Predicting Pelargonium Responses to Climate Change in a Biodiversity

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Doctoral Dissertations University of Connecticut Graduate School

8-8-2019 Predicting Pelargonium Responses to Climate Change in a Biodiversity Hotspot throughout South Africa Tanisha Marie Williams University of Connecticut, [email protected]

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Predicting Pelargonium Responses to Climate Change in a Biodiversity Hotspot

throughout South Africa

Tanisha Marie Williams, PhD

University of Connecticut, 2019

Climate change is causing major shifts in species distributions, which fundamentally alters the species composition and functioning of biological communities across the globe.

Projections suggest that by 2100 up to one of every six species will become extinct. Such drastic changes will have significant impacts on biodiversity patterns and ecosystem functioning.

Ecologists are faced with the pressing work of trying to understand how plants will respond to changing and increasingly stressful environments. To predict the long-term effects and magnitude of species responses, it is imperative that species adaptive responses are understood across the entirety of their geographic ranges. My dissertation employs an integrative approach to study the mechanisms driving interactions between functional traits, current and future distributions, historic phenological shifts, and species survival in changing environments. I use the highly diverse and charismatic flowering plant genus, Pelargonium L’Hér., as my model system.

Chapter 1 assesses whether mean annual temperatures and flowering times have changed over the century and if the changes are correlated. This phenology study provides evidence of historic responses to climate change and highlighted shifts in flowering phenology associated with increases in temperatures.

Tanisha Marie Williams – University of Connecticut, 2019

Chapter 2 uses common gardens to understand how species would respond to novel environments they are projected to face in the future, and identify traits related to fitness. Species exhibited significant differences among traits and across gardens, suggesting that plasticity may have a role in aiding species persistence.

Lastly, Chapter 3 evaluates the relationship between environmental predictors and species occurrences using the principle of maximum entropy and then forecasts habitat suitability into the future. Results showed a wide range of responses, with most species ranges shifting towards more hot and dry conditions, which suggests species may find themselves in unfavorable conditions if they are not able to adapt or be plastic.

My research strengthens our understanding of plastic responses along an environmental gradient, assesses intraspecific and interspecific variation, identifies key indicator traits needed for species adaptability, forecast species range responses, and provides the practical framework for conservation protection and management in the highly diverse ecosystems found in South

Africa.

Predicting Pelargonium Responses to Climate Change in a Biodiversity Hotspot

throughout South Africa

Tanisha Marie Williams

B.S., Pennsylvania State University, 2007

M.S., California State University Los Angeles, 2012

A Dissertation

Submitted in Partial Fulfillment of the

Requirements for the Degree of

Doctor of Philosophy

at the

University of Connecticut

2019

Tanisha Marie Williams

2019

APPROVAL PAGE

Doctor of Philosophy Dissertation

Predicting Pelargonium Responses to Climate Change in a Biodiversity Hotspot

throughout South Africa

Presented by

Tanisha Marie Williams, B.S., M.S.

Co-Major Advisor ______

Kent E. Holsinger

Co-Major Advisor ______

Carl D. Schlichting

Associate Advisor ______

Elizabeth L. Jockusch

University of Connecticut 2019

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Dedicated to my great-grandmother, Grace Alice Hawkins.

For without her unconditional love, support and laughter this

journey would have not been possible. Thank you so much.

ACKNOWLEDGEMENTS

“It takes a village.” – African Proverb

This journey is not one that is taken alone. It requires a lot of support, thought-provoking conversations, laughter, early mornings, late nights, encouragement and love. I have been fortunate to have many people form my village. For the many people past and present, thank you for sharing your lives with me and being a part of this journey. Whether you have listened, encouraged, supported, cooked, laughed, given advice, etc., I thank you for your support. I am truly grateful for you!

I want to thank both of my advisors, Dr. Kent E. Holsinger and Dr. Carl D. Schlichting.

They accepted me into their labs but most importantly, they believed in me. I thank Kent for always reminding me of the positives and teaching me so much, especially about Bayesian statistics. I thank Carl for pushing me to critically think about my research questions and what is experiments are needed to answer these questions. I thank both my advisors for their guidance, and for allowing me to find my own path. I want to especially thank them for their valuable input throughout this journey and for being my introduction into South African ecology. I also want to thank Dr. Elizabeth L. Jockusch for being an incredible committee member and always having an open door for the many questions, setbacks, and triumphs that come with a dissertation. I couldn’t have done this without many extended advisors. I must thank Dr. Cynthia Jones, Dr.

John Silander, Dr. Gregory Anderson, and Dr. Paul Lewis for their support and encouragement throughout this journey.

I also want to thank the many scientists from South Africa who have supported me

throughout this journey. My external South African committee has helped with research logistics, helping me familiarize myself with South African ecology, and having me over for a meal or braai. Many thanks go to: Dr. Jasper Slingsby, Dr. Tony Verboom, Dr. Tony Rebelo,

Mashudu Nndanduleni, Dr. Francis Lewu, Dr. Sussi Vetter, Dr. Terry Trinder-Smith, Timothy

Jasson, Philip Crous, and Tony Dold.

I have to thank the many EEB and CLAS personnel who have helped along the way. I want to thank Pat Anderson for always having a smile, supportive words and being such a caring friend. I also want to thank all those who have supported my degree: Kathleen Tebo, Anne St.

Onge, Lois Limberger, Mary Brescia, Dr. Robert Capers, Clinton Morse, Nicholas McIntosh,

Nicholas Boston, and Madeline Hennessey. Thank you so much for all your help and for allowing me to always drop by and ask questions.

I have to thank the EEB Department for their continued support throughout my degree. I also want to thank the many faculty and staff members who have helped along the way. Thank you all for asking how my day was going, how research was treating me, and having such kind words of support. I also want to thank the EEB graduate students for the many study sessions, reading groups, and social gatherings.

I want to thank a few special people for their friendship and for always going above and beyond. I want to thank Kristen Nolting for being the best lab sister and friend. I truly can’t thank you enough for all the support, especially during the final months (and years) of this degree. I want to thank Kaitlin Gallagher, Katherine Taylor, and Lauren Stanley for being great friends and a great support group. There were so many friends along the way, but I have to

give special thanks to: Dr. Holly Milton Brown, Dr. Johana Goyes, Dr. Jessie Rack, Dr. Suman

Neupane, Dr. Cera Fisher, and Dr. Veronica Bueno.

I was fortunate enough to have two labs, dubbed the Schlolsinger Lab. I want to thank the

Holsinger Lab for the many great times in the US and South Africa! I also thank the lab members for cultivating my love for the UConn Women’s basketball team. Thank you, Kristen Nolting and Dr. Nora Mitchell for all your support! I want to thank past members of the Holsinger Lab for their valuable help with research and during the postdoc/job search phase. Thanks go to:

Dr. Jane Carlson, Chris Adams, Dr. Rachel Prunier, and Dr. Krissa Skogen. I have to thank the

Schlichting Lab for the many lab meetings, social outings, barbecues, and laughs. Many thanks go to: Dr. Timothy Moore, Dr. James Mickley, Dr. Kat Shaw, and Dr. Jessica Lodwick.

I thank the wonderful people from around UConn that have helped support my journey. I have met so many wonderful people through the Fulbright, McNair Scholars, LSAMP, and the

Bridge to Doctorate STEM programs. I thank all of the faculty, staff, and students that I was able to work with throughout the years. Special thanks go to: Dr. Rowena Grainger, LuAnn Saunders-

Kanabay, Dr. Renee Gilberti, and Dr. Aida Ghiaei.

I would like to thank my family for all your love and support during my life, this journey would not be possible without you. You continue to always be a source of happiness. I want to thank my mother, Christine C. Hawkins, my grandfather, William Hawkins, and my great- grandmother, Grace Alice Hawkins for all their love. I want to recognize my aunts Beatrix D.

Fields, Esq. and Dr. Bonnie Davis, and my grandmother Alice T. Davis for all your support and

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love. Many thanks to: Dr. Alaina N. Fields, Shauna M. Fields, Kimberly McPherson, Christopher

Diggs, Julie Xu, Dr. Trent W. Davis, Cathye Fabrega, Alvena Wright, Wanda Sterling, Kia

Martinson + Kelker and Rutger, Dr. Geoffrey Ecker, Naomi Amos, Dr. Heather Marco, Dr. Gerd

Gade, Dr. Terri Braxton, Peter Hunt, and the best fur babies, Monte and Carlo.

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Some of the wonderful Pelargonium diversity seen on my many collecting trips throughout South Africa.

TABLE OF CONTENTS

INTRODUCTION ………………………………………………………………………..…...... 1

REFERENCES ………………………………………………………………………...….9

CHAPTER 1: Herbarium records demonstrate changes in flowering phenology associated with climate change over the past century within the Cape Floristic Region, South Africa ……….…18

ABSTRACT ………….………………………………………………………………….18

INTRODUCTION ………………………………………………………………..……...19

METHODS …………..………………………………………………………………….22

RESULTS ……………………………………………………………………………….26

DISCUSSION ….....……………………………………………………………………..26

ACKNOWLEDGEMENTS ……….…………………………………………………….30

TABLES ……………..……………………………………………………………….….31

FIGURES …………….………………………………………………………………….32

REFERENCES ……………………………………………………………………….….35

CHAPTER 1 SUPPORTING INFORMATION ……..…………………………………..47

CHAPTER 2: Using common gardens to understand the role phenotypic plasticity and functional trait variation have in the potential responses to climate change in Pelargonium species throughout South Africa ……...…………………………………………………………62

ABSTRACT ………….………………………………………………………………….62

INTRODUCTION …………………………………………………………………….…63

METHODS …………..………………………………………………………………….65

RESULTS ……………………………………………………………………………….71

DISCUSSION ….....……………………………………………………………………..73

ACKNOWLEDGEMENTS ……….…………………………………………………….76

TABLES ……………..…………………………………………………………………..77

FIGURES …………….………………………………………………………………….79

REFERENCES ………………………………………………………………….……….82

CHAPTER 2 SUPPORTING INFORMATION ……..…………………………………..94

CHAPTER 3: Species distribution modeling methods predict drastic distribution shifts in response to environmental changes within South Africa …………..………………….………...99

ABSTRACT ………….………………………………………………………………….99

INTRODUCTION ……………………………………………………………….……..100

METHODS …………..………………………………………………………………...102

RESULTS ……………………………………………………………………………...108

DISCUSSION ….....………………………………………………………………...….111

ACKNOWLEDGEMENTS ……….…………………………………………………...115

TABLES ……………..………………………………………………………………....116

FIGURES …………….……………………………………………………………..….124

REFERENCES ………………………………………………………………………....129

CHAPTER 3 SUPPORTING INFORMATION ……..………………………………....141

INTRODUCTION

As the fields of ecology and evolutionary biology continue to advance, research questions still remain regarding the fundamental topics of species diversity, speciation, phenotypic plasticity, functional trait relationships, and adaptation within natural populations (Orr and

Coyne 1992; Anderson et al. 2011; Sutherland et al. 2013). Scientists are still seeking to understand how organisms interact with their abiotic environments and biotic interactions, and the complexities that result in the dynamic biodiversity that is seen across the world (Sutherland et al. 2013). Increased interests in ecological and evolutionary research has risen due to climate change risks and impacts (Urban 2015; Humphreys et al. 2019). Understanding the mechanisms that drive species diversity, community structure, and the ecological strategies needed for survival will not only increase our knowledge about the diversity patterns observed and how species will respond to novel abiotic and biotic conditions, but will also increase our abilities to conserve and manage populations, communities and ecosystems.

One country with remarkable biodiversity, long marveled over from Darwin to current scientists, is South Africa (Darwin 2004). Its heterogenous topography, vegetation and biomes promote the astounding diversity of plants, animals and marine life indigenous to this country.

South Africa occupies only 2% of the world’s land area, yet is home to 10% of the world’s plant species and 7% of all reptiles, birds, and mammal species, and its marine ecosystem represents

15% of all marine species (Goldblatt and Manning 2002). South Africa is also home to three globally recognized biodiversity hotspots, areas with both high levels of species diversity and endemism (Myers et al. 2000). The three regions are the Cape Floristic Region, the Succulent

Karoo, shared with Namibia, and the Maputaland-Pondoland-Albany hotspot, shared with

Swaziland and Mozambique.

The majority of my work was focused within the Cape Floristic Region (CFR). The CFR is the smallest of six global floral kingdoms, with an area of just 88,000 km2 (Goldblatt and

Manning 2002). It is one of the most biologically diverse regions in the world (Myers et al. 2000;

Allsopp et al. 2014). It also has some of the world’s highest plant species richness densities, averaging 94 unique species per 1,000 km2, but in some areas the average explodes to 150-170 unique species per 1,000 km2, which is two to three times that of tropical rainforests (Manning

2007). The CFR is located on the southwestern tip of Africa boarded to the north by the

Succulent Karoo biome and the Atlantic and Indian oceans to the west and south, respectively

(Forest, Colville, Cowling 2018). It has mountain ranges throughout and is characterized by the dominate vegetation (and biome) Fynbos or fine bush (Forest, Colville, and Cowling 2018). It accounts for less than 5% of the total land area in southern Africa, but contains 44% of all southern African species (Myers et al. 2000; Goldblatt and Manning 2002; Linder 2005). Sixty- nine percent (69%) of these plant species are endemic to the CFR (Goldblatt and Manning 2000,

2002).

The CFR is an arid Mediterranean-type ecosystem, with nutrient-poor sandstone soils, dominated by sclerophyllous shrubs in the Fynbos biome (Cowling and Hilton-Taylor 1994;

Milton et al. 1997; Goldblatt and Manning 2002; Linder 2005; Mucina and Rutherford 2006).

The climate of the western and northern CFR is strictly Mediterranean (winter rainfall) with little to no summer precipitation, while the rainfall in the eastern CFR is more evenly spread through the year (Goldblatt and Manning 2002). As a result of the breakup of Gondwana approximately

200 MYA, the region is topographically diverse (Barker et al. 2007). This tectonic event produced the Great Escarpment (Partridge and Maud 2000) and unearthed the quartzose sandstone core of the Cape Flat Belt (Tinker et al. 2008). These events led to the development of

highly infertile, sandy soils, and often mountainous terrain. The high environmental and geographic heterogeneity in such a small area is hypothesized to play a significant role in promoting differential adaptation/niche differentiation and geographical isolation that produced the high levels of biodiversity within the region (Carrick 2003; Silvertown 2004; Ellis and Weis

2006; Slingsby 2011).

Around half of all species (N = ~4,500) in the hyperdiverse CFR have originated from radiations in only 30 lineages (Linder 2003). One such radiation occurred approximately 18

MYA in the genus Pelargonium L’Hér. (Geraniaceae), and it generated over 200 species and is now the third largest genus in the CFR (Bakker et al. 1999; Goldblatt and Manning 2002; Linder

2003). Pelargonium is a highly diverse flowering plant genus of herbaceous perennials, evergreen perennials, subshrubs and geophytes. It consists of 280 species, of these ~160 occur in the CFR and 89% are endemic to the region (Goldblatt 1997; Goldblatt and Manning 2002).

Members of the genus occur in a broad range of habitats from nutrient-poor to moderately rich soils and in mountainous and lowland habitats (Goldblatt 1997; Bakker et al. 1999). They exhibit high levels of morphological variation in growth form and leaf traits (Jones et al. 2009;

Martínez-Cabrera et al. 2012), making them a model system for studying plant evolution, diversity and phenotypic plasticity. The strong association of several traits with environmental variables across the CFR (Jones et al. 2013; Carlson, Adams, and Holsinger 2015; Mitchell et al.

2015; Moore et al. 2018) suggests that climate change could lead to mismatches between the environments to which populations are adapted and the local climate in which they find themselves.

Global climate change projections suggest that by 2100 up to one of every six species may become extinct (Urban 2015). Recent research on plant extinction rates shows an increased

rate of extinction of ~2.3 species per year (18–26 extinctions per million species years), which is much faster than that of the normal species turnover rates (0.05–0.35 extinctions per million species years), and may even be higher for newly described or undescribed species with less historic data available (Humphreys et al. 2019). Moreover, climate change is causing major shifts in species distributions and abundances and in species composition of communities across the globe. These shifts are intensified through development, overexploitation, deforestation, disease and other threats that increase the rate of extinction (Davis and Shaw 2001; Myers et al. 2000;

Midgley et al. 2003; Thomas et al. 2004; Salamin et al. 2010; McMahon et al. 2011). Such impacts have posed a greater need for understanding how phenotypic plasticity, genetic variation and local adaptation will allow species to persist (Parmesan 2006; Nicotra et al. 2010; Duputie et al. 2015). To predict the long-term effects and magnitude of species responses to climate change, it is imperative to understand what has driven historic responses and what is driving the current observed responses.

