Accelerating joint species distribution modelling with Hmsc-HPC by GPU porting.

ABSTRACT: Joint species distribution modelling (JSDM) is a widely used statistical method that analyzes combined patterns of all species in a community, linking empirical data to ecological theory and enhancing community-wide prediction tasks. However, fitting JSDMs to large datasets is often computationally demanding and time-consuming. Recent studies have introduced new statistical and machine learning techniques to provide more scalable fitting algorithms, but extending these to complex JSDM structures that account for spatial dependencies or multi-level sampling designs remains challenging. In this study, we aim to enhance JSDM scalability by leveraging high-performance computing (HPC) resources for an existing fitting method. Our work focuses on the Hmsc R-package, a widely used JSDM framework

SUBMITTER: Rahman AU 

PROVIDER: S-EPMC11398642 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature