;£EPA
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1
United States
Environmental Protection
Agency
Office of Water
4503F
Washington, DC 20460
EPA841-D-97-002
July 1997
(Draft)
Revision to Rapid
Bioassessment Protocols for
Use in Streams and Rivers
Periphyton, Benthic
Macroinvertebrates, and Fish
U.S. Environmental Protection Agency
Re-ion VII
Information Resource Center
901 N. 5th Street
Kansas City, KS 66101
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EPA REGION VII IRC
097881
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DRAFT REVISION-^July 29,1997
EPA 841-D-97-002
jBt) Revision to Rapid Bioassessment Protocols
For Use in Streams and Rivers:
Periphyton, Benthic Macroinvertebrates, and Fish
By:
Michael T. Barbour
Jeroen Gerritsen
Blaine D. Snyder
James B. Stribling
Project Officer:
Chris Faulkner
Office of Water
US EPA
401 M Street, NW
Washington, DC 20460
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DRAFT REVISION—July 29,1997
NOTICE
This document will be reviewed in accordance with U.S. Environmental Protection Agency policy
and approved for publication. Mention of trade names or commercial products does not constitute
endorsement or recommendation for use.
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DRAFT REVISION—July 29,1997
FOREWORD
In December 1986, U.S. EPA's Assistant Administrator for Water initiated a major study of the
Agency's surface water monitoring activities. The resulting report, entitled "Surface Water
Monitoring: A Framework for Change" (U.S. EPA 1987), emphasizes the restructuring of existing
monitoring programs to better address the Agency's current priorities, e.g., toxics, nonpoint source
impacts, and documentation of "environmental results." The study also provides specific
recommendations on effecting the necessary changes. Principal among these are:
1. To issue guidance on cost-effective approaches to problem identification and trend
assessment.
2. To accelerate the development and application of promising biological monitoring
techniques.
In response to these recommendations, the Assessment and Watershed Protection Division developed
the rapid bioassessment protocols (RBPs) designed to provide basic aquatic life data for water
quality management purposes such as problem screening, site ranking, and trend monitoring, and
produced a document in 1989 (Plafkin et al. 1989). Although none of the protocols were meant to
provide the rigor of fully comprehensive studies, each was designed to supply pertinent, cost-
effective information when applied in the appropriate context.
As the technical guidance for biocriteria has been developed by EPA, states have found these
protocols useful as a framework for their monitoring programs. This document was meant to have a
self-corrective process as the science advances; the implementation by state water resource agencies
has contributed to refinement of the original RBPs for regional specificity. This revision reflects the
advancement in bioassessment methods since 1989 and provides an updated compilation of the most
cost-effective and scientifically valid approaches.
Rapid Bioassessment Protocols for Use in Streams and Rivers
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DRAFT REVISION-July 29,1997
DEDICATION
All of us who have dealt with the evaluation and diagnosis of perturbation to our aquatic resources
owe an immeasurable debt of gratitude to Dr. James L. Plafkin. In addition to developing the
precursor to this document in 1989, Jim was a driving force within EPA to increase the use of
biology in the water pollution control program until his untimely death on February 6, 1990.'
Throughout his decade-long career with EPA, his expertise in ecological assessment, his dedication,
and his vision were instrumental in changing commonly held views of what constitutes pollution and
the basis for pollution control programs. Jim will be remembered for his love of life, his enthusiasm,
and his wit. As a small token of our esteem, we dedicate this revised edition of the RBPs to his
memory.
\
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DRAFT REVISION—July 29,1997
ACKNOWLEDGMENTS
Dr. James L. Plafkin of the Assessment and Watershed Protection Division (AWPD) in USEPA's
Office of Water, served as principal editor and coauthor of the original document in 1989. Other
coauthors of the original RBPs were consultants to the AWPD, Michael T. Barbour, Kimberly D.
Porter, Sharon Gross and Robert M. Hughes. Many others also contributed to the development of the
original RBP document. Special thanks goes to the original Rapid Bioassessment Workgroup. The
Workgroup, composed of both State and EPA Regional biologists (listed in Chapter 1), was
instrumental in providing a framework for the basic approach and served as primary reviewers of
various drafts. Dr. Kenneth Cummins and Dr. William Hilsenhoff provided invaluable advice on
formulating certain assessment metrics in the original RBP approach. While not directly involved
with the development of the RBPs, Dr. James Karr provided the framework and theoretical
underpinnings for "re-inventing" bioassessment for water resource investigations. Since 1989,
extensive use and application of the RBP concept has helped to refine specific elements and
strengthen the overall approach. The insights and consultation provided by these numerous
biologists have provided the basis for the improvements presented in this current document.
This revision of the RBPs could not have been accomplished without the support and oversight of
Chris Faulkner of the USEPA Office of Water. Special thanks go to Ellen McCarron and Russell
Frydenborg of Florida DEP, Kurt King of Wyoming DEQ, John Maxted of Delaware DNREC, Dr.
Robert Haynes of Massachusetts DEP, and Elaine Major of University of Alaska, who provided the
opportunity to test and evaluate various technical issues and regional specificity of the protocols in
unique stream systems throughout the U.S. Editorial, production, report design, and HTML
formatting were provided by a team of Terra Tech staff— Brenda Fowler, Michael Bowman, Erik
Leppo, James Kwon, and Susan (Abby) Markowitz. Technical assistance and critical review were
provided by Drs. Jerry Diamond and James (Sam) Stribling, both of Tetra Tech.
Much appreciation is due to the biologists in the field (well over a hundred) who contributed their
valuable time to review both the original and current documents and provide constructive input.
Their help in this endeavor is sincerely appreciated.
iii Rapid Bioassessment Protocols for Use in Streams and Rivers
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DRAFT REVISION—July 29,1997
TABLE OF CONTENTS
FOREWORD i
DEDICATION ii
ACKNOWLEDGMENTS iii
LIST OF FIGURES v
LIST OF TABLES vi
1. THE CONCEPT OF RAPID BIOASSESSMENT :.... 1-1
1.1 PURPOSE OF THE DOCUMENT 1-1
1.2 HISTORY OF THE RAPED BIOASSESSMENT PROTOCOLS 1-2
1.3 ELEMENTS OF THIS REVISION 1-3
2. APPLICATION OF RAPID BIOASSESSMENT PROTOCOLS (RBPs) 2-1
2.1 A FRAMEWORK FOR IMPLEMENTING THE RAPID BIOASSESSMENT
PROTOCOLS 2-1
2.2 CHRONOLOGY OF TECHNICAL GUIDANCE 2-1
2.3 PROGRAMMATIC APPLICATIONS OF BIOLOGICAL DATA 2-4
2.3.1 Section 305(b)—Water Quality Assessment 2-4
2.3.2 Section 319— Nonpoint Source Program 2-5
2.3.3 Watershed Protection Approach 2-5
2.3.4 The TMDL Program 2-6
2.3.5 NPDES Permits and Individual Control Strategies 2-7
2.3.6 Risk Assessment 2-7
2.3.7 EPA Water Quality Criteria and Standards 2-8
3. ELEMENTS OF BIOMONITORING 3-1
3.1 BIOSURVEYS, BIO ASSAYS, AND CHEMICAL MONITORING 3-1
3.2 USE OF DIFFERENT ASSEMBLAGES IN BIOSURVEYS 3-2
3.2.1 Advantages of Using Periphyton 3-3
3.2.2 Advantages of Using Benthic Macroinvertebrates 3-3
3.2.3 Advantages of Using Fish 3-4
3.3 IMPORTANCE OF HABITAT ASSESSMENT 3-5
3.4 THE ECOREGION CONCEPT 3-5
3.5 STATION SITING 3-9
3.6 DATA MANAGEMENT AND ANALYSIS 3-10
3.7 TECHNICAL ISSUES FOR SAMPLING THE PERIPHYTON
ASSEMBLAGE 3-11
3.7.1 Seasonality 3-11
3.7.2 Sampling Methodology 3-12
3.8 TECHNICAL ISSUES FOR SAMPLING THE BENTHIC
MACROINVERTEBRATE ASSEMBLAGE 3-13
3.8.1 Seasonality for Benthic Collections 3-13
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DRAFT REVISION—July 29,1997
3.8.2 Benthic Sampling Methodology 3-13
3.9 TECHNICAL ISSUES FOR THE SURVEY OF THE FISH ASSEMBLAGE .. 3-15
3.9.1 Seasonality for Fish Collections 3-15
3.9.2 Fish Sampling Methodology 3-15
3.9.2.1 Advantages and Disadvantages of Electrofishing 3-16
3.9.2.2 Advantages and Disadvantages of Seining 3-16
3.10 SAMPLING REPRESENTATIVE HABITAT 3-17
4. PERFORMANCE-BASED METHODS SYSTEM (PBMS) 4-1
4.1 APPROACHES FOR ACQUIRING COMPARABLE BIOASSESSMENT
DATA 4-1
4.2 ADVANTAGES OF A PBMS APPROACH FOR CHARACTERIZING
BIOASSESSMENT METHODS 4-5
4.3 QUANTIFYING PERFORMANCE CHARACTERISTICS 4-6
4.4 RECOMMENDED PROCESS FOR DOCUMENTATION OF METHOD
COMPARABILITY ... 4-9
4.5 CASE EXAMPLE DEFINING METHOD PERFORMANCE
CHARACTERISTICS 4-12
5. HABITAT ASSESSMENT AND PHYSICOCHEMICAL PARAMETERS 5-1
5.1 PHYSICAL CHARACTERISTICS AND WATER QUALITY 5-1
5.1.1 Header Information (Station Identifier) 5-2
5.1.2 Weather Conditions 5-2
5.1.3 Site Location/Map 5-2
5.1.4 Stream Characterization 5-2
5.1.5 Watershed Features 5-2
5.1.6 Riparian Vegetation 5-2
5.1.7 Instream Features 5-2
5.1.8 Aquatic Vegetation 5-4
5.1.9 Water Quality 5-4
5.1.10 Sediment/Substrate 5-4
5.2 HABITAT ASSESSMENT 5-5
6. PERIPHYTON PROTOCOLS 6-1
6.1 FDZLD SAMPLING PROCEDURES: NATURAL SUBSTRATES 6-1
6.2 FIELD SAMPLING PROCEDURES: ARTIFICIAL SUBSTRATES 6-4
6.3 LABORATORY ANALYSIS 6-5
6.4 SEMI-QUANTITATIVE ASSESSMENTS OF BENTHIC ALGAL
BIOMASS AND TAXONOMIC COMPOSITION 6-8
6.5 PERIPHYTON METRICS 6-9
6.5.1 Diatom Metrics 6-9
6.6 TAXONOMIC REFERENCES FOR PERIPHYTON 6-13
7.. BENTHIC MARCROINVERTEBRATE PROTOCOLS .... 7-1
7.1 SINGLE HABITAT APPROACH: 1-METER KICK NET 7-3
7.1.1 Field Sampling Procedures for Single Habitat 7-3
7.2 MULTIHABITAT APPROACH: D-FRAME DIP NET 7-5
7.2.1 Habitat Types 7-6
7.2.2 Field Sampling Procedures for Multihabitat 7-7
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7.3 LABORATORY PROCESSING FOR MACROEWERTEBRATE
SAMPLES 7-9
7.3.1 Subsampling and Sorting 7-9
7.3.2 Identification of Macroinvertebrates 7-12
7.4 BENTfflC METRICS 7-14
7.5 BIOLOGICAL RECONNAISSANCE (BioRecon) OR PROBLEM
IDENTIFICATION SURVEY 7-22
7.5.1 Sampling, Processing, and Analysis Procedures 7-23
7.6 TAXONOMIC REFERENCES FOR MACROINVERTEBRATES 7-25
8. FISH PROTOCOLS 8-1
8.1 FISH COLLECTION PROCEDURES: ELECTROFISHING 8-2
8.1.1 Field Sampling Procedures 8-3 .
8.2 LABORATORY IDENTIFICATION AND VERIFICATION 8-6
8.3 DESCRIPTION OF FISH METRICS 8-6
8.3.1 Species Richness and Composition Metrics 8-7
8.3.2 Trophic Composition Metrics 8-11
8.4 TAXONOMIC REFERENCES FORFISH 8-14
9. BIOLOGICAL DATA ANALYSIS 9-1
9.1 THE MULTIMETRIC APPROACH 9-2
9.1.1 Metric Selection, Calibration, and Aggregation into an Index 9-3
9.1.2 Assessment of Biological Condition 9-13
9.2 RIVER INVERTEBRATE PREDICTION AND CLASSIFICATION
SCHEME (RIVPACS) 9-14
9.2.1 Building Predictive Models Using AusRivAS 9-14
9.2.2 Reporting of AusRivAS Results 9-20
10. DATA INTEGRATION AND REPORTING 10-1
10.1 DATA INTEGRATION 10-1
10.1.1 Data Integration of Assemblages 10-1
10.1.2 Relationship Between Habitat and Biological Condition 10-2
10.2 REPORTING 10-4
10.2.1 Graphical Display 10-4
10.2.2 Report Format 10-7
11. LITERATURE CITED 11-1
APPENDIX A: SAMPLE DATA FORMS FOR THE PROTOCOLS A-l
APPENDIX B: TOLERANCE AND FUNCTIONAL FEEDING GROUP
ASSIGNMENTS FOR BENTHOS B-l
APPENDIX C: TOLERANCE AND TROPHIC GUILDS OF SELECTED
FISH SPECDZS C-l
APPENDIX D: SURVEY APPROACH FOR COMPILATION OF HISTORICAL
DATA D-l
Rapid Bioassessment Protocols for Use in Streams and Rivers vi
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DRAFT REVISION—July 29,1997
LIST OF FIGURES
Figure 3-1. Level HI ecoregion map 3-7
Figure 3-2. Example of the relationship of data tables in a typical relational database 3-12
Figure 3-3. Example input or lookup form in a typical relational database 3-12
Figure 4-1. Flow chart summarizing the steps necessary to quantify performance characteristics
of a bioassessment method (taken from Diamond et al. 1996) 4-6
Figure 4-2. Comparison of the discriminatory ability of the SCI between the Peninsula and the
Northeast Bioregions 4-15
Figure 9-1. Comparison of the developmental process for the multimetric and multivariate
approaches to biological data analysis (modified from Reynoldson, Rosenberg,
and Resh, unpublished data) 9-1
Figure 9-2. Process for developing assessment thresholds (modified from Paulsen et al. [1991]
and Barbour et al. [1995]) 9-3
Figure 9-3. Species richness versus stream size (taken from Fausch et al. 1984) 9-4
Figure 9-4. Classification of reference stream sites in Florida into bioregions 9-5
Figure 9-5. Example of discrimination between reference and impaired sites 9-8
Figure 9-6. Basis of metric scores for bioassessment 9-9
Figure 9-7. Discriminatory power analysis of t he Stream Condition Index (SCI) for Florida
(modified from Barbour et al. 1996c). See Figure 9-8 for illustration of
thresholds for condition categories of very good (VG), good (G), poor (P), and
very poor (VP). The minimum index value for the SCI is 8 9-11
Figure 9-8 Ordinal rating scale for each bioregion and index period based on quadrisection
of scoring ranges. Note that "even" numbers are included for instances where
replicates are averaged (taken from Barbour et al. 1996c) 9-12
Figure 9-9. Example of a cluster dendrogram, illustrating similarities and clustering of sites
(x-axis) using biological data 9-16
Figure 10-1. Spatial and temporal trend of Ohio's Invertebrate Community Index (contributed
by Ohio EPA) 10-2
Figure 10-2. Relationship between the condition oft he biological community and physical
habitat 10-2
Figure 10-3. Comparison of integrated assessment (habitat, fish, and benthos) among stream
sites in Pennsylvania. Station 16 is a reference site. (Taken from Snyder et al. 1996) 10-3
vii Rapid Bioassessment Protocols for Use in Streams and Rivers
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DRAFT REVISION—July 29,1997
Figure 10-4. Standardized values for EPT abundance from 7 Wyoming streams as a function
of the number of Surber samples composited at each site (Taken from Diamond
et al. 1996) 10-4
Figure 10-5. Comparison of bioassay and biosurvey results in Ohio streams. (Taken from
Barbour et al. 1996c) 10-5
Figure 10-6. The population of values of the IBI in reference sites within each of the ecoregions
of Ohio. (Contributed by Ohio EPA) 10-5
Figure 10-7. Use of multidimensional scaling on benthic data to ascertain stream classification.
(Taken from Barbour et al. 1996b) 10-6
Figure 10-8. Integration of data from habitat, fish, and benthic assemblages 10-6
Figure 10-9. Results of the benthic assessment of streams in the Mattaponi Creek watershed
of southern Prince George's County, Maryland. (Taken from Stribling et al. 1996b) 10-7
Figure 10-10. Guidance for Florida Ecosummary - A Bioassessment Report. (Contributed
by FloridaDEP) 10-8
Rapid Bioassessment Protocols for Use in Streams and Rivers viii
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DRAFT REVISION—July 29,1997
LIST OF TABLES
Table 2-1. Chronology of U.S. EPA bioassessment guidance (relevant to streams and rivers) 2-1
Table 4-1. Progression of a generic bioassessment field and laboratory method and corresponding steps
requiring performance criteria characterization 4-3
Table 4-2. Translation of some performance criteria, derived for laboratory analytical systems, to
biological systems (taken from Diamond et al. 1996) 4-4
Table 4-3. Recommended sampling design and parameters for documenting performance parameters
and comparability of 2 different bioassessment approaches using 2 habitat types, regions, or
site classes (taken from Diamond et al. 1996) 4-8
Table 4-4. Suggested arithmetic expressions for deriving performance characteristics that can be
compared between 2 or more methods. In all cases, |i = mean value, X = test site value, s =
standard deviation, t = students t value (one-tailed). Subscript code for parameters is as
follows: capital letter refers to habitat type or ecoregion (A or B); numeral refers to method 1
or 2; and lower case letter refers to reference (r) or test site (t) (taken from Diamond et al.
1996) 4-10
Table 6-1. Summary of collection techniques for periphyton from wadeable streams (adapted from
Kentucky DEP 1993, Bahls 1993) 6-19
Table 6-2. Indicator taxa (taken from Kentucky DEP 1993) 6-12
Table 7-1. Examples of metric suites used for analysis of macroinvertebrate assemblages (modified
from Barbour et al. 1995) 7-15
Table 7-2. Definitions of potential benthic metrics and expected direction of metric response to
increasing perturbation (compiled from Kerans and Karr 1994, Fore et al. 1996, and Barbour
et al. 1996b) 7-18
Table 8-1. Fish IBI metrics used in various regions of North America 8-8
Table 9-1. Some potential periphyton metrics that could be considered for streams. Redundancy of
metrics can be evaluated during analysis phase 9-6
Table 9-2. Some potential benthic macroinvertebrate metrics that could be considered for streams.
Redundancy of metrics can be evaluated during analysis phase 9-6
Table 9-3. Some potential fish metrics that could be considered for streams. Redundancy of metrics
can be evaluated during analysis phase 9-7
Table 9-4. Statistics of repeated samples in the Peninsula Bioregion of Florida and the detectable
difference (effect size) at 0.05 significance level. One-tailed test, depicting comparison of a
single site to the criterion (taken from Barbour et al. 1996c) 9-12
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DRAFT REVISION-JTuIy 29,1997
Table 9-5. An example of the calculation of the expected number of taxa and the expected
SIGNAL score (taken from Simpson et al. 1996) 9-17
Table 9-6. Advantages and disadvantages of using O/E Families and O/E SIGNAL (taken
from Simpson et al. 1996) : 9-18
Table 9-7. Division of the indices into bands or categories for reporting (taken from Simpson
et al. 1996). The names of the bands refer to the relationship of the index value
to the reference condition (Band A). Under comments, for each index, the verbal
interpretation of the Bank is stated first, followed by likely causes as dot-points 9-19
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DRAFT REVISION-^Tuly 23,1997
THE CONCEPT OF RAPID
BlOASSESSMENT
1.1 PURPOSE OF THE DOCUMENT
The primary purpose of this document is to provide States and local water quality monitoring
agencies with a practical technical reference for conducting cost-effective biological assessments of
lotic systems. The protocols presented are not necessarily intended to replace those already in use by
State agencies nor is it intended to be used as a rigid protocol without regional modifications.
Instead, they provide options for agencies that wish to implement rapid biological assessment
techniques. This guidance, therefore, is intended to provide basic, cost-effective biological methods
for states that (1) have no established bioassessment procedures, (2) are looking for alternative
methodologies, or (3) may need to supplement their existing programs (not supersede other
bioassessment approaches that have already been successfully implemented).
The Rapid Bioassessment Protocols (RBPs) are essentially a synthesis of existing methods that have
been employed by various State Water Resource Agencies (e.g., Ohio Environmental Protection
Agency [EPA], Florida Department of Environmental Protection [DEP], Delaware Department of
Natural Resources and Environmental Control [DNREC], Massachusetts DEP, Kentucky DEP, and
Montana Department of Environmental Quality [DEQ]). Protocols for 3 aquatic assemblages (i.e.,
periphyton, benthic macroinvertebrates, fish) and habitat assessment are presented. All of these
protocols have been tested in streams in various parts of the country. The choice of a particular
protocol should depend on the purpose of the bioassessment, the need to document conclusions with
confirmational data, and available resources. The original rapid bioassessment protocols were
designed as inexpensive screening tools for determining if a stream is supporting or not supporting a
designated aquatic life use. The basic information generated from these methods would enhance the
coverage of broad geographical assessments, such as State and National 305 (b) Water Quality
Inventories. However, members of a 1986 benthic Rapid Bioassessment Workgroup and reviewers
of this document indicated that the rapid bioassessment protocols can also be applied to other
program areas, for example:
• Characterizing the existence and severity of impairment to the water resource
• Helping to identify sources and causes of impairment
• Evaluating the effectiveness of control actions and restoration activities
• Supporting use attainability studies and cumulative impact assessments
• Characterizing regional biotic attributes of reference conditions
Therefore, the scope of this guidance might now be considered applicable to a wider range of
planning and management purposes than originally envisioned, i.e., they may be appropriate for
priority setting, point and nbnpoint-source evaluations, use attainability analyses, and trend
monitoring, as well as initial screening.
Rapid Bioassessment Protocols for Use in Streams and Rivers 1-1
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DRAFT REVISION—July 23,1997
1.2 HISTORY OF THE RAPID BIOASSESSMENT PROTOCOLS
In the mid-1980's, the need for cost-effective biological survey techniques was realized because of
rapidly dwindling resources for monitoring and assessment for states and the extensive miles of un-
assessed stream miles in the United States. It was also recognized that it was crucial to collect,
compile, analyze, and interpret environmental data rapidly to facilitate management decisions and
resultant actions for control and/or mitigation of impairment. Therefore, the principal conceptual
underpinnings of the RBPs were:
• cost-effective, yet scientifically valid, procedures
• provisions for multiple site investigations in a field season
• quick turn-around of results for management decisions
• easily translated to management and the public
• environmentally-benign procedures.
The original RBPs were developed in two phases. The first phase centered on the development and
refinement of the benthic protocols. The second phase involved the addition of analogous protocols
pertinent to the assessment offish assemblages.
The benthic protocols were originally developed by consolidating procedures in use by various State
water quality agencies. In 1985, a survey was conducted to identify States that routinely perform
screening-level bioassessments and believed that such efforts were important to their monitoring
programs. Guidance documents and field methods in common use were evaluated in an effort to
identify successful bioassessment methods that used different levels of effort. Original survey
materials and information obtained from direct personal contacts were used to develop the draft
protocols.
Missouri Department of Natural Resources (DNR) and Michigan Department of Natural Resources
both used an approach upon which the screening protocol (RBP I) in the original document was
based. The second (RBP II) was more time and labor intensive, incorporating field sampling and
family-level taxonomy, and was a less intense version of RBP III. The concept of family-level
taxonomy was based on the approach used by the Virginia State Water Control Board (SWCB) in the
late 1980s. The third protocol (RBP III) incorporated certain aspects of the methods used by the
North Carolina Division of Environmental Management (DEM) and the New York Department of
Environmental Conservation (DEC) and was the most rigorous of the 3 approaches.
In response to a number of comments received from State and U.S. EPA personnel on an earlier
version of the RBPs, a set offish protocols was also developed. Fish protocol V was based on Karr's
work (1981) with the Index of Biological Integrity (IBI), Gammon's Index of Well Being (1980), and
standard fish population assessment models, coupled with certain modifications for implementation
in different geographical regions. Ohio EPA has developed biological criteria using the IBI and
Index of Well Being (IWB), and a substantial database on their use for site-specific fish and
macroinvertebrate assessments exists. In the intervening years since 1989, several other states have
followed suit with similar methods.
1-2 Chapter 1: The Concept of Rapid Bioassessment
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DRAFT REVISION-July 23,1997
A workgroup of State and U.S. EPA Regional biologists (listed below) was formed to review and
refine the original draft protocols. The Rapid Bioassessment Workgroup was convened from 1987
through 1989 and included biologists using the State methods described above and biologists from
other regions where pollution sources and aquatic systems differed from those areas for which the
draft protocols were initially developed.
U.S. EPA
James Plafkin1, Assessment and Watershed Protection Division (AWPD)
Michael Bilger2, Region I
Michael Bastian2, Region VI
William Wuerthele, Region VIII
Evan Homig2, Region X
STATES
Brenda Sayles, Michigan DNR
John Rowland2, Missouri DNR
Robert Bode, New York DEC
David Lenat, North Carolina DEM
Michael Shelor2, Virginia SWCB
Joseph Ball, Wisconsin DNR
The original RBPs (Plafkin et al. 1989) have been widely distributed and extensively tested across
the United States. Under the direction of Chris Faulkner, Monitoring Branch of AWPD, a series of
workshops has been conducted across the Nation since 1989 that have been directed to training and
discussions on the concept and approach to rapid bioassessment. As a result of these discussions and
the opportunity of applying the techniques in various stream systems, the procedures have been
improved and refined, while maintaining the basic concept of the RBPs. This document reflects
those improvements and functions as an update to U.S. EPA's Rapid Bioassessment Protocols.
1.3 ELEMENTS OF THIS REVISION
Refinements to the original RBPs have occurred from regional testing and adaptation by state agency
biologists and basic researchers. The original concept of large, composited samples, and multimetric
analyses has remained intact for the aquatic assemblages, and habitat assessment has remained
integral to the assessment. However, the specific methods for benthic macroinvertebrates have been
refined, and protocols for periphyton surveys have been added. Various technical issues, e.g., the
testing of subsampling, selection of index period, selection and calibration of biological metrics for
regional application have been refined since 1989. Many of these technical issues, e.g., development
of reference condition, selection of index period and selection/calibration of metrics, have been
discussed in other documents and sources (Barbour et al. 1995, Gibson et al. 1996, Barbour et al.
1996a). This revision draws heavily upon the original RBPs (Plafkin et al. 1989) as well as other
sources that detail relevant modifications. This document is a compilation of the basic approaches to
conducting rapid bioassessment in streams and wadable rivers and focuses on the periphyton, benthic
macroinvertebrates, and fish assemblages.
1 deceased
2 no longer with state or EPA agency or department relevant to water resource assessments of
ecosystem health.
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DRAFT REVISION-July 29,1997
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1 -4 Chapter 1: The Concept of Rapid Bioassessment
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DRAFT REVISION-^FuIy 23,1997
APPLICATION OF RAPID BIOASSESSMENT
PROTOCOLS (RBPs)
2.1 A FRAMEWORK FOR IMPLEMENTING THE RAPID
BIOASSESSMENT PROTOCOLS
The Rapid Bioassessment Protocols advocate an integrated assessment, comparing habitat (e.g.,
physical structure, flow regime), water quality and biological measures with empirically defined
reference conditions (via actual reference sites, historical data, and/or modeling or extrapolation).
Reference conditions are best established through systematic monitoring of actual sites that represent
the natural range of variation in "minimally" disturbed" water chemistry, habitat, and biological
conditions (Gibson et al. 1996). Of these three components of ecological integrity, ambient water
quality may be the most difficult to characterize because of the complex array of chemical
constituents (natural and otherwise) that affect it. The implementation framework is enhanced by the
development of an empirical relationship between habitat quality and biological condition that is
refined for a given region. As additional information is obtained from systematic monitoring of
potentially impacted and site-specific control sites, the predictive power of the empirical relationship
is enhanced. Once the relationship between habitat and biological potential is understood, water
quality impacts can be objectively discriminated from habitat effects, and control and rehabilitation
efforts can be focused on the most important source of impairment.
2.2 CHRONOLOGY OF TECHNICAL GUIDANCE
Since the publication of the original RBPs in 1989, U.S. Environmental Protection Agency (EPA)
has produced substantial guidance and documentation on both bioassessment strategies and
implementation policy on biological surveys and criteria for water resource programs. Much of this
effort has led to the national trend of adapting biological assessment and monitoring approaches for
detecting problems, evaluating Best Management Practices (BMPs) for mitigation, and monitoring
ecological health over time. The chronology of the crucial EPA guidance relevant to bioassessment
in streams and rivers is presented in Table 2-1.
Table 2-1. Chronology of U.S. EPA bioassessment guidance (relevant to streams and rivers).
Year
1987
1988
Document Title
Surface Water Monitoring: A Framework for
Change
Proceedings of the First National Workshop on
Biological Criteria (Lincolnwood, Illinois)
Relationship to Bioassessment
EPA calls for efficacious methods to assess and
determine the ecological health of the nation's
surface waters.
EPA brings together agency biologists and
"basic" researchers to establish a framework for
the initial development of biological criteria and
associated biosurvey methods.
Citation
U.S. EPA
1987
U.S. EPA
1988
Rapid Bioassessment Protocols for Use in Streams and Rivers
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Year
Document Title
Relationship to Bioassessment
Citation
1989
Rapid Bioassessment Protocols for Use in
Streams and Rivers: Benthic Macroinvertebrates
and Fish
The initial development of cost-effective methods
in response to the mandate by EPA (1987), which
are to provide biological data on a national scale
to address the goals of the Clean Water Act.
Plafkin et
al. 1989
1989
Regionalization as a Tool for Managing
Environmental Resources
EPA develops the concept of ecoregions and
partitions the contiguous U.S. into homogeneous
regions of ecological similarity, providing a basis
for establishment of regional reference
conditions.
Gallant et
al. 1989
1990
Second National Symposium on Water Quality
Assessment: Meeting Summary
EPA holds a series of National Water Quality
Symposia. In this second symposium, biological
monitoring is introduced as an effective means to
evaluating the quality of water resources.
U.S. EPA
1990a
1990
Biological Criteria: National Program Guidance
for Surface Waters
The concept of biological criteria is described for
implementation into state water quality programs.
