In this dissertation, I assess data collection methods for avian surveys and develop and apply integrated models to estimate demographic rates and trends for bird species of conservation concern. The work in this dissertation advances the capacity of science to inform management decisions for bird conservation through evaluation and improvement of data collection and analysis methods, and further demonstrates the application of new modeling techniques to understand ecological phenomena. I focus on survey methods and modeling approaches for shorebird and waterbird species—migratory birds that experience numerous challenges and population-level threats due to their long-distance migrations, reliance on water bodies and coastal habitats, and sensitivity to anthropogenic-induced climate change. In Chapter One, I review data collection issues associated with aerial surveys, a common survey method to estimate waterbird population abundance, and provide evidence-based solutions for overcoming issues such as, non-detection, counting error, and species misidentification. The challenges associated with aerial survey data are highlighted using a case study featuring waterbirds in the Gulf of Mexico. In the remaining chapters, I use integrated modeling methods to combine multiple data types into unified analyses of target species to simultaneously estimate demographic rates and inform management needs of migratory species in decline. In Chapter Two, I develop an integrated population model of black terns (Chlidonias niger) in the Upper Midwestern United States and combine it with a Bayesian population viability analysis to predict the effects of various management strategies on population persistence. The results reveal that current conservation efforts, taking place almost exclusively during the breeding season, are unlikely to reverse the black tern declines observed over the last decade. My assessments indicate that to be effective, black tern conservation must also include interventions targeting adult survival during nonbreeding periods of the annual cycle. In Chapter Three, I estimate demographic rates, including the effects of environmental drivers, for three species of Arctic-breeding shorebirds using single-species integrated population models. I assess species’ demographic rate correlations (i.e., population synchrony), which indicate that breeding season dynamics are relatively synchronous for the three species while nonbreeding season dynamics vary asynchronously. The disparities observed in shorebird species dynamics suggest that differential responses to environmental conditions, such as differences in responses to climactic variation and distinct nonbreeding season conditions arising from different migratory pathways and nonbreeding season ranges, could be driving shorebird population dynamics. The results from chapters two and three highlight the importance of enhancing management efforts for migratory species during migration and nonbreeding periods, which constitute a much larger, and generally riskier, proportion of the avian annual cycle compared to the breeding season. In Chapter Four, I extend the integrated population model from a single-species context to a multi-species framework to estimate demographic rates at both the species- and community-levels for a community of eight Arctic-breeding shorebirds, including species for which few data exist. The application and extension of integrated models to sparse and disparate community-level datasets allows researchers to estimate species’ demographic rates and trends in cases when traditional, independent analyses of such data are not likely to yield robust inferences. My dissertation demonstrates how the continued assessment and improvement of data collection techniques and integrated modeling approaches can provide conservation scientists with accurate and precise estimates of population and community dynamics that can be used to inform biodiversity monitoring and conservation initiatives for birds and other taxa.
Read
