Biological soil crusts (biocrusts hereafter) cover a substantial proportion of dryland ecosystem and play crucial roles in ecological processes such as biogeochemical cycles, water distribution and soil erosion. Consequently, studying the spatial distribution of biocrusts holds great significance for drylands, which is still lacking, especially in a global scale. In this work, we first compiled a global database of ~3,800 biocrusts cover from field surveys, literatures, and open datasets. By training the relationship between biocrust cover and environmental indicators using machine learning approach, we obtained high-resolution (1 km) maps and determining factors of biocrust and its major subtypes (cyanobacterial, lichen, and moss biocrusts) in global drylands. This study found that biocrusts had an average cover of ~30% in drylands, with moss as the major component. Climate was the most important factors for biocrusts, with cyanobacteria and lichens mainly affected by precipitation while mosses mainly by temperature. Finally, to further advance this field, we proposed several potential research topics and aspects, including building standardized database of biocrusts, enhancing non-vascular vegetation dynamic models, integrating multi-sensor monitoring, making full use of machine learning, and focusing on regional research co-development. This work is supposed to significantly contribute to the studies on biocrusts, and thereby to advance our understandings of dryland ecosystem management and restoration.
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Wang et al. (2024) studied this question.
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