Arts, Faculty of
Geography, Department of
DSpace
UBCV
Loesberg, Jenna
Doctor of Philosophy - PhD
University of British Columbia
The widespread loss of biodiversity expected under climate change is ultimately driven by how individual populations respond to climate. Predicting whether populations will persist under climate change first requires understanding how climate directly and indirectly influences the demographic rates (survival, growth, and reproduction) underlying population growth. In my dissertation, I paired a rainfall manipulation experiment in a natural plant community with mesocosm experiments and population modeling (integral projection models, count-based population viability analysis, and competition models) to understand the climatic and demographic drivers of population change under climate change in Garry oak savannas. Each chapter addresses a potential indirect effect of climate variation on population dynamics: in my first two data chapters, I examined how seasonal climate and reproductive trade-offs jointly influence population growth in a long-lived perennial, Primula hendersonii. Specifically, Chapter 2 examines how costs of sexual reproduction vary across climate and changes in flowering effort. Chapter 3 incorporates trade-offs between clonal and sexual reproduction with interactions with conspecific neighbors. In Chapter 4, I examine direct climate impacts and indirect effects of competitive interactions on demographic rates and population growth for two native annual forbs, Plectritis congesta and Claytonia perfoliata, and an annual invasive grass, Bromus sterilis. While I found demographic rate-specific responses to seasonal climate, rainfall manipulation, and reproductive trade-offs, I primarily found negative effects of increased spring temperature for two of the native species, positive effects of increased summer temperature for the perennial species, and positive effects of increased winter temperature on an increasingly common invasive grass. Overall, only density-dependence had a large indirect effect, where predictions of population growth rates and effects of climate were dependent on the local density of conspecifics. I did not see evidence for costs of reproduction influencing variation in population growth rate across time, despite observing such costs across multiple experiments. Interspecific competition was not influenced by rainfall manipulation or seasonal climate variation over time, and no focal species were competitively dominant. Thus, direct climate effects paired with density-dependent responses may be most important for understanding population persistence under climate change.
Effects of reproductive trade-offs, conspecific density, and invasive species on oak savanna wildflower populations under climate change by Jenna Loesberg B.A., Pitzer College, 2018 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Geography) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) October 2025 © Jenna Loesberg, 2025 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Effects of reproductive trade-offs, conspecific density, and invasive species on oak savanna wildflower populations under climate change submitted by Jenna Loesberg in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Geography Examining Committee: Jennifer Williams, Associate Professor, Geography, UBC Supervisor Amy Angert, Professor, Botany, UBC Supervisory Committee Member Tara Martin, Professor, Forest and Conservation Sciences, UBC University Examiner Michelle Tseng, Associate Professor, Botany and Zoology, UBC University Examiner Additional Supervisory Committee Members: Rachel Germain, Assistant Professor, Zoology, UBC Supervisory Committee Member Jeannette Whitton, Associate Professor, Botany, UBC Supervisory Committee Member iii Abstract The widespread loss of biodiversity expected under climate change is ultimately driven by how individual populations respond to climate. Predicting whether populations will persist under climate change first requires understanding how climate directly and indirectly influences the demographic rates (survival, growth, and reproduction) underlying population growth. In my dissertation, I paired a rainfall manipulation experiment in a natural plant community with mesocosm experiments and population modeling (integral projection models, count-based population viability analysis, and competition models) to understand the climatic and demographic drivers of population change under climate change in Garry oak savannas. Each chapter addresses a potential indirect effect of climate variation on population dynamics: in my first two data chapters, I examined how seasonal climate and reproductive trade-offs jointly influence population growth in a long-lived perennial, Primula hendersonii. Specifically, Chapter 2 examines how costs of sexual reproduction vary across climate and changes in flowering effort. Chapter 3 incorporates trade-offs between clonal and sexual reproduction with interactions with conspecific neighbors. In Chapter 4, I examine direct climate impacts and indirect effects of competitive interactions on demographic rates and population growth for two native annual forbs, Plectritis congesta and Claytonia perfoliata, and an annual invasive grass, Bromus sterilis. While I found demographic rate-specific responses to seasonal climate, rainfall manipulation, and reproductive trade-offs, I primarily found negative effects of increased spring temperature for two of the native species, positive effects of increased summer temperature for the perennial species, and positive effects of increased winter temperature on an increasingly iv common invasive grass. Overall, only density-dependence had a large indirect effect, where predictions of population growth rates and effects of climate were dependent on the local density of conspecifics. I did not see evidence for costs of reproduction influencing variation in population growth rate across time, despite observing such costs across multiple experiments. Interspecific competition was not influenced by rainfall manipulation or seasonal climate variation over time, and no focal species were competitively dominant. Thus, direct climate effects paired with density-dependent responses may be most important for understanding population persistence under climate change. v Lay Summary Climate (temperature and precipitation) can impact the birth and death rates of individuals within populations of plants and animals. Changes in these birth and death rates ultimately determine whether populations will persist or become extinct. Identifying the effects of climate on these rates is key for informing ecosystem conservation and management under climate change. I studied how climate affected populations of native and invasive plant species in the Garry oak savanna ecosystem, one of the most threatened ecosystems in Canada. I found that individual plants with many neighbors responded differently to climate compared to plants with few neighbors. My research suggests that predictions that ignore population size are likely to overestimate persistence for uncrowded populations, and underestimate predictions in crowded populations. Ultimately, to determine how native plants will be affected by climate change and to manage ecosystems to ensure their persistence requires incorporating population size into future predictions. vi Preface This dissertation is composed of three original research chapters: A version of Chapter 2 has been accepted for publication in Ecology with co-author J. Williams. J. Williams and I conceived the idea, and I led data collection, data analysis, and manuscript writing in collaboration with J. Williams. A version of Chapter 3 is under review for publication with co-author J. Williams. I conceived the idea in consultation with J. Williams. I led the experiment design, data collection, data analysis, and wrote the manuscript with critical contributions from J. Williams. Chapter 4 is in preparation for submission with co-author J. Williams. I conceived the idea in consultation with J. Williams. I collected data, led data analysis, wrote the manuscript, and led the editing process with critical contributions from J. Williams. Statement on AI: Generative AI was not used in preparation of this dissertation. vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ........................................................................................................................ vii List of Tables ................................................................................................................................ xi List of Figures .............................................................................................................................. xii List of Symbols ........................................................................................................................... xiv List of Abbreviations ...................................................................................................................xv Acknowledgements .................................................................................................................... xvi Chapter 1: Introduction ................................................................................................................1 1.1 Climate Change and Demography .................................................................................. 1 1.2 Reproductive Trade-offs under Climate Variation ......................................................... 2 1.3 Interactions with Neighbors of the Same and Different Species .................................... 4 1.4 Experimental and Observational data ............................................................................. 7 1.5 Study System and Experimental Design ......................................................................... 7 1.6 Thesis Overview ............................................................................................................. 9 Chapter 2: Seasonal climate drives population growth but not costs of reproduction of a perennial wildflower ....................................................................................................................10 2.1 Introduction ................................................................................................................... 10 2.2 Methods......................................................................................................................... 14 2.2.1 Study System and Species .................................................................................... 14 viii 2.2.2 Rainfall Manipulation Experiment ....................................................................... 16 2.2.3 Clipping Experiment ............................................................................................. 17 2.2.4 Climate Variables.................................................................................................. 19 2.2.5 Climate-Dependent Demographic Rates............................................................... 19 2.2.6 Integral Projection Model ..................................................................................... 20 2.3 Results ........................................................................................................................... 21 2.3.1 Clipping Experiment ............................................................................................. 21 2.3.2 Long-Term Demographic Rates ........................................................................... 23 2.3.3 Population Growth ................................................................................................ 26 2.4 Discussion ..................................................................................................................... 28 Chapter 3: Conspecific density and reproductive trade-offs govern population response to climate in a clonal wildflower .....................................................................................................33 3.1 Introduction ................................................................................................................... 33 3.2 Methods......................................................................................................................... 36 3.2.1 Study System and Species .................................................................................... 36 3.2.2 Rainfall Experiment .............................................................................................. 38 3.2.3 Manipulated Density Experiment ......................................................................... 40 3.2.4 Demographic Rate Analysis in Rainfall and Density Manipulation Experiments 41 3.2.5 Integral Projection Model ..................................................................................... 44 3.3 Results ........................................................................................................................... 45 3.3.1 Demographic Rates in the Rainfall and Density Manipulation Experiment ......... 45 3.3.2 Population Growth Rates in the Rainfall Manipulation Experiment .................... 52 3.4 Discussion ..................................................................................................................... 54 ix 3.4.1 Clonal and Sexual Reproduction .......................................................................... 55 3.4.2 Influence of Density on Demographic Rates and Population Growth .................. 56 3.4.3 Conclusions ........................................................................................................... 59 Chapter 4: Direct temperature effects are more impactful than competition for population growth of native annual forbs and an invasive grass in Pacific Northwest savannas ...........60 4.1 Introduction ................................................................................................................... 60 4.2 Methods......................................................................................................................... 65 4.2.1 Study System ........................................................................................................ 65 4.2.2 Study Species ........................................................................................................ 66 4.2.3 Annual Census and Estimated Seed Production ................................................... 67 4.2.4 Time Series Analysis and Count-based PVA ....................................................... 69 4.2.5 Competition Models.............................................................................................. 70 4.2.6 Analysis................................................................................................................. 72 4.3 Results ........................................................................................................................... 74 4.3.1 Climate impacts density-dependent population growth ........................................ 74 4.3.2 Climate-specific impacts on seed production and competition metrics................ 79 4.4 Discussion ..................................................................................................................... 85 Chapter 5: Conclusion .................................................................................................................92 5.1 Summary ....................................................................................................................... 92 5.2 Implications for Management ....................................................................................... 96 5.3 Future Research ............................................................................................................ 97 Works Cited ..................................................................................................................................98 Appendices ..................................................................................................................................128 x Appendix A Appendix for Chapter 2 ...................................................................................... 128 A.1 Climate Variable Selection ..................................................................................... 128 A.2 Demographic rate Model Selection ........................................................................ 133 A.3 Integral Projection Model ....................................................................................... 136 Appendix B Appendix for Chapter 3 ...................................................................................... 140 B.1 Seed addition experiment and per capita seed production ...................................... 140 B.2 Integral Projection Model ....................................................................................... 141 B.3 Field demographic rates .......................................................................................... 144 B.4 Effect of past summer temperature on population growth rate (λ(N)) ................... 151 Appendix C Appendix for Chapter 4 ...................................................................................... 152 C.1 Plectritis congesta seed production ........................................................................ 152 C.2 Climate variables .................................................................................................... 154 C.3 Predicted seed production from competition models.............................................. 155 xi List of Tables Table 2.1 Results from likelihood ratio tests for variable selection in the flower clipping experiment..................................................................................................................................... 22 Table 2.2 Best fit models from model selection for climate- and size-dependent demographic rates ............................................................................................................................................... 24 Table 3.1 Best supported models from model selection for climate, size, and density-dependent demographic rates in the long-term demographic field data ........................................................ 46 Table 3.2 Results from stepwise selection for the density manipulation experiment ................... 51 Table 4.1. Model selection for Rt for Plectritis congesta, Bromus sterilis, and Claytonia perfoliata using AIC ..................................................................................................................... 74 Table 4.2. Model selection for nonlinear competition models for each species and rainfall manipulation treatment ................................................................................................................. 80 Table A.1 Loadings for each seasonal variable for PC axes 1 and 2 .......................................... 129 Table A.2 AIC table for demographic rate models selection showing the candidate models with the lowest 10 ΔAIC ..................................................................................................................... 133 Table B.1 AIC table for demographic rate models selection showing the candidate models with the lowest 10 ΔAICs ................................................................................................................... 144 Table C.1 Model selection for Plectritis congesta seed production based on proxies for plant fitness .......................................................................................................................................... 152 xii List of Figures Figure 2.1 Seed production reduces plant growth in the flower clipping experiment from 2021 to 2022............................................................................................................................................... 23 Figure 2.2 Size-dependent demographic rate models ................................................................... 25 Figure 2.3 Population growth rate (λ) in rainfall treatments across varying spring, summer, and winter temperature ........................................................................................................................ 27 Figure 3.1 Size-dependent demographic rate models for the field demographic data.................. 48 Figure 3.2 Size-dependent demographic rate models for the density manipulation (pot) experiment..................................................................................................................................... 52 Figure 3.3 Population growth rate (λ(N)) across neighbor density, past reproduction, spring temperature, and rainfall treatment ............................................................................................... 54 Figure 4.1. Population density per 1 m2 and Rt, or log(Nt + 1/Nt), per 1 m2 .................................. 76 Figure 4.2 Density-dependent Rt, or log(Nt + 1/Nt), and the probability of quasi-extinction ........ 77 Figure 4.3. Parameter estimates for Plectritis congesta and Bromus sterilis ............................... 82 Figure 4.4 Competition coefficients and low conspecific density seed production for the native forb, Claytonia perfoliata ............................................................................................................. 84 Figure A.1. Scree plot showing proportion of explained variance for each PCA ...................... 128 Figure A.2. Correlation matrix of climate variables used in the PCA ........................................ 130 Figure A.3. Histograms of climate variables (2013 – 2022) that were selected from the PCA to include in model selection .......................................................................................................... 131 Figure A.4. Scatterplot matrix of climate variables included in the PCA .................................. 132 Figure A.5. Effect of varying values of transition probabilities in IPM ..................................... 138 xiii Figure A.6. Elasticity analysis of discrete parameters and demographic rate model coefficients used in the IPM ........................................................................................................................... 139 Figure B.1 Effect of varying survival and transition probabilities for the one-leaf discrete stage on population growth rate for IPMs. ........................................................................................... 143 Figure B.2 Demographic rates in the field experiment for spring temperature dependent survival, size-dependent flower production, and bulblet production ......................................................... 150 Figure B.3 Population growth rate (λ(N)) across neighbor density, past reproduction, past summer temperature, and rainfall treatment in the field ............................................................. 151 Figure C.1 Seed production of P. congesta plants in rainfall manipulation plots ...................... 153 Figure C.2 Climate histograms displaying the range of climate (2016-2024) included in the Rt (log population growth rate) model selection ............................................................................. 154 Figure C.3 Predicted seed production from competition models for Plectritis congesta, Bromus sterilis, and Claytonia perfoliata ................................................................................................ 155 xiv List of Symbols λ Per capita population growth rate (in chapter 2) λ(N) Density-dependent population growth rate, where N is population density (in chapter 3) λi Per capita seed production without competition (in chapter 4) Rt Population growth rate, or ln(Nt + 1/Nt), where N is population density K Competitive ability αii Competition coefficient that describes the per capita effect a conspecific (species i) on predicted fecundity of species i αij Competition coefficient that describes the per capita effect a heterospecific individual (species j) on predicted fecundity of species i xv List of Abbreviations AIC - Akaike Information Criterion GLM - Generalized linear model GLMM - Generalized linear mixed effect model IPM - Integral Projection Model PVA - Population Viability Analysis RCP - Representative Concentration Pathway xvi Acknowledgements Garry oak savannas are ecosystems that have been cultivated for millennia by First Nation stewardship through frequent burning and crop cultivation. My research took place in what is now known as the Cowichan Valley of Vancouver Island, which is the ancestral and unceded territory of Cowichan Tribes and the Hul'qumi'num people who have stewarded the land and plant communities since time immemorial. I acknowledge the tremendous privilege I have to be able to access this land and to do research in one of the few remaining remnant oak savannas on Vancouver Island. I want to thank my supervisor Dr. Jennifer Williams for supporting me through the long process of completing a PhD. In particular, thank you for always making time and for your uncanny ability to solve problems. I appreciate your dedication and willingness to advocate for your students, and it’s been a honor to do research with you for the past few years. I thank my committee members, Drs. Amy Angert, Rachel Germain, and Jeannette Whitton, for their astute and helpful feedback on these projects and on the resulting manuscripts. I am grateful to all of the past undergraduate and graduate students who contributed to the demographic data collection at the Cowichan Garry Oak Preserve before I joined the lab: Hailey Bloom, Sophie Duncan, Jens Johnson, Deirdre Loughnan, and Charlotte Trowbridge. This dissertation would not have been possible without the hard work of many people who helped with my data collection: Piper Battersby, Erin McHugh, Scott Nordstrom, Ria Raut, Lauren Smith, and Lindsay Worden. I am eternally grateful to Dr. Jennifer Taylor for sharing her beautiful home and for the opportunity to call Shawnigan Lake home for a couple of months each year during the field season. xvii The Williams Lab has been a wonderful place to do science. In particular, thank you to Carla Urquhart for always providing the best writing feedback that I could have ever hoped for. Finally, thank you to my family for their unwavering support, and Evan Muise for general graduate school support, proofreading my manuscripts, and sharing his mastery of ggplot and patchwork.1 Chapter 1: Introduction 1.1 Climate Change and Demography How climatic variation affects population and community dynamics is a fundamental question in ecology (Andrewartha and Birch, 1954; Nicholson, 1954). Under climate change, this question is increasingly relevant to understand how current populations are influenced by climate, and to predict future population persistence under novel climates. Identifying the effects of climate on the demography of populations is a key step in predicting effects on species, communities, and biodiversity under climate change. Changes in population persistence, or population growth rates, are ultimately driven by changes in the rates of births and deaths of individuals within the population. These can be modeled as the demographic rates (i.e., survival, growth, and reproduction) that underlie population growth rates, which in turn scale to species persistence and community composition. Statistical models of demographic rates can incorporate climate variables, such as precipitation and temperature, to examine explicit climate drivers of population growth rates using population models (Iler et al., 2019; Williams et al., 2015), or climate can be incorporated in time series data of population sizes (Roland and Matter, 2016, 2013). However, understanding both the direct and indirect pathways of climate impacts on demography is key for accurately modeling population dynamics. Climate can have direct physiological effects that influence demographic rates, or it can indirectly alter demographic rates via its influence on competitive interactions or resource allocation. These pathways are often complex. Identifying the extent to which these pathways influence current population dynamics, and when they should be incorporated into models, is key for accurately predicting population growth or decline under future conditions. In 2 this dissertation, I examine the role of life-history trade-offs, conspecific density dependence, and interspecific competition in influencing population response to climate. 1.2 Reproductive Trade-offs under Climate Variation Life history theory predicts that organisms must partition available resources across the demographic rates underlying fitness, which result in trade-offs when resources are limited (Reznick, 1985; Stearns, 1989; Williams, 1966). If reproduction carries a cost, allocating more resources to reproduction will have a trade-off with reduced current or future demographic rates (Obeso, 2002; Stearns, 1989; Williams, 1966). As reproductive effort increases, demographic costs of reproduction will be expressed as correlated reductions in other demographic rates. For life histories that allocate resources to form reproductive structures in the year prior to the current growing season, for example overwintering iteroparous perennials (Schnablová et al., 2021), the effects of reproduction in one year would mostly likely be realized as changes in demographic rates in the following year. Costs of reproduction may be influenced by climate change through increased stress that makes reproduction relatively more costly, or through changes in resource availability that alters the proportion of resources that are allocated to flowering (Lambrecht et al., 2007; van Noordwijk and de Jong, 1986). For example, climate change can shift reproductive effort across an individual’s lifespan resulting in earlier or later flowering and shifting resource allocation to diverge from optimal reproductive strategies, thereby increasing the fitness cost of flowering (Hamann et al., 2021). Importantly, shifts in costs of reproduction may influence population growth rates and indirectly influence population responses to climate, in addition to direct effects of climate variation on demographic rates. Although climate has been shown to influence costs 3 of reproduction (Hamann et al., 2021; Sletvold and Ågren, 2015a), few studies have examined the extent to which these factors influence population responses to climate change (although see Williams et al., 2015). In addition to the predicted trade-off between reproduction and other demographic rates, life history theory also predicts a trade-off between sexual and asexual reproduction, or reproductive modes. Increased resource investment in clonal reproduction is expected to result in decreased sexual reproduction, and vice versa (Fischer and Van Kleunen, 2001; Loehle, 1987; Lovett Doust, 1989; Silvertown et al., 1993; Stearns, 1992). This trade-off influences whether recruitment comes from clones or seeds, which has consequences for population dynamics when clones and seeds have different contributions to population growth (Harper, 1977; Hesse et al., 2008; Lin et al., 2016; Sarukhan and Gadgil, 1974) and for local conspecific densities (Benot et al., 2013). Clones may have higher establishment and survival probabilities than seeds because of their larger starting size and accelerated development achieved by skipping seed stages, which can result in greater competitive ability and tolerance for harsh conditions than seeds and seedlings (Eriksson, 1989; Harper, 1977). Further, clonal reproduction may bypass the costs associated with sexual reproduction if reproductive effort is lower for producing clones than for reproductive structures like flowers, fruits, and seeds (Obeso, 2002; Wang et al., 2018). However, there can also be fitness costs associated with clonal spread. For example, in phalanx growth forms, ramets may compete against clonal offspring when independent clones establish close-by and local density increases, whereas seeds have the opportunity to disperse away from parents (Eriksson, 1993, 1989). While trade-offs between reproductive modes can be driven by resource availability and local conspecific density, there is no consensus on which conditions favor sexual reproduction or 4 clonal growth (Barkham, 1980; Fischer and Van Kleunen, 2001; Obeso, 2002). High resource availability can lead to increased sexual reproduction and decreased clonality (Yang and Kim, 2016). Conversely, some species show the opposite relationship, perhaps in response to increased intraspecific competition in patches that are already at high density (Barkham, 1980; Van Kleunen et al., 2001). Clonality may be more common than sexual reproduction in low density conditions (Diez et al., 2014), although the opposite occurs as well (Barkham, 1980), suggesting a complicated relationship between density, resource availability, and clonality. 