Download Assignment_7[1]_GIS

Alyssa Corbett
March 29, 2008
Assignment 7: Initial Project Description
Project Title: A spatial analysis of potential human-wolf conflict in Montana
Project Description:
As wolf populations in Montana continue to rebound under the protection of the Endangered
Species Act, the number of negative interactions between wolves and humans, specifically those that own
livestock farming operations on the fringe of wolf habitat. The proximity of these carnivores to human
settlements, especially in the farming regions of Montana, causes a chronic conflict that threats the
persistence of wolf populations and the prosperity of Montana farmers. Therefore, it is imperative to
identify these human-wolf interfaces in order to: 1) provide the farmers with the tools to prevent wolves
from accessing their domestic livestock, and 2) locate regions that may need to be set aside as protected
areas for wildlife. Recognition of these conflict areas will allow proper management of the wolves and the
domestic stock. Past efforts to encourage wolf-friendly methods livestock protection have failed because
they were implemented over a large, generalized region – an impractical task. The use of spatial analysis
via GIS allows researchers to anticipate the locations of potential human-wildlife conflict and focus
preventative methods in these specific regions. This being said, I propose to compile and overlay datasets
of different environmental and anthropogenic factors to produce a map that clearly delineates regions of
Montana that are prone to human-wolf conflicts. Layer datasets will include: current region of potential
wolf territory, domestic livestock density, infrastructure, current areas of protected habitat, and habitat
suitability (landuse). The final product will help inform wildlife management and urban planners as well
as educate the public about potential zones for human-wolf conflict.
Annotated Bibliography:
Le Lay, G., Clergeau, P., and Hubert-Moy, L. 2001. Computerized map of risk to manage wildlife species
in urban areas. Environmental Management 27(3): 451-461.
This article, put together by three French scientists from the University of Rennes, summarizes a
methodology for assessing and mapping the risk in cases of human-wildlife conflict in urban areas for the
use of public education and the comprehension and communication between people and wildlife agencies.
The article delineates the two potential components of “human-wildlife conflict risk”: 1) the potential
distribution and activities of species in an urban setting and 2) the vulnerability of human life, such as
townspeople, pets, possessions, buildings, or any other aspect of a town. It is suggested that once these
two separate components are visually mapped, they can be combined to form the overall risk factor for the
region in question. This idea can be visually conceptualized in Figure 1 (next page).
The European starling (Sturnus vulgaris) in Rennes, Franch is used as a case study to demonstrate
the usefulness of risk mapping in advising urban planners in aspects of urban wildlife management. The
starling is a good study species because it has been considered a pest to agricultural regions since the
1960s, when the species began to considerably increase. The case study listed the different urban
vulnerabilities, including epidemiological risks, ecological risks, sociological risks, economical pressures,
as well as the wildlife potentials of the starling, such as size of roost, roost seasons, nesting availability,
and forest fragmentation. Each factor was weighted (with reference to experts knowledge and to the link
observed between activities of wildlife species and humans) and the integrated with the others to produce
a risk map for starling management. It highlights sites that are at a high risk for conflict if used for a
nesting area, and thus allows for wildlife managers and urban planners to make educated plans for
development.
Figure 1: Configuration of the GIS built to draw the map of risk for wildlife management
Peralvo, M.F., Cuesta, F., and Manen, F. 2005. Delineating priority habitat areas for the conservation of
Andean bears in northern Ecuador. Ursus 16(2): 222-233.
This 2005 article describes how different geographic layers can be combined to locate prime
habitat for Andean bears in the northern portion of the eastern Andean cordillera in Ecuador. Andean
bears are quickly losing their cloud forest habitats due to habitat destruction, and thus it is imperative that
conservationists find large tracts of undeveloped land that can be set aside as protected bear habitat. In
order to locate a suitable parcel of land, these researchers built a raster dataset that included seven
environmental variables, including elevation (meters), slope (degrees), terrain shape (difference in
elevation between a central pixel and its neighbors in a 5x5 pixel block), distance to rivers (meters), road
density (in a 10 km radius), percentage of developed land (percent in a 10 km radius), and land-cover type
at a resolution of 30 meters. Habitat suitability was determined using a multivariate statistic called the
Mahalanobis distance, which is a measure of dissimilarity. More specifically, the statistic represents how
dissimilar habitats that are being proposed for potential protection status are from current Andean bear
habitat. The Mahalanobis distances and the environmental variables were then overlain to produce to
produce a habitat suitability map. Areas that were deemed “suitable habitat” were then scrutinized for
three other quantitative criteria: altitudinal range, human influence, and current conservations status.
