2026 Great Lakes Coastal Symposium

Through funding from NASA's MUREP program, research was done on the resilience of Great Lakes coastal wetlands to environmental disturbance and how wetland behavior impacts Great Lakes nearshore water quality. In particular, the ability of wetlands to retain nutrients was assessed under different inundation levels using the Mondrian ecosystem model, revealing that nutrient retention is significantly reduced under flooded conditions. The impact of nutrient retention on water quality was then assessed using satellite remote sensing. This analysis revealed that flooding leads to high dissolved organic matter loading in the nearshore environment, but relationships with chlorophyll-a concentration and submerged aquatic vegetation presence varied by site, baseline nutrient conditions and wetland condition. This work integrated in situ sampling, ecosystem modeling, and remote sensing analysis to assess the vulnerability of Great Lakes aquatic ecosystems, providing a framework for future integrated research. Further, it helped to increase the engagement of underrepresented groups in research, providing unique educational opportunities for a cohort of students from Texas State University who were able to get direct experience with remote sensing, data science, simulation modeling, and field sampling.

The Ohio Department of Natural Resources has constructed, restored, or enhanced wetlands throughout the state with the goal of capturing nutrients to improve water quality, and mitigate harmful algal blooms in Lake Erie. The H2Ohio Wetland Monitoring Program was developed to evaluate a subset of those wetlands, measuring inflowing and outflowing nutrient concentrations to understand both the amount of nutrients wetlands retain, and what drives that retention. Three years of monitoring data shows that wetlands that are more connected to flowing water are more successful at nutrient retention. While monitoring is focused on only a small subset of the full number of constructed wetlands our data can be used to understand the potential for the full number of wetlands to treat nutrients across the entire watershed. Our results suggest that when wetlands are hydrologically connected, they are an effective method to mitigate nutrient loading to larger waterbodies such as Lake Erie.

Managing and monitoring wetland restorations for nutrient retention is a critical component of Ohio’s H2Ohio Water Quality Initiative, which aims to reduce nutrient loading to Lake Erie in part through wetland restoration. Leveraging data from the H2Ohio Wetland Monitoring Program and funding from the Harmful Algal Bloom Research Initiative (HABRI), this study advances understanding of mechanisms behind wetland vegetation nutrient cycling and links nutrient retention outcomes to the species and functional traits that contribute most to nutrient retention. We selected 12 plant species commonly found in H2Ohio wetland restorations and quantified biomass and nutrient concentrations in the summer (peak biomass), fall (resorption), and following spring (overwinter storage) to capture annual nutrient cycling. Additionally, we conducted annual litter-bag decay experiments and monitored flow-event-driven sedimentation in areas where the species were dominant. Our results identify species that may enhance wetland nutrient retention through distinct mechanisms, including high rates of overwinter nutrient retention (e.g. Carex vulpinoidea), slower decay (e.g. Juncus effusus), and increased sediment capture (e.g. Asclepias incarnata). These findings inform species selection for wetland restorations by emphasizing functional traits and mechanisms associated with annual nutrient retention, providing guidance for practitioners beyond individual species selections.

Environmental monitoring programs can generate actionable data that informs management and policy, but they require intentional design of shared data infrastructures that consider the timeline of decision-making activities, the primary audiences they serve, and potential pain points to ensure robust data-to-action systems. The H2Ohio Wetland Monitoring Program (WMP) was established in 2020 to evaluate the nutrient removal effectiveness of varied wetland restoration projects implemented by the Ohio Department of Natural Resources (ODNR). The data stewardship and governance policies for the WMP have focused on strategies and tools that ensure data preservation, guarantee scientific rigor, and optimize efficiencies from data collection to dissemination by adopting FAIR principles. Our data infrastructure has been built in a stepwise iterative process, adapting and improving systems using on-the-ground feedback from data collectors. We have used custom digital surveys to centralize data entry and automated workflows to optimize and document critical data lifecycle steps. We built standardized quality checks to produce verified and integrated datasets. These approaches address challenges with information centralization and standardization, and ensure robust data toward actionable results, repeatable analyses, and usability in other applications.

Soil plays a major role in wetland effects on watershed transport of phosphorus and nitrogen which drive the formation of Harmful Algal Blooms. Determining soil chemical characteristics is important for estimating and predicting wetland performance and understanding how performance can be improved. Soil in wetlands is spatially heterogeneous and unlike water, is relatively stationary and doesn’t mix as water does. An important question for evaluating soil function in wetlands is where and how many samples must be collected to generate an estimate of soil chemical composition with the desired level of confidence and accuracy. To answer this question we applied geostatistical techniques to the analysis of high-density soil sampling of pools in five wetland projects with a variety of conditions, hydroperiods, vegetation, and soil characteristics. We will summarize the results and explain how they can be used to decide the minimum number of soil samples that should be collected to achieve different levels of accuracy. We will also explain the limitations and give examples of the variation and considerations for applying this to wetland monitoring.