1.3 Interactions with Neighbors of the Same and Different Species Populations are structured by both internal regulation through density dependence, and external factors like resource availability (Hairston et al., 1960). Intraspecific competition may reduce or increase the effects of changing climate on demographic rates and population dynamics (Ehrlén and Morris, 2015), but few studies incorporate intraspecific competition when examining population responses to changing climate (but see Dahlgren et al., 2016; Herzog et al., 2025). The effects of climate variation on demographic rates likely depend on current local population densities, where any potential benefits from increasingly mesic conditions, for example lower temperatures or increased rainfall, are negated by increased competition (Bertness and Callaway, 1994). Alternatively, microenvironment modification by neighbors is a key mechanism of species interactions (Bertness and Callaway, 1994; Ray et al., 2023) and positive effects of neighbors, or facilitation, may occur if neighbors ameliorate increased stress, for example, if shading by neighbors reduces the impact of warmer temperatures (Bertness and Hacker, 1994; Wright et al., 2015). An additional mechanism of increased performance at high conspecific 5 density, also known as an Allee effect, is meditation of pollen limitation, where high densities lead to increased pollination and thus decreased pollen limitation (Stephens et al., 1999). Population growth rates will likely respond to changes in climate in density-dependent ways. If decreased precipitation leads to smaller environmental carrying capacities, populations currently at high density will face stronger negative density dependence than populations at low density (Casper, 1996; Ehrlén and Morris, 2015). Alternatively, the benefits of increased precipitation may be intensified if densities differ from equilibrium population size, although competition under mesic conditions is expected to be greater than under adverse conditions (Brooker and Callaghan, 1998; Dahlgren et al., 2016, 2014; Maestre et al., 2009; Zhang and Tielbörger, 2020). If these factors influence population responses to changes in climate but are not incorporated into population models, models may fail to predict likelihoods of population persistence. Changes in climate that benefit or inhibit competitors may also indirectly impact how focal species respond to climate (Christiansen et al., 2024; White, 2013). Climate may influence competitive abilities and outcomes of competition or have indirect effects through increased or decreased density of neighboring species. Predictions of species coexistence depend on environmental contexts, including climate (Van Dyke et al., 2022; Wainwright et al., 2019), but coexistence theory is rarely applied to natural communities or across time. Competition between species may depend on how each species uses and consumes a shared resource, with the species that can survive at the lowest resource level outcompeting the other when resources are limited (resource-ratio theory or R* theory; Miller et al., 2005; Tilman, 1982). On the other hand, the strength of competition is predicted to increase as productivity increases, or under mesic conditions (Bertness and Callaway, 1994; Grime, 1977; Maestre et al., 2009). For example, 6 negative species interactions like competition have been shown to reduce population growth more in relatively mesic environments compared to arid environments (Brooker and Callaghan, 1998; Louthan et al., 2018). Thus, indirect effects of altered competitive interactions under changing abiotic contexts may be impactful for population response to climate. In addition to shifting intraspecific competition within species, changes in interspecific competition between co-occurring plant species may also influence how populations respond to climate (Adler et al., 2018, 2012). In the context of invasive species co-occurring with native species, climate change is predicted to benefit invasive plants over native plants (Dukes and Mooney, 1999; Liu et al., 2017). This hypothesis is in part driven by the broader environmental tolerance and higher phenotypic plasticity that invasive species can have compared to native species (Davidson et al., 2011), allowing a greater ability to survive in novel environmental conditions. This also implies that native species, which may be more closely adapted to their specific environment, would be more strongly driven by direct impacts of changes in climate. In support of this hypothesis, a meta-review saw evidence for greater performance of invasive species under conditions associated with climate change (elevated temperature, carbon dioxide enrichment) relative to native species (Liu et al., 2017). However, native species decline could also be driven by changes in competitive interactions, which are specific to environmental contexts (Catling et al., 2024; Cervantes-Loreto et al., 2023; Hallett et al., 2019; Lanuza et al., 2018). Thus, indirect changes in the relative strengths of intra- and interspecific competition under climate change may lead to competitive exclusion and population declines independent from direct climate impacts on species. 7 1.4 Experimental and Observational data Climate manipulation experiments are needed to understand the mechanistic and causal role of climate on demographic rates and population growth (Lucas et al., 2008). Many studies use observational time series or demographic datasets that link variation in plant demography to climate, but these relationships are correlational and may not be causal (Ehrlén et al., 2016). In addition, experiments that manipulate reproductive effort or density, or those that examine pairwise competition between species, are likely to be required to detect costs of reproduction, density-dependence, and competitive outcomes, respectively (Fowler et al., 2006; Hart et al., 2018; Obeso, 2002). However, employing resource manipulation experiments and accounting for climate variation across time may make it possible to detect these effects using observational demographic data under natural densities (Dahlgren et al., 2014). Notably, comparisons between results from manipulative experiments and results from observational data under variation in climate are uncommon. 1.5 Study System and Experimental Design Garry oak savannas are highly endangered ecosystems historically found across the Pacific coast of North America from central California to their northern extent in southwestern British Columbia. These regions have sub-Mediterranean climates with mild and wet winters, short spring growing seasons, and summer droughts. In Canada, Garry oak savannas are restricted to the drier rain shadow regions of the Olympic and Vancouver Island Insular Mountains in southwestern British Columbia (Meidinger and Pojar, 1991). Prior to European contact, First Nations, including the Coash Salish peoples in Canada, stewarded savannas for millennia for use as camas bulb (Camassia quamash) croplands with frequent, low intensity fires and bulb 8 cultivation (Bjorkman and Vellend, 2010; McCune et al., 2013). These ecosystems are now restricted to 5% of their historic range in Canada, in part due to the loss of indigenous stewardship including prescribed fires, habitat conversion, woody encroachment of native species, and exotic species (Fuchs, 2001; McCune et al., 2013). Savannas are characterized by sparse Garry oak (Quercus garyanna) overstories with a diverse forb and grass understory. Remnant savannas are commonly invaded by exotic perennial and annual grasses, forbs, and shrubs (MacDougall and Turkington, 2006). Climate projections for drier, hotter summers are common across the range of Garry oak savannas (Bachelet et al., 2011). This dissertation research was conducted in a single meadow at the Cowichan Garry Oak Preserve (Nature Conservancy of Canada) in Duncan, BC, Canada. Local climate projections for the Cowichan Valley estimate an 11% increase in annual total precipitation by 2080 (specifically, an increase in total precipitation for winter, spring, and fall, but a decrease in summer precipitation) and increased annual temperature by 3°C by 2050 and 4.5°C by 2080 under “business as usual” emission scenarios (Cowichan Valley Regional District, 2017). To examine understory plant community response to changes in total precipitation, a year-round rainfall manipulation experiment with control, drought, and irrigation treatments was established in 2015 to simulate changes in the amount of rainfall received per rain event (Knapp et al., 2017; Smith and Williams, 2023). Control plots consisted of 2 x 2 m plots with 50 cm buffers that received ambient rainfall. Wooden drought shelters with strips of corrugated clear plastic roofing covering 50% of a 3 x 3 m area reduced incoming rainfall by 50% in the focal 2 x 2 m drought plots. Water runoff from the shelters were diverted to passively irrigate 2 x 2 m irrigation plots. 9 1.6 Thesis Overview Under climate change, it is increasingly important to understand how populations respond to climate, and which demographic processes are important to incorporate in predictions. In this dissertation, I ask how reproductive trade-offs, conspecific density, and heterospecific density interact with climate to influence population growth rates. Specifically: • In Chapter Two, I examine the role of costs of reproduction in population-responses to climate. I paired the rainfall manipulation experiment described in the previous section with a one-year experiment that manipulated reproductive effort. • In Chapter Three, I examine how conspecific density influences demographic rates, trade-offs between clonal and sexual reproduction, and population growth. I paired the rainfall manipulation experiment with a manipulative density experiment. • In Chapter Four, I examine the direct impact of climate on population growth rates and the indirect impacts of climate on competition between two native and an invasive species. I paired long term observational subpopulation counts with competition models within the rainfall manipulation experiment. 10 Chapter 2: Seasonal climate drives population growth but not costs of reproduction of a perennial wildflower 2.1 Introduction Accurately predicting population growth under environmental change requires a clear understanding of the climatic drivers and demographic trade-offs influencing individual performance and population demographic rates (e.g., survival, growth, and reproduction that contribute to population growth rates) (Morris et al., 2020; Villellas et al., 2015). However, both the magnitude and direction of demographic trade-offs are expected to depend on environmental conditions (Reznick, 1985); for example, stronger trade-offs are predicted under decreased resource availability (van Noordwijk and de Jong, 1986). Thus, to make more accurate predictions of population responses to climate change, and here we focus on plants, requires understanding how the climate drivers that influence demographic rates and population growth (Ehrlén and Morris, 2015; Morris et al., 2020) interact with life-history trade-offs to jointly influence plant population response to environmental change (Compagnoni et al., 2016; Jongejans et al., 2010). Climate directly affects plant population dynamics through effects on underlying demographic rates (Andrzejak et al., 2025; Christiansen et al., 2023; Ehrlén et al., 2016; Lindell et al., 2022; van de Pol et al., 2016). Yet, making the explicit link between climate drivers and population growth rates is challenging. For example, the importance of dormant season and lagged climate is becoming increasingly clear (Anderson, 2023; Evers et al., 2023, 2021; Schnablová et al., 2021; Scott et al., 2022), but these factors are uncommonly examined in demographic studies. Second, different climate variables can influence different demographic 11 rates, even in opposing directions, leading to responses that are specific to each demographic rate (Boggs and Inouye, 2012; Dalgleish et al., 2011; Doak and Morris, 2010; Lindell et al., 2022; Louthan et al., 2021; Williams et al., 2015), and require population models to scale individual demographic rate responses to population growth rates (Ehrlén and Morris, 2015). Finally, much of what is known relies on correlative relationships between climate and demographic rates across time (Compagnoni et al., 2021), even as these correlative relationships can covary with other factors, making predictions under future climates difficult. Climate manipulation experiments can help identify and confirm the causal influence of climate on demographic rates and population growth. Despite the importance of identifying causal relationships, these climate manipulation experiments may have low biological realism (Carlyle et al., 2011; Korell et al., 2020) which highlights the need for pairing long-term observational demographic data with climate manipulations. In addition to direct effects of climate on demographic rates, climate may also influence demographic trade-offs between demographic rates, leading to indirect effects on population growth. The cost of reproduction hypothesis predicts that reproduction carries a fitness cost, such that allocating finite resources to current reproduction reduces resources available to allocate to future reproduction, growth, or survival (Reznick, 1985; Stearns, 1989; Williams, 1966). Fitness costs are thus predicted to be detected demographically through negative relationships between current reproduction and future demographic rates (Compagnoni et al., 2016; Ehrlén and Morris, 2015; Obeso, 2002). In iteroparous herbaceous plants, current reproduction has been shown to reduce subsequent reproduction (Hamann et al., 2021; Horvitz et al., 2010; Sherman et al., 2019; von Euler et al., 2012), survival (Aragón et al., 2009), growth (Jacquemyn et al., 2010; Miller et al., 2012; Williams et al., 2015), or a combination of these demographic rate responses (Kellett 12 and Shefferson, 2018; Sletvold and Ågren, 2015a, 2015b, 2011; Sun et al., 2022), but how changes in climate influence costs is less clear. Costs are in part determined by the total resources available and an individual’s reproductive effort, here defined as the proportion of the total resource budget that is allocated to reproductive processes (Obeso, 2002). If climate variation impacts resource availability and resource allocation to reproduction, then climate change may indirectly influence costs of reproduction in addition to directly influencing reproduction (Iler et al., 2019; Lambrecht et al., 2007; Sletvold and Ågren, 2015a; Williams et al., 2015). Importantly, because environmentally driven demographic trade-offs may scale to changes in population growth rates (Laughlin et al., 2020; Siefert and Laughlin, 2023), these indirect effects may buffer or exacerbate population responses to climate (Hamann et al., 2021; Jacquemyn et al., 2010). For example, if the lifetime fitness cost of reproducing increases under more stressful conditions, population decline could be exacerbated (Hamann et al., 2021; Jacquemyn et al., 2010). Quantifying the effect of climate on costs of reproduction requires quantifying costs, which is notoriously difficult (Obeso, 2002). Individual variation in resource acquisition can overshadow the detection of costs, for example if individuals have poor future performance independent of previous reproduction (van Noordwijk and de Jong, 1986; Villellas and García, 2018). Further, spurious correlations can occur if an environmental driver has a positive effect on growth but a negative effect on reproduction, but does not influence resource allocation to different demographic rates (Knops et al., 2007). Multiple methods have been used interchangeably to detect and measure costs of reproduction, but whether manipulative and correlational studies result in similar observations of costs remains unclear (Fox and Peter, 1991). Costs of reproduction can be measured experimentally by manipulating reproductive 13 effort, namely through flower clipping (lowering reproductive effort) or pollen supplementation (increasing reproductive effort). In observational demographic data, costs can be detected by comparing the relative effects of initiating reproduction on future demographic rates of non-reproductive plants (low reproductive effort) and reproductive plants (high reproductive effort) (Miller et al., 2012; Obeso, 2002). However, the expression of costs may differ if energy allocation differs between initiating reproduction and producing seeds, and reproductive effort is measured in different ways across study types. Experimental and observational studies also differ in duration, with manipulative experiments usually lasting only one to two years, and observational studies requiring longer term data (Miller et al., 2012; Obeso, 2002). Finally, costs may not be evident at low environmental stress if resources are abundant, leading to small or undetectable trade-offs. Thus, manipulating environmental conditions can aid in cost detection, particularly through climate manipulations (Hamann et al., 2021), comparing populations with different habitat types or stress (Sletvold and Ågren, 2015b, 2015a), or examining the effects of reproduction across long-term natural variation in climate (Miller et al., 2012; Williams et al., 2015). Here, we paired experiments with observational demographic data to examine the influence of seasonal climate on costs of reproduction, vital rates, and population growth in an herbaceous perennial plant, Primula hendersonii, in Garry oak savanna in the Pacific Northwest of North America. In this system, water limits ecosystem productivity (Smith and Williams 2023) and future climate projections predict changes in seasonal precipitation intensity. To disentangle direct effects of climate variation on vital rates from indirect effects through costs of reproduction, we paired a one-year flower clipping experiment that manipulated reproductive effort with observational demographic and climate data across seven years (2017-2023), all 14 collected within a rainfall manipulation experiment. Using this combined approach, we evaluated (1) how individuals express demographic costs of reproduction across two experiment types; (2) how interannual variation in seasonal climate variables and simulated rainfall change (i.e., manipulated water availability) influence costs of reproduction; (3) which seasonal climate variables influence vital rates and how those then translate to effects on population growth rate; and finally (4) the extent to which costs of reproduction influence population growth rate across variation in climate. We compared results from the short-term manipulative experiment of costs of reproduction with those from observed longer-term natural variation in climate and reproduction. We hypothesized that: increased reproductive effort would decrease future reproduction and growth; reproductive costs would be higher under drier and hotter conditions; and simulated drought and hotter temperatures would contribute to lower population growth rates. 2.2 Methods 2.2.1 Study System and Species This study took place at the Cowichan Garry Oak Preserve in Duncan, BC, Canada. Garry oak ecosystems are sub-Mediterranean oak savannas spanning from southern British Columbia to northern California. Average annual precipitation at the site is approximately ~1100 mm/year (460 mm in winter, 210 mm in spring, 81 mm in summer, and 325 mm in fall for 1980-2010 climate normals (Wang et al., 2016)). Under a high emission scenario (RCP 8.5), annual precipitation is predicted to increase by 11% by 2080, primarily in rainfall intensity in winter (14% increase), spring (7% increase) and fall (19% increase), while summer precipitation is projected to decline by 26% (Cowichan Valley Regional District, 2017). Annual average 15 temperatures are predicted to increase by 3°C by 2050 and 4.5°C by 2080 (Cowichan Valley Regional District, 2017). Primula hendersonii (A. Gray) A.R. Mast & Reveal is a long-lived early spring wildflower common in oak savannas of the Pacific Northwest of North America (Hall and Mathew, 1997). The above-ground growing period is restricted to February through June, and plants perennate underground from the onset of summer drought through mid-winter. Basal rosette growth and flower production within a season is determinate and draws resources from perennating structures. Plants spend 2-5 growing seasons as vegetative plants before flowering. Flower primordia are preformed prior to leaf out, inflorescence buds emerge with leaves in late winter, and plants put out a single flowering stalk with 1-17 hermaphroditic flowers that open in April to May. Flowers are described as requiring buzz pollination for seed set (Douglas et al., 1999; Hall and Mathew, 1997), though one source suggests that unpollinated flowers can self-pollinate (Gielens et al., 2014). In a seed addition experiment, seeds only germinated within one year of planting, so we assumed the population does not have a seed bank (Loesberg, unpublished data). Seeds germinate in late winter and spend one growing season with only cotyledons, emerge in their second year with one true leaf, and emerge in their third year with more than one leaf which we considered an adult rosette. Plants also reproduce asexually through bulblets, which form underground and emerge as one-leaf plants in their first year, although asexual reproduction was not quantified in this study. Vertebrate herbivores (deer and small mammals) consume 20-60% of stalks of flowers and fruits across the field site (Loesberg, unpublished data). Insects (Lepidoptera larvae) bore into individual capsules and consume seeds. 16 2.2.2 Rainfall Manipulation Experiment We used a rainfall manipulation experiment established in 2015 to simulate changes in precipitation intensity following the International Drought Experiment protocol (Knapp et al., 2017). Treatments included ambient rainfall, simulated drought, and simulated increased precipitation (n = 5 per treatment, 2 x 2 m plots). Drought treatments simulated 100-year drought conditions using 3 x 3 m wood structures with corrugated clear plastic intercepting 50% of incoming rainfall, allowing for a buffer around the focal 2 x 2 m study area. Irrigated treatments simulated higher rainfall intensity by irrigating the focal 2 x 2 m study areas via polyethylene emitter tubing with the intercepted 50% of ambient rainfall from drought shelters. For further protocols and design, see Knapp et al. (2017) and Smith and Williams (2023). In response to three consecutive years (2018-2020) with low spring rainfall, resulting in little difference in water availability between treatments, in 2021-2023 we added an additional sixty liters of water two or three times per week in April-June (15 – 22.5 mm/m2 per week) to ensure that treatments received different amounts of water. We added a third round of watering in weeks with air temperatures above 20°C to account for water loss from increased evapotranspiration under warmer temperatures. Air temperature and ambient precipitation were measured hourly on site with a HOBO U30 weather station. Instances of missing daily climate data were imputed using data from the nearest weather station (North Cowichan, from Environment and Climate Change Canada Historical Climate Data at https://climate.weather.gc.ca/index_e.html) using a linear regression model between climate variables from each site (r2 = 0.87 for precipitation; r2 = 0.99 for temperature). In the 15 rainfall experiment plots, P. hendersonii naturally occur in five control plots, four drought plots, and four irrigated plots. Beginning in 2017, we marked adult P. hendersonii 17 plants in 1 x 2 m2 plots within the rainfall experiment focal study area with permanent aluminum tags. Annually in early spring from 2017-2023, we collected demographic measurements of rosette width and length (total leaf area estimated as the area of an ellipse), survival, flowering status and number of flowers for marked adult individuals. For plots with greater than 100 adults, we only marked new adult plants in 1 m2 subsets of the plots. We counted the number of seeds produced by each tagged plant that flowered and made seeds (64 -157 plants per year) in 2021-2023. Vegetative dormancy was rare, with 49 of 1212 total plants reemerged as adult plants after not emerging for one growing season across all years of the experiment, so dormancy was not examined in analyses. We estimated one-leaf plant (the juvenile stage) survival and probability of becoming an adult by counting the total number of one-leaf plants annually from 2017-2023, and in addition, marked a subset of one-leaf plants in 2021-2023 with toothpicks and relocated them the following year. We quantified seed germination rates in a pot experiment and a seed addition experiment (Loesberg, unpublished data). To estimate the transition from seedlings to one-leaf plants, we counted the number of seedlings in 2023 and number of one-leaf plants in 2024 in two 10 x 10 cm2 areas in each plot. 2.2.3 Clipping Experiment To determine the effect of reproductive effort on future demographic rates (growth, survival, and reproduction) within the rainfall experiment, we used a simulated flower herbivory experiment to manipulate reproductive effort for P. hendersonii in 2021 and examined the effect on demographic rates in 2022. These treatments have natural analogs for this population, where deer and rabbits clip flower stalks and insects consume individual flowers and capsules. In April 18 2021, when greater than 75% of flowers on each plant had opened, we assigned flowering plants in each plot randomly to one of three clipping treatments: control treatments (high reproductive effort) received no clipping or herbivory for the season; half clipped (medium reproductive effort) treatments had half of their flowers clipped; all clipped (low reproductive effort) treatments had all flowers clipped. Wire mesh cages were placed over individuals that still had flowers to prevent further natural mammal florivory. Sample sizes varied due to varying numbers of flowering individuals in each rainfall experiment plot, ranging from one to five plants in each clipping treatment per plot, for a total of 147 plants. At the end of the growing season, we counted the number of seeds produced by each plant. In April 2022, we relocated and determined the survival, growth, flowering status, and flower production of each individual. We used linear mixed effects models to evaluate the effects of reproductive effort in 2021 across rainfall treatments on growth, probability of flowering, survival, and number of flowers produced in 2022. All models included size in 2021 as a fixed effect and plot as a random intercept effect to account for non-independence of observations. We used successful seed set in 2021 as a continuous measure of reproductive effort to capture the range of reproductive effort since flower production varied among individuals within a clipping treatment due to size. To test the significance of fixed effects (size in 2021, seed set in 2021, rainfall treatment, all two-way interactions, and a three-way interaction between all variables), we used likelihood ratio tests on nested models. Growth was modeled with a linear mixed effect model with a Gaussian distribution, probability of flowering and survival were modeled with generalized linear mixed effect models (GLMMs) with binomial distributions, and number of flowers was modeled with GLMMs using a Poisson distribution. 19 2.2.4 Climate Variables We used Principal Component Analysis (PCA) to select seasonal climate variables to include in demographic rate model selection (Appendix A.1). The PCA included seasonal fall (September-November), winter (December-February), spring (March-May), and summer (June-August) climate variables (total precipitation, maximum temperature, minimum temperature, and mean temperature) for 2013-2022. We included mean, maximum, and minimum temperature in the PCA to identify the temperature variable that contributed most to variation in the PCA because these three summary statistics were often correlated. We chose to include seasonal values of climate, rather than annual or monthly climate, because seasonal values directly relate to season-specific climate projections for the Cowichan Valley and for rainfall received in the rainfall manipulation experiments. We selected the two seasonal variables with the highest absolute loadings from PC axes 1 and 2, for a total of four variables, to include in demographic rate model selection (Appendix A.1; Table A.1). Climate variables were standardized to a mean of zero and a standard deviation of one. If the two highest loadings included temperature from the same season (for example, summer maximum and summer mean temperature) we selected mean temperature. Selected variables from the PCA were mean winter, spring, and summer temperature and spring total precipitation. Distributions of these climate variables can be found in Appendix A.1: Figure A.3. 2.2.5 Climate-Dependent Demographic Rates To evaluate costs of reproduction across time, climate, and rainfall treatment, we modeled demographic rates with mixed effects models for the long-term demographic data from 2017-2023. The four demographic rates examined were: size in t + 1 (growth), survival in t + 1, 20 probability of flowering in t + 1, and flower production in t + 1, each as a function of size in t. Demographic rates were modeled with the same error distributions as described in the clipping experiment. To select models that provided the best fit to the data, full models that included plant size in t, climate variable in t, flowering status in t (flowering or vegetative), rainfall treatment, and two-way interactions were compared with all possible model subsets using Akaike Information Criteria (AIC; 105 candidate models per demographic rate). Including flowering status in t as a fixed effect in candidate models tested the effect of current flowering and reproductive effort on future demographic rates. To simplify models and to make examining interactions among predictor variables tractable, we included only one climate variable in each demographic rate candidate model. Preliminary analyses showed that size in t was a better predictor than size in t + 1 for demographic rates in t + 1 indicating support for incorporating climate from the previous year. Early spring forbs, including the Primula genera, that overwinter are predicted to preform leaves and inflorescences prior to emergence in the growing season (Schnablová et al., 2021). Thus, we only included climate variables in year t and assume that demographic rates in t + 1 for this species are primarily influenced by the previous year’s climate. All model subsets included size in t as a fixed effect and plot and year as random intercept effects. We selected the candidate model with the lowest ΔAIC, but if there was more than one model with a ΔAIC of less than 2, we chose the model with the fewest number of parameters. All models were fit in R (version 4.2.2, R Core Team, 2024); mixed effect models were fit using the lme4 package (version 1.1-35.1; Bates et al., 2015). 2.2.6 Integral Projection Model We used the best supported demographic rate models outlined in the previous section to parameterize climate, reproduction, rainfall treatment, and size-dependent integral projection 21 models (IPMs) to integrate the effect of seasonal climate, reproductive effort, and rainfall treatment on the deterministic population growth rate, λ. We used a continuously size-structured population of adult individuals with greater than one-leaf and two discrete stages: seedlings and one-leaf plants. The continuous IPM kernel describes the size distribution of adult plants in t + 1, across the possible range of plant sizes, based on functions for survival and growth of adult plants in t and the discrete probability of one-leaf plants entering the adult population. Fecundity is estimated as the number of seedlings in t + 1 produced by the adult population in time t. IPM equations and discrete transition probabilities can be found in Appendix A.3. We built separate IPMs for each combination of parameters specific to the best supported demographic rate models and estimated the deterministic population growth rate, λ, as the dominant eigenvector of each IPM kernel. To evaluate the influence of costs of reproduction on λ, we compared λ from IPMs built from functions that incorporate a cost of reproduction, where it was supported at the level of the demographic rate, to when reproduction carried no cost. We estimated 95% confidence intervals for λ with parametric bootstrapping by resampling coefficients of each demographic rate function 1000 times and recalculated λ for each bootstrapped replicate. 