Combined Zonation Results 2024 [U.S. Fish and Wildlife Service]
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- Title
- Combined Zonation Results 2024 [U.S. Fish and Wildlife Service]
- Description
- We used version 5 of Zonation for the entire Southeast Blueprint. We ran Zonation within 6 zones. Zonation ranks the pixels in each zone according to their indicator scores, using a modeling approach that tries to conserve high-value representations of all indicators collectively. Pixels that rank higher in Zonation become higher priority in the Blueprint. INLAND CONTINENTAL REMOVING RESERVOIRS Reasoning Though reservoirs are highly altered systems, they still have conservation value. Unfortunately, the current set of indicators used in the Blueprint do not do a good job of capturing important parts of reservoirs or distinguishing the relative value of different reservoirs. As a result, we remove reservoirs from the zones used to define the boundaries of each Zonation run so that they are not eligible to be prioritized in the Blueprint. However, the indicators do capture the value of areas surrounding reservoirs, and those areas are not removed. The areas around reservoirs are also where most conservation actions occur to improve reservoir condition. Input Data USGS National Hydrography Database (High Resolution) in FileGDB 10.1 format (published 08-30-2021) - NHDWaterbody and NHDFlowlines, accessed 10-14-2021 National Inventory of Dams (NID), accessed 10-15-2021; download the data 2019 National Land Cover Database (NLCD) Floodplain Inundation Frequency Southeast version: available on request by emailing yvonne_allen@fws.gov Base Blueprint 2022 extent Mapping Steps Make copies of the NHDWaterbody and NHDFlowlines layers for editing. Extract features identified as either "LakePond" or "Reservoir" from the NHDWaterbody layer. Most reservoirs in the Southeast region are coded as "LakePond" in this dataset. Make a geospatial layer of the National Inventory of Dams (NID) from the source .csv file. Select NHD waterbodies that are within 200 m of NID locations. Add to the selection all NHD waterbodies that are within 5 m of the selection generated in the previous step to ensure that all parts of a single waterbody are selected. Select NHD flowlines that are within 50 m of NID locations. Select NHD waterbodies that are within 50 m of the selection generated in the previous step. At this stage, we did some hand-editing to add in obvious large reservoirs (especially in Texas) that were omitted from the above selections because the NID did not capture the dam locations. We used Inundation Frequency and the 2019 NLCD to assist in this step. In addition, the NHD contains some misclassified reservoirs (e.g., reservoirs classified as swamp/marsh or stream/river) that we manually added in. Note: The NID is also missing many dam locations associated with small farm ponds, which are too numerous to add by hand. Convert to raster using the ArcPy Polygon to Raster function and clip to the spatial extent of Base Blueprint 2022. Create a mask to use for Zonation runs by creating a raster where everything in the SECAS extent is 1 except for the reservoirs, which are NoData. For rebalancing later, we also create a raster where everything in the SECAS extent is 1 and the reservoirs have a value of 0. Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code. ZONATION INPUTS To create the inputs for each inland continental Zonation run: Clip the reservoirs mask defined above to the 4 inland continental zones. This effectively removes reservoirs from each zone so that reservoirs are not included in each Zonation run. This step is not required in the continental marine or the Caribbean areas because the reservoir layer does not extend there. Clip the inland continental indicators to the 4 inland continental zones. Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code. The zones with reservoirs masked out serve as the boundaries of each Zonation run. Each Zonation run in the inland continental geography includes: All indicators that occur in a given zone, clipped to that zone All subregions that occur in a given zone ZONATION RUN SETTINGS Determining Indicator Weights We use Zonation to identify a network of priorities that includes the areas most important for all indicators collectively. Since the goal is to create a balanced portfolio where all indicators are represented, in a perfect world, we would weight each indicator equally. However, in some cases, we have to downweight indicators that would otherwise have an outsized impact on the Blueprint priorities. The way Zonation ranks pixels on the landscape can be influenced by factors like an indicator's spatial rarity within a given zone and the distribution of high and low values (i.e., is the indicator's distribution top-heavy or bottom-heavy). In addition, some indicators are based on coarse-scale data. Other indicators have a wide spread of high to low values, but the data provider only intends the highest values to be considered good candidates for conservation action. In these situations, we use weighting to limit the disproportionate influence of certain indicators. We developed four standard indicator weighting rules to ensure that: Coarse-scale indicators have less of an influence Spatially limited (i.e., rare) indicators were