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Spiny lizards (genus Sceloporus) have long served as important systems for studies of behavior, thermal physiology, dietary ecology, vector biology, speciation, and biogeography. The western fence lizard, Sceloporus occidentalis, is found across most of the major biogeographical regions in the western United States and northern Baja California, Mexico, inhabiting a wide range of habitats, from grassland to chaparral to open woodlands. As small ectotherms, Sceloporus lizards are particularly vulnerable to climate change, and S. occidentalis has also become an important system for studying the impacts of land use change and urbanization on small vertebrates. Here, we report a new reference genome assembly for S. occidentalis, as part of the California Conservation Genomics Project (CCGP). Consistent with the reference genomics strategy of the CCGP, we used Pacific Biosciences HiFi long reads and Hi-C chromatin-proximity sequencing technology to produce a de novo assembled genome. The assembly comprises a total of 608 scaffolds spanning 2,856 Mb, has a contig N50 of 18.9 Mb, a scaffold N50 of 98.4 Mb, and BUSCO completeness score of 98.1% based on the tetrapod gene set. This reference genome will be valuable for understanding ecological and evolutionary dynamics in S. occidentalis, the species status of the California endemic island fence lizard (S. becki), and the spectacular radiation of Sceloporus lizards.

 

Genetic diversity plays a key role in maintaining population viability by preventing inbreeding depression and providing the building blocks for adaptation. Understanding how genetic diversity varies across space is, therefore, of key interest in conservation and population genetics.

Here, we introducewingen, anrpackage for calculating continuous maps of genetic diversity, including nucleotide diversity, allelic richness, and heterozygosity, from standard genotypic and spatial data using a spatial moving window approach. We provide functions to account for variation in sample size across space using rarefaction, to create kriging‐interpolated maps of genetic diversity, and to mask any areas that are outside the area of interest.

Tests with simulated and empirical datasets demonstrate thatwingencan successfully capture variation in genetic diversity across landscapes from both reduced‐representation and whole genome sequencing datasets. For reduced‐representation datasets,wingen's functions can be run easily on a standard laptop computer, and we provide options for parallelization to increase the efficiency of running larger whole genome datasets.

wingenprovides novel and computationally tractable tools for creating informative maps of genetic diversity with applications for conservation prioritization as well as population and landscape genetic analyses.

 

Abstract Understanding the drivers of spatial patterns of genomic diversity has emerged as a major goal of evolutionary genetics. The flexibility of forward-time simulation makes it especially valuable for these efforts, allowing for the simulation of arbitrarily complex scenarios in a way that mimics how real populations evolve. Here, we present Geonomics, a Python package for performing complex, spatially explicit, landscape genomic simulations with full spatial pedigrees that dramatically reduces user workload yet remains customizable and extensible because it is embedded within a popular, general-purpose language. We show that Geonomics results are consistent with expectations for a variety of validation tests based on classic models in population genetics and then demonstrate its utility and flexibility with a trio of more complex simulation scenarios that feature polygenic selection, selection on multiple traits, simulation on complex landscapes, and nonstationary environmental change. We then discuss runtime, which is primarily sensitive to landscape raster size, memory usage, which is primarily sensitive to maximum population size and recombination rate, and other caveats related to the model’s methods for approximating recombination and movement. Taken together, our tests and demonstrations show that Geonomics provides an efficient and robust platform for population genomic simulations that capture complex spatial and evolutionary dynamics. 

Cryptogenic species are those whose native and introduced ranges are unknown. The extent and long history of human migration rendered numerous species cryptogenic. Incomplete knowledge regarding the origin and native habitat of a species poses problems for conservation management and may confound ecological and evolutionary studies. The Lesser Antilles pose a particular challenge with regard to cryptogenic species because these islands have been anthropogenically connected since before recorded history. Here, we use population genetic and phylogeographic tools in an attempt to determine the origin ofEleutherodactylus johnstonei, a frog species with a potentially widespread introduced range and whose native range within the Lesser Antilles is unknown. Based on elevated estimates of genetic diversity and within-island geographic structure not present elsewhere in the range, we identify Montserrat as the native island ofE. johnstonei. We also document two major clades withinE. johnstonei, only one of which is the primary source of introduced populations throughout the Americas. Our results demonstrate the utility of genetic tools for resolving cryptogenic species problems and highlightE. johnstoneias a potential system for understanding differences in invasive potential among sister lineages.

