EFFECTS OF OFF-ROAD RECREATION ON MULE DEER AND ELK
Michael J. Wisdom Research Wildlife Biologist
USDA Forest Service, Pacific Northwest Research Station
Forestry and Range Sciences Laboratory
1401 Gekeler Lane, La Grande, Oregon 97850
Phone (541-962-6532), fax (541-962-6504), Email mwisdom@fs.fed.us
Alan A. Ager
Operations Research Analyst
USDA Forest Service Umatilla National Forest
2517 Hailey Ave., Pendleton, OR 97801
Phone (541-278-3740) fax (541-278-3730)
Email aager@fs.fed.us
Haiganoush K. Preisler
Statistician
USDA Forest Service, Pacific Southwest Research Station
800 Buchanan St., Albany, CA 94710
Phone (510-559-6484), fax (510-559-6440)
Email hpreisler@fs.fed.us
Norman J. Cimon
Systems Analyst
USDA Forest Service, Pacific Northwest Research Station
Forestry and Range Sciences Laboratory
1401 Gekeler Lane, La Grande, Oregon 97850
Phone (541-962-6551), fax (541-962-6504)
Email ncimon@fs.fed.us
Bruce K. Johnson
Starkey Project Leader
Oregon Department of Fish and Wildlife
Forestry and Range Sciences Laboratory
1401 Gekeler Lane, La Grande, Oregon 97850
Phone (541-962-6556), fax (541-962-6504) Email johnsobd@eou.edu
Suggested Citation:
Wisdom, M. J., H. K. Preisler, N. J. Cimon, B. K. Johnson. 2004. Effects
of Off-Road Recreation on Mule Deer and Elk. Transactions of the North
American Wildlife and Natural Resource Conference 69: in press. Wisdom
et al. 2
Effects of Off-Road Recreation on Mule Deer and Elk
Michael J. Wisdom
U.S. Department of Agriculture, Forest Service, Pacific Northwest
Research Station
La Grande, Oregon
Alan A. Ager
U.S. Department of Agriculture, Forest Service, Umatilla National Forest
Pendleton, Oregon
Haiganoush K. Preisler
U.S. Department of Agriculture, Forest Service, Pacific Southwest
Research Station
Berkeley, California
Norman J. Cimon
U.S. Department of Agriculture, Forest Service, Pacific Northwest
Research Station
La Grande, Oregon
Bruce K. Johnson
Oregon Department of Fish and Wildlife
La Grande
Introduction
Off-road recreation is increasing rapidly in the United States,
especially on public lands (Havlick 2002, U.S. Department of Agriculture
Forest Service 2004). An expansive network of roads provides easy access
to many public lands, which facilitates off-road uses in the form of
all-terrain vehicles (ATVs), horses, mountain bikes, and foot traffic.
No research, however, has evaluated effects of these off-road activities
on vertebrate species in a comparative and experimental manner (see
review by Gaines et al. 2003). One recent study (Taylor and Knight
2003a) evaluated bison (Bison bison), pronghorn (Antilocapra americana),
and mule deer (Odocoileus hemionus) responses to mountain biking and
hiking. This study, however, did not include ATV or horseback riding,
nor did it include experimental controls needed to assess cause-effect
relations.
To address these knowledge gaps, we initiated a manipulative, landscape
experiment in 2002 to measure effects of off-road recreation on mule
deer and elk (Cervus elaphus), two charismatic species of keen
recreational, social, and economic interest across western North
America. Our objectives were to (1) document cause-effect relations of
ATV, horseback, mountain bike, and hiking activities on deer and elk,
using these off-road activities as experimental treatments and periods
of no human activity as experimental controls; (2) measure effects with
response variables that index changes in animal or population
performance, such as movement rates, flight responses, resource
selection, spatial distributions, and use of foraging versus security
areas; (3) use these response variables to estimate the energetic and
nutritional costs associated with each activity and the resultant
effects on deer and elk survival; and (4) interpret results for
recreation management.
Our research began in 2002 and ends in 2004. In this paper, we present
findings from 2002 to address parts of objectives 1, 2, and 4. We
specifically focus on changes in movement rates and flight responses of
mule deer and elk in relation to the off-road activities, as compared to
periods of no human activity. We then describe potential uses of the
results for recreation management.
