2021-07-12 14575500 2021-07-12 16:50:03.67 editor:esri.dijit.metadata.editor 1.0 FGDC CSDGM Metadata FGDC false 1626133791425r5641627884770835 2020-06-23 16:49:51.04 These data layers are meant to be used for conversation and dialogue. They are draft data layers. Many of the data layers are derived from data layers produced by others and synthesized/summarized by CCLC hexagons. <?xml version="1.0" encoding="utf-8"?>
<metadata>
  <idinfo>
    <citation>
      <citeinfo>
        <title>Impacts and Resiliency within the Cascades to Coast Landscape Collaborative Region</title>
        <geoform>vector digital data</geoform>
      </citeinfo>
    </citation>
    <descript>
      <abstract>Overview of Spatial Design Goals and Process: 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;amp; Willemen, 2009; Melillo et al., 2014).”Analytical Question:  Create a prototype landscape design 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. We can include social network perspective as part of a potential suite of design products, such as implementation capacity.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 is a synthesis of best available data to map land management, ownership and zoning across the Cascades to Coast Landscape Collaborative region.  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>Working Lands</themekey>
        <themekey>forestry</themekey>
        <themekey>zoning</themekey>
        <themekey>land ownership</themekey>
        <themekey>land management</themekey>
        <themekey>protected area status</themekey>
        <themekey>zoning</themekey>
        <themekey>parcels.</themekey>
      </theme>
    </keywords>
    <accconst>None</accconst>
    <useconst>These data layers are meant to be used for conversation and dialogue.  They are draft data layers.  Many of the data layers are derived from data layers produced by others and synthesized/summarized by CCLC hexagons.  </useconst>
    <datacred>Tom Miewald, USFWS; Erin Butts, USFWS; John Mankowski, Mankowski Environmental. </datacred>
    <native>Esri ArcGIS 12.3.2.15850</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>ThreatsAndResiliency_052120</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>AUs_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>AUs_SLR</attrlabl>
        <attrdef>(Source) These data were derived from the NOAA Sea Level Rise Inundation national dataset (2017).  Methods for these data are here:  https://coast.noaa.gov/data/digitalcoast/pdf/slr-inundation-methods.pdf.  

(Processing Abstract) NOAA data were clipped to the CCLC study area for both Oregon and Washington and then merged into one data set.  Three feet SLR was chosen as the unit as a mid-range projection.  This is NOT meant to be the absolute SLR that is chosen for the eventual results.  The 3 feet value is meant as a starting point and this data set can be altered based upon feedback through the prototyping process.  

(Final) SLR data were summarized by 500 acre Hexagon using ArcGIS Zonalstatistics as Table.  These hexagons represent mean sea level rise at a projected three feet level rise.</attrdef>
      </attr>
      <attr>
        <attrlabl>AUs_Dasymetric</attrlabl>
        <attrdef>(Source) These data were derived from the EPA Enviroatlas (2020).  Methods for these data are here: https://edg.epa.gov/metadata/catalog/search/resource/details.page?uuid=%7BBBDAEC9A-F9B6-490F-8CA0-40401C47DEBA%7D.  Essentially:  "This EnviroAtlas dataset intelligently reallocates 2010 population from census blocks to 30 meter pixels based on land cover and slope."

(Processing Abstract) NOAA data were clipped to the CCLC study area for both Oregon and Washington and then merged into one data set.  Three feet SLR was chosen as the unit as a mid-range projection.  This is NOT meant to be the absolute SLR that is chosen for the eventual results.  The 3 feet value is meant as a starting point and this data set can be altered based upon feedback through the prototyping process.  

