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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.

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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.

References

  1. Global Nutrition Report. Global Nutrition Report 2017: Nourishing the SDGs (Development Initiatives, 2017).

  2. Horton, S. & Steckel, R. H. in How Much Have Global Problems Cost the World? (ed. Lomborg, B.) 247–272 (Cambridge Univ. Press, 2013).

  3. Haddad, L. et al. A new global research agenda for food. Nature 540, 30–32 (2016).

    CAS  ADS  PubMed  Google Scholar 

  4. Kawarazuka, N. & Béné, C. The potential role of small fish species in improving micronutrient deficiencies in developing countries: building evidence. Public Health Nutr. 14, 1927–1938 (2011).

    PubMed  Google Scholar 

  5. Vaitla, B. et al. Predicting nutrient content of ray-finned fishes using phylogenetic information. Nat. Commun. 9, 3742 (2018).

    ADS  PubMed  PubMed Central  Google Scholar 

  6. Thilsted, S. H. et al. Sustaining healthy diets: the role of capture fisheries and aquaculture for improving nutrition in the post-2015 era. Food Policy 61, 126–131 (2016).

    Google Scholar 

  7. Hotz, C. & Gibson, R. S. Traditional food-processing and preparation practices to enhance the bioavailability of micronutrients in plant-based diets. J. Nutr. 137, 1097–1100 (2007).

    CAS  PubMed  Google Scholar 

  8. Hixson, S. M., Sharma, B., Kainz, M. J., Wacker, A. & Arts, M. T. Production, distribution, and abundance of long-chain omega-3 polyunsaturated fatty acids: a fundamental dichotomy between freshwater and terrestrial ecosystems. Environ. Rev. 23, 414–424 (2015).

    CAS  Google Scholar 

  9. McGill, B. J., Enquist, B. J., Weiher, E. & Westoby, M. Rebuilding community ecology from functional traits. Trends Ecol. Evol. 21, 178–185 (2006).

    PubMed  Google Scholar 

  10. Gelman, A., Meng, X.-L. & Stern, H. Posterior predictive assessment of model fitness via realized discrepancies. Stat. Sin. 6, 733–760 (1996).

    MathSciNet  MATH  Google Scholar 

  11. Black, R.E. et al. Maternal and child undernutrition and overweight in low-income and middle income-countries. The Lancet 382, 427–451 (2013).

    ADS  PubMed  PubMed Central  Google Scholar 

  12. Beal, T., Massiot, E., Arsenault, J. E., Smith, M. R. & Hijmans, R. J. Global trends in dietary micronutrient supplies and estimated prevalence of inadequate intakes. PLoS ONE 12, e0175554 (2017).

    PubMed  PubMed Central  Google Scholar 

  13. Lal, R. Managing soils for a warming earth in a food-insecure and energy-starved world. J. Plant Nutr. Soil Sci. 173, 4–15 (2010).

    CAS  Google Scholar 

  14. Marinda, P. A., Genschick, S., Khayeka-Wandabwa, C., Kiwanuka-Lubinda, R. & Thilsted, S. H. Dietary diversity determinants and contribution of fish to maternal and under-five nutritional status in Zambia. PLoS ONE 13, e0204009 (2018).

    PubMed  PubMed Central  Google Scholar 

  15. Thilsted, S. H., Roos, N. & Hassan, N. The role of small indigenous fish species in food and nutrition security in Bangladesh. Naga 20, 82–84 (1997).

    Google Scholar 

  16. Calder, P. C. Marine omega-3 fatty acids and inflammatory processes: effects, mechanisms and clinical relevance. Biochim. Biophys. Acta 1851, 469–484 (2015).

    CAS  PubMed  Google Scholar 

  17. Parrish, C. C. Lipids in marine ecosystems. ISRN Oceanogr. 2013, 604045 (2013).

    Google Scholar 

  18. Arts, M. T., Brett, M. T. & Kainz, M. J. Lipids in Aquatic Ecosystems (Springer, 2009).

  19. Pauly, D. & Zeller, D. Catch reconstructions reveal that global marine fisheries catches are higher than reported and declining. Nat. Commun. 7, 10244 (2016).

