, Volume 818, Issue 1, pp 163–175 | Cite as

Enhanced ecological indication based on combined planktic and benthic functional approaches in large river phytoplankton ecology

  • Chao Wang
  • Viktória B-Béres
  • Csilla Stenger-Kovács
  • Xinhui Li
  • András Abonyi
Primary Research Paper


The occurrence of benthic diatoms in large river plankton is considered to be highly stochastic. Accordingly, the widely applied phytoplankton functional group concept sensu Reynolds (FG) classifies all benthic diatom taxa together. Based on data of a high-frequency 1-year long phytoplankton survey of the Pearl River (China), we tested whether the combination of the FG system with various trait-based classifications of benthic diatoms enhances our ability in predicting the community composition from the local environment. Using the Self-Organizing Map approach, we identified characteristic community compositions based on (i) taxonomic data, (ii) the FG approach, and (iii) the FG system combined with trait-based functional approaches of benthic diatoms: size structure, ecological guilds, and eco-morphological groups. All combined functional approaches enabled better predictions for the community composition than the taxonomic data or the FG system alone. The most reliable approach was the combination of the FG system with ecological guilds of benthic diatoms. Therefore, the occurrence of benthic diatoms in large river phytoplankton can be assessed ecologically in a meaningful way based on combined planktic and benthic functional classifications. The application of this approach seems to be highly relevant in large river phytoplankton ecology, ecological modelling, or in ecological status indication.


Benthos Diatoms Ecological guilds Functional groups Functional traits Potamoplankton 



This work was supported by the Science and Technology Program of Guangzhou, China (ref. no.: NO.201707010310), by the Central Public-interest Scientific Institution Basal Research Fund, CAFS (ref. no.: NO.2016RC-LX01), and by the National Natural Science Foundation of China (ref. no.: NO.41403071). AA acknowledges the support by the National Research, Development and Innovation Office (NKFIH, ref. no.: PD 124681). VBB is thankful for the support of the National Research, Development and Innovation Office (NKFIH, ref. no.: GINOP-2.3.2-15-2016-00019). CSS-K acknowledges the support by the Széchenyi 2020 program (ref. no.: EFOP-3.6.1-16-2016-00015). We thank Elaine Monaghan, BSc (Econ), from Liwen Bianji, Edanz Editing China ( for editing the English text of the first draft version of our manuscript.

Author contributions

CW and XL collected the data; AA formulated the idea; AA and VBB classified algae according to functional approaches; CW performed statistical analysis; CW, VBB, and AA wrote the first draft of the manuscript and then all authors contributed to revisions substantially.

Supplementary material

10750_2018_3604_MOESM1_ESM.docx (1 mb)
Supplementary material 1 (DOCX 1029 kb)


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Pearl River Fisheries Research InstituteChinese Academy of Fishery ScienceGuangzhouChina
  2. 2.MTA Centre for Ecological Research, Institute of Ecology and BotanyVácrátótHungary
  3. 3.MTA Centre for Ecological ResearchGINOP Sustainable Ecosystems GroupTihanyHungary
  4. 4.MTA-DE Lendület Functional and Restoration Ecology Research GroupDebrecenHungary
  5. 5.Department of LimnologyUniversity of PannoniaVeszprémHungary

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