Title

Machine learning identifies specific habitats associated with genetic connectivity in Hyla squirella

Authors

Authors

T. D. Hether;E. A. Hoffman

Comments

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Abbreviated Journal Title

J. Evol. Biol.

Keywords

amphibian; clusters; ensemble learning; gene flow; landscape genetics; Random Forest; southeastern USA; LANDSCAPE GENETICS; POPULATION-GENETICS; COMPOUND CLASSIFICATION; TEMPORARY WETLANDS; UNITED-STATES; BUFFER ZONES; FRAGMENTATION; SOFTWARE; ECOLOGY; FORESTS; Ecology; Evolutionary Biology; Genetics & Heredity

Abstract

The goal of this study was to identify and differentiate the influence of multiple habitat types that span a spectrum of suitability for Hyla squirella, a widespread frog species that occurs in a broad range of habitat types. We collected microsatellite data from 675 samples representing 20 localities from the southeastern USA and used machine-learning methodologies to identify significant habitat features associated with genetic structure. In simulation, we confirm that our machine-learning algorithm can successfully identify landscape features responsible for generating between-population genetic differentiation, suggesting that it can be a useful hypothesis-generating tool for landscape genetics. In our study system, we found that H. squirella were spatially structured and models including specific habitat types (i.e. upland oak forest and urbanization) consistently explained more variation in genetic distance (median pR2 = 47.78) than spatial distance alone (median pR2 = 23.81). Moreover, we estimate the relative importance that spatial distance, upland oak and urbanized habitat have in explaining genetic structure of H. squirella. We discuss how these habitat types may mechanistically facilitate dispersal in H. squirella. This study provides empirical support for the hypothesis that habitat-use can be an informative correlate of genetic differentiation, even for species that occur in a wide range of habitats.

Journal Title

Journal of Evolutionary Biology

Volume

25

Issue/Number

6

Publication Date

1-1-2012

Document Type

Article

Language

English

First Page

1039

Last Page

1052

WOS Identifier

WOS:000304033000003

ISSN

1010-061X

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