Cooperative Fish and Wildlife Research Units Program: Massachusetts
Education, Research and Technical Assistance for Managing Our Natural Resources

Wilson, T.L., L.M. Phillips, and B. Mangipane. 2017. Improving bald eagle nest monitoring with a second spring survey. The Journal of Wildlife Management 81: 545-551.


Failure to account for observation error can cause bias in estimators of variables important for wildlife management and conservation, such as abundance, occupancy, and species composition. Therefore, long‐term monitoring programs need to evaluate sources of bias to ensure that objectives of the program can be met. Although trend estimation is possible using biased estimators, population status, and management decisions can be sensitive to random and systematic heterogeneity in the observation process. For example, standard methods used to monitor reproductive attempts at bald eagle (Haliaeetus leucocephalus) nests fail to quantify observation error. We used bald eagle nest surveys from 3 national parks in Alaska, USA, to examine their ability to accurately estimate nesting attempts and hypothesized that our ability to perfectly observe nesting activity was related to the timing of the survey relative to peak nest attendance by eagles. We therefore, developed 3 scenarios that evaluated our ability to correctly identify nesting status metrics: peak nesting remained unchanged (observed variation scenario), reduced ability to predict peak nest attendance (high heterogeneity scenario), and peak nest attendance occurred earlier than the survey window (low detection scenario). Eagle nest attendance was consistently underestimated using naïve estimators, but productivity (predicted no. chicks/nesting attempt) was not affected by imperfect detection. All scenarios showed that using replicate observations to model imperfect detection corrected estimator bias for nest attendance and improved the estimator precision for both metrics. Our study demonstrates that imperfect observation of bald eagle nesting activity using standard methods results in estimator bias and reduced precision if not corrected. Further, our simulations show that changes in observation conditions can affect estimator bias and precision, which is an important consideration for long‐term monitoring programs. Therefore, formally modeling imperfect detection should be completed for monitoring programs designed to use nesting status to inform management or conservation decisions.