This dissertation is primarily focused on closely-related species’ coexistence and how it is both a function and a driver of phenotypic evolution. To understand how phenotypic diversity evolves in relation to a species’ interactions and evolutionary history, I focus on ecomorphology—an organisms’ ecological role and morphological adaptations. My dissertation uses field and museum-based methods to quantify the dynamics of ecomorphological evolution within the genus Sceloporus lizards, a radiation of ~100 species, and how it influences species’ resource use and distributions, from sites to landscapes to continents, and from populations to communities to the entire genus.Chapter I of my dissertation examines the relationship between resource use and phenotypic variation among four species of co-occurring lizards in a sky island system, the Chiricahua Mountains of southeastern Arizona. This isolated environment provides an opportunity to quantify resource use where forces of selection are expected to be strong. I quantified niche use of 300+ co-occurring lizards across multiple axes to determine their relative importance and found that perch height and type explained most variance between species, but also uncovered variation in the temporal and thermal activity of these interacting species. By measuring phenotypic traits for all individuals, I found that species differed in traits associated with climbing ability, thus suggesting a coevolved relationship between physical traits and divergent niches.

In Chapter II, I explored another resource axis along which many co-occurring species differentiate by quantifying the trophic ecology of the Sceloporus species in the Chiricahua Mountains. Using DNA metabarcoding, I found significant differences in species’ diets concomitant with their respective ecologies as uncovered in Chapter 1. As sit-and-wait predators, diet is a function of microhabitat, underscoring the importance of spatial niche partitioning in allowing co-occurrence in this system.

In Chapter III, I examined the dynamics of ecomorphological evolution Sceloporus and its role in facilitating coexistence between congeners. By collecting ecological and morphological data for 80 taxa from 300+ specimens at 5 museums, I found that multivariate morphology is a good predictor of ecology and that Sceloporus species occur in six ecological modes with associated morphologies (ecomorphs). I show that the evolution of arboreality at the base of a rapid radiation led to further ecological and morphological evolution. Examination of the spatial distribution of ecomorphology revealed a pattern of spatial overdispersion: across their range Sceloporus assemblages are comprised of more ecomorphs than expected by chance, suggesting ecological and morphological diversity play a key role in spatial sorting.

Together, the results of my dissertation suggest that across the Sceloporus radiation, ecology andmorphology have evolved in concert and these traits influence the spatial distribution of populations, species, and assemblages.

This dissertation is primarily focused on understanding the generation and maintenance of phenotypic diversity through ecological selection. Natural selection acts on the phenotype because the phenotype lies at the interface between an organisms genotype and its environment. Thus, the study of phenotypic diversity is central to our understanding of evolution. I have primary addressed questions of phenotypic evolution using the radiations of Caribbean Anolis lizards. Chapter I of my dissertation examines if claw and toepad morphology formed an integrated phenotype correlated with microhabitat partitioning in Greater Antillean anoles. The Greater Antilles forms an exceptional system to study convergent evolution due to the repeated evolution of structural microhabitat specialist ‘ecomorphs’. As locomotor traits associated with clinging performance, claw and toepad traits are likely highly relevant to partitioning of vertical arboreal habitat. I found that correlative selection did drive the evolution of claw and toepads as an integrated phenotype to optimize performance on specific microhabitats. Additionally, rates of evolution were coupled between claws and toepads across most species, except twig specialists which experienced a dramatic reduction in claw structure. Thus, this study provides a convincing case study for correlative selection facilitating ecological divergence between species. Chapter II studies claw and toepad integration in the context of the species depauperate Lesser Antillean anole fauna. These anoles represent broad habitat generalists with only one or two species per island. Broadly, claw and toepad morphology correlated with overall island habitat availability on one species islands, but not two species islands. Here, I found that Anolis bimaculatus series anoles closely-related to the Greater Antillean adaptive radiations followed predictable patterns of divergence on two species islands in that they partitioned vertical habitat space and showed associated phenotypic integration between claws and toepads. However, the distantly-related A. roquet series anoles did not show predictable patterns of divergence on two species islands and a decoupling of claw and toepad traits. These results suggest that phylogenetic history may influence the capacity for adaptive divergence. Chapter III tests for parallel intraspecific patterns of divergence across environmental gradients in nine species of A. bimaculatus series anoles inhabiting the northern Lesser Antilles. Species within the A. bimaculatus series are broadly generalist and occur widely throughout their native islands. Thus, each encounters a wide range of environmental variation ranging from xeric coastal scrub to montane rainforests. I found that patterns of morphological divergence across these environmental gradients were idiosyncratic. It is possible that due to many-to-one mapping of form to function, adaptation to environmental gradients can be achieved through different ecologically-relevant traits. However, strong patterns of convergence did emerge for some traits, notably dorsal coloration. Chapter IV examines the phenomenon of species richness-genetic diversity in the context of island biogeography and extends this framework to include phenotype diversity. Specifically, I examined the relative roles of island size, distance from mainland, environmental variance, and topographic complexity across the Lesser Antilles. I partitioned biogeographical effects across species, phenotypic, and genetic diversity using both native and introduced herpetofauna. I found that island size correlates with both species richness and genetic diversity in all contexts. Secondary island characteristic effects on species and genetic diversity differed between groups likely due to differences in their natural history. Phenotypic diversity was not correlated with tested island variables in all cases suggesting that macroecological forces which shape species and genetic diversity are decoupled from phenotypic diversity.

