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

TL;DR

This study employs a deep learning model to predict earthworm distributions across France, identifying key environmental drivers and ecological response groups, thereby enhancing soil biodiversity understanding and conservation strategies.

Contribution

It introduces a multi-species deep learning approach with interpretability to analyze large-scale earthworm distribution data, revealing environmental influences and ecological groupings.

Findings

High predictive accuracy (TSS >= 0.7) for earthworm species.

Precipitation, temperature seasonality, and land cover are key predictors.

Distinct ecological response groups linked to climate and land use.

Abstract

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. Species clustering revealed distinct ecological strategies tied to climatic and land use gradients. 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 support improved soil biodiversity monitoring and conservation planning in the face of global environmental change.