Visual Gradation of Biological Soil Crust Development: A Simple and Effective Recording Method

Xinyu Zhang, Ping He () and Jie Xu
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Xinyu Zhang: Ecological Institute, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
Ping He: Ecological Institute, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
Jie Xu: Ecological Institute, Chinese Research Academy of Environmental Sciences, Beijing 100012, China

Land, 2025, vol. 14, issue 1, 1-15

Abstract: Biological soil crusts are important components of dryland ecosystems, showing variations in appearance, morphology, and function across developmental stages. However, the methods for recording biocrust developmental stages have not been simplified and standardized. In this study, three developmental grades for both cyanobacterial crust and moss crust were defined based on visual indicators such as color, thickness, and moss height. A field survey was conducted across three precipitation regions in northern China, during which the developmental grades of cyanobacterial and moss crusts were visually recorded. Key biocrust developmental indicators, including shear strength, penetration resistance, coverage, chlorophyll a content, and bulk density were measured for each grade. The results showed that both cyanobacterial and moss crusts could be effectively classified into three developmental grades based on these indicators, with a 90% concordance between the measured indicators and the defined grading method. This finding validated that the method could accurately reflect biocrust developmental stages while simplifying field recordings. Developmental indicators in various grades of cyanobacterial and moss crusts showed a moderate (30% < CV < 100%) to strong (CV > 100%) variation, highlighting the importance of environmental heterogeneity at the regional scale. Moreover, the grading method proved effective across varying spatial scales, highlighting its broad applicability. However, its validation across the comprehensiveness of target objects and the geographical scope remains limited. Future research should focus on expanding the grading method to include lichen crust, refining it across diverse ecosystems, and exploring the integration of advanced technologies such as hyperspectral imaging and machine learning to automate and improve the classification process. This study provides a simple and effective grading method for visually recording the developmental stages of biological soil crusts, which is useful for ecological research and field applications.

Keywords: dryland; successional stage; classification; precipitation; developmental indicators (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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