Computationally efficient joint species distribution modeling of big spatial data.

Area of Science:

• Ecology
• Macroecology
• Biodiversity Science

Background:

• Global change necessitates analysis of large-scale species community data for biodiversity forecasting.
• Joint species distribution models (JSDMs) handle multiple species and spatial structure but face computational limits with large datasets.
• Scalability is a major constraint for applying JSDMs to extensive spatial ecological data.

Purpose of the Study:

• To overcome the computational scalability limitations of joint species distribution models for large spatial datasets.
• To develop and implement efficient Bayesian methods for analyzing extensive species community data.
• To facilitate macroecological research by enabling JSDMs on large spatial scales.

Main Methods:

• Utilized Gaussian predictive process and nearest-neighbor Gaussian process techniques to enhance scalability.
• Developed an efficient Gibbs posterior sampling algorithm for Bayesian model fitting.
• Implemented these methods as an extension to the hierarchical modeling of species communities (HMSC) framework.

Main Results:

• Successfully analyzed large community datasets with hundreds of species across hundreds of thousands of spatial units.
• Demonstrated the performance of the proposed methods using an extensive plant dataset with 30,955 spatial units.
• Provided a practical and efficient solution for applying JSDMs to spatially extensive biodiversity data.

Conclusions:

• The proposed methods significantly alleviate scalability constraints in joint species distribution modeling.
• This advancement enables robust analysis of macroecological patterns and biodiversity changes across large spatial extents.
• The implemented methods offer a valuable tool for ecological research on global change impacts.