United States
Environmental Protection
Agency
Goryallis Environmental
Rea reh Laboratory
Corvallla, Oregon 97333
September 1988
THE CONCEPT OF TIME
Supplement
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THE CONCEPT OF TIME
TEMPORALLY INTEGRATED MONITORING OF ECOSYSTEMS (TIME)
Supplement
Prepared by
Forrest E. Payne
FTN Associates, Ltd.
Little Rock, AR
and
Jesse Ford
NCASI
Environmental Research Laboratory - Corvallis
U.S. Environmental Protection Agency
Corvallis, OR
Prepared for
U.S. Environmental Protection Agency
Corvallis Environmental Research Laboratory
200 SW 35th Street
Corvallis, OR
12 September 1988
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TABLE OF CONTENTS
1.0 INTRODUCTION 1-1
1.1 Purpose of Supplement 1-1
12 TIME 1-1
2.0 THE CONCEPT OF TIME 2-1
2.1 Conceptual Design 2-1
22 Wheel and Axle Design 2-1
3.0 STATUS OF STUDIES AND WORKSHOPS 3-1
3.1 Available Reports and Reports in Development 3-1
32 Trend Detection - In Time Series Data 3-2
33 Exploratory Analyses 3-6
3.4 QA/QC Interpretation 3-7
3.5 Multivariate Trend Detection in Time Series Data 3-9
3.6 Assessment of Regional Trends and Model Based
Approach 3-10
3.7 Role of Biomonitoring 3-12
3.8 Deposition Network Evaluation 3-14
4.0 SITE SELECTION 4-1
5.0 LITERATURE CITED .5-1
GLOSSARY
i
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LIST OF FIGURES
Figure 2.1. Regions and subregions of the United States used to define
target populations for the National Surface Water Survey..........—2-2
Figure 22. The conceptual "Wheel and Axle" design frame for the Northeast ..2-3
Figure 23. The conceptual "Wheel and Axle" design frame for the
Mid-Atlantic and Southeast 2-4
Figure 2.4. The conceptual "Wheel and Axle" design frame for the Upper
Midwest and the Southern Rocky Mountains 2-5
Figure 25. The conceptual "Wheel and Axle" design frame for Florida, in the
West other than in the Southern Rocky Mountains, and Alaska
(Kenai Peninsula) 2-6
Figure 3.1. Level of detectable trend for a = 0.10 and fi = 0.10 for five
configurations of number of lakes and spatial correlation = 0.0
and 02 (Loftis et aL 1988) 3-5
Figure 4.1. Overview of site selection process 4-2
ii
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THE CONCEPT OF TIME
TEMPORALLY INTEGRATED MONITORING OF ECOSYSTEMS (TIME)
SUPPLEMENT
1.0 INTRODUCTION
1.1 PURPOSE OF SUPPLEMENT
In August 1987, The Concept of TIME (Thornton et al. 1987), a conceptual plan
for the Temporally Integrated Monitoring of Ecosystems (TIME) project, became
available for review and comment Since the production of The Concept of TIME, a
redirection of priorities within the Environmental Protection Agency (EPA) has
modified the scope and implementation schedule of TIME. The purpose of this
document is to:
o Describe the modifications in the TIME project
o Update the reader as to additional research efforts and workshops (since
August 1987) that relate to development of the TIME Research Plan.
Much of the information in the original conceptual plan is still pertinent and will
not be repeated in this supplement
12 TIME
As was stated in The Concept of TIME (Thornton et aL 1987), the TIME project
is a proposed long-term monitoring program designed to assess the effects of acidic
deposition on aquatic ecosystems. The TIME project is intended to:
o Provide early warning signals of surface water acidification or recovery in
regions of interest
o Provide an ongoing assessment of regional patterns or trends in surface
water acidification or recovery,
o Assess the extent to which observed patterns and trends in surface water
1-1
-------
chemistry correspond with model forecasts of surface water chemistry
changes (e.g., from the EPA Direct/Delayed Response Project),
o Assess the relationships between the observed patterns and trends in
surface water chemistry, and patterns and trends in atmospheric deposition.
The TIME Project is currently scheduled for implementation in the spring of 1991.
1-2
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2.0 THE CONCEPT OF TIME
2.1 CONCEPTUAL DESIGN
The original conceptual design of the TIME project was a hierarchical frame of
four tiers (Section 3.4, Thornton et al. 1987). The purpose of Tier 1 (Regional Her)
was to describe broad regional patterns and trends in ecosystem attributes such as water
chemistry. In Tier 2 (Seasonal Tier), a smaller number of ecosystems in each region
were to be sampled seasonally in lakes or bimonthly in streams to identify seasonal
patterns or trends in system attributes. The purpose of Tier 3 (Research Tier) was to
integrate information from process oriented study sites or intensively monitored sites
with the TIME sampling regime, and the purpose of Tier 4 (Special Studies Tier) was to
investigate specific patterns of change within and among subregions or regions, and
address issues raised in the ongoing work in the underlying tiers. This hierarchical
design frame has been replaced by a "wheel and axle" design frame that emphasizes the
early warning aspects of the program. This design, like the previous design, takes
advantages of two of the elements of the existing Aquatic Effects Research Program
(AERP), namely the National Surface Water Surveys (NSWS) (Brakke et al. 1988, Eilers
et al. 1988a, Eilers et al. 1988b, Eilers et al. 1988c, Kaufmann et al. 1988, Landers et al.
1988a, Landers et aL 1988b, Messer et al. 1986, and Sale et al. 1988) and the temporally
intensive Long Term Monitoring (LTM) project (Newell et aL 1987). Figure 2.1
illustrates the regions and subregions used to define the target populations for the
National Surface Water Surveys.
