Clustering Species With Residual Covariance Matrix in Joint Species Distribution Models
Clustering Species With Residual Covariance Matrix in Joint Species Distribution Models
复制标题
DOI：
10.3389/fevo.2021.601384
复制
发表时间：
2021-03-09
影响因子：
3
通讯作者：
Thuiller, Wilfried
中科院分区：
文献类型：
--
作者：
Bystrova, Daria;Poggiato, Giovanni;Thuiller, Wilfried
模拟物种在空间和时间上的分布是生态学和保护生物学的主要研究课题之一。联合物种分布模型（JSDM）最近被引入作为一种工具，以更好地模拟社区数据，通过推断物种之间的残差协方差矩阵，占物种对环境的反应。然而，这些模型的计算要求很高，即使当潜在因素，一个常见的工具，用于降维，使用。为了解决这个问题，Taylor-Rodriguez等人（2017）提出使用Dirichlet过程，贝叶斯非参数先验，通过在残差协方差矩阵中聚类物种来进一步降低模型维数。在这里，我们建立在这种方法的基础上，包括潜在的集群数量的先验知识，而是使用Pitman-Yor过程来解决Dirichlet过程的一些关键限制。因此，我们提出了一个框架，包括先验知识的残差协方差矩阵，提供了一个工具来分析集群的物种，共享相同的残差协会与其他物种。我们应用我们的方法的植物群落在法国阿尔卑斯山（Bauges区域公园）的保护区的案例研究，并证明了我们的扩展提高降维和揭示更多的信息，从残差协方差矩阵，特别是显示如何估计集群是兼容的植物性状，认可他们的重要性，在塑造社区。
Modeling species distributions over space and time is one of the major research topics in both ecology and conservation biology. Joint Species Distribution models (JSDMs) have recently been introduced as a tool to better model community data, by inferring a residual covariance matrix between species, after accounting for species' response to the environment. However, these models are computationally demanding, even when latent factors, a common tool for dimension reduction, are used. To address this issue, Taylor-Rodriguez et al. (2017) proposed to use a Dirichlet process, a Bayesian nonparametric prior, to further reduce model dimension by clustering species in the residual covariance matrix. Here, we built on this approach to include a prior knowledge on the potential number of clusters, and instead used a Pitman-Yor process to address some critical limitations of the Dirichlet process. We therefore propose a framework that includes prior knowledge in the residual covariance matrix, providing a tool to analyze clusters of species that share the same residual associations with respect to other species. We applied our methodology to a case study of plant communities in a protected area of the French Alps (the Bauges Regional Park), and demonstrated that our extensions improve dimension reduction and reveal additional information from the residual covariance matrix, notably showing how the estimated clusters are compatible with plant traits, endorsing their importance in shaping communities.