UC Berkeley

Estimating the spatiotemporal distributions of species and understanding how variation in those distributions is explained by the environment are central goals in ecology. Observations of animals generated by participatory science (or "citizen science") are an increasingly important resource for ecologists interested in estimating species distributions because they are high-volume and high-resolution. However, statistical inference with these data is more challenging than inference with data collected under standardized sampling, because participatory science observations contain substantial unmeasured variation in sampling effort and observer behavior. Ecologists need tools and methodological guidance that support the estimation of computationally efficient, flexible statistical models useful for robust inference with participatory science data. In this dissertation, I advance the field of species distribution modeling with participatory science data via contributions across three chapters. First, I present a new software tool, nimbleEcology, that supports the efficient and flexible estimation of hierarchical ecological models, alongside a brief review of the use of such models in ecology and three worked examples of model estimation. Second, I undertake a comparison of two modeling approaches useful for estimating relative abundance from participatory science data, making practical recommendations for model selection. Finally, I apply these methodological developments to data obtained from an important participatory science dataset, eBird, to investigate how common birds respond to drought in California's Central Valley ecoregion. This project demonstrates the application of modeling principles to an important ecological case study and produces new evidence to characterize critical dimensions of birds' drought responses.