We present findings from our first year of study because of the urgent
need for timely management information to address the rapid growth in
off-road recreation (U.S. Department of Agriculture Forest Service
2004). For example, ATV use on public lands has increased seven-fold
during Wisdom et al. 3
the past 20 years, and many conservation groups are calling for
widespread restrictions on ATV travel (U.S. Department of Agriculture
Forest Service 2004). Yet no studies have evaluated the role of ATVs
compared to other off-road activities, such as mountain biking and
horseback riding, which also are increasing rapidly. Without
comprehensive studies of ATV effects in relation to other recreation,
the debate over ATV is likely to intensify. Our study was designed to
measure a variety of ungulate responses to address this debate, such
that results can be used to identify compatible mixes of different
off-road recreational opportunities in relation to deer and elk
management.
Throughout our paper, we refer to off-road recreation, both motorized
and non-motorized, as that occurring on trails, primitive (unpaved)
roads, or areas without trails or roads. This definition complements the
term "off-highway vehicle (OHV) use," which refers to motorized vehicle
use on any surface beyond highways (U.S. Department of Agriculture
Forest Service 2004), but which does not include other forms of
non-winter recreation that typically occur on primitive roads and
trails, such as hiking, horseback riding, and mountain biking.
Study Area and Technologies
We conducted our research at the Starkey Experimental Forest and Range
(Starkey, Figure 1) in northeast Oregon, a facility equipped to evaluate
real-time and landscape-level responses of deer and elk to human
activities under controlled experimentation (Rowland et al. 1997, Wisdom
et al. 2004a). The facility encompasses spring, summer, and fall ranges
typical of those used by mule deer and elk in the western United States.
Timber harvest, livestock grazing, motorized traffic, hunting, camping
and other public uses of Starkey also are managed like those on National
Forests in the western United States, thus providing a large inference
space for research findings (Rowland et al. 1997, Wisdom et al. 2004a).
An essential research component at Starkey is the ungulate-proof
enclosure, one of the largest in the world, which allows scientists to
evaluate ungulate responses to human activities over large areas and
under controlled conditions (Bryant et al. 1993, Rowland et al. 1997).
Another key technology is the automated tracking system (ATS), which can
generate up to one animal location every 20 seconds, 24 hours a day,
from April through December each year (Rowland et al. 1997, Kie et al.
2004). Additional technologies include maps and databases of more than
100 environmental variables to relate animal movements to the landscape
experiments, and supporting methods and software to analyze these data
(Rowland et al. 1997, 1998).
Implementing the Recreation Treatments
To meet our objectives, a network of off-road transects was established
and run in 2002, using ATV, horseback, mountain bike, and hiking
activities as experimental treatments in the 3,590-acre (1,453-ha)
northeast study area (Figure 1). Approximately 20 miles (32 km) of
transects were established (Figure 1), over which ATV, horseback,
mountain bike, and foot traffic was experimentally applied from
mid-April through October. Locations of each transect were established
with Global Positioning System (GPS) units (Figure 1). Transects were
located on flat or moderate terrain typically used by off-road
activities. Primitive roadbeds, like those often established by off-road
vehicles (U.S. Department of Agriculture Forest Service 2004), were
included in the transects. Use of roadbeds and trails to implement human
activities is referred to as a "tangential" experimental approach
because animals are not targeted directly by the activities (Taylor and
Knight 2003b). This is in contrast to a direct experimental approach,
such as testing the reaction of nesting birds to designed encounters
with humans at nest sites.
A sufficient number and length of transects were established to
encompass all portions of the northeast study area (Figure 1). Each
off-road activity was run on a given transect twice daily, once in the
morning and once in the afternoon, during a 5-day period; this daily
frequency of activity corresponds to traffic frequency on Starkey roads
that produced an avoidance response by elk in earlier research (Wisdom
1998, Wisdom et al. 2004b). Wisdom et al. 4
A particular activity for a given morning or afternoon was completed by
one to three people who rode ATVs ("four-wheelers" or "quads"), mountain
bikes, or horses, or hiked as a group. On most days, group size
consisted of two people moving as a pair; that is, by two people hiking
or each riding ATVs, mountain bikes, or horses. A group size of two,
with a range of one to three people, often is typical for these
recreation activities in non-wilderness portions of National Forests
(personal communication, D. Barrett, Wallowa-Whitman National Forest).
