Predictive modelling of burn probability and burn severity in a desert spring ecosystem
Stephanie O. Sunderman A B and Peter J. Weisberg A C
+ Author Affiliations
- Author Affiliations
A Department of Natural Resources and Environmental Science, University of Nevada, Reno Mail Stop 186, Reno, NV 89557, USA.
B Present address: Florida Department of Environmental Protection, 2600 Blair Stone Road, Tallahassee, FL 32399, USA.
C Corresponding author. Email: pweisberg@cabnr.unr.edu
International Journal of Wildland Fire 21(8) 1014-1024 https://doi.org/10.1071/WF11172
Submitted: 3 December 2011 Accepted: 30 April 2012 Published: 30 July 2012
Abstract
Little is known about the fire ecology of desert springs, despite their importance for biodiversity and for provision of ecosystem services. Desert spring ecosystems are characterised by high and continuous fuel loads compared with surrounding uplands, suggesting that fire may play a significant ecological role. For the Ash Meadows spring complex in the south-western USA, we used ecological-niche factor analysis and a Bayesian model averaging regression technique to characterise the environmental conditions associated with spatially explicit burn probability and burn severity over a 24-year period. Burn probability and burn severity were both more strongly associated with fuel availability than with proximity to anthropogenic ignition sources; however, areas with more homogeneous vegetation cover were positively associated with high-severity burns but were negatively associated with burn probability. Burn probability was greater near areas of high anthropogenic influence, whereas areas further from anthropogenic alteration were more likely to experience high-severity fire. Riparian forest and emergent wetland vegetation were most likely to burn although they were among the rarest vegetation types. Human activities may strongly influence fire regimes in desert spring wetlands through groundwater pumping and introductions of exotic plants that alter fuelbed heterogeneity and shift the balance among woody and herbaceous vegetation.
Additional keywords: cienaga, fire ignition, fuel heterogeneity, niche factor analysis, spatial modelling.
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