This DATSETNAMEreadme.txt file was generated on 2021-08-20 by KRN FLORKO GENERAL INFORMATION 1. Title of Dataset: Predicting how climate change threatens the prey base of Arctic marine predators 2. Author Information Investigator Contact Information Name: Katie RN Florko Institution: Institute for the Oceans and Fisheries, University of British Columbia Email: katieflorko@gmail.com 3. Date of data collection (single date, range, approximate date): NA 4. Geographic location of data collection: NA, raw data from FishBase and AquaMaps 5. Information about funding sources that supported the collection of the data: NSERC (Canada Graduate Scholarship, Discovery program), Canada Research Chairs, Northern Scientific Training Program, and Ocean Leaders Fellowship. This research was enabled in part by advanced research computing support provided by Compute Canada (www.computecanada.ca). SHARING/ACCESS INFORMATION 1. Licenses/restrictions placed on the data: Copyright:© 2021 John Wiley & Sons Ltd. 2. Links to publications that cite or use the data: Article DOI: 10.1111/ele.13866 3. Links to other publicly accessible locations of the data: NA 4. Links/relationships to ancillary data sets: NA 5. Was data derived from another source? yes/no A. If yes, list source(s): Yes, FishBase and AquaMaps 6. Recommended citation for this dataset: Florko, K.R.N., Tai, T.C., Cheung, W.W.L., Sumaila, U.R., Ferguson, S.H., Yurkowski, D.J., Auger-Méthé, M. 2021. Predicting how climate change threatens the prey base of Arctic marine predators. Ecology Letters, doi:10.1111/ele.13866 DATA & FILE OVERVIEW 1. File List: All data file names correspond with the figure they were used to create. fig2A.csv: x: latitude grid y: longitude grid years: consolidated years summarized bottomtemp_celsius: temperature in celsius bottomO2_milmolperL: bottom oxygen in mL/L bottompH_logH: bottom pH in log[hydrogen] iceconverage_percent: ice coverage in percentage primaryproduction_pgCL: surface primary productivity in pgCL fig2B_O2_btm.txt Year: Year modelled RegArea: Large Marine Ecosystem Area, Hudson Bay System == 63 Model: the CMIP5 model RCP: Relative concentration pathway, where 26 = 2.6 and 85 = 8.5 Value: bottom oxygen in mL/L fig2B_htotal_btm.txt Year: Year modelled RegArea: Large Marine Ecosystem Area, Hudson Bay System == 63 Model: the CMIP5 model RCP: Relative concentration pathway, where 26 = 2.6 and 85 = 8.5 Value: bottom hydrogen fig3_bodysize.csv Year: Year modelled Spp: Species, corresponds with species codes defined in README_species_codes.csv ChMnw: change in relative body size Model: the CMIP5 model RCP: Relative concentration pathway, where 26 = 2.6 and 85 = 8.5 fig3_biomass.csv Year: Year modelled Spp: Species, corresponds with species codes defined in README_species_codes.csv ChBio: biomass in tonnes Model: the CMIP5 model RCP: Relative concentration pathway, where 26 = 2.6 and 85 = 8.5 fig3_abundance.csv Year: Year modelled Spp: Species, corresponds with species codes defined in README_species_codes.csv ChMnw: change in relative abundance Model: the CMIP5 model RCP: Relative concentration pathway, where 26 = 2.6 and 85 = 8.5 fig4.csv Year: Year modelled Spp: Species, corresponds with species codes defined in README_species_codes.csv ChBio: biomass in tonnes Model: the CMIP5 model RCP: Relative concentration pathway, where 26 = 2.6 and 85 = 8.5 fig2B_IceExt.txt Year: Year modelled RegArea: Large Marine Ecosystem Area, Hudson Bay System == 63 Model: the CMIP5 model RCP: Relative concentration pathway, where 26 = 2.6 and 85 = 8.5 Value: sea ice coverage in percentage fig2B_botTemp.txt Year: Year modelled RegArea: Large Marine Ecosystem Area, Hudson Bay System == 63 Model: the CMIP5 model RCP: Relative concentration pathway, where 26 = 2.6 and 85 = 8.5 Value: bottom temperature in celsius fig2B_pH_btm.txt Year: Year modelled RegArea: Large Marine Ecosystem Area, Hudson Bay System == 63 Model: the CMIP5 model RCP: Relative concentration pathway, where 26 = 2.6 