r/EPA
United States Environmental
Protection Agency
Office of Water
Washington, DC 20460
EPA822-R-02-015
March 2002
METHODS FOR EVALUATING WETLAND CONDITION
#4 Study Design for
Monitoring Wetlands
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United States Environmental Office of Water EPA 822-R-02-015
Protection Agency Washington, DC 20460 March 2002
METHODS FOR EVALUATING WETLAND CONDITION
#4 Study Design for
Monitoring Wetlands
Errata sheet
Principal Contributor
Warnell School of Forest Resources, University of Georgia
Amanda K. Parker
Prepared jointly by:
The U.S. Environmental Protection Agency
Health and Ecological Criteria Division (Office of Science and Technology)
and
Wetlands Division (Office of Wetlands, Oceans, and Watersheds)
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NOTICE
The material in this document has been subjected to U.S. Environmental Protection Agency (EPA)
technical review and has been approved for publication as an EPA document. The information
contained herein is offered to the reader as a review of the "state of the science" concerning wetland
bioassessment and nutrient enrichment and is not intended to be prescriptive guidance or firm advice.
Mention of trade names, products or services does not convey, and should not be interpreted as
conveying official EPA approval, endorsement, or recommendation.
APPROPRIATE CITATION
U.S. EPA. 2002. Methods for Evaluating Wetland Condition: Study Design for Monitoring
Wetlands. Office of Water, U.S. Environmental Protection Agency, Washington, DC.
EPA-822-R-02-015.
This entire document can be downloaded from the following U.S. EPA websites:
http ://www.epa.gov/ost/standards
http://www.epa.gov/owow/wetlands/bawwg
-------
r/EPA
United States Environmental Office of Water EPA 822-R-02-015
Protection Agency Washington, DC 20460 March 2002
METHODS FOR EVALUATING WETLAND CONDITION
#4 Study Design for
Monitoring Wetlands
Principal Contributor
Amanda K. Parker
Warnell School of Forest Resources, University of Georgia
Prepared jointly by:
The U.S. Environmental Protection Agency
Health and Ecological Criteria Division (Office of Science and Technology)
and
Wetlands Division (Office of Wetlands, Oceans, and Watersheds)
-------
-------
NOTICE
The material in this document has been subjected to U.S. Environmental Protection Agency (EPA)
technical review and has been approved for publication as an EPA document. The information
contained herein is offered to the reader as a review of the "state of the science" concerning wetland
bioassessment and nutrient enrichment and is not intended to be prescriptive guidance or firm advice.
The views expressed by the author are his or her own and do not necessarily reflect those of EPA.
Mention of trade names, products or services does not convey, and should not be interpreted as
conveying official EPA approval, endorsement, or recommendation.
APPROPRIATE CITATION
Parker, Amanda K. 2001. Methods for Evaluating Wetland Condition: Study Design for
Monitoring Wetlands..EPA 822-R-02-015. U.S. Environmental Protection Agency; Office of
Water; Washington, DC.
This entire document can be downloaded from the following U.S. EPA websites:
http://www.epa.gov/ost/standards
http://www.epa.gov/owow/wetlands/bawwg
11
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CONTENTS
FOREWORD iv
LIST OF "METHODS FOR EVALUATING WETLAND
CONDITION" MODULES : v
SUMMARY 1
PURPOSE 1
INTRODUCTION 1
CONSIDERATIONS FOR SAMPLING DESIGN 2
SAMPLING PROTOCOI 6
SUGGESTED WEBSITES 13
REFERENCES 14
i
LIST OF TABLES
TABLE 1: COMPARISON OF STRATIFIED RANDOM,
TARGETED, AND BACI SAMPLING DESIGNS 7
111
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FOREWORD
In 1999, the U.S. Environmental Protection Agency (EPA) began work on this series of reports entitled
Methods for Evaluating Wetland Condition. The purpose of these reports is to help States and
Tribes develop methods to evaluate (1) the overall ecological condition of wetlands using biological
assessments and (2) nutrient enrichment of wetlands, which is one of the primary stressors damaging
wetlands in many parts of the country. This information is intended to serve as a starting point for States
and Tribes to eventually establish biological and nutrient water quality criteria specifically refined for
wetlands.
This purpose was to be accomplished by providing a series of "state of the science" modules concerning
wetland bioassessment as well as the nutrient enrichment of wetlands. The individual module format
was used instead of one large publication to facilitate the addition of other reports as wetland science
progresses and wetlands are further incorporated into water quality programs. Also, this modular
approach allows EPA to revise reports without having to reprint them all. A list of the inaugural set of
20 modules can be found at the end of this section.
