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Evaluating Tradeoffs Between Economic Value and Wildlife Habitat Suitability in
Buffer Zones for Protected Areas in the Northern Rocky Mountains, USA
Author(s): Tony Prato
Source:
Mountain Research and Development
, Feb 2009, Vol. 29, No. 1 (Feb 2009), pp. 46-
58
Published by: International Mountain Society
Stable URL: https://www.jstor.org/stable/mounresedeve.29.1.46
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Evaluating Tradeoffs Between Economic Value and
Wildlife Habitat Suitability in Buffer Zones for
Protected Areas in the Northern Rocky
Mountains, USA
Tony Prato
pratoa@missouri.edu
Center for Applied Research and Environmental Systems (CARES), 212 Mumford Hall, University of Missouri, Columbia, MO 65211, USA
Open access article: please credit the authors and the full source.
Future economic growth
and land development
have the potential to
produce tradeoffs in
which economic values
increase at the expense
of environmental values.
Although such tradeoffs
have not been empirically
verified in mountain
ecosystems, they are likely to exist for an ecosystem
containing abundant natural resources and environmental
amenities that is undergoing rapid economic and population
growth. Quantifying future tradeoffs between economic and
environmental values is important because it provides
information for natural resource managers and community
planners that is useful in alleviating the adverse impacts of
future growth and development on wildlife. Such tradeoffs are
quantified for Flathead County, Montana, located in the
Northern Rocky Mountains of the United States, using the
Ecosystem Landscape Modeling System (ELMS). In particular,
the ELMS is used for the following: (1) to simulate the extent
of the tradeoffs between economic values (ie total output of
goods and services) and wildlife habitat suitability (ie extent of
habitat disturbance and the degree of loss in habitat security)
in buffer zones for 5 protected areas in Flathead County
between 2005 and 2024; and (2) to determine whether
implementing a more restrictive land use policy than existed in
2005 would reduce future adverse impacts of growth and
development on wildlife habitat. Simulation results indicate
that future growth in Flathead County increases total output of
goods and services, and the resulting land development
reduces the suitability of wildlife habitat in the buffer zones.
Degradation in habitat suitability can be alleviated by
implementing a more restrictive land use policy. The methods
used in the study provide a coarse assessment of the
tradeoffs between economic values and wildlife habitat
suitability in buffer zones for mountain protected areas.
Keywords: Economic growth; land development; economic
value; wildlife habitat suitability; tradeoffs; Rocky Mountains;
Montana; United States.
Peer-reviewed: October 2008 Accepted: November 2008
Introduction
Land development caused by economic growth often
reduces the ecological integrity of mountain ecosystems
through loss and fragmentation of wildlife habitat,
increases wildlife mortality resulting from human–wildlife
conflicts, boosts soil erosion and water pollution,
increases the spread of exotic (nonnative) species, raises
temperatures in streams, lakes, and ponds, and
accelerates the natural processes of ecosystem change
(Adger and Brown 1994; Ojima et al 1994; Turner and
Meyer 1994; Vitousek 1994; Vitousek et al 1997; IIASA
1998; Baron et al 2000; Miller and Brown 2001; Solecki
2001). Environmental impacts of land development,
including landscape fragmentation, have been widespread
and extensive in the United States (eg Gonzalez-Abraham
et al 2007). Over 90% of the land in the Lower 48 states
has been logged, plowed, mined, overgrazed, paved, or
otherwise modified from presettlement conditions
(Terborgh and Soule 1999). Between 1982 and 1997,
121,000 km
2
of undeveloped nonfederal lands in the
United States were transformed into urban areas (USDA
2000), which has contributed to species extinction
(Stoltzenburg 1996). During the last 3 centuries, nearly
1.2 million km
2
of forest and woodland and
5.6 million km
2
of grassland and pasture have been
converted to other uses on a global basis (Ramankutty and
Foley 1999).
Economic growth in the Northern Rocky Mountains of
the United States is being driven by lower crime rates, less
traffic congestion, cleaner air and water, and more diverse
outdoor recreational opportunities and environmental
amenities than exist in other regions of the country (Prato
2004; Gude et al 2006; Gruver 2007). Although economic
growth increases economic values, notably total output,
jobs, and personal income, the resulting land development
changes land cover and land use, which can diminish
environmental amenities and the quality of life (Meyer
1993; Rasker and Hansen 2000; Swanson et al 2003; Rasker
et al 2004; Prato and Fagre 2005).
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Mountain Research and Development Vol 29 No 1 Feb 2009: 46–58 http://dx.doi.org/doi:10.1659/mrd.992 ß2009 by the authors46
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In the Northern Rocky Mountains, metropolitan as
well as gateway communities for protected areas, such as
national parks, wilderness areas, and wildlife refuges, are
experiencing substantial growth and development (Howe
et al 1997; Rasker et al 2004). Between 1970 and 2000, rural
residential development in the Montana and Wyoming
portions of the Greater Yellowstone Ecosystem increased
400% (Williams 2001), causing degradation and
fragmentation of current and potential grizzly bear habitat
on private lands. Double-digit growth in residential
subdivisions adjacent to the National Elk Refuge in
Jackson, Wyoming, has reduced the winter range for the
10,000 elk that use the refuge and displaced corridors that
elk use to reach summer range in Yellowstone and Grand
Teton national parks (Howe et al 1997). Cumulative
impacts of residential and resource development (ie timber
harvesting and energy development) near Glacier National
Park in northwest Montana threaten the park’s natural
resources (Keiter 1985; Prato 2004; Sax and Keiter 2007).
