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Demographic inference in a spatially-explicit ecological model from genomic data: a proof of concept for the Mojave Desert Tortoise

View ORCID ProfileJaime Ashander, View ORCID ProfilePeter Ralph, View ORCID ProfileEvan McCartney-Melstad, H. Bradley Shaffer
doi: https://doi.org/10.1101/354530
Jaime Ashander
1Department of Ecology and Evolutionary Biology, 610 Charles E. Young Drive East, University of California, Los Angeles, Los Angeles, CA 90095 USA
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Peter Ralph
2Department of Mathematics and Institute of Ecology and Evolution, University of Oregon, Eugene, OR 97403, USA
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Evan McCartney-Melstad
1Department of Ecology and Evolutionary Biology, 610 Charles E. Young Drive East, University of California, Los Angeles, Los Angeles, CA 90095 USA
3La Kretz Center for California Conservation Science, Institute of the Environment and Sustainability, University of California, Los Angeles, Los Angeles, CA 90095, USA
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H. Bradley Shaffer
1Department of Ecology and Evolutionary Biology, 610 Charles E. Young Drive East, University of California, Los Angeles, Los Angeles, CA 90095 USA
3La Kretz Center for California Conservation Science, Institute of the Environment and Sustainability, University of California, Los Angeles, Los Angeles, CA 90095, USA
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Abstract

In this paper, we study the general problem of extracting information from spatially explicit genomic data to inform inference of ecologically and geographically realistic population models. We describe methods and apply them to simulations motivated by the demography of the Mojave desert tortoise (Gopherus agassizii). The tortoise is an example of a long-lived, threatened species for which we have an excellent understanding of range, habitat preference, and certain aspects of demography, but inadequate information on other life history components that are important for conservation management. We use an individual-based model on a discretized geographic landscape with overlapping generations and age and sex-specific dispersal, fecundity, and mortality to develop and test a method that uses genomic data to infer demographic parameters. We do this by seeking parameters that best match a set of spatial statistics of genomes, which we introduce and discuss. We find that for inferring only overall population density and mean migration distance, a simple statistical learning method performs well using simulated training data, inferring parameters to within 10% accuracy. In the process, we introduce spatial analogues of common population genetics statistics, and discuss how and why they are expected to contain signal about the geography of population dynamics that are key for ecological modeling generally and conservation of endangered taxa.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted June 23, 2018.
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Demographic inference in a spatially-explicit ecological model from genomic data: a proof of concept for the Mojave Desert Tortoise
Jaime Ashander, Peter Ralph, Evan McCartney-Melstad, H. Bradley Shaffer
bioRxiv 354530; doi: https://doi.org/10.1101/354530
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Demographic inference in a spatially-explicit ecological model from genomic data: a proof of concept for the Mojave Desert Tortoise
Jaime Ashander, Peter Ralph, Evan McCartney-Melstad, H. Bradley Shaffer
bioRxiv 354530; doi: https://doi.org/10.1101/354530

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