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   "description": "<DIV STYLE=\"text-align:Left;\"><DIV><DIV><P><SPAN>The following methods apply for the Rocky Mountians and east.  For Califonia methods see: <\/SPAN><A href=\"https://omniscape.codefornature.org/\" STYLE=\"text-decoration:underline;\"><SPAN><SPAN>https://omniscape.codefornature.org/#/analysis-tour<\/SPAN><\/SPAN><\/A><SPAN>.  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<\/SPAN><\/P><P><SPAN>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: <\/SPAN><\/P><UL STYLE=\"margin:0 0 0 0;padding:0 0 0 0;\"><LI><P><SPAN><SPAN>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. <\/SPAN><\/SPAN><\/P><\/LI><LI><P><SPAN><SPAN>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. <\/SPAN><\/SPAN><\/P><\/LI><LI><P><SPAN><SPAN>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. <\/SPAN><\/SPAN><\/P><\/LI><LI><P><SPAN><SPAN>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. <\/SPAN><\/SPAN><\/P><\/LI><LI><P><SPAN><SPAN>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.<\/SPAN><\/SPAN><\/P><\/LI><\/UL><P><SPAN>In the national map we use the diffuse and concentated flow areas.<\/SPAN><\/P><P><SPAN>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 \u201cconcentrated\u201dbecause 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 \u201cdiffuse\u201dbecause 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.<\/SPAN><\/P><P><SPAN STYLE=\"font-weight:bold;\">Climate Flow<\/SPAN><\/P><P><SPAN><SPAN>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. <\/SPAN><\/SPAN><\/P><P><SPAN>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.<\/SPAN><\/P><P><SPAN /><\/P><P STYLE=\"margin:0 0 14 0;\"><SPAN /><SPAN /><\/P><P STYLE=\"margin:0 0 14 0;\"><SPAN /><SPAN /><\/P><P STYLE=\"margin:0 0 14 0;\"><SPAN><SPAN>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; 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. <\/SPAN><\/SPAN><\/P><P STYLE=\"margin:0 0 0 0;\"><SPAN /><\/P><P STYLE=\"margin:0 0 0 0;\"><SPAN /><\/P><\/DIV><\/DIV><\/DIV>",
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   "description": "<DIV STYLE=\"text-align:Left;\"><DIV><DIV><P><SPAN>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.<\/SPAN><\/P><P><SPAN>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. <\/SPAN><\/P><P><SPAN>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.<\/SPAN><\/P><P><SPAN /><\/P><P><SPAN>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.<\/SPAN><\/P><\/DIV><\/DIV><\/DIV>",
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