Date of Graduation
8-2024
Document Type
Thesis
Degree Name
Master of Science in Statistics and Analytics (MS)
Degree Level
Graduate
Department
Statistics and Analytics
Advisor/Mentor
Chakraborty, Avishek A.
Committee Member
Zhang, Qingyang
Second Committee Member
Arnold, Mark E.
Keywords
Areal data analysis; Bayesian analysis; Hierarchical modeling; Spatial modeling
Abstract
Predicting the distribution and abundance of migratory shorebirds is crucial for effective conservation planning. This research applies hierarchical spatial models to predict counts and spatial variations of three shorebird species: Semipalmated sandpiper (sesa), Ruddy turnstone (rutu), and Whimbrel (whim). Different versions of the Poisson, Negative Binomial, and Hurdle regression models are employed to tackle specific data characteristics, such as overdispersion and excess zeros. Model comparisons are performed in terms of likelihood measures and cross-validation. The Hurdle model for sesa and rutu and the Negative Binomial model for whim effectively captured spatial patterns, highlighting potential hotspots. Mean predictive count further emphasized spatial variability, with the Hurdle model predicting high counts for sesa and rutu in specific areas, identifying critical habitats. The Negative Binomial model efficiently handled overdispersion by accurately depicting regions with high predicted counts for whim. This study's approach provides valuable information on model effectiveness and spatial patterns, identifying specific areas requiring focused conservation efforts and offering guidance for optimal habitat management.
Citation
Alam, M. (2024). Hierarchical Spatial Abundance Models for Migratory Shorebirds. Graduate Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/5460