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1980-·81
ALASKA DEPAFTMENT OF FISH AND GAME
JUNEAU, ALASKA
STATE OF ALASKA
Jay S. Hammond, Governor
DEPARTMENT OF FISH AND GAME
Ronald 0. Skoog, Commissioner
DIVISION OF GAME
Ronald J. Somerville, Director
Gregory N. Bos, Acting Research Chief
MOOSE SURVEY PROCEDURES DEVELOPMENT
BY
William C. Gasaway
Stephen D. DuBois
and
Samuel J. Harbo HABITAT DIVJSfON LIB
ALASKA Dr:JPAP.if"fN T -RAiY
,., .. '· · OF FISH &. GAME
A ,..., v33 R.A.S PBERRY ROAD
.N c HORAGE, ALASKA 99518 -1599 Volume IV
Final Report
Federal Aid in Wildlife Restoration
Projects W-17-9 through W-17-11, W-21-1 and W-21-2,
Jobs 1.17R, 1.18R and 1.19R
and
Project Progress Report
Federal Aid in Wildlife Restoration
Project W-21-2, Job 1.26R
:sons are free to use material in these reports for educational or
:ormational purposes. However, since most reports treat only part of
Ltinuing studies, persons intending to use this material in scientific
1lications should obtain prior permission from the Department of Fish
l Game . In all cases, tentative conclusions should be identified as
:h in quotation, and due credit would be appreciated .
(Printed December 1981)
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State:
Cooperators:
JOB FINAL REPORT (RESEARCH)
Alaska
William C. Gasaway, Stephen D. DuBois, and
Samuel J. Harbo
&3~
/CJJO-ZI
Project No. : W-17-9 through Project Title: Big Game Investigations
W-17-11, W-21-l
Job No.:
Job No.:
Job No.:
Period Covered:
and W-21-2
l.l7R Job Title:
1.18R Job Title:
1.19R Job Title:
Development of Sampling
Procedures for Estimating
Moose Abundance
Determination of Sightability
of Moose During Aerial Surveys
Standardization of Tec~~igues
for Estimating Modse Abundance
July 1, 1980 through June 30, 1981
SUMMARY
Moose sightability during aerial surveys was determined and
methods for estimating numbers and sex and age composition of
moose were developed. A techniques manual was drafted
incorporating the pertinent sightability findings, and two
training workshops were held. Personnel attending workshops
conducted five population estimation surveys during November 1980
and estimated moose numbers were greater in each area than
biologists had previously realized. The new survey method
provides more representative sex and age composition data than
traditional composition surveys used by the ADF&G. Calf:cow
ratios were substantially underestimated by use of traditional
composition surveys.
ARLIS
Alaska Resources
Library & Inform~tion Services
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CONTENTS
Summ.ary. . . . . . . . . . . . . . . . . . . i
Background . . . . . . • . . . . . . . . . . . . . . 1
Objectives . . . . . . . . . . . . . . . . . 3
Study Area . . . . . . . . . . . . . . . 3
Methods. . . . . . . . . . • • • 3
Results and Discussion . . . . . ....... .
Development of Sampling System. . . . . . .
Determination of Moose Sightability ....
Standardization of Moose Survey Techniques.
4
4
4
4
Recommendations. . • . . . . . . . . • • • • 9
Acknowledgments. . . . . . . . . • • • • • • 9
Literature Cited . . . . . . . . . . . . . . • • . • 12
BACKGROUND
The greatest problem in effective moose (Alces alces) management
and research is the inability to accurately estimate their
numbers. Accurate population estimates are extremely difficult
to obtain because of moose behavior and the type of habitat they
prefer. A completely satisfactory method of inventorying moose
has yet to be devised (Timmermann 1974)~ therefore, we selected
this area of technique development for study.
Aerial survey methods for large mammals generally underestimate
the number of animals present because some animals are not seen
during surveys (Caughley and Goddard 1972). Therefore, sighta-
bili ty estimates for animals seen under varying survey methods
and environmental conditions are needed to correctly estimate
actual animal numbers. In the words of Caughley (1974):
Sightabili ty may be defined as the probability
that an animal within an observer's field of
search will be seen by the observer. The proba-
bility is determined by the distance between the
animal and the observer; by such characteristics
of location as thickness of cover, background, and
lighting; by such characteristics of the animals
as color, size, and movement; and by observer's
eyesight, speed of travel, and level of fatigue.
Few sightability estimates exist for moose or other large animals
from which reliable correction factors can be developed. Sighta-
bility estimates for moose in four, 2.6 km2 pens were reported by
LeResche and Rausch (1974). They found that experienced
observers who had recently conducted surveys saw an average of 68
percent of the moose under their experimental conditions.
Unfortunately, search methods employed, terrain, and habitat
types available limited the application of findings to other
situations. Novak and Gardner (1975) estimated 90 percent
sightability of moose during aerial transect surveys over 25 km2
plots in a forested portion of Ontario. As a basis for
calculating sightabili ty, they assumed that all moose present
during the aerial surveys were later found by intensive searching
of the plots by helicopter. Floyd et al. (1979) reported seeing
50 percent of the radio-collared deer in 1.3 to 26 km 2 forested
test plots when these areas were intensively surveyed. Several
studies have demonstrated that increasing search intensity
increased moose sightability and population estimates (Fowle and
Lumsden 1958, Evans et al. 1966, Lynch 1971, Mantle 1972);
however, an unknown proportion of the moose present was probably
not seen during even the most intensive searches. This, of
course, precluded calculation of sightability.
In Alaska, variations of transect surveys have been used
extensively to obtain sex and age composition data. When
compared from year to year, these data provide useful insight
into population trends. In a few cases, these data have been
extrapolated to form crude estimates of population size, but the
technique is generally considered inadequate for population
estimation. Basically, the transect method involves flying
parallel lines at prescribed altitudes and airspeed and counting
moose seen in prescribed transect widths (Banfield et al. 1955).
Population estimates derived in this manner are inaccurate
because of two major problems: 1) determination of transect
width is difficult and 2) the number of unseen moose is not known
and varies greatly with habitat types and environmental factors.
Timmermann ( 1974) concluded the transect method was inadequate
for the needs of wildlife management agencies and that quadrat
sampling methods for the estimation of moose abundance should be
adopted. However, Thompson (1979) proposed a variation of the
transect method that overcomes some of the difficulties with past
transect methods.
Aerial surveys in which quadrats were exhaustively searched were
first introduced in the 1950's {Cumming 1957, Trotter 1958,
Lumsden 1959). Quadrat sampling tends to give higher estimates
of moose numbers than transect methods. For example, Evans et
al. (1966) and Lynch {1971) found that transect surveys provided
population estimates of only 25 and 67 percent, respectively, of
estimates obtained by the quadrat method. Using the quadrat
sampling technique, each randomly selected plot is thoroughly
searched until the observer is satisfied that further searching
will not reveal additional moose. The increased counting effort
per unit of area increases the percentage of moose seen compared
with the transect method, and accounts for the higher· and more
accurate population estimates. This method assumes that all
moose are seen in a quadrat, although some animals are inevitably
missed. The number of undetected moose varies according to the
density of canopy cover, environmental factors, moose behavior,
and pilot/observer effectiveness {LeResche and Rausch 1974).
Given that less than 100 percent sightabili ty of moose was
achievable, we tested aerial search patterns, and intensities in
search of combinations ·which would provide high sightabili ties
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under varying conditions. These search patterns and
sightabili ties were then used in the development of population
estimation procedures. Our sampling design was a modification of
the random, stratified procedures reported by Siniff and Skoog
(1964) and Evans et al. (1966). Linear transect sampling methods
were rejected because they were not adaptable to specific terrain
and habitat types found in Alaska.
Findings from our research were used to produce a preliminary
technique manual for the estimation of moose population size.
Workshops have been used to introduce biologists to this survey
method.
OBJECTIVES
To develop sampling procedures for estimating moose abundance and
to evaluate moose survey methods presently employed.
To quantify the sightability of moose in relationship to habitat,
environmental factors, diurnal and seasonal behavior patterns,
sex, age, and aggregation size, and to calculate sightabili ty
correction factors for variables when appropriate and/or minimize
the influence of variables in the design of survey methods.
To demonstrate the relationship of search intensity and method to
numbers and sex and age composition of moose seen so biases in
observed sex and age ratios can be interpreted and minimized.
To prepare an illustrated manual describing the application of
the population estimation method and the calculation of popula-
tion parameters, and to assist game biologists in application of
survey techniques through workshops and field training programs.
STUDY AREA
The study area is diverse and represents most habitat and terrain
types used by moose in Interior Alaska. Included are mountains,
mountainous foothills, rolling hills, flats, and both forested
and subalpine river channels. Botanical descriptions of habitat
types were reported by Coady (1976) and include alpine,
herbaceous, low shrub, tall shrub, deciduous, and coniferous
types. The study area includes drainages of the Chena and Salcha
Rivers in Game Management Unit (GMU) 20B and much of GMU 20A.
METHODS
Methods used to estimate sightabili ty of moose and develop the
sampling scheme have been described in previous reports (Gasaway
1977, 1978, 1980; Gasaway et al. 1979).
3
RESULTS AND DISCUSSION
Development of a Sampling System
A moose population estimation techniques
describing the sampling design was drafted.
final report for Job 1.17R.
Determination of Moose Sightability
manual (Appendix I)
This manual is the
Analysis of sightabili ty data continued during the reporting
period. S'ightabili ty data applicable to May and June surveys
were analyzed and reported in Gasaway et al. ( 1979) and are
currently being prepared for publication. Analysis of winter
sightability data has not advanced beyond that reported in
Gasaway et al. (1979).
Improvements were made in the application of a sightabili ty
correction factor ( SCF) to population estimation survey data
(Appendix I). The SCF was adjusted upward by 3 percent to
account for moose not seen during the intensive searches in early
winter (Gasaway et al. 1979). Additional refinements to the SCF
are being made by calculating a variance component for the SCF
and will be incorporated into the manual. This variance
component is necessary to accurately estimate the confidence
interval ( CI) about the estimated number of moose. It will
result in a wider CI for a specified probability.
standardization of Moose Survey Techniques
The moose. population estimation manual (Appendix I) provides
guidelines for Alaska Department of Fish and Game personnel
conducting moose surveys. The manual serves both as a training
aid during 2-day workshops on population estimation techniques
and as a field reference.
Biologists produced 5 population estimates during November 1980
using techniques presented in the manual and at the workshops.
Each survey resulted in an estimated population larger than
expected based on presurvey data (Table 1). For example, upper
and lower Nowi tna River surveys produced population estimates
that were four times greater than the expected number of moose in
those areas.
Two factors account for th~ large discrepancy between expected
and estima.ted moose numbers .· in the Nowi tna surveys. First, very
little previous survey effort had occurred there, and second,
moose were thought to be scarce because they were scattered at a
low density throughout a very large, heavily forested drainage.
Precision of moose population estimates is defined by the width
of the 90 percent CI about the population estimate. We suggest
that a 90 percent CI equal to or less than 20 percent of the
estimated number of moose is acceptable for many uses
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(Appendix I). However, the acceptable level of precision must be
established by biologists for each study area. These precision
levels will vary with study objectives or management needs.
Confidence intervals ranged from 4 to 19 percent of the
population estimates for the five population estimates produced
in November 1980 (Table 1). Narrower CI's could have been
attained by surveying a larger percentage of the sample units in
each survey; however, costs would have increased.
The costs of all five surveys were high (Table 1), due primarily
to three factors. First, population estimates in small areas
required a very high percentage of the areas to be sampled, and
this increased costs per unit area. Two of the surveys (CA3 and
Tok) covered quite small areas 274 mi 2 and 450 mi 2 of moose
habitat. A more detailed discussion of the cost of estimating
populations in small areas is found in Appendix I. Second, all
population estimates were conducted in remote areas which
increased aircraft charter costs as well as food and lodging
expenses for personnel. The third factor contributing to high
costs was the vast expanse of some survey areas such as the upper
and lower Nowitna drainages. Because of the size and remoteness
of the Nowitna, aircraft had to fly up to 4 hours round trip just
to reach distant sample units. Experience gained during the 1980
surveys will result in more accurate cost estimates in the
future.
Numbers of moose seen on sex and age composition surveys substan-
tially underestimated the number of moose in three areas where
both composition surveys and population estimation surveys were
conducted; approximately 65 percent of the estimated population
was seen on these sex and age composition surveys (Fig. 1) .
Rough population estimates should be possible from composition
survey data by multiplying the number of moose seen by a SCF.
Because sightability of moose can vary drastically among
composition surveys throughout Alaska and within a single survey
area among years, more data are needed to develop sightabili ty
correction factors for sex and age composition surveys conducted
under variable conditions.
The stratification process can be used alone to provide a rapid
and inexpensive measure of moose distribution in large areas
where little or no prior knowledge is available. Stratification
allows for a rapid and systematic accumulation of data in a form
that maximizes knowledge. The number of moose seen during three
stratification flights (Fig. 1) was approximately 30 percent of
the estimated population. Therefore, multiplying the number of
moose seen by three and four gives a crude estimate of moose
abundance in those areas. Stratification also produces a moose
distribution map containing relative moose densities. These data
provide a basis for selecting sites for composition or trend
surveys, initiating management strategies, and addressing
resource use issues.
_ ___ Po:p_u_l_a"ti_Qn _es_:timation __ sur¥e¥S--pi"oduced--high.eJ;--Ga-1-f-:-Gew---Ea--ties-----[_
r:-c-than sex and age composition surveys (Table 2). These differ-
5
Table 1. Results of population estimation surveys during November 1980.
Game Management
Unit, Count Area
13, CA3
13, CA7+14
12, Tok River
21, Upper Nowitna
21, Lower Nowitna
Area of
Moose
Habitat
(mi 2 )
274
945
450
3,800
2,770
1 Sightability correction
during intensive search
Sightability
Correction
Factor1
1.06
1.06
1.38
1.11
1.19
90% Confidence
Interval
Estimated (% of
No. Moose Estimate) Cost ($)
501 9 3,000
2,105 19 8,000
872 4 4,000
1,891 16 25,000
2,376 18 15,000
Estimated Number of
Moose Relative to
the Number Expected
higher density than
expected
higher density than
expected
expected 700 moose
expected 400 moose
expected 600 moose
factor calculated during the survey times 1.03 fo.rmoose _not. seen
(see Appendix I for details).
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Survey Estimate
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TYPE OF SURVEY
Fig. 1. The percentage of moose seen or estimated
by surveys during November 1980.
7
Table 2. Sex and age ratios in moose populations calculated from composition survey and
population estimation survey data.
CalvesLlOO~ MalesLlOO~
Composition Population Estimat1on Composition Population Estimation
Survey Surve~ Survey Survey
Game Management Pooled Pooled Unlnas 2 Pooled Pooled Unbias
Unit, Count Area Data 1 N Data 1 Method N Data Data Method
13, CA3 31 344 44 45 459 37 30 29
13, CA7+14 23 1,393 32 32 742 13 13 13
12, Tok River 20 525 24 26 526 25 26 29
21, Upper Nowitna 27 26 434 71 69
21, Lower Nowitna 34 34 405 71 74
1 Ratio calculated from all moose observed.
2 Ratio weighted by the composition and nunilier of moose within each stratum
(see Appendix I for details).
