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Monitoring Environmental Impact in the Upper Sonoran
Lifestyle: A New Tool for Rapid Ecological Assessment
Casey D. Allen
Received: 18 September 2007 / Accepted: 28 August 2008 / Published online: 11 October 2008
Springer Science+Business Media, LLC 2008
Abstract Characterized by expensive housing, high
socioeconomic status, and topographic relief, Upper Son-
oran Lifestyle communities are found primarily along the
Wildland-Urban Interface (WUI) in the Phoenix, Arizona
metro area. Communities like these sprawl into the wild-
lands in the United States Southwest, creating a distinct
urban fringe. This article, through locational comparison,
introduces and evaluates a new field assessment tool for
monitoring anthropogenic impact on soil–vegetation
interactions along the well-maintained multi-use recrea-
tional trails in Upper Sonoran Lifestyle region. Comparing
data from randomly selected transects along other multi-
use trails with data from a control site revealed three key
indicators of anthropogenic disturbances on soil–vegetation
interactions: soil disturbance, vegetation disturbance, and
vegetation density. Soil and vegetation disturbance dis-
played an average distance decay exponent factor of -
0.60, while vegetation density displayed a reverse decay
average of 0.60. Other important indicators of disturbance
included vegetation type, biological soil crusts, and soil
bulk density. The predictive ability of this new field tool
enhances its applicability, offering a powerful rapid eco-
logical assessment method for monitoring long-term
anthropogenic impact in the Upper Sonoran Lifestyle, and
other sprawling cities along the WUI.
Keywords Rapid ecological assessment Trail impact
Upper Sonoran Lifestyle Biological soil crusts
Distance decay modeling Field methods
Introduction
This article explores the development of a rapid ecological
assessment tool for monitoring the long-term impact that
burgeoning desert metropolitan suburbs have on adjacent
wildlands. Monitoring environmental impact in the Upper
Sonoran Lifestyle (USL) allows for better management of the
limited and precious resources available in desert Wildland-
Urban Interface (WUI, cf. Pulido and Wolch 1996) settings.
To bolster its monitoring potential, the creation and use of this
new tool is explored in the context of its predictive ability for
assessing anthropogenic soil–vegetation impact along USL
multi-use recreational trails. Findings in the USL demonstrate
the tool’s ease-of-use and applicability for land managers of
USL and non-USL communities alike.
In this article I first introduce the USL as an ideal location
for studying the urban fringe and exurban development at the
WUI, and outline the need for a new assessment tool for
anthropogenic trail impact. Next, I discuss the importance of
soil–vegetation interactions for understanding anthropo-
genic impact of multi-use recreational trails in the USL, and
the important role biological soil crusts play at the WUI in
arid regions. This is followed with an overview of methods
used to select and compare the tool test sites, as well as
techniques used to assess spatial parameters. Then, follow-
ing a detailed discussion of the results, I conclude that the
abundant multi-use recreational trails in the USL should be
monitored/managed, and the field assessment tool outlined
in this article lays the groundwork for a rapid ecological
assessment that is easy-to-use, replicable, inexpensive, and
non-invasive. This new tool also focuses on assessing
anthropogenic impacts of soil–vegetation interactions along
multi-use trails in this locale, making ecological and envi-
ronmental management study data more relatable for policy
makers (Schiller and others 2001).
C. D. Allen (&)
Department of Geography and Environmental Sciences,
University of Colorado Denver, P.O. Box 173364, CB 172,
Denver, CO 80217-3364, USA
e-mail: casey.allen@ucdenver.edu
123
Environmental Management (2009) 43:346–356
DOI 10.1007/s00267-008-9212-5
The Upper Sonoran Lifestyle as an Urban Fringe Ideal
Considered a ‘‘trendsetter by residential development,’’ the
USL and adjacent wildland landscape was introduced in
the north Phoenix metropolitan area (Romig 2005, p. 67),
but has spread throughout the burgeoning fringes of
metropolitan Arizona. The Upper Sonoran Lifestyle is not
the same as the ‘‘upper Sonoran lifezone.’’ The latter rep-
resents a biome based mainly on climate, vegetation, and
elevation, while the former represents a double entendre:
‘‘upper’’ representing (1) a literal move higher in elevation
from the valley floor and (2) a higher (‘‘upper’’ class)
socioeconomic status. This type of development is not
endemic to Arizona. Similar urban and exurban sprawl
continue to increase around the world, and especially so in
the United States Southwest.
