Conservation Biology, Volume 0, No. 0, 1–14 © 2020 Society for Conservation Biology DOI: 10.1111/cobi.13519
Landscape planning that ensures the ecological integrity of ecosystems is critical in the face of rapid human‐driven habitat conversion and development pressure. Wildlife tracking data provide unique and valuable information on animal distribution and location‐specific behaviors that can serve to increase the efficacy of such planning. Given the spatiotemporal complexity inherent to animal movements, the interaction between movement behavior and a location is often oversimplified in commonly applied analyses of tracking data. We analyzed GPS‐tracking‐derived metrics of intensity of use, structural properties (based on network theory), and properties of the movement path (speed and directionality) with machine learning to define homogeneous spatial movement types. We applied our approach to a long‐term tracking data set of over 130 African elephants (Loxodonta africana ) in an area under pressure from infrastructure development. We identified 5 unique location‐specific movement categories displayed by elephants, generally defined as high, medium, and low use intensity, and 2 types of connectivity corridors associated with fast and slow movements. High‐use and slow‐movement corridors were associated with similar landscape characteristics associated with productive areas near water, whereas low‐use and fast corridors were characterized by areas of low productivity farther from water. By combining information on intensity of use, properties of movement paths, and structural aspects of movement across the landscape, our approach provides an explicit definition of the functional role of areas for movement across the landscape that we term the movescape . This combined, high‐resolution information regarding wildlife space use offers mechanistic information that can improve landscape planning.