacobs and Latimer, 2012; Lambrinos, 2002; Zas et al., 2011), and bio-control efforts (Haye et al., 2005; Roderick and Navajas, 2003, but see Barratt et al., 1997). Of course, in the study of bio-control preliminary laboratory work is often a necessity given regulatory restrictions on the release of non-native organisms. But my findings indicate that researchers conducting laboratory work on the effectiveness of bio-control agents need to comment on the potential differences that might exist between the lab and “real-world” settings.

In response to the measured loss of biodiversity that occurs during biological invasions, there has been much recent interest in trying to understand the importance of rare and unique species that might be the most sensitive to invasion. In this regard, many recent studies have been concerned not only with how rare species persist in a community, but also how they can be better characterized both in terms of their functional structure and their contribution to ecosystem functionality. By integrating recently developed trait-based methods with species distributions (Violle et al. 2017), I demonstrate how the biodiversity of plant species can be characterized across different forms of “functional rarity”. Specifically, I show how some plants exhibit considerable uniqueness in their functional traits composition relative to the regional species pool, but also how trait uniqueness occurs independent of abundance and geographical distribution. This study was also one of the first to evaluate the relative importance of different forms of rarity in plants for ecosystem functioning. I found little support for rare species (functional or abundance-based) having a significant effect on ecosystem functioning. And although I did find abundant species to significantly affect functioning, this occurred in both a positive and negative fashion; potentially indicating that different forms of rarity might be important for those functions that are negatively affected by abundant species. With respect to the effect of V. rossicum invasion on the presence of

131 different forms of rarity, I did not find a relationship between increasing V. rossicum abundance and plant functional uniqueness. In other words, the plants that are being locally extirpated by V. rossicum are not unique in their functional trait composition. Of course, this does not mean that we shouldn’t be concerned about the advancement of V. rossicum, as I show in Chapter 2 that it is causing drastic declines in other facets of biodiversity. Finally, as pointed out in Chapter 4, the quantification of functional uniqueness is undoubtedly affected by the number and type of traits considered. For this study, to quantify functional uniqueness I chose to integrate trait values that have been shown to affect several ecosystem functions. Though, it is likely the case that trait selection should be targeted towards the function of interest in order to more closely explore individual trait-function relationships and the significance of trait uniqueness therein (Zhu et al., 2017).

Within the discipline of invasion ecology, it is not only our job to quantify the manner in which invasions are impacting biodiversity but also to engage the public and policy-makers in order to promote the preservation of native biodiversity. And although the majority of this dissertation is focused on the ecological dynamics of plant invasion, all of the aforementioned analyses examine the ecology of peri-urban environments, where human populations can have a strong influence on the functioning and governance of local ecosystems (Sterling et al., 2017). In order to investigate the social dynamics related to the above ecological analyses I used stakeholder analysis to assess public perceptions of invasion and valuation of the ecosystem services provided by Rouge NUP. I observed “ecological engagement” (awareness of invasive species) to result in significantly greater valuations of ecosystem services. While this was not a surprising result, it points to the need to for creative communication strategies that can promote interest in local ecology. And while I found that many people are concerned about the ecological impact of V. rossicum, there are still many others that either disagree or are neutral about its impact. This finding points to the general challenge regarding the “uptake” of conservation-focused science among the general public (Troumbis, 2017). Certainly, there is no shortage of scientific studies documenting the drastic manner by which current anthropogenic pressures are impacting biodiversity (Ceballos et al., 2017; Doherty et al., 2016; Johnson et al., 2017), which has also been widely communicated in the “popular literature” (Drake et al., 2015; Hance, 2015; Kunzig et al., 2014). Yet, there is still a significant amount of indifference regarding the manner in which humans are affecting biodiversity as well as potential pathways for remediation (Miller, 2005). My findings suggest

132 that there is strong potential for heightened valuation of ecosystem services by engaging the public in local ecological issues. This perspective dovetails with the increasingly recognized importance of local actions for global biodiversity conservation objectives, particularly in urban and peri-urban environments (Ostrom et al., 1999; Paudyal et al., 2016; Schewenius et al., 2014).

In closing, it is clear that the disciplines of both urban and invasion ecology face the significant challenge of articulating the nuanced nature of biodiversity conservation in human dominated environments. Urban and peri-urban ecosystems typically contain an abundance of non- native species (Cadotte et al, in press), many of which can coexist with native species and provide ecosystem services (Perring et al., 2013). Recently, there has been a push to conceptualize these kinds of species assemblages as components of larger “novel ecosystems” that have been created by human actions (Hobbs et al., 2006). Though, there has also been significant backlash to this concept with some claiming that it attempts to delineate a type of system that actually exists along a continuous axis (i.e. native/pristine to non-native/degraded)(Miller, 2005; Murcia et al., 2014). In fact, the renowned invasion ecologist Dan Simberloff (2015) goes as far as to say that the “novel ecosystem” designation itself only serves to obfuscate the larger goals of biodiversity conservation. This dissertation has shown that peri-urban ecosystems are still recognized by the public as being extremely valuable for the ecosystem services they provide, despite the fact that they contain an abundance of non-native and invasive species. Moreover, I have shown that in the face of invasion by an extremely aggressive species that more “benign” non-native species can act to support ecosystem functioning and services. However, it is undoubtedly the case that the spread of the non-indigenous invasive vine Vincetoxicum rossicum is causing a significant decline in native biodiversity and alterations to ecosystem functionality; with significant impliations for the continued provisioning of ecosystem services. As a recommendation then, based on the findings of this thesis, local conservation practitioners would do well to 1) continue to investigate control options for V. rossicum, 2) recognize that more “benign” non-native species can play a vital role in the persistence of native species and ecosystem functioning, and 3) develop creative and engaging public communication strategies for biodiversity conservation, focusing on the threats posed by invasive species. By quantifying the significant ecological impacts of an invasive species and detailing the variability in the general functionality of peri-urban ecosystems, this dissertation illustrates the types of nuanced ecological perspectives that are necessary to characterize the functionality of urban and peri-urban ecosystems.

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7 Appendices Table 8-1: Rouge National Urban Park site coordinates Site UTM_E UTM_N 1 644322.84 4855672.65 2 644477.87 4855663.05 3 644860.14 4855129.36 4 645126.87 4855326.24 5 645332.51 4855394.01 6 646691.93 4855561.84 7 647627.71 4855709.11 8 648005.31 4853579.64 9 648338.14 4853927.83 10 647399.53 4853110.9 11 647440.99 4852904.09 12 647016.74 4852520.59 13 647112.87 4852479.81 14 648591.62 4852784.92

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Table 8-2: Mean relative abundance (±SE) of all observed herbaceous plant species across 14 meadow communities in Rouge NUP.

