Southwest Florida Water Management District Conservation Land Acquisition Project Boundary Review Ecological GIS Decision Support System Final Report

Transcription

1 Southwest Florida Water Management District Conservation Land Acquisition Project Boundary Review Ecological GIS Decision Support System Final Report Introduction By: Tom Hoctor, Ph.D. Director, Center for Landscape and Conservation Planning Research Associate, GeoPlan Center College of Design, Construction, and Planning University of Florida Gainesville, Florida Phone: The Project Boundary Review Ecological GIS Decision Support System (DSS) is intended to provide integrated information on ecological and wildlife conservation priorities within the Southwest Florida Water Management District (District). Though there are many potential uses of this DSS, it was created to assess potential additions and deletions to current project boundaries for the District s conservation land acquisition projects. The DSS combines statewide GIS data layers from the Critical Lands and Waters Identification Project (CLIP) ( relevant GIS District data and new GIS analyses adapted from the Greater Ridge Conservation Planning Tool ( GreaterRidgeConservationPlanningTool-final.pdf). All of these data were combined into a hierarchical system of data including core data, natural resource categories, major categories, and cumulative aggregated models. Though the cumulative aggregated models combine all data to provide a complete synthesis of all ecological priorities, all levels of the DSS data hierarchy can potentially provide information relevant for making specific land conservation or other related decisions. In this report, development of all levels of the DSS data hierarchy are explained including the alternative aggregated models to aid using these data both for conservation acquisition decision-making and also determine whether these data may be useful for other policy decisions. Finally, though these data represent scientifically valid models identifying ecological conservation priorities, they are general in resolution and do contain some errors based on inaccuracies of base data (such as land cover or land use data) or generalizations inherent in modeling. Therefore, these data are not intended to represent survey quality mapping information and should not be used to make specific decisions regarding precise boundaries between potential conservation lands and other areas. Such decisions must include a combination of DSS data with other information including high resolution aerial photography, parcel data, and ground-truthing to determine project or other boundaries that best represent conservation priority areas or other objectives. These considerations are similar to data disclaimers discussed in the CLIP project, which is a good source for more information regarding limitations of these types of state and regional GIS data. 1

2 Process Beginning in September 2008, Tom Hoctor met with District staff led by Kevin Love and staff from Wildlands Conservation (the Team) in a series of five meetings through April 2009 to identify and prioritize data and to develop and adopt methods for integrating data into resource categories and cumulative aggregated models. This process included review of CLIP and District and other state or regional data potentially relevant to development of the DSS. Gaps in existing data were identified and rectified when possible through additional analyses of District or other data developed collaboratively by Tom Hoctor and District staff. Once core data layers were developed and prioritized, the Team discussed and agreed on alternatives for integrating these data into models identifying regional ecological conservation priorities in the District. The core data layers, prioritization, and integration methods were all agreed to by Team members, so the DSS represents a collaborative scientific, consensus approach for identifying regional ecological conservation priorities. However, the Team also recognized various opportunities to improve or add data in any future versions of the DSS, which will be discussed below in the Discussion section of this report. Model Structure and Methods The PBR DSS is a hierarchical GIS data library with four levels. The first level is called Core Data, which is made up of individual GIS data layers that identify ecological conservation priorities for specific features such as rare species habitat, floodplains, natural communities, wetlands, ecological greenways, and springsheds. The Core Data layers are organized into Resource Categories that represent natural resources with commonalities such as Surface Water, Species, Groundwater, and Landscapes. The Resource Categories are then organized into Major Categories, which represent the two major goals for District land acquisition, Water Resources and Natural Systems. Finally, the two Major Categories are integrated into a set of Aggregated Priorities that represent the collective priorities for all Core Data layers and Resource Categories (See Figure 1). The following sections describe each Core Data layer, the methods for compatibly prioritizing each Core Data layer, the methods and rationale for combining Core Data layers into Resource Categories, Major Categories, and Aggregated Priorities. 2

3 Aggregated Priorities Water Resources Major Categories Resource Categories Natural Systems Surface Water Floodplains Groundwater Species Natural Communities Landscapes CLIP Surface Water Model CLIP Natural Floodplain District Recharge CLIP Rare Species Habitat CLIP and District Natural Communities CLIP Florida Ecological Greenways Network CLIP/PBR Wetlands Model District Floodplain District Springsheds Core Data CLIP SHCAs CLIP Landscape Integrity CLIP Surface Water Supply District Sensitive Karst CLIP Biodiversity Hotspots Wide-ranging Species Habitat Figure 1. Project Boundary Review Ecological GIS Decision Support System model hierarchy 3

