The effects of future urban development on habitat fragmentation in... Santa Monica Mountains Jennifer J. Swenson & Janet Franklin

The effects of future urban development on habitat fragmentation in... Santa Monica Mountains Jennifer J. Swenson & Janet Franklin
Landscape Ecology 15: 713–730, 2000.
© 2000 Kluwer Academic Publishers. Printed in the Netherlands.
713
The effects of future urban development on habitat fragmentation in the
Santa Monica Mountains
Jennifer J. Swenson1 & Janet Franklin
Department of Geography, San Diego State University, San Diego, California 92182-4493, U.S.A. 1 (Corresponding
author: Current address: Department of Forest Science, Oregon State University, Corvallis, OR 97331-7501, USA,
(E-mail: [email protected])
Received 27 May 1999; Revised 23 February 2000; Accepted 1 April 2000
Key words: coastal sage scrub, habitat fragmentation, landscape pattern indices, land use change, simulation
modeling, Santa Monica Mountains
Abstract
A site suitability model of urban development was created for the Santa Monica Mountains in southern California,
USA, to project to what degree future development might fragment the natural habitat. The purpose was to help
prioritize land acquisition for the Santa Monica Mountains National Recreation Area and examine to what extent
projected urban development would affect distinct vegetation classes. The model included both environmental constraints (slope angle), and spatial factors related to urban planning (proximity to roads and existing development,
proposed development, and areas zoned for development). It implemented a stochastic component; areas projected
to have high development potential in the suitability model were randomly selected for development. Ownership
tracts were used as the spatial unit of development in order to give the model spatial realism and not arbitrarily
‘develop’ grid cells. Using different assumptions and parameters, the model projected the pattern of development
from ∼ 5 to ∼ 25 years hence (based on recent development rates in the area). While < 25% of the remaining
natural landscape is removed under these scenarios, up to 30% of core (interior) habitat area is lost and edge length
between natural vegetation and development increases as much as 45%. Measures of landscape shape complexity
increased with area developed and number of patches of natural habitat increased four- to nine-fold, depending
upon model parameters. This increase in fragmentation occurs because of the existing patterns of land ownership,
where private (‘developable’) land is interspersed with preserved park lands.
Introduction
The coastal Santa Monica Mountains in California
(Figure 1), host numerous habitats from valley oak
savanna to coastal sage scrub, many of which are
becoming increasingly rare in the face of rapid development in southern California (Westman 1981;
Schoenherr 1990; O’Leary et al. 1992). The mountain
range is located within the Southwest Ecoregion of
California that contains the highest number of endangered plant species in the U.S. except Hawaii (Dobson
it et al. 1997). The National Park Service’s Santa
Monica Mountains National Recreation Area (SMMNRA) exists as more of an administrative unit than
a typical protected area. Its boundary encompasses
various parklands, public beaches, cities, and substantial amounts of private residential and commercial
land with varying levels of development. Urban and
residential development continues in the semi-rural
area as increasing amounts of Los Angeles residents
seek refuge from the surrounding city. Although the
area is ‘patchily preserved’ through efforts by federal
and local agencies and private conservation groups,
the majority of the land is mostly undeveloped and
remains in private ownership (Figure 2a).
The National Park Service (NPS) is attempting to
acquire land within this patchwork of ownership and
jurisdictions guided by its Land Protection Plan. Prioritization of land acquisition according to the biological
or cultural significance of parcels is crucial due to the
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Figure 1. The Santa Monica Mountains in Southern California.
extremely high land prices, and the difficulty in acquiring land held fast by private owners. Generally, conservation in urban areas proves difficult, as land costs
are prohibitive due to high demand, and local political
entities usually favor development over preservation
(Murphy 1988). By predicting where future development is likely to occur, conservation agencies and
land managers may assess what areas most urgently
need acquisition and subsequent preservation in order
to prevent large-scale loss and fragmentation of the
remaining natural habitat.
In this study a simulation model projecting future
urban development in the Santa Monica Mountains
was implemented in a Geographic Information System
(GIS) to examine the effects that future development
patterns could have on different vegetation communities and the spatial pattern and connectivity of the
natural habitat.
Background
Habitat fragmentation results from increasing the
number of landscape pieces, decreasing interior habitat area, increasing the extent of forest-opening edges
[or other habitat types], or increasing isolation of
residual forest patches’ (Li et al. 1993; p. 67). Habitat fragmentation has been recognized as the leading
factor in species loss, on both a local and global level
(Wilcove et al. 1986; Wilcox and Murphy 1985).
Animals with relatively large ranges (umbrella
species sensu Wilcox 1994), for example, birds and
large mammals, are often the first to be affected by
habitat fragmentation due to its effects on population viability (Beier 1993). If reserves are designed
to meet the needs of these species, other species with
smaller area requirements may be protected under this
‘umbrella’ (Murphy 1988; Noss 1990). Conversely,
reptiles and small mammals with limited mobility may
be separated into distinct populations by narrow geographic barriers (Quinn 1990) such as non-primary
roads or urban structures easily crossed by more mobile creatures. These more mobile species can often be
sustained on a network of smaller patches, as long as
they are, in some way, linked (Soulé 1991b).
