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Detection Distance and Environmental Factors in Conservation Detection Dog
Surveys
Author(s): Sarah E. Reed, Allison L. Bidlack, Aimee Hurt and Wayne M. Getz
Source: Journal of Wildlife Management, 75(1):243-251.
Published By: The Wildlife Society
URL: http://www.bioone.org/doi/full/10.1002/jwmg.8
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Research Article
Detection Distance and Environmental
Factors in Conservation Detection Dog Surveys
SARAH E. REED,
1,2
Department of Environmental Science, Policy and Management, University of California, Berkeley, 137 Mulford Hall #3110,
Berkeley, CA 94720, USA
ALLISON L. BIDLACK, Department of Environmental Science, Policy and Management, University of California, Berkeley, 137 Mulford Hall
#3110, Berkeley, CA 94720, USA
AIMEE HURT, Working Dogs for Conservation, 52 Eustis, Three Forks, MT 59752, USA
WAYNE M. GETZ, Department of Environmental Science, Policy and Management, University of California, Berkeley, 137 Mulford Hall #3110,
Berkeley, CA 94720, USA
ABSTRACT Surveys using conservation detection dogs have grown increasingly popular as an efficient means to gather monitoring data,
particularly for elusive and low-density species such as carnivores. Working with dogs can greatly increase the area surveyed for wildlife and the
detection rate of survey targets. Due to the confounding effects of scent dispersion and dog movement, however, it can be difficult to estimate the
area searched in a survey. Additionally, although detection dogs have been used in studies under a wide range of air temperature, humidity, and
wind conditions, little research has examined how environmental factors affect detection dogs’ effectiveness for wildlife surveys. Between 2003
and 2005, we trained 2 dogs to assist us with surveys for mammalian carnivore scats in northern California. We conducted controlled search trials
to assess how the dogs’ scat detection rates were affected by the distance of scats from the transect search line, as well as variation in six
environmental factors. Both dogs detected >75% of scats located within 10 m, and the dogs’ detection rates decreased with increasing distance of
scats from the transect line. Among environmental factors, precipitation was the most important variable explaining variation in scat detection
rates for both dogs. Precipitation likely degrades or removes scats from the landscape over time, and detection rates increase as scat begins to
accumulate following the last substantial (>5 mm) rain event of the year. If scat accumulation is not controlled for in ecosystems with a strong
seasonal pattern of rainfall, it could lead to considerable bias in study results. We recommend that researchers report the conditions under which
conservation detection dog surveys took place and analyze how detection rates vary as a function of distance, temperature, precipitation, humidity,
wind, and other locally important environmental factors. ß2011 The Wildlife Society.
KEY WORDS carnivore, detection distance, detection dog, environmental conditions, monitoring, non-invasive,
scat survey.
Conservation and management of rare and wide-ranging
species require monitoring data that can be collected
inexpensively and repeatedly. Ideally, wildlife monitoring
techniques should maximize the geographic area and number
of animals surveyed, while minimizing cost and risk of
disturbance or injury to individual animals. Non-invasive
survey methods, which do not require direct observation
or capture of wildlife, have increased in popularity as efficient
techniques for gathering monitoring data, particularly for
low-density and elusive species such as carnivores (Long et al.
2008).
One method for increasing the sample size and improving
the accuracy of non-invasive surveys is the use of conserva-
tion detection dogs. ‘‘Conservation detection dog’’ is an
umbrella term for detection dogs trained to locate or dis-
criminate biological targets in a natural setting for research or
management applications, distinguished from dogs that are
trained to find wildlife contraband in a law enforcement
context (Hurt and Smith 2009). Although dogs have
been assisting biologists with wildlife surveys for more than
a century (Zwickel 1980, Hill and Hill 1987, Gutzwiller
1990), recent applications have expanded both the scope and
sophistication of their contributions, particularly through
scent detection and discrimination work (Browne et al.
2006). Dogs have been trained to recover carcasses
(Homan et al. 2001, Arnett 2006), locate invasive or endan-
gered species (Engeman et al. 2002, Cablk and Heaton
2006), detect species’ scent trails (Akenson et al. 2004),
and identify occupied burrows (Reindl-Thompson et al.
2006). Dogs have also been trained to detect the scats of
kit foxes (Smith et al. 2003), black bears (Wasser et al. 2004,
Long et al. 2007), grizzly bears (Wasser et al. 2004), bobcats
(Harrison 2006, Long et al. 2007), and fishers (Long et al.
2007) in terrestrial surveys and right whales in marine surveys
(Rolland et al. 2006). Scats collected in detection dog surveys
can be combined with recent advancements in laboratory
techniques to generate a wealth of information about
wildlife populations, including species- and individual-level
identification, diet, disease, reproductive status, and physio-
logical condition (Kohn and Wayne 1997).
