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TECHNICAL REPORTS

Landscape and Watershed Processes

Fire Effects on Stable Isotopes in a Sierran Forested Watershed

^a Dep. of Natural Resources and Environmental Science, Univ. of Nevada Reno, Mail Stop 186, 1000 Valley Road, Reno, NV 89512
^b The Nature Conservancy, One E. First St., Suite 1007, Reno, NV 89501
^c 1475 Folsom Street, #362, Boulder, CO 80302

^* Corresponding author (lsaito{at}cabnr.unr.edu)

Received for publication June 16, 2006. This study tested the hypothesis that stable C and N isotope values in surface soil and litter would be increased by fire due to volatilization of lighter isotopes. The hypothesis was tested by: (1) performing experimental laboratory burns of organic and mineral soil materials from a watershed at combinations of temperature ranging 100 to 600°C and duration ranging from 1 to 60 min; (2) testing field samples of upland soils before, shortly after, and 1 yr following a wildfire in the same watershed; and (3) testing field soil samples from a down-gradient ash/sediment depositional area in a riparian zone following a runoff event after the wildfire. Muffle furnace results indicated the most effective temperature range for using stable isotopes for tracing fire impacts is 200 to 400°C because lower burn temperatures may not produce strong isotopic shifts, and at temperatures 600°C, N and C content of residual material is too low. Analyses of field soil samples were inconclusive: there was a slightly significant effect of the wildfire on ¹⁵N values in upland watershed analyses 1 yr postburn, while riparian zone analyses results indicated that ¹³C values significantly decreased 0.71 over a 9 mo post-fire period (p = 0.015), and ash/sediment layer ¹³C values were 0.65 higher than those in the A horizon. The lack of field confirmation may have been due to overall wildfire burn temperatures being <200°C and/or microbial recovery and vegetative growth in the field. Thus, the muffle furnace experiment supported the hypothesis, but it is as yet unconfirmed by actual wildfire field data.

Abbreviations: GLM, generalized linear model • MSE, mean squared error • SS, sum of squares • UNR, University of Nevada Reno