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Should I stay or should I go? : assessing range stasis versus range shifts of plants in the North Cascades Wilson, Rachel 2017

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SHOULD I STAY OR SHOULD I GO? ASSESSING RANGE STASIS VERSUS RANGE SHIFTS OF PLANTS IN THE NORTH CASCADES by  Rachel Wilson  B.Sc., Queen’s University, 2014  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Botany)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  January 2017  © Rachel Wilson, 2017 ii  Abstract  Under the pressure of anthropogenic climate change, species that are negatively impacted must rapidly respond or risk extirpation. The most immediate option for many species will be to track changing distributions of suitable habitat. Comparisons of contemporary data to historical baseline data indicate that climate change has already altered ranges and abundances of numerous species. Though general patterns are slowly emerging, there is still considerable variation in responses among species. Further, a number of species do not appear to be undergoing any change in their distributions or abundances, despite possible fitness costs of stasis. Given this variation, mechanisms underlying whether species shift or do not shift must be elucidated to allow for the creation of a predictive framework that can be extended to other systems. One way to achieve this end is to associate species functional traits with their magnitude of response. To detect elevational range shifts and changes in abundance of plant species, I and a team of surveyors resurveyed historical vegetation plots in North Cascades National Park. Since the original 1983 survey, the area has warmed by approximately 0.8  C. I then tested whether variation in range shifts among species could be associated with functional traits. Overall, most species exhibited range stasis. Of the species that initially appeared to exhibit a range shift, more than half were eliminated after accounting for fires and differences in survey effort between years. Species tended to decrease in abundance within their range, though this trend was often not significant. Predictions from trait models were inconsistent, depending on the modeling framework, the metric used for range shifts, and the inclusion of an outlying species. Range stasis was likely driven by dispersal limitation, but may have also resulted from acclimation, slow demographic processes, microclimate buffering of atmospheric temperatures by landscape features, or some combination of these and other factors. The variation in the degree of range shifts could not be explained satisfactorily by functional traits, casting doubt on their use in a general framework to predict future responses. iii  Preface  This thesis contains the original, unpublished work of the author, Rachel Wilson. The research program was designed by Dr. Amy Angert and Rachel Wilson. Field surveys were directed by Dr. Amy Angert, Dr. Christopher Kopp and Rachel Wilson with the assistance of Miquel Anglès-Marín, Jennifer Chen, Erin Fitz, Gabrielle Hindley, Peter Lambert, Laura Super, Erin Warkman, Derek Wiens, and Jecoliah Williams. Historical data were obtained with permission from Jim Agee. iv  Table of Contents  Abstract .......................................................................................................................................... ii Preface ........................................................................................................................................... iii Table of Contents ......................................................................................................................... iv List of Tables ............................................................................................................................... vii List of Figures ............................................................................................................................. viii Acknowledgements ...................................................................................................................... ix Dedication ...................................................................................................................................... x Chapter 1: Introduction ............................................................................................................... 1 Chapter 2: Should I stay or should I go? Assessing range stasis versus range shifts of plants in the North Cascades ................................................................................................................. 10 2.1 Synopsis ........................................................................................................................ 10 2.2 Methods ........................................................................................................................ 12 2.2.1 Field resurveys .......................................................................................................... 12 2.2.2 Curation and collection of trait data.......................................................................... 15 2.2.3 Statistical analyses .................................................................................................... 16 2.2.3.1 Data manipulation ............................................................................................. 16 2.2.3.2 Controlling for disturbance ............................................................................... 17 2.2.3.3 Changes in presence .......................................................................................... 18 2.2.3.4 Rarefaction ........................................................................................................ 20 v  2.2.3.5 Changes in cover ............................................................................................... 22 2.2.3.6 Traits ................................................................................................................. 23 2.3 Results .......................................................................................................................... 24 2.3.1 Changes in presence .................................................................................................. 24 2.3.2 Rarefaction ................................................................................................................ 29 2.3.3 Changes in cover ....................................................................................................... 29 2.3.4 Controlling for disturbance ....................................................................................... 32 2.3.5 Traits ......................................................................................................................... 32 2.4 Discussion..................................................................................................................... 35 2.4.1 Most species show range stasis ................................................................................. 35 2.4.2 Species tend to decrease in abundance ..................................................................... 38 2.4.3 Traits cannot consistently explain range shifts ......................................................... 39 2.4.4 Caveats ...................................................................................................................... 40 2.4.5 Summary and future directions ................................................................................. 42 Chapter 3: Conclusion ................................................................................................................ 45 Bibliography ................................................................................................................................ 48 Appendices ................................................................................................................................... 56 Appendix A Trait curation and collection ............................................................................ 56 A.1 Growth form.............................................................................................................. 56 A.2 Dispersal syndrome ................................................................................................... 56 A.3 Leaf textures.............................................................................................................. 56 A.4 Shade tolerance ......................................................................................................... 56 vi  A.5 SLA ........................................................................................................................... 57 A.6 Seed mass .................................................................................................................. 57 A.7 Plant height ............................................................................................................... 57 Appendix B Controlling for disturbance .............................................................................. 59 B.1 Effect of fire on presence .......................................................................................... 59 B.2 Effect of fire on cover ............................................................................................... 59 Appendix C Changes in cover ................................................................................................ 61 Appendix D Supplemental tables .......................................................................................... 62  vii  List of Tables  Table 1. Number of species that include a given coefficient in their set of model-averaged coefficients predicting presence .................................................................................................... 25 Table 2. Number of species that include a given coefficient in their set of model-averaged coefficients predicting cover ......................................................................................................... 30 _ Table D1. 43 focal species’ four-letter code, latin names, common name, and functional type, including moss .............................................................................................................................. 62 Table D2. Generalized Variance Inflation Factors (GVIFs) and degrees of freedom for five traits entering a multivariate model ....................................................................................................... 65 Table D3. Species code, number of occurrences in either survey, AIC relative to the top model, weight based on AIC, r2, and coefficients of top models predicting presence ........................... 66 Table D4. Species code, dataset (original vs. rarefied), number of measurements in either survey, and model coefficients .................................................................................................................. 76 Table D5. Species code, number of measurements in either survey, AIC relative to the top model, weight based on AIC, r2, and coefficients of top models predicting cover .................... 81 Table D6. Coefficients of top models describing the association between standardized traits and five different range metrics. .......................................................................................................... 90 Table D7. Coefficients of top models describing the association between standardized traits and five different range metrics, with Achillea millefolium excluded ................................................. 92  viii  List of Figures  Figure 1. Location of legacy plots in North Cascades National Park ............................................ 9 Figure 2. Schematic showing setup of resurvey plots .................................................................. 14 Figure 3. Raw edge displacement among species at the (a) lower and (b) upper range edge ..... 24 Figure 4. Probability of presence with elevation for (a) A. uva-ursi, (b) R. parviflorus, (c) H. discolor, and (d) O. horridus ........................................................................................................ 26 Figure 5. Modelled displacement among species at the (a) lower range edge, (b) upper range edge, and (c) elevation of peak presence ...................................................................................... 28 Figure 6. Cover class (%) with elevation for (a) A. alnifolia, (b) V. membranaceum, (c) L. borealis, and (d) R. spectabilis ..................................................................................................... 31 Figure 7. Association of (a) different growth forms with modelled upper range edge displacement, (b) leaf texture with modelled upper edge displacement, and (c) leaf texture with modelled peak-presence displacement. ......................................................................................... 33  ix  Acknowledgements Heartfelt thanks to Dr. Amy Angert for her tireless support, invaluable expertise, and good humour in advising me through the ups and downs of a Master’s degree. Thanks to Dr. Chris Kopp for advice, organizational skills in the field, and cooking a mean Dutch oven chicken. This work could not have been completed without the aid of an army of dedicated and talented field assistants: Miquel Anglès-Marín, Jennifer Chen, Erin Fitz, Gabrielle Hindley, Peter Lambert, Catriona Leven, Laura Super, Erin Warkman, Derek Wiens, and Jecoliah Williams. Many thanks to committee members Dr. Chris Harley and Dr. Greg Henry for their expert guidance and prompt responses to my frantic, statistics-related emails. Thanks to lab members past and present, official and honorary, for indispensable insight, advice, and commiseration: Matthew Bayly, Megan Bontrager, Barb Gass, Dr. Rachel Germain, Dr. Anna Hargreaves, Evan Hersch, Qin Li, Dr. Chris Muir, and Cora Skaien. Thanks also to Dr. Mary O’Connor for commentary on this thesis. I am grateful to Dr. Janneke Hille-Ris Lambers for support and guidance, and to her lab for helping me get acquainted with Pacific Northwest flora. I am also grateful to staff in the North Cascades National Park Service Complex for logistical assistance, accommodations, and access to their geodatabase of historical fires. Thanks to Dr. Jim Agee for undertaking the original survey work, without which this research would not have been possible, and for answering questions such as, “what are non-dimensional plots?” I am forever grateful for the support of my parents, Jack and Lauren, as well as my sister, Hannah, for their unflagging encouragement and ability to ask probing questions about the status of my degree. Thanks to my boyfriend, Alex, for supporting me through many adrenaline-fueled tirades about the fickle nature of R. And finally, thanks to countless individuals who supported me both socially and academically – you know who you are. This research was generously funded by the National Sciences and Engineering Research Council of Canada, a Cordula and Gunter Paetzold UBC-Affiliated Fellowship, and a Mazamas Research Grant. This thesis has been supported by the TRY initiative on plant traits (http://www.try-db.org). The TRY initiative and database is hosted, developed and maintained by J. Kattge and G. Bönisch (Max Planck Institute for Biogeochemistry, Jena, Germany). TRY is currently supported by DIVERSITAS/Future Earth and the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig.  x  Dedication         I dedicate this thesis to every pet in my life, past and present: cats Jasper (R.I.P.), Cleo (R.I.P.), Molly (R.I.P.), Jesse, and Nala; fish Mars (R.I.P.), Zephyr (R.I.P.), Blue Moon (R.I.P.), Noodle (R.I.P.), Spike, and Spock; and several nameless plants (mostly, R.I.P.).1  Chapter 1: Introduction  Understanding and predicting biological responses to anthropogenic climate change is a pressing goal in ecology. At the current rate of climate change, as many as one in six species globally are likely to face extinction (Urban 2015), and a number of species extinctions have already been directly or indirectly attributed to climate change (Pounds et al. 2006, Sinervo et al. 2010, Cahill et al. 2012, Gynther et al. 2016). Under this urgent threat, species negatively impacted by climate change must be able to rapidly respond in order to persist on the landscape (Jump and Penuelas 2005). Viable responses include swiftly adapting or acclimatizing to novel climate. Species may also respond by dispersing from newly unsuitable habitat into unchanged or newly suitable habitat, possibly leading to range expansions or contractions (i.e., shifts) on a larger scale. For ranges determined primarily by climate, tracking of suitable habitat should be largely limited by dispersal ability of the species, but if ranges are also determined by biotic factors, unanticipated patterns of shifts may arise. Regardless, given that the rapid pace of climate change may negate the ability of all but the most short-lived or plastic species to adapt or acclimatize, the immediately viable option for many species will be to track suitable habitat by shifting their range. Preliminary but widespread change in the ranges and abundances of species have already been documented, with measurable consequences for community structure and functioning (Walther et al. 2002, Parmesan 2006, Damschen et al. 2010, Elmendorf et al. 2012b). However, there appears to be considerable heterogeneity in responses among species and systems, and much of this variation is unexplained (Angert et al. 2011). As climate change continues to progress, attempting to manage biological responses requires rigorous projections 2  for the likely direction and magnitude of these responses among species and, more generally, among functional groups. A common method for predicting how distributions will respond to climate change is to model a species probability of occurrence on the landscape given some combination of climatic variables, and then to model predicted distributions based on likely alterations of those variables under climate change scenarios (Iverson and Prasad 2001, Elith and Leathwick 2009). The ability of these distribution models to accurately predict species future distributions has been questioned (Pearson and Dawson 2003), prompting tests of existing models (Araújo et al. 2005, Pearman et al. 2008) and