2.2 WHEEL AND AXUE DESIGN
The wheel and axle design frame is presented schematically in Figures 22
through 2.5. The dashed axle and the wheels on the dashed axle represent existing
AERP program elements on which the TIME project will be built The axle represents
the temporally intensive LTM project (Newell et aL 1987) and the wheels represent the
regionally extensive Phase I Eastern (Iinthurst et al. 1986, Brakke et aL 1988, Eilers et
aL, 1988a, Eilers et al., 1988b, Landers et aL 1988a, and Landers et al. 1988b), and
2-1
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West
Pacific NW1
California'
Northern Rockies2
Upper Midwest
NE Minnesota1
NC Wisconsin'
Adirondack!1
UP of
Michigan*
Poconos Catskllls
r.
. Appalachian Plateau?
Ridge and Valley9
Central Rockies*
Upper G
Southern Rockies2
Ozark Plateau4
Piedmont4
Florida'
~ NLS
B NSS
¦ Overlap NLS/NSS
'ELS Phase-t
*WLS Phase-1
'NSS Pilot
4NSS Screening
*NSS Phase-1
Northeast
Maine4
Central and
Southern
New England*
Glaciated Highlands
of PA, NJ, and NY®
Chesapeake1
Mid-Atlantic
Southern Blue Ridge"3
Southeast
Figure 2.1.
Regions and subregions
Water Survey.
of the United States used to define target populations for the National Surface
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Analysis & Interpretation
LTM
ELS-B
1986
NORTHEAST
PROBABILITY SAMPLE:
Regional Populotion Trends
X] Fixed sites
(trend detection)
Floating sites
(pop. characterization)
EARLY WARNING NETWORK*
Individual Sites
f PI Subset of probability sample
\ M Hand-picked sites *
* (seasonal sampling)
—TIME
Figure 2.Z The conceptual "Wheel and Axle" design frame for the Northeast
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LTM
Pilot
Stream
Survey
(SBR only-
54 sites)
1985
National
Stream
Survey
(Mid-Atl.*SE-
450 sites)
1986
MID-ATLANTIC
& SOUTHEAST
(streams)
Analysis & Interpretation
PROBABILITY SAMPLE'
Regional Population Trends
Fixed sites
(trend detection)
Floating sites
(pop. characterization)
EARLY WARNING NETWORK:
Individual Sites
{Q Subset of probability sample
¦ Hand-picked sites «
• (seasonal sampling)
TIME
Figure 23. The conceptual "Wheel and Axle" design frame for the Mid-Atlantic and Southeast
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LTM
Analysis & Interpretation
ELS-I •WLS
(1984.1985)
N>
&
UPPER MIDWEST
WEST
(S. Rocky Mtns.)
PROBABILITY SAMPLE:
Regional Population Trends
Fixed sites
(trend detection)
Floating sites
(pop. characterization)
EARLY WARNING NETWORK:
Individual Sites
/ Q Subset of probability sample
\ H Hand-picked sites *
» (seasonal sampling)
TIME
Figure 2.4. The conceptual "Wheel and Axle" design frame for the Upper Midwest and the Southern Rocky Mountains.
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£
ELS-I *WLS
(1984,1985)
FLORIDA
WEST
(other than
S. Rocky Mtns.)
ALASKA
(Kenai Peninsula)
Analysis & Interpretation
PROBABILITY SAMPLE:
Regional Population Trends
Fixed sites
(trend detection)
Floating sites
(pop. characterization)
EARLY WARNING NETWORK:
Individual Sites
( Q Subset of probability sample
\ HI Hand-picked sites *
(seasonal sampling)
TIME
Figure 2.5. The conceptual "Wheel and Axle" design frame for Florida, in the West other than in the Southern
Rocky Mountains, and Alaska (Kenai Peninsula).
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Western (Landers et aL, 1987, and Eilers et al., 1988c) Lake Survey, the Phase II
Eastern Lake Survey (Thornton et aL 1986), the Pilot Stream Survey (Messer et al.
1986), and the National Stream Survey (Kaufmann et al. 1988 and Sale et aL 1988).
The array of wheels varies from region to region and some subregions lack the
temporally intensive LTM sites.
HME is currently scheduled for implementation in 1991 when the "axle" sites will
come on-line. The hand-picked sites in the "axle" will form the core of the early
warning system. The "axle" also includes a subset of the probability sites that comprise
the regionally extensive "wheels". This subset of probability samples will be tracked
seasonally to provide an ongoing assessment of seasonal and annual variability in order
to assist in the interpretation of regional patterns and trends made from the "wheels" or
periodic resuiveys.
Hand-picked sites will be chosen from a pool of low acid neutralizing capacity
(ANC) candidate sites that meet one or more of the following criteria:
o Previous monitoring information.
o Ancillary watershed studies relevant to tracking acidic deposition effects.
o Ancillary sites to meet particular information needs for an early warning
network (e.g., position along a deposition gradient).
This pool includes, but is not limited to, existing LTM sites. Other potential sites
include those currently under study by other federal or state agencies or university
researchers. Some higher ANC reference sites may be included in this set in order to
separate out the potential influence of other factors (e.g., short-term hydrologic
fluctuations).
The "wheels" represent periodic resurveys and the sites in this group will be
composed of probability samples in each region. The sites that make up the wheels will
be of two types: fixed and floating probability samples. Fixed sites for repetitive
sampling are necessary in order to specifically evaluate regional trends. Floating sites,
which will change from survey to survey, will ensure an ongoing characterization of
populations within each region.
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The spacing of wheels in Figures 22 through 2.5 does not necessarily imply
sampling at regular intervals. Spacing will vary depending on results from the early
warning (axle) sites, regional needs, and budgetary constraints.
