APPLICATION OF DQO TO BENTHOS DATA
  Application of the Great Lakes
 National Program Office's Data
Quality Objective to Benthos Data
  Generated by the Annual Water
            Quality Survey
              Richard P. Barbiero

                   CSC
            1359 West Elmdale Avenue
                  Suite #2
             Chicago, Illinois 60660
                Prepared for:
     United States Environmental Protection Agency
         Great Lakes National Program Office
            77 West Jackson Boulevard
             Chicago, Illinois 60604

           Louis Blume, Project Officer

                February 2003

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APPLICATION OF DQO TO BENTHOS DATA

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       APPLICATION OF DQO TO BENTHOS DATA
Acknowledgements
This report was prepared under the direction of Louis Blume, Project Officer, Great Lakes Na-
tional Program Office (EPA Contract No. 68-C-01-091).

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         APPLICATION OF DQO TO  BENTHOS DATA
Summary
A Data Quality Objective (DQO) has been developed by the Great Lakes National Program Office
(GLNPO) to ensure that data collected from their Water Quality Surveys are of suitable quality to
provide  decision makers with sufficient certainty to make educated ecological management deci-
sions. The current GLNPO DQO states that data quality should be sufficient for there to be an
80% chance of detecting a 20% change, at the 90% confidence level, between current and historical
measurements of a variable made in a particular lake during a particular season.

This report assesses the  extent to which benthos data collected in summer, 1999 comply with the
GLNPO DQO. The most important findings are summarized below:

    •   Sufficient inter-site variation  exists between offshore stations within each lake basin to
        consider each station representative of a separate statistical population, rather than a repli-
        cate of a larger, basin-wide statistical population.
    •   When densities  of the most dominant species/taxonomic groups were examined, the tar-
        get contained in the DQO was by and large not met by the present sampling effort.
    •   Sample sizes required to meet the DQO when just the most dominant species/taxonomic
        groups are considered are unfeasibly large (> 12 in almost all cases; > 35 in the majority of
        cases).
    •   Variation was in some cases higher at nearshore than offshore stations. However, because
        of the higher overall densities of organisms at nearshore stations, sample sizes required to
        satisfy the DQO tended to be slightly higher at deeper stations.
    •   The current detection target of a 20% change is much lower than actual interannual differ-
        ences seen in Diporeia densities between 1997 and 2001, even where no consistent trends
        (e.g., declines) were noted.  Changes this small are therefore likely to be within the range of
        natural fluctuation for most benthic  organism  populations, and as such are probably of
        limited inherent ecological interest or use.
    •    In spite of the inability to meet the DQO, the current level of replication was sufficient to
        detect interannual  changes in Diporeia densities from 1997 to 2001 at almost all sites for
        which data were available.
    •   In general, the current DQO is ambiguous with regard to specifically which differences are
        of interest.   Several interpretations are possible,  each requiring a different statistical  ap-
        proach.
    •   The current DQO does not address which  biological variables are of interest, and there-
        fore which should be subject to its specifications.
                                                                                        4

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          APPLICATION  OF DQO  TO BENTHOS DATA
1  Introduction
1.1 Benthos program

The Great Lakes National Program Office of
the US EPA has been involved in regular sur-
veillance monitoring of the open waters of the
Laurentian Great Lakes since 1983. This sur-
veillance monitoring is meant to  satisfy  the
provisions of the Great Lakes Water Quality
Agreement (International  Joint Commission
1978), which  calls for periodic monitoring of
the lakes to evaluate water quality trends over
time.  In 1997, a benthic invertebrate monitor-
ing  program  was   added  to  complement
GLNPO's existing open water program. This
program differs from the open water surveys
in a  number  of important ways.  Given  the
homogeneity  of most of the  open waters  of
the Great Lakes, the  open water survey is  de-
signed to detect changes on a fairly large spa-
tial scale.   The  statistical  populations which
the sampling program of this survey were  de-
signed to estimate correspond to lake basins,
thus the half dozen or so sampling stations es-
tablished within each basin serve as replicates.
In contrast, the benthic  program originally
employed  a  sampling strategy designed  to
characterize  communities  from two habitat
types, the nearshore  (<50 m) and offshore
(>50 m). The rationale behind this design was
that the offshore benthic  communities would
serve as integrators of conditions on  a larger,
basin-wide  scale, while nearshore locations
would exhibit a stronger dependence  on local
conditions  and offer a better indication  of
relatively short-term responses to local stress-
ors.  While representative stations were estab-
lished in both  offshore and nearshore loca-
tions, coverage was  not sufficient to  provide
replication within even the larger, basin-wide
areas.  Instead, replication was introduced at
the level of each station, and therefore the sta-
tistical   populations   in   question  coincide
roughly  with the immediate area of each sam-
pling station.   This  proved to  be  particularly
fortuitous since subsequent data has indicated
that substantial biological changes can and do
occur in  the offshore at spatial scales consid-
erably smaller than whole lake basins.
1.2 Objectives of study

The primary goal of this study was to deter-
mine if GLNPO's DQO is being met with the
current level of sampling effort in the benthic
program.  Specifically, the goals of this study
were several fold:
   •   To assess whether inter-site variability
       at offshore stations  precludes replica-
       tion on a basin-wide  basis;
   •   To determine the minimum detectable
       differences under the current sampling
       regime;
   •   To  determine the  sample sizes  re-
       quired to meet the DQO;
   •   To assess the current DQO in relation
       to the magnitude of variability seen in
       benthos data.

In addition, different possible interpretations
of the GLNPO DQO, and its general suitabil-
ity for biological data will be discussed.
2  GLNPO's Data Quality
   Objectives
2.1 DQO for GLNPO water quality sur-
vey

In order to assess lake health using data gener-
ated from the benthic program, or from any
aspect of the water quality survey, sufficient
data quality must be obtained to permit detec-
tion of 'significant' changes in these variables.
For the purposes of the water quality surveys,
GLNPO has defined a significant change as a
20% difference between current and historical
measurements, made for a particular variable

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         APPLICATION OF DQO TO BENTHOS DATA
in a particular lake during a particular season.
Data quality should be sufficient for there to
be an 80% chance of detecting such a change
at the 90% confidence level.
2.2 Application of DQO to benthic data

As  currently  formulated,  GLNPO's  DQO
might not be strictly applicable to the benthic
program since, as noted above, data generated
from nearshore stations were only intended to
be  representative  of local  conditions,  while
even  offshore  stations might capture  too
much  site-specific  variation  to  be  usefully
pooled  to represent broader, basin- or lake-
wide areas.   In this case,  changes might be
more  profitably assessed on  a  station by  sta-
tion basis, rather than on a whole-lake, or even
basin-wide, basis.

