Visibility Bias during Aerial Surveys of Elk in Northcentral Idaho

Visibility Bias during Aerial Surveys of Elk in Northcentral Idaho
Author(s): Michael D. Samuel, Edward O. Garton, Michael W. Schlegel and Robert G. Carson
Source: The Journal of Wildlife Management, Vol. 51, No. 3 (Jul., 1987), pp. 622-630
Published by: Wiley on behalf of the Wildlife Society
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BIAS DURINGAERIALSURVEYSOF ELKIN
VISIBILITY
IDAHO
NORTHCENTRAL
D. SAMUEL,'Departmentof Fish and Wildlife,Universityof Idaho,Moscow, ID83843
MICHAEL
EDWARDO. GARTON,Departmentof Fishand Wildlife,Universityof Idaho,Moscow, ID83843
W. SCHLEGEL,
IdahoDepartmentof Fishand Game, P.O. Box 25, Boise, ID83707
MICHAEL
ROBERTG. CARSON,2Departmentof Fish and Wildlife,Universityof Idaho,Moscow ID83843
Abstract: Radio-collaredelk (Cervuselaphus) were used to assessthe importanceof visibilityfactorsduring
winter helicopter surveysin northcentralIdaho. Radio collarsfacilitated monitoringelk groupsto determine
whether elk were observedor missed during helicopter counts. Multivariateanalysisindicated that visibility
was significantlyinfluenced by group size and vegetation cover. Snow cover, search rate, animal behavior,
and different observersdid not significantlyaffect visibility of elk. A sightability model was developed to
predict the probabilityof observingelk groups during winter aerial counts.
J. WILDL.
MANAGE.51(3):622-630
Aerial surveys are an important method for
estimating populations of ungulates. However,
aerial surveys underestimate animal abundance
(Caughley 1977). A major goal for the improvement of aerial survey estimates is to determine
the number of animals missed during surveys.
Failure to observe all animals is called visibility
bias and is generally a major cause of inaccuracies in aerial surveys (Caughley 1974, 1977).
The magnitude of visibility bias depends on numerous factors, including animal behavior and
dispersion, observers, weather, vegetation cover,
and equipment. Visibility bias also may confound the estimation of age and sex ratios when
males, females, or young have different visibility
factors.
Recognition of and correction for visibility
bias have been subjectively and quantitatively
used to adjust wildlife population surveys. Graham and Bell (1969) contended that visibility
of animals was influenced by environmental
conditions and by factors attributable to the observers. However, assessment has generally focussed on those factors that can be controlled
during the survey: observer factors (Caughley
1974, LeResche and Rausch 1974) and aircraft
factors (Caughley et al. 1976). Even when these
factors are rigorously standardized considerable
flight-to-flight variability is possible. In part, this
variability is due to environmental factors that
1
Present address: U.S. Fish and Wildlife Service,
National Wildlife Health Center, 6006 Schroeder Rd.,
Madison, WI 58711.
2 Present address:
Fish and Wildlife Department,
Colville Confederated Tribes, 605 NE Pearl Hill Road,
Bridgeport, WA 98813.
cannot be controlled by the survey technique,
including cover type (LeResche and Rausch
1974, Floyd et al. 1979, Biggins and Jackson
1984, Gasaway et al. 1985), animal group size
(Cook and Martin 1974, Samuel and Pollock
1981, Gasaway et al. 1985), animal behavior
(Gasaway et al. 1985), snow conditions (Lovaas
et al. 1966, LeResche and Rausch 1974, Biggins
and Jackson 1984), and weather (Anderson 1958).
Elk population trends have been monitored
by aerial census since 1932 (Anderson 1958).
There is considerable evidence that counts from
the ground or from the air do not enumerate
all the elk. Large discrepancies in the numbers
of elk seen on repeated aerial counts made during 1 winter have been reported in Washington
(Buechner et al. 1951), Montana (Lovaas et al.
