Full Title: Building Resilience for Oysters, Blue Crabs, and Spotted Seatrout to Environmental Trends and Variability in the Gulf of Mexico
This project explores how oyster, blue crab, and spotted seatrout populations respond to human and environmental changes with the goal of improving the management of these economically and culturally important species.
The Team: John C. Lehrter (lead investigator, University of South Alabama, Dauphin Island Sea Lab firstname.lastname@example.org), Ronald Baker (University of South Alabama, Dauphin Island Sea Lab), Just Cebrian (Mississippi State University, Northern Gulf Institute), Brian Dzwonkowski (University of South Alabama, Dauphin Island Sea Lab), Latif Kalin (Auburn University), Lisa Lowe (North Carolina State University), Dan Petrolia (Mississippi State University), Sean Powers (University of South Alabama, Dauphin Island Sea Lab), Di Tian (Auburn University), and Seong Yun (Mississippi State University)
Technical Monitors: Kelly Samek (email@example.com) and Eric Weissberger (firstname.lastname@example.org)
Science Program Liaison: Becky Allee (email@example.com)
Federal Program Officer: Frank Parker (firstname.lastname@example.org)
Award Amount: $2,887,250
Award Period: This project began in September 2019 and will end in August 2024.
Why it matters: The abundance of oysters, blue crabs, and spotted seatrout is rapidly declining in the Gulf of Mexico. These species have provided valuable food, raw material, recreation, and cultural resources to humans since the Gulf was settled. Today, the ecosystem services provided by these species are threatened, or near collapse in Gulf estuaries. This is partially due to human activities and environmental trends such as fisheries harvest and changes in water and habitat quality. Many of the underlying mechanisms that relate long-term trends and short-term variability in the environment to changing populations of oyster, blue crab and spotted seatrout are unquantified or unknown.
What the team is doing: This project will identify temperature, salinity (freshwater), oxygen (hypoxia), and pH (acidity) thresholds for oyster, blue crab, and spotted seatrout populations based on current and future habitat conditions, including climate variability and human-induced stressors. Thresholds will be quantified in mesocosm experiments, from field observations, and with numerical models. By linking multiple data sets of species recruitment, growth, and survival rates with natural and human induced environmental conditions across time, the project team will identify the large scale drivers and stressors of these populations in Mobile Bay, Alabama. Next numerical models will be created based off these data that can forecast population, ecosystem services, and socio-economic changes based on scenarios of future conditions. Public preferences about changes to the ecosystem will be gauged through a survey and incorporated into the models to calculate the costs and benefits of potential management actions.
Expected Outcome: This project will provide Mobile Bay decision-makers a process for evaluating various scenarios, management actions, and outcomes based on single and multiple thresholds for oyster, blue crab, and spotted seatrout populations. It will help identify what individual or combined stressors affect these economically and culturally important species plus evaluate how management actions may improve the resilience of these populations to environmental change.
Li, Y., Tian, D., & Medina, H. (2021). Multimodel Subseasonal Precipitation Forecasts over the Contiguous United States: Skill Assessment and Statistical Postprocessing. Journal of Hydrometeorology, 22(10), 2581-2600. https://doi.org/10.1175/JHM-D-21-0029.1
Wang, F., Tian, D., Lowe, L., Kalin, L., & Lehrter, J. (2021). Deep Learning for Daily Precipitation and Temperature Downscaling. Water Resources Research, 57(4). https://doi.org/10.1029/2020WR029308
Haas, H., Dosdogru, F., Kalin, L., & Yen, H. (2021). Soft Data in Hydrologic Modeling: Prediction of Ecologically Relevant Flows with Alternate Land Use/Land Cover Data. Water, 13(21), 2947.
Haas, H., Kalin, L., & Srivastava, P. (2022). Improved forest dynamics leads to better hydrological predictions in watershed modeling. Science of The Total Environment, 153180.
Wang, F., & Tian, D. (2022). On deep learning-based bias correction and downscaling of multiple climate models simulations. Climate Dynamics, 1-18.
Takhellambam, B. S., Srivastava, P., Lamba, J., McGehee, R. P., Kumar, H., & Tian, D. (2022). Temporal disaggregation of hourly precipitation under changing climate over the Southeast United States. Scientific Data, 9(1), 211.
Domeisen, D. I. V., White, C. J., Afargan-Gerstman, H., Muñoz, Á. G., Janiga, M. A., Vitart, F., Wulf, C. O., Antoine, S., Ardilouze, C., Batté, L., Bloomfield, H. C., Brayshaw, D. J., Camargo, S. J., Charlton-Pérez, A., Collins, D., Cowan, T., del Mar Chaves, M., Ferranti, L., Gómez, R., González,P., Romero, C., Infanti, J., Karozis, S., Kim, H., Kolstad, E., LaJoie, E., Lledó, L., Magnusson, L., Malguzzi, P., Manrique-Suñén, A., Mastrangelo, D., Materia, S., Medina, H., Palma,L.,Pineda, L., Sfetsos, A., Son, S., Soret, A., Strazzo, S., & Tian, D. (2022). Advances in the subseasonal prediction of extreme events: relevant case studies across the globe. Bulletin of the American Meteorological Society, 103(6), E1473–E1501.
Fung, M. S., Phipps, S. W., & Lehrter, J. C. (2022). Abrupt chlorophyll shift driven by phosphorus threshold in a small subtropical estuary. Frontiers in Marine Science, 1949.
Dzwonkowski, B., Fournier, S., Lockridge, G., Coogan, J., Liu, Z., & Park, K. (2022). Hurricane Sally (2020) shifts the ocean thermal structure across the inner core during rapid intensification over the shelf. Journal of Physical Oceanography, 52(11), 2841-2852.
Liu, Z., Lehrter, J., Dzwonkowski, B., Lowe, L. L., & Coogan, J. (2022). Using dissolved oxygen variance to investigate the influence of nonextreme wind events on hypoxia in Mobile Bay, a shallow stratified estuary. Frontiers in Marine Science, 9.
Wang, F., Tian, D., & Carroll, M. (2022). Customized Deep Learning for Precipitation Bias Correction and Downscaling. Geoscientific Model Development, 16, 535–556