Use of step selection functions to model movement of three neotropical primates using LiDAR-derived measures of forest structure

Background/Question/Methods

Forest structure has long been acknowledged as an important factor in how primates select arboreal pathways that are repeatedly used. Studies that include forest structure often rely on observers measuring percent cover by height while following animals, which may feasible at the scale of a daily trajectory or localized home range, but not at the landscape or regional scale at which habitat suitability is assessed. High-resolution airborne Light Detection and Ranging (LiDAR) has the potential to solve this scaling issue by measuring detailed three-dimensional forest structure relevant to individual movement decisions at the landscape scale. Using LiDAR-derived forest structure variables I applied step selection functions to assess the role of forest structure in the movement behavior of three neotropical primate species with distinct ranging behavior.

Results/Conclusions

The best performing models for each species based on AIC showed k-fold cross-validation values of 0.25, 0.35, and 0.89 (out of 1) for Alouatta palliata, Cebus capuchinus, and Ateles geoffroyi, respectively. Lower k-fold cross-validation values for A. palliata and C. capuchinus, both of which have limited ranging behavior on within the study area, may indicate that structure is not the primary driver in selection of movement pathways. Conversely, the high cross-validation value for A. geoffroyi, which exhibits long-range movement throughout the study area, indicates that structure is an important driver of pathway selection. Canopy height and distance to gap were significant variables for all three species, while crown thickness and density were significant only for A. geoffroyi. This study lays the groundwork for behaviorally-informed canopy habitat suitability modeling for neotropical forests, in which up to 60% of mammals are at least partially arboreal. Integrating behavioral ecology with high-resolution information about the habitat in which behavior occurs may provide a useful approach from which subsequent research and management decisions can be made.