Projected climate risk of aquatic food system benefits

• Main Authors: Tigchelaar, Michelle, Cheung, William, Mohammed, Essam, Phillips, Michael, Payne, Hanna, Selig, Elizabeth, Wabnitz, Colette, Oyinlola, Muhammed, Frölicher, Thomas, Gephart, Jessica, Golden, Christopher, Allison, Edward, Bennett, Abigail, Cao, Ling, Fanzo, Jessica, Halpern, Benjamin, Micheli, Fiorenza, Naylor, Rosamond, Sumaila, Rashid, Tagliabue, Alessandro, Troell, Max
• Format: Dataset
• Language: English
• Published: Dryad 2021
• Subjects: climate risk; aquatic foods; food systems; Fuzzy logic; FOS Earth and related environmental sciences; Laplace; Sea ice
• Online Access: https://dx.doi.org/10.5061/dryad.70rxwdbz3; http://datadryad.org/stash/dataset/doi:10.5061/dryad.70rxwdbz3

Aquatic foods from marine and freshwater systems are critical to the nutrition, health, livelihoods, economies and culture of billions of people worldwide – but climate-related hazards may compromise their ability to provide these benefits. This analysis estimates national-level aquatic food system climate risk using a fuzzy logic modeling approach that connects climate hazards impacting marine and freshwater capture fisheries and aquaculture to their contributions to sustainable food system outcomes, and vulnerability to losing those contributions. Estimates are presented for a high and a low emissions scenario in three different time windows (2030, 2050, 2090). : This analysis computes quantitative indices of climate risk for four aquatic food system outcomes – nutrition & health, economic, social, and environmental – adopting a fuzzy logic modeling approach to implement the risk assessment framework used by the Intergovernmental Panel on Climate Change. In this framework, climate risk results from the interaction between climate-change induced hazards, exposure to those climate hazards, and vulnerabilities of components of the aquatic food systems. For our purposes, we conceptualized climate hazards as the dominant climate variables that impact aquatic food production and supply chains, exposure as the degree to which aquatic foods contribute to the various food system outcomes at a national-level, and vulnerability as a combination of sensitivity to and adaptive capacity of the nationally-aggregated food systems in the face of the loss of aquatic food contributions. Through two rounds of virtual workshops, the team of co-authors – who were selected for their expertise spanning marine and freshwater ecosystems, fisheries and aquaculture production systems, and multiple food system outcomes – selected hazard, exposure, and vulnerability indicators based on their expert knowledge, published literature, and data availability for most of the countries included in this study. Climate hazards Climate hazard scores were calculated for six different components of aquatic food systems: marine fisheries, freshwater fisheries, marine aquaculture, freshwater aquaculture, brackish aquaculture, post-production processes. The following variables were selected for each of these components: Marine fisheries: Maximum catch potential (from an ecology model based on ocean temperature, circulation, dissolved oxygen, net primary production in the top 100m, salinity and sea ice) surface and bottom pH; marine heatwave frequency Freshwater fisheries: Near-surface air temperature; freshwater balance; percent extraction of renewable freshwater Marine aquaculture: Maximum mariculture potential (from an ecology model based on ocean conditions, suitable marine area for farming, fishmeal and fish oil production) marine heatwave frequency; percent of population inundated by sea level; cyclone strength in Low Elevation Coastal Zone; global cropland temperature; feed Crude Protein index Freshwater aquaculture: Near-surface air temperature; freshwater balance; percent extraction of renewable freshwater; global cropland temperature; fishmeal/fish oil availability; feed Crude Protein index Brackish aquaculture: Near-surface air temperature; percent of population inundated by sea level; cyclone strength in Low Elevation Coastal Zone; global cropland temperature; fishmeal/fish oil availability; feed Crude Protein index Post-production: Near-surface air temperature; percent of population inundated by sea level; cyclone strength in Low Elevation Coastal Zone; change in sea ice extent; % of landings from small-scale operations Where possible, projections from three different Earth system models (ESM) were used to represent uncertainties in projections of environmental changes, all available from the Coupled Models Intercomparison Project Phase 6 (CMIP6): Geophysical Fluid Dynamics Laboratory (GFDL)-ESM4, The Institut Pierre-Simon Laplace (IPSL)-CM6A-LR, and Max Planck Institute (MPI)-ESM1-2-HR. We calculated climate hazards using two contrasting scenarios – Shared Socio-economic Pathway (SSP) 1 - Representative Concentration Pathway (RCP) 2.6 (SSP1-2.6) and SSP5-8.5. The SSP1-2.6 and SSP5-8.5 represent a ‘strong mitigation’ low-emissions pathway and a ‘no mitigation’ high-emissions pathway, respectively. For the marine heatwave variable, CMIP6 results were not yet available so CMIP5 equivalents were used. Results were calculated for the near future (2021-2040), middle (2041-2060) and end (2081-2100) of the 21st century. Exposure The following exposure indicators were selected for each of the four food system outcomes: Nutrition & health: Per capita supply of marine and freshwater aquatic foods; percentage of a nation’s consumption of vitamin B-12 and DHA+EPA fatty acids derived from aquatic foods Economic: Contribution of aquatic food production to Gross Domestic Product (GDP); economic multipliers of marine supply chains; net aquatic food trade balance relative to GDP Social: Contribution of marine fisheries, aquaculture, and inland fisheries to employment; ratio of indigenous to national-average consumption of seafood Environmental: Average greenhouse gas emissions, nitrogen