Model animal home range

Fish and Wildlife Agencies are tasked with making management decisions about game and non-game species in their states in order to ensure these populations are stable and sustainable over time. An important aspect of making these management decisions is understanding information about these populations; namely where they are and how they move over time. These decisions can be complicated when animals move across state and national boundaries.

Using a subset of data collected by the long-term monitoring program of elk in southwest Alberta, southeast British Columbia, and northwest Montana, you'll investigate the home range of this elk population.

Depending on the data you have available and the motives for research, there are many ways to calculate an animal's home range. In this tutorial, you'll use four different approaches to calculating a home range, building them into a model so you can replicate the results. These approaches include minimum bounding geometry, kernel density, density-based clustering, and directional distribution.

This tutorial was last tested on January 3, 2025, using ArcGIS Pro 3.4. 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

Model animal home range

Define Home Range with a Minimum Convex Polygon

In this section, you’ll use the Minimum Bounding Geometry tool to understand the largest observed area occupied by the elk in this region. The Minimum Bounding Geometry tool calculates polygons representing the smallest area needed to enclose the input data. While the tool can calculate shapes such as circles and rectangles, you’ll use the Convex Hull type, which draws a straight line between the outer vertices of the input dataset and is simplest method to employ when examining an animal’s home range.

1. Download the Elk_Home_Range project package.

   A file named Elk_Home_Range.ppkx is downloaded to your computer.

   Note:

   A .ppkx file is an ArcGIS Pro project package and may contain maps, data, and other files that you can open in ArcGIS Pro. Learn more about managing .ppkx files in A guide to ArcGIS Pro project packages (.ppkx files).

2. Locate the downloaded file on your computer. Double-click the Elk_Home_Range.ppkx file to open the project in ArcGIS Pro.
3. If necessary, sign in to ArcGIS Pro 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 map shows locations of elk telemetry data collected in 2009 in southwest Alberta, southeast British Columbia, and northwest Montana. The data used in this tutorial represents a subset of data collected in the original study, which spans multiple years. This subset of data has been projected and preprocessed to include an attribute indicating what season the point was recorded. This attribute will be used as the case field in the directional distribution analysis to show change over time. To learn more about the study and access the full dataset, see the study on MoveBank.

4. On the ribbon, click the Analysis tab. In the Geoprocessing group, click ModelBuilder.

   

   A blank ModelBuilder window appears. You’ll start to build your model by dragging tools in from the Analysis pane.

5. On the ModelBuilder ribbon, in the Insert group, click Tools.

   

   The Geoprocessing pane appears.

6. In the Geoprocessing pane, search for the Minimum Bounding Geometry tool. Drag the tool to the model.

   

   A rectangle showing the Minimum Bounding Geometry geoprocessing tool and an oval representing the tool’s output feature class are added to the model.

7. In the model, double-click the Minimum Bounding Geometry rectangle to open the parameters.

   The first parameter is the input features that you’ll calculate the minimum bounding geometry for.

8. For Input Features, choose the Elk_in_Southwestern_Alberta_2009 feature class.
9. For Geometry Type parameter, choose Convex Hull, and for Output Feature Class type Elk_data_MBG.

   

10. Click OK.

    

    A blue circle representing the Elk_data_2009 feature class is added to the model with an arrow pointing to the Minimum Bounding Geometry tool to show that this layer is an input to the tool.

11. In your model, right-click the green output and choose Add to Display.

    This will add the output to the map after it is run.

12. Right-click the Minimum Bounding Geometry tool in the model and click Run.

    

    The tool runs. When it’s finished, the new Elk_data_MBG layer will be added to the map.

13. Click the Map tab. If necessary, in the Contents pane, drag the Elk_in_Southwestern_Alberta_2009 layer above the ModelBuilder group layer.

    

    Note:

    The color of the layer is randomly generated and may differ from the example image, but it does not impact the results of the analysis.

The resulting layer depicts the minimum bounding geometry of the elk herd. Individual animals don’t spend time in these ranges equally across their range, however. In the next section, you’ll explore their range further with kernel density to understand where the elk spend more or less time.

Examine where elk congregate with the Kernel Density Tool

Having an outline of the observed range is often helpful, but it can overestimate the home range. Other analyses like kernel density can help more accurately define where the animals congregate. Kernel density estimation produces an output raster showing estimates of the likelihood of space use by animals. Based on locations where elk were observed, or the animals’ known space use, kernel density estimates how likely it is that elk would be observed in surrounding areas. The Kernel Density function creates this estimation by assuming that the more elk are observed around a given location N, the more likely that location N is to also have elk.

Because a higher density value at a location means a higher likelihood of observing elk around that location, the kernel density output raster can help visualize the animals’ home range. Density values will be comparatively higher within the home range and then drop off precipitously at the edge of the home range. Different threshold values can be used to define the perimeter of the home range.