4 Core Data Methods and Priority Rankings All Core Data layers were ranked on a priority scale of 1 to 9, where 9 = highest priority, 5 = moderate priority, and 1 = low or no priority. This ranking scale was selected to allow for mathematical combination of data layers if desired when aggregating Core Data layers into Resource, Major, or Aggregated priorities. A consistent ranking scale is required to make each layer comparable so that mathematical combinations will maintain the integrity of the ranking scale (Carr and Zwick 2007). In decision support statistics, it is not considered appropriate to combine data ranked on different scales. If there are perceived differences in relative significance of core data layers, then differential weighting can be applied when these data are combined. The following descriptions are intended to provide basic descriptions of how each Core Data layer was created. For CLIP Core Data, the relevant sections from the CLIP report are copied below to serve as the basic description. Then, if applicable, any modifications to the original CLIP data are also described. A. Water Resources Major Category 1. Surface Water Resource Category a. CLIP Surface Water Priorities Model This data layer was created by Florida Natural Areas Inventory, in consultation with state water resource experts, specifically for the Florida Forever statewide environmental land acquisition program. It is intended to show areas that have statewide significance for land acquisition to protect significant surface waters with good water quality. This data layer is not intended to address surface waters with substantial restoration needs, only surface waters that are currently in a relatively natural condition and are a priority for protecting Florida's water resources. The Surface Water model is a combination of seven water resource submodels: Special Outstanding Florida Water (OFW) rivers as defined by DEP, other OFWs (on conservation lands), OFW lakes and Aquatic Preserves, coastal surface waters, the Florida Keys, springs, and rare fish basins. For each resource category, drainage basins that contributed to the resource were selected and buffers to water bodies applied. For more information see the CLIP report (2009) and the FNAI Florida Needs Assessment technical report. See Table 1 for the assignments of these priorities into the priority classes of 1 to 9. b. CLIP/PBR Wetlands Model This data layer was created by modifying the methods used to create the CLIP wetlands priority layer. The Wetlands data layer used for the CLIP analysis was developed by FNAI specifically for the Florida Forever statewide environmental land acquisition program. It is based on the National Wetlands Inventory (NWI) dataset developed by the U.S. Fish & Wildlife Service. The NWI layer was modified by removing all recently developed areas and prioritizing the remaining wetlands into 4

5 four classes: 1) wetlands in FNAI Potential Natural Areas (PNA) priorities 1-4; 2) wetlands in PNA priority 5; 3) wetlands in FLUCCS natural land cover categories; and 4) any remaining wetlands. The FNAI PNA data layer represents areas of intact natural vegetation as determined by interpretation of aerial photography. Potential Natural Areas were assigned ranks of Priority 1 through Priority 5 based on size, perceived quality and type of natural community present. For the PBR DSS we modified the modeling process in two ways. First, instead of using NWI wetlands as wetlands source data, we used wetlands from the District FLUCCS land use data. All areas with codes from were included. In addition to including all wetlands that overlapped with FNAI PNAs priorities 1-4 as highest priority, we also included all wetlands within existing conservation lands as highest priority. Then, the two other priority levels were identified using the same methods described above (except for starting with District wetland instead of NWI wetlands (Table 1). For more information see the CLIP report (2009) and the FNAI Florida Needs Assessment technical report. c. CLIP Surface Water Supply (FNAI submodel) This core data layer is a submodel of the CLIP Surface Water Model described above. It was included as a surrogate for more detailed analyses of potable surface water supply protection priorities that may be done for the entire District at some point in the future. Some models already exist for some surface water supply water bodies, but not all such waters. The FNAI Surface Water Supply model does something similar for at least those surface waters designated Class 1 (potable water supply) by the Florida Department of Environmental Protection (DEP), though in our meetings to explore these data it appeared that other various surface water supply water bodies in the District may not be included in these data. However, the FNAI Surface Water Supply model serves as a sufficient surrogate for more thorough analyses that may be done in the future. In the FNAI model, Class 1 waters and their tributaries were buffered by 1,000 feet and 1 mile. All sub-basins were then scored based on proximity to the Class 1 waters. Sub-basins contiguous to the resource were given a proximity score of 1, sub-basins adjacent to proximity 1 were scored proximity 2, and so on (the least proximal sub-basin scored 14). For the purposes of creating the priorities for the PBR DSS, we followed the recommendation of Jon Oetting from FNAI to reclassify the original priorities into priorities of 1 to 9 (Table 1). For more information on the Surface Water submodels used to create this model and the CLIP Surface Water Priorities Model, see the Florida Forever Conservation Needs Assessment Technical Report, Version 3 (FNAI 2008). 5

6 Table 1. Priority Rankings for the Surface Water Resource Category Layer and Original Priorities PBR Priority CLIP Surface Water Model Priority 1 9 Priority 2 8 Priority 3 7 Priority 4 6 Priority 5 6 Priority 6 5 Priority 7 5 All other areas 1 CLIP/PBR Wetlands Model Priority 1 (including all wetlands on existing conservation lands) 9 Priority 2 8 Priority 3 7 All other areas 1 CLIP Surface Water Supply (FNAI submodel) Priority 1 9 Priority 2 8 Priority 3 7 Priority Priority Priority 9 5 Priority All other areas 1 2. Floodplains Resource Category a. CLIP Natural Floodplain Like the Surface Waters model, the Natural Floodplain data layer was created by FNAI, in consultation with state water resource experts, specifically for the Florida Forever statewide environmental land acquisition program. It is intended to show areas that have statewide significance for land acquisition to protect natural floodplain. This model includes natural riverine floodplain identified from Florida Fish and Wildlife Conservation Commission (FWC) LandSat satellite imagery land cover vegetation types. Bottomland hardwoods, hardwood swamp, freshwater marsh, and mixed wetland forest vegetation types that were adjacent to major river systems were included. The model was prioritized into three classes: 1) floodplain in FNAI Potential Natural Areas (PNA) priorities 1-4; 2) floodplain in PNA priority 5; and 3) floodplain outside PNAs. 6