Burchell et al (2005) observe that ‘‘[e]ach year, development
disrupts wildlife habitat by claiming millions of acres of
wetlands and forests. This loss often results in habitat
fragmentation, in which animals are forced to live in
smaller areas isolated from other members of their own
species and sometimes unable to forage or migrate
effectively. Habitat destruction is the main factor
threatening 80% or more of the species listed under the
[U.S.] Endangered Species Act.’’
The objectives of this paper are the following: (1) to
simulate the extent of the tradeoffs between economic
value (ie total output of goods and services produced) and
wildlife habitat suitability (ie extent of habitat
disturbance and the degree of loss in habitat security) in
buffer zones for 5 protected areas in Montana’s Flathead
County; and (2) to determine whether a more restrictive
land use policy than existed in 2005, which is the base year
for simulating changes in land use, would reduce adverse
impacts of future growth and development on wildlife
habitat through 2024. Quantifying prospective tradeoffs
between economic and environmental values is important
because it provides information to natural resource
managers and community planners that is useful in
supporting decisions to alleviate the adverse impacts of
future growth and development on wildlife.
Study area
The study area is Flathead County, Montana (Figure 1),
which is located in the Northern Rocky Mountains of the
United States. The county has spectacular mountain
landscapes that sustain diverse employment and outdoor
recreational opportunities and afford a high quality of life.
From 1990 to 2000 the total population of the county
increased 25.8% to 74,471 (United States Census Bureau
2007), compared to 12.9% for the state of Montana and
13.1% for the nation. Between April 2000 and July 2005, the
population of the county grew 11.7%, compared to 3.7%
for Montana and 5.3% for the United States. The July 2005
population of Flathead County was 83,000 (United States
Census Bureau 2007), and the population is projected to
grow to 113,140 by 2025 (NPA Data Services 2003).
Much of the growth and development in Flathead
County has occurred in the Flathead Valley, an area
containing rich farmland, low rolling timbered hills, and
residential and commercial areas. In the last 30 years,
42,998 ha of farmland in the Flathead Valley have been
converted to developed uses (Anonymous 2003).
Ranchland in the county is at risk of being converted to
low-density residential development (Wenger 2004). As a
result of growth and development, most old-growth
forests that once existed outside protected areas in the
county have been harvested, rivers have been altered by
hydroelectric power development, significant acreage has
been converted from farms and forests to residential and
commercial developments, lakes and streams have
become polluted by agricultural and urban runoff, fish
and wildlife habitat has been lost or degraded, large areas
have been invaded by nonnative species, and air pollution
has increased.
Despite these changes, Flathead County has plentiful
and relatively clean water and air, numerous world-class
natural protected areas, and abundant wildlife. Six rivers
flow through Flathead Valley: the North, Middle, and
South Forks of the Flathead River; Stillwater River;
Whitefish River; and Swan River. The Flathead River flows
into Flathead Lake, one of the 300 largest lakes in the
world and the largest body of fresh water west of the
Mississippi River. The county contains several natural
protected areas, including the Great Bear Wilderness, the
northern portion of the Bob Marshall Wilderness,
roadless areas in the Flathead National Forest, the west
side of Glacier National Park, and the Lost Prairie
National Wildlife Refuge (Figure 2). Glacier National Park
is a UNESCO Biosphere Reserve, and Waterton-Glacier
International Peace Park is the world’s first international
peace park and a World Heritage Site (FCVB 2008).
Despite its temperate climate, Flathead County has a
highly diverse flora and fauna with 300 species of aquatic
insects, 22 native and introduced species of fish, and
nearly all of the large mammals of North America. Five of
these species are threatened, and 2 are endangered (Norse
et al 1986; Prato and Fagre 2007).
The land area of the county is about 13,605 km
2
(approximately the size of the state of Connecticut in the
United States), of which 78.6% is managed by the federal
government, and 82.5% is controlled by federal, state, and
tribal agencies (Flathead County Planning and Zoning 2007).
The climate of Flathead County falls in the transition
zone between continental and Pacific maritime. Average
annual temperature is 5.9uC (averaging 2.2uC in the
winter and 25.6uC in the summer), average annual rainfall
is 419 mm, and average annual snowfall is 1402 mm (GCS
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Research 2005). Elevation in the county ranges from
approximately 900 to 3000 m, resulting in a range of
vegetation communities.
Methods
The primary method of analysis is the Ecosystem
Landscape Modeling System (ELMS) (Prato et al 2007).
The ELMS was used to simulate future land use changes in
Flathead County for 9 economic growth–land use policy
scenarios. A scenario-based approach is used in the ELMS
because of uncertainty regarding future growth rates and
land use policies. The ELMS consists of an economic
growth model, a land use change model, and a wildlife
habitat assessment model, which are described in the next
three sections.
