Digging deeper: deep joint species distribution modeling reveals environmental drivers of Earthworm Communities

Earthworms are key drivers of soil function, influencing organic matter turnover, nutrient cycling, and soil structure. Understanding the environmental controls on their distribution is essential for predicting the impacts of land use and climate change on soil ecosystems. While local studies have identified abiotic drivers of earthworm communities, broad-scale spatial patterns remain underexplored. We developed a multi-species, multi-task deep learning model to jointly predict the distribution of 77 earthworm species across metropolitan France, using historical (1960–1970) and contemporary (1990–2020) records. The model integrates climate, soil, and land cover variables to estimate habitat suitability. We applied SHapley Additive exPlanations (SHAP) to identify key environmental drivers and used species clustering to reveal ecological response groups. The joint model achieved high predictive performance (TSS > 0.7) and improved predictions for rare species compared to traditional species distribution models. Shared feature extraction across species allowed for more robust identification of common and contrasting environmental responses. Precipitation variability, temperature seasonality, and land cover emerged as dominant predictors of earthworm distribution but differed in ranking across species and functional groups. Species clustering into response groups to climatic, land use and soil revealed distinct ecological strategies including a gradient of sensitivity to precipitation seasonality, differential habitat preferences in terms of vegetation cover and wetness and trade-offs between soil acidity and organic matter quality. Our study advances both the methodological and ecological understanding of soil biodiversity. We demonstrate the utility of interpretable deep learning approaches for large-scale soil fauna modeling and provide new insights into earthworm habitat specialization. These findings highlight land cover and seasonal climate variability as efficient proxies for soil biodiversity, providing actionable indicators for global monitoring initiatives and helping to identify habitat requirements of earthworm species to guide emerging earthworm conservation strategies in the face of global environmental change. • Earthworms are key drivers of soil function, influencing organic matter turnover, nutrient cycling, and soil structure. Understanding the environmental controls on their distribution is essential for predicting the impacts of land use and climate change on soil ecosystems. While local studies have identified abiotic drivers of earthworm communities, broad-scale spatial patterns remain underexplored. • We developed a multi-species, multi-task deep learning model to jointly predict the distribution of 77 earthworm species across metropolitan France, using historical (1960–1970) and contemporary (1990–2020) records. The model integrates climate, soil, and land cover variables to estimate habitat suitability. The joint model achieved high predictive performance (TSS > 0.7) and improved predictions for rare species compared to traditional species distribution models. Shared feature extraction across species allowed for more robust identification of common and contrasting environmental responses. • We applied SHapley Additive exPlanations (SHAP) to identify key environmental drivers and used species clustering to reveal ecological response groups. Precipitation variability, temperature seasonality, and land cover emerged as dominant predictors of earthworm distribution but differed in ranking across species and functional groups. Species clustering into response groups to climatic, land use and soil revealed distinct ecological strategies including a gradient of sensitivity to precipitation seasonality, differential habitat preferences in terms of vegetation cover and wetness and trade-offs between soil acidity and organic matter quality. • Our study advances both the methodological and ecological understanding of soil biodiversity. We demonstrate the utility of interpretable deep learning approaches for large-scale soil fauna modeling and provide new insights into earthworm habitat specialization. • These findings highlight land cover and seasonal climate variability as efficient proxies for soil biodiversity, providing actionable indicators for global monitoring initiatives and helping to identify habitat requirements of earthworm species to guide emerging earthworm conservation strategies in the face of global environmental change.