Additionally, application of the DQO requires
an explicit statement of how  the benthos data
are to  be used to assess differences between
current and historical measurements. As  de-
tailed in the next section, 'historical  measure-
ments' can be interpreted in a number of  dif-
ferent ways.  While perhaps these differences
might seem to be largely semantic, the ramifi-
cations  of the  different  interpretations  can
dramatically change the fundamental questions
being asked by the monitoring program,  and
the statistical techniques used to answer those
questions.  By referring to difference between
current and 'historical'  measurements, rather
than, say, year to year differences, the current
DQO  implies  that historical  measurements
refer to the pooling of all  past data. In  this
case, changes  in a  given variable in the past
would contribute to variability in the  historical
measurements, and  hinder the detection of
further changes without continual increases in
sample size.  If changes are not unidirectional
(e.g., are cyclical), then pooling historical data
could completely preclude detection of further
changes.  Two other possible  interpretations
of the  DQO include the ability to detect  dif-
ferences between any two (or more) years, and
the  ability  to  detect  directional  changes
(trends).   The statistical implications  of each
of these interpretations is discussed in  the next
section.
2.3  Choice of response variables

While a precise definition of historical meas-
urements is necessary before the data can be
assessed  for its ability to satisfy the stated
DQO, so  is a precise definition of the re-
sponse variable.  Unlike most chemical  data,
biological data, including that generated from
the  benthos  program, is multivariate,  and
therefore  offers a  number of  potential re-
sponse variables.  For instance, changes in the
densities  of individual species can be assessed,
potentially  limited  to either specific indicator
species or to dominant species.   In this  case,
determination of the ability of the data to sat-
isfy the DQO would require multiple analyses,
one  for  each species of interest.  The  total
density of the benthic community could be
also  be used, or the total densities of individu-
als within  more broadly  defined taxonomic
categories  (e.g.,  oligochaetes,   chironomids,
etc.). Alternatively,  community-level  metrics,
such as diversity or species richness, could be
used.  Finally, specific indicators of benthic
community  structure,  such as  the  Milbrink
(Milbnnk,  1983),   Goodnight   and  Whitley
(Goodnight and Whitley, 1960),  or Brinkhurst
(Brinkhurst,  1967) oligochaete indices, could
be employed. Presumably the variables to be
subjected to the DQO criterion should be
ones which are conceptually tractable, and for
which changes would have some understood
ecological meaning.

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          APPLICATION OF  DQO TO BENTHOS  DATA
3  Possible Interpretations of
   GLNPO's DQO
Determination of the adequacy of the current
benthos   sampling  regime   in   satisfying
GLNPO's DQO involves an  assessment of
the precision in estimating the mean (fj,) of the
statistical population in question.  The preci-
sion  of a given statistical estimate is affected
by the natural variation, or variance (o?\ of
the population under study, and the sampling
variability. Since natural variation is not under
investigator control, increases in precision can
only be effected through  decreases in  sam-
pling variability, which is mainly accomplished
by increasing sample size (»), although  theo-
retically improvements in sampling methodol-
ogy can also result in  reduced  sampling vari-
ability. If a desired precision in estimating a
parametric mean is known in  advance, as  is
the case with a DQO,  and the desired prob-
ability of attaining that precision for a given a
is also specified, then the number of samples
needed to achieve the desired precision can be
calculated according to the statistical test to be
used.

GLNPO's DQO addresses the detection of a
change in a population mean, relative to an
historical value.  The precision with which a
change can be detected will be  a function not
only of the number of observations in the cur-
rent  sample, but also of the number of obser-
vations making up  the historical sample. The
adequacy of a given sample size to detect such
a change will therefore  depend  upon the con-
stitution of the historical sample, and this in
turn  depends upon the  exact definition of the
'historical sample',  and on the statistical test
which is to be used to assess differences be-
tween it and the current sample.  A number of
possible interpretations  of the DQO exist, and
three of the most likely are outlined below,
along with the  statistical  considerations  in-
volved in their assessment.
3.1   Detection  of differences  between
current year and  all  previous years
combined

In the simplest scenario, the current sample is
compared to all past samples  pooled.  This
corresponds to the most literal interpretation
of GLNPO's  DQO, and it  assumes that all
past samples  estimate  the  same  population
mean, i.e., that there have been no changes in
the variable of interest prior to the current
year.  In this case, the appropriate formula for
determining sample size is:
       n = -

where: Sp2 =

        d =
             sample  estimate   of  pooled
             population variance; and
             the  minimum detectable  differ-
             ence specified by the DQO.
The  assumption  that  there  have been  no
changes in past years would require statistical
testing to assess, and therefore a de facto testing
for interannual differences between  all years
would be required under this interpretation of
the DQO, whether or  not that testing were,
strictly speaking, being used to  assess differ-
ences of interest (i.e., differences between  the
current year and all previous years combined) .
Furthermore, the assumption that all historical
data are statistically the same contains an ele-
ment of self-contradiction, since by definition
each current  year's data becomes historical
data with the collection of the next year's data.
3.2 Detection of changes between any
year

A second, and  more likely, interpretation of
the DQO is that the detection of any interan-
nual differences that have occurred  over the
course of the monitoring program is of inter-
est. In this case, each year's data would con-
 7

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         APPLICATION OF DQO TO BENTHOS DATA
stitute a different  'treatment', and a one-way
ANOVA, or equivalent, would be used to as-
sess   differences  between 'treatments'  (i.e.,
years).  The sample size necessary to detect a
specified difference (8) in  mean values can be
calculated using the following equation:
where: k =
                                         in
       8 =
number of treatments
(in this case years)
number  of  observations
each treatment
within groups variance
the minimum detectable  differ
ence, specified by the DQO
(f> is  a parameter related to the power of the
performed test.  This equation is solved itera-
tively, inputting successive estimates of n until
a  (p  corresponding to  the  desired  1-|3 is
achieved.

Alternatively, the following equation can be
used  to test for the  minimum detectable  dif-
ference with a specified n:
                       2 J.2
              5 =
                    2ks2
                      n
Under this scenario, the target minimum de-
tectable difference  of 20% would apply  to
changes seen  between  any two  years, even
non-consecutive years.   Of course with an
analysis of variance procedure, exactly which
treatments (in  this case years)  are  different
from which others are not specified; what can
be  concluded  from  the   analysis  is  only
whether or not any of the treatments are dif-
ferent from any other treatments.
3.3  Detection of trends overtime

A third possible  interpretation of the DQO
would be to enable detection of a trend in the
variable of interest, in other words, to test for
a change over time.  This can be  done in one
of two ways: through the use  of a regression
analysis, with the variable of interest regressed
against time (i.e., year), or by using a correla-
tion analysis, again with the variable of interest
correlated with time.  In the first case, the mag-
nitude of change over time can be  assessed,
specifically by examination of the regression
coefficient, b.  Simple linear regression,  how-
ever, has at least two drawbacks in the context
of the current application.  First,  it assumes a
cause and  effect relationship between the in-
dependent  and  dependent variables.  Strictly
speaking, it is unlikely that time in and of itself
would be the  causative factor  in  increases  or
decreases in benthos densities.  Second, and
more importantly,  it assumes that the  depend-
ent variable (e.g., benthos density at a  site) has
a constant, linear relationship to the  independent
variable (time).  If the detection of any change
in a variable over  time is  of interest,  and not
just a strictly linear one, then linear regression
analysis is an inappropriate tool.

