Mapping of suitable habitats for earthworms in China

Abstract

Earthworms are important soil organisms that play critical roles in ecosystem material cycling and energy flows. Discovering and predicting the distribution of earthworm habitats is critical for managing biodiversity conservation projects and improving ecosystem health. However, earthworm data are challenging to obtain, and studies on the distribution of earthworms and factors affecting this have mainly been conducted in fields at a small scale; the spatial distribution of earthworms throughout China remains unclear. Species distribution models have been effectively used in macro-scale species suitability distribution studies; however, they have certain limitations. Thus, here, we optimized the maximum entropy model (MaxEnt) to achieve low complexity and high transferability, and the model was capable of predicting the potential distribution of earthworms in China. Modeling was based on the use of a developed database containing 286 earthworm occurrence records and 31 environmental variables (19 climatic, 9 soil, and 3 topographic variables). Results show that earthworm distribution is mainly controlled by the following environmental variables (with corresponding contribution rates): minimum temperature of the coldest month (18.47%), digital elevation model (17.65%), coarse fragments (16.72%), soil organic carbon (9.65%), soil type (7.53%), mean diurnal range (5.35%), and soil thickness (5.05%). The variables with the strongest influence on distribution are climate followed by landforms and soils. The relationship between the effect of environmental variables and earthworm distribution is not simple and linear, and each element has a certain threshold range. Only 50.67% of the total land area of China provides a suitable habitat for earthworms, and there are remarkable spatial differences. Of the various ecosystems, woodland ecosystems provide most of the suitable habitats, followed by cropland and grassland ecosystems, which together account for 45.74% of the land area. This study can be used as a reference for understanding and assessing ecosystem health, sustainability, and for enabling biodiversity conservation.