    CAS  ADS  PubMed  PubMed Central  Google Scholar 

  20. Willett, W. et al. Food in the Anthropocene: the EAT-Lancet Commission on healthy diets from sustainable food systems. Lancet 393, 447–492 (2019).

    PubMed  Google Scholar 

  21. Achouba, A., Dumas, P., Ouellet, N., Lemire, M. & Ayotte, P. Plasma levels of selenium-containing proteins in Inuit adults from Nunavik. Environ. Int. 96, 8–15 (2016).

    CAS  PubMed  Google Scholar 

  22. Vedtofte, M. S., Jakobsen, M. U., Lauritzen, L. & Heitmann, B. L. Dietary α-linolenic acid, linoleic acid, and n-3 long-chain PUFA and risk of ischemic heart disease. Am. J. Clin. Nutr. 94, 1097–1103 (2011).

    CAS  PubMed  Google Scholar 

  23. Golden, C. D. et al. Nutrition: fall in fish catch threatens human health. Nature 534, 317–320 (2016).

    ADS  PubMed  Google Scholar 

  24. Kummu, M. et al. Over the hills and further away from coast: global geospatial patterns of human and environment over the 20th–21st centuries. Environ. Res. Lett. 11, 034010 (2016).

    ADS  Google Scholar 

  25. Micha, R. et al. Global, regional and national consumption of major food groups in 1990 and 2010: a systematic analysis including 266 country-specific nutrition surveys worldwide. BMJ Open 5, e008705 (2015).

    PubMed  PubMed Central  Google Scholar 

  26. National Academies of Sciences, Engineering and Medicine. Dietary Reference Intakes Tables and Application http://nationalacademies.org/hmd/Activities/Nutrition/SummaryDRIs/DRI-Tables.aspx (2017).

  27. Allen, L., de Benoist, B., Dary, O. & Hurrell, R. Guidelines on Food Fortification with Micronutrients (WHO, 2006)

  28. Sen, A. Poverty and Famines: An Essay on Entitlement and Deprivation (Oxford Univ. Press, 1982).

  29. Fréon, P., Avadí, A., Vinatea Chavez, R. A. & Iriarte Ahón, F. Life cycle assessment of the Peruvian industrial anchoveta fleet: boundary setting in life cycle inventory analyses of complex and plural means of production. Int. J. Life Cycle Assess. 19, 1068–1086 (2014).

    Google Scholar 

  30. Smith, M. R., Micha, R., Golden, C. D., Mozaffarian, D. & Myers, S. S. Global expanded nutrient supply (GENuS) model: a new method for estimating the global dietary supply of nutrients. PLoS ONE 11, e0146976 (2016).

    PubMed  PubMed Central  Google Scholar 

  31. Hunter, J. D. Matplotlib: a 2D graphics environment. Comput. Sci. Eng. 9, 90–95 (2007).

    Article  Google Scholar 

  32. Rittenschober, D., Stadlmayr, B., Nowak, V., Du, J. & Charrondiere, U. R. Report on the development of the FAO/INFOODS user database for fish and shellfish (uFiSh) — challenges and possible solutions. Food Chem. 193, 112–120 (2016).

    CAS  PubMed  Google Scholar 

  33. Rittenschober, D., Nowak, V. & Charrondiere, U. R. Review of availability of food composition data for fish and shellfish. Food Chem. 141, 4303–4310 (2013).

    CAS  PubMed  Google Scholar 

  34. FAO. FAO/INFOODS Food Composition Databases v.4.0 http://www.fao.org/infoods/infoods/tables-and-databases/faoinfoods-databases/en/ (2017).

  35. Bogard, J. R. et al. Nutrient composition of important fish species in Bangladesh and potential contribution to recommended nutrient intakes. J. Food Compos. Anal. 42, 120–133 (2015).

    CAS  Google Scholar 

  36. Charrondiere, U. R. et al. Improving food composition data quality: three new FAO/INFOODS guidelines on conversions, data evaluation and food matching. Food Chem. 193, 75–81 (2016).

    CAS  PubMed  Google Scholar 

  37. Sidhu, K. S. Health benefits and potential risks related to consumption of fish or fish oil. Regul. Toxicol. Pharmacol. 38, 336–344 (2003).