Climate change and associated pressures are especially acute in biodiversity hotspots.

Although such hotspots account for only 1.4% of the Earth’s surface, they contain 44% of all vascular plant and 35% of all vertebrate species (Myers et al. 2000). Having such large amounts of biodiversity and high levels of endemism in relatively small areas makes hotspots highly susceptible to environmental changes (Myers et al. 2000; Thomas et al. 2004). Climate predictions for South Africa project drier and warmer seasons in the winter rainfall region and wetter and warmer seasons in the summer rainfall region (Midgley et al. 2003). Such shifts in climate could produce a scenario where species located on range edges might have more favorable ecological responses to climate changes than species in more central ranges (Hill et al.

2011), e.g., species located on range edges may already be constantly adapting to variable

environmental conditions and will be the first to colonize newly habitable land (Davis and Shaw

2001). Pelargonium species might see a diversity of outcomes, as some species are widely distributed (e.g., Pelargonium zonale), where other species (e.g., P. barklyi) are restricted to very narrow ranges. Investigating the genetic (G), environmental (E) and the genetic variation for plasticity (G x E interaction) components of Pelargonium’s responses to novel environments will provide insight into the potential for species adaptation across current and potential distributional ranges (see review in Eckert et al. 2008).

Patterns of variation, and the research being done to understand physiological, molecular and ecological processes producing these patterns, give the framework to make inferences about future species-climate interactions. A plant’s phenotype is the result of the interaction between its environment, physiology and genetic structure, and an individual’s ability to change its phenotype in response to external conditions (i.e., plasticity, referring to character changes of an individual) may give it more opportunities to adapt to future climate change. Phenotypic plasticity is the change in phenotypic expression of a genotype across environments (Bradshaw

1965; Schlichting 1986; Sultan 1987). Phenotypic plasticity may be a starting point for adaptation (West-Eberhard 1989; Nicotra et al. 2010), as it determines fitness in heterogeneous environments (Parmesan 2006; Chevin et al. 2010; Franks et al. 2014; Valladares et al. 2014).

Differences among genotypes in responses to environments are referred to as genotype-by- environment interaction (G x E) (Scheiner 1993; Pham and McConnaughay 2014); these are often the product of differential gene expression or physiological responses to environmental cues (Mateus et al. 2014). Species responses and adaptations can be evaluated along a continuum: at one end, plant responses to environmental change through the use of non-genetic changes in individual phenotypes (e.g., phenotypic plasticity), while at the other end plant

responses may be due to adaptive evolutionary changes in the genetic structure of populations.

Responses may also involve a combination of both strategies along this continuum.

To understand strategies used along this continuum functional traits, proxies for ecological strategies, can be used (Cornelissen et al. 2003; Pérez-Harguindeguy et al. 2013).

Analyzing their relationship to environmental gradients and genetic variation is a promising approach to understanding species responses, population structures, and community dynamics in these ecosystems. Understanding how plants adapt to and persist in their environment – for example, how will species adapt to a decrease in water availability throughout the Western Cape of South Africa – is important for enhancing survival of geographically restricted species and will provide insight useful for conservation management of these systems. My research investigated multiple mechanisms affecting species survival along an environmental gradient in the CFR using a successive timing approach that looks at historic, current and future species responses. I assessed the physiological and morphological mechanisms of individual plant performance in different environments and across different Pelargonium species using herbarium records, common garden experiments, and species distribution modeling methods. These methods were used to evaluate how genetic differences and phenotypic plasticity would impact species responses to climate change.

In Chapter 1, I investigated mean annual temperature changes over the past century, if flowering phenology has changed over this same period, and if this change is associated with the change in temperatures. I examined herbarium data from over 130

Pelargonium species collected during 1900–2009 and related these records to ~1,700 weather station temperature records for the same time period from throughout the CFR. Using a Bayesian generalized linear mixed model (GLMM) I found mean annual temperatures had increased by

2.9oC. This increase in temperature was associated with a flowering date advancement of 12 d over the century (approximately 4 d oC-1).

In Chapter 2, I used common garden experiments to measure phenotypic plasticity and Pelargonium species growth and survival across two rainfall regimes found in South

Africa. Common garden experiments are a powerful tool used to show adaptive and plastic changes in plant traits, and are also used to test fitness variation across environments. I carried out a large-scale common garden experiment using two gardens in different rainfall regimes

(Western Cape (winter) and Eastern Cape (summer)) and five species of Pelargonium. Seven functional traits were measured repeatedly over three years. The aims of this study were as follows: (i) to understand how species respond to novel environmental conditions they are projected to face in the future, (ii) to measure plastic responses, and (iii) to identify growth and leaf traits that were related to performance. I found significant differences in traits and growth rates among species and across gardens (P < 0.05), with some traits (e.g., height) showing strong phylogenetic and environmental associations. Leaf thickness and height were strongly associated with survival across gardens; taller plants with thicker leaves were more likely to survive at the warm, wet garden (Western Cape) (P < 0.0001). Plasticity may have a significant role in aiding species persistence throughout environmentally and ecologically diverse habitats in South

Africa.

In Chapter 3, I evaluated the environmental correlates of current occurrences for

150 Pelargonium species. Using these relationships, models were used to predict range shifts in response to climate change at two time periods, 2050 and 2070, and across 16 scenarios (4 general circulation climate models x 4 climate scenarios (representative concentration pathways)). Species distribution models (SDMs) estimate the relationship

between species’ occurrence and environmental characteristics associated with these occurrences, and are frequently used to predict historic and future distributional ranges in biogeographic, ecological and evolutionary research. The principal of maximum entropy

(MaxEnt) method was used to model species distributions. All models were highly accurate based on the SDM evaluation metrics, with significant area under the receiver operating characteristic curve (AUC) values (current = 0.961; future = 0.956) and True Skill Statistic

(TSS) values (current = 0.801; future = 0.787). Overall, mean annual temperature of the wettest quarter, precipitation seasonality and precipitation of the wettest quarter were the top three environmental predictors of current species occupancy (total mean contribution = 83.4%). Future distribution predictions showed a strong trend towards north and west range shifts. There was also evidence of many range contractions, especially species with ranges along the coastlines.

These range shifts are interesting as South Africa’s climate is projected to be much warmer and drier in the north-west region (Western Cape) of the country. This climate profile has been shown to negatively impact Pelargonium species persistence (see Chapter 2). These results suggest that although precipitation is important for species occurrence, other abiotic interactions might be driving species distributions throughout South Africa.

In summary, this dissertation examines the genetic and plastic responses of functional traits along an environmental gradient, identifies key indicator traits needed for species adaptability, investigates current and future species responses to environmental changes and provides a practical framework for conservation protection and management in a highly diverse ecosystem.

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CHAPTER 1:

Herbarium records demonstrate changes in flowering phenology associated with climate

change over the past century within the Cape Floristic Region, South Africa

ABSTRACT

Climate change is affecting species composition and diversity across the globe.

Phenological changes provide a sensitive indicator of biological responses to changes in climate.

Recent studies using herbarium records in Europe and North America have shown changes in flowering time and other phenological events in response to changing climate conditions, such as warming temperatures and chilling winters, but few studies have been carried out in the southern hemisphere.

A Bayesian approach was used to examined changes in flowering time from 1900–2009 in South Africa in the widespread, diverse genus Pelargonium. I also combined records from more than 6,100 herbarium specimens with historical weather data on temperature to examine the impact of climate change on flowering phenology. Records for 129 Pelargonium species throughout South Africa were analyzed. I then used data from over 464 weather stations in South

Africa to estimate historical climate conditions for each of the 4,600 geographic sites included in our sample.

During this time period there was a 2.9oC increase in mean annual temperature across

South Africa. Flowering date advanced by 7.1 days, with nearly all of the advance attributed to the increase in temperature (2.9oC x 2.4 days/oC = 6.96 days) during this time. Pelargonium

species are showing similar phenological responses when compared to Canada, the U.S., and

Europe.

This study is one of the first to assess large-scale climate and phenological patterns throughout a highly diverse African genus, and it illustrates that herbarium records provide an effective method for detecting effects of climate change on flowering phenology across large geographic scales.

INTRODUCTION

Climate change is causing major shifts in species distributions, species abundances, and community composition across the globe. As the climate continues to warm (IPCC 2013), the timing of life history events like flower initiation, leafing out, bird breeding behavior (e.g., nesting, egg laying, breeding season length), and insect development and emergence, are shifting to track the changing climate (Dewer and Watt 1992; Crick et al. 1997; Dunn and Winkler 1999;

Hill et al. 1999; Parmesan et al. 1999; Bradshaw and Holzapfel 2001; Fitter and Fitter 2002;

Parmesan and Yohe 2003; Primack et al. 2004; Menzel et al. 2006; Parmesan 2006; 2007;

Miller-Rushing and Primack 2008; Park et al. 2008; Willis et al. 2008; Gallagher et al. 2009;

Anderson et al. 2012; Cleland et al. 2012; MacLean et al. 2018; Meineke et al. 2018). Such changes can have large impacts on reproduction, survival, and distributions of both the individual species and on those species that depend on them. Understanding how climate change affects populations is imperative to gaining insights into whether and how species will persist.

Phenology refers to the timing of seasonal events - flowering, leaf out and fruiting in plants, and seasonal migration in animals. Changes in phenology have been highlighted as one of the most convincing lines of evidence of species responses to climate change (use of herbarium

records to assess phenological shifts: Primack et al. 2004; Gallagher et al. 2009; MacLean et al.

2018). Increasing temperature often leads to the earlier onset of spring events, and this is associated with the advancement in phenological responses across a wide variety of plant taxa

(Post and Stenseth 1999; Fitter and Fitter 2002; Primack et al. 2004; Miller-Rushing and Primack

2008), both within localized regions (Bradley et al. 1999; Visser and Holleman 2001; Primack et al. 2004; Miller-Rushing and Primack 2008) and across broad geographic regions (Inouye et al.

2000; Chmielewski and Rötzer 2001; Parmesan and Yohe 2003; Lavoie and Lachance 2006;

Parmesan 2007). Much of this work has focused on the north temperate regions of North

America (Bradley et al. 1999; Primack et al. 2004; Parmesan 2007; Miller-Rushing and Primack

2008), Europe (Menzel and Fabrian 1999; Post and Stenseth 1999; Chmielewski and Rötzer

2001; Fitter and Fitter 2002; Menzel et al. 2006), and throughout Asia (Aono and Kazui 2008;

Ge et al. 2015).

Flowers are initiated 15–51 d earlier now than in the past 35–100 years in Canada

(Lavoie and Lachance 2006), and a mean of 2.5 d earlier per decade for over 500 species across

21 European countries from 1971–2000 (Menzel et al. 2006). Associated with these phenological advancements, Canada’s mean winter and April temperatures have increased by 2.3oC and 0.8oC, respectively (Lavoie and Lachance 2006). Europe is experiencing a similar trend: temperatures over the last decade have risen between 1.6–1.7oC (European Environment Agency 2018).

Within the New England region of the U.S., flowering is now about 8 d earlier and temperatures about 1.5oC warmer than in 1885 (Primack et al. 2004). Earlier flowering, though common, is not seen in all species: some studies have shown no apparent effects of increasing temperature on phenology, while others have shown delays (Parmesan and Yohe 2003).

Temperature plays a key role in flower initiation, bud burst, leafing out, and other developmental events. It is highly correlated with plant performance and helps synchronize life history events to favorable environmental conditions (Reeves and Coupland 2000; Fitter and

Fitter 2002; Parmesan and Yohe 2003; Menzel et al. 2006). Previous studies within temperate regions have highlighted the role of temperature, more so than other climatic factor, in accounting for the majority of the variation observed in plant phenology (Primack et al. 2004;

Menzel et al. 2006; Doi 2007; Robbirt et al. 2010; Calinger et al. 2013). In this study I tested this relationship for a region different than the most commonly prescribed studies. Therefore, I assessed the association between temperature and flowering phenology in the southern hemisphere, Mediterranean climate of the Cape Floristic Region (CFR).

Herbarium records capture moments in time, but by compiling enough ‘moments’ changes in phenology can be detected. With ~ 2.5 billion herbarium specimens worldwide, these data provide a unique opportunity to study the impacts of climate change with long-term spatiotemporal data (Soberon 1999; Krishtalka and Humphrey 2000; Graham et al. 2004; Soltis

2017; Willis et al. 2017; Lang et al. 2019). Most of the work using herbarium records is from the northern hemisphere because of the extensive herbarium and natural history collections in North

America, Europe, and Asia. Long-term records across taxa and dense time series are not frequent in other parts of the world (Schwartz and Reiter 2000). In the southern hemisphere, the exceptions are Australia (e.g., Gallagher et al. 2009; Rawal et al. 2015: focused on temperate regions) and South Africa (Daru et al. 2019).

In this study, I wanted to understand whether flowering phenology has changed for the genus Pelargonium over the past century in relation to temperature in the CFR, a biodiversity hotspot, of South Africa. The primary research questions were:

1) Have mean annual temperatures changed throughout the CFR over the past

century?

2) Has flowering phenology changed over this same time period? If flowering

phenology has shifted, is this change associated with the changes in temperature?

3) Are the results observed comparable to trends found when assessing phenological

shifts throughout the northern hemisphere and within temperate forests?

METHODS

Study Region and System

The study region is the world’s smallest biodiversity hotspot, the Cape Floristic Region, with a total area of ~91,000 km2 (Manning and Goldblatt 2012). This area harbors significant floristic diversity and endemism comparable to tropical rainforests (Goldblatt and Manning

2002). I extended the analyses ~600 km to the east and 200 km to the north of the CFR because of the large number of Pelargonium records and availability of long-term weather station data comparable to that from the CFR. The focal region (Fig. 1) included over 70% of all herbarium observations for Pelargonium in South Africa and had weather/climate data since 1850. The study region lies between 30o and 35o S latitude and 17o and 28o E longitude.

Mean annual temperatures are coolest (7–15oC) in the month of July and warmest (15–

25oC) during the mid-summer month of January. The hottest temperatures (30–40oC) within the

CFR are found in the interior valleys. Rainfall averages between 250–650 mm throughout the

CFR, except for the interior valleys (<250 mm) and mountains of southwest and southern Cape

(>1,000 mm). The climate of the western and northern CFR is strictly Mediterranean (winter rainfall) with little to no summer precipitation, while the rainfall in the eastern CFR (Mossel Bay

to Port Elizabeth) is spread more evenly throughout year. There is extensive environmental, geographic, and topographic heterogeneity across the CFR, thought to play a key role in the diversification of some plant lineages (Verboom et al. 2009).

Pelargonium L’Hér. (Geraniaceae) is a highly diverse genus of herbaceous perennials, evergreens, subshrubs and geophytes. With approximately 280 species, it is among the twenty largest genera of South African vascular plants (Goldblatt 1997; Goldblatt and Manning 2002).

Eighty-nine percent of the approximately 160 species that occur in the CFR are endemic to South

Africa (Goldblatt 1997; Goldblatt and Manning 2002). Members of the genus occur in a broad range of habitats from nutrient-poor to moderately rich soils and in mountainous and lowland habitats (Goldblatt 1997; Bakker et al. 1999) and are found in nine of the eleven recognized

South African biomes (Mucina and Rutherford 2006). They exhibit high levels of leaf and growth form variation (Jones et al. 2009; Martínez-Cabrera et al. 2012) and show strong associations of leaf traits with environmental variables (Jones et al. 2013; Mitchell et al. 2015), suggesting that climate change could lead to mismatches between the environments to which populations are adapted and the local climate in which they find themselves.

Historical Climate Data

Daily minimum and maximum temperature data for the CFR were derived from 464 weather stations of the South Africa Weather Service (SAWS). The gridded data spanned the herbarium collection dates (1900–2009). The spatial distribution of the weather stations is shown in Fig. 1.

Herbarium Data

Herbarium records were obtained from the Bolus Herbarium at the University of Cape

Town, the Selmar Schonland Herbarium at Rhodes University, and the Plants of southern Africa

(PRECIS) database curated by the South African National Biodiversity Institute (SANBI).

19,978 digitized herbarium records were manually reviewed for suitability. Records and species satisfying the following criteria were included in the final data set:

1) Complete date (day, month, year), and georeferenced locality or accurate description of

locality that could be georeferenced.

