Does environmental selection for plant functional traits influence species' population trajectories? A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF Master of Science in Ecology AT THE UNIVERSITY OF CANTERBURY SCHOOL OF BIOLOGICAL SCIENCES BY Zane W. Lazare UNIVERSITY OF CANTERBURY 2017 This thesis is dedicated to my loved ones. I could not have accomplished this without you. i Acknowledgements I would like to offer thanks to the innumerable amount of people who have offered and pro- vided support to me throughout my thesis journey. I am so grateful to have had the support of my friends and family throughout this time. Mum, Nan, and my closest friends, Lydia, 'Javo' and Hench, I could not have achieved this without your unconditional support. I do not believe I would still be here today without you. This also applies to my supervisor Jason. Your complete faith in my abilities has carried me through this process. The knowledge you have imparted to me will have a life long effect. I would also like to acknowledge and thank Hannah Buckley for providing the data this thesis was based upon. I am incredibly lucky to have had access to such a magnificent dataset. To the Stouffer-Tylianakis lab, thank you for being available to bounce ideas off and generally rant to. Finally, partial funding for this project was covered by a Rutherford Discovery Fellowship to Jason Tylianakis, administered by the Royal Society of New Zealand. I will be forever grateful for this support. ii Abstract Humans appropriate ecosystem goods and services to maintain their quality of life. Conse- quently, anthropogenic activities have modified 40% of terrestrial ecosystems in a relatively short time. A major component of these modified systems are invasive species. Invasive species can drive a decline in native species richness and alter community structure, influence resource use and availability in their community, and in turn alter ecosystem functioning. Approaches analysing the similarity of traits between invasive and native species have be- come an increasingly common means to infer the processes underlying successful invasions and to predict future invaders. These approaches comprise part of a larger research con- text of understanding how species' traits link their response to environment, influence on ecosystem functioning, and interactions with other species. In this thesis I used two overarching research questions to investigate whether environ- mental selection for plant functional traits influences species' population trajectories, which bears relevance for both native species decline and spread of invasives. These two questions were: a) How do environmental drivers of change influence species and trait composition (and can these influences be detected over the influence of spatial processes)? and b) how does the trade-off between competitive release (associated with novel traits) and environmental filters on community traits determine the success of species? I addressed these questions by testing a number of hypotheses pertaining to each. To do this, I used data on plant communities over 30 years from grasslands across the South Island of NZ and sourced trait data to describe these communities from online databases. I used iii a multivariate analysis framework to test for trait|environment relationships and describe how these relationships varied across multiple spatial scales. Additionally, I used measures of trait novelty and fit to environment, within a mixed modelling framework, to test how differences in these measures related to change in each species' abundance over time. In doing so, I did not find support for my hypothesis that it is advantageous for species to have a novel (unique or original) set of traits, and that this advantage would be reflected in novel species increasing in abundance over time. Instead, I found that the environment shaped species traits, and that species with traits that deviated from those determined by environmental filters decreased in abundance. My findings suggest that searching for traits of 'invasiveness', or even comparing invader traits to those of the community, may have limited value in predicting the success of invaders unless these are considered within the context of environmental trait selection. iv Contents Acknowledgements ii Abstract iii Table of contentsv List of figures viii List of tables ix 1 Introduction1 1.1 Context . .2 1.1.1 Global drivers of change . .2 1.1.2 Functional traits . .3 1.1.3 Functional traits and species invasions . .5 1.2 Grasslands as a model system . .8 1.2.1 Case study: New Zealand's high country grasslands in the South Is- land, and their change from pre-human to modern state . .9 1.2.1.1 Fire: The first wave of disturbance . 10 1.2.1.2 Pastoralism: The second wave of disturbance . 11 1.2.1.3 Current states of New Zealand's high country . 12 1.2.1.4 Conservation of high-country dry-grassland ecosystems . 