Characterizing tree species richness in Indigenous Peoples’ lands: addressing often-overlooked data limitations and biases

Abstract

Abstract Several studies have characterized species richness distribution in Indigenous Peoples’ Lands (IPL) using global biodiversity datasets. However, these datasets contain substantial limitations and biases which may compromise the accuracy of spatial biodiversity assessments. In this study, we characterize tree species richness across IPL in Bolivia based on three different datasets to examine how spatial and taxonomic biases, as well as the data’s institutional provenance, shape characterization outcomes. Results closely reflect identified spatial and taxonomic biases, and as data generally come from various sources, the individual collections’ scope and our characterization do not align. We also notice that most data derive from international institutions without information about contributions from Indigenous Peoples, complicating sensitive interpretation and weakening visibility of Indigenous knowledge systems. Our findings underscore the limitations of relying solely on global biodiversity datasets to map species distributions in IPL and emphasize the need for locally grounded, ethically framed data collection practices.