and 85 = 8.5 Value: bottom pH in log[hydrogen] fig5.csv x: latitude grid y: longitude grid years: consolidated years summarized capelin: relative abundance of capelin arccod: relative abundance of arctic cod nsandla: relative abundance of northern sand lance psandla: relative abundance of pacific sand lance README_species_codes.csv Species: scientific name of species Common name: common name of species Taxon code: FishBase code for species 2. Relationship between files, if important: NA 3. Additional related data collected that was not included in the current data package: NA 4. Are there multiple versions of the dataset? no METHODOLOGICAL INFORMATION 1. Description of methods used for collection/generation of data: Florko, K.R.N., Tai, T.C., Cheung, W.W.L., Sumaila, U.R., Ferguson, S.H., Yurkowski, D.J., Auger-Méthé, M. 2021. Predicting how climate change threatens the prey base of Arctic marine predators. Ecology Letters, doi:10.1111/ele.13866 SUPPLEMENTAL INFORMATION. 2. Methods for processing the data: From Florko et al. 2021 Ecology Letters Methods: We used a dynamic bioclimate envelope model (DBEM) to predict the abundance, expressed as number of fish, biomass in tonnes and distribution of the Arctic marine mammal prey species in the Hudson Bay System from 1950 to 2100 under two climate change scenarios (see Supplementary Methods). This modelling approach allows for species‐specific physical and biological conditions that are suitable habitat, whereas other methods (e.g. BiOeconomic mArine Trophic Size‐spectrum [BOATS], EcoOcean, Apex Predators ECOSystem Model [APECOSM]) are based on functional groups (see for model comparisons: Bryndum‐Buchholz et al., 2020; Bryndum‐Buchholz et al., 2019). DBEM baseline data are realised through empirical species occurrence data and the associated biogeographical characteristics. Since no empirical fish distribution or abundance data are available for Hudson Bay, the DBEM uses empirical data from other regions to determine baseline equilibrium; as such, our model is best able to capture relative differences in biomass, abundance, and body size between species and through space (Fernandes et al., 2013). Abundance and distribution across time can then be predicted by evaluating how climate change scenarios alter biogeographical characteristics (Pauly & Cheung, 2018). Our DBEM uses Earth system model (oceanographic) data from CMIP5 (Intergovernmental Panel on Climate Change's Coupled Model Intercomparison Project; we used three earth system models: GFDL, IPSL and MPI) as primary biophysical drivers to past, present and future environmental conditions (Bryndum‐Buchholz et al., 2020; Cheung et al., 2011). The DBEM mechanistically models how spatiotemporal changes in ocean conditions (Figure 2) affect the physiology, growth, population dynamics, habitat suitability and movement of a species, and are used to model changes in species distribution through time (Jones et al., 2012). Our DBEM simulates the yearly relative abundance of species on a 0.5° longitude by 0.5° latitude grid. The model uses a species‐specific algorithm derived von Bertalanffy growth function (VBGF) model for physiology and growth, considering oxygen supply and demand, temperature, and ocean acidity, a logistic growth function to model population growth, and a fuzzy logic model to determine movement based on habitat suitability (Cheung et al., 2011; Tai et al., 2018) (see Supplementary Methods). All species were modelled separately. 3. Instrument- or software-specific information needed to interpret the data: All models were ran in Fortran and figures created in R. 4. Standards and calibration information, if appropriate: NA 5. Environmental/experimental conditions: NA 6. Describe any quality-assurance procedures performed on the data: NA 7. People involved with sample collection, processing, analysis and/or submission: Florko, K.R.N., Tai, T.C., Cheung, W.W.L., Sumaila, U.R., Ferguson, S.H., Yurkowski, D.J., Auger-Méthé, M.