Toward a better understanding of forest spatial patterns:A generalisation of the uniform angle index

IF 2.6 3区 环境科学与生态学 Q2 ECOLOGY Ecological Modelling Pub Date : 2025-03-03 DOI:10.1016/j.ecolmodel.2025.111070
Yuankun Bu , Weizhong Li , Klaus von Gadow , Jiangtao Wei , Pengxiang Zhao , Yanzheng Yang , Chaofan Zhou , Boheng Wang , Xuan Zhao
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Abstract

Spatial structure is important for characterizing a forest ecosystem. Among a myriad of spatial structure indices, the Uniform Angle Index (UAI) is rather special. The UAI quantifies a spatial pattern based on angles between neighbouring trees, thereby offering new insights into close range tree arrangements, competition, and stand dynamics. However, previous theoretical studies of the UAI have primarily relied on simulation stand and hence the uncertainty associated with them since its inception. Therefore, a mathematical derivation is still lacking. In this study, we present a theoretical framework for the UAI with the aim of broadening its applicability in quantifying the intensity of interactions among trees. Our theory is developed at two levels, the individual tree level and the stand level. The objective is to eliminate any simulation-induced uncertainty and bias and to enrich the theoretical foundation and applicability. We present a significant improvement of the UAI for estimating interaction strength among trees by the distance between an ideal and a real stand. This research highlights the opportunities for point pattern research in a new multidisciplinary science of forest ecology by growing knowledge and information along scientifically meaningful lines.
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来源期刊
Ecological Modelling
Ecological Modelling 环境科学-生态学
CiteScore
5.60
自引率
6.50%
发文量
259
审稿时长
69 days
期刊介绍: The journal is concerned with the use of mathematical models and systems analysis for the description of ecological processes and for the sustainable management of resources. Human activity and well-being are dependent on and integrated with the functioning of ecosystems and the services they provide. We aim to understand these basic ecosystem functions using mathematical and conceptual modelling, systems analysis, thermodynamics, computer simulations, and ecological theory. This leads to a preference for process-based models embedded in theory with explicit causative agents as opposed to strictly statistical or correlative descriptions. These modelling methods can be applied to a wide spectrum of issues ranging from basic ecology to human ecology to socio-ecological systems. The journal welcomes research articles, short communications, review articles, letters to the editor, book reviews, and other communications. The journal also supports the activities of the [International Society of Ecological Modelling (ISEM)](http://www.isemna.org/).
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