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ences are caused by bias. The composition survey method contains
greater bias than population estimation surveys because sighta-
bili ty of moose is lower and a smaller portion of low moose
density area is generally surveyed. These two factors result in
unrepresentative, low calf:cow ratios from composition survey
data because cows with calves are more frequently missed during
low to moderate intensity searches (Table 3), and because cows
with calves are disproportionately abundant in areas of low moose
density (Fig. 2). So far, we have been unable to detect consist-
ent differences in bull: cow ratios produced between the two
survey techniques.
The calculation of population composition ratios from population
estimation data is described in Appendix I. The population
composition ratios are calculated for each stratum in the survey
area; an overall ratio is then calculated using weighted
estimates for each stratum. Previously, we suggested simply
pooling all moose observed during population estimation surveys
and then calculating composition ratios.
The new and old methods of calculating composition produced quite
similar values for November 1980 data (Table 2); however, larger
differences can occur with certain combinations of sampling
effort and distribution of calves among strata.
In those areas where both a population estimation survey and sex
and age composition survey were done in the same area (Tok, CA3,
and CA7+14 in Table 2), ten calves:lOO cows was the mean increase
in the ratio.
No insurmountable problems were encountered during the 5 popula-
tion estimation surveys in November 1980. The techniques manual
(Appendix I) has been revised to solve or minimize the problems
that were identified.
RECOMMENDATIONS
A more comprehensive population estimation manual should be
prepared during the next 2 years. Analysis of sightability data
should be completed and written up for publication. Workshops
should be continued so that personnel can learn methods for
making population and composition estimates. The method should
be applied when population estimates and representative composi-
tion data are needed for management and research.
ACKNOWLEDGMENTS
We thank Warren Ballard, David Kelleyhouse, and Roland Quimby for
use of survey data. Dale Haggstrom, Warren Ballard, Suzanne I, Miller, Sterling Miller, and David Kelleyhouse provided valuable
L discussion of and improvements to the survey method. Jim Raymond
designed the HP 97 program. Wayne Heimer and Joann Barnett
r , reviewed the manuscript. I
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Table 3. Composition of moose missed during moderate intensity
aerial surveys.
Survey Conditions
and Intensity Calves/100 Cows Number of Cows
Moose seen on first
search at 4 min/mi 2 34 120
Additional moose observed
when re-searched at
12/min/mi2 41 29
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Fig. 2.
A 0 Nowitna
1.0-1.9 2.0-2.9 2:3.0
Hoo.se Density (moose/mi2)
Calf:cow ratios of moose with respect
to moose density in sample units surveyed
during-the estimation of population size,
November 1980. Lines were fit by linear
regression.
1 1
LITERATURE CITED
Banfield, A. W. F., D. R. Flook, J. P. Kelsall, and A. G.
Loughrey. 1955. An aerial survey technique for northern
big game. Trans. N. Am. Wildl. Conf. 20:519-530.
Caughley, G. 1974. Bias in aerial survey. J. Wildl. Manage.
38 ( 4): 921-933.
and J. Goddard. 1972. Improving the estimates from
inaccurate censuses. J. Wildl. Manage. 36(1):135-140~
Coady, J. w. 1976. Interior moose and moose disease studies.
Alaska Dept. Fish and Game, Fed. Aid Wildl. Rest. Proj.
Prog. Rep.
cumming, H. G. 1957. Geraldton District plan for a statisti-
cally sound aerial moose survey. Fish Wildl. Branch,
Ontario Dept. Lands and Forests.
Evans, c. D., w. A. Troyer, and c. J. Lensink. 1966.
census of moose by quadrat sampling units. J.
Manage. 30(4):767-776.
Aerial
Wildl.
Floyd, T. J., L. D. Mech, and M. E. Nelson. 1979. An improved
method of censusing deer in deciduous-coniferous forest. J.
Wildl. Manage. 43(1):258-261.
Fowle, c. D. and H. G. Lumsden. 1958. Aerial censusing of big
game with special reference to moose in· Ontario. Meeting
Can. Wildl. Biologists, Ottawa.
Gasaway, W. c. 1977. Moose survey procedures
Alaska Dept. Fish and Game, Fed. Aid Wildl.
Prog. Rep. W-17-9.
development.
Rest. Proj.
1978. Moose survey procedures development. Alaska
Dept. Fish and Game, Fed. Aid Wildl. Rest. Proj. Prog. Rep.
W-17-10.
1980. Interior moose studies. Alaska Dept. Fish
and Game, Fed. Aid Wildl. Rest. Proj. Final Rep. W-17-9.
__________ , S. J. Harbo, and S. D. DuBois. 1979. Moose survey
procedures development. Alaska Dept. Fish and Game, Fed.
Aid Wildl. Rest. Proj. Prog. Rep. W-17-11.
LeResche, R. E. and R. A. Rausch. 1974. Accuracy and precision
of aerial moose censusing. J. Wildl. Manage. 38(2):175-182.
Lynch, G. M. 1971.
Edison region.
Unpubl.
Ungulate population surveys conducted in the
Alberta Dept. Lands and Forests, Edmonton.
1 2
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Lumsden, H. G. 1959. Ontario moose inventory winter 1958-59.
Rep. Ontario Dept. Lands and Forests, Toronto.
Mantle, E. F. 1972. A special moose inventory, 1971
Aubinadong moose study area, Sault Ste. Marie forest
district, Ontario. Proc. N. Am. Moose Conf. and Workshop,
Thunder Bay, Ontario 8:124-133.
Novak, M. and J. Gardner. 1975. Accuracy of
surveys. Proc. N. Am. Moose Conf. 11:154-180.
moose aerial
Siniff, D. B. and R. 0. Skoog. 1964. Aerial censusing of
caribou using stratified random sampling. J. Wildl. Manage.
28(2):391-401.
Thompson, I. D. 1979. A method of correcting population and sex
and age estimates from aerial transect surveys for moose.
Proc. N. Am. Moose Conf. and Workshop 15:148-168.
Timmermann, H. R. 19 7 4. Moose inventory methods : a review.
Nat. can. 101:615-629.
Trotter, R. H. 1958. An aerial census technique for moose. NE
Sect. Wildl. Soc.
PREPARED BY:
William c. Gasaway
Game Biologist III
SUBMITTED BY:
John W. Coady
Regional Research
Coordinator
APPROVED BY:
1 3
State:
Cooperators:
Project No. :
Job No.:
Period Covered:
JOB PROGRESS REPORT (RESEARCH)
Alaska
William c. Gasaway, Stephen D. DuBois, and
Diane J. Preston
W-21-2
1.26 R
Project Title: Big Game Investigations
Job Title: Movements of Juvenile
Moose
July 1, 1980 through June 30, 1981
SUMMARY
During 1980, radio collars were placed on 10 yearling offspring
of radio-collared cows to continue monitoring dispersal of sub-
adult moose from a low-density, rapidly growing population.
Additionally, nine 2-year-old moose and three 3-year-old moose
were available for study from previous collarings. Subadult
moose were usually relocated twice per month to assess dispersal
from the home range occupied by the offspring while accompanying
its dam. Radio collar malfunctions and hunters claimed a total
of 8 subadul t moose from the sample. Extensive overlap between
the home range of the subadult moose and the home range it
occupied while accompanying its dam was recorded for all remain-
ing subadult moose. No long-range dispersal was recorded.
i
CONTENTS
Summary. . . . . . . . . . . . . . . . . . . . . . . . . . i
Background . . . . . . . . . . . . . . . . . . . . . . 1
Objectives . . . . . . . . . . • . . . . . . . . . . . 2
Study Area . . . . . . . . . . . . . . . . . . . . . . 2
Methods . . • . • . . . . . . . . . . . . . . 4
Results and Discussion . . . . . . . . . . . . . . 4
Recommendations. . . . . . . . . . . . . . . . 5
Acknowledgments. . . . . . . . . . . . . . . . . . . . 5
Literature Cited . . . . . . . . . . . . . . . . . 5
BACKGROUND
The extent of dispersal from a moose (Alces alces) population can
alter the management strategy for that populat1.on and adjacent
populations which may receive dispersing moose. Therefore, it is
useful to predict when dispersal may occur, which sex and age
classes are prone to disperse, and the approximate magnitude of
dispersal.
Expansion of moose range through dispersal has been documented in
North America (Houston 1968; Mercer and Kitchen 1968; Peek 1974a,
1974b; Coady 1980), the Soviet Union (Likhachev 1965;
Yurlov 1965; Filonov and Zykov 1974), and Europe
(Pullainen 1974). In those studies for which age specific
dispersal was determined, yearling and 2-year-old moose dispersed
more frequently than adults (Likhachev 1965; Houston 1968;
Peek 1974a; Roussel et al. 1975; Lynch 1976). Adult bull and cow
moose were relatively faithful to previously established seasonal
home ranges (Houston 1968; Goddard 1970; Berg 1971; Saunders and
Williamson 1972; Phillips et al. 1973; LeResche 1974; Coady 1976;
VanBallenberghe 1977, 1978). Therefore, the fidelity that adult
moose demonstrate toward their home ranges minimizes their role
in the colonization of new ranges through dispersal.
Dispersal of moose appears to be associated with relatively high
population density ( Likhachev 1965; Yurlov 1965; Houston 1968;
Filonov and Zykov 1974; ~eResche 1974; Peek 1974a, 1974b;
Irwin 1975; Roussel et al. 1975; Coady 1980). Although not
specifically stated by most of the above authors, the densities
of moose populations from which dispersal was recorded may have
approached or exceeded the carrying capacity of the range based
on our interpretations of information presented in those studies.
Dispersal from a moose population that was clearly at low density
relative to carrying capacity was found only in Mercer and
Kitchen (1968).
Many moose populations in Alaska are presently at low densities
relative to the carrying capacities of their ranges. Management
of moose should consider dispersal patterns of moose .in these
low-density populations as well as in populations with densities
closer to carrying capacity.
This study was designed to investigate the frequency, direction,
and distance of dispersal as well as the age and sex of dispers-
ing moose in a low density moose population. The population
selected for study had an estimated peak density of approximately
0.8-0.9 moose/km during the late 1960's (Bishop and Rausch 1974);
however, reappraisal of past data suggests the density may have
been nearly twice the earlier estimates. During the mid-1960's,
heavily browsed vegetation and winter die-offs suggested that
these moose exceeded the carrying capacity of the range. Density
had declined to approximately 0.23 moose/km by 1975 as a result
of severe winter weather, malnutrition, high harvest by hunters,
and hign rates of wolf (Canis lupus) predation (Bishop and
Rausch 1974; Gasaway et al. 1979). Following harvest reductions
since 1975 and wolf control since 1976, this population has
steadily increased through 1979. The mean density of moose in
the study area had increased to an estimated 0. 27 moose/km by
fall 1978 (Gasaway et al. 1979), and it is still c'onsidered to be
below the range's carrying capacity. This is a preliminary
report on a continuing study.
OBJECTIVES
To determine sightability differences between yearling and adult
moose and evaluate biases in sex and age ratios determined from
composition surveys.
To determine the extent to which moose offspring adopt movement
patterns different from those of the dam.
To determine the extent to which young adult moose contribute to
breeding groups other than those in which they were produced.
To determine if yearling and young adult moose produced in
rapidly increasing populations contribute substantially to
adjacent declining populations through emigration, thereby
reducing the predation burden on declining populations.
To determine the extent to which rapidly increasing populations
can provide hunting recreation in adjacent areas as a result of
emigration of young moose.
STUDY AREA
The study area in Interior Alaska (Fig. 1) includes the lowlands
of the Tanana Flats, the rolling hills of the Tanana Hills, and
the alpine zones and mountainous terrain of the north side of the
Alaska Range. The Tanana Flats is a mosaic of habitat types,
ranging from herbaceous bogs to deciduous and white spruce (Picea
glauca) forest and including shrub-dominated seres following
wildfires. Habitat of the Tanana Flats is described in detail by
LeResche et al. (1974). Vegetation on hillsides and river
bottoms of the Tanana Hills is influenced by aspect of the slope.
Warm, well-drained soils support white spruce, quaking aspen
(Populus tremuloides), and paper birch (Betula papyrifera)
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3
whereas extensive stands of black spruce (Picea mariana) grow on
water-saturated, cold soils. Shrub communities are located along
creek and river bottoms and in recent burns. Vegetation in the
Alaska Range is characterized as an upland climax community
(LeResche et al. 1974). Willows (Salix spp.) are found along
streams and intergrade into a shrub zone and eventually into
alpine tundra on ridgetops and higher elevations. Spruce, aspen,
and birch are characteristic of lower elevations.
METHODS
Radio collars were placed on 10 . yearling offspring of
radio-collared cows in early May 1980 prior to separation of the
dam and offspring. Each pair had :Peen radio-tracked for the
previous 12 months. Yearlings were immobilized with a mixture of
5 mg M99 (Etorphine hydrochloride, D-M Pharmaceuticals, Inc.,
Rockfield, MD), 200 mg Rompun, (Xylazine hydrochloride, Chemagro
Division of Bay Chemical Corp., Kansas City, WO), and 375
national formulary units Wydase (Wyeth Laboratories, Kent, WA)
injected by a dart fired from a Palmer Capture Gun. Radio
collars (Telonics, Mesa, AZ) were placed on each moose, and a
yellow canvas visual collar 15 em wide with 13 em high black
numbers was attached to each radio collar.
Moose were generally located twice per month from fixed-wing
aircraft, although during some months they were located only
once. Locations were plotted on 1:63,360 topographic maps or
1:60,000 aerial photographs. Movements of yearlings, their dams,
nine 2-year-olds, and three 3-year-olds, were monitored. All
moose other than yearlings had been collared in previous years
(Gasaway et al. 1980).
We defined dispersal as the spatial separation between the home
range of the independent offspring and the home range occupied by
the offspring while accompanying its dam. Hence, the extent
offspring disperse can range from no dispersal, if the offspring
remains within the home range occupied while associated with its
dam, to lengthy distances if the offspring moves to a new home
range.
No qualitative analysis was performed on dispersal data collected
from July 1980 to June 1981. Rather, a subjective evaluation was
performed to determine if new data seemed to confirm or alter the
conclusions we reached after analyzing dispersal data from
previous cohorts of subadult moose (Gasaway et al. 1980). Convex
polygons enclosing the year-round home range of independent
subadul t moose during their first year of independence were
drawn. The home ranges of the independent yearling were compared
to their home ranges during the year they were with their dams.
