Pilot Study For Montgomery County and Maryland DNR Data Integration: Comparison of Benthic Macroinvertebrate Sampling Protocols for Freshwater Streams

United States
Environmental Protection
Agencf
for

for

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EPA903/R-03/005
May 2003
Pilot Study for Montgomery County
and Maryland DNR Data Integration:
Comparison of Benthic Macroinvertebrate
Sampling Protocols for Freshwater Streams
Prepared for:
U.S. Environmental Protection Agency
Office of Environmental Information and
Mid-Atlantic Integrated Assessment Program
701 Mapes Road
Fort Meade, MD 20755-5350
Wayne Davis, Project Officer
Under subcontract from:
Technology Planning & Management Corporation
Wharf Plaza, Suite 208
Scituate, MA 02066
Prepared by:
J.H. V0lstad, N.E. Roth,
M.T. Southerland, G. Mercuric

Versar, Inc.
ESM Operations
9200 Rumsey Road
Columbia, MD 21045
Printed on chlorine free 100% recycled paper with
100% post-consyrner fiber using vegetable-based ink.
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Notice
NOTICE

This document has been reviewed and approved in accordance with U.S. Environmental
Protection Agency policy. Mention of trade names, products, or services does not convey and
should not be interpreted as conveying official EPA approval, endorsement, or recommendation
for use.

Funding was provided by the U.S. Environmental Protection Agency under U.S. Department of
Commerce, Commerce Information Technical Solutions Contract No. 50-CMAA-900065 with
Technology Planning and Management Corporation.

The appropriate citation for this report is:

V01stad, J.H, N.E. Roth, M.T. Southerland and G. Mercurio. 2003. Pilot Study for Montgomery
County and Maryland DNR Data Integration: Comparison of Benthic Macroinvertebrate
Sampling Protocols for Freshwater Streams. Prepared by Versar, Inc., Columbia,
Maryland for U.S. Environmental Protection Agency, Office of Environmental
Information and Mid-Atlantic Integrated Assessment Program, Region 3, Ft. Meade, MD.

This document can be downloaded from EPA's website for Biological Indicators of Watershed
Health:

http://www.epa.gov/bioindicators
11
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Acknowledgements
ACKNOWLEDGEMENTS

EPA funded this study through contract no. 50CMAA900065 to TPMC, Project No. 6073-012 to
Versar subcontract. We are grateful to Wayne Davis (EPA, Office of Environmental
Information), Keith Van Ness (Montgomery County), and Ron Klauda and Paul Kazyak
(Maryland Department of Natural Resources) for supporting this project. Montgomery County
staff collected all field data, sorted organisms from debris, and subsampled organisms in the
laboratory. The Versar benthic laboratory performed the taxonomic identification of organisms
in all samples.
in
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Abstract
ABSTRACT

At both state and local levels, bioassessment programs supply valuable information to guide
stream resource management. For example, a regulatory decision-making framework is currently
being developed by the Maryland Department of the Environment (MDE) for listing watersheds
(Maryland 8-digit and 12-digit watersheds) as impaired based on indices of biotic integrity
(IBIs), initially for freshwater, non-tidal streams. The primary source of data for developing and
implementing the biocriteria framework is the Maryland Biological Stream Survey (MBSS)
conducted by the Maryland Department of Natural Resources (DNR). Several counties in
Maryland conduct biological sampling of streams and produce more spatially intensive results
that can be of use for biocriteria and other stream management activities.

To successfully integrate IBI data collected by both county and state monitoring in the same
watersheds, differences in sampling protocols must be evaluated. This report presents the results
of a quantitative comparison of benthic sampling protocols used by MBSS and Montgomery
County to assess freshwater, non-tidal streams. Montgomery County Department of
Environmental Protection (DEP) has monitored streams since 1994 and is currently exploring
adopting MBSS protocols for benthic macroinvertebrates. This comparison study involved
paired sampling at a random subset of sites. The experimental sites were allocated in a balanced
manner into catchments with both high and low percentage urban land use and small and large
stream size, ensuring that paired sampling was conducted across a range of stream condition.

This study supports the contention that Montgomery County and Maryland DNR stream
monitoring of benthic macroinvertebrate communities can be effectively integrated. In the case
of sampling protocol differences, integration options include (1) continuing to use different
protocols when the mean results are comparable but of differing precision; (2) adjusting the
result from one protocol to match the other, usually with a loss of precision; and (3) agreeing to
adopt the same protocol.

The study demonstrates that D-Net sampling protocol can provide more reliable benthic indices
of biotic integrity (B-IBI) indices than the Kick Seine protocol because sampling from more
plots is more representative of the stream segment. This study also indicates that Montgomery
County could improve the precision of their B-IBIs by increasing the level of chironomid and
oligochaete identification to genus level. For the same overall survey cost, however, we conclude
that the identification of chironomids to tribe, in conjunction with an appropriate increase in the
number of sampling sites, could yield a similar level of precision in mean B-IBI scores. For some
monitoring programs, the moderate improvements in IBI precision obtained by identifying
chironomids to genus may not warrant the needed investments in equipment and training. One
option for such programs is to identify these taxa to tribe as part of a B-IBI for watershed
screening and to identify these taxa to genus only at impaired stations to support stressor
identification.
IV
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Contents
CONTENTS
Page
NOTICE ii
ACKNOWLEDGEMENTS in
ABSTRACT iv
FIGURES vi
TABLES vii
1.0 INTRODUCTION 1-1
2.0 METHODS 2-1
2.1 Experimental Design 2-1
2.2 Replicate Sampling 2-3
2.3 Compilation of Field and Laboratory Data 2-3
2.3.1 Field Sampling 2-3
2.3.2 Laboratory Sub sampling and Taxonomic Identification 2-4
2.4 Analytical Methods 2-6
3.0 RESULTS 3-1
3.1 Comparisons by Stream Order and Human Disturbance Class 3-1
3.2 Comparisons of MBSS and Montgomery County Field
and Laboratory Methods 3-2
3.2.1 MBSSB-IBI and Individual Metrics 3-2
3.3 Comparisons of 100- versus 200-organism Subsampling 3-5
3.3.1 MB SSB-IBI and Individual Metrics 3-5
3.4 Effects of Sampling Method and Laboratory Protocol on the Precision
of Mean B-IBI Scores 3-9
3.5 Comparisons by Taxonomic Level for Chironomids and Oligochaetes 3-9
3.6 Cost-Benefit Analysis of Taxonomic Identification Level 3-14
4.0 DISCUSSION 4-1
5.0 CONCLUSIONS 5-1
6.0 REFERENCES 6-1

APPENDIX
Frequency Distribution (%) of Individual Taxa Across All 24 Sites by
Sampling Method and Laboratory Protocol A-l
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Figures
FIGURES

Figure No. Page

2-1 Locations of 24 stream sites in Montgomery County, Maryland, used for paired
comparison of field sampling methods during spring 2001 2-2

3-1 Mean B-IBI (MBSS method) for D-Net "100-organism" samples by urban land use class
and stream order (SO) 3-3
3-2 Mean B-IBI (MBSS method) for Kick Seine "200-organism" samples by urban land use
class and SO 3-3
3-3 Mean B-IBI scores (MBSS method) by urban land use class and SO for (1) D-Net
samples with "100-organism" subsample, and (2) Kick Seine samples with "200-
organism" subsample 3-4
3-4 Paired comparison of B-IBI scores (MBSS method) for D-Net samples with "100-
organism" subsample, and Kick Seine samples with "200-organism" subsample, using
linear regression 3-4
3-5 Mean B-IBI scores (MBSS method) by urban land use class and SO for
(1) D-Net samples with "100-organism" subsample, and (2) D-Net with
"200-organism" subsample 3-6
3-6 Mean B-IBI scores (MBSS method) by urban land use class and stream order for
Kick Seine samples with "100-organism" and "200-organism" subsamples 3-6
3-7 Paired comparison of B-IBI scores (MBSS method) for D-Net samples with "100-
organism" versus "200-organism" subsamples using linear regression 3-7
3-8 Paired comparison of B-IBI scores (MBSS method) for Kick Seine samples with "100-
organism" versus "200-organism" subsamples using linear regression 3-7
3-9 Mean B-IBI (MBSS method) across all sites (n=24) by field method and laboratory
sub sampling procedure 3-10
3-10 Relative SE (RSE= SE/x) of mean B-IBI (MBSS method) scores across all sites
(n=24) by field method and laboratory subsampling procedure 3-10
3-11 Relationship between standard B-IBI scores for D-Net "100 organism" samples with
chironomids and oligochaetes identified to genus, and B-IBI scores with chironomids
and oligochaetes lumped to tribe 3-11
3-12 Relationship between standard B-IBI scores for D-Net "100 organism" samples with
chironomids and oligochaetes identified to genus, and B-IBI scores with oligochaetes
lumped to family 3-11
3-13 Relationship between standard B-IBI scores for D-Net "100 organism" samples with
chironomids and oligochaetes identified to genus, and B-IBI scores from Kick Seine
"200 organism" samples with chironomids and oligochaetes lumped to tribe 3-12
3-14 Relationship between standard B-IBI scores for D-Net "100 organism" samples with
chironomids and oligochaetes identified to genus, and B-IBI scores from Kick Seine
"200 organism" samples with chironomids and oligochaetes lumped to family 3-12
VI
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Tables

TABLES

Table No. Page

2-1 Summary of experimental design 2-1

2-2 Description of different taxonomic identification methods compared, and the
associated laboratory processing time for chironomids and oligochaetes 2-6

3-1 Number of organisms in the laboratory subsamples for "100 organism" and
"200 organism" target sample size 3-1

3-2 Comparison of MBSS B-IBI and individual metrics from paired samples using MBSS
and Montgomery County methods (D-Net "100-organism" versus Kick Seine "200-
organism") with test statistic for paired t-test 3-5

3-3 Mean difference between MBSS B-IBI scores and individual metrics for D-Net
"100-organism" and "200-organism" subsamples with associated SEs and p-values
for paired t-test 3-8