This series of reports is the product of a collaborative effort between EPA's Health and Ecological
Criteria Division of the Office of Science and Technology (OST) and the Wetlands Division of the
Office of Wetlands, Oceans and Watersheds (OWOW). The reports were initiated with the support
and oversight of Thomas J. Danielson (OWOW), Amanda K. Parker and Susan K. Jackson (OST),
and seen to completion by Douglas G. Hoskins (OWOW) and Ifeyinwa F. Davis (OST). EPA relied
heavily on the input, recommendations, and energy of several panels of experts, which unfortunately
have too many members to list individually:
• Biological Assessment of Wetlands Workgroup
• Wetlands Nutrient Criteria Workgroup
More information about biological and nutrient criteria is available at the following EPA website:
http://www.epa.gov/ost/standards
More information about wetland biological assessments is available at the following EPA website:
http://www.epa.gov/owow/wetlands/bawwg
IV
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LIST OF "METHODS FOR EVALUATING WETLAND
CONDITION" MODULES
MODULE #
MODULE TITLE
1 INTRODUCTION TO WETLAND BIOLOGICAL ASSESSMENT
2 INTRODUCTION TO WETLAND NUTRIENT ASSESSMENT
3 THE STATE OF WETLAND SCIENCE
4 STUDY DESIGN FOR MONITORING WETLANDS
5 ADMINISTRATIVE FRAMEWORK FOR THE IMPLEMENTATION OF A
WETLAND BIOASSESSMENT PROGRAM
6 DEVELOPING METRICS AND INDEXES OF BIOLOGICAL INTEGRITY
7 WETLANDS CLASSIFICATION
8 VOLUNTEERS AND WETLAND BIOMONITORING
9 DEVELOPING AN INVERTEBRATE INDEX OF BIOLOGICAL
INTEGRITY FOR WETLANDS
10 USING VEGETATION TO ASSESS ENVIRONMENTAL CONDITIONS
IN WETLANDS
11 USING ALGAE TO ASSESS ENVIRONMENTAL CONDITIONS IN
WETLANDS
1 2 USING AMPHIBIANS IN BlOASSESSMENTS OF WETLANDS
13 BIOLOGICAL ASSESSMENT METHODS FOR BIRDS
14 WETLAND BIOASSESSMENT CASE STUDIES
15 BIOASSESSMENT METHODS FOR FISH
16 VEGETATION-BASED INDICATORS OF WETLAND NUTRIENT
ENRICHMENT
17 LAND-USE CHARACTERIZATION FOR NUTRIENT AND SEDIMENT
RISK ASSESSMENT
1 8 BlOGEOCHEMICAL INDICATORS
19 NUTRIENT LOAD ESTIMATION
2O SUSTAINABLE NUTRIENT LOADING
v
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SUMMARY
Otate and Tribal monitoring programs
kJ should be designed to assess wetland condi-
tion with statistical rigor while maximizing available
management resources. The three study designs
described in this module—stratified random sam-
pling, targeted/tiered approach, and before/after,
control/impact (BACI)—allow for collection of a
significant amount of information for statistical analy-
ses with relatively minimal effort. The sampling de-
sign selected for a monitoring program will depend
on the management question being asked. Sam-
pling efforts should be designed to collect informa-
tion that will answer management questions in a way
that will allow robust statistical analysis. In addi-
tion, site selection, characterization of reference sites
or systems, and identification of appropriate index
periods are all of particular concern when selecting
an appropriate sampling design. Careful selection
of sampling design will allow the best use of finan-
cial resources and will result in the collection of high-
quality data for evaluation of the wetland resources
of a State or Tribe. Examples of different sampling
designs currently in use for State and Tribal wet-
land monitoring are described in the Case Study
(Bioassessment) module and on http://
www.epa.gov/owow/wetlands/bawwg/case.htnil.
PURPOSE
rhe purpose of this module is to provide tech-
nical guidance information on designing
effective sampling programs for State and Tribal wet-
land water quality monitoring.
INTRODUCTION
T/rTetlands are included as waters of the United
W States in the Federal regulations (40 CFR
122.2,40 CFR230.3, and40 CFR232.2) imple-
menting the Clean Water Act [Section 502(7)].
Wetlands are important waterbodies; they can pro-
vide many functions that are beneficial to the local
landscape, for example, water storage, water qual-
ity improvement, and wildlife habitat. Wetlands are
also valuable as ecosystems in their own right, pro-
viding carbon storage, biogeochemical transforma-
tions, and aquifer recharge (Mitsch and Gosselink
1993). However, few States or Tribes (only six
States and Tribes reported attainment of designated
uses in the "National Water Quality Inventory 1996
Report to Congress") include wetland monitoring
in their routine water quality monitoring programs
(U.S. EPA 1996, 1998). Yet, wetland chemical
and biological water quality monitoring data are
scarce to nonexistent in many States and most Fed-
eral databases. The need for water quality moni-
toring data on wetlands is obvious; the vast major-
ity of data about wetlands are collected in relation
to dredge and fill permitting. Indeed, only 4% of
the nation's wetlands were surveyed and only 11
States and Tribes reported information concerning
wetland designated use attainment in the "National
Water Quality Inventory 1998 Report to Congress"
(U.S. EPA 1998). This module is intended to pro-
vide guidance to State and Tribal water quality man-
agers on designing wetland monitoring programs to
be included as a part of their routine water quality
sampling. ; .
Most States and Tribes will need to begin wet-
land monitoring programs to collect water quality
and biological data (U.S. EPA 1990). The best
monitoring programs are designed to assess wet-
land condition with statistical rigor while maximiz-
ing available management resources. At the broadest
level, monitoring should include the following:
• Detecting and characterizing the ambient condi-
tion of existing wetlands
• Describing whether wetland condition is improv-
ing, degrading, or staying the same
• Defining seasonal patterns in wetland conditions
• Identifying thresholds for system stressors, that
is, how much the system can be disturbed with-
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out causing unacceptable changes in wetland sys-
tem quality or degradation of beneficial uses.
Water quality monitoring programs at the State
and Tribal level are often poorly and inconsistently
funded or are improperly designed and carried out,
making it difficult to collect a sufficient number of
samples over time and space to identify changes in
system condition or to estimate average conditions
with statistical rigor. Three approaches to study
design for assessing water quality as well as bio-
logical and ecological condition and for identifying
degradation in wetlands are described in this mod-
ule. Specific issues to consider in designing moni-
toring programs for wetland systems are also dis-
cussed in this module. The study designs presented
can be tailored to fit the specific goals of monitoring
programs. The assemblage-specific modules will
discuss detailed sampling considerations.
The three approaches described—stratified ran-
dom sampling; targeted/tiered approach; and
BACI—present study designs that allow one to
obtain a significant amount of information with rela-
tively minimal effort. Stratified random sampling
begins with a large-scale random monitoring design
that is reduced as the wetland system or habitat
conditions are characterized. This approach is used
to find the mean condition of each wetland class, or
type, in a specific region. Stratified random sam-
pling design is frequently used for new large-scale
monitoring programs at the State and Federal level
(e.g., the Environmental Monitoring and Assess-
ment Program, the Regional Environmental Moni-
toring and Assessment Program, and State pro-
grams in Maine, Montana, and Wisconsin). The
tiered or targeted approach to monitoring begins
with coarse screening and proceeds to more de-
tailed monitoring protocols as impaired and high-
risk systems are identified for further investigation.
Targeted sampling design provides atriage approach
to identifying wetland systems in need of restora-
tion, protection, and intensive management. Sev-
eral State pilot proj ects use this method or a modi-
fication of this method for wetland assessment (e.g.,
Florida, Ohio, Oregon, and Minnesota). The syn-
optic approach described in Kentula et al. (1993)
uses a modified targeted sampling design. The
BACI design and its modifications are frequently
used to assess the success of restoration efforts or
other management experiments. BACI design al-
lows for comparisons in similar systems over time
to determine the rate of change in relation to the
management activity, for example, to assess the suc-
cess of a wetland hydrologic restoration.