RESULTS AND DISCUSSION
Radio collar malfunctions and moose hunters claimed a total of
8 subadul t moose from the available sample during the reporting
4
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period. Four of the 10 radio collars placed on yearling moose
failed within 3.5 months of collaring, and one radio collar fell
off within 2 weeks of collaring. Hunters also shot two
2-year-old moose in September 1980, and radio collars failed on
two 3-year-olds. The remaining five yearlings, seven
2-year-olds, and one 3-year-old were relocated 21-25 times each
from 1 July 1979 to 1 June 1980.
Extensive overlap between the home range of all subadult moose
and the home range they established while accompanying their dams
was recorded. No long-range dispersal was documented during this
reporting period. Convex polygons showing the overlap between
the home ranges of the yearling moose and their dams are shown in
Fig. 2. Based on a subjective evaluation of the new data we
found no reason to alter our earlier conclusions pertaining to
dispersal of subadult moose from a low-density population
(Gasaway et al. 1980). A detailed discussion on the management
implications of dispersal from a low-density moose population can
be found in Gasaway et al. 1980.
RECOMMENDATIONS
Continue analysis of dispersal data and preparation of a
manuscript discussing the results.
ACKNOWLEDGMENTS
We thank Larry Jennings for assistance with fieldwork.
LITERATURE CITED
Berg, w. E. 1971. Habitat use, movements, and activity patt.erns
of moose in northwestern Minnesota. Unpubl. Ph.D. Thesis,
Univ. Minnesota. 98pp.
Bishop, R. H., and R. A.
fluctuations in Alaska,
Rausch. 1974.
1950-1972. Nat.
Moose population
Can. 101:559-593.
Coady, J. W. 1976. Interior moose and moose disease studies.
Alaska Dept. Fish and Game, Fed. Aid Wildl. Rest. Proj.
Rep., Job W-17-7 and W-17-8. Juneau. 26pp.
1980. History of moose in northern Alaska and
adjacent regions. Can. Field-Nat. 94(1):61-68.
Filonov, c. P., and c. D. Zykov. 1974. Dynamics of moose
populations in the forest zone of the European part of the
USSR and in the Urals. Nat. Can. 101:605-613.
Gasaway, W. c., s. J. Harbo, and s. D. DuBois. 1979. Moose
survey procedures development. Alaska Dept. Fish and Game,
Fed. Aid Wildl. Rest. Proj. Rep., Job W-17-11. Juneau.
48pp.
!5
\
A
c
0 16 32 48 km
·~·-·-·
B
D
D
Home range of yearling
while accompaning its dam
Home range of independant
yearling
Fig. 2. Home ranges of 4 subadult moose in Interior Alaska.
[
[
[
fJ u
[
l
[
r .
L
_ _j
' ---s-u--=b-a-d-=-u--=--1 t
Alaska.
s. D. DuBois, and K. L. Brink. 1980.
moose from a low-density population
Proc. N. Am. Moose Conf. Workshop,
Dispersal of
in Interior
16:314-337.
Goddard, J. 1970. Movements of moose in a heavily hunted area
of Ontario. J. Wildl. Manage. 34(2):439-445.
Houston, D. B. 1968. The Shiras moose in Jackson Hole, Wyoming.
Grand Teton Nat. Hist. Assoc. Tech. Bull. No. 1. llOpp.
Irwin, L. L. 1975. Deer-moose relationships
northeastern Minnesota. J. Wildl. Manage.
on a burn in
39 ( 4): 653-662.
LeResche, R. E. 1974. Moose migrations in North America. Nat.
Can. 101:393-415.
, R. H. Bishop,
-----=-b-u-=t,_l~. o-n--a-nd habitats of
101:143-178.
and J. W. Coady. 1974. Distri-
moose in Alaska. Nat. Can.
Likhachev, G. N. 1965. Moose in the Tula Zaseky (game pre-
serves) during the years 1935-1951. Pages 66-80 In The
biology and commercial hunting of the moose. Symposium 2.
Rossel'Khozizdat, Moscow.
Lynch, G. M. 1976. Some long-range movements of radio-tagged
moose in Alberta. Proc. N. Am. Moose Conf. and Workshop
12:220-235.
Mercer, W. E., and D. A. Kitchen. 1968. A preliminary report on
the extension of moose range in the Labrador Peninsula.
Proc. N. Am. Moose Conf. and Workshop. 5:62-81.
Peek, J. M. l974a. Initial response of moose to a forest fire
in northeastern Minnesota. Am. Midl. Nat. 91(2):435-438.
l974b. On the winter habitats of Shiras moose.
Nat. Can. 101:131-141.
Phillips, R. L., W. E. Berg, and D. B. Siniff. 1973. Moose
movement patterns and range use in northwestern Minnesota.
J. Wildl. Manage. 37(3):266-278.
Pullainen, E. 1974. Seasonal movements of moose in Europe.
Nat. Can. 101:379-392.
Roussel, Y. E., E. Audy, and F. Potvin. 1975. Preliminary study
of seasonal moose movements in Laurentides Provincial Park,
Quebec. Can. Field-Nat. 89(1):47-52.
Saunders, B. P., and J. C. Williamson. 1972. Moose movements
from ear tag returns. Proc. N. Am. Moose Conf. and Workshop.
8:177-184.
7
VanBallenberghe,
southcentral
13:103-109.
v. 1977.
Alaska.
Migratory behavior
Proc. Int. Congr.
of moose in
Game Biol.
1978. Final report on the effects of the
Trans-Alaska Pipeline on mo.ose movements. Spec. Rep.
No. 23. Joint State/Federal Fish and Wildlife Advisory
Team, Anchorage. 4lpp.
Yurlov, K. T. .1965. The change in the range of moose in the
southern part of the West-Siberian lowland. Pages 17-27 In
The biology and commercial hunting of the moos~
Symposium 2. Rossel'Khozizdat, Moscow.
PREPARED BY:
Stephen D. DuBois
Game Biologist II
SUBMITTED BY:
John Vl. Coady
Regional Research
Coordinator
APPROVED BY:
of Game
8
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CONTENTS
Introduction. . . . . . . . .
Selection of the Survey Area.
Defining Sample Units . . . .
Stratification of Survey Area
Selecting Sample Units •...
Survey Methods and Search Effort.
Estimating Sightability . . . . .
Recording Observations ..... .
Calculation of Moose Population Estimate and Confidence
Sample Calculations of a Population Estimate ..... .
Interval.
1
2
3
8
25
28
32
35
40
48
Hewlett-Packard 97 Moose Survey Program for Calculation of
Population Estimate ...... .
Optimum Allocation of Search Effort .
Precision of the Population Estimate.
Experience and Currency of Pilots and Observers .
Costs of Surveys. . . . . . . . . . . . . . •
Materials List. . . . • . . . . . . . . . . .
Calculation of Population Sex and Age Composition
Introduction
. . 51
55
56
58
59
63
64
1
Estimates of moose population size and composition are often requirements
of successful management and research programs. Methods of estimating
these population parameters need to be unbiased and contain a measure of
precision or goodness, i.e., a confidence interval with a known probability
level.
This manual describes a method of estimating population size and composition
that minimizes bias and measures the precision of the population estimates.
The manual functions as a survey training aid, field reference, and a
means of maintaining consistency in surveys. The method is suited for
most terrain and habitat occupied by moose in Alaska, and the sampling
scheme is compatible with the distribution of moose in Alaska.
This manual is in an intermediate stage of development. A more comprehen-
sive version will be produced, but in the meantime, this manual provides
adequate guidelines for conducting surveys and calculating results.
...
APPENDIX I
ESTIMATING MOOSE ABUNDANCE ANn COMPOSITION
by
William Gasaway ·
Alaska Department of Fish and Game
1300 College Road
Fairbanks, AK 99701
and
Samuel Harbo
Department of Fisheries and Wildlife
University of Alaska
Fairbanks, AK 99701
July 1981
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I. Selection of the Survey Area
A. ·The initial selection of a study area may be based on major
factors such as one of the following situations:
B.
1. A particular river drainage the biologist desires to
study
2. A discrete moose population that requires a population
estimate
3. An area that will be influenced by industrial development
such as a pipeline or dam.
Once the study area has been identified, the biologist must
then consider the size of the area to be surveyed.
1. The survey area must be small enough to be sampled
adequately and rapidly. It may be necessary to survey
only a portion of the entire study area at a time in
order to accomplish this goal.
2. The biologist also needs to consider other variables that
may influence the quality of the population estimate such
as economics, logistics, and weather.
a. Economic considerations include such factors as the
available budget and projected cost of the survey.
b. Logistical considerations include the availability
and number of aircraft, qualified pilots and
observers, fuel. etc.
c. Weather considerations include the dominant weather
patterns at the time of the proposed survey and the
likelihood of a prolonged stretch of good flying
weather to allow the survey to be completed in a
timely manner.
3
II. Definin~ Sample Units
A. A sample unit (SU) is the smallest delineated portion of the
B.
BETWEEN
LAKES
g. 1. Straight
nes between topo-
aphic references
e 11 sed to define
ges of sample units.
survey area that has a probability of being selected and
searched in its entirety for moose.
All possible SU's are drawn in pencil on a 1:63,360 scale map
of the survey area.
1. The size of SU's should range from 12-15 mi 2 ; however,
some may be out of this range because of the lack of
sufficient natural boundaries. Avoid making SU's smaller
than 8 mi 2 and larger than 20 mi 2 . Sample unit area is
large compared to most other sampling methods used for
estimation of numbers of moose. Experiments in Alaska
have demonstrated that sampling variance and confidence
interval width can be reduced by the use of large SU's.
2. Boundaries of SU's are generally creeks, rivers, and
ridges; however, straight lines between two identifiable
points can be commonly utilized when necessary topo-
graphic features are not·present on the map. Forks or
bends in creeks, lakes, or peaks on ridges are convenient
points of origin for straight boundary lines (Fig. 1).
SU boundaries drawn on maps must be identifiable from the
air. The person drawing SU's should be familiar enough
with the area and topographic features on maps to draw
boundaries that are easily identified from the air.
a. There will be occasions when boundaries become vague
due to uniform topography. At that time boundaries
should be selected which have a very low probability
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sample units when no topo-
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4
of having moose along them. For example, dense
spruce forest may have a very low moose density,
hence a poorly defined boundary through it presents
little problem because few moose will be
encountered. A compass or visual heading may be
flown across the area while observations are made
from only one side of the aircraft. This flight
path establishes the boundary, and subsequent flight
lines are made towards the interior of the SU
(Fig. 2).
4. Moose distribution within the survey area should also be
taken into consideration while drawing SU boundaries.
Attempt to draw SU's that encompass large areas having
similar moose distribution. Avoid drawing boundaries
where concentrations of moose are thought to occur.
a. An example of optional ways of drawing SU's is taken
from the Yanert River drainage during fall where
moose concentrate at or above timberline.
l) Sample Unit A (Fig. 3A) was drawn to include
alpine areas from the upper limit of moose
habitat (4000' contour) on the north side of
the river, a lowland portion of the drainage,
and alpine habitat on the south side of the
river. Therefore, SU-A probably contains a
heterogeneous mixture of moose densities
ranging from high density on the side hills to
low density in the river bottom. This SU can
be divided (see below) in a manner that can
Fig. 3A, B, and C. Example of drawing a su to ~nclude areas of
varying densiti~s of moose and redrawing it
to enclose areas of similar moose density.
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Fig. 3B. Sample Unit B includes predominantly upland moose habitat
and uniform high densities of moose.
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Fig. 3C. Sample Unit c includes predominantly lowland moose
habitat and uniform low densities of moose.
29
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5.
7
lead to improved precision of the population
estimate.
2) Sample Unit B (Fig. 3B) was drawn to enclose
the predominantly subalpine and alpine habitat
in anticipation of high moose densities relative
to the lowlands. Sample Unit C (Fig. 3C) was
drawn to incorporate mostly lowland habitat
which should have a low moose density relative
to SU-B.
a) Sample Unit B and C therefore have sub-
divided the area into units that should
have uniform moose distributions. This
type of SU construction should result in a
more precise population estimate than that
from SU-A because stratification of SU's
will be easier.
Each SU is given a unique number for identification. The
numbers are color coded for rapid relocation on the map.
Use one color for each 50 SU's and keep the color in a
tight block (Fig. 4).
Fig. 4. Each sample unit should
have a unique number and be color
coded in gr,oups of fifty.
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III. Stratification of the Survey Area
A. Stratification is the partitioning of the survey area into
several subunits (strata) with each stratum containing SU's of
similar moose density but with moose density differing widely
among strata.
1. Stratification of the survey area is one of the most
IMPORTANT aspects of estimating moose abundance. Without
accurate stratification, all time and money spent on the
survey will be wasted because an imprecise population
estimate will result.
B. Reasons for stratification of the survey area are:
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UNSTRATIFIED POPULATION
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STRATIFIED POPULATION
r'ig. 5. Stratificat'ion is the
lf-----roce9s of subdividin~ the moose
_,opulation into areas of homogen-
--~pus moose density.
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densities that vary from high moose density in some
locations to low or zero moose density in others.
a. Stratification divides the total moose population in
the survey area into subpopulations that are charac-
terized by homogeneous moose densities within each
subpopulation (Fig. 5).
b. When an accurate stratification is achieved, a
relatively small sample from each stratum can be
used to calculate an estimated moose density for the
corresponding stratum. The strata estimates are
combined to calculate a total population estimate.
c. A population estimate from a properly stratified
moose population will be more precise than an estimate
calculated from a nonstratified population.
9
2. Stratification allows a more precise population estimate [
to be made with a given amount of effort and dollars
because increas·ed sampling effort can easily be applied [
to strata where the sampling variance is greatest.
a. The sampling variance among SU's in high density
strata is generally greater than in lower density [
strata. Therefore, the variance can be reduced in
the high density stratum by increasing the proportion
of the area sampled. The result is a more accurate
population estimat·e. [-
c. Several strata are generaily formed.
1. The number of strata is based largely on the accuracy [
with which biologists are able to identify areas with n
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homogeneous densities of moose.
a. Generally, only 3-4 strata can be identified accurately.
2. Suggested possibilities for designatiohs include the
following:
a. High moose density 0
b. Medium moose density
c. Low moose density
d. Zero moose density
1) The zero density stratum includes only those
portions of the survey area that are non-moose
habitat, such as large lakes or glaciated
mountains.
3. Moose densities within strata designations are relative
L values within a particular survey area only.
l
10
a. For example, a high density stratum may contain 0.8
moose per mi 2 in one area, while in another area it
may contain 3.2 moose per mi 2 .
D. The stratification process.
1. In its simplest form, stratification is a process of
superficially assessing the relative number of moose in
each SU and placing SU's of similar densities into groups
called strata.
2. Several biologists will generally participate in the
stratification, so it is important for each biologist
involved to have a similar concept of the moose density
criterion for each strat~~.
a. This is referred to as "calibrating the strati-
fiers."
b. Calibrating the stratifiers requires that each
biologist be capable of subjectively evaluating
moose densities within the census area and assigning
SU's with comparable densities to the same strata.
c. A practical method for calibrating each stratifier
is to begin the initial stratification flight with
all biologists in one aircraft until each person has
a similar concept of the relative moose densities in
the various strata and can then assign the same
stratum classification to areas of similar moose
density.