Working with dogs can greatly increase the area surveyed
for wildlife, as well as the detection rate of survey targets. For
example, dogs searched up to 5 times as much distance as
humans in 70% of the time (Nussear et al. 2008), and dogs’
detection rates were 2–4 times greater than those of humans
searching visually (Smith et al. 2001, Homan et al. 2001).
Due to the confounding effects of scent dispersion and dog
movement, however, it can be difficult to estimate the
Received: 13 August 2009; Accepted: 23 April 2010
1
E-mail: sarah.reed@colostate.edu
2
Present Address: Department of Fish, Wildlife, and Conservation
Biology, Colorado State University, Fort Collins, CO 80523-1474.
Journal of Wildlife Management 75(1):243–251; 2011; DOI: 10.1002/jwmg.8
Reed et al. Factors Affecting Conservation Dog Surveys 243
distance at which the target is likely to be detected, and
subsequently, to quantify the total area searched in a survey.
Few studies have reported detection distances from conser-
vation detection dog surveys, and measurements differed
widely, ranging from mean detection distances of 4.8 m
(Ralls and Smith 2004), 13.9 m (Cablk et al. 2008), and
29.3 m (Shivik 2002) in terrestrial surveys, to detection
distances up to 1.9 km in marine surveys (Rolland et al.
2006).
To our knowledge, no researcher using conservation detec-
tion dogs has attempted to model the probability of detection
to estimate the actual density or abundance of survey targets
(Thomas et al. 2010). Yet as is true for many other
wildlife survey methods, knowledge of the detection function
is critical for designing research studies, selecting transect
locations, and estimating species’ densities.
Variability in habitat or weather conditions and the phys-
iological condition of dogs can affect detection rates and
potentially bias the results of wildlife surveys (Gutzwiller
1990). Air temperature, vapor pressure, and the direction
and variability of wind currents all affect how scent disperses
through the air (Syrotuck 1972, Snovak 2004). Environmental
factors are also believed to affect the production of scent
through the decomposition of its source, but there is
currently little scientific evidence to support these theories
(Stockham et al. 2004). Air temperature and relative humid-
ity may influence the evaporation rate of the scent source
(Pearsall and Verbruggen 1982) or the bacterial activity
that releases scent vapors (Syrotuck 1972). In general, higher
temperatures correspond to higher rates of evaporation and
bacterial activity (Wasser et al. 2004), but this intensity may
be short-lived, and temperatures that are too high can kill
bacteria and halt scent production. On the other hand, air
moisture slows evaporation rates and is essential to maintain
bacterial activity, but prolonged precipitation may dampen or
wash away scent vapors near the ground (Syrotuck 1972).
Over time, wildlife researchers have noted that precipitation
and other weather factors contribute to degradation or
disappearance of survey targets such as scats (Smith et al.
2005, Harrison 2006).
Environmental conditions also influence the physiological
state of detection dogs. Observations indicate that higher
temperatures can lead to excessive panting (Smith et al.
2003) and more rapid fatigue (Homan et al. 2001,
Nussear et al. 2008). Panting is a dog’s primary means
of cooling its body, and because a dog cannot sniff and
pant simultaneously, increased panting causes a decrease
in sniffing rate and scent detection (Gazit and Terkel
2003). Dry conditions can cause dehydration or a dry nose,
which also limit a dog’s ability to detect scent (Snovak 2004).
Detection dogs have been used under a wide range of
air temperature, humidity, and wind conditions (Table 1).
However, as Shivik (2002) and Cablk and Heaton (2006)
note, little research has examined how these environmental
factors affect conservation detection dogs’ abilities or their
effectiveness for wildlife surveys. Although most papers
published on detection dog methods discussed potential
effects of environmental factors, few reported the
environmental conditions in which surveys took place
(Homan et al. 2001, Smith et al. 2003, Harrison 2006) or
analyzed the relationship between environmental variables
and dogs’ detection rates (Shivik 2002, Cablk and Heaton
2006, Long et al. 2007, Cablk et al. 2008, Nussear et al.
2008).
Here, we present the results of controlled search trials
investigating the influence of distance and environmental
conditions on the scat detection rates of two conservation
detection dogs in northern California. We empirically
assessed how dogs’ detection of mammalian carnivore scats
was affected by the distance of scats from the transect search
line, as well as variation in several environmental factors.
We also examined the influence of environmental conditions
on scat detection rates in uncontrolled field searches in
30 research sites located throughout the San Francisco
Bay Area. Our objectives were to 1) estimate the dogs’
probability of detecting a target species’ scat by distance from
the transect search line, and 2) assess how temperature,
precipitation, humidity, vapor pressure, and wind affect dogs’
scat detection rates.
STUDY AREA
We conducted our research in California oak woodlands,
located to the north and east of San Francisco Bay (378510N,
1228260W). This region experienced rapid conversion of
undeveloped land to residential and agricultural uses, and
remaining oak woodlands primarily occurred along hillslopes
ranging from 50 m to 1,000 m in elevation. Woodlands in
our study area were dominated by several oak species: coast
live oak (Quercus agrifolia), Oregon oak (Q. garryana), valley
oak (Q. lobata), black oak (Q. kelloggii), and blue oak
(Q. douglasii), with small patches of bay laurel (Laurus nobilis)
and Douglas fir (Pseudotsuga menziesii), and chaparral at
higher elevations.