Genus/Species Site 1 Site 2 Site 3 Site 4 Site 5 Site 6 Site 7 Achillea millefolium 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0.05 ± 0.05 Agrostis gigantea 2.4 ± 0.74 2.43 ± 0.91 0.22 ± 0.09 0.08 ± 0.06 0 ± 0 0.35 ± 0.35 18.83 ± 2.98 Allaria Petiolata 0.03 ± 0.03 0 ± 0 0.06 ± 0.06 0 ± 0 0 ± 0 0 ± 0 0.05 ± 0.05 Anaphalis margaritacea 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 Anemone canadensis 1.95 ± 0.7 0 ± 0 0.03 ± 0.03 0.05 ± 0.05 1.16 ± 0.81 0 ± 0 2.64 ± 0.62 Anemone virginiana 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0.87 ± 0.43 Asclepias syriaca 2.44 ± 0.83 9.48 ± 1.88 4.6 ± 1.12 7.87 ± 1.46 3.85 ± 1.14 0.65 ± 0.56 0.05 ± 0.05 Bromus inermis 4.13 ± 1.31 5.14 ± 2.05 15.59 ± 2.79 13.4 ± 2.65 6.53 ± 1.38 7.42 ± 3.33 2.29 ± 2.29 Carex sp 2.17 ± 0.83 0.71 ± 0.71 0.58 ± 0.38 0.36 ± 0.25 0.07 ± 0.05 0 ± 0 1.13 ± 0.54 Centaurea maculosa 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 Cirsium arvense 2.59 ± 0.96 2.46 ± 0.6 0.21 ± 0.13 1.78 ± 0.52 0.51 ± 0.29 1.49 ± 0.66 0.21 ± 0.11 Clematis virginiana 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 Cornus stolonifera 2.42 ± 1.75 1.84 ± 1.69 3.11 ± 1.34 0.54 ± 0.5 0 ± 0 0 ± 0 1.49 ± 1.27 Daucus carota 4.04 ± 1.23 0.47 ± 0.47 0.31 ± 0.1 0.68 ± 0.28 1.99 ± 0.71 2.47 ± 0.86 6.52 ± 1.12 Dianthus armeria 0.58 ± 0.3 0 ± 0 0.03 ± 0.03 0 ± 0 0 ± 0 0.08 ± 0.06 0 ± 0 Echium vulgare 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 Equisetum arvense 3.3 ± 1.42 1.13 ± 0.54 0.28 ± 0.15 0 ± 0 0 ± 0 0 ± 0 0 ± 0 Erigeron annuus 2.63 ± 1.25 1.7 ± 0.91 0.04 ± 0.04 0 ± 0 0 ± 0 1.11 ± 0.51 0.07 ± 0.07 Euphorbia cyparissias 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 Fragaria virginiana 0.54 ± 0.3 0.04 ± 0.04 0.07 ± 0.07 0.81 ± 0.52 0.09 ± 0.07 0.04 ± 0.04 12.26 ± 3.04 Geum urbanum 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0.1 ± 0.07 Hieracium vulgatum 0 ± 0 1.26 ± 1.2 0.06 ± 0.05 2.03 ± 2 0 ± 0 0.63 ± 0.49 0 ± 0 Hypericum perforatum 1.01 ± 0.55 0 ± 0 0.09 ± 0.06 0 ± 0 0.05 ± 0.05 0 ± 0 0.03 ± 0.03 Inula helenium 0.84 ± 0.81 0 ± 0 0 ± 0 6.75 ± 2.28 0 ± 0 0 ± 0 2.31 ± 1.45 Leucanthemum vulgare 0.07 ± 0.07 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0.07 ± 0.07 Linaria vulgaris 0.81 ± 0.6 0.9 ± 0.38 0.3 ± 0.12 0.18 ± 0.09 0.17 ± 0.09 0.08 ± 0.06 0.12 ± 0.08 Lonicera canadensis 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 Lotus corniculatus 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 Melilotus albus 0.3 ± 0.26 0 ± 0 0 ± 0 0 ± 0 0.04 ± 0.03 2.35 ± 0.92 0 ± 0 Medicago lupulina 1.02 ± 0.47 0.49 ± 0.34 0.07 ± 0.05 0.1 ± 0.06 0.04 ± 0.04 0.57 ± 0.3 2.76 ± 1.01 Monarda fistulosa 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0.79 ± 0.74 0 ± 0 Oxalis stricta 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0.03 ± 0.03 Phalaris arundinacea 2.55 ± 1.76 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 6.19 ± 2.24 Phleum pratense 4.3 ± 1.45 4.29 ± 2.43 2.07 ± 1.73 0 ± 0 0 ± 0 0.04 ± 0.04 4.12 ± 1.31 Plantago lanceolata 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 2.61 ± 0.98 Plantago major 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0.55 ± 0.39 Poa pratensis 3.06 ± 0.82 12.34 ± 2.51 8.45 ± 1.84 16.11 ± 2.07 25.34 ± 2.45 7.75 ± 2.08 3.02 ± 1.01 Potentilla recta 0.5 ± 0.4 0.07 ± 0.07 0.34 ± 0.11 0 ± 0 0.05 ± 0.05 0.08 ± 0.05 0 ± 0 Ranunculus acris 0.61 ± 0.3 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0.81 ± 0.31 Rudbeckia hirta 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0.05 ± 0.05 0 ± 0 Sonchus arvensis 2.21 ± 0.92 8.00E-02 ± 0.06 0.41 ± 0.23 0.79 ± 0.46 0 ± 0 2.45 ± 0.98 1.11 ± 0.97 Solidago sp 25.24 ± 3.82 43.04 ± 4.7 23.98 ± 4.89 22.8 ± 4.16 32.41 ± 3.66 52.38 ± 4.02 2.62 ± 0.82 Symphyotrichum cordifolium 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 Symphyotrichum ericoides 2.25 ± 1.12 1.36 ± 1.01 2.44 ± 1.3 2.23 ± 0.77 4.08 ± 1.22 2.19 ± 0.91 6.24 ± 1.93 Symphyotrichum lanceolatum 0.87 ± 0.4 0.58 ± 0.42 0.37 ± 0.14 0 ± 0 0.26 ± 0.26 1.98 ± 0.93 3.14 ± 0.93 Symphyotrichum novae-angliae 2.66 ± 0.9 0.7 ± 0.59 1.97 ± 0.86 1.41 ± 0.48 0.48 ± 0.31 1.71 ± 0.69 2.19 ± 0.76 Taraxacum officinale 3.13 ± 1.01 0.05 ± 0.05 0.06 ± 0.06 0.08 ± 0.06 0 ± 0 0.93 ± 0.5 0.32 ± 0.2 Trifolium hybridum 0.13 ± 0.07 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0.45 ± 0.4 0 ± 0 Trifolium pratense 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 7.14 ± 2.28 5.5 ± 1.82 Valeriana officinalis 2.29 ± 1.13 1.36 ± 0.99 0.33 ± 0.24 0.03 ± 0.03 0 ± 0 1.05 ± 0.82 0 ± 0 Veronica officinalis 2.21 ± 0.91 0.61 ± 0.4 0 ± 0 0.04 ± 0.04 0 ± 0 0 ± 0 0.08 ± 0.05 Vicia cracca 8.61 ± 2.02 3.24 ± 0.82 5.3 ± 0.78 5.99 ± 0.94 5.7 ± 1.06 1 ± 0.94 0 ± 0 Vitis riparia 1.55 ± 0.8 0.93 ± 0.52 0.81 ± 0.55 0.55 ± 0.39 4.16 ± 1.71 0.71 ± 0.71 1.6 ± 0.57 Vincetoxicum rossicum 4.56 ± 1.09 3.3 ± 1.38 27.84 ± 3.98 15.36 ± 2.82 13.03 ± 2.44 2.06 ± 1.06 8.03 ± 2.34

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Table 8-2 (cont.)

Genus/Species Site 8 Site 9 Site 10 Site 11 Site 12 Site 13 Site 14 Achillea millefolium 0.36 ± 0.3 1.29 ± 0.71 0 ± 0 0.66 ± 0.39 0 ± 0 0 ± 0 0 ± 0 Agrostis gigantea 0 ± 0 1.88 ± 0.79 5.96 ± 2.47 2.17 ± 0.74 0 ± 0 0.38 ± 0.23 0 ± 0 Allaria Petiolata 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 Anaphalis margaritacea 0 ± 0 0.04 ± 0.04 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 Anemone canadensis 0 ± 0 0.06 ± 0.06 0 ± 0 0.74 ± 0.32 0 ± 0 0 ± 0 0 ± 0 Anemone virginiana 0 ± 0 3.1 ± 0.8 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 Asclepias syriaca 0.3 ± 0.3 0 ± 0 1.57 ± 0.72 1.54 ± 0.52 0 ± 0 0.62 ± 0.35 1.34 ± 0.97 Bromus inermis 0 ± 0 0.04 ± 0.04 6.74 ± 2.7 17.85 ± 3.18 12.69 ± 3.41 10.78 ± 2.67 45.53 ± 4.65 Carex sp 0 ± 0 0.26 ± 0.21 0.21 ± 0.21 0.3 ± 0.17 0.22 ± 0.1 0 ± 0 0 ± 0 Centaurea maculosa 0.03 ± 0.03 0.27 ± 0.27 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 Cirsium arvense 0 ± 0 0 ± 0 1.2 ± 0.51 0.08 ± 0.08 1.6 ± 0.64 0.05 ± 0.05 6.55 ± 2.54 Clematis virginiana 0 ± 0 0.65 ± 0.47 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 Cornus stolonifera 0 ± 0 0.46 ± 0.45 0 ± 0 2.69 ± 2.06 0 ± 0 0 ± 0 0 ± 0 Daucus carota 2.35 ± 0.96 3.11 ± 0.69 0 ± 0 2.96 ± 0.75 0 ± 0 0 ± 0 0 ± 0 Dianthus armeria 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 Echium vulgare 0.28 ± 0.18 0.05 ± 0.05 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 Equisetum arvense 0 ± 0 0 ± 0 1.99 ± 1.27 0.23 ± 0.23 0 ± 0 0 ± 0 0 ± 0 Erigeron annuus 0.17 ± 0.14 0.1 ± 0.07 0 ± 0 0.1 ± 0.07 0 ± 0 0 ± 0 0 ± 0 Euphorbia cyparissias 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0.36 ± 0.21 0.19 ± 0.19 0 ± 0 Fragaria virginiana 0 ± 0 5.79 ± 1.34 0 ± 0 0.04 ± 0.04 0 ± 0 0 ± 0 0 ± 0 Geum urbanum 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 Hieracium vulgatum 0 ± 0 0.04 ± 0.04 0 ± 0 0.16 ± 0.16 0 ± 0 0 ± 0 0 ± 0 Hypericum perforatum 0.22 ± 0.16 0.59 ± 0.54 0.13 ± 0.11 0.87 ± 0.3 0 ± 0 0 ± 0 0 ± 0 Inula helenium 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 Leucanthemum vulgare 0.2 ± 0.14 0.04 ± 0.04 0 ± 0 0.05 ± 0.05 0 ± 0 0 ± 0 0 ± 0 Linaria vulgaris 0.22 ± 0.07 0.57 ± 0.42 0.96 ± 0.91 3.95 ± 0.96 0.25 ± 0.16 0.4 ± 0.21 0.03 ± 0.03 Lonicera canadensis 0.14 ± 0.14 2.57 ± 0.87 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 Lotus corniculatus 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 1.97 ± 1.11 Melilotus albus 13.55 ± 1.93 3.49 ± 0.75 0 ± 0 0.05 ± 0.05 0 ± 0 0 ± 0 0.04 ± 0.04 Medicago lupulina 0.66 ± 0.27 0.56 ± 0.25 0 ± 0 0.08 ± 0.06 0 ± 0 0.08 ± 0.06 0.06 ± 0.06 Monarda fistulosa 1.14 ± 0.47 9.39 ± 1.6 0 ± 0 5.82 ± 1.37 0 ± 0 0 ± 0 0 ± 0 Oxalis stricta 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 Phalaris arundinacea 0 ± 0 0 ± 0 37.04 ± 5.56 0 ± 0 0 ± 0 0 ± 0 2.8 ± 1.74 Phleum pratense 0 ± 0 0 ± 0 0 ± 0 0.23 ± 0.09 0 ± 0 0.55 ± 0.39 2.51 ± 2.23 Plantago lanceolata 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 Plantago major 0 ± 0 0.04 ± 0.04 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 Poa pratensis 25.28 ± 4.11 6.35 ± 1.12 0.69 ± 0.49 9.39 ± 2.06 33.78 ± 4.32 17.3 ± 2.55 8.27 ± 2.55 Potentilla recta 0.12 ± 0.06 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 Ranunculus acris 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 Rudbeckia hirta 0.03 ± 0.03 0 ± 0 0 ± 0 1.67 ± 0.64 0 ± 0 0 ± 0 0 ± 0 Sonchus arvensis 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0.04 ± 0.04 0 ± 0 Solidago sp 0.2 ± 0.2 13.73 ± 2.55 9.28 ± 2.78 10.49 ± 2.74 0 ± 0 0 ± 0 1.5 ± 1.17 Symphyotrichum cordifolium 0 ± 0 2.82 ± 0.8 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 Symphyotrichum ericoides 1.45 ± 0.42 2.96 ± 0.59 0 ± 0 1.97 ± 0.71 0.64 ± 0.42 0.44 ± 0.44 1.1 ± 0.72 Symphyotrichum lanceolatum 0 ± 0 0 ± 0 2.89 ± 1.75 0 ± 0 0 ± 0 0 ± 0 0 ± 0 Symphyotrichum novae-angliae 0.18 ± 0.18 0.25 ± 0.1 2.56 ± 1.09 0 ± 0 0 ± 0 0 ± 0 0 ± 0 Taraxacum officinale 2.42 ± 1.24 0.39 ± 0.29 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 Trifolium hybridum 0 ± 0 0 ± 0 0 ± 0 0.18 ± 0.18 0 ± 0 0 ± 0 0 ± 0 Trifolium pratense 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 Valeriana officinalis 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 Veronica officinalis 0 ± 0 0.04 ± 0.04 0 ± 0 0 ± 0 0 ± 0 0 ± 0 0 ± 0 Vicia cracca 0 ± 0 1.02 ± 0.52 4.74 ± 1.13 4.04 ± 0.45 9.06 ± 2.18 5.67 ± 1.5 2.38 ± 0.63 Vitis riparia 0 ± 0 0 ± 0 0 ± 0 0.83 ± 0.75 0 ± 0 0 ± 0 0 ± 0 Vincetoxicum rossicum 50.69 ± 3.35 38.06 ± 5.39 24.05 ± 4.2 30.88 ± 2.86 41.41 ± 4.81 63.51 ± 3.79 25.93 ± 3.84