the development of more realistic models (Boulangeat et al. 2012). However, many models attempting to forecast distributions still ignore important factors such as overlap between a shifting species and novel competitors (Alexander et al. 2015), the role of pollen dispersal in facilitating adaptation and slowing range expansion (Aguilee et al. 2016), and habitat modification (Archaux 2004). These factors and others may give rise to lagged responses or counterintuitive patterns of movement such as downslope elevational range shifts (Lenoir et al. 2010). Another approach has been to experimentally warm communities in the field and measure responses (Walker et al. 2006, Elmendorf et al. 2012a). The ability of these experiments to predict climate change responses has been variable. Comparisons of warming experiments to long-term observational records suggest that experiments may under-predict phenological advances in response to warming (Wolkovich et al. 2012), but Elmendorf et al. (2015) found that experimental community responses were in good agreement with long-term observations. Ultimately, the only way to reliably test the validity of predictions is to wait for the species to respond, which is obviously not practical from a conservation standpoint. As such, historical surveys of presence and abundance of species provide an invaluable baseline against which to 3  measure shifts that have already occurred due to recent climate change (Vellend et al. 2013). Ideally, these recent shifts can be used to predict future responses with the greatest degree of accuracy available to us. There are numerous challenges associated with resurveying historical plots, such as the ability to relocate plots, taxonomic discrepancies, and outdated surveying methods (Vellend et al. 2013). Despite this, a considerable number of resurveys of historical plots have been undertaken. Often, the goal of these surveys is to characterize responses of community composition to climate change or anthropogenic disturbance (for example, Rogers et al. 2009, Keith et al. 2009, Damschen et al. 2010). More rarely, the historical plots covered sufficient latitudinal or elevational breadth for modern surveyors to assess changes in species ranges (for example, Lenoir et al. 2008, Moritz et al. 2008, Tingley et al. 2012, Ash et al. 2016). In general, many surveys report an overall trend of species shifting higher in latitude or elevation, in accordance with the expectation that increasing temperatures will improve suitability at upper range edges and decrease suitability at lower range edges (for example, Beckage et al. 2008, Kelly and Goulden 2008, Lenoir et al. 2008). However, a number of species do not follow these patterns and vary in their magnitude and direction of responses (Angert et al. 2011). Observational and experimental abundance responses to warming have also been shown to vary among functional groups, such that forbs and shrubs tend to respond positively while mosses, ferns and lichens tend to respond negatively (Walker et al. 2006, Elmendorf et al. 2012b). These idiosyncratic responses present a significant challenge to making general predictions of distributional shifts under climate change. Most surprisingly, many species have shown little to no change in distribution or abundance. These species are rarely the focal point of publications, even if the majority of sampled species appear to show no measurable climate change response (for example, Wolf et al. 4  2016), so the frequency of this response may be overlooked. However, a number of resurveys have reported no response or a response lagging behind the pace of climate change, particularly among trees (Bertrand et al. 2011, Forero-Medina et al. 2011, Zhu et al. 2011, Devictor et al. 2012, Ash et al. 2016). An absent response (hereafter referred to as stasis) may be driven either by “resistance”, referring to internal or external factors that buffer the immediate fitness consequences of climate change, or time lags that create delayed susceptibility to climate change. Resistance may be conferred by the ability of a species to rapidly acclimatize or adapt (Jump and Penuelas 2005), or by having a broad thermal niche, the extremes of which extend beyond current conditions (Sunday et al. 2012). Additionally, buffering of atmospheric warming by geologic features or dense forest may create cooler microhabitats for sheltered species, negating the need for a measurable climate change response (De Frenne et al. 2013, Frey et al. 2016). Stasis driven by time-lagged susceptibility is more concerning from a conservation perspective, as it may arise if slow demography (for example, that of trees) prevents immediate population extinctions at the trailing edge. Slow demography may also delay colonization at the expanding edge, though long-distance dispersal or increasing their reproductive effort may compensate for this. Colonization at the expanding edge may also be delayed if established species are generally able to outcompete newcomers (Bjorkman et al. 2016) or if species are dispersal-limited (Schloss et al. 2012). Given the variation in responses, a simple, predictive framework is urgently needed so that management decisions can be informed with a high degree of accuracy and a negligible expenditure of resources. One way to achieve this end is to associate species functional traits with known responses (i.e., those measured from a historical baseline), then extrapolate those responses to other species possessing similar traits. Functional traits have been increasingly 5  employed in attempts to produce general principles and predictions for ecology (Westoby and Wright 2006, McGill et al. 2006), leading to the expansion of data available through online databases (for example, the TRY database; Kattge et al. 2011). Predictions for how traits relate to range shifts may be drawn from invasion biology, as certain traits are likely to influence both invasions and range shifts via their effects on colonization or persistence. For example, species with a high dispersal capacity may be more likely to colonize new habitat, and highly dispersive individuals often characterize populations at the leading edge of invasions (Simmons and Thomas 2004). As such, species with traits enhancing dispersal may be more likely to undergo range shifts. Similarly, propagule pressure, or the number of offspring released into the environment, increases the likelihood of colonization during invasions by increasing the number of dispersal events (Simberloff 2009). Species with high fecundity and frequent reproductive bouts may therefore be better able to shift their range. Ecological generalization is also likely to aid species in range shifts, since generalists may be better able to persist in novel habitat and take advantage of diverse resources. Despite well-developed predictions for how traits may relate to range shifts (Aubin et al. 2016), support for the ability of traits to predict observed responses to climate change is highly conflicting among and even within studies. Generalizing is further complicated by the fact that studies often use slightly different metrics to quantify range shifts. Early research suggested that individual traits may associate with range shifts in some cases (Perry et al. 2005, Dulvy et al. 2008, Lenoir et al. 2008, Pöyry et al. 2009) but not others (Hill et al. 2002, Moritz et al. 2008, Forero-Medina et al. 2011). For example, a synthesis of four resurvey studies of plants, birds and mammals by Angert et al. (2011) found that a few traits did associate with range shifts for each taxonomic group. However, even multivariate trait models generally had low power to explain 6  variation among shifts (Angert et al. 2011). A review by Buckley and Kingsolver in 2012 found only ten published records of attempts to relate traits to range shifts, four of which were undertaken by Angert et al. (2011), with poor agreement of trait predictive ability among and within taxonomic groups. More recent studies have often found that one or more traits can associate with range shifts, though some studies do not evaluate the explanatory power of trait models (Betzholtz et al. 2012, Reif and Flousek 2012, Feary et al. 2013, Powney et al. 2014, Sunday et al. 2015, Aguirre-Gutiérrez et al. 2016). Others have found the relationships to be weak (Zhu et al. 2011, Ash et al. 2016) or counter to theoretical expectations (Tingley et al. 2012). Only five studies have used resurvey data across a distributional gradient to relate traits to range shifts for plants, all of which occurred in the Swiss alps, the Eastern U.S., or Britain, and only two of which assessed elevational gradients (Lenoir et al. 2008, Angert et al. 2011, Zhu et al. 2011, Powney et al. 2014, Ash et al. 2016). Given the confusing picture that arises when comparing among and even within study systems and taxonomic groups, much more replication and expansion to other systems is needed, with range shifts quantified using a variety of metrics and numerous traits tested for associations. In this thesis, I aim to use resurvey data to characterize range and abundance shifts of Pacific Northwest plants in response to recent climate change, then relate functional traits to five different metrics of range shifts. To achieve this, I and a team of surveyors relocated historical vegetation plots in and around the North Cascades (NOCA) National Park Service Complex in Washington, USA (Fig. 1.1). These plots were originally surveyed in 1983 and are ideal for detecting elevational range shifts, as they encompass a wide range of elevations (162 m – 2122 m), show considerable topographic variation, and are supplemented by detailed notes from the original surveyors (Agee and Kertis 1987). Further, the area has experienced approximately a 0.8 7   C increase in warming since the original survey (based on unpublished analyses conducted in ClimateWNA; Wang et al. 2012), a temperature change equivalent to descending approximately 125 m in elevation. We resurveyed these plots in a manner parallel to the original survey, then constructed historical and contemporary distributions of the presence of 42 species plus the functional group moss. We also assessed temporal change in species abundances within their ranges. To determine whether functional traits might predict range shifts, I assembled data on dispersal syndrome, growth form, leaf texture, plant height, shade tolerance, specific leaf area, and seed mass from the TRY database (Kattge et al. 2011) and supplemented those data by collecting trait measurements in the field and collating from other sources. I then tested whether magnitude of species shifts associate with their traits. I also evaluated whether associations vary among different range shift metrics in magnitude or direction. I expect my findings to generally support those of other elevational resurveys, such that species show variation in their magnitude and direction of responses. On average, however, I suspect that (1) most species will tend to shift their range higher in elevation, and (2) shrubby species will tend to increase in cover while ferns and moss will tend to decrease in cover. Considering the variable conclusions from the literature, I tentatively hypothesize that (3) traits will only weakly associate with climate change responses, though traits strongly affecting colonization ability or persistence in novel habitat may show some importance. Colonization ability may be affected by: dispersal syndrome, such that dispersal strategies involving wind (anemochory), animal consumption (endozoochory), or sticking externally to animals (epizoochory) have greater potential for long-distance dispersal than passive dispersal (barochory); seed mass, in which smaller seeds may be more easily carried by dispersal vectors; plant height, such that seeds released from a greater height are more likely to travel farther; and 8  growth form, where groups with faster generation times may undergo more colonization events. Persistence in novel habitat may be affected by traits known to directly or indirectly associate with ecological generalism or competitive ability, such as: leaf texture, such that species with fine-textured leaves may be more susceptible to herbivory than competitors (Cornelissen et al. 1999); plant height, for example that taller species may outcompete smaller ones for light; shade tolerance, such that intermediately tolerant species may be able to capitalize on a greater variety of niches; specific leaf area, such that photosynthetic rates increase with increasing SLA (Reich et al. 1997); and seed mass, where heavier seeds may reflect improved maternal provisioning and increased likelihood of establishment. Ultimately, my thesis aims to add to the relatively scarce literature relating traits to range shifts and to characterize climate change responses of flora in the Pacific Northwest of North America. These responses and their trait associations could be used to predict future change.  9   Figure 1. Location of legacy plots in North Cascades National Park (boundary shown in dashed green) in relation to historical fires. Yellow polygons indicate areas that burned prior to 1983 and red polygons indicate areas that burned after 1983. Plots that overlap with historical fires are drawn with O’s and plots that were never burned are drawn with X’s. Blue polygons represent lakes and rivers. Map created in QGIS version 2.14.3-1 using OpenStreetMap as the topographic base layer.   10  Chapter 2: Should I stay or should I go? Assessing range stasis versus range shifts of plants in the North Cascades  2.1  Synopsis In 2014 and 2015, I and a team of surveyors resurveyed historical vegetation plots (established and sampled in 1983) to detect temporal changes in plant elevational ranges and abundances. Plots were surveyed using methods analogous to the original survey to allow for direct comparisons between years (Agee and Kertis 1987). At each plot, presence and percent cover (abundance) were recorded for understory species. Elevations were then associated with plots to create elevational distributions of species presence/absence and abundance.   Change in species elevational distributions were quantified both as “raw” shifts (i.e., absolute change in range edge based on highest- or lowest-elevation presence in each survey year) and “modelled” shifts. Modelled shifts were measured from temporal change in predicted probability of presence based on elevation, elevation2, survey year, survey year*elevation, survey year*elevation2, and fires (see below). Top models (AIC < 2) predicting species probability of presence were averaged based on Akaike weight, and species whose model-averaged coefficients included an interaction between survey year and elevation were interpreted as likely to have exhibited a range shift. Shifts in range edges and elevations of peak presence were also calculated for each species based on their AIC-averaged model. I then assessed whether percent cover of species varied between survey years using a similar AIC model selection framework. 11  Although the region has experienced ~0.8 C of climate warming since 1983 (climateWNA; Wang et al. 2012), temporal changes may not be entirely attributable to climate change. First, many more species were detected in 2014/2015 surveys than in 1983 surveys, suggesting an overall greater ability of 2014/2015 surveyors to detect presence of species. To account for this difference, 2014/2015 survey data were rarefied and presence analyses were repeated, then compared to results from unrarefied data. Second, numerous plots have historically experienced fires. To account for this, fire history of the plot was included as a covariate in models of species presence and cover. I also assessed whether fire interacted with year to affect probability of presence or percent cover for each species.   I was additionally interested in the ability of species functional traits to associate with magnitude and direction of range shifts. For all my species of interest, I downloaded data on traits (dispersal syndrome, growth form, leaf texture, plant height, shade tolerance, specific leaf area, and seed mass) from the TRY database (Kattge et al. 2011). As a number of traits were not measured for my focal species, I supplemented the TRY data using values from the literature and my own measurements from the field. Traits were then tested for the ability to predict five different range shift metrics in both a univariate and multivariate context. Across range metrics, most species did not show strong evidence of range shifts, though there was some variation in magnitude and direction of shifts among species. Of the few species that did exhibit shifts, over half were no longer characterized as such after rarefying and accounting for fire. Most species appeared to decrease in cover between survey years, though these models often had low explanatory power. Univariate and multivariate analyses of associations between traits and shifts were inconsistent among range metrics, and removing an outlying species substantially altered any definitive patterns. Ultimately, overwhelming range 12  stasis and a trend of abundance decreases suggest many species are suffering fitness consequences of climate change within their ranges, but are limited in their ability to disperse to mitigate these consequences. Furthermore, traits cannot reliably explain what little variation was observed.  