Spatial Bayesian Learning in Ecology and Climate Change

This project focuses on the application of Bayesian learning techniques to analyze and interpret Ecological data, aiming to improve our understanding of ecological processes, biodiversity patterns, and the impacts of climate change. By using hierarchical models and spatial analysis, the project addresses key ecological questions such as species distribution, genetic diversity, and environmental impacts. The integration of Bayesian methods provides robust statistical frameworks to handle uncertainty and variability inherent in ecological data. This approach enhances predictive modeling and supports decision-making in conservation and environmental management.