Statistical models for animal movement and landscape connectivity

Abstract

This dissertation considers statistical approaches to the study of animal movement behavior and landscape connectivity, with particular attention paid to modeling how movement and connectivity are influenced by landscape characteristics. For animal movement data, a novel continuous-time, discrete-space model of animal movement is proposed. This model yields increased computational efficiency relative to existing discrete-space models for animal movement, and a more flexible modeling framework than existing continuous-space models. In landscape genetic approaches to landscape connectivity, spatially-referenced genetic allele data are used to study landscape effects on gene flow. An explicit link is described between a common circuit-theoretic approach to landscape genetics and variogram fitting for Gaussian Markov random fields. A hierarchical model for landscape genetic data is also proposed, with a multinomial data model and latent spatial random effects to model spatial correlation.