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SEIB–DGVM: A new Dynamic Global Vegetation Model using a spatially explicit individual-based approach

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  • Sato, Hisashi
  • Itoh, Akihiko
  • Kohyama, Takashi

Abstract

We report the development of a new spatially explicit individual-based Dynamic Global Vegetation Model (SEIB–DGVM), the first DGVM that can simulate the local interactions among individual trees within a spatially explicit virtual forest. In the model, a sample plot is placed at each grid box, and then the growth, competition, and decay of each individual tree within each plot is calculated by considering the environmental conditions for that tree as it relates to the trees that surround it. Based on these parameters only, the model simulated time lags between climate change and vegetation change. This time lags elongated when original biome was forest, because existing trees prevent newly establish trees from receiving enough sunlight and space to quickly replace the original vegetation. This time lags also elongated when horizontal heterogeneity of sunlight distribution was ignored, indicating the potential importance of horizontal heterogeneity for predicting transitional behavior of vegetation under changing climate. On a local scale, the model reproduced climate zone-specific patterns of succession, carbon dynamics, and water flux, although on a global scale, simulations were not always in agreement with observations. Because the SEIB–DGVM was formulated to the scale at which field biologists work, the measurements of relevant parameters and data comparisons are relatively straightforward, and the model should enable more robust modeling of terrestrial ecosystems.

Suggested Citation

  • Sato, Hisashi & Itoh, Akihiko & Kohyama, Takashi, 2007. "SEIB–DGVM: A new Dynamic Global Vegetation Model using a spatially explicit individual-based approach," Ecological Modelling, Elsevier, vol. 200(3), pages 279-307.
  • Handle: RePEc:eee:ecomod:v:200:y:2007:i:3:p:279-307
    DOI: 10.1016/j.ecolmodel.2006.09.006
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    2. Miao Yu & Guiling Wang & Dana Parr & Kazi Ahmed, 2014. "Future changes of the terrestrial ecosystem based on a dynamic vegetation model driven with RCP8.5 climate projections from 19 GCMs," Climatic Change, Springer, vol. 127(2), pages 257-271, November.
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    5. Bellassen, V. & Le Maire, G. & Dhôte, J.F. & Ciais, P. & Viovy, N., 2010. "Modelling forest management within a global vegetation model—Part 1: Model structure and general behaviour," Ecological Modelling, Elsevier, vol. 221(20), pages 2458-2474.
    6. Vance, Richard R. & Steele, Mark A. & Forrester, Graham E., 2010. "Using an individual-based model to quantify scale transition in demographic rate functions: Deaths in a coral reef fish," Ecological Modelling, Elsevier, vol. 221(16), pages 1907-1921.
    7. Wramneby, Anna & Smith, Benjamin & Zaehle, Sönke & Sykes, Martin T., 2008. "Parameter uncertainties in the modelling of vegetation dynamics—Effects on tree community structure and ecosystem functioning in European forest biomes," Ecological Modelling, Elsevier, vol. 216(3), pages 277-290.
    8. Piponiot, Camille & Derroire, Géraldine & Descroix, Laurent & Mazzei, Lucas & Rutishauser, Ervan & Sist, Plinio & Hérault, Bruno, 2018. "Assessing timber volume recovery after disturbance in tropical forests – A new modelling framework," Ecological Modelling, Elsevier, vol. 384(C), pages 353-369.
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    10. Bohn, Friedrich J. & Frank, Karin & Huth, Andreas, 2014. "Of climate and its resulting tree growth: Simulating the productivity of temperate forests," Ecological Modelling, Elsevier, vol. 278(C), pages 9-17.
    11. Fischer, Rico & Bohn, Friedrich & Dantas de Paula, Mateus & Dislich, Claudia & Groeneveld, Jürgen & Gutiérrez, Alvaro G. & Kazmierczak, Martin & Knapp, Nikolai & Lehmann, Sebastian & Paulick, Sebastia, 2016. "Lessons learned from applying a forest gap model to understand ecosystem and carbon dynamics of complex tropical forests," Ecological Modelling, Elsevier, vol. 326(C), pages 124-133.
    12. Badouard, Vincyane & Schmitt, Sylvain & Salzet, Guillaume & Gaquiere, Thomas & Rojat, Margaux & Bedeau, Caroline & Brunaux, Olivier & Derroire, Géraldine, 2024. "LoggingLab: An R package to simulate reduced-impact selective logging in tropical forests using forest inventory data," Ecological Modelling, Elsevier, vol. 487(C).
    13. Bellassen, V. & le Maire, G. & Guin, O. & Dhôte, J.F. & Ciais, P. & Viovy, N., 2011. "Modelling forest management within a global vegetation model—Part 2: Model validation from a tree to a continental scale," Ecological Modelling, Elsevier, vol. 222(1), pages 57-75.
    14. Taubert, Franziska & Frank, Karin & Huth, Andreas, 2012. "A review of grassland models in the biofuel context," Ecological Modelling, Elsevier, vol. 245(C), pages 84-93.
    15. Runqing Zhang & Xiaoyu E & Zhencheng Ma & Yinghe An & Qinggele Bao & Zhixiang Wu & Lan Wu & Zhongyi Sun, 2024. "Drought Sensitivity and Vulnerability of Rubber Plantation GPP—Insights from Flux Site-Based Simulation," Land, MDPI, vol. 13(6), pages 1-16, May.
    16. Nakagawa, Yoshiaki & Yokozawa, Masayuki & Ito, Akihiko & Hara, Toshihiko, 2017. "Effectively tuning plant growth models with different spatial complexity: A statistical perspective," Ecological Modelling, Elsevier, vol. 361(C), pages 95-112.
    17. Manusch, Corina & Bugmann, Harald & Wolf, Annett, 2014. "Sensitivity of simulated productivity to soil characteristics and plant water uptake along drought gradients in the Swiss Alps," Ecological Modelling, Elsevier, vol. 282(C), pages 25-34.
    18. Zhang, Tao & Lichstein, Jeremy W. & Birdsey, Richard A., 2014. "Spatial and temporal heterogeneity in the dynamics of eastern U.S. forests: Implications for developing broad-scale forest dynamics models," Ecological Modelling, Elsevier, vol. 279(C), pages 89-99.

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