1966), and Idaho (Robel 1960). In the Sun River
herd only 17% of marked elk were resighted
during some counts (Lovaas et al. 1966). Anderson (1958) found that heavy snowfall concentrated animals at lower elevations where
counts could be made more easily. Conversely,
during years of light snowfall, elk had a tendency to scatter widely over a large area. The
highest counts in the Sun River area were consistently made during severe winters when snow
depths in the mountains were at the maximum
(Lovaas et al. 1966). Late winters caused downward movement and congregation of elk in Idaho (Dalke et al. 1965).
Assessment of visibility factors for elk populations and for helicopter surveys are notably
lacking from the literature. We radiocollared
elk in northcentral Idaho to determine the influence of environmental factors and observer
factors on the sightability of elk during winter
622
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J. Wildl. Manage. 51(3):1987
VISIBILITY BIAS IN ELK SURVEYS * Samuel et al.
helicopter counts. Our approach attempted to
standardize most factors that are controllable by
observers and to measure those biases that could
not be controlled. A 2nd objective was to develop a model to predict elk sightability from
the important factors. The sightability model
was then used to estimate total population and
sex and age ratio parameters (Samuel 1985; Garton et al., unpubl. data).
Funding for the project was provided by the
Idaho Dep. Fish and Game. Field operations
were greatly aided by D. L. Davis, L. Kuck, G.
N. Power, G. S. McNeil, and M. D. Scott. D.
Petit and J. Pope provided expert flying skills.
L. J. Nelson and R. K. Steinhorst provided statistical consultation and assistance. J. M. Peek
was instrumental in initiating the project. This
is Contrib. 292 from the For., Wildl. and Range
Exp. Stn., Univ. of Idaho.
STUDYAREAS
Four representative study areas (Samuel 1984)
were chosen from the vegetation commonly used
by elk on winter range in northcentral Idaho.
The Salmon River Breaks (SRB) consisted primarily of open grassland with pockets of ponderosa pine (Pinus ponderosa) in the draws and
at higher elevation. The topography was relatively steep and dissected by numerous drainages flowing into the Salmon River. The Fish
Creek Drainage (FCD) was predominantly open
deciduous shrubs on south facing slopes with
scattered patches of Douglas fir (Pseudotsuga
menziesii) and ponderosa pine. The FCD was
less rugged than SRB and consisted of a few
broad drainages. The Hungry Ridge (HR) area
had the flattest topography of the 4 sites, where
forest vegetation was primarily mature, open
ponderosa pine broken by large clearcuts. Deadman Creek (DC) had the densest cover of the
4 areas. Topography of DC was intermediate
between FCD and SRB, and vegetation was
mostly dense Douglas-fir with some open grass
areas along ridge tops. These study areas were
selected to represent a range of vegetation cover
conditions and were ranked from most open to
and DC (Samuel
most closed as SRB, FCD,
HR,
1984). Study areas were divided into units based
on topographic features formed by creek
boundaries and ridgetops that were easily distinguishable from the air. Units ranged in size
between 0.37 and 12.7 km2 and averaged 3.0
km2 (N = 45).
623
METHODS
Elk were immobilized by darting from a helicopter or from the ground. Twenty animals (3
yrl and 3 ad bulls, and 14 ad cows) were fitted
with white radio collars for subsequent relocation. Fourteen adult cows with active radio collars from previous studies in the SRB and HR
areas also were used. White collars were chosen
to enhance the identification of marked animals
from the helicopter once elk were located and
to avoid the possibility of visually locating an
animal due to a bright-colored collar. Thirtyseven sightability survey flights were made between February 1982 and March 1985. Sightability surveys within the same study area were
week to allow radiotypically separated by
collared animals to mix>-1
with other elk and to
prevent helicopter observers from "learning"
where to find individual animals. Units were
flown systematically at 150-m contour intervals
until the entire unit was searched. Search patterns typically started at low elevation and progressed upslope.
