This KOEHN_ETAL_PlosONE_2022__DATA_README.txt file was generated on 2022-08-12 by Laura E. Koehn GENERAL INFORMATION 1. Title of Dataset: Data for: Social-ecological vulnerability of fishing communities to climate change: a U.S. West Coast case study 2. Author Information Author/Principal Investigator Information Name: Dr. Laura E. Koehn ORCID: 0000-0002-3292-1504 Institution: School of Environmental and Forest Services, University of Washington Email: laura.koehn216@gmail.com Co-investigator 1 Name: Dr. Laura K. Nelson Institution: School of Environmental and Forest Services, University of Washington Co-investigator 2 Name: Dr. Jameal F. Samhouri Institution: NOAA, National Marine Fisheries Service, Northwest Fisheries Science Center Email: jameal.samhouri@noaa.gov Co-investigator 3 Name: Dr. Karma C. Norman Institution: NOAA, National Marine Fisheries Service, Northwest Fisheries Science Center Email: karma.norman@noaa.gov Co-investigator 4 Name: Dr. Michael G. Jacox Institution: NOAA, National Marine Fisheries Service, Southwest Fisheries Science Center Email: michael.jacox@noaa.gov Co-investigator 5 Name: Dr. Alison C. Cullen Institution: Evans School of Public Policy and Governance, University of Washington Co-investigator 6 Name: Dr. Jerome Fiechter Institution: Ocean Sciences Department, University of California at Santa Cruz Co-investigator 7 Name: Dr. Mercedes Pozo Buil Institution: Institute of Marine Sciences, University of California Santa Cruz Institution: NOAA, National Marine Fisheries Service, Southwest Fisheries Science Center Co-investigator 8 Name: Dr. Phillip S. Levin Institution: School of Environmental and Forest Services, University of Washington Institution: The Nature Conservancy in Washington 3. Date of data compilation: 2019-2020 (for data that represents 2009-2018) 4. Geographic location of data representation: Data collated for communities or species along the U.S. West Coast (Washington, Oregon, and California) 5. Information about funding sources that supported the collection of the data: Lenfest Ocean Program SHARING/ACCESS INFORMATION 1. Links to other publicly accessible locations of the data: See below - data derived from another source 2. Data derived from another source: Much data used in this analysis to generate metrics and indicators comes from other sources and was not produced by methods in this study. Certain data underlying the values presented in the study are publicly available: Aquamaps: https://www.aquamaps.org/ for species ranges to determine ecological risk or by contacting Aquamaps at info.aquamaps@gmail.com and are not replicated here. Additional raster files needed to construct species range files are available here through the GitHub page cited in this paper: O'Hara CC, Afflerbach JC, Scarborough C, Kaschner K, Halpern BS. Aligning marine species range data to better serve science and conservation. PLoS One. 2017 May 3;12(5):e0175739. https://doi.org/10.1371/journal.pone.0175739 and at the associated git repository https:// github.com/OHI-Science/IUCN-AquaMaps (used as part of the code to rasterize species range data available at the time of publication on GitHub here: https://github.com/koehnl/CommunityVuln_PlosOne). Social metric data for calculating adaptive capacity for communities are available through the CDC here: https://www.atsdr.cdc.gov/ placeandhealth/svi/data_documentation_download.html and therefore not replicated here. Confidential vessel-level landings data may be acquired by direct request from the Pacific Fisheries Information Network (PacFIN) (https://pacfin.psmfc.org/) or the Departments of Fish and Wildlife in California, Oregon, and Washington, subject to a non-disclosure agreement. Aggregated data used to determine top species landed for each community and percent revenue from each species for each community, and all associated R code will be publicly available at the time of publication at https://github.com/koehnl/CommunityVuln_PlosOne for R code and this repository submission for aggregated data (aggregated landings by ports can also be found here https://reports.psmfc.org/pacfin/f?p=501:1000:::::: and go to “All species by port group”. Values for community reliance (from NOAA California Current Integrated Ecosystem Assessment) and tables on pacFIN ports and species used, formulated from https://pacfin.psmfc.org/pacfin_pub/codes.php, are provided in this repository submission. 