    CAS  PubMed  Google Scholar 

  38. Budge, S. M., Iverson, S. J., Bowen, W. D. & Ackman, R. G. Among- and within-species variability in fatty acid signatures of marine fish and invertebrates on the Scotian Shelf, Georges Bank, and southern Gulf of St. Lawrence. Can. J. Fish. Aquat. Sci. 59, 886–898 (2002).

    CAS  Google Scholar 

  39. Balter, M. What made humans modern? Science 295, 1219–1225 (2002).

    CAS  PubMed  Google Scholar 

  40. Mouillot, D., Graham, N. A. J., Villéger, S., Mason, N. W. H. & Bellwood, D. R. A functional approach reveals community responses to disturbances. Trends Ecol. Evol. 28, 167–177 (2013).

    PubMed  Google Scholar 

  41. Froese, R. & Pauly, D. (eds). FishBase https://www.fishbase.org, version Aug 2016.

  42. Willis, J. N. & Sunda, W. G. Relative contributions of food and water in the accumulation of zinc by two species of marine fish. Mar. Biol. 80, 273–279 (1984).

    CAS  Google Scholar 

  43. Christensen, V. & Pauly, D. ECOPATH II — a software for balancing steady-state ecosystem models and calculating network characteristics. Ecol. Modell. 61, 169–185 (1992).

    Google Scholar 

  44. van der Oost, R., Beyer, J. & Vermeulen, N. P. Fish bioaccumulation and biomarkers in environmental risk assessment: a review. Environ. Toxicol. Pharmacol. 13, 57–149 (2003).

    PubMed  Google Scholar 

  45. Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).

    Google Scholar 

  46. Barlow, J. et al. The future of hyperdiverse tropical ecosystems. Nature 559, 517–526 (2018).

    CAS  ADS  PubMed  Google Scholar 

  47. Dee, L. E. et al. Functional diversity of catch mitigates negative effects of temperature variability on fisheries yields. Proc. R. Soc. Lond. B 283, 20161435 (2016).

    Google Scholar 

  48. Willmer, P., Stone, G. & Johnston, I. Environmental Physiology of Animals (John Wiley & Sons, 2009).

  49. Salvatier, J., Wiecki, T. V. & Fonnesbeck, C. Probabilistic programming in Python using PyMC3. PeerJ Comput. Sci. 2, e55 (2016).

    Google Scholar 

  50. Gelman, A. et al. Bayesian Data Analysis, vol. 2 (CRC, Boca Raton 2014).

  51. Nash, K. L., Watson, R. A., Halpern, B. S., Fulton, E. A. & Blanchard, J. L. Improving understanding of the functional diversity of fisheries by exploring the influence of global catch reconstruction. Sci. Rep. 7, 10746 (2017).

    ADS  PubMed  PubMed Central  Google Scholar 

  52. United Nations DESA Population Division. World Population Prospects https://population.un.org/wpp/Download/Standard/Population/ (2017).

  53. SEDAC. Gridded Population of the World (GPW), v4 http://sedac.ciesin.columbia.edu/data/collection/gpw-v4/sets/browse (2018).

  54. Cunningham, S. in International Relations and the Common Fisheries Policy: Proc. 4th Concerted Action Workshop on Economics and the Common Fisheries Policy (eds Hatcher, A. & Tingley, D.) 255–272 (Center for the Economics and Management of Aquatic Resources, 2000).

  55. Alder, J. & Sumaila, U. R. Western Africa: a fish basket of Europe past and present. J. Environ. Dev. 13, 156–178 (2004).

    Google Scholar 

  56. Ruddle, K. Fisheries for global welfare and environment. In Proc. 5th World Fisheries Congress (eds Tsukamoto, K. et al.) 399–411 (2008).

  57. Asche, F., Bellemare, M. F., Roheim, C., Smith, M. D. & Tveteras, S. Fair enough? Food security and the international trade of seafood. World Dev. 67, 151–160 (2015).

    Google Scholar 

  58. FAO. Fishery Statistical Collections: Fishery Commodities and Trade http://www.fao.org/fishery/statistics/global-commodities-production/en (2019).

Download references

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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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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