We conducted dog training and experimental trials at
the Hopland Research & Extension Center (HREC), a
2,139-ha field station managed by the University of
California, Davis in southern Mendocino County (Fig. 1).
The 30 field sites we surveyed were located in Alameda,
Contra Costa, Marin, and Sonoma counties and were owned
and managed by several public agencies and conservation
organizations.
Table 1. Ranges of air temperature, humidity, and wind conditions reported
for prior conservation detection dog surveys conducted in the United States,
1997–2005.
Temp
(C)
Relative
humidity (%)
Wind
speed (m/s) References
3–11 23–70 Homan et al. (2001)
19–31 25–91 Homan et al. (2001)
8–22 Smith et al. (2003)
14–32 Smith et al. (2003)
11–23 Harrison (2006)
12.2–26.7 15.8–87.9 0–8.7 Cablk and Heaton (2006);
Cablk et al. (2008)
9.4–29.9 9.8–85.8 0–3.2 Nussear et al. (2008)
244 The Journal of Wildlife Management 75(1)
METHODS
Dog and Handler Training
The non-profit organization Working Dogs for Conservation
(WDC) led dog and handler training seminars in August
2003 and January 2004. Handler training included dog
selection and testing, positive reinforcement techniques,
scent discrimination training, and field handling skills.
We searched for candidate detection dogs in shelters
and rescue organizations throughout northern California.
We tested candidates for their level of object obsession
(i.e., toy drive) and agility, using methods modified from
the Brownell–Marsolais motivation and drive test (Brownell
et al. 2000), standard search-and-rescue (SAR) techniques
(Snovak 2004), and the early stages of scent discrimination
training. Each handler tested >300 dogs before selecting the
final training candidates from Bay Area shelters. Dog 1 was a
1.5-year-old female Labrador retriever mix adopted in fall
2003, and dog 2 was a 2-year-old male pit bull terrier mix
adopted in spring 2004 (University of California Berkeley
Animal Care and Use Committee Protocol no. R245-0503).
We trained the 2 dogs to detect the scats of different
suites of species. We trained dog 1 to detect mountain lion
(Puma concolor), bobcat (Lynx rufus), and domestic cat scats,
whereas we trained dog 2 to detect red fox (Vulpes vulpes),
gray fox (Urocyon cinereoargenteus), and kit fox (Vulpes
macrotis) scats. We collected training scats from zoos and
animal rehabilitation centers throughout California, and for
each target species, we collected scats from several captive
individuals fed a variety of diets.
Dog training followed methods previously described by
Smith et al. (2003). We taught the dogs to associate the
scent of the target species scats with a reward (i.e., a
play session with a tennis ball) and to ignore the scent of
non-target species scats. Training progressed through
increasingly complicated search conditions until, at the
end of the training period, each handler was able to walk
along a transect line up to several kilometers in length, with
the dog air-scenting and signaling detection of target species
scats in the surrounding landscape. Total time required to
train each dog was approximately 12 weeks.
Detection Distance Trials
When the dogs had successfully passed through all stages of
scat detection training and were capable of long-distance
field searches, we designed an experiment to test how
distance from the transect search line affected dogs’ ability
to detect scats. We conducted search trials in a small pasture
at HREC that was fenced to exclude livestock and wildlife
for many years (Fig. 1). Because most of our target species
occur at HREC, we thoroughly searched the pasture with
each dog to ensure the pasture was cleared of target species
scats before beginning the test. We conducted detection
distance trials with dog 1 from December 2003 to January
2004 and trials with dog 2 from January to February 2005.
We established a fixed 200-m line transect running north
northwest–south southeast through the center of the pasture
and marked it using visual landmarks and flagging. For each
dog, we used scats collected from one captive animal, to
minimize any potential variation in detectability due to
Figure 1. Location of the Hopland Research & Extension Center (HREC) and additional research sites in northern California where we used conservation
detection dogs to survey for mountain lion, bobcat, domestic cat, red fox, gray fox, and coyote scats. Randomly selected placement locations for 3 scats in one
controlled search trial are shown in a diagram of the fenced pasture at HREC where experimental trials occurred from 2003 to 2005.
Reed et al. Factors Affecting Conservation Dog Surveys 245
species or individual scent. We used a random number
generator (Microsoft Excel, Microsoft Corporation,
Redmond, WA) to select placement locations for each of
3 scats per search trial. We randomly stratified scat locations
by distance along the transect line (in 10-m intervals), direc-
tion from the transect line (east or west), and distance from
the transect line (in 5-m intervals).
