{"title":"Spatial confounding in joint species distribution models","authors":"Francis K. C. Hui, Quan Vu, Mevin B. Hooten","doi":"10.1111/2041-210x.14420","DOIUrl":null,"url":null,"abstract":"<jats:list> <jats:list-item>Joint species distribution models (JSDMs) are a popular method for analysing multivariate abundance data, with important applications such as uncovering how species communities are driven by environmental processes, model‐based ordination to visualise community composition patterns across sites and variance partitioning to quantify the relative contributions of different processes in shaping a species community.</jats:list-item> <jats:list-item>One issue that has received relatively little attention in the study of joint species distributions is that of spatial confounding: when one or more of the environmental predictors exhibit spatial correlation, and spatially structured random effects such as spatial factors are also included in the model, then these two components may be collinear with each other.</jats:list-item> <jats:list-item>Through a combination of simulations and case studies, we show that if not managed properly, spatial confounding can result in misleading inference on covariate effects in a spatially structured JSDM, along with difficulties in interpreting ordination results and incorrect attribution of variation to environmental processes in a species community.</jats:list-item> <jats:list-item>We present one approach to treat spatial confounding called restricted spatial factor analysis, which is designed to ensure that the covariate effects retain their full explanatory power, and ordinations constructed using the spatial factors explain species covariation beyond that accounted for by the measured predictors. We encourage ecologists to consider the inferences they seek to make from spatially structured JSDMs and to ensure that the covariate effects and ordinations they estimate and interpret are aligned with their scientific questions of interest.</jats:list-item> </jats:list>","PeriodicalId":208,"journal":{"name":"Methods in Ecology and Evolution","volume":null,"pages":null},"PeriodicalIF":6.3000,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Methods in Ecology and Evolution","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1111/2041-210x.14420","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Joint species distribution models (JSDMs) are a popular method for analysing multivariate abundance data, with important applications such as uncovering how species communities are driven by environmental processes, model‐based ordination to visualise community composition patterns across sites and variance partitioning to quantify the relative contributions of different processes in shaping a species community.One issue that has received relatively little attention in the study of joint species distributions is that of spatial confounding: when one or more of the environmental predictors exhibit spatial correlation, and spatially structured random effects such as spatial factors are also included in the model, then these two components may be collinear with each other.Through a combination of simulations and case studies, we show that if not managed properly, spatial confounding can result in misleading inference on covariate effects in a spatially structured JSDM, along with difficulties in interpreting ordination results and incorrect attribution of variation to environmental processes in a species community.We present one approach to treat spatial confounding called restricted spatial factor analysis, which is designed to ensure that the covariate effects retain their full explanatory power, and ordinations constructed using the spatial factors explain species covariation beyond that accounted for by the measured predictors. We encourage ecologists to consider the inferences they seek to make from spatially structured JSDMs and to ensure that the covariate effects and ordinations they estimate and interpret are aligned with their scientific questions of interest.
期刊介绍:
A British Ecological Society journal, Methods in Ecology and Evolution (MEE) promotes the development of new methods in ecology and evolution, and facilitates their dissemination and uptake by the research community. MEE brings together papers from previously disparate sub-disciplines to provide a single forum for tracking methodological developments in all areas.
MEE publishes methodological papers in any area of ecology and evolution, including:
-Phylogenetic analysis
-Statistical methods
-Conservation & management
-Theoretical methods
-Practical methods, including lab and field
-This list is not exhaustive, and we welcome enquiries about possible submissions. Methods are defined in the widest terms and may be analytical, practical or conceptual.
A primary aim of the journal is to maximise the uptake of techniques by the community. We recognise that a major stumbling block in the uptake and application of new methods is the accessibility of methods. For example, users may need computer code, example applications or demonstrations of methods.