Self-thinning due to density-dependent mortality usually occurs during the forest development. To improve predictions of such processes during forest successions under climate change, reliable stand-level models are needed. In this study, we developed an integrated system of tree- and stand-level models by deriving tree diameter and survival models from stand growth and survival models based on climate-sensitive self-thinning rule of Chinese fir plantations in subtropical China. The resulting integrated system, having a unified mathematical structure, should provide consistent estimates at both tree and stand levels. Predictions were reasonable at both stand and tree levels. Because stand-level values aggregated from the tree model outputs are different from those predicted directly from the stand models, the disaggregation approach was applied to provide numerical consistency between models of different resolutions. Compared to the unadjusted approach, predictions from the disaggregation approach were slightly worse for tree survival but slightly better for tree diameter. Because the stand models were developed under the climate-sensitive self-thinning trajectory, the integrated system could offer reasonable predictions that could aid in managing Chinese fir plantations under climate change.
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Citation
Zhang X, Cao QV, Qu Y, Zhang J (2022). Deriving tree growth models from stand models based on the self-thinning rule of Chinese fir plantations. iForest 15: 1-7. - doi: 10.3832/ifor3792-014
Academic Editor
Alessio Collalti
Paper history
Received: Feb 24, 2021
Accepted: Nov 17, 2021
First online: Jan 13, 2022
Publication Date: Feb 28, 2022
Publication Time: 1.90 months
© SISEF - The Italian Society of Silviculture and Forest Ecology 2022
Open Access
This article is distributed under the terms of the Creative Commons Attribution-Non Commercial 4.0 International (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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