3-4 Mean difference between MBSS B-IBI scores and individual B-IBI metrics for Kick
Seine "100-organism" and "200-organism" subsamples with associated SEs and
p-values for paired t-test 3-8

3-5 B-IBI scores for MBSS 8-digit watersheds for 2000 for three levels of taxonomic
identification for chironomids and oligochaetes 3-13

3-6 Mean predicted B-IBI scores for 8-digit watersheds (at genus level) for MBSS 2000
from samples where chironomids and oligochaetes were lumped to tribe or family .... 3-13
vn
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Introduction
1.0 INTRODUCTION

To meet the requirements of the Clean Water Act (CWA), the MDE, with the assistance of the
Biological Criteria Advisory Committee, has developed an interim regulatory framework for the
application of biocriteria to Maryland's water quality inventory (305(b) report) and list of
impaired waters (303(d) list). The proposed biocriteria apply to wadeable, non-tidal (first- to
fourth-order) streams, and rely on two biological indicators from the Maryland Biological
Stream Survey (MBSS or the Survey; Klauda et al. 1998), the fish Index of Biotic Integrity (IBI)
(Roth et al. 1998) and the benthic macroinvertebrate IBI (Stribling et al. 1998). The MDE applies
the results of MBSS biological sampling for management and regulatory purposes (e.g., CWA
§303(d) listing) at the same spatial resolution (Maryland 8-digit watershed) currently used in the
state's Water Quality Inventory (305(b) report). Maryland defines 8-digit watersheds and 12-digit
subwatersheds at a scale finer than the USGS 8-digit Hydrologic Unit Codes (HUCs). In some
but not all cases, these state-defined units are true topographic watersheds (Omernik and Bailey
1997). Maryland 8-digit watersheds (average area 194 km2) are subunits of USGS 8-digit HUCs
(average area in Maryland 1295 km2). The first round of MBSS (1995-1997) focused on the
basin level. When sample sizes are sufficient, the results from the first round can also be applied
to the 8-digit watershed level because the inclusion probabilities of all samples are known. In this
case, each 8-digit watershed is considered to be a sub-population (domain) of the basins (see
Cochran, 1977; V01stad et al. 2003a).

The primary source of data for developing and implementing the biocriteria framework is the
MBSS conducted by the Maryland DNR. However, other bioassessment programs conducted at
both state and local levels supply valuable information to guide stream resource management.
When local programs are probability-based, and provide IBI data that are compatible with data
from the MBSS, it is desirable to include these data in the state's biocriteria framework to
increase sample sizes and enhance the reliability of the water quality assessments. The interim
biocriteria framework for Maryland requires that fish or benthic IBI data from ten or more
MBSS sites be used to evaluate impairment of a Maryland 8-digit watershed. To apply
biocriteria where sample size is insufficient to characterize the 8-digit watershed, the smaller 12-
digit subwatersheds (statewide average area 21 km2) contained within the 8-digit watershed are
evaluated to determine impairment. A 12-digit subwatershed is determined to be impaired if
either the fish or the benthic IBI fails to meet a predetermined threshold (as defined in the
biocriteria framework) at any site. In the future, fish and benthic IBI data from biological surveys
conducted by the counties could increase the sampling coverage. In addition, the potential exists
for integrating volunteer monitoring data that have a probability-based study design with state
and local program data.

In Maryland, several counties conduct biological sampling of streams, and IBI data are currently
available from more than one survey for some watersheds. When the surveys are probability-
based, as is the case for one component of the Montgomery County stream survey, mean IBIs
that are more precise than the separate estimates can be achieved by using composite estimators
1-1
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Introduction
(Korn and Graubard 1999; V01stad et al. 2003a). Both state and local program managers
recognize the advantages of integrating stream monitoring. Potential advantages to monitoring
program integration include (1) consistent statements to the public about stream condition, (2)
increased accuracy in estimates of stream condition, and (3) reduced cost of sampling programs.
Specifically, integrated data analysis has the potential to increase the precision of stream
condition estimates (e.g., mean IBIs) for each program. In addition, the potential exists for
integrating volunteer monitoring data with state and local program data. An evaluation of the
effects of field and laboratory protocols on estimates of stream condition is one critical
component of an effective integration of stream monitoring programs.

The objective of this project was to provide a quantitative comparison of how differences in
benthic sampling protocols used by MBSS and Montgomery County affect the assessments of
freshwater, non-tidal streams based on IBIs. Montgomery County Department of Environmental
Protection (DEP) has monitored streams since 1994 and is currently exploring adoption of
MBSS protocols for benthic macroinvertebrates. This methods comparability study was
conducted to (1) evaluate how the change in methods could affect Montgomery County
assessment results and (2) facilitate use of County data by MBSS in developing consistent
assessments of stream condition. This comparison study, involving two established programs,
also affords an opportunity to develop a statistical design and methods comparison approach that
has general applicability for the integration of county and state monitoring programs.

Historically, Montgomery County has sampled benthic organisms with field and laboratory
methods that differed somewhat from those used by MBSS, as detailed in Section 2, with two
Kick Seine samples (2.00 m2 total) in riffle habitat only. The organisms collected from these two
samples were composited, and a target of 200 organisms were subsampled from this composite
sample in the lab and identified. Most taxa were identified to genus, but chironomids and
oligochaetes were only identified to family level. Starting in 2001, Montgomery County began
using D-Nets as the standard gear for sampling benthic macroinvertebrates following MBSS
protocol. In the MBSS, a composite sample of benthic organisms is collected at each station
from 20 jabs (1.85 m2 total) with a D-Net in a variety of habitats (primarily riffles). A 100-
organism subsample is identified in the laboratory. Chironomids and oligochaetes are slide
mounted and identified to genus or lowest possible taxonomic level.

A previous study to compare Montgomery County Kick Seine and MBSS D-Net methods was
conducted jointly by the two programs in 1997, with paired sampling at 12 sites selected ad hoc.
Although scores from the two programs generally were in the same (or neighboring) assessment
categories (Roth et al. 2001), the results were inconclusive because of (1) low sample sizes and
(2) a study design that resulted in little spread in IBI scores among the experimental sites.
Therefore, a more extensive study that covers a wide range of stream conditions was
recommended. In this study, we applied a stringent experimental design that was implemented
by Montgomery County to effectively compare the effects of differences in the Montgomery
County and MBSS benthic sampling and laboratory processing protocols on IBI scores.
Sampling for this comparison study was conducted in spring 2001.
1-2
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Methods
2.0 METHODS
2.1 EXPERIMENTAL DESIGN
A stringent experimental design was implemented for the 2001 comparison study to increase the
power for (1) detecting differences between field sampling methods and (2) determining the
effects of different laboratory protocols for subsampling organisms prior to identification. A
randomized paired comparison design (Box et al. 1978) was employed to study the effects of
benthic sampling protocols and gear (D-Net versus Kick Seine) on IBI scores and individual
metrics under a variety of stream conditions. The experiment involved paired sampling at a
random selection of sites within four blocks defined by stream order and urban land use (Table
2-1). These blocks were introduced to eliminate unwanted sources of variability; the
randomization within blocks supports valid inferences in the face of the remaining variability
which could not be controlled. The percentage of impervious surface in the catchments was used
as a proxy variable for poor and good stream condition. Streams were also classified into size
according to their stream order: The sites were grouped into two land use classes: high urban
(catchments > 15% impervious) and low urban (catchments < 15% impervious). Stream order 1-
2 (small streams) versus 3-4 (large streams), with the stream order being based on the Strahler
convention (Strahler 1957), using the Montgomery County 1:24,000 scale map. The impervious
area percentages were based on the County-wide Stream Protection Strategy (CSPS), 1998.
These impervious area percentages are based on actual ground cover from aerial photos
performed in 1998. Any development that may have occurred between 1998 through 2002 was
minimal in the rural station areas.
Table 2-1. Summary of experimental design. Paired D-Net and Kick Seine sampling was
conducted at each site.
Percentage of Impervious Surface
Catchments with > 15% impervious surface
Catchments with < 15% impervious surface
Both Classes of Land Use
Stream Order
(1:24Kmap)
1,2
3+
1,2
3+
1-4
Number of Sites
6
6
6
6
24
2-1
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Methods
Sites for paired sampling methods comparisons (Kick Seine and D-Nets) were randomly selected
from a list of Montgomery County sites within each block. This experimental design ensured that
paired sampling was conducted across a wide range of stream conditions (Figure 2-1). Paired
comparisons were conducted at six sites per block, for a total number of 24 sites. Because of the
relatively low total sample size, it was critical that the design be balanced (i.e., that paired
sampling is conducted at an equal number of sites within each of the four blocks) to maintain
adequate statistical power.
..--•• 3 44
388
388
;,, » 4 11

, »4.33

,."' 255 ,:'
389
366
Map features
Streams
Sub-watersheds

B-IBI ranges
Good
Fair
Poor
Very Pool
Figure 2-1. Locations of 24 stream sites in Montgomery County, Maryland, used for paired
comparison of field sampling methods during spring 2001. The benthic indices of B-IBI are
based on the MBSS method applied to the D-Net 100-organism subsamples.
2-2
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Methods
2.2 REPLICATE SAMPLING

In addition to obtaining data for the comparison study, Montgomery County DEP is interested in
quantifying variability in its B-IBI scores resulting from repeated sampling at a single site using
the MBSS protocols. Similar replicate sampling (collection of two MBSS samples at a site) is
conducted at approximately 5% of sites per year in the MBSS; these data have provided
Maryland DNR with an estimate of small-scale variance, which is useful in data interpretation.
Montgomery County DEP requested guidance on how many replicate samples it should take to
estimate the within-site variance. We recommended that Montgomery County follow the MBSS
protocol and collect two samples during each site visit (replicates) with D-Nets within a random
subset of 5-10% of the total number of sites sampled in each year. Over time, this will provide
sufficient data for evaluating measurement error in IBI scores. Such information is particularly
useful when evaluating temporal trends in IBI scores at individual sites. The County reported
that it was able to sample this minimum number of sites as replicates during spring 2001. When a
sufficient number of samples becomes available, analysis of these replicate sample data can be
performed at a future time, but this is outside the scope of this project.
2.3 COMPILATION OF FIELD AND LABORATORY DATA

2.3.1 Field Sampling

At each experimental site, paired sampling was conducted in a 75-m stream segment using
the two field sampling methods:

(1) D-Net method: 20 jabs in multiple habitats with 600-micron-mesh D-frame dipnet
(D-Net) used in MBSS (Kazyak 2001), and

(2) Kick Seine method: two Kick Seine collections in riffles with a mesh size of 530
microns used by the Montgomery County (Van Ness et al. 1997) from 1994 to 2001.