Alternatively, correlation  analysis  can  be used
to assess whether changes in the magnitude of
one variable are associated with changes in the
magnitude  of a second variable. In correlation
analysis, no cause and effect relationship is as-
sumed.  However, in simple linear correlation
analysis, a linear relationship between the two
variables of interest is assumed.   This restric-
tion can be circumvented  by  use of a  rank-
based  correlation  procedure,  such  as  the
Spearman  rank  correlation procedure.  With
this procedure,  data are converted  to  ranks
prior  to conducting the analysis,  so that only
trends in the two  variables are assessed, and
not quantitative  changes of one in relation to
the other variable. So in this case, the magni-
tude of the change is not  assessed, rather the
strength of association  between the two  variables
is.  For this reason, the GLNPO DQO as cur-
rently formulated  cannot  be  applied to  an
analysis  of this  nature, since  the DQO  ad-
                                                                                          8

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          APPLICATION OF DQO TO BENTHOS DATA
dresses a specific magnitude of change (i.e., a
20% change over some historical value).

For  the purposes  of this report, it was as-
sumed that  the GLNPO DQO  requires the
ability to detect a 20% change in a variable be-
tween any two (or more) years for which data
are available.  The approach  adopted here,
therefore,  follows that outlined above in sec-
tion  3.2.
4 Overview of Current
   Benthos  Methods
In 1999, the most current year for which full
benthos data is available, a total of 53 stations
were visited during the summer survey (Figure
1).  Between  seven  (Lake Erie)  and fourteen
(Lake Michigan)  stations were visited in each
lake.  At each station, three replicate samples
were  collected,  using a Ponar grab sampler.
Samples were sieved through a 500-|J,m sieve
in the field, preserved, and transported to the
laboratory.  In the laboratory, all animals were
removed from any remaining sediment under
a dissecting microscope and sorted  by major
taxonomic  group.  All oligochaetes and chi-
ronomids were mounted and identified under
a compound microscope; all other inverte-
brates were identified and  counted under  a
dissecting  microscope.   Counts  were  con-
verted into areal units (#/m2) by multiplying
by 19.12, a  factor which takes into account the
area sampled by the Ponar.
5 Approach
In all cases, data from 1999 were used, since
this is the  most recent year for which com-
plete benthos data are available.  For the pur-
poses of this study, offshore stations are de-
fined  as those with a depth > 64 m.  This is a
slightly narrower definition than the one origi-
nally employed in  the design of the benthic
survey, and was adopted largely because of the
high percentage of stations with depths within
a few meters of 50 m, the original demarcation
between  near-  and offshore stations.   The
variables  considered for offshore stations in
this study were areal densities  (#/m2) of the
total benthos community, the total oligochaete
community (excluding fragments,  but includ-
ing immatures), and the two common profun-
dal organisms Diporeia and  Stylodrilus.  Because
of greater species richness,  additional variables
were examined at nearshore stations.  These
included  total areal densities of the Naididae,
the Tubificidae, the Chironomidae,  and  the
Sphaeriidae. Sites were excluded from analysis
where  numbers were  extremely  small,  or
where organisms were completely absent from
one or more replicate.

Since the original rationale  of the sampling de-
sign was  that offshore stations within a lake
would be estimating the same statistical popu-
lation, an initial analysis was undertaken to as-
sess  within lake differences in each  of  the
above four variables.  These potential differ-
ences were assessed using  a one way analysis
of variance (with stations as a random factor).
Where assumptions of  homoscedasticity  or
normality were  not met, a  Kruskal-Wallis one
way analysis of variance  on ranks was  per-
formed.

Minimum detectable differences  were deter-
mined for all variables tested at all sites using
the equation in section 3.2.  Data were  not
tested for homoscedasticity or normality prior
to computation of minimum differences, nor
were any transformations  used.  It  is recog-
nized that  in some cases  these assumptions
were probably not met.  However, given the
number of analyses, it was not deemed feasi-
ble to assess the need for transformations in
each case, particularly given the robustness of
a one way  ANOVA, the  statistical test upon
which most calculations were  based.  Mini-
mum detectable differences were  determined

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         APPLICATION OF DQO TO BENTHOS DATA
                                              Offshore Benthos Sites
                                              Nearshore Benthos Sites
Fig. 1. Locations of GLNPO's water quality survey (WQS) benthos sampling stations for summer,
1999 survey. Sites considered offshore are indicated in blue; sites considered nearshore are indi-
cated in red. .
for n (i.e., number of replicates taken)  - 3, 4
and 5, and for k (i.e., number of years com-
pared) = 2 through 9.  In all cases, |3 was taken
to equal 1-0.80 and a was taken to equal 0.05.
The most recent GLNPO DQO assumes an a
= 0.10; however, tabled values for (f)  given a =
0.10 and varying degrees of freedom could not
be found, so the more commonly used a of
0.05 was used instead. Therefore, estimates of
minimum detectable differences will be some-
what more conservative than are required by
the current DQO.  General trends, however,
should not be affected by the difference in a.

The adequacy of the current sampling regime
in satisfying the GLNPO DQO was examined
assuming that  any  between-year  differences
are of interest.  Natural variation at each site
was estimated using sample variance calculated
from 1999 data. The number of replicates re-
quired to satisfy the stated GLNPO DQO was
calculated  for each variable according to  the
equation in section 3.2, using the mean value
of the variable  in 1999  as the value in relation
to which a 20% change should be detectable,
and assuming a comparison of 5 years, a =
0.10 and p = 1-0.80.
6  Results
6.1 Within Lake Variation in Offshore
Sites

ANOVA analyses were  conducted on  off-
shore sites within each lake to determine if
                                                                                   10

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          APPLICATION  OF DQO TO  BENTHOS DATA
Table 1. One way analysis of variance results examining between site differences in Lakes Huron,
Michigan, Ontario and Superior for offshore stations.  For all tests, a = 0.05. Where assumptions
of normality or homos cedasticity were not met, the Kruskall-Wallis one way analysis of variance on
ranks was used.
LAKE
Z Benthos       Z Oligochaetes
Diporeia
Stylodrilus
HURON
MICHIGAN
ONTARIO
SUPEROR
5.08
94.6
10.6*
28.7
0.01
<0.001
NS
<0.001
4.35
24.7
13.5*
26*
0
<0
0
0,
.01
.001
.02
.002
5,
55,
38,
26,
.32
.2
.1
.5*
0,
<0.
<0
0,
.01
.001
.001
.002
17.6*
14.2
15*
23.3*
0.01
<0.001
0.01
0.01
  Kruskal-Wallis One Way Analysis of Variance on Ranks used: statistic - H.
these sites should  serve as replicates within
each lake.  Variables  tested were densities of
total benthos, total oligochaetes,  Diporeia  and
Stylodrilus.  For  all  variables tested,  with  the
sole exception of total benthos  densities in
Lake  Ontario,  significant  differences  were
found between  sites  at a  =  0.05 (Table  1).
Therefore all sites were considered separately
in subsequent analyses.
6.2  Comparison of variability between
offshore and nearshore sites