The areas of interest and the focus of the TIME program are low ANC regions,
as in the NSWS. These areas include the Northeast, the Mid-Atlantic and Southeast,
Florida, the Upper Midwest, and the West The types of systems targeted for
monitoring in each region are primarily:
o Northeast: Drainage lakes and (possibly) streams
o Mid-Atlantic/Southeast: Streams
o Florida: Precipitation dominated seepage lakes
o Upper Midwest: Precipitation dominated seepage lakes
o West: Drainage lakes and (possibly) streams
2-8
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3.0 STATUS OF STUDIES AND WORKSHOPS
3.1 AVAILABLE REPORTS AND REPORTS IN DEVELOPMENT
Reports and workshops that have been completed since the publication of the
conceptual plan include:
o Loftis et aL (1988) - Detecting trends in time series data,
o Newell (1987) - Summary of the exploratory TIME cluster analysis
workshop, October 1987, Corvallis, Oregon,
o Pollard et aL (1987) - Workshop on quality assurance for Temporally
Integrated Monitoring of Ecosystems (TIME) Project, held in Las Vegas,
Nevada, November 18-20, 1987: Preliminary Summary,
o Pollard et aL (1988) • Expanded summary of the workshop on quality
assurance for Temporally Integrated Monitoring of Ecosystems, Las Vegas,
Nevada, November 18-20, 1987.
In addition, several reports and summaries are being developed. These reports
and summaries include:
o Marmorek et aL (In Preparation) • Biological monitoring for acidification
effects: U.S. - Canadian Workshop, March 21-23, 1988, Burlington,
Ontario.
o Report on multivariate trend detection in time series data,
o Report on regional trend assessment and model-based
approach.
o Individual manuscripts from the Biomonitoring Workshop to be published
in a journal or special publication,
o Evaluation of deposition networks,
o TIME Research Plan,
o TIME QA Plan.
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o TIME Data Analysis Plan.
These recent contributions will be summarized in the following sections.
3.2 TREND DETECTION IN TIME SERIES DATA
In Section 4.7 of The Concept of TIME, two progress reports that examined trend
detection procedures were summarized (Loftis and Ward 1987a; Loftis et aL 1987b).
The final draft report for that project is now complete (Loftis et al. 1988).
Loftis et al. (1988) tested various methods for detecting water quality trends in
individual and groups of lakes impacted by acidic deposition. Data sources used in the
study included the LTM data set, data from Environment Canada for Clearwater Lake,
Ontario, and data from the U. S. Bureau of Reclamation for Twin Lakes, Colorado.
These data were used to make generalizations regarding the level of seasonal behavior,
serial correlation, and non-normality anticipated from TIME data.
Several candidate tests for trend detection were selected by Loftis et al. (1988)
for evaluation. These candidate tests included:
0
Analysis of covariance
0
Analysis of covariance on ranks
0
"Modified t"
0
"Modified t" on ranks
0
Kendall tau following removal of seasonal means
0
Seasonal Kendall with serial correlation correction
0
Seasonal Kendall
Monte Carlo simulation studies, designed to reproduce data characteristics
observed in existing data sets and anticipated for TIME sites, were used to compare the
performance of the candidate tests. The performance indices were actual significance
level and power of trend detection. The significance level of a test, in a Monte Carlo
evaluation, is determined by generating a large number (e.g., 500) of sequences of data
with known characteristics and no trend. The trend detection method is applied to each
3-2
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sequence, and the significance level is computed as the fraction of trials in which a trend
is falsely detected.
The power of a given test is estimated in the same manner, except that a trend of
known magnitude is added to each sequence of data. Power is the fraction of sequences
in which the trend is correctly detected. The upper bound on the trend magnitude used
by Loftis et aL (1988) was similar to that observed at Clearwater Lake, Ontario, an
acidic lake in the Sudbury region that is undergoing rapid chemical recovery following
dramatic decreases in deposition from Sudbury stacks. The magnitude of the trend for
ANC recovery at that site is currently 2.8 ueq/L per year or 0.7 standard deviations per
year.
To adequately represent the range of characteristics anticipated from TIME data
and to compare alternative tests, a very large number of simulations were performed in
each experiment. Parameters varied by Loftis et al. (1988) include:
o Seasonal patterns in mean
o Seasonal patterns in standard deviation
o Ratios of largest to smallest quarterly standard deviation
o Ratio of largest to smallest quarterly mean
o Trend magnitude
o Length of record
o Underlying distribution
o Lag-one autocorrelation coefficient
o Nominal significance level
There were 3456 combinations of parameters evaluated, and for each
combination at least 500 sequences were generated to empirically determine the power
or significance level of the candidate test
For annual data (in which no prior removal of seasonal means is necessary), the
Kendall tau test is recommended. For seasonal (quarterly) sampling, either the analysis
of covariance on ranks or the Seasonal Kendall test is recommended. Both tests
performed well on the Monte Carlo simulations under conditions of seasonal variation
3-3
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and both non-normal and log-normal noise. Loftis et al. (1988) recommended that if a
choice must be made between the two methods, the Seasonal Kendall should be used,
especially for large data records. However, neither test performed well when
observations were serially correlated. Loftis et al. (1988) noted that the Seasonal
Kendall test with corrections for correlation is sufficient for large data records and small
correlation.
Ongoing work extends these approaches from univariate to multivariate analyses.
These approaches will also permit evaluation of the relationships between changes in
surface water quality and changes in atmospheric deposition.
