Abstract: Biological soil crust (biocrust) is widely distributed on the Loess Plateau and plays multiple roles in regulating ecosystem stability and multifunctionality. Few reports are available on the distribution characteristics of biocrust in this region, which limits the assessment of its ecological functions. Based on 388 sampling points in different precipitation zones on the Loess Plateau from 2009 to 2020, we analyzed the coverage, composition, and influencing factors of biocrust across different durations since land abandonment, precipitation levels, topography (slope aspect and position), and utilization of abandoned slopelands (shrubland, forest, and grassland). On this base, with the assistance of machine learning and spatial modeling methods, we generated a distribution map of biocrust and its composition at a resolution of 250 m × 250 m, and analyzed the spatial distribution of biocrust on the Loess Plateau. The results showed that the average biocrust coverage in the woodlands and grasslands was 47.3%, of which cyanobacterial crust accounted for 25.5%, moss crust 19.7%, and lichen crust 2.1%. There were significant temporal and spatial variations. Temporally, the coverage of biocrust in specific regions fluctuated with the extension of the abandoned durations and coverage of cyanobacterial crust, while moss crust showed a reverse pattern. In addition, the coverage of biocrust in the wet season was slightly higher than that in the dry season within a year. Spatially, the coverage of biocrusts on the sandy lands area on the Loess Plateau was higher and dominated by cyanobacterial crusts, while the coverage was lower in the hilly and gully area. Precipitation and utilization of abandoned land were the major factors driving biocrust coverage and composition, while slope direction and position did not show obvious effect. In addition, soil organic carbon content, pH, and texture were related to the distribution of biocrust. This study uncovered the spatial and temporal variability of biocrust distribution, which might provide important data support for the research and management of biocrust in the Loess Plateau region.

Key words: biocrust, random forest, environmental factor, distribution pattern, spatial prediction