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EPA/600/A-86/104
Adaptive Sampling in Environmental Studies
Steven K. Thompson
Department of Statistics
326 Classroom Building
Pennsylvania State University
University Park, PA 16802-2111
Abstract
Adaptive sampling refers to designs in which the pro-
cedure for selecting units or sites at which to make ob-
servations may depend on observations made during the
survey. For example, in pollution assessment studies,
additional observations may be made in the vicinity of
observed hot spots. In surveys of animal and plant popu-
lations, sampling intensity may be increased in the vicin-
ity of high observed abundance. In this paper, examples
of adaptive sampling procedures in survey situations in-
volving whales, shrimp, moose, waterfowl, forest birds,
fish, hardwood tree species, and environmental restora-
tion are described.
Introduction
Adaptive sampling refers to designs in which the pro-
cedure for selecting units or sites on which to make
observations may depend on observations made during
the survey. Thus, the sampling plan has the flexibility
to change during the course of the survey in response
to observed patterns in the population. For example, in
a survey of a rare, clustered animal population, when-
ever unusually high abundance is observed at a sample
site, neighboring sites may be added to the sample. In
a survey of an environmental pollutant, additional ob-
servations may be added in the vicinity of observed hot
spots. Descriptions of a variety of adaptive sampling
designs are found in Thompson (1992) and Thompson
and Seber (1996).
Some types of adaptive designs include adaptive clus-
ter sampling, adaptive allocation, ordinary sequential
designs, and optimal Bayes designs, In adaptive cluster
sampling, an initial sample is selected by some conven-
tional design such as simple random sampling, strat-
ified sampling, unequal probability sampling, system-
atic sampling, or conventional cluster sampling. Then,
whenever a unit in the sample satisfies a specified condi-
tion, such as having a high value, neighboring units are
added to the sample. Still more units may then be added
because some of the neighboring units also satisfy the
condition. With adaptive allocation designs the alloca-
tion of sampling effort among strata is determined se-
quentially during the survey, usually based on observed
sample means or variances (Seber and Thompson 1994,
Thompson 1990, 1991a,b, 1993,1994,1996).
Examples
An example of a situation in which adaptive allocation
is used is the annual moose survey conducted by the
Alaska Department of Fish and Game in interior Alaska
(Gasaway et al. 19S6). Moose are surveyed from air-
craft flying over selected sample plots within a large
study region. The study region is stratified based on the
quality of habitat. At the end of each day during die
survey, sample variances are computed for each stratum
and the sampling effort for the next day is allocated to
approximate the optimal Neyman allocation.
Adaptive allocation designs have also been used and
investigated for surveys of commercial fish species in-
cluding mackerel and orange roughy (Francis 1991),
anchovy (lolly 1993, Jolly and Hampton 1990, 1991),
and shrimp (Thompson, Ramsey, and Seber 1992). The
inherent problem in such surveys is that die mobility of
the populations makes the patterns of abundance unpre-
dictable prior to the survey so that classical allocation
1