The use of biocriteria for evaluating attainment of
"aquatic life use" is discussed.
U.S. EPA
1990b
1990
Macroinvertebrate Field and Laboratory Methods
for Evaluating the Biological Integrity of Surface
Waters
This EPA document is a compilation of the
current "state-of-the-art" field and laboratory
methods used for surveying benthic
macroinvertebrates in all surface waters (i.e.,
streams, rivers, lakes, and estuaries).
Klemm et
al. 1990
1991
Biological Criteria: State Development and
Implementation Efforts
The status of biocriteria and bioassessment
programs as of 1990 is summarized here.
U.S. EPA
1991a
1991
Biological Criteria Guide to Technical Literature
A limited literature survey of relevant research
papers and studies is compiled for use by state
water resource agencies.
U.S. EPA
1991b
1991
Technical Support Document for Water
Quality-Based Toxics Control
EPA describes the approach for implementing
water quality-based toxics control of the nation's
surface waters, and discusses the value of
integrating three monitoring tools, i.e., chemical
analyses, toxicity testing, and biological surveys.
U.S. EPA
1991c
1991
Biological Criteria: Research and Regulation,
Proceedings of the Symposium
This national symposium focuses on the efficacy
of implementing biocriteria in all surface waters,
and the proceedings documents the varied
applicable approaches to bioassessments.
U.S. EPA
1991d
1991
Report of the Ecoregions Subcommittee of the
Ecological Processes and Effects Committee
The SAB (Science Advisory Board) reports
favorably that the use of ecoregions is a useful
framework for assessing regional fauna and flora.
Ecoregions become more widely viewed as a
basis for establishing regional reference
conditions.
U.S. EPA
1991e
1991
Guidance for the Implementation of Water
Quality-Based Decisions: The TMDL Process
The establishment of the TMDL (total maximum
daily loads) process for cumulative impacts
(nonpoint and point sources) supports the need
for more effective monitoring tools, including
biological and habitat assessments.
U.S. EPA
1991f
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Year
Document Title
Relationship to Bioassessment
Citation
1991
Design Report for EMAP, the Environmental
Monitoring and Assessment Program
EPA's Environmental Monitoring and
Assessment Program (EMAP) is designed as a
rigorous national program for assessing the
ecological status of the nation's surface waters.
Overton et
al. 1991
1992
Procedures for Initiating Narrative Biological
Criteria
A discussion of the concept and rationale for
establishing narrative expressions of biocriteria is
presented in this EPA document.
Gibson
1992
1992
Ambient Water-Quality Monitoring in the U.S.
First Year Review, Evaluation, and
Recommendations
Provide first-year summary of task force efforts
to develop and recommend framework and
approach for improving water resource quality
monitoring.
ITFM
1992
1993
Fish Field and Laboratory Methods for
Evaluating the Biological Integrity of Surface
Waters
A compilation of the current "state-of-the-art"
field and laboratory methods used for surveying
the fish assemblage and assessing fish health is
presented in this document.
Klemm et
al. 1993
1994
Surface Waters and Region 3 Regional
Environmental Monitoring and Assessment
Program: 1994 Pilot Field Operations and
Methods Manual for Streams
EPA focuses its EMAP program on streams and
wadable rivers and initiates an approach in a pilot
study in the Mid-Atlantic Appalachian
mountains.
Klemm
and
Lazorchak
1994
1994
Watershed Protection: TMDL Note #2,
Bioassessment and TMDLs
EPA describes the value and application of
bioassessment to the TMDL process.
U.S. EPA
1994a
1994
Report of the Interagency Biological Methods
Workshop
Summary and results of workshop designed to
coordinate monitoring methods among multiple
objectives and states.
Gurtzand
Muir 1994
1995
Generic Quality Assurance Project Plan Guidance
for Programs Using Community Level Biological
Assessment in Wadable Streams and Rivers
EPA develops guidance for quality assurance and
quality control for biological survey programs.
U.S. EPA
1995a
1995
Volunteer Stream Monitoring: A Methods
Manual (Field Test Draft)
EPA provides guidance for citizen monitoring
groups to use biological and habitat assessment
methods for monitoring streams. These methods
are based in part on the RBPs.
U.S. EPA
1995b
1995
The Strategy for Improving Water Quality
Monitoring in the United States: Final Report of
the Intergovernmental Task Force on Monitoring
Water Quality
An Intergovernmental Task Force (ITFM)
comprised of several federal and state agencies
draft a monitoring strategy intended to provide a
cohesive approach for data gathering, integration,
and interpretation.
ITFM
1995a
1995
The Strategy for Improving Water Quality
Monitoring in the United States: Final Report of
the Intergovernmental Task Force on Monitoring
Water Quality, Technical Appendices
Various issue papers are compiled in these
technical appendices associated with ITFM's
final report.
ITFM
1995b
1995
Environmental Monitoring and Assessment
Program Surface Waters: Field Operations and
Methods for Measuring the Ecological Condition
of Wadeable Streams
A revision and update of the 1994 Methods
Manual for EMAP.
Klemm
and
Lazorchak
1995
1996
Biological Assessment Methods, Biocriteria, and
Biological Indicators: Bibliography of Selected
Technical, Policy, and Regulatory Literature
EPA compiles a comprehensive literature survey
of pertinent research papers and studies for
biological assessment methods. This document is
expanded and updated from EPA 1991b.
Stribling
etal.
1996a
Rapid Bioassessment Protocols for Use in Streams and Rivers
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Year
1996
1996
1996
1996
1996
Document Title
Summary df State Biological Assessment
Programs for Wadeable Streams and Rivers
Biological Criteria: Technical Guidance for
Streams and Small Rivers
The Volunteer Monitor's Guide to Quality
Assurance Project Plans
Nonpoint Source Monitoring and Evaluation
Guide
Biological Criteria: Technical Guidance for
Survey Design and Statistical Evaluation of
Biosurvey Data
Relationship to Bioassessment
The status of bioassessment and biocriteria
programs in state water resource programs is
summarized in this document, providing an
update of EPA 199 la.
Technical guidance for development of
biocriteria for streams and wadable rivers is
provided as a follow-up to the Program Guidance
(EPA 1 990b). This technical guidance serves as
a framework for developing guidance for other
surface water types.
EPA develops guidance for quality assurance for
citizen monitoring programs, which becomes a
companion document to EPA 1995a.
EPA describes how biological survey methods
are used in nonpoint-source investigations, and
explains the value of biological and habitat
assessment to evaluating BMP implementation
and identifying impairment.
EPA describes and define different statistical
approaches for biological data analysis and
development of biocriteria.
Citation
Davis et
al. 1996
Gibson et
al. 1996
U.S. EPA
1996a
U.S. EPA
1996b
Reckhow
and
Warren-
Hicks
1996
2.3 PROGRAMMATIC APPLICATIONS OF BIOLOGICAL DATA
States are responsible for identifying water quality problems, those waters needing total maximum
daily loads (TMDLs), and evaluating the effectiveness of point and nonpoint source water quality
controls. The biological monitoring protocols presented in this guidance document will strengthen a
state's monitoring program if other bioassessment and monitoring techniques are not already in
place. An effective and thorough biological monitoring program can help to improve reporting (e.g.,
305(b) reporting), increase the effectiveness of pollution prevention efforts, and document the
progress of mitigation efforts. This section provides suggestions for the application of biological
monitoring to streams and small rivers through existing state programs.
2.3.1 Section 305(b)—Water Quality Assessment
Section 305(b) establishes a process for reporting information about the quality of the Nation's water
resources (U.S. EPA 1993, U.S. EPA 1994b). States, the District of Columbia, territories, some
tribes, and certain River Basin Commissions have developed programs to monitor surface and
ground waters and to report the current status of water quality biennially (changed recently to once
every 5 years) to EPA. This information is compiled into a biennial National Water Quality
Inventory report to Congress.
Use of biological assessment in §305(b) reports helps to define an understandable endpoint of
relevance to society—-the biological integrity of waterbodies. Many of the better-known and widely
reported pollution cleanup success stories have involved the recovery or reappearance of valued
sport fish and other pollution-intolerant species to systems from which they had disappeared (U.S.
2-4
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EPA 1980). Improved coverage of biological integrity issues, based on monitoring protocols with
clear bioassessment endpoints, will make the §305(b) reports more accessible and meaningful to
many segments of the public.
Biological monitoring provides data that could augment several of the §305(b) reporting
requirements. In particular, the following assessment activities and reporting requirements could be
enhanced through the use of the biological monitoring information:
• Determine the status of the water resource (Are the designated/beneficial and aquatic
life uses being met?).
• Evaluate the causes of degraded water resources and the relative contributions of
pollution sources.
• Report on the activities underway to assess and restore water resource integrity.
• Determine the effectiveness of control and mitigation programs.
• Measure the success of watershed management plans.
2.3.2 Section 319—Nonpoint Source Program
The 1987 Water Quality Act Amendments to the Clean Water Act (CWA) added section 319, which
established a national program to assess and control nonpoint source (NFS) pollution. Under this
program, states are asked to assess their NFS pollution problems and submit these assessments to
EPA. The assessments include a list of "navigable waters within the state which, without additional
action to control nonpoint source of pollution, cannot reasonably be expected to attain or maintain
applicable water quality standards or the goals and requirements of this Act." Other activities under
the section 319 process require the identification of categories and subcategories of NPS pollution
that contribute to the impairment of waters, descriptions of the procedures for identifying and
implementing BMPs, control measures for reducing NPS pollution, and descriptions of state and
local programs used to abate NPS pollution. Based on the assessments, states have prepared
nonpoint source management programs.
Assessment of biological condition is the most effective means of evaluating cumulative impacts
from nonpoint sources, which may involve habitat degradation, chemical contamination, or water
withdrawal (Karr 1991). Biological assessment techniques can improve evaluations of nonpoint
source pollution controls (or the combined effectiveness of current point and nonpoint source
controls) by comparing biological indicators before and after implementation of controls. Likewise,
biological attributes can be used to measure site-specific ecosystem response to remediation or
mitigation activities aimed at reducing nonpoint source pollution impacts or response to pollution
prevention activities.
2.3.3 Watershed Protection Approach
Since 1991, EPA has been promoting the Watershed Protection Approach (WPA) as a framework for
meeting the Nation's remaining water resource challenges (U.S. EPA 1994c). EPA's Office of Water
has taken steps to reorient and coordinate point source, nonpoint source, surface waters, wetlands,
coastal, ground water, and drinking water programs in support of the watershed approach. EPA has
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also promoted multi-organizational, multi-objective watershed management projects across the
Nation.
The watershed approach is an integrated, inclusive strategy for more effectively protecting and
managing surface water and ground water resources and achieving broader environmental protection
objectives using the naturally defined hydrologic unit (the watershed) as the integrating management
unit. Thus, for a given watershed, the approach encompasses not only the water resource, such as a
stream, river, lake, estuary, or aquifer, but all the land from which water drains to the resource. The
watershed approach places emphasis on all aspects of water resource quality—physical (e.g.,
temperature, flow, mixing, habitat); chemical (e.g., conventional and toxic pollutants such as
nutrients and pesticides); and biological (e.g., health and integrity of biotic communities,
biodiversity).
As states develop their watershed protection approach (WPA), biological assessment and monitoring
offer a means of conducting comprehensive evaluations of ecological status and improvements from
restoration/rehabilitation activities. Biological assessment integrates the condition of the watershed
from tributaries to mainstem through the exposure/response of indigenous communities.
2.3.4 The TMDL Program
The technical backbone of the WPA is the TMDL process. A total maximum daily load (TMDL) is a
tool used to achieve applicable water quality standards. The TMDL process quantifies the loading
capacity of a waterbody for a given stressor and ultimately provides a quantitative scheme for
allocating loadings (or external inputs) among pollutant sources (U.S. EPA 1994a). In doing so, the
TMDL quantifies the relationships among sources, stressors, recommended controls, and water
quality conditions. For example, a TMDL might mathematically show how a specified percent
reduction of a pollutant is necessary to reach the pollutant concentration reflected in a water quality
standard.
Section 303(d) of the CWA requires each state to establish, in accordance with its priority rankings,
the total maximum daily load for each waterbody or reach identified by the state as failing to meet,
or not expected to meet, water quality standards after imposition of technology-based controls. In
addition, TMDLs are vital elements of a growing number of state programs. For example, as more
permits incorporate water quality-based effluent limits, TMDLs are becoming an increasingly
important component of the point-source control program.
TMDLs are suitable for nonchemical as well as chemical stressors (U.S. EPA 1994a). These include
all stressors that contribute to the failure to meet water quality standards, as well as any stressor that
presently threatens but does not yet impair water quality. TMDLs are applicable to waterbodies
impacted by both point and nonpoint sources. Some stressors, such as sediment deposition or
physical alteration of instream habitat, might not clearly fit traditional concepts associated with
chemical stressors and loadings. For these nonchemical stressors, it might sometimes be difficult to
develop TMDLs because of limitations in the data or in the technical methods for analysis and
modeling. In the case of nonpoint source TMDLs, another difficulty arises in that the CWA does not
provide well-defined support for regulatory control actions as it does for point source controls, and
controls based on another statutory authority might be necessary.
Because TMDLs directly measure the aquatic community's response to pollutants or stressors,
biological surveys can provide compelling evidence of water resource impairment. Biological
assessments and criteria address the cumulative impacts of all stressors, especially habitat
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DRAFT REVISION-July 23,1997
degradation, loss of biological diversity, and nonpoint source pollution. Biological information can
help provide an ecologically based assessment of the status of a waterbody and as such can be used
to decide which waterbodies need TMDLs (U.S. EPA 1993) and aid in the ranking process by
targeting waters for TMDL development with a more accurate link between bioassessment and
ecological integrity.
Finally, the TMDL process is a geographically-based approach to preparing load and wasteload
allocations for sources of stress that might impact waterbody integrity. The geographic nature of this
process will be complemented and enhanced if ecological regionalization is applied as part of the
bioassessment activities. Specifically, similarities among ecosystems can be grouped into
homogeneous classes of streams and rivers that provides a geographic framework for more efficient
aquatic resource management.
2.3.5 NPDES Permits and Individual Control Strategies
All discrete sources of wastewater must obtain a National Pollutant Discharge Elimination System
(NPDES) permit (or state equivalent), which regulates the facility's discharge of pollutants. The
approach to controlling and eliminating water pollution is focused on the pollutants determined to be
harmful to receiving waters and on the sources of such pollutants. Authority for issuing NPDES
permits is established under Section 402 of the CWA (U.S. EPA 1989).
Point sources are generally divided into two types—industrial and municipal. Nationwide, there are
approximately 50,000 industrial sources, which include commercial and manufacturing facilities.
Municipal sources, also known as publicly owned treatment works (POTWs), number about 15,700
nationwide. Wastewater from municipal sources results from domestic wastewater discharged to
POTWs, as well as the "indirect" discharge of industrial wastes to sewers.
USEPA does not recommend the use of biological survey data as the basis for deriving an effluent
limit for an NPDES permit (U.S. EPA 1994d). Unlike chemical-specific water quality analyses,
biological data do not measure the concentrations or levels of chemical stressors. Instead, they
directly measure the impacts of any and all stressors on the resident aquatic biota. Because of this,
biological criteria do not definitively establish the causal relationship between a biological impact
and its source. This is not to say that biological criteria have no role in the permitting process.
Where appropriate, biological assessment can be used within the NPDES process (U.S. EPA 1994d)
to obtain information on the status of a waterbody where point sources might cause, or contribute to,
a water quality problem. In conjunction with chemical water quality and whole-effluent toxicity
data, biological data can be used to detect previously unmeasured chemical water quality problems
and to evaluate the effectiveness of implemented controls.
Some states have already demonstrated the usefulness of biological data under certain circumstances
to indicate the need for additional or more stringent permit limits (e.g., sole-source discharge into a
stream where there is no significant nonpoint source discharge, habitat degradation, or atmospheric
deposition) (U.S. EPA 1994d). In these situations, the biological findings triggered additional
investigations to establish the cause-and-effect relationship and to determine the appropriate limits.
In this manner, biological data support regulatory evaluations and decision making. Biological data
can also be useful in monitoring highly variable or diffuse sources of pollution that are treated as
point sources such as wet-weather discharges and stormwater runoff (U.S. EPA 1994d). Traditional
chemical water quality monitoring is usually only minimally informative for these types of point
source pollution, and a biological survey of their impact might be critical to effectively evaluate
these discharges and associated treatment measures.
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2.3.6 Risk Assessment
Risk assessment is a scientific process that includes stressor identification, receptor characterization
and endpoint selection, stress-response assessment, and risk characterization (U.S. EPA 1992, Suter
et al. 1993). Risk management is a decision-making process that involves all the human-health and
ecological assessment results, considered with political, legal, economic, and ethical values, to
develop and enforce environmental standards, criteria, and regulations (Maughan 1993). Risk
assessment can be performed on an on-site basis or can be geographically-based (i.e., watershed or
regional scale), and it can be used to assess human health risks or to identify ecological impairments.
Results of regional bioassessment studies can be used in watershed ecological risk assessments to
develop broad scale (geographic) empirical models of biological responses to stressors. Such models
can then be used, in combination with exposure information, to predict risk due to stressors or to
alternative management actions. Risks to biological resources are characterized, and sources of
stress can be prioritized. Watershed risk managers can use such results for critical management
decisions.
2.3.7 EPA Water Quality Criteria and Standards
The water quality standards program, as envisioned in Section 303 (c) of the Clean Water Act, is a
joint effort between the states and EPA. The states have primary responsibility for setting,
reviewing, revising, and enforcing water quality standards. EPA develops regulations, policies, and
guidance to help states implement the program and oversees states' activities to ensure that their
adopted standards are consistent with the requirements of the CWA and that water quality standards
regulations (40 CFR Part 131) are met. EPA has authority to review and approve or disapprove state
standards and, where necessary, to promulgate federal water quality standards.
A water quality standard defines the goals of a waterbody, or a portion thereof, by designating the
use or uses to be made of the water, setting criteria necessary to protect those uses, and preventing
degradation of water quality through antidegradation provisions. States adopt water quality
standards to protect public health or welfare, enhance the quality of water, and protect biological
integrity.
Chemical, physical, or biological stressors impact the biological characteristics of an aquatic
ecosystem (Gibson et al. 1996). For example, chemical stressors can result in impaired functioning
or loss of a sensitive species and a change in community structure. Ultimately, the number and
intensity of all stressors within an ecosystem will be evidenced by a change in the condition and
function of the biotic community. The interactions among chemical, physical, and biological
stressors and their cumulative impacts emphasize the need to directly detect and assess the biota as
indicators of actual water resource impairments.
Sections 303 and 304 of the CWA require states to protect biological integrity as part of their water
quality standards. This can be accomplished, in part, through the development and use of biological
criteria. As part of a state or tribal water quality standards program, biological criteria can provide
scientifically sound and detailed descriptions of the designated aquatic life use for a specific
waterbody or segment. They fulfill an important assessment function in water quality-based
programs by establishing the biological benchmarks for (1) directly measuring the condition of the
aquatic biota, (2) determining water quality goals and setting priorities, and (3) evaluating the
effectiveness of implemented controls and management actions.
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The biological integrity of an aquatic system reveals itself in the condition, abundance, and diversity
of its biota, including terrestrial species that depend on the system for food or habitat. Biological
criteria for aquatic systems help meet this need by providing an evaluation benchmark for direct
assessment of the condition of the biota that live either part or all of their lives in aquatic systems
(Gibson et al. 1996) by describing (in narrative or numeric criteria) the expected biological
condition of an unimpaired aquatic community (U.S. EPA 1990b). They can be used to define
ecosystem rehabilitation goals and assessment endpoints. Biological criteria supplement traditional
measurements (for example, as backup for hard-to-detect chemical problems) and will be particularly
useful in assessing impairment due to nonpoint source pollution and nonchemical (e.g., physical and
biological) stressors. Thus, biological criteria fulfill a function missing from EPA's traditionally
chemical-oriented approach to pollution control and abatement (U.S. EPA 1994d).
Biological criteria can also be used to refine the aquatic life use classifications for a state. Each state
develops its own designated use classification system based on the generic uses cited in the CWA,
including protection and propagation of fish, shellfish, and wildlife. States frequently develop
subcategories to refine and clarify designated use classes when several surface waters with distinct
characteristics fit within the same use class or when waters do not fit well into any single category.
As data are collected from biosurveys to develop a biological criteria program, analysis may reveal
unique and consistent differences between aquatic communities that inhabit different waters with the
same designated use. Therefore, measurable biological attributes can be used to refine aquatic life
use or to separate one class of aquatic life into two or more subclasses.
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ELEMENTS OF BIOMONITORING
3.1 BIOSURVEYS, BIOASSAYS, AND CHEMICAL MONITORING
The water quality-based approach to pollution assessment requires various types of data. Biosurvey
techniques, such as the rapid bioassessment protocols (RBPs), are best used for detecting aquatic life
impairments and assessing their relative severity. Once an impairment is detected, however,
additional chemical and biological (toxicity) testing is usually necessary to identify the causative
agent, its source, and to implement appropriate mitigation (U.S. Environmental Protection Agency
[EPA] 1991c). Following mitigation, biosurveys are important for evaluating the effectiveness of
such control measures. Biosurveys may be used within a planning and management framework to
prioritize water quality problems for more stringent assessments and to document "environmental
recovery" following control action and rehabilitation activities. Some of the advantages of using
biosurveys for this type of monitoring are:
• Biological communities reflect overall ecological integrity (i.e., chemical, physical,
and biological integrity). Therefore, biosurvey results directly assess the status of a
waterbody relative to the primary goal of the Clean Water Act (CWA).
• Biological communities integrate the effects of different stressors and thus provide a
broad measure of their aggregate impact.
• Communities integrate the stresses over time and provide an ecological measure of
fluctuating environmental conditions.
• Routine monitoring of biological communities can be relatively inexpensive,
particularly when compared to the cost of assessing toxic pollutants, either
chemically or with toxicity tests (Ohio EPA 1987).
• The status of biological communities is of direct interest to the public as a measure
of a pollution free environment, whereas reductions in chemical pollutant loadings
are not as readily understood by the layperson as positive environmental results.
• Where criteria for specific ambient impacts do not exist (e.g., nonpoint-source
impacts that degrade habitat), biological communities may be the only practical
means of evaluation.
Biosurvey methods have a long-standing history of use for "before and after" monitoring. However,
the intermediate steps in pollution control, i.e., identifying causes and limiting sources, require
integrating information of various types—chemical, physical, toxicological, and/or biosurvey data.
These data are needed to:
Identify the specific stress agents causing impact: This may be a relatively simple task; but, given
the array of potentially important pollutants (and their possible combinations), it is likely to be both
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DRAFT REVISION—July 29,1997
difficult and costly. In situations where specific chemical stress agents are either poorly understood
or too varied to assess individually, toxicity tests can be used to focus specific chemical
investigations or to characterize generic stress agents (e.g., whole effluent or ambient toxicity). For
situations where habitat degradation is prevalent, a combination of biosurvey and physical habitat
assessment is most useful (Barbour and Stribling 1991).
Identify and limit the specific sources of these agents: Although biosurveys can be used to help
locate the likely origins of impact, chemical analyses and/or toxicity tests are usually necessary to
confirm the point sources and develop appropriate discharge limits.
Design appropriate treatment to meet the prescribed limits and monitor compliance:
Treatment facilities are designed to remove identified chemical constituents with a specific
efficiency. Chemical data are therefore required to construct such facilities and evaluate treatment
effectiveness. To some degree, a biological endpoint resulting from toxicity testing can also be used
to evaluate the effectiveness of prototype treatment schemes and can serve as a design parameter. In
most cases, these same parameters are limited in discharge permits and, after controls are in place,
are used to monitor for compliance. Where discharges are not controlled through a permit system
(e.g., nonpoint-source runoff, combined sewer outfalls, and dams) compliance must be assessed in
terms of ambient standards. Improvement of the ecosystem both from restoration or rehabilitation
activities are best monitored by biosurvey techniques.
Effective implementation of the water quality-based approach requires that various monitoring
techniques be considered within a larger context of water resource management. Both biological and
chemical methods play critical roles in a successful pollution control program. They should be
considered complementary rather than mutually exclusive approaches that will enhance overall
program effectiveness when used appropriately.
3.2 USE OF DIFFERENT ASSEMBLAGES IN BIOSURVEYS
The bioassessment techniques presented in this document focus on the evaluation of water quality,
habitat parameters, and the analysis of periphyton, benthic macroinvertebrates, and fish assemblages.
Many State water quality agencies employ trained and experienced benthic biologists, have
accumulated considerable background data on macroinvertebrates, and consider benthic surveys a
useful assessment tool. However, water quality standards, legislative mandate, and public opinion
are more directly related to the status of a waterbody as a fishery resource. For this reason, separate
protocols were developed for fish and were incorporated as Chapter 8 in this document. The fish
survey protocol is based largely on Karr's Index of Biotic Integrity (IBI) (Karr 1981, Karr et al. 1986,
Miller et al. 1988), which uses the structure of the fish assemblage to evaluate water quality. The
integration of functional and structural/compositional metrics, which forms the basis for the IBI, is a
common element to the rapid bioassessment approaches.
The periphyton assemblage (primarily algae) is also useful for water quality monitoring, but has not
been incorporated widely in monitoring programs. They represent the primary producer trophic
level, exhibit a different range of sensitivities, and will often indicate effects only indirectly observed
in the benthic and fish communities. As in the benthic macroinvertebrate and fish assemblages,
integration of structural/compositional and functional characteristics provides the best means of
assessing impairment (Rodgers et al. 1979).
Periphyton structural/compositional analyses may be focused upon taxonomic or non-taxonomic
features. Taxonomic descriptors (metrics) (e.g., diversity indices, taxa richness, indicator species)
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are commonly used, and are described in studies by Patrick (1973), Palmer (1977), Rodgers et al.
(1979), Weitzel (1979). Non-taxonomic measures, such as biomass and chlorophyll, can also be
useful for detecting effects not indicated by taxonomic analysis. For example, toxic pollutants may
cause sublethal (i.e., reproductive) effects which would not immediately be detected by taxonomic
descriptors such as taxa richness, but would be indicated by low biomass (Patrick 1973). A summary
of nontaxonomic measurements is presented in Weitzel (1979).
Functional aspects of algal communities, such as primary productivity rates, can also be assessed.
These analyses, which are described in Rodgers et al. (1979), can be performed at the taxonomic
level (e.g., determination of species colonization rate) or at the non-taxonomic level (e.g.,
community respiration) to evaluate effects of toxicants or nutrient enrichment.
In selecting the aquatic assemblage appropriate for a particular biomonitoring situation, the
advantages of using each assemblage must be considered along with the objectives of the program.
Some of the advantages of using periphyton, macroinvertebrates, and fish in a biomonitoring
program are presented in this section. References for this list are Caims and Dickson (1971),
American Public Health Association et al. (1971), Patrick (1973), Rodgers et al. (1979), Weitzel
(1979), Karr (1981), U.S. EPA (1983), Hughes et al. (1982), and Plafkin et al. (1989).
3.2.1 Advantages of Using Periphyton
• Algae generally have rapid reproduction rates and very short life cycles, making
them valuable indicators of short-term impacts.
• As primary producers, algae are most directly affected by physical and chemical
factors.
• Sampling is easy, inexpensive, requires few people, and creates minimal impact to
resident biota.
• Relatively standard methods exist for evaluation of functional and non-taxonomic
structural (biomass, chlorophyll measurements) characteristics of algal communities.
• Algal assemblages are sensitive to some pollutants which may not visibly affect
other aquatic assemblages, or may only affect other organisms at higher
concentrations (i.e., herbicides).
3.2.2 Advantages of Using Benthic Macroinvertebrates
• Macroinvertebrate assemblages are good indicators of localized conditions. Because
many benthic macroinvertebrates have limited migration patterns or a sessile mode
of life, they are particularly well-suited for assessing site-specific impacts
(upstream-downstream studies).
• Macroinvertebrates integrate the effects of short-term environmental variations.
Most species have a complex life cycle of approximately one year or more.
Sensitive life stages will respond quickly to stress; the overall community will
respond more slowly.
Rapid Bioassessment Protocols for Use in Streams and Rivers 3-3
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DRAFT REVISION—July 29,1997
• Degraded conditions can often be detected by an experienced biologist with only a
cursory examination of the benthic assemblage. Macroinvertebrates are relatively
easy to identify to family; many "intolerant" taxa can be identified to lower
taxonomic levels with ease.
• Benthic macroinvertebrate assemblages are made up of species that constitute a
broad range of trophic levels and pollution tolerances, thus providing strong
information for interpreting cumulative effects.
• Sampling is relatively easy, requires few people and inexpensive gear, and has no
detrimental effect on the resident biota.
• Benthic macroinvertebrates serve as a primary food source for many recreationally
and commercially important fish.
• Benthic macroinvertebrates are abundant in most streams. Many small streams (1st
and 2nd order), which naturally support a diverse macroinvertebrate fauna, only
support a limited fish fauna.
• Most state water quality agencies that routinely collect biosurvey data focus on
macroinvertebrates (Southerland and Stribling 1995). Many states already have
background macroinvertebrate data. Most state water quality agencies have more
expertise with invertebrates than fish.
3.2.3 Advantages of Using Fish
• Fish are good indicators of long-term (several years) effects and broad habitat
conditions because they are relatively long-lived and mobile (Karr et al. 1986).
• Fish assemblages generally include a range of species that represent a variety of
trophic levels (omnivores, herbivores, insectivores, planktivores, piscivores). They
tend to integrate effects of lower trophic levels; thus, fish community structure is
reflective of integrated environmental health.
• Fish are at the top of the aquatic food web and are consumed by humans, making
them important for assessing contamination.
• Fish are relatively easy to collect and identify to the species level. Most specimens
can be sorted and identified in the field by experienced fisheries professionals, and
subsequently released unharmed.
• Environmental requirements of common fish are comparatively well known. Life
history information is extensive for many species, and information on fish
distributions is commonly available.
• Aquatic life uses (water quality standards) are typically characterized in terms of
fisheries (coldwater, coolwater, warmwater, sport, forage). Monitoring fish provides
direct evaluation offish propagation" and "fishability", which emphasizes the
importance of fish to anglers and commercial fishermen.
3-4 Chapter 3: Elements of Biomonitoring
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DRAFT REVISION-July 29,1997
• Fish account for nearly half of the endangered vertebrate species and subspecies in
the United States.