Loftis et al. (1988) also considered the expected performance of monitoring (Le.,
the power of trend detection) for various numbers of sites and levels of spatial
correlations, assuming linear trend. Figure 3.1 illustrates the level of detectable trend
for a = 0.10 and fi - 0.10 (where a is the probability of rejecting the null hypothesis
of no trend when it is true and fi is the probability of accepting the null hypothesis when
a real trend exists). Five configurations of number of lakes and spatial correlation are
presented. These configurations are:
o Curve a -1 lake (no spatial correlation, by definition)
o Curve b - 4 lakes with no spatial correlation
o Curve c -16 lakes with no spatial correlation
o Curve d - 4 lakes with spatial correlation = 0.2
o Curve e -16 lakes with spatial correlation = 0.2
As an example of how to use Figure 3.1, assume that the "average" temporal
standard deviation of a constituent in individual lakes is 10 ueq/L Also assume that
sampling is annual and that a total change of two standard deviations is the desired level
of detectability. It would take approximately 28 years to detect a change in a
constituent at a single lake of 10(2)=20 ueq/L (curve a). Suppose that one desired to
see a change of this magnitude in a shorter time span (e.g., 5 years). Figure 3.1
indicates a 90% probability that a trend in the means of this magnitude could be seen in
5 years using 16 sites that exhibited no spatial correlation (curve c). (In this case, the
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w
n
o
4.0
3.5
3.0
0
1 2.5 h
o
•o
c
o
(/>
•
o*
c
o
o
2.0
1.5
1.0
0.5
0
Curve
No. of
Lakes
Spatial
Correlation
A
1
0
B
4
0
C
16
0
D
4
0.2
E
16
0.2
B.E
1
i
1
l
1
10
20 30
Number of Observations
40
50
Figure 3.1. Level of detectable trend for a = 0.10 and $ = 0.10 for five configurations of number of lakes and spatial
correlation = 0.0 and 0.2 (Loftis et al. 1988).
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probability of falsely detecting a trend also is only 10%.) However, because inter-lake
correlation has an impact on the power of detecting trends in a group of lakes, spatial
correlation needs to be taken into account Figure 3.1 indicates that for a mean spatial
correlation of 02 among 16 lakes, it would take about 9 years to detect the 2 standard
deviation change (curve e). Analysis of the best (based on length and completeness of
record) 11 records from the Adirondack lakes indicates that pairwise spatial correlations
generally fall in the range of 02 - OA for this subregion (Loftis et al. 1988). This type of
analysis specifically applies to trends in the mean when multiple sites are considered,
and only linear trends are considered. Despite these constraints, however, the analysis is
useful as a scoping activity to indicate the trade-offs between numbers of sites and
length of records for realistic values of temporal variance and spatial correlation.
3-3 EXPLORATORY ANALYSES
Ouster analyses were used to identify subpopulations of lakes and their
characteristics during the ELS-Phase n design (Section 4.9, Thornton et al. 1987). A
similar exercise was performed for TIME using exclusion criteria developed specifically
for TIME.
On October 26-28, 1987, a small workshop was held at the Environmental
Research Laboratory in Corvallis, Oregon (ERL-C), to interpret cluster analyses on the
National Lake Survey (NLS) data. These exploratory cluster analyses were used to
describe the types of lakes in regional populations and to aid in the site selection design
for the TIME project
The analyses on the NLS data set stratified various combinations of factors,
including:
o ANG
o Atmospheric S04 deposition levels,
o Hydrologic type and retention time.
o Silica, to separate precipitation dominated seepage lakes from groundwater
dominated seepage lakes in the Upper Midwest
o Potassium, to separate precipitation dominated seepage lakes from
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groundwater dominated seepage lakes in Florida.
The workshop helped identify issues of potential importance for TIME site
selection, and additional analyses are currently being conducted by ERL-C. Principal
component analysis/factor analysis is being used to explore meaningful combinations of
chemical and physical variables that will help identify populations of interest in the
various subregions.
3.4 OA/OO INTERPRETATION
The QA/QC workshop referred to in Section 4.11 of The Concept of TIME was
held on November 18-20, 1987 at the Environmental Monitoring Systems Laboratory in
Las Vegas, Nevada (EMSL-LV). The purpose of the workshop was to provide a forum
to discuss QA/QC issues relevant to the TIME project (Pollard et aL 1987) and to
initiate discussions on optimizing TIME'S QA plan based on experience with the
strengths and weaknesses of the QA plan for the various surveys.
Major topics discussed at the workshop included precision, bias, detectability, and
characterization of system error. Numerous issues were raised during the workshops,
many of which were not resolved. Recommendations from the workshop were as
follows:
1. It was resolved to focus effort on benchtop and field QC throughout the
project rather than on post-hoc QA analyses. This effort will include
providing known QC standards to the analytical laboratories so the analysts
will know if the system is achieving desired precision and accuracy. In
addition, rapid analysis and feedback of QA/QC data to field and
laboratory personnel is a priority item, in order to achieve the best
possible analyses on an ongoing basis.
2. Bias estimation should be evaluated using measurements of absolute bias
rather than relative bias. A method for accomplishing this was not
formally established during the workshop. However, in order to use
absolute bias, any audit should have a defined reference value that is
3-7
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highly defensible (i.e., measured confidence bounds of the reference should
be at least 3 times, and preferably 10 times, better than the desired
accuracy of the analytical laboratories receiving the samples).
3. Bias information needs to be quantified and reported, but the data should
not be corrected in the data base. This was viewed as necessary for the
data user to be able to interpret the patterns in the data sets. No
resolution was reached on how data corrections might be performed, or on
how to interpret interlaboratory bias.
4. While the data generator needs estimates of precision, bias, and accuracy
to maintain the system within the control bounds established by the data
quality objectives, the data user needs estimates of precision, bias, and
accuracy to evaluate the effect of the measurement error on data
interpretation, and to assess the extent to which the quality control
procedures kept the system in control. Therefore, there is still a need
for QA assessment with blind QA audits, even though benchtop QC is a
priority concern.
5. If the measurement error is small in comparison to the overall error
(population sampling or within-site error), it may not be necessary to know
the components of the measurement error. On the other hand, if the
measurement error is large in comparison to the overall error, it will be
important to know all the components of the sources of error.
