2021-08-25 11212400 2021-08-25 11:21:55.24 editor:esri.dijit.metadata.editor 1.0 FGDC CSDGM Metadata FGDC false 1629915708597r6383894783887036 2020-06-10 11:21:48.11 Although these data and information have been processed successfully on a computer system at the USFWS, no warranty expressed or implied is made regarding the accuracy or utility of the data and information on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. This disclaimer applies both to individual use of the data, and information, and aggregate use with other data and information. It is also strongly recommended that careful attention be paid to the contents of the metadata file associated with these data and information. The USFWS shall not be held liable for improper or incorrect use of the data and information described and/or contained herein. <?xml version="1.0" encoding="utf-8"?>
<metadata>
  <idinfo>
    <citation>
      <citeinfo>
        <title>Hexagons_EcosystemServicesInputs</title>
        <geoform>vector digital data</geoform>
      </citeinfo>
    </citation>
    <descript>
      <abstract>◦Overview: A key principle of Landscape Conservation Design is that “Stakeholders design landscape configurations that promote resilient and sustainable social-ecological systems” (Campellone et al, 2018). From Campellone et al: (2018): “A beneficial aspect of stakeholder engagement in spatial design is the development of a deeper trust that the models used to identify priorities integrate their interests with other information and knowledge, which furthers social learning and collective agreement on resource allocation and landscape objectives” (Melillo et al., 2014). Overall, the co-development of a spatial design helps organize landscape elements while maintaining and improving stakeholder buy-in” (De Groot, Alkemade, Braat, Hein, &amp;amp; Willemen, 2009; Melillo et al., 2014).”◦Analytical Question: Create a prototype landscape design (blueprint) that integrates multiple values on the landscape including wildlife conservation, forest and agriculture production, recreation, cultural and human health. The prototype will be created based upon readily available data.This analysis will be used to understand landscape-scale conservation and working landscape priorities, while incorporating other important values.The blueprint will be used to represent a sustainable landscape in the future. ◦Desired Outcome: A map or maps that represents a balance of multiple values on the landscape, with a focus on conservation and working landscape values.</abstract>
      <purpose>This feature class represents key attributes that are important for Ecosystem Services.  This is for version 1.0 of the spatial design process of the Cascades to Coast Landscape Collaborative. </purpose>
    </descript>
    <spdom>
      <bounding>
        <westbc>-127.067133</westbc>
        <eastbc>-119.544736</eastbc>
        <northbc>48.995982</northbc>
        <southbc>41.787941</southbc>
      </bounding>
    </spdom>
    <keywords>
      <theme>
        <themekt>None</themekt>
        <themekey>Cascades to Coast Landscape Collaborative</themekey>
        <themekey>Spatial Design</themekey>
        <themekey>Oregon</themekey>
        <themekey>Washington</themekey>
        <themekey>Coastal Ecoregion</themekey>
        <themekey>Landscape Conservation Design</themekey>
        <themekey>Ecosystem Services</themekey>
      </theme>
    </keywords>
    <accconst>None</accconst>
    <useconst>These data are produced for dialogue on the intersection of several different values on the landscape, including working lands, conservation and ecosystem services.</useconst>
    <datacred>Tom Miewald, USFWS; Erin Butts, USFWS; John Mankowski, Mankowski Environmental. </datacred>
    <native> Version 6.2 (Build 9200) ; Esri ArcGIS 10.6.1.9273</native>
  </idinfo>
  <spdoinfo>
    <direct>Vector</direct>
    <ptvctinf>
      <sdtsterm>
        <sdtstype>GT-polygon composed of chains</sdtstype>
        <ptvctcnt>60007</ptvctcnt>
      </sdtsterm>
    </ptvctinf>
  </spdoinfo>
  <eainfo>
    <detailed>
      <enttyp>
        <enttypl>Hexagons_EcosystemServicesInputs</enttypl>
      </enttyp>
      <attr>
        <attrlabl>OBJECTID</attrlabl>
        <attrdef>Internal feature number.</attrdef>
        <attrdefs>Esri</attrdefs>
        <attrdomv>
          <udom>Sequential unique whole numbers that are automatically generated.</udom>
        </attrdomv>
      </attr>
      <attr>
        <attrlabl>Shape</attrlabl>
        <attrdef>Feature geometry.</attrdef>
        <attrdefs>Esri</attrdefs>
        <attrdomv>
          <udom>Coordinates defining the features.</udom>
        </attrdomv>
      </attr>
      <attr>
        <attrlabl>GRID_ID</attrlabl>
      </attr>
      <attr>
        <attrlabl>WillametteAddOn</attrlabl>
        <attrdef>Willamette Valley hexagons were created but not evaluated. This field indicates if the hexagon is part of the Willamette Valley, or not. </attrdef>
        <attrdefs>USFWS</attrdefs>
        <attrvai>
          <attrva>Y yes Willamette Valley add on. N no</attrva>
        </attrvai>
      </attr>
      <attr>
        <attrlabl>HexID</attrlabl>
        <attrdef>This is the ID for the 500 acre hexagon.  This can be linked used as the key field to link to other data sets in this geodatabase. </attrdef>
        <attrdefs>USFWS</attrdefs>
      </attr>
      <attr>
        <attrlabl>Area_km2</attrlabl>
        <attrdef>Area of hexagon in square kilometers</attrdef>
        <attrdefs>USFWS</attrdefs>
      </attr>
      <attr>
        <attrlabl>MEAN_FreshwaterFishing_RecreationDemand_FF_Demand</attrlabl>
        <attrdef>Source: US EPA EnviroAtlas. This EnviroAtlas dataset includes the total number of recreational days per year demanded by people ages 18 and over for freshwater fishing by location in the contiguous United States. These values are based on 2010 population distribution, 2011 U.S. Fish and Wildlife Service (FWS) Fish, Hunting, and Wildlife-Associated Recreation (FHWAR) survey data, and 2011 U.S. Department of Agriculture (USDA) Forest Service National Visitor Use Monitoring program data, and have been summarized by 12-digit hydrologic unit code (HUC). This dataset was produced by the US EPA to support research and online mapping activities related to the EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).&lt;/

