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<idAbs>&lt;DIV STYLE="text-align:Left;"&gt;&lt;DIV&gt;&lt;DIV&gt;&lt;P&gt;&lt;SPAN&gt;Canadian ecologist Stanley Rowe called landform "the anchor and control of terrestrial ecosystems." It breaks up broad landscapes into local topographic units, and in doing so provides for more detailed meso- and micro-climatic expression of general macroclimatic character. It is largely responsible for local variation in solar radiation, soil development, moisture availability, and susceptibility to wind and other disturbance. As one of the five "genetic influences" in the process of soil formation, it is tightly tied to rates of erosion and deposition, and therefore to soil depth, texture, and nutrient availability. These are, with moisture, the primary edaphic controllers of plant productivity and species distributions. If the other four influences on soil formation (climate, time, parent material, and biota) are constant over a given space, it is variation in landform that drives variation in the distribution and composition of natural communities.&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN&gt;Landform is a compound measure, which can be deconstructed into the primary terrain attributes of elevation, slope, aspect, surface curvature, and upslope catchment area. The wide availability and improving quality of digital elevation data has made the quantification of primary terrain attributes a simple matter. Compound topographic indices have been derived from these primary attributes to model various ecological processes. We adopted the Fels and Matson (1997) approach to landform modeling. They describe a metric that combines information on slope and landscape position to define topographic units such as ridges, sideslopes, coves, and flats on the landscape. &lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN&gt;The current 30m resoluation grid landform dataset is a base 17-part model comprising cliffs, flat summits/ridge tops, slope crests, steep slopes warm aspect and cool aspect, sideslopes warm aspect and cool aspect, cove slopes warm aspect and cool aspect, flats at the bottom of steep slopes, hilltop flats, hill gentle slopes, valley/toe slopes, dry flats, wet flats, moist flats, and large open water bodies. Topographic landforms were developed using 30m digital elevation data where landposition, slope, and moisture accumulation were calculated and integrated to define landforms. For open water and wetflats, vector and raster wetlands and water data were compiled for this analysis region and integrated into the landforms as well. Please see the source report documents for more information on landform detailed methods.&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN /&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN&gt;This dataset was downloaded for the City of Fayetteville of October 19th 2022. It was then clipped and reclassified to show only areas within the planning area.&lt;/SPAN&gt;&lt;/P&gt;&lt;/DIV&gt;&lt;/DIV&gt;&lt;/DIV&gt;</idAbs>
<idPurp>Resilience concerns the ability of a living system to adjust to climate change, moderate potential damages, take advantage of opportunities, or cope with consequences; in short, the capacity to adapt. The Nature Conservancy’s resilience analysis develops an approach to conserve biological diversity while allowing species and communities to rearrange in response to a continually changing climate. Together, the diversity of physical features and the ability for local movement define a landscape’s resiliency.
See more at: http://nature.org/climate_resilience
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<idCredit>The Nature Conservancy, Eastern Resource Office, Eastern Conservation Science (ECS), Boston, MA. Source reports from which this national dataswet was compiled:
Anderson, M.G., A. Barnett, M. Clark, C. Ferree, A. Olivero Sheldon, J. Prince. 2016. Resilient Sites for Terrestrial Conservation in Eastern North America. The Nature Conservancy, Eastern Conservation Science. Anderson, M.G., M. M. Clark, M.W. Cornett, K.R. Hall, A. Olivero Sheldon, J. Prince. 2018. Resilient Sites for Terrestrial Conservation in the Great Lakes and Tallgrass Prairie. The Nature Conservancy, Eastern Conservation Science and North America Region. Anderson, M.G., M.A. Ahlering, M. M. Clark, K.R. Hall, A. Olivero Sheldon, J. Platt and J. Prince. 2018. Resilient Sites for Terrestrial Conservation in the Great Plains. The Nature Conservancy, Eastern Conservation Science and North America Region.
Anderson, M.G., M. M. Clark, A. Olivero, and J. Prince. 2019. Resilient Sites and Connected Landscapes for Terrestrial Conservation in the Lower Mississippi-Ozark Region. The Nature Conservancy, Eastern Conservation Science.
Anderson, M.G., M. M. Clark, A. Olivero, and J. Prince. 2019. Resilient Sites and Connected Landscapes for Terrestrial Conservation in the Rocky Mountains and Southwest Desert Region. The Nature Conservancy, Eastern Conservation Science.