Economic growth model
Economic growth scenarios for Flathead County specify
the annual percentage increase in the total output of
goods and services produced in 11 economic sectors of
the county between 2000 and 2024 (see Prato et al [2007])
for a complete specification of the growth scenarios). The
11 sectors are: (1) agricultural, forestry, and fishery; (2)
construction; (3) farming and ranching; (4) government;
(5) finance, insurance, and real estate; (6) manufacturing,
including forest products; (7) mining; (8) services; (9) retail
trade; (10) transportation; communications, and public
utilities; and (11) wholesale trade. A diverse group of
stakeholders from the county reached consensus on the
annual growth rates for total output of goods and services
produced in the 11 sectors during the periods 2000–2014
and 2014–2024 for low, moderate, and high growth
scenarios. Annual average growth rates for the 11 sectors
are 8.78%, 6.26%, and 3.91% for the low, moderate, and
high growth scenarios, respectively, between 2000 and
2014, and 4.39%, 3.13%, and 1.95% for the low, moderate,
and high growth scenarios, respectively, between 2014
and 2024.
The economic growth model uses the IMPLAN
program (Minnesota IMPLAN Group 2008) to estimate
the increases in total output and total jobs in Flathead
County between 2000 and 2024 for 3 economic growth
scenarios (ie low, moderate, and high). IMPLAN is a
menu-driven computer software program developed by
the USDA Forest Service that permits nonsurvey regional
input-output analysis of any county or combination of
counties in the United States. A productivity adjustment
estimated using forecasts of productivity increases over
time (Berman 2004) was applied to the IMPLAN
multipliers to account for increases in labor productivity
over time (2000 to 2024) due to technological change. The
starting year for the IMPLAN analysis is 2000. This was
the most recent year for which IMPLAN data were
available for Flathead County at the time the ELMS was
developed.
Job increases by sector between 2000 and 2024 were
estimated by applying the jobs-to-output ratios calculated
using the IMPLAN model to the increases in sector
outputs for each growth scenario. Increases in total
housing units from 2005 to 2024 were estimated for each
growth scenario as follows:
H~ðJ|PJÞ7PH
½
|HL,
where H 5estimated increase in total housing units, J 5
estimated increase in total jobs, PJ 5population-to-jobs
ratio (1.5), PH 5average persons-per-household ratio
(2.48), and HL 5housing units-to-households ratio (1.18).
The PJ, PH, and HL ratios were developed as part of the
FIGURE 1 Location of Flathead County, Montana, USA. (Map by Christian Hergarten)
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FIGURE 2 Location of 8-km-wide buffer zones for 5 protected areas in Flathead County, Montana.
(Map by the Center for Applied Research and Environmental Systems, University of Missouri)
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2000 Census for the United States. The calculation of H
accounts for housing units that are either vacant or
occupied by nonpermanent county residents of the
county.
Acreage required for new commercial, institutional,
and industrial (CI&I) units under each growth scenario
was determined by multiplying the estimated increase in
total jobs and the estimated CI&I acreage per job in
Flathead County in 2004. The latter is 114.36 m
2
per job,
which is the product of the square meters per worker
(Nelson 2004) and the number of workers per job in
Montana (DOE 2002; Nelson 2004).
Land use change model
The land use change model simulates the conversion of
developable parcels to new residential housing units and
new CI&I units based on the following: (1) the number of
additional CI&I units and housing units required between
2005 and 2024 with each growth scenario; (2) the
proportion of additional housing units in each of 6
density classes; (3) the number and location of
developable parcels in 2005; (4) the desirability of
developable parcels for CI&I units and residential
housing units; and (5) the order in which parcels are
converted to CI&I units and residential housing units. The
number of additional CI&I units and housing units
required between 2005 and 2024 with each growth
scenario are determined by the economic growth model.
The land use policies specify the proportion of additional
housing units in each of the following density classes: (1)
high (2.8 units per hectare); (2) urban (2.2 units per
hectare); (3) suburban (0.8 units per hectare); (4) rural
(1 unit per 0.4 hectares); (5) exurban (1 unit per
3 hectares); and (6) agricultural (1 unit per 19 hectares).
The number and location of developable parcels in
2005 (ie baseline developable parcels) are determined by
eliminating from the set of all parcels ones that: (1)
cannot be developed because they are located on public
land; (2) are already developed and are too small to
accommodate additional development after imposing the
setbacks of housing units and CI&I units from water
bodies as specified by a land use policy; and (3) cannot be
developed because of restrictions imposed by county
ordinances and county subdivision regulations. A parcel
is excluded from development based on item (3) if more
than half of the area of the parcel has an average slope
that exceeds 30% or more than half of the area of the
parcel is in the designated 100-year floodplain. The size,
location, and other features of developed and developable
parcels obtained from the 2005 parcel database created
by the Montana Cadastral Mapping Project (Montana
Cadastral Mapping 2008) were incorporated into a
geographic information system.