Urban development creates a high contrast edge
around natural vegetation, and may introduce exotic ornamental plant species (Alberts et al. 1993),
domestic cats and dogs, air- and water-borne toxins, increased run-off, lowered water tables, and increased general disturbance by human activities (Murphy 1988). Where native vegetation has been cleared
for firebreaks around residences and along ridge tops
in the Santa Monica Mountains, exotic grass species
dominate. Naturally sparse Mediterranean scrub habitats, such as those in the Santa Monica Mountains,
are especially vulnerable to edge effects and exotic
invasions. This vegetation is physically more susceptible to disturbance such as trampling, and slower to
recover, than that of a forest community, for example
(Soulé et al. 1992; O’Leary 1995; Zink et al. 1995).
Various preservation agencies, principally the National Park Service, are working to acquire more land
to maintain the existing natural areas within the National Recreation Area through the Land Protection
Plan, (NPS 1984; 1987; 1991; 1994). The National
Recreation Area can be considered to be in a state
of reserve planning or design as over one third of
the Santa Monica Mountains consists of privatelyowned undeveloped land (NPS 1994), which could
potentially be purchased by the NPS. The overall
goal of designing a biological reserve is to conserve
a diversity of ecosystems and thereby conserve the
highest diversity of species possible (Franklin 1993).
A number of approaches have been suggested for
accomplishing this goal where land resources are limited. These include maintaining requisite habitat area
for viable populations of animal species, maximizing
habitat connectivity, and prioritizing the conservation
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value of landscape patches based on multiple criteria
(Margules and Usher 1981; Church et al. 1996; Stoms
et al. 1998).
Landscape linkages can enhance biotic movement
between patches, provide extra foraging area, serve as
refuges during disturbances such as fire, and often significantly add to the preserved areas of native vegetation (Saunders et al. 1991). If the area to be preserved
has already been subjected to habitat fragmentation,
Soulé (1991a) recommends promoting habitat contiguity and connectedness through landscape linkages
(Hudson 1991). In the case of the Santa Monica Mountains, by selectively purchasing land, areas that are
currently conserved may be enlarged and the linkage
between habitat patches could be maintained.
Habitat pattern and connectivity can be quantified
using various indices of landscape structure (O’Neill
et al. 1988; Turner 1989; Ripple et al. 1991; Turner
and Gardner 1991; Li et al. 1993; Haines-Young and
Chopping 1996). Quantification of landscape pattern
at large spatial scales is crucial in landscape-scale
reserve design (Noss 1983), and in management of
processes that are dependent on a particular landscape
structure (Mladenoff et al. 1993).
The process of landscape fragmentation is generally not random (Usher 1987), as in the case of urban
development. An advantage of landscape simulation
models is that they allow analysis of hypothetical disturbances that may be generated without altering the
actual landscape (Turner 1989). The strength of these
models lies in their ability to project future landscape
scenarios given current circumstances and to test certain practices or dynamics of disturbance. The models
discussed in the following paragraphs contributed to
the formulation of the simulation model used in this
study, which incorporates elements from landscape
ecology and urban development modeling.
Urban simulation modeling is a multidisciplinary
field that originated in the late 1960s and has since
expanded to incorporate simulations such as residence
location models, urban employment locations, and
transportation flow models (Wilson 1974; Putnam
1983; Kain 1986; Landis 1994). Urban development
models for land-use planning often incorporate economic and socio-political factors as well as spatial
features such as transportation networks and existing
land use. Related to these models are site suitability
models, which evaluate areas in terms of their suitability for urban development (Hopkins 1977). While site
suitability models strive to find places that are ideal
for development, the model developed in this study
projects where development is most likely to occur.
Urban models differ from landscape ecology models (Baker 1989; Sklar and Costanza 1991; Turner and
Dale 1991) by depending heavily on socioeconomic
data, and incorporating theories of urban development and transportation modeling. The Santa Monica
Mountains are impacted strongly by urbanization, yet
it does not appear to be easily predicted by the typical
growth models used in urban planning. In our study
area there are few central places of business, and travel
time to and from work is not an essential factor as
many of the residences are second homes or have been
built for retirement. Because of the complexity of the
socioeconomic conditions present in the area, we do
not attempt to model these factors, but incorporate into
our model spatial factors that appear to influence the
urban development of the Santa Monica Mountains.
Therefore, the model does not predict future socioeconomic factors that would accompany development,
but assumes current conditions will continue.
Recently, models of landscape change have striven
to simulate urban development or land use change using concepts from both urban modeling and landscape
ecology. These models incorporate socioeconomic and
transportation factors such as land tenure or land ownership, population, immigration patterns and distance
to roads, rivers and cities with spatial and ecological
factors (e.g., lot size, vegetation type, soil type, elevation, and slope), when modeling land use change from
regional to global scales (Wilkie and Finn 1988; LaGro and DeGloria 1992; Dale et al. 1993; Spies et al.
1994; Turner et al. 1996; Theobald and Hobbs 1998).
The objective of many of these studies is to project
and identify the effects of land use change on biodiversity (Steinitz et al. 1998) and to guide ecosystem
and resource management (Wear et al. 1996; Spies and
Johnson 1999).