1) During the calibration flights, be sure to look
at all variations in moose density within the
survey area.
11
a) Begin stratification in those areas that
are most familiar and which have the
highest and lowest densities.
(1) This method allows all biologists the
opportunity to observe and discuss
the various strata designations while
together in the air.
(2) Once all stratifiers are thinking
alike, they can then separate and
complete the stratification more
rapidly by working independently.
3. Spend the minim'um flight time required to ACCURATELY
stratify SU's.
a. Spend more time stratifying SU's that are difficult
to classify and spend less time in the easy areas.
Standardized transect flights over the entire SU are
not necessary before stratifying. Remember that the
stratification flight is only a superficial survey.
b. The best airplane for stratification is probably a
C-185 because it is fast and will carry 2-3 biologists
during the initial stratification.
1) Because stratification is essentially a superficial
survey it is not necessary to have a slow
flying survey aircraft, such as a Super Cub,
even after the biologists have been "calibrated."
Instead, a faster aircraft is more desirable
for the entire stratification process. If the
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only aircraft available for stratification is a
slow-flying plane such as a Super Cub, have the
pilot fly at cruising speed.
4. Stratification is based on a subjective evaluation of
moose densities, and this evaluation is based upon any
clues that will give an idea of moose density within and
between SU's. The following clues should be used during
stratification flights:
a. Prior knowledge of moose distribution.
b.
1) Before the stratification begins, biologists
will have some idea of where the highest and
lowest moose densities occur. This knowledge
will be based on such factors as previous
surveys or habitat distribution.
a) Since composition survey data from previous
years can be used to facilitate stratification,
it is a good practice to record the flight
routes and locations of moose observed
during all future composition surveys
(Fig. 6).
The number and distribution of moose seen during the
stratification flight.
1) The relative density of moose observed is
usually the most useful clue for stratification.
Remember that approximately 70 percent of the
moose will be overlooked during stratification
so other moose density clues should also be
used.
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c. Density of moose tracks observed.
1) The abundance of moose tracks in an area will
give a good clue of moose density if major
movements of moose have not occurred since the
last snowfall.
d. Quality and extent of moose habitat.
1) Habitat is one of the most important clues used
_in stratifying. Habitat type is easily
observed from aircraft, and habitat type and
moose density are often closely related.
Ecotones should be used to anticipate signifi-
cant changes in moose density.
2) Even though habitat is an important clue to
moose density, in most situations SU's should
not be stratified solely on habitat. Instead,
combine habitat clues with direct observations
of moose density to arrive at the final stratum
classification. For example, a SU may have an
abundance of high quality moose habitat, and
yet the moose density may be very low. Based
on habitat alone, the SU would probably be
classified as high or medium moose density.
But in reality, it should be classified as low
moose density because very few moose actually
occur there.
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15
Stratification of SU's should be based
solely on habitat only when large expanses
of a homogeneous habitat type are encountered.
A portion of a block of habitat should be
stratified using direct observations of
moose density and the remainder of the
area can be stratified using habitat only.
(1) This procedure is best applied to low
density areas only. For example, a
150 mi 2 block of muskeg and black
spruce forest may be subdivided into
10 SU's of 15 mi 2 each. The entire
area has virtually no moose. The
biologist flies over 3 of the SU's
noting the poor quality habitat and
no moose or moose tracks. He places
the 3 SU's in the low density stratum,
and if he is confident the remaining 7
SU's also have a comparable low
density of moose, he also classifies
them as low density without flying
over the SU's. These 7 SU's would be
stratified based on habitat rather
than moose density. SU's stratified
solely on habitat should be noted.
The reasons for distinguishing between
the manner of stratification will
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16
become important when restratification
during the survey is discussed later.
5. Some SU boundaries should be redrawn during and after the
stratification flight to make the density of moose within
SU's more uniform and to minimize problems of moose
movement between SU's.
a. Two situations will arise that require the redrawing
of certain SU boundaries.
1) Some SU's will contain a wide range of moose
densities within their boundaries despite the
initial attempt to draw SU's having similar
moose densities. If it is difficult to assign
the SU's to a stratum, redraw the boundaries.
a) For example, Fig. 7A illustrates 3 SU's
(SU A, B, and C) which were drawn using
topographic features. During the stratifica-
tion flight, high densities of moose were
observed in the upstream portion of each
SU, and low densities of moose were observed
in the downstream portion of each SU. The
SU's were then redrawn (Fig. 7B) so that
all of the high moose density was contained
within SU-D. Sample units E and F were
then classified as low density, and stratifi-
cation was simplified and made much more
accurate.
--~~~·---------~----~--·
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19
2) The second situation requiring the redrawing of
SU boundaries occurs whenever concentrations of
moose are discovered on or near SU boundaries
and the potential exists for moose to move
between SU's. Localized movement of moose may
occur between adjacent SU's from the time of
stratification to the time a SU is surveyed.
The problem is most critical for movements of
moose from high or medium SU's to low density
SU's.
a) For example, if a high density SU is
adjacent to a low density SU, the potential
exists for a large number of moose to move
across SU boundaries from the high density
SU into the low density SU after stratification,
If this movement occurred and the low
density SU were surveyed, the actual density
of this low density SU would be increased
well abov~ the average density of the low
stratum, and the variance of the population
estimate for the low density stratum would
be increased. The result would be a less
precise population estimate.
b) Use of the following rule will help alleviate
the problem of moose movement between
SU's. Never draw SU boundaries near
concentrations of moose: redraw SU's when
r L_:
[
~-
L __ )
[:
~~
I . f--i u
Fl
l
[
[
r L_
L
""···
I
*
Ecotone serving as
original SU J
boundary
,.--..,..;--~r
Moose
*' * * ~ *K
BLACK SPRUCE +-
Lot-1 DENSITY
v_ ~
I
-'~ ~ §!1.1
*" -1E--k
* * * -*• * **" \,
* -f\
"* New SU boundary beyond <he~
influence of the edge
c)
Fig. 8. Example· of drawing SU boundaries
to accomodate moose movements across
strata boundaries.
20
concentrations of moose are found near SU
boundaries.
Another solution to the problem of moose
movement between strata is to include some
lower density cou~try within the perimeter
of a high density SU whenever movements
are anticipated. This area of low density
country should help ensure that all moose
within a medium or high density SU will
still be there when the SU is surveyed.
(1) For example, suppose a burned area of
30 mi 2 is subdivided into two SU's of
15 mi 2 , and each SU is stratified as
high density. The two high density
SU's are surrounded by SU's of black
spruce forest that are classified as
low density. A large number of moose
may utilize the edge of the burn and
wander between it and the spruce.
Therefore, the best strata boundary
would not be the ecotone between the
two habitat types but would be somewhere
inside the spruce forest thereby
including the spruce forest that is
influenced by the ecotone within the
high density stratum (Fig. 8). A
subjective judgment must be made to
21
determine where the influence of the
edge grades into true low moose
density. This is a difficult line to
draw, and we generally recommend
extending the higher density SU
boundary 0.25 mi or more into the
lower density area.
b. Sometimes the lack of identifiable topographic
features precludes moving SU and strata boundaries
when boundaries go through areas where moose concentrate.
l) An example is where SU's from the low and
medium strata are separated by a creek. Usually
the center of the creek is the boundary; however,
since moose tend to concentrate along the
riparian willow, many of the moose associated
with the medium density SU could be using the
shrubs along either side of the creek. To
ensure these moose are counted in the medium
density SU, the entire riparian willow strip
can be included in the medium SU prior to
surveying. This technique is very useful, but
remember the decision to include the entire
riparian strip must be done prior to observing
the distribution of moose in the SU. Bias must
be held to a minimum.
a) The simplest way to indicate this special.
boundary situation is to color the outside
[
[
[
[:
[
r-,
I :
f--' c__;
b
[
[
=
LOW
SPRUCE
FOREST
HI LITER
STRIP
b.
22
of the higher density SU boundary with a
colored marker (Hiliter) (Fig. 9). The
marker can indicate any predesignated
situation, i.e., the entire riparian
strip, a 50-yard strip beyond the creek,
or something similar.
Important: Even though many SU's will be
redrawn after stratification, drawing all
possible SU's prior to stratification is
necessary and helps stratification proceed
Fig. 9. Special survey
conditions along strata
boundaries are marked with
a colored Hiliter marker.
c.
at a rapid pace.
To assist in redrawing SU's, flight routes
and other notes should be recorded on
1:63,360 topographic maps during the
stratification flight.
1) Information such as the location and
number of moose observed, notes on
habitat, occurrence of tracks, or any
other clue that will assist with the
stratification should be recorded.
E. Upon completion of the stratification, stratum classifications
for SU's are transferred to an acetate overlay that covers the
survey area map. Adjust.Jllents to SU boundaries made dudng
stratification should also be transferred to the survey area
map.
1. Hang the map and acetate overlay on a wall. Use a grease
pencil to make notes on the map.
23 [
F. Changes in stratification during the survey.
1. Once the survey has begun, additional knowledge may
reveal areas that were stratified incorrectly. This
information may be gained while flying to and from SU's [
and while actually surveying SU's.
a. If· an error were made on the initial stratification,
the area in question can be restratified even if
some of the SU's have already been surveyed. The
basis for the initial stratification, i.e., moose
density or a non-moose density clue such as habitat,
determines the manner in which the correction of the
stratification is accomplished.
1) r: 1--~ When the initial stratification was based on
'~
observed moose density (i.e., abundance of n
tracks or numbers of moose seen), then SU's l
that have been counted prior to the change in
boundaries must stay in the initial stratum
category. The SU's that have not been surveyed
may be changed to a new stratum and sampled at
the intensity of the new stratum.
2) When the initial stratification was based on
factors other than observed moose density, then
those SU's that have already been surveyed as [
well as those not yet surveyed may be reclassified [ to the new stratum.
3) Therefore, during the stratification it is
important to note those SU's that were strati-
fied based on clues other than moose density. L
24
G. Timing of stratification.
1. Stratification should be conducted just prior to the
survey.
a. Wait for proper survey conditions (snow, wind, and
light) and then rapidly stratify the area. Begin
surveying immediately upon completion of the stratifi-
cation to minimize moose movements between SU's.
b. Always survey adjacent SU's consecutively to minimize
the effects of moose movements between SU's.
2. Be aware of the migratory movements of moose during the
proposed survey period. If moose are migratory at this
time, consider rescheduling the survey to a time period·
when moose are less mobile. If the survey cannot be
rescheduled, then stratify and sample as quickly as
possible.
H. The timing of moose surveys for moose population estimates
conflicts with routine sex and age composition surveys during
early winter. Unfortunately, the number of good flying days
during early winter is very limited, and biologists may be
= tempted to conduct composition surveys and stratification
flights simultaneously.
a. The requirements of the two are unique enough that neither
the composition survey nor stratification would be adequate
if both were done simultaneously. However, if both types
of surveys are to be made at nearly the same time, then
mapping aggregations of moose during composition surveys
first can speed up the stratification.
25
b. Be aware that sex and age ratios collected during a
survey, as described in this manual, are not comparable
with ratios obtained during a composition survey. The
differences in the data will be discussed later.
IV. Selecting Sample Units
A. SU's to be surveyed are selected by a simple random sampling
procedure.
1. From a table of random numbers (Table 1), select SU's by
their unique identifying numbers. Sampling is without
replacement of SU's selected. List SU's in .the order of
selection on a sheet of paper. Select more SU's than you
think will be needed. Indicate the stratum classification
of each SU by placing a symbol (L, M, H) along side each
SU number. On a second sheet of paper, arrange in a
column all low density SU's listed on the above sheet so
that SU's are in the order of selection. Do the same for
SU's from the remaining strata.
B. The order in which SU's are surveyed is important.
1. At least five SU's should be surveyed in each stratum, and
these SU's can be done in the most efficient order.
However, after the first five or a greater predetermined
minimum number of SU's to be surveyed, SU's should be
surveyed in the order in which they were selected within
each stratum. By surveying SU's in the order selected,
the survey can be terminated when an adequate population
estimate has been attained and a simple random sample of
SU's is ensured.
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TABLE
TEN THOUSAND RANDOM DIGITS
11 '1
0\LII L'IJ c ..... J
1 oo-01 1 os-09 110-14115-19 1 20-241 25-29 1 30-341 35-39 1 40~ 45-49
-----------
00 88758 66605 33843 43623
01 35661 42832 16240 77410
02 26335 03771 46115 88133
03 60826 74718 56527 29508
04 95044 99896 13763 31764
05 83746 47694 06143 42741
06 . 27998 42562 63402 10056
07 . 82685 32323 74625 14510
08 . 18386 13862 10988 04197
09 : 21717 13141 22707 68165
10 : 18446 83052 31842 08634
II : ~n~6 75177 47398 66423
12 96779 54309 87456
13 ' 27045 62626 73159 91149
14 : 13094 17725 14103 00067
15 92382 62518 17752 53163
16 16215 50809 49326 77232
17 09342 14528 64727 71403
18 38148 79001 03509 79424
19 23689 19997 72382 15247
20 25407 37726 73099 51057
21 25349 69456 19693 85568
22 02322 77491 56095 03055
23 15072 33261 99219 43307
24 27002 31036 85278 H547
.25166181 83316 4@36--54316
26 09779 01822 45537 13128
27 10791 07706 87481 2G107
28 74833 55767 31312 76611
29 17583 24038 83701 28570
30 45601 4G977 39325 09286
31 60G83 33112 65995 64203
32 , 29956 81169 18877 15296
33 I 91713 8·1235 75296 69875
34: 85704 86588 82837 67822
'
35 ' 17921 26111 35373 8G494
3G 1 13929 71341 804!l!l 89!l27
37 : 03248 IB880 21667 01311
38' 50583 17972 12(;90 00152
39 10636 46975 09449 45986
40 43B% 41278 42205 10125
41 7fi714 !l09G3 74907 HiB90
42 22393 46719 020B3 62428
43 70942 92042 22776 47761
H 92011 60326 86316 26738
45 li6·t56 00121) 45685 67G07
46 %292 4·n1B
1
20B9B 02227
4 7119fi!l0 071·16 53951 10935
4fJ (i73H 51442 24531i GO I 51
49 95BB!l 59255 I ()(i89B 99137
6277 4
206 6
4072
8
7
7
I
919 5
939 0
383' 3
6
2
7
4
8
816 8
859 7
187 0
584 0
118
701
789
965
688
638
901
841
396
802
687
938
377
392
848
295
511
248
673
635
411
180
943
824
S59
482
482
618
937
8
6
6
0
4
5
5
5
2
0
7
0
7
9
3
2
5
6
5
5
3
6
8
3
7
3
3
0
9
9
0
2
5
8
6
3
7
6
5
8
7
9
I
3
0
8
14
6 3
66
77
06
66
316 72
6!i5 60
15
451
4
5
9
13.