Detenbeck et al. (1996) used BACI design for
monitoring water quality of wetlands in the Minne-
apolis/St. Paul, Minnesota, metro area.
Monitoring programs should be designed to an-
swer questions such as how, when, where, and at
what levels do unacceptable wetland conditions
occur? These questions are interrelated, and a well-
designed monitoring program can contribute to an-
swering them. Sampling design is dependent on
the management question being asked. Sampling
efforts should be designed to collect information that
will answer the management question. For example,
stratified random sampling might be good for ambi-
ent monitoring programs, BACI for evaluating res-
toration, and targeted sampling for developing an
Index of Biological Integrity or nutrient criteria
thresholds, hi fact, some State programs will likely
need to use a combination of approaches.
CONSIDERATIONS FOR
SAMPLING DESIGN
DESCRIBING THE MANAGEMENT
QUESTION
Clearly defining the question being asked (identi-
fying the hypothesis) encourages the use of appro-
priate statistical analyses, reduces the occurrence
of false-positive (Type I) errors, and increases the
efficient use of management resources (Suter 1993,
Leibowitz et al. 1992, Kentula et al. 1993). Begin-
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ning a study or monitoring program with carefully
defined questions and objectives helps to identify
the statistical analyses most appropriate for the study
and reduces the chance that statistical assumptions
will be violated. Management resources are opti-
mized because resources are directed at monitor-
ing that is most likely to answer management ques-
tions. In addition, defining the specific hypotheses
to be tested, carefully selecting reference sites, and
identifying the most useful sampling interval can help
reduce the uncertainty associated with the results
of any sampling design as well as further conserve
management resources (Kentula et al. 1993).
SITE SELECTION
Site selection is arguably the most important task
in developing a monitoring program (Kentula et al.
1993). Site selection for a monitoring program is
based on the need to sample a relatively large num-
ber of wetlands to establish the range of wetland
quality in a specific regional setting. Protecting or
improving the quality of a wetland system often de-
pends on the ability of the monitoring program to
identify dose-response relationships, for example,
the relationship of nutrient concentration (dose) to
periphyton abundance (response). Dose-response
relationships can be identified using large sample
sizes and systems that span the gradient (low to
high) of wetland quality. All ranges of responses
should be observed along the dosing gradient from
low levels to high levels of human disturbance. In
addition, wetland monitoring frequently requires an
analysis of both watershed/landscape characteris-
tics and wetland-specific characteristics (Kentula
et al. 1993, Leibowitz et al. 1992). Therefore,
wetland sampling sites should be selected on the
basis of land use in the region so that watersheds
range from minimally impaired with few expected
stressors to high levels of development (e.g., agri-
culture, forestry, or urban) with multiple expected
stressors (see Module 17: Land-Use Character-
ization for Nutrient and Sediment Risk Assessment).
Establishing dose-response relationships may be
confounded due to time lags between stressor oc-
currence and biological or functional response. The
duration of the time lag between stressor and re-
sponse depends on many factors, including the type
of stressor, climate, and system hydrology. These
factors should be considered when selecting wet-
land sites to establish the range of wetland quality
within a region.
The synoptic approach described in Leibowitz et
al. (1992) provides a method of rapid assessment
of wetlands at the regional and watershed levels
that can help identify the range of wetland quality
within a region. Leibowitz et al. (1992) recommend
an initial assessment for site selection based on cur-
rent knowledge of watershed- and landscape-level
features; modification of such an assessment can
be made as more data are collected. Assessing
watershed characteristics through the use of aerial
photography and geographical information systems
linked to natural resource and land-use databases
can aid in identifying reference and degraded sys-
tems (see Module 17: Land-Use Characterization
for Nutrient and Sediment Risk Assessment)
(Johnston etal. 1988,1990,Gwinetal. 1999,Palik
et al. 2000). Some examples of watershed char-
acteristics that can be evaluated using aerial pho-
tography and geographical information systems in-
clude land use, land cover (including riparian veg-
etation), soil, bedrock, hydrography, and infrastruc-
ture (e.g., roads and railroads).
IDENTIFYING AND CHARACTERIZING
REFERENCE WETLANDS
The term "reference" in this module refers to those
systems that are least impaired by anthropogenic
effects. This term can be confusing because of the
different meanings that are currently in use in differ-
ent classification methods (particularly in
hydrogeomorphic wetland classification). A dis-
cussion of the term "reference" and its multiple
meanings is provided in Module 7: Wetlands
Classification.
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Watersheds with little or no development that re-
ceive minimal anthropogenic inputs could potentially
contain wetlands that would serve as minimally im-
paired reference sites. Watersheds with a high per-
centage of the drainage basin occupied by urban
areas, agricultural land, and altered hydrology are
likely to contain wetlands that are impaired or could
potentially be considered "at risk" for developing
problems. Wetland loss in the landscape should
also be considered when assessing watershed char-
acteristics for reference wetland identification.
Biodiversity can become impoverished due to wet-
land fragmentation or decreases in regional wet-
land density, even in the absence of site-specific
land-use activities. Reference wetlands may be
more difficult to locate if fragmentation of wetland
habitats is significant, and they may no longer rep-
resent the biodiversity of minimally disturbed wet-
lands in the region. The continued high rate of wet-
land loss in most States and Tribes requires that
multiple reference sites be selected to ensure some
consistency in reference sites for multiple-year sam-
pling programs (Leibowitz et al. 1992, Kentula et
al. 1993). Once the watershed level has been con-
sidered, a more site-specific investigation can be
initiated to better assess wetland condition.
Potential reference wetlands should be charac-
terized to allow for the identification of appropriate
reference wetland systems. Appropriate reference
sites will have similar soils, vegetation, hydrologic
regimes, and landscape settings to other wetlands
in the region (Adamus 1992, Leibowitz etal. 1992,
Kentulaetal. 1992,Detenbecketal. 1996). Clas-
sification of wetlands, as discussed in Module 7:
Wetlands Classification, will aid in identifying ap-
propriate reference wetlands for specific regions and
wetland classes. Wetland classification should be
supplemented with information on hydroperiod and
flood frequency to ensure that the selected refer-
ence wetlands are truly representative of wetlands
in the region, class, or subclass of interest. Refer-
ence wetlands may not be available for all wetland
classes. In this case, data from systems that are as
close as possible to the assumed unimpaired state
of wetlands in the wetland class of interest should
be sought from within the same geologic province.
