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1 January 2008 A Hierarchical Model for Estimating Change in American Woodcock Populations
John R. Sauer, William A. Link, William L. Kendall, James R. Kelley, Daniel K. Niven
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The Singing-Ground Survey (SGS) is a primary source of information on population change for American woodcock (Scolopax minor). We analyzed the SGS using a hierarchical log-linear model and compared the estimates of change and annual indices of abundance to a route regression analysis of SGS data. We also grouped SGS routes into Bird Conservation Regions (BCRs) and estimated population change and annual indices using BCRs within states and provinces as strata. Based on the hierarchical model–based estimates, we concluded that woodcock populations were declining in North America between 1968 and 2006 (trend = −0.9%/yr, 95% credible interval: −1.2, −0.5). Singing-Ground Survey results are generally similar between analytical approaches, but the hierarchical model has several important advantages over the route regression. Hierarchical models better accommodate changes in survey efficiency over time and space by treating strata, years, and observers as random effects in the context of a log-linear model, providing trend estimates that are derived directly from the annual indices. We also conducted a hierarchical model analysis of woodcock data from the Christmas Bird Count and the North American Breeding Bird Survey. All surveys showed general consistency in patterns of population change, but the SGS had the shortest credible intervals. We suggest that population management and conservation planning for woodcock involving interpretation of the SGS use estimates provided by the hierarchical model.

John R. Sauer, William A. Link, William L. Kendall, James R. Kelley, and Daniel K. Niven "A Hierarchical Model for Estimating Change in American Woodcock Populations," Journal of Wildlife Management 72(1), 204-214, (1 January 2008).
Published: 1 January 2008

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American woodcock
Hierarchical model
route regression
Scolopax minor
Singing-ground Survey
trend analysis
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