All surveys were flown in a Hiller 12E helicopter. The Hiller affords good forward and
lateral visibility with an observer on either side
of the pilot. Both observers and the pilot located
groups of animals, but groups were usually
counted and classified by the observers. Classification data were recorded by the most experienced (primary) observer. One of 3 primary
observers was used for each survey with a variety of secondary observers. All primary observers were experienced. Experience levels of
the secondary observers varied.
Each helicopter survey was preceded by a
fixed-wing flight to locate radio-collared animals. Locations were plotted on large-scale (12.6
cm/km) aerial photos and the number of animals in the group, percent vegetation cover,
percent snow cover, and behavior (bedded,
standing, or moving) were noted. A helicopter
survey was conducted within 2 hours after the
fixed-wing flight. Helicopter observers were informed of the units containing radio-collared
elk and the radio frequencies of collared animals. However, they did not know the location
of collared elk within the unit. Typically a single
radio-collared animal might be searched for in
2 units or 2 animals might be searched for in
2-3 units.
The helicopter observers surveyed the required units and recorded elk group size, group
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624
VISIBILITY BIAS IN ELK SURVEYS * Samuel et al.
J. Wildl. Manage. 51(3):1987
Table1. Elksightability
surveyresultsbyindependentvariable helicopter. Group size, group composition, befrom4 study areas in northcentralIdaho,1982-85.
No. of groups
Variable
Missed
Seen
Visibilitya
18
27
10
9
0.95
0.73
0.34
0.35
5
6
5
6
9
4
14
10
5
0.22
0.46
0.50
0.60
0.69
0.40
0.82
1.00
1.00
26
9
2
12
5
8
2
0.90
0.90
1.00
0.66
0.50
0.38
0.10
4
34
26
0.29
0.56
0.72
9
5
5
45
0.90
0.56
0.56
0.54
10
17
20
34
14
16
0.77
0.45
0.44
10
9
2
8
11
7
8
11
15
14
10
6
0.44
0.55
0.88
0.64
0.48
0.46
Study areasb
1
SRB
FCD
10
HR
19
17
DC
Group size
1
18
2
7
5
3
4
4
4
5
6
6
7-15
3
16-30
0
0
30+
cover
class
(%)
Vegetation
0-12
3
1
13-27
0
28-42
6
43-57
58-72
5
13
73-87
19
88-100
Behavior
10
Bedded
27
Standing
10
Moving
%snow cover
1
0-19
4
20-50
4
51-99
100
38
Observerse
MWS
LXK
GNP
Search rate (min/km2)
2.00-4.99
5.00-6.19
6.20-7.39
7.40-9.89
9.90-12.39
12.40+
a Visibility = (no. of groups seen) + (no. of
groups seen + no. of groups
missed).
b SRB = Salmon River Breaks, FCD = Fish Creek Drainage, HR
Hungry Ridge, and DC = Deadman Creek.
c Initials of primary observers.
composition, behavior, percent vegetation cover, and percent snow cover for each group seen
that contained a radio-collared animal. Upon
completion of the search, radio-collared animals
not seen were located using a receiver in the
havior, percent vegetation cover, and percent
snow cover also were recorded for these missed
animals. Time required to search each unit was
recorded in either case.
The dichotomous classification of groups seen
or missed was treated as the dependent variable.
Group size, percent snow cover, percent vegetation cover, animal behavior, primary observer, and study area formed the independent
variables. Search rates (min/km2) also were
computed as an additional independent variable. On 3 occasions 2 radio-collared animals
were found in the same group. Each of these 3
cases was treated as a single observation to maintain independent samples. Samples of seen or
missed groups were analyzed by stepwise logistic regression procedures (Dixon et al. 1981) to
determine which independent variables had a
significant influence on the dependent variable.