Generally active, the USL population has more dispos-
able income to afford leisure time and recreational
amenities, and offers private and paved recreation trails
within the community (Romig 2005). The numerous
nearby multi-use recreational trails are almost exclusively
used by USL residents (Romig 2005). As ‘‘landscape pat-
terns…are important indicators of land-use impacts, past
and present, upon the landscape,’’ monitoring trails in the
USL offers a way to assess what kind of impacts urban
sprawl might have on soil–vegetation interactions (Olsen
and others 2007, p. 137).
Most USL communities are resultant of urban sprawl
and located at/along the urban fringe and/or WUI. The
WUI, described by Alavalapati and others (2005, p. 705) as
‘‘areas of urban sprawl where development pressures are
pressed against public and private wildlands,’’ poses
environmental challenges such as ‘‘ecosystems fragmen-
tation, increased exposure to invasive species, water and air
pollution, wildfires, and loss of habitat for wildlife…to
both rural and urban communities.’’ Indeed, as Alavalapati
and others (2005) note, anthropogenic changes occurring at
WUIs like the USL are extremely rapid, affect land use
changes more than any other process in history, and usually
center on economically based phenomena. Urban sprawl
models by Carruthers and Vias (2005, p. 21, emphasis
added) for example, further demonstrate that USL com-
munities’ ‘‘long-term [economic] prosperity…depends on
the preservation of the high quality of life it [the urban,
suburban, and exurban sprawl region] offers,’’ including
recreational opportunities.
The Need for a New Assessment Tool
Researchers and land managers focusing on the wildland
impact of urban sprawl, exurban development, and the
WUI’s where USL communities reside, have no tool for
assessing specifically USL environmental impacts on soil
vegetation interactions. Traces of field metrics that could
be used in this situation are seen in studies such as Wilson
and others (2003), who make good use of the Normalized
Difference Vegetation Index (NDVI) to assess urban eco-
systems, and Laymon and others (1998) and Olsen and
others (2007) who use remote sensing techniques to study
trail impact and land cover, respectively. In all these
studies however, techniques are associated with high alti-
tude remote sensing applications (e.g., satellite data) or
orthophotography, and not with ground-level fieldwork.
Furthermore, no single method results in a usable tool—a
single, simple-to-use environmental assessment metric that
could be used by land managers, researchers, and policy-
makers alike to understand the collective impact of a
relatively new form of land use at the WUI in the rapidly-
expanding U.S. Southwest.
With specific focus on arid lands and anthropogenic
disturbances, models by Okin and others (2001, p. 135)
reveal that ‘‘destruction of soil crusts and vegetation
cover can cause indirect disturbance of adjacent areas by
initiating the disintegration of islands of fertility’’ and
that ‘‘reduced precipitation or increased temperature may
exacerbate landscape vulnerability.’’ This model has
profound effects on USL communities that are econom-
ically driven to expansion, possibly leading to an areal
expansion of the urban heat island (cf. Baker and others
2002; Gober and Burns 2002; Grimm and Redman
2004). These models seem to hold true in the case of
Phoenix, and specifically at the sites used in this
research.
Driven by economic expansion and lying at the WUI,
USL communities offer a unique vantage point from
which to assess anthropogenic impacts on soil–vegetation
interactions. Romig (2005) discovered that USL com-
munities have especially clean thoroughfares and houses,
and a population with ample monetary resources and
leisure time. From simple, ground-level observation, it is
clear USL communities also include a system of well-
maintained, multi-use, and easily accessible recreational
trails. While the trails might seem well-manicured at first
glance, anthropogenic impact does occur. If impact can
be predicted using a replicable rapid ecological assess-
ment tool, then it can then be used by land managers in
USL and USL-like communities. To demonstrate a new
tool for addressing this situation, preliminary field data
were gathered and analyzed for specific indicators of
anthropogenic impacts on soil–vegetation interactions at
an initial USL site. From these data, a field assessment
tool was created and then tested at other USL sites and
one, distinct non-USL urban fringe locale, for
replicability.
