The Influence of Climate and Topography in Modeling Distributions For

The influence of climate and topography in modeling distributions for species with restricted ranges: A case study using the Hawaiian endemic plant genus, Schiedea (Caryophyllaceae)

A dissertation submitted to the Graduate School of the University of Cincinnati in partial fulfillment of the requirements for the degree of,

Doctor of Philosophy

In the Department of Biological Sciences of the McMicken College of Arts and Sciences by

Sunita Yadav

M.A. Geography, University of Kansas, December 1996 B.S. Computer Science, University of Miami, 1991

Committee Chair: Theresa M. Culley, Ph.D. Dr. Hongxing Liu, Ph.D. Dr. Eric Maurer, Ph.D. Dr. Steven H. Rogstad, Ph.D. Dr. Ann K. Sakai, Ph.D. Dr. E. Emiel van Loon, Ph.D.

ABSTRACT

Dynamic environments affect species distributions and as a consequence also influence intraspecific genetic variation in both space and time. Many factors determine why a species persists in a particular location, some related to environmental tolerances or colonization history, while others are attributable to biological competition or dispersal limitations, such as that occurring on oceanic island systems. Islands are hotspots of endemism where the potential impacts of habitat modification on biodiversity could be substantial. Therefore, the goal of this dissertation was to investigate the influence of the abiotic environment on species geographic distribution patterns and on breeding system distribution within an island genus, in addition to examining genetic diversity within a broadly distributed endemic species.

Field collected species presence and absence data for Schiedea globosa were used with climate and topographic predictors to evaluate four different species distribution models (SDM):

GLM, GAM, Maxent, and Random Forests. The most accurate model was then used to predict the impact of average shoreline change on suitable habitat at two future time periods. Additionally, I investigated the intraspecific genetic diversity and fine-scale spatial genetic structure for the same species using 11 microsatellite markers from seven populations on the Hawaiian Islands of Maui and

O’ahu. Finally, a community-level SDM approach examined the association of abiotic variables with different breeding systems within Schiedea. Abiotic niches for five breeding groups (hermaphroditic- outcrossing, hermaphroditic-selfing, gynodioecy, subdioecy, and dioecy) composed of 33 Schiedea taxa are described from models developed with georeferenced species occurrence records and environmental data.

ii At a species level, the most accurate SDM describing S. globosa habitat was the Random

Forests model that included six predictors with topographic predictors as the top three predictors.

Two of the seven populations are predicted to be critically affected by loss of suitable habitat due to shoreline change, with higher predicted losses on Maui than on O’ahu. Results from genetic analyses indicate that diversity is relatively high in S. globosa (Ho=0.256 and NA=5.6). Strong spatial structure was detected between islands, and within populations with a larger geographical area and demographic size. Estimated average shoreline change in the next 100 years, assuming no change in genetic diversity, was predicted to affect two of the seven populations with loss of one unique allele.

At the genus level, there were large differences among breeding system SDMs in their responses to environmental predictors and little to moderate amounts of niche overlap among breeding systems.

The largest niche overlap was between the gynodioecious and dioecious groups with the lowest between the hermaphroditic-selfing and subdioecious groups.

This research on a narrowly distributed endemic species provides useful information on variable and model selection to make inferences on the sensitivity of such species to future habitat change. The combination of genetic and spatial analytical tools utilized here provides useful predictions to forecast biodiversity consequences while taking into account projected habitat change.

Results from pairwise niche comparisons among breeding systems within the entire genus highlight abiotic factors that are associated with the distribution and diversification of breeding systems within

Schiedea.

iii

Sunita Yadav

iv ACKNOWLEDGEMENTS

I would like to thank my advisor, Dr. Theresa Culley, for her encouragement and flexibility to pursue this research project. She taught me DNA extraction and field techniques, edited my manuscripts and grant submissions, in addition to providing some amazing opportunities to work on other projects. Thank you Dr. Culley for patiently explaining the complexity of plant breeding systems and your inspiring enthusiasm. I was fortunate to have some wonderful minds on my committee, Dr. Eric Maurer, Dr. Hongxing Liu, Dr. Ann Sakai, Dr. Steven Rogstad and Dr. Emiel van Loon; you have all made this dissertation better with your knowledge and your comments, and challenged me to be a better scientist. I would like to thank Dr. Sakai for her endless knowledge on Schiedea and Hawaii. I also want to express my gratitude to Dr. Rogstad, you have been my Botany guru here at UC and I truly appreciate your kindness and sharp intellect. A special thank you to Dr. van Loon who taught me everything I know about species distribution models, this work would not have been possible without your guidance. Dr. Maurer, I truly appreciate your insightful questions related to conservation issues and Dr. Liu made sure I did not forget my Geography roots. I am grateful to my funding sources, the Yates Scholars Program during my first year at UC, the Choose Ohio First award from 2009-2013, the Benedict Botany Award and the Wieman- Wendel-Benedict Award from the Biological Sciences Department. I am indebted to several people who facilitated this work. Alex Roy at the OCCL provided collection permits and guidance about where to apply for additional permits and Charmian Dang provided contact information. Tara Hirohata and Martha McDaniel at the Hanauma Bay Nature Preserve (HBNP), and John S. Cumming at the Hawai’i DLNR helped with permits. Joshlyn Sand at the Honululu Botanic Gardens was kind enough to meet me on my first field trip and was especially helpful with advice on contacting people. Pomaka’i Kaniaupio-Crozier at the Maui Land and Pineapple Company gave us access and support for the Maui locations. Joseph Ward helped us to locate one population of S. globosa on Maui. Scott Fischer at the Hawaiian Islands Land Trust helped us in the field on Maui. Clyde Imada at the Bishop Museum provided access to herbarium specimens. All the people in the permitting agencies were enormously helpful and took time to provide either contacts or permits. Some even gave recommendations for the best Hawaiian Shaved Ice. Mahalo nui loa! My two field assistants were indispensable on site, working long hours in often dangerous environments. Thank you to Megan Philpott and Meagan Rathjen for all your hard work, competence, and delightful company in the field. Thanks to my friends Ben T. and Ben M. for answering my GIS questions when I was stuck. I am grateful for my fellow graduate students who taught someone with little background in Biology about evolution, animal behavior, and many other topics. A special thanks to my lab mates Alina, Yamini, Megan, Francis, Susan, Jessica, Rick, Allison, Ben, and Rob for creating a friendly lab environment and many memories. Thanks to the UH students who adopted me as part of their group during conferences and taught me about Hawaiian culture. I am so grateful to UH housing for letting me stay on campus my second field season.

I could not have done completed this dissertation without the love and support of my family and friends, especially my mother Sharbati, my sister Sangita, and my friends Gladys and Seema who often had to act as surrogate mothers to my kids. Thank you for keeping me sane, believing in me, and being there when I needed you. Thank you Anissa for help with data-entry and Sohan for providing many laughs. Lastly, my kids Nitin and Aditya had to sacrifice much in order for me to pursue this goal and they helped me with data-entry, planting S. globosa cuttings in the greenhouse, and counting hundreds of S. globosa flowers. I could not always help you with homework or pick you up early from daycare, and there were many days when I never saw you, but you are wonderful people and I appreciate your generous and beautiful souls.

TABLE OF CONTENTS

Abstract………………………………………………………………………………………………….……………………….………. ii

Acknowledgements………………………………………………………………………………………………………………….. v

Table of Contents………………………………………………………………………………………………………….………… vii

List of Tables……………………………………………………………………………………………..…………………………… viii

List of Figures………………………….…………………………………………………………………………….…….….…..….. ix

CHAPTER 1: GENERAL INTRODUCTION …………………………………………………………………………………………....… 1

CHAPTER 2: COMPARISON OF FOUR SPECIES DISTRIBUTION MODELS TO PREDICT HABITAT CHANGE DUE TO ESTIMATED SHORELINE SHIFT FOR SCHIEDEA GLOBOSA IN THE HAWAIIAN ISLANDS……………………………………….. 10

CHAPTER 3: THE EFFECTS OF SHORELINE CHANGE ON THE FINE SCALE SPATIAL GENETIC DIVERSITY OF THE HAWAIIAN HERB SCHIEDEA GLOBOSA…………………………………………………………………………...... 61

CHAPTER 4: THE ASSOCIATION BETWEEN BREEDING SYSTEM AND THE ENVIRONMENT IN THE HAWAIIAN ENDEMIC GENUS, SCHIEDEA ……………………………………………………...... 107

CHAPTER 5: GENERAL CONCLUSION……………………………………………………………………………….….………….. 150

Appendix 1: Preliminary unpublished results on inflorescence architecture………………………... 158

vii LIST OF TABLES

Chapter 2 Table 2.1 - List of all predictors considered for SDM variable selection. Table 2.2 - Comparison of suitable habitat pixels predicted from each model. Table A2.1 - List of S. globosa occurrences used to develop models.

Chapter 3 Table 3.1 - Descriptive statistics for all populations of Schiedea globosa sampled on the Hawaiian Islands of Maui and O'ahu for 10 microsatellite loci. Table 3.2 - Summary AMOVA table displaying how genetic variation is partitioned. Table 3.3 - Spatial autocorrelation distances and geographic extents for each population in meters. Table 3.4 - Current and predicted genetic diversity for Schiedea globosa for samples collected on Maui and O’ahu.

Table A3.1 - The pairwise Fst distance matrix for 11 loci across seven populations on Maui and O’ahu.

Chapter 4 Table 4.1 - List of all species used in species distribution modeling. Table 4.2 - All predictors considered a priori for SDM variable selection. Table 4.3 - Final list of the six variables selected for each breeding system group.

viii LIST OF FIGURES

Chapter 2 Figure 2.1 - Study Sites on Maui and O’ahu displaying the distribution of sampling locations for Schiedea globosa. Figure 2.2 - Evaluation metrics for four different models using AUC and TSS with 4-fold partitioning. Figure 2.3 - Predicted habitat suitability maps for Schiedea globosa for Climate Only models. Figure 2.4 - Predicted habitat suitability maps for Schiedea globosa on Maui for Climate+ models. Figure 2.5 - Predicted habitat suitability maps for Schiedea globosa on O’ahu for Climate+ models. Figure 2.6 - Examples of shoreline change and habitat loss on west Maui and southeast O’ahu predicted by the RF Climate+ model. Figure A2.1 - Variable selection using Random Forests for modeling the distribution of Schiedea globosa in the Hawaiian Islands. Figure A2.2 - Null Model ROC plots from Climate Only and Climate+ models.

Chapter 3 Figure 3.1 - Topographic map of the sampling locations for Schiedea globosa with four sites on southeast O'ahu and three sites on West Maui.

Figure 3.2 - Plot of the first two axes of the principal coordinate analysis based on pairwise Fst in Schiedea globosa populations on Maui and O’ahu. Figure 3.3 - Spatial autocorrelation distance classes for populations 951 and 313 on Maui and 906 and 844 on O’ahu. Figure 3.4 - Distribution of alleles for two markers (SA15 and SA06) within the 951 population on Maui, overlaid on a background slope map. Figure 3.5 - Distribution of alleles for two markers (SA15 and SA06) within the 906 population on O’ahu overlaid on a background slope map.

Figure 3.6 - Map of population most affected by shoreline change on Maui at Pohakupule (population 951) where approximately half the individuals could be lost. Figure A3.1 - Monmonier graph depicting geographic and genetic distance for S. globosa populations on O'ahu and Maui. Figure A3.2 - Change in number of alleles for all loci across populations of S. globosa on Maui and O’ahu. Figure A3.3 - Change in allele frequency by marker for population 951 on Maui from present day (left pie-chart) to a future time period at 100 years (right pie-chart).

Chapter 4 Figure 4.1 - Distribution of breeding systems within the genus Schiedea in the Hawaiian Islands overlaid with an elevation layer. Figure 4.2 - Distribution of outcrossing and selfing species that display hermaphroditic breeding systems within Schiedea in the Hawaiian Islands. Figure 4.3 - Response curves for the three most important predictors for each breeding group as determined by the MaxEnt algorithm. Figure 4.4 - Receiver operating curve plots for final models for each breeding system group. Figure 4.5 - Breeding system predicted suitability of presence from Maxent Models. Figure 4.6 - Pairwise comparison of ecological niches for breeding system groups using Schoener’s D metric and the Identity (I) metric.

CHAPTER 1

General Introduction

Sunita Yadav

Department of Biological Sciences,

University of Cincinnati,

614 Rieveschl Hall, Cincinnati, OH 45221, USA

[email protected]

Biodiversity losses are predicted to accelerate in the next century due to combined threats from global climate change and from anthropogenic causes, such as urbanization, deforestation, agricultural practices, and invasive species (Guisan 2014). Endemic island species are at particular risk because they often have small habitat ranges and lower population sizes

(Leisz et al. 2008; Marcer et al. 2013). Furthermore, human population expansion and the associated activities pose a serious threat to island biotas as evidenced by substantial extinction rates and the increasing number of threatened species added on IUCN red lists (Whittaker &

Fernandez-Palacios, 2007; US Census Bureau 2010; García-Verdugo et al. 2014). Despite their small land area, oceanic islands are high priority conservation areas because they are biodiversity hotspots with high rates of endemism (Caujapé-Castells et al. 2010). Isolated oceanic islands, those never connected to continental landmasses, have characteristic biogeographic patterns and unique geologic histories that make them particularly useful to develop ecological or evolutionary hypotheses.

Islands have a long history of scientific investigation since the time of Darwin and

Wallace and are often portrayed as natural laboratories to test biogeographic hypotheses

(Darwin 1839; Wallace 1881; MacArthur & Wilson 1967; Carlquist 1974). The geological cycle of islands presents unique conditions for biological evolution; isolation acts as a filter for colonizers from a mainland, mountain building affects local climate by modifying wind patterns, whereas constant habitat change via erosion and subsidence offers evolutionary opportunities for speciation (Stuessy et al. 2014; Harter et al. 2015). García-Verdugo et al. (2014) suggest that island plants display higher phenotypic variation than mainland plants, which may increase the ability of island flora to colonize new habitats. Plant species on oceanic islands also reflect

2 complex evolutionary pathways; for example, Givnish et al. (2009) found that diversification patterns in Hawaiian lobeliads display hierarchical adaptive radiation with habitat partitioning, then elevation and floral-tube length.

The Hawaiian Islands are the most remote oceanic islands on the planet with a high percentage of endemic plant species, c. 90% in angiosperm floras and over half that flora at risk

(Sakai et al. 2002; Caujapé -Castells et al. 2010; Keeley & Funk 2011). Habitat loss due to urbanization, competition with invasive species, reduction in pollinators, and generally small population sizes contribute increasing pressures to Hawai’i’s rare flora. Recent studies have examined the explicit effects of invasive species, large ungulates, and climate change on

Hawaiian plant species (Joe & Daehler 2008; Weller et al. 2011; Eversole & Andrews 2014).

Although there is uncertainty surrounding specific predictions from global climate change,

Pacific Ocean warming is projected to intensify the hydrologic cycle, which could potentially modify trade wind patterns (Timm & Diaz 2009). The IPCC (2014) and other researchers

(Romine et al. 2012) predict a change in precipitation patterns, sea level rise, and increases in extreme weather events. Current local scale models forecast an increase in precipitation in the dry season and a decrease in the wet season (Timm & Diaz 2009). Historical records indicate that local temperature and rainfall have responded to past climate change (Chu & Chen 2005;

Diaz et al. 2005). As threats to biodiversity intensify, there is an urgent need to study existing species and ecosystems at multifaceted levels in order to predict risks, and to preserve diversity.

One group that has been widely studied is the genus Schiedea. It is the fifth largest adaptive radiation of a plant group in the Hawaiian archipelago consisting of 32 extant species

(34 in total), with the greatest diversity in breeding systems (Wagner et al. 2005). Over half of all extant Schiedea species are federally listed as endangered while the remainder are all considered threatened (USFWS 2010). In this dissertation, I provide new information to understand current species distributions and genetic diversity for members of this genus at different scales: Schiedea globosa at a species level using species distribution modeling methods (SDM), the same species at a genetic level with microsatellite markers, and at a taxonomic level of the genus itself to understand the environmental factors affecting breeding system distributions.

Evolutionary processes combined with ecological and geographical isolation determine to a great degree the distribution of plant species on oceanic islands (Price & Wagner 2004).

Species distribution models (SDM) are commonly used to map species distributions in past, current, or future time. The theoretical underpinnings of SDMs are based in ecological niche concepts, with the primary aim to accurately predict the species distribution (Araújo & Williams

2000; Guisan & Thuiller 2005). Species occurrence data and environmental predictor variables are used to develop a statistically derived probability distribution (Franklin 2009). While there have been vast improvements in SDM methods, modeling rare or uncommon species is still a challenge because of the unique characteristics of such taxa (Engler et al. 2004; Marcer et al.

2013). Rare species have restricted ranges, small population sizes and are generally habitat specialists, all factors that contribute to methodological challenges, and require careful selection of model variables at appropriate spatial scales. Given that there are many types of models and associated evaluation metrics, I investigated four different model types in Chapter

1 using two evaluation metrics to determine the models that work best for species with narrow

4 distributions such as S. globosa at a high spatial resolution. I then used the best model to predict habitat loss given predicted shoreline change in the Hawaiian Islands at two time periods.

Shoreline change due to multiple causal factors, including climate-change induced sea- level rise, can also affect overall genetic diversity in populations (Vinceti et al. 2013). This is turn has implications for population genetic structure at broad and fine spatial scales (Pauls et al.

2013). The question is whether species will be able to keep up with the rate of change via migration or adaptation. Vulnerability to threats from climate change and information on current population genetic structure are necessary to assess management goals, especially for threatened plant species that are highly susceptible to habitat alteration. To further investigate the effects of shoreline change on S. globosa, I then tested the hypothesis that shoreline change would impact fine-scale population genetic structure of seven populations on the two islands of Maui and O’ahu from the current time to 100 years from now in Chapter 2.

Lastly, there is an important debate in the scientific community at the moment regarding niche conservatism during the course of evolution (Peterson et al. 2011; Araújo et al.

2013). Because there is a great variety of breeding systems within the genus Schiedea, it is an excellent model to examine the question of ecological niche conservatism or divergence.

Evidence suggests that niche divergence is relevant in this system because of the pattern of breeding system evolution associated with habitat and/or pollinator shifts (Culley et al. 2002;

Sakai et al. 2006). In Chapter 3 of this dissertation, I test the hypothesis that there is little niche overlap among the five different breeding systems (hermaphroditic outcrossing, hermaphroditic selfing, gynodioecy, subdioecy, and dioecy) in the genus Schiedea using SDM

5 techniques (comparing hermaphroditic outcrossing, hermaphroditic selfing, gynodioecious, subdioecious, and dioecious groups of taxa). Ecological niche data were subsequently assessed to determine whether niche overlap is associated with observed habitat shifts of breeding systems, to address the main question of whether abiotic niches are conserved in this genus.

Endemic plant species in the Hawaiian Islands face multiple threats from a high urbanization over the past 100 years (US Census Bureau 2010), global climate change, and competition from invasive species. Therefore, evaluating risks to island biodiversity will be important to predict and mitigate extinction risks of individual taxa. The tools to quantify biodiversity are improving and therefore, we can now effectively estimate biodiversity stock and make predictions for the future.

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Harter, D.E. et al. 2015. Impacts of global climate change on the floras of oceanic islands– Projections, implications and current knowledge. Perspectives in Plant Ecology, Evolution and Systematics, 17(2): 160-183. IPCC. 2013. Summary for Policymakers. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change Edited by Stocker, T.F., D. Qin, G.-K. Plattner, M.M.B. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Joe, S.M., & Daehler, C.C. 2008. Invasive slugs as under-appreciated obstacles to rare plant restoration: evidence from the Hawaiian Islands. Biological Invasions, 10(2), 245-255. Keeley, S.C., & Funk, V.A. 2011. Origin and evolution of Hawaiian endemics: new patterns revealed by molecular phylogenetic studies. The biology of island floras, 57-88. Leisz, S.J. et al. 2008. Climate change and biodiversity in Melanesia. MacArthur, R.H., & Wilson, E.O.(1967. The theory of island biogeography (Vol. 1). Princeton University Press. Marcer, A. et al. 2013. Using species distribution modelling to disentangle realised versus potential distributions for rare species conservation. Biological Conservation, 166: 221-230. Nogueira-Filho, S.L.G. et al. 2009. Ecological impacts of feral pigs in the Hawaiian Islands. Biodiversity and Conservation 18:3677-3683. Pauls, S.U. et al. 2013. The impact of global climate change on genetic diversity within populations and species. Molecular ecology, 22(4):925-946. Peterson, A.T. 2011. Ecological niche conservatism: A time‐structured review of evidence. Journal of Biogeography, 38(5), 817-827. Price, J.P. and Wagner, W.L. 2004. Speciation in Hawaiian angiosperm lineages: cause, consequence, and mode. Evolution 58(10):2185-2200. Romine, B.M., & Fletcher, C.H. 2012. A summary of historical shoreline changes on beaches of Kauai, Oahu, and Maui, Hawaii. Journal of Coastal Research, 29(3):605-614. Sakai, A.K. et al. 2002. Patterns of endangerment in the Hawaiian flora. Systematic Biology, 51(2):276-302. Sakai, A.K. et al. 2006. Adaptive radiation and evolution of breeding systems in Schiedea (Caryophyllaceae), an endemic Hawaiian genus. Annals of the Missouri Botanical Garden, 93:49-63. Stuessy, T.F. et al. 2014. Interpretation of patterns of genetic variation in endemic plant species of oceanic islands. Botanical Journal of the Linnean Society, 174(3):276-288.