6. Detectability issues (detection limits, decision limits) should not pose
major problems. Detection limits become important for variables at low
concentration levels. Variables on a regional basis, for which detectability
is important, need to be identified and a method for measuring detection
or decision limits needs to be developed.
Several important QA/QC elements identified at the workshop that need to be
accomplished include:
1. Developing the number and types of audits to be used in the HME
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project, and determining what information will be provided.
2. Evaluating QA/QC data from the NSWS to develop "system goals" for the
TIME project, It is important to determine which variables should be
evaluated for system contamination levels, and if the levels observed in the
NSWS are within the ranges tolerable for the TIME project
3. Defining protocols for determination of both system and method detection
and decision limits.
4. Developing detailed protocols for control chart use for both method-level
and system-level considerations. These control goals should be based on
data quality objectives for precision, accuracy, bias, and detectability.
5. Building a variance component experimental design that will demonstrate
how the components of variance of measurement error could be
partitioned. The example should include a cost estimate based on real
analytical cost per sample and use realistic assumptions about the
number of laboratories and number of crews sending samples to each
laboratory.
6. Comparing the overall error with the total measurement error in the
NSWS data to determine the relative magnitude of measurement versus
population sampling error. This will be based upon various stratification
schemes, beginning with the clustering scheme provided by ERL-C.
3.5 MULTIVARIATE TREND DETECTION IN TIME SERIES DATA
Although substantial work has been done on identifying appropriate statistical
techniques to apply to trend detection, these explorations have concerned only univariate
data. Multivariate approaches potentially give better power for trend detection and
allow the detection of trends in a vector of variables. It is likely that trends in a vector
of variables will be more meaningful than examining variables one by one.
The ongoing multivariate trend detection work is focused on:
1. Identifying all multivariate techniques potentially applicable to issues of
trend detection and drawing statistical inferences about the relationships
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between changes in acid deposition and changes in surface water
chemistry.
2. Identifying characteristics of the data base necessary to satisfy the
requirements of each technique. Examples of characteristics might include
specific water quality and deposition variables of interest, length of
records, number and spatial density of stations, sampling frequency, and
completeness of records.
3. Proposing procedures for evaluating the statistical tests and data sets.
4. Exploring the sensitivity to assumptions and power against specified
hypotheses of each candidate procedure on at least four real long-term
data sets.
5. Exploring the sensitivity and power of each of the candidate procedures on
simulated data.
3.6 ASSESSMENT OF REGIONAL TRENDS AND MODELrBASED APPROACH
The surface waters in any region can be conceptualized as an assemblage of
specific types of lakes or streams with different characteristics. One way to survey a
specific type of lake or stream within a given region and be able to extrapolate results to
the larger population of all lakes or streams of that type in the region is to define a
population identity (i.e., population of interest) and devise a statistical sampling frame.
Because populations will vary in terms of characteristics that affect the rate, timing, and
magnitude of acidification and recovery, each population should be considered
independently. The issue of regional trends therefore becomes an issue of defining the
populations of interest within the region, and picking the appropriate size/probability
sample from each population to ensure accurate population expansions.
Once the populations of interest are defined, population trends can be studied.
Several approaches to determining population trends are being considered. For
example, population distributions can be described at successive points in time and
representation of trends in the distributions can be made using simple distribution-free
tests of trends in moments (e.g., mean and variance); quantiles (median and percentile);
3-10
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or reference proportions (e.g., pc). Other methods might include parametric
representations of these trends (i.e., fitting an explicit distribution, and characterizing the
trend in the parameters of the distribution). A different approach is to describe trends
in individual systems (e.g., Loftis et al. 1988; Section 32 of this report) and characterize
the population as a distribution of trend characteristics. The rigor with which these
latter results can be associated with populations of interest depends critically on the
relationship of the individual systems to the larger sampling frame.
The most straightforward way to relate population trends back to a statistical
sampling frame (e.g., the Phase I National Surface Water Survey (NSWS)) is to choose
the sites composing the population as a probability sample from the larger sampling
frame. However, in some cases it may be desirable to analyze records from handpicked
sites that are not part of the larger sampling frame (e.g., LTM sites). To put such sites
into a regional population context, model-based extrapolation procedures must be
developed.
Model-based extrapolation procedures will allow the implications of observed
changes in hand-picked sites that are not part of the original NSWS sample to be
extrapolated to the larger regional surface water resource. The model-based
extrapolation procedure is still under development, although some of the groundwork
has been conducted during analysis of data from the Phase II of the Eastern Lake
Survey (ELS-II) (as summaiized in the TIME Conceptual Plan, Section 43, Thornton et
aL 1987). The use of sites included in the statistical frame of the NSWS will probably
involve some form of calibration. One simple way to do this is to identify homogeneous
clusters or populations of interest within the larger sampling frame and associate hand-
picked sites with clusters as appropriate (Overton 1988a). It is important to recognize,
however, that hand-picked sites do not constitute a probability sample of a cluster, and
that assignment of sites to clusters involves some degree of informed judgement, even
when objective techniques such as discriminant analysis are applied. Therefore, the
extrapolation from patterns and trends in one or several hand-picked sites to population
patterns or trends is necessarily based on the assumption of representativeness (Overton
1988a). Careful description of the populations and their associated hand-picked sites
will provide the information necessary to assess the validity of the population expansions.
3-11
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Work on both regional population trends and the model-based approach is
ongoing (Overton 1988b), and will be summarized in the TIME Data Analysis Plan.
Techniques developed in this project will also be applied to extrapolating the
implications of observed patterns and trends in the existing LTM data set to populations
of interest within the various regions. The utility of this approach will be compared to
that of a probability double sample that can lead to more rigorous inferences.
