Build a model to connect mountain lion habitat

Mountain lions, also known as cougars, need room to roam, and the rugged mountains northwest of Los Angeles in the United States provide this protected species the space it needs to hunt and breed. The problem? The mountain lion habitat is becoming fragmented into isolated patches because of the increase in human population. The solution? Connect the habitat patches by creating wildlife corridors. This connected habitat will provide adequate space for the mountain lion population and will also allow mountain lions in the different patches to breed, keeping up the population and genetic diversity. In this tutorial, you'll use ArcGIS Pro to create a geoprocessing model identifying the optimal routes for these corridors.

This tutorial was last tested on August 13, 2026, using ArcGIS Pro 3.7. If you're using a different version of ArcGIS Pro, you may encounter different functionality and results.

Requirements

• ArcGIS Pro (see options for software access)
• ArcGIS Spatial Analyst extension

Explore the study area and data

To get started, you'll download an ArcGIS Pro project that contains all the data you'll need for this workflow. You'll explore the mountain lion habitat areas, understand the proposed analysis strategy, and review the data.

Explore the habitat areas

First, you'll download a project and open it in ArcGIS Pro. Then, you'll explore the mountain lion habitat areas.

1. Download the Mountain_Lion_Corridors compressed (zipped) folder. Extract the folder to location of your choice.
2. Browse to the extracted folder and double-click Mountain_Lion_Corridors.aprx to open the project in ArcGIS Pro. If necessary, sign in using your licensed ArcGIS account.

   Note:

   If you don't have access to ArcGIS Pro or an ArcGIS organizational account, see options for software access.

   The project contains a map of the study area.

   

   On the map, two layers are currently turned on. The Study Area layer shows the area of interest for this workflow, represented as a rectangular polygon (in black). The World Topographic basemap provides general geographic context. The study area is located in the greater Los Angeles area in the United States. It includes the San Fernando Valley, Burbank, and Santa Clara, as well as several mountain ranges spread out throughout the area.

3. In the Contents pane, check the box next to Core Mountain Lion Habitats.

   

   The layer turns on, making it visible on the map. The study area contains four core mountain lion habitats, represented in purple. These are the habitats that you want to connect by proposing locations for wildlife corridors. Mountain lions can be present in a larger zone around each core habitat, but the purple polygons show the areas of highest mountain lion concentration.

   

   Note:

   In this tutorial, you'll focus on this specific study area to limit the time it takes for the analysis tools to run. However, there are other mountain lion habitat areas in Northern California, and the same workflow could be applied to a larger study area to connect more of these habitats with each other.

   The three habitats identified as Los Padres, Santa Monica, and San Gabriel represent true core mountain lion habitat. They are set in spacious, mountainous natural areas and contain significant mountain lion populations. The Santa Susana location, which was established by the National Park Service and is located just outside of Pico Canyon Park, doesn't represent a significant core area, but it serves as an important geographic gateway for linking the other three geographically disconnected habitat patches to each other.

   Instead of managing the mountain lions present in the four habitats as four separate populations, you want to manage them as a single metapopulation—a population of populations. Ensuring that this overall population of mountain lions can move around from one core habitat patch to another will help it remain healthy and genetically diverse. And if the mountain lions go locally extinct in one of the patches, the lions from other patches can recolonize it. This is why it is essential to establish wildlife corridors to connect these existing habitat patches.

   You'll turn the labels off, as you won't need them any longer.

4. In the Contents pane, right-click the Core Mountain Lion Habitats layer and choose Label.

   

   On the map, the labels disappear.

Review data for the cost surface

So, how will you determine the best corridors for mountain lions?

The best corridors are the ones that the mountain lions will find the least stressful and dangerous as they move through the landscape; the ones they will feel the most comfortable with. For instance, passing through an urbanized area would be very high stress and dangerous. Instead, passing through a protected forest that is devoid of human beings and cars would be low stress and low danger. You'll identify the best corridors by performing a least-cost analysis, in which you'll determine which paths through the landscape have the lowest cost for the mountain lions, in terms of stress and danger.

Your analysis will have two parts:

Part 1: Create a cost surface for the entire study area. This cost surface will be a raster (that is, a grid of cells or pixels) where a cost value has been assigned to every cell. Low numeric values will correspond to low cost, and high numeric values to high cost. The following image shows a cost surface with values varying from 1 to 5:

Part 2: Determine the optimal paths connecting the core habitats patches over the cost surface. A path accumulates cost values as it goes from cell to cell on the cost surface raster. The optimal paths are the ones that have the lowest accumulated cost. In the following image, the line shows the least cost path to go from point A to point B:

There are five steps to create a cost surface:

1. Identify several cost criteria that make sense for the topic (here mountain lions moving through the landscape).
2. Identify datasets to represent these criteria.
3. If necessary, preprocess the datasets, so that they can fully represent the criteria.
4. Transform the criteria datasets to a common scale (from 1 to 10).
5. Weigh and combine the criteria datasets into a single cost surface raster.

You'll decide on several cost criteria that make sense for mountain lions. As a reminder, you want to represent the cost for mountain lions as they move through the landscape, in terms of potential danger and stress. This would also include the ability to find prey along the way and not go hungry. You'll use the following criteria:

• Terrain ruggedness—Rugged terrain serves multiple purposes allowing the mountain lions to hide within the terrain and helping them stalk their prey. It also discourages human presence and human development. The more ruggedness, the lower the cost.
• Dense vegetation land cover—The presence of trees and bushes provides security when moving and cover for stalking prey. The mountain lions' preferred prey also tends to live in this type of land cover, lowering the chances for the lions to go hungry. The more dense vegetation in an area, the lower the cost.
• Protected areas—Although the mountain lions themselves don't know whether an area is protected, such areas offer the natural environment that the lions prefer, with minimal human presence and no urban development. They are also a guarantee that the natural environment will remain intact in the future. The more protected an area, the lower the cost.
• Distance to roads—Not only are roads dangerous for mountain lions, but they also serve as a proxy for human development. The further distance from roads, the lower the cost.

Note:

You can learn more about cost surface and cost analysis in the articles Distance Analysis Simplified and Determining how distance is encountered by the traveler. You can learn more about mountain lions on the Cougar Overview and Cougar Family Life pages, created by the Cougar Fund.

To represent each cost criterion in your analysis, you must have an appropriate dataset. Next, you'll review the four datasets selected. The first one is the Elevation layer that you'll use to represent the terrain ruggedness criterion.

1. In the Contents pane, check the box next to Elevation.

   The Elevation layer appears on the map.

   

   This layer is a raster layer where each cell represents elevation above sea level. Cells with lower elevation appear in dark tones and cells with higher elevation appear in lighter tones. The mountainous elevated areas appear white and light gray, while flatter low-lying valleys appear black and dark gray. Based on the legend in the Contents pane, the elevation values range from about 2,444 meters at the highest to a very small fraction of a meter at the lowest (basically, sea level).