(Final) SLR data were summarized by 500 acre Hexagon using ArcGIS Zonalstatistics as Table.  These hexagons represent mean sea level rise at a projected three feet sea level rise.</attrdef>
      </attr>
      <attr>
        <attrlabl>AUs_Landcast30</attrlabl>
        <attrdef>Source: Oak Ridge National Laboratory; Jacob J. McKee, Amy N. Rose, Edward A. Bright, Timmy Huynh, 2015.  This represents a model of the spatial distribution of people across the US for the year 2030.  "The model presented here departs from other spatially explicit projection models by accounting for socioeconomic and cultural characteristics that influence spatial population growth at smaller scales, while still projecting population at a large scale. The resulting projected population distribution can be exploited for long-term urban and infrastructure planning, and scientific modeling for climate change. "/////
Processing: Data were summarized by 500 acre Hexagon using ArcGIS Zonalstatistics as Table. /////
Summary Use: The 2030 data were not used in prototype maps for the CCLC.  Rather, we focused attention on models that depict 2050.  </attrdef>
      </attr>
      <attr>
        <attrlabl>AUs_Landcast50</attrlabl>
        <attrdef>Source: Oak Ridge National Laboratory; Jacob J. McKee, Amy N. Rose, Edward A. Bright, Timmy Huynh, 2015.  This represents a model of the spatial distribution of people across the US for the year 2050.  "The model presented here departs from other spatially explicit projection models by accounting for socioeconomic and cultural characteristics that influence spatial population growth at smaller scales, while still projecting population at a large scale. The resulting projected population distribution can be exploited for long-term urban and infrastructure planning, and scientific modeling for climate change. "/////
Processing: Data were summarized by 500 acre Hexagon using ArcGIS Zonalstatistics as Table. /////
Summary Use: The 2050 data were used to compare with the 2010 dasymetric census data (AUs_dasymetric).  </attrdef>
      </attr>
      <attr>
        <attrlabl>AUs_DevRisk</attrlabl>
        <attrdef>Source: Summarize data from Dr. David M. Theobald, Natural Resource Ecology Lab, Colorado State University, 25 August 2007. The development risk data layer is intended to emphasize areas that are projected to experience increased housing development in the next 30 years.
Processing: Data were summarized by 500 acre Hexagon using ArcGIS Zonalstatistics as Table.
Summary: These data can be used to project what areas might be at risk to future development.  </attrdef>
      </attr>
      <attr>
        <attrlabl>AUs_PopChange2010to50</attrlabl>
        <attrdef>Source: Data for 2050 are derived from Oak Ridge National Laboratory (2015).  Data representing 2010 census data was derived from US EPA (see AUs_dasymetric).  /////
Processing: We used the original raster data to identify areas of and magnitude of population change.    ESRI Conditional Statement:  " Con("landcast2050_ProjectRaster" &gt;"Aggrega_Dasy1" ,("landcast2050_ProjectRaster" -  "Aggrega_Dasy1"),0)".  /////
Use Summary:  This data represents a commonly articulated issue/impact/threat across the CCLC: population change.  Articulated by stakeholders:  Pressure to convert working land to meet housing demands of an even greater population; population increase in Oregon and on the coast; and The projected increase in Oregon’s overall population will most likely present new challenges to housing supply, food systems, and other infrastructure.  The output data represent modeled areas where population is expected to increase.  Note that these are based purely on models.  </attrdef>
      </attr>
      <attr>
        <attrlabl>Zstats_PopulationChange_MEAN</attrlabl>
        <attrdef>DELETE THIS FIELD</attrdef>
      </attr>
      <attr>
        <attrlabl>VALUE</attrlabl>
        <attrdef>DELETE THIS FIELD
</attrdef>
      </attr>
      <attr>
        <attrlabl>ThreatsWork_AREA</attrlabl>
        <attrdef>DELETE THIS FIELD</attrdef>
      </attr>
      <attr>
        <attrlabl>MEAN</attrlabl>
        <attrdef>DELETE THIS FIELD</attrdef>
      </attr>
      <attr>
        <attrlabl>MA_2080</attrlabl>
        <attrdef>Source: NorWest Stream Temperature Database. https://www.fs.fed.us/rm/boise/AWAE/projects/NorWeST.html.
Processing:  Values for stream temperature were summarized within 500 acre Hexagons. 
Summary:  This represents the average Mean Annual modeled stream temperature for 2080.  </attrdef>
      </attr>
      <attr>
        <attrlabl>MS_2080</attrlabl>
        <attrdef>Source: NorWest Stream Temperature Database. https://www.fs.fed.us/rm/boise/AWAE/projects/NorWeST.html.
Processing:  Values for stream temperature were summarized within 500 acre Hexagons. 
Summary:  This represents the average Mean summer modeled stream temperature for 2080.  </attrdef>
      </attr>
      <attr>
        <attrlabl>MAUG_2080</attrlabl>
        <attrdef>Source: NorWest Stream Temperature Database. https://www.fs.fed.us/rm/boise/AWAE/projects/NorWeST.html.
Processing:  Values for stream temperature were summarized within 500 acre Hexagons. 
Summary:  This represents the average Mean August modeled stream temperature for 2080.  </attrdef>
      </attr>
      <attr>
        <attrlabl>Shape_Length_1</attrlabl>
      </attr>
      <attr>
        <attrlabl>Shape_Area_1</attrlabl>
      </attr>
      <attr>
        <attrlabl>Change80to11</attrlabl>
        <attrdef>Source: NorWest Stream Temperature Database. https://www.fs.fed.us/rm/boise/AWAE/projects/NorWeST.html.
Processing:  Values for 2011 mean summer stream temperature were subtracted from modeled mean summer temperatures for 2080 to identify the change in temperature. 
Summary:  The represents the absolute (not percent) change in stream temperature from 2011 to 2080. </attrdef>
      </attr>
      <attr>
        <attrlabl>Shape_Length_12</attrlabl>
      </attr>
      <attr>
        <attrlabl>Shape_Area_12</attrlabl>
      </attr>
      <attr>
        <attrlabl>TNCResilience</attrlabl>
        <attrdef>Source:  Resilience stratified by land facet and ecoregion as evaluated in the 2015 report.  Buttrick, S., K. Popper, M. Schindel, B. McRae, B. Unnasch, A. Jones, and J. Platt. 2015. Conserving Nature’s Stage:  Identifying Resilient Terrestrial Landscapes in the Pacific Northwest. The Nature Conservancy, Portland Oregon.  104 pp. Available online at: http://nature.org/resilienceNW. 

