Research ArticleENVIRONMENTAL STUDIES

Escaping the perfect storm of simultaneous climate change impacts on agriculture and marine fisheries

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Science Advances  27 Nov 2019:
Vol. 5, no. 11, eaaw9976
DOI: 10.1126/sciadv.aaw9976
  • Fig. 1 IPCC vulnerability framework (AR4), adapted for our cross-sector analysis.

    Exposure refers here to the extent to which a food production sector is subject to a driver of change. Sensitivity refers to the strength of reliance, or dependency, on this sector in terms of employment, revenue, and food security. Adaptive capacity refers to the preconditions that enable a country to mobilize resources and adjust its food system in response to climate change–induced impacts of agriculture and fisheries. Note that IPCC now bridges the AR4 definition of vulnerability with the concept of risk (AR5).

  • Fig. 2 Dimensions of agriculture and marine fisheries vulnerability to climate change.

    (A and B) Average relative changes in agriculture productivity (maize, rice, soy, and wheat) and in maximum catch potential within exclusive economic zones projected by 2100 (RCP8.5) were used to estimate exposure of agriculture and fisheries, respectively. (C and D) Sensitivity on each sector is a composite metric of dependence for food, jobs, and revenue. (E and F) Adaptive capacity is based on future gross domestic product (GDP) per capita and is not sector specific. Socioeconomic indicators (C to F) are normalized between 0 (lowest possible value) and 100 (largest possible value). The right panels are latitudinal trends. Class intervals are quantiles.

  • Fig. 3 Vulnerability of agriculture and marine fisheries as a function of exposure, sensitivity, and adaptive capacity to the impacts of climate change.

    The bivariate map shows linked vulnerabilities of agriculture and fisheries for each country under RCP8.5. The 10 most vulnerable countries are indicated for agriculture (A) and marine fisheries (F). Right panel indicates latitudinal trends.

  • Fig. 4 Magnitude of changes in agriculture and marine fisheries productivity and affected population size, according to two CO2 emission scenarios.

    (A and B) Radial diagrams show projected concomitant changes in agriculture and marine fisheries productivity, where the angle describes the relative contribution of each sector to overall change (0°, gain in agriculture only; 90°, gain in fisheries only; 180°, loss in agriculture only; 270°, loss in fisheries only) and thus describes win-win (green), lose-lose (red), and win-lose (orange and blue) exposure categories. Each diagram consists of two rings. The inner ring represents the overall magnitude of the projected changes, measured as the distance between each country’s projected change and the origin (i.e., no change) in an orthogonal coordinate system. The outer ring indicates human population projected to be living at each bearing by 2100. (C) Alluvial diagram illustrates how the total number of people projected to experience win-win (green), win-lose (blue and orange), and lose-lose (red) situations varies according to the emission scenario. Numbers are in billions (summations may not be exact owing to rounding) and only account for the projected population by 2100. See fig. S1 for global maps of each exposure category and fig. S5 for model uncertainty surrounding these estimates.

  • Fig. 5 Climate mitigation benefits for agriculture and marine fisheries productivity at the country-level.

    (A) Countries’ net change in future agriculture and fisheries productivity potential induced by climate mitigation plotted against their corresponding vulnerability under RCP8.5. Net change represents the projected differences in changes in productivity potential from RCP8.5 (business as usual) to RCP2.6 (highly successful reduction of greenhouse gas emissions); negative and positive values thus indicate net loss (i.e., lower gains, higher losses, or gains to losses) and net gain (i.e., higher gains, lower losses, or losses to gains) from climate mitigation, respectively. The 15 most vulnerable countries are indicated. (B) Countries’ net change in future agriculture and fisheries productivity potential plotted against annual CO2 production with the top 15 CO2 producers indicated. Density plots show the distribution of the world’s population, and values report net change in sectors’ productivity at the 10th, 25th, 50th, and 90th percentiles of the distribution. See fig. S7 for global estimates on mitigation benefits and table S2 for details on the most vulnerable countries and top CO2 producers.

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/5/11/eaaw9976/DC1

    Fig. S1. Spatial variation in agriculture and marine fisheries exposure, and associated levels of sensitivity and adaptive capacity according to emission scenarios RCP2.6 and RCP8.5.

    Fig. S2. Relationships between agriculture and marine fisheries vulnerability to climate change under RCP8.5 and RCP2.6.

    Fig. S3. Changes in productivity for maize, rice, soy, and wheat crops under RCP2.6 and RCP8.5.

    Fig. S4. Changes in productivity for six other crops under RCP2.6 and RCP8.5.

    Fig. S5. Uncertainty in projected changes in agriculture and marine fisheries productivity.

    Fig. S6. Regional changes in agriculture and marine fisheries productivity under RCP2.6 and RCP8.5.

    Fig. S7. Net gains and losses in agriculture and fisheries productivity from climate mitigation.

    Fig. S8. Spearman’s rank correlations among pairs of agricultural crop changes in productivity under RCP2.6 and RCP8.5.

    Fig. S9. Projected changes in finfish and bivalve aquaculture production potential under climate change.

    Fig. S10. Correlations between historical and present-day indicators of sensitivity.

    Fig. S11. Spearman’s rank correlations among pairs of adaptive capacity indicators.

    Table S1. Indicators and main data sources used to measure country-level metrics of agriculture and marine fisheries vulnerability to climate change.

    Table S2. Effect of strong climate mitigation on top CO2 producers and on the most vulnerable countries.

    References (5154)

  • Supplementary Materials

    This PDF file includes:

    • Fig. S1. Spatial variation in agriculture and marine fisheries exposure, and associated levels of sensitivity and adaptive capacity according to emission scenarios RCP2.6 and RCP8.5.
    • Fig. S2. Relationships between agriculture and marine fisheries vulnerability to climate change under RCP8.5 and RCP2.6.
    • Fig. S3. Changes in productivity for maize, rice, soy, and wheat crops under RCP2.6 and RCP8.5.
    • Fig. S4. Changes in productivity for six other crops under RCP2.6 and RCP8.5.
    • Fig. S5. Uncertainty in projected changes in agriculture and marine fisheries productivity.
    • Fig. S6. Regional changes in agriculture and marine fisheries productivity under RCP2.6 and RCP8.5.
    • Fig. S7. Net gains and losses in agriculture and fisheries productivity from climate mitigation.
    • Fig. S8. Spearman’s rank correlations among pairs of agricultural crop changes in productivity under RCP2.6 and RCP8.5.
    • Fig. S9. Projected changes in finfish and bivalve aquaculture production potential under climate change.
    • Fig. S10. Correlations between historical and present-day indicators of sensitivity.
    • Fig. S11. Spearman’s rank correlations among pairs of adaptive capacity indicators.
    • Table S1. Indicators and main data sources used to measure country-level metrics of agriculture and marine fisheries vulnerability to climate change.
    • Table S2. Effect of strong climate mitigation on top CO2 producers and on the most vulnerable countries.
    • References (5154)

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