We began by placing the 3 scats and recording their Global
Positioning System (GPS) locations. We stored the scats
frozen but allowed them to thaw for 0.5 hr before placing
them. We did not walk directly to placement locations, but
instead walked all around the pasture to disperse the human
scent trail. After placing scats, we waited an average of 1.0 hr
(range: 0.3–4.8 hr) before searching to allow the scent to
disperse in the air.
We returned with the dog for a search trial, walking
slowly in one direction down the transect line. The dog
worked off-lead, moving freely around the handler. When
the dog signaled a detection, we marked the position where
we stopped walking on the transect line and approached the
dog. If the dog successfully located a scat, we rewarded him
or her with a play session, collected the scat, and recorded its
GPS point location. We then returned to the position where
we stopped on the transect line and continued searching.
We took a short break at the end of the line and then
searched the transect in the opposite direction. At the end
of each search trial, we left all uncollected scats in the pasture.
We completed 14 search trials with dog 1 (n¼41 scats)
and 15 search trials (n¼45 scats) with dog 2. We used a
Geographic Information System (GIS) database in ArcView
3.2 to match scat locations and calculate the delay time
between placement and collection of each scat. We used
JMP (SAS Institute, Inc., Cary, NC) to perform all statistical
analyses, and we analyzed data sets for each dog separately.
We considered only those scats found during the first search
trial after placement, to minimize any effect of scat decompo-
sition on detection distance. We assigned a value of 1 to scats
detected during the first search trial following placement and
a value of 0 to those that were not.
Wind direction and strength could affect the dogs’ detec-
tion of scats placed on either side of the search transect.
We collected data on wind direction and wind speed
during search trials using an automated weather station
located at HREC and managed by the California
Irrigation Management Information System (CIMIS). We
calculated wind direction relative to the direction of scat
placement from the transect line; in other words, values
ranged from 0 (wind blowing from the scat toward the
transect) to 180 (wind blowing away from the transect
toward the scat). We used logistic regression to estimate
the dogs’ detection functions as a function of scat placement
distance, wind direction, wind speed, and the interaction
between wind direction and speed (WINDDIR
WINDSP; Buckland et al. 2006), and we used a model
selection approach to compare the resulting models
(Burnham and Anderson 2002). We compared a balanced
set of 11 models for combinations of 2 explanatory
variables using the Akaike Information Criterion with a
small sample-size adjustment (AIC
c
). We present results
for the model with the greatest support (min. AIC
c
) and
for competing models (DAIC
c
<3).
Environmental Conditions
Controlled trials.– We also used the results of the controlled
search trials to test for effects of variable environmental
conditions on the dogs’ scat detection rates. We examined
the dogs’ detection rates in search trials conducted in the
experimental pasture described above. For the environmental
conditions analysis, we included all scats left in the field from
previous search trials to account for possible effects of scat
decomposition over time. Thus, we knew the total number of
target scats available for detection during each search trial,
allowing us to calculate the dogs’ actual detection rates.
We recorded several environmental variables for each
search trial from HREC’s automated weather station: air
temperature, vapor pressure, wind speed, wind variability
(SD of wind direction), and relative humidity. We averaged
values for all variables for the nearest hour to the search trial.
We also recorded cumulative precipitation for each search
trial, beginning with the start date of the experiment
(Table 2).
We used a model selection approach to examine the
relationships between environmental conditions and scat
detection rates. We calculated the proportion of available
scats (scats that were placed or were remaining in the pasture)
detected during each search trial. We compared a balanced
set of 22 models for combinations of 2 environmental
factors using AIC
c
. We present results for the model with
the greatest support (min. AIC
c
) and for competing models
(DAIC
c
<3).
Uncontrolled trials.– To better understand how environ-
mental factors affect actual field surveys, we repeated our
Table 2. Mean values and ranges of environmental variables in controlled and uncontrolled field searches using conservation detection dogs in northern
California, 2003–2005.
Variable Code
Controlled trials Uncontrolled trials
xRange xRange
Temp (C) TEMP 11.1 4.4–21.8 17.1 9.3–28.7
Vapor pressure (kPa) VP 0.9 0.6–1.3 1.3 0.9–1.8
Wind speed (m/s) WINDSP 1.6 0.7–3.4 1.6 0.8–3.3
Variability of wind direction (8) WINDVAR 34.5 18.5–58.9 40.7 13.4–68.5
Relative humidity (%) RH 68 22–100 71 45–95
Cumulative precipitation (mm) PRECIP1 138 1–310
Days since precipitation (>5 mm) PRECIP2 41 5–115
246 The Journal of Wildlife Management 75(1)
analysis of the relationships between environmental con-
ditions and dogs’ scat detection rates using data from uncon-
trolled field searches conducted in 30 additional research
sites. All sites were located in similar oak woodland habitats,
and because the 2 dogs were trained to locate the scats of
different target species, we analyzed variation in detection
rates among surveys for each dog separately.