7 For the PBR DSS we made one change to the CLIP model: all floodplain vegetation identified by FNAI in the original model that overlapped with existing conservation lands was also given the highest priority (Table 2). For more information see the CLIP report (2009) and the FNAI Florida Needs Assessment technical report (2008). b. District Floodplain Land Use Option (using Q3 floodplains as base data) This Core Data layer was created specifically for the PBR DSS. We used District Q3 floodplains data as the base. Then we used District land use data to separate floodplains into priority classes based on intensity of land uses. The classes were: 1) natural floodplains; 2) floodplains that overlap with semi-natural land uses such as pine plantations, rangelands, unimproved pastures, and open lands; 3) floodplains that overlap with agricultural land uses such as improved pasture, row and field crops, citrus, etc.; 4) floodplains that overlap with low density development such as residential areas with less than 2 units per acre; and 5) floodplains that overlap with higher density or intensity development including residential, commercial, and industrial These 5 floodplain land use classes were then ranked on the same priority scale of 1-9 (Table 2). Table 2. Priority Rankings for the Floodplains Resource Category Layer and Original Priorities PBR Priority CLIP Natural Floodplain Priority 1 (including all riparian floodplain vegetation on existing conservation lands) 9 Priority 2 8 Priority 3 7 All other areas 1 District Floodplain Land Use Option (using Q3 floodplains as base data) Floodplain in natural cover 9 Floodplain in seminatural land use 7 Floodplain in agricultural land use 5 Floodplain in intensive land use (low density) 3 Floodplain in intensive land use (high density) 2 All other areas 1 7

8 3. Groundwater Resource Category a. District Recharge We separated District Recharge 2002 data into seven classes based on inches of recharge per year and general land use classes from District land use data that can be used to indicate potential level of functional recharge. First, we lumped recharge into four classes: inches of recharge per year; 3-10 inches of recharge per year; inches of recharge per year; and areas of discharge. Then, the three recharge classes were subdivided into two classes each based on whether these areas of recharge overlap with natural/semi-natural/pasture or more intensive agriculture. All areas of intensive development were given the lowest priority regardless of the overlapping recharge level. To see all of the priority classes see Table 3. b. District Springsheds District springsheds were combined with spring location and spring magnitude to prioritize areas within springsheds based on spring magnitude and distance from springs. For magnitude 1-2 springs areas within 2.5 miles were given the highest priority, areas within 5 miles were given the next highest priority, and then areas beyond 5 miles were given a moderately high priority. For magnitude 3-5 springs, springs within 1 mile were given a moderate priority, areas within 2.5 miles were given the next highest priority, and areas beyond 2.5 miles were given moderately low priority. Buffers of magnitude 1-2 springs were always given precedence over buffers for magnitude 3-5 springs when they overlapped. Not all springs in the District have delineated springsheds. In these cases, a 2.5 mile circular buffer was applied and prioritized according to the rules described above for within springsheds. See Table 3 to see the all of the priority classes. c. District Sensitive Karst District sensitive karst areas were prioritized based on their overlap with two general land use categories from the District land use data. Sensitive karst areas that overlap with natural/semi-natural/pasture land use were given the highest priority. Sensitive karst areas that overlap with more intensive agriculture were given a moderately high priority. All other area including sensitive karst areas in areas of intensive development were given the lowest priority (Table 3). 8

9 Table 3. Priority Rankings for the Groundwater Resource Category PBR Priority Land Use Class Description (where applicable) Layer and Original Priorities District Recharge 2002 Recharge inches a year 9 natural/semi-natural/pasture Recharge inches a year 8 other ag Recharge 3.01 to 10.0 inches/year 7 natural/semi-natural/pasture Recharge 3.01 to 10.0 inches/year 6 other ag Recharge 0.01 to 3 inches/year 5 natural/semi-natural/pasture Recharge 0.01 to 3 inches/year 4 other ag all other cells 1 District Springsheds within 2.5 mi of 1-2 magnitude spring 9 within 5 mi of 1-2 magnitude spring 8 beyond 5 mi of 1-2 magnitude spring 7 within 1 mi of 3-5 magnitude spring 6 within 2.5 mi of 3-5 magnitude spring 5 beyond 2.5 mi 3-5 magnitude spring 4 all other areas 1 District Sensitive Karst Sensitive Karst 9 natural/semi-natural/pasture Sensitive Karst 7 other ag all other cells 1 B. Natural Systems Major Category 1. Species Resource Category a. CLIP Rare Species Habitat This data layer was created by FNAI specifically for the Florida Forever statewide environmental land acquisition program. It is intended to show areas that have a high statewide priority to protect habitat for Florida s rarest plant and animal species. The model was designed explicitly to identify areas important for species habitat based on both species rarity and species richness. FNAI mapped occurrence-based potential habitat for 248 species of plants, invertebrates, and vertebrates, including aquatic species. Because land acquisition was the focus, species were included according to their need for additional habitat placed in conservation. All federally listed species were included, as well as many state listed species and several species not listed at either the federal or state levels. Suitable habitat was mapped only in the vicinity of known occurrences. Species habitat was mapped based on remotely sensed vegetation data (FWC LandSat satellite imagery land cover and aerial photography classed into FLUCCS codes by Florida s Water Management Districts), as well as information 9