The desirability of developable parcels for
development is determined using a multiple attribute
evaluation procedure that calculates development
attractiveness scores for all developable parcels and ranks
the parcels based on their scores (Herath and Prato 2006).
Parcel development attractiveness scores for residential
development are based on 4 parcel attributes: (1)
maximum acceptable distance from a major highway; (2)
maximum acceptable distance from the edge of town; (3)
maximum acceptable distances from 7 amenities (ie lake,
river, preserve/park, golf course, ski resort, forest, and
elevation difference between the parcel and valley floor);
and (4) minimum acceptable distances from 5
disamenities (ie industrial facility or park, trailer park,
commercial center, railroad tracks, and airport). Parcel
development attractiveness scores for CI&I development
are based on 2 parcel attributes: (1) the maximum
acceptable distance from a major highway; and (2) the
maximum acceptable distance from the edge of town.
Maximum and minimum acceptable distances of
developable parcels from landscape features are specified
in Prato et al (2007), and actual distances are determined
using a geographic information system. Since it was not
possible to obtain sufficient feedback from community
stakeholders about attribute weights, assumed values were
used for the attribute weights (see Prato et al 2007 for
actual weights).
The order of parcel development is the following: (1)
CI&I units; (2) high-density housing units; (3) urban
density housing units; (4) suburban density housing units;
(5) rural density housing units; (6) exurban density
housing units; and (7) agricultural density housing units.
This order constrains the number and area of parcels
developed when the amount of land available for
development is less than the amount of land required for
development, which occurs with the current land use
policy at the moderate and high growth rates.
Three land use policies are simulated with the ELMS:
current or baseline; moderately restrictive; and highly
restrictive. These policies differ with respect to the
following: (1) the percentage of new residential housing
units allocated to the 6 density classes; (2) the setbacks of
residential housing and CI&I units from water bodies; (3)
the kinds of new residential housing units and CI&I units
permitted in environmentally sensitive areas; and (4) the
types of residential housing and CI&I units allowed on
parcels that are not sewer accessible. As the land use
policy becomes more restrictive (ie current to moderately
restrictive to highly restrictive), the proportion of housing
units in higher-density classes increases, and the
proportion of housing units in lower-density classes
decreases. Setbacks of housing units and CI&I units from
water bodies are 6.1 m for the current policy, 10.7 m for
the moderately restrictive policy, and 15.2 m for the
highly restrictive policy. The land use policies impose
restrictions on development in 1.61-km-wide buffer areas
around environmentally sensitive areas (ie national parks,
wilderness areas, wildlife refuges, county parks, and state
parks). The current land use policy, which closely
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approximates the land use policy in effect in 2005, allows
housing units in all 6 density classes to be constructed in
1.61-km-wide buffer areas for environmentally sensitive
areas. The moderately restrictive land use policy allows
housing units in the urban, suburban, rural, exurban, and
agricultural density classes to be constructed in the 1.61-
km-wide buffer areas. The highly restrictive land use
policy allows housing units in the suburban, rural,
exurban, and agricultural density classes to be
constructed in the 1.61-km-wide buffer areas. None of the
land use policies allow the construction of additional
CI&I units in the 1.61-km-wide buffer areas for
environmentally sensitive areas.
Finally, development of a parcel is restricted based on
whether or not it is sewer accessible. A parcel is
considered sewer accessible if it is located within the 2003
growth boundaries for the incorporated cities in Flathead
County or within the boundaries of the unincorporated
areas in Flathead County. Only CI&I units and housing
units in the high, urban, and suburban density classes are
allowed on sewer-accessible parcels. Rural, exurban, and
agricultural housing units are allowed within and outside
sewer-accessible areas.
Wildlife habitat assessment model
Since protected areas and lands adjacent to protected
areas provide important habitat for many wildlife species
in the study area, the wildlife habitat assessment model
evaluates the impacts of land development on the
suitability of wildlife habitats in 8-km- and 16-km-wide
buffer zones around 5 protected areas in Flathead
County: Glacier National Park; the Great Bear Wilderness
plus the northern portion of the Bob Marshall Wilderness;
a northern unit of roadless areas in the Flathead National
Forest; a southern unit of roadless areas in the Flathead
National Forest; and the Lost Trail National Wildlife
Refuge. The buffer width (ie 8 km and 16 km) represents
the straight-line distance between the boundary of the
protected area or inner boundary of the buffer zone and
the outer boundary of the buffer zone. Since the
simulations of land use change for the 9 economic
growth–land use policy scenarios were based on economic
data for Flathead County, it was not possible to simulate
land use change outside the boundaries of the county. For
this reason, the buffer zones around the 5 protected areas
do not extend beyond the boundaries of the county. A
geographic information system is used to delineate the
buffer zones. Figure 2 illustrates the 8-km buffer zones for
the 5 protected areas.
Ideally, the potential impacts of future land
development on the suitability of wildlife habitat in the
buffer zones should be assessed using landscape metrics
(Forman and Godron 1986; Turner 1989) calculated from
land cover data using computer programs, such as
FRAGSTATS (McGarigal and Marks 1995) and APACK
(Mladenoff and Dezonia 1997). Although landscape
metrics were calculated using APACK, it has been difficult
to interpret the wildlife habitat implications of those
metrics because they are based on simulated land use.
