The History and Future of the Rare, Endemic Plant Species, Draba Yukonensis (Yukon Draba)

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2021-02-11 The history and future of the rare, endemic plant species, Draba yukonensis (Yukon Draba)

Jasper, Caroline

Jasper, C. (2021). The history and future of the rare, endemic plant species, Draba yukonensis (Yukon Draba) (Unpublished master's thesis). University of Calgary, Calgary, AB. http://hdl.handle.net/1880/113098 master thesis

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The history and future of the rare, endemic plant species, Draba yukonensis (Yukon Draba)

Caroline Jasper

A THESIS SUBMITTED TO THE FACULTY OF GRADUATE STUDIES IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE

GRADUATE PROGRAM IN BIOLOGICAL SCIENCES

CALGARY, ALBERTA

FEBRUARY, 2021

ABSTRACT

Many species in Canada remain poorly characterized regarding the projected impact of climate change. Here, I report my results from species distribution modeling for the rare, Yukon endemic plant, Draba yukonensis. Predicted change in range size in 2070 is an increase of 171375.1-

184318.0 km2, which is surprising considering its limited known range. Overlap of predicted future and current ranges is 96.9-97.4%. Occurrence of protected areas in the predicted future range is 9.7-10.5%. It is possible that D. yukonensis represents a recently originated variety of a more common species that recently experienced polyploidization. I therefore performed phylogenetic analyses to elucidate relationships of D. yukonensis with other Draba species in the

Yukon. I found that D. yukonensis is appropriately delineated as a Canadian endemic species and may be an allopolyploid of D. fladnizensis and D. lactea, or, likelier, is closely related to D. lactea. Overall, this work provides important predictions regarding where D. yukonensis could require interventions to aid its conservation.

ACKNOWLEDGEMENTS

First and foremost, I would like to thank my incredible Supervisor and mentor, Dr. Jana Vamosi, whose sage advice, dedication to my project, and constant encouragement were immeasurably appreciated. You were there with the perfect thing to say every time I needed it, and I truly admire you. To my committee members, Dr. Marco Musiani and Dr. Mindi Summers, who put in a great deal of effort in reviewing and commenting on my work, thank you for all of your support. Also, I thank Renna Truong of SANDS at the University of Calgary, whose patience while teaching me ArcGIS is commendable. To the teams at the Yukon Conservation Data

Centre and Canadian Wildlife Services in the Yukon (including Syd Cannings, Bruce Bennett,

Randi Mulder, Kristy Kennedy, and Caitlin Willier), thank you for showing me your beautiful part of the world and helping me attain the data needed to better understand it. Thank you to Dr.

Jenny McCune of the University of Lethbridge, whose important work in species distribution modeling is paving the way for newcomers like me. To the Champagne and Aishihik First

Nations, thank you for your continued and invaluable land stewardship, which helps to protect

Yukon Draba and the other wildlife in your territories. To my family and friends, thank you for constantly checking in and always being there for me. To my best friends, my husband Jeremy and our cats, Crookshanks and Siam, thank you for your sacrifices and infinite love while I pursue my aspirations.

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DEDICATION

To my husband, Jeremy. You gave me courage when I felt intimidated, happiness when I cried, and peace when I worried. Thank you for being you.

TABLE OF CONTENTS

ABSTRACT ...... ii ACKNOWLEDGEMENTS ...... iii DEDICATION ...... iv TABLE OF CONTENTS ...... v LIST OF TABLES ...... vi LIST OF FIGURES ...... vii CHAPTER 1: GENERAL INTRODUCTION ...... 1 CHAPTER 2: PREDICTING THE FUTURE DISTRIBUTION OF YUKON DRABA DUE TO CLIMATE CHANGE ...... 5 INTRODUCTION ...... 5 METHODS ...... 10 RESULTS ...... 18 DISCUSSION ...... 20 TABLES ...... 28 FIGURES...... 31 CHAPTER 3: UNDERSTANDING THE ORIGIN AND PHYLOGENETIC POSITION OF YUKON DRABA ...... 47 INTRODUCTION ...... 47 METHODS ...... 52 RESULTS ...... 55 DISCUSSION ...... 57 TABLES ...... 66 FIGURES...... 68 CHAPTER 4: GENERAL CONCLUSIONS...... 78 REFERENCES ...... 82 SUPPLEMENTARY MATERIALS ...... 98

LIST OF TABLES

TABLE 2.1: LIST OF ENVIRONMENTAL VARIABLES ...... 28 TABLE 2.2: AREAS OF CALCULATIONS ...... 30

TABLE 3.1: LIST OF ACCESSIONS...... 66

LIST OF FIGURES

FIGURE 2.1: SPECIES DISTRIBUTION MODELING ...... 31 FIGURE 2.2: YUKON DRABA IMAGE AND KNOWN DISTRIBUTION ...... 33 FIGURE 2.3: SYSTEMATIC SAMPLING GRIDS ...... 35 FIGURE 2.4: TRANSFORMATION OF CONTUNIOUS MAPS TO BINARY MAPS USING THRESHOLDS...... 37 FIGURE 2.5: MEAN CONTINUOUS PROBABILITY DISTRIBUTION MAPS ...... 38 FIGURE 2.6: BINARY DISTRIBUTION MAPS ...... 40 FIGURE 2.7: OVERLAP OF PREDICTED CURRENT AND FUTURE DISTRIBUTIONS MAPS ...... 43 FIGURE 2.8: OVERLAP OF PROTECTED AREAS AND PREDICTED FUTURE DISTRIBUTION MAPS ...... 45

FIGURE 3.1: PHYLOGENETIC TREES AND NETWORKS ...... 68 FIGURE 3.2: RANGES OF DRABA SPECIES USED IN THIS STUDY ...... 70 FIGURE 3.3: MAXIMUM LIKELIHOOD TREE FOR CONCATENATED GENES ... 72 FIGURE 3.4: NETWORK ANALYSIS OF CONCATENATED GENES ...... 74 FIGURE 3.5: IMAGES OF D. YUKONENSIS, D. FLADNIZENSIS, & D. LACTEA ..... 76 FIGURE 3.6: OVERLAP OF RANGES OF D. YUKONENSIS, D. FLADNIZENSIS, & D. LACTEA ...... 77

FIGURE S1: MAPS OF YUKON TERRITORY, CANADA ...... 98 FIGURE S2: MAXIMUM PARSIMONY TREE FOR CONCATENATED GENES .. 100 FIGURE S3: BAYESIAN INFERENCE TREE FOR CONCATENATED GENES .... 101 FIGURE S4: MAXIMUM LIKELIHOOD TREE FOR ITS2 ...... 102 FIGURE S5: MAXIMUM PARSIMONY TREE FOR ITS2 ...... 103 FIGURE S6: BAYESIAN INFERENCE TREE FOR ITS2 ...... 104 FIGURE S7: NETWORK ANALYSIS FOR ITS2 ...... 105 FIGURE S8: MAXIMUM LIKELIHOOD TREE FOR RBCL ...... 106 FIGURE S9: MAXIMUM PARSIMONY FOR RBCL ...... 107

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FIGURE S10: BAYESIAN INFERENCE TREE FOR RBCL ...... 108 FIGURE S11: NETWORK ANALYSIS FOR RBCL ...... 109

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Chapter 1: General introduction

Human activity is driving the decline of Earth’s biodiversity (Ceballos et al., 2015;

Ceballos et al., 2017). In fact, our planet is now in the midst of its sixth mass extinction

(Ceballos et al., 2015; Ceballos et al., 2017), indicating that the current rate of species extinctions is much greater than the “background” (or typical) extinction rates that prevailed between the previous five mass extinctions (Cellabos et al., 2015). Anthropogenic factors contributing to this elevated extinction include habitat destruction, introduction of alien invasive species (Barnosky et al., 2011; Dirzo & Raven, 2003), and climate change (Ceballos et al., 2015; Shivanna, 2019;

Millennium Ecosystem Assessment [MEA], 2005 as cited in Wagler, 2011). Climate change is also responsible for ecosystem alterations we are currently seeing in nature, such as range shifts

(Chen et al., 2011) and phenological changes (Edwards & Richardson, 2004; Porto, 2019).

Global warming is occurring at an accelerated rate in northern latitudes (Bradshaw &

Holzapfel, 2006; Karl & Trenberth, 2003). This is causing phenomena such as shrubification

(encroachment into herb-dominated habitats by shrubs) (Formica et al., 2014; Kettenbach et al.,

2017), which can disrupt native plant communities by, for instance, changing availability of resources (Kettenbach et al., 2017; Wilson & Nilsson, 2009). The Yukon Territory in Canada is a northern region that has also been dramatically affected by climate change. For example, a recent infestation of the spruce bark beetle in the Champagne and Aishihik First Nations

Traditional Territory was considered to be the largest on record in Canada (Arctic Climate

Impact Assessment [ACIA], 2004, as cited in Ogden & Innes, 2008). Additionally, the Yukon has already seen increased risk of fire, changes in salmon and caribou migrations and populations, and biodiversity loss due to climate change (Streicker, 2016). Therefore, conservation efforts focused on areas such as the Yukon Territory are of the utmost importance.

The Yukon is located in northwestern Canada between the 60th parallel and the Beaufort

Sea, and covers 482,443 km2. The Yukon is geologically diverse, with river valleys, plateaus, permafrost, glaciers, and the world’s largest non-polar icefield (a large expanse of ice and glaciers surprisingly occurring outside of polar environments, in Kluane National Park, YT)

(Travel Yukon, 2017). Additionally, the Yukon hosts the Saint Elias Mountain range, which is home to Mount Logan, the highest mountain in Canada (Encyclopædia Britannica, 2017). The climate in the Yukon ranges from sub-arctic to arctic, with average summer and winter temperatures in Whitehorse, the capital city, being 15 oC and -18 oC, respectively (Travel Yukon,

2017), and average annual precipitation being 267 millimetres (Yukon Community Profiles,

2014). See Figure S1 for general Yukon map.

Plants are a key group that are adversely affected by climate change. For example, plants have already been found to have undergone genetic alterations due to climate change (Bradshaw

& Holzapfel, 2006; Jump & Peñuelas, 2005), which may result in their inability to recover from further environmental disturbances, thus leading to increased extinction risks (Jump & Peñuelas,

2005). Rare species, including rare plants, may be disproportionately at risk of extinction due to climate change (Enquist et al., 2019; Schwartz et al., 2006; University of Arizona, 2019).

Therefore, research focusing on rare plants is vital for effective conservation planning.

Draba yukonensis (Yukon Draba) is a rare plant species endemic to the Yukon Territory

(Figure 2.2). Draba yukonensis provides an intriguing model system in investigating how increased information can change the status designation of a species at risk. It is a species of interest with the Canadian Wildlife Service (CWS) and the Yukon Conservation Data Centre

(CDC), such that information on community assemblages, forest encroachment of habitat, soil variables, and DNA barcode data has already been collected, yet there has been little further

2 research on characterizing the genetic diversity of this species or examining whether the genetic diversity is sufficient for climate change adaptation. Ongoing efforts to conserve Yukon Draba have been summarized in the COSEWIC (Committee on the Status of Endangered Wildlife in

Canada) Status Reports (2011, 2018) (COSEWIC, 2011; COSEWIC, 2018a) and, within the

2018 report, the NatureServe Climate Change Vulnerability Index (CCVI) (Young &

Hammerson, 2016).

In this project, I perform species distribution modeling (SDM) to elucidate Yukon

Draba’s future distribution due to climate change, thus allowing for further important information, which is currently lacking, to be included in an updated CCVI for a more accurate assessment. Additionally, I perform phylogenetic analyses on Yukon Draba to clarify its taxonomic status and relationships. Use of these two techniques together can help conservation efforts by, for example, elucidating historical evolutionary patterns of species and using this information to identify refugia networks and dispersal corridors that are important for preservation (Buerki, 2015). In my work, I use species distribution modeling and phylogenetic analyses to aid in Yukon Draba conservation efforts by filling knowledge gaps, including consideration of the reasons Yukon Draba has a limited range.

In Chapter 2 of this work, I perform species distribution modeling of D. yukonensis to elucidate its potential current and future distributions due to climate change. I ask research questions relating to 1) the predicted future (2070) change in range size for D. yukonensis; 2) the overlap of predicted future (2070) range with current range for D. yukonensis; and 3) the occurrence of protected areas in the predicted future (2070) distribution for D. yukonensis. In

Chapter 3, I perform phylogenetic analyses to help clarify the phylogenetic position and taxonomic distinctiveness of D. yukonensis, and relate my findings to the relationship of D.

