Quantifying drivers of biodiversity change through increasing data availability and improved analytical frameworks

Abstract

Research Highlight: Guilbault, E., Sihvonen, P., Suuronen A., Huikkonen, I.-M., Pöyry, J., Laine, A.-L., Roslin, T., Saastamoinen, M., Vanhatalo, J. (2025). Strong context dependence in the relative importance of climate and habitat on nation-wide macro-moth community changes. Journal of Animal Ecology, https://doi.org/10.1111/1365-2656.70107. Distributions of species are linked directly to extinction risk. Different threats can drive range contractions and reshape biodiversity patterns, yet their relative importance is rarely apparent. Additionally, the spatial and temporal distributions of species—and the resulting biodiversity patterns—are often incomplete or biased in existing datasets. Guilbault et al. (2025) present a productive framework to analyse biodiversity-monitoring data with spatiotemporal gaps by combining joint species distribution modelling (jSDM) with variance partitioning. Using a Finnish moth monitoring dataset, they demonstrate the effectiveness of this approach in identifying the dominant drivers of (and potentially, threats to) species' distributions. Their results reveal how these drivers vary across environments (environmental dependency) and between moths with different functional traits (functional dependency). Expanding this analytical framework to additional datasets with broad spatial and/or temporal coverage will further our understanding of how threats to biodiversity vary across time and space. Advances in modelling methods and the growing availability of high-quality data are substantially improving our capability to pinpoint and address threats to biodiversity—we hope that by leveraging results from such efforts, we may increase capacity for managing these threats to slow biodiversity loss.