ESRM 450 Animal Movement and Home Range Lab
Brian Kertson and John Marzluff
We will explore the Hawth’s Tools extension for ArcMap that allows us to investigate
and characterize the movement of animals. If you need an ArcMap refresher, we suggest:
http://courses.washington.edu/gis250/main/schedule.html
The basis of our analysis is a file of point locations from tracking an animal (in our case
cougars in Snoqualmie Forest). We are interested in first learning if their movements are
random or directional and second relating their movements to properties of the forest they
live within.
1. Getting Data into ArcMap and exploring a typical data file from a GPS-collared
animal.
Click on Add Data Button:
Navigate to cd drive (usually the D drive), double click on cougar gps telemetry files
(f136_2007_esrm_450 and m323_2007_esrm_450)
Note: ArcMap will only import comma delimited (.csv), database (.dbf), and tab
delimited (.txt) files for display. Excel workbooks (.xls) must be saved in one of these
formats. Make sure there are no spaces in the file name or column headings as
ArcMap will not allow you to import the file.
You will notice you are now on the Source tab (lower left corner):
Right click on the .csv file, scroll down to "Display XY data", for X Field select
"EASTING", for Y Field select "NORTHING" (red arrow)
Before the data is displayed, assign a coordinate system and projection. Click on "Edit"
(green arrow)
>Select >Projected Coordinate System > Utm > WGS 1984 > WGS 1984 UTM Zone
10N.prj > click OK
Click "OK" again, data should display with name: "f136_2007_esrm_450.csv Events"
Note: It is critical you know what datum and coordinate system your data was
collected in. The use of the incorrect datum or projection will result in significant
spatial errors in the display and analysis of the data. For a more detailed discussion of
datum and projection systems click on this link (copy and paste link into browser):
http//:courses.washington.edu/esrm590/lessons/projection/index.html
2) Investigate the cougar gps telemetry files:
Right click on the events table > Open Attribute Table: What are the various fields?
COUGAR_ID: the unique cougar identification number
LINE_NO: the gps location number
UTC_DATE: Date
UTC_TIME: Time
EASTING: the UTM east/west coordinate
NORTHING: the UTM north/south coordinate
HEIGHT: the elevation of the gps location, estimated by the gps satellites (often not
accurate)
DOP: (Positional) Dilution of Precision, a measure of the quality of the gps fix (1 is
highest quality)
NAV: 2D or 3D; another measure of the quality of the gps fix (3D is better than 2D)
VALIDATED: yes, no; another measure of gps fix quality
SATS_USED: the number of satellites used in the fix
MAIN: the charge of the main collar battery
BACK: the charge of the backup collar battery
TEMP: air temperature next to the animal in degrees Celsius
3) Create a shapefile from the Events file:
Right click on the Events File > Data > Export Data
Note: Name the shapefile and save it on your USB drive. Make sure the box for "this
layer's source data" is checked. (You will use a random sample of this shapefile later to
generate the kernels for these cougars).
"Yes" you want to add this data to the ArcMap view
WORKING WITH HAWTH’s TOOLS
1. Explore the tool at http://www.spatialecology.com/htools/tooldesc.php
2. Visualize Movements
Pick the male or female cougar shape file (or you can use the events file)
Hawth’s Tools>Animal Movement>Convert Locations to Paths
If locations are sorted chronologically, then you get a quick track of the
movement between locations.
For advanced users you might want to consider animating movement along this path.
3. Describe and Characterize Movements
Hawth’s Tools>Animal Movement>Calculate Movement Parameters
Enter your point file
And the field that identifies the
animal of interest
Use Web Help for more details
If locations are sorted chronologically, then you get a quick calculation of lengths and
turn angles between each location. These are added point by point to the attribute table
(see next page). These can be summarized quickly by clicking on the column in the
attribute table, right clicking, and then clicking on statistics (see below).
Attribute table with path variables added
Statistics of a
column
3.Consider the Randomness of Movements
Hawth’s Tools>Animal Movement>CRW Simulation Tools
Use the simple option and then use the first UTM coordinate for a cougar
as the starting point, do a pure random walk. See how this walk compares to the
observed track of the cougar.
Can any of these results be applied to your research questions or do they suggest
new questions?
Look at attribute table of the random walk. How does it compare to the actual walk?
Think about how you could use these analyses to investigate resource selection.ra
For Advanced Users: Explore other statistics in ArcMap that would be useful.
