Dataset for Gelatinous zooplankton-mediated carbon flows in the global oceans: A data-driven modeling study

• Main Authors: Luo, Jessica Y (10503573), Condon, Robert H (10503576), Stock, Charles A (10503579), Duarte, Carlos M (8155743), Lucas, Cathy H (10503582), Pitt, Kylie A (10503585), Cowen, Robert K (10503588)
• Format: Dataset
• Language: unknown
• Published: 2020
• Subjects: Biochemistry; Microbiology; Genetics; Molecular Biology; Ecology; Marine Biology; Inorganic Chemistry; Biological Sciences not elsewhere classified; gelatinous zooplankton; carbon biomass; global; gridded; Antarctic; Southern Ocean; Antarctic Peninsula; Atkinson; Duarte; Antarc*
• Online Access: https://doi.org/10.5281/zenodo.3891704

Gridded dataset of gelatinous zooplankton (GZ) biomass (mg C m -3 ) and numeric density (individuals m -3 ), time-averaged, in a 1-degree grid. Data are separated by phyla: Cnidaria, Ctenophora, and Chordata (pelagic tunicates). Original data compiled as part of the Jellyfish Database Initiative Project (JeDI; Condon et al. 2015, doi:10.1575/1912/7191) and converted to carbon biomass units for Lucas et al. 2014. Cnidarian additions to this dataset include records from the northern California Current (Brodeur et al., 2014) and Gulf of Mexico (Robinson et al., 2015). Chordata additions include salps from the Bermuda Atlantic Time Series (BATS; Stone & Steinberg, 2014), Western Antarctic Peninsula (WAP; Steinberg et al., 2015), and Southern Ocean, from KRILLBASE (Atkinson et al., 2017). Note that we excluded the KRILLBASE records from the WAP region that to prevent double-counting. See Methods in Luo et al. (2020) for details on biometric conversions to carbon biomass. Data were averaged by time (season, then year), and then within each 1-degree grid cell. Code for the model using this dataset is available at: https://github.com/jessluo/gz_biogeochem_pub Luo, Jessica Y. , Condon, R. H., Stock, C. A., Duarte, C. M., Lucas, C. H., Pitt, K. A., & Cowen, R. K. (2020). Gelatinous zooplankton‐mediated carbon flows in the global oceans: A data‐driven modeling study. Global Biogeochemical Cycles , 34, e2020GB006704. https://doi.org/10.1029/2020GB006704