   The Elevation layer represents elevation, not ruggedness. Later in the workflow, you'll use it to derive a new layer that represents the ruggedness criterion. Then, you'll assign the lowest cost to the highest ruggedness values.

   Next, you'll review the dataset that you'll use for the second criterion, dense vegetation land cover.

2. In the Contents pane, uncheck the Elevation layer to turn it off. Turn on the Land Cover layer.

   The Land Cover layer appears on the map.

   

   This layer is another raster layer, although it doesn't have a simple black-to-white color gradient like the Elevation layer. To understand what this layer shows, you'll investigate its symbology.

3. In the Contents pane, click the arrow next to the Land Cover layer.

   The layer's legend expands.

   

   This layer represents different land cover types, like Evergreen Forest or Cultivated Crops. Each of these land types is symbolized with a color: the red colors are developed urban areas, while the green colors correspond to forests or grasslands. The brown colors are shrubland or herbaceous areas. This layer doesn't need to be preprocessed and can be used directly in the least-cost analysis, where you'll assign the lowest cost to dense vegetation cover types, such as forest and shrub.

   Next, you'll review the layer you'll use for the third criterion: protected areas.

4. In the Contents pane, click the arrow next to the Land Cover layer to collapse the symbology.
5. Turn off the Land Cover layer and turn on the Protected_Status layer.

   The Protected_Status layer appears on the map.

   

   This raster layer indicates the level of protection for all lands contained in the Protected Areas Database of the United States (PAD-US), from highly protected in dark green to not protected in light green. An example of highly protected land would be a national forest. An example of not protected land would be a recreation area. Areas without a color on the map are not tracked in PAD-US, and usually correspond to urban, agriculture, or other forms of privately exploited areas—they will usually be the least favorable to mountain lions. This layer doesn't need to be preprocessed and can be used directly in the least-cost analysis, where you'll assign the lowest cost to the highest levels of protection.

   Note:

   This layer was derived from an older version of PAD-US and may be out of date. This layer should only be used for the purposes of this tutorial.

   Last, you'll review the dataset related to the fourth criterion: distance from roads.

6. In the Contents pane, turn off the Protected_Status layer and turn on the Roads layer.

   On the map, the Roads layer appears.

   

   The Roads layer is not a raster layer; it's a vector layer that represents features as lines. (Other types of vector layers can contain points or polygons.) Each line represents a road, ranging from small roads to major highways, although you won't distinguish between these different types for most of the workflow. To use this layer in the least-cost analysis, you'll need to preprocess it and derive from it a raster representing the distance from any cell to the nearest roads. Then, you'll assign the lowest cost to the highest distance-to-road cell values.

   Note:

   Like the PAD-US layer, this layer was derived from an older road network dataset and may be out of date. This layer should only be used for the purposes of this tutorial.

7. Turn off the Roads layer.

Create a hillshade layer

Before you prepare your data for analysis, you'll create a hillshade layer to add more context to the map and help you understand the landscape. A hillshade layer highlights the landscape's relief with shade and light for a rendering familiar to the human eye. You'll derive it from the Elevation layer.

1. On the ribbon, click the Imagery tab. In the Analysis group, click the Raster Functions button.

   

   The Raster Functions pane appears, listing functions that can be used on raster data.

2. In the Raster Functions pane, in the search box, type Hillshade. In the list of results, click Hillshade.

   

3. In the Hillshade Properties parameters, for Raster, choose Elevation.

   

   You don't need to change the remaining parameters. They change the shading used for the hillshade.

4. At the bottom of the Raster Functions pane, click Create new layer.

   A new Hillshade_Elevation layer appears on the map.

   

   The layer makes it clear where mountain ranges and valleys are located throughout the study area. While you won't use this layer in the analysis itself, you'll use it to help visualize the various layers you'll generate throughout the analysis.

   You'll rename the hillshade layer.

5. In the Contents pane, click Hillshade_Elevation to make the layer name editable. Type Hillshade and press Enter.

   

   Since this layer will be used mostly as a background, you'll move it below most of the other layers.

6. In the Contents pane, drag the Hillshade layer under the Protected_Status layer.

   

7. Close the Raster Functions pane.
8. On the Quick Access Toolbar, click the Save Project button.

   

So far in this workflow, you've reviewed mountain lion habitats in the area of study, learned the principles of least-cost analysis, and identified four criteria for your analysis. Next, you'll process the default data for use in your analysis.

Prepare the data for analysis

Next, you'll prepare your data for analysis. You'll process two of the layers, Elevation and Roads, to create derived raster layers for the ruggedness of the land and distance to roads. The other two layers can be used directly.

Whether it is to prepare the data or to perform the analysis itself, you must run several geoprocessing tools. You can run each of these tools individually; however, in this tutorial, you'll learn how to gather them all into a geoprocessing model. A model is a series of geoprocessing tools that run in an order you specify. You can run a model as many times as you want, use it to automate a workflow, or share it with colleagues. It also allows you to fine-tune an analysis quickly, as you can modify any of the tools' parameters and re-run the whole analysis in one go.

Set the environment parameters

Before you start building your model, you'll set the geoprocessing environment parameters. These parameters apply to all the geoprocessing tools you run and are helpful for limiting the extent of your processing or determining how data will be saved.

First, you'll ensure that output layers created by geoprocessing tools will overwrite any existing layers with the same name. This option is useful when you intend to repeat an analysis workflow multiple times but don't want to clutter your project with extra layers. The option may be enabled by default, but you'll confirm it's active before you begin the analysis.

1. On the ribbon, click the Project tab.

   

2. In the list of side tabs, click Options.

   

   The Options window appears.

3. In the Options window, click the Geoprocessing tab.

   

4. Confirm that Allow geoprocessing tools to overwrite existing datasets is checked.

   

5. Click the ModelBuilder tab.

   

6. Uncheck the Add output to model group layer option.

   

7. Click OK.

   Settings changed in the Options window will be saved across all your projects in ArcGIS Pro. The rest of the settings you'll choose are specific to this project, so you'll change them within the project itself.

8. On the ribbon, click the Analysis tab. In the Geoprocessing group, click Environments.

   

   The Environments window appears. First, you'll ensure the default workspace (the location where new layers created by geoprocessing tools are saved) is the geodatabase connected to your project.

9. In the Environments window, under Workspace, confirm that both Current Workspace and Scratch Workspace are set to Mountain_Lion_Corridors.gdb.

   

   Both workspaces indicate the location where data is saved, but the scratch workspace is intended for data you don't want to keep. You'll keep the same location for both.

10. Under Output Coordinates, for Output Coordinate System, choose Elevation.

    The coordinate system automatically changes to the coordinate system of the Elevation layer, NAD 1983 UTM Zone 11N. By choosing a default coordinate system, you ensure that all the output layers you create will use the same coordinate system.

    

11. Under Processing Extent, for Extent, click the Extent of a layer button and choose Study Area.

    

    Under Extent, the X and Y Extent values update to the coordinates of the Study Area's corners.

