Table_1_Graphical Analysis of A Marine Plankton Community Reveals Spatial, Temporal, and Niche Structure of Sub-Communities.pdf

Species-rich communities are structured by environmental filtering and a multitude of associations including trophic, mutualistic, and antagonistic relationships. Graphs (networks) defined from correlations in presence or abundance data have the potential to identify this structure, but species with very high absence rates or abundances frequently near detection limits can result in biased retrieval of association graphs. Here we use graph clustering analysis to identify five sub-communities of plankton from the North Atlantic Ocean. We show how to mitigate the challenges of high absence rates and detection limits. The sub-communities are distinguished partially by their constituent functional groups: one group is dominated by diatoms and another by dinoflagellates, while the other three sub-communities are mixtures of phytoplankton and zooplankton. Diagnosing pairwise taxonomic associations and linking them to specific processes is challenging because of overlapping associations and complex graph topologies. Our approach presents a robust approach for identifying candidate associations among species through sub-community analysis and quantifying the aggregate strength of pairwise associations emerging in natural communities.

物种丰富的群落由环境过滤作用以及多种相互作用（包括营养、互利与拮抗关系）共同塑造其结构。基于物种存在与否或丰度数据的相关性构建的图（网络），有望揭示这类群落的结构特征，但对于缺失率极高，或丰度常接近检测限的物种，此类方法常会导致关联图的检索结果出现偏差。本研究通过图聚类分析，对北大西洋海域的浮游生物进行聚类，得到五个亚群落，并提出了缓解高缺失率与检测限问题带来的挑战的可行方案。这些亚群落的区分部分依赖于其组成功能类群：其中一个亚群落以硅藻（diatoms）为主，另一个以甲藻（dinoflagellates）占优，其余三个亚群落则为浮游植物（phytoplankton）与浮游动物（zooplankton）的混合类群。由于种间关联存在重叠且网络拓扑结构复杂，解析成对分类群间的关联并将其与特定生态过程相联系极具挑战。本研究提出的方法通过亚群落分析实现物种间候选关联的识别，并量化自然群落中出现的成对关联的总强度，为相关研究提供了稳健的分析方法。