3.3 IMPORTANCE OF HABITAT ASSESSMENT
The procedure for assessing physical habitat quality presented in this document (Chapter 5) is an
integral component of the final evaluation of impairment. The matrix used to assess habitat quality
is based on key physical characteristics of the waterbody and surrounding land. All of the habitat
parameters evaluated are related to overall aquatic life use and are a potential source of limitation to
the aquatic biota.
Habitat, as structured by instream and surrounding topographical features, is a major determinant of
aquatic community potential (Southwood 1977, Plafkin et al. 1989, and Barbour and Stribling 1991).
Both the quality and quantity of available habitat affect the structure and composition of resident
biological communities. Effects of such features on biological assessment results can be minimized
by sampling similar habitats at all stations being compared. However, when all stations are not
physically comparable, habitat characterization is particularly important for proper interpretation of
biosurvey results.
Where physical habitat quality at a test site is similar to that of a reference, detected impacts can be
attributed to water quality factors (i.e., chemical contamination) or other stressors. However, where
habitat quality differs substantially from reference conditions, the question of appropriate aquatic life
use designation and physical habitat alteration/restoration must be addressed. Final conclusions
regarding the presence and degree of biological impairment should thus include an evaluation of
habitat quality to determine the extent that habitat may be a limiting factor. The habitat
characterization matrix included in the rapid bioassessment protocols provides an effective means of
evaluating and documenting habitat quality at each biosurvey station.
3.4 THE ECOREGION CONCEPT
Innate regional differences exist in forests, lands with high agricultural potential, wetlands, and
waterbodies. These regional differences have been mapped by Bailey (1976), U.S. Department of
Agriculture (USDA) Soil Conservation Service (1981), Energy, Mines and Resources Canada
(1986), and Omernik (1987). All four maps were developed from examination of several mapped
land variables. Waterbodies reflect the lands they drain (Omernik 1987, Hunsaker and Levine 1995)
and it is assumed that similar lands should produce similar waterbodies. This ecoregional approach
provides more robust and ecologically-meaningful regional maps than could be attained by mapping
a single variable. For example, hydrologic unit maps are useful for mapping drainage patterns, but
have limited value for explaining the substantial changes that occur in water quality and biota
independent of stream size and river basin. Recognition of these changes stimulated Warren's (1979)
work, and Ohio's and Arkansas' development of ecoregional standards. Currently, 75% of the states
have developed or are in the process of developing an ecoregional or subecoregional framework for
stream bioassessment (Davis et al. 1996).
Omernik (1987) provided an ecoregional framework for interpreting spatial patterns in state and
national data. The geographical framework is based on regional patterns in land-surface form, soil,
potential natural vegetation, and land use, which vary across the country. Geographic patterns of
similarity among ecosystems can be grouped into ecoregions or subecoregions. Naturally occurring
biotic assemblages, as components of the ecosystem, would be expected to differ among ecoregions
but be relatively similar within a given ecoregion. The ecoregion concept thus provides a geographic
Rapid Bioassessment Protocols for Use in Streams and Rivers 3-5
-------
DRAFT REVISION—July 29,1997
framework for efficient management of aquatic ecosystems and their components (Hughes 1985,
Hughes et al. 1986, and Hughes and Larsen 1988). For example, studies in Ohio (Larsen et al. 1986),
Arkansas (Rohm et al. 1987), and Oregon (Hughes et al. 1987, Whittier et al. 1988) have shown that
distributional patterns offish communities approximate ecoregional boundaries as defined a priori
by Omemik (1987). This, in turn, implies that similar water quality standards, criteria, and
monitoring strategies are likely to be valid throughout a given ecoregion, but should be tailored to
accommodate the innate differences among ecoregions (Ohio EPA 1987). Figure 3-1 shows the
Level HI ecoregions developed by Omernik (1987) and refined by the U.S. EPA (1996c).
Within an ecoregion (Omernik 1987), additional qualifiers such as stream size, hydrologic regime,
elevation, and natural riparian vegetation need to be considered for partitioning variability. Stream
size has been found to be a covariate within ecoregions (Ohio EPA 1987), rather than as an
additional partitioning variable. However, some programs, such as EMAP (Klemm and Lazorchak
1994) and the Maryland Biological Stream Survey (MBSS) (Volstad et al. 1995) have found that a
surrogate measure of stream size (catchment size) is useful in partitioning the variability of stream
segments for assessment. Hydrologic regime can include flow regulation, water withdrawal, and
whether a stream is considered intermittent or perennial. Elevation has been found to be an
important classification variable when using the benthic macroinvertebrate assemblage (Barbour et
al. 1992, Barbour et al. 1994, Spindler 1996). Although riparian vegetation is closely linked to
ecoregions; its use as a descriptor at the smaller scale of physical habitat is needed to characterize
canopy cover. For example, even though a given stream segment is classified within an ecoregion or
subecoregion, it may be wooded (deciduous or coniferous) or open within a perennial or intermittent
flow regime, and represent one of several orders of stream size.
Individual descriptors will not apply to all ecoregions, nor will all conditions (i.e., deciduous,
coniferous, open) be present in all stream sizes. Those streams or stream segments that represent
characteristics atypical for that particular ecoregion should be excluded from the regional aggregate
of sites and treated as a special situation. Ohio EPA (1987) considered these unique systems to be a
separate aquatic life use designation (exceptional warmwater aquatic life use) on a statewide basis.
Although the final rapid bioassessment guidance should be generally applicable to all ecoregions of
the United States, each agency will need to evaluate the generic criteria suggested in this document
for inclusion into specific programs. To this end, the application of the ecoregion concept versus the
site-specific control approach will need to be examined. When rapid bioassessment protocols
(RBPs) are used to assess impact sources (upstream-downstream studies), reference criteria may not
be as important if an unimpacted site-specific control station can be sampled. However, when a
synoptic ("snapshot") or trend monitoring survey is being conducted in a watershed or river basin,
use of idealized criteria may be the only means of discerning use impairment or assessing impact.
Additional investigation will be needed to: delineate areas that differ significantly in their innate
biological potential; locate reference sites within each ecoregion (or subecoregion) that fully support
aquatic life uses; develop biological criteria (e.g., define optimal values for the metrics
recommended) using data generated from each of the assemblages.
3-6 Chapter 3: Elements of Biomonitoring
-------
Level III Ecoregions of the Continental United States
II Coast Range
LI 2.1'ugcl Lowland
•J 3. Willamette Valley
J 4. Cascades
J 5. Sierra Nevada
1 6. Southern and Central California Plains
and Hills
3 7. Central California Valley
L_l 8. Southern California Mountains
I 1 9. Eastern Cascades Slopes anil Foothills
I I 10. Columbia Plateau
CD 11 • Blue Mountains
f~'112. Snake River Basin/High Desert
9 13. Northern Basin and Range
CD 14. Southern Basin and Range
CD 15. Northern Rockies
3 16. Montana Valley and Foothill
Prairies
CJ 17. Middle Rockies
CJ 18. Wyoming Basin
CJ 19. Wasaleh and Uinla Mountains
I | 20. Colorado Plateaus
I 12 [.Southern Rockies
I 1 22. Ari/ona/New Mexico
Plateau
II23. Arizona/New Mexico
Mountains
CD 24. Southern Deserts
a 25. Western High Plains \
l~l 26. Southwestern 'I'ahleliuids i
CJ 27. Central Great Plains \
CJ 28. Plinl Hills
CJ 29. Central Olkahoma/Texas Plains
l'~l 30. Edwards Plateau
n 31. Southern Texas Plains
W 32. Texas Blackland Prairies
(gfl 33. East Central Texas Plains
I I 34. Western Oulf Coastal Plain
CD 35. South Central Plains
I I 36. Ouachita Mountains
ED 37. Arkansas Valley
r I 38. Boston Mountains
CD, 39. Ozark Highlands
r~l 40. Central Irregular Plains
CD 41 Northern Montana Glaciated Plains
C] 42. Northwestern (ilacialnl Plains
I I 43. Northwestern Urea! Plains
'Level III ccoregions identified in the ecoregion revision and
subdivision process subsequent to the original map
compilation (Omernik 1987).
JJTJ] 44. Nebraska Sand Hills
ESI 45. Piedmont*
Bi 46. Northern Glaciated Plains
E£J 47. Western Corn Bell Plains
|~| 48. Lake Agassiz Plain
[71 49. Northern Minnesota Wetlands
5° Northern lakes and I-'orests
"] 51 North C'entral Hardwood l;oresls
p~'l 52. Drililess Area
[ | 53. Southeastern Wisconsin Till Plains
fjg) 54. Central Corn Belt Plains
HI 55. Eastern Com Belt Plains
•'•] 56. S. Michican/N. Indiana Till Plains
SH 57.1 lurmi/l-jie I ake Plains
f_" ] 58. Northeastern Highlands
f~:i 59. Northeastern Coastal /one
("7] 60. Northern Appalachian Plateau anil
Uplands
LU 61. Erie/Ontario Lake Hills and Plain
CJ 62. North Central Appalachians
CJ 63. Middle Atlantic Coastal Plain
CJ 64. Northern Piedmont
C3 65. Southeastern Plains
CJ 66. Blue Ridge Mountains
f,T3 67. Ridge and Valley
CD °8 Southwestern Appalachians
Erg] 69. C'entral Appalachians
I—| 70. Western Allegheny Plateau
CJ 71. Interior Plateau
[££] 72. Interior River Lowland
£2] 73. Mississippi Alluvial Plain
Kg 74. Mississippi Valley Loess Plains
CD 75. Southern Coastal Plain
jgjj 76. Southern Florida Coastal Plain
Et] 77. North Cascades*
iffl 78. Klamath Mountains*
-------
DRAFT REVISION-July 29,1997
Level III Ecoregions of the Alaska
101. Arctic Coastal Plain
102. Arcnc Foothills
io3. Brooks Range
104. iDleiior Forested Lowlands
and Uplands
^>
iZW"' -~j^
*?&M-^2?&'
.^T^!-^r5^
^Jfey-' ., S,~ <•*/!*
^v-^r/~*
-•( .11 l-^c. >".:V-*a
) ./ ' ' 3 ^ t-'-l
'••' x- 5 112 ~~2j^ "
-0-/?-A-7f ^ X°
v' .-' .-^-"X ,"
-' i ^ ^S>.
.^- , -^j- 100 330 300 400 Mi
Sc«k LI6.0OO.OOO
Altien Equi Am
-^ 113. .Alaska Peninsula Mountains
-— 114. Aleulun Islands (Western portion not shown)
- 115 Cook tola
— 116. Alaska Range
3 117 Copper Plateau
—: 118. Wrangell Mounoins
!_] 119 Pacific Coastal Mountains
S 120. Coastal Western Hemioclt-Sitka Spruce Forests
105. Imcnor Higblmds
106. ImtnorBonoinUixis
107. Yukon Fl«o
108. Olgivie Mounoms
109. Sutarcac Coual Plains
110. Seward Peninsula
111 Ahkhinand Kilbuck Mountains
112. Bnslc4 Bay-Nushagak Lowlands
Figure 3-1. Level III ecoregion map.
3-8
Chapter 3: Elements ofBiomonitoring
-------
DRAFT REVISION-July 29,1997
3.5 STATION SITING
Although the specific bioassessment objective is an important consideration in locating sampling
stations, all assessments require an identification of a reference condition. This is best done by
compiling reference data from comparable sites within the same region when acceptable sites are
available. These regional reference stations are desirable because they allow evaluation of
conditions on a larger scale than single river reaches or subwater sheds. This approach (regional
reference condition) is valid for any type of bioassessment or chemical-specific surveys. However, a
site-specific control (usually upstream of a problem source) is generally thought to be most
representative of "best attainable" conditions for a particular waterbody and may be useful when
assessing overall potential. Where feasible, effects should be bracketed by establishing a series or
network of sampling stations at points of increasing distance from the impact source(s). These
stations will provide a basis for delineating impact and recovery zones. In significantly altered
systems (i.e., channelized or heavily urbanized streams), suitable reference sites are usually not
available (Gibson et al. 1996). In these cases, historical data or simple ecological models may be
necessary to establish reference conditions. See Gibson et al. (1996) for more detail.
All of the RBPs include the collection of biological samples to assess the biological condition of a
given site; because of this, sites must be similar in their general structure and characteristics. To
meaningfully evaluate biological condition, sampling locations must be similar enough to have
similar biological expectations, which, in rum, provides a basis for comparison of impairment. If the
goal of an assessment is to evaluate the effects of water chemistry degradation, comparable physical
habitat should be sampled at all stations, otherwise, the differences in the biology attributable to a
degraded habitat will be difficult to separate from those resulting from chemical pollution water
quality degradation. Availability of appropriate habitat at each sampling location can be established
during preliminary reconnaissance. In evaluations where several stations on a waterbody will be
compared, the station with the greatest habitat constraints (in terms of productive habitat availability)
should be noted. The station with the least number of productive habitats available will often
determine the type of habitat to be sampled at all stations of comparison.
Locally modified sites, such as small impoundments and bridge areas, should be avoided unless data
are needed to assess their effects. Sampling near the mouths of tributaries entering large waterbodies
should also be avoided since these areas will have habitat more typical of the larger waterbody (Karr
et al. 1986).
For bioassessment activities where the concern is non-chemical stressors, e.g., the effects of habitat
degradation or flow alteration, or cumulative impacts, a different approach to station selection is
used. Physical habitat differences between sites can be substantial for two reasons: (1) one or a set
of sites is more degraded (physically) than another, or (2) is unique for the stream class or region due
to the essential natural structure resulting from geological characteristics. Because of these
situations, the more critical part of the siting process comes from the recognition of the habitat
features that are representative of the ecoregion or stream class. In basin-wide or watershed studies,
sample locations should not be avoided due to habitat degradation or to physical features that are
well-represented in the stream class.
Site selection for assessment and monitoring can either be "targeted", i.e., relevant to special studies
that focus on potential problems, or "random", which provides information of the overall status or
condition of the watershed, basin, or region. In a random or probabilistic sampling regime, stream
characteristics may be highly dissimilar among the sites, but will provide a more accurate assessment
of biological condition throughout the area than a targeted design. Most studies conducted by state
Rapid Bioassessment Protocols for Use in Streams and Rivers 3-9
-------
DRAFT REVISION-July 29,1997
water quality agencies for identification of problems and sensitive waters are done with a targeted
design. Studies for aquatic life use determination can be done with a random (watershed or higher
level) or targeted (site-specific) design.
3.6 DATA MANAGEMENT AND ANALYSIS
U.S. EPA has developed a biological data management system linked to STORET, which provides a
centralized system for storage of biological data and associated analytical tools for data analysis.
The field survey file component of STORET provides a means of storing, retrieving, and analyzing
biosurvey data, and will process data on the distribution, abundance, and physical condition of
aquatic organisms, as well as descriptions of their habitats. Data stored in STORET become part of a
comprehensive database that can be used as a reference, to refine analysis techniques or to define
ecological requirements for aquatic populations. Data from the rapid bioassessment protocols can be
readily managed with the STORET field survey file using header information presented on the field
data forms (Appendix A) to identify sampling stations.
Habitat and physical characterization information may also be stored in the field survey file with
organism abundance data. Parameters available in the field survey file can be used to store some of
the environmental characteristics associated with the sampling event, including physical
characteristics, water quality, and habitat assessment. Physical/chemical parameters include stream
depth, velocity, and substrate characteristics, as well as many other parameters. STORET also
allows storage of other pertinent station or sample information in the comments section.
Entering data into a computer system can provide a substantial time savings. An additional
advantage to computerization is analysis documentation, which is an important component for a
Quality Assurance/Quality Control (QA/QC) plan. An agency conducting rapid bioassessment
programs can choose an existing system within their agency or utilize the STORET system
developed as a national database system.
Data collected as part of state bioassessment programs are usually entered, stored and analyzed in
easily obtained spreadsheet programs. This method of data management becomes cumbersome as
the database grows in volume. An alternative to spreadsheet programs is a multiuser relational
database management system. Most relational databases currently run in the windows operating
system and offer menu driven interfaces and ranges of toolbars that provide quick access to many
routine database tasks. Automated assistants help users quickly create forms for data input and
lookup, tables, reports, and complex queries about the data.
By using only tables and queries, a user can enter, manipulate, and print data. The metrics used in
most bioassessments can be calculated with simple queries. Queries can be saved so metrics can be
re-calculated at the click of the mouse each time data are updated or changed. If an operation on the
data is too complex for one of the many default functions then the operation can be written in code
(e.g., visual basic access) and stored in a module for use in any query. Repetitive steps can be
handled with macros. As the user develops the database other database elements such as forms and
reports can be added.
Table design is the foundation of the relational database (Figure 3-2), because they function as data
containers. Tables are related through the use of a unique identifier or index. In the example
database "Stationld" links the tables "ChemSamp", "HabSamp", and "BenSamp" to the "Stations"
table. The chemical parameters and habitat parameters table act as metadata tables and contain
descriptive data (e.g., measurement units, detection limits). This method of storing data is more
3-10 Chapter 3: Elements of Biomonitoring
-------
DRAFT REVISION-July 29,1997
efficient than spreadsheets, because it eliminates a lot of redundant data. A Master Taxa table is
created for the biological data to contain all relevant information about each taxon. This information
does not have to be repeated each time a taxon is entered into the database.
Input or lookup forms (Figure 3-3) are screens that are designed to aid in entering or retrieving data.
Forms are linked to tables so data go to the right cell in the right table. Because of the relationships
among the tables, data can be updated across all the tables that are linked to the form (e.g., providing
cascading updates). Reports can be generated in a variety of styles, and data can be exported to other
databases or spreadsheet programs.
3.7 TECHNICAL ISSUES FOR SAMPLING THE PERIPHYTON
ASSEMBLAGE
3.7.1 Seasonality
Stream periphyton have distinct seasonal cycles, with peak abundance and diversity typically
occurring in late summer or early fall (Bahls 1993). High flows may scour and sweep away
periphyton. For these reasons, the index period for periphyton sampling is usually late summer or
early fall, when stream flow is relatively stable (Kentucky DEP 1993, Bahls 1993).
Algae are light limited, and may be sparse in heavily shaded streams. Early spring, before leafout,
may be a better index period in shaded streams.
Finally, since algae have short generation times (one to several days), they respond rapidly to
environmental changes. Samples of the algal community are "snapshots" in time, and do not
integrate environmental effects over entire seasons or years.
3.7.2 Sampling Methodology
Artificial substrates (periphytometers) have long been used in algal investigations, typically using
glass slides as the substrate, but also with glass rods, plastic plates, ceramic tiles and other
substances. However,-many agencies are sampling periphyton from natural substrates in order to
characterize the natural community. Advantages of artificial and natural substrates are summarized
below (Caims 1982, Bahls 1993).
Advantages of Artificial Substrates:
• Artificial substrates allow sample collection in locations that are typically difficult to
sample effectively (e.g., bedrock, boulder, or shifting substrates; deep or high
velocity water).
• As a "passive" sample collection device, artificial substrates permit standardized
sampling by eliminating subjectivity in sample collection technique. Direct
sampling of natural substrate requires similar effort and degree of efficiency for the
collection of each sample. Use of artificial substrates requires standardization of
setting and retrieval; however, colonization provides the actual sampling
mechanism.
Rapid Bioassessment Protocols for Use in Streams and Rivers 3-11
-------
DRAFT REVISION—July 29,1997
STAINDEX
StatonID
STREAMNAME
LOCATION
REFTYPE
Index
SampteD
StationID
Cod ate
CoHMeth
Collector
TauENIJD
Sample
TAXAJD
tt Individuals
Excluded Taxa
Comments
Entered Date
ChParmID
Lab.lD
StatioriD
ChemDate
CompSeason
Season
ChemTime
ChemParam
Value
Remark
Status
HaUndnt
StationID
HabParam
Value
Comments
TAXHJD
Phjilum
Class
Oidei
Famty
Genus
Species
FnallD
TolVal
Authority
Auth.FFG
Enteied.by
Entered Date
Comments
HabPMB
Score Range
Comments
ChPatmID
ChemParam
Field Name / Dcfonct
Meas.Unit
Detjm
Quart_lim
SOP
EPA Me*
Figure 3-2. Example of the relationship of data tables in a typical relational database.
ES
E<* Y»~
b«*>
>
StationID | ECO65A01
5«nthic cwnpl* input and display
form
I SamplcIO |
_MB9609112-115
jj
CollDal. |
9/9/96!
i
R«cocd '« I ' 1 1 l » l»i t»*i e< 1
Benthic Taxa input and display form | p(^ '•»« ''••<• "P'^k T
-------
DRAFT REVISION-July 29,1997
• Confounding effects of habitat differences are minimized by providing a
standardized microhabitat. Microhabitat standardization may promote selectivity for
specific organisms if the artificial substrate provides a different micro habitat than
that naturally available at a site.
• Sampling variability is decreased due to a reduction in microhabitat patchiness,
improving the potential for spatial and temporal similarity among samples.
• Sample collection using artificial substrates may require less skill and training than
direct sampling of natural substrates.
Disadvantages of Artificial Substrates:
• Artificial substrates require a return trip; this may be a significant consideration in
large states or those with limited technical resources.
• Artificial substrates are prone to loss, natural damage or vandalism.
• The material of the substrate will influence the composition and structure of the
community; solid artificial substrates will favor attached forms over motile forms
and compromise the usefulness of the siltation index.
• Orientation and length of exposure of the substrate will influence the composition
and structure of the community.
3.8 TECHNICAL ISSUES FOR SAMPLING THE BENTHIC
MACROINVERTEBRATE ASSEMBLAGE
3.8.1 Seasonally for Benthic Collections
The ideal sampling procedure is to survey the biological community with each change of season,
then select the appropriate sampling periods that accommodate seasonal variation (Gibson et al.
1996). Such indexing makes the best use of the biological data. However, resident assemblages
integrate stress effects over the course of the year, and their seasonal cycles of abundance and taxa
composition are fairly predictable within the limits of interannual variabliry (Gibson et al. 1996).
Many programs have found that a single index period provides a strong database that allows all of
their management objectives to be addressed. However, if one goal of a program is to understand
seasonal variability, then establishing index periods during multiple seasons is necessary (Gibson et
al. 1996). Although a single index period would not likely be adequate for assessing the effects of
catastrophic events, such as spills (Gibson et al. 1996), those assessments should be viewed as
special studies requiring sampling of reference sites during the same time period.
Ultimately, selection of the appropriate sampling period should be based on three factors that reflect
efforts to:
1) minimize year-to-year variability resulting from natural events,
2) maximize gear efficiency, and
Rapid Bioassessment Protocols for Use in Streams and Rivers 3-13
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DRAFT REVISION-July 29,1997
3) maximize accessibility of targeted assemblage.
Sampling and comparisons of data from the same seasons (or index periods) as the previous year's
sampling provides some correction and minimization of annual variability. The season of the year
during which sampling gear is most effective is an important consideration for selecting an index
period. For example, low flow or freezing conditions may hamper an agency's ability to sample with
its selected gear. Seasons where those conditions are prevalent should be avoided. The targeted
assemblage(s) should be accessible and not be inhabiting hard-to-reach portions of the sampling
area. For example, if benthos are primarily deep in the substrate in winter, beyond normal sampling
depth, that period should be avoided and another index period chosen. If high flows are typical of
spring runoff periods, and sampling cannot occur, the index period should be established during
typical flow periods.
3.8.2 Benthic Sampling Methodology
The benthic RBPs employ direct sampling of natural substrates. Because routine evaluation of a
large number of sites is a primary objective of the RBPs, artificial substrates were eliminated from
consideration due to time required for both placement and retrieval, and the amount of exposure time
required for colonization. However, where conditions are inappropriate for the collection of natural
substrate samples, artificial substrates may be an option. The Science Advisory Board (SAB 1993)
cautioned that the only appropriate type of artificial substrates to be used for assessment are those
that are "introduced substrates", i.e., substrates that are representative of the natural substrate of the
stream system, such as rock-filled baskets in cobble- or gravel-bottomed streams. Ohio EPA and
Maine DEP, are examples of states that use artificial substrates for their water resource
investigations (Davis et al. 1996).
Advantages and disadvantages of artificial substrates (Caims 1982) relative to the use of natural
substrates are presented below.
Advantages of Artificial Substrates:
• Artificial substrates allow sample collection in locations that are typically difficult to
sample effectively (e.g., bedrock, boulder, or shifting substrates; deep or high
velocity water).
• As a "passive" sample collection device, artificial substrates permit standardized
sampling by eliminating subjectivity in sample collection technique. Direct
sampling of natural substrate requires similar effort and degree of efficiency for the
collection of each sample. Use of artificial substrates requires standardization of
setting and retrieval; however, colonization provides the actual sampling
mechanism.
• Confounding effects of habitat differences are minimized by providing a
standardized microhabitat. Microhabitat standardization may promote selectivity for
specific organisms if the artificial substrate provides a different microhabitat than
that naturally available at a site (see second bullet under Disadvantages below). Most
artificial substrates, by design, select for the Scraper and Filterer components of the
benthic assemblages or for Collectors if accumulation of debris has occured in the
substrates.
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DRAFT REVISION-^July 29,1997
• Sampling variability is decreased due to a reduction in microhabitat patchiness,
improving the potential for spatial and temporal similarity among samples.
• Sample collection using artificial substrates may require less skill and training than
direct sampling of natural substrates. Depending on the type of artificial substrate
used, properly trained technicians could place and retrieve the substrates. However,
an experienced specialist should be responsible for the selection of habitats and
sample sites.
Disadvantages of Artificial Substrates:
• Two trips (one to set and one to retrieve) are required for each artificial substrate
sample; only one trip is necessary for direct sampling of the natural substrate.
Artificial substrates require a long (8-week average) exposure period for
colonization. This decreases their utility for certain rapid biological assessments.
• Samples may not be fully representative of the benthic assemblage at a station if the
artificial substrate offers different microhabitats than those available in the natural
substrate. Artificial substrates often selectively sample certain taxa, misrepresenting
relative abundances of these taxa in the natural substrate. Artificial substrate
samples would thus indicate colonization potential rather than the resident
community structure. This could be advantageous if a study is designed to isolate
water quality effects from substrate and other microhabitat effects. Where habitat
quality is a limiting factor, artificial substrates could be used to discriminate between
physical and chemical effects and assess a site's potential to support aquatic life on
the basis of water quality alone.
• Sampler loss or perturbation commonly occurs due to sedimentation, extremely high
or low flows, or vandalism during the relatively long (at least several weeks)
exposure period required for colonization.
• Depending on the configuration of the artificial substrate used, transport and storage
can be difficult. The number of artificial substrate samplers required for sample
collection increases such inconvenience.
3.9 TECHNICAL ISSUES FOR THE SURVEY OF THE FISH
ASSEMBLAGE
3.9.1 Seasonality for Fish Collections
Seasonal changes in the relative abundances of the fish community primarily occur during
reproductive periods and (for some species) the spring and fall migratory periods. However, because
larval fish sampling is not recommended in this protocol, reproductive period changes in relative
abundance are not of primary importance.
Generally, the preferred sampling season is mid to late summer, when stream and river flows are
moderate to low, and less variable than during other seasons. Although some fish species are
capable of extensive migration, fish populations and individual fish tend to remain in the same area
during summer (Funk 1957, Gerking 1959, Caims and Kaesler 1971). The Ohio Environmental
Rapid Bioassessment Protocols for Use in Streams and Rivers 3-15
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DRAFT REVISION—July 29,1997
Protection Agency (1987) stated that few fishes in perennial streams migrate long distances. Hill
and Grossman (1987) found that the three dominant fish species in a North Carolina stream had
home ranges of 13 to 19 meters over a period of 18 months. Ross et al. (1985) and Matthews (1986)
found that stream fish assemblages were stable and persistent for 10 years, recovering rapidly from
droughts and floods indicating that large population fluctuations are not likely to occur in response to
purely natural environmental phenomena. However, comparison of data collected during different
seasons is discouraged, as are data collected during or immediately after major flow changes.
3.9.2 Fish Sampling Methodology
Although various gear types are routinely used to sample fish, electrofishers and seines are
the most commonly used collection methods in fresh water habitats. Each method has advantages
and disadvantages (Hendricks et al. 1980, Nielsen and Johnson 1983). However, electrofishing is
recommended for most fish field surveys because of its greater applicability and efficiency. Local
conditions may require consideration of seining as an optional collection method. Advantages and
disadvantages of each gear type are presented below.
3.9.2.1 Advantages and Disadvantages of Electrofishing
Advantages of Electrofishing:
• Electrofishing allows greater standardization of catch per unit of effort.
• Electrofishing requires less time and a reduced level of effort than some sampling
methods (e.g., use of ichthyocides) (Hendricks et al. 1980).
• Electrofishing is less selective than seining (although it is selective towards size and
species) (Hendricks et al. 1980). (See second bullet under Disadvantages below).
• If properly used, adverse effects on fish are minimized.
• Electrofishing is appropriate in a variety of habitats.
Disadvantages of Electrofishing:
• Sampling efficiency is affected by turbidity and conductivity.
• Although less selective than seining, electrofishing is size and species selective.
Effects of electrofishing increase with body size. Species specific behavioral and
anatomical differences also determine vulnerability to electroshocking (Reynolds
1983).
• Electrofishing is a hazardous operation that can injure field personnel if proper
safety procedures are ignored.
3.9.2.2 Advantages and Disadvantages of Seining
Advantages of Seining:
• Seines are relatively inexpensive.
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DRAFT REVISION-July 29,1997
• Seines are lightweight and are easily transported and stored.
• Seine repair and maintenance are minimal and can be accomplished onsite.
• Seine use is not restricted by water quality parameters.
• Effects on the fish population are minimal because fish are collected alive and are
generally unharmed.
Disadvantages of Seining:
• Previous experience and skill, knowledge of fish habitats and behavior, and
sampling effort are probably more important in seining than in the use of any other
gear (Hendricks et al. 1980).
• Sample effort and results for seining are more variable than sampling with
electrofishing or rotenoning.
• Use of seines is generally restricted to slower water with smooth bottoms, and is
most effective in small streams or pools with little cover.
• Standardization of unit of effort to ensure data comparability is difficult.
3.10 SAMPLING REPRESENTATIVE HABITAT
Effort should be made when sampling to avoid regionally unique natural habitat. Samples from such
situations, when compared to those from sites lacking the unique habitat, will appear different, i.e.,
assess as in either better or worse condition, than those not having the unique habitat. This is due to
the usually high habitat specificity that different taxa have to their range of habitat conditions;
unique habitat will have unique taxa. Thus, all RBP sampling is focused on representative sampling
of habitat.
Composite sampling is the norm for RBP investigations to better characterize the reach, rather than
individual small replicates that can skew the within-site variability. A major source of variance can
result from taking too few samples for a composite. Therefore, each of the protocols (i.e., for
periphyton, benthos, fish) advocate compositing several samples or efforts throughout the stream
reach.