not overprioritized Subregional indicators that don't cover most of their target ecosystem were not overprioritized Indicators with limited overlap of below-average values in a given zone were not overprioritized In a handful of edge cases, we developed additional exceptions to those four rules. The section below describes the application of the standard rules and additional exceptions. Coarse-scale indicators Two indicators (amphibian and reptile areas and estuarine coastal condition) have a much coarser spatial scale than the other indicators in the Blueprint. Amphibian and reptile areas uses generalized, expert-defined polygons, while estuarine coastal condition interpolates data from a relatively small number of sampling points into broad condition scores. These datasets oversimplify more site-specific variation in ecosystem health and habitat value, so we reduce their weight by 0.5 across all zones to limit their influence on the Blueprint priorities. This allows other finer-scale indicators to play a stronger role in teasing out key places within broad areas of importance for these coarser indicators. Spatially limited indicators With equal weights, the entire area of many indicators that cover a limited part of a zone (e.g., South Atlantic beach birds, greenways and trails) is identified as a high priority. To address this issue, we weighted indicators based on the proportion of their total area with values ≥1 in a given zone. For indicators where the proportion of ≥1 values was between 0.1 and 0.5, we set their weights equal to that proportion. For example, we set the imperiled aquatic species indicator weight to approximately 0.26 in the Coastal Zone because that was the proportion of indicator pixels analyzed by Zonation in that zone with a value ≥1. For indicators where the proportion of ≥1 values was ≤0.1, we set their weights equal to that proportion multiplied by 3. We found that indicators in this range began to be underprioritized when only using the unadjusted proportion. For indicators where the proportion of ≥1 values was 0.5 and higher, we set their weight to 1.0 unless they were covered by the exceptions discussed below. Subregional indicators that don't cover most of their target ecosystem Nine indicators use source datasets that only cover specific subregions and do not apply to the majority of their target ecosystem (Mississippi Alluvial Valley forest birds - protection, Mississippi Alluvial Valley forest birds - reforestation, West Coastal Plain and Ouachitas forested wetland birds, West Coastal Plain and Ouachitas open pine birds, West Gulf Coast mottled duck nesting, South Atlantic forest birds, South Atlantic beach birds, South Atlantic low-urban historic landscapes, South Atlantic maritime forest). For example, South Atlantic forest birds does not capture important forest bird habitat that occurs elsewhere in the Southeast. While these indicators help the Blueprint identify important areas within these subregions, they can also unfairly penalize areas that aren't covered by the models that would otherwise score highly due to their habitat or ecosystem value. This can cause Zonation to overprioritize the subregions that happen to be covered by these indicators. To reduce these unintended negative impacts, we reduce the weight of these indicators by 0.5. Indicators with limited overlap of below-average values in a given zone In a few cases, an indicator that targets a specific ecosystem will occur primarily in one zone, but spill over the boundary slightly into a neighboring zone. When this handful of overlapping pixels scores relatively low for that indicator, Zonation will often overprioritize these low-scoring areas. This occurs because Zonation can only see the range of indicator values that occur in a given zone—so while they score low overall, they are the highest values in that zone, which makes these areas seem more important than they actually are. To address this, we multiply the weight of the four indicators that have a small amount of overlapping below-average values in a given zone by 0.1. This rule applies to East Coastal Plain open pine birds and resilient coastal sites in the Greater Appalachians, Gulf coral and hardbottom in the Central West, and imperiled aquatic species in the Marine. Other exceptions Landscape condition & intact habitat cores in the Arid West The Arid West Zone is composed of much drier ecosystems that are climatically distinct from the rest of the SECAS geography. Landscape condition and intact habitat cores do not perform as well in this zone for distinguishing natural, unfragmented areas from more altered areas. As a result, the standard "spatially limited indicators" weighting rule caused Zonation to overprioritize the Arid West. To mitigate this, we reduced the weight of these indicators to 0.7 in the Arid West only. Spatially limited Indicators on the edge of weighting thresholds In some cases, indicators that are right on the edge of weighting thresholds can cause issues. Intact habitat cores in the Coastal Zone is just below the 0.5 "spatially limited indicators" threshold and was being underrepresented. Being above that threshold would've resulted in a weight of 1.0 instead of <0.5. To address this, we doubled the weight to bring it closer to 1.0. Some subregional inputs in the Central West