Environmental Management (2009) 43:346–356 347
123
Soil–Vegetation Interactions
In a single glance, a researcher would notice that multi-use
trails in and near USL communities are clean and well-
maintained on the surface, allowing users to experience a
sense of quality in their recreational use (Moore and Polley
2007). Yet, according to Bunting (1967) and others (cf.
Okin and others 2001; Brady and Weil 2002; Dwyer and
Childs 2004; D’Odorico and others 2005), anthropogenic
disturbances directly and indirectly affect soil, and if soil is
disturbed—through compaction (higher bulk density) or
(un)conscious wanton destruction—plants have difficulty
taking root. This is the most important soil–vegetation
interaction as both Roovers and others (2004) and Li and
others (2005) discovered when assessing environmental
conditions of recreational trails.
In continuing soil–vegetation mutualism, however,
desert regions have a key stabilizing agent: biological soil
crusts (BSCs). Consisting of various lichens, mosses, and
algae, BSCs are a unique and beneficial contributor to
desert ecosystems. In many arid ecosystems BSCs are the
first step for protecting soil from erosion, aiding in water
retention and dispersion, and providing nutrients for higher
plant growth (Belnap and Lange 2003). In some arid
regions where they are especially prevalent, BSCs can
create microclimates known to have impacts on surround-
ing ecosystems (Smith and others 1987; Dukes and
Mooney 2004; Hassett and Zak 2005).
Further, Bornyasz and others (2005) studied rhizomes in
granite bedrock with thin soils, similar in composition to
soils at this article’s study sites. They found that ectomy-
corrhizal root tips were present as deep as 4 m, sometimes
growing into the bedrock through minute fissures, creating
soil via rock-vegetation interactions. When these processes
are interrupted or disturbed by anthropogenic means,
however, the soil–vegetation interaction ceases (or decrea-
ses dramatically), and neither soil nor vegetation develop
(Bunting 1967; Bornyasz and others 2005).
While anthropogenic impacts on BSCs are widely
studied, and techniques constantly developed to assess
specific environmental characteristics associated with them
(cf. Belnap and Lange 2003; Allen 2005), few rapid eco-
logical assessment tools exist to quickly and efficiently
study specifically soil–vegetation interactions (including
BSC–soil–vegetation interactions, cf. Belnap and Lange
2003). Roovers and others (2004), for example, use forests
outside of Flanders, but do not link their results to the
nearby WUI; likewise with Li and others (2005) and
Campbell and Gibson (2001). Even though studying soil–
vegetation along the urban–rural gradient is a prevalent
topic in ecological research (cf. Green and Oleksyszyn
2002; Sukopp 2004; Williams and others 2005), these
studies have not been conducted specifically in conjunction
with USL communities, nor can their data be fit to any USL
environmental model. While Roovers and others (2004,p.
107), for example, provide the researcher with a strong data
analysis technique for assessing soil–vegetation interac-
tions, their predictive models focus on species diversity
along ‘‘path ecotones’’ rather than specifically on the soil–
vegetation interactions.
Methods
Site Selection
The control site (Fig. 1) was selected based on established
criteria of the USL suggested by Romig (2005), i.e.,
located in the Sonoran Desert, expensive houses and land,
high socioeconomic status, and segregation from the rest of
the valley by elevation. Anchored in the northern foothills
of the Phoenix metropolitan region and built on alluvial
fans and pediments, communities in this area offer the
archetypal USL, with house prices beginning in the
$900,000s. Near the boundary between the cities of
Scottsdale and Cave Creek, running parallel to Stage Coach
Pass Road, is a nearly 5-km long multi-use recreational
trail that services at least three separate USL communities.
Throughout the length of the site, topography is varied but
most pediment interfluves are relatively small with devel-
oped drainages.
Three additional sites (L2, L3, and L4; Fig. 1)—one
near the control site and two others away from the initial
site—were chosen for comparison. The first of these sites
(L2) is approximately 1 km west of the initial site on the
same multi-use trail, but on the crest of a large interfluve
with nearly level topography. Level landforms affect soil
and vegetation factors, especially in arid environments and
along the urban fringe and WUI (cf. Laymon and others
1998; Bornyasz and others 2005; Hara and others 2005).