 

Abstract 1. Estimating biologically meaningful geographic distances is essential for research in disciplines ranging from landscape genetics to community ecology. Topographically correcting distances to account for the total overland distance between locations imposed by topographic relief provides one method for calculating geographic distances that account for landscape structure. 2. Here, I present TOPODISTANCE, an R package for calculating shortest topographic distances, weighted topographic paths and topographic least cost paths (LCPs). Topographic distances are calculated by weighting the edges of a graph by the hypotenuse of the horizontal and vertical distances between raster cells and then finding the shortest total path between cells of interest. The package also includes tools for mapping topographic paths and plotting elevation profiles. 3. Examples from a species with moderate dispersal abilities, the western fence lizard, inhabiting a topographically complex landscape, Yosemite National Park (USA), demonstrate that topographic distances can vary significantly from straight-line distances, and topographic LCPs can trace very different routes from LCPs and shortest topographic paths. 4. Topographic paths and distances are broadly useful for modelling geographic isolation resulting from dispersal limitation for organisms that interact with the topographic structure of a landscape during movement and dispersal. 

Estimating biologically meaningful geographic distances is essential for research in disciplines ranging from landscape genetics to community ecology. Topographically correcting distances to account for the total overland distance between locations imposed by topographic relief provides one method for calculating geographic distances that account for landscape structure.

Here, I presenttopoDistance, anrpackage for calculating shortest topographic distances, weighted topographic paths and topographic least cost paths (LCPs). Topographic distances are calculated by weighting the edges of a graph by the hypotenuse of the horizontal and vertical distances between raster cells and then finding the shortest total path between cells of interest. The package also includes tools for mapping topographic paths and plotting elevation profiles.

Examples from a species with moderate dispersal abilities, the western fence lizard, inhabiting a topographically complex landscape, Yosemite National Park (USA), demonstrate that topographic distances can vary significantly from straight‐line distances, and topographic LCPs can trace very different routes from LCPs and shortest topographic paths.

Topographic paths and distances are broadly useful for modelling geographic isolation resulting from dispersal limitation for organisms that interact with the topographic structure of a landscape during movement and dispersal.

 

A primary challenge for modern phylogeography is understanding how ecology and geography, both contemporary and historical, shape the spatial distribution and evolutionary histories of species. Phylogeographic patterns are the result of many factors, including geology, climate, habitat, colonization history and lineage‐specific constraints. Assessing the relative influences of these factors is difficult because few species, regions and environments are sampled in enough detail to compare competing hypotheses rigorously and because a particular phylogeographic pattern can potentially result from different evolutionary scenarios. The silky anoles (Anolis sericeuscomplex) of Central America and Mexico are abundant and found in all types of lowland terrestrial habitat, offering an excellent opportunity to test the relative influences of the factors affecting diversification. Here, we performed a range‐wide statistical phylogeographic analysis on restriction site‐associated DNA (RAD) markers from silky anoles and compared the phylogeographic patterns we recovered to historical and contemporary environmental and topographic data. We constructed niche models to compare niche overlap between sister lineages and conducted coalescent simulations to characterize how the major lineages of silky anoles have diverged. Our results revealed that the mode of divergence for major lineage diversification events was geographic isolation, resulting in ecological divergence between lineages, followed by secondary contact. Moreover, comparisons of parapatric sister lineages suggest that ecological niche divergence contributed to isolation by environment in this system, reflecting the natural history differences among populations in divergent environments.