A major goal of landscape genomics is to understand how spatiotemporal variability in complex environments influences evolutionary dynamics, and consequently geographic patterns of genomic diversity, in natural populations. Recent computational advances enable this spatiotemporal complexity to be described and analyzed in unprecedented detail. One such advance is the improvement of forward-time landscape genomic simulation, allowing arbitrarily complex evolutionary scenarios that mimic real-world systems to be created and studied in silico. In chapter 1, I present Geonomics, a new, user-friendly Python package for performing complex, spatially explicit, landscape genomic simulations on changing landscapes and with full spatial pedigrees. I describe the structure and function of Geonomics in detail, show that its results are consistent with expectations for a variety of validation tests based on classical population genetics, then demonstrate its utility and flexibility with example scenarios featuring polygenic selection, selection on multiple traits, and non-stationary environmental change on realistic landscapes. Taken together, these tests and demonstrations establish Geonomics as a robust platform for population genomic simulations that incorporate complex spatiotemporal dynamics.

In chapter 2, I apply the software developed in chapter 1 to one of the major areas of theoretical and applied interest in landscape genomics: the evolutionary consequences of climate change. I model climate change realistically, as the decoupling of historical environmental gradients that generates novel multivariate selective environments. I then simulate evolutionary responses to that climate change event across a range of genomic architectures, defined as the full factorial crossing of discretized levels of three key architectural components: the number of genes per trait (polygenicity), the recombination rate between neighboring genes (linkage), and the number of distinct genotypes generating identical phenotypes (genotypic redundancy). I use the results to test a series of hypotheses about the influence of polygenicity, linkage, and redundancy on gene flow, maladaptation, and demographic decline. Results show that a commonly assumed mechanism of evolutionary rescue, adaptive gene flow from populations whose current climates approximate future projection, can be less effective than in situ adaptation under some architectures, likely because of maladaptive introgression caused by the decoupling of environmental gradients. I also find that high polygenicity aggravates maladaptation and demographic decline, a concerning result given the likely polygenic nature of many climate-adapted traits, but that higher genotypic redundancy increases adaptive capacity across all scenarios, adding to the growing recognition of its importance. Overall, this chapter shows that genomic architecture, though it is often ignored, can exert large influence over the effectiveness and relative magnitudes of adaptive gene flow and in situ adaptation in a spatially distributed population subjected to climate change.

Another major computational advance facilitating the study of spatiotemporal evolutionary dynamics is the advent of massive and distributed geocomputation. In chapter 3, I use this tool set to study, in unprecedented detail, global geographic variability in the seasonality of terrestrial plant productivity – i.e., land surface phenology (LSP). Not only does the geography of LSP convey critical information about environmental controls on plant function and carbon cycling, but it has important implications for evolutionary biogeography: spatial asynchrony in LSP can indicate the potential for spatial asynchrony in reproductive phenology, and thus for increased genetic isolation and divergence between conspecific populations. Thus, whereas chapter 2 provides an example of the spatial nature of an evolving system influencing its temporal dynamics, this chapter provides an example of the less-appreciated inverse situation: the potential for a system’s temporal complexity to influence its evolutionary dynamics. Despite its importance, LSP research lacks mapping methodologies that can characterize the full diversity of terrestrial phenologies, and LSP asynchrony mapping is even less developed. Here, I develop a multivariate, generalized, and robustly-validated LSP mapping methodology, based on simple harmonic regression, then apply it to a 10-year, 0.05° dataset of MODIS near-infrared reflectance of vegetation (NIRV, a proxy of plant productivity). This produces a global LSP map that reveals surprising diversity, including both regional patterns of heterogeneity that are corroborated by prior research and intercontinental patterns of convergence that recapitulate major bioclimatic and biogeographic gradients. Next, I calculate and present a global map of LSP asynchrony, and use machine learning to explore regional variability in its potential climatic and physiographic drivers. I describe LSP asynchrony hotspots in the world’s five Mediterranean climate regions, where asynchrony appears driven by precipitation asynchrony and spatial variability in vegetation structure, and in tropical montane regions, where minimum temperature asynchrony and precipitation asynchrony appear to be interacting drivers. Lastly, I use an ensemble of regressions within global high-asynchrony regions to demonstrate that phenological asynchrony between climatically similar sites is most frequent at lower latitudes, supporting the notion that phenological asynchrony is most likely to cause allochrony and consequent evolutionary divergence in the tropics.