Each dog-handler team completed 15 uncontrolled field
searches. We surveyed sites between April and June 2004
with dog 1 and between July and September 2005 with dog 2.
We used a handheld GPS device to record our transect
search line and the point location of each scat collected.
We downloaded and differentially corrected the GPS data
and exported it to ArcGIS 9.0, and we corrected transect
lines using the Smooth Line tool in ArcToolbox (PAEK
algorithm with a smoothing tolerance of 50 m).
We determined the nearest CIMIS weather station to each
field site and averaged the values of environmental variables
for the hours searched in each site. We collected the same
set of environmental variables as for controlled field
trials (Table 2), but we calculated the effect of precipitation
differently. We conducted controlled searches repeatedly
in one site during the rainy season of California’s
Mediterranean climate, which allowed us to examine the
effect of daily variation in precipitation on scat detection. We
conducted the uncontrolled searches in 30 dispersed field
sites, and we surveyed each site only once during the dry
season. There is considerable variation in rainfall across the
study region, and cumulative precipitation values are not
likely to be comparable among sites. Because we assume that
the primary effect of precipitation is likely to be removal or
decomposition of scats, for uncontrolled searches we calcu-
lated the number of days that passed since a substantial
precipitation event occurred in each field site. Exploratory
analysis of weather data indicated that >5 mm of rainfall in
1 day was an appropriate threshold for a substantial precipi-
tation event in our study area.
We searched an average of 4.1 km (range: 1.5–10.2 km)
of transects in each site, and we used a model selection
approach to examine effects of environmental conditions
on scat detection. We counted the number of scats collected
in each site, and we calculated the scat detection rate as the
number of scats found per distance searched. We compared
a balanced set of 22 models for combinations of 2 environ-
mental factors using AIC
c
and we present results for
the model with the greatest support (min. AIC
c
) and for
competing models (DAIC
c
<3).
RESULTS
Dog 1 detected 68% of scats and dog 2 detected 77% of scats
during the first search following placement, and the dogs’
detection rates decreased with increasing distance of scats
from the transect line (Fig. 2). Scat placement distance was
the most important variable identified in the full model sets
for dog 1 (w
þ
(DIST)¼1.00) and dog 2 (w
þ
(DIST)¼0.90)
and appeared in all of the top-ranked models selected for
both dogs (Table 3). Confidence intervals for all coefficients
of the wind conditions variables overlapped zero.
In controlled search trials at HREC, top-ranked models
(DAIC
c
<3) for detection rates of dog 1 and dog 2 included
both air temperature and cumulative precipitation (Table 4).
Cumulative precipitation was the most important variable
identified in the full, balanced model sets for dog 1
(w
þ
(PRECIP1) ¼0.96) and dog 2 (w
þ
(PRECIP1) ¼1.00),
and it was negatively related to scat detection rate for
both dogs. Air temperature (w
þ
(TEMP)¼0.47) had a weak
positive relationship to detection rate for dog 1, whereas for
dog 2, air temperature (w
þ
(TEMP)¼0.20) was negatively
related to detection rate. However, confidence intervals
for the coefficient of air temperature for dog 2 and the
coefficients of relative humidity for both dogs overlapped
zero.
In the uncontrolled field trials for dog 1, we identified 7
competing models (DAIC
c
<3) to explain the relationship
between scat detection rate and environmental conditions
Figure 2. Results of detection distance trials conducted at the Hopland
Research & Extension Center (HREC) using (a) dog 1 in 2003–2004 and
(b) dog 2 in 2004–2005. Both dog 1 and dog 2 detected placed scats less
frequently as the distance from the transect line increased. Model-averaged
estimates of scat detection functions are shown for each dog, given mean
wind conditions during the search trials. In addition, mean detection rates
(SE) are given for scat placement distances to the nearest 5 m. Dog 1
searched for a mean of 6.8 scats and dog 2 searched for a mean of 7.5 scats
at each distance.
Reed et al. Factors Affecting Conservation Dog Surveys 247
(Table 4). The null model was the top-ranked model, and the
environmental variables with the greatest relative importance
in the 6 models were days since precipitation (w
þ
(PRECIP2) ¼
0.32) and wind variability (w
þ
(WINDVAR)¼0.24). However,
confidence intervals for the coefficients of both variables over-
lapped zero. Only one model had strong support (DAIC
c
<3)
to explain the relationship between detection rate and environ-
mental conditions for dog 2 (Table 4). Days since precipitation
(w
þ
(PRECIP2) ¼1.00) and relative humidity (w
þ
(RH)¼0.90)
were both positively related to detection rate.
DISCUSSION
We were unsurprised to find that the dogs’ scat detection
rates declined by distance from the transect search line.
