Zooplankton validation in a physical-biogeochemical model with potentially valuable applications to fisheries management in the Gulf of Mexico

Taylor Shropshire, Florida State University, Tallahassee, United States, Michael R Stukel, Florida State University, Tallahassee, FL, United States, Eric Chassignet, Center for Ocean-Atmospheric Prediction Studies, Tallahassee, FL, United States, Steven Morey, Florida A&M University, School of the Environment, Tallahassee, FL, United States, Victoria Coles, University of Maryland Center for Environmental Science, Horn Point Laboratory, Cambridge, MD, United States, Sang-Ki Lee, University of Miami, Cooperative Institute for Marine and Atmospheric Studies, Miami, FL, United States, Mandy Karnauskas, National Oceanic and Atmospheric Administration, Southeast Fisheries Science Center, Miami, FL, United States, Glenn Zapfe, National Oceanic and Atmospheric Administration, Southeast Fisheries Science Center, Pascagoula, MS, United States and Michael R Landry, Scripps Inst Oceanography, La Jolla, CA, United States
Zooplankton play an important role in global biogeochemistry and their secondary production supports socio-economically important fish species and/or their planktonic larvae. Despite their importance, synoptic estimates of zooplankton abundance are absent in most of the world ocean as they are difficult to study in the field and their abundances cannot currently be estimated using remote sensing techniques. Hence, coupled physical-biogeochemical models (PBMs) provide an important oceanographic research tool for studying zooplankton on regional and global scales. However, evaluating the accuracy of zooplankton biomass estimates from PBMs has been a major challenge as a result of sparse ship-based observations. Consequently, zooplankton dynamics have been under studied and under validated in PBMs. In this study, we configure a PBM for the Gulf of Mexico (GoM) and validate the model against a combination of in situ zooplankton biomass and rate measurements. Once validated, the model was used to investigate prey limitation for pelagic larval fish. To evaluate larval fish susceptibility to starvation we developed a methodology to estimate prey limitation based on simulated mesozooplankton fields and larval metabolic requirements. From our analysis we find that persistent prey limiting conditions exist in significant regions of the oligotrophic GoM. Coupling to an individual-based Lagrangian fish model enables further investigation of starvation-driven mortality throughout the pelagic larval duration. Ocean modeling tools such as the ones used in this study have the potential to provide valuable information for fisheries management. Further integrative modeling studies that examine zooplankton are needed to inform decision making to manage the impacts of future climate on Gulf of Mexico fisheries.