Model-based simultaneous clustering and ordination of multivariate abundance data in ecology
Model-based simultaneous clustering and ordination of multivariate abundance data in ecology
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DOI：
10.1016/j.csda.2016.07.008
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发表时间：
2017-01-01
影响因子：
1.8
通讯作者：
Hui, Francis K. C.
中科院分区：
文献类型：
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作者：
Hui, Francis K. C.
在研究多变量丰度数据时，生态学家通常感兴趣的主要模式之一是，这些地点是否在代表物种组成的低维排序空间上表现出聚类。一种新的基于模型的方法，称为CORAL（聚类和排序回归分析），开发用于解决这个问题，基于执行同时聚类和排序使用潜变量回归。通过从有限的混合密度中提取潜在变量，CORAL基于它们在底层信号空间中的位置对站点进行概率分类。这类似于因子分析器的混合，除了CORAL是为非正态响应设计的，并且使用物种特异性而不是集群特异性因子载荷（回归系数）。通过贝叶斯MCMC抽样进行估计，代码见补充材料。模拟结果表明，通过利用联合信息的数据进行分类和降维，CORAL优于几个流行的，基于算法的方法，聚类和排序的生态学。CORAL适用于数据集的存在-不存在的记录收集在站点沿着杜布斯河附近的法国-瑞士边境，结果显示两个集群或生态区域部分类似的空间分离的上游和下游的网站。(C)© 2016 Elsevier B. V.版权所有。
When studying multivariate abundance data, one of the main patterns ecologists are often interested in is whether the sites exhibit clustering on the low-dimensional, ordination space representing species composition. A new model-based approach called CORAL (Clustering and Ordination Regression AnaLysis) is developed for tackling this question, based on performing simultaneous clustering and ordination using latent variable regression. By drawing the latent variables from a finite mixture density, CORAL probabilistically classifies sites based on their positions on an underlying signal space. This is similar to mixtures of factor analyzers, except CORAL is designed for non-normal responses and uses species-specific rather than cluster-specific factor loadings (regression coefficients). Estimation is performed via Bayesian MCMC sampling, with code provided in the Supplementary Material. Simulations demonstrate that, by utilizing the joint information available in the data for both classification and dimension reduction, CORAL outperforms several popular, algorithm-based methods for clustering and ordination in ecology. CORAL is applied to a dataset of presence-absence records collected at sites along the Doubs River near the France-Switzerland border, with results revealing two clusters or ecological regions partly resembling the spatial separation of upstream and downstream sites. (C) 2016 Elsevier B.V. All rights reserved.