George Olah, Maldwyn John Evans, Gregory P Asner
Our results indicate that (i) the antenna beam-based position-finding method outperforms common methods in both accuracy and yield, (ii) the novel introduced per-position error estimation model reliably reflects measured PE from ground-truth data, and (iii) the resulting setup provides a robust foundation for high-resolution wildlife movement analyses.
Tropical forests harbour exceptional biodiversity, yet anthropogenic change is rapidly altering forest composition and structure in ways that shape species' ecological strategies and persistence. While advances in remote sensing now allow detailed characterisation of canopy function, links between forest functional diversity and animal traits remain poorly understood. Here, using linear model selection and variance partitioning, we test whether canopy functional groups (clusters of forest types sharing similar structure, chemistry, and phenology) predict variation in bird community (>1300 species) functional traits, life history traits, vulnerability, and taxonomy across Andean-Amazonian forests in Peru, accounting for a comprehensive baseline of environmental and structural predictors. Distinct forest functional groups are associated with distinct bird traits, levels of anthropogenic impact, and taxonomic assemblages, providing explanatory power beyond traditional abiotic gradients. For example, Southern Amazonian lowlands and Northern Amazonian Swamps contain large-bodied, slow-reproducing, and non-passerine-dominated assemblages. In contrast, Amazonian floodplains support small, fast-reproducing, and passerine-dominated assemblages. These findings underscore the potential of integrating habitat functionality with species traits to map critical resource landscapes and revolutionise conservation prioritisation strategies such as gap analyses.