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Seasonality of primary production explains the richness of pioneering benthic communities

Author

Listed:
  • Matteo Cecchetto

    (University of Genoa)

  • Agnès Dettai
  • Cyril Gallut

    (UA Station Marine de Concarneau)

  • Matthias Obst

    (University of Gothenburg)

  • Piotr Kuklinski

    (Institute of Oceanology, Polish Academy of Sciences)

  • Piotr Balazy

    (Institute of Oceanology, Polish Academy of Sciences)

  • Maciej Chelchowski

    (Institute of Oceanology, Polish Academy of Sciences)

  • Magdalena Małachowicz

    (Institute of Oceanology, Polish Academy of Sciences)

  • Anita Poćwierz-Kotus

    (Institute of Oceanology, Polish Academy of Sciences)

  • Małgorzata Zbawicka

    (Institute of Oceanology, Polish Academy of Sciences)

  • Henning Reiss

    (Faculty of Biosciences and Aquaculture)

  • Marc P. Eléaume
  • Gentile Francesco Ficetola

    (Università degli Studi di Milano)

  • Christina Pavloudi

    (European Marine Biological Resource Centre (EMBRC-ERIC))

  • Katrina Exter

    (Flanders Marine Institute (VLIZ), InnovOcean Campus)

  • Diego Fontaneto

    (National Research Council of Italy—Water Research Institute (CNR-IRSA)
    National Biodiversity Future Center (NBFC))

  • Stefano Schiaparelli

    (University of Genoa
    University of Genoa)

Abstract

A pattern of increasing species richness from the poles to the equator is frequently observed in many animal taxa. Ecological limits, determined by the abiotic conditions and biotic interactions within an environment, are one of the major factors influencing the geographical distribution of species diversity. Energy availability is often considered a crucial limiting factor, with temperature and productivity serving as empirical measures. However, these measures may not fully explain the observed species richness, particularly in marine ecosystems. Here, through a global comparative approach and standardised methodologies, such as Autonomous Reef Monitoring Structures (ARMS) and DNA metabarcoding, we show that the seasonality of primary production explains sessile animal richness comparatively or better than surface temperature or primary productivity alone. A Hierarchical Generalised Additive Model (HGAM) is validated, after a model selection procedure, and the prediction error is compared, following a cross-validation approach, with HGAMs including environmental variables commonly used to explain animal richness. Moreover, the linear effect of production magnitude on species richness becomes apparent only when considered jointly with seasonality, and, by identifying world coastal areas characterized by extreme values of both, we postulate that this effect may result in a positive relationship in environments with lower seasonality.

Suggested Citation

  • Matteo Cecchetto & Agnès Dettai & Cyril Gallut & Matthias Obst & Piotr Kuklinski & Piotr Balazy & Maciej Chelchowski & Magdalena Małachowicz & Anita Poćwierz-Kotus & Małgorzata Zbawicka & Henning Reis, 2024. "Seasonality of primary production explains the richness of pioneering benthic communities," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-52673-z
    DOI: 10.1038/s41467-024-52673-z
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    References listed on IDEAS

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    1. Vincent Ranwez & Sébastien Harispe & Frédéric Delsuc & Emmanuel J P Douzery, 2011. "MACSE: Multiple Alignment of Coding SEquences Accounting for Frameshifts and Stop Codons," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-10, September.
    2. Simon N. Wood & Natalya Pya & Benjamin Säfken, 2016. "Smoothing Parameter and Model Selection for General Smooth Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1548-1563, October.
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