Name: Small Animal - Wetland Model (Fine Scale)

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Description: Florida Wetlands Small Animal Road Hotspots Model (8/26) This data layer was developed for the Florida Department of Transportation (FDOT). The analysis used data layers of important aquatic and wetland resources vital for sustaining populations of wetland-dependent species. The intent was to use the resource data layers to indicate locations where road conflicts are most likely to occur. Results were presented using a buffered, one-mile, road-segment base layer. Using ArcGIS (ESRI, Inc) we performed the analysis on the following Florida Natural Areas Inventory (FNAI) resource data: • Significant Surface Waters • Functional Wetlands • Natural Floodplain These data layers were created as part of the Florida Forever Conservation Needs Assessment and establish conservation priorities for each respective resource type. Each layer offers a set of priorities that were translated into likelihood of presence of important wetland and aquatic species that may be adversely impacted by roads: • Relative water resource quality • Potential habitat suitability • Priorities for conservation For more detailed descriptions of these data layers, visit FFCNA_TechReport_v5_3.pdf (fnai.org). Observation point data (American Alligator and American Crocodile) and Affinity (+/-) habitat models (Alligator, Crocodile, River Otter, Spotted Turtle) were used for coarse validation of the results. Step 1: Establish equivalent sets of values among the three resource data layers. Data in the Floodplain priorities and Functional Wetlands layers were ranked from 1 to 6 (1 being the highest ranking), while Significant Surface Waters was ranked from 1 to 7. After reviewing attributes of the Significant Surface Waters layer, priorities 6 and 7 (the lowest priorities) were consolidated, resulting in the same number of values as the other two data layers. Step 2: Use the Combine function to identify overlap among the three resource data layers. The process includes stacking the three layers and calculating the sum of values for each raster cell. Prior to performing the Combine, we preferred that the highest combined value reflect the greatest importance. Therefore, the original scale of values was inverted so that 6 would be the most important priority and 1 would be the least important priority. The results of the Combine operation identified locations of overlap with the highest scores indicating greatest likelihood of potential habitat of multiple wetland-dependent species. The highest possible score was 18 and the lowest possible score was 0.Step 3: Perform a reclassification using “Jenks Natural Breaks”. This step condensed and generated the final set of priority levels for the final raster layer - Combined ReclassifiedValue Value 18 6 highest17 5 12 to 16 4 8 to 11 34 to 7 2 1 to 3 1 lowest0 0 out* value zero represents raster cells outside of designated priority areas** only the top three values, limited to roads designated as rural in the FDOT major roads layer, are displayed when viewed from the layer file, all other values are available through the attribute table.Step 4: Enhancements to the output data layer. First, areas that overlapped with the major river connectivity layer created by the Center for Landscape Conservation Planning at the University of Florida had +1 added to their final value. This was done to represent the increased priority of riverine features that may have been underrepresented in the Significant Surface Water data layer. Second, areas designated as high-intensity urban in the cooperative landcover layer (FWC 2025) were set to 0 and removed from consideration.Step 5: Clip the final, combined and reclassified priorities data layer to the roads base layer. We used the Annual Average Daily Traffic (FDOT 2025) roads layer, excluded minor roads without traffic counts and converted it to a 1-mile segmented, buffer layer. We used the "Generate Points Along Lines" ArcGIS Pro tool to create points at every one mile interval, then used the "Generate Thiessen Polygons" to create non-overlapping polygons of approximately 1-mile width, then clipped the result to the buffered roads. To the base layer we added the analysis results and other important attribute data for road segment comparisons. The fine-scale version was a direct Intersect of the output and the road segments, showing the results as unequal polygons limited to the roads. Each road segment was set to the highest value from the fine-scale version to create the coarse scale results. For this reason, the fine-scale version of this data is most applicable for evaluating specific road projects while the coarse-scale, consisting of whole road segments, serves as a quick reference for initial review. Notes: 1) A key for the attribute table is provided in the attached file: hotspots data layer attribute table key.pdf 2) The results are presented at a coarse, statewide scale (including urban and rural versions) and also at a fine, district level scale. For more detail on the analysis and results, please consult the project report (Small Animal Wetlands Road Hotspots Model.pdf) or visit us at the UF Center for Landscape Conservation Planning (https://conservation.dcp.ufl.edu/).

Copyright Text: University of Florida, Center for Landscape Conservation Planning; Data layer created by Daniel Smith and Crystal Gagne.

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