13 v 1.3 Summary, aims and hypotheses . 14 2 Study Data 16 2.1 Data . 17 2.1.1 Species Data . 17 2.1.2 Trait Data . 18 2.1.3 Study Site Data . 19 3 Methods: How do environmental drivers of change influence community species and trait composition? 22 3.0.1 Summary . 23 3.1 Methods to detect spatial patterns in species and trait composition . 24 3.1.1 Moran's Eigenvector Maps (MEMs) and spatial model selection . 24 3.1.1.1 Computation of Spatial Weighting Matrix . 24 3.1.2 Visualisation of spatial patterns in species and trait composition . 25 3.1.3 Local and regional structures: Eigenvector decomposition . 26 3.1.3.1 Variation partitioning . 26 3.1.3.2 Spatial sub-model selection . 28 3.2 Environmental selection of traits . 29 3.2.1 Multivariate species-environment-trait relationships: RLQ method . 29 3.2.2 Bi-variate trait-environment relationships: Fourth-corner method . 30 3.2.3 The combined fourth-corner procedure . 31 3.2.4 Spatial scaling in environmental selection for traits: MEMs and the combined fourth-corner . 32 4 Results: How do environmental drivers of change influence community species and trait composition? 34 4.0.1 Summary . 35 4.1 Environmental selection of species and traits . 35 vi 4.1.1 Global assessment of environment, traits and species covariance: RLQ analysis . 35 4.1.2 Univariate environment trait associations: Fourth Corner Analysis . 37 4.1.2.1 Vegetative traits and environmental associations . 37 4.1.2.2 Reproductive traits and environmental associations . 39 4.2 Spatial and environmental structuring of species and traits . 41 4.2.1 Classification of global spatial models: MEM model selection . 41 4.2.2 Environmental and spatial structuring in species and trait diversity . 44 4.2.2.1 Species composition . 44 4.2.2.2 Vegetative-trait diversity . 50 4.2.2.3 Reproductive-trait diversity . 53 4.2.3 Landscape and regional patterns: MEM sub-model analysis . 56 4.3 Spatial scales in trait-environment relationships . 59 4.3.1 Multi-scale assessment of environment, traits and species covariance: Spatial RLQ analysis . 59 4.3.2 Multiscale environment-trait associations: Fourth Corner Analysis . 61 4.3.2.1 Vegetative trait{environment associations . 61 4.3.2.2 Reproductive trait{environment associations . 64 5 Methods: Can novel or environmentally-selected traits predict the success of species? 65 5.0.1 Summary . 66 5.1 Functional diversity metrics . 67 5.1.1 Trait Originality and Uniqueness . 67 5.1.2 Distance from trait optimum . 68 5.2 Testing changes of abundance in response to functional metrics: Mixed-effects regression . 69 vii 6 Results: Does change in abundance of species depend on competition or the environment? 71 6.1 The effect of trait uniqueness on change in abundance . 71 6.2 Effect of trait originality on change in species abundance . 73 6.3 Effect of distance from community-trait optimum on change in species abun- dance . 74 6.4 Effect of uniqueness and originality, combined with distance from community- trait optimum, on change in species abundance . 75 7 Discussion 77 7.1 Summary . 78 7.2 How do environmental drivers influence community trait composition? . 79 7.2.1 Environmental drivers of vegetative trait composition . 80 7.2.2 Environmental drivers of reproductive trait composition . 83 7.3 Does past community trait composition influence future trait composition? . 85 7.4 Study limitations . 89 7.5 Conclusion . 89 References 91 Appendices 124 A Appendix A 125 B Appendix B 130 C Appendix C 136 D Appendix D 140 E Appendix E 148 viii List of Figures 2.1 Map of study sites . 18 4.1 Vegetative trait combined RLQ and Fourth Corner analyses factorial maps . 39 4.2 Reproductive trait combined RLQ and Fourth Corner analyses factorial maps 41 4.3 Site connection networks used for calculating Moran's Eigenvector Maps . 43 4.4 Maps of the spatially-explicit analysis of species composition . 47 4.5 Environmental correlations with the first and second axes of the redundancy analysis carried out on species and trait community composition. 48 4.6 Species and trait composition variation partitioning in response to environ- ment and spatial variables . 49 4.7 Maps of the spatially-explicit analysis of vegetative-trait composition . 52 4.8 Maps of the spatially-explicit analysis of reproductive trait composition . 55 4.9 Variograms of Moran's Eigenvector Maps (MEMs) for vegetative and repro- ductive traits . 58 4.10 Trait composition variation partitioning in response to environment variables and spatial sub-models . 59 7.1 Conceptual summary of results by section . 79 ix List of Tables 2.1 Environmental explainatory variable descriptions . 21 4.1 Vegetative trait-environment relationships . 38 4.2 Reproductive trait-environment relationships . 40 4.3 Environmental variables included in spatial modelling following forward selection 46 4.4 Regional-scale vegetative trait-environment relationships .