{ "currentVersion": 11.1, "cimVersion": "3.1.0", "serviceDescription": "A changing climate and its effects on ecosystem services will have broad impacts, however, not all people and communities will be equally affected. This assessment of vulnerability is concerned with identifying communities and geographic areas where climate-change-driven ecological changes have the potential to adversely affect human well-being due to changes in the provision of ecosystem services. Communities that are at greater risk of ecological changes and that lack adaptive capacity are considered more vulnerable. We analyzed vulnerability components of exposure, sensitivity, and adaptive capacity based on available socioeconomic and ecological data. Reporting here includes quantitative and spatially based summaries on community risk, resource sector dependence, and capacity to adapt, as well as an integration of the three vulnerability components. This report extends existing vulnerability reporting focused on national forests by assessing all lands, regardless of ownership, in Arizona and New Mexico.\n\nVulnerability in the Triepke et al. (2019) study is defined as how likely the predominant vegetation is to change under future climate. Vulnerability to climate change was categorized as low, moderate, high, and very high likelihood of change, according to the difference between historic and future climate. The original dataset segments all lands into ecological response units (ERUs)\u2014a classification of lands into 26 ecosystem types (for example, spruce-fir forest, ponderosa pine forest, Juniper grass, semi-desert grassland, sagebush shrubland) to provide a fine subregional landscape analysis. Each ERU is assigned a vulnerability classification and an uncertainty classification based on the agreement of their modeling projections.\n\nThe polygon geospatial layer was summarized as the percentage of each geography (e.g. census tract) with high or very high likelihood of vegetative change when uncertainty is low or moderate. Calculation using Triepke, F. J., E. H. Muldavin, and M. M. Wahlberg. 2019. Using climate projections to assess ecosystem vulnerability at scales relevant to managers. Ecosphere 10(9):e02854. 10.1002/ecs2.2854", "mapName": "r03_SEVA_County_01", "description": "A changing climate and its effects on ecosystem services will have broad impacts, however, not all people and communities will be equally affected. This assessment of vulnerability is concerned with identifying communities and geographic areas where climate-change-driven ecological changes have the potential to adversely affect human well-being due to changes in the provision of ecosystem services. Communities that are at greater risk of ecological changes and that lack adaptive capacity are considered more vulnerable. We analyzed vulnerability components of exposure, sensitivity, and adaptive capacity based on available socioeconomic and ecological data. Reporting here includes quantitative and spatially based summaries on community risk, resource sector dependence, and capacity to adapt, as well as an integration of the three vulnerability components. This report extends existing vulnerability reporting focused on national forests by assessing all lands, regardless of ownership, in Arizona and New Mexico.\n\nVulnerability in the Triepke et al. (2019) study is defined as how likely the predominant vegetation is to change under future climate. Vulnerability to climate change was categorized as low, moderate, high, and very high likelihood of change, according to the difference between historic and future climate. The original dataset segments all lands into ecological response units (ERUs)\u2014a classification of lands into 26 ecosystem types (for example, spruce-fir forest, ponderosa pine forest, Juniper grass, semi-desert grassland, sagebush shrubland) to provide a fine subregional landscape analysis. Each ERU is assigned a vulnerability classification and an uncertainty classification based on the agreement of their modeling projections.\n\nThe polygon geospatial layer was summarized as the percentage of each geography (e.g. census tract) with high or very high likelihood of vegetative change when uncertainty is low or moderate. Calculation using Triepke, F. J., E. H. Muldavin, and M. M. Wahlberg. 2019. Using climate projections to assess ecosystem vulnerability at scales relevant to managers. Ecosphere 10(9):e02854. 10.1002/ecs2.2854", "copyrightText": "Triepke, F. J., E. H. Muldavin, and M. M. Wahlberg. 