3 yukonensis to other species of Draba in the Yukon. In the final chapter, Chapter 4, I summarize the findings stemming from the analyses and provide my concluding remarks and recommend future research directions.

Chapter 2: Predicting the future distribution of Yukon Draba due to climate change

INTRODUCTION

Climate change

Anthropogenic climate change is causing the disruption of almost every natural ecosystem worldwide. Climate change has been shown to cause changes in species traits

(Bjorkman et al., 2018; Root et al., 2003), alter species communities (Manish et al., 2016;

Parmesan & Yohe, 2003), cause range shifts (Parmesan et al., 1999; Telwala et al., 2013), greatly accelerate rates of species extinctions and extirpations (Wiens, 2016), and likely facilitate the success of non-native species (Willis et al., 2010). Factors such as these are expected to be amplified under future climate change scenarios. For example, range shifts may continue to occur to the extent that suitable habitat is lost completely for some species (Upson et al., 2016), and invasive species may encroach into novel habitats, possibly threatening native species

(Hannah et al., 2019).

Climate change and rare species

Assessment of species’ vulnerabilities to climate change is important in conservation planning for rare species (Still et al., 2015). Risks are reduced and outcomes are greatly improved when actions are taken quickly (Naujokaitis‐Lewis et al., 2018). This is especially true for rare species, which are expected to experience population decreases, extinctions, and range shifts as a result of climate change sooner than their more widespread counterparts (Lawson et al., 2010; Schwartz et al., 2006). This is due to their limited suitable habitat types and small population sizes (California Department of Fish and Wildlife, n.d.).

Conservation efforts

Climate change in Canada has and will continue to have many drastic effects on natural ecosystems. On a national scale, increased natural disturbances, permafrost decrease, and shorter sea-ice seasons are expected. At regional scales, factors such as sea-level rise, increased ocean- surface temperatures, and greater storm intensity are predicted (Suffling & Scott, 2002).

Additionally, the range expansion of invasive species (e.g., Sharma & Jackson, 2008; Sharma et al., 2009) and the range contraction of native species (e.g., Kerr & Packer, 1998) may occur or worsen.

Canadian conservation efforts usually begin with assessments by COSEWIC (Committee of the Status of Endangered Wildlife in Canada) and SARA (Species at Risk Act). COSEWIC was established to assess species’ risks of extinction using scientific and traditional ecological knowledge. These assessments, called COSEWIC Status Reports, are then used to inform SARA, which dictates which species will be eligible for legal protection under Canadian law

(COSEWIC, n.d.). At-risk species in Canada are listed under SARA’s Schedule 1, ensuring that actions to protect and recover the species are taken (Government of Canada, 2019). As of 2019,

810 species were considered to be at risk in Canada, with 253 of those being plant species

(Freedman & Morton, 2020).

Two important methods to measure climate change vulnerability are species distribution models (SDMs), and climate change vulnerability indices (CCVIs). While these two techniques can be used individually, it is often preferred to use both methods to complement one another and increase confidence in results (Anacker et al., 2013; Shank & Nixon, 2014; Still et al., 2015).

SDMs relate environmental variables (e.g., soil type, temperature, precipitation, etc.) to known species occurrences in order to predict habitat suitability within the study area, representing species’ potential distributions (Fois et al., 2018; Gogol-Prokurat, 2011; Rus et al.,

2018) (see Figure 2.1). SDMs have been used in a variety of contexts to aid conservation efforts, such as establishment of conservation areas (Bosso et al., 2018; Fois et al., 2018; Guisan et al.,

2013; McCune, 2016), predicting the range shifts of species due to climate change (Garzón et al.,

2008; Manish et al., 2016; Mod & Luoto, 2016; Thomas et al., 2004; Upson et al., 2016; Vessella et al., 2017; Zhang et al., 2017), guiding the search for new populations of rare species (Fois et al., 2018; McCune, 2016; Rhoden et al., 2017; Williams et al., 2009), and predicting the potential future distribution of invasive species (Chai et al., 2016; Hannah et al., 2019).

SDMs have been used several times in order to predict species’ range shifts due to climate change. For example, Mod & Luoto (2016) used SDMs to demonstrate that woody plants will continue to expand due to climate warming, resulting in both negative and positive effects.

This demonstrates the mediating role shrubs can have with respect to the effects of climate warming on tundra vegetation (Mod & Luoto, 2016). Furthermore, Manish et al. (2016) studied angiosperm species endemic to the Himalaya and found that approximately 18% of the study species are likely to lose their habitat by 2070. The results of this study can assist policy makers so that climate change mitigation and management plans can be more effective (Manish et al.,

2016). Additionally, it has been demonstrated that climate change may result in the expansion of many western plant species of the Falkland Islands, while the complete loss of habitats for many upland species is predicted. This demonstrates the need to implement long-term monitoring and can assist in informing conservation decisions (Upson et al., 2016).

NatureServe’s Climate Change Vulnerability Index (CCVI) (Young & Hammerson,

2016) uses available information to assess species and their habitats in terms of A) exposure to local climate change, B) indirect exposure to climate change, C) sensitivity and adaptive capacity, and D) modeled response to climate change (Young & Hammerson 2016). These categories include factors that are usually estimated using available information, and, therefore,

Section D/modeled response to climate change is oftentimes missing. This assessment subsequently determines a species’ vulnerability to climate change as either Extremely

Vulnerable, Highly Vulnerable, Moderately Vulnerable, or Less Vulnerable to climate change

(Young & Hammerson, 2016). The CCVI can be used as part of the COSEWIC status assessment (e.g., COSEWIC, 2017). Likewise, for COSEWIC assessed species, many of the

CCVI factors can be verified using the applicable COSEWIC Status Reports. Additionally,

SDMs can assist in determining a species’ modeled response to climate change, further bolstering the CCVI results. Thus, these can be powerful, interconnected tools when assessing species’ risks.

CCVIs with the use of SDMs for modeled response to climate change are becoming popular tools used in order to help meet conservation goals. For example, the Alberta

Biodiversity Monitoring Institute (ABMI) used the CCVI and available SDMs in order to better understand how 173 of Alberta’s bird, amphibian, insect, plant, and mammal species could fair in light of climate change in the 2050s. This review allowed ABMI to provide data to decision makers for land use and natural resource policies (Shank & Nixon, 2014). Furthermore, Anacker et al. (2013) assessed the vulnerability of 156 Californian rare plants to climate change using the

CCVI, as well as SDMs built specifically for their study. The data derived from this study can be used to identify, monitor, and manage those plants that are most vulnerable to climate change,

8 and can help in developing conservation policies for rare plants (Anacker et al., 2013). Studies such as these demonstrate that the use of CCVIs with SDMs provide powerful and insightful tools when considering conservation actions.

Climate change and Yukon Draba (Draba yukonensis)

Draba yukonensis, also known as Yukon Draba, is a small, herbaceous plant in the

Brassicaceae family. Yukon Draba can be distinguished from its congeners by its leaves and stems that are covered with unforked hairs, and it produces small, white, four-petaled flowers. It typically grows in dry meadows (COSEWIC, 2018a) (Figure 2.2). Draba yukonensis is a rare plant species, endemic to Yukon Territory, Canada. The species is currently known from 19 sites there and has an extent of occurrence of 7295 km² (COSEWIC, 2018a). Draba yukonensis was assessed by COSEWIC in 2011 as being endangered (COSEWIC, 2011) and was reassessed in

2018 by COSEWIC as Special Concern, although it has not yet been listed on SARA’s Schedule

1 (COSEWIC, 2018a). Additionally, only one completed CCVI for Yukon Draba is available, and it is part of the 2018 COSEWIC Status Report for this species. This overall CCVI score is

Moderately Vulnerable.

Research questions and objectives

The CCVI in the 2018 COSEWIC Status Report for D. yukonensis lists all of Section D

(Documented or modeled response to climate change) as Unknown. My aim here is to model D. yukonensis’s projected future response to climate change in an attempt to help elucidate three of four of the unknown factors of this section. My research questions based on these factors are:

 What is the predicted future (2070) change in range size for D. yukonensis?

 What is the overlap of predicted future (2070) range with current range for D.

yukonensis?

 What is the occurrence of protected areas in predicted future (2070) distribution for D.

yukonensis?

To answer these research questions, I performed species distribution modeling with Maxent to model the rare plant D. yukonensis’s vulnerability to climate change, allowing me to fill knowledge gaps on three of the data-deficient factors in the CCVI for Yukon Draba, and examine how sensitive CCVI scores are to common knowledge gaps. Note that while the CCVI stipulates using 2050 for projections, I have used 2070, and feel this is valid since Yukon Draba is not expected to be assessed again by COSEWIC for many years. Thus, by then, it is expected that the CCVI guidelines will be updated to reflect a more distant date than 2050 (e.g., 2070 or

2080). Additionally, I expect that 2070 outputs would be qualitatively similar to 2050 outputs, and so using this data to complete the CCVI is valid.

METHODS

Occurrence data

Draba yukonensis occurrence data was obtained from the Yukon Conservation Data Centre and used for the purposes of this work with their permission. This data consisted of centroids of mapped patches of Yukon Draba’s known locations, as well as the geographic coordinates of the start and end points of transects used for research purposes in the Alsek location only. I bisected the transects with recorded Yukon Draba occurrences using ArcGIS Pro 2.2.0 (Esri, 2018) and these center points were used to augment the sample size of occurrences. Because the Alsek subpopulation is, by far, the most populous site in which D. yukonensis is found (COSEWIC,

2018a), adding these data points more accurately reflects the geographic distribution of Yukon

Draba. Spatial autocorrelation, in which nearby points are more similar than distant points

(Tobler, 1970), can affect estimates and increase type I error rates (falsely rejecting a no-effect null hypothesis) (Dormann et al, 2007). Spatial autocorrelation in the data can occur because some locations are more accessible by humans and thus have undergone greater search efforts.

Therefore, to examine the effects of spatial autocorrelation, I ran two sets of models: 1) using data unaltered, and 2) by performing systematic sampling, in which any duplicate points per cell are removed from a coarser resolution than that used for data analysis (Fourcade et al., 2014;

Ihlow et al., 2012). To accomplish this procedure, I used a 250 m2 cell size for data analysis.

This cell size was chosen because of Yukon Draba’s dispersal capability, which is expected to be poor (likely less than 10-100 m in unsuitable habitat (CCVI in COSEWIC, 2018a)), possibly due to, for example, its seeds lacking barbs and wings (Al-Shehbaz, et al., 2010; COSEWIC, 2018a).

Therefore, it is not expected that Yukon Draba can disperse more than 250 m, and, hence, the risk of double counting the same “individual” is reduced, allowing for more independent observations. I then overlaid a 500 m2 cell size grid on the occurrence data in ArcGIS Pro and manually removed all but one occurrence point within each cell (see Figure 2.3). Thus, systematic sampling analyses were run using 39 points, rather than 73 when systematic sampling was not employed and the “unaltered” data was used.

Environmental data

When determining which environmental variables to use, it is important to understand a species’ habitat preferences so as to selectively add these parameters to the model in order to avoid overfitting (Fois et al., 2018; Hamilton, 2015; Ingegno, 2017; Manish et al., 2016) (see

Figure 2.1). Overfitting occurs when too many variables cause the occurrence data to fit

11 completely to the model so that the model also shows the relationship between the noise of the occurrence data and the environmental variables, thus producing imprecise results (Ingegno,

2017). Similarly, variable selection is important because parsimonious models (with fewer predictor variables) are expected to have better predictive power (Johnson & Omland, 2004;

Wisz & Guisan, 2009). Likewise, understanding the ecological niche of a species is important in ensuring that no critical environmental predictors for the species of interest are excluded from the model (McCune, 2016). Thus, I consulted the 2018 COSEWIC Status Report for Draba yukonensis and contacted the Yukon Conservation Data Centre and the Canadian Wildlife

Service in order to best determine which factors play important roles in the ecology of D. yukonensis. I found that elevation, land cover, aspect, slope, soil drainage, soil type, and glacial limits may be important, and used these ecological variables for analyses (Table 2.1).

