Species-level tree crown maps improve predictions of tree recruit abundance in a tropical landscape

Predicting forest recovery at landscape scales will aid forest restoration efforts. The first step in successful forest recovery is tree recruitment. Forecasts of tree recruit abundance, derived from the landscape-scale distribution of seed sources (i.e. adult trees), could assist efforts to identify sites with high potential for natural regeneration. However, previous work has revealed wide variation in the effect of seed sources on seedling abundance, from positive to no effect. We quantified the relationship between adult tree seed sources and tree recruits, and predicted where natural recruitment would occur in a fragmented tropical agricultural landscape. We integrated species-specific tree crown maps generated from hyperspectral imagery and property boundaries data on individual property ownership with field data on the spatial distribution of tree recruits from five species. We then developed hierarchical Bayesian models to predict landscape-scale recruit abundance. Our models re...