3.7 ROLE OF BIOMONHDRINfl
In The Concept of TIME (Thornton et al. 1987), Sections 4.4 and 4.5 considered
the role of biological data and biologically relevant chemistry. After these sections had
been written, a joint United States/Canada Biological Monitoring Workshop was held
on March 21-23, 1988 in Burlington, Ontario, at the Canada Center for Inland Waters.
The goal of the workshop was to answer two questions:
1. What is gained (relative to chemical measurements alone) by incorporating
biological measurements into a regionally extensive monitoring program
focused on surface water acidification and recovery?
2. If biological measurements are worthwhile, what is the most informative
and cost effective sampling strategy for each ecoregion of concern?
Prior to the convening of the workshop, eight background papers were prepared
discussing the use of phytoplankton (diatoms (Smol 1987), chrysophytes (Siver 1987),
dinoflagellates (Holt 1987), and bluegreen/greens (Baker 1987)), periphyton (Stokes and
Howell 1987), benthic invertebrates (Singer and Smith 1987), zooplankton (Marmorek
and Bernard 1987), and fish (Baker 1987) as early warning indicators. Five overview
papers, one for each organismal group, were presented at the workshop. After the
summary presentations, the participants broke into groups of seven to nine in each area
to discuss topics such as:
o Best response variables to use as early warning indicators (indicator
species, species composition, community indices such as species richness or
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diversity, biomass, interorganismal group indicators, etc),
o Regional differences in response variables and sampling methodologies,
o Inferences obtainable at different levels of monitoring frequency (e.g.,
annual, seasonal, etc.), and sampling and data analysis methods required,
o Estimated total costs of data collection.
o Types of analyses that can be done using existing data sets to improve
sampling design.
In addition to the specialists in each work group, statisticians and generalists were
spread among the groups. The consensus of the participants (Marmorek et aL, in
preparation) was that:
o Biomonitoring greatly improves our knowledge of the actual response of
aquatic ecosystems, as distinguished from the inferred response obtained
by linking chemical monitoring data with laboratory or field bioassays.
o Biomonitoring provides a sound mechanism for integrating and evaluating
the aquatic effects of seasonally varying surface water chemistry,
o To obtain the maximum value per dollar invested in biomonitoring,
biomonitoring programs should provide data and insight into both incipient
biological changes (i.e., early warning indicators) and "biologically
important" changes, where "biologically important" changes are taken to
mean those significant changes that are important to either (1) ecosystem
functioning, or (2) human ecosystem utilization, particularly, fishing and
other water based recreation.
A series of specific technical recommendations for TIME biomonitoring design was
made. These are summarized in Marmorek et aL (in preparation).
There was a consensus among the 39 workshop participants that many of the
common perceptions about biological monitoring are erroneous (e.g., it is unmanageably
complicated, is not amenable to QA/QC, and is unreasonably costly). Participants felt
that although the issues raised in biological monitoring design are complex, they are no
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more intractable for biologists than are similar issues in chemical monitoring design for
chemists. The primary response variable of interest identified by most of the work
groups was species composition. Biomonitoring need not be more expensive than
chemical monitoring, nor are biological data base management problems unique. For
example, the large biological data base collected by the Electric Power Research
Institute (EPRI) - funded Paleoecological Investigations of Recent Lake Acidification
(PIRLA) Project (Charles et aL, 1986) demonstrated that compositional data are both
amenable to QA/QC and can be adequately managed. Although in some instances
biological results are not straightforward, this generally occurs because the questions to
be answered are not adequately defined. Clearly defined questions obviate this problem.
In short, the consensus of the 39 participants was that biological monitoring can be as
cost effective as chemical monitoring if the program is well-defined, and can supplement
chemical monitoring with respect to important short-term transient phenomena such as
episodes of low pH and ANC and high AL Finally, biological monitoring can provide a
context within which the significance of subtle chemical changes can be evaluated.
Statistical aspects of the biomonitoring program also were considered. Three
main areas of statistical research were identified to serve this program:
o Multivariate methods for monitoring
o Definition of the detection threshold for species presence/absence
o Time series analysis on proportional (relative abundance) data
Marmorek et al. (in preparation) are summarizing the results of the biological
workshop. The eight various literature reviews prepared as background papers for the
workshop will be published as an EPA report and/or as a set of papers in the peer
reviewed literature.
3.8 DEPOSITION NETWORK EVALUATION
One of the questions the TIME project is intended to address is, "What are the
relationships between the observed patterns and trends in surface water chemistry and
regional patterns and trends in atmospheric deposition?" This question was briefly
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addressed in Section 4.13 of The Concept of TIME (Thornton et al. 1987).
Regional trend detecdon in deposition is being addressed by the U.S. Geological
Survey (USGS), the EPA Atmospheric Sciences Research Laboratory in Research
Triangle Park, North Carolina (ASRL-RTP), and the statistical group at Battelle's
Pacific Northwest Laboratory in Richland, Washington (PNL). Close coordination
among the above mentioned laboratories and agencies and the TIME project will be
necessary to minimis duplication of effort and to provide the linkages required for
achieving the TIME objective of assessing the relationships between patterns and trend
in surface water chemistry and patterns and trends in depositions.
The adequacy of existing deposition networks for TIME'S purposes will be
evaluated. In addition, work has been initiated to evaluate the most appropriate
statistical techniques for assessing relationships between changes in surface water quality
and changes in atmospheric deposition. Finally, the issue of how to analyze temporal
trends in spatially complex data is also currently under study.
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4.0 SITE SELECTION
In The Concept of TIME (Thornton et aL 1987), site selection was discussed in
Section 7.0. The site selection model in Section 7.0 of that document is still current, with
only slight modifications. In particular, Figure 7.1 has been replaced by Figure 4.1 of this
supplement
Step A identifies the desired target populations for each class (i.e., probability
samples, rapid response sites, and special interest sites), and may apply to any level of the
classification process. The cluster analyses and factor analyses discussed in Section 33 of
this document are major components of step B.
