Define the regions
To define the regions for conservation planning purposes, you'll use layers extracted from ArcGIS Living Atlas of the World to identify areas with high summed range-size rarity of species. These areas have more rare species with small ranges, so they are important locations to conserve to maximize protection of these species. Your objective is to define which 5,000 additional square kilometers in Ohio to conserve. The areas should be outside of existing parks, which are already protected, and urban areas, where land is more expensive.
You'll experiment with the Locate Regions tool, examining ways of growing compact regions that maximize these values.
Open and explore the map
You'll start by downloading the tutorial data.
- Download the Identify_Connect.ppkx project package and locate the downloaded file on your computer.
Note:
Most web browsers download files to your computer's Downloads folder by default.
- Double-click the Identify_Connect.ppkx project package to open it in ArcGIS Pro. If prompted, sign in with your ArcGIS organizational account.
Note:
If you don't have access to ArcGIS Pro or an ArcGIS organizational account, see options for software access.
The project package extracts, and the project opens to a map showing a topographic basemap and the outline of Ohio.
- In the Contents pane, check the Range Size Rarity layer.
The map shows the layer.
Darker areas on the map have higher numbers of rare, geographically limited species. Species with small range sizes are weighted higher because there are fewer opportunities to protect them.
This layer is a local raster layer clipped to the state of Ohio from the Summed Range-size Rarity of Imperiled Species in the United States layer from NatureServe Network created in June 2024. It is part of the Map of Biodiversity Importance.
NatureServe reserves all rights in all intellectual property provided. Distribution of the data or any intellectual property in whole or in part, or any products derived from the data or any intellectual property for commercial purposes is strictly prohibited. See the full terms of use.
This layer is used here with permission.
- Check the Urban Areas and the Parks layers.
These two layers are derived from layers hosted in ArcGIS Living Atlas of the World. They are local polygon feature layers, clipped to the state of Ohio.
Urban areas, as defined by the United States Census Bureau, are shown with a gray hatch fill.
Parks—including national and state parks and forests, along with county, regional and local parks within the United States.
You'll use these layers to limit the proposed conservation analysis. You do not want to include current parkland, as those areas are already protected. You also do not want to include urban areas, due to the higher cost of acquiring land in urban areas.
- Check the Global Human Modification layer.
The layer turns on.
Browner areas on the map are more modified by human activity, including urbanization and agriculture, while greener areas are less modified by human activity.
The urban areas are mostly shaded dark brown, but there are large areas, especially in western Ohio, that are heavily modified by agriculture.
This layer is a local raster layer clipped to the state of Ohio from the Global Human Modification raster developed by Kennedy, Oakleaf, Theobald, Baruch‐Mordo, and Kiesecker in their article Managing the middle: A shift in conservation priorities based on the global human modification gradient in Global Change Biology.
- Check the Range Size Rarity - No Urban layer.
The layer turns on.
This map is shows the richness and rarity layer with urban areas removed from it. This preprocessing step was done using the Extract by Mask tool to copy only the areas outside of urban polygons to a new raster. The layer is named Range Size Rarity - No Urban.
Find the region with the highest summed range size rarity
First, you'll run the tool to determine the single 5,000 square kilometer region with the highest summed range size rarity.
- On the ribbon, click Analysis. In the Geoprocessing section, click Tools.
- In the Geoprocessing pane, in the search box, type Locate regions. In the search results, click the Locate Regions tool.
- In the Locate Regions pane, for Input raster, choose the Range Size Rarity - No Urban layer.
The Locate Regions tool uses a parameterized region-growing algorithm to identify high value cells from the input raster and grow regions to maximize these values, constrained by the input parameters.
In this case, the value is the summed range size rarity of each cell in the Range Size Rarity - No Urban layer. The tool places seed candidate cells at high value locations across the raster and then adds adjacent cells to grow regions. Since the urban areas have been removed from the input raster, the proposed region expands around those areas.
A notification button appears beside the Input raster parameter.
- Click the notification icon.
This warning indicates that because the raster has more than 500,000 cells, a resampled version of it will be used.
- Close the message.
- For Total area, type 5000.
- Accept the default Area units value of Square kilometers.
The tool will continue adding cells to the region until it reaches an area of 5,000 square kilometers.
- For Output raster, type Cons_region_opt_1.
.
This name indicates that this is the first conservation region option that you are generating in this exploratory process.
- Accept the default Number of regions value of 1.
The tool will define a single region that maximizes the number range size rarity raster values.
- Accept the default Region shape value of Circle.
As each cell is considered by the region growing algorithm, the tool will weight its cell value against its utility in maintaining a roughly circular overall shape. The degree to which the shape of the region is controlled by the Shape/Utility tradeoff (%) parameter, which has a 50 percent default value.
- Accept the remaining default values and click Run.