Significant independent variables were used to
develop a model to predict the probability of
sighting elk groups. The logistic regression model for predicting sightability is:
exp(u)
Y= -(1)
1 + exp(u)
(1)
where y is the sighting probability and
u = bo + bx, +
+ bkxk (2)
+...
is the usual linear regression equation of variables (x1, x2 ...
xk) significantly influencing
..
sightability.
Preliminary results showed that the natural
log transformation of group size was consistently
a better predictor than the untransformed values. Therefore, the transformed group sizes were
used in subsequent analyses.
The significance of independent variable contribution to sightability was judged by forward
stepwise logistic regression. A variable was considered to be important in predicting sightability when its stepwise improvement Chi-square
exceeded a 10% significance level based on improvement in the likelihood ratio (Dixon et al.
1981). The logistic model was judged appropriate for the data by Brown's goodness-of-fit
Chi-square test (Prentice 1975, 1976; Dixon et
al. 1981). This procedure tests the null hypothesis that the data can be appropriately modelled
by logistic
regression.
b2x2
Adequate
prediction
of
the model was judged by D. Hosmer's goodnessof-fit test (Dixon et al. 1981). A t-test was used
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J. Wildl. Manage. 51(3):1987
VISIBILITY BIAS IN ELK SURVEYS * Samuel et al.
to evaluate the significance of individual model
coefficients.
625
Table 2. Finallogisticregressionresults (N = 111) fromelk
sightabilitysurveys on 4 study areas in northcentralIdaho,
1982-85. Minimumlevel of significancefor inclusionof variables set at P = 0.10.
RESULTS
Factors InfluencingSightability
Sightability data were obtained on 111 groups
of elk during the study (Table 1). The overall
probability of observing elk groups was different
for each study area. This trend follows the general pattern of increasing visibility with decreasing vegetation cover on the study areas. Average
visibility increased from 0.22 for single animals
to 1.0 for groups >15. Groups in open cover
were more visible than those in dense cover.
Percent snow cover did not appear to influence
visibility except under sparse snow conditions.
Increased activity (from bedded to standing to
moving) resulted in higher visibility during surveys. One observer (MWS), who also was the
most knowledgeable of the study areas, appeared to be more skilled at locating elk. Helicopter search rate showed no consistent visibility trend. However, visibility increased in the
intermediate categories of search rate.
The 1st step of the logistic regression analysis
indicated that group size (P < 0.0001), percent
vegetation cover (P < 0.0001), animal behavior
(P = 0.0165), study area (P < 0.0001), observer
(P = 0.0025), and percent snow cover (P =
0.0593) each was significantly related to sightability. Only helicopter search rate (P = 0.5903)
had no significant relationship to sightability.
These results are analogous to those expected
from univariate correlation analysis of each factor with sightability. However, subsequent steps
from the logistic regression analysis indicated
that group size and percent vegetation cover
were predominant and had nearly equal influence on sightability (Table 2). Independent variables measuring animal behavior, study areas,
percent snow cover, observers, and search rate
did not have a significant influence on sightability once these 2 variables were removed (Table 2).
SightabilityModels
The significant relationships identified in the
logistic regression analysis were used to build 2
predictive models. Model I was developed to
predict sightability from equation (1) and Table
2, where the linear regression portion of the
model is:
Variable
Constant
Group size
%vegetation cover
Observersd
Study areasd
Behaviord
%snow coverd
Search rated
Level of
significancea
0.0000
0.0000
0.1081
0.1891
0.4387
0.7554
0.8960
Final coefficientb
Coefficient
+ SEc
1.22
1.55
-0.05
1.81
4.14
-4.90
a Probability that variable has no significant influence on sightability.
b Regression coefficients for sightability model.
eCoefficient divided
by SE is equivalent to a t-test for the coefficient =
0.0.
d Variables not included in final logistic regression model.
u = 1.22 + 1.55 In(group size)
- 0.05% vegetation cover.
Model coefficients determined the pattern of
each variable's influence on sightability. Negative coefficients indicated that larger values of
that variable cause sightability to decrease; positive coefficients caused sightability to increase.