The second comparison site (L3), also a multi-use trail, but
with obvious equine usage, is located south of Pinnacle
Peak just outside a Troon North development along
Dynamite Road. Equine activity can affect vegetation
diversity and invasion, as well as vegetation disturbance
and compaction (cf. Campbell and Gibson 2001), as well as
soil erosion and decreasing soil bulk density—especially
along dry trails (cf. Deluca and others 1998).
The third comparison site (L4), in Queen Creek, was
chosen specifically for contrast and to test the field
assessment tool’s predictability. Near the base of the San
Tan Mountains, L4 is the antithesis of the USL from a
topographic and socioeconomic standpoint. Although the
region is expanding just as rapidly as the rest of the
Phoenix metropolitan region, environmental regulations
are less in this once-rural area. And, similar to Esbah’s
348 Environmental Management (2009) 43:346–356
123
(2007) situation, if left unmonitored, any rapid expansion
will undoubtedly have negative environmental effects.
Each site is located in the Sonora desert and considered
part of the metropolitan Phoenix region (Gober and Burns
2002). They all have similar vegetation, landform, and
Natural Resources Conservation Service soil classification
schemes (Saminiego series). BSCs are also present at each
site in smooth or rugose form (cf. Belnap and Lange 2003),
although they are more prevalent and less disturbed at the
USL sites.
Control and Comparison Sites
At the control site (L1), factors known to have an effect on
soil–vegetation interactions, as well as those factors
indicative of anthropogenic environmental impacts, were
assessed. Initially based on the established assessment
parameters of Roovers and others (2004), this resulted in a
matrix that included transect reference data (e.g., date,
time, location, descriptions, etc.), vegetation parameters,
pedologic traits, human impact factors, and BSC charac-
teristics. Once all data were gathered and assessed for
initial site, patterns began to emerge that placed higher
relevance on certain indicators than others. For example,
the amount of soil disturbance was a major factor for
assessing impact, while the root depth of vascular plants
displayed very little relevance. After analyzing the control
site data for trends and patterns, a precise, compact, and
easily measurable field metric was developed (Fig. 2).
Equipment and Parameters Used for Assessment
of Indicators
Developing a tool for assessing anthropogenic impact on
soil–vegetation interactions requires a multitude of eco-
logical, environmental, and social factors. Ecologists and
other environmental scientists continue to debate anthro-
pogenic impact factors of the ‘‘natural’’ landscape in urban
fringe and WUI environments (cf. Fry and Sarlo
¨v-Herlin
1997; Tjallingii 2000; Dwyer and Childs 2004; Fujihara
Fig. 1 Site locations: L1, L2,
and L3 are located in the
‘‘Upper Sonoran Lifestyle’’
area. L4 is in a distinctly non-
Upper Sonoran Lifestyle area
(map by author)
Environmental Management (2009) 43:346–356 349
123
and others 2005; Alavalapati and others 2005). Some
researchers go as far as suggesting a human comfort index
(Barradas 1991; McGregor 1993; Hartz and others 2005;
Toros and others 2005) or using agent-based modeling to
assess anthropogenic impacts (cf. Brown and others 2004;
Parker and Meretsky 2004). Determination of key indica-
tors to include in a field assessment tool was based on
current research and established parameters (cf. Roovers
and others 2004; Li and others 2005; Dale and others
2005), the suggestion of an easily communicative metric
(Schiller and others 2001), and my own criteria of an
inexpensive, easily replicable, easy-to-use, non-invasive
metric.
Based on previously established techniques by Roovers
and others (2004) indicators and parameters for recording
anthropogenic impacts on soil–vegetation interactions
included:
1. Soil disturbance: a visual assessment of the trail and its
surroundings quickly reveals the extent of human
impact, and another visual assessment of 25 cm on
either side of the transect (a total of 50 cm), recorded
in the matrix as a percentage.
2. Vegetation density: a visual assessment of 25 cm on
either side of the transect (a total of 50 cm), recorded
in the matrix as a percentage. Generally, the more
dense the vegetation, the less human impact and more
fertile the soil (Williams and others 2005).
3. Vegetation disturbance: a visual assessment of 25 cm
on either side of the transect (a total of 50 cm),
recorded in the matrix as a percentage. Broken
branches, uprooted tufts of grass, etc. are effective
indicators of human impact (Okin and others 2001;
Williams and others 2005).