The Montgomery County Water Quality Monitoring Program staff conducted D-Net
sampling within each stream segment (mainly from riffles) to collect organisms from
habitats likely to support the greatest taxonomic diversity; the benthic macroinvertebrates
collected from the 20 jabs were pooled into one composite sample. The Montgomery
County staff received training from Maryland DNR in the standard 20 jabs D-Net method
used in the MBSS before the experimental field data were collected. Within the same 75-m
stream segments, the Montgomery County staff collected a paired sample using a Kick
Seine, following standard Montgomery County protocol. On each sampling event, two
Kick Seine samples were collected from the same stream segment - one from an area of
fast current velocity and one from an area of slower current velocity. These two Kick Seine
samples were then combined to provide one composite sample for each site.
2-3
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Methods
2.3.2 Laboratory Subsampling and Taxonomic Identification

For this comparison, the B-IBI scores for all samples were calculated using the standard
MBSS B-IBI method (Stribling et al. 1998). The standard Montgomery County and MBSS
laboratory protocols were modified to improve the sensitivity of the study to detect
differences in B-IBI scores and B-IBI metrics attributable specifically to a "100-organism"
versus "200-organism" subsampling protocol in the laboratory. The standard laboratory
subsampling of benthos conducted by MBSS and Montgomery County involves the
distribution of organisms in the composite sample across a gridded tray; organisms are then
picked from randomly selected grids. When the cumulative number of organisms from
random grids reaches the target sample size of 100 or 200 organisms, the remaining
organisms in the last grid are also included in what is called the "100-organism" or "200-
organism" subsample. Thus, the actual number of organisms in a sample may exceed the
target sample size. For this methods comparison study, the subsamples were selected in two
stages: First a "100-organism" subsample was collected from the required number of
random grids; and second, additional random grids were picked until a "200-organism"
subsample was achieved. The organisms collected in each stage were placed in two
separate containers for identification. An example of this three-stage procedure is as
follows:

The laboratory technician picks grids up to and including the grid containing the 100th
organism. This first subsample contains 134 organisms. All go into the first container
(subsample 1).
The 135th organism goes into a second container. The technician continues picking
organisms from random grids up to and including the grid containing the 200th
organism. These organisms go into the second container (subsample 2).
After the laboratory identification, data from the two containers are combined to make
up the 200-organism subsample.

By identifying the two groups of organisms separately for each sample, we calculated
separate IBI scores for the first group (the "100-organism" subsample) and for the
combined groups ("200-organism" subsample). This information was used to assess how
subsample size affects B-IBI scores and individual B-IBI metrics. Note that if the first
"100-organism" subsample contains a large number of individuals (i.e., approaching or
exceeding 200), then few, if any, additional organisms are needed to achieve the combined
"200-organism" subsample, and the two subsamples will be similar or identical.

In addition, the remaining "sortate" (debris containing the remainder of individuals in the
composite sample) was preserved and retained. These data could be analyzed at a later
stage to evaluate the effects of larger subsample sizes, different grid sampling techniques,
or other questions of interest.
2-4
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Methods
For this study, the taxonomic identifications for all samples were conducted to genus level
so that the effects of taxonomic identification level on IBI scores could be evaluated.
Montgomery County protocol involves identifying chironomids and oligochaetes to family,
and all other specimens to genus. For this comparison, all chironomids and oligochaetes
were slide mounted and identified to genus; these data can be aggregated to family during
data analysis for the comparison of IBI scores. Identification procedures employed here
differed slightly from the MBSS standard protocol, which employs some subsampling of
chironomids. As outlined in Boward and Friedman (2000), MBSS standard laboratory
protocols are to identify most organisms to genus, if possible. Exceptions, and their
corresponding target taxonomic level, include chironomids and oligochaetes (family),
Nematoda (phylum), Nematomorpha (family). Those taxa not identifiable to genus (due to
small size or damage) may be identified to family level or higher. The MBSS process for
identifying chironomid larvae (Boward and Friedman 2000) is as follows:

Divide chironomid larvae into subfamily (i.e., Chironominae, Orthocladiinae,
Tanypodinae, Diamesinae) or tribe (i.e., Tanytarsini, Chironomini) and count the total
number in each group.

Identify using slide mounts for a subsample of approximately 20% of the individual larvae
within each subfamily or tribe. Once these subsamples are identified, multiply the counts of
all genera by five and record the total extrapolated number of genera for the entire
chironomid group.

If either the total number of chironomids or the total number of individuals within a
subfamily or tribe is ten or less, all larvae are identified (no subsampling is performed).

The four levels of taxonomic identification that were compared in this study and the
estimated laboratory processing time are summarized in Table 2-2. We assume that
collection of each benthic field sample takes 2 hours, with a crew of two.
2-5
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Methods
Table 2-2. Description of different taxonomic identification methods compared, and the
associated laboratory processing time for chironomids and oligochaetes. The additional
laboratory processing time for other macroinvertebrates is estimated as 1 - % hour per 100-
organism subsample.
Method Name as Used in
Text and Tables
Genus
Genus - Chironomidae
only
Tribe
Family
Taxonomic Level of
Identification
Chironomidae - Genus
Oligocheata - Genus
Chironomidae - Genus
Oligocheata - Family
Chironomidae - Tribe
Oligocheata - Family
Chironomidae - Family
Oligocheata - Family
Relative Level of
Laboratory Effort
Most

\
Least
Effort

/
Effort
Estimated Number of Hours
for Laboratory Processing
(100-organism subsample)
1
3/4
1/2
1/4
2.4 ANALYTICAL METHODS

The randomization and blocking of sites by stream order and land use in conjunction with the
paired comparison of methods support the use of paired t tests for testing differences in B-IBI
scores and the suite of individual B-IBI metrics (see Box et al. 1978, p. 101). Analysis of
variance (ANOVA) for a two-factor experiment was also used to further evaluate differences in
mean B-IBI scores between stream orders and the two classes of urban land use using the model:
(1.1)
where Yijk is the B-IBI score for sample k in stream order / and urban land use classy, A
represents the stream order factor (/'=!,2); B represents the urban land use factor (/'=!,2), and
£=1,2,...,6 signify the observations collected within each cell. This model was applied separately
to D-Net "100-organism" samples and Kick Seine "200-organism" samples.

Model 1.1 was also expanded to include the effects of field and laboratory methods on
differences in
B-IBI scores, using the following model:
(1.2)
where ^represents the combination of field method and laboratory subsampling configurations
(/ = 1,2,..,4), and T^AB^) is the effect of method within each cell defined by stream order and
2-6
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Methods
land use. The factor of interest here is T, (alone, or within stream order and land use); the other
factors were introduced to remove or lessen the effects of stream order and land use on B-IBI
scores and thereby increase the sensitivity of the analysis for detecting significant differences in
B-IBI scores caused by the choice of sampling method. We used model 1.2 to examine the
variation in B-IBI scores between D-Net with "100-organism" subsamples and Kick Seine with
"200-organism" subsamples, as well as variation caused by subsampling sizes within each field
method (e.g., D-Net with "100-organism" versus "200-organism" subsamples).

We also conducted a linear regression analysis of B-IBI scores from replicated paired samples of
the same stream segments using the model,

Y = a+j3X (1.3)

where Y is the IBI score for the second sample, and X is the IBI score for the first sample.
When equation 1.3 is used to predict mean IBI scores, the standard error will be inflated because
of uncertainty in the regression parameters. In the prediction of mean genus level B-IBI from
mean tribe or family B-IBIs, the intercept in equation 1.3 was not significant, and was, therefore,
not included in the regression. An approximate estimator for the variance (S*) of a predicted
mean IBI when the intercept or = 0 is

S2y = (faj)2 + (Xd^2 -d*xa* (1.4)

where o~ is the standard deviation of the mean variable X, a^ is the standard deviation of the

estimated slope (J3). This equation is based on the variance estimator for a product of two
independent random variables (Goodman 1960; Kendall et al. 1987, p. 342). The square root of
model 1.4 is an estimator of the standard error (SE) of the mean predicted score.

Model 1.3 was used to compare paired benthic IBI scores for (1) D-Net "100-organism" versus
"200-organism"; (2) Kick Seine "100-organism" versus "200-organism"; (3) D-Net "100-
organism" versus Kick Seine "200-organism", and (4) IBI scores at different taxonomic
identification levels for chironomids and oligochaetes. The regressions can be used to calibrate
B-IBI results from Montgomery County and MBSS and to evaluate effects of taxonomic levels
on the rating of stream condition. The similarity of the IBI scores was assessed by the slopes and
the R2. The regression plots also offered a simple visual means of determining whether the
variability in IBI scores within stream segments tended to be greater for high or low mean
scores.
2-7
-------
Results
3.0 RESULTS

3.1 COMPARISONS BY STREAM ORDER AND HUMAN DISTURBANCE CLASS

The 24 experimental sites represent a wide range of stream sizes and degree of anthropogenic
disturbance as intended by the experimental design (Table 3-1). On average, the "100 organism"
subsamples for each method significantly exceeded the target number of 100 specimens (at 5%
alpha level). In three cases the target "100 organism" subsample exceeded 200 organisms, and
thus the "100 organism" and the "200 organism" samples were identical. Two sites (GSGN 104
and GSLD 110) did not have sufficient number of specimens to achieve the target sample sizes.
Table 3-1. Number of organisms in the laboratory subsamples for "100 organism" and "200
organism" target sample size. The percent impervious area in the catchments is indicated for the
urban class. The mean number of organisms across sites and the associated SEs by sampling
method and laboratory protocol are shown in the last row.
Station
GSCB 111
GSGN 104
GSLD 110
GSLS101
GSLS102
GSMS112
LSBL110
GSCB 207
GSGN 205
LSBL 203
LSLS 202
LSLS 203
GSGB 303
GSGN 302
GSGS 303
GSWR 302
GSWR 305
GSGS 402
GSLS 430
GSLS 438
GSMS 404
GSMS 406
GSMS 413
GSMS 415
Stream
Order
1
1
1
1
1
1
1
2
2
2
2
2
3
3
3
3
3
4
4
4
4
4
4
4
Urban
High, 23%
High, 23%
High, 28%
Low, 4%
Low, 5%
High, 30%
Low, 4%
High, 23%
High, 23%
Low, 5%
Low, 5%
Low, 5%
Low, 5%
High, 23%
Low, 5%
High, 33%
High, 33%
Low, 5%
Low, 5%
Low, 4%
Low, < 1 5%
High, 21%
High, 21%
High, 21%
Mean count across all sites (SE in brackets)
D-Net
"100
Organism"
123
32
101
99
121
95
101
108
96
130
109
184
292
118
145
157
108
216
209
84
177
152
133
164
132(10)
"200
Organism"
240
32
101
208
248
168
219
210
235
244
228
368
292
241
248
266
251
216
209
195
266
285
212
276
227(13)
Kick Seine
"100
Organism"
179
32
97
136
111
105
111
108
122
106
194
237
252
96
184
156
175
198
142
97
104
407
135
109
150(15)
"200
Organism"
282
32
198
228
261
245
204
227
206
219
314
237
406
203
235
289
319
263
284
208
202
677
223
258
259 (22)
3-1
-------
Results
For this analysis, MBSS B-IBI scores were computed following the protocols described in
Stribling et al. (1998). Sites with high urban land use (catchments >15% impervious surface)
generally had significantly lower mean B-IBI scores than sites with low urban land use
(catchments <15% impervious surface), while only small differences in mean scores by stream
order were observed (Figures 3-1 and 3-2).

The model 1.1 ANOVA applied to B-IBI values based on D-Net "100-organism" samples was
highly significant (F=22.72; p<0.0001) with R2=0.77. The ANOVA showed highly significant
differences in mean B-IBI scores between the two urban land use classes (F = 56.59; p <0.001)
and for the interaction between land use and stream order factors (F = 10.67; p = 0.0032), while
stream order alone had no significant effect on IBI scores (F = 0.39; p = 0.54) from the D-Net
samples. The same ANOVA model applied to the Kick Seine "200-organism" samples also was
highly significant (F=18.12; p<0.0001), with R2 =0.73. As for the D-Net, a highly significant
difference for urban land use (F=49.84; p<0.001) was observed, but neither stream order
(F=2.52; p=0.13) nor the interaction between stream order and urban land use class (F=1.99;
p=0.17) had a significant effect.
3.2 COMPARISONS OF MBSS AND MONTGOMERY COUNTY FIELD AND
LABORATORY METHODS

3.2.1 MBSS B-IBI and Individual Metrics

The standard MBSS D-Net and the Montgomery County Kick Seine sampling protocols
resulted in similar mean B-IBI scores by stream order and urban land use (Figure 3-3). The
paired t-test showed no significant difference in B-IBI scores (MBSS method, Stribling et
al. 1998) between the D-Net with "100-organism" and the Kick Seine "200-organism"
samples (Table 3-2). However, the Kick Seine "200-organism" samples had significantly
larger values on average for many of the individual B-IBI metrics (e.g., over 13 more taxa
per site were collected on average by the Kick Seine) as compared to the D-Net "100-
organism" samples. The linear relationship and a coefficient of determination (R2) of 0.77
suggest that the scores from the two methods are comparable on average. However, the
fairly large spread of scores around the regression line suggest that the prediction of stream
condition at individual sites could vary substantially depending on the sampling protocol
with increasing uncertainty for streams that had B-IBI scores above 3.0 (Figure 3-4).
3-2
-------
Results
D-Net 100
CO
(0
0)
iJ.U
4 n
Q n

1 n
I .U
n n
T
T

f*l
I
l

T
rh

T Ti
T
1
±

• All
DS012
DS034

High Low All

Urban land use

Figure 3-1. Mean B-IBI (MBSS method) for D-Net "100-organism" samples by urban land use class
and stream order (SO). High and Low urban land use defines catchments with >15% and <15%
impervious surface, and All represents both classes. SO 12 represents orders 1 and 2 combined, SO
34 represents orders 3 and 4 combined, and All represents stream orders 1-4 combined.
Kick Seine 200
u.u
4n
.U
CO
«i ^ n
CQ 15% and <15% impervious
surface, and All represents both classes. SO 12 represents orders 1 and 2 combined, SO 34
represents orders 3 and 4 combined, and All represents orders 1-4 combined.
3-3
-------
Results
MBSS B-IBI
u.u
5 4n
i 4.U
m
_ Q 0
(0
a) 9 n
2 2'°
1 n

T T
T~

r^h

T
j.

T
1

•

T
1

D D-Net 100
DKick Siene200
High Low High Low

12 12 34 34

Urban class and stream order
Figure 3-3. Mean B-IBI scores (MBSS method) by urban land use class and SO for (1) D-Net samples
with "100-organism" subsample, and (2) Kick Seine samples with "200-organism" subsample. High and
Low urban land use define catchments with >15% and <15% impervious surface, respectively. SO 12
represents order 1 and 2 combined, while SO 34 represents order 3 and 4 combined.

B-IBI Scores
o
o
0)

Q
5.0

4.0

3.0

2.0

1.0
y = 0.81x + 0.56 n
R2 = 0.78
D Q0D
1.0 2.0 3.0 4.0 5.0

Kick Seine 200
Figure 3-4. Paired comparison of B-IBI scores (MBSS method) for D-Net samples with "100-organism"
subsample, and Kick Seine samples with "200-organism" subsample, using linear regression. High and
low urban land uses are defined by catchments with >15% and <15% impervious surface, respectively.
The regression coefficients were estimated using SAS (SAS Institute 1999) and may not correspond
exactly with the regression line fitted Microsoft® Excel.
5-4
-------
Results
Table 3-2. Comparison of MBSS B-IBI and individual metrics from paired samples using MBSS and
Montgomery County methods (D-Net "100-organism" versus Kick Seine "200-organism") with test
statistic for paired t-test. Total number of paired samples across stream order and land use is n =24.
Parameter
B-IBI
Number of Taxa
Number of EPT Taxa
Number of Diptera Taxa
Percentage Ephemeroptera Individuals of Total
Number of Individuals
Percent Tanytarsini of Total Number of
Individuals
Number of Intolerant Taxa
Percent Tolerant Individuals
Percent Collectors
A= Difference in D-Net and Kick
Seine paired B-IBI values
Mean A
-0.07
-13.58
-3.42
-9.33
0.60
3.35
-1.88
-0.26
4.59
SE
0.09
2.27
0.67
1.80
1.15
1.19
0.58
5.00
1.80
Pr> /
0.42
<0.0001
<0.0001
<0.0001
0.61
0.0097
0.0038
0.96
0.018
The ANOVA using model 1.2 was highly significant (F=17.22; p<0.0001) with R2 =0.75,
showing no significant effect of method (7]) alone (F=0.29; p=0.59), nor of method within
stream order and land use cells (F=0.70; p=0.56) on B-IBI scores. This indicates that the four
combinations of field method and subsampling procedures produce comparable B-IBI scores.
Regression results (model 1.3) suggest that B-IBI scores from the two sampling protocols could
be used interchangeably but at a cost of increased standard errors. The estimated intercept and
slope in the linear regression of D-Net 100 IBI values against Kick Seine 200 values (model 1.3)
were 0.56 (SE=0.32) and 0.81 (SE=0.92), respectively. The mean predicted scores had a standard
error of 0.47 (model 1.4), as compared to 0.17 and 0.19 for the mean IBI scores from D-Net 100
and Kick Seine 200 samples, respectively.
3.3 COMPARISONS OF 100- VERSUS 200-ORGANISM SUBSAMPLING

3.3.1 MBSS B-IBI and Individual Metrics

The regression analysis of scores from "100-organism" versus "200-organism"
subsamples by method show that the B-IBI score for a "200-organism" subsample was 8%
higher for D-Net and 12% higher for Kick Seine. The mean B-IBI scores for the "200-
organism" samples were significantly higher than for the "100-organism" (Tables 3-3 and
3-4) as expected, and were consistently larger in both small and large streams, and for
both high and low urban land use (Figures 3-5 and 3-6). The high coefficient of
3-5
-------
Results
determination (R2 >0.91) for the regressions also suggests that the scores for a "200-
organism" sample can be predicted quite accurately from the first sample of "100-
organism" (Figures 3-7 and 3-8).

MBSS B-IBI
m
(0
5.0
5.0
1.0

T
1
T
1

rj-

-X-
- JL
T

T
T

±
1

D D-Net 100
D D-Net 200

High Low High Low

12 12 34 34
Urban class and stream order
Figure 3-5. Mean B-IBI scores (MBSS method) by urban land use class and SO for (1) D-Net samples
with "100-organism" subsample, and (2) D-Net with "200-organism" subsample. High and Low urban
land use define catchments with >15% and < 15% impervious surface, respectively. SO 12 represents
order 1 and 2 combined, while SO 34 represents order 3 and 4 combined.
MBSS B-IBI
^>.\j
mA C\
_ 4.U
^ n
^ o.u
re
a) 2 n
1 n

T
1
T
1

T
j.