Group size can vary substantially, however, with larger groups of 5-10
ATV riders or horseback riders, for instance. We had neither the
resources nor the experimental options to include these larger groups as
treatments in our study. Moreover, group size of mountain bikers and
hikers often does not approach 5-10 people, and we wanted to maintain
approximately the same group size across all four activities. A group
size of two people, with a range of one to three people, provided this
consistency.
For ATV travel, a pair of riders could easily cover the 20 miles (32 km)
of transects during a given morning or afternoon. A pair of mountain
bike riders, however, could cover about 50 percent of the 20 miles (32
km) in a morning or afternoon. Horseback riders and hikers could cover
about 30 percent. Because we wanted to standardize the experiment by the
same number of transect runs or "passes" (twice daily) among all four
off-road activities, two different groups of mountain bikers, and three
groups of horseback riders or hikers, were used to obtain complete
coverage of transects for a given morning or afternoon. For mountain
biking, the transects were divided in half, with each of the two groups
assigned to ride a different half of the 20 miles (32 km) in a morning
or afternoon. Similarly, three groups of horseback riders or hikers,
each assigned to hike a different third of the transect length, were
used for each morning and afternoon to obtain complete coverage of
transects.
Each of the four off-road activities was implemented under an
"interrupted" movement design, where humans were allowed to momentarily
stop to view animals for less than 1 minute when animals were observed.
This is in contrast to a "continuous" movement design, where human
activities are not delayed or stopped when animals are observed (Taylor
and Knight 2003b).
Each 5-day period of off-road activity was followed by a 9-day control
period, during which no human activities occurred in the study area.
This pattern was followed from mid-April through October, resulting in
three replicates of each of the four off-road activities. Each 5-day
replicate of an off-road activity thus was paired with a 9-day control
period that immediately followed the replicate. Only one type of
off-road activity (ATV, horseback, mountain bike, or hiking) occurred on
transects during a given 5-day replicate. The chronological order of
each off-road activity, in terms of which activity occurred during the
first 5-day replicate in late April, versus the next 5-day replicate in
early May, and so on, was randomly chosen.
Throughout the experiment, all human entry beyond the four off-road
activities, including administrative use of roads, was prohibited to
eliminate the confounding effects of other human activities with animal
response to the off-road activities. Consequently, human activities such
as timber harvest, road traffic, camping, and hunting did not occur
during the study because of their confounding effects.
Measuring Animal Responses
To monitor animal responses, 12 female mule deer and 12 female elk were
radio-collared among a larger population of approximately 25 female deer
and 100 female elk present in the northeast study area in early April.
Movements of these radio-collared animals were monitored with the ATS
(Rowland et al. 1997). During periods of off-road activity, locations of
each radio-collard deer or elk were generated at approximately 10-minute
intervals. Locations of humans engaged in each off-road activity were
generated at approximately 1-minute intervals, using GPS units carried
by one of the persons in each group of hikers or riders of ATVs, horses,
or mountain bikes. Use of the automated telemetry system to track animal
movements, combined with the use of GPS units to track human movements,
provided real-time, unbiased estimates of the distances between each
ungulate and group of humans.
Our method of estimating distances between ungulates and humans
contrasts strongly with the use of direct observation, using
rangefinders or other devices, to measure distances. Direct observation
as Wisdom et al. 5
a means of estimating distances between ungulates and humans is likely
to be biased by the proportion of deer or elk whose reactions to human
activities cannot be observed because such reactions are different than
those of animals that can be observed. For example, some animals may run
from human activity at distances beyond the view of observers, while
other animals may react at close distances to, and in view of,
observers. This bias in observed distances would result in
underestimation of the true distance at which animals react to the human
activity. In other cases, animals may flee from humans at close
distances, but not be viewed because such animals seek dense cover
during flight; this bias would result in overestimation of distances. We
avoided such biases with the use of our automated telemetry system and
GPS units to continuously monitor the movements of ungulates and humans
throughout our study.
We also located radio-collared animals during the 9-day periods of no
human activity, or control period. Approximately two locations of each
radio-collared animal were obtained every hour during control periods,
to establish baseline information about areas of deer and elk use,
habitat selection, movement rates, and flight responses in the absence
of human activities. For this paper, we analyzed two types of animal
reactions: (1) movement rate and (2) probability of flight response. We
evaluated movement rate and probability of flight response because both
can ultimately be used to estimate the energetic costs of animal
reactions to off-road activities (see Conclusions and Interpretations).