4. Vegetation ‘‘type’’: type in this instance refers to the
area a plant occupies. A visual assessment of 25 cm on
either side of the transect (a total of 50 cm), recorded
in the matrix as a percentage. For example, larger,
more developed plants, such as a Palo Verde tree,
would be avoided by the trail-user (Williams and
others 2005).
5. Biological Soil Crust presence: a visual assessment of
25 cm on either side of the transect (a total of 50 cm),
recorded in the matrix as a ‘‘yes’’ or ‘‘no’’. The
presence of BSCs indicates an extended period of soil
stability, since they are extremely sensitive to anthro-
pogenic impacts and have life spans of decades and
hundreds of years (Belnap and Lange 2003).
6. Biological Soil Crust disturbance: a visual assessment
of 25 cm on either side of the transect (a total of 50 cm),
recorded in the matrix as a percentage. Once disturbed,
especially during dry periods, BSCs can take decades to
return to full nitrogen-fixing potential (Belnap and
Lange 2003). In heavily populated regions, such as
metropolitan Phoenix, most disturbances of BSCs are
human-induced (Belnap and Lange 2003).
7. Biological Soil Crust ‘‘type’’: a visual assessment of
25 cm on either side of the transect (a total of 50 cm),
recorded in the matrix as ‘‘smooth’’ or ‘‘rugose’’, per
Belnap and Lange’s (2003) visual classification sys-
tem. Smooth BSCs are more resilient to disturbances
Fig. 2 A rapid ecological assessment tool for USL communities
350 Environmental Management (2009) 43:346–356
123
(minimal topography), while rugose BSCs are more
susceptible to disturbances (more topography).
8. Soil bulk density (Db): in relation to number 1, above,
bulk density assesses the compactness of soil. A small
soil pit was dug at the 0-, 1.5- and 3-m marks, soil
samples were taken to a lab, and Db calculated by
established formulas. Bulk density on the trail should
be significantly more than a few meters off the trail,
assuming a few meters off-trail displays no anthropo-
genic impacts (Brady and Weil 2002).
Indicators 1–7, in rank order by importance as deter-
mined from the control site findings, were then used to
create the metric. Indicator 8, while useful for assessing
soil compaction and thus disturbance, was not used because
it is invasive, time consuming, and training-intensive.
Other indicators tested, below, showed no direct relation-
ship with anthropogenic impact and soil–vegetation
interactions in the USL.
•Root depth of most abundant plant species. Root depth
is related to the soil profile, and in the desert leaching
causes formation of carbonates which, given enough
time, form an impermeable petrocalcic (or other hard)
layer, making root depth assessment extremely difficult
(Brady and Weil 2002).
•O horizon depth of the soil. Characterized by the
amount of decomposing organic matter present, O
horizons in arid environments are usually negligible
(Brady and Weil 2002).
•A horizon depth of the soil. The A horizon in arid
regions represents the most favorable root placement
region in the soil profile, but sometimes requires a
fairly large and deep soil pit to be dug (no small feat on
a pediment interfluve!) to assess it correctly (Brady and
Weil 2002).
•Human debris and type. While this anthropogenic
feature is easily identifiable, it varies widely across
socioeconomic boundaries, has no specific pattern, but
is a major factor in determining trail quality (cf. Moore
and Polley 2007). In the kilometers of trails I walked in
the USL, I found one small piece of trash, while at the
non-USL site, human debris was strewn next to the
road, but not the trails.
Rather than solely recording measurements, as Roovers
and others (2004) did, once the transect was measured and
demarcated with small flags at the center of the trail and at
1.5- and 3-m intervals, a panoramic photo was taken of each
transect. This non-invasive documentation of each transec t is
used to double-check data along transects, and allows
researchers to juxtapose sites, perhaps employing photo
survey methods (cf. Kim and others 2003), alongside lon-
gitudinal datasets.
Random Sampling Technique
While site locations were chosen because they were either
USL or non-USL, a random sampling technique was
employed to generate the quantity of and distance between
transects at each site. Two factors were kept constant at
each site: transect length and transect width. Following
Roovers and others (2004) parameters, transect width was
assessed in 50 cm swaths from its center. Because of the
varied desert topography however, transect length was
shortened from 10 m, as Roovers and others (2004) used,
to 3 m, with sample points at .3, .6, .9, 1.2, 1.5, 1.8, 2.1,
2.4, 2.7, and 3 m, keeping the points constant for all
transects at all locations.