Development of a conceptual reference may be nec-
essary if appropriate reference sites cannot be found
in the local region or geologic province. Techniques
for defining a conceptual reference are discussed at
length hi Harris et al. (1995), Trexler (1995), and
Toth etal. (1995).
Reference wetlands should be selected on the basis
of low levels of human alteration in their watersheds
(Leibowitz et al. 1992, Kentula et al. 1993, U.S.
EPA 2000). Selecting reference wetlands usually
involves the assessment of land use within water-
sheds as well as visits to individual wetland systems
to ground-truth expected land use and check for
unsuspected impacts. Ground-truthing visits to ref-
erence wetlands are crucial for the identification of
ecological impairment that may not be apparent from
land-use and local habitat conditions. Again, a suf-
ficient sample size is important to characterize the
range of conditions that can be expected in the least.
impacted systems of the region (Detenbeck et al.
1996). Reference wetlands should be identified for
each ecoregion or geological province in the State
or Tribal lands and then characterized with respect
to ecological integrity. A minimum of three low-
impact reference systems is recommended for each
wetland class for statistical analyses. However,
power analysis can be performed to determine the
degree of replication necessary to detect an impact
to the systems being investigated (Detenbeck et al.
1996, Urquhart et al. 1998; see also http://www.
mpl-pwrc.usgs.gov/powcase/index.html). Highest
priority should be given to identifying reference sys-
tems for those wetland classes considered to be at
the greatest risk from anthropogenic stress.
WHEN TO SAMPLE
Sampling may be targeted to the period when
problems are most likely to occur—the index pe-
riod. The appropriate index period will be defined
by what the investigator is trying to investigate and
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by what taxonomic assemblage or parameters are
being used for that investigation (Barbour et al.
1999). For example, increased nutrient concen-
trations and sedimentation from nonpoint sources
may occur following periods of high runoff during
the spring and fall, whereas point sources of nutri-
ent pollutants may cause plankton blooms and/or
increased water and soil nutrient concentrations in
wetland pools during times of low rainfall. Hence,
different index periods may be needed for nonpoint
source and point source nutrients. Each taxonomic
assemblage studied will also have an appropriate
index period—usually in the growing season (see
assemblage methods in the Minnesota case study:
http://www.epa.gov/owow/wetlands/bawwg/case/
mnl .html). The index period window may be early
in the growing season for amphibians and algae.
Other assemblages, such as vegetation and birds,
may require a different sampling window for the
index period (see the assemblage-specific modules
for recommendations). Once wetland condition has
been characterized, one-time annual sampling dur-
ing the appropriate index period may be adequate
for multiple-year monitoring of indicators of nutri-
ent status, designated use, and biotic integrity. How-
ever, criteria and ecological indicator development
may require more frequent sampling to define con-
ditions that relate to the stressor or the impact of
interest (Karr and Chu 1999, Stevenson 1996,
1997).
Ideally, water quality monitoring programs pro-
duce long-term data sets compiled over multiple
years to capture the natural, seasonal, and year-to-
year variations in biological communities and
waterbody constituent concentrations (Tate 1990,
Doddsetal. 1997,McCormicketal. 1999). Mul-
tiple-year data sets can be analyzed with statistical
rigor to identify the effects of seasonality and vari-
able hydrology. Once the pattern of natural varia-
tion has been described, the data can be analyzed
to determine the ecological state of the waterbody.
Long-term data sets have also been important in
influencing management decisions about wetlands,
most notably in the Everglades, where long-term
data sets have induced Federal, State, and Tribal
actions for conservation and restoration of the larg-
est wetland system in the United States (see Davis
and Ogden 1994, Redfield 1999, 2000, 2001;
1994 Everglades Forever Act, Florida Statute
§373.4592).
In spite of the documented value of long-term data
sets, there is a tendency to intensively study a
waterbody for 1 year before and 1 year after treat-
ment. A more cost-effective approach would be
to measure only the indices most directly related to
the stressor of interest (i.e., those parameters or
indicators that provide the best information to an-
swer the specific management question), but to
double or triple the monitoring period. Two or more
years of data are often needed to identify the ef-
fects of years with extreme climatic or hydrologic
conditions. Comparisons over time between refer-
ence and at-risk or degraded systems can help de-
scribe biological response and annual patterns in
the presence of changing climatic conditions. Long-
term data sets can also help describe regional trends.
Flooding or drought may significantly affect wet-
land biological communities and the concentration
of water column and soil constituents. Effects of
uncommon climatic events can be characterized to
discern the overall effect of management actions
(e.g., nutrient reduction and water diversion) if sev-
eral years of data are available to identify the long-
term trends. At the very minimum,-2 years of data
before and 2 after specific management actions, but
preferably 3 of more each, are recommended to
evaluate the cost-effectiveness of management ac-
tions with some degree of certainty (U.S. EPA
2000). If funds are limited, restricting sampling fre-
quency and/or numbers of indices analyzed should
be considered to preserve a long-term data set.
Reducing sampling frequency or numbers of pa-
rameters measured will allow for effectiveness of
management approaches to be assessed against the
high annual variability that is common in most wet-
land systems. Wetlands witiihighhydrologicalvaria-
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tion from year to year may require more years of
sampling before and after mitigation procedures to
identify the effects of the natural hydrologic vari-
ability (Kadlec and Knight 1996).
Using the B ACI study design may also provide
substantial benefitfor determining the effectiveness
of management activities. Tracking both reference
(control) and impacted systems within a region over
tune will help determine the rate and direction of
change in monitored systems regardless of system
variability. The B ACI design may require more fre-
quent monitoring of reference systems than is sug-
gested for random or targeted designs, but it would
allow useful comparison of system change regard-
less of variability (Detenbecketal. 1996).
