Accounting for surveyor effort in large-scale monitoring programs

Accounting for errors in wildlife surveys is necessary for reliable status assessments and quantification of uncertainty in estimates of population size. We apply a hierarchical log-linear Poisson regression model that accounts for multiple sources of variability in count data collected for the Integrated Waterbird Management and Monitoring Program during 2010–2014. In some large-scale monitoring programs (e.g., Christmas Bird Count) there are diminishing returns in numbers counted as survey effort increases; therefore, we also explore the need to account for variable survey duration as a proxy for effort. In general, we found a high degree of concordance between counts and effort-adjusted estimates of relative abundance from the Integrated Waterbird Management and Monitoring Program (x̄_difference = 0.02%; 0.25% SD). We suggest that the model-based adjustments were small because there is only a weak asymptotic relationship with effort and count. Whereas effort adjustments are reasonable and effective when applied to count data from plots of standardized area, such adjustments may not be necessary when the area of sample units is not standardized and surveyor effort increases with number of birds present. That is, large units require more effort only when there are many birds present. The general framework we implemented to evaluate effects of varying survey effort applies to a wide variety of wildlife monitoring efforts.