Ecological Model
The ecological model assesses the potential impacts of alternative futures on wildlife
habitat. It is still under development. Attached is a preliminary method for assessing
potential and effective wildlife habitat, followed by a link to downloadable images for the
Flathead River Corridor.
Preliminary Proposed Wildlife Habitat Analysis
A method is proposed for assessing the wildlife impacts of historical or potential future
land use change in Flathead County. The method makes a distinction between potential
habitat and realized habitat. Potential habitat is the habitat area potentially available for a
particular species, and does not consider impacts of human disturbances, such as roads,
structures, logging, and recreation on habitat quality. Realized habitat is potential habitat
times the proportion of potential habitat that is useable by the species, which takes into
account the impacts of human disturbances on the occurrence and persistence of a species
in a potential habitat area. Habitat effectiveness is essentially a ratio of realized to
potential habitat. Potential and realized habitat can be evaluated for an individual species
and/or multiple species. In our study, habitat analyses can be done for five time periods:
three historical time periods (1982, 1995 and 2002), and two future time periods (2012
and 2022). The remainder of this paper describes: (1) potential habitat analysis for
individual species; (2) potential habitat analysis for multiple species; (3) realized habitat
analysis for individual species; and (4) realized habitat analysis for multiple species.
Potential habitat analysis for individual species
The potential habitat analysis for individual species divides the county into polygons,
which can be a single pixel or grid cell, or a moving window of cells. Polygons can be
aggregated into habitat types, habitat patches, or wildlife management units. Potential
habitat can be assessed as follows.
1. Calculate the following area-weighted potential habitat score (HP) for each species:
n
n
i 1
i 1
HPjt =  ai Sijt/  ai,
where ai is the area of polygon i, Sijt is the potential habitat score for polygon i and
n
species j in time period t, and  ai equals the size of the area being evaluated, i.e., the
i 1
total area covered by all polygons.
There are several ways to calculate Sijt:
 Deuling et al. (2000) estimated Sijt as a geometric mean of four habitat
suitability indices for woodland caribou, namely elevation, slope, habitat
unit, and stand age, i.e., HSItotal = (HSIelevation * HSIslope * HSIhabitat unit *
HSIstand age)1/4 .



Johnson et al. (2004) determined Sijt by applying multiple attribute
evaluation methods to habitat quality variables for black-tailed prairie dog.
This approach requires determining weights for the habitat quality
variables.
Root et al. (2003) developed Sijt scores based on known habitat
preferences and available species distribution data. Sijt scores ranged from
0 to 5.
The Miistakis Institute’s cumulative effects analysis for grizzly bear
(2002) determined habitat values for different habitat types (see Table 1
on p. 5). Sijt was defined as the habitat value on a [0, 1] interval for each
habitat type in a polygon. It is doubtful we can identify all habitat types
listed in Table 1 (for grizzly bear) using 30m land cover data.
2. Divide the HPjt scores into classes and map the classes to determine changes in their
spatial pattern over time. For example, Deuling et al. (2000) divided habitat scores into
10 classes, and mapped those classes for different areas and time periods using a GIS as
illustrated below:
3. Determine the habitat implications for a species of changes over time in the relevant
landscape pattern metrics for the three highest habitat classes for HPjt. For example,
Deuling et al. (2000) determined the following changes in landscape metrics over two
time periods for habitat classes 8, 9, and 10, which are identified in the bottom right-hand
corner of the above map:
Class
(Ha)
Areas
Number
Patches
of
HSI
Class
1975
1997
Change
%
HSI
Class
1975
1997
Change
%
8
6073.75
5402.62
-671.13
-11.05
8
1566
1752
186.00
11.88
9
3233.81
3084.69
-149.12
-4.61
9
554
617
63.00
11.37
10
460.00
447.12
-12.88
-2.80
10
248
264
16.00
6.45
Patch Density
Mean
Patch
Size (Ha)
HSI
Class
1975
1997
Change
%
HSI
Class
1975
1997
Change
%
8
0.89%
1.00%
0.001
11.88
8
3.88
3.08
-0.800
-20.62
9
0.32%
0.35%
0.000
11.37
9
5.84
5.00
-0.840
-14.38
10
0.14%
0.15%
0.000
6.45
10
1.85
1.69
-0.160
-8.65
Edge Density
Mean
Index
Shape
HSI
Class
1975
1997
Change
%
HSI
Class
1975
1997
Change
%
8
9.29
9.04
-0.250
-2.69
8
1.48
1.46
-0.02
-1.35
9
4.22
4.43
0.210
4.98
9
1.54
1.54
0.00
0.00
10
1.05
1.06
0.010
0.95
10
1.48
1.47
-0.01
-0.68
Mean Proximity Index
Mean Core Area
(Ha)
HSI
Class
1975
1997
Change
%
HSI
Class
1975
1997
Change
%
8
153.50
82.37
-71.13
-46.34
8
2.14
1.84
-0.30
-14.02
9
247.77
155.92
-91.85
-37.07
9
3.20
2.20
-1.00
-31.25
10
17.96
14.63
-3.33
-18.54
10
0.61
0.60
-0.01
-1.64
Potential habitat analysis for multiple species
1. Impacts of land cover/use changes on potential habitat for multiple species can be
assessed using the following index-based multispecies conservation value (Root et al.
2003):
m
MCV(P)it =

