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  1. Abstract

    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.

  2. Wilson, Melissa (Ed.)
    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 spatialmore »and evolutionary dynamics.« less
  3. 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.