In step C the desired number of sites in each category will be determined. Then,
all the potential sites in the candidate pool will be listed (step D), and exclusion criteria
applied to these sites (step E). If there are not enough sites, the exclusion criteria will be
re-evaluated, and if there are too many sites, some will be eliminated based on ancillary
inclusion criteria. The process sketched here is discussed in greater detail in Section 7.0
(Thornton et aL 1988).
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(A) Define Target Population.
(B) Classify into Categories.
(C) Determine Total Number of
Sites in Each Category.
V
(D) List All (or draw sample of)
Potential Sites in
Target Population.
v
XE) Apply Exclusion Criteria.
Figure 4.1. Overview of site selection process.
V
If number of sites
now < C,
re-evaluate.
V
If number of sites
stHI > C,
eliminate sites:
Apply ancillary inclusion
criteria to prioritize;
Eliminate sites
from bottom up.
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5.0 LITERATURE CITED
Baker, J. P. 1987. The use of fish in long-term monitoring programs for lake and stream
acidification (and recovery). Report prepared for the U.S. Environmental Protection
Agency, Corvallis Environmental Research Laboratory, Corvallis, OR. 99 pp.
Baker, K. K. 1987. A literature review on the usefulness of bluegreen and green algae to
detect chronic or episodic acidification and/or recovery in the context of a long-term
monitoring program. Report prepared for the U.S. Environmental Protection Agency,
Corvallis Environmental Research Laboratory, Corvallis, OR. 77 pp.
Brakke, D. R, D. H. Landers, and J. M. Eilers. 1988. Chemical and physical characteristics
of lakes in the Northeastern United States. Environ. Sci. and Tech. 22:155-163.
Charles, D. F., D. R. Whitehead, D. S. Anderson, R. Bienert, K. E. Camburn, R. B. Cook,
T. L. Crisman, R. B. Davis, J. Ford, B. D. Fry, R. A. Hites, J. S. Kahl, J. C Kingston, R.
G. Kreis, Jr., M. J. Mitchell, S. A. Norton, L. A. Roll, J. P. Smol, P. R. Sweets, A. J. Uutala,
J. R. White, M. C Whiting, and R. J. Wise. 1986. The PIRLA Project (Paleoecological
Investigation of Recent Lake Acidification): Preliminary results for the Adirondacks, New
England, N. Great Lakes States, and N. Florida. Water, Air, and Soil Pollut 30:355-365.
Eilers, J. M., D. F. Brakke, and D. H. Landers. 1988a. Chemical and physical
characteristics of lakes in the Upper Midwest Environ. Sci. and Tech. 22:164-172.
Eilers, J. M., D. H. Landers, and D. F. Brakke. 1988b. Chemical and physical
characteristics of lakes in the Southeastern United States. Environ. Sci. and Tech.
22:172-177.
Eilers, J. M., D. F. Brakke, D. H. Landers, and P. E. Kellar. 1988c Characteristics of
lakes in mountainous areas of the Western United States. Verb. Internal Verein. Limnol.
23:144-151.
Holt, J. R. 1987. The use of four common armored dinoflagellates as indicators of the
early stages of acidification. Report prepared for the U.S. Environmental Protection
Agency, Corvallis Environmental Research Laboratory, Corvallis, OR. 35pp.
Kaufmann, P. R., A. T. Herlihy, J. M. Elwood, M. E. Mitch, W. S. Overton, M. J. Sale, J.
J. Messer, K. A. Cougan, D. V. Peck, K. H. Reckhow, A. J. Kinney, S. J. Christie, D. D.
Brown, C A. Hagley, and FL L lager. 1988. Chemical characteristics of streams in the
Mid-Atlantic and Southeastern United States. Volume L Population descriptions and
physico-chemical relationships. EPA/600/3-88/0021a. United States Environmental
Protection Agency, Washington, DC
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Landers, D. H., J. M. Eilers, D. F. Brakke, W. S. Overton, P. E. Kellar, M. E. Silverstein,
R. D. Schonbrod, R. E. Crowe, R. A. Linthurst, J.M. Omemik, S.A. Teague, and E. P.
Meier. 1987. Characteristics of lakes in the Western United States. Volume I. Population
descriptions and physico-chemical relationships. EPA-600/3-86/054a. United States
Environmental Protection Agency, Washington, DC.
Landers, D. H., J. M. Eilers, D. F. Brakke, and P. E. Kellar. 1988a. Characteristics of
acidic lakes in the Eastern United States. Verh. Internat Verein. Limnol. 23:152-162.
Landers, D. H., W. S. Overton, R. A. linthurst, and D. F. Brakke. 1988b. Eastern Lake
Survey, regional estimate of lake chemistry. Environ. Sci. and Tech. 22:128-135.
linthurst, R. A., D. H. Landers, J. M. Eilers, D. F. Brakke, W. S. Overton, E. P. Meier, and
R. E. Crowe. 1986. Characteristics of lakes in the Eastern United States. Volume I.
Population descriptions and physico-chemical relationships. EPA-600/4-86/007a. United
States Environmental Protection Agency, Washington, DC.
Loftis, J. C. and R. C. Ward. 1987a. Progress Report Long-term monitoring project:
Statistical tests for trend analysis. Dept. of Agri. and Chem. Engr. Colorado State
University. 14 pp.
Loftis, J. C, R. C Ward, R. D. Phillips, and C. H. Taylor. 1987b. Progress report No. 2
TIME Project: Statistical tests for trend analysis. Dept. of Agri. and Chem. Engr.
Colorado State University. 48 pp.
