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A pathway for multivariate analysis of ecological communities using copulas

机译:使用copulas进行生态群落多变量分析的途径

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摘要

We describe a new pathway for multivariate analysis of data consisting of counts of species abundances that includes two key components: copulas, to provide a flexible joint model of individual species, and dissimilarity‐based methods, to integrate information across species and provide a holistic view of the community. Individual species are characterized using suitable (marginal) statistical distributions, with the mean, the degree of over‐dispersion, and/or zero‐inflation being allowed to vary among a priori groups of sampling units. Associations among species are then modeled using copulas, which allow any pair of disparate types of variables to be coupled through their cumulative distribution function, while maintaining entirely the separate individual marginal distributions appropriate for each species. A Gaussian copula smoothly captures changes in an index of association that excludes joint absences in the space of the original species variables. A permutation‐based filter with exact family‐wise error can optionally be used a priori to reduce the dimensionality of the copula estimation problem. We describe in detail a Monte Carlo expectation maximization algorithm for efficient estimation of the copula correlation matrix with discrete marginal distributions (counts). The resulting fully parameterized copula models can be used to simulate realistic ecological community data under fully specified null or alternative hypotheses. Distributions of community centroids derived from simulated data can then be visualized in ordinations of ecologically meaningful dissimilarity spaces. Multinomial mixtures of data drawn from copula models also yield smooth power curves in dissimilarity‐based settings. Our proposed analysis pathway provides new opportunities to combine model‐based approaches with dissimilarity‐based methods to enhance understanding of ecological systems. We demonstrate implementation of the pathway through an ecological example, where associations among fish species were found to increase after the establishment of a marine reserve.
机译:我们描述了一种新的数据多元分析方法,该方法包括物种丰富度计数,其中包括两个关键组成部分:copulas(提供单个物种的灵活联合模型)和基于异种的方法,以整合跨物种的信息并提供整体视图社区。使用适当的(边际)统计分布来表征单个物种,并允许平均值,过分分散程度和/或零通货膨胀在各采样单位的先验组之间变化。然后使用copulas对物种之间的关联进行建模,该模型允许通过其累积分布函数将任何一对不同类型的变量耦合在一起,同时完全保留适用于每个物种的单独的单独边际分布。高斯系动词平滑地捕获了关联指数的变化,该变化排除了原始物种变量空间中的联合缺失。具有精确族序误差的基于置换的滤波器可以有选择地先验地使用,以减少copula估计问题的维数。我们详细描述了蒙特卡洛期望最大化算法,用于有效估计具有离散边际分布(计数)的copula相关矩阵。所得的完全参数化的系模型可用于在完全指定的原假设或替代假设下模拟现实的生态群落数据。然后,可以从具有生态意义的相异空间的顺序中可视化从模拟数据得出的社区质心的分布。在基于差异的设置中,从copula模型中提取的数据的多项混合也可以生成平滑的幂曲线。我们提出的分析途径提供了将基于模型的方法与基于不相似性的方法相结合的新机会,以增强对生态系统的理解。我们通过一个生态实例证明了该途径的实施,在建立海洋保护区后,鱼类种类之间的联系增加了。

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