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Updated: Jan 28, 2026

Non-radioactive in situ Hybridization Protocol Applicable for Norway Spruce and a Range of Plant Species
Published on: April 17, 2009
Hybridization and introgression during density-dependent range expansion: European wildcats as a case study
Claudio S Quilodrán1,2,3, Beatrice Nussberger4, Juan I Montoya-Burgos2,5
1Laboratory of Anthropology, Genetics and Peopling History, Department of Genetics and Evolution, Anthropology Unit, University of Geneva, Geneva, Switzerland.
Abstract:
Interbreeding between historically allopatric species with incomplete reproductive barriers may result when species expand their range. The genetic consequences of such hybridization depend critically on the dynamics of the range expansion. Hybridization models during range expansion have been developed but assume dispersal to be independent from neighboring population densities. However, organisms may disperse because they are attracted by conspecifics or because they prefer depopulated areas. Here, through spatially explicit simulations, we assess the effect of various density-dependent dispersal modes on the introgression between two species. We find huge introgression from the local species into the invasive one with all dispersal modes investigated, even when the hybridization rate is relatively low. This represents a general expectation for neutral genes even if the dispersal modes differ in colonization times and amount of introgression. Invasive individuals attracted by conspecifics need more time to colonize the whole area and are more introgressed by local genes, whereas the opposite is found for solitary individuals. We applied our approach to a recent expansion of European wildcats in the Jura Mountains and the hybridization with domestic cats. We show that the simulations explained better the observed level of introgression at nuclear, mtDNA, and Y chromosome markers, when using solitary dispersal for wildcats instead of random or gregarious dispersal, in accordance with ecological knowledge. Thus, use of density-dependent dispersal models increases the predictive power of the approach.
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