Using GIS to Map an Individual Animal’s Home Range

Caitlin Dempsey

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A crucial piece of information for natural resource management is understanding the spatial needs of individual animals, often referred to as the home range. Home range estimation not only sheds light on the extent of the area used by individuals of a specie for its regular activities but also provides insights into habitat preferences, resource needs, and potential threats from human encroachment or environmental changes.

GIS can be used to model the home range of species based on the collected point locations of individual animals as they move through an environment.

Methods to collect animal movement data

Mapping the movement of animals is usually done by one of two field collection methods. One method is to use is Very High Frequency (VHF) radio telemetry and the other method is to use GPS.

Very High Frequency (VHF) radio telemetry

With radio telemetry, animals are outfitted with devices like radio or satellite tags. These devices then send signals back so scientists can track location, movements, and in some cases, vital signs from a distance. Sensors in the tag can track not only the location of the animal but information about the physiological state (such as temperature, heart rate, and oxygen levels), behaviors (including vocalizations, breathing patterns, and tail movements), and the environment around them (like ambient sound, temperature, salinity, and light levels).

GPS tracking of animals

GPS tracking is similar to radio telemetry in that tags are attached to the animal being tracked and signals containing the movement information are transmitted back to the receiver. GPS trackers tend to be heavier and to consume more battery power than VHF tags. This can make them less optimal than radio telemetry for small animals like song birds or in situations where it may be a while before the tag can be retrieved. Compared to radio tracking, GPS units have an advantage of being able to collect large amounts of data passively and remotely.

Estimating an animal’s home range

Both approaches generate a dataset with point locations showing where individual animals have traveled across a landscape. By tracking these locations over a certain period, researchers can gain insights into the environment that an animal regularly utilizes.

Once an adequate sample size of animal movements has been collected, it’s possible apply spatial analysis in order to delineate a boundary showing the estimated home range of individual animals.

There are numerous methods available for estimating the home ranges of animals, each with its unique approach to mapping the spatial boundaries that animals use. Minimum convex polygon (MCP) and kernel density estimation (KDE) are the two of the easiest and most popular methods for estimating home range that will be discussed in this article.

Minimum convex polygon

Minimum convex polygon, also known as convex hull, is often selected for its ease of use. MCP is the smallest polygon around all animal sighting locations where no internal angle is greater than 180°. This is the digital equivalent of placing a string around all the thumbtacks marking animal sightings on a paper map.

Since this method is simply drawing an outline around all known animal location points, it doesn’t require extensive statistical or mathematical calculations. As a result, MCP doesn’t provide context within the home range boundary. There are no estimates of which parts of a home range are most used by a species.

The minimum convex polygon method, however, is known for introducing bias into home range estimations. Since minimum convex polygon creates a home range boundary around all known points, a single outlier can create overestimation of home ranges.

The minimum convex polygon has fallen out of favor with many spatial ecologists due to this issue with home range overestimation and the method’s inherent biases. For example, PennState’s Walter Applied Spatial Ecology Lab cautions, “MCP can be used to describe the extent of distribution of locations of an animal but NOT as an estimation of home range size. In fact, reporting size of home range using MCP should be avoided at all costs unless you can justify its use.”

Researchers from the University of Melbourne compared home range estimations produced by convex hulls (MCP) with Bergman and (2003) concluded that “There is little to recommend convex hulls for range estimation. They have the unpleasant properties that biases increase as sample sizes increase, and that biases may be very substantial, even when errors in the location of observations are small.”

Where minimum convex polygon estimates can come in handy is for temporal comparisons to home range estimations found in older research that also used this methodology.

Kernel Density Estimation

Kernel density estimation (KDE) calculates how many times an animal has been spotted in each part of a study area and uses this information to figure out how likely it is for the animal to move to nearby areas. The output is a statistical estimation of the likelihood that an individual animal will occupy a given space within a home range based on location data.

Percentage volume contours (PVCs) in KDEs indicate the core range (where an individual can be found 50% of the time) and the total home range (where an individual can be found 95% of the time).

A benefit of KDE is that it provides insights into how animals use their space by highlighting areas of high and low use within the home range. This feature allows researchers to identify geographic areas where the animal spends most of its time.

Kernel density estimation is often preferred by spatial ecologists over MCP as this method is generally less sensitive to outlier location points than MCP because the impact of each point is smoothed over its surrounding area. This characteristic often results in more accurate and realistic home range estimates, although some home range estimation is still present.

Another drawback to using kernel density estimation is that, by itself, it doesn’t account for any barriers to movement like a body of water for land mammals or a road. There is, however, the ability to use an additional polygon or line dataset in GIS applications as a barrier layer when running kernel density estimations.

When setting up a Kernel Density Estimation (KDE), it’s important to choose the right sizes for grid cells, the overall area to be covered, and the search radius that matches the specific needs of your animals and species.

References

Burgman, M. A., & Fox, J. C. (2003, February). Bias in species range estimates from minimum convex polygons: implications for conservation and options for improved planning. In Animal Conservation Forum (Vol. 6, No. 1, pp. 19-28). Cambridge University Press. https://core.ac.uk/reader/14989660

Walter, W. D., Onorato, D. P., & Fischer, J. W. (2015). Is there a single best estimator? Selection of home range estimators using area-under-the-curve. Movement Ecology3, 1-11. https://doi.org/10.1186/s40462-015-0039-4

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About the author
Caitlin Dempsey
Caitlin Dempsey is the editor of Geography Realm and holds a master's degree in Geography from UCLA as well as a Master of Library and Information Science (MLIS) from SJSU.

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