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Predicting taxonomic, functional and phylogenetic diversity of plant assemblages in the Okanagan ecoregion Chelick, Carmen Christine 2019

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    Predicting taxonomic, functional and phylogenetic diversity of plant assemblages in the Okanagan Ecoregion   by   Carmen Christine Chelick   B.Sc. (Honours with Distinction), The University of British Columbia Okanagan, 2015     A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF  THE REQUIREMENTS FOR THE DEGREE OF   MASTER OF SCIENCE   in   THE COLLEGE OF GRADUATE STUDIES   (Biology)       THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan)    January 2019   © Carmen Christine Chelick 2019 ii  The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled:  Predicting taxonomic, functional and phylogenetic diversity of plant assemblages in the Okanagan Ecoregion  submitted by     Carmen Christine Chelick      in partial fulfillment of the requirements of   the degree of   Master of Science      .    Dr. Jason Pither, Biology Department, Irving K. Barber School of Arts and Sciences Supervisor Dr. Lael Parrot, Biology Department, Irving K. Barber School of Arts and Sciences Supervisory Committee Member Dr. Adam Ford, Biology Department, Irving K. Barber School of Arts and Sciences Supervisory Committee Member Dr. Jon Corbett, Community, Culture and Global Studies Department, Irving K. Barber School of Arts and Sciences University Examiner            iii  Abstract Taxonomic diversity, or species richness, has traditionally been the focus of biodiversity conservation efforts, but attention is increasingly being paid to measures of functional and phylogenetic diversity, which consider the range of ecological functions and unique evolutionary histories of assemblages.  Despite the important benefits to using this multi-dimensional approach, especially in the context of rapid climate and land use change, regional-scale conservation initiatives continue to consider taxonomic diversity alone within their strategies.  My general objective is to inform biodiversity conservation efforts in the Okanagan Ecoregion by quantifying and documenting, for the first time, current and future geographic patterns of taxonomic, functional, and phylogenetic diversity among the region’s vascular plant assemblages. My specific objectives are to: 1) Use species distribution models to predict current and future distributions of plant species inhabiting the Okanagan Ecoregion; 2) Quantify, map and compare current taxonomic, functional, and phylogenetic diversity patterns and hotspots in the Okanagan Ecoregion and compare to the current protected area network; and 3) Use climate projections to assess how future patterns and hotspots of taxonomic, functional, and phylogenetic diversity may differ from current. Using a reproducible protocol, I produced current and future species distribution models for 1,541 plant species. I then used three plant traits that represent the ecological variation of plant life history strategies as well as a recently published phylogeny to quantify and map patterns of functional and phylogenetic diversity for plant assemblages in the Okanagan Ecoregion. Overall, I found that there was significant geographic variation between patterns of taxonomic, functional, and phylogenetic diversity.  While current functional and phylogenetic diversity hotspots had the greatest amount of congruence, hotspots of species richness and functional diversity had essentially no congruence.  With future climate projections, all hotspots showed significant decreases in congruence. Elevation became more positively correlated with all three diversity measures in the future, indicating that plant diversity may be shifting to higher elevation areas in response to climate change.   iv  The novel findings I provide here concerning patterns of functional and phylogenetic diversity should complement the taxonomic diversity patterns that inform conservation efforts in the Okanagan Ecoregion.                           v  Lay Summary The method that is most commonly used to measure biodiversity assumes that all species in an ecosystem have equal conservation value.  There are two other methods that can be used to quantify biodiversity that allow different conservation values to be considered. Functional diversity considers the physical traits of species and how they contribute to the health and function of the ecosystem. Phylogenetic diversity measures the unique evolutionary histories of the species in the community. I produced a series of maps that show where areas of high and low biodiversity of plant species are in the Okanagan Ecoregion based on these measures of biodiversity.  I also produced a series of maps that show how these biodiversity patterns may change in the future with climate change.  This is the first time that functional and phylogenetic diversity patterns have been mapped in the Okanagan Ecoregion.                  vi  Preface This research was conducted at the University of British Columbia Okanagan under the supervision of Dr. Jason Pither. All of the research, including data collection, methodological development, and data display, analyses and interpretation, was conducted by myself with collaboration from Dr. Jason Pither. The initial research concept was created collaboratively with Dr. Jason Pither, Dr. Lael Parrott, and Dr. Karen Hodges.  This thesis was written by myself, with guidance and review from Dr. Jason Pither, as well as my supervisory committee: Dr. Lael Parrott and Dr. Adam Ford. A version of chapter 3 will be submitted to a peer-reviewed journal.  I performed all of the background research, methodology, analyses, and writing for the manuscript with guidance and editing from Dr. Jason Pither.                      vii  Table of Contents  Abstract ............................................................................................................................. iii Lay Summary .................................................................................................................... v Preface ............................................................................................................................... vi Table of Contents ............................................................................................................ vii List of Tables .................................................................................................................... ix List of Figures .................................................................................................................... x Acknowledgements .......................................................................................................... xi Dedication ........................................................................................................................ xii 1  Background .............................................................................................................. 1 1.1 Quantification of biodiversity .............................................................................. 1 1.2 A Multi-Faceted Approach to Biodiversity Conservation ................................... 2 1.2.1 Defining functional diversity and conservation applications ........................ 2 1.2.2 Defining phylogenetic diversity and conservation applications ................... 4 1.2.3 Challenges related to the quantification of functional diversity that may be compensated for by coupling with phylogenetic diversity assessments ................... 5 1.2.4 Rationale for consideration of both functional and phylogenetic diversity in conservation strategies ............................................................................................... 7 1.3 The Okanagan Ecoregion and associated conservation efforts ............................ 8 2 Quantifying and mapping taxonomic, functional and phylogenetic diversity at a regional scale: a review of methods and a proposed reproducible protocol .......... 18 2.1 Using species distribution models (SDMs) to quantify taxonomic, functional, and phylogenetic diversity ........................................................................................... 18 2.2 Creating species distribution models using MaxEnt .......................................... 20 2.2.1 How does MaxEnt work? ............................................................................ 21 2.2.2 Occurrence data .......................................................................................... 24 2.2.3 Predictor variables ...................................................................................... 25 2.2.4 Model Performance & Validation ............................................................... 27 2.3 Functional Diversity ........................................................................................... 30 2.3.1 Trait data collection .................................................................................... 30 2.3.2 Trait Selection ............................................................................................. 31 2.3.3 LHS traits .................................................................................................... 34 2.3.4 Intraspecific Trait Variability and Phenotypic Plasticity ............................ 35 viii  2.3.5 Data Scaling ................................................................................................ 36 2.3.6 Measures of Functional Diversity ............................................................... 37 2.3.7 Functional Diversity Null Models .............................................................. 39 2.4 Phylogenetic Diversity ....................................................................................... 40 2.4.1 Phylogenies used ......................................................................................... 40 2.4.2 Measures of Phylogenetic Diversity ........................................................... 42 2.4.3 Null model considerations .......................................................................... 44 2.5 Development of a reproducible protocol ............................................................ 45 3 Patterns of Taxonomic, Functional and Phylogenetic Diversity of Vascular plants in the Okanagan Ecoregion ................................................................................ 47 3.1 Synopsis ............................................................................................................. 47 3.2 Methods .............................................................................................................. 50 3.2.1  Study Region and Species Occurrence Data ........................................... 50 3.2.2 Species Distribution Models and Species Richness .................................... 52 3.2.3 Functional and Phylogenetic Diversity measurement ................................. 53 3.3.4 Congruence between diversity facets, environmental variables, and protected areas ......................................................................................................... 54 3.3 Results ................................................................................................................ 56 3.4 Discussion .......................................................................................................... 62 4 Conclusion .............................................................................................................. 68 Bibliography .................................................................................................................... 71 Appendices ....................................................................................................................... 84 A1. Final Species List .................................................................................................. 84 A2. Spearman correlation coefficient matrix for current environmental variables and current diversity metrics ............................................................................................. 124 A3. Spearman correlation coefficient matrix for future environmental variables and future diversity metrics............................................................................................... 125 A4. Summary of current and future environmental variables within the Okanagan Ecoregion ................................................................................................................... 126 A5. Summary of diversity measures from current and future climate projections for the Okanagan Ecoregion .................................................................................................. 128 A6. Heatmaps and Hotspot Congruence Maps for Standardized Diversity Measures129   ix  List of Tables Table 2.1 The common challenges faced by plants and some suggested traits ................ 33 Table 3.1 Number of occurrences used in the MaxEnt model as well as the AUC value, a measure of predictive performance ................................................................................... 56 Table 3.2 Spearman correlation coefficients (ρ) between all diversity metrics according to current climate .................................................................................................................. 57 Table 3.3 Spearman correlation coefficients (ρ) between all diversity metrics according to future climate .................................................................................................................... 58                        x  List of Figures Figure 1.1 Illustration comparing taxonomic, functional, and phylogenetic diversity ....... 2 Figure 1.2 Okanagan Ecoregion (Nature Conservancy) ..................................................... 9 Figure 1.3 Biogeoclimatic zones found within the Okanagan Ecoregion ........................ 11 Figure 1.4 Three plant species-at-risk that occur in the Okanagan Ecoregion ................. 13 Figure 1.5 Protected areas in the Okanagan Ecoregion .................................................... 16 Figure 2.1 AUC plot showing an example of an AUC curve illustrating the predictive performance of the MaxEnt model based on a given threshold value .............................. 29 Figure 2.2 Schematic showing a summary of the analysis ............................................... 46 Figure 3.1 Okanagan Ecoregion (Nature Conservancy) ................................................... 51 Figure 3.2 Protected areas found throughout the Okanagan Ecoregion ........................... 55 Figure 3.3  Heatmaps depicting areas of high diversity and low diversity ....................... 60 Figure 3.4  Maps showing the congruence between hotspots (top 5% of values) ............ 61 Figure 3.5  Grouped barplot depicting hotspot protection by the current protected area for the three diversity metrics according to current and future climate .................................. 62 Figure A6.1  Heatmaps depicting areas of high diversity and low diversity for standardized diversity measures ..................................................................................... 129 Figure A6.2  Maps showing the congruence between hotspots (top 5% of values) between standardized diversity hotspots ....................................................................................... 130             xi  Acknowledgements There are many great people and organizations that made this research possible, and I can’t express enough how much I have appreciated this opportunity to develop my skills as a researcher.  I would first like to thank my thesis supervisor, Dr. Jason Pither for teaching me to be a more confident researcher, for teaching me patience (especially when coding in R), and for allowing me to be a part of the BLERF family. The lessons and skills I have learned while being in your lab will surely stay with me into the next chapters of my life. I would also like to thank Dr. Lael Parrot, Dr. Karen Hodges, and Dr. Adam Ford for being a part of my supervisory committee and for all of the valuable contributions they made to the formation and the execution of this research idea.  I would also like to acknowledge the funding support that I received from the Canadian Council on Ecological Areas (CCEA), the Society of Woman Geographers, the Natural Sciences and Engineering Research Council (NSERC), Irving K. Barber British Columbia Scholarship Society, and the UBCO College of Graduate Studies.  Thank you to my friends Madie and Kristine for helping to keep my writing motivation up by meeting with me at coffee shops on the evenings and weekends to write our theses together.  I am very grateful for your friendship and for your continual support. I would also like to thank all of the past and current BLERF lab members and other graduate student friends for always providing constructive feedback, a different point-of-view, and an excuse to get out of the lab once and a while.  I am very grateful for the friendships we have created.  Many thanks to the Biology faculty for the administrative support that you have provided me with over the years. Thank you also to the staff at the Aboriginal Programs and Services for the support you have given me over the years. The work that you do and the people that you are is truly making an impact on the Aboriginal student population at UBCO.  Thank you to my family for showing me what hard work looks like and for being my biggest cheerleaders.  Finally, I would like to thank my husband, Dallas, for always encouraging me to grow by doing the things that I am most scared to do, for supporting me when times are hard and I am at my worst, and for being the person that I aspire to be more like every day. I truly couldn’t have done this without you. Thank you all for supporting me on this journey.  This accomplishment would not have been possible without all of your support.    xii  Dedication     For Dallas   1  1  Background 1.1 Quantification of biodiversity The Convention of Biological Diversity (1992) defines biodiversity as “the variability among living organisms from all sources including terrestrial, marine and other aquatic ecosystems and the ecological complexes of which they are part: this includes diversity within species, between species and of ecosystem” (Convention on Biological Diversity, 1992). While the concept of biodiversity is naturally perceivable by humans, biodiversity conservation and research only began to emerge in the 1980s.  During this time, biologists were becoming increasingly aware of and concerned with species extinctions, and they began devising ways to conserve species.  The most common method for quantifying biodiversity became the measure of species richness, or the number of species or taxa within an ecological assemblage. Species richness, which is a measure of taxonomic diversity, continues to be the most commonly used measure of biodiversity in conservation research (Marchese, 2015).  This is likely partially attributed to the ease at which species richness can be quantified among most taxa.  Generalizing biodiversity with this single measure takes away from the multi-dimensional nature of biodiversity.  As shown in Figure 1.1, and expanded upon in Section 1.2, two assemblages could have identical species richness, but could differ dramatically with respect to functional and / or phylogenetic diversity – facets that, respectively, address the range of ecological functions and unique evolutionary histories of assemblages. This multi-faceted approach to quantifying biodiversity has become increasingly common in biodiversity conservation research due to the identification of links between functional traits and ecosystem functioning (Tilman et al., 1997; Loreau, 2000; Diaz & Cabido, 2001; Hooper et al., 2005; Cadotte, 2017), as well as increased efforts to conserve unique evolutionary histories (Mace et al., 2003; Isaac et al., 2007; Devictor et al., 2010). The following section describes these two facets of biodiversity, functional and phylogenetic diversity, and how and why they have been incorporated into biodiversity conservation research.   2   Figure 1.1 Illustration comparing taxonomic, functional, and phylogenetic diversity. Both assemblages have the same taxonomic diversity, with three species. The species in assemblage a) have a wider range of functions (greater functional diversity) than assemblage b). Similarly, the species in assemblage a) also have a wider phylogenetic distribution (greater phylogenetic diversity) compared to assemblage b). Figure modified from Brent Mishler (2014) Scientists enlist big data to guide conservation efforts. Nature Communications, University of California – Berkeley https://phys.org/news/2014-07-scientists-big-efforts.html.  1.2 A Multi-Faceted Approach to Biodiversity Conservation 1.2.1 Defining functional diversity and conservation applications  Swenson (2014) defines functional diversity as the “diversity or dissimilarity of the ecological strategies or performance of species upon the basis of their morphological physiological traits”.  Functional diversity is an important facet of biodiversity that has gained a lot of attention in recent decades. This is largely attributed to an increased focus on mechanistic approaches that directly link traits to ecosystem functions, competitive interactions, and how traits relate to a species’ resilience to disturbances.   There are two main types of functional traits: functional effect and functional response traits (Diaz & Cabido, 2001).  Functional effect traits are traits that contribute to ecosystem functions and ecosystem services. Functional effect traits are typically related to nutrient cycling, trophic transfer, and an individual’s ability to capture and conserve   3  resources (Grime, 2001; Leps et al., 2006).  The ability to fix nitrogen is an example of a functional effect trait, and one that can have desirable ecosystem effects. For example, Spehn et al. (2002) found that the presence of nitrogen-fixing legume species in an assemblage had a significant impact on the productivity of the assemblage, such that all species within an assemblage with legumes had significantly more above-ground biomass than assemblages without legumes. Notably, however, the presence of nitrogen-fixing plants can also negatively impact ecosystems: in one greenhouse experiment, it was shown that assemblages with soil enriched with fixed nitrogen from a native lupine species were more likely to have fewer native species and lower species richness than assemblages without lupine (Maron & Connors, 1996).  Functional response traits are traits that relate to a species’ response to the abiotic and biotic disturbances, and typically measure the sensitivity and adaptive capacity of a plant.  These can include traits that relate to dispersal, fecundity and resistance to disturbance, such as dispersal method, seed production, and bark thickness.  Thus, as landscapes and climates change at a rapid pace, the resilience of the affected ecosystems will depend in part on the diversity of functional response traits within the constituent plant assemblages. When an assemblage possesses individuals with distinct environmental responses, the assemblage can be buffered to the stressor via compensatory dynamics (Gonzalez & Loreau, 2009). This means that if two competing species respond differently to environmental stressors, while one species is heavily impacted, the competing species is able to increase in abundance, and vice versa.  If instead, all species in an assemblage responded in the same way to an environmental stressor, a whole assemblage can be devastated to one stressor or disturbance.  Functional insurance, or the ability for an assemblage to maintain long-term ecosystem functioning due to variation in functional responses, is an important mechanism related to assemblage resilience.   Functional response traits can also be used to assess how an assemblage will respond to restoration efforts.  Clark et al. (2012) found that plant trait models explained as much variability in plant responses to restoration efforts as species identity models, and thus could be used to make generalizations as to how plant assemblages would respond to restoration efforts. Most restoration efforts are species or site-specific; plant   4  traits may allow for more general characterizations of species responses to restoration treatments. Kane et al. (2017) also classified grassland species into functional types in order to evaluate how these groups would respond to different restoration efforts under different climate change scenarios.  While the species within the functional groups did not have consistent responses to the climate changes scenarios (ie. within a given group, some species would have increased habitat suitability while some would have decreased habitat suitability), they did find that habitat suitability for shrub and tree species, as well as tall fescue (Schedonorus arundinacea), an invasive species in the region, would decrease. They emphasized that restoration efforts should consider how plant traits will impact a species’ ability to persist in the future.  1.2.2 Defining phylogenetic diversity and conservation applications Phylogenetic diversity measures the amount of evolutionary history that is represented by a given assemblage. The importance of conserving unique evolutionary histories, as well as advances in molecular sequencing and more readily available phylogenetic information, have resulted in the increased assessments of phylogenetic diversity (Davies & Buckley, 2011). The majority of phylogenetic diversity measures are based on the assumption that evolution occurs according to the Brownian motion model, assuming that traits evolve at the same rate across a phylogenetic tree, and that phylogenetic distances are representative of the amount of time since divergence from a common ancestor (Cadotte et al., 2013). These assumptions are not always realistic, as exemplified by rapid diversification and niche filling among cichlid fish in African lakes, and convergence of traits amongst distantly related species under harsh environmental conditions (eg. Cactaceae and Euphorbiaceae) (Cadotte et al. (2013)). Faith (1992) and Vane-Wright et al. (1991) were among the first to describe the importance of phylogenetic diversity in conserving biodiversity. Taxonomic diversity measures implicitly regard species as equal. Yet, species do not necessarily carry the same weight in terms of our conservation priorities. Vane-Wright et al. (1991) emphasizes this in asking the questions, “To a conservationist, regardless of relative abundance, is Welwitschia equal to a species of Taraxacum? Is the panda equivalent to one species of rat?” The answer may seem obvious, considering that the Welwitschia is   5  considered to be a living fossil, in a family with no other species, while the Taraxacum, or dandelion, genera is one of the most speciose. Phylogenetic diversity measures provide a method for quantifying this conservation value.  Although the importance of preserving evolutionary history is generally considered to be important in conservation prioritization, it is rarely implemented into conservation planning. The Evolutionarily Distinct and Globally Endangered (EDGE) of Existence program introduced by the Institute of Zoology in London, UK brings to light a prioritization scheme that combines both the evolutionary distinctiveness of a species as well as its global threat of extinction (Isaac et al., 2007). This EDGE metric has been used to identify species of conservation priority around the world. Earlier work by Heard et al. (2000) and Mooers et al. (2008) suggests that extinction risk may be phylogenetically related. Later work by Davies & Buckley (2011) also found that plant species-at-risk in the South African Cape were phylogenetically clustered.  In terms of phylogenetic diversity conservation, it is also important to consider the affects that climate change will have.  Thuiller et al. (2011) assessed how climate change would impact plant, bird, and mammal assemblages in Europe across the phylogenetic tree.  Their results showed that phylogenetic diversity would be greatly reduced in high latitude and altitude areas, and that spatial turnover may be reduced by as much as 34% by 2080 in Europe, leading to continent-wide trend towards phylogenetic homogenization. These results emphasize the importance of considering phylogenetic diversity in biodiversity conservation efforts, as there has already been evidence of a trend towards phylogenetic homogenization.  1.2.3 Challenges related to the quantification of functional diversity that may be compensated for by coupling with phylogenetic diversity assessments While focus on functional and phylogenetic biodiversity assessments is increasingly emphasized in biodiversity conservation research, there are still significant challenges to quantifying these facets.  For one, functional diversity assessments depend on decisions about the set of functional traits that best signal the ecosystem function or response of interest.  These decisions can be hard to make considering that biological systems are incredibly complex and traits that may be important to assess may be   6  overlooked, or trait data may be limited (Cadotte et al., 2013; Davies et al., 2016).  Although phylogenies are continually being updated with new genetic information, phylogenetic diversity is considered to be easier to quantify and to more directly represent the actual degree of phylogenetic diversity represented by an ecological assemblage.  Phylogenetic diversity measures are therefore often used to represent the evolutionary divergence of traits (Cadotte et al., 2013; Chalmandrier et al., 2015).  If a suite of traits has a strong phylogenetic signal that means that closely related species are likely to retain the same ancestral functional traits.  If this is the case, phylogenetic diversity may be a good predictor of functional diversity, as it may be representative of functional diversity based on unmeasured traits (Davies et al., 2016).  Cadotte et al. (2008) found that phylogenetic diversity was a better predictor of ecosystem productivity than species richness and functional diversity, where assemblages composed of more distantly related species had more stability than those more closely related. Although phylogenetic diversity may be a good indicator of functional complementarity in the absence of complete trait data, it must be acknowledged that this is not always considered to be a good proxy. One reason for this is that phylogenetic diversity estimations are based on the neutral or Brownian motion model of evolution, which assumes that evolution occurs randomly across evolutionary history. However, it is likely that the traits that are relevant to specific ecosystem functions are likely under evolutionary selection (Davies et al., 2016).  Two methods of community assembly that are commonly linked to phylogenetic relatedness are competitive exclusion and habitat filtering. Competitive exclusion assumes that closely related species are less likely to co-occur, and assemblages structured by competitive exclusion are typically considered to be “phylogenetically overdispersed” (Miller et al., 2016). Habitat filtering on the other hand assumes that assemblages are structured based on their ability to withstand the environmental conditions or habitat that they are found in, resulting in assemblages that are “phylogenetically clustered”, with species that are more closely related than expected by chance, and possess similar traits (Miller et al., 2016).     7  1.2.4 Rationale for consideration of both functional and phylogenetic diversity in conservation strategies Biodiversity quantification and conservation is commonly based on species richness or other measures of taxonomic diversity alone (Devictor et al., 2010; Marchese, 2015). Species-at-risk are another common priority in conservation decisions. While it is reasonable to prioritize areas that are known to support many species, or species that are at risk of extirpation or extinction, these conservation prioritization methods do not consider the distinct ecological functions and evolutionary histories that species may possess. For example, Hidasi-Neto et al. (2013), found that bird species that were put on the “Red List” in Brazil did not comprise greater FD or PD than expected by selecting an equal number of species at random, meaning that they were not ecologically or evolutionarily distinct species. Daugherty et al. (1990) also describes the taxonomic debate regarding the tuatara (Sphenodon spp.) in New Zealand in the early 20th century.  Until 1990, the tuatara were considered to be a single taxonomic species, and was not considered to be taxonomically rare or threatened, being one of 6000 species in the suborder Rhynchocephalia (ie. lizards and snakes), and therefore did not receive significant conservation focus (VaneWright et al., 1991; Redding & Mooers, 2006). Since 1990, genetic research has identified multiple subspecies or geographic variants of tuatara and identified the tuatara as being evolutionary distinct members of the Rhynchocephalia suborder. By 1990, however, one subspecies of tuatara that resided on the Cook Islands had become extinct.  The tuatara are now considered to be “living fossils”, representing unique evolutionary histories and contributions to ecosystem functioning, and are now being considered as conservation priorities.  These examples emphasize that all three facets of biodiversity, taxonomic, functional, and phylogenetic diversity, should be considered in order to fully encapsulate the variation in biological functions and evolutionary history, especially in the context of conservation.  