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CSIRO Publishing, International Journal of Wildland Fire, 8(22), p. 1021

DOI: 10.1071/wf13026

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A comparison of targeted and systematic fire-scar sampling for estimating historical fire frequency in south-western ponderosa pine forests

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Abstract

Fire history researchers employ various forms of search-based sampling to target specimens that contain visible evidence of well preserved fire scars. Targeted sampling is considered to be the most efficient way to increase the completeness and length of the fire-scar record, but the accuracy of this method for estimating landscape-scale fire frequency parameters compared with probabilistic (i.e. systematic and random) sampling is poorly understood. In this study we compared metrics of temporal and spatial fire occurrence reconstructed independently from targeted and probabilistic fire-scar sampling to identify potential differences in parameter estimation in south-western ponderosa pine forests. Data were analysed for three case studies spanning a broad geographic range of ponderosa pine ecosystems across the US Southwest at multiple spatial scales: Centennial Forest in northern Arizona (100 ha); Monument Canyon Research Natural Area (RNA) in central New Mexico (256 ha); and Mica Mountain in southern Arizona (2780 ha). We found that the percentage of available samples that recorded individual fire years (i.e. fire-scar synchrony) was correlated strongly between targeted and probabilistic datasets at all three study areas (r ¼ 0.85, 0.96 and 0.91 respectively). These strong positive correlations resulted predictably in similar estimates of commonly used statistical measures of fire frequency and cumulative area burned, including Mean Fire Return Interval (MFI) and Natural Fire Rotation (NFR). Consistent with theoretical expectations, targeted fire-scar sampling resulted in greater overall sampling efficiency and lower rates of sample attrition. Our findings demonstrate that targeted sampling in these systems can produce accurate estimates of landscape-scale fire frequency parameters relative to intensive probabilistic sampling.