Spatial Statistics Tool>Analyzing Patterns>Average Nearest Neighbor
Spatial Statistics Tool>Measuring Geographic Distributions>Linear Directional
Mean
4. Home Range Analysis
Estimating an Animal’s Use of Space
There are many different analytical techniques designed to
measure the area routinely utilized by an animal. Most attempt
to quantify the “home range” (following Burt (1943); the area
covered by an individual in its normal daily activities, omitting
occasional long-distance excursions) from a sample of points
where the animal was observed. Early methods included the
Concave
minimum covex (or concave) polygons (figure to right), the
bivariate normal (below to right), and the harmonic mean (see
Polygon
White and Garrott 1990 for details). These techniques may
still be useful in some situations, but most modern estimates of space use rely on
the utilization distribution (Van Winkle 1975) and its estimation with kernel
techniques (Worton 1989).
MCP
Unsuitable
Lake Habitat
for Terrestrial
Animal
The utilization distribution is a probability density function (Silverman 1986) that
quantifies an individual’s or group's relative use of space
(Kernohan et al. 2001). It depicts the probability of an
animal occurring at each location within its home range as a
function of relocation points (White and Garrott; Figures
below).
Utilization
distributions
can be
estimated
from point
processes,
such as observed (Jennrich and Turner 1969)
locations of animals, using probability
density functions, such as kernel
techniques (Worton 1989,
Kernohan et al. 2001).
Kernel density estimation
techniques have been
applied in the statistical literature for many years and have recently been
evaluated as estimators of space use by animals (Seaman and Powell
1996, Seaman et al. 1999). Accurate kernel estimation assumes sampling
is sufficient to quantify relative differences in use. Simulation evaluations
demonstrate that kernel-based estimators better represent differential space
use than other UD techniques with adequate sample sizes (>30-50 point
estimates) and perform well under complex spatial point patterns (Seaman et al. 1999).
Consequently, kernel-based estimators have become the standard for non-mechanistic
models of animal movements (Worton 1989, Kernohan et al. 2001).
Home range estimation is possible within Arcmap using the Hawth’s Tools extension.
1. Determining the home range of cougars.
Your starting layers are the point locations of male and female cougars.
Calculate the Minimum Convex Polygon home range
Hawth’s Tools>Animal Movements>Create minimum convex polygon
Calculate the Fixed Kernel home range estimate
First, we need to estimate h, the smoothing factor or range of spatial dependence
in your data. The HRT tool in ArcMap is unreliable, so we need to
use ArcView at this point. For our purposes we also need a smaller
random sample of points to estimate the smoothing factor. This can
be done within ArcView, using the extension Animal Movements.
Open Arc View and use your cougar shape file of points.
Movement>Random Selection>0 (to take a set # of
points)>300>NO (without replacement)
With the selection in force (yellow points are those 300 you
selected) determine h using Least Squares Cross Validation
Home Range>Kernel, use the default LSCV technique
Write down the h value and finish the run
Now, return to ArcMap to calculate the fixed Kernel Home Range
Hawth’s Tools>Kernel Tools>Fixed Kernel Density Estimator
You will need to enter your point layer (shape file of male or female points)
And your h (smoothing) value from ArcView
And change the output cell size to 30m
And identify an empty folder for the
resulting grid
When the analysis is done you have to add the new grid you created as usual and
change the color ramp used from black and white to the color of
your choice (invert it for max effect!)
Home Range Analysis Questions
1. How do the minimum convex polygon, fixed kernel estimates of cougar use areas
compare? Overlay the graphics to compare visually.
2. How does the specification of h (smoothing factor) affect the estimation of the
fixed kernel? Try some values other than that obtained by LSCV and compare
results.
2. Overlap in home range.
There are two ways to compare the overlap in space use by animals. To illustrate
these methods use the male and female cougar from Snoqualmie, who share a bit of
their ranges.
A. Calculate the 2-dimensional overlap – the amount of space shared by the cats
assuming each spot in a home range is used equally.
Hawth’s Tools>Analysis Tools>Polygon in Polygon Analysis
When the analysis is run a new field is added to the Zonal Polygon (the female cougar’s
mcp in my example (you enter the other animal of interest in the Summary Polygon
Layer (the male in my example).
Open the attribute table of the zonal polygon shape file and the area of overlap is the new
variable and the total area is the “Area” variable. The proportional, 2-dimensional overlap
is simply the area of overlap / total area of range (0 in my example).
B. Calculate the 3-dimensional overlap—the amount of space shared by the
cougars assuming unequal use within the home range.
Before you can calculate the 3-dimensional overlap of the male and female cougar, the
kernels must be converted from a kernel density estimate (does not sum to 1) to a true
probability density function (sums to 1)
Open Hawth's Tools > Sampling Tools > Generate Regular Points
Specify the layer in which
the point layer's extent will
be based = the kernel
Make sure the Point Spacing is
the same as the cell size of the
raster (30 m) and is locked into
a 1:1 ratio (default option)
Name the point grid shapefile
and specify the destination
where it will be saved
Click OK and you will see the prompt below asking if you would like to center the first
point in the center of the cell in the upper left corner, click Yes.
