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Modelling Spatial Compositional Data: Reconstructions of past land cover and uncertainties
Tech Univ Denmark, Denmark;Lund University.
Lund University.
Lund University;Tallinn Univ Technol, Estonia.
Linnaeus University, Faculty of Health and Life Sciences, Department of Biology and Environmental Science.ORCID iD: 0000-0002-2025-410X
2018 (English)In: Spatial Statistics, E-ISSN 2211-6753, Vol. 24, p. 14-31Article in journal (Refereed) Published
Abstract [en]

In this paper we construct a hierarchical model for spatial compositional data which is used to reconstruct past land-cover compositions (in terms of coniferous forest, broadleaved forest, and unforested/open land) for five time periods during the past 6000 years over Europe. The model consists of a Gaussian Markov Random Field (GMRF) with Dirichlet observations. A block updated Markov chain Monte Carlo (MCMC), including an adaptive Metropolis adjusted Langevin step, is used to estimate model parameters. The sparse precision matrix in the GMRF provides computational advantages leading to a fast MCMC algorithm. Reconstructions are obtained by combining pollen-based estimates of vegetation cover at a limited number of locations with scenarios of past deforestation and output from a dynamic vegetation model. To evaluate uncertainties in the predictions a novel way of constructing joint confidence regions for the entire composition at each prediction location is proposed. The hierarchical model's ability to reconstruct past land cover is evaluated through cross validation for all time periods, and by comparing reconstructions for the recent past to a present day European forest map. The evaluation results are promising, and the model is able to capture known structures in past land-cover compositions. (C) 2018 Elsevier B.V. All rights reserved.

Place, publisher, year, edition, pages
Elsevier, 2018. Vol. 24, p. 14-31
Keywords [en]
Gaussian Markov Random Field, Dinchlet observation, Adaptive Metropolis adjusted Langevin, Pollen records, Confidence regions
National Category
Biological Sciences
Research subject
Environmental Science, Paleoecology
Identifiers
URN: urn:nbn:se:lnu:diva-76853DOI: 10.1016/j.spasta.2018.03.005ISI: 000432788000002Scopus ID: 2-s2.0-85044647476OAI: oai:DiVA.org:lnu-76853DiVA, id: diva2:1232897
Available from: 2018-07-13 Created: 2018-07-13 Last updated: 2019-08-29Bibliographically approved

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Gaillard, Marie-José

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CiteExportLink to record
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  • apa
  • harvard1
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  • Other locale
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Output format
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