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CHAPTER 2

Comparison of four species distribution models and prediction of habitat change

due to estimated shoreline shift for Schiedea globosa in the Hawaiian Islands

Sunita Yadav

Department of Biological Sciences,

University of Cincinnati,

614 Rieveschl Hall, Cincinnati, OH 45221, USA

ABSTRACT

Identifying the connections between habitat modification and species distributions is essential to understand why species persist in particular locations. While many models exist to explain the distribution of common and invasive species, there is still a need to accurately predict distributions for rare and uncommon species. To do so, we evaluated four different species distribution models (GLM, GAM, Maximum Entropy, and Random Forests) for Schiedea globosa (Caryophyllaceae), an endemic Hawaiian coastal species. This species is considered uncommon, although it is not listed as a federally endangered species. Field collected presence and absence data were used with climate and topographic predictors to develop species distribution models (SDM) in order to (1) determine the most accurate model and (2) to predict the impact of changing shoreline on suitable habitat at two time horizons, 100 year and 200 year.

Models that included climate and landscape predictors displayed much narrower distributions for all four model types with good model metrics whereas climate-only models had poor to fair model fit with a much broader distribution pattern. The best discriminating model was the Random Forests model that included six predictors with topographic predictors as the top three. Of the seven populations sampled in the field, two are predicted to be critically affected with half of their current recorded presences lost directly due to shoreline change. Results suggest that there will be a much higher loss of suitable habitat on Maui than on O’ahu due to average shoreline change. SDMs have proven useful to describe current distributions for many common species, however, selecting the ideal combination of model type and predictor variables can vary depending on the species characteristics. Thus, case

11 studies of endemic species, which often have narrow ecological ranges, are needed to make inferences on the sensitivity of such species to future habitat change. The results presented here indicate that choice of model, spatial scale and variable selection can result in dramatically different predictions. Overall, algorithmic models provide predictions that are more accurate and a higher spatial resolution is critical for narrowly distributed species.

2.1 INTRODUCTION

Conservation efforts often focus on critically endangered rare species because they are the most in need of assistance and financial resources, while less vulnerable species are less likely to be given resources for protection. However, once a species has reached federally endangered status, it likely has small population sizes and may consequently suffer from the loss of genetic variability because of drift and inbreeding, as well as demographic fluctuations that make the species vulnerable to environmental change (Barrett and Kohn 1991, Marcot and

Molina 2007). Conservation efforts for rare species may often be unsuccessful because of issues related to their small population sizes and little remaining suitable habitat. Therefore, identifying species before their population sizes dwindle may lead to more success with conservation efforts by avoiding small population size problems (Primack 2010, Negrón-Ortiz

2014). While there is not as much focus on less-threatened species, these species also face risks due to habitat destruction and changes in their environment (Engler 2004, Gogol-Prokurat

2011, Vorsino et al. 2013), and in contrast to highly endangered species, their conservation may be more easily addressed (Primack 2010). Thus, it is crucial to analyze and compare methods that detect the elements in the abiotic environment that impact species distributions, in order to answer questions related to impacts from climate or land-use change.

The area and size of a species range is key to understanding the extinction risk of the species (Araujo and Williams 2000, Rodrigues et al. 2006, Attore et al. 2012). Threatened species pose a unique problem for conservation biologists because many such species have narrow ecological ranges; therefore, a modification to their habitat has a potentially major effect on their future survival. Unfortunately, species occurrence data is often scarce with poor

13 spatial accuracy for rare or uncommon species (Engler et al. 2004, Newbold 2010, Marcer et al.

2013). Increasingly, conservation efforts use species distribution modeling (SDM) approaches to assess the range and size of suitable habitat as well as threats to such habitat (Fordham et al.

2012, Franklin and Regan 2014). SDMs extrapolate species occurrence, along with environmental data, in space and/or time based on a statistical model (Franklin 2009). If the model provides a good fit between the species presence and the environmental predictors, it can be applied to detect species habitat ranges, predictive mapping of previously unknown habitat, persistence under climate change, and to test evolutionary hypotheses (Guisan and

Thuiller 2005, Williams et al. 2009, Stohlgren et al. 2010, Franklin et al. 2013).

The primary goal of SDMs is to understand the associations between environmental factors and known species occurrences to identify the environmental conditions where the target species can persist. The underlying theory in modeling a species distribution is based on the definition of a species fundamental niche (Hutchinson, 1957, Guisan and Thuiller 2005). A common aim for modeling species distributions is to tease apart the environmental predictors that most accurately define the realized niche, which may be smaller than the fundamental niche of a species (Soberón and Nakamura 2009, Marcer et al. 2013). Nevertheless, many species are not at an optimal equilibrium (i.e. occupying a realized niche) but at a pseudo- equilibrium; thus, in many instances, SDMs predict a distribution in between a fundamental and realized distribution (McInerny and Etienne 2012, Sillero 2011).

Most modeling tools currently available use presence-only models that are overly optimistic in predicting distributions of species with few known localities (Williams et al. 2009).

Habitat suitability models that employ presence and absence (or pseudoabsence) data have

14 been proposed as a better alternative to presence-only models for uncommon species (Engler et al. 2004, Williams et al. 2009). There are numerous challenges for developing SDMs for species with narrow distributions, including the reality that environmental predictors are usually unavailable at the desired resolution, absences may not be accurately determined, and a dataset that may be too small for model calibration and evaluation (therefore lacking independent test data to validate models). In spite of these challenges, the limited number of studies published on habitat suitability models for rare species offers some measure of predictive success for habitat identification and at finding new occurrences of these taxa

(Guisan et al. 2006, Williams et al. 2009, Gogol-Prokurat 2011).

Many endemic species have narrow geographic ranges, which places them at higher risk of extinction (Sakai et al. 2002, Marcer et al. 2013). The Hawaiian Islands present an ideal laboratory to study the relationship between the environment and species distribution because of high rates of endemism and rarity; more than 90% of native flowering plant and native land bird species are endemic, making this location a biodiversity hotspot (Wagner et al. 1999, Pratt

2009). The continuous volcanic formation and eventual demise of islands through subsidence and erosion keeps habitats fluid; thus, plant species inhabiting the islands have to keep recolonizing newer habitats or islands to survive on this geologic conveyer belt (Fleischer et al. 1998; Price and

Wagner 2004). As a consequence, rapid diversification is evident in many plant lineages on the islands due to dispersal and adaptation (Price and Wagner 2004, Sakai et al. 2006). The endemic plant genus Schiedea (Caryophyllaceae) has been used as a model to understand the evolution of separate sexes (dioecy) in angiosperms (e.g., Sakai et al. 2006). It is the fifth largest adaptive radiation of flowering plants in Hawai'i with 32 extant species that differ in breeding systems and

15 habitat (Sakai et al. 1995, Wagner et al. 2005). The majority of species within Schiedea are listed on the United States Endangered Species List and at least two species are already extinct (Sakai et al. 2006).

In this study, our major goal was to evaluate the usefulness of four commonly used

SDMs in predicting the distribution of a narrowly distributed Hawaiian endemic plant species with relatively large overall population sizes. Schiedea globosa (Caryophyllaceae) H. Mann, the focal species, occurs in narrow coastal areas that are highly susceptible to shoreline erosion and sea-level rise associated with climate change. Over the past century, the principal Hawaiian

Islands of O'ahu, Maui and Hawai'i have lost about 9% of beach area, mostly due to sea-level rise and storm surges (Fletcher et al. 2012, Romine et al. 2012). Coastal erosion is a significant problem in the Hawaiian Islands leading to beach loss and potentially affecting coastal species

(Romine et al. 2012). Some islands show relatively minor average erosion rates over time

(O'ahu, Kaua'i) while others (Maui) show higher erosion rates with differences attributed to storm events, wind patterns, and urban construction close to beaches (Romine et al. 2013).

More recently, rapid urban development has drastically reduced the size of some populations of S. globosa. Human population increased within the state at approximately 12% within the past decade (Stein et al. 2014). As a result of increasing population pressure, native landscapes have been converted to pasture, roads, or to housing developments and the ensuing disturbance has led to an increase in invasive species. Consequently, S. globosa is of conservation concern and species distribution modeling techniques may identify potential habitat for this species. Using different techniques to model the distribution of a narrow range endemic species, such as S. globosa, may provide clues about which types of models may best

16 predict the relationship between a species presence and a set of environmental conditions

(Williams et al. 2009). In this study, we develop and apply a species distribution modeling workflow to compare four different models and to predict the impact of shoreline change on the habitat of a narrowly distributed Hawaiian endemic species, S. globosa (Caryophyllaceae).

2.2 METHODS

Study area and species

The Hawaiian archipelago stretches over 1600 km in the Northern Pacific, sitting on a stationary hot-spot that is under the Pacific tectonic plate which is moving in a northwesterly direction (Fleischer et al. 1998; Price and Wagner 2004). The island chain comprises eight high islands and numerous smaller islands and atolls. Newer volcanoes protrude above the ocean surface and in some instances coalesce to form islands, such as the island of Hawai'i; over time, some islands separated as sea levels rose after the Pleistocene and land subsided, for example, the island of Maui Nui separated into Maui, Moloka'i, Lana'i, and Kaho'olawe (Carlquist, 1980).

Island isolation and habitat shifts played an important role in inter-island dispersal events and intra-island adaptive radiations within Hawaiian lineages (Wagner and Funk, 1995, Cowie and

Holland 2006). There are a wide variety of habitats on the Hawaiian Islands, ranging from tropical lowlands to alpine regions, xeric regimes to one of the wettest places on the planet

(Wagner and Funk, 1995; Price 2004). Average annual temperatures are tropical but vary from

25°C (sea level) to 5°C (high elevation) and similarly rainfall varies from 250 - 1200 mm/yr in leeward areas to over 2500-7000 mm/yr in windward habitats and over 11,000 mm in wet rainforest (Price et al. 2007; Giambelluca et al. 2013).

Schiedea globosa is a woody subshrub that grows on steep rocky coastal cliffs on the islands of Maui, Moloka'i, O'ahu, and Hawai’i (Wagner et al. 2005). Although not officially listed as federally endangered like many species within Schiedea, it is considered globally rare occurring from sea level to 492 m elevation in dry regions on volcanic origin substrate. The breeding system of S. globosa is subdioecy - with male and female individuals, as well as plants with a few hermaphroditic flowers intermixed on male inflorescences (Sakai and Weller 1991); its flowering period is typically from December to April after winter rains commence.

Pollination occurs mostly via wind (Weller et al. 1998) while seed dispersal is thought to occur through gravity and occasional inter-island rafting of whole plants and possibly seeds (Wagner et al. 2005).

In general, S. globosa populations are found on the windward side of the islands subject to winter storms. Study sites on West Maui and O'ahu in the Hawaiian Islands (Figure 2.1) are characterized by rugged coastal terrain with an active disturbance regime including severe seasonal storms that may erode rock cliffs (S. Yadav, personal observation). The species is also found on the islands of Hawai’i and the north shore of Moloka'i, in addition to offshore islets

(Wagner et al. 2005), but these locations were difficult to access and consequently were not used in modeling. On Maui, S. globosa is more often found on the western side of the island although it also occurs on a rocky offshore islet (Alau islet) near the eastern side (Wagner et al.

2005). The eastern and western sides of Maui are separated by a valley formed by two volcanoes: Haleakala and the West Maui volcano (approximately 2 million and 1.1 million years respectively). In contrast to East Maui, the western half of the island is arid and receives more

18 solar radiation (Giambelluca et al. 2013). On O'ahu, S. globosa occurs primarily in the eastern to southeastern corner of the island (Wagner et al. 2005).

Species Occurrence Data

Georeferenced presence data from seven populations and absence data were collected in the field in 2012 and 2013. A total of 130 presence and 157 absence pixels were used in model development after removal of duplicates. Field sampling was accomplished by on-site surveys to determine the extent of the species distribution at each site. For larger sites, we sampled along transects placed next to the cliff base of the site with parallel transects going up the elevation gradient on the cliff face. Absence data were sampled within 5km of the coast.

Models were evaluated only on presence/absence locations collected in the field, as spatial accuracy is important for narrowly distributed species (Engler et al. 2004). All field collected presence and absence points have an overall accuracy of 5m or less. As S. globosa displays a narrow coastal distribution, some presence points at the edge of the range occurred in the ocean due to resolution issues. We chose to reassign these 'sea-pixels' to the closest land pixel rather than delete them because of the limited data set and because these pixels are bona fide presence data (as described in Marcel et al. 2013).

Environmental Data

Three types of environmental predictors were selected for this study because they may affect species persistence, dispersal, and physiological limits: climatic, topographic and edaphic factors (Table 2.1). In addition, a physical variable, distance to coast, was used as a proxy for

19 moisture from ocean spray as S. globosa is noticeably distributed in areas directly exposed to such moisture. Distance to coast was calculated using boundary files obtained from the USGS.

While the native resolution of environmental data is at varying spatial scales, we disaggregated the data to the smallest available resolution. Data at coarser resolutions were resampled to

10m using a nearest-neighbor interpolation to maintain the accuracy of the original data. All predictors were assembled in a GIS database (ArcGIS v.10.2, ESRI Inc.) and a spatial mask was set to 5km from the coast as natural populations of S. globosa have not been observed further inland than this distance.

Climate Data

Long-term climate normals (PRISM 2013, Giambelluca et al. 2013) were used to generate annual trends, seasonal indices and extreme conditions that play a direct effect on plant physiologic limits (Austin and Van Niel 2011a). The seasonal indices used were winter monthly mean and total (October-April) and summer monthly mean and total (May-

September), which correspond to the wet and dry seasons on the Hawaiian Islands

(Giambelluca et al. 2013). The derived extreme predictors (Table 2.1), such as rainfall in driest months, are commonly used in species distribution modeling as they may have a role in species fitness and survival (Zimmerman and Kienast 1999). We used a two-month window rather than a three-month (quarter) to have the most extreme value for the climate predictor.

Topographic Data

The topographic factors derived from the USGS 10m digital elevation model (DEM) for the Hawaiian Islands (Gesch et al. 2007) were percent slope, aspect, solar radiation, and four different topographic indices (Table 2.1). While slope may not directly affect a species

20 distribution, it may explain some of the gravitational processes in steep areas (Zimmerman and

Kienast 1999) and it is used to calculate aspect, which has a direct effect on soil moisture and microclimate. Aspect was used as a categorical predictor with four cardinal directions and eight intermediate directions (with zero aspect for flat areas). Solar radiation (Kcal/m2/day) was calculated as an annual average, summer average, and winter growing season average. Annual solar radiation was calculated for a 6-month period at 14-day and one hour intervals, while the seasonal indices were calculated for a 7-day and half hour intervals. The winter period was chosen because this is the flowering season for the species and the summer period was selected because this is the driest time when total radiation may affect species survival

(Zimmerman and Kienast 1999).

Due to the active disturbance regime of the study area and to account for microclimatic setting, four topographic predictors were calculated to evaluate the effect of terrain on models.

Topographic Position Index (TPI) was calculated for a 5x5 neighborhood. TPI is the relative topographic position of each pixel calculated with respect to a defined neighborhood and is thought to indicate dominant geomorphic processes relating to soil formation (Guisan et al.

1999). In contrast, Terrain Ruggedness Index (TRI) corresponds to the mean elevation difference between a pixel and its surrounding eight-cell neighborhood (Riley et al. 1999).

Standard deviation of slope for a 3x3 neighborhood was also calculated as previous studies have shown the relevance of this variable in rapidly changing terrain as found in the Hawaiian

Islands (Grohman et al. 2011). Topographic Wetness Index (TWI) uses flow accumulation and flow direction to differentiate areas with a low slope that accumulate water (concave shape)

21 and those with a high slope that shed water (convex shape; Bevin and Kirkby 1979). The formula used for TWI is:

TWI = ln (local upslope contributing area / tan local slope angle)

Edaphic Data

Rare or little-known species are often associated with specific soil types (Gogol-Prokurat

2011, Wu and Smeins 2000). Soil predictors downloaded from the USDA GSURGO database were as follows: pH, soil taxonomic order, and erodibility factor (KFFACT). The latter is calculated by accounting for soil composition, particle size and soil structure; the soil erodibility factor is a measure of erosion due to detachment of soil particles and movement by water

(USDA Soil Survey Staff 2013). It is used here to assess risks due to erosion and as a proxy for soil drainage characteristics (S. globosa prefers well drained soils). Areas with 'no data' were on bare rock and these were reclassified to zero with the assumption that bare rock has zero erodibility. Soil taxonomic order was used as a categorical variable in models with S. globosa occurring only on Entisols and Ultisols. Although a more direct measure of soil moisture and drainage would have been preferred, such data were unavailable or incomplete in the STATSGO database.

Statistical Model Development

A major goal of this project was to determine which modeling approaches work well for rare or uncommon species. Four statistical models were examined: Generalized Linear Models

(GLM), Generalized Additive Models (GAM), a maximum entropy model (MaxEnt - MX), and

Random Forests (RF). GLMs form the basis for more advanced models and are still useful with

22 true presence/absence data (Austin 2007). GAMs were included because they permit a smoothing function for predictors that may model the functional form of a predictor better than a linear model. Maxent and RF models are both algorithmic approaches that previously have been useful to model species with low occurrences (Elith et al. 2006, Williams et al. 2009).

In addition, two categories of models were built for each of these four models: Climate Only models (hereafter referred to as Climate Only), and Climate+ topography and all remaining predictors (referred to as Climate+ from here onwards).

Models are often evaluated by an independent data set from the data used to build the models. However, for rare or uncommon species, finding adequate data is a challenge and methods such as data splitting and k-fold partitioning are commonly used (Williams et al. 2009).

In this study, a 4-fold stratified partitioning by population was done with k-1 sets used for model calibration and each k was used for model testing. Sampling was stratified across the seven sites for both presence and absence points.

Variable Selection

Prior to modeling, variable selection was performed using all predictors and the complete data set by running full models in RF and MaxEnt for ClimateOnly and Climate+ models. Both MaxEnt and RF have recently been used to identify informative variables and lead to models that perform satisfactorily (Genuer et al. 2010, Stohlgren et al. 2010, Elith et al.

2011). Choosing only the most relevant predictors was important to avoid model overfitting where model predictions are too constrained in novel areas and to obtain parsimonious models that can be interpreted ecologically (Elith et al. 2011). The subset of predictor variables

23 obtained from RF and MaxEnt models were subsequently chosen as a starting point for GLM and GAM models.

Climate Only Models Variable Selection

Using the RF approach, backwards variable elimination and the importance spectrum based upon the out-of-bag (OOB) error estimate were used to select the core set of predictors

(Genuer 2010). Four minimum climate variables were important for the best RF model

(Supplementary material Appendix I, Fig. A2.1): rainfall in the warmest months, average annual maximum temperature, temperature in the driest months, and temperature in the warmest months (Table 2.1). The settings for the variable selection process were 1000 trees for the first forest, 300 trees for additional forests, 20% drop fraction in predictors, default settings for the number of input variables at each split in the classification tree (mtry), and variable importance computed at each iteration.

Variable selection was likewise done in MaxEnt using the complete dataset and all predictors. A combination of variable percent contribution, permutation importance and jackknife of regularized training gain was used to determine relevant predictors. All predictors contributing less than 2% to the full model were initially removed. In subsequent steps, variables were removed if the model AUC value without the predictor was higher than with the predictor variable. The final variables used were temperature in the warmest months, rainfall in the warmest months, and rainfall in the coolest months.

Climate+ Models Variable Selection

As already described, variable selection in RF for Climate+ models initially arrived at 10 predictors, however, further variable elimination using the OOB error estimate led to a final six

24 important predictors for the best RF model (Supplementary material Appendix I, Fig. A2.2).

These predictors were solar radiation in the winter season, topographic ruggedness index, standard deviation of slope (3x3 neighborhood), temperature in warmest months, distance to coast, and soil erodibility factor. MaxEnt variable selection arrived at the same six predictors independently using the previously described steps.

Model fitting

Each of the four model types was calibrated on the 4-fold training data and models were evaluated on calibration and test data with Area Under the Curve (AUC) and the True Skill

Statistic (TSS) criteria. GLM, GAM and RF models were examined in the R programming environment v.3.03 (R Core Team 2013), using the lme4, mgcv, and randomForest packages.

The Java software MaxEnt v.3.3.3k (Phillips et al. 2006) was used for the Maximum Entropy method.

(a) Generalized Linear Models

Generalized Linear Models (GLM) were implemented with a binomial error distribution and logistic link function. GLM’s are a category of linear regression models that use different link functions to define the relation between the response and explanatory variables (Guisan et al.

2002). The full GLM models included all the variables resulting from the RF and MaxEnt variable selection analysis as a starting point. Non-significant predictors were removed and model comparison using AUC and the TSS criteria determined the most parsimonious predictors (Table

2.1). Due to a small sample dataset and issues with model overfitting, recursive predictor elimination was employed to obtain the most parsimonious model. The best significant model,

25 with the lowest AUC and TSS values was chosen for comparison purposes. Climate Only model predictors were temperature in warmest months, rainfall in warmest months, rainfall in coolest months and rainfall in driest months; Climate+ predictors were winter solar radiation, distance to coast, soil erodibility factor, and standard deviation of slope.

(b) Generalized Additive Models

Generalized Additive Models (GAM) are similar to GLMs with added flexibility in defining the relationship between response and predictor variables with smoothing terms (Guisan et al.

2002). Analogous to GLMs, the GAMs developed here used a binomial error distribution and logistic link function with smoothing terms. The major issue related to GAM modeling is that models can have low deviance but also low parsimony due to increased degrees of freedom associated with smoothing terms (Austin 2007). Ideally, one should balance minimizing deviance with the most parsimonious model. The predictors in Climate Only models were temperature in warmest months, rainfall in warmest months, rainfall in coolest months and rainfall in driest months; Climate+ predictors were winter solar radiation, distance to coast, soil erodibility factor, and standard deviation of slope.

A few studies have shown RF Models to be useful in modeling the distribution of rare species

(Cutler et al. 2007, Williams et al. 2009). This model is a classification and regression tree method that grows a collection of regression trees trained on a sample of the original data

(Breiman 2001). Data not used for model construction are used to validate the model and to calculate variable importance (Cutler et al. 2007). The random forest algorithm was run with

2000 trees for the Climate Only and 1500 for the Climate+ models respectively with default

26 settings for the number of input variables at each split in the classification tree (mtry).

