Additive multivariate Gaussian processes for joint species distribution modeling with heterogeneous data

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http://hdl.handle.net/10138/315088

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Vanhatalo , J , Hartmann , M & Veneranta , L 2020 , ' Additive multivariate Gaussian processes for joint species distribution modeling with heterogeneous data ' , Bayesian analysis , vol. 15 , no. 2 , pp. 415-447 . https://doi.org/10.1214/19-BA1158

Title: Additive multivariate Gaussian processes for joint species distribution modeling with heterogeneous data
Author: Vanhatalo, Jarno; Hartmann, Marcelo; Veneranta, Lari
Contributor: University of Helsinki, Department of Mathematics and Statistics
University of Helsinki, Department of Computer Science
Date: 2020-06
Number of pages: 33
Belongs to series: Bayesian analysis
ISSN: 1931-6690
URI: http://hdl.handle.net/10138/315088
Abstract: 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. Here, we propose JSDMs where the responses to environmental covariates are modeled with additive multivariate Gaussian processes coded as linear models of coregionalization. These allow inference for wide range of functional forms and interspecific correlations between the responses. We propose also an efficient approach for inference with Laplace approximation and parameterization of the interspecific covariance matrices on the euclidean space. We demonstrate the benefits of our model with two small scale examples and one real world case study. We use cross-validation to compare the proposed model to analogous semi-parametric single species models and parametric single and joint species models in interpolation and extrapolation tasks. The proposed model outperforms the alternative models in all cases. We also show that the proposed model can be seen as an extension of the current state-of-the-art JSDMs to semi-parametric models.
Subject: ABUNDANCE
COMMUNITY
COOCCURRENCE
EUTROPHICATION
FINLAND
FRAMEWORK
GULF
INFERENCE
Laplace approximation
PATTERNS
VARIABLES
covariance transformation
heterogeneous data
hierarchical model
linear model of coregionalization
model comparison
spatial prediction
112 Statistics and probability
113 Computer and information sciences
119 Other natural sciences
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