Alyssa Corbett
January 24, 2008
Intro to GIS: Assignment 1
Treves, A., Naughton-Treves, L., Harper, E.K., Mladenoff, D.J., Rose, R.A., Sickley,
T.A., and Wydeven, A.P. 2003. Predicting human-carnivore conflict: a spatial
model derived from 25 years of data on wolf predation on livestock. Conservation
Biology 18(1): 114-125.
http://www.geography.wisc.edu/livingwithwolves/publications/spatial_model_of_depred
ation_risk_Treves_etal_2004.pdf
In 2003, Adrian Treves et al. from the New York Wildlife Conservation Society
and the University of Wisconsin-Madison published a paper that used spatial modeling to
predict potential zones of human-wildlife conflict between the farmers of Minnesota and
Wisconsin and resident wolf populations. The study was important for a number of
reasons. First, as wolf populations in Wisconsin and Minnesota continue to grow under
the protection of the Endangered Species Act, so do the number of interactions between
the wolves and the human population that also inhabits the area. Therefore, it is crucial to
identify these human-wildlife interfaces in order to provide tools to the farmers to prevent
wolves from accessing the domestic livestock. Second, the study was among the first to
use mapping and spatial analysis as methods for ameliorating human-wildlife tensions.
Therefore, this research can be a stepping stone for wildlife managers who wish to
accurately locate potential areas of conflict and find practical solutions.
The study mentioned above assigned each township in Wisconsin and Minnesota
a color-code ranging from red (highest risk) to blue (lowest risk). The risk factors were
predicted using three sets of spatial data from the two stations: 1) the range of the 1998
wolf population, 2) locations of 975 sites of wolf predation on livestock over the past 25
years (1976-2000), and 3) recent cenusus and remotely sensed land-cover data. The first
data set was collected by the researchers by mapping the wolf population range using
radiotelemetry, track surveys, howl surveys. Additionally, the population was estimated
by handing out questionnaires for land management agencies who were asked to roughly
sketch the range of the wolf populations. Records of domestic livestock losses were
verified and georeferenced using information from the respective, state-specific
Departments of Natural Resources. Lastly, land-cover census data was retrieved from the
National Land Cover Data (NLCD) of 1993. The researchers then used a series of
statistical tests to determine the contribution of each dataset to the overall human-wildlife
risk factor.
Two maps were created to show the relative risk of wolf predation on livestock
across Wisconsin and Minnesota. The first (A) assumes that there is a continuous
statewide distribution of wolves, while the second, more conservative map (B) assumes
that wolves will only occupy territories with a road density of less than 0.88 kilometers of
road per squared kilometer. The maps revealed that southwest Wisconsin, an area where
breeding packs of wolves have not yet re-colonized, faced moderate to high risk. The
map also revealed that highest risk townships were clustered along the edge of the wolves
homeranges. This finding was supported by the fact that these problem areas have the
A)
B)
lowest habitat suitability for wolves and are in zones where landowners have little recent
experience with human-wildlife conflict since the wolf packs have only recently
established populations in the area. Recognition of these trouble areas will allow proper
management of the wolves and the domestic stock, preventing the needless killing of
wildlife. Techniques such as guard animals, improved fending, and new scare devices
that use random sounds and light can deter wolves from preying on livestock. Past efforts
to modify farmer’s practices have failed because they have been implemented on a largescale – an impractical task. The use of GIS has allowed researchers to anticipate the
locations of potential human-wildlife conflict and thus the focusing of preventative
methods in these smaller sets of area, deeming mapping and spatial analysis as a
necessary tool for human-wildlife management.
However, the greater significance of this study is that it has shown that the
mapping technique can be adapted to other areas where human-wildlife conflicts occur,
provided enough geographic data can be gathered, to define more precise management
zones. For example, a specific interest of mine is the human-wildlife conflicts in East
Africa. Human populations continue to grow in regions of East Africa while,
simultaneously, land use is shifting away from the non-invasive pastime of pastoralism
and towards energy-intensive agriculture. This shift has caused the displacement of huge
herds of wildlife into smaller tracts of a fragmented landscape. At the human-wildlife
interface there is considerable strain on the agriculturalists as they attempt to keep out
hungry, and potentially dangerous, visitors such as elephants, buffalo, and antelope.
Currently, any situation that arises at the interface is usually solved with a gun or the toss
of a spear. The use of mapping and spatial analysis to identify potential risk zones would
help local persons to properly equip farmers with the preventative measures needed to
protect their farms. Additionally, wildlife managers could attempt to turn high risk zones
into protected spaces, and instead displace the human populations into areas that are less
used by wildlife.