Estimating Movement Rates
We defined movement rate as the speed of animal movement (yards
moved/minute), as estimated hourly, 24 hours per day, for a given
species, treatment, and control period. We calculated the speed of
animal movement for each radio-collared deer or elk for each pair of
successive locations; that is, the horizontal distance between two
successive locations divided by the elapsed time between locations (Ager
et al. 2003). Each measurement of animal speed for a given
radio-collared animal was assigned to the time recorded for the first
location of each pair of animal locations used in the calculation.
Only successive locations with consistent elapsed times were included in
the calculation of movement rates to eliminate the bias of excessively
short and long elapsed times. Short elapsed times (less than 5 minutes)
between locations falsely inflate the movement rate because of random
location errors in the ATS over such short time periods (Findholt et al.
1996, 2002). Long elapsed times (e.g., greater than 35 minutes) between
locations allow animals to move back and forth between the documented
locations, thus biasing the estimate of movement rate downward (Ager et
al. 2003).
To estimate overall patterns of movement rates for each species, rates
calculated for each individual radio-collared animal were averaged among
all animals, for mule deer and for elk, by hourly interval, for each
off-road treatment and the paired control period that immediately
followed that treatment. For this analysis, we minimized random
variation by summarizing results across each 5-day treatment and across
each subsequent 9-day control. We did this after exploratory plots of
data provided no evidence of change in movement rates of animals from
day one through day five of each treatment period, or for day one
through nine of each control period, as examined on an hourly basis. We
then pooled hourly results for each species across the three replicates
of each off-road activity, and across control periods, after finding no
evidence of differences in like replicates across time, or in control
periods across time.
Estimating Probabilities of a Flight Response
We used a stimulus response model to estimate the probability of a
flight response by a deer or elk with changing distance between each
animal and off-road activity. We defined a flight response as the speed
of animal movement, or movement rate, that exceeded the 95th percentile
of all deer or elk speeds calculated for each hour from data collected
during the control periods. Specifically, a flight response was any
animal movement for a given hour of day that exceeded the 95th
percentile of all deer or elk speeds calculated for that same hour of
day during the paired 9-day control period that immediately followed a
given 5-day period of off-road activity. Thus by definition, when no
stimulus was present (no human Wisdom et al. 6
activity), a deer or elk would register a response (i.e., travel at
speeds greater than the 95th percentile of all deer or elk speeds for
that hour during the control period) 5 percent of the time.
Probabilities of response were estimated using logistic regression
within the generalized additive model framework (Hastie and Tibshirani
1990).
Each estimated probability of a flight response for a given
radio-collared animal was linked to the estimated distance between that
animal and each group of humans conducting an off-road activity, thus
allowing an examination of how probabilities changed with distance
between animals and humans. As with our analyses of movement rates, we
pooled the probability data for each species across the three replicates
of each off-road activity, and across control periods. We pooled data
after initial analyses showed that results for deer and elk were similar
across the three replicates of each off-road activity, and across all
control periods.
Movement Rates of Elk
Movement rates of elk were substantially higher during periods of all
four off-road activities as compared to periods of no human activity
(Figure 2). Responses of elk to the morning and afternoon runs were
clearly evident, with the most pronounced increase in movement rates
observed during the hours when each off-road activity occurred (Figure
2). For example, our morning pass on transects began between 0830 and
0930 Pacific Daylight Time (PDT), and highest movement rates for elk
occurred in the hours immediately after, from 0900-1100, during all four
activities (Figure 2). Moreover, lunch break for participants in the
experiment occurred at or near noon, and movement rates for elk dipped
to their lowest level at noon during all activities. Finally, we resumed
each activity at 1230-1300 PDT, and movement rates for elk substantially
increased immediately after (Figure 2).
Movement rates were substantially higher for elk during the morning pass
compared to the afternoon pass for all four activities (Figure 2).
Movement rates of elk during the afternoon pass, however, stayed well
above the rates observed during the periods of no human activity
(control period, Figure 2). Movement rates during the afternoon pass
declined after 1500 PDT, when afternoon activities ended.
For the morning pass, movement rates of elk were highest during ATV
riding, second-highest during mountain bike riding, and lowest during
hiking and horseback riding (Figure 2). Movement rates of elk also
stayed higher, over a longer period, during the afternoon ATV run,
compared to lower rates during afternoon horseback riding, mountain bike
riding, and hiking. Peak movement rates of elk during the morning pass
were highest for ATV riding (21 yards/minute [19 m/min]), followed by
mountain bike riding (17 yards/minute [16 m/min]) and horseback riding
and hiking (both about 15 yards/minute [14 m/min]). For the afternoon
run, movement rates of elk again were highest during ATV riding (13
yards/minute [12 m/min]), followed by horseback riding (about 11
yards/minute [10 m/min]) and hiking and mountain bike riding (about 10
yards/minute [9 m/min]).
