This session will focus on the application of modeling approaches to assess the impacts of global change on aquatic ecosystems, emphasizing the resolution of biological problems. By incorporating a range of modeling methodologies, including but not limited to biophysical modeling, biogeochemical modeling, and ecosystem modeling, the session will advance the understanding of how environmental stressors affect aquatic ecosystems. Additionally, it will discuss how these models can be utilized to develop better management strategies for policymakers facing the stressors of global change.
We will examine how modeling can elucidate the effects of multiple stressors — such as climate change, land use intensification, contaminants, nutrients, habitat destruction, and overfishing — on environmental and biological issues. These issues include, but are not limited to, algal blooms, water quality, shifts in species distributions, and changes in ecosystem functions. This session also aims to advance our understanding of the complex dynamics of global change impacts on aquatic systems and address pressing environmental challenges effectively through fostering interdisciplinary collaborations. On the one hand, the robustness and predictive capabilities of modeling can be enhanced by integrating real-time observational data, remote sensing, in situ measurements, and machine learning/data analysis techniques. On the other hand, modeling work can provide better guidance for observed fieldwork and public policymaking.
We invite contributions that demonstrate innovative modeling approaches, case studies, and practical applications to promote knowledge sharing and the development of effective solutions. Specific areas of focus will include but are not limited to, modeling aquatic greenhouse gas emissions, harmful algal blooms (HABs) occurrences, assessing changes in water quality indicators, and understanding nutrient dynamics under varying climatic conditions. This session will provide a platform for exchanging ideas and strategies to enhance the resilience of aquatic ecosystems in the face of various stressors, leveraging modeling approaches to resolve critical biological and environmental problems.
We encourage submissions not only from researchers but also from practitioners and policymakers to promote interdisciplinary collaborations and advance our understanding of climate change impacts on aquatic ecosystems. We particularly welcome submissions from early career researchers and those from BIPOC, LGBTQIA+, and other marginalized communities.
Lead Organizer: Chuyan Zhao, Michigan Technological University (zhaocy0516@gmail.com)
Co-organizers:
Pengfei Xue, Michigan Technological University (pexue@mtu.edu)
Mark Rowe, NOAA (mark.rowe@noaa.gov)
Xing Zhou, Georgia Institute of Technology (xzhou473@gatech.edu)
Reza Valipour, Environment and Climate Change Canada (reza.valipour@ec.gc.ca)
Presentations
04:30 PM
ESTIMATING DYNAMIC ENERGY BUDGET MODEL PARAMETERS FOR QUAGGA MUSSELS (8873)
Primary Presenter: Tongyao Pu, University of Michigan (tongyaop@gmail.com)
Dreissena spp. impacts on infrastructure and ecosystems are well-documented, particularly their significant effects on nutrient cycling and productivity within the Great Lakes. These effects are influenced by mussel density, growth, and reproduction. To predict mussel growth, filtration, temperature dependence, and nutrient fluxes, we compiled existing data to calibrate a Dynamic Energy Budget (DEB) model specifically for Quagga Mussels (Dreissena rostriformis bugensis). We estimated DEB parameters including shape coefficient, Arrheinius temperature, surface area-specific maximum ingestion rate, and surface area-specific maximum searching rate. Our model incorporates an innovative allometric analysis of a substantial, long-term dataset from field-collected mussels in the Great Lakes and derives two distinct temperature-dependent relationships based on oxygen consumption and filtration rates. Additionally, it uniquely considers both food quantity and quality, enhancing its realism. However, since field-collected data are not designed for precise parameter estimation, significant uncertainties in parameter estimates currently persist, highlighting the need for well-controlled laboratory experiments. The components of this model hold potential for integration into biogeochemical models to elucidate mussel effects on nutrient cycling and their dependencies on future environmental changes. This integration could significantly enhance our understanding of ecosystem dynamics and inform effective management strategies for invasive species in aquatic environments.
04:45 PM
Development of Chesapeake Bay Program’s Phase 7 Water Quality Bay Model (9179)
Primary Presenter: Zhengui Wang, Virginia Institute of Marine Science (wangzg@vims.edu)
As the largest estuary in the United States, Chesapeake Bay’s ecosystem health is important both ecologically (for the wildlife) and economically (for the fishing industry and coastal community). The Virginia Institute of Marine Science (VIMS) is leading the development of EPA’s next-generation management model for the estuarine portion of the Bay, known as the Phase 7 Main Bay Model (MBM) based on the unstructured-grid model, SCHISM (schism.wiki). In this presentation, we will discuss the motivation for the Phase 7 MBM development and the progress made over the past three years. The MBM domain includes the entire bay and all its tributaries, along with a large portion of adjacent coastal ocean. We will present the linkage of MBM to watershed, airshed and ocean, as well as the changes of these drivers under climate changes. The water quality model (ICM) for the bay is fully coupled with hydrodynamic model, wave model and sediment transport model. ICM also comprises many sub-models for sediment nutrient diagenesis, submerged aquatic vegetation (SAV), wetland, oyster and pH, etc. It can simulate many water quality parameters such as chlorophyll-a (CHL-A), carbon, nitrogen, phosphorus and dissolved oxygen (DO), and living resources in shallow waters that impact water quality. To build a robust and reproducible modeling system, we have significantly restructured the code and the entire workflow, the latter based on a user-friendly python-based interface. In addition, we have developed many easy-to use pre/post-processing, visualization and diagnostic tools. Comprehensive model assessment is an on-going process and we will present the spatiotemporal variations of key variables like dissolved oxygen (DO) and chlorophyll-a (CHL-A), as well as the lessons we have so far learned from the development of MBM and future plans. The flexibility of the MBM provides significant advantages for holistic decision making across multiple scales (from pelagic to shallow embayment).