Characterizing precision of estimates
Estimates of dose-response relationships, nutri-
ent and biological conditions in reference systems,
and wetland conditions in a region are based on
sampling, hence precision must be assessed. Pre-
cision is defined as "measure of the degree of agree-
ment among the replicate analyses of a sample, usu-
ally expressed as the standard deviation" (Eaton et
al. 2000). Determining precision of measurements
for one-time assessments from single samples in a
wetland is often necessary. The variation associ-
ated with one-time assessments from single samples
can often be determined by resampling a specific
number of wetlands during the survey. Measure-
ment variation among replicate samples can then
be used to establish the expected variation for one-
time assessment of single samples. Resampling does
not establish the precision of the assessment pro-
cess; it identifies the precision of an individual mea-
surement (Kentula et al. 1993). Resampling fre-
quency is often conducted for one wetland site in
every block of 10 sites. However, investigators
should adhere to the objectives of resampling (of-
ten considered an essential element of quality as-
sessment/quality control) to evaluate the variation
in a one-time assessment from a single sample. The
larger the sample size, the better (smaller) will be
the estimate of that variation. Often, more than 1 in
10 samples need to be replicated in monitoring pro-
grams to provide a reliable estimate of measure-
ment precision (Barbouretal. 1999).
SAMPLING PROTOCOL
APPROACHES TO SAMPLING DESIGN
Three approaches to sampling design—stratified
random, targeted design, and B ACI—have advan-
tages and disadvantages that under different circum-
stances warrant the choice of one approach over
the other (Table 1). The decision as to the best
approach for sample design in a new monitoring
program must be made by the water quality resource
manager or management team after carefully con-
sidering different approaches. Justification of a dose-
response relationship is confounded by lack of ran-
domization and replication, and must be considered
in choosing a sampling design for a monitoring pro-
gram. Direct identification of a cause-response re-
lationship is not possible in observational (monitor-
ing) studies. However, inferences of causality can
be argued if appropriate information is collected.
Beyers (1998) describes assembly rules for causal
arguments that can be used to infer causality for
stressors of interest. The number of sites to be
sampled and the sampling frequency are determined
by the type of sampling design chosen by the re-
source managers. Power analyses can be used to
help make this decision by estimating the number of
sites to be sampled and replicates needed to pro-
duce a statistically significant result. The U.S. Geo-
logical Survey provides an excellent website that
can assist the resource manager in determining the
number of sites and replicates needed to produce
the desired analytical power for a particular sam-
pling design: http://www.mpl-pwrc.usgs.gov/
powcase/howhtml.
6
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TABLE 1: COMPARISON OF STRATIFIED RANDOM, TARGETED, AND BACI
SAMPLING DESIGNS
STRATIFIED RANDOM
TARGETED
BACI
Random selection of wetland systems
from entire population within a
region.
This design requires minimal prior
knowledge of wetlands within the
sample population for stratification.
This design may require more
resources (time and money) to
randomly sample wetland classes,
because more wetlands may need to
be sampled.
System characterization for a class of
wetlands is more statistically robust.
Rare wetlands may be under-
represented or absent from the
sampled wedands.
This design is potentially best for
regional characterization of wetland
classes, especially water quality
conditions are not known.
Targeted selection of wedands based
on problematic (wedand systems
known to have problems) and
reference wetlands.
This design requires prior knowledge
of wetlands within the sample
population.
This design utilizes fewer resources
because only targeted systems are
sampled.
System characterization for a class of
wedands is less statistically robust,
although characterization of a
targeted wedand may be statistically
robust.
This design may miss important
wetland systems if they are not
selected for the targeted
investigation.
This design is potentially best for
site-specific and watershed-specific
criteria development when water
quality conditions for the wedand of
interest are known.
Selection of wetlands based on a
known impact.
This design requires knowledge of a
specific impact to be analyzed.
This design utilizes fewer resources
because only wedands with known
impacts and associated control
systems are sampled.
Characterization of the investigated
systems is statistically robust.
The information gained in this type
of investigation is not transferable to
wetland systems not included in the
study.
This design is potentially best for
monitoring restoration or creation of
wedands and systems that have
specific known stressors.
STRATIFIED RANDOM
SAMPLING DESIGN
Probabilistic sampling—a sampling process in
which randomness is a requisite (Hayek 1994)—
can be used to characterize the status of water qual-
ity conditions and biotic integrity in a region's wet-
land systems. This type of sampling design is used
to describe the average conditions of a wetland
population, identify the variability among sampled
wetlands, and to help determine the range of wet-
land system conditions in a region. However, the
data collected from a probabilistic random sample
design will generally be characteristic of the domi-
nant class of wetland in the region, and rare wet-
lands may be underrepresented or absent from the
probabilistically sampled wetlands. Additional sam-
pling sites may need to be added to include the com-
plete range of wetland conditions and classes in the
region.
Probabilistic designs are often modified by strati-
fication (such as classification). Stratification, or
stratified random sampling, is a type of probability
sampling in which atarget population is divided into
relatively homogenous groups (strata) before sam-
pling based on factors that influence variability in
that population (Hayek 1994). Analysis of vari-
ance can be used to identify statistically different
parameter means among the sampling strata. The
strata are the analysis of variance treatments (Poole
7
-------
1972). The result of collecting and assessing water
quality and biotic responses with a stratified ran-
dom sample is, presumably, an unbiased estimate
of the descriptive statistics (e.g., means, variances,
modes, and quartiles) of all wetlands in a stratum.
Stratification by wetland size and class provides
more information about different types of wetlands
within a region. Sample statistics from random se-
lection alone would be most characteristic of the
dominant wetland class in a region if the population
of wetlands is not stratified.
Many State 305b and watershed monitoring pro-
grams use stratified random sampling designs, for
example, Maine, Montana, and Wisconsin pilot
projects use this design. Details of these monitor-
ing designs can be found in the Module 14: Case
Studies (Bioassessment) and at http://
wvwv.epa.gov/owow/wetlands/bawwg/index.html.
Stratification is based on identifying wetland sys-
tems in a region (or watershed) and then selecting
an appropriate sample of systems from the defined
population. The determination of an appropriate
sample population depends on the management
questions being asked. A sample population of iso-
lated depressional wetlands could be identified as a
single stratum, but investigations of these wetlands
would notprovide any information on riparian wet-
lands in the same region. If the goal of the monitor-
ing program is to identify wetland condition for all
wetland classes within a region, then a sample
population of wetlands should be randomly selected
from all wetlands within each class. In practice,
most State and Tribal programs stratify random
populations by size, wetland class (see Module 7:
Wetlands Classification), and landscape character-
istics or location (see http://www.epa.gov/owow/
wetlands/bawwg/case/me.html, http://www.
epa.gov/owow/wetlands/bawwg/case/wa.html, and
http://www.epa.gov/owow/wetlands/bawwg/case/
\\ilJitrnl).