Predictors used for RF models were described earlier in the section on variable selection.

(d) MaxEnt

Maximum Entropy is a technique commonly used to predict potential distributions of rare species (Williams et al. 2009, Gogol-Prokurat 2011, Elith et al. 2011). The algorithm is based on predicting the best probability distribution with constraints imposed by the predictor variables and sample presence data (Philips et al. 2006). MaxEnt's merit lies in that it can successfully handle small data sets (Gogol-Prokurat 2011), incorporate categorical predictor data, and identify curves of any shape between presence and predictor data (Merow et al. 2013). For this study, samples with data (SWD) format was used for presence, background (true absences in this case) data, and test data. Model parameters were set as follows: regularization multiplier=1, maximum iterations=500, and convergence threshold=10-5 . MaxEnt predicts a probability of occurrence in each pixel from 0 to 1 and threshold rules can then be used to produce binary classification habitat suitability maps. Predictors in MaxEnt models were described in the section on variable selection.

Model evaluation

Two main evaluation criteria were used to find the model with the best predictive ability from each of the three models using calibration and test data from 4-fold cross validation:

1. AUC of the receiver-operating-characteristic (ROC) vs Null Models

2. TSS vs Null Models

The receiver-operating-characteristic (ROC) plot and associated area-under-the-curve

(AUC) metric is commonly used to evaluate predictive ability of a model. An ROC plots sensitivity (true positive fraction) by 1-specificity (false positive fraction) across the range of possible thresholds (Fielding and Bell 1997). AUC values calculated from ROC plots are threshold independent and thus often used to compare overall performance from different models. (Pearson et al. 2007). TSS accounts for omission and commission errors and is not affected by prevalence, like AUC is, and when TSS may be affected by prevalence, the reasons tend to be ecological and not statistical (Allouche et al. 2006, Jimenez-Valverde et al. 2007,

Lobo et al. 2008). For true presence/absence data, an AUC value equal to 0.5 is thought to fit at random whereas an AUC of 1.0 signifies a perfect fit (Swets 1988). Values for TSS range from -1 to +1 with values below zero being no better than random and +1 indicating a perfect fit. We used the scheme proposed by Landis and Koch (1977) where TSS<0.4 is poor, 0.4< TSS<0.75 is good and TSS > 0.75 is excellent.

AUC and TSS evaluation criteria rely on calculations from confusion matrices that may be biased by the actual geographical area of the species distribution; therefore, a perfect AUC may not be one but much less than one due to the actual area occupied by a species (Philips et al. 2006). One technique that avoids this problem is Null Model comparison where presence/absence data are randomized (Raes and ter Steege 2007). This is similar to null hypothesis testing in statistics. Here, presence/absence data were randomized for 99 null models for each model type and the 95% confidence intervals determined. Then, AUC and TSS values for the full model were separately compared to the AUC and TSS values for the 99 null models for each model type. For example, AUC values from the full Climate Only GLM model

28 were compared to AUC values from 99 randomized Climate Only GLM Null models; the rank of the non-randomized model in this comparison was assigned as the probability value (Raes and ter Steege 2007).

Habitat Suitability Maps and Shoreline Change

Because a single 10m pixel could contain a viable population, every pixel predicted to be suitable for occupancy was counted as suitable habitat. Threshold criteria were selected with the aim to provide the most accurate maps using omission and commission errors; thus, an equal sensitivity and specificity threshold was chosen. The full data set was used to produce binary habitat suitability maps for each model type. Only the best model, determined by all evaluation metrics, was selected to predict change in habitat due to shoreline change. Urban land use areas were then subtracted from this final map for further analysis of habitat loss due to shoreline change.

Shoreline erosion is spatially variable around islands; for example, the north and west shores on O'ahu and Maui are particularly affected by wave action in the winter months

(Vitousek and Fletcher 2008). There appears to be a direct influence of mean sea-level rise (SLR) on shoreline change where Maui has significantly higher SLR (2.32+0.53 mm/yr) and shoreline change (0.15 m/yr) compared to O'ahu (SLR 1.5+0.25 mm/yr, shoreline change 0.03 m/yr;

Romine et al. 2013). For the purposes of this study, we used the average island rate for each individual island to calculate loss in suitable habitat due to shoreline change because we were interested to look at habitat suitability for the entire island. A finer resolution by transect could

29 be used for portions of each island but shoreline sampling data do not cover the entire island, therefore some interpolation is necessary (Romine et al. 2013).

2.3 RESULTS

Important Predictors

In the Climate Only models, the relevant predictors differed slightly among the four main model types, with four or less predictors in each case. The most important predictor in all models was temperature in the warmest months; rainfall in the coolest months was relevant to all models except in RF. This latter predictor also led to the greatest decrease in training gain for the MX model, which means that by itself it contains the most useful information.

In the Climate+ models, the same six predictors were relevant in the RF and MX models

(Table2.1) although their importance in each model varied. The top three predictors in MX were distance to coast followed by topographic ruggedness index and standard deviation of slope whereas the top three predictors in RF were winter solar radiation, topographic ruggedness index, and standard deviation of slope. GLM and GAM models both had the same four relevant predictors (winter solar radiation, standard deviation of slope, temperature in the warmest months, and distance to coast).

Model Comparison

AUC Comparison

Overall, training AUC was much higher than test AUC as expected, except for the RF models (Figure 2.2). The highest training AUC values were reported from the GAM and RF

30 models at 0.98 (Climate Only) and 0.98/1 (Climate+). The lowest training AUC was adequate at

0.71 for the GLM Climate Only model. However, the lowest test AUC values for the GLM

Climate Only model and MX Climate+ models (0.52 and 0.54, respectively) were no better than random predictions. The inclusion of additional predictors (topographic, edaphic) led to more accurate GLM models (training/test AUC increased from 0.71 to 0.91 and 0.52 to 0.62), but resulted in worse MX models. The only models with good test AUC values were the MaxEnt

Climate Only and the RF Climate Only and Climate+ ones (AUC >= 0.7).

Climate Only and Climate+ full data models both ranked above the 95% confidence interval for random Null models. However, when compared to the Null Model AUC values (0.55-

0.56 at 95% CI), the GLM Climate Only was a poor model (test AUC = 0.52). The Climate+ models had AUC values above the 95% confidence limits for all the NULL models except for the

MX model (test AUC within the 95% CI; Supplementary material Appendix I, Fig. A2.2).

TSS Comparison

The True Skill Statistic training values ranged from 0.28 to 0.91 and test values ranged from 0.17 to 0.85. All models showed good TSS values (TSS > 0.4) except the MX Climate Only models (TSS = 0.28 and 0.17 for training and test data). The inclusion of additional predictors did improve the accuracy of GLM, MX, and RF models (TSS > 0.4; Figure 2.2). The only model that displayed a drop in TSS was the GAM model with the test TSS value decreasing from 0.74 to 0.58, still judged as a good value (TSS > 0.4). As with AUC, the highest values were for the RF models (Figure 2.2). TSS values for all randomized data models were poor (<0.4) and worse than both Climate Only and Climate+ data models (Supplementary material Appendix I, Fig. A2.2).

Habitat Suitability and Shoreline Change

Climate Only models were generally more variable and tended to over predict suitable habitat (Figure 2.3, Table2.2). Climate+ models were much more similar to one another in the suitable area predicted (Figures 1.4 and 1.5, Table2.2). When topography and physical attributes (e.g. distance to coast) are included, a common pattern is that the species is restricted to a narrow coastal strip within the 5 km buffer (Figures 1.4 and 1.5). The RF Climate+ model had the smallest suitable area and the GLM Climate Only model had the largest suitable area. These were also the most and least accurate models respectively.

Suitable habitat in urban areas was only a factor for the island of O'ahu and was subtracted prior to calculating habitat loss due to shoreline change. Based on the RF Climate+ model, loss of suitable habitat for S. globosa using average shoreline for the entire island of

Maui was predicted to be 7% for the 100 year time frame and 23% for 200 years (Figure 2.6a).

O'ahu has a lower predicted shoreline change and would lose 0.5% and 1.3% of suitable habitat over 100 and 200 year timelines respectively (Figure 2.6b). On Maui, only one sampled population would be affected whereas on O'ahu, all four populations would lose at least some individuals due to an encroaching shoreline.

2.4 DISCUSSION

One of the continuing debates in species distribution modeling is the utility of such models in specific case studies and consequently, one of our goals was to investigate which model more accurately predicts narrowly distributed species. Not all modeling algorithms are appropriate for every project. Here, we used four commonly used statistical models to predict

32 the distribution of a narrowly distributed Hawaiian endemic plant species, S. globosa. In addition to sampled regions, most of the models indicated additional suitable habitat for both

Maui and O'ahu; on Maui, habitat was confined to the northwestern, northern, and eastern coasts, while on O'ahu, suitable habitat was limited to southeastern and eastern portions of the island as well as Ka'ena Point.

Climate is likely a main driver of species distributions at broad spatial scales whereas landscape and soil become important at finer spatial scales (Gogol-Prokurat 2011, Eskildson et al. 2013). Therefore, although evaluation metrics for Climate Only models may be quite good, such models generally overpredict the species distribution and need careful interpretation to make ecologically accurate predictions (Austin and Van Niel 2011b, Wilson et al. 2013). Overall, the RF models performed the most consistently for both the Climate Only and Climate+ data with the RF Climate+ model the most accurate based on AUC and TSS metrics. Although the confidence limits for both RF models overlapped somewhat (Fig. A2.2; Supplementary Material

Appendix I), the resulting habitat suitability maps from the Climate Only models were further refined only after addition of landscape and soil variables.

Selection of model evaluation metrics is important when interpreting results because metrics have different inherent properties (Jiménez-Valverde and Lobo 2007, Liu et al. 2011,

Smith 2013). In our study, we compared values from a threshold independent metric (AUC) and a threshold dependent metric (TSS) to a set of 99 random Null models. High AUC values tended to be biased towards a species actual distribution with low representation of unsampled areas

(Allouche et al. 2006, Lobo et al. 2008, Smith 2013). Because we used true absences, this bias should be minimal; however, we further validated model results by comparing the true model

AUC's to random Null models. The resulting p-values indicate that all the models were better than random models. We observed that Climate Only GLM models had test AUC values within the upper confidence limits of Null models and these were the least accurate models. Although

Climate+ GLM models were above the 95% confidence limits, their AUC values were only slightly above that of the random models. GAM and MX models performed better than GLMs in spite of the fact that both of these models had test metrics just above random models. TSS does not suffer from the same bias as AUC but generally tends to have greater variability

(Allouche et al. 2006). In general, we found that random model TSS scores were much lower than the cross-validated and full model TSS scores. As with AUC, the worst model fit using TSS values was the GLM Climate Only model.

The habitat occupied by a species is a reflection of the combined abiotic conditons needed directly or indirectly for optimal growth and reproduction. In the case of environmental conditions affecting the distribution of S. globosa in the Hawaiian Islands, we found that climate and landscape predictors associated with the dry season were some of the most important predictors at a fine spatial resolution (10m). Climate Only models indicate that summer temperature and rainfall significantly affect the distribution of the species, i.e., the species is not found in regions where summer temperature is above 30°C. The shapes of the predictor response curves confirm some of our own observations in greenhouse trials. Plant specimens grown in a greenhouse setting died when temperature rose above 30°C; thus, it is probable that high summer temperature could lead to seasonal mortality. Solar radiation in the winter months may also indicate a thermal limit as predicted probability falls when radiation rises above 275 Kwh. The predictor, distance to coast, was used as a proxy for onshore moisture

34 coming from the ocean. The intolerance for drier conditions and reduced competition may be a potential reason why this species is limited to narrow coastal zones, which receive moisture even during dry summer months and have fewer competitors. The response curve for this predictor demonstrates a sharp decline after one km from the coast. The species also tends to occur in areas of low erodibility (due to movement by water), mainly because it typically grows on thin soil or bare rock. In the greenhouse, plants displayed better growth in well drained soil or rock substrate relative to a more developed soil medium, which is consistent with the observed occurrences in the field. We expected aspect to play a larger role influencing S. globosa distribution because of our field observations indicating occurrences on north-facing slopes; however, it was not a significant predictor in models. The scale of 10m may not be fine enough to resolve differences in aspect observed in the field and thus future work may need to use an even finer spatial resolution.

In this study, we used true presence and absence data, which lead to a more accurate representation of the realized species distribution (Soberón and Nakamura 2009). For rare and uncommon species, true absence data are critical as pseudo-absences may lead to spurious conclusions, i.e. that the species is absent in an area when in fact it simply has not been observed. It is interesting to note that all the models predicted suitable habitat in an area of

West Maui (Waihe'e) where a population once existed but disappeared within the last 15 years due to coastal erosion (this population location was not used in the modeling). Extant locations that were not used in model development were differentially predicted by each model. For example, a location on Maui near Pa'ia (Wagner et al. 2005) was predicted as suitable by all models; however, an unsampled occurrence of S. globosa near Hāna was predicted by all

35 models except the MX and GAM Climate Only models. Another known occurrence on O'ahu

(Ka'a'awa on the windward side) was predicted by all the Climate Only models but only by the

RF and MX Climate+ models. A second known occurrence of the species on O'ahu at

Kanehoelani (higher elevation in the Ko'olau Mts. of eastern O'ahu) was not predicted by any of the models, although areas close to this location were identified as suitable. This location is at the top of a peak and may be a novel habitat dissimilar to other locations; thus, we would suggest to use this location to train models in future efforts. It is encouraging that the models predicted a high probability for locations not used in the models (including an extinct population) which were at least 15 km away from the closest sampled occurrence.

Based on genetic evidence (Wallace et al. 2009), S. globosa likely evolved on one of the current older islands in the archipelago, such as O'ahu, and migrated to the other islands. In contrast to other taxa, where species may be confined to small areas (sometimes less than 4 km2; Williams et al. 2009, Sousa-Silva et al. 2014), S. globosa inhabits four main islands, although confined to a narrow coastal zone. While the species does not fully occupy the entire habitat predicted by the models, this may be due to factors that were not included in the models. For example, our field observations suggest that the species may be restricted to steep cliff faces due to competitive pressures from other species, dispersal limitations, herbivory, and/or time to occupancy, i.e., length of time in a location (historical phylogeography; Sakai et al. 2002, Soberón and Peterson 2005, Marcer et al. 2013). One proposed solution to more accurately predict the actual extent of occurrence from models is to limit the area analyzed in models to those areas historically accessible to species (Barve et al. 2011). Dispersal limitations were difficult to model because data such as wind direction were unavailable. We were unable

36 to find high resolution wind data which may be important in S. globosa distribution (as the species exhibits wind pollination). While seed and pollen may be dispersed by wind, actual wind patterns may limit the species to a zone of initial establishment. We limited the area of S. globosa to a 5 km buffer, but, this buffer may need to be further restricted to the furthest natural occurrence inland.

It is also possible that narrow range species may be constrained by edaphic characters such as pH and geologic substrate (Williams et al. 2009, Gogol-Prokurat 2011). If edaphic limitations play a prominent role, sampling absences extensively within and outside these bounds would result in 'better' models. We initially used three edaphic variables (pH, taxonomic order, and soil erodibility factor) but only one (soil erodibility) was relevant in models. Schiedea globosa prefers well-drained soils, often growing in patches of thin soil on steep rocky cliffs or directly on rocks in small soil pockets; however, we do not know if S. globosa is an edaphic specialist. Additional soil variables may have improved model accuracy but these data were unavailable in the USDA soil database. Once these databases are updated with gap-filling, additional soil information can be incorporated to build more comprehensive models.

To demonstrate the applicability of the species distribution model, the highest performing model was then applied to investigate habitat loss due to shoreline change over two time horizons for this species. We used a conservative estimate to model shoreline change and habitat loss using the best performing model from the analysis, i.e. the RF Climate+ model.

Within the 100 year timeframe, the threats to S. globosa habitat loss due to shoreline change appear minimal (7% for Maui and less than 1% for O'ahu), but habitat on Maui is predicted to

37 suffer close to a 25% loss within a 200 year timeframe. The north and west shores of the

Hawaiian Islands experience high erosion rates during winter storms, therefore, habitat loss could potentially be even greater in those areas (Romine et al. 2012). Future improvements could include data on extreme events and climate change for the relevant predictors to enhance the current models. An additional factor promoting habitat reduction on the Hawaiian

Islands (Vorsino et al. 2013, Fortini et al. 2014) is rapid urbanization; in this study, suitable habitat for the island of O'ahu was further reduced because some suitable areas were in highly urbanized locations.

Summary

Our approach highlights the need to select appropriate models and a broad choice of predictor variables. As with previous studies, we found that RF and MX models displayed the highest performance based on AUC and TSS scores with RF being the better of the two

(Williams et al. 2009), that landscape variables increased the accuracy of models (Eskildsen et al. 2013), and that GLMs have low predictive ability for rare species. Although smoothing the predictors via GAMs improved the evaluation metrics somewhat, algorithmic models achieved greater success in distinguishing habitat than either GLMs or GAMs.

Restricted range species are problematic for modeling actual population distributions with high confidence due to limited occurrences, a frequent feature of rare species (Guisan and

Thuiller 2005, Pearson et al. 2007). Species with a narrow geographical range such as S. globosa test the limits of SDMs that mostly predict species ranges (Williams et al. 2009). Because S. globosa occurs on multiple Hawaiian Islands with sufficient environmental variation, and we

38 validated models with independent data on where the species currently occurs or previously existed, we have some confidence in the robustness of the models developed. In addition, we also used true absence data in contrast to earlier studies (Araujo et al. 2000, William et al.

2009, Vorsino et al. 2013). Nevertheless, we recognize that the models developed here could be improved with more extensive sampling of true absences within the 5 km buffer and/or limiting the area of analysis to less than 5 km from the coast.

Results from SDMs may be a good approximation of a species' realized distribution.

However, it is possible that models may not capture all the details of where the species can persist, but may be useful as a guide to protect prime habitat from current and potential future threats. Suitability scores from models may be useful to set conservation priorities with existing occurrences holding the highest priority. We specifically used two data sets, climate and climate as well as landscape predictors to determine differences in model predictions; while finding the minimum predicted area is often used as a criteria for evaluating models, another factor that can be considered is the applicability of the model. If the goal is to preserve large conservation areas, then climate predictors may prove more useful than landscape predictors; nevertheless, this decision should be based on individual species properties and model accuracy. In this study,

Climate Only and Climate+ models had similar evaluation metrics, but the predictive ability of

Climate Only models was low as these models overpredicted suitable areas. For rare or uncommon species, landscape factors are especially necessary to improve the predictive ability of models as they further refine the suitable habitat where a species can occur within a climatically suitable area. Furthermore, species are often not at equilibrium with abiotic conditions and could have wider tolerances (range underfilling), thus accounting for broader

39 climatic tolerances in models will be useful to predict how species will respond to climate change (Feeley 2015).

This study provides an estimation of the threat posed by shoreline change that may be useful information for conservation managers. However, the predicted habitat loss may be underestimated, given that the species occupies only a fraction of the predicted suitable habitat and extreme weather events were not incorporated here. Hawaiian plant species are particularly vulnerable to projected climate change effects in the next century because of the strong link between small range sizes and vulnerability (Caujapé-Castells et al. 2010, Warren et al. 2013, Fortini et al. 2013). The main abiotic factors that appear to affect the habitat distribution of S. globosa are those associated with moisture availability, particularly in the dry season; furthermore, predicted habitat moisture zones ranged from seasonally mesic to very dry and could therefore be highly susceptible to climate anomalies (Price et al. 2012). Modeling species distributions for island endemics is thus an important first step to identify the most influential set of abiotic variables and to conserve such habitats for possible re-introductions amidst rapid urbanization.

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Chapter 2 – FIGURES and TABLES

TABLE 2.1. All predictors considered a priori for SDM variable selection are listed. Climate Only models included climate data only and all data categories were included for Climate+ models. Data used in different models are indicated by symbols as follows: Maxent (MX), RF, GLM, and GAM. Temperature data were obtained from the PRISM datasetA, rainfall from the Hawaii Rainfall AtlasB, elevation data from the USGS Digital Elevation datasetC, and soil data from USDA GSURGOD. Distance to coast was calculated in ArcGIS v.10.2 as a physical distance variable.

Data Type Predictor Scale Climate Only Climate+ Climate Annual average rainfall 250m Summer average rainfall 250m Winter average rainfall 250m Summer total rainfall 250m Winter total rainfall 250m Rainfall in wettest 2 months 250m Rainfall in driest 2 months 250m MX, GLM, GAM Rainfall in warmest 2 months 250m MX, RF, GLM, GAM Rainfall in coldest 2 months 250m MX, GLM, GAM Annual maximum temperature 400m RF Annual minimum temperature 400m Winter minimum average temperature 400m Summer maximum average temperature 400m Maximum Temperature of warmest 2 months 400m MX, RF, GLM, GAM MX ,RF Minimum Temperature of coldest 2 months 400m Minimum temperature in wettest 2 months 400m Maximum temperature in driest 2 months 400m RF Topographic Elevation 10m Slope 10m Aspect (4 directions) 10m Aspect (8 directions) 10m Annual solar radiation 10m Solar radiation in winter growing season 10m MX, RF, GLM, GAM Solar radiation around summer solstice 10m Relative topographic position (TPI) 10m Terrain Ruggedness Index (TRI) 10m MX, RF Standard deviation of slope (3x3) 10m MX, RF, GLM, GAM Topographic Wetness Index (TWI) 10m Physical Distance to coast 10m MX, RF, GLM, GAM Soil Soil pH 30m Soil type 30m Soil erodibility factor 30m MX, RF, GLM, GAM A http://www.prism.oregonstate.edu B http://rainfall.geography.hawaii.edu C http://pubs.usgs.gov D hhttp://soils.usda.gov

Table 2.2. Comparison of suitable habitat pixels for Schiedea globosa in the Hawaiian Islands as predicted from each model. Each pixel is 10x10 meters in area.

