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  5. Prevalent fingerprint of marine macroalgae in arctic surface sediments

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Article
en
2023

Prevalent fingerprint of marine macroalgae in arctic surface sediments

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en
2023
Vol 898
Vol. 898
DOI: 10.1016/j.scitotenv.2023.165507

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Carlos M. Duarte
Carlos M. Duarte

King Abdullah University of Science and Technology

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Sarah B. Ørberg
Carlos M. Duarte
Nathan R. Geraldi
+4 more

Abstract

Macroalgal forests export much of their production, partly supporting food webs and carbon stocks beyond their habitat, but evidence of their contribution in sediment carbon stocks is poor. We test the hypothesis that macroalgae contribute to carbon stocks in arctic marine sediments. We used environmental DNA (eDNA) fingerprinting on a large-scale set of surface sediment samples from Greenland and Svalbard. We evaluated eDNA results by comparing with traditional survey and tracer methods. The eDNA-based survey identified macroalgae in 94 % of the sediment samples covering shallow nearshore areas to 1460 m depth and 350 km offshore, with highest sequence abundance nearshore and with dominance of brown macroalgae. Overall, the eDNA results reflected the potential source communities of macroalgae and eelgrass assessed by traditional surveys, with the most abundant orders being common among different methods. A stable isotope analysis showed a considerable contribution from macroalgae in sediments although with high uncertainty, highlighting eDNA as a great improvement and supplement for documenting macroalgae as a contributor to sediment carbon stocks. Conclusively, we provide evidence for a prevalent contribution of macroalgal forests in arctic surface sediments, nearshore as well as offshore, identifying brown algae as main contributors.

How to cite this publication

Sarah B. Ørberg, Carlos M. Duarte, Nathan R. Geraldi, Mikael K. Sejr, Susse Wegeberg, Josie Hansen, Dorte Krause‐Jensen (2023). Prevalent fingerprint of marine macroalgae in arctic surface sediments. , 898, DOI: https://doi.org/10.1016/j.scitotenv.2023.165507.

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Publication Details

Type

Article

Year

2023

Authors

7

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1016/j.scitotenv.2023.165507

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