Evaluating pollinator diversity in the Brazilian Atlantic Forest biome using geospatial and Machine Learning Tools

Abstract. Pollinators play a central role in sustaining biodiversity and ecosystem services, consequently their response to forest regeneration in tropical landscapes needs to be quantified at large scales. Here, we assess how land cover composition and forest age influence pollinator diversity in the Brazilian Atlantic Forest — a global biodiversity hotspot undergoing extensive regeneration. We integrated land-use and forest age data from MapBiomas with 56,593 bee occurrence records from GBIF, focusing on five bee families. Using Random Forest models, we evaluated the importance of land cover types and secondary forest age intervals for predicting total occurrences and genus richness. Our results show that primary forest cover is the dominant predictor of bee genus richness, followed by late-stage secondary forests aged > 26 years and riparian-associated water surfaces. In contrast, younger secondary forests (< 25 years) contributed negligibly and urban dominated landscapes support less diversity overall. While total occurrence data reflected strong spatial bias towards non-vegetated and agricultural areas, genus richness emerged as a more robust parameter, avoiding bias, and mitigating over-representation from anthropic landscapes. Our findings highlight the ecological value of mature secondary forests for pollinator conservation and reinforce the need to incorporate the time dimension into restoration monitoring. Our results underscore the conservation value of mature secondary forests and the need to integrate forest age into restoration monitoring. Our approach demonstrates the utility of combining biodiversity data, geospatial data derived from remote sensing, and machine learning to produce scalable, spatially explicit insights into ecological recovery and pollination services in tropical biomes.