For the more generalized historic climate data, I downloaded 19 bioclimatic variables from WorldClim (Fick & Hijmans, 2017) (Table 2.1). These variables correspond to annual trends and limiting or extreme environmental factors (WorldClim, n.d.a), which should also influence plant distribution. I then calculated the correlation coefficient between these variables, and, for any variable pairs that showed a correlation greater than +/-0.7, only one was included for further analysis, which is acceptable following methods by Gogol-Prokurat (2011). After removal of correlates, the remaining bioclimatic variables were BIO1 (Mean Annual

Temperature), BIO7 (Temperature Annual Range), BIO8 (Mean Temperature of Wettest

Quarter), BIO12 (Annual Precipitation), and BIO15 (Precipitation Seasonality (Coefficient of

Variation)) (WorldClim, n.d.a). A correlation matrix for these remaining bioclimatic variables and the ecological variables was also generated (Gogol-Prokurat, 2011). Elevation was found to be correlated with BIO 7 (-0.84); however, while these effects can therefore not be considered

12 independent of one another, both these variables are considered important and thus both were kept in the models to help to determine where Yukon Draba can be found. Elevation is important because members of Draba are known to typically be high-altitude species (Jordon-Thaden et al., 2010; Jordon-Thaden & Koch, 2008). BIO 7 (Annual Temperature Range) is important because Yukon Draba is presumed to be adapted to extreme cold (COSEWIC, 2018a), and so the temperature range it can tolerate may better elucidate how it will manage climate change.

Maxent penalizes models with additional variables, thus downplaying the importance of variables that are redundant. These procedures thereby regulate model complexity, and so the removal of highly correlated variables does not actually greatly influence Maxent models (Feng et al., 2019).

Global circulation models, or GCMs, represent, for example, oceanic and atmospheric circulation models in response to increased greenhouse gas concentrations (Intergovernmental

Panel on Climate Change [IPCC], n.d.). These are used in species distribution models to predict the potential future distribution of species. Four GCMs that are appropriate for use in the northern hemisphere and are used in this study are CCSM4, GFDL-CM3, HAD-GEM2-ES, and

MPI-ESM-LR (Lee et al., 2019). The representative concentration pathways (RCPs) indicate greenhouse gas concentrations and, as per the CCVI, “middle of the road” climate scenarios should be used. Therefore, I used the GCMs listed above for RCP 4.5 for 2070 (WorldClim, n.d.b) (Table 2.1). These variables were converted to match the units of the historical climate variables using the Raster Calculator tool in ArcGIS Pro. Both historical and future variables are required in order to generate SDM predictions for the future, as needed by Section D of the

CCVI.

I cropped all variables to the extent of the study area, the entirety of the Yukon Territory, using the Extract by Mask tool in ArcGIS Pro and the Ecoregions_2014_1M raster as the mask

(GeoYukon, n.d.) (Table 2.1). Layers were resampled as necessary to 250 m2 cell size using the

Resample tool in ArcGIS Pro. I used Projection Coordinate System NAD 1983 Yukon Albers for all analyses.

Use of maximum entropy approach and Maxent settings

Maxent version 3.4.1 (Phillips et al., n.d.) was used for analyses. Maxent, or maximum entropy, uses presence-only data and environmental variables added by the modeler to determine the species distribution that is most uniform within the constraints of the input data (Biodiversity and Climate Change Virtual Laboratory [BCCVL], 2019). Maxent is considered a dependable method for modeling the distribution of species that are endemic, rare, and/or have small populations (Manish et al., 2016; McCune, 2019; Pearson et al., 2007; Williams et al., 2009).

The Maxent settings I used for my SDM mostly followed Manish et al. (2016), who also modeled the future distribution of plants in the northern hemisphere due to climate change.

These settings included: subsample replicated run type, 15 replicates, 0.00001 as the convergence threshold, and 5000 iterations (Manish et al., 2016). However, for random test percentage, 25 was used, as per McCune (2016). This indicates that 25% of the occurrence records will be set aside to test the model, which will be run, or trained, with 75% of the occurrence records (Udemy, n.d.). For the threshold rule to apply, I followed Fouquet et al.

(2010) and used the 10th percentile training presence, meaning that if a cell’s suitability score is larger than the 10th percentile of occurrence points in the training set, then that cell will be scored as suitable (Fouquet et al., 2010). Additionally, Escalante et al. (2013) found that the 10th

14 percentile training presence threshold is the most effective threshold when identifying areas of endemism of North American mammals with Maxent (Escalante et al., 2013).

Consistent with other studies performing species distribution modeling in Maxent

(Anacker et al., 2013; Pearson et al., 2007; Still et al., 2015), the ‘Auto features’ setting, allowing

Linear, Quadratic, Product, Threshold, and Hinge features, was used in order to allow Maxent to automate the choosing of the feature type using a sample size-based algorithm (Plantecology, n.d.). I chose the default regularization parameter setting (1), as simulations have shown that default settings, including for the regularization parameter, perform as well as modified settings

(Baldwin, 2009; Phillips & Dudík, 2008). These procedures are consistent with Forbis de

Queiroz (2012), Pearson et al. (2007), and Rus et al. (2018), who also used the default regularization parameter in practice while performing species distribution modeling for rare species.

I used the logistic output, similar to other studies predicting future distributions of species

(Fouquet et al., 2010; Manish et al., 2016; Riordan et al., 2018; Still et al., 2015). Logistic output is recommended as it affords probability of occurrence estimates based on the chosen environmental variables, resulting in improved model calibration and greater ease in interpretation, as large variances in output values correspond better to large variances in habitat suitability (Baldwin, 2009; Phillips & Dudík, 2008).

In order to reduce sampling bias, a bias file was created from which background points were generated. Because Maxent uses presence-only data (i.e., no true absence data), I generated a file in ArcGIS Pro, which encircled all the occurrence points used (either unaltered or systematically sampled) for analysis. This file was then used as the bias file in Maxent, from which the program generated 10000 background points. This procedure limits sampling bias

15 because, rather than showing the background points as occurring throughout the study region

(i.e., the extent of the Yukon Territory), it limits background points to just the areas that are known to have been searched for the species. This therefore results in both the presence and background points having the same biases (Phillips et al., 2009; Udemy, n.d.).

Running the Maxent models

Similar to McCune (2016), I used only the variables that contributed most to model gains.

I began by running all of the uncorrelated WorldClim variables, and then removed the one that contributed least to the model (BIO 1). I then ran all of the ecological variables and again removed the one that contributed least to model gains (glacial limits). Note that soil type did not contribute to model gains, and so was not considered further for analysis. I then combined the remaining variables and removed the WorldClim variable and the ecological variable with the lowest contributions. Thus, I ran the final models with the GCM data and using BIO 7, BIO 12,

BIO 15, elevation, land cover, northness (aspect), and slope.

I ran four models, one for each GCM. Each Maxent run provides averaged predicted current and future distribution continuous maps and threshold values for all replicates. I calculated the average threshold values across all replicates across all GCMs (sensu Still et al.,

2015). I averaged the projections across all GCMs (sensu Manish et al., 2016; Still et al., 2015). I then used the mean threshold values to convert these averaged continuous probability maps into binary (suitable/unsuitable) maps (sensu Still et al., 2015) (Figure 2.4).

Statistical Analysis

Following Manish et al. (2016), Rus et al. (2018), Still et al. (2015), and Qin et al. (2017),

I used the area under the curve (AUC) to assess my models. AUC can be interpreted as the

16 probability that a presence location is ranked higher in suitability than a background point

(Merow et al., 2013; Slater & Michael, 2012). Thus, the AUC value can range from 0 (model predictions are poorer than random) to 1 (model perfectly distinguishes between background and presence points), with 0.5 indicating model predictions are equal to random (Riordan et al.,

2018). AUC values above 0.7 represent good discrimination between presence and background points, values above 0.8 represent very good, and values above 0.9 represent excellent (Chai et al., 2016). Maxent records the average AUC of all the replicates for each model run. I took the average of these AUCs over all four GCM models and used this as my AUC value. To assess the importance of a variable to the models, I used the percent contribution output in Maxent

(Fouquet et al., 2010; Hannah et al., 2019; Upson et al., 2016; Vessella et al., 2017). The average of each percent contribution over all four GCMs is reported in this paper thesis.

Converting SDM results for use in a CCVI

To determine the modeled future (2070) change in range size, I calculated the total area of the predicted future and current distributions using ArcGIS Pro. To determine the occurrence of protected areas in modeled future (2070) distribution, I first downloaded a file of protected areas in Canada from the Canadian Wildlife Service, https://www.canada.ca/en/environment- climate-change/services/national-wildlife-areas/protected-conserved-areas-database.html, and clipped it to the extent of the Yukon.

Change in range size (factor D2 in the CCVI) was calculated by subtracting the predicted current range and the future range. In order to calculate the percent overlap of the current and future range (factor D3), the area of overlap of the predicted current and future ranges was divided by the total area of the predicted current range, and this value was multiplied by 100

(sensu Still et al., 2015; Young & Hammerson, 2016). In order to calculate the percentage

17 overlap of the future range and protected areas (factor D4), the area of overlap of the predicted future range and protected areas was divided by the total area of the predicted future range, and this value was multiplied by 100 (sensu Still et al., 2015).

RESULTS

The average AUC using the unaltered data was 0.906 and the average AUC using the systematically sampled data was 0.876. When considering the unaltered dataset, the two variables that contributed the most model gains were BIO 15/Precipitation Seasonality (38.4%), and BIO 12/Annual precipitation (30.6%).When considering the systematically sampled dataset, the two variables that contributed most to model gains were BIO 12/Annual precipitation

(29.8%), and BIO 15/Precipitation Seasonality (28.5%).

The mean continuous probability distribution maps for predicted current and future distributions for both the unaltered and the systematically sampled datasets can be seen in Figure

2.5. The maps using the different datasets show similar patterns, with many of the areas showing the lowest probability of occurrence on the current maps projected to increase in probability of occurrence in the future.

The averaged threshold for the systemically sampled data was ~0.25 and the averaged threshold for the unaltered data was ~0.22. Applying these thresholds to the respective continuous probability distribution maps produced the binary maps for the predicted current and future distributions for both datasets (Figure 2.6). The maps using the two datasets again show a similar pattern, with much of the Yukon occurring above the respective thresholds and, thus, being considered suitable habitat for Yukon Draba in 2070.

Using the systemically sampled dataset, the area of Yukon Draba’s predicted current range is 204953.9 km2 and the area of predicted future range is 376329.0 km2. Thus, by 2070, the suitable habitat range is expected to increase by 171375.1 km2 (83.6%). The maps of the overlaps of the predicted future range with the predicted current range can be seen in Figure 2.7.

The area of overlap of the predicted current and future ranges is 197978.0 km2 (96.6%). These results are outlined in Table 2.2.

Using the unaltered dataset, the area of Yukon Draba’s predicted current range is

203354.9 km2 and the area of predicted future range is 387672.9 km2. Thus, by 2070, the suitable habitat range is expected to increase by 184318.0 km2 (90.6%). The maps of the overlaps of the predicted future range with the predicted current range can be seen in Figure 2.7. The area of overlap of the predicted current and future range is 197970.8 km2 (97.4%). These results are outlined in Table 2.2.

The area of overlap of the predicted future range and protected areas in the Yukon is

39685.1 km2 when considering the systematically sampled dataset, and the area of overlap of the predicted future range and protected areas in the Yukon is 37599.5 km2 when considering the unaltered dataset. This results in the occurrence of protected areas in the projected future range being 10.5% and 9.7%, respectively. These maps can be seen in Figure 2.8, and the results are outlined in Table 2.2.

The total increase or decrease in range size is required for factor D2 of the CCVI. As my model shows that Yukon Draba is likely to increase in range size in the future, regardless of which dataset is used, I have scored this factor as Neutral in the CCVI. The percent overlap of the current and future range is required for factor D3 of the CCVI. When considering either dataset, my model shows that Yukon Draba’s predicted future range and predicted current range

19 are expected to overlap by 96.6-97.4% (i.e., > 60% as stipulated in the CCVI), and so I have also scored this factor as Neutral. The percent that the future range is expected to overlap with protected areas is required for factor D4. As my model shows that 9.7-10.5% of Yukon Draba’s future range is expected to occur in protected areas in (i.e., between 5-30%, as stipulated in the

CCVI), I have scored this factor as Somewhat Increase Vulnerability. Changing the scores for these factors from Unknown to the scores as described above in the CCVI calculator did not result in a change of the overall vulnerability index score for Yukon Draba (i.e., it remains at

Moderately Vulnerable).