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Adaptive Sampling in Environmental Studies
91
formulas are of limited usefulness. In the adaptive ver-
sion, the pattern is assessed in the initial part of the
survey, for example as the research vessel travels west-
ward through the study region, and sampling effort re-
allocated for the second part of the survey, as the vessel
returns westward in the example. With adaptive alloca-
tion the conventional stratified sampling estimator is not
in general unbiased. Unbiased estimation is possible,
however, through a variety of methods.
Adaptive cluster sampling or similar designs have
been investigated for ecological populations such as wa-
terfowl (Smith, Conroy, and Brakhage 1995), and trees
(Roesch, 1993). A related procedure has sometimes
been used in whale surveys (Kishino and Kasamatsu
1987, Schweder and 0ien 1993). Other adaptive sam-
pling designs have been examined for salmon (Geiger
1994)	and for soil remediation (Englund and Herari
1995).	The populations of interest tend to be rare and
unevenly distributed spatially, with aggregation or so-
cial linkage tendencies. By adding neighboring units
whenever high or interesting values are observed in the
sample, the sample total of the variable of interest may
be substantially increase—increasing for example the
number of animals of the species that are observed—
so that unbiased estimation needs to take the adaptive
selection procedure into account.
The possibilities with adaptive designs are very wide.
Much work is needed to determine effective designs
for specific populations to compare the efficiency of
adaptive designs with conventional designs and to de-
termine which type of design will be most effective for
the population of interest. For rare, clustered popula-
tions, adaptive cluster sampling can produce substantial
gains in efficiency relative to conventional designs of
equivalent sample size or cost. Factors influencing the
relative efficiency include within-network variation, rar-
ity within the study region, and cost issues. Adaptive
allocation designs have also been shown to give gains
in efficiency for unpredictably distributed populations
including schooling species of fish and shrimp.
Nonsampling errors such as imperfect detection of
animals, variable catchability of nets for fish, and in-
correct self reporting of drug use must be taken into
account with adaptive as well as with conventional de-
signs. Generally, it is straightforward to adjust estimates
for imperfect detectability by dividing observed values
by detection probabilities. Imperfect detectability also
adds components to the variance which must be esti-
mated (Thompson and Seber 1994).
Conclusions
Motivation for adaptive sampling is provided both by
real world situations such as those described above and
by results in sampling theory showing that the optimal
sampling strategy in many cases will be an adaptive
one. The practical motivations for using adaptive sam-
pling procedures for rare, clustered populations, from
whales to rare plant species, have led field researchers
to suggest adaptive procedures or to use them on an im-
provised basis. The theoretically optimal strategies tend
to be too complex or require too much prior knowledge
for practical implementation. Even so the theoretical
results are suggestive of practical adaptive procedures
with die potential to improve efficiency in surveys of
real populations. Consideration of adaptive along with
conventional sampling procedures greatly increases the
possibilities in survey sampling.
References
Brown, J.A. (1994). The application of adaptive cluster
sampling to ecological studies. In D J. Fletcher and
B .FJ. Manly (Eds), Statistics in Ecology and Environ-
mental Monitoring, pp. 86-97. Otago Conference
Series No. 2. Dunedin, New Zealand: University of
Otago Press.
Englund, EX, and Herari, N. (1995). Phased sampling
for soil remediation. Environmental and Ecological
Statistics, to appear.
Francis, RI.C.C. (1984). An adaptive strategy for strat-
ified random trawl surveys. New Zealand Journal of
Marine and Freshwater Research 18,59-71.
Francis, R.I.C.C. (1991). Statistical properties of two-
phase surveys: comment. Canadian Journal of Fish~
eries and Aquatic Sciences 48,1228.
Gasaway, W.C., DuBois, S.D., Reed, DJ., and Harbo,
SJ. (1986). Estimating Moose Population Parame-
ters from Aerial Surveys. Biological Papers of the
University of Alaska (Institute of Arctic Biology)
Number 22. Fairbanks: University of Alaska.