2.3 Results 2.3.1 Clipping Experiment Reproductive effort, quantified as seed set in t, had a marginally significant effect on growth from 2021 to 2022: seed production reduced plant size the following year (Figure 2.1). Plants that flowered but made zero seeds (all-clipped, low-reproductive effort treatment) were, on average, 12% larger than plants with medium reproductive effort (10th percentile of seed production) and 21% larger than plants with high reproductive effort (90th percentile of seed 22 production). This effect did not interact with plant size in t (P = 0.073; Table 2.1; Figure 2.1). Reproductive effort in 2021 did not have a significant direct effect on the probability of flowering or flower or seed production in the subsequent year (2022), although few candidate models converged for the flower production models. Survival models had low instances of converge due to high survival rates (131 of 141 plants survived), so we do not report results from the probability of survival models. Overall, probability of flowering and flower production increased with plant size. The effect of reproductive effort was not influenced by rainfall treatments for any demographic rate (Table 2.1). Table 2.1 Results from likelihood ratio tests for variable selection in linear mixed effect models (growth) and generalized linear mixed effect models (GLMMs; probability of flowering, number of flowers, and number of seeds) for Primula hendersonii in the flower clipping experiment. All models included plot as a random intercept effect and plant size as a fixed effect. Variables listed in the first column refer to the variable removed from the nested models in stepwise selection, ordered from most complex to least; statistics are results from likelihood ratio tests (sample size = 141 individuals). The degrees of freedom for each likelihood ratio test are 1. Dashes indicate models that did not converge, x indicates interaction terms, and bolded values indicate marginally significant values. Variable removed Growth Probability of flowering Number of flowers χ² P-value χ² P-value χ² P-value Plant size  total seeds in 2021  rainfall treatment 0.277 0.871 2.465 0.292 0.842 0.656 Total seeds in 2021  rainfall treatment 3.255 0.196 0.286 0.867 0.501 0.779 Plant size  total seeds in 2021 0.785 0.376 0.501 0.479 - - Plant size  rainfall treatment 0.283 0.868 3.845 0.146 0.023 0.989 Total seeds in 2021 3.221 0.073 3.914 0.141 1.002 0.606 Rainfall treatment 0.101 0.951 1.711 0.191 0.448 0.503 23 Figure 2.1 Seed production reduces plant growth in the flower clipping experiment from 2021 to 2022. Lines indicate predicted growth under different amounts of seed production in 2021. The dark purple line indicates growth for 90th percentile seed production, medium purple line indicates 10th percentile seed production, light pink line indicates zero seeds produced. Points are individual plants and size of point indicates how many seeds were produced by the individual. The grey 1:1 line shows stasis (no change in plant size from 2021 to 2022). The log transformation is the natural logarithm. 2.3.2 Long-Term Demographic Rates Across the long-term demographic study, the best supported model for individual growth included summer mean temperature in time t, flowering in time t, and rainfall treatment (Table 2.2). The effect of flowering was size-dependent. Only large plants paid a cost of reproduction, albeit small: large plants (90th size percentile) that flowered in t were 1.6% smaller in t + 1 than those that were vegetative in t. However, small plants (10th percentile) that flowered in t were 33% larger in t + 1 than small vegetative plants (Figure 2.2a). Plants in irrigated treatments were, on average, 5% and 15% larger in t + 1 than plants in control and drought treatments, respectively. Summer temperature had a positive association with growth: plants were larger 24 after years with warmer summers. Importantly, summer temperature interacted with rainfall treatment, such that cooler temperatures were associated with larger growth differences between irrigated and drought plots, and growth did not differ across treatments in warmer summers (Figure 2.2a). Table 2.2 Best fit models from model selection for climate and size-dependent demographic rates in the long-term demographic data. All models include plot and year as random intercept effects and fixed effects are from time t. For all functions, plant size [log(rosette area cm2)] in time t is indicated by x, temperature in year t is indicated by z (spring temperature in probability of flowering and survival functions, summer temperature for growth functions, and winter temperature for the number of flowers function), plant size in year t + 1 is indicated by y, probability of flowering in t + 1 is β, survival in year t + 1 is indicated by θ, and number of flowers in time t + 1 is φ. “F” and “V” subscripts on response variables indicate functions for estimates of flowering and vegetative individuals, respectively. Logit is the logistic function, where logit(k) = ek/(1+ ek). Mixed effect models were fit using the lme4 package (version 1.1-35.1; Bates et al. 2015). Demographic rate Climate variable in best fit model (scaled) Fixed effects in best-fit model Parameter Estimates Growth Summer mean temperature Summer mean temperature  rainfall treatment log(rosette area) in t + 1 ~ log(rosette area) + p(flower) + log(rosette area) x p(flower) + rainfall treatment + summer mean temp. + rainfall treatment x summer mean temp. Drought functions: yF = 2.13 + 0.32z + 0.45x yV = 1.54 + 0.32z + 0.58x Control functions yF = 2.21 + 0.20z+ 0.45x yV = 1.63 + 0.20z + 0.58x Irrigated functions yF = 2.26 + 0.18z + 0.45x yV = 1.67+ 0.18z + 0.58x Probability of flowering Spring mean temperature p(flower) in t + 1 ~ log(rosette area) + p(flower) + log(rosette area) x p(flower) + spring mean temp. β(x)F = logit(-4.44 + 0.92x -0.36z) β(x)V = logit(-7.36 + 1.54x -0.36z) Survival Spring mean temperature p(survival) in t + 1 ~ log(rosette area) + spring mean temp. θ = logit(0.61 + 0.59x – 0.36z) Number of flowers Winter mean temperature number of flowers in t + 1 ~ log(rosette area) + winter mean temp. φ = logit(0.54 + 0.20x – 0.08z) 25 Figure 2.2 Size-dependent demographic rate models showing the influence of (A) summer mean temperature (t), rainfall treatment, and past reproduction (t) on growth, (B) spring mean temperature (t) on survival, (C) past flowering (t), and spring mean temperature (t) on the probability of flowering in t + 1, and (D) winter mean temperature (t) on flower production in t + 1. Warmer seasonal temperatures were associated with lower survival, probability of flowering, and flower production, but higher growth. For growth (A) and probability of flowering (C), solid curves show predicted relationships of plants that flowered in t, dotted curves show plants that were vegetative in t. Color of curve refers to scaled seasonal mean temperature (dark blue is cool or 1 SD lower than the 2013-2022 average, light blue is average, and orange is warm or 1 SD higher than the average). The black 1:1 line in panel A shows stasis (no change in plant size from t to t + 1). The log transformation is the natural logarithm. Probability of flowering in t + 1 was best predicted by size in t, spring mean temperature in time t, and flowering in time t (Table 2.2). Similar to the cost of reproduction detected for growth, the effect of flowering in t was size-dependent and only large plants faced a cost of reproduction. Large plants that flowered in time t had a lower probability of flowering again in time t + 1 than vegetative plants, and small plants that flowered had a higher probability of flowering again. Threshold flowering size (the size at which 50% of plants flower) in t + 1 for 26 low reproductive effort plants was 4% smaller than for high reproductive effort plants. Warmer springs were associated with lower flowering in t + 1 (Figure 2.2c). As an indicator of the effect of spring temperature on flowering, the threshold flowering size (size at which 50% of plants flower) under cool springs was 37% smaller than warm springs (-1 and 1 SD from mean spring temperature, respectively). Rainfall treatments did not influence the probability of flowering, survival, or flower production. Survival was dependent on size in time t and spring temperature in time t (Table 2.2), and lower survival was associated with warmer springs (Figure 2.2b). Flower production in t + 1 was influenced by size and winter temperature in time t (Table 2.2), where warmer winter temperatures resulted in lower flower production (Figure 2.2d). See Appendix A.2 and Table A.2 for AIC rankings of top candidate models for each demographic rate. 2.3.3 Population Growth Population growth rates (deterministic λ) were highest in irrigated plots and lowest in drought plots across interannual variation in all seasonal climate variables (Figure 2.3), and past seasonal temperatures had a strong effect on λ. Specifically, warmer spring temperature and cooler summer temperature were associated with declines in λ, variation in winter temperature did not influence λ, and the associated negative effects of cool summer temperatures on λ were exacerbated in drought treatments (Figure 2.3). Within drought treatments, IPMs built from demographic estimates of high reproductive effort showed slightly lower λ than IPMs built from demographic estimates of low reproductive effort, especially under cool summers. However, 95% confidence intervals overlap (Figure 2.3), suggesting that even though costs were evident in growth and probability of flowering for large plants, they did not significantly influence λ under natural variation in seasonal climate or rainfall manipulation. 27 Figure 2.3 Population growth rate (λ) in rainfall treatments across varying (A) spring, (B) summer, and (C) winter temperature. For each row, the indicated temperature variable varies while the others are held at the mean. The x-axis is shown in units of standard deviation, a zero x-axis values refer to the mean temperature from 2013-2023, and absolute 1 and 2 values indicate temperatures that are 1 or 2 standard deviations from the mean. Bold lines show mean λ with 95% confidence intervals obtained from 1000 parametric bootstrap samples from demographic rate function coefficients. Color indicates rainfall treatment. Solid lines refer to models built from demographic functions of plants that flowered in time t. Dotted lines refer to models built from demographic functions of plants that were vegetative in time t. Black line indicates a λ values above one, indicating a growing population, λ values under one indicate population decline 28 2.4 Discussion Costs of reproduction are predicted to depend on climate, but whether they influence how populations respond to climate variation is unclear. We detected weak size-dependent costs of reproduction in an observational demographic study in a long-lived perennial. However, the observed costs did not influence λ nor did they meaningfully impact how λ changed across variation in climate or rainfall manipulation. Climate variables influenced vital rates and λ individualistically and only hotter spring temperatures were associated with reductions in λ. Here we discuss our results and the implications. Despite past research examining the influence of climate drivers on demographic rates and population growth rates, the role of dormant and past season climate is rarely incorporated in demographic analyses (van de Pol et al., 2016). We found that all demographic rates were climate-dependent and responded individualistically to climate variables of the past seasons, in line with previous studies (Dalgleish et al., 2011; Iler et al., 2019; Williams et al., 2015). Notably, we found that for P. hendersonii, flower production and growth were influenced by dormant-season (winter and summer) temperatures, but winter temperature did not influence λ. Any explanations we have for how dormant season temperature influences P. hendersonii are conjecture, but there is evidence that dormant season climate can be more important for demographic rates than growing season climate (Evers et al., 2021). One hypothesis is that the beneficial impact that hotter summers have on P. hendersonii may be mediated through the effects of summer temperature on other late season species, notably invasive perennial grasses that produce thatch that may negatively impact native species in subsequent growing seasons (Thomson, 2005; Williams and Crone, 2006). Although this hypothesis has not been tested in this system, warmer summers could reduce grass productivity through lower soil moisture 29 (Smith and Williams, 2023), thereby decreasing the amount of thatch grasses produce. On the other hand, cooler winters were associated with higher flower production; this could be due to chilling requirements or vernalization of the species or a benefit of cool weather when plants emerge (Anderson, 2023; Chouard, 1960). These results support that dormant season climate is important for demographic rates and λ, and should not be excluded when examining the influence of climate on population dynamics. Although precipitation has been shown to limit ecosystem productivity within the rainfall manipulation experiment at this study site, especially in the spring growing season (Smith and Williams, 2023), we did not detect effects of natural rainfall in any season on any demographic rates. However, the rainfall manipulation treatments did influence both individual plant growth and population growth, mainly within the drought treatments. The magnitude of water manipulation ultimately relied on natural variation on rainfall, with the exception of the irrigated treatments in spring 2021-2023. This may have obscured our detection of effects of water availability in this experiment, where weak effects of manipulated rainfall were ultimately influenced by small differences in treatments due to unexpectedly low ambient rainfall. This highlights the need for both observational and manipulative climate studies to detect subtle impacts of climate. In a previous experiment in a Pacific Northwest savanna, experimental warming with open top chambers reduced population growth rates of annuals within the growing season, but treatments simulating small changes in spring precipitation did not influence population growth (Reed et al., 2021). However, there were key differences between our study and the previous study: the previous experiment was conducted much further south in the ecosystem’s range using annual species under only 20% addition and 40% reduction in rainfall. Spring temperature was associated with larger shifts in λ than winter temperature and summer 30 temperature, likely because spring temperature influenced the demographic rates (survival and probability of flowering) with high elasticities, or high proportional influences on λ (Appendix A.3: Figure A.6, Franco and Silvertown, 2004; Pfister, 1998). Climate projections for our study site indicate season-specific changes in precipitation but warmer conditions across all seasons (Cowichan Valley Regional District, 2017), and our results imply that while increases in spring temperature are negative for this species, warmer summers and wetter springs may benefit λ. Observational and manipulative studies may lead to differences in the detection and strength of costs of reproduction (Fox and Peter, 1991). In support of this, we detected a size-dependent cost of reproduction in the observational demographic experiment, and little evidence for a cost of reproduction in the clipping experiment. A key difference in the two methods is the way that reproductive effort is measured, i.e., seed production in the manipulative clipping experiment as opposed to initiating reproduction in the observational data (Obeso, 2002). Primula hendersonii have determinate growth and emerge in the winter with buds already formed, suggesting that stored resources are already allocated to reproduction prior to flowering, and making seeds is not the most expensive stage of reproducing. However, this may be specific to overwintering species with organ preformation, where resource storage from previous growing seasons influences current reproduction (Ehrlén and Van Groenendael, 2001; Schnablová et al., 2021). Due to small sample sizes in the clipping experiment resulting in models of some demographic rates failing to converge, it is possible that we were not able to detect other existing costs of seed production. Alternatively, this weak influence of manipulating reproductive effort through flower clipping could be an adaptation to frequent floral and capsule herbivory by mammals and insects in this population (J. Loesberg, personal observation). In populations with a high risk of floral herbivory, low resource investment in flowers and fruits may buffer the 31 negative effects of herbivory if flowers and fruits are lost (Ehrlén, 1991), which may have led to small differences in reproductive effort across clipping treatments if plants had already allocated resources prior to flowering. Thus, identifying when resources and effort are allocated to reproduction may be key to detecting costs, as well as considering life histories of study species (Sun et al., 2022). Costs of reproduction that are ameliorated or exacerbated by climate change can influence future population persistence and dynamics (Hamann et al., 2021). Overall there was little evidence for costs: only large plants paid a cost for reproducing, but small individuals experienced positive effects of reproduction. Positive correlations are not uncommon in studies that have looked for costs of reproduction (Lehtilä and Syrjänen, 1995; Obeso, 2002; Stearns, 1989). Under abundant resources, costs can remain undetected, reproduction and future demographic rates may be positively correlated, or costs can appear in complex ways (e.g., costs that differ with plant size) (Miller et al., 2012; Obeso, 2002; van Noordwijk and de Jong, 1986). Theory predicts that costs of reproduction increase with plant size, where small plants allocate a lower proportion of total resources to reproducing than large plants to increase lifetime fitness (Hemborg and Karlsson, 1998; Obeso, 2002; Reekie and Bazzaz, 1992; Worley and Harder, 1996), even as other studies have found that small plants pay higher costs of reproduction than large plants (Miller et al., 2012). Regardless, the lack of change in λ across reproductive effort may be driven by individuals that are able to flower at small sizes or young ages, suggesting that these “lucky individuals” contribute strongly to population growth rates (Snyder and Ellner, 2018). This has been detected in ungulates, where variation in individual quality masks costs of reproduction (Hamel et al., 2009; Weladji et al., 2008). Our combined rainfall manipulation experiment and clipping experiment aimed to control for the effects of individual heterogeneity 32 and resource availability, but the climate of the year of the clipping experiment had low spring precipitation but particularly warm summer temperatures (Appendix A.1: Figure A.3). Failing to detect effects of water availability may have been driven by little difference between water availability in control and drought treatments. Here we show that climate influences both vital rates and population growth rates, but not costs of reproduction of a long-lived iteroparous wildflower. We add to the growing number of studies that show that costs of reproduction can be detected in long-term observational data. We highlight that clipping experiments may be less practical, as samples sizes are limited to only reproducing individuals, are time intensive, and are difficult to maintain over a long time period. Future studies should incorporate further indirect responses of climate change, for example how neighboring plant species, which are also responding to changes in climate, influence target species responses. 33 Chapter 3: Conspecific density and reproductive trade-offs govern population response to climate in a clonal wildflower 3.1 Introduction Predicting how future climate conditions will drive changes in biodiversity requires understanding the impacts of climate on populations of individual species (Benton et al., 2006; Urban et al., 2016). This involves linking climate drivers to the demographic rates (survival, growth, and reproduction) that underlie population growth rates, and scaling these effects to population growth rate with population models (Campbell, 2019; Iler et al., 2019). Often, this method assumes that demographic rates are independent of each other, are not influenced by local conspecific density, and are driven directly by climate (Crone et al., 2011; Ehrlén et al., 2016; Iles et al., 2019). However, the effect of a climate driver on an individual’s demographic rates likely depends on the local density that the individual experiences, and on the resulting effects on other demographic rates (Doak and Morris, 2010; Ehrlén and Morris, 2015). Thus, the density of conspecifics may mediate the effects of climate drivers, which would bias estimates of population responses to drivers when these effects are not accounted for. The effects of changes in environmental drivers on populations can be regulated by density through (1) changes in the equilibrium population size, that is, how close the current population size or density is to the environmental carrying capacity, or (2) if conspecific neighbors modify how individuals experience the abiotic environment, e.g. by regulating the effects of temperature through shading (Brooker, 2006; Herrando-Pérez et al., 2012). While the density of interspecific competitors may also moderate population response to climate drivers 34 (Nomoto and Alexander, 2021), although not always (Adler et al., 2012; Fox and Morin, 2001), here we isolate the effects of intraspecific density even as we acknowledge that effects of interspecific competition could also be important. While it is well-established that interactions between plants control the magnitude and direction of individual plant responses to environmental drivers (Brooker and Callaghan, 1998; Dahlgren et al., 2016, 2014; Maestre et al., 2009; Zhang and Tielbörger, 2020), environmentally explicit population models rarely incorporate population density (but see Dahlgren et al., 2016, 2014). This is in part because of the complexities of detecting the effects of density in natural populations. While manipulative density experiments are recommended over detecting effects of density in observational demographic data (Fowler et al., 2006; Ramula and Buckley, 2009), manipulative experiments are time and resource intensive and are difficult to use for long-lived, rare or protected species, and for populations where density influences numerous ages or stages. However, theory predicts that detecting density effects in observational demographic data will depend on how close the population or local environment is to the equilibrium population size, or carrying capacity (Krebs, 2002). Since populations fluctuate around equilibrium, especially where there is variation in environmental conditions (Ehrlén and Morris, 2015; Krebs, 2002; Slobodkin, 1955), the effects of density should be detectable when accounting for environmental variation (Dahlgren et al., 2016, 2014). In addition, under climate change, many populations are predicted to deviate from, but not fluctuate around, equilibrium population sizes (Diez et al., 2014; Svenning and Sandel, 2013). The experimental manipulation of environmental drivers related to climate should also aid in the detection of density effects without manipulating density. Further adding to the complexity of this relationship between demography, density, and climate drivers, clonality is common in perennial plant species and often occurs in addition to 35 reproducing sexually through seeds. Sexual reproduction and clonality can differ in their contributions to population growth, for example, if clones establish at a greater size than seedlings or bypass life stages like seeds and seedlings (Harper, 1977; Hesse et al., 2008; Lin et al., 2016; Sarukhan and Harper, 1973), and likely are influenced individually by density and environmental drivers. Importantly, these two reproductive modes are predicted to trade-off, with investment in clonal reproduction resulting in decreased investment in sexual reproduction, and vice versa (Abrahamson, 1980; Barrett, 2015; Fischer and Van Kleunen, 2001; Herben et al., 2015; Loehle, 1987; Lovett Doust, 1989; Obeso, 2002; Silvertown et al., 1993; Stearns, 1992). The production of flowers, fruits, and seeds associated with sexual reproduction may be more costly than the production of clonal offspring, or the fitness benefits of one mode over the other may be realized under environmental variation (Obeso, 2002). Consequently, clonal reproduction may allow individuals to bypass the costs of sexual reproduction and provide reproductive assurance in adverse conditions where flowering is rare, or for individuals too small to flower (Aarssen, 2008). Flowering may also be reduced in patches that are already at or exceed equilibrium density in response to high intraspecific competition (Barkham, 1980; Gadgil and Solbrig, 1972), or increased to increase offspring dispersal (Stoll and Prati, 2001; Van Kleunen et al., 2001). In low density conditions, clonality may also increase the competitive ability of a genet over a beneficial resource patch (Diez et al., 2014), although not always (Barkham, 1980). Thus, clonal reproduction and density will likely interact to influence demographic rates and population growth under climate variation. Here, we ask (1) how conspecific density influences population response to variation in climate, (2) if there is a trade-off between clonal reproduction and sexual reproduction across climate and density, and (3) if these factors ultimately influence population growth across 36 manipulated rainfall treatments and temperature. To answer these questions we used Primula hendersonii, a perennial herb native to western North America that reproduces sexually and clonally. To compare results from observational and manipulated density experiments, we paired observational density and demographic data with a pot experiment planted with different manipulated densities and water conditions. We predicted that: the negative effects of conspecific density would be ameliorated by climatically stressful conditions; reproductive mode would occur as a trade-off, where flowering would be more common under benign conditions and at high density, and clonality would be more common under stressful conditions and low density; and that high density would decrease population growth rates under benign, but not adverse, conditions. 3.2 Methods 3.2.1 Study System and Species We conducted our study at the Cowichan Garry Oak Preserve in Duncan, BC, Canada, which contains a remnant of Garry oak savanna, a sub-Mediterranean oak savanna ecosystem in the Pacific Northwest of North America. Now restricted to roughly 5% of its historical distribution (Fuchs, 2001), Garry oak habitat is characterized by open savannas with a forb and grass understory, wet spring growing seasons, warm dry summers, and mild winters. Mean annual precipitation at the study site is approximately 1100mm (460 mm in winter, 210 mm in spring, 81 mm in summer, and 325 mm in fall) and mean annual temperature is 10°C (1980-2010 climate normals; Wang et al., 2016). Climate projections in the Cowichan Valley estimate an 11% increase in total annual rainfall but season-specific changes in rainfall intensity (14%, 7%, and 9% increase in winter, spring, and fall, respectively, and 26% decrease in summer rainfall) 37 and annual temperature increases of 3°C by 2050 and 4.5°C by 2080 under “business as usual” emission scenarios (Cowichan Valley Regional District, 2017). Primula hendersonii (A. Gray) A.R. Mast & Reveal is an iteroparous early spring wildflower common in Garry oak savannas (Hall & Mathew, 1997) and ranges from central California to southwestern British Columbia. Its growing season at the field site is February to June, after which plants perennate underground. Similar to other early spring perennials, rosette size, flower initiation, and flower production are expected to draw from resources acquired in the previous growing season (Schnablová et al., 2021). Primula hendersonii reproduce sexually via seeds and clonally via bulblets. After 3-5 years of vegetative growth, adult plants can produce a single flowering stalk with 1-17 hermaphroditic flowers that open in April to May. Of plants that flower at the field site, 55% do not flower again and are vegetative the following year. Flowers require buzz pollination (Douglas et al., 1999; Hall and Mathew, 1997). One study observed seed production by P. hendersonii within pollinator exclusion cages, suggesting that unpollinated flowers may be able to self-pollinate, but the authors explain that the exclusion design may have allowed insect access (Gielens et al., 2014). Regardless, the potential selfing ability may not be common given that outcrossed pollen was not found to be limited (Gielens et al., 2014). In a seed addition experiment, seeds only germinated within one year of planting, suggesting little or no role of seed banking (Appendix B.1). After germination in the early spring, seedlings spend one growing season with only cotyledons and emerge in their second year with one true leaf, taking at least three years to make an adult rosette. One-leaf plants and adult rosettes can produce offspring clonally through the production of genetically identical bulblets. Bulblets separate from parent plants underground and grow independently but close to the parent. They emerge as a one-leaf plant, bypassing the one year of cotyledon growth that sexual recruits require. We refer 38 to clonality as a type of reproduction (rather than growth), as bulblets develop independently of parent plants and create a secondary pathway for recruitment (Aarssen, 2008). 3.2.2 Rainfall Experiment Our study was conducted within a rainfall manipulation experiment established in 2015 to simulate changes in rainfall and predict community response to projected future climate conditions. The year-round experiment consists of drought, control, and irrigated treatments (five 2 x 2 m plots per treatment). Drought treatments decreased intensity of every natural rain event using 3 x 3 m permanent wood drought shelters with strips of corrugated clear plastic roofing covering 50% of the shelter area, with the aim of reducing incoming rainfall by 50% in the focal 2 x 2 m drought plots. In irrigated plots, intensity of every natural rain event was doubled in 2 x 2 m focal plots (4 m2) with water captured from with the intercepted ambient rainfall from drought treatments (9 m2) added via polyethylene emitter tubing. To account for low ambient spring rainfall in April, May, and June 2018-2020, beginning in 2021 an additional sixty liters of water was added twice per week (15 mm/m2 per week), or three times per week (22.5 mm/m2 per week) if maximum daily temperatures within a week exceeded 20°C to account for water loss with increased evapotranspiration. Hourly air temperature and ambient precipitation were recorded on site with a HOBO U30 weather station. In cases of missing data, local weather station data were imputed using a linear regression model (r2 = 0.87 for total precipitation; r2 = 0.99 for minimum, maximum, and average temperature) using data from a nearby weather station (North Cowichan, from Environment and Climate Change Canada Historical Climate Data at https://climate.weather.gc.ca/index_e.html) 39 Primula hendersonii occurs in 13 of the 15 total rainfall experiment plots (five control plots, four drought plots, and four irrigated plots). Beginning in 2017, naturally occurring P. hendersonii ramets with more than one-leaf (adults) were permanently marked with aluminum tags in 1 x 2 m2 areas of the 2 x 2 m2 rainfall experiment focal plots. In late April, we collected demographic measurements of rosette size (total leaf area estimated as the area of an ellipse), survival, and fecundity of each tagged individual. New adult rosettes were given permanent tags. For plots with high densities of P. hendersonii, (containing more than 100 or 200 tagged individuals per m2), only new adult plants in 1 m2 or 0.5 m2 subsets of the demography plots were marked, respectively, although previously tagged individuals were surveyed every year. This was done for logistic feasibility, but was still a representative sample of the plot as densities did not vary substantially within plots already at high density. From 2021 through 2024, we quantified local conspecific density by counting the number of neighbors in a circle of fixed radius of 10 cm (area of 415 cm2) around each marked adult plant. This radius was chosen because it allowed us examine local density at the same scale as the density manipulation experiment (see next section) and because previous studies have detected effects of density at this scale (Silander and Pacala, 1985; Sletvold, 2005; Sletvold and Hestmark, 2000). To estimate one-leaf (one-year old) plant survival and probability of one-leaf plants becoming adults, we counted the total number of one-leaf plants annually, marked a subset of one-leaf plants with toothpicks, and relocated them the following year. We estimated seed production by counting seed production per capsule in a haphazardly chosen subset of tagged flowering individuals (64 -157 plants annually for 2021-2024). We averaged the number of seeds produced per flower across rainfall treatments (Appendix B.1). Clonal reproduction was estimated as the number of individual small clonal leaves within 1 cm of adult rosettes. We 40 interpreted these to be clones, as we never observed seedlings within 1 cm of adult plants, but it is possible some leaves were the product of sexual reproduction. To estimate the transition from seedlings to one-leaf plants, we counted the number of seedlings in 2023 and the number of subsequent one-leaf plants in 2024 in two 10 x 10 cm2 areas in each plot. We assumed these were the product of sexual reproduction because the seedling subplots were not near adult rosettes. We quantified seed germination in a seed addition experiment (Appendix B.1). Vegetative dormancy was rare (4% of plants reemerged as adult plants after not emerging for one growing season across all years of the rainfall experiment, 2017-2024), so vegetative dormancy was not included in analyses. 