T
1
TT

-~r
1
i

D Kick Seine 100

D Kick Seine 200
High Low High Low

12 12 34 34

Urban class and stream order

Figure 3-6. Mean B-IBI scores (MBSS method) by urban land use class and stream order for Kick Seine
samples with "100-organism" and "200-organism" subsamples. High and Low urban land use define
catchments with >15% and <15% impervious surface, respectively. SO 12 represents order 1 and 2
combined, while SO 34 represents order 3 and 4 combined.
3-6
-------
Results
B-IBI
5

3 -j

1
y = 0.91x + 0.05

R2 = 0.92
3 4

D-Net 100
Figure 3-7. Paired comparison of B-IBI scores (MBSS method) for D-Net samples with "100-organism"
versus "200-organism" subsamples using linear regression. The regression coefficients were estimated
using SAS (SAS Institute 1999) and may not correspond exactly with the regression line fitted in
Microsoft® Excel.

B-IBI
CM
O

13
y=0.93x-0.12
R2 = 0.92
Kick Seine 100
Figure 3-8. Paired comparison of B-IBI scores (MBSS method) for Kick Seine samples with "100-
organism" versus "200-organism" subsamples using linear regression. The regression coefficients were
estimated using SAS (SAS Institute 1999) and may not correspond exactly with the regression line fitted
in Microsoft® Excel.
3-7
-------
Results
Table 3-3. Mean difference between MBSS B-IBI scores and individual metrics for D-Net "100-
organism" and "200-organism" subsamples with associated SEs and p-values for paired t-test.
Total number of paired samples across SO and urban land use is n =24.
Parameter
B-IBI
Number of Taxa
Number of EPT Taxa
Number of Diptera Taxa
Percentage Ephemeroptera Individuals of Total
Number of Individuals
Proportion Tanytarsini of Total Number of
Individuals
Number of Intolerant Taxa
Percent Tolerant Individuals
Percent Collectors
A= Difference between 100-
and 200-organism subsamples
Mean A
-0.27
-22.21
-5.04
-9.33
-0.31
0.22
-2.38
-0.26
-0.08
SE
0.05
2.59
0.98
1.80
0.33
0.37
0.62
5.00
0.59
Pr> /
<0.0001
<0.0001
<0.0001
<0.0001
0.36
0.56
0.0008
0.96
0.88
Table 3-4. Mean difference between MBSS B-IBI scores and individual B-IBI metrics for Kick
Seine "100-organism" and "200-organism" subsamples with associated SEs and p-values for
paired t-test. Total number of paired samples across SO and urban land use is n =24.
Parameter
B-IBI
Number of Taxa
Number of EPT Taxa
Number of Diptera Taxa
Percentage Ephemeroptera Individuals of Total
Number of Individuals
Percent Tanytarsini of Total Number of
Individuals
Number of Intolerant Taxa
Percent Tolerant Individuals
Percent Collectors
A= Difference between 100-
and 200-organism subsamples
Mean A
-0.36
-18.83
-4.37
-11.75
0.60
-0.12
-2.04
0.79
0.46
SE
0.05
1.71
0.68
1.14
1.15
0.20
0.42
0.54
0.57
Pr> |/|
<0.0001
<0.0001
<0.0001
<0.0001
0.61
0.0097
<0.0001
0.15
0.43
3-8
-------
Results
3.4 EFFECTS OF SAMPLING METHOD AND LABORATORY PROTOCOL ON THE
PRECISION OF MEAN B-IBI SCORES

The mean B-IBI scores and their associated precision varied across field sampling methods and
laboratory sub-sampling protocols. The D-Net produced mean B-IBIs with a slightly lower
relative SE than for the Kick Seine samples (Figure 3-10). D-Net samples are taken from 20
small locations (1.89 m2 total) within the stream segment as opposed to 2 large locations (2.00 m2
total) within the segment for the Kick Seine. Because of the patchy distribution of benthic
organisms, the larger number of small samples appeared to produce a composite sample that is
more representative of the overall composition in the 75-m stream segment than a composite
sample based on few samples of larger size. The frequency distribution of individual taxa by
sampling method and laboratory protocol is provided in the Appendix.

This study indicates that the precision of mean B-IBI scores only marginally improves when the
target subsample sizes increase from 100 to 200 organisms for either D-Net or Kick Seine
samples (Figures 3-9 and 3-10).
3.5 COMPARISONS BY TAXONOMIC LEVEL FOR CHIRONOMIDS AND
OLIGOCHAETES

The regression of B-IBI scores for D-Net "100 organism" samples where chironomids are
identified to genus on scores based on their identification to tribe or family suggests that the
standard MBSS B-IBI scores can be fairly accurately predicted from the samples lumped to tribe
(Figures 3-11), with,/?2 of 0.89. The prediction of standard MBSS B-IBI scores from samples
where the chironomids are identified to family is less reliable (Figure 3-12) with R2 of 0.53. The
same tendency holds when predicting standard MBSS B-IBI scores from Kick Seine samples,
with R2 = 0.67 when the chironomids are identified to tribe, and R2 = 0.54 when they are
identified to family (Figures 3-13, 3-14). The consequences of these different levels of
taxonomic identification is that in the experimental study (which provides the widest range of
B-IBI conditions), the percentage of sites designated as degraded (B-IBI < 3) using tribe is not
significantly different than the percentage designated as degraded using genus (9% fewer using
tribe) (Figure 3-11). In contrast, the percentage of sites designated as degraded using family was
significantly different (p = .05) than the percentage designated as degraded using genus (36%
more using family) (Figure 3-12). We conducted further investigations to evaluate the
implications of these different taxonomic approaches on watershed estimates, a spatial level at
which bioassessment results are often employed (e.g., in the Maryland biocriteria framework).

As an example, we calculated the mean B-IBI scores for eleven 8-digit watersheds sampled by
MBSS during 2001. The mean scores based on family and tribe were biased downwards
compared to standard MBSS B-IBI scores based on genus, as expected, because the number of
different taxa are reduced (Table 3-5). The predicted B-IBI scores from samples lumped to tribe
using the fitted regression reduced the bias, but the predicted scores were generally lower than
the observed (Table 3-6). The standard errors (model 1.4) for the predicted standard mean IB I by

3-9
-------
Results
watershed from samples where chironomids were lumped to family was slightly larger, on
average, than the SEs for the IBI based on genus.
Mean B-IBI across all sites
m
D-Net100 D-Net200
Kick Seine
100
Kick Seine
200
Method
Figure 3-9. Mean B-IBI (MBSS method) across all sites (n=24) by field method and
laboratory subsampling procedure. Error bars represent 95% confidence intervals.
Relative standard error of mean
B-IBI across all sites
VJ. 1 VJ
n OR
\J.\JO
m 0.06
* 0.04
0.02
0.00

ID-

Net 1

' 1 '
DO D-

Net2(

' 1 '
DO Ki<

:kSei
100

' 1 '
ne Kii

DkSei
200

ne
Method

Figure 3-10. Relative SE (RSE= SE/J) of mean B-IBI (MBSS method) scores across all
sites (n=24) by field method and laboratory subsampling procedure.
3-10
-------
Results
D-Net"100-organism"
6

to 4
c

5 3

1
y = 1.01x

R2 = 0.89
12345

Tribe

Figure 3-11. Relationship between standard B-IBI scores for D-Net "100 organism" samples with
chironomids and oligochaetes identified to genus, and B-IBI scores with chironomids and oligochaetes
lumped to tribe. The SE of the regression coefficient is 0.02, estimated using SAS (SAS Institute 1999).
D-Net "100 organism"
« 4
D
C
0)
O 3
2

1
y = 1.29x

R2 = 0.53
12345

Family

Figure 3-12. Relationship between standard B-IBI scores for D-Net "100 organism" samples with
chironomids and oligochaetes identified to genus, and B-IBI scores with oligochaetes lumped to family.
The SE of the regression coefficient is 0.05, estimated using SAS (SAS Institute 1999).
3-11
-------
Results
Tribe (Kick Seine 200)

Figure 3-13. Relationship between standard B-IBI scores for D-Net "100 organism" samples with
chironomids and oligochaetes identified to genus, and B-IBI scores from Kick Seine "200 organism"
samples with chironomids and oligochaetes lumped to tribe. The SE of the regression coefficient is
0.03, estimated using SAS (SAS Institute 1999).
-, 4
o
o
•4-1
0)
W
D
C
0)
O
y= 1.08x
R2 = 0.56
Family (Kick Seine 200)
Figure 3-14. Relationship between standard B-IBI scores for D-Net "100 organism" samples with
chironomids and oligochaetes identified to genus, and B-IBI scores from Kick Seine "200 organism"
samples with chironomids and oligochaetes lumped to family. The SE of the regression coefficient is
0.04, estimated using SAS (SAS Institute 1999).
3-12
-------
Results
Table 3-5. B-IBI scores for MBSS 8-digit watersheds for 2000 for three levels of taxonomic identification
for chironomids and oligochaetes.
Watershed
Casselman River
Liberty Reservoir
Little Patuxent River
Lower Monocacy River
Mattawoman CR
Patapsco River Lower
North BR
Potomac R WA County/
Marsh Run/Tonoloway/Little Ton
Prettyboy Reservoir
Town CR
Upper Choptank
Upper Monocacy River
N
10
16
13
21
10
14
12
10
10
11
17
Average
Genus
Mean
3.38
3.6
2.79
3.32
3.34
2.87
2.81
3.96
3.82
2.38
3.2
3.22
SE
0.4
0.14
0.29
0.21
0.3
0.16
0.15
0.18
0.21
0.27
0.19
0.23
Tribe
Mean
3.04
3.17
2.68
2.92
2.82
2.35
2.52
3.71
3.44
2.29
2.88
2.89
SE
0.41
0.17
0.19
0.19
0.26
0.19
0.16
0.20
0.23
0.17
0.20
0.22
Family
Mean
2.58
2.47
2.21
2.41
2.38
2.00
2.07
2.89
2.91
1.61
2.57
2.37
SE
0.36
0.16
0.17
0.18
0.27
0.15
0.17
0.20
0.22
0.13
0.16
0.20
Ratio
Genus/
Tribe
1.11
1.14
1.04
1.14
1.18
1.22
1.12
1.07
1.11
1.04
1.11
1.12
Genus/
Fam
1.31
1.46
1.26
1.38
1.40
1.44
1.36
1.37
1.31
1.48
1.25
1.36
Table 3-6. Mean predicted B-IBI scores for 8-digit watersheds (at genus level) for MBSS 2000 from
samples where chironomids and oligochaetes were lumped to tribe or family. The SEs for the predicted IBI
means were estimated using model 1.4.
Watershed
Casselman River
Liberty Reservoir
Little Patuxent River
Lower Monocacy River
Mattawoman CR
Patapsco River Lower North BR
Potomac R WA County/Marsh Run/
Tonoloway/Little Ton
Prettyboy Reservoir
Town CR
Upper Choptank
Upper Monocacy River
N
10
16
13
21
10
14
12
10
10
11
17
Average
From Tribe
Mean
3.07
3.20
2.71
2.95
2.85
2.37
2.55
3.75
3.47
2.31
2.91
2.92
SE
0.42
0.18
0.20
0.20
0.27
0.20
0.17
0.22
0.24
0.18
0.21
0.23
From Family
Mean
3.33
3.19
2.85
3.11
3.07
2.58
2.67
3.73
3.75
2.08
3.32
3.06
SE
0.48
0.24
0.25
0.26
0.37
0.22
0.24
0.30
0.32
0.19
0.24
0.28
3-13
-------
Results
3.6 COST-BENEFIT ANALYSIS OF TAXONOMIC IDENTIFICATION LEVEL