10 from various species experts. Each species received a Conservation Needs score based on rarity (FNAI Global rank), total habitat area, and percent of habitat protected on existing conservation lands. Species were then grouped into five Conservation Needs Weighting Groups (A through E). In the CLIP version of Rare Species Habitat, Priority 1 includes high suitability habitat for any G1S1 species, plus areas of overlap of multiple less-rare species. Priority 2 includes high suitability habitat for any G2S1 or G3S1 species, plus areas of overlap of multiple less-rare species. Priority 3 includes high suitability habitat for any G2S2, G3S2, G4S1, or G5S1 species, plus areas of overlap of multiple less-rare species. Priority 4 includes high suitability habitat for any G3S3, G4S2, or G5S2 species, plus areas of overlap of multiple less-rare species. Priority 5 includes high suitability habitat for any G4S3 or G5S3 species, plus areas of overlap of multiple less-rare species. Priority 6 includes all remaining habitat for G4S4, G5S4, and G5S5 species. See Table 4 to see how these priority classes were ranked in the PBR DSS. For more information see the CLIP report (2009) and the FNAI Florida Needs Assessment technical report (2008). b. CLIP Strategic Habitat Conservation Areas This data layer was created by FWC to identify gaps in the existing statewide system of wildlife conservation areas, and to inform ongoing land acquisition and conservation efforts. FWC modeled areas of habitat that are essential to sustain a minimum viable population for focal species of terrestrial vertebrates that were not adequately protected on existing conservation lands. Individual species potential habitat models were developed from FWC 2003 LandSat satellite imagery land cover overlaid with FNAI element occurrences, FWC wildlife observations, or other data relevant for identifying potential habitat. Individual SHCAs for each species were identified as the additional areas beyond existing conservation lands that were needed to ensure a minimum viable population for species that require additional habitat protection. The final SHCA data layer is an aggregation of the individual species SHCAs. For the CLIP analysis, SHCAs were prioritized into five classes for the Century Commission CLIP analysis. Priority 1 is species with Heritage ranks of S1 and G1-G3. Priority two is species with ranks of S1, G4-G5 or S2, G2-G3. Priority 3 is species with Heritage ranks of S2, G4-G5 or S3, G3. Priority 4 is species with ranks of S3, G4. Priority 5 is species with ranks of S3, G5 or S4, G4. See Table 4 to see how these priority classes were ranked in the PBR DSS. For more information see the CLIP report (2009). c. CLIP Hotspots Because SHCAs do not address species richness, FWC also developed Biodiversity Hotspots to identify areas of overlapping species habitat (Fig. 2). FWC created a statewide potential habitat model for each species included in their analysis. The Biodiversity Hotspots layer includes the entire potential habitat model for each species and provides a count of the number of species with potential habitat occurring at each location. The highest number of focal species co-occurring at any location in the model is 13. Unlike SHCAs, the Biodiversity Hotspots layer does not address 10

11 species rarity, rather it is a simple additive overlay of focal species habitat models. For CLIP, Biodiversity Hotspots are prioritized by the species count, with higher species counts given higher priority over lower species counts. See Table 4 to see how these priority classes were ranked in the PBR DSS. For more information see the CLIP report (2009). d. Wide-ranging Species Habitat The wide-ranging species habitat model was created specifically for the PBR DSS using a black bear habitat connectivity model used in various project s including the District s Land Use and Land Management GIS Decision Support System and the Greater Ridge Conservation Planning Tool project (TNC 2008). This model identifies large landscapes containing blocks of primary and secondary bear habitat connected either through primary or secondary habitat or by other traversable land uses that bears can move through functionally. In order to be identified as potential bear habitat, primary and secondary habitat must occur in functionally connected blocks of 10,000 acres or larger. Therefore, the black bear habitat model can be considered an indicator model that identifies larger, intact landscapes that may be suitable for other wide-ranging species including the Florida panther, bobcats, otters, etc. Unlike the species Core Data from CLIP it is more focused on identifying larger connected blocks of habitat needed by species that require large, intact areas to support individuals or viable populations. The three classes of habitat identified in this model, primary, secondary, and traversable matrix were given high, moderately high, and moderate ranks respectively (Table 4). 11

12 Table 4. Priority Rankings for the Species Resource Category Layer and Original Priorities PBR Priority CLIP Rare Species Habitat Priority 1 9 Priority 2 9 Priority 3 8 Priority 4 7 Priority 5 6 Priority 6 6 All other areas 1 CLIP SHCAs Priority 1 9 Priority 2 9 Priority 3 8 Priority 4 8 Priority 5 7 All other areas 1 CLIP Hotspots 8-13 species 9 7 species species species 6 1 species 5 All other areas 1 Wide-ranging Species Habitat Primary habitat 9 Secondary habitat 7 Traversable matrix 5 All other areas 1 2. Natural Communities Resource Category a. Natural Communities The Natural Communities Resource Category in the PBR DSS consists of one core data layer that combines the CLIP Underprotected Natural Communities data layer with natural communities identified using District land use data. The CLIP Underprotected Natural Communities data layer was created by FNAI specifically for the Florida Forever statewide environmental land acquisition program. It is intended to map high priority natural communities that are under-represented on existing conservation lands. FNAI mapped the statewide range of 11 natural community types: upland glades, pine rocklands, seepage slopes, scrub, sandhill, tropical hardwood hammock, upland hardwood forest, pine flatwoods, dry prairie, coastal uplands, and 12

