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A Bayesian MCMC approach to reconstruct spatial vegetation dynamics from sparse vegetation maps

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Abstract

In studies of vegetation dynamics, data points describing the changes are often sparse, because changes were not recognized in early stages or investigations were part of different projects. The snapshots at hand often leave the nature of the dynamics unrevealed and only give a rough estimation of the directions of changes. Extrapolation of the dynamics with traditional cellular automaton modeling is also complicated in such cases, because rules often cannot be deduced from field data for each interaction. We developed a Bayesian MCMC method, using a discrete time stochastic cellular automaton model to reconstruct vegetation dynamics between vegetation maps available and provide estimation of vegetation pattern in years not surveyed. Spread capability of each vegetation type was characterized by a lateral spread parameter and another for establishment from species pool. The method was applied to a series of three vegetation maps depicting vegetation change at a grassland site following abandonment of grazing in north-eastern Hungary. The Markov chain explored the missing data space (missing maps) as well as the parameter space. Transitions by lateral expansion had a greater importance than the appearance of new vegetation types without spatial constraints at our site. We estimated the trajectory of change for each vegetation type, which bore a considerable non-linear element in most cases. To our best knowledge, this is the first work that tries to estimate vegetation transition parameters in a stochastic cellular automaton based on field measurements and provides a tool to reconstruct past dynamics from observed pattern.

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Acknowledgments

Klára Virágh was supported by the Hungarian Research Fund (OTKA) grant no.SUP062338, István Miklós by that of no. F61730. Imelda Somodi was supported by both listed grants, as well as by the Hungarian Research Fund (OTKA) grant no. NI68218. The authors are grateful to Beáta Oborny for her support during model development.

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Authors and Affiliations

  1. Institute for Ecology and Botany of the Hungarian Academy of Sciences, 2-4 Alkotmány utca, 2163, Vácrátót, Hungary

    Imelda Somodi & Klára Virágh

  2. Department of Plant Taxonomy and Ecology, Institute of Biology, Eötvös Loránd University, 1/C Pázmány Péter sétány, 1117, Budapest, Hungary

    Imelda Somodi

  3. Alfréd Rényi Institute of Mathematics, Hungarian Academy of Sciences, 13-15 Reáltanoda utca, 1053, Budapest, Hungary

    István Miklós

Authors
  1. Imelda Somodi
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  2. Klára Virágh
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Correspondence to Imelda Somodi.

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Appendix 1. (JPEG 3.3 Mb)

10980_2011_9610_MOESM2_ESM.doc

Appendix 2. Transition matrices estimated with a quantification of uncertainty of the estimation. Letters refer to vegetation types, for more information see Table 1. (DOC 184 kb)

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Somodi, I., Virágh, K. & Miklós, I. A Bayesian MCMC approach to reconstruct spatial vegetation dynamics from sparse vegetation maps. Landscape Ecol 26, 805–822 (2011). https://doi.org/10.1007/s10980-011-9610-6

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