When sampling large streams, rivers, or waterbodies with complex habitats, a complete inventory of
the entire reach is not necessary for bioassessment. However, the sampling area should be
representative of the reach, incorporating riffles, runs, and pools if these habitats are typical of the
stream in question. Midchannel and wetland areas of large rivers, which are difficult to sample
effectively, may be avoided. Sampling effort may be concentrated in near-shore habitats where most
species will be collected. In doing so, some deep water or wetland species may be undersampled,
however, the data should be adequate for the objective of bioassessment.
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PERFORMANCE-BASED METHODS
SYSTEM (PBMS)
Regardless of the type of data being collected, field methods share one important feature in
common—they cannot tell you whether the information collected is an accurate portrayal of the
system of interest (Intergovernmental Task Force on Monitoring Water Quality [ITFM] 1995). We
may know, with some accuracy, properties of a given sample taken from the field, but typically, we
are interested in answering questions on much larger spatial and temporal scales. To grapple with
this problem, environmental scientists and statisticians have long recognized that field methods must
strive to obtain samples and (or) data that are representative of the field conditions at the time of
sampling.
An accurate assessment of lotic biological data is difficult because natural variability cannot be
controlled (Resh and Jackson 1993). Unlike analytical assessments conducted in the laboratory, in
which accuracy can be verified in a number of ways, accuracy of macroinvertebrate assessments in
the field cannot be objectively verified; we are unable, for example, to "spike" a stream with a
known species assemblage or a known level of impairment and then determine the accuracy of a
bioassessment method. This problem is not theoretical. Different techniques may yield conflicting
interpretations at the same sites, underscoring the question of accuracy in bioassessment. Depending
on which methods are chosen, the actual structure and condition of the benthic assemblage present,
or trends in the status of the benthos over time, may be misinterpreted. Even with considerable
convergence in methods used in the U.S.A. by states and other agencies (Southerland and Stribling
1995, Davis et al. 1996), direct sharing of data among agencies may cause problems because of
uncertainty associated with unfamiliar methods, misapplication of familiar methods, or varied data
analyses and interpretation (Diamond et al. 1996). The contents of this chapter are taken liberally
from Diamond et al. 1996, which is a refinement of the PBMS approach developed for ITFM
(1995b).
4.1 APPROACHES FOR ACQUIRING COMPARABLE
BIOASSESSMENT DATA
There are 2 general approaches for acquiring comparable bioassessment data among programs or
among states. The first is for everyone to use the same method on every study, as attempted by most
water resource agencies in the U.S. by developing standard operating procedures (SOPs). These
SOPs would be adhered to throughout statewide or regional areas to provide comparable assessments
within each program. The Rapid Bioassessment Protocols (RBPs) developed by Plafkin et al. (1989)
and refined here are attempts to provide a framework for agencies to develop SOPs. However, the
use of a single method, even for a particular type of habitat, is probably not likely among different
agencies, no matter how exemplary (Diamond et al. 1996). Different researchers or programs may
have different reasons for doing bioassessments and these different reasons may not necessarily
require the same level or type of effort in sample collection, taxonomic identification, and data
analysis (Gurtz and Muir 1994). However, different methods of sampling and analysis may yield
comparable data for certain objectives despite differences in effort.
The 2nd approach to acquiring comparable data from different organizations, and one recommended
by ITFM, is to encourage the documentation of quality control characteristics for all methods and to
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DRAFT REVISION-July 23,1997
use those characteristics to determine comparability of different methods (ITFM 1995b). This
documentation is known as a performance-based method system (PBMS) which, in the context of
biological assessments, is defined as a system that permits the use of any method (to sample and
analyze stream assemblages) that meets established requirements for data quality (Diamond et al.
1996). Data quality objectives (DQOs) are qualitative and quantitative expressions that define
requirements for data precision, bias, method sensitivity, and range of conditions over which a
method yields satisfactory data (Klemm et al. 1990). The determination of DQOs for a given study
or agency program is central to all data collection and to a PBMS, particularly, because these
objectives establish not only the necessary quality of a given method (Klemm et al. 1990) but also
the types of methods that are likely to provide satisfactory information.
In practice, DQO's are developed in 3 stages: 1) determine what information is needed and why and
how that information will be used; 2) determine methodological and practical constraints and
technical specifications to achieve the information desired; and 3) compare different available
methods and choose the one that best meets the desired specifications within identified practical and
technical limitations (U.S. Environmental Protection Agency [U.S. EPA] 1984, 1986, Klemm et al.
1990, U.S. EPA 1995c). Clearly, one cannot make an informed choice of method if data quality
characteristics for different methods are unavailable, as is presently the case for most assessment
methods using benthic macroinvertebrates. The successful introduction of the PBMS concept in
laboratory chemistry, and more recently in laboratory toxicity testing (U.S. EPA 1990c, American
Society of Testing and Materials [ASTM] 1995), suggests that adapting such a system to the problem
of comparing bioassessments could be worthwhile.
The recommendation by the ITFM (1995b) to encourage PBMS was an outgrowth of more than 3
years of discussion among most federal and state agencies in the U.S. that monitor water quality.
With a multitude of agencies and academic institutions currently performing benthic
macroinvertebrate assessments in the U.S. alone, trends in benthic assemblages over time in specific
streams and rivers are obscured by the difficulty of comparing data from different methods at these
same sites. Determining the comparability of different methods and the use of data of known quality
was recognized by the ITFM as singularly important in the overall process of better defining status
and trends in water quality and natural aquatic resources on a national scale (Diamond et al. 1996).
If different methods are similar with respect to the quality of data each produces, then data from
those methods may be used interchangeably or together, aggregated, or used to demonstrate trends.
As an example, a method for sample sorting and organism identification, through repeated
examination using trained personnel, could be used to determine that the proportion of missed
organisms is less than 10% of the organisms present in a given sample and that taxonomic
identifications (to the genus level) have an accuracy rate of at least 90% (as determined by samples
verified by recognized experts). A study could require the above percentages of missed organisms
and taxonomic accuracy as DQOs to ensure the collection of satisfactory data (Ettinger 1984,
Clifford and Casey 1992, Cuffhey et al. 1993a). In a PBMS approach, any laboratory sorting and
identification method that documented the attainment of these DQOs would yield comparable data
and the results would therefore be satisfactory for the study.
For the PBMS approach to be useful, 4 basic assumptions must be met (ITFM 1995b): 1) DQOs
must be set that realistically define and measure the quality of the data needed; reference (validated)
methods must be made available to meet those DQOs. 2) To be considered satisfactory, an
alternative method must be as good or better than the reference method in terms of its resulting data
quality characteristics. 3) There must be proof that the method yields reproducible results that are
sensitive enough for the program. 4) The method must be effective over the prescribed range of
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DRAFT REVISION—July 23,1997
conditions in which it is to be used. For bioassessments, the above assumptions imply that a given
method for sample collection and analysis produces data of known quality, including precision, the
range of habitats over which the collection method yields a specified precision, and the magnitude of
difference in data among sites with different levels or types of impairment (Diamond et al. 1996).
Thus, for multimetric assessment methods, such as RBPs, the precision of the total multimetric score
is of interest as well as perhaps the individual metrics that make up the score (Diamond et al. 1996).
Several performance parameters must be characterized for a given method to utilize a PBMS
approach. These parameters include method precision, bias, performance range, interferences, and
method detection limit. These parameters, as well as method accuracy, are typically demonstrated in
analytical chemistry systems through the use of blanks, standards, spikes, blind samples,
performance evaluation samples, and other techniques to compare different methods and eventually
to derive a reference method for a given analyte. Many of these performance parameters are
applicable to biological laboratory and field methods and other prelaboratory procedures as well
(Table 4-1). It is known that a given collection method is not equally accurate over all ecological
conditions even within a general aquatic system classification (e.g., streams, lakes, estuaries).
Therefore, assuming a given method is a "reference method" on the basis of regulatory or
programmatic reasons does not allow for possible translation or sharing of data derived from
different methods because the performance characteristics of different methods have not been
quantified. One can evaluate performance characteristics of methods in 2 ways: with respect to the
collection method itself and with respect to the overall assessment process. Method performance is
characterized using quantifiable data (metrics, scores) derived from data collection and analysis.
Assessment performance, on the other hand, is a step removed from the actual data collected.
Interpretive criteria (which may be based on a variety of approaches) are used to rank sites and thus,
PBMS in this case is concerned with performance characteristics of the ranking procedures as well as
the methods that lead up to the assessment.
Table 4-1. Progression of a generic bioassessment field and laboratory method and
corresponding steps requiring performance criteria characterization.
Step
1
2
3
4
5
Procedure
Sampling device
Sampling method
Field sample processing
(subsampling, sample
transfer, preservation)
Laboratory sample
processing (sieving.
sorting)
Taxonomic
enumeration
Examples of Performance Criteria
Performance range - different efficiency in various habitat types or
substrates.
Bias - exclusion of certain taxa (mesh size).
Interferences - matrix or physical limitations (current velocity, depth).
Precision - of metrics or measures among replicate samples at a site.
Performance range - limitations in certain habitats or benthic substrates.
Precision - of metrics among splits of subsamples.
Accuracy - of transfer process.
Performance range - of preservation and holding time.
Precision - among split samples.
Accuracy - of sorting method; equipment used.
Performance range - of sorting method depending on sample matrix
(detritus, mud).
Bias - in sorting certain taxonomic groups or organism size.
Precision - split samples.
Accuracy - of identification and counts
Performance range - dependent on taxonomic group and (or) density.
Bias - counts and identifications for certain taxonomic groups.
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DRAFT REVISION—July 23,1997
Data quality and performance characteristics of methods for analytical chemistry are typically
validated through the use of quality control samples including blanks, calibration standards, and
samples spiked with a known quantity of the analyte of interest. Table 4-2 summarizes some
performance criteria used in analytical chemistry and how these might be translated to biological
methods.
Table 4-2. Translation of some performance criteria, derived for laboratory analytical
systems, to biological laboratory systems (taken from Diamond et al. 1996).
Performance Criteria
Precision
Bias
Performance range
Interferences
Method detection limit
Accuracy
Analytical Chemical Methods
Replicate samples
Matrix-spiked samples; standard
reference materials; performance
evaluation samples
Standard reference materials at
various concentrations; evaluation of
spiked samples by using different
matrices
Occurrence of chemical reactions
involved in procedure; spiked
samples; procedural blanks
Standards, instrument calibration
Performance standards, procedural
blanks
Biological Methods
Multiple taxonomists identifying 1 sample;
split sample for sorting, identification,
enumeration; multiple subsamples in adjacent
reaches at a site having similar habitat and
stressors
Taxonomic reference samples; "spiked"
organism samples
Efficiency of field sorting procedures under
different sample conditions (mud, detritus,
sand, low light)
Excessive detrital material or mud in sample;
identification of young life stages; taxonomic
uncertainty
Organism-spiked samples; standard level of
identification
Confirmation of identification, percentage of
"missed" specimens
The collection of high-quality data, particularly for bioassessments, depends on having adequately
trained people. One way to document satisfactory training is to have newly trained personnel use the
method and to compare their results with those previously considered acceptable. Although new
field crews and new laboratory personnel in many organizations are trained in this way (Cuffhey et
al. 1993b), the results are rarely documented or quantified. As a result, an organization cannot assure
either itself or other potential data users that different personnel performing the same method at the
same site yield comparable results and that data quality specifications of the method (e.g., precision
of metrics or scores) are consistently met. Some of this information is published for certain
bioassessment sampling methods, but is defined qualitatively (see Elliott and Tullett 1978, Peckarsky
1984, Resh et al. 1990, Merritt et al. 1996 for examples), not quantitatively. Quantitative
information needs to be more available so that the quality of data obtained by different methods is
documented.
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•It is imperative that the specific range of environmental conditions (or performance range) is
quantitatively defined for a sampling method (Diamond et al. 1996). As an example, the
performance range for macroinvertebrate sampling is usually addressed qualitatively by
characterizing factors such as stream size, hydrogeomorphic reach classification, and general habitat
features (riffle vs. pool, shallow vs. deep water, rocky vs. silt substrate; Merritt et al. 1996). In a
PBMS framework, different methods could be classified based on the ability of the method to
achieve specified levels of performance characteristics such as data precision and sensitivity to
impairment over a range of appropriate habitats. Thus, the precision of individual metrics or scores
obtained by different sampling methods can be directly and quantitatively compared for different
types of habitats.
4.2 ADVANTAGES OF A PBMS APPROACH FOR CHARACTERIZING
BIOASSESSMENT METHODS
Two fundamental requirements for a biological assessment are: that the sample taken and analyzed
is representative of the site or the assemblage of interest and that the data obtained are an accurate
reflection of the sample. The latter requirement is ensured using proper quality assurance and
quality control (QA/QC) in the laboratory including the types of performance criteria summarized in
Table 4-2. The 1st requirement is met through appropriate field sampling procedures, including
random selection of sampling locations within the habitat type(s) of interest, choice of sampling
device, and sample preservation methods. The degree to which a sample is representative of the
environment depends on the type of sampling method used (including subsampling) and the
ecological endpoint being measured. For example, many benthic samples may be needed from a
stream to obtain 95% confidence intervals that are within 50% of the mean value for
macroinvertebrate density, whereas fewer benthic samples may be needed to determine the dominant
species in a given habitat type at a particular time (Needham and Usinger 1956, Resh 1979, Plafkin
etal. 1989).
Several questions have been raised concerning the appropriateness or "accuracy" of methods such as
RBPs, which take few samples from a site and base their measures or scores on subsamples.
Subsampling methods have been debated because the "accuracy" of data derived from different
methods is questionable (Courtemanch 1996, Barbour and Gerritsen 1996, Vinson and Hawkins
1996). Using a PBMS_ framework, the question is not which subsampling method is more "accurate"
or precise but rather what accuracy and precision level can a method achieve, and do those
performance characteristics meet the DQOs of the program? Looking at bioassessment methods in
this way, including subsampling and taxonomic identification, forces the researcher or program
manager to quantitatively define beforehand the quality control characteristics necessary to make the
type of interpretive assessments required by the study or program.
Once the objectives and data quality characteristics are defined for a given study, a method is chosen
that meets those objectives. Depending on the data quality characteristics desired, several different
methods for collecting and sorting macroinvertebrates may be suitable. Once data precision and
"accuracy" are quantified for measures derived from a given bioassessment method, the method's
sensitivity (the degree of change in measures or endpoints between a test site and a control or
reference site that can be detected as a significant difference) and reliability (the degree to which an
objectively defined impaired site is identified as such) can be quantified and compared with other
methods. A method may be modified (e.g., more replicates or larger samples taken) to improve the
precision and "accuracy" of the method and meet more stringent data requirements. Thus we believe
a PBMS framework has the advantage of forcing us as scientists to focus on the ever-important issue:
what type of sampling program and data quality are needed to answer the question at hand?
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A 2nd advantage of a PBMS framework is that data users and resource managers could potentially
increase the amount of available information by combining data based on known comparable
methods. The 305(b) process of the U.S. Environmental Protection Agency (National Water Quality
Inventory, U.S. EPA 1995c) is a good example of an environmental program that would benefit from
a PBMS framework. This program is designed to determine status and trends of surface water
quality in the U.S. A PBMS framework would make explicit the quality and comparability of data
derived from different bioassessment methods, would allow more effective sharing of information
collected by different states, and would improve the existing national database. Only those methods
that met certain DQOs would be used. Such a decision might encourage other organizations to at
least meet those data requirements, thus
increasing the amount of usable information.
For example, the RBPs used by many state
agencies for water resources (Southerland and
Stribling 1995) could be modified for field and
laboratory procedures and still meet similar
data quality objectives. The overall design
steps of the RBPs, and criteria' for determining
useful metrics or community measures, would
be relatively constant across regions and states
to ensure similar quality and comparability of
data.
4.3 QUANTIFYING
PERFORMANCE
CHARACTERISTICS
The following suggested sampling approach
(Figure 4-1) need only be performed once for a
particular method and by a given agency or
research team; it need not be performed with
each bioassessment study. Once data quality
characteristics for the method are established,
limited quality control sampling and analysis
should supplement the required sampling for
each bioassessment study to ensure that data
quality characteristics of the method are met
(U.S. EPA 1995a). We believe that the
additional effort and expense of such quality
control are negligible in relation to the potential
unknown quality.
Stepl
Step 2
Step 3
Step 4
Steps
Step6
Step?
Sample 'replicate* reaches or sub-reaches within a site,
using different trained personnel. Repeat for different site
' classes (stream size, habitat, ecoregion).
'
| Sample at least 5 reference sites in the same site class
(habitat type, stream size, ecoregion).
\
• Sample processing and organism identification
1
Compute measures/metrics for each site.
1
Compute precision of each measure among
sites.
J
Repeat steps 3 and 4 for at least 3 test sites in each site
class examined in step 1. Test sites should have different
types and apparent levels of impairment
1
'
Compare data precision, bias, and method sensitivity for
each site class.
~]
Figure 4-1. Flow chart summarizing the steps
necessary to quantify performance characteristics of a
bioassessment method (modified from Diamond et al.
1996).
environmental cost of producing data of poor or
The 1st step is to define precision of the collection method itself. This is accomplished by sampling
adjacent reaches (pseudoreplicates) within a site (see Hannaford and Resh 1995). Reaches must be
similar with respect to habitat and any stresses present. It is recommended that the same personnel
sample "duplicate reaches". The samples collected are processed and analyzed separately and their
metrics compared to obtain a more realistic measure of the method precision and consistency with all
other factors being equal. Use of a different sampling team in duplicate reaches would document
adequacy of personnel training and sensitivity of method to operator bias. Finally, it is desirable to
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DRAFT REVISION^Iuly 23,1997
sample a range of site classes (stream size, habitat type) over which the method is likely to be used.
This kind of sampling, processing, and analysis should reveal potential biases.
Once the precision of the method is known, one can determine the actual variability associated with
sampling "replicate" reference sites within an ecoregion or habitat type. The degree of assemblage
similarity observed among "replicate" reference streams, along with the precision of the collection
method itself, will determine the overall precision, accuracy, and sensitivity of the bioassessment
approach as a whole. This kind of checking has been done, at least in part, by several states (Bode
and Novak 1995; Yoder and Rankin 1995; Homig et al. 1995; Barbour et al. 1996b), some U.S. EPA
programs (Gibson et al. 1996), and the U.S. Geological Survey (USGS) National Water Quality
Assessment Program (Cuffhey et al. 1993b, Gurtz 1994). Evaluation of metric or score variability
among supposedly replicate reference sites can result in wiser choices of stream classification and
improved data precision. For example, the Arizona Department of Environmental Quality (DEQ)
determined that macroinvertebrate assemblage structure varied substantially within ecoregions
resulting in large metric variability among reference sites and poor method precision (Spindler
1996). Using detrended correspondence and cluster analysis, the state agency determined that
discrimination of sites by elevation and watershed area, corresponding to montane upland, desert
lowland, and transition zones, resulted in much lower variability among reference sites and
potentially greater sensitivity to impairment.
If multiple reference sites are sampled in different site classes such as different habitat types or
ecoregions (where the sampling method is judged to be appropriate), several important method
performance characteristics are quantified, including: 1) precision for a given metric or assessment
score across replicate reference sites within a site class; 2) relative precision of a given metric or
score among reference sites in different habitat types or ecoregions; 3) range of habitat types over
which a given method yields similar precision and "accuracy"; 4) potential interferences to a given
method that are related to ecoregional or habitat qualities; and 5) bias of a given metric, method, or
both, owing to differences in ecoregions or habitats (Diamond et al. 1996).
A study by Barbour et al. (1996b) for Florida, U.S.A., streams illustrates the importance of
documenting method performance characteristics using multiple reference sites in different regions
or habitat types. Using the same method at all sites, these authors observed fewer taxa in reference
sites from the Florida Peninsula compared to the Florida Panhandle, resulting in much lower
reference values for taxa richness metrics in the Peninsula. Although metric precision was similar
among reference sites in each region, method sensitivity (i.e., the ability of a metric to discern a
difference between a test site and reference sites) was probably poorer in the Peninsula for taxa
richness. Thus, bioassessment "accuracy" may be more uncertain for the Florida Peninsula; that is,
the probability of committing a Type II error (concluding a test site is no different from reference —
therefore unimpaired — when, in fact, it is) may be greater in the Peninsula region. In the context of
a PBMS, the state agency can recognize and document differences in method performance
characteristics between regions and incorporate them into their DQOs. The state in this case can also
use the method performance results to identify those regions or habitat types for which the indicator
fauna may not be naturally sensitive to impairment; i.e., the fauna is naturally species-poor and thus
less likely to reflect water quality changes. If the state agency desires greater sensitivity than the
current method provides, it may have to develop and test different region-specific methods and
perhaps different indicators.
In the last step of the process, a method is used over a range of impaired conditions so as to
determine the method's sensitivity or ability to detect impairment. As discussed earlier, we do not
have sites with known levels of impairment or analogous standards by which to create a calibration
Rapid Bioassessment Protocols for Use in Streams and Rivers 4-7
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DRAFT REVISION—July 23,1997
curve for a given bioassessment method. In lieu of this limitation, sampling sites are chosen that
have known stresses (e.g., urban runoff, toxic pollutants, livestock intrusion, sedimentation,
pesticides). Because different sites may or may not have the same level of impairment within a
habitat type or region (i.e., they are not replicate sites), precision of a method in impaired sites may
best be examined by taking and analyzing multiple samples from the same site or adjacent reaches
(Hannaford and Resh 1995).
The quantification of performance characteristics is a compromise between statistical power and
cost. We recommend sampling at least 5 reference sites in each of 2 different site classes based on
stream size or habitat (Table 4-3, Figure 4-1). More reference sites in each site'class would further
refine precision and, possibly, discriminatory power (sensitivity), of the method. Given the often
wide variation of natural geomorphic conditions and landscape ecology, even within supposedly
"uniform" ecoregions or habitats (Corkum 1989, Hughes 1995), it is desirable to examine 10 or more
reference sites in a given region or habitat type (Yoder and Rankin 1995, Gibson et al. 1996). More
site classes in the evaluation process would improve documentation of the performance range and
bias for a given method. Using the sampling design suggested in Table 4-3 and Figure 4-1, data
from at least 30 sites (reference and test sites combined), sampled within a brief time period (so as to
minimize seasonal changes in the benthic assemblage), are needed to define performance
characteristics.
Table 4-3. Recommended sampling design and parameters for documenting performance
parameters and comparability of 2 different bioassessment approaches using 2 habitat types,
regions, or site classes (taken from Diamond et al. 1996).
Endpoint
Metric or
Assessment
score
Metric or
Assessment
score
Habitat or Region A
Reference Sites 1-5
Method 1
Mean
^Alr
C.V.
cvAlr
Method 2
Mean
MA2r
C.V.
cvA2r
Impaired or Test Site
Method 1
Value
XAU
Method 2
Value
XA2t
Habitat or Region B
^Blr
cvBlr
MB2r
cvB2r
XBU
^B2t
We suggest that a range of "known" impaired sites within a site class is sampled to test the
performance characteristics of a given method. It is important that impaired sites meet the following
criteria: 1) they are similar in habitat and geomorphology to the reference sites examined, implicit in
the ecoregional approach; 2) they clearly have been receiving some chemical, physical, or biological
stress(es) for some time (months at least); and 3) impairment is not obvious without sampling; i.e.,
impairment is not extreme.
The 1st criterion is necessary to reduce potential interferences owing to habitat differences between
the test site and the reference sites. Thus, the reference site will have high probability of serving as a
true blank as discussed earlier. If method performance should be assessed over different levels of
4-8
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DRAFT REVISION—July 23,1997
physical habitat impairment, then sampling would include sites with different habitat problems (for
example, siltation, channelization, or lack of riparian vegetation) and lacking chemical stresses.
The 2nd criterion, which is the documented presence of potential stresses, is necessary to ensure the
likelihood that the test site is truly impaired (Resh and Jackson 1993). A potential test site might
include a body of water that receives toxic chemicals from a point-source discharge or from nonpoint
sources, or a water body that has been colonized by introduced or exotic "pest" species (for example,
zebra mussel or grass carp). Stresses at the test site should be measured quantitatively to document
potential cause(s) of impairment.
The 3rd criterion, that the site is not obviously or heavily impaired, provides a reasonable test of
method sensitivity or "detection limit". Severe impairment (e.g., a site that is dominated by 1 or 2
invertebrate species, or a site apparently devoid of aquatic life) generally requires no biological .
sampling for detection.
4.4 RECOMMENDED PROCESS FOR DOCUMENTATION OF
METHOD COMPARABILITY
Although a comparison of methods at the same reference and test sites at the same time is preferable
(same seasons and similar conditions), it is not essential. The critical requirement when comparing
different sampling methods is that performance characteristics for each method are derived using
similar habitat conditions and site classes at similar times/seasons (Diamond et al. 1996). This
approach is most useful when examining the numeric scores upon which the eventual assessment is
based. Thus, for a method such as RBP that sums the values of several metrics to derive a single
score for a site, the framework described in Table 4-3 should use the site scores. If one were
interested in how a particular multimetric scoring system behaves, or one wishes to compare the
same metric across methods, then individual metrics could be examined using the framework in
Table 4-3. For multivariate assessment methods that do not compute metric scores, one could
instead examine a measure of community similarity or other variable that the researcher uses in
multivariate analysis (Morris 1995).
Method comparability is based on: 1) the relative magnitude of the coefficients of variation in
measurements within and between habitat types or ecoregions, and 2) the relative differences in
measurements between reference and test sites. We emphasize that we are not basing comparability
on the measurements themselves, because different methods may produce different numeric scores
or metrics and some sampling methods may explicitly ignore certain taxonomic groups, which will
influence the metrics examined. Instead, we advise the detection of a systematic relationship among
indices or the same measures among methods. If 2 methods are otherwise comparable based on
similar performance characteristics, then results of the 2 methods can be numerically related to each
other. This outcome is a clear benefit of examining method comparability using a PBMS
framework.
Table 4-3 summarizes a suggested test design, and Table 4-4 summarizes recommended analyses for
documenting both the performance characteristics of a given method (as outlined in Figure 4-1), and
the degree of data comparability between 2 or more methods. We emphasize that the process
outlined in Table 4-3 is not one that is implemented with every study. Rather, the process should be
performed at least once to document the limitations and range of applicability of the methods, and
should be cited with subsequent uses of the method(s).
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DRAFT REVISION—July 23,1997
The following performance characteristics are quantified for each bioassessment method and
compared: 1) the within-habitat or ecoregion coefficient of variation for a given metric score or
index (by examining reference-site data for each habitat type or ecoregion separately (e.g., CVAlr and
CVBlr; Table 4-3), 2) difference or bias in precision related to habitat type or ecoregion for a given
metric or score (by comparing reference site coefficient of variation from each habitat type or
ecoregion: CVAIr/CVBlr; Table 4-4) and 3) estimates of method sensitivity or discriminatory power,
by comparing test site data with reference site data within each habitat type or ecoregion as a
function of reference site variability (Table 4-4) e.g.,
Air
Table 4-4. Suggested arithmetic expressions for deriving performance characteristics that can
be compared between 2 or more methods. In all cases, /j. = mean value, X = test site value, S =
standard deviation, t = students t value (one-tailed). Subscript code for parameters is as
follows: capital letter refers to habitat type or ecoregion (A or B); numeral refers to method 1
or 2; and lower case letter refers to reference (r) or test site (t) (taken from Diamond et al.
1996).
Performance characteristic
Parameters for quantifying method comparability
Relative precision of metric or score within a habitat type
or ecoregion
CVAlr and CVA2r CVBlr and CVB2r
Relative metric or score precision between habitat types or
ecoregions
CV
A1r
CV
Air
CV
B1r
CV
B2r
Relative sensitivity or "detection limit" of metric or score
within a habitat type or ecoregion
Alt
A2t
SA1r SA2r
Bit
B2t
SB1r SB2r
Relative sensitivity of metric or score between habitat
types or ecoregions
Alt
Bit
= 0?
B1r
A2t
B2t
= 0?
A2r
B2r
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DRAFT REVISION—July 23,1997
Performance characteristic
Parameters for quantifying method comparability
Relative accuracy of metric or score within a habitat type
or ecoregion
_y
-Y
-~
JA1r
A2r
X
B1t
B1r
B2r
(at some predefined probability level [e.g., p =
0.05] and df = number of reference sites sampled •
1).
Relative accuracy of metric or score between habitats or
ecoregions
J51 = 1.0? -11 = 1.0?
A method that yields a smaller difference between test-sites and reference score in relation to the
reference site variability measured (Table 4-4) would indicate less discriminatory power or
sensitivity; that is, the test site is erroneously perceived to be similar to or better than the reference
condition and not impaired (Type II error). The ratio suggested in Table 4-4 is similar to a t statistic
and its value could be defined to establish impaired conditions as part of the data quality
requirements for an acceptable method (Underwood 1994). For example, a ratio > 2.132 (based on
the Table t value (1-tailed) at p=0.05 and data for 5 reference sites (4 df), could be used as the
criterion for assessing a test site as impaired. In this case, there is only a 5% probability of making a
Type 1 error: assessing the test site as impaired when in fact it is similar to the reference condition.
Relatively few methods may be able to consistently meet the above data quality criterion and also
maintain high sensitivity to impairment because both characteristics require a method that produces
relatively precise, accurate data. If an agency or program wants to reduce the probability of a Type
II error and is willing to sacrifice some accuracy for increased method sensitivity, the criterion could
be relaxed to 0.941 (based on Table t score of p=0.20, or a 20% probability of a Type I error). In
this case, a wider range of methods may be available that meet this DQO. For example, if the
agency's intent is to screen many sites so as to prioritize "hot spots" or significant impairment in
need of corrective action, then a method that is inexpensive, quick, and tends to show impairment
when significant impairment is actually present (such as the volunteer monitoring method discussed
earlier) can meet prescribed DQOs with less cost and effort. In this case, the data requirements
dictate high priority for method sensitivity or discriminatory power (potentially lower Type II error
rate), understanding that there is likely also to be a high Type I error rate (misidentification of
unimpaired sites).
Relative accuracy of each method is addressed to the extent that the test sites chosen are likely to be
truly impaired on the basis of independent factors such as the presence of chemical stresses or
Rapid Bioassessment Protocols for Use in Streams and Rivers
4-11
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DRAFT REVTSION-^July 23,1997
suboptimal habitat. Relative accuracy should be evident in the magnitude of the t-statistic ratios in
Table 4-4. Also, a method with relatively low precision (high variance) among ecoregional
reference sites compared with another method may suggest lower method accuracy. Note that a
method having lower precision may still be satisfactory for some programs if it has other advantages,
such as high ability to detect impaired sites with less cost and effort to perform.
