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ABSTRACT Landscape genomic approaches for detecting genotype‐environment associations (GEA), isolation by distance (IBD) and isolation by environment (IBE) have seen a dramatic increase in use, but there have been few thorough analyses of the influence of sampling strategy on their performance under realistic genomic and environmental conditions. We simulated 24,000 datasets across a range of scenarios with complex population dynamics and realistic landscape structure to evaluate the effects of the spatial distribution and number of samples on common landscape genomics methods. Our results show that common analyses are relatively robust to sampling scheme as long as sampling covers enough environmental and geographic space. We found that for detecting adaptive loci and estimatingIBE, sampling schemes that were explicitly designed to increase coverage of available environmental space matched or outperformed sampling schemes that only considered geographic space. When sampling does not cover adequate geographic and environmental space, such as with transect‐based sampling, we detected fewer adaptive loci and had higher error when estimatingIBDandIBE. We found thatIBDcould be detected with as few as nine sampling sites, while large sample sizes (e.g., greater than 100 individuals) were crucial for detecting adaptive loci andIBE. We also demonstrate that, even with optimal sampling strategies, landscape genomic analyses are highly sensitive to landscape structure and migration—when spatial autocorrelation and migration are weak, commonGEAmethods fail to detect adaptive loci. 

Synopsis As species move into new environments through founder events, their phenotypes may diverge from native populations. Identifying the drivers underlying such variation, and the constraints on the adaptive potential of this variation, is essential for understanding how organisms respond to new or rapidly changing habitats. Such phenotypic divergence may be especially evident in populations introduced to new environments via human-assisted transport or populations in dramatically altered environments such as cities. Sexually dimorphic species beg the additional questions of how these new environments may influence sexes differently and how dimorphism may shape the range of potential responses. The repeated translocation, establishment, and spread of wall lizards (Podarcis spp.) from native European populations to new locations in North America provide an excellent natural experiment to explore how phenotypes may differ after establishment in a new environment. Here, we quantify body shape and the multivariate morphological phenotype (incorporating limb dimensions and head length) of common wall lizards (P. muralis) and Italian wall lizards (P. siculus) in replicated North American introductions. In both species, males are larger and have larger head length and limb dimensions than females across all sampled groups. Sexual dimorphism in the multivariate morphological phenotype was of similar magnitude when comparing native and introduced populations for both species, though the trajectory angles in multivariate trait space differed in P. siculus. When comparing introduced lizards from contemporary and historically collected museum specimens, we identified differences of similar magnitude but in different trajectories between sexes in P. siculus, and differences in both magnitude and direction of sexual dimorphism in P. muralis. These idiosyncratic patterns in phenotypic trajectories provide insight to the potential array of processes generating phenotypic variation within species at the intersection of invasion biology and urban evolution. 

Abstract Landscape genomics can harness environmental and genetic data to inform conservation decisions by providing essential insights into how landscapes shape biodiversity. The massive increase in genetic data afforded by the genomic era provides exceptional resolution for answering critical conservation genetics questions. The accessibility of genomic data for non‐model systems has also enabled a shift away from population‐based sampling to individual‐based sampling, which now provides accurate and robust estimates of genetic variation that can be used to examine the spatial structure of genomic diversity, population connectivity and the nature of environmental adaptation. Nevertheless, the adoption of individual‐based sampling in conservation genetics has been slowed due, in large part, to concerns over how to apply methods developed for population‐based sampling to individual‐based sampling schemes. Here, we discuss the benefits of individual‐based sampling for conservation and describe how landscape genomic methods, paired with individual‐based sampling, can answer fundamental conservation questions. We have curated key landscape genomic methods into a user‐friendly, open‐source workflow, which we provide as a new R package, A Landscape Genomics Analysis Toolkit in R (algatr). Thealgatrpackage includes novel added functionality for all of the included methods and extensive vignettes designed with the primary goal of making landscape genomic approaches more accessible and explicitly applicable to conservation biology. 

Abstract 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. 

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.