Processing:  The Resilience data were summarized by zonal mean with the 500 acre hexagons. 

Usage:  "The central idea is that by mapping key geophysical features and evaluating them for landscape characteristics that buffer against the effects of climate change, we can identify the most resilient places in order to guide future conservation investments."  Mapping resiliency has been a primary objective of the CCLC since its inception.  ".Develop a collaborative community climate resilient strategy among all stakeholders" has been articulated in several meetings. </attrdef>
      </attr>
      <attr>
        <attrlabl>BackwardVelocity</attrlabl>
        <attrdef>Source:  Carroll, C., J. J. Lawler, D. R. Roberts, and A. Hamann. 2015. Biotic and climatic velocity identify contrasting areas of vulnerability to climate change. PLoS ONE 10:e0140486.  "the distance from projected future climate pixels back to analogous current climate locations; note that backward velocity reflects the minimum distance, given the projected future conditions at a site, that a climate-adapted organism would have to migrate to colonize the site."

Processing:  Data were summarized by 500 acre Hexagon using ArcGIS Zonalstatistics as Table.

Usage:  These data can be used to identify areas that might be more vulnerable or resilient to climate change.  </attrdef>
      </attr>
      <attr>
        <attrlabl>ForwardVelocity</attrlabl>
        <attrdef>Source:  Carroll, C., J. J. Lawler, D. R. Roberts, and A. Hamann. 2015. Biotic and climatic velocity identify contrasting areas of vulnerability to climate change. PLoS ONE 10:e0140486.  "the distance from current climate locations to the nearest site with an analogous future climate; note that forward velocity reflects the minimum distance an organism in the current landscape must migrate to maintain constant climate condition"

Processing:  Data were summarized by 500 acre Hexagon using ArcGIS Zonalstatistics as Table.

Usage:  These data can be used to identify areas that might be more vulnerable or resilient to climate change.  </attrdef>
      </attr>
      <attr>
        <attrlabl>Refugia</attrlabl>
        <attrdef>Source:  Michalak, J. L., J. J. Lawler, D. R. Roberts, and C. Carroll. 2018. Distribution and protection of climatic refugia in North America. Conservation Biology.  The dataset can be downloaded from https://adaptwest.databasin.org/pages/distribution-and-protection-climatic-refugia.  We chose Refugia_RCP85_2080s_MIROC5_allsptypes.tif as the data set for further analysis.  

Processing: Data were summarized by 500 acre Hexagon using ArcGIS Zonalstatistics as Table.