In recent decades, aquatic ecosystem conditions have been monitored using a tool known as an index of biotic integrity (IBI). An IBI combines taxon-specific metrics into one score for easy interpretation of ecosystem health. The Great Lakes are an example of a highly monitored ecosystem where the Great Lakes Coastal Wetland Monitoring Program (CWMP) uses multiple taxonomic IBIs for assessing conditions in over 1,000 coastal wetlands. The program monitors fishes, macroinvertebrates, birds, and vegetation to get one IBI per taxa group. This method provides us with a more detailed look at wetland conditions, though having multiple IBI scores can cause complications when variation occurs across taxa IBIs. The goal of this study was to develop an approach for understanding cross-taxa IBI variation to better understand wetland conditions. We modeled the four CWMP IBIs against a set of environmental variables to determine influences on IBI scores and variation among IBIs. Our results indicate that each taxonomic group was influenced by a different set of environmental variables. Water level fluctuations, land cover, and local wetland characteristics all influenced each of the taxonomic IBIs in varying ways, which helps explain IBI variation. Understanding the environmental influences on each taxonomic IBI will allow restoration managers to build site-specific restoration targets that better align with ecological objectives.

Using a multi-season SAR-optical approach, we mapped historical changes in extents of invasive Phragmites australis and Typha spp. in wetland ecosystems of Lake Huron from circa 1998, 2010, and 2018. Field data on dominant vegetation cover, water depth, and presence of invasive plants from >500 locations were used to train supervised machine learning algorithm Random Forest. We applied hybrid change detection between the map years using radiometric and categorical data. Integrating radiometric change magnitude information, derived from a change vector analysis into traditional categorical change can improve accuracy by reducing commission error. Final hybrid-change layers were used to assess wetland change trends from 1998-2010 and 2010-2018 for the overlapping mapped extents and for coastal wetlands. Change results found from 1998 to 2010 there were decreases in most wetland types (> 8,000 ha) and high conversation to Phragmites (5000 ha increase). From 2010 to 2018, as water levels increased, the trend flipped with wetland areas increasing. Major trends included wetlands shifting type across hydrology zones (shrub to emergent, emergent to floating), wetland conversion to Phragmites, and Phragmites flooding out to open water. Linking our change analysis to the shifting zone of coastal influence allows us to more accurately quantify the gain, loss, and change dynamics in wetland ecotypes.

Quantifying biodiversity outcomes remains one of the greatest challenges in evaluating wetland restoration success. The Bird-Friendliness Index (BFI) was developed as a practical biodiversity indicator that uses bird community composition to assess ecosystem condition by integrating bird abundance, conservation importance, and functional diversity into a standardized metric of habitat quality. Here, we demonstrate application of an adapted marshbird-focused BFI to evaluate wetland restoration outcomes across the Great Lakes region. We highlight two complementary use cases: (1) measuring marshbird response through time following Sustain Our Great Lakes (SOGL) restoration actions; and (2) comparing ecological condition among sites in the Calumet region (Indiana and Illinois). Across applications, the BFI provided an interpretable indicator of wetland bird community condition and allowed restoration outcomes to be evaluated beyond single-species responses. In the Calumet region, restored wetlands exhibited higher marshbird friendliness than sites not yet restored, and longitudinal analyses further illustrate how BFI trajectories can be used to demonstrate positive biodiversity change through time at SOGL-restored sites relative to broader regional variation.

Marsh birds are important indicators of wetland health, many of which face decline due to wetland loss and degradation. Monitoring is therefore crucial for assessing wetland restoration success, but their secretive nature and inaccessible habitat present challenges. We evaluated whether autonomous recording units (ARUs) combined with deep neural network (DNN) sound recognition software can improve marsh bird detections and occupancy compared to traditional call-broadcast surveys. In collaboration with the National Fish and Wildlife Foundation (NFWF) and Indiana DNR (IN DNR), we collected and analyzed 60 ARU deployments at 16 sites and four states between 2022-2024, each paired with call-broadcast surveys using the Conway protocol. ARUs recorded for 46 days and recordings were classified using BirdNET Analyzer for 16 focal marsh bird species. We validated fifteen days of recordings for each species using classifier-guided listening. ARUs obtained higher richness than point counts in 65% of point-years and detected species of high conservation concern that point counts did not (e.g., King Rail). ARUs also yielded more detections for use in species-specific occupancy models than point counts due to the longer sampling period. Overall, our results indicate that ARUs can considerably improve marsh bird monitoring and strengthen our assessments of wetland restoration.

