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Quantitative Biology > Populations and Evolution

arXiv:2106.01867 (q-bio)
[Submitted on 30 May 2021 (v1) , last revised 21 Jun 2021 (this version, v2)]

Title: Plasticity as a link between spatially explicit, distance-independent, and whole-stand forest growth models

Title: 塑性作为空间显式、距离无关和全林生长模型之间的联系

Authors:Oscar García
Abstract: Models at various levels of resolution are commonly used, both for forest management and in ecological research. They all have comparative advantages and disadvantages, making desirable a better understanding of the relationships between the various approaches. It is found that accounting for crown and root plasticity creates more realistic links between spatial and non-spatial models than simply ignoring spatial structure. The article reviews also the connection between distance-independent models and size distributions, and how distributions evolve over time and relate to whole-stand descriptions. In addition, some ways in which stand-level knowledge feeds back into detailed individual-tree formulations are demonstrated. The presentation intends to be accessible to non-specialists. Study implications: Introducing plasticity improves the representation of physio-ecological processes in spatial modelling. Plasticity explains in part the practical success of distance-independent models. The nature of size distributions and their relationship to individual-tree and whole-stand models are discussed. I point out limitations of various approaches and questions for future research.
Abstract: 不同分辨率的模型在森林管理和生态研究中都被广泛使用。 它们各自有比较优势和劣势,因此更好地理解各种方法之间的关系是有益的。 研究发现,考虑树冠和根系的可塑性比简单忽略空间结构更能创建出更真实的时空与非时空模型之间的联系。 文章还回顾了无距离依赖模型与大小分布之间的联系,以及这些分布如何随时间演变并与整个林分描述相关联。 此外,展示了林分层面的知识如何反馈到详细的单木公式中。 报告旨在让非专业人士也能理解。 研究意义:引入可塑性改善了空间建模中的生理生态过程表示。 可塑性部分解释了无距离依赖模型的实际成功。 讨论了大小分布的本质及其与单木和整体林分模型的关系。 我指出了各种方法的局限性和未来研究的问题。
Comments: 9 pages, 8 figures. Submitted manuscript. Version 2 with minor changes: expanded caption in Fig. 2, new Fig. 6, corrected Wikipedia reference, fix typos
Subjects: Populations and Evolution (q-bio.PE) ; Applications (stat.AP)
Cite as: arXiv:2106.01867 [q-bio.PE]
  (or arXiv:2106.01867v2 [q-bio.PE] for this version)
  https://doi.org/10.48550/arXiv.2106.01867
arXiv-issued DOI via DataCite
Journal reference: Forest Science 68(1), 1-7. 2022
Related DOI: https://doi.org/10.1093/forsci/fxab043
DOI(s) linking to related resources

Submission history

From: Oscar García [view email]
[v1] Sun, 30 May 2021 23:59:03 UTC (1,358 KB)
[v2] Mon, 21 Jun 2021 16:19:32 UTC (519 KB)
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