However, estimates of the dogs’ detection functions are
useful for calculating the area searched around the survey
transect and the overall density or abundance of survey
targets (Thomas et al. 2010). We defined detection distance
as the distance between the scat’s placement location and the
transect search line. We recognize that although the handler
Table 3. Top-ranked models from an Akaike Information Criterion (AIC)-based model selection of scat detection rates in detection distance trials at Hopland
Research & Extension Center for dog 1 (2003–2004) and dog 2 (2005). For each model we included parameter estimates (b
1
,b
2
) and 95% confidence intervals
(CI) for 1 or 2 variables, log-likelihood (Log(L)), number of parameters (K), AIC
c
(small-sample correction to AIC), and Akaike weight (w
i
). Scat placement
distance was the most important explanatory variable in the full model sets for dog 1 (w
þ
(DIST)¼1.00) and dog 2 (w
þ
(DIST)¼0.90).
Model, by dog b
1
CI (b
1
)b
2
CI (b
2
) Log(L)KAIC
c
w
i
Dog 1
DIST
a
þWINDDIR WINDSP
b
0.221 0.148 0.010 0.012 838.35 3 41.69 0.410
DIST
a
þWINDDIR
c
0.208 0.139 0.012 0.017 860.80 3 42.79 0.237
DIST
a
0.171 0.119 871.03 2 42.90 0.224
DIST
a
þWINDSP
d
0.183 0.128 1.09 2.14 886.95 3 44.06 0.125
Dog 2
DIST
a
þWINDSP
d
0.153 0.125 1.08 1.28 1041.46 3 47.01 0.412
DIST
a
0.116 0.101 1066.89 2 47.79 0.278
DIST
a
þWINDDIR WINDSP
b
0.115 0.104 0.002 0.007 1102.90 3 49.74 0.105
DIST
a
þWINDDIR
c
0.123 0.103 0.004 0.013 1103.48 3 49.76 0.104
a
Distance of scat from the search transect.
b
Interaction between wind direction and wind speed during the search trial.
c
Wind direction relative to the direction of scat placement from the search transect.
d
Wind speed during the search trial.
Table 4. Top-ranked models from an Akaike Information Criterion (AIC)-based model selection of scat detection rates in controlled search trials at Hopland
Research & Extension Center (2003–2005) and uncontrolled searches of 30 research sites in northern California (2004–2005). For each model we included
parameter estimates (b
1
,b
2
), and 95% confidence intervals (CI) for 1 or 2 environmental variables, log-likelihood (Log(L)), number of parameters (K), AIC
c
(small-sample correction to AIC), and Akaike weight (w
i
). In the controlled trials, cumulative precipitation was the most important explanatory variable in the
full model sets for both dog 1 (w
þ
(PRECIP1) ¼0.96) and dog 2 (w
þ
(PRECIP1) ¼1.00). In the uncontrolled trials, the most important explanatory variables in
models selected for dog 1 were days since precipitation (w
þ
(PRECIP2) ¼0.32) and wind variability (w
þ
(WINDVAR)¼0.24). For dog 2, they were days since
precipitation (w
þ
(PRECIP2) ¼1.00) and relative humidity (w
þ
(RH)¼0.90).
Model, by trial and dog b
1
CI (b
1
)b
2
CI (b
2
) Log(L)KAIC
c
w
i
Controlled trials—dog 1
PRECIP1
a
þTEMP
b
0.0028 0.0014 0.058 0.056 23.54 4 34.08 0.460
PRECIP1
a
0.0023 0.0016 20.80 3 32.93 0.258
PRECIP1
a
þRH
c
0.0029 0.0017 0.0064 0.0088 22.29 4 31.58 0.131
Controlled trials—dog 2
PRECIP1
a
0.0039 0.0010 33.36 3 58.55 0.436
PRECIP1
a
þTEMP
b
0.0033 0.0013 0.012 0.018 34.51 4 57.02 0.203
PRECIP1
a
þRH
c
0.0034 0.0014 0.0018 0.0039 34.01 4 56.02 0.123
Uncontrolled trials—dog 1
Null 7.61 2 20.15 0.240
PRECIP2
d
0.045 0.070 6.61 3 21.21 0.141
WINDVAR
e
0.042 0.085 7.00 3 21.99 0.095
PRECIP2
d
þWINDVAR
e
0.054 0.069 0.055 0.082 5.40 4 22.44 0.076
WINDSP
f
0.71 3.23 7.49 3 22.97 0.058
TEMP
b
0.054 0.357 7.55 3 23.11 0.055
RH
c
0.015 0.102 7.56 3 23.11 0.054
Uncontrolled trials—dog 2
PRECIP2
d
þRH
c
0.162 0.055 0.201 0.105 8.72 4 29.88 0.902
a
Cumulative amt of precipitation since the beginning of the study.
b
Temp during the search trial.
c
Relative humidity during the search trial.
d
Days since precipitation (>5 mm).
e
Variability of wind direction during the search trial.
f
Wind speed during the search trial.
