IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v220y2009i18p2272-2280.html
   My bibliography  Save this article

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

Author

Listed:
  • Toda, Motomu
  • Yokozawa, Masayuki
  • Sumida, Akihiro
  • Watanabe, Tsutomu
  • Hara, Toshihiko

Abstract

A simulation study was carried out to investigate simultaneously the effects of eco-physiological parameters on competitive asymmetry, self-thinning, stand biomass and NPP in a temperate forest using an atmosphere–vegetation dynamics interactive model (MINoSGI). In this study, we selected three eco-physiological relevant parameters as foliage profiles (i.e. vertical distribution of leaf area density) of individual trees (distribution pattern is described by the parameter η), biomass allocation pattern in individual tree growth (χ) and the maximum carboxylation velocity (Vmax). The position of the maximal leaf area density shifts upward in the canopy with increasing η. For scenarios with η<4 (foliage concentrated in the lowest canopy layer) or η>12 (foliage concentrated in the uppermost canopy layer), a low degree of competitive asymmetry was produced. These scenarios resulted in the survival of subordinate trees due to a brighter lower canopy environment when η<4 or the generation of spatially separated foliage profiles between dominant and subordinate trees when η>12. In contrast, competition between trees was most asymmetric when 4≤η≤12 (vertically widespread foliage profile in the canopy), especially when η=8. In such cases, vertically widespread foliage of dominant trees lowered the opportunity of light acquisition for subordinate trees and reduced their carbon gain. The resulting reduction in carbon gain of subordinate trees yielded a higher degree of competitive asymmetry and ultimately higher mortality of subordinate trees. It was also shown that 4≤η≤12 generated higher self-thinning speed, smaller accumulated NPP, litter-fall and potential stand biomass as compared with the scenarios with η<4 or η>12. In contrast, our simulation revealed small effects of χ or Vmax on the above-mentioned variables as compared with those of η. In particular, it is notable that greater Vmax would not produce greater potential stand biomass and accumulated NPP although it has been thought that physiological parameters relevant to photosynthesis such as Vmax influence dynamic changes in forest stand biomass and NPP (e.g. the greater the Vmax, the greater the NPP). Overall, it is suggested that foliage profiles rather than biomass allocation or maximum carboxylation velocity greatly govern forest dynamics, stand biomass, NPP and litter-fall.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:ecomod:v:220:y:2009:i:18:p:2272-2280
    DOI: 10.1016/j.ecolmodel.2009.05.011
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380009003342
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2009.05.011?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Peter M. Cox & Richard A. Betts & Chris D. Jones & Steven A. Spall & Ian J. Totterdell, 2000. "Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model," Nature, Nature, vol. 408(6809), pages 184-187, November.
    2. Peter M. Cox & Richard A. Betts & Chris D. Jones & Steven A. Spall & Ian J. Totterdell, 2000. "Erratum: Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model," Nature, Nature, vol. 408(6813), pages 750-750, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Toda, Motomu & Yokozawa, Masayuki & Emori, Seita & Hara, Toshihiko, 2010. "More asymmetric tree competition brings about more evapotranspiration and less runoff from the forest ecosystems: A simulation study," Ecological Modelling, Elsevier, vol. 221(24), pages 2887-2898.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Francesco Lamperti & Giovanni Dosi & Mauro Napoletano & Andrea Roventini & Alessandro Sapio, 2018. "And then he wasn't a she : Climate change and green transitions in an agent-based integrated assessment model," Working Papers hal-03443464, HAL.
    2. Govind, Ajit & Chen, Jing Ming & Bernier, Pierre & Margolis, Hank & Guindon, Luc & Beaudoin, Andre, 2011. "Spatially distributed modeling of the long-term carbon balance of a boreal landscape," Ecological Modelling, Elsevier, vol. 222(15), pages 2780-2795.