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Table 8-3: mean values (±SE) for biodiversity metrics and ecosystem functions for all Rouge NUP study sites. cwm SLA Site cwm C:N cwm height (cm) cwm lcc (%) cwm lnc (%) (cm^2/g) FDis FDiv FEve FRic 1 21.66 ± 0.47 79.19 ± 2.95 43.24 ± 0.45 2.17 ± 0.05 182.72 ± 6.3 1.84 ± 0.09 0.73 ± 0.02 0.57 ± 0.03 12.71 ± 1.1 2 22.79 ± 0.29 85.69 ± 3.5 42.26 ± 0.58 1.95 ± 0.03 152.03 ± 4.13 1.42 ± 0.07 0.61 ± 0.03 0.55 ± 0.03 6.59 ± 0.71 3 22.52 ± 0.22 97.18 ± 1.7 42.46 ± 0.39 1.99 ± 0.03 179.99 ± 5.69 1.48 ± 0.08 0.62 ± 0.03 0.51 ± 0.03 8.77 ± 0.95 4 21.76 ± 0.35 94.28 ± 2.92 39.78 ± 0.56 1.96 ± 0.03 167.42 ± 4.52 1.8 ± 0.09 0.69 ± 0.02 0.58 ± 0.03 9.16 ± 0.8 5 22.81 ± 0.21 82.34 ± 2 39.75 ± 0.44 1.85 ± 0.03 163.33 ± 3.75 1.73 ± 0.07 0.66 ± 0.02 0.51 ± 0.03 4.92 ± 0.41 6 22.37 ± 0.42 88.32 ± 3.57 42.7 ± 0.42 2.03 ± 0.06 149.2 ± 4.97 1.5 ± 0.12 0.78 ± 0.02 0.54 ± 0.03 7.36 ± 0.96 7 21.13 ± 0.43 60.64 ± 4.45 41.13 ± 0.49 2.05 ± 0.04 183.76 ± 5.53 1.79 ± 0.07 0.75 ± 0.02 0.63 ± 0.02 6.44 ± 0.59 8 22 ± 0.19 86.42 ± 3.31 40.63 ± 0.67 1.9 ± 0.04 216.74 ± 3.51 1.46 ± 0.07 0.71 ± 0.04 0.57 ± 0.04 2.91 ± 0.46 9 22.24 ± 0.18 83.19 ± 2.82 42.83 ± 0.21 1.96 ± 0.02 201.24 ± 6.15 1.36 ± 0.06 0.67 ± 0.03 0.61 ± 0.04 4.27 ± 0.43 10 22.2 ± 0.3 104.94 ± 3.73 42.51 ± 0.35 2 ± 0.02 200.72 ± 5.92 1.1 ± 0.1 0.57 ± 0.02 0.49 ± 0.03 3.83 ± 0.49 11 23.55 ± 0.16 92.25 ± 2.91 42.77 ± 0.38 1.9 ± 0.02 189.93 ± 3.8 1.5 ± 0.06 0.61 ± 0.02 0.53 ± 0.03 7.89 ± 0.75 12 22.45 ± 0.38 82.62 ± 4.13 37.99 ± 0.78 1.85 ± 0.05 210.87 ± 5.34 1.75 ± 0.11 0.67 ± 0.03 0.65 ± 0.04 4.49 ± 0.69 13 22.45 ± 0.27 96.67 ± 2.26 41.23 ± 0.43 1.93 ± 0.04 232.84 ± 4.79 1.39 ± 0.1 0.61 ± 0.04 0.58 ± 0.04 4.18 ± 0.46 14 24.24 ± 0.36 110.6 ± 2.97 41.75 ± 0.41 1.8 ± 0.04 172.93 ± 6.55 1.31 ± 0.08 0.74 ± 0.03 0.51 ± 0.03 3.8 ± 0.34

inverse inorganic N Site pd richness evenness shannon H simpson D V. rossicum soil moisture (%) ph (%) (µg/g) 1 2.3 ± 0.05 13.04 ± 0.85 0.79 ± 0.02 1.98 ± 0.08 0.8 ± 0.02 4.4 ± 1.08 0.18 ± 0.01 7.22 ± 0.05 -20.13 ± 1.06 2 1.88 ± 0.04 7.44 ± 0.49 0.71 ± 0.02 1.38 ± 0.06 0.65 ± 0.02 3.25 ± 1.38 0.15 ± 0.03 7.29 ± 0.04 -19.91 ± 0.93 3 2.12 ± 0.04 9.48 ± 0.61 0.69 ± 0.03 1.51 ± 0.07 0.68 ± 0.02 27.28 ± 4.04 0.16 ± 0.03 7.15 ± 0.08 -18.21 ± 2.73 4 2.02 ± 0.03 8.76 ± 0.44 0.77 ± 0.02 1.65 ± 0.06 0.74 ± 0.02 15.34 ± 2.83 0.16 ± 0.03 7.1 ± 0.09 -20.04 ± 2.2 5 1.9 ± 0.03 7.16 ± 0.31 0.78 ± 0.02 1.52 ± 0.05 0.72 ± 0.02 12.99 ± 2.43 0.18 ± 0.04 7.26 ± 0.05 -19.17 ± 3.67 6 1.89 ± 0.05 7.76 ± 0.62 0.63 ± 0.03 1.26 ± 0.08 0.6 ± 0.03 2.06 ± 1.06 0.1 ± 0.02 7.51 ± 0.03 -11.9 ± 0.71 7 2.15 ± 0.07 10.52 ± 0.79 0.83 ± 0.02 1.9 ± 0.09 0.79 ± 0.02 8.09 ± 2.44 0.14 ± 0.03 7.65 ± 0.06 -10.81 ± 2.11 8 1.8 ± 0.05 6.04 ± 0.4 0.65 ± 0.02 1.13 ± 0.06 0.58 ± 0.02 50.43 ± 3.36 0.08 ± 0.02 7.65 ± 0.1 -7.33 ± 2.86 9 2.16 ± 0.07 10.56 ± 0.78 0.75 ± 0.03 1.74 ± 0.12 0.72 ± 0.03 36.79 ± 5.3 0.14 ± 0.04 7.59 ± 0.07 -17.82 ± 2.45 10 1.85 ± 0.02 5.24 ± 0.31 0.71 ± 0.02 1.15 ± 0.06 0.6 ± 0.03 24.05 ± 4.2 0.14 ± 0.05 7.56 ± 0.04 -17.4 ± 3.17 11 1.96 ± 0.04 10.12 ± 0.86 0.77 ± 0.02 1.74 ± 0.1 0.75 ± 0.03 30.28 ± 2.94 0.07 ± 0.02 7.47 ± 0.04 -11.02 ± 3.43 12 1.7 ± 0.03 4.64 ± 0.24 0.69 ± 0.03 1.02 ± 0.05 0.55 ± 0.03 41.37 ± 4.81 0.16 ± 0.04 7.6 ± 0.04 -9.13 ± 1.21 13 1.73 ± 0.03 4.76 ± 0.25 0.61 ± 0.03 0.94 ± 0.06 0.49 ± 0.03 62.55 ± 3.79 0.14 ± 0.01 7.46 ± 0.04 -8.25 ± 4.1 14 1.79 ± 0.02 4.8 ± 0.24 0.69 ± 0.03 1.05 ± 0.05 0.56 ± 0.02 25.93 ± 3.84 0.14 ± 0.03 7.77 ± 0.05 -12.12 ± 3.4