Once the wetlands for each stratum have been
selected, the sample population is often modified
by deleting systems that are too close to other wet-
lands to be different, thereby reducing redundant
collection efforts. For example, the Environmental
Monitoring and Assessment Program limits redun-
dant collection efforts by applying a regular (hex-
agonal) grid'to a map of the area. Sampling sites
are chosen by randomly selecting grid cells and ran-
domly sampling wetland resources within the cho-
sen grid cells (Paulsen et al. 1991). Estimatesof
ecological conditions from these kinds of modified
. probabilistic sampling designs can be used to char-
acterize the water quality conditions and biological
integrity of wetland systems in a region and, over
time, to distinguish trends in ecological condition
within a region (see http://www.epa.gov/owow/wet-
lands/bawwg/case/mtdev.html and http://
www.epa.gov/owow/wetlands/bawwg/case/
flLhtml).
TARGETED DESIGN
A targeted approach to sampling design may be
more appropriate when resources are limited. Tar-
geted sampling is a specialized case of random
stratified sampling. The approach described here
involves defining a gradient of impairment. Once
the gradient has been defined and systems have been
placed in categories of impairment, investigators
focus the most effort on identifying and characteriz-
ing wetland systems or sites likely to be impacted
by anthropogenic stressors and on relatively undis-
turbed wetland systems or sites (see "Identifying
and Characterizing Reference Wetlands") that can
serve as regional, subregional, or watershed ex-
amples of natural biological integrity. The Florida
Department of Environmental Protection uses a tar-
geted sampling design for developing thresholds of
impairment with macroinvertebrates (http://
www.epa.gov/owow/wetlands/bawwg/case/
fl2.html). Choosing sampling stations that best al-
low the comparison of ecological integrity at refer-
ence wetland sites of known condition can conserve
financial resources. A sampling design that tests
specific hypotheses (e.g., the study by the Florida
8
-------
Department of Environmental Protection tested the
effect of elevated water column phosphorus on
macroinvertebrate species richness) can generally
be analyzed with statistical rigor and can conserve
resources by answering specific questions. Fur-
thermore, the identification of systems with prob-
lems and reference conditions eliminates the need
for selecting a random sample of the population for
monitoring.
Targeted sampling assumes some knowledge of
the systems sampled. Systems with evidence of
degradation are compared with reference systems
that are similar in physical structure (i.e., in the same
class of wetlands). Targeted sampling requires that
the wetlands be characterized by a gradient of im-
pairment. Wetland systems should be placed along
a continuum from reference to most impacted. An
impaired or degraded wetland is simply a system in
which anthropogenic impacts exceed acceptable
levels or interfere with beneficial uses. Compari-
son of the monitoring data with the data collected
from reference wetlands will allow characterization
of the sampled systems. Wetlands identified as "at
risk" should be evaluated through a sampling pro-
gram to characterize the degree of degradation.
Once characterized, the wetlands should be placed
in categories such as the following:
• Degraded wetlands—wetlands in which the level
of anthropogenic perturbance interferes with des-
ignated uses
• High-risk wetlands—wetlands in which anthro-
pogenic stress is high but does not significantly
impair designated uses (Tnhigh-risk systems, im-
pairment is prevented by one or a few factors
that could be changed by human actions, although
characteristics of ecological integrity are already
marginal.)
• Low-risk wetlands—wetlands in whichmany fac-
tors prevent impairment, stressors are maintained
below problem levels, and/or no development is
contemplated that would change these conditions
• Reference wetlands—wetlands in which the eco-
logical characteristics most closely represent the
pristine or minimally impaired condition.
Once wetland systems have been classified on the
basis of their physical structure (see Module 7:
Wetlands Classification) and placed into the cat-
egories previously defined, specific wetlands need
to be selected for monitoring. At this point, ran-
domness is introduced; wetlands should be ran-
domly selected within each class and risk category
for monitoring. An excellent example of categoriz-
ing wetlands in this manner is given in the Ohio En-
vironmental Protection Agency's case study at
http://www.epa.gov/owow/wetlands/bawwg/case/
ohl.html. It used the Ohio Rapid Assessment
Method to categorize wetlands by degree of im-
pairment. The Minnesota Pollution Control Agency
also used a targeted design for monitoring wetlands
(see http://www.epa.gov/owow/wetlands/bawwg/
case/mnl .html). It used the best professional judg-
ment of local resource managers to identify refer-
ence sites as well as sites with known impairment
from identified stressors (e.g., agriculture and
stormwater runoff).
Monitoring efforts are oftenprioritizedto best utilize
limited resources. For example, case study investi-
gators in Oregon chose not to monitor depressional
wetlands because of funding constraints; they fur-
ther tested the degree of independence of selected
sites (and thus the need to monitor all of those sites)
by using cluster analysis and other statistical tests
(seehttp://www.epa.gov/owow/wetlands^awwg/
case/or.html). Frequency of monitoring is deter-
mined by the management question being asked and
the intensity of monitoring necessary to collect
enough information to answer the question. In ad-
dition, monitoring should identify the watershed-level
activities that are likely to result in ecological deg-
radation of wetland systems (Suter et al. 1993).
Targeted sampling design involves monitoring iden-
tified degraded systems and comparable reference
systems most intensively. Low-risk systems are
9
-------
monitored less frequently (after initial identification),
unless changes in the watershed indicate an in-
creased risk of degradation.
Activities surrounding impaired wetland systems
may be used to help identity which actions nega-
tively affect wetlands, and therefore may initiate
more intensive monitoring of at-risk wetlands. Moni-
toring should focus on factors likely to identify eco-
logical degradation and anthropogenic stress and
on any actions that might alter those factors. State/
Tribal water quality agencies should encourage
adoption of local watershed protection plans to
minimize ecological degradation of natural wetland
systems. Development plans in a watershed should
be evaluated to identify potential future stressors.