Process: Intersect hexagons with HUC 12 Enviroatlas data. Summary statistics to calculate the mean value.

Final: Mean recreation demand in hexagon.</attrdef>
      </attr>
      <attr>
        <attrlabl>MEAN_BigGameHunting_RecreationDemand_BG_Demand</attrlabl>
        <attrdef>Source: US EPA EnviroAtlas. This EnviroAtlas dataset includes the total number of recreational days per year demanded by people ages 18 and over for big game hunting by location in the contiguous United States. Big game includes deer, elk, bear, and wild turkey. These values are based on 2010 population distribution, 2011 U.S. Fish and Wildlife Service (FWS) Fish, Hunting, and Wildlife-Associated Recreation (FHWAR) survey data, and 2011 U.S. Department of Agriculture (USDA) Forest Service National Visitor Use Monitoring program data, and have been summarized by 12-digit hydrologic unit code (HUC). This dataset was produced by the US EPA to support research and online mapping activities related to the EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

Process: Intersect hexagons with HUC 12 Enviroatlas data. Summary statistics to calculate the mean value.

Final: Mean recreation demand in hexagon.</attrdef>
      </attr>
      <attr>
        <attrlabl>MEAN_BirdWatching_RecreationDemand_BW_Demand</attrlabl>
        <attrdef>Source: US EPA EnviroAtlas. This EnviroAtlas dataset includes the total number of recreational days per year demanded by people ages 18 and over for bird watching by location in the contiguous United States. These values are based on 2010 population distribution, 2011 U.S. Fish and Wildlife Service (FWS) Fish, Hunting, and Wildlife-Associated Recreation (FHWAR) survey data, and 2011 U.S. Department of Agriculture (USDA) Forest Service National Visitor Use Monitoring program data, and have been summarized by 12-digit hydrologic unit code (HUC). This dataset was produced by the US EPA to support research and online mapping activities related to the EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

Process: Intersect hexagons with HUC 12 Enviroatlas data. Summary statistics to calculate the mean value.

Final: Mean recreation demand in hexagon.</attrdef>
      </attr>
      <attr>
        <attrlabl>MEAN_MigratoryBirdHunting_RecreationDemand_MB_Demand</attrlabl>
        <attrdef>Source: US EPA EnviroAtlas. This EnviroAtlas dataset includes the total number of recreational days per year demanded by people ages 18 and over for migratory bird hunting by location in the contiguous United States. These values are based on 2010 population distribution, 2011 U.S. Fish and Wildlife Service (FWS) Fish, Hunting, and Wildlife-Associated Recreation (FHWAR) survey data, and 2011 U.S. Department of Agriculture (USDA) Forest Service National Visitor Use Monitoring program data, and have been summarized by 12-digit hydrologic unit code (HUC). This dataset was produced by the US EPA to support research and online mapping activities related to the EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

Process: Intersect hexagons with HUC 12 Enviroatlas data. Summary statistics to calculate the mean value.