Along each transect, measurements were recorded in a
matrix, and once all data was compiled for each site, an
average rate of distance decay for soil and vegetation dis-
turbance was established using an exponent factor of -
0.60; for vegetation density, the established rate of increase
was an exponent factor of 0.60.
Results and Discussion
Exponential Distance Decay
From simple observation, it is clear that people in USL
communities stay predominantly on the marked trail, yet a
visual assessment of any USL multi-use trail also supports
a notion of exponential distance decay. As Findlay and
Zheng (1997, 265) note, ‘‘for a simple model of expo-
nential distance-decay of average stressor values away
from the source, the characteristic distance…for a stressor
increases with (1) decreasing spatial signal strength, (2)
decreasing spatial noise, (3) increasing sample size and (4)
decreasing sampling resolution.’’
Establishing a Predictive Average Rate of ‘‘Increase’’
Soil Disturbance. At L1, exponential distance decay is a
prominent feature (Fig. 3). Data collected at L1 imply an
average soil disturbance rate of -0.60, and this lambda
holds true at the other USL sites (Fig. 4). When applied to
the non-USL site, however, it does not correlate with any
transect (Fig. 5). Disturbance of soil and anthropogenic
disturbance via trail use are the key stressors at all sites,
and the data suggest that major disturbance at USL sites
tends to occur within an average distance of 1.5 m on
either side of the trail. These data follow a distinct pattern
with only a few explicable outliers, discussed below.
Vegetation Disturbance. Along multi-use recreational
trails in the USL, vegetation disturbance exhibit a distinct
Environmental Management (2009) 43:346–356 351
123
distance decay pattern (Fig. 6), suggesting replicability for
USL settings (cf. Kumsap and others 2005). While vege-
tation can recover quickly from anthropogenic impacts (cf.
Dale and others 2005) however, Williams and others
(2005; see also Fujihara and others 2005) note that along
the urban–rural gradient, recovery takes longer because of
the constant human presence. Anthropogenic vegetation
disturbance also generate stresses on vegetation and those
effects, whether direct or indirect, are often due to soil
compaction, emphasizing even more the delicate link
between soil and vegetation (Roovers and others 2004).
All transects at L4 display only randomness in associ-
ation with vegetation disturbance (Fig. 7). This same
randomness is also observed with soil disturbance, and the
obvious explanation is the lack of clearly-defined and
maintained multi-use trails, like those found in USL
communities.
Vegetation Density. Vegetation density increases with
outward movement from the center of all USL sites, at an
average ‘‘reverse decay’’ rate of 0.60 (Fig. 8). This can be
attributed to the minimal impact USL communities have on
multi-use trails. In their studies of impact on vegetation, Li
and others (2005) and Dale and others (2005) observed that
vegetation density tends to increase with outward move-
ment from the trail, regardless of type of impact. Focusing
specifically on the urban fringe, Fry and Sarlo
¨v-Herlin
(1997) found the same phenomena occurred, though they
all failed to record specific measurements of increase. The
reverse decay pattern established at the control site, how-
ever, is consistent across the other USL sites assessed.
Fig. 3 Soil disturbance over 3-m transects at the USL control site
(L1) and established average exponential distance decay rate
Fig. 4 Soil disturbance over 3-m transects at L2 and L3 and
established average exponential distance decay rate
Fig. 5 Established average rate of exponential distance decay of soil
disturbance applied to all 3-m transects at site L4
Fig. 6 Vegetation disturbance along all 3-m USL transects (L1, L2,
L3) and established average exponential distance decay
352 Environmental Management (2009) 43:346–356
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At L4, as with other data recorded at the site, there is no
distinguishable pattern except randomness (Fig. 9). If soil
and vegetation disturbances occur randomly at the non-
USL site, it follows that vegetation density—and any other
characteristic associated with soil–vegetation interac-
tions—would also be random there. Indeed, in every
instance (see Other Prominent Indicators…, below), this is
precisely the case.