2.2 Methods 2.2.1 Field resurveys My colleague, Dr. Christopher Kopp, and I led two survey teams (each including 2-4 assistants) to resurvey 374 legacy vegetation plots in the NOCA (Fig. 1). Originally surveyed in 1983, these plots were ideal for resurvey as they encompassed a wide range of elevations and topographic variation and were supplemented by detailed notes from the original surveyors (Agee and Kertis 1987). Furthermore, the region has experienced an increase of ~0.8C in mean annual temperatures since the original survey, while mean annual precipitation has not changed (climateWNA; Wang et al. 2012). We resurveyed 60 plots from mid-July 2014 to August 2014 and resurveyed the remaining plots from June 2015 to September 2015. Plots surveyed each year were chosen to span a range of elevations to ensure that inter-annual variation was not confounded with elevational variation. Sampling methods were designed to be analogous to the 1983 survey (Agee and Kertis 1987; Agee 2014, pers. comm.), with two exceptions. First, the original survey consisted of circular plot ranging in size from 25 – 400 m2 depending on stand density, while our circular plots were uniformly 500 m2 in size. However, we recorded trees as occurring either inside or outside of the historical plot radius to allow for direct comparison with historical data. Second, the original survey assessed cover within a 50 m2 circle (Agee 2014, pers. comm.), whereas we assessed 13  cover within four 1 m2 quadrats. These differences were necessary to allow comparison between our resurvey and a parallel resurvey in Mount Rainier National Park. We relocated historical plots using Universal Transverse Mercator (UTM) data transferred from pencil dots on paper maps by the NOCA National Park Service and site descriptions provided by the original surveyors. In the absence of permanent, on-site markers, some uncertainty in relocating plots remained. However, as I am focusing on elevational distributions of species and not plot-specific comparisons (e.g., richness), this uncertainty is unlikely to affect my results. Furthermore, other resurveys have been shown to be robust to relocation error (Kopecký and Macek 2015). Once we reached the approximate historical plot area, we established the plot centre haphazardly by throwing a flag and recorded its coordinates using a GPS device (GPSmap76CSx). The 500 m2 plot was established by using an electronic distance-measuring tool (Sonin 10300) to measure a radius of 12.62 m from the plot centre. We placed flags in each cardinal direction at this distance (Fig. 2). The radius of the historical plot was also marked with flagging tape in each cardinal direction to help distinguish whether trees fell inside or outside the historical plot. Photographs were taken at each outmost flag facing the plot centre to help future researchers relocate our plots. We recorded slope and aspect using an inclinometer and compass. Since some plots were historically subject to logging and forest fires, we noted evidence of natural and anthropogenic disturbance, such as charring or cut trees. 14    Figure 2. Schematic showing setup of resurvey plots. Flags (red diamonds) were placed in each cardinal direction at distances of 12.62 m and 3.98 m from the plot centre. Trees were surveyed within the 500 m2 plot (white and yellow circles), understory species were surveyed within the 50 m2 subplot (yellow circle), and understory species cover and tree seedling tallies were surveyed within the 4 x 1 m2 quadrats (yellow squares).  After the plot was established, we surveyed understory and tree species. Note that the analysis of tree data was beyond the scope of this thesis, but I describe the sampling procedure here anyway for transparency and completeness. Trees taller than 1.4 m whose centre fell within the 500 m2 plot area were identified and measured for diameter at breast height (DBH). We additionally recorded whether these trees fell inside or outside the historical plot radius. Next, a South  North  East 50 m2 subplot West     1 m2 quadrat 500 m2 plot 15  circular subplot of 50 m2 was established by placing flags at a distance of 3.98 m from the centre in each cardinal direction (Fig. 2). Within this subplot, we recorded the presence of understory vascular plant species and moss (as a functional group, not identified to species). Finally, 1 m2 quadrats were set up at each of the 4 flags marking the 50 m2 subplot (Fig. 2). We recorded percent cover of understory species and moss within each of the four quadrats. Since estimations of percent cover can vary among individuals, percent cover was assessed by more than one person for each of the first several quadrats until estimations became consistent among all observers. We also identified and counted tree seedlings within each quadrat, defining seedlings as trees less than 1.4 m tall. From this point forward, I will focus on the understory species data. 2.2.2 Curation and collection of trait data Traits hypothesized to directly or indirectly affect species elevational shifts (see Changes in presence) were downloaded from the TRY database on June 6, 2016 (Kattge et al. 2011). In total, seven traits (dispersal syndrome, growth form, leaf texture, plant height (m), shade tolerance, specific leaf area (SLA; mm2/mg), and seed mass (mg)) were assessed. A number of species had not been measured for my traits of interest, so I filled gaps wherever possible using published materials. I also incorporated observations, collections and measurements I made during the summers of 2015 and 2016 (details in Appendix A). Traits were condensed to a single value per species. For categorical variables, species were assigned their most commonly reported classification across TRY datasets. For numerical variables, a mean was calculated among all measurements. In cases of species redundancy between my collected data and TRY data, I used my data only. Although this potentially overlooks relevant intraspecific variation, my data were based on samples collected from plants at or near my study sites, and are therefore more likely to 16  be relevant to my study. Moss was excluded from all trait analyses, as few traits can be confidently assigned to such a general group. 2.2.3 Statistical analyses  All analyses were conducted in R (version 3.3.1). I first adapted the 2014/2015 measurements to be directly comparable with 1983 data (see Data manipulation). I accounted for confounding disturbances (see Controlling for disturbance). Next, I associated plot locations with elevations, then constructed elevational distributions for species at both timepoints to detect range shifts (see Changes in presence). 2014/2015 data were rarefied to account for increased resurvey effort relative to the historical survey (see Rarefaction). I also assessed change in species abundance between surveys (see Changes in cover). Finally, I associated functional traits with species magnitude of shifts (see Traits). 2.2.3.1 Data manipulation  The 1983 survey assessed species cover per plot in six unequally divided classes (0.1-5%, 5-25%, 25-50%, 50-75%, 75-95%, and 95-100%) for one sample area, whereas the 2014/2015 survey assessed cover to the nearest 1% in four quadrats. To standardize the 2014/2015 covers to the 1983 covers, I averaged a given species cover among our four quadrats and then assigned this value to its corresponding 1983 cover class, repeating this process for all species in all plots. If a species was present only in our 50 m2 subplot but not in the four quadrats, then I included it in analyses of presence/absence but excluded it from cover analyses. Elevations associated with 2014/2015 plot coordinates were obtained from Google Earth (version 7.1.5.1557) and assigned to corresponding 2014/2015 and 1983 plots to create elevational distributions. Finally, a set of focal species were selected from the total set of species. As the goal of this study was, in part, to assess changes in species distributions, I excluded any species with fewer than 10 occurrences in 17  either survey, resulting in a focal set of 42 species plus moss (Table D1). This safeguarded against the possibility that any changes observed in a species range were due to sampling error alone. Non-native species, putative hybrids, and species that were unable to be classified beyond genus were also excluded. 2.2.3.2 Controlling for disturbance  Because a number of plots were subject to prior disturbances, some of the observed changes between survey years may be successional and unrelated to climate change. For example, two plots were inferred to have a history of clearcutting based on uniform stand age, proximity to logging roads, and observations from the 1983 surveyors. These plots were excluded from the final set of plots for analysis. A more difficult confounding factor was fire, to which numerous plots had historically been subjected. It was important to distinguish between fires occurring before the 1983 survey and after the 1983 survey. For example, old growth forest that experienced a severe fire after the 1983 survey would appear to have progressed “backwards” in successional time upon resurvey, whereas the same forest burned before the 1983 survey would appear to have progressed “forwards”. Either may confound estimates of a given species climate change response, depending on whether that species is positively or negatively affected by fire or climate change.  To account for the effect of fires, a geodatabase containing polygons of natural and human-prescribed fires was obtained from the National Park Service and joined to plot coordinates in QGIS (version 2.14.3-1). Plots were categorized as unburned, burned before 1983, and burned after 1983. The year of most recent fire was used for the few plots that burned multiple times. Fire was then included as a covariate in analyses of species presence and cover. 18  2.2.3.3 Changes in presence Range shifts are here defined as temporal change in a species probability of presence as a function of elevation. Describing range shifts of species can be challenging, as shifts may be measured as shifts in elevation of the peak probability of occurrence of a given species, or expansion or contraction at a species upper range edge, lower range edge, or both edges. Furthermore, detecting the position of range edges is sensitive to sampling effort and species detectability. To account for this complexity, I quantified range shifts as (1) range edge displacement in metres at upper and/or lower range edges, with range edges defined from a species highest and lowest recorded elevations in each survey period; and (2) in a formal modelling context to detect whether a year*elevation interaction affected probability of presence.  Upper range edge displacement for a given species was measured by subtracting that species highest historical occurrence from its highest contemporary occurrence. For example, a positive value would indicate a shift upwards in the range boundary (i.e., a range expansion at the upper edge). Lower elevational range displacement was calculated in a similar manner, such that a positive difference would indicate a range contraction at the lower edge. These values will be referred to as “raw” shifts from this point onwards.  Range shifts were also more formally quantified using a generalized linear model (glm) with a binomial distribution testing for the effect of elevation, elevation2, year, fires, year*elevation interaction, and year*elevation2 interaction on a given species probability of presence. A subset of “top” models (those having  Akaike Information Criterion < 2; Burnham and Anderson 2002) were obtained from the full set of possible models. Pseudo-R-squared based on McFadden’s log likelihood was calculated for each top model. To obtain model-averaged coefficients, I calculated Akaike weights for each top model, multiplied by each coefficient in 19  that model, and then summed across models for each coefficient (Burnham and Anderson 2002). Variables not present in models were assigned a value of zero before summing across models to avoid inflating the Akaike weights of rarely-included coefficients. I interpret species whose model-averaged coefficients include a non-zero year*elevation interaction term to be likely to have undergone a range shift, such that their probability of presence as a function of elevation has changed through time. To shape modelling results into a format more analogous to the raw shifts, I also calculated range edge displacement from my modelled predicted values. In this framework, range edges were considered to be the elevation at which the predicted probability of a species presence dropped below 0.05 in a given model. For each top model, elevations corresponding to predicted upper and lower range edges were calculated for 1983 and 2014/2015, then the difference between years was taken following the raw shift framework above. This yielded upper and lower edge displacement for each top model. If presence did not drop below 0.05 at a given edge, that edge was assigned the elevation of the lowest or highest survey plot. Similarly, if the top model did not include an effect of year, lower and upper edges were assigned the elevation of the lowest and highest survey plot. The elevation at which a given species had the greatest probability of presence was also calculated for each survey year, then the difference was calculated. This yielded peak-presence displacement for each top model. For models not including an effect of year, peak-presence was assumed to be the mean elevation of plots at which the species was present. Finally, these upper and lower range edge displacements and peak-presence displacements were weighted by the associated model’s Akaike weight and summed across models. These values will be referred to as “modelled” shifts from this point onwards. Ideally, this approach controls for situations in which one far-outlying occurrence causes the measured 20  raw shift to be greatly inflated. Furthermore, the correlation between raw and modelled shifts was significant but less than 0.7 (upper: Pearson’s r = 0.56, P = 0.0001; lower: Pearson’s r = 0.34, P = 0.03), indicating that analyzing both raw and modelled shifts is not redundant. To assess magnitude and direction of change overall, among-species means of the raw lower edge displacement, raw upper edge displacement, modelled lower edge displacement, modelled upper edge displacement, and peak-presence displacement were calculated. One-sample t-tests were used to assess whether most species have expanded, contracted, or remained unchanged at either range edge for raw or modelled range metrics. A one-sample t-test was also used to test whether species have shifted in their point of peak presence. 2.2.3.4 Rarefaction  Many more species were detected in the 2014/2015 surveys compared to the 1983 survey (210 species and 356 species, respectively; refer to Results – Rarefaction). In particular, small or hard-to-see species tended to be recorded in many more 2014/2015 plots than in the 1983 survey. The enhanced ability of 2014/2015 surveyors to detect species may increase the likelihood of erroneously describing range expansions at either edge, as many species tend to be sparsely distributed at range edges; these edge individuals may have been overlooked in 1983, yet detected in 2014/2015 surveys. To compensate for this problem, I rarefied the 2014/2015 data, recomputed all analyses, and compared results from rarefied to unrarefied datasets.   A subset of species consisting of those common to both surveys were used for rarefaction analysis, excluding non-native species, hybrids, and species not identified beyond the family level. Among these, mean richness per plot was calculated for the 1983 survey and the 2014/2015 survey. Note that here I include individuals classified to the level of genus, whereas I excluded these individuals when compiling the set of focal species. While genus-level 21  classifications of individuals may mask true species richness per plot only rarely, it is possible that they more frequently underestimate species richness across the scope of each survey. For example, a low-elevation and high-elevation species pair belonging to the same genus will rarely occur in the same plot, but an inability to distinguish between the two will result in only one genus-level record across the full scope of the survey, and will also mask range shifts experienced by either species. For that reason, genus-level IDs were excluded from the focal set of species for analysis, but included when generally estimating species richness in and among plots.  The difference in mean richness per plot between surveys was considered to be a proxy for the difference in survey effort, and so was used as the number of species to be randomly removed from each 2014/2015 plot. A species probability of random removal from the plot increased with decreasing percent cover recorded at that plot in 2014/2015, such that rare or otherwise hard-to-see species were more likely to be discarded during rarefaction. Species recorded in the 50 m2 subplot only were assumed to be rare and so were assigned a proxy cover of 0.1%, though this likely underestimates the abundance of large, patchily distributed species. Acer circinatum and A glabrum were only measured for DBH in 2014 surveys and so did not have cover values associated with those plots. As a proxy, I calculated the mean cover among plots in 2015 for both A. circinatum and A. glabrum and assigned those cover values to the 2014 plot occurrences.  To perform the rarefaction, species were randomly removed from each 2014/2015 plot using the “sample” function, incorporating percent cover as a probability weight. For plots containing as many as or fewer than the designated number of species for removal, no species were removed. The new, rarefied 2014/2015 dataset was then joined to the original 1983 dataset and analyzed following the previously-described procedure (see Changes in presence), 22  ultimately producing model-averaged coefficients for each species. The rarefaction and subsequent analysis were repeated 100 times, giving each species 100 sets of model-averaged coefficients. Finally, for each species, the median value and the 5th and 95th percentiles of the set of rarefied coefficients were compared to the original, un-rarefied coefficients. 2.2.3.5 Changes in cover As cover was recorded in unequally-sized classes, with each class representing a range of possible values (see Data Manipulation), I used interval regression (package “survival”; Therneau 2014) to test for an effect of elevation, elevation2, survey year, fires before and after 1983, year*elevation interaction, and year*elevation2 interaction on species cover. Interval regression is appropriate when analyzing variables with distinct upper and lower bounds but exact values are unknown. First, all possible models incorporating elevation, year, fire, and/or a year*elevation interaction were computed. This full set of models was reduced to a subset of top models with AIC < 2. Pseudo-R-squared for each model was calculated from the correlation between each model’s predicted cover values and the true cover values. Since true cover values were intervals rather than discrete values, the interval’s midpoint was used in the calculation of pseudo-R-squared. Model-averaged coefficients were calculated following the previously described procedure (Changes in presence). I interpret species whose model-averaged coefficients included an effect of year or a year*elevation interaction as likely to have responded to climate change. Unlike interpretations of the range shift models, year alone is also considered to be of importance here, as increases or decreases in abundance may occur irrespective of elevation as a result of climate change. Rarefaction was not performed on the cover data, as the 2014/2015 survey bias arguably lies in detectability and not cover assessment. However, this leaves the possibility that 23  the 2014/2015 survey recorded greater cover of species growing underneath other species, which may inflate 2014/2015 abundance measures. 