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DNA metabarcoding highlights cyanobacteria as the main source of primary production in a pelagic food web model

  • Andreas Novotny*
  • , Baptiste Serandour
  • , Susanne Kortsch
  • , Benoit Gauzens
  • , Kinlan M. G. Jan
  • , Monika Winder
  • *Korresponderande författare för detta arbete

Forskningsoutput: TidskriftsbidragArtikelVetenskapligPeer review

26 Citeringar (Scopus)
139 Nedladdningar (Pure)

Sammanfattning

Models that estimate rates of energy flow in complex food webs often fail to account for species-specific prey selectivity of diverse consumer guilds. While DNA metabarcoding is increasingly used for dietary studies, methodological biases have limited its application for food web modeling. Here, we used data from dietary metabarcoding studies of zooplankton to calculate prey selectivity indices and assess energy fluxes in a pelagic resource-consumer network. We show that food web dynamics are influenced by prey selectivity and temporal match-mismatch in growth cycles and that cyanobacteria are the main source of primary production in the investigated coastal pelagic food web. The latter challenges the common assumption that cyanobacteria are not supporting food web productivity, a result that is increasingly relevant as global warming promotes cyanobacteria dominance. While this study provides a method for how DNA metabarcoding can be used to quantify energy fluxes in a marine food web, the approach presented here can easily be extended to other ecosystems.
OriginalspråkEngelska
Artikelnummereadg1096
TidskriftScience Advances
Volym9
Nummer17
DOI
StatusPublicerad - 26 apr. 2023
MoE-publikationstypA1 Tidskriftsartikel-refererad

Finansiering

We are grateful for the sampling support from staff at R/V Aranda and R/V Svea and to A.-K. Thell at the SMHI for welcoming us onboard their monitoring surveys. We also acknowledge the support from the National Genomics Infrastructure in Stockholm funded by Science for Life Laboratory, the Knut and Alice Wallenberg Foundation and the Swedish Research Council, and SNIC/Uppsala Multidisciplinary Centre for Advanced Computational Science for assistance with massively parallel sequencing and access to the UPPMAX computational infrastructure.The research was funded by the Swedish Research Council VR (grant 2016-04685 to M.W.) and Formas (grant 2020-01128 to M.W.). BG gratefully acknowledges the support of iDiv funded by the German Research Foundation (DFG–FZT 118,202548816).

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