Climate Only Suitable Habitat Pixels Climate+ Suitable Habitat pixels

Maui O’ahu Maui O’ahu GLM 6, 472,119 6, 445, 111 43, 007 28, 562 GAM 1, 288, 670 2, 190, 952 35, 997 21, 496 MaxEnt 3, 218, 415 386, 939 29, 047 5, 898 Random Forests 500, 369 300, 543 20, 651 9, 360

(a) Sampling on Maui

(b) Sampling on Oahu

Figure 2.1. Study Sites on Maui and Oahu displaying the distribution of sampling locations for Schiedea globosa, indicating known points of species presence (green circles) and absence (red triangles). Model predictions were limited to the shaded 5 km zone because S. globosa is not known to naturally occur further inland.

0.8

0.6 Climate Only Training

0.4 Climate Only Test Climate+ Training 0.2

Climate+ Test AUC AUC discriminant values 0 GLM GAM MX RF Model Type

(a) AUC metrics

0.8

0.6 Climate Only Training

0.4 Climate Only Test Climate+ Training 0.2

TSS TSS discriminant values Climate+ Test 0 GLM GAM MX RF Model Type

(b) TSS metrics Figure 2.2. Evaluating the predictive accuracy of four different models for Hawaiian populations of Schiedea globosa using AUC and TSS metrics with 4-fold partitioning. Metrics are shown for the best models from the 4-fold cross-validation. Predictors used in each model are displayed in Table 1. Training values are from model calibration and test values from the withholding data used in model evaluation. Model success for each metric is as follows: for AUC, poor (AUC<0.7), good (0.70.9), for TSS, poor (TSS<0.4), good (0.40.75).

GLM GAM

MX RF (a) Predicted habitat suitability for S. globosa on Maui.

GLM GAM

MX RF (b) Predicted habitat suitability for S. globosa on Oahu.

Figure 2.3. Predicted habitat suitability maps for Schiedea globosa for Climate Only models. Green areas indicate suitable habitat and beige areas unsiutable habitat (suitability was determined on a greater than 70% probability using model thresholds) for GLM, GAM, RF (Random Forest), and MX (MaxEnt) models.

Area of detail

GLM

GAM

Figure 2.4. Predicted habitat suitability maps for Schiedea globosa on Maui for Climate+ models. Green areas indicate suitable habitat and beige areas unsuitable habitat (suitability was determined on a greater than 70% probability using model thresholds) for GAM, RF (Random Forest), and MX (MaxEnt) models. Maps on the left show predictions for entire islands and to the right is shown an enlarged high resolution local area.

Area of detail

GLM

GAM

RF Figure 2.5. Predicted habitat suitability maps for Schiedea globosa on Oahu for Climate+ models. Green areas indicate suitable habitat and beige areas unsuitable habitat (suitability was determined on a greater than 70% probability using model thresholds) for GAM, RF (Random Forest), and MX (MaxEnt) models. Maps on the left show predictions for entire islands and to the right is shown an enlarged high resolution local area. 53

(a) Section of west Maui coast (b) Section of southeast Oahu coast

Figure 2.6. Examples of shoreline change and habitat loss at a section of (a) west Maui and (b) southeast Oahu predicted by the RF Climate+ model. The seaward strip in light blue is lost by 100 years while the landward strip in darker blue is lost in addition at a 200 year time frame. Presences of Schiedea globosa individuals are indicated by blue crosses and absences are indicated by red asterisks.

(a) soil erodibility factor (b) distance to coast

Figure 2.7. Sample response curves for predictors in the Maxent (MX) model for the distribution of Schiedea globosa in the Hawaiian Islands.

Supplementary Material APPENDIX I

(a) Climate Only models (b) Climate+ models

Figure A2.1. Variable selection using Random Forests for modeling the distribution of Schiedea globosa in the Hawaiian Islands. The most parsimonious Climate Only model with the lowest Out-of-Bag error had four climate variables: rainfall in warmest months, average annual maximum temperature, temperature in driest months and temperature in warmest months. The initial Climate+ model had 10 predictors and the most parsimonious Climate+ model had six predictors; solar radiation in winter, standard deviation of slope, temperature in warmest months, soil erodibility factor, and distance to coast.

(a) Climate Only GLM (b) Climate+ GLM

(a) Climate Only GAM (b) Climate+ GAM

(a) Climate Only MX (b) Climate+ MX

(a) Climate Only RF (b) Climate+ RF

Figure A2.2. Null Model ROC plots from Climate Only and Climate+ models for Schiedea globosa. Red lines are plots of the 99 randomized null models whereas the black line represents the full data model. Comparison of the full model to the random models resulted in p-values less than 0.05 in all model types and data sets. Although the Climate+ random RF model had high AUC values, the true model AUC was nevertheless ranked higher than the 99 random models.

Table A2.1 - List of S. globosa occurrences used to develop models.

GPS_point Easting Northing Island GPS88 746543 2326485 Maui GPS110 746613 2326463 Maui GPS112 746606 2326464 Maui GPS115 746593 2326465 Maui GPS121 746586 2326460 Maui GPS124 746584 2326450 Maui GPS128 746577 2326472 Maui GPS131 746565 2326471 Maui GPS135 746561 2326465 Maui GPS136 746564 2326466 Maui GPS141 755064 2323977 Maui GPS142 755048 2323998 Maui GPS144 755042 2323996 Maui GPS147 755023 2323985 Maui GPS149 754987 2323957 Maui GPS152 754945 2323959 Maui GPS154 754936 2323961 Maui GPS156 754948 2323951 Maui GPS161 755038 2323966 Maui GPS419 746579 2326457 Maui GPS420 746575 2326450 Maui GPS424 748600 2326728 Maui GPS426 748600 2326731 Maui GPS429 748591 2326733 Maui GPS431 748584 2326729 Maui GPS436 748593 2326725 Maui GPS440 755066 2323974 Maui GPS441 755048 2323987 Maui GPS442 755041 2323988 Maui GPS447 755005 2323972 Maui GPS448 754988 2323953 Maui GPS449 754956 2323950 Maui GPS456 754938 2323966 Maui GPS460 746580 2326471 Maui GPS466 746543 2326487 Maui GPS467 746524 2326493 Maui GPS471 746486 2326489 Maui GPS472 746555 2326484 Maui GPS474 746565 2326471 Maui GPS475 746558 2326472 Maui GPS482 746538 2326450 Maui GPS483 746542 2326443 Maui GPS484 746549 2326445 Maui GPS485 746537 2326451 Maui GPS486 746530 2326462 Maui GPS488 746530 2326454 Maui GPS489 746529 2326441 Maui 58

GPS_point Easting Northing Island GPS491 755034 2323967 Maui GPS492 755031 2323957 Maui GPS495 755037 2323980 Maui GPS500 755031 2323990 Maui GPS501 755023 2323994 Maui GPS502 755012 2323990 Maui GPS503 755005 2323982 Maui GPS504 754944 2323966 Maui GPS510 746619 2326474 Maui GPS515 746612 2326471 Maui GPS520 746562 2326483 Maui GPS521 746556 2326486 Maui GPS523 746628 2326451 Maui GPS524 746622 2326448 Maui GPS525 746591 2326445 Maui GPS526 746593 2326448 Maui GPS527 746578 2326440 Maui GPS528 746581 2326441 Maui USR529 746615 2326452 Maui USR530 746607 2326447 Maui GPS196 639367 2356990 Oahu GPS198 639390 2356990 Oahu GPS200 639422 2356994 Oahu GPS201 639422 2356988 Oahu GPS204 639441 2356984 Oahu GPS219 635200 2352374 Oahu GPS220 635203 2352368 Oahu GPS221 635195 2352370 Oahu GPS222 635191 2352370 Oahu GPS223 635193 2352371 Oahu GPS237 635452 2352053 Oahu GPS238 635446 2352069 Oahu GPS239 635452 2352069 Oahu GPS240 635472 2352057 Oahu GPS241 635482 2352055 Oahu GPS242 635463 2352045 Oahu GPS248 635518 2352036 Oahu GPS252 635577 2352050 Oahu GPS253 635585 2352047 Oahu GPS255 635517 2352024 Oahu GPS306 635577 2352045 Oahu GPS308 635575 2352056 Oahu GPS309 635561 2352045 Oahu GPS310 635526 2352034 Oahu GPS316 635497 2352043 Oahu GPS317 635510 2352040 Oahu GPS318 635510 2352038 Oahu GPS321 635476 2352042 Oahu GPS324 639369 2356989 Oahu

GPS_point Easting Northing Island GPS325 639389 2356985 Oahu GPS329 639426 2356992 Oahu GPS330 639427 2356986 Oahu GPS336 639441 2356991 Oahu GPS337 639444 2356997 Oahu GPS340 639446 2356983 Oahu GPS341 639466 2357001 Oahu GPS343 639479 2357007 Oahu GPS345 639505 2357021 Oahu GPS347 639524 2357024 Oahu GPS351 635447 2352067 Oahu GPS355 635460 2352060 Oahu GPS356 635467 2352061 Oahu GPS358 635474 2352057 Oahu GPS359 635470 2352059 Oahu GPS360 635453 2352053 Oahu GPS361 635461 2352044 Oahu GPS362 635465 2352032 Oahu GPS366 635487 2352045 Oahu GPS368 635474 2352032 Oahu GPS370 635492 2352039 Oahu GPS372 638985 2357022 Oahu GPS373 638965 2357021 Oahu GPS375 638973 2357017 Oahu GPS376 638979 2357016 Oahu GPS379 638987 2357015 Oahu GPS380 638973 2357008 Oahu GPS381 638989 2357004 Oahu GPS386 638985 2356998 Oahu GPS387 638988 2356998 Oahu GPS390 638979 2357002 Oahu GPS391 638989 2357004 Oahu GPS397 638996 2357013 Oahu GPS398 639528 2357025 Oahu

CHAPTER 3

The predicted effects of shoreline change on the spatial genetic diversity of the Hawaiian subshrub Schiedea globosa

Sunita Yadav

Department of Biological Sciences,

University of Cincinnati,

614 Rieveschl Hall, Cincinnati, OH 45221, USA

ABSTRACT

Dynamic environments affect species distributions and genetic variation in both space and time. Islands are particularly vulnerable to such change and predictions for the Hawaiian Islands indicate shifting rainfall patterns and rising sea levels. Because islands are hotspots of endemism, the potential impacts of habitat change on biodiversity could be substantial. We investigated the intraspecific genetic diversity and fine-scale spatial genetic structure for an endemic plant species, Schiedea globosa (Caryophyllaceae) on the Hawaiian islands of Maui and

O’ahu. Additionally, we predicted changes in genetic diversity due to estimated shoreline change, mostly attributable to sea-level rise. We used microsatellite data from 11 loci for specimens collected from seven populations on two islands. Genetic diversity was relatively high as indicated by observed heterozygosity and number of alleles per locus (0.256 and 5.6 respectively). Strong spatial structure was detected between islands and within populations with a larger geographical area and demographic size. Estimated average shoreline change in the next 100 years was predicted to affect two of the seven populations with the loss of one unique allele, assuming that current genotypes remain in the same general location. Extreme storm events, not analyzed in this study, could affect the coastal habitat of S. globosa to a greater degree, potentially leading to a higher loss of genetic diversity. The combination of genetic and spatial analytical tools utilized here provides useful predictions to forecast biodiversity consequences under varying habitat and climate scenarios.

3.1 INTRODUCTION Climate oscillations often play a large role in determining biogeographic distributions of species. Temperature changes may push the thermal tolerance limits of a species while precipitation can affect interspecies interactions by shifting community composition (Iverson &

Prasad 1998). In addition, isolated species with narrow distributions are especially vulnerable to reduced habitat due to climatic fluctuations if species are unable to adapt or shift their ranges

(Thomas 2011; Fordham et al. 2012; Travis et al. 2013). Coastal species are particularly at risk because of synergistic effects of sea-level rise, storm surges and shoreline erosion (Romine et al. 2013; Courchamp et al. 2014) and because their narrow habitat zone could potentially be constricted between the ocean and adjacent human settlements or topographic features. While much research has been devoted to analyzing the risks from climate change on species abundance and distributions (Van der Putten et al. 2010; Austin et al. 2011; Bellard et al. 2012), relatively less attention has been paid to assessing the consequences of climate change on population spatial genetic structure (Pauls et al. 2013; Vinceti et al. 2013). Fluctuations in the environmental space of species may affect gene flow, which in turn can lead to changes in the spatial genetic structure of populations. Studies suggest that even for species that are able to migrate, most of the genetic variation remains in source locations (Hewitt 2004; Pauls et al.

2013). Therefore, climate change can potentially lead to reduced connectivity among populations, which is in turn often associated with reduced genetic diversity and a lower ability to respond to a changing climate (Vinceti et al. 2013). Therefore, the challenge for such species will be whether they can keep up with the rate of change and adapt to new environments, given a reduction in genetic variation ultimately caused by climate change. Knowledge of

63 current population genetic structure and vulnerability to climate change threat is crucial to creating effective management goals, especially for rare, narrowly endemic plant species that are most susceptible to habitat loss and alteration. Here, we assess spatial genetic structure at broad and fine scales, and predict changes to such genetic diversity based on an environmental factor shown previously to be important in the distribution of the species.

In the Hawaiian Islands, global climate change is predicted to have significant impacts including rising temperatures, modified precipitation patterns, sea level rise, and increases in extreme weather events (Keener et al. 2012; IPCC 2013). Regional scale models across islands predict a decrease in precipitation of 5-10% in the wet winter season and an increase in precipitation of 5% in the dry season with longer drought periods (Timm & Diaz 2009) because of decreased trade winds. This is consistent with the annual historical precipitation trend decreasing by 15% over a 15 year period from 1995-2010 (Chu & Chen 2005; Diaz et al. 2005;

Kruk et al. 2015). The persistence of the trade winds in an east-to-west direction is a dominant feature on the archipelago and thus any change to the pattern or intensity would lead to a significant impact on local climate. Furthermore, sea-level has risen 1.5-3.3 cm per decade in the past 100 years but the rate is expected to accelerate to a 0.3-1.0 m rise by 2100 (Eversole &

Andrews 2014); this will likely lead to saltwater intrusion, potentially damaging large areas of coastal habitats and threatening species in these areas. Moreover, actual biodiversity losses in island systems are predicted to be higher because of greater rates of endemism (Caujapé-

Castells et al. 2010; Harter et al. 2015).

Many endemics in the Hawaiian Islands have restricted distributions with small population sizes (Wagner & Funk 1995). But even species with large population sizes could be

64 at risk if their distribution is highly localized and prone to significant threats (Primack 2010). A species exhibiting these characteristics is Schiedea globosa H. Mann (Caryophyllaceae), a coastal Hawaiian endemic subshrub occurring on four main islands (O'ahu, Maui, Moloka'i, and

Hawai'i) and a few rocky islets. Although S. globosa belongs to a genus in which most species are single island endemics and highly threatened, S. globosa in contrast, has several populations on multiple islands (Sakai et al. 2006). The species exhibits subdioecy (the presence of males, females, and individuals with a few hermaphroditic flowers on staminate inflorescences), and is wind-pollinated (Sakai & Weller 1991; Weller et al. 1998; Wagner et al.

2005) with low inter-island dispersal rates (Wallace et al. 2009). There is also a high level of genetic variation in the species that differs among islands (Wallace et al. 2009; Dixon et al.

2011). However, gene flow could be limited as the species occupies only a small proportion of suitable habitat with large intervening unsuitable areas (Chapter 2). Furthermore, local adaptation of sexes within a location could contribute to increased genetic variability within populations if it increases outcrossing (Kawecki & Ebert 2004), whereas drift in small isolated populations may lead to reduced genetic diversity and greater differentiation among populations (Stuessy et al. 2014).

Molecular markers such as simple sequence repeats (microsatellites) can be used to determine overall genetic diversity within a species, and such data could then be used to make predictions under various climate change scenarios. Recent landscape genetic studies in a variety of species have connected dispersal ability to environmental barriers and climate,

(Muñoz-Fuentes et al. 2009; Jaquiéry et al. 2011; Sexton et al. 2014) which impact intra-specific variation at broad and fine scales. Dispersal affects gene flow and it may be substantially limited

65 in restricted geographic settings such as mountains or coastal areas, locations also predicted to be highly impacted by climate change. Response to climate can occur not only at the population scale but also within populations; for example, Triticum dicoccoides and Hordeum spontaneum displayed fine scale genetic differences in response to temperature and water availability

(Owuor et al. 1997; Li et al. 1999; Huang et al. 2002). Furthermore, populations of H. spontaneum exhibited higher genetic diversity in shaded areas with soil substrate versus sun- exposed areas with rock substrate (Huang et al. 2002), with unique alleles confined to a particular soil type (Owour et al. 1997). In other studies, spatial genetic structure of alpine grass populations displayed an association with altitude (Byars et al. 2009), and spatial genetic structure was also correlated to an ecological soil substrate variable (Alvarez et al. 2009). Such differences could be amplified in dioecious species where there could be fine scale local adaptation if the different sexes are adapted to separate microenvironments (Mooney et al.

2010). Similarly, in the case of outcrossed species, genetic variation could be further amplified within populations and reduced among populations (Charlesworth 2003; Mable & Adam 2007).

In addition, reduced genetic diversity due to drift, associated with climate drivers, will likely increase a species susceptibility to pest and disease (Jump & Peñuelas 2005; Garrett et al.

2006).

Landscape genetic methods are valuable tools to assess the effects of habitat and geographical features on patterns of genetic variation due to gene flow (Holderegger et al.

2010, Storfer et al. 2010). As S. globosa occupies a narrow coastal zone on multiple Hawaiian

Islands where populations are isolated by topographical features, we should be able to detect genetic patterns affected by the landscape. In this study, we examine the fine scale population

66 genetic diversity of S. globosa on the Hawaiian Islands of Maui and O'ahu using spatial autocorrelation analyses. We subsequently predict the differences in genetic diversity and structure, assuming alleles remain in current locations, from the present day to 100 years into the future due to projected mean shoreline change.

3.2 METHODS

Study species and sampling

Schiedea globosa (Caryophyllaceae: Alsinoideae), is a perennial woody subshrub endemic to the Hawaiian archipelago. The species occurs in rocky coastal scrub habitat on four of the main Hawaiian Islands in addition to a few rocky offshore islets. Temperature ranges within S. globosa habitat vary from a winter minimum of 18°C to a summer maximum of 30°C.

For these locations, the majority of rainfall occurs during winter months, from December to

April, with a significant dry season from June-August. Natural populations of S. globosa are primarily restricted to a narrow coastal zone, within 200 meters of the shoreline, on cliff faces near the ocean and subject to ocean spray. The flowering season is dependent upon winter rain and varies among populations, generally occurring between December to April (Sakai & Weller

1991). Pollen dispersal is mainly through wind (Weller et al. 1998) whereas seed dispersal is largely attributed to gravity with occasional long distance seed dispersal. Rafting of whole plants (and/or seeds) between islands has also been documented (Wagner et al. 2005).

Individuals may live up to five years with little to no seed dormancy (Wagner et al. 2005).

Seven populations on Maui and O'ahu were sampled in 2012 and 2013 during the winter flowering season, with sample sizes ranging from 6-86 individuals per population and totaling

248 individuals (Figure 3.1). We were unable to collect sufficient samples for three of the populations due to small population size and inaccessibility of the terrain; we recognize that sample size may influence measures of genetic diversity (Pruett & Winker 2008; Landguth et al.

2012). Sampling was maximized to cover the extent of the local species distribution, with transects placed across the width of each population and random walks on either side of the main transect. For larger populations, an additional 1-2 transects were sampled along the elevation gradient from shore to along the cliff face. Plants were sampled at least 5m apart. We collected fresh leaf tissue for each plant and recorded sex (when possible) and GPS coordinates with a hand held device (Garmin CSX 60TM) at a spatial accuracy of 5m or less. Leaf tissue samples were stored in glassine envelopes with desiccant and stored at 0°C until DNA could be extracted.

Genetic markers

DNA was extracted with the stored leaf tissue using a modified CTAB protocol (Doyle &

Doyle 1987). Eleven polymorphic nuclear microsatellite (nSSR) loci were amplified for each sample using primers designed for related S. adamantis (Culley et al. 2008) using the multiplexing technique from Culley et al. (2013). Two primer mixes were used in multiplexed reactions, one containing 8 loci and the other 3 loci (SA04, SA05, SA06, SA15, SA16, SA30, SA31,

SA32, SA35, SA36, SA38). PCR was conducted in a total reaction volume of 10 µl with 0.2 µl DNA template, 3.8 µl dH2O, 5 µl QIAGEN Multiplex Master Mix (Qiagen Inc., Valencia, CA), and 1 µl nucleotide primer mix. The PCR reaction was run on an Eppendorf MasterCycler for 15 min at

95°C, followed by 30 cycles each of 30s at 94°C, 45s at 57°C, and 45s at 72°C, subsequently

68 followed by eight cycles each of 30s at 94°C, 45s at 53°C, and 45s at 72°C, then with a final elongation of 10 minutes at 72°C. After PCR was performed, a LIZ-500TM internal size standard was added to each sample before all PCR products were processed at the Cornell University

BioResource Center using a 3730xlsequencer (Applied Biosystems, Fortune City, CA). The software program GeneMarker v. 1.85 (SoftGenetics, State College, PA) was used for scoring loci.

Genetic diversity and structure

Microsatellite data were analyzed in GenAlEx vers. 6.5 (Peakall and Smouse 2006, 2012) using the following descriptive statistics metrics: the number of alleles per locus (A), the mean number of alleles per polymorphic loci (Ap), observed heterozygosity (Ho), expected heterozygosity (He), and the percentage of polymorphic loci (Pp). Variance partitioning of microsatellite data was calculated with Weir and Cockerham’s (1984) statistics, f and θ, within and among populations. The inbreeding coefficient (f) and θ are estimators for Wright's (1951)

Fis and Fst with the additional benefit that they take into account small and unequal sample sizes; f measures the degree of relatedness among genes within individuals in populations whereas θ is a measure of variance among sub-populations relative to the total variation. In addition to Fst, deviations from Hardy-Weinberg equilibrium, and linkage disequilibrium were also calculated in GDA 1.1 (Lewis & Zaykin 2001).