By contrast, peak movement rates of elk during the control periods did
not exceed 9 yards/minute (8 m/min). Moreover, peak movement rates
during the control periods stayed below 8 yards/minute (7 m/min) during
daylight hours of 0800-1500, the comparable period of each day when
off-road treatments were implemented.
Interestingly, movement rates of elk also were higher than control
periods at times encompassing sunrise and sunset for the days in which
an off-road activity occurred, even though humans were not present at
these times of day (Figure 2). These higher movement rates near sunrise
and sunset suggest that elk were displaced from preferred security and
foraging areas as a result of flight behavior during the daytime
off-road activities. In particular, movement rates of elk at or near
sunrise and sunset were higher during the 5-day treatments of mountain
bike and ATV activity (Figure 2). This finding will be studied in detail
in future analyses. Wisdom et al. 7
Flight Responses of Elk
The estimated probability of elk flight from a human disturbance was
highly dependent on distance. When elk and humans were close to one
another, the maximum probability of a flight response was approximately
0.65 during ATV, mountain bike, and hiking activity, and about 0.55
during horseback riding (Figure 3). Higher probabilities of flight
response occurred during ATV and mountain bike activity, in contrast to
lower probabilities observed during hiking and horseback riding (Table
1). Probability of a flight response declined most rapidly during
hiking, with little effect when hikers were beyond 550 yards (500 m)
from an elk. By contrast, higher probabilities of elk flight continued
beyond 820 yards (750 m) from horseback riders, and 1,640 yards (1,500
m) from mountain bike and ATV riders (Figure 3).
Movement Rates of Deer
In contrast to elk, mule deer showed less change in movement rates
during the four off-road activities compared to the control periods
(Figure 4). During the period of day from 0800 to1500 when off-road
activities occurred, movement rates of deer during ATV riding were
similar to rates during control periods. By contrast, daytime movement
rates of deer were higher, as compared to control periods, during
mountain bike riding, horseback riding, and hiking, especially in the
morning (Figure 4).
Interestingly, the increased movement rates observed for elk near
sunrise and sunset also were evident for mule deer. Movement rates at
these times were particularly high during all four activities as well as
during the control periods, suggesting that these times were peak
foraging periods (Ager et al. 2003).
Flight Responses of Deer
Estimated probabilities of flight response for mule deer were similar
among all four activities versus control periods (Table 1, Figure 5).
These probabilities were nearly identical among all four activities and
not significantly different than the null probability of 0.05 set for
control periods, suggesting that deer were not exhibiting the same
tendency for flight as shown by elk in relation to off-road activities
(Table 1).
Conclusions and Interpretations
Elk
Movement rates and probabilities of flight response for elk were
substantially higher during all four off-road activities compared to
control periods of no human activity. Consequently, off-road
recreational activities like those evaluated in our study appear to have
a substantial effect on elk behavior. The energetic costs associated
with these treatments deserve further analysis to assess potential
effects on elk survival. For example, if the additional energy required
to flee from an off-road activity reduces the percent body fat of elk
below 9 percent as animals enter the winter period, the probability of
surviving the winter is extremely low (Cook et al. 2004). Animal energy
budgets also may be adversely affected by the loss of foraging
opportunities while responding to off-road activities, both from
increased movements, and from displacement from foraging habitat. These
potential effects will be evaluated as part of future analyses.
Our results from 2002 also show clear differences in elk responses to
the four off-road activities. Elk reactions were more pronounced during
ATV and mountain bike riding, and less so during horseback riding and
hiking. Both movement rates and probabilities of flight responses were
higher for ATV and mountain bike riding than for horseback riding and
hiking. Wisdom et al. 8
Interestingly, the maximum probability of flight was approximately 0.65
for the treatments, meaning that about 35 percent of the time elk did
not exhibit a flight response when close to an off-road activity. Most
likely the response depends on local topography, cover, and other
factors that we have not yet analyzed as part of our flight response
model. Future work will include terrain and vegetation measures as
covariates in the probability models to examine whether these effects
can be detected and quantified (per recommendations and discussion by
Taylor and Knight 2003b).