187

Table 8.3 (cont.) inverse soil N flower cover pollinator pollinator Site (g/kg) litter (log g) biomass (log g) soil C(g/kg) k constant (log count/cover) abundance richness 1 -0.31 ± 0.02 4.67 ± 0.22 6.38 ± 0.07 3.53 ± 0.2 0.049 ± 0.003 5.12 ± 0.48 14.24 ± 2.06 1.84 ± 0.26 2 -0.31 ± 0.03 5.91 ± 0.13 7.1 ± 0.1 3 ± 0.34 0.046 ± 0.009 8.08 ± 0.51 11.92 ± 2.58 3.46 ± 0.31 3 -0.32 ± 0.03 4.88 ± 0.24 6.11 ± 0.17 2.51 ± 0.3 0.052 ± 0.009 4.79 ± 0.23 4.76 ± 1.24 1.18 ± 0.23 4 -0.49 ± 0.1 4.79 ± 0.15 6.04 ± 0.18 3.32 ± 0.26 0.047 ± 0.004 5.57 ± 0.53 2.21 ± 0.74 0.86 ± 0.17 5 -0.67 ± 0.02 4.82 ± 0.09 5.95 ± 0.07 2.46 ± 0.29 0.088 ± 0.017 7.87 ± 0.49 6.29 ± 1.44 1.87 ± 0.26 6 -0.68 ± 0.02 5.86 ± 0.11 6.88 ± 0.09 2 ± 0.06 0.042 ± 0.006 6.51 ± 0.39 13.41 ± 2.08 3.06 ± 0.33 7 -0.88 ± 0.1 4.04 ± 0.2 5.72 ± 0.1 2.67 ± 0.59 0.046 ± 0.01 3.88 ± 0.49 54.84 ± 7.89 5.42 ± 0.6 8 -0.89 ± 0.18 4.88 ± 0.17 6.2 ± 0.06 5.26 ± 1.31 0.028 ± 0.003 3.81 ± 0.19 17.19 ± 3.78 2.57 ± 0.45 9 -0.45 ± 0.07 4.84 ± 0.2 5.9 ± 0.16 5.21 ± 0.77 0.037 ± 0.003 3.85 ± 0.16 34.43 ± 8.19 3.19 ± 0.5 10 -0.35 ± 0.02 5.1 ± 0.13 6.49 ± 0.21 3.73 ± 0.21 0.035 ± 0.004 4.8 ± 0.31 11.04 ± 2.14 2.16 ± 0.45 11 -0.43 ± 0.17 5.14 ± 0.12 6.18 ± 0.11 3.21 ± 0.39 0.038 ± 0.006 4.11 ± 0.26 11.75 ± 2.79 2.4 ± 0.36 12 -0.69 ± 0.16 5.24 ± 0.13 6.44 ± 0.06 2.19 ± 0.2 0.036 ± 0.003 5.25 ± 0.51 7.21 ± 1.07 2.07 ± 0.25 13 -0.2 ± 0.03 5.35 ± 0.13 6.56 ± 0.07 2.54 ± 0.42 0.031 ± 0.005 5.16 ± 0.39 2.48 ± 0.56 1.04 ± 0.16 14 -0.18 ± 0.04 5.44 ± 0.2 6.65 ± 0.1 3.2 ± 0.19 0.048 ± 0.009 4.1 ± 0.24 8.57 ± 1.33 1.73 ± 0.26

188

Table 8-4: Model outputs from linear and polynomial regressions investigating the relationship between V. rossicum invasion and plant community biodiversity at the regional scale (note: model needs to be update to account for intra-site variability) Response Predictor r^2 adj r^2 Sigma df Estimate SE Statistic p value CWM C:N V. rossicum^2 0.018 0.013 1.716 347 0.029 0.012 2.438 0.015 CWM plant height V. rossicum^2 0.123 0.118 18.314 347 0.465 0.128 3.621 <0.001 CWM leaf C (%) V. rossicum^2 0.047 0.042 2.744 347 -0.047 0.019 -2.418 0.016 CWM leaf N (%) V. rossicum^2 0.045 0.04 0.205 347 -0.006 0.001 -3.945 <0.001 CWM SLA V. rossicum 0.615 0.614 21.524 348 1.118 0.047 23.589 <0.001 Evenness V. rossicum^2 0.27 0.266 0.116 347 0.005 0.001 5.992 <0.001 Functional dispersion V. rossicum^2 0.151 0.146 0.434 347 0.005 0.003 1.707 0.089 Functional divergenceV. rossicum^2 0.073 0.067 0.145 346 -0.003 0.001 -3.252 0.001 Functional evenness V. rossicum^2 0.01 0.004 0.167 346 0.001 0.001 1.196 0.232 Functional richness V. rossicum 0.104 0.101 4.077 347 -0.057 0.009 -6.341 <0.001 PD V. rossicum 0.083 0.08 0.263 348 -0.003 0.001 -5.615 <0.001 Species richness V. rossicum 0.146 0.143 3.466 348 -0.059 0.008 -7.706 <0.001 Shannon's H V. rossicum^2 0.249 0.245 0.422 347 0.003 0.003 1.015 0.311 Simpson's D V. rossicum^2 0.342 0.338 0.126 347 0.004 0.001 5.058 <0.001

189

Table 8-5: preliminary analysis of the relationship between V. rossicum abundance and ecosystem functionality using site means along the invasion gradient. Response Predictor r^2 adj r^2 sigma df estimate se statistic p Pollinator richness Relative V. rossicum 0.13 0.06 1.15 12 -0.02 0.02 -1.34 0.21 Pollinator abundance Relative V. rossicum 0.04 -0.04 14.43 12 -0.15 0.22 -0.71 0.49 Flower cover Relative V. rossicum 0.24 0.17 1.28 12 -0.04 0.02 -1.92 0.08 Litter (log) Relative V. rossicum 0.001 -0.08 0.51 12 0.0003 0.01 0.03 0.97 Biomass (log) Relative V. rossicum 0.01 -0.07 0.4 12 -0.003 0.01 -0.42 0.68 k constant Relative V. rossicum 0.51 0.47 0.01 12 -0.0005 0.0001 -3.53 0.004 Soil carbon Relative V. rossicum 0.06 -0.02 1.07 12 0.01 0.02 0.87 0.4 Inverse Inorganic N Relative V. rossicum 0.6 0.56 3.57 12 0.22 0.05 4.2 0.001 Inverse total N Relative V. rossicum 0 -0.08 0.29 12 -0.0009 0.004 -0.21 0.84

190

Table 8-6: Mixed effects models for general biodiversity-ecosystem function relationships for Rouge National Urban Park (site coded as random factor) (±95% C.I.)