DISCUSSION

This study set out to elucidate 1) The predicted future (2070) change in range for Draba yukonensis, 2) The overlap of the predicted future (2070) range with current range for Draba yukonensis, and 3) The occurrence of protected areas in the predicted future (2070) distribution for Draba yukonensis. These were factors that were data deficient in the CCVI for D. yukonensis

(factors D2, D3, and D4, respectively) (COSEWIC, 2018a). In endeavouring to answer these research questions, I performed species distribution modeling (SDM) for D. yukonensis using the program Maxent. With the occurrence data available, I was able to achieve excellent to very good SDMs (average AUC was 0.906 when considering the unaltered dataset, and 0.876 when considering the systematically sampled dataset) for discriminating between presence and background points (Chai et al., 2016). Additionally, both datasets produced similar results with respect to the predicted current and future distributions, and, thus, similar results in the overlaps between the predicted current and future distributions and between the future distribution and protected areas in the Yukon (see Figures 2.5-2.8 and Table 2.2). Somewhat surprisingly, both datasets led to the same scores for the respective CCVI factors. Overall, updating these factors in

20 the CCVI did not result in a change in the CCVI calculator score, and the score remained as

Moderately Vulnerable. A score of Moderately Vulnerable indicates that the “abundance and/or range extent within [the] geographical area assessed [is] likely to decrease” in the future (Young

& Hammerson, 2016). This result shows that, while the SDM results can detect changes at a finer scale than the CCVI, using the SDM alone may underestimate vulnerability to climate change due to its lack of incorporation of life history traits (Still et al., 2015). This result therefore reiterates the importance of using multiple techniques in conservation planning

(Anacker et al., 2013; Still et al., 2015).

What is the precited future (2070) change in range for Draba yukonensis?

The change in range size for Yukon Draba is 184318.0 or 171375.1 km2, depending on which dataset (unaltered or systematically sampled, respectively) is used (Figures 2.5-2.6, Table

2.2). In either case, these values represent an increase in suitable habitat for Yukon Draba by

2070, despite the finding that some currently extant sites will become unsuitable. This is important because range contractions due to climate change can put species more at risk of extinction (Borges et al., 2019). Thus, these results demonstrate that Yukon Draba is expected to have more suitable habitat available than many other species in the face of climate change. This is unexpected because Yukon Draba is a native herbaceous flower, and range expansions are more commonly expected with shrubby plants (Manish et al., 2016; Walker et al., 2006). For example, Manish et al. (2016) studied plant communities in the Himalayas and found that shrublands are projected to expand in the future, somewhat hindering meadow species (Manish et al., 2016). Additionally, many studies show range contractions of native species. For instance,

Upson et al. (2016) studied the potential impact on the distribution of plant species native to the

Falkland Islands due to climate change. They found that species in upland regions were expected

21 to experience range contractions of 93-98% (Upson et al., 2016). However, similar to my results, range expansions of native species have also been projected. For example, Upson et al. (2016) also found that some plant species native to the western Falkland Islands are expected to increase their range in the future by 49-96% (Upson et al., 2016). Similarly, Qin et al. (2017) studied the effects of climate change on the range distribution of the endangered conifer, Thuja sutchuenensis, in China. They found that this species could expand in the face of climate change

(Qin et al., 2017). Furthermore, in studying whiptail lizards, Alvarez et al. (2017) found that the generalist species, which occurs in woodlands, deserts, and in many US states, as well as

Mexico, could also expand its future range (Alvarez et al., 2017). While this lizard is not a plant species, it could identify clues as to why D. yukonensis is expected to thrive due to climate change. Similar to the reptile species, Yukon Draba also has a wide range of ecological tolerances. For example, it can tolerate six different land cover types (out of twelve), elevations from 617 m to 1309 m, and precipitation ranges between 248 mm and 360 mm. This wide range in environmental tolerances could therefore explain why the range of Yukon Draba may not be projected to be as severely reduced in size by climate change as might be expected. Additionally, this could explain why Yukon Draba currently occupies habitats that are considered dry relative to randomly sampled sites in its range (Wilcoxon two-sample test, W=938.5, p<0.05) but why the model indicates that, when considering all factors, it can tolerate moister sites as well, thus predicting increased habitat suitability in the future. Future research could further investigate the relationship between Yukon Draba and habitat moisture.

Additionally, Yukon Draba may be a polyploid species (see Chapter 3). Studies have shown that polyploids have outperformed their diploid counterparts when faced with other mass extinction events. For instance, Fawcett et al. (2009) demonstrated that, during the Cretaceous–

Tertiary (KT) extinction, survival rates of plants were enhanced by polyploidization (Fawcett et al., 2009). This could perhaps be due to factors such as increased number of alleles accessible for selection, and hybrid vigor due to innovative gene function (Fawcett et al., 2009; Otto &

Whitton, 2000; Rieseberg et al., 2003). Thus, Yukon Draba’s expanded range due to climate change may be consistent with findings such as these.

What is the overlap of the predicted future (2070) range with current range for Draba yukonensis?

The overlap of the predicted future range with the predicted current range is very high,

96.6% and 97.4% (Figure 2.7, Table 2.2), when considering the systematically sampled dataset and the unaltered dataset, respectively. This finding again indicates that Yukon Draba may not be particularly vulnerable to climate change. This is because the overlap of the current and future ranges of a species can be considered its future distribution when a limited dispersal scenario is considered (Hodd et al., 2014). Hence, if the future distribution is the same or very similar to the present distribution, the species may require less intervention, such as increasing connectivity between suitable habitats (Cabrelli et al., 2014). This finding is especially important to Yukon

Draba, which is known to have limited dispersal capabilities (COSEWIC, 2018a), and thus may have limited capability to disperse to new areas. Given the large changes anticipated in climate in the Yukon, this result is somewhat unexpected. However, large current and future range overlaps have been seen in studies involving other species. For example, an overlap of greater than 67% is expected for the Colorado-endemic cactus, Sclerocactus glaucus (Still et al., 2015).

What is the occurrence of protected areas in the predicted future (2070) distribution for Draba yukonensis?

The occurrence of protected areas in the predicted future range is 9.7% and 10.5% when considering the unaltered dataset and the systematically sampled dataset, respectively (Figure

2.8, Table 2.2). This result would categorize Yukon Draba as Somewhat Vulnerable to climate change in the CCVI. However, while this is less than the 30% required by NatureServe for this factor to be scored Neutral, this projected area of protection is actually increased compared to how much range is protected currently (0%) (COSEWIC, 2018a). This result is important because protected areas allow for favourable ecological conditions in which species can survive, including being less vulnerable to human activities such as habitat destruction (Young &

Hammerson, 2016). Therefore, when future ranges occur completely outside of protected areas, this can jeopardize species’ enduring viability (Williams et al., 2005; Young & Hammerson,

2016). Thus, habitat fragmentation and habitat loss should be regulated via a network of protected areas that sufficiently covers species’ habitats (Spiers et al., 2018). In consideration of the total area of protected areas in the Yukon as compared to the total area predicted for Yukon

Draba’s predicted future distribution, this result was somewhat expected. For example, Spiers et al. (2018) studied endemic plants of Trinidad and Tobago and found that even species with relatively large predicted distributions tended to have little (<25%) occurrence in protected areas.

This finding reflects the importance of establishing new (Spiers et al., 2018) and expanding existing protected areas in all parts of the world so as to better conserve biodiversity.

Additionally, it should be noted that, while the range of Yukon Draba is expected to increase overall, there are occurrence localities that are currently considered suitable habitat that are considered unsuitable habitat in the future. For example, when considering the systematically

24 sampled dataset, there are three localities that are currently considered suitable habitat but are considered unsuitable habitat in the future. These are “Aishihik, 5 km N,” “Aishihik, W of,” and

“Isaac Creek, N side” (localities as per the COSEWIC 2018 Status Report and the occurrence data from the Yukon CDC) (Figure 2.6). This may be considered especially concerning because

“Aishihik, 5 km N” is the second largest subpopulation of Yukon Draba, with 48,910-87,370+ individuals (COSEWIC, 2018a).

Performing species distribution modeling requires understanding the assumptions and limitations of this technique. According to Riordan et al. (2018), there are three fundamental

SDM assumptions. The first is that the current species distribution is in equilibrium with the climate and, thus, that the species has inhabited all suitable areas despite factors such as longevity and biotic interactions (e.g., with humans). So, while this is an unavoidable assumption, it should nevertheless be recognized when interpreting species distribution models

(Riordan et al., 2018; Svenning & Sandel, 2013). The second is that species’ distributions are assumed to result from the models’ input variables; however, the relationships between the chosen variables and the distributions may not be correlated with the processes at the population- level that drive species persistence. Ultimately, SDMs that consider population dynamics are required in order to tackle population persistence under future climate change (Franklin et al.,

2016; Riordan et al., 2018). The third is niche conservatism, which is that the relationship between a species and its environment is fixed over space and time, and, thus, that no local adaptations currently exist and that no adaptations to climate change will develop in the species

(Riordan et al., 2018; Wiens & Graham, 2005). This is unlikely to be the case for many species; however, more detailed information about the genetic variability of the species would be required to address this question (Riordan et al., 2018).

One limitation of my work is that considerations such as land changes (e.g., anthropogenic modifications, vegetation alterations, etc.), population dynamics, and functional characteristics are not considered in the models (Fouquet et al., 2010; Manish et al., 2016).

However, factors such as these tend to be indicators of climatic changes and, thus, are indirectly accounted for by the SDM (Manish et al., 2016; Pearson & Dawson, 2003). Additionally, while soil type did not contribute to model gains in this work, more detailed soil work related to Yukon

Draba’s habitat and the relationship between soil type and Yukon Draba could further be explored in the future. Another limitation of my models is that the larger cell size and small scale/large geographic area assessed may not appropriately capture a species with such a narrow range. Furthermore, the chosen environmental variables may not represent the true most important variables to D. yukonensis. Additionally, Yukon Draba’s dispersal capabilities are likely to be rather limited (COSEWIC, 2018a), and so it is unlikely to naturally colonize much of its predicted current and future suitable habitats. However, my work assumes unlimited dispersal capabilities of Yukon Draba (Riordan et al., 2018), which may underestimate the vulnerability of the species to climate change (Still et al., 2015). Considerations such as seed dispersal and other life history traits are captured in the CCVI (Young & Hammerson, 2016), potentially accounting for these factors in informing species management plans (Still et al., 2015), but these variables were not limited in Yukon Draba enough to categorize it at a heightened vulnerability score.

Thus, there seems to be a trade-off between CCVI and SDM methods for assessing climate change vulnerability. The CCVI methods are coarse yet include an enriched description of the species, while SDM methods have more precision with regard to how climate change and the species’ ecological tolerance will interact at particular sites occupied currently by the species, yet omit other important variables (e.g., seed dispersal).

My work adds to the body of knowledge in helping to identify where to search to locate other, as yet undiscovered, populations of Yukon Draba, as well as helping in identifying its predicted future distribution due to climate change. Moreover, conservation plans often only consider past and current threats, and not future threats; however, management strategies may require different considerations when future threats are considered (Still et al., 2015), and so this work offers insight to help better prioritize and manage conservation efforts. Thus, in light of

Yukon Draba’s projected increase in suitable habitat by 2070, this species may not require high prioritization for conservation effort unless other proximate threats (e.g., mining) compromise its existence. Furthermore, the use of various climate scenarios/GCMs enables conservation planners to address uncertainty regarding future climate change and therefore establish approaches that can be successfully applied across a range of future climate conditions (sensu

Riordan et al., 2018). Additionally, the maps provided in this work elucidate locations where

Yukon Draba might be most successful and therefore, in light of its modest dispersal capabilities, a preliminary assessment of areas that could be suitable for its translocation should the need arise

(Riordan et al., 2018). Similarly, SDMs such as this can help to provide information regarding important land to protect for future dispersal corridors (Guisan & Thuiller, 2005; Williams et al.,

2005). Overall, this work can help in the conservation of the rare, endemic plant species, Draba yukonensis.

TABLES

Table 2.1. List of environmental variables used for this project. Variable Cell size/ scale Source Comments

Elevation 30 m http://files.environmentyukon.ca/2001/ (Accessed ~Nov.2019; link no longer

available.)

Slope 30 m Derived from Elevation file.