3.2.3 Manipulated Density Experiment Because plant density could not be manipulated at the preserve where we conducted the field experiment, and to compare how results differ when comparing natural variation in density versus manipulated density, we established a complementary density experiment in pots to isolate the effect of intraspecific density on individual plants and demographic rates. In March 2021, we planted adult Primula plants sourced from Satinflower Native Plants (Victoria, BC, Canada) under four treatments in 2 L pots with a surface area of 415 cm2: one plant alone (replicated with 12 pots), four plants (8 pots), eight plants (8 pots), and 16 plants (12 pots). Plants were up to 5 years old, but varying in age (Satinflower Native Plants, pers. comm.). Initially, only four 16-plant density pots were planted, but eight extra replicates were added in October 2021 to increase the number of replicates. Treatment densities were determined from the highest observed density in the field, which was four adult plants per 100 cm2 (corresponding to about 16 plants per 415 cm2). This high density was extremely rare in the field (median number 41 of neighbors was two adult plants per 415 cm2). Individuals were marked with plastic toothpicks to identify them across years. In four of 12 of the highest density pots, it was difficult to mark and track individual plants due to crowded conditions, and we measured all plants without tracking individuals. To estimate individual plant demographic rates from year t to year t + 1 for these pots, we estimated individual fates in t + 1 by using the average plant size and standard deviation within the pot in t + 1 to randomly simulate t + 1 measurements to assign to each plant. Pots were kept outdoors in a fenced area at the Cowichan Garry Oak Preserve and allowed to experience natural weather conditions. A shade cloth was added in early June to correspond with leaf-out of overstory Garry oak trees. All pots received the same amount of water in 2021. To examine the effect of water availability on demographic rates, pots were randomly assigned to one of two water treatments in April 2022: wet treatments were watered with 500 mL of water when soil was dry to the touch, and dry treatments received only ambient rainfall. Supplemental water was only added in April and May. In April of 2021-2024, we measured the size of each individual rosette, flowering status and number of flowers produced, and counted the number of small (smaller than 2 cm x 2 cm, indicative of clonal growth) and large (larger than 2 cm x 2 cm, indicative of rosette growth) leaves for each individual plant. Soil used was a 50/50 mix of Sunshine Mix #4 (Sungro Horticulture, Agawam, MA, USA) and Island’s Finest Potting Mix (Cinnabar Farms, Nanaimo, BC, Canada). 3.2.4 Demographic Rate Analysis in Rainfall and Density Manipulation Experiments To evaluate the effects of observed conspecific density, climate, and rainfall treatment on field demographic rates for 2021-2024, the years where density was recorded, we used model selection with Akaike’s Information Criteria (AIC) to determine the best supported demographic 42 rate models for the demographic data from the rainfall experiment. The six demographic rates examined were: size in t + 1 (growth), survival in t + 1, probability of flowering in t + 1, flower production in t + 1, probability of making a clone in t + 1, and number of clones produced in t + 1. Full models included four variables: size in t, rainfall experiment treatment (drought, irrigated, control), number of conspecific neighbors, and seasonal climate. We also included all possible 2-way interaction terms in addition to main effects. Candidate models included subsets of all combinations of variables, but always included size in t with the exception of an intercept-only model. To examine trade-offs between reproductive modes for the probability of clonality and probability of flowering, we included the alternate reproductive mode in candidate models (i.e., the probability of clonality in t was included as a predictor variable in the probability of flowering in t + 1 model, and vice versa). A trade-off between the two modes of reproduction, that is, between flowering and clonal reproduction, would be observed as a reduced probability of clonality for plants that flowered in the year prior, and vice versa for flowering on clonality. We tested the effect of reproductive mode as a binary (i.e. did or did not flower previously) rather than a continuous measure (how many flowers were made previously) to improve model convergence, although differences in past resource allocation (more or fewer flowers) may have influenced the trade-off. To limit the number of possible candidate models, we tested the seasonal climate variable that was included in best supported models from model selection for each demographic rate in previous analyses: summer temperature in t for growth in t +1, spring temperature in t for survival and probability of flowering in t +1, and winter temperature in t for number of flowers in t +1 (determined in Chapter 2). There were 16 candidate models for demographic rates with the exception of the probability of flowering and the probability of 43 clonality, which had 37 candidate models to account for the added effect of the alternate reproductive mode. For the density manipulation experiment, we used likelihood ratio tests on nested models with single term removals to examine how water availability and density treatment influenced demographic rates. Density treatment was a categorical effect, rather than a continuous count of neighbors, so that we could account for nonlinear effects of density and for the effects of starting density on future demographic rates. Full models varied between demographic rates because of issues of model convergence and because of the inclusion of reproductive mode in models: the full growth model included a three-way interaction between size, density treatment, and water treatment, two- and three-way interactions between these variables, and main effects. The probability of clonality and flowering models also included the other reproductive mode (probability of flowering or probability of clonality) as a fixed effect. The survival, probability of clonality, and number of bulblets models only converged with main effects. The probability of flowering models would not converge for any combination of fixed effects, so year was dropped as a random effect to simplify the random effect structure, and the full model included two-way interactions between the above variables and the probability of clonality in t. The number of flowers model did not converge, even without the year random effect. For both analyses, growth was modeled with linear mixed effects models with a Gaussian error distribution; probability of flowering, survival, and probability of clonality were modeled with generalized linear mixed effect models (GLMMs) with binomial distributions; and number of flowers and number of clones produced were modeled with GLMMs using Poisson distributions. The density experiment models also included replicate (pot) as a random effect and the rainfall experiment included plot as a random effect, in both cases to account for repeated 44 measures. All analyses were done in R (version 4.4.1, R Core Team, 2024) and mixed effect models were fit using the lme4 package (version 1.1-35.1, Bates et al., 2015)). 3.2.5 Integral Projection Model We used the best supported field demographic rate statistical models outlined in the previous section to parameterize integral projection models (IPMs) to examine the effect of seasonal climate, reproductive mode, and rainfall treatment on the local density-dependent population growth rate, λ(N), where N is the number of conspecific neighbors in a 415 cm2 circle around the focal plant. We used a continuously size-structured population of adult individuals and two discrete stages: seedlings and one-leaf plants. The continuous IPM kernel described the size distribution of adult plants in t + 1, across the possible range of plant sizes, based on functions for survival and growth of adult plants in t and the discrete probability of one-leaf plants entering the adult population. Clonal reproduction was estimated as the number of bulblets (one-leaf plants) produced by individuals from the adult population into the one-leaf discrete stage. Fecundity was estimated as the number of seedlings in t + 1 produced by the adult population in time t. IPM equations and discrete transition probabilities can be found in Appendix B.2. We built IPMs for each combination of parameters specific to the best supported demographic rate models and estimated the density-dependent growth rate, λ(N), as the dominant eigenvector of each IPM kernel, across the range of observed number of conspecific neighbors (0 to 9 plants per 415 cm2). When the effect of an alternative reproductive mode on a demographic rate was supported by the statistical model, we compared λ(N) from IPMs built from functions that incorporated past reproduction. We estimated 95% confidence intervals for λ with parametric 45 bootstrapping by resampling coefficients of each demographic rate function 1000 times and recalculated λ for each bootstrapped replicate. 3.3 Results 3.3.1 Demographic Rates in the Rainfall and Density Manipulation Experiment Across the long-term demographic study, the best supported model for individual growth included summer mean temperature, the number of conspecific neighbors, and rainfall treatment (Table 3.1, Figure 3.1). Higher growth and larger plant size were associated with high neighbor density (Figure 3.1). Plant growth (individual plant size increment from t to t + 1) was higher in years following warmer summers, especially in the drought treatments. Similarly, in drought treatments, plants were more likely to flower in years following warm springs (i.e. highest probabilities of flowering in t + 1) (Figure 3.1). However, the effect of spring temperature on the probability of flowering was the opposite for irrigated and control plots, where flowering was higher following cooler springs. The probability of flowering was not influenced by the probability of producing clones. Warm springs were associated with reduced plant survival, and flower production was dependent only on plant size but not seasonal temperature or rainfall treatment (Appendix B.3, Figure B2). 46 Table 3.1 Best supported models from model selection for climate, size, and density-dependent demographic rates in the long-term demographic field data. All models include plot and year as random effects and fixed effects are from time t. For all functions, plant size [log(rosette area cm2)] in time t is indicated by x, temperature in year t is indicated by z (spring temperature in probability of flowering and survival functions, summer temperature for growth functions, and winter temperature for the number of flowers function), n indicates number of neighbors, plant size in year t + 1 is indicated by y , probability of flowering in t + 1 is β, survival in year t + 1 is indicated by θ, number of flowers in time t + 1 is φ, probability of clonality in t + 1 is H, and number of clones produced in t + 1 is K. X indicates interaction terms. “F” and “V” subscripts on response variables indicate functions for estimates of flowering and vegetative individuals, respectively. Logit is the logistic function, where logit(k) = ek/(1+ ek). Demographic Rate Fixed effects in best-fit model Parameter Estimates Growth log(rosette area) in t + 1 ~ log(rosette area) + neighbors + log(rosette area) x neighbors + rainfall treatment + summer temp. + rainfall treatment x summer temp. yControl = 1.60 + 0.12n + 0.13z - 0.03nx + 0.60x yIrrigation = 1.75 + 0.12n + 0.04z - 0.03nx + 0.60x yDrought = 1.52+ 0.12n + 0.32z - 0.03nx + 0.60x Probability of flowering Probability of flowering in t + 1 ~ log(rosette area) + spring temp. + rainfall treatment + rainfall treatment x spring temp. β(x,z)Control = logit(-6.80 + 1.63x -0.03z) β(x,z)Irrigated = logit(-7.36 + 1.63x -0.13z) β(x,z)Drought = logit(-6.94 + 1.63x + 0.57z) Number of flowers Number of flowers in t + 1 ~ log(rosette area) φ(x) = logit(0.60 + 0.19x) Probability of clonality Probability of clonality in t + 1 ~ log(rosette area) + p(flower) + log.ros.area x p(flower) + neighbors + neighbors x p(flower) + rainfall treatment HControl, V(x, n) = logit(-1.54 + 0.27x - 0.17n) HControl, F(x, n) = logit(-0.06 - 0.11x + 0.008n) HDrought, V(x, n)= logit(-1.52 + 0.27x - 0.17n) HDrought, F(x, n) = logit(-0.04 - 0.11x + 0.008n) HIrrigated, V(x, n) = logit(-1.95 + 0.27x - 0.17n) 47 Demographic Rate Fixed effects in best-fit model Parameter Estimates HIrrigated, F(x, n) = logit(-0.47 - 0.11x + 0.008n) Number of bulblets Number of bublets in t + 1 ~ log(rosette area) + neighbors + rainfall treatment + neighbors x rainfall treatment Kcontrol(x, n) = logit(0.38 + 0.05x - 0.007n) KIrrigated(x, n) = logit(0.38 + 0.05x + 0.02n) KDrought(x, n) = logit(0.71 + 0.05x - 0.10n) Survival p(survival) in t + 1 ~ log(rosette area) + spring mean temp. + log(rosette area) x spring mean temp. θ(x,z) = logit(1.13 + 0.35x – 0.81z - 0.36xz) 48 Figure 3.1 Size-dependent demographic rate models for the field demographic data showing the effect of density, rainfall treatment, and past summer temperature on plant growth under (A) no neighbors and (B) five neighbors (the 95th percentile of number of neighbors); size and rainfall treatment dependent probability of producing a clone under (C) no neighbors and (D) five neighbors; and (E) rainfall treatment and spring temperature on the probability of flowering in t +1. For panels A, B, and E, solid lines show warm temperatures and dot-dashed lines show cool temperatures (summer for growth, and spring for probability of flowering and survival. Cool indicates the coolest 49 seasonal temperature experienced in 2014-2024). For C and D, dotted lines indicate predicted clonal reproduction for plants that were vegetative in t, solid lines indicate predicted clonal reproduction for plants that flowered in t. Line color indicates rainfall manipulation treatment (drought, control, or irrigated). In C and D, lines for the flowering control treatment are obscured by the irrigated lines. The probability of producing clones was dependent on plant size, local density, rainfall treatment, and whether an individual flowered in the previous year (Table 3.1, Figure 3.1). For plants that did not flower in the previous year, the probability of clonality was higher for plants without neighbors and increased with plant size (Figure 3.1). In contrast, for plants that flowered previously, the probability of clonality was not dependent on local density but decreased at larger plant sizes. Small plants not yet reaching the threshold size needed to flower were able to produce clones (Figure 3.1). When clones were produced, the number of clones produced was dependent on both density and rainfall treatment, where clone production was highest at low densities in drought plots (Appendix B.2, Figure B.2). In the density manipulation pot experiment, density treatment had a marginally significant effect on plant growth (p = 0.067, Table 3.2) and the effect of watering varied with plant size: plants were larger but grew more slowly in the wet treatment (marginally significant water treatment x plant size in t interaction term in Table 3.2, p = 0.052). In contrast to the expectation that increased density would decrease demographic rates but in support of the field demographic rates, individual plant growth was higher under increased neighbor density, but not when density exceeded the number of neighbors found naturally in the field (Table 3.2, Figure 3.2). Across watering treatments, plants were 29% and 52% larger in four and eight neighbor treatments (the highest number of neighbors found naturally in the field) than with zero neighbors, respectively, while the highest density (16 neighbors, which exceeded densities found naturally in the field) had the lowest growth, and plants were 16% smaller than the zero-neighbor treatment. The probability of producing a clone in t +1 was size-dependent and reduced at high 50 density (Table 3.2, Figure 3.2). The probability of producing a clone in t +1 was also higher for plants that did not flower in t than for plants that did, suggesting that initiating flowering reduced clonal reproduction. The probability of flowering in t +1 was marginally lower in wet treatments than dry treatments (p = 0.071; Table 3.2, Figure 3.2), and was not influenced by the past production of clones. The number of clones produced and probability of survival increased with increasing plant size (p = 0.012 and p = 0.004, respectively; Table 3.2), but not by treatment (p = 0.099 and p = 0.519, respectively; Table 3.2). 51 Table 3.2 Results from stepwise selection for linear mixed effects models (growth) and generalized linear mixed effects models (GLMMs; probability of flowering, probability of clonality, survival, number of bulblets) for Primula hendersonii in the density manipulation experiment. All models include replicate (pot) and year as a random intercept and plant size as a fixed effect, except for the probability of flowering, which only has pot as a random effect. Variables listed in the first column refer to the variable removed from the full model in stepwise selection; statistics are results from likelihood ratio tests (sample size = 312 individuals; the degrees of freedom for each likelihood ratio test is one). Size refers to log(rosette area) in t, density refers to density treatment, water refers to water treatment, p(clonal) refers to the probability of clonality in t + 1, p(flower) refers to the probability of flowering in t + 1. Dashes indicate variables that were not tested in a demographic rate model, NA’s indicate models that did not converge (for example, for survival, full models with three- and two-way interaction terms did not converge, and the influence of reproduction was not tested), x indicates interaction terms, bolded values indicate significant values, bolded values with an asterisk indicate marginally significant values. Significant terms were variables included in final models. Growth (size in t+1) Survival in t+1 P(flower) in t+1 P(clonal) in t+1 Number of clones t+1 Variable removed χ² P-value χ² P-value χ² P-value χ² P-value χ² P-value size x density x water 0.6 0.897 NA NA NA NA NA NA NA NA size x density 4.976 0.174 NA NA 1.252 0.741 NA NA NA NA density x water 1.467 0.69 NA NA 0.929 0.819 NA NA NA NA size x water 3.776 0.052 NA NA 3.26 0.071* NA NA NA NA density 7.171 0.067* 2.266 0.519 2.649 0.449 10.576 0.014* 6.282 0.099 water 0.217 0.642 2.111 0.146 - - 0.168 0.682 1.679 0.195 size - - 8.491 0.004* - - 7.136 0.008* 6.246 0.012* size x p(clonal) - - - - 1.023 0.312 NA NA NA NA density x p(clonal) - - - - 3.703 0.295 NA NA NA NA water x p(clonal) - - - - 0.28 0.597 NA NA NA NA P(clonal) - - - - 0.231 0.631 - - - - P(flower) - - - - - - 3.106 0.078* - - 52 Figure 3.2 Size-dependent demographic rate models for the density manipulation (pot) experiment showing the effect of density treatment on plant growth (size in t + 1) in dry (A) and wet (B) treatments; the influence of flowering and density on the probability of producing a clone in t + 1 (C); and water treatment on the probability of flowering in t + 1 (D). Grey lines in A and B indicate 1:1 growth (same size from year t to year t +1). Line color in panels A, B, and C indicate neighbor treatment, and D indicates water treatment. Dotted and solid lines in C show predicted relationships of plants that were vegetative and that flowered in t, respectively. The curve for the “four” density treatment in panel C is obscured by the “eight” density curve. 3.3.2 Population Growth Rates in the Rainfall Manipulation Experiment Overall, population growth rates were slightly reduced following years with warm springs (the growing season; Figure 3.3) and cool summer temperatures (the dormant season; Figure B3). λ(0), or population growth rates simulating a population where individuals have zero neighbors, 53 were highest in drought treatments and lowest in irrigated treatments (Figure 3.3; Figure B3). Across all climates, λ(N) declined with increasing neighbor density where flowering did not reduce clonal reproduction (i.e., IPMs that were parameterized with demographic functions of previously vegetative plants that predicted a higher probability of clonality in t + 1). In these simulations, the negative effect of density was strongest in the drought treatment. However, when the effect of flowering was included (i.e., simulations built from parameters where previous flowering resulted in a reduced probability of making a clone), there was only a negative effect of density on λ(N) under drought conditions (Figure 3.3; Figure B3). λ(N) was reduced when the effect of past flowering was not included, likely because the increased probability of clonal reproduction did not compensate for the fitness cost of not flowering. The effect of summer temperature on λ was also influenced by this trade-off, where λ(0) after warm summers was slightly higher than after cool summers in models without the negative effect of flowering on clonal reproduction, but not for models that included the trade-off (Figure B3). We only observed predictions of population decline (λ < 1) after warm spring conditions in simulations without trade-offs and thus high predicted probabilities of clonality (Figure 3.3). 54 Figure 3.3 Population growth rate (λ(N)) across neighbor density, past reproduction, spring temperature, and rainfall treatment in the field rainfall manipulation experiment. Line color indicates spring temperature, where spring temperature varies while summer temperature is held constant at the mean. Solid lines refer to models built from demographic functions of plants that flowered in time t (and experienced a trade-off). Dotted lines refer to models built from demographic functions of plants that were vegetative in time t, which did not experience reduced probabilities of making clones due to previous flowering. Black horizontal lines indicate a λ value of one (stasis). Shaded areas show 95% confidence intervals of 1000 parametric bootstrap samples from demographic rate function coefficients, edges of confidence intervals correspond to past reproduction and temperature. The mean number of neighbors was 2.50 (SD = 1.75) in irrigated treatments, 2.15 (SD = 1.8) in control treatments, and 1.83 (SD = 1.54) in drought treatments. 3.4 Discussion Density and clonality interact to shape population demography and responses to climate drivers, and incorporating these processes is necessary to make accurate predictions under future climate conditions (Benton et al., 2006). Here we show that population responses to climate depend on local density, clonal reproduction, and trade-offs between reproductive modes. Predicted λ(N) was reduced by increasing neighbor density under simulated drought, and in simulated populations without a flowering-clonal trade-off. This indicates that the interactive 55 effect between reproductive trade-offs and density are key for determining climatic impacts on population growth. 3.4.1 Clonal and Sexual Reproduction Negative correlations between clonal reproduction and flowering have been shown to maximize plant fitness under varying environmental conditions (Herben et al., 2015; Vallejo-Marín et al., 2010) and across density (Barkham, 1980; Diez et al., 2014). Generally, we would expect that life histories with a phalanx style of growth, where ramets are produced in clusters, have decreased clonal reproduction at high density (Lovett Doust, 1989). Our results support this prediction, where we found that the probability of clonal reproduction was highest for plants growing under low density conditions in both the field rainfall manipulation and the pot experiment where density and water was manipulated. Greater clonality may increase the competitive ability of a genet occurring at low density, but at high density, clonal reproduction would exacerbate the negative effects of high density (Diez et al., 2014). Further, there may be a fitness benefit of long distance dispersal methods to prevent inbreeding and pollen discounting if plants only receive pollen from the same genet, for example if all neighbors are genetically identical (Vallejo-Marín et al., 2010). However, while flowering was not influenced by density in either of our experiments, primarily relying on flowering for the recruitment of new individuals at high local densities would increase the dispersal and survival of offspring (seeds) and likely increase ramet and genet fitness (Diez et al., 2014). Notably, lower probabilities of clonality at high density contributed to the reduction in population growth between low and high local densities, which was primarily mediated by the negative effect of flowering on clonal reproduction. Local density and abiotic environment have 56 been shown to drive the trade-off between clonal reproduction and flowering (Abrahamson, 1980; Barrett, 2015; Fischer and Van Kleunen, 2001; Herben et al., 2015; Loehle, 1987; Lovett Doust, 1989; Obeso, 2002), and here we show that both factors interact to influence population growth. The cost of reproduction hypothesis predicts that reproduction results in a fitness cost and that flowering may require more resources than vegetative reproduction (Obeso, 2002). Although the exact costs of producing bulblets versus seeds in this species is unknown, we only detected a flowering cost on clonality, and not vice versa, which scaled to differences in population growth rates and were more pronounced in drought and high density conditions. This suggests that while clonality may not be a form of reproductive assurance under adverse conditions in this population, it does ultimately impact estimates of population growth response to climate. 3.4.2 Influence of Density on Demographic Rates and Population Growth Negative density dependence, where population growth rates decrease with neighbor density, is predicted to be stronger under low abiotic stress and benign conditions (Zhang and Tielbörger, 2020). At the population level, we saw that density primarily decreased λ(N) in drought treatments. It is surprising that we only saw evidence of negative density effects under drought treatments, as dry, warm conditions are expected to be adverse conditions for species in this system. A previous study examining perennial plants in Pacific Northwest prairie ecosystems under simulated climate treatments detected negative impacts of temperature and drought on λ within species’ current ranges, but positive impacts for some species planted beyond their current northern range (Reed et al., 2020). This may suggest that this species’ early spring growing season at the northern edge of its range may be relatively abiotically stressful 57 with cold temperatures and wet conditions, especially if P. hendersonii’s performance at our field site, which lies at the northern extent of P. hendersonii’s range, is limited by climate (Louthan et al., 2015). The summer drought season, which was influential for plant growth and population growth, is a dormant period for the species, which may indicate that dormant season conditions after growing seasons are important physiologically for this species. Although so far only shown in Arctic systems, growing seasons may be longer underground than above ground, and so dormant seasons may be physiologically important for perennating structures belowground if physiological processes, for example bud formation, occur outside of the growing season (Blume-Werry et al., 2016; Lubbe et al., 2021). Future analyses should not exclude dormant season climate from analyses, and further research is needed examining why dormant season climate is influential for plant demography (Evers et al., 2021). In this study we detected similar effects of density using a density manipulation experiment and observation density in the field coupled with environmental data. Density manipulation experiments are recommended to both detect and quantify the effects of density (Fowler et al., 2006), although recent studies suggest that observational density dependence is detectable when accounting for environmental drivers (Dahlgren et al., 2014; Schultz et al., 2017). However, comparisons between manipulative and observational studies of density dependence are rare. This study provides further evidence that effects of observational density, supported from our results from the manipulated density experiment, can be found in observational demographic datasets when coupled with the environmental drivers of demography (Dahlgren et al., 2014). This is especially important for demographic studies of rare species or where destructive or manipulative density experiments are not feasible. Despite detecting effects of density using both approaches, we found nuanced differences in the effects of density across 58 experiments. Plant size increased under higher densities (increasing intercepts in demographic rate models), but only in the observational data did we detect slower rates of growth at high density (smaller slopes at high density). However, in the pot experiment, density treatments at the upper range of what is naturally found in the field experiment (highest density treatment) had the smallest plants, suggesting that there are positive effects of density on growth until the equilibrium density of an area is reached. Positive effects of density, or facilitation, are expected in high abiotic stress environments (Zhang and Tielbörger, 2020). When grown under high densities, P. hendersonii neighbors may ameliorate an abiotic stressor like temperature and contribute toward beneficial microhabitats, as shown in alpine systems (Callaway et al., 2002). This abiotic stress also could have been exacerbated in the pot experiment, which aimed to replicate natural conditions but likely was more abiotically stressful than the field because of higher temperature fluctuation than plants experience in the field. One place where we were not able to examine the effect of density was in the discrete stages in the IPM (seedlings and one-leaf plants). Self-thinning, or low recruitment and seedling survival at high densities (Harper, 1977), could be an important driver of survival for seedlings, and bulblets may compete against mother ramets. Further, in the IPMs, we assumed that the survival of one-leaf plants was the same for one-leaf plants that were made by adults who reproduce clonally and for one-leaf plants from seedlings that survive. However, survival may also differ between offspring of each reproductive mode. For example, clones may have higher establishment and survival probabilities than seeds and seedlings because of their larger starting size, leading to greater competitive ability and tolerance for adverse conditions (Eriksson, 1989; Harper, 1977). Despite this, simulations of differing survival and transition probabilities of one-leaf plants on λ showed small effects of changes in these probabilities on λ (Figure B1). In 59 addition, increased population densities may negate positive impacts of population growth at low density over time (Ehrlén and Morris, 2015). We emphasize here that we do not characterize population regulation, which implies a feedback between population growth rate and population size or density (Herrando-Pérez et al., 2012), and instead we focus on the effects of local density on demographic processes and predicted population growth. 3.4.3 Conclusions Two thirds of vascular plants reproduce clonally (Herben and Klimešová, 2020), but clonality is rarely incorporated in demographic studies or under the context of climate drivers of demography. Here we show that intraspecific density influences population response to variation in climate, that flowering reduces clonality, and that these processes ultimately influence predictions of λ across manipulated rainfall treatments and changes in climate across years. Thus, at least for this species, if we ignore the influence of clonality and density on population response to climate change, predictions of population decline will likely be underestimated for dense populations and overestimated for sparse populations. In addition, there are still key physiological unknowns for how plants respond to climate drivers, especially dormant season climates, highlighting that a priori predictions for relationships between demography and climate drivers may be difficult to make. Importantly, we show that incorporating density effects is achievable in observational data, and manipulative density experiments may not be needed. 