Abstract Meta-analyses are powerful tools for synthesizing wildlife-habitat relationships, but small sample sizes and complex species-habitat relationships often preclude correlative meta-analyses on endangered species. In this study, we demonstrate qualitative… Click to show full abstract
Abstract Meta-analyses are powerful tools for synthesizing wildlife-habitat relationships, but small sample sizes and complex species-habitat relationships often preclude correlative meta-analyses on endangered species. In this study, we demonstrate qualitative comparative analysis (QCA) as a tool that can reliably synthesize habitat-fitness relationships from small sample sizes for species with narrow habitat requirements. We apply QCA to results from a habitat threshold regression tree model and identify habitat thresholds with consistent positive effects on fitness of the federally endangered red-cockaded woodpecker (Dryobates borealis; RCW) on the Savannah River Site, USA. We reformulated regression tree results in a QCA framework to examine the consistency of threshold effects on RCW fledgling production at the individual group level (n = 47). Synthesizing regression tree results with QCA revealed alternative combinations of habitat thresholds that in conjunction with group size consistently led to above-average fledgling production for 41 of 47 (88%) individual RCW groups. Importantly, QCA identified unique combinations of habitat thresholds and group size related to above-average fledgling production that were not retained in the regression tree model due to small sample sizes. Synthesizing a small habitat-fitness dataset using QCA provided a tractable method to identify unique combinations of habitat and group size conditions that are consistently important to individual fitness, but may not be detected by meta-analyses that can be biased by small sample sizes. QCA offers a viable approach for synthesis of habitat-fitness relationships and can be extended to address many complex issues in endangered species recovery when correlative meta-analyses are not possible.
               
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