2) Greater than or equal to 10 herbarium records across time [mean: 48 records per species].

3) Collection times were limited to records that fell between 1900–2009.

Personal observations at herbaria indicated that the majority of Pelargonium specimens had been collected with flowers and so all digitized specimens that met the above criteria were used in the following analyses. A subset (N = 8,808) of digitized records had flower or fruit counts; of these,

7,686 (87%) were recorded as flowering. No species in this study were listed on the IUCN Red

List (accessed on 1 Jan 2019). Duplicate records and clearly mistaken (e.g., georeferenced localities falling in bodies of water or other countries) were removed. The final data set consisted of 6,171 herbarium specimens representing 129 Pelargonium species (Table S1). Collection dates were used as proxies for peak flowering times as done in similar studies (Primack et al.

2004; Robbirt et al. 2010). A modified Julian date of collection was calculated for each record

(date package v1.2-38 in R; Therneau et al. 2018). Specifically, I used July 1 as the start of a year, corresponding to mid-winter in the Southern Hemisphere.

Matching Climate Data to Herbarium Data

All analyses were done using R v3.3.2 (R Development Core Team 2016). I calculated great circle distances between all herbarium and weather station location points (N = 2,863,344 total) (fields package v9.6; Nychka et al. 2017). Next, I determined the five closest weather stations (or all stations < 10 km from the collection site) to each herbarium record and extracted

available data for daily minimum and maximum temperatures from those stations. I used all available weather data for the period to fit a Bayesian generalized additive mixed model

(stan_gamm4()) to understand the relationship between temperature [(mean daily maximum + mean daily minimum)/2] and year (rstanarm package v2.13.1; Stan Development Team 2016) with station treated as a random effect.

MAT ~ Year + (1|Station).

I used posterior_predict() to impute a posterior distribution of the mean annual temperature

(MAT) for every weather station in every year and took the mean of the posterior distribution as our estimate of MAT for every weather station and year combination. For each herbarium record

I used the average of MAT imputed in the year of collection at the five closest weather stations as our estimate of MAT for each collection site in the year of collection. Imputation methods were used because no weather station had complete daily minimum and maximum temperature recordings for the entire time period. Imputation results closely aligned with raw temperature records and thus were used in the following analysis. Rainfall, photoperiod, and three additional measures of MAT were accessed, though none were significant, and thus not used in further analyses (see Supporting Information A-C).

Statistical Analyses

To assess the relationships between flowering phenology and its association with climate

I used a Bayesian linear mixed-effects model implemented in stan_lmer() (rstanarm package;

Stan Development Team 2016). I used the following model to assess this relationship:

Phenology ~ MAT + (1|Locality*Species).

Phenology is the Julian date calculated to represent the floral observation. MAT was treated as a fixed predictor variable. Locality, species, and their interaction were treated as random effects

(see Table S2 for model selection results). This reflected our presumption that local weather conditions are the predominant influence on date of maximum flowering.

RESULTS

Mean annual temperature has increased by 2.9oC across the CFR from 1850–2009 (Fig.

2). The relationship between MAT and year was significant (95% credible interval: -4.03, -0.94) and corresponds to an average temperature rise of 0.18oC/decade over this period (Table 1).

Almost two-thirds of this increase (1.9oC) occurred since the mid-1980s.

I found evidence that phenological shifts were associated with MAT throughout the past century (Fig. 3). MAT was negatively associated with flowering phenology (Table 1).

Pelargonium species advanced their flowering by 7.1 days, and nearly all of the advancement was attributed to an association with MAT. I found a 2.4 day advancement per degree increase in temperature (95% credible interval: -4.47, -0.39). When combined with the observed change in

MAT, 6.96 days of the advancement are attributed to the 2.9oC increase in MAT over the time period. Although, I observed a strong response between phenology and temperature, only ~37% of the phenological variation was explained by its association to temperature (R2 = 0.368). I found comparable results for the digitized records that had floral scores (results not shown).

DISCUSSION

To our knowledge, this study is one of the first studies to assess phenological shifts using herbarium records from South Africa and is one of just a handful of studies from the southern hemisphere. Mean annual temperatures throughout the study region have risen by an average of

2.9oC since 1850 and are increasing at a much faster rate than the global trend of 0.85 oC (range:

0.65 to 1.06) over a similar time period, 1880-2012 (IPCC 2013). The increased rate of temperature change throughout South Africa is negatively associated with an advancement (2.4 days/oC) in flowering phenology (Fig. 3). The results also highlighted that MAT was not the only environmental component impacting species responses. This suggests that Pelargonium species are responding to multiple long-term, contemporary changes, which involve mean annual temperature, but might also involve other measures like precipitation, that have not been assessed in this study. The responsiveness to long-term, contemporary change suggests that

Pelargonium could be an indicator of species’ responses to other aspects of climate change throughout South Africa (Gallagher et al. 2009). With the use of herbarium records, indicator species can be used to measure the rate of change happening throughout an ecological system, capture the current status of that system, and when linked with continued monitoring can provide robust data to assist in conservation planning and implementation (Kenney et al. 2016; Hufft et al. 2018).

Climate change is altering the way plants function and interact with their environment.

Increases in extreme events, such as heat waves, droughts, and fires, disruptions in precipitation cycles, and more unpredictable weather patterns are being observed (Meehl et al. 2006, 2007;

IPCC 2013). These changes magnify the need to understand temperature, carbon and water relations, as they are key elements of plant performance and productivity. Temperature is only one of the primary abiotic components affecting plant growth and development including the initial development and continued growth of buds, leaves, flowers, and biomass accumulation.

Here I showed a significant response to the changing environment, but the driver of this response was not only temperature, as only 37% of the variation shown was due to temperature changes.

Photoperiod and precipitation have long been established as prominent environmental cues for

developmental processes as well (Lobo et al. 2003; Grogan and Schulze 2012). Plants in habitats with pronounced dry seasons have been shown to use precipitation as an environmental cue to produce flowers and leaves (Lobo et al. 2003; Grogan and Schulze 2012). Similarly, photoperiod has shown to induce developmental processes in habitats where there is high rainfall, within temperate zones, and in higher latitudes (Borchert and Rivera 2001; Kudoh 2018). Although, temperature is the standard measure of environment change in most herbarium studies, I am showing that other environmental variables may be acting to cause phenological changes.

Because many organisms are currently and will be impacted by changes to environmental patterns, I urge researchers to look at a suite of environmental variables that may impact plant phenology.

Our study and similar herbarium-based research (Fitter and Fitter 2002; Primack et al.

2004; Menzel et al. 2006; Miller-Rushing and Primack 2008; Gallagher et al. 2009) continues to confirm the value of using long-term phenological datasets to understand how plants are responding to climate change. The CFR, a biodiversity hotspot in South Africa, is showing similar phenological responses to those observed in northern temperate environments. Although phenological responses have been showed, I know phenology is a complex phenomenon. Other factors that covary with phenological development (e.g., photoperiod, precipitation, and topographic location) may be impacting the flowering responses shown throughout the region.

Future work using large datasets, such as the Pelargonium data discussed in this paper, can be used to assess responses to additional environmental and physical factors (such as mean annual precipitation, seasonality of rainfall, photoperiod, or north- vs. south-facing slope locality), habitat preferences and nice partitioning, range shifts, and other ecological processes.

The herbarium data on which this study relies on does have several shortcomings. The most important are the following: 1) data were not systematically collected for our analysis; i.e., records are rarely available at the same location on the same date every year, and are rarely collected throughout the flowering season, and may be weak indicators of peak flowering, and 2) other abiotic and biotic factors could be responsible for the observed shifts in phenology.

Although, the data was not collected for the current climate change impact analysis, it has been shown that herbarium collections are reliable proxies for peak flowering times when compared to annual field observations (Robbirt et al. 2010). It has also been shown that temperature associations and responses are the most well-studied relationship within the phenology literature

(Fitter and Fitter 2002; Primack et al. 2004; Menzel et al. 2006; Miller-Rushing and Primack

2008; Gallagher et al. 2009; Daru et al. 2019). Nonetheless, herbarium data provides important clues about the impacts of climate change, and they provide a baseline for comparison with present and future field observations (see Primack et al. 2004; Panchen et al. 2012; Davis et al.

2015).

Long-term phenological records, derived from herbarium records, have provided credible evidence of species adjustment to climate change (Visser and Holleman 2001; Fitter and Fitter

2002; Gallagher et al. 2009; Meineke et al. 2018). For many species, long-term data is only possible through herbarium collections. With such a large number of specimens worldwide (~

2.5 billion), this information is becoming more and more valuable in light of climate change

(Soberon 1999; Krishtalka and Humphrey 2000; Graham et al. 2004). Using these historical collections and long-term environmental data the investigation of associations with phenological responses and patterns can be conducted across large geographical ranges and species rich

datasets. These data not only document past responses, but they may help identify sensitive species and habitats especially in need of conservation attention.

ACKNOWLEDGEMENTS

I thank the staff at the University of Cape Town’s Bolus Herbarium (especially Dr. Terry

Trinder-Smith), Rhodes University’s Selmar Schonland Herbarium (especially Mr. Tony Dold), and the South African National Biodiversity Institute’s (SANBI) herbaria for all their help and access to their facilities. I know that the work curated by these facilities has taken many, many years, and offer retrospective thanks to the countless botanists and naturalists who collected the specimens and provided the data. I also want to highlight the importance for the continued support of such institutions. This work was supported by the National Science Foundation’s

Dimension of Biodiversity grant (DEB-1046328). This work was also supported by the National

Science Foundation’s LSAMP Bridge to Doctorate Fellowship (No. 1249283) and the University of Connecticut’s Graduate School Harriott Fellowship.

TABLES

Table 1. Regression coefficients and credible intervals for Pelargonium temperature and phenology associations. Standard deviation (SD); credible interval (CI); mean annual temperature (MAT). Italicized and starred fixed predictor variables indicate significance.

Mean SD 95% CI MAT ~ Year + (1| Weather Station)

Year* -2.90 0.79 (-4.03, -0.94)

Phenology ~ MAT + (1|Locality * Species)

MAT* -2.44 1.04 (-4.47, -0.39)

FIGURES

Fig. 1. Map of herbarium collection sites and weather station localities throughout the study area. Herbarium sites are portrayed with light gray filled circles and weather stations with black filled triangles.

Fig. 2. Mean annual temperature from 1900–2009 in the Cape Floristic Region and surrounding areas of South Africa reconstructed using climatic data from 464 weather stations (see Fig. 1 for weather station localities).

Fig. 3. The associate between flowering date and mean annual temperature show a significant negative relationship (95% credible interval: -4.47, -0.39). Pelargonium species show a trend toward advancing flowering times by 2.4 d per 1oC increase in temperature throughout the Cape Floristic Region and surrounding region. R2 = 0.368. Julian Day 1 = July 1.

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CHAPTER 1 SUPPORTING INFORMATION

Table S1. Pelargonium species with the number of specimens, and collection year and Julian date ranges per species.

Earliest Latest Earliest Latest Species No. Collection Collection Julian Julian Year Year Date Date abrotanifolium 163 1904 2007 7 363 alchemilloides 155 1908 2006 28 351 alpinum 30 1934 2008 115 229 alternans 96 1904 2009 15 346 althaeoides 45 1913 2005 31 315 anceps 49 1905 1985 62 336 anethifolium 28 1932 2009 32 141 antidysentericum 10 1916 2004 62 304 appendiculatum 13 1925 2009 52 120 aridum 27 1901 2005 63 315 articulatum 27 1922 2003 16 267 asarifolium 37 1921 2007 31 365 auritum 71 1903 1996 31 249 barklyi 10 1940 1985 46 366 betulinum 65 1908 2007 19 258 caespitosum 10 1953 1985 92 290 caffrum 10 1915 1983 123 276 caledonicum 14 1930 2001 1 268 candicans 110 1904 2004 10 357 capillare 52 1902 1999 32 275 capitatum 94 1904 2005 26 353 carneum 42 1912 2006 49 333 carnosum 68 1911 2008 63 336 caucalifolium 81 1904 2008 10 340 chelidonium 34 1903 2009 34 153 citronellum 24 1923 2003 1 332 columbinum 13 1925 2007 62 183 cordifolium 90 1904 2007 8 366 coronopifolium 69 1912 2009 60 306 crassipes 15 1933 1983 1 351 crispum 76 1907 2000 1 355 crithmifolium 50 1907 2007 1 347 cucullatum 110 1903 2008 1 233 dasyphyllum 18 1908 2003 57 335

denticulatum 71 1903 2005 4 365 dichondrifolium 30 1900 1999 95 365 dipetalum 45 1917 1999 1 335 divisifolium 14 1940 1988 93 304 echinatum 43 1907 2009 1 365 elegans 37 1928 2006 50 260 ellaphieae 15 1900 1987 123 263 elongatum 38 1913 2006 81 304 englerianum 59 1904 1995 32 294 exstipulatum 22 1905 2003 1 361 fergusoniae 13 1931 1981 107 320 fissifolium 17 1962 2001 47 215 fruticosum 112 1905 2004 1 354 fulgidum 58 1905 2009 3 364 gibbosum 16 1901 2008 9 275 glutinosum 92 1904 2006 1 355 gracillimum 13 1907 1983 91 271 grandicalcaratum 10 1948 1989 66 335 grandiflorum 39 1909 2008 62 333 graveolens 14 1903 2004 45 365 griseum 15 1951 2005 34 252 grossularioides 67 1909 2007 31 337 hermaniifolium 57 1915 2000 1 299 hirtum 18 1905 2003 45 300 hispidum 64 1904 1999 8 313 hypoleucum 11 1923 1988 82 304 hystrix 23 1921 1972 93 304 incarnatum 36 1905 2001 93 288 incrassatum 43 1925 2009 31 327 inquinans 25 1914 1999 54 365 iocastum 29 1924 2006 76 198 karooicum 18 1907 1990 8 294 laevigatum 118 1903 2009 1 365 lanceolatum 43 1907 2009 11 291 laxum 19 1906 2009 32 365 leipoldtii 10 1919 1981 27 120 lobatum 43 1903 1991 17 276 longicaule 74 1910 2005 17 362 longifolium 155 1901 2009 19 336 luteolum 17 1919 2004 7 320 magenteum 84 1919 2007 8 366 minimum 30 1901 2008 28 289 moniliforme 32 1917 2008 27 223

multicaule 62 1904 2007 57 363 multiradiatum 16 1926 1980 32 357 myrrhifolium 210 1908 2009 12 365 nervifolium 16 1921 2006 24 145 odoratissimum 45 1907 2004 30 366 oenothera 16 1905 1987 63 307 ovale 159 1904 2008 1 304 panduriforme 51 1909 2003 11 337 papilionaceum 35 1923 2003 38 336 patulum 130 1902 1994 32 356 peltatum 85 1908 2004 4 365 pillansii 24 1919 2009 1 362 pilosellifolium 11 1920 2007 17 256 pinnatum 95 1901 2008 3 364 plurisectum 10 1919 1991 63 250 polycephalum 14 1907 1990 48 205 praemorsum 54 1917 2009 45 153 proliferum 33 1908 1995 93 336 psammophilum 20 1915 1969 113 185 pulchellum 25 1930 2006 57 335 pulverulentum 11 1902 1979 32 316 quercifolium 45 1908 2003 3 362 radens 21 1908 2000 6 244 radicatum 19 1915 1985 7 314 radulifolium 28 1905 1977 32 276 rapaceum 70 1912 2009 51 248 reflexum 10 1930 1996 51 209 reniforme 79 1907 2003 1 365 ribifolium 43 1908 2004 57 244 scabroide 21 1933 2005 33 306 scabrum 194 1907 2007 3 361 senecioides 69 1914 2009 32 285 setulosum 26 1935 1991 65 227 sidoides 36 1900 2006 75 351 stipulaceum 19 1908 2006 71 366 sublignosum 17 1936 1983 103 320 suburbanum 45 1912 2003 32 344 tabulare 68 1915 2004 62 338 ternatum 48 1908 2003 29 340 ternifolium 39 1919 2005 32 336 tetragonum 39 1904 2004 62 199 tomentosum 12 1904 1993 1 215 tragacanthoides 45 1909 2005 1 339

tricolor 72 1904 1996 71 305 trifidum 49 1904 1995 33 365 trifoliolatum 10 1932 1953 63 180 triphyllum 15 1940 1988 36 280 triste 160 1902 2009 10 366 undulatum 19 1910 1983 63 124 viciifolium 29 1929 1982 31 183 vitifolium 34 1916 1980 62 274 zonale 80 1901 2003 9 331

Table S2. Phenology model selection results.