Predictors Response fixed effect random effect df.residual sigma Estimate S.E. Statistic Lower 95% C.I.Upper 95% C.I. Marginal R2 Conditional R2 biomass (log) simpson site 66 0.27 -0.443 0.284 -1.56 -0.999 0.114 0.022 0.628 biomass (log) shannon site 66 0.268 -0.172 0.088 -1.955 -0.345 0.0005 0.042 0.629 biomass (log) richness site 66 0.266 -0.021 0.011 -1.962 -0.042 -1.73E-05 0.046 0.651 biomass (log) rel.native site 66 0.272 -0.0003 0.002 -0.147 -0.004 0.004 0.0003 0.651 biomass (log) rel.exotic site 66 0.272 0.0003 0.002 0.147 -0.004 0.004 0.0003 0.651 biomass (log) pd site 66 0.271 -0.234 0.154 -1.517 -0.537 0.068 0.019 0.627 biomass (log) FRic site 66 0.245 -0.027 0.008 -3.481 -0.042 -0.012 0.078 0.739 biomass (log) FEve site 66 0.269 -0.327 0.244 -1.338 -0.806 0.152 0.01 0.652 biomass (log) FDiv site 66 0.263 -0.47 0.264 -1.782 -0.986 0.047 0.022 0.684 biomass (log) FDis site 66 0.255 -0.274 0.087 -3.155 -0.445 -0.104 0.076 0.666 biomass (log) evenness site 66 0.272 -0.315 0.333 -0.946 -0.967 0.337 0.007 0.633 biomass (log) cwm.vh site 66 0.253 0.007 0.002 3.48 0.003 0.011 0.117 0.668 biomass (log) cwm.sla site 66 0.27 0.001 0.001 0.555 -0.002 0.003 0.003 0.659 biomass (log) cwm.lnc site 66 0.272 -0.094 0.203 -0.465 -0.493 0.304 0.002 0.644 biomass (log) cwm.lcc site 66 0.271 0.014 0.015 0.907 -0.016 0.044 0.009 0.646 biomass (log) cwm.c.n site 66 0.273 0.013 0.027 0.475 -0.04 0.066 0.002 0.636 flower cover simpson site 253 1.9 -2.302 0.941 -2.448 -4.146 -0.459 0.025 0.336 flower cover shannon site 253 1.901 -0.786 0.322 -2.442 -1.417 -0.155 0.03 0.335 flower cover richness site 253 1.89 -0.126 0.042 -3.006 -0.208 -0.044 0.045 0.344 flower cover rel.native site 253 1.921 0.012 0.007 1.785 -0.001 0.026 0.019 0.285 flower cover rel.exotic site 253 1.921 -0.012 0.007 -1.785 -0.026 0.001 0.019 0.285 flower cover pd site 253 1.917 0.523 0.527 0.992 -0.511 1.556 0.004 0.328 flower cover FRic site 252 1.913 -0.062 0.035 -1.796 -0.13 0.006 0.013 0.332 flower cover FEve site 252 1.924 0.526 0.744 0.707 -0.932 1.984 0.001 0.318 flower cover FDiv site 252 1.915 1.359 0.873 1.556 -0.352 3.071 0.008 0.33 flower cover FDis site 253 1.921 -0.166 0.274 -0.607 -0.703 0.371 0.001 0.318 flower cover evenness site 253 1.919 -0.98 0.978 -1.002 -2.898 0.937 0.003 0.315 flower cover cwm.vh site 253 1.923 -0.001 0.008 -0.162 -0.016 0.014 0.0001 0.315 flower cover cwm.sla site 253 1.921 0.001 0.005 0.208 -0.008 0.01 0.0002 0.322 flower cover cwm.lnc site 253 1.92 0.48 0.628 0.766 -0.75 1.711 0.002 0.319 flower cover cwm.lcc site 253 1.905 0.098 0.05 1.982 0.001 0.196 0.013 0.339 flower cover cwm.c.n site 253 1.92 0.075 0.077 0.965 -0.077 0.226 0.003 0.314 inv. inorganic N simpson site 52 4.605 -2.05 4.521 -0.454 -10.91 6.81 0.003 0.525 inv. inorganic N shannon site 52 4.606 -0.972 1.764 -0.551 -4.43 2.487 0.006 0.523 inv. inorganic N richness site 52 4.609 -0.11 0.302 -0.363 -0.702 0.483 0.003 0.524 inv. inorganic N rel.native site 52 4.668 -0.029 0.032 -0.897 -0.091 0.034 0.142 0.479 inv. inorganic N rel.exotic site 52 4.668 0.029 0.032 0.897 -0.034 0.091 0.066 0.543 inv. inorganic N pd site 52 4.412 -7.306 3.003 -2.433 -13.193 -1.42 0.094 0.551 inv. inorganic N FRic site 52 4.322 0.373 0.229 1.627 -0.076 0.822 0.04 0.65 inv. inorganic N FEve site 52 4.609 0.895 4.334 0.207 -7.599 9.389 0.001 0.525 inv. inorganic N FDiv site 52 4.592 2.498 4.115 0.607 -5.566 10.563 0.004 0.528 inv. inorganic N FDis site 52 4.594 0.308 1.427 0.216 -2.489 3.105 0.0005 0.533 inv. inorganic N evenness site 52 4.597 -1.636 4.511 -0.363 -10.478 7.205 0.002 0.53 inv. inorganic N cwm.vh site 52 4.569 -0.043 0.047 -0.917 -0.134 0.049 0.014 0.535 inv. inorganic N cwm.sla site 52 4.556 0.061 0.023 2.663 0.016 0.106 0.122 0.482 inv. inorganic N cwm.lnc site 52 4.423 4.468 2.955 1.512 -1.324 10.26 0.021 0.588 inv. inorganic N cwm.lcc site 52 4.597 0.011 0.26 0.041 -0.5 0.521 1.96E-05 0.532 inv. inorganic N cwm.c.n site 52 4.498 -0.512 0.367 -1.394 -1.231 0.208 0.019 0.552

191

Table 8-6 (cont.) Predictors Response fixed effect random effect df.residual sigma Estimate S.E. Statistic Lower 95% C.I.Upper 95% C.I. Marginal R2 Conditional R2 inv. total soil N simpson site 52 0.076 -0.076 0.081 -0.947 -0.234 0.082 0.002 0.929 inv. total soil N shannon site 52 0.074 -0.051 0.032 -1.575 -0.114 0.012 0.008 0.933 inv. total soil N richness site 52 0.072 -0.011 0.006 -1.952 -0.023 4.70E-05 0.016 0.939 inv. total soil N rel.native site 52 0.074 -0.001 0.001 -1.624 -0.002 0.0002 0.009 0.935 inv. total soil N rel.exotic site 52 0.074 0.001 0.001 1.624 -0.0002 0.002 0.009 0.935 inv. total soil N pd site 52 0.074 -0.092 0.056 -1.628 -0.203 0.019 0.008 0.935 inv. total soil N FRic site 52 0.076 -0.001 0.004 -0.116 -0.009 0.008 1.00E-04 0.929 inv. total soil N FEve site 52 0.074 -0.122 0.073 -1.669 -0.265 0.021 0.006 0.929 inv. total soil N FDiv site 52 0.075 -0.099 0.069 -1.44 -0.234 0.036 0.004 0.931 inv. total soil N FDis site 52 0.076 -0.002 0.024 -0.067 -0.049 0.046 7.63E-06 0.928 inv. total soil N evenness site 52 0.076 -0.064 0.078 -0.821 -0.217 0.089 0.001 0.928 inv. total soil N cwm.vh site 52 0.075 0.002 0.001 1.794 -0.0001 0.003 0.011 0.926 inv. total soil N cwm.sla site 52 0.075 0.001 0.0004 1.234 -0.0003 0.001 0.005 0.932 inv. total soil N cwm.lnc site 52 0.076 0.013 0.052 0.246 -0.089 0.115 0.0001 0.928 inv. total soil N cwm.lcc site 52 0.076 0.001 0.004 0.177 -0.008 0.01 1.00E-04 0.928 inv. total soil N cwm.c.n site 52 0.076 0.003 0.006 0.396 -0.01 0.015 0.0003 0.928 k constant simpson site 52 0.017 0.017 0.015 1.179 -0.012 0.046 0.027 0.242 k constant shannon site 52 0.017 0.006 0.006 1.041 -0.005 0.017 0.023 0.256 k constant richness site 52 0.017 0.001 0.001 0.631 -0.001 0.002 0.009 0.267 k constant rel.native site 52 0.017 4.56E-05 0.0001 0.452 -0.0002 0.0002 0.004 0.239 k constant rel.exotic site 52 0.017 -4.56E-05 0.0001 -0.452 -0.0002 0.0002 0.004 0.239 k constant pd site 52 0.016 0.024 0.01 2.446 0.005 0.043 0.11 0.374 k constant FRic site 52 0.016 -0.001 0.001 -1.654 -0.003 0.0002 0.055 0.429 k constant FEve site 52 0.017 -0.018 0.015 -1.224 -0.047 0.011 0.025 0.253 k constant FDiv site 52 0.016 0.018 0.014 1.298 -0.009 0.046 0.026 0.3 k constant FDis site 52 0.017 0.003 0.005 0.58 -0.007 0.013 0.005 0.247 k constant evenness site 52 0.017 0.018 0.015 1.176 -0.012 0.048 0.024 0.255 k constant cwm.vh site 52 0.017 8.91E-07 0.0002 0.006 -0.0003 0.0003 7.22E-07 0.268 k constant cwm.sla site 52 0.017 -1.00E-04 1.00E-04 -1.045 -0.0002 0.0001 0.022 0.208 k constant cwm.lnc site 52 0.017 -0.01 0.011 -0.966 -0.031 0.011 0.014 0.287 k constant cwm.lcc site 52 0.017 1.00E-04 0.001 0.109 -0.002 0.002 0.0002 0.258 k constant cwm.c.n site 52 0.017 0.001 0.001 0.858 -0.001 0.004 0.011 0.264 litter (log) simpson site 66 0.359 -0.975 0.372 -2.618 -1.705 -0.245 0.068 0.586 litter (log) shannon site 66 0.359 -0.312 0.116 -2.696 -0.539 -0.085 0.086 0.587 litter (log) richness site 66 0.366 -0.025 0.014 -1.733 -0.053 0.003 0.04 0.589 litter (log) rel.native site 66 0.369 0.001 0.003 0.316 -0.005 0.006 0.001 0.611 litter (log) rel.exotic site 66 0.369 -0.001 0.003 -0.316 -0.006 0.005 0.001 0.611 litter (log) pd site 66 0.369 -0.288 0.209 -1.376 -0.697 0.122 0.017 0.58 litter (log) FRic site 66 0.357 -0.021 0.011 -1.946 -0.043 0.0002 0.033 0.635 litter (log) FEve site 66 0.352 -0.798 0.32 -2.493 -1.425 -0.171 0.036 0.634 litter (log) FDiv site 66 0.368 -0.154 0.366 -0.422 -0.872 0.563 0.001 0.611 litter (log) FDis site 66 0.336 -0.435 0.114 -3.819 -0.658 -0.212 0.117 0.645 litter (log) evenness site 66 0.354 -1.19 0.431 -2.762 -2.034 -0.346 0.062 0.599 litter (log) cwm.vh site 66 0.363 0.008 0.003 2.591 0.002 0.013 0.081 0.568 litter (log) cwm.sla site 66 0.367 0.001 0.002 0.475 -0.003 0.005 0.002 0.617 litter (log) cwm.lnc site 66 0.368 0.117 0.274 0.428 -0.419 0.653 0.001 0.616 litter (log) cwm.lcc site 66 0.367 0.022 0.021 1.051 -0.019 0.062 0.013 0.605 litter (log) cwm.c.n site 66 0.366 -0.017 0.036 -0.473 -0.088 0.054 0.002 0.629

192

Table 8-6 (cont.)