To determine if within site variability was re-
lated to depth, i.e., if nearshore sites were
more or less variable than offshore  sites, the
standard deviation of total benthos  estimates
for individual sites was plotted against depth
for all sites.  The more extreme standard de-
viations were associated  with shallower sites,
although not all shallow sites exhibited a large
standard  deviation  (Figure  2).   Since  the
GLNPO DQO is formulated in terms  of per-
cent, rather  than  absolute,  change, though,
high standard deviations would not necessarily
result in large required sample sizes if those
standard deviations were associated with high
values of the  variable of interest.   Benthos
densities do in fact tend  to be higher at shal-
                                lower sites, and  this  might counteract the
                                higher variances seen at shallow sites.
                                The coefficient of variation:
                                                       X

                                provides a measure of the variance relative to
                                the mean, and when this was  plotted against
                                depth,  no relationship was seen (Figure 2).
                                This indicates that the higher standard devia-
                                tion  of some shallow sites was due to higher
                                total numbers.

                                When the natural logarithm of the sample size
                                required to satisfy the DQO for the estimation
                                of total  benthos  at  each site was  plotted
                                against site depth, an apparent positive rela-
                                tionship was seen (Figure 3). This relationship
                                was just statistically significant at a =  0.10, as
                                determined by least  squares regression (F  =
                                2.77, P = 0.10).  A more significant relation-
                                ship  was  found  between the sample  size re-
                                quired  to  estimate  total  numbers  of oli-
                                gochaetes and depth  (F = 5.59, P = 0.02). In
                                both cases  depth  explained a  relatively low
                                percentage of the variance in required sample
                                size  (total  benthos:  R2  =  0.03;  total oli-
                                gochaetes: R2 =  0.09).  This suggests that, on
                                the whole,  deeper sites  tend to  require  a
                                greater degree of replication to enable detec-
                                tion  of a 20% change for these variables than
11

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        APPLICATION OF DQO TO BENTHOS DATA
              4000
              3000 -
           c
           o
           >  2000
           Q
           CD
           T3
           C
           JD
           CO
   1000 -
                                              • Ontario
                                              • Erie
                                              O Michigan
                                              O Huron
                                              O Superior
                           oo  o
oo  .6*
                                                         o
                           50
                       100
                       ^50
                                        Depth (m)
               100
                80-
o
"CD
-£   40 -
CD
           O
200
                                       -O
250
300
                                                  O
                                 0
                                      0
                        O

                     O
                    O    O
                                                         o
                                 0
                                   (D
                           50
                       100
                       ^50
                                        Depth (m)
200
250
300
Figure 2. Standard deviation (top panel) and coefficient of variation (bottom panel) of estimates of
total benthos densities at each site, graphed against site depth.  Variance estimates based on 1999
values.
do shallower sites, although this  tendency is
not marked. As noted, this is due in large part
to the smaller densities of organisms seen at
deeper  sites.  Such a relationship was not
found,  however,  between  sample  sizes  re-
                               quired to satisfy the DQO for densities of Di-
                               poreia and station depth (Figure 3).  The prob-
                               able reason for this  is that, unlike total ben-
                               thos  and total oligochaete densities, Diporeia
                               numbers tend to increase with depth.
                                                                               12

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        APPLICATION OF DQO TO BENTHOS DATA
         1000
                                              Total Oligochaetes
                                                        Ontario
                                                        Erie
                                                      O Michigan
                                                      O Huron
                                                      O Superior
                                                              300
                                                              300
                     50
100
150
200
250
300
                                  Depth (m)

Figure 3. Relationship between sample size required to satisfy DQO and site depth for estimation
of total benthos, total oligochaete and Dipomaareai densities. In all cases, k = 5, a = 0.10 and |3 =
1 - 0.80.
13

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         APPLICATION OF DQO TO BENTHOS DATA
6.3 Minimum detectable differences for
interannual comparisons at offshore
sites

Average lake-wide minimum detectable differ-
ences for the current level  of replication, as-
suming a comparison of 5 years of data and a
= 0.05, were less  than 1,000 individuals/m2 in
all cases except in Lake Huron, where the av-
erage minimum detectable difference for Dio-
reia was 1,692/m2 and for  the total benthos
was 2,343/m2 (Table 2).  This is  reflective of
the generally higher variances,  and  hence
higher minimum detectable differences, seen
at the offshore sites in that lake compared to
the other lakes.  Average minimum detectable
differences for the four variables were broadly
similar in Lakes Ontario and Michigan,  and
somewhat lower in Lake Superior, due to the
lower densities of organisms in that lake.

Since the GLNPO DQO is stated in  terms of
a percent, rather than an absolute, change, a
more relevant measure of the adequacy of the
current sampling program  in fulfilling the re-
quirements of the DQO is minimum percent
detectable difference.   In most cases,  the
Table 2.  Maximum, minimum and average minimum detectable differences (#/m2) and percent
detectable differences for offshore (z > 64 m) stations in Lakes Michigan, Huron, Ontario and Su-
perior, under the current sampling regime. In all cases, n = 3, k = 5, a = 0.05 and |3 = 0.80.
LAKE Minimum Detectable
Variable Max Min
MICHIGAN
Diporeia
Stylodrilus
Z Oligochaetes
Z Benthos
HURON
Diporeia
Stylodrilus
Z Oligochaetes
Z Benthos
ONTARIO
Diporeia
Stylodrilus
Z Oligochaetes
Z Benthos
SUPERIOR
Diporeia
Stylodrilus
Z Oligochaetes
Z Benthos

1,144
1,109
1,331
1,040

3,055
2,289
2,093
5,407

1,020
1,570
1,954
2,084

1,513
1,396
1,454
1,791

314
157
157
595

491
104
68
579

343
39
39
245

68
104
39
68
Difference
Ave

794
580
589
864

1,692
651
965
2,343

741
443
443
951

437
539
531
855
Percent Detectable Difference
Max Min Ave

89%
617%
178%
81%

175%
310%
206%
166%

72%
168%
392%
127%

356%
592%
617%
432%

34%
46%
27%
24%

22%
21%
14%
15%

40%
62%
21%
32%

30%
44%
35%
45%

52%
188%
88%
41%

108%
165%
152%
95%

61%
123%
143%
71%

168%
236%
253%
169%
                                                                                   14