The number of samples that can be collected and processed for a fixed survey cost depends on
the taxonomic identification level for chironomids and oligochaetes. Specifically, the
identification of these organisms below the family level (i.e., to tribe or genus) requires more
time and taxonomic expertise. Most programs follow EPA recommended sampling methods that
utilize an "index period" within which all benthic sampling is done to minimize seasonal
variability between sampling years. There is a maximum number of samples that can be
collected during this index period dependent on staff, hours allocated to the sampling effort,
costs of processing, and weather. The identification of chironomids is more pertinent since very
few oligochaetes were collected in our samples. To address this issue, we surveyed several
taxonomic experts from Maryland, Ohio, and New York and chose the most often reported time
estimates for laboratory processing, a four-fold increase in time associated with identification to
genus as compared to family. It should be noted that some of the surveyed taxonomists estimated
a two-fold increase in time; running the analysis below with this estimate did not significantly
change the conclusions, so the more conservative estimate was used.

For simplicity, we have expressed these laboratory costs in terms of personnel hours, and have
assumed that the field and laboratory personnel hours are equivalent in terms of cost. As a
reference, the total cost of collecting and processing 100 samples to genus level (taxonomic
level 1), with a laboratory sub-sample size of 100, is 625 personnel hours. We have assumed that
collection of each field sample takes two hours with a staff of two biologists (i.e., 4 personnel
hours). Monitoring programs should strive to collect and process the number of samples that are
required to achieve adequate precision in the IBIs for the sampling design employed, but often
they are constrained by a shortage of staff and resources. For a given monitoring cost, the sample
size depends on the field effort, as well as the cost of processing the samples. It costs more to
identify chironomids to genus than to tribe or family, and this additional cost would only be
justified if it resulted in increased accuracy and precision in the resulting assessment. In some
cases, the same accuracy and precision may be achieved at a lower taxonomic level if it allows
for more samples to be processed for the same cost. To a certain extent, the increased sample
size compensates for the higher variability in genus IBI scores that are predicted from lower
taxonomic levels. Using the laboratory processing times for four different taxonomic levels
(Table 2-2), we estimated that for a fixed cost of 625 personnel hours, it would be possible to
collect and process:

100 samples for taxonomic level 1 (genus);
104 samples for taxonomic level 2 (genus for chironomids only);
109 samples for taxonomic level 3 (tribe); and
114 samples for taxonomic level 4 (family).

In evaluating expected precision for a given sample size, we used the mean coefficient of
variation (cv) for the eleven 8-digit watersheds sampled in MBSS 2000 as a measure of the
natural variability between samples in a Maryland 8-digit watershed. Precision in estimated
mean B-IBI from a sample of size n is measured by the RSE. The mean coefficient of variation

3-14
-------
Results
for the taxonomic level 1 B-IBI (genus level) was 26%, as compared to 28% and 33% when the
B-IBI was predicted from samples processed to level 3 (tribe) or level 4 (family), respectively.
The variability between B-IBI values that are predicted from level 3 or level 4 taxonomic levels
is higher then the variability in genus level B-IBI values as a result of prediction errors. We used
the respective cvs to estimate expected RSE for mean B-IBI as a function of taxonomic level and
sample size, using the relationship RSE = cv/Jn (Cochran 1977). On average, ten samples
identified to genus level would be expected to produce a mean B-IBI with RSE of 8.2%. For the
same cost as collecting and processing 10 samples to genus level, the sample size could be
increased by 9% if processed only to tribe (level 3) and 14% if processed to family (level 4). The
resulting precision (including the additional samples that could be collected and processed for
the fixed cost) in predicted B-IBI (compared to taxonomic level 1) would only marginally
decrease (RSE = 8.5%) for taxonomic level 3, and decrease further (RSE = 9.8%) for taxonomic
level 4. Thus, the 14% increase in sample size that could be achieved if chironomids and
oligochaetes were identified to family level, as compared to genus, would not be sufficient to
offset the added variability from predicting B-IBI (i.e., reduced precision in estimating stream
condition).
3-15
-------
Discussion
4.0 DISCUSSION

The balanced allocation of the 24 experimental sites into catchments with both high and low
percentage of urban land use and a small and large stream size ensured that this study could
compare the field sampling and laboratory protocols across a wide range of stream condition, as
intended. The similarity in mean B-IBI scores for large and small streams suggest that the
calibration for stream order in the scoring method is effective. As expected, the sites in
catchments with high urban land use (>15% impervious surface) had lower B-IBI scores than
sites with low urban land use (< 15% impervious surface), on average. This is consistent with
results in V01stad et al. (2003b), which indicated that the likelihood of stream sites failing
biocriteria in Maryland doubles for every 10% increase in the extent of urban land use in their
catchments. Many other studies have linked stream degradation to increases in impervious
surface (Center for Watershed Protection 1998; Schueler 1994; Smart et al. 1981; Wang et al.
1997). Steedman (1988) found that more than 25-50% urban land use led to severe impacts on
stream quality in southern Ontario. We chose the 15% impervious surface cut-off in the study
design, because it sufficiently separated the scores in each group while covering a range of
stream degradation. We did not target sites in catchments with very high impervious surface for
this comparison study, because B-IBI scores at these sites would have low expected values,
regardless of the field sampling and laboratory protocols we tested. V01stad et al. (2003b) found
that sites in catchments with more than 40-50% urban land use had greater than 80% probability
of failing the Maryland interim biocriteria, on average.

Using this design, our study produced robust answers to the following questions.

Are D-Net or Kick Seine sampling protocols comparable?
Are 100- and 200-organism subsampling protocols comparable?
Are different levels of taxonomic identification for chironomids and oligochaetes
comparable?

D-Net versus Kick Seine Sampling. The MBSS and Montgomery County sampling methods
produced similar mean B-IBI scores across a wide range of stream conditions, suggesting that
results from the two field sampling programs could be integrated. However, the MBSS D-Net
sampling appears to produce slightly more precise area-wide estimates of mean B-IBI than the
Kick Seine sampling. This is likely a result of the D-Net collecting benthic macroinvertebrates
from more areas within each stream segment than does the Kick Seine. In principle, both
programs employ two-stage sampling (Cochran 1977; Gilbert 1987). In the first stage, a
representative sample of streams segments is selected from each watershed, and in the second
stage, macroinvertebrates are collected from representative areas (plots) within each stream
segment. Macroinvertebrates generally have a patchy distribution, both at the local spatial scale
(e.g., 75-m stream segment) and at the larger scales, such as a Maryland 8-digit watershed.
Hence, the sampling from many small plots may better characterize the benthic community
within a stream segment than sampling from a few larger plots. The subsampling of benthos
4-1
-------
Discussion
within stream segments adds to the variance of estimated mean B-IBI scores for streams, but the
exact amount of added uncertainty cannot be assessed from the standard MBSS and Montgomery
County data, because the within-segment samples are composited. The increased plot-size in
Kick Seine sampling by Montgomery County does not appear to compensate for the uncertainty
associated with sampling fewer plots within each stream segment when compared to D-Net
sampling from more plots. The use of a small plot sizes is also supported by Karr and Chu
(1999).

100-organism Versus 200-organism Subsampling. For the Maryland DNR and Montgomery
County data evaluated, it appears that the precision in area-wide estimates of mean B-IBI scores
only marginally improves if the target subsample size in the laboratory is increased from 100 to
200 organisms, regardless of the field sampling method. For a fixed survey cost, the optimum
subsample size depends on the cost of collecting field samples and the cost of the taxonomic
identification. A net loss in precision of mean B-IBI scores could result if an increased
subsample size reduces the number of stream segments that can be collected in the field.

The choice of number of individual organisms to be counted and identified from each field
sample is controversial (Karr and Chu 1999). Recommendations range from 100 organisms to a
complete count of all organisms. A number of other studies have shown that species richness as a
function of sample size reaches an asymptotic level for a count of between 100 and 900
organisms depending on overall richness in the sample (May 1975, Barbour and Gerritsen 1996).
Somers et al. (1998) conclude that counts of 100 animals are sufficient to distinguish the littoral
benthic communities of small inland lakes in south-central Ontario. Karr and Chu (1999)
advocate subsample sizes of more than 100 organisms and point out that a fixed subsample size
is a potential source of bias. The subsampling protocol employed by Montgomery County and
the MBSS does not involve a fixed number of organisms (i.e., can exceed the 100- or 200-count
target because all the organisms in the last grid are included) and thus do not introduce this type
of bias. The actual number of organisms selected is a random variable, although the target
sample size is 100 or 200 organisms. Although the target sample size of 100 organisms appears
to be sufficient for characterizing Maryland streams well, a greater number of organisms would
likely encounter more rare taxa and potentially produce a more sensitive index. However, a
larger count would also add significant laboratory costs for a fixed number of field samples.

