Recursive Bayesian Methods in Ecology and Environmental Science

Modern computing environments continue to improve allowing us to fit models to data faster than ever before. In particular, multi-processor and distributed computing resources have become widely available in academics, agencies, and industry, and the computing cores per machine continues to increase. More emphasis on parallel computing using graphical processing units (GPUs) and the demand for massive high-performance computing centers driven by the large language model (LLM) training needs of the AI-based industry has led to unprecedented accessibility of such resources. Ecological and environmental data sets continue to grow in size and complexity due to technological advances and increased interest. Recursive (i.e., multi-stage) approaches for fitting statistical models often allow us to leverage modern parallel computing environments and they have proliferated in the Bayesian and machine learning fields. Despite these advancements however, many approaches are either too complicated or too narrowly applicable for ecologists and environmental scientists to adopt. Fortunately, ongoing developments in recursive statistical computing have led to simple and intuitive methods that can be refined and generalized with ease. Combined with readily available software for parallelization, these new recursive computing approaches have enabled practitioners and led to improvements in scientific inquiry based on large data sets. Recursive computing methods have also inspired new approaches to formulate statistical models that can be both more general and economize implementation at the same time. This proposed invited session comprises statisticians across a range of career stages who work on ecological and environmental applications. Each will present new methodology in recursive Bayesian computing, developing ways to make implementation of statistical models scalable and accurate and demonstrating the approaches using a variety of ecological and environmental data sets.