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A simple and effective approach to quantitatively characterize structural complexity

This study brings insight into interpreting forest structural diversity and explore the classification of individuals according to the distribution of the neighbours in natural forests. Natural forest communities with different latitudes and distribution patterns in China were used. Each tree and it...

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Autores principales: Zhang, Gongqiao, Hui, Gangying, Yang, Aiming, Zhao, Zhonghua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7809123/
https://www.ncbi.nlm.nih.gov/pubmed/33446718
http://dx.doi.org/10.1038/s41598-020-79334-7
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author Zhang, Gongqiao
Hui, Gangying
Yang, Aiming
Zhao, Zhonghua
author_facet Zhang, Gongqiao
Hui, Gangying
Yang, Aiming
Zhao, Zhonghua
author_sort Zhang, Gongqiao
collection PubMed
description This study brings insight into interpreting forest structural diversity and explore the classification of individuals according to the distribution of the neighbours in natural forests. Natural forest communities with different latitudes and distribution patterns in China were used. Each tree and its nearest neighbours form a structural unit. Random structural units (or random trees) in natural forests were divided into different sub-types based on the uniform angle index (W). The proportions of different random structural units were analysed. (1) There are only two types of random structural units: type R1 looks similar to a dumbbell, and type R2 looks similar to a torch. These two random structural units coexist in natural forests simultaneously. (2) The proportion of type R1 is far less than that of R2, is only approximately 1/3 of all random structural units or random trees; R2 accounts for approximately 2/3. Furthermore, the proportion of basal area presents the same trend for both random structural units and random trees. R2 has approximately twice the basal area of R1. Random trees (structural units) occupy the largest part of natural forest communities in terms of quantity and basal area. Meanwhile, type R2 is the largest part of random trees (structural units). This study finds that the spatial formation mechanism of natural forest communities which is of great significance to the cultivation of planted forests.
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spelling pubmed-78091232021-01-15 A simple and effective approach to quantitatively characterize structural complexity Zhang, Gongqiao Hui, Gangying Yang, Aiming Zhao, Zhonghua Sci Rep Article This study brings insight into interpreting forest structural diversity and explore the classification of individuals according to the distribution of the neighbours in natural forests. Natural forest communities with different latitudes and distribution patterns in China were used. Each tree and its nearest neighbours form a structural unit. Random structural units (or random trees) in natural forests were divided into different sub-types based on the uniform angle index (W). The proportions of different random structural units were analysed. (1) There are only two types of random structural units: type R1 looks similar to a dumbbell, and type R2 looks similar to a torch. These two random structural units coexist in natural forests simultaneously. (2) The proportion of type R1 is far less than that of R2, is only approximately 1/3 of all random structural units or random trees; R2 accounts for approximately 2/3. Furthermore, the proportion of basal area presents the same trend for both random structural units and random trees. R2 has approximately twice the basal area of R1. Random trees (structural units) occupy the largest part of natural forest communities in terms of quantity and basal area. Meanwhile, type R2 is the largest part of random trees (structural units). This study finds that the spatial formation mechanism of natural forest communities which is of great significance to the cultivation of planted forests. Nature Publishing Group UK 2021-01-14 /pmc/articles/PMC7809123/ /pubmed/33446718 http://dx.doi.org/10.1038/s41598-020-79334-7 Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Zhang, Gongqiao
Hui, Gangying
Yang, Aiming
Zhao, Zhonghua
A simple and effective approach to quantitatively characterize structural complexity
title A simple and effective approach to quantitatively characterize structural complexity
title_full A simple and effective approach to quantitatively characterize structural complexity
title_fullStr A simple and effective approach to quantitatively characterize structural complexity
title_full_unstemmed A simple and effective approach to quantitatively characterize structural complexity
title_short A simple and effective approach to quantitatively characterize structural complexity
title_sort simple and effective approach to quantitatively characterize structural complexity
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7809123/
https://www.ncbi.nlm.nih.gov/pubmed/33446718
http://dx.doi.org/10.1038/s41598-020-79334-7
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