2.2.3.6 Traits  First, using univariate linear models, I tested for the ability of individual traits to predict range shifts among species. A total of five metrics for range shifts were used: (1) raw lower edge displacement, (2) raw upper edge displacement, (3) modelled lower edge displacement, (4) modelled upper edge displacement, and (5) modelled peak-presence displacement. Each of these five metrics were assessed for a relation with each of the seven previously described traits. A Bonferroni-adjusted significance threshold was used to correct for multiple comparisons (Dunnett 1955).  Next, a multivariate analysis was performed in an AIC model-selection framework to assess which combinations of traits best predict range shifts. Most traits were missing values for a few species, which was problematic as the AIC framework cannot incorporate missing values. After excluding plant height, which contained the most missing values, the total number of species for which all traits were available was 28 (reduced from 42, and moss was not included). Dispersal syndrome was also excluded from multivariate analyses as it was nearly perfectly collinear (aliased) with growth form (for example, all ferns were anemochorous). Generalized variance inflation factors (GVIFs) were calculated to assess for collinearity among the remaining set of predictors (growth form, leaf texture, shade tolerance, seed mass, and SLA) and were concluded to be acceptable (Table D2; package "car"; Fox and Monette 1992, Fox and Weisberg 2011); additionally, correlation among numerical variables was found to be acceptably  low (Pearson’s r < 0.7). AIC corrected for small sample sizes (AICc) was used to rank models. Models were restricted to include no more than three predictor variables (approximately one-24  tenth the sample size). Top models (delta AIC < 2) were identified using the “dredge” function and model averages were calculated using the “model.avg” function (package “MuMIn”; Bartoń 2016).  2.3 Results 2.3.1 Changes in presence  Among 42 focal species and moss, mean raw shift at the lower edge was marginally significantly less than zero (P < 0.1), although there was considerable variation (mean: -32.4 m  38, minimum: -376 m, maximum: 216 m; Fig. 3a). Mean raw shifts at the upper edge did not differ from zero among species, though a few species exhibited a substantial contraction or expansion (mean: 13.0 m  59, minimum: -544 m, maximum: 669 m; Fig. 3b).  Figure 3. Raw edge displacement among species at the (a) lower and (b) upper range edge. Mean displacement among species is indicated by the red line. Confidence intervals from a one-sample t-test are indicated by the black dashed lines. ~ indicates a marginally significant trend (P < 0.1). 25   Formal modeling revealed similar patterns, with only 15 of 42 species and moss including a year*elevation interaction term in their set of top models (Table 1). Explanatory power of models varied considerably among species (Table D3). Of the species that showed evidence of range shifts (those that included a year*elevation interaction term), direction and magnitude of responses were highly variable (Fig. 4) and only 5 species interaction coefficients were significant predictors of presence in their respective models (Table D3). Furthermore, the range shift observed for Chamerion angustifolium was found to be largely attributable to fire (see Controlling for disturbance and Appendix B); excluding it from the previous counts results in only 14 total species showing evidence of range shifts, with 4 species including a significant interaction coefficient.  Table 1. Number of species that include each coefficient in their set of model-averaged coefficients predicting presence, separated by direction of effect.  Intercept Year Elevation (m) Elevation (m2) Fires After 1983 Fires Before 1983 Year X Elevation Year X Elevation2 + – 3  40 29 14 36 7 1 42 9 30 17 22 9 6 7 8 Total (43) 43 43 43 43 39 39 15 15 26    Figure 4. Probability of presence with elevation for (a) Arctostaphylos uva-ursi, (b) Rubus parviflorus, (c) Holodiscus discolor, and (d) Oplopanax horridus. All four species include a year*elevation coefficient in their suite of model-averaged coefficients. Points indicate plots at which the species was either present or absent, with the 1983 survey indicated with blue triangles and the 2014/2015 survey indicated with red circles. Points have been displaced for clarity. Lines indicate probability of presence based on top models.   27   Following patterns similar to those observed for raw shifts, mean modelled shift at the lower edge was significantly (P < 0.05) less than zero, again with some variation among species (mean: -47.6 m  39, minimum: -504 m, maximum: 229 m; Fig. 5a). Unlike the pattern observed for raw upper edge shifts, however, mean modelled shift at the upper edge was significantly (P < 0.01) greater than zero (mean: 77.8 m  48, minimum: -220 m, maximum: 696 m; Fig. 5b). Peak-presence shift did not differ significantly from zero (mean: 9.7 m  32, minimum: -214 m, maximum: 695 m; Fig. 5c).   28    Figure 5. Modelled displacement among species at the (a) lower range edge, (b) upper range edge, and (c) elevation of peak presence. Mean displacement among species is indicated by the red line. Confidence intervals from a one-sample t-test are indicated by the black dashed lines. * indicates a significant trend (P < 0.05).    29  2.3.2 Rarefaction  In 1983, a total of 210 species were recorded with an average of 6.9 species per plot, while in the 2014/2015 surveys, a total of 356 species were recorded with an average of 10.2 species per plot. Only 131 species were recorded in both surveys, of which a mean of 5.7 were recorded per 1983 plot and 7.6 were recorded per 2014/2015 plot. As such, 2 species were removed from each 2014/2015 plot, unless that plot contained 2 or fewer species. After 100 permutations of rarefaction, of the 14 species that originally showed evidence of range shifts, 11 species median rarefied year*elevation coefficients did not significantly differ from their original model-averaged year*elevation coefficients, such that the rarefied coefficient’s 5th and 95th percentiles overlapped with the original coefficient (Table D4). Thus, rarefaction did not significantly change the magnitude of range shift we originally observed for most species. However, the 5th and 95th percentiles of the rarefied interaction coefficients overlapped with zero for 8 species. This suggests that these 8 species putative range shifts can be attributed, at least in part, to differences in survey effort. Ultimately, in this formal modelling context, I detected strong evidence of range shifts for 5 species plus moss (Arctostaphylos uva-ursi, Holodiscus discolor, Oplopanax horridus, moss, Trientalis borealis, Vaccinium alaskaense), and weak evidence of range shifts for 8 species (Achillea millefolium, Cornus sericea, Luetkea pectinata, Menziesia ferruginea, Paxistima myrsinites, Rubus parviflorus, Rubus spectabilis, Sorbus sitchensis). The remaining 29 species showed no evidence of climate-change-induced range shifts (Table D4). 2.3.3 Changes in cover  Year had non-zero model-averaged coefficients for all species (Table 2), suggesting that all species changed in abundance. Most species tended to decrease in cover, though a diversity of responses were observed (Fig. 6). Additionally, 11 species included a year*elevation interaction 30  term in their set of model-averaged coefficients. However, among the full set of focal species, explanatory power of models tended to be low and the effect of year or year*elevation was significantly or marginally significantly different from zero for only 13 species (Table D5). Seven focal species were excluded from cover analyses; see Appendix C for further detail and some additional caveats. Shrubs did not conform in the direction of their responses, but all ferns and moss showed negative responses.  Table 2. Number of species that include each coefficient in their set of model-averaged coefficients predicting cover, separated by direction of effect.  Intercept Year Elevation (m) Elevation (m2) Fires After 1983 Fires Before 1983 Year X Elevation Year X Elevation2 + – 34 2 11 25 16 19 0 3 3 5 3 5 6 5 1 0 Total (40) 36 36 35 3 8 8 11 1 31   Figure 6. Cover class (%) with elevation for (a) Alnus alnifolia, (b) Vaccinium membranaceum, (c) Linnaea borealis, and (d) Rubus spectabilis. All four species include a year and/or year*elevation coefficient in their suite of model-averaged coefficients, yet show a variety of responses. Solid lines are predicted cover values with elevation and the dashed lines are model confidence intervals, with the 1983 survey indicated in blue and the 2014/2015 survey indicated in red.  32  2.3.4 Controlling for disturbance  Of 372 plots, 36 plots experienced fire after 1983, 23 plots experienced fire before 1983, and 313 plots did not experience fire. Plots experiencing fire either before or after 1983 were distributed relatively evenly across elevations (analysis not shown). Fires had a significant effect on cover and the likelihood of presence for some species (Table D3; Table D5). For this reason, further analyses were conducted to test for an interactive effect of survey year and fire on probability of presence and cover (Appendix B). Ultimately, only probability of presence of C. angustifolium was found to be confounded with fire. 2.3.5 Traits Herbaceous species were significantly (P < 0.05) more likely to experience a shift in the upward direction at their modelled upper range edge (Fig. 7a). Similarly, species with fine-textured leaves were significantly (P < 0.05) more likely to experience an uphill shift at their modelled upper range edge (Fig. 7b). Fine-leaved species were also significantly (P < 0.05) more likely to experience an uphill shift in their elevation of modelled peak presence (Fig. 7c). However, patterns with leaf texture became nonsignificant when the outlier Achillea millefolium (raw and modelled upper-edge displacement of 669 m and 695.5 m, respectively) was removed, and growth form became marginally significant (P < 0.1; analysis not shown). Furthermore, a Bonferroni-corrected significance threshold level of 0.001 made all patterns nonsignificant. Traits were not significant predictors of raw shifts. 33   Figure 7. Association of (a) different growth forms (Fe denotes fern, Fo denotes forb, Gr denotes graminoid, Sh denotes shrub, S-Sh denotes semi-shrub, T-Sh denotes tree-like shrub) with modelled upper range edge displacement, (b) leaf texture with modelled upper edge displacement, and (c) leaf texture with modelled peak-presence displacement. For each plot, the median is the central black line, the lower edge of each box is the 25th percentile, the upper edge is the 75th percentile, and the dashed line extends between the minimum and maximum points or 1.5 interquartile ranges from the median, whichever is less. Points beyond this are indicated by open circles. Significant effect of a group (P < 0.05) is indicated by *. Note that these figures include data from A. millefolium. 34  Of the five traits included in the multivariate analysis, growth form, leaf texture, seed mass, and SLA were included in a top model for at least one range metric, while shade tolerance was never included in a model (Table D6). In agreement with results from univariate analyses, herbaceous plants were positively associated with uphill shift in modelled upper range edge, as evidenced by the inclusion of a positive model-averaged coefficient. Similarly, fine leaf texture was associated with an upward shift in modelled upper edge and elevation of peak presence. However, it was additionally associated with an upward shift in the modelled lower range edge and raw upper range edge, an effect not detected in univariate analyses. Model averaging also revealed that seed mass tended to positively associate with upward shifts at the raw lower and upper range edges, the modelled lower range edge, and the elevation of peak presence. Finally, SLA showed a negative relation with raw upper edge displacement, modelled lower edge displacement, and peak-presence displacement, but showed a positive relation with raw lower edge displacement. Explanatory power for models predicting raw and modelled lower edge displacement tended to be low, while it tended to be reasonably high for raw and modelled upper edge displacement as well as peak-presence displacement. The multivariate analysis was also repeated excluding Achillea millefolium, yielding different results (Table D7). Shade tolerance became an important predictor for raw upper edge displacement, such that shade-intolerant species were more likely to undergo a range contraction at the upper edge. Leaf texture was no longer included in top models for raw upper edge, modelled lower edge, or modelled lower edge, remaining in the top models for peak-difference displacement only. Additionally, SLA showed a positive relation with modelled upper edge displacement, whereas before it was not included in that set of top models. Conclusions for the role of seed mass and growth form were qualitatively unchanged. Consistent with results 35  including Achillea millefolium, explanatory power for models predicting raw and modelled lower edge displacement tended to be low, while it tended to be reasonably high for raw and modelled upper edge displacement, though explanatory power for models predicting peak-presence displacement became lower. In summary, the results of multivariate trait analyses were not consistent among range metrics or with univariate analyses and changed substantially when a single outlying species was removed.  2.4 Discussion  Overall, two thirds (28/42) of species did not experience a range shift, and more than half (8/14) of the shifting species responses could be attributed to increased 2014/2015 survey effort or fire. All species showed changes in abundance between surveys, with more than two thirds of species showing a decrease in abundance, though these patterns tended to be weak. Some traits appeared to associate with one or more range shift metrics, but patterns were not consistent am ong metrics, and repeating the analysis following removal of an outlying species yielded different results. 2.4.1 Most species show range stasis  Counter to expectations given 0.8 C of warming from 1983 to 2015, two thirds of species did not exhibit a range shift. Of the remaining species which initially appeared to undergo range shifts, more than half were later shown to be artefacts of increased 2014/2015 survey effort, and one was found to be driven by patterns of fire. Ultimately, only five species, plus the functional group moss, showed strong evidence of range shifts in response to climate change. Additionally surprising was a tendency of species to move downhill at the lower edge. As NOCA was designated a protected area in only 1969, it is possible that reduced disturbance at lower 36  elevations altered competitive dynamics or habitat such that previously excluded species could shift downslope (Archaux 2004, Lenoir et al. 2010), although I removed plots with a suspected history of anthropogenic disturbance. However, given that species also tended to show a slight expansion at their modelled upper edge, and given also that we would expect the greater 2014/2015 survey effort to increase the likelihood of detecting range expansions on either edge, these patterns cannot be confidently categorized as a general climate change response. The trend could be investigated more robustly by repeating calculations of raw and modelled edge displacements for the rarefied dataset. In the absence of this analysis, I conservatively conclude that nearly all species showed stasis in their ranges.  Why might species fail to shift their ranges? Two disparate mechanisms may drive this pattern. First, species in NOCA may be adapting or acclimatizing to temperatures rapidly enough that fitness remains high within the historical range. The viability and frequency of this mechanism as a response to recent climate change is somewhat uncertain. Jump and Penuelas (2005) caution that even in populations with considerable standing genetic variation, the pace of climate change and its additive effects in fragmented habitats may prevent adaptation from successfully mitigating fitness consequences of climate change. Acclimatization buffers the need for adaptation and may be less capable of mitigating the fitness costs of extreme events, which are likely to increase with climate change (Jump and Penuelas 2005). Although phenotypic responses to climate change have been observed, it can be difficult to separate adaptive responses from those due to acclimatization (i.e., phenotypic plasticity; Jump and Penuelas 2005, Merilä and Hendry 2014). Genetic responses to climate change seem to be rarer than plastic responses, though robust tests are scarce (Merilä and Hendry 2014). Given that only 31-32 years has passed since the original survey, that all focal species are perennials, and that genetic 37  responses may occur only rarely, it is unlikely that genetic responses are driving stasis in this case. Acclimatization cannot be completely ruled out as an underlying mechanism. However, the changes in abundance observed among species suggest that acclimatization, if it is occurring, is unable to nullify all responses to the changing climate. Furthermore, acclimatization and adaptation would not limit colonization at the upper range edge, yet upper edge range expansion was rarely observed. Resistance may also lead to stasis when species have a broad thermal niche that encompasses even extreme fluctuations imposed by climate change, as has been observed in terrestrial ectotherms (Sunday et al. 2012). This mechanism cannot be confidently ruled out unless species thermal niches are quantified and compared to their degree of shift. Finally, stasis may be related to microclimate buffering of atmospheric temperatures by closed-canopy forest (De Frenne et al. 2013, Frey et al. 2016). However, focal species were fairly diverse in terms of canopy preference (inferred from shade tolerance and anecdotal observations in the field), suggesting that microclimate buffering may not fully explain the stasis I observed, especially among species that prefer open canopies.  