Zone also had weight threshold issues. In this case, some fell under the 0.1 threshold which gave them an extra upward adjustment. That resulted in inconsistent representation across the subregions in the Central West Zone. To address this, we did not do the extra upward weight adjustment for subregions under 0.1 in the Central West Zone. This resulted in better balance across subregions. Marginal Loss Rule CAZMAX: This setting is consistent with basic core-area Zonation (commonly CAZ) in Zonation 4, which was used in previous versions of the Blueprint. It tries to always cover high-value locations for all indicators, even if this comes with the cost of achieving lower average coverage across all indicators. Additional Connectivity Methods To increase the overall aggregation of the priorities, we included each indicator twice in each zone. The first version of the indicator in the input file is just the indicator layer as-is. For the second version, we have Zonation do a connectivity transformation. This approach of using both regular and connectivity transformed versions of indicators is described in the Zonation manual. It helps balance locally important places and nearby connectivity. For the connectivity transformation, we used the negative exponential dispersal kernel. This is commonly used in modeling animal movements (Moilanen et al. 2024). We used a parameter of 1, which aligns with a mean dispersal distance of 1 pixel. For the cut parameter, which sets a distance to stop the connectivity transformation, we used a value of 5 pixels. Beyond that distance, the distribution already has only a minimal impact on values anyway. Zonation Weights for Each Zone To see the weights used for each indicator and subregion in each inland continental zone, see the Zonation feature list files see the Zonation feature list files available in the Southeast Blueprint 2024 Data Download under > 6_Code. REBALANCING ZONATION RESULTS Rebalancing the Results for Each Zone The Zonation software outputs a raster layer with values ranging from a very small fraction close to 0 to 1 (the actual minimum value depends on the size of the input area). The values in the Zonation output represent the percent of the input area ranked from highest priority to lowest priority, according to the indicators. So, if we take the values in the Zonation output that range between 0.9 and 1, this represents the best 10% of the input area. In the inland continental zones, we removed reservoirs before we ran Zonation (described in the Removing Reservoirs section above). As a result, the outputs from Zonation represent a subset of each zone's area, and not the entire zone. So, we need to convert the Zonation output values into the percent of the whole zone (currently, they are the percent of the portion of the zone we gave it, which excludes reservoirs). To account for this, we rescaled the Zonation outputs using a linear rescale. The goal is to have the top 10% of each zone score "highest priority," the next 15% score "high priority," and the next 20% score "medium priority." First, we calculated the percentage of each zone that was left out of Zonation (reservoirs). Then we used the ArcPy Rescale "Linear" function to convert every pixel from the original Zonation output to a new value, so that the new minimum is equal to the percent of the zone that was left out of Zonation and the new maximum value is 100. The linear rescale both shifts the values up and condenses the values (because the percent of zone area covered by each pixel is smaller now that we have a bigger denominator). Then we added back in the areas that were left out of Zonation (reservoirs) and gave them a value of 0. This puts them in the bottom of the stack and ensures they don't interfere with any of the other values when we add in the 5% of the area covered by corridors later. The result is that we have more priority areas in the linear rescale output than we had in the Zonation output, because we have made the "highest priority" class cover 10% of the entire zone and not just 10% of a portion of the zone. These methods allow priorities that would have gone to reservoirs (if they had ranked highly enough to get prioritized in Zonation based on their indicator scores) to be applied to other areas inside the zone. So, even if a zone has a lot of reservoirs taking up space, it still gets 10% of the total zone as highest priority, 15% as high priority, and 20% as medium priority. The resulting Blueprint output matches the result we would get if we had given Zonation all the reservoirs and they just had very low indicator scores. SECAS staff are actively working on developing indicators that specifically measure the value or importance of reservoirs. Since we do not have those indicators for Blueprint 2024, we cannot score reservoirs relative to each other. Considering this lack of information, we decided this was the best option for Blueprint 2024, as it does not punish zones that have a lot of reservoirs by limiting the total amount of priority areas those zones are allotted. Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code. MARINE CONTINENTAL ZONATION INPUTS To create the inputs for the marine continental Zonation run: Clip the marine continental indicators to the Continental Marine Zone. Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code. The Continental Marine Zone serves as the boundaries of the Zonation run. This Zonation run includes: All indicators that occur in the Continental Marine Zone, clipped to that zone All marine continental subregions ZONATION RUN SETTINGS Determining Indicator Weights We developed indicator weighting rules to ensure that: Spatially limited indicators were not overprioritized Priorities were relatively balanced between Gulf and Atlantic areas Spatially limited indicators With equal weights, the entire area of many indicators that cover a limited part of the Continental Marine Zone (e.g., estuarine coastal condition, Atlantic estuarine fish habitat) is identified as a high priority. To address this issue, we weighted indicators based on the proportion of their total area with values ≥1. For indicators where the proportion of ≥1 values was between 0.1 and 0.5, we set their weights equal to that proportion. For example, we set the Gulf coral and hardbottom indicator weight to approximately 0.217 because that was the proportion of pixels analyzed by Zonation in the Continental Marine Zone with a value ≥1. For indicators where the proportion of ≥1 values was ≤0.1, we set their weights equal to that proportion multiplied by 3. We found that indicators in this range began to be underprioritized when only using the unadjusted proportion. For indicators where the proportion of ≥1 values was 0.5 and higher, we set their weight to 1.0 unless they were covered by the "balancing Atlantic and Gulf" rule below. Balancing Atlantic & Gulf priorities The Continental Marine Zone includes many indicators that only cover either the Atlantic or the Gulf. In order to better balance priorities across those areas, we made two modifications to the "spatially limited indicators" weighting rule. The first modification was only weighting bird, mammal, and turtle indicators when the proportion of ≥1 values was less than 0.3. The second change was increasing the weight of two indicators that were getting significantly under-represented in the Blueprint priorities (Atlantic coral and hardbottom, Gulf deep-sea coral richness) from 0.3 to 0.6. Marginal Loss Rule CAZMAX: This setting is consistent with basic core-area Zonation (commonly CAZ) in Zonation 4, which was used in previous versions of the Blueprint. It tries to always cover high-value locations for all indicators, even if this comes with the cost of achieving lower average coverage across all indicators. Additional Connectivity Methods To increase the overall aggregation of the priorities, we included each indicator twice in each zone. The first version of the indicator in the input file is just the indicator layer as-is. For the second version, we have Zonation do a connectivity transformation. This approach of using both regular and connectivity transformed versions of indicators is described in the Zonation manual. It helps balance locally important places and nearby connectivity. For the connectivity transformation, we used the negative exponential dispersal kernal. This is commonly used in modeling animal movements (Moilanen et al. 2024). We used a parameter of 1, which aligns with a mean dispersal distance of 1 pixel. For the cut parameter, which sets a distance to stop the connectivity transformation, we used a value of 5 pixels. Beyond that distance, the distribution already has only a minimal impact on values anyway. Zonation Weights for Each Zone To see the weights used for each indicator in the Continental Marine Zone, see the Zonation feature list files available in the Southeast Blueprint 2024 Data Download under > 6_Code. CARIBBEAN Note: Since this portion of the Blueprint was not updated in 2024, to create the Caribbean portion of the 2024 Zonation results, we simply clipped the 2023 Zonation results to the Caribbean subregion. However, we provide the previous year's input data and mapping steps for clarity. As a result, the following documentation will continue to refer to version 2023. ZONATION INPUTS To create the inputs for the Caribbean Zonation run: Clip the Caribbean indicators to the Caribbean Zone. Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code. The Caribbean Zone serves as the boundaries of the Zonation run. This Zonation run includes: All indicators that occur in the Caribbean Zone, clipped to that zone Subregions are not used as inputs in the Caribbean because the Caribbean Zone is not subdivided into multiple subregions. ZONATION RUN SETTINGS Determining Indicator Weights We developed indicator weighting rules to ensure that: Spatially limited indicators were not overprioritized Spatially limited indicators With equal weights, the entire area of many indicators that cover a limited part of the Caribbean Zone (e.g., Caribbean beach habitat, Caribbean greenways and trails) is identified as a high priority. To address this issue, we weighted indicators based on the proportion of their total area with values ≥ 1. For indicators where the proportion of ≥ 1 values was between 0.1 and 0.5, we set their weights equal to that proportion. For example, we set the Caribbean karst habitat indicator weight to approximately 0.228 because that was the proportion of pixels analyzed by Zonation in the Caribbean Zone with a value ≥ 1. For indicators where the proportion of ≥ 1 values was ≤ 0.1, we set their