Clustering of individuals was visualized by means of a principal coordinate analysis

(PCoA) using Nei's standard genetic distance matrix in GenAlEx. Additional variance partitioning was quantified with a hierarchical analysis of molecular variance test (AMOVA) among

69 individuals between islands, among individuals across all populations, and within individuals within populations. The overall migration rate (Nm) was calculated as [(1/Fst)-1]/4 (Peakall &

Smouse 2006, 2012).

Spatial genetic structure was measured at the scale of all populations (regardless of location), island, and individual population with an autocorrelation approach for codominant markers in GenAlEx. A correlation coefficient (r) was calculated via pairwise genetic and geographic matrices for evenly spaced distance classes at 20m intervals up to the extent of each population. This metric is a measure of the pairwise genetic similarity between individuals when their geographic separation occurs in a particular distance class. Geographic distances were calculated from field collected GPS coordinates and Nei's standard genetic distance was calculated in GenAlEx. Additionally, the package, adegenet, in the R programming environment v.3.03 (Jombart 2008; R Core Team 2013) was used to calculate isolation by distance (IBD) and spatial genetic boundaries within and among populations using Monmonier's algorithm.

Isolation by distance (IBD) across all levels was tested using Monte Carlo simulation with

Mantel tests using 999 random permutations. To further analyze spatial genetic structure, a spatial Principal Component Analysis (sPCA) was applied in the adegenet package to determine the alleles and markers contributing to spatial structure. Differences in the spatial distribution of alleles within a population were analyzed within ArcGIS v 10.2 using Ripley’s K-function for cluster analysis (Boots & Getis 1988).

Barriers to gene flow - resistance surfaces

More recently, cost resistance distances have been shown to more effectively account for the variation in movement within a heterogeneous landscape than Euclidean distances

(Cushman et al. 2006; Storfer et al. 2010) because they may better describe landscape barriers to gene flow. For example, a topographic feature such as a mountain could act as a barrier to pollen movement across a landscape. Landscape and climate data were used to develop species distribution models for S. globosa (see Chapter 1) and the most accurate model was utilized here as a resistance surface. Therefore, the most suitable habitat would offer the least resistance whereas the least suitable habitat offers the greatest barrier to gene flow. Relative costs were assigned for the probability of suitable habitat at five levels based on suitability scores from SDMs (described in Chapter 2) in ArcGIS 10.2 (ESRI Inc.); these cost rasters were then used to calculate geographical distance matrices. The matrices were subsequently compared to the genetic distance matrices in the vegan package implemented in R vers. 3.03 (R

Core Team 2013; Oksanen et al. 2015). The important predictors in the SDM were: topographic ruggedness index (TRI), standard deviation of slope, distance to coast, maximum temperature of warmest two months, soil erodibility factor, and solar radiation in the winter (see Chapter 1 for model development). Resistance surfaces were used for fine scale analysis for the largest populations on each island if they displayed spatial structuring.

Parentage analysis

Parentage testing was conducted in Cervus 3.0.7 (Kalinowski et al. 2007) using likelihood tests for co-dominant markers, based on the microsatellite data. Simulation of parent pairs was

71 carried out for known and unknown candidate parent sex with observed allelic frequencies.

Cervus determines parentage by calculating the difference in logarithm of likelihood ratios between the top two most likely parents and then determines confidence levels with simulation. We used a relaxed confidence level of 80% to maximize the detection of parentage.

Input parameters for the simulation analysis included a minimum of five loci for typing, all individuals sampled as candidate mothers and fathers, and 10,000 cycles.

Predicted genetic impacts due to shoreline change

To calculate population loss due to shoreline change, we used an average rate of shoreline change calculated by Romine and Fletcher (2012) for each island. It was not possible to use an inundation approach for sea-level rise because we would need to use sub-meter accuracy data which was not feasible for this study. Therefore, we chose to use the shoreline dataset that is correlated with mean sea-level rise in the Hawaiian Islands (Romine et al. 2013).

For this study, a rate of 0.15 m/yr was used for Maui while the rate for O'ahu was 0.03 m/yr

(Romine & Fletcher 2012). The main driver responsible for the difference in shoreline erosion rates between the two islands is the larger rate of sea-level rise on Maui because of wave exposure and local erosional dynamics (65% greater on Maui than Oahu; Romine et al. 2013). In addition, sea-surface temperature, local currents, and island subsidence due to volcanic activity on the island of Hawai’i have a larger influence on Maui than on O’ahu. Because we had precise

GPS coordinates for every sampled plant, the individuals lost due to predicted shoreline change over a 100 year time span were removed from the dataset and the genetic data were reanalyzed as described previously. Subsequently, current genetic diversity and structure was

72 compared to the predicted future genetic composition to assess changes within and among populations with no new recruitment, assuming that genotypes stay in the same location and do not change over time. We realize this is a relatively simplistic approach as it does not account for generation time and drift, but this is a first step to understand how the environment affects genetic diversity.

3.3 RESULTS

Genetic diversity and structure

A total of 248 individuals were genotyped in seven populations for eleven microsatellite loci (Table 3.1). One locus (SA35) was not used in the analysis because of limited success in only 20% of samples. The average number of alleles across loci varied among populations

(NA=1.9-4.0), with population 844 on O’ahu exhibiting the highest value and the greatest number of private alleles (NA=4.0 and NPA=13 respectively). As previously reported for S. globosa (Weller et al. 1996; Wallace et al. 2009; Dixon et al. 2011), most loci exhibited a high degree of polymorphism (percentage polymorphic loci at 80%) with the mean observed heterozygosity at 0.256 (range from 0.222 to 0.317). Expected heterozygosity ranged from

0.272 to 0.444 (mean at 0.341). The number of alleles per locus across populations varied from

3 (locus SA30) to 8 (locus SA16) with a mean of 5.6 alleles per locus. The average number of effective alleles was generally lower than the average number of alleles per locus suggesting that many alleles occur in low frequencies. Significant deviation from Hardy-Weinberg equilibrium was detected at some loci in all, but the smallest population (845 on O’ahu), with the largest population on O’ahu (844) displaying deviation in six out of ten loci. The remaining

73 populations had an average of three loci out of Hardy-Weinberg equilibrium. These results are not unexpected given that S. globosa populations are relatively small and the species occupies marginal coastal habitat. Linkage disequilibrium was observed in four loci pairs in one to three populations.

Average Fst calculated per locus across populations was significantly different from zero with an overall mean of 0.338 (ranging from 0.046 to 0.812, SE=0.076), although most of the variation was found within populations. The inbreeding coefficient, Fis, varied with a mean of

0.287 (range from -0.053 to 1.0, SE=0.113). Comparison of pairwise Fst values revealed that the greatest differentiation occurred between populations 906 on O’ahu and 951 on Maui

(Fst=0.381) with the smallest differentiation between 844 and 845 on O’ahu (Fst=0.035;

Appendix II, Table A3.1). Migration rates between populations on separate islands were all less than one migrant per generation (Nm ranged from 0.406 to 0.781, SE=0.483) whereas Nm within island populations ranged from two to six migrants per generation.

The principal coordinate analysis (PCoA) indicated some clustering of Maui and O’ahu populations with a few exceptions (Figure 3.2). The first three axes explained 26.5, 16.25, and

7.6% of the genetic variation among populations with a combined total of 50.35%. The greatest amount of differentiation is displayed between islands and much less so among populations. An hierarchical analysis of molecular variance (AMOVA) corroborates this evidence in which 29% variation was partitioned between islands and only 5% was found among populations, with the remainder occurring within (30%) and among individuals (36%; Table 3.2).

Genetic connectivity and spatial genetic structure

Spatial analyses across the species range did not show isolation by distance (IBD) by population (r=0.133, p=0.175) but they did indicate strong IBD at the individual level across all populations (r=0.64, p=0.001). Furthermore, analysis within each island displayed greater IBD at the individual level but not at the population level. In addition, only the island of O’ahu showed significant correlation of genetic and geographic distance among populations (r=0.174, p=0.001); populations on Maui did not appear to be differentiated by distance (r=0.034, p=0.127). On the other hand, at a fine-scale within populations, the two largest populations exhibited significant IBD (p values for correlograms < 0.01; Figure 3.3) at varying spatial scales.

On Maui, population 951 displayed significant IBD up to 80m for 20m, 30m, and 40m evenly spaced distance categories, whereas population 906 on O’ahu displayed significant IBD up to

30m for the 10m distance category (example correlograms in Figures 3.3a, 3.3b; Table 3.3). The remaining populations had significant spatial autocorrelation (therefore, lack of IBD) up to

120m (populations 852 and 844), up to 30m (populations 313 and 845), and up to 60m

(population md; Figures 3.3c and 3.3d; Table 3.3). In the case of populations 313 and 845, their range size was less than 30m so the spatial autocorrelation was limited to this length. It appears that spatial autocorrelation occurs at the geographic extent of most populations except the two largest populations on each island (Table 3.3).

Patterns of spatial autocorrelation varied by marker based on Ripley’s K function, some markers displayed maximal autocorrelation at 15 m whereas other markers displayed it at greater distances up to 60 m (Figures 3.4 and 3.5). The markers contributing the greatest amounts to spatial structure within the two largest populations, determined by sPCA, were

SA15 (allele 181) in population 951 and marker SA06 (allele 193) in population 906. When examining all individuals across populations, the alleles with the largest influence were allele

181 and 187 (both for locus SA15). We did not observe significant spatial autocorrelation of sex within populations.

Barriers to gene flow - resistance surfaces

Isolation by resistance surface was analyzed within a population for the largest populations on each island based upon habitat suitability. Isolation by resistance distance was significant for both populations (r=0.52, p=0.001 for 951 on Maui and r=0.24, p=0.01 for 906 on

O’ahu).

Parentage analysis

Simulation of parentage analyses in Cervus with the candidate parent of known sex was low at

1% at the relaxed level (80% confidence). With the candidate parent pair set at an unknown sex, assignment remained low at 2% (80% confidence). This analysis may have resulted in low parent assignment because our sampling efforts were focused on flowering individuals that may be part of an adult cohort, pollen travels farther than we expected, or because more loci are needed. A sampling design that incorporates age-structure within populations and a greater sample size are likely necessary to capture all potential parents in the sampling pool adequately.

Predicted genetic impacts due to shoreline change

Using average shoreline change values for each island and assuming the same genetypes, only two populations were affected: 951 on Maui (n=86 total sample size) and 845 on O’ahu (n=6 total). Within each of these two populations, roughly half of all individuals were lost (Figure 3.6). When these individuals were removed from the genetic data set, there was little change in the number of alleles across loci for each population (Appendix II, Figure A3.2) and only slight changes in allele frequencies per locus (Table 3.4; Appendix II, Figure A3.3).

However, one unique allele was lost in the O’ahu 845 population, which was the smallest population in our sample. Fst varied slightly from 0.338 to 0.334 and genetic structure was also similar with the greatest variance between islands, and subsequently within and among individuals.

3.4 DISCUSSION

The primary goal of this study was to examine the fine-scale population genetic structure of S. globosa and the influence of shoreline change on the genetic diversity in the species. Fine-scale spatial analyses detected IBD in the larger populations with greater geographic extent, but not within smaller populations. Using a conservative estimate of average shoreline change (Romine & Fletcher 2012) associated with mean sea-level rise, genetic diversity was predicted to be altered in two out of seven populations; one is the largest in size

(951 on Maui) and the other is the smallest (845 on O’ahu). In both populations, about half the individuals would be lost. As genetic diversity is spatially well distributed within each population, there appears to be a minimal impact on allelic diversity and frequency in these

77 populations. One private allele, from a total of 21 unique alleles, was predicted to be lost on

Maui.

In this study, the evidence suggests that the genetic variation found in S. globosa is likely associated with the geologic evolution of the Hawaiian archipelago with rare dispersal events from source populations on older islands to new locations on younger islands. This is in line with studies indicating that floristic ontogeny (immigration, establishment, development of genetic diversity, loss of diversity, and extinction) is parallel with island ontogeny for many island endemics (Stuessy et al. 2014). The highest genetic diversity was found on populations on O’ahu (NA = 4 with 15 private alleles), the oldest island sampled (3.0-2.6 myrs; Price & Clague

2002), with greater genetic distance between populations on this island (Appendix II, Figure

A3.1). In contrast, populations on Maui (island age 1.2-1.5 myrs; Price & Clague 2002) exhibited less allelic diversity (highest NA = 3.2 with six private alleles; also observed by Filatov & Burke

2004) and little genetic distance among populations (Appendix II, Figure A3.1). This is consistent with previous studies, based on allozyme data for five populations (Weller et al. 1996) and a single gene locus for three populations (Filatov & Burke 2004), but it is inconsistent with another study based on two nuclear loci and one plastid locus in 10 populations (Wallace et al.

2009). While the allozyme data and gene loci could be subject to drift and selection, microsatellite loci are purportedly neutral and generally retain variation even during population establishment (Zhang & Hewitt 2003). The difference with the more recent study (Wallace et al.

2009) could possibly be due to dissimilarities in sample size or the loci used (Landguth et al.

2012). Wallace et al. (2009) used up to 10 individuals per population and an additional six populations; this study used 6-86 individuals where only one population contained 6

78 individuals, whereas sample sizes for the remaining populations were all larger than 16 individuals.

Partitioning of genetic variation displayed the greatest amount of nuclear allelic diversity within individuals, consistent with a previous study (66.5 and 91.4 for two nuclear loci;

Wallace et al. 2009). Average Fst across all populations was comparable to recent data from

Dixon et al. (2011) for 19 nuclear regions from five sites (0.338 reported in this study vs 0.270 in

Dixon et al. 2011). In addition, Fst estimates from both previous studies indicated greater population differentiation between islands than the variation detected within islands. In accordance with other investigations in the Hawaiian Islands (Givnish et al. 2009; Harbaugh et al. 2009; Dunbar-Co et al. 2011), the Pacific Ocean seems to be the principal barrier between the two island populations and explains the relatedness of populations on each island.

However, landscape barriers may also be a factor, for example, the pairwise Fst was significantly different from zero for populations 845 and 906 on O’ahu, which are separated by tens of meters, whereas the pairwise value between 845 and 844 was low and these populations have a larger geographic distance between them. The pattern of migration, less than one migrant per generation, suggests weak gene flow between islands and greater gene flow within islands, consistent with the Fst data. Nevertheless, populations on Maui appear to have retained a moderate amount of genetic diversity and sufficient temporal divergence as exemplified by their six private alleles.

Although S. globosa technically possesses a subdioecious breeding system, the rarity of hermaphroditic individuals renders the species functionally dioecious (Sakai & Weller 1991;

Delph & Wolf 2005), with approximately equal potential mothers and fathers. In this case,

79 nearest neighbor mating should be reduced under random mating and we would expect low spatial genetic structure (Schroeder et al. 2014). Overall, we observed greater within- population spatial structure in the populations with a larger geographical area and demographic size, possibly suggesting that there is an association to distance. The lack of IBD when examining loci across all populations (r=0.133, p=0.175) could be a reflection of on-going migration from O’ahu to Maui or it could be associated with sample size. However, separate analysis of IBD for each island revealed that there was significant IBD on O’ahu but not on Maui across populations. In contrast, IBD was observed at the individual level using all individuals on both islands and also separately for each island. Fine-scale spatial autocorrelation patterns for each population revealed autocorrelation at the extent of the population range size for smaller populations (Table 3.3), whereas larger populations displayed autocorrelation at distances shorter than the range size. The patterns observed here suggest that the extent of spatial autocorrelation measured is a function of the geographic extent of the population and of population size at a fine-scale (Fenster et al. 2003). As pollen is wind-dispersed and likely travels longer distances, the structure observed at this fine scale is likely due to limited seed dispersal, while the broad-scale structure among populations could be due to both pollen and seed dispersal. Further analysis comparing nuclear and chloroplast markers could elucidate the differing patterns from pollen and seed dispersal.

In the current study, sample size was not associated with any measures of genetic diversity but it did influence spatial associations (data not reported here). Recent studies have suggested that population size, the number of loci, and number of alleles sampled influence measures of population genetic structure, with increasing numbers related to decreasing

80 relatedness (Pruett & Winker 2008; Landguth et al. 2012; Prunier et al. 2013). The one anomaly in our dataset was population 852 on Maui. In contrast to expectations, it did not exhibit genetic patterns similar to population 906 on O’ahu, although it was of a similar population size and geographic extent. These differences could be due to historical reasons related to a reduced sample of founders during population establishment, consistent with migration from

O’ahu to Maui.

Patterns of genetic diversity potentially encode a species history, geologic history and changes over time. It has been suggested that a high level of genetic variation is required for species to persist in dynamic environments such as Hawai'i (Lande & Shannon 1996; North et al.

2011). When populations are at a hypothetical equilibrium, there is a balance between migration and genetic drift, at which point, a linear relationship should be observed between pairwise genetic and geographic distances (Hutchison & Templeton 1999). Isolation by distance

(IBD) patterns can thus be observed over small or large spatial scales depending on the amount of gene flow between populations, spatial organization of populations, and the time of establishment of new populations (Slatkin 1993). Barriers linked to the abiotic environment and ecological factors as well as natural selection (resulting in local adaptation) may also be important, in addition to geographic distance, with gene flow being restricted due to limited dispersal when populations occupy different environments (Wang et al. 2013; Rhodes et al.

2014).

The distinct pattern of IBD within the larger populations indicates that landscape barriers could limit dispersal. In this study, we used overall habitat suitability categories to represent the landscape; however, more in-depth analysis could include specific landscape

81 categories that were used in the SDM such as slope and topographic ruggedness. While pollen is wind dispersed in S. globosa, seeds are most likely dispersed by gravity, thus resulting in a core area of spatial relatedness with differentiation at the population edges (as also observed in

Oenothera harringtonii by Rhodes et al. 2014). Differences in spatial autocorrelation by allele were similar to overall genotype spatial structuring within the two largest populations. Within population 951 on Maui, the area of spatial autocorrelation occurs along the lower slopes with greater differentiation along the slope axis (Figure 3.4), suggesting an influence of topographic heterogeneity. Previous estimates of the ratio of pollen:seed dispersal (10:1; Dixon et al. 2011) suggest greater pollen movement and much less seed dispersal, also suggested by large areas of unoccupied suitable habitat (see Chapter 2). Further sampling and a full-cohort paternity analysis could more clearly reveal the separate patterns from pollen and seed dispersal

(Schroeder et al. 2014).

These results and the higher genetic diversity found on O’ahu suggest that O’ahu populations are more ancient in relation to Maui, which is consistent with the ages of the islands. There is little differentiation among Maui populations, consistent with the colonization history of the species. The existing allelic diversity on Maui (Table 3.1) and the pairwise Fst comparisons possibly reflect the short time horizon since geographic isolation. Theory suggests that gene flow estimates may only be valid after there has been sufficient time for drift and migration to reach equilibrium (Slatkin 1993). In the case of the Hawaiian Islands where there is constant geologic upheaval, populations may rarely be at equilibrium. Colonization by angiosperms on O’ahu is predicted at 2.6-3 mya and Maui at 1.2-1.5 mya (Price & Clague 2002).

A recent study highlighted this observation in which island age and volcanic maturation

82 promoted genetic population differentiation in multiple species (Roderick et al. 2012).

Additional studies also indicate similar patterns, for example in Hawaiian Plantago species

(Dunbar-Co et al. 2011), lobeliads (Givnish et al. 2009), and Schiedea (Willyard et al. 2011), in which migration events occurred from geologically older islands to newer islands. The absence of conspicuous spatial genetic structure inferred via spatial autocorrelation in the smaller populations indicates that relatively low amounts of gene flow play a key role in structuring of populations, rather than random drift. Two loci had a disproportionate role in fine-scale structure within the two largest populations and development of additional markers could reveal further insights into spatial patterns.

Change in genetic diversity

We used average shoreline change to calculate genetic losses for each population, when in fact shoreline change is highly spatially variable around an island (Romine & Fletcher 2012).

In reality, the impact at a particular location could be higher or lower than average. For example, the models we used assume a mean sea-level rise without accounting for extreme storm events such as hurricanes that could potentially cause more extensive and catastrophic population losses (Michener et al. 1997). Studies indicate that extreme storm events cause substantial change in ecosystems with recovery sometimes taking decades due to uprooting, defoliation, and snapping of plant tissue (Brokaw & Walker 1991; Potter 2014). Damage is greatest to plants in the path of the wind, which is where populations of S. globosa occur.

Accounting for extreme storm events in future studies may provide a more comprehensive outlook for S. globosa and similar coastal species.

The detrimental effects of removal of individuals on genetic diversity have been documented in multiple studies on commercial harvesting (Cruse-Sanders & Hamrick 2004;

Wernsdörfer et al. 2011; Baldauf et al. 2013). These investigations have shown that removal is effective in reducing genetic diversity dependent on the life stage. For example, in one study, harvesting appeared to have a greater impact on seedling allelic diversity than on adult allelic diversity (Baldauf et al. 2013). Climate related effects on genetic variability have also been documented at spatial scales varying from tens of meters to over one km (Huang et al. 2002;

Schroeder et al. 2014). In these findings, spatial structuring occurred due to differential microclimatic conditions and if these conditions change, the question is how these locally adapted populations would fare. A recent review (Jump & Penuelas 2005) highlighted that population genetic diversity in multiple species varied considerably over space and time, most critically due to seedling establishment. Furthermore, as genetic diversity in general is higher in core versus peripheral populations, conservation efforts are focused on preserving core populations. In our study, we found little impact on overall allelic diversity but our sampling efforts were concentrated in adult life stages of plants with an estimated five-year lifespan.

