engleski

Spatial genetic analysis for delineating boundaries to gene flow

With increased anthropogenic pressure, landscape becomes more and more fragmented. This fragmentation induces appearance of boundaries to gene flow, isolates the populations and increases possibility to go extinct. This PhD thesis focuses on spatial methods for detecting boundaries to gene flow. It is composed of three parts, first of which presents state of the art of the spatial methods for boundary detection and describes Bayesian clustering and edge detection methods. In the second part, the results from the comparative evaluation of three Bayesian spatial clustering methods and two edge detection methods are presented. The performance of the methods is compared on the simulated datasets. The results show that the Bayesian clustering methods (spatial or not) outperformed edge detection methods, but also that all the methods incorrectly detected boundaries in the presence of strong isolation by distance, even in the absence of true boundaries. Performance of the methods is additionally compared on two previously published empirical datasets. Final part presents the results of an analysis of the genetic spatial structure of common frog (Rana temporaria) on a local scale. The results show that for datasets with low amount of genetic information, inclusion of a priori population membership (for example: sampling site) in the models can improve the results.

Landscape genetics, Spatial clustering, Edge detection, Genetic boundaries

Doktorat je izrađen i obranjen uz dvojno mentorstvo (cotutelle) sa Universite Joseph Fourier, Grenoble, Francuska. Francuska mentorica: Stephanie Manel.