Scores of 0 to 1 were assigned for each factor and then added together to create and overall ranking of
each habitat. Lastly, qualitative variables of hydrography and vegetation types were added to the model,
and five classes of habitat suitability were finally mapped. The study found that almost twenty eight
percent of the area in question was suitable for Andean bear populations, and much of the land was a
contiguous tract between that lay in the cloud forest zone. Thus, a suitable area for Andean bear
preservation was located using a full range of ecological requirements of large carnivores.
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.
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 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 censuses 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 geo-referenced 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 assumes that there is a continuous statewide distribution of wolves, while the
second, more conservative map 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’ home-ranges. This
finding was supported by the fact that these problem areas have the
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.
Wilson, S.M., Madel, M.J., Mattson, D.J., Graham, J.M., Burchfield, J.A., and Belsky, J.M. 2005. Natural
landscape features, human-related attractants, and conflict hotspots: a spatial analysis of humangrizzly bear conflicts. Ursus 16(1): 117-129.
In an attempt to ameliorate human-grizzly bear conflict on privately owned agricultural lands in
Montana, this team of researchers used spatial analysis to better understand the components of conflict
between these two species. They selected a study site in central Montana on the Rocky Mountain East
Front because of the history of grizzly bear encounters in privately owned agriculture land. The scientists
collected data of all human-grizzly bear conflicts from 1986 to 2001, which consisted of incidents of all
different severities - from an attack to a bear being spotted nearby a home – which were then input into a
GIS mapping program. Also, a digital 30 meter Landsat Thematic Mapper image of vegetation cover and
a vector-based hydrography layer were used to show riparian and other wetland regions, both of which are
crucial habitats for bear communities. Then, the scientists used density mapping as a tool to identify areas
of past concentrated conflicts, weighted by season length. Conflict “hotspots” were then defined as areas
that had greater than or equal to three conflicts for over two years, indicating human-grizzly bear
encounters were a chronic issue. Next, the linear distance from the conflict location to the nearest river or
creek, calving or lambing area, or cattle/sheep pasture was mapped, using centroids of each polygon as
the point of measurement. This is because grizzlies’ main method of predation is typically ambush from
the cover of woody riparian vegetation. Researchers used these distances as the protocol for “buffer
zones” around agricultural and farm lands. Interestingly, results showed that human-grizzly bear conflicts
were concentrated in only a small portion of the study area; in fact, about seventy-five percent of all
conflicts occurred in only eight percent of the study area (hotspots). This implies that spatial analysis is an
important tool for wildlife management, as effort can be focused on this specific area rather than over a
generalized region.
Methods and Data Layers:
Table 1: Factors in Risk Analysis of Human-Wolf Conflict in Montana
Layer
Wolf Habitat
Cattle Density Highways
Designated
Wilderness
Areas
Montana Fish,
Department of
TIGER
Montana
Source
Wildlife, and
Agriculture
Natural
Parks
Heritage
Program
2005
2006
1990
2003
Year
n/a
n/a
± 50 m
± 170 ft. (~51
Estimated
meters)
Accuracy (in
Metadata)
3000 m (2 mi.) n/a
50 m
1500 m (1 mi.)
Necessary
Accuracy
Will have to
Layer is excel
Will either use Will have to
Notes
rasterize this
spreadsheet (no. the buffer tool rasterize this
data in order to of cattle per
or the
data, unless I
use spatial
county) joined
“distance
choose to use
analysis tool
to Montana
from” spatial
“points” of ea.
county shape
analysis tool
Protected
file. A new field to create these region instead
calculates cattle zones
of the entire
density per sq.
wilderness area
mi.
Distance from
Density per
Distance from Distance from
Analysis
county
5: 1 = wolf
5: 1 = 0-8 head 5: 1 = road, 2 5: 1 = protected
No. of
habitat, 2 =
of cattle per sq. = closest to
area, 2 = closest
Classes
closest to wolf
mi., 2 = 9-16, 3 road… 5 =
to protected
habitat… 5 =
= 17-24, 4 = 25- furthest from
areas… 5 =
furthest from
32, 5 = >32
road
furthest from
wolf habitat
protected areas
Weight
40%
20%
15%
15%
Landuse
Suitability
Montana State
Library
1993
± 3800 m
3000 m (2 mi.)
Will have to
rasterize this
data in order to
use spatial
analysis tool
Range of
suitability
3: 1 = suitable
(ungrazed
woodland or
grassland), 2 =
conflict risk
(grazed
woodland,
cropland,
irrigation land),
3 = unsuitable
(water feature,
urban area)
10%