Topographical features and forest cover influence landscape connectivity and gene flow of the Caucasian pit viper, Gloydius caucasicus (Nikolsky, 1916), in Iran
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The populations of the Caucasian pit viper are declining and face genetic threats because of habitat fragmentation. Landscape genetics facilitates effective connectivity conservation actions by providing methods to predict factors affecting gene flow.
We described the genetic diversity and structure of Caucasian pit viper populations in Iran. We also adopted an individual-based landscape genetics approach to determine the effects of landscape features on gene flow across the species’ range.
We evaluated the degree of genetic structuring using spatial and non-spatial clustering methods. Then, we used restricted multivariate optimization and maximum-likelihood population effects modeling to predict landscape resistance to gene flow. Finally, we compared the predictions based on maximum-likelihood population effects modeling and reciprocal causal modelling in the context of multivariate optimization.
Strong genetic structure was found between populations. Landscape genetics analysis showed that gene flow was related to forest cover density and topographic roughness. Gene flow was much higher in areas of low topographical roughness and was impeded by closed forests. We found an overall high similarity in multivariate optimization results obtained through reciprocal causal modeling and maximum-likelihood population effects modeling, suggesting that both approaches may produce consistent predictions.
Caucasian pit viper has limited dispersal abilities resulting in genetic structuring at short distances and gene flow appears to be influenced by natural features, in particular topographic roughness and forest cover density. Our findings have important implications for the species’ conservation and can be used to develop empirically supported prioritization of core habitats and corridors.
KeywordsCaucasian pit viper Gene flow Landscape genetics Landscape resistance Multivariate optimization
This study was licensed by the Iranian Department of Environment under permits Nos. 94/6049 and 96/3631.
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