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<idAbs>&lt;DIV STYLE="text-align:Left;"&gt;&lt;DIV&gt;&lt;DIV&gt;&lt;P&gt;&lt;SPAN&gt;The following methods apply for the Rocky Mountians and east. For Califonia methods see: &lt;/SPAN&gt;&lt;A href="https://omniscape.codefornature.org/" STYLE="text-decoration:underline;"&gt;&lt;SPAN&gt;&lt;SPAN&gt;https://omniscape.codefornature.org/#/analysis-tour&lt;/SPAN&gt;&lt;/SPAN&gt;&lt;/A&gt;&lt;SPAN&gt;. For the Pacific Northwest, the base flow was calculated using omniscape and the climate flow was using eastern methods. For more information see: https://www.conservationgateway.org/ConservationByGeography/NorthAmerica/UnitedStates/oregon/science/Documents/McRae_et_al_2016_PNW_CNS_Connectivity.pdf&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN&gt;The wall to wall results reveal how the human-modified landscape is configured. The results allow you to identify where population movements and potential range shifts may become concentrated or where they are well dispersed, and it is possbile to quantify the importance of an area by measuring how much flow passes through it and how concentrated that flow is. The four prevalent flow types found here each suggest a different conservation strategy: &lt;/SPAN&gt;&lt;/P&gt;&lt;UL STYLE="margin:0 0 0 0;padding:0 0 0 0;"&gt;&lt;LI&gt;&lt;P&gt;&lt;SPAN&gt;&lt;SPAN&gt;Diffuse flow: areas that are extremely intact and consequently facilitate high levels of dispersed flow that spreads out to follow many different and alternative pathways. A conservation aim might be to keep these areas intact and prevent the flow from becoming concentrated. This might be achievable through land management or broad-scale conservation easements. &lt;/SPAN&gt;&lt;/SPAN&gt;&lt;/P&gt;&lt;/LI&gt;&lt;LI&gt;&lt;P&gt;&lt;SPAN&gt;&lt;SPAN&gt;Concentrated flow: areas where large quantities of flow are concentrated through a narrow area. Because of their importance in maintaining flow across a larger network, these pinch points are good candidates for land conservation. &lt;/SPAN&gt;&lt;/SPAN&gt;&lt;/P&gt;&lt;/LI&gt;&lt;LI&gt;&lt;P&gt;&lt;SPAN&gt;&lt;SPAN&gt;Constrained flow: areas of low flow that are neither concentrated nor fully blocked but instead move across the landscape in a weak reticulated network. These areas present large conservation challanges. &lt;/SPAN&gt;&lt;/SPAN&gt;&lt;/P&gt;&lt;/LI&gt;&lt;LI&gt;&lt;P&gt;&lt;SPAN&gt;&lt;SPAN&gt;In some cases restoring a riparian network might end up concentrating the flow and creating a linkage that will be easier to maintain over time. &lt;/SPAN&gt;&lt;/SPAN&gt;&lt;/P&gt;&lt;/LI&gt;&lt;LI&gt;&lt;P&gt;&lt;SPAN&gt;&lt;SPAN&gt;Blocked/Low flow: areas where little flow gets through and is consequently deflected around these features. Some of these might be important restoration areas where restoring native vegetation or altering road infrastructure might reestablish a historic connection.&lt;/SPAN&gt;&lt;/SPAN&gt;&lt;/P&gt;&lt;/LI&gt;&lt;/UL&gt;&lt;P&gt;&lt;SPAN&gt;In the national map we use the diffuse and concentated flow areas.&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN&gt;To create a categorical classification of flow pattern, we applied the following method. First, we calculated the amount and the variation of flow in every local neighborhood (1000 acres) around every cell (The size of the neighborhood was determined by testing a variety of distances and picking the one that best captured flow pattern and still retained local detail). Next, within each neighborhood we calculated the mean amount of flow, and the variation in flow as indicated by the standard deviation. Areas that had high flow and a high standard deviation were considered “concentrated”because they not only channel a large amount of flow but are different from their surrounding cells. Areas that had above-average flow and low standard deviation were considered “diffuse”because they move a lot of flow but are similar to their neighboring cells. We divided the mean and standard deviation into 7 quantiles classes by area and analyzed the combinations to classify the wall-to-wall continuous grid.&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN STYLE="font-weight:bold;"&gt;Climate Flow&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN&gt;&lt;SPAN&gt;For our final model, we weighted the regional flow model with the upslope, downslope and northward models to simulate species populations could flow through the natural landscape finding climate refuge both by moving up or down slopes and mostly in a northward direction. The goal was to approximate a species population expanding locally then northward as allowed by the anthropogenic resistance within its neighborhood. &lt;/SPAN&gt;&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN&gt;When combining the factors, a challenge was how to weight the influence of each factor in a way that most closely approximates the real world. We wanted to keep the emphasis on the areas that are important for regional flow, while boosting slightly the areas that channel slope-based and northward movements. We accomplished this by using the northward regional flow map as our based dataset and boosted the score of cells if they were important for upslope or downslope movement. For each of the two factors we took the areas that were above-average with respect to their factor. The areas for climate flow are sperate categories in the map.&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;SPAN /&gt;&lt;/P&gt;&lt;P STYLE="margin:0 0 14 0;"&gt;&lt;SPAN /&gt;&lt;SPAN /&gt;&lt;/P&gt;&lt;P STYLE="margin:0 0 14 0;"&gt;&lt;SPAN /&gt;&lt;SPAN /&gt;&lt;/P&gt;&lt;P STYLE="margin:0 0 14 0;"&gt;&lt;SPAN&gt;&lt;SPAN&gt;We created categorical classification of the climate flow patterns, using the similar method described previously for the regional flow. The amount of flow was calculated by looking at the mean flow within a 1000-acre circle of each cell (Figure 7.22 &amp;amp; Figure 7.23). We used the anthropogenic regional flow weighted towards northward movement (66%) as the base map and added in the areas of upslope and downslope flow wherever those areas had higher flow than the base flow. The outcome looks superficially like the regional flow map but has higher flow along gradients important for temperature and moisture relief. This allowed us to parse out more levels of diffuse flow and identify key climate pathways within the relatively intact landscape. &lt;/SPAN&gt;&lt;/SPAN&gt;&lt;/P&gt;&lt;P STYLE="margin:0 0 0 0;"&gt;&lt;SPAN /&gt;&lt;/P&gt;&lt;P STYLE="margin:0 0 0 0;"&gt;&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. See more at: http://nature.org/climateresilience. In the Resilient and Connected Network analysis, sites and linkages between them identified by the combination of resilience, flow, and biodiversity were integrated into a single network. The network is designed to represent resilient examples with all the characteristic physical environments of the region while maximizing the amount of diversity contained within in them and the natural flow that connects them. By building the network around the natural pathways that allow species populations to shift and then identifying representative resilient sites situated within those pathways, the network is specifically configured to sustain biological diversity while allowing nature to adapt and change.
The Resilient and Connected Landscapes analysis was tailored to regions based on input from regional steering committees that modified factors to capture local ecological functions important to each region. This national dataset is directly derived from the 9 regional datasets and care should be taken to understand the different methods in each of the regions.
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.</idPurp>
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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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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 &lt;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 final step all wetflats &lt;2 acres in size were removed unless they were in the playa source dataset or confirmed with a wetland community Heritage occurrence to remove the smallest features which were often anthropogentically created 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>
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