APACK is typically applied to land cover.
Potential impacts of simulated future land use changes
on wildlife habitat suitability in the buffer zones are
evaluated using 2 indicators of wildlife habitat suitability:
the extent of habitat disturbance (E) and the degree of loss in
habitat security (S). Indicator E measures the percentage of
the total area of a buffer zone that is developed in 2005
under the current land use policy and in 2024 under a
particular economic growth–land use policy scenario. If
the value of E is higher (or lower) in 2024 than in 2005,
then habitat suitability decreases (or increases) from 2005
to 2024.
Indicator S equals [(LD) / (LD +MD +HD)] 3[100],
where LD, MD, and HD are the area of parcels developed
into low, moderate, and high density land uses,
respectively, in 2005 and 2024 under a particular
economic growth–land use policy scenario. Low density
land uses include residential housing units in the exurban
and agricultural density classes. Moderate density land
uses include residential housing units in the suburban and
rural density classes. High density land uses include
residential housing units in the high density and urban
density classes plus CI&I units. S measures the percentage
of the total developed area of a buffer zone in low density
land uses in 2005 under the current land use policy and in
2024 with a particular economic growth–land use policy
scenario. For a given growth scenario, higher (or lower)
values of S indicate that a higher (or lower) percentage of
the developed area of a buffer zone is in patches
disturbed by human activity. In other words, a higher (or
lower) value of S implies a greater (or lesser) degree of loss
in habitat security for wildlife species in the buffer zone,
especially species that are intolerant of human
disturbance (Turner et al 2001). Less (or more) human
disturbance in buffer zones decreases (or increases) the
likelihood of injury or death to species with large home
ranges, such as grizzly bear, wolverine, and mountain lion,
all of which inhabit the study area.
Tradeoff analysis
Tradeoffs between wildlife habitat suitability in the buffer
zones (E and S) for the 5 protected areas and total output
of goods and services for the 11 sectors (T) are evaluated
using 2 tradeoff elasticities: DE
fij
/DT
j
and DS
fij
/DT
j
.
DE
fij
is the percentage change in the extent of wildlife
disturbance in buffer size f between 2005 and 2024 with
land use policy i and growth scenario j (f 51 for the 8-km
buffer zone and f 52 for the 16-km buffer zone; i 51 for
the current land use policy, i 52 for the moderately
restrictive land use policy; and i 53 for the highly
restrictive land use policy; j 51 for the low growth
scenario, j 52 for the moderate growth scenario, and j 5
3 for the high growth scenario). DS
fij
is the percentage
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change in the degree of loss in habitat security in buffer f
between 2005 and 2024 with land use policy i and growth
scenario j. DT
j
is the percentage change in total output of
goods and services produced in Flathead County between
2005 and 2024 with growth scenario j. For example, if
DE
811
/DT
1
50.03, then for each 1% increase in total
output for the low growth scenario there is a 0.03%
increase in the developed area of the 8-km buffer zone
under the current land use policy and low growth
scenario. Similarly, if DS
811
/DT
1
50.02, then for each 1%
increase in total output for the low growth scenario there
is a 0.02% increase in the area in low density land uses in
the 8-km buffer zone under the low economic growth–
current land use policy scenario. Positive (or negative)
values of DE
fij
/DT
j
and DS
fij
/DT
j
imply that future growth
and development decrease (or increase) the suitability of
wildlife habitat in the buffer zones. Additionally, a higher
(or lower) absolute value of the tradeoff ratio implies a
greater (or lesser) tradeoff between total output and
wildlife habitat suitability.
Results and discussion
The size of the developed area in the 8-km- and 16-km-
wide buffer zones under the baseline (2005) and 9
economic growth–land use policy scenarios in 2024 are
summarized in Table 1. In all cases (ie combinations of
buffer sizes and scenarios), the developed area of the
buffer zone is largest for the southern roadless area and
smallest for the wilderness areas. For the most part, the
ranking of the other 3 protected areas according to the
size of the developed area is Lost Trail National Wildlife
Refuge, northern roadless area, and Glacier National
Park. As expected, the 16-km buffer zones have a larger
developed area than the 8-km buffer zones, and the
developed area of the buffer zones for the 5 protected
areas (last column in Table 1) increases as growth rates
increase, except between the moderate and high growth
rate scenarios under the current land use policy. This
exception occurs because all of the land available for
development in the buffer zones is developed under the
moderate economic growth–current land use policy
scenario. Except for the buffer zone for Glacier National
Park between the current and moderately restrictive land
use policy under the low growth scenario, the developed
area of the buffer zones for each protected area decreases
as the land use policy becomes more restrictive (ie current
to moderately restrictive to highly restrictive) for each
growth scenario. Therefore, a more restrictive land use
policy than existed in 2005 moderates the increase in
the developed area of the buffer zone between 2005
and 2024.