248 The Journal of Wildlife Management 75(1)
walked along the transect search line, the dog had freedom of
movement and may have been to the right or left of the line at
any given time. Therefore, scent from a scat placed 10 m to
the left of the line may have been detected by a dog when it
was 5 m to the right of the line, for an actual detection
distance of 15 m (Cablk et al. 2008). Because we did not
directly assess the point at which the dog detected the scent,
similar to distance sampling methods, our approach defined
a nominal detection function around the transect search
line, which should be kept in mind when interpreting the
distances discussed below.
The average distance at which dog 1 detected a scat during
the first search trial following placement was 9.6 m, whereas
the average distance for dog 2 was 10.4 m, and at a placement
distance of 10 m, both dogs detected scats >75% of the time
(Fig. 2). Results for these 2 dogs are similar to one another
and comparable to detection distances reported for other
terrestrial surveys (Ralls and Smith 2004, Cablk et al.
2008). It would be helpful to repeat similar controlled trials
with several more dogs to understand the degree to which the
detection function varies by individual dog. In addition,
controlled search trials should be repeated with scats placed
at greater distances from the survey transect to get a better
estimate of the distance at which the detection rate
approaches zero.
We did not find evidence for the influence of wind con-
ditions on the dogs’ detection functions (Table 3). In all
cases, coefficients for wind speed, direction, and the inter-
action of wind speed and direction substantially overlapped
zero, which may be because wind speeds were mild during
the search trials (Table 2) or because the dogs’ movement
around the transect line compensated for any effect of wind.
In addition, we searched the transect in 2 directions during
each trail, which likely enhanced the dogs’ overall detection
rates. Wind conditions could have a greater effect on surveys
conducted with detection dogs on-lead, when a dog has less
freedom to search for scents from multiple directions.
Although results of our search trials indicate there is a clear
relationship with scat placement distance, the top-ranked
models explained only part of the variation in the detection
rates of dog 1 (R
2
¼0.32) and dog 2 (R
2
¼0.19). Many
other factors could affect the probability of detection around
a transect line. For example, topography and vegetation
can influence the distribution of scent in space. Search
dog professionals report that scent pools in drainages or
depressions, along walls or fences, or in areas of dense
vegetation (Syrotuck 1972, Snovak 2004). We selected the
pasture where we worked because it was a flat and open grassy
area, but the dogs’ detection functions might change in areas
with variable terrain and vegetation structure.
Scent contamination is another potential confounding fac-
tor for conservation detection dog surveys (Snovak 2004).
Although we used the same pasture for all of our controlled
trials, we were unable to control for possible temporal
sources of variability in the olfactory environment, such as
the proximity of livestock, vehicle exhaust, and residual scent
from prior searches. In addition, when dogs are trained to
detect multiple survey targets (e.g., scats from multiple
species), researchers must account for the possibility that
dogs detect targets from different species or individuals
at variable distances or rates, especially when independent
verification of target detection is difficult or impossible
(Reindl-Thompson et al. 2006). We were able to control
for this factor in the trials at HREC by using scats from only
one target individual per dog, but we could not account for it
in uncontrolled field searches. Lastly, perhaps the most
important unmeasured variable in all of our search trials
was the rate of communication between dog and handler.
We measured not only the ability of dogs to detect scats at
different placement distances and under variable environ-
mental conditions, but also the ability of the dog to success-
fully communicate its alert when it found a scat and the
ability of the handler to correctly interpret and reinforce the
alert. Some of these issues could be addressed by establishing
consistent standards for obedience and search skills through a
certification program for conservation detection dog-handler
teams (Cablk and Heaton 2006, Long et al. 2008).
Researchers frequently discuss the potential for environ-
mental conditions to influence the results of conservation
detection dog surveys (e.g., in 100% of the studies we
reviewed), but most of the available information is anecdotal
or drawn from the popular literature (Pearsall and
Verbruggen 1982), and relationships between environmental
factors and detection rate are rarely examined empirically.
Cablk and Heaton (2006) and Nussear et al. (2008) did not
observe significant variation in detections of desert tortoises
(Gopherus agassizii) by wind, temperature, or humidity,
and Long et al. (2007) found that the same factors did
not influence detection of forest carnivore presence in scat
surveys. Shivik (2002) observed a positive relationship
between wind variability and time to detection, suggesting
that highly variable wind may disperse scent and make
it more difficult for a dog to follow it to its source.
However, many of these previous studies were not explicitly
designed to examine the influence of environmental factors,
and any effect of environmental conditions may have been
overwhelmed by other sources of variation in the study
systems (Smith et al. 2005) or obscured by model analyses
that focused on species presence rather than detection rate
(Long et al. 2007).
Our controlled search trials allowed us to examine the
relationship between the dog’s actual detection rate of placed
scats and a realistic range of environmental conditions for our
study area. The same group of top-ranked models explained
the variation in detection rates for the 2 dogs (Table 4),
and the most important variable in all of the models was
cumulative precipitation. We conducted controlled search
trials during the rainy season of California’s Mediterranean
climate, and we suspect that regular rainfall during the study
led to degradation or removal of placed scats over time.
