Submerged, “reef-style” breakwaters may be a viable nature-based solution for shoreline protection and ecological enhancement. However, the lack of quantitative data on the effectiveness of such nature-based solutions limits the ability of managers to implement them within Great Lakes coastal communities.

Our goals are to use monitoring data to (a) inform habitat restoration and protection efforts around Lake Michigan and other Great Lakes, and (b) provide the much needed geomorphic and sediment-routing context to help assess the regional impacts of such structures (and their broader utility). We will accomplish these goals through the following objectives: Obj 1) Determine the effect of the two different artificial reefs on aquatic species abundance and diversity through comparisons to long-term (2016-present) ecological monitoring sites co-located with the reefs. Obj 2) Quantify bathymetric changes to the nearshore environment surrounding the reefs and topographic changes to the adjacent beach environment, evaluating post-reef morphodynamics in context of available pre-construction data (post-2018 at Site 1). Obj 3) Engage a variety of coastal stakeholder groups by way of discussion forums, workshops, fact sheets, or other meeting types. 

• Examine diets of sympatric and allopatric populations of mottled sculpins and round gobies
• Determine the relationship between mottled sculpin and round goby feeding on benthic invertebrate
  communities
• Assess the role of round gobies as trophic replacements for mottled sculpins

• Quantify contaminant levels in salmon and stream-resident fish at three sites at the southern end of Lake Michigan

• To quantify the current environmental quality, both in terms of metal concentrations and ambient conditions (pH, Eh temperature. groundwater flow directions and velocities), in different subenvironments (defined in terms of hydrologic, ecological and disturbance histories) of the Great Marsh, Indiana Dunes National Lakeshore
• To develop simple models/relationships to explain the spatial distribution of heavy metals across the Great Marsh and with depth within the sediments, and the environment conditions associated with enhanced mobility
• To use these models/relationships to determine the potential for heavy metal release associated with certain changes in hydrology, hydrochemistry and sediment chemistry that may occur during and following wetland restoration at the Great Marsh

Wetlands provide ecosystem services critical to the well-being of human populations, yet they have undergone massive loss and degradation. Illinois and Indiana alone have lost 85-90% of their historical wetland extent, which could impact the region’s resilience to climatic events and stressors. In response, agencies are dedicating substantial resources to restoring wetlands and their ecological functions. However, maintaining high quality, resilient habitats in human-dominated landscapes is challenging. Current literature reports a wide range of response to restoration interventions. Gathering long-term, consistent data on restored and protected wetlands is key to advancing our understanding of the root causes of this variability. This project will identify remote sensing-based indicators of vegetation composition and ecological functions to facilitate the consistent and large-scale monitoring of Lake Michigan wetlands. As a result, the project will generate three outputs aligned with the strategic goals of the IISG: (1) a literature review, to be published in a peer-review journal, summarizing current knowledge on the relationships between remote sensing-based indicators and transformations in plant communities; (2) a detailed script and tutorial, to be made available to scholars and stakeholders, showing users how to derive indicators of wetland health and recovery from free remote sensing datasets; and (3) a case study in a subsample of wetlands to serve as a proof of concept for the larger proposal.   

During the given one-year research period, I plan to develop a data-driven model integrating the physical model of infrastructure vulnerable to hazard and artificial intelligence machine learning algorithms to offer precautions and suggestions to resist natural hazards and enhance infrastructure flood resilience for the southern Lake Michigan communities. The proposed research targets to provide coastal communities with on-time and accessible suggestions to resist flooding attacks, support coastal industrial development without interference, give organizations reasonable, efficient recommendations to minimize the flooding impact on infrastructure, and offer the government customized design advice for infrastructure in the southern Lake Michigan region. Most importantly, this research will call public attention to the resilience of coastal communities and infrastructure.