{ "currentVersion": 11.4, "cimVersion": "3.4.0", "id": 9, "name": "Small Animal - Upland Model (Fine Scale)", "type": "Feature Layer", "description": "Florida Uplands Small Animal Road Hotspots Model (9/26)This data layer was developed for the Florida Department of Transportation (FDOT). The analysis used Florida Fish and Wildlife Conservation Commission (FWC) species-habitat predictive models to identify and rank likelihood of the presence of key, representative small-animal, upland species. Results were presented using a buffered, one-mile, road-segment base layer. Using ArcGIS (ESRI, Inc) we performed the analysis on the following 11 representative species predictive models: \u2022 Florida mouse \u2022 Eastern diamondback rattlesnake \u2022 Eastern indigo snake \u2022 Florida pine snake \u2022 Southern hognose snake \u2022 Florida gopher frog \u2022 Flatwoods salamander \u2022 Striped newt \u2022 Tiger salamander \u2022 Gopher tortoise \u2022 Mimic glass lizardThe predictive models rank potential habitat suitability on a continuous scale from 0 to 1, with one representing the highest probability. The first step was to Reclassify each continuous data model for the nine species discussed above. FWC provided binary threshold values for each species-habitat model. We used all values above the binary threshold value and applied Jenks Natural Breaks using three categories \u2013 high (3), medium (2) and low (1) \u2013 to represent likelihood of potential habitat and species presence. High intensity urban areas were then removed using the cooperative landcover. In step two we used the Combine function to identify overlap among the individual eleven species-habitat models using the high, medium and low scale. This process identified locations with highest combined scores indicating greatest likelihood of potential habitat of multiple species. The highest possible score was 33 and the lowest possible score was 0. The range of actual scores was 0 to 28. The third step was to consolidate the scores and establish a set of priorities. We used standard deviation (SD=3.44) to create a scale of ranked values and then performed a Reclassification to generate the final raster data layer prior to converting to polygon and clipping to the roads layer. Ranked Value - Score - SD6 - 24 to 28 - 6+ 5 - 21 to 23 - 5+ 4 - 17 to 20 - 4+3 - 14 to 16 - 3+ 2 - 11 to 13 - 2+1 - 7 to 10 - 1+0 - 0 to 6 - <1+ * mean value was 2.64 ** only the top five values, limited to rural roads, are displayed when viewed from the layer file, all other values are available through the attribute table.Additionally, we elevated areas by a value of 1 that overlapped with the xeric connectivity layer (one of the criteria used in the creation of the Florida Ecological Greenways Network; UF Center for Landscape Conservation Planning) extending the possible values from 0 to 7 (7 being the highest ranking). This was performed to recognize increased value for connected xeric communities.The fourth step included Clipping the combined, reclassified data layer to a roads base layer. We used the Annual Average Daily Traffic layer (FDOT 2025) for this purpose and excluded roads without traffic counts. This was then converted to a 1-mile roads segment 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. We added the analysis results and other important attribute data for road segment comparisons to the roads base layer. The results are presented at a statewide coarse-scale (including split urban and rural versions), as well as at the district level with finer scale detail. 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. For more detail on the analysis and results, please consult the project report (Small Animal Uplands Road Hotspots Model.pdf) or visit us at the UF Center for Landscape Conservation Planning (https://conservation.dcp.ufl.edu/). 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