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<useLimit>&lt;DIV STYLE="text-align:Left;"&gt;&lt;DIV&gt;&lt;DIV&gt;&lt;P&gt;&lt;SPAN&gt;The Nature Conservancy reserves all rights in data provided. All data are provided as is. This is not a survey quality dataset. The Nature Conservancy makes no warranty as to the currency, completeness, accuracy or utility of any specific data. This disclaimer applies both to individual use of the data and aggregate use with other data. It is strongly recommended that careful attention be paid to the contents of the metadata file associated with these data.&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN&gt;The Nature Conservancy compiled this data set from publicly available data sources and this data is freely distributable without permission from Eastern Division Conservation Science. This data set must be cited on all electronic and hard copy products using the language of the Data Set Credit. The Nature Conservancy shall not be held liable for improper or incorrect use of the data described and/or contained herein. Any sale, distribution, loan, or offering for use of these digital data, in whole or in part, is prohibited without the approval of The Nature Conservancy. The use of these data to produce other GIS products and services with the intent to sell for a profit is prohibited without the written consent of The Nature Conservancy. All parties receiving these data must be informed of these restrictions. The Nature Conservancy shall be acknowledged as data contributors to any reports or other products derived from these data.&lt;/SPAN&gt;&lt;/P&gt;&lt;/DIV&gt;&lt;/DIV&gt;&lt;/DIV&gt;</useLimit>
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<attrdef>Simple 17 Landform Value: Use this field for a simplier set of landforms, 17 rather than the full 23. Landforms that were used in only parts of the U.S.A. were merged into most similar landform class in this simplification.</attrdef>
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<attrdef>Simple 17 Landform Name: Use this field for a simplier set of landforms, 17 rather than the full 23. Landforms that were used in only parts of the U.S.A. were merged into most similar landform class in this simplification.</attrdef>
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<stepDesc>To quantify the microclimates, we developed a GIS model that divides and classifies a continuous surface into one of 17 elementary landforms. Our methods are described in detail elsewhere (Anderson 1999, Anderson et al. 2012) and are based on those of Fels and Matson (1997). In brief, the landform model was built from a 30-m digital elevation model (DEM; Gesch 2007, Canada Digital Elevation Data 2011), and metrics derived from the DEM were used to estimate the variables of: slope, aspect, land position, and moisture accumulation. Thresholds were used to partition the ranges of each variable into zones that corresponded with recognizable distinctions between component landforms that could be verified in the field. Major divisions in the model were based on relative land position and slope, with slopes further subdivided by aspect, and flats further subdivided by moisture accumulation.
To create the landform model, we generated the following initial datasets as grids from the 30-m DEM:
• Topographic Position Index: We evaluated the elevation differences between any cell and the surrounding cells within a specified search radius (roughly 350 m) and scored it using a topographic position index (TPI). For example, if the model cell was, on average, higher than the surrounding cells, then it was considered closer to the hill top (a more positive position value), and conversely, if the model cell was, on average, lower than the surrounding cells, then it was considered closer to the slope bottom (a more negative position value).
• Slope: Degree of slope was calculated as the difference in elevation between two neighboring cells, expressed in degrees.
• Aspect: Aspect was calculated using the GIS Aspect tool which fits a plane to the z-values of a 3 x 3 cell neighborhood around a center cell. The direction the plane faces is the aspect for the center cell. • Moisture Index: We calculated a moisture index using a flow accumulation model which maps variation in moisture accumulation and soil residence time using the equation: Moist index = ln [(flow accumulation + 1) / (slope + 1)].
We used National Wetlands Inventory (NWI) datasets (U. S. Fish and Wildlife Service 2017) and playas from the Playa Lakes Joint Venture (PLJV Version 4. 2017) to calibrate the moisture index and set a threshold for dividing flat areas into those where moisture is more likely to accumulate and be retained from those which were drier. We defined moist flat areas as those with a moisture index &gt;= the ecoregional “mean plus half a standard deviation” of the moisture index values within known wetlands and playas in each ecoregion. This mean plus half a standard deviation area did a good job spatially capturing the known wetlands without spilling far outside these known wet places. The moisture index threshold ranged from a low value of 2302 in the Osage/Flint Hills ecoregion to a high value of 3638 in the Southern Shortgrass Prairie ecoregion where you needed a much higher moisture index value to get any likely expression or retention of moisture. Areas with confirmed wetlands, playas lakes, or small waterbodies 2-100 acres were labeled as “wet flats” to highlight confirmed wetness of these landforms, while the remaining moist flat areas in upland landcover were labeled “moist flats”. Wetland and water sources included Canada Agriculture and Agri-Food Canada (AAFC) Landuse Wetlands defined as pixels classified as wetland &gt;2 years out of 2012-2016 period (AAFC 2012-2016) , Canada Vector Waterbodies 1:50,000 , Alberta Merged Wetland Inventory (Alberta Environment and Parks, 2017), US National Wetland Inventory wetlands of type EM/FO/SS (USFWS 2017), US National Land Cover Database Emergent or Forested Wetlands (NLCD 2011), US National Hydrography High Resolution Waterbodies and Areas (USGS 2017), and Playa Lakes for parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma and Texas (Playa Lakes Joint Venture (PLJV Probably Playas V.4.). Water bodies &lt;100 acres were also all put in the wetflat category given that in the Great Plains these small waterbodies were considered by ecologists similar to playa lakes and on the wetland ecosystem continuum. Spatial comparison of these small waterbodies with existing NWI, wetland, and playa datasets also showed much spatial confusion and overlap between and among the wetlands, playas, and small waterbodies. It was determined more spatially and ecologically correct to treat all small waterbodies in this region as part of the larger wetland “continuum” and place them into the “confirmed “wetflats” category for the landforms and subsequent wetland density analysis. The open water landform was then restricted to larger waterbodies &gt;100 acres which provided substantial permanent open water habitat which was thought of as different from the wetland continuum of habitats which included the wetflat mixture of permanent and intermittent wetlands and small shallow playa lakes and waterbodies in this region. As a last step all features &lt;2 acres were removed unless they were in the playa dataset or confirmed with wetland ecosystem element occurrences from Heritage program surveys. This removal of the smallest wetlands focused the wetflats and furthre analysis on larger more stable features on the landscape and removed many antrhopogentically created small stock ponds/wetlands. The above topographic position, slope, aspect, wet flats, and moist flat girds grids were combined in a structured way to create the landforms (Figure 3.2). For example, a south-facing side-slope was defined by land position, slope and aspect, whereas a moist flat was defined by land position, slope and moisture accumulation. The landform model can distinguish any number of units, but we used a 17-unit model that matched recognizable environments with differing vegetation, especially in the herbaceous layer. We tested whether these 17 units capture the major differences in temperature and moisture by looking at the current distribution of the biota in the region.
</stepDesc>
</prcStep>
</dataLineage>
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