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Case Study: Home Range Estimations for Coyotes
Lab Objectives
Estimate home ranges
MBG – Minimum Bounding Geometry (Convex Polygons)
KDE – Kernel Density Estimator
Calculate 50% and 95% quantile areas for the whole population from the Kernel Density surface and using the reclassify tool.
Calculate Use v. Availability ratio for resources
Minimum Convex Polygon (MCP): Draws the smallest polygon around all the exterior points. Can potentially include area not used by the coyote resulting in an overestimation of the home range.
Fixed Kernel Density Estimate (KDE): Density surfaces that represent the probability that an animal will occupy any given area of its home range. Leading to a more accurate result.
The map on the left shows the Core (50%) and General (95%) polygons for each animal. The map on the right is a close up of the home ranges for individual coyotes with points so you can see the distribution.
I used a Python script that automats the entire process of calculating home range (core 50% and general 95%) for individual coyotes with only a few inputs. I calculate availability which determines what landcover types are considered “available” to the coyotes in this population group and calculated utilization which determine what vegetation or land cover is actually “utilized” by the coyotes.
We used a Pivot Table which summarizes, sorts, reorganizes, group, count, total or average data stored in a table. It allows us to transform columns into rows and rows into columns. It allows grouping by any field (column), and using advanced calculations on them.
Expected Count: (total number of 95% HR coyote sample points) * (proportion of each available landcover type)
Preference Ratio: (Utilized count) / (Expected count)
I choose the top 12 landcover. If the preference ration is greater than one it means coyotes are preferring it while values less than one are being under-utilized.
Data Sources
This dataset for this exercise was provided by Dr. Eric Gese at Utah State University.
The python script was written by Chris Garrard and Tyler Hatch in 2020 but updated for ArcGIS Pro in 20201.
The data was collected by Bryan Kluever of Utah State University during the period 2010-2014. The data used in this project was only a subset of a much larger dataset.
Map Created: March 25th, 2021
Author: Steph Aristizabal
Page updated
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