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Harnessing global fisheries to tackle micronutrient deficiencies

Nature volume 574, pages 95–98 (2019)Cite this article

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Abstract

Micronutrient deficiencies account for an estimated one million premature deaths annually, and for some nations can reduce gross domestic product1,2 by up to 11%, highlighting the need for food policies that focus on improving nutrition rather than simply increasing the volume of food produced3. People gain nutrients from a varied diet, although fish—which are a rich source of bioavailable micronutrients that are essential to human health4—are often overlooked. A lack of understanding of the nutrient composition of most fish5 and how nutrient yields vary among fisheries has hindered the policy shifts that are needed to effectively harness the potential of fisheries for food and nutrition security6. Here, using the concentration of 7 nutrients in more than 350 species of marine fish, we estimate how environmental and ecological traits predict nutrient content of marine finfish species. We use this predictive model to quantify the global spatial patterns of the concentrations of nutrients in marine fisheries and compare nutrient yields to the prevalence of micronutrient deficiencies in human populations. We find that species from tropical thermal regimes contain higher concentrations of calcium, iron and zinc; smaller species contain higher concentrations of calcium, iron and omega-3 fatty acids; and species from cold thermal regimes or those with a pelagic feeding pathway contain higher concentrations of omega-3 fatty acids. There is no relationship between nutrient concentrations and total fishery yield, highlighting that the nutrient quality of a fishery is determined by the species composition. For a number of countries in which nutrient intakes are inadequate, nutrients available in marine finfish catches exceed the dietary requirements for populations that live within 100 km of the coast, and a fraction of current landings could be particularly impactful for children under 5 years of age. Our analyses suggest that fish-based food strategies have the potential to substantially contribute to global food and nutrition security.

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Fig. 1: Bayesian hierarchical predictive model of nutrient concentrations in fish.
Fig. 2: Nutrient concentration of fisheries and total catch by EEZ.
Fig. 3: The contribution that fisheries could make to closing dietary nutrient gaps.

Data availability

Data used to produce our nutrient models and estimated global catch can be found at https://github.com/mamacneil/GlobalFishNutrients.

Code availability

Code for Bayesian hierarchical model used to predict nutrient concentrations from standardized covariates and code used to produce our estimated global catch can be found at https://github.com/mamacneil/GlobalFishNutrients.

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Acknowledgements

This research was supported by a European Research Council Starting Grant awarded to C.C.H. (ERC grant number: 759457), Lancaster University, the ARC Centre of Excellence for Coral Reef Studies, a Royal Society University Research Fellowship to N.A.J.G. (UF140691), a NSERC Tier II Canada Research Chair awarded to M.A.M, the Australian Centre for International Agricultural Research through projects FIS/2017/003 and FIS/2015/031, and a USAID Feed the Future Innovation Lab for Nutrition – Asia (award number AIDOAA-1-10-00005) to A.L.T.-L. This work was undertaken as part of the CGIAR Research Program (CPR) on Fish Agri-Food Systems (FISH) led by WorldFish, supported by contributors to the CGIAR Trust Fund. We are grateful for the support provided by the FishBase and FAO/INFOODS database teams, J. Robinson for help with Fig. 3, and N. Swan, J. Silveira and E. Maire for help sourcing data.

Author information

Authors and Affiliations

  1. Lancaster Environment Centre, Lancaster University, Lancaster, UK

    Christina C. Hicks & Nicholas A. J. Graham

  2. Australian Research Council, Centre of Excellence for Coral Reef Studies, James Cook University, Townsville, Queensland, Australia

    Christina C. Hicks, Philippa J. Cohen, Nicholas A. J. Graham & David J. Mills

  3. WorldFish, Bayan Lepas, Malaysia

    Philippa J. Cohen, Edward H. Allison, Coralie D’Lima, David J. Mills, Matthew Roscher & Shakuntala H. Thilsted

  4. Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Tasmania, Australia

    Kirsty L. Nash

  5. Centre for Marine Socio-ecology, University of Tasmania, Hobart, Tasmania, Australia

    Kirsty L. Nash

  6. School of Marine and Environmental Affairs, University of Washington, Seattle, WA, USA

    Edward H. Allison

  7. Center for Human Nutrition, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

    Andrew L. Thorne-Lyman

  8. Ocean Frontier Institute, Department of Biology, Dalhousie University, Halifax, Nova Scotia, Canada

    M. Aaron MacNeil

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Contributions

C.C.H. conceived the study with P.J.C., N.A.J.G., K.L.N., A.L.T.-L., C.D’L., E.H.A., S.H.T. and D.J.M.; C.C.H., P.J.C., N.A.J.G., K.L.N., C.D’L. and M.R. collected the data; C.C.H., M.A.M. and K.L.N. developed and implemented the analyses; C.C.H. led the manuscript with input from all authors.