Once performance characteristics are defined for each method, data comparability can be
ascertained. If 2 methods are similarly precise, sensitive, and biased over the habitat types sampled,
then the different methods should produce comparable data. Interpretive judgements could then be
made concerning the quality of aquatic life using data produced by either or both methods combined.
Alternatively, the comparison may show that 2 methods are comparable in their performance
characteristics in certain habitats or regions and not others. If this is so, results of. the 2 methods can
be combined for the type of habitats in which data comparability was demonstrated, but not for other .
regions or habitat types.
In practice, comparability of bioassessment methods would be judged relative to a reference method
that has already been fully characterized (using the framework summarized in Figure 4-1) and which
produces data with the quality needed by a certain program or agency. The qualities of this reference
method are then defined as method performance criteria. If an alternative method yields less
precision among reference sites within the same habitat type or ecoregion than the reference method
(e.g., CVAlr > CVfa in Table 4-3), then the alternative method probably is not comparable to the
reference method. A program or study could require that alternative methods are acceptable only if
they are as precise as the reference method. A similar process would be accomplished for other
performance characteristics that a program or agency deems important based on the type of data
required by the program or study.
4.5 CASE EXAMPLE DEFINING METHOD PERFORMANCE
CHARACTERISTICS
Florida Department of Environmental Protection (DEP) has developed a statewide network for
monitoring and assessing Florida's surface waters using macroinvertebrate data. Florida DEP has
rigorously examined performance characteristics of their collection and assessment methods to
provide better overall quality assurance of their biomonitoring program and to provide defensible
and appropriate assessments of the state's surface waters (Barbour et al. 1996b, c). Much of the
method characterization process developed for Florida DEP is easily communicated in the context of
a PBMS approach.
In addition to characterizing data quality and method performance based on ecoregional site classes,
Florida DEP also characterized their methods based on season (summer vs. winter sampling index
periods), and size of subsample analyzed (100, 200, or 300-organism subsample). In addition,
analyses were performed on the individual component metrics which comprised the Florida stream
condition index (SCI). For the sake of brevity, we will summarize the characterization process and
results for the SCI in the summer index period and the Penninsula and Northeast bioregions. The
same process was used for other bioregions in the state and in the winter index period.
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DRAFT REVISION—July 23,1997
PERFORMANCE CRITERIA CHARACTERISTICS OF FLORIDA SCI
(See Figure 4-1 for Process)
Characterize Method Precision Within a Site or Measurement Error
A total of 7 sites in the Penninsula ecoregion were subjected to multiple sampling (adjacent
reaches). The DEP observed a mean SCI = 28.4 and a C.V. (within a stream) = .6.8%. These
data suggest low measurement error associated with the method and the index score.
Determine Precision of the Method Based on a Population of Reference Sites
A total of 36 reference sites were sampled in the Penninsula ecoregion (Step 1, Figure.4-1).
The SCI score could range from a minimum of 7 to a theoretical maximum of 38 based on
the component metric scores. However, in the Penninsula, reference site SCI scores
generally ranged between 20 and 31. A mean SCI score of 27.9 was observed with a C.V. of
13.3%. Given this degree of precision in the reference condition SCI score, power analysis
indicated that 80% of the time, a test site with an SCI 5 points less (based on only a single
sample at the test site) than the reference criterion, could be distinguished as impaired with
95% confidence. This analysis also indicated that if duplicate samples were taken at the test
site, a difference of 3 points in the SCI score between the test site and the reference criterion
could be distinguished as impaired with 95% confidence.
Determine Method and Index Sensitivity and "Accuracy"
"reference ~ Xtest
Using the formula in Table 4-4 _IL!I!5£! !L the above data can be translated into a
(2*S . ) '
reference
theoretical "detection limit" or sensitivity which for the Penninsula bioregion and summer
index period is equal to an SCI score of approximately 20. SCI scores lower than this value
are distinguishable from reference with approximately 95% confidence. The criterion for
accuracy of the SCI score (and therefore the assessment), using this method in the
Penninsula ecoregion and summer
^reference ~ SCItest
index period is given by _H!I!^f! !!l > t (Table 4-4) where t = 1.697 based on 36
reference
reference sites. This formula yields an SCI accuracy criterion < 21. Actual "accuracy" of
the method, using "known" impaired sites, indicated that approximately 80% of the test sites
had SCI scores < 20 (Fig. 4-2a); thereby meeting the t-test criterion. In other words, an
impaired site would be assessed as such 80% of the time using the collection method in the
Penninsula bioregion in the summer.
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DRAFT REVISION—July 23,1997
Determination of Method Bias and Relative Sensitivity in Different Site Classes or
Ecoregions
A comparative analysis of precision, sensitivity, and ultimately bias, can be performed for
the Florida DEP method and the SCI index outlined in Table 4-4. For example, the mean
SCI score in the Northeast bioregion, during the same summer index period, was 26.3 with a
C.V. = 12.8% based on 16 reference sites. Comparing this C.V. to the one reported for the
Penninsula in the previous step, it is apparent that the precision of this method in the
Northeast was similar to that observed in the Penninsula bioregion.
1 ft 3 — "5f
Relative detection limit, as defined in Table 4-4, was —• or a SCI score of 19
(2*3.4)
similar to the limit observed for the Penninsula suggesting that the collection method and
index score can theoretically detect impairment with similar sensitivity in both the Northeast
and the Penninsula bioregions. However, actual sensitivity of the method in the Northeast
was slightly lower than in the Penninsula bioregion. This was indicated by examining the
Xref ~ SCItest
"accuracy" criterion of the index — — > t = 1.75, based on Preference sites;
(SreP
which yields an SCI criterion < 19. This SCI criterion is slightly lower than that observed
for the Penninsula bioregion (SCI = 21). Actual accuracy, however, appeared to be much
lower in the Northeast than in the Penninsula bioregion. Using the "accuracy" criterion of
SCI < 19 noted above, and data from actual "known" impaired sites, 50% of the impaired
test sites had SCI scores 2:20 (Fig. 4-2b) and would have failed the t-test criterion. This
suggests that an impaired site would be assessed as such only 50% of the time in the
Northeast bioregion in the summer as opposed to 80% of the time in the Penninsula
bioregion during the same index period.
Part of the difference in accuracy of the method among the two bioregions can be attributed
to differences in sample size. Data from only four "known" impaired sites were available in
the Northeast region while the Penninsula region had data from 12 impaired sites. The
above analyses show, however, that there may be differences in method performance
between the two regions (probably attributable to large habitat differences between the
regions) which should be further explored using data from additional "known" impaired
sites, if available.
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DRAFT REVISION—July 23,1997
3. Peninsula Stream Sites (Summer)
35
I 30
c ot;
zo
1 20
T3
i 15
°e 10
(0
2 5
b.
35
£ 30
•o
-E 25
•I 20
1 «
» 10
ro c
05 5
3> 0
r "* n
1 ^^Bl^««»
8
i
O
•
-1-
4
4
4
4
4
1
I
Reference - Impaired
Northeast Stream Sites (Summer)
»
1 • 1
Reference
•
Impaired
1
• 1
4
I
4
4
«
1C Non-Outlier Max
Non-Outlier Min
D 75%
25%
• Median
© Outliers
31 Non-Outlier Max
Non-Outlier Min
O 75%
25%
• Median
Figure 4-2. Comparison of the discriminatory ability of the SCI between Peninsula and Northeast
Bioregions.
Rapid Bioassessment Protocols for Use in Streams and Rivers
4-15
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DRAFT REVISION—July 29,1997
This Page Intentionally Left Blank
4-16 Chapter 4: Performance-Based Methods System (PBMS)
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DRAFT REVISION-^Julv 28,1997
HABITAT ASSESSMENT AND
PHYSICOCHEMICAL PARAMETERS
An evaluation of habitat quality is critical to any assessment of ecological integrity and should be
performed at each site at the time of the biological sampling. In the truest sense, "habitat'"
incorporates all aspects of physical and chemical constituents along with the biotic interactions. In
these protocols, the definition of "habitat" is narrowed to the quality of the instream and riparian
habitat that influences the structure and function of the aquatic community in a stream. The
presence of a degraded habitat can sometimes obscure investigations on the effects of toxicity
and/or pollution. The assessments performed by many water resource agencies include a general
description of the site, a physical characterization and water quality assessment, and a visual
assessment of instream and riparian habitat quality. Some states (e.g., Idaho DEQ and Illinois
EPA) include quantitative measurements of physical parameters in their habitat assessment.
Together these data provide a comprehensive and integrated picture of the biological condition of
a stream system.
The habitat quality evaluation can be accomplished by characterizing selected physicochemical
parameters in conjunction with a systematic assessment of physical structure. Through this
approach, key features can be rated or scored to provide a useful assessment of habitat quality.
5.1 PHYSICAL CHARACTERISTICS AND WATER QUALITY
Both physical characteristics and water quality parameters are pertinent to characterization of the
stream habitat. An example of the data sheet used to characterize the physical characteristics and
water quality of a site is shown in Appendix A. The information required includes measurements
of physical characterization and water quality made routinely to supplement biological surveys.
Physical characterization includes documentation of general land use. description of the stream
origin and type, summary of the riparian vegetation features, and measurements of instream
parameters such as width, depth, flow, and substrate. The water quality discussed in these
protocols are in situ measurements of standard parameters that can be taken with a water quality
instrument. These are generally instantaneous measurements taken at the time of the survey.
Measurements of certain parameters, such as temperature, dissolved oxygen, and turbidity, can be
taken over a diurnal cycle and will require instrumentation that can be left in place for extended
periods or collects water samples at periodic intervals for measurement. In addition, water
samples may be desired to be analyzed for selected chemicals as part of a chemical monitoring
program. These chemical samples are transported to an analytical laboratory for processing. The
combination of this information (physical characterization and water quality) will provide insight
as to the ability of the stream to support a healthy aquatic community, and to the presence of
chemical and non-chemical stressors to the stream ecosystem. Information requested in this
section (Appendix A-l. Form 1) is standard to many aquatic studies and allows for some
comparison among sites. Additionally, conditions that may significantly affect aquatic biota are
documented.
Rapid Bioassessment Protocols for Use in Streams and Rivers 5-1
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DRAFT REVISION—July 28, 1997
5.1.1 Header Information (Station Identifier)
The header information is identical on all data sheets and requires sufficient information to
identify the station and location where the survey was conducted, date and time of survey, and the
investigators responsible for the quality and integrity of the data. The stream name and river basin
identify the watershed and tributary; the location of the station is described in the narrative to help
identify access to the station for repeat visits. The rivermile (if applicable) and latitude/longitude
are specific locational data for the station. The station number is a code assigned by the agency
that will associate the sample and survey data with the station. The STORET number is assigned
to each datapoint for inclusion in USEPA's STORET system. The stream class is a designation of
the grouping of homogeneous characteristics from which assessments will be made. For instance.
Ohio EPA uses ecoregions and size of stream. Florida DEP uses bioregions (aggregations of
subecoregions), and Arizona DEQ uses elevation as a means to identify stream classes. Listing
the agency and investigators assigns responsibility to the data collected from the station at a
specific date and time. The reason for the survey is sometimes useful to an agency that conducts
surveys for various programs and purposes.
5.1.2 Weather Conditions
Note the present weather conditions on the day of the survey and those immediately preceding the
day of the survey. This information is important to interpret the effects of storm events on the
sampling effort.
5.1.3 Site Location/Map
To complete this phase of the bioassessment. a photograph may be helpful in identifying station
location and documenting habitat conditions. Any observations or data not requested but deemed
important by the field observer should be recorded. A hand-drawn map is useful to illustrate
major landmarks or features of the channel morphology or orientation, vegetative zones.
buildings, etc. that might be used to aid in data interpretation.
5.1.4 Stream Characterization
Stream Subsystem: In regions where the perennial nature of streams is important, or where the
tidal influence of streams will alter the structure and function of communities, this parameter
should be noted.
Stream Type: Communities inhabiting coldwater streams are markedly different from those in
warmwater streams, many states have established temperature criteria that differentiate these 2
stream types.
Stream Origin: Note the origination of the stream under study, if it is known. As the size of the
stream or river increases, a mixture of origins of tributaries is likely.
5.1.5 Watershed Features
Collecting this information usually requires some effort initially for a station. However.
subsequent surveys will most likely not require an in-depth research of this information.
5-2 Chapter 5: Habitat Assessment and Physicochemical Parameters
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DRAFT REVISION-^July 28,1997
Predominant Surrounding Land Use Type: Document the prevalent land-use type in the
catchment of the station (noting any other land uses in the area which, although not predominant,
may potentially affect water quality). Land use maps should be consulted to accurately document
this information.
Local Watershed Nonpoint-Source Pollution: This item refers to problems and potential
problems in the watershed. Nonpoint-source pollution is defined as diffuse agricultural and urban
runoff. Other compromising factors in a watershed that may affect water quality include feedlots.
constructed wetlands, septic systems, dams and impoundments, mine seepage, etc.
Local Watershed Erosion: The existing or potential detachment of soil within the local
watershed (the portion of the watershed or catchment that directly affects the stream reach or
station under study) and its movement into the stream is noted. Erosion can be rated through
visual observation of watershed and stream characteristics (note any turbidity observed during
water quality assessment below).
5.1.6 Ripa rian Vegetation
An acceptable riparian zone includes a buffer strip of a minimum of 18 m from the stream on
either side. The vegetation within the riparian zone is documented here as the dominant type and
species, if known.
5.1.7 Instream Features
Estimated Stream Width (in meters, m): Estimate the distance from bank to bank at a transect
representative of the stream width in the reach.
Estimated Stream Depth (m): Estimate the vertical distance from water surface to stream
bottom at a representative depth to obtain average depth.
Proportion of Reach Represented by Stream Morphological Types: The proportion
represented by riffles, runs, and pools should be noted to describe the morphological
heterogeneity of the reach.
Estimated Reach Length: This information is important if variable length reaches are surveyed
and assessed.
Velocity: Measure the surface velocity in the thalweg of a representative run area. If
measurement is not done, estimate the velocity as slow, moderate, or fast.
Channelized: Indicate whether or not the area around the sampling reach or station is
channelized.
Dam Present: Indicate the presence or absence of a dam upstream or downstream of the
sampling reach or station. If a dam is present, include specific information relating to alteration of
flow.
Canopy Cover: Note the general proportion of open to shaded area which best describes the
amount of cover at the sampling reach or station. A densiometer may be used in place of visual
estimation.
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DRAFT REVISION—July 28,1997
High Water Mark (m): Estimate the vertical distance from the stream bank to the peak overflow
level, as indicated by debris hanging in riparian or floodplain vegetation, and deposition of silt or
soil. In instances where bank overflow is rare, a high water mark may not be evident.
5.1.8 Aquatic Vegetation
The general type and relative dominance of aquatic plants are documented in this section. Only an
estimation of the extent of aquatic vegetation is made. Besides being an ecological assemblage
that responds to perturbation, aquatic vegetation provides refugia and food for aquatic fauna. List
the species of aquatic vegetation, if known.
5.1.9 Water Quality
Temperature (°C), Dissolved Oxygen (fig/L), pH, Conductivity (uohms), Turbidity:
Measure and record values for each of the water quality parameters indicated, using the appropri-
ate calibrated water quality instrument(s). Note the type of instrument and unit number used.
Water Odors: Note those odors described (or include any other odors not listed) that are
associated with the water in the sampling area.
Water Surface Oils: Note the term that best describes the relative amount of any oils present on
the water surface.
Turbidity: If turbidity is not measured directly, note the term which, based upon visual
observation, best describes the amount of material suspended in the water column.
5.1.10 Sediment/Substrate
Sediment Odors: Disturb sediment in pool or other depositional areas and note any odors
described (or include any other odors not listed) which are associated with sediment in the
sampling reach.
Sediment Oils: Note the term which best describes the relative amount of any sediment oils
observed in the sampling area.
Sediment Deposits: Note those deposits described (or include any other deposits not listed) that
are present in the sampling reach. Also indicate whether the undersides of rocks not deeply
embedded are black (which generally indicates low dissolved oxygen or anaerobic conditions).
Inorganic Substrate Components: Visually estimate the relative proportion of each of the seven
substrate/particle types listed that are present over the sampling reach.
Organic Substrate Components: Indicate relative abundance of each of the three substrate types
listed.
5-4 Chapter 5: Habitat Assessment and Physicochemical Parameters
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DRAFT REVISION-^JuIy 28,1997
5.2 HABITAT ASSESSMENT
Biological potential is limited by the quality of the physical habitat, forming the template within
which biological communities develop (Southwood 1977). Thus, habitat assessment is defined as
the evaluation of the structure of the surrounding physical habitat that influences the quality of the
water resource and the condition of the resident aquatic community (Barbour et al. 1996a). For
streams, an encompassing approach to assessing structure of the habitat includes an evaluation of
the variety and quality of the substrate, channel morphology, bank structure, and riparian
vegetation. Habitat parameters pertinent to the assessment of habitat quality include those that
characterize the stream "micro scale" habitat (e.g.. estimation of embeddeddness). the "macro
scale" features (e.g., channel morphology), and the riparian and bank structure features that are
most often influential in affecting the other parameters.
Rosgen (1985, 1994) presented a
stream and river classification system
that is founded on the premise that
dynamically-stable stream channels
have a morphology that provides
appropriate distribution of flow
energy. Further, he identifies eight
major variables that affect the
stability of channel morphology, but
are not mutually independent:
channel width, channel depth, flow
velocity, discharge, channel slope,
roughness of channel materials,
sediment load and sediment particle
size distribution. When streams have
one of these characteristics altered,
some of their capability to dissipate
energy is lost (Leopold et al. 1964,
Rosgen 1985) and will result in
accelerated rates of channel erosion.
Some of the habitat structural
components that function to dissipate
• sinuositv
EQUIPMENT/SUPPLIES NEEDED FOR HABITAT
ASSESSMENT AND PHYSICAL/WATER QUALITY
CHARACTERIZATION
• Physical Characterization and Water Quality Field
Data Sheet'
Habitat Assessment Field Data Sheet*
clipboard
pencils or waterproof pens
35 mm camera (may be digital)
video camera (optional)
upstream/downstream "arrows" or signs for
photographing and documenting sampling reaches
Flow or velocity meter
In situ water quality meters
Global Positioning Satellite (GPS) Unit
' It is helpful to copy fieldsheets onto "Rite in the Rain" '
paper for use in wet weather conditions
flow enerev are:
. • roughness of bed and bank materials
• presence of point bars (slope is an important characteristic)
• vegetative conditions of stream banks and the riparian zone
• condition of the floodplain (accessibility from bank, overflow, and size are
important characteristics).
Measurement of these parameters or characteristics serve to stratify and place streams into distinct
classifications. However, none of these habitat classification techniques attempt to differentiate
the quality of the habitat and the ability of the habitat to support the optimal biological condition
of the region. Much of our understanding of habitat relationships in streams has emerged from
Rapid Bioassessment Protocols for Use in Streams and Rivers
5-5
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DRAFT REVISION—July 28,1997
comparative studies that describe statistical relationships between habitat variables and abundance
of biota (Hawkins et al. 1993). However, in response to the need to incorporate broader scale
habitat assessments in water resource programs, 2 types of assessment approaches have been
developed. The Environmental Monitoring and Assessment Program (EMAP) of the USEPA and
the National Water-Quality Assessment Program (NAWQA) of the USGS developed techniques
that incorporate measurements of various features of the instream, channel, and bank morphology
(Meader et al. 1993, Klemm and Lazorchak 1994). These techniques provide a relatively
comprehensive characterization of the physical structure of the stream sampling reach and its
surrounding floodplain. The second type was a more rapid and qualitative habitat assessment
approach that was developed to describe the overall quality of the physical habitat (Ball 1982,
Ohio EPA 1987. Plafkin et al. 1989. Barbour and Stribling 1991, 1994. Rankin 1991. 1995). The
habitat assessment matrix developed for the Rapid Bioassessment Protocols (RBPs) in Plafkin et
al. (1989) were originally based on the Stream Classification Guidelines for Wisconsin developed
by Ball (1982) and "Methods of Evaluating Stream, Riparian, and Biotic Conditions" developed
by Platts et al. (1983). Barbour and Stribling (1991. 1994) modified the habitat assessment
approach originally developed for the RBPs to include additional assessment parameters for high
gradient streams and a more appropriate parameter set for low gradient streams (Appendix A-l,
Forms 2,3). All parameters are evaluated and rated on a numerical scale of 0 to 20 (highest) for
each sampling reach. The ratings are then totaled and compared to a reference condition to
provide a final habitat ranking. Scores increase as habitat quality increases. To ensure consistency
in the evaluation procedure, descriptions of the physical parameters and relative criteria are
included in the rating form.
A biologist who is well-versed in the ecology and zoogeography of the region can generally
recognize optimal habitat structure as it relates to the biological community. The ability to
accurately assess the quality of the physical habitat structure using a visual-based approach
depends on several factors:
• the parameters selected to represent the various features of habitat structure need
to be relevant and clearly defined
• a continuum of conditions for each parameter must exist that can be characterized
from the optimum for the region or stream type under study to the poorest
situation reflecting substantial alteration due to anthropogenic activities
• the judgement criteria for the attributes of each parameter should minimize
subjectivity through either quantitative measurements or specific categorical
choices
• the investigators are experienced in or adequately trained for stream assessments
in the region under study
• adequate documentation and ongoing training is maintained to evaluate and
correct errors resulting in outliers and aberrant assessments
Habitat evaluations are first made on instream habitat, followed by channel morphology, bank
structural features, and riparian vegetation. Generally, a single, comprehensive assessment is
made that incorporates features of the entire sampling reach as well as selected features of the
catchment. Additional assessments may be made on neighboring reaches to provide a broader
evaluation of habitat quality for the stream ecosystem. The actual habitat assessment process
involves rating the 10 parameters as optimal, suboptimal. marginal, or poor based on the criteria
5-6 Chapter 5: Habitat Assessment and Physicochemical Parameters
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DRAFT REVISION-Juty 28,1997
included on the Habitat Assessment Field Data Sheets (Appendix A-l, Forms 2,3). Some state
programs, such as Florida Department of Environmental Protection (DEP) (1996) and Mid-
Atlantic Coastal Streams Workshop (MACS) (1996) have adapted this approach using somewhat
fewer and different parameters.
Reference conditions are used to scale the assessment to the "best attainable" situation. This
approach is critical to the assessment because stream characteristics will vary dramatically across
different regions (Barbour and Stribling 1991). The ratio between the score for the test station and
the score for the reference condition provides a percent comparability measure for each station.
The station of interest is then classified on the basis of its similarity to expected conditions
(reference condition), and its apparent potential to support an acceptable level of biological health.
Use of a percent comparability evaluation allows for regional and stream-size differences which
affect flow or velocity, substrate, and channel morphology. Some regions are characterized by
streams having a low channel gradient, such as coastal plains or prairie regions.
Other habitat assessment approaches or a more rigorously quantitative approach to measuring the
habitat parameters may be used (See Klemm and Lazorchak 1994, Meader et al. 1993). However,
holistic assessment of a wide variety of habitat attributes along with other types of data is critical
if physical measurements are to be used to best advantage in interpreting biological data.
A more detailed discussion of the relationship between habitat quality and biological condition is
presented in Chapter 10.
A generic habitat assessment approach based on visual observation can be separated into 2 basic
approaches—one designed for high-gradient streams and one designed for low-gradient streams.
High-gradient or riffle/run prevalent streams are those in moderate to high gradient landscapes.
Natural high-gradient streams have substrates primarily composed of coarse sediment particles
(i.e., gravel or larger) or frequent coarse paniculate aggregations along stream reaches. Low-
gradient or glide/pool prevalent streams are those in low to moderate gradient landscapes. Natural
low-gradient streams have substrates of fine sediment or infrequent aggregations of more coarse
(gravel or larger) sediment particles along stream reaches. The entire sampling reach is evaluated
for each parameter. Descriptions of each parameter and its relevance to instream biota are
presented in the following discussion. Parameters that are used only for high-gradient prevalent
streams are marked with an "a"; those for low-gradient dominant streams, a "b". If a parameter is
used for both stream types, it is not marked with a letter. A brief set of decision criteria is given
for each parameter corresponding to each of the 4 categories reflecting a continuum of conditions
on the field sheet.
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DRAFT REVISION—July 28,1997
PROCEDURE FOR PERFORMING HABITAT ASSESSMENT
1. Select the reach to be assessed. The habitat assessment is performed on the same 100 m reach (or
other reach designation) from which the biological sampling is conducted. Some parameters
require an observation of a broader section of the catchment than just the sampling reach.
2. Complete the station identification section of each field data sheet and habitat assessment form.
3. Generally, it is best to perform the biological sampling first so the investigators obtain a close look
at the habitat features. If the physical and water quality characterization and habitat assessment are
done before the biological sampling, care must be taken to avoid disturbing the sampling habitat.
After scrutinizing the instream habitat during sampling, refinement of the visual-based habitat
assessment may be necessary.
4. Complete the Physical Characterization and Water Quality Field Data Sheet. Sketch a map of
the sampling reach on the back of this form.
5. Complete the Habitat Assessment Field Data Sheet, in teams of two or more biologists, if
possible, to come to a consensus on determination of quality.
QUALITY ASSURANCE PROCEDURES
1. Each biologist is to be trained in the visual-based habitat assessment technique for the applicable
region or state.
2. The judgment criteria for each habitat parameter are calibrated for the stream classes under study
to be applicable to an assessment.
3. Periodic checks of assessment results are completed using pictures of the sampling reach and
discussions among the biologists in the agency.
Parameters to be evaluated in sampling reach:
1. Epifaunal Substrate/Available Cover:
Includes the relative quantity and variety of natural structures in
the stream, such as cobble (riffles), large rocks, fallen trees, logs
and branches, and undercut banks, available as refugia, feeding.
or sites for spawning and nursery functions of aquatic
macrofauna. A wide variety and/or abundance of submerged
structures in the stream provides macroinvertebrates and fish with
a large number of niches, thus increasing habitat diversity. As
variety and abundance of cover decreases, habitat structure
becomes monotonous, diversity decreases, and the potential for
recovery following disturbance decreases. Riffles and runs are
critical for maintaining a variety and abundance of insects in
most high-gradient streams and serving as spawning and feeding
refugia for certain fish. The extent and quality of the riffle is an
important factor in the support of a healthy biological condition
in high-gradient streams. Riffles and runs offer a diversity of
habitat through variety of particle size, and, in many small high-
gradient streams, will provide the most stable habitat. Snags and
5-8 Chapter 5: Habitat Assessment and Physicochemical Parameters
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DRAFT REVISION—July 28,1997
Selected References:
submerged logs are among the most productive habitat structure
for macroinvertebrate colonization and fish refugia in low-
gradient streams. However, "new fall" will not yet be prepared
for colonization.
Wesche et al. 1985, Pearsons et al. 1992, Gorman 1988. Rankin
1991, Barbour and Stribling 1991, Plafkin et al. 1989, Platts et
al. 1983, Osborne et al. 1991. Benke et al. 1984, Wallace et al.
1996, Ball 1982, MacDonald et al. 1991, Reice 1980, Clements
1987, Hawkins etal. 1982.
Habitat
Parameter
1. Epifaunal
Substrate/
Available Cover
SCORE
Condition Category
Optimal
Greater than 70% (50%
for low gradient streams)
of substrate favorable for
epifaunai colonization
and fish cover, most
favorable is a mix of
snags, submerged logs,
undercut banks, cobble or
other stable habitat and at
stage to allow full
colonization potential
(i.e., logs/snags that are
not new fall and not
transient).
20 19 18 17 16
Suboptimal
40-70% (30-50% for low
gradient streams) mix of
stable habitat; well-suited
for full colonization
potential; adequate
habitat for maintenance of
populations: presence of
additional substrate in the
form of newfall, but not
yet prepared for
colonization (may rate at
high end of scale).
15 14 13 12 11
Marginal
20-40% (10-30% for low
gradient streams) mix of
stable habitat; habitat
availability less than
desirable: substrate
frequently disturbed or
removed.
10 9 8 7 6
Poor •
Less than 20% (10% for
low gradient streams)
stable habitat lack of
habitat is obvious;
substrate unstable or
lacking.
543210
Epifaunal Substrate/Available Cover—High Gradient
Poor Range
Optimal Range
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DRAFT REVISION—July 28,1997
Epifaunal Substrate/Available Cover—Low Gradient
Optimal Range
Poor Range
2a. Embeddedness (High Gradient):
Refers to the extent to which rocks (gravel, cobble, and boulders)
and snags are covered or sunken into the silt, sand, or mud of the
stream bottom. Generally, as rocks become embedded, the
surface area available to macroinvertebrates and fish (shelter,
spawning, and egg incubation) is decreased. Embeddedness is a
result of large-scale sediment movement and deposition, and is a
parameter evaluated in the riffles and runs of high-gradient
streams. The rating of this parameter may be variable depending
on where the observations are taken. To avoid confusion with
sediment deposition (another habitat parameter), observations of
embeddedness should be taken in the upstream and central
portions of riffles and cobble substrate areas.
Selected References:
Ball 1982, Osbome et al. 1991. Barbour and Stribling 1991,
Platts et al. 1983, MacDonald et al. 1991. Rankin 1991. Reice
1980, Clements 1987, Benke et al. 1984, Hawkins et al. 1982,
Burton and Harvev 1990.
Habitat
Parameter
2.a Embeddedness
SCORE
Condition Category
Optimal
Gravel, cobble, and
boulder particles are 0-
25% surrounded by fine
sediment. Layering of
cobble provides diversity
of niche space.
20 19 18 17 16
Suboptimal
Gravel, cobble, and
boulder particles are 25-
50% surrounded by fine
sediment.
15 14 13 12 11
Marginal
Gravel, cobble, and
boulder panicles are 50-
75% surrounded by fine
sediment.
10 9 8 7 6
Poor
Gravel, cobble, and
boulder particles are more
than 75% surrounded by
fine sediment.
543210
5-10
Chapter 5: Habitat Assessment and Physicochemical Parameters
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DRAFT REVISION—July 28,1997
Embeddedness—High Gradient
Poor Range
Optimal Range
2b.
Pool Substrate Characterization (Low Gradient):
Evaluates the type and condition of bottom substrates found in
pools. Firmer sediment types (e.g., gravel, sand) and rooted
aquatic plants support a wider variety of organisms than a pool
substrate dominated by mud or bedrock and no plants. In
addition, a stream that has a uniform substrate in its pools will
support far fewer types of organisms than a stream that has a
variety of substrate types.