When these three facets of biodiversity are used congruently to assess biodiversity patterns of assemblages, they can provide a more holistic approach that simultaneously considers multiple conservation objectives when considering areas for conservation priority, and also considers how a diversity of ecological functions and phylogeny in a community can provide resilience against disturbances such as climate change. For   8  example, when assessing global patterns of biodiversity hotspots based on species, functional and phylogenetic diversity of mammals, Mazel et al. (2014) found large spatial discrepancies in the three diversity measures, emphasizing the need for a multifaceted approach when assessing biodiversity for conservation strategies. Strecker et al. (2011) assessed taxonomic, functional, and phylogenetic diversity patterns of freshwater fish in the Lower Colorado River Basin, and although they found 75% congruence between the three diversity facets, they did highlight areas with disproportionately low diversity for each of the facets as areas of conservation focus. When assessing taxonomic, functional, and phylogenetic diversity patterns of bird species in protected areas in France, Devictor et al. (2010) found that functional diversity was not well represented, while taxonomic diversity was over-represented in protected areas. Thuiller et al. (2014) also found large discrepancies in patterns of current and future taxonomic, functional, and phylogenetic diversity for plant assemblages at different elevations in the French Alps. Albouy et al. (2017) identified hotspots for all three facets of biodiversity for marine mammals at a global scale and found that functional diversity hotspots were least correlated with species richness hotspots. While considering all three facets simultaneously can help overcome the limitations and assumptions associated with all three facets, these three facets also encapsulate the broad range of ecological functions and evolutionary histories that assemblages may possess.  While there are many more studies that have adopted this multi-faceted biodiversity approach, species richness remains the main method of quantifying and conserving biodiversity at a regional scale. As most other studies have found a lack of spatial congruence for the three biodiversity facets, developing conservation strategies based on species richness alone may be resulting in the loss of functional and phylogenetic diversity.  1.3 The Okanagan Ecoregion and associated conservation efforts The Okanagan Ecoregion (shown in Figure 1.2), defined by the Nature Conservancy, is an area of over 9.6 million hectares (96,000 km2) located within the rain shadow of the Cascade and Columbia mountain ranges, spanning the international boundary between south-central British Columbia and north-central Washington. Many species in the Okanagan Ecoregion are at their northern range limit but thrive in the mild   9  and dry climate that is found year-round in the Okanagan Ecoregion.  Some of the most endangered ecosystems in British Columbia occur in this ecoregion, including low-elevation grasslands, shrub-steppe, and dry Ponderosa Pine forests. The physiography of the region is defined by repeated glaciation events during the Pleistocene Epoch 2.5 mya to 10 mya, which resulted in the wide valleys, large lakes, and rolling hills and plateaus, that are characteristic of the Okanagan (Nasmith, 1962).  The elevation varies from 300 masl in the valleys to over 3,000 masl in the mountain ranges, resulting in a particularly high variance in climate across the region (Pryce et al., 2006). The west is found within the rain shadow of the Cascade Mountains, resulting in a dry and hot climate giving the desert-like conditions characteristic of the Great Basin. To the east and in high elevation areas, temperatures decrease drastically and greater volumes of precipitation are typical.   Figure 1.2 Okanagan Ecoregion (Nature Conservancy). Figure created by Carmen Chelick.    10  Biogeoclimatic (BEC) zones were developed by Dr. V.J. Krajina and his students from the University of British Columbia in the 1960s, characterizing ecosystems based on climate, soil, and vegetation (Meidinger & Pojar, 1991).  Over 90% of the Okanagan Ecoregion is characterized by five BEC zones (Figure 1.3).  BC Ministry of Forests (Meidinger & Pojar, 1991) generally describes these zones as follows. The Bunchgrass Zone (BG) is found at lower elevations in the region, and is dominated by bluebunch wheatgrass (Agropyron spicatum), with shrubs such as big sagebrush (Artemisia tridentata) found interspersed, and a defined cryptogram crust covering the ground.  This zone has been impacted by overgrazing by livestock, which has dramatically altered the natural condition and structure of these plant assemblages.  The Ponderosa Pine Zone (PP), with its dry, open canopied forest stands made up of Ponderosa pine (Pinus ponderosa) and understory of bluebunch wheatgrass, forbs, and the occasional shrubs, is found at middle elevations. Frequent fires are also a large part of the natural ecological cycles of this zone, with many serotinous species and species that depend on periodic fires to carry out life history stages. Fire suppression causing a build-up of fuel, as well as climate change, has increased the severity and frequency of fires in this zone in recent years.  Along the elevational gradient, Interior Douglas-fir Zones (IDF) are typically found above the PP zone, although they are known to also occur at lower elevations.  IDF zone forests are comprised of Interior Douglas-fir (Pseudotsuga menziesii), typically have more of a closed canopy, and can have a variable understory assemblage, depending on moisture and temperature conditions. IDF zones have experienced impacts from historical logging practices and cattle grazing. The Montane Spruce (MS) Zone typically occurs above the IDF zone and is comprised of open spruce (Picea spp.) and subalpine fir (Abies lasiocarpa) forests, with shrubs such as black huckleberry (Vaccinium membranaceum) and grouseberry (Vaccinium scoparium), and a variety of forbs and grasses. Logging and mountain pine beetle outbreaks have had significant impacts climax MS ecosystems. The Englemann Spruce – Subalpine Fir (ESSF) zone occurs at the highest alpine elevations. While closed canopy forests of Englemann spruce (Picea engelmannii) and subalpine fir (Abies lasiocarpa) are typical of the climax condition of this zone, subalpine meadows containing a diverse herbaceous plant assemblages are also characteristic of this zone.  Logging practices have also had significant impacts on the plant assemblages in this   11  zone.  Most of these zones are in the “xh” subzone, which is indicative of the very hot, dry climates characteristic of the Okanagan Ecoregion. This ecoregion is considered to be a biodiversity hotspot in Canada, while also harbouring plant, bird and other animal species that do not occur anywhere else in Canada.  Figure 1.3 Biogeoclimatic zones found within the Okanagan Ecoregion. Figure created by Carmen Chelick.  The favourable climate and presence of mineral-rich soil created from sediments left behind from the last glacial cycle has also brought a great deal of development to the valley. Irrigation advancements in the 1930s resulted in the rapid growth of commercial orchards and vineyards.  Currently, the Okanagan produces a large portion of the countries fruit, particularly apricots and sweet cherries. The Okanagan is also the second largest wine producing region in Canada. There are currently 172 licensed wineries in the central Okanagan valley alone, covering 8,619 acres of land, all benefitting from the   12  warm climate, variable terrain and mineral-rich soil. The Okanagan is also visited by millions of tourists each year.  The rapid growth of the Okanagan’s agricultural and tourism industries over the last century, along with other industries such as manufacturing and forestry, has endangered the diverse habitats and unique species of the Okanagan. Using aerial photographs dating back to the 1800s, Lea (2008) shows the rapid change that has occurred over the Okanagan landscape. Some of his results include a 53% decline in Ponderosa Pine-Bluebunch Wheatgrass ecosystems, a 92% decline in Water Birch-Red-osier Dogwood ecosystems, and an 84% decline in low-elevation wetlands. According to the BC Ministry of Environment, the BGxh grasslands and open PPxh forests are considered to be two of the most endangered ecosystems in Canada, and approximately 30% of the BC’s Red-listed wildlife species and 46% of BC’s Blue-listed wildlife species reside in the South Okanagan. Figure 1.4 shows examples of three plant species-at-risk that have global or continental ranges that are limited to the Okanagan Ecoregion. Species populations are likely to become even more impacted as development pressures continue to limit the availability of habitat, and climate change alters the conditions of the habitat.    13     The deterioration of the Okanagan’s natural environment has not gone unnoticed by local government and non-government organizations that aspire to protect and restore biodiversity in the Okanagan. The South Okanagan Similkameen Conservation Program (SOSCP) began in 2000 as a partnership between various organizations that had the common interest of maintaining “A healthy environment that sustains the diversity of indigenous plants and animals while enriching people’s lives” (SOSCP, 2016). Their a) b) c) Figure 1.4 Three plant species-at-risk that occur in the Okanagan Ecoregion, including: a) the North American range of Lemmon’s holly fern (Polystichum lemmonii) – Threatened; b) the global range of Lyall’s Mariposa lily (Calochortus lyalli) – Special Concern; c) the global range of whitebark pine (Pinus albicaulis) – Endangered. Photo and map credit: a) Southern Interior Rare Plants Recovery Implementation Group (2007) Recovery Strategy for the Lemmon’s holly fern (Polystichum lemmonii) in British Columbia. Prepared for the British Columbia Ministry of Environment; b) Southern Interior Rare Plants Recovery Implementation Group (2008) Recovery Strategy for Lyall’s Mariposa Lily (Calochortus lyallii) in British Columbia. Prepared for the British Columbia Ministry of Environment; c) COSEWIC (2010) COSEWIC Assessment and Status Report on the Whitebark Pine (Pinus albicaulis) in Canada. Committee on the Status of Endangered Wildlife in Canada.    14  work has focussed on helping to assess the status of biodiversity in the South Okanagan and develop recommendations for maintaining biodiversity in the region (OCCP, 2014). Building on this, the Okanagan Collaborative Conservation Program (OCCP) worked to do the same assessments for Central and North Okanagan. These assessments involved the development of four decision support tools, maps representing conservation rankings, land management classes, habitat connectivity, and relative biodiversity. Relative biodiversity was assessed based on biodiversity proxies such as distance from roads, size of natural areas, and the presence of important ecosystems, habitat features, or species at risk. Although this is a good starting point, these proxies do not necessarily represent the multi-faceted nature of biodiversity. For example, while prioritizing the conservation of species-at-risk is a prominent conservation tactic and there are often legislative requirements for their protection, they may not represent species that have diverse ecological traits and evolutionary histories.  Species-at-risk assessments nationally, via the Canadian Species at Risk Act, are based on the IUCN’s guidelines for identifying threatened species (Redding & Mooers, 2006). These guidelines are characterizing a species’ conservation worth based on the degree of threat that they are faced with and assume equal worth for all species (Redding & Mooers, 2006).  While prioritizing species based on decreasing population sizes and likelihood of extinction is a good tactic, increasing pressures will make it more difficult to continue to prioritize threatened species for conservation as there become more and more species with the risk of extinction.  Therefore, when faced with prioritization of species for conservation outside of extinction risk, the distinct evolutionary histories and ecological traits, including traits that contribute to ecosystem services and their resilience to disturbances, should be considered.  The Nature Conservancy of Canada also used MARXAN software to produce conservation portfolio’s identifying priority conservation areas in the Okanagan Ecoregion (Pryce et al., 2006). This assessment used terrestrial and aquatic species and ecosystems as conservation targets, which were given scores of irreplaceability and vulnerability.  Irreplaceability is measured as the number of sites that have the same ecological composition or representation as a given site and vulnerability is measured as the likelihood of an area losing biodiversity value (Margules & Pressey, 2000).  To date,   15  functional and phylogenetic diversity have not been considered within evaluations of irreplaceability and vulnerability in the Okanagan Ecoregion.  One of the most common and effective methods for conserving species is the establishment of protected areas.  While the establishment of protected areas is typically based on the ability to procure land, they are often chosen based on biodiversity hotspots or ecological representation.  A network of protected areas, including provincial and regional parks, wildlife management areas, and private conservations lands with varying levels of protection is shown in Figure 1.5. These protected areas encompass around 800,000 hectares (8,000 km2) of land, approximately 8% of the Okanagan Ecoregion.  In 2016, a national park was proposed to be established in the South Okanagan in order to have these arid, low elevation grassland ecosystems represented in the national park network.  Another initiative within the Okanagan Ecoregion, the Transboundary Climate-Connectivity Project1, explored the impacts that climate change will have on wildlife movement through the Washington-British Columbia transboundary area (Krosby et al., 2016).  By modelling future changes in the distributions of wildlife species of interest and vegetation assemblages, they identified corridors that would optimize wildlife movement throughout the region as climate changes.  As protected areas and movement corridors are established in the Okanagan Ecoregion, maps representing hotspots of taxonomic, functional, and phylogenetic diversity would contribute to land acquisition decisions by ensuring that species richness is not the only facet of biodiversity being represented.                                                     1 https://cig.uw.edu/resources/analysis-tools/the-washington-british-columbia-transboundary-climate-connectivity-project/   16   Figure 1.5. Protected areas in the Okanagan Ecoregion. Figure created by Carmen Chelick.  1.4 Thesis objectives My general objective in this thesis is to inform biodiversity conservation efforts in the Okanagan Ecoregion by quantifying and documenting, for the first time, current and future geographic patterns of taxonomic, functional, and phylogenetic diversity among the region’s vascular plants. My specific objectives are as follows:   1) To develop a reproducible protocol for predicting current and future patterns of taxonomic (TD), functional (FD), and phylogenetic diversity (PD) at regional extents; 2) To apply this protocol to plant assemblages in the Okanagan Ecoregion of southern British Columbia and northern Washington State, in order to:   17  i) generate novel biodiversity maps that include estimates of current taxonomic, functional, and phylogenetic diversity, and that identify potential ‘hotspots’ of biodiversity; ii) generate maps of predicted future biodiversity patterns, based on predicted future climate data; iii) quantify geographic congruence among the three diversity facets; iv) determine the degree to which the three facets of diversity overlap with the current protected area network.  There are a number of aspects of this research that are novel, including the use of species distribution models (SDMs) to predict functional and phylogenetic diversity of such a wide range of taxa at a resolution high enough to produce maps that could be interpreted on a regional level.  Chapter 2 of this thesis reviews the tools and data used to assess taxonomic, functional, and phylogenetic diversity patterns. Chapter 3 outlines the identification of hotspots of taxonomic, functional, and phylogenetic diversity of plant assemblages in the Okanagan Ecoregion.  Chapter 4 concludes the thesis and summarizes future directions for this work.          18  2 Quantifying and mapping taxonomic, functional and phylogenetic diversity at a regional scale: a review of methods and a proposed reproducible protocol 2.1 Using species distribution models (SDMs) to quantify taxonomic, functional, and phylogenetic diversity In order to quantify the three facets of biodiversity, distributions of all species within the region of interest are required. Range maps or presence point-to-grid maps are often used as the basis for predictions of taxonomic diversity (Devictor et al., 2010; Mouillot et al., 2011; Mazel et al., 2014; Albouy et al., 2017).  However, these methods have been used more often for national or global estimates of diversity, and therefore do not have fine enough resolution to be useful at a regional level, where conservation planning typically takes place (Graham & Hijmans, 2006).  The development of new, open source software, such as R Statistical Programming Software, as well as the increased availability of species occurrence data and environmental predictor data have resulted in the increased use of species distribution models (SDMs) to create predictions of multiple facets of biodiversity. SDMs have been used for a variety of species and for a variety of purposes.  Some SDMs, such as MaxEnt, have been demonstrated to produce accurate predictions of the ranges of rare and threatened species, even when limited occurrence data is available.  Hernandez et al. (2006) demonstrated that MaxEnt was able to produce accurate predictions of ranges for threatened animal species in California, even for species with as few as 5 occurrences.  Murray-Smith et al. (2009) used SDMs to predict areas of endemism for threatened species of Myrtaceae in coastal Brazilian forests.  SDMs have also been used as cost-effective methods to predict invasion potential for introduced species.  After creating SDMs for 15 invasive plant species in Southeast Asia, Truong et al. (2017) found that shrub species had the highest risk of invasion, and native species had an equal or even greater risk of becoming invasive, or expanding into areas that they were not currently known to inhabit, compared to non-native species.  Ensing et al. (2013) used SDMs to predict the invasion of Pilosella glomerata, a hawkweed species, in   19  British Columbia, Canada, while also emphasizing that the reliability of these predictions is based on reliability in taxonomic identification. SDMs have also become increasingly used to predict species richness.  There are two approaches that have been commonly used to model the species richness of ecological assemblages: stacked species assemblage modelling and direct assemblage or environment-regression modelling approaches (Algar et al., 2009; Ko et al., 2016). Direct assemblage models estimate species richness using a “top-down” approach that relates the number of species in an assemblage to environmental predictors, and predicts outwards using regression/correlative approaches.  One of the drawbacks to direct assemblage modelling techniques is that it does not consider assemblage composition, or the identity of the species within the assemblage, which is important for functional and phylogenetic diversity assessments.  Generally, stacked species distribution models (S-SDMs) predict the individual distributions of multiple species, based on known occurrences and environmental predictors, and stacks them to obtain a measure of species richness.  The main drawback of this approach is that it assumes that species distributions are based on their relationships with environmental variables only, and does not consider the effect that biotic interactions, adaptive or evolutionary processes, or dispersal limitations may have on their distributions (Drake, 2014; Zhang et al., 2015).  S-SDMs therefore often result in an overestimate of species richness.  The range of environmental conditions that a species is able to live in is typically referred to as a species’ “fundamental niche”.  Typically, stacked SDMs are not able to consider a species’ “realized niche”, or the range within which a species is actually found within, taking biotic interactions, adaptive or evolutionary processes, and dispersal limitations into account. With the assumption that S-SDMs are only able to consider species’ fundamental niche, assessments of taxonomic, functional, and phylogenetic diversity based on S-SDMs should be considered as predictions of “potential” diversity.  Nevertheless, when compared to direct assemblage modelling approaches, S-SDMs typically yield accurate predictions of species richness (Ko et al., 2016; Zurell et al., 2016; Da Mata et al., 2017).    Since S-SDMs are based on environmental predictors including climate, future climate scenarios are able to be used to predict future patterns of taxonomic, functional,   20  and phylogenetic diversity.  Considering how assemblages will be impacted by climate change and other disturbances is an important aspect of conservation planning. Conservation efforts are largely focussed on how species and ecosystem services are responding to current environmental conditions. However, climate projections show that environmental conditions are predicted to change drastically in the next century, which will have significant impacts on ecological assemblages (Hamann & Wang, 2006; Shafer et al., 2015).  Kane et al. (2017) used S-SDMs to predict how climate change will impact the habitat suitability of grassland species in the US Midwest, and therefore, how effective current restoration activities will be.  Thuiller et al. (2014) also used S-SDMs to predict taxonomic, functional, and phylogenetic diversity for over 2,500 plant species in the French Alps, based on current and future climate conditions. They generally found that the current protected area network sufficiently protected current and future diversity patterns for all three facets of biodiversity.   2.2 Creating species distribution models using MaxEnt Within the last two decades, many methods for creating SDMs have been developed. When compared to other models, MaxEnt often emerges with the highest predictive performance, and is especially robust to small sample sizes (Elith et al., 2006; Fourcade et al., 2014; Wan & Zhang, 2016; Kane et al., 2017; Truong et al., 2017). Its consistently high performance when predicting species distributions has been attributed to its ability to handle complex interactions between the species occurrences and the environmental covariates or predictor variables (Fourcade et al., 2014; Truong et al., 2017).  While MaxEnt can be employed using a downloadable desktop application, it can also be accessed through functions available in multiple packages in R Statistical Programming software.  Due to its ease of use coupled with robust results, it has been used in a wide range of applications (Elith et al., 2011a), from complex assemblage ecology modelling, to species range and biodiversity mapping produced by government and non-government organizations, including the Point Reyes Bird Observatory online application and the Atlas of Living Australia.     21  2.2.1 How does MaxEnt work? There are two main categories of SDMs: correlative and mechanistic (Shabani et al., 2016). Although they have the potential to produce more accurate models, mechanistic approaches to SDMs require a vast amount of data and computational effort to properly model the complexities of the interactions involved in estimating species distributions. Correlative models on the other hand are easier to parameterize and can produce useful model outputs.  MaxEnt, created by Phillips et al. (2006) is a correlative model that uses a machine learning approach and the Principle of Maximum Entropy to relate species occurrences with environmental data. According to this principle, the probability distribution that gives the best estimate for the system of interest is the distribution that maximizes entropy or uncertainty, while remaining within the constraints of the moments of the measured data. In the case of MaxEnt models, the constraints of the model are given by the statistical moments (the mean, variance, etc.) representing the environmental conditions at the locations of the species presences. The unknown distribution, or relative suitability of habitat for the given species, across the area of interest is therefore constrained by the environmental conditions experienced at the presence locations.   MaxEnt does not require absence data, but instead takes a presence-background modelling approach. Random samples from within the subject area are collected (default is typically 10,000 points), and represent the “background” conditions, or the range and variation in environmental conditions across the study area (Elith et al., 2011b; Kane et al., 2017). This background data represents the null condition where without occurrence data, a given species has no particular suitability for one environment over another and would instead be found in certain environmental conditions based on their availability (Elith et al., 2011b). With the estimation from the background data as the base, the probability distribution becomes further constrained by the moments of the environmental conditions at the species occurrence locations. Because the MaxEnt model must be constrained to the moments of multiple covariates at once, the MaxEnt model performs transformations of the covariates, turning them into “features”, in order to allow complex relationships between covariates to be considered simultaneously (Elith et al., 2011b). It is often the case that there are more features than covariates. According to Elith   22  et al. (2011a), MaxEnt has six classes of features: linear, product, quadratic, hinge, threshold and categorical, and by default, MaxEnt restricts the features based on the number of samples given. Linear features are always used, quadratic features are used with a minimum of 10 samples, hinge with 15 samples, and threshold and product features with more than 80 samples (Elith et al., 2011b; Zhang et al., 2016). However, the features used can also be set manually. With the potential to create many different features to fit the distribution to, MaxEnt models can quickly become overfit. In order to reduce overfitting, regularization can be applied to the distribution. Regularization penalizes complexity and creates a distribution that is more smooth, with higher values creating models that are overfit (Elith et al., 2011b; Truong et al., 2017) The final output of the MaxEnt model comes as a raw representation of the exponential model or can be given in logistic output, with values between 0 and 1 that are generally representative of relative habitat suitability. Due to the nature of the conversion from raw output to logistic output, a number of assumptions about species prevalence across the landscape are made, so when possible, the raw output should be used to represent a specie’s predicted distribution (Elith et al., 2011b; Merow et al., 2013). However, when trying to predict presence/absence for a species, the logistic output is often used (Merow et al., 2013; Norris, 2014). In order to convert a logistic output to a presence/absence output, a threshold value at above which to call a species “present” and below which to call a species “absent” must be chosen. It has been demonstrated that the choice of threshold can significantly alter the presence/absence output (Liu et al., 2013; Norris, 2014). Thresholds are chosen either as an arbitrary value between 0 and 1, or are chosen statistically in relation to the model fitting. Martinson et al. (2016) for example used a threshold of 50% to delineate the range of 30 vascular plant species in North America. Less subjective thresholds however are produced as a result of the MaxEnt model. The minimum training presence threshold for example is a threshold that will result in a binary surface where all the training samples (discussed in further detail in Section 2.2.4) will be included as presences. Another set of commonly used thresholds produced by MaxEnt are the fixed cumulative value thresholds (5, 10, 15), which result in a binary surface that, in the case of the fixed cumulative value 5 threshold, will include all but 5%   23  of the training samples as presences. MaxEnt produces a number of different thresholds based on different aspects of the model inputs and model fitting, and it can be difficult to determine which threshold suits the data best.  Some things to consider when choosing a threshold are how broad you want your prediction to be, the types of error that you are most concerned about limiting, and your confidence in the input occurrence data (Norris, 2014). This largely depends on the goal of the model; whether it is being used to identify potential habitat for an endangered species for example, or is being used to identify where a species may currently be found. Norris (2014) found that the areas of unsuitable habitat predicted for lowland tapir in Brazilian forests ranged from 18-85% across seven different threshold values available in MaxEnt.  This means that depending on the threshold used, the presence/absence output can be a significant overprediction of the actual distribution of the species. This may be a suitable result in the case of the endangered species, where an overprediction is identifying potential habitat for conservation efforts. However, in the case where the goal is to identify the actual distribution of a species, identifying too large of an unsuitable area may not be ideal. In these cases, more stringent thresholds such as the fixed cumulative value thresholds may be used. The type of error that is more important to minimize and the reliability of the occurrence records should also play a role in threshold choice. Type I error, false positives, or error of commission is an error where the MaxEnt model would predict a species is absent where we have presence records.  Type II error, false negatives, or error of omission would produce an error where the model output would predict that a species is present in an area that it is not known to be. While both should be minimized whenever possible, in some cases, one is more detrimental than the other.  In the example of the endangered species, which may have reliable but few occurrence records, commission error should be minimized as much as possible, in order to make sure that the known occurrences are included as presences in the model output (Pearson et al., 2007). In this case, a minimum training presence threshold should be used. In the second case, where the goal is to try to predict the range of a species, an overprediction may not give the best representation of the actual range, so a more stringent threshold such as a fixed cumulative value threshold should be used. Although   24  there are many choices, one should choose a threshold as carefully as possible or consider multiple thresholds and the affect that has on the outcome of the model.   2.2.2 Occurrence data One of the benefits of MaxEnt is that it is able to use presence-only data. Presence-only occurrence data for plant species is now easily accessible for download from online repositories such as the Botanical Information and Ecology Network (BIEN) and the Global Biodiversity Information Facility (GBIF). There are multiple packages in R Statistical Programming Software that allow occurrence data to be extracted from online sources with ease. Both data sources are able to integrate data from a variety of sources, including peer-reviewed publications, botanical inventories, and museum specimens collected since the 1800s, and create standardization schemes that scrub data for consistent taxonomic nomenclature. GBIF was established in 2001, and includes biodiversity data from all biological organisms around the globe, and has been used in conjunction with MaxEnt to create SDMs for plant species (Martinson et al., 2011; Truong et al., 2017). Work on the BIEN database began in 2008, and was made available in 2016. This database includes vegetation data for North and South America. Both of these databases provide dependable and easily accessible data on which SDMs can be built. MaxEnt is able to produce a robust model from very few occurrence records due to its ability to consider complex relationships between multiple covariates and the occurrence data.  According to Hernandez et al. (2006), a minimum of 10 distinct occurrence records is needed to produce an accurate model (Hernandez et al., 2006).  Algar et al. (2009) used 10 as the minimum number of occurrence records used for their analysis of the distributions of Canadian butterfly species using MaxEnt. Occurrence data used for MaxEnt modelling typically also excludes duplicate records, or points that occur within the same grid cell based on the resolution of the environmental data (Martinson et al., 2011; Shabani et al., 2016; Truong et al., 2017), and records that have low geographic accuracy (i.e. longitudes and latitudes with less than two decimal places). One should also consider the temporal range of the occurrence data (i.e. contemporary or historical) that suites the research question, as species adapt to different conditions and   25  migrate over time, as well as the source of the occurrence records (ie. herbarium or survey data), as these decisions both introduce bias into the resulting SDM.  