Animal tracking moves community ecology: Opportunities and challenges.

Individual decisions regarding how, why and when organisms interact with one another and with their environment scale up to shape patterns and processes in communities. Recent evidence has firmly established the prevalence of intraspecific variation in nature and its relevance in community ecology, yet challenges associated with collecting data on large numbers of individual conspecifics and heterospecifics have hampered integration of individual variation into community ecology. Nevertheless, recent technological and statistical advances in GPS-tracking, remote sensing and behavioural ecology offer a toolbox for integrating intraspecific variation into community processes. More than simply describing where organisms go, movement data provide unique information about interactions and environmental associations from which a true individual-to-community framework can be built. By linking the movement paths of both conspecifics and heterospecifics with environmental data, ecologists can now simultaneously quantify intraspecific and interspecific variation regarding the Eltonian (biotic interactions) and Grinnellian (environmental conditions) factors underpinning community assemblage and dynamics, yet substantial logistical and analytical challenges must be addressed for these approaches to realize their full potential. Across communities, empirical integration of Eltonian and Grinnellian factors can support conservation applications and reveal metacommunity dynamics via tracking-based dispersal data. As the logistical and analytical challenges associated with multi-species tracking are surmounted, we envision a future where individual movements and their ecological and environmental signatures will bring resolution to many enduring issues in community ecology.

Incorporating animal spatial memory in step selection functions.

Memory is among the most important and neglected forces that shapes animal movement patterns. Research on the movement-memory interface is crucial to understand how animals use spatial learning to navigate across space because memory-based navigation is directly linked to animals' space use and home range behaviour; however, because memory cannot be measured directly, it is difficult to account for. Here, we incorporated spatial memory into step selection functions (SSF) to understand how resource selection and spatial memory affect space use of feral hogs (Sus scrofa). We used Biased Random Bridge kernel estimates linked to residence time as a surrogate for memory and tested four conceptually different dynamic maps of spatial memory. We applied this memory-based SSF to a data set of hog relocations to evaluate the importance of land cover type, time of day and spatial memory on the animals' space use. Our approach has shown how the incorporation of spatial memory into animal movement models can improve estimates of habitat selection. Memory-based SSF provided a feasible way to gain insight into how animals use spatial learning to guide their movement decisions. We found that while hogs selected forested areas and water bodies and avoided grasslands during the day (primarily at noon), they had a strong tendency to select previously visited areas, mainly those held in recent memory. Beyond actively updating their memory with recent experiences, hogs were able to discriminate among spatial memories encoded at different circadian phases of their activity. Even though hogs are thought to have long memory retention, they likely relied on recent experiences because the local food resources are quickly depleted and slowly renewed, yielding an uncertain spatial distribution of resources.