The percentages of developed area in a buffer zone (E)
under the 9 economic growth–land use policy scenarios
are summarized in Table 2. Recall that an increase (or
decrease) in E implies a decrease (or increase) in the
suitability of wildlife habitat in the buffer zones between
2005 and 2024. As expected, E increases between 2005 and
2024 for all buffer zones and economic growth–land use
policy scenarios, increases as growth rates increase, and,
for each growth scenario, generally decreases as the land
use policy becomes more restrictive except between the
current and moderately restrictive land use policy for the
high growth scenario. Consequently, as growth rates
increase, the extent of disturbance to wildlife habitat
increases, and implementing a more restrictive land use
policy than existed in 2005 moderates that disturbance.
The ranking of buffer zones for protected areas from
highest-to-lowest extent of habitat disturbance, or
equivalently highest-to-lowest habitat vulnerability to
development in buffer zones, is Lost Trail National
Wildlife Refuge, northern roadless area, southern roadless
area, Glacier National Park, and the wilderness areas.
The percentages of the developed portion of a buffer
zone in low density uses (S) with the 9 economic growth–
land use policy scenarios are summarized in Table 3.
Recall that a higher (or lower) value of S implies less
secure (or more secure) habitat in the buffer zones,
especially for wildlife species that are highly intolerant to
human disturbance (eg grizzly bear). For the most part,
between 2005 and 2024 the security of wildlife habitat in
the buffer zones decreases between the low and moderate
growth scenarios and increases between the moderate and
high growth scenarios. With one exception, the security of
wildlife habitat for both buffer sizes increases as the land
use policy becomes more restrictive for the northern and
southern roadless areas and wilderness areas. For Glacier
National Park and the Lost Trail National Wildlife
Refuge, there are some cases for which the security of
wildlife habitat decreases and other cases for which it
increases as the land use policy becomes more restrictive.
However, the decreases are rather small. The Lost Trail
National Wildlife Refuge, northern roadless area, and
Glacier National Park have the lowest, second lowest, and
third lowest habitat security, respectively, for all
economic growth–land use policy scenarios. The ranking
of habitat security in buffer zones for the southern
roadless area and wilderness areas varies with the buffer
width. For 6 of the 9 scenarios, habitat security is less in
the 8-km buffer zone for the southern roadless area than
in the 8-km buffer zone for the wilderness areas. The
converse is true for all 9 economic growth–land use policy
scenarios in the 16-km buffer zone.
Tradeoff elasticities DE/DT and DS/DT for the 9
economic growth–land use policy scenarios and 2 buffer
widths are given in Table 4. As a point of reference,
estimated total output (T) is US$ 5,437 million in 2005
and US$ 12,825 million, US$ 15,329 million, and US$
18,860 million in 2024 for the low, medium, and high
economic growth scenarios, respectively. All tradeoff
elasticities are positive, indicating that increases in total
output between 2005 and 2024 are accompanied by
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increases in E and S and hence degradation in wildlife
habitat suitability. For both buffer widths, DE/DT
increases or remains the same as growth rates increase,
which indicates the tradeoffs between habitat disturbance
and total output stay the same or increase as growth rates
increase. The value of DE/DT increases between the 8-km
and 16-km buffer zones, indicating that tradeoffs between
habitat disturbance and total output are greater in the 16-
km buffer zone than in the 8-km buffer zone. Conversely,
for all cases DS/DT decreases with growth rates for both
TABLE 1 Developed area in 8-km- and 16-km-wide buffer zones for Glacial National Park (GNP), Lost Trail National Wildlife Refuge (LTWR), Northern Roadless Area
(NRA), Southern Roadless Area (SRA), and Great Bear Wilderness and northern portion of Bob Marshall Wilderness (Wilderness) in Flathead County for the baseline
(2005) and 9 economic growth–land use policy scenarios (2024).
Economic growth–land use
policy scenario
Protected area
GNP LTWR NRA SRA Wilderness Total
Developed area in 8-km buffer zone (31000 ha)
Baseline 2263 3845 2980 5483 594 15,165
Low growth
Current 9452 19,688 12,079 25,288 2408 68,915
Moderately restrictive 10,198 15,718 11,264 20,327 1812 59,319
Highly restrictive 5866 13,314 8165 13,847 1283 42,475
Moderate growth
Current 17,682 29,778 34,057 37,882 3369 122,768
Moderately restrictive 15,984 26,031 19,601 31,921 2991 96,528
Highly restrictive 10,864 16,183 13,251 20,205 1682 62,185
High growth
Current 17,682 29,778 34,057 37,882 3369 122,768
Moderately restrictive 17,658 29,776 33,979 37,864 3368 122,645
Highly restrictive 13,436 25,839 16,520 33,558 3068 92,421
Developed area in 16-km buffer zone (31000 ha)
Baseline 4138 12,351 5959 13,540 1584 37,572
Low growth
Current 18,387 41,667 27,036 50,797 5213 143,100
Moderately restrictive 16,983 35,284 23,042 40,884 4400 120,593
Highly restrictive 11,212 27,394 16,755 29,626 3153 88,140
Moderate growth
Current 30,871 61,774 62,819 73,842 7751 237,057
Moderately restrictive 27,832 54,447 40,234 63,839 6842 193,194
Highly restrictive 18,409 34,723 27,646 41,981 4290 127,049
High growth
Current 30,871 61,774 62,819 73,842 7751 237,057
Moderately restrictive 30,847 61,761 62,704 73,816 7750 236,878
Highly restrictive 25,087 54,219 37,307 66,187 7071 189,871
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buffer widths, indicating that tradeoffs between loss in
habitat security and total output decrease as growth rates
increase. DS/DT decreases between the 8-km and 16-km
buffer zones, indicating the tradeoffs between loss in
habitat security and total output decrease as the buffer
width increases. Except for DE/DT for both buffer widths
at the high growth scenario and DS/DT for the 8-km
buffer width at the high growth scenario, DE/DT and
DS/DT decrease as the land use policy becomes more
restrictive for all growth scenarios. Therefore, a more
restrictive land use policy appears to be effective in
reducing the magnitude of the tradeoffs between habitat
TABLE 2 Percentage of developed area (E) in the 8-km- and 16-km-wide buffer zones for Glacial National Park (GNP), Lost Trail National Wildlife Refuge (LTWR),
Northern Roadless Area (NRA), Southern Roadless Area (SRA), and Great Bear Wilderness and northern portion of Bob Marshall Wilderness (Wilderness) in Flathead
County for the baseline (2005) and 9 economic growth–land use policy scenarios (2024).