Final: Mean recreation demand in hexagon.</attrdef>
      </attr>
      <attr>
        <attrlabl>LandscapeAesthetics_mean</attrlabl>
        <attrdef>Source: Brandt, P.; Abson, D. J.; DellaSala, D.; Feller, R.; von Wehrden, H. (2014): Multifunctionality and biodiversity: Ecosystem services in temperate rainforests of the Pacific Northwest, USA. Biological Conservation, 169, 362-371.
The compiled dataset consists of several spatial layers weighted according to their naturalness. Terrain roughness was incorporated as proxy for physical landscape heterogeneity. Each layer was weighted either positively or negatively except for terrain roughness that was weighted based on three states, low roughness as negative, medium roughness as neutral and high roughness as positive. Data were transformed to a standardized scale so all values range between 0 and 1.

Process: Zonal statistics to determine mean landscape aesthetics in hexagons.

Final: Average landscape aesthetics in hexagon.</attrdef>
      </attr>
      <attr>
        <attrlabl>Imp_drinkingWater</attrlabl>
        <attrdef>Source: Forests to Faucets Surface Drinking Water Importance, USFS. https://www.fs.fed.us/ecosystemservices/FS_Efforts/forests2faucets.shtml
Relative Important Areas for Surface Drinking Water: The final model of surface drinking water importance combines the drinking water protection model (PRn),
capturing the flow of water and water demand, with Brown et al’s (2008) model of mean annual water supply (Qn). The values generated by the drinking water protection model are simply multiplied by the results of the model of mean annual water supply to create the final surface drinking water importance index. 

Process: Intersect data with hexagons. Use summary statistics to calculate mean of F2F_OUT_IMP1 (Surface drinking water importance)

Final: Mean surface drinking water importance in hexagon.
</attrdef>
      </attr>
      <attr>
        <attrlabl>Trails_Lengthm</attrlabl>
        <attrdef>Source: gtrn_pub_trails_arc: Publication dataset showing both BLM inventoried and non-inventoried trails in Oregon &amp; Washington. https://www.blm.gov/or/gis/data-details.php?id=18 ; TrailNFS_Publish USFS ; NPS__Trails__Web_Mercator USNPS.

Process: Tabulate intersection was run on the trail datasets. Length in meters was attributed to hexagons. Since the datasets overlapped BLM trails was used as the primary dataset, and any hexagons without BLM trail data used NFS and NPS trails in order to fill in any gaps without double counting trails. 

Final: Length of trails in meters in hexagon.</attrdef>
      </attr>
      <attr>
        <attrlabl>Mean_NEP</attrlabl>
        <attrdef>Net Ecosystem Production (NEP) is a measurement of the net gain (or loss) of energy/carbon in a system over a period of time. 

Source: Carbon Potential (Net ecosystem production): Estimating carbon sequestration in the Pacific Northwest. Turner, D.P., W.D. Ritts, R.E. Kennedy, A.N. Gray, and Z. Yang. 2016. NACP Biome-BGC Modeled Ecosystem Carbon Balance, Pacific Northwest, USA, 1986-2010. ORNL DAAC, Oak Ridge, Tennessee, USA.
http://dx.doi.org/10.3334/ORNLDAAC/1317
This data set provides Biome-BGC modeled estimates of carbon stocks and fluxes in the U.S. Pacific Northwest for the years 1986-2010. 

Process: Zonal statistics to calculate the mean NEP within hexagons.