1. Click the Model tab. In the Geoprocessing pane, search for the Kernel Density tool. Drag the tool into the model below the Minimum Bounding Geometry tool.
2. Click the Elk_in_Southwestern_Alberta_2009 oval and drag your mouse to connect it to the Kernel Density tool. In the window that opens, choose Input point or polyline feature.

   

   The blue Elk_in_Southwestern_Alberta_2009 oval now has two lines connecting it to two different tools.

3. Double-click the Kernel Density tool to open the parameters.

   The Input point or polyline features parameter is set to the Elk_in_Southwestern_Alberta_2009 layer.

4. For Output raster, type Elk_KernelDensity.

   

   You’ll accept all the other defaults. The area units and output cell size are determined by the map’s projection and inputs. When left blank, the Search radius value will be calculated based on the input dataset, and the value used can be found in the tool’s messages after processing is complete.

5. Click OK.
6. Right-click the Elk_KernelDensity output in the model and choose Add to Display.
7. Right-click Kernel Density and choose Run.

   Note:

   Clicking the Run button on the toolbar will run the entire model. Because you've already run the Minimum Bounding Geometry tool and don’t want to run it again, you’re choosing to run just the Kernel Density tool.

8. Click the Map tab. In the Contents pane, uncheck the Elk_in_Southwestern_Alberta_2009 and Elk_data_MBG layers to review the results of your Kernel Density analysis.

   

   Now that you’ve calculated kernel density, you can use the output to help visualize the potential home range. The default symbology doesn't show much of the data, so you'll change this to better understand the result.

9. In the Contents pane, right-click the kernel density layer and choose Symbology.

   

   The Symbology pane opens. The layer is currently being styled using the Equal Interval method, which creates classes at equal ranges regardless of the data’s spread. This method works best for describing familiar ranges, such as percentages or temperatures, where it’s meaningful to emphasize the value of an attribute relative to other attributes. Instead, you want to use the kernel density results to describe whether it’s more or less likely that elk are found in a location.

10. In the Symbology pane, for Primary symbology, click Classify and choose Stretch.

    

    The raster is now drawn using a black to white color ramp that shows values of 0, or no presence likely, as black. You’ll change the color ramp to better visualize the kernel density output.

11. For Color scheme, click the color ramp. Under Format color scheme, check Show names and Show all.
12. Choose the Heat Map : Dark Metal-Blue-White-Semitransparent color ramp.

    

    The Elk_KernelDensity layer updates to show the new heat map color ramp. Areas in bright white are most likely to have elk while areas in darker shades of blue are less likely to have elk.

    

The Kernel Density tool produces a continuous surface showing the likelihood of elk presence at a location within the home range.

Find clusters using the Density-based Clustering tool

The kernel density tool can help you find clusters across the total population. Now, you’ll use the Density-based Clustering tool to find where an individual elk tends to spend time. The Density-based Clustering tool identifies clusters and noise in point data. You’ll use the observed points for a single elk, E106, who has both the most observed points in the dataset and a large geographic area of travel. Because this elk traveled so far during the year 2009, you’ll use the tool to find clusters where it spent a lot of time versus what observed points might be outliers or noise in the data.

1. In the Contents pane, check the Elk_in_Southwestern_Alberta_2009 layer to show it on the map.
2. On the ribbon, click the Map tab. In the Selection group, click Select By Attributes.

   

   The Select By Attributes tool opens.

3. For Input rows, choose Elk_in_Southwestern_Alberta_2009.
4. For Expression, click the Select a field box and choose the ind_ident field. In the last box, click the drop-down menu and choose E106.

   

   The full expression should read Where ind_ident is equal to E106.

5. Click OK.

   The points associated with elk E106 are highlighted on the map. You’ll run the next analysis with this selection active to analyze clusters specific to this individual.

6. Click the Model tab and add the Density-based Clustering geoprocessing tool below the Kernel Density tool.
7. Connect the Elk_in_Southwestern_Alberta_2009 oval to the Density-based Clustering tool as the Input Point Features.
8. Double-click Density-based Clustering to open the tool’s parameters and rename the Output features to Elk_E106_DBC.

   Next you’ll choose the Clustering Method option. Defined Distance, or DBSCAN, finds clusters based on a search distance that you specify in the tool. Self-adjusting, HBDSCAN, will find clusters based on the probability that a data point belongs in a specific group.

9. For Clustering Method, choose Self-adjusting (HDBSCAN), and for Minimum Features per Cluster, type 100. Click OK.

   

   The Minimum Features per Cluster value of 100 was chosen to create a smaller number of clusters. To test other cluster values and methods, you can change the Clustering Method and Minimum Features parameters.

10. Right-click the output to choose Add to Display.
11. Right-click the Density-based Clustering tool and click Run.

    When the tool finishes running, the Messages window shows that four clusters were identified.

12. Click the Map tab to see the results. In the Contents pane, uncheck all layers except for the Elk_E106_DBC layer and the basemap.