It is important to note that designing our study to maintain the same
number of daily passes on transects among all four activities required
most effort for hiking and horseback riding, and least effort for ATV
riding. Specifically, to accomplish two runs per day required three
groups of hikers or horseback riders (with each group hiking
approximately 33 percent of transect length), but only one group of ATV
riders. By contrast, accomplishing two runs per day required two groups
of mountain bikers (with each group covering approximately 50 percent of
transect length).
Our results for elk might be different had we designed the study to test
animal response to an equal number of groups, or equal density, of
people engaged in the four off-road activities (i.e., the same number of
groups of people engaged in each activity, regardless of the number of
passes that could be accomplished), rather than testing for effects of
equal saturation of the study area (i.e., two daily passes on transects
for all four activities). In future analyses, we plan to explore the use
of the amount of time spent by each off-road activity as a covariate,
and possibly weight the movement rates and probabilities of flight
response by the inverse of time spent by each of the four off-road
activities. This weighting would help account for differences in effort
required among the four activities to achieve equal saturation of the
study area.
Our results may also change if elk eventually become "habituated" to
some or all of the off-road activities. We will evaluate this
possibility in future analyses by formally testing for replicate and
year effects under a random effects model, with repeated measures taken
on radio-collared animals over time (Kirk 1982). Analyses to test for
animal habituation to the off-road activities will be possible when all
three years of data are collected.
Mule Deer
In contrast to elk, mule deer showed little measurable response to the
off-road treatments. Movement rates increased slightly, however, during
periods of all four-off road activities except ATV riding. Deer may well
be responding to the treatments with fine-scale changes in habitat use,
rather than substantial increases in movement rates and flight
responses.
For example, it is possible that deer may respond to an off-road
activity by seeking dense cover, rather than running from the activity.
If mule deer are spending more time in dense cover, in reaction to any
of the off-road activities, this could result in reduced foraging
opportunities, and a subsequent reduction in opportunities to put on fat
reserves during summer that are needed for winter survival. Such
potential responses will be evaluated as part of future analyses.
Utility of Response Variables
Taylor and Knight (2003b) defined a variety of terms for measuring
animal responses to human activity. Neither movement rate nor
probability of a flight response was defined, however, because these
types of animal responses apparently have not been measured in past
research. We measured these two responses to human activity because both
variables can ultimately be used to estimate the energetic costs of
animal reactions to human activities. For example, movement rate can be
used as a background index of the rate of animal speed without human
activities, versus periods of human activities, to estimate the
additional energetic costs of increased movement, if any, in relation to
human activities (Ager et al. 2003).
Similarly, the probability of a flight response indicates how likely an
animal is to move at high speed in relation to its distance from a
human. This probability indicates how likely an animal is to run Wisdom
et al. 9
from a human activity, and thereby disrupt the animal's activities
related to energy acquisition (foraging) or energy conservation
(resting). Any movement away from an area in relation to human activity
has the potential disrupt these foraging and resting patterns, and
thereby cost energy (Johnson et al. 2004).
Future analyses will focus on the energetic costs, if any, to mule deer
and elk from exposure to each off-road activity. Additional analyses
also will include estimates of (1) the distance moved by an animal,
given a flight response; (2) the time required for an animal that
exhibits a flight response to return within a specified distance of the
animal's location before the flight; (3) the change in space use by an
animal, during or following periods of human activity, which may suggest
or reflect an animal seeking greater refuge from the human activity, as
compared to background or "null" use of space during periods of no human
activity; and (4) the degree to which animals spend time in forage
areas, gaining energy, versus time spent in non-foraging areas, during
each off-road activity versus control periods.
Implications for Recreation Management
Laws and policies of public land management emphasize multiple resource
uses. Management of timber, grazing, roads, minerals, and wilderness are
examples of traditional uses on lands administered by the U.S.
Department of Agriculture Forest Service and U.S. Department of Interior
Bureau of Land Management (BLM), the two largest federal landowners in
the United States. Public land managers now face the additional
challenge of serving a variety of off-road recreational uses that are
increasing rapidly, and that can be difficult to accommodate on the same
land area at the same time (Taylor and Knight 2003a).