A 2nd model (Model II), incorporating group
size and 7 categories of percent vegetation cover
(Table 1), also was developed. The resulting
model is specified by equation (1) where
u = 1.74 + 1.60 In(group size)
- 0.80 vegetation cover class.
Vegetation cover class takes values of 1-7 for
increasing percent vegetation cover categories.
All coefficients for both models were significantly different (P < 0.05) from 0. The differences in sightability predicted for the 2 models
were insignificant. Predicted sightability for
Model II increased with group size for fixed
vegetation cover class (Fig. 1). For fixed group
size, increases in percent vegetation cover (Model I) caused sightability to decrease (Fig. 2).
Approximate standard errors for the sightability
predicted by Model I are illustrated in Figure
3. The error pattern is similar to that of a binomial distribution. The standard error patterns
from Model II were indistinguishable from
Model I.
DISCUSSION
Sightability models for winter helicopter
counts of elk indicated that group size and percent vegetation cover were the primary factors
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626
VISIBILITY BIAS IN ELK SURVEYS * Samuel et al.
J. Wildl. Manage. 51(3):1987
1.0 I
0.70.90.5,,/
I,
.I
/
0.*2
0.01
1
5
10
15
20
GROUPSIZE
Fig. 1. Predictedsightabilityby groupsize for 0-12 (), 43-57 (- - -), and 73-87 (- - -)% vegetationcover (ModelII),
based on 111 elk groupsobserved duringhelicoptersurveys in northcentralIdaho,1982-85.
influencing observability. The interpretation of
factors influencing elk sightability using an approach similar to univariate correlation analysis
indicated that many factors are significantly related to sightability. Our sightability models
showed that many of the factors influencing
sightability are interrelated. This multivariate
approach has not been used to determine factors
that influence visibility in other studies, making
direct comparison with our results difficult. Estimating sightability factors through simple univariate analyses will overestimate the number
of factors that significantly influence sightability.
Despite the differences in analytical approaches, some comparisons between our results
and other research can be drawn. First, group
size is generally believed to be an important
factor influencing sightability (Cook and Martin
1974, Cook and Jacobson 1979, Samuel and Pol-
lock 1981). The inability of other studies to measure group size may have caused related factors
to appear as important influences on sightability.
For example, Biggins and Jackson (1984) speculated that group size might have been a causative factor in the seasonal observability differences of mule deer (Odocoileus hemionus) bucks
and does. They observed a positive relationship
between deer density and visibility, which may
have been partially due to the influence of group
size.
Secondly, the importance of vegetation cover
has been stressed by nearly every study that has
attempted to examine ungulate visibility. Previous authors have suggested dividing a study
area into discrete habitats or cover types and
estimating visibility in each type (Floyd et al.
1979). Such an approach might require substantially more data to estimate a separate sightability function for each habitat. In addition,
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VISIBILITY BIASIN ELK SURVEYS* Samuel et al.
J. Wildl. Manage. 51(3):1987
1
.
0
---.....
627
_
__.
"of
0.9
N
N.
0. 88
0
"
0.7
0.6
0.3
x
0.20.10.0
0
10
20
30
40
50
60
70
80
90
100
x VEGETATIVE
COVER
), 5 (- - -), and 10 (-Fig.2. Predictedsightabilityby percentvegetativecoverfor groupsof 1 (
on 111 elk groupsobserved duringhelicoptersurveys in northcentralIdaho,1982-85.
problems may arise from assuming uniform
vegetative cover within a habitat. Both of these
problems are avoided by estimating percent
vegetative cover directly. Further, taking this
approach offers the possibility that sightability
functions developed in 1 area may be applicable
over broad areas. The sightability functions we
developed gave good predictions over 4 study
areas spanning a range of habitats. Further research in other habitats might broaden the applicability of the models or identify habitats that
require a more complex approach.