60 Chapter 4: Direct temperature effects are more impactful than competition for population growth of native annual forbs and an invasive grass in Pacific Northwest savannas 4.1 Introduction Despite the general prediction that climate change will disproportionately benefit invasive over native species (Dukes and Mooney, 1999; Liu et al., 2017), it is unclear how climate drives invasive species success. Many mechanisms describe invasive species’ displacement of native species (Vilà et al., 2011), some of which may be exacerbated by climate change, such as the greater ability to plastically respond to disturbance (Daehler, 2003; Hille Ris Lambers et al., 2010; MacDougall and Turkington, 2005) and changes in resources (Davidson et al., 2011; Dickson et al., 2012; Wainwright et al., 2012), higher performance relative to native species under particular resource or environmental conditions (Daehler, 2003; Van Kleunen et al., 2010), or increased competitive superiority (Flory and Clay, 2010). Disentangling the direct effects of climate and the indirect changes in competitive interactions for populations in natural communities is needed to predict community change under climate change. This is especially important for the management of ecosystems where climate-induced increases of invasive species and declines of native species are already occurring, such as grasslands and savannas on the west coast of North America (Harrison et al., 2015; Pfeifer-Meister et al., 2016). Precipitation (Hallett et al., 2019; Suttle et al., 2007; Van Dyke et al., 2022) and temperature (Reed et al., 2021) have both been shown to directly influence the population growth of annual forbs and grasses in west coast grasslands and savannas. Increasing temperature and 61 drought severity (aridification) have been associated with increases in the abundance of annual invasive plants and declines in native forb and perennial grasses (Everard et al., 2010; Harrison et al., 2020, 2015; Pfeifer-Meister et al., 2016; Prevéy and Seastedt, 2015). Similarly, climate manipulations, particularly warming, across a range of Pacific Northwest savannas resulted in decreased native diversity (Reed et al., 2020) and increased abundance of invasive annuals that are hypothesized to better tolerate increasingly hot and dry conditions (Pfeifer-Meister et al., 2016). These relationships imply direct effects of climate on populations, that is, climate impacts that are not mediated by other processes. However, shifts in competitive dynamics may indirectly affect these relationships between climate and population growth. For example, greater spring precipitation was also shown to drive native forb loss in California grasslands indirectly through increased competition with invasive annual grasses (Suttle et al., 2007). Indirect effects of climate on species interactions also have the potential to influence population persistence, especially as environmental conditions influence competitive outcomes (Catling et al., 2024; Cervantes-Loreto et al., 2023; Hallett et al., 2019; Lanuza et al., 2018)), although not always (Adler et al., 2009; Levine et al., 2010). In a planted California annual grassland community, despite differences in rainfall having only a small direct effect on individual plants grown without competitions, manipulated rainfall idiosyncratically shifted predictions for competitive exclusion and coexistence for most species in pairwise competition (Van Dyke et al., 2022). Mechanisms for environment-dependent competition include differences in traits across environmental contexts as a result of phenotypic plasticity, leading to changes in individual resource requirements, or reduced or increased fitness and niche differences between species (Germain et al., 2018; Turcotte and Levine, 2016). Alternatively, stress could limit the ability to acquire resources, leading to competitive exclusion by the species 62 that can tolerate the lowest resource levels (resource-ratio theory or R* theory; (Miller et al., 2005; Tilman, 1982). Lastly, the density of conspecific neighbors can also regulate how populations respond to climate, for example if shifting climate influences local carrying capacity and equilibrium population sizes (Dahlgren et al., 2016, 2014); Data Chapter 2) or microenvironments are modified by neighbors (Ray et al., 2023). Importantly, shifts in intraspecific competition under changing environmental context can also have strong impacts on population growth, potentially even greater than shifts in interspecific competition when species have small niche overlap (Adler et al., 2018; Fox and Morin, 2001). Thus, determining population persistence under climate change requires identifying how changes in competition that are mediated by climate indirectly affect population growth. Understanding the direct impact of climate on annual plant populations requires identifying the links between climate variation, plant demographic performance, and population growth. Climate variables can be included as covariates in population models to relate variation in annual estimates of population growth rate with variation in climate variables (Boggs and Inouye, 2012; Dalgleish et al., 2011; Iler et al., 2019). When demographic data are not available, or for unstructured populations, variation in annual census counts or time series data can be correlated with climate variation (Knape and de Valpine, 2010; Roland and Matter, 2016, 2013; WallisDeVries et al., 2011). When census counts are conducted within climate manipulation experiments, the effects of manipulated conditions on population growth can be identified. Further, conspecific density can be incorporated as past population size or density (Brook and Bradshaw, 2006) to estimate density-dependent population growth and risk of population extinction, i.e. population viability analysis (PVA; Chaudhary and Oli, 2020; Morris and Doak, 2002). Count-based PVA methods have commonly been used for single species analysis of bird, 63 mammal, and insect populations, but are now increasingly being used for plant species for which detailed demographic data are lacking (Bernardo et al., 2018; Scholl et al., 2022). Despite the utility of PVA for understanding population persistence of single species under environmental variation over time, there is also a need to understand to what extent interspecific competition is driven by climate. Modern coexistence theory (Chesson, 2000), which partitions species competition into fitness and niche difference to predict coexistence outcomes and coexistence metrics like competitive ability (Hart et al., 2018), is a framework used to understand the relative roles of environmental drivers and intra- and interspecific competition in influencing predicted population persistence (Aoyama et al., 2022; Lanuza et al., 2018). Specifically, competitive ability is defined here as a metric that incorporates the ability for a species to have high fecundity in the absence of competition and to tolerate both intra-and interspecific competition (Hart et al., 2018). While experiments examining coexistence mechanisms have provided insights into competition between species, their results can be difficult to extend to naturally occurring field systems (Cadotte and Tatsumi, 2025). For example, manipulative density experiments where species are grown alone or with one neighboring species often predict competitive exclusion for species pairs that commonly persist over long periods of time in nature (Bowler et al., 2022b; Tuck et al., 2018; Wainwright et al., 2019; Adler et al., 2018) and environmental manipulations used may not accurately simulate future climate conditions. In addition, these experiments rarely extend beyond one year (but see (Adler et al., 2018), making it difficult to link results to observed population change over time or to variation in environmental conditions (Aoyama et al., 2022). Methods incorporating and comparing both coexistence theory and population viability analysis in tandem will be helpful to understand climate impacts on populations (Jeltsch et al., 2019). 64 To disentangle the impacts of climate on population growth rates and competition between species, we use a rainfall manipulation experiment where we conducted census counts taken over two to nine years for three naturally occurring annual species with similar phenologies (two native forbs and one invasive grass). We pair this with 2-4 years of density-dependent fecundity collected within the same rainfall experiment. We examine how seasonal climate and manipulated rainfall (1) influence density-dependent population growth estimated from census counts for each species, and (2) influence competitive ability, a competition metric derived from coexistence theory which would indicate an indirect effect of climate. Our work occurs in Garry oak savanna ecosystems, which are threatened by invading species as well as shifting climate (Pfeifer-Meister et al., 2016; Reed et al., 2021). These fire-dependent, wet savanna ecosystems have historically been invaded by perennial grass species introduced by European settlers (Pinto and MacDougall, 2010), but increasing temperatures and changing precipitation are predicted to promote annual grass invaders that can better tolerate drier, hotter conditions (Pfeifer-Meister et al., 2016; Poppenwimer et al., 2023). Since species with similar phenologies are predicted to compete (Alexander and Levine, 2019; Blackford et al., 2020; Godoy et al., 2023; Levine et al., 2022), we expected to detect competition between our focal species that would be exacerbated under drier and warmer conditions. If the invasive grass species contributes to the decline of the native species, we would expect to see higher competitive ability, here quantified as a metric that incorporates difference in population growth, or fitness, and tolerance for competition (Hart et al., 2018), of the invasive grass, particularly under warmer, drier conditions. 65 4.2 Methods 4.2.1 Study System Pacific Northwest Garry oak ecosystems are characterized by oak savanna with a forb and grass understory, with mild winters and short spring flowering seasons. These ecosystems are now restricted to 5% of their historic range in Canada (Fuchs, 2001) and historically experienced fire under First Nations stewardship. Remnant prairies are commonly invaded by exotic perennial and annual grasses, forbs, and shrubs (MacDougall and Turkington, 2006). Our study was conducted in a single prairie at the Cowichan Garry Oak Preserve (Nature Conservancy of Canada) in Duncan, BC, Canada. Mean annual precipitation at the study site is approximately 1100 mm and mean annual temperature is 10°C (1980-2010 climate normals; (Wang et al., 2016). Local climate projections estimate an 11% increase in annual precipitation by 2080 (specifically, an increase in total precipitation for winter, spring, and fall, but a decrease in summer precipitation) and increased annual temperature by 3°C by 2050 and 4.5°C by 2080 under “business as usual” emission scenarios (Cowichan Valley Regional District, 2017). Hourly air temperature and ambient precipitation was recorded on site with a HOBO U30 weather station. Using the International Drought Experiment protocol (Knapp et al., 2017; Smith and Williams, 2023), we established a year round rainfall manipulation experiment with control, drought, and irrigation treatments (five 2 x 2 m plots per treatment) in 2015 to simulate changes in the amount of rainfall received per rain event to examine community response to changes in total precipitation. Control plots consisted of 2 x 2 m plots with 50 cm buffers. Wooden drought shelters with strips of corrugated clear plastic roofing covering 50% of a 3 x 3 m area reduced incoming rainfall by 50% in the focal 2 x 2 m drought plots. Water runoff from the shelters was 66 diverted to passively irrigate 2 x 2 m irrigation plots. After three consecutive years of low spring precipitation, an additional sixty liters of water were added to irrigated treatments during the growing season beginning in 2021. Water additions occurred twice per week (15 mm/m2 per week), but if maximum daily temperatures within a week exceeded 20°C water was added three times per week (22.5 mm/m2 per week) to account for water loss with increased evapotranspiration. This ensured that irrigated treatments received water even when drought and control plots received no rainfall from natural rain events. 4.2.2 Study Species Plectritis congesta, Bromus sterilis, and Claytonia perfoliata are locally common, co-occurring annuals common to Garry oak ecosystems, and are the three most common annuals occurring in the study plots (Loesberg & Williams, unpublished data). Plectritis congesta (Lindl.) DC. (Valerianaceae) is a native winter annual forb that typically germinates in fall or late winter, flowers in late spring, and sets seeds and dies by mid-June. Plectritis congesta seeds have high germination rates and low dormancy, so in line with previous studies on this species, we assumed that seeds do not survive in a seed bank after germination occurs in the fall (Johnson and Williams, 2020). Bromus sterilis L. is an invasive annual European grass, similar in morphology and closely related to the widely invasive cheatgrass (Bromus tectorum; Fenesi et al., 2011). At our study site, B. sterilis germinates in fall or late winter, flowers mid-May through mid-June, and sets seeds and dies in late June. High abundances of B. sterilis at the site are recent in comparison to invasive perennial grasses (i.e. Dactylis glomerata and Poa pratensis; Pinto and MacDougall 2010), although its date of introduction is unknown. A common weed in winter 67 cereal croplands, B. sterilis has been shown to have low seed dormancy and does not persist in soil longer than a year (Andersson et al., 2002; Holec and Soukup, 2014; Peters et al., 2000; Theaker et al., 1995), so we assume this population does not form a seedbank. Claytonia perfoliata Donn. (Portulacaceae) is a shade tolerant annual native to western North America, and most commonly occurs in moist soil (Miller, 1976). At the site, plants germinate in early spring, flower in April and May, and set seed and die at the onset of warm, dry conditions in late May. Seed dormancy or seed banking is unknown; for the purposes of the models, we assumed no contributions from the seed bank. This assumes that recruitment comes from seed production in the year prior, although some recruitment could come from seeds produced in earlier years. 4.2.3 Annual Census and Estimated Seed Production In 2 x 1 m focal areas of each of the 15 rainfall manipulation plots, annual censuses of each species were taken at peak flowering in four permanent 25 cm x 25 cm subplots (2016-2024 for P. congesta, 2021-2024 for B. sterilis, and 2023-2024 for C. perfoliata). If the subplots contained zero or few individuals, we counted all individuals in the entire focal area. Plot density was determined by adding densities of subplots together and scaling to 1 m2, which effectively pooled subplot densities. We could not count seed production for each plant in the experiment, so we evaluated the relationship between traits and seed set for a sample of individuals from each species and determined the best fitness proxies from which to predict seed production. To estimate seed production of each individual in the census subplots, we measured traits for up to 20 individuals 68 per subplot and predicted seed production based on relationships between seed production and fitness proxies from best supported models. For P. congesta, we measured plant height, number of inflorescences, and width and length of the main inflorescence for 10-20 individuals per subplot. To estimate seed production from these traits, in 2021 and 2022 we measured the fitness proxies on individual plants that fell within the rainfall plots but outside of the subplots where censuses were counted to avoid influencing seed production within the subplots. When plants started seeding, organza bags were placed on main inflorescences to collect seeds. Seeds were collected, counted, and returned to plots. To determine the best predictors of seed production (plant height, rainfall treatment, area of main inflorescence, and volume of main inflorescence), we fit generalized linear models with Poisson error distributions for collected seed counts as a response and compared model fit with AIC. Rainfall treatment was included as a predictor of seed production because we hypothesized that seed production would be influenced by rainfall treatments based on prior work on another species in the experiment (Chapters 2 and 3). Data was pooled across plots and years because models including plot and year as fixed or random effects would not converge. Year was not included as a fixed effect because we aimed to predict seed production across all years of fecundity data. The best supported model to predict seed production included plant height, rainfall treatment, and a height x treatment interaction (Table C.1; Figure C.1), which is similar to previous estimates of P. congesta seed production predicted from fitness measurements (Johnson and Williams, 2020). This relationship was used to predict the seed production of individuals within the census subplots based on measured plant height. For B. sterilis, we counted the number of spikelets on 10-15 plants per subplot. On average, plants produced five seeds per spikelet (standard deviation ± 2.2). Per plant seed 69 production was estimated as the number of spikelets produced multiplied by 5 seeds. For C. perfoliata, we counted the number of racemes for up to 10 individuals per subplot. To estimate how many flowers were produced on each raceme on average, we haphazardly counted flowers on 10 racemes within plots (3.5 ± 2.0 to 12.4 ± 2.7 flowers per raceme per plot). Each flower produces one capsule that contains three seeds (Miller, 1976; Miller and Chambers, 1993), and we used this relationship to predict seed production per plant. 4.2.4 Time Series Analysis and Count-based PVA For each species in each rainfall experiment plot, we calculated the log(Nt + 1/Nt), or Rt, from one growing season (t) to the next (t + 1) where N is the intraspecific density per m2. Values greater than zero indicate population increase and values less than zero indicate decline. To facilitate log-transformation of the data, we added one individual to the summed plot densities, as some plots had zero individuals at the beginning of the experiment (Figure 1). For example, six rainfall experiment plots had zero P. congesta at the beginning of the experiment, but had individuals in subsequent years (Figure 1). This is likely because of seed dispersal from nearby plants rather than recruitment from a seed bank. This may mean that densities of subplots are in part driven by seed dispersal from other areas; however, P. congesta has low dispersal (Trowbridge, 2015) and we assume seed rain is generally localized around parent plants and net immigration within subplots is similar to net emigration. One plot was excluded from the analysis of P. congesta because it had only one individual for multiple years. To estimate the effects of seasonal climate on Rt, we examined: spring mean temperature (March-May), spring total precipitation, fall mean temperature (September-November), winter mean minimum temperature (December-February), summer total precipitation (June-August), or 70 summer mean maximum temperature (see Figure C.2 for ranges of climate variables). We examined the effects of these “short-listed” climate variables on Rt because we hypothesized that they would be most influential for fall germinating annuals that survive as seedlings through the winter, flower in the spring, and set seed in early summer. Climate variables for 2013-2024 were scaled to a mean of 0. We tested the effect of both seasonal climate (precipitation and temperature) and rainfall manipulation experiment because we expected interactive effects between the experiment and observed climate. For example, the difference in rainfall experienced across treatments depended on ambient precipitation, with the exception of irrigated plots in the growing season, and manipulated rainfall may have been more important under warmer or cooler temperatures. Soil moisture was not used as a covariate because soil depth heterogeneity resulted in large differences in soil moisture within plots. 4.2.5 Competition Models To compare competition coefficients and competitive ability between P. congesta and B. sterilis across rainfall manipulation treatments, we parameterized Beverton-Holt annual plant competition models for each rainfall treatment to determine the influence of intra- and interspecific competition on seed production, 𝐹𝑖 (Beverton and Holt, 1957; Hart et al., 2018; Levine and HilleRisLambers, 2009). We modified the following Beverton-Holt function to include the effect of seasonal climate variation on competition coefficients and low density fecundity (Godoy and Levine, 2014; Lanuza et al., 2018): 𝐹𝑖,𝑡 = 𝜆𝑖,𝑡(1 + 𝛽𝑡𝐶𝑡)1 + (𝛼𝑖𝑖,𝑡+𝛾𝑖𝑖𝐶𝑡)𝑁𝑖,𝑡+ (𝛼𝑖𝑗,𝑡+𝛾𝑖𝑗𝐶𝑡)𝑁𝑗,𝑡 (Equation 4.1) 71 where 𝐹𝑖 is the per-individual fecundity of each species, 𝜆𝑖 is estimated low density seed prediction, 𝛼𝑖𝑖,𝑡 is the intraspecific interaction coefficient which describes the per capita effect of conspecific density, 𝛼𝑖𝑗,𝑡 is the interspecific interaction coefficient which describes the per capita effect of the competing species, and Ni and Nj are the population densities of species i and j. 𝐶𝑡 refers to standardized climate (see analysis section for climate variable selection), with 𝛽𝑡, 𝛾𝑖𝑖, and 𝛾𝑖𝑗 describing the effect of climate on 𝜆𝑖, 𝛼𝑖𝑖, and 𝛼𝑖𝑗, respectively. This “full” model was compared to reduced models (models with (1) only a climate effect on 𝜆𝑖, (2) only climate effects on 𝛼𝑖𝑖,𝑡 and 𝛼𝑖𝑗,𝑡, and (3) no climate effects) using AIC. 𝜆𝑖 is the estimated seed production without intraspecific competition or the competing species, but it does include the competitive effects of the background community. Although this is an imperfect measurement of low density seed production, plants from each species grown alone in the field did not produce seeds (Loesberg, unpublished data). By incorporating variation in environmental drivers and replicating estimates of 𝜆𝑖across plots and years, we aimed to account for the resulting variation in this estimate. Further, where species occurred at low densities of intraspecific and the other focal species, there were not other abundant annuals aside from a late season annual grass (B. hordeaceus). Models were built for pairwise competition between P. congesta and B. sterilis using data from 2021-2024. We then used estimated parameters to estimate competitive ability (𝐾) for P. congesta and B. sterilis, specific to each rainfall treatment and climate, if the addition of climate improved the fecundity model (Hart et al., 2018): 𝐾𝑖 =𝜆𝑖 − 1√𝛼𝑖𝑖𝛼𝑖𝑗 (Equation 4.2) This metric describes the ability for a species to have high seed production under low competition, and to withstand intra and interspecific competition. High values of K indicate 72 higher competitive ability, and would predict competitive exclusion of the competitor if there are no niche differences. Because there were only two years of C. perfoliata fecundity data, climate independent models were built for the effects of P. congesta and B. sterilis on C. perfoliata fecundity for 2023 and 2024 within the rainfall experiment, with an added coefficient term to estimate the impact of a third species (k) on fecundity: 𝐹𝑖 = 𝜆𝑖1 + (𝛼𝑖𝑖 𝑁𝑖+ 𝛼𝑖𝑗𝑁𝑗+ 𝛼𝑖𝑘𝑁𝑘) (Equation 4.3) Even though we could not test the effect of observed climate on fecundity in models that included C. perfoliata, we could still test the effect of rainfall manipulation on competition between these three species. 4.2.6 Analysis To determine the density-dependent effect of climate and rainfall treatment on Rt, or log population growth rate, and which climate variable influenced P. congesta and B. sterilis population dynamics, we used linear mixed effect models with Gaussian errors of observed log(Nt + 1/Nt) with density in year t (Nt), rainfall treatment, and one of the six climate variables described above as predictors. We also tested two-way interactions between density and rainfall treatment, density and climate, and rainfall treatment and climate. To test for nonlinear effects of density, we included a quadratic term for density but these models would not converge, so they were excluded from the set of candidate models. Plot and year were included as random effects to account for spatial autocorrelation and repeated measures. The best-supported model was selected as the model with the lowest AICc, and with fewest number of parameters if 𝛥AICc was 73 less than 2 for more than one model. To quantify uncertainty in parameters in the best supported model, we did 1000 parametric bootstraps and calculated 95% confidence intervals. We did not examine seasonal climate effects on C. perfoliata because there was only one population growth transition year; instead, we built candidate linear regressions for candidate models that included population density, rainfall treatment, and density x treatment interactions. To determine the relative influence of density and climate on population persistence, we projected time to quasi-extinction using the best supported models for P. congesta and B. sterilis (Morris and Doak, 2002). We define and estimate “quasi-extinction” as the subpopulation size at which we could consider the population too small to return to higher densities within the plot (Morris and Doak, 2002). We projected population densities 20 years into the future 50,000 times with a quasi-extinction threshold of 1 individual/m2. We chose this threshold because very low densities were rarely observed, so we hypothesized that low densities rarely rebounded. Starting population densities in the simulations were set at the median density across all years of data for each species. Variance in the predicted population growth rate was incorporated as the residual variance from the mixed effect models. We then determined the percentage of simulated populations that reached densities of 1 individual per m2 under each climate scenario to simulate relative population change under a scenario of fixed warmer or cooler temperatures. To parameterize competition models (Equation 2 and models 1-3) for P. congesta and B. sterilis, we used nonlinear mixed effects models. Year was included as a random effect to account for nonindependence of measurements across years. Plot was not included as a random effect, as models including both random effects would not converge and the year random effect explained more variance. Full and reduced models (models 1-3) with winter mean minimum 74 temperature or summer mean maximum temperature (climate variables included in the best supported time series models) were compared using AIC. Separate models were parameterized for each rainfall treatment as the nonlinear models do not accept categorical predictors. To quantify uncertainty in parameters and competitive ability, we bootstrapped 1000 datasets, re-estimated the parameters, and calculated 95% confidence intervals. All analyses were performed in R version 4.4.1 (R Core Team, 2024). Linear mixed effects models were fit using the lme4 package (Bates et al., 2015), and nonlinear mixed effect models were fit using the nlme package (Pinheiro et al., 2025). For the C. perfoliata fecundity models, we parameterized nonlinear models using maximum likelihood with the nlsLM function in the minpack.lm package (Elzhov et al., 2023). 4.3 Results 4.3.1 Climate impacts density-dependent population growth For all species, density-dependent models were better predictors of the log population growth rate (R t = log(Nt + 1/Nt) per 1 m2) than density-independent models (lower ΔAIC, Table 4.1). Rt and population sizes within each plot for all species varied across years (Figure 4.1). Claytonia perfoliata population growth within plots was negatively affected by density of conspecifics at 1 m2 (P < 0.001, r2 = 0.77; Table 4.1; Figure 4.2e), and was not influenced by rainfall treatment. Table 4.1. Model selection for linear models of Rt for Plectritis congesta, Bromus sterilis, and Claytonia perfoliata using AIC. Top ten models are shown, in addition to the intercept-only model. Bolded rows indicate the best supported model (ΔAIC < 2 with fewest number of parameters, with the exception of B. sterilis, where we chose the temperature variable). K is the number of estimated parameters for each model. Plectritis congesta and B. sterilis models were linear mixed effects models that include plot and year as random intercepts, C. perfoliata linear models did not have random effects nor did they include effects of climate. Abbreviations in model predictor variables are: N is the log transformed conspecific population density (individuals per m2) in time t, trt is rainfall treatment, SuT indicates summer mean maximum temperature, SpP indicates total spring precipitation, WT indicates winter mean 75 minimum temperature, SpT indicates spring mean temperature, FT indicates fall mean temperature. “X” indicates an interaction term between variables Species Model K AICc ΔAICc Rank Plectritis congesta N x SuT + N + SuT 7 223.752 0 1 N x SuP + N + SuP 7 225.149 1.398 2 N + SuT 6 225.491 1.739 3 N x SuT + trt + N + SuT 9 226.131 2.379 4 N x SuP + N + SuP + trt 9 226.709 2.958 5 N + SpP 6 226.719 2.967 6 N + SuP 6 226.987 3.236 7 N + FT x trt + FT + trt 10 227.249 3.497 8 N 5 227.309 3.558 9 N + SuT + trt 8 227.345 3.594 10 1 4 257.037 33.286 36 Bromus sterilis N + WT 6 110.659 0 1 N x WT + N + WT 7 111.01 0.351 2 N + SpP 6 112.209 1.551 3 N + SuP 6 112.515 1.856 4 N x SpP + N + SpP 7 112.66 2.001 5 N x SuP + N + SuP 7 113.058 2.399 6 N + SuT 6 113.662 3.003 7 N x SuT + N + SuT 7 114.591 3.932 8 N 5 114.939 4.28 9 N + WT + trt 8 115.364 4.706 10 1 4 130.49 19.832 39 Claytonia perfoliata N 3 32.556 0 1 N + trt 5 40.672 8.116 2 1 2 49.822 17.266 3 N x trt + N + trt 7 53.376 20.82 4 trt 4 57.014 24.458 5 76 Figure 4.1. Population density per 1 m2 (A-C) and Rt, or log(Nt + 1/Nt), per 1 m2 (D-F) for the native annual forb Plectritis congesta (A, D), introduced annual grass Bromus sterilis (B, E), and native annual forb Claytonia perfoliata (C, F). Each line shows one plot in the rainfall manipulation experiment, color indicates rainfall manipulation treatment. Values of Rt > 0 indicate population increase and values < 0 indicate decline. Horizontal grey line indicates Rt = 0 (stasis). Rt does not have values for 2024 because they indicate transitions from t to t + 1. 77 Figure 4.2 Density-dependent Rt, or log(Nt + 1/Nt), and the probability of quasi-extinction. Panels A, C, and E show the Rt, or log(Nt + 1/Nt), of P. congesta for cool (-1 standard deviation from the mean from 2016-2024) and warm (1 standard deviation from the mean) maximum summer temperatures, B. sterilis for cool (-1 standard deviation from the mean from 2016-2024) and warm (1 standard deviation from the mean) minimum winter temperature, and C. perfoliata across population density, where the effect of temperature was not tested, respectively. Points indicate one value of Rt for an individual 1 m2 plot from t to t + 1. Sizes of points in A and C indicate seasonal temperature (summer mean max for P. congesta and winter mean minimum for B. sterilis; small points indicate cooler temperatures, larger points indicate warmer temperatures). Plectritis congesta and B. sterilis models were mixed effects models that include plot and year as random intercepts, the C. perfoliata model is linear regression. Shaded areas in A and C indicate 95% confidence intervals from 1000 parametric bootstraps from model parameters. Note the log scale for density. Panels B and D show the probability of quasi-extinction projected for 20 years under warm and cool climate scenarios for P. congesta and B. sterilis. Quasi-extinction is defined as the proportion of simulations reaching population densities of one individual per m2 from a starting population of the median species density from across the experiment. 78 Rt for P. congesta was negatively associated with high local conspecific density and warm summer temperatures (Table 4.1; Figure 4.2a), although multiple models were within ΔAIC < 2 (Table 4.1). Population growth rates declined at high density, and this effect was weaker in years with cooler summer maximum temperatures, where population decline (Rt < 0) only occurred at the very highest observed conspecific density (Figure 4.2a). In contrast, warm summers were associated with increasing population size (Rt > 0) only when local population density was low. However, 95% confidence intervals overlapped between mean maximum summer temperatures that were one standard deviation above and below the mean (corresponding to summer maximum temperatures of 26.3°C and 24.5°C). We did not detect effects of rainfall manipulation treatment or annual variation in seasonal rainfall on Rt for P. congesta. For the invasive grass, B. sterilis, Rt per 1 m2 plot was negatively associated with higher local conspecific density and lower minimum winter temperature (Table 4.1; Figure 4.2c). Two other models were within ΔAIC < 2 and had the same number of parameters (these models included density and negative effects of spring precipitation (ΔAIC = 1.56) or summer precipitation (ΔAIC = 1.86)). In subsequent analyses, we examined only the effects of winter temperature to reduce the number of candidate models while staying consistent with examining temperature effects on the other focal species. Predicted Rt was significantly higher for warm minimum winter temperature (one standard deviation higher than the mean, or 2.2°C) than cool minimum winter temperature (one standard deviation lower than the mean, or -0.28°C). Rt under cool winters was not significantly greater than zero, indicating population decline, across all densities (Fig 4.2c). 79 Simulated P. congesta populations under warm summer mean maximum temperatures (one standard deviation higher than the 2013-2024 mean), which were held constant over the projections, had accelerated estimates of quasi-extinction compared to simulated populations under cool temperature (one standard deviation lower than the 2013-2024 mean), although simulations for both temperature scenarios reached 100% quasi-extinction within 20 years (Figure 4.2b). For B. sterilis, simulations under cool temperatures, 46% of simulations reached quasi-extinction within one year, compared to 5% of simulations under warm temperatures. Despite this, all simulated B. sterilis populations were estimated to reach quasi-extinction within 20 years (Figure 4.2d). These high predictions of quasi-extinction could be an artifact of high residual variation from the models, which were used to stochastically simulate future population size. Further, these predictions are ultimately dependent on the cut off for quasi-extinction which we conservatively set at one individual per m2. Alternatively, these results could suggest a pessimistic scenario which assumes no evolution or adaptation to warmer or cooler average climates. Regardless, the relative times to quasi-extinction, rather than absolute predictions, between climate scenarios is a better indicator of population persistence. 