Changes in point sources can be monitored through
the National Pollutant Discharge Elimination Sys-
tem permit program (U.S. EPA 2000). Changes in
nonpoint sources can be evaluated through the iden-
tification and tracking of wetland loss and/or deg-
radation, increased residential development, in-
creased tree harvesting, and shifts to more inten-
sive agriculture with greater fertilizer use or increases
in livestock numbers. Local planning agencies
should be informed of the risk of increased anthro-
pogenic stress and encouraged to guide develop-
ment accordingly. Ecological degradation often
gradually increases as a result of many growing
sources of anthropogenic stress. Therefore, fre-
quent monitoring is warranted for high-risk wetlands
if sufficient resources remain after meeting the needs
of degraded wetlands. Whenever development
plans appear likely to alter factors that maintain eco-
logical integrity in a high-risk wetland (e.g., veg-
etated buffer zones), monitoring should be initiated
at a higher sampling frequency in order to enhance
the understanding of baseline conditions (U.S. EPA
2000).
BACI SURVEY DESIGN
An ideal impact survey has several features: The
type of impact, time of impact, and place of occur-
rence should be known in advance; the impact
should not have occurred yet; and control areas
should be available (Green 1979). The first feature
allows surveys to be efficiently planned to account
for the probable change in the environment. The
second feature allows a baseline study to be estab-
lished and to be extended as needed. The third
feature allows the surveyor to distinguish between
temporal effects unrelated to the impact and changes
related to the impact. In practice, however, ad-
vance knowledge of specific impacts is rare and
the ideal impact survey is rarely conducted. BACI
designs modified to monitor impacts during or after
their occurrence can still provide information, but
there is an increase in the uncertainty associated
with the results, and the likelihood of finding a sta-
tistically significant change caused by the impact is
much less probable. Power analyses of after-only
studies were conducted by Osenberg et al. (1994).
They determined that because of the time constraints
of most studies, relatively few of the population and
chemical/physical parameters could provide ad-
equate analytical power. They suggest expending a
greater effort on monitoring individual-based pa-
rameters (e.g., body size and recruitment density)
in addition to population and chemical/physical pa-
rameters for environmental impact assessments
(Osenberg et al. 1994). Defining the study objec-
tives, and identifying the specific hypotheses being
tested, greatly increases the certainty of the results.
In addition, other aspects of survey design are de-
pendent on the study objectives: the sampling inter-
val, the length of time the survey is conducted (i.e.,
sampling for acute vs. chronic effects), and the sta-
tistical analyses appropriate for analyzing the data
(Suterl993).
The best interval for sampling is determined by
the objectives of the study (Kentulaetal. 1993). If
the objective is to detect changes in trends (e.g.,
regular monitoring for detection of changes in wa-
ter quality or biotic integrity), regularly spaced in-
tervals are preferred because the analysis is easier.
However, if the objective is to assess differences
-------
before and after impact, then samples at random
time points are advantageous. Random sample in-
tervals reduce the likelihood that cyclic differences
unforeseen by the sampler will influence the size of
the difference before and after the impact. For ex-
ample, surveys taken every summer for several years
before and after a clear-cut may show little differ-
ence in system quality; however, differences may
exist that can be detected only in the winter and,
therefore, they may go undetected if sampling oc-
curs only during summer.
The simplest impact survey design involves taking
a single survey before and after the impact event
(Green 1979). This type of design has the obvious
pitfall that there may be no relationship between the
observed event and the changes in the response
variable—the change may be entirely coincidental.
This pitfall is addressed in B ACI design by com-
paring before and after impact data with data col-
lected from a similar control system nearby. Data
are collected before and after a potential distur-
bance in two areas (treatment and a control), with
measurements on biological and environmental vari-
ables in all combinations of time and area (Green
1979). For example, consider a study in which the
investigators want to identify the effects of clear-
cutting on wetland systems. In the simplest B ACI
design, two wetlands would be sampled. One wet-
land would be adjacent to the clear-cut (the treat-
ment wetland); the other wetland would be adja-
cent to a control site that is not clear-cut. The con-
trol site should have characteristics (i.e., soil, veg-
etation, structure, and functions) similar to the treat-
ment wetland and should be exposed to climate and
weather similar to the first wetland. Both wetlands
are sampled at the same time points before and af-
ter the clear-cut occurs. This design is technically
known as an area-by-time factorial design. Evi-
dence of an impact is found by comparing the con-
trol site samples (before and after) with the treat-
ment site before and after samples. Area-by-time
factorial design allows for both natural wetland-to-
wetland variation and coincidental time effects. If
the clear-cut has had no effect, then the change in
system quality between the two time points should
be the same. If the clear-cut has had an effect, then
the change in system quality between the two time
points should be different.
There are some potential problems with B ACI
design. First, because the control and impact sites
are not randomly assigned, observed differences
between sites may be related solely to some other
factor that differs between the two sites. One could
argue that it is unfair to ascribe the effect to the
impact (Hurlbert 1984, Underwood 1991). How-
ever, as pointed out by Stewart-Oaten et al. (1986),
the survey is concerned about a particular impact in
a particular place, not about the average of several
impacts when the survey is replicated in many dif-
ferent locations. Consequently, it may be possible
to detect a difference between these two specific
sites. However, if there are no randomized repli-
cate treatments, the results of the study cannot be
generalized to similar events at different wetlands.
However, the likelihood that the differences between
sites are due to factors other than the impact can be
reduced by monitoring several control sites
(Underwood 1991). If one assumes that the varia-
tion in the (before and after) measurements of mul-
tiple control sites is the same as the variation among
potentially impacted sites, and that the variability
over time between the control sites is not corre-
lated, one can estimate the likelihood that the im-
pact caused the observed difference at the impacted
site, given the observed variability in the control sites.
That is, several control wetlands could be moni-
tored at the same time points as the single impact
wetland. If the observed difference in the impact
wetland is much different than could be expected
based on the multiple control wetlands, the event is
said to have caused an impact. The lack of ran-
domization is less of a concern when several con-
trol sites are monitored, because the multiple con-
trol sites provide some information about potential
effects of other factors.
1 1
-------
The second and more serious concern with the
simple before and after design with a single sam-
pling point before and after the impact is that it fails
to recognize that natural fluctuations may occur in
the characteristic of interest that are unrelated to
any impact (Hurlbert 1984, Stewart-Oaten 1986).