Materials and methods
In this study, the current degree of landscape fragmentation in the SMMNRA was examined using indices
of fragmentation. Next, a spatial simulation model
was developed to predict the pattern of future urban
growth on the landscape. The model contains a stochastic component and was replicated while changing
the input variables and assumptions, and as a result
produces a range of future development scenarios. The
spatial pattern of habitat in the simulated landscapes
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was summarized using the same habitat fragmentation
indices as used on the current landscape. The effects
of different land development scenarios on landscape
fragmentation were measured with these indices and
effects of development on different vegetation classes
were examined for disproportionate impacts.
Study area
The Santa Monica Mountains are an east-west trending mountain range adjacent to the Los Angeles
Metropolitan Area. The SMMNRA administrative
boundary encompasses 60,000 ha and is bordered on
the south by the Pacific Ocean, to the north by suburban communities, to the east by the Los Angeles
metropolitan area, and to the west by agricultural
lands. The study was conducted within the area defined by a one km buffer outside the park boundary, though numerical data are reported only for the
area within the recreation area boundary. Within this
boundary, approximately half of the land is publicly
owned and protected park land, while the remaining
half is private land of which an estimated 25% is developed (NPS 1994). The Santa Monica Mountains
have been subjected to many waves of development
and are currently the object of further urban growth
encouraged by the booming economy, less expensive
real estate, and the increasing population of nearby
Los Angeles (Markman 1994). Existing urban development within the National Recreation Area boundary
ranges from sparsely distributed ranch-style homes to
relatively dense subdivisions.
The mountains are topographically complex, with
steep slopes that rise from the Pacific Ocean to 600 m.
Because of this dissected topography, there are areas within the park boundary that are relatively isolated from infrastructure and the surrounding urban
development. The vegetation of the Santa Monica
Mountains reflects the Mediterranean climate conditions of cool moist winters, warm dry summers, and
frequent fires. SMMNRA’s ecological communities
of coastal sage scrub, chaparral, coast live oak and
walnut woodlands, valley oak savanna, grasslands,
wetlands, riparian and coastal areas (Keeley and Keeley 1988; Mooney 1977; Sawyer and Keeler-Wolf
1995; Figure 2b) host 50 species of mammals, 384
bird species, and 36 herptiles (NPS 1994). There are
multiple species of small mammals occurring in the
recreation area that are endemic to chaparral habitats (Quinn 1990) and various animal species that are
found on state or federal rare, threatened, or endan-
gered species lists (NPS 1994). The mountain lion
(Felix concolor), resides in the area to date, yet its
long-term persistence is regarded as precarious due to
habitat fragmentation by urbanization (NPS 1993).
Coastal sage scrub comprises 25% of the Santa
Monica Mountains and is of special conservation interest due to its relatively high native biodiversity and
increasing scarcity (O’Leary et al. 1992; O’Leary
1995). Once a widespread vegetation type in southern
California, it was estimated to occupy only 10–15%
of its original range in ca. 1980 (Westman 1981), and
continues to decline due to urbanization. Almost 100
species of plants and animals associated with coastal
sage scrub are listed as rare, sensitive, threatened,
or endangered by federal and state agencies (California Department of Fish and Game 1994). Valley oak
(Quercus lobata) is in serious decline in California
due to its low recruitment rates and increasing destruction for development and farming as it occupies
valley bottoms and low foothills (Pavlik et al. 1992).
Grasslands (now occupying less than 5% of the study
area), are prime hunting habitat for raptors, such as
the golden eagle (Aguila chrysaetos), and have decreased by thousands of hectares due to large-scale
urbanization (Plantrich 1990).
Digital map data
The primary digital map data used in this study were
provided by the NPS and include a map of existing vegetation/land cover, land ownership, the County
General Plan, elevation, hydrology, and roads (Table 1). The vegetation/land cover map is of special
importance to this study, and was produced by the
authors for the NPS (Franklin et al. 1997). The map
was based on satellite multispectral imagery from the
Landsat Thematic Mapper sensor. Vegetation stands
were delineated in two ha minimum mapping units
and labeled using image segmentation and unsupervised spectral classification (according to the methods
of Woodcock et al. 1994; Franklin and Woodcock
1997), and extensively edited based on air photo interpretation and field reconnaissance. The area-weighted
thematic accuracy of the vegetation-land cover map
was 93%, based on an accuracy assessment method
using fuzzy sets (the RIGHT operator; see Gopal and
Woodcock 1994). Vegetation class accuracy ranged
from 62–100%. The accuracy of a map depicting land
cover can affect the measurement of landscape pattern
(Wickham et al. 1997). From this digital map, a layer
of existing development was extracted (based on areas
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Figure 2. a) Land ownership in the Santa Monica Mountains (NPS 1984, see Table 1). b) Map of existing vegetation/land cover ca. 1995
(SDSU/NPS, see Table 1). c) Development likelihood map; based on additive overlay of five criteria and widest buffer widths for roads and
existing development. ‘Low’, ‘low to medium’, ‘medium’, and ‘high’ classes correspond to the intersection of 1, 2, 3, 4–5 input variables,
respectively.