Underestimation of rare species can reduce the sensitivity of community-based assessment
methods to detect ecological changes and thus reduce the effectiveness of bioassessment (Cao et
al. 1998; Karr and Chu 1999). Karr and Chu (1999) and Cao et al. (1998) advocate a larger
sample size than the standard of 100 to 300 individuals used in EPA rapid bioassessment
protocols (RBPs) to reliably differentiate between reference and impacted sites. However, the
actual count of organisms required to achieve adequate power for distinguishing between
reference and impaired sites depends on characteristics of the biota and on how each site is
sampled in the field and thus may differ among studies. Somers et al. (1998), for example,
compared biological indices for assessing health of lakes based on counts of 100, 200, and 300
organisms and found that doubled or tripled effort resulted in little improvement in the ability to
distinguish between lakes.

4-2
-------
Discussion
Our study indicates that the number of plots sampled in each stream segment may have a larger
affect on the precision in estimates of species richness and in particular the likelihood of
detecting rare species than the subsample size of individuals from each composite sample.
Because virtually all species have a patchy distribution, the sampling of only a few plots from a
stream segment may not provide accurate data on the benthic community in that segment even if
all specimens from the composite samples are identified. Therefore, this effect should be taken
into account when evaluating the effects of subsample sizes on mean IBI scores.

Taxonomic Identification Level for Chironomids and Oligochaetes. Identification of
chironomids and oligochaetes to genus improved the precision of B-IBI scores. When the genus
data were aggregated to tribe or family, the resulting B-IBI scores were biased downwards, as
expected. Although this bias could be adjusted for somewhat by predicting genus B-IBI values
from regressions, the use of such predictions reduces the precision of mean B-IBI scores. The
reason for lowered precision is that the estimated regression parameters also have associated
variances that influence the predictions.

The cost-benefit analysis conducted indicates that finer levels of taxonomic identification of
chironomids and oligochaetes improve the precision in mean B-IBI for the same survey cost. An
approximately constant level of precision can be obtained by only identifying these taxa to tribe,
if the laboratory cost saving is converted into additional samples. In contrast, this same level of
precision cannot be achieved by converting cost savings from identification to the family level. It
should be noted that considerable investments in equipment and training are needed to identify
chironomids and oligochaetes to genus, and that the moderate improvements in IBI precision
may not warrant these investments for some programs. One option would be identification of
chironomids to tribe for use in a B-IBI for watershed screening purposes. Those stations that are
identified as impaired could then be reevaluated by having the chironomids in these fewer
stations identified to genus to provide information on potential stressors.
4-3
-------
Conclusions
5.0 CONCLUSIONS

The study demonstrates that D-Net sampling protocol can provide more reliable B-IBI indices
than the Kick Seine protocol, possibly because sampling from 20 smaller plots is more
representative than sampling from 2 larger plots of the stream segment. This study also indicates
that Montgomery County could improve the precision of their B-IBIs by increasing the level of
chironomid and oligochaete identification to genus level. For the same overall survey cost,
however, we conclude that the identification of chironomids to tribe, in conjunction with an
appropriate increase in the number of sampling sites, could yield a similar level of precision in
mean B-IBI scores. For some monitoring programs, the moderate improvements in IBI precision
obtained by identifying chironomids to genus may not warrant the needed investments in
equipment and training. One option for such programs is to identify these taxa to tribe as part of
a B-IBI for watershed screening and to identify these taxa to genus only at impaired stations to
support stressor identification.

The determination of an optimum subsample size is complex and would require detailed
information about the cost of both the taxonomic identification step and field sampling step. For
the stream networks sampled in this study (and using the B-IBI developed based on 100
organisms), it appears that Montgomery County could reduce its subsampling to 100 organisms
to save costs with only a marginal loss in precision of mean B-IBI scores. Before such a decision
is made, further study should be undertaken to determine if a B-IBI developed using 200
organisms would produce cost-effective benefits in precision.
5-1
-------
References
6.0 REFERENCES

Barbour, M.T. and J. Gerritsen. 1996. Subsampling of benthic samples: A defense of the fixed-
count method. J. N. Am. Benthol. Soc. 15: 386-391.

Box, G.E.P., W.G. Hunter, and J.S. Hunter. 1978. Statistics for Experimenters. John Wiley &
Sons. New York. 653pp.

Boward, D. and E. Friedman. 2000. Maryland Biological Stream Survey: Laboratory Methods
for Benthic Macroinvertebrate Processing and Taxonomy. Maryland Department of
Natural Resources, Monitoring and Non-tidal Assessment Division. CBWP-MANTA-
EA-00-6

Cao, Y., D.D. Williams, and N.E. Williams. 1998. How important are rare species in aquatic
community ecology and bioassessment? Limnol. Oceanogr. 43(7): 1403-1409.

Center for Watershed Protection. 1998. Rapid Watershed Planning Handbook: A Comprehensive
Guide for Managing Urbanizing Watersheds. Center for Watershed Protection, Ellicott
City, MD.

Cochran, W.G. 1977. Sampling Techniques. 3rd Ed. John Wiley & Sons. New York. 428pp.

Gilbert, R.O. 1987. Statistical methods for Environmental Pollution Monitoring. VanNostrand,
New York. 320pp.

Goodman, L.A. 1960. On the exact variance of products. J. Amer. Statist. Ass. 55: 708.

Karr, J.R. and E.W. Chu. 1999. Restoring Life in Running Waters. Island Press, Washington DC.
206pp.

Kazyak, P.P. 2001. Maryland Biological Stream Survey Sampling Manual. Maryland
Department of Natural Resources, Monitoring and Non-tidal Assessment Division.

Kendall, M.A. Stuart, and J.K. Ord. 1987. Kendall's Advanced Theory of Statistics - 5th Ed.
Volume 1: Distribution Theory. Charles Griffin & Co. London.

Klauda, R., P. Kazyak, S. Stranko, M. Southerland, N. Roth, and J. Chaillou. 1998. Maryland
Biological Stream Survey: A state agency program to assess the impact of anthropogenic
stresses on stream habitat quality and biota. Environmental Management and Assessment
51:299-316

Korn, E.L. and B.I. Graubard. 1999. Analysis of Health Surveys. John Wiley & Sons. New York.
382pp.

May, R.M. 1975. Patterns of species abundance and diversity. In: M.L. Cody and J.M. Diamond
(editors). Ecology and evolution of communities. Harvard University Press, Cambridge,
Massachusetts, pp. 81-120.

6-1
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References
Omernik, J.M. and R.G. Bailey. 1997. Distinguishing between watersheds and ecoregions.
Journal of the American Water Resources Association 33: 935-949

Roth, N., M. Southerland, J. Chaillou, R. Klauda, P. Kazyak, S. Stranko, S. Weisberg, L. Hall,
Jr., and R. Morgan II. 1998a. Maryland Biological Stream Survey: Development of a
Fish Index of Biotic Integrity. Environmental Management and Assessment 51:89-106.

SAS Institute, Inc. 1999. SAS/STAT User's Guide, Version 8.0, SAS Institute Inc., Gary, North
Carolina.

Schueler, T. 1994. The importance of imperviousness. Watershed Protection Techniques 1:100-
111.

Smart, .M., T. Barney, and J.R. Jones. 1981. Watershed impact on stream quality: a technique
for regional assessment. Journal of Soil and Water Conservation 36:297-300.

Somers, K.M., R.A. Reid, and S.M. David. 1998. Rapid biological assessments: how many
animals is enough? J. N. Am. Benthol. Soc. 17: 348-358.

Steedman, R.J. 1988. Modification and assessment of an index of biotic integrity in southern
Ontario. Can. J. Fish. Aquat. Sci. 45: 492-501.

Strahler, A.N. 1957. Quantitative analysis of watershed geomorphology. Transactions of the
American Geophysical Union 38(6): 913-920.

Stribling, J.B., B.K. Jessup, J.S. White, D. Boward, and M. Kurd. 1998. Development of a
Benthic Index of Biotic Integrity for Maryland Streams. Prepared by Tetra Tech, Inc.,
Owings. Mills, MD and Maryland Department of Natural Resources, Monitoring and
Non-Tidal Assessment Division. CBWP-MANTA-EA-98-3.

Van Ness, K., K. Brown, M.S. Haddaway, D. Marshall, and D. Jordahl. 1997. Montgomery
County Water Quality Monitoring Program Stream Monitoring Protocols. Montgomery
County Department of Environmental Protection, Watershed Management Division,
Rockville, MD. February 20, 1997.

V01stad, J.H., N.K. Neerchal, N.E. Roth and M.T. Southerland. 2003a. Combining biological
indicators of watershed condition from multiple sampling programs—a case study from
Maryland. Ecological Indicators 3:13-25.

V01stad, J.H., N. Roth, G. Mercuric, M. Southerland, and Don Strebel. 2003b. Incorporating
environmental factors for predictive screening of stream condition based on biological
indicators. Environmental Monitoring and Assessment (In press).