Model df AIC Log-Likelihood Phenology ~ MAT 3 70606 -35300

Phenology ~ MAT + (1|Locality) 4 70244 -35118

Phenology ~ MAT + (1|Species) 4 70244 -35118

Phenology ~ MAT + (1|Locality*Species) 6 69639 -34813

CHAPTER 1 SUPPORTING INFORMATION A

Table S1A. Rainfall seasonality (winter, summer, aseasonal) and photoperiod model results.

Mean Standard Error t-value

Year ~ Rainfall + (1|Species)

Intercept 2035.09 8.53 238.5

Summer Rainfall -3.97 1.17 -3.4

Winter Rainfall -5.24 1.16 -4.5

Phenology ~ Rainfall + Year + (1|Species)

Intercept 1.94e2 6.13 31.8

Summer Rainfall 1.10 3.17e-1 3.4

Winter Rainfall 4.43e-1 2.96e-1 1.5

Year 2.11e-3 3.13e-3 0.7

Phenology ~ Rainfall + Photoperiod + Year + (1|Species)

Intercept -2.6e2 4.0 -63.6

Summer Rainfall -5.7 1.8e-1 -31.3

Winter Rainfall -1.9 1.8e-1 -10.9

Photoperiod 4.5e1 2.5e-1 177.2

Year 3.2e-4 1.6e-3 0.2

Mean Annual Precipitation ~ Rainfall + Year + (1|Species)

Intercept -116.7 150.9 -0.8

Summer Rainfall 76.2 8.2 9.3

Winter Rainfall 149.3 8.2 18.2

Year -0.6 0.1 -9.1

CHAPTER 1 SUPPORTING INFORMATION B

Table S1B. Minimum, maximum and average photoperiod of all locations were Pelargonium specimens were collected.

Minimum Maximum Average Species No. Day Length Day Length Day Length (hrs) (hrs) (hrs) abrotanifolium 163 9.85 14.46 12.30 alchemilloides 155 9.81 14.50 13.20 alpinum 30 9.92 14.39 14.04 alternans 96 9.89 14.41 12.47 althaeoides 45 9.84 14.47 13.11 anceps 49 9.81 14.50 12.86 anethifolium 28 9.87 14.43 12.48 antidysentericum 10 9.92 14.39 11.63 appendiculatum 13 10.02 14.27 12.03 aridum 27 9.94 14.37 13.05 articulatum 27 9.92 14.39 13.03 asarifolium 37 9.87 14.43 12.05 auritum 71 9.84 14.47 13.46 barklyi 10 9.94 14.37 12.33 betulinum 65 9.81 14.50 12.39 caespitosum 10 9.98 14.32 12.80 caffrum 10 9.87 14.43 13.56 caledonicum 14 9.81 14.50 12.60 candicans 110 9.84 14.47 12.19 capillare 52 9.89 14.41 13.63 capitatum 94 9.81 14.50 12.48 carneum 42 9.83 14.48 13.32 carnosum 68 9.88 14.42 12.48 caucalifolium 81 9.83 14.48 12.94 chelidonium 34 9.88 14.42 12.30 citronellum 24 9.89 14.42 12.36 columbinum 13 9.85 14.46 12.75 cordifolium 90 9.83 14.48 12.13 coronopifolium 69 9.89 14.41 13.16 crassipes 15 9.91 14.39 11.21 crispum 76 9.87 14.43 12.37 crithmifolium 50 9.92 14.39 11.75 cucullatum 110 9.83 14.48 13.12 dasyphyllum 18 9.84 14.47 12.22

denticulatum 71 9.87 14.43 12.04 dichondrifolium 30 9.89 14.41 12.47 dipetalum 45 9.82 14.49 12.05 divisifolium 14 9.87 14.43 13.90 echinatum 43 9.89 14.41 11.57 elegans 37 9.82 14.49 13.17 ellaphieae 15 9.86 14.44 13.67 elongatum 38 9.85 14.46 13.36 englerianum 59 9.88 14.42 13.54 exstipulatum 22 9.87 14.43 11.56 fergusoniae 13 9.83 14.48 13.04 fissifolium 17 9.89 14.41 12.73 fruticosum 112 9.87 14.44 12.43 fulgidum 58 9.93 14.38 11.62 gibbosum 16 9.85 14.46 12.44 glutinosum 92 9.88 14.43 12.45 gracillimum 13 9.90 14.41 13.13 grandicalcaratum 10 9.92 14.39 11.81 grandiflorum 39 9.88 14.42 13.27 graveolens 14 9.87 14.43 12.27 griseum 15 10.02 14.28 13.62 grossularioides 67 9.83 14.48 12.95 hermaniifolium 57 9.85 14.46 12.44 hirtum 18 9.87 14.43 11.72 hispidum 64 9.85 14.46 13.27 hypoleucum 11 9.84 14.47 13.20 hystrix 23 9.94 14.37 12.96 incarnatum 36 9.83 14.48 13.75 incrassatum 43 10.00 14.30 11.50 inquinans 25 9.87 14.43 12.19 iocastum 29 9.85 14.46 13.56 karooicum 18 9.89 14.41 12.61 laevigatum 118 9.89 14.42 12.95 lanceolatum 43 9.89 14.41 12.91 laxum 19 9.89 14.41 11.83 leipoldtii 10 9.94 14.37 11.74 lobatum 43 9.82 14.49 12.26 longicaule 74 9.83 14.48 12.81 longifolium 155 9.83 14.48 13.31 luteolum 17 9.87 14.43 12.08 magenteum 84 9.94 14.37 11.48 minimum 30 9.94 14.37 12.50 moniliforme 32 9.87 14.44 12.19

multicaule 62 9.85 14.46 12.87 multiradiatum 16 9.86 14.45 11.96 myrrhifolium 210 9.81 14.50 12.57 nervifolium 16 9.95 14.35 11.92 odoratissimum 45 9.84 14.46 12.05 oenothera 16 9.89 14.41 12.81 ovale 159 9.83 14.48 12.93 panduriforme 51 9.89 14.42 12.50 papilionaceum 35 9.85 14.46 12.65 patulum 130 9.87 14.43 13.55 peltatum 85 9.85 14.46 12.63 pillansii 24 9.86 14.44 11.75 pilosellifolium 11 9.85 14.46 12.79 pinnatum 95 9.83 14.48 13.56 plurisectum 10 9.85 14.46 12.65 polycephalum 14 9.89 14.41 12.08 praemorsum 54 9.94 14.37 11.92 proliferum 33 9.84 14.47 13.13 psammophilum 20 9.85 14.46 14.01 pulchellum 25 10.12 14.17 11.64 pulverulentum 11 9.87 14.43 12.66 quercifolium 45 9.85 14.46 12.39 radens 21 9.87 14.43 12.51 radicatum 19 9.89 14.42 13.09 radulifolium 28 9.87 14.44 12.70 rapaceum 70 9.81 14.50 13.62 reflexum 10 10.10 14.19 12.41 reniforme 79 9.87 14.43 12.40 ribifolium 43 9.87 14.43 12.88 scabroide 21 9.89 14.41 13.11 scabrum 194 9.85 14.46 12.50 senecioides 69 9.85 14.46 12.22 setulosum 26 9.85 14.46 13.34 sidoides 36 9.85 14.46 13.17 stipulaceum 19 9.95 14.36 12.40 sublignosum 17 9.87 14.43 12.83 suburbanum 45 9.81 14.50 13.05 tabulare 68 9.83 14.48 13.03 ternatum 48 9.87 14.43 12.03 ternifolium 39 9.89 14.42 12.36 tetragonum 39 9.87 14.43 12.87 tomentosum 12 9.87 14.43 13.37 tragacanthoides 45 9.89 14.41 13.06

tricolor 72 9.87 14.43 13.11 trifidum 49 9.89 14.41 12.74 trifoliolatum 10 9.90 14.40 13.54 triphyllum 15 9.89 14.42 13.38 triste 160 9.81 14.50 12.19 undulatum 19 9.89 14.41 12.35 viciifolium 29 9.89 14.41 13.10 vitifolium 34 9.83 14.48 13.08 zonale 80 9.85 14.46 12.75

Table S2B. Representative Pelargonium species, spanning across the three rainfall regimes found throughout the country, showing the minimum and maximum photoperiods at the time of specimen collection.

Minimum Maximum Rainfall Photoperiod at Photoperiod at Species Seasonality Time of Time of Collection (hrs) Collection (hrs) P. triste Winter 13.8 14.3 P. longifolium Winter 13.8 14.3 P. patulum Winter 13.8 14.3 P. praemorsum Winter 13.5 14.0 P. aridum Summer 13.3 13.6 P. sidoides Summer 13.9 14.0 P. capitatum Aseasonal 14.4 14.6 P. alchemilloides Aseasonal 13.7 14.3 P. scabrum Aseasonal 14.1 14.2

Minimum Day Length = 10 hours Maximum Day Length = 14.8 hours

Density

10 11 12 13 14 15 Day Length (hours) Fig. S1B. Minimum and maximum day lengths across all sites where Pelargonium species were collected were 10 and 14.8 hours,

respectively. All Pelargonium species assessed in this study showed a strong correlation to flowering during long days. Based on the

photoperiod of the collection date and record location, the minimum (June 21) day length for all collections was 12.7 hours and

maximum (December 21) day length was 14.8 hours.

CHAPTER 1 SUPPORTING INFORMATION C

Since most phenological studies are done in temperate regions that have harsh winters and chilling periods before bud and floral development, I wanted to assess what measure of temperature would be appropriate within Mediterranean systems, which have warm, dry summers and wet, cool winters with rare frost events. The four measures of mean annual temperature (MAT) were calculated as follows: 1) MAT during the adjusted southern hemisphere calendar year: Jul 1 – Jun 30, 2) MAT during the year preceding the collection date,

3) MAT during the (unadjusted) calendar year of collection, and 4) MAT during the three months preceding the collection date, which is the most common measure of MAT used throughout the phenological literature (see Primack et al. 2004; Robbirt et al. 2011; Rawal et al.

2015). Models were run in R v3.3.2 using a Bayesian linear mixed-effects model (stan_lmer) in the rstanarm package v2.13.1 (R Development Core Team 2016; Stan Development Team 2016).

I used the following model: Phenology ~ MAT + Year + (1|Locality*Species). Model comparison was done using the Bayesian leave-one-out cross-validation method (loo package v2.1.0; Vehtari et al. 2019). The best model was method one, which used the adjusted southern hemisphere calendar.

REFERENCES

Primack, D., Imbres, C., Primack, R. B., Miller‐Rushing, A. J., and Del Tredici, P. (2004).

Herbarium specimens demonstrate earlier flowering times in response to warming in

Boston. American Journal of Botany, 91: 1260-1264.

R Development Core Team. (2016). R: A language and environment for statistical computing. R

Foundation for Statistical Computing, Vienna, Austria. URL http://www.r-project.org/.

Stan Development Team. (2016). rstanarm: Bayesian applied regression modeling via Stan. R package version 2.13.1. URL http://mc-stan.org/.

Vehtari, A., Gabry, J., Yao, Y., and Gelman, A. (2019). loo: Efficient leave-one-out cross- validation and WAIC for Bayesian models. R package version 2.1.0. URL https://CRAN.R- project.org /package= loo.

Table S1C. Phenology model with varying mean annual temperature (MAT) measures and model comparison results.

Significance Mean SD 2.5% 97.5% Loo Results ± SE† Model 1: Southern Hemisphere Calendar Date

MAT *** -2.90 0.79 -4.03 -0.94 70593.6 (162.8) Year *** -2.40 1.04 -4.47 -0.39

Model 2: Preceding Year of Collection Date 73991.3 MAT 2.17 1.87 -1.48 5.77 (150.7) Year 0.02 0.03 -0.09 0.04

Model 3: Calendar Year of Collection Date 73988.4 MAT *** -5.76 1.94 -9.57 -2.05 (150.7) Year 0.04 0.03 -0.02 0.11

Model 4: Three Months Preceding Collection Date 73602.2 MAT *** -4.292 2.685 -9.54 -0.82 (162.6) Year *** -0.12 0.02 -0.15 -0.08

†SE: Standard error.

CHAPTER 2:

Using common gardens to understand the role phenotypic plasticity and functional trait

variation have in the potential responses to climate change in Pelargonium species

throughout South Africa

ABSTRACT

Knowing how phenotypic differences are influenced by plasticity and genetic differences is important for understanding how plants respond to environmental changes. Common garden studies are often used to control for environmental variation and identify the contribution of genetic differences to trait variation. From 2016–2018, experiments in two common gardens were carried out in the Western Cape (winter rainfall) and Eastern Cape (summer rainfall) of

South Africa. The genus Pelargonium was used as a model system because of its geographic, morphological, and genetic diversity. The aims of this study were as follows: (i) to understand how Pelargonium species will respond to novel environmental conditions they are projected to face in the future, (ii) to determine if species perform better in environments typical of where they occur, and (iii) to identify growth and leaf traits that are related to performance across the two rainfall regimes. Significant differences were found in traits and growth rates among species across the two gardens. All traits showed significant phenotypic plasticity (P ≤ 0.05), except for stem diameter. Some traits (e.g., height) showed strong phylogenetic and environmental associations, while other traits (e.g., leaf mass per area) had less consistent associations. Leaf thickness and plant height were strongly associated with the probability of survival at both

gardens. Plasticity may have a significant role in aiding species persistence throughout environmentally and ecologically diverse habitats in South Africa.

INTRODUCTION

The largest threat facing many species is climate change, whose effect on weather patterns influences the phenology, abundance, and geographic distribution of species across the world (Davis and Shaw 2001; Parmesan and Yohe 2003; Aitken et al. 2008; Kelly and Goulden

2008; Lenoir et al. 2008; Amano et al. 2014). Historically, species migrated poleward and toward higher altitudes during postglacial periods of warming (Parmesan and Yohe 2003; Hickling et al.

2006; Lenoir et al. 2008; Chen et al. 2011). These movements were accompanied by local extinctions and expansions over thousands to millions of years. Contemporary climate change, exacerbated by deforestation, agriculture, and urban development, is happening so quickly that species may not move rapidly enough to persist (Jump and Peñuelas 2005; Kelly and Goulden

2008; Chen et al. 2011).

Phenotypic plasticity, the ability of a single genotype to produce different phenotypes in different environmental conditions (Bradshaw 1965; Schlichting 1986; Sultan 1987), will play a crucial role in species’ responses to climate change (Nicotra et al. 2010). Plasticity may provide a buffer against environmental change and may promote rapid responses to such change

(Bradshaw 1965; Davis and Shaw 2001; Ackerly 2003; Chevin et al. 2010). Genotypes, populations, and species with greater phenotypic plasticity may respond more effectively in the short-term allowing populations to persist until genetic adaptation occurs in the long-term

(Sultan and Spencer 2002; Chevin et al. 2010; Matesanz et al. 2010). In plants, phenotypic plasticity often influences seed longevity, leaf and floral phenology, leaf lifespan, temperature

responses, and ecological breadth, all which could allow a species to persist until it can adapt to changing environments (e.g. Anderson et al. 2012; Matesanz et al. 2010; see citations within).

Nonetheless, little is known about the magnitude of phenotypic plasticity in long-lived plant species, even though they often experience extensive changes in environmental conditions within their lifetime.

Common garden experiments are a powerful tool for identifying the role of genetic differences and phenotypic plasticity in differences in plant traits at different life history stages and in different environmental conditions (Turesson 1925; Clausen, Keck, and Heisey 1948).

Partitioning observed variation into genetically and environmentally influenced components can reveal the role that local adaptation plays in population differences. In addition, common gardens allow examination of fitness responses within and beyond the present range limits (Clausen,

Keck, and Heisey 1948; Kawecki and Ebert 2004; Ghalambor et al. 2007). Here, a common garden experiment was used in South Africa with five Pelargonium species to examine how environmental conditions within and outside of each species’ range (contrasting current environmental conditions with those they are expected to experience in 30+ years) affects fitness components and morphology over a two-year period.

