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Biophysical influences on the spatial distribution of fire in the desert grassland region of the southwestern USA
Authors:Matthew R Levi  Brandon T Bestelmeyer
Institution:1.USDA-ARS Jornada Experimental Range, MSC 3JER,New Mexico State University,Las Cruces,USA
Abstract:

Context

Fire is an important driver of ecological processes in semiarid systems and serves a vital role in shrub-grass interactions. In desert grasslands of the southwestern US, the loss of fire has been implicated as a primary cause of shrub encroachment. Where fires can currently be re-introduced given past state changes and recent restoration actions, however, is unknown and controversial.

Objectives

Our objective was to evaluate the interactive effects of climate, urban development, and topo-edaphic properties on fire distribution in the desert grassland region of the southwestern United States.

Methods

We characterized the spatial distribution of fire in the Chihuahuan Desert and Madrean Archipelago ecoregions and investigated the influence of soil properties and ecological site groups compared to other commonly used biophysical variables using multi-model inference.

Results

Soil-landscape properties significantly influenced the spatial distribution of fire ignitions. Fine-textured bottomland ecological site classes experienced more fires than expected in contrast to upland sites with coarse soil textures and high fragment content that experienced fewer fire ignitions than expected. Influences of mean annual precipitation, distance to road/rail, soil available water holding capacity (AWHC) and topographic variables varied between ecoregions and political jurisdictions and by fire season. AWHC explained more variability of fire ignitions in the Madrean Archipelago compared to the Chihuahuan Desert.

Conclusions

Understanding the spatiotemporal distribution of recent fires in desert grasslands is needed to manage fire and predict responses to climate change. The use of landscape units such as ecological sites presents an opportunity to improve predictions at management scales.
Keywords:
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