j1
m
Ej HP*ijt/  Ej,
j1
where MCV(P)it is the value of the index for polygon i in time period t, Ej is an
endangerment or threat index value for species j, and HP*ijt is the standardized (to the [0,
1] interval) value of HPijt = ai Sijt. Root et al. (2003) used the following four sets of values
for Ej:
The area-weighted potential habitat score for multiple species is:
m
MCV(P)t =

j1
n
ai MCV(P)it/  ai.
i 1
This metric distills the potential habitat information for a suite of species at a single point
in time into one summary indicator. Changes over time in MCV(P)t can be evaluated.
2. Map and analyze the MCV(P)it values for different polygons and time periods. Root et
al. (2003) mapped MCV(P)it for 40 species in 10 counties in southern California, as
follows:
Realized habitat analysis for individual species
The realized habitat analysis for individual species is similar to the potential habitat
analysis for individual species. Realized habitat can be assessed as follows.
1. Calculate the following area-weighted realized habitat score (HR) for each species:
n
HRjt =  ai Rijt for Rijt = Dijt Sijt,
i 1
n
where ai, Sijt and  ai are as previously defined, and Rijt is realized habitat and Dijt is the
i 1
human disturbance coefficient for polygon i and species j in time period t. If Dijt cannot
be determined for a species, then realized habitat cannot be analyzed.
2. Same as step 2 for the potential habitat analysis for an individual species, except that
HRjt scores are divided into classes and the classes are mapped over time.
3. Same as step 3 for the potential habitat analysis for an individual species, except that
the assessment focuses on the habitat implications for the species of changes over time in
the relevant landscape pattern metrics for the three highest habitat classes for HRjt.
The Miistakis Institute’s cumulative effects analysis for grizzly bear in the Castle and
Flathead watersheds (2002) assessed realized habitat in terms of habitat types for 50m
grid polygons within bear management units (BMU). Habitat types were as follows:
Each grid would be assigned to one of these habitat types based on its location. As an
example, for a forest polygon Rijt = (0.046)(Dijt). The Miistakis Institute study (2002)
assigned human disturbance coefficients (Dijt) to zones of influence based on the same
50m polygon grid. The coefficients were developed by Gibeau et al. (1998) for national
parks in the Rocky Mountains, and are as follows:
Note: A value of 0 denotes total displacement and a value of 1 denotes
no disturbance.
As an example, all 50 m2 polygons that have a forest habitat type, ‘cover’ security class,
high intensity of use, and located within 800 m of a linear motorized area, R ijt =
(0.046)(0.37) = 0.02. By averaging the Rijt values for all polygons in a BMU, the
Miistakis Institute study created the following realized habitat map for grizzly bear in the
Castle and Flathead watersheds:
A similar map can be created that shows Rijt values for all polygons in the study area.
Changes in mean realized habitat can be evaluated over time developing maps for
different years.
The study also determined habitat effectiveness (HE) for each BMU as follows:
HEk =
R