3. Recommended citation for this dataset: Koehn, Laura E. et al. (2022), Data for: Social-ecological vulnerability of fishing communities to climate change: a U.S. West. Dryad, Dataset AND Koehn, Laura E. et al. (2022), Social-ecological vulnerability of fishing communities to climate change: a U.S. West. Plos One (please also cite references listed in "data derived from another source") 4. Code availability: related code is available on GitHub at https://github.com/koehnl/CommunityVuln_PlosOne DATA & FILE OVERVIEW 1. Description of dataset Data collated from other sources and/or produced through models to calculate an index of community vulnerability to climate change for 200+ fishing communities on the U.S. West Coast. Data are used to generate metrics of: ecological exposure, ecological sensitivity, ecological risk, community exposure, community sensitivity, community risk, community adaptive capacity, which are combined mathematically to produce an index of community vulnerability. ecological exposure: Degree to which a species is subject to (exposed to) changing environmental conditions due to climate change. Calculated as the expected change in environmental conditions a species will face in their range (overlap between the historic range and future range of climate values a species experiences in its spatial range where greater overlap = lower exposure). Species range data comes from Aquamaps (Kaschner et al. 2019). Average exposure for each species for each climate model across four climate factors: temperature, pH, chlorophyll, and oxygen ecological sensitivity: Conditions determining how likely a species will tolerate future changes in environmental conditions. Here, use a proxy calculated as present-day breadth of environmental conditions a species experiences in its range (where greater breadth implies greater tolerance). Average sensitivity for each species for each climate model across four climate factors: temperature, pH, chlorophyll, and oxygen ecological risk: Degree to which a species is susceptible to climate change. Combination of ecological exposure and ecological sensitivity to climate change (calculated as the Euclidean distance between sensitivity and exposure). Calculated for each species and climate model community exposure: Degree to which a community is subject to impacts of climate change based on the species targeted by the community. Ecological risk for each species in the community’s top 90% of landings by metric tonnes, weighted by the percent revenue of each species for that community community sensitivity: Conditions determining how likely a community will be impacted by climate change. Calculated as community economic reliance on commercial fishing (from IEA [Harvey et al. 2020] and see Jepson and Colburn, 2013). community risk: Likelihood a community will be adversely affected by a climate change. Combination of community exposure and community sensitivity calculated for each community community adaptive capacity: Ability to adapt, absorb, and recover from climate change. Demographic and social indicators are known to directly impact a community’s adaptability (Flanagan et al. 2011). Made up of a combination of social indicators from CDC 2020) for each community where highest adaptive capacity = 0 and lowest = 1, so that for all axes - adaptive capacity, exposure, sensitivity - higher values equate to higher vulnerability. community vulnerability: Cumulative measure of potential climate change effects based on community exposure, community sensitivity, and adaptive capacity calculated for each community; calculated as the Euclidean distance between community sensitivity, exposure, and adaptive capacity (where higher values of each are associated with higher vulnerability) 2. File List: File 1 Name: FINALspecieslist_used.csv File 1 Description: List of species (including scientific name and Integrated Taxonomic Information System #) in the top 90% of fisheries landings for communities on the U.S. West Coast (2009-2018) and used in analysis to calculate ecological exposure and sensitivity File 2 Name: species_code_noduplicates.csv File 2 Description: For each species used in analysis, pacFIN four letter/number code associated with that species (see https:// pacfin.psmfc.org/pacfin_pub/data_rpts_pub/code_lists/sp_tree.txt) File 3 Name: speciesrisk_UPDATED_12.13FORUPLOAD.csv File 3 Description: Ecological risk calculated for each fisheries species in the analysis File 4 Name: 2017_reliance_CalCurIEA_forpaper.xlsx File 4 Description: Metrics calculated for fishing communities for the California Current Integrated Ecosystem Assessment including commercial reliance (see examples in reports here: https://www.integratedecosystemassessment.noaa.gov/regions/california-current/california-current- reports#ecosystem-status-report or Harvey et al. 2020) File 5 Name: pacFINcommunities.csv File 5 Description: Spreadsheet of community names File 6 Name: PacFINports.csv File 6: Description: Spreadsheet of codes used for fishing ports by pacFIN - see: https://pacfin.psmfc.org/pacfin_pub/data_rpts_pub/code_lists/ pc_tree.txt File 7 Name: AggregatedPercentmtonlandings_4.2020_noMSC2_revenue-PacFIN.csv File 7 Description: Summarized landings data for various fishing ports across 2009-2018 to find the top 90% of species landed by metric tonnes by port. Landings data is collated by pacFIN and aggregated landings by ports can also be found here https://reports.psmfc.org/pacfin/f? p=501:1000:::::: and go to “All species by port group”. Data for certain ports is confidential if landed by <3 vessels. File 8 Name: 2030-2060_climatefactoroverlap.zip File 8 Description: A series of files used to calculate ecological exposure. Ecological exposure is defined as the overlap between the historic and future distributions of climate conditions a species experiences. Therefore, files provided give, for various oceanographic factors (temperature, pH, oxygen, chlorophyll) and for each species, the percent of the distribution of future values that falls in the 5th-95th (or 25th-75th) percentile range of the historical distribution of values of climate experienced for each species. File 9 Name: 2030-2060_climatefactorspercentiles.zip File 9 Description: A series of files used to calculate ecological sensitivity for each species for each climate factor. Ecological sensitivity is the climatic breadth experienced by a species and is the percentile range (5th-95th) experienced by each species in its historical spatial distribution (calculated as the 95th percentile value minus the 5th percentile value and inversed). 3. Additional related data collected that was not included in the current data package: see "Data derived from another source" METHODOLOGICAL INFORMATION We focus our analysis on U.S. fishing communities in the California Current Ecosystem and define a fishing community as a geographic location that is a specified census designated place, with at least some level of commercial fishing activity associated with commercial fisheries along the continental U.S. West Coast (as defined by the National Oceanic and Atmospheric Administration and California Current Integrated Ecosystem Assessment, IEA (Harvey et al. 2020)). We adapted the vulnerability assessment framework outlined by Marshall et al. (2013) and Thiault et al. (2021) to investigate the vulnerability of fishing communities to climate change. We first determine ecological risk, a combination of the ecological exposure and ecological sensitivity of target species to changing climate conditions specifically for changes in pH, temperature, chlorophyll, and oxygen. To determine target species for each community, we looked at landings data from pacFIN and we calculated the total landed weight over the 10-year span, found the percent of total landed weight each species comprised, and only considered the species that contributed to the top 90% of landed weight. Fish ticket data for 2009-2016 and 2017-2018 were downloaded in April 2017 and June 2019, respectively (the final quarter of 2016 was not complete at the time of the