    This parameter guarantees that all output layers will automatically be clipped to your study area. By reducing the size of your output, you lower the processing time and ensure that your results cover only the area you are interested in.

12. Under Raster Analysis, for Cell Size, choose Same as layer Elevation.

    The Elevation layer and other raster layers in this project have a resolution of 30 meters, meaning each cell represents an area of 30 square meters in the real world. The Cell Size parameter ensures that all output raster layers generated during the analysis will have the same cell size.

    Tip:

    To check the resolution of a raster layer, double-click its name in the Contents pane to open its Layer Properties window. Click Source and expand Raster Information. The Cell Size parameters give the resolution of the layer.

13. For Snap Raster, choose Elevation or confirm that it is already chosen.

    The Snap Raster parameter causes all output raster layers to match the cell alignment of the chosen layer, meaning that the cells will overlap exactly.

    

14. Click OK.

Create ruggedness from elevation

Next, you'll create a new geoprocessing model and use it to generate the ruggedness layer from the elevation layer.

1. On the ribbon, on the Analysis tab, in the Geoprocessing group, click ModelBuilder.

   

   The Model view appears, containing a new model. First, you'll add the Elevation layer to the model.

2. In the Contents pane, drag the Elevation layer to the blank area in the model.

   

   The layer is displayed in the model as a rounded rectangle, outlined in blue with the layer's name. Blue rounded rectangles are input data variables: they represent the initial data that you'll process with a geoprocessing tool.

   Next, you'll add the geoprocessing tool. For this workflow, ruggedness will be defined by large and dramatic shifts in elevation. You'll use the Focal Statistics geoprocessing tool to generate a layer that shows the amplitude in elevation variation. For each cell in the Elevation raster, the tool will look at the cells surrounding it (also known as its neighborhood) and compute the range between the lowest and highest cell values. For instance, if the lowest and highest values in a cell's neighborhood are 430 and 450 meters, the range will be 20 meters, and that will be the value assigned to that cell. Higher range values correspond to more pronounced ruggedness. First, you'll locate the Focal Statistics tool.

3. On the ribbon, on the ModelBuilder tab, in the Insert group, click the Tools button.

   

   The Geoprocessing pane appears.

4. In the Geoprocessing pane, search for Focal Statistics.

   

5. In the search results, drag the Focal Statistics (Spatial Analyst Tools) tool into the model to the right of the Elevation input data element.

   

   The tool is currently depicted as a rectangle outlined in gray, connected to an output that is also outlined in gray. Both elements are grayed out because they are currently inactive. You must connect them to an input layer to make them active.

   Note:

   You can reposition any model elements in any way you like by dragging them. You can also resize them. Throughout the tutorial, the example images will show the model at various sizes and positions. You do not need to replicate the sizes and positions of the example images exactly to complete the workflow.

6. Click anywhere in the blank space in the model.

   All selected elements are deselected. Selected elements are surrounded by a blue box.

7. Draw an arrow from the Elevation element to the Focal Statistics tool.

   

   When you release the mouse button, the two model elements are connected and a list of connection options appears.

8. Click Input raster.

   

   The Elevation element is now the input raster for the tool. The tool element changes from gray to yellow and the output element changes from gray to green, indicating the tool is ready to run. Before you run the tool, you'll set additional parameters.

9. In the model, double-click the Focal Statistics tool.

   The tool parameters appear. You'll change the name of the output and choose the statistics type to compute the neighborhood range.

10. For Output raster, delete the existing text and type Ruggedness. For Statistics type, choose Range.

    

    Note:

    To learn more about a tool parameter, point to the parameter and point to the information icon that appears next to it.

11. Click OK.

    The name of the output element changes to Ruggedness. You'll modify the output element so that the output layer is added to the map when the tool runs.

12. Right-click the Ruggedness model element and choose Add To Display.

    

    You'll save the model before you run it.

13. On the ribbon, on the ModelBuilder tab, in the Model group, click the Save drop-down arrow and choose Save As.

    

14. In the Save Model As window, double-click Toolboxes and double-click Mountain_Lion_Corridors.tbx.
15. For Name, type Least_Cost_Analysis. Click Save.

    The model is saved. For now, it contains only the elements to produce the Ruggedness layer. However, throughout this workflow, you'll add more tools until you have created the complete least-cost analysis.

16. On the ribbon, on the ModelBuilder tab, in the Run group, click Run.

    

    The model runs. A window appears and informs you about the status of the process. After a few moments, the resulting Ruggedness layer is added to the Contents pane.

    The Focal Statistics element has a green check mark, indicating the tool ran successfully.

    

17. Close the process information window.
18. Click the Map view.

    

    The map becomes active. The Ruggedness layer displays on the map.

    

    This layer depicts the ruggedness level in black to white tones. Darker cells have the least ruggedness, while lighter cells have the most.

Symbolize the ruggedness layer

You'll change the layer's symbology to make the differences in ruggedness more apparent.

1. In the Contents pane, click the symbol for the Ruggedness layer.

   

   The Symbology pane appears.

2. In the Symbology pane, under Primary symbology, click the Color scheme drop-down menu and choose More color schemes.

   

   The Choose a color scheme window appears. You'll limit the styles to those in the current project before searching for a specific scheme.

3. Click All styles and choose Project styles.

   

4. In the search bar, type Red-Yellow-Green (Continuous) and press Enter. In the list of results, click Red-Yellow-Green (Continuous).

   

5. click OK.

   The map updates to the new symbology.

   

   The green areas are the most rugged and the red areas the least. To add more context, you'll make the Ruggedness layer transparent so it appears at the same time as the Hillshade layer.

6. If necessary, in the Contents pane, click the Ruggedness layer to select it. On the ribbon, click the Raster Layer tab.

   Note:

   The Raster Layer tab is a contextual tab. It will only appear if an appropriate layer is selected in the Contents pane. If the Ruggedness layer is not selected, the tab may not appear.

7. In the Effects group, for Transparency, type 20%. For Layer Blend, choose Multiply.

   

   The map updates, showing the Ruggedness layer and the Hillshade layer at the same time.

   

   Tip:

   Blending two layers with Multiply generates a result with vibrant colors. Adding some transparency can soften the colors obtained to fine-tune the result.

   Looking at the ruggedness in relation to the hillshade, it's clear that high, rocky mountain peaks tend to be more rugged, while low-lying valleys tend to be flatter and smoother. More ruggedness signifies a lower cost for the mountain lions in terms of stress and danger. The mountain lions core habitat patches are, unsurprisingly, located in highly rugged areas.

8. Close the Symbology pane.

Create distance to roads

Next, you'll derive a distance-to-road raster layer using the Roads layer. The distance-to-road layer will indicate each cell's distance to the nearest road.

1. Click the Least_Cost_Analysis view.

   

   You return to your model.

