Land-use change and biodiversity: Challenges for assembling evidence on the greatest threat to nature.

Land-use change is considered the greatest threat to nature, having caused worldwide declines in the abundance, diversity, and health of species and ecosystems. Despite increasing research on this global change driver, there are still challenges to forming an effective synthesis. The estimated impact of land-use change on biodiversity can depend on location, research methods, and taxonomic focus, with recent global meta-analyses reaching disparate conclusions. Here, we critically appraise this research body and our ability to reach a reliable consensus. We employ named entity recognition to analyze more than 4000 abstracts, alongside full reading of 100 randomly selected papers. We highlight the broad range of study designs and methodologies used; the most common being local space-for-time comparisons that classify land use in situ. Species metrics including abundance, distribution, and diversity were measured more frequently than complex responses such as demography, vital rates, and behavior. We identified taxonomic biases, with vertebrates well represented while detritivores were largely missing. Omitting this group may hinder our understanding of how land-use change affects ecosystem feedback. Research was heavily biased toward temperate forested biomes in North America and Europe, with warmer regions being acutely underrepresented despite offering potential insights into the future effects of land-use change under novel climates. Various land-use histories were covered, although more research in understudied regions including Africa and the Middle East is required to capture regional differences in the form of current and historical land-use practices. Failure to address these challenges will impede our global understanding of land-use change impacts on biodiversity, limit the reliability of future projections and have repercussions for the conservation of threatened species. Beyond identifying literature biases, we highlight the research priorities and data gaps that need urgent attention and offer perspectives on how to move forward.