-------
         APPLICATION OF DQO TO  BENTHOS  DATA
   I
   I
   !§
   T3
   .0
   8
   CD
   T3
   E
   c
2000
1800 \
1600
1400
1200
1000
 800
      Diporeia
n = 3
n = 4
n = 5
       2600
2400 J
2200
2000
1800
1600
1400
                                            1200
             Total Individuals
               2      4      6      8     10
                Number of years compared
                                              2468
                                              Number of years compared
                                           10
   CD
   O
   c
    .
   •I
   Q)
   .0
   .2
   "O
   £
   "CD
   T3
   E
   'c
                                      800
               2      4      6      8     10
                Number of years compared
                                              2      4      6      8     10
                                              Number of years compared
Figure 4. Average minimum detectable differences in areal densities of Diporeia, total benthos, total
oligochaete and Stylodrilus at offshore sites in Lake Huron for different numbers of replicates (ri)
when comparing 2-9 years of data. Variance estimates are based on 1999 data.
 smallest  percent difference  that the  current
 sampling regime permits detection of was sub-
 stantially larger than 20%,  reaching several
 hundred percent in  a large  number of cases
 (Table 2). In only two cases was the current
 level of replication adequate to  permit detec-
 tion of a 20% change: given  estimates of vari-
 ance from 1999, current levels  of replication
 permit detection of 14%  and 15% changes in
 total oligochaete and total benthos densities,
                                      respectively, at station HU 93 in Lake Huron.
                                      Average minimum percent detectable differ-
                                      ences were highest in Lake Superior; this was
                                      due to not to higher variance, but rather to
                                      lower overall densities of organisms in  the
                                      lake.  Thus, while in general smaller differ-
                                      ences are able to be detected in Lake Superior,
                                      the low densities seen in the lake counteract
                                      this to make minimum percent detectable  dif-
                                      ferences larger than in the other lakes.
15

-------
         APPLICATION OF DQO TO BENTHOS DATA
  CD
  O
  .0
  CO
  t5
  CD
  -t->
  CD
  T3
  E
  'c
   1300
   1200
   1100 -
   1000 -
    900
    800 \
    700
    600
    500
            Diporeia
n = 3
n = 4
n = 5
1000

 900

 800

 700

 600 -

 500 -

 400
             Total Individuals
              2      4      6      8      10
               Number of years compared
                                                  2468
                                                  Number of years compared
                                           10
   8
   (D
   .Q
   CO
    700
      400
CD
T3
|
1
i  300
            Total oligochaetes
   £  600 -
      500 -
        700
                                          600
                                          500
        400
                                            300
           2      4      6      8
            Number of years compared
                                          10
                                               Stylodrilus
                2468
                Number of years compared
                                    10
Figure 5.  Average minimum detectable differences in areal densities of Diporeia, total benthos, total
oligochaete and Stylodrilus at offshore sites in Lake Michigan for different numbers of replicates (n)
when comparing 2-9 years of data. Variance estimates are based on 1999 data.
Minimum detectable differences were  calcu-
lated  for interannual  comparisons  in which
between 2 to 9 years of data were compared,
and with 3, 4 and 5 replicates per site, for a =
0.05.  For any given level of replication, mini-
mum detectable  differences increase as  the
number of years  in the comparison increases
(Figures 4-7).   In  general, increasing  the
number of replicates from 3 to 4 resulted in a
decrease in minimum detectable differences of
                                          approximately 17-23%, while increasing repli-
                                          cation to 5 further reduced minimum detect-
                                          able differences by approximately 10-15%.  In
                                          absolute terms, taking 4 replicate samples at
                                          each site reduced the minimum detectable dif-
                                          ference by an average of 170 individuals/m2
                                          for the four variables considered (range: 88 -
                                          470/m2), while 5  replicate samples per site re-
                                          duced the minimum detectable  difference by
                                          an average of 259/m2 (range: 133 - 714/m2),
                                          compared  to 3 replicates (Table 3).
                                                                                  16

-------
        APPLICATION OF DQO TO BENTHOS DATA
  0
  o
  !§
  T3
0
-I—»
0
T3
E
3
E
1000
 900
 800 •
 700 •
 600 •
 500 •
 400
 300
           Diporeia
                                n = 3
                                n = 4
                                n = 5
                            1200
                            1100
                            1000 -I
                            900
                            800 -I
                            700
                            600 -I
                            500
                            400
                                          Total Individuals
             2      4     6     8    10
             Number of years compared
                                         2468
                                         Number of years compared
                                                           10
   0
   o
   c
   0
   0
   m
   0
   -i—»
   0
   T3
   |
   E
   c
 600
 550
 500
 450
 400
 350
 300
 250
 200
Total oligochaetes
                            450
                                       150
            2     4      6      8
             Number of years compared
                               10
                                   2468
                                   Number of years compared
                                                                   10
Figure 6. Average minimum detectable differences in areal densities of Diporeia, total benthos, total
oligochaete and Stylodrilm at offshore sites in Lake Ontario for different numbers of replicates (ri)
when comparing 2-9 years of data. Variance estimates are based on 1999 data.
17

-------
        APPLICATION OF DQO TO  BENTHOS DATA
  8  600
            Number of years compared
                                         2     4     6     8     10
                                         Number of years compared
  CD
  o
  c
  .
  CD
 t
 T3
 JD
 .Q

 fi
 £
  CD
 T3
  E
  D
  E
  c
600
550
500
450
400
350
300
250
200
         Total oligochaetes
800
750
700
650
600
550
500
450
400
350
300
           2468
            Number of years compared
                               10
Stvlodrilus
       2468
       Number of years compared
                        10
Figure 7.  Average minimum detectable differences in areal densities of Diporeia, total benthos, total
oligochaete and Stylodrilus at offshore sites in Lake Superior for different numbers of replicates (»)
when comparing 2-9 years of data. Variance estimates are based on 1999 data.
                                                                       18

-------
          APPLICATION OF DQO TO  BENTHOS DATA
           Table 3. Estimated average minimum detectable differences (#/m2) for off-
           shore (z > 64 m) stations in Lakes Michigan, Huron, Ontario and Superior, for
           samples sizes of 3, 4 and 5.  In all cases, k = 5, a = 0.05 and |3 = 1-0.80.
LAKE
Variable
MICHIGAN
Diporeia
Stylodrilus
Z Oligochaetes
Z Benthos
HURON
Diporeia
Stylodrilus
Z Oligochaetes
Z Benthos
ONTARIO
Diporeia
Stylodrilus
Z Oligochaetes
Z Benthos
SUPERIOR
Diporeia
Stylodrilus
Z Oligochaetes
Z Benthos
Sample Size
345