initial data query and not included in analysis, specifically 9/9/2016 through 12/31/2016). We assumed landings data for each individual port are the same as the PacFIN port group that the individual port is a member of (see https://pacfin.psmfc.org/pacfin_pub/codes.php for all port groupings). We excluded ports where a high percent of landings data were confidential. Species specific distributions for all species in the top 90% of landings were collated from Aquamaps (Kaschner et al. 2019) and we rasterized species range data using methods presented in O’Hara et al (2017). We used a California Current configuration of the Regional Ocean Modeling System (ROMS) coupled with a biogeochemical model (NEMUCSC) to simulate historical and future climate conditions for temperature, pH, chlorophyll, or oxygen within species geographic ranges (Fiechter et al. 2021, Pozo Buil et al. 2021) to calculate ecological exposure and sensitivity for three climate models (Geophysical Fluid Dynamics Laboratory Earth System’s Model GFDL-ESM2M climate model(Dunne et al. 2012, 2013); Met Office Hadley Centre Earth Systems Model HadGEM2-ES Climate model (Collins et al. 2011); Institut Pierre Simon Laplace Model IPSL-CM5A-MR climate model (Dufresne et al. 2013)). Ecological exposure was defined as the overlap between the historic and future distributions of climate conditions a species experiences. Ecological sensitivity was defined as the inverse of the current breadth of climate conditions experienced by a species within its current spatial range. We calculate ecological risk for approximately 50 years into the future so that community vulnerability reflects medium-term future conditions. Ecological risk directly informs community exposure, such that ecological risk of each target species is weighted by economic importance (percent of total revenue for each species from pacFIN data) of those species for each community. Community sensitivity is determined by the economic reliance of communities on the fishing industry (taken from the California Current IEA (see Harvey et al. 2020), which when combined with community exposure, gives community risk to climate change. We then consider the adaptive capacity of fishing communities, or the ability to adapt, absorb, and recover from climate change impacts, which is influenced by demographic and social factors (Flanagan et al. 2011) that is taken from available data from the CDC Social Vulnerability index (https://www.atsdr.cdc.gov/placeandhealth/svi/data_documentation_download.html). Combining community risk with community adaptive capacity generates the overall extent that communities are vulnerable to climate change, or “community vulnerability”. Instrument- or software-specific information needed to interpret the data: R / Rstudio DATA-SPECIFIC INFORMATION FOR: FINALspecieslist_used.csv 1. Number of variables: 7 2. Number of cases/rows: 69 3. Variable List: Common name: name of fisheries species Scientific name: Latin name of the species ITIS Taxonomic Serial #: Integrated Taxonomic Information System taxonomic serial number for each species Alternative Scientific name: Alternate Latin scientific name used habitat: pelagic or benthic to determine which climate variables to use when calculating ecological exposure and sensitivity in_landings: confirmation in landings data comment: additional information 4. Missing data codes: None 5. Specialized formats or other abbreviations used: ITIS: Integrated Taxonomic Information System DATA-SPECIFIC INFORMATION FOR: species_code_noduplicates.csv 1. Number of variables: 6 2. Number of cases/rows: 72 3. Variable List: spid: Species ID used by pacFIN for landings (see https://pacfin.psmfc.org/pacfin_pub/data_rpts_pub/code_lists/sp_tree.txt) Species: species common name Special: special notes to identify specific species for more general groups (for example: "Surfperch species" are Black, Striped surfperch) spid_use: Species ID used by pacFIN but modified for certain species where a general broad group is defined but more specific info is known on species. species_risk_use: name of species to be used in code when matching risk calculation to each species