Bayesian model selection to investigate meaningful spatial scales
Links
- More information: Publisher Index Page (via DOI)
- Open Access Version: Publisher Index Page
- Download citation as: RIS | Dublin Core
Abstract
Ecologists and other statistical practitioners with access to high-resolution spatial data lack guidance on best approaches for discerning meaningful spatial scales for environmental covariates which is necessary when spatial factors influence environmental processes. Recently developed methods have attempted to automate investigating spatial scales for covariates by evaluating models for which potential explanatory variables are derived from concentric circles of increasing size centered at survey locations. However, these methods make a strong assumption on the inclusion of the covariate and do not help discern whether a covariate should be included in the model. We present an approach that utilizes researcher guidance to create informative priors on the model space that, along with parallelizable Reversible Jump MCMC techniques, enables efficient estimation of posterior model probabilities to assist with the choice of meaningful spatial scales for environmental covariates.
| Publication type | Preprint |
|---|---|
| Publication Subtype | Preprint |
| Title | Bayesian model selection to investigate meaningful spatial scales |
| Series title | Authorea |
| DOI | 10.22541/au.173685828.82162349/v1 |
| Publication Date | January 14, 2025 |
| Year Published | 2025 |
| Language | English |
| Publisher | Authorea |
| Contributing office(s) | Northern Rocky Mountain Science Center |
| Description | 26 p. |