and phosphorus emissions, land use and freshwater use of different types of wild-capture and farmed aquatic food production Vulnerability The following vulnerability indicators were selected for each of the four food system outcomes: Nutrition & Health: Percent of population below national poverty line; percent secondary educational attainment; percent stunted children under 5; Summary Exposure Values for Vitamin B-12 and omega-3 fatty acids; Economic: GDP per capita; GINI coefficient; percent of population with access to bank account; R&D expenditures relative to GDP; average of Worldwide Governance Indicators; percent of landings from small-scale operations Social: Percent of population below national poverty line; GINI coefficient; percent of population with access to bank account; average of Worldwide Governance Indicators; percent secondary educational attainment; percent of landings from small-scale operations Environmental: GDP per capita; GINI coefficient; R&D expenditures relative to GDP; average of Worldwide Governance Indicators; Environmental Performance Index – Biodiversity & Habitat, Fisheries, and Climate Change; percent landings from small-scale operations Fuzzy logic system The fuzzy logic modeling system consists of three steps: Categorizing each indicator variable into one or more levels of ‘low’, ‘medium’, ‘high’ and ‘very high’ simultaneously, with the degree of membership defined by fuzzy membership functions (“fuzzification”) Accumulating the degree of membership associated with each level using the MYCIN algorithm for each of the subcomponents of climate risk (hazard, exposure and vulnerability) and applying a set of heuristic rules to combine the components into an aggregate risk score (“fuzzy reasoning”) Calculating a final score from the accumulated memberships in order to express climate risk on a scale from 1 to 100 (“defuzzification”). For more information on the fuzzy logic methodology, see: Cheung, W. W. L., Pitcher, T. J. & Pauly, D. A fuzzy logic expert system to estimate intrinsic extinction vulnerabilities of marine fishes to fishing. Biol. Conserv. 124, 97–111 (2005). Cheung, W. W. L., Jones, M. C., Reygondeau, G. & Fr.licher, T. L. Opportunities for climate-risk reduction through effective fisheries management. Glob. Chang. Biol. 24, 5149–5163 (2018). Jones, M. C. & Cheung, W. W. L. Using fuzzy logic to determine the vulnerability of marine species to climate change. Glob. Chang. Biol. 24, e719–e731 (2018). : The datafile contains 6 tabs, for combinations of 2 emissions scenarios (SSP1-2.6, low-emissions; SSP5-8.5, high-emissions) and 3 time frames (2030, 2050, 2090). Each tab contains 23 variables for 240 countries and territories. All variables are of a unit-less score ranging from 1-100, where <25 indicates 'Low', 25-50 indicates 'Medium', 50-75 indicates 'High', and >75 indicates 'Very High'. Missing data are marked by empty cells, shaded grey. Countries and territories marked with an asterisk (*) are ones for which data availability was low, indicating reduced confidence in resulting risk scores. The following variables are included: Hazard - Aggregate: Climate hazard score aggregated across all production systems based on present-day production weights; in all countries the 'post-production' component is assigned a weight of 10% Hazard – Marine fisheries: Climate hazard score for marine fisheries Hazard – Freshwater fisheries: Climate hazard score for freshwater fisheries Hazard – Marine aquaculture: Climate hazard score for marine aquaculture Hazard – Freshwater aquaculture: Climate hazard score for freshwater aquaculture Hazard – Brackish aquaculture: Climate hazard score for brackish aquaculture Hazard – Post-production: Climate hazard score for post-production processes Exposure – Nutrition_Health: Exposure score for the Nutrition & Health food systems outcome Exposure – Economic: Exposure score for the Economic food systems outcome Exposure – Social: Exposure score for the Social food systems outcome Exposure – Environmental: Exposure score for the Environmental food systems outcome Exposure to Hazard – Nutrition_Health: Exposure to Hazard score for the Nutrition & Health food systems outcome; scores were aggregated across all production systems based on present-day production weights; in all countries the 'post-production' component is assigned a weight of 10% Exposure to Hazard – Economic: Exposure to Hazard score for the Economic food systems outcome; scores were aggregated across all production systems based on present-day production weights; in all countries the 'post-production' component is assigned a weight of 10% Exposure to Hazard – Social: Exposure to Hazard score for the Social food systems outcome; scores were aggregated across all production systems based on present-day production weights; in all countries the 'post-production' component is assigned a weight of 10% Exposure to Hazard – Environmental: Exposure to Hazard score for the Environmental food systems outcome; scores were aggregated across all production systems based on present-day production weights; in all countries the 'post-production' component is assigned a weight of 10% Vulnerability – Nutrition_Health: Vulnerability score for the Nutrition & Health food systems outcome Vulnerability – Economic: Vulnerability score for the Nutrition & Health food systems outcome Vulnerability – Social: Vulnerability score for the Nutrition & Health food systems outcome Vulnerability – Environmental: Vulnerability score for the Nutrition & Health food systems outcome Risk – Nutrition_Health: Climate risk score for the Nutrition & Health food systems outcome Risk – Economic: Climate risk score for the Nutrition & Health food systems outcome Risk – Social: Climate risk score for the Nutrition & Health food systems outcome Risk – Environmental: Climate risk score for the Nutrition & Health food systems outcome More information can be found in the associated README file.