Selected References: Beschta and Platts 1986, U.S. EPA 1983.
Habitat
Parameter
2b. Pool Substrate
Characterization
SCORE
Condition Category
Optimal
Mixture of substrate
materials, with gravel and
firm sand prevalent: root
mats and submerged
vegetation common
20 19 18 17 16
Suboptimal
Mixture of soft sand.
mud. or clay: mud may be
dominant: some root mats
and submerged vegetation
present
1< 14 n i: 11
Marginal
All mud or clay or sand
bottom; little or no root
mat; no submerged
vegetation.
10 9 8 7 6
Poor
Hard-pan clay or bedrock;
no root mat or submerged
vegetation.
543210
Rapid Bioassessment Protocols for Use in Streams and Rivers
5-11
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DRAFT REVISION-July 28,1997
Pool Substrate Characterization—Low Gradient
Optimal Range
Poor Range
3a. Velocity/Depth Combinations (High Gradient):
Patterns of velocity and depth are included for high-gradient
streams under this parameter as an important feature of habitat
diversity. The best streams in most high-gradient regions will
have all four patterns present: (1) slow-deep, (2) slow-shallow.
(3) fast-deep, and (4) fast-shallow. The general guidelines are
0.5 m depth to separate shallow from deep, and 0.3 m/sec to
separate fast from slow. The occurrence of these four patterns
relates to the stream's ability to provide and maintain a stable
aquatic environment.
Selected References:
Ball 1982, Brown and BruSsock 1991, Gore and Judy 1981,
Oswood and Barber 1982.
Habitat
Parameter
3a. Velocity/ Depth
Regimes
SCORE
Condition Category
Optimal
All four velocity /depth
regimes present (slow-
deep, slow-shallow, fast-
deep, fast-shallow).
20 19 18 17 16
Suboptimal
Only 3 of the 4 regimes
present (if fast-shallow is
missing, score lower than
if missing other regimes).
15 14 13 12 11
Marginal
Only 2 of the 4 habitat
regimes present (if fast-
shallow or slow-shallow
are missing, score low).
10 9 8 7 6
Poor
Dominated by 1 velocity/
depth regime (usually
slow-deep).
543210
5-12
Chapter 5: Habitat Assessment and Physicochemical Parameters
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DRAFT REVISION—July 28,1997
Velocity/Depth—High Gradient
Optimal Range
Poor Range
3b. Pool Variability (Low Gradient):
Rates the overall mixture of pool types found in streams,
according to size and depth. The four basic types of pools are
large-shallow, large-deep, small-shallow, and small-deep. A
stream with many pool types will support a wide variety of
aquatic species. Rivers with low sinuosity (few bends) and
monotonous pool characteristics do not have sufficient quantities
and types of habitat to support a diverse aquatic community.
General guidelines are any pool dimension (i.e., length, width.
oblique) greater than half the cross-section of the stream for
separating large from small and 1 m depth separating shallow and
deep.
Selected References: Beschta and Platts 1986. U.S. EPA 1983.
Habitat
Parameter
3b. Pool
Variability
SCORE
Condition Category
- Optimal
Even mix of large-
shallow, large-deep,
small-shallow, small-deep
pools present.
20 19 18 17 16
Suboptimal
Majority of pools large-
deep; very few shallow.
15 14 13 12 11
Marginal
Shallow pools much more
prevalent than deep pools.
10 9 8 7 6
Poor
Majority of pools small-
shallow or pools absent.
543210
Rapid Bioassessment Protocols for Use in Streams and Rivers
5-13
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DRAFT REVISION—July 28,1997
Pool Variability—Low Gradient
Optimal Range
Poor Range
4. Sediment Deposition: Measures the amount of sediment that has accumulated in pools
and the changes that have occurred to the stream bottom as a
result of deposition. Deposition occurs from large-scale
movement of sediment. Sediment deposition may cause the
formation of islands, point bars (areas of increased deposition
usually at the beginning of a meander that increase in size as the
channel is diverted toward the outer bank) or shoals, or result in
the filling of runs and pools. Usually deposition is evident in
areas that are obstructed by natural or manmade debris and areas
where the stream flow decreases, such as bends. High levels of
sediment deposition are symptoms of an unstable and continually
changing environment that becomes unsuitable for many
organisms.
Selected References:
MacDonald et al. 1991. Platts et al. 1983, Ball 1982, Armour et
al. 1991, Harbour and Stribling 1991, Rosgen 1985.
Habitat
Parameter
4. Sediment
Deposition
SCORE
Condition Category
Optimal
Little or no enlargement
of islands or point bars
and less than 5% (<20%
for low-gradient streams)
of the bottom affected b>
sediment deposition.
20 19 18 17 16
Suboptimal
Some new increase in bar
formation, mostlv from
gravel, sand or fine
sediment:
5-30% (20-50°. for low-
gradient) of the bottom
affected: slight deposition
in pools.
15 14 1? i: 11
Marginal
Moderate deposition of
new gravel, sand or fine
sediment on old and new
bars: 30-50% (50-80% for
low-gradient) of the
bottom affected; sediment
deposits at obstructions,
constrictions, and bends;
moderate deposition of
pools prevalent.
10 9 8 7 6
Poor
Heavy deposits of fine
material, increased bar
development: more than
50% (80% for low-
gradient) of the bottom
changing frequently;
pools almost absent due
to substantial sediment
deposition.
543210
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Chapter 5: Habitat Assessment and Physicochemical Parameters
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DRAFT REVISION—July 28,1997
Sediment Deposition—High Gradient
Optimal Range
Poor Range
Sediment Deposition—Low Gradient
Poor Range
Optimal Range
5. Channel Flow Status: The degree to which the channel is filled with water. The flow
status will change as the channel enlarges (e.g.. aggrading stream
beds with actively widening channels) or as flow decreases as a
result of dams and other obstructions, diversions for irrigation, or
drought. When water does not cover much of the streambed, the
amount of suitable substrate for aquatic organisms is limited. In
high-gradient streams, riffles and cobble substrate are exposed; in
low-gradient streams, the decrease in water level exposes logs
and snags, thereby reducing the areas of good habitat. Channel
flow is especially useful for interpreting biological condition
under abnormal or lowered flow conditions. This parameter
becomes important when more than one biological index period
Rapid Bioassessment Protocols for Use in Streams and Rivers
5-15
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DRAFT REVISION-July 28,1997
is used for surveys or the timing of sampling is inconsistent
among sites or annual periodicity.
Selected References: Rankin 1991, Rosgen 1985, Hupp and Simon 1986. MacDonald
et al. 1991, Ball 1982, Hicks et al. 1991.
Habitat
Parameter
5. Channel Flow
Status
SCORE
Condition Category
Optimal
Water reaches base of
both lower banks, and
minimal amount of
channel substrate is
exposed.
20 19 18 17 16
Suboptimal
Water fills >75% of the
available channel; or
<25% of channel
substrate is exposed.
15 14 13 12 11
Marginal
Water fills 25-75% of the
available channel, and/or
riffle substrates are
mostly exposed.
10 9 8 7 6
Poor
Very little water in
channel and mostly
present as standing pools.
543210
Channel Flow Status—High Gradient
Poor Range
Optimal Range
5-16
Chapter 5: Habitat Assessment and Physicochemical Parameters
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DRAFT REVISION-^July 28,1997
Channel Flow Status—Low Gradient
Optimal Range
Poor Range
Parameters to be evaluated broader than sampling reach:
6.
Channel Alteration:
Selected References:
Is a measure of large-scale changes in the shape of the stream
channel. Many streams in urban and agricultural areas have been
straightened, deepened, or diverted into concrete channels, often
for flood control or irrigation purposes. Such streams have far
fewer natural habitats for fish, macroinvertebrates, and plants
than do naturally meandering streams. Channel alteration is
present when artificial embankments, riprap, and other forms of
artificial bank stabilization or structures are present; when the
stream is very straight for significant distances; when dams and
bridges are present; and when other such changes have occurred.
Scouring is often associated with channel alteration.
Harbour and Stribling 1991, Simon 1989a, b, Simon and Hupp
1987. Hupp and Simon 1986, Hupp 1992, Rosgen 1985, Rankin
1991, MacDonald et al. 1991.
Habitat
Parameter
6. Channel
Alteration
-
SCORE
Condition Category
Optimal
Channelization or
dredging absent or
minimal; stream with
normal pattern.
20 19 18 17 16
Suboptimal
Some channelization
present. usuaJly in areas
of bridse abutments:
evidence of past
channelization, i.e..
dredging. ( greater than
past 20 yr> may be
present, but recent
channelization is not
present
15 14 1? 12 11
Marginal
Channelization may be
extensive; embankments
or shoring structures
present on both banks;
and 40 to 80% of stream
reach channelized and
disrupted.
10 9 8 7 6
Poor
Banks shored with gabion
or cement: over 80% of
the stream reach
channelized and
disrupted. Instream
habitat greatly altered or
removed entirely.
543210
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5-17
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DRAFT REVISION—July 28,1997
Channel Alteration—High Gradient
Optimal Range
Poor Range
Channel Alteration—Low Gradient
Optimal Range
Poor Range
7a. Frequency of Riffles (or bends)/(High Gradient)
7b. Channel Sinuosity (Low Gradient):
Is a way to measure the sequence of riffles and thus the
heterogeneity occurring in a stream. Riffles are a source of high-
quality habitat and diverse fauna, therefore, an increased
frequency of occurrence greatly enhances the diversity of the
stream community. For areas where distinct riffles are
uncommon, a run/bend ratio can be used as a measure of
meandering or sinuosity. A high degree of sinuosity provides for
diverse habitat and fauna, and the stream is better able to handle
surges when the stream fluctuates as a result of storms. The
absorption of this energy by bends protects the stream from
5-18
Chapter 5: Habitat Assessment and Physicochemical Parameters
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DRAFT REVISION-^July 28,1997
excessive erosion and flooding and provides refugia for benthic
invertebrates and fish during storm events. To gain an
appreciation of this parameter in some streams, a longer segment
or reach than that designated for sampling may be incorporated
into the evaluation. In some situations, this parameter may be
rated from viewing accurate topographical maps. The
"sequencing'" pattern of the stream morphology is important in
rating this parameter. In headwaters, riffles are usually
continuous and the presence of cascades or boulders provides a
form of sinuosity and enhances the structure of the stream. In
"oxbow" streams of coastal areas and deltas, meanders are highly
exaggerated and transient. Natural conditions in these streams
are shifting channels and bends, and alteration is usually in the
form of flow regulation and diversion. A stable channel is one
that does not exhibit progressive changes in slope, shape, or
dimensions, although short-term variations may occur during
floods (Gordon et al. 1992).
Selected References: Hupp and Simon 1991, Brussock and Brown 1991, Platts et al.
1983, Rankin 1991, Rosgen 1985, 1994, 1996, Osborne and
Hendricks 1983. Hughes and Omernik 1983. Cushman 1985,
Bain and Boltz 1989, Gislason 1985, Hawkins et al. 1982,
Statzner et al. 1988.
Habitat
Parameter
7a. Frequency of
Riffles (or bends
SCORE
Condition Category
Optimal
Occurrence of riffles
relatively frequent: ratio •
of distance between riffles
divided by width of the
stream <7:1 (generally 5
to 7): variety of habitat is
key. In streams where
riffles are continuous.
placement of boulders or
other large, natural
obstruction is important.
20 19 18 17 16
Suboptimal
Occurrence of riffles
infrequent: distance
between riffles divided by
the width of the stream is
between 7 to 15.
15 14 13 12 11
Marginal
Occasional riffle or bend:
bottom contours provide
some habitat: distance
between riffles divided by
the width of the stream is
between 15 to 25.
10 9 8 7 6
Poor
Generally all flat water or
shallow riffles: poor
habitat: distance between
riffles divided by the
width of the stream is a
ratio of >25.
543210
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DRAFT REVISION—July 28,1997
Frequency of Riffles (or bends)/Velocity-Depth Combinations—High Gradient
Poor Range
Optimal Range
Habitat
Parameter
7b. Channel
Sinuosity
SCORE
Condition Category
Optimal
The bends in the stream
increase the stream length
3 to 4 times longer than if
it was in a straight line.
(Note - channel braiding
is considered normal in
coastal plains and other
low-lying areas. This
parameter is not easily
rated in these areas.
20 19 18 17 16
Suboptimal
The bends in the stream
increase the stream length
2 to 3 times longer than if
It was in a straight line.
15 14 13 12 11
The bends in the stream
increase the stream length
1 to 2 times longer than if
it was in a straight line.
10 9 8 7 6
Channel straight;
waterway has been
channelized for a long
distance.
5432 10
Channel Sinuosity—Low Gradient
Optimal Range
Poor Range
5-20
Chapter 5: Habitat Assessment and Physicochemical Parameters
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DRAFT REVISION—July 28,1997
8. Bank Stability (condition of banks):
Measures whether the stream banks are eroded (or have the
potential for erosion). Steep banks are more likely to collapse
and suffer from erosion than are gently sloping banks, and are
therefore considered to be unstable. Signs of erosion include
crumbling, unvegetated banks, exposed tree roots, and exposed
soil. Eroded banks indicate a problem of sediment movement
and deposition, and suggest a scarcity of cover and organic input
to streams.
Selected References: Ball 1982, MacDonald et al. 1991, Armour et al. 1991, Barbour
and Stribling 1991, Hupp and Simon 1986, 1991, Simon 1989a,
Hupp 1992, Hicks et al. 1991, Osbome et al. 1991, Rosgen
1994, 1996.
Habitat
Parameter
8. Bank Stability
(score each bank)
Note: determine
left of right side by
facing downstream
SCORE (LB)
SCORE (RB)
Condition Category
Optimal
Banks stable; evidence of
erosion or bank failure
absent or minimal; little
potential for future
problems. <5% of bank
affected.
Left Bank 10 9
Right Bank 10 9
Suboptimal
Moderately stable;
infrequent, small areas of
erosion mostly healed
over. 5-30% of bank in
reach has areas of
erosion.
8 7 6
876
Marginal
Moderately unstable; 30-
60% of bank in reach has
areas of erosion; high
erosion potential during
floods.
543
543
Poor .
Unstable; many eroded
areas; "raw" areas
frequent along straight
sections and bends;
obvious bank sloughing:
60- 100% of bank has
erosional scars.
2 1 0
2 1 0
Bank Stability (condition of banks)—High Gradient
Optimal Range
Poor Range
Rapid Bioassessment Protocols for Use in Streams and Rivers
5-21
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DRAFT REVISION—July 28,1997
Bank Stability (condition of banks)—Low Gradient
Optimal Range
Poor Range
9. Bank Vegetative Protection:
Measures the amount of vegetative protection afforded to the
stream bank and the near-stream portion of the riparian zone.
The root systems of plants growing on stream banks help hold
soil in place, thereby reducing the amount of erosion that is likely
to occur. This parameter supplies information on the ability of
the bank to resist erosion as well as some additional information
on the uptake of nutrients by the plants, the control of instream
scouring, and stream shading. Banks that have full, natural plant
growth are better for fish and macroinvertebrates than are banks
without vegetative protection or those shored up with concrete or
riprap. This parameter is made more effective by defining the
natural vegetation for the region and stream type (i.e.. shrubs,
trees, etc.). In areas of high grazing pressure from livestock or
where residential and urban development activities disrupt the
riparian zone, the growth of a natural plant community is
impeded and can extend to the bank vegetative protection zone.
Selected References: Platts et al. 1983, Hupp and Simon 1986, 1991, Simon and Hupp
1987. Ball 1982, Osborne et al. 1991, Rankin 1991, Barbour and
Stribling 1991, MacDonald et al. 1991, Armour et al. 1991.
Myers and Swanson 1991, Bauer and Burton 1993.
5-22
Chapter 5: Habitat Assessment and Physicochemical Parameters
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DRAFT REVISION—July 28,1997
Habitat
Parameter
•
9. Vegetative
Protection (score
each bank)
Note: determine left
or right side by
facing downstream.
SCORE (LB)
SCORE (RB)
Condition Category
Optimal
More than 90% of the
streambank surfaces and
immediate riparian zones
covered by native
vegetation, including
trees, understory shrubs.
or nonwoody
macrophytes; vegetative
disruption through
grazing or mowing
minimal or not evident;
almost all plants allowed
to grow naturally.
Left Bank 10 9
Risht Bank 10 9
Suboptimal
70-90% of the
streambank surfaces
covered by native
vegetation, but one class
of plants is not well-
represented: disruption
evident but not affecting
full plant growth potential
to any great extent; more
than one-half of the
potential plant stubble
height remaining.
876
876
Marginal
50-70% of the
streambank surfaces
covered by vegetation;
disruption obvious;
patches of bare soil or
closely cropped
vegetation common; less
than one-half of the
potential plant stubble
height remaining.
543
543
Poor
Less than 50% of the
streambank surfaces
covered by vegetation;
disruption of streambank
vegetation is very high;
vegetation has been
removed to
5 centimeters or less in
average stubble height.
2 1 0
2 1 0
Bank Vegetative Protection—High Gradient
Optimal Range
Poor Range
Bank Vegetative Protection—Low Gradient
Optimal Range
Poor Range
Rapid Bioassessment Protocols for Use in Streams and Rivers
5-23
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DRAFT REVISION—July 28,1997
10. Riparian Vegetative Zone Width:
Measures the width of natural vegetation from the edge of the
stream bank out through the riparian zone. The vegetative zone
serves as a buffer to pollutants entering a stream from runoff,
controls erosion, and provides habitat and nutrient input into the
stream. A relatively undisturbed riparian zone supports a robust
stream system; narrow riparian zones occur when roads, parking
lots, fields, lawns, bare soil, rocks, or buildings are near the
stream bank. Residential developments, urban centers, golf
courses, and rangeland are the common causes of anthropogenic
degradation of the riparian zone. The presence of "old field"
(i.e., a previously developed field not currently in use), paths, and
walkways in an otherwise undisturbed riparian zone may be
judged to be inconsequential to destruction of the riparian zone.
In some regions of the country, an increase in the specified width
of a desirable riparian zone is warranted.
Selected References: Barton et al. 1985, Naiman et al. 1993, Hupp 1992, Gregory et
al. 1991, Platts et al. 1983, Rankin 1991, Harbour and Stribling
1991, Bauer and Burton 1993.
Habitat
Parameter
10. Riparian
Vegetative Zone
Width (score each
bank riparian zone)
SCORE (LB)
SCORE (RBI
Condition Category
Optimal
Width of riparian zone
>18 meters; human
activities (i.e., parking
lots, roadbeds, clear-cuts,
lawns, or crops) have not
impacted zone.
Left Bank 10 9
RiehtBank 10 9
Suboptimal
Width of riparian zone
12-18 meters; human
activities have impacted
zone only minimally.
876
876
Marginal
Width of riparian zone 6-
12 meters: human
activities have impacted
zone a great deal.
543
543
Poor
Width of riparian zone <6
meters: little or no
riparian vegetation due to
human activities.
2 1 0
2 1 0
Riparian Vegetative Zone Width—High Gradient
Optimal Range
Poor Range
5-24
Chapter 5: Habitat Assessment and Physicochemical Parameters
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DRAFT REVISION-^July 28,1997
Riparian Vegetative Zone Width—Low Gradient
Optimal Range
Poor Range
Rapid Bioassessment Protocols for Use in Streams and Rivers
5-25
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DRAFT REVISION—July 28,1997
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5-26 Chapter 5: Habitat Assessment and Physicochemical Parameters
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DRAFT REVISION—July 23,1997
PERIPHYTON PROTOCOLS
Benthic (attached) algae are primary producers and are sensitive indicators of environmental change
in lotic waters. Because periphyton is attached to the substrate, this assemblage integrates physical
and chemical disturbances to the stream reach. The periphyton assemblage serves as a good
biological indicator because of naturally high number of species and a rapid response time to both
exposure and recovery. Diatoms in particular are useful indicators of biological condition because
they are ubiquitous and found in all lotic systems. In addition, most periphyton taxa can be
identified to species by experienced biologists, and tolerance or sensitivity to specific changes in
environmental condition are known for many species (Rott 1991, Dixit et al. 1992). By using algal
data in association with macroinvertebrate and fish data, the strength of biological assessments is
optimized. The objectives of a rapid bioassessment protocol for periphyton could include, but would
not be limited to, assessment of biomass (chlorophyl a or ash-free dry mass), species, composition
and biological condition of periphyton assemblages.
Presently, few states have developed protocols for the periphyton assemblage. The protocols
presented in this document are a "composite" of the techniques used in Kentucky, Montana, and
Oklahoma (Kentucky Department of Environmental Protection [DEP] 1993, Bahls 1993, Oklahoma
Conservation Commission [CC] 1993). The protocols are quite general and have a broad potential
for application elsewhere. The following table summarizes microhabitats and appropriate collection
techniques for various types of periphyton.
Table 6-1. Summary of collection techniques for periphyton from wadeable streams (adapted from
Kentucky DEP 1993, Bahls 1993).
Substrate Type
Collection Technique
Removable substrates (hard): gravel, pebbles,
cobble, and woody debris
Remove representative substrates from water;
brush or scrape representative area of algae
from surface and rinse into sample jar.
Removable substrates (soft): mosses,
macroalgae, vascular plants, root wads
Place a portion of the plant in a sample
container with some water and shake
vigorously; remove plant.
Large substrates (not removable): boulders,
bedrock, logs, trees, roots
Place PVC pipe with a neoprene collar at one
end on the substrate so that the collar is sealed
against the substrate. Dislodge algae in the
collar with a toothbrush, nail brush or scraper
and pick them up with a pipet.
6.1 FIELD SAMPLING PROCEDURES: NATURAL SUBSTRATES
For an accurate assessment of the assemblage, samples should be collected during periods of stable
in-stream flow. High flows can scour the stream bed, flushing the periphyton downstream.
Recolonization of substrates can be faster after less severe floods and in streams with nutrient
enrichment. Peterson and Stevenson (1990) recommend a three week delay following high, bottom-
Rapid Bioassessment Protocols for Use in Streams and Rivers
6-1
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DRAFT REVISION—July 23,1997
scouring stream flows to allow for recolonization and succession to a mature periphyton community.
The following procedures have been adapted from the Kentucky and Montana protocols (Kentucky
DEP 1993, Bahls 1993).
1. Collect periphyton from
all available
microhabitats in the
sampling reach. Collect
a composite qualitative
sample by sampling
microhabitats in roughly
the proportion that they
occur at the site e.g.,
pebbles and cobbles in
riffles, woody debris or
aquatic vegetation in
pools. Sample both
riffles and pools. (Note--
one major habitat type
[usually riffle] may be
selected if it occurs at all
sites to be compared in
the study—see below).
2. Collect samples during
stable low—flow
conditions. During
low-flow periods, pools
may be the only habitat
available. After
FIELD EQUIPMENT/SUPPLIES NEEDED FOR
PERIPHYTON SAMPLING
—NATURAL SUBSTRATES
• pocket knife, toothbrush, or similar brushing/scraping tools
(toothbrush with handle bent back is ideal for brushing in
PVC pipe)
stainless steel teaspoon
a section of PVG pipe (3" diameter or larger) fitted with a
rubber collar at one end
data sheets*
white plastic or enamel pan
filter flask and filter basket
hand operated or portable electric pump and hoses
glass fiber filters for chlorophyll samples
forceps, suction bulb and disposable pipettes
water bottle with distilled water
aluminum foil
first aid kit
sample containers (4 oz. Vials)
sample container labels
field notebook
3^4% buffered formalin, Lugol's solution, or 2%
glutaraldehyde
cooler with ice
* It is helpful to copy fieldsheets onto "Rite in the Rain" ® paper for
use in wet weather conditions
APPROACH FOR SINGLE HABITAT
COLLECTIONS
• An alternative to compositing several
microhabitats is to select the single habitat
type that sufficiently characterizes the study
reach. The most accurate way to decrease
sample variability is to collect from only one
type of habitat within a reach and to
composite many samples within that habitat
(Rosen 1995). If separate habitats are
sampled, keep them separate for analysis.
As a general rule, sample hard substrates
(pebble and cobble) from riffles and runs
with current velocities of 10-20 cm/sec. The
objective is to collect a single composite
sample that is representative of the
periphyton assemblage present at that site.
extremes of flooding or drought, allow at least a 3
week recolonization period before sampling.
3. Collect a representative sample of
removable substrates (rocks and woody
debris). Thoroughly remove all algae,
(with a pocketknife, toothbrush or similar
hand tool) rinsing with distilled water as
necessary. Scrape or brush material from
substrate into a white plastic or enamel
pan, and then wash into a sampling
container. If replicate samples are
desired, choose rocks of similar size.
Samples can either be composited or
analyzed separately. If quantitative data
are required, measure the area of surfaces
scraped clean.
6-2
Chapter 6: Periphyton Protocols
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DRAFT REVISION-^IuIy 23,1997
For bedrock substrates, a suction device can be used to collect samples. Press a 3-5" length
of PVC pipe (3" diameter) fitted with a rubber or foam gasket against the bedrock and
dislodge the periphyton in the enclosed area with a stiff brush. A small nail brush or
toothbrush with a bent handle works well. The material can then be suctioned into a filter
flask with a hand-operated or small electric pump. Replicate, quantitative samples can
easily be obtained by measuring the area of the substrate sampled.
Slide of suction device and description here
5. Algal mats or other soft-bodied algal
forms can be collected from depositional
areas with forceps or a suction bulb and
disposable pipette. A spoon or
eyedropper may also be useful. Samples
can be collected quantitatively from soft
sediments (sand and silt) with a petri
dish. Insert the petri dish into the
sediments. Slide a spatula under the petri
dish and transfer the sample to a
container.
6. Epiphytes on vascular plants, mosses or
macroalgae are sampled by placing
sections of the plant in a sample jar with
some water and shaking vigorously. The
plant sections are then removed.
Alternatively, the epiphytes can be
scraped or brushed from the vascular plants into a white plastic or enamel pan, and then
washed into a sampling container.
7. All samples should be placed in water tight, unbreakable, wide-mouthed containers. A four
ounce (125 ml) sample is usually sufficient for analysis (Bahls 1993). Add enough Lugol's
solution (iodine potassium iodide), buffered 4% formalin or other preservative to preserve
sample.
8. For chlorophyll analysis thoroughly scrape periphyton from a fixed area (i.e., 9 cm2) into a
glass fiber filter on the filter apparatus. Filters are wrapped in foil and frozen (on dry ice if
necessary) for transport to the laboratory.
9. Place a permanent label on the outside of the container with the following information: water
body name, location or site number, date, and name of collector. Put another label inside the
sample container with the same information. This and any other ecological information can
be recorded in a field notebook or on a field data sheet (Appendix A-2, Form 1).
10. Transport samples back to the laboratory in a cooler, log in all incoming samples (Appendix
A-2, Form 2) and store in a dark refrigerator until they are processed. Check preservative
every few weeks and replenish as necessary until taxonomic evaluation is completed.
Rapid Bioassessment Protocols for Use in Streams and Rivers
6-3
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DRAFT REVISION^July 23,1997
QUALITY CONTROL (QC) IN THE FIELD
1. Sample labels must be properly completed, including the sample identification code date, stream name,
sampling location, and collector's name and placed into the sample container. The outside of the
container should be labeled with the same information. Chain-of-custody forms, if needed, must
include the same information as the sample container labels.
2. After sampling has been completed at a given site, all brushes, suction devices, etc. that have come in
contact with the sample will be rinsed thoroughly, examined carefully, and picked free of algae or
debris. Any additional algae found should be placed into the sample containers. The equipment should
be examined again prior to use at the next sampling site.
3. Replicate (1 duplicate sample) 10% of the sites to evaluate precision or repeatability of sampling
technique or collection team.
6.2 FIELD SAMPLING PROCEDURES: ARTIFICIAL SUBSTRATES
Periphyton can also be sampled by collecting from artificial substrates that are placed in aquatic
habitats and colonized over a period of time. This procedure is particularly useful in non-wadeable
streams, rivers with no riffle areas, wetlands, or the littoral zones of lentic environments. Both
natural and artificial techniques are useful in monitoring and assessing waterbody condition, and
have corresponding advantages and disadvantages (Stevenson and Lowe 1986, Aloi 1990). The
methods summarized here are a composite of those specified by Kentucky (Kentucky DEP 1993),
Florida (Florida DEP 1996), and Oklahoma (Oklahoma CC 1993).
1.
4.
Microslides should be thoroughly cleaned before placing in periphytometer. Rinse slides in
acetone and clean with Kimwipes®.
Place surface (floating) or benthic (bottom) periphytometers fitted with glass slides, glass
rods, clay tiles, plexiglass plates or similar substrates in the study area. Allow 2 to 4 weeks
for periphyton recruitment and colonization.
Replicate a minimum of three periphytometers at each site to account for spatial variability,
depending upon the
research design and
hypotheses being tested.
Samples can either be
composited or analyzed
individually.
Attach periphytometers
to a stable structure and
hidden from view to
minimize disturbance or
vandalism. The
periphytometer should be
oriented with the shield
directed upstream.
FIELD EQUIPMENT/SUPPLIES NEEDED FOR
PERIPHYTON SAMPLING
—ARTIFICIAL SUBSTRATES
periphytometers
8 microslides
forceps, suction bulb and disposable pipettes
water bottle with distilled water
aluminum foil
first aid kit
sample containers (to fit microslides)
sample container labels
field notebook
3—4% buffered formalin or.Lugol's solution
6-4
Chapter 6: Periphyton Protocols
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DRAFT REVISION—July 23,1997
5. If flooding or similar scouring event occurs, allow waterbody to equilibrate, and reset
periphytometers.
6. After the incubation period (2-4 weeks), slides are collected and split for determination of
periphyton chlorophyll a and taxonomic analysis. Storage containers for chlorophyll a are
filled with deionized (DI) water and those for taxonomic analysis are filled with ambient
water.
7. Microslides for taxonomic analysis may be scraped and samples preserved in the field, as
with natural substrates, or the substrates may be returned to the laboratory for scraping and
preservation. If entire substrates are returned to the laboratory they must be kept on ice in
the dark (e.g., in a whirl-pak or a sample jar with a small amount of water). -If travel and
holding time are not excessive (less than 12 hours), laboratory sampling and preservation are
preferred.
8. Place a permanent label on the outside of the container with the following information: water
body name, location or site number, date, and name of collector. Put another label inside of
the sample container with the same information. This and any other ecological information
can be recorded in a field notebook or on a field data sheet.
9. Transport samples back to the laboratory in a cooler, log in all incoming samples (Appendix
A-2, Form 2), and store in a dark refrigerator until they are processed. Check preservative
every few weeks and replenish as necessary until taxonomic evaluation is completed.
