To address the lack of monitoring, the goal of this study is to develop a
framework to initiate assessment of habitat restoration in the Tampa Bay region. This assessment structure will be evaluated to consider how it can meet the needs of the
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SWIM Program, and potentially, that of natural resource managers in the bay region. Specifically, the research intends to address the following questions: 1) Is the SWIM Program implementing projects in accordance with the “Restoring the Balance” paradigm outlined in the TBEP CCMP? What habitat types and extents have been created since the inception of SWIM and how have they contributed to the overall CCMP goals? 2) How are these projects developing over time and which project types are most and/or least self-sustainable, or “successful”? 3) Can the research monitoring methodologies demonstrate whether the restoration projects are providing an ecological benefit within the bay segments and/or drainage basins in which they are constructed and to the Tampa Bay watershed overall?
Pure restoration, or projects not used for mitigation credit like those implemented by the SWIM Program, incorporates similar features and goals as projects constructed to compensate for environmental impacts due to development. Habitat assessments, particularly for wetland communities, are regularly reviewed for permitted impacts and are a required part of mitigation. Methods for these appraisals can take the form of remote sensing analyses, onsite “rapid” techniques, and intensive field measurements where floral, faunal, and biogeochemical data are collected (Mack 2006).
These methods have been used for many years, have been highly scrutinized, and have a structured accounting process for easy comparison of results. Therefore, the assessment tools used for mitigation also seemed appropriate for quantification of community development within habitat restoration projects. Using a selected subset of projects from the SWIM database, a combination of approaches, taken from the
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mitigation realm, will be considered to ascertain the overall “success” of these types of projects in the Tampa Bay area.
2.1.5.1 Introduction to the Uniform Mitigation Assessment Method (UMAM) Since 2004, the Uniform Mitigation Assessment Method (UMAM) (Chapter 62- 345, F.A.C.) has been used throughout the state of Florida to evaluate any type of impact and determine the amount of mitigation required to offset adverse wetland and surface water impacts encountered through urban development (FDEP 2014).
Using qualitative and quantitative components, an appraisal is completed via the UMAM on the area of interest. The qualitative portion of the assessment form considers the ecological community’s former and current condition, hydrologic connection,
uniqueness, location, and fish, wildlife, and public utilization of each project site (FDEP 2014). For the latter section, three categories are used to numerically score the degree of impairment on a scale of zero to 10, where 10 indicates minimal impairment and zero indicates a non-functioning system. The first category, Location and Landscape
Support, appraises the ecological relationship between the assessment area and the surrounding landscape. The second, Water Environment, examines hydrologic alteration and water quality impairment. The third category focuses on Community Structure, reviewing the vegetation and structural habitat within the assessment area (Bardi et al. 2005).
The method provides a standardized procedure for evaluating the ecological functions provided by the created, restored, or enhanced wetlands and surface waters (FDEP 2014). Final scoring results in a number between 0.00, non-functioning, and 1.00, optimal habitat conditions. Currently, wetlands with UMAM scores from 0.49 to
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0.69 are considered to have some level of degradation and fees can be paid for
impacting these areas. However, wetlands with UMAM scores exceeding 0.70 cannot be destroyed due to their “overriding public benefit.” The result of the UMAM appraisal in this study will be used to describe projects on an individual level, with the ultimate goal of all project trajectories moving toward 1.00. Restoration projects within the SWIM subset receiving a score of 0.70 or greater will, therefore, be deemed “successful.”
2.1.5.2 Introduction to the Landscape Development Intensity (LDI) index The Landscape Development Intensity (LDI) index was developed by Brown and Vivas in 2005. This index is used to determine the effect of human disturbance on wetland systems. It is calculated spatially in a GIS framework using coefficients that are applied to delineated land use cover types within a watershed (Table 2.3, Brown and Vivas 2005). In Florida, the primary land use cover type classification scheme used by government agencies is the Florida Land Use and Cover Classification System
(FLUCCS), developed by the Department of Transportation. The system is hierarchical and includes three broad classes, urban, agriculture, and natural, which are subdivided to a finer level of detail with increasing resolution (Brown and Vivas 2005).
The LDI coefficients are correlated with the intensity of human activity within any given area based on non-renewable energy use, e.g. electricity, fuel, fertilizer, and pesticides. The index reflects changes in environmental condition on the structure, process, and function of ecosystems (Reiss et al. 2014). More natural area classes are lower on the LDI scale with a value closer to 1.0, while more intensive land use classes have values closer to 10. Because land use and land cover data are now readily
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assessment tool” (Stuber et al. 2016). In this analysis, the LDI scoring will be calculated per the Brown and Vivas (2005) equation below and used as a tool to evaluate how the projects have affected the drainage basins in which they were implemented and the region overall. Decreases noted in LDI where restoration has been completed would provide a marker of “success,” thus supporting restoration contributions within Tampa Bay.
LDItotal = ∑%LUi * LDIi, Where,
• LDItotal = LDI ranking for landscape unit
• %LUi = percent of the total area of influence in land use i
• LDIi = landscape development intensity coefficient for land use I