Future modeling should take into consideration earlier life stages as these may influence the patterns observed here. Additionally, the effects of changing shoreline on genetic diversity needs further in-depth investigation, such as accounting for changes in population size, gene flow, genetic drift, and Allee effects that could affect the genetic structure of the species over time. With an estimated generation time of 5 years, S. globosa will likely undergo at least 20 generations in a 100 year interval. The precarious habitat of S. globosa is also subject to extreme storm events that have the potential to dramatically change the demographics of the

84 species; a reduction in population size may leave it more susceptible to Allee effects and genetic drift, which could ultimately impact its population genetics.

Conservation implications

Although S. globosa is not immediately at risk of extinction due to mean shoreline change, some populations will likely be impacted in the near future. As genetic diversity forms the basis for evolutionary adaptation, preserving those populations and regions with the highest genetic diversity should be a priority. Given that the largest and most genetically diverse population on O’ahu has already been drastically reduced due to urban development

(i.e. being bisected by construction of a road), the synergistic effects of climate change and development need to be taken into account at various life stages. The large escalation in the human population in the Hawaiian archipelago over the past 100 years (over 600%, US Census

Bureau 2010) and within the past 10 years (over 9%) has led to rapid urban development with significant changes to island ecosystems and biological diversity. The S. globosa populations on

O’ahu with the highest genetic diversity are the most susceptible to threats from urban development while the populations on Maui have a greater threat from shoreline change.

Increased drought periods predicted for the Hawaiian Islands would likely increase mortality, especially for individuals at the margins of the population, such as within populations 906 and md, both on O’ahu. As the overall genetic diversity is lower on Maui than O’ahu, rising sea- levels could increase the distance for dispersal, which could limit recruitment; in that scenario, populations on Maui would be limited to mutation and recombination for genetic diversity

(Stuessy et al. 2014). The relatively high neutral genetic diversity in S. globosa indicates that

85 one of the best strategies would be to conserve as many current populations as possible and to protect the most diverse populations, especially on O’ahu.

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Chapter 3 - FIGURES and TABLES

O’ahu

Maui

Relative island locations

Southeast O'ahu 313 West Maui 951

852

md 844

845 906

Figure 3.1. Topographic map of the sampling locations for Schiedea globosa with four sites on southeast O'ahu and three sites on West Maui.

Table 3.1. Descriptive statistics for all populations of Schiedea globosa sampled on the Hawaiian Islands of Maui and O'ahu for 10 microsatellite loci.

Shown are the sample size; N, number of individuals per population used in genetic analyses (these may differ from sample size as not all loci are amplified); NA, average number of alleles across all loci per population; Ne number of effective alleles across all loci per population; NPA, average number of private alleles across all loci per population; HO, observed level of heterozygosity; HE, expected level of heterozygosity; F, Fixation index.

Island Sample Population: N N N N H H F Size A E PA O E

313 (3 sisters) Maui 13 11.60 2.60 1.775 2 0.317 0.388 0.134

852 (Kahakuloa) Maui 46 36.60 2.30 1.456 0 0.247 0.276 0.164

951 (Pohakupule) Maui 86 67.00 3.20 1.634 4 0.222 0.322 0.269

844 (Makapu’u) Oahu 30 21.70 4.00 2.166 13 0.250 0.444 0.463

845 (Hanauma-bch) Oahu 6 4.90 1.90 1.618 0 0.225 0.272 0.125

906 (Hanauma) Oahu 51 37.00 2.40 1.785 2 0.273 0.357 0.296 md (ditch) Oahu 16 9.60 2.20 1.751 0 0.255 0.331 0.201

Table 3.2. Summary Analysis of Molecular Variance (AMOVA) displaying how variation is partitioned for microsatellite markers in Hawaiian populations of Schiedea globosa on Maui and O’ahu.

Source Df SS MS Est. Var. %

Among Islands 1 260.656 260.656 0.999 29%

Among Pops 5 65.951 13.190 0.167 5%

Among Individuals 241 858.931 3.564 1.253 36%

Within Individuals 248 262.500 1.058 1.058 30%

Total 495 1448.038 3.477 100%

Principal Coordinates (PCoA)

313

852 Maui

951

844 Coord.2 845 Oahu 906 md Coord. 1

Figure 3.2. Plot of the first two axes of the principal coordinate analysis based on pairwise Fst in Schiedea globosa populations on Maui and Oahu. Populations 313, 852, and 951 are on Maui and the remaining are on O'ahu as represented by legend symbols. The first two axes explained 26.5 and 16.2 of the genetic variation among populations.

Results of Spatial Structure Analysis

0.100

r 0.000 r -0.100 U 20 40 60 80 100 120 L Distance Class (End Point)

(a) Spatial autocorrelation for population 951 on Maui, (omega=41.6, p=0.004)* Results of Spatial Structure Analysis

0.100

r 0.000 r -0.100 U 20 40 60 80 100 120 L Distance Class (End Point)

(b) Spatial autocorrelation for population 906 on O'ahu, (omega=31.5, p=0.01)* Results of Spatial Structure Analysis 0.400

0.200

r 0.000 r -0.200 -0.400 U 20 40 60 80 100 120 L Distance Class (End Point)

0.200

r 0.000 r -0.200 U 10 20 30 L Distance Class (End Point)

(d) Spatial autocorrelation for population 313 on Maui, (omega=31.5, p=0.3)

Figure 3.3. Spatial autocorrelation distance classes for populations 951 and 313 of Schiedea globosa on Maui and 906 and 844 on Oahu; r=correlation coefficient, U and L are the 95% confidence envelopes of the correlation coefficient.*Correlogram is significant, p values indicated below each correlogram

Table 3.3. Spatial autocorrelation distances and geographic extents for each population of Schiedea globosa in meters. Isolation by distance was observed for populations 951 on Maui and 906 on O’ahu at the distances listed below. Population Island Pop. Size Spatial autocorrelation distance (m) Geographic extent (m) 951 Maui 86 80* 155 852 Maui 46 120 145 313 Maui 13 30 25 906 O'ahu 51 40* 165 844 O'ahu 30 120 180 845 O'ahu 6 30 30 md O'ahu 16 60 60

Distribution of alleles for marker SA06 Distribution of heterozygosity for SA06 (blue=heterozygous, red=homozygous)

Distribution of alleles for marker SA15 Distribution of heterozygosity for SA15 (blue=heterozygous, red=homozygous)

0 5 10 20 30 40 ± m Distribution of sex in population 951, Maui

Figure 3.4. Distribution of alleles for two Schiedea globosa markers (SA15 and SA06) and distribution of sex within the 951 population on Maui, overlaid on a background slope map. Spatial autocorrelation for each allele varied from 15 meters to 40 meters with spatial autocorrelation for all alleles maximal at 40 meters.

Distribution of alleles for marker SA06 Distribution of heterozygosity for SA06 (blue=heterozygous, red=homozygous)

Distribution of alleles for marker SA15 Distribution of heterozygosity for SA15 (blue=heterozygous, red=homozygous)

0 15 30 60 90 120 m Distribution of sex in population 906, O’ahu

Figure 3.5. Distribution of alleles for two Schiedea globosa markers (SA15 and SA06) and distribution of sex within the 906 population on O’ahu overlaid on a background slope map. Spatial autocorrelation for each allele varied from 20 meters to 57 meters with spatial autocorrelation for all alleles maximal at 30 meters.

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Table 3.4. Current and predicted genetic diversity for S. globosa for samples collected on Maui and O’ahu. Shown are N, number of individuals per population; NA, average number of alleles across all loci per population; HO, observed level of heterozygosity. Italicized rows in red font indicate the populations that are predicted to be affected by average shoreline change.

Current Genetic Diversity Predicted Genetic Diversity

Population N Na Ho Island Population N Na Ho Island

844 21.70 4.00 0.250 O'ahu 844 21.70 4.00 0.250 O'ahu

845 4.90 1.90 0.225 O'ahu 845 2.70 1.80 0.250 O'ahu

md 9.60 2.20 0.255 O'ahu md 9.60 2.20 0.255 O'ahu

906 37.00 2.40 0.273 O'ahu 906 37.00 2.40 0.273 O'ahu

852 36.60 2.30 0.247 Maui 852 36.60 2.30 0.247 Maui

951 67.00 3.20 0.222 Maui 951 38.60 2.90 0.223 Maui

313 11.60 2.60 0.317 Maui 313 11.60 2.60 0.317 Maui

101

Pacific Ocean

Figure 3.6. Map of Schiedea globosa population most affected by shoreline change on Maui at Pohakupule (population id 951) where approximately half the individuals could be lost, indicated by area within the blue strip.

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APPENDIX II

Table A3.1 The pairwise Fst distance matrix for 11 loci across 7 populations of Schiedea globosa on Maui and O’ahu. All comparisons were significantly different from zero (p<0.05) except for populations 844 and 845.

313 852 951 844 845 906 md

313 0

852 0.100 0

951 0.084 0.038 0

844 0.242 0.299 0.276 0

845 0.339 0.322 0.313 0.035 0

906 0.358 0.379 0.381 0.072 0.062 0

md 0.315 0.332 0.316 0.044 0.079 0.107 0

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O'ahu populations

Maui populations

genetic distance between islands

Figure A3.1 Monmonier graph depicting geographic and genetic distance for Schiedea globosa populations on O'ahu and Maui. The largest differentiation is between islands, then among O'ahu populations, whereas Maui populations have little differentiation.

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Current Predicted

Figure A3.2. Change in the number of alleles for all loci across populations of Schiedea globosa on Maui and O’ahu. The differences compare present allelic diversity to future allelic diversity after accounting for individual losses due to shoreline change at a 100 year time point from present.

105

Locus- SA04 Locus –SA30

Locus –SA05 Locus –SA32

Locus –SA06 Locus -SA36

Locus –SA15 Locus –SA38

Locus –SA16

Figure A3.3. Change in allele frequency by marker for population 951 on Maui from present day (left pie-chart) to a future time period at 100 years (right pie-chart) for Schiedea globosa.

106

CHAPTER 4

The association between plant breeding systems and geography in the Hawaiian genus Schiedea

Sunita Yadav

Department of Biological Sciences,

University of Cincinnati,

614 Rieveschl Hall, Cincinnati, OH 45221, USA

107

ABSTRACT

The geographic distributions of species differ for many reasons, some of which are related to environmental tolerances, while others are attributable to biological competition or dispersal limitations. Additional factors affecting the distribution of species are colonization history and adaptation to novel environments, such as those occurring on oceanic island systems. Adaptation to a different environment is often associated with changes in traits such as evolution of dioecy from hermaphroditism in multiple plant species. The broad goal of this research was to investigate abiotic spatial patterns that are associated with breeding system variation using the Hawaiian endemic plant genus Schiedea (Caryophyllaceae) as a model. Here, we use a community-level modelling approach and group species by breeding system prior to analyzing their distributions in species distribution models. Abiotic niches for five breeding groups (hermaphroditic outcrossing, hermaphroditic selfing, gynodioecy, subdioecy, and dioecy) composed of 33 Schiedea taxa are described from models developed with georeferenced species occurrence records and environmental data in three categories (climate, topography, and soils). Additionally, we investigated the relationships between the environment and breeding system by comparing variable importance and niche overlap across breeding categories. We found significant differences in model responses to environmental predictors among breeding systems (e.g., average summer rainfall and topographic position) and little to moderate amounts of niche overlap among breeding systems. The largest niche overlap was between the gynodioecious and dioecious groups, with the lowest overlap detected between the hermaphroditic selfing and subdioecious groups. Contrary to expectations, there was a significant niche overlap between hermaphroditic outcrossing and dioecious groups. Overall, the ecological niche similarities and differences detected here among breeding systems within Schiedea provide

108 support both for and against the hypothesis that species with different breeding systems occupy different ecological niches. These results highlight abiotic factors that may contribute to the spatial distribution of species as breeding systems diversified within Schiedea.

109

4.1 INTRODUCTION

Unlike most animals, plants possess a wide diversity of breeding systems. While dioecy (separate sexes) in flowering plants is found within only 6% of species, it has arisen independently in 38% of flowering plant families (Renner & Ricklefs 1995). Various pathways have been proposed for the evolution of breeding systems from hermaphroditism to dioecy and the conditions that favor this transition (e.g. Charlesworth & Charlesworth 1978; Bawa 1980; Charlesworth & Charlesworth 1987;

Renner & Ricklefs 1995; Barrett 2002; Charlesworth 2006). Changes in breeding systems often occur during shifts in habitat and/or pollination strategy (e.g., Sakai et al. 1995b; Culley et al. 2002). Thus far, variation in breeding system spatial distribution has not been quantitatively investigated with regard to the abiotic environment. While there are a limited number of comparative studies on niche differences between mating systems (Johnson et al. 2014; Grossenbacher et al. 2015), there are no studies examining a wide diversity of breeding systems within the same genus. The evolution of dioecy from hermaphroditism requires differences in sex allocation in response to resource limitation, in addition to underlying genetic variation (Ashman 1999; Delph 2009). Because resource requirements vary by sex (Spigler & Ashman 2011), it is critical to understand the associations with environmental factors that determine the ecological niches for different breeding systems.

One of the main pathways from hermaphroditism to dioecy is via gynodioecy (presence of females and hermaphrodites in a population; Darwin 1877; Charlesworth & Charlesworth 1978).

Although most instances of gynodioecy are caused by nuclear cytoplasmic male sterility, in the case concerning nuclear inheritance of male sterility, a genetic mutation arises in a hermaphroditic population that leads to male sterility in some individuals, resulting in the presence of female and hermaphroditic individuals (Charlesworth & Charlesworth 1978). Next, another mutation in the

110 hermaphrodites may result in female sterility with populations subsequently containing separate male and female individuals. Often, an intermediate breeding system with male, females and hermaphrodites also exists – this is known as subdioecy (Charlesworth & Charlesworth 1978; Sakai et al. 1995a). The two prevailing explanations for the evolution of gynodioecy are differential resource allocation by sex and avoidance of inbreeding depression (Charlesworth & Charlesworth 1978;

Charlesworth & Charlesworth 1987), in addition to ecological habitat partitioning by sex (Cox 1981).

Numerous studies document higher seed production in females relative to hermaphrodites in gynodioecious populations, (Ashman 1994; Gouyon 1987; Weller & Sakai 2005) and this is thought to affect selection for the female-sterility gene. Combined with resource limitation, this selection pressure would result in hermaphrodites shifting resources to male function in order to maximize reproductive output (i.e., fitness), especially in habitats with limited resources (Sakai et al. 2006;

Spigler & Ashman 2011). Once sex ratios are equivalent in a population, divergent selection may lead to reproductive isolation and separate sexes (Llyod 1974).

Dioecy has a relatively high incidence in remote island archipelagos (Carlquist 1974; Weller et al. 1990), with initial colonization likely due to hermaphrodites (Baker 1955); although in the Hawaiian

Islands, colonists were both hermaphroditic and dimorphic (Sakai et al. 1995a). Following establishment of a population, there appears to be a strong selection for evolution of separate sexes and an association with a shift in habitat from mesic to dry environments (Barrett et al. 1996). During the course of such rapid adaptive radiations on islands, niches may be conserved or diverge. Evidence suggests that rapid radiations often lead to niche divergence (Ackerly et al. 2006; Losos & Ricklefs

2009; Salamin et al. 2010), with closely related species often occupying different habitats (Sakai et al.

2006). In such cases, divergent natural selection can lead to adaptation to local conditions that could

111 then result in reproductive barriers within and among populations (Rundle & Nosil 2005). In the

Hawaiian Islands, habitat shifts are closely linked to breeding system shifts for some genera (Sakai et al. 1995b), which in turn affect genetic diversity within populations. Consequently, overall heritable variation can influence how species respond to their environment.

Due to their sessile nature, plant species are highly sensitive to fine-scale environmental differences, and ecological factors may influence their evolutionary trajectories to a greater degree than geographic isolation (Sobel 2014; Anacker & Strauss 2015). Environmental conditions, in addition to influencing morphology, may provide barriers to mating as pollination may be dependent on wind and/or biotic vectors (Culley et al. 2002). Thus, it is expected that autogamous species (those exhibiting self-fertilization) display larger range sizes than outcrossing species because of the added challenge of finding a mate for outcrossing species (Johnson et al. 2014). Currently, there is a lack of sufficient evidence to support this hypothesis; in Collinsia and many other taxa, self-compatible species do exhibit a wider distributional range (Randle et al. 2009; Grossenbacher et al. 2015), while in

Oenothera, this is not the case (Johnson et al. 2010).

Therefore, to understand the association between the abiotic environment and distribution of breeding systems, I investigated the Hawaiian endemic genus Schiedea, in part because of the potential effects of island biogeography and the reproductive traits of the genus itself. Adaptation and dispersal have led to rapid diversification in many plant lineages on the Hawaiian Islands (Price &

Clague 2002; Sakai et al. 2006). In addition, the Hawaiian Islands have the highest frequency of dioecy within angiosperms (14.7%) with in situ evolution of different breeding systems within many genera

(Wagner & Funk 1995; Sakai et al. 1995a). The in-situ evolution of breeding systems is associated with niche divergence, for example within the genus Schiedea (Willyard et al. 2011). Islands are thus ideal

112 for studying niche divergence for two reasons: first, because islands are often young with dynamic habitats and second, because there are fewer species, it is easier to study evidence for shifts to novel habitats for species to survive the constant erosion of existing habitats (Fleischer et al. 1998). Schiedea is a well-studied genus for the evolution of dioecy from a hermaphroditic ancestor with a nuclear inheritance system, and it exhibits the fifth largest adaptive radiation in Hawaii with 32 extant species

(Sakai et al. 1995a; Wagner et al. 2005; Sakai et al. 2006). While all mesic habitat species within

Schiedea are hermaphroditic, dry habitat species display various breeding systems ranging from hermaphroditism to dioecy (Sakai et al. 2006). The presence of different breeding systems in dry habitats may indicate that habitat shifts occurred prior to breeding system shifts (Sakai et al. 1995b).

The standard approach to investigate the relationship between species niches and the environment has been reciprocal transplant experiments; however, these experiments are time- consuming and costly. Recent work combines geo-referenced species and environmental data to make predictions of a species realized niche. Such species distribution models (SDM), often also referred to as ecological niche models (ENM), are increasingly being used to test biogeographic hypothesis about species-environment relationships. Because SDMs offer statistical explanations for species tolerances and abiotic trends based on current occurrences, they can be informative to detect species habitat ranges or to test evolutionary hypotheses (Guisan &Thuiller 2005; Peterson et al.

1999). They have been used to reconstruct past distributions or model future ones (Svenning et al.

2011; Scoble & Lowe 2010), examine the effect of plant mating on geographic distribution (Johnson et al. 2010; Grossenbacher et al. 2015), or test niche and lineage divergence (Kalkvik et al. 2012; Loera et al. 2012). More recently, SDM output was used to test niche conservatism or divergence - i.e. whether niches are similar or different in comparison to a null model (Theodoridis et al. 2013). While SDMs are

113 commonly used to understand ecological processes (Manel et al. 2007; Nakazato et al. 2008), they have only recently been used to explore evolutionary processes (Elith & Leathwick 2009).

Spatially referenced abiotic data combined with species location data offer a distinct advantage to shed light on the evolution of breeding systems and their realized niches. There is considerable debate in the current literature arguing that the fundamental niche (Grinnellian niche) is generally conserved over evolutionary time periods, and this idea has only recently been quantitatively tested (Peterson et al. 1999, Warren et al. 2008). In a recent review, Peterson et al.

(2011) stated that niches tend to be conserved over shorter time scales such as invasions and distribution shifts, with observed differences attributable to methodological artefact. Therefore, the niche shifts observed in many studies were in fact shifts in species realized niches and not species fundamental niches. However, they do suggest that niche conservatism (defined as the tendency to maintain the ancestral ecological niche, Peterson et al. 1999) breaks down over longer time periods, such as speciation events and across phylogenies, and over deep evolutionary time (Peterson et al.

2011). Speciation on islands by vicariance, such as that observed in the Hawaiian Islands, suggests that a species evolving on newly formed islands should display a reduced fundamental niche (due to habitat differences) with a smaller range size in comparison to species on source islands (Randle et al.

2009; Grossenbacher et al. 2015). Diversification within Schiedea is thought to have involved habitat shifts to different environments on the same island and colonization to younger islands (Sakai et al.

2006). This reflects ecological isolation, with partitioning of environmental space by individual species

(e.g., some species occupying more mesic environments at higher elevations and others drier environments at lower elevations), and geographic isolation due to dispersal.

114

In this chapter, I investigate how spatially heterogeneous abiotic factors are associated with the spatial distribution of breeding systems in the genus Schiedea, especially in terms of the association of dimorphic breeding system (i.e. dioecy, gynodioecy) with drier habitats. For this purpose, I test three main hypotheses: (1) species displaying different breeding systems within

Schiedea (hermaphroditic outcrossing, hermaphroditic selfing, gynodioecy, subdioecy, and dioecy) show variation in their responses to abiotic variables because breeding systems differ in their ecological niches, (2) ecological niche overlap decreases from hermaphroditic outcrossers to dioecious groups following the pattern for the evolution of dioecy in Schiedea, and (3) there are differences in niche overlap between selfing and outcrossing hermaphroditic species.

4.2 METHODS

Species Data

The genus Schiedea is endemic to the Hawaiian Islands and comprises 34 species, with two subspecies, in total. These species display an exceptionally wide variety of morphology associated with different habitats. Broad-leaved species tend to occur in more mesic environments while narrow-leaved species occupy drier habitats. Similarly, inflorescence structure is distinctly different among species, with mesic-habitat species displaying diffuse inflorescence structure, sometimes with pendent individual flowers, whereas dry-habitat species contain hundreds of relatively small flowers in condensed inflorescences (Wagner et al. 1995). Inflorescence condensation appears to be an adaptation to wind-pollination in dry environments (Weller et al. 1998). Species within Schiedea exhibit diversity in habit as well, varying from forest shrubs to woody vines with a variety of breeding systems: obligately autogamous (only reproduce via selfing), facultatively autogamous (reproduce via

115 outcrossing or selfing), outcrossing hermaphroditic, gynodioecious (females and hermaphrodites), subdioecious (males, females, and hermaphrodites) and dioecious (males and females). Pollination is diverse as well, with many hermaphrodites pollinated by native moths, and pollination shifting to wind as a vector in some gynodioecious species but all subdioecious and dioecious species (Norman et al. 1997). Seeds are thought to disperse short distances within Schiedea, although the mechanisms are poorly understood (Wagner et al. 2005).