Sampling bias relating to the occurrence data used can also alter the outcome of the MaxEnt model. Errors of omission can occur in MaxEnt models if, for example, herbarium records are biased towards certain taxa, seasons, and time periods for which collectors were focussing on (Graham et al., 2016; Martinson et al., 2011). Herbarium records and survey data can also be geographically biased towards areas more easily accessed by surveyors (ie. roadsides). In order to produce the most accurate model possible, it is optimal to have occurrence records that span the full range of environmental conditions that a species can inhabit. Sampling bias is commonly reduced by first removing occurrence records that occur in the same environmental grid cell (Fourcade et al., 2014). In order to further reduce sampling bias, less weight can be applied to occurrence records from areas of dense sampling and more weight given to areas with few records (Shabani et al., 2016; Elith et al., 2010). Another approach to reducing sampling bias involves choosing background points based on a grid representing sampling bias, where each cell is scaled to represent the survey effort given to that cell (Elith et al., 2011a). Regardless of the inevitable biases associated with occurrence data, MaxEnt models have proven to produce informative SDMs.  2.2.3 Predictor variables The second input into a MaxEnt model is the predictor variables. Climate variables tend to be the main predictors for MaxEnt models. WorldClim makes global climate data freely accessible at resolutions as fine as 30 arcseconds (~1 km2 at the equator), and packages are also available to download this data directly from the database into an R environment. BioClim variables, or variables that are considered to have more biological meaning, were derived from temperature, precipitation and seasonality measures and are commonly used in MaxEnt modelling (Hamann & Wang, 2006; Martinson et al., 2011; Shabani et al., 2016; Kane et al., 2017; Truong et al., 2017). WorldClim data is also available for historic, current and future climate. Current climate data from WorldClim data is derived from interpolations of observed data, representative of the years 1960-1990. WorldClim allows the user to select future climate data based on   26  representative concentration pathways (RCPs) and numerous global climate models (GCMs). GCMs create future climate projections using different mathematical models of the physical processes in the Earth’s atmosphere, oceans and land. The “MPI-ESM-LR” GCM from the Max Planck Institute for Meteorology in Germany, for example, considered to be an improvement on the groups previous model, ECHAM5/MPIOM, accounts for surface albedo, aerosol, interactive vegetation dynamics, and the coupled carbon cycle. This GCM is considered to produce a median climate projection for North American climate (Batllori et al., 2017). The four different RCPs were implemented by the Intergovernmental Panel on Climate Change’s (IPCC’s) Fifth Assessment Report (AR5), and are used to characterize four potential trajectories for atmospheric greenhouse gas concentrations. The range from RCP 2.6, which represents the situation where stringent climate policies that significantly reduce greenhouse gas emissions are implemented, to RCP8.5 which represents the “business as usual” scenario which assumes no change in climate policies than what is currently in place. RCP4.5 and RCP 6.0 fall in the middle of these two scenarios. The availability of such an extensive dataset makes it possible for many different scenarios to be considered for SDMs that look how species distributions may change in the future. Climate is not the only abiotic variable that may play in to a species’ distribution. Variables that relate to the topography of the land, such as elevation, slope, and aspect, also influence species distributions, and are also typically included in MaxEnt models (Martinson et al., 2011; Truong et al., 2017). Soil characteristics, such as soil moisture, nutrient content, and texture are also particularly important to consider when modelling species distributions, particularly plants (Martinson et al., 2011; Zhang et al., 2015; Truong et al., 2017).  Biogeoclimatic (BEC) zones characterize plant communities across British Columbia based mainly on climate, topography, and soil characteristics.  While climate and topography data is easily accessible, obtaining good quality soil data with the appropriate resolution for a given area is more challenging.  MaxEnt is also capable of handling categorical predictors, such as landcover, although they are often given too much weight (Truong et al., 2017).   It is common practice to remove highly correlated predictor variables in order to decrease the noise and flexibility in the model and increase the performance of the model   27  (Fourcade et al., 2014; Kane et al., 2017; Truong et al., 2017). This is considered to be best practice in most cases. On the other hand, Elith et al. (2011) suggests that high collinearity is less of a problem for machine learning methods such as MaxEnt that chooses the predictors that are most important in the model via regularization. This would suggest that if the predictive accuracy of the SDM output is the main goal, and the identification of the relative contributions of the predictor variables in the model is not an important aspect, then all predictor variables should be used as inputs (Merow et al., 2013). Nevertheless, as with any ecological model, predictors selected by experts who are familiar with the species of interest should be given highest priority (Elith et al., 2011a). Once the desired predictor variables are chosen, one needs to consider the resolution of the data required to satisfy the scope of the study. Kane et al. (2017) used a resolution of 30 arcseconds (~1km2) for their regional assessment of a grassland conservation priority area in Missouri, USA, which encompasses an area of 28,000 hectares. Truong et al. (2017) used the same resolution in order to model the invasion potential for plants in Southeast Asia. Zhang et al. (2015) used an 8x8 km resolution, however, they were producing their model at a national scale, for all of China. The tradeoff between computational time versus resolution is a limiting factor in producing a MaxEnt model. Another thing to consider when preparing predictor variables is whether or not your predictor variables are in a geographic (unprojected) or projected coordinate system. If your predictor variables are covering a large area, the raster cell size will likely differ longitudinally, which is problematic as MaxEnt assumes that all predictor cells are equal in area when sampling for background data  (Elith et al., 2011a).  2.2.4 Model Performance & Validation Using occurrence data that is independent of the data used to generate the MaxEnt model is the best way to test the accuracy of the MaxEnt model.  However, in many cases, independent data is not available. MaxEnt allows the performance of the model outputs to be tested by partitioning the occurrence data into training data and test data. By default, MaxEnt uses 70% of the data for training the model, and sets aside 30% for testing purposes (Algar et al., 2009; Truong et al., 2017), while depending on the amount of data available, other studies have set aside less testing data (Martinson et al., 2011;   28  Graham et al., 2017). MaxEnt then calculates the sensitivity, or True Positive Rate (TPR), which is the number of the test points that were predicted within the suitable area predicted by MaxEnt. Similarly, the specificity, or True Negative Rate (TNR) calculates the number of test points that fall outside of the suitable area defined by the MaxEnt model. The Receiving Operator Characteristics (ROC) curve which plots the False Positive Rate (FPR), which is 1 – specificity and represents Type I error, is then used to determine whether or not the model produced a prediction that performs better or worse than a random guess (the background data). Figure 2.1 shows an example of an AUC plot, an output of the MaxEnt model that shows a red line that represents the ROC curve based on different thresholds in MaxEnt. An AUC value of 0.5 means that the model performed similarly to if the suitability was chosen by random chance, while a value greater than 0.5 means that the MaxEnt model had higher predictive power than expected by chance. AUC is commonly reported as a measure of the predictive performance of MaxEnt models and is often favoured over other measures because it is considered to be threshold-independent, as it gives a single value of performance based on many possible thresholds (Phillips et al., 2006; Wan & Zhang, 2016; Graham et al., 2017; Kane et al., 2017).     29    Figure 2.1. AUC plot showing an example of an AUC curve illustrating the predictive performance of the MaxEnt model based on a given threshold value. Illustration credit: Moghaddam-Gheshlagh et al. (2017) Climate change impact on Olneya tesota A. Gray (Ironwood) distribution in Sonoran desert using MaxEnt Modeling approach. Journal of Wildlife and Biodiversity 1(2): 110-117.  In some cases, independent occurrence data is available and can be used to validate the MaxEnt model. In a study on the distribution of invasive cheatgrass (Bromus tectorum) in Colorado, USA, MaxEnt models were created using presence data from 2007 and 2008 to 2013 (West et al., 2016). A GLM was developed using presence/absence data from 2008 to 2013 and it was found that the MaxEnt model based on 2008 to 2013 presence data had an AUC of 0.96 while the GLM model had an AUC of 0.83. Even in the case of the MaxEnt model produced using only 2007 (ie. a smaller sample size), the AUC value was comparable to the GLM model (AUC=0.80). These results remained true even when using threshold dependent measures of model performance, indicating that MaxEnt could be appropriately used when absence data is not available and when occurrence data is limited.     30  2.3 Functional Diversity 2.3.1 Trait data collection While functional diversity analyses have largely become more prevalent due to the mechanistic links identified between functional traits and ecosystem functioning (Tilman et al., 1997; Loreau, 2000; Diaz & Cabido, 2001; Hooper et al., 2005; Cadotte, 2017), increases in data availability have also facilitated more research in this field.  Since the development of standardized plant trait data collection methods (Cornelissen et al., 2003), trait data is constantly being contributed to large databases by researchers working around the world.  Databases such as TRY Plant Trait Database have been used prominently as a data repository in the past. However, this database requires data to be requested from the originating authors who may then set up user agreements to their specifications.  It can therefore take a long time to obtain the data required to do functional diversity analyses. Large data repositories that compile data from various research groups and provide an easy way to access it are becoming more common. Currently, the BIEN trait database comprises of 34 plant traits, 52,363 plant species with at least one trait observation, and 296,958 trait observations compiled from 550 different contributors including research groups, published literature, and data repositories. This data is now integrated into an R package that allows trait data to be pulled directly into R for analysis. Similarly, trait data from the Global Biodiversity Information Facility (GBIF) can also be accessed in R, however, it has also been integrated into the BIEN database. Plant trait data for North American plants is also available through the United States Department of Agriculture (USDA) Plants Database. Although it has not yet been integrated into an R package, it can be easily downloaded from the database. Trait data is available for approximately 2,000 plant species and includes continuous and categorical traits related to morphology/physiology, growth requirements, reproduction, and as well as commercial uses. This data has been compiled from both peer-reviewed literature and grey literature. Although compiling complete trait data for a full set of species for a given assemblage can take some time, trait data is more readily available than ever. The question then becomes which traits should functional diversity be calculated for?   31   2.3.2 Trait Selection Ecological assemblages are complex systems and one of the biggest challenges when quantifying functional diversity is choosing traits that best signal the diversity of functions and responses of individuals in an assemblage (Cadotte et al., 2013). Classically, functional traits are classified as either functional effect or functional response traits. Functional effect traits relate to the ecological role or ecosystem service that the species contributes to the assemblage, while functional response traits are related to a species’ resilience to disturbances. Functional effect traits are typically associated with an individual’s ability to capture and conserve resources such as nutrient cycling (Grime, 2001; Leps et al., 2006). For example, Maron & Connors (1996) showed that the presence of bush lupine (Lupinus arboreus), a native nitrogen-fixing shrub with high productivity that grows along the Central Californian coast, can change the overall assemblage composition and potentially facilitate the establishment of invasive species. Spehn et al. (2002) also showed that in a cross-European study, the presence of nitrogen-fixing legumes significantly influenced the accumulation of nitrogen as well as the above-ground biomass of the assemblage.  Functional effect traits such as timing of bloom, nectar resource traits, and morphological traits such as height of the plant and colour of flowers, have also been linked to pollinator richness and pollinator visitation frequency (Fornoff et al., 2017).  Functional response traits measure the sensitivity and adaptive capacity of an individual’s response to competition or environmental disturbances, and include traits such as dispersal, fecundity, seed production, dispersal method, fire and drought tolerance, and bark thickness. Spasojevic et al. (2015) used a set of continuous and categorical traits in order to assess the relationship between the diversity of traits relating to response to fire and the resilience of forest ecosystems in southwestern United States. The categorical traits they used included growth habit (eg. graminoid, shrub, tree), fire tolerance (yes or no), fire resistance (low, medium, high), and resprout ability (yes or no), which were all gathered from the USDA Plants Database. Although these seem to be broad categories, they are based on a suite of other traits, both categorical and numerical. For example, fire resistance is based on traits such as plant height, and whether or not   32  their height may be taller than flames, as well as bark thickness (Lavorel & Garnier, 2002; Spasojevic et al., 2016). Spasojevic et al. (2016) found that diversity in fire tolerance, fire resistance and resprout ability had a strong effect on the recovery of a forest assemblage after a wildfire, while species richness and seed mass were not as strong. Weiher et al. (1999) gives an outline of some of the challenges that plants face and the traits associated with these challenges (Table 1). They categorize these traits as either hard or easy traits, relating to their ease of measurement, stating that while some of the hard traits may be close to impossible to measure, the easy traits can act as analogs for the hard traits and are collected with much more ease. Some easy traits are able to act as a good signal for a given challenge. For example, seed mass and shape are considered to be good indicators of propagule longevity and therefore a good indicator of seed dispersal in time. Seed mass is also a reasonable indicator for seed dispersal distance for wind dispersed seeds, yet is not able to be related to other types of dispersal such as endozoochory or exozoochory. If easy traits are being used as analogs for a certain function or plant response, the degree to which the trait actually relates to the function or response needs to be considered.                33  Table 2.1 The common challenges faced by plants and some suggested traits (Weiher et al., 1999). Challenge Hard Trait Easy Trait 1. Dispersal              Dispersal in space Dispersal distance Seed mass, Dispersal mode            Dispersal in time Propagule longevity Seed mass, Seed shape 2. Establishment              Seedling growth Seed mass Seed mass  Relative growth rate Specific Leaf Area (SLA)   Leaf Water Content (LWC) 3. Persistence              Seed production Fecundity Seed mass   Above ground biomass            Competitive ability Competitive effect and response Height Above ground biomass            Plasticity Reaction norm SLA, LWC            Holding space/             longevity Life span Life history, Stem density            Acquiring space Vegetative spread Clonality            Respond to             disturbance: stress             and disturbance             avoidance Resprouting ability  Phenology Palatability Resprouting ability  Onset of flowering SLA, LWC  Another example of a hard trait that is difficult to represent using easy traits is a plants ability to obtain water and nutrients from the soil, which can also relate to a plant’s ability to withstand disturbances such as drought or nutrient depletion in soil. While below ground aspects of plants, such as rooting structure, depth, and mycorrhizal associations are significant aspects of plant life that should be included into functional diversity assessments (Vojtko et al., 2017), these trait data are generally lacking.  However, it is often presumed that these hard traits are associated with soft traits from aboveground (Cornelissen et al., 2001; Leps et al., 2006). There is a vast number of other traits that could be used in functional diversity analyses: life span (ie. annual, perennial), propagation type (ie. sexual, vegetative), mode of seed dispersal (ie. autochory, endochory, exochory), pollination system (ie. insect,   34  wind), sexual expression, inflorescence structure, leaf mass and structure, and even traits relating to socio-ecological groupings (ie. food plants) (Markowicz et al., 2015; Pardo et al., 2017).  While focussing on traits that are known to be direct signals of the function of interest will give the best estimate of functional diversity, it is most likely that not all traits that are important for a given function are able to be measured or are even known.    2.3.3 LHS traits  Comparable to Grime’s CSR life-history strategy model, comprising of competitors, stress-tolerators, and ruderals as the vertices of the triangle, Westoby's (1998) leaf-height-seed (LHS) plant strategy scheme characterizes plant species based on three plant traits: specific leaf area (SLA), plant height, and seed mass. SLA is measured as the area of one side of a complete, mature leaf divided by the dry mass (Cornelissen et al., 2003), and is considered to represent the photosynthetic capacity of the leaf or the amount of light that a leaf is able to capture (Westoby, 1998). The trade-off associated with SLA is that although a high SLA means that there is a high rate of return for the plant, it is also associated with faster leaf degradation (Westoby, 1998). Plants with low SLA often have more robust leaves, where defensive and structural compounds are found in place of photosynthetic compounds. Plants with high SLA invest more energy into their leaf production at the expense of the lifespan of the leaf. Plant height relates to plant assemblage dynamics, where although the taller plants may receive benefits such as more light, they are also more susceptible to damage from disturbances like strong winds. Westoby (1998) describes this succession as different plants being “leaders in the race” at different points of successional time. The trade-offs associated with this trait are therefore related to their competitive ability and ability to respond to environmental stress (Chalmandrier et al., 2015). Seed mass relates to the likelihood of establishment, where the trade-off is that a larger seed has a better chance of establishment, but takes more metabolic energy to create and is therefore also correlated with seed production, and smaller seeds, although they are created in more abundance, have a lower likelihood of establishment. The location of a plant species along these three trait axes captures the overall variation in plant ecological life history strategies in an assemblage, and also   35  characterizes both functional effects and functional responses of plant species (Chalmandrier et al., 2015). LHS traits have been used in various applications, from assemblage ecology looking at the relationship between functional diversity, environmental filtering and assemblage structure (Bello et al., 2013; Herben et al., 2013), to assessing the responses of functional diversity in plant assemblages to environmental management and restoration efforts (Lavorel et al., 2011).  LHS traits have continually been demonstrated to capture the ecological variation of plant life history strategies, and is therefore commonly used to assess the overall functional diversity of plant assemblages.  2.3.4 Intraspecific Trait Variability and Phenotypic Plasticity Plant trait databases, such as TRY, TOPIC, and BIEN, have increased access to trait data for functional diversity analyses.  When using trait data collected outside of the study region of interest, one must consider the implications that intraspecific trait variability has on functional diversity analyses.  A trait for a given species can vary considerable depending on the climate and ecosystem it is found in, as well as biotic interactions with neighboring plant species (Abakumova et al., 2016).  The collection of plant trait data typically follows standardized sampling techniques (Cornelissen et al., 2003).  Plant height, for example, must be collected from healthy, adult plants that have foliage exposed to full sunlight, and since it can be extremely variable, it must be collected on at least 25 individuals in a given sampling effort (Cornelissen et al., 2003).  If these standard sampling techniques are followed for trait data collection, intraspecific trait variation is typically attributed to trait plasticity.  Phenotypic, or trait, plasticity, is the ability of a species to alter traits in response to changes in both abiotic and biotic aspects of its environment (Weiher et al., 1999).  While trait plasticity has been included as a trait in itself in functional diversity analyses, quantified as the variation of the trait for the species, it is generally difficult to properly assess trait plasticity without controlled greenhouse experiments (Weiher et al., 1999).  Most typically, trait data for a given species is averaged in order to obtain a representative trait value.  Although studies have found that species hierarchies for the different traits are still maintained (Craven et al.   36  ,2016; Cordlandwehr et al., 2013; Kazakou et al., 2014), properly incorporating measures of trait plasticity into functional diversity analyses remains a challenge.   2.3.5 Data Scaling Functional diversity can be calculated on a single trait or on a suite of traits that represent the function of interest. If multiple traits are used, it is important to consider that especially for plants, trait values may be measured on drastically different scales. When using the LHS traits for example, seed mass is typically measured in milligrams and plant height in meters.  Since the range of values or scales of these traits can vary by orders of magnitude, traits with larger values may be given greater weight in the calculation of functional diversity. In order to correct for this, trait data can be transformed using an algebraic function, or be standardized according to the range of values in your dataset (Leps et al., 2006). Trait data is commonly log-transformed, which can give an approximate normal distribution (Swenson, 2014). While this is a good approach for most continuous trait data, log transformation may not be appropriate in every case, especially when the trait data includes negative values, zeros that represent trait absence, or data on an interval scale (for example, phenological data like flowering onset) (Leps et al., 2006). Another approach to scaling data would be to scale according to the range of values in the dataset. For example, a Z-score could be calculated by subtracting each trait value from the mean of the trait then dividing by the standard deviation of the trait. This results in a set of traits expressed in units of standard deviation represented by their relation to the mean.  After trait data has been scaled based on the other values in the dataset, or using an algebraic function, multiple traits can be used to calculate functional diversity with each trait without the magnitude of the trait values affecting the weighting in the calculation. However, the correlation between traits still needs to be considered. For example, if multiple leaf measurements are used to calculate functional diversity, such as leaf dry mass and leaf area, it is likely that the two leaf traits are highly correlated and likely represent the same axis of function (Swenson, 2014). Principal Components Analysis (PCA) can be used to determine the distinct functional axes and eliminate trait   37  redundancy (Leps et al., 2006; Swenson, 2014). Swenson (2014) suggests that only axes that explain over 90% of the variation in trait data should be selected. The PCA scores representing where all of the species fall on the given number of axes chosen can then be used to calculate the trait distance matrices and functional diversity metrics. These scaling methods allow traits that are measured on different magnitudes (ie. seed mass versus plant height), and traits that may co-vary to be scaled and therefore be used to calculate functional diversity in a way that does not give too much weight to a certain trait and span the broad range of functions and responses of individuals in an assemblage.  2.3.6 Measures of Functional Diversity In recent years, many indices have been created to calculate the functional diversity of an assemblage. Swenson (2014) describes some of the metrics that can be used to quantify or describe functional diversity patterns within an assemblage for a single trait. In order to get a first glimpse of an assemblage structure, four statistical moments regarding the trait distributions of an assemblage can be calculated – mean, standard deviation, skewness, and kurtosis (Swenson, 2014). The mean trait value gives the central tendency of the trait in the assemblage, and the standard deviation would give a measure of the spread of trait values in the assemblage, such that a higher standard deviation would indicate that species in the assemblage are functionally dissimilar. Skewness is a measure of the symmetry of traits in the assemblage, such that a high value of skew could indicate that while the majority of species are functionally similar, there are species with dissimilar trait values that would increase the functional diversity of the overall assemblage.  Kurtosis measures the degree of flatness or “peakedness” of the traits in the assemblage, such that a low kurtosis value indicates that there is more trait disparity between species in the assemblage. All of these moments can also be weighted by abundance, or any other weighting such as percent cover (Swenson, 2014). The assemblage-weighted mean (CWM) is a commonly used metric to do just that. Grime's (1998) mass ratio theory, which states that the dominant plants and traits in an assemblage have the most impact on ecosystem functions, is accounted for by the CWM measurement (Dubuis et al., 2013).   38  Together, these statistical moments can give a clear picture of the distribution of traits within the assemblage. However, on their own, statistical moments such as the mean trait value for an assemblage, do not necessarily give a measure of the overall diversity of functional traits in an assemblage, especially because they only describe the diversity of a single trait. One should therefore be wary of what these moments represent before using them in functional diversity analyses. The overall objective of functional diversity analyses is to measure the range of functions in an assemblage based on species traits (Swenson, 2014). Functional diversity based on a single trait is therefore the range of values of the trait. When there are multiple traits, the range of functions being measured is calculated instead as the multi-dimensional volume of the convex hull that encapsulates all species and their traits in trait space (Swenson, 2014). The volume of the convex hull of a multi-dimensional trait space is known as Functional Richness (FRic) and gives a relative measure of how packed species are within a trait space. Two other common classes of functional diversity metrics are based on trait distances: mean pair-wise trait distance (PW), and mean nearest-neighbor trait distance (NN) (Swenson et al., 2012; Swenson, 2014). In order to calculate these metrics, trait distance matrices or dengrograms of traits must first be generated. Trait distance matrices are created using either Euclidean distances between species within a generated trait space or dendrogram branch lengths. The Euclidean distance approach is more favorable and straight-forward to calculate, however, trait dendrograms are sometimes preferred when the goal is to eventually relate functional traits to a phylogenetic tree, as these two formats are directly comparable. When creating distance matrices for multiple traits, where some of the traits are categorical as opposed to continuous, the distance matrix can be converted to Gower Distances or Gower Dissimilarity, which is measured from 0 (identical) to 1 (maximally dissimilar). PW is calculated by summarizing the average distances between all pairs of species within an assemblage (Swenson, 2014). This metric gives the overall dissimilarity of the species in the assemblage, such that a high PW indicates that species in the assemblage are highly functionally dissimilar. NN on the other hand gives a more detailed measure of functional diversity by averaging the distance between each species   39  and it’s nearest functional neighbour in the assemblage. The standard deviation of these nearest neighbour distances can also be taken and indicates the variation in nearest neighbour values. As with the other metrics, these can also be weighted by the abundances of each species in the assemblage if this data is available. Other measures of functional diversity related to their distribution in multi-dimensional trait space include functional evenness (FEve), functional dispersion (FDis), and functional divergence (FDiv). FEve calculates the minimum spanning tree (MST) required to connect all species in the multi-dimensional trait space. FDis calculates the average distance from each species to the centroid in the trait space. FDiv gives an understanding of whether or not the species are dispersed more towards the maximum or minimum of the range of traits (Villeger et al., 2008; Swenson, 2014).  2.3.7 Functional Diversity Null Models If the goal of calculating functional diversity is to gain additional information about an assemblage above what is given with species richness, the functional diversity metric used should not be correlated with species richness. Swenson (2014) demonstrates that the NN and FRic metrics may be correlated with species richness. Although he also demonstrates that while the PW metric is not necessarily correlated with species richness, the variance in PW decreases with increasing species richness, suggesting that it is not completely independent of species richness. Swenson (2014) therefore suggests that although any given metric for functional diversity may not be correlated with species richness, a null model should still be used to compare any functional diversity metric to in order to minimize any underlying bias associated with species richness. There are two main approaches to functional diversity null models; a null model where the functional trait data is constant and the assemblage data matrix is randomized, and a null model where the functional trait data is randomized and the assemblage data matrix is fixed (Swenson, 2014). The former was the first approach used, however, as null models based on the randomization of observed assemblage data often inadvertently end up randomizing other aspects of the data other than the pattern of interest, they end up inflating type I error (Swenson, 2014). The latter approach creates null models that, instead of basing the null model comparison on randomly constructed assemblages,   40  compares observed values to those based on randomized functional trait data (Swenson, 2014).  Swenson (2014) describes an unconstrained and constrained approach at randomizing functional trait data. An unconstrained model involves shuffling just the species names in the trait matrix, which allows the overall phenotypes or combinations of traits to be maintained and only randomizes the species that possesses this phenotype (Swenson, 2014). The constrained version of this null model involves pruning the species that can be randomly shuffled down to those that fall within the observed multi-dimensional trait volume, in order to account for the fact that these combinations of traits in the assemblage were filtered by abiotic factors and then by their similarity to other species in the assemblage. The combinations created by randomly shuffling all species could potentially create combinations of species that could not occur in real life. Although the constrained model makes more sense ecologically, it takes more computational effort and may not provide enough random combinations to provide statistical power (Swenson, 2014).  The output of the randomization is expressed as the standardized effect size (SES) or Z-score, calculated as:   𝑆𝐸𝑆 =𝑂𝑏𝑠𝑒𝑟𝑣𝑒𝑑 𝐷𝑖𝑣𝑒𝑟𝑠𝑖𝑡𝑦 − 𝑀𝑒𝑎𝑛 𝑜𝑓 𝑁𝑢𝑙𝑙 𝐷𝑖𝑠𝑡𝑟𝑖𝑏𝑢𝑡𝑖𝑜𝑛𝑆𝐷 𝑜𝑓 𝑁𝑢𝑙𝑙 𝐷𝑖𝑠𝑡𝑟𝑖𝑏𝑢𝑡𝑖𝑜𝑛  A positive value for the SES therefore reflects functional evenness, or a greater functional distance between the species in the assemblage than expected by chance (Swenson, 2014). On the other hand, a negative SES is indicative of functional clustering, or smaller functional distances among species in the assemblage than expected by chance (Swenson, 2014).  