    3. Eliseev, Alexey V. & Mokhov, Igor I., 2008. "Eventual saturation of the climate–carbon cycle feedback studied with a conceptual model," Ecological Modelling, Elsevier, vol. 213(1), pages 127-132.
    4. Brovkin, Victor & Cherkinsky, Alexander & Goryachkin, Sergey, 2008. "Estimating soil carbon turnover using radiocarbon data: A case-study for European Russia," Ecological Modelling, Elsevier, vol. 216(2), pages 178-187.
    5. Ulaganathan, Kandasamy & Goud, Sravanthi & Reddy, Madhavi & Kayalvili, Ulaganathan, 2017. "Genome engineering for breaking barriers in lignocellulosic bioethanol production," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 1080-1107.
    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. Kai Yin & Dengsheng Lu & Yichen Tian & Qianjun Zhao & Chao Yuan, 2014. "Evaluation of Carbon and Oxygen Balances in Urban Ecosystems Using Land Use/Land Cover and Statistical Data," Sustainability, MDPI, vol. 7(1), pages 1-27, December.
    8. Agudelo, César Augusto Ruiz & Bustos, Sandra Liliana Hurtado & Moreno, Carmen Alicia Parrado, 2020. "Modeling interactions among multiple ecosystem services. A critical review," Ecological Modelling, Elsevier, vol. 429(C).
    9. Ouardighi, Fouad El & Sim, Jeong Eun & Kim, Bowon, 2016. "Pollution accumulation and abatement policy in a supply chain," European Journal of Operational Research, Elsevier, vol. 248(3), pages 982-996.
    10. Kim, Hyeyoung & House, Lisa A. & KIm, Tae-Kyun, 2016. "Consumer perceptions of climate change and willingness to pay for mandatory implementation of low carbon labels: the case of South Korea," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 19(4), October.
    11. Guoju, Xiao & Weixiang, Liu & Qiang, Xu & Zhaojun, Sun & Jing, Wang, 2005. "Effects of temperature increase and elevated CO2 concentration, with supplemental irrigation, on the yield of rain-fed spring wheat in a semiarid region of China," Agricultural Water Management, Elsevier, vol. 74(3), pages 243-255, June.
    12. Sogol Moradian & Farhad Yazdandoost, 2021. "Seasonal meteorological drought projections over Iran using the NMME data," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 108(1), pages 1089-1107, August.
    13. Viola, Flavio M. & Paiva, Susana L.D. & Savi, Marcelo A., 2010. "Analysis of the global warming dynamics from temperature time series," Ecological Modelling, Elsevier, vol. 221(16), pages 1964-1978.
    14. Farrelly, Damien J. & Everard, Colm D. & Fagan, Colette C. & McDonnell, Kevin P., 2013. "Carbon sequestration and the role of biological carbon mitigation: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 712-727.
    15. Marc Kennedy & Clive Anderson & Anthony O'Hagan & Mark Lomas & Ian Woodward & John Paul Gosling & Andreas Heinemeyer, 2008. "Quantifying uncertainty in the biospheric carbon flux for England and Wales," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(1), pages 109-135, January.
    16. Yonghua Li & Song Yao & Hezhou Jiang & Huarong Wang & Qinchuan Ran & Xinyun Gao & Xinyi Ding & Dandong Ge, 2022. "Spatial-Temporal Evolution and Prediction of Carbon Storage: An Integrated Framework Based on the MOP–PLUS–InVEST Model and an Applied Case Study in Hangzhou, East China," Land, MDPI, vol. 11(12), pages 1-22, December.
    17. Sara J. Germain & James A. Lutz, 2020. "Climate extremes may be more important than climate means when predicting species range shifts," Climatic Change, Springer, vol. 163(1), pages 579-598, November.
    18. Xiongwen Chen & Wilfred Post & Richard Norby & Aimée Classen, 2011. "Modeling soil respiration and variations in source components using a multi-factor global climate change experiment," Climatic Change, Springer, vol. 107(3), pages 459-480, August.
    19. Wang, Tao & Yu, Wei & Le Moullec, Yann & Liu, Fei & Xiong, Yili & He, Hui & Lu, Jiahui & Hsu, Emily & Fang, Mengxiang & Luo, Zhongyang, 2017. "Solvent regeneration by novel direct non-aqueous gas stripping process for post-combustion CO2 capture," Applied Energy, Elsevier, vol. 205(C), pages 23-32.
    20. Lamperti, F. & Dosi, G. & Napoletano, M. & Roventini, A. & Sapio, A., 2018. "Faraway, So Close: Coupled Climate and Economic Dynamics in an Agent-based Integrated Assessment Model," Ecological Economics, Elsevier, vol. 150(C), pages 315-339.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecomod:v:220:y:2009:i:18:p:2272-2280. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.