weights equal to that proportion multiplied by 3. We found that indicators in this range began to be underprioritized when only using the unadjusted proportion. For indicators where the proportion of ≥ 1 values was 0.5 and higher, we set their weight to 1.0. Removal Rule CAZMAX: This setting is consistent with basic core-area Zonation (commonly CAZ) in Zonation 4, which was used in previous versions of the Blueprint. It tries to always cover high-value locations for all indicators, even if this comes with the cost of achieving lower average coverage across all indicators. Zonation Weights for Each Zone To see the weights used for each indicator in the Caribbean Zone, see the Zonation feature list files available in the Southeast Blueprint 2024 Data Download under > 6_Code. COMBINING CONTINENTAL AND CARIBBEAN RESULTS Next, we mosaiced together the rebalanced Zonation results for all the inland continental zones, the Continental Marine Zone, and the Caribbean Zone. In this mosaiced, rebalanced Zonation results layer, each pixel in the Southeast Blueprint geography has a continuous value ranging from 0 to 100 according to its rank by the Zonation prioritization, rebalanced by linear rescale. The output of this step, the combined Zonation results for the Southeast Blueprint , is available on the Blueprint page of the SECAS Atlas . The continuous Zonation results are also available for download in the Southeast Blueprint 2024 Ancillary Data Download , but are provided as separate files for the continental and Caribbean geographies to reduce file size. Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code. LITERATURE CITED Allen, Y. 2016. Landscape Scale Assessment of Floodplain Inundation Frequency Using Landsat Imagery. River Research and Applications 32:1609-1620. [ https://doi.org/10.1002/rra.2987 ]. Moilanen, A., I. Kohonen, P. Lehtinen, I. Kivistö, J. Jalkanen, E. Virtanen, H. Kujala. March 2024. Zonation 5 v2.0 User Manual. [ https://github.com/zonationteam/Zonation5/releases/download/v2.0/manual_and_example_setups.zip] . Moilanen A, Lehtinen P, Kohonen I, Virtanen E, Jalkanen J, Kujala H. 2022. Novel methods for spatial prioritization with applications in conservation, land use planning and ecological impact avoidance. Methods in Ecology and Evolution. [ https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.13819 ]. OpenStreetMap. Download OpenStreetMap data for this region: North America. Accessed March 11, 2021. [ https://download.geofabrik.de/north-america.html ]. U.S. Geological Survey. National Hydrography in FileGDB 10.1 format (08-30-2021) - NHDWaterbody. Accessed 14 October 2021. [ https://www.usgs.gov/national-hydrography/access-national-hydrography-products ]. U.S. Geological Survey (USGS). Published June 2021. National Land Cover Database (NLCD) 2019 Land Cover Conterminous United States. Sioux Falls, SD. [ https://doi.org/10.5066/P9KZCM54 ]. Yang, L., Jin, S., Danielson, P., Homer, C., Gass, L., Case, A., Costello, C., Dewitz, J., Fry, J., Funk, M., Grannemann, B., Rigge, M. and G. Xian. 2018. A New Generation of the United States National Land Cover Database: Requirements, Research Priorities, Design, and Implementation Strategies, ISPRS Journal of Photogrammetry and Remote Sensing, 146, pp.108-123. [ https://doi.org/10.1016/j.isprsjprs.2018.09.006 ].
- Creator
- U.S. Fish & Wildlife Service
- Temporal Coverage
- 2024
- Date Issued
- 2024-08-26
- Rights
- The United States Fish and Wildlife Service (Service) shall not be held liable for improper or incorrect use of the data described and/or contained herein. While the Service makes every reasonable effort to ensure the accuracy and completeness of data provided for distribution, it may not have the necessary accuracy or completeness required for every possible intended use. The Service recommends that data users consult the associated metadata record to understand the quality and possible limitations of the data. The Service creates metadata records in accordance with the standards endorsed by the Federal Geographic Data Committee. As a result of the above considerations, the Service gives no warranty, expressed or implied, as to the accuracy, reliability, or completeness of the data. It is the responsibility of the data user to use the data in a manner consistent with the limitations of geospatial data in general and these data in particular. Although these data have been processed successfully on a computer system at the Service, no warranty, expressed or implied, is made regarding the utility of the data on another system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. This applies to the use of the data both alone and in aggregate with other data and information.
- Access Rights
- Public
- Format
- ArcGIS ImageMapLayer
- Language
- English
- Date Added
- August 10, 2025
- Provenance Statement
- The metadata for this resource was last retrieved from U.S. Fish and Wildlife Service Open Data on 2025-08-24.
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Local Collection
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Citation
U.S. Fish & Wildlife Service (2024). Combined Zonation Results 2024 [U.S. Fish and Wildlife Service]. . https://gis-fws.opendata.arcgis.com/content/fws::combined-zonation-results-2024 (web service) -
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