Data availability impacts the predictive accuracy of pressure-based biodiversity models

This is a Preprint and has not been peer reviewed. This is version 2 of this Preprint. You must log in to post a comment. There are no comments or no comments have been made public for this article. This is a Preprint and has not been peer reviewed. This is version 2 of this Preprint. Add a Comment You must log in to post a comment. Comments There are no comments or no comments have been made public for this article. Amidst the biodiversity crisis, there is high demand for spatially explicit biodiversity monitoring. Global models that quantify impacts of human pressures provide important insights for conservation, but their accuracy in spatial projections has yet to be systematically tested. Here we evaluate this using a global dataset of 25,987 species inventories from 681 studies. Despite estimated land-use impacts in line with previous research, our results highlight the challenging gap between effect size inference and prediction. We find that mixed models with study attributes as random effects – common in meta-analysis and used in several indicators – exhibit generally low predictive accuracy. This is driven by reliance on a small set of averaged fixed effects. In contrast, a biogeographic-taxonomic model structure with explicit environmental covariates shows higher but still modest interpolation accuracy. However, performance when extrapolating to other contexts remains low, due to distribution shifts in environmental conditions. These patterns apply to site-level diversity and differences between sites. Models are essential for informed conservation efforts, but their applicability is fundamentally constrained by data availability. Whereas countries with extensive data can build high-fidelity national indicators, accelerated data collection and model development are needed to better support data-poor regions with localized, actionable biodiversity insights. https://doi.org/10.32942/X2507T Biodiversity, Ecology and Evolutionary Biology Model-based biodiversity indicators, Spatial biodiversity models, Pressue-based biodiversity models, Ecological model evaluation, Cross-study validation, Spatially explicit predictions, Spatial projection, Model generality, Ecological interpolation, Ecological extrapolation, biodiversity data Published: 2025-12-23 00:26 Last Updated: 2026-03-04 11:42 CC BY Attribution 4.0 International Data and Code Availability Statement: Open data/code will be made available during and after the review process. Language: English