Status and trends of American Woodcock Scolopax minor populations in the eastern and central US and Canada are monitored 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, eastern and central management regions, and survey-wide. In recent years, a log-linear hierarchical model that defines year effects as random effects in the context of a slope parameter (the S Model) has been used to model population change. Recently, alternative models have been proposed 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 D model was favored by the Bayesian predictive information criterion but a pairwise t -test indicated that model D 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.
Monitoring and Quantitative Human Risk Assessment of Municipal Solid Waste Landfill Using Integrated Satellite–UAV–Ground Survey Approach