GEOMORPHIC CONTROLS ON SHRUB CANOPY SIZE AND SPACING OF CREOSOTE BUSH IN NORTHERN MOJAVE DESERT, USA

The influencing role of geomorphology on vegetation assemblage and structure has been studied at the regional and shrub scales, but studies at the basin scale are less available. In this study, we posit that pedogenic development in the form of increased horizonation and clay-content (and perhaps desert pavement formation in some areas) ultimately controls the size and spacing of the common creosote bush Larrea tridentate shrubs via modulation of biotic competition. The 35,500 hectare site for this research is within the Boulder City Conservation Easement in Eldorado Valley, Nevada, USA, in the northeastern portion of the Mojave Desert. To test our hypothesis, we use airborne LiDAR data (5-7 pts/m²) and high-resolution imagery (4 bands, 0.15 m spatial resolution) as the base data layer. These data were used to identify approximately 30 million individual L. tridentata shrubs. Geomorphic surfaces and soil properties were obtained from the USDA SSURGO database and a recent geologic map of the NW portion of the study area. Results indicate significant relationships between local geomorphology, soil type and land aspect on the canopy characteristics (canopy size, volume, and spatial distribution) of these shrubs, supporting our hypothesis. Specifically, relatively young surfaces with less horizon development and lower clay content allow more water entry into the soil, leading to intra-species competitive behavior for (water) resources and shrubs that are larger and spaced further apart. With increased time and at a hypothetical pedogenic “threshold,” soil properties restrict water entry and deeper movement of soil water, diminishing intra-species competition and leading to small shrubs with shorter interspaces. This scale of inquiry could inform future research into habitats, desert ecology, and soil identification, potentially enhancing the ability to infer geomorphologic and subsurface characteristics of a remote desert region using remotely-sensed vegetation data.