2. From the Contents pane, drag the Roads layer into the model below the Elevation layer.

   

   To calculate distance from roads, you'll use the Distance Accumulation tool, which calculates the straight-line distance from each cell to the closest road.

3. In the Geoprocessing pane, search for Distance Accumulation.

   Tip:

   If you closed the Geoprocessing pane or can't find it, you can open it from the ModelBuilder tab on the ribbon by clicking the Tools button.

4. Drag the Distance Accumulation tool into the model next to the Roads input.

   

   Note:

   If necessary, you can rearrange the elements by dragging them.

   This tool has several outputs, but you are only interested in the distance accumulation raster, so you'll ignore the other output rasters.

5. Draw an arrow from the Roads input to the Distance Accumulation tool to connect them and choose Input Raster or Feature Sources.

   

   The tool and the Output Distance Accumulation Raster element become active, but the other output rasters do not. The unactivated output rasters are optional and don't get created by default. Next, you'll open the tool parameters to change the name of the distance accumulation output.

6. Double-click the Distance Accumulation tool.
7. In the tool parameters, for Output Distance Accumulation Raster, delete the existing text and type Distance_to_Roads.

   

8. Click OK.
9. In the model, right-click the Distance_to_Roads output element and choose Add To Display.
10. In the ribbon, on the ModelBuilder tab, in the Model group, click the Save button.

    

    Tip:

    By default, the model is stored in your project toolbox. If you accidentally close your model, you can open it again though the Catalog pane. To do so, on the ribbon, on the View tab, choose Catalog Pane. In the Catalog pane, expand Toolboxes and expand Mountain_Lion_Corridors.tbx. Right-click the Least_Cost_Analysis model and choose Edit.

    Next, you'll run the tool. However, you don't want to run the Focal Statistics tool again. You can run only part of a model by selecting the tool you want to run.

11. In the model, right-click Distance Accumulation and choose Run.

    

    The tool runs. After a few moments, the resulting layer is added to the Contents pane.

12. Close the processing window. Click the Map view to make it active.

    Before you look at the output layer in more detail, you'll change itss symbology.

13. In the Contents pane, click the symbol for the Distance_to_Roads layer.
14. In the Symbology pane, for Color scheme, choose More color schemes. Search for and choose Red-Yellow-Green (Continuous).

    The map updates to the new symbology. Like before, you'll make the layer transparent to compare it to the Hillshade layer.

15. In the Contents pane, turn off the Ruggedness layer. If necessary, click the Distance_to_Roads layer to select it.
16. On the ribbon, on the Raster Layer tab, for Transparency, type 30%. For Layer Blend, choose Multiply.

    The map updates.

    

    The layer's legend indicates that the cell values range from 0 to over 10,000 meters (approximately 7 miles). Most of the study area is close to a road (in red) and farther areas (in light yellow or green) are few and fragmented, indicating how widespread human development has become in the area. The few areas away from roads tend to be on higher mountainous peaks.

    The mountain lion core habitat patches are mostly located in these rare areas away from roads. More distance to roads signifies a lower cost for the mountain lions in terms of stress and danger. However, when roads can be found almost anywhere, they can't be entirely avoided.

17. Close the Symbology pane. Save the project.

You've set geoprocessing environment parameters to prepare for your analysis and created a model to process the Elevation and Roads layers. You've derived new layers that represent the ruggedness and distance to road criteria. Your other two data layers don't need to be processed. Next, you'll continue building your model and implement the least-cost analysis.

Generate a cost surface

You now have four data layers that represent the analysis criteria: terrain ruggedness, distance to roads, dense vegetation land cover, and protected areas. However, they all have different measurement methods and value ranges. To combine the four layers meaningfully, you'll first transform them to a common cost scale from 1 to 10, where 1 is the lowest cost and 10 the highest. Then, you'll combine the four resulting layers into a single cost surface: a raster that represents the overall cost for mountain lions as they move through the landscape in terms of stress and danger.

The tool you'll use to transform each raster depends on the type of raster. There are two main types: continuous and categorical.

Distance_to_Roads is a continuous raster, meaning its cell values represent quantities (distances, in this case) and have meaning relative to one another. If one of its cells has a value of 600 meters and another one 650 meters, you can say that the second cell represents a greater distance than the first one.

In contrast, Land Cover is a categorical raster, meaning its cell values represent categories and do not have meaning relative to each other. For instance, it would be meaningless to say that Evergreen Forest is greater than Cultivated Crops.

Note:

Categorical rasters are also sometimes called thematic or discrete. Learn more about continuous and categorical rasters on the Introduction to image and raster data page.

The four criteria layers belong to the following types:

• Ruggedness—Continuous
• Distance to Roads—Continuous
• Land Cover—Categorical
• Protected Status—Categorical

You'll transform the continuous rasters into a common scale with the Rescale by Function tool, which applies continuous linear and nonlinear functions. In the output rasters, each cell will be assigned continuous cost value (floating-point value) between 0 and 10.

You'll transform the categorical rasters into a common scale with the Reclassify tool, which allows you to set the cost value for each individual category. For instance, you could decide that the Evergreen Forest land cover type should receive a low cost value of 1 and that the Developed, High Intensity type should receive a high cost value of 10. In the output rasters, each cell will be assigned an integer cost value between 0 and 10.

Transform ruggedness

First, you'll transform the Ruggedness dataset to the common scale using the Rescale by Function tool. Higher ruggedness values represent less stress and danger for mountain lions, so they should be transformed into lower cost values (close to 1 in the 1 to 10 scale).

1. Click the Least_Cost_Analysis view to return to your model.
2. In the Geoprocessing pane, search for Rescale by Function.
3. Drag the Rescale by Function (Spatial Analyst Tools) tool into the model next to the Ruggedness output.
4. Draw an arrow to connect the Ruggedness output to the Rescale by Function tool. Choose Input raster.

   

   The tool and its output become active. Next, you'll specify the output name and the transformation function.

5. In the model, double-click the Rescale by Function tool.

   The tool parameters appear. Some of the parameters were populated automatically based on the Ruggedness layer input. The Lower threshold and Upper threshold parameters indicate that the ruggedness values can vary from 0 to 223.39.

6. Set the following parameters:
   • For Output raster, delete the existing text and type Ruggedness_Cost.
   • For Transformation function, choose MS Large.
   • For From scale, type 10.
   • For To scale, type 1.

   

   Now, Ruggedness values close to 0 will be transformed into the highest cost of 10. Ruggedness values close to 223 will be transformed into the lowest cost of 1.

7. Click OK. In the model, right-click the Ruggedness_Cost output element and choose Add To Display.
8. On the ribbon, on the ModelBuilder tab, click the Save button.

   Next, you'll run the tool.

9. In the model, right-click the Rescale by Function tool and choose Run.

   The tool runs and the output layer is added to the Contents pane.

10. Close the processing window. Click the Map view.

    You'll invert the symbology of the new layer so that low cost values appear in green and high cost values in red. You'll also add transparency to view the layer with the hillshade.