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92
S. K. Thompson
Geiger, H J. (1994). A Bayesian approach for estimating
hatchery contribution in a series of salmon fisheries.
Alaska Fishery Research Bulletin 1, 66-75.
Jolly, G.M. (1993). Bias in two-phase stratified ran-
dom sampling for optimum allocation. Unpublished
manuscript.
Jolly, G.M., and Hampton, I. (1990). A stratified random
transect design for acoustic surveys of fish stocks.
Canadian Journal of Fisheries and Aquatic Science
47, 1282-1291.
Jolly, G.M., and Hampton, I. (1991). Reply to comment
by R.I.C.C. Francis. Canadian Journal of Fisheries
and Aquatic Science 48, 1228-1229.
Kishino, H., and Kasamatsu, F. (1987). Comparison
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Commission 37, 253-258.
Munholland, P.L., and Borkowski, J.J. (1993b). Adap-
tive Latin square sampling + 1 designs. Technical
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Roesch, F.A. Jr. (1993). Adaptive cluster sampling for
forest inventories. Forest Science 39, 655-669.
Schweder, T., 0ien, N., and Host, G. (1993). Estimates
of abundance of northeastern Atlantic minke whales
in 1989. Report of the International Whaling Com-
mission 43,323-331.
Seber, G.A.F., and Thompson, S.K. (1994). Environ-
mental adaptive sampling. In G.R Patil and C.R.
Rao (Eds), Handbook of Statistics, Vol. 12 (Environ-
mental Sampling), pp. 201-220. New York: North
Holland/Elsevier Science Publishers.
Smith, D.R., Conroy, M.J., and Brakhage, D.H. (1995).
Efficiency of adaptive cluster sampling for estimat-
ing density of wintering waterfowl. Biometrics, to
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Thompson, S.K. (1990). Adaptive cluster sampling.
Journal of the American Statistical Association 85,
1050-1059.
Thompson, S.K. (1991a). Adaptive cluster sampling:
Designs with
primary and secondary units. Biometrics 47, 1103-
1115.
Thompson, S.K. (1991b). Stratified adaptive cluster
sampling. Biometrika 78, 389-397.
Thompson, S.K. (1993). Multivariate aspects of adap-
tive cluster sampling. In G.P. Patil and C.R. Rao
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572. New York: North Holland/Elsevier Science
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94-0301, Center for Statistical Ecology and Environ-
mental Statistics, Department of Statistics, Pennsyl-
vania State University, University Park.
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based on order statistics. Environmetrics 7 123-133.
Thompson, S.K., Ramsey, F.L., and Seber, G.A.F.
(1992). An adaptive procedure for sampling animal
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ity in conventional and adaptive sampling. Biometrics
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Sampling. New York: Wiley. In press.

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Computing Science
and Statistics
Volume 27
Statistics and Manufacturing with Subthemes in
Environmental Statistics, Graphics and Imaging
Proceedings of the
27th Symposium on the Interface
Pittsburgh, PA, June 21-24, 1995
Editors
Michael M. Meyer
James L. Rosenberger
INTERFACE
FOUNDATION
OF NORTH AMERICA

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TECHNICAL REPORT DATA
(Please read instructions on the reverse before com''
1. REPORT NO.
EPA/600/A-96/104
2.

4. TITLE AND SUBTITLE
Adaptive sampling in environmental studies
5. REPORT DATE
6. PERFORMING ORGANIZATION CODE
7. AUTHOR(S|
S.K. Thompson
8. PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
Department of Statistics
326 Classroom Building
Pennsylvania State University
University Park, PA 16802-2111
10. PROGRAM ELEMENT NO.
11. CONTRACT/GRANT NO.
12. SPONSORING AGENCY NAME AND ADDRESS
US EPA ENVIRONMENTAL RESEARCH LABORATORY
200 SW 35th Street
Corvallis, OR 97333
13. TYPE OF REPORT AND PERIOD COVERED
Symposium paper
14. SPONSORING AGENCY CODE
EPA/600/02
16. SUPPLEMENTARY NOTES
1995. Proceedings of the 27th Symposium on the Interface. Computer Science and Statistics, Statistics &
Manufacturing with Subthemes in Environmental Statistics, Graphics, and Imaging, Pittsburgh, Pa, June 21-24, 1995.
16. ABSTRACT
Adaptive sampling refers to designs in which the procedure for selecting units or sites at which the procedure for
selecting units or sites at which to make observations may depend on observations made during the survey. For
example, in pollution assessment studies, additinal observatioons may be made in the viciniity of observed hot spots.
In surveys of animal and plant populations, sampling intensity may be increased in the vicinitiy of observed abundance.
In this paper, examples of adaptive sampling procedures in survey situations involving whales, shrimp, moose,
waterfowl, forest birds, fish, hardwood tree species, and environmental restoration are described.
17. KEY WORDS AND DOCUMENT ANALYSIS
a. DESCRIPTORS
b. IDENTIFIERS/OPEN ENDED TERMS
c. COSATI Field/Group
Adaptive sampling, environmental
statistics.


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