Stasis may also be driven by time-lagged susceptibility to the fitness consequences of climate change. Time-lagged susceptibility could result from one or the combination of a number of factors. For example, species with long generation times, such as trees, could show range stasis if the pace of climate change exceeds rates of colonization of suitable habitat and population extinctions in unsuitable habitat. Susceptibility is delayed, rather than alleviated, as demography will eventually catch up with climate change and fitness consequences will become evident. Trees and other long-lived plants are generally expected to show lags in climate responses (Aitken et al. 2008). Furthermore, Perry et al. (2005) found that fish with shorter generation times are more likely to undergo range shifts. Considering that all NOCA focal 38  species are perennials and that some species may have cycled through only a few generations since 1983, it is possible that insufficient time has passed or intensity of warming has occurred for responses to become apparent. However, all species showed weak but detectable changes in abundance, suggesting that fitness consequences may not have been fully buffered. Given that very few species show range responses, but many species show abundance responses, dispersal limitation could be a key force in driving stasis, if stasis reflects a failure to move despite fitness consequences. This would be surprising, given that elevational range limits are not often constrained by dispersal (Hargreaves et al. 2014). However, capitalizing on newly suitable habitat above the range may require long-distance dispersal events, which are rare and often unpredictable (Nathan et al. 2008); it is therefore unlikely that sufficient time has passed for the dispersal and establishment of individuals above the historical range. Range shifts could also be impeded by prevention of successful establishment above the range due to biotic or abiotic factors (Bjorkman et al. 2016). Preliminary tests of this assertion could be evaluated by planting species above their current range in the presence of local competitors. 2.4.2 Species tend to decrease in abundance  More than two thirds of species appeared to decrease in cover, though year coefficients were often not statistically significant in top models and explanatory power of models was low. Only 11 species included an interaction coefficient in their top models, indicating that responses often appear to be occurring irrespective of location within the range. This may be especially troubling for species that tended to decrease in abundance. For example, if a population in the middle of the range decreased in abundance to a lesser degree than neighbouring populations, the healthier population could act as a seed source to support neighbours; in the absence of this interaction coefficient, populations may begin to suffer the combined effects of high extinction 39  rates and low immigration rates. I also expected shrubs to generally increase in cover and ferns and moss to decrease in cover (Walker et al. 2006, Elmendorf et al. 2012b). Although shrubs showed mixed directional responses, all ferns and moss decreased between surveys. The latter agrees with experimental results and, taken together, suggests that ferns and mosses may continue to decline in this region. Overall, these results suggest unfavourable outcomes for a number of species with respect to climate change, echoing the general conclusions from myriad other studies (for example, Walther et al. 2002, Parmesan 2006, Sinervo et al. 2010, Urban 2015). Again, these conclusions must be weighted in the context of poor explanatory power and nonsignificant coefficients; a more cautious interpretation suggests that just under a third of species showed changes in abundance due to climate change. 2.4.3 Traits cannot consistently explain range shifts  The ability of traits to associate with range shifts was highly inconsistent. Univariate analyses suggested that herbaceous species or those having fine-textured leaves may be more likely to undergo range expansion at the upper edge, but this pattern was driven almost entirely by the outlying A. millefolium. In a multivariate context, herbaceous plants were more likely to undergo a range expansion, even after the exclusion of A. millefolium, but only at the modelled upper edge. Similar to patterns from univariate analyses, patterns of leaf texture were driven almost entirely by A. millefolium. SLA shows discordant patterns at raw and modelled range edges; for example, it associated positively with raw lower edge displacement but negatively with modelled lower edge displacement. Its negative association with raw upper edge displacement was significant, even in the absence of A. millefolium, although this is counter both to its relation with modelled upper edge displacement and general expectations from another resurvey study (Rosbakh et al. 2014). Seed mass was consistently positively associated with 40  uphill shifts, except in the case of modelled upper edge displacement. Most reliably, smaller seeds were more likely to move downhill at lower range edges. This generally agrees with the expectation that smaller seeds can be dispersed more easily, and so having smaller seeds may increase the likelihood of range expansion. However, this pattern was not observed at upper edges and was observed only among the subgroup of species used for multivariate analysis; univariate analysis of the relation between seed mass and edge displacement among all species did not yield significant patterns.  Overall, the seven traits assessed here cannot be reliably used to predict traits in this system. Furthermore, vastly different conclusions could be drawn depending on the species included and the range metric used for prediction, casting concern on the validity of attempting to relate traits to these range shifts at all. The analysis also suffered from numerous drawbacks, including the fact that many Pacific Northwest flora have not been measured for a suite of traits, reducing the sample size of species available for multivariate analyses; the ability of researchers to detect trait-based relationships is only as good as the suite of traits that are included. 2.4.4 Caveats  The findings of this thesis should be interpreted in the context of several caveats. First, resurveys such as this one suffer from uncertainty in plot relocation, which may lead to inflated measurements of plot-level change through time (Vellend et al. 2013). This uncertainty can be alleviated, at least in part, by grouping plots prior to analysis (Damschen et al. 2010). I took a similar approach when I constructed elevational distributions of species among plots, rather than attempting to make paired plot comparisons between years, so I do not expect relocation error to affect my results. Furthermore, Kopecký and Macek (2015) found that measurements of temporal change were robust to relocation error. 41   A second caveat is that I compared change between only two time points. This could potentially exclude interesting inter-annual variation, especially when considering the substantial fluctuation in various climate variables between years (Hijmans et al. 2005). Repeat sampling at the NOCA sites in future years could help to confirm the patterns I found, but this remains a limitation within the scope of this thesis.  These analyses and interpretations also assume that all temporally varying confounding factors have been accounted for. I expected that fire and logging history were the most influential confounding factors, considering that a disturbance event associated with either may vastly alter plant communities for decades following (Agee 1994, Halpern and Spies 1995). However, infrequent disturbances such as major avalanches (which may have changed in intensity, frequency or paths since the original survey) and minor disturbances such as foot traffic from trails were not accounted for in this study. Analyses could be reassessed after incorporating data from historical satellite images describing change in avalanche regimes and trail construction. A number of plots were in avalanche chutes; these could be isolated to see if patterns in avalanche chutes differ from the park as a whole. However, as previously stated, the effect of these infrequent or minor disturbances on our conclusions is likely to be negligible.  A fourth drawback is that effect of temperature was never explicitly tested. Although temperature is known to decrease with increasing elevation, and climate change is known to have warmed the study area, species shifts along a temperature gradient could be explicitly tested using high-resolution point temperature data from ClimateWNA (Wang et al. 2012).  A caveat of using functional traits to predict shifts among species is that species may not be independent, given their phylogenetic relationships. Although a formal phylogenetic analysis was beyond the scope of this thesis, relatedness was superficially assessed by constructing 42  phylogenies of focal species and visually assessing trees for clustering of traits. Phylogenies were constructed using the online program Phylomatic (version 3; Webb and Donoghue 2005) and viewed as a tree using the ETE 3 online tool (Huerta-Cepas et al. 2016). Phylogenetic relations were superficially determined not to affect these results, though some groupings are worth noting: (1) shrub-like growth forms tended to cluster, especially in Eriaceae, and ferns were unsurprisingly clustered outside of vascular species; (2) all ferns showed anemochorous dispersal strategies and all Rubus showed endozoochorous dispersal strategies. These biases are unlikely to affect our conclusions, given that significant associations with range shifts were not observed for non-herbaceous growth forms or dispersal strategies of any kind.  Finally, this survey was restricted to only the most common species between both surveys. This was a necessary artefact of attempting to describe species ranges, but could potentially overlook interesting responses of rare or patchily distributed species. However, this restriction was also a necessary artefact of differences in effort between survey years. Any differences in effort not accounted for by rarefaction could be attributable to differences in identification effort, which could be parsed out by comparing numbers of higher-level classifications among surveys. Ideally, my focus on common, charismatic flora, for which both surveys would have a well-established search image, controls for this problem. 2.4.5 Summary and future directions  Despite warming, few species show strong evidence of range shifts, whereas most species appear to have weakly decreased in abundance across their ranges. Isolation of the primary mechanism driving this pattern depends on interpretation of the abundance results. If abundance decreases are real and indicative of fitness consequences of climate change, species may benefit from shifting their range but are limited by their ability to disperse into or establish in novel 43  habitat. If abundance decreases are so weak as to be ecologically irrelevant or an artefact of model selection, microclimate buffering by landscape features, slow demography, acclimatization, or some combination of all of these may be driving the absence of climate change responses. It is also possible that warming has not yet exceeded the thermal niche extremes of most species, failing to prompt a response. Finally, what little variation in range responses I observed could not be satisfyingly explained by variation in species functional traits, based on discordant conclusions among range metrics and species groupings and an overall paucity of data for ecologically relevant traits.  Predictions for future changes of NOCA flora depend whether stasis is due to resistance (acclimatization, microclimate buffering) or time-lagged susceptibility (dispersal limitation or slow demography). If resistance is driving stasis, species will likely continue to be able to resist to climate change for the foreseeable future, although the ability of phenotypic plasticity to cope with extreme climate events is uncertain (Jump and Penuelas 2005). This resistance may not be sustainable should climate warming continue on a business-as-usual trajectory. Alternatively, if time-lagged susceptibility is driving stasis, species have yet to measurably respond to climate change, and we cannot know the direction of these responses or whether they will be sufficient to avoid extinction given the rapid pace of climate change. Transplanting of tractable species above and within their existing range should be undertaken to further elucidate the causes of stasis and to provide a preliminary evaluation of the viability of assisted migration in this system (Aitken and Whitlock 2013). Furthermore, a repeat survey should be established at these sites to capture inter-annual variation and to allow for immediate detection of climate responses. Ultimately, given that abundance estimates suggest that fitness consequences may already be underway and 44  that traits cannot offer an informative predictive framework, the absence of range responses by Pacific Northwest flora to climate change is concerning.    45  Chapter 3: Conclusion   In this thesis, I aimed to describe elevation-wide climate change responses of Pacific Northwest flora. I also hoped to create a predictive, trait-based framework that could be extended to accurately predict responses in less-studied systems. I capitalized on an extensive historical survey undertaken in 1983 to use as a baseline against which to measure responses (Agee and Kertis 1987). I and my colleagues resurveyed these historical plots in 2014 and 2015. Elevational distributions were created for each time point based on species presences and absences across the full elevational range of plots. Range shifts were quantified as raw change at either boundary, change in either boundary based on modelled probability of presence, and change in point of peak presence. I also assessed temporal change in species percent cover across elevation. Fires may also impact presence and abundance, so I included fire history of each plot as a covariate in presence and cover analyses. I also accounted for greater 2014/2015 survey effort by rarefying 2014/2015 data and comparing to results from unrarefied analyses.  Given ~0.8 C of climate warming in NOCA, I expected species to generally shift their ranges uphill, either by shifting upwards in their point of peak presence or by expanding at their upper edge and contracting at their lower edge. Though some variation in responses was observed, the majority of focal species showed stasis in their ranges, especially after accounting for the effects of survey effort and fires. This was unexpected, considering trends of upward shifts found in previous studies (for example, Lenoir et al. 2008, Moritz et al. 2008). However, others have reported either no response or a response lagging behind the pace of climate change (Bertrand et al. 2011, Forero-Medina et al. 2011, Zhu et al. 2011, Devictor et al. 2012, Ash et al. 2016). I also predicted that species abundances would respond differentially depending on their 46  functional group; specifically, I predicted that shrubby species would tend to increase in abundance while ferns and moss would tend to decrease in abundance. Though shrubs showed directionally variable responses in abundance, all ferns and moss tended to decrease between years, supporting expectations from experimental warming studies (Walker et al. 2006, Elmendorf et al. 2012b). Among all focal species, more than two thirds were found to decrease in abundance since the original survey, though models often had low explanatory power. Taken together, these patterns suggest that species may have experienced fitness consequences of climate change within their ranges, as evidenced by decreases in abundance, but are limited in their ability to disperse (i.e., shift their range) to mitigate these consequences. If a more conservative viewpoint is applied, most species were unaltered in their ranges and abundances, a pattern which may be driven by microclimate buffering, slow demography, and/or acclimatization (Perry et al. 2005, Jump and Penuelas 2005, De Frenne et al. 2013, Frey et al. 2016).  I also predicted that traits influencing colonization ability and/or persistence in novel habitat may associate with species magnitude or direction of range shift. I collated data describing seven traits that were hypothesized to affect these functions. Traits were assessed for the univariate and multivariate ability to predict five metrics of range shifts. Depending on the range metric used, the group of species being analyzed, and the modelling framework, different traits and directions of trait effect were associated with range shifts. Ultimately, traits could not reliably predict range shifts of species in this system.  This thesis contributes to the growing body of literature that compares contemporary data to historical data to describe recent climate change responses. Notably, I describe an overwhelming pattern of range stasis in my focal species, which is unexpected given atmospheric 47  warming in the region and general expectations from previous resurveys of elevational ranges. This work also acts as a voice of caution in attempting to relate functional traits to range shifts. I drew variable conclusions depending on the analytic framework used, suggesting that multiple metrics should be assessed for trait relations. Additionally, replication among systems and greater breadth of traits available for analysis is needed. However, based on this thesis and synthesis of existing literature, I do not believe that traits can be reliably used to predict responses to climate change. Even in studies that purport to show associations between traits and shifts, unexplained variation is often large or unreported, suggesting that management decisions could not be made on this basis alone.  