4.3.2 Climate-specific impacts on seed production and competition metrics Overall, we found little evidence that climate influenced the effects of competition on fecundity for either the invasive grass B. sterilis or the native forb P. congesta. We examined the effects of rainfall treatment, seasonal climate, and the interaction between treatments and climate, as we predicted the effect of treatments would depend on the current climate. For both B. sterilis and P. congesta., models that included climate were better predictors of fecundity than models that did not include climate (lower ΔAIC, Table 4.2). Predicted λi, or low density seed production, for P. 80 congesta was significantly reduced in warm summers compared to cool summers in irrigated plots, although there was no effect of summer temperature in control or drought plots nor was there a difference between rainfall treatments (overlapping 95% confidence intervals in Figure 4.3c). Bromus sterilis λ was significantly higher in control plots compared to drought and irrigated treatments (Figure 4.3c) but was not influenced by temperature. Claytonia perfoliata λ was decreased in drought plots relative to control but not irrigated plots, where we did not examine effects of seasonal climate (Figure 4.4). Table 4.2. Model selection for nonlinear competition models for each species and rainfall manipulation treatment. Selected models are indicated by bolded rows. “No climate” models refer to base models without climate; WT indicates winter mean minimum temperature and SuT indicates summer mean maximum temperature; “Temp effect on 𝜶𝒊𝒊 and 𝜶𝒊𝒋” indicates models that only include a climate effect on 𝜶 ; “Temp on λ” indicates models that only include a climate effect on low density seed production; and “Full models” are the full climate model, where there is a climate effect on 𝜶𝒊𝒊, 𝜶𝒊𝒋, and λ. Species Treatment Model K AIC ΔAIC Rank Bromus sterilis Control WT on 𝛼𝒊𝒊 and 𝛼𝒊𝒋 7 4429.316 0 1 Full WT 8 4430.252 0.936 2 Full SuT 8 4437.377 8.061 3 SuT on 𝛼𝑖𝑖 and 𝛼𝑖𝑗 7 4438.627 9.312 4 No climate 5 4456.768 27.452 5 SuT on λ 6 4458.084 28.769 6 WT on λ 6 4458.738 29.422 7 Drought WT on 𝛼𝒊𝒊 and 𝛼𝒊𝒋 7 3422.734 0 1 Full WT 8 3424.178 1.445 2 SuT on λ 6 3432.549 9.816 3 No climate 5 3433.109 10.375 4 WT on λ 6 3435.027 12.293 5 Full SuT 8 3436.068 13.334 6 81 Species Treatment Model K AIC ΔAIC Rank SuT on 𝛼𝑖𝑖 and 𝛼𝑖𝑗 7 3436.305 13.571 7 Irrigated Full SuT 8 2973.826 0 1 WT on 𝛼𝒊𝒊 and 𝛼𝒊𝒋 7 2974.063 0.236 2 Full WT 8 2975.98 2.154 3 SuT on 𝛼𝑖𝑖 and 𝛼𝑖𝑗 7 2975.993 2.166 4 No climate 5 2983.583 9.757 5 SuT on λ 6 2983.637 9.811 6 WT on λ 6 2985.056 11.23 7 Plectritis congesta Control WT on 𝛼𝒊𝒊 and 𝛼𝒊𝒋 7 6259.215 0 1 Full WT 8 6260.915 1.7 2 SuT on λ 6 6268.358 9.143 3 WT on λ 6 6268.854 9.639 4 No climate 5 6269.335 10.12 5 Full SuT 8 6271.813 12.598 6 SuT on 𝛼𝑖𝑖 and 𝛼𝑖𝑗 7 6273.02 13.806 7 Drought SuT on 𝛼𝒊𝒊 and 𝛼𝒊𝒋 7 6839.661 0 1 Full SuT 8 6841.552 1.89 2 WT on 𝛼𝑖𝑖 and 𝛼𝑖𝑗 7 6843.378 3.717 3 Full WT 8 6844.243 4.582 4 SuT on λ 6 6854.901 15.24 5 No climate 5 6857.643 17.982 6 WT on λ 6 6857.95 18.289 7 82 Figure 4.3. Parameter estimates for Plectritis congesta and Bromus sterilis from best-supported non-linear mixed effect models across each rainfall manipulation treatment. Competition coefficients, 𝜶𝒊𝒊 (intraspecific coefficient, hollow circles, or “Bromus on Bromus” and “Plectritis on Plectritis” panels) and 𝜶𝒊𝒋 (interspecific coefficient, solid triangles or “Plectritis on Bromus” and “Bromus on Plectritis” panels), are the rate of decline of per capita seed production with increasing conspecific and heterospecific density, respectively, for B. sterilis (a) and P. congesta (b). Positive values indicate competition, negative numbers indicate facilitation. (c-d) Predicted per capita seed production under low density (𝝀𝒊) for B. sterilis (c) and P. congesta (d). (e) Competitive ability (K) for each species, where larger values indicate greater competitive ability. For visualization, K values are log-transformed. Colors indicate climate terms, where grey indicates parameters that were not influenced by climate in best supported models. Climate variables are not common across all species and rainfall manipulation treatments because best supported models incorporated different climate variables. Hot and wet climate refers to standardized climate 1 SD above the 2013-2024 mean max summer temperature and mean total summer precipitation, and cool and dry indicate -1 SD. Error bars indicate 95% confidence intervals from 1000 nonparametric bootstrapping samples. 83 Competition coefficients, which describe the per capita effect of either a conspecific (𝛼𝑖𝑖) or heterospecific individual (𝛼𝑖𝑗) on predicted fecundity, showed variable responses to climate for all species. In the pairwise analysis between P. congesta and B. sterilis, for which there were more years of data, differences in competition coefficients in control treatments were influenced by climate, where the effect of B. sterilis on itself was higher than the effect of P. congesta on B. sterilis under warm but not cool winters. The effect of B. sterilis on P. congesta was greater in warm winters in control plots. Likewise, in drought plots, the effect of B. sterilis on P. congesta was greatly increased in warm summers, but not significantly from the effect of P. congesta on itself under these conditions (Figure 4.3b). In drought plots when winters were cool, B. sterilis had a greater effect on itself than P. congesta had on B. sterilis, but not in the other rainfall treatments (Figure 4.3a). In irrigated plots, generally, 𝛼𝑖𝑖 were greater than or equal to 𝛼𝑖𝑗 coefficients (Figure 4.3a,b), suggesting that species were more influenced by their own density than the density of their competitor. Plectritis congesta density had a negative effect (𝛼𝑖𝑗 > 0) on B. sterilis fecundity under drought and warm temperatures, but estimates of 𝛼𝑖𝑗 were less than zero under control and irrigated treatments, which may imply facilitation. In the analysis with three species, the effect of B. sterilis on C. perfoliata was greatest in irrigated plots, the effect of P. congesta on C. perfoliata was highest in drought plots (Figure 4.4). 84 Figure 4.4 Competition coefficients (a) and low conspecific density seed production (b) for the native forb, Claytonia perfoliata. Colors in panels A and B indicate rainfall manipulation experiment treatments. Panels in (a) indicate competition coefficients: “Bromus on Claytonia” is the per capita effect of B. sterilis on C. perfoliata (𝜶𝒊𝒋, or closed triangles), “Plectritis on Claytonia” is the per capita effect of P. congesta on C. perfoliata (𝜶𝒊𝒌, or closed triangles), and “Claytonia on Claytonia” is the per capita effect of C. perfoliata on itself (𝜶𝒊𝒊, or open circles). Error bars show standard error of parameters. Predicted per capita fecundity increased with conspecific density for B. sterilis under cool winters in irrigated plots, and under warm winters in drought plots (Figure C.3). Seed production of P. congesta was predicted to be lowest under warm, drought conditions when in competition with high densities of B. sterilis (Figure C.1). Rainfall manipulation treatment did not greatly influence predictions of density-dependent fecundity for C. perfoliata (Figure C.3). Ultimately, neither species showed evidence of competitive dominance (higher K) under the environmental conditions we examined, as shown by overlapping 95% CI (Figure 3d). The only significant reduction in competitive ability (K), and thus predicted competitive dominance, 85 occurred for P. congesta under warm winter temperatures compared to cool in control plots (Figure 3b). Across bootstrapped samples, there were many instances of negative competition coefficients, or potential facilitation where a competitive coefficient was below zero. This prevented the calculation of competitive ability, mainly in models of warm temperature for B. sterilis (up to 85-98% of bootstrapped datasets indicated facilitation), and in cool control and warm irrigated plots for P. congesta (87% and 71% of models indicated facilitation). Confidence intervals for these conditions may be large because of few cases of competitive interactions. 4.4 Discussion With climate change, populations of invasive species may benefit from increased competitive ability under certain environmental conditions, direct effects of climate, or a combination of both factors. To disentangle these effects, we examined the population dynamics of native annual forbs and an invasive grass under rainfall manipulation and interannual variation in seasonal climate in Garry oak savanna. We detected effects of climate on density-dependent population growth estimated from census counts for all three species, but did not see evidence for indirect effects of climate on inter- or intra-specific competition or competitive ability. Here, we discuss the implications and limitations of our results. In support of the hypothesis that climate change will favor invasive species, warmer temperatures benefitted the invasive grass but not the native forb. Our results are also in line with previous studies that have detected negative effects of temperature, but not precipitation, on population growth rates of native annual (Reed et al., 2021) and perennial species in Garry oak savanna ecosystems (Reed et al., 2020); Data Chapters 1 and 2). Previous warming experiments resulted in severe decreases in P. congesta population growth rates under warmed temperatures 86 (Reed et al., 2021), which we also observed with accelerated predictions of quasi-extinction for P. congesta., These predictions of time to subpopulation extinction are likely overestimated, in part because they assume no evolutionary change in response to warming, even as some research suggests adaptation may not allow populations to cope with changing climates (Anderson et al., 2025) or seed dispersal from other areas. In a previous competition experiment in pots, P. congesta intrinsic population growth rates were also not influenced by water availability (Johnson and Williams, 2020), which suggests that temperature is a stronger driver of P. congesta population dynamics than precipitation. In contrast, the invasive grass B. sterilis was negatively affected by cool minimum winter temperatures (December-February). For a similar annual invasive grass species, B. tectorum, warming treatments increased population growth rates, especially at high elevations where population growth was limited by adverse climate conditions (Compagnoni and Adler, 2014). This result coupled with our findings suggests that the future success of B. sterilis and other invasive European annual grasses in the system could be primarily driven by warmer winter temperatures that increase overwintering survival, while native forb persistence is separately reduced by warmer summer temperatures. While increased aridity has been shown to increase invasive annual dominance (Harrison et al., 2015; Pfeifer-Meister et al., 2016), here we specifically identify that cool season temperature changes are important for the invasive annual grass. We also expect that our results from the northern edge of the range could differ from the drier, southern end. Particularly, precipitation may be more important in drier regions of the range (Reed et al., 2021, 2020) or may become more important under future warmer temperatures (Tietjen et al., 2017). Despite theoretical predictions and past studies showing that climate influences competitive interactions, we did not see evidence for changes in competition between species 87 across yearly variation in climate or the rainfall manipulation experiment. In agreement with our results, prior field studies saw separate effects of climate on focal and competitor annual species, but no evidence for changes in competition influencing population responses (Levine et al., 2010). This includes an observational study that examined natural variation in population dynamics over 5-12 years (Levine et al., 2008). However, our results contrast with previous studies examining the influence of rainfall manipulation on coexistence (Van Dyke et al., 2022; Wainwright et al., 2019). In both of these previous experiments, predictions for coexistence for most species pairs changed with changes in rainfall. Similarly, a pot experiment examining coexistence for P. congesta, one of the native forbs in our study, and an introduced forb resulted in differences in competitive outcomes across water availability (Johnson and Williams, 2020). In the latter example, field surveys also indicated a negative correlation between P. congesta and the other focal annual forb competitor, which may suggest that those species compete more strongly than P. congesta and B. sterilis, despite their large overlap in phenology. Competition has also been shown to control community responses to abiotic changes in large experiments that manipulate environmental drivers (Going et al., 2009; Suttle et al., 2007). Despite not detecting changes in interspecific competition, we did detect influences of intraspecific competition on population growth: Rt decreased with local conspecific density for all focal species. Under the rainfall manipulation experiment, the only significant effect of climate was on the competitive ability (K) of P. congesta between cool and warm winters, where competitive ability was decreased under warm conditions. Thus, we highlight that predictions of coexistence are likely specific to the context where they are measured, and are still difficult to apply to natural communities with a diversity of species. 88 Our study occurred in a natural system that also included a background community of other species, which may have obscured the pairwise competitive interactions quantified between focal species as well as artificially lowered estimation of low density growth rates. Competition may have been mediated through other species that we did not account for, or the observed effects were the result of correlational rather than causal relationships. For example, the differences in interspecific competition (𝛼𝑖𝑗), especially the predictions of facilitation, could have been caused by differences in microsite quality or from release of competition from another species. On the other hand, competition experiments might overestimate competitive exclusion between species pairs, as shown by predictions for exclusion for species pairs that co-occur in natural communities (Bowler et al., 2022a; Hart, 2023; Johnson and Williams, 2020; Wainwright et al., 2019). Further, species pairs with large niche differences are predicted to experience small indirect effects of climate on competition, and may be more influenced by shifts in intraspecific competition (Adler et al., 2012). Regardless, we were only able to detect density-dependent effects of climate in the observational dataset using the population count data, rather than in the competition models. While coexistence experiments commonly only use a year or two of experimental data, our study occurred over multiple years with varying seasonal climates. Incorporating more years of data may increase variation in competitive interactions (Bowler et al., 2022b), which has been shown to be a mechanism of coexistence that reduces the impact of interspecific competitive effects (Adler et al., 2006). We should also note the statistical shortcomings for hierarchical datasets that require nonlinear models, particularly where we could not incorporate categorical fixed effects or more than one random effect due to limitations in the modeling framework. Considering this, count-based population modeling methods may be more useful to incorporate density-dependent predictions of climate impacts on populations than using 89 competition models to incorporate the influence of other species, especially when data on demographic rates are lacking or may be too resource intensive to collect. Our study only examined three species, and competitive interactions may still influence species response to climate change, especially if different species have smaller niche differences (Adler et al., 2012). While we hypothesized competition between our focal species given their tight overlap in phenology and similar roles as abundant annuals in the community, it is possible that these species had little niche overlap which resulted in the weak response of competition to climate (Barabás et al., 2018). Classic theory predicts that to be able to coexist, species must limit their own population growth more than they limit the population growth of competitors (Chesson, 2000). However, there is increasing evidence of facilitation between native species and invaders in annual plant systems (Bimler et al., 2018; Bowler et al., 2022a; Wainwright et al., 2019), as well as an important role of facilitation for species interactions in communities (Bertness and Hacker, 1994; Hart, 2023; Hart and Marshall, 2013; Ray et al., 2023). We saw some evidence for both intra- and interspecific facilitation, although our detection of facilitation may have been influenced by artificially low estimates of low density fecundity. Although coexistence theory is beginning to incorporate facilitation, metrics like competitive ability cannot be calculated if a coefficient is negative (Adler et al., 2018; Bimler et al., 2018; Hart, 2023). While we cannot disentangle the potential facilitative effects of neighboring species or conspecifics from the benefits of a beneficial microsite using our dataset, we can say from the rainfall manipulation experiment that precipitation and temperature influenced the direction of the interactions: for example, B. sterilis was only significantly limited by its own growth under control and drought treatments under cool climates (where 𝛼𝑖𝑖 > 0). In an observational field survey, P. congesta was shown to have higher 90 seed production as surrounding grass cover increased, although which grass species is unclear (Johnson, 2018). Further, in the census data, we only saw a negative effect of intraspecific competition on P. congesta population growth under warm temperatures. Our results support predictions for decreased competition coefficients, or per capita competition strength, under increasing abiotic stress (Bertness and Callaway, 1994; Zhang and Tielbörger, 2020), although how species experience stress (i.e., cool or warm temperatures) seem to be species-specific in our experiment. Although seed production was the only demographic rate that we examined in this study, our results suggest that there is a negative effect of warm summer climate on P. congesta, which is when the species produces seeds. However, the negative effects of minimum winter temperature on B. sterilis, which would affect the seedling life stage, would suggest that seedling survival is likely important for B. sterilis population growth, potentially more so than seed production. Other mechanisms and demographic rates that may influence competition between species that we were not able to account for are changes in germination and seedling survival. Higher seed germination of species that make fewer seeds could counteract climate impacts that reduce seed production (Goldberg et al., 2001). Further, earlier life stages like germination, seedling establishment, and survival for invasive species may be limited by competition from established native species (Going et al., 2009). On the other hand, litter following years of high grass density may also reduce native species recruitment (Williams and Crone, 2006). Although we were able to account for the entire life cycle of each species using the count-based demographic data, we may not have pinpointed the exact demographic rate that competition acts on. 91 Disentangling direct climate effects and indirect changes to competitive interactions for populations in natural communities may be needed to predict community change under changing climates. Our results show that climate effects and conspecific density-dependence can be detected in census-based population counts. Further, the differences in interspecific competition under different climate conditions were weak and, in some cases, suggested facilitative interactions. We did not see evidence that invasive species are increasingly competitive under drier, warmer conditions. Rather, predictions for invasive annual species success and native species decline were ultimately influenced by direct climate effects acting independently on invasive and native species. As a result, regional climate projections of increased temperature across all seasons will be particularly beneficial to the invasive grass and detrimental to the native forb.92 Chapter 5: Conclusion 5.1 Summary Predicting how future climate conditions will drive changes in biodiversity requires understanding the direct and indirect impacts of climate on populations of individual species (Benton et al., 2006; Urban et al., 2016). Making predictions about the persistence of plant populations under climate change requires explicitly incorporating environmental drivers into population models. However, demographic processes that may influence how populations respond to environmental drivers, including reproductive trade-offs and competition, are rarely incorporated into these models. In this dissertation, I tested three potential indirect effects of climate on population growth. In Chapter 2, I examined how seasonal climate and costs of reproduction jointly influenced demographic rates and population growth in a long-lived perennial, Primula hendersonii, using long-term demographic data collected in a rainfall manipulation experiment. Within this experiment, I also examined how reproductive costs vary across manipulated plant reproduction and natural variation in reproduction. The experimental component is a key contribution, because historically, experiments to detect costs of reproduction were used interchangeably with detection via demographic data, even though it is not clear whether the detection of costs differs between approaches. Simulated drought reduced population growth rates, and demographic rates responded individualistically to variation in winter, spring, and summer temperatures, although not to variation in seasonal precipitation. However, only warmer spring temperatures were associated with a decline in population growth rates. Although there was a weak negative effect of past reproduction on growth and future reproduction for large individuals, these costs of reproduction ultimately did not influence population growth. 93 In the third chapter, I paired a manipulative density experiment where P. hendersonii was grown in pots with the rainfall manipulation experiment to examine the influence of variation in environmental drivers (ambient temperature and manipulated rainfall) and local density on demographic rates (survival, growth, sexual and clonal reproduction) and density-dependent population growth rates. Using integral projection models, I found that projected population growth rates declined with increasing conspecific density mainly under simulated drought conditions. The probability of making clones was lower for plants that previously flowered and for plants at high density. For simulated populations without the negative effect of flowering on clonal reproduction, population growth was reduced compared to simulations where the negative effect of flowering on clonal reproduction was included. In the fourth chapter, I examined competitive interactions in natural populations of native annual forbs, Plectritis congesta and Claytonia perfoliata, and an invasive annual grass, Bromus sterilis, within the long-term rainfall manipulation experiment. Using population viability analysis of census-based estimates of population growth, competition models, and competitive metrics derived from coexistence theory, I evaluated how seasonal climate influences fecundity and population growth, how competitive interactions shift with changes in climate, and the extent to which competitive interactions influence responses to climate variation. Competitive ability was not influenced by rainfall manipulation or seasonal climate variation over time, and neither species was competitively dominant. Rather, I found that a negative effect of hot summer temperatures, but not rainfall treatment (drought or increased rainfall), was the main factor that reduced density-dependent population growth for the native P. congesta. Cool winters were associated with decreased population growth of the invasive grass, and accelerated predictions for time to quasi-extinction. Importantly, warmer summers and winters are predicted under local 94 climate projections. For the native annual C. perfoliata, for which the time series was shorter, I found reduced population growth rates at high conspecific density. I show that direct effects of climate on population growth were larger than indirect effects of climate on competition, suggesting that identifying direct effects may be more crucial for predicting future population persistence. Collectively, these results suggest that density-dependent responses to climate variation are important to include in population models and predictions of population growth or decline under climate change. In Chapters 3 and 4, local density influenced predictions of population growth rates. Particularly, in Chapter 3, the effect of density was influenced by the trade-off between clonal and sexual reproduction, which scaled up to influence population growth. In both chapters, there was evidence for facilitation, or positive effects on demographic rates with increasing density, for natural density levels. However, as shown in the density manipulation experiment in Chapter 3, if density was greater than what was observed in the field, I detected negative effects of density. In Chapter 3, I observed increased growth when plants had many neighbors compared to few neighbors in the observational demography data, which was supported by similar results in the density manipulation experiment. In Chapter 4, the competitive ability of the native species was reduced under warm winter temperatures, but there was little evidence of climate- or rainfall treatment-dependent interspecific competition between the species. However, predicted population growth rates were density-dependent, and the negative effects of climate were exacerbated by high conspecific density. Increased temperatures, mainly in the spring and summer, were associated with reduced population growth rates in all the native species examined in this work. In Chapter 3, there were negative effects of density on population growth under drought treatments. Otherwise, there was 95 not a strong influence of rainfall manipulation on the focal species, both perennials and annuals. This could be because where we conducted these experiments lies at the northern edge of the Garry oak savanna ecosystem as well as for the species ranges of P. henderosonii and P. congesta. Thus, these species may be more limited by temperature than precipitation. Because the invasive species population growth rate was increased by warmer winter temperatures, warmer temperature predictions for both seasons by local climate projections suggest declines in the native species and increases in the invasive species that are directly driven by climate. Chapter 2 and 3 used the same focal species but modeled their populations in different ways. The IPM in Chapter 2 only had flowering as the contributor of new individuals, and modeled demographic data for 2017-2023. In Chapter 3, the IPM was modified to account for clonal reproduction for a subset of data from 2021-2024. Predicted population growth rates were reduced in Chapter 2 compared to Chapter 3. The IPM used in Chapter 2 likely underestimated population growth without the contribution of clonal reproduction, although population growth was also modeled for different subsets of years which could have also influenced absolute λ values. In addition, the absolute values of λ from these deterministic models may not be entirely representative of actual population growth because other components of temporal variability, aside from climate variability, were not accounted for (Morris and Doak, 2002). Despite this, the absolute values of λ should not have influenced the conclusions about the relative effects of climate and flowering on population growth, which was the objective of these studies. In Chapter 4, projections of population sizes into the future resulted in estimated quasi-extinction for both annual species within 20 years, even across all simulated climates. It is surprising that all subpopulations are predicted to go extinct, although times to quasi-extinction are most useful in population viability analysis to compare relative times to extinction, rather 96 than absolute predictions of extinction for a given population (Morris and Doak, 2002). Climate is only one of the potential drivers of populations, and other threats to the ecosystem like the loss of fire regimes or disturbance could also contribute to native species decline (Reed et al., 2021). Likewise, these stochastic models assume that there is no selection or evolutionary adaptation across time. On the other hand, annuals may be more strongly affected by climate change than long lived perennials because their fitness is reliant on one growing season, especially if there is no seedbank. In comparison, long lived perennials can buffer low fecundity in one growing season with reproduction in subsequent years (Compagnoni et al., 2021; Poppenwimer et al., 2023). Thus, native annual species in this system may be at particular risk of local extinction. 5.2 Implications for Management Identifying the role of direct and indirect climate impacts on populations is important to make accurate forecasts of population, community, and ecosystem dynamics under climate change. Although this dissertation does not examine future projections of climate, I identify that density dependence is an important process to consider in future projections. In addition, I identify two conservation implications. First, the flower clipping experiment in Chapter 2 has a natural analog of flower stalk clipping, which is commonly done by mammals at the field site. Floral herbivory is cited as a management concern for many Garry oak species due to overabundant white tailed deer and rabbits (Skaien and Arcese, 2018). My results suggest that flower clipping, which had little impact on predicted population growth, is likely not a strong contributor to population decline for P. hendersonii. Likewise, for other long lived perennial species for which reproduction may not contribute most strongly to population growth compared to other demographic rates (Franco and Silvertown, 2004), floral herbivory may not be an 97 important concern. The second management implication is the increase in abundance of invasive winter annual grasses under climate change. Traditional management techniques such as mowing likely are not feasible considering the phenological overlap with the native species. Thus, increasing populations and spread of invasive annual grasses will be an increasing management concern in the future. 5.3 Future Research My results support that climate in the summer season, where most plants in this ecosystem are dormant, is an important driver of plant populations (Evers et al., 2021). However, the physiological and mechanistic processes behind these results are still unclear, and future research should aim to examine potential indirect effects that influence how species respond to dormant season climate. For example, while P. hendersonii is senesced in the summer, species that have not senesced above ground include large shrubs and perennial grasses. Thus, warmer summer temperatures may have a negative effect on these species, but a potential positive effect on P. hendersonii and other native species. Indirect effects of competing species may still influence population responses, but identifying when is an open question. Likewise, demographic factors such as costs of reproduction may still influence some species responses to changes in climate. Finally, management in this ecosystem should particularly focus on invasive annual grasses, which my research predicts will become more common under climate change. 