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Fisheries track the future redistribution of marine species

Nature Climate Change volume 14, pages 1093–1100 (2024)Cite this article

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Abstract

The redistribution of fish stocks induced by climate change is expected to have global implications for fisheries, particularly the poleward shifts of species. However, the responses of different fishing gears and fleet of countries and their potential attempts to spatially redistribute catches remain unknown. Here, by developing environmental niche models for industrial fisheries of 82 countries and 13 fishing gears, we demonstrate that without management, global fleets are expected to shift poleward by the end of the century. This is driven by polar fishing gears moving to higher Arctic areas and tropical fishing gears expanding both within the tropics and poleward. Most nations, particularly tropical ones, may struggle to track these shifts, as they largely rely on coastal and nearshore fishing gears, such as trawlers. Our findings highlight the need to consider future shifts of fisheries in their management, to ensure the long-term sustainability and accessibility of fish stocks.

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Fig. 1: Projected changes in the global industrial fishing fleet distribution in 2100.
Fig. 2: Projected changes in industrial fishing gears distribution and ocean area fished in 2100.
Fig. 3: Projected changes in the industrial fishing fleet distribution of countries in 2100.
Fig. 4: Influence of environmental variables on fishing gears occurrence.
Fig. 5: Projected changes in cell-based number and composition of fishing countries in 2100.

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Data availability

All the data used in this article are public and freely available. The fishing fleet data are from GFW (https://globalfishingwatch.org/), the environmental layers are from Bio-ORACLE (https://www.bio-oracle.org/) and the EEZs boundaries used to create the figures are from Marine Regions (https://marineregions.org/). The data necessary to reproduce this article are available via figshare63.

Code availability

The code is available via GitHub at https://github.com/leonardocruz37/Fishing-fleet-modeling.git and via figshare63.

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Acknowledgements

This work was partially financed by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) through a masters’ scholarship awarded to L.C. P.L. thanks Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) for a productivity grant (no. 302365/2022-2). P.L. was supported by the Romanian Ministry of Research, Innovation and Digitalization (grant no. 760054-JUST4MPA), in the PNRR-III-C9-2022-I8 call.

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Authors and Affiliations

  1. Department of Ecology, Federal University of Rio Grande do Norte, Natal, Brazil

    Leonardo Cruz & Priscila Lopes

  2. Centro Oceanográfico de Madrid, Instituto Español de Oceanografía, Madrid, Spain

    Maria Pennino

  3. Research Institute of the University of Bucharest, University of Bucharest, Bucharest, Romania

    Priscila Lopes

Authors
  1. Leonardo Cruz
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Contributions

L.C. conceived the idea. L.C., M.P. and P.L. designed the methodology. L.C. performed the analyses and wrote the paper. All authors contributed to the paper writing and reviewing.

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Correspondence to Leonardo Cruz.

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Nature Climate Change thanks Eudriano F. S. Costa, Lida Teneva and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Spatial uncertainty for the global fishing fleet model predictions.

(a, b) Uncertainty in the predictions for the industrial fishing fleet under SSP 1-1.9 (a) and SSP 4-6.0 (b). Uncertainty is measured as the width of the 95% credible interval. This uncertainty ranges from 0 to 1, reflecting the variability within the model predictions, where wider intervals signify higher uncertainty and narrower intervals denote greater confidence in the predictions.

Extended Data Fig. 2 Spatial uncertainty for the fishing gears model predictions.

(a, b) Uncertainty in the predictions for drifting longlines under SSP 1-1.9 (a) and SSP 4-6.0 (b). (c, d) Uncertainty in the predictions for trawlers under SSP 1-1.9 (c) and SSP 4-6.0 (d). (e, f) Uncertainty in the predictions for tuna purse seines under SSP 1-1.9 (e) and SSP 4-6.0 (f). Uncertainty is measured as the width of the 95% credible interval. This uncertainty ranges from 0 to 1, reflecting the variability within the model predictions, where wider intervals signify higher uncertainty and narrower intervals denote greater confidence in the predictions.

Extended Data Fig. 3 Spatial uncertainty for the countries’ fleet model predictions.

(a, b) Uncertainty in the predictions for the Icelandic fishing fleet under SSP 1-1.9 (a) and SSP 4-6.0 (b). (c, d) Uncertainty in the predictions for the Brazilian fishing fleet under SSP 1-1.9 and (c) SSP 4-6.0 (d). (e, f) Uncertainty in the predictions for the Chinese fishing fleet under SSP 1-1.9 (e) and SSP 4-6.0 (f). Uncertainty is measured as the width of the 95% credible interval. This uncertainty ranges from 0 to 1, reflecting the variability within the model predictions, where wider intervals signify higher uncertainty and narrower intervals denote greater confidence in the predictions.

Extended Data Fig. 4 Net changes in countries’ industrial fishing fleet occurrences in 2100.

(a, b) Same as main text Fig. 3a,b, showing the net change in countries’ fishing fleet occurrence probabilities (that is, sum of increases and decreases, expressed as percentages), 95% confidence intervals are hidden for better visualization. Colors represent the geographic region of each country according to the World Bank.

Extended Data Fig. 5 The spatial footprint, fleet size and fishing gear composition of the country’s geographic region.

This summary is based on the broadcasting industrial fishing fleet cumulative occurrence data (2013–2020) from the Global Fishing Watch at 10th degree resolution, but only including the 82 countries (out of 167) used in our modeling.

Supplementary information

Supplementary Information

Supplementary Figs. 1–21 and Tables 1–3.

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Cruz, L., Pennino, M. & Lopes, P. Fisheries track the future redistribution of marine species. Nat. Clim. Chang. 14, 1093–1100 (2024). https://doi.org/10.1038/s41558-024-02127-7

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