2.4 Phylogenetic Diversity 2.4.1 Phylogenies used Calculating the phylogenetic diversity of an assemblage relies on an accurate phylogenetic tree structure and branch length estimation. According to Qian and Jin   41  (2015), over 10 versions of angiosperm megatrees already exist, and it is likely that these will continually be refined each year, with advances in molecular sequencing. The Angiosperm Phylogeny Group, an international collaboration between systematic biologists, created a number of megatrees that are continually being updated and are commonly used. These include the R20091110 (Brum et al., 2012; Duarte et al., 2012; Soliveres et al., 2012; Qi et al., 2014),  R20100701 (Giehl & Jarenkow, 2012; Seger et al., 2013; Brunbjerg et al., 2014), and R20120829 (Cianciaruso et al., 2013; Gonzalez-Caro et al., 2014; Feng et al., 2015; Matos et al., 2016) supertrees which were all derived from APG III, the third update of the supertree from the group. The problem with using these supertrees for phylogenetic diversity analyses is two-fold. For one, these trees include only a small portion of all angiosperm families and species, and secondly, these trees do not include information on branch length, which is the basis for most phylogenetic diversity measures (Qian & Jin, 2015). Branch lengths for this tree are typically estimated using Phylomatic and BLADJ along with plant clade estimates from Wikstro et al. (2001), which itself only includes branch length estimates for less than 30% of the all angiosperm families that are incorporated by APG III (Qian & Jin, 2015). A species-level phylogeny that includes branch length estimates has now been created by Zanne et al. (2014), based on seven gene regions, both slow evolving to quickly evolving regions (Qian & Jin, 2015). This phylogeny includes over 30,000 extant plant species and over 50% of the genera of all extant seed plants in the world (Zanne et al., 2014; Qian & Jin, 2015). According to Web of Science, the Zanne et al. (2014) paper has been cited 228 times as of January 2018, with the phylogeny being used in many different applications. The one critique of this megatree according to Qian & Jin (2015) is that the taxonomy in this phylogeny is not necessarily consistent with international authority plant databases, such as The Plant List, in terms of naming conventions and the presence of species synonyms. Qian & Jin (2015) created an updated version of Zanne et al. (2014) megatree that eliminated any discrepancies in the taxonomy. Their tree, however, was not freely available at the time of this work. BIEN has also developed a plant phylogeny that will include branch lengths, consistent taxonomy, and be easily accessible. The megatree developed by BIEN was estimated using a standardized list of New World plant species using a method similar to   42  that of Zanne et al. (2014), which involved querying GenBank for data regarding certain gene sequences. Although this tree is considered to be a work in progress, it has the benefit of being used in conjunction with occurrence data, trait data, and other data that are based on the same taxonomic naming scheme.   2.4.2 Measures of Phylogenetic Diversity As more attention has been turned to phylogenetic diversity in recent decades, indices are continually being developed to represent this metric. Faith's (1992) PD index, one of the most widely used metrics, quantifies phylogenetic diversity as the total minimum length of the phylogenetic branches required to span all of the taxa within an assemblage on a phylogenetic tree. This metric is considered to be a form of “richness” metric, and can also be weighted by the abundance of each species in the assemblage, if the data is available (Swenson, 2014). Another option when using this metric is whether or not to include the root of the phylogeny in the summation of the total branch length. When this index was created, the root was not included in the calculation, yet recently, the root is typically included based on the rationale that the root includes information about the complete evolutionary history that lead up to the species being found in the assemblage of interest (Swenson, 2014). When the root is included, the metric is often termed “Evolutionary History” or “Evolutionary Heritage” (EH), and is considered as a historical diversity of an assemblage, which is typically preferred in conservation applications (Mooers & Heard, 2005; Swenson, 2014). As with functional diversity, other methods that have been developed to represent phylogenetic diversity usually fall under the categories of pair-wise distance methods or nearest-neighbour distance methods (Swenson, 2014). Mean pair-wise distance, or MPD, developed by Webb et al. (2002) is the most commonly used pair-wise distance metric. It is calculated as:  where n is the number of species in the assemblage, δ are the pairwise phylogenetic distances between species i and j. MPD is therefore calculated as the average pairwise   43  distance between all species in the assemblage. Because this metric considers all pairwise distances between all species in an assemblage, it is considered to capture the overall phylogenetic dissimilarity of an assemblage, although unable to detect finer scale phylogenetic patterns (Swenson, 2014). As with Faith’s PD index, MPD can be weighted by the abundances of the species in the assemblage, if this data is available. Although other pairwise distance methods have been developed, including Rao’s Dalpha (Rao, 1982), which is similar to the abundance weighted MPD index, and Hardy et al.'s (2007) Dk, which has been likened to the phylogenetic version of the Shannon Index (Swenson, 2014), they are all significantly correlated to Webb et al.'s  (2002) MPD index. Another category of phylogenetic diversity indices that are not considered to be conceptually or mathematically related to pairwise distance measures (Swenson, 2014) are called nearest neighbor measures. While pairwise distance measures are considered to “basal”, representing the entire evolutionary history of the whole assemblage, nearest neighbour measures are considered to incorporate “terminal” evolutionary information, meaning that they give an idea of the phylogenetic distances between each species and its closest relative in the assemblage (Swenson, 2014). As in functional diversity analyses, nearest neighbour measures give an idea of the spread of species across the phylogeny. Webb et al. (2002) produced the mean nearest taxon distance (MNTD) metric, calculated as:   where n is the number of species in the assemblage and minδi,j is the minimum phylogenetic distance between species I and all other species in the assemblage. As with the other indices, this index can be weighted by abundances of each species in the assemblage if this data is available. Although many phylogenetic diversity indices or metrics to measure evolutionary distinctiveness have been produced, many of them are correlated and it is crucial that the question of interest is matched with the appropriate metric. Davies et al. (2016) introduce a network theory approach that identifies important nodes in a phylogeny that may contribute to ecosystem functions using two measures of network centrality: betweenness and closeness. This approach allows one to consider branches in a phylogeny that may   44  have keystone effects on the ecosystem functioning of an assemblage, compared to tree-based measures such as Faith’s PD that would assume that two branches of the same length would have equal contributions to phylogenetic diversity. Davies et al. (2016) were therefore able to identify a node representing the evolution of a trait that contributes greatly to the ecosystem function of the assemblage, nitrogen-fixation. Another metric that is considered to be a phylogenetic richness metric, similar to Faith’s PD index, is called Evolutionary Distinctiveness (ED), which looks at the phylogenetic isolation of each taxa in an assemblage (Safi et al., 2011).  2.4.3 Null model considerations As with functional diversity metrics, null models are typically required in order to provide phylogenetic diversity measurements that are independent from species richness. For tree-based measures such as Faith’s PD index, adding species simply increases the sum of the branch lengths, and is therefore directly related to species richness. Distance-based measures, such as MPD and MNTD, are less directly impacted by species richness.  Matos et al. (2016) however, found that in their case, MPD was significantly correlated with species richness while MNTD was not. In order to remove this bias, the metric should be compared to a null distribution of phylogenetic diversity values based on randomized data. Also analogous to a null model associated with functional diversity data, there are two main approaches to phylogenetic diversity null models: a null model where the phylogenetic tree is constant and the assemblage data matrix is randomized, and a null model where the phylogenetic tree is randomized and the assemblage data is fixed (Swenson, 2014). Although one can choose to randomize the observed assemblage data to create a null distribution of phylogenetic diversity metrics, this is not the preferred approach. Instead, Swenson (2014) suggests comparing observed values of the phylogenetic diversity metric for an assemblage to those based on randomized phylogenetic distances between species that are observed in the assemblage (Swenson, 2014). An example of one approach at doing this is to repeatedly shuffle the taxa labels across the phylogeny, essentially randomizing who is most closely related to whom (Swenson, 2014).    45  The standardized effect size can again be used to assess whether or not the phylogenetic diversity for an assemblage is higher or lower than expected given the number of species in the assemblage (Boesing, 2016). The choice of null model requires careful consideration, as the significance of the phylogenetic diversity metric depends on the null model used (Miller et al., 2016). In order to address issues relating to null model selection, Miller et al. (2016) suggest creating a set of possible metrics and a set of possible null models, which undergo repeated matrix-wise randomizations, in order to give the most appropriate null model to compare the metric to. Regardless of the null model method used, standardization of phylogenetic diversity metrics allows a meaningful comparison between phylogenetic diversity, functional diversity, and taxonomic diversity or species richness to be made.  2.5 Development of a reproducible protocol The data and computational tools needed to produce SDMs and to carry out functional and phylogenetic diversity analyses have become increasingly easy to access in recent years. One of the objectives of this project was to create reproducible methods that use SDMs to produce estimates of species richness, phylogenetic diversity and functional diversity at a regional scale. Figure 2.2 shows a summary of the analysis that carries out data compilation and manipulation, the output of MaxEnt SDM models, as well as estimations of functional and phylogenetic diversity based on multiple measures. The blue boxes represent analysis inputs, white dashed boxes represent data manipulations/pruning decisions, yellow circles represent computational analyses, green diamonds represent analysis decisions, and purple shapes represent final model outputs. BIEN, GBIF, and WorldClim data are able to be accessed directly within R while USDA and Kew data was downloaded from their respective websites before being loaded into R and pruned.  At various stages in this process, the modeller has the power to change the inputs in various ways in order to make it suit their needs. Throughout the process, there is very little need for manual data manipulation, which allows for this methodology to be reproducible. Figure 2.2 outlines the work flow process including the inputs needed at   46  various stages as well as some of the options that can be changed based on the needs of the modeller.  Figure 2.2. Schematic showing a summary of the analysis that carries out data compilation and manipulation, the output of MaxEnt SDM models, as well as estimations of functional and phylogenetic diversity based on multiple measures. The blue boxes represent analysis inputs, white dashed boxes represent data   47  manipulations/pruning decisions, yellow circles represent computational analyses, green diamonds represent analysis decisions, and purple shapes represent final model outputs. 3 Patterns of Taxonomic, Functional and Phylogenetic Diversity of Vascular plants in the Okanagan Ecoregion 3.1 Synopsis An ongoing challenge in ecology and conservation biology is to improve upon methods used to quantify biodiversity, and to devise conservation management strategies that successfully conserve biodiversity and its associated benefits, now and into the future.  Taxonomic diversity, commonly represented as species richness, has long been the focus of conservation research and practical efforts (Myers et al., 2000; Davies & Cadotte, 2011; Marchese, 2015).  However, quantifying biodiversity using taxonomic diversity in isolation implies that all taxa have equal conservation value.  In recent years there has been increased emphasis on other facets of biodiversity that can offer important information about the structure and conservation value of an ecological assemblage.  Phylogenetic diversity measures the evolutionary distinctiveness of an assemblage, while functional diversity, which is based on species traits, can give insight into the range of functions that species in an assemblage play, the ecosystem services they provide, as well as the potential resilience of an assemblage to disturbances such as climate change (Dubuis et al., 2013; Spasojevic et al., 2016).  Considering all three facets of biodiversity simultaneously, as opposed to using measures of taxonomic diversity alone, can facilitate consideration of multiple conservation objectives (Devictor et al., 2010). The Okanagan Ecoregion, located in south central British Columbia, is considered to be a biodiversity hotspot in Canada, harbouring a large proportion of Canada’s species and ecosystems at risk.  The Okanagan Ecoregion is also considered to be a transition zone between various biomes and ecosystems (Pryce et al., 2006), and may also be an important corridor for species moving across the landscape and species that may migrate north in response to climate change (Krosby et al., 2016). Rapid urban and agricultural development in the region in the past century has had significant impacts on the species and ecosystems in the Okanagan Ecoregion. The Okanagan Collaborative Conservation Program (OCCP), a partnership between various government and non-government   48  organizations, initiated the Okanagan Biodiversity Strategy, which involved the identification of high priority conservation areas.  Relative biodiversity was mapped for the region and was based on biodiversity proxies such as distance from roads, size of natural areas, and the presence of important ecosystems, habitat features, or species-at-risk, which are equivalent to taxonomic diversity measures. The Nature Conservancy of Canada also identified areas of significant conservation priority in the Okanagan Ecoregion using MARXAN software (Pryce et al., 2006). This assessment identified conservation targets based on the relative scores of irreplaceability and vulnerability for terrestrial species, aquatic species, and rare plant communities; taxonomic diversity was the only facet of biodiversity that was considered. While this work provides good baseline information for planning biodiversity conservation efforts, all of this work has been largely based on taxonomic diversity, or species richness, alone and has not incorporated assessments of functional and phylogenetic diversity.  Including these facets in biodiversity assessments and conservation prioritization decision making frameworks will help to identify areas in the Okanagan Ecoregion that have greater range of functions or encompass more evolutionary history than might be suggested based on the number of species present.  For these reasons, maintaining functional aspects of the landscape as well as maintaining species that are evolutionarily distinct will add significant conservation value to the Ecoregion, especially as new protected areas are being considered (Parks Canada, 2018). Traditionally, conservation efforts have largely focussed on how species and ecosystem services respond to contemporary stressors.  However, projections have shown that climate is predicted to change drastically in the next century, which will have significant impacts on ecological assemblages (Hamann & Wang, 2006; Shafer et al., 2015).  Thus, considering how assemblages will be impacted by climate change and other disturbances is an important aspect of modern conservation planning.  Using a climate envelope modelling approach for forest communities in British Columbia, Hamann & Wang (2006) found that suitable habitat for conifer species is estimated to significantly decrease in size under future climate scenarios while tree species that are currently at their northern limit in British Columbia will gain habitat. Hamann & Aitken (2013) also demonstrated using a similar approach that the current protected area network in British Columbia would be able to maintain between 35% and 85% of locally adapted forest   49  communities under climate change scenarios.  They also emphasize that predictions of range change based on future climate are dependent on a species’ migration ability (ie. ability to move to suitable habitat) and adaptive capacity (ie. ability to adapt to new climates and remain in place).  In the Okanagan Ecoregion, annual temperatures are projected to increase by 1.8°C by 2050, with annual precipitation increasing by 6% (PCIC, 2013). Summer climate is projected to have a greater increase in temperature than other seasons and become drier, with an approximate increase of 2.2°C and precipitation decreasing by 9% by 2050 (PCIC, 2013).  Despite these predictions, there is limited research addressing the impact that climate change will have on ecological assemblages and maintenance of biodiversity in the Okanagan Ecoregion.   Our objective here is to fill these important knowledge gaps, by addressing the following specific objectives: 1) Use species distribution models to predict current and future distributions of plant species inhabiting the Okanagan Ecoregion; 2) Quantify, map and compare taxonomic, functional, and phylogenetic diversity patterns and hotspots in the Okanagan Ecoregion and compare to the current protected area network; and 3) Use climate projections to assess how future patterns and hotspots of taxonomic, functional, and phylogenetic diversity may differ from current. We also use null models to quantify functional and phylogenetic diversity independent of species richness, and to identify areas that have greater functional or phylogenetic diversity than expected given species richness.  Functional and phylogenetic diversity measures are often confounded by species richness, such that more species typically yields greater functional and phylogenetic diversity (Swenson, 2014).  Null models are therefore used to complement the main findings by highlighting areas that are unusually rich or poor in the given diversity measure after accounting for the observed species richness. This multi-faceted approach to quantifying biodiversity and identifying biodiversity hotspots allows different conservation values to be considered and will broaden our understanding of biodiversity patterns within the Okanagan.    50  3.2 Methods 3.2.1  Study Region and Species Occurrence Data The Okanagan Ecoregion, defined by the Nature Conservancy, is a 96,000 km2 area that spans the international boundary south-central British Columbia, Canada, and north-central Washington, USA (Figure 3.1). The large variance in climate and physiography found throughout the region has resulted in the presence of ecosystems that are not found anywhere else in Canada. Interior Douglas-fir (Pseudotsuga menziesii) and Ponderosa Pine (Pinus ponderosa) forests are characteristic of this region and transition to shrub-steppe and grasslands in the low-elevation valleys, with lodgepole pine (Pinus contorta) and subalpine fir (Abies lasiocarpa) forests dominating in the high elevation alpine (Pryce et al., 2006). These ecosystems support the greatest diversity of breeding birds found in British Columbia as well as large assemblages of plant species that are nationally rare in Canada (Pryce et al., 2006). These ecosystems have been highly impacted by agriculture, urban and industrial development, especially in the low elevation areas where grassland and wetland ecosystems have decreased by as much as 84% and 92% since the 1800s  (Lea, 2008).     51   Figure 3.1 Okanagan Ecoregion (Nature Conservancy). Figure created by Carmen Chelick.  We used the BIEN package version 1.2.2 (Maitner et al., 2018) in R version 3.4.2 (R Core Team, 2017) to obtain a list of plant species known to occur in the Okanagan Ecoregion.  We excluded species if they belonged to the following categories: non-vascular species, obligate wetland species, hybrid, variant or subspecies.  Non-vascular and obligate wetland species were excluded because their distributions would not be properly represented using the climate and topographic predictor variables used for the species distribution models.  Hybrids, variants and subspecies were excluded to simplify the taxonomy.  We also excluded species that were not present in the BIEN phylogeny. The final species list included 1,541 species (Appendix A1), comprising 1,221 native species and 320 exotic species, 982 forbs, 262 graminoids, 81 shrubs, 130 subshrubs (low growing shrubs under 1.0 m tall at maturity), and 86 trees.   52  Occurrence data for all species in the final plant list were extracted from the Global Biodiversity Information Facility (GBIF) using the spocc package version 0.7.0. Occurrences extracted from GBIF were limited to those found within North America, and records from 1980 to present.  Kane et al. (2017) express that it is good practice to model distributions based on a larger area that encompasses the smaller geographic area, in order to increase the background data provided for the MaxEnt model and ensure that a broad range of environmental conditions are represented for each species.  Also, by encompassing a much broader extent than the focal area, we ensure that when predicting future distributions in response to climate change (see below), we accommodate species whose ranges may shift into the focal region from elsewhere.   3.2.2 Species Distribution Models and Species Richness We used the MaxEnt algorithm (Phillips et al., 2006) to create current and future species distribution models for all species. MaxEnt is considered to be one of the most robust approaches to modeling species distributions, especially when using presence-only data and small sample sizes (Elith et al., 2006; Fourcade et al., 2014; Wan & Zhang, 2016; Kane et al., 2017; Truong et al., 2017). Distribution models were created using the default settings in Maxent. Predictor variables used in the MaxEnt models included 19 bioclimatic variables from Worldclim for both current (1960-1990) and future (2070) climate (Hijmans et al., 2005) as well as elevation, aspect and slope derived from a Digital Elevation Model (DEM) for North America. All predictor variables had a resolution of 30 arc-seconds, which is approximately 1km2 at the equator.  For the future climate projection we used the “MPI-ESM-LR” general circulation model from the Max Planck Institute for Meteorology in Germany, which is considered to produce a median climate projection for North American climate (Batllori et al., 2017), along with the representative concentration pathway (RCP) 8.5 which represents a “business as usual” scenario for potential trajectories of future atmospheric greenhouse gas concentrations.  Although it is generally recommended that collinear variables are eliminated from MaxEnt models (Fourcade et al., 2014; Kane et al., 2017; Truong et al., 2017), Elith et al. (2011) suggests that since MaxEnt uses the process of regularization to choose predictors and features (transformations of predictors) that contribute most to the   53  distribution of a given species, all available predictors can be used to improve the predictive performance of the model. This would not be the case if the goal was to identify predictors that contribute most to the final SDM, however, this was outside of the scope of this work and since many species were being modelled concurrently, predictive performance was more important. We input current environmental variables and occurrences for each species into the maximum entropy model Maxent version 3.3.3 k (Phillips et al., 2006). To predict the future distribution for each species we input future environmental variables into the Maxent model.  After the raw Maxent output in logistic format was created for current and future species distributions, the “fixed cumulative value 5 logistic threshold” was applied to the output in order to produce presence/absence outputs for each species. This generally means that roughly 5% of the presences used as model inputs will be predicted as absences. This was used in order to account for some misidentification error or other errors in the input data, as opposed to using a less stringent threshold, such as minimum training presence, which generally gives minimal omission error, but produces outputs with a larger area of suitability (Norris, 2014). Presence/absence outputs for all species were stacked and a species richness map for the study region was created by summing all binary rasters representing each species’ presence and absences.   3.2.3 Functional and Phylogenetic Diversity measurement All data mining and analyses described below were conducted using R statistical software (R Core Team, 2018) using the BIEN, caper, ape, picante, and geometry packages. Different suites of traits are known to represent different functions and different responses to environmental disturbance. The leaf-height-seed (LHS) plant life strategy scheme, created by Westoby (1998), has been commonly used to represent different life history strategies in plants, similar to the Grime’s CSR scheme. Trait data for the Okanagan Ecoregion plant list was extracted from the BIEN database using the “BIEN_trait_mean” function in the BIEN package. This dataset incorporates data from larger botanical databases and independent studies that use standardized measurement   54  methods. Out of the 1,541 species in the overall plant list for the region, 1,220 had trait data for all three LHS traits: specific leaf area (SLA), plant height, and seed mass. These traits were then scaled and principal components analysis (PCA) was used to eliminate trait redundancy and to identify distinct functional axes (Leps et al., 2006; Swenson, 2014).  Functional Richness (FRic) was calculated as the convex hull volume of each assemblage in three-dimensional trait space using the trait PCA scores.   The BIEN complete phylogeny (Maitner et al., 2018) was used to create the phylogenetic tree representing all plant species in the Okanagan Ecoregion. Faith’s Phylogenetic Diversity (PD) index, calculated as the sum of the branch lengths of all species in an assemblage, was quantified and used as a measure of phylogenetic richness.  The observed diversity metrics were then standardized using a null model. The taxa labels for both the phylogenetic and functional distance matrices were randomized 100 times. Each of the phylogenetic and functional diversity metrics were then recalculated based on the 100 randomized distance matrices, to create null distributions of each diversity metric. The standardized diversity metrics were then calculated as the standardized effect size, given as:  𝑆𝐸𝑆 =𝑂𝑏𝑠𝑒𝑟𝑣𝑒𝑑 𝐷𝑖𝑣𝑒𝑟𝑠𝑖𝑡𝑦 − 𝑀𝑒𝑎𝑛 𝑜𝑓 𝑁𝑢𝑙𝑙 𝐷𝑖𝑠𝑡𝑟𝑖𝑏𝑢𝑡𝑖𝑜𝑛𝑆𝐷 𝑜𝑓 𝑁𝑢𝑙𝑙 𝐷𝑖𝑠𝑡𝑟𝑖𝑏𝑢𝑡𝑖𝑜𝑛  This gives measures of functional and phylogenetic diversity that are independent of species richness.  3.3.4 Congruence between diversity facets, environmental variables, and protected areas We used Spearman correlation test to assess the degree of congruence between the current and future biodiversity facets as well as the current and future environmental variables.  P-values were not reported, as spatial autocorrelation between the metrics and the large sample size would greatly inflate Type-I error. Instead, only the strength of the correlation was given.  Hotspots for all metrics were defined as the cells with the top 5%   55  of values (Mouillot et al., 2011; Albouy et al., 2017). Areas of congruence or overlap between hotspots were mapped for species richness (SR), standardized functional diversity (FRicses), and standardized phylogenetic diversity (PDses).  Protected areas found throughout the Okanagan Ecoregion (Figure 3.2) were also overlaid with the hotspots in order to identify the current and future protection of biodiversity under the current protected area network.  The protected areas used included BC Parks, Ecological Areas, and Protected Areas, and Washington Protected Areas, all within IUCN Protected Area Categories I to V (Worboys, 2015). These protected areas encompass a total area of approximately 8,000 km2, about 8% of the Okanagan Ecoregion.  Figure 3.2 Protected areas found throughout the Okanagan Ecoregion. Figure created by Carmen Chelick.      56  3.3 Results All scripts required to achieve computational reproducibility of this research are available on the Open Science Framework (https://osf.io/cxdj8/).  The SDMs used to predict taxonomic, functional, and phylogenetic diversity patterns for the 1,541 vascular plant species had a high predictive performance overall.  Table 2 shows the mean number of occurrences extracted from GBIF for each species, the occurrences that actually went into the model, and the AUC of the model, which is a measure of the model’s predictive performance.  A summary of the current and future diversity metrics and environmental variables across the Okanagan Ecoregion is found in Appendix A4 and A5.   Table 3.1. Number of occurrences used in the MaxEnt model as well as the AUC value, a measure of predictive performance. Summary Mean SD Min Max Total Occurrences (GBIF) 17,172 44,066 22 200,000 Occurrences Used   (MaxEnt) 426 594 2 10,348 AUC                         (model performance) 0.96 0.031 0.81 0.9996  Appendix A2 and A3 show the correlations between the current and future diversity metrics and the current and future environmental predictor variables that were used to create the SDMs. While current SR was strongly (ρ ≥ |0.75|) positively correlated with temperature of driest quarter (BIO 9), current PD, and FRic were not strongly correlated with any of the environmental predictor variables.  None of the future diversity metrics were strongly correlated with any of the future environmental predictor variables.  Current SR and PD went from being negatively correlated with elevation (ρ = -0.44 and ρ = -0.67, respectively) to being positively correlated with elevation (ρ = 0.49 and ρ = 0.25, respectively) in the future.  Current FRic and elevation went from having ρ = -0.65 to ρ = -0.05 in the future.   57  All three diversity measures (SR, FRic, PD) showed highly concordant associations with the abiotic predictor variables; a Kendall’s coefficient of concordance analysis using data from Appendix S2 was highly significant (Kendall’s W = 0.89; P < 0.001).  Thus, in general, if one of the measures exhibited a positive association with, for example, elevation, then the other two measures tended to also. Current SR and PD were strongly positively correlated, while SR and FRic, and FRic and PD were weakly positively correlated (Table 3).  After being standardized using the null model, FRicses and PDses were only weakly correlated with SR.  Current SR was highest in the southern parts of the region, with areas of high SR also occurring in some of the valleys in the northwest (Figure 3.3a). Current FRic was high in the northeast, southeast, and within the northwestern valleys (Figure 3.3b and 10c). PD was also high in the northwestern valleys, as well as in the southeast and southwest (Figure 3.3d and 3.3e).  Hotspots of current FRic and PD, and SR and FRic had 2,179 km2 and 2,280 km2 area of congruence (Figure 3.4), respectively.  Current SR and PD had the greatest area of congruence at 5,236 km2. The hotspot congruence between current SR and FRic, SR and PD, and FRic and PD occurred mainly in the southeast and in some of the valleys in the northwest (Figures 3.4).  Table 3.2. Spearman correlation coefficients (ρ) between all diversity metrics according to current climate. Diversity Metric SR FRic FRicses PD PDses SR  0.622 0.211 0.940 0.258 FRic 0.622  0.889 0.739 0.595 FRicses 0.211 0.889  0.395 0.614 PD 0.940 0.739 0.395  0.564 PDses 0.258 0.595 0.614 0.564   None of the diversity metrics for future climate were strongly correlated (Table 4).  Future SR was weakly negatively correlated with FRic, FRicses, and PDses, and   58  weakly positively correlated with PD.  FRic and PD, and FRicses and PDses were also weakly positively correlated.  Future SR and FRic had an area of congruence of 2,705 km2, and future FRic and PD had an area of congruence of 2,514 km2. Future SR and PD hotspots had the most congruence, with an area of 4,211 km2. Hotspot congruence between SR, FRic and PD occurred mainly in the southwest (Figure 3.4).  Table 3.3 Spearman correlation coefficients (ρ) between all diversity metrics according to future climate. Diversity Metric SR FRic FRicses PD PDses SR  -0.058 -0.380 0.215 -0.525 FRic -0.058  0.003 0.269 0.033 FRicses -0.380 0.003  0.167 0.216 PD 0.215 0.269 0.167  -0.254 PDses -0.525 0.033 0.216 -0.254   Overall, the current protected area network in the Okanagan Ecoregion will more effectively protect future diversity hotspots than current diversity hotspots. The current protected area network currently protects 292 km2 of the current SR hotspots, and will protect 7,193 km2 of future SR hotspots, an increase in protection of 2,363% (Figure 3.5).  FRic and PD hotspots also both have an increase in the areas protected, with FRic increasing from 471 km2 currently protected to 1,072 km2 protected in the future (127% increase), and PD increasing from 422 km2 currently protected to 1,683 km2 protected in the future (299% increase) (Figure 3.5).   The standardized measures of FRic and PD were independent of SR, as shown by the weak correlations.  Appendix A6 shows the heatmaps and hotspot congruence for SR, FRicses, and PDses.  Current SR and FRicses hotspots had essentially no congruence (an area of 1 km2), FRicses and PDses had 2,016 km2 area of congruence, and SR and PDses had 1,599 km2 area of congruence.  These areas of congruence occurred mainly in the northeast area of the region.  Future SR hotspots had only 170 km2 area of congruence   59  with PDses hotspots and essentially no congruence with FRicses hotspots (an area of 1 km2).  Future FRicses and PDses had 280 km2 area of congruence.    