Predictors Response fixed effect random effect df.residual sigma Estimate S.E. Statistic Lower 95% C.I.Upper 95% C.I. Marginal R2 Conditional R2 pollinator abundance simpson site 253 18.312 -23.018 9.153 -2.515 -40.958 -5.079 0.024 0.414 pollinator abundance shannon site 253 18.254 -8.437 3.146 -2.682 -14.603 -2.272 0.031 0.438 pollinator abundance richness site 253 18.253 -1.12 0.412 -2.719 -1.928 -0.313 0.033 0.435 pollinator abundance rel.native site 253 18.475 -0.115 0.068 -1.69 -0.248 0.018 0.016 0.384 pollinator abundance rel.exotic site 253 18.475 0.115 0.068 1.69 -0.018 0.248 0.016 0.384 pollinator abundance pd site 253 18.586 7.99 5.11 1.563 -2.026 18.006 0.01 0.329 pollinator abundance FRic site 252 18.541 -0.531 0.337 -1.575 -1.191 0.13 0.01 0.357 pollinator abundance FEve site 252 18.616 6.902 7.201 0.958 -7.212 21.015 0.003 0.342 pollinator abundance FDiv site 252 18.614 -5.238 8.499 -0.616 -21.895 11.419 0.001 0.353 pollinator abundance FDis site 253 18.506 -4.434 2.643 -1.678 -9.615 0.747 0.009 0.362 pollinator abundance evenness site 253 18.493 -15.312 9.457 -1.619 -33.847 3.224 0.008 0.373 pollinator abundance cwm.vh site 253 18.453 0.123 0.073 1.696 -0.019 0.266 0.01 0.393 pollinator abundance cwm.sla site 253 18.578 0.048 0.045 1.063 -0.04 0.136 0.005 0.354 pollinator abundance cwm.lnc site 253 18.464 -11.487 6.049 -1.899 -23.343 0.37 0.011 0.371 pollinator abundance cwm.lcc site 253 18.614 -0.28 0.486 -0.577 -1.232 0.672 0.001 0.347 pollinator abundance cwm.c.n site 253 18.543 0.928 0.749 1.239 -0.54 2.397 0.005 0.365 pollinator richness simpson site 253 1.788 -1.778 0.883 -2.014 -3.509 -0.048 0.018 0.315 pollinator richness shannon site 253 1.801 -0.289 0.303 -0.952 -0.883 0.306 0.005 0.291 pollinator richness richness site 253 1.808 0.028 0.04 0.71 -0.05 0.106 0.003 0.263 pollinator richness rel.native site 253 1.803 -0.005 0.006 -0.749 -0.018 0.008 0.003 0.289 pollinator richness rel.exotic site 253 1.803 0.005 0.006 0.749 -0.008 0.018 0.003 0.289 pollinator richness pd site 253 1.799 0.868 0.49 1.771 -0.092 1.828 0.014 0.269 pollinator richness FRic site 252 1.808 0.023 0.033 0.713 -0.041 0.087 0.002 0.275 pollinator richness FEve site 252 1.81 -0.229 0.699 -0.328 -1.598 1.141 0.0003 0.27 pollinator richness FDiv site 252 1.806 -0.664 0.821 -0.809 -2.274 0.945 0.002 0.279 pollinator richness FDis site 253 1.794 -0.469 0.255 -1.838 -0.97 0.031 0.011 0.288 pollinator richness evenness site 253 1.776 -2.461 0.905 -2.721 -4.234 -0.688 0.025 0.321 pollinator richness cwm.vh site 253 1.808 0.0004 0.007 0.056 -0.013 0.014 1.32E-05 0.27 pollinator richness cwm.sla site 253 1.784 0.01 0.004 2.228 0.001 0.018 0.023 0.324 pollinator richness cwm.lnc site 253 1.808 -0.045 0.589 -0.076 -1.2 1.11 2.02E-05 0.268 pollinator richness cwm.lcc site 253 1.804 0.049 0.047 1.053 -0.042 0.141 0.004 0.271 pollinator richness cwm.c.n site 253 1.807 0.032 0.073 0.442 -0.11 0.174 0.001 0.273 soil total C simpson site 52 0.847 2.37 0.847 2.799 0.71 4.029 0.095 0.63 soil total C shannon site 52 0.786 1.224 0.317 3.867 0.604 1.845 0.179 0.717 soil total C richness site 52 0.818 0.185 0.057 3.231 0.073 0.297 0.17 0.696 soil total C rel.native site 52 0.879 0.011 0.006 1.826 -0.001 0.024 0.053 0.615 soil total C rel.exotic site 52 0.879 -0.011 0.006 -1.826 -0.024 0.001 0.053 0.615 soil total C pd site 52 0.895 1.042 0.62 1.68 -0.174 2.258 0.044 0.576 soil total C FRic site 52 0.908 0.051 0.047 1.101 -0.04 0.143 0.021 0.566 soil total C FEve site 52 0.929 0.561 0.872 0.643 -1.148 2.27 0.005 0.517 soil total C FDiv site 52 0.931 0.168 0.833 0.202 -1.465 1.802 0.0005 0.517 soil total C FDis site 52 0.922 0.235 0.286 0.821 -0.326 0.796 0.007 0.531 soil total C evenness site 52 0.871 1.977 0.86 2.298 0.291 3.664 0.057 0.597 soil total C cwm.vh site 52 0.928 -0.006 0.009 -0.689 -0.025 0.012 0.008 0.521 soil total C cwm.sla site 52 0.915 -0.004 0.005 -0.842 -0.014 0.005 0.012 0.551 soil total C cwm.lnc site 52 0.908 0.757 0.605 1.25 -0.43 1.943 0.016 0.553 soil total C cwm.lcc site 52 0.91 0.065 0.052 1.257 -0.036 0.166 0.018 0.549 soil total C cwm.c.n site 52 0.923 -0.067 0.075 -0.892 -0.214 0.08 0.008 0.526

193

Table 8-7: Mean functional trait values for herbaceous plant species observed at Rouge NUP study sites (n=20-40 for height & SLA, n=5 for LCC, LNC & LCN) code Family Genus/Species Origin height (cm) se SLA (cm^2/g) se LCC (%) se LNC (%) se LCN se ACMI Asteraceae Achillea millefolium Exotic 22.28 ± 0.89 174.53 ± 14.56 44.4 ± 0.24 2.4 ± 0.06 18.53 ± 0.5 AGGI Poaceae Agrostis gigantea Exotic 35.35 ± 2.71 145.3 ± 4.52 45.65 ± 0.2 1.63 ± 0.09 28.29 ± 1.51 ALPE Brassicaceae Allaria Petiolata Exotic 25.02 ± 1.31 137.03 ± 4.34 35.21 ± 0.49 1.84 ± 0.01 19.11 ± 0.22 ANCA Ranunculaceae Anemone canadensis Native 29.75 ± 1.04 245.94 ± 17.67 44.59 ± 0.27 1.67 ± 0.01 26.64 ± 0.23 ANMA Asteraceae Anaphalis margaritacea Native 44.03 ± 3.28 58.3 ± 3.16 40.1 ± 0.56 1.39 ± 0.06 28.98 ± 0.96 ANQU Ranunculaceae Anemone quinquefolia Exotic ANVI Ranunculaceae Anemone virginiana Native 40.53 ± 2.6 140.23 ± 5.54 44.7 ± 0.41 1.76 ± 0.02 25.44 ± 0.38 AQCA Ranunculaceae Aquilegia canadensis Native ASSY Apocynaceae Asclepias syriaca Native 91.25 ± 1.85 130.27 ± 3.06 40.8 ± 0.32 2.73 ± 0.04 14.97 ± 0.24 BRIN Poaceae Bromus inermis Exotic 140.91 ± 2.02 116.65 ± 7.97 43.12 ± 0.48 1.59 ± 0.01 27.1 ± 0.5 CAREX Cyperaceae Carex sp Native 33.63 ± 1.14 457.64 ± 153.7 49.39 ± 0.73 2.94 ± 0.05 16.83 ± 0.5 CEMA Asteraceae Centaurea maculosa Exotic 44.81 ± 3.07 159.6 ± 5.35 45.08 ± 0.17 2.04 ± 0 22.08 ± 0.09 CIAR Asteraceae Cirsium arvense Exotic 55.3 ± 7.22 121.3 ± 6.25 40.14 ± 1.09 1.52 ± 0.05 26.54 ± 1.46 CLVIR Ranunculaceae Clematis virginiana Native 115.78 ± 12.6 159.07 ± 14.43 38.32 ± 0.43 2.21 ± 0.05 17.34 ± 0.44 COST Cornaceae Cornus stolonifera Native 51.06 ± 2.78 136.73 ± 6.4 47.4 ± 0.81 2.21 ± 0.16 22 ± 1.84 DACA Apiaceae Daucus carota Exotic 92.9 ± 2.96 158.32 ± 9.79 51.54 ± 0.39 2.13 ± 0.03 24.27 ± 0.5 DIAR Caryophyllaceae Dianthus armeria Exotic 33.07 ± 2.06 128.25 ± 4.52 45.34 ± 0.63 2.04 ± 0.1 22.5 ± 1.26 ECVU Boraginaceae Echium vulgare Exotic 48.32 ± 4.25 155.25 ± 6.25 38.54 ± 0.54 1.93 ± 0.01 20.02 ± 0.35 EQAR Equisetaceae Equisetum arvense Native 21.62 ± 1.9 92.25 ± 3.25 54.2 ± 0.48 1.55 ± 0.01 34.93 ± 0.31 ERAN Asteraceae Erigeron annuus Native 67.43 ± 1.66 229.86 ± 31.32 54.02 ± 0.63 1.62 ± 0.06 33.5 ± 1.13 EUCY Euphorbiaceae Euphorbia cyparissias Exotic 54.64 ± 2.95 179.64 ± 8.5 38.2 ± 0.48 1.65 ± 0.01 23.13 ± 0.25 CYST Woodsiaceae Cystopteris tenuis Exotic FRVI Rosaceae Fragaria virginiana Native 10.49 ± 0.59 150.86 ± 18.92 31.81 ± 0.56 2.08 ± 0.05 15.32 ± 0.59 GEUR Rosaceae Geum urbanum Exotic 58.25 ± 4.31 115.81 ± 2.86 47.21 ± 0.48 1.85 ± 0.01 25.49 ± 0.19 HIVU Asteraceae Hieracium vulgatum Exotic 18.49 ± 1.56 204.35 ± 27.42 39.23 ± 0.43 2.4 ± 0.04 16.37 ± 0.3 HYPE Hypericaceae Hypericum perforatum Exotic 32.93 ± 1.17 235.43 ± 24.57 46.37 ± 0.2 2.18 ± 0.04 21.27 ± 0.34 INHE Asteraceae Inula helenium Exotic 179.8 ± 3.09 142.07 ± 6.07 30.55 ± 0.52 1.79 ± 0.12 17.38 ± 1.23 LEVU Asteraceae Leucanthemum vulgare Exotic 43.85 ± 1.94 215.36 ± 10.02 26.91 ± 0.69 1.18 ± 0.03 22.83 ± 0.85 LIVUL Plantaginaceae Linaria vulgaris Exotic 31.09 ± 1.42 220.14 ± 16.82 44.94 ± 0.46 1.48 ± 0.09 30.8 ± 1.9 LOCAN Caprifoliaceae Lonicera canadensis Native 87.25 ± 3.34 147.81 ± 7.14 29.87 ± 0.55 2.05 ± 0.02 14.55 ± 0.22 LOCO Fabaceae Lotus corniculatus Exotic 29.45 ± 1.35 182.18 ± 1.72 34.21 ± 0.48 3.35 ± 0.01 10.21 ± 0.13