Responses of NOCA flora to future climate change remains uncertain, with the possible exception of the five species exhibiting strong evidence of range shifts. For example, the lower-edge contraction and upper-edge expansion shown by A. uva-ursi may progress, moving the species generally uphill and possibly altering interspecific interactions at either edge. The tendency of species to decrease in abundance, however, suggests that the future of many NOCA flora may be grim. As such, continued research in this area is desperately needed. Species should be planted above their existing range to identify the causes of stasis and provide a preliminary evaluation of the viability of assisted migration in this system (Aitken and Whitlock 2013). Furthermore, a repeat survey should be established at these sites to capture inter-annual variation and to allow for immediate detection of climate responses in the future.  48  Bibliography  Agee, J. K. 1994. Fire ecology of Pacific Northwest forests. 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A.2 Dispersal syndrome  Plants were categorized as anemochorous (wind-dispersed), endozoochorous (internally animal-dispersed), epizoochorous (externally animal-dispersed), or barochorous (passively- or gravitationally-dispersed) based on observations collected in the Fire Effects Information System (FIES) created by the US Forest Service and Department of Agriculture (Habeck 1991, 1992, Snyder 1991, 1993, Matthews 2000, Meyer 2005, Reeves 2006a, 2006b, Fryer 2010, Gucker 2012, Zouhar 2015), guidebooks (Pojar and MacKinnon 1994, 2013), published literature (Piper 1986, Moral et al. 1993, Fuller and del Moral 2003), and my qualitative assessment of their dispersal structures. A.3 Leaf textures  Leaves were categorized qualitatively in the field as being either coarse, medium, or fine based on how easily they could be torn. A.4 Shade tolerance  Plants were categorized as tolerant, intermediately tolerant, or intolerant of shade based on observations in guidebooks (Pojar and MacKinnon 1994, 2013) and the FIES (Snyder 1991, Habeck 1992, Howard 1993, Meyer 2005, Reeves 2006a). A fourth category, intermediate-57  intolerant, was assigned to plants that were recorded as intermediately tolerant or intolerant an equal number of times in the TRY database. A.5 SLA  Most leaf samples were pressed flat and dried at room temperature for several months, then weighed using a Mettler-Toledo AL104 microbalance. Leaves for a few species (Holodiscus discolor, Goodyera oblongifolia, Spiraea betulifolia, and Trientalis borealis) were collected shortly before lab processing and so were dried in a Shellab drying oven at 60C for 72 hours. To calculate leaf area, photographs of leaves beside a ruler (to standardize size) were taken using an iPhone 5S (8 megapixels) and analyzed in ImageJ (version 1.51f). SLA was then calculated by dividing leaf area by mass. Additional values were taken from the literature (Antos and Zobel 1984, Antos 1988, Lange 1998, Gorzelak et al. 2012, Caplan and Yeakley 2013). A.6 Seed mass  “Dry” infructescences (i.e., non-fleshy fruits) were stored in seed envelopes at room temperature for several months. Any dispersal-related structures were removed prior to weighing. “Wet” (i.e., fleshy fruits) infructescences were stored in a 70% ethanol solution at room temperature. These seeds were separated from fruit flesh, air-dried for 12 hours, then dried in a Shellab drying oven at 45C for 60 hours. Seeds were then weighed using a Mettler-Toledo AL104 microbalance. Extremely small seeds were weighed in groups, then divided by the number of seeds. Only mature and putatively viable seeds were measured. A.7 Plant height  Plant height was measured in the field as height from ground level to the tallest vegetative structure, measured with a ruler. A number of height values from TRY were assessed 58  to be well outside the normal range and so were removed before calculating the species’ trait mean.   59  Appendix B  Controlling for disturbance B.1 Effect of fire on presence  Fire interacted marginally significantly (P < 0.1) with survey year for C. rubescens and C. umbellata, such that 2014/2015 surveys had a decreased likelihood of finding the species if the plot had burned after 1983. Fire also interacted marginally significantly (P < 0.1) with year for A. millefolium and significantly (P < 0.05) for C. angustifolium and moss, though in these cases, the 2014/2015 survey had greater probability of detection if the plot had burned after 1983. If fires were clustered near distributional edges, any putative range contractions (in the case of C. rubescens and C. umbellata) or expansions (in the case of A. millefolium, C. angustifolium and moss) shown by these groups would be confounded with the effect of fire. Fires after 1983 were spread evenly across the distributions of plots (analysis not shown), and range contractions were not observed for C. rubescens or C. umbellata (Table D3). However, A. millefolium, C. angustifolium and moss did show evidence of expansions at both range edges. To account for this, plots burned after 1983 were removed and the presence analysis was repeated for these three groups. Although A. millefolium and moss retained their year*elevation interaction term among top models, C. angustifolium no longer included a top model containing an interaction term. The possibility that this species’ range expansion was driven primarily by fire cannot be excluded, and so its response cannot be exclusively attributed to climate change. As such, it was removed from the list of species showing evidence of range shifts. B.2 Effect of fire on cover  Fire significantly (P < 0.05) interacted with survey year for moss, such that 2014/2015 surveys were more likely to find increased cover of moss if the plot had burned before 1983. Moss was found to decrease significantly between years, so the positive effect of the year*fire 60  interaction did not confound the overall pattern; furthermore, the top models describing moss cover did not change substantially when plots burned before 1983 were removed.    61  Appendix C  Changes in cover Acer circinatum and A. glabrum were excluded from cover analyses. These species were delineated as trees in the 2014 survey and so were only measured for DBH. Although the 2015 survey did measure percent cover of A. circinatum and A. glabrum, considering only the 2015 sites and their 1983 counterparts for this analysis would have been inappropriate; many 2014 occurrences of A. circinatum and A. glabrum tended to be clustered at low elevations whereas the 2015 occurrences covered only the upper half of their ranges. Hieracium albiflorum was also excluded, as its cover class never deviated from 1 among both surveys and so the model was unable to fit any distribution to the data. Chimaphila umbellata, Goodyear oblongifolia, and Trientalis borealis were not excluded from analyses; however, their cover varied insufficiently to fit all possible models. Despite being included in the presence analyses, Gaultheria ovatifolia, Holodiscus discolor, Polystichum munitum, and Vaccinium alaskaense occurred fewer than 10 times in the percent cover grouping in either or both surveys and so were excluded.     62  Appendix D  Supplemental tables Table D1. 43 focal species’ four-letter code, latin names, common name, and functional type, including moss. Code Latin name Common name Growth form ACCI Acer circinatum Vine maple Tree-like shrub ACGL Acer glabrum Douglas maple Tree-like shrub ACMI Achillea millefolium Yarrow Forb AMAL Amelanchier alnifolia Saskatoon berry Tree-like shrub ARUV Arctostaphylos uva-ursi Kinnikinnick Shrub ATFI Athyrium filix-femina Lady fern Fern CAME Cassiope mertensiana White mountain-heather Shrub CARU Calamagrostis rubescens Pinegrass Graminoid CEVE Ceanothus velutinus Snowbrush ceanothus Shrub CHAN Chamerion angustifolium Fireweed Forb CHUM Chimaphila umbellata Prince’s pine Forb CLUN Clintonia uniflora Queen’s cup Forb COCA Cornus canadensis Bunchberry Forb COSE Cornus sericea Red osier dogwood Shrub GAOV Gaultheria ovatifolia Western teaberry Shrub GASH Gaultheria shallon Salal Shrub 63  Code Latin name Common name Growth form GOOB Goodyera oblongifolia Rattlesnake plantain Forb GYDR Gymnocarpium dryopteris Oak fern Fern HIAL Hieracium albiflorum White hawkweed Forb HODI Holodiscus discolor Oceanspray Shrub LIBO Linnaea borealis Twinflower Semi-shrub LUPE Luetkea pectinata Partridge-foot Semi-shrub MANE Mahonia nervosa Oregon grape Shrub MEFE Menziesia ferruginea False azalea Shrub OPHO Oplopanax horridus Devil’s club Shrub PAMY Paxistima myrsinites Oregon falsebox Shrub PHEM Phyllodoce empetriformis Purple mountain-heather Shrub POMU Polystichum munitum Sword fern Fern PTAQ Pteridium aquilinum  Bracken fern Fern RHAL Rhododendron albiflorum White-flowered rhododendron Shrub RULA Rubus lasiococcus Dwarf bramble Semi-shrub RUPA Rubus parviflorus Thimbleberry Shrub RUPE Rubus pedatus Five-leaved bramble Semi-shrub RUSP Rubus spectabilis Salmonberry Shrub 64  Code Latin name Common name Growth form SOSI Sorbus sitchensis Sitka mountain-ash Tree-like shrub SPBE Spiraea betulifolia White spiraea Shrub TITR Tiarella trifoliata Foamflower Forb TRBO Trientalis borealis Starflower Forb VAAL Vaccinium alaskaense Alaskan bluberry Shrub VADE Vaccinium deliciosum Cascade blueberry Shrub VAME Vaccinium membranaceum Black huckleberry Shrub VASI Valeriana sitchensis Sitka valerian Forb    65  Table D2. Generalized Variance Inflation Factors (GVIFs) and degrees of freedom for five traits entering a multivariate model. GVIF(1/(2*Df)) allows for comparison among dimensions by standardizing based on degrees of freedom. Trait GVIF DF GVIF(1/(2*Df)) Growth form 4.70 4 1.21 Leaf texture 2.45 2 1.25 Shade tolerance 2.32 1 1.52 Seed mass 3.77 3 1.25 SLA 3.01 1 1.73        66  Table D3. Species code (refer to Table D1), number of occurrences in either survey (1983 N and 2014/2015 N, where “2015 N” is used as shorthand for the latter), AIC relative to the top model, weight based on AIC (w), r2, and coefficients of top models (AIC < 2) predicting presence. r2 is a pseudo-r-squared calculated by McFadden’s log-likelihood method. Model-averaged coefficients appear as the last row of each species section (weighted by w). Annotations on the coefficients indicate significance of that coefficient in its associated model, where ~ corresponds to P < 0.1, * corresponds to P < 0.05, ** corresponds to P < 0.01, and *** corresponds to P <0.001. An F annotation on the species name indicates that that species range shift is attributable to fire. Species 1983 N 2015 N AIC w r2 Intercept Year Elevation (m) Elevation (m2) Fires After 1983 Fires Before 1983 Year X Elevation Year X Elevation2 ACCI 72 65 0.00 0.67 0.30 -2.372** 0.000 7.27E-03** -6.30E-06*** -16.994 0.679* 0.00E+00 0.00E+00    1.42 0.33 0.30 -2.290* -0.167 7.28E-03** -6.31E-06*** -16.998 0.681* 0.00E+00 0.00E+00 Weighted average NA NA NA -2.345 -0.055 7.27E-03 -6.30E-06 -16.995 0.680 0.00E+00 0.00E+00 ACGL 15 18 0.00 0.70 0.07 -4.784*** 0.000 5.73E-03~ -3.53E-06* 0.000 0.000 0.00E+00 0.00E+00    1.70 0.30 0.07 -4.887*** 0.199 5.73E-03~ -3.53E-06* 0.000 0.000 0.00E+00 0.00E+00 Weighted average NA NA NA -4.815 0.060 5.73E-03 -3.53E-06 0.000 0.000 0.00E+00 0.00E+00 ACMI 10 31 0.00 0.40 0.15 -4.293*** 1.293*** 0.00E+00 0.00E+00 2.306*** 1.599** 0.00E+00 0.00E+00    0.81 0.27 0.16 -3.599*** -0.068 -7.12E-04 0.00E+00 2.312*** 1.706** 1.29E-03 0.00E+00    1.54 0.19 0.15 -4.570*** 1.293*** 2.40E-04 0.00E+00 2.298*** 1.700** 0.00E+00 0.00E+00    1.99 0.15 0.17 -6.539*** 3.529 5.86E-03 -3.07E-06 2.335*** 1.708** -6.65E-03 3.65E-06 Weighted average NA NA NA -4.492 1.262 7.22E-04 -4.55E-07 2.310 1.662 -6.42E-04 5.40E-07 AMAL 42 59 0.00 0.71 0.14 -3.283*** 0.442~ 4.90E-03** -3.45E-06*** 0.601 0.935** 0.00E+00 0.00E+00    1.81 0.29 0.13 -3.042*** 0.000 4.89E-03** -3.44E-06*** 0.591 0.927** 0.00E+00 0.00E+00 Weighted average NA NA NA -3.213 0.314 4.90E-03 -3.45E-06 0.598 0.933 0.00E+00 0.00E+00 67  Species 1983 N 2015 N AIC w r2 Intercept Year Elevation (m) Elevation (m2) Fires After 1983 Fires Before 1983 Year X Elevation Year X Elevation2 ARUV 42 32 0.00 0.13 0.04 -0.751~ -1.330* -1.33E-03*** 0.00E+00 0.558 -0.735 1.05E-03~ 0.00E+00    0.22 0.12 0.03 -0.795* -1.335* -1.27E-03** 0.00E+00 0.000 0.000 1.06E-03~ 0.00E+00    0.43 0.11 0.04 -2.297* -0.662 2.18E-03 -1.68E-06 0.639~ -0.684 -6.39E-04 8.81E-07    0.58 0.10 0.03 -2.425** 0.000 1.48E-03 -1.06E-06 0.639~ -0.678 0.00E+00 0.00E+00    1.05 0.08 0.03 -2.241* -0.653 2.05E-03 -1.59E-06 0.000 0.000 -6.54E-04 8.87E-07    1.10 0.08 0.03 -1.361*** 0.000 -8.34E-04** 0.00E+00 0.566 -0.723 0.00E+00 0.00E+00    1.11 0.08 0.03 -2.285** -0.301 1.49E-03 -1.06E-06 0.638~ -0.679 0.00E+00 0.00E+00    1.23 0.07 0.02 -2.364** 0.000 1.35E-03 -9.69E-07 0.000 0.000 0.00E+00 0.00E+00    1.37 0.07 0.02 -1.405*** 0.000 -7.66E-04** 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    1.64 0.06 0.03 -1.219*** -0.301 -8.35E-04** 0.00E+00 0.564 -0.724 0.00E+00 0.00E+00    1.74 0.06 0.02 -2.224** -0.302 1.36E-03 -9.71E-07 0.000 0.000 0.00E+00 0.00E+00    1.89 0.05 0.02 -1.262*** -0.301 -7.67E-04** 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 Weighted average NA NA NA -1.670 -0.534 2.98E-04 -6.18E-07 0.333 -0.390 1.46E-04 1.65E-07 ATFI 24 34 0.00 0.26 0.11 -1.596~ 0.000 6.84E-04 -1.33E-06 -16.093 -0.273 0.00E+00 0.00E+00    0.02 0.25 0.12 -1.805* 0.398 6.79E-04 -1.33E-06 -16.087 -0.274 0.00E+00 0.00E+00    0.10 0.24 0.11 -0.526 0.000 -1.91E-03*** 0.00E+00 -16.167 -0.302 0.00E+00 0.00E+00    0.11 0.24 0.11 -0.735~ 0.400 -1.92E-03*** 0.00E+00 -16.162 -0.303 0.00E+00 0.00E+00 Weighted average NA NA NA -1.177 0.199 -5.88E-04 -6.80E-07 -16.126 -0.287 0.00E+00 0.00E+00 68  Species 1983 N 2015 N AIC w r2 Intercept Year Elevation (m) Elevation (m2) Fires After 1983 Fires Before 1983 Year X Elevation Year X Elevation2 CAME 32 34 0.00 0.53 0.43 -24.00** 0.000 2.20E-02* -4.88E-06 -3.005** -12.442 0.00E+00 0.00E+00    1.32 0.27 0.43 -11.00*** 0.000 5.91E-03*** 0.00E+00 -3.340** -13.170 0.00E+00 0.00E+00    1.90 0.20 0.44 -24.05** 0.099 2.20E-02* -4.88E-06 -3.006** -12.442 0.00E+00 0.00E+00 Weighted average NA NA NA -20.49 0.020 1.76E-02 -3.56E-06 -3.096 -12.639 0.00E+00 0.00E+00 CARU 33 29 0.00 0.70 0.11 -5.581*** 0.000 6.83E-03** -3.42E-06*** 1.964*** 0.746 0.00E+00 0.00E+00    1.74 0.30 0.11 -5.515*** -0.142 6.84E-03** -3.42E-06*** 1.964*** 0.747 0.00E+00 0.00E+00 Weighted average NA NA NA -5.561 -0.042 6.84E-03 -3.42E-06 1.964 0.746 0.00E+00 0.00E+00 CEVE 26 25 0.00 0.73 0.16 -7.411*** 0.000 9.82E-03*** -4.70E-06*** 2.407*** -0.114 0.00E+00 0.00E+00    1.99 0.27 0.16 -7.395*** -0.034 9.83E-03*** -4.70E-06*** 2.407*** -0.114 0.00E+00 0.00E+00 Weighted average NA NA NA -7.407 -0.009 9.82E-03 -4.70E-06 2.407 -0.114 0.00E+00 0.00E+00 CHAN F 18 34 0.00 0.59 0.09 -9.063*** 5.710* 1.17E-02* -5.13E-06** 1.684*** -0.954 -9.89E-03* 4.31E-06*    0.76 0.41 0.08 -5.342*** 0.723* 4.39E-03* -1.93E-06* 1.666*** -0.954 0.00E+00 0.00E+00 Weighted average NA NA NA -7.553 3.687 8.74E-03 -3.83E-06 1.677 -0.954 -5.88E-03 2.56E-06 CHUM 62 73 0.00 0.46 0.17 -3.704*** 0.000 8.03E-03*** -5.24E-06*** -0.827~ 0.378 0.00E+00 0.00E+00    0.70 0.32 0.17 -3.827*** 0.233 8.04E-03*** -5.24E-06*** -0.826~ 0.379 0.00E+00 0.00E+00    1.51 0.22 0.16 -3.720*** 0.000 8.04E-03*** -5.25E-06*** 0.000 0.000 0.00E+00 0.00E+00 Weighted average NA NA NA -3.747 0.076 8.04E-03 -5.24E-06 -0.648 0.296 0.00E+00 0.00E+00 CLUN 44 62 0.00 1.00 0.17 -6.996*** 0.453* 1.33E-02*** -7.32E-06*** -1.773* -0.734 0.00E+00 0.00E+00 69  Species 1983 N 2015 N AIC w r2 Intercept Year Elevation (m) Elevation (m2) Fires After 1983 Fires Before 1983 Year X Elevation Year X Elevation2 COCA 20 29 0.00 0.51 0.17 -3.036** 0.000 4.46E-03~ -3.63E-06* -16.925 -17.655 0.00E+00 0.00E+00    0.09 0.49 0.17 -3.267** 0.427 4.47E-03~ -3.63E-06* -16.919 -17.656 0.00E+00 0.00E+00 Weighted average NA NA NA -3.149 0.209 4.47E-03 -3.63E-06 -16.922 -17.655 0.00E+00 0.00E+00 COSE 12 15 0.00 0.51 0.19 -7.821** 0.000 1.61E-02* -1.13E-05* -1.056 -15.961 0.00E+00 0.00E+00    1.37 0.26 0.21 -5.096 -8.249 1.02E-02 -8.86E-06 -1.100 -15.921 1.87E-02 -9.27E-06    1.63 0.23 0.19 -7.954** 0.247 1.61E-02* -1.13E-05* -1.053 -15.961 0.00E+00 0.00E+00 Weighted average NA NA NA -7.146 -2.078 1.46E-02 -1.07E-05 -1.067 -15.951 4.85E-03 -2.40E-06 GAOV 11 12 0.00 0.73 0.13 -11.43*** 0.000 1.69E-02** -7.78E-06** -15.82 -16.09 0.00E+00 0.00E+00    1.95 0.27 0.13 -11.48*** 0.093 1.69E-02** -7.78E-06** -15.82 -16.09 0.00E+00 0.00E+00 Weighted average NA NA NA -11.45 0.025 1.69E-02 -7.78E-06 -15.82 -16.09 0.00E+00 0.00E+00 GASH 22 25 0.00 0.52 0.23 0.824~ 0.000 -4.02E-03*** 0.00E+00 -2.014~ -2.188* 0.00E+00 0.00E+00    1.31 0.27 0.23 0.110 0.000 -1.93E-03 -1.35E-06 -1.983~ -2.163* 0.00E+00 0.00E+00    1.76 0.21 0.23 0.744 0.159 -4.03E-03*** 0.00E+00 -2.013~ -2.189* 0.00E+00 0.00E+00 Weighted average NA NA NA 0.615 0.034 -3.46E-03 -3.62E-07 -2.006 -2.181 0.00E+00 0.00E+00 GOOB 20 72 0.00 1.00 0.17 -3.536*** 1.535*** 3.89E-03* -2.76E-06** -0.672 -1.324* 0.00E+00 0.00E+00 GYDR 17 29 0.00 0.68 0.10 -4.193*** 0.586~ 4.55E-03~ -2.87E-06* -15.72 -0.072 0.00E+00 0.00E+00    1.48 0.32 0.09 -3.860*** 0.000 4.54E-03~ -2.86E-06* -15.73 -0.071 0.00E+00 0.00E+00 Weighted average NA NA NA -4.086 0.397 4.55E-03 -2.87E-06 -15.72 -0.072 0.00E+00 0.00E+00 70  Species 1983 N 2015 N AIC w r2 Intercept Year Elevation (m) Elevation (m2) Fires After 1983 Fires Before 1983 Year X Elevation Year X Elevation2 HIAL 12 14 0.00 0.50 0.08 -1.438** 0.000 -2.05E-03*** 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    1.06 0.30 0.08 -2.422* 0.000 4.47E-04 -1.33E-06 0.000 0.000 0.00E+00 0.00E+00    1.83 0.20 0.08 -1.525** 0.169 -2.05E-03*** 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 Weighted average NA NA NA -1.747 0.034 -1.31E-03 -3.93E-07 0.000 0.000 0.00E+00 0.00E+00 HODI 17 16 0.00 0.41 0.19 -2.950* 0.000 3.43E-03 -4.22E-06 0.810 1.160* 0.00E+00 0.00E+00    0.75 0.28 0.21 -3.194~ -3.168 3.10E-03 -3.22E-06 0.842 1.132* 1.44E-02 -1.41E-05    1.84 0.16 0.18 -0.736 0.000 -3.08E-03*** 0.00E+00 0.762 1.181* 0.00E+00 0.00E+00    1.98 0.15 0.19 -2.922* -0.057 3.43E-03 -4.22E-06 0.809 1.161* 0.00E+00 0.00E+00 Weighted average NA NA NA -2.656 -0.895 2.28E-03 -3.26E-06 0.811 1.156 4.03E-03 -3.93E-06 LIBO 48 74 0.00 1.00 0.25 -5.172*** 0.628** 1.17E-02*** -7.84E-06*** -16.801 0.038 0.00E+00 0.00E+00 LUPE 35 46 0.00 0.48 0.47 -39.58*** 0.490 4.06E-02** -1.03E-05** -1.980** -13.02 0.00E+00 0.00E+00    0.68 0.34 0.47 -39.08*** 0.000 4.03E-02** -1.03E-05** -1.958** -13.03 0.00E+00 0.00E+00    1.88 0.19 0.48 -25.76* -29.545 2.41E-02 -5.52E-06 -1.992** -12.99 3.56E-02 -1.04E-05 Weighted average NA NA NA -36.84 -5.267 3.74E-02 -9.42E-06 -1.975 -13.02 6.62E-03 -1.94E-06 MANE 62 68 0.00 0.68 0.31 -4.320*** 0.000 1.24E-02*** -9.64E-06*** -1.660** 1.094** 0.00E+00 0.00E+00    1.53 0.32 0.31 -4.400*** 0.153 1.24E-02*** -9.65E-06*** -1.659** 1.096** 0.00E+00 0.00E+00 Weighted average NA NA NA -4.345 0.049 1.24E-02 -9.65E-06 -1.660 1.095 0.00E+00 0.00E+00 71  Species 1983 N 2015 N AIC w r2 Intercept Year Elevation (m) Elevation (m2) Fires After 1983 Fires Before 1983 Year X Elevation Year X Elevation2 MEFE 26 31 0.00 0.43 0.03 -5.811*** 0.000 6.18E-03** -2.49E-06** 0.000 0.000 0.00E+00 0.00E+00    0.37 0.36 0.04 -4.224** -3.778 2.60E-03 -8.81E-07 0.000 0.000 8.29E-03~ -3.75E-06*    1.51 0.20 0.03 -5.913*** 0.195 6.18E-03** -2.49E-06** 0.000 0.000 0.00E+00 0.00E+00 Weighted average NA NA NA -5.258 -1.325 4.89E-03 -1.91E-06 0.000 0.000 2.99E-03 -1.36E-06 MOSS 195 328 0.00 0.41 0.18 0.848*** 2.072*** -5.35E-04** 0.00E+00 -1.499*** -0.323 0.00E+00 0.00E+00    0.93 0.26 0.18 0.262 5.022*** 6.02E-04 -4.61E-07 -1.550*** -0.330 -5.46E-03* 2.17E-06*    1.67 0.18 0.18 0.768** 2.352*** -4.64E-04* 0.00E+00 -1.494*** -0.321 -2.37E-04 0.00E+00    1.94 0.16 0.18 0.975~ 2.072*** -7.95E-04 1.10E-07 -1.508*** -0.329 0.00E+00 0.00E+00 Weighted average NA NA NA 0.703 2.879 -2.71E-04 -1.01E-07 -1.513 -0.326 -1.44E-03 5.58E-07 OPHO 29 23 0.00 0.31 0.13 -1.708~ 0.000 9.24E-04 -1.69E-06 -15.91 0.243 0.00E+00 0.00E+00    0.48 0.24 0.13 -0.463 0.000 -2.22E-03*** 0.00E+00 -15.99 0.224 0.00E+00 0.00E+00    1.21 0.17 0.14 -1.580~ -0.265 9.22E-04 -1.69E-06 -15.91 0.244 0.00E+00 0.00E+00    1.35 0.16 0.15 -0.223 -3.787~ -2.03E-03 -3.99E-07 -15.92 0.255 7.59E-03 -3.41E-06    1.69 0.13 0.13 -0.334 -0.266 -2.23E-03*** 0.00E+00 -15.99 0.225 0.00E+00 0.00E+00 Weighted average NA NA NA -0.976 -0.669 -7.06E-04 -8.62E-07 -15.94 0.238 1.18E-03 -5.31E-07 PAMY 127 138 0.00 0.51 0.06 -1.714*** 0.000 3.04E-03** -1.70E-06*** 1.188*** 0.895** 0.00E+00 0.00E+00    1.13 0.29 0.07 -1.790*** 0.149 3.04E-03** -1.70E-06*** 1.191*** 0.896** 0.00E+00 0.00E+00    1.94 0.19 0.07 -2.674*** 1.832~ 4.68E-03** -2.34E-06*** 1.200*** 0.904** -3.13E-03 1.23E-06 72  Weighted average NA NA NA -1.923 0.400 3.36E-03 -1.82E-06 1.191 0.897 -6.08E-04 2.39E-07 Species 1983 N 2015 N AIC w r2 Intercept Year Elevation (m) Elevation (m2) Fires After 1983 Fires Before 1983 Year X Elevation Year X Elevation2 PHEM 61 66 0.00 0.68 0.55 -34.52*** 0.000 3.68E-02*** -9.44E-06*** -2.869*** -13.41 0.00E+00 0.00E+00    1.51 0.32 0.55 -34.67*** 0.198 3.68E-02*** -9.45E-06*** -2.874*** -13.41 0.00E+00 0.00E+00 Weighted average NA NA NA -34.57 0.063 3.68E-02 -9.44E-06 -2.870 -13.41 0.00E+00 0.00E+00 POMU 20 15 0.00 0.32 0.20 0.265 0.000 -4.10E-03*** 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    0.55 0.24 0.21 0.470 0.000 -4.23E-03*** 0.00E+00 -1.548 -0.215 0.00E+00 0.00E+00    1.21 0.17 0.20 0.421 -0.324 -4.10E-03*** 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    1.65 0.14 0.20 0.728 0.000 -5.48E-03* 8.72E-07 0.000 0.000 0.00E+00 0.00E+00    1.72 0.13 0.21 0.632 -0.333 -4.24E-03*** 0.00E+00 -1.557 -0.216 0.00E+00 0.00E+00 Weighted average NA NA NA 0.454 -0.100 -4.34E-03 1.21E-07 -0.580 -0.080 0.00E+00 0.00E+00 PTAQ 47 29 0.00 0.56 0.10 -2.360** -0.562* 2.99E-03~ -2.22E-06* -0.886 0.490 0.00E+00 0.00E+00    0.50 0.44 0.09 -2.367** -0.554* 3.03E-03~ -2.26E-06* 0.000 0.000 0.00E+00 0.00E+00 Weighted average NA NA NA -2.363 -0.558 3.01E-03 -2.24E-06 -0.498 0.276 0.00E+00 0.00E+00 RHAL 24 19 0.00 0.66 0.27 -43.42*** 0.000 5.31E-02*** -1.67E-05*** 0.000 0.000 0.00E+00 0.00E+00    1.30 0.34 0.28 -43.36*** -0.282 5.32E-02*** -1.67E-05*** 0.000 0.000 0.00E+00 0.00E+00 Weighted average NA NA NA -43.40 -0.097 5.32E-02 -1.67E-05 0.000 0.000 0.00E+00 0.00E+00 RULA 18 20 0.00 0.72 0.19 -17.99*** 0.000 2.31E-02*** -8.17E-06*** -16.48 -15.64 0.00E+00 0.00E+00    1.88 0.28 0.19 -18.05*** 0.120 2.31E-02*** -8.17E-06*** -16.48 -15.64 0.00E+00 0.00E+00 73  Weighted average NA NA NA -18.01 0.034 2.31E-02 -8.17E-06 -16.48 -15.64 0.00E+00 0.00E+00 Species 1983 N 2015 N AIC w r2 Intercept Year Elevation (m) Elevation (m2) Fires After 1983 Fires Before 1983 Year X Elevation Year X Elevation2 RUPA 33 26 0.00 0.30 0.09 -4.786*** 0.000 7.23E-03** -4.32E-06*** 0.000 0.000 0.00E+00 0.00E+00    0.11 0.28 0.10 -7.577*** 4.692~ 1.39E-02** -7.81E-06** 0.000 0.000 -1.14E-02* 5.84E-06*    1.09 0.17 0.09 -4.665*** -0.265 7.24E-03** -4.32E-06*** 0.000 0.000 0.00E+00 0.00E+00    1.83 0.12 0.11 -7.882*** 4.823~ 1.44E-02** -8.01E-06** -0.160 0.654 -1.17E-02* 6.00E-06*    1.85 0.12 0.09 -4.989*** 0.000 7.47E-03** -4.40E-06*** -0.151 0.631 0.00E+00 0.00E+00 Weighted average NA NA NA -5.956 1.869 1.00E-02 -5.76E-06 -0.037 0.154 -4.64E-03 2.38E-06 RUPE 23 30 0.00 0.62 0.07 -6.141*** 0.000 6.91E-03** -2.80E-06** -15.966 -1.164 0.00E+00 0.00E+00    0.97 0.38 0.07 -6.302*** 0.294 6.92E-03** -2.80E-06** -15.963 -1.165 0.00E+00 0.00E+00 Weighted average NA NA NA -6.202 0.112 6.92E-03 -2.80E-06 -15.965 -1.164 0.00E+00 0.00E+00 RUSP 15 24 0.00 0.21 0.08 -1.679*** 0.509 -1.43E-03*** 0.00E+00 -15.70 0.018 0.00E+00 0.00E+00    0.16 0.20 0.08 -2.892** 0.508 1.42E-03 -1.41E-06 -15.61 0.050 0.00E+00 0.00E+00    0.27 0.18 0.07 -1.402*** 0.000 -1.43E-03*** 0.00E+00 -15.71 0.017 0.00E+00 0.00E+00    0.43 0.17 0.08 -2.612* 0.000 1.42E-03 -1.40E-06 -15.62 0.050 0.00E+00 0.00E+00    0.96 0.13 0.08 -1.203~ -0.247 -2.02E-03** 0.00E+00 -15.70 0.015 9.07E-04 0.00E+00    1.35 0.11 0.09 -1.184 -2.693 -2.09E-03 3.59E-08 -15.61 0.056 6.48E-03 -2.70E-06 Weighted average NA NA NA -1.908 -0.116 -5.37E-04 -5.10E-07 -15.66 0.033 8.16E-04 -2.91E-07 74  Species 1983 N 2015 N AIC w r2 Intercept Year Elevation (m) Elevation (m2) Fires After 1983 Fires Before 1983 Year X Elevation Year X Elevation2 SOSI 48 36 0.00 0.40 0.18 -11.19*** -0.364 1.38E-02*** -4.59E-06*** -1.659* -15.52 0.00E+00 0.00E+00    0.17 0.37 0.18 -11.33*** 0.000 1.38E-02*** -4.57E-06*** -1.654* -15.52 0.00E+00 0.00E+00    1.11 0.23 0.18 -9.442*** -6.397 1.15E-02*** -3.86E-06** -1.662* -15.50 8.04E-03 -2.56E-06 Weighted average NA NA NA -10.84 -1.619 1.33E-02 -4.41E-06 -1.658 -15.51 1.85E-03 -5.88E-07 SPBE 17 36 0.00 0.57 0.14 -4.371*** 0.861** 5.45E-03* -3.92E-06** 0.961* 0.116 0.00E+00 0.00E+00    0.59 0.43 0.13 -4.038*** 0.846** 5.02E-03* -3.72E-06** 0.000 0.000 0.00E+00 0.00E+00 Weighted average NA NA NA -4.229 0.855 5.27E-03 -3.84E-06 0.551 0.066 0.00E+00 0.00E+00 TITR 34 46 0.00 0.51 0.07 -2.391** 0.353 2.03E-03 -1.56E-06* -1.452* -0.235 0.00E+00 0.00E+00    0.12 0.49 0.06 -2.204** 0.000 2.02E-03 -1.56E-06* -1.452* -0.234 0.00E+00 0.00E+00 Weighted average NA NA NA -2.300 0.182 2.03E-03 -1.56E-06 -1.452 -0.234 0.00E+00 0.00E+00 TRBO 10 36 0.00 0.53 0.30 -4.336~ 0.574 4.82E-03 -4.03E-06 -16.61 1.041* 6.32E-03 -6.85E-06    0.24 0.47 0.29 -4.250** 1.516*** 6.72E-03 -6.79E-0*6 -16.59 1.026* 0.00E+00 0.00E+00 Weighted average NA NA NA -4.295 1.017 5.72E-03 -5.33E-06 -16.60 1.034 3.35E-03 -3.63E-06 VAAL 11 11 0.00 0.53 0.22 -37.26** 21.604 6.61E-02** -3.06E-05* 0.000 0.000 -3.48E-02 1.25E-05    0.23 0.47 0.24 -36.80** 20.332 6.57E-02** -3.06E-05* -15.480 0.428 -3.25E-02 1.14E-05 Weighted average NA NA NA -37.05 21.004 6.59E-02 -3.06E-05 -7.302 0.202 -3.37E-02 1.20E-05 VADE 34 73 0.00 1.00 0.54 -51.15*** 1.614*** 5.54E-02*** -1.50E-05*** -2.572*** 1.724 0.00E+00 0.00E+00 VAME 96 152 0.00 1.00 0.13 -6.706*** 0.791*** 1.06E-02*** -4.23E-06*** -0.893* -0.201 0.00E+00 0.00E+00 75  Species 1983 N 2015 N AIC w r2 Intercept Year Elevation (m) Elevation (m2) Fires After 1983 Fires Before 1983 Year X Elevation Year X Elevation2 VASI 24 35 0.00 0.36 0.21 -12.27*** 0.459 1.16E-02** -3.11E-06* -1.023 -14.410 0.00E+00 0.00E+00    0.51 0.28 0.21 -11.99*** 0.000 1.16E-02** -3.11E-06* -1.017 -14.412 0.00E+00 0.00E+00    1.09 0.21 0.20 -12.75*** 0.454 1.22E-02*** -3.32E-06** 0.000 0.000 0.00E+00 0.00E+00    1.58 0.16 0.20 -12.48*** 0.000 1.22E-02*** -3.31E-06** 0.000 0.000 0.00E+00 0.00E+00 Weighted average NA NA NA -12.32 0.257 1.18E-02 -3.18E-06 -0.645 -9.109 0.00E+00 0.00E+00      76  Table D4. Species code (refer to Table D1), dataset (original vs. rarefied), number of measurements in either survey (1983 N and 2014/2015 N, where “2015 N” is used as shorthand for the latter), and model coefficients. The first row within each species’ section is model-averaged coefficients from original, un-rarefied data (as in Table D3) and the second row is median values of coefficients from rarefaction analysis. Annotations on the rarefied coefficient indicate that the coefficient differs significantly the original coefficient (*) and/or that the coefficient does not differ significantly from zero (†) based on its 5th and 95th percentiles. An F annotation on the species name indicates that its range shift is attributable to fires.  Species Dataset 1983 N 2015 N Intercept Year Elevation (m) Elevation2 (m2) Fires After 1983 Fires Before 1983 Year X Elevation Year X Elevation2 ACCI Original 72 65 -2.345 -0.055 7.27E-03 -6.30E-06 -17.00 0.680 0.00 0.00  Rarefied 72 61.37 -2.291 -0.097* 7.01E-03 -6.10E-06 -16.96* 0.693 0.00† 0.00† ACGL Original 15 18 -4.815 0.060 5.73E-03 -3.53E-06 0.000 0.000 0.00 0.00  Rarefied 15 13.53 -4.352* -0.032*† 4.45E-03* -2.98E-06* 0.000† 0.000† 0.00† 0.00† ACMI Original 10 31 -4.492 1.262 7.22E-04 -4.55E-07 2.310 1.662 -6.42E-04 5.40E-07  Rarefied 10 25.03 -4.385 0.713* 7.29E-04† -4.62E-07† 2.070* 1.777 -2.79E-04† 5.08E-07† AMAL Original 42 59 -3.213 0.314 4.90E-03 -3.45E-06 0.598 0.933 0.00 0.00  Rarefied 42 47.57 -3.296 0.046* 5.05E-03 -3.50E-06 0.598 0.907 0.00† 0.00† ARUV Original 42 32 -1.670 -0.534 2.98E-04 -6.18E-07 0.333 -0.390 1.46E-04 1.65E-07  Rarefied 42 29.19 -1.540 -0.861 2.24E-04† -7.19E-07 0.390 -0.817 2.92E-04 2.59E-07 ATFI Original 24 34 -1.177 0.199 -5.88E-04 -6.80E-07 -16.13 -0.287 0.00 0.00  Rarefied 24 30.00 -1.674 0.069* 4.33E-04† -1.13E-06 -16.01* -0.162* 0.00† 0.00† CAME Original 32 34 -20.49 0.020 1.76E-02 -3.56E-06 -3.096 -12.639 0.00 0.00  Rarefied 32 31.44 -19.20 0.000*† 1.57E-02 -2.91E-06 -3.109 -12.560* 0.00† 0.00† 77  Species Dataset 1983 N 2015 N Intercept Year Elevation (m) Elevation2 (m2) Fires After 1983 Fires Before 1983 Year X Elevation Year X Elevation2 CARU Original 33 29 -5.561 -0.042 6.84E-03 -3.42E-06 1.964 0.746 0.00 0.00  Rarefied 33 23.75 -5.522 -0.163* 6.69E-03 -3.36E-06 2.023 0.636 0.00† 0.00† CEVE  26 25 -7.407 -0.009 9.82E-03 -4.70E-06 2.407 -0.114 0.00 0.00  Original 26 21.83 -7.267 -0.058* 9.37E-03 -4.47E-06 2.405 -0.031*† 0.00† 0.00† CHAN F Rarefied 18 34 -7.553 3.687 8.74E-03 -3.83E-06 1.677 -0.954 -5.88E-03 2.56E-06  Original 18 30.30 -7.816 4.364 9.29E-03 -4.07E-06 1.624 -0.902 -7.44E-03 3.24E-06 CHUM Rarefied 62 73 -3.747 0.076 8.04E-03 -5.24E-06 -0.648 0.296 0.00 0.00  Original 62 57.21 -3.759 -0.025* 7.81E-03 -5.14E-06 -0.262* 0.129 0.00† 0.00† CLUN Rarefied 44 62 -6.996 0.453 1.33E-02 -7.32E-06 -1.773 -0.734 0.00 0.00  Original 44 54.89 -7.089 0.131 1.36E-02 -7.42E-06 -2.395 -0.637 0.00† 0.00† COCA Rarefied 20 29 -3.149 0.209 4.47E-03 -3.63E-06 -16.92 -17.66 0.00 0.00  Original 20 25.43 -3.190 0.097* 4.44E-03 -3.52E-06 -16.84* -17.56* 0.00† 0.00† COSE Rarefied 12 15 -7.146 -2.078 1.46E-02 -1.07E-05 -1.067 -15.95 4.85E-03 -2.40E-06  Original 12 13.48 -7.865* -2.078† 1.70E-02* -1.26E-05 -0.977* -15.92* 4.85E-03† -2.40E-06† GAOV Rarefied 11 12 -11.447 0.025 1.69E-02 -7.78E-06 -15.82 -16.09 0.00 0.00  Original 11 9.12 -12.060 -0.046* 1.75E-02 -7.90E-06 -11.68* -11.83* 0.00† 0.00† GASH Rarefied 22 25 0.615 0.034 -3.46E-03 -3.62E-07 -2.006 -2.181 0.00 0.00  Original 22 22.7 0.574 0.010*† -3.49E-03 -3.62E-07 -1.947* -2.124* 0.00† 0.00† 78  Species Dataset 1983 N 2015 N Intercept Year Elevation (m) Elevation2 (m2) Fires After 1983 Fires Before 1983 Year X Elevation Year X Elevation2 GOOB Rarefied 20 72 -3.536 1.535 3.89E-03 -2.76E-06 -0.672 -1.324 0.00 0.00  Original 20 48.29 -4.045* 1.000* 4.86E-03 -3.20E-06 -0.231 -0.385* 0.00† 0.00† GYDR Original 17 29 -4.086 0.397 4.55E-03 -2.87E-06 -15.72 -0.072 0.00 0.00  Rarefied 17 24.83 -4.386 0.193* 5.11E-03 -3.10E-06 -15.61* 0.058* 0.00† 0.00† HIAL Original 12 14 -1.747 0.034 -1.31E-03 -3.93E-07 0.000 0.000 0.00 0.00  Rarefied 12 11.19 -1.945* -0.016*† -1.21E-03 -3.75E-07 0.000† 0.000† 0.00† 0.00† HODI Original 17 16 -2.656 -0.895 2.28E-03 -3.26E-06 0.811 1.156 4.03E-03 -3.93E-06  Rarefied 17 11.82 -2.437 -0.662 1.61E-03† -2.74E-06 0.639 1.111 2.52E-03 -2.62E-06 LIBO Original 48 74 -5.172 0.628 1.17E-02 -7.84E-06 -16.80 0.038 0.00 0.00  Rarefied 48 69.55 -5.068 0.539* 1.15E-02 -7.73E-06 -16.75* 0.087* 0.00† 0.00† LUPE Original 35 46 -36.839 -5.267 3.74E-02 -9.42E-06 -1.975 -13.02 6.62E-03 -1.94E-06  Rarefied 35 37.12 -33.319 0.026*† 3.32E-02 -8.21E-06 -1.800 -12.38 0.00*† 0.00*† MANE Original 62 68 -4.345 0.049 1.24E-02 -9.65E-06 -1.660 1.095 0.00 0.00  Rarefied 62 60.35 -4.051* -0.013*† 1.14E-02* -9.04E-06* -1.590* 0.864* 0.00† 0.00† MEFE Original 26 31 -5.258 -1.325 4.89E-03 -1.91E-06 0.000 0.000 2.99E-03 -1.36E-06  Rarefied 26 16.12 -5.087 -1.521 4.19E-03* -1.55E-06* 0.000† 0.000† 2.17E-03† -9.60E-07† MOSS Original 195 328 0.703 2.879 -2.71E-04 -1.01E-07 -1.513 -0.326 -1.44E-03 5.58E-07  Rarefied 195 304.0 0.299 4.859 5.61E-04† -4.47E-07* -1.537 -0.244 -5.81E-03 2.13E-06 79  Species Dataset 1983 N 2015 N Intercept Year Elevation (m) Elevation2 (m2) Fires After 1983 Fires Before 1983 Year X Elevation Year X Elevation2 OPHO Original 29 23 -0.976 -0.669 -7.06E-04 -8.62E-07 -15.94 0.238 1.18E-03 -5.31E-07  Rarefied 29 17.35 -0.223* -5.921 -2.03E-03* -3.99E-07* -15.81* 0.207† 1.14E-02* -5.19E-06* PAMY Original 127 138 -1.923 0.400 3.36E-03 -1.82E-06 1.191 0.897 -6.08E-04 2.39E-07  Rarefied 127 115.8 -2.136 -0.048* 3.63E-03 -1.92E-06 1.056* 0.835 0.00*† 0.00*† PHEM Original 61 66 -34.57 0.063 3.68E-02 -9.44E-06 -2.870 -13.41 0.00 0.00  Rarefied 61 55.55 -34.04 -0.080* 3.60E-02 -9.20E-06 -2.648* -13.22* 0.00† 0.00† POMU Original 20 15 0.454 -0.100 -4.34E-03 1.21E-07 -0.580 -0.080 0.00 0.00  Rarefied 20 11.04 0.386 -0.493* -4.18E-03 7.07E-08† -0.286* -0.056 0.00† 0.00† PTAQ Original 47 29 -2.363 -0.558 3.01E-03 -2.24E-06 -0.498 0.276 0.00 0.00  Rarefied 47 23.15 -2.506 -0.814* 3.38E-03 -2.45E-06 -0.355* 0.211 0.00† 0.00† RHAL Original 24 19 -43.40 -0.097 5.32E-02 -1.67E-05 0.000 0.000 0.00 0.00  Rarefied 24 14.79 -40.57* -0.309* 4.93E-02* -1.54E-05* 0.000† 0.000† 0.00† 0.00† RULA Original 18 20 -18.01 0.034 2.31E-02 -8.17E-06 -16.48 -15.64 0.00 0.00  Rarefied 18 13.29 -24.57* -0.134* 3.21E-02* -1.12E-05* -16.25 -15.30 0.00† 0.00† RUPA Original 33 26 -5.956 1.869 1.00E-02 -5.76E-06 -0.037 0.154 -4.64E-03 2.38E-06  Rarefied 33 22.14 -6.067 1.120† 1.03E-02 -5.88E-06 0.000*† 0.000*† -3.28E-03† 1.74E-06† RUPE Original 23 30 -6.202 0.112 6.92E-03 -2.80E-06 -15.965 -1.164 0.00 0.00  Rarefied 23 24.52 -6.649 0.013*† 7.61E-03 -3.09E-06 -15.825* -1.032* 0.00 0.00 80  Species Dataset 1983 N 2015 N Intercept Year Elevation (m) Elevation2 (m2) Fires After 1983 Fires Before 1983 Year X Elevation Year X Elevation2 RUSP Original 15 24 -1.908 -0.116 -5.37E-04 -5.10E-07 -15.663 0.033 8.16E-04 -2.91E-07  Rarefied 15 22.35 -1.875* 0.140† -5.84E-04 -4.81E-07 -15.621 0.055 1.09E-04† 0.00*† SOSI Original 48 36 -10.84 -1.619 1.33E-02 -4.41E-06 -1.658 -15.51 1.85E-03 -5.88E-07  Rarefied 48 21.68 -10.23* -2.871 1.25E-02* -4.14E-06* -1.458 -15.34* 2.41E-03† -7.55E-07† SPBE Original 17 36 -4.229 0.855 5.27E-03 -3.84E-06 0.551 0.066 0.00 0.00  Rarefied 17 33.45 -3.959 0.779* 4.66E-03* -3.52E-06* 0.360† 0.051† 0.00† 0.00† TITR Original 34 46 -2.300 0.182 2.03E-03 -1.56E-06 -1.452 -0.234 0.00 0.00  Rarefied 34 41.38 -2.381 0.070* 2.13E-03 -1.58E-06 -1.140 -0.137 0.00† 0.00† TRBO Original 10 36 -4.295 1.017 5.72E-03 -5.33E-06 -16.60 1.034 3.35E-03 -3.63E-06  Rarefied 10 29.22 -4.055 1.029 5.23E-03 -5.08E-06 -16.45* 0.831 2.02E-03 -2.26E-06 VAAL Original 11 11 -37.05 21.00 6.59E-02 -3.06E-05 -7.302 0.202 -3.37E-02 1.20E-05  Rarefied 11 9.66 -37.08* 21.47 6.59E-02 -3.06E-05* -7.002 0.232 -3.54E-02 1.31E-05 VADE Original 34 73 -51.15 1.614 5.54E-02 -1.50E-05 -2.572 1.724 0.00 0.00  Rarefied 34 67.57 -47.85 1.388* 5.17E-02 -1.40E-05 -2.772 1.722 0.00† 0.00† VAME Original 96 152 -6.706 0.791 1.06E-02 -4.23E-06 -0.893 -0.201 0.00 0.00  Rarefied 96 119.9 -6.363* 0.435 9.88E-03* -3.96E-06* -0.402* -0.094 0.00† 0.00† VASI Original 24 35 -12.32 0.257 1.18E-02 -3.18E-06 -0.645 -9.109 0.00 0.00  Rarefied 24 28.98 -11.68* 0.073* 1.09E-02* -2.88E-06* -0.444* -7.182* 0.00† 0.00†   81  Table D5. Species code (refer to Table D1), number of measurements in either survey (1983 N and 2014/2015 N, where “2015 N” is used as shorthand for the latter), AIC relative to the top model, weight based on AIC (w), r2, and coefficients of top models (AIC < 2) predicting cover. r2 is a pseudo-r-squared calculated from the correlation between model predicted values and the midpoint of the measured cover interval. Model-averaged coefficients appear as the last row of each species section (weighted by w). Annotations on the coefficients indicate significance of that coefficient in its associated model, where ~ corresponds to P < 0.1, * corresponds to P < 0.05, ** corresponds to P < 0.01, and *** corresponds to P <0.001. An X annotation on the species name indicates that insufficient variance was present to reliably fit one or more of that species’ models. Species 1983 N 2015 N AIC w r2 Intercept Year Elevation (m) Elevation (m2) Fires After 1983 Fires Before 1983 Year X Elevation Year X Elevation2 ACMI 10 24 0.00 0.43 0.06 5.022*** 0.000 -5.14E-04 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    0.18 0.39 NA 4.410 0.000 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    1.76 0.18 0.07 4.948*** 0.179 -5.47E-04 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 Weighted average NA NA NA 4.769 0.032 -3.18E-04 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 AMAL 42 45 0.00 0.36 0.06 9.165~ -10.82 -4.64E-03 0.00E+00 0.000 0.000 1.70E-02* 0.00E+00    0.44 0.29 NA 6.804*** 0.000 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    1.22 0.20 0.01 5.502*** 2.529 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    1.67 0.16 0.01 3.928 0.000 3.64E-03 