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Cited by 644Microbial Community Ecology and PhysiologyMarine Biology and Ecology ResearchMethane Hydrates and Related Phenomena

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Global Patterns of Bacterial Beta-Diversity in Seafloor and Seawater Ecosystems

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Zinger, Lucie, Amaral-Zettler, Linda A., Fuhrman, Jed A., Horner-Devine, M. Claire, Huse, Susan M., Welch, David B. Mark, Martiny, Jennifer B. H., Sogin, Mitchell, Boetius, Antje, Ramette, Alban·2011·PLoS ONE
DOI 10.1371/journal.pone.0024570PMC PMC3169623
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Abstract

BACKGROUND: Marine microbial communities have been essential contributors to global biomass, nutrient cycling, and biodiversity since the early history of Earth, but so far their community distribution patterns remain unknown in most marine ecosystems. METHODOLOGY/PRINCIPAL FINDINGS: The synthesis of 9.6 million bacterial V6-rRNA amplicons for 509 samples that span the global ocean's surface to the deep-sea floor shows that pelagic and benthic communities greatly differ, at all taxonomic levels, and share <10% bacterial types defined at 3% sequence similarity level. Surface and deep water, coastal and open ocean, and anoxic and oxic ecosystems host distinct communities that reflect productivity, land influences and other environmental constraints such as oxygen availability. The high variability of bacterial community composition specific to vent and coastal ecosystems reflects the heterogeneity and dynamic nature of these habitats. Both pelagic and benthic bacterial community distributions correlate with surface water productivity, reflecting the coupling between both realms by particle export. Also, differences in physical mixing may play a fundamental role in the distribution patterns of marine bacteria, as benthic communities showed a higher dissimilarity with increasing distance than pelagic communities. CONCLUSIONS/SIGNIFICANCE: This first synthesis of global bacterial distribution across different ecosystems of the World's oceans shows remarkable horizontal and vertical large-scale patterns in bacterial communities. This opens interesting perspectives for the definition of biogeographical biomes for bacteria of ocean waters and the seabed.

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Figure 1 Global map of sample locations. Sample locations were plotted on a SeaWiFS satellite image of chlorophyll pigment concentration, with redder colour representing higher concentrations. The realm origin of the samples is indicated by circles (pelagic) and triangles (benthic), whereas ecosystem types are indicated by the colour orange (coastal), light blue (open ocean surface waters), dark blue (deep-sea), red (anoxic), and purple (hydrothermal vents). Further visualization of sample distribution e.g. on a bathymetric map is available at http://vamps.mbl.edu/mapper/index.php.
Figure 1 Global map of sample locations. Sample locations were plotted on a SeaWiFS satellite image of chlorophyll pigment concentration, with redder colour representing higher concentrations. The realm origin of the samples is indicated by circles (pelagic) and triangles (benthic), whereas ecosystem types are indicated by the colour orange (coastal), light blue (open ocean surface waters), dark blue (deep-sea), red (anoxic), and purple (hydrothermal vents). Further visualization of sample distribution e.g. on a bathymetric map is available at http://vamps.mbl.edu/mapper/index.php.CC BY · PubMed Central
Figure 2 Bacterial community composition according to realms and ecosystem types. (a) Average sequence frequency for the ten most abundant bacterial classes in the pelagic and benthic realms. Error bars represent standard deviation values (number of samples indicated in Table 1). Vents and anoxic ecosystems were not taken into account for the average bacterial community composition in pelagic and benthic realms. (b) Average proportions of the main bacterial taxa per realm and ecosystem type. P = Pelagic, B = Benthic. Notice that taxonomic levels displayed here are not necessarily of the same level, but reflect the most common levels whose ecology and diversity are usually investigated in marine microbiology.
Figure 2 Bacterial community composition according to realms and ecosystem types. (a) Average sequence frequency for the ten most abundant bacterial classes in the pelagic and benthic realms. Error bars represent standard deviation values (number of samples indicated in Table 1). Vents and anoxic ecosystems were not taken into account for the average bacterial community composition in pelagic and benthic realms. (b) Average proportions of the main bacterial taxa per realm and ecosystem type. P = Pelagic, B = Benthic. Notice that taxonomic levels displayed here are not necessarily of the same level, but reflect the most common levels whose ecology and diversity are usually investigated in marine microbiology.CC BY · PubMed Central
Figure 3 Global beta-diversity patterns of marine bacterial communities according to realms and ecosystem types. (a) NMDS ordination of the dissimilarity in bacterial community composition (OTU0.03 level, NMDS stress = 0.18). (b) Heat map showing pairwise community overlap (upper-half symmetric matrix displayed) according to realm/ecosystem. All ANOSIM R values were significant (1000 Monte Carlo permutations, Bonferroni-corrected P<0.05). (c) Heat map of the average number of OTU shared between ecosystems types, based on random resampling of 100 subdatasets. (d) Boxplots of the variation in beta-diversity in each ecosystem type. Different letters on each box represent significant differences in variance homogeneity between ecosystem types as determined by Mann–Whitney rank sum tests followed by Bonferroni correction (P<0.01).
Figure 3 Global beta-diversity patterns of marine bacterial communities according to realms and ecosystem types. (a) NMDS ordination of the dissimilarity in bacterial community composition (OTU0.03 level, NMDS stress = 0.18). (b) Heat map showing pairwise community overlap (upper-half symmetric matrix displayed) according to realm/ecosystem. All ANOSIM R values were significant (1000 Monte Carlo permutations, Bonferroni-corrected P<0.05). (c) Heat map of the average number of OTU shared between ecosystems types, based on random resampling of 100 subdatasets. (d) Boxplots of the variation in beta-diversity in each ecosystem type. Different letters on each box represent significant differences in variance homogeneity between ecosystem types as determined by Mann–Whitney rank sum tests followed by Bonferroni correction (P<0.01).CC BY · PubMed Central
Figure 4 Global beta-diversity patterns of marine bacterial communities within each realm. The respective contributions of ecosystem type, geographic location (transformed latitudes and longitudes, water depth), time (number of days since the first sampling) and upper water productivity (as defined by Longhurst's primary production index and classes of capture fisheries yield) were assessed by using variation partitioning and are displayed as a Venn diagram. The blue boxes (i.e. upper water productivity) correspond to the fourth category of the model, which cannot be represented by a circle in such a display. The significance of each pure effect was validated by performing partial RDA with 1000 Monte Carlo permutation tests. Significance levels: *** P<0.001, **P<0.01, *P<0.05. Covariation parts cannot be tested for significance because they are numerically deduced from the pure parts [74].
Figure 4 Global beta-diversity patterns of marine bacterial communities within each realm. The respective contributions of ecosystem type, geographic location (transformed latitudes and longitudes, water depth), time (number of days since the first sampling) and upper water productivity (as defined by Longhurst's primary production index and classes of capture fisheries yield) were assessed by using variation partitioning and are displayed as a Venn diagram. The blue boxes (i.e. upper water productivity) correspond to the fourth category of the model, which cannot be represented by a circle in such a display. The significance of each pure effect was validated by performing partial RDA with 1000 Monte Carlo permutation tests. Significance levels: *** P<0.001, **P<0.01, *P<0.05. Covariation parts cannot be tested for significance because they are numerically deduced from the pure parts [74].CC BY · PubMed Central
Figure 5 Consistency of patterns of community variation at various taxonomic levels. (a) NMDS ordination of the dissimilarity in bacterial community composition at the Phylum level (NMDS stress = 0.15). The goodness-of-fit between this ordination and the ordination presented in Fig. 3a is shown in Table S4. Pelagic and benthic origins are indicated by the letters P and B, respectively. (b) Partitioning of the variation of bacterial composition at different taxonomic levels using the same approach as presented in Fig. 4. Units are expressed in adjusted R2 values, i.e. in proportion of explained variance.
Figure 5 Consistency of patterns of community variation at various taxonomic levels. (a) NMDS ordination of the dissimilarity in bacterial community composition at the Phylum level (NMDS stress = 0.15). The goodness-of-fit between this ordination and the ordination presented in Fig. 3a is shown in Table S4. Pelagic and benthic origins are indicated by the letters P and B, respectively. (b) Partitioning of the variation of bacterial composition at different taxonomic levels using the same approach as presented in Fig. 4. Units are expressed in adjusted R2 values, i.e. in proportion of explained variance.CC BY · PubMed Central