Corresponding author

Correspondence to Christina C. Hicks.

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The authors declare no competing interests.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Peer review information Nature thanks Ray Hilborn, Edoardo Masset, Daniel Pauly and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Fig. 1 Bayesian diagnostic plots I.

Forest plots for each nutrient, given their respective model, including highest posterior densities (filled cicles), 50% (thick line) and 95% (thin line) uncertainty intervals for each parameter (left). Also included are R-hat (Gelman–Rubin) statistics (right), showing evidence of model convergence from four independent model runs (chains). R-hat values close to one show a consistent, stable relationship between the within-chain and among-chain variances, suggesting no evidence of non-convergence.

Extended Data Fig. 2 Bayesian diagnostic plots II.

The 25 randomly chosen posterior predictive distributions (small blue histograms; left) for observed values (red vertical lines) of individual nutrients under each nutrient-specific model. Red lines on top of the blue distribution indicate evidence of model fit; whereas red lines beyond the posterior suggest observations that are not consistent with the underlying model. Posterior predictive distributions (large blue histograms; right) for the observed overall mean (blue vertical lines) under each nutrient-specific model. Blue lines on top of the histogram indicate evidence of model fit, with a posterior predictive mean consistent with the observed data.

Extended Data Fig. 3 Reported nutrient yield of fisheries and total fishery yield by EEZ.

Data based on average taxon composition of annual reported catches19 from 2010 to 2014, showing calculated yields of calcium, iron, selenium, zinc, vitamin A, omega-3 fatty acids and protein and the total catch. Data are plotted at the scale of EEZ areas as previously defined19. Base maps were generated using the matplotlib library31 (https://matplotlib.org) in Python.

Extended Data Fig. 4 Relationships between nutrient yield, nutrient concentration and total catch.

Relationships are shown for calcium, iron, selinium, zinc, vitamin A, omega-3 fatty acids and protein, showing Pearson product–moment correlation coefficients, calculated using the corrcoef function in the ‘numpy’ library of Python (n = 280 EEZ areas, as previously defined19).

Extended Data Fig. 5 Nuisance parameters for Bayesian hierarchical predictive model of nutrient concentrations.

Standardized effect sizes for known factors that may influence results but that are not of interest to the study (nuisance parameters) are shown. These factors were included in the overall model, meaning their influence was accounted for in the predictions. Parameter estimates are Bayesian posterior median values, 95% highest posterior density uncertainty intervals (thin lines) and 50% uncertainty intervals (thick lines). Black dots indicate that the 50% uncertainty intervals do not overlap zero, indicating that more than 75% of the posterior density was either positive or negative; and open squares indicate the baseline category in the statistical model. Underlying sample sizes are as follows: calcium, n = 170 biologically independent samples; iron, n = 173; selenium, n = 134; zinc, n = 196; vitamin A, n = 69; omega-3 fatty acids, n = 176; and protein, n = 627.

Extended Data Fig. 6 Reported and unreported nutrient yield of fisheries and total fishery yield by EEZ.

Data based on average taxon composition of annual reported and unreported catches19 from 2010 to 2014, showing calculated yields of calcium, iron, selenium, zinc, vitamin A, omega-3 fatty acids and protein and the total catch. Data are plotted at the scale of EEZ areas as previously defined19. Base maps were generated using the matplotlib library31 (https://matplotlib.org) in Python.

Extended Data Fig. 7 Relationships between reported only and reported and unreported catch nutrient yields per EEZ.

Pearson product–moment correlation coefficients were calculated using the corrcoef function in the numpy library of Python (n = 280 EEZ areas, as previously defined19).

Extended Data Fig. 8 Relationships between reported only and reported and unreported catch nutrient concentration per EEZ.

Pearson product–moment correlation coefficients were calculated using the corrcoef function in the numpy library of Python (n = 280 EEZ areas, as previously defined19).

Extended Data Table 1 Top 20 countries by nutrient concentration
Full size table
Extended Data Table 2 Foreign fishing and trade (2010–2014)
Full size table

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Hicks, C.C., Cohen, P.J., Graham, N.A.J. et al. Harnessing global fisheries to tackle micronutrient deficiencies. Nature 574, 95–98 (2019). https://doi.org/10.1038/s41586-019-1592-6

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