Final: Mean Net ecosystem production in hexagons.</attrdef>
      </attr>
      <attr>
        <attrlabl>Shape_Length</attrlabl>
        <attrdef>Length of feature in internal units.</attrdef>
        <attrdefs>Esri</attrdefs>
        <attrdomv>
          <udom>Positive real numbers that are automatically generated.</udom>
        </attrdomv>
      </attr>
      <attr>
        <attrlabl>Shape_Area</attrlabl>
        <attrdef>Area of feature in internal units squared.</attrdef>
        <attrdefs>Esri</attrdefs>
        <attrdomv>
          <udom>Positive real numbers that are automatically generated.</udom>
        </attrdomv>
      </attr>
    </detailed>
  </eainfo>
  <metainfo>
    <metd>20200610</metd>
    <metstdn>FGDC Content Standard for Digital Geospatial Metadata</metstdn>
    <metstdv>FGDC-STD-001-1998</metstdv>
    <mettc>local time</mettc>
    <metuc>Although these data and information have been processed successfully on a computer system at the USFWS, no warranty expressed or implied is made regarding the accuracy or utility of the data and information on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. This disclaimer applies both to individual use of the data, and information, and aggregate use with other data and information. It is also strongly recommended that careful attention be paid to the contents of the metadata file associated with these data and information. The USFWS shall not be held liable for improper or incorrect use of the data and information described and/or contained herein.</metuc>
  </metainfo>
</metadata> Hexagons_EcosystemServicesInputs vector digital data ◦Overview: A key principle of Landscape Conservation Design is that “Stakeholders design landscape configurations that promote resilient and sustainable social-ecological systems” (Campellone et al, 2018). From Campellone et al: (2018): “A beneficial aspect of stakeholder engagement in spatial design is the development of a deeper trust that the models used to identify priorities integrate their interests with other information and knowledge, which furthers social learning and collective agreement on resource allocation and landscape objectives” (Melillo et al., 2014). Overall, the co-development of a spatial design helps organize landscape elements while maintaining and improving stakeholder buy-in” (De Groot, Alkemade, Braat, Hein, &amp; Willemen, 2009; Melillo et al., 2014).”◦Analytical Question: Create a prototype landscape design (blueprint) that integrates multiple values on the landscape including wildlife conservation, forest and agriculture production, recreation, cultural and human health. The prototype will be created based upon readily available data.This analysis will be used to understand landscape-scale conservation and working landscape priorities, while incorporating other important values.The blueprint will be used to represent a sustainable landscape in the future. ◦Desired Outcome: A map or maps that represents a balance of multiple values on the landscape, with a focus on conservation and working landscape values. This feature class represents key attributes that are important for Ecosystem Services. This is for version 1.0 of the spatial design process of the Cascades to Coast Landscape Collaborative. Tom Miewald, USFWS; Erin Butts, USFWS; John Mankowski, Mankowski Environmental. Version 6.2 (Build 9200) ; Esri ArcGIS 10.6.1.9273 WashingtonLandscape Conservation DesignCoastal EcoregionOregonCascades to Coast Landscape CollaborativeSpatial DesignEcosystem Services Washington Landscape Conservation Design Coastal Ecoregion Oregon Cascades to Coast Landscape Collaborative Spatial Design Ecosystem Services -127.067133 -119.544736 41.787941 48.995982 These data are produced for dialogue on the intersection of several different values on the landscape, including working lands, conservation and ecosystem services. Other Constraints Other Constraints 60007 Hexagons_EcosystemServicesInputs OBJECTID Internal feature number. Esri Sequential unique whole numbers that are automatically generated. Shape Feature geometry. Esri Coordinates defining the features. GRID_ID WillametteAddOn Willamette Valley hexagons were created but not evaluated. This field indicates if the hexagon is part of the Willamette Valley, or not. USFWS Y yes Willamette Valley add on. N no HexID This is the ID for the 500 acre hexagon. This can be linked used as the key field to link to other data sets in this geodatabase. USFWS Area_km2 Area of hexagon in square kilometers USFWS MEAN_FreshwaterFishing_RecreationDemand_FF_Demand Source: US EPA EnviroAtlas. This EnviroAtlas dataset includes the total number of recreational days per year demanded by people ages 18 and over for freshwater fishing by location in the contiguous United States. These values are based on 2010 population distribution, 2011 U.S. Fish and Wildlife Service (FWS) Fish, Hunting, and Wildlife-Associated Recreation (FHWAR) survey data, and 2011 U.S. Department of Agriculture (USDA) Forest Service National Visitor Use Monitoring program data, and have been summarized by 12-digit hydrologic unit code (HUC). This dataset was produced by the US EPA to support research and online mapping activities related to the EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).