Plant communities in South Africa, especially those found within the highly biodiverse Cape Floristic Region (CFR), are among the most speciose in the floral kingdoms

(Cowling et al. 1996). High environmental heterogeneity across short distances has been suggested as the mechanism that promotes the many species-rich ecosystems found throughout the country (Verboom et al. 2009). Rainfall seasonality, steep climatic gradients, fire disturbance, and resource availability variation all contribute to the high species and niche diversity observed (Verboom et al. 2009), making this region especially exciting for

investigating questions concerning climate change responses. With this backdrop, the highly diverse plant genus Pelargonium is an ideal study system in which to investigate such topics. Not only is this predominantly South African genus among the largest genera within the CFR, it also exhibits high morphological variation in growth form and leaf traits (Goldblatt and Manning

2002; Jones et al. 2013). Moreover, previous research has shown strong trait-environment associations across plant taxa and within Pelargonium species found throughout South Africa

(Nicotra et al. 2007; Carlson et al. 2011; West et al. 2012; Jones et al. 2013; Mitchell et al. 2015;

Moore et al. 2018).

Understanding local patterns and processes underlying species performance and fitness allows for more accurate assessments of adaptation, survival, and the effectiveness of possible conservation strategies. Recent research has combined common gardens and trait-based theory to predict extinction risks, assess local adaptation, understand disturbance responses, assess community assembly and shifts in association to environmental gradients, as well as assess the trait-environment role in species diversification (Warren et al. 2006; Premoli,

Raffaele, and Mathiasen 2007; Ravenscroft, Fridley, and Grime 2014; Benito Garzón et al. 2019;

Gárate‐Escamilla et al. 2019). In this study, I used common gardens to assess the following in five species of Pelargonium in two different environments:

1) The survival of species beyond their geographic and/or environmental range,

2) The relative contribution of genetic differences and phenotypic plasticity to trait

differences, and

3) The relationship between traits and fitness components in each garden.

METHODS

Study Species

The diverse genus, Pelargonium L’Hér. (Geraniaceae), consist of herbaceous perennials, evergreen perennials, subshrubs and geophytes. It is among the twenty largest genera of vascular plants occurring in the Cape Floristic Region (CFR), a global biodiversity hotspot

(Goldblatt 1997; Goldblatt and Manning 2002). It includes 280 species; approximately 160 of them occur and are endemic to the CFR (Goldblatt 1997; Goldblatt and Manning 2002). Species of the genus occur in a wide variety of habitats from lowland to mountainous habitats consisting of nutrient-poor to moderately rich soils (Goldblatt 1997; Bakker et al. 1999). They exhibit high levels of morphological variation (e.g., within and across species, across populations, and among individuals within a population) in growth form and leaf traits, making them an excellent system for studying plant evolution, diversity, and phenotypic plasticity (Jones et al. 2009; Martínez-

Cabrera et al. 2012).

The five Pelargonium species were selected because they represent different evolutionary lineages and rainfall seasonality regimes (Fig. 1, Table S1). Pelargonium cucullatum’s distribution occurs within the winter rainfall regime (Mediterranean climate), P. transvaalense’s distribution occurs within the summer rainfall regime, P. capitatum occurs widely in coastal areas in all rainfall regimes, while P. quercifolium and P. alchemilloides occur in the aseasonal rainfall regime (Fig. 1, Table S1). Pelargonium cucullatum (evergreen woody shrub), P. capitatum (evergreen woody subshrub) and P. quercifolium (evergreen woody shrub) are from

Clade A1, section Pelargonium, a clade consisting of evergreen shrubs, subshrubs and woody perennials (Jones et al. 2009). P. transvaalense (herbaceous subshrub) and P. alchemilloides

(herbaceous perennial) are from Clade C2, section Ciconium, a clade consisting of herbaceous perennials, shrubs, subshrubs and geophytes (Jones et al. 2009).

Common Garden Sites

Climate change predictions for South Africa project drier and warmer winters in the winter rainfall region and wetter and warmer summers in the summer rainfall region (Midgley et al. 2003). The common garden experiment was conducted in two gardens 872 km apart (Cape

Town, Western Cape and Grahamstown, Eastern Cape; Fig. 1). These sites were selected because they approximate two of the temperature and precipitation regimes species in this region will face with the advancement of climate change (Midgley et al. 2003). The Cape Town garden site (referred to as the Kirstenbosch garden) also represents one study species’ native range (Fig.

1). The Cape Town garden (-33.9876 S, 18.4309 E) is in the winter rainfall regime region, which is characterized by rainy, cool winter months and dry, hot summers (Fig. 1, Table S2). It is warmer and wetter than the Grahamstown garden (-33.3136 S, 26.5143 E), with mean annual temperature is 17.3 oC and mean annual precipitation is 1295 mm vs 15.8 oC and 689 mm for the

Grahamstown garden (referred to as the Rhodes garden). The Grahamstown garden is located in the summer rainfall regime region, which is characterized by warm, wet summers and dry, cold winters (Fig. 1, Table S2).

Germination and Early Growth Procedure

Seed was obtained from two different nurseries (Kirstenbosch Botanical Garden’s

Seed Facility in Cape Town, South Africa and Silverhill Seeds in Kenilworth, South Africa) that collect materials from natural populations within South Africa. A total of 2,500 seeds (N =

500/species) were sown on March 1, 2016 in a greenhouse facility at the Cape Peninsula

University of Technology in Bellville, Western Cape, South Africa. Seeds were sown in fine vermiculite in 7.62 cm deep 96-well germination trays. Seeds were placed on top of the medium with 0.64 to 1.27 cm vermiculite covering. Both vermiculite and seedling trays were sterilized

before use. Conditions in the germination facility were set for warm days (12 hours at 22–23 oC) and moderate nights (12 hours at 15–16 oC) for the duration of the germination period. Relative humidity was kept within the range of 67–70%. Seeds were initially watered daily with a misting system for 10 seconds every 30 minutes. Upon germination, seeds were watered every 2–3 days depending on soil moisture levels. Germination was recorded at the emergence of the cotyledons.

Growth measurement were recorded for a period of six months. After seedlings were ≥ 10 cm

(average time = 3.5 months), they were transplanted to individual half gallon seedling bags

(grow bags) in a potting soil mixture of three parts standard potting soil and one-part sand under the same growing conditions. After ~4 months of growth, plants were moved to harden off with

50% shade for one month and then full sun for one to six months before transplantation into gardens. The Kirstenbosch garden was planted in August 2016 (near the end of the rainy season, after two months of full sun hardening), while the Rhodes garden was planted in January 2017

(near the end of the rainy season, after six months of full sun hardening; Fig. S1).

Experimental Design

Each site was cleared by hand weeding and tilling (machine tilling at Kirstenbosch and manual tilling at Rhodes) to minimize competition with established plants (Fig. S1).

Resprouting vegetation was hand weeded during the course of the experiment. The plot area for each garden was 19 x 3 m. A 20 cm buffer was cleared around the plot area of each garden to prevent shading from neighboring vegetation. The Kirstenbosch site was 20 cm to one meter from any neighboring plots and the Rhodes site was over one meter from any neighboring plots.

The experiment was set up as a completely randomized plot design with four blocks. Since gardens were planted at the appropriate rainy season, fewer plants survived for planting at

Rhodes. The initial plantings consisted of 238 individuals at Kirstenbosch and 164 individuals at

Rhodes. Each block contained 59 (Kirstenbosch) or 41 (Rhodes) plants, and included individuals from all five study species. Blocks along the 19 m edge had 100 cm between them, while blocks along the 3 m edge had 50 cm between them. Seedlings were watered in at the initial transplantation and then daily for two to three weeks during the initial adjustment period.

Functional Trait Measurements

Eight traits related to fitness were measured throughout the growing season (Table

S3). Leaf mass per area (LMA) is related to plant growth rate, photosynthesis rates and competitive vigor (Westoby et al. 2002; Wright et al. 2004). Height is related to resources acquisition, lifespan, and light interception (Westoby et al. 2002; Moles et al. 2009). Leaf area is associated with photosynthesis rate, water balance, and light interception, while leaf mass to growth rate and water balance (Cornelissen et al. 2003). Leaf length:width ratio is associated with resource acquisition and light interception (Westoby et al. 2002; Cornelissen et al. 2003;

Wright et al. 2004), while leaf thickness is related to the environmental conditions in which an individual is found (Pérez-Harguindeguy et al. 2013). Stem diameter is related to the aboveground storage of carbon, disease resistance, and other abiotic resistance factors

(Cornelissen et al. 2003). Lastly, survival is associated with competitive vigor and life history strategy, and was monitored yearly as a key component of fitness (Violla et al. 2007). Traits were measured at Kirstenbosch in August 2016 (initial establishment), January 2017, and August

2017. Traits were measured at Rhodes in January 2017 (initial establishment) and August 2017.

All traits, if available (e.g., if plant died, only the survival status was recorded), were recorded at each date. Stem diameter and leaf thickness were measured to the nearest 0.01 mm using an electronic caliper (iGaging IP54), and height was measured to the nearest 0.1 cm using a tape measure (Stanley Max). Two to three fully expanded, undamaged leaves were removed, and

fresh leaf material was scanned (Cannon LiDE 120) within 24-36 hours for further analysis.

When possible, leaves of similar age on a given plant were chosen for consistency. After harvest, leaf area (cm2), leaf length (cm), and leaf width (cm) were assessed by using image analysis software (ImageJ, National Institutes of Health, MD, USA). Leaf length:width ratios were calculated. Leaves were then dried at 60 oC for 72 h and weighed on a balance to the 0.0001 g accuracy (Mettler AE 200) to measure dry mass. LMA (g cm-2) was calculated as dry leaf mass per unit leaf area (Cornelissen et al. 2003; Pérez-Harguindeguy et al. 2013).

Statistical Analyses

To assess survival performance a Bayesian generalized linear model (stan_glm) with binomial error distribution and logit link function was fit using the rstanarm package version

2.18.2 (Stan Development Team 2016). This analysis included the interaction between garden and species. To determine if survival rates differed by year, a Chi-Square contingency analysis was performed. The relative contribution of genetic differences and plastic responses for each trait were analyzed using ANOVA including the interaction between species and garden.

Pairwise comparisons were performed only after a significant ANOVA result using a Tukey post hoc test. The relationship between traits and survival (fitness component) at each garden was tested using a Bayesian generalized linear mixed-effects model (stan_glmer) with binomial error distribution and logit link function (rstanarm package; Stan Development Team 2016). All traits were treated as fixed predictor variables and species identity was included as a random effect.

Each garden was tested separately. All traits were scaled and centered to normalize their distributions prior to analyses. Initial trait measurements, i.e., measurements at each garden’s establishment, were used for the fitness measure (survival) analysis. Trait measures after initial

establishment, i.e., traits measured on or after the second visit, were used for all other analyses.

All analyses were performed using R version 3.3.2 (R Development Core Team 2016).

RESULTS

Survival

The generalized linear model revealed significant survival differences between gardens (95% credible interval: -3.51, -1.84) and among species. Pelargonium cucullatum (95% credible interval: -2.29, -0.59) and P. transvaalense (95% credible interval: -2.49, -0.72) had significantly lower survival than the other species. Interestingly, survival followed the same rank order across both gardens. P. capitatum had the highest survival in both gardens (Kirstenbosch

(K) = 92% and Rhodes (R) = 100%); P. quercifolium K = 86% and R = 50%; P. alchemilloides

K = 84% and R = 34%; P. cucullatum K = 61% and R = 22%; and P. transvaalense K = 53% and R = 10%. Species survival varied between years at both: Kirstenbosch (푋2 = 26.1, P <

0.00001) and Rhodes (푋2 = 13.7, P = 0.0084).

Trait Variation and Phenotypic Plasticity

Traits exhibited a wide range of morphological responses both within and between species, and across gardens (Fig. 2, Table 1, Table S4). Of the 10 traits analyzed, eight showed significant genetic differences among species (all P ≤ 0.01); the exceptions were flower number and leaf length:width ratio (Table S4). These differences were aligned with differences in species’ morphology, life history strategies, and phylogenetic relationships. Plant height, leaf mass, leaf thickness and stem diameter all showed significant clade effects (A1 vs C2; Tukey: P

≤ 0.00001). Clade A1 plants were taller and had thicker leaves with larger leaf areas, and larger stem diameters than clade C2 plants (Fig. 2).

Eight traits, all except stem diameter, showed significant environmental (plastic) responses (all P ≤ 0.02) (Table S4). In general, Kirstenbosch individuals had thicker leaves and larger LMAs, while Rhodes individuals had larger leaf areas and length:width ratios (Fig. 2).

Significant plastic responses of P. alchemilloides resulted in thicker leaves, and smaller leaf area, length:width ratio and overall height at Kirstenbosch (Fig. 2). P. capitatum had decreased leaf area and length:width ratio at Kirstenbosch (Fig. 2). P. cucullatum had increased leaf mass and leaf thickness, and decreased height and length:width ratio at Kirstenbosch (Fig. 2). P. quercifolium increased leaf thickness, while P. transvaalense decreased length:width ratio at

Kirstenbosch. Four traits (leaf area, leaf mass, leaf thickness, and length:width ratio) showed significant differences in plastic responses (G x E interactions; all P ≤ 0.04; Table S4).

Interestingly, strong plasticity of leaf thickness resulted in significantly thicker leaves at the

Kirstenbosch garden (Fig. 2, Table S4).

There were two trait responses, leaf length:width ratio and LMA, that warranted closer examination due to their calculation from multiple measured components. The plastic response for length:width ratio was significant for all species except P. quercifolium (Fig. 2), with larger leaf length:width ratios at Rhodes. This change in ratio was achieved through narrower leaves, resulting from increased leaf length in most species. P. cucullatum and P. quercifolium achieved narrower leaves through the reduction of leaf width. Plants at the Rhodes garden had smaller LMAs (Fig. 2), and this was achieved by having thinner leaves than those on plants at Kirstenbosch.

Functional Traits and Survival (Fitness)

At Kirstenbosch, survival was significantly associated with just two traits: positively with plant height (95% credible interval: 0.33, 0.56) and negatively with leaf thickness (95%

credible interval: -0.17, -0.008). Interestingly, the same two traits, height (95% credible interval:

0.30, 0.55) and leaf thickness (95% credible interval: -0.14, -0.01), were associated with survival at Rhodes (Table 2).

DISCUSSION

Species compositions and distributions are being significantly impacted by climate change due to anthropogenic activities (Davis and Shaw 2001; Parmesan and Yohe 2003; Aitken et al. 2008; Kelly and Goulden 2008; Lenoir et al. 2008; Amano et al. 2014). The central goal of this work was to evaluate responses of species in the genus Pelargonium to environmental conditions projected for the year 2050 (Midgley et al. 2003). South Africa’s divergent climate is predicted to have even more extreme conditions, and rainfall regimes will essentially swap between winter and summer. In the winter rainfall region (Kirstenbosch location) there will be much hotter and drier conditions and in the summer rainfall region (Rhodes location) there will be hotter and wetter conditions (Midgley et al. 2003). Here, Pelargonium species exhibited significant differences in survival across gardens, with higher survival at the warmer, wetter garden (Kirstenbosch). Although plasticity was present in many of the measured traits, the species’ plastic responses varied.

Functional traits are commonly used to reflect the relationship between an organism’s morphology and its response to environmental pressures via impacts on growth, dispersal, and reproduction (Westoby et al. 2002; Wright et al. 2004; Violle et al. 2007). Functional trait analyses help connect plant strategies (or traits) across environmental gradients that filter species’ persistence, and provide one way to determine if changes in traits are adaptive or influence fitness (Westoby et al. 2002; Wright et al. 2004; Violle et al. 2007). The use of

functional traits has increased because of the mounting evidence that plant traits tend to be associated with environmental variables in predictable patterns. For example, plants in drier environments have high LMAs, smaller leaf areas, higher length:width ratios, and thicker leaves than plants in wetter environments (Westoby et al. 2002; Wright et al. 2004; Moles et al. 2009).

In this study, complex patterns of phenotypic plasticity and functional trait responses across environments (Fig. 2, Table 1, Table S4).

The extensive morphological variation observed had both genetic and plastic sources contributing to trait differences among individuals and gardens (Fig. 2, Table S4). As expected, the variation also showed strong phylogenetic patterns (Fig. 2, Table 1, Table S4). These results suggest that Pelargonium species have the potential to plastically respond to novel selection imposed on traits due to climate change. Phenotypic plasticity is often seen as a potential mechanism that can be used to buffer against climate change (Nicotra et al. 2010). As environments continue to change, there is growing literature supporting the notion that plasticity may be favored under climate change (Matesanz, Gianoli, and Valladares 2010; Nicotra et al.

2010; Franks, Weber, and Aitken 2014).