Single samples before and after impact would be
sufficient to detect the effects of the impact if no
natural fluctuations occurred over time. However,
if the population also has natural fluctuations over
and above the long-term average, then it is impos-
sible to distinguish between cases in which no ef-
fect occurs from cases in which an impact does
occur. Consequently, measured differences in sys-
tem quality may be artifacts of the sampling dates,
and natural fluctuations may obscure differences or
lead one to believe differences are present when
they are not.
The simple BACI design was extended by
Stewart-Oaten et al. (1986) by pairing surveys at
several selected time points before and after the
impact to help resolve the issue of pseudoreplication
(Hurlbert 1984). This modification of the BACI
design is referred to as the BACI-paired series (PS)
design. The selected sites are measured at the same
time points. The rationale behind this paired design
is that repeated sampling before the impact gives
an indication of the pattern of differences of poten-
tial change between the two sites. BACI-PS study
design provides information on the mean difference
in the wetland system quality before and after im-
pact and on the natural variability of the system qual-
ity measurements. An effect is detected if the
changes in the mean difference are large relative to
natural variability. Considerations for sampling at
either random and regularly spaced intervals also
apply here.
BACI-PS study design also has potential pitfalls.
As with all studies, numerous assumptions need to
be made during the analysis (Stewart-Oaten et al.
1992, Smith etal. 1993). The primary assumption
for BACI-PS design is that the responses over time
are independent of each other. A lack of indepen-
dence over time tends to produce false-positive
(Type I) errors, which may lead a manager to de-
clare that an effect has occurred when, in fact, none
has. Formal time series analysis methods or re-
peated measures analysis may be necessary
(Rasmussen et al. 1993) to eliminate Type I errors.
(The analysis of time series is easiest with regularly
spaced sampling points.) In addition, the differ-
ence in mean level between control and impact sites
is assumed to be constant over time in the absence
of an impact effect. The effect of the impact is as-
sumed to change the arithmetic difference. In the
clear-cut example given previously, the difference
in mean system quality between the two sites is as-
sumed to be constant over time. That is, mean sys-
tem quality measurements may fluctuate over time,
but both sites are assumed to fluctuate in the same
manner simultaneously, thereby maintaining the same
average arithmetic difference. This assumption is
violated if the response variable at the control site is
a constant multiple of the response variable at the
impact site. For example, suppose that the read-
ings of water quality at two sites at the first time
point were 200 vs. 100, which has an arithmetic
difference of 100, and at the second time point were
20 vs. 10, which has an arithmetic difference of 10,
but both pairs are in a 2:1 ratio at both time points.
The remedy is simple: A logarithmic transform of
the raw data converts a multiplicative difference into
a constant arithmetic difference on the logarithmic
scale. This is a common problem when system qual-
ity measurements are concentrations (e.g., pH).
Smith et al. (1993) pointed out that this may not
solve the issue of pseudoreplication. Trends are
common in most natural populations, but BACI
design assumes that trends are not present in the
populations or that the control and impact sites have
the same trends, so that differences between the
sites are identified as associated with the impact,
not with differences in trends of natural populations
(Smith etal. 1993). Violation of the BACI assump-
tions may invalidate conclusions drawn from the
data. Enough data must be collected before the
impact to identify the trends in the communities of
-------
each sampling site if the B ACI assumptions are to
be met. Clearly defining the objectives of the study
and identifying a statistically testable model of the
relationships the investigator is studying can help
resolve these issues (Suter 1993).
Underwood (1991) also considered two varia-
tions on the B ACI-PS design. First, it may not be
possible to sample both sites simultaneously for tech-
nical or logistical reasons. Underwood (1991) dis-
cussed a modification in which sampling is done at
different times in each site before and after impact
(i.e., sampling times are no longer paired), but notes
that this modification cannot detect changes that
occurred in the two sites before the impact. For
example, differences in system quality may show a
gradual change over time in the paired design be-
fore impact. Without paired sampling, it would be
difficult to detect this change, hi addition, sampling
only a single control site still has the problem iden-
tified previously, that is, it is not known whether
observed differences in the impact and the control
sites are site specific. Again, Underwood (1991)
suggests that multiple control sites should be moni-
tored. The variability in the difference between each
control site and the impact site provides informa-
tion on transferability of the impact effects to other
sites (i.e., it either refutes or supports the site speci-
ficity of the impact and associated system response).
The designs described are suitable for detecting
long-term, or chronic, effects in the mean level of
the variable of interest. However, the impact may
have a short-term, or acute, effect, or it may change
the variability in response (e.g., seasonal changes
become more pronounced) in some cases. The
sampling schedule can be modified to occur at two
temporal scales (enhanced BACI-PS design) that
encompass both acute and chronic effects
(Underwood 1991). The modified temporal de-
sign introduces randomization by randomly choos-
ing sampling occasions in two periods (before and
after) in the control or impacted sites. The two
temporal scales (sampling periods vs. sampling oc-
- casions) allow the detection of a change in mean
and in yariability after impact. For example, groups
of surveys could be conducted every year, with five
surveys one week apart randomly located within
each group. The analysis of such a design is pre-
sented in Underwood (1991). Again, multiple con-
trol sites should be used to confound the argument
that detected differences are specific to the sampled
site.
BACI-PS design is also useful when there are
multiple objectives. For example, the objective for
one variable may be to detect a change in trend.
The pairing of sample points on a long time scale
leads to efficient detection of trend changes. The
objectives for another variable may be to detect
differences in the mean level. A short time scale
surveys randomly located in time and space are ef-
ficient for detecting differences in the mean level.
The September 2000 issue of the Journal of Agri-
cultural, Biological, and Environmental Statis-.
tics discusses many of the advantages and disad-
vantages of the B ACI design and provides several
examples of appropriate statistical analyses for evalu-
ation of BACI studies.
SUGGESTED WEBSITES
1 http://ebook.stat.ucla.edu/calculators/
powercalc/
2 http://www.math.sfu.ca/stats/Courses/Stat-
650/Notes/Handouts/nodel .html
3 http://www.mpl-pwrc.usgs.gov/powcase/
index-html
4 http://www.salnionweb.org/salmonweb/pubs/
pacnwfin.htnil
5 http://trochim.human.comeU.edu/tutorial/flynn/
multivarJilm
6 http://wvvw.ttitts.edu/~gdaUal/STUDY.HTM
7 http://www.umass.edu/tei/mwwp/studydes.html
13
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