01
70
76
23'
7
5
3
1
8
05
50
92
77
03
!33
9G
12
:~3
!l8
71
255171 09560
26656 59698
06787 95962
. 13695 25215
60987 14692
97694 69300
.48744 08400
28017 80588
72757 71418
19187 08421
86070 08464
16232 67343
79638 68869
44204 92237
63565 93578
44840 02592
69955 93892
34083 35613
73315 18811
58090 43804
75768 77991
18661 69018
18216 81781
79712 94753
36252 09373
86032 34563
B2703 75350
27805 42710
04691 39687
00098 60784
34031 94867
65437 13624
16317 34239
05197 66596
83021 90732
01888 65735
07229 71953
80201 47889
16414 01212
46916 63881
599G7 90139
2741!9 06067
575fi2 492<}3
16037 30875
04186 41388
0481!9 ,, 98128
53IB5 03057
76233 13706
64678 87569
81265 42223
41880 85126 60755
86241 13152 49187
60841 91788 86386
72237 06337 73439
71039 34165 21297
99864 19641 15083
83124 19896 18805
14756 54937 76379
81133 69503 44037
23872 03036 34208
20565 74390 36541
36205 50036 59411
49062 02196 55109
29969 49315 11804
24756 10814 15185
88572 03107 90169
70445 00906 57002
35670 10549 07468
86230 99682 82896
94548 82693 22799
72641 95386 70138
10332 83137 88257
32245 84081 18436
41450 30944 53912
69471 15606 77209
93204 72973 90760
25179 86104 40638
63471 08804 23455
13596 88730 8G850
76098 84217 34997
11849 75171 57682
90896 80945 71987
03643 66081 12242
13083 46278 73498
32661 64751 83903
05315 79328 13367
16128 65074 28782
8'3052 31029 06023
27964 0276G 2B786
83117 53947 95218
73563 29875 79033
222B7 19760 13056
31748 64278 05731
80754 47-191 96012
03fl48 78354 14964
13599 93710 23974
61375 10760 261389
20502 60405 09745
6501i6 17790 55413
83303 4B694 1!1953
{, ,j 'j , ,. I ~ I j, 1 ,J
TADLE 1 (Continued)
TEN THOUSAND RANDOM DIGITS
1 50-54 1 55-59 1 60-64 1 65-69 1 70-741 75-79 1 80-84 1 85-89
1
90-94 1 95-99
--·--------------------· ------------
00 70896 44520 64720 49898 78088 I 76740 I 47460 183150 789051591!70 OJ 56809 42909 25853 47624 29486 14196 75841 00393 42390 24847
02 66109 84775 07515 49949 61482 91836 48126 I 80778 21302 24975 03 18071 36263 14053 52526 44347 04923 68100 57805 19521 15345 04 98'132 15120 91754 12657 74675 78500 01247 49719 47635 55514
05 36075 83967 22268 77971 31169 68584 21336 725-H 61i959 39708
06 04110 45061 78062 18911 27855 09419 56459 001)95 70323 04538
07 75658 58509 24479 10202 13150 959-16 55087 31!398 18718 955GI
08 8H03 19142 27208 35149 34889 27003 14181 441!13 17784 41036
09 00005 52142 65021 64438 69610 12154 98422 65320 79996 01935
'
10 43674 47103 48614 70823 78252 82403 93424 05236 54588 27757
II 68597 68874 35567 98463 99671 05634 1!1533 47406 17228 4H55
12 91874 70208 06308 40719 02772 69589 79936 07514 44!l50 35190
13 73854 19470 53014 29375 62256 77488 74388 53949 4%07 19816
14 65926 34117. 55344 68155 38099 56009 03513 05926 35584 ' 42328
15 40005 35246 49440 40295 44390 83043 26090 80201 029J1 I 49:!60
16 46686 29890 14821 69783 34733 11803 64845 32065 H527 3B702
17 02717 61518 39583 72863 50707 96115 07416 05041 36756 61065
18 17048 22281 35573 28944 96889 51823 57268 03866 27658 91950 19 75304 53248 42151 93928 17343 88322 28683 11252 10355 65175
20 97844 62947 62230 30500 92816 85232 27222 91701 11057 83257
21 07611 71163 82212 20653 21499 51496 40715 78952 33029 6·!207
22 47744 04603 44522 62783 39347 72310 41460 31052
406 .. I'"" 23 54293 43576 88116 67416 34908 15238 40561 73940 56850 31078
24 67556 93979 73363 00300 11217 74405 18937 79000 68834 48307
25 86581 73041 95809 73986 49408 53316 90841 73808 53421 82315
26 28020 86282 1!3365 76600 11261 74354 20968 60770 12141 09539
27 42578 32471 37840 30872 75074179027 57813 62831 54715 21ili93
28 47290 15997 86163 10571 81911 92124 92971 80860 41012 586G6
29 24856 63911 13221 77028 06573 33667 30732 47280 12926 67276
30 16352 24836 60799 76281 83402 44709 78930 8291i9 84468 36910
31 89060 79852 97854 21!324 39638 86936 06702 74304 39873 19496
32 07637 30412 04921 26471 09605 07355 2lJ.l66 49793 40539 21077
33 37711 47786 37468 31963 16908 50283 8088+ 08252 72()55 58926
34 82994 53232 58202 73318 6247! 49650 15888 73370 98748 69181
35 31722 67288 12110 04776 15168 68862 92347 90789 lit>% I I 0·11 G2
36 93819 78050 19364 38037 25706 90879 05215 00260 IH21i 88207
37 65557 24496 04713 23688 26623 41356 47049 60676 72236 01214
38 88001 91382 05129 36041 10257 55558 89979 58061 28957 10701
39 96648 70303 18191 62404 26558 92804 15415 02865 52H9 78509
40 04118 51573 59356 02426 35010 37104 9B316 44(i02 96478 O!H:l:l
41 19317 27753 39431 26996 OH65 69695 61374 06317 12n5 62025
42 37182 91221 17307 61!507 85 7:!5 8IB98 22588 222-ll B0337 8!lO:l:l
43 82990 03607 29560 60413 59743 75000 03806 13741 79G71 25·1lli
44 9729-! 21991 11217 98087 79124 52275 31088 32085 23089 21498
45 86771 69504 13345 42544 596lli 078G7 78717 828·10 746119 21515
46 26046 55559 12200 95106 56·196 76662 44880 ll!H57 84209 0133:!
47 39689 05999 92290 79024 70271 93352 90272 9H95 26842 5H77
48 83265 89573 01437 43786 52986 49Ml 17952 35035 8B985 811i71
49 15128 35791 11296 45319 06330 82027 90808 54351 43091 3031l7
N
0\
,._
\
TAnLE I (Continued)
TEN THOUSAND RANDOM DIGITS
1 oo-04 1 os-o9\10-14 115-19 1 20-241 25-29 1 30-34 1 3s-_:i~-~~~-4:_
·------------------~----------------~---------~-··-----------
50 5·H41 6·1681 93190 00993 62130 44484 46293 60717 50239 76319
51 08573 52937 84274 95106 891 I 7 65849 41356 65549 78787 50442
52 81067 68052 14270 19718 88·199 63303 13533 91882 51136 60828
53 39737 58891 75278 980·16 52284 40164 72442 77824 72900 14886
54 34958 76090 08827 61623 311H 86952 83645 91786 29633 78294
55 61417 72-124 92626 71952 69709 81259 58472 43409 84454 88648.
56 99187 14149 57474 32268 85424 90378 34682 47606 89295 02420
57 13130 13064 36485 48133 35319 05720 76317 70953 50823 06793
58 65563 11831 82402 46929 91446 72037 17205 89600 59084 55718
59 28737 49502 06060 52100 43704 50839 22538 56768 83467 19313
60 50353 74022 59767 49927 45882 74099 18758 57510 58560 07050
61. 65208 96466 29917 22862 69972 35178 3291 I 08172 06277 62795
62 21323 38148 26696 817+1 25131 200!37 67452 19670 35898 50636
63 67875 29831 5.9330 46570 6976!3 3€671 01031 95995 68<117 6!lG65
64 82631 2G2GO 86554 31881 70512 37899 38851 40568 54284 24056
65 91989 39633 59039 12526 37730 68848 71399 28513 69018 10289
66 12950 31418 93425 69756 34036 55097 97241 92480 49745 42461
67 00328 27427 95474 97217 05034 26676 49629 13594 50525 13485
68 63986 16698 82804 01524 39919 323!31 67488 05223 89537 59490
69 55775 75005 57912 20977. 35722 51931 89565 77579 93085 06467
70 24761 56877 56357 78809· 407-18 69727 56652 12462 40528 75269
71 43820 80926 26795 5755.3 2!3319 25376 51795 26123 51102 89853
72 66669 02880 02987 33615 54206 20013 75872 88678 17726 606·10
73 49944 66725 19779 50416 42800 71733 82052 28504 15593 51799
74 71003 87598 61296 95019 21568 86134 66096 65403 47166 78638
75 52715 04593 69484 9341 I 38046 13000• 04293 60830 03914 75357
76 21998 31729 89963 I 1573 49442 69467 40265 56066 36024 25705
77 58970 96827 18377 31564 23555 86338 79250 43168 96929 97732
78 67592 59149 42554 42719 13553 48560 81167 10747 92552 19867
79 18298 18429 09357 96436 11237 88039 81020 00428 75731 37779
80 88420 28841 42628 84647 59024 52032 31251 72017 43875 48320
81 07627 88424 23381 29680 14027 75905 27037 22113 77873 78711
H2 37917 93581 04979 21041 95252 62450 05937 81670 44894 47262
83 ].1783 95119 68464 08726 74818 91700 05961 23554 74649 50540
!34 05378 32640 64562 15303 13168 23189 8819!3 63617 58566 56047
85 19640 96709 220-17 07825 405!33 99500 39989 96593 32254 37158
8G 20514 11081-. 51131 SG·I69 33947 77703 35679 45774 06776 67062
87 9G763 56249 812'13 62416 84451 14696 38195 70435 45948 67690
88 49439 61075 31558 59740 52759 55323 95226 01385 20158 54054
89 16294 50518 71317 32168 86071 47314 65393 56367 16910 51269
90 31381 9·4301 79273 32843 05862 36211 93960 00671 67631 23952
91 98032 87203 03227. 6()021 99666 98368 39222 36056 81992 20121
92 10700 31826 94774 11366 81391 33602 69608 84119 93204 26825
93 68692 668•19 29366 77540 14978 06508 10824 65416 23629 63029
9·1 19017 10781 19607 20296 31804 72984 60060 50353 23260 58909
95 82867 ()9266 50733 62()30 00956 61500 89913 30049 82321 62367
96 2G528 213928 52600 72997 !30943 04084 BG662 90025 14360 64867
97 SIJG6 OOG07 499G2 30724 81707 145413 25844 47336 57492 02207
98 97245 15440 551132 153GB 85136 98869 33712 95152 50973 913658
99 54998 88fl30 956~9 45104 72676 I 2H220 82576 57381 34438 2-1565
Sounr;E: l'n·pared by Fn:d Cruenbcrgcr, Numerocal Analys1s Laboratory, Umvcrs1ty of
\\"isconsin, J\fadison, \\'is., 1!152.
r--l..J • ,-----, l, --··~--·· '. ___ )
TAnLE 1 (Continued)
TEN THOUSAND RANDOM DIGITS
__ j_~~-~~J~~~~J~-o~~-~~69 1 70-741 75-791 !l0-841 85-89 i 90-9~ 1 95-99
5o 58649 85086 16502 . 97s4iT7661i 194229-· 34987 ; 86718 8720;; lo:i·IZG--
51 97306 52419 5559G 66739 36525 97563 29-IG9 :H235 7927!i 1101131
52 09942 79344 78160 11015 55777 22047 57615 15717 !36239 3G:,7B
53 83842 28631 74893 4 7911 92170 38181 30416 5·18GO H 120 73031
54 73778 30395 20163 76111 13712 33449 99224 18206 514113 7000G
59G63 i 6 Ill 7
21357 30772
81106 11740
37425 80832
73825 16927
55 88381 56550 47467
56 31044 21404 159GB
57 00909 63837 91328
58 69882 37028 41732
59 26059 78324 22501
60 38573
61 70G24
62 49806
6:3 05461
6•1 76582
98078
00063
23976
67523
62153
38982
81455
05640
18316
53801
33078
16924
2980·1
14613
51219
9352·1
12!H8
38988
08541
30424
39716
81482
50193
03320
31545
45606
23BOI
25024
35231
32599
32927
38807
Bli806
20690
ISG95
534G3
55481
7G951
313312
49099
OGIGB
67231
21931
321)53
74216
20391
78~l7H
02341
1491)9
83959
06217 45·P7
8·12fl3 63552
IB054 4%01
90145 03029
98372 2!3547
fllli37
2G795
(i3219
67279
68·108
372G'l
10553
75Bfi-l
50502
20147
65 16660 80470 75062 75588 24384 27874
66 60166 42424 97470 88451 ' 81270 80070
67 28953 03272 31460 41691 57736 72052
68 47536 86439 95210 96386 38704 15484
69 73457 266s7 36983 72410 30244 1 97711
20018 11428
72959,26220
22762 96323
07426 70675
25652 09373
322G5 07692
59939 31127
27Glli 53123
OG888 81203
6G218 6·1077
70 11190 66193 66287
71. 5 7062 78964 44455
72 99624 67254 67302
73 97521 83669 85968
74 40273 04838 13661
75 57260
76 03451
77 62331
78 32290
79 28014
80 18950
81 17403
82 27999
83 87076
84 89044
85 98048
86 09345
87 07086
88 93128
!39 85137
90 32798
91 62496
92 62707
93 05500
94 79476
06176
47098
20492
51079
80428
16091
69503
50489
53174
45974
64400
12956
77628
25657
70964
39024
26371
81825
28982
31445
49963
63495
15393
06512
92853
29543
01866
66613
12165
14524
24705
49770
76195
46872
29947
138f4
89880
40987
86124
59498
09116
14036
18991
16135
64757
29760
71227
84~70 38 06
31 33
65817
13049
21843
84495
46906
75711
80311
47584
11206
27795
985·16
52078
97656
19554
85132
48140
36098
97687
30133
17461
69546
79304
24396
93327
32648
07002
07263
71746
47947
26052
36232
32319
62411
06831
255·17
46585
47781
89714
80818
24582
37669
40773
54099
51312
78085
02932
I 1688
9488+
17831
60094
61336 39429
29753 99131
32962 21632
80086 19088
16734 43418
I
73JJ5 I 94115
13039 83844
65868 16208
60706 6·1034
51851 84197
57624.
18238
40397
87944
37682
84108
95260
52177
9·1935
26024
41424
16952
71857
97914
96105
7-1603.