11. In the Contents pane, click the symbol for the Ruggedness_Cost layer.
12. In the Symbology pane, under Primary symbology, check the Invert box.

    

13. In the Contents pane, turn off the Distance_to_Roads layer. If necessary, click the Ruggedness_Cost layer to select it.
14. On the ribbon, on the Raster Layer tab, for Transparency, type 20%. For Layer Blend, choose Multiply.

    The map updates.

    

    The Ruggedness_Cost layer looks relatively similar to the Ruggedness layer, but it represents cost values varying between 1 and 10.

15. Close the Symbology pane.

Transform distance to roads

Next, you'll transform the Distance_to_Roads raster to the common cost scale. Since it's a continuous raster, you'll use the Rescale by Function tool again. Areas that are farther from roads represent less stress and danger for mountain lions, so you'll transform them into lower cost values.

1. Click the Least_Cost_Analysis view to return to your model.
2. If necessary, in the Geoprocessing pane, search for Rescale by Function. Drag the Rescale by Function (Spatial Analyst Tools) tool into the model next to the Distance_to_Roads output.

   The tool is added as Rescale by Function (2) to differentiate it from the first iteration of the tool, which you used for Ruggedness.

3. Draw an arrow to connect the Distance_to_Roads output to the Rescale By Function (2) tool. Choose Input raster.

   

4. Double-click the Rescale by Function (2) tool to open its parameters.

   The original Distance_to_Roads values vary from 0 to 10,961.62.

5. Set the following parameters:
   • For Output raster, delete the existing text and type Distance_to_Roads_Cost.
   • For Transformation function, choose MS Large.
   • For From scale, type 10.
   • For To scale, type 1.

   Values close to 0 will be transformed into the highest cost of 10, while values close to 10,961 will be transformed into the lowest cost of 1.

6. Click OK.
7. In the model, right-click the Distance_to_Roads_Cost output element and choose Add To Display.
8. Save the model.
9. In the model, right-click the Rescale By Function (2) tool and choose Run.

   The tool runs. The output layer is added to the Contents pane.

10. Close the processing window. Click the Map view to make it active.

    Like before, you'll invert the symbology of the new layer so that the low cost values appear in green and the high cost values in red.

11. In the Contents pane, click the symbol for the Distance_to_Roads_Cost layer. In the Symbology pane, check the Invert box.
12. In the Contents pane, turn off the Ruggedness_Cost layer. If necessary, click the Distance_to_Roads_Cost layer to select it.
13. On the ribbon, on the Raster Layer tab, for Transparency, type 30%. For Layer Blend, choose Multiply.

    The map updates.

    

    The Distance_to_Roads_Cost layer looks relatively similar to the Distance_to_Roads layer, but it represents cost values varying between 1 and 10.

14. Close the Symbology pane.

Transform land cover

Next, you'll transform the Land Cover dataset. It's a categorical raster, so you'll use the Reclassify tool, which allows you to decide the cost value for each category individually.

Land cover types that are high in dense vegetation represent less stress and danger for mountain lions, so they should be transformed into the lower cost values in the 1 to 10 scale. Barren or developed land should be transformed into the highest cost values.

1. Click the Least_Cost_Analysis view to return to your model.

   First, you'll add the Land Cover layer as an input variable.

2. From the Contents pane, drag the Land Cover layer into ModelBuilder and drop it under the Output Source Location Raster element.

   

3. In the Geoprocessing pane, search for Reclassify. Drag the Reclassify (Spatial Analyst Tools) tool into the model next to the Land Cover element.
4. Draw an arrow to connect the Land Cover element to the Reclassify tool. Choose Input raster.

   

   Next, you'll specify the output name and the one-to-one transformations.

5. Double-click the Reclassify tool.

   The tool parameters appear. In the Reclassification table, the Value column is populated with the land cover types. In the New column, you'll replace the default values with 1 to 10 cost values based on your assessment of each land cover type.

   Mountain lions feel the most safe in forest and shrub land cover types, although they can also make use of agricultural or open areas. Developed lands are not safe and should receive a high cost value. Finally, water-covered areas will get the highest cost value, since they are inhospitable for mountain lions.

6. In the Reclassification table, update the New column with the following values.

   Value	New
   Open Water	10
   Developed, Open Space	8
   Developed, Low Intensity	7
   Developed, Medium Intensity	8
   Developed, High Intensity	9
   Barren Land	6
   Deciduous Forest	2
   Evergreen Forest	1
   Mixed Forest	2
   Shrub/Scrub	3
   Herbaceous	3
   Hay/Pasture	4
   Cultivated Crops	6
   Woody Wetlands	4
   Emergent Herbaceous Wetlands	4

7. For Output raster, delete the existing text and type Land_Cover_Cost.

   

8. Click OK.
9. In the model, right-click the Land_Cover_Cost output element and choose Add To Display.
10. Save the model.

    Next, you'll run the tool.

11. In the model, right-click the Reclassify tool and choose Run.

    The tool runs and the output layer is added to the Contents pane.

12. Close the processing window. Click the Map view to make it active.
13. In the Contents pane, turn off the Distance_to_Roads_Cost layer.

    The map shows the Land_Cover_Cost layer.

    Note:

    The layer's symbology is random and may differ from the example image.

    

    By default, not only is the symbology random, but the layer is still symbolized by the original land cover classes, not the reclassified values. You'll change the random symbology to represent the values.

14. In the Contents pane, click any of the symbols for the Land_Cover_Cost layer.
15. In the Symbology pane, for Field 1, choose Value.

    

16. For Color scheme, click the drop-down menu and choose More color schemes. In the Choose a color scheme window, search for Red-Yellow-Green (9 Classes) and choose Red-Yellow-Green (9 Classes).

    

    Note:

    There are only 9 classes and not 10, because the cost value 5 was not assigned to any categories.

17. Click OK.

    By default, low values have red symbols and high values have green symbols. You'll invert the symbology.

18. In the Symbology pane, in the Values tab, click More, point to Symbols, and choose Reverse order.

    

19. If necessary, in the Contents pane, click the Land_Cover_Cost layer to select it. On the ribbon, on the Raster Layer tab, for Layer Blend, choose Multiply.

    The map updates.

    

    The lower cost land cover types are mostly located in the mountain ranges with a lot of forest and shrubland. The higher cost land cover types tend to be located in the flatter low-lying valleys where most of the human development has taken place. The mountain lions core habitats are nested in midst of low cost areas, which makes sense; the lions would naturally gravitate toward low stress land cover.

20. Close the Symbology pane.

Transform protected status

Next, you'll transform the last criteria dataset, Protected_Status. It's a categorical raster, so you'll use the Reclassify tool again. Areas that are the most protected represent less stress and danger for mountain lions, so they should be transformed into the lower cost values.

1. Click the Least_Cost_Analysis view to return to your model.
2. From the Contents pane, drag the Protected_Status layer into the model under the Land Cover element.