13 coastal wetlands. The natural communities were mapped based on a combination of remotely sensed vegetation data (FWC satellite imagery land cover and aerial photography classed into FLUCCS codes by Florida s Water Management Districts) and field observations. The natural communities are mutually exclusive types (any given location can be classed as only one community type), so there is no overlay model of the communities. For the CLIP analysis, the natural communities were classed by State rarity rank (S-rank). For the PBR DSS, the following natural communities from the FNAI natural communities data were given the highest priority: coastal uplands, dry prairie, sandhill, sandhill upland lake, scrub, seepage slope/bog, and upland glade (no tropical hammock or pine rockland in the District). Pine flatwoods were given a high priority, and coastal wetlands and upland hardwood forest were given a moderately high priority (Table 5). We then used District land use data to identify additional natural communities, which were given a moderate priority. Semi-natural land use types (such as pine plantations, rangelands, and unimproved pastures) were given a moderately low priority (Table 5). For more information see the CLIP report (2009) and the FNAI Florida Needs Assessment technical report (2008). Table 5. Priority Rankings for the Natural Communities Resource Category Layer and Original Priorities PBR Priority CLIP Underprotected Natural Communities Priority 1 (coastal uplands, dry prairie, sandhill, sandhill upland lake, scrub, seepage slope/bog, upland glade) 9 Priority 2 (pine flatwoods) 8 Priority 3 (coastal wetlands and upland hardwood forest) 7 WMD 2007 FLUCCS Land Use Data Natural Cover Types Natural Cover Types 5 Semi-natural Cover Types 3 All other areas 1 Note: The two ranking systems above are used to create one PBR core data layer and, since there is only one core data layer in this resource category, it also becomes the resource category data layer by default. 3. Landscape Resource Category a. CLIP Florida Ecological Greenways Network The Florida Ecological Greenways Network model was created to delineate the ecological component of a Statewide Greenways System plan developed by the DEP Office of Greenways and Trails, under guidance from the Florida Greenways Coordinating Council and the Florida Greenways and Trails Council. This plan guides OGT land acquisition and conservation efforts, and promotes public awareness of the need for and benefits of a statewide greenways network. It is also used as the 13

14 primary data layer to inform the Florida Forever conservation land acquisition program regarding the location of the most important conservation corridors and large, intact landscapes in the state. This data layer is intended to represent a statewide network of ecological hubs and linkages designed to maintain large landscape-scale ecological functions including focal species habitat and ecosystem services throughout the state. The model started with an aggregation of a variety of existing habitat models including FWC SHCAs, FWC Biodiversity Hotspots, FWC Priority Wetlands for Listed Species, FNAI Potential Natural Areas, FNAI Areas of Conservation Interest, existing and proposed conservation lands, and vegetation from FWC satellite imagery land cover. These data were used to identify a series of hubs, or core areas, of large, landscape-scale ecological significance, and a network of landscape linkages and corridors connecting the hubs into a statewide ecological greenways system. The entire model was updated in 2004 to include newly identified areas of ecological significance (including the FNAI Rare Species Habitat Conservation Priorities and Surface Water models) and to remove recently developed areas. The 2004 base boundary was prioritized by assigning individual corridors to six priority classes, based on contribution to the statewide ecological network. The top priority is called Critical Linkages, which are considered most important for protecting the Florida Ecological Greenways Network. Critical Linkages were identified based on both ecological value and threat from development pressure. Priority 2 areas are also very significant and together with Critical Linkages they represent the best opportunities to protect large, connected landscapes throughout Florida from the Everglades to the western tip of the Florida panhandle. For the CLIP analysis, a subset of the top two Greenways priorities was identified for further prioritization as critical parcels. These parcels represent the best opportunity for making a corridor connection in each Critical Linkage ( critical parcels 1 ) or in each Priority 2 corridor ( critical parcels 2 ). Therefore, in the CLIP version of the prioritized Florida Ecological Greenways Network has 8 priority levels: Critical Parcels 1 = Parcels within Critical Linkages that are critical for completing a corridor connection; Critical Parcels 2 = Parcels within Priority 2 that are critical for completing a corridor connection; Priority 1 (Critical Linkages) = Remaining areas of Critical Linkages not covered by Critical Parcels 1; Priority 2 = Remaining areas of Priority 2 greenways not covered by Critical Parcels 2; Priority 3 = Priority 3 ecological greenway corridors; Priority 4 = Priority 4 ecological greenway corridors; Priority 5 = Priority 5 ecological greenway corridors; Priority 6 = Priority 6 ecological greenway corridors. Priority 6 includes existing conservation lands. For Priorities 2-6, the ecological greenways corridors are prioritized based on: 1) potential importance for maintaining or restoring populations of wide-ranging species (e.g. Florida black bear and Florida panther); 2) importance for maintaining a statewide, connected reserve network from south Florida through the panhandle; 3) other important landscape linkages that provide additional opportunities to maintain statewide connectivity especially in support of higher priority linkages; 4) provide important riparian corridors within Florida and to other states; and 5) other regionally significant opportunities to protect large intact landscapes. See Table 6 to see the 14

