Graphical Analysis of A Marine Plankton Community Reveals Spatial, Temporal, and Niche Structure of Sub-Communities
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-08-01
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| Series: | Frontiers in Marine Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2022.943540/full |
| _version_ | 1811221330225266688 |
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| author | Joseph T. Siddons Andrew J. Irwin Zoe V. Finkel |
| author_facet | Joseph T. Siddons Andrew J. Irwin Zoe V. Finkel |
| author_sort | Joseph T. Siddons |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T07:57:30Z |
| format | Article |
| id | doaj.art-5eadf4dab61146ee8d0f78b488115594 |
| institution | Directory Open Access Journal |
| issn | 2296-7745 |
| language | English |
| last_indexed | 2024-04-12T07:57:30Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Marine Science |
| spelling | doaj.art-5eadf4dab61146ee8d0f78b4881155942022-12-22T03:41:26ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452022-08-01910.3389/fmars.2022.943540943540Graphical Analysis of A Marine Plankton Community Reveals Spatial, Temporal, and Niche Structure of Sub-CommunitiesJoseph T. Siddons0Andrew J. Irwin1Zoe V. Finkel2Department of Mathematics and Statistics, Dalhousie University, Halifax, NS, CanadaDepartment of Mathematics and Statistics, Dalhousie University, Halifax, NS, CanadaDepartment of Oceanography, Dalhousie University, Halifax, NS, CanadaSpecies-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.https://www.frontiersin.org/articles/10.3389/fmars.2022.943540/fullplanktoncommunitygraphassociationclusteringsub-community |
| spellingShingle | Joseph T. Siddons Andrew J. Irwin Zoe V. Finkel Graphical Analysis of A Marine Plankton Community Reveals Spatial, Temporal, and Niche Structure of Sub-Communities Frontiers in Marine Science plankton community graph association clustering sub-community |
| title | Graphical Analysis of A Marine Plankton Community Reveals Spatial, Temporal, and Niche Structure of Sub-Communities |
| title_full | Graphical Analysis of A Marine Plankton Community Reveals Spatial, Temporal, and Niche Structure of Sub-Communities |
| title_fullStr | Graphical Analysis of A Marine Plankton Community Reveals Spatial, Temporal, and Niche Structure of Sub-Communities |
| title_full_unstemmed | Graphical Analysis of A Marine Plankton Community Reveals Spatial, Temporal, and Niche Structure of Sub-Communities |
| title_short | Graphical Analysis of A Marine Plankton Community Reveals Spatial, Temporal, and Niche Structure of Sub-Communities |
| title_sort | graphical analysis of a marine plankton community reveals spatial temporal and niche structure of sub communities |
| topic | plankton community graph association clustering sub-community |
| url | https://www.frontiersin.org/articles/10.3389/fmars.2022.943540/full |
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