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Red-Cockaded Woodpeckers in the Red Hills Region: A GIS-Based Assessment

James A. Cox, W. Wilson Baker and R. Todd Engstrom
Wildlife Society Bulletin (1973-2006)
Vol. 29, No. 4 (Winter, 2001), pp. 1278-1288
Published by: Wiley on behalf of the Wildlife Society
Stable URL: http://www.jstor.org/stable/3784154
Page Count: 11
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Red-Cockaded Woodpeckers in the Red Hills Region: A GIS-Based Assessment
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Abstract

Nearly a decade has passed since the Red Hills population of red-cockaded woodpeckers (Picoides borealis) was surveyed. This is the largest population on private lands and supports an estimated 3-4% of the remaining population of this endangered species. We initiated a new survey in 1998 to determine the current status of the Red Hills population and to investigate factors that might affect cluster activity. We geo-referenced cavity trees (n=2,047) using a global positioning system and entered them into a geographic information system (GIS). We then used GIS to compare habitat and spatial features associated with each cluster. Estimated numbers of active (n=179) and inactive (n=90) clusters were similar to those found in previous surveys, but other factors (e.g., a high rate of cluster inactivation) made it difficult to conclude that the population was stable. Average number of cavity trees/cluster was 7.6 (SD=5.2). Average number of active trees in active clusters was 2.8 (SD=1.6). The most common species used as cavity trees were longleaf pine (Pinus palustris) and loblolly pine (P. taeda), and a greater proportion of longleaf cavity trees was active (26.9% versus 11.8% for loblolly). Active clusters had more active neighbors within 2 and 4.5 km and shorter distances to an active neighboring cluster than inactive clusters. Active clusters also were surrounded by more-uniform forest cover, smaller areas of unsuitable habitat types, less total edge habitat, and fewer total patches of unsuitable habitat than inactive clusters. Proportion of cavity trees in longleaf pine, number of active neighbors within 2 km, and proportion of unsuitable habitat within 804 m of cluster centers were the best predictors of cluster activity. These variables correctly classified 75% of the clusters. Clusters misclassified by a discriminant analysis (i.e., active clusters classified as inactive and inactive clusters classified as active) should be the focus of attempts to stabilize or expand this population by constructing artificial cavities.

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