    

    The four clusters in this elk’s recorded locations are shown on the map. To investigate the results of the clustering parameters used, two charts are created with the Distribution of Membership Probability and Features Per Cluster layer.

13. In the Contents pane, right-click the Elk_data_DBC layer and choose Attribute Table.

    In the Elk_data_DBC attribute table, each point identified as being part of a cluster shows the probability that the point is part of the cluster as well as the stability of the cluster. Points are also labeled as Outliers or Exemplars. Exemplars are points most representative of the cluster, while outliers are scored for how close to the exemplar they are. Learn more about interpreting Density-based Clustering.

14. Close the attribute table.
15. On the ribbon, in the Selection group, click Clear.

    

    The selected data is cleared, and you can continue your analysis on all data points in the Elk_in_Southwestern_Alberta_2009 layer.

Use the Standard Deviational Ellipses tool to understand range and change over time

The Standard Deviational Ellipses tool is another useful tool in examining a species home range. While the convex hull polygon you calculated with the Minimum Bounding Geometry tool outlines the extent of observations, the Standard Deviational Ellipses can statistically determine a home range at either 1, 2 or 3 standard deviations based on the central tendency, dispersion, and directional trends of the features. While it can be helpful to understand the overall distribution of the observed animal ranges, directional distribution can also show patterns over time. You’ll use this tool twice, first to show an overall distribution, and second seasonal changes in the elk’s home range. The season in which each point was recorded is reflected in the summer_indicator field. In the summer_indicator field, values of 1 represent points collected in December, January, and February; values of 2 represent points collected in March, April, and May; values of 3 represent points collected in June, July, and August; and values of 4 represent points collected in September, October, and November.

1. Click the model tab and add the Directional Distribution (Standard Deviational Ellipse) tool below the Density-based Clustering tool.
2. Connect the Elk_in_Southwestern_Alberta_2009 oval to the Directional Distribution tool as the Input Feature Class.
3. Double-click Directional Distribution to open the parameters. Name the Output Ellipse Feature Class parameter value Elk_data_DD.
4. For Ellipse Size, choose 2 standard deviations. Leave the remaining variables empty and click OK.

   Creating ellipses of 2 standard deviations will capture approximately 95 percent of the population. Because every animal location point has the same importance, you won’t use the Weight Field parameter. The Case Field parameter is used to group features for calculation; you’ll use this parameter later to calculate directional distribution ellipses by observation month.

5. Right-click the green output in the model and check Add to Display, and run the Directional Distribution tool.

   The output is added to the map.

   

   This distribution is centered on the mean center for all features. Because elk migrate seasonally for grazing and reproduction, it can also be helpful to find the directional distribution for summer and other seasons.

6. On the Model tab, right-click the Directional Distribution tool from the previous section and choose Copy.
7. Right-click the model pane below Directional Distribution and choose Paste to duplicate this tool inside the model.

   Because you copied the tool, the parameters you set previously will still be applied.

8. Double-click the pasted Directional Distribution tool and rename the Output Ellipse Feature Class parameter Elk_data_DD_season.
9. Under the Case Field parameter, choose summer_indicator and click OK.
10. Add the output to the display and run the tool.

    

    The Elk_data_DD_season result is drawn on the map. With the current symbology, all the ellipses showing monthly data are symbolized the same. You’ll change the symbology so that each month has a unique color.

11. In the Contents pane, right-click Elk_data_Year and choose Symbology.
12. In the Symbology pane, for Primary symbology, choose Unique Values. For Field, choose summer_indicator.

    

    Now each ellipse is drawn with a different color, but because the geometry type is polygon, they all have a fill that makes comparison difficult.

13. Next to Color scheme, click the Color scheme options button and choose Apply to fill and outline.

    

    The polygon outlines are updated from gray to the same color as the fill. Now, you’ll remove the fill so you can see all the ellipses.

14. Under Classes, click More and choose Format all symbols.

    

    The Format Multiple Polygon Symbols pane appears.

15. Click the Properties tab and expand Appearance, and click Color. Choose No color and click Apply.

    

    The ellipses are redrawn showing just the outline. You’ll make these outlines larger to stand out against the basemap.

16. For Outline width, change the symbol to 3 pt and click Apply.

    

17. Save the project.

You’ve now learned five ways to explore the home range of an elk herd, using Minimum Bounding Geometry, Kernel Density, Density-based Clustering, Directional Deviation and Directional Deviation by Season. The model you built is saved and can be reused or shared across your organization.

Acknowledgements

• The telemetry data used in this tutorial is a subset of the Elk in Southwestern Alberta study. The full dataset and details about the study can be found on MoveBank.
• Boyce MS, and Ciuti S (2020). Data from: Human selection of elk behavioural traits in a landscape of fear. Movebank Data Repository. https://www.doi.org/10.5441/001/1.j484vk24 [doi.org]