Increasing the intensity of search effort is
commonly believed to increase the proportion
of animals seen and may reduce the effects of
some environmental factors such as weather,
lighting, and snow conditions. Thus, there appears to be a distinct advantage of using intensive helicopter quadrat surveys over transect
surveys with fixed-wing aircraft (Gasaway et al.
-) elk (ModelI),based
1985). Helicopters further enhance the observers' ability to minimize terrain and cover problems (Kufeld et al. 1980). In addition, the noise
of the helicopter generally causes animals to
move (perhaps increasing visibility) and may be
used to flush animals from patches of cover (Kufeld et al. 1980). Helicopters also may reduce
observer fatigue problems by minimizing disorientation and airsickness (Kufeld et al. 1980)
and by allowing more frequent breaks (for more
frequent refueling). Helicopter search rate was
not a significant predictor of visibility for our
elk surveys. However, we believe that the search
rates we measured may reflect other factors,
such as time required to classify animals, overall
density of animals in units flown, vegetation
cover, and pilot skill. Further research should
attempt a more rigorous assessment of the influence of search rate on visibility.
Gasaway et al. (1985) found that bedded ra-
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628
VISIBILITY
BIASIN ELK SURVEYS* Samuel et al.
J. Wildl. Manage.51(3):1987
0. 125
0.100
o 0.075
o 0.050
0.025
0.000
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
SIGHTABILITY
(ModelI)forelkgroups(N= 111)observedduringhelicoptersurveysinnorthcentral
Fig.3. Standarderrorof predictedsightability
Idaho,1982-85.
dio-collared moose (Alces alces) were more likely to be missed during intensive fixed-wing surveys. This corresponds with our simple
correlation results for elk sightability. However,
the significant influence of elk behavior vanished when group size and percent vegetation
cover were considered. Apparently, the behavior of elk was correlated with the group size and
vegetation characteristics. Gasaway et al. (1985)
made similar conclusions for moose activity and
group size.
Observer differences, study area differences,
and percent snow cover apparently did not have
a substantial influence on elk visibility after more
significant variables were considered. Previous
studies have shown contradictory evidence regarding the influence of snow cover (quality and
quantity) on sightability. LeResche and Rausch
(1974) believed that snow quality had an important influence on visibility of moose, whereas
Biggins and Jackson (1984) found no effect on
deer visibility. Snow cover may interact with
other factors (e.g., habitat use, group size, and
behavior), thereby reducing its importance in
sightability. Further, the effect of snow cover
may be reduced during the intensive quadrat
surveys we conducted on elk. The reason for a
lack of significant influence of percent snow cover and observers in our study may be partially
attributed to the limited range over which these
factors were evaluated. Our research was designed to standardize observer differences as
much as practical within the context of normal
field procedures. Other research (LeResche and
Rausch 1974, Caughley et al. 1976) has described important effects of observer experience
on sightability. However, these studies generally
looked at a much larger range of experience
and ignored the interaction of these factors with
group size and vegetation cover.
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J. Wildl. Manage. 51(3):1987
* Samuel et al.
VISIBILITY
BIASIN ELKSURVEYS
629
A variety of precautions are necessary before
vegetation. In these situations the determination
applying our specific models developed for elk of correction factors for groups may not be feasightability. Foremost, is that many factors that sible.
In general, the development of sightability
can be controlled were standardized in our study
to reduce their influence on sightability. There models proposed here is related to the closed
is little doubt that sightability will vary (perhaps mark-recapture methods presented by Otis et
dramatically) due to the experience levels of al. (1978). In both cases the goal is to estimate
observers and pilots or to the type of aircraft the capture probability and to apply these paused. Our models presume that expert observers rameters to population estimation. The develand pilots are involved in conducting the heli- opment of sightability models has the advantage
copter surveys. Although the sightability models of adequately handling problems associated with
appear to have general applicability, their use capturing groups of animals and of estimating
in geographically different and vegetatively dif- the influence of environmental factors.