New planning approaches are now underway in the Forest Service to
accommodate increasing off-road recreational demands while mitigating
the negative effects on species like elk (U.S. Department of Agriculture
Forest Service 2004). These approaches could consider two related
concepts: (1) off-road use rates and (2) off-road recreational
equivalents. We define off-road use rates as the number of passes per
unit time on a given linear route (primitive road or trail that we
referred to as transects) traveled by an off-road activity. Our results
show that one pass per day by any of the four off-road activities causes
increased movement rates and flight responses by elk.
We define off-road recreational equivalents as the ratio of ATV riders,
mountain bikers, horseback riders, and hikers that results in
approximately the same effect on a given resource, given the same
off-road use rate. In the case of elk, movement rates and probabilities
of flight were highest during ATV riding and lowest during horseback
riding and hiking. These effects were a result of one group of ATV
riders, two groups of mountain bikers, and three groups of horseback
riders or hikers required to complete one pass on the transects each
morning or afternoon. Consequently, the stronger effects posed by ATV
riding, combined with differences in the number of groups required of
each activity to achieve one pass on the transects, suggest that
recreational equivalents would exceed three groups of horseback riders
or hikers to every group of ATV riders, and exceed two groups of
mountain bike riders to every group of ATV riders.
While the formal methods of calculating the specific recreational
equivalents could be a subject of lengthy debate, the idea that
different levels of each off-road activity are required to approximate
the same effect on a given resource is logical and defensible.
Accordingly, off-road use rates and recreational equivalents could be
tested as potential concepts in helping allocate recreational activities
within and across watersheds on a given National Forest or BLM Field
Office. These concepts may be particularly relevant when derived from a
combination of response variables or resource uses. For example, effects
of each off-road activity on water quality, soil productivity, invasion
of exotic plants, and species sensitive to human activities could be
considered in deriving use rates and recreational equivalents.
Such an approach would demand a substantial increase in research on
effects of off-road activities. For management of elk, results from our
study will be most useful when estimates of the energetic costs, if any,
are derived for each of the four off-road activities in terms of use
rates and recreational equivalents. Energetic costs to elk from one pass
per day on a given linear route traveled by a Wisdom et al. 10
given off-road activity could be estimated, and the equivalent energetic
costs, given the same use rates, could be estimated among all off-road
activities.
Although these details are not yet available, managers could begin to
consider holistic management strategies for all off-road activities
based on our current findings. Some watersheds might feature
opportunities for ATV or mountain bike riding, for example, while other
watersheds might focus on opportunities for horseback riding or hiking.
Importantly, the watersheds identified for horseback riding or hiking
could accommodate a substantially higher number of groups engaged in
these off-road activities before realizing the same effects on elk as
would be expected in watersheds where ATV or mountain bike riding are
featured. This type of holistic management of different mixes of all
off-road activities contrasts with management approaches that focus on a
single off-road activity, without consideration of all off-road uses and
the cumulative effects from all activities.
Other strategies for watershed planning might simply focus on
restricting each recreational activity to specified trails or roads. In
this case, our results suggest that the effectiveness of such a strategy
would depend on how much area is affected by the network of trails or
roads allowed for use. If the linear distance of trails or roads open to
recreation is small relative to the total area of the watershed, the
effect on elk is likely to be minor or negligible. If the linear
distance is large relative to the size of the watershed, the negative
effect on elk could increase substantially. The specific effects could
be analyzed in the same manner as outlined for estimating effects of
motorized road traffic on elk, as done with "distance band" models
(Rowland et al. 2004).
Effective and defensible strategies to meet off-road recreation demands,
while also mitigating negative resource effects, are likely to require a
substantial increase in budgets of public land agencies for research,
management, and monitoring of these activities. Managers currently have
little knowledge with which to develop effective strategies in
partnership with the many public recreation users. Without such
knowledge, the debate about off-road recreation is likely to intensify,
with few scientifically based options for resolution in relation to
mitigating potential negative effects on species like elk that are
sensitive to human activities.
Acknowledgments
J. Boyd, A. Christensen, B. Dick, M. Rowland, R. Sparrowe, and M. Vavra
provided helpful reviews of our paper. The Oregon Department of Parks
and Recreation and the Pacific Northwest Research Station of U.S.
Department of Agriculture Forest Service provided funding. K. Munday and
B. Dick coordinated fieldwork among 33 people who implemented the
off-road activities. The ATV Grant Allocation Advisory Committee of
Oregon Department of Parks and Recreation, particularly N. Arbogast, P.
Harris, T. Hart, J. Barrell, and R. Parmelee, provided valuable
discussions about study designs most relevant to recreation management
on public lands.