15 rankings of the FEGN into PBR DSS priority classes. For more information see the CLIP report (2009). b. CLIP Landscape Integrity The landscape integrity layer is comprised of two related landscape indices assessing ecological integrity based on land use intensity and patch size of natural communities and semi-natural land uses. The landscape integrity layer was developed as part of the CLIP TAG process after discussion about the need for an additional landscape layer that identified areas of high ecological integrity based on land use intensity and patch size, where areas dominated by large patches of natural and semi-natural land use are assigned the highest significance. Since these analyses are dependent on landscape scale analysis, buffer areas in Georgia and Alabama were included to provide accurate assessment of the areas of Florida near the Georgia or Alabama border. Please note that this index is intended to primarily characterize terrestrial ecosystems and therefore values for large water bodies are not considered significant. The land use intensity index characterizes the intensity of land use across the state based on five general categories of natural, semi-natural (such as rangelands and plantation silviculture), improved pasture, agricultural/low-intensity development, and high intensity development. The assumption is that areas dominated by high intensity land uses are more likely to have severe ecological threats and much lower ecological integrity than areas dominated by natural land cover. The land use data is from the Water Management Districts 2004 data for most of the state (SJRWMD, SWFWMD, SFWMD, parts of SRWMD) and a hybrid between either 1995 land use, the 2003 FWC land cover data, and the 2004 FNAI development layer wherever 2004 land use data was not available (small parts of the SRWMD and all of the NWFWMD). The patch size index combines the land use data with major roads data to identify contiguous patches of natural and semi-natural land cover and ranks them based on area. In addition all pasturelands within the south-central prairies region were also considered "intact" and potentially part of patches. This region was defined using the Davis Potential Natural Vegetation map for Florida. Major roads were defined as all roads that have 4 or more through lanes and all roads with average annual daily traffic of 5,000 or more vehicles per day. These roads were selected because they are considered to be the most likely to fragment habitat through a combination of road width and traffic level. The assumption is that small patches are likely to have the highest threat and lowest ecological integrity and large patches are likely to have the lowest threat and highest ecological integrity. The combination of the land use intensity and patch size indices was created by adding the two together and dividing by two to create a non-weighted average of the two indices. Values of 10 represent areas with the highest potential ecological integrity based on these landscape indices and 1 represents the lowest ecological integrity. See Table 6 to see the rankings of the FEGN into PBR DSS priority classes. For more information see the CLIP report (2009). 15

16 Table 6. Priority Rankings for the Landscape Resource Category Layer and Original Priorities PBR Priority CLIP Florida Ecological Greenways Network Priority 1 Strategic Linkages 9 Priority 2 Strategic Linkages 9 Priority 1 7 Priority 2 7 Priority 3 7 Priority 4 7 Priority 5 5 Priority 6 5 All other areas 1 CLIP Landscape Integrity Index Value 10 9 Index Value 9 9 Index Value 8 8 Index Value 7 8 Index Value 6 7 Index Value 5 5 Index Value 4 4 Index Value 3 3 Index Value 2 2 Index Value 1 1 Methods for Creating Resource Category, Major Category, and Aggregated Priority Models Through the series of PBR DSS meetings and preliminary analyses, we explored various methods for combining Core Data into the combined priorities represented by the Resource and Major Categories and then an Aggregated Priorities model that combined all Core Data into one set of cumulative priorities. The two primary combination options are overlay and rules-based approaches. Overlay or weighted overlays involve combining individual data layers through mathematical combination. The standard form of mathematical combination is averaging, where data layers with the same rank scale (in this case 1 to 9) are added together and then divided by the total number of layers. If the modelers determine that one or more of the input data layers are more important than others, then differential weighting can be used. The effect of such models is that they emphasize overlap of priorities across data layers, which can be useful when the modeler determines that such overlap has significance for the modeling purpose, but it also can lead to averaging effects that result in many areas having only average priority and very few areas having higher priority. In contrast, rules-based approaches can be any combination of user-defined rules that are used to delineate priority classes. For example, it might be determined that all higher priority classes from one Core Data layer would be considered highest priority in the combined Resource Category data layer but only the 16

