Associate Professor University of Michigan, Michigan, United States
Vegetation structure is a crucial dimension of wildlife habitat, responsive to global changes in human activities and ecosystem processes. NASA’s recent Global Ecosystem Dynamics Investigation (GEDI) provides an exciting opportunity to explore how spaceborne high-resolution laser ranging observations can improve our ability to measure wildlife habitat and advance animal ecology in the Anthropocene. We tested the utility of GEDI data in univariate multi-season occupancy models to estimate habitat use in a remote mountain system in central Idaho, USA. We collected data from 49 camera trap stations from two surveys in 2018-2019 and modeled the occupancy for each of seven mammal species representing different trophic levels and feeding strategies: American black bear (Ursus americanus), deer (Odocoileus hemionus), elk (Cervus canadensis), moose (Alces alces), coyote (Canis latrans), wolf (Canis lupus), and mountain lion (Puma concolor). We first derived structural diversity indices (richness, evenness, and divergence) of GEDI-derived canopy height, plant area index, and foliage height diversity to represent different dimensions of vegetation structure. This spatial aggregation is necessary due to gaps in GEDI footprints and parallels commonly used functional diversity metrics applied to biological communities that are calculated using trait probability densities. We measured these indices across three spatial scales that reflect different species movement and habitat selection patterns. We found the structural diversity indices of canopy height, foliage height diversity, and plant area index had the strongest effects on the occupancy of most mammals compared to two-dimensional variables (e.g., tree cover, NDVI). The spatial extent of these indices also influenced the strength of response, highlighting the importance of selecting a scale large enough to capture sufficient GEDI footprints but small enough to reflect site-level variance. Compared to two-dimensional covariates, our results suggest that GEDI variables allow researchers to generate more detailed inference on the forms of habitat that wildlife use. We discuss the implications of these findings for habitat management and future wildlife research from local to global scales.
