A framework for linking dispersal biology to connectivity across landscapes

Fletcher, R. J., Jr., Iezzi, M.E., Guralnick, R. Marx, A. J., Ryan, S. J., Valle, D. (2023) Landscape Ecology 0: 0-0 10.1007/s10980-023-01741-8

Abstract: Dispersal typically consists of three components—departure, transience and settlement—each of which can be influenced by the landscape. A fundamental aspect of dispersal is the dispersal kernel, which describes how the likelihood of settlement varies as a function of the distance from the departure location. Dispersal concepts are often closely connected to the interpretation of landscape connectivity, yet models of landscape connectivity often do not generate dispersal kernels nor explicitly capture the three components of dispersal. Objectives We apply Markov chain theory for the generation of random-walk dispersal kernels that are based on the three components of dispersal to better link dispersal processes to landscape connectivity. Methods We extend the spatial absorbing Markov chain (SAMC) framework, which is aimed at addressing a broad range of problems in landscape connectivity, to explicitly model dispersal kernels that acknowledge each component of the dispersal process and how the landscape can alter each of these components. We provide an example with the Florida black bear (Ursus americanus floridanus), a species of conservation and management concern, where we contrast expected connectivity between key subpopulations when models do and do not consider random-walk dispersal kernels. Results Our extensions show how the SAMC can generate different types of random-walk kernels that include information on how the landscape alters departure, transience and settlement processes. Importantly, this framework can also readily incorporate mortality into predictions and be applied to make time-explicit predictions across landscapes. Connectivity for the Florida black bear is predicted to be much lower when acknowledging dispersal kernels and suggests that the settlement process may be more influential to connectivity predictions than landscape resistance. Conclusion These results provide a foundation for applying the SAMC to dispersal kernels. Not only do these extensions provide a formal linkage of connectivity to concepts in dispersal biology, but also help to bring together concepts from common connectivity models (e.g., circuit theory and least-cost resistant kernels) to facilitate predicting connectivity across landscapes.