60                   Figure 3.3 Maps depicting areas of high diversity (red hues), moderate diversity (yellow and green hues), and low diversity (blue hues), where a) represents current species richness; b) represents current observed FRic; c) represents current observed PD; d) represents future species richness; e) represents future observed FRic; f) represents future observed PD.   a) b) c) d) e) f)   61                        Figure 3.4 Maps showing the congruence between hotspots (top 5% of values) of a) current SR and observed FD; b) current SR and observed PD; c) current observed FD and observed PD; d) future SR and observed FD; e) future SR and observed PD; and f) future observed FD and observed PD.  a)  d)  f)  c)  e)  b)    62   Figure 3.5  Grouped barplot depicting hotspot protection by the current protected area network for the three diversity metrics according to current and future climate.  3.4 Discussion While taxonomic diversity is typically the only facet of biodiversity that is incorporated into regional conservation efforts, including functional and phylogenetic diversity in biodiversity assessments can allow other conservation priorities to be addressed, including the conservation of unique ecological and ecosystem functions (Tilman et al., 1997; Loreau, 2000; Diaz & Cabido, 2001; Hooper et al., 2005; Cadotte, 2017) and unique evolutionary histories (Mace et al., 2003; Isaac et al., 2007; Devictor et al., 2010).  Here we have provided novel quantitative analyses and maps of functional and phylogenetic plant diversity for the Okanagan Ecoregion, with the aim of broadening the information base available to inform regional conservation efforts, which thus far have exclusively considered taxonomic diversity. Protected area establishment is considered to be one of the most common and effective methods for conserving biodiversity.  According to these results, the Okanagan Ecoregion’s current protected area network does not effectively protect hotspots of current taxonomic, functional, and phylogenetic diversity of plant communities.    63  However, we did find that hotspots of all three facets of biodiversity increased in protection in the future, with taxonomic diversity having a substantial increase in protection.  Figure 3.3 illustrates shifts in taxonomic, functional and phylogenetic diversity from the east to the west in the future.  The increase in the protection of all three facets of biodiversity in the future may be attributed to the presence of a large network of protected areas in the west, made up of Cathedral Provincial Park, Snowy Protected Area, and E.C. Manning Provincial Park in British Columbia, and the Wenatchee National Forest in Washington, which together make up almost 1.8 million hectares of protected land.   The overall increase in protection of all three biodiversity facets in the future may also be attributed to a shift in diversity towards higher elevation areas.  While current observed biodiversity measures were negatively correlated with elevation, future observed biodiversity measures were found to be more positively correlated with elevation.  Since the current protected area network encapsulates a number of mountain ranges and higher elevation areas within the Okanagan Ecoregion, a shift in biodiversity up in elevation may therefore result in increased protection in the future. Thuiller et al. (2014) also found a shift in plant diversity towards higher elevation areas in the French Alps, which resulted in increased protection within their current protected area network.  It may therefore be important to focus protected area establishment on higher elevation areas in the Okanagan Ecoregion to compensate for the potential shift in plant diversity up in elevation. Unlike the maps produced as a result of this research, the relative biodiversity maps created by the OCCP, based on biodiversity proxies such as distance from roads, size of natural areas, and the presence of important ecosystems, habitat features, or species-at-risk, show high biodiversity occurring mainly in the low elevation valleys in the Okanagan.  This difference is likely attributed to the fact that many of the species- and ecosystems-at-risk in the Okanagan Ecoregion occur in low elevation grasslands and wetlands, and the goal of the OCCP maps is to highlight areas of conservation priority for the region.  The maps of taxonomic, functional, and phylogenetic diversity, on the other hand, do not take extinction risk or regional significance into account.    64  It is also important to consider that spatial shifts observed between current and future biodiversity patterns are only possible if species have the ability to disperse to and track their preferred environmental conditions (Dullinger et al., 2012; Thuiller et al., 2014), are not outcompeted and displaced in new habitats by native and non-native competitors (Svenning et al., 2014; Thuiller et al., 2014), and are not able to adapt to climate variability and remain in their current habitats (Zimmermann et al., 2009; Thuiller et al., 2014). Taking these potential limitations into consideration, this work still emphasizes that protected area establishment and other conservation efforts need to not only consider protecting multiple facets of biodiversity, but also consider how these facets of biodiversity may change in the future.  Apart from the observed shift in biodiversity to higher elevation areas in the future, there were also lower or moderate elevation areas that were found to have high taxonomic, functional, and phylogenetic diversity according to current climate conditions.  The Botanie Valley south of Lillooet, BC, also had high current taxonomic, functional, and phylogenetic diversity.  In an ethnobotanical study by Turner et al. (2011), they found that the of the Nlaka’pmx Interior Salish people have been travelling to Pt-e´n’i (Botanie Valley) for hundreds of years to forage on the bounty of plant life available in this valley.  Another area that had a significantly high current functional diversity is the area north of Vernon, BC.  This area is considered to be a transition zone from the hot and dry ecosystems that are characteristic of the Okanagan Ecoregion, into moist and warm ecosystems of the Interior Cedar-Hemlock (ICH) biogeoclimatic zone.   Overall, our results illustrated significant geographic variation between patterns of taxonomic, functional, and phylogenetic diversity. There have been a number of other studies that have found discrepancies between diversity patterns of these three facets across a variety of taxonomic groups (Devictor et al., 2010; Strecker et al., 2011; Albouy et al., 2017; Pardo et al., 2017).  Congruence between hotspots of functional and taxonomic diversity, and functional and phylogenetic diversity for both current and future climate was relatively low.  Current and future hotspots of taxonomic and phylogenetic diversity had greater congruence.  This means that while conservation efforts that focus on protecting hotspots of taxonomic diversity may result in the protection of phylogenetic diversity hotspots, but may not sufficiently protect functional diversity hotspots.  We also   65  found, however, that all three current observed diversity measures were positively correlated.  Positive relationships between the three facets of biodiversity have also been demonstrated in literature, with the relationship between functional and phylogenetic diversity being continually explored and debated.  Although it is generally predicted that closely related species will be more functionally similar than distantly related species, this prediction does not hold true for all models of evolution, especially for traits that undergo strong selection pressure.  While the debate within literature is ongoing, phylogenetic diversity is sometimes found to be a better signal of ecosystem function than species richness, and sometimes even functional diversity (Cadotte et al., 2008).  Using a long-term empirical data set on plant productivity, Davies et al. (2016) found no association between evolutionary distinctiveness and ecosystem functioning. They however do suggest that phylogenetic diversity may be a better predictor of functional diversity than species richness alone. They also suggest that one of the reasons why phylogenetic diversity may be a good proxy for ecosystem function is that it is often difficult to collect functional trait data that directly relates to a given ecosystem function and is therefore poorly represented in functional diversity assessments.  Phylogenetic diversity based on up-to-date phylogenies has the potential to incorporate traits that are hard to measure into predictions of ecosystem functioning (Flynn et al., 2011; Davies et al., 2016). Forest et al. (2010) found that phylogenetic diversity and species richness patterns differed in plant assemblages in the Cape of South Africa, but phylogenetic diversity was more effectively related to species with greater feature diversity and economic and medicinal use than species richness.  Phylogenetic signalling of traits of plant species in the Okanagan Ecoregion would need to be assessed in order to determine the potential for phylogenetic diversity to be used as a proxy for functional diversity.   While we observed significant geographic variation in taxonomic, functional, and phylogenetic diversity among plant communities in the Okanagan Ecoregion that emphasizes the need for a multi-faceted approach to conservation efforts, the positive correlation observed between the facets and the evidence found in literature suggests that phylogenetic diversity may be used as a proxy for functional diversity when sufficient data to quantify functional diversity is lacking.     66  As part of this analysis, a null model was used in order to create measures of functional and phylogenetic diversity that are independent of taxonomic diversity.  While these standardized measures are typically used to assess mechanisms of community assembly by identifying functional or phylogenetic overdispersion or clustering (Weiher et al., 1995; Lessard et al., 2012; Miller et al., 2016), these assessments require fine-grain sampling in order to adequately measure these processes. Instead, we use these standardized measures of functional and phylogenetic diversity to identify areas that have greater functional or phylogenetic diversity than expected given taxonomic diversity.  These standardized measures of functional and phylogenetic diversity show greater diversity in the northern areas of the Okanagan Ecoregion compared to the observed measures which are concentrated in the south.  While the primary focus of this research was to determine how conservation measures focussed on taxonomic diversity relate to the conservation of observed measures of functional and phylogenetic diversity, the standardized measures of functional and phylogenetic diversity identify sites that harbour plant communities with greater functional and phylogenetic diversity than expected given taxonomic diversity, and could result in an overall increased protection of plant species that are functionally and evolutionarily unique. Using individual species distribution models to develop patterns of taxonomic, phylogenetic, and functional diversity is uncommon in literature, but can be a useful method for identifying areas of conservation interest in regions that have not been extensively surveyed.  The overall high AUC values that we obtained from the individual SDMs suggest that the taxonomic, functional, and phylogenetic patterns produced from these SDMs are good representations of the relative diversity patterns.  One limitation of this approach however is that SDMs based on the abiotic requirements of species are only modelling a species’ fundamental niche, and do not directly take into account biotic interactions, adaptive or evolutionary processes, or dispersal limitations (Drake, 2014; Zhang et al., 2015).  Therefore, the diversity patterns produced from these SDMs likely overestimate the actual diversity, and should instead be interpreted as predictions of “potential” diversity. Overall, this research demonstrates the importance of considering multiple facets of biodiversity simultaneously as well as considering how these facets of biodiversity   67  may change with changing climate.  While the current protected area established in the Okanagan Ecoregion does not sufficiently protect hotspots of any of the three biodiversity facets for plant communities, protection may increase in the future if species move west into larger protected area networks and move into protected areas in higher elevation areas.  While the importance of all three facets of biodiversity has been increasingly demonstrated in scientific literature, there remains a need for more studies to bring the importance of these three facets of biodiversity into conservation efforts occurring at a regional scale.  Using SDMs to predict diversity patterns, this research provides baseline estimates of the geographic variations in the three facets of biodiversity in plant communities across the Okanagan Ecoregion that may offer guidance for future conservation decisions.                     68  4 Conclusion  The goal of this research was to predict current and future patterns of taxonomic, functional, and phylogenetic diversity for plant assemblages in the Okanagan Ecoregion.  As complete survey data for the region was not available, stacked SDMs were used to first predict the ranges of all species known to occur in the Okanagan Ecoregion. These species ranges were then stacked to produce estimates of species richness, or taxonomic diversity, which was subsequently used to predict functional and phylogenetic diversity patterns.  The species ranges were modelled for both current and future climate, which allowed for comparisons of current and future patterns of taxonomic, functional, and phylogenetic diversity. Hotspots of all three facets of biodiversity based on current and future climate were then compared to the current protected area network within the Okanagan Ecoregion. The approach used here highlighted the tools and data that is freely available to carry out multi-faceted biodiversity assessments, and this approach could therefore be used to do similar assessments within any region or for any group of species of interest.  Null models were also used in order to create standardized measures of functional and phylogenetic diversity that are independent of species richness.  We found that hotspots of taxonomic, functional, and phylogenetic diversity had a limited amount of congruence and were also found to shift with changing climate.  While current functional and phylogenetic diversity hotspots had the greatest amount of congruence, hotspots of species richness and functional diversity had essentially no congruence.  With future climate projections, all hotspots showed significant decreases in congruence. Species richness surprisingly had the lowest level of protection from the current protected area network, however, with future climate projections, protection of species richness hotspots increased, functional diversity hotspot protection decreased, and phylogenetic diversity hotspots protection remained mostly constant.  This multi-faceted biodiversity approach, which also considers current and future climate conditions, will allow local conservation practitioners to consider multiple conservation priorities simultaneously.  While taxonomic diversity is the only facet of biodiversity that is currently being considered by conservation efforts in the Okanagan Ecoregion, this project emphasizes the importance of considering functional and phylogenetic diversity facets in future efforts.     69  It is also important to consider the drastic impacts that climate change will have in the Okanagan Ecoregion.  This research has shown that biodiversity patterns will shift in response to climate change, and while species richness may have increased protection in the current protected area network, functional diversity protection may decrease. Future conservation efforts in the Okanagan Ecoregion should therefore not only consider functional and phylogenetic diversity patterns alongside species richness, but should also consider how current biodiversity patterns will be impacted by climate change. This research can be considered as a preliminary assessment of current and future patterns of taxonomic, functional, and phylogenetic diversity, and there are a number of steps that could be taken in the future to refine this work. Because stacked SDMs were used as the basis from which the diversity measures were quantified, the accuracy of the SDMs for each species impacts the subsequent diversity patterns. Although the predictive performance of the SDMs appeared to be high, field validations would have helped assess the accuracy of these models.  If field validations are not possible, these results could also be validated against species lists that may exist for protected areas such as provincial parks. Validating these SDMs in the field would help to better understand the overall accuracy of using stacked SDMs to produce estimates of taxonomic, functional, and phylogenetic diversity patterns. While LHS traits are commonly used to assess the overall range of life history strategies in plant assemblages, functional diversity of other plant traits could be assessed to determine the diversity of specific ecological functions and resilience to disturbances.  Due to the increased frequency and intensity of forest fires in the region, for example, mapping the functional diversity of plant assemblages based on traits that relate to fire tolerance and resistance, such as seed dispersal method and bark thickness, would identify assemblages that may be more resilient to disturbance by fire, as well as assemblages that would be most vulnerable to fire. Mapping functional diversity based on traits that are related to pollinators, such as nectar resource traits and timing of flower bloom, could also identify potential restoration areas that could contribute to the connectivity of habitats for pollinator species.  These assessments could also be produced for more specific species groups, such that they show where areas of high and low   70  taxonomic, functional and phylogenetic diversity of species-at-risk, for example, occur in the Okanagan Ecoregion. This research provides the first baseline assessment of taxonomic, functional, and phylogenetic diversity for the region. Identifying hotspots of multiple facets of biodiversity for both current and future climate will allow conservation practitioners in the region to consider multiple conservation objectives and scenarios of landscape change as new protected areas are established. While these approaches are prominent in scientific literature, I have demonstrated a methodology that utilizes open source software and easily accessible data that could be used to create assessments at a scale that is meaningful for regional conservation decisions.                      71  Bibliography Abakumova, M., Zobel, K., Lepik, A. & Semchenko, M. 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Final Species List Scientific Name Common Name Family Duration Growth Form BC Status Abies amabilis Pacific silver fir Pinaceae Perennial Tree Native Abies grandis grand fir Pinaceae Perennial Tree Native Abies lasiocarpa subalpine fir Pinaceae Perennial Tree Native Abutilon theophrasti velvetleaf Malvaceae Annual Forb Exotic Acer campestre hedge maple Aceraceae Perennial Tree Native Acer circinatum vine maple Aceraceae Perennial Tree Native Acer glabrum Rocky Mountain maple Aceraceae Perennial Shrub Native Acer macrophyllum bigleaf maple Aceraceae Perennial Tree Native Acer negundo boxelder Aceraceae Perennial Tree Exotic Acer platanoides Norway maple Aceraceae Perennial Tree Exotic Acer saccharinum silver maple Aceraceae Perennial Tree Native Achillea millefolium common yarrow Asteraceae Perennial Forb Native Achlys triphylla sweet after death Berberidaceae Perennial Forb Native Achnatherum lemmonii Lemmon's needlegrass Poaceae Perennial Graminoid Native Achnatherum lettermanii Letterman's needlegrass Poaceae Perennial Graminoid Native Achnatherum nelsonii Columbia needlegrass Poaceae Perennial Graminoid Native Achnatherum occidentale western needlegrass Poaceae Perennial Graminoid Native Achnatherum richardsonii Richardson's needlegrass Poaceae Perennial Graminoid Native Achnatherum thurberianum Thurber's needlegrass Poaceae Perennial Graminoid Native Aconitum columbianum Columbian monkshood Ranunculaceae Perennial Forb Native Actaea rubra red baneberry Ranunculaceae Perennial Forb Native Adenocaulon bicolor American trailplant Asteraceae Perennial Forb Native Adiantum aleuticum Aleutian maidenhair Pteridaceae Perennial Forb Native Adiantum pedatum northern maidenhair Pteridaceae Perennial Forb Native Aegilops cylindrica jointed goatgrass Poaceae Annual Graminoid Exotic Aegopodium podagraria bishop's goutweed Apiaceae Perennial Forb Exotic Agastache urticifolia nettleleaf giant hyssop Lamiaceae Perennial Forb Native Agoseris aurantiaca orange agoseris Asteraceae Perennial Subshrub Native Agoseris glauca pale agoseris Asteraceae Perennial Forb Native Agoseris grandiflora bigflower agoseris Asteraceae Perennial Forb Native Agoseris heterophylla annual agoseris Asteraceae Annual Forb Native Agrimonia gryposepala tall hairy agrimony Rosaceae Perennial Forb Native Agropyron cristatum crested wheatgrass Poaceae Perennial Graminoid Exotic Agropyron desertorum desert wheatgrass Poaceae Perennial Graminoid Exotic Agrostemma githago common corncockle Caryophyllaceae Annual Forb Native Agrostis capillaris colonial bentgrass Poaceae Perennial Graminoid Exotic   85  Scientific Name Common Name Family Duration Growth Form BC Status Agrostis exarata spike bentgrass Poaceae Perennial Graminoid Native Agrostis gigantea redtop Poaceae Perennial Graminoid Exotic Agrostis hyemalis winter bentgrass Poaceae Perennial Graminoid Native Agrostis idahoensis Idaho bentgrass Poaceae Perennial Graminoid Native Agrostis mertensii northern bentgrass Poaceae Perennial Graminoid Native Agrostis oregonensis Oregon bentgrass Poaceae Perennial Graminoid Native Agrostis pallens seashore bentgrass Poaceae Perennial Graminoid Native Agrostis scabra rough bentgrass Poaceae Perennial Graminoid Native Ailanthus altissima tree of heaven Simaroubaceae Perennial Tree Exotic Alcea rosea hollyhock Malvaceae Biennial Forb Exotic Allium acuminatum tapertip onion Liliaceae Perennial Forb Native Allium ampeloprasum broadleaf wild leek Liliaceae Perennial Forb Native Allium cernuum nodding onion Liliaceae Perennial Forb Native Allium crenulatum Olympic onion Liliaceae Perennial Forb Native Allium douglasii Douglas' onion Liliaceae Perennial Forb Native Allium fibrillum Cuddy Mountain onion Liliaceae Perennial Forb Native Allium geyeri Geyer's onion Liliaceae Perennial Forb Native Allium nevii Nevius' garlic Liliaceae Perennial Forb Native Allium schoenoprasum wild chives Liliaceae Perennial Forb Native Allium textile textile onion Liliaceae Perennial Forb Native Alnus incana gray alder Betulaceae Perennial Tree Native Alnus rhombifolia white alder Betulaceae Perennial Tree Native Alnus rubra red alder Betulaceae Perennial Tree Native Alopecurus pratensis meadow foxtail Poaceae Perennial Graminoid Exotic Alyssum alyssoides pale madwort Brassicaceae Annual Forb Exotic Alyssum desertorum desert madwort Brassicaceae Annual Forb Exotic Amaranthus albus prostrate pigweed Amaranthaceae Annual Forb Exotic Amaranthus blitoides mat amaranth Amaranthaceae Annual Forb Exotic Amaranthus blitum purple amaranth Amaranthaceae Annual Forb Exotic Amaranthus californicus California amaranth Amaranthaceae Annual Forb Native Amaranthus cruentus red amaranth Amaranthaceae Annual Forb Native Amaranthus powellii Powell's amaranth Amaranthaceae Annual Forb Exotic Amaranthus retroflexus redroot amaranth Amaranthaceae Annual Forb Native Ambrosia acanthicarpa flatspine bur ragweed Asteraceae Annual Forb Native Ambrosia artemisiifolia annual ragweed Asteraceae Annual Forb Exotic Ambrosia psilostachya Cuman ragweed Asteraceae Annual Forb Exotic Amelanchier alnifolia Saskatoon serviceberry Rosaceae Perennial Tree Native Amelanchier pumila dwarf serviceberry Rosaceae Perennial Shrub Native Amelanchier utahensis Utah serviceberry Rosaceae Perennial Tree Native   86  Scientific Name Common Name Family Duration Growth Form BC Status Amsinckia lycopsoides tarweed fiddleneck Boraginaceae Annual Forb Native Amsinckia menziesii Menzies' fiddleneck Boraginaceae Annual Forb Native Amsinckia tessellata bristly fiddleneck Boraginaceae Annual Forb Native Anagallis minima chaffweed Primulaceae Annual Forb Native Anaphalis margaritacea western pearly everlasting Asteraceae Perennial Forb Native Androsace occidentalis western rockjasmine Primulaceae Annual Forb Native Androsace septentrionalis pygmyflower rockjasmine Primulaceae Annual Forb Native Anemone drummondii Drummond's anemone Ranunculaceae Perennial Forb Native Anemone multifida Pacific anemone Ranunculaceae Perennial Forb Native Anemone occidentalis white pasqueflower Ranunculaceae Perennial Forb Native Anemone oregana blue windflower Ranunculaceae Perennial Forb Native Angelica arguta Lyall's angelica Apiaceae Perennial Forb Native Angelica genuflexa kneeling angelica Apiaceae Perennial Forb Native Antennaria alpina alpine pussytoes Asteraceae Perennial Forb Native Antennaria anaphaloides pearly pussytoes Asteraceae Perennial Forb Native Antennaria argentea silver pussytoes Asteraceae Perennial Forb Native Antennaria dimorpha low pussytoes Asteraceae Perennial Subshrub Native Antennaria flagellaris whip pussytoes Asteraceae Perennial Forb Native Antennaria friesiana Fries' pussytoes Asteraceae Perennial Forb Native Antennaria howellii Howell's pussytoes Asteraceae Perennial Forb Native Antennaria lanata woolly pussytoes Asteraceae Perennial Forb Native Antennaria luzuloides rush pussytoes Asteraceae Perennial Subshrub Native Antennaria media Rocky Mountain pussytoes Asteraceae Perennial Forb Native Antennaria microphylla littleleaf pussytoes Asteraceae Perennial Forb Native Antennaria neglecta field pussytoes Asteraceae Perennial Forb Native Antennaria parvifolia small-leaf pussytoes Asteraceae Perennial Forb Native Antennaria pulcherrima showy pussytoes Asteraceae Perennial Forb Native Antennaria racemosa raceme pussytoes Asteraceae Perennial Forb Native Antennaria rosea rosy pussytoes Asteraceae Perennial Forb Native Antennaria stenophylla narrowleaf pussytoes Asteraceae Perennial Forb Native Antennaria umbrinella umber pussytoes Asteraceae Perennial Forb Native Anthemis cotula stinking chamomile Asteraceae Annual Forb Exotic Anthoxanthum aristatum annual vernalgrass Poaceae Annual Graminoid Exotic Anthoxanthum monticola alpine sweetgrass Poaceae Perennial Graminoid Native Anthoxanthum odoratum sweet vernalgrass Poaceae Perennial Graminoid Exotic Anthriscus sylvestris wild chervil Apiaceae Annual Forb Exotic Apera interrupta dense silkybent Poaceae Annual Graminoid Exotic Aphanes arvensis field parsley piert Rosaceae Annual Forb Exotic Apium graveolens wild celery Apiaceae Perennial Forb Native   87  Scientific Name Common Name Family Duration Growth Form BC Status Apocynum androsaemifolium spreading dogbane Apocynaceae Perennial Forb Native Apocynum cannabinum Indianhemp Apocynaceae Perennial Forb Native Aquilegia flavescens yellow columbine Ranunculaceae Perennial Forb Native Aquilegia formosa western columbine Ranunculaceae Perennial Forb Native Arabidopsis lyrata lyrate rockcress Brassicaceae Biennial Forb Native Arabidopsis thaliana mouseear cress Brassicaceae Annual Forb Exotic Arabis eschscholtziana Eschscholtz's hairy rockcress Brassicaceae Annual Forb Native Arabis hirsuta hairy rockcress Brassicaceae Annual Forb Native Arabis nuttallii Nuttall's rockcress Brassicaceae Perennial Forb Native Arabis pycnocarpa creamflower rockcress Brassicaceae Annual Forb Native Arachis hypogaea peanut Fabaceae Annual Forb Native Aralia nudicaulis wild sarsaparilla Araliaceae Perennial Subshrub Native Arctium minus lesser burdock Asteraceae Biennial Forb Exotic Arctostaphylos nevadensis pinemat manzanita Ericaceae Perennial Subshrub Native Arctostaphylos uva-ursi kinnikinnick Ericaceae Perennial Subshrub Native Arenaria serpyllifolia thymeleaf sandwort Caryophyllaceae Annual Forb Exotic Aristida purpurea purple threeawn Poaceae Annual Graminoid Native Armoracia rusticana horseradish Brassicaceae Perennial Forb Exotic Arnica chamissonis Chamisso arnica Asteraceae Perennial Forb Native Arnica cordifolia heartleaf arnica Asteraceae Perennial Forb Native Arnica fulgens foothill arnica Asteraceae Perennial Forb Native Arnica lanceolata lanceleaf arnica Asteraceae Perennial Forb Native Arnica latifolia broadleaf arnica Asteraceae Perennial Forb Native Arnica longifolia spearleaf arnica Asteraceae Perennial Forb Native Arnica mollis hairy arnica Asteraceae Perennial Forb Native Arnica ovata sticky leaf arnica Asteraceae Perennial Forb Native Arnica parryi Parry's arnica Asteraceae Perennial Forb Native Arnica rydbergii Rydberg's arnica Asteraceae Perennial Forb Native Arnica sororia twin arnica Asteraceae Perennial Forb Native Arrhenatherum elatius tall oatgrass Poaceae Perennial Graminoid Exotic Artemisia absinthium absinthium Asteraceae Perennial Subshrub Exotic Artemisia annua sweet sagewort Asteraceae Annual Forb Native Artemisia arbuscula little sagebrush Asteraceae Perennial Shrub Native Artemisia arctica boreal sagebrush Asteraceae Perennial Subshrub Native Artemisia biennis biennial wormwood Asteraceae Annual Forb Native Artemisia campestris field sagewort Asteraceae Biennial Forb Native Artemisia douglasiana Douglas' sagewort Asteraceae Perennial Forb Native Artemisia dracunculus tarragon Asteraceae Perennial Subshrub Native Artemisia frigida prairie sagewort Asteraceae Perennial Subshrub Native   88  Scientific Name Common Name Family Duration Growth Form BC Status Artemisia longifolia longleaf wormwood Asteraceae Perennial Subshrub Native Artemisia michauxiana Michaux's wormwood Asteraceae Perennial Subshrub Native Artemisia tilesii Tilesius' wormwood Asteraceae Perennial Subshrub Native Artemisia tridentata big sagebrush Asteraceae Perennial Tree Native Artemisia tripartita threetip sagebrush Asteraceae Perennial Shrub Native Artemisia vulgaris common wormwood Asteraceae Perennial Forb Exotic Aruncus dioicus bride's feathers Rosaceae Perennial Forb Native Asarum caudatum British Columbia wildginger Aristolochiaceae Perennial Forb Native Asclepias fascicularis Mexican whorled milkweed Asclepiadaceae Perennial Forb Native Asclepias speciosa showy milkweed Asclepiadaceae Perennial Forb Native Asparagus officinalis garden asparagus Liliaceae Perennial Forb Exotic Aspidotis densa Indian's dream Pteridaceae Perennial Forb Native Asplenium trichomanes maidenhair spleenwort Aspleniaceae Perennial Forb Native Asplenium viride brightgreen spleenwort Aspleniaceae Perennial Forb Native Astragalus agrestis purple milkvetch Fabaceae Perennial Forb Native Astragalus alpinus alpine milkvetch Fabaceae Perennial Forb Native Astragalus beckwithii Beckwith's milkvetch Fabaceae Perennial Forb Native Astragalus canadensis Canadian milkvetch Fabaceae Perennial Forb Native Astragalus cicer chickpea milkvetch Fabaceae Perennial Forb Exotic Astragalus eucosmus elegant milkvetch Fabaceae Perennial Forb Native Astragalus filipes basalt milkvetch Fabaceae Perennial Forb Native Astragalus lentiginosus freckled milkvetch Fabaceae Annual Shrub Native Astragalus lotiflorus lotus milkvetch Fabaceae Perennial Forb Native Astragalus miser timber milkvetch Fabaceae Perennial Forb Native Astragalus purshii woollypod milkvetch Fabaceae Perennial Forb Native Astragalus robbinsii Robbins' milkvetch Fabaceae Perennial Forb Native Astragalus sclerocarpus woodypod milkvetch Fabaceae Perennial Forb Native Astragalus tenellus looseflower milkvetch Fabaceae Perennial Forb Native Athysanus pusillus common sandweed Brassicaceae Annual Forb Native Atriplex argentea silverscale saltbush Chenopodiaceae Annual Forb Native Atriplex hortensis garden orache Chenopodiaceae Annual Forb Exotic Atriplex micrantha twoscale saltbush Chenopodiaceae Annual Forb Exotic Atriplex patula spear saltbush Chenopodiaceae Annual Forb Exotic Atriplex rosea tumbling saltweed Chenopodiaceae Annual Forb Exotic Atriplex truncata wedgescale saltbush Chenopodiaceae Annual Forb Native Avena fatua wild oat Poaceae Annual Graminoid Exotic Avena sativa common oat Poaceae Annual Graminoid Exotic Axyris amaranthoides Russian pigweed Chenopodiaceae Annual Forb Exotic Balsamorhiza careyana Carey's balsamroot Asteraceae Perennial Forb Native   89  Scientific Name Common Name Family Duration Growth Form BC Status Balsamorhiza sagittata arrowleaf balsamroot Asteraceae Perennial Forb Native Barbarea vulgaris garden yellowrocket Brassicaceae Biennial Forb Exotic Bassia hyssopifolia fivehorn smotherweed Chenopodiaceae Annual Forb Exotic Berberis aquifolium hollyleaved barberry Berberidaceae Perennial Shrub Native Berberis nervosa Cascade barberry Berberidaceae Perennial Shrub Native Berberis repens creeping barberry Berberidaceae Perennial Shrub Native Berberis thunbergii Japanese barberry Berberidaceae Perennial Shrub Exotic Berberis vulgaris common barberry Berberidaceae Perennial Shrub Exotic Berteroa incana hoary alyssum Brassicaceae Annual Forb Exotic Betula occidentalis water birch Betulaceae Perennial Tree Native Betula papyrifera paper birch Betulaceae Perennial Tree Native Betula pendula European white birch Betulaceae Perennial Tree Exotic Bidens frondosa devil's beggartick Asteraceae Annual Forb Exotic Bistorta vivipara alpine bistort Polygonaceae Perennial Forb Native Boechera collinsii Collins' rockcress Brassicaceae Biennial Forb Native Boechera divaricarpa spreadingpod rockcress Brassicaceae Biennial Forb Native Boechera holboellii Holboell's rockcress Brassicaceae Biennial Forb Native Boechera lemmonii Lemmon's rockcress Brassicaceae Perennial Forb Native Boechera lignifera desert rockcress Brassicaceae Perennial Forb Native Boechera lyallii Lyall's rockcress Brassicaceae Perennial Forb Native Boechera microphylla littleleaf rockcress Brassicaceae Perennial Forb Native Boechera retrofracta second rockcress Brassicaceae Biennial Forb Native Boechera sparsiflora sicklepod rockcress Brassicaceae Biennial Forb Native