Understanding how species interact with their environment is the first step in being able to craft effective conservation and management plans. One of the most common ways to evaluate species-habitat relationships is to use Resource Selection Functions (RSFs). RSFs work on the assumption that animals prefer landscape features that are used more often than expected and avoid landscape features that are used less often than expected, based on their availability. Therefore by comparing areas animals used versus those that were available, but not used, we can estimate the strength of preference or avoidance of different landscape features and, across a study area, predict the probability that an individual will use a specific location.
My research has focused on using GPS telemetry data from collared animals for estimating RSFs. GPS telemetry data provides a wealth of information that allows us to differentiate habitat use based on different behavioral states of individual animals. For example, I have shown that preference for a habitat type can change depending on whether an animal is in a ‘resource-use’ state versus when an animal is in a ‘movement’ state. GPS data can also be used to examine multi-scale RSFs, incorporating the fact that species respond to different landscape features at different spatial scales. I have developed novel methods for estimating multi-scale RSFs to examine habitat use during animal movement. I have also taken a look at how often GPS telemetry points are collected may affect the results of RSFs and shown that for movement, longer intervals between fixes can introduce a surprising amount of bias into the results.