Northness/aspect 30 m Derived from Elevation file using

formula “northness = cosine(aspect in

radians)” (Gogol-Prokurat, 2011).

Land cover 30 m https://open.canada.ca/data/en/dataset/4e615eae-b90c-420b-adee- Title per link: “2015 Land Cover of

2ca35896caf6 Canada.”

Soil data 1:1 million http://sis.agr.gc.ca/cansis/nsdb/slc/v3.2/index.html Citation: Soil Landscapes of Canada

Working Group, 2010.

Glacial limits 1:250,000 http://data.geology.gov.yk.ca/Compilation/29#InfoTab © Government of Yukon 2020.

Historical climate ~1km http://www.worldclim.com/version2

Future climate ~1km https://worldclim.org/data/cmip5_30s.html Latest version (2.1), did not have ~ 1

km resolution data available at time of

research, and so the previous version

(1.4) was used.

Ecoregions_2014_1M https://geoweb.gov.yk.ca/geoportal/catalog/search/browse/browse.page Used to clip rasters to extent of Yukon.

Table 2.2. Areas of various calculations for Yukon Draba using the systematically sampled

dataset and the unaltered dataset.

Area (km2) Systematically sampled Unaltered

Predicted current range 204953.9 203354.9

Predicted future range (2070) 376329.0 387672.9

Suitable habitat range increase from predicted current 171375.1 (83.6%) 184318.0 (90.6%) range

Overlap – predicted future and current ranges 197978.0 (96.6%) 197970.8 (97.4%)

Overlap – predicted future range and protected areas 39685.1 (10.5%) 37599.5 (9.7%)

FIGURES

Future emissions scenarios

Species occurrence data and study region

Predictor variables

SPECIES

DISTRIBUTION Background points

Model testing MODEL

Figure 2.1. Species distribution modeling (sensu IUCN Standards and Petitions Committee, 2019).

Obtain accurate species occurrence data from reputable sources (capture entire range of the

species). Avoid extrapolation of model results beyond the border of the study region. Select an

appropriate study region (i.e., the region from where the species has been observed to occur).

Generate background points from an appropriate area (i.e., points from a similar study region

where the species has not been found). Create polygon around just the known occurrences and take

background points from only there so that they are subject to the same biases as the presence points. 31

Choose predictor variables that influence species’ distributions by using bioclimatic variables and species-specific variables (e.g., elevation) determined via literature review that are available as GIS rasters. Future emissions scenarios, i.e., RCPs (representative concentration pathways) are various socioeconomic “storylines” (IUCN Standards and Petitions Committee, 2019, p. 102) that represent different assumptions about future land use, GHG emissions, etc. Global circulation models

(GCMs) (e.g., ACCESS1-0, CCSM4) for future projections of environmental variables (e.g., temperature, precipitation) that match the coordinates of the study area should be selected and are available for a range of RCPs. Model testing (e.g., using AUC, Kappa) is important because it gives an indication of how well the environmental variables predict the presence of the species. It is important to recognize “that the predictive skill of the bioclimatic models under climate change remains untested” (IUCN Standards and Petitions Committee, 2019, p. 102). In other words, in many cases it is not possible to go to each site that is predicted to be suitable for the species and determine the accuracy of the model. Source for Future emissions scenarios image: Nazarenko et al., 2015 (https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2014MS000403) (this image has been made smaller than original image). Model testing image from Maxent.

Figure 2.2. A) Photo of Yukon Draba. B) “Map showing localities where the Yukon Draba has been recorded and the sites of targeted searches from 2011 to 2016 in southwestern Yukon”

(COSEWIC, 2018a). Image sources: COSEWIC (COSEWIC, 2018a, https://www.canada.ca/en/environment-climate-change/services/species-risk-public- registry/cosewic-assessments-status-reports/yukon-draba-2018.html). Letters are my additions.

Figure 2.3. Systematic sampling grids. A) 500 m2 grid over points at Alsek meadows. B) 250 m2 grid with points that remained after systematic sampling/removing all points but one from each cell of the 500 m2 grid. Analyses were performed at 250 m2. Systematic sampling was performed to reduce the effects of spatial autocorrelation. Basemaps from ArcGIS Pro.

Continuous probability map Binary suitability map

Apply threshold

Figure 2.4. The continuous probability distribution map and its transformation to a binary distribution map. Thresholds and continuous maps are used to make binary distribution maps in

GIS (showing suitable (above the threshold) and unsuitable (below the threshold) habitat).

Performing GIS analyses can then determine whether an increase or decrease in area is predicted over time due to climate change, the area of overlap of current and future distributions, etc.

A B

C D

Figure 2.5. Mean continuous probability distribution maps for Yukon Draba in Yukon, Canada.

A) Predicted current distribution using the unaltered dataset. B) Predicted future distribution using the unaltered dataset. C) Predicted current distribution using the systemically sampled dataset. D) Predicted future distribution using the systemically sampled dataset.

A B

C D

Figure 2.6. Binary distribution maps for Yukon Draba in Yukon, Canada using the respective threshold values to delineate suitable and unsuitable habitat. A) Predicted current distribution using the unaltered dataset. B) Predicted future distribution using the unaltered dataset. C)

Predicted current distribution using the systemically sampled dataset. D) Predicted future distribution using the systemically sampled dataset. E) Systematically sampled points on predicted current distribution map, with enhanced area of “Aishihik, 5 km N” population. F)

Systematically sampled points on predicted future distribution map, with enhanced area of

“Aishihik, 5 km N” population.

Figure 2.7. Maps of overlap of predicted current and future distributions for the unaltered dataset

(A) and the systematically sampled dataset (B).

Figure 2.8. Maps of the overlap of the predicted future range with protected areas in the Yukon using the unaltered dataset (A) and the systematically sampled dataset (B).

Chapter 3: Understanding the origin and phylogenetic position of Yukon Draba

INTRODUCTION

Conserving species and ecosystems can be best achieved with an understanding of the distribution of genetic diversity in natural populations. As described in Chapter 1, determining how genetic diversity clusters along environmental gradients helps us to define species (or subspecies and varieties) for conservation. It can also help us to define intraspecific clusters that can sometimes make more sense for conservation. These clusters can be genetic and are often referred to as conservation units (also known as evolutionarily significant units (ESUs)), and/or can be predicated based on biogeographical barriers (Mee et al., 2015) (in the Canadian system, these are referred to as designatable units (DUs) (COSEWIC, 2018b)). Phylogenetics in particular is emerging as an important way to delineate the most appropriate ESU or DU for conservation via the use of phylogenetic trees and networks.

In Chapter 2, I find that Yukon Draba does not occupy all of the existing suitable habitat.

This could be due to Yukon Draba being a young species or that it cannot compete with closely related species that may already occupy those areas. Examining the evidence for either of these scenarios requires investigation of the phylogeny of Draba in order to elucidate whether Yukon

Draba is of recent origin, as well as the identity of its closest relatives. Performing phylogenetic analyses can be particularly helpful when identifying incidents of hybridization (the

“interbreeding of individuals from what are believed to be genetically distinct populations, regardless of the taxonomic status of such populations” (Rhymer & Simberloff, 1996, p. 84)).

This is important because hybrids are typically underrepresented in conservation strategies, with, for example, only 7% of reviewed policy documents providing potential for the conservation of hybrids (Jackiw et al., 2015). This may occur for a number of reasons. For instance, hybrids may

47 threaten their progenitor species, which may harbour genetic diversity and local adaptations

(Allendorf et al., 2004; Jackiw et al., 2015; Vilà et al, 2000). Additionally, community structures can be altered by hybrids, which can be responsible for limiting or promoting important components such as pollinators and pathogens (Jackiw et al., 2015; Vilà et al, 2000).

Therefore, it is important to identify whether species are hybrids or “pure” species in order to ensure that conservation resources are suitably distributed. Hybridization can result in allopolyploidy, in which interspecific hybridization results in a doubling of the genome (Parisod et al., 2010). Another type of polyploidy (doubling of the genome) is autopolyploidy, in which each chromosome within the genome within a single species is duplicated (Parisod et al., 2010).

Autopolyploids are often not considered species in their own rights and are instead lumped in with their predecessors (Soltis et al., 2007). This process of lumping can have serious implications in conservation efforts because it can underrepresent biodiversity (Soltis et al.,

2007; Soltis & Gitzendanner, 1999). It is therefore important to identify and recognize species in an appropriate manner. One concept that can resolve issues from polyploid units is to categorize them as separate conservation units based on, for example, genetic lineages, such as ESUs or

DUs (Mee et al., 2015).

Understanding conservation units is important in conservation efforts because genetic diversity may not be fully captured within the existing taxonomy (Avise, 1989; Fraser &

Bernatchez, 2001). Additionally, effective conservation strategies can only come to fruition when well-defined conservation units are the focus (de Magalhães et al., 2017; Albani Rocchetti et al., 2021). Indeed, the concept of conservation units is used by many wildlife protection bodies. For instance, COSEWIC (the Committee on the Status of Endangered Wildlife in

Canada) and SARA (the (Canadian) Species at Risk Act) “recognize that conservation of

48 biological diversity requires protection for taxonomic entities below the species level”

(COSEWIC, 2018b), and use the term designatable units/DUs in their procedures. Similarly, many research papers have focused on ESUs to help with conservation efforts (Parker et al.,

1999; Stockwell et al., 1998). The term ESU was first coined by Ryder (1986) to assist in conservation efforts focused on captive breeding programs (Ryder, 1986; Stockwell et al., 1998).

In 1991, Waples (1991) extended the concept to include the management of populations in the wild, for which resources are scarce, and defined an ESU as a population that represents a valuable component of the evolutionary history of the species and that is reproductively isolated

(Stockwell et al., 1998; Waples, 1991). In this work, we will use the term conservation units, which is a broader term encompassing similar ideas that can be based on morphological, geographical, and/or genetic differences in the units. Overall, then, recognizing accurate conservation units is crucial in ensuring that biodiversity is effectively preserved. Phylogenetic analyses can help in the appropriate delineation of conservation units.

Phylogenetic trees and networks

A phylogenetic tree is a bifurcating depiction of evolutionary relationships among taxa, with tips, or leaves (Huson & Bryant, 2006; Schliep et al., 2016) being labelled to identify the most recent evolutionary outcomes (descendants), nodes identifying earlier evolutionary events

(common ancestors), and branches connecting the two (Understanding Evolution, 2004a). In this way, phylogenetic trees can also allow us to see when a species arose relative to other species depicted (Understanding Evolution, 2004b), which can influence conservation-relevant parameters, such as range size (sensu Liow & Stenseth, 2007). Phylogenetic trees can be derived using several statistical methods, for example, maximum parsimony and maximum likelihood

(Kumar et al., 2018). On the other hand, phylogenetic networks represent evolutionary

49 relationships that may not necessarily conform to the bifurcating assumption of phylogenetic tree inference (Bapteste et al., 2013; Schliep et al., 2016). There are many different kinds of phylogenetic networks and ways to derive them. One method that is popular in analyzing the evolutionary history of plants is using the Neighbor-net algorithm (as in Bombarely et al., 2014;

Cires et al., 2014; Jordon-Thaden, 2009; Krak et al., 2016; Sherman‐Broyles et al., 2017) and a distance matrix derived from the data to form a splits graph, which shows signals in the data in the form of splits (Bryant & Moulton, 2004; Bryant et al., 2007). The splits of conflicting signals are parallel edges (or boxes) (Bryant & Moulton, 2004; Huson & Bryant, 2006), and, while they do not explicitly represent evolutionary patterns (Bryant & Moulton, 2004; and Strimmer et al.,

2001), they can depict evidence of, for example, possible hybridization events (Bryant et al.,

2007). In this way, therefore, phylogenetic networks generalize phylogenetic trees (Fitch, 1997, as cited Bryant & Moulton, 2004), allowing for conflicting phylogenetic signals in the data to be represented graphically, e.g., via a Neighbor-net network (Bryant et al., 2007). This is important because these conflicts may be missed by traditional tree-based methods (Esser et al., 2004;

Huson & Bryant, 2006). See Figure 3.1.

Research objectives

These new advances in understanding evolutionary history can be brought to the field of conservation for help in delineating appropriate conservation units and refining our understanding of the origin of rare species. Although phylogenetic analyses can elucidate when species are lumped or split inappropriately, which can help optimize conservation priorities and decisions (Robuchon et al., 2019), they are often time-consuming and can require prohibitively expensive additional field work, training, as well as destructive sampling of rare plants. Genbank

(https://www.ncbi.nlm.nih.gov/genbank/) has been steadily accumulating genetic resources for a

50 large number of species on many genetic markers that can be phylogenetically informative.