83464
23778
61924
24002
50799 17255 OGI81
33150 07459 3GI27
42283 G325R SOliS!
75016 80278 GB953
27010 80945 li6·139
41985118572
18419 71791
9291i5 I 38610
59887 . 9!HIG
90124 15086
20271 50250
80143 390·18
•1678 I 93·102
31635 65lli'J
6169+ 57-129
9B1~B
Bl515
+1!123
2·19113
·l!l·J...H
25061
!i21i5·1
12:123
93070
63395
7i366
92088
54823
li-!670
26848
52790 H·1705
51222 ll2Bti5
2li53li 51i792
4571i0 34353
093B9 64326
9·1812
(i5942
07482
3l!l2B
li3718
73'llifl 687ti(j
9171il 53727
9Hi7B 40121!
7931i9 23507
79164 43556
N 95 10653
96 30524
97 69050
98 27908
99 64520
29954 97568 91541
06495 00886 40666
22019 74066 14500
78802 63446 07674
16618 47-109 I 195H
33139 84525
68574 49574
14506 06423
98871 63831
78136 4tiOD
72271
19705
38332
72H9
01277
025·16
16429
:HI91
42705
7914!i
6·18 18 143!1 I -....!
9091!1 II liB 103 82ti63 B5:l2:l
2ti513 19BB:l
95759 :lti781
I-
!
28
2. Some SU's which were selected for surveying may be skipped
because of localized bad flying weather or poor snow.
Simply replace this SU with the next one on the list from
the same stratum which is in an area with suitable weather
conditions.
V. Survey Methods and Search Effort
A. Search effort will average approximately 4-5 min/mi 2 for each
SU. At this rate, it will be possible to survey approximately
one SUper hour plus the flight time required between SU's.
l. The minimum acceptable time is 4 min/mi 2 . This search
intensity is greater than used on routine aerial composition
surveys (Table 2) and requires flight lines at 0.25 mi
intervals.
a. Most moose are seen during surveys with 4-5 min/mi 2
search effort during early winter in most moose
habitat of Interior Alaska (Table 3, Fig.' 10).
b. A high sightability of moose must be maintained
during the survey. The best way to assure a high
sightability is to maintain a high search intensity
to compensate for day-to-day variations in survey
conditions and variations in survey conditions
between SU's.
Table 2. Time searched per square mile during composition surveys
conducted between 1974 and 1980 in Alaska.
Game
Hana~?;ement Flats
Unit
20A 1.4(1-1.9)
20B
13 0.8
Mean min per mi sg (Range)
Hills Mtn. Foothills
l. 9 ( l. 5-2 . 2)
2.1(1.5-3.0)
1.6 (1.2-2.0)
a These are examples of typical surveys conducted by the Alaska Department
of Fish and Game. Transects were -used over flat terrain while contour
flights were flown in irregular terrain.
Table 3. Percent radio-collared moose seen in quadrats as categorized
by dominant habitat type. Transect/contour data for quadrats
with snow given a "poor" rating have been excluded.
Percent Collared Moose Seen (No. Radio-collared Moose)
Dominant Habitat
.Shrub-dominated
Recent burn
Subalpine
Forest-Shrub mixture
Shrub-dominated
Deciduous-dominated
Spruce-dominated
Total
Q)
til
0
0 s
4-1
0
75
50
25
Transect/Contour Intensive Search
Oct/Nov Feb/Mar Oct/Nov Feb/Mar
90(21) 73(15) 100(20) 94(18)
100(8) 80(10) 100 (8) 100(16)
80(15) 61(23) 100(15) 97(29)
83(6) 100(9) 100(6) 100(10)
85 (13) 51(51) 86(14) 86(56)
88(64) 63(108) 97(63) 92(130)
8 10 12 14 00
SEARCH EFFORT (min/mi2)
Fig. 10. Sightability of moose
during aerial surveys.
29 [
k~
['"
. ~
[
L
[
f ·.
[
r
I f-i u
[
D
D
D
c
[
c ~·~
L
~~
l._;
[
L~
30
2. The appropriate search time for a SU can be calculated by
estimating its area in mi 2 from the map and multiplying
by 4.5 min/mi 2 .
a. Practice will be required in gauging your flight
pattern so as to complete the SU survey in the
appropriate time. However, in order to maintain a
high sightability of moose, it is better to over
search than under search. Practice should occur
prior to the survey, and both pilot and observer
should be familiar with the technique.
3. The search pattern flo~~ varies with the topography.
Fig. 11. Flight pattern
for sample units in flat
terrain.
a. Flat land: parallel transects are flown at 0.25 mi
intervals.
1) Transects should be short. Choose a compass
heading that is perpendicular to the long axis
of the SU.
2) Short transects allow you and the pilot to stay
oriented, i.e., not miss areas or overlap too
much.
3) Estimate the number of transects that should be
made during the search, i.e., 4x the length of
the SU in miles. Make sure no fewer are flown
(Fig. 11) .
a) Predrawing transects on the map before the
survey can be helpful in monitoring your
progress while in the SU.
'igure 12.
1attern in heads of
md ends of ridges.
b.
31
4) Mark the approximate location of the transect
on the map while turning between transects.
5) Mark the location of moose on the map while
between transects if time permits.
Hills and mountains: the flight path generally
follows topographic features .and consists of contour
routes, circles, and flights along ridges and creeks.
l) Circles are very effective at the heads of
valleys and at the ends of ridges (Fig. 12).
2) Concentrate search effort out of one side of
theplane. This reduces the chance of overlooking
a portion of the SU. Generally the down slope
side of the plane is preferred (Fig. 13).
However, there are many occasions when viewing
from the upslope side will be more practical
and effective. For example, very steep slopes
f'1g .. j3. (A) Aa:lunt of hidden ground and perspective of terrain
obtained by viewing upel01)e and dOVD.alope durina a contour·
flight; (B) Observer• s view d.ovulope illuatratiD& top
aspect of crees; and (C) Ob•erver's vtev upelope illua-
trar.iq dde aspect of tree•·
and the ends of gently rounded ridges are best
viewed from.the upslope side of the aircraft.
3) The interval between flight lines is approximately
0.25 mi.
c. The pilot's first responsibility is to fly the
appropriate search pattern and keep the plane oriented
with respect to SU boundaries. But also expect
pilots to help look for moose when they can.
[
I'
I : J-;
L_,
[
I' Ll
[
32
VI. Estimating Sightability of Moose with Approximately 4 min/mi 2
Aerial Search Effort
A. Sightability is defined as the percentage of moose seen during
an aerial survey.
B. Sightability of moose must be estimated so that the total
number of moose present in the survey area can be estimated.
Upon completion of the 4 min/mi2 search of a SU, a search
effort of approximately 12 min/mi 2 is repeated in some of the
SU's to estimate the total number of moose present. We assume
97 percent of all moose are seen during the intensive search.
l. The sightability correction factor (SCF) is estimated
only from those SU's having the two levels of search.
SCF = # moose seen during the intensive search
# moose seen during low search effort X 1.03
a. The value 1.03 in the above formula is the correction
b.
c.
for the 3 percent of the moose that were estimated
to have been missed during the intensive search.
The SCF will be greater than 1.0 since more moose
will be seen with the intensive search.
The corrected total moose estimated to be present in
the survey area is calculated as follows:
corrected estimate = SCF x (estimated no. moose 2 of number of moose seen during 4 min/mi
search effort)
d. This SCF is also used to adjust the confidence
interval (CI) of the final population estimate for
the survey area. Details for adjusting the CI will
be discussed later.
igure l4.
2.
33
Experimental data demonstrate that the number of moose
seen on high intensity searches during early winter is a
good estimator of the true number of moose present in
Interior Alaska.
a. 97 percent of radio-collared moose were seen with an
intensive search effort of approximately 12 min/mi 2
(Table 3).
b. When applying this finding to other areas, habitat
selection and social behavior of moose are assumed
to be similar. If moose differ significantly in a
way that reduces their sightability from those in
the experimental area, this assumption cannot be
applied. Experimental work with radio-collared
moose in many areas would be needed to verify this
assumption; however, in the meantime we have
incorporated a correction component of 1.03 in the
SCF for early winter surveys only.
3. . The high intensity search of approximately 12 min/mi 2
uses a different flight pattern than the lower intensity
search.
a. Flat land: a series of continuous slightly overlapping
circles or ovals should be flown (Fig. 14).
l) The pilot is responsible for ensuring all
surface area has been viewed.
2) The radii of circles should be 0.2-0.3 mi. As
vegetational canopy height and density increase,
light patcern (cop view) uaed during intensive search of
lac tl!!rrain illustrating the elongated, overlapping parallel
ircling pattern to ea.ure comPlece coverage of a quadrat.
the turning radius should decrease.
[
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~j
[
r I : f-1
L_,
u
c
b
[
L
~'
--,
~
-il
--;;
-c==~
34
3) Observations are made from the low wing side.
b. Hills and mountains: Fly close contours and make
frequent circles. This search pattern is similar to
that used for the SU except contours are closer and
circling is more frequent.
4. Selection of high intensity search plots.
a. Approximately 20 plots should be intensively searched.
b. Plots are located within SU's from the high and
medium density strata only.
1) Select a random sample of 20 SU's from those
2)
3)
4)
5)
previously selected for the survey.
Divide each of these 20 sample units into
approximately four quarters and randomly select
one quarter from each SU. The plot to be
intensively searched should be located in this
quarter.·
Are~ of plots should be approximately 2 mi 2 so
as not to take more than 0.5 hours to search.
The exact plot boundaries will be identified
from the air immediately prior to searching the
SU. Upon completion of the search at an intensity
of approximately 4 min/mi 2 , the plot is intensively
searched at 12 min/mi2 .
Moose observed in the SU's must be mapped
accurately with reference to the plot boundaries
during the low and high intensity searches.
35
6) Do not search the plot with different effort
during the low intensity search than you normally
would use for the low intensity.
7) Do not inform the pilot of the location of the
plot until it is time for the high intensity
search.
5. The SCF should be calculated on a daily basis and maintained
at a mean value of no greater than 1.18 during early
winter surveys.
a. Increase the initial search effort in future SU's to
increase sightability.
b. SCF of 1.06 has been achieved in Alaska although the
financial expense required to produce a very low SCF
may be prohibitive in many areas.
VII. Recording Observations on the Moose Survey Form (Forms 1 and 2)
A. Routine information includes the following:
l. Sample unit number
2. Date
3. Start and stop time of the sample unit survey
4. Page
5. Location
6. Weather
B. Additional information includes:
1. Habitat description
a. The dominant habitat within the SU should be classified
as one of two major types, with further subdivisions
under each general category as follows:
r:
L
[
L
r l~
[
c
b
[
[
c l "
L_;
L
36
FORM 1 MOOSE SURVEY FORM FOR POPULATION ESTIMATION
I
s-AMPLE Start Time"_" ____ _
~T ~o. ______ Date'----Stop nme _____ _ Page __ of __
Location: map Pilot/Observer
Location description -------------------------
Habitat description~-------------------------------------------------
~eather:
----~--~----~------~-------------------~-------------------~ Age: Fresh~--Cover: Complete:,.--~-----
Moderat:e Some low veg sho,.n.ng,~---
old'---Di -f stracting amount:s o
Estimated Sample
Unit Area ___ _cmi2
bare ground showing~--
Snow on trees and shrubs ---Measured Sample
Unit Area mi 2 __ ___:
Type of Survey: Ll 4-5 mim/mi2
Time .:Jf Search: 4-5 min/mi2
u Intensive
Intensive
Remarks. __________________________________________________________ ___
HABITAT
BULLS/activ. COHS/ac1;iv. IJNID~T
Agg.
vrlglmadllge
t:l/0 H/1
1
W/2
1
Total
·• No. calf I calf calf Moose'
I I I l H LS TS D C!C! s I I ......
I
2 H LS TS D ss s
3 H LS TS D ss s
4 H LS TS D ss s
5 H LS TS D ss s
6 H LS TS D ss s
'J H LS TS D ss s
~ H LS TS D ss s
~ H LS TS D ss s
IQ H LS TS D ss s
II H LS TS D ss s
'J;J...
H LS TS D ss s
l
1 Total Moose= -
I
L I
L
L
L
L
L
..
L
L
L
L
L
L I
37
FORM 2. MOOSE SURVEY FORM FOR POPULATION ESTIMATION
""eatht!r: ~ 1 ._,_ 3<:::~ ... r-, g wtPn t..·-h··--./L
SNC!t-1 Age: Fresh Cover: Complet:e. __ -;;.~-----
Moderace .-Some low veg showing,~---
Estimated Samplt!
Unit Area ____ mi 2
Old Distracting amounts of
bare ground showing, ___ _
Snow on trees and shrubs. __
l'Ieasured Sample
Unit Area ___ mi 2
Type of Survey: /~4-5 mim/mi 2
Time of S.:arch: ~ 4..,.5 min/mi2
Ll Intensive
Intensive
Remarks·--------------------------------------------------------------
HABITAT
BULLS/activ. COl·7S I act:iv. ~IDENT
Agg.
vrlsd med llge
tv/0 H/1 W/2
1
Total
-. No. calflcalf_lcalf Moose
H LS TS D SS S L
I I I I I .-, I I I
2 ·x "' 0
H . LS TS D ss 5 L
3 ;:_ ~ H LS TS D ss s L
0
4 /{_ d-H LS TS D ss s L
5 ~ 3 H LS TS D ss s L
6 YL I H LS TS D ss s L
..
'1 ~ ' H LS TS D ss s L
~ X (}-H LS TS D ss 5 L
~ K d-H LS TS D ss s L
IQ j;
jL
..... ......
-J H LS TS D ss 5 L
II E ~)( II' If I '(t1) DL ~f. ~
~ H LS TS D ss s L F-
'I~ 1
t
--H LS T5 D ss s L
. l Total Moose= & / I
[
[
r .
l ~
[
n I ; l !
L._J
[J
[
E
t
L
[
38
1) shrub-dominated
a) recent burn
b) subalpine
2) forest-shrub mixture
a) shrub-dominated forest (greater than 50%
shrub)
b) deciduous-dominated forest (greater than
50% forest)
c) spruce-dominated forest
2. Snow conditions
a. Snow conditions have a profound influence on moose
sightability (Table 4). Snow conditions should be
classified based on the following subjective components.
1) age of the snow
a) fresh
b) moderate
c) old
2) snow cover
a) complete
b) some low vegetation showing
c) distracting amounts of bare ground or
herbaceous vegetation showing
.;•' ,.
··· .. · .. ·· d) fresh snow on trees and shrubs
3) a combination of snow cover and age can be used
to rank the quality of snow conditions in each
sample unit as good, moderate, or poor (Table 5).
39
Table 4. The influence of activity, habitat selected by moose, and search intensity or. the
sightability of moose during aerial surveys under good, moderate, and poor snow conditions.