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Table 8-7 (cont) code Family Genus/Species Origin height (cm) se SLA (cm^2/g) se LCC (%) se LNC (%) se LCN se LOPE Poaceae Lolium perenne Exotic LYAM Lamiaceae Lycopus americanus Native LYCI Primulaceae Lysimachia ciliata Native MEAL Fabaceae Melilotus albus Exotic 122.23 ± 5.95 116.45 ± 8.7 44.17 ± 0.36 2.58 ± 0.09 17.22 ± 0.63 MELU Fabaceae Medicago lupulina Exotic 45.72 ± 3.73 392.63 ± 10.09 45.21 ± 0.16 3.05 ± 0.02 14.82 ± 0.09 MEPI Lamiaceae Mentha piperita Exotic MOFI Lamiaceae Monarda fistulosa Native 77.02 ± 1.14 178.9 ± 13.44 47.41 ± 0.47 1.9 ± 0.09 25.17 ± 1.33 OXST Oxalidaceae Oxalis stricta Exotic 8.21 ± 0.48 498.18 ± 32.94 40.2 ± 0.48 2.35 ± 0.01 17.08 ± 0.17 PHAR Poaceae Phalaris arundinacea Exotic 125.5 ± 6.51 220.65 ± 9.85 41.16 ± 0.2 1.91 ± 0 21.51 ± 0.09 PHPR Poaceae Phleum pratense Exotic 95.93 ± 1.97 270 ± 15.25 44.76 ± 0.54 2.15 ± 0.03 20.8 ± 0.17 PLLA Plantaginaceae Plantago lanceolata Exotic 42.23 ± 3.73 158 ± 7.55 28.62 ± 0.23 2.08 ± 0.03 13.8 ± 0.29 PLMA Plantaginaceae Plantago major Exotic 22.45 ± 1.33 128.29 ± 2.42 48.21 ± 0.48 1.85 ± 0.01 26.06 ± 0.21 POPR Poaceae Poa pratensis Exotic 32.5 ± 1.53 174 ± 8.25 28.84 ± 0.41 1.18 ± 0.02 24.43 ± 0.5 PORE Rosaceae Potentilla recta Native 19.78 ± 2.32 14.87 ± 0.43 42.96 ± 0.06 1.6 ± 0.05 26.89 ± 0.82 RAAC Ranunculaceae Ranunculus acris Exotic 31.16 ± 1.72 273.59 ± 18.78 48.04 ± 0.25 2.36 ± 0.08 20.42 ± 0.72 RHCA Rhamnaceae Rhamnus cathartica Exotic RUHI Asteraceae Rudbeckia hirta Native 47.78 ± 1.79 157.8 ± 10.21 38.04 ± 0.39 1.52 ± 0.04 25.17 ± 0.92 RULA Asteraceae Rudbeckia lacinata Native SOARV Asteraceae Sonchus arvensis Exotic 61.33 ± 3.47 611.19 ± 39.58 48.23 ± 0.51 2.64 ± 0.06 18.33 ± 0.45 SOSP Asteraceae Solidago sp Native 106.65 ± 2.07 117.54 ± 6.69 45.48 ± 0.37 1.88 ± 0.08 24.46 ± 1.2 SYCO Asteraceae Symphyotrichum cordifolium Native 34.24 ± 4.06 244.25 ± 24.36 41.56 ± 0.22 1.74 ± 0.08 24.11 ± 1.13 SYER Asteraceae Symphyotrichum ericoides Native 36.75 ± 1.55 134.64 ± 18.02 38.55 ± 0.26 1.75 ± 0.09 22.29 ± 1.15 SYLA Asteraceae Symphyotrichum lanceolatum Native 82.78 ± 3.35 127.54 ± 19.86 33.36 ± 0.16 2.03 ± 0.05 16.46 ± 0.55 SYNO AsteraceaeSymphyotrichum novae-angliae Native 55.35 ± 2.65 88.74 ± 6.39 43.65 ± 0.22 2.35 ± 0.09 18.7 ± 0.67 TAOF Asteraceae Taraxacum officinale Exotic 23.27 ± 2.55 206.69 ± 14.74 35.5 ± 0.33 2.62 ± 0.05 13.55 ± 0.31 TORA Anacardiaceae Toxicodendron radicans Native TRHY Fabaceae Trifolium hybridum Exotic 22.72 ± 1.28 219.84 ± 3.88 32.2 ± 0.48 4.15 ± 0.01 7.76 ± 0.11 TRPRA Fabaceae Trifolium pratense Exotic 13.5 ± 0.44 163.97 ± 10.29 35.81 ± 0.48 3.72 ± 0.01 9.62 ± 0.12 VAOF Caprifoliaceae Valeriana officinalis Exotic 116.35 ± 2.9 195.25 ± 7.58 42.96 ± 0.39 2.35 ± 0.03 18.28 ± 0.3 VEOF Plantaginaceae Veronica officinalis Exotic 12.34 ± 0.7 232.66 ± 6.38 44.86 ± 0.2 2.25 ± 0 19.92 ± 0.11 VICR Fabaceae Vicia cracca Exotic 73.65 ± 5.29 219.15 ± 28.36 31.62 ± 0.48 4.01 ± 0.01 7.87 ± 0.11 VIRI Vitaceae Vitis riparia Native 72.44 ± 5.02 173.86 ± 16.54 44.86 ± 0.2 1.81 ± 0.07 24.97 ± 1.05 VIRO Apocynaceae Vincetoxicum rossicum Exotic 110.19 ± 4.04 271.59 ± 15 45.08 ± 0.17 2.01 ± 0.06 22.54 ± 0.7

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Table 8-8: Linear and polynomial regression models showing the relationship between individual biodiversity- ecosystem function coefficients and V. rossicum invasion (statistically significant relationships have bolded p values)

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Table 8-8 (cont.)

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Table 8-8 (cont.)