Usage:  These data can be used to identify areas that might be more vulnerable or resilient to climate change.  

</attrdef>
      </attr>
      <attr>
        <attrlabl>PctFlowChng</attrlabl>
        <attrdef>Source:  Detailed methods are described in: (1) Wenger, S.J., C.H. Luce, A.F. Hamlet, D.J. Isaak, and H.M Neville. 2010. Macroscale hydrologic modeling of ecologically relevant flow metrics. Water Resources Research. 46: W09513.  "This feature class represents the percent change in modeled streamflow metrics between the historical (1997-2006) and late 21st century (2070-2099) time periods in the western United States.".

Processing: Data were summarized by 500 acre Hexagon using ArcGIS Zonalstatistics as Table.

Usage:  These data can be used to identify areas that might be more vulnerable or resilient to climate change.  </attrdef>
      </attr>
      <attr>
        <attrlabl>PctTempChng</attrlabl>
      </attr>
      <attr>
        <attrlabl>TNCResilienceIndex</attrlabl>
      </attr>
      <attr>
        <attrlabl>BackwardIndex</attrlabl>
      </attr>
      <attr>
        <attrlabl>ForwardIndex</attrlabl>
      </attr>
      <attr>
        <attrlabl>ResilienceSynth</attrlabl>
      </attr>
      <attr>
        <attrlabl>HydroChange</attrlabl>
      </attr>
      <attr>
        <attrlabl>Shape_Length_12_13</attrlabl>
      </attr>
      <attr>
        <attrlabl>Shape_Area_12_13</attrlabl>
      </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>20200623</metd>
    <metstdn>FGDC Content Standard for Digital Geospatial Metadata</metstdn>
    <metstdv>FGDC-STD-001-1998</metstdv>
    <mettc>local time</mettc>
    <metuc>These data layers are meant to be used for conversation and dialogue.  They are draft data layers.  Many of the data layers are derived from data layers produced by others and synthesized/summarized by CCLC hexagons.  </metuc>
  </metainfo>
</metadata> Impacts and Resiliency within the Cascades to Coast Landscape Collaborative Region vector digital data Overview of Spatial Design Goals and Process: 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, & Willemen, 2009; Melillo et al., 2014).”Analytical Question: Create a prototype landscape design 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. We can include social network perspective as part of a potential suite of design products, such as implementation capacity.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 is a synthesis of best available data to map land management, ownership and zoning across the Cascades to Coast Landscape Collaborative region. 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. Esri ArcGIS 12.3.2.15850 Washingtonparcels.Working LandsLandscape Conservation DesignCoastal Ecoregionzoningland managementforestryprotected area statusland ownershipOregonCascades to Coast Landscape CollaborativeSpatial Design Washington parcels. Working Lands Landscape Conservation Design Coastal Ecoregion zoning land management forestry protected area status land ownership Oregon Cascades to Coast Landscape Collaborative Spatial Design -127.067133 -119.544736 41.787941 48.995982 These data layers are meant to be used for conversation and dialogue. They are draft data layers. Many of the data layers are derived from data layers produced by others and synthesized/summarized by CCLC hexagons. Other Constraints Other Constraints 60007 ThreatsAndResiliency_052120 OBJECTID Internal feature number. Esri Sequential unique whole numbers that are automatically generated. Shape Feature geometry. Esri Coordinates defining the features. AUs_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 AUs_SLR (Source) These data were derived from the NOAA Sea Level Rise Inundation national dataset (2017). Methods for these data are here: https://coast.noaa.gov/data/digitalcoast/pdf/slr-inundation-methods.pdf. (Processing Abstract) NOAA data were clipped to the CCLC study area for both Oregon and Washington and then merged into one data set. Three feet SLR was chosen as the unit as a mid-range projection. This is NOT meant to be the absolute SLR that is chosen for the eventual results. The 3 feet value is meant as a starting point and this data set can be altered based upon feedback through the prototyping process. (Final) SLR data were summarized by 500 acre Hexagon using ArcGIS Zonalstatistics as Table. These hexagons represent mean sea level rise at a projected three feet level rise. AUs_Dasymetric (Source) These data were derived from the EPA Enviroatlas (2020). Methods for these data are here: https://edg.epa.gov/metadata/catalog/search/resource/details.page?uuid=%7BBBDAEC9A-F9B6-490F-8CA0-40401C47DEBA%7D. Essentially: "This EnviroAtlas dataset intelligently reallocates 2010 population from census blocks to 30 meter pixels based on land cover and slope." (Processing Abstract) NOAA data were clipped to the CCLC study area for both Oregon and Washington and then merged into one data set. Three feet SLR was chosen as the unit as a mid-range projection. This is NOT meant to be the absolute SLR that is chosen for the eventual results. The 3 feet value is meant as a starting point and this data set can be altered based upon feedback through the prototyping process. (Final) SLR data were summarized by 500 acre Hexagon using ArcGIS Zonalstatistics as Table. These hexagons represent mean sea level rise at a projected three feet sea level rise. AUs_Landcast30 Source: Oak Ridge National Laboratory; Jacob J. McKee, Amy N. Rose, Edward A. Bright, Timmy Huynh, 2015. This represents a model of the spatial distribution of people across the US for the year 2030. "The model presented here departs from other spatially explicit projection models by accounting for socioeconomic and cultural characteristics that influence spatial population growth at smaller scales, while still projecting population at a large scale. The resulting projected population distribution can be exploited for long-term urban and infrastructure planning, and scientific modeling for climate change. "///// Processing: Data were summarized by 500 acre Hexagon using ArcGIS Zonalstatistics as Table. ///// Summary Use: The 2030 data were not used in prototype maps for the CCLC. Rather, we focused attention on models that depict 2050. AUs_Landcast50 Source: Oak Ridge National Laboratory; Jacob J. McKee, Amy N. Rose, Edward A. Bright, Timmy Huynh, 2015. This represents a model of the spatial distribution of people across the US for the year 2050. "The model presented here departs from other spatially explicit projection models by accounting for socioeconomic and cultural characteristics that influence spatial population growth at smaller scales, while still projecting population at a large scale. The resulting projected population distribution can be exploited for long-term urban and infrastructure planning, and scientific modeling for climate change. "///// Processing: Data were summarized by 500 acre Hexagon using ArcGIS Zonalstatistics as Table. ///// Summary Use: The 2050 data were used to compare with the 2010 dasymetric census data (AUs_dasymetric). AUs_DevRisk Source: Summarize data from Dr. David M. Theobald, Natural Resource Ecology Lab, Colorado State University, 25 August 2007. The development risk data layer is intended to emphasize areas that are projected to experience increased housing development in the next 30 years. Processing: Data were summarized by 500 acre Hexagon using ArcGIS Zonalstatistics as Table. Summary: These data can be used to project what areas might be at risk to future development. AUs_PopChange2010to50 Source: Data for 2050 are derived from Oak Ridge National Laboratory (2015). Data representing 2010 census data was derived from US EPA (see AUs_dasymetric). ///// Processing: We used the original raster data to identify areas of and magnitude of population change. ESRI Conditional Statement: " Con("landcast2050_ProjectRaster" >"Aggrega_Dasy1" ,("landcast2050_ProjectRaster" - "Aggrega_Dasy1"),0)". ///// Use Summary: This data represents a commonly articulated issue/impact/threat across the CCLC: population change. Articulated by stakeholders: Pressure to convert working land to meet housing demands of an even greater population; population increase in Oregon and on the coast; and The projected increase in Oregon’s overall population will most likely present new challenges to housing supply, food systems, and other infrastructure. The output data represent modeled areas where population is expected to increase. Note that these are based purely on models. Zstats_PopulationChange_MEAN DELETE THIS FIELD VALUE DELETE THIS FIELD ThreatsWork_AREA DELETE THIS FIELD MEAN DELETE THIS FIELD MA_2080 Source: NorWest Stream Temperature Database. https://www.fs.fed.us/rm/boise/AWAE/projects/NorWeST.html. Processing: Values for stream temperature were summarized within 500 acre Hexagons. Summary: This represents the average Mean Annual modeled stream temperature for 2080. MS_2080 Source: NorWest Stream Temperature Database. https://www.fs.fed.us/rm/boise/AWAE/projects/NorWeST.html. Processing: Values