2019", "supportsDynamicLayers": true, "layers": [ { "id": 0, "name": "SEVA_County", "parentLayerId": -1, "defaultVisibility": true, "subLayerIds": null, "minScale": 5000000, "maxScale": 0, "type": "Feature Layer", "geometryType": "esriGeometryPolygon", "supportsDynamicLegends": true } ], "tables": [], "spatialReference": { "wkt": "PROJCS[\"North_America_Lambert_Conformal_Conic\",GEOGCS[\"GCS_North_American_1983\",DATUM[\"D_North_American_1983\",SPHEROID[\"GRS_1980\",6378137.0,298.257222101]],PRIMEM[\"Greenwich\",0.0],UNIT[\"Degree\",0.0174532925199433]],PROJECTION[\"Lambert_Conformal_Conic\"],PARAMETER[\"False_Easting\",0.0],PARAMETER[\"False_Northing\",0.0],PARAMETER[\"Central_Meridian\",-108.0],PARAMETER[\"Standard_Parallel_1\",32.0],PARAMETER[\"Standard_Parallel_2\",36.0],PARAMETER[\"Latitude_Of_Origin\",0.0],UNIT[\"Meter\",1.0]]", "xyTolerance": 0.001, "zTolerance": 0.001, "mTolerance": 0.001, "falseX": -37986000, "falseY": -24548100, "xyUnits": 10000, "falseZ": -100000, "zUnits": 10000, "falseM": -100000, "mUnits": 10000 }, "singleFusedMapCache": false, "initialExtent": { "xmin": -695475.879633749, "ymin": 3585159.823611643, "xmax": 521788.51493374456, "ymax": 4427322.8639883585, "spatialReference": { "wkt": "PROJCS[\"North_America_Lambert_Conformal_Conic\",GEOGCS[\"GCS_North_American_1983\",DATUM[\"D_North_American_1983\",SPHEROID[\"GRS_1980\",6378137.0,298.257222101]],PRIMEM[\"Greenwich\",0.0],UNIT[\"Degree\",0.0174532925199433]],PROJECTION[\"Lambert_Conformal_Conic\"],PARAMETER[\"False_Easting\",0.0],PARAMETER[\"False_Northing\",0.0],PARAMETER[\"Central_Meridian\",-108.0],PARAMETER[\"Standard_Parallel_1\",32.0],PARAMETER[\"Standard_Parallel_2\",36.0],PARAMETER[\"Latitude_Of_Origin\",0.0],UNIT[\"Meter\",1.0]]", "xyTolerance": 0.001, "zTolerance": 0.001, "mTolerance": 0.001, "falseX": -37986000, "falseY": -24548100, "xyUnits": 10000, "falseZ": -100000, "zUnits": 10000, "falseM": -100000, "mUnits": 10000 } }, "fullExtent": { "xmin": -639874.6525000036, "ymin": 3684059.8849, "xmax": 466187.2877999991, "ymax": 4328422.802700002, "spatialReference": { "wkt": "PROJCS[\"North_America_Lambert_Conformal_Conic\",GEOGCS[\"GCS_North_American_1983\",DATUM[\"D_North_American_1983\",SPHEROID[\"GRS_1980\",6378137.0,298.257222101]],PRIMEM[\"Greenwich\",0.0],UNIT[\"Degree\",0.0174532925199433]],PROJECTION[\"Lambert_Conformal_Conic\"],PARAMETER[\"False_Easting\",0.0],PARAMETER[\"False_Northing\",0.0],PARAMETER[\"Central_Meridian\",-108.0],PARAMETER[\"Standard_Parallel_1\",32.0],PARAMETER[\"Standard_Parallel_2\",36.0],PARAMETER[\"Latitude_Of_Origin\",0.0],UNIT[\"Meter\",1.0]]", "xyTolerance": 0.001, "zTolerance": 0.001, "mTolerance": 0.001, "falseX": -37986000, "falseY": -24548100, "xyUnits": 10000, "falseZ": -100000, "zUnits": 10000, "falseM": -100000, "mUnits": 10000 } }, "datesInUnknownTimezone": false, "minScale": 5000000, "maxScale": 0, "units": "esriMeters", "supportedImageFormatTypes": "PNG32,PNG24,PNG,JPG,DIB,TIFF,EMF,PS,PDF,GIF,SVG,SVGZ,BMP", "documentInfo": { "Title": "Socioeconomic Vulnerability County to Ecological Changes in the Southwest", "Author": "", "Comments": "A changing climate and its effects on ecosystem services will have broad impacts, however, not all people and communities will be equally affected. This assessment of vulnerability is concerned with identifying communities and geographic areas where climate-change-driven ecological changes have the potential to adversely affect human well-being due to changes in the provision of ecosystem services. Communities that are at greater risk of ecological changes and that lack adaptive capacity are considered more vulnerable. We analyzed vulnerability components of exposure, sensitivity, and adaptive capacity based on available socioeconomic and ecological data. Reporting here includes quantitative and spatially based summaries on community risk, resource sector dependence, and capacity to adapt, as well as an integration of the three vulnerability components. This report extends existing vulnerability reporting focused on national forests by assessing all lands, regardless of ownership, in Arizona and New Mexico.\n\nVulnerability in the Triepke et al. (2019) study is defined as how likely the predominant vegetation is to change under future climate. Vulnerability to climate change was categorized as low, moderate, high, and very high likelihood of change, according to the difference between historic and future climate. The original dataset segments all lands into ecological response units (ERUs)\u2014a classification of lands into 26 ecosystem types (for example, spruce-fir forest, ponderosa pine forest, Juniper grass, semi-desert grassland, sagebush shrubland) to provide a fine subregional landscape analysis. Each ERU is assigned a vulnerability classification and an uncertainty classification based on the agreement of their modeling projections.\n\nThe polygon geospatial layer was summarized as the percentage of each geography (e.g. census tract) with high or very high likelihood of vegetative change when uncertainty is low or moderate. Calculation using Triepke, F. J., E. H. Muldavin, and M. M. Wahlberg. 2019. Using climate projections to assess ecosystem vulnerability at scales relevant to managers. Ecosphere 10(9):e02854. 10.1002/ecs2.2854", "Subject": "Socioeconomic Vulnerability to Ecological Change County", "Category": "", "Version": "3.1.0", "AntialiasingMode": "Fast", "TextAntialiasingMode": "Force", "Keywords": "Socioeconomic,Vulnerability,Ecological,USFS,Southwest Region,Arizona,New Mexico" }, "capabilities": "Map,Query,Data", "supportedQueryFormats": "JSON, geoJSON, PBF", "hasVersionedData": true, "exportTilesAllowed": false, "referenceScale": 0.0, "supportsDatumTransformation": true, "archivingInfo": {"supportsHistoricMoment": false}, "supportsClipping": true, "supportsSpatialFilter": true, "supportsTimeRelation": true, "supportsQueryDataElements": true, "mapUnits": {"uwkid": 9001}, "maxRecordCount": 2000, "maxImageHeight": 4096, "maxImageWidth": 4096, "supportedExtensions": "" }