794
580
589
864

1,692
651
965
2,343

741
594
443
951

437
539
531
855

635
464
471
691

1,353
520
771
1,873

593
474
354
760

349
431
424
684

552
403
409
601

1,176
452
671
1,629

515
413
308
661

304
375
369
595
 6.4  Required sample sizes for offshore
 sites

 The number of replicates needed to  detect a
 20% change in the  four variables tested, as-
 suming a five year comparison, ranged from 3
 for the site in Lake  Huron noted above (HU
 93), to 702 to detect a 20% change in total
 benthos densities at  site SU 16 in Lake  Supe-
 rior (Figure 8).  The median number  of repli-
 cates needed to meet the DQO  for all vari-
 ables was 39, while fully 80% of the variable/
 site combinations tested would require at least
 12  replicates  to satisfy current  the DQO.
However, since each variable is not sampled
independently at each site, the number of rep-
licate samples required on a site by site basis
would be determined by the variable requiring
the greatest number of replicates at that site to
satisfy the DQO. Examined on this basis, the
median number of replicates at offshore  sites
needed to meet the DQO was estimated to be
97, while 80%  of sites would require 19 repli-
cates or more.  These estimates obviously rep-
resent  an  impractical  degree  of replication,
both from  the standpoint of ship  time re-
quired  to amass such a number of samples,
and from the standpoint of time required for
sample analysis.
19

-------
        APPLICATION OF DQO TO BENTHOS DATA
      1000
   CD   100
 ^ N
 ^W

 'M
 o-o.
 CD E
 o: ro
   w
                                                          S Benthos

                                                          S Oligochates

                                                          Diporeia

                                                          Stylodrilus
             HU32     HU38     HU 48     HU 54
HU61
HU 93     HU 95
       1000
    CD   100
    N
  
-------
          APPLICATION OF DQO TO BENTHOS DATA
 6.5  Minimum detectable differences for
 interannual comparisons at nearshore
 sites

 Minimum detectable  differences were calcu-
 lated for each nearshore site, assuming a com-
 parison of five years and a = 0.05, for the fol-
 lowing  six variables:  areal densities  of total
 benthos, total oligochaetes, total Tubificidae,
 total Chironomidae,  Diporeia and  Sphaeridae.
 In only five cases was the current level of rep-
 lication sufficient to  detect a 20% difference
 when comparing five  years of data (Figure 9).
 Most values for Lakes Erie, Huron and Michi-
 gan  were between 50 and 100% (medians  =
 65%, 59% and 79%,  respectively), while mini-
 mum detectable  differences were  somewhat
 higher in Lake Ontario (median = 166%). Of
 the three  nearshore stations in the latter lake,
 two  exhibited substantial variability in all vari-
 ables examined.  No  substantial differences in
 minimum detectable  differences were appar-
 ent between the different variables.
 6.6   Required  sample  sizes for  near-
 shore sites

 The number of replicates needed to detect a
 20% change in the six variables tested, assum-
 ing a  five year comparison  and a =  0.10,
 ranged from three for several variables at three
 sites in Lakes Erie and Michigan, to  837 repli-
 cates to estimate total chironomids at a station
 in Lake Ontario (Figure  10).   Overall, a me-
 dian sample size of 25 was required when all
 variables at all sites were considered,  with 80%
 of  site/variable combinations requiring nine
 replicates  or more.  When just the maximum
 required sample size  at  each site for  all  six
 variables was considered, which is a  better in-
 dication of the required site-wise level of repli-
 cation,  the  median  number  of  replicates
 needed to satisfy the DQO at all sites was  77,
 with 80% of the sites requiring 51 replicates or
more for the DQO to be met for all variables
tested. Given the greater number of variables
under consideration here, this is a more rigor-
ous  test  of the  sampling program than was
used for offshore stations.  Considering just
those variables that were also assessed in the
offshore  sites, namely total benthos, total oli-
gochaetes and Diporeia (Stylodrilus does not ap-
pear in substantial numbers at shallower sites),
the median number  of replicates needed  to
satisfy the DQO at each site was 38, with 80%
of the sites requiring  at  least  13  replicates.
These numbers are lower than  those seen in
the offshore sites.  On average, then, near-
shore sites  would  require less  replication  to
satisfy GLNPO's DQO,  compared  to off-
shore sites.  Even so, the level  of replication
required at  nearshore sites for acceptable esti-
mates of total benthos, total oligochaetes and
Diporeia densities, given the current DQO, are
in the vast  majority of cases  infeasible  from a
practical standpoint.
7  Relation of DQO to 1997-
   2001 Diporeia data
To determine the relation of  the  GLNPO
DQO to a benthic variable of ecological inter-
est, and  for which data from 1997-2001 are
available, changes in the density of Diporeia
were examined.  The amphipod Diporeia has
historically  been one  of the  most abundant
and  widespread  benthic  organisms  in  the
Great Lakes (Dermott and Corning, 1988) and
is  an important link in the  Great Lakes  food
chain,  feeding  on  pelagic-derived  detritus
(Gardner et al., 1990)  and in turn providing an
important food source for  many fish  species
(Scott  and  Grossman,  1973).   Recently its
numbers have  been  declining  in  significant
portions  of its range in the  Great Lakes, with
potentially huge consequences for community
structure and energy flow through the Great
Lakes food web.
21

-------
        APPLICATION OF DQO TO BENTHOS DATA
CD
o

I
CD
      300 -
   o  200 -
  .0
  8
CD
O
-*->

CD
a
CD
Q_
   CD
   O
      100 -
                                                    Z Benthos
                                                    Z Oligochates
                                                    Z Tubificidae
                                                    Z Chironomidae
                                                    Diporeia
                                                    Sphaeridae
ER15
                    ER43
                                ER61
    ER78
      ER91
                                         ER 93
                                          ER 95
      500
   CD  40° -

  b
   0)  300 -
  .a
   CO
      200 -
      100 -
   CD
  Q
   CD
  Q_
   CD
   o
   I
  !§
  b
   o  200 -
  .0
  8
HU06
HU96
HU97
HU 98
ON 65
                                          ON 67
                                                            ON 69
                                                                       SU 22
   300 -
   CD
  Q
  -*->

   CD
   a
   CD
  Q.
   100 -
            Ml 30   Ml 31
                           42    Ml 46    Ml 48
                                         Station
                                       49   Ml 50    Ml 52    Ml 53
Figure 9.  Minimum detectable differences, as a percent, for estimates of total benthos, total oli-
gochaete and total Tubificidea, total Chironomidae Diporeia and Sphaeridae areal densities. In all

cases, k = 5, a = 0.05 and P = 1 - 0.80. Reference line represents current data quality objective of
20%
                                                                              22

-------
         APPLICATION OF DQO TO BENTHOS DATA
      1000
CD   100
 o: ro
   CO    10
                                                       E Benthos
                                                       E Oligochates
                                                       E Tubificidae
                                                       E Chironomidae
                                                       Diporeia
                                                       Sphaeridae
             ER15     ER43
                             ER61
ER78     ER91     ER 93     ER 95
      1000
             HU 06    HU 96    HU 97    HU 98    ON 65    ON 67   ON 69    SU 22
      1000
   CD   100
   N
 a:
   CO    10
             Ml 30
                                                               52   Ml 53
                                         Station
Figure 10. Sample sizes required at nearshore sites to detect a 20% change in total benthos, total
oligochaete, total  Tubificidae, total Chironomidae, Diporeia and total Sphaeridae areal densities
when comparing 5 years, assuming a = 0.10 and |3 = 1 - 0.8.  Variance estimates and initial densi-
ties are based on 1999 values. Reference line represents n = 3.
23