17 highest priority from another Core Data layer would be. This approach was emphasized in the CLIP model. However, for the PBR DSS, the primary rules-based approach selected was a maximum model. In the maximum model at all cell locations within the raster data for the study area, the output value for a combined priority model is the highest priority for any of the input layers. For example, if there are three input data layers all prioritized on a scale of 1 to 9 and only one of these layers had a value of 9 and the other two layers had a value of 1 at a grid cell location, then the output value would be a value of 9. In contrast, an averaging overlay model would have a value of 3.66 at the same grid cell location. For GIS decision support modeling, there is not a right or wrong answer when making choices about the best approach for combining data layers into combined priorities. The choices should be based on examining input data in relation to the goals of the project and selecting the combination methods that provide the most or best information relevant to supporting the decision making process. For more information on these method alternatives, see Carr and Zwick (2007). After examining the alternatives for combining Core Data, we selected a hybrid modeling approach that used both average overlay and maximum rules-based approaches from creating combined priorities. More specifically, Resource Category combinations (Surface Water, Floodplains, Groundwater, Species, Natural Communities, and Landscapes) were created using the maximum rules-based approach. One of the reasons this was selected was the fact that each of the Resource Categories contained different numbers of Core Data layers, and we desired that all Core Data layers would have an equal effect on all model combinations. If an average overlay approach was used with Resource Categories, then Core Data layers in Resource Categories with fewer Core Data sets would have a greater effect on the combined model priorities. An additional reason for selecting the maximum rules-based approach with Resource Categories was to create a compromise between the impact of the rules-based approach (which can result in a lot of area being identified as high priority, which might reduce usefulness in making decision-making) and average overlays. Then, the Major Category data layers (Water Resources and Natural Systems) were created using the average overlay approach. Finally, we created two alternative Aggregated Priorities models using the two different combination methods. Therefore, one Aggregated Priorities model combines the Water Resources and Natural Systems Major Categories using the average overlay approach and the other Aggregated Priorities model was created using the maximum rules-based approach. 17

18 Results This result section includes maps of all Resource Category results, the two Major Category results, and the two model options for the Aggregated Priorities (Figures 2-11). Figure 2. Surface Water Resource Category Maximum Model Results 18

19 Figure 3. Floodplains Resource Category Maximum Model Results 19

20 Figure 4. Groundwater Resource Category Maximum Model Results 20

21 Figure 5. Species Resource Category Maximum Model Results 21

22 Figure 6. Natural Communities Resource Category Maximum Model Results 22

23 Figure 7. Landscape Resource Category Maximum Model Results 23

24 Figure 8. Water Resources Major Category Model Results 24

25 Figure 9. Natural Systems Major Category Model Results 25

26 Figure 10. Aggregated Priorities Maximum-Average-Maximum Option Model Results 26

27 Figure 11. Aggregated Priorities Maximum-Average-Average Option Model Results 27

28 Discussion The Project Boundary Review Ecological GIS Decision Support System (DSS) is intended to provide integrated information on ecological and wildlife conservation priorities within the Southwest Florida Water Management District to inform decisions regarding additions or deletions within conservation land acquisition projects as part of the Project Boundary Review (PBR) project. The PBR DSS is a hierarchical GIS database that provides a variety of data on biodiversity and water resource conservation priorities within the District by integrating data from the Critical Lands and Waters Identification Project with District data and other relevant information. The PBR DSS was developed using a team approach that included Tom Hoctor from the Center for Landscape and Conservation Planning at the University of Florida, staff from Wildlands Conservation, and staff from the District. The team developed and prioritized all Core Data and integrated Core Data into 3 sets of combined priorities (Resource Categories, Major Categories, and Aggregated Priorities) using a combination of rules-based methods and mathematical overlays. Through this process, the team recognized that all levels of the PBR DSS data hierarchy are useful for informing the conservation land acquisition planning process. In other words, though the goal was to create one or two maps that depicted combined priorities for all resources (the Aggregated Priority data layers), individual Core Data layers and the Resource Category and Major Category combinations can also have utility for more specific land acquisition planning purposes. We also discussed the possibility that these data could be used for other planning activities by District staff in a variety of applications. One future goal may be to explore these other potential applications and whether any modifications of the PBR DSS might make it more useful for other such applications. More specifically, the PBR DSS should be treated as an iterative decision support tool that is updated and improved over time. Basic updates could include modifying Core Data based on changes in District land use data in the yearly updates to these land use data. In addition, CLIP data may be updated as frequently as once every year to two years, which would affect some of the Core Data layers in the PBR DSS. The PBR DSS team also discussed other potential future modifications subject to data availability, time, and future funding constraints. Future modifications to the DSS could include: 1) Develop a more thorough surface water supply protection priorities Core Data layer. This could include working with FNAI to make sure they include all potable surface water supply sources in their modeling of Surface Water protection priorities and/or the development of surface water supply protection priorities by District staff. 2) Consider using the new FNAI Groundwater Protection priorities layer that is available in final draft form but was not available for this version of the PBR DSS. 3) Develop a more refined Groundwater Recharge layer with cell resolutions similar to other PBR DSS data. Consider modifying Groundwater Recharge priorities with more refined comparisons with land use or other factors that may affect recharge function. In other words, the focus of the Groundwater Recharge layer, 28