ferent areas should not be attempted without
Application of sightability models does not
prior validation. Likewise, the application of eliminate the need to design an appropriate
these models during seasons other than winter sampling strategy for conducting surveys. Sightshould be approached skeptically. Optimum
ability models may be used in conjunction with
for
elk
conditions
are
to
occur
counting
likely
sophisticated sampling approaches such as stratduring winter (or early spring) when animals ified, proportional, or multiple survey designs
occur in larger groups and in areas of open (Samuel 1985; Garton et al., unpubl. data). These
habitat.
applications also should consider the minimum
Estimates of the number of animals present sampling area or the number of groups required
on the survey area may be computed by apply- to produce reliable estimates. Of primary iming an appropriate correction factor to each ob- portance in any survey design will be a clear
served group. The correction factors for each formulation of objectives, a careful choice of
the required level of precision, and a knowledge
group are obtained by inverting the estimated
sighting probability (Samuel 1985; Samuel and of population distribution. When multiple obSteinhorst, unpubl. data). The sample size re- jectives, such as population estimation and herd
quired for adequate estimation of sighting prob- composition, are specified survey design probability will depend upon the complexity of the lems are likely to become complex. Neverthesightability model (primarily the number of sig- less, the combination of sightability models with
nificant independent variables). Model standard optimum survey design methods could provide
errors (Fig. 3) provide a means for comparison wildlife biologists with a valuable tool for popamong alternative models. In addition, these ulation estimation from aerial surveys.
standard errors provide information on the relCITED
ative precision of the sightability model and the LITERATURE
influence of model variance on the precision of ANDERSON, C. C. 1958. The elk of JacksonHole.
Wyo. Game and Fish Comm. Bull. 10. 184pp.
the estimated population (Samuel and SteinD. E., ANDM. R. JACKSON.1984. Biases
BIGGINS,
horst, unpubl. data).
in aerial surveysof mule deer. Thorne Ecol. Inst.
The use of sightability models will be restrictTech. Publ. 14:60-65.
ed to animals that have some probability of being BUECHNER, H. K., I. O. Buss, AND H. F. BRYAN.
1951. Censusing elk by airplane in the Blue
seen. If a segment of the population has 0 probMountainsof Washington.J. Wildl. Manage. 15:
ability of being seen then no correction factor
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OF MULEDEERAND ELK
MEASUREMENTS
BODY-SURFACE
KATHERINE
L. PARKER,'WildlifeBiologyProgram,WashingtonState University,Pullman,WA99164
Abstract: Body-surfacemeasurementswere taken on 6 captive mule deer (Odocoileus hemionus) and 8
captive elk (Cervuselaphus nelsoni). Surfacearea, characteristicdimension,and mid-ribheight, which affect
thermal exchange between the animal and its environment, increase exponentially with increasing body
weight. Back lower-leg length and chest circumference may serve as simple indices of body weight under
field conditions.
J. WILDL.
MANAGE.51(3):630-633
Body-surface measurements of animals have
several applications. Often used as indicators of
growth rate (e.g., Bandy [1965]), representative
body weights, heights, lengths, and total animal
surface areas also are used to examine the energetic constraints of wild ungulates. For example, leg lengths of mule deer, elk, and caribou
(Rangifer tarandus) are directly related to the
energetic cost of travel in snow (Parker et al.
1984) and cratering to obtain forage (Fancy and
IPresent address:Instituteof Arctic Biology, University of Alaska,Fairbanks,AK 99775.
White 1985). An animal's height aboveground
and the representative distance over which the
wind travels on its surface influence energy expenditures for thermoregulation. Effective surface area for thermal exchange may be altered
by changes in orientation or posture and seasonal pelage or insulation (Morrison 1966, Moen
1973, Chappel 1980, Jacobsen 1980). This paper
reports several surface measurements of mule
deer and elk that have not previously been documented, obtained from tractable research animals in good condition. These morphometric
variables are useful in understanding the relative effects of an animals's thermal environment
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