Using Pointing Dogs and Hierarchical Models to Evaluate American Woodcock Winter Occupancy and Densities
Use of dogs has increased for multiple wildlife research purposes ranging from carnivore scat detection to estimation of reptile abundance. Use of dogs is not particularly novel for upland gamebird biologists, and pointing dogs have been long considered an important research tool. However, recent advances in Global Positioning System (GPS) technology and the development of hierarchical modeling approaches that account for imperfect detection may improve estimates of occupancy and density of cryptic species such as the American woodcock (Scolopax minor; hereafter, woodcock). We conducted surveys for woodcock using a trained pointing dog wearing a GPS collar during the winters of 2010–2011 and 2011–2012 in East Texas, USA. We surveyed 0.5-km-radius circular plots (n = 24; survey sites) randomly placed along secondary roads in Davy Crockett National Forest and on private timber property. Surveys lasted 1.5 hrs and were repeated 3–5 times each winter. We estimated woodcock occupancy and density using multiple modeling approaches at the survey site and forest stand scales within survey sites. Woodcock occupied 88% (21/24) of survey sites and 48% (39/82) of forest stands (i.e., unique cover types) within sites. Using a modified distance sampling technique, we estimated an average density of 0.16 birds/ha (SE = 0.13) throughout both study areas. We describe the first attempt to blend use of pointing dogs with hierarchical modeling approaches to derive estimates of regional diurnal woodcock occupancy and density, and describe relationships between these estimates of abundance and habitat covariates. Although forest stand occupancy estimates had the lowest coefficients of variation, our estimates of density provided the most useful inference of habitat use. Surveys using pointing dogs paired with hierarchical models of occupancy and density may provide a cost-efficient and effective approach to estimate habitat abundance at broad spatial scales.
Copyright (c) 2019 Daniel S. Sullins, Warren C. Conway, David A. Haukos, Christopher E. Comer
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