Abstract
Distance, environmental heterogeneity and local adaptation can strongly influence population structure and connectivity. Understanding how these factors shape the genomic landscape of threatened species is a major goal in conservation genomics and wildlife management. Herein, we use thousands (6,859) of single nucleotide polymorphism markers and spatial data from hundreds of individuals (n = 646) to re-evaluate the population structure of Agassiz’s desert tortoise (Gopherus agassizii). Analyses resolve from 4 to 8 spatially well-defined clusters across the range. Western, central, and southern populations within the Western Mojave recovery unit are consistent throughout, while analyses sometimes merge other recovery units depending on the level of clustering. Causal modeling consistently associates genetic connectivity with least-cost distance, based on multiple landscape features associated with tortoise habitat, better than geographic distance. Some features include elevation, soil depth, rock volume, precipitation, and vegetation coverage, suggesting that physical, climatic, and biotic landscape features have played a strong evolutionary role restricting gene flow between populations. Further, 12 highly differentiated outlier loci have associated functions that may be involved with neurogenesis, wound healing, lipid metabolism, and possibly vitellogenesis. Together, these findings have important implications for recovery programs, such as translocations, population augmentation, reproduction in captivity and the identification of ecologically important genes, opening new venues for conservation genomics in desert tortoises.
Highlights
A major goal in conservation genetics involves understanding how landscape features influence population connectivity and structure[1,2]
Environmental friction can be quantified by the least-cost distance (LCD), which is the path between two points that accumulates less friction and resistance-distance (RD), which uses circuit-theory to simultaneously weigh many possible routes across a landscape[18]
Landscape genomics extends the amalgamation of population genetics and landscape ecology on two fronts: (1) access to thousands of putatively independent markers across the genome, which should increase analytical accuracy; and (2) access to genetic data that may be subject to evolutionary forces other than drift, such as natural selection and linkage[29]
Summary
A major goal in conservation genetics involves understanding how landscape features influence population connectivity and structure[1,2]. Hagerty et al.[13] used habitat suitability scores from a model of the distribution of desert tortoises[19] to quantify landscape friction with LCD and RD. Their results suggested distance due to barriers, such as mountains and deep valleys, are major landscape features limiting gene flow. Their barrier model was not better than the null IBD expectation[13]. The distribution of genetic variation across landscapes can reflect intricate interactions between the environment and evolutionary processes affecting population structure and adaptation to local conditions[30]
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