Loftis, J. C., R. C. Ward, R. D. Phillips, and C. H. Taylor. 1988. Detecting trends in time
data series. Dept. of Agri. sind Chem. Engr. Colorado State University. 162 pp.
Marmorek, D. R., D. P. Bernard. 1987. The use of zooplankton to detect lake acidification
and recovery. Report prepared for the U.S. Environmental Protection Agency, Corvallis
Environmental Research Laboratory, Corvallis, OR. 61 pp.
Marmorek, D. R., D. P. Bernard, and J. Ford. (In Preparation). Biological monitoring for
acidification effects: U.S. Canadian. Workshop, 21-23 March 1988 in Burlington Ontario.
U.S. Environmental Protection Agency, Corvallis-Environmental Research Laboratory
(Draft).
Messer, J. J., C. W. Ariss, R. Baker, S. K. Drouse , K. N. Eshleman, P. R. Kaufmann, R.
A. Linthurst, J. M. Omeraik, W. S. Overton, M. J. Sale, R. D. Schonbrod, S. M. Stambaugh,
and J. R. Tuschall, Jr. 1986. National Surface Water Survey: National Stream Survey
Phase I - Pilot Study. EPA/600/4-86/026. United States Environmental Protection Agency,
Washington, DC. 179 pp.
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Newell, A. D., C F. Powers, and S. J. Christie. 1987. Analysis of data from long-term
monitoring of lakes. EPA/600/4-87/014. United States Environmental Protection Agency,
Office of Acid Deposition, Environmental Monitoring and Quality Assurance, Washington,
DC, 150 pp.
Overton, W. S. 1988a. Outline of topics - study of regional trends (memo).
Overton, W. S. 1988b. Cooperative Agreement 1989-1990 between the U.S. Environmental
Protection Agency and Oregon State University.
Pollard, J. E., K. Howe, and D. T. Haggen. 1987. Workshop on quality assurance for the
Temporally Integrated Monitoring of Ecosystems (TIME) Project held in Las Vegas,
Nevada, November 18-20, 1987: Preliminary Summary, United States Environmental
Protection Agency, Office of Acid Deposition, Environmental Monitoring and Quality
Assurance, Washington, DC, 55 pp.
Pollard, J. E., K. Howe, and D. T. Haggen. 1988. Expanded summary of workshop on
quality assurance for the Temporally Integrated Monitoring of Ecosystems (TIME) Project.
Las Vegas, Nevada. November 18-20, 1987. United States Environmental Protection
Agency, Office of Add Deposition, Environmental Monitoring and Quality Assurance,
Washington, DC, 74 pp. (Draft).
Sale, M. J., P. R. Kaufmann, H. I. Jager, J. M. Coe, K. A. Cougan, A. J. Kinney, M. E.
Mitch, and W. S. Overton. 1988. Chemical characteristics of streams in the Mid-Atlantic
and Southeastern United States. Volume II. Streams sampled, descriptive statistics, and
compendium of physical chemical data. EPA/600/3-88/0021b. United States
Environmental Protection Agency, Washington, DC.
Singer, R., D. Smith. 1987. The use of benthic macroinvertebrates as indicators of
acidification. Report prepared for the U.S. Environmental Protection Agency, Corvallis
Environmental Research Laboratory, Corvallis, OR. 47 pp.
Siver, P. A. 1987. A critical literature review on the usefulness of chrysophytes to detect
lake chronic or episodic acidification and/or recovery in the context of a long-term
monitoring program. Report submitted to U.S. Environmental Protection Agency, Corvallis
Environmental Research Laboratory, Corvallis, OR. 112 pp.
Smol, J. P. 1987. Usefulness of diatoms in detecting acidification and/or recovery in a
long-term monitoring progrsun. Report prepared for the U.S. Environmental Protection
Agency, Corvallis Environmental Research Laboratory, Corvallis, OR. 124pp.
Stokes, P. M., E. T. Howell. 1987. The usefulness of periphyton to detect lake chronic or
episodic acidification and/or recovery in the context of a long-term monitoring program.
Report prepared for the U.S. Environmental Protection Agency, Corvallis Environmental
Research Laboratory, Corvallis, OR. 88 pp.
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Thornton, K. W., J. P. Baker, K. H. Reckhow, D. H. Landers, and P. J. Wigington, Jr.
1986. National Surface Water Survey - Eastern Lake Survey - Phase II Research Plan.
U.S. Environmental Protection Agency, Corvallis Environmental Research Laboratory,
Corvallis, OR.
Thornton, K. W., F. E. Payne, J. Ford, and D. H. Landers. 1987. The Concept of TIME.
U.S. Environmental Protection Agency, Environmental Research Laboratory - Corvallis,
Corvallis, OR.
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GLOSSARY
AERP
Aquatic Effects Research Program
ANC
Acid Neutralizing Capacity
ASRL-RTP -
U.S. Environmental Protection Agency, Atmospheric Sciences
Research Laboratory, Research Triangle Park, North Carolina.
ELS
Eastern Lake Survey - Phase I
ELS-H
Eastern Lake Survey - Phase II
EMSI^LV -
U.S. Environmental Protection Agency, Environmental Monitoring
Systems Laboratory, Las Vegas, Nevada
EPA
Environmental Protection Agency
EPRI
Electric Power Research Institute
ERL-C
U.S. Environmental Protection Agency, Environmental Research
Laboratory, Corvallis, Oregon
LTM
Long Term Monitoring Project
NLS
National Lake Survey
NSS
National Stream Survey
NSWS
National Surface Water Survey
PERLA
Paleoecological Investigations of Recent Lake Acidification
PNL
Batelle's Pacific Northwest Laboratory, Richland, Washington.
QA/QC
Quality Assurance and Quality Control
TIME
Temporally Integrated Monitoring of Ecosystems
USGS
United States Geological Survey
WLS
Western Lake Survey - Phase II
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