Only one trait followed the trait-based global pattern predictions: lower length:width ratios at Kirstenbosch, the wetter garden. Three traits, LMA, leaf area and leaf thickness, showed relationships opposite than those proposed in the literature. Plants in the cool and dry garden had smaller LMAs and larger leaf areas than the warmer, wetter garden, and leaves were significantly thinner at Rhodes than at Kirstenbosch (Fig. 2). These results mirror those from field studies on a diversity of Pelargonium species, showing that only some leaf economic spectrum relationships held consistently, and others were clade specific (Moore et al. 2018). Other studies

have also shown a lack of concordance of global and regional functional trait patterns (Marks and Lechowicz 2006; Mitchell et al. 2015).

The relationship between traits and fitness components in both gardens showed two traits, plant height and leaf thickness, associated with survival (Table 2). Plants that were taller and had thinner leaves had the highest survival. Pelargonium species responded to drought conditions by producing thin leaves that could increase photosynthetic capacity and carbon gain without investing resources into producing more morphologically complex and long-lived leaf structures.

Strikingly, plants at Rhodes had even thinner leaves than those at Kirstenbosch, indicating the ability of plastic responses to further enhance fitness. These results add to the many cases indicating that plasticity can be advantageous for higher survival and growth rates, or increased adaptive potential in novel environments (Nicotra et al. 2010; Leffler, Monaco, and James, 2011;

Skalova, Havlickova, and Pysek, 2012; Franks, Weber, and Aitken 2013; Wei et al. 2018; Taylor et al. 2019). Our results underscore the importance of better understanding the functional trait plasticity across environmental conditions.

It should be noted, that during this experiment South Africa was in the fourth year of the worst drought recorded in over a century (Wilson, Latimer, and Silander 2015). Although

Kirstenbosch is historically the wetter site, neither garden had much precipitation over the course of this experiment, and supplemental watering was necessary to prevent mass mortality.

Prolonged drought episodes will likely decrease reproductive success and increase mortality rates that could lead to local extinctions throughout the country (survival rates across both gardens were low due to drought conditions).

Plant species can respond to environmental changes through genetic diversity, distribution shifts, phenotypic plasticity, or a combination of these mechanisms. It has been

highlighted throughout the literature that phenotypic plasticity and genetic differentiation will play key roles in species responses and survival to climate change (see reviews in Parmesan

2006; Nicotra et al. 2010). However, increasingly concerns are being raised about the adaptability of species in the face of rapid acceleration of climate change (IPCC 2013) – will plasticity and genetic variation be sufficient to keep pace (Jump and Penuelas 2005; Parmesan

2006; Visser 2008)? Species loss, local extinctions, and the latest extinction threat of ~1 million species due to anthropogenic and climate-related processes highlights the importance of understanding species responses to changes in their environment (Urban 2015; Jezkova and

Wiens 2016; Humphreys et al. 2019). Plant taxa that show high levels of genetic and phenotypic variation along environmental gradients may have a higher capacity to respond to global climate change. My results show that there is significant variation among species in both their traits

(including survival) and for several traits plastic responses, suggesting both the possibility of plasticity driven acclimation but also differential species success due to trait variation.

ACKNOWLEDGMENTS

I am grateful to all the collectors of seed from the South African National Biodiversity Institute and Kirstenbosch Botanical Garden, and Silverhill Seeds. I appreciate the logistical support and germination knowledge provided by Mr. Timothy Jasson and Mr. Anton Nel (Cape Peninsula

University of Technology, Bellville glasshouses). I thank Mr. Mashudu Nndanduleni, Dr.

Anthony Hitchcock, and groundskeepers for their valuable assistance at the Kirstenbosch garden.

I thank Dr. Susanne Vetter, Mr. Philip Crous and groundskeepers for their valuable assistance at the Rhodes garden. This work was supported by a U.S. Fulbright Grant and a National Science

Foundation Dimensions of Biodiversity Grant (DEB-1046328).

TABLES

Table 1. Functional trait means ± standard deviation in parentheses across the five Pelargonium species and two common gardens.

Functional Traits Leaf Leaf Stem Leaf Height LMA LWR Species Garden -2 Area Mass Diameter Thickness (cm) (g cm ) 2 (cm) (cm ) (g) (mm) (mm)

Kirstenbosch 53.2 (26.1) 0.012 (0.013) 56.4 (44.6) 0.43 (0.25) 1.2 (0.5) 10.1 (5.5) 0.72 (0.32) P. cucullatum Rhodes 65.5 (22.0) 0.009 (0.014) 49.4 (41.8) 0.21 (0.15) 2.5 (0.9) 9.4 (2.6) 0.26 (0.15)

Kirstenbosch 47.7 (20.9) 0.011 (0.007) 27.9 (21.7) 0.24 (0.08) 1.3 (0.3) 6.6 (3.7) 0.56 (0.25) P. capitatum Rhodes 34.6 (9.3) 0.005 (0.004) 78.2 (30.3) 0.29 (0.17) 2.4 (1.4) 9.0 (1.5) 0.37 (0.17)

Kirstenbosch 41.6 (24.5) 0.013 (0.008) 33.7 (17.6) 0.37 (0.19) 1.5 (0.4) 7.5 (3.4) 0.75 (0.53) P. quercifolium Rhodes 60.8 (20.3) 0.007 (0.005) 50.8 (28.4) 0.26 (0.13) 2.0 (0.8) 6.9 (1.3) 0.29 (0.15)

Kirstenbosch 20.2 (15.2) 0.008 (0.008) 23.7 (17.4) 0.11 (0.12) 1.3 (0.4) 3.9 (3.2) 0.34 (0.16) P. transvaalense Rhodes 15.2 (3.5) 0.010 (0.014) 40.2 (30.9) 0.21 (0.09) 2.0 (0.9) 2.6 (0.8) 0.21 (0.12)

Kirstenbosch 20.8 (16.6) 0.008 (0.009) 20.0 (11.9) 0.14 (0.15) 1.3 (0.5) 3.1 (2.2) 0.40 (0.15) P. alchemilloides Rhodes 16.7 (9.5) 0.005 (0.008) 48.5 (32.6) 0.16 (0.09) 2.1 (1.1) 2.7 (0.7) 0.23 (0.15) LMA = Leaf mass per area; LWR = Leaf length:width ratio.

Table 2. Regression coefficients (95% credible interval) for trait and survival associations across gardens.

Kirstenbosch Garden: Survival ~ Traits Parameter Mean SD 2.5% 97.5% Height* 0.44 0.06 0.33 0.56 LMA -0.03 0.04 -0.09 0.05 Leaf Area -0.04 0.05 -0.15 0.05 Leaf Mass -0.001 0.04 -0.07 0.07 LW Ratio -0.02 0.02 -0.07 0.03 Leaf Thickness* -0.08 0.04 -0.17 -0.008 Stem Diameter -0.001 0.03 -0.06 0.06

Rhodes Garden: Survival ~ Traits Parameter Mean SD 2.5% 97.5% Height* 0.42 0.06 0.30 0.55 LMA -0.02 0.05 -0.12 0.05 Leaf Area -0.01 0.03 -0.06 0.04 Leaf Mass 0.01 0.03 -0.05 0.08 LW Ratio 0.04 0.03 -0.02 0.11 Leaf Thickness* -0.07 0.03 -0.14 -0.01 Stem Diameter -0.02 0.05 -0.11 0.09 *Bold and italicized terms indicate significance (i.e., credible intervals that do not overlap zero).

FIGURES

Fig. 1. Distribution map of the five Pelargonium species used in this study and common garden localities. Species occurrences are marked with filled circles. Common garden sites are indicated by a red star and arrow. Rainfall seasonality is shaded in grey. Graphs show common garden environmental profiles.

Fig. 2. Boxplots for each functional trait measured. Letters below boxplots represent significant

ANOVA and subsequent Tukey post hoc groups (species (genetic) effects). Significant phenotypic plasticity (ANOVA: Table S4) is marked as follows: *** P < 0.00001, ** P = 0.001,

* P ≤ 0.05. LMA = Leaf mass per area; LWR = Leaf length:width ratio; alch. = P.

alchemilloides; capi. = P. capitatum; cucu. = P. cucullatum; quer. = P. quercifolium; and trans.

= P. transvaalense.

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CHAPTER 2 SUPPORTING INFORMATION

Table S1. Phylogenetic relationships and rainfall seasonality associations of the five

Pelargonium species used in this study.

Rainfall Seasonality Clade Section Winter Summer Aseasonal

A1 Pelargonium P. cucullatum P. capitatum P. quercifolium

C2 Ciconium -- P. transvaalense P. alchemilloides

Table S2. Common garden site locality, rainfall seasonality regime, mean annual temperature (MAT), mean annual precipitation

(MAP), elevation (ELEV), and other environmental profile measures.

Mean Mean Mean Mean Temp. Temp. Precip. Precip. Lat. Lon. Rainfall MAT MAP ELEV Garden Warmest Coldest Wettest Driest (S) (E) Seasonality (oC) (mm) (m) Month Month Month Month (oC) (oC) (mm) (mm)

Kirstenbosch -33.98760 18.43098 Winter 17.3 1295 22 26 7 100 20 (Jan) (Aug) (Jul) (Dec)

Rhodes -33.31361 26.51433 Summer 15.3 689 558 24 6 62 29 (Jan) (Jul) (Oct) (Jul)

Temp. = Temperature; Precip. = Precipitation.

Table S3. Functional traits used in this study with their ecological and environmental associations, along with selected references.

Survival was used as a fitness proxy because of its association with competitive vigor and life history strategy.

Functional Trait Ecological Importance Environmental Correlation Citations Leaf Mass per Area Related to plant growth rate, High LMA associated with low nutrients, Westoby et al. 2002; (LMA) photosynthetic rates, environmental low rainfall and high temperatures. Wright et al. 2004 tolerance and competitive vigor. Height Related to resource acquisition, light Taller heights are often associated with Westoby et al. 2002; interception, lifespan, seed mass and higher fitness. Height is also highly Moles et al. 2009 competitive vigor. impacted by environmental stressors (i.e., fire, acidic soils, etc.). Leaf Area Related to photosynthesis rates, light Larger areas are associated with higher Cornelissen et al. 2003 interception, water balance and temperatures. biomass estimation. Leaf Mass Related to growth rate, leaf lifespan Higher mass is associated with higher Cornelissen et al. 2003 and water balance. resistance to damage and herbivory. Lower masses are associated with highly disturbed environments. Leaf Length:Width Ratio Related to resource acquisition and Larger length:width ratios (narrower leaf) Westoby et al. 2002; (LWR) light interception. are associated with dry, arid environments. Cornelissen et al. 2003; Wright et al. 2004 Leaf Thickness Related to environmental conditions Thicker leaves associated with low Pérez-Harguindeguy et al. (thicker as an adaptation to drought temperature and arid environments; also, 2013 or frost). with sunnier, drier and less fertile environment and in longer-living leaves. Stem Diameter Related to aboveground storage of Larger stem diameters are associated with Cornelissen et al. 2003 carbon, growth rate and resistance to taller plants and smaller stem diameters for disease and abiotic factors (i.e., shorter plants. Larger stem diameters are drought, frost). also associated with higher defenses against pathogens, drought and frost.

Table S4. Analysis of variance (ANOVA) results for each fitness and functional trait measured.

Source of F value variance df Survival Height LMA Leaf Leaf LWR Leaf Stem Area Mass Thickness Diameter Species (G) 4 12.00*** 25.29*** 3.21* 7.63*** 47.25*** 0.35 27.82*** 87.20***

Garden (E) 1 115.97*** 31.03*** 5.65* 46.48*** 6.48* 122.15*** 102.29*** 2.04

G x E 4 1.11 0.99 1.78 6.03** 10.20*** 2.51* 8.02*** 2.12

Significant effects are shown in bold. *P <0.05; ** P<0.003; ***P<0.00001 LMA = Leaf mass per area; LWR = Leaf length:width ratio.

Fig. S1. Planting of common gardens before (A, B) and after (C, D). The Kirstenbosch garden (A, C) is located in the Western Cape and the Rhodes Garden (B, D) is located in the Eastern Cape. 99

CHAPTER 3:

Species distribution modeling methods predict drastic distribution shifts in response to

environmental changes within South Africa

ABSTRACT

Changes in species distributions and abundances due to climate change and other ecological processes are having great impacts on biodiversity and ecosystem services. Species distribution models evaluate the relationship between environmental predictors and species’ occurrences, and can provide insights into what environmental variables influence species persistence. Here, current and future habitat suitability for 150 Pelargonium species were assessed to i) understand how environmental predictors affect current species occurrences

(current niche), ii) evaluate the importance of these environmental predictors, and iii) forecast species distributions to understand potential range shifts and responses to climate change.

Forecast averages for years 2050 and 2070 of four general circulation models across all four representative concentration pathways were used to analyze distribution shifts. Overall, mean annual temperature of the wettest quarter, precipitation seasonality and precipitation of the wettest quarter were the top three environmental predictors of current species occupancy (mean total contribution = 83.4%). Future distribution predictions showed a strong trend towards northwest range shifts and there was also evidence of many northward range contractions, especially along the coastal regions of the country. These range shifts are interesting as South

Africa’s climate is projected to be much warmer and drier in the north-west region (Western

Cape) of the country. This climate profile has been shown to negatively impact Pelargonium

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species persistence (seen in Chapter 2). These results suggest that although precipitation is important for species occurrence, other abiotic interactions might be driving species distributions throughout South Africa.

INTRODUCTION

Climate change and associated anthropogenic pressures are causing environmental changes and severely impacting species diversity across the globe, with suggestions that the

“sixth mass extinction” is now upon us (Wake and Vredenburg 2008; Ceballos et al. 2015;

Humphreys et al. 2019). By 2100, one in six species could be extinct due to climate change, with the remaining taxa more than likely encountering novel ecological communities that could alter their abundances and distributions (Parmesan 2006; Urban 2015). Such changes will have great impacts on human welfare, ecosystem services and biodiversity (Grimm et al. 2013; Garcia et al.

2014). Understanding habitat characteristics important to demographic processes (i.e., survival, reproduction, development) will have a significant impact on biodiversity persistence and conservation efforts.

Plant species are among the most dramatically affected organisms on the planet. Though there are relatively few (<150) declared extinct plant species (IUCN 2019; likely due to the

IUCN’s stringent standardized criteria for Red List species), more than 50,000 plant species have been estimated to be near extinction (Willis et al. 2017). Recent research suggests that as many as 571 plant species have gone extinct over the last 2.5 centuries (Humphreys et al. 2019). This extinction rate of ~2.3 species per year, scales to 18-26 extinctions per million species years

(E/MSY)); this far exceeds estimates of background rates (0.05-0.35 E/MSY) (Humphreys et al.

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2019). The rapid extinction rates highlight the urgent need to understand what causes and maintains species diversity, as well as how those mechanisms change in light of climate change.

Species distribution models (SDMs) estimate the relationship between species occurrences and environmental characteristics across localities (Franklin 2009). SDMs can be used to estimate relationships for both abiotic (e.g., climatic, edaphic) and biotic (e.g., vegetation, species interactions) properties of species occurrences, yielding an estimate of suitability for the species (Dark et al. 2004; Elith et al. 2006; Beaumont et al. 2009; Elith and

Leathwick 2009; Platts et al. 2010; Dubis et al. 2011; Junker et al. 2012; Naujokaitis-Lewis et al.

2013; Gavin et al. 2014). SDMs are also an important tool for projections of future species distributions, in conjunction with global climate models (GCMs) that present a range of plausible future climate scenarios based on representative concentration pathways (RCPs; IPCC 2007,

2013; Rosentrater 2010; Weaver et al. 2013). GCMs provide an envelope of accurate predictions, for the years 2041-2060 and 2061-2080 (Porfirio et al. 2014).

South Africa is home to some of the world’s most diverse ecosystems. A recent study highlights that within the Western Cape of South Africa, home to several biodiversity hotspots,

37 plant species have already gone extinct since 1900 (Humphreys et al. 2019). This places

South Africa second to Hawaii in plant species extinction rates (Humphreys et al. 2019). Climate change is projected to have devastating impacts, such as extinctions, on the extensive biodiversity found throughout South Africa, as well as impacts on human health and well-being

(Midgley et al. 2003). Using a highly charismatic and representative plant genus of the region, my primary aim for this study was to understand how species will respond to climate change projections for 2050 and 2070. I specifically used SDMs to understand how environmental predictors affect current species occurrences (current niche), evaluate the importance of these

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environmental predictors, and forecast habitat suitability to understand potential range shifts and responses to climate change.