for stream temperature were summarized within 500 acre Hexagons. Summary: This represents the average Mean summer modeled stream temperature for 2080. MAUG_2080 Source: NorWest Stream Temperature Database. https://www.fs.fed.us/rm/boise/AWAE/projects/NorWeST.html. Processing: Values for stream temperature were summarized within 500 acre Hexagons. Summary: This represents the average Mean August modeled stream temperature for 2080. Shape_Length_1 Shape_Area_1 Change80to11 Source: NorWest Stream Temperature Database. https://www.fs.fed.us/rm/boise/AWAE/projects/NorWeST.html. Processing: Values for 2011 mean summer stream temperature were subtracted from modeled mean summer temperatures for 2080 to identify the change in temperature. Summary: The represents the absolute (not percent) change in stream temperature from 2011 to 2080. Shape_Length_12 Shape_Area_12 TNCResilience Source: Resilience stratified by land facet and ecoregion as evaluated in the 2015 report. Buttrick, S., K. Popper, M. Schindel, B. McRae, B. Unnasch, A. Jones, and J. Platt. 2015. Conserving Nature’s Stage: Identifying Resilient Terrestrial Landscapes in the Pacific Northwest. The Nature Conservancy, Portland Oregon. 104 pp. Available online at: http://nature.org/resilienceNW. Processing: The Resilience data were summarized by zonal mean with the 500 acre hexagons. Usage: "The central idea is that by mapping key geophysical features and evaluating them for landscape characteristics that buffer against the effects of climate change, we can identify the most resilient places in order to guide future conservation investments." Mapping resiliency has been a primary objective of the CCLC since its inception. ".Develop a collaborative community climate resilient strategy among all stakeholders" has been articulated in several meetings. BackwardVelocity Source: Carroll, C., J. J. Lawler, D. R. Roberts, and A. Hamann. 2015. Biotic and climatic velocity identify contrasting areas of vulnerability to climate change. PLoS ONE 10:e0140486. "the distance from projected future climate pixels back to analogous current climate locations; note that backward velocity reflects the minimum distance, given the projected future conditions at a site, that a climate-adapted organism would have to migrate to colonize the site." Processing: Data were summarized by 500 acre Hexagon using ArcGIS Zonalstatistics as Table. Usage: These data can be used to identify areas that might be more vulnerable or resilient to climate change. ForwardVelocity Source: Carroll, C., J. J. Lawler, D. R. Roberts, and A. Hamann. 2015. Biotic and climatic velocity identify contrasting areas of vulnerability to climate change. PLoS ONE 10:e0140486. "the distance from current climate locations to the nearest site with an analogous future climate; note that forward velocity reflects the minimum distance an organism in the current landscape must migrate to maintain constant climate condition" Processing: Data were summarized by 500 acre Hexagon using ArcGIS Zonalstatistics as Table. Usage: These data can be used to identify areas that might be more vulnerable or resilient to climate change. Refugia Source: Michalak, J. L., J. J. Lawler, D. R. Roberts, and C. Carroll. 2018. Distribution and protection of climatic refugia in North America. Conservation Biology. The dataset can be downloaded from https://adaptwest.databasin.org/pages/distribution-and-protection-climatic-refugia. We chose Refugia_RCP85_2080s_MIROC5_allsptypes.tif as the data set for further analysis. Processing: Data were summarized by 500 acre Hexagon using ArcGIS Zonalstatistics as Table. Usage: These data can be used to identify areas that might be more vulnerable or resilient to climate change. PctFlowChng Source: Detailed methods are described in: (1) Wenger, S.J., C.H. Luce, A.F. Hamlet, D.J. Isaak, and H.M Neville. 2010. Macroscale hydrologic modeling of ecologically relevant flow metrics. Water Resources Research. 46: W09513. "This feature class represents the percent change in modeled streamflow metrics between the historical (1997-2006) and late 21st century (2070-2099) time periods in the western United States.". Processing: Data were summarized by 500 acre Hexagon using ArcGIS Zonalstatistics as Table. Usage: These data can be used to identify areas that might be more vulnerable or resilient to climate change. PctTempChng TNCResilienceIndex BackwardIndex ForwardIndex ResilienceSynth HydroChange Shape_Length_12_13 Shape_Area_12_13 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.