Boechera stricta Drummond's rockcress Brassicaceae Biennial Forb Native Borago officinalis common borage Boraginaceae Annual Forb Exotic Botrychium lanceolatum lanceleaf grapefern Ophioglossaceae Perennial Forb Native Botrychium lunaria common moonwort Ophioglossaceae Perennial Forb Native Botrychium minganense Mingan moonwort Ophioglossaceae Perennial Forb Native Botrychium montanum mountain moonwort Ophioglossaceae Perennial Forb Native Botrychium multifidum leathery grapefern Ophioglossaceae Perennial Forb Native Botrychium pinnatum northern moonwort Ophioglossaceae Perennial Forb Native Botrychium virginianum rattlesnake fern Ophioglossaceae Perennial Forb Native Brassica juncea brown mustard Brassicaceae Annual Forb Exotic Brassica nigra black mustard Brassicaceae Annual Forb Exotic Brassica rapa field mustard Brassicaceae Annual Forb Exotic Brickellia grandiflora tasselflower brickellbush Asteraceae Perennial Subshrub Native Brickellia oblongifolia Mojave brickellbush Asteraceae Perennial Subshrub Native Brodiaea coronaria crown brodiaea Liliaceae Perennial Forb Native Bromus arvensis field brome Poaceae Annual Graminoid Native   90  Scientific Name Common Name Family Duration Growth Form BC Status Bromus briziformis rattlesnake brome Poaceae Annual Graminoid Exotic Bromus carinatus California brome Poaceae Annual Graminoid Native Bromus ciliatus fringed brome Poaceae Perennial Graminoid Native Bromus diandrus ripgut brome Poaceae Annual Graminoid Native Bromus hordeaceus soft brome Poaceae Annual Graminoid Exotic Bromus inermis smooth brome Poaceae Perennial Graminoid Exotic Bromus marginatus mountain brome Poaceae Perennial Graminoid Native Bromus porteri Porter brome Poaceae Perennial Graminoid Native Bromus racemosus bald brome Poaceae Annual Graminoid Exotic Bromus secalinus rye brome Poaceae Annual Graminoid Exotic Bromus sitchensis Alaska brome Poaceae Perennial Graminoid Native Bromus squarrosus corn brome Poaceae Annual Graminoid Exotic Bromus suksdorfii Suksdorf's brome Poaceae Perennial Graminoid Native Bromus tectorum cheatgrass Poaceae Annual Graminoid Exotic Bromus vulgaris Columbia brome Poaceae Perennial Graminoid Native Buddleja davidii orange eye butterflybush Buddlejaceae Perennial Shrub Exotic Buglossoides arvensis corn gromwell Boraginaceae Annual Forb Exotic Cacaliopsis nardosmia silvercrown Asteraceae Perennial Forb Native Calamagrostis canadensis bluejoint Poaceae Perennial Graminoid Native Calamagrostis purpurascens purple reedgrass Poaceae Perennial Graminoid Native Calamagrostis rubescens pinegrass Poaceae Perennial Graminoid Native Calamagrostis stricta slimstem reedgrass Poaceae Perennial Graminoid Native Calamovilfa longifolia prairie sandreed Poaceae Perennial Graminoid Native Calochortus apiculatus pointedtip mariposa lily Liliaceae Perennial Forb Native Calochortus lyallii Lyall's mariposa lily Liliaceae Perennial Forb Native Calochortus macrocarpus sagebrush mariposa lily Liliaceae Perennial Forb Native Calystegia sepium hedge false bindweed Convolvulaceae Perennial Forb Exotic Camassia quamash small camas Liliaceae Perennial Forb Native Camelina microcarpa littlepod false flax Brassicaceae Biennial Forb Exotic Camissonia andina Blackfoot River evening primrose Onagraceae Annual Forb Native Camissonia subacaulis diffuseflower evening primrose Onagraceae Perennial Forb Native Camissonia tanacetifolia tansyleaf evening primrose Onagraceae Perennial Forb Native Campanula lasiocarpa mountain harebell Campanulaceae Perennial Forb Native Campanula parryi Parry's bellflower Campanulaceae Perennial Forb Native Campanula rapunculoides rampion bellflower Campanulaceae Perennial Forb Exotic Campanula rotundifolia bluebell bellflower Campanulaceae Perennial Forb Native Canadanthus modestus giant mountain aster Asteraceae Perennial Forb Native Cannabis sativa marijuana Cannabaceae Annual Forb Native   91  Scientific Name Common Name Family Duration Growth Form BC Status Capsella bursa-pastoris shepherd's purse Brassicaceae Annual Forb Exotic Capsicum annuum cayenne pepper Solanaceae Annual Subshrub Native Cardamine bellidifolia alpine bittercress Brassicaceae Perennial Forb Native Cardamine breweri Brewer's bittercress Brassicaceae Perennial Forb Native Cardamine cordifolia heartleaf bittercress Brassicaceae Perennial Forb Native Cardamine hirsuta hairy bittercress Brassicaceae Annual Forb Exotic Cardamine oligosperma little western bittercress Brassicaceae Annual Forb Native Cardamine parviflora sand bittercress Brassicaceae Annual Forb Native Cardamine pratensis cuckoo flower Brassicaceae Perennial Forb Exotic Carduus acanthoides spiny plumeless thistle Asteraceae Biennial Forb Exotic Carduus nutans nodding plumeless thistle Asteraceae Biennial Forb Exotic Carex aenea dryspike sedge Cyperaceae Perennial Graminoid Native Carex albonigra blackandwhite sedge Cyperaceae Perennial Graminoid Native Carex athrostachya slenderbeak sedge Cyperaceae Perennial Graminoid Native Carex atrata sedge Cyperaceae Perennial Graminoid Native Carex atrosquama lesser blackscale sedge Cyperaceae Perennial Graminoid Native Carex backii Back's sedge Cyperaceae Perennial Graminoid Native Carex bigelowii Bigelow's sedge Cyperaceae Perennial Graminoid Native Carex bolanderi Bolander's sedge Cyperaceae Perennial Graminoid Native Carex brevicaulis shortstem sedge Cyperaceae Perennial Graminoid Native Carex brevior shortbeak sedge Cyperaceae Perennial Graminoid Native Carex capillaris hair-like sedge Cyperaceae Perennial Graminoid Native Carex capitata capitate sedge Cyperaceae Perennial Graminoid Native Carex concinna low northern sedge Cyperaceae Perennial Graminoid Native Carex concinnoides northwestern sedge Cyperaceae Perennial Graminoid Native Carex cordillerana Cordilleran sedge Cyperaceae Perennial Graminoid Native Carex deflexa northern sedge Cyperaceae Perennial Graminoid Native Carex deweyana Dewey sedge Cyperaceae Perennial Graminoid Native Carex douglasii Douglas' sedge Cyperaceae Perennial Graminoid Native Carex duriuscula needleleaf sedge Cyperaceae Perennial Graminoid Native Carex engelmannii Engelmann's sedge Cyperaceae Perennial Graminoid Native Carex filifolia threadleaf sedge Cyperaceae Perennial Graminoid Native Carex fracta fragile sheath sedge Cyperaceae Perennial Graminoid Native Carex garberi elk sedge Cyperaceae Perennial Graminoid Native Carex geyeri Geyer's sedge Cyperaceae Perennial Graminoid Native Carex hassei salt sedge Cyperaceae Perennial Graminoid Native Carex haydeniana cloud sedge Cyperaceae Perennial Graminoid Native Carex heteroneura different-nerve sedge Cyperaceae Perennial Graminoid Native Carex hoodii Hood's sedge Cyperaceae Perennial Graminoid Native   92  Scientific Name Common Name Family Duration Growth Form BC Status Carex illota sheep sedge Cyperaceae Perennial Graminoid Native Carex infirminervia weak-nerved sedge Cyperaceae Perennial Graminoid Native Carex inops long-stolon sedge Cyperaceae Perennial Graminoid Native Carex laeviculmis smoothstem sedge Cyperaceae Perennial Graminoid Native Carex leptopoda taperfruit shortscale sedge Cyperaceae Perennial Graminoid Native Carex macloviana thickhead sedge Cyperaceae Perennial Graminoid Native Carex media closedhead sedge Cyperaceae Perennial Graminoid Native Carex mertensii Mertens' sedge Cyperaceae Perennial Graminoid Native Carex microptera smallwing sedge Cyperaceae Perennial Graminoid Native Carex multicostata manyrib sedge Cyperaceae Perennial Graminoid Native Carex nardina spike sedge Cyperaceae Perennial Graminoid Native Carex nigricans black alpine sedge Cyperaceae Perennial Graminoid Native Carex obtusata obtuse sedge Cyperaceae Perennial Graminoid Native Carex parryana Parry's sedge Cyperaceae Perennial Graminoid Native Carex peckii Peck's sedge Cyperaceae Perennial Graminoid Native Carex petasata Liddon sedge Cyperaceae Perennial Graminoid Native Carex phaeocephala dunhead sedge Cyperaceae Perennial Graminoid Native Carex praegracilis clustered field sedge Cyperaceae Perennial Graminoid Native Carex praticola meadow sedge Cyperaceae Perennial Graminoid Native Carex preslii Presl's sedge Cyperaceae Perennial Graminoid Native Carex pyrenaica Pyrenean sedge Cyperaceae Perennial Graminoid Native Carex raynoldsii Raynolds' sedge Cyperaceae Perennial Graminoid Native Carex rossii Ross' sedge Cyperaceae Perennial Graminoid Native Carex saximontana Rocky Mountain sedge Cyperaceae Perennial Graminoid Native Carex scirpoidea northern singlespike sedge Cyperaceae Perennial Graminoid Native Carex spectabilis showy sedge Cyperaceae Perennial Graminoid Native Carex sychnocephala manyhead sedge Cyperaceae Perennial Graminoid Native Carex tahoensis Tahoe sedge Cyperaceae Perennial Graminoid Native Carex tenera quill sedge Cyperaceae Perennial Graminoid Native Carex vallicola valley sedge Cyperaceae Perennial Graminoid Native Carex vernacula native sedge Cyperaceae Perennial Graminoid Native Carthamus tinctorius safflower Asteraceae Annual Forb Exotic Cassiope mertensiana western moss heather Ericaceae Perennial Subshrub Native Cassiope tetragona white arctic mountain heather Ericaceae Perennial Subshrub Native Castanea dentata American chestnut Fagaceae Perennial Tree Exotic Castilleja cervina deer Indian paintbrush Scrophulariaceae Perennial Forb Native Castilleja cusickii Cusick's Indian paintbrush Scrophulariaceae Perennial Forb Native Castilleja elmeri Wenatchee Indian paintbrush Scrophulariaceae Perennial Subshrub Native   93  Scientific Name Common Name Family Duration Growth Form BC Status Castilleja hispida harsh Indian paintbrush Scrophulariaceae Perennial Subshrub Native Castilleja lutescens stiff yellow Indian paintbrush Scrophulariaceae Perennial Forb Native Castilleja miniata giant red Indian paintbrush Scrophulariaceae Perennial Subshrub Native Castilleja occidentalis western Indian paintbrush Scrophulariaceae Perennial Subshrub Native Castilleja parviflora mountain Indian paintbrush Scrophulariaceae Perennial Subshrub Native Castilleja raupii Raup's Indian paintbrush Scrophulariaceae Perennial Forb Native Castilleja suksdorfii Suksdorf's Indian paintbrush Scrophulariaceae Perennial Forb Native Castilleja sulphurea sulphur Indian paintbrush Scrophulariaceae Perennial Subshrub Native Castilleja tenuis hairy Indian paintbrush Scrophulariaceae Annual Forb Native Castilleja thompsonii Thompson's Indian paintbrush Scrophulariaceae Perennial Forb Native Catalpa bignonioides southern catalpa Bignoniaceae Perennial Tree Native Ceanothus sanguineus redstem ceanothus Rhamnaceae Perennial Shrub Native Ceanothus velutinus snowbrush ceanothus Rhamnaceae Perennial Tree Native Celastrus scandens American bittersweet Celastraceae Perennial Forb Native Celtis laevigata sugarberry Ulmaceae Perennial Tree Native Cenchrus longispinus mat sandbur Poaceae Annual Graminoid Exotic Centaurea diffusa diffuse knapweed Asteraceae Annual Forb Exotic Centaurea solstitialis yellow star-thistle Asteraceae Annual Forb Native Centaurea stoebe spotted knapweed Asteraceae Biennial Forb Exotic Cerastium arvense field chickweed Caryophyllaceae Perennial Forb Exotic Cerastium beeringianum Bering chickweed Caryophyllaceae Perennial Forb Native Cerastium fontanum common mouse-ear chickweed Caryophyllaceae Biennial Forb Exotic Cerastium glomeratum sticky chickweed Caryophyllaceae Annual Forb Exotic Cerastium nutans nodding chickweed Caryophyllaceae Annual Forb Native Cerastium pumilum European chickweed Caryophyllaceae Annual Forb Exotic Cerastium semidecandrum fivestamen chickweed Caryophyllaceae Annual Forb Exotic Ceratocephala testiculata curveseed butterwort Ranunculaceae Annual Forb Native Cercidiphyllum japonicum katsura tree Cercidiphyllaceae Perennial Tree Native Chaenactis douglasii Douglas' dustymaiden Asteraceae Biennial Forb Native Chaenorhinum minus dwarf snapdragon Scrophulariaceae Annual Forb Exotic Chamaerhodos erecta little rose Rosaceae Biennial Forb Native Cheilanthes feei slender lipfern Pteridaceae Perennial Forb Native Cheilanthes gracillima lace lipfern Pteridaceae Perennial Forb Native Chelidonium majus celandine Papaveraceae Biennial Forb Exotic Chenopodium atrovirens pinyon goosefoot Chenopodiaceae Annual Forb Native Chenopodium berlandieri pitseed goosefoot Chenopodiaceae Annual Forb Native   94  Scientific Name Common Name Family Duration Growth Form BC Status Chenopodium capitatum blite goosefoot Chenopodiaceae Annual Forb Native Chenopodium chenopodioides low goosefoot Chenopodiaceae Annual Forb Native Chenopodium desiccatum aridland goosefoot Chenopodiaceae Annual Forb Native Chenopodium fremontii Fremont's goosefoot Chenopodiaceae Annual Forb Native Chenopodium glaucum oakleaf goosefoot Chenopodiaceae Annual Forb Exotic Chenopodium leptophyllum narrowleaf goosefoot Chenopodiaceae Annual Forb Native Chenopodium simplex mapleleaf goosefoot Chenopodiaceae Annual Forb Native Chenopodium strictum lateflowering goosefoot Chenopodiaceae Annual Forb Exotic Chimaphila menziesii little prince's pine Pyrolaceae Perennial Subshrub Native Chimaphila umbellata pipsissewa Pyrolaceae Perennial Subshrub Native Chorispora tenella crossflower Brassicaceae Annual Forb Exotic Cichorium intybus chicory Asteraceae Biennial Forb Exotic Circaea alpina small enchanter's nightshade Onagraceae Perennial Forb Native Cirsium arvense Canada thistle Asteraceae Perennial Forb Exotic Cirsium brevistylum clustered thistle Asteraceae Annual Forb Native Cirsium edule edible thistle Asteraceae Perennial Forb Native Cirsium flodmanii Flodman's thistle Asteraceae Perennial Forb Native Cirsium hookerianum white thistle Asteraceae Perennial Forb Native Cirsium undulatum wavyleaf thistle Asteraceae Perennial Forb Native Cirsium vulgare bull thistle Asteraceae Biennial Forb Exotic Cistanthe tweedyi Tweedy's pussypaws Portulacaceae Perennial Forb Native Cistanthe umbellata Mt. Hood pussypaws Portulacaceae Annual Forb Native Citrus aurantium sour orange Rutaceae Perennial Shrub Exotic Citrus reticulata tangerine Rutaceae Perennial Tree Native Clarkia pulchella pinkfairies Onagraceae Annual Forb Native Clarkia rhomboidea diamond clarkia Onagraceae Annual Forb Native Claytonia caroliniana Carolina springbeauty Portulacaceae Perennial Forb Native Claytonia cordifolia heartleaf springbeauty Portulacaceae Perennial Forb Native Claytonia lanceolata lanceleaf springbeauty Portulacaceae Perennial Forb Native Claytonia parviflora streambank springbeauty Portulacaceae Annual Forb Native Claytonia perfoliata miner's lettuce Portulacaceae Annual Forb Native Claytonia rubra redstem springbeauty Portulacaceae Annual Forb Native Claytonia sibirica Siberian springbeauty Portulacaceae Annual Forb Native Clematis columbiana rock clematis Ranunculaceae Perennial Forb Native Clematis hirsutissima hairy clematis Ranunculaceae Perennial Subshrub Native Clematis ligusticifolia western white clematis Ranunculaceae Perennial Forb Native Clematis occidentalis western blue virginsbower Ranunculaceae Perennial Forb Native Clintonia uniflora bride's bonnet Liliaceae Perennial Forb Native Collinsia parviflora maiden blue eyed Mary Scrophulariaceae Annual Forb Native   95  Scientific Name Common Name Family Duration Growth Form BC Status Collinsia sparsiflora spinster's blue eyed Mary Scrophulariaceae Annual Forb Native Collomia grandiflora grand collomia Polemoniaceae Annual Forb Native Collomia heterophylla variableleaf collomia Polemoniaceae Annual Forb Native Collomia linearis tiny trumpet Polemoniaceae Annual Forb Native Collomia tinctoria staining collomia Polemoniaceae Annual Forb Native Comandra umbellata bastard toadflax Santalaceae Perennial Subshrub Native Conium maculatum poison hemlock Apiaceae Biennial Forb Exotic Conringia orientalis hare's ear mustard Brassicaceae Annual Forb Exotic Consolida ajacis doubtful knight's-spur Ranunculaceae Annual Forb Exotic Convolvulus arvensis field bindweed Convolvulaceae Perennial Forb Exotic Conyza canadensis Canadian horseweed Asteraceae Annual Forb Native Coreopsis tinctoria golden tickseed Asteraceae Annual Forb Native Corispermum americanum American bugseed Chenopodiaceae Annual Forb Native Corispermum pallasii Siberian bugseed Chenopodiaceae Annual Forb Native Corispermum villosum hairy bugseed Chenopodiaceae Annual Forb Native Cornus florida flowering dogwood Cornaceae Perennial Tree Native Cornus nuttallii Pacific dogwood Cornaceae Perennial Tree Native Cornus sericea redosier dogwood Cornaceae Perennial Tree Native Cornus unalaschkensis western cordilleran bunchberry Cornaceae Perennial Subshrub Native Corydalis aurea scrambled eggs Fumariaceae Annual Forb Native Corydalis sempervirens rock harlequin Fumariaceae Annual Forb Native Corylus cornuta beaked hazelnut Betulaceae Perennial Tree Native Cota tinctoria golden chamomile Asteraceae Perennial Forb Exotic Cotoneaster lucidus shiny cotoneaster Rosaceae Perennial Shrub Exotic Crataegus castlegarensis hawthorn Rosaceae Perennial Tree Native Crataegus chrysocarpa fireberry hawthorn Rosaceae Perennial Tree Native Crataegus douglasii black hawthorn Rosaceae Perennial Shrub Native Crataegus enderbyensis hawthorn Rosaceae Perennial Tree Native Crataegus monogyna oneseed hawthorn Rosaceae Perennial Shrub Exotic Crataegus okanaganensis Okanagan Valley hawthorn Rosaceae Perennial Shrub Native Crataegus okennonii O'kennon's hawthorn Rosaceae Perennial Shrub Native Crataegus phippsii Phipps' hawthorn Rosaceae Perennial Tree Native Crepis acuminata tapertip hawksbeard Asteraceae Perennial Forb Native Crepis atribarba slender hawksbeard Asteraceae Perennial Forb Native Crepis bakeri Baker's hawksbeard Asteraceae Perennial Forb Native Crepis capillaris smooth hawksbeard Asteraceae Annual Forb Exotic Crepis intermedia limestone hawksbeard Asteraceae Perennial Forb Native Crepis modocensis Modoc hawksbeard Asteraceae Perennial Forb Native Crepis nana dwarf alpine hawksbeard Asteraceae Perennial Forb Native   96  Scientific Name Common Name Family Duration Growth Form BC Status Crepis occidentalis largeflower hawksbeard Asteraceae Annual Forb Native Crepis runcinata fiddleleaf hawksbeard Asteraceae Perennial Forb Native Crepis tectorum narrowleaf hawksbeard Asteraceae Annual Forb Exotic Crocidium multicaule common spring-gold Asteraceae Annual Forb Native Cryptantha affinis quill cryptantha Boraginaceae Annual Forb Native Cryptantha ambigua basin cryptantha Boraginaceae Annual Forb Native Cryptantha celosioides buttecandle Boraginaceae Biennial Forb Native Cryptantha circumscissa cushion cryptantha Boraginaceae Annual Forb Native Cryptantha intermedia Clearwater cryptantha Boraginaceae Annual Forb Native Cryptantha nubigena Sierra cryptantha Boraginaceae Perennial Forb Native Cryptantha pterocarya wingnut cryptantha Boraginaceae Annual Forb Native Cryptantha torreyana Torrey's cryptantha Boraginaceae Annual Forb Native Cryptantha watsonii Watson's cryptantha Boraginaceae Annual Forb Native Cryptogramma acrostichoides American rockbrake Pteridaceae Perennial Forb Native Cryptogramma cascadensis Cascade rockbrake Pteridaceae Perennial Forb Native Cryptogramma stelleri fragile rockbrake Pteridaceae Perennial Forb Native Cuscuta californica chaparral dodder Cuscutaceae Perennial Forb Native Cuscuta cephalanthi buttonbush dodder Cuscutaceae Perennial Forb Native Cuscuta pentagona fiveangled dodder Cuscutaceae Annual Forb Native Cynodon dactylon Bermudagrass Poaceae Perennial Graminoid Exotic Cynoglossum officinale gypsyflower Boraginaceae Biennial Forb Exotic Cynoglossum virginianum wild comfrey Boraginaceae Perennial Forb Native Cytisus scoparius Scotch broom Fabaceae Perennial Shrub Exotic Dactylis glomerata orchardgrass Poaceae Perennial Graminoid Exotic Danthonia californica California oatgrass Poaceae Perennial Graminoid Native Danthonia intermedia timber oatgrass Poaceae Perennial Graminoid Native Danthonia spicata poverty oatgrass Poaceae Perennial Graminoid Native Danthonia unispicata onespike danthonia Poaceae Perennial Graminoid Native Dasiphora fruticosa shrubby cinquefoil Rosaceae Perennial Shrub Native Datura stramonium jimsonweed Solanaceae Annual Forb Exotic Datura wrightii sacred thorn-apple Solanaceae Annual Forb Native Delphinium bicolor little larkspur Ranunculaceae Perennial Forb Native Delphinium depauperatum slim larkspur Ranunculaceae Perennial Forb Native Delphinium glareosum Olympic larkspur Ranunculaceae Perennial Forb Native Delphinium glaucum Sierra larkspur Ranunculaceae Perennial Forb Native Delphinium menziesii Menzies' larkspur Ranunculaceae Perennial Forb Native Delphinium nuttallianum twolobe larkspur Ranunculaceae Perennial Forb Native Deschampsia cespitosa tufted hairgrass Poaceae Perennial Graminoid Native Deschampsia danthonioides annual hairgrass Poaceae Annual Graminoid Native   97  Scientific Name Common Name Family Duration Growth Form BC Status Deschampsia elongata slender hairgrass Poaceae Perennial Graminoid Native Descurainia incana mountain tansymustard Brassicaceae Annual Forb Native Descurainia pinnata western tansymustard Brassicaceae Annual Forb Native Descurainia sophia herb sophia Brassicaceae Annual Forb Exotic Descurainia sophioides northern tansymustard Brassicaceae Biennial Forb Native Dianthus armeria Deptford pink Caryophyllaceae Annual Forb Exotic Dianthus deltoides maiden pink Caryophyllaceae Perennial Forb Exotic Dicentra cucullaria dutchman's breeches Fumariaceae Perennial Forb Native Dicentra formosa Pacific bleeding heart Fumariaceae Perennial Forb Native Dicentra uniflora longhorn steer's-head Fumariaceae Perennial Forb Native Dichanthelium acuminatum tapered rosette grass Poaceae Perennial Graminoid Native Dichanthelium oligosanthes Heller's rosette grass Poaceae Perennial Graminoid Native Digitaria ischaemum smooth crabgrass Poaceae Annual Graminoid Exotic Digitaria sanguinalis hairy crabgrass Poaceae Annual Graminoid Exotic Dipsacus fullonum Fuller's teasel Dipsacaceae Biennial Forb Native Draba albertina slender draba Brassicaceae Annual Forb Native Draba aurea golden draba Brassicaceae Perennial Forb Native Draba borealis boreal draba Brassicaceae Perennial Forb Native Draba breweri cushion draba Brassicaceae Perennial Forb Native Draba crassifolia snowbed draba Brassicaceae Annual Forb Native Draba densifolia denseleaf draba Brassicaceae Perennial Forb Native Draba incerta Yellowstone draba Brassicaceae Perennial Forb Native Draba lonchocarpa lancepod draba Brassicaceae Perennial Forb Native Draba macounii Macoun's draba Brassicaceae Perennial Forb Native Draba nemorosa woodland draba Brassicaceae Annual Forb Native Draba nivalis yellow arctic draba Brassicaceae Perennial Forb Native Draba oligosperma fewseed draba Brassicaceae Perennial Forb Native Draba paysonii Payson's draba Brassicaceae Perennial Forb Native Draba praealta tall draba Brassicaceae Biennial Forb Native Draba reptans Carolina draba Brassicaceae Annual Forb Native Draba ruaxes Rainier draba Brassicaceae Perennial Forb Native Draba stenoloba Alaska draba Brassicaceae Annual Forb Native Draba verna spring draba Brassicaceae Annual Forb Exotic Dracocephalum parviflorum American dragonhead Lamiaceae Annual Forb Native Dryas octopetala eightpetal mountain-avens Rosaceae Perennial Subshrub Native Dryopteris carthusiana spinulose woodfern Dryopteridaceae Perennial Forb Native Dryopteris expansa spreading woodfern Dryopteridaceae Perennial Forb Native Dryopteris filix-mas male fern Dryopteridaceae Perennial Forb Native Dysphania ambrosioides Mexican tea Chenopodiaceae Annual Subshrub Native   98  Scientific Name Common Name Family Duration Growth Form BC Status Dysphania botrys Jerusalem oak goosefoot Chenopodiaceae Annual Forb Exotic Dysphania pumilio clammy goosefoot Chenopodiaceae Annual Forb Exotic Echinocystis lobata wild cucumber Cucurbitaceae Annual Forb Exotic Elaeagnus angustifolia Russian olive Elaeagnaceae Perennial Tree Exotic Elaeagnus commutata silverberry Elaeagnaceae Perennial Shrub Native Eleocharis atropurpurea purple spikerush Cyperaceae Annual Graminoid Native Eleocharis bella beautiful spikerush Cyperaceae Annual Graminoid Native Eleocharis macrostachya pale spikerush Cyperaceae Perennial Graminoid Native Eleocharis ovata ovate spikerush Cyperaceae Annual Graminoid Native Elliottia pyroliflora copperbush Ericaceae Perennial Shrub Native Elmera racemosa yellow coralbells Saxifragaceae Perennial Forb Native Elymus canadensis Canada wildrye Poaceae Perennial Graminoid Native Elymus elymoides squirreltail Poaceae Perennial Graminoid Native Elymus glaucus blue wildrye Poaceae Perennial Graminoid Native Elymus lanceolatus thickspike wheatgrass Poaceae Perennial Graminoid Native Elymus macrourus tufted wheatgrass Poaceae Perennial Graminoid Native Elymus multisetus big squirreltail Poaceae Perennial Graminoid Native Elymus repens quackgrass Poaceae Perennial Graminoid Exotic Elymus scribneri spreading wheatgrass Poaceae Perennial Graminoid Native Elymus trachycaulus slender wheatgrass Poaceae Perennial Graminoid Native Elymus villosus hairy wildrye Poaceae Perennial Graminoid Native Empetrum nigrum black crowberry Empetraceae Perennial Subshrub Native Epilobium anagallidifolium pimpernel willowherb Onagraceae Perennial Forb Native Epilobium brachycarpum tall annual willowherb Onagraceae Annual Forb Native Epilobium clavatum talus willowherb Onagraceae Perennial Forb Native Epilobium densiflorum denseflower willowherb Onagraceae Annual Forb Native Epilobium foliosum leafy willowherb Onagraceae Annual Forb Native Epilobium glaberrimum glaucus willowherb Onagraceae Perennial Forb Native Epilobium halleanum glandular willowherb Onagraceae Perennial Forb Native Epilobium hirsutum codlins and cream Onagraceae Perennial Forb Exotic Epilobium hornemannii Hornemann's willowherb Onagraceae Perennial Forb Native Epilobium lactiflorum milkflower willowherb Onagraceae Perennial Forb Native Epilobium latifolium dwarf fireweed Onagraceae Perennial Forb Native Epilobium luteum yellow willowherb Onagraceae Perennial Forb Native Epilobium minutum chaparral willowherb Onagraceae Annual Forb Native Epilobium torreyi Torrey's willowherb Onagraceae Annual Forb Native Equisetum arvense field horsetail Equisetaceae Perennial Forb Native Equisetum hyemale scouringrush horsetail Equisetaceae Perennial Forb Native Equisetum laevigatum smooth horsetail Equisetaceae Perennial Forb Native   99  Scientific Name Common Name Family Duration Growth Form BC Status Equisetum palustre marsh horsetail Equisetaceae Perennial Forb Native Equisetum scirpoides dwarf scouringrush Equisetaceae Perennial Forb Native Equisetum sylvaticum woodland horsetail Equisetaceae Perennial Forb Native Equisetum variegatum variegated scouringrush Equisetaceae Perennial Forb Native Eragrostis cilianensis stinkgrass Poaceae Annual Graminoid Exotic Eragrostis minor little lovegrass Poaceae Annual Graminoid Exotic Eragrostis pectinacea tufted lovegrass Poaceae Annual Graminoid Native Eragrostis pilosa Indian lovegrass Poaceae Annual Graminoid Exotic Eragrostis virescens Mexican lovegrass Poaceae Annual Graminoid Native Erechtites hieraciifolius American burnweed Asteraceae Annual Forb Native Eremogone capillaris slender mountain sandwort Caryophyllaceae Perennial Forb Native Eremogone congesta ballhead sandwort Caryophyllaceae Perennial Forb Native Eremogone kingii King's sandwort Caryophyllaceae Perennial Forb Native Eremopyrum triticeum annual wheatgrass Poaceae Annual Graminoid Exotic Ericameria bloomeri rabbitbush Asteraceae Perennial Shrub Native Ericameria greenei Greene's goldenbush Asteraceae Perennial Subshrub Native Ericameria nauseosa rubber rabbitbrush Asteraceae Perennial Shrub Native Erigeron acris bitter fleabane Asteraceae Annual Forb Native Erigeron aureus alpine yellow fleabane Asteraceae Perennial Forb Native Erigeron compositus cutleaf daisy Asteraceae Perennial Forb Native Erigeron corymbosus longleaf fleabane Asteraceae Perennial Forb Native Erigeron divergens spreading fleabane Asteraceae Biennial Forb Native Erigeron filifolius threadleaf fleabane Asteraceae Perennial Forb Native Erigeron flagellaris trailing fleabane Asteraceae Biennial Forb Native Erigeron glabellus streamside fleabane Asteraceae Perennial Forb Native Erigeron humilis arctic alpine fleabane Asteraceae Perennial Forb Native Erigeron linearis desert yellow fleabane Asteraceae Perennial Forb Native Erigeron lonchophyllus shortray fleabane Asteraceae Biennial Forb Native Erigeron peregrinus subalpine fleabane Asteraceae Perennial Forb Native Erigeron philadelphicus Philadelphia fleabane Asteraceae Biennial Forb Native Erigeron pumilus shaggy fleabane Asteraceae Perennial Forb Native Erigeron speciosus aspen fleabane Asteraceae Perennial Subshrub Native Erigeron strigosus prairie fleabane Asteraceae Annual Forb Native Erigeron subtrinervis threenerve fleabane Asteraceae Perennial Forb Native Eriogonum baileyi Bailey's buckwheat Polygonaceae Annual Forb Native Eriogonum compositum arrowleaf buckwheat Polygonaceae Perennial Forb Native Eriogonum douglasii Douglas' buckwheat Polygonaceae Perennial Subshrub Native Eriogonum elatum tall woolly buckwheat Polygonaceae Perennial Subshrub Native Eriogonum flavum alpine golden buckwheat Polygonaceae Perennial Subshrub Native   100  Scientific Name Common Name Family Duration Growth Form BC Status Eriogonum heracleoides parsnipflower buckwheat Polygonaceae Perennial Forb Native Eriogonum ovalifolium cushion buckwheat Polygonaceae Perennial Subshrub Native Eriogonum pyrolifolium Shasta buckwheat Polygonaceae Perennial Subshrub Native Eriogonum strictum Blue Mountain buckwheat Polygonaceae Perennial Subshrub Native Eriogonum thymoides thymeleaf buckwheat Polygonaceae Perennial Subshrub Native Eriogonum umbellatum sulphur-flower buckwheat Polygonaceae Perennial Subshrub Native Eriogonum vimineum wickerstem buckwheat Polygonaceae Annual Forb Native Eriophyllum lanatum common woolly sunflower Asteraceae Annual Subshrub Native Eritrichium nanum arctic alpine forget-me-not Boraginaceae Perennial Forb Native Erodium cicutarium redstem stork's bill Geraniaceae Annual Forb Exotic Erucastrum gallicum common dogmustard Brassicaceae Biennial Forb Exotic Erysimum arenicola cascade wallflower Brassicaceae Perennial Forb Native Erysimum capitatum sanddune wallflower Brassicaceae Biennial Forb Native Erysimum cheiranthoides wormseed wallflower Brassicaceae Annual Forb Native Erysimum inconspicuum shy wallflower Brassicaceae Biennial Forb Native Erysimum repandum spreading wallflower Brassicaceae Annual Forb Native Erythronium grandiflorum yellow avalanche-lily Liliaceae Perennial Forb Native Eurybia conspicua western showy aster Asteraceae Perennial Forb Native Eurybia merita subalpine aster Asteraceae Perennial Forb Native Eurybia sibirica arctic aster Asteraceae Perennial Forb Native Euthamia graminifolia flat-top goldentop Asteraceae Perennial Forb Native Fallopia convolvulus black bindweed Polygonaceae Annual Forb Exotic Fallopia japonica Japanese knotweed Polygonaceae Perennial Forb Exotic Fallopia sachalinensis giant knotweed Polygonaceae Perennial Forb Exotic Fallopia scandens climbing false buckwheat Polygonaceae Perennial Forb Native Festuca altaica Altai fescue Poaceae Perennial Graminoid Native Festuca arundinacea tall fescue Poaceae Perennial Graminoid Exotic Festuca brachyphylla alpine fescue Poaceae Perennial Graminoid Native Festuca campestris rough fescue Poaceae Perennial Graminoid Native Festuca hallii plains rough fescue Poaceae Perennial Graminoid Native Festuca idahoensis Idaho fescue Poaceae Perennial Graminoid Native Festuca occidentalis western fescue Poaceae Perennial Graminoid Native Festuca ovina sheep fescue Poaceae Perennial Graminoid Exotic Festuca pratensis meadow fescue Poaceae Perennial Graminoid Exotic Festuca rubra red fescue Poaceae Perennial Graminoid Exotic Festuca saximontana Rocky Mountain fescue Poaceae Perennial Graminoid Native Festuca subulata bearded fescue Poaceae Perennial Graminoid Native Festuca viridula greenleaf fescue Poaceae Perennial Graminoid Native Festuca washingtonica Washington fescue Poaceae Perennial Graminoid Native   101  Scientific Name Common Name Family Duration Growth Form BC Status Floerkea proserpinacoides false mermaidweed Limnanthaceae Annual Forb Native Fragaria virginiana Virginia strawberry Rosaceae Perennial Forb Native Frangula purshiana Cascara buckthorn Rhamnaceae Perennial Tree Native Frasera albicaulis whitestem frasera Gentianaceae Perennial Forb Native Fraxinus pennsylvanica green ash Oleaceae Perennial Tree Exotic Fritillaria affinis checker lily Liliaceae Perennial Forb Native Fritillaria camschatcensis Kamchatka fritillary Liliaceae Perennial Forb Native Fritillaria pudica yellow fritillary Liliaceae Perennial Forb Native Gaillardia aristata blanketflower Asteraceae Perennial Forb Native Galeopsis tetrahit brittlestem hempnettle Lamiaceae Annual Forb Exotic Galinsoga parviflora gallant soldier Asteraceae Annual Forb Exotic Galium aparine stickywilly Rubiaceae Annual Forb Native Galium bifolium twinleaf bedstraw Rubiaceae Annual Forb Native Galium boreale northern bedstraw Rubiaceae Perennial Forb Native Galium mexicanum Mexican bedstraw Rubiaceae Perennial Forb Native Galium serpenticum Intermountain bedstraw Rubiaceae Perennial Subshrub Native Galium triflorum fragrant bedstraw Rubiaceae Perennial Forb Native Gaultheria humifusa alpine spicywintergreen Ericaceae Perennial Shrub Native Gaultheria ovatifolia western teaberry Ericaceae Perennial Shrub Native Gaultheria shallon salal Ericaceae Perennial Shrub Native Gaura coccinea scarlet beeblossom Onagraceae Perennial Subshrub Native Gaura mollis velvetweed Onagraceae Annual Forb Native Gayophytum diffusum spreading groundsmoke Onagraceae Annual Forb Native Gayophytum humile dwarf groundsmoke Onagraceae Annual Forb Native Gayophytum racemosum blackfoot groundsmoke Onagraceae Annual Forb Native Gayophytum ramosissimum pinyon groundsmoke Onagraceae Annual Forb Native Gentiana affinis pleated gentian Gentianaceae Perennial Forb Native Gentiana calycosa Rainier pleated gentian Gentianaceae Perennial Forb Native Gentiana glauca pale gentian Gentianaceae Perennial Forb Native Gentianella propinqua fourpart dwarf gentian Gentianaceae Annual Forb Native Gentianella tenella Dane's dwarf gentian Gentianaceae Annual Forb Native Geocaulon lividum false toadflax Santalaceae Perennial Forb Native Geranium bicknellii Bicknell's cranesbill Geraniaceae Annual Forb Native Geranium carolinianum Carolina geranium Geraniaceae Annual Forb Native Geranium pusillum small geranium Geraniaceae Annual Forb Exotic Geranium richardsonii Richardson's geranium