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Table 8-9: Pearson correlations for response variables from simulated herbivory plots. (bolded p-values indicate statistically significant relationships) Variable Treament i j r p-value 100% Delta NH4 Delta NO3 0.28 0.58 (n=6) Delta NH4 Delta CO2 Efflux 0.08 0.88 Delta NO3 Delta CO2 Efflux 0.57 0.24 Delta NH4 k -0.44 0.39 Delta NO3 k -0.19 0.72 Delta CO2 Efflux k 0.27 0.6

75% Delta NH4 Delta NO3 -0.06 0.88 (n=8) Delta NH4 Delta CO2 Efflux 0.58 0.13 Delta NO3 Delta CO2 Efflux 0.04 0.93 Delta NH4 k 0.18 0.68 Delta NO3 k 0.36 0.38 Delta CO2 Efflux k 0.01 0.99

50% Delta NH4 Delta NO3 0.33 0.47 (n=7) Delta NH4 Delta CO2 Efflux 0.13 0.78 Delta NO3 Delta CO2 Efflux 0.79 0.03 Delta NH4 k -0.8 0.03 Delta NO3 k 0.03 0.95 Delta CO2 Efflux k -0.02 0.97

25% Delta NH4 Delta NO3 0.71 0.03 (n=9) Delta NH4 Delta CO2 Efflux -0.28 0.47 Delta NO3 Delta CO2 Efflux -0.72 0.03 Delta NH4 k -0.67 0.05 Delta NO3 k -0.32 0.4 Delta CO2 Efflux k -0.06 0.87

control Delta NH4 Delta NO3 0.04 0.9 (n=10) Delta NH4 Delta CO2 Efflux 0.03 0.93 Delta NO3 Delta CO2 Efflux -0.26 0.46 Delta NH4 k 0.11 0.77 Delta NO3 k 0.07 0.86 Delta CO2 Efflux k -0.1 0.79

25+50+75+100 % Delta NH4 Delta NO3 0.27 0.14 (n=40) Delta NH4 Delta CO2 Efflux 0.08 0.66 Delta NO3 Delta CO2 Efflux 0.02 0.92 Delta NH4 k -0.34 0.07 Delta NO3 k 0.13 0.51 Delta CO2 Efflux k -0.08 0.69

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Table 8-10: Relative abundance, functional uniqueness, restrictedness and functional rarity or herbaceous plant species in Rouge National Urban Park

Relative abundance Functional Functional genus species (%, regional scale) uniquness Restrictedness rarity Achillea millefolium 0.223 0.055 0.714 1.128 Agrostis gigantea 2.762 0.044 0.286 0.595 Allaria Petiolata 0.011 0.068 0.786 1.291 Anemone canadensis 0.004 0.051 0.5 0.872 Anemone virginiana 0.32 0.044 0.857 1.21 Asclepias syriaca 2.927 0.092 0.143 0.755 Bromus inermis 14.227 0.096 0.071 0.704 Carex sp 0.446 0.089 0.286 0.89 Centaurea maculosa 0.022 0.029 0.857 1.112 Cirsium arvense 1.633 0.046 0.143 0.455 Cornus stolonifera 1.385 0.044 0.5 0.826 Daucus carota 2.089 0.078 0.286 0.818 Dianthus armeria 0.068 0.029 0.786 1.036 Echium vulgare 0.029 0.049 0.857 1.243 Equisetum arvense 0.586 0.116 0.643 1.45 Erigeron annuus 0.496 0.116 0.429 1.22 Euphorbia cyparissias 0.061 0.04 0.857 1.184 Fragaria virginiana 1.622 0.074 0.357 0.868 Hieracium vulgatum 0.457 0.068 0.571 1.059 Hypericum perforatum 0.255 0.045 0.429 0.756 Inula helenium 1.029 0.153 0.786 1.846 Leucanthemum vulgare 0.04 0.053 0.643 1.039 Linaria vulgaris 0.762 0.056 0 0.366 Lonicera canadensis 0.219 0.053 0.857 1.269 Medicago lupulina 0.514 0.065 0.5 0.963 Melilotus albus 2.165 0.089 0.143 0.736 Monarda fistulosa 1.543 0.033 0.714 0.984 Phalaris arundinacea 4.729 0.078 0.714 1.278 Phleum pratense 1.482 0.042 0.429 0.736 Plantago major 0.04 0.057 0.857 1.295 Poa pratensis 16.281 0.053 0 0.346 Potentilla recta 0.101 0.093 0.571 1.222 Ranunculus acris 0.173 0.045 0.857 1.217 Rudbeckia hirta 0.162 0.04 0.786 1.108 Solidago sp 20.204 0.072 0.143 0.625 Sonchus arvensis 0.5 0.124 0.5 1.349 Symphyotrichum cordifolium 2.525 0.051 0.929 1.333 Symphyotrichum ericoides 0.899 0.049 0.071 0.397 Symphyotrichum lanceolatum 1.27 0.053 0.5 0.885 Symphyotrichum novae-angliae 0.633 0.077 0.286 0.811 Trifolium hybridum 0.068 0.074 0.786 1.33 Trifolium pratense 0.888 0.066 0.857 1.354 Valeriana officinalis 0.424 0.063 0.643 1.104 Veronica officinalis 0.263 0.051 0.643 1.025 Vicia cracca 5.2 0.074 0.143 0.638 Vincetoxicum rossicum 33.482 0.042 0 0.275 Vitis riparia 0.899 0.033 0.429 0.677 200

Table 8-11: Mean z-score (±SE) for the effect of plant functional uniqueness (categorical) on ecosystem function. Functional uniqueness category calculation detailed in section 4.2.3

Functional Uniqueness Category Ecosystem function mean z score S.E. lower 95% C.I. Upper 95% C.I. Pollinator species richness 0.004 0.036 -0.066 0.075 Pollinator abundance 0.022 0.014 -0.004 0.049 Flower cover 0.003 0.051 -0.096 0.103 Biomass(log) 0.031 0.046 -0.058 0.121 High Litter(log) 0.016 0.074 -0.128 0.16 Soil total C -0.047 0.039 -0.123 0.029 k constant (decomposition) 0.02 0.033 -0.045 0.085 Inverse inorganic N -0.031 0.016 -0.062 0.0004 Inverse total soil N -0.013 0.051 -0.112 0.086 Pollinator species richness -0.062 0.066 -0.19 0.067 Pollinator abundance -0.099 0.094 -0.284 0.085 Flower cover -0.085 0.089 -0.26 0.09 Biomass(log) -0.183 0.227 -0.629 0.262 Medium Litter(log) 0.076 0.077 -0.076 0.227 Soil total C -0.34 0.383 -1.09 0.41 k constant (decomposition) 0.004 0.056 -0.106 0.113 Inverse inorganic N 0.306 0.237 -0.157 0.77 Inverse total soil N -0.143 0.125 -0.387 0.101 Pollinator species richness 0.15 0.142 -0.129 0.43 Pollinator abundance 0.143 0.143 -0.137 0.423 Flower cover -0.045 0.04 -0.123 0.032 Biomass(log) -0.088 0.086 -0.257 0.08 Low Litter(log) -0.087 0.064 -0.212 0.039 Soil total C -0.013 0.067 -0.144 0.119 k constant (decomposition) 0.011 0.042 -0.07 0.093 Inverse inorganic N -0.015 0.154 -0.317 0.287 Inverse total soil N -0.169 0.194 -0.548 0.211

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Table 8-12: mean z score (±SE) for the effect of plant relative abundance (categorical) on ecosystem function (mean z scores that differ significantly from zero are bolded)

Relative Abundance Category Ecosystem function mean z score S.E. Lower 95% C.I. Upper 95% C.I. Pollinator species richness -0.016 0.001 -0.017 -0.014 Pollinator abundance 0.001 0.001 -0.001 0.003 Flower cover 0.004 0.003 -0.002 0.009 Biomass(log) 0.004 0.001 0.002 0.006 High Litter(log) 0.008 0.001 0.006 0.009 Soil total C -0.001 0.001 -0.003 0.001 k constant (decomposition) 0.001 0.002 -0.004 0.005 Inverse inorganic N 0.005 0.002 0.0001 0.009 Inverse total soil N -0.004 0.003 -0.009 0.001 Pollinator species richness -0.004 0.012 -0.028 0.02 Pollinator abundance 0.013 0.015 -0.017 0.043 Flower cover -0.001 0.009 -0.019 0.018 Biomass(log) 0.005 0.012 -0.02 0.029 Medium Litter(log) -0.001 0.017 -0.034 0.031 Soil total C -0.002 0.006 -0.014 0.01 k constant (decomposition) 0.008 0.007 -0.007 0.023 Inverse inorganic N -0.007 0.006 -0.019 0.005 Inverse total soil N -0.009 0.007 -0.023 0.005 Pollinator species richness 0.068 0.119 -0.165 0.301 Pollinator abundance 0.024 0.13 -0.231 0.279 Flower cover -0.099 0.084 -0.263 0.065 Biomass(log) -0.195 0.195 -0.576 0.187 Low Litter(log) 0.011 0.094 -0.174 0.195 Soil total C -0.316 0.312 -0.928 0.297 k constant (decomposition) 0.017 0.058 -0.097 0.131 Inverse inorganic N 0.222 0.223 -0.215 0.658 Inverse total soil N -0.235 0.171 -0.57 0.099

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Figure 8-2: Relationship between increasing V. rossicum abundance and plant community biodiversity metrics at the regional scale

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Figure 8-3: preliminary analysis showing the relationship between ecosystem functions and increasing V. rossicum abundance. Points represent site means, bars represent standard errors. n=25 for flower cover, pollinator abundance and pollinator richness, n=5 for biomass and litter, n=4 for soil N and C and decomposition rate. Solid lines indicate statistically significant relationship. Dashed line represents a near-statistically significant relationship. See Table 8-5 below for model output.

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Figure 8-4: Bray-Curtis dissimilarity values between Rouge NUP study sites a) calculated by species presence/absence, b) calculated by species relative abundance

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Figure 8-13: canopy photos from experimental Hypena opulenta release site in Kirkfield, Ontario.

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Figure 8-14: Initial plot conditions for simulated herbivory study. Error bars denote standard error of the mean, n values for each treatment are shown in figure legend.

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Figure 8-15: Photos from control and experimental plots of shade and sun treatments approx. three weeks following the application of H. opulenta. a) shade plots, b) sun plots.

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