   

3. From the Geoprocessing pane, drag the Reclassify (Spatial Analyst Tools) tool into the model next to the Protected_Status element.
4. Draw an arrow to connect the Protected_Status element to the Reclassify (2) tool. Choose Input raster.

   

5. In the model, double-click the Reclassify (2) tool.

   The tool parameters appear. In the Reclassification table, the Value column is populated with the Protected_Status levels. In the New column, you'll replace the defaults with cost values between 1 and 10 based on your assessment of each protection status category.

   Mountain lions feel the least stress in the most protected areas, so these should receive the lowest cost value. Unlike with land cover, you'll also reclassify NODATA cells, which correspond to locations that are not tracked in the Protected Areas database. These areas are usually very high stress areas like urbanized and other development-prone locations. NODATA cells should receive the highest cost value.

6. In the Reclassification table, update the New column with the following values:

   Value	New
   1 – Very high	1
   2 – High	3
   3 – Medium	6
   4 – Not protected	9
   NODATA	10

7. For Output raster, delete the existing text and type Protected_Status_Cost.

   

8. Click OK.
9. In the model, right-click the Protected_Status_Cost output element and choose Add To Display.
10. Save the model. Right-click the Reclassify (2) tool and choose Run.

    The tool runs and the output is added to the Contents pane.

11. Close the processing window. Click the Map view to make it active.

    The colors representing the cost values were assigned at random. You'll change the symbology to make it more meaningful.

12. In the Contents pane, turn off the Land_Cover_Cost layer. Click any of the symbols for the Protected_Status_Cost layer to open the Symbology pane.
13. In the Symbology pane, for Field 1, choose Value.
14. For Color scheme, choose More color schemes. In the Choose a color scheme window, search for Red-Yellow-Green (5 Classes).
15. Choose Red-Yellow-Green (5 Classes) and click OK.
16. In the Symbology pane, on the Values tab, click More, point to Symbols, and choose Reverse order.
17. If necessary, in the Contents pane, click the Protected_Status_Cost layer to select it.
18. On the ribbon, on the Raster Layer tab, for Transparency, type 30%. For Layer Blend, choose Multiply.

    The map updates.

    

    The lowest cost areas (in dark green and light green) in terms of protection status are located in the mountain ranges. Many areas are not protected (in orange or red). It doesn't mean that mountain lions couldn't go through them at all, but they would tend to be higher stress.

19. Close the Symbology pane.

Create a cost surface layer

Now that you've converted the four criteria data layers to the same 1 to 10 cost scale, you'll combine these cost layers into a single cost surface that simulates how the mountain lions will experience the landscape as they move through it. You'll use the Weighted Sum tool to add the values in the four cost layers, with weights given to each layer. The weights allow you to make some layers play a heavier role than others (making certain features more costly) in the creation of the combined cost surface. For each cell, the tool multiplies the values for each input raster by the given weight and then adds the four values together to create a new value representing the total cost.

1. Click the Least_Cost_Analysis view to return to your model.
2. In the Geoprocessing pane, search for Weighted Sum.
3. Drag the Weighted Sum (Spatial Analyst Tools) tool into the model next to the four cost layers.

   

   You'll connect the four cost layers to the tool.

4. Drag an arrow from Ruggedness_Cost to the Weighted Sum tool. Choose Input rasters.

   

5. Draw an arrow from the other cost layers to the tool and choose Input rasters for each.

   

   Next, you'll edit the tool parameters to provide a weight for each input.

6. In the model, double-click the Weighted Sum tool.

   The tool parameters appear. The four transformed layers are listed as input rasters. The default weight for each is 1, which means they all have the same weight. In this case, you decide the land cover criterion should play a heavier role, as you don't want the mountain lions passing through urban areas. You also decide the ruggedness criterion is very significant, as mountain lions don't do well in flat, non-rugged areas. As a result, you'll increase the weights of these two criteria.

7. For Ruggedness_Cost and Land_Cover_Cost, change Weight to 1.25.
8. For Output raster, delete the existing text and type Cost_Surface.

   

9. Click OK.
10. In the model, right-click the Cost_Surface output and choose Add To Display.
11. Save the model. Right-click the Weighted Sum tool and choose Run.

    The output layer is added to the Contents pane.

12. Close the processing window. Click the Map view to make it active.

    You'll invert the symbology of the new layer so low cost values appear in green and the high cost values appear in red.

13. In the Contents pane, click the symbol for the Cost_Surface layer. In the Symbology pane, check the Invert box.
14. Turn off the Protected_Status_Cost layer. If necessary, click the Cost_Surface layer to select it.
15. On the ribbon, on the Raster Layer tab, for Transparency, type 20%. For Layer Blend, choose Multiply.

    The map updates.

    

    This Cost_Surface layer simulates how the mountain lions will experience the landscape as they move through it. The green areas have the lowest cost in terms of stress and danger, while the red areas have the highest cost and the beige areas have a medium cost. You can see that almost all low and medium cost areas are located in mountainous regions. The core mountain lion habitats are all located in areas that are low cost (dark green or light green). This makes sense, since mountain lions would naturally want to live in low stress and low danger areas. It is a good indication that, although it's your first attempt and your model could be refined further, your results are good.

16. Close the Symbology pane.
17. Save the project.

You've transformed the four criteria datasets into a common scale from 1 to 10 and combined the resulting layers into a single cost surface. Next, you'll determine the best locations for mountain lion corridors.

Generate least-cost paths

You'll use your cost surface to determine the optimal paths connecting the core habitat patches. Modeling the location of corridors is like determining the path of least resistance. In a least-cost path, you want to travel between specified locations accumulating as little cost as possible.

Identify least-cost paths

To create the optimal paths, you'll use the Optimal Region Connections tool, which identifies an optimum network of least-cost paths between identified regions. The tool will use two inputs: the cost surface and the core mountain lion habitats.

1. Click the Least_Cost_Analysis view to return to your model.
2. From the Contents pane, drag the Core Mountain Lion Habitats layer into the model under the Cost_Surface output.

   

3. In the Geoprocessing pane, search for Optimal Region Connections. Drag the Optimal Region Connections tool into the model next to the Cost_Surface and Core Mountain Lion Habitats inputs.

   

4. Drag an arrow to connect the Cost_Surface input to the Optimal Region Connections tool and choose Input Cost Raster.
5. Drag an arrow to connect the Core Mountain Lion Habitats input to the Optimal Region Connections tool and choose Input Raster or Feature Regions.

   

6. Double-click the Optimal Region Connections tool.

   The tool parameters appear. You'll enter the names for the tool's two output layers.

7. For Optimal Output Connectivity Lines, change the output name to Mountain_Lion_Paths.

   This output will contain the optimal network of paths (or lines) for mountain lions to move from one habitat patch to any other patches (possibly passing through another patch).