Georeferenced species occurrence data, primarily collected from the Schiedea monograph

(Wagner et al. 2005), were supplemented from the BISON online database (downloaded July 2015) for species with less than five occurrences (i.e., populations) whenever possible. Of the 34 Schiedea taxa, two are extinct but we were able to use 33 taxa for the ecological niche modeling. Because a few species have only single locations and could not be modeled, species were grouped into breeding system groups (Table 4.1). This follows a community level approach from a macroecology framework, is corroborated by recent publications (Baselga & Araújo 2010; Guisan & Rahbek 2011; Madon et al.

2013), and is especially useful to model rare species distributions. In such an approach, species are grouped together based on some criteria and the distribution for multiple species is modeled simultaneously (Guisan & Rahbek 2011). For this study, species were grouped together based on breeding system; thus the SDMs predict habitat suitability for each breeding system. The final number of species by breeding systems was as follows: hermaphroditic outcrossing (15), hermaphroditic selfing (8), gynodoecious (4), subdioecious (3), and dioecious (3) with 261 total occurrences (Figure 4.1 and 4.2). All species location data were spatially filtered to obtain only one occurrence point per grid cell at a 30-meter resolution.

116

Pseudo-Absences

The model utilized in this study, Maxent v.3.3.3 (Phillips et al. 2006; Phillips & Dudik 2008), uses presences and pseudo-absences (or background data) to generate predictions. As false absence data can potentially have significant negative consequences on SDM performance, careful selection of absences is crucial (Barbet-Massin et al. 2012). In this study, false absences were randomly generated for each breeding group separately with these following criteria: they should be outside of a 150- meter buffer around presence points, every absence was at minimum 150 meters away from another absence point, and absences were created only for islands where the breeding group had occurrences. The Maui Nui group of islands (Maui, Moloka’i, Lana’i, and Kaho’olawe) were treated as a single island because they were connected during the Pleistocene glaciation and for more than 75% of their existence (Price & Clague 2002). As many species within Schiedea are single-island endemics with limited dispersal, restricting absences to islands containing presences avoids extrapolation issues to novel environmental spaces (Barbet-Massin et al. 2012). Additionally, a ratio of 20 absences per presence point was used to produce absences, which maintains prevalence across groups as this can influence model output (Iturbide et al. 2015).

Abiotic Data

Abiotic environmental data were collected within a Geographic Information system (ArcGIS v. 10.2) for the islands of: Kaua’i, O’ahu, Moloka’i, Maui, Lana’i, Kaho’olawe, and Hawai’i. Three categories of abiotic data were used: climate data, edaphic data, and geographic layers associated primarily with topography (elevation, slope, aspect, solar radiation; Table 4.2). Temperature and precipitation variables have a direct influence on species physiological tolerance limits and influence species

117 distributions at broad geographic scales (Guisan et al. 2005). All remaining topographic variables were calculated as previously described (Chapter 1) with the exception of aspect and the compound topographic index (CTI). Here, circular aspect was also transformed to a continuous variable, which varies from a value of zero to areas oriented north-northeast (generally the coolest and wettest land areas), and a value of one to land oriented south-southwest (typically hotter and dryer slopes;

Roberts & Cooper 1989). The predictor CTI, also known as the steady state wetness index, quantifies a site relative to its position in the local landscape (Moore et al. 1993). It is a function of the slope and the upstream contributing area or catchment (Moore et al. 1993) and is calculated with the following equation:

CTI = ln(As / (tan (beta)) where; As=specific contributing area [(flow accumulation + 1 ) * (pixel size in m)] and beta=slope in radians Pearson’s correlation was used to remove collinearity in climate and topographic predictors. After correlation analyses of climate data, only two of the 13 climatic variables remained for building SDMs

(where r ≤ 0.8); these were annual average maximum temperature and summer average rainfall

(averaged from May-September). This is consistent with other studies (Austin & Van Niel 2011) indicating that extreme predictors play a role in species physiological limits and are central to determining species ranges. Two topographic predictors, elevation and standard deviation of slope, displayed collinearity with several other topographic variables and were removed from further analyses (here also the threshold of r ≤ 0.8 was used). Additionally, percent organic matter and soil erodibility factor were also correlated and we decided to retain percent organic matter for model development as it has a more direct effect on plant growth. All remaining predictors (15 in total) were then used in Maxent for variable selection prior to model development.

118

Variable Selection

The same suite of environmental predictors is required across breeding groups to conduct hypothesis tests for niche overlap. To select the common predictors across breeding groups, I pooled all occurrences across groups, recoded them as presences and pooled all absence data. These binary data were then used in Maxent to determine the most important predictors across breeding groups.

Step-wise removal was employed to eliminate predictor variables with lower importance in model runs. While there is continuing debate on the appropriate number of predictors per occurrence, the consensus is that models with fewer predictors have a higher predictability (Elith & Leathwick 2009).

In this study, I limited the number of predictors to a ratio of one predictor per five occurrences.

Therefore, only the top six predictor variables were selected for the final models because the smallest breeding category consisted of 29 occurrences. The same method was used to select six common predictors for the comparison among hermaphroditic groups. We could have selected additional predictors for some groups because they had many more occurrences, but we decided to keep the overall number of predictors the same to be consistent when comparing models (Elith & Leathwick

2009; Austin & Van Niel 2011).

Model Development

Species distribution models (SDM) were developed in Maxent v. 3.3.3 (Phillips et al. 2006;

Phillips & Dudik 2008) using five-fold cross-validation with a logistic output. Maxent calculates a suitability of species presence in a geographic area using species occurrences and environmental data

(Royle et al. 2012). The maximum entropy algorithm has repeatedly been shown to be robust for

119 species with low occurrences as it gives an estimate of the probability distribution for a species constrained by observed presences and the associated environmental conditions (Elith et al. 2006;

Phillips et al. 2006; Pearson et al. 2007). The data in the models included species occurrence data for each group and the six predictor variables, in a samples-with-data (swd) format. Default parameters were used for Maxent with the exception of pseudo-absence background points that were generated separately as described in a previous section in this chapter. All predictors were cropped to match the extent and cell size for the digital elevation raster at a 30-meter resolution in a UTM projection and then analyzed within Maxent without extrapolation. The cross-validation method used here is a five- fold cross-validation whereby (in five alternating configurations) four partitions are used to train the model and one to test the model.

Model performance was evaluated using the area under the receiver operating curve (ROC),

AUC. The ROC plots sensitivity (ratio of correctly classified presences) versus 1-specificity (ratio of correctly classified absences) and ranges from zero to one. Typically, models with AUC values higher than 0.9 are considered highly accurate, 0.7-0.9 are useful, 0.6-0.7 poorly accurate, and below 0.6 are no better than chance (Swets 1988). In the case of presence only data, maximum AUC is slightly less than one and while more predictors and large pseudo-absences increase AUC scores, there is a danger of overfitting (Wiley et al. 2003), which leads to poor predictability from models.

Niche Comparison

To determine whether niches diverge as breeding systems evolved within Schiedea, SDM predictions were compared at multiple levels. The first comparison was to analyze broad differences in abiotic niches for breeding categories (hermaphroditic outcrossing, hermaphroditic selfing,

120 gynodioecious, subdioecious, and dioecious species). This comparison was repeated after removing S. spergulina, from the dioecious group as this species displays introgression and may skew results. It is not known if the introgression and back-migration observed within S. spergulina is recent or historical

(Willyard et al. 2011); if recent, we would expect greater niche overlap with hermaphroditic outcrossers due to a short time for divergence. On the other hand, if back-migration were historical, then we would expect less niche overlap with hermaphroditic outcrosses due to a longer time for divergence. The final comparison was to examine ecological niches among facultative selfing, obligate selfing, and outcrossing hermaphroditic species. Niche overlap is often compared using Hellinger’s identity I, and Schoener’s D metrics described by Warren et al. (2008). These metrics range from zero

(no overlap) to one (species potential distributions are identical). The D metric represents the difference in the predicted probability of presences between two distributions and is analogous to a percentage overlap for two distributions. It uses the raw probability distribution from SDMs and is thus unaffected by threshold choice which is often arbitrary (Warren et al. 2008). A D value below 0.2 represents a low overlap, between 0.4-0.6 a moderate amount of overlap, and over 0.6 is a high niche overlap (Rödder et al. 2011). The I metric is a niche similarity index that represents the similarity of two distributions. In addition, the observed D and I metrics were then compared to the distribution of randomly generated values from 100 pseudoreplicates to evaluate the statistical significance of the observed metrics for each pairwise comparison (Warren et al. 2008; Alvarado-Serrano 2014).

121

4.3 RESULTS

Variable Selection

The full environmental data set included 30 abiotic predictor variables (Table 4.2), which were first reduced to 15 after correlation analyses. As the total number of occurrences for the smallest breeding system group was only 29, these remaining variables were further reduced to avoid overfit models, using the variable importance and response curves within Maxent. The six common variables across breeding categories were summer average rainfall, annual average maximum temperature, slope, topographic position index, sine of aspect, and annual average insolation (Table 4.3). For the hermaphroditic group comparisons, the six relevant predictors were: summer average rainfall, annual average maximum temperature, topographic position index, topographic ruggedness index, compound topographic index, and annual average insolation. Table 4.3 lists the relative contributions of different variables to predict breeding system distributions. The two climate predictors were in the top three predictors in four of the five breeding categories and were particularly dominant in the hermaphroditic categories.

The shape of the marginal response curves displays how model prediction changes when a predictor is varied keeping all other predictors constant. Models for both hermaphroditic groups show higher predictive ability at a narrower range of annual maximum temperature than the other groups (Figure 4.3). Higher summer average rainfall had a substantial effect on the models for the hermaphroditic selfing group whereas lower values for this variable were important in all remaining groups. Both the hermaphroditic selfing and subdioecious models also show higher predictions with lower insolation although the reasons for it may be different. Topographic position index displays a bimodal response with higher prediction for flat areas and ridge tops for the hermaphroditic

122 outcrossing group. Slope is an important factor for all three dimorphic groups, with higher slope increasing the probability of prediction for these groups, with up to an 84% slope for the subdioecious group.

The rank of important predictors showed some differences for the analysis with only hermaphroditic groups. Annual average maximum temperature displayed the greatest increase in gain to explain distribution patterns for both the obligate and facultative selfing categories, though it had a broader range for the obligate group and contained the most useful information for this group.

In contrast, summer average rainfall was the most useful variable for the facultative selfing category, with higher rainfall increasing suitability of occurrences. Topographic predictors, ruggedness and positon indices, played a larger role in models for the outcrossing group.

Model evaluation

Over 75 models were processed in Maxent (using five-fold cross validation and multiple model runs per group to reduce variables). The final correlative models in Maxent provided geographic distributions that displayed good accuracy (AUC >= 0.8, ROC plots in Figure 4.4) with low omission. However, as with most narrow-range species, commission error had a broad range and was relatively high for some SDMs. Generally, groups with more presence data resulted in more accurate models. Maxent provides many thresholds to evaluate models and all final models predicted presences in the test data significantly better than random (p < 0.001). Predicted suitability maps from the Maxent models of breeding system presence in geographic space are displayed in Figure 4.5.

123

Niche comparison

When comparing all five breeding groups in geographic space, Schoener’s D metric indicated that the two pairs of breeding groups with the greatest percent overlap were: the gynodioecious and dioecious groups (D=0.66) and the two hermaphroditic groups (D=0.57; Table 4.6a). The breeding group pairs with the least amount of percent overlap in geographic space were each of the gynodioecious and subdioecious groups with the hermaphroditic selfing group (D=0.27 and D=0.23 respectively). After removing S. spergulina from the dioecious group, the percent overlap of the dioecious group with the hermaphroditic outcrossing group showed the greatest reduction (from

D=0.57 to D=0.49), followed by the comparison with the gynodeiocious group (D=0.66 to D=0.60;

Table 4.6b). However, there was little change in the pairwise comparisons with the hermaphroditic selfing and subdioecious groups (D=0.46 to D=0.36 after removal). While the niche identity metric, I, was generally higher than the D metric, the overall pattern was similar to the D metric. Thus, the most similar distributions also had the greatest percent overlap.

The last niche comparison test was to evaluate niche overlap among hermaphroditic groups after splitting the selfing species into facultative and autogamous selfing categories. These tests indicate that outcrossers have greater percent overlap with the facultative selfing group than with the obligate selfing group (D=0.56 vs D=0.36 respectively; Table 4.6c). The two selfing groups had a moderate amount of overlap at D=0.42.

4.4 DISCUSSION

The results presented here show differences in the abiotic variables associated with each breeding category. The observed differentiation between the ecological niches of the five

124 breeding systems within Schiedea provides evidence of niche shifts, possibly during the evolution of breeding systems in this group. Evolution via gradualism would suggest that niches are widely conserved (Peterson et al. 2011), however, our results indicate that niches within

Schiedea were not conserved during diversification. While the outcrossing and selfing groups displayed one of the greatest niche overlap, as expected, pairwise niche differences among the remaining groups was not in accordance with the purported trajectory of breeding system evolution (from a hermaphroditic outcrosser to gynodioecy, then subdioecy, and lastly to dioecy). There could be several reasons for this disparity, such as, imprecise geographic coordinates, variable selection, the relatively low number of species within a group, variation in the abiotic niche of hermaphroditic outcrossers, or lack of knowledge about speciation mechanisms during breeding system evolution within Schiedea. Here, we discuss some possible reasons for model uncertainty and provide further suggestions for improving ecological niche comparison analysis.

Variable selection and dimensionality

Many niche comparison studies use the same suite of environmental predictors in niche models (Theodoridis et al. 2013; Johnson et al. 2014) to compare niches in environmental space. Although we used the same set of predictors, they have a different effect on model predictions dependent on the breeding system. The most important variables for predicting the distribution of the hermaphroditic selfing group appear to indicate a preference for mesic environments, higher summer average rainfall coupled with lower annual insolation and a sharp drop in suitability beyond 20°C. In contrast, the relevant predictors for the

125 hermaphroditic outcrossing group showed a more mixed picture: a narrow temperature tolerance at the lower range, bimodality in solar insolation and topographic position, and a higher model prediction at the lower range of summer average rainfall. Some of these differences may be due to the variation in responses within a breeding group for an individual species. Further analyses at a species level would help to tease out these species level differences. The dimorphic breeding categories show greater probability of presence in areas of higher slope, low summer average rainfall, and diverge in their responses to annual average maximum temperature, with gynodioecious and dioecious groups displaying a wider range, and the subdioecious category present in an extremely narrow temperature range. All these predictors indicate that these groups occur in more dry environments with the higher slope possibly reflecting low soil moisture retention.

As we were interested to observe potential differences in the environmental factors associated with each breeding system, we also ran an alternate analysis by selecting the six most important predictors for each individual breeding category. There was a different set of predictors that was important in models using this selection criterion. In this analysis, the two hermaphroditic breeding groups had four out of a total of six common variables (topographic ruggedness index, annual average maximum temperature, summer average rainfall, and topographic position index), the most overlap of any two groups. Response curves indicated that the optimal temperature for the outcrossing group was higher than for the selfing group, and that the selfing group occupied less rugged terrain and areas of higher average summer rainfall than the outcrossing group. Similar to the analyses using the same set of predictors, dimorphic groups tended to occur in regions of relatively low summer rainfall (a variable that

126 was common to all breeding groups) in comparison to hermaphroditic species. There were also differences in aspect with the gynodioecious group occupying more north facing aspects.

Likewise, slope factors were common in both analyses, particularly for the subdioecious group, which may be related to either colonization history or a preference for well-drained soils. In contrast, slope is important in the dioecious group both as a first and second derivative predictor. The first derivative may simply show a preference for soil drainage characters and the second derivative of slope may in fact be associated with a preference for northeast slopes.

Annual average maximum temperature was important in four groups, soil moisture in three groups, whereas some measure of aspect was important in all except the hermaphroditic selfing group.

The differences in important predictors for each breeding group highlight a need for methods to compare SDMs using a different set of predictors. With current methods, hypothesis tests can only be conducted using the same set of predictors. We think aspect may be an important predictor for some of these breeding groups, however, aspect was not important when selecting the same set of variables across breeding groups. In contrast, during the individual selection of variables by breeding category, aspect was important in four of the groups.

A final consideration with variable selection is the issue of dimensionality, which has often been raised when comparing niches across taxa (Austin & Van Niel 2011; Peterson et al.

2011). Niche models utilizing too many environmental variables will be overfitted, thus losing predictive power (Elith et al. 2011). In such cases, one could erroneously conclude that there is little niche overlap, when in fact the specified suite of variables uniquely detects each occurrence. In this study, we reduced error from multiple dimensions via a two-step process.

First, we removed highly correlated variables and subsequently, we removed variables that

127 were less important during multiple model runs. In addition, using the same number of variables for each breeding group added another layer of consistency when comparing niches.

In future studies, we would also consider averaging models using different environmental predictors to capture the effect of variables that may have low but important effects (Breiner et al. 2015), and analyzing possible spatial autocorrelation during variable selection.

Niche comparison

Recent studies have observed niche differences between breeding systems in plants;

Randle et al. (2009) found that greater selfing ability led to significantly larger range size within

Collinsia. This is corroborated by additional studies (Johnson et al. 2014; Grossenbacher et al.

2015) and is consistent with Baker’s Law (Baker 1955) which states that selfers display greater range size due to higher colonization ability because they are not restricted by mate availability.

However, dimorphic species appear to be successful colonists in Hawaii, indicating that they were not limited by access to mates (Sakai et al. 1995). In fact, of the three species found on multiple islands, one is subdioecious and two are hermaphroditic outcrossing species. One of these species is S. globosa, which is predicted to be evolving increasing dioecy because its populations contain almost equal numbers of males and females with a few hermaphrodites (Sakai & Weller 1991; Spigler &

Ashman 2011).

Niche comparison among all five breeding systems

Species that respond in similar ways to environmental predictors are likely to share some common functional traits (Kalkvik et al. 2012; Johnson et al. 2014); for such species, we

128 would expect to see greater niche overlap as in the case of the two hermaphroditic Schiedea species groups. Current evidence indicates that breeding systems within Schiedea evolved from hermaphroditic ancestors and then diversified into gynodioecious, subdioecious, and dioecious species (Sakai et al. 2006; Willyard et al. 2011). Thus, dioecious species are the most recent products of such an adaptive radiation. We should expect decreasing niche overlap from hermaphroditic to gynodioecious, subdioecious, and dioecious groups. While we observe a step-wise reduction in niche overlaps between both hermaphroditic groups to gynodioecious and subdioecious groups, there remains a high overlap between hermaphroditic and dioecious groups (Table 4.6a). Some of the high degree of overlap could be explained due to introgression in S. spergulina (a dioecious species), a possible sister species to S. stellaroides (hermaphrodite outcrossing) based on plastid data (Willyard et al. 2011). However, even after removing S. spergulina from the analysis (Table 4.6b), the hermaphroditic groups continued to display high niche overlap to the dioecious group. This may seem surprising at first glance, but evidence suggests that intermediate forms (such as gynodioecy and subdioecy) during speciation tend to occupy smaller niches, and thus emphasize niche dissimilarity between groups (Wiens et al.

2010). If we contend that gynodioecious and subdioecious groups are intermediate forms, then our results are consistent with the intermediate form hypothesis. The niche overlap patterns for the three dimorphic groups do not show any clear pattern common with breeding system evolution within Schiedea. Is this a reflection of the unresolved phylogeny for these groups? A further combined analysis comparing species level SDMs and phylogenetic data may help to clarify the patterns observed in this study. Schiedea species hybridize easily and there are some

129 cases of introgression (Weller & Sakai 1999; Willyard et al. 2011) which could affect niche comparison results, i.e., introgression may yield greater niche overlap.

Niche comparison among hermaphroditic groups

Percent niche overlap was greatest between the hermaphroditic outcrossing and facultative selfing groups, even more than the two selfing groups. The phylogenetic evidence would suggest that the two selfing groups would display greater niche overlap as they are more closely related (Sakai et al. 2006; Willyard et al. 2011). However, the degree of selfing within the facultative selfing group may vary, as one species (S. hawaiiensis) is thought to be wind pollinated and others indicate a history of possible biotic pollinated in the past (S. viscosa) suggested by the high levels of nectar present (Weller et al. 1998). As selfing has evolved three separate times within Schiedea, perhaps some of the facultative selfing species have retained some aspects of their ancestral abiotic niches, consistent with niche conservatism (Sakai et al.

2006). Additional comparisons between species level SDMs and phylogenetic signal may help to clarify responses to environmental gradients that lead to niche divergence between the facultative and obligate selfing categories. The overall pattern suggests that breeding systems within the genus are highly labile with several exceptions to general patterns within breeding system categories; these exceptions tend to be restricted to one island or few populations and may not be stable with respect to breeding. For example, S. hawaiiensis is a facultative selfing species that occurs in dry regions unlike most facultative species, similarly, S. lydgateii is thought to have reverted to hermaphroditism from a dimorphic breeding system (Weller et al.

1998).

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The case for niche divergence

Recent discussion has highlighted the need for further research into mechanisms and scenarios to evaluate niche conservatism and divergence (Nakazato et al. 2010; Wiens et al.