METHODS

Study Area

South Africa is home to 10% of the world’s plant species, yet occupies only 2% of the world’s terrestrial surface area (Goldblatt and Manning 2002). It is also home to three biodiversity hotspots, areas with high levels of both species’ richness and endemism (Myers et al. 2000). The study region lies between 22o and 35o south latitude and 15o and 35o east longitude, with a surface area of 1.22 million km2. It is bounded to the north by Namibia,

Botswana, Zimbabwe and Mozambique. To the west, south and east it is bounded by the Atlantic and Indian Oceans.

South Africa has a subtropical climate, which is relatively dry. Temperatures range from

15–36 oC in the summer and -2–26 oC in the winter. Rainfall average is ~464 mm per year, but highly variable across the country. The climate of the western portion of the country is strictly

Mediterranean with warm, dry summers and cool, rainy winters. The majority of the country sees most of its rainfall during the summer months (November – March). Along the southern coast

(Mossel Bay to Port Elizabeth) rainfall is aseasonal and spread more evenly throughout the year.

Study Species

Members of the genus Pelargonium L’Hér. (Geraniaceae) exhibit a wide variety of growth forms ranging from perennials, evergreens, shrubs and geophytes (Jones et al. 2009;

Martínez-Cabrera et al. 2012). Species within the genus are found across a broad range of habitats that include nutrient-poor to moderately rich soils and mountainous and lowland regions

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(Goldblatt 1997; Bakker et al. 1999). Pelargonium species also show strong associations of functional traits with environmental variables (Jones et al. 2013; Mitchell et al. 2015; Moore et al. 2018), suggesting that changes in climate could lead to mismatches, making the genus

Pelargonium a model for using SDM methods.

Modeling Approach

For most regions, extensive biological collections and survey data are limited, both temporally and spatially. Species records are normally gathered from museums, natural history collections and herbarium collections that are often, if not always, presence-only data. To maximize use of this type of data, the SDM method maximum entropy modeling (MaxEnt) method was formulated using the principles of maximum entropy (Elith et al. 2011). MaxEnt compares probability densities in covariate space, in other words it estimates a species’ distribution across geographic space based on environmental predictors, species occurrences and background points (random sampling of environmental profiles that describe the entire study region, not to be confused with absences) to calculate habitat suitability of species within the focal region (Phillips, Anderson, and Schapire 2006; Elith et al. 2011). Moreover, MaxEnt quantifies statistical relationships between predictor variables at locations where a species has been observed versus background locations in the study region (Phillips, Anderson, and Schapire

2006; Phillips et al. 2009; Merow, Smith, and Silander 2013; Muscarella et al. 2014). MaxEnt has been proven to be the best method for SDM and niche modeling of presence-only data

(Phillips, Anderson, and Schapire 2006; Phillips and Dudik 2008; Elith et al. 2011; Saupe et al.

2015) with robust predictive performance and model stability and good performance even with small sample sizes (Phillips, Anderson, and Schapire 2006; Phillips and Dudik 2008; Elith et al.

2011; Saupe et al. 2015).

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Occurrence/Distributional Data

I obtained species occurrence data from the Bolus Herbarium at the University of Cape

Town, the Selmar Schonland Herbarium at Rhodes University, and the Plants of southern Africa

(PRECIS) database curated by the South African National Biodiversity Institute (SANBI).

19,978 digitized records were manually reviewed for suitability and duplication; unique records with a date and georeferenced locality were retained. All were examined for geographic outliers using the raster v2.9 (Hijmans et al. 2019) and maptools v0.9 (Bivand et al. 2019) packages in R v3.3.2 (R Development Core Team 2016). Additionally, to handle the issue of spatial autocorrelation often due to sampling bias (e.g., sampling close to roads), spatial thinning using a

10 km boundary around points was used within the open source GUI application program

Wallace (Hijmans et al. 2012; Kass et al. 2017; Moore et al. 2018). The final data set consisted of 11,304 herbarium record collections (range: 10–520 records per species; mean: 74 records per species) representing 150 Pelargonium species (Fig. 1).

Environmental Data

All nineteen bioclimatic variables were downloaded from the WorldClim v1.4 database

(Hijmans et al. 2005; www.worldclim.org). Current climate data is averaged from 1950–2000

(Hijmans et al. 2005). The spatial resolution of all environmental predictors was set at 30 seconds (ca. 1 km). These bioclimatic variables, which are widely used in SDMs, describe different aspects of climate by summarizing temperature (e.g., temperature in the warmest quarter) and precipitation (e.g., precipitation seasonality) components. The data are available as individual raster layers spanning all continents. All analyses were done using R v3.3.2 (R

Development Core Team 2016). The bioclimatic layers were cropped and stacked to match the extent of South Africa using the raster v2.9 package (Hijmans et al. 2019). Using excess

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environmental predictors unnecessarily increases the complexity of the model and reduces model accuracy (Merow et al. 2014), and an understanding of the organism’s ecophysiology can help in selecting ecologically relevant environmental correlates. In this study, I performed a correlation analysis (Fig. S1) and principal component analysis (PCA) across all nineteen environmental layers to identify a core set of environmental predictors. The correlation analysis was performed in the corrplot v0.84 package (Wei et al. 2017) and PCA in base R using prcomp (R

Development Core Team 2016). Environmental predictors were chosen based on the following criteria: a) correlations with other predictors were ≤ 64%, and b) predictors had high PC loading scores. The first five PC axes explained 94% of the variation, and the components that had loading scores ≥ 30 were evaluated (the four highest scores per axes): Bio1- Mean Annual

Temperature; Bio3 - Isothermality; Bio8 - Mean Temperature of the Wettest Quarter; Bio9 -

Mean Temperature of the Driest Quarter; Bio15 - Precipitation Seasonality; Bio16 - Precipitation of the Wettest Quarter; Bio18 - Precipitation of the Warmest Quarter.

For future projections, GCMs were downloaded from the WorldClim v1.4 database with the same resolution and extent as the current layers (Hijmans et al. 2005). GCMs are not meant to provide specific climate projections for any given point in time (Porfirio et al. 2014). Because of this, and how GCMs are modeled, best practice methods average responses across a variety of

GCMs for several RCPs to gather a more accurate understanding of species responses to climate change. We chose four GCMs (Table S1) for analysis because of the variability (and sensitivity) found across model methods (Porfirio et al. 2014). The seven bioclimatic predictors used for the current models were also used for future projections. Each GCM was run for both 2050 and 2070 projections under all four RCPs (N = 16 models per species). The low RCP, RCP2.5 (Table S2),

o represents a CO2 concentration of 490 ppm and temperature increases of 1.5 C (IPCC 2007).

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The medium RCPs, RCP4.5 and RCP6.0, model climate under CO2 levels of 650 and 850 ppm, and temperature increases of 2.4 oC and 3.0 oC, respectively (IPCC 2007). Lastly, the high RCP,

o RCP8.5, represents a climate with CO2 levels of 1370 ppm and 4.9 C increase in temperature

(IPCC 2007).

Model Procedure and Analyses

The MaxEnt v3.4.1 software (Phillips, Anderson, and Schapire 2006; Elith et al. 2011) with modified parameters was accessed using the dismo v1.1 package in R (Hijmans et al. 2017).

For every species, one model was developed for the current period and then projected to 2050 and 2070 across the four GCMs (Table S1) for all four RCPs (Table S2). Seventy-five percent of the occurrence data was used for model training, while the remaining 25% was used for model testing. Jackknife tests to determine environmental predictor importance isolated each variable and compared results to a model with all variables (Phillips, Anderson, and Schapire 2006). Ten thousand random background points were used for every model drawn from the entirety of South

Africa. Models were run ten times and averaged. Further analyses used R packages dismo v1.1

(Hijmans et al. 2017), ggplot2 v3.2.0 (Wickham et al. 2019), raster v2.9 (Hijmans et al. 2019), rgdal v1.4 (Bivand et al. 2019), SDMTools v1.1 (VanDerWal et al. 2019), sp v1.3 (Pebesma et al. 2018), adehabitatHR v0.4.16 (Calenge 2006), and geosphere v1.5 (Hijmans et al. 2019).

Model results return many features that can be used to further assess species’ ranges. One such feature is the area under the receiver operating characteristic curve (AUC). This measures the model fit to the training and testing data (Fielding and Bell 1997). AUC greater than 0.5 denotes high predictive power, while values equal to or less than 0.5 denotes a random chance model or worse than random model, respectively. The true skill statistic (TSS) measures model prediction accuracy, and accounts for both the sensitivity and specificity (Allouche et al. 2006).

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It is used as a supplement to the AUC score because the AUC score is dependent on the prevalence and spatial extent of occurrence data and has been criticized (Lobo et al. 2008), although it is still an informative statistic for fit and predictive power (Phillips et al. 2017). TSS scores from -1 to 0 denote that model performance is no better than chance, while a score > 0 to

1 performs better than chance. MaxEnt also produces a raster layer that displays habitat suitability with ranges from 0 (not suitable) to 1 (highly suitable); thresholding was set at 90% sensitivity.

Environmental predictor significance was assessed by an analysis of variance (ANOVA).

For significant ANOVA results, pairwise comparisons were performed using a Tukey post hoc test. To assess whether suites of environmental predictors were associated with certain phylogenetic and morphological groupings (clades, sections, growth forms, etc.), k-means clustering was used (R Development Core Team 2016). K-means clustering is a commonly used unsupervised learning algorithm that partitions similar data based on their Euclidean distance to each group’s centroid, and is also used to discover underlying patterns within the data (Forgey

1965).

The range size or area of suitable habitat for both the current and projection models was calculated as the number of raster cells above a threshold of presence > 60%. Calculations of habitat reduction or expansion were done from these raster counts. Bayesian generalized linear mixed models, stan_lmer(), were performed in the rstanarm package (Stan Development Team

2016) were used to assess the relationship between changes in range sizes and phylogenetic and morphological groupings. The following models were used:

Range_Size(Current) ~ Clade + Rainfall_Regime + Growth_Form + (1|Species), and

ΔRange_Size ~ Year*Rainfall_Regime*Growth_Form*Clade + (1|Species).

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To understand what direction and distance species ranges were moving and to interpret the velocity of climate change, I drew a polygon around the 60% presence pixels. I calculated the centroid point (latitude/longitude) for each model using the raster v2.9 (Hijmans et al. 2019),

SDMTools v1.1 (VanDerWal et al. 2019), sp v1.3 (Pebesma et al. 2018), adehabitatHR v0.4.16

(Calenge 2006), and geosphere v1.5 (Hijmans et al. 2019) packages, and then subtracted the future centroid from the current. I measured the Euclidean distance between current and future habitats to assess range shift distances. To assess the velocity of change and associations with phylogenetic and morphological groupings, two Bayesian generalized linear mixed models, stan_lmer(), were performed in the rstanarm package (Stan Development Team 2016). The following models were used:

Distance(Current-2050) ~ Clade + Rainfall_Regime + Growth_Form + (1|Species), and

Distance(Current-2070) ~ Clade + Rainfall_Regime + Growth_Form + (1|Species).

The direction of habitat suitability movement was manually assessed using the geographic information system, QGIS v3.4 (QGIS Development Team 2019). Lastly, I identified species of concern, based on the criterion of a decline of ≥ 50% of suitable habitat by 2050 or

2070.

RESULTS

Model Performance

For both current and future models, prediction scores (AUC) were greater than 0.90 (0.99 and 0.95, respectively), indicating that the models were highly informative (Table 1). TSS scores for both the current and future models scores were greater than 0.6, (0.63 and 0.72, respectively), also indicating good model performance (Table 1). Model performance was also manually

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checked and for each species to confirm that the simulated current habitat suitability matched the present-day species distribution.

Environmental Predictor Importance

Mean temperature of the wettest quarter (Bio 8) produced the highest average model contribution (37.8%; maximum contribution = 99.9%) (Table 2). Precipitation seasonality (Bio

15) and precipitation of the wettest quarter (Bio 16) also ranked highly, averaging 22.9%

(maximum contribution = 94.7%) and 22.7% (maximum contribution = 91.7%), respectively

(Table 2). ANOVA results showed the same importance relationships as the SDM models described and indicated highly significant difference among environmental predictors were found (P < 0.00001). Post hoc tests confirmed that mean temperature of the wettest quarter (Bio

8), precipitation seasonality (Bio 15), and precipitation of the wettest quarter (Bio 16) were significantly different from the remaining variables (P < 0.001).

K-means clustering identified four clusters with distinct environmental profiles (Fig. 2).

There were no phylogenetic associations found, but there were rainfall seasonality and growth form relationships found. Cluster 1 (N = 35) described species whose most important predictor was precipitation seasonality (Bio 15: mean contribution = 68.6%). Within this cluster, the dominant growth form was woody subshrubs (N = 26), accounting for 37.1% of all woody species. Cluster 2 (N = 55) was described by mean temperature in the wettest quarter (Bio 8: mean contribution = 72.5%). The majority of species were from the winter rainfall region (N =

47), representing 52.8% of the species within this rainfall regime. 38.6% of all woody subshrubs were assigned to this cluster, as well. Cluster 3 (N = 33) was described by two environmental predictors, mean temperature in the wettest quarter (Bio 8: mean contribution = 33.4%) and mean temperature in the driest quarter (Bio 9: mean contribution = 23.5%). Species within this

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cluster were largely from the winter rainfall regime, representing 21.3% of the species from this regime. The dominant growth form was geophytes, accounting for 28.9% of all species within this growth form. Lastly, cluster 4 (N = 29) was characterized by precipitation in the wettest quarter (Bio 16: mean contribution = 65.8%). This cluster had the high representation of succulent species (N = 13), making up 68.4% of all succulent species assessed in this study.

Additionally, a majority of the species were also from the winter rainfall regime, representing

24.7% of winter rainfall species.

Range Sizes and Range Shifts (Directions and Distances)

Current range sizes (area) showed significant rainfall seasonality and growth form associations (Table 3). Species that occur within the aseasonal (95% credible interval: -847.6,

-4030.1) and winter (95% credible interval: -1106.4, -3497.6) rainfall regimes had significantly smaller range sizes. This is expected, and helps demonstrate that the models are performing reasonably, both the winter and aseasonal rainfall regime areas are much smaller than the summer rainfall region. Interestingly, species with succulent (95% credible interval: -83.5,

-7106.0) or woody subshrub (95% credible interval: -153.5, -5468.0) growth forms also had significantly smaller ranges.

Assessing the changes in range sizes (future – current) found that the change was highly associated with plant growth form and rainfall regime (Table 4). In 2050, herbaceous perennials

(95% credible interval: -67.7, -4160.6) and succulents (95% credible interval: -942.7, -4758.9) are projected to have significantly smaller range sizes than they do currently. By 2070, the same relationship is projected for herbaceous perennials (95% credible interval: -233.3, -4441.7) and succulents (95% credible interval: -551.5, -4419.2). Additionally, species within the summer

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rainfall regime are also projected to have significantly smaller range sizes (95% credible interval:

-17.5, -2641.5).

Range shift distances, across all models, showed no significant associations between any of the phylogenetic, environmental or morphological relationships tested. A number of interesting habitat suitability shift patterns were observed: (a) some habitats are tracking climate conditions and moving their ranges northwest, (b) others are staying in place and reducing their peripheral habitat areas, (c) while a number of habitats are expanding north and others east, and lastly, (d) there are a number of habitats moving southward toward the Indian Ocean coastline

(Fig. 3, Fig. S2).

By 2070, 85% (127) of the 150 Pelargonium species used in this study are projected to have reduced range sizes (range loss: 3 to 27,007 pixels) (Fig. S3, Table 5). Range expansions

(range gain: 3 to 9,657 pixels) were projected for the remaining 23 (15%) species (Fig. S3, Table

5).

Species of Concern

There were four species of critical concern: Pelargonium incrassatum, P. magenteum, P. praemorsum, and P. pulchellum (Fig. 4, Table 5). These species are projected to lose all of their suitable habitat by 2050 (Table 5). All are restricted to the winter rainfall regime, with the majority of their distributions confined to the Northern Cape. An additional 85 species will have

≥50% of their current suitable habitat reduced by 2070 (Fig. S3). Of these 89 (including the four of critical concern), 63 species are from Clade A, 42 species from the winter rainfall region, and

73 either woody subshrubs (N = 47) or geophytes (N = 26).

