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Landscape influences on dispersal behaviour: a theoretical model and empirical test using the fire salamander, Salamandra infraimmaculata.

Authors
  • Kershenbaum, Arik
  • Blank, Lior
  • Sinai, Iftach
  • Merilä, Juha
  • Blaustein, Leon
  • Templeton, Alan R
Type
Published Article
Journal
Oecologia
Publisher
Springer-Verlag
Publication Date
Jun 01, 2014
Volume
175
Issue
2
Pages
509–520
Identifiers
DOI: 10.1007/s00442-014-2924-8
PMID: 24648023
Source
Medline
License
Unknown

Abstract

When populations reside within a heterogeneous landscape, isolation by distance may not be a good predictor of genetic divergence if dispersal behaviour and therefore gene flow depend on landscape features. Commonly used approaches linking landscape features to gene flow include the least cost path (LCP), random walk (RW), and isolation by resistance (IBR) models. However, none of these models is likely to be the most appropriate for all species and in all environments. We compared the performance of LCP, RW and IBR models of dispersal with the aid of simulations conducted on artificially generated landscapes. We also applied each model to empirical data on the landscape genetics of the endangered fire salamander, Salamandra infraimmaculata, in northern Israel, where conservation planning requires an understanding of the dispersal corridors. Our simulations demonstrate that wide dispersal corridors of the low-cost environment facilitate dispersal in the IBR model, but inhibit dispersal in the RW model. In our empirical study, IBR explained the genetic divergence better than the LCP and RW models (partial Mantel correlation 0.413 for IBR, compared to 0.212 for LCP, and 0.340 for RW). Overall dispersal cost in salamanders was also well predicted by landscape feature slope steepness (76%), and elevation (24%). We conclude that fire salamander dispersal is well characterised by IBR predictions. Together with our simulation findings, these results indicate that wide dispersal corridors facilitate, rather than hinder, salamander dispersal. Comparison of genetic data to dispersal model outputs can be a useful technique in inferring dispersal behaviour from population genetic data.

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