r 1
R
(realized habitat)r (area of grid cell)r/  (potential habitat)r (area of grid cell)r,
r 1
where r is an index for grid cells in BMU k. HEk indicates the percent of BMU k that is
capable of supporting grizzly bear. The mean habitat effectiveness map for BMUs in the
Castle and Flathead watersheds is as follows:
Changes in mean habitat effectiveness can be evaluated over time by developing maps
for different years.
Realized habitat analysis for multiple species
1. Impacts of land cover/use changes on realized habitat for multiple species can be
assessed using the same index-based multispecies conservation value (Root et al. 2003)
as in the potential habitat assessment for multiple species. The index-based multispecies
conservation value for potential habitat is:
m
MCV(R)it =

j1
m
Ej HR*ijt/  Ej,
j1
where MCV(R)it is the value of the index for polygon i in time period t, Ej is an
endangerment or threat index value for species j, and HR*ijt is the standardized (to the [0,
1] interval) value of HRijt = ai Rijt.
The area-weighted realized habitat score for multiple species is:
m
MCV(R)t =

j1
ai MCV(R)it.
2. Map and analyze the MCV(R)it values for different polygons, management units, and
time periods.
References
Apps, C.D. 2002. Grizzly bear habitat suitability modeling in the Central Purcell
Mountains, British Columbia. Ministry of Water, Land and Air Protection, Victoria, B.C.
Deuling, M.J., C.G. Woudsma, and S.E. Franklin. 2000. Temporal analysis of habitat
fragmentation: Integrating GIS, landscape ecology, and improved RS classification
methods. 4th International Conference on Integrating GIS and Environmental Modeling
(GIS\EM4): Problems, Prospects, and Research Needs. Alberta, CA, September 2-8.
Gibeau, M. 1998. Grizzly bear habitat effectiveness model for Banff, Yoho, and
Kootenay National Parks, Canada. Ursus 10: 235-241.
Hulse, D., J. Eilers, K. Freemark, C. Hummon, and D. White. 2000. Planning alternative
future landscapes in Oregon: Evaluating effects on water quality and biodiversity.
Landscape Journal 19: 1-19
Johnson, L.E., J. Wyckoff, R. Wostl, B. Blaser, H. Liu, D. McDermott, F. Nuszdorfer,
N.T. Phan, and U. Vanderchoi. 2004. Cumulative effects assessment modeling along
Colorado’s Front Range. ASPRS Annual Conference Proceedings, Denver, CO.
Miistakis Institute for the Rockies. 2002. Transboundary Cumulative Effects Analysis for
Grizzly Bear. University of Calgary, AL.
Root, K.V., H.R. Akcakaya and L. Ginzburg. 2003. A multispecies approach to
ecological valuation and conservation. Conservation Biology 17: 1523-1739.
White, D., P.G. Minotti, M.J. Barczak, J.C. Sifneos, K.E. Freemark, M.V. Santelmann,
C.F. Steinitz, A.R. Kiester, and E.M. Preston. 1997. Assessing risks to biodiversity from
future landscape change. Conservation Biology 11: 349-360.
Images for Flathead River Corridor
A downloadable hyperspectral image and land cover classification for the Flathead
Valley River Corridor as of fall 2004 are available.
Click here to download image and classification. Image should be viewed using one of
the following: Microsoft Office Picture Manager, Adobe Photoshop CS, Windows
Picture and Fax Viewer, and Microsoft Paint 5.1.