DISCUSSION

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Climate change is impacting the ecological ranges of plant species. Most studies report up slope or poleward shifts of species tracking favorable climates (Walther et al. 2002; Parmesan and Yohe 2003; Root et al. 2003; Hickling et al. 2006; Tingley et al. 2009; Chen et al. 2011). Yet many species have migration limitations, such as dispersal capacity and restricted ranges (e.g., geographic restrictions: on a mountain top), that hinder their abilities to track favorable conditions. Such species will face increased rates of extinction if adaptation in place, phenotypic plasticity or a combination of strategies are not viable options. Here, I focused on an endemic plant genus found within a biodiversity hotspot in South Africa. The goals of this study were to evaluate the current niche and environmental associations of 150 Pelargonium species, identify distribution shifts under various future climate scenarios, and identify species of concern. Model accuracies obtained here match those of other SDM studies done in Africa (Blach-Overgaard et al. 2010; Merow, Smith, and Silander 2013; Scholtz et al. 2014). Results suggest that changes in climate will have profound effects on Pelargonium species.

Three environmental variables (Mean Temperature of the Wettest Quarter, Precipitation

Seasonality, and Precipitation of the Wettest Quarter) had high predictive power for predicting

Pelargonium occurrence. Precipitation variants were highly important to Pelargonium species.

Not only was it important to know the seasonality of rainfall, but in many cases, it was also important to know the amount of rainfall and the mean annual temperature in the quarter with the most rainfall. While these three environmental predictors were the most important across species as a whole, they are not the only environmental variables that are important to Pelargonium persistence. For example, mean annual temperature was highly predictive for P. leucophyllum, with a percentage contribution of 91%. Whether the three main predictors are limiting or regulating species occurrence, these environmental associations are shaping Pelargonium

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occurrence. These and related environmental variables have also been shown to be important in other studies of Pelargonium species, a number of studies assessing functional trait-environment relationships have shown that rainfall seasonality and other precipitation variants significantly impact leaf shape, leaf thickness, plant structure and canopy area of Pelargonium species (Jones et al. 2013; Mitchell et al. 2015; Moore et al. 2018).

Given that these three environmental predictors are not important for every species and have varied importance rankings, it was surprising to see how strongly species grouped towards one to two of these environmental predictors. The results showed that all cluster groupings had strong relationships with winter rainfall, which can be explained by high species densities within the region. Many species have highly restricted distributions found within this rainfall regime, while others have broader distribution often overlapping into the aseasonal and summer rainfall regimes. Interestingly, woody subshrubs and geophytes are more attuned to rainfall seasonality and mean annual temperatures, while succulent species are sensitive to the amount of rainfall.

Range size dynamics revealed interesting environmental and morphological associations.

Current range sizes are associated with rainfall and growth form. Larger range sizes are found within the summer rainfall region, which amounts to most of the country’s area, when compared to the winter and aseasonal rainfall regions. Current range sizes are also associated with growth form. Succulents and woody subshrubs have much smaller ranges than annuals, herbaceous perennials and geophytes. As the climate in South Africa continues to change, with many areas projected to experience more severe drought conditions (Midgley et al. 2003), rainfall seasonality will no longer be an adequate predictor of habitat suitability. Future range sizes are only associated with plant growth form. Two growth forms, herbaceous perennials and succulents, are projected to see reductions in range size in response to climate change.

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Methods for modeling species distributions, like MaxEnt, have limitations, but are still very useful for assessing climate change impacts, vulnerability, ecological niche parameters and spatial dynamics (Phillips and Dudik 2008; Elith et al. 2011; Merow, Smith, and Silander 2013).

Some limitations include the assumption that species ranges are in equilibrium with current climates, competition is not a significant hinderance to species presence, the use of biased occurrence data (spatial autocorrelation), and the use of presence-only data. Yet, with careful model selection, training and evaluation, SDMs has widespread applications for answering practical and theoretical questions in ecology, evolutionary biology, epidemiology, conservation biology, climate change biology, biological invasions and biogeography (Phillips and Dudik

2008; Elith et al. 2011; Merow, Smith, and Silander 2013).

The effects of climate change are predicted to include severe reductions in habitat suitability for 85% of the 150 Pelargonium species assessed in this study, with four species’ habitats projected to vanish by 2050. This lineage of ~280 species that radiated over 18 MYA is seeing an increased rate of habitat destruction over a short period of time (<100 years), causing major declines in populations’ and species’ abilities to cope (Bakker et al. 1999). These drastic reductions are due to projections that climate will become much warmer and more arid in the winter rainfall region, and warmer and wetter (with more extreme rainfall events) in the aseasonal and summer regions (Midgley et al. 2003).

There were a number of habitat suitability shift patterns were observed within this study, with many species projected to have great reductions in habitat. With such projections,

Pelargonium species will have to find ways to mitigate climate change impacts through adaptation, dispersal or phenotypic plasticity. In Chapter 1, there were significant phenological responses to increases in mean annual temperatures that showed potential adaptive capacity of

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Pelargoniums throughout the Cape Floristic Region. The common garden experiments, presented in Chapter 2, showed tremendous plastic responses to drought conditions in the winter and summer rainfall regions. Both results highlight that although habitat suitability is projected to be profoundly impacted, Pelargonium species may have the ecological and evolutionary tools necessary to respond and mitigate negative effects.

ACKNOWLEDGMENTS

I am grateful to all the collectors and curators of herbarium specimens from the University of

Cape Town’s Bolus Herbarium (especially Dr. Terry Trinder-Smith), Rhodes University’s

Selmar Schonland Herbarium (especially Mr. Tony Dold), and the South African National

Biodiversity Institute’s (SANBI) herbaria for access to their facilities. I would also like to thank

Kristen Nolting, Timothy Moore and Corey Merow for insightful conversations about theory, methods and coding advice. This work was supported by a National Science Foundation

Dimensions of Biodiversity Grant (DEB-1046328).

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TABLES

Table 1. MaxEnt model evaluation for current and averaged future models.

Model Evaluation

Test Mean SE Min Max AUC Train 0.961 -- 0.763 0.999 Current AUC Test 0.956 0.008 0.613 0.999 TSS 0.801 ------AUC Train 0.965 -- 0.763 0.998 RCP 2.6 AUC Test 0.954 0.008 0.613 0.999 TSS 0.796 ------AUC Train 0.961 -- 0.763 0.999 RCP 4.5 AUC Test 0.956 0.008 0.613 0.999 2050 TSS 0.787 ------AUC Train 0.961 RCP 6.0 AUC Test 0.956 0.008 0.613 0.999 TSS 0.867 ------AUC Train 0.961 -- 0.763 0.999 RCP 8.5 AUC Test 0.961 0.007 0.613 0.999 TSS 0.849 ------AUC Train 0.962 -- 0.762 0.998 RCP 2.6 AUC Test 0.955 0.008 0.613 0.999 TSS 0.843 ------AUC Train 0.962 -- 0.785 0.999 RCP 4.5 AUC Test 0.956 0.008 0.613 0.999 TSS 0.899 ------2070 AUC Train 0.961 -- 0.763 0.999 RCP 6.0 AUC Test 0.956 0.008 0.613 0.999 TSS 0.879 ------AUC Train 0.963 -- 0.765 0.913 RCP 8.5 AUC Test 0.957 0.007 0.615 0.999 TSS 0.852 ------

AUC: Area under the curve; Train: Data used to train model; Test: Independent of training data, test data is used to determine the predictive power of the model; TSS: True Skill Statistic is used in conjunction with the AUC score to determine the model fit and accuracy of model prediction.

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Table 2. WorldClim bioclimatic layers. The bold layers indicate the seven environmental predictors used in this study with the accompanying mean contribution percentages across all species and all models.

Layer Environmental Parameter Units Mean Percent Percent Contribution Contribution Range Bio 1 Annual Mean Temperature oC 2.9 0 – 91.0 Bio 2 Annual Mean Diurnal Range oC Bio 3 Isothermality % 3.1 0 – 27.3 Bio 4 Temperature Seasonality oC Bio 5 Max Temperature of Warmest Month oC Bio 6 Min Temperature of Coldest Month oC Bio 7 Annual Temperature Range oC Bio 8 Mean Temperature of Wettest Quarter oC 37.8 0 – 99.9 Bio 9 Mean Temperature of Driest Quarter oC 8.9 0 – 58.8 Bio 10 Mean Temperature of Warmest Quarter oC Bio 11 Mean Temperature of Coldest Quarter oC Bio 12 Annual Precipitation mm Bio 13 Precipitation of Wettest Month mm Bio 14 Precipitation of Driest Month Bio 15 Precipitation Seasonality % 22.9 0 – 94.7 Bio 16 Precipitation of Wettest Quarter mm 22.7 0 – 91.7 Bio 17 Precipitation of Driest Quarter mm Bio 18 Precipitation of Warmest Quarter mm 1.7 0 – 19.3 Bio 19 Precipitation of Coldest Quarter mm

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Table 3. Regression coefficients for current Pelargonium range size dynamics and morphological and environmental associations.

Model Predictor Classes Mean SD 95% CI

Intercept 4856.8 1209.5 (2608.0, 7299.8)

Aseasonal* -2386.7 830.2 (-4030.1, -847.6) Rainfall Summer 1332.2 823.3 (-226.3, 2900.1) Regime Winter* -2242.6 627.3 (-3497.6, -1106.4)

B 638.9 958.8 (-1214.9, 2502.3) Current Clade C 888.4 634.2 (-294.7, 2111.6)

Geophyte 178.0 1149.9 (-2028.1, 2371.0)

Growth Herbaceous Perennial -882.7 1313.9 (-3416.0, 1620.5)

Form Succulent* -3524.8 1105.2 (-83.5, -7106.0)

Woody Subshrub* -2786.6 861.3 (-153.5, -5486.0)

*Significant predictor class; Standard deviations (SD); Credible intervals (CI).

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Table 4. Regression coefficients for changes in Pelargonium range size dynamics and their morphological and environmental associations.

Model Predictor Classes Mean SD 95% CI Intercept 1150.1 1104.5 (-623.7, 3228.9) Aseasonal -480.6 663.2 (-1833.2, 753.9) Rainfall Summer -1129.7 719.6 (-2358.3, 337.9) Regime Winter -35.2 506.5 (-1057.3, 905.7) B -669.5 810.4 (-2273.4, 689.5) 2050 Clade C -70.4 551.2 (-1179.6, 906.2) Geophyte -1693.2 1088.1 (-3749.6, 79.1) Growth Herbaceous Perennial* -2073.0 1113.3 (-4160.6, -67.7) Form Succulent* -2718.3 1041.0 (-4758.9, -942.7) Woody Subshrub -1145.1 861.0 (-2867.7, 453.3) Intercept 1167.7 963.3 (-685.6, 2998.5) Aseasonal -405.8 648.5 (-1678.2, 861.1) Rainfall Summer* -1363.9 675.7 (-2641.5, -17.5) Regime Winter -212.2 492.1 (-1209.8, 744.2) B -690.7 730.4 (-2159.4, 625.7) 2070 Clade C -81.8 514.0 (-1116.1, 966.9) Geophyte -1470.9 949.8 (-3302.7, 390.2)

Herbaceous Perennial* -2359.1 1078.3 (-4441.7, -233.3) Growth Succulent* -2539.2 991.4 (-4419.2, -551.5) Form Woody Subshrub -917.2 876.5 (-2577.8, 867.2) *Significant predictor class; Standard deviations (SD); Credible intervals (CI).

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Table 5. Pelargonium species whose habitat suitability will be reduced by ≥ 50% by 2070.

Bolded species are of concern, losing ≥ 80% of habitat suitability by 2070. Bolded and starred species are of critical concern, losing 100% of habitat suitability by 2050.

Habitat Suitability Loss (%) Species 2050 2070 P. incrassatum* 100.0 P. magnetum* 100.0 P. praemorsum* 100.0 P. pulchellum* 100.0 P. abrotanifolium 65.4 96.5 P. acetosum 57.4 69.1 P. acraeum 90.2 P. alchemilloides 88.3 P. anethifolium 62.1 63.9 P. aridum 73.9 75.0 P. aristatum 61.0 80.1 P. asarifolium 90.3 P. betulinum 70.1 77.5 P. bowkeri 79.4 81.5 P. caffrum 68.8 89.5 P. caledonicum 88.3 P. candicans 94.0 P. capillare 56.1 P. capitatum 81.7 P. carneum 65.2 76.5 P. carnosum 89.1 P. caucalifolium 55.8 91.2 P. chamaedryfolium 64.2 P. citronellum 51.3 P. cordifolium 73.9 77.1 P. coronopifolium 71.2 71.5 P. crassipes 67.4 67.5 P. crispum 95.2 P. cucullatum 88.1 P. denticulatum 50.9 P. dipetalum 94.5 P. dispar 83.5 84.7 P. divisifolium 65.0 P. dolomiticum 67.7 71.6

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P. elegans 86.2 P. englerianum 59.5 75.6 P. exstipulatum 79.6 87.1 P. fergusoniae 98.8 P. fruticosum 54.3 72.4 P. fumariifolium 62.4 64.2 P. gibbosum 67.1 P. glutinosum 50.9 P. gracillimum 64.8 78.9 P. greytonense 74.1 90.9 P. hispidum 50.4 69.0 P. hypoleucum 66.2 P. iocastum 79.9 P. karooicum 60.1 64.9 P. klinghardtense 62.0 P. lanceolatum 71.8 P. laxum 90.1 90.8 P. longicaule 97.3 P. longifolium 90.4 P. luridum 93.5 P. luteolum 58.9 P. minimum 54.3 P. multiradiatum 80.3 P. nanum 56.4 57.3 P. oblongatum 93.2 P. ovale 77.4 90.9 P. panduriforme 82.8 90.2 P. papilionaceum 92.3 98.8 P. patulum 68.0 68.9 P. pilosellifolium 86.4 P. plurisectum 79.2 P. psammophilum 71.6 P. pseudofumarioides 53.6 P. pseudoglutinosum 56.8 P. quercifolium 62.3 P. radens 98.7 P. radiatum 51.6 P. radulifolium 69.1 74.3 P. ranunculophyllum 54.3 60.1 P. rapaceum 80.2 P. ribifolium 70.3 85.7 P. scabrum 98.1 122

P. schizopetalum 84.4 P. setulosum 95.2 P. sidoides 97.6 P. spinosum 57.7 P. suburbanum 80.7 P. ternatum 61.3 P. tetragonum 85.4 87.8 P. tragacanthoides 51.2 58.8 P. triandrum 73.6 74.8 P. tricolor 59.7 P. trifidum 65.1 78.3 P. triphyllum 60.4 P. triste 88.0

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FIGURES

Fig. 1. Map of all 150 Pelargonium occurrence points used in this study.

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Fig. 2. K-means clustering groupings (K = 4) projected onto principal components (PCA) space. PCA analysis was done using the

BioClim (environmental) data that characterizes Pelargonium occurrence and the K-means clustering positions are illustrated on the first two environmental components. 125

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Fig. 3. Habitat suitability maps of twelve representative Pelargonium species. These species represent the range shifts patterns commonly found across the 150 species assessed in this study.

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Fig. 4. Species of critical concern that are projected to lose all of their habitat suitability by 2050. The four species are: a)

Pelargonium incrassatum, b) P. magenteum, c) P. praemorsum, and d) P. pulchellum. Illustrations by J. J. A. van der Walt (1977,

1981). 128

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CHAPTER 3 SUPPORTING INFORMATION Table S1. Overview of the global climate models (GCMs) used in this study.

Model Abbreviation Institution CCSM4 CCSM National Center for Atmospheric Research, USA GISS-E2-R GISS NASA Goddard Institute for Space Studies, USA HadGEM2-ES HAD Met Office Hadley Center, UK MRI-CGCM3 MRI Meteorological Research Institute, Japan

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Table S2. The four Intergovernmental Panel on Climate Change (IPCC) emission scenarios used in this study. Temperature and sea level represent the potential increases based on the emission scenario and year.

2050 2070

RCP2.6 RCP4.5 RCP6.0 RCP8.5 RCP2.6 RCP4.5 RCP6.0 RCP8.5

Temp (oC) 1.0 1.4 1.3 2.0 1.0 1.8 2.2 3.7

Sea Level (m) 0.24 0.26 0.25 0.30 0.40 0.47 0.48 0.63

RCP: Representative Concentration Pathway.

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Fig. S1. Correlation matrix of all 19 bioclimatic layers from WorldClim.

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Fig. S2. Pelargonium species habitat suitability maps averaged over the four global climate models (GCMs). The white squares show presence localities used for training. The purple squares show the localities used for testing the models.

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Fig. S3. Forecasted change in range sizes of the 150 Pelargonium species. By 2070, 85% of the species are projected to have reduced range sizes.

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