10. Microslides for chlorophyll analysis should be thoroughly scraped and rinsed with DI water
onto a glass-fiber filter (Whatman GFC or equivalent). Filters with captured algal cells are
wrapped in foil and frozen to await extraction and analysis.
QUALITY CONTROL (QC) IN THE FIELD
1. Sample labels must be properly completed, including the sample identification code date, stream name,
sampling location, and collector's name and placed into the sample container. The outside of the
container should be labeled with the same information. Chain-of-custody forms, if needed, must
include the same information as the sample container labels.
2. After sampling has been completed at a given site, all brushes, suction devices, and periphytometers
that have come in contact with the sample will be rinsed thoroughly, examined carefully, and picked
free of algae or debris. Any additional algae found should be placed into the sample containers. The
equipment should be examined again prior to use at the next sampling site.
3. Replicate (1 duplicate sample) 10% of the sites to evaluate precision or repeatability of sampling
technique or collection team.
6.3 LABORATORY ANALYSIS
Methods summarized here are a composite of those used by Kentucky (Kentucky DEP 1993), Florida
(Florida DEP 1996), and Montana (Bahls 1993). For greater detail and for alternative methods, see
American Public Health Association (APHA) (1992).
Rapid Bioassessment Protocols for Use in Streams and Rivers 6-5
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DRAFT REVISION—July 23,1997
'Soft" Algae (non-diatoms)
1.
2.
Thoroughly shake the
sample container to
dislodge epiphytes from
filamentous taxa and
randomly mix all algal
organisms.
Pour the contents into a
shallow dish or bowl so
that all the filamentous
and mat-forming taxa
can be separated.
LABORATORY EQUIPMENT/SUPPLIES NEEDED
FOR PERIPHYTON ANALYSIS
• compound microscope with at least 20X, 40X, and
100X (oil) objectives
tally counter
microscope slides and supplies
fume hood
oxidation reagents (HNO3, H,SO4, K2Cr2O7, H;O2)
safety glasses and clothing
tissue grinder (for chlorophyll)
acetone
fluorometer and/or spectrophotometer
Deionized (DI) water
3.
Using dissecting probes or needle-nosed forceps, place representative filamentous taxa on a
pre-cleaned microscope slide with a shallow well. Return remainder of sample to sample
jar.
NOTE: When making laboratory observations, soft
algal units are considered instead of cells. Because
colonial, or filamentous cells normally do not occur
singly in a natural environment, counting each cell
does not accurately portray relative abundance for that
specific taxon. For example, although Pediastrum
duplex is composed of several cells, the colony as a
whole is counted as one unit. Likewise, coenobia and
unicells are each considered one unit.
4. With a pipet, add several drops of
microalgae from the sample jar.
Gently place a coverslip over the
subsample, completing the wet
mount.
5. Examine each slide at 200x, then at
400x to ensure that smaller
organisms are not overlooked.
Identify all soft algae to the lowest
taxonomic level using taxonomic
references. Scan each slide until no
new organisms are seen.
6. If resources allow replication, examine several slides for each sample.
7. Record observed taxa on a bench sheet or in a logbook, along with taxonomic division,
estimated relative abundance (abundant, common, rare), estimated relative rank according to
biovolume, and any autecological information known for a taxon.
Diatoms
1. After the soft algae have been identified, clear diatom frustules of organic and intercellular
material using one of several standard oxidation methods (van der Werff 1955, APHA 1992)
(see attached oxidation methods).
2. Mount diatoms in Naphrax or Permount mounting medium to make permanent slides.
(Other possible media are Pleimax, Carmount 165, Meltmount, and Cumar R-9 11).
3. Examine slide under oil immersion at a magnification of lOOOx; identify diatoms to species
whenever possible, using current taxonomic references.
6-6
Chapter 6: Periphyton Protocols
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DRAFT REVISION—July 23,1997
4. For data on abundance, count a minimum of 300 to 500 valves or frustules and record taxa
and number counted on bench sheets. Data assessment is based on the completed species
list.
5. For data on taxon richness, scan the slide recording all species encountered.
6. Continue to scan the slide until several minutes pass without producing any new taxa.
7. Record results of taxonomic identifications on laboratory bench sheets (Appendix A-2, Form
3).
OXIDATION METHODS FOR PREPARING DIATOMS FROM KENTUCKY DEP 1993
NITRIC ACID OXIDATION:
1. Shake the sample vigorously and immediately pour a portion (about 20-30 ml) of the sample into a
large 2000 ml 'erlenmeyer flask.
2. Under a fume hood, add 50 ml of concentrated nitric acid. Allow to oxidize overnight, then fill the
flask with distilled water.
3. Let the sample resettle overnight, siphon off the supernatant, and pour the remaining diatom
solution into a 1000 ml graduated cylinder.
4. Fill with distilled water, settle and siphon supernatant at least twice more, until the yellow color
changes to clear.
HYDROGEN PEROXIDE/POTASSIUM DICHROMATE OXIDATION:
1. Prepare samples as in nitric acid method above, but use 50 ml of 50% H2O2 instead of nitric acid.
2. Allow to oxidize overnight; then add a microspatula of potassium dichromate. This will cause a
violent exothermic reaction, so be careful and perform this method only under a fume hood.
3. When the sample color changes from purple to yellow and boiling stops, fill the flask with distilled
water.
4. Repeat rinsing steps as outlined for the nitric acid method until the yellow color is gone.
"BURN MOUNT" INCINERATION METHOD: This method is more rapid than the chemical oxidation methods,
and microscope slides can be prepared in a day. However, the diatoms may not be cleared sufficiently to
identify more difficult taxa to species level. This technique is not preferred for periphyton.
1. Using a disposable pipette, drop a small amount of well-mixed sample onto a coverslip that has
been placed on a hot plate.
2. Dry at low heat until the moisture has been removed; then heat on high for one hour to incinerate
all organic material.
Rapid Bioassessment Protocols for Use in Streams and Rivers 6-7
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DRAFT REVISIONWuly 23,1997
Chlorophyll a analysis
Chlorophyll is analyzed fluorometrically or spectrophotometrically following disruption of cells (by
grinding) and extraction with acetone (APHA 1992):
1. Grind filter paper with algal cells in tissue grinder with 10 ml acetone until filter paper and
all cells are homogenized.
2. Centrifuge and analyze chlorophyll and phaeophytin pigments in supernatant using standard
fluorometric or spectrophotometric methods (APHA 1992).
QUALITY CONTROL (QC) IN THE LABORATORY
1. Upon delivery to the laboratory, complete entries on sample log-in form (Appendix A, Form 5).
2. Ten percent of the samples should have a replicate composite wetmount prepared for identification to
ensure representative subsampling.
3. Replicate diatom slides should be analyzed by another phycologist.
4. For sample lots of less than 10, one replicate diatom slide should be prepared and analyzed.
6.4 SEMI-QUANTITATIVE ASSESSMENTS OF BENTHIC ALGAL
BIOMASS AND TAXONOMIC COMPOSITION
Semi-quantitative assessments of benthic algal biomass and taxonomic composition can be made
rapidly with a viewing bucket marked with a grid and biomass scoring system (Stevenson 1996).
The advantage of using this
technique is that it enables rapid
assessment of algal biomass over
larger spatial scales than
substrate sampling and
laboratory analysis. Coarse-level
taxonomic characterization of
communities is also possible
with this technique. This
technique is a survey of the
natural substrate and does not
require laboratory processing. It
is a technique developed by
Stevenson1 (personal comm.)
and may be an alternative
screening technique to other
procedures in this chapter.
FIELD EQUIPMENT NEEDED/SUPPLIES NEEDED FOR
SEMI-QUANTITATIVE ASSESSMENT
• Viewing bucket with grided bottom
~ remove the bottom from a large bucket
— leave a 3 cm rim around the bottom
— drop a piece of clear acrylic plastic in the bottom of
the bucket that fits inside the edges of the bucket
and overlaps the 3 cm rim
— attach the clear acrylic to the bucket with a clear
silicon cement or caulk
~ using a permanent marker, mark a grid of 50 dots
on the acrylic in a uniform pattern within the rim of
the bucket
~ typically, a 7 x 7 square grid is used with a final dot
placed randomly outside the grid.
'Dr. Jan Stevenson is a phycologist and Professor of Biology at University of Louisville.
6-8
Chapter 6: Periphyton Protocols
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DRAFT REVISION—July 23,1997
1. Establish at least 3 transects across the habitat being sampled (preferably a riffle or run in
which benthic algal accumulation is readily observed and characterized).
2. Select 3 locations objectively in at least 3 locations on each transect (e.g., establish stratified
random locations near the right bank, middle of transect, and near the left bank).
3. Characterize algae in each location by immersing the bucket with grid in the water. While
observing the bottom of the stream through the bottom of the viewing bucket, count and
record the number of dots covered by macroalgae (e.g., Cladophora or Spirogyra) under
which substrates can not be seen. Measure and record the maximum length of the
macroalgae. If two types of macroalgae are present, count the dots, measure, and record
information for each type of macroalgae separately. While viewing the same area, record the
number of dots under which substrata occur that are suitable size for microalgal
accumulation (gravel > 2 cm in size). Determine the kind of microalgae (usually diatoms
and blue-green algae) and estimate the density under each dot using the following thickness
scale:
0 - substrate rough with no evidence of microalgae
0.5 - substrate slimy, but no accumulation of microalgae is evident
1 - a thin layer of microalgae is evident
2 - accumulation of microalgal layer from 0.5-1 mm thick is evident
3 - accumulation of microalgae layer from 1 mm to 5 mm thick is evident
4 - accumulation of microalgal layer from 5 mm to 2 cm thick is evident
5 - accumulation of microalgal layer greater than 2 cm thick is evident
4. Characterize the density of algae on substrate by calculating the average percent cover of the
habitat by each type of macroalgae, maximum length of each type of macroalgae, mean
density of each type of microalgae on suitable substrate, and maximum density of each type
of macroalgae on suitable substrate.
5. QA/QC between observers and calibration between algal biomass (chl a, AFDM, cell density
and biovolume biovolume/cm2 and taxonomic composition can be developed by collecting
samples that have specific microhabitat rankings and assaying the periphyton.
6.5 PERIPHYTON METRICS
The periphyton metrics summarized below are in use by several states (Kentucky DEP 1993, Bahls
1993, Florida DEP 1996). Two metrics are taxa richness metrics (total taxa and Shannon diversity);
these are estimated from the count of taxa encountered in a set number of cells (usually 500 cells). If
the cell counts vary by more than 20% from the target number (500), then it may be necessary to
adjust the taxa richness estimate with a ratification formula (see Hurlbert 1971, Barbour and
Gerritsen 1996).
6.5.1 Diatom Metrics
1. Total Number of Diatom Taxa (TNDT) is an estimate of diatom species richness. High
species richness is assumed for unimpacted sites, and species richness is expected to decrease with
increasing pollution. Slight levels of nutrient enrichment, however, may increase species richness in
headwater or naturally unproductive, nutrient-poor streams (Bahls et al. 1992).
Rapid Bioassessment Protocols for Use in Streams and Rivers 6-9
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DRAFT REVISION—July 23,1997
2. Shannon Diversity (for diatoms). The Shannon Index is affected by both the number of
species in a sample and the distribution of individuals among those species (Klemm et al. 1990).
Because species richness and evenness may vary independently, under certain conditions Shannon
diversity values can be misleading e.g., when total number of taxa is less than 10. Assessments for
low richness samples can be improved by comparing the assemblage Shannon Diversity to the
Maximum Shannon Diversity value (David Beeson2, personal communication). Species diversity,
despite the controversy surrounding it, has historically been used with success as an indicator of
organic (sewage) pollution (Wilhm and Dorris 1968, Weber 1973, Cooper and Wilhm 1975). Bahls
et al. (1992) uses Shannon diversity because of its sensitivity to water quality changes, and
Stevenson (1984) suggests that changes in species diversity, rather than the diversity value, may be
useful indicators of changes in water quality.
3. Percent Community Similarity (PSC) of Diatoms. The percent community similarity (PSC)
index, discussed by Whittaker (1952), was used by Whittaker and Fairbanks (1958) to compare
planktonic copepod communities. It was chosen for use in diatom bioassessments because it shows
community similarities based on relative abundances, and in doing so, gives more weight to
dominant taxa than rare ones. Percent similarity only applies to comparison to a control site, or to
multivariate cluster analysis. If emphasis is comparison to regional reference condition (i.e., a
composite of sites), % similarity will not be useful. Percent community similarity values range from
0 (no similarity) to 100%.
The formula for calculating percent community similarity is:
PS = 100-.5SS a.-b.| = 2s min(a.,b.)
C 1=1 1 I1 1=1 V 1 /
where:
3j = percentage of species i in sample A
bj = percentage of species i in sample B
4. Pollution Tolerance Index for Diatoms. The pollution tolerance index (PTI) used by
Kentucky DEP is most similar to that of Lange-Bertalot (1979) and resembles the Hilsenhoff biotic
index for macroinvertebrates (Hilsenhoff 1987). Lange-Bertalot distinguished three categories of
diatoms according to their tolerance to increased pollution, with species assigned a value of 1 for
most tolerant taxa (e.g., Nitzschia palea or Gomphonema parvuluni) to 3 for relatively sensitive
species. For the PTI, Lange-Bertalot's list has been adapted to four categories to differentiate a large
moderately tolerant group of species (similar to his splitting of category 2 diatoms into 2a and 2b);
the Kentucky DEP diatom pollution tolerance values range from one (most tolerant) to four (most
sensitive). Tolerance values have been generated from several sources, including Lowe (1974),
Patrick and Reimer (1966, 1975), Patrick (1977), Lange-Bertalot (1979), Descy (1979), Sabater et al.
(1988), Bahls et al. (1992), and Oklahoma Conservation Commission (1993).
The formula used to calculate PTI is:
Snt.
PTI =
N
2David Beeson is a phycologist with S.M. Stoller Corporation.
6-10 Chapter 6: Periphyton Protocols
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DRAFT REVISION—July 23,1997
where:
n, = number of cells counted for species i
tj = tolerance value of species i (1, 2, or 3)
N = total number of cells counted
5. Percent Sensitive Diatoms. The percent sensitive diatoms metric is the sum of the relative
abundances of all intolerant species. This metric is especially important in smaller-order streams
where primary productivity may be naturally low, causing the other metrics to underestimate water
quality.
6. Percent Motile Diatoms. The percent motile diatoms is a siltation index, as the relative
abundance ofNavicula + Nitzschia + Surriella. It has shown promise in Montana (Bahls et al. 1992).
The three genera are able to crawl towards the surface if they are covered by silt; their abundance is
thought to reflect the frequency of siltation.
7. Percent Achnanthes Minutissima. This species is a cosmopolitan diatom that has a very
broad ecological amplitude. It is an attached diatom and often the first species to pioneer a recently
scoured site, sometimes to the exclusion of all other algae. A. minutissima is also frequently
dominant in streams subjected to acid mine drainage (e.g., Silver Bow Creek, Montana) and to other
chemical insults. The percent abundance of A. minutissima has been found to be directly
proportional to the time that has elapsed since the last scouring flow or episode of toxic pollution.
For use in bioassessment, the quartiles of this metric from a population of sites has been used to
establish judgment criteria, e.g., 0-25% = no disturbance, 25-50% = minor disturbance, 50-75% =
moderate disturbance, and 75-100% = severe disturbance. Least-impaired streams in Montana may
contain up to 50% A. minutissima (Loren Bahls3, personal communication).
6.5.2 Non-diatom Metrics
8. Taxa Richness of Non-diatoms. In general, an inverse relationship exists between the
number of soft algae present and impairment. Extremely low taxa richness of non-diatoms indicates
the possible occurrence of a toxicity problem (for example, acid mine drainage), while high taxa
richness suggests clean water. However, extremely high taxa richness in low-order streams may
indicate a minor degree of nutrient enrichment, while low taxa richness may be natural in low-order
streams with low nutrient inputs.
9. Indicator Non-diatom Taxa. Certain taxa are good indicators of pollution. Autecological
information on these indicator taxa is available in published references (Palmer 1969, 1977, Prescott
1968, Lowe 1974, and Patrick and Reimer 1966, 1975). Indicator categories are provided in Table 6-
2. Presence and relative abundance of indicator taxa is recorded and used in conjunction with other
data to determine water quality impairment.
3 Dr. Loren Bahls is a retired phycologist and Chief of Nonpoint Section of the Montana Department
of Environmental Quality.
Rapid Bioassessment Protocols for Use in Streams and Rivers 6-11
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DRAFT REVISION—July 23,1997
Table 6-2. Indicator taxa (taken from Kentucky DEP 1993).
Acidophilic taxa
Alkaliphilic taxa
Heterotrophic taxa
Halophilic taxa
Eutrophic taxa
Aberrant diatoms
Taste and odor taxa
Occur at a pH of 7 or below.
Occur at a pH of 7 or above.
Have a growth requirement for organic
nitrogen; often associated with wastewater
treatment plant effluents.
Tolerate elevated chloride concentrations
(including brackish water forms).
Characteristic of water with high nutrient
concentrations.
Morphological changes are an indication of
physiological stress often found in association
with toxic materials (e.g., metals).
All taxa that cause water to taste and/or smell
noxious; taxa will be identified in streams used
for domestic water supplies.
10. Relative Abundance of All Taxa. Can be calculated from counting a pre-determined
number of cells or, relative abundance of each taxon (diatoms are combined under the heading
Bacillariophyceae) can be estimated as follows:
Rare Present in <25% of the examined fields and only 1 unit per field
Common Present in 25-75% of the examined fields and 2-10 units per field
Abundant Present in >75% of the examined fields and >10 units per field.
11. Number of Divisions Represented All Taxa. Representatives from several phyla of algae
are common from sites with good water quality. The number of phyla represented is reported as an
indicator of diversity. -
12. Chlorophyll a. Benthic chlorophyll a values are used as an estimate of algal biomass.
Chlorophyll a values can be extremely variable because of the patchiness of periphyton distribution;
therefore, assessments are based on a mean of three or more replicate samples. These values are
used to compare biomass accrual at the same station over time or between stations during the same
sampling period. High chlorophyll a values may indicate nutrient enrichment, while low values may
either indicate low nutrient availability, toxicity, or low-light availability because of shading,
sedimentation, or high turbidity. Chlorophyll a values are used only in support of other analyses.
13. Ash-free Dry-mass (AFDM). Benthic AFDM values are used as an estimate of total
organic material accumulated on the artificial substrate. This organic material includes all living
organisms (algae, bacteria, fungi, protozoa, and macroinvertebrates) as well as non-living detritus.
Ash-free dry-mass values have been used in conjunction with chlorophyll a as a means of
determining the trophic status (autotrophic vs. heterotrophic) of streams. The Autotrophic Index
(AI) is calculated as follows:
6-12
Chapter 6: Periphyton Protocols
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DRAFT REVISION—July 28,1997
AFDM (mg/m")
AI =
Chlorophyll a (mg/m 2)
High AI values (>200) indicate the community is dominated by heterotrophic organisms, and
extremely high values indicate poor water quality (Weber 1973, Weitzel 1979, Matthews et al.
1980). This index should be used with discretion, as non-living organic detritus can artificially
inflate the APDW value.
Stevenson4 (1996) recommends that the AI be modified as chl/AFDM. The index is then positively
related to the autotrophic proportion of the assemblage and not the heterotrophic component. Also,
the index will have better statistical properties as a proportion or percent (chl/AFDM is usually about
0.1% of the assemblage by mass) than in the original form as AFDM/chl.
6.6 TAXONOMIC REFERENCES FOR PERIPHYTON
Camburn, K.E., R.L. Lowe, and D.L. Stonebumer. 1978. The haptobenthic diatom flora of Long
Branch Creek, South Carolina. Nova Hedwigia 30:149-279.
Collins, G.B. and R.G. Kalinsky. 1977. Studies on Ohio diatoms: I. Diatoms of the Scioto River
Basin. Bull. Ohio Biological Survey. 5(3): 1-45.
Czarnecki, D.B. and D.W. Blinn. 1978. Diatoms of the Colorado River in Grand Canyon National
Park and vicinity. (Diatoms of Southwestern USA II). Bibliotheca Phycologia 38. J. Cramer. 181 pp.
Dawes, C. J. 1974. Marine Algae of the West Coast of Florida. University of Miami Press.
Dillard, G.E. 1989a. Freshwater algae of the Southeastern United States. Parti. Chlorophyceae:
Volvocales, Testrasporales, and Chlorococcales. Bibliotheca, 81.
Dillard, G.E. 1989b. Freshwater algae of the Southeastern United States. Part 2. Chlorophyceae:
Ulotrichales, Microsporales, Cylindrocapsales, Sphaeropleales, Chaetophorales, Cladophorales,
Schizogoniales, Siphonales, and Oedogoniales. Bibliotheca Phycologica, 83.
Dillard, G.E. 1990. Freshwater algae of the Southeastern United States. Part 3. Chlorophyceae:
Zygnematales: Zygenmataceae, Mesotaeniaceae, and Desmidaceae (Section 1). Bibliotheca
Phycologica, 85.
Dillard, G.E. 1991. Freshwater algae of the Southeastern United States. Part 4. Chlorophyceae:
Zygnemateles: Desmidaceae (Section 2). Bibliotheca Phycologica, 89.
Drouet, F. 1968. Revision of the classification of the oscillatoriaceae. Monograph 15. Acad. Nat.
Sci. Phila. Fulton Press, Lancaster, Pensylvania.
Hohn, M.H. and J. Hellerman. 1963. The taxonomy and structure of diatom populations from three
North American rivers using three sampling methods. Trans. Am. Microsc. Soc. 82:250-329.
"Dr. Jan Stevenson is a phycologist and Professor of Biology at University of Louisville.
Rapid Bioassessment Protocols for Use in Streams and Rivers 6-13
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DRAFT REVISION-^Tuly 28,1997
Hustedt, F. 1927-1966. Die kieselalgen In Rabenhorst's Kryptogamen-flora von Deutschland
Osterreich und der Schweiz VII. Leipzig, West Germany.
Hustedt, F. 1930. Bacillariophyta (Diatomae). In Pascher, A. (ed). Die suswasser Flora
Mitteleuropas. (The freshwater flora of middle Europe). Gustav Fischer Verlag, Jena, Germany.
Jarrett, G.L. and J.M. King. 1989. The diatom flora (Bacillariphyceae) of Lake Barkley. U.S. Army
Corps of Engineers, Nashville Dist. #DACW62-84-C-0085.
Krammer, K. and H. Lange-Bertalot. 1986-1991. Susswasserflora von Mitteleuropa. Band 2. Parts
1-4. Bacillariophyceae. Gustav Fischer Verlag. Stuttgart. New York.
Lange-Bertalot, H. and R. Simonsen. 1978. A taxonomic revision of the Nitzschia lanceolatae
Grunow: 2. European and related extra-European freshwater and brackish water taxa. Bacillaria
1:11-111.
Lange-Bertalot, H. 1980. New species, combinations and synonyms in the genus Nitzschia.
Bacillaria 3:41-77.
Patrick, R. and C.W. Reimer. 1966. The diatoms of the United States, exclusive of Alaska and
Hawaii. Monograph No. 13. Acad. Nat. Sci. Phila., Philadelphia, Pennsylvania.
Patrick, R. and C.W. Reimer. 1975. The Diatoms of the United States. Vol. 2, Parti. Monograph
No. 13. Acad. Nat. Sci. Phila., Philadelphia, Pennsylvania.
Prescott, G.W. 1962. The algae of the Western Great Lakes area. Wm. C. Brown Co., Dubuque,
Iowa.
Prescott, G.W., H.T. Crousdale, and W.C. Vinyard. 1975. A Synopsis of North American desmids.
Part II. Desmidaceae: Placodermae. Section 1. Univ. Nebraska Press, Lincoln, Nebraska.
Prescott, G.W., H.T. Crousdale, and W.C. Vinyard. 1977. A synopsis of North American desmids.
Part II. Desmidaceae: Placodermae. Section 2. Univ. Nebraska Press, Lincoln, Nebraska.
Prescott, G.W. 1978. How to know the freshwater algae. 3rd Ed. Wm. C. Brown Co., Dubuque,
Iowa.
Simonsen, R. 1987. Atlas and catalogue of the diatom types of Friedrich Hustedt. Vol. 1-3. J.
Cramer. Berlin, Germany.
Smith, M. 1950. The Freshwater Algae of the United States. McGraw-Hill, New York, New York.
Taylor, W. R. 1960. Marine algae of the eastern tropical and subtropical coasts of the Americas.
University of Michigan Press, Ann Arbor, Michigan.
Whitford, L.A. and G.J. Schumacher. 1973. A manual of freshwater algae. Sparks Press, Raleigh,
North Carolina.
Wujek, D.E. and R.F. Rupp. 1980. Diatoms of the Tittabawassee River, Michigan. Bibliotheca
Phycologia 50:1-100.
6-14 Chapter 6: Periphyton Protocols
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DRAFT REVISION-^JuIy 29,1997
BENTHIC MACROINVERTEBRATE
PROTOCOLS
Rapid bioassessment using the benthic macroinvertebrate assemblage has been the most popular set of
protocols among the state water resource agencies since 1989 (Southerland and Stribling 1995). Most
of the development of benthic Rapid Bioassessment Protocols (RBPs) has been oriented toward RBP
III (described in Plafkin et al. 1989). As states have focused attention on regional specificity, which
has included a wide variety of physical characteristics of streams, the methodology of conducting
stream surveys of the benthic assemblage has advanced. Some states have preferred to retain more
traditional methods such as the Surber or Hess samplers (e.g., Wyoming Department of
Environmental Quality [DEQ]) over the kick net in cobble substrate. Other agencies have developed
techniques for streams lacking cobble substrate, such as those streams in coastal plains. State water
resource agencies composing the Mid-Atlantic Coastal Streams (MACS) Workgroup, i.e., New Jersey
Department of Environmental Protection (DEP), Delaware Department of Natural Resources and
Environmental Control (DNREC), Maryland Department of Natural Resources (DNR) and Maryland
Department of the Environment (MDE), Virginia DEQ, North Carolina Department of Environmental
Management (DEM), and South Carolina Department of Health and Environmental Control (DHEC),
and a workgroup within Florida Department of Environmental Protection (DEP) were pioneers in this
effort. These 2 groups developed a multihabitat sampling procedure using a D-frame dip net. Testing
of this procedure by these 2 groups indicates that this technique is scientifically valid for low-gradient
streams. Research conducted by the U.S. Environmental Protection Agency (EPA) for their
Environmental Monitoring and Assessment Program (EMAP) program and the United States
Geological Survey (USGS) for their National Water Quality Assessment Program (NAWQA) program
STANDARD BENTHIC MACROINVERTEBRATE SAMPLING GEAR TYPES FOR STREAMS
(assumes standard mesh size of 500 ji nytex screen)
• Kick net: Dimensions of net are 1 meter (m) x 1 m attached to 2 poles and functions similarly to a
fish kick seine. Is most efficient for sampling cobble substrate (i.e., riffles and runs) where velocity
of water will transport dislodged organisms into net. Designed to sample 1 m2 of substrate at a time
and can be used in any depth from a few centimeters to just below 1m (Note — Depths of 1m or
greater will be difficult to sample with any gear).
• D-frame dip net: Dimensions of frame are 0.3 m width and 0.3 m height and shaped as a "D"
where frame attaches to long pole. Net is cone or bag-shaped for capture of organisms. Can be
used in a variety of habitat types and used as a kick net. or for ^'jabbing", "dipping", or "'sweeping".
• Rectangular dip net: Dimensions of frame are 0.5 m width and 0.3 m height and attached to a long
pole. Net is cone or bag-shaped. Sampling is conducted similarly to the D-frame.
• Surber: Dimensions of frame are 0.3 m x 0.3 m. which is horizontally placed on cobble substrate to
delineate a 0.09 m2 area. A vertical section of the frame has the net attached and captures the
dislodged organisms from the sampling area. Is restricted to depths of less than 0.3 m.
• Hess: Dimensions of frame are a metal cylinder approximately 0.5 m in diameter and samples an
area 0.8 m:. Is an advanced design of the Surber and is intended to prevent escape of organisms and
contamination from drift. Is restricted to depths of less than 0.5 m.
Rapid Bioassessment Protocols for Use in Streams and Rivers 7-1
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DRAFT REVISION—July 29, 1997
have indicated that the rectangular dip net is a reasonable compromise between the traditional Surber
or Hess samplers and the RBP kick net described the original RBPs.
From the testing and implementation efforts that have been conducted around the country since 1989,
refinements have been made to the procedures while maintaining the original concept of the RBPs.
Two separate procedures that are oriented toward a "single, most productive" habitat and a
multihabitat approach represent the most rigorous benthic RBP and are essentially a replacement of
the original RBP III. The primary differences between the original RBP II and III are the decision, on
field versus lab sorting and level of taxonomy. These differences are not considered sufficient reasons
to warrant separate protocols. In addition, a third protocol has been developed as a more standardized
biological reconnaissance or screening and replaces RBP I of the original document.
Kicknet
D-frame Dipnet
Picture of Hess sampler
Picture 4
do not have a picture
Rectangular Dipnet
Chapter ~: Benthic \lacroinvertebrate Protocols
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DRAFT REVISION—July 25,1997
7.1 SINGLE HABITAT APPROACH: 1-METER KICK NET
The original RBPs (Plafkin et al. 1989) emphasized the sampling of a single habitat, in particular
riffles or runs, as a means to standardize assessments among streams having those habitats. This
approach is still valid, because macroinvertebrate diversity and abundance are usually highest in
cobble substrate (riffle/run) habitats. Where cobble substrate is the predominant habitat, this
sampling approach provides a representative sample of the stream reach. However, some streams
naturally lack the cobble substrate. In cases where the cobble substrate represents less than 30% of
the sampling reach in reference streams (i.e., those streams that are representative of the region),
alternate habitat(s) will need to be sampled (See Section 7.2). The appropriate sampling method
should be selected based on the habitat availability of the reference condition and not potentially
impaired streams. For example, methods would not be altered for situations where the extent of
cobble substrate in streams influenced by heavy sediment deposition may be substantially reduced
from that expected for the region.
7.1.1 Field Sampling Procedures for Single Habitat
A 100-m reach
representative of the
characteristics of the
stream should be
selected. Whenever
possible, the area should
be at least 100 meters
upstream from any road
or bridge crossing to
minimize its effect on
stream velocity, depth,
and overall habitat
quality. There should be
no major tributaries
discharging to the stream
in the study area.