Anomalous Data. Anomalous data (e.g., large spikes on
the graph) from sites L1, L2, and L3 are explained by a
quick review of the raw data and field notes, including the
photos. Although only three off-trail disturbances were
observed along all transects assessed at L1, L2, and L3,
there was an occasional plant—either dead, such as a
fouquieria splendens (ocotillo) at L2, or alive, such as a
large cercidium microphyllum (palo verde) at L1. The
anomaly at site L3 occurred because of obvious equine
usage, which is known to increase erosion, thus widening
trails (cf. Campbell and Gibson 2001; Deluca and others
1998).
Other Prominent Indicators Revealed in Comparisons
Changes in Vegetation ‘‘Type.’’ The majority of vegetation
type in USL community transects were grasses. This is not
surprising, since grasses are quick to reproduce and need
very little soil to take root (Bazzaz 2000). Being smaller,
however, anthropogenic disturbance was at times more
difficult to discern in grasses, often requiring close-to-the-
ground inspection. There is evidence that larger vegeta-
tion—presumably matured before the trail and USL
communities were established—is less disturbed, and at
USL sites where shrubs were present, soil and vegetation
disturbance are almost non-existent. This trend holds true
across all USL sites only.
Biological Soil Crusts (BSCs). The data demonstrate
that there is less BSC disturbance at USL sites than at the
non-USL site. A good indicator of soil disturbance because
of their longevity and resilience, BSCs provide a quick
assessment of soil stability (Belnap and Lange 2003).
While BSCs are present at every site, they are certainly
more prevalent at USL sites. Rugose BSCs, while more
susceptible to disturbances because of their higher relief
and varied topography, can indicate the presence of nitro-
gen, and provide vegetation a deeper ‘‘soil’’ in which to
take root (Belnap and Lange 2003). At the USL sites,
Fig. 7 Established average rate of exponential distance decay of
vegetation disturbance applied to all 3-m transects at site L4
Fig. 8 Average rate of vegetation density increase, by percent, and
respective increases of vegetation density along all 3-m USL transects
(L1, L2, L3, moving outward from center of trail)
Fig. 9 Percent of vegetation density along all 3-m transects at site
L4. Note the randomness of vegetation density due to anthropogenic
disturbances
Environmental Management (2009) 43:346–356 353
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rugose BSCs dominated, and in one instance, even though
they were anthropogenically disturbed, the BSCs retained
their composition and remained attached to a few milli-
meters of soil.
BSCs at L4 are predominantly ‘‘smooth.’’ While smooth
BSCs are more resistant to disturbance, they can also
represent recently disturbed soil (Belnap and Lange 2003).
Since soil classification is the same at each site, the smooth
BSCs at L4 were most likely recently disturbed. The ran-
domness of BSCs (and soil and vegetation) disturbances
and impacts at L4, also has an acute affect on vegetation
density. Prevalent at site L4 is the abundant off-road
vehicle (ORV use), and in assessing short-term impacts of
ORVs on BSCs, Belnap (2002) found that nitrogenase
activity decreased with disturbance, and decreasing nitro-
gen leads to less vegetation growth. Converting
atmospheric nitrogen into vegetation-usable nitrogen is a
main function of BSCs, and if left undisturbed, they can fix
nitrogen to soils for centuries (Belnap and Lange 2003).
Soil Bulk Density (Db). Very compacted soils, such as
those from a tractor tire, usually have a Db in the range of
1.4–1.6 g/cm
-3
; open, friable soil with good organic matter
content, such as hearty agricultural soils, tend to have a Db
of less than 1.0 g/cm
-3
(Brady and Weil 2002). Following
established patterns of anthropogenic soil compaction (cf.
Jenny 1941; Lovich and Bainbridge 1999; Bazzaz 2000)
then, Db at all USL sites was as expected: more compacted
on and near the trail, and decreasing with outward move-
ment along the transect. Although Db was relatively high,
too high to support major agriculture for example, it is
within established parameters of desert soils (Brady and
Weil 2002). At L4 however, Db was not only higher, but
there was no significant difference between sites or along
transects. Barely a 0.25 g/cm
-3
difference is noted between
the most and least compacted transects at L4, while along
each transect, the biggest variance is only 0.09 g/cm
-3
.