29 at least for the purposes of conservation land acquisition planning should likely be on intact areas providing the most functional recharge. Also consider whether additional or different classes of priority recharge levels should be included in the future (e.g., whether the current three levels are sufficient or should be modified). 4) Consider identifying springshed for all springs within the District. Consider refining relationship between distance from springs and prioritization, e.g., should other distances be used or should/could distances vary in specific springsheds. 5) Consider refining District Sensitive Karst to reflect the impact of current land use or any other relevant factors that might affect protection of karst areas versus simply separating Sensitive Karst areas into two priority classes based on general land use categories. 6) Consider whether there are any District focal species that should be addressed more specifically than in the CLIP species Core Data layers or the Wide-Ranging Species layer. 7) Consider whether there are any District natural communities that should be elevated in priority similar to how the CLIP Underprotected Natural Communities are ranked. Work with FNAI to make sure that natural communities in the CLIP Underprotected Natural Communities layer are mapped correctly with the District. 8) Consider adding a regional/local riparian corridor data layer to the Landscape Resource Category to augment the state to regional corridors identified in the Florida Ecological Greenways Network. A potential model for doing so is included in the Greater Ridge Conservation Planning Tool (2008). All of these proposed modifications to the PBR DSS would refine the model in future iterations; however, the current model is a very useful tool for informing the decisionmaking process regarding additions or deletions to current District conservation land acquisition projects. Citations Carr, M. H. and P. D. Zwick Smart land-use analysis: the LUCIS model land use identification strategy. ESRI Press, Redlands, California. Florida Natural Areas Inventory Florida Forever Conservation Needs Assessment Technical Report, Version 3. Tallahassee. Hoctor, T.S., J. Oetting, and S. Beyeler Critical Lands and Waters Identification Project: Report on completion of the CLIP Version 1.0 Database. Report for the Century Commission for a Sustainable Florida, Tallahassee. The Nature Conservancy, Archbold Biological Station, and University of Florida The Greater Ridge Conservation Planning Tool. Babson Park, Florida. 29

30 Appendix: Guide to the PBR DSS GIS Data Layers The following list is organized by the folder structure in the GIS database provided to the District. Some ancillary data is also included to maximize potential utility of data. Ancillary data is defined here as additional layers created during the model development process but not used to create the final Aggregated Priorities layer options. In addition, District staff requested two additional Aggregated Priorities options that excluded the District Recharge and District Sensitive Karst Core Data layers from the determination of combined priorities. All such ancillary data are highlighted in yellow below to distinguish them from primary data. 1) Aggregated Model (Aggregated_Model): contains the model alternatives combining the Water Resources and Natural Systems prioritization models. a. agg_maxavav = average of the maximum-average models for Water Resources and Natural Systems b. agg_maxavmax = maximum model where the highest value from either the Water Resources model or Natural Systems model is retained in this product grid (this is the same methodology used in all maximum models described below). c. agg_maxavhy = hybrid model where a value of 9 in either the Water Resources or Natural Systems models are retained in this product grid but all other values are averaged (this is the same methodology used in all hybrid models described below). d. agg_maxavavs = average of the maximum-average models for Water Resources and Natural Systems but EXCLUDING the District Recharge and Sensitive Karst Core Data layers. e. agg_maxavmaxs = maximum model where the highest value from either the Water Resources model or Natural Systems model is retained in this product grid but EXCLUDING the District Recharge and Sensitive Karst Core Data layers. 2) Natural Systems (Natural_Systems): contains the Species, Natural Communities, and Landscape Resource Category grids and another folder containing the Natural Systems Major Category model alternatives that combine the Species, Natural Community, and Landscape Resource Categories. a. Species Resource Category (Species) 1. fnaihabr = FNAI Rare Species Habitat PBR reclassification 2. fwchotspr = FWC vertebrate species hotspots PBR reclassification 3. fwcshcar = FWC Strategic Habitat Conservation Areas PBR reclassification 4. wrsp_hab = T. Hoctor wide-ranging species habitat and connectivity model 5. biodiv_max = maximum model of the four Species Resource Category core data grids 6. biodiv_av = average of the four Species Resource Category core data grids 30

31 7. biodiv_hy = hybrid model of the four Species Resource Category core data grids b. Landscape Resource Category (Landscapes) 1. ecogwayr = Florida Ecological Greenways Network PBR reclassification 2. land_intrr = Landscape Integrity PBR reclassification 3. landscape_max = maximum model of the two Landscape Resource Category core data grids 4. landscape_av = average of the two Landscape Resource Category core data grids 5. landscape_hy = hybrid model of the two Landscape Resource Category core data grids c. Natural Community Resource Category (Natural_Communities) 1. natcommf = combination of prioritized FNAI Underprotected Natural Communities and natural and semi-natural land cover types for District 2007 land use data (this grid serves as a core data layer and the Resource Category layer given there is only one layer in this Resource Category) f. Natural Systems Major Category Models (Natural_Systems_Model) 1. natsys_maxav = average model combining the three Natural Systems Resource Category grids that were created by maximum models 2. natsys_av = average of the three Natural Systems Resource Category grids that were also created by averaging (except for the Natural Community Resource Category) 3. natsys_hy = hybrid model combining the three Natural Systems Resource Category grids that were also created by hybrid models (except for the Natural Community Resource Category) 4. natsys_max = maximum model combining the three Natural Systems Resource Category grids that were also created by maximum models (except for the Natural Community Resource Category) 5. natsys_maxhy = hybrid model combining the three Natural Systems Resource Category grids that were created by maximum models 3) Water Resources: contains the Floodplains, Groundwater, and Surface Water Resource Category grids and another folder containing the Water Resources Major Category model alternatives that combine the Floodplains, Groundwater, and Surface Water Resource Categories. a. Floodplains Resource Category (Floodplains) 1. flood_cliprc = FNAI Riparian Floodplains PBR reclassification 2. flood_luse = District Q3 floodplains prioritized by District land use categories 3. flood_max = maximum model of the two Floodplains Resource Category core data grids 31