-------
         APPLICATION  OF DQO TO BENTHOS  DATA
For this analysis, only those sites for which all
five years of data were available were used.
Within  site  differences  in  densities  between
any two years were assessed using a  one way
ANOVA with year as the factor.  This corre-
sponds to the approach adopted above, and is
the interpretation of the DQO  used through-
out this report.  In accordance with the DQO,
a significance level of ot = 0.10 was used.  In
the case of Diporeia, what is of interest is spe-
cifically a  [decreasing] trend over time, rather
than any year to year differences.  To test for
this, the non-parametric  Spearman rank corre-
lation coefficient was calculated for each site.
While data from most sites conformed to the
assumptions of normality and homoscedastic-
ity, and in cases where these were not met, de-
viations were relatively small, a non-parametric
test was used for correlations to enable detec-
tion  of any,  rather than just linear,  relation-
ships  between year and  density.  For consis-
tency,  a significance level of a  = 0.10 was
used.

Both  the maximum  difference in areal densi-
ties between any two years,  and the maximum
difference between any two consecutive years,
were calculated for each site.  Maximum dif-
ferences between any two years ranged from
108/m2 to 10,510/m2, with a median value of
1,399/m2  (Table 4). Since the DQO is formu-
lated in terms of a percent, maximum percent-
age differences were also  calculated,  using a
conservative approach in which the larger of
the two numbers was used as  the denomina-
tor. Thus  the equation used was:

          MaxAnnual - MinAnnual
                MaxAnnual
Maximum percentage differences ranged from
43% to 100% (i.e., where the minimum annual
value =  0).  The median percent difference
was 72%, and 80% of values were greater than
55%.  Therefore, in all cases an interannual
difference substantially higher than the target
detection limit of the  DQO was seen.  Since
an  alternate  interpretation of  the  GLNPO
DQO requires the detection of a 20% change
between  values  in  consecutive  years, maxi-
mum differences between  consecutive  years
were also computed for all sites.  In this case,
maximum differences  ranged  from 108/m2 to
5,487/m2, with a median difference of 1,316/
m2.  These corresponded to percentage differ-
ences ranging from 29% to 100%, with a me-
dian of 64%.  80% of sites had a maximum
difference between consecutive years  of 49%
or more.  Thus  all sites experienced  interan-
nual changes in  Diporeia density greater than
the 20% targeted in  the  DQO, even when
these differences were limited to those occur-
ring in consecutive years.

Using  the criterion for  significance stated in
the DQO (a = 0.10), 26 of the 30 sites exam-
ined were found  to have statistically significant
interannual  differences  in  Diporeia  densities,
when  assessed  using a one way  ANOVA
(Table 4). Since  interannual differences can be
a consequence of natural fluctuation in popu-
lation  sizes, rather than an  indication  of a
trend, which is presumably of greater inherent
interest, Spearman rank  sum correlation  coef-
ficients were also calculated.  In this case, 15
of the 30 sites examined exhibited statistically
significant increasing or decreasing trends with
time, indicating  that in 11 of the  cases for
which  interannual differences were  detected,
these differences were not directional.
                                                                                        24

-------
         APPLICATION OF DQO TO BENTHOS DATA
 Table 4. Analysis of Diporeia areal densities at all sites for which five (1997 - 2001) years of data are
 available. Maximum differences (A) in density estimates between any two years, and between any
 two consecutive years, are shown as both absolute values (#/m2) and percentages. Results of one
 way analysis of variance, with year as factor, and Spearman rank correlation analysis, are shown.
 Significant results, determined at a = 0.10, are shown in bold.
Site
HU06
HU32
HU38
HU48
HU54
HU61
HU93
HU95
HU96
HU97
Mill
MI 18
MI 27
MI 41
MI 46
MI 48
MI 50
MI 52
ON 25
ON 41
ON 60
SU01
SU10
SUB
SU15
SU16
SU17
SU19
SU21
SU22
Maximum A
(any years")
3,996
3,060
1,358
2,830
4,871
1,759
2,435
2,282
3,295
1,574
1,396
3,811
4,512
5,481
1,294
918
1,434
10,510
778
1,377
1,402
535
255
185
433
153
108
223
166
1,211
100%
66%
73%
83%
85%
48%
79%
71%
86%
49%
63%
84%
71%
95%
43%
100%
100%
100%
57%
71%
55%
48%
63%
81%
68%
80%
85%
81%
49%
54%
Maximum A
(consecutive years")
2,428
1,791
1,358
1,740
2,014
1,128
1,848
2,263
1,402
931
1,396
2,855
4,512
4,640
1,294
918
1,338
5,487
720
1,377
1,134
351
249
178
433
121
108
204
166
1,052
61%
49%
73%
75%
70%
31%
65%
70%
53%
29%
63%
63%
71%
80%
43%
100%
93%
78%
53%
71%
45%
31%
62%
78%
68%
63%
85%
74%
49%
47%
ANOVA
F P
16.6
6.9
12.1
6.7
29.8
5.1
88.0
15.0
5.3
11.8
2.7
6.2
9.1
18.3
3.7
16.0
6.7
84.8
1.8
16.1
4.4
4.9
3.5
2.1
3.7
3.2
2.1
6.2
1.7
16.9
<0.001
0.006
<0.001
0.007
<0.001
0.017
<0.001
<0.001
0.015
<0.001
0.096
0.009
0.002
<0.001
0.042
<0.001
0.007
<0.001
0.214
<0.001
0.025
0.019
0.049
0.161
0.043
0.061
0.152
0.009
0.23
<0.001
Spearman Corr.
r, P
-0.95
1.78
-0.34
-0.46
-0.68
-0.74
-0.86
-0.31
-0.81
-0.35
-0.29
-0.69
-0.13
-0.74
0.53
0.17
-0.82
-0.90
-0.40
-0.18
-0.81
0.05
-0.29
-0.41
-0.22
-0.49
-0.22
-0.59
-0.20
-0.16
<0.001
<0.001
0.209
0.083
0.005
0.001
<0.001
0.257
<0.001
0.194
0.293
0.004
0.629
0.003
0.061
0.578
<0.001
<0.001
0.134
0.514
<0.001
0.842
0.287
0.124
0.410
0.059
0.426
0.020
0.457
0.566
25

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         APPLICATION OF DQO TO  BENTHOS DATA
8 Discussion
In almost all cases examined, the current level
of sampling effort in the benthos program is
inadequate  to  satisfy GLNPO's  DQO,  as
presently formulated.  Even when just consid-
ering the limited number of variables used in
this  report,  the great majority of sites would
require over a dozen replicate Ponar samples
to meet the current DQO,  and  over half
would require more than three dozen repli-
cates. This degree of sampling effort is clearly
infeasible  in the  content of  the  GLNPO
monitoring program,  and it  is in  fact unclear
the extent to which it is required to make edu-
cated ecological management decisions using
the benthos  data.