2010; Peterson et al. 2011; Araújo et al. 2013). While the fundamental abiotic niche is generally thought to be well-conserved, except in rare situations, the realized niche which includes biotic interactions and movement is assumed to be more disposed to change (Peterson et al. 2011). In

Schiedea, we would expect to see significant differences in realized and possibly fundamental niches because of the adaptive radiation mode of speciation in this group. Theory indicates that in groups where there is rapid diversification, history of multiple colonization events, strong population structure, and differential selective pressure in novel habitats, niches would tend to diverge (Peterson et al. 1999). The results presented here suggest that ancestral ecological niches may not have been conserved during diversification to obligate selfing but moderate niche conservatism is observed for the transitions to facultative selfing and dioecy as breeding systems evolved within Schiedea.

Future directions

Most studies investigating ecological niche comparison use low-resolution data from the

WorldClim dataset, and the geographic scale can have a potential significant effect on results.

Because plant species cannot relocate, they are more sensitive than animal species to fine-scale environmental differences that may not be captured at low spatial resolution. In our study, we used a high spatial resolution of 30 meters that is sufficient to characterize fine-scale spatial

131 heterogeneity in environmental predictors and improve identification of occurrences. On the other hand, a few studies suggest that fine-scale studies may highlight niche differences

(Kalkvik et al. 2012; Alvarado-Serrano 2014) because fine-scale topographic variables often include some attributes of biotic interactions. Thus, the models produced with these variables possibly reflect the abiotic plus biotic niche (Wisz et al. 2013), but nonetheless still miss the crucial component of movement that also affects species distributions (Peterson et al. 1999).

Therefore, we recommend that future such studies examine geographic scale factors or utilize multi-scale approaches, in addition to including more explicit biotic and movement variables, when considering niche comparisons.

While there is much literature devoted to techniques for selecting pseudo-absences and the importance of such absences in model discrimination, there is little consensus on best practices (Barbet-Massin et al. 2012; Iturbide et al. 2015). Pilot experimentation in this study with a 1:10, 1:20, and a random 10,000 absence generation by Maxent indicate that more absences result in higher AUC values but do not reflect higher accuracy. In this study, we picked an intermediate ratio of presence to absence data for lack of reliable information on generating absences, and further refined absence selection by spatially limiting absences to islands of occurrences and using a buffer of 180 meters away from presences. However, some have suggested using a larger buffer distance when selecting absences (Iturbide et al. 2015).

Analyses that are more rigorous for absence selection are required for studies involving rare or uncommon species.

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Concluding remarks

Our analyses provides support for both niche divergence between some breeding system pairs, and also support for niche conservatism between some breeding categories within the

Hawaiian endemic plant genus Schiedea. The results highlight abiotic factors that may be associated with the spatial distribution of species with different breeding systems as they radiated into different environments within Schiedea. Niche conservatism may be more prevalent in regions with higher environmental stability than the Hawaiian Islands as similar findings have been reported in European bird assemblages that were subject to environmental stochasticity (Pearman et al. 2014). As far as we are aware, there are no current studies that have investigated ecological niches in such a variety of breeding systems within a single genus. Thus, our work provides new insights into comparative niche analyses across taxa.

Quantifying niche differences using species occurrence data represents a combination of the environmental niches of species, some biotic interactions (dependent on the geographic scale used), the colonization history, dispersal limitations, and the available suitable habitat (Guisan et al. 2005;

Elith et al. 2006). The models developed here provide analytical evidence clarifying the environmental conditions associated with breeding system shifts in plants using Schiedea as a model system and whether there is a spatial pattern in the distribution. Since phylogenetic relationships could strongly influence niche convergence or divergence, evidence of phylogenetic signal is essential to interpret niche comparisons (Wiens et al. 2010). We would highly recommend a parallel comparison of ecological niche comparison to phylogenetic signal to further validate the results presented here.

Because more than half the species within Schiedea are on the U.S. Federal Endangered

Species List, it is vital to understand the environmental factors that affect species distributions and

133 ranges within this Hawaiian endemic group. Many of these species have restricted ecological ranges and face increasing threats from urbanization and potentially climate change. In addition to their geographic location, specific characteristics of plant species, such as breeding system, can act in concert with habitat loss to exacerbate extinction risks (Ashman 1999).

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Chapter 4 – FIGURES AND TABLES

Table 4.1. List of all Schiedea species used in species distribution modeling; di=dioecious, sd=sub-dioecious, gy=gynodioecious, hm_out=hermaphrodite (outcrossing), hm_self=hermaphrodite selfing. All species with georeferenced locations were used from the Schiedea monograph (Wagner et al. 2005) and supplemented from BISON (BISON USGS).

Breeding Species List #Occurrences #Taxa hermaphrodite S. attenuata, S. helleri, S. hookeri, S. implexa, S. kaalae, S. kauaiensis, 109 15 outcrossing S. lydgatei, S. membranaceae, S. menziesii, S. nuttalii, S. nuttalii_S. implexa, S. pentandra, S. perlmanii, S. pubescens, S. stellariodes hermaphrodite S. diffusa diffusa, S. diffusa_macrae, S. jacobii, S. laui, S. lychnoides, S. 57 8 selfing obovata, S. trinervis, S. viscosa gynodioecious S. adamantis, S. apokremnos, S. salicaria, S. sarmentosa 29 4 sub-dioecious S. globosa, S. kealiae, S. mannii 37 3 dioecious S. haleakalensis, S. spergulina, S. ligustrina, 29 3

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Distribution of breeding systems within Schiedea

Figure 4.1 Distribution of breeding systems within the genus Schiedea in the Hawaiian Islands overlaid with an elevation layer; di=dioecious, sd=subdioecious, gy=gynodioecious, hm_self= hermaphroditic selfing, hm_out=hermaphroditic outcrossing. Facultatively and obligately selfing species were combined in this analysis. Projection: UTM

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Distribution of hermaphroditic species within Schiedea

Figure 4.2 Distribution of outcrossing and selfing species that display hermaphroditic breeding systems within Schiedea in the Hawaiian Islands. Facultatively and obligately autogamous species were combined into the selfing category. Projection: UTM

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Table 4.2. All predictors considered a priori for SDM variable selection are listed. Variable selection was optimized for each breeding system group and final variables were selected after three initial model runs. Temperature data were obtained from the PRISM datasetA, rainfall from the Hawaii Rainfall AtlasB, elevation data from the USGS Digital Elevation datasetC, and soil data from USDA GSURGOD.

Data Type Predictor Scale Climate Annual average rainfall 250m Summer average rainfall 250m Winter average rainfall 250m Rainfall in wettest 2 months 250m Rainfall in driest 2 months 250m Rainfall in warmest 2 months 250m Rainfall in coldest 2 months 250m Winter minimum average temperature 400m Summer maximum average temperature 400m Maximum Temperature of warmest 2 months 400m Minimum Temperature of coldest 2 months 400m Minimum temperature in wettest 2 months 400m Maximum temperature in driest 2 months 400m Topographic Elevation 30m Slope 30m 2nd derivative of Slope 30m Standard deviation of slope (3x3) 30m Transformed aspect 30m Sine of aspect 30m Cosine of aspect 30m Annual solar radiation 30m Relative topographic position (RTP) 30m Terrain Ruggedness Index (TRI) 30m Compound Topographic Index (CTI) 30m Topographic Position Index (TPI) 30m Soil Soil pH 30m Available water supply (0-100cm) 30m Soil moisture (0-100 cm) 30m Percent organic matter 30m Erodibility factor (Kffact) 30m A http://www.prism.oregonstate.edu B http://rainfall.geography.hawaii.edu C http://pubs.usgs.gov D hhttp://soils.usda.gov

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Table 4.3 Final list of the six variables selected for each breeding system group in Schiedea; variables are ordered according to percent contribution as determined by the Maxent algorithm.

Breeding system Predictors Percent contribution (ordered predictors) annual maximum temperature, average summer rainfall, 52.5, 21, 11.7, 11.1, Hermaphrodite topographic position index, slope, sine of aspect, average 3.3, 0.4 outcrossing annual insolation annual maximum temperature, average annual insolation, 64.2, 19.1, 11.4, 3.8, Hermaphrodite average summer rainfall, topographic position index, slope, 1, 0.5 selfing sine of aspect slope, average summer rainfall, annual maximum 57.5, 36.2, 2.4, 2.3, Gynodioecious temperature, average annual insolation, sine of aspect, , 1.5, 0.1 topographic position index slope, topographic position index, average annual insolation, 23.6, 18.1, 18.1, 17.6, Subdioecious annual maximum temperature, average summer rainfall, 13.4, 9.2 sine of aspect average summer rainfall, slope, annual maximum 54.1, 38.2, 5.1, 1.4, 1, Dioecious temperature, sine of aspect, topographic position index, 0.2 average annual insolation

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Hermaphroditic outcrossing

Hermaphroditic selfing

Gynodioecious

Subdioecious

Dioecious Figure 4.3 Marginal response curves for the three most important predictors for each breeding group as determined by the MaxEnt algorithm. These display the change in prediction in the multi-variate model as each predictor variable is varied keeping all other variables at their average value.

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(a)hermaphroditic outcrossing breeding group (b) hermaphroditic selfing breeding group mean AUC=0.873 (sd=0.038) mean AUC=0.873 (sd=0.073)

(c)gynodioecious breeding group (d) subdioecious breeding group mean AUC=0.877 (sd=0.049) mean AUC=0.917 (sd=0.047)

(e)dioecious breeding group with S. spergulina (f)dioecious breeding group without S. spergulina mean AUC=0.936 (sd=0.027) mean AUC=0.934 (sd=0.022)

Figure 4.4 Receiver operating curve plots for final models for each breeding system group. The red curve displays the AUC curve relative to the random prediction (black line). The blue area is the one standard deviation from the mean curve.

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With S. spergulina Without S. spergulina

Figure 4.5 Breeding system predicted presence suitabilities from Maxent Models. Bright yellow areas represent high habitat suitability and dark blue regions low habitat suitability. Red dots indicate occurrences for each breeding category.

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0.90 0.80 0.70 0.60 0.50 0.40

D/I Metric D/I 0.30 D Metric 0.20 0.10 I Metric 0.00

(a) Niche comparison with all 33 taxa included within groups

0.90 0.80

0.70

0.60 0.50 0.40

D/I Metrics D/I 0.30 D Metric 0.20 0.10 I Metric 0.00

(b) Niche comparison after removing S. spergulina from the dioecious group

D Metric 1.00 I Metric

0.80 0.60 0.40 D/I Metric D/I 0.20 0.00 hm_out/oa hm_out/fa oa/fa

Figure 4.6 Pairwise comparison of ecological niches for breeding system groups in Schiedea using Schoener’s D metric and the Identity (I) metric, all comparison were significantly different from random null models; di=dioecious, sd=subdioecious, gy=gynodioecious, hm_self= hermaphroditic selfing, hm_out=hermaphroditic outcrossing, oa=obligate selfing; fa=facultative selfing. 149

CHAPTER 5

Main Conclusions

Sunita Yadav

Department of Biological Sciences,

University of Cincinnati,

614 Rieveschl Hall, Cincinnati, OH 45221, USA

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Endemic species on oceanic islands generally have restricted distributions and face increasing threats to their habitat due to habitat loss and global climate change. Such negative impacts on a species can have demographic and genetic consequences that lead to a reduced population size. Furthermore, species with small population sizes have less genetic potential to respond to environmental change (Barret & Kohn 1991). Therefore, preservation of suitable habitat and predicting threats to habitat early can avoid potential future population losses

(Negrón-Ortiz 2014). Species distribution modeling (SDM) techniques quantify a species relationship to its environment to predict a species’ realized niche. They are thus a useful tool for addressing fundamental questions in ecology relating to estimating species ranges, mapping areas of species reintroductions or ecosystem restoration, or predicting potential threats to current and future habitat (Guisan & Thuiller 2005; Soberón and Nakamura 2009). However, modeling the distribution of rare or uncommon species is a continuing challenge in the field

(Guisan et al. 2006; Wisz and Guisan, 2009; Lomba et al. 2010). Therefore, one goal of this dissertation was to provide a new case study of modeling an uncommon species with a narrow distribution to evaluate and compare different methods (Chapter 1).

In this dissertation, I collected species presence and absence locations and abiotic environmental data to model the distribution of S. globosa. Our results show that algorithmic modeling approaches, such as Random Forest and Maxent, perform better than simple statistical methods (GLM and GAM) using two evaluation criteria (threshold independent AUC, and threshold dependent TSS). In addition, we confirm that climate variables appear to be more important in models at broad spatial scales but lose importance at finer scales. Model accuracy was also considerably improved for all four modelling approaches after inclusion of

151 fine spatial resolution topographic variables. Past studies rarely used landscape variables, but evidence is accumulating that such data are critical to produce relevant models, particularly to model distributions of rare species. Finally, the most accurate model was applied to estimate shoreline change at a 100 year and 200 year time period. These results predict greater potential habitat losses on Maui than O’ahu. While the short-term habitat losses are minimal, long-term losses in 200 years on Maui could be significant.

A second goal of this dissertation was to investigate current genetic diversity within S. globosa and evaluate changes to this diversity due to predicted average shoreline change.

Schiedea globosa contains high levels of genetic diversity with strong isolation by distance (IBD) within populations with larger sizes but no evidence of IBD in the smaller populations at different distance classes up to the extent of the population. I also found that genetic diversity and structure were higher on the older island of O’ahu relative to Maui. Results indicate that loss to the genetic diversity due to mean shoreline change at a 100-year time period are negligible with no significant change to allelic frequency or diversity. Two of the seven populations would be affected with a loss of two private alleles out of a total of 21. This suggests that genetic diversity is spatially well distributed within populations and not localized at a fine-scale.

Lastly, to examine how the environment influences the distributions of breeding systems within the entire genus Schiedea, I used SDM methods to predict the distribution of each breeding system on the eight major Hawaiian Islands. The questions I was interested in exploring related to issues of niche conservatism or divergence at a genus level, which are currently fiercely debated topics within the SDM community. I found that niches display little to

152 moderate overlap between the five breeding groups examined here (hermaphroditic outcrossing, hermaphroditic selfing, gynodioecy, subdioecy, and dioecy). I expected less overlap between the hermaphrotidic outcrossing and dioecious groups, however, these two groups had a moderate amount of overlap even after removal of S. spergulina (a species with an unresolved evolutionary history; Willyard et al. 2011). The least amount of niche overlap was found between hermaphroditic outcrossing and obligate selfing groups. Responses to the environment also indicated differences among breeding groups, for example, hermaphroditic outcrossers tend to occur in areas with warmer temperatures and lower average rainfall than hermaphrotidic selfers. Future additions to this research area would need to examine the role of the phylogenetic signal and assess if they correspond to the results observed in this study.

The results presented in this dissertation offer new methodological insights relating to modeling distributions of rare species that are broadly applicable. The data on species threats from shoreline changes can be valuable to make decisions on conservation priorities based on both habitat prioritization and genetic diversity measures. Endemic species on island systems often have small population sizes and narrow geographic distributions (Wagner & Funk 1995;

Caujapé-Castells et al. 2010). These characteristics make such species particularly vulnerable to risk. However, results in this dissertation demonstrate that even species with large enough population sizes, such as S. globosa, could be at risk because their distribution is highly localized and prone to significant threat (Primack 2010). Populations of S. globosa on O’ahu are the most genetically diverse but face the most significant threats from human activity. The largest population of S. globosa was already severely impacted due to road construction. Thus, while the short-term outlook remains stable for S. globosa, extreme storm events or anthropogenic

153 activity could place individual populations or overall genetic diversity at risk. Consequently, such population losses then affect the ability of S. globosa to respond to environmental change.

The habitat suitability areas identified by the SDMs in this dissertation combined with the genetic diversity analysis offer important information about areas that need further protection or could be used for ex-situ conservation.

Habitat loss and species extinctions remain core issues within the ecological community

(Sakai et al. 2002; Fordham et al. 2012). Many studies indicate that climate change is already affecting species, for example by shifting phenological patterns or habitat displacement, and that may eventually lead to increased rates of extinctions (Memmott et al. 2007; Parmesan

2007). Species on the Hawaiian Islands face particularly rising threats due to urbanization with a

600% increase of the human population on the islands in the past 100 years (US Census Bureau

2010). Modelling of species vulnerability to climate change scenarios indicates that large numbers of islands plants are threatened by extinction, habitat shifts, and even complete loss of climatic niches (Fortini et al. 2013). Insights from past climate change analogs, contemporary trends, and future projections provide clues that species on island systems are greatly affected by climatic oscillations that often lead to extinction. A disproportionate proportion of vascular plant diversity exists on islands, about one quarter by some estimates, and extinctions on islands therefore vastly affect global biodiversity (Harter et al. 2015).

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FUTURE DIRECTIONS

The current methods of evaluating species habitats such as correlative SDMs are useful to identify the geographical area and habitat quality and quantity. However, recent discussions

(see Anderson et al. 2009; Kearney et al. 2010; Fordham et al. 2012, Araújo et al. 2013) call for an improvement to models to address mechanistic processes and biotic interactions that influence species distributions more explicitly. A limited amount of evidence from mechanistic models suggests that species traits are important to understand species distributions in dynamic environments (Buckley et al. 2010). The current study could greatly benefit from linking data on demographic processes, for example spatially structured population models to

SDMs to more accurately encapsulate ecological processes. In addition, species traits also affect predictive ability of SDMs; Dobrowski et al. (2011) found that predictive ability was higher for

SDMs of species with greater relative dispersal capability then species with low dispersal capability. Comparisons of correlative-only and coupled population-SDM approaches are currently lacking but much needed to evaluate and predict species responses to habitat modification.

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REFERENCES Anderson, B.J. et al. 2009. Dynamics of range margins for metapopulations under climate change. Proceedings of the Royal Society of London B 276:1415–1420. Araújo, M. B. et al. 2013. Heat freezes niche evolution. Ecology Letters, 16(9), 1206-1219. Barrett, S. C. H. and Kohn, J. 1991. The genetic and evolutionary consequences of small population size in plant: implications for conservation. - In: Falk, D., and Holsinger, K. E. (eds), Genetics and Conservation of Rare Plants. Oxford University Press, pp. 3–30. Buckley, L.B .2010. The range implications of lizard traits in changing environments. Global Ecology and Biogeography, 19, 452–464. Caujapé-Castells, J. et al. 2010. Conservation of oceanic island floras: present and future global challenges. Perspectives in Plant Ecology, Evolution and Systematics, 12(2): 107-129. Dobrowski, S. et al. 2011. Modeling plant ranges over 75 years of climate change in California, USA: relating transferability to species traits. Ecological Monographs, 81, 241–257. Engler, R. et al. 2004. An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data. Journal of Applied Ecology, 41(2): 263–274. Fordham, D. A. et al. 2012. Plant extinction risk under climate change: are forecast range shifts alone a good indicator of species vulnerability to global warming? Global Change Biology, 18(4): 1357-1371. Fortini, L. et al. 2013. A landscape-based assessment of climate change vulnerability for all native Hawaiian plants. Hawaii Cooperative Studies Unit Technical Report HCSU-044. Guisan, A. and Thuiller, W. 2005. Predicting species distribution: offering more than simple habitat models. Ecology Letters, 8(9): 993–1009. Guisan, A. et al. 2006. Using niche-based models to improve the sampling of rare species. Conservation Biology, 20(2): 501–511. Harter, D.E. et al. 2015. Impacts of global climate change on the floras of oceanic islands– Projections, implications and current knowledge. Perspectives in Plant Ecology, Evolution and Systematics, 17(2):160-183. Kearney M.R. et al. 2010. Correlative and mechanistic models of species distribution provide congruent forecasts under climate change. Conservation Letters, 3:203–213. Lomba, A. et al. 2010. Overcoming the rare species modelling paradox: a novel hierarchical framework applied to an Iberian endemic plant. Biological Conservation, 143(11), 2647- 2657. Memmott, J. et al. 2007. Global warming and thedisruption of plant–pollinator interactions. Ecology Letters, 10, 710–717.

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Negrón-Ortiz, V. 2014. Pattern of expenditures for plant conservation under the Endangered Species Act. Biological Conservation, 171: 36-43. Parmesan, C., 2007. Influences of species, latitudes and methodologies on estimatesof phenological response to global warming. Global Change Biology, 13, 1860–1872. Primack, R.B. 2010. Essentials of conservation biology. Fifth Edition. Sunderland, Massachusetts: Sinauer Associates. Sakai, A.K. et al. 2002. Patterns of endangerment in the Hawaiian flora. Systematic Biology, 51(2): 276-302. Soberón, J. and Nakamura, M. 2009. Niches and distributional areas: concepts, methods, and assumptions. Proceedings of the National Academy of Sciences, 106(Supplement 2): 19644- 19650. United States Census Bureau / American FactFinder. Profile of General Demographic Characteristics: 2000 Census 2000 Summary File 1 (SF 1) 100% Data. Wagner, W. L. and Funk, V.A. 1995. Hawaiian Biogeography. Evolution on a Hot Spot Archipelago. Smithsonian Institution Press, Washington, DC. Wisz, M. S. et al. 2008. Effects of sample size on the performance of species distribution models. Diversity and Distributions, 14(5): 763-773.

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APPENDIX 1

Preliminary results on inflorescence architecture

The following are preliminary results for an additional project to analyze inflorescence architecture among sexes in Schiedea globosa.

Comparison of peduncle length, inflorescence length, total flower count, and lateral branch length among sexes in S. globosa. A one-way analysis of variance (ANOVA) test by sex and island for floral characters showed significant differences for all but the inflorescence length (data was log transformed ). Except for lateral branch length, all other floral characters differed markedly between islands. These are a subset of data analyzed for samples collected in the field on Maui and O’ahu during 2012 and 2013.

By Sex (Male, Female, By Island (Maui, O’ahu) Hermaphrodite)

P (α=0.05, df=2) F P (α=0.05, df=1) F

Peduncle Length 0.022* 4.02 0.0001* 21.69

Inflorescence Length 0.594 0.524 0.039* 4.39

Total Flower Number 0.013* 4.55 0.034* 4.65

Lateral Branch Length 0.0083* 5.12 0.85 0.04

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