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Table 1. Metadata for spatial data (digital maps) used in the analysis.
Variable
Origins
Vegetation
Satellite image/aerial photo
processing
Department of Geography
San Diego State University (SDSU)
NPS
Land ownership,
tract numbers, owners
proposed developments
County General Plan
Year of last
update
Permission of use
granted by
Original data format
1995
SDSU/NPS
Raster, 30 × 30 m
1984
1995
NPS
Vector
1995
SCAG
Vector
Elevation
Southern California Association of
Governments (SCAG)
United States Geological Survey
variable
NPS
Hydrology
United States Geological Survey
variable
NPS
Roads
Thomas Brothers
1994
NPS
Raster, 30 × 30 m,
1:24,000
Vector, digital line
graph, 1:24,000
Vector
mapped as ‘urban’), as it was the most recent mapped
information on land cover for the SMMNRA.
The 1984 land ownership map delineates ‘ownership tracts’ or contiguous parcels owned by the same
person or entity. This digital map also includes information on proposed developments dated as late as May
1995, which range from large urban subdivisions to
small structures such as room additions.
Spatial modeling was carried out using the Grid
module of the Arc-Info GIS software (Arc-Info 6.0,
ESRI 1991). Digital geographic layers obtained from
the NPS in Arc-Info vector format were converted to
raster format with a cell resolution of 30 by 30 m
(0.09 ha). The model results were exported from ArcInfo to the FRAGSTATS software (version 2.0; McGarigal and Marks 1994) for the analysis of landscape
pattern.
Landscape simulation model
The landscape simulation model developed in this
study estimates a location’s likelihood of future development using geographic planning data, land ownership data, development constraints, and a stochastic selection process. This model can be classified
as a whole mosaic cartographic model in which
all variables, as Boolean data layers, are combined
or summed (Tomlin 1991). The input variables are
equally weighted, each consists of layers that are ‘twostate’ or binary, and they represent variables in a
multivariate analysis (Hunsaker et al. 1993). The areas
in which all or most of the binary variables intersect
geographically are considered more likely to be developed than areas where few or no variables intersected.
The five input variables are each represented as binary
map layers of the presence or absence of likely future
development. The layers are overlain geographically
and summed (Figure 3a).
Several methods (or simulation variations) of selecting areas for development were tested and are
discussed later in this section. The number of points
used in the selection of ownership tracts was varied to
simulate different amounts of development. The values of two parameters, the buffer widths around roads
and buffer widths around existing development, were
varied to examine the effect on the simulation results.
The assumptions underlying this model are that development is most likely to occur (1) relatively level
slopes, (2) in areas of proposed developments, (3) in
privately-owned areas that have been zoned for development, (4) on lands in close proximity to roads
and (5) in close proximity to existing development.
Thus, input variables of the urban development model
consist of maps of buffered areas of varying widths
around roads and around existing development, areas
zoned for development in the County General Plan,
areas of moderate slope, and housing tracts in which
developments are currently proposed.
Our rationale in selecting these five variables is as
follows:
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Figure 3. a) Illustration of how the simulation model produces the likelihood of development layer. The five model input variables are combined
spatially and areas of extreme slopes (over 60%) and areas already developed are spatially removed. This results in a layer consisting of low,
medium, and high likelihood of development. b) Illustration of how developed area is chosen from likelihood of development layer for the three
different modeling scenarios.
1. We limited potential development to slopes of less
than 60%, and allowed development on slopes
from 0 to 25% after consulting the literature (Whitley et al. 1993; Moser 1991; Landis 1994), Park
Service personnel, and the geographic data of the
study area (examining the distribution of current
development with respect to slope). This was accomplished by assigning slopes of less than 25%
the value 1 as a model input variable, while using slopes greater that 60% as a mask or filter
after the five variables were combined. The limit
of 60% may appear quite high, but because of the
strong demand for new housing in southern California and the desire for panoramic views, the
building industry often develops remote areas as
well as hillsides when flatter lands have already
720
been developed (Moser 1991 and National Park
Service personnel). Building on steeper slopes is
quite common in the Santa Monica Mountains,
where a third of the area has slopes of over forty
percent.
2. All housing tracts with proposed developments
were given equal weight regardless of type or
extent of development, due to the difficulty of
classifying the 1100 proposed developments.
3. The County General Plan demarcates areas to be
developed, according to density or type, and areas
to be preserved. Areas that were zoned for higher
density development were selected for likely development. The ‘zoned for development’ input
variable was not weighted more heavily because
the General Plan could be amended in the future.
For example, many of the proposed developments
(the second input variable) occur on lands that are
not zoned for development.
4. Existing roads and highways indicate where development has occurred in the Santa Monica Mountains (Los Angeles County 1981). It was assumed
that future development would more likely occur
along main roads or highways that enable access
out of the mountains into surrounding urbanized
areas. In order to estimate the buffer width to
use around roads and highways, the current landscape was examined in terms of the percentage
of development that existed within a distance of
a given road. This was done to identify a road
buffer width that included a high percentage of development, but did not contain large amounts of
non-developed areas. For this project we wished
to have a range of buffer widths to simulate different urbanization strategies and to examine this
effect on the natural landscape. After analyzing
the current patterns of development, three sets of
road buffer distances were chosen because they
represented an adequate range of scenarios for the
simulation model. The road buffer widths each had
two distances, one for primary roads and highways, and the other for secondary roads, and
consisted of: 50/70, 70/100, and 100/140 m.
