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Effectively tuning plant growth models with different spatial complexity: A statistical perspective

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  • Nakagawa, Yoshiaki
  • Yokozawa, Masayuki
  • Ito, Akihiko
  • Hara, Toshihiko

Abstract

Forest gap models (non-spatial, patch- and individual-based models) and size structure models (non-spatial stand models) rely on two assumptions: the mean field assumption (A-I) and the assumption that plants in one patch do not compete with plants in other patches (A-II). These assumptions lead to differences in plant size dynamics between these models and spatially explicit models (or observations of real forests). Therefore, to more accurately replicate dynamics, these models require model tuning by (1) adjusting model parameter values or (2) introducing a correction term into models. However, these model tuning methods have not been systematically and statistically investigated in models using different patch sizes.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ecomod:v:361:y:2017:i:c:p:95-112
    DOI: 10.1016/j.ecolmodel.2017.07.018
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    References listed on IDEAS

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    1. 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.
    2. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    3. M. E. Atkinson & D. C.M. Dickson, 2000. "An Introduction to Actuarial Studies," Books, Edward Elgar Publishing, number 2155.
    4. anonymous, 2000. "Fed introduces nationally standardized check services," Financial Update, Federal Reserve Bank of Atlanta, vol. 13(Jul), pages 1-4.
    5. Toda, Motomu & Yokozawa, Masayuki & Sumida, Akihiro & Watanabe, Tsutomu & Hara, Toshihiko, 2009. "Foliage profiles of individual trees determine competition, self-thinning, biomass and NPP of a Cryptomeria japonica forest stand: A simulation study based on a stand-scale process-based forest model," Ecological Modelling, Elsevier, vol. 220(18), pages 2272-2280.
    6. Brazhnik, Ksenia & Shugart, H.H., 2016. "SIBBORK: A new spatially-explicit gap model for boreal forest," Ecological Modelling, Elsevier, vol. 320(C), pages 182-196.
    7. Adams, Thomas & Ackland, Graeme & Marion, Glenn & Edwards, Colin, 2011. "Effects of local interaction and dispersal on the dynamics of size-structured populations," Ecological Modelling, Elsevier, vol. 222(8), pages 1414-1422.
    8. Nakagawa, Yoshiaki & Yokozawa, Masayuki & Hara, Toshihiko, 2015. "Competition among plants can lead to an increase in aggregation of smaller plants around larger ones," Ecological Modelling, Elsevier, vol. 301(C), pages 41-53.
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