8. For Output Neighboring Connecting Lines, type Mountain_Lion_Paths_Neighbors.

   

   This secondary output identifies all paths from each patch to each of its closest (or lowest cost) neighbors. The paths to lowest cost neighbors can possibly provide alternative paths to reach specific patches.

9. Click OK.
10. Right-click each of the two outputs and choose Add To Display.
11. Save the model. Right-click Optimal Region Connections and choose Run.

    After a few moments, the two output layers, Mountain_Lion_Paths and Mountain_Lion_Paths_Neighbors, are added to the Contents pane.

12. Close the processing window. Click the Map view to make it active.

    The new layers are line vector layers. The default symbology is difficult to see, so you'll change it.

13. In the Contents pane, click the symbol for the Mountain_Lion_Paths_Neighbors layer.
14. In the Symbology pane, on the Gallery tab, search for 2.5 Point. In the list of search results, click the 2.5 Point symbol.

    

15. In the Contents pane, click the symbol for the Mountain_Lion_Paths layer. In the Symbology pane, apply the 2.5 Point symbol to the layer.

    Both layers are more visible on the map.

    

16. In the Contents pane, turn off the Mountain_Lion_Paths_Neighbors layer.

    Now, only the Mountain_Lion_Paths layer is visible.

    

    This layer represents the optimal network of paths to connect the four habitat patches with the least cost. The paths primarily go through darker green and lighter green areas of the Cost_Surface layer. This pattern makes sense, as these are the lower cost areas. The paths only go through red areas when there are no other alternatives. This network is the most efficient way of connecting the four patches and it represents the optimal locations for creating mountain lion corridors.

17. Turn on the Mountain_Lion_Paths_Neighbors layer.

    This layer contains the same paths as the previous layer, but also offers additional paths that connect each habitat patch to its lowest cost neighbors. These extra routes are not necessary, but they provide supplementary options. They primarily go through dark green and light green areas whenever possible.

18. Close the Symbology pane.

Clean up the model

Before evaluating your results, you'll clean up and finalize your model.

1. Click the Least_Cost_Analysis view to return to your model.
2. On the ribbon, on the ModelBuilder tab, in the View group, click Fit to Window.

   

   The model redraws to fit entirely in the window.

   

   The positioning of your model elements might not be perfectly aligned and equally spaced. You'll use the Auto Layout functionality to reorganize the model optimally.

3. On the ribbon, on the ModelBuilder tab, in the View group, click Auto Layout.

   

   The model redraws to optimize the placement of the elements.

   

   Tip:

   If you are not fully satisfied with the automated placement, you can reposition the elements manually.

   To better show the model's structure, you'll reorganize it into three groups:

   • Preprocess datasets
   • Create cost surface
   • Find least-cost paths
4. In the model, draw a box around the Focal Statistics and Distance Accumulation tools and their associated output elements.

   

   The elements are selected.

5. On the ribbon, on the ModelBuilder tab, in the Group group, click Group.

   

   The elements you selected are grouped. You'll rename the group.

6. Double-click the Group name.

   

   The group name becomes editable.

7. Type Preprocess datasets and press Enter.

   

   Before you create the next group, you'll move the Core Mountain Lion Habitats element out of the way.

8. Click the Core Mountain Lion Habitats element to select it. Drag it under the Optimal Region Connections tool.

   

9. Draw a box around the Rescale by Function, Reclassify, and Weighted Sum tools and their associated output elements.
10. On the ribbon, on the ModelBuilder tab, click Group.
11. Rename the new group to Create cost surface.

    

12. Draw a box around the Optimal Region Connections tool, its associated output elements, and the Core Mountain Lion Habitats element.
13. On the ribbon, on the ModelBuilder tab, click Group.
14. Rename the new group to Find least-cost paths.

    

15. Drag the groups and elements slightly away from each other to increase the model's legibility.

    

16. Save the model.

Close the access the model

For future use, you can always retrieve the model from the Catalog pane.

1. For the Least_Cost_Analysis model, click the Close button.

   

2. On the ribbon, click the View tab. In the Windows group, click Catalog Pane.

   

3. In the Catalog pane, expand Toolboxes and Mountain_Lion_Corridors.tbx.

   Your model is located in this toolbox.

   Note:

   The Results.tbx toolbox contains a copy of the model provided for reference purposes.

4. Right-click the Least_Cost_Analysis model and choose Edit.

   

   The model reopens.

5. On the ribbon, on the ModelBuilder, in the Run group, click Validate.

   

   The model is validated, indicating that all data elements and parameter values are valid. The model status is reset, making it ready to be run again.

   You can run the entire model as many times as you want by clicking the Run button. However, the symbology styles you applied to the result layers earlier in the workflow would be lost, so you won't run it now.

   Tip:

   While it is beyond the scope of this tutorial, it is possible to apply symbology styles automatically to the output layers within your model by incorporating the Apply Symbology From Layer tool.

6. Save the project.

You've added a tool to your model to determine the least-cost paths between the core mountain lion habitats. You also ran the tool and symbolized the output layers. Finally, you cleaned up your model to finalize it and learned how you can continue using it in the future.

Evaluate the results

Lastly, you'll evaluate the results of your analysis.

Compare the result layers

First, you'll compare the proposed paths to the different cost layers.

1. Click the Map view to make it active.
2. In the Contents pane, turn off the Mountain_Lion_Paths_Neighbors layer.

   Overall, the paths in the Mountain_Lion_Paths layer look like good options for developing wild corridors to connect the core mountain lion habitats. You'll look at the four individual criteria cost layers individually to check how the paths fit each one.

3. In the Contents pane, turn off Cost_Surface and turn on Ruggedness_Cost.

   

   The paths are almost only through high ruggedness terrain. From this criteria's standpoint, the paths show great promise for high quality mountain lion corridors.

4. Turn off Ruggedness_Cost and turn on Distance_to_Roads_Cost.

   

   Though the paths are as low cost as possible, there is no way to avoid near-road locations in this study area. Later, you'll make a more fine-grained distinction between highways and small roads and learn about possible approaches to tackle this challenging high-road density situation.

5. Turn off Distance_to_Roads_Cost and turn on Land_Cover_Cost.

   

   The paths are almost only through low-cost land cover. From this criteria's standpoint, the paths show again great promise for high quality mountain lion corridors.

6. Turn off Land_Cover_Cost and turn on Protected_Status_Cost.

   

   The optimal paths do their best to go through protected areas, but much of the valleys and some of the mountainous regions are unprotected and the paths can't avoid going through them. Later, you'll learn how this situation could be improved by changing the protection status in the stretches of land along the paths.

Visualize areas to protect

Implementing wildlife corridors along the proposed paths would necessitate several actions. One of them would be to preserve the remaining undeveloped land along these routes. For instance, the state of California could launch a program to give protected status to these strips of land. You'll visualize the land that would be the highest priority for such a program.