5. According to the Interim Area Plan for the Malibu/Santa Monica Mountains area, new developments in the Santa Monica Mountains will be
located next to existing developments, because of
the limited availability of infrastructure and public
services throughout the mountains (Los Angeles
County 1981). Based on literature review (Iverson
1988; Landis 1994; Theobald and Hobbs 1998)
and consultation with park personnel, buffer zones
of 100, 200, 500, and 1000 m surrounding existing
development were chosen to delineate this model
variable.
The first model variation, that includes the stochastic component, is the ‘Standard’ simulation, which is
an equally-weighted five variable site suitability model
with random selection of ownership tracts. The five
input variables are summed to form an output layer
containing values ranging from 0 to 5, signifying areas of low to high development potential (Figure 3a).
Figure 2c shows a map of the resulting values based
on a model that used the larger values for buffer widths
around roads and existing development. From this output map, as with all model variations, areas of extreme
slopes and existing development are removed, so as
not to be selected. This results in a site suitability
map of privately owned, non-developed land on slopes
of less than 60% with various levels of development
potential. To simulate development, points are then
randomly selected from the areas of high and medium
development likelihood (classes 3, 4 and 5), and the
ownership tracts into which the points fall are considered ‘developed’ (Figure 3b, Standard Simulation).
Each Standard simulation thus results in a map of predicted development, which is subsequently overlain on
the existing vegetation map.
Test runs of the model were performed to determine the number of spatially random points that could
be located in the areas of high development potential
without exhausting the possibility of selecting a new
ownership tract. The number of points was varied in
order to simulate different amounts of development.
When the number of random points is held constant,
the areal extent of development varies with each replication, as the tracts of private land vary from 0.1 to
5462 ha and, with each model run, different tracts are
selected.
In order to determine the variability of area projected to be developed as a result of the model’s
stochastic component, three replicate runs of one Standard model were performed with 50 and then with 120
points while using the same buffer width parameters.
The other simulation types do not require replication
because they lack the stochastic component of the
Standard simulation.
The next type of simulation, the ‘Overlay’, produces a map of development as a direct result of overlaying the binary input variables. The Overlay simulation has no stochastic component and does not use
ownership tracts as the unit of development. Instead,
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all grid cells classified as medium to high likelihood
of development (classes 3–5) in the site suitability map
are converted to development (Figure 3b). Therefore,
the edges or spatial units of development are the direct
result of the spatial coincidence of the input variables.
To simulate an even greater extent of urban development, a simulation termed ‘Maximum Development’, that does not include a stochastic component,
was implemented. This site-suitability model simulates development by ownership tracts. All ownership
tracts that partially or completely coincide with areas
of high or medium development likelihood (classes 3–
5) are converted to development (Figure 3b). Finally,
to simulate a hypothetical maximum urban development, all private lands having natural vegetation are
converted to development in the ‘Total Build-out’
scenario.
Most main and primary roads were not mapped in
the vegetation/land cover map (due to the 2 ha minimum mapping unit that was utilized), and therefore
are not represented as edges on the vegetation map
(Figure 2b). Because these roads can act as barriers to
smaller vertebrates we performed an additional analysis to explore how main and primary roads may affect
the fragmentation of the contiguous landscape. In the
GIS, the main and primary roads of the Santa Monica
Mountains were buffered slightly in order to displace
a single line of 30 m pixels. These buffered areas were
then overlain on the natural vegetation map that resulted from the Maximum Development and Standard
model simulations.
The fragmentation of the natural vegetation resulting from all modeling simulations described above
was then quantified. Two different versions of each
map were analyzed; one contained 14 vegetation
classes (generalized classes in Figure 2b), while the
other clumped all vegetation classes as a single land
cover class (natural vegetation). The fragmentation
analysis of vegetation classes considered separately
provides insight to the effects that predicted urban
development could have upon those different vegetation classes, while using a single natural vegetation
class emphasizes the high contrast urban and natural
interface across the landscape.
Landscape pattern analysis
The degree of habitat fragmentation that could be
caused by potential future development was analyzed
by comparing indices of landscape pattern for the map
of existing vegetation to their values for the maps
derived from each of the urban growth models. The
following research questions were posed:
1. In fragmented landscapes, core (interior) area represents habitat unaffected by edges better than
the measure of total area (McGarigal and Marks
1994), therefore, while using an edge zone or
buffer distance of 100 m to delineate the interior
of patches (Franklin and Forman 1987; Spies et al.
1994), will the amount of core area habitat decrease at a greater rate than the decrease in total
area?
2. As urban development increases in the Santa Monica Mountains, will the fragmentation of the landscape increase as shown by (a) an non-linear increase in the amount of edge between natural vegetation and developed land when compared with
the increased urban area, (b) an increase in the
Landscape Shape Index (LSI), a measure of shape
complexity and (c) an increase in the number of
patches of habitat?
