文章基本信息

标题：Additive Multivariate Gaussian Processes for Joint Species Distribution Modeling with Heterogeneous Data
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作者：Jarno Vanhatalo ; Marcelo Hartmann ; Lari Veneranta 等
期刊名称：Bayesian Analysis
印刷版ISSN：1931-6690
电子版ISSN：1936-0975
出版年度：2020
卷号：15
期号：2
页码：415-447
DOI：10.1214/19-BA1158
语种：English
出版社：International Society for Bayesian Analysis
摘要：Species distribution models (SDM) are a key tool in ecology, conservation and management of natural resources. Two key components of the state-of-the-art SDMs are the description for species distribution response along environmental covariates and the spatial random effect that captures deviations from the distribution patterns explained by environmental covariates. Joint species distribution models (JSDMs) additionally include interspecific correlations which have been shown to improve their descriptive and predictive performance compared to single species models. However, current JSDMs are restricted to hierarchical generalized linear modeling framework. Their limitation is that parametric models have trouble in explaining changes in abundance due, for example, highly non-linear physical tolerance limits which is particularly important when predicting species distribution in new areas or under scenarios of environmental change. On the other hand, semi-parametric response functions have been shown to improve the predictive performance of SDMs in these tasks in single species models.
关键词：linear model of coregionalization; hierarchical model; heterogeneous data; spatial prediction; model comparison; Laplace approximation; covariance transformation