A number of ambiguities exist  in the current
DQO, some of which are specific to its appli-
cation to the biological program, and some of
which are more general.  As noted  in  the in-
troduction, the  current DQO does  not make
entirely clear what should be compared when
assessing changes in the magnitude  of a vari-
able.  For the most part in this report it was
assumed that what the DQO required detec-
tion  of was a change between any two years of
data.  However, as was seen with Diporeia, a
20% fluctuation in density between  two years
was  the rule, rather than  the  exception, and
while this was due in part to actual basin wide
declines seen in populations of this organism
in recent years,  even those sites which did not
exhibit a trend  (i.e., for which rs was not sig-
nificant)  showed  differences  between years
that were several times greater than 20%. This
suggests that the normal range of natural fluc-
tuations in biological populations can be con-
siderably  higher  than the  target  difference
specified in  the DQO, and therefore that  the
DQO is probably unnecessarily stringent  for
biological data.  In spite of not meeting DQO
requirements with regard to  being able to  de-
tect  changes in Diporeia populations, the cur-
rent  sampling program nonetheless found sta-
tistically significant differences in annual Di-
poreia densities at nearly every site for which
data were available. As suggested above, in at
least some cases these differences were proba-
bly just due to natural fluctuations, and would
not  necessarily  be indicative  of an overall
change in the ecological character of the ben-
thos.

More restrictive interpretations of the DQO
would  include the ability to  detect a  20%
change between consecutive  years, and  being
able to  detect a trend in a variable. In the for-
mer interpretation, as  seen with the  Diporeia
data, a 20% change might still be too stringent
a requirement for benthos data;  nearly every
site examined exhibited a difference between
consecutive years at least twice this great.  In
the case of the  latter interpretation, unless a
linear trend were assumed (an assumption for
which there would be no  apriori support),  it
would be difficult, and likely counter  produc-
tive, to set a specific criterion for the magni-
tude of the trend to be detected.  Presumably
the detection  of any trend would be of inter-
est.  Instead, a DQO might best be stated in
terms  of detecting a relationship between  a
variable and  time (e.g.,  year)  of a  given
strength.

The main deficiency in the current DQO, as it
relates specifically to biological data (including
the benthos data), is that the  variable of inter-
est is not specified.  When assessing  changes
in the  chemistry of a  lake,  determining the
variable of interest is usually relatively straight-
forward (though not necessarily; e.g., instances
involving detection of a large number of con-
geners  of an organic pollutant).  Such is not
the case with biological data, however, which
is  multivariate.  As pointed out in the  intro-
duction, potential variables include each indi-
vidual species (of which there might  be doz-
ens, or even hundreds), groupings  of species
under broader taxonomic categories, commu-
nity-level metrics such as species  richness or
diversity,  and specific indices such as Good-
                                                                                        26

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          APPLICATION OF DQO TO BENTHOS DATA
night-Whitley (Goodnight and Whitley, 1960)
or Milbrmk's (Milbrmk, 1983). Between 1997
and 1999, a total of 86 taxonomic entities were
recorded from the benthos samples collected
by  GLNPO, each one  constituting a potential
response variable to which the DQO could be
applied.  In many cases these species appeared
in relatively low numbers at the sites at which
they were found, which makes a given percent
difference  more difficult to discern, and also
more difficult to interpret. This report limited
the taxonomic groups  under consideration to
only those few that contained relatively high
densities of individuals. Even with this restric-
tion, sample  sizes required to meet the DQO
were  unrealistically high.  If the number of
replicates required  at  each  site were  deter-
mined by that needed  to meet the DQO re-
quirement for all species encountered, the re-
sulting sampling effort would undoubtedly be-
come not  only infeasible, but physically im-
possible.

It is  also unclear  how such changes,  if de-
tected, would be interpreted; e.g., what eco-
logical meaning a 20%  increase or decrease in
Stempellinella  would  have.   In  some  cases,
changes  in the biological populations  of cer-
tain species are of inherent interest. One such
case is Diporeia, as noted above.  Large varia-
tions  in the population sizes of dominant or-
ganisms might also be  of interest, though the
ecological  meaning of such variations  might
not be readily apparent.  Currently, the DQO
does  not provide guidance on which  species
or taxonomic groups are of interest, and there-
fore on which variables should be subject to
the target contained in  the DQO. Ideally, the
benthos data should also be assessed in a way
that enables detection of changes in the entire
benthic  community.    This  would  require
adopting a multivariate  approach towards data
analysis, and would probably require reformu-
lation of the DQO to specify a probability of
detecting a deviation from a 'baseline' commu-
nity,  rather than trying  to detect a percent
change in a single variable.  This is because of
the difficulty of reducing multivariate data to a
single metric. As such, though, the definition
of a baseline community, as well as what con-
stitutes  an ecologically  significant deviation
from such a community, would need to be de-
fined.  There is  some precedent for  such  an
approach in the Great Lakes (Reynoldson and
Day, 1998); however further consideration of
this alternative is beyond the scope of the pre-
sent report. Ultimately,  however, a more de-
tailed statement  of the DQO  target with re-
gard to the benthos data will depend on an ex-
plicit understanding of what the data will  be
used for, and how they will be interpreted.
27

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         APPLICATION OF DQO TO BENTHOS DATA
9  References
Brinkhurst, R.O. 1967. The distribution of aquatic oligochaetes in Saginaw Bay, Lake Huron. Lim-
     nol. Oceanogr. 12:137-143.

Dermott, R. and K. Corning.  1988. Seasonal ingestion rates of Pontoporeia hoyi (Amphipoda) in Lake
     Ontario. Can.]. Fish. Aquat. Sd. 45:1886-1895.

Gardner, W.S., M.A. Quigley, G.L. Fahnenstiel, D. Scavia and W.A. Frez. 1990. Pontoporeia hoyi - a
     direct trophic link between spring diatoms and fish in Lake Michigan. In 'Large Lakes: Ecologi-
     cal Structure and Function, eds.  M.M. Tilzer and C. Serruya, pp. 632-644. Springer-Verlag, New
     York, NY.

Goodnight, C.I. and T.S Whitley. 1960.  Oligochaetes  as indicators of pollution. Proc.  15th Industr.
     Waste Conf. Purdue Univ.  Ext. Eng. 106:139-142.
International Joint Commission. 1978. Great Lakes Water Qualit
     tional Joint Commission.
of 1978. Ottawa: Interna-
Milbrink, G. 1983. An improved environmental index based on the relative abundance of oli-
     gochaete species. Hydrobiologia 102:89-97.

Reynoldson, T.B. and K.E. Day.  1998. Biological Guidelines for the Assessment of S ediment Quality in the
     Laurentian Great Lakes. National Water  Res. Inst. Rpt.  No. 98-232, Environment Canada,
     Burlington Ont.
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