The LSI metric represents the complexity of the
shapes over the landscape. It is calculated using the
total length of edge of the landscape, divided by the
square root of the total area, which is adjusted by a
constant for a standard simple shape (in this case a
square). LSI is equal to one for a perfect square (or
circle in the case of vector data) and increases without
limit as the shapes become more irregular across the
landscape.
3. Because the coastal sage scrub and valley oak
woodland vegetation classes often exist along
edges of current development and on lower elevation slopes (thereby appearing to be more susceptible to development), will habitat fragmentation
affect coastal sage scrub and valley oak woodland disproportionately when compared to other
vegetation classes?
4. With a constant search radius of 300 m (Gustafson
and Parker 1992), will the Mean Proximity Index
(MPI) for each vegetation class decrease as future
development scenarios convert more natural vegetation to urban land use? Will the patches of a
particular vegetation type become more dispersed?
The MPI uses the nearest neighbor distance and
patch area to calculate the average proximity between
patches of like vegetation classes, and is a unitless
measure representing isolation and fragmentation. The
MPI decreases as patches in the landscape become
more fragmented and isolated from similar patches.
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Table 2. Results of the replicate runs of the Standard
model (using middle buffer values).
Measurement
Area converted, ha
50 points
120 points
Core area remaining, ha
50 points
120 points
Edge, km
50 points
120 points
Rep 1
Rep 2
Rep 3
2519
3900
2644
3527
2451
5455
48906
47318
48795
47801
48910
45652
1461
1546
1454
1499
1485
1569
Results and discussion
Varying the buffer widths around existing development (four values) as well as buffer widths around
roads (three values) produces twelve different results
(runs) of each model. The model results shown in
Figures 4b and 4c were achieved using the middle
values for buffer widths (70/100 m road buffers and
200 m development buffers) and all vegetation as one
category. For the Standard model, the area of land converted to urban development ranges from ∼ 2900 ha
(5% of the existing natural vegetation of the study
area) for the most restrictive model, to ∼ 5000ha
(8% of the natural vegetation) for the model with the
widest buffers. The majority of the Standard model
runs produces values ranging from 4000 to 4900 ha.
The influence of the buffer widths around development
(100–1000 m) plays a more significant role in the resulting fragmentation of the landscape than the road
buffer widths (50–140 m), because of its larger range
of buffer distances (details not show here; see Swenson 1995). Each model replication results in different
amounts of area developed due to its stochastic component and due to wide variability of ownership tract
sizes (Table 2). The map of existing natural vegetation
(Figure 4a) can be compared with the results of one
run of the Standard model (Figure 4b).
The Overlay models result in far less area developed than other models, as it disregards boundaries of
ownership, as long as the land was privately owned.
Areas developed range from 995 to 3574 ha depending
on the buffer widths. The Overlay simulation results
are not shown cartographically owing to the small
amount of area changed.
Areas converted to development in the Maximum Development simulations range from 10,701 to
13,295 ha (18 to 22% of the natural landscape) depending on the buffer widths used (Figure 4c). In this
simulation, the model with the largest buffers predictably develops the most area, since all ownership
tracts that include areas of high or medium development likelihood are developed (and not by a number of
randomly selected points like in the Standard Model).
Changing model parameters (from smallest to largest,
road and development buffer widths) in the Maximum
Development Model results in a 10 percent increase in
edge length, a 24% increase in number of patches, and
a 8% decrease in core area.
The Total Build-out scenario develops 57%
(34,000 ha) of the natural existing landscape, the remainder of which is currently protected. The development of all remaining private lands is unlikely to occur,
although assuming current trends continue, it could
hypothetically take place in 71 years (Figure 4d).
The area developed by the models can be compared
to recent rates of urbanization, which were estimated
in this study by analyzing the differences between the
area mapped as Developed in the vegetation maps of
1995 and 1984. The development rate was estimated
to be about 500 ha per year for that period or 0.8% of
the National Recreation Area per year. Another study
of this area estimated the development rate at 570 ha
per year (Kamrant 1995).
Throughout the Standard, Overlay and Maximum
Development simulations, fragmentation increases
when compared to the existing landscape in all cases.
It is difficult to visually distinguish differences between the current landscape and the results of the
Standard model (Figures 4a vs. 4b), and therefore
illustrates the utility of the fragmentation indices for
enhancing visual interpretations. As fragmentation increases with the modeling simulations (Figures 4a–c),
most of the remaining vegetation continues to exist
as one large interconnected patch. This large area
becomes increasingly perforated by development but
the majority of the vegetation remains connected and
is not cleanly broken up until Total Build-out (Figure 4d). This phenomenon is partly due to current
location of existing development (one of the model’s
input variables) but also due to data resolution; if the
main roads had been mapped during the vegetation
mapping, this main patch would have been initially
split up (refer to Figure 2a for main roads).
