Modelling landscape connectivity for highly-mobile terrestrial animals: a continuous and scalable approach

• Main Author: Galpern, Paul
• Other Authors: Manseau, Micheline (Natural Resources Institute)
• Published: Elsevier 2012
• Subjects: landscape ecology; natural resource management; landscape connectivity; landscape genetics
• Online Access: http://hdl.handle.net/1993/9224

Assessments of landscape connectivity are increasingly required in natural resource management. Understanding how landscape structure affects the movement and dispersal of animals may be essential for ensuring the long-term persistence of species of conservation concern. Functional connectivity models describing how features on the landscape influence animal movement behaviour have been produced in two different ways. The resistance surface models landscape connectivity as its inverse, the resistance to movement and dispersal, while the landscape graph represents landscape connectivity by describing the relationships among resource patches. Both methods have limitations that make them less effective for modelling highly-mobile and wide-ranging species such as ungulates and carnivores. This thesis develops a method called grains of connectivity that combines the continuous representation of landscape connectivity provided by resistance surfaces and the scalability provided by landscape graphs to create a flexible modelling framework for these species. The first half of the thesis reviews the conceptual origins of the grains of connectivity method and examines its properties using simulated landscapes. In the second half, empirical evidence of movement and dispersal in a boreal woodland caribou (Rangifer tarandus caribou) population is used to validate functional connectivity hypotheses generated using the method. Connectivity for caribou at the temporal scale of generations is examined using a landscape genetics approach, while connectivity at the seasonal scale is assessed using the distribution of caribou telemetry locations. Grains of connectivity may be most useful for study systems where animals are not found exclusively in well-defined resource patches and there is uncertainty in the behavioural parameters influencing movement and dispersal. Additionally, the scalability of the analysis can be used to selectively remove spatial heterogeneity that may be uncorrelated with movement and dispersal giving an improved description of the pattern affecting the landscape connectivity process.