LiDAR-derived forest structure metrics show ecologically defined scales outperform grids when predicting mammal diversity

Vegetation structure has emerged as a key determinant of terrestrial biodiversity based on studies using randomly placed sampling grids, often at broad spatial scales. The resultant grid cells often contain substantial heterogeneity in ecological conditions that are highly relevant for the taxa of interest, potentially undermining our ability to detect relevant drivers of diversity. Here we use 15 structural metrics measured using ground-based LiDAR to model mammalian diversity at 58 sampling locations across seven distinct tropical forest types in Indonesian Borneo. We conducted analyses at four spatial scales using over five years of camera trap data. Models predicting mammal diversity based on ecologically defined scales (i.e., forest type boundaries) outperformed models using a grid scale of comparable resolution. Our results underscore the value of LiDAR in capturing forest structural metrics relevant to mammals and highlight the importance of incorporating ecologically meaningful spatial scales in biodiversity studies.