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Table 1. Estimated probabilities (and approximate 95 percent Confidence
Limits) of a flight response by elk and mule deer as a function of
distance between animals and humans riding all-terrain vehicles (ATV),
mountain bikes (BIKE), horses (HORSE), or hiking (HIKE). On average
there were 128 deer or elk locations obtained during a given day of each
off-road activity (treatment periods). During periods of no human
activity (control periods), the null probability of a flight response is
0.05 (see text). Thus, any values >0.05 reflect an increased probability
of a flight response in relation an off-road activity.
Distance1 ATV BIKE HORSE HIKE
100 meters (109 yards) from elk 0.62 (0.52-0.73) 0.58
(0.46-0.68) 0.50 (0.40-0.59) 0.52 (0.42-0.64)
500 meters (545 yards) from elk 0.43 (0.36-0.49) 0.31
(0.26-0.35) 0.22 (0.19-0.26) 0.15 (0.12-0.18)
1000 meters (1090 yards) from elk 0.25 (0.20-0.30) 0.13
(0.10-0.16) 0.07 (0.05-0.08) 0.06 (0.04-0.08)
All distances from elk 0.19 (0.17-0.21) 0.14 (0.12-0.16)
0.11 (0.09-0.12) 0.08 (0.07-0.10)
100 meters (109 yards) from deer 0.06 (0.01-0.11) 0.08
(0.02-0.14) 0.11 (0.03-0.19) 0.10 (0.04-0.17)
500 yards (545 yards) from deer 0.05 (0.02-0.07) 0.07
(0.04-0.10) 0.05 (0.03-0.07) 0.04 (0.02-0.05)
1000 meters (1090 yards) from deer 0.03 (0.01-0.06) 0.06
(0.03-0.08) 0.04 (0.02-0.06) 0.04 (0.02-0.06)
All distances from deer 0.03 (0.02-0.05) 0.05 (0.04-0.07)
0.04 (0.03-0.05) 0.04 (0.03-0.06)
1 Distance between an animal and human during each off-road activity.
Wisdom et al. 13
Figure 1. Boundaries of ungulate-proof enclosures at the Starkey
Experimental Forest and Range in northeast Oregon (bottom left), and
location of transects used for ATV activities in the 3,590-acre
(1,453-ha) northeast study area (upper right), site of the off-road
recreation study. Transects were similar in length and location for
mountain biking, hiking, and horseback riding as that shown here for ATV
activities. Wisdom et al. 14
Figure 2. Mean movement rate (speed, meters/minute) of elk, estimated
hourly on a 24-hour basis, Pacific Daylight Time (PDT), during periods
of no human activity (C) versus periods of ATV activity (ATV), hiking
(HIK), mountain bike riding (BIK), and horseback riding (HRS), from
April through October, 2002, in northeast study area of Starkey. Wisdom
et al. 15
Figure 3. Estimated probability (solid line encompassed by dashed lines
of the approximate 95 percent pointwise confidence interval) of a flight
response by elk during 2002 in relation to distance (meters [m]) from
humans riding an ATV, mountain bike, horseback, or hiking. A flight
response is defined as an animal movement with a speed exceeding the
95th percentile of speeds observed during periods of no human activity
(control period). The horizontal dashed line at the bottom of each graph
is the probability of a flight response by elk during periods of no
human activity, and this line represents the background or "null"
condition, above which significant elk response to the off-road
activities exists. Wisdom et al. 16
Figure 4. Mean movement rate (speed, meters/minute) of mule deer,
estimated hourly on a 24-hour basis, Pacific Daylight Time (PDT), during
periods of no human activity (C) versus periods of ATV activity (ATV),
hiking (HIK), mountain bike riding (BIK), and horseback riding (HRS)
during 2002 in northeast study area of Starkey. Wisdom et al. 17
Figure 5. Estimated probability (solid line encompassed by dashed lines
of the approximate 95 percent pointwise confidence interval) of a flight
response by mule deer during 2002 in relation to distance (meters, [m])
from humans riding an ATV, mountain bike, horseback, or hiking. A flight
response is defined as an animal movement with a speed exceeding the
95th percentile of speeds observed during periods of no human activity
(control period). The horizontal dashed line at the bottom of each graph
is the probability of a flight response by deer during periods of no
human activity, and this line represents the background or "null"
condition, above which significant deer response to the off-road
activities exists.