Wang, L., J. Lyons, P. Kanehl, and R. Gatti. 1997. Influences of watershed land use on habitat
quality and biotic integrity in Wisconsin streams. Fisheries 22(6): 6-12.
6-2
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Appendix
APPENDIX
FREQUENCY DISTRIBUTION (%) OF INDIVIDUAL
TAXA ACROSS ALL 24 SITES BY SAMPLING METHOD
AND LABORATORY PROTOCOL
A-l
-------
Appendix
Table A-1. Frequency distribution of individual taxa across all 24 sites by sampling method
and laboratory protocol.
Name
Cheumatopsyche
Cricotopus
Orthocladius
Rheotanytarsus
Parametriocnemus
Rheocricotopus
Tanytarsus
Hydrobaenus
Polypedilum
Conchapelopia
Meropelopia
Microtendipes
Simulium
Antocha
Clinocera
Paratanytarsus
Tvetenia
Ephemerella
Eurylophella
Stenelmis
Amphinemura
Brillia
Chelifera
Hemerodromia
Hydropsyche
Micropsectra
Phaenopsectra
Nais
Stenonema
Ablabesmyia
Ceratopsyche
Dubiraphia
Limnodrilus
Neophylax
Tipula
Zavrelimyia
Cricotopus/Orthocladius
Imm. Tubificid w/o Cap. Chaete
Parakiefferiella
D-Net
100
3.26
3.11
2.81
2.81
2.37
2.37
2.37
2.22
2.22
2.07
2.07
2.07
2.07
1.63
1.63
1.63
1.63
1.48
1.48
1.48
1.33
1.33
1.33
1.33
1.33
1.33
1.33
1.19
1.19
1.04
1.04
1.04
1.04
1.04
1.04
1.04
0.89
0.89
0.89
D-Net
200
3.31
2.91
2.75
2.67
2.67
2.34
2.34
2.10
2.58
2.26
2.10
2.02
1.94
1.53
1.21
1.53
1.53
1.45
1.78
1.62
1.29
1.53
1.29
1.45
1.53
1.29
1.53
1.21
1.29
0.89
1.05
0.97
0.73
1.05
1.37
0.89
0.97
0.65
0.73
Kick Seine
100
3.98
3.29
3.63
3.11
2.77
2.25
2.94
1.90
2.08
2.08
2.42
1.90
1.90
2.25
1.73
0.52
2.08
1.90
1.21
2.60
1.56
1.04
1.04
2.25
1.90
1.21
0.87
2.77
1.73
0.35
1.38
0.35

1.38
1.56
0.35
0.87
0.52
1.21
Kick Seine
200
4.17
3.69
3.98
3.11
3.01
2.52
2.91
2.14
2.43
1.94
2.23
2.23
1.94
2.33
2.04
0.39
2.14
2.04
1.07
2.43
1.46
1.07
0.97
1.94
2.04
1.17
0.68
2.72
1.75
0.19
1.26
0.19
0.10
1.17
1.26
0.19
1.17
0.58
1.17
A-2
-------
Appendix
Table A-1 (cont'd). Frequency distribution of individual taxa across all 24 sites by
sampling method and laboratory protocol.
Name
Prosimulium
Thienemannimyia grp.
Argia
Corynoneura
Helopelopia
Hydropsychidae
Potthastia
Rhyacophila
Thienemanniella
Ameletus
Calopteryx
Chaetocladius
Crangonyx
Diamesa
Enchytraeidae
Leptophlebiidae
Lumbricidae
Macronychus
Optioservus
Orthocladiinae
Oulimnius
Physella
Stempellinella
Acerpenna
Caenis
Dicrotendipes
Helichus
Nigronia
Pycnopsyche
Rheopelopia
Strophopteryx
Tanytarsini
Trissopelopia
Bezzia/Palpomyia grp.
Boyeria
Caecidotea
Coenagrionidae
Collembola
Corbicula fluminea
Corixidae
D-Net
100
0.89
0.89
0.74
0.74
0.74
0.74
0.74
0.74
0.74
0.59
0.59
0.59
0.59
0.59
0.59
0.59
0.59
0.59
0.59
0.59
0.59
0.59
0.59
0.44
0.44
0.44
0.44
0.44
0.44
0.44
0.44
0.44
0.44
0.30
0.30
0.30
0.30
0.30
0.30
0.30
D-Net
200
0.81
0.73
0.81
0.65
0.65
0.48
0.73
0.57
0.89
1.05
0.81
0.57
0.73
0.57
0.57
0.65
0.81
0.65
0.48
0.40
0.65
0.57
0.81
0.32
0.57
0.32
0.32
0.57
0.32
0.40
0.40
0.24
0.73
0.40
0.32
0.16
0.24
0.32
0.32
0.16
Kick Seine
100
1.04
0.17

0.52
0.69
0.52
1.38
0.52
0.35
0.17

0.52
0.17
1.04
0.35
0.17
0.87
0.35
0.69
0.17
1.56

1.04
0.69
0.52
0.17
0.17
0.35

0.35
0.52
0.52
0.69

Kick Seine
200
0.97
0.10

0.29
0.58
0.58
1.36
0.68
0.39
0.19

0.39
0.29
0.97
0.39
0.19
0.78
0.49
0.68
0.19
1.36

0.87
0.58
0.58
0.19
0.19
0.39

0.39
0.39
0.49
0.49

0.19

A-3
-------
Appendix
Table A-1 (cont'd). Frequency distribution of individual taxa across all 24 sites by
sampling method and laboratory protocol.
Name
Dasyhelea
Diphetor
Diplectrona
Empididae
Ephemerellidae
Gomphidae
Heptageniidae
Hexatoma
Hydroptila
Isonychia
Limnophyes
Nanocladius
Natarsia
Nematoda
Ostrocerca
Sparganophilus
Stegopterna
Stenacron
Stenochironomus
Sublettea
Tanypodinae
Acentrella
Agabus
Allocapnia
Anchytarsus
Ancyronyx
Aulodrilus
Chaetogaster
Chimarra
Crangonyctidae
Cryptochironomus
Cura
Dixella
Dugesia
Eccoptura
Ephemera
Eukiefferiella
Glossosoma
Glyptotendipes
Habrophlebia
D-Net
100
0.30
0.30
0.30
0.30
0.30
0.30
0.30
0.30
0.30
0.30
0.30
0.30
0.30
0.30
0.30
0.30
0.30
0.30
0.30
0.30
0.30
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
D-Net
200
0.16
0.24
0.24
0.24
0.32
0.16
0.32
0.32
0.16
0.16
0.16
0.57
0.40
0.16
0.40
0.16
0.16
0.16
0.16
0.24
0.16
0.24
0.16
0.16
0.16
0.08
0.08
0.08
0.16
0.08
0.24
0.16
0.08
0.08
0.08
0.08
0.08
0.32
0.08
0.08
Kick Seine
100
0.17
0.17
0.17
0.35
0.17
0.17
0.17
0.17
0.17
0.52

0.69
0.52
0.17
0.35
0.52

0.17
0.69
0.17

0.35

0.17

0.17
0.17

0.69
0.35

0.17
Kick Seine
200
0.19
0.19
0.29
0.19
0.19
0.39
0.29
0.19
0.19
0.58

0.10
0.58
0.58
0.10
0.29
0.49
0.10
0.10
0.87
0.10
0.10

0.10
0.19

0.39

0.10

0.10
0.10

0.97
0.19

0.19
A-4
-------
Appendix
Table A-1 (cont'd). Frequency distribution of individual taxa across all 24 sites by
sampling method and laboratory protocol.
Name
Haploperla
Heterotrissocladius
Imm. Tubificid w/ Cap. Chaete
Ironoquia
Ischnura
Leptophlebia
Limnephilus
Microcylloepus
Neoporus
Paraleptophlebia
Paraphaenocladius
Pedicia
Perlesta
Pilaria
Pisidium
Procloeon
Prodiamesa
Prostoia
Prostoma
Pseudochironomus
Serratella
Sialis
Sigara
Slavina
Spirosperma
Stygonectes
Sympotthastia
Syrphidae
Taeniopteryx
Triaenodes
Tribelos
Trichocorixa
Acariformes
Acroneuria
Apsectrotanypus
Baetidae
Branchiura
Capniidae
Cardiocladius
Chaoborus
D-Net
100
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15

D-Net
200
0.08
0.08
0.16
0.08
0.24
0.08
0.08
0.08
0.08
0.08
0.16
0.08
0.16
0.16
0.16
0.08
0.08
0.16
0.08
0.08
0.16
0.08
0.08
0.16
0.24
0.16
0.24
0.08
0.08
0.16
0.08
0.08

0.08

0.16
0.08
Kick Seine
100
0.17
0.35

0.17

0.17
0.17

0.35

0.17
0.17

0.17

Kick Seine
200
0.10
0.19

0.10

0.10
0.10

0.29

0.10
0.29

0.10

0.19

0.10
0.10
0.10

0.10
0.10

A-5
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Appendix
Table A-1 (cont'd). Frequency distribution of individual taxa across all 24 sites by
sampling method and laboratory protocol.
Name
Cladotanytarsus
Corydalus
Cultus
Dicranota
Diplocladius
Diura
Dolophilodes
Dytiscidae
Eclipidrilus
Elmidae
Enallagma
Endochironomus
Ferrissia
Helochares
Lanthus
Leptoceridae
Limnophila
Limonia
Lype
Microvelia
Molophilus
Nemouridae
Orconectes
Paracladopelma
Paratendipes
Paratrichocladius
Perlodidae
Planariidae
Polycentropus
Rhithrogena
Rhyacodrilus
Saetheria
Sperchon
Sphaerium
Stempellina
Stictochironomus
Stilocladius spp.
Synorthocladius
Taeniopterygidae
D-Net
100

D-Net
200

0.16

0.08
0.08
0.08
0.08
0.08

0.08
0.08

0.08
0.08
0.16
0.08

0.08

0.16
0.16

0.08

0.08
0.24

Kick Seine
100

0.17

0.17
0.17

0.52
0.17

0.17

0.17
0.17

0.17

0.35

0.17
0.17
0.17
0.17

0.17

0.17
0.17
0.17
0.17

0.17
0.17

Kick Seine
200
0.19
0.10
0.19

0.19
0.10

0.29
0.19

0.10
0.10

0.10

0.10
0.10
0.10
0.10

0.19
0.10

0.10
0.19
0.19
0.10
0.19
0.10
0.10
0.10
0.10
0.19
0.10
0.10
0.10
0.10
0.10
A-6
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