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 Weighted average NA NA NA 6.950 -3.396 -1.10E-03 0.00E+00 0.000 0.000 6.12E-03 0.00E+00 ARUV 42 29 0.00 0.31 0.12 27.16*** 0.000 -9.32E-03~ 0.00E+00 9.125 -11.07 0.00E+00 0.00E+00    0.32 0.27 0.06 28.34*** 0.000 -9.69E-03* 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    1.49 0.15 0.12 27.68*** -2.805 -8.64E-03~ 0.00E+00 8.930 -11.11 0.00E+00 0.00E+00    1.65 0.14 0.06 17.97*** 0.000 0.00E+00 0.00E+00 11.14* -7.491 0.00E+00 0.00E+00    1.72 0.13 0.07 28.85*** -3.141 -8.89E-03~ 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 82  Weighted average NA NA NA 26.51 -0.834 -7.98E-03 0.00E+00 5.719 -6.150 0.00E+00 0.00E+00 Species 1983 N 2015 N AIC w r2 Intercept Year Elevation (m) Elevation (m2) Fires After 1983 Fires Before 1983 Year X Elevation Year X Elevation2 ATFI 24 30 0.00 0.54 NA 15.66*** 0.000 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    1.69 0.23 0.01 12.55* 0.000 3.95E-03 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    1.75 0.23 0.00 16.95*** -2.343 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 Weighted average NA NA NA 15.23 -0.529 9.21E-04 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 CAME 32 33 0.00 0.48 NA 19.58*** 0.000 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    0.63 0.35 0.02 17.41*** 4.308 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    1.98 0.18 0.00 17.23 0.000 1.32E-03 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 Weighted average NA NA NA 18.41 1.497 2.33E-04 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 CARU 33 26 0.00 0.26 0.04 15.89*** -6.990 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    0.29 0.23 0.07 8.674 -7.616~ 7.71E-03 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    0.57 0.20 NA 12.79*** 0.000 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    0.72 0.18 0.10 2.605 6.833~ 1.42E-02 0.00E+00 0.000 0.000 -1.48E-02 0.00E+00    1.38 0.13 0.02 6.407 0.000 6.57E-03 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 Weighted average NA NA NA 9.966 -2.305 5.21E-03 0.00E+00 0.000 0.000 -2.70E-03 0.00E+00 CEVE 26 20 0.00 0.50 NA 12.04*** 0.000 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    0.91 0.32 0.02 10.62*** 3.389 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    1.99 0.18 0.00 12.41* 0.000 -3.60E-04 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 83  Weighted average NA NA NA 11.66 1.072 -6.63E-05 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 Species 1983 N 2015 N AIC w r2 Intercept Year Elevation (m) Elevation (m2) Fires After 1983 Fires Before 1983 Year X Elevation Year X Elevation2 CHAN 18 30 0.00 0.57 NA 5.465*** 0.000 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    1.91 0.22 0.00 6.131* 0.000 -5.96E-04 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    2.00 0.21 0.00 5.445*** 0.030 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 Weighted average NA NA NA 5.607 0.006 -1.31E-04 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 CHUM X 62 60 0.00 0.51 NA 4.134 0.000 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    1.08 0.30 0.01 4.464*** 0.000 -4.25E-04 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    1.87 0.20 0.00 4.218 0.065 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 Weighted average NA NA NA 4.248 0.013 -1.26E-04 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 CLUN 44 53 0.00 0.29 0.03 6.965*** 0.000 -2.44E-03 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    0.48 0.23 NA 4.763*** 0.000 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    0.57 0.22 0.05 0.000NA 0.000 1.37E-02*** -8.67E-06*** 0.000 0.000 0.00E+00 0.00E+00    1.18 0.16 0.06 7.438*** 0.000 -2.78E-03~ 0.00E+00 -3.755 -2.404 0.00E+00 0.00E+00    2.00 0.11 0.03 6.953*** 0.019 -2.44E-03 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 Weighted average NA NA NA 5.026 0.002 1.58E-03 -1.88E-06 -0.602 -0.386 0.00E+00 0.00E+00 COCA 20 26 0.00 0.47 0.06 4.571 -0.478 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    0.64 0.34 NA 4.279 0.000 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    1.91 0.18 0.06 4.372*** -0.547 1.87E-04 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 84  Weighted average NA NA NA 4.434 -0.326 3.40E-05 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 Species 1983 N 2015 N AIC w r2 Intercept Year Elevation (m) Elevation (m2) Fires After 1983 Fires Before 1983 Year X Elevation Year X Elevation2 COSE 12 13 0.00 0.46 0.28 31.71*** -23.70** 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    0.86 0.30 0.31 13.38 -25.77*** 2.76E-02 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    1.21 0.25 0.36 29.01*** -21.04** 0.00E+00 0.00E+00 33.488~ 0.000NA 0.00E+00 0.00E+00 Weighted average NA NA NA 25.61 -23.66 8.17E-03 0.00E+00 8.316 0.000 0.00E+00 0.00E+00 GASH 22 23 0.00 0.36 0.35 54.71*** -18.33*** -3.81E-02** 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    0.39 0.30 0.37 65.42*** -37.96* -5.68E-02** 0.00E+00 0.000 0.000 3.19E-02 0.00E+00    1.38 0.18 0.39 53.20*** -17.17** -3.76E-02** 0.00E+00 30.84 -2.510 0.00E+00 0.00E+00    1.60 0.16 0.41 65.19*** -37.80* -5.92E-02** 0.00E+00 31.16~ 5.911 3.44E-02 0.00E+00 Weighted average NA NA NA 59.31 -27.10 -4.70E-02 0.00E+00 10.62 0.505 1.50E-02 0.00E+00 GOOB X 20 52 0.00 0.66 NA 4.017*** 0.000 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    1.34 0.34 0.01 2.860 1.215 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 Weighted average NA NA NA 3.626 0.411 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 GYDR 17 24 0.00 0.28 0.10 1.803 0.000 5.18E-03~ 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    0.11 0.26 0.13 2.608 -2.607 6.01E-03~ 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    0.87 0.18 0.15 -1.037 3.097 1.10E-02~ 0.00E+00 0.000 0.000 -7.31E-03 0.00E+00    1.10 0.16 NA 6.191*** 0.000 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    1.66 0.12 0.12 0.000NA 0.000 1.04E-02** -3.16E-06 0.000 0.000 0.00E+00 0.00E+00 85  Weighted average NA NA NA 1.989 -0.130 6.25E-03 -3.83E-07 0.000 0.000 -1.31E-03 0.00E+00 Species 1983 N 2015 N AIC w r2 Intercept Year Elevation (m) Elevation (m2) Fires After 1983 Fires Before 1983 Year X Elevation Year X Elevation2 LIBO 48 72 0.00 0.25 0.06 3.642*** 2.212* 0.00E+00 0.00E+00 0.000 3.420* 0.00E+00 0.00E+00    0.34 0.21 0.03 4.073*** 2.175* 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    1.17 0.14 0.04 6.076** 2.129* -2.59E-03 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    1.20 0.14 0.06 3.108 7.312~ 1.23E-03 0.00E+00 0.000 0.000 -6.75E-03 0.00E+00    1.27 0.13 0.07 5.210** 2.173* -2.00E-03 0.00E+00 0.000 3.248* 0.00E+00 0.00E+00    1.33 0.13 0.09 2.318 7.199~ 1.73E-03 0.00E+00 0.000 3.221* -6.55E-03 0.00E+00 Weighted average NA NA NA 4.037 3.531 -2.35E-04 0.00E+00 0.000 1.701 -1.77E-03 0.00E+00 LUPE 35 46 0.00 0.70 0.07 8.377*** -3.479* 0.00 0.00 0.000 0.000 0.00 0.00    1.71 0.30 0.07 4.461 -3.457* 2.22E-03 0.00 0.000 0.000 0.00 0.00 Weighted average NA NA NA 7.209 -3.472 6.61E-04 0.00 0.000 0.000 0.00 0.00 MANE 62 60 0.00 0.52 0.07 16.29*** 0.000 -1.04E-02** 0.00E+00 -7.961 3.827 0.00E+00 0.00E+00    1.40 0.26 0.03 16.33*** 0.000 -9.86E-03* 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    1.72 0.22 0.08 16.80*** -0.930 -1.04E-02* 0.00E+00 -8.446 3.706 0.00E+00 0.00E+00 Weighted average NA NA NA 16.41 -0.205 -1.03E-02 0.00E+00 -6.009 2.810 0.00E+00 0.00E+00 MEFE 26 17 0.00 0.28 0.18 -1.483 -6.375~ 1.30E-02** 0.00E+00 -15.37* -6.770 0.00E+00 0.00E+00    0.47 0.22 NA 10.13*** 0.000 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    0.51 0.22 0.05 2.759 0.000 5.99E-03* 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 86     1.00 0.17 0.13 -1.145 0.000 1.03E-02 0.00E+00 -10.73~ -4.842 0.00E+00 0.00E+00    1.84 0.11 0.18 -2.809 -1.207 1.43E-02* 0.00E+00 -16.31* -6.490 -4.27E-03 0.00E+00 Weighted average NA NA NA 1.923 -1.918 8.30E-03 0.00E+00 -7.939 -3.440 -4.75E-04 0.00E+00 Species 1983 N 2015 N AIC w r2 Intercept Year Elevation (m) Elevation (m2) Fires After 1983 Fires Before 1983 Year X Elevation Year X Elevation2 MOSS 195 326 0.00 0.59 0.21 55.61*** -4.149* -2.35E-02*** 0.00E+00 -11.24** -6.098 0.00E+00 0.00E+00    0.70 0.41 0.21 59.05*** -9.415~ -2.67E-02*** 0.00E+00 -11.60** -6.208 4.93E-03 0.00E+00 Weighted average NA NA NA 57.03 -6.323 -2.48E-02 0.00E+00 -11.39 -6.144 2.04E-03 0.00E+00 OPHO 29 14 0.00 0.54 NA 12.26*** 0.000 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    1.69 0.23 0.01 8.766 0.000 4.60E-03 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    1.77 0.22 0.01 13.02*** -2.399 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 Weighted average NA NA NA 11.62 -0.537 1.07E-03 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 PAMY 127 115 0.00 0.22 0.06 0.000NA 0.000NA 3.23E-02*** -1.57E-05*** 0.000 0.000 -1.42E-02** 8.81E-06*    0.04 0.22 0.05 19.60*** -13.93** -5.36E-03~ 0.00E+00 0.000 0.000 9.41E-03* 0.00E+00    0.32 0.19 0.06 20.78*** -13.43* -5.57E-03** 0.00E+00 -4.038 -3.681 8.95E-03* 0.00E+00    1.06 0.13 0.03 14.20*** -4.506* 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    1.08 0.13 0.04 15.20*** -4.452* 0.00E+00 0.00E+00 -4.423~ -3.416 0.00E+00 0.00E+00    1.61 0.10 0.03 0.000NA 0.000 2.57E-02*** -1.16E-05*** 0.000 0.000 0.00E+00 0.00E+00 Weighted average NA NA NA 12.166 -6.820 7.59E-03 -4.69E-06 -1.353 -1.153 6.05E-04 1.98E-06 87  Species 1983 N 2015 N AIC w r2 Intercept Year Elevation (m) Elevation (m2) Fires After 1983 Fires Before 1983 Year X Elevation Year X Elevation2 PHEM 61 62 0.00 0.62 0.04 21.51*** -6.006* 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    1.00 0.38 0.05 8.880 -6.014* 7.34E-03 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 Weighted average NA NA NA 16.74 -6.009 2.77E-03 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 PTAQ 47 21 0.00 0.36 0.03 19.40*** 0.000 -8.31E-03 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    0.24 0.32 NA 12.39*** 0.000 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    1.68 0.16 0.03 19.73*** -2.158 -7.92E-03 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    1.70 0.16 0.01 13.26*** -2.851 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 Weighted average NA NA NA 16.23 -0.783 -4.27E-03 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 RHAL 24 18 0.00 0.48 0.07 -21.74 0.000 2.66E-02~ 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    0.84 0.32 NA 20.31*** 0.000 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    1.77 0.20 0.07 -22.96 -2.553 2.80E-02~ 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 Weighted average NA NA NA -8.621 -0.510 1.84E-02 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 RULA 18 19 0.00 1.00 0.22 27.89** -24.96* -1.55E-02** 0.00E+00 0.000 0.000 1.57E-02* 0.00E+00 RUPA 33 21 0.00 0.33 0.06 26.82*** 0.000 -1.48E-02~ 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    0.04 0.32 0.12 41.21*** -28.82* -3.00E-02** 0.00E+00 0.000 0.000 2.98E-02* 0.00E+00    1.13 0.19 NA 14.20*** 0.000 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    1.47 0.16 0.06 28.23*** -3.283 -1.49E-02~ 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 Weighted average NA NA NA 29.34 -9.860 -1.70E-02 0.00E+00 0.000 0.000 9.67E-03 0.00E+00 88  Species 1983 N 2015 N AIC w r2 Intercept Year Elevation (m) Elevation (m2) Fires After 1983 Fires Before 1983 Year X Elevation Year X Elevation2 RUPE 23 29 0.00 0.47 0.05 10.92*** -5.230~ 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    0.87 0.30 NA 8.016*** 0.000 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    1.40 0.23 0.06 15.05** -5.544~ -3.29E-03 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 Weighted average NA NA NA 11.00 -3.724 -7.62E-04 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 RUSP 15 24 0.00 0.56 NA 13.94*** 0.000 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    1.87 0.22 0.00 16.51* 0.000 -3.09E-03 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    1.95 0.21 0.00 14.77** -1.390 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 Weighted average NA NA NA 14.69 -0.297 -6.87E-04 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 SOSI 48 24 0.00 0.53 NA 6.007*** 0.000 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    1.58 0.24 0.01 6.388*** -1.130 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    1.62 0.23 0.01 8.402* 0.000 -1.67E-03 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 Weighted average NA NA NA 6.659 -0.271 -3.90E-04 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 SPBE 17 32 0.00 0.49 NA 8.073*** 0.000 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    0.84 0.32 0.02 11.79** 0.000 -4.83E-03 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    1.98 0.18 0.00 8.298*** -0.362 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 Weighted average NA NA NA 9.318 -0.066 -1.56E-03 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 TITR 34 43 0.00 0.52 NA 7.965*** 0.000 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    1.31 0.27 0.01 8.933*** -1.790 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 89     1.82 0.21 0.00 6.880* 0.000 1.24E-03 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 Weighted average NA NA NA 7.999 -0.483 2.60E-04 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 Species 1983 N 2015 N AIC w r2 Intercept Year Elevation (m) Elevation (m2) Fires After 1983 Fires Before 1983 Year X Elevation Year X Elevation2 TRBO X 10 30 0.00 0.48 NA 3.955 0.000 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    0.81 0.32 0.02 1.703 2.834 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    1.78 0.20 0.00 4.326*** 0.000 -6.65E-04 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 Weighted average NA NA NA 3.306 0.909 -1.31E-04 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 VADE 34 72 0.00 0.63 0.08 28.04*** -9.381** 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    1.05 0.37 0.09 43.07** -9.607** -8.65E-03 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 Weighted average NA NA NA 33.62 -9.465 -3.21E-03 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 VAME 96 135 0.00 1.00 0.23 -2.608 2.202 1.85E-02*** 0.00E+00 0.000 0.000 -9.91E-03* 0.00E+00 VASI 24 30 0.00 0.55 NA 6.304*** 0.000 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    1.71 0.24 0.00 3.963 0.000 1.46E-03 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    1.91 0.21 0.00 6.044*** 0.451 0.00E+00 0.00E+00 0.000 0.000 0.00E+00 0.00E+00 Weighted average NA NA NA 5.698 0.096 3.44E-04 0.00E+00 0.000 0.000 0.00E+00 0.00E+00    90  Table D6. Coefficients of top models (AICc<2) describing the association between standardized traits and five different range metrics. Traits are growth form (GF; including S for semi-shrubs and T for tree-like shrubs), leaf texture (LT), shade tolerance (ST), seed mass (SM), and specific leaf area (SLA). The last row of each metric’s section indicates the weighted average of coefficients in top models calculated from Akaike weights (w). Annotations on coefficients indicate significance of that coefficient in its associated model, where ~ corresponds to P < 0.1, * corresponds to P < 0.05, and ** corresponds to P < 0.01.  Range metric AICc w r2 Intercept GF Forb GF Shrub GF S-Shrub GF T-Shrub LT Coarse LT Fine ST SM SLA Original Lower edge displacement 0.00 0.66 0.06 -30.55        26.77   1.32 0.34 0.01 -31.70         12.20  Weighted average    -30.95        17.66 4.151  Upper edge displacement 0.00 0.57 0.09 36.01         -64.72  1.90 0.22 0.12 37.40        35.55 -82.55~   1.93 0.21 0.21 11.33     -4.625 225.5~   -87.34~  Weighted average    31.00     -0.996 48.58  7.773 -73.49 Mod Lower edge displacement 0.00 0.35 0.02 -35.73        16.53   0.13 0.33 0.02 -38.53         -16.07   1.43 0.17 0.06 -37.40        29.03 -30.63   1.79 0.14 0.05 -57.35~     34.78 84.55     Weighted average    -40.06     5.009 12.18  10.85 -10.60  Upper edge displacement 0.00 0.50 0.36 -163.8 409.9* 216.1 198.6 180.4       0.00 0.50 0.19 66.64     2.805 249.3*     Weighted average    -48.71 205.2 108.2 99.42 90.30 1.401 124.5    91   Range metric AICc w r2 Intercept GF Forb GF Shrub GF S-Shrub GF T-Shrub LT Coarse LT Fine ST SM SLA Mod Peak-difference displacement 0.00 0.60 0.35 9.985     -48.36 242.2**   -48.92~  0.81 0.40 0.26 7.137     -15.98 212.1**      1.63 0.21 0.39 9.972     -53.35 265.2**  29.11 -66.71*  Weighted average    9.081     -39.15 237.5**  6.106 -37.17       92  Table D7. Coefficients of top models (AICc<2) describing the association between standardized traits and five different range metrics, with Achillea millefolium excluded. Traits are growth form (GF; including S for semi-shrubs and T for tree-like shrubs), leaf texture (LT; including C for coarse and F for fine), shade tolerance (ST; including II for intermediate-intolerant, I for intolerant, and T for tolerant), seed mass (SM), and specific leaf area (SLA). The last row of each metric’s section indicates the weighted average of coefficients in top models calculated from Akaike weights (w). Annotations on coefficients indicate significance of that coefficient in its associated model, where ~ corresponds to P < 0.1 and * corresponds to P < 0.05.  Range metric AICc w r2 Int. GF - F GF - S F - S GF - T LT - C LT - F ST - II ST - I ST - T SM SLA Original Lower edge displacement 0.00 0.65 0.05 -30.13          26.59   1.26 0.35 0.01 -31.03           11.90  Weighted average    -30.44          17.36 4.132  Upper edge displacement 0.00 0.70 0.43 82.83       44.24 -209.4* -62.94  -68.02*  1.69 0.30 0.47 98.07       17.25 -219.0* -80.88 36.23 -85.70*  Weighted average    87.42       36.10 -212.3* -68.35 10.92 -73.35* Mod Lower edge displacement 0.00 0.46 0.04 -45.56*          20.65   0.78 0.32 0.01 -47.85*           -11.87  1.48 0.22 0.08 -46.88*          31.89 -27.74  Weighted average    -46.58*          16.64 -9.870  Upper edge displacement 0.00 0.52 0.33 -163.8 345.7* 198.6 216.1~ 180.4         1.36 0.26 0.02 70.90*          -16.03    1.69 0.22 0.00 72.62*           8.458  Weighted average      -49.91 178.5 102.6 111.6 93.15      -4.196 1.877                  93   Range metric AICc w r2 Int. GF - F GF - S F - S GF - T LT - C LT - F ST - II ST - I ST - T SM SLA Mod Peak-difference displacement 0.00 0.32 0.04 0.271          5.832   0.31 0.27 0.03 -0.531           -5.436   0.46 0.25 0.12 -0.226          10.06 -10.45   1.39 0.16 0.09 7.137     -15.98 -26.14       Weighted average    1.016     -2.533 -4.144    4.389 -4.112     

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