98 Works Cited Aarssen, L.W., 2008. Death without sex—the ‘problem of the small’ and selection for reproductive economy in flowering plants. Evolutionary Ecology 22, 279–298. Abrahamson, W.G., 1980. Demography and Vegetative Reproduction. Demography and the evolution of plant populations 89–106. Adler, P.B., Dalgleish, H.J., Ellner, S.P., 2012. Forecasting plant community impacts of climate variability and change: When do competitive interactions matter? Journal of Ecology 100, 478–487. https://doi.org/10.1111/j.1365-2745.2011.01930.x Adler, P.B., HilleRisLambers, J., Kyriakidis, P.C., Guan, Q., Levine, J.M., 2006. Climate variability has a stabilizing effect on the coexistence of prairie grasses. Proceedings of the National Academy of Sciences 103, 12793–12798. https://doi.org/10.1073/pnas.0600599103 Adler, P.B., Leiker, J., Levine, J.M., 2009. Direct and Indirect Effects of Climate Change on a Prairie Plant Community. PLoS ONE 4, e6887. https://doi.org/10.1371/journal.pone.0006887 Adler, P.B., Smull, D., Beard, K.H., Choi, R.T., Furniss, T., Kulmatiski, A., Meiners, J.M., Tredennick, A.T., Veblen, K.E., 2018. Competition and coexistence in plant communities: intraspecific competition is stronger than interspecific competition. Ecology Letters 21, 1319–1329. https://doi.org/10.1111/ele.13098 Alexander, J.M., Levine, J.M., 2019. Earlier phenology of a nonnative plant increases impacts on native competitors. Proceedings of the National Academy of Sciences of the United States of America 116, 6199–6204. https://doi.org/10.1073/pnas.1820569116 99 Anderson, J.T., 2023. The consequences of winter climate change for plant performance. American Journal of Botany 110, e16252. https://doi.org/10.1002/ajb2.16252 Anderson, J.T., DeMarche, M.L., Denney, D.A., Breckheimer, I., Santangelo, J., Wadgymar, S.M., 2025. Adaptation and gene flow are insufficient to rescue a montane plant under climate change. Science 525–531. Andersson, L., Milberg, P., Schütz, W., Steinmetz, O., 2002. Germination characteristics and emergence time of annual Bromus species of differing weediness in Sweden. Weed Research 42, 135–147. https://doi.org/10.1046/j.1365-3180.2002.00269.x Andrewartha, H., Birch, L., 1954. The distribution and abundance of animals. University of Chicago Press, Chicago, Illinois, USA. Andrzejak, M., Knight, T.M., Plos, C., Korell, L., 2025. Changes in reproduction mediate the effects of climate change and grassland management on plant population dynamics. Ecological Applications 35, e3063. https://doi.org/10.1002/eap.3063 Aoyama, L., Shoemaker, L.G., Gilbert, B., Collinge, S.K., Faist, A.M., Shackelford, N., Temperton, V.M., Barabás, G., Larios, L., Ladouceur, E., Godoy, O., Bowler, C., Hallett, L.M., 2022. Application of modern coexistence theory to rare plant restoration provides early indication of restoration trajectories. Ecological Applications 32, e2649. https://doi.org/10.1002/eap.2649 Aragón, C.F., Méndez, M., Escudero, A., 2009. Survival costs of reproduction in a short-lived perennial plant: live hard, die young. American Journal of Botany 96, 904–911. https://doi.org/10.3732/ajb.0800223 100 Bachelet, D., Johnson, B.R., Bridgham, S.D., Dunn, P.V., Anderson, H.E., Rogers, B.M., 2011. Climate Change Impacts on Western Pacific Northwest Prairies and Savannas. Northwest Science 85, 411–429. https://doi.org/10.3955/046.085.0224 Barabás, G., D’Andrea, R., Stump, S.M., 2018. Chesson’s coexistence theory. Ecological Monographs 88, 277–303. https://doi.org/10.1002/ecm.1302 Barkham, J., 1980. Population Dynamics of the Wild Daffodil (Narcissus pseudonarcissus): I . Clonal Growth, Seed Reproduction, Mortality and the Effects of Density. Journal of Ecology 68, 607–633. Barrett, S.C.H., 2015. Influences of clonality on plant sexual reproduction. Proceedings of the National Academy of Sciences of the United States of America 112, 8859–8866. https://doi.org/10.1073/pnas.1501712112 Bates, D., Mächler, M., Bolker, B.M., Walker, S.C., 2015. Fitting linear mixed-effects models using lme4. Journal of Statistical Software 67. https://doi.org/10.18637/jss.v067.i01 Benot, M.L., Bittebiere, A.K., Ernoult, A., Clément, B., Mony, C., 2013. Fine-scale spatial patterns in grassland communities depend on species clonal dispersal ability and interactions with neighbours. Journal of Ecology 101, 626–636. https://doi.org/10.1111/1365-2745.12066 Benton, T.G., Plaistow, S.J., Coulson, T.N., 2006. Complex population dynamics and complex causation: Devils, details and demography. Proceedings of the Royal Society B: Biological Sciences 273, 1173–1181. https://doi.org/10.1098/rspb.2006.3495 Bernardo, H.L., Vitt, P., Goad, R., Masi, S., Knight, T.M., 2018. Count population viability analysis finds that interacting local and regional threats affect the viability of a rare plant. Ecological Indicators 93, 822–829. https://doi.org/10.1016/j.ecolind.2018.05.083 101 Bertness, M., Callaway, R.M., 1994. Positive interactions in communities. Trends in Ecology & Evolution 9, 191–193. https://doi.org/10.1201/9780203738559 Bertness, M.D., Hacker, S.D., 1994. Physical Stress and Positive Associations Among Marsh Plants. The American Naturalist 144, 363–372. Beverton, R.J.H., Holt, S.J., 1957. On the dynamics of exploited fish populations. Ministry of Agriculture and Fisheries, London, UK. Bimler, M.D., Stouffer, D.B., Lai, H.R., Mayfield, M.M., 2018. Accurate predictions of coexistence in natural systems require the inclusion of facilitative interactions and environmental dependency. Journal of Ecology 106, 1839–1852. https://doi.org/10.1111/1365-2745.13030 Bjorkman, A.D., Vellend, M., 2010. Defining historical baselines for conservation: Ecological changes since European settlement on Vancouver Island, Canada. Conservation Biology 24, 1559-68. https://doi.org/10.1111/j.1523-1739.2010.01550.x. Blackford, C., Germain, R.M., Gilbert, B., 2020. Species differences in phenology shape coexistence. American Naturalist 195, E168–E180. https://doi.org/10.1086/708719 Blume-Werry, G., Wilson, S.D., Kreyling, J., Milbau, A., 2016. The hidden season: growing season is 50% longer below than above ground along an arctic elevation gradient. New Phytologist 209, 978–986. https://doi.org/10.1111/nph.13655 Boggs, C.L., Inouye, D.W., 2012. A single climate driver has direct and indirect effects on insect population dynamics. Ecology Letters 15, 502–508. https://doi.org/10.1111/j.1461-0248.2012.01766.x 102 Bowler, C.H., Shoemaker, L.G., Weiss-Lehman, C., Towers, I.R., Mayfield, M.M., 2022a. Positive effects of exotic species dampened by neighborhood heterogeneity. Ecology 103, e3779. https://doi.org/10.1002/ecy.3779 Bowler, C.H., Weiss-Lehman, C., Towers, I.R., Mayfield, M.M., Shoemaker, L.G., 2022b. Accounting for demographic uncertainty increases predictions for species coexistence: A case study with annual plants. Ecology Letters 25, 1618–1628. https://doi.org/10.1111/ele.14011 Brook, B.W., Bradshaw, C.J.A., 2006. Strength of Evidence for Density Dependence in Abundance Time Series of 1198 Species. Ecology 87, 1445–1451. https://doi.org/10.1890/0012-9658(2006)87[1445:SOEFDD]2.0.CO;2 Brooker, R., Callaghan, T., 1998. The Balance between Positive and Negative Plant Interactions and Its Relationship to Environmental Gradients: A Model. Oikos 81, 196–207. Brooker, RW., 2006. Plant–plant interactions and environmental change. New Phytologist 171, 271–284. https://doi.org/10.1111/j.1469-8137.2006.01752.x Cadotte, M.W., Tatsumi, S., 2025. Uncovering the mechanisms underpinning divergent environmental change impacts on biodiversity and ecosystem functioning. Ecology 106, e70040. https://doi.org/10.1002/ecy.70040 Callaway, R.M., Brooker, R.W., Choler, P., Kikvidze, Z., Lortie, C.J., Michalet, R., Paolini, L., Pugnaire, F.I., Newingham, B., Aschehoug, E.T., Armas, C., Kikodze, D., Cook, B.J., 2002. Positive interactions among alpine plants increase with stress. Nature 417, 844–848. https://doi.org/10.1038/nature00812 Campbell, D.R., 2019. Early snowmelt projected to cause population decline in a subalpine plant. PNAS 116, 12901–12906. https://doi.org/10.1073/pnas.1820096116 103 Carlyle, C.N., Fraser, L.H., Turkington, R., 2011. Tracking Soil Temperature and Moisture in a Multi-Factor Climate Experiment in Temperate Grassland: Do Climate Manipulation Methods Produce their Intended Effects? Ecosystems 14, 489–502. https://doi.org/10.1007/s10021-011-9425-y Casper, B.B., 1996. Demographic consequences of drought in the herbaceous perennial Cryptantha flava: Effects of density, associations with shrubs, and plant size. Oecologia 106, 144–152. https://doi.org/10.1007/BF00328593 Catling, A.A., Mayfield, M.M., Dwyer, J.M., 2024. Individual vital rates respond differently to local-scale environmental variation and neighbour removal. Journal of Ecology 112, 1369–1382. https://doi.org/10.1111/1365-2745.14308 Cervantes-Loreto, A., Pastore, A.I., Brown, C.R.P., Marraffini, M.L., Aldebert, C., Mayfield, M.M., Stouffer, D.B., 2023. Environmental context, parameter sensitivity, and structural sensitivity impact predictions of annual-plant coexistence. Ecological Monographs 93, e1592. https://doi.org/10.1002/ecm.1592 Chaudhary, V., Oli, M.K., 2020. A critical appraisal of population viability analysis. Conservation Biology 34, 26–40. https://doi.org/10.1111/cobi.13414 Chesson, P., 2000. Mechanisms of maintenance of species diversity. Annual Review of Ecology and Systematics 31, 343–66. https://doi.org/10.1146/annurev.ecolsys.31.1.343 Chouard, P., 1960. Vernalization and its Relations to Dormancy. Annu. Rev. Plant. Physiol. 11, 191–238. https://doi.org/10.1146/annurev.pp.11.060160.001203 Christiansen, D.M., Ehrlén, J., Hylander, K., 2024. Competitive interactions modify the direct effects of climate. Ecography e07322, 1–9. https://doi.org/10.1111/ecog.07322 104 Christiansen, D.M., Romer, G., Dahlgren, J.P., Borg, M., Jones, O., Merinero, S., Hylander, K., Ehrlén, J., 2023. High-resolution data are necessary to understand the effects of climate on plant population dynamics of a forest herb. Ecology 1, 731–737. https://doi.org/10.1002/ecy.4191 Compagnoni, A., Adler, P.B., 2014. Warming, competition, and Bromus tectorum population growth across an elevation gradient. Ecosphere 5(9), 1–34. https://doi.org/10.1890/ES14-00047.1 Compagnoni, A., Bibian, A.J., Ochocki, B.M., Rogers, H.S., Schultz, E.L., Sneck, M.E., Elderd, B.D., Iler, A.M., Inouye, D.W., Jacquemyn, H., Miller, T.E.X., 2016. The effect of demographic correlations on the stochastic population dynamics of perennial plants. Ecological Monographs 86, 480–494. https://doi.org/10.1002/ecm.1228 Compagnoni, A., Levin, S., Childs, D.Z., Harpole, S., Paniw, M., Römer, G., Burns, J.H., Che-Castaldo, J., Rüger, N., Kunstler, G., Bennett, J.M., Archer, C.R., Jones, O.R., Salguero-Gómez, R., Knight, T.M., 2021. Herbaceous perennial plants with short generation time have stronger responses to climate anomalies than those with longer generation time. Nature Communications 12, 1–8. https://doi.org/10.1038/s41467-021-21977-9 Cowichan Valley Regional District, 2017. Climate projections for the Cowichan Valley Regional District [WWW Document]. URL https://www.cvrd.ca/DocumentCenter/View/81884/Climate-Projections-Report?bidId= Crone, E.E., Menges, E.S., Ellis, M.M., Bell, T., Bierzychudek, P., Ehrlén, J., Kaye, T.N., Knight, T.M., Lesica, P., Morris, W.F., Oostermeijer, G., Quintana-Ascencio, P.F., Stanley, A., Ticktin, T., Valverde, T., Williams, J.L., 2011. How do plant ecologists use 105 matrix population models? Ecology Letters 14, 1–8. https://doi.org/10.1111/j.1461-0248.2010.01540.x Daehler, C.C., 2003. Performance Comparisons of Co-Occurring Native and Alien Invasive Plants: Implications for Conservation and Restoration. Annual Review of Ecology, Evolution, and Systematics 34, 183–211. https://doi.org/10.1146/annurev.ecolsys.34.011802.132403 Dahlgren, J.P., Bengtsson, K., Ehrlén, J., 2016. The demography of climate-driven and density-regulated population dynamics in a perennial plant. Ecology 97, 899–907. https://doi.org/10.1890/15-0804.1 Dahlgren, J.P., Östergård, H., Ehrlén, J., 2014. Local environment and density-dependent feedbacks determine population growth in a forest herb. Oecologia 176, 1023–1032. https://doi.org/10.1007/s00442-014-3073-9 Dalgleish, H.J., Koons, D.N., Hooten, M.B., Moffet, C.A., Adler, P., 2011. Climate influences the demography of three dominant sagebrush steppe plants. Ecology 92, 75–85. Davidson, A.M., Jennions, M., Nicotra, A.B., 2011. Do invasive species show higher phenotypic plasticity than native species and, if so, is it adaptive? A meta-analysis. Ecology Letters 14, 419–431. https://doi.org/10.1111/j.1461-0248.2011.01596.x Dickson, T.L., Hopwood, J.L., Wilsey, B.J., 2012. Do priority effects benefit invasive plants more than native plants? An experiment with six grassland species. Biological Invasions 14, 2617–2624. https://doi.org/10.1007/s10530-012-0257-2 Diez, J.M., Giladi, I., Warren, R., Pulliam, H.R., 2014. Probabilistic and spatially variable niches inferred from demography. Journal of Ecology 102, 544–554. https://doi.org/10.1111/1365-2745.12215 106 Doak, D.F., Morris, W.F., 2010. Demographic compensation and tipping points in climate-induced range shifts. Nature 467, 959–962. https://doi.org/10.1038/nature09439 Douglas, G.W., Meidinger, D.V., Pojar, J., 1999. Illustrated Flora of British Columbia. Volume 4: Dicotyledons (Orobanchaceae Through Rubiaceae). B.C. Ministry of Environment, Lands & Parks and B.C. Ministry of Forests., Victoria. Dukes, J.S., Mooney, H.A., 1999. Does global change increase the success of biological invaders? Trends in Ecology and Evolution 14, 135–139. Ehrlén, J., 1991. Why do Plants Produce Surplus Flowers? A Reserve-Ovary Model. The American Naturalist 138, 918–933. Ehrlén, J., Morris, W.F., 2015. Predicting changes in the distribution and abundance of species under environmental change. Ecology Letters 18, 303–314. https://doi.org/10.1111/ele.12410 Ehrlén, J., Morris, W.F., Euler, T.V., Dahlgren, J.P., 2016. Advancing environmentally explicit structured population models of plants. Journal of Ecology 104, 292–305. https://doi.org/10.1111/1365-2745.12523 Ehrlén, J., Van Groenendael, J., 2001. Storage and the delayed costs of reproduction in the understorey perennial Lathyrus vernus. Journal of Ecology 89, 237–246. https://doi.org/10.1046/j.1365-2745.2001.00546.x Elzhov, T.V., Mullen, K.M., Spiess, A.-N., Bolker, B., 2023. minpack.lm: R Interface to the Levenberg-Marquardt Nonlinear Least-Squares Algorithm Found in MINPACK, Plus Support for Bounds. r package Version 1.2-4, 1.2-4. Eriksson, O., 1993. Dynamics of genets in clonal plants. Trends in Ecology and Evolution 8, 313–316. https://doi.org/10.2144/98243bm15 107 Eriksson, O., 1989. Seedling Dynamics and Life Histories in Clonal Plants. Oikos 55, 231–238. Everard, K., Seabloom, E.W., Harpole, W.S., De Mazancourt, C., 2010. Plant water use affects competition for nitrogen: Why drought favors invasive species in California. American Naturalist 175, 85–97. https://doi.org/10.1086/648557 Evers, S.M., Knight, T.M., Compagnoni, A., 2023. The inclusion of immediate and lagged climate responses amplifies the effect of climate autocorrelation on long-term growth rate of populations. Journal of Ecology 111, 1985–1996. https://doi.org/10.1111/1365-2745.14155 Evers, S.M., Knight, T.M., Inouye, D.W., Miller, T.E.X., Salguero-Gómez, R., Iler, A.M., Compagnoni, A., 2021. Lagged and dormant season climate better predict plant vital rates than climate during the growing season. Global Change Biology 00, 1–15. https://doi.org/10.1111/gcb.15519 Fenesi, A., Rédei, T., Botta-Dukát, Z., 2011. Hard traits of three Bromus species in their source area explain their current invasive success. Acta Oecologica 37, 441–448. https://doi.org/10.1016/j.actao.2011.05.012 Fischer, M., Van Kleunen, M., 2001. On the evolution of clonal plant life histories. Evolutionary Ecology 15, 565–582. https://doi.org/10.1023/A:1016013721469 Flory, S.L., Clay, K., 2010. Non-native grass invasion alters native plant composition in experimental communities. Biol Invasions 12, 1285–1294. https://doi.org/10.1007/s10530-009-9546-9 Fowler, A.N.L., Overath, R.D., Pease, C.M., 2006. Detection of Density Dependence Requires Density Manipulations and Calculation of λ. Ecology 87, 655–664. 108 Fox, J.F., Peter, S., 1991. Costs of Reproduction in a Willow: Experimental Responses Vs. Natural Variation. Ecology 72, 1013–1023. Fox, J.W., Morin, P.J., 2001. Effects of intra- and interspecific interactions on species responses to environmental change. Journal of Animal Ecology 70, 80–90. https://doi.org/10.1111/j.1365-2656.2001.00478.x Franco, M., Silvertown, J., 2004. A Comparative Demography of Plants Based upon Elasticities of Vital Rates. Ecology 85, 531–538. Fuchs, M., 2001. Recovery Strategy for Garry Oak and Associated Ecosystems and their Associated Species at Risk in Canada 2001 - 2006. Environment Canada, Canadian Wildlife Service, Ladner, BC, Canada. Gadgil, M., Solbrig, O., 1972. The Concept of r- and K-Selection: Evidence from Wild Flowers and Some Theoretical Considerations. The American Naturalist 106, 14–31. Germain, R.M., Mayfield, M.M., Gilbert, B., 2018. The ‘filtering’ metaphor revisited: Competition and environment jointly structure invasibility and coexistence. Biology Letters 14. https://doi.org/10.1098/rsbl.2018.0460 Gielens, G., Gillespie, S., Neame, L., Elle, E., 2014. Pollen limitation is uncommon in an endangered oak savannah ecosystem. Botany 92, 743–748. https://doi.org/10.1139/cjb-2014-0054 Godoy, O., González-Muñoz, N., Hart, S.P., 2023. Effects of intraspecific variation in a native species’ phenology on its coexistence with non-native plants. Oikos 1–12. https://doi.org/10.1111/oik.10149 109 Godoy, O., Levine, J.M., 2014. Phenology effects on invasion success: Insights from coupling field experiments to coexistence theory. Ecology 95, 726–736. https://doi.org/10.1890/13-1157.1 Going, B.M., Hille Ris Lambers, J., Levine, J.M., 2009. Abiotic and biotic resistance to grass invasion in serpentine annual plant communities. Oecologia 159, 839–847. https://doi.org/10.1007/s00442-008-1264-y Goldberg, D.E., Turkington, R., Olsvig-Whittaker, L., Dyer, A.R., 2001. DENSITY DEPENDENCE IN AN ANNUAL PLANT COMMUNITY: VARIATION AMONG LIFE HISTORY STAGES. Ecological Monographs 71, 423–446. https://doi.org/10.1890/0012-9615(2001)071[0423:DDIAAP]2.0.CO;2 Griffith, A.B., 2017. Perturbation approaches for integral projection models. Oikos 126, 1675–1686. https://doi.org/10.1111/oik.04458 Grime, J., 1977. Evidence for the Existence of Three Primary Strategies in Plants and Its Relevance to Ecological and Evolutionary Theory. The American Naturalist 111, 1169–1194. Hairston, N.G., Smith, F.E., Slobodkin, L.B., 1960. Community Structure, Population Control, and Competition. The American Naturalist 94, 421–425. https://doi.org/10.1086/282146 Hall, T., Mathew, B., 1997. DODECATHEON HENDERSONII Primula. Curtis’s Botanical Magazine 14, 137–141. Hallett, L.M., Shoemaker, L.G., White, C.T., Suding, K.N., 2019. Rainfall variability maintains grass-forb species coexistence. Ecology Letters 22, 1658–1667. https://doi.org/10.1111/ele.13341 110 Hamann, E., Wadgymar, S.M., Anderson, J.T., 2021. Costs of reproduction under experimental climate change across elevations in the perennial forb Boechera stricta. Proceedings of the Royal Society B: Biological Sciences 288. https://doi.org/10.1098/rspb.2020.3134 Hamel, S., Côté, S.D., Gaillard, J.M., Festa-Bianchet, M., 2009. Individual variation in reproductive costs of reproduction: High-quality females always do better. Journal of Animal Ecology 78, 143–151. https://doi.org/10.1111/j.1365-2656.2008.01459.x Harper, J., 1977. Population biology of plants. Academic Press, New York. Harrison, S., Gornish, E.S., Copeland, S., 2015. Climate-driven diversity loss in a grassland community. Proc. Natl. Acad. Sci. U.S.A. 112, 8672–8677. https://doi.org/10.1073/pnas.1502074112 Harrison, S., Spasojevic, M.J., Li, D., 2020. Climate and plant community diversity in space and time. Proc. Natl. Acad. Sci. 117, 4464–4470. https://doi.org/10.1073/pnas.1921724117 Hart, S.P., 2023. How does facilitation influence the outcome of species interactions? Journal of Ecology 111, 2094–2104. https://doi.org/10.1111/1365-2745.14189 Hart, S.P., Freckleton, R.P., Levine, J.M., 2018. How to quantify competitive ability. Journal of Ecology 106, 1902–1909. https://doi.org/10.1111/1365-2745.12954 Hart, S.P., Marshall, D.J., 2013. Environmental stress, facilitation, competition, and coexistence. Ecology 94, 2719–2731. https://doi.org/10.1890/12-0804.1 Hemborg, Å.M., Karlsson, P.S., 1998. Altitudinal variation in size effects on plant reproductive effort and somatic costs of reproduction. Ecoscience 5, 517–525. https://doi.org/10.1080/11956860.1998.11682495 Herben, T., Klimešová, J., 2020. Evolution of clonal growth forms in angiosperms. New Phytologist 225, 999–1010. https://doi.org/10.1111/nph.16188 111 Herben, T., Šerá, B., Klimešová, J., 2015. Clonal growth and sexual reproduction: tradeoffs and environmental constraints. Oikos 124, 469–476. Herrando-Pérez, S., Delean, S., Brook, B.W., Bradshaw, C.J.A., 2012. Density dependence: an ecological Tower of Babel. Oecologia 170, 585–603. https://doi.org/10.1007/s00442-012-2347-3 Herzog, S., Kueppers, L., Louthan, A., 2025. Neighbor effects on population growth rate differ among populations due to variation in demographic rate sensitivities in Sedum lanceolatum. Oikos 2025, e10928. https://doi.org/10.1111/oik.10928 Hesse, E., Rees, M., Schärer, H.M., 2008. Life-History Variation in Contrasting Habitats: Flowering Decisions in a Clonal Perennial Herb (Veratrum album). The American Naturalist 172, E196–E213. https://doi.org/10.1086/591683 Hille Ris Lambers, J., Yelenik, S.G., Colman, B.P., Levine, J.M., 2010. California annual grass invaders: the drivers or passengers of change? Journal of Ecology 98, 1147–1156. https://doi.org/10.1111/j.1365-2745.2010.01706.x Holec, J., Soukup, J., 2014. Seed ecology of Bromus sterilis L . 156–164. https://doi.org/10.5073/jka.2014.443.018 Horvitz, C.C., Ehrlén, J., Matlaga, D., 2010. Context-dependent pollinator limitation in stochastic environments: Can increased seed set overpower the cost of reproduction in an understorey herb? Journal of Ecology 98, 268–278. https://doi.org/10.1111/j.1365-2745.2009.01628.x Iler, A.M., Compagnoni, A., Inouye, D.W., Williams, J.L., CaraDonna, P.J., Anderson, A., Miller, T.E.X., 2019. Reproductive losses due to climate change-induced earlier 112 flowering are not the primary threat to plant population viability in a perennial herb. Journal of Ecology 107, 1931–1943. https://doi.org/10.1111/1365-2745.13146 Iles, D.T., Rockwell, R.F., Koons, D.N., 2019. Shifting vital rate correlations alter predicted population responses to increasingly variable environments. American Naturalist 193, E57–E64. https://doi.org/10.1086/701043 Jacquemyn, H., Brys, R., Jongejans, E., 2010. Size-dependent flowering and costs of reproduction affect population dynamics in a tuberous perennial woodland orchid. Journal of Ecology 98, 1204–1215. https://doi.org/10.1111/j.1365-2745.2010.01697.x Jeltsch, F., Grimm, V., Reeg, J., Schlägel, U.E., 2019. Give chance a chance: from coexistence to coviability in biodiversity theory. Ecosphere 10, e02700. https://doi.org/10.1002/ecs2.2700 Johnson, J., 2018. Mechanisms of coexistence between native and exotic plant species (MSc Thesis). University of British Columbia. Johnson, J., Williams, J.L., 2020. A native annual forb locally excludes a closely related introduced species that co-occurs in oak-savanna habitat remnants. AoB PLANTS 12, 1–11. https://doi.org/10.1093/aobpla/plaa045 Jongejans, E., de Kroon, H., Tuljapurkar, S., Shea, K., 2010. Plant populations track rather than buffer climate fluctuations. Ecology Letters 13, 736–743. https://doi.org/10.1111/j.1461-0248.2010.01470.x Kellett, K.M., Shefferson, R.P., 2018. Temporal variation in reproductive costs and payoffs shapes the flowering strategy of a neotropical milkweed, Asclepias curassavica. Population Ecology 60, 77–87. https://doi.org/10.1007/s10144-018-0618-5 113 Knape, J., de Valpine, P., 2010. Effects of weather and climate on the dynamics of animal population time series. Proceedings of the Royal Society B: Biological Sciences 278, 985–992. https://doi.org/10.1098/rspb.2010.1333 Knapp, A.K., Avolio, M.L., Beier, C., Carroll, C.J.W., Collins, S.L., Dukes, J.S., Fraser, L.H., Griffin-Nolan, R.J., Hoover, D.L., Jentsch, A., Loik, M.E., Phillips, R.P., Post, A.K., Sala, O.E., Slette, I.J., Yahdjian, L., Smith, M.D., 2017. Pushing precipitation to the extremes in distributed experiments: recommendations for simulating wet and dry years. Global Change Biology 23, 1774–1782. https://doi.org/10.1111/gcb.13504 Knops, J.M.H., Koenig, W.D., Carmen, W.J., 2007. Negative correlation does not imply a tradeoff between growth and reproduction in California oaks. Proceedings of the National Academy of Sciences of the United States of America 104, 16982–16985. https://doi.org/10.1073/pnas.0704251104 Korell, L., Auge, H., Chase, J.M., Harpole, S., Knight, T.M., 2020. We need more realistic climate change experiments for understanding ecosystems of the future. Global Change Biology 26, 325–327. https://doi.org/10.1111/gcb.14797 Krebs, C.J., 2002. Two complementary paradigms for analysing population dynamics. Phil. Trans. R. Soc. Lond. B 357, 1211–1219. https://doi.org/10.1098/rstb.2002.1122 Lambrecht, S.C., Loik, M.E., Inouye, D.W., Harte, J., 2007. Reproductive and physiological responses to simulated climate warming for four subalpine species. New Phytologist 173, 121–134. https://doi.org/10.1111/j.1469-8137.2006.01892.x Lanuza, J.B., Bartomeus, I., Godoy, O., 2018. Opposing effects of floral visitors and soil conditions on the determinants of competitive outcomes maintain species diversity in 114 heterogeneous landscapes. Ecology Letters 21, 865–874. https://doi.org/10.1111/ele.12954 Laughlin, D.C., Gremer, J.R., Adler, P.B., Mitchell, R.M., Moore, M.M., 2020. The Net Effect of Functional Traits on Fitness. Trends in Ecology and Evolution 35, 1037–1047. https://doi.org/10.1016/j.tree.2020.07.010 Lehtilä, K., Syrjänen, K., 1995. Positive effects of pollination on subsequent size, reproduction, and survival of Primula veris. Ecology 76, 1084–1098. Levine, J.I., Levine, J.M., Gibbs, T., Pacala, S.W., 2022. Competition for water and species coexistence in phenologically structured annual plant communities. Ecology Letters 00, 1–16. https://doi.org/10.1111/ele.13990 Levine, J.M., HilleRisLambers, J., 2009. The importance of niches for the maintenance of species diversity. Nature 461, 254–257. https://doi.org/10.1038/nature08251 Levine, J.M., McEachern, A.K., Cowan, C., 2010. Do competitors modulate rare plant response to precipitation change? Ecology 91, 130–140. Levine, J.M., McEachern, A.K., Cowan, C., 2008. Rainfall effects on rare annual plants. Journal of Ecology 96, 795–806. https://doi.org/10.1111/j.1365-2745.2008.01375.x Lin, C., Miriti, M.N., Goodell, K., 2016. Demographic consequences of greater clonal than sexual reproduction in Dicentra canadensis. Ecology and Evolution 6, 3871–3883. https://doi.org/10.1002/ece3.2163 Lindell, T., Ehrlén, J., Dahlgren, J.P., 2022. Weather-driven demography and population dynamics of an endemic perennial plant during a 34-year period. Journal of Ecology 110, 582–592. https://doi.org/10.1111/1365-2745.13821 115 Liu, Y., Oduor, A.M.O., Zhang, Z., Manea, A., Tooth, I.M., Leishman, M.R., Xu, X., van Kleunen, M., 2017. Do invasive alien plants benefit more from global environmental change than native plants? Global Change Biology 23, 3363–3370. https://doi.org/10.1111/gcb.13579 Loehle, C., 1987. Partitioning of Reproductive Effort in Clonal Plants: A Benefit-Cost Model. Oikos 49, 199–208. Louthan, A.M., Doak, D.F., Angert, A.L., 2015. Where and When do Species Interactions Set Range Limits? Trends in Ecology & Evolution 30, 780–792. https://doi.org/10.1016/j.tree.2015.09.011 Louthan, A.M., Pringle, R.M., Goheen, J.R., Palmer, T.M., Morris, W.F., Doak, D.F., 2018. Aridity weakens population-level effects of multiple species interactions on Hibiscus meyeri. Proceedings of the National Academy of Sciences of the United States of America 115, 543–548. https://doi.org/10.1073/pnas.1708436115 Louthan, A.M., Walters, J.R., Terando, A.J., Garcia, V., Morris, W.F., 2021. Shifting correlations among multiple aspects of weather complicate predicting future demography of a threatened species. Ecosphere 12. https://doi.org/10.1002/ecs2.3740 Lovett Doust, J., 1989. Plant reproductive strategies and resource allocation. Trends in Ecology and Evolution 4, 230–234. https://doi.org/10.1016/0169-5347(89)90166-3 Lubbe, F.C., Klimešová, J., Henry, H.A.L., 2021. Winter belowground: Changing winters and the perennating organs of herbaceous plants. Functional Ecology 35, 1627–1639. https://doi.org/10.1111/1365-2435.13858 116 Lucas, R.W., Forseth, I.N., Casper, B.B., 2008. Using rainout shelters to evaluate climate change effects on the demography of Cryptantha flava. Journal of Ecology 96, 514–522. https://doi.org/10.1111/j.1365-2745.2007.01350.x MacDougall, A.S., Turkington, R., 2006. Dispersal, competition, and shifting patterns of diversity in a degraded oak savanna. Ecology 87, 1831–1843. https://doi.org/10.1890/0012-9658(2006)87[1831:DCASPO]2.0.CO;2 MacDougall, A.S., Turkington, R., 2005. ARE INVASIVE SPECIES THE DRIVERS OR PASSENGERS OF CHANGE IN DEGRADED ECOSYSTEMS? Ecology 86, 42–55. Maestre, F.T., Callaway, R.M., Valladares, F., Lortie, C.J., 2009. Refining the stress-gradient hypothesis for competition and facilitation in plant communities. Journal of Ecology 97, 199–205. https://doi.org/10.1111/j.1365-2745.2008.01476.x McCune, J.L., Pellatt, M.G., Vellend, M., 2013. Multidisciplinary synthesis of long-term human–ecosystem interactions: A perspective from the Garry oak ecosystem of British Columbia. Biological Conservation 166, 293–300. https://doi.org/10.1016/J.BIOCON.2013.08.004 Meidinger, D., Pojar, J., 1991. Ecosystems of British Columbia. BC Ministry of Forests, Victoria, BC. Miller, J.M., 1976. Variation in Populations of Claytonia perfoliata (Portulacaceae). Systematic Botany 1, 20–34. https://doi.org/10.2307/2418836 Miller, J.M., Chambers, K.L., 1993. Nomenclatural Changes and New Taxa in Claytonia (Portulacaceae) in Western North America. Missouri Botanical Garden Press (Novon) 3, 268–273. https://doi.org/10.2307/3391469 117 Miller, T.E., Burns, J.H., Munguia, P., Walters, E.L., Kneitel, J.M., Richards, P.M., Mouquet, N., Buckley, H.L., 2005. A critical review of twenty years’ use of the resource-ratio theory. American Naturalist 165, 439–448. https://doi.org/10.1086/428681 Miller, T.E.X., Williams, J.L., Jongejans, E., Brys, R., Jacquemyn, H., 2012. Evolutionary demography of iteroparous plants: Incorporating non-lethal costs of reproduction into integral projection models. Proceedings of the Royal Society B: Biological Sciences 279, 2831–2840. https://doi.org/10.1098/rspb.2012.0326 Morris, W.F., Doak, D., 2002. Quantitative Conservation Biology: Theory and Practice of Population Viability Analysis. Sinauer Associates, Sunderland, MA. Morris, W.F., Ehrlén, J., Dahlgren, J.P., Loomis, A.K., Louthan, A.M., 2020. Biotic and anthropogenic forces rival climatic/abiotic factors in determining global plant population growth and fitness. Proceedings of the National Academy of Sciences of the United States of America 117, 1107–1112. https://doi.org/10.1073/pnas.1918363117 Nicholson, A.J., 1954. Experimental Demonstrations of Balance in Populations. Nature 173, 862–863. https://doi.org/10.1038/173862a0 Nomoto, H.A., Alexander, J.M., 2021. Drivers of local extinction risk in alpine plants under warming climate. Ecology Letters 24, 1157–1166. https://doi.org/10.1111/ele.13727 Obeso, J., 2002. The costs of reproduction in plants. New Phytologist 321–348. Peters, Atkins, Brain, 2000. Evidence of differences in seed dormancy among populations of Bromus sterilis. Weed Research 40, 467–478. https://doi.org/10.1046/j.1365-3180.2000.00202.x Pfeifer-Meister, L., Bridgham, S.D., Reynolds, L.L., Goklany, M.E., Wilson, H.E., Little, C.J., Ferguson, A., Johnson, B.R., 2016. Climate change alters plant biogeography in 118 Mediterranean prairies along the West Coast, USA. Global Change Biology 22, 845–855. https://doi.org/10.1111/gcb.13052 Pfister, C., 1998. Patterns of variance in stage-structured populations: Evolutionary predictions and ecological implications. PNAS 95, 213–218. Pinheiro, J., Bates, D., R Core Team, 2025. nlme: Linear and Nonlinear Mixed Effects Models. Pinto, S.M., MacDougall, A.S., 2010. Dispersal limitation and environmental structure interact to restrict the occupation of optimal habitat. American Naturalist 175, 675–686. https://doi.org/10.1086/652467 Poppenwimer, T., Mayrose, I., DeMalach, N., 2023. Revising the global biogeography of annual and perennial plants. Nature 624, 109–114. https://doi.org/10.1038/s41586-023-06644-x Prevéy, J.S., Seastedt, T.R., 2015. Effects of precipitation change and neighboring plants on population dynamics of Bromus tectorum. Oecologia 179, 765–775. https://doi.org/10.1007/s00442-015-3398-z R Core Team, 2024. R: A language and environment for statistical computing. Ramula, S., Buckley, Y.M., 2009. Multiple Life Stages with Multiple Replicated Density Levels Are Required to Estimate Density Dependence for Plants. Oikos 118, 1164–1173. Ray, C.A., Kapas, R.E., Opedal, Ø.H., Blonder, B.W., 2023. Linking microenvironment modification to species interactions and demography in an alpine plant community. Oikos 2023, e09235. https://doi.org/10.1111/oik.09235 Reed, P.B., Bridgham, S.D., Pfeifer-Meister, L.E., DeMarche, M.L., Johnson, B.R., Roy, B.A., Bailes, G.T., Nelson, A.A., Morris, W.F., Doak, D.F., 2021. Climate warming threatens the persistence of a community of disturbance-adapted native annual plants. Ecology 102, 1–13. https://doi.org/10.1002/ecy.3464 119 Reed, P.B., Peterson, M.L., Pfeifer-meister, L.E., Morris, W.F., Doak, D.F., Roy, B.A., Johnson, B.R., Bailes, G.T., Nelson, A.A., Bridgham, S.D., Program, S., 2020. Climate manipulations differentially affect plant population dynamics within versus beyond northern range limits. Journal of Ecology 0–27. https://doi.org/10.1111/1365-2745.13494 Reekie, E.G., Bazzaz, F.A., 1992. Cost of reproduction as reduced growth in genotypes of two congeneric species with contrasting life histories. Oecologia 90, 21–26. https://doi.org/10.1007/BF00317804 Reznick, D., 1985. Costs of Reproduction: An Evaluation of the Empirical Evidence. Oikos 44, 257–267. Roland, J., Matter, S.F., 2016. Pivotal effect of early-winter temperatures and snowfall on population growth of alpine Parnassius smintheus butterflies. Ecological Monographs 86, 412–428. https://doi.org/10.1002/ecm.1225 Roland, J., Matter, S.F., 2013. Variability in winter climate and winter extremes reduces population growth of an alpine butterfly. Ecology 94, 190–199. https://doi.org/10.1890/12-0611.1 Sarukhan, J., Gadgil, M., 1974. Studies on Plant Demography: Ranunculus repens L ., R . bulbosus L . and R . acris L .: III . A Mathematical Model Incorporating Multiple Modes of Reproduction. Journal of Ecology 62, 921–936. Sarukhan, J., Harper, J.L., 1973. Studies on Plant Demography: Ranunculus repens L ., R . bulbosus L . and R . acris L: I. Population Flux and Survivorship. The Journal of Ecology 61, 675. https://doi.org/10.2307/2258643 Schnablová, R., Huang, L., Klimešová, J., Šmarda, P., Herben, T., 2021. Inflorescence preformation prior to winter: a surprisingly widespread strategy that drives phenology of 120 temperate perennial herbs. New Phytologist 229, 620–630. https://doi.org/10.1111/nph.16880 Scholl, J.P., Urbina-Casanova, R., Iler, A.M., 2022. The importance of negative density dependence for rare species persistence. Biological Conservation 274, 1–10. https://doi.org/10.1016/j.biocon.2022.109729 Schultz, E.L., Eckberg, J.O., Berg, S.S., Louda, S.M., Miller, T.E.X., 2017. Native insect herbivory overwhelms context dependence to limit complex invasion dynamics of exotic weeds. Ecology Letters 20, 1374–1384. https://doi.org/10.1111/ele.12833 Scott, E.R., Uriarte, M., Bruna, E.M., 2022. Delayed effects of climate on vital rates lead to demographic divergence in Amazonian forest fragments. Global Change Biology 28, 463–479. https://doi.org/10.1111/gcb.15900 Sherman, D.A., Dahlgren, J.P., Ehrlén, J., García, M.B., 2019. Sex and the cost of reproduction through the life course of an extremely long-lived herb. Oecologia 191, 369–375. https://doi.org/10.1007/s00442-019-04491-0 Siefert, A., Laughlin, D.C., 2023. Estimating the net effect of functional traits on fitness across species and environments. Methods in Ecology and Evolution 14, 1035–1048. https://doi.org/10.1111/2041-210X.14079 Silander, J.A., Pacala, S.W., 1985. Neighborhood predictors of plant performance. Oecologia 66, 256–263. https://doi.org/10.1007/BF00379863 Silvertown, J., Franco, M., Pisanty, I., Mendoza, A., 1993. Comparative Plant Demography--Relative Importance of Life-Cycle Components to the Finite Rate of Increase in Woody and Herbaceous Perennials. Journal of Ecology 81, 465–476. 