Therefore, one research objective is to explore the use of Genbank for readily available material that could help to elucidate the conservation unit structure pertinent to the conservation of Draba yukonensis.

Several studies have addressed the evolutionary history of species in the genus Draba

(e.g., Beilstein & Windham, 2003; Jordon-Thaden, 2009; Koch & Al-Shehbaz, 2002), yet some

Draba groups remain unresolved (Beilstein & Windham, 2003; Jordon-Thaden, 2009; Koch &

Al-Shehbaz, 2002), perhaps due to evolutionary factors such as hybridization. However, resolution can be increased in phylogenies by using approaches such as restriction-site associated

(RAD) DNA sequences (Wang et al., 2020) or Neighbor-net network analyses (Bryant &

Moulton, 2004; Jordon-Thaden, 2009). Furthermore, none of these studies have included D. yukonensis or limited the research area to its endemic territory, Yukon, Canada. Thus, I explore

Yukon Draba’s evolutionary history here to elucidate the relationship of D. yukonensis to other species of Draba in the Yukon. To examine the relationships and origin of Yukon Draba, I performed phylogenetic analyses using maximum likelihood, maximum parsimony, and

Bayesian inference phylogenetic trees, and Neighbor-net phylogenetic networks. The use of various techniques was not to compare these methods, but rather to employ them in unison in order to more fully understand and appreciate the evolutionary history of Yukon Draba. Analyses were performed with the ITS2 and rbcL datasets attained from Genbank, which had sequence material for this purpose. Ten other species of Draba that are known to exist in the Yukon were selected for analyses.

METHODS

Species selection

Draba L. is the largest genus in the Brassicaceae family (Al-Shehbaz et al, 2006; Jordon-

Thaden et al., 2010), with more than 370 species (Jordon-Thaden et al., 2010). Approximately 45 species of Draba occur in Canada (Al-Shehbaz & Mulligan, 2014). Draba species are typically perennial and are distributed in alpine, subarctic, and arctic regions, as well as in many mountainous areas (Jordon-Thaden et al., 2010). I chose Draba species for phylogenetic analysis based on their groupings per Jordon-Thaden et al. (2010), as well as their geographic proximity

(Lawrence & Datwyler, 2016) to Draba yukonensis. Because D. yukonensis has not been formally assessed, I first arbitrarily chose species from Jordon-Thaden et al. (2010)’s groups II and III, which mostly represent North and South American Cordillera and circumarctic/Beringia locations, respectively. These groups were sampled as they seemed most likely to contain the closest relatives of D. yukonensis. Nine species were chosen from the subset of these species that occurred in the Yukon (determined via an online search). Additionally, while D. kluanei was not included in the Jordon-Thaden et al. (2010) analysis, I chose to add this species due to its many commonalities with D. yukonensis, such as its Yukon endemism/range. Overall, the Draba species chosen were D. kluanei, D. cana, D. crassifolia, D. praealta, D. breweri, D. albertina, D. aurea, D. lactea, D. nivalis, and D. fladnizensis. Additionally, many of these species are delineated into either the white-petaled Leucodraba group with D. yukonensis (Mulligan, 1976

(as per Schulz, 1927)) or the yellow-petaled second group by Mulligan, 1976 (Table 3.1). The ranges of these species can be seen in Figure 3.2. Because the genus Arabis is morphologically distinct from Draba, the species A. eschscholtziana in this genus served as the outgroup/root for the phylogenetic trees (Jordon-Thaden, 2009).

Gene selection and sequence alignments

Gene selection for phylogenetic reconstruction is often based on maternally inherited genes (e.g., plastid genes) and biparentally inherited nuclear genes (Jordon-Thaden, 2009;

Lawrence & Datwyler, 2016; Lundberg et al., 2009). For D. yukonensis, the sequences available in Genbank were for the plastid rbcL gene and the ITS2/5.8S ribosomal RNA/large ribosomal subunit nuclear genes (hereafter known as ITS2). Thus, for the other species, I retrieved accessions that most closely resembled the sequences available for Yukon Draba, preferring accessions assembled by Kuzmina et al. (2017) when available (to maintain as much consistency as possible with the methodology used to obtain the Yukon Draba accessions). I used a maximum of five accessions per gene per species (Table 3.1). Sequences for each gene were aligned using the ClustalW algorithm (Lawrence & Datwyler, 2016) in MEGA (Kumar et al.,

2018). Default settings in MEGA were kept (Hall, 2013) (Gap opening penalty: 15.00, Gap extension penalty: 6.66), and manual adjustments (i.e., deleting all nucleotides before the first asterisk (*) and after the last) were made as necessary.

Phylogenetic reconstructions

Using the Find Best DNA/Protein Models (ML) feature with default settings in MEGA

(Hall, 2013) (Tree to use: neighbour-joining tree, Statistical method: maximum likelihood,

Substitutions type: nucleotide, Gaps/missing data treatment: use all sites, Select codon positions:

1st, 2nd, 3rd, noncoding sites selected, Branch swap filter: none, number of threads: 3), I found the best substitution models for each aligned gene based on the lowest BIC (Bayesian Information

Criterion) score (Kumar et al., 2018).

The maximum likelihood phylogenetic trees were constructed in MEGA for each alignment using the following settings: Test of phylogeny: bootstrap method, Substitution type: nucleotide, Rates among sites: uniform rates, Gaps/missing data treatment: use all sites, Select codon positions: 1st, 2nd, 3rd, noncoding sites selected, ML Heuristic method: Nearest-neighbor interchange (NNI), Initial tree for ML: make initial tree automatically (default NJ/BioNJ),

Branch swap filter: none, Number of threads: 3, 3000 bootstrap replicates, and the respective best substitution model. The maximum parsimony phylogenetic trees were also constructed in

MEGA for each gene, using settings similar to Cires et al. (2014). This included using the Tree-

Bisection-Reconnection (TBR) algorithm, and 3000 bootstrap replicates. The following settings were also used: Search level: 3, Test of phylogeny: bootstrap method, Substitution type: nucleotide, Gaps/missing data treatment: use all sites, Select codon positions: 1st, 2nd, 3rd, noncoding sites selected, Number of initial trees: 10, Number of threads: 3.

The FASTA gene sequences from MEGA were converted to NEXUS files using ALTER

(Glez-Peña et al, 2010). These files were uploaded to MrBayes (Ronquist et al., 2012) to generate the Bayesian inference phylogenetic trees for each gene, using the following settings:

Number of generations: 10000000, Sample frequency: 100, Burn in: 20% (Cires et al., 2014),

Diagnostic frequency: 1000 (Ronquist et al., 2011), default priors (Lundberg et al., 2009), and the respective best substitution model. MrBayes is a commonly used program for running

Bayesian analyses across a large range of evolutionary and phylogenetic models (Ronquist et al.,

2011). The outputs from MrBayes were visualized using TreeGraph2 (Stöver & Müller, 2010).

I performed an ILD (incongruence length difference) test/ partition homogeneity test

(Cires et al., 2014) in PAUP4 (Swofford, 2003) to determine whether the sequences from the different genes could be combined. I then used MEGA to concatenate the sequences manually

54 and proceeded using only the number of individuals for which sequences from both genes were available.

For the network analysis in SplitsTree4 , accessions for A. eschscholtziana were removed, following Cires et al. (2014), thereby creating unrooted phylogenetic networks.

Alignments were imported into SplitsTree4, and, using default settings (Bombarely et al., 2014)

(Lambda frac: 1.0, Variance: ordinary least squares, Character transformations: (uncorrected_P),

Handle ambiguous states: ignore, Normalize: (checked), Splits transformations: equal angle,

Daylight iterations: 0, Optimize boxes iterations: 0, Spring embedder iterations: 0, Use weights and Run convex hull (checked)), with 1000 bootstrap replicates (Cires et al., 2014), Neighbor-net phylogenetic networks (Bombarely et al., 2014; Cires et al., 2014) were generated for each alignment. Neighbor-net network analyses can better resolve lineages that are displayed as a polytomies in phylogenetic trees (Bryant & Moulton, 2004; Jordon-Thaden, 2009). SplitsTree4

(Huson & Bryant, 2006) is a program that allows for the analysis of both phylogenetic trees and networks using a multitude of input types. When using nucleotide sequences and the Neighbor- net algorithm as I did, the splits are computed via the generated pairwise distance matrix (Huson

& Bryant, 2006).

RESULTS

Alignment of the 52 ITS2 accessions and the 35 rbcL accessions resulted in sequences consisting of 261 base pairs for the ITS2 data and 503 base pairs for the rbcL data. The substitution models with the lowest BIC and, thus, the best fit for the data were K2 (Kimura2) for the ITS2 and concatenated datasets and JC (Jukes Cantor) for the rbcL dataset. The p-value of the ILD/partition homogeneity test was >0.01 (p=1), therefore indicating that the gene datasets could be combined (Cunningham, 1997).

ITS2 dataset

The maximum likelihood tree for ITS2 can be seen in Figure S4. While this tree shows

Yukon Draba as a monophyletic group, support was low with a value of 58%, and weak resolution was seen in other relationships of these species of Draba. Results were qualitatively similar for the ITS2 maximum parsimony tree (Figure S5), Bayesian inference tree (Figure S6), and Neighbor-net network analysis (Figure S7). rbcL dataset

The maximum likelihood tree for the rbcL dataset can be seen in Figure S8. This figure shows D. yukonensis as part of a large polytomy, which includes all Draba. Results were qualitatively similar for the rbcL maximum parsimony tree (Figure S9), Bayesian inference tree

(Figure S10), and Neighbor-net network analysis (Figure S11).

Concatenated dataset

The maximum likelihood tree representing the concatenated dataset (Figure 3.3) shows similar results as the network analysis, but with slightly weaker support and less resolution. For instance, D. yukonensis forms a monophyletic group with 57% support, and this is part of a polytomy with the accessions of D. lactea and D. fladnizensis, which has 46% support. Results were qualitatively similar for the concatenated maximum parsimony tree (Figure S2), and the

Bayesian inference tree (Figure S3).

Draba yukonensis exhibits some separation from the rest of the network (with 62.1% support) by way of a simple split (Figure 3.4), and it is connected to the rest of the network via three accessions of D. lactea and one accession of D. fladnizensis, which are in turn clearly

56 separated from the rest of the network (with 62.4% support), also through a simple split (Figure

3.4).

DISCUSSION

Many areas of the Yukon, including those where D. yukonensis occurs such as Alsek, have relatively high species richness (Douglas, 1974). However, the evolutionary relationships of many species that can be found in the Yukon are still being investigated (as in Hoot et al., 1994;

Roalson & Friar, 2004; Saarela et al., 2010). This is also the case with Draba species, whose relationships continue to be explored at various geographic scales. For example, Jordon-Thaden et al. (2010) explored the evolutionary history of approximately 45% of all known Draba species around the world (Jordon-Thaden et al., 2010). Similarly, Beilstein & Windham (2003) explored the phylogenetic relationships of 17 Draba species in western North America (Beilstein &

Windham, 2003). Research efforts such as these help to identify appropriate units of conservation so that conservation resources can be more appropriately delegated. In this work, I examined the evolutionary history of Yukon Draba with ten other species of Draba found in the

Yukon to determine the relationships of D. yukonensis to other species of Draba in the Yukon. In order to achieve this goal, I performed phylogenetic analyses for the chosen species based on

ITS2, rbcL, and concatenated datasets. These genetic resources were available on Genbank and thus provide an indication of the level of insight we can rapidly gain into conservation unit delineations.

Similar to previous studies (Beilstein & Windham, 2003; Koch & Al-Shehbaz, 2002), the distinctions between some Draba groups can be weak and inconsistent when using standard phylogenetic analyses. This may be due to the young age of the genus and its propensity to hybridize. For example, the maximum likelihood tree for the ITS2 dataset (Figure S4) indicates

57 that the accessions of D. yukonensis appear to form a monophyletic species (with weak support), yet the maximum likelihood tree for the rbcL dataset (Figure S8) did not. Unresolved and conflicting relationships such as these are a hallmark of phylogenies of genera where polyploidization events have commonly occurred. For example, Ourari et al. (2011) noted that their findings using nuclear gene sequences to elucidate the relationships of Hordeum murinum were congruent with many studies but incongruent with other studies, and they recognized that these conflicting trees possibly reflect reticulation events in this polyploid complex (Ourari et al.,

2011). Indeed, Soltis et al. (2004) remarked that phylogenetic reconstruction may be difficult for polyploid groups due to their reticulate nature, particularly when analyzing allopolyploids (Soltis et al., 2004). This may hinder progress on conservation unit designation for conservation biology.

