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American woodcock (Scolopax minor; woodcock) are monitored, in part, by counts of displaying male woodcock collected via the American Woodcock Singing Ground Survey (SGS), which suggests long‐term, range‐wide declines in woodcock populations. Data from the SGS have been used extensively to develop conservation plans, direct management actions, and understand causes of decline. To avoid bias, the SGS should be timed to avoid spring migration, and the distribution of survey routes should coincide with woodcock breeding distribution. Our objectives for this research were to evaluate SGS timing with the phenology of male woodcock migration, relate the spatial coverage of the SGS to male woodcock breeding distributions, and explore other sources of variation in woodcock migration timing. We marked 133 male woodcock captured throughout eastern North America with global positioning system (GPS) transmitters during 2019–2022, and compared the timing of their spring migration with the spatiotemporal stratification of the SGS. Most woodcock (74%) completed migration prior to the onset of the SGS. In the northernmost SGS zone, a greater percentage of males (34%) continued migration during the survey window; however, the influence of this mismatch is offset because SGS routes were run more frequently during the second half of the window. Young woodcock completing their first spring migration took 8.6 days longer to do so, on average, compared to adults, and so were more likely to migrate during the SGS window. We found little evidence that timing of migration varied among years. Existing SGS routes cover the majority of male woodcock post‐migratory breeding distribution, with 90% of male woodcock establishing final breeding sites within the spatial coverage of the SGS. Our results confirm the SGS includes some migrating males, with the proportion relative to resident breeding males increasing in more northern survey strata. Our data suggests these errors are unlikely to bias trend estimates at large scales (e.g., within woodcock management regions), but there may be potential for bias at more local scales (e.g., state or provincial population indices). [ABSTRACT FROM AUTHOR]

Wildlife biologists monitor the status and trends of American woodcock Scolopax minor populations in the eastern and central United States and Canada via a singing-ground survey, conducted just after sunset along roadsides in spring. Annual analyses of the survey produce estimates of trend and annual indexes of abundance for 25 states and provinces, management regions, and survey-wide. In recent years, researchers have used a log-linear hierarchical model that defines year effects as random effects in the context of a slope parameter (the S model) to model population change. Recently, researchers have proposed alternative models suitable for analysis of singing-ground survey data. Analysis of a similar roadside survey, the North American Breeding Bird Survey, has indicated that alternative models are preferable for almost all species analyzed in the Breeding Bird Survey. Here, we use leave-one-out cross-validation to compare model fit for the present singing-ground survey model to fits of three alternative models, including a model that describes population change as the difference in expected counts between successive years (the D model) and two models that include t-distributed extra-Poisson overdispersion effects (H models) as opposed to normally distributed extra-Poisson overdispersion. Leave-one-out cross-validation results indicate that the Bayesian predictive information criterion favored the D model, but a pairwise t-test indicated that the D model was not significantly better-fitting to singing-ground survey data than the S model. The H models are not preferable to the alternatives with normally distributed overdispersion. All models provided generally similar estimates of trend and annual indexes suggesting that, within this model set, choice of model will not lead to alternative conclusions regarding population change. However, as in Breeding Bird Survey analyses, we note a tendency for S model results to provide slightly more extreme estimates of trend relative to D models. We recommend use of the D model for future singing-ground survey analyses. [ABSTRACT FROM AUTHOR]