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

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Zeitschriftentitel: Journal of Evolutionary Biology
Personen und Körperschaften: HETHER, T. D., HOFFMAN, E. A.
In: Journal of Evolutionary Biology, 25, 2012, 6, S. 1039-1052
Format: E-Article
Sprache: Englisch
veröffentlicht:
Oxford University Press (OUP)
Schlagwörter:
Ecology, Evolution, Behavior and Systematics
author_facet HETHER, T. D.
HOFFMAN, E. A.
HETHER, T. D.
HOFFMAN, E. A.
author HETHER, T. D.
HOFFMAN, E. A.
spellingShingle HETHER, T. D.
HOFFMAN, E. A.
Journal of Evolutionary Biology
Machine learning identifies specific habitats associated with genetic connectivity in Hyla squirella
Ecology, Evolution, Behavior and Systematics
author_sort hether, t. d.
spelling HETHER, T. D. HOFFMAN, E. A. 1010-061X 1420-9101 Oxford University Press (OUP) Ecology, Evolution, Behavior and Systematics http://dx.doi.org/10.1111/j.1420-9101.2012.02497.x <jats:title>Summary</jats:title><jats:p>The goal of this study was to identify and differentiate the influence of multiple habitat types that span a spectrum of suitability for <jats:italic>Hyla squirella</jats:italic>, 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 <jats:italic>H</jats:italic>. <jats:italic>squirella</jats:italic> were spatially structured and models including specific habitat types (i.e. upland oak forest and urbanization) consistently explained more variation in genetic distance (median <jats:italic>pR</jats:italic><jats:sup>2</jats:sup> = 47.78) than spatial distance alone (median <jats:italic>pR</jats:italic><jats:sup>2</jats:sup> = 23.81). Moreover, we estimate the relative importance that spatial distance, upland oak and urbanized habitat have in explaining genetic structure of <jats:italic>H. squirella</jats:italic>. We discuss how these habitat types may mechanistically facilitate dispersal in <jats:italic>H. squirella</jats:italic>. 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.</jats:p> Machine learning identifies specific habitats associated with genetic connectivity in <i>Hyla squirella</i> Journal of Evolutionary Biology
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title Machine learning identifies specific habitats associated with genetic connectivity in Hyla squirella
title_unstemmed Machine learning identifies specific habitats associated with genetic connectivity in Hyla squirella
title_full Machine learning identifies specific habitats associated with genetic connectivity in Hyla squirella
title_fullStr Machine learning identifies specific habitats associated with genetic connectivity in Hyla squirella
title_full_unstemmed Machine learning identifies specific habitats associated with genetic connectivity in Hyla squirella
title_short Machine learning identifies specific habitats associated with genetic connectivity in Hyla squirella
title_sort machine learning identifies specific habitats associated with genetic connectivity in <i>hyla squirella</i>
topic Ecology, Evolution, Behavior and Systematics
url http://dx.doi.org/10.1111/j.1420-9101.2012.02497.x
publishDate 2012
physical 1039-1052
description <jats:title>Summary</jats:title><jats:p>The goal of this study was to identify and differentiate the influence of multiple habitat types that span a spectrum of suitability for <jats:italic>Hyla squirella</jats:italic>, 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 <jats:italic>H</jats:italic>. <jats:italic>squirella</jats:italic> were spatially structured and models including specific habitat types (i.e. upland oak forest and urbanization) consistently explained more variation in genetic distance (median <jats:italic>pR</jats:italic><jats:sup>2</jats:sup> = 47.78) than spatial distance alone (median <jats:italic>pR</jats:italic><jats:sup>2</jats:sup> = 23.81). Moreover, we estimate the relative importance that spatial distance, upland oak and urbanized habitat have in explaining genetic structure of <jats:italic>H. squirella</jats:italic>. We discuss how these habitat types may mechanistically facilitate dispersal in <jats:italic>H. squirella</jats:italic>. 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.</jats:p>
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author HETHER, T. D., HOFFMAN, E. A.
author_facet HETHER, T. D., HOFFMAN, E. A., HETHER, T. D., HOFFMAN, E. A.
author_sort hether, t. d.
container_issue 6
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description <jats:title>Summary</jats:title><jats:p>The goal of this study was to identify and differentiate the influence of multiple habitat types that span a spectrum of suitability for <jats:italic>Hyla squirella</jats:italic>, 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 <jats:italic>H</jats:italic>. <jats:italic>squirella</jats:italic> were spatially structured and models including specific habitat types (i.e. upland oak forest and urbanization) consistently explained more variation in genetic distance (median <jats:italic>pR</jats:italic><jats:sup>2</jats:sup> = 47.78) than spatial distance alone (median <jats:italic>pR</jats:italic><jats:sup>2</jats:sup> = 23.81). Moreover, we estimate the relative importance that spatial distance, upland oak and urbanized habitat have in explaining genetic structure of <jats:italic>H. squirella</jats:italic>. We discuss how these habitat types may mechanistically facilitate dispersal in <jats:italic>H. squirella</jats:italic>. 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.</jats:p>
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spelling HETHER, T. D. HOFFMAN, E. A. 1010-061X 1420-9101 Oxford University Press (OUP) Ecology, Evolution, Behavior and Systematics http://dx.doi.org/10.1111/j.1420-9101.2012.02497.x <jats:title>Summary</jats:title><jats:p>The goal of this study was to identify and differentiate the influence of multiple habitat types that span a spectrum of suitability for <jats:italic>Hyla squirella</jats:italic>, 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 <jats:italic>H</jats:italic>. <jats:italic>squirella</jats:italic> were spatially structured and models including specific habitat types (i.e. upland oak forest and urbanization) consistently explained more variation in genetic distance (median <jats:italic>pR</jats:italic><jats:sup>2</jats:sup> = 47.78) than spatial distance alone (median <jats:italic>pR</jats:italic><jats:sup>2</jats:sup> = 23.81). Moreover, we estimate the relative importance that spatial distance, upland oak and urbanized habitat have in explaining genetic structure of <jats:italic>H. squirella</jats:italic>. We discuss how these habitat types may mechanistically facilitate dispersal in <jats:italic>H. squirella</jats:italic>. 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.</jats:p> Machine learning identifies specific habitats associated with genetic connectivity in <i>Hyla squirella</i> Journal of Evolutionary Biology
spellingShingle HETHER, T. D., HOFFMAN, E. A., Journal of Evolutionary Biology, Machine learning identifies specific habitats associated with genetic connectivity in Hyla squirella, Ecology, Evolution, Behavior and Systematics
title Machine learning identifies specific habitats associated with genetic connectivity in Hyla squirella
title_full Machine learning identifies specific habitats associated with genetic connectivity in Hyla squirella
title_fullStr Machine learning identifies specific habitats associated with genetic connectivity in Hyla squirella
title_full_unstemmed Machine learning identifies specific habitats associated with genetic connectivity in Hyla squirella
title_short Machine learning identifies specific habitats associated with genetic connectivity in Hyla squirella
title_sort machine learning identifies specific habitats associated with genetic connectivity in <i>hyla squirella</i>
title_unstemmed Machine learning identifies specific habitats associated with genetic connectivity in Hyla squirella
topic Ecology, Evolution, Behavior and Systematics
url http://dx.doi.org/10.1111/j.1420-9101.2012.02497.x