使用 Dirichlet 过程混合物模型进行聚类和无约束排序

IF 6.3 2区 环境科学与生态学 Q1 ECOLOGY Methods in Ecology and Evolution Pub Date : 2024-08-02 DOI:10.1111/2041-210X.14389
Christian Stratton, Andrew Hoegh, Thomas J. Rodhouse, Jennifer L. Green, Katharine M. Banner, Kathryn M. Irvine
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引用次数: 0

摘要

评估不同采样地点物种组成或丰度的相似性是多物种监测计划的共同目标。现有的顺序排列技术通过将高维群落数据投射到代表物种组成的低维潜在生态梯度中,为根据物种组成对采样地点进行聚类提供了一个框架。然而,这些技术需要指定潜空间中存在的不同生态群落的数量,而这很难事先确定。我们开发了一种能够同时进行聚类和排序的排序模型,可以估算出潜在生态梯度中存在的聚类数量。该模型从 Dirichlet 过程混合物模型中提取每个样本位置的潜在坐标,为研究人员提供了关于潜在生态梯度中存在的聚类数量的概率声明。我们通过模拟将该模型与现有的同时聚类和排序方法进行了比较,并将其应用于两个经验数据集;附录中提供了拟合拟议模型的 JAGS 代码。第一个数据集涉及法国东部杜布河鱼类的存在-消失记录,第二个数据集描述了美国爱达荷州月球环形山国家纪念碑和保护区(CRMO)植物物种的存在-消失记录。这两项分析的结果与每个地点现有的生态梯度一致。Dirichlet 过程排序模型的开发为野生动物管理者提供了以数据为依据的推断,即在监测地点存在的不同群落的数量,从而能够为保护管理提供更具成本效益的监测和可靠的决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Clustering and unconstrained ordination with Dirichlet process mixture models

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来源期刊
CiteScore
11.60
自引率
3.00%
发文量
236
审稿时长
4-8 weeks
期刊介绍: A British Ecological Society journal, Methods in Ecology and Evolution (MEE) promotes the development of new methods in ecology and evolution, and facilitates their dissemination and uptake by the research community. MEE brings together papers from previously disparate sub-disciplines to provide a single forum for tracking methodological developments in all areas. MEE publishes methodological papers in any area of ecology and evolution, including: -Phylogenetic analysis -Statistical methods -Conservation & management -Theoretical methods -Practical methods, including lab and field -This list is not exhaustive, and we welcome enquiries about possible submissions. Methods are defined in the widest terms and may be analytical, practical or conceptual. A primary aim of the journal is to maximise the uptake of techniques by the community. We recognise that a major stumbling block in the uptake and application of new methods is the accessibility of methods. For example, users may need computer code, example applications or demonstrations of methods.
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