The changes in area, core area, edge, number of
patches, and landscape complexity as shown by the
723
Figure 4. a) Natural vegetation (habitat) patches of the current landscape. b) Habitat patches remaining after Standard model with medium
buffers (70/100 m for roads, 200 m for development). c) Habitat patches remaining after Maximum Development model with medium buffers.
d) Habitat patches remaining after Total Build-out scenario.
724
Figure 5. a) Percent of landscape occupied by natural vegetation vs. percent of landscape occupied by core (interior) habitat area (natural
vegetation patches less a 100 m buffer). b) The total length of edges between natural vegetation and development vs. vegetation removed by
models, ha. c) Number of habitat patches in landscape vs. percent of natural landscape removed by models. d) Landscape Shape Index vs.
percent of natural landscape removed by models (ha). ‘No model’= current landscape.
Landscape Shape Index, for the models just discussed
are shown in Figure 5a–d. The proportion of core area
is, by definition, less than that of the remaining total
area, and declines (diverges from the 1:1 line) throughout the modeling scenarios. The Maximum Development models remove a higher proportion of core
area relative to total area than do the Standard models
(Figure 5a). The amount of edge generally increases
with the removal of area by the model with a pattern that appears slightly non-linear (increasing rate)
when the current landscape, Standard, and Maximum
Development models are compared (Figure 5b). One
exception is the Total Build-out simulation that results
in little remaining natural vegetation area and con-
sists of simple shapes delineated by land ownership
boundaries, and therefore has little edge. The Overlay model results in a high amount of edge because it
converts all areas of high development likelihood, and
ignores ownership tracts that generally consist of simple shapes. Actual development, however, may only
occur on portions of ownership tracts and therefore
the pattern depicted by the Overlay model may not be
entirely unrealistic. As more land is converted to development, more individual habitat patches are formed
and the number of patches doubles from the Standard
model simulations to those of the Maximum Development model (Figure 5c). In Figure 5d, an increase in
habitat patch complexity is also shown by an increase
725
Table 3. Fragmentation change when main roads are barriers for different simulations (middle value
buffers).
Model type
Ha. lost
with road
removal
Total core
area, ha
# Core
areas
Mean core
area, ha
Edge, km
Standard
Standard with roads as barriers
Max. Dev.
Max. Dev. with roads as barriers
N/A
224
N/A
287
47,024
45,947
38,333
37,626
112
141
166
201
130
119
41
39
1519
1699
1935
2063
Table 4. Mean proximity index (MPI) for different simulations using middle value buffers.
Model type
Chaparral
Coastal sage
scrub
Valley oak
Coast live oak
No model (current
landscape)
Overlay
Standard
Maximum Development
Total Build-out
60,585.4
700.6
24.1
25.7
59,761.4
33,310.0
11,910.7
5,211.7
not calculated
603.5
417.7
157.0
25.2
16.9
11.5
1.2
23.5
16.3
19.1
17.4
Table 5. Losses by vegetation class for the Standard and Maximum Development simulations
(+/− = more and less than expected by chance, based on χ 2 -square test).
Principal vegetation classes
Mixed chaparral
Coastal sage scrub
Grassland
Coast live oak woodland
Riparian area
Valley oak woodland
Walnut woodland
Total area loss (converted
to development)
Mixed chaparral
Coastal sage scrub
Grassland
Coast live oak
Riparian area
Valley oak woodland
Walnut woodland
Total area loss (converted
to development)
Loss
(ha)
% of original
landscape
Loss per class/
total loss(%)
Loss per class/
area of class (%)
Standard Model M, S
2164
59.1
50.8−
1232
25.6
29.0+
265
4.7
6.2+
229
3.0
5.4+
58
1.8
1.3
38
0.6
0.9
13
0.2
0.3
4255
6.1
8.1
9.4
12.5
5.4
11.1
10.2
Maximum Development, M, S
6313
59.1
54.4
3506
25.6
30.2
592
4.7
5.1
381
3.0
3.2
177
1.8
1.5
71
0.6
0.6
61
0.2
0.5
11,597
17.8
22.8
21.0
20.8
16.4
20.7
47.6
726
Figure 6. (a) Patches in current landscape with roads as barriers, ranked by size. (b) Patches after Standard model with medium buffers
(70/100 m for roads, 200 m for development) and roads as barriers. (c) Patches after Maximum Development model with medium buffers and
roads as barriers.
727
in the LSI as development increases throughout the
modeling scenarios.
The geographical distribution of patch sizes with
main roads considered as barriers to dispersal for the
current landscape is shown in Figure 6. The roads
comprise only 224 to 287 ha when they are buffered
and removed, yet considering them to be barriers
increases the fragmentation of these simulated landscapes as measured by core area, number of core areas,
mean core area size, and edge length (Table 3).
change is supported in all cases concerning coastal
sage scrub, and partially supported concerning valley
oak depending on the method of evaluation. The impact of development, as a function of the loss of each
class area (fourth column, Table 5), shows the small
woodland classes of valley oak, coast live oak, and
walnut consistently have high percentage losses.
Individual vegetation classes
The fragmentation indices have been calculated for inventory purposes, representation of biological and environmental processes, and for identification of areas
for the maintenance of interior habitat. These indices
provide quantitative descriptions about the landscape
pattern that may not be visually apparent in a map.