</ Process: Intersect hexagons with HUC 12 Enviroatlas data. Summary statistics to calculate the mean value. Final: Mean recreation demand in hexagon. MEAN_BigGameHunting_RecreationDemand_BG_Demand Source: US EPA EnviroAtlas. This EnviroAtlas dataset includes the total number of recreational days per year demanded by people ages 18 and over for big game hunting by location in the contiguous United States. Big game includes deer, elk, bear, and wild turkey. These values are based on 2010 population distribution, 2011 U.S. Fish and Wildlife Service (FWS) Fish, Hunting, and Wildlife-Associated Recreation (FHWAR) survey data, and 2011 U.S. Department of Agriculture (USDA) Forest Service National Visitor Use Monitoring program data, and have been summarized by 12-digit hydrologic unit code (HUC). This dataset was produced by the US EPA to support research and online mapping activities related to the EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets). Process: Intersect hexagons with HUC 12 Enviroatlas data. Summary statistics to calculate the mean value. Final: Mean recreation demand in hexagon. MEAN_BirdWatching_RecreationDemand_BW_Demand Source: US EPA EnviroAtlas. This EnviroAtlas dataset includes the total number of recreational days per year demanded by people ages 18 and over for bird watching by location in the contiguous United States. These values are based on 2010 population distribution, 2011 U.S. Fish and Wildlife Service (FWS) Fish, Hunting, and Wildlife-Associated Recreation (FHWAR) survey data, and 2011 U.S. Department of Agriculture (USDA) Forest Service National Visitor Use Monitoring program data, and have been summarized by 12-digit hydrologic unit code (HUC). This dataset was produced by the US EPA to support research and online mapping activities related to the EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets). Process: Intersect hexagons with HUC 12 Enviroatlas data. Summary statistics to calculate the mean value. Final: Mean recreation demand in hexagon. MEAN_MigratoryBirdHunting_RecreationDemand_MB_Demand Source: US EPA EnviroAtlas. This EnviroAtlas dataset includes the total number of recreational days per year demanded by people ages 18 and over for migratory bird hunting by location in the contiguous United States. These values are based on 2010 population distribution, 2011 U.S. Fish and Wildlife Service (FWS) Fish, Hunting, and Wildlife-Associated Recreation (FHWAR) survey data, and 2011 U.S. Department of Agriculture (USDA) Forest Service National Visitor Use Monitoring program data, and have been summarized by 12-digit hydrologic unit code (HUC). This dataset was produced by the US EPA to support research and online mapping activities related to the EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets). Process: Intersect hexagons with HUC 12 Enviroatlas data. Summary statistics to calculate the mean value. Final: Mean recreation demand in hexagon. LandscapeAesthetics_mean Source: Brandt, P.; Abson, D. J.; DellaSala, D.; Feller, R.; von Wehrden, H. (2014): Multifunctionality and biodiversity: Ecosystem services in temperate rainforests of the Pacific Northwest, USA. Biological Conservation, 169, 362-371. The compiled dataset consists of several spatial layers weighted according to their naturalness. Terrain roughness was incorporated as proxy for physical landscape heterogeneity. Each layer was weighted either positively or negatively except for terrain roughness that was weighted based on three states, low roughness as negative, medium roughness as neutral and high roughness as positive. Data were transformed to a standardized scale so all values range between 0 and 1. Process: Zonal statistics to determine mean landscape aesthetics in hexagons. Final: Average landscape aesthetics in hexagon. Imp_drinkingWater Source: Forests to Faucets Surface Drinking Water Importance, USFS. https://www.fs.fed.us/ecosystemservices/FS_Efforts/forests2faucets.shtml Relative Important Areas for Surface Drinking Water: The final model of surface drinking water importance combines the drinking water protection model (PRn), capturing the flow of water and water demand, with Brown et al’s (2008) model of mean annual water supply (Qn). The values generated by the drinking water protection model are simply multiplied by the results of the model of mean annual water supply to create the final surface drinking water importance index. Process: Intersect data with hexagons. Use summary statistics to calculate mean of F2F_OUT_IMP1 (Surface drinking water importance) Final: Mean surface drinking water importance in hexagon. Trails_Lengthm Source: gtrn_pub_trails_arc: Publication dataset showing both BLM inventoried and non-inventoried trails in Oregon & Washington. https://www.blm.gov/or/gis/data-details.php?id=18 ; TrailNFS_Publish USFS ; NPS__Trails__Web_Mercator USNPS. Process: Tabulate intersection was run on the trail datasets. Length in meters was attributed to hexagons. Since the datasets overlapped BLM trails was used as the primary dataset, and any hexagons without BLM trail data used NFS and NPS trails in order to fill in any gaps without double counting trails. Final: Length of trails in meters in hexagon. Mean_NEP Net Ecosystem Production (NEP) is a measurement of the net gain (or loss) of energy/carbon in a system over a period of time. Source: Carbon Potential (Net ecosystem production): Estimating carbon sequestration in the Pacific Northwest. Turner, D.P., W.D. Ritts, R.E. Kennedy, A.N. Gray, and Z. Yang. 2016. NACP Biome-BGC Modeled Ecosystem Carbon Balance, Pacific Northwest, USA, 1986-2010. ORNL DAAC, Oak Ridge, Tennessee, USA. http://dx.doi.org/10.3334/ORNLDAAC/1317 This data set provides Biome-BGC modeled estimates of carbon stocks and fluxes in the U.S. Pacific Northwest for the years 1986-2010. Process: Zonal statistics to calculate the mean NEP within hexagons. Final: Mean Net ecosystem production in hexagons. Shape_Length Length of feature in internal units. Esri Positive real numbers that are automatically generated. Shape_Area Area of feature in internal units squared. Esri Positive real numbers that are automatically generated.