I found that SplitsTree approaches helped better clarify relationships of Yukon Draba.

The long branch extending to D. yukonensis in the Neighbor-net phylogenetic network for the concatenated dataset (Figure 3.4) also shows that D. yukonensis is a well-resolved species.

Unlike with standard phylogenetic analyses, Neighbor-net approaches show where conflict in the trees occurs (as alternative splits, rather than collapsing bifurcating trees into uninformative polytomies). Importantly, because the concatenated network shows a simple split (no conflicts) to three accessions of D. lactea and only one accession of D. fladnizensis, it can be deduced that

D. yukonensis is likely most closely related to D. lactea. I also conclude that D. yukonensis appears to show closer relationships with D. lactea and D. fladnizensis than to other species of

Draba in the Yukon, although the relationships between these three species are not well-resolved in this analysis.

The origin of D. yukonensis could have arisen through (1) an autopolyploidization/typical bifurcating/progenitor-derivative speciation event, or (2) possibly (but less likely), D. yukonensis is an allopolyploid, with D. lactea and D. fladnizensis acting as parental progenitors.

1) Draba yukonensis is an autopolyploid or sister-species of D. lactea.

The fact that Figures 3.3, 3.4, and S2, S3, S4, S5, and S6 show several accessions of D. lactea closely related to D. yukonensis indicates that D. yukonensis could be an autopolyploid of

D. lactea, or it arose from a typical bifurcating event. While autopolyploidy is relatively rare in nature compared to allopolyploidy (Soltis et al., 1993), and there have been few occasions of autopolyploids being formally considered separate species (Soltis et al., 2007), autopolyploids can nevertheless often exhibit unique attributes such as geographic ranges and morphological characteristics (Soltis et al., 2007). For example, while their ranges overlap, D. yukonensis is found only in the Yukon, Canada (COSEWIC, 2018a), while D. lactea has a circumpolar distribution (Grundt et al., 2004) (Figure 3.2). Additionally, while they share many morphological characteristics, the two species are visually distinguishable, with, for example, D. lactea having larger flowers (1.2-1.6 mm vs. 1.8-3 mm (eFloras, 2008)), as can be seen in Figure

3.5.

The sister species of D. yukonensis may be D. lactea (Figures 3.3, 3.4, and S2, S3, S4,

S5, and S6). Given the larger range of D. lactea (which is seen with D. yukonensis being endemic to the Yukon (COSEWIC, 2018a) and D. lactea being circumpolar (Grundt et al.,

2004)) (Crawford, 2010; López et al., 2012) (see Figure 3.2), it seems likely that the origin of

Yukon Draba could be a case of progenitor-derivative speciation. Progenitor-derivative speciation occurs when an isolated population of a species diverges and forms a derivative species, with the progenitor species remaining relatively unchanged (Gottlieb, 1973 and

Jaramillo‐Correa & Bousquet, 2003, as cited in López et al., 2012). According to Crawford

(2010) and López (2012), progenitor-derivative speciation may be deduced when, for example: a) There is a close phylogenetic and morphological relationship between the taxa (which is seen in the phylogenetic analyses with D. yukonensis and D. lactea (Figures 3.3, 3.4, and S2, S3, S4,

S5, and S6), as well as their relatively similar morphologies (Figure 3.5)); b) The derivative species appears to have lower genetic diversity than the progenitor (which seems probable, considering D. yukonensis is typically monophyletic, while D. lactea is not); and c) The derivative species nests with the progenitor species in phylogenetic analyses (which can be seen in Figures 3.3, 3.4, and S2, S3, S4, S5, and S6 with D. yukonensis and several accessions of D. lactea) (Crawford, 2010; López et al., 2012).

2) Draba yukonensis is an allopolyploid, with D. lactea and D. fladnizensis acting as the parental progenitors.

While there is weaker evidence to support this conclusion, the fact that Figures 3.3, 3.4, and S2, S3, S4, S5, and S6 show D. yukonensis as most closely related to D. lactea and D. fladnizensis (albeit for only one accession of D. fladnizensis) could reflect the idea that D. yukonensis is an allopolyploid, with D. lactea and D. fladnizensis acting as the parental progenitors. The concatenated network analysis (Figure 3.4) shows only weak evidence of D. yukonensis appearing intermediate between D. lactea and D. fladnizensis (with most accessions of D. fladnizensis separated from D. yukonensis by a long branch (62.4)). While this result may indicate that a potential allopolyploidy event is possible (sensu Cires et al., 2014; Sherman‐

Broyles et al., 2017), the distinction between D. lactea and D. fladnizensis is weak, as has been observed in other studies (Grundt et al., 2004), and the weight of evidence provides more support for a closer relationship between D. yukonensis and D. lactea. For example, the concatenated

60 network analysis (Figure 3.4) shows no conflicts regarding the respective D. lactea, D. fladnizensis, and D. yukonensis accessions, perhaps indicating that the lack of support/respective polytomy in the phylogenetic tree is not caused by conflict in the ITS2 and rbcL genes, as would be expected if allopolyploidy were at play, further suggesting that traditional speciation or autopolyploidy are the most likely modes of D. yukonensis speciation. However, it can be seen

(Figure 3.6) that D. fladnizensis and D. lactea are often sympatric, including in the Yukon, illustrating that D. yukonensis could reasonably be a hybrid of these two species.

If D. yukonensis is an allopolyploid of D. lactea and D. fladnizensis, this could be supported by morphological traits that D. yukonensis shares with each of its putative progenitors

(despite it being visually distinguishable from them, as per Figure 3.5). For example, D. yukonensis and D. fladnizensis both have spatulate flowers and oblong seeds. On the other hand,

D. yukonensis and D. lactea both always have ebracteate racemes and straight fruiting pedicels

(eFloras, 2008). Additionally, while D. yukonensis was not included in the phylogenetic assessment performed on Draba species by Jordon-Thaden (2009), both D. fladnizensis and D. lactea were categorized together as part of Group III, and all three species were categorized together in the white-petaled Leucodraba group by Mulligan (1976)/Schulz (1927) (Table 3.1).

Moreover, Ekman (1932a, 1932b, 1936, as cited in Scheen et al., 2002) acknowledged the existence of a hybrid between D. fladnizensis and D. lactea, further illustrating that this hybrid could exist and be viable. Overall, then, the concept that D. yukonensis is an allopolyploid, with

D. lactea and D. fladnizensis acting as the parental progenitor species could warrant further study.

Determining the evolutionary history of D. yukonensis in relation to D. lactea and D. fladnizensis is impeded by the fact that D. lactea and D. fladnizensis are not well resolved

61 species. While Scheen et al. (2002) found that D. fladnizensis and D. lactea were definitively two unique species in the Svalbard populations, this “taxonomic controversy” (Scheen et al.,

2002, p. 60) has long plagued Draba research (Scheen et al., 2002). For example, Bocher (1966, as cited in Grundt et al., 2004) suggested that D. fladnizensis is a progenitor of D. lactea (and

Scheen et al. (2002) could also not discount this hypothesis), while GBIF (Global Biodiversity

Information Facility) lists D. lactea var. nidificans as a variety of D. fladnizensis (Draba fladnizensis Wulfen in GBIF Secretariat, 2019). This ambiguity is also reflected in the phylogenetic analyses presented in this work, with respective accessions of D. fladnizensis and

D. lactea rarely occurring fully separated from one another nor clustered as two distinctive species. Additionally, the chromosome counts of each species varies, with D. fladnizensis exhibiting either 2n=16 or 2n=32 and D. lactea exhibiting either 2n=16 (rarely), 2n=32, or

2n=48 (per Chromosome Counts Database [CCBD], Rice et al., 2015). This within-species variation can indicate intra-species autopolyploidization, which can render phylogenetic trees inaccurate, providing another reason why performing network analyses can be important in phylogenetic research. In any event, future work focusing on resolving this delineation for North

American populations of these species would be beneficial.

In contrast, it appears that D. yukonensis is comparatively well resolved, with all accessions occurring together often (albeit, with the exception of the ITS2 and concatenated

Bayesian inference trees (0.96-0.97), with only weak support (47%-64.2%)). This reflects the fact that all accessions are very similar/identical. This result is unsurprising considering Yukon

Draba is a rare and endemic species, which tend to have relatively lower genetic diversity (Cole,

2003; Hamrick & Godt, 1996; Wang, 2019). Because all D. yukonensis accessions are identical, this finding could also mean that the individuals were sampled from the same local area and

62 during the same time frame, potentially leading to false conclusions regarding monophyly of this species. However, upon inspection of Genbank, it appears that the accessions are from different localities in the Yukon, for example, from Haines Junction/Alsek and from Lake Terrace Creek.

This finding can further bolster the idea that D. yukonensis is properly defined as its own species, and thus that it is not a subspecies/variety and should continue to be defined as a single conservation unit for COSEWIC purposes; however, further insights regarding, for example,

Traditional Ecological Knowledge (TEK), are required. Nonetheless, it can be gleaned that D. yukonensis’s closest Draba relations in the Yukon are D. fladnizensis and, especially, D. lactea.

Moreover, because Figure 3.3 shows D. yukonensis as occurring further from the base of the tree, it seems that Yukon Draba is likely a younger species than, for example, D. cana, which occurs closer to the base of the tree (Figure 3.3). Thus, Yukon Draba may be especially young considering recent estimates date core Draba species as being only ~2.3 million years old

(Jordon-Thaden, 2009). While timing is difficult to estimate without knowledge of the genes’ mutation rates, this finding may provide one reason why Yukon Draba has not yet dispersed to and established within all of the regions that provide suitable habitat in the Yukon (see Chapter

2). Many Draba species are thought to have originated during recent glacial cycles (Jordon-

Thaden, 2009), and these results are consistent with the idea that Yukon Draba is one of them.

Perhaps Yukon Draba has not dispersed from its area of origination or glacial refugia (for example, the Cordilleran icesheet receded from southern Yukon 10,000 years ago (Jackson et al.,

1991)). Future genetic research should be able to determine the timing of origination.

The limitations of my work include the fact that the genes chosen for these analyses do not discriminate well between the species D. lactea and D. fladnizensis; however, these were the only genes available for D. yukonensis when I was conducting my research, and it is assumed

63 that the selected accessions represent the species listed. Additionally, the analyses could have benefited from the use of more sequences, which would have increased the power; however, I feel that there was sufficient material for this preliminary investigation of the taxonomic position and relationships of D. yukonensis to other Draba species in the Yukon. Further, this work does not acknowledge chromosome count and therefore I cannot make firm conclusions regarding evolutionary history; however, no chromosome counts for Yukon Draba were available when I was conducing my research. Future work could involve determining the chromosome count for

Yukon Draba in order to help better elucidate its evolutionary history. Moreover, the ten Draba species chosen for exploration may not incorporate the true progenitor(s) of D. yukonensis; however, the species I chose represent an appropriate cross section of Draba species in the

Yukon for this preliminary examination. Future research could explore other species in the

Jordon-Thaden et al. (2010) and Mulligan (1976)/Schulz (1927) groupings, such as D. porsildii and D. stenopetala (III/L and II/2, respectively (see Table 3.1)). Additionally, the splits graphs produced by the Neighbor-net algorithm allow for data exploration and representation, but not diagnosis (Bryant & Moulton, 2004). Future work could involve further investigating these results to better define Yukon Draba’s evolutionary history while at the same time including a greater number of Draba species.

The implications of this work are that new techniques to refine intricate relationships can assist in clarifying the complex evolutionary history (Lawrence & Datwyler, 2016) of Draba species, including Yukon Draba. Studies based on molecular analyses of narrowly distributed species are important in establishing conservation priorities (Cires et al., 2014), as there is often some skepticism as to whether an endemic plant species is truly a species, or simply a polyploid variant of an older, more widespread species. Phylogenetic assessments such as these are

64 important because understanding the evolutionary history of a species has implications in species conservation (Semple & Steel, 2003, as cited in Gross et al., 2020) but are sometimes not attempted due to the perception that new field work and molecular analysis would be needed.

