Geomorphic controls on biological soil crust distribution: A conceptual model from the Mojave Desert (USA)

Abstract

Biological soil crusts (BSCs) are bio-sedimentary features that play critical geomorphic and ecological roles in arid environments. Extensive mapping, surface characterization, GIS overlays, and statistical analyses explored relationships among BSCs, geomorphology, and soil characteristics in a portion of the Mojave Desert (USA). These results were used to develop a conceptual model that explains the spatial distribution of BSCs. In this model, geologic and geomorphic processes control the ratio of fine sand to rocks, which constrains the development of three surface cover types and biogeomorphic feedbacks across intermontane basins. (1) Cyanobacteria crusts grow where abundant fine sand and negligible rocks form saltating sand sheets. Cyanobacteria facilitate moderate sand sheet activity that reduces growth potential of mosses and lichens. (2) Extensive tall moss–lichen pinnacled crusts are favored on early to late Holocene surfaces composed of mixed rock and fine sand. Moss–lichen crusts induce a dust capture feedback mechanism that promotes further crust propagation and forms biologically-mediated vesicular (Av) horizons. The presence of thick biogenic vesicular horizons supports the interpretation that BSCs are long-lived surface features. (3) Low to moderate density moss–lichen crusts grow on early Holocene and older geomorphic surfaces that display high rock cover and negligible surficial fine sand. Desert pavement processes and abiotic vesicular horizon formation dominate these surfaces and minimize bioturbation potential. The biogeomorphic interactions that sustain these three surface cover trajectories support unique biological communities and soil conditions, thereby sustaining ecological stability. The proposed conceptual model helps predict BSC distribution within intermontane basins to identify biologically sensitive areas, set reference conditions for ecological restoration, and potentially enhance arid landscape models, as scientists address impacts of climate change and anthropogenic disturbances.