12/13/2023 0 Comments Graphical analysis![]() In a diverse community, it may be helpful to know about more than just pairwise associations. Consequently, one can define a community graph by examining the co-occurrence patterns between all pairs of taxa and determining if there is an association between the pair. A graph is composed of vertices (sometimes referred to as nodes), representing taxa, and edges, representing some connection between a pair of taxa. These associations can be directly observed interactions, such as pollination networks ( Blüthgen et al., 2008), or inferred from abundance data using correlations or similar measures of similarity ( Steele et al., 2011 Zhang, 2011 Friedman and Alm, 2012 Faust et al., 2012). Graphical models (sometimes referred to as networks, or network models) are being developed as a tool to study ecological communities by identifying and visualizing a wide range of associations between pairs of taxa ( Ings et al., 2009 Faust and Raes, 2012 Lima-Mendez et al., 2015 Sunagawa et al., 2015 Poisot et al., 2016 Zhou et al., 2018 Delmas et al., 2019). In communities with strong associations between member species, community structure is an emergent phenomenon as a consequence of species traits, environmental forcing, and inter-species associations. Depending on the intensity or quantity of these associations, they may have a significant effect on community compositions. Associations - relationships and/or interactions - can develop between pairs, or groups, of co-occurring species and can influence growth rate and biomass to an extent that is not described by environmental forcing and niches alone. The selection of species from the broader species pool according to their niches is known as environmental filtering ( Lebrija-Trejos et al., 2010). Ultimately, the traits of species determine the optimal environmental conditions under which each species grows, and thrives, which is summarized as the species’ fundamental niche ( Hutchinson, 1957). ![]() Species can be characterized by traits, which describe their maximal growth rate, trophic role, and biogeochemical function. ![]() These diverse communities can vary widely over space, time, and with environmental forcing. Oceanic phytoplankton and copepods form species-rich communities at the base of the marine food web ( Medlin et al., 2006 de Vargas et al., 2015 Fuhrman et al., 2015). 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. Diagnosing pairwise taxonomic associations and linking them to specific processes is challenging because of overlapping associations and complex graph topologies. 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. We show how to mitigate the challenges of high absence rates and detection limits. Here we use graph clustering analysis to identify five sub-communities of plankton from the North Atlantic Ocean. 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. Species-rich communities are structured by environmental filtering and a multitude of associations including trophic, mutualistic, and antagonistic relationships. 2Department of Oceanography, Dalhousie University, Halifax, NS, Canada.1Department of Mathematics and Statistics, Dalhousie University, Halifax, NS, Canada.
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