Varying more than 0.50 g/cm
-3
between sites and more
than 0.25 g/cm
-3
along transects, USL sites have more than
twice the Db range of non-USL sites. While these data are
inline with previous studies (cf. Sukopp 2004; Li and others
2005; Dale and others 2005), they are not included in the
rapid ecological assessment tool because measuring Db is
invasive, time consuming, and takes extended training.
From data collected, however, it is clear that Db plays a role
in assessing USL impacts on soil–vegetation interactions.
Establishing Tool Usability
Soil, vegetation, BSCs, and Db are, with the exception of
Db, easily identifiable and quantifiable in the field using
basic field techniques. To further examine the field
assessment tool’s feasibility, undergraduate students from
an upper-division field methods course were trained in its
use and then conducted their own environmental assess-
ment of USL multi-use trails. Each group (2–3 students per
group, n=20 students total) yielded similar results, with
the established distance decay and ‘‘reverse’’ decay factors
(0.60 and -0.60, respectively) holding constant.
Desert vegetation, including BSCs, ‘‘respond primarily to
changes in their resource base’’ and anthropogenic distur-
bances of vegetation ‘‘alter the resource base of a site’’
(Bazzaz 2000, p. 61). Vegetation helps breakdown parent
material to form soil, and soil in turn provides nutrients for
the vegetation to survive (Jenny 1941). Since vegetation is
expressly linked to soil via this resource base, the relation-
ship has profound implications for multi-use recreational
trails along the WUI (Green and Oleksyszyn 2002; Williams
and others 2005), such as the USL. As the WUI increases,
monitoring multi-use trails in USL and USL-like commu-
nities of the desert US Southwest for major disturbances
should be a priority.
Conclusions
Extensive growth rates in metropolitan Phoenix has led to a
widespread creeping of isolated homes into the surrounding
foothills and mountains. Typical throughout the northern
metropolitan region, the Upper Sonoran Lifestyle epitomizes
the concept of sprawl (Romig 2005). As upper income
dwellings become subdivisions and subdivisions become
incorporated into cities, nearby recreational opportunities
develop alongside them. But there is, at present, no appro-
priate technique available to monitor the long-term
environmental impact on adjacent trails in this region.
This article presents a rapid ecological assessment field
tool that focuses on USL anthropogenic impacts of soil–
vegetation interactions along multi-use recreational trails,
and then tests that tool for efficiency, ease of use, non-
invasiveness, and predictability. The resultant field assess-
ment tool meets the need of land managers by making
ecological and environmental assessment data quick and
cost-efficient to collect and also understandable by policy
makers (cf. Schiller and others 2001).
Data gathered from the control and test sites reveal a
distinct distance decay pattern in soil disturbance and
vegetation disturbance along trails at the Upper Sonoran
Lifestyle sites. Calculated based on methods established by
Findlay and Zheng (1997), the findings suggest that an
exponent factor of -0.60 can be applied to soil and veg-
etation disturbance indicators along multi-use, recreational
trails in the USL, allowing prediction of anthropogenic
environmental impacts on soil–vegetation interactions.
Initial findings also demonstrate that vegetation density at
USL sites increases with outward movement from the trail
at a factor of 0.60, but at the non-USL site, vegetation
354 Environmental Management (2009) 43:346–356
123
density—and soil and vegetation disturbance—is exacer-
bated by extreme and random anthropogenic impacts. This
predictability enhances the tool’s validity as a rapid eco-
logical assessment, the result of which produced an easy-
to-use, replicable, inexpensive, and non-invasive technique
for assessing anthropogenic impacts of soil–vegetation
interactions in Upper Sonoran Lifestyle communities. It
also represents a preliminary step for other USL-like
communities at the WUI to assess anthropogenically-
caused environmental stresses.
Acknowledgments This article is the result of the School of Geo-
graphical Sciences’ two-week PhD Research and Field Exam at
Arizona State University (see Oberle and others 2005). As such,
appreciation is forwarded to the field exam committee: Drs. Daniel D.
Arreola, Kevin McHugh, and Anthony J. Brazel. Profuse thanks also
go to Dr. Ronald I. Dorn for his numerous reviews and precious
feedback on earlier manuscripts and to Dr. Robert Edsall for his
statistical review. Further data collection was graciously provided by
students in my Geographic Field Methods course at Arizona State
University. And finally, as field assistant for the project, express
gratitude goes to my brilliant spouse, Dawn Renee
´.
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