05:00 PM
ASSESSING THE INFLUENCE OF SEA-LEVEL RISE ON SOUTHERN FLOUNDER RECRUITMENT PROCESSES IN ALBEMARLE-PAMLICO SOUND (9291)
Primary Presenter: Katherine Boot, University of North Carolina Wilmington (keb3424@uncw.edu)
Albemarle-Pamlico Sound (APS) is a lagoonal estuary and key nursery habitat for southern flounder (Paralichthys lethostigma) in northeastern North Carolina. Over the past decade, flounder abundance has declined due to overfishing and poor recruitment linked to environmental changes. It is crucial to monitor and investigate physical processes that may influence southern flounder recruitment in APS, including sea level rise (SLR). Semi-Implicit Cross-Scale Hydrodynamic System Model (SCHISM) will be used to evaluate how changes in mean sea level affect hydrodynamic circulation and salt flux in APS. A hindcast simulation of 2019 was built and validated using in-situ observations, demonstrating the model’s fidelity. The model grid includes spatial resolutions ranging from the river mouth (~10 m) to the coastal ocean (~4 km) and extends ~ 411 km offshore into the Atlantic Ocean. Model inputs include atmospheric forcings, tides, river discharge and open boundary conditions. The impacts of SLR will be explored by isolating mean sea level (MSL) changes and examining the resulting hydrodynamic shifts in APS through three scenarios: low MSL from 2002, moderate SLR projections from CMIP6 SSP370, and high SLR projections from SSP585. Integrating a statistical model based on trawl survey data from 1987 to 2021 with these scenario-based simulation outputs may provide critical insights into how changes in MSL impact southern flounder recruitment and inform management strategies to preserve this resource in North Carolina.
05:15 PM
Oxythermal Habitat Characterization and Predictive Modeling of Upper Midwestern Glacial Lakes (8806)
Primary Presenter: Emma Blackford, University of Wisconsin- Madison (eblackford@wisc.edu)
Cold-water fish species in the Upper Midwest are culturally, economically, and ecologically important; however, they are especially sensitive to climate warming and variability. Threats to habitat conditions and availability for cold-water fish intensify as glacial lakes become warmer and nutrient loading increases. Summertime warm water temperatures and hypolimnion hypoxia cause fish to be “squeezed” into a thin layer of oxythermal habitat during lake stratification, causing physiological stress and possible death. Considerable variation in sufficient oxythermal habitat availability both between and within summers has been observed in Northern Wisconsin lakes, making it well-suited for predictive modeling and management. We leverage data from Wisconsin’s North Temperate Lakes- Long Term Ecological Research program (NTL-LTER) and the NOAA Physical Sciences Laboratory to create statistical models predicting various summertime average oxythermal stress metrics for cisco (Coregonus artedi) at a season-ahead time scale. Predictions are issued pre-summer, utilizing machine learning models conditioned on local hydrology, lake characteristics, and large-scale climate features as predictors. Results indicate skillful prediction of vertical habitat thickness; notably global climate features represent strong predictors. Responding to requests from natural resource managers, we expanded the analysis to thousands of glacial lakes across Upper Midwestern states. Our study highlights how season-ahead predictions can inform actionable resource decisions and compliment short-term and long-term adaptation strategies for cold-water fish in glacial lakes. Management strategies by the Department of Natural Resources and other lake organizations are often reacting to adverse conditions, whereas pre-summer season prediction models may uniquely offer prospects for proactive management.
05:30 PM
Modeling the diversity of life history, through growth and development, in Snake River Chinook salmon using an Integral Projection Model framework (9255)
Primary Presenter: Grace Veenstra, University of Alaska Fairbanks (gveenstra.ak@gmail.com)
As climate change shifts temperatures, it is impacting the phenology of species with temperature-dependent growth. One such species is wild Chinook salmon (Oncorhynchus tshawytscha) in the Idaho Snake River Basin. Chinook salmon life history is complex, their growth and movement heavily influenced by temperature and food availability. In order to predict the impact of climate change on juvenile Chinook salmon life history, I developed a temperature-dependent integral projection model to evaluate potential freshwater growth and development, tracking salmon from spawning to smolt outmigration. After eggs emerge, juveniles follow one of two alternative life history strategies, either overwintering in their natal tributary or downriver in mainstem reaches, each with a distinct temperature regime. Both strategies culminate in spring outmigration in the Snake River. The model predicts the size of juvenile Chinook salmon through time, allowing comparison with observed size distributions. Furthermore, the model illustrates variation in smolt size intra- and inter-annually, and the proportion of fish that followed a particular life history strategy. Initial results show the model is accurately predicting within the range of observed smolt sizes, even with the relatively simple set of parameters being used. The computational efficiency of an integral projection model is well suited for use in large-scale simulations, including predicting distributions under many different climate scenarios.
SS20C - Leveraging Modeling Approaches to Understand and Mitigate Global Change Impacts on Aquatic Ecosystems
Description
Time: 4:30 PM
Date: 28/3/2025
Room: W205CD