Great Lakes water-level fluctuations drive coastal wetland plant species composition and abundance. Receding levels stimulate seed-bank germination and increase plant diversity, while increasing levels drown flood-intolerant species, reducing diversity. While short-term studies have quantified aspects of these dynamics, long-term studies of natural wetlands undergoing multiple water-level cycles are necessary to clarify plant responses and provide a baseline for predicting future conditions. Nearly every year since 1971, students and researchers based at the University of Michigan Biological Station have collected vegetation and physical data from Cecil Bay, a Great Lakes coastal wetland in the Straits of Mackinac. We examined this unique 50-year vegetation dataset that includes multiple periods of high and low water-levels. We found that the entire marsh maintained a surprisingly stable breadth throughout the 50 years, with consistent emergent plant composition defining the outer wetland. Notably, the conventional models of vegetation change with water-level fluctuation underestimate the tolerance of rhizomatous emergent plants to long-term flooding, a capacity critical for maintaining Great Lakes coastal wetlands. We incorporated our findings into a new conceptual model of Great Lakes coastal wetland vegetation dynamics. This long-term study enhances understanding of the resilience of these remarkably dynamic ecosystems.

Competing land-use demands along the Canadian Lake Ontario Coastal Margin necessitate a strategic approach to coastal wetland conservation, protection, and restoration. This study presents a prioritization assessment of 81 wetlands that can be used to identify coastal wetlands sites in most need of conservation action. Ecological significance and environmental pressure were evaluated by integrating spatial data from the Canadian Baseline Coastal Habitat Survey, Coastal Wetland Monitoring Program, and other available data for the Canadian shoreline of Lake Ontario. Climate change resilience was also assessed using a Coastal Wetland Response Model developed by Environment and Climate Change Canada. This assessment provides a science-based framework for identifying high-priority conservation targets and optimizing the allocation of limited conservation resources.

The interior of large coastal wetlands can be difficult to access, making monitoring the presence and extent of wetland vegetation communities/invasives in the wetland problematic. Collecting data on wetland vegetation community composition by way of transects across wetlands in boats or on foot can be challenging and may cause damage to sensitive vegetation or miss features/communities of interest. Small UAS (drones) offer a repeatable way to collect very high spatial resolution, multispectral aerial imagery, documenting presence/extent of species in a wetland without sending research staff into the wetland. UAS-acquired aerial imagery provides high spatial resolution (2 - 5 cm per pixel) imagery of a wetland, which can be collected as needed with no disturbance to sensitive plant communities. It simplifies the coordination of field crews with aerial imagery collection, allowing simultaneous collection of ground reference data and aerial imagery. Object-oriented image classification and expert photointerpretation techniques are applied to create a map of presence and extent of plant communities in the study areas. This presentation offers an application of UAS technology for monitoring plant communities at Little Portage River and Turtle Creek wetlands in the western basin of Lake Erie that are monitored by vegetation crews from the H2Ohio Wetland Monitoring Program.

Gain a comprehensive overview of the geospatial datasets, tools, and technical training resources developed and disseminated by NOAA’s Office for Coastal Management and the Digital Coast. Key topics include the development of high-resolution land cover data through the Coastal Change Analysis Program (C-CAP), enhanced functionality within the Lake Level Viewer, and the current progress of the hardened shorelines inventory. The session will also examine projects funded through the Great Lakes Restoration Initiative including the U.S. Collaborative Benthic Habitat Mapping Project and recent work on the Wild Rice Project.

The State of Michigan has long recognized the importance of protecting its freshwater sand dunes. In response to development pressures and the recognition of the need to protect especially important dunes, in 1989 the Michigan Department of Environment, Great Lakes, and Energy (EGLE) designated approximately 74,000 acres of critical dune areas (CDAs) throughout the state that require special protections. From 2024-2026, EGLE contracted with GEI Consultants, Michigan Natural Features Inventory, Michigan Technological University, and Michigan State University to update the 1989 CDA atlas. The effort combined desktop reviews and field-based assessments to designate new CDAs, de-designate existing CDAs that no longer meet designation criteria, or correct CDA boundaries based on geomorphological and ecological conditions. This presentation will provide an overview of the methodology used to holistically assess the morphology, coastal processes, impacts, and ecology of Michigan’s sand dunes.

The overall state of surface water and groundwater systems in the Great Lakes Basin (GLB) remains insufficiently understood to accurately predict water storage and flow across the region. A USGS assessment of GLB science needs (Carl et al. 2021) concluded that limited groundwater quantity and quality information is available to support water-resource management, and that groundwater’s role in the Great Lakes system is largely unknown. Climate change is increasing hydrologic variability, while demands for water for drinking, agriculture, and industry continue to grow. Improving estimates of water storage and movement from the land to the coasts is therefore essential. As part of a broader effort to develop an integrated surface-groundwater hydrologic model for the GLB, this work focuses on monitoring inundation and groundwater dynamics using satellite remote sensing. We present a novel approach for measuring inundation and estimating groundwater level fluctuations across the basin every 12 days using synthetic aperture radar (SAR) observations. Surface water and wetland extent derived from Sentinel-1 SAR growing-season data (April–October, 2016–2025) are mapped and combined with high-resolution LiDAR elevation models in a wetland connectivity framework to infer groundwater elevations. The resulting surface water, inundation and elevation datasets are openly available through a web viewer to support coastal monitoring and GLB hydrologic model development and calibration.