Percent Radio-collared ~loose Seen During Quadrat Searches (no. of moose)
Transect/Contour Search Intensive Search
Standing L;t:ing Stand ins Lying
Habitat Selected Good Hod Poor Good Mod Poor Good Mod Poor Good Mod Poor
Nonsprucea 94 93 85 82 78 44 100 100
(32) (14) (13) (44) (27) (9) (3~) (31)
Spruceb 70 so 0 ss 17 0 78· 88
(10) (8) (1) (20) (12) (4) (9) (8)
a Includes herbaceous, low shrub, tall shrub, deciduous forest and larch.
b Includes spruce forest aad sparse spruce forest.
100
(13)
0
(1)
Table~ Classification of snow conditions for sightability of moose
during aerial surveys.
98
(40)
90
(21)
Age of Snow Coverage Classification
Fresh Complete
Some low vegetation showing
Bare or herbaceous vegetation ground s~owing
Hoderate Complete
Some low vegetation showing
Bare or herbaceous vegetation ground showing
Old Complete
Some low vegetation showing
Bare or herbaceous vegetation ground showing
Good
Moderate
Poor
Good
Moderate
Poor
Moderate
Poor
Poor
93
(27)
83
(12)
100
(9)
75
(4)
[
[
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[
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b .
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l
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r
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40
a) We do not recommend surveys be conducted
when snow conditions are ranked as poor.
3. Habitat use by moose can be evaluated during this survey.
5.
6.
a. Any habitat classification system familiar to the
observers will work.
b. We use the following habitat categories in our work:
1) herbaceous
2) low shrub--shrubs up to 6 feet in height
3) tall sh-rub--shrubs greater than 6 feet in
height
4) deciduous forest
5) sparse spruce forest
6) spruce forest
7) larch forest
c. The survey form has a check list of these habitat
types for each aggregation of observed moose. An X
can be placed over the habitat used, and habitats
available can be circled (Form 2).
Moose spotted during SU surveys should be recorded by
aggregations.
The activity of moose on the initial sighting can be
recorded as lying or standing by putting a S or L below
the number of moose seen (Form 2).
VIII. Calculation of the Moose Population Estimate and Confidence Interval
A. The calculated population estimate is the number of moose that
could have been seen if the entire survey area had been searched
B.
41
at approximately 4-5 min/mi2 . This calculation results in an
underestimation of the number of moose present in the survey
•area because some moose were missed during the survey. The
SCF will be incorporated in the population estimate later to
correct for those moose not seen.
The population estimate and its variance is obtained by estimating
the number of moose and variance for each stratum and then
summing all strata estimates to arrive at the total for the
survey area. Formulas are presented in this section that show
how to calculate estimates for only one stratum. The next
section combines estimates for strata into estimates for the
entire survey area.
1. The following symbols are used in the calculation of each
individual stratum population estimate and variance.
A = total surface area (square miles) in a particular
stratum
y. = number of moose in the ith su
1
x. = number of square miles in the ith su
1
x =mean size of all SU's surveyed in a particular
stratum
n =number of SU's selected in a particular stratum
N =total number of SU's in a particular stratum
T = total population estimate for a particular
stratum
a. Prior to performing any calculations, determine the
total area per stratum (A) and the number of square
miles in each SU (x.) that was surveyed.
1
[
[
[
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D
0
E
[
[
_ _;
L3
[
r :
I L.
r-
L-
2.
1)
2)
3)
42
Solve for A by adding the areas of all SU
within each stratum.
The area of each SU (x.) can be easily estimated
].
in the field by counting the 1 mile square
sections on the map.
The area of each SU (x.) should be determined
].
with a polar compensating planimeter before
final calculations are made.
a) This task is simplified by tracing the
perimeter of each SU onto a piece of
tracing paper rather than attempting to
operate the planimeter directly on the
map.
The following calculations will be performed for each
stratum:
a.
r =
b.
c.
V(T)
The density of moose for each stratum (r) is the
number of moose per square mile.
total no. of moose observed in all SU's
that were surveyed
total surface area of all SU's (mi 2 )
that were surveyed
=
The population estimate for each stratum.
n
.L y.
i=l l.
n
L X.
l. i=l
T = density of moose X total surface area of the stratum
or
T = r · A
Variance {V(T)} for the stratum population estimate.
=
2 s
~
n
43 [
2
1)
2)
1 -~ = Finite Population Correction Factor
a) One advantage of using a simple random sample
versus other sampling types (i.e., sampling
proportional to size of SU's) is that a
finite .population correction factor can be
incorporated into the calculations. The
finite population correction factor reduces
the variance of the estimate as the number
of SU's surveyed increases.
In order to solve for
necessary to solve for
V(T) it is first
s 2 as follows: q
2 I No. of moose
in each SU
Surface area 2 r • L of each SU X
No. moose in the
corresponding SU
r 2 • ' s f 2 .e.. ur ace area
s = of each SU q
n -1
OR.
2 s =
n 2 ·n 2 n 2 I y. -~r • I X. y. + r I X .
i=l 1 i=l 1 1 i=l 1
q
n-1
2 The value of s can then be inserted into the
variance formu~a to solve for V(T).
C. The population estimate (Tt) uncorrected for sightability and
the variance of the population estimate {V(Tt)} for the entire
survey area are determined by summing estimates for individual
strata.
[
l:
[
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L
[
[
+ n I b
I L~
c
u
[
b
[
[
L
[
I
(
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D.
44
1. Total population estimate = L strata population estimates
Tt = Th + Tm + T! =
(rh • Ah) + (rm • Am) + (r! • A!)
where h = high density stratum, m = medium, and ! = low
2. Variance of the =
population estimate
L variance of the strata
population estimates
V(Tt) = V(Th) + V(Tm) + V(T!) =
[A2h • V(rh)] + [A2m • V(rm)]
Calculation of the confidence interval (CI) for the population
estimate of the survey area.
1. An estimate of the number of moose is useful to the
biologist; however, it is of limited value unless the
quality of that estimate can be specified. Although it
is impossible to know the true number of moose present in
the study area, a range of values or interval in which
the true value is likely to lie can be described. This
interval is the confidence interval, and the specification
of such an interval is as important in moose population
estimation as the estimation of the number of moose
(Simpson, G. G., A. Roe, and R. C. Lewontin. 1960.
Quantitative Zoology. Harcourt, Brace and Co., NY.
440pp.)
a. A CI gives you a known probability that the true
number of moose lies within that interval. The
b.
c.
45
known probability is the Cl level used in calculating r-
the CI. Unfortunately, as the CI is decreased, the
confidence that the true number of moose is within
the range also decreases. In each case, the biologist
must decide whether it is better to be nearly sure
that the number of moose lies within some large
range, or to be less sure that it lies in a smaller
range. No statistical technique is available to
make that decision.
It is solely up to the wildlife biologist to choose
the level of confidence for each case. Ideally, a
narrow CI with a high probability of containing the
true number of animals is desired, such as a 95
percent CI which is ! 5 percent of the estimate.
Wildlife biologists cannot usually expect levels of
confidenc~ this great when making population estimates
because the large sampling effort required makes it
prohibitive. Therefore, a reasonable compromise
must be sought and accepted for moose population
estimates.
We recommend striving for precision equal to or
greater than a 90 percent CI which has outer limits
of ! 20 percent of the population estimate.
1) The undesirable alternative in Alaska is to
continue the present system of making the "best
guess" with no definable degree of confidence.
[
[_-
[
[j
[
E
_-_jj
2.
46
CI = Total population estimate ± (ta,v)\ variance of the
total population
estimate
where t is the Student's t value
for a specified probability
a. Table 6 lists Student's t probabilities for confidence
intervals used for determining t (a,v)
b. The degrees of freedom (v) are calculated as follows~
rvcrt)J2
c.
v =
rv<rm)J 2 + cv<Tt)J 2
nm-1 n!-1
where nh' nm, and n! are the number of sample
units flown in the high, medium, and low strata,
respectively.
a is the probability level.
3. Evaluation of the CI for the total population estimate of
the survey area (or, how precise was the population
estimate).
a.
(total population\ -(lower end)
estimate J of CI = % of population estimate
Total population estimate
= % of population estimate
TABLE h. Cumulative Student's t distribution. The body of the
table contains values of Student's t; n is the number
of degrees of freedom.
Probabilitits for confidence intervals
n /_.9_1~!
I 16.314 I 12.7061
2 : 2.920 1 4.303 :
3 2.353' 3.182
4 2.132 ~ 2.776
5 2.015 ~ 2.571
6 1.943 2.447
7 1.895 2.365
8 1.860 2.306
9 1.833 2.262
10 1.812 2.228
II 1.796 2.201
12 1.782 2.179
13 1.771 2.160
14 1.761 2.145
IS 1.753 2.131
16 1 1.746 2.120
17 ' 1.740 2.110
18 i 1.734 2.101
19 i 1.729 2.093
'20 1.725 2.086
i
21 !1.721 2.080
22 I 1.111 2.074
23 1.714 2.069
24 1.711 2.064
25 1.708 2.060
26 1.706 2.056!
27 1.703 2.052 i
28 1.701 2.0481 29 1.699 2.045
30 1.697 2.042
40 1.684 2.021 i
60 1.671 2.000 I
120 1.658 1.980 f
00 1.645 1.960 1
47 [~
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48
IX. Sample Calculations of a Population Estimate
A. The following data were collected during a 1978 survey of the
Tanana Flats, Alaska.
Table 7. Moose survey data for the Tanana Flats, November 1978.
Stratum
Low Medium High
Sample Moose Area Moose Area Moose Area
Unit (no.) (mi'2) (no.) (mi 2 ) (no.) (mi 2)
l1f.
1 7 22.1 3 8.2 21 13.6
2 4 35.0 13 14.3 27 20.6
3 0 20.1 4 12.1 2 6.2
4 4 29.6 0 14.4 15 10.8
5 2 18.3 6 9.6 25 . 16.0
6 11 27.7 24 10.8
7 5 16.4
8 5 16.2
9 6 21.1
10 4 10.4
--Sample
Total 17 125.1 57 150.4 114 78.0
Total Area
Per Stratum (A) 1144.0 1388.0 294.0
Total SU possible
Per Stratum (N) 74.0 93.0 19.0
B. Population estimate and variance for low density stratum
l. Ratio estimator of moose density
r~ = 17 moose observed in low density SU's
125.1 mi 2 surveyed in low density stratum
r~ = 0.136 moose/mi 2 in the low density stratum
2. Population estimate
T~ = (0.136 moose/mi 2) (1144 mi 2 in low density stratum)
T~ = 156 moose in low density stratum
49
3. Variance of the population estimate, V(T~)
F . 1 f 2 . h . f 1 1rst so ve or s 1n t e var1ance ormu a q
{2(0.136) x [(7x22.1) + (4x35.0) + (Ox20.1) + (4x29.6) + (2xl8.3)]} +
{(0.136)2 X [(22.1)2 + (35.0)2 + (20.1)2 + (29.6)2 + (18.3)2 )}
5-l
2 85 -122.318 + 59.912 s = ~--~~~--~~~ q 4
A 2 sq = 5.649, use this value to solve for V(T~)
veT ) ~ (1144)2 r-l ? . X 5.649 (l -'~~
Q ~25.020)-5 " ~~_J
V(i~) = (1144)2 [0.002 X 1.130 (0.932)]
V(T~) = (1144)2 [0.002] note: V(r) = 0.002
note: The variance may differ
somewhat depending on the number
of significant digits used in
rounding; however, this will not
cause significant errors in the
calculations.
C. Population estimate and variance for the medium density stratum
1. r = 0.379 moose/mi 2
m
2. T ·= 526 moose m
3. V(T ) = 7706 m
a. s~ = 11.236
D. Population estimate and variance for the high density stratum
1. rh = 1.462 moose/mi 2
2. Th = 430 moose
c
r ~
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r~
(
L
r": I . I ,
L
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3. V(Th) = 1556
2 -a. s -26.197 q
50
E. Total population estimate and variance for the Tanana Flats
survey area (uncorrected for sightability)
l.
2.
Tt = 156 + 526 + 430
T = 1112 total moose t
"'
V(Tt) = 2617 + 7706 + 1556
V(Tt) = 11,879
F. Calculation of the CI for the total population estimate of the
survey area.
G.
H.
1. CI = 1112 ± 1.746 J 11,879
CI = 1112 ± 190
2. The total population estimate is between 922 and 1302
moose (still uncorrected for sightability)
Evaluation of the CI for the total population estimate of the
Tanana Flats survey area.
1. 1112 .. 922 = 17% of the population eetimate
1112
Sightability correction of total population estimate and
variance.
1. Correction of the estimate for sightability was discussed
in Section VI and is calculated at this point, Simply
multiply the SCF times the population estimate and the
CI.
x.
51
Hewlett-Packard 97 Moose Survey Program will make all Calculations
for the Population Estimate
A. The following description is a step-by-step procedure for
calculating the survey area population estimate and variance,
with the HP 97 calculator.
1. Put HP 97 on "Run" and "Man" and turn "On."
2. Load program card number 1 on side 1 and push A.
3. Load program card number 2 on side 1 and side 2, then
push A.
a. The display will read "10.0" and indicates that the
HP 97 is ready for step 4.
4. Enter total surface area (A) of the first stratum and
push R/S.
5. Enter the total number of possible SU's (N) in the first
stratum and push R/S.
6. Enter the number of moose observed in the first SU surveyed
(y) for the stratum and push R/S.
7. Enter the number of square miles in the first SU surveyed
(x) for the stratum and push R/S.
a. Display will read the number of SU's entered as each
set of y and x data is entered.
8. Repeat step 6 and 7 until y and x have been entered for
all SU's surveyed in the first stratum (HP 97 will handle
a maximum of x =50 per stratum).
9. Push B and HP 97 prints the following parameters of the
first stratum.
a. r
b. T
I
I L.
[
r I . I-l_j
~;
L
52
c. V(r)
d. V(T)
10. Display will read "10" and indicates that the HP 97 is
ready for steps 4-9 again for the next stratum.
a. Repeat steps 4-9 for each stratum.
b. The program will handle a total of 5 strata in this
procedure.
11. When the data have been entered for all strata, push C
and the HP 97 will print the following:
a. Tt
"' b. V(T .. ) ...
c. v
12. The display now reads "20." This is an indication to
select either the 90 or 95 percent confidence level.
Enter either 90 or 95 and push R/S. The HP 97 prints the
following:
a. 90 or 95
' ~ b. tav
-'
1) The program will calculate ± values when v ~ 4. --_ _:;;
c. CI -upper end
~ d. CI lower end d
e. CI as a % of the population estimate
13. The display now reads "30." Enter the ratio of number of
moose seen during high intensity searches of SU's divided
by number of moose seen during low intensity searches of
SU's and push R/S (assume 1.15 for this example).
B.
c.
53
14. Enter the correction factor for percentage of moose
missed during high intensity searches of SU's and push
R/S.
a. Use a correction factor of 1.03 for October-November
surveys.
b. Use a correction factor of 1.09 for February, March,
and April surveys.
c. HP 97 prints the following parameters:
1) Corrected Tt
2) Corrected CI -upper end
3) Corrected CI -lower end
15. HP 97 displays "40" to indicate that the program is
finished.
a. To recycle the program, simply push A and return to
step 4.