Economic growth–land use policy
scenario
Protected area
GNP LTWR NRA SRA Wilderness
Developed area in 8-km buffer zone (%)
Baseline 0.6 9.7 1.4 1.2 0.1
Low growth
Current 2.5 49.6 5.6 5.6 0.6
Moderately restrictive 2.7 39.6 5.2 4.5 0.4
Highly restrictive 1.5 33.5 3.8 3.1 0.3
Moderate growth
Current 4.6 75.0 15.7 8.4 0.8
Moderately restrictive 4.2 65.6 9.1 7.1 0.7
Highly restrictive 2.8 40.8 6.1 4.5 0.4
High growth
Current 4.6 75.0 15.7 8.4 0.8
Moderately restrictive 4.6 75.0 15.7 8.4 0.8
Highly restrictive 3.5 65.1 7.6 7.4 0.7
Developed area in 16-km buffer zone (%)
Baseline 0.8 13.7 1.8 2.1 0.3
Low growth
Current 3.5 46.3 8.3 8.0 0.9
Moderately restrictive 3.3 39.2 7.0 6.4 0.8
Highly restrictive 2.2 30.4 5.1 4.7 0.6
Moderate growth
Current 5.9 68.6 19.2 11.6 1.4
Moderately restrictive 5.4 60.5 12.3 10.0 1.2
Highly restrictive 3.5 38.6 8.4 6.6 0.8
High growth
Current 5.9 68.6 19.2 11.6 1.4
Moderately restrictive 5.9 68.6 19.2 11.6 1.4
Highly restrictive 4.8 60.2 11.4 10.4 1.3
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disturbance and total output, and between habitat
security and total output. Tradeoff elasticities indicate
that implementing a more restrictive land use policy is
generally most effective in reducing tradeoffs at low
growth rates, moderately effective at moderate growth
rates, and least effective at high growth rates.
Conclusions
Economic growth and land development between 2005
and 2024 in Flathead County, Montana, are expected to
generate economic benefits in the form of increased
production of goods and services and increased
TABLE 3 Percentage of the developed area in low density land uses (S) in the 8-km- and 16-km-wide buffer zones for Glacial National Park (GNP), Lost Trail National
Wildlife Refuge (LTWR), Northern Roadless Area (NRA), Southern Roadless Area (SRA), and Great Bear Wilderness and northern portion of Bob Marshall Wilderness
(Wilderness) in Flathead County for the baseline (2005) and 9 economic growth–land use policy scenarios (2024).
Economic growth–land use policy scenario
Protected area
GNP LTWR NRA SRA Wilderness
Developed area in low density land uses in 8-km buffer zone (%)
Baseline 66.8 97.7 86.2 51.1 43.2
Low growth
Current 90.0 99.4 94.9 85.0 83.0
Moderately restrictive 90.7 99.3 93.4 81.3 77.2
Highly restrictive 85.0 99.3 90.6 73.7 70.8
Moderate growth
Current 92.9 98.8 97.2 85.6 86.8
Moderately restrictive 93.0 97.7 94.3 86.8 86.4
Highly restrictive 90.4 99.3 93.3 80.1 77.2
High growth
Current 92.9 96.9 94.5 84.8 87.3
Moderately restrictive 93.0 97.7 94.3 86.8 86.4
Highly restrictive 90.4 98.6 91.4 84.6 86.3
Developed area in low density land uses in 16-km buffer zone (%)
Baseline 66.2 97.6 69.1 51.4 60.7
Low growth
Current 87.8 98.5 88.7 81.7 84.2
Moderately restrictive 85.0 98.7 85.9 76.4 81.3
Highly restrictive 76.2 98.7 78.3 68.2 74.0
Moderate growth
Current 90.0 98.8 93.1 83.8 87.3
Moderately restrictive 88.0 96.8 88.4 82.5 86.3
Highly restrictive 83.3 98.6 84.1 75.4 79.3
High growth
Current 85.6 95.9 88.7 81.3 85.9
Moderately restrictive 86.7 96.6 89.6 81.2 84.2
Highly restrictive 80.8 98.6 82.9 79.0 83.5
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employment at the expense of reducing the suitability of
wildlife habitat in buffer zones for 5 protected areas.