Although this type of analysis is not a substitute for
detailed biological research such as population viability analysis, it would be advantageous to use studies
of landscape fragmentation in conjunction with such
research. While we examined a number of indices
of landscape fragmentation in our exploratory analyses of model results, we found direct measures such
as core (interior habitat) area and edge length, and
other simple indices based on direct measurements,
to be most useful for comparing model outcomes.
The indices in general were useful in comparing the
same landscape undergoing different simulations, as a
method to quantify change.
Particular vegetation classes such as valley oak,
coast live oak, and coastal sage scrub are impacted
more than other classes by the modeled development
projections in this study. The preservation of these
vegetation types requires special attention because of
their increasing rarity in the region. The differential
impact on vegetation classes does not appear to be random and most likely results from the modeling input
variables. In particular, the modeling assumption that
more homes would be built on gentler slopes, which
are often covered in coastal sage or valley oak, than on
steep topography, commonly covered in chaparral, has
integrated biogeographical factors of vegetation type
into the model.
Where data on species habitat use and minimum
area requirements are available, it appears that the best
species preservation strategy is the identification of
landscape linkages for wide-ranging species such as
the bobcat, while keeping in mind the habitat needs
and dispersal capabilities of more sedentary small-area
species like the avifauna of coastal sage scrub (see
The MPI is shown for four vegetation classes as they
occur on the current landscape, and in all modeling scenarios using the mid-sized buffers (Table 4).
In their current coverage, chaparral and coastal sage
scrub constitute over 85% of the area and are shown
with data for two small oak woodland classes of special ecological interest. The index decreases when
similar patches become more isolated from one another. As expected, the index decreases with increased
conversion of area to development, with the exception of some minor discrepancies in the oak classes,
possibly due to their small areal extent.
Classes of vegetation were affected to different degrees by the development models. Note the percent of
the area of each class that was lost and the change in
the percent area for each vegetation class (Table 5).
For the Standard and the Maximum Development
models, the area lost to development for each class is
shown in the first column (Table 5), the percent of the
study area the class currently occupies is in the second
column, the amount of area lost by the class divided by
the total area removed by development is shown in the
third column, and the percent loss of the class itself in
shown in the fourth column. If development randomly
occurred across the landscape, it would be expected
that the percent of the original landscape occupied by
a given vegetation class would be proportional to the
percent lost by the class after development (in this case
the second and third columns of Table 5 would agree).
These models impact coastal sage scrub more than
chaparral, and in most cases, impact valley oak to a
slightly higher degree than would be expected of random development. Coastal sage scrub, grassland and
coast live oak are impacted significantly more by modeled development than expected by random patterns
of development, while mixed chaparral is impacted
significantly less by modeled development (Table 5).
Thus, the research question regarding vegetation class
Conclusions
728
Soulé et al. 1988). In general, maintenance of cohesive
habitat at many different scales is necessary, yet the
projection models of potential patterns of urban development in this study show that without future land
acquisitions, this landscape will be come increasingly
fragmented.
This model used a simple overlay approach based
on a site suitability framework in which all variables
are given equal weight and entire ownership tracts are
developed. This model could be altered by giving ‘importance weights’ for each variable involved according
to expert opinion (Hopkins 1977). Further steps could
be taken to simulate development in only portions of
ownership tracts and at various levels of density for
a more detailed prediction of development, as actual
development sometimes occurs across entire ownership tracts and sometimes on only a small portion of
the tract, with implications for patterns of fragmentation. This modeling framework could also be used in
conjunction with predicted future social and economic
scenarios to create a simulation model that assumes a
changing economy.
Soulé (1991a) concludes from his extensive studies of chaparral canyon fragments in an urban matrix
that ‘the best way to maintain wildlife and ecosystem
values is to minimize habitat fragmentation’ (p. 319).
The amount of area converted to development in each
of the modeling simulations plays a significant role in
the resulting fragmentation of the landscape, but the
pattern and placement of development is important as
well. If simulated development had taken place in a
few isolated clusters rather than scattered across the
landscape, the indices of core area, edge, LSI, number of patches, and MPI would have shown far less
fragmentation. Under some patterns of development
(around the circumference of a preserve, or in one
compact area), the fragmentation represented by these
indices could stay roughly the same with a decline
in overall habitat area. Controlling or decreasing the
amount of development for this area is a partial solution, but special attention should be given to the
spatial pattern of present and planned future urban and
residential development. According to the projections
made by this modeling study, the SMMNRA, where
possible, should focus its land acquisition efforts on
areas in the west-central portion of the NRA to reduce overall habitat fragmentation (Figure 6), and on
land which supports the most disproportionately affected habitat types: coastal sage, grassland and oak
woodland.
Acknowledgements
We thank the following people for their contributions:
Paul Chamberland, George Cox, Allen Hope, Sherri
L. Johnson, Denise Kamrant, Dave McKinsey, Ray
Sauvajot, David Shaari, Joseph Shandley, and Ralph
Warbington. This study was supported in part by a
Rocky Mountain Region National Park Service Student Cooperative Agreement (to J. Swenson). The
manuscript was greatly improved by the comments of
two anonymous reviewers.
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