The results of the HP 97 may vary from calculations performed
by hand on a desk calculator. However, these variations will
[
r
I ' L_;
I L __ ,
only produce small changes in the total population estimates [J
(approx. 2-4 moose/1000 moose in the final estimate).
1. These discrepancies are due to the rounding difference that [
may occur between hand calculations and the HP 97 program.
a. The HP 97 performs all calculations with numbers
carried 10 decimal places and exponents through 2
digits even though the display may indicate only 2
decimal places.
The following is a sample HP 97 population estimation program
using the data in Table 8.
[
~;
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L
L
54
Table 8. Sample print out from HP 97 population estimation program.
LD..J j 1144. e .Ul-
1
74. :;-f:·J#:
-I 'f 7. :f;ll:~-t-\tbH 294.0 *** I
I 7-?":' ~ t.Ji:li: .l9a *** I ~-·J.
~ *** !
'T.
?5.0 *** .-.1 t.:t::+: I l..•· ' e. .tJ!::t: 1-, *'::+: -~~-0 I 20.1 Jf;*=f
2.7. *ll:: .. · I 4. l-:t:~ 20.6 t:f:* I
I 29.£ :f;ll·:, I' .-. *~* l I .c:..
I
•"I :t:lf:* !• 6.2 ***
~.
I
I 18.3 *t.;: I,
15". *~~· I, I I I 18.8 f::+:-*: I u ! l t' 0.176 *** I I 25. *** "' 155. t:U 1 f6.B
_c; T
I
i ~:t.Ji; tl{r) e. B0t.9 *H: I 24. :t:f;;.r: i
I "tt) ..,.., --: U* ! f ·• 0 **;+. ......... o._,.
..u.u
-
I ! -
I ME l)l "'1"\ !3SB.e *** 1.46£:1 **~· 93. **:ot: I 430·. *"* 6.0177 -**Jt: ."'!. tl*::f; I 1534. *** ....... .ttt ! c . .:: I =e 13. t:t::+: I
i4.J *':;. I
-Ti: " 1111. t.:t::t. ...:.; 4 . • f;:f::<: I 12359. 'f.~:;t: 12.1 *~* I
! V{T-t.) e. ;:~:li: I l ~ 16. .;:»:-* i ~ !4. 4 *** I c::.:t. 90. *** I
J
I I I 6. flt:* ! i t 1.746 **=+ I 9.6 tli':t it
1Je5. t.:t* 11. *** I ' Q;-up
I ;. c::::r-,_, 917. t.f.Jt: 27.7 t:f::f; I t 17.5
:_j
I ..,. :j;j;Jt:
I s. "*-*'' I ;
i' 16.4 t~r:* j ~c.F~ 1.15 *** I 5. **'~· I !
1. 03 **·':
~
!
.-.]:
16.2 **'· I ~-{~ 1316. *'n: 6. nt I II. 1546. *:f:* 21cl :n:ll·
I f c:f'.-Uf 1986. ·~*·f: 4. .t:JJ* e. 4-lo....,
10.4 ~f:· ~ .
I 0
8.:!79 t:H: I 526. tn
1?.0€)44 '~·*· i I 8462. *:t:~ I
~
55
XI. Optimum Allocation of Search Effort (or how to get the most accurate [
population estimate for your dollar)
A. Optimum allocation of search effort is the process of distributing [
B.
the available survey time in the most efficient manner to
produce the best possible population estimates.
l. Optimum allocation of search effort involves monitoring
the variance of each stratum as the survey progresses,
and adjusting the number of SU's to be surveyed to produce [
the smallest variance in each stratum.
2. Discussion of optimum allocation was delayed until this
point because it is advantageous to first understand how
to calculate the population estimate. However, optim~T.
allocation must be considered much earlier in the survey
process, and the allocation of SU's is continually revised
during the survey.
Adjustment of the sampling effort among strata is accomplished
by calculating strata variances as soon as at least 3-5 SU's
have been surveyed in each stratum. Strata with the largest
variances will receive a higher proportion of the remaining
sampling effort.
l. Use the HP 97 to calculate variances.
a. For example, a survey is being conducted on the
Tanana Flats and economics dictate that a maximum of
50 SU's can be surveyed. After 2 days of flying,
the first 21 SU's to be surveyed produced the fol-
lowing strata variances:
r
L
[
C" I ; I !
l_;
c
[J
[
L
[.
I ~
L
l
c.
1) Density
High
Medium
Low
No. SU's Surveyed
6
10
5
56
Stratum Variance
1556
7706
2617
2) By calculating the variance for each stratum
based on the first 21 SU's, it is apparent that
the largest variance is produced from the
medium density stratum.
3) At this point, the biologist has 29 SU's remain-
ing to produce the best possible population
estimate.
4) Even though the medium density stratum has
received over 50 percent of the first 21 SU's,
it is apparent that the greatest variation in
moose density occurs there. Therefore, even
greater sampling effort must be directed into
that stratum in an attempt to reduce its variance.
The process o·f reapportioning sampling effort is influenced by
the rate that the survey is progressing and the variation in
observed moose density within strata. But, in order to maintain
optimum allocation of sampling effort, the variance within
strata should be calculated as frequently as deemed necessary
(usually daily).
XII. Precision of the Population Estimate
A. No estimate of numbers of moose will be absolutely accurate.
Several sources of error exist which always cause a discrepancy
between the estimated and the true number of moose.
57
1. Sampling error
a. If the entire area were searched, there would be no
need for sample units and no sampling error would
exist. However, we are conducting surveys in areas
too large for total count procedures.
b. The mean density of moose found in the area sampled
will always differ slightly from the true density,
but it will approach the true density as the number
of sample units increases.
2. Error in sightability estimate
a. We see less than 100 percent of the moose; therefore,
a sightability correction factor must be estimated.
The estimated SCF is not exact.
3. Errors in calculations
a. The area of each stratum cannot be measured exactly,
thus an error of several percent could result from
this source alone.
B. How accurate is the estimate? Since you can never know the
true-density of number of moose, you cannot directly evaluate
the quality of the estimates.
1. However, a probability that the true value is within a
certain range of the estimated value can be assigned.
This is the CI.
a) As the CI decreases at a particular probability, you
have reason to develop increasing confidence in the
accuracy of the estimate.
[
f )
[
f"" I ;
I " L_:
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~ .
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58
C. Ways to improve accuracy
l. Choose a SU area which minimizes variation between SU.
2. Stratify accurately.
3. Maintain a search effort which provides a high sightability.
4. Spend the effort to make a good estimate of sightability
of moose.
5. Practice survey procedures prior to the survey.
6. Fly when the weather and snow conditions are acceptable
to reduce variation in sightability of moose.
XIII. Experience and Currency of Pilots and Observers
A. All personnel piloting or observing should be trained in the
methods to ensure consistency among survey teams.
l. Biologists and pilots should practice methods prior to
the survey, so proper search effort and search pattern
can be used from the first SU counted. Locating bound-
aries of SU's requires a little practice. The pilot is
primarily responsible for maintaining the flight path
within the SU while searching. The pilot must be able to
read 1:63,360 scale maps on a very detailed basis.
2. Pilots should be fully briefed on reasons for the survey,
overall methods, type of search pattern to be flown,
expected results of the survey, and the importance of
their participation in achieving a precise population
estimate.
59 r
Free flowing communication should start prior to flying
and continue during the survey. Pilot and observer
should discuss the search pattern and flying techniques
early in the survey, so an effective team is built.
Observers are often reluctant to tell the pilot to alter
the flight pattern, and similarly, pilots are often
unsure of what is expected of them because of poor direc-
tions from the observer. Teamwork is built by communica-
tions--so talk!
3. Periodic breaks during the day will help reduce fatigue
and maintain good counting efficiency. Take a short
break every 2 hours or so if possible. A good survey
[
requires that you are mentally sharp during the search of p
f '
L
SU's. Use the flight time between SU's to relax in the
plane (pilot should not relax too much).
4. The aircraft choice is a two-place plane with tandem
seating.
[J
XIV. Cost of Surveys
A. The labor and financial expenditures required to make a popula-[
tion estimate are substantially greater than conducting composi-
tion surveys in comparable areas.
B. Financial expenditures for a population estimate can be subdi-
vided into fixed and variable costs.
l. Fixed costs are expenses that will be incurred regardless
of the location of the survey area.
a. Purchase of materials such as topographic maps, [
acetate, and miscellaneous supplies are fixed. I :
I
.' .. :~
60
b. Aircraft charter costs that are fixed consist of
flight time'actually spent within the survey area
itself and include:
1) Stratification flight time
2) Flight time required to search SU's and fly
intensive searches within SU's
3) Flight time between SU's within a census area.
2. Variable costs are those expenses that are dependent on
the accessibility of the survey area.
a. Aircraft flight time required to fly between the
airport and the survey area can be quite large.
1) For example, the survey area may be located
30-45 min flying time from the airport and
20-60 min may be required to traverse a large
survey area. Therefore, an hour or more of
flight is needed to simply get to some SU's.
b. Food and lodging expenses are also quite variable
for the survey crews. If survey crews are able to
return to their own homes each day, expenses are
considerably less than when they are lodged in
commercial facilities.
Variable expenses can be 25 to 50 percent of the cost
and must be given serious consideration.
3. An example is given for labor and aircraft flight times
during a survey in a 5,000 mi 2 area which had 333 SU's
. 15 ·2 averag1ng m1 . Assume that the SU's are searched at
4.0 min/mi 2 , and 20 intensive searches are flown at 12
. I ·2 f 2 o ·2 m1n m1 or . m1 areas.
c.
a. Fixed aircraft charter times total 134 hr.
1) Stratification was calculated at the rate of
650 mi 2 stratified per aircraft per 6 hr day
and total 46 hr.
61
2) Surveying SU's, intensive searches, and travel
time between SU's totals 88 hr.
b. Fixed labor expenditures total 224 hr of effort
1) Presurvey preparation (purchasing supplies,
preparing maps, and drawing SU's) requires
21 hr.
2) Stratification (flight time within the survey
area, transferring data to the survey area map,
and preparation for flying) requires 60 hr.
3) Surveying SU's (flight time within the survey
area, preparation for sampling, selecting SU's
to be surveyed, and calculating optimum allocation
of search effort) require 88 hr.
4) Data analyses (m,easuring areas of SU' s and
calculating population estimates) require
25 hr.
Small survey areas (300-700 mi 2 ) require a proportionally
larger total area to be surveyed than large survey areas.
1. A small survey area may have only 25-50 total SU's that
are subdivided into several strata.
a. To have an acceptable variance, it may be necessary
to sample most if not all SU's in a stratum. This
is especially true for the high and medium density
strata.
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2.
3.
4.
62
As the size of a survey area decreases, the financial and
labor expenditures per mi 2 increase, but the total cost
decreases.
a. Only 20-25 percent of a large survey area may have
to be sampled versus 50-90 percent of a small area
to produce a population estimate of comparable
precision.
Moose abundance and distribution in a small survey area
will play an important part in determining the proportion
of the survey area to be sampled.
a. If moose density is high and a large proportion of
the area is stratified as high or medium density,
75-80 percent of the total survey area may have to
be sampled.
b. If moose density is low and there is a large area
stratified as low density, then perhaps only 50
percent of the survey area may have to be sampled.
c. If moose distribution is very uniform and the survey
area is subdivided into only l-2 stratum, then a
smaller proportion of the total area will have to be
sampled than if three strata are used.
Survey areas smaller than approximately 300mi 2 can
generally be surveyed in their entirety, thereby saving
the expense of stratification.
XV. Materials List for Moose Population Estimation Surveys
Mapping supplies
topographic maps (5-7 sets)
lead pencils
colored pencils
grease pencils (3 colors, 3 each)
large erasers (4)
scissors (3 pair)
large felt tip markers (2)
transparent colored markers (3)
clear tape (6-8 rolls)
masking tape (1 roll)
heavy gauge acetate at least 40 in. wide (enough to cover
maps of the survey area)
expandable file folders to store maps and data sheet (6)
Flying supplies
clipboards
lead pencils
topographic maps of SU's
data sheets
watch
intercom and headsets
spare batteries for intercom
survival gear
foam pad to sit on
63 [
[
[
r_ r-·
L
L
-~
64
"Preparation H" (in case you forget the foam pad) (use of trade
name does not imply government endorsement of commercial
products)
air sickness pills
sunglasses
yellow glasses
ear plugs
Data calculation supplies
Hewlett-Packard 97 and population estimation program
extra batteries and paper for HP 97
instruction manual for HP 97
polar compensating planimeter
pad of writing paper
extra battery-powered calculator that can perform the required
calculations
Other
notebook to store all forms, calculations, and notes
XVI. Calculation of Unbiased Sex and Age Ratios from Moose Observed
During a Population Estimation Survey.
A. Sex and age ratios are calculated for each stratum based on
the number of moose observed during 4 min/mi 2 searches of the
SU's.
1. For example, the following data were collected during a
November 1980 census of Count Area 7 and 14 in GMU 13.
65
Stratum
Type of
Moose
No. Moose Observed
During 4 min/mi 2
Searches (%)
Sex-Age Ratios
by Stratum
High
ca
35 (9.9)
254 (72.0)
64 (18.1)
14 d'/100 ~
25 ca/100 ~
Medium 19 (8.8)
146 (67.3)
52 (24.0)
13 d'/100 ~
36 ca/100 C(
Low
B.
c.
ca
ca
14 (8.1)
lll (64.5)
47 (27.3)
13 d'/100 ~
42 ca/100 ~
Next calculate the number·of bulls, cows, and calves in
each stratum based on the stratum population estimate
from the HP-97 program.:. ·The estimated number of moose is
uncorrected for sightability.
Percentage Estimated Estimated
Stratum of PoEulation X PoEulation = Number
High 9.9 d' 954 94 d'
72.0 ~ 687 ~
18.1 ca 173 ca
Medium 8.8 d' 655 . 58 d'
67.3 ~ 441 ~
24.0 ca 157 ca
Low 8.1 d' 375 30 d'
64.5 C( 242 ~
27.3 ca 102 ca
Now the SCF is applied to the estimated number of moose
for each stratUm as follows:
Uncorrected Corrected
Stratum No. of Moose X SCF = No. of Moose
High 94 d' 1.06 100 d'
687 ~ 1.06 728 ~
173 ca 1.06 183 ca
[
[
[
66
Medium 58 (j 1.06 61 (j
441 ~ 1.06 467 ~
157 ca 1.06 166 ca
Low 30 (j 1.06 32 (j
242 ~ 1.06 257 ~
102 ca 1.06 108 ca
TOTALS 193 (j
1,452 ~
457 ca
D. In order to compute the unbiased sex and age ratios for
the entire survey area, calculate the appropriate ratios
based on the corrected number of bulls, cows, and calves
as follows:
Corrected
No. of Moose
193 (j
1,452 !?
457 ca
1}.
Sex-Age Ratios
l3 cfjlOO 9
31 ca/100 9