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
06:00 PM
Applying community structure data and machine learning to improve microbial diversity in polar ecosystem models (9260)
Primary Presenter: Emelia Chamberlain, Woods Hole Oceanographic Institution (emelia.chamberlain@whoi.edu)
Numerical modeling is a valuable tool for understanding ecosystem processes in the rapidly changing polar oceans. Due to their roles in biogeochemical cycling, climate change feedbacks, and the polar food web, it is increasingly important to explicitly quantify microbial contributions to modeled processes. However, current approaches are typically not informed by diversity data, despite clearly observed functional differences among microbial groups and increased ‘omics data availability. This results in a major discrepancy between observed and modeled biological complexity. Here, we highlight how machine learning, such as self-organizing maps and network analyses, can be used to segment the microbial community into functionally distinct ecotypes that can inform discrete variables in mechanistic single-column models. Specifically, we showcase a microbially-oriented regional test bed model that has been optimized for the western Antarctic Peninsula, but whose features can be altered for use in any region. For instance, we will use the biogeochemical, environmental, and genomic time-series data collected during the 2019-2020 MOSAiC Expedition to optimize this model for the warming central Arctic Ocean. Implementing our machine learning results, we will then test the variable contributions of specific archetypes within the microbial community to key ecological processes under climate change. We expect adding greater explicit functional diversity within this microbial-oriented numerical model will result in more accurate predictions of polar ecosystem function.
06:00 PM
WHAT ARE THE ODDS? EXTREME VALUE THEORY INDICATES THERE IS A 30% CHANCE OF A CATASTROPHIC OIL SPILL IN THE GULF OF MEXICO BEFORE 2050 (9740)
Primary Presenter: Susan Lubetkin, Elemental Statistics (susan.c.lubetkin@gmail.com)
The Bureau of Ocean Energy Management (BOEM) calculates risk = frequency x consequence, where frequency is the number of expected spills over a given amount of exposure, and exposure is often measured in billions of barrels of oil that may be produced. BOEM considers spills which have an expected frequency of >1, or a >63% chance of occurrence under a Poisson distribution, as statistically expected. The most recent Gulf of Mexico (GOM) catastrophic discharge event (CDE) (>159,000 m3) risk analysis from BOEM maps possible trajectories of oil released in a CDE but does not include any estimates of the expected frequency or probability of a CDE, which BOEM has described as “low probability” and “unlikely” in recent proposed oil and gas development environmental impact statements. BOEM estimates that the return period for CDEs for the United States outer continental shelf is 165 years based on an extreme value theory (EVT) model fitted to 49 years of annual maximum spill data from 1964-2012. Using only GOM spills from 1964-2024, I refit the model to 61 oil production volume blocks (60,678,000 m3) instead of annual blocks. The return period for CDEs in the GOM was 107 production volume blocks. The estimated oil production in the GOM from 2025-2050 is 2.37 billion m3 (39 production volume blocks). With that level of oil production, the probability of an oil spill >159,000 m3 is 30%, and at least one spill >43,826 m3 (95% CI: 9,920 to 193,622 m3) would be expected.
06:00 PM
Investigating the Fate of Microplastic Pollution in the Northern Gulf of Mexico and its impact on Marine Habitats (9668)
Primary Presenter: Mireya Ramirez, Georgia Institute of Technology (mramirez63@gatech.edu)
Microplastic (MP) pollution is an anthropogenic contaminant and stressor, posing complex threats to aquatic ecosystems. MPs can directly cause severe health issues for marine creatures through ingestion or absorption and can bioaccumulate up the food chain, impacting predatory species that consume them. The Gulf of Mexico (GoM) is the world's largest gulf, and its north part is one of the bodies of water most affected by MPs due to an input of materials from commercial and residential pollution being transported through the Mississippi-Atchafalaya River System, amongst others. Moreover, the coastal areas of the northern Gulf of Mexico provide critical habitats for diverse marine species, including coral, zooplankton, fish, and turtles, and serve as popular fishing destinations—all of which are under threat from the hazards posed by microplastic pollution. In this study, we applied a Lagrangian particle-tracking model coupled with a high-resolution (1 km) 3D ocean model to examine the transport of MPs particles released from major rivers and urban cities in the northern GoM. We identified the area near the coast west of the Mississippi River Delta as a major zone of accumulation, along with many other coastal areas in the GoM. We further assess the risks posed to various species by linking simulated MPs transport patterns with habitat data from previous studies and reports.
06:00 PM
Pocketization and Hardening of the Southeastern Lake Huron Shoreline: Impacts on Beach Morphology and Bluff Retreat (8738)
Primary Presenter: Ben Woodward, University of Waterloo (bwoodwar@uwaterloo.ca)
Rising water levels and corresponding bluff retreat in Lake Michigan-Huron during the mid 1980s and late 2010s caused many landowners to erect protective shoreline infrastructure. In southeastern Lake Huron, this infrastructure included shoreline hardening solutions, such as rock and metal break walls, as well as structures designed to trap sand, such as groynes. 11 groynes were present along an ~25 km stretch of shoreline between Bayfield and Grand Bend in 1978, but this figure jumped to 89 by 2015. These groynes often form groyne fields, leading to the pocketization of beaches along some portions of the shoreline. The long-term impacts of these coastal engineering solutions on the sediment dynamics of southeastern Lake Huron are only beginning to materialize due to the decadal nature of water level oscillations in Lake Michigan-Huron. Previous research on engineered pocket beaches in Illinois has shown that their sediment dynamics are particularly sensitive to lake level fluctuations. Field studies and analysis of air photos from between the 1950s and 2020 has shown that during the water level rise of the late 2010s, bluff retreat primarily occurred in areas south (downdrift) of groynes and on the northern side of groyne field pockets. The pocketization of parts of the southeastern Lake Huron shoreline coupled with climate change impacts such as reduced protective winter ice cover may worsen bluff retreat in specific areas during future decadal lake level rises.
06:00 PM
Mind the GAP: Integrating In-Situ and Earth Observation Data to Assess Climate Change Impacts on African and Latin American Lakes (9686)
Primary Presenter: Lorena Pinheiro-Silva, UMCES (lsilva@umces.edu)
Climate change poses a significant threat to lake ecosystems and the essential services they provide to society. One of the most direct impacts of climate change on lakes is the alteration of surface water temperature, which affects their energy, chemical, and water budgets. While international networks like the Global Lake Ecological Observatory Network (GLEON) and the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) have generated extensive research on climate change impacts on lakes at local and global scales, there is a notable underrepresentation of lakes from Latin America and Africa. Notably, this gap arises from challenges associated with poor data quality and restrictive data-sharing policies in these regions, hindering their inclusion in global assessments. To address this critical issue, we aim to: (i) combine available in-situ surface water temperature data with satellite observations (Sentinel-2 and CCI lakes) to create and validate synthetic time series for African and Latin American lakes, and (ii) investigate their responses to climate change.
SS20P - Leveraging Modeling Approaches to Understand and Mitigate Global Change Impacts on Aquatic Ecosystems
Description
Time: 6:00 PM
Date: 29/3/2025
Room: Exhibit Hall A