The tool runs and produces a single region in the central-southern part of the state.
- Zoom in to the southern part of the gray region layer.
The proposed region has cells that fall within existing parks. You'll make changes and run the tool again to take existing parks into account.
Locate a region while avoiding parks
The urban areas were avoided because they had been clipped out and set to no-data values. You'll adjust the input parameters of the tool and re-run it to avoid parks.
- On the Locate Regions tool, edit the Output raster name to be Cons_region_opt_2.
- For the Input raster or feature of existing regions parameter, choose the Parks layer.
- Click Run.
The Cons_region_opt_2 layer is added to the map.
- Turn off the Cons_region_opt_1 layer.
The Cons_region_opt_2 layer no longer overlaps parks, and it extends a bit further north and east.
- On the ribbon, on the Map tab, in the Navigate section, click Bookmarks and choose Ohio.
The map zooms to show the full extent of the state of Ohio.
Locate multiple regions
While this region covers some high value areas for summed species rarity, there are many areas outside of it that are also of high value. You'll run the tool again and grow multiple regions to get better coverage across the state.
- In the Locate Regions tool, edit the Output raster name to be Cons_region_opt_3.
- For Number of regions, type 10.
A notification icon appears beside the Number of regions parameter.
- Click the notification icon.
When the Number of regions parameter is greater than eight, the tool uses the Sequential selection method. For smaller numbers of regions, the tool will use the computationally expensive Combinatorial selection method by default.
- Close the notification.
- Click Run.
The tool runs and the layer is added to the map.
- Turn off the Cons_region_opt_2 layer.
These regions are distributed across the state in a roughly north-south band and cover more of the high value areas. In the northern part of the state the regions tend to be more widely separated, while some of the regions in the south are closer together.
The gray patch for the 1 region is a little hard to see. You'll change the color of that region.
- In the Contents pane, for the Cons_region_opt_3 layer, right-click the gray color patch for the 1 value region. In the color picker choose a light purple color, such as Amethyst.
You've explored the data and generated regions outside of urban areas that cover areas with predicted high species rarity. Next, you'll define connections between these regions.
Connect the regions
Now that you've identified some regions distributed across the state, the next step is to find connections between them. You'll use the Global Human Modification layer as a cost surface to identify routes between the regions that are least affected by human modification. Areas that are heavily modified will have higher cost to traverse, while areas that are less modified will have a lower cost to traverse. The tool will find the path between the regions that minimizes cost.
Find connections
You'll use the Optimal Region Connections tool to find the best paths between the regions you defined.
- In the Geoprocessing pane, in the search box, type Optimal region connections. In the search results, click the Optimal Region Connections tool.
- For the Input Raster or Feature Regions parameter, choose the Cons_region_opt_3 layer.
- For the Output Optimal Connection Lines parameter, type Optimal_path_1.
- Leave the Input Raster or Feature Barriers parameter empty.
- For the Input Cost Raster parameter, choose the Global Human Modification layer.
- Accept the default values for the other parameters and click Run.
The tool runs and the Optimal_path_1 layer of lines connecting the regions is added to the map. The tool pane also shows a warning.
- Click View Details.
The warning indicates that some lines may not have connected because they cross NoData or extend beyond the data extent.
- Close the warning.
- In the Contents pane, for the Optimal_path_1 layer, click the line symbol.
- In the Symbology pane, find and click the 1.5 Point black line symbol.
The symbol is updated on the map. You can see the connections between the regions better with this symbol.
- In the Contents pane, uncheck the Global Human Modification layer.
- Close the Symbology pane.
The connecting lines between the regions are the most direct routes that minimize the transit cost of human modification. However, some of them cross urban areas.
Find connections that avoid urban areas
For some species, traversing urban areas is not challenging. However, for many rare and endangered species, it is more difficult or impossible. You'll re-run the tool, this time using the urban areas as barrier features.
- On the Optimal Region Connections tool, change the Output Optimal Connection Lines parameter from Optimal_path_1 to Optimal_path_2.
- For the Input Raster or Feature Barriers parameter, choose the USACensusUrbanAreas_Ohio layer.
- Click Run.
The tool runs and the Optimal_path_2 layer of lines connecting the regions is added to the map.
- Change the symbol for the layer to the 3.0 Point black line symbol.
The new lines avoid the urban areas.
These features represent safer paths for rare and endangered species to move between protected areas. You could devote some of your resources to protecting these areas for better habitat connectivity.
Some species require broader corridors for travel. You could buffer these lines to identify such corridors, or you could use the Optimal Corridor Connections tool to create corridors.
- Save the project.
You have used the Locate Regions tool to find regions with higher values for protecting rare and endangered species. You used the Optimal Region Connections tool to find paths to enable migration between these regions.
You can find more tutorials in the tutorial gallery.