The new point shapefile will be generated, this may take a couple of minutes.
Your new point shapefile will look like below:
Notice the points are centered
in each cell, the full extent
looks black because of the
sheer number of points
Full Extent
Zoomed In
Repeat these steps for the other cougar.
Note: These point files are huge, for the female cougar, >400,000 points; for the male,
>1,000,000! Be sure to remember where you saved these point shapefiles, they will be
used in next week's lab to append use values from the UD and landscape metrics
associated with these values for the RUF analysis.
You will now need the determine the sum of cougar kernel to convert the units of the
kernel from density to volume.
Hawth's Tools > Analysis Tools > Intersect Point Tool (this will add a field with the
kernel value for each point)
The point grid shapefile that the
kernel values will be appended too
too.
The raster from which the
values will be extracted
Click OK, the analysis will take several minutes.
Right Click on the point grid shapefile > Open Attribute Table.
You will see a new field with the abbreviated name of your raster.
Right Click on the field > Statistics (same as before)
Write down the sum value
New field with value from raster
Repeat these steps for the other cougar.
To convert the kernel density estimate to a probability density function, you will need the
Raster Calculator within the Spatial Analyst Toolbar:
Tools > Extensions > check box for Spatial Analyst (dock the new toolbar in the menu)
Spatial Analyst > Raster Calculator > enter equation: "kernel_name / sum" > Evaluate
This will create a new raster identical in appearance and characteristics, but with
converted values (ArcMap will assign the name Calculation, you can rename the raster
whatever you would like)
Note: If you summed the values of this new raster it would be 1, a true PDF.
Repeat these steps for the other cougar.
Create a new raster with the minimum Use values from 2 different cougar UDs:
Spatial Analyst > Raster Calculator > enter equation: "min([raster1], [raster2]) > Evaluate
You now have a new raster with the minimum Use value of the two cougars at each 30 x
30 m pixel
To get the total Volume of Intersection (VI = 3d overlap) you will need the sum of the
minimum use raster you just created which requires you to follow the same steps you
performed to get the sum of the kernel.
Create a point grid:
Hawth's Tools > Sampling Tools > Generate Regular Points (follow previous steps)
Append the raster values to the point grid:
Hawth's Tools > Analysis Tools > Intersect Point Tool (follow steps from above)
-This will take a few minutes, be patient
Right click on your point grid shapefile > Open Attribute Table > right click on the new
field > Statistics: Sum = Volume of Intersection (3d overlap)
OVERLAP QUESTION: How do the 2D and 3D overlap estimates compare?
Literature Cited
Burt, W.H. 1943. Territoriality and home range concepts as applied to mammals. Journal
of Mammalogy 24:346-352.
Hooge, P. N., and B. Eichenlaub. 1997. Animal movement extension to Arcview:
version 1.1. Alaska Biological Science Center, U.S. Geological Survey,
Anchorage, Alaska, USA.
Kernohan, B. J., R. A. Gitzen, and J. J. Millspaugh. 2001. Analysis of animal space use
and movements. Pages 126-166 in J. J. Millspaugh and J. M. Marzluff, editors.
Radio Tracking and Animal Populations. Academic Press, Inc., San Diego,
California, USA.
Ostro, L. E. T., T. P. Young, S. C. Silver, and F. W. Koontz. 1999. A geographic
information system method for estimating home range size. Journal of Wildlife
Management 63:748-755.
Samuel, M.D. and M. R. Fuller. 1996. Wildlife radiotelemetry. Pp. 370-418. In:
T.A.Bookhout (ed.) Research and management techniques for wildlife and
habitats. The Wildlife Society, Bethesda, Md.
Seaman, D. E., and R. A. Powell. 1996. An evaluation of the accuracy of kernel density
estimators for home range analysis. Ecology 77:2075-2085.
Seaman, D. E., J. J. Millspaugh, B. J. Kernohan, G. C. Brundige, K. J. Raedeke, and R.
A. Gitzen. 1999. Effects of sample size on kernel home range estimates. Journal
of Wildlife Management 63:739-747.
van Winkle, W. 1975. Comparison of several probabilistic home-range models. Journal
of Wildlife Management 39:118-123.
White, G. C. and R. A. Garrott. 1990. Analysis of wildlife radio-tracking data.
Academic Press, Inc., San Diego, California, USA.
Worton, B. J. 1989. Kernel methods for estimating the utilization distribution in homerange studies. Ecology 70:164-168.