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Ecological variables for developing a global deep-ocean monitoring and conservation strategy

Nature Ecology & Evolution volume 4, pages 181–192 (2020)Cite this article

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Matters Arising to this article was published on 16 November 2020

Abstract

The deep sea (>200 m depth) encompasses >95% of the world’s ocean volume and represents the largest and least explored biome on Earth (<0.0001% of ocean surface), yet is increasingly under threat from multiple direct and indirect anthropogenic pressures. Our ability to preserve both benthic and pelagic deep-sea ecosystems depends upon effective ecosystem-based management strategies and monitoring based on widely agreed deep-sea ecological variables. Here, we identify a set of deep-sea essential ecological variables among five scientific areas of the deep ocean: (1) biodiversity; (2) ecosystem functions; (3) impacts and risk assessment; (4) climate change, adaptation and evolution; and (5) ecosystem conservation. Conducting an expert elicitation (1,155 deep-sea scientists consulted and 112 respondents), our analysis indicates a wide consensus amongst deep-sea experts that monitoring should prioritize large organisms (that is, macro- and megafauna) living in deep waters and in benthic habitats, whereas monitoring of ecosystem functioning should focus on trophic structure and biomass production. Habitat degradation and recovery rates are identified as crucial features for monitoring deep-sea ecosystem health, while global climate change will likely shift bathymetric distributions and cause local extinction in deep-sea species. Finally, deep-sea conservation efforts should focus primarily on vulnerable marine ecosystems and habitat-forming species. Deep-sea observation efforts that prioritize these variables will help to support the implementation of effective management strategies on a global scale.

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Fig. 1: Deep-sea monitoring strategy based on DEEVs.
Fig. 2: Expert elicitation results.
Fig. 3: Tools for investigating the deep ocean.

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Data availability

The dataset generated and analysed during the current study is available from the corresponding author on reasonable request.

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Acknowledgements

We deeply thank M. Rex (University of Massachusetts) for valuable discussion and suggestions on an early draft of the manuscript. We are very grateful to M. Baker for supporting the authors in the distribution of the Qualtrics survey to the INDEEP and DOSI communities and to the deep-sea scientists that participated to the survey and J. Cerri for the analysis of Qualtrics results. This work was supported by the H2020 project MERCES (GA N. 689518) and IDEM (GA N. 11.0661/2017/750680/SUB/EN V.C2.). E.R.-L. was supported by the Norwegian project MarMine (247626), the Norwegian Institute for Water Research and the H2020 project MERCES (GA N. 689518). P.V.R.S. was supported by the NSERC Canadian Healthy Oceans Network and CFREF Ocean Frontier Institute. L.T. is supported by JPI Oceans2 and ONC. J.A. is supported by ARIM (Autonomous Robotic sea-floor Infrastructure for bentho-pelagic Monitoring; MartTERA ERA-Net Cofound). L.L. acknowledges NSF grant OCE 1634172 and the Deep-Ocean Observing Strategy subcontract from the Consortium for Ocean Leadership. H.R. was supported by the EMSO-Link project of the European Commission (Grant agreement ID: 731036).

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Authors and Affiliations

  1. Department of Life and Environmental Sciences, Polytechnic University of Marche, Ancona, Italy

    Roberto Danovaro, Emanuela Fanelli, Laura Carugati & Antonio Dell’Anno

  2. Stazione Zoologica Anton Dohrn, Naples, Italy

    Roberto Danovaro

  3. Instituto de Ciencias del Mar (ICM-CSIC), Paseo Marítimo de la Barceloneta, Barcelona, Spain

    Jacopo Aguzzi

  4. National Oceanography Centre, Southampton, UK

    David Billett & Henry Ruhl

  5. Department of Sciences and Engineering of Materials, Environment and Urban Planning (SIMAU), Polytechnic University of Marche, Ancona, Italy

    Cinzia Corinaldesi

  6. IUCN Global Marine and Polar Programme, Gland, Switzerland

    Kristina Gjerde

  7. School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, UK

    Alan J. Jamieson

  8. The Biodiversity Research Group, The School of Biological Sciences, Centre for Biodiversity and Conservation Science, The University of Queensland, Brisbane, Queensland, Australia

    Salit Kark

  9. Louisiana Universities Marine Consortium, Chauvin, LA, USA

    Craig McClain

  10. Center for Marine Biodiversity and Conservation and Integrative Oceanography Division, Scripps Institution of Oceanography, University of California, La Jolla, CA, USA

    Lisa Levin

  11. Department of Geography, The Hebrew University of Jerusalem, Jerusalem, Israel

    Noam Levin

  12. Norwegian Institute for Water Research, Oslo, Norway

    Eva Ramirez-Llodra

  13. Monterey Bay Aquarium Research Institute, Moss Landing, CA, USA

    Henry Ruhl

  14. Department of Oceanography, University of Hawaii at Mano’a, Honolulu, HI, USA

    Craig R. Smith

  15. Departments of Ocean Sciences and Biology, Memorial University of Newfoundland. St, John’s, Newfoundland, Canada

    Paul V. R. Snelgrove

  16. Jacobs University, Bremen, Germany

    Laurenz Thomsen

  17. Division of Marine Science and Conservation, Nicholas School of the Environment, Duke University, Durham, NC, USA

    Cindy L. Van Dover

  18. School of Biological Sciences and Swire Institute of Marine Science, The University of Hong Kong, Hong Kong SAR, China

    Moriaki Yasuhara

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Contributions

R.D., E.F., J.A., D.B., L.C., C.C., A.D., K.G., A.J.J., S.K., C.M., L.L., N.L., E.R.-L., H.R., C.R.S., P.V.R.S., L.T., C.L.V.D. and M.Y. contributed equally to the work, discussion of the data and manuscript writing. All authors critically revised the drafts and the amended version and gave final approval for publication.

Corresponding author

Correspondence to Roberto Danovaro.

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The authors declare no competing interests.

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Extended data

Extended Data Fig. 1.

Ranking of the essential variables for biodiversity measures. Results of the Expert Elicitation obtained by using the Plackett–Luce model for the analysis about the prioritization of essential variables for biodiversity measures (y axis). The worth of each variable is reported on log scale (x axis). Average weighted Cohen’s κ is also reported on the upper part of the graph. ES, expected species number.

Extended Data Fig. 2.

Ranking of the readiness of the available technologies for deep-sea ecological monitoring. Results of the Plackett–Luce model for the analysis of responses about the readiness of technology for deep-sea monitoring according to the essential variables identified for each scientific area. The Cohen’s κ value is reported on the upper part of each graph.

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Danovaro, R., Fanelli, E., Aguzzi, J. et al. Ecological variables for developing a global deep-ocean monitoring and conservation strategy. Nat Ecol Evol 4, 181–192 (2020). https://doi.org/10.1038/s41559-019-1091-z

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