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Research works printed but not linked: 2

  1. C Turley, 2002, The importance of ‘marine snow’., Microbiol Today, 29, 177
  2. P Legendre, 1998, Numerical Ecology, 2nd English edition.

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Authors
Zinger, L., Amaral-Zettler, L. A., Fuhrman, J. A., Horner-Devine, M. C., Huse, S. M., Welch, D. B. M., Martiny, J. B. H., Sogin, M., Boetius, A., & Ramette, A.
Affiliations
  • Max Planck Institute for Marine Microbiology (DE)
  • University of Southern California (US)
  • Marine Biological Laboratory (US)
  • University of California, Irvine (US)
  • Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung (DE)
Corresponding author
  • Zinger, Lucie
  • Fuhrman, Jed A.
  • Huse, Susan M.
  • Welch, David B. Mark
  • Martiny, Jennifer B. H.
  • Sogin, Mitchell
  • Ramette, Alban
ORCID
Venue
PLoS ONE
Publisher
Public Library of Science (PLoS)
Published
2011-09-08
Volume
6
Issue
9
Pages
e24570
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0024570
PMC
PMC3169623
OpenAlex
W2169157436
Cited by
643
References
79
Citations by year
2026: 29 · 2025: 28 · 2024: 23 · 2023: 34 · 2022: 55 · 2021: 43 · 2020: 48 · 2019: 47 · 2018: 72 · 2017: 57 · 2016: 43 · 2015: 54 · 2014: 44 · 2013: 39 · 2012: 24
FWCI
30.72
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
  • Bern Open Repository and Information System (University of Bern) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Helmholtz-Zentrum für Polar-und Meeresforschung (Alfred-Wegener-Institut) (submittedVersion)
  • eScholarship (California Digital Library) (submittedVersion, cc-by)
  • Max Planck Digital Library (submittedVersion)
  • Figshare (submittedVersion, cc-by)
  • Open Access CRIS of the University of Bern
Funders
  • Deutsche Forschungsgemeinschaft
  • Max-Planck-Gesellschaft
  • Leibniz-Gemeinschaft
Topics
  • Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
  • Physical Sciences › Earth and Planetary Sciences › Oceanography › Marine Biology and Ecology Research
  • Physical Sciences › Environmental Science › Environmental Chemistry › Methane Hydrates and Related Phenomena
Keywords
marine bacteria, benthic-pelagic coupling, seafloor ecosystems, deep-sea floor, hydrothermal vents, coastal ecosystems, surface water productivity, particle export, ocean mixing, bacterial biogeography
MeSH terms
Bacteria, Classification, Oceans and Seas, Phylogeny, Seawater, Water Microbiology, Cluster Analysis, Databases, Factual, Ecosystem, Biomass, Computational Biology, Biodiversity

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