121 Skaien, C.L., Arcese, P., 2018. Spatial variation in herbivory, climate and isolation predicts plant height and fruit phenotype in Plectritis congesta populations on islands. Journal of Ecology 106, 2344–2352. https://doi.org/10.1111/1365-2745.12982 Sletvold, N., 2005. Density-dependent growth and survival in a natural population of the facultative biennial Digitalis purpurea. Journal of Ecology 93, 727–736. https://doi.org/10.1111/j.1365-2745.2005.01008.x Sletvold, N., Ågren, J., 2015a. Climate-dependent costs of reproduction: Survival and fecundity costs decline with length of the growing season and summer temperature. Ecology Letters 18, 357–364. https://doi.org/10.1111/ele.12417 Sletvold, N., Ågren, J., 2015b. Nonlinear costs of reproduction in a long-lived plant. Journal of Ecology 103, 1205–1213. https://doi.org/10.1111/1365-2745.12430 Sletvold, N., Ågren, J., 2011. Among-population variation in costs of reproduction in the long-lived orchid Gymnadenia conopsea: An experimental study. Oecologia 167, 461–468. https://doi.org/10.1007/s00442-011-2006-0 Sletvold, N., Hestmark, G., 2000. A comparative test of the predictive power of neighbourhood models in natural populations of Lasallia pustulata. Can. J. Bot. 77, 1655–1661. https://doi.org/10.1139/b99-112 Slobodkin, L.B., 1955. Conditions for Population Equilibrium. Ecology 36, 530–533. https://doi.org/10.2307/1929600 Smith, L.J., Williams, J.L., 2023. Plant and herbivorous insect communities respond in complex ways to rainfall manipulation in an oak savanna grassland. Journal of Ecology 111, 655–665. https://doi.org/10.1111/1365-2745.14053 122 Snyder, R.E., Ellner, S.P., 2018. Pluck or luck: Does trait variation or chance drive variation in lifetime reproductive success? American Naturalist 191, E90–E107. https://doi.org/10.1086/696125 Stearns, S., 1992. The evolution of life histories. Oxford University Press, Oxford. Stearns, S., 1989. Trade-Offs in Life-History Evolution. Functional Ecology 3, 259–268. Stephens, P.A., Sutherland, W.J., Freckleton, R.P., 1999. What Is the Allee Effect? Oikos 87, 185-190. https://doi.org/10.2307/3547011 Stoll, P., Prati, D., 2001. Intraspecific Aggregation Alters Competitive Interactions in Experimental Plant Communities. Ecology 82, 319–327. https://doi.org/10.1890/0012-9658(2001)082[0319:IAACII]2.0.CO;2 Sun, H.Q., Zhou, T.H., Sletvold, N., 2022. The expression of demographic costs of reproduction varies among coexisting plants with different life history traits. Journal of Ecology 110, 2343–2358. https://doi.org/10.1111/1365-2745.13951 Suttle, K.B., Thomsen, M.A., Power, M.E., 2007. Species interactions reverse grassland responses to changing climate. Science 315, 640–642. https://doi.org/10.1126/science.1136401 Svenning, J.-C., Sandel, B., 2013. Disequilibrium vegetation dynamics under future climate change. American Journal of Botany 100, 1266–1286. https://doi.org/10.3732/ajb.1200469 Theaker, A.J., Boatman, N.D., Froud-Williams, R.J., 1995. Variation in Bromus sterilis on Farmland: Evidence for the Origin of Field Infestations. Journal of Applied Ecology 32, 47–55. https://doi.org/10.2307/2404414 123 Thomson, D.M., 2005. Matrix models as a tool for understanding invasive plant and native plant interactions. Conservation Biology 19, 917–928. https://doi.org/10.1111/j.1523-1739.2005.004108.x Tietjen, B., Schlaepfer, D.R., Bradford, J.B., Lauenroth, W.K., Hall, S.A., Duniway, M.C., Hochstrasser, T., Jia, G., Munson, S.M., Pyke, D.A., Wilson, S.D., 2017. Climate change-induced vegetation shifts lead to more ecological droughts despite projected rainfall increases in many global temperate drylands. Glob Chang Biol 23, 2743–2754. https://doi.org/10.1111/gcb.13598 Tilman, D., 1982. Resource competition and community structure. Princeton Monographs in Population Biology 17. Princeton University Press, Princeton, NJ. Trowbridge, C.C., 2015. Long-term effects of prairie restoration: community structure and native plant population dynamics 6 years after experimental management (MSc Thesis). University of British Columbia. Tuck, S.L., Porter, J., Rees, M., Turnbull, L.A., 2018. Strong responses from weakly interacting species. Ecology Letters 21, 1845–1852. https://doi.org/10.1111/ele.13163 Turcotte, M.M., Levine, J.M., 2016. Phenotypic Plasticity and Species Coexistence. Trends in Ecology & Evolution 31, 803–813. https://doi.org/10.1016/j.tree.2016.07.013 Urban, M.C., Bocedi, G., Hendry, A.P., Mihoub, J.-B., Pe’er, G., Singer, A., Bridle, J.R., Crozier, L.G., De Meester, L., Godsoe, W., Gonzalez, A., Hellmann, J.J., Holt, R.D., Huth, A., Johst, K., Krug, C.B., Leadley, P.W., Palmer, S.C.F., Pantel, J.H., Schmitz, A., Zollner, P.A., Travis, J.M.J., 2016. Improving the forecast for biodiversity under climate change. Science 353, aad8466. https://doi.org/10.1126/science.aad8466 124 Vallejo-Marín, M., Dorken, M.E., Barrett, S.C.H., 2010. The ecological and evolutionary consequences of clonality for plant mating. Annual Review of Ecology, Evolution, and Systematics 41, 193–213. https://doi.org/10.1146/annurev.ecolsys.110308.120258 van de Pol, M., Bailey, L.D., McLean, N., Rijsdijk, L., Lawson, C.R., Brouwer, L., 2016. Identifying the best climatic predictors in ecology and evolution. Methods in Ecology and Evolution 7, 1246–1257. https://doi.org/10.1111/2041-210X.12590 Van Dyke, M.N., Levine, J.M., Kraft, N.J.B., 2022. Small rainfall changes drive substantial changes in plant coexistence. Nature 611, 507–511. https://doi.org/10.1038/s41586-022-05391-9 Van Kleunen, M., Fischer, M., Schmid, B., 2001. Effects of intraspecific competition on size variation and reproductive allocation in a clonal plant. Oikos 94, 515–524. https://doi.org/10.1034/j.1600-0706.2001.940313.x Van Kleunen, M., Weber, E., Fischer, M., 2010. A meta-analysis of trait differences between invasive and non-invasive plant species. Ecology Letters 13, 235–245. https://doi.org/10.1111/j.1461-0248.2009.01418.x van Noordwijk, A., de Jong, G., 1986. Acquisition and Allocation of Resources: Their Influence on Variation in Life History Tactics. The American Naturalist 128, 137–142. Vilà, M., Espinar, J.L., Hejda, M., Hulme, P.E., Jarošík, V., Maron, J.L., Pergl, J., Schaffner, U., Sun, Y., Pyšek, P., 2011. Ecological impacts of invasive alien plants: A meta-analysis of their effects on species, communities and ecosystems. Ecology Letters 14, 702–708. https://doi.org/10.1111/j.1461-0248.2011.01628.x 125 Villellas, J., Doak, D.F., García, M.B., Morris, W.F., 2015. Demographic compensation among populations: What is it, how does it arise and what are its implications? Ecology Letters 18, 1139–1152. https://doi.org/10.1111/ele.12505 Villellas, J., García, M.B., 2018. Life-history trade-offs vary with resource availability across the geographic range of a widespread plant. Plant Biology 20, 483–489. https://doi.org/10.1111/plb.12682 von Euler, T., Ågren, J., Ehrlén, J., 2012. Floral display and habitat quality affect cost of reproduction in Primula farinosa. Oikos 121, 1400–1407. https://doi.org/10.1111/j.1600-0706.2012.20433.x Wainwright, C.E., HilleRisLambers, J., Lai, H.R., Loy, X., Mayfield, M.M., 2019. Distinct responses of niche and fitness differences to water availability underlie variable coexistence outcomes in semi-arid annual plant communities. Journal of Ecology 107, 293–306. https://doi.org/10.1111/1365-2745.13056 Wainwright, C.E., Wolkovich, E.M., Cleland, E.E., 2012. Seasonal priority effects: Implications for invasion and restoration in a semi-arid system. Journal of Applied Ecology 49, 234–241. https://doi.org/10.1111/j.1365-2664.2011.02088.x WallisDeVries, M.F., Baxter, W., Van Vliet, A.J.H., 2011. Beyond climate envelopes: effects of weather on regional population trends in butterflies. Oecologia 167, 559–571. https://doi.org/10.1007/s00442-011-2007-z Wang, T., Hamann, A., Spittlehouse, D., Carroll, C., 2016. Locally downscaled and spatially customizable climate data for historical and future periods for North America. PLoS ONE 11, 1–17. https://doi.org/10.1371/journal.pone.0156720 126 Wang, Z., Xie, L., Prather, C.M., Guo, H., Han, G., Ma, C., 2018. What drives the shift between sexual and clonal reproduction of Caragana stenophylla along a climatic aridity gradient? BMC Plant Biology 18, 1–10. https://doi.org/10.1186/s12870-018-1313-6 Weladji, R.B., Loison, A., Gaillard, J.M., Holand, Ø., Mysterud, A., Yoccoz, N.G., Nieminen, M., Stenseth, N.C., 2008. Heterogeneity in individual quality overrides costs of reproduction in female reindeer. Oecologia 156, 237–247. https://doi.org/10.1007/s00442-008-0961-x White, J., 2013. Competitive and demographic leverage points of community shifts under climate warming. Proceedings of the Royal Society B 280. https://doi.org/10.1098/rspb.2013.0572 Williams, G., 1966. Natural Selection, the Costs of Reproduction, and a Refinement of Lack’s Principle. The American Naturalist 100, 687–690. Williams, J.L., Crone, E.E., 2006. The impact of invasive grasses on the population growth of Anemone patens, a long-lived native forb. Ecology 87, 3200–3208. https://doi.org/10.1890/0012-9658(2006)87[3200:TIOIGO]2.0.CO;2 Williams, J.L., Jacquemyn, H., Ochocki, B.M., Brys, R., Miller, T.E.X., 2015. Life history evolution under climate change and its influence on the population dynamics of a long-lived plant. Journal of Ecology 103, 798–808. https://doi.org/10.1111/1365-2745.12369 Williams, J.L., Miller, T.E.X., Ellner, S.P., 2012. Avoiding unintentional eviction from integral projection models. Ecology 93, 2008–2014. Worley, A.C., Harder, L.D., 1996. Size-Dependent Resource Allocation and Costs of Reproduction in Pinguicula vulgaris (Lentibulariaceae). Journal of Ecology 84, 195–206. 127 Wright, A., Schnitzer, S.A., Reich, P.B., 2015. Daily environmental conditions determine the competition–facilitation balance for plant water status. Journal of Ecology 103, 648–656. https://doi.org/10.1111/1365-2745.12397 Yang, Y.Y., Kim, J.G., 2016. The optimal balance between sexual and asexual reproduction in variable environments: a systematic review. Journal of Ecology and Environment 40, 1–18. https://doi.org/10.1186/s41610-016-0013-0 Zhang, R., Tielbörger, K., 2020. Density-dependence tips the change of plant–plant interactions under environmental stress. Nature Communications 11, 1–9. https://doi.org/10.1038/s41467-020-16286-6 128 Appendices Appendix A Appendix for Chapter 2 A.1 Climate Variable Selection Figure A.1 Scree plot showing proportion of explained variance for each principal component axis. PC1 accounts for 44% of variance and PC2 accounts for 16% of variation. 129 Table A.1 Table showing loadings for each seasonal variable for PC axes 1 and 2. Table is ordered according to the absolute values of PC1. Bolded PC values and italicized seasonal variable names indicate chosen climate variables according to their PC1 or PC2 value, italicized seasonal variable indicates the four chosen variables to include in demographic rate models. Seasonal Variable PC1 PC2 Winter maximum temperature 0.360317 -0.02665 Winter mean temperature 0.355853 -0.00266 Spring mean temperature 0.350131 0.095964 Spring maximum temperature 0.343858 0.230508 Winter minimum temperature 0.342224 -0.02436 Fall minimum temperature 0.290285 -0.17699 Winter total precipitation 0.253591 0.318539 Spring minimum temperature 0.250918 -0.14118 Fall total precipitation 0.214342 -0.04909 Fall mean temperature 0.188825 -0.28547 Spring total precipitation -0.18412 -0.33646 Fall maximum temperature -0.14192 -0.30328 Summer maximum temperature 0.124461 -0.42025 Summer mean temperature 0.107408 -0.44217 Spring minimum temperature 0.080311 -0.34974 Summer total precipitation -0.0703 0.009085 130 Figure A.2 Correlation matrix of climate variables used in the PCA. Colors correspond to correlations: blue (-1) to red (1) being highly correlated, white (0) being not correlated, and numbers in cells correspond to p-values, where α > 0.05 indicates variables that are not significantly correlated. “Sp” is spring, “Wt” is winter, “Su” is summer, “Fa” is fall, “precip” is total precipitation, “MinT” is minimum temperature, “MaxT” is maximum temperature, “MeanT” is mean temperature. 131 Figure A.3 Histograms of climate variables (2013 – 2022) that were selected from the PCA to include in model selection. Dashed blue vertical lines show variable means and dashed grey lines show location of ±1 standard deviation of the mean. Purple points on x-axis indicates climate values in the year of the clipping experiment (2021) 132 Figure A.4 Scatterplot matrix of climate variables included in the PCA. Points indicate climate variables from one year. “Sp” is spring, “Wt” is winter, “Su” is summer, “Fa” is fall, “precip” is total precipitation, “MinT” is minimum temperature, “MaxT” is maximum temperature, “MeanT” is mean temperature. Climate variables are standardized to a mean of 0 and a standard deviation of 1. 133 A.2 Demographic rate Model Selection Table A.2 AIC table for demographic rate models selection showing the candidate models with the lowest 10 ΔAICs. Bolded rows indicate the selected best supported model. K is the number of estimated parameters for each model. All models include plot and year as random intercepts. Size indicates plant size (rosette area) in time t, F indicates flowering status in time t, Trt indicates rainfall treatment, SuT indicated summer mean temperature, SpP indicates total spring precipitation, WT indicates winter mean temperature, SpT indicates spring mean temperature, “x” indicates an interaction term. Climate variables are for time t. Mixed effect models were fit using the lme4 package (version 1.1-35.1; Bates et al. 2015). Demographic rate Candidate model K AICc ΔAICc Growth S x F + T x SuT + S + F + Trt + SuT 12 5541.35 0.00 S + F + Trt x SuT + Trt + SuT 11 5548.63 7.28 S x F x SpP + S + F + SpP 11 5549.68 8.34 S x F x SpP + S + F + SpP + Trt 13 5551.78 10.43 S x F x WT + S + F + WT 11 5553.01 11.66 S x Trt x SuT + F + S + Trt + SuT 16 5553.16 11.81 S + SuT x Trt + SuT + Trt 10 5553.66 12.32 S x WT x F + Trt + S + WT + F 13 5555.11 13.76 S + SuT x F x Trt + SuT + F + Trt 16 5556.86 15.51 S x SuT x Trt + S + SuT + Trt 15 5558.23 16.88 Probability of Flowering Size x F + Trt x WT + Size + F + Trt + WT 11 2675.54 0.00 Size x F + SpT + Size + F 7 2675.77 0.23 Size x F + Trt x SpP + Size + F + Trt + SpP 11 2676.47 0.93 134 Demographic rate Candidate model K AICc ΔAICc Size x F + Trt + WT + Size + F 9 2678.72 3.18 Size x F + Size + F 6 2679.33 3.78 Size x F + SpP + Size + F 7 2679.33 3.79 Size x F + WT + Size + F 7 2681.31 5.77 Size x SpT + F + Trt + Size + SpT 9 2683.69 8.15 Size + T+ F x SpP + F + SpP 9 2684.85 9.31 Size + SpP x F x Trt + F + SpP + Trt 15 2685.39 9.85 Survival Size + SpT 5 1686.46 0.00 Size + F + SpT 6 1688.20 1.74 Size x SpT + Size + SpT 6 1688.35 1.89 Size 4 1688.65 2.19 Size x WT + Size + WT 6 1688.85 2.39 Size + SpT + Trt 7 1688.93 2.47 Size + Trt x F + SpT + Trt + F 10 1689.42 2.97 Size + Trt x F + SpT + Trt + F 5 1689.45 2.99 135 Demographic rate Candidate model K AICc ΔAICc Size x SpP + Size + SpP 6 1689.70 3.25 Size + F x SpT + F + SpT 7 1689.95 3.49 Number of flowers Size + WT + Trt 7 2530.53 0.00 Size + WT 5 2530.95 0.42 Size x WT + Trt + Size + WT 8 2531.49 0.95 Size + WT x Trt + WT + Trt 9 2531.77 1.24 Size + SpP x Trt + SpP + Trt 9 2531.83 1.30 Size x WT + Size + WT 6 2531.86 1.33 Size + Trt + F + WT 8 2532.44 1.91 Size + SpP + Trt 7 2532.47 1.94 Size + SpP 5 2532.65 2.12 Size + F + WT 6 2532.87 2.34 136 A.3 Integral Projection Model The continuous IPM kernel is: 𝑛(𝑦, 𝑡 + 1) = ∫ [𝑠(𝑥)𝑔(𝑦, 𝑥) + 𝑓(𝑥) ] ∗ 𝑛(𝑥, 𝑡)𝑑𝑥 + [𝑝𝑎𝑓𝑎(𝑦)𝐿(𝑡)]𝛺 eqn A.1 where 𝑛(𝑦, 𝑡 + 1) is the size distribution of the population at time t +1, Ω is the possible range of adult plant sizes, s(x) is the survival of individuals of size x, g(y,x) is the growth of an individual of size x in time t to size y in t+1, f(x) is the fecundity function that describes the number of seedings in t + 1 produced by an x sized plant (see equation 2 below); and 𝑛(𝑥, 𝑡)𝑑𝑥 summarizes the size distribution of the continuously sized adult population at time t. The second half of the equation is the term for the number of new adults entering the population, where 𝑝𝑎 is the probability that a one-leaf plant becomes an adult, 𝑓𝑎(𝑦) is the size distribution of new adults, and 𝐿(𝑡) is the number of one-leaf plants in t. The fecundity function, which describes the number of seedlings produced by adult plants that enter the discrete seedling stage, consists of: 𝑓(𝑥) = 𝑝𝑓𝑙𝑜𝑤𝑒𝑟(𝑥) ∗ 𝑓𝑓𝑙𝑜𝑤𝑒𝑟(𝑥) ∗ 𝑓𝑠𝑒𝑒𝑑𝑠 ∗ 𝑝𝑠𝑒𝑒𝑑𝑙𝑖𝑛𝑔 eqn A.2 where 𝑝𝑓𝑙𝑜𝑤𝑒𝑟(𝑥) is the probability of flowering in t + 1 for an individual of size x in t, 𝑓𝑓𝑙𝑜𝑤𝑒𝑟(𝑥) is the number of flowers in t + 1 produced by a flowering plant that was size x in time t, 𝑓𝑠𝑒𝑒𝑑𝑠 is the average number of seeds produced by each flower (13 seeds per flower in irrigated plots, 11 in control, and 9 in drought), and 𝑝𝑠𝑒𝑒𝑑𝑙𝑖𝑛𝑔is the probability that a seed establishes as a seedling in t + 1. The number of seedlings in t (the discrete seedling stage, described by S(t)) either die or become one-leaf plants in t + 1 (described by L(t + 1)) after one transition, and one-leaf plants can either die or stay as a one-leaf plant: 𝐿(𝑡 + 1) = 𝑝𝑠𝑒𝑒𝑑𝑙𝑖𝑛𝑔 𝑠𝑢𝑟𝑣𝑖𝑎𝑙𝑆(𝑡) + 𝑝𝑜𝑛𝑒−𝑙𝑒𝑎𝑓 𝑠𝑢𝑟𝑣𝑖𝑣𝑎𝑙 ∗ (1 − 𝑝𝑔𝑟𝑜𝑤𝑡ℎ 𝑡𝑜 𝑎𝑑𝑢𝑙𝑡)𝐿(𝑡) eqn A.3 137 where 𝑝𝑠𝑒𝑒𝑑𝑙𝑖𝑛𝑔 𝑠𝑢𝑟𝑣𝑖𝑣𝑎𝑙 is the probability of a seedling surviving and becoming a one-leaf plant, 𝑝𝑜𝑛𝑒−𝑙𝑒𝑎𝑓 𝑠𝑢𝑟𝑣𝑖𝑣𝑎𝑙 is the probability of a one-leaf plant surviving, 𝑝𝑔𝑟𝑜𝑤𝑡ℎ 𝑡𝑜 𝑎𝑑𝑢𝑙𝑡 is the probability of a one-leaf plants growing to an adult, 𝑓𝑏𝑢𝑙𝑏𝑙𝑒𝑡(𝑥) is the number of bulblets made, and 𝐿(𝑡) is the number of one-leaf plants in t (the discrete one-leaf stage). Discrete stage probability values are described in Figure A.5. We corrected for eviction of small and large individual from the IPM kernel using the ceiling/floor approach described in (Williams et al., 2012). 138 Figure A.5 Effect of varying values of transition probabilities for (a) the probability of a seedling transition to a one-leaf plant, (b) the probability of a one-leaf plant surviving and staying a one-leaf plant, and (c) the probability of a one-leaf plant surviving and growing to an adult (>2 leaves). Vertical dashed lines indicate the values calculated through existing data and used in IPMs. Values used in IPMs did not greatly influence values of population growth rates. 139 Figure A.6 Elasticity analysis of discrete parameters and demographic rate model coefficients used in the IPM under mean climate for reproducing individuals. Elasticity values were determined with equation 3 in (Griffith, 2017) with a perturbation of 0.01. 140 Appendix B Appendix for Chapter 3 B.1 Seed addition experiment and per capita seed production To estimate seed germination in the field and determine whether Primula hendersonii has a seed bank at the field site, we established a seed addition experiment in the IDE plots in mid-June 2021, around the timing of natural seed set for the species at the field site. Seeds were collected locally at the field site in 2021 shortly before the seed addition. Two 10 x 10 cm quadrats were established in each rainfall experiment plot with toothpicks. One quadrat acted as a control, and 30 Primula hendersonii seeds were added to the other quadrat. To determine if seed predation influenced germination rates, this design was also replicated with a wire mesh cage over seeds to deter vertebrates. In 2022 and 2023, we counted the number of P. hendersonii seedlings and one-leaf plants in each quadrat in late May (probability of seed germination was 0.022 in 2022). There was no further germination of seeds in 2023. Germination did not differ between caged and uncaged treatments, suggesting that no seed predation occurred. In the rainfall manipulation experiment, the number of seeds produced by each capsule was 13 (SD = 7.0) for irrigated plots, 11 (SD = 5.5) for control plots, and 6 (SD = 3.2) for drought plots for 64 -157 plants annually for 2021-2024. 141 B.2 Integral Projection Model The continuous IPM kernel is: 𝑛(𝑦, 𝑡 + 1) = ∫ [𝑠(𝑥)𝑔(𝑦, 𝑥) + 𝑓(𝑥) ] ∗ 𝑛(𝑥, 𝑡)𝑑𝑥 + [𝑝𝑎𝑓𝑎(𝑦)𝐿(𝑡)]𝛺 eqn B.1 where n(y,t+1) is the size distribution of the population at time t + 1, Ω is the possible range of adult plant sizes, s(x) is the survival of individuals of size x, g(y,x) is the growth of an individual of size x in time t to size y in t + 1, f(x) is the fecundity function that describes the number of seedings in t + 1 produced by an x sized plant (see equation 2 below); and n(x,t)dx summarizes the size distribution of the continuously sized adult population at time t. The second half of the equation is the term for the number of new adults entering the population, where pa is the probability that a one-leaf plant becomes an adult, fa(y) is the size distribution of new adults, and L(t) is the number of one-leaf plants in t. The fecundity function, which describes the number of seedlings produced by adult plants that enter the discrete seedling stage, consists of: 𝑓(𝑥) = 𝑝𝑓𝑙𝑜𝑤𝑒𝑟(𝑥) ∗ 𝑓𝑓𝑙𝑜𝑤𝑒𝑟(𝑥) ∗ 𝑓𝑠𝑒𝑒𝑑𝑠 ∗ 𝑝𝑠𝑒𝑒𝑑𝑙𝑖𝑛𝑔 eqn B.2 where pflower (x) is the probability of flowering in t + 1 for an individual of size x in t, fflower (x) is the number of flowers in t + 1 produced by a flowering plant that was size x in time t, fseeds is the average number of seeds produced by each flower (13 seeds per flower in irrigated plots, 11 in control, and 9 in drought), and pseedling is the probability that a seed establishes as a seedling in t + 1. The number of seedlings in t (the discrete seedling stage, described by S(t)) either die or become one-leaf plants in t + 1 (described by L(t + 1)) after one transition, and one-leaf plants can either die or stay as a one-leaf plant: 142 𝐿(𝑡 + 1) = 𝑝𝑠𝑒𝑒𝑑𝑙𝑖𝑛𝑔 𝑠𝑢𝑟𝑣𝑖𝑣𝑎𝑙𝑆(𝑡) ∗ 𝑝𝑜𝑛𝑒−𝑙𝑒𝑎𝑓 𝑠𝑢𝑟𝑣𝑖𝑣𝑎𝑙 ∗ (1 − 𝑝𝑔𝑟𝑜𝑤𝑡ℎ 𝑡𝑜 𝑎𝑑𝑢𝑙𝑡)𝐿(𝑡) +𝑝𝑏𝑢𝑙𝑏𝑙𝑒𝑡(𝑥) ∗ 𝑓𝑏𝑢𝑙𝑏𝑙𝑒𝑡(𝑥) eqn B.3 where pseedling survival is the probability of a seedling surviving and becoming a one-leaf plant, pone-leaf survival is the probability of a one-leaf plant surviving, pgrowth to adult is the probability of a one-leaf plants growing to an adult in t + 1, pbulblet (x) is the probability of producing a bulblet of a x sized plant, fbulblet (x) is the number of bulblets made by a x sized plant, and L(t) is the number of one-leaf plants in t (the discrete one-leaf stage). Discrete stage probability values are described in Figure B1. We corrected for eviction of small and large individuals from the IPM kernel using the ceiling/floor approach described in Williams et al. (2012). 143 Figure B.1 Effect of varying survival and transition probabilities for the one-leaf discrete stage on population growth rate for IPMs built from parameters for control treatments under average spring and summer temperature. Panel A describes the influence of the probability that a one-leaf individual survives but stays in the one-leaf discrete stage on λ, and panel B describes the influence of the probability that a one-leaf individual survives but stays in the one-leaf discrete stage on λ. Vertical dashed lines show values used in IPMs (0.17 for A, 0.24 for B). Panels indicate IPMs built from parameters for vegetative and flowering plants. 144 B.3 Field demographic rates Table B.1 AIC table for demographic rate models selection showing the candidate models with the lowest 10 ΔAICs. Bolded rows indicate the selected best supported model. K is the number of estimated parameters for each model. All models include plot and year as random intercepts. Size indicates plant size in time t, neighbors is the number of neighbors per 415 cm2, Trt indicates rainfall treatment, SuT indicates summer mean temperature in t, WT indicates winter mean temperature, SpT indicates spring mean temperature. P(clonal) and p(flower) indicate the probability of producing clones and the probability of producing flowers in t, respectively. “x” indicates interaction terms. Demographic Rate Candidate Model K AICc Delta_AICc Growth Size + Neighbors + Size x Neighbors + Trt + SuT + Trt:SuT 12 3421.61 0.00 Size + Trt x SuT + Trt + SuT 10 3429.35 7.74 Size + Neighbors + Trt x SuT + Trt + SuT 11 3431.35 9.74 Size+Trt x SuT + Neighbors + Trt + SuT 11 3431.35 9.74 Size x Neighbors + Size + Neighbors 7 3433.90 12.29 Size x Neighbors:SuT + Trt + Size + Neighbors + SuT 13 3434.24 12.63 Size x Neighbors + Trt + SuT + Size + Neighbors 10 3436.73 15.12 Size 5 3439.57 17.95 Size + Neighbors x Trt x SuT + Neighbors + SuT + Trt 16 3440.03 18.42 145 Demographic Rate Candidate Model K AICc Delta_AICc Size x Neighbors x Trt + SuT + Size + Neighbors + Trt 16 3440.41 18.80 Probability of Flowering Size + SpT x Trt + SpT + Trt 9 1487.20 0.00 Size + Neighbors + p(clonal) + Trt x SpT + SpT + Trt 11 1487.24 0.04 Size x Neighbors + Size + Neighbors + p(clonal)+ Trt x SpT + SpT + Trt 12 1487.63 0.44 Size + Neighbors + SpT x Trt + SpT + Trt 10 1488.63 1.43 Size x Neighbors + Size + Neighbors + SpT x Trt + SpT + Trt 11 1489.00 1.80 Size + Neighbors x p(clonal) + Trt x SpT + SpT + Trt + Neighbors + p(clonal) 12 1489.27 2.07 Size + Neighbors x Trt + SpT x Trt + SpT + Trt + Neighbors 12 1492.19 4.99 Size x p(clonal) + Size + p(clonal) 6 1494.18 6.98 Size x p(clonal) + Neighbors + Size + p(clonal) 7 1495.70 8.50 Size x p(clonal) + Trt + Size + p(clonal) 8 1496.19 8.99 146 Demographic Rate Candidate Model K AICc Delta_AICc Number of Flowers Size + Neighbors 5 2522.03 0.00 Size 4 2522.07 0.04 Size + WT + Neighbors 6 2523.30 1.28 Size + WT 5 2523.35 1.33 Size x Neighbors + Size + Neighbors 6 2524.06 2.04 Size x WT + Size + WT 6 2525.17 3.14 Size + Trt + Neighbors 7 2525.42 3.39 Size + Trt + Neighbors + WT 8 2526.66 4.63 Size + Trt + WT 7 2526.66 4.63 Probability of Survival Size x SpT + Size + SpT 6 1041.99 0.00 Size + Neighbors + Trt x SpT + Trt + SpT 10 1045.68 3.69 Size + SpT + Trt 7 1045.84 3.86 147 Demographic Rate Candidate Model K AICc Delta_AICc Size + Trt 6 1047.00 5.02 Size x Neighbors + Trt x SpT + Trt + SpT + Size + Neighbors 11 1047.12 5.13 Size + SpT 5 1047.26 5.27 Size + Trt + Neighbors + SpT 8 1047.68 5.69 Size + Trt x Neighbors + Trt + Neighbors + SpT 10 1047.99 6.01 Size 4 1048.27 6.28 Size + Trt + Neighbors 7 1048.86 6.87 Probability of Clonal Reproduction Size x p(flower)+ Neighbors x p(flower) + Trt + Size + p(flower) + Neighbors 10 2300.27 0.00 Size + Neighbors x p(flower) + Trt + p(flower) + Neighbors 9 2304.02 3.76 Size x p(flower)+ Neighbors + Trt + Size + p(flower) 9 2304.12 3.86 Size x p(flower) + Neighbors x Trt + Size + p(flower) + Neighbors + Trt 11 2306.12 5.86 148 Demographic Rate Candidate Model K AICc Delta_AICc Size + Neighbors 5 2308.45 8.19 Size + Neighbors + Trt 7 2308.57 8.30 Size + Neighbors x Trt + p(flower) + Neighbors + Trt 10 2309.75 9.49 Size x Neighbors + Size + Neighbors 6 2310.14 9.88 Size + Neighbors x Trt + Trt+ Neighbors 9 2310.84 10.57 Size x p(flower) + Size + p(flower) 6 2311.96 11.70 Number of bulblets Size + Neighbors x Trt + SpT x Trt + Neighbors + Trt + SpT 9 2186.67 0.00 Size + Neighbors + Trt 7 2189.059 2.39 Size + Trt 6 2189.742 3.07 Size + Neighbors 5 2190.186 3.52 Size 4 2191.44 4.77 Trt 5 2191.738 5.07 149 Demographic Rate Candidate Model K AICc Delta_AICc Size x Trt + Size + Trt 8 2192.04 5.37 Size x Neighbors + Size + Neighbors 6 2192.102 5.43 Intercept 3 2193.172 6.50 150 Figure B.2 Demographic rates in the field experiment for (A) spring-temperature dependent survival, (B) size-dependent flower production, and (C) bulblet production that is dependent on number of neighbors and rainfall treatment. 151 B.4 Effect of past summer temperature on population growth rate (λ(N)) Figure B.3 Population growth rate (λ(N)) across neighbor density, past reproduction, past summer temperature, and rainfall treatment in the field. Line color indicates summer temperature, where summer temperature varies while spring temperature is held constant at the mean. Solid lines refer to models built from demographic functions of plants that flowered in time t (and experienced a trade-off). Dotted lines refer to models built from demographic functions of plants that were vegetative in time t, where flowering did not reduce clonality. Black horizontal lines indicate a λ value of one (stasis). Shaded areas show 95% confidence intervals of 1000 parametric bootstrap samples from demographic rate function coefficients, edges of confidence intervals correspond to past reproduction and temperature. 152 Appendix C Appendix for Chapter 4 C.1 Plectritis congesta seed production Table C.1 Model selection for Plectritis congesta seed production based on proxies for plant fitness. Ht refers to plant height (from base to top of inflorescence), trt to rainfall manipulation experiment treatment, flower area is the main inflorescence area (estimated as the area of a cylinder), flower volume is the main inflorescence volume (estimated as the volume of a cylinder), 1 as an intercept-only model. “X” indicates interaction effects that include the main effects of each variable. Bold text indicates the best supported model. R2 values were calculated from GLMs as 𝟏 −𝐑𝐞𝐬𝐢𝐝𝐮𝐚𝐥 𝐝𝐞𝐯𝐢𝐚𝐧𝐜𝐞𝐍𝐮𝐥𝐥 𝐝𝐞𝐯𝐢𝐚𝐧𝐜𝐞 . Model K AICc Delta_AICc R2 Rank ht x trt + ht + trt 6 2910.751 0 0.44 1 ht + trt 4 3007.969 97.218 0.41 2 ht 2 3043.486 132.735 0.40 3 flower area x trt + flower area + trt 6 3153.149 242.398 0.37 4 flower area + trt 4 3165.337 254.586 0.36 5 flower area 2 3184.782 274.031 0.36 6 flower volume x trt + flower volume + trt 6 3203.833 293.082 0.35 7 flower volume + trt 4 3208.134 297.383 0.35 8 flower volume 2 3232.004 321.253 0.34 9 trt 3 4353.33 1442.579 0.0032 10 1 1 4359.795 1449.044 0 11 153 Figure C.1 Seed production of P. congesta plants in rainfall manipulation plots in 2021 and 2022. Curves indicate the best supported model where seed production was best predicted by plant height and rainfall manipulation treatment. Color and panels indicate rainfall manipulation treatments and points indicate seed production by one plant. 154 C.2 Climate variables Figure C.2 Climate histograms displaying the range of climate (2016-2024) included in the Rt (log population growth rate) model selection. Blue dashed lines indicate mean climate for the 2016-2024 period, and grey lines one standard deviation around the mean. Values used in analyses were standardized to a mean of zero, where one is one standard deviation from the mean. Summer refers to June-August, winter December-February, spring March-May, and summer June- August. 155 C.3 Predicted seed production from competition models Figure C.3 Predicted seed production from competition models for Plectritis congesta (a), Bromus sterilis (b), and Claytonia perfoliata (c). Curves show predicted seed production from competition models built from mean coefficients from bootstrapped samples. Line type indicates the density of heterospecific neighbors (solid line is zero heterospecific neighbors, dashed line indicates the effect of thirty neighbors on seed production). Points show seed production of plants in the rainfall manipulation experiment plots. Colors in panel C refer to the density of competitors. Grey lines indicate seed production under varying conspecific density but low Plectritis congesta and Bromus sterilis density (one individual). Pink lines indicate predicted seed production under varying conspecific density, high P. congesta density (30 individuals), and low B. sterilis density (one individual). Green lines indicate predicted seed production under varying conspecific density, low P. congesta density (1 individual), and high B. sterilis density (30 individuals).
Thesis/Dissertation
eng
Geography
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Williams, Jennifer L.
Effects of reproductive trade-offs, conspecific density, and invasive species on oak savanna wildflower populations under climate change
Text