Tiger abundance and gene flow in Central India are driven by disparate combinations of topography and land cover

Abstract

AbstractAimEffective conservation of the endangered tiger depends upon reliable knowledge of factors driving genetic differentiation and population connectivity. Connectivity models frequently use resistance surfaces not optimized with actual movement or genetic data which limits reliability. Our aim is to use empirical data on genetic diversity of tiger populations to optimize landscape resistance to gene flow and identify factors that predict local population abundance across Central India.LocationThe study area covers 697,000 km2 across Madhya Pradesh and parts of Rajasthan, Jharkhand and Maharashtra.MethodsWe used genetic data of 309 tigers and restricted multivariate optimization of correlation between landscape variables and genetic distance in a reciprocal causal modelling framework to parameterize a resistance surface for gene flow. We further evaluated the association between effective population size and landscape connectivity using all‐subsets logistic regression with model averaging based on AICc.ResultsGene flow is primarily related to topographic roughness and slope position and secondarily to human footprint and land cover. It is much higher in areas of rough topography and ridge tops and is facilitated by forest cover in areas with low human footprint. In contrast, effective population size in protected areas is primarily driven by extent of protected areas and surrounding forest cover, and is not significantly related to resistant kernel connectivity value.Main ConclusionsThis is the first study to use a rigorous multivariate optimization approach to identify factors which limit gene flow of tigers. Tiger movement is highly affected by landscape features, and dispersing tigers move through rough terrain along forested ridges, avoiding non‐forest areas with high human footprint, while local tiger population density is driven primarily by the extent of protected forested habitat. These results have important implications for tiger conservation and can be used to develop empirically supported prioritization of core areas and corridors.