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

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.

Available for download on Friday, September 11, 2026

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