Geraniaceae Perennial Forb Native Geranium robertianum Robert geranium Geraniaceae Annual Forb Exotic Geranium viscosissimum sticky purple geranium Geraniaceae Annual Forb Native Geum aleppicum yellow avens Rosaceae Perennial Forb Native   102  Scientific Name Common Name Family Duration Growth Form BC Status Geum macrophyllum largeleaf avens Rosaceae Perennial Forb Native Geum triflorum old man's whiskers Rosaceae Perennial Forb Native Gilia aggregata scarlet gilia Polemoniaceae Biennial Forb Native Gilia sinuata rosy gilia Polemoniaceae Annual Forb Native Glandularia gooddingii southwestern mock vervain Verbenaceae Perennial Forb Native Glechoma hederacea ground ivy Lamiaceae Perennial Forb Exotic Gleditsia triacanthos honeylocust Fabaceae Perennial Shrub Native Glycine max soybean Fabaceae Annual Forb Exotic Glycyrrhiza lepidota American licorice Fabaceae Perennial Forb Native Gnaphalium stramineum cottonbatting plant Asteraceae Annual Forb Native Gnaphalium uliginosum marsh cudweed Asteraceae Annual Forb Exotic Graphephorum wolfii Wolf's trisetum Poaceae Perennial Graminoid Native Gutierrezia sarothrae broom snakeweed Asteraceae Perennial Subshrub Native Gypsophila elegans showy baby's-breath Caryophyllaceae Annual Forb Native Gypsophila paniculata baby's breath Caryophyllaceae Perennial Forb Exotic Hackelia deflexa nodding stickseed Boraginaceae Annual Forb Native Hackelia diffusa spreading stickseed Boraginaceae Perennial Forb Native Hackelia floribunda manyflower stickseed Boraginaceae Biennial Forb Native Hackelia micrantha Jessica sticktight Boraginaceae Perennial Forb Native Halenia deflexa American spurred gentian Gentianaceae Annual Forb Native Hedera helix English ivy Araliaceae Perennial Forb Exotic Hedysarum boreale Utah sweetvetch Fabaceae Perennial Forb Native Hedysarum sulphurescens white sweetvetch Fabaceae Perennial Forb Native Helenium autumnale common sneezeweed Asteraceae Perennial Forb Native Helianthus annuus common sunflower Asteraceae Annual Forb Exotic Helianthus cusickii Cusick's sunflower Asteraceae Perennial Subshrub Native Helianthus giganteus giant sunflower Asteraceae Perennial Forb Native Helianthus grosseserratus sawtooth sunflower Asteraceae Perennial Forb Native Helianthus maximiliani Maximilian sunflower Asteraceae Perennial Forb Exotic Helianthus nuttallii Nuttall's sunflower Asteraceae Perennial Forb Native Helianthus tuberosus Jerusalem artichoke Asteraceae Perennial Forb Exotic Heracleum maximum common cowparsnip Apiaceae Perennial Forb Native Herniaria hirsuta hairy rupturewort Caryophyllaceae Annual Forb Native Hesperis matronalis dames rocket Brassicaceae Biennial Forb Exotic Hesperochiron californicus California hesperochiron Hydrophyllaceae Perennial Forb Native Hesperochiron pumilus dwarf hesperochiron Hydrophyllaceae Perennial Forb Native Hesperostipa comata needle and thread Poaceae Perennial Graminoid Native Hesperostipa curtiseta shortbristle needle and thread Poaceae Perennial Graminoid Native Hesperostipa spartea porcupinegrass Poaceae Perennial Graminoid Native   103  Scientific Name Common Name Family Duration Growth Form BC Status Heterocodon rariflorum rareflower heterocodon Campanulaceae Annual Forb Native Heterotheca villosa hairy false goldenaster Asteraceae Perennial Subshrub Native Heuchera cylindrica roundleaf alumroot Saxifragaceae Perennial Forb Native Heuchera glabra alpine heuchera Saxifragaceae Perennial Forb Native Heuchera micrantha crevice alumroot Saxifragaceae Perennial Forb Native Hieracium albiflorum white hawkweed Asteraceae Perennial Forb Native Hieracium scouleri Scouler's woollyweed Asteraceae Perennial Forb Native Hieracium umbellatum narrowleaf hawkweed Asteraceae Perennial Forb Native Holodiscus discolor oceanspray Rosaceae Perennial Shrub Native Holosteum umbellatum jagged chickweed Caryophyllaceae Annual Forb Exotic Hordeum brachyantherum meadow barley Poaceae Perennial Graminoid Native Hordeum jubatum foxtail barley Poaceae Perennial Graminoid Native Hordeum murinum mouse barley Poaceae Annual Graminoid Exotic Hordeum vulgare common barley Poaceae Annual Graminoid Exotic Hornungia procumbens prostrate hutchinsia Brassicaceae Annual Forb Native Humulus lupulus common hop Cannabaceae Perennial Forb Exotic Huperzia occidentalis western clubmoss Lycopodiaceae Perennial Forb Native Hydrophyllum capitatum ballhead waterleaf Hydrophyllaceae Perennial Forb Native Hydrophyllum fendleri Fendler's waterleaf Hydrophyllaceae Perennial Forb Native Hyoscyamus niger black henbane Solanaceae Annual Forb Exotic Hypericum perforatum common St. Johnswort Clusiaceae Perennial Forb Exotic Hypericum scouleri Scouler's St. Johnswort Clusiaceae Perennial Forb Native Hypochaeris radicata hairy cat's ear Asteraceae Perennial Forb Exotic Idahoa scapigera oldstem idahoa Brassicaceae Annual Forb Native Impatiens aurella paleyellow touch-me-not Balsaminaceae Annual Forb Native Impatiens capensis jewelweed Balsaminaceae Annual Forb Native Impatiens noli-tangere western touch-me-not Balsaminaceae Annual Forb Native Inula helenium elecampane inula Asteraceae Perennial Forb Exotic Ipomoea purpurea tall morning-glory Convolvulaceae Annual Forb Native Ipomopsis aggregata scarlet gilia Polemoniaceae Biennial Forb Native Ipomopsis congesta ballhead ipomopsis Polemoniaceae Perennial Subshrub Native Iris missouriensis Rocky Mountain iris Iridaceae Perennial Forb Exotic Iva axillaris povertyweed Asteraceae Perennial Subshrub Native Juglans regia English walnut Juglandaceae Perennial Tree Exotic Juncus brachyphyllus tuftedstem rush Juncaceae Perennial Graminoid Native Juncus confusus Colorado rush Juncaceae Perennial Graminoid Native Juncus conglomeratus common rush Juncaceae Perennial Graminoid Exotic Juncus drummondii Drummond's rush Juncaceae Perennial Graminoid Native Juncus dudleyi Dudley's rush Juncaceae Perennial Graminoid Native   104  Scientific Name Common Name Family Duration Growth Form BC Status Juncus ensifolius swordleaf rush Juncaceae Perennial Graminoid Native Juncus longistylis longstyle rush Juncaceae Perennial Graminoid Native Juncus nevadensis Sierra rush Juncaceae Perennial Graminoid Native Juncus parryi Parry's rush Juncaceae Perennial Graminoid Native Juncus regelii Regel's rush Juncaceae Perennial Graminoid Native Juncus saximontanus Rocky Mountain rush Juncaceae Perennial Graminoid Native Juncus tenuis poverty rush Juncaceae Perennial Graminoid Native Juncus torreyi Torrey's rush Juncaceae Perennial Graminoid Native Juncus triglumis three-hulled rush Juncaceae Perennial Graminoid Native Juniperus communis common juniper Cupressaceae Perennial Tree Native Juniperus horizontalis creeping juniper Cupressaceae Perennial Subshrub Native Juniperus occidentalis western juniper Cupressaceae Perennial Tree Native Juniperus scopulorum Rocky Mountain juniper Cupressaceae Perennial Tree Native Juniperus virginiana eastern redcedar Cupressaceae Perennial Tree Native Kalmia latifolia mountain laurel Ericaceae Perennial Tree Native Kelloggia galioides milk kelloggia Rubiaceae Perennial Forb Native Knautia arvensis field scabiosa Dipsacaceae Annual Forb Exotic Kobresia myosuroides Bellardi bog sedge Cyperaceae Perennial Graminoid Native Kochia scoparia burningbush Chenopodiaceae Annual Forb Exotic Koeleria macrantha prairie Junegrass Poaceae Perennial Graminoid Native Lactuca biennis tall blue lettuce Asteraceae Annual Forb Native Lactuca canadensis Canada lettuce Asteraceae Annual Forb Exotic Lactuca ludoviciana biannual lettuce Asteraceae Biennial Forb Exotic Lactuca saligna willowleaf lettuce Asteraceae Annual Forb Native Lactuca sativa garden lettuce Asteraceae Annual Forb Native Lactuca tatarica blue lettuce Asteraceae Biennial Forb Native Lagophylla ramosissima branched lagophylla Asteraceae Annual Forb Native Lamium amplexicaule henbit deadnettle Lamiaceae Annual Forb Exotic Lamium maculatum spotted henbit Lamiaceae Perennial Forb Native Lamium purpureum purple deadnettle Lamiaceae Annual Forb Exotic Lappula occidentalis flatspine stickseed Boraginaceae Annual Forb Native Lappula squarrosa European stickseed Boraginaceae Annual Forb Exotic Lapsana communis common nipplewort Asteraceae Annual Forb Exotic Larix lyallii subalpine larch Pinaceae Perennial Tree Native Larix occidentalis western larch Pinaceae Perennial Tree Native Lathrocasis tenerrima delicate gilia Polemoniaceae Annual Forb Native Lathyrus lanszwertii Nevada pea Fabaceae Perennial Forb Native Lathyrus latifolius perennial pea Fabaceae Perennial Forb Exotic Lathyrus nevadensis Sierra pea Fabaceae Perennial Forb Native   105  Scientific Name Common Name Family Duration Growth Form BC Status Lathyrus ochroleucus cream pea Fabaceae Perennial Forb Native Lathyrus pauciflorus fewflower pea Fabaceae Perennial Forb Native Lens culinaris lentil Fabaceae Annual Forb Native Leonurus cardiaca common motherwort Lamiaceae Perennial Forb Exotic Lepidium appelianum hairy whitetop Brassicaceae Perennial Forb Exotic Lepidium campestre field pepperweed Brassicaceae Annual Forb Exotic Lepidium densiflorum common pepperweed Brassicaceae Annual Forb Native Lepidium draba whitetop Brassicaceae Perennial Forb Exotic Lepidium latifolium broadleaved pepperweed Brassicaceae Perennial Forb Exotic Lepidium perfoliatum clasping pepperweed Brassicaceae Annual Forb Exotic Lepidium ramosissimum manybranched pepperweed Brassicaceae Annual Forb Exotic Lepidium ruderale roadside pepperweed Brassicaceae Annual Forb Exotic Lepidium virginicum Virginia pepperweed Brassicaceae Annual Forb Native Leptarrhena pyrolifolia fireleaf leptarrhena Saxifragaceae Perennial Forb Native Leptosiphon liniflorus narrowflower flaxflower Polemoniaceae Annual Forb Native Leucanthemum vulgare oxeye daisy Asteraceae Perennial Forb Exotic Lewisia columbiana Columbian lewisia Portulacaceae Perennial Forb Native Lewisia pygmaea alpine lewisia Portulacaceae Perennial Forb Native Lewisia rediviva bitter root Portulacaceae Perennial Forb Native Lewisia triphylla threeleaf lewisia Portulacaceae Perennial Forb Native Leymus cinereus basin wildrye Poaceae Perennial Graminoid Native Leymus condensatus giant wildrye Poaceae Perennial Graminoid Native Leymus mollis American dunegrass Poaceae Perennial Graminoid Native Leymus triticoides beardless wildrye Poaceae Perennial Graminoid Native Ligusticum canbyi Canby's licorice-root Apiaceae Perennial Forb Native Ligusticum grayi Gray's licorice-root Apiaceae Perennial Forb Native Ligustrum vulgare European privet Oleaceae Perennial Shrub Exotic Lilium columbianum Columbia lily Liliaceae Perennial Forb Native Linanthus harknessii Harkness' flaxflower Polemoniaceae Annual Forb Native Linanthus septentrionalis northern linanthus Polemoniaceae Annual Forb Native Linaria dalmatica Dalmatian toadflax Scrophulariaceae Perennial Forb Native Linaria vulgaris butter and eggs Scrophulariaceae Perennial Forb Exotic Linnaea borealis twinflower Caprifoliaceae Perennial Forb Native Linum lewisii Lewis flax Linaceae Perennial Subshrub Native Linum perenne blue flax Linaceae Perennial Forb Exotic Linum usitatissimum common flax Linaceae Annual Forb Exotic Lithospermum incisum narrowleaf stoneseed Boraginaceae Perennial Forb Native Lithospermum ruderale western stoneseed Boraginaceae Perennial Forb Native Lloydia serotina common alplily Liliaceae Perennial Forb Native   106  Scientific Name Common Name Family Duration Growth Form BC Status Lobelia inflata Indian-tobacco Campanulaceae Annual Forb Exotic Logfia arvensis field cottonrose Asteraceae Annual Forb Exotic Lolium perenne perennial ryegrass Poaceae Perennial Graminoid Exotic Lomatium ambiguum Wyeth biscuitroot Apiaceae Perennial Forb Native Lomatium bicolor Wasatch desertparsley Apiaceae Perennial Forb Native Lomatium brandegeei Brandegee's desertparsley Apiaceae Perennial Forb Native Lomatium dissectum fernleaf biscuitroot Apiaceae Perennial Forb Native Lomatium farinosum northern biscuitroot Apiaceae Perennial Forb Native Lomatium geyeri Geyer's biscuitroot Apiaceae Perennial Forb Native Lomatium gormanii Gorman's biscuitroot Apiaceae Perennial Forb Native Lomatium grayi Gray's biscuitroot Apiaceae Perennial Forb Native Lomatium macrocarpum bigseed biscuitroot Apiaceae Perennial Forb Native Lomatium martindalei cascade desertparsley Apiaceae Perennial Forb Native Lomatium nudicaule barestem biscuitroot Apiaceae Perennial Forb Native Lomatium piperi Indian biscuitroot Apiaceae Perennial Forb Native Lomatium triternatum nineleaf biscuitroot Apiaceae Perennial Forb Native Lomatium utriculatum common lomatium Apiaceae Perennial Forb Native Lonicera ciliosa orange honeysuckle Caprifoliaceae Perennial Forb Native Lonicera conjugialis purpleflower honeysuckle Caprifoliaceae Perennial Shrub Native Lonicera involucrata twinberry honeysuckle Caprifoliaceae Perennial Shrub Native Lonicera tatarica Tatarian honeysuckle Caprifoliaceae Perennial Shrub Exotic Lonicera utahensis Utah honeysuckle Caprifoliaceae Perennial Shrub Native Lotus corniculatus bird's-foot trefoil Fabaceae Perennial Forb Exotic Lotus tenuis narrowleaf trefoil Fabaceae Perennial Forb Exotic Luetkea pectinata partridgefoot Rosaceae Perennial Subshrub Native Luina hypoleuca littleleaf silverback Asteraceae Perennial Subshrub Native Lupinus albicaulis sicklekeel lupine Fabaceae Annual Forb Native Lupinus arbustus longspur lupine Fabaceae Perennial Subshrub Native Lupinus arcticus arctic lupine Fabaceae Perennial Forb Native Lupinus argenteus silvery lupine Fabaceae Perennial Subshrub Native Lupinus burkei largeleaf lupine Fabaceae Perennial Forb Native Lupinus latifolius broadleaf lupine Fabaceae Perennial Forb Native Lupinus lepidus Pacific lupine Fabaceae Perennial Forb Native Lupinus leucophyllus velvet lupine Fabaceae Perennial Forb Native Lupinus lyallii dwarf mountain lupine Fabaceae Perennial Subshrub Native Lupinus polyphyllus bigleaf lupine Fabaceae Perennial Forb Native Lupinus sellulus Donner Lake lupine Fabaceae Perennial Forb Native Lupinus sericeus silky lupine Fabaceae Perennial Subshrub Native Lupinus sulphureus sulphur lupine Fabaceae Perennial Forb Native   107  Scientific Name Common Name Family Duration Growth Form BC Status Luzula comosa Pacific woodrush Juncaceae Perennial Graminoid Native Luzula hitchcockii Hitchcock's smooth woodrush Juncaceae Perennial Graminoid Native Luzula multiflora common woodrush Juncaceae Perennial Graminoid Exotic Luzula parviflora smallflowered woodrush Juncaceae Perennial Graminoid Native Luzula piperi Piper's woodrush Juncaceae Perennial Graminoid Native Luzula spicata spiked woodrush Juncaceae Perennial Graminoid Native Lycium barbarum matrimony vine Solanaceae Perennial Forb Exotic Lycopodium alpinum alpine clubmoss Lycopodiaceae Perennial Subshrub Native Lycopodium annotinum stiff clubmoss Lycopodiaceae Perennial Subshrub Native Lycopodium clavatum running clubmoss Lycopodiaceae Perennial Subshrub Native Lycopodium complanatum groundcedar Lycopodiaceae Perennial Subshrub Native Lycopodium dendroideum tree groundpine Lycopodiaceae Perennial Subshrub Native Lycopodium sitchense Sitka clubmoss Lycopodiaceae Perennial Subshrub Native Lygodesmia juncea rush skeletonplant Asteraceae Perennial Forb Native Lysimachia ciliata fringed loosestrife Primulaceae Perennial Forb Native Lysimachia vulgaris garden yellow loosestrife Primulaceae Perennial Forb Exotic Machaeranthera canescens hoary tansyaster Asteraceae Annual Forb Native Machaeranthera pinnatifida lacy tansyaster Asteraceae Perennial Subshrub Native Madia exigua small tarweed Asteraceae Annual Forb Native Madia glomerata mountain tarweed Asteraceae Annual Forb Native Madia gracilis grassy tarweed Asteraceae Annual Forb Native Madia sativa coast tarweed Asteraceae Annual Forb Exotic Maianthemum racemosum feathery false lily of the valley Liliaceae Perennial Forb Native Maianthemum stellatum starry false lily of the valley Liliaceae Perennial Forb Native Malva neglecta common mallow Malvaceae Annual Forb Exotic Malva parviflora cheeseweed mallow Malvaceae Annual Forb Exotic Malva pusilla low mallow Malvaceae Annual Forb Exotic Malvella leprosa alkali mallow Malvaceae Perennial Forb Native Marrubium vulgare horehound Lamiaceae Perennial Subshrub Exotic Matricaria discoidea disc mayweed Asteraceae Annual Forb Native Matteuccia struthiopteris ostrich fern Dryopteridaceae Perennial Forb Native Medicago lupulina black medick Fabaceae Annual Forb Exotic Medicago sativa alfalfa Fabaceae Annual Forb Exotic Melampyrum lineare narrowleaf cowwheat Scrophulariaceae Annual Forb Native Melica bulbosa oniongrass Poaceae Perennial Graminoid Native Melica fugax little oniongrass Poaceae Perennial Graminoid Native Melica spectabilis purple oniongrass Poaceae Perennial Graminoid Native Melica subulata Alaska oniongrass Poaceae Perennial Graminoid Native   108  Scientific Name Common Name Family Duration Growth Form BC Status Melilotus officinalis sweetclover Fabaceae Annual Forb Exotic Mentha arvensis wild mint Lamiaceae Perennial Forb Native Mentha spicata spearmint Lamiaceae Perennial Forb Exotic Mentzelia aspera tropical blazingstar Loasaceae Annual Forb Native Mentzelia dispersa bushy blazingstar Loasaceae Annual Forb Native Mentzelia laevicaulis smoothstem blazingstar Loasaceae Biennial Forb Native Menziesia ferruginea rusty menziesia Ericaceae Perennial Shrub Native Mertensia longiflora small bluebells Boraginaceae Perennial Forb Native Mertensia oblongifolia oblongleaf bluebells Boraginaceae Annual Forb Native Mertensia paniculata tall bluebells Boraginaceae Perennial Subshrub Native Microseris nutans nodding microseris Asteraceae Perennial Forb Native Mimulus breviflorus shortflower monkeyflower Scrophulariaceae Annual Forb Native Mimulus breweri Brewer's monkeyflower Scrophulariaceae Annual Forb Native Mimulus lewisii purple monkeyflower Scrophulariaceae Perennial Forb Native Mimulus suksdorfii Suksdorf's monkeyflower Scrophulariaceae Annual Forb Native Minuartia biflora mountain stitchwort Caryophyllaceae Perennial Forb Native Minuartia michauxii Michaux's stitchwort Caryophyllaceae Annual Forb Native Minuartia obtusiloba twinflower sandwort Caryophyllaceae Perennial Subshrub Native Minuartia rubella beautiful sandwort Caryophyllaceae Annual Forb Native Mirabilis albida white four o'clock Nyctaginaceae Perennial Forb Exotic Mirabilis jalapa marvel of Peru Nyctaginaceae Perennial Forb Native Mirabilis nyctaginea heartleaf four o'clock Nyctaginaceae Perennial Forb Exotic Mitella breweri Brewer's miterwort Saxifragaceae Perennial Forb Native Mitella caulescens slightstemmed miterwort Saxifragaceae Perennial Forb Native Mitella pentandra fivestamen miterwort Saxifragaceae Perennial Forb Native Mitella stauropetala smallflower miterwort Saxifragaceae Perennial Forb Native Mitella trifida threeparted miterwort Saxifragaceae Perennial Forb Native Moehringia lateriflora bluntleaf sandwort Caryophyllaceae Perennial Forb Native Moehringia macrophylla largeleaf sandwort Caryophyllaceae Perennial Forb Native Mollugo verticillata green carpetweed Molluginaceae Annual Forb Exotic Monarda fistulosa wild bergamot Lamiaceae Perennial Subshrub Native Monardella odoratissima mountain monardella Lamiaceae Perennial Subshrub Native Moneses uniflora single delight Pyrolaceae Perennial Forb Native Monolepis nuttalliana Nuttall's povertyweed Chenopodiaceae Annual Forb Native Montia dichotoma dwarf minerslettuce Portulacaceae Annual Forb Native Montia linearis narrowleaf minerslettuce Portulacaceae Annual Forb Native Montia parvifolia littleleaf minerslettuce Portulacaceae Perennial Forb Native Muhlenbergia andina foxtail muhly Poaceae Perennial Graminoid Native Muhlenbergia asperifolia scratchgrass Poaceae Perennial Graminoid Native   109  Scientific Name Common Name Family Duration Growth Form BC Status Muhlenbergia mexicana Mexican muhly Poaceae Perennial Graminoid Native Muhlenbergia minutissima annual muhly Poaceae Annual Graminoid Native Muhlenbergia racemosa marsh muhly Poaceae Perennial Graminoid Native Muhlenbergia richardsonis mat muhly Poaceae Perennial Graminoid Native Myosotis arvensis field forget-me-not Boraginaceae Annual Forb Exotic Myosotis asiatica Asian forget-me-not Boraginaceae Perennial Forb Native Myosotis discolor changing forget-me-not Boraginaceae Annual Forb Exotic Myosotis stricta strict forget-me-not Boraginaceae Annual Forb Exotic Myosotis sylvatica woodland forget-me-not Boraginaceae Perennial Forb Exotic Myosotis verna spring forget-me-not Boraginaceae Annual Forb Native Myosoton aquaticum giantchickweed Caryophyllaceae Perennial Forb Exotic Nassella viridula green needlegrass Poaceae Perennial Graminoid Native Navarretia divaricata divaricate navarretia Polemoniaceae Annual Forb Native Navarretia intertexta needleleaf navarretia Polemoniaceae Annual Forb Native Nemophila breviflora basin nemophila Hydrophyllaceae Annual Forb Native Nemophila parviflora smallflower nemophila Hydrophyllaceae Annual Forb Native Nepeta cataria catnip Lamiaceae Perennial Forb Exotic Neslia paniculata ballmustard Brassicaceae Annual Forb Exotic Nicotiana acuminata manyflower tobacco Solanaceae Annual Forb Native Nothochelone nemorosa woodland beardtongue Scrophulariaceae Perennial Subshrub Native Oenothera biennis common evening primrose Onagraceae Biennial Forb Exotic Oenothera elata Hooker's evening primrose Onagraceae Biennial Forb Native Oenothera pallida pale evening primrose Onagraceae Biennial Forb Native Oenothera villosa hairy evening primrose Onagraceae Perennial Forb Exotic Olsynium douglasii Douglas' grasswidow Iridaceae Perennial Forb Native Onobrychis viciifolia sainfoin Fabaceae Perennial Forb Exotic Onopordum acanthium Scotch cottonthistle Asteraceae Biennial Forb Exotic Oplopanax horridus devilsclub Araliaceae Perennial Shrub Native Oreostemma alpigenum tundra aster Asteraceae Perennial Forb Native Origanum vulgare oregano Lamiaceae Perennial Forb Exotic Orobanche corymbosa flat-top broomrape Orobanchaceae Annual Forb Native Orobanche fasciculata clustered broomrape Orobanchaceae Annual Forb Native Orobanche uniflora oneflowered broomrape Orobanchaceae Annual Forb Native Orthilia secunda sidebells wintergreen Pyrolaceae Perennial Subshrub Native Orthocarpus luteus yellow owl's-clover Scrophulariaceae Annual Forb Native Oryzopsis asperifolia roughleaf ricegrass Poaceae Perennial Graminoid Native Oryzopsis hymenoides Indian ricegrass Poaceae Perennial Graminoid Native Osmorhiza chilensis sweetcicely Apiaceae Perennial Forb Native Osmorhiza depauperata bluntseed sweetroot Apiaceae Perennial Forb Native   110  Scientific Name Common Name Family Duration Growth Form BC Status Osmorhiza occidentalis western sweetroot Apiaceae Perennial Forb Native Osmorhiza purpurea purple sweetroot Apiaceae Perennial Forb Native Oxalis corniculata creeping woodsorrel Oxalidaceae Annual Forb Exotic Oxalis dillenii slender yellow woodsorrel Oxalidaceae Perennial Forb Exotic Oxalis stricta common yellow oxalis Oxalidaceae Perennial Forb Exotic Oxyria digyna alpine mountainsorrel Polygonaceae Perennial Forb Native Oxytropis campestris field locoweed Fabaceae Perennial Forb Native Oxytropis deflexa nodding locoweed Fabaceae Perennial Forb Native Oxytropis sericea white locoweed Fabaceae Perennial Forb Native Packera cana woolly groundsel Asteraceae Perennial Forb Native Packera cymbalaria dwarf arctic ragwort Asteraceae Perennial Forb Native Packera indecora elegant groundsel Asteraceae Perennial Forb Native Packera macounii Siskiyou Mountain ragwort Asteraceae Perennial Forb Native Packera pauciflora alpine groundsel Asteraceae Perennial Forb Native Packera paupercula balsam groundsel Asteraceae Perennial Forb Native Packera plattensis prairie groundsel Asteraceae Biennial Forb Native Packera pseudaurea falsegold groundsel Asteraceae Perennial Forb Native Packera streptanthifolia Rocky Mountain groundsel Asteraceae Perennial Forb Native Panicum capillare witchgrass Poaceae Annual Graminoid Native Panicum miliaceum proso millet Poaceae Annual Graminoid Exotic Papaver rhoeas corn poppy Papaveraceae Annual Forb Exotic Parietaria pensylvanica Pennsylvania pellitory Urticaceae Annual Forb Native Parthenocissus quinquefolia Virginia creeper Vitaceae Perennial Forb Native Pascopyrum smithii western wheatgrass Poaceae Perennial Graminoid Native Paxistima myrsinites Oregon boxleaf Celastraceae Perennial Shrub Native Pedicularis bracteosa bracted lousewort Scrophulariaceae Perennial Forb Native Pedicularis contorta coiled lousewort Scrophulariaceae Perennial Forb Native Pedicularis langsdorffii Langsdorf's lousewort Scrophulariaceae Perennial Forb Native Pedicularis ornithorhyncha ducksbill lousewort Scrophulariaceae Perennial Forb Native Pedicularis racemosa sickletop lousewort Scrophulariaceae Perennial Forb Native Pellaea atropurpurea purple cliffbrake Pteridaceae Perennial Forb Native Pellaea glabella smooth cliffbrake Pteridaceae Perennial Forb Native Pennisetum glaucum pearl millet Poaceae Annual Graminoid Native Penstemon attenuatus sulphur penstemon Scrophulariaceae Perennial Subshrub Native Penstemon barbatus beardlip penstemon Scrophulariaceae Perennial Forb Native Penstemon confertus yellow penstemon Scrophulariaceae Perennial Subshrub Native Penstemon davidsonii Davidson's penstemon Scrophulariaceae Perennial Subshrub Native Penstemon deustus scabland penstemon Scrophulariaceae Perennial Subshrub Native Penstemon ellipticus rocky ledge penstemon Scrophulariaceae Perennial Forb Native   111  Scientific Name Common Name Family Duration Growth Form BC Status Penstemon eriantherus fuzzytongue penstemon Scrophulariaceae Perennial Subshrub Native Penstemon fruticosus bush penstemon Scrophulariaceae Perennial Subshrub Native Penstemon gairdneri Gairdner's beardtongue Scrophulariaceae Perennial Subshrub Native Penstemon glandulosus stickystem penstemon Scrophulariaceae Perennial Forb Native Penstemon hirsutus hairy beardtongue Scrophulariaceae Perennial Forb Native Penstemon montanus cordroot beardtongue Scrophulariaceae Perennial Subshrub Native Penstemon procerus littleflower penstemon Scrophulariaceae Perennial Subshrub Native Penstemon pruinosus Chelan beardtongue Scrophulariaceae Perennial Forb Native Penstemon richardsonii cutleaf beardtongue Scrophulariaceae Perennial Subshrub Native Penstemon rupicola cliff beardtongue Scrophulariaceae Perennial Forb Native Penstemon rydbergii Rydberg's penstemon Scrophulariaceae Perennial Subshrub Native Penstemon serrulatus serrulate penstemon Scrophulariaceae Perennial Subshrub Native Penstemon speciosus royal penstemon Scrophulariaceae Perennial Forb Native Penstemon triphyllus Riggin's penstemon Scrophulariaceae Perennial Subshrub Native Penstemon venustus Venus penstemon Scrophulariaceae Perennial Subshrub Native Penstemon wilcoxii Wilcox's penstemon Scrophulariaceae Perennial Subshrub Native Persicaria longiseta Oriental lady's thumb Polygonaceae Annual Forb Exotic Petasites frigidus arctic sweet coltsfoot Asteraceae Perennial Forb Native Phacelia franklinii Franklin's phacelia Hydrophyllaceae Annual Forb Native Phacelia hastata silverleaf phacelia Hydrophyllaceae Perennial Forb Native Phacelia heterophylla varileaf phacelia Hydrophyllaceae Biennial Forb Native Phacelia humilis low phacelia Hydrophyllaceae Annual Forb Native Phacelia incana hoary phacelia Hydrophyllaceae Annual Forb Native Phacelia leptosepala narrowsepal phacelia Hydrophyllaceae Perennial Subshrub Native Phacelia linearis threadleaf phacelia Hydrophyllaceae Annual Forb Native Phacelia procera tall phacelia Hydrophyllaceae Perennial Forb Native Phacelia ramosissima branching phacelia Hydrophyllaceae Perennial Subshrub Native Phacelia sericea silky phacelia Hydrophyllaceae Biennial Subshrub Native Phalaris canariensis annual canarygrass Poaceae Annual Graminoid Exotic Phalaris paradoxa hood canarygrass Poaceae Annual Graminoid Native Philadelphus lewisii Lewis' mock orange Hydrangeaceae Perennial Shrub Native Phleum alpinum alpine timothy Poaceae Perennial Graminoid Native Phleum pratense timothy Poaceae Perennial Graminoid Exotic Phlox caespitosa tufted phlox Polemoniaceae Perennial Subshrub Native Phlox diffusa spreading phlox Polemoniaceae Perennial Subshrub Native Phlox hoodii spiny phlox Polemoniaceae Perennial Forb Native Phlox longifolia longleaf phlox Polemoniaceae Perennial Subshrub Native Phlox pulvinata cushion phlox Polemoniaceae Perennial Forb Native Phlox speciosa showy phlox Polemoniaceae Perennial Subshrub Native   112  Scientific Name Common Name Family Duration Growth Form BC Status Phoenicaulis cheiranthoides wallflower phoenicaulis Brassicaceae Perennial Forb Native Phragmites australis common reed Poaceae Perennial Subshrub Exotic Phyllodoce empetriformis pink mountainheath Ericaceae Perennial Subshrub Native Phyllodoce glanduliflora yellow mountainheath Ericaceae Perennial Subshrub Native Physalis longifolia longleaf groundcherry Solanaceae Perennial Forb Native Physalis philadelphica Mexican groundcherry Solanaceae Annual Forb Exotic Physaria didymocarpa common twinpod Brassicaceae Perennial Forb Native Physocarpus malvaceus mallow ninebark Rosaceae Perennial Shrub Native Picea engelmannii Engelmann spruce Pinaceae Perennial Tree Native Picea glauca white spruce Pinaceae Perennial Tree Native Pinus contorta lodgepole pine Pinaceae Perennial Tree Native Pinus monticola western white pine Pinaceae Perennial Tree Native Pinus ponderosa ponderosa pine Pinaceae Perennial Tree Native Piptatherum micranthum littleseed ricegrass Poaceae Perennial Graminoid Native Piptatherum pungens mountain ricegrass Poaceae Perennial Graminoid Native Pityrogramma triangularis goldback fern Pteridaceae Perennial Forb Native Plagiobothrys scouleri Scouler's popcornflower Boraginaceae Annual Forb Native Plagiobothrys tenellus Pacific popcornflower Boraginaceae Annual Forb Native Plantago lanceolata narrowleaf plantain Plantaginaceae Annual Forb Exotic Plantago patagonica woolly plantain Plantaginaceae Annual Forb Native Plectritis brachystemon shortspur seablush Valerianaceae Annual Forb Native Plectritis macrocera longhorn plectritis Valerianaceae Annual Forb Native Poa alpina alpine bluegrass Poaceae Perennial Graminoid Native Poa annua annual bluegrass Poaceae Annual Graminoid Exotic Poa arctica arctic bluegrass Poaceae Perennial Graminoid Native Poa bulbosa bulbous bluegrass Poaceae Perennial Graminoid Exotic Poa compressa Canada bluegrass Poaceae Perennial Graminoid Exotic Poa cusickii Cusick's bluegrass Poaceae Perennial Graminoid Native Poa fendleriana muttongrass Poaceae Perennial Graminoid Native Poa glauca glaucous bluegrass Poaceae Perennial Graminoid Native Poa howellii Howell's bluegrass Poaceae Annual Graminoid Native Poa interior inland bluegrass Poaceae Perennial Graminoid Native Poa nemoralis wood bluegrass Poaceae Perennial Graminoid Exotic Poa nervosa Wheeler bluegrass Poaceae Perennial Graminoid Native Poa palustris fowl bluegrass Poaceae Perennial Graminoid Native Poa paucispicula Alaska bluegrass Poaceae Perennial Graminoid Native Poa pratensis Kentucky bluegrass Poaceae Perennial Graminoid Exotic Poa secunda Sandberg bluegrass Poaceae Perennial Graminoid Native Poa stenantha northern bluegrass Poaceae Perennial Graminoid Native   113  Scientific Name Common Name Family Duration Growth Form BC Status Poa wheeleri Wheeler's bluegrass Poaceae Perennial Graminoid Native Podagrostis humilis alpine bentgrass Poaceae Perennial Graminoid Native Polemonium californicum moving polemonium Polemoniaceae Perennial Forb Native Polemonium elegans elegant Jacob's-ladder Polemoniaceae Perennial Forb Native Polemonium micranthum annual polemonium Polemoniaceae Annual Forb Native Polemonium occidentale western polemonium Polemoniaceae Perennial Forb Native Polemonium pulcherrimum Jacob's-ladder Polemoniaceae Perennial Forb Native Polemonium viscosum sticky polemonium Polemoniaceae Perennial Forb Native Polygonum achoreum leathery knotweed Polygonaceae Annual Forb Native Polygonum aviculare prostrate knotweed Polygonaceae Annual Forb Exotic Polygonum douglasii Douglas' knotweed Polygonaceae Annual Forb Native Polygonum majus large knotweed Polygonaceae Annual Forb Native Polygonum minimum broadleaf knotweed Polygonaceae Annual Forb Native Polygonum polygaloides milkwort knotweed Polygonaceae Annual Forb Native Polygonum ramosissimum bushy knotweed Polygonaceae Annual Forb Native Polygonum sawatchense Johnston's knotweed Polygonaceae Annual Forb Native Polygonum spergulariiforme scatter knotweed Polygonaceae Annual Forb Native Polypodium glycyrrhiza licorice fern Polypodiaceae Perennial Forb Native Polypodium hesperium western polypody Polypodiaceae Perennial Forb Native Polystichum andersonii Anderson's hollyfern Dryopteridaceae Perennial Forb Native Polystichum imbricans narrowleaf swordfern Dryopteridaceae Perennial Forb Native Polystichum kruckebergii Kruckeberg's hollyfern Dryopteridaceae Perennial Forb Native Polystichum lemmonii Lemmon's hollyfern Dryopteridaceae Perennial Forb Native Polystichum lonchitis northern hollyfern Dryopteridaceae Perennial Forb Native Polystichum munitum western swordfern Dryopteridaceae Perennial Forb Native Polystichum scopulinum mountain hollyfern Dryopteridaceae Perennial Forb Native Populus alba white poplar Salicaceae Perennial Tree Native Populus balsamifera balsam poplar Salicaceae Perennial Tree Native Populus deltoides eastern cottonwood Salicaceae Perennial Tree Exotic Populus nigra Lombardy poplar Salicaceae Perennial Tree Exotic Populus tremuloides quaking aspen Salicaceae Perennial Tree Native Populus trichocarpa black cottonwood Salicaceae Perennial Tree Native Portulaca oleracea little hogweed Portulacaceae Annual Forb Exotic Potentilla argentea silver cinquefoil Rosaceae Perennial Forb Exotic Potentilla biennis biennial cinquefoil Rosaceae Annual Forb Native Potentilla diversifolia varileaf cinquefoil Rosaceae Perennial Forb Native Potentilla drummondii Drummond's cinquefoil Rosaceae Perennial Forb Native Potentilla flabellifolia high mountain cinquefoil Rosaceae Perennial Forb Native Potentilla gracilis slender cinquefoil Rosaceae Perennial Subshrub Native   114  Scientific Name Common Name Family Duration Growth Form BC Status Potentilla hippiana woolly cinquefoil Rosaceae Perennial Forb Native Potentilla hookeriana Hooker's cinquefoil Rosaceae Perennial Forb Native Potentilla nivea snow cinquefoil Rosaceae Perennial Subshrub Native Potentilla norvegica Norwegian cinquefoil Rosaceae Annual Forb Native Potentilla pectinisecta combleaf cinquefoil Rosaceae Perennial Subshrub Native Potentilla pensylvanica Pennsylvania cinquefoil Rosaceae Perennial Forb