Spatial variability of surface fuels in treated and untreated ponderosa pine forests of the southern Rocky Mountains
Emma Vakili A , Chad M. Hoffman A D , Robert E. Keane B , Wade T. Tinkham A and Yvette Dickinson C
+ Author Affiliations
- Author Affiliations
A Department of Forest and Rangeland Stewardship, Colorado State University, Fort Collins, CO 80524, USA.
B USDA Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory, PO Box 8089, Missoula, MT 59807, USA.
C School of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI 49931, USA.
D Corresponding author. Email: c.hoffman@colostate.edu
International Journal of Wildland Fire 25(11) 1156-1168 https://doi.org/10.1071/WF16072
Submitted: 2 August 2015 Accepted: 18 August 2016 Published: 3 October 2016
Abstract
There is growing consensus that spatial variability in fuel loading at scales down to 0.5 m may govern fire behaviour and effects. However, there remains a lack of understanding of how fuels vary through space in wildland settings. This study quantifies surface fuel loading and its spatial variability in ponderosa pine sites before and after fuels treatment in the southern Rocky Mountains, USA. We found that spatial semivariance for 1- and 100-h fuels, litter and duff following thin-and-burn treatments differed from untreated sites, and was lower than thin-only sites for all fuel components except 1000-h fuels. Fuel component semivariance increased with mean fuel component loading. The scale of spatial autocorrelation for all fuel components and sites ranged from <1 to 48 m, with the shortest distances occurring for the finest fuel components (i.e. duff, litter). Component mean fuel particle diameter strongly predicted (R2 = 0.88) the distance needed to achieve sample independence. Additional work should test if these scaling relationships hold true across forested ecosystems, and could reveal fundamental processes controlling surface fuel variability. Incorporating knowledge of spatial variability into fuel sampling protocols will enhance assessment of wildlife habitat, and fire behaviour and effects modelling, over singular stand-level means.
Additional keywords: Colorado, forest heterogeneity, New Mexico, Pinus ponderosa, spatial scaling.
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