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

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dc.contributor University of Helsinki, Department of Mathematics and Statistics en
dc.contributor University of Helsinki, Department of Computer Science en
dc.contributor.author Vanhatalo, Jarno
dc.contributor.author Hartmann, Marcelo
dc.contributor.author Veneranta, Lari
dc.date.accessioned 2020-05-20T18:29:01Z
dc.date.available 2020-05-20T18:29:01Z
dc.date.issued 2020-06
dc.identifier.citation 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 en
dc.identifier.issn 1931-6690
dc.identifier.other PURE: 123975941
dc.identifier.other PURE UUID: 6813e876-5082-4ccc-9404-c1e53f17afb8
dc.identifier.other WOS: 000533646400004
dc.identifier.uri http://hdl.handle.net/10138/315088
dc.description.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. en
dc.format.extent 33
dc.language.iso eng
dc.relation.ispartof Bayesian analysis
dc.rights en
dc.subject ABUNDANCE en
dc.subject COMMUNITY en
dc.subject COOCCURRENCE en
dc.subject EUTROPHICATION en
dc.subject FINLAND en
dc.subject FRAMEWORK en
dc.subject GULF en
dc.subject INFERENCE en
dc.subject Laplace approximation en
dc.subject PATTERNS en
dc.subject VARIABLES en
dc.subject covariance transformation en
dc.subject heterogeneous data en
dc.subject hierarchical model en
dc.subject linear model of coregionalization en
dc.subject model comparison en
dc.subject spatial prediction en
dc.subject 112 Statistics and probability en
dc.subject 113 Computer and information sciences en
dc.subject 119 Other natural sciences en
dc.title Additive multivariate Gaussian processes for joint species distribution modeling with heterogeneous data en
dc.type Article
dc.description.version Peer reviewed
dc.identifier.doi https://doi.org/10.1214/19-BA1158
dc.type.uri info:eu-repo/semantics/other
dc.type.uri info:eu-repo/semantics/publishedVersion
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