Withdrawn - 12. A Preferential Sampling Model for Harmful Algal Blooms

Harmful Algal Blooms (HABS) can produce toxins that pose serious health risks to humans, pets, and livestock. Data from Utah Lake over the past years have largely been collected in a preferential manner, meaning samples are primarily gathered when HABS are already suspected to be present. This introduces bias, as it fails to capture the full range of bloom conditions, including periods of absence or low risk. A further challenge is distinguishing between presence-only data and systematic presence/absence data, both of which are available and require different modeling strategies. To address these issues, we are developing a Bayesian hierarchical framework that incorporates seasonal patterns and environmental covariates to estimate latent bloom behavior. This ongoing work aims to improve inference on HAB dynamics and support more reliable risk assessment.

Harmful Algal Blooms 

Mid-Level

Knowledge

Women in Statistics and Data Science 2025