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Multiyear analysis uncovers coordinated seasonality in stocks and composition of the planktonic food web in the Baltic Sea proper

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  • معلومة اضافية
    • بيانات النشر:
      Linnéuniversitetet, Institutionen för biologi och miljö (BOM)
      University of Gothenburg, Sweden
      GEOMAR Helmholtz Centre for Ocean Research Kiel, Germany
      Halmstad University, Sweden
    • الموضوع:
      2023
    • Collection:
      Linnaeus University Kalmar Växjö: Publications
    • نبذة مختصرة :
      The planktonic realm from bacteria to zooplankton provides the baseline for pelagic aquatic food webs. However, multiple trophic levels are seldomly included in time series studies, hampering a holistic understanding of the influence of seasonal dynamics and species interactions on food web structure and biogeochemical cycles. Here, we investigated plankton community composition, focusing on bacterio-, phyto- and large mesozooplankton, and how biotic and abiotic factors correlate at the Linnaeus Microbial Observatory (LMO) station in the Baltic Sea from 2011 to 2018. Plankton communities structures showed pronounced dynamic shifts with recurring patterns. Summarizing the parts of the planktonic microbial food web studied here to total carbon, a picture emerges with phytoplankton consistently contributing > 39% while bacterio- and large mesozooplankton contributed ~ 30% and ~ 7%, respectively, during summer. Cyanophyceae, Actinobacteria, Bacteroidetes, and Proteobacteria were important groups among the prokaryotes. Importantly, Dinophyceae, and not Bacillariophyceae, dominated the autotrophic spring bloom whereas Litostomatea (ciliates) and Appendicularia contributed significantly to the consumer entities together with the more traditionally observed mesozooplankton, Copepoda and Cladocera. Our findings of seasonality in both plankton composition and carbon stocks emphasize the importance of time series analyses of food web structure for characterizing the regulation of biogeochemical cycles and appropriately constraining ecosystem models.Â
    • File Description:
      application/pdf
    • Relation:
      Scientific Reports, 2023, 13:1; orcid:0000-0003-4871-7441; orcid:0000-0002-1149-6852; orcid:0000-0003-3083-7437; orcid:0000-0003-4787-7021; orcid:0000-0002-6570-5525; orcid:0000-0002-6405-1347; orcid:0000-0001-7155-3604; orcid:0000-0002-3740-5998; http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-123829; ISI:001178658600020; Scopus 2-s2.0-85165356529
    • الرقم المعرف:
      10.1038/s41598-023-38816-0
    • الدخول الالكتروني :
      http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-123829
      https://doi.org/10.1038/s41598-023-38816-0
    • Rights:
      info:eu-repo/semantics/openAccess
    • الرقم المعرف:
      edsbas.33244866