Overall, it is my hope that this work can help in the conservation of Yukon Draba.

TABLES

Table 3.1. List of accessions for each species and gene from GenBank.

Species rbcL ITS2 Draba group Grouping per accessionsa accessionsa per Jordon- Mulligan (1976) (L = Thaden et al. Leucodraba; (2010) 2 = yellow-petaled 2nd group) MG248995 FJ187898 KX678417 FJ187899 Arabis eschscholtziana - - FJ187954

FJ187957 MG237072 MG247911 AY047661 Draba albertina 2 MG249002 MG236180 II

MG237075 JN965473 DQ467507 JN965474 DQ467506 Draba aurea II/III 2 DQ467638

DQ467639 DQ467508 DQ467519 DQ467520 Draba breweri (var. DQ467629 III - Cana b) GU202455 GU202456 MG247555 MG235909 MG247883 MG236160 III Draba cana MG248033 MG237045 L

MG248958 MG236285 MG249894 MG237788 KC482588 AY047666 AF146482 Draba crassifolia II DQ467616 2

DQ467631 DQ467632 MG248198 DQ467323 MG246054 DQ467326 Draba fladnizensis MG245977 DQ467488 III L KC482589 GU202472 KT960505 MG236419 Draba kluanei MG247239 MG235654 - -

KC482610 AY134134 KC482609 AY134138 Draba lactea KC482612 AY573518 III L KC482611 AY134179 MG249294 MG237299 KC482622 AY134180 JN965489 AY047677 Draba nivalis JN965490 GU202510 III L KC482621 AY134133 JN965491 AY573473 MG246462 DQ467610 Draba praealta MG248571 MG235026 III 2 MG236726 MG249157 MG237190 MG249429 MG237624 Draba yukonensis MG249697 MG237411 - L MG249848 MG237749 MG249910 MG237803

a As I was not able to perform my own sequencing, and therefore could not ensure that the two gene sequences came from the same individual, concatenation of genes within each species was completed arbitrarily for each individual; however, I expect the effect (if any) on analysis is minor, as the rbcL gene sequences exhibited negligible within-species variation. b Some of these accessions of D. breweri are “var. Cana.” Because D. cana is now considered a variety of D. breweri, and D. cana is, indeed, found in the Yukon, D. breweri is also considered to be found in the Yukon. However, it is important to note that D. breweri as it is now delineated cannot be found outside of California, USA (see Figure 3.2).

FIGURES

Figure 3.1. A) (Image and description sensu Bryant & Moulton, 2004.) A basic splits graph/Neighbor-net network. This graph represents four simple splits (no conflict) that separate

A, B, C, and D from each other (single, orange, diagonal edges), one split clustering together

A,B and separating them from C,D (green, horizontal edges), and a final split clustering together

A,C and separating them from B,D (blue, heavy, vertical edges) (sensu Bryant & Moulton,

2004). These splits indicate conflict in the data. The longer the edge, the more support/weight

68 there is for a split (Huson & Bryant, 2006). Thus, A could be clustered with C or B, but there is a longer edge (more support) for A clustering with B (77.3 vs 9.1) and A split from C. B) This image represents a simple phylogenetic tree. For example, E + F (along with their ancestral node) represent a clade/monophyletic group, as do E, F + G (and their respective ancestral nodes). Branch lengths in some trees can scale with time (with longer branches representing taxa that have been in existence for more time than those with shorter branches).

A F

B G

C H

D I

E J

Figure 3.2. Ranges of Draba species used in these phylogenetic analyses. A) D. albertina, B) D. breweri, C) D. crassifolia, D) D. kluanei, E) D. nivalis, F) D. aurea, G) D. cana, H) D. fladnizensis, I) D. lactea, J) D. prealta, K) D. yukonensis. Points from GBIF/GeoCat (Bachman et al., 2011). Basemaps from ArcGIS Pro.

78/84/0.97

46/56/0.96

57/58/0.97

100/100/-

64/57/0.98

64/64/0.99

49/56/0.66

63/62/0.98

Figure 3.3. Maximum likelihood tree for the ITS2 and rbcL concatenated dataset. Symbols next to names represent groupings per Jordon-Thaden et al. (2010) and Mulligan (1976) (Table 3.1).

Numbers near nodes indicate support values (maximum likelihood/maximum parsimony/Bayesian inference; "-" indicates relationships with support less than 0.5 in the

Bayesian analysis).

62.1

62.4

Figure 3.4. A) Neighbor-net network of ITS2 and rbcL concatenated dataset. Numbers along edges indicate support values. B)

Enhanced portion of A) to more clearly show the relationships of D. yukonensis. 75

A B C

Figure 3.5. Images of Draba species. A) D. yukonensis (Photo credit: © sydcannings, https://www.inaturalist.org/observations/9670543). B) D. fladnizensis (Photo credit: © Игорь

Поспелов, https://www.inaturalist.org/observations/38364831; this image has been cropped). C)

D. lactea (Photo credit: © sydcannings, https://www.inaturalist.org/observations/52333227).

Letters are my additions.

Figure 3.6. A) Worldwide overlap of ranges of D. yukonensis (pink), D. lactea (green), and D. fladnizensis (orange). B) Yukon-wide overlap of ranges of D. yukonensis (pink), D. lactea

(green), and D. fladnizensis (orange). Points from GBIF/GeoCat (Bachman et al., 2011).

Basemaps from ArcGIS Pro.

Chapter 4: General conclusions

Effective conservation requires characterization of the (1) species (or intraspecific taxon) needing management, and (2) factors that threaten the species now or will threaten it in the future. In Chapter 2, I describe my analyses conducted for filling knowledge gaps in the CCVI

COSEWIC (2018a) completed for Draba yukonensis. Although COSEWIC assessed this species in 2018, it had to do so without information on: 1) the predicted future change in range for

Draba yukonensis, 2) the overlap of the predicted future range with current range for Draba yukonensis, and 3) the occurrence of protected areas in the predicted future distribution for

Draba yukonensis, which are all knowledge gaps that can be estimated using species distribution modeling. Thus, it is my hope that my work can be used for the forthcoming COSEWIC assessment for D. yukonensis. In performing species distribution modeling using Maxent, I found that the predicted future change in range size is an increase of 171375.1 km2 (83.6%) or

184318.0 km2 (90.6%) (depending on which dataset is used). The overlap of the predicted future range with the predicted current range is very high, at 96.6% or 97.4% (depending on which dataset is used) because climate change is predicted to bring conditions that are suitable for the expansion of Yukon Draba. These promising results for D. yukonensis led to these two factors being scored as Neutral in the CCVI. The occurrence of protected areas in the predicted future range is 10.5% or 9.7%, depending on which dataset is used, and so this factor was scored as

Somewhat Vulnerable. Interestingly, the completion of these factors did not change the overall

CCVI score, and it remains at Moderately Vulnerable, despite the expectation that these favourable future conditions would result in a lower vulnerability score. This lack of sensitivity in the CCVI, which focuses more on life history traits, might indicate that this technique is only able to provide a rather coarse approximation of climate change vulnerability. Similarly, species

78 distribution modeling, which provides a finer estimation of climate change effects but does not account for life history traits, may underestimate vulnerability to climate change (Still et al.,

2015). Therefore, users should try to combine the CCVI tool with techniques like SDM to increase confidence when estimating the effect of climate change on their chosen study system

(Anacker et al., 2013; Still et al., 2015). My SDM research allowed me to see that Yukon Draba has a great deal of additional possible suitable habitat in the Yukon that it is not occupying.

While other populations may be found as search efforts continue, perhaps this indicates that it is a young species that has not had sufficient time to establish in these areas.

In Chapter 3, I explored Yukon Draba’s evolutionary history to elucidate the relationship of D. yukonensis to other species of Draba in the Yukon via the use of phylogenetic trees and networks. With network analysis, I found that Yukon Draba is relatively well differentiated from other species of Draba and would be defined as a species under most species concepts commonly used in conservation biology. It is likely most closely related to D. lactea and, possibly, D. fladnizensis. Future study will be needed to determine if D. yukonensis is an allopolyploid of D. lactea and D. fladnizensis, but there is stronger evidence that D. yukonensis is a close species to D. lactea (potentially derived as a result of autopolyploidization or progenitor-derivative speciation). The fact that D. yukonensis occurs further from the root of the phylogenetic trees than other species within the genus Draba also indicates that it is perhaps a younger species than some of the other Draba species in the Yukon. This finding may further suggest that Yukon Draba may not be occupying all of its suitable habitat because it is young and thus has not had time to populate these areas, yet more refined phylogenetic analyses with increased taxon sampling and dating techniques will be required to determine if young species have smaller ranges (in Draba), as suggested by Willis (1916) (as referenced in De Vries, 1917).

It was surprising that Yukon Draba has a large predicted suitable habitat range, despite its modest known range. Future directions could include performing species distribution modeling with the other closely related species, D. fladnizensis and D. lactea, to determine if they fill their predicted suitable habitats in the Yukon. It was also unexpected that Yukon Draba’s predicted suitable habitat would expand due to climate change. In the future, it would be important to also perform species distribution modeling for species of shrubs that are currently encroaching on

Yukon Draba’s habitat (COSEWIC, 2018a) to see if this biotic factor could potentially negate the abiotic factors in the model. Generally speaking, perhaps another reason that Yukon Draba does not occupy much of its predicted suitable habitat is because it is able to compete neither with shrubs nor its closest relatives. To my knowledge, this work represents the first SDM done for a Draba species in northern Canada. Additionally, very few studies have performed species distribution modeling for plants in the Yukon (e.g., Conway & Danby, 2014). Therefore, much remains to be done regarding assessing vulnerability to climate change of plant species in the northern Canada. Also surprising was the lack of strong phylogenetic distinction between D. fladnizensis and D. lactea. Future work could use more advanced techniques in an attempt to clarify the taxonomy for these two species in the Yukon, as was done for the Svalbard populations (Scheen et al., 2002).

Filling knowledge gaps with respect to Yukon Draba’s potential current and future distributions due to climate change with an SDM allows for pinpointing potential areas within a species’ range that are predicted to become unsuitable in the future, which assists us in properly planning for how to maintain valuable genetic diversity in Canada’s rare species. This work can help in providing a preliminary assessment of possible appropriate areas for D. yukonensis translocation and protection, which may be required for those subpopulation locations such as

“Aishihik, 5 km N,” “Aishihik, W of,” and “Isaac Creek, N side,” which are predicted to be unsuitable for Yukon Draba in the future. My research helps to confirm that Yukon Draba is indeed one of Yukon’s endemic species, and not a variety of a more common widespread species, which will provide evidence that it warrants continued monitoring within its currently limited range. In general, this work can help in conservation planning and management of Draba yukonensis.

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SUPPLEMENTARY MATERIALS

Figure S1. A) Map of Canada with Yukon Territory highlighted. Source: MapGrid,

https://en.wikipedia.org/wiki/Yukon#/media/File:Yukon_in_Canada_2.svg. B) Map of Yukon

Territory, Canada to show locations of landmarks (e.g., Whitehorse (the capital city), the St.

Elias Mountain Range/Mount Logan, and major rivers). Source: Daniel Feher,

https://www.freeworldmaps.net/northamerica/canada/yukon/map.html.

Figure S2. Maximum parsimony tree for the ITS2 and rbcL concatenated dataset. Numbers near nodes indicate support values.

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Figure S3. Bayesian inference tree for the ITS2 and rbcL concatenated dataset. Numbers near nodes indicate support values.

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Figure S4. Maximum likelihood tree for the ITS2 dataset. Numbers near nodes indicate support values. 102

Figure S5. Maximum parsimony tree for the ITS2 dataset. Numbers near nodes indicate support values.

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Figure S6. Bayesian inference tree for the ITS2 dataset. Numbers near nodes indicate support values.

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Figure S7. Neighbor-net network for the ITS2 dataset. Numbers along edges indicate support values.

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Figure S8. Maximum likelihood tree for the rbcL dataset. Numbers near nodes indicate support values.

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Figure S9. Maximum parsimony tree for the rbcL dataset. Numbers near nodes indicate support values.

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Figure S10. Bayesian inference tree for the rbcL dataset. Numbers near nodes indicate support values.

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Figure S11. Neighbor-net network for the rbcL dataset. Numbers along edges indicate support values. “…” indicates that some accessions have been removed from the label for visualizing.

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