Open Access Kent State (OAKS)

Green roofs are an innovative method of revitalizing urbanized areas and capturing stormwater. However, the conditions of a green roof ecosystem pose unique difficulties that can limit the success of plant growth. To help maximize the benefits of green roofs, we performed research examining the effects of mycorrhizal inoculation on the vitality of green roofs. The eventual goal is to use this information to create an optimum green roof, reflecting natural ecosystems by utilizing symbiotic organisms. This study aims to determine the most important factors that impact mycorrhizal colonization. We examined the effect of a commercial mycorrhizal fungal inoculum, and three substrate types, on mycorrhizal colonization of the plant species Liatris apera over the span of a year. The controlled experiment was set up using 36 plots at the Cleveland Industrial Innovation Center (CIIC). The substrate types included “Movable Meadow” (MM) with sandy loam soil, “Conventional Green Roof” (CGR) of engineered clay and shale media, and “Quasi-Traditional Green Roof” (QTR) which utilized worm castings. Half of the plots were inoculated with mycorrhizae and the other half uninoculated. Root samples of Liatris apera were collected, stained, and examined under the microscope to quantify mycorrhizal colonization. Preliminary results indicate that mycorrhizal colonization on average was lower in the inoculated treatment. However, it appears that this difference leveled out over time. These results suggest that mycorrhizal inoculation may not be necessary to promote colonization on green roofs. Additional research is being conducted to examine the effects of mycorrhizal inoculation on other plant species.

Human-managed and occupied ecosystems may mimic naturally occurring habitats, either spontaneously or by design. Understanding how communities of organisms assemble and use these novel spaces provides a key opportunity to understand, and potentially shape, the ecosystem functions and services delivered in human-dominated landscapes. For example, green roofs are a type of living architecture in which plants are intentionally grown on top of a human-built structure. Structurally analogous natural ecosystems are relatively rare, but some thin-soil environments can be found here in the Great Lakes Basin. As the natural habitats provide vital ecosystem functions, green roofs have the potential to provide urban areas with many services. Insects are the ideal focal taxa to examine for this project: in addition to their ubiquity, facilitating large scale data collection, insects play a variety of critical roles in ecosystem function and service, making them ideal sentinel organisms. The project focuses on characterizing insect communities and vegetation in green roof and natural thin soil environments to examine and quantify the services those insects provide (i.e. pollination, pest control, and decomposition). Characterizing the function and worth of insect services in natural and urban ecosystems is critical to supporting conservation decision-making in these human-managed ecosystems.

Microplastics (plastic debris with diameter <5mm) are of particular concern to the environment. However, there is a scarcity of information concerning the effects of conditioning films on bacterial colonization of microplastics in freshwater ecosystems. The formation of conditioning films on substrate surfaces is a critical step in the priming of substrates for bacterial colonization in aquatic systems. Conditioning films are comprised of dissolved organic solutes that are deposited on to surfaces of substrates, which attract bacterial colonizers. Moreover, the thicknesses of conditioning films are influenced by the physicochemical properties of substrate surfaces. This study aimed to understand the effects of different conditioning films on bacterial colonization of microplastic surfaces in freshwater. Five types of conditioning films were analyzed: Bovine Serum Albumin (BSA), sodium alginate (medium and very low viscosity), humic and fulvic acid; all are components of biofilms on four types of microplastic disks (diameter <5mm): polypropylene (PP), polystyrene (PS), high-density polyethylene (HDPE), and low-density polyethylene (LDPE). The disks were analyzed for conditioning film thicknesses using AFM (atomic force microscopy) and 16S rRNA sequencing to determine compositions of bacterial communities in the presence of different conditioning films. Understanding these questions will provide insights on fates of microplastic debris in freshwater.

Natural wetlands intrinsically heterogeneous, and are typically composed of a mosaic of ecosystem patches with different plant types. The adaptation of these plants communities to water-dominated environment is the basis for their use in improving the water quality in constructed wetlands. The understanding of wetland vegetation effects on the environment is the key to determine which plant to grow in a constructed wetland in term of nutrients removal. Wetland vegetation can influence water movement. The plant density and life form affect the drag and thus controls the residence time of water in different parts of the wetlands, as well as the rate of deposition of suspended solids. Furthermore, emergent plants with high transpiration rates can lower the water level. Accurately identifying the vegetation patches is important to understanding their hydrological effects and further effects on nutrients removal. Compared to labor consuming field survey, remote sensing is an efficient way to monitor plant communities in wetlands. However, wetlands are typically small and vegetation patches within wetland vary at an even smaller scale, such that moderate resolution will not be able to discern the different vegetation. Alternatively, the NASA’s Harmonized Landsat Sentinel-2 (HLS) makes it possible to acquire moderate-high spatial resolution imagery at high temporal resolution, which creates the opportunity to build time series of wetland vegetation characteristics at sufficient spatial and temporal resolutions. This study aims to use NDVI time series generated from NASA’s HLS dataset to classify vegetation patches at an estuarine wetland. We collected HLS data for the year of 2019 and generated the NDVI time series for each pixel of the wetland. Unsupervised classification was then applied on these pixels using the time series. And results will be evaluated with ground truth points.