You'll define a wildlife corridor as a swath of land of a certain width that runs along a proposed path. In this workflow, you'll choose the width of your corridors to be 2 kilometers (1.24 miles) wide. You'll visualize the wildlife corridors by creating a buffer of 1 kilometer (or 1,000 meters) on each side of the proposed paths.

1. At the bottom of the Catalog pane, click the Geoprocessing tab.

   

2. In the Geoprocessing pane, search for Pairwise Buffer. In the list of results, click the Pairwise Buffer tool.
3. In the Pairwise Buffer tool, set the following parameters:
   • For Input Features, choose Mountain_Lion_Paths.
   • For Output Feature Class, type Wildlife_Corridors.
   • For Distance, type 1000 and choose Meters.
   • For Dissolve Type, choose Dissolve all output features into a single feature.

   

4. Click Run.

   The Wildlife_Corridors layer appears on the map in semitransparent blue.

   

   Note:

   In this workflow, you created a basic buffer (named a Euclidean buffer). A more advanced approach would be to create a cost buffer, which would take into account the cost surface. This can be done in the Distance Accumulation tool using the paths and cost surface and setting a maximum distance. It can also be done using the Least Cost Corridor tool. Learn more on the Connect locations with corridors documentation page. See also the Connecting locations with optimal paths article.

   On the Protected_Status_Cost layer, the areas that are not protected appear in red and orange. Red and orange areas within the buffer are the areas that would need to be turned into protected land, whenever possible.

   Tip:

   While it is beyond the scope of this tutorial, you could also extract the stretches of land you just identified visually using the Reclassify tool to derive a new raster that only contains unprotected land (that is, values 9 and 10), the other values being reclassified to NODATA. Then, you would use the Extract by Mask tool to extract the raster cells that represent unprotected land within the Wildlife_Corridor buffer.

Assess wildlife overpass locations

Another type of action needed to implement wildlife corridors would be to help the mountain lions pass through the more dangerous areas along the way, such as highways. While in an ideal world the wildlife corridors would not cross any roads, this is impossible in the study area due to the high level of development. However, it can be useful to distinguish between different types of roads.

Mountain lions can cross smaller roads with a relatively low level of casualty, whereas they are in tremendous danger whenever attempting to cross a highway. When a wildlife corridor can't avoid crossing a highway, a well-established approach is to build a wildlife overpass to go over it, such as in the following example image:

This photo shows an example of wildlife overpass in Estonia.

You'll identify where the proposed paths cross highways in your study area. First, you'll create a copy of the Roads layer and use a definition query to display only highways.

1. In the Contents pane, right-click the Roads layer and choose Copy.

   

2. In the Contents pane, right-click Map and choose Paste.

   

   A copy of the Roads layer is added to the pane. You'll rename it.

3. Click the new Roads layer to make its name editable. Type Highways and press Enter.

   

   You'll create the definition query.

4. Right-click Highways and choose Properties.
5. In the Layer Properties window, click the Definition Query tab. Click New definition query.

   

6. Under Query 1, create the query Carto is less than or equal to 2.

   

   The Carto attribute stores a numeric value that encodes the road types from largest (1) to smallest (6). This query will keep only the roads with Carto values of 1 and 2, which are highways and highway ramps.

7. For Query 1, click Apply. At the bottom of the Layer Properties window, click OK.

   The definition query is applied.

8. In the Contents pane, turn on the Highways layer. Turn off the Wildlife_Corridors, Protected_Status_Cost, and Hillshade layers.

   

   You'll choose a more visible symbol for the Highways layer.

9. In the Contents pane, click the Highways symbol.
10. In the Symbology pane, on the Gallery tab, click Highway.

    

11. In the Contents pane, drag the Mountain_Lion_Paths layer above the Highways layer.

    

    On the map, the highways now display as thick lines. There are four locations where a proposed path crosses a highway.

    

    This number of intersections between paths and highways is not surprising, as the Los Angeles area is known for its extensive highway system. It's not possible for the paths to avoid crossing these highways, even when considering longer detours. Construction of overpasses should be considered at these four intersections. You'll look closer at one location in particular.

12. On the ribbon, click the Map tab. In the Navigate group, click Bookmarks and choose State Route 126.

    

    The map navigates to one of the intersections. You'll switch to an imagery basemap to get a better sense of the situation on the ground.

13. On the ribbon, on the Map tab, in the Layer group, click Basemap and choose Imagery Hybrid.

    

    The map updates, displaying a photographic view of the area along with some cartographic indications.

    

14. Zoom in on the intersection between the highway and the proposed path.

    A single overpass could probably go over the highway and any adjacent routes or railroads. A bit further south, it might be necessary to review whether the Santa Clara River is passable by mountain lions. If it isn't, some type of passage would also need to be arranged there.

    Note:

    The overpass wouldn't need to be built at the exact location proposed. It could be judicious to move it slightly if it can avoid a steep slope or provide denser vegetation cover for the mountain lions to feel safer.

15. Optionally, use the other bookmarks to review the other three crossing locations.
16. Save the project.

Undoubtedly, implementing wildlife corridors in the study area present challenges, as it is already quite developed. However, you now have a concrete and well-informed plan to propose to the local government and other relevant institutions. The analysis method and the model you created, while simplified for clarity, is an excellent starting point for those seeking to carry out similar analyses in their own regions of the world. You can continue to edit the tool parameters in your model to fine-tune the results. What if you changed the weights for the Weighted Sum tool? What if you added a fifth criterion to the cost surface? With a documented and editable workflow, you can test theories and make adjustments as needed to perfect your analysis. You might also be able to enhance your analysis by adding supplementary data on local conservation efforts, land ownership, and agriculture.

To learn more about mountain lions and efforts to protect it, check out the Cougar Fund.

You can find more tutorials in the tutorial gallery.

Acknowledgements

• The Elevation layer was derived from USGS's 3DEP Elevation data via the Ground Surface Elevation - 30m feature layer.
• The Roads layer was derived from the United States Census Bureau's TIGER/Line Geodatabase via the USA Roads feature layer, which has since been deprecated. For an updated version of similar data, see Transportation (Roads and Railroads).
• The Land Cover layer was derived from MRLC's National Land Cover Database via the USA NLCD Land Cover feature layer.
• The Protected_Status layer was derived from USGS's Protected Areas Database of the United States via the USA Protected Areas - GAP Status 1-4 feature layer, which has since been deprecated. For an updated version of similar data, see PAD-US Protection Status by GAP Status Code.
• The Study area and Core mountain lion habitats layers were created by Esri staff.
• Photo of an overpass in Estonia: Sillerkiil via Wikimedia Commons.
• Gallery image: National Park Service.
• Topographic map sources: Esri, TomTom, Garmin, FAO, NOAA, USGS, OpenStreetMap contributors, and the GIS User Community
• Imagery Hybrid map sources: Esri, Vantor, Earthstar Geographics, TomTom, Garmin, FAO, NOAA, USGS, OpenStreetMap contributors, and the GIS User Community