Land development is expected to increase wildlife habitat
disturbance (E) in 8-km- and 16-km-wide buffer zones
around the 5 protected areas. The extent of the
disturbance is expected to be greatest in the buffer zone
for the Lost Trail National Wildlife Refuge and least in
the buffer zone for the Bob Marshall and Great Bear
wilderness areas. Consequently the protected areas in
Flathead County that strictly control human activities (ie
national parks and wilderness areas) have buffer zones
that are less vulnerable to land development than buffer
zones for protected areas that impose fewer controls on
human activities (ie roadless areas and a national wildlife
refuge). The areal extent of the disturbance to wildlife
habitat from development increases as growth rates
increase. Implementing a more restrictive land use policy
than existed in 2005 appears to assuage future human
disturbances to wildlife habitat.
For the most part, between 2005 and 2024, the
security of wildlife habitat in the buffer zones for the 5
protected areas decreases between the low and moderate
growth scenarios and increases between the moderate
and high growth scenarios. For 87% of the cases
evaluated, the security of wildlife habitat in the buffer
zones improves between 2005 and 2024 as the land use
policy becomes more restrictive. Wildlife habitat security
is lowest in the buffer zone for the Lost Trail National
Wildlife Refuge, second lowest in the buffer zone for the
northern roadless area, and third lowest in the buffer
zone for Glacier National Park for all 9 economic
growth–land use policy scenarios. For the most part,
habitat security is highest in the 8-km buffer zones for
wilderness areas and second highest in the 8-km buffer
zones for the southern roadless area. Habitat security is
highest in the 16-km buffer zones for the southern
roadless area and second highest in the 16-km buffer
zones for wilderness areas.
The tradeoff analysis indicates that although increases
in total output of goods and services in Flathead County
between 2005 and 2024 would be beneficial for the
economy, they degrade the suitability of wildlife habitat.
TABLE 4 Estimated tradeoff elasticities for the percentage change in the developed area of buffer zones with respect to a
1% increase in total output (DE/DT) and the percentage change in low density uses in buffer zones with respecttoa1%
increase in total output (DS/DT) in Flathead County between the baseline (2005) and 9 economic growth–land use policy
scenarios (2024).
Land use policy scenario
Economic growth scenario
Low Moderate High
Tradeoff elasticities for DE/DT for 8-km buffer zone
Current 0.05 0.07 0.05
Moderately restrictive 0.04 0.05 0.05
Highly restrictive 0.02 0.03 0.04
Tradeoff elasticities for DE/DT for 16-km buffer zone
Current 0.07 0.13 0.13
Moderately restrictive 0.05 0.10 0.13
Highly restrictive 0.03 0.06 0.10
Tradeoff elasticities for DS/DT for 8-km buffer zone (%)
Current 0.27 0.22 0.15
Moderately restrictive 0.24 0.21 0.15
Highly restrictive 0.20 0.18 0.14
Tradeoff elasticities for DS/DT for 16-km buffer zone (%)
Current 0.24 0.20 0.12
Moderately restrictive 0.20 0.17 0.13
Highly restrictive 0.13 0.14 0.11
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For most of the 9 scenarios, tradeoff elasticities between
the extent of habitat disturbance and total output
increase as growth rates increase and development
expands. Also, tradeoffs are greater in the 16-km buffer
zone than in the 8-km buffer zone. For all 9 scenarios, the
tradeoff elasticities between the loss of wildlife habitat
security and total output increase as growth rates increase
and land development expands. For all 3 economic
growth scenarios, a more restrictive land use policy
reduces the tradeoffs between total output and wildlife
habitat suitability in Flathead County. For the most part,
however, implementing a more restrictive land use policy
than existed in 2005 is most effective in reducing such
tradeoffs when growth rates are low, moderately effective
when growth rates are moderately high, and least effective
when growth rates are high. Although this result suggests
that lower growth rates would reduce tradeoffs between
total output and habitat suitability, it is highly unlikely
that Flathead County would develop a growth policy that
limits economic growth as evidenced by the current
growth policy for the county (Flathead County Planning
and Zoning 2007). The ELMS can be used to quantify the
tradeoffs between total output and suitability of wildlife
habitat in buffer zones for protected areas in other
mountain ecosystems provided the data/information
required by the ELMS are available.
This study did not consider the potential negative
impacts of land development on air and water quality,
water supply, carbon sequestration, and other ecosystem
services. Accounting for such impacts would result in a
more comprehensive evaluation of the economic and
environmental tradeoffs implied by future economic
growth–land use policy scenarios than the one presented
here.
ACKNOWLEDGMENTS
This study was supported, in part, by the National Research Initiative of the
USDA Cooperative State Research, Education and Extension Service, grant
number 2006-55101-17129.
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