type: species type between invert and fish 4. Missing data codes: NA: not applicable 5. Specialized formats or other abbreviations used: spid: species ID DATA-SPECIFIC INFORMATION FOR: speciesrisk_UPDATED_12.13FORUPLOAD.csv 1. Number of variables: 16 2. Number of cases/rows: 72 3. Variable List: spid: Species ID used by pacFIN for landings (see https://pacfin.psmfc.org/pacfin_pub/data_rpts_pub/code_lists/sp_tree.txt) Species: species common name Special: special notes to identify specific species for more general groups (for example: "Surfperch species" are Black, Striped surfperch) spid_use: Species ID used by pacFIN but modified for certain species where a general broad group is defined but more specific info is known on species. species_risk_use: name of species to be used in code when matching risk calculation to each species type: species type between invert and fish gfdlrisk: species ecological risk calculated from Geophysical Fluid Dynamics Laboratory Earth System’s Model GFDL-ESM2M climate model(Dunne et al. 2012, 2013) hadrisk: species ecological risk calculated from Met Office Hadley Centre Earth Systems Model HadGEM2-ES Climate model (Collins et al. 2011) ipslrisk: species ecological risk calculated from the Institut Pierre Simon Laplace Model IPSL-CM5A-MR climate model (Dufresne et al. 2013) gfdlriskeuc: ecological risk calculated using GFDL climate model and euclidean distance between exposure and sensitivity hadriskeuc: ecological risk calculated using HAD climate model and euclidean distance between exposure and sensitivity ipslriskeuc: ecological risk calculated using IPSL climate model and euclidean distance between exposure and sensitivity avgrisk: ecological risk averaged across the three climate models scaledrisk: (not used in final analysis) average ecological risk (across climate models) scaled to be between 0 and 1 scaledrisk2:(not used in final analysis) rescaled ecological sensitivity and exposure separately before calculating risk scaledavg: (not used in final analysis) risk per climate model is scaled between 0 and 1 before averaged to find average risk 4. Missing data codes: NA: not applicable - species not included in final analysis so risk not calculated 5. Specialized formats or other abbreviations used: HAD: Met Office Hadley Centre Earth Systems Model HadGEM2-ES Climate model (Collins et al. 2011) GFDL: Geophysical Fluid Dynamics Laboratory Earth System’s Model GFDL-ESM2M climate model(Dunne et al. 2012, 2013) IPSL: Institut Pierre Simon Laplace Model IPSL-CM5A-MR climate model (Dufresne et al. 2013) DATA-SPECIFIC INFORMATION FOR: 2017_reliance_CalCurIEA_forpaper.xlsx 1. Number of variables: 31 2. Number of cases/rows: 1161 3. Variable List: See Harvey et al. 2020 (California Current IEA) and Jepson and Colburn (2013) references for more explanation. Year: year that data represents (2017) GEO_ID2: unique geographic identifier REGION: Region of the United States STATEABBR: WA for Washington, OR for Oregon, CA for California GEO_NAME: Geographic name of the community including state MAPNAME: Geographic community name without state PRIMARY_LATITUDE: Latitude of community PRIMARY_LONGITUDE: Longitude of the community PerDis: Personal disruption factor score PopCom: Population composition factor score Pvrty: Poverty factor score LabFrc: Labor force score HsChr: Housing characteristics factor score HsDis: Housing disruption factor score RetMig: Retiree migration factor score UrbSpl: Urban sprawl metric LabFrc_Rev: Labor force score reversed HsChr_Rev: Housing characteristics factor score reversed ComEng: Commercial fisheries engagement score ComRel: Commercial fisheries reliance score PerDis_ct: Personal disruption categorical ranking PopCom_ct: Population composition categorical ranking Pvrty_ct: Poverty categorical ranking LabFrc_ct: Labor force categorical ranking HsChr_ct: Housing characteristics categorical ranking HsDis_ct: Housing disruption categorical ranking RetMig_ct: Retiree migration categorical ranking UrbSpl_ct: Urban sprawl categorical ranking ComEng_ct: Commercial fisheries engagement ranking ComRel_ct: Commercial fisheries reliance ranking 4. Missing data codes: NA: not applicable, data not provided by source (California Current IEA) 5. Specialized formats or other abbreviations used: See variable names DATA-SPECIFIC INFORMATION FOR: pacFINcommunities.csv 1. Number of variables: 3 2. Number of cases/rows: 573 3. Variable List: Name: Geographic community name ALT_NAME: alternative name - same as "name" column if no alternative name YES_ALT: code to identify communities that use an alternative name (0 = yes, alternative name used) 4. Missing data codes: None 5. Specialized formats or other abbreviations used: WA = Washington OR = Oregon CA = California DATA-SPECIFIC INFORMATION FOR: PacFINports.csv 1. Number of variables: 6 2. Number of cases/rows: 572 3. Variable List: PCID: PacFIN port identifier code (see https://pacfin.psmfc.org/pacfin_pub/data_rpts_pub/code_lists/pc_tree.txt) PortName: Geographic name of the port based on the port identifier (see https://pacfin.psmfc.org/pacfin_pub/data_rpts_pub/code_lists/ pc_tree.txt) Description: capitalized name where certain names include further description State: U.S. West Coast state (WA = Washington, OR = Oregon, CA = California) City: Geographic city name alone (no state) Community: Combined city name, ",", state abbreviation 4. Missing data codes: NA: not found 5. Specialized formats or other abbreviations used: WA = Washington OR = Oregon CA = California DATA-SPECIFIC INFORMATION FOR: AggregatedPercentmtonlandings_4.2020_noMSC2_revenue-PacFIN.csv 1. Number of variables: 13 2. Number of cases/rows: 433 3. Variable List: PACFIN_PORT: Port code used by PacFIN and see data file PacFINports.csv PORT: Geographic name of port where species were landed STATE: U.S. West Coast state (WA = Washington, OR = Oregon, CA = California) dollars: total revenue for each species (US$) spid: species ID from pacFIN and see data file species_code_noduplicates.csv Species: species common name Special: special notes to identify specific species for more general groups (for example: "Surfperch species" are Black, Striped surfperch) mtons: metric tonnes (mt) landed of that species percent.mtons: metric tonnes landed for that species out of all total metric tonnes landed for that port num.vessels: number of vessels that landed that species cum.percent.mtons: cumulative running total of percent metric tonnes for that port spid_use: Species ID used by pacFIN but modified for certain species where a general broad group is defined but more specific info is known on species. testing: column used to test species ID in code (not used in final analysis) 4. Missing data codes: NA: not applicable CONFIDENTIAL: Data is confidential as defined by pacFIN. (species landed by <3 boats). See data access policy but is not needed to replicate our analysis as we removed ports with confidential data. https://pacfin.psmfc.org/pacfin_pub/confagree.php unknown: could not find using pacFIN sources 5. Specialized formats or other abbreviations used: spid: species ID nom: nominal - interpreted as landings of the species identified following "nom." 6. Additional info: see https://reports.psmfc.org/pacfin/f?p=501:1000:::::: for reported data that was used ZIP FOLDERS For both .zip folders: Variable information: Variable Description Units -------- ----------- ----- temp_surface Sea surface temperature ˚C temp_bottom Sea bottom temperature ˚C chl_surface Log of surface chlorophyll concentration mg m-3 chl_50m Log of chlorophyll concentration integrated over upper 50 m mg m-2 oxygen_bottom Oxygen concentration at ocean floor mmol m-3 depth_oxygen_2.0 Depth at which oxygen concentration = 2.0 ml L-1 m depth_oxygen_3.5 Depth at which oxygen concentration = 3.5 ml L-1 m ph_surface pH at ocean surface none ph_bottom pH at ocean bottom none ------------------------ ANALYSIS NOTES ------------------------ 1. This historical reference period is 1980-2010 and the future period is 2030-2060 2. The full model domain is 30-48˚N and from the coast to 134˚W. To avoid boundary artifacts, data were only extracted from 31-47˚N and from the coast to 133˚W. 3. Hippoglossus stenolepis was removed from the analysis as its range was only a small area north of 47˚N 4. The depth of oxygen levels (e.g., depth_oxygen_2.0) is undefined if the whole water column has oxygen concentration above that threshold. In some locations the depth is undefined in the historical period but not in the future period, or vice versa. Such cases were excluded from the analysis to enable clear comparison. DATA-SPECIFIC INFORMATION FOR: 2030-2060_climatefactoroverlap.zip 1. File naming structure: Climate factor considered_climate model_overlap.csv Example: "ph_surface_had_overlap.csv" Climate factor considered was surface ph Climate model was HAD ( Met Office Hadley Centre Earth Systems Model HadGEM2-ES Climate model (Collins et al. 2011) 2. Number of cases/rows per file: 78 3. Variable List: Species_Name: Latin scientific name of the species overlap_5_95: percentage of the future distribution of climate experienced by a species that falls between historical percentiles (5th-95th) overlap_25_75: percentage of the future distribution of climate experienced by a species that falls between historical percentiles (25th-75th) 4. Missing data codes: None 5. Specialized formats or other abbreviations used: HAD: Climate model from Met Office Hadley Centre Earth Systems Model HadGEM2-ES Climate model (Collins et al. 2011)) IPSL: Climate model from Institut Pierre Simon Laplace Model IPSL-CM5A-MR (Dufresne et al. 2013) GHDL: Climate model from Geophysical Fluid Dynamics Laboratory Earth System’s Model GFDL-ESM2M climate model (Dunne et al. 2012, 2013) DATA-SPECIFIC INFORMATION FOR: 2030-2060_climatefactorspercentiles.zip 1. File naming structure: Climate factor considered_climate model_percentile.csv Example: "ph_surface_had_percentiles.csv" Climate factor considered was surface ph Climate model was HAD ( Met Office Hadley Centre Earth Systems Model HadGEM2-ES Climate model (Collins et al. 2011)) 2. Number of cases/rows per file: 78 3. Variable List for each file: Species_Name: Latin scientific name of the species Values of historical and future percentiles (5th, 25th, 50th, 75th, 95th) of climate experienced by a species as well as the mean for each model/variable/species where his = historic and fut = future: hist_5 hist_25 hist_50 hist_75 hist_95 hist_mean fut_5 fut_25 fut_50 fut_75 fut_95 fut_mean 4. Missing data codes: None 5. Specialized formats or other abbreviations used: HAD: Climate model from Met Office Hadley Centre Earth Systems Model HadGEM2-ES Climate model (Collins et al. 2011)) IPSL: Climate model from Institut Pierre Simon Laplace Model IPSL-CM5A-MR (Dufresne et al. 2013) GHDL: Climate model from Geophysical Fluid Dynamics Laboratory Earth System’s Model GFDL-ESM2M climate model (Dunne et al. 2012, 2013) References: Centers for Disease Control [CDC] and Prevention/ Agency for Toxic Substances and Disease Registry/ Geospatial Research, Analysis and SP. CDC/ATSDR Social Vulnerability Index 2018 Database Washington, Oregon, and California. 2020 [cited 29 Apr 2020]. Available: https://www.atsdr.cdc.gov/placeandhealth/svi/documentation/pdf/SVI2018Documentation_01192022_1.pdf Collins WJ, Bellouin N, Doutriaux-Boucher M, Gedney N, Halloran P, Hinton T, et al. Development and evaluation of an Earth-System model - HadGEM2. Geosci Model Dev. 2011;4: 1051–1075. doi:10.5194/gmd-4-1051-2011 Dufresne JL, Foujols MA, Denvil S, Caubel A, Marti O, Aumont O, et al. Climate change projections using the IPSL-CM5 Earth System Model: From CMIP3 to CMIP5. Clim Dyn. 2013;40: 2123–2165. doi:10.1007/s00382-012-1636-1 Dunne JP, John JG, Adcroft AJ, Griffies SM, Hallberg RW, Shevliakova E, et al. GFDL’s ESM2 global coupled climate-carbon earth system models. Part I: Physical formulation and baseline simulation characteristics. J Clim. 2012;25: 6646–6665. doi:10.1175/JCLI-D-11-00560.1 Dunne JP, John JG, Shevliakova S, Stouffer RJ, Krasting JP, Malyshev SL, et al. GFDL’s ESM2 global coupled climate-carbon earth system models. Part II: Carbon system formulation and baseline simulation characteristics. J Clim. 2013;26: 2247–2267. doi:10.1175/JCLI-D-12-00150.1 Fiechter J, Pozo Buil M, Jacox MG, Alexander MA, Rose KA. Projected Shifts in 21st Century Sardine Distribution and Catch in the California Current. 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