Interestingly, air temperature appeared to affect the
dogs differently. Higher temperatures correlated with
higher detection rates for dog 1, whereas dog 2’s detection
rate declined with increasing temperature. Both variable
relationships were weak, and the confidence interval
for the coefficient of temperature for dog 2 substantially
Reed et al. Factors Affecting Conservation Dog Surveys 249
overlapped zero. These divergent results may be attributable
to differences in the conditions in which we conducted the
surveys, differences in the bacterial activity or scent volatility
of the target scats, or most likely, differences in the heat
tolerances of the individual dogs. Increased panting leads to
decreased sniffing and scent detection (Gazit and Terkel
2003), and prior observations indicate that detection dogs
can have highly variable panting rates in response to the same
environmental conditions (Smith et al. 2003).
Selection of models for environmental conditions in the
uncontrolled field searches further underscored the influence
of precipitation on detection rates. Although there was
substantial uncertainty in model selection for dog 1, the
most important variable overall was days since precipitation,
which was positively related to detection rate (Table 4).
Dog 1’s detection rate increased with days since precipi-
tation, but the confidence interval for the coefficient of
precipitation overlapped zero. The best model for dog 2
showed an even stronger positive relationship between days
since precipitation and detection rate (Fig. 3). Dog 2’s
detection rate also increased with increasing levels of
relative humidity (Table 4). Some search dog professionals
have suggested that humidity may slow the evaporation rate
or increase bacterial activity at the scent’s source, thereby
increasing the intensity of the scent (Syrotuck 1972, Pearsall
and Verbruggen 1982).
Our results indicate that precipitation plays a strong role
in degrading or removing scats from the landscape and
that detection rates increase as scat begins to accumulate
following the last substantial (>5 mm) rain event of the year.
Although some researchers have suggested that precipitation
plays a role in scat removal (Smith et al. 2005, Harrison
2006), we are not aware of any other studies specifically
examining this relationship. We speculate that the stronger
relationship for dog 2 was primarily attributable to surveys
using that dog being conducted later in the dry season,
allowing more time for scats to accumulate (Fig. 3). For
ecosystems with a strong seasonal pattern of rainfall such as
coastal California, scat accumulation could lead to significant
bias in study results if researchers do not control or account
for this factor. On the other hand, given the positive relation-
ship with relative humidity shown for dog 2 (Table 4), the
complex relationship between air moisture, precipitation,
and detection rate warrants further investigation in ecosys-
tems with more variable rainfall patterns.
MANAGEMENT IMPLICATIONS
As recent contributions to the literature suggest, surveys
using conservation detection dogs will become increasingly
common in wildlife research and management. Because
the influence of environmental conditions is likely to vary
substantially by study environment and individual dog, it is
important for researchers to quantify the factors affecting
detection rates and minimize potential biases. At a mini-
mum, we recommend that researchers report the conditions
under which wildlife detection surveys took place and analyze
whether detection rates vary as a function of temperature,
humidity, wind, precipitation, and other locally important
environmental factors.
In addition, wildlife surveys should be designed to maxi-
mize the abilities of the detection dog. Ideally, controlled
search trials should be conducted under a variety of con-
ditions, similar to the experiments we described, to
assess detection distances and environmental thresholds of
individual dogs. Where applicable, trials should be repeated
for multiple survey targets, to verify whether dogs’ detection
rates vary by target species or individual. This information
would allow researchers to calibrate their survey design
(i.e., the spatial arrangement of transects, time of day, and
duration of searches) to individual dogs’ abilities.
ACKNOWLEDGMENTS
Funding for this project was provided by an American Society of
Mammalogists Grant-In-Aid-of-Research (ALB), Budweiser
Conservation Scholarship (SER), National Science Foundation
Graduate Research Fellowship (SER), Phi Beta Kappa Doctoral
Fellowship (SER), Sigma Xi Grant-In-Aid-of-Research
(SER), and the Department of Environmental Science,
Policy and Management. We thank the staff of Working
Dogs for Conservation (WDC) for dog and handler training
seminars and ongoing consultation, and A. M. Merenlender
and the Hopland Research & Extension Center (HREC) for
providing facilities for the experimental trials and assistance
with care of the detection dogs. The California Living
Museum, Coyote Point Museum, Folsom Zoo, Lindsay
Wildlife Museum, Oakland Zoo, and Six Flags Marine
World contributed scats of known origin for dog training
and field surveys. Finally, this project would not have been
possible without the patience and persistence of our dogs
Maggie and Seth.
Figure 3. Scat accumulation over time in 30 field sites in northern
California. Model-averaged estimates of the relationship between the num-
ber of days since precipitation (>5 mm) and scat detection rate are shown for
uncontrolled field searches using dog 1 (*) during April–June 2004 and dog
2(&) during July–September 2005, given mean values for other environ-
mental factors.
250 The Journal of Wildlife Management 75(1)
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