Climate change, land use change, and other anthropogenic pressures are increasing species extinctions, phenology shifts, and drastic population declines. Avian populations and communities are particularly vulnerable to global change given their mobile and migratory life history strategies. Avian abundance has drastically declined throughout North America over several decades, which is compounded by phenological shifts in breeding periods and migratory patterns. Informed management and conservation of avian populations and communities requires large-scale monitoring programs, as well as associated inferential tools to provide statistically robust inference using multiple data sources. In this dissertation, I develop a suite of hierarchical modeling approaches to understand avian soundscapes, populations, and communities. I leverage a hierarchical Bayesian modeling framework, which is ideally suited for complex wildlife data with numerous types of observation error and dependencies among data points. In Chapter 1, I provide a brief overview of avian monitoring approaches and their associated statistical analysis frameworks. In Chapters 2 and 3, I develop hierarchical models for the analysis of complex avian soundscape data, and apply these approaches to two case studies. In Chapter 2, I apply a two-stage hierarchical beta regression model to quantify the relationship between anthropogenic and biological sounds in avian soundscapes in western New York. In Chapter 3, I use a multivariate linear mixed model to assess disturbance impacts of a shelterwood logging on avian soundscapes in northern Michigan. In Chapter 4, I develop a multi-region, multi-species abundance model to quantify trends of avian species and communities using point count data across a network of National Parks in the northeastern US. In Chapters 5 and 6, I use a model-based data integration approach to yield improved inference on avian population and communities. In Chapter 5, I integrate automated acoustic recording data with point count data to estimate avian abundance, which I apply to a case study on the Eastern Wood Pewee (Contopus virens) in a National Historical Park in Vermont. In Chapter 6, I develop an integrated community occupancy model that combines multiple types of detection-nondetection data for inference on species-specific and community level occurrence dynamics, which I use to assess occurrence dynamics of a foliage-gleaning bird community in New Hampshire. These results exhibit the value of hierarchical models to partition ecological data into distinct observation and ecological components for improved inference on avian population and community dynamics. Future work should continue to leverage complex data sources within hierarchical modeling frameworks to address pressing conservation and management questions on avian populations, communities, and the ecosystem services they provide.
Read