Native Potentilla pulcherrima beautiful cinquefoil Rosaceae Perennial Forb Native Potentilla recta sulphur cinquefoil Rosaceae Perennial Forb Exotic Potentilla rivalis brook cinquefoil Rosaceae Annual Forb Native Potentilla uniflora oneflower cinquefoil Rosaceae Perennial Forb Native Prunella vulgaris common selfheal Lamiaceae Perennial Forb Exotic Prunus emarginata bitter cherry Rosaceae Perennial Tree Native Prunus pensylvanica pin cherry Rosaceae Perennial Tree Native Prunus virginiana chokecherry Rosaceae Perennial Tree Native Psathyrostachys juncea Russian wildrye Poaceae Perennial Graminoid Exotic Pseudognaphalium canescens Wright's cudweed Asteraceae Annual Forb Native Pseudoroegneria spicata bluebunch wheatgrass Poaceae Perennial Graminoid Native Pseudostellaria jamesiana tuber starwort Caryophyllaceae Perennial Forb Native Pseudotsuga menziesii Douglas-fir Pinaceae Perennial Tree Native Psilocarphus brevissimus short woollyheads Asteraceae Annual Forb Native Psilocarphus elatior meadow woollyheads Asteraceae Annual Forb Native Pteridium aquilinum western brackenfern Dennstaedtiaceae Perennial Forb Native Pterospora andromedea woodland pinedrops Monotropaceae Perennial Forb Native Pteryxia terebinthina turpentine wavewing Apiaceae Perennial Forb Native Purshia tridentata antelope bitterbrush Rosaceae Perennial Shrub Native Pyrola asarifolia liverleaf wintergreen Pyrolaceae Perennial Subshrub Native Pyrola chlorantha greenflowered wintergreen Pyrolaceae Perennial Subshrub Native Pyrola elliptica waxflower shinleaf Pyrolaceae Perennial Subshrub Native Pyrola picta whiteveined wintergreen Pyrolaceae Perennial Subshrub Native Pyrrocoma carthamoides largeflower goldenweed Asteraceae Perennial Forb Native Pyrrocoma hirta tacky goldenweed Asteraceae Perennial Forb Native Quercus garryana Oregon white oak Fagaceae Perennial Tree Native Quercus imbricaria shingle oak Fagaceae Perennial Tree Native Ranunculus abortivus littleleaf buttercup Ranunculaceae Biennial Forb Native Ranunculus acris tall buttercup Ranunculaceae Perennial Forb Exotic Ranunculus eschscholtzii Eschscholtz's buttercup Ranunculaceae Perennial Forb Native Ranunculus glaberrimus sagebrush buttercup Ranunculaceae Perennial Forb Native Ranunculus gmelinii Gmelin's buttercup Ranunculaceae Perennial Forb Native Ranunculus inamoenus graceful buttercup Ranunculaceae Perennial Forb Native   115  Scientific Name Common Name Family Duration Growth Form BC Status Ranunculus occidentalis western buttercup Ranunculaceae Perennial Forb Native Ranunculus orthorhynchus straightbeak buttercup Ranunculaceae Perennial Forb Native Ranunculus pedatifidus surefoot buttercup Ranunculaceae Perennial Forb Native Ranunculus pygmaeus pygmy buttercup Ranunculaceae Perennial Forb Native Ranunculus repens creeping buttercup Ranunculaceae Perennial Forb Exotic Ranunculus uncinatus woodland buttercup Ranunculaceae Annual Forb Native Raphanus raphanistrum wild radish Brassicaceae Annual Forb Exotic Reseda lutea yellow mignonette Resedaceae Biennial Forb Exotic Rhinanthus minor little yellow rattle Scrophulariaceae Annual Forb Native Rhodiola integrifolia ledge stonecrop Crassulaceae Perennial Forb Native Rhododendron albiflorum Cascade azalea Ericaceae Perennial Shrub Native Rhododendron macrophyllum Pacific rhododendron Ericaceae Perennial Shrub Native Rhus glabra smooth sumac Anacardiaceae Perennial Tree Native Rhus trilobata skunkbush sumac Anacardiaceae Perennial Shrub Native Ribes acerifolium mapleleaf currant Grossulariaceae Perennial Shrub Native Ribes aureum golden currant Grossulariaceae Perennial Shrub Exotic Ribes bracteosum stink currant Grossulariaceae Perennial Shrub Native Ribes cereum wax currant Grossulariaceae Perennial Shrub Native Ribes divaricatum spreading gooseberry Grossulariaceae Perennial Shrub Native Ribes inerme whitestem gooseberry Grossulariaceae Perennial Shrub Native Ribes lacustre prickly currant Grossulariaceae Perennial Shrub Native Ribes laxiflorum trailing black currant Grossulariaceae Perennial Forb Native Ribes oxyacanthoides Canadian gooseberry Grossulariaceae Perennial Shrub Native Ribes rubrum cultivated currant Grossulariaceae Perennial Shrub Exotic Ribes sanguineum redflower currant Grossulariaceae Perennial Shrub Native Ribes viscosissimum sticky currant Grossulariaceae Perennial Shrub Native Robinia pseudoacacia black locust Fabaceae Perennial Tree Exotic Romanzoffia sitchensis Sitka mistmaiden Hydrophyllaceae Perennial Forb Native Rorippa tenerrima Modoc yellowcress Brassicaceae Annual Forb Native Rorippa teres southern marsh yellowcress Brassicaceae Annual Forb Native Rosa acicularis prickly rose Rosaceae Perennial Subshrub Native Rosa canina dog rose Rosaceae Perennial Forb Exotic Rosa gymnocarpa dwarf rose Rosaceae Perennial Subshrub Native Rosa nutkana Nootka rose Rosaceae Perennial Subshrub Native Rosa rubiginosa sweetbriar rose Rosaceae Perennial Subshrub Exotic Rosa woodsii Woods' rose Rosaceae Perennial Subshrub Native Rostraria cristata Mediterranean hairgrass Poaceae Annual Graminoid Native Rubus arcticus arctic raspberry Rosaceae Perennial Subshrub Native Rubus idaeus American red raspberry Rosaceae Perennial Subshrub Native   116  Scientific Name Common Name Family Duration Growth Form BC Status Rubus lasiococcus roughfruit berry Rosaceae Perennial Forb Native Rubus occidentalis black raspberry Rosaceae Perennial Subshrub Native Rubus parviflorus thimbleberry Rosaceae Perennial Subshrub Native Rubus pubescens dwarf red blackberry Rosaceae Perennial Subshrub Native Rubus spectabilis salmonberry Rosaceae Perennial Forb Native Rubus ursinus California blackberry Rosaceae Perennial Subshrub Native Rudbeckia hirta blackeyed Susan Asteraceae Annual Forb Exotic Rudbeckia occidentalis western coneflower Asteraceae Perennial Forb Native Rumex acetosa garden sorrel Polygonaceae Perennial Forb Exotic Rumex acetosella common sheep sorrel Polygonaceae Perennial Forb Exotic Rumex crispus curly dock Polygonaceae Perennial Forb Exotic Rumex obtusifolius bitter dock Polygonaceae Perennial Forb Exotic Rumex patientia patience dock Polygonaceae Perennial Forb Exotic Rumex paucifolius alpine sheep sorrel Polygonaceae Perennial Forb Native Rumex salicifolius willow dock Polygonaceae Perennial Forb Native Rumex stenophyllus narrowleaf dock Polygonaceae Perennial Forb Exotic Rumex triangulivalvis Mexican dock Polygonaceae Perennial Forb Native Rumex venosus veiny dock Polygonaceae Perennial Forb Native Sagina saginoides arctic pearlwort Caryophyllaceae Biennial Forb Native Salix amygdaloides peachleaf willow Salicaceae Perennial Tree Native Salix arctica arctic willow Salicaceae Perennial Shrub Native Salix barrattiana Barratt's willow Salicaceae Perennial Shrub Native Salix bebbiana Bebb willow Salicaceae Perennial Tree Native Salix brachycarpa shortfruit willow Salicaceae Perennial Shrub Native Salix cascadensis cascade willow Salicaceae Perennial Shrub Native Salix drummondiana Drummond's willow Salicaceae Perennial Shrub Native Salix eriocephala Missouri River willow Salicaceae Perennial Tree Native Salix exigua narrowleaf willow Salicaceae Perennial Tree Native Salix glauca grayleaf willow Salicaceae Perennial Tree Native Salix hookeriana dune willow Salicaceae Perennial Tree Native Salix lasiandra Pacific willow Salicaceae Perennial Shrub Native Salix lasiolepis arroyo willow Salicaceae Perennial Tree Native Salix lucida shining willow Salicaceae Perennial Shrub Native Salix myrtillifolia blueberry willow Salicaceae Perennial Tree Native Salix nivalis snow willow Salicaceae Perennial Subshrub Native Salix pseudomonticola false mountain willow Salicaceae Perennial Shrub Native Salix pseudomyrsinites firmleaf willow Salicaceae Perennial Shrub Native Salix scouleriana Scouler's willow Salicaceae Perennial Tree Native Salix sitchensis Sitka willow Salicaceae Perennial Tree Native   117  Scientific Name Common Name Family Duration Growth Form BC Status Salix tweedyi Tweedy's willow Salicaceae Perennial Shrub Native Salsola kali Russian thistle Chenopodiaceae Annual Forb Native Salvia dorrii purple sage Lamiaceae Perennial Subshrub Native Sambucus racemosa red elderberry Caprifoliaceae Perennial Tree Native Sanguisorba annua prairie burnet Rosaceae Annual Forb Native Sanguisorba canadensis Canadian burnet Rosaceae Perennial Forb Native Sanguisorba minor small burnet Rosaceae Perennial Forb Native Sanguisorba officinalis great burnet Rosaceae Perennial Forb Native Sanicula graveolens northern sanicle Apiaceae Perennial Forb Native Sanicula marilandica Maryland sanicle Apiaceae Perennial Forb Native Saponaria officinalis bouncingbet Caryophyllaceae Perennial Forb Exotic Saxifraga adscendens wedgeleaf saxifrage Saxifragaceae Perennial Forb Native Saxifraga arguta brook saxifrage Saxifragaceae Perennial Forb Native Saxifraga bronchialis yellowdot saxifrage Saxifragaceae Perennial Forb Native Saxifraga cernua nodding saxifrage Saxifragaceae Perennial Forb Native Saxifraga lyallii redstem saxifrage Saxifragaceae Perennial Forb Native Saxifraga nelsoniana heartleaf saxifrage Saxifragaceae Perennial Forb Native Saxifraga odontoloma brook saxifrage Saxifragaceae Perennial Forb Native Saxifraga oppositifolia purple mountain saxifrage Saxifragaceae Perennial Forb Native Saxifraga tolmiei Tolmie's saxifrage Saxifragaceae Perennial Forb Native Saxifraga tricuspidata three toothed saxifrage Saxifragaceae Perennial Forb Native Scirpus acutus hardstem bulrush Cyperaceae Perennial Graminoid Native Scleranthus annuus German knotgrass Caryophyllaceae Annual Forb Exotic Sclerochloa dura common hardgrass Poaceae Annual Graminoid Exotic Scrophularia lanceolata lanceleaf figwort Scrophulariaceae Perennial Forb Native Scutellaria angustifolia narrowleaf skullcap Lamiaceae Perennial Forb Native Secale cereale cereal rye Poaceae Annual Graminoid Exotic Sedum debile orpine stonecrop Crassulaceae Perennial Forb Native Sedum divergens Pacific stonecrop Crassulaceae Perennial Forb Native Sedum lanceolatum spearleaf stonecrop Crassulaceae Perennial Forb Native Sedum oreganum Oregon stonecrop Crassulaceae Perennial Forb Native Sedum rosea roseroot stonecrop Crassulaceae Perennial Forb Native Sedum stenopetalum wormleaf stonecrop Crassulaceae Perennial Forb Native Selaginella densa lesser spikemoss Selaginellaceae Perennial Forb Native Selaginella scopulorum Rocky Mountain spikemoss Selaginellaceae Perennial Forb Native Selaginella wallacei Wallace's spikemoss Selaginellaceae Perennial Forb Native Senecio elmeri Elmer's ragwort Asteraceae Perennial Forb Native Senecio fremontii dwarf mountain ragwort Asteraceae Perennial Forb Native Senecio hydrophiloides tall groundsel Asteraceae Biennial Forb Native   118  Scientific Name Common Name Family Duration Growth Form BC Status Senecio integerrimus lambstongue ragwort Asteraceae Biennial Forb Native Senecio lugens small blacktip ragwort Asteraceae Perennial Forb Native Senecio sylvaticus woodland ragwort Asteraceae Annual Forb Exotic Senecio vulgaris old-man-in-the-Spring Asteraceae Annual Forb Exotic Setaria verticillata hooked bristlegrass Poaceae Annual Graminoid Exotic Setaria viridis green bristlegrass Poaceae Annual Graminoid Exotic Shepherdia argentea silver buffaloberry Elaeagnaceae Perennial Tree Exotic Shepherdia canadensis russet buffaloberry Elaeagnaceae Perennial Shrub Native Sibbaldia procumbens creeping sibbaldia Rosaceae Perennial Subshrub Native Silene acaulis moss campion Caryophyllaceae Perennial Forb Native Silene antirrhina sleepy silene Caryophyllaceae Annual Forb Native Silene csereii Balkan catchfly Caryophyllaceae Biennial Forb Exotic Silene douglasii Douglas's catchfly Caryophyllaceae Perennial Forb Native Silene drummondii Drummond's campion Caryophyllaceae Perennial Forb Native Silene latifolia bladder campion Caryophyllaceae Biennial Forb Exotic Silene menziesii Menzies' campion Caryophyllaceae Perennial Forb Native Silene noctiflora nightflowering silene Caryophyllaceae Annual Forb Exotic Silene oregana Oregon silene Caryophyllaceae Perennial Forb Native Silene parryi Parry's silene Caryophyllaceae Perennial Forb Native Silene repens pink campion Caryophyllaceae Perennial Forb Native Silene scouleri simple campion Caryophyllaceae Perennial Forb Native Silene suksdorfii Suksdorf's silene Caryophyllaceae Perennial Forb Native Silene vulgaris maidenstears Caryophyllaceae Perennial Forb Exotic Sinapis alba white mustard Brassicaceae Annual Forb Exotic Sinapis arvensis charlock mustard Brassicaceae Annual Forb Exotic Sisymbrium altissimum tall tumblemustard Brassicaceae Annual Forb Exotic Sisymbrium loeselii small tumbleweed mustard Brassicaceae Annual Forb Exotic Sisymbrium officinale hedgemustard Brassicaceae Annual Forb Exotic Sisyrinchium angustifolium narrowleaf blue-eyed grass Iridaceae Perennial Forb Native Sisyrinchium montanum strict blue-eyed grass Iridaceae Perennial Forb Native Smelowskia calycina alpine smelowskia Brassicaceae Perennial Forb Native Smelowskia ovalis alpine false candytuft Brassicaceae Perennial Forb Native Solanum americanum American black nightshade Solanaceae Annual Subshrub Exotic Solanum dulcamara climbing nightshade Solanaceae Perennial Subshrub Exotic Solanum melongena eggplant Solanaceae Perennial Subshrub Native Solanum physalifolium hoe nightshade Solanaceae Annual Forb Exotic Solanum rostratum buffalobur nightshade Solanaceae Annual Forb Exotic Solanum triflorum cutleaf nightshade Solanaceae Annual Forb Exotic Solidago altissima Canada goldenrod Asteraceae Perennial Forb Exotic   119  Scientific Name Common Name Family Duration Growth Form BC Status Solidago canadensis Canada goldenrod Asteraceae Perennial Forb Exotic Solidago elongata rough Canada goldenrod Asteraceae Perennial Forb Native Solidago gigantea giant goldenrod Asteraceae Perennial Forb Native Solidago missouriensis Missouri goldenrod Asteraceae Perennial Forb Native Solidago multiradiata Rocky Mountain goldenrod Asteraceae Perennial Subshrub Native Solidago simplex Mt. Albert goldenrod Asteraceae Perennial Subshrub Native Sonchus arvensis field sowthistle Asteraceae Perennial Forb Exotic Sonchus asper spiny sowthistle Asteraceae Annual Forb Exotic Sorbus aucuparia European mountain ash Rosaceae Perennial Tree Exotic Sorbus scopulina Greene's mountain ash Rosaceae Perennial Tree Native Sorbus sitchensis western mountain ash Rosaceae Perennial Tree Native Sorghum bicolor sorghum Poaceae Annual Graminoid Exotic Spartina gracilis alkali cordgrass Poaceae Perennial Graminoid Native Spergula arvensis corn spurry Caryophyllaceae Annual Forb Exotic Spergularia rubra red sandspurry Caryophyllaceae Annual Forb Exotic Sphaeralcea munroana Munro's globemallow Malvaceae Perennial Subshrub Native Sphenopholis intermedia slender wedgescale Poaceae Perennial Graminoid Native Sphenopholis obtusata prairie wedgescale Poaceae Annual Graminoid Native Spiraea betulifolia white spirea Rosaceae Perennial Shrub Native Spiraea densiflora rose meadowsweet Rosaceae Perennial Shrub Native Spiraea douglasii rose spirea Rosaceae Perennial Shrub Native Sporobolus airoides alkali sacaton Poaceae Perennial Graminoid Native Sporobolus compositus composite dropseed Poaceae Perennial Graminoid Native Sporobolus cryptandrus sand dropseed Poaceae Perennial Graminoid Native Sporobolus neglectus puffsheath dropseed Poaceae Annual Graminoid Native Sporobolus vaginiflorus poverty dropseed Poaceae Annual Graminoid Exotic Stachys pilosa hairy hedgenettle Lamiaceae Perennial Forb Native Stellaria calycantha northern starwort Caryophyllaceae Annual Forb Native Stellaria crispa curled starwort Caryophyllaceae Perennial Forb Native Stellaria graminea grass-like starwort Caryophyllaceae Perennial Forb Exotic Stellaria longifolia longleaf starwort Caryophyllaceae Perennial Forb Native Stellaria media common chickweed Caryophyllaceae Annual Forb Exotic Stellaria nitens shiny chickweed Caryophyllaceae Annual Forb Native Stellaria obtusa Rocky Mountain chickweed Caryophyllaceae Perennial Forb Native Stellaria umbellata umbrella starwort Caryophyllaceae Perennial Forb Native Stenanthium occidentale western featherbells Liliaceae Perennial Forb Native Suaeda calceoliformis Pursh seepweed Chenopodiaceae Annual Forb Native Symphoricarpos albus common snowberry Caprifoliaceae Perennial Subshrub Native Symphoricarpos mollis creeping snowberry Caprifoliaceae Perennial Subshrub Native   120  Scientific Name Common Name Family Duration Growth Form BC Status Symphoricarpos occidentalis western snowberry Caprifoliaceae Perennial Shrub Native Symphoricarpos oreophilus mountain snowberry Caprifoliaceae Perennial Shrub Native Symphyotrichum ascendens western aster Asteraceae Perennial Forb Native Symphyotrichum campestre western meadow aster Asteraceae Perennial Forb Native Symphyotrichum ciliatum rayless alkali aster Asteraceae Annual Forb Native Symphyotrichum ciliolatum Lindley's aster Asteraceae Perennial Forb Native Symphyotrichum eatonii Eaton's aster Asteraceae Perennial Forb Native Symphyotrichum ericoides white heath aster Asteraceae Perennial Forb Native Symphyotrichum falcatum white prairie aster Asteraceae Perennial Forb Native Symphyotrichum foliaceum alpine leafybract aster Asteraceae Perennial Forb Native Symphyotrichum frondosum short-rayed alkali aster Asteraceae Annual Forb Native Symphyotrichum laeve smooth blue aster Asteraceae Perennial Forb Native Symphyotrichum spathulatum western mountain aster Asteraceae Perennial Forb Native Symphyotrichum subspicatum Douglas aster Asteraceae Perennial Forb Native Symphytum asperum prickly comfrey Boraginaceae Perennial Forb Exotic Symphytum officinale common comfrey Boraginaceae Perennial Forb Exotic Syringa vulgaris common lilac Oleaceae Perennial Shrub Exotic Taeniatherum caput-medusae medusahead Poaceae Annual Graminoid Native Tamarix parviflora smallflower tamarisk Tamaricaceae Perennial Shrub Native Tamarix ramosissima saltcedar Tamaricaceae Perennial Tree Native Tanacetum vulgare common tansy Asteraceae Perennial Forb Exotic Taraxacum erythrospermum rock dandelion Asteraceae Perennial Forb Exotic Taraxacum officinale common dandelion Asteraceae Perennial Forb Exotic Taxus brevifolia Pacific yew Taxaceae Perennial Tree Native Teesdalia nudicaulis barestem teesdalia Brassicaceae Annual Forb Exotic Tellima grandiflora bigflower tellima Saxifragaceae Perennial Forb Native Tetradymia canescens spineless horsebrush Asteraceae Perennial Subshrub Native Teucrium canadense Canada germander Lamiaceae Perennial Forb Native Thalictrum occidentale western meadow-rue Ranunculaceae Perennial Forb Native Thalictrum venulosum veiny meadow-rue Ranunculaceae Perennial Forb Native Thelypodium integrifolium entireleaved thelypody Brassicaceae Biennial Forb Native Thelypodium laciniatum cutleaf thelypody Brassicaceae Biennial Forb Native Thelypodium milleflorum manyflower thelypody Brassicaceae Biennial Forb Native Thelypteris quelpaertensis queen's-veil maiden fern Thelypteridaceae Perennial Forb Native Thinopyrum intermedium intermediate wheatgrass Poaceae Perennial Graminoid Exotic Thinopyrum ponticum tall wheatgrass Poaceae Perennial Graminoid Exotic Thlaspi arvense field pennycress Brassicaceae Annual Forb Exotic Thuja plicata western redcedar Cupressaceae Perennial Tree Native Thymus praecox mother of thyme Lamiaceae Perennial Subshrub Native   121  Scientific Name Common Name Family Duration Growth Form BC Status Thysanocarpus curvipes sand fringepod Brassicaceae Annual Forb Native Tiarella trifoliata threeleaf foamflower Saxifragaceae Perennial Forb Native Tiarella unifoliata oneleaf foamflower Saxifragaceae Perennial Forb Native Tofieldia pusilla Scotch false asphodel Liliaceae Perennial Forb Native Tolmiea menziesii youth on age Saxifragaceae Perennial Forb Native Torreyochloa pallida pale false mannagrass Poaceae Perennial Graminoid Native Toxicodendron radicans eastern poison ivy Anacardiaceae Perennial Shrub Native Toxicodendron rydbergii western poison ivy Anacardiaceae Perennial Shrub Native Tragopogon dubius yellow salsify Asteraceae Annual Forb Exotic Tragopogon porrifolius salsify Asteraceae Biennial Forb Exotic Trautvetteria caroliniensis Carolina bugbane Ranunculaceae Perennial Forb Native Triantha occidentalis western false asphodel Liliaceae Perennial Forb Native Tribulus terrestris puncturevine Zygophyllaceae Annual Forb Exotic Trichostema oblongum oblong bluecurls Lamiaceae Annual Forb Native Trientalis latifolia broadleaf starflower Primulaceae Perennial Forb Native Trifolium aureum golden clover Fabaceae Annual Forb Exotic Trifolium cyathiferum cup clover Fabaceae Annual Forb Native Trifolium dubium suckling clover Fabaceae Annual Forb Exotic Trifolium fragiferum strawberry clover Fabaceae Perennial Forb Exotic Trifolium hybridum alsike clover Fabaceae Annual Forb Exotic Trifolium longipes longstalk clover Fabaceae Perennial Forb Native Trifolium microcephalum smallhead clover Fabaceae Annual Forb Native Trifolium wormskioldii cows clover Fabaceae Annual Forb Native Trillium ovatum Pacific trillium Liliaceae Perennial Forb Native Trillium petiolatum Idaho trillium Liliaceae Perennial Forb Native Triodanis perfoliata clasping Venus' looking-glass Campanulaceae Annual Forb Native Tripleurospermum inodorum scentless false mayweed Asteraceae Annual Forb Exotic Trisetum cernuum tall trisetum Poaceae Perennial Graminoid Native Trisetum spicatum spike trisetum Poaceae Perennial Graminoid Native Triteleia grandiflora largeflower triteleia Liliaceae Perennial Forb Native Triteleia hyacinthina white brodiaea Liliaceae Perennial Forb Native Triticum aestivum common wheat Poaceae Annual Graminoid Exotic Trollius albiflorus American globeflower Ranunculaceae Perennial Forb Native Tropaeolum majus nasturtium Tropaeolaceae Annual Forb Native Tsuga heterophylla western hemlock Pinaceae Perennial Tree Native Tsuga mertensiana mountain hemlock Pinaceae Perennial Tree Native Turritis glabra tower rockcress Brassicaceae Annual Forb Native Ulmus pumila Siberian elm Ulmaceae Perennial Tree Exotic Uropappus lindleyi Lindley's silverpuffs Asteraceae Annual Forb Native   122  Scientific Name Common Name Family Duration Growth Form BC Status Urtica dioica stinging nettle Urticaceae Perennial Forb Exotic Vaccaria hispanica cow soapwort Caryophyllaceae Annual Forb Exotic Vaccinium caespitosum dwarf bilberry Ericaceae Perennial Shrub Native Vaccinium deliciosum Cascade bilberry Ericaceae Perennial Shrub Native Vaccinium membranaceum thinleaf huckleberry Ericaceae Perennial Shrub Native Vaccinium myrtilloides velvetleaf huckleberry Ericaceae Perennial Subshrub Native Vaccinium myrtillus whortleberry Ericaceae Perennial Shrub Native Vaccinium ovalifolium oval-leaf blueberry Ericaceae Perennial Shrub Native Vaccinium parvifolium red huckleberry Ericaceae Perennial Shrub Native Vaccinium scoparium grouse whortleberry Ericaceae Perennial Subshrub Native Vahlodea atropurpurea mountain hairgrass Poaceae Perennial Graminoid Native Valeriana dioica marsh valerian Valerianaceae Perennial Forb Native Valeriana edulis tobacco root Valerianaceae Perennial Forb Native Valeriana officinalis garden valerian Valerianaceae Perennial Forb Exotic Valeriana scouleri Scouler's valerian Valerianaceae Perennial Forb Native Valeriana sitchensis Sitka valerian Valerianaceae Perennial Forb Native Valerianella locusta Lewiston cornsalad Valerianaceae Annual Forb Exotic Ventenata dubia North Africa grass Poaceae Annual Graminoid Exotic Veratrum viride green false hellebore Liliaceae Perennial Forb Native Verbascum blattaria moth mullein Scrophulariaceae Biennial Forb Exotic Verbascum thapsus common mullein Scrophulariaceae Biennial Forb Exotic Verbena bracteata bigbract verbena Verbenaceae Annual Forb Native Verbena hastata swamp verbena Verbenaceae Biennial Forb Native Verbena officinalis herb of the cross Verbenaceae Annual Forb Native Verbena stricta hoary verbena Verbenaceae Annual Forb Native Veronica arvensis corn speedwell Scrophulariaceae Annual Forb Exotic Veronica biloba twolobe speedwell Scrophulariaceae Annual Forb Exotic Veronica cusickii Cusick's speedwell Scrophulariaceae Perennial Forb Native Veronica officinalis common gypsyweed Scrophulariaceae Perennial Forb Exotic Veronica peregrina neckweed Scrophulariaceae Annual Forb Exotic Veronica persica birdeye speedwell Scrophulariaceae Annual Forb Exotic Veronica verna spring speedwell Scrophulariaceae Annual Forb Exotic Veronica wormskjoldii American alpine speedwell Scrophulariaceae Perennial Forb Native Viburnum edule squashberry Caprifoliaceae Perennial Shrub Native Viburnum lentago nannyberry Caprifoliaceae Perennial Tree Native Viburnum opulus European cranberrybush Caprifoliaceae Perennial Tree Exotic Vicia americana American vetch Fabaceae Perennial Forb Native Vicia cracca bird vetch Fabaceae Perennial Forb Exotic Vicia villosa winter vetch Fabaceae Annual Forb Exotic   123  Scientific Name Common Name Family Duration Growth Form BC Status Viola adunca hookedspur violet Violaceae Perennial Forb Native Viola arvensis European field pansy Violaceae Annual Forb Exotic Viola canadensis Canadian white violet Violaceae Perennial Forb Native Viola glabella pioneer violet Violaceae Perennial Forb Native Viola labradorica alpine violet Violaceae Perennial Forb Native Viola nephrophylla northern bog violet Violaceae Perennial Forb Native Viola nuttallii Nuttall's violet Violaceae Perennial Forb Native Viola praemorsa canary violet Violaceae Perennial Forb Native Viola purpurea goosefoot violet Violaceae Perennial Forb Native Viola selkirkii Selkirk's violet Violaceae Perennial Forb Native Viola sempervirens evergreen violet Violaceae Perennial Forb Native Viola septentrionalis northern woodland violet Violaceae Annual Forb Native Viola trinervata Rainier violet Violaceae Perennial Forb Native Viola vallicola sagebrush violet Violaceae Perennial Forb Native Vitis vinifera wine grape Vitaceae Perennial Forb Native Vulpia bromoides brome fescue Poaceae Annual Graminoid Exotic Vulpia microstachys small fescue Poaceae Annual Graminoid Native Vulpia myuros annual fescue Poaceae Annual Graminoid Exotic Vulpia octoflora sixweeks fescue Poaceae Annual Graminoid Native Woodsia oregana Oregon cliff fern Dryopteridaceae Perennial Forb Native Woodsia scopulina Rocky Mountain woodsia Dryopteridaceae Perennial Forb Native Xanthium spinosum spiny cocklebur Asteraceae Annual Forb Native Xanthium strumarium rough cocklebur Asteraceae Annual Forb Exotic Xerophyllum tenax common beargrass Liliaceae Perennial Forb Native Zeltnera exaltata desert centaury Gentianaceae Annual Forb Native Zeltnera muehlenbergii Muhlenberg's centaury Gentianaceae Annual Forb Native Zigadenus elegans mountain deathcamas Liliaceae Perennial Forb Native Zigadenus paniculatus foothill deathcamas Liliaceae Perennial Forb Native Zigadenus venenosus meadow deathcamas Liliaceae Perennial Forb Native         124  A2. Spearman correlation coefficient matrix for current environmental variables and current diversity metrics Predictor Variables for Current Climate Species Richness PD PDses FRic FRicses Annual Mean Temperature (BIO 1) 0.51 0.72 0.77 0.64 0.55 Mean Diurnal Range (BIO 2) 0.48 0.48 0.21 0.32 0.18 Isothermality (BIO 3) 0.38 0.26 -0.17 0.06 -0.10 Temperature Seasonality (BIO 4) 0.39 0.55 0.59 0.48 0.42 Max Temperature of Warmest Month (BIO 5) 0.55 0.72 0.70 0.62 0.51 Min Temperature of Coldest Month (BIO 6) 0.53 0.73 0.76 0.64 0.53 Temperature Annual Range (BIO 7) 0.41 0.51 0.44 0.42 0.35 Mean Temperature of Wettest Quarter (BIO 8) -0.31 -0.14 0.37 -0.05 0.13 Mean Temperature of Driest Quarter (BIO 9) 0.78 0.73 0.18 0.49 0.17 Mean Temperature of Warmest Quarter (BIO 10) 0.51 0.71 0.76 0.63 0.54 Mean Temperature of Coldest Quarter (BIO 11) 0.52 0.72 0.78 0.66 0.55 Annual Precipitation (BIO 12) 0.16 0.06 -0.21 0.07 -0.07 Precipitation of Wettest Month (BIO 13) 0.34 0.22 -0.18 0.16 -0.05 Precipitation of Driest Month (BIO 14) -0.07 -0.18 -0.35 -0.11 -0.17 Precipitation Seasonality (BIO 15) 0.66 0.56 0.03 0.36 0.06 Precipitation of Wettest Quarter (BIO 16) 0.34 0.21 -0.18 0.16 -0.05 Precipitation of Driest Quarter (BIO 17) -0.10 -0.22 -0.37 -0.11 -0.16 Precipitation of Warmest Quarter (BIO 18) -0.53 -0.53 -0.21 -0.27 -0.11 Precipitation of Coldest Quarter (BIO 19) 0.32 0.19 -0.19 0.14 -0.06 Elevation -0.44 -0.67 -0.81 -0.65 -0.59 Aspect -0.04 -0.02 0.05 -0.02 -0.01 Slope -0.10 -0.07 0.05 0.12 0.17       125  A3. Spearman correlation coefficient matrix for future environmental variables and future diversity metrics Predictor Variables for Future Climate Species Richness PD PDses FRic FRicses Annual Mean Temperature (BIO 1) -0.48 -0.25 0.61 0.01 0.18 Mean Diurnal Range (BIO 2) -0.03 -0.03 0.02 0.001 0.01 Isothermality (BIO 3) 0.13 0.06 -0.18 -0.009 -0.04 Temperature Seasonality (BIO 4) -0.27 -0.16 0.31 0.01 0.09 Max Temperature of Warmest Month (BIO 5) -0.35 -0.18 0.47 0.01 0.13 Min Temperature of Coldest Month (BIO 6) -0.47 -0.23 0.66 -0.003 0.16 Temperature Annual Range (BIO 7) -0.15 -0.1 0.17 0.02 0.06 Mean Temperature of Wettest Quarter (BIO 8) -0.58 -0.51 0.27 -0.12 0.12 Mean Temperature of Driest Quarter (BIO 9) 0.25 0.34 0.24 0.12 0.04 Mean Temperature of Warmest Quarter (BIO 10) -0.43 -0.23 0.55 0.009 0.15 Mean Temperature of Coldest Quarter (BIO 11) -0.48 -0.24 0.65 0.003 0.17 Annual Precipitation (BIO 12) 0.45 0.48 0.04 0.16 0.01 Precipitation of Wettest Month (BIO 13) 0.52 0.56 0.09 0.22 0.05 Precipitation of Driest Month (BIO 14) 0.26 0.25 -0.05 0.08 -0.01 Precipitation Seasonality (BIO 15) 0.48 0.54 0.16 0.22 0.06 Precipitation of Wettest Quarter (BIO 16) 0.52 0.57 0.10 0.22 0.04 Precipitation of Driest Quarter (BIO 17) 0.22 0.21 -0.07 0.06 -0.01 Precipitation of Warmest Quarter (BIO 18) -0.06 -0.05 -0.007 -0.02 -0.001 Precipitation of Coldest Quarter (BIO 19) 0.50 0.53 0.07 0.20 0.04 Elevation 0.49 0.25 -0.66 -0.05 -0.22 Aspect 0.06 0.16 0.24 0.27 0.28 Slope -0.07 -0.06 0.04 -0.05 -0.02         126  A4. Summary of current and future environmental variables within the Okanagan Ecoregion Current Predictor Variables Mean SD Min. Max. Elevation 1148.88 556.71 -54.00 3114.00 Aspect 179.07 105.15 -1.00 359.98 Slope 7.99 5.64 0.00 34.74 BIO 1 (Annual Mean Temp °C) 4.22 3.21 -6.40 11.30 BIO 2 (Mean Diurnal Range °C) 10.59 1.46 6.00 14.90 BIO 3 (Isothermality) 0.33 0.03 0.27 0.44 BIO 4 (Temp Seasonality °C) 7.33 1.05 3.71 9.18 BIO 5 (Max Temp of Warmest Month °C) 22.11 3.91 9.20 31.80 BIO 6 (Min Temp of Coldest Month °C) -10.07 4.29 -19.90 2.20 BIO 7 (Temp Annual Range °C) 32.19 4.36 17.80 41.20 BIO 8 (Mean Temp of Wettest Quarter °C) -1.67 7.00 -13.80 19.90 BIO 9 (Mean Temp of Driest Quarter °C) 7.57 7.79 -11.60 21.50 BIO 10 (Mean Temp of Warmest Quarter °C) 13.64 3.27 2.60 22.10 BIO 11 (Mean Temp of Coldest Quarter °C) -5.03 3.64 -14.50 5.50 BIO 12 (Annual Precip mm) 761.13 459.77 199.00 3141.00 BIO 13 (Precip of Wettest Month mm) 106.06 76.53 31.00 533.00 BIO 14 (Precip of Driest Month mm) 32.12 12.67 6.00 66.00 BIO 15 (Precip Seasonality mm) 33.38 13.01 14.00 79.00 BIO 16 (Precip of Wettest Quarter mm) 297.74 221.56 82.00 1451.00 BIO 17 (Precip of Driest Quarter mm) 109.97 43.93 23.00 245.00 BIO 18 (Precip of Warmest Quarter mm) 132.95 43.38 30.00 266.00 BIO 19 (Precip of Coldest Quarter mm) 272.85 204.47 72.00 1392.00  Future Predictor Variables Mean SD Min. Max. Elevation 1148.88 556.71 -54.00 3114.00 Aspect 7.99 5.64 0.00 34.74 Slope 179.07 105.15 -1.00 359.98 BIO 1 (Annual Mean Temp °C) 8.36 3.09 -1.90 14.90 BIO 2 (Mean Diurnal Range °C) 9.56 1.53 5.20 14.70   127  Future Predictor Variables Mean SD Min. Max. BIO 3 (Isothermality) 31.02 2.98 24.00 42.00 BIO 4 (Temp Seasonality °C) 7.00 1.01 3.67 9.13 BIO 5 (Max Temp of Warmest Month °C) 26.47 4.13 13.70 37.00 BIO 6 (Min Temp of Coldest Month °C) -3.91 3.86 -12.90 7.10 BIO 7 (Temp Annual Range °C) 30.38 4.08 17.60 40.00 BIO 8 (Mean Temp of Wettest Quarter °C) 1.35 5.04 -9.00 21.90 BIO 9 (Mean Temp of Driest Quarter °C) 13.81 6.78 -5.70 26.20 BIO 10 (Mean Temp of Warmest Quarter °C)  17.57 3.37 6.40 26.50 BIO 11 (Mean Temp of Coldest Quarter °C) -0.25 3.39 -9.10 9.60 BIO 12 (Annual Precip mm) 817.30 479.84 204.00 3174.00 BIO 13 (Precip of Wettest Month mm) 120.28 84.56 33.00 570.00 BIO 14 (Precip of Driest Month mm) 32.77 13.17 6.00 67.00 BIO 15 (Precip Seasonality mm) 35.45 13.73 11.00 77.00 BIO 16 (Precip of Wettest Quarter mm) 320.37 230.17 83.00 1467.00 BIO 17 (Precip of Driest Quarter mm) 108.98 42.73 21.00 219.00 BIO 18 (Precip of Warmest Quarter mm) 123.50 42.28 26.00 223.00 BIO 19 (Precip of Coldest Quarter mm) 302.31 228.49 71.00 1467.00            128  A5. Summary of diversity measures from current and future climate projections for the Okanagan Ecoregion  Diversity Measure (Current Climate) Mean SD Min. Max. Species Richness 655.63 118.44 283.00 987.00 MNTD (Observed) 19.05 1.85 14.35 31.97 MNTD (SES) -0.81 0.93 -3.84 3.32 MPD (Observed) 322.55 7.70 297.02 360.11 MPD (SES) 0.76 1.19 -2.96 5.92 Faith's PD (Observed) 13384.56 1408.32 8801.53 17761.91 Faith's PD (SES) -1.33 1.16 -4.85 3.98 Fric (Observed) 129.05 36.38 40.10 223.22 Fric (SES) -0.75 1.01 -3.92 2.54 Trait-MNTD (Observed) 0.17 0.01 0.13 0.24 Trait-MNTD (SES) -1.78 1.47 -7.97 3.47 Trait-MPD (Observed) 1.82 0.07 1.53 2.09 Trait-MPD (SES) -2.59 0.96 -5.58 1.57  Diversity Measure (Future Climate) Mean SD Min. Max. Species Richness 717.63 117.66 284.00 1044.00 MNTD (Observed) 19.62 2.79 13.86 38.66 MNTD (SES) 0.24 1.15 -3.58 3.74 MPD (Observed) 319.62 8.80 295.36 353.47 MPD (SES) 0.50 1.61 -3.10 4.88 Faith's PD (Observed) 14751.23 1110.15 9031.76 17880.51 Faith's PD (SES) 0.23 1.14 -3.52 4.72 Fric (Observed) 135.72 35.24 47.13 219.10 Fric (SES) -1.05 1.20 -4.92 2.19 Trait-MNTD (Observed) 0.17 0.02 0.13 0.26 Trait-MNTD (SES) -0.85 1.32 -5.91 4.60 Trait-MPD (Observed) 1.87 0.06 1.63 2.08 Trait-MPD (SES) -2.12 1.37 -6.47 1.92    129  A6. Heatmaps and Hotspot Congruence Maps for Standardized Diversity Measures                               Figure A6.1 Maps heatmaps of a) current SR and FDses; b) current SR and PDses; c) current FDses and PDses; d) future SR and FDses; e) future SR and PDses; and f) future FDses and PDses, where red colours are high diversity areas and blue colours are low diversity areas.  a)  b)  c)  d)  e)  f)    130                                Figure A6.2 Maps showing the congruence between hotspots (top 5% of values) of a) current SR and FDses; b) current SR and PDses; c) current FDses and PDses; d) future SR and FDses; e) future SR and PDses; and f) future FDses and PDses.  a)  b)  c)  d)  e)  f)  

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