Before sampling,
complete the
physical/chemical field
sheet (see Chapter 5;
Appendix A-l, Form 1) to document site description, weather conditions, and land use.
After sampling, review this information for accuracy and completeness.
Draw a map of the sampling reach. This map should include in-stream attributes (e.g.,
riffles, falls, fallen trees, pools, bends, etc.) and important structures, plants, and attributes of
the bank and near stream areas. Use an arrow to indicate the direction of flow. Indicate the
areas that were sampled for macroinvertebrates on the map. Estimate "river mile" for
sampling reach for probable use in data management of the water resource agency. If
available, use hand-held Global Positioning Satellite (GPS) for latitude and longitude
determination and take at the furthest downstream point of the sampling reach.
FIELD EQUIPMENT/SUPPLIES NEEDED FOR BENTHIC
MACROINVERTEBRATE SAMPLING
—SINGLE HABITAT APPROACH
Standard kick-net, 500 fj. opening mesh, 1.0 meter width
sieve bucket, with 500 /u. opening mesh
95% ethanol
sample containers, sample container labels
forceps
pencils, clipboard
Benthic Macroinvertebrate Field Data Sheet*
first aid kit
waders (chest-high or hip boots)
rubber gloves (arm-length)
camera
.Global Positioning Satellite (GPS) Unit
* It is helpful to copy fieldsheets onto "Rite in the Rain" <
use in wet weather conditions
> paper for
Rapid Bioassessment Protocols for Use in Streams and Rivers
7-3
-------
DRAFT REVISION—July 25,1997
All riffle and run areas within the 100-
m reach are candidates for sampling
macroinvertebrates. A composite
sample is taken from individual
sampling spots in the riffles and runs
representing different velocities.
Generally, a minimum of 2 m2
composited area is sampled for RBP
efforts.
Sampling begins at the downstream end
of the reach and proceeds upstream.
Using aim kick net, 2 or 3 kicks are
sampled at various velocities in the
riffle or series of riffles. A kick is a
stationary sampling accomplished by
positioning the net and disturbing one
square meter upstream of the net.
Using the toe or heel of the boot,
dislodge the upper layer of cobble or
gravel and scrap the underlying bed.
Larger substrate particles should be
picked up and rubbed by hand to remove
D-frame or rectangular net), a composite
ALTERNATIVES FOR STREAM REACH
DESIGNATION
• Fixed-distance designation—A standard
length of stream, such as a reach, is
commonly used to obtain an estimate of
natural variability. Conceptually, this
approach should provide a mixture of
habitats in the reach and provide, at a
minimum, duplicate physical and
structural elements such as a riffle/pool
sequence.
• Proportional-distance designation—
Alternatively, a standard number of stream
"widths" is used to measure the stream
distance, e.g., 40 times the stream width is
defined by EMAP for sampling (Klemm
andLazorchak 1995). This approach
allows variation in the length of the reach
based on the size of the stream.
attached organisms. If different gear is used (e.g., a
is obtained from numerous kicks (See Section 7.2).
9.
The jabs or kicks collected from different locations in the cobble substrate will be
composited to obtain a single homogeneous sample. After every kick, wash the collected
material by running clean stream water through the net 2 to 3 times. If clogging does occur,
discard the material in the net and redo that portion of the sample in a different location.
Remove large debris after rinsing and inspecting it for organisms; place any organisms found
into the sample container. Do not spend time inspecting small debris in the field.
Transfer the sample from the net to sample container(s) and preserve in enough 95 percent
ethanol to cover the sample. Forceps may be needed to remove organisms from the dip net.
Place a label indicating the sample identification code or lot number, date, stream name,
sampling location, and collector name into the sample container. The outside of the
container should include the same information and the words "preservative: 95% ethanol".
If more that one container is needed for a sample, each container label should contain all the
information for the sample and should be numbered (e.g., 1 of 2, 2 of 2, etc.). This
information will be recorded in the "Sample Log" at the biological laboratory (Appendix A-
3, Form 2).
Complete the top portion of the "Benthic Macroinvertebrate Field Data Sheet" (Appendix A-
3, Form 1), which duplicates the "header" information on the physical/chemical field sheet.
Record the percentage of each habitat type in the reach. Note the sampling gear used, and
comment on conditions of the sampling, e.g., high flows, treacherous rocks, difficult access
to stream, or anything that would indicate adverse sampling conditions.
7-4
Chapter 7: Benthic Macroinvertebrate Protocols
-------
DRAFT REVISION-July 25,1997
10. Document observations of aquatic flora and fauna. Make qualitative estimates of
macroinvertebrate composition and relative abundance as a cursory estimate of ecosystem
health and to check adequacy of sampling.
11. Perform habitat assessment (Appendix A-l, Form 2) after sampling has been completed.
Having sampled the various microhabitats and walked the reach helps ensure a more
accurate assessment. Conduct the habitat assessment with another team member, if possible.
12. Return samples to laboratory and complete log-in form (Appendix A-3, Form 2).
QUALITY CONTROL (QC) IN THE FIELD
1. Sample labels must be properly completed, including the sample identification code, date, stream
name, sampling location, and collector's name, and placed into the sample container. The outside of
the container should be labeled with the same information. Chain-of-custody forms, if needed, must
include the same information as the sample container labels.
2. After sampling has been completed at a given site, all nets, pans, etc. that have come in contact with
the sample will be rinsed thoroughly, examined carefully, and picked free of organisms or debris. Any
additional organisms found should be placed into the sample containers. The equipment should be
examined again prior to use at the next sampling site.
3. Replicate (1 duplicate sample) 10% of the sites to evaluate precision or repeatability of sampling
technique or collection team.
7.2 MULTIHABITAT APPROACH: D-FRAME DIP NET
Streams in many states vary from
high gradient, cobble dominated
to low gradient streams with
sandy or silry sediments.
Therefore, a method suitable to
sampling a variety of habitat
types is desired in these cases.
The method which follows is
based on Mid-Atlantic Coastal
Streams Workgroup
recommendations designed for
use in streams with variable
habitat structure (MACS 1996)
and was used for statewide
stream bioassessment programs
in Florida (FL DEP 1996) and
Massachusetts (MA DEP 1995).
This method focuses on a
multihabitat scheme designed to
sample major habitats in
proportional representation
within a sampling reach. Benthic
macroinvertebrates are collected systematically from all available instream habitats by kicking the
FIELD EQUIPMENT/SUPPLIES NEEDED FOR BENTHIC
MACROINVERTEBRATE SAMPLING
—MULTI-HABITAT APPROACH
Standard D-frame dip net, 500 // opening mesh, 0.3 m
width (~ 1.0 ft frame width)
sieve bucket, with 500 n opening mesh
95% ethanol
sample containers, sample container labels
forceps
pencils, clipboard
Benthic Macroinvertebrate Field Data Sheet*
first aid kit
waders (chest-high or hip boots)
rubber gloves (arm-length)
camera
Global Positioning Satellite (GPS) Unit
' It is helpful to copy fieldsheets onto "Rite in the Rain" i
use in wet weather conditions
' paper for
Rapid Bioassessment Protocols for Use in Streams and Rivers
7-5
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DRAFT REVISION—July 25,1997
substrate or jabbing with a D-frame dip net. A total of 20 jabs (or kicks) are taken from all major
habitat types in the reach resulting in sampling of approximately 3.1m2 of habitat. An organism-
based subsampl'e (usually 100, 200, 300, or 500 organisms) is sorted in the laboratory and identified
to the lowest practical taxon, generally genus or species.
7.2.1 Habitat Types
The major stream habitat types used here are in reference to those that are colonized by
macroinvertebrates and generally support the diversity of the macroinvertebrate assemblage in
stream ecosystems. Some combination of these habitats would be sampled in the multihabitat
approach to benthic sampling.
Cobble (hard substrate) - Cobble will be prevalent in the riffles (and runs), which are a common
feature throughout most mountain and piedmont streams. In many high-gradient streams, this habitat
type will be dominant. However, riffles are not a common feature of most coastal or other low-
gradient streams. Sample shallow areas with coarse (mixed gravel, cobble or larger) substrates by
holding the bottom of the dip net against the substrate and dislodging organisms by kicking the
substrate for 0.5 m upstream of the net.
Snags - Snags and other woody debris that have been submerged for a relatively long period (not
recent deadfall) provide excellent colonization habitat. Sample submerged woody debris by jabbing
in medium-sized snag material (sticks and branches). The snag habitat may be kicked first to help
dislodge organisms, but only after placing the net downstream of the snag. Accumulated woody
material in pool areas are considered snag habitat. Large logs should be avoided because they are
generally difficult to sample adequately.
Vegetated Banks - When lower banks are submerged and have roots and emergent plants associated
with them, they are sampled in a fashion similar to snags. Submerged areas of undercut banks are
good habitats to sample. Sample banks with protruding roots and plants by jabbing into the habitat.
Bank habitat can be kicked first to help dislodge organisms, but only after placing the net
downstream.
Submerged macrophytes - Submerged macrophytes are seasonal in their occurrence and may not be
a common feature of many streams, particularly those that are high-gradient. Sample aquatic plants
that are rooted on the bottom of the stream in deep water by drawing the net through the vegetation
from the bottom to the surface of the water (maximum of 0.5 m each jab). In shallow water sample
by bumping or jabbing the net along the bottom in the rooted area, avoiding sediments where
possible.
Sand (and other fine sediment) - Usually the least productive macroinvertebrate habitat in streams,
this habitat may be the most prevalent in some streams. Sample banks of unvegetated or soft soil by
bumping the net along the surface of the substrate rather than dragging the net through soft
substrates; this reduces the amount of debris in the sample.
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DRAFT REVISION—July 25,1997
7.2.2 Field Sampling Procedures for Multihabitat
6.
ALTERNATIVES FOR STREAM RJEACH
DESIGNATION
• Fixed-distance designation—A standard
length of stream, such as a reach, is
commonly used to obtain an estimate of
natural variability. Conceptually, this
approach should provide a mixture of
habitats in the reach and provide, at a
minimum, duplicate physical and
structural elements such as a riffle/pool •
sequence.
• Proportional-distance designation—
Alternatively, a standard number of stream
"widths" is used to measure the stream
distance, e.g., 40 times the stream width is
defined by EMAP for sampling (Klemm
and Lazorchak 1995). This approach
allows variation in the length of the reach
based on the size of the stream.
A 100 m reach that is representative of
the characteristics of the stream should
be selected. Whenever possible, the
area should be at least 100 m upstream
from any road or bridge crossing to
minimize its effect on stream velocity,
depth and overall habitat quality. There
should be no major tributaries
discharging to the stream in the study
area.
Before sampling, complete the
physical/chemical field sheet (see
Chapter 5; Appendix A-l, Form 1) to
document site description, weather
conditions, and land use. After
sampling, review this information for
accuracy and completeness.
Draw a map of the sampling reach.
This map should include in-stream
attributes (e.g., riffles, falls, fallen trees,
pools, bends, etc.) and important structures, plants, and attributes of the bank and near
stream areas. Use an arrow to indicate the direction of flow. Indicate the areas that were
sampled for macroinvertebrates on the map. Approximate "river mile" to sampling reach for
probable use in data management of the water resource agency.
Different types of habitat are to be sampled in rough proportion to their representation of
surface area of the total macroinvertebrate habitat in the reach. For example, if snags
comprise 50% of the habitat in a reach and riffles comprise 20%, then 10 jabs should be
taken in snag material and 4 jabs should be take in riffle areas. The remainder of the jabs (6)
would be .taken in any remaining habitat type. Habitat types contributing less than 5% of the
stable habitat in the stream reach should not be sampled. In this case, allocate the remaining
jabs proportionately among the predominant substrates. The number of jabs taken in each
habitat type should be recorded on the field data sheet.
Sampling begins at the downstream end of the reach and proceeds upstream. A total of 20
jabs or kicks will be taken over the length of the reach; a single jab consists of forcefully
thrusting the net into a productive habitat for a linear distance of 0.5 m. A kick is a
stationary sampling accomplished by positioning the net and disturbing the substrate for a
distance of 0.5 m upstream of the net.
The jabs or kicks collected from the multiple habitats will be composited to obtain a single
homogeneous sample. Every 3 jabs, more often if necessary, wash the collected material by
running clean stream water through the net two to three times. If clogging does occur that
may hinder obtaining an appropriate sample, discard the material in the net and redo that
portion of the sample in the same habitat type but in a different location. Remove large
Rapid Bio assessment Protocols for Use in Streams and Rivers
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DRAFT REVISION-^July 25,1997
debris after rinsing and inspecting it for organisms; place any organisms found into the
sample container. Do not spend time inspecting small debris in the field.
7. Transfer the sample from the net to sample container(s) and preserve in enough 95% ethanol
to cover the sample. Forceps may be needed to remove organisms from the dip net. Place a
label indicating the sample identification code or lot number, date, stream name, sampling
location, and collector name into the sample container. The outside of the container should
include the same information and the words "preservative: 95% ethanol". If more that one
container is needed for a sample, each container label should contain all the information for
the sample and should be numbered (e.g., 1 of 2, 2 of 2, etc.). This information will be
recorded in the "Sample Log" at the biological laboratory (Appendix A-3, Form 2).
8. Complete the top portion of the "Benthic Macroinvertebrate Field Data Sheet" (Appendix A-
3, Form 1), which duplicates the "header" information on the physical/chemical field sheet.
9. Record the percentage of each habitat type in the reach. Note the sampling gear used, and
comment on conditions of the sampling, e.g., high flows, treacherous rocks, difficult access
to stream, or anything that would indicate adverse sampling conditions.
10. Document observations of aquatic flora and fauna. Make qualitative estimates of
macroinvertebrate composition and relative abundance as a cursory estimate of ecosystem
health and to check adequacy of sampling.
11. Perform habitat assessment (Appendix A-1, Form 3) after sampling has been completed.
Having sampled the various microhabitats and walked the reach helps ensure a more
accurate assessment. Conduct the habitat assessment with another team member, if possible.
12. Return samples to laboratory and complete log-in forms (Appendix A-3, Form 2).
QUALITY CONTROL (QC) IN THE FIELD
1. Sample labels must be properly completed, including the sample identification code, date, stream
name, sampling location, and collector's name and placed into the sample container. The outside of
the container should be labeled with the same information. Chain-of-custody forms, if needed, must
include the same information as the sample container labels.
2. After sampling has been completed at a given site, all nets, pans, etc. that have come in contact with
the sample will be rinsed thoroughly, examined carefully, and picked free of organisms or debris. Any
additional organisms found should be placed into the sample containers. The equipment should be
examined again prior to use at the next sampling site.
3. Replicate (1 duplicate sample) 10% of the sites to evaluate precision or repeatability of sampling
technique or collection team.
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DRAFT REVISION—July 25,1997
7.3 LABORATORY PROCESSING FOR MACROINVERTEBRATE
SAMPLES
Macroinvertebrate samples collected by either intensive method, i.e., single habitat or multihabitat,
are best processed in the laboratory under controlled conditions. Aspects of laboratory processing
including subsampling, sorting, and identification are presented below.
All samples should be dated and
recorded in the "Sample Log"
notebook or on sample log form
(Appendix A-3, Form 2) upon receipt
by laboratory personnel. All
information from the sample
container label should be included on
the sample log sheet. If more than
one container was used, the number of
containers should be indicated as
well. All samples should be sorted in
a single laboratory to enhance quality
control.
7.3.1 Subsampling and
Sorting
LABORATORY EQUIPMENT/SUPPLIES NEEDED
FOR BENTHIC MACROINVERTEBRATE SAMPLE
PROCESSING
• log-in sheet for samples
• standardized gridded pan (30 cm x 36 cm) with
approximately 30 grids (6 cm x 6 cm)
• 500 micron sieve
• forceps
• white plastic or enamel pan (15 cm x 23 cm) for
sorting
• specimen vials with caps or stoppers
• sample labels
• standard laboratory bench sheets for sorting and
identification
• dissecting microscope for organism identification
• fiber optics light source
• compound microscope with phase contrast for
identification of mounted organisms (e.g., midges)
• 70% ethanol for storage of specimens
• appropriate taxonomic keys
Subsampling reduces the effort
required for the sorting and
identification aspects of
macroinvertebrate surveys and
provides a more accurate estimate of
time expenditure (Barbour and
Gerritsen 1996). The RBPs use a fixed-count approach to subsampling and sorting the organisms
from the sample matrix of detritus, sand, and mud. The following protocol is based on a 100-
organism subsample, but it could be used for any subsample size (200,300, 500, etc.). The
subsample is sorted and preserved separately from the remaining sample for quality control checks.
1. Prior to processing any samples in a lot (i.e., samples within a collection date, specific
watershed, or project), complete the sample log-in sheet to verify that all samples have
arrived at the laboratory, and are in proper condition for processing.
2. Thoroughly rinse sample in a 500 um-mesh sieve to remove preservative and fine sediment.
Large organic material (whole leaves, twigs, algal or macrophyte mats, etc.) not removed in
the field should be rinsed, visually inspected, and discarded. If the samples have been
preserved in alcohol, it will be necessary to soak the sample contents in water for about 15
minutes to hydrate the benthic organisms, which will prevent them from floating on the
water surface during sorting. If the sample was stored in more than one container, the
contents of all containers for a given sample should be combined at this time. Gently mix the
sample by hand while rinsing to make homogeneous.
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DRAFT REVISION—July 25,1997
Subsample Procedure Modifications
Subsampling procedures developed by
Hilsenhoff (1987) and modified by Plafkin et al.
(1989) were used in the original RBPII and RBP
III protocols. As an improvement to the
mechanics of the technique, Caton (1991)
designed a sorting tray consisting of two parts, a
rectangular plastic or plexiglass pan (36 cm x 30
cm) with a rectangular sieve insert. The sample
is placed on the sieve, in the pan and dispersed
evenly. When a random grid(s) is selected, the
sieve is lifted to temporarily drain the water. A
"cookie-cutter" like metal frame 6 cm x 6 cm is
used to clearly define the selected grid; debris
overhanging the grid may be cut with scissors. A
6 cm flat scoop is used to remove all debris and
organisms from the grid. The contents are then
transferred to a separate sorting pan with water
for removal of macroinvertebrates.
These modifications have allowed for rapid
isolation of organisms within the selected grids
and easy removal of all organisms and debris
within a grid while eliminating investigator bias.
After washing, spread the sample
evenly across a pan marked with
grids approximately 6 cm x 6 cm.
On the laboratory bench sheet, note
the presence of large or obviously
abundant organisms; do not remove
them from the pan. However,
Vinson and Hawkins (1996) present
an argument for including these large
organisms in the count, because of
the high probability that these
organisms will be excluded from the
targeted grids.
Use a random numbers table to select
4 numbers corresponding to squares
(grids) within the gridded pan.
Remove all material (organisms and
debris) from the four grid squares,
and place the material into a shallow
white pan and add a small amount of
water to facilitate sorting. If there
appear (through a cursory count or
observation) to be 100 organisms ±
20% (cumulative of 4 grids), then
subsampling is complete.
Any organism that is lying over a line separating two grids is considered to be on the grid
containing its head. In those instances where it may not be possible to determine the
location of the head (worms for instance), the organism is considered to be in the grid
containing most of its body.
If the density of organisms is high enough that many more than 100 organisms are contained
in the 4 grids, transfer the contents of the 4 grids to a second gridded pan. Randomly select
grids for this second level of sorting as was done for the first, sorting grids one at a time until
100 organisms ± 20% are found. If picking through the entire next grid is likely to result in a
subsample of greater than 120 organisms, then that grid may be subsampled in the same
manner as before to decrease the likelihood of exceeding 120 organisms. That is, spread the
contents of the last grid into another gridded pan. Pick grids one at a time until the desired
number is reached. The total number of grids for each subsorting level should be noted on
the laboratory bench sheet.
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Chapter 7: Benthic Macroinvertebrate Protocols
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DRAFT REVISION—July 25,1997
TESTING OF SUBSAMPLING
Ferraro et al. (1989) describe a procedure for calculating the "power-cost efficiency" (PCE), which
incorporates both the number of samples and the cost (i.e. time or money) for each alternative sampling
scheme. With this analysis, the optimal subsampling size is that by which the costs of increased effort are
offset by the lowest theoretical number of samples predicted from the power analysis to provide reliable
resolution (Barbour and Gerritsen 1996).
There are four primary steps in assessing the PCE of a suite of alternative subsampling strategies:
Stepl: For each subsampling strategy (i.e., 100-, 200-, or 300- organism level) collect samples at several
reference and impaired stations. The observed differences in each of the core metrics is defined to
be the magnitude of the difference desired to be detected. The difference is the "effect size" and is
equivalent to the inverse CV described above.
Step 2: Assess the "cost" (c,), in time or money, of each subsampling scheme i at each site. The cost can
include labor hours for subsampling, sorting, identification, and documentation. Total cost of each
subsampling alternative is the product of cost per site and required sample size.
Step 3: Conduct statistical power analyses to determine the minimum number of replicate samples (n,)
needed to detect the effect size with an acceptable probability of Type I (<*; the probability that the
null hypothesis [e.g., "sites are good"] is true and it is rejected. Commonly termed the significance
level.) and Type II (P; the probability that the null hypothesis is false and it is accepted) error.
Typically, « and P are set at 0.05. This step may be deleted for those programs that already have
an established number of replicate samples.
Step 4: Calculate the PCE for each sampling scheme by:
PCE =-
where (n X c)min = minimum value of (n X c) among the i sampling schemes. The PCE formula is
equivalent to the "power efficiency" ratio of the sample sizes attained by alternative tests under
similar conditions (Ferraro et al. 1989) with the n's multiplied by the "cost" per replicate sample.
Multiplying n by c puts efficiency on a total "cost" rather than on a sample size basis. The
reciprocal of PCE, is the factor by which the optimal subsampling scheme is more efficient than
alternative scheme i. When PCE is determined for multiple metrics, the overall optimal
subsampling scheme may be defined as that which ranks highest in PCE for most metrics of
interest.
5. Save the sorted debris residue in a separate container. Add a label that includes the words
"sorted residue" in addition to all prior sample label information and preserve in 95%
ethanol. Save the remaining unsorted sample debris residue in a separate container labeled
"sample residue"; this container should include the original sample label. Length of storage
and archival is determined by the laboratory or benthic section supervisor.
6. Place the sorted 100-organism (± 20%) subsample into glass vials, and preserve in 70%
ethanol. Label the vials inside with the sample identifier or lot number, date, stream name,
sampling location and taxonomic group. If more than one vial is needed, each should be
labeled separately and numbered (e.g., 1 of 2, 2 of 2). For convenience in reading the labels
inside the vials, insert the labels left-edge first. If identification is to occur immediately after
sorting, a petri dish or watch glass can be used instead of vials.
Rapid Bioassessment Protocols for Use in Streams and Rivers 7-11
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DRAFT REVISION—July 25,1997
7. Midges (Chironomidae) should be mounted on slides in an appropriate medium (e.g.,
Euperal, CMC-9); slides should be labeled with the site identifier, date collected, and the
first initial and last name of the collector. As with midges, worms (Oligochaeta) must also
be mounted on slides and should be appropriately labeled.
8. Fill out header information on Laboratory Bench Sheet as in field sheets (see Chapter 5).
Also check subsample target number. Complete back of sheet for subsampling/sorting
information. Note number of grids picked, time expenditure, and number of organisms. If
QC check was performed on a particular sample, person conducting QC should note findings
on the back of the Laboratory Bench Sheet. Calculate sorting efficiency to determine
whether sorting effort passes or fails.
9. Record date of sorting and slide monitoring, if applicable, on Log-In Sheet as documentation
of progress and status of completion of sample lot.
QUALITY CONTROL (QC) FOR SORTING
1. Ten percent of the sorted samples in each lot should be examined by laboratory QC personnel or a
qualified co-worker. (A lot is defined as a special study, basin study, entire index period, or individual
sorter.) The QC worker will examine the grids chosen and tray used for sorting and will look for
organisms missed by the sorter. Organisms found will be added to the sample vials. If the QC worker
finds less than 10 organisms (or 10% in larger subsamples) remaining in the grids or sorting tray, the
sample passes; if more than 10 (or 10%) are found, the sample fails. If the first 10% of the sample lot
fails, a second 10% of the sample lot will be checked by the QC worker. Sorters in-training will have
their samples 100% checked until the trainer decides that training is complete.
2. After laboratory processing is complete for a given sample, all sieves, pans, trays, etc., that have come
in contact with the sample will be rinsed thoroughly, examined carefully, and picked free of organisms
or debris; organisms found will be added to the sample residue.
7.3.2 Identification of Macroinvertebrates
Taxonomy can be at any level, but should be consistent among samples. In the original RBPs, two
levels of identification were suggested — family (RBP II) and genus/species (RBP III) level (Plafkin
et al. 1989). Genus/species will provide more accurate information on ecological/environmental
relationships and sensitivity to impairment. Family level will provide a higher degree of precision
among samples and taxonomists, requires less expertise to perform, and accelerates assessment
results. In either case, only those taxonomic keys that have been peer-reviewed and are published in
some way to be available to other taxonomists should be used. Unnamed species (i.e., species A, B,
1 or 2) may be ecologically informative, but will contribute to variability and inconsistency when a
statewide database is being developed.
1. Most organisms are identified to the lowest practical level (generally genus or species) by a
qualified taxonomist using a dissecting microscope. Midges (Diptera: Chironomidae) are
mounted on slides in an appropriate medium and identified using a compound microscope.
Each taxon found in a sample is recorded and enumerated in a laboratory bench notebook
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DRAFT REVISION—Julv 25,1997
and then transcribed to the laboratory bench sheet for subsequent reports. Any difficulties
encountered during identification (e.g., missing gills) are noted on these sheets.
2. Labels with specific taxa names (and taxonomist's initials) are added to the vials of
specimens by the taxonomist. Individual specimens may be extracted from the sample to be
included in a reference collection or to be verified by a 2nd taxonomist. Slides are initialed
by the identifying taxonomist. A separate label may be added to slides to include the taxon
(taxa) name(s) for use in a voucher or reference collection.
3. Record the identity and number of organisms on the Laboratory Bench Sheet (Appendix A-3,
Form 3). Either a tally counter or "slash" marks on the bench sheet can be done to keep
track of the cumulative count. Also,.record the life stage of the organisms, taxonomist's
initials and taxonomic certainty rating (TCR) as a measure of confidence.
4. Complete the back of the bench sheet to explain certain TCR ratings or condition of
organisms. Other comments can be included to provide additional insights for data
interpretation. If QC was performed, record on back of sheet.
5. For archiving samples, specimen vials, grouped by station and date, are placed in jars with a
small amount of denatured 70% ethanol and tightly capped. The ethanol level in these jars
must be examined periodically and replenished as needed, before ethanol loss from the
specimen vials takes place. A stick-on label is placed on the outside of the jar indicating
sample identifier, date, and preservative (denatured 70% ethanol).
QUALITY CONTROL (QC) FOR TAXONOMY
1. A voucher collection of all samples and subsamples should be maintained. These specimens should be
properly labeled, preserved, and stored in the laboratory for future reference. A taxonomist (the
reviewer) not responsible for the original identifications should spot check samples corresponding to
the identifications on the bench sheet.
2. The reference collection of each identified taxon should also be maintained and verified by a second
taxonomist. The word "val." and the 1" initial and last name of the person validating the identification
should be added to the vial label. Specimens sent out for taxonomic validations should be recorded in
a "Taxonomy Validation Notebook" showing the label information and the date sent out. Upon return
of the specimens, the date received and the finding should also be recorded in the notebook along with
the name of the person who performed the validation.
3. Information on samples completed (through the identification process) will be recorded in the "sample
log" notebook to track the progress of each sample within the sample lot. Tracking of each sample
will be updated as each step is completed (i.e., subsampling and sorting, mounting of midges and
worms, taxonomy).
4. A library of basic taxonomic literature is essential in aiding identification of specimens and should be
maintained (and updated as needed) in the taxonomic laboratory (see attached list). Taxonomists
should participate in periodic training on specific taxonomic groups to ensure accurate identifications.
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DRAFT REVISION—July 25,1997
7.4 BENTHIC METRICS
Benthic metrics have undergone evolutionary developments and are documented in the Invertebrate
Community Index (ICI) (DeShon 1995), RBPs (Shackleford 1988, Plafkin et al. 1989, Barbour et al.
1992, 1995, 1996a, Hayslip 1993), and the benthic IBI (Kerans and Karr 1994, Fore et al. 1996).
Metrics used in these indices evaluate aspects of both elements and processes within the
macroinvertebrate assemblage. Although these indices have been regionally developed, they are
typically appropriate over wide geographic areas with minor modification (Barbour et al. 1995)
(Table 7-1). Selected metrics are listed in broad classes for each index (Table 7-2).
The process for testing the efficacy and calibrating the metrics is described in Chapter 10. While the
metrics described here are ecologically sound, they are candidates that require testing oh a regional
basis. Resh and Jackson (1993) tested the ability of 20 metrics used in 30 different assessment
protocols for the benthic assemblage to discriminate between impaired and unimpaired sites in
California. The most effective measures, from their study, were the richness measures, two
community indices (Margalef s and Hilsenhoff s family biotic index), and a functional feeding group
metric (percent scrapers). Resh and Jackson emphasized that both the measures (metrics) and
protocols need to be calibrated for different regions of the country, and, perhaps, for different impact
types (stressors). In a study of 28 invertebrate metrics, Kerans and Karr (1994) demonstrated
significant patterns for 18 metrics and used 13 in their final B-IBI (Benthic Index of Biotic Integrity).
Richness measures were useful as were selected trophic and dominance metrics. One of the unique
features of the fish IBI presently lacking in benthic indices is the ability to incorporate metrics on
individual condition, although chironomid larvae deformities have recently been advocated (Lenat
1993).
Taxa richness, or the number of distinct taxa, represents the diversity within a sample. Use of taxa
richness as a key metric in a multimetric index include the ICI (DeShon 1995), the fish IBI (Karr et
al. 1986), the benthic IBI (Kerans et al. 1992, Kerans and Karr, 1994), and RBP's (Plafkin et al.
1989, Barbour et al. 1996a). Taxa richness usually consists of species level identifications but can
also be evaluated as designated groupings of taxa, often as higher taxonomic groups (i.e., genera,
families, orders, etc.) in assessment of invertebrate assemblages. Richness measures reflect the
diversity of the aquatic assemblage (Resh et al. 1995). The expected response to increasing
perturbation is summarized, as an example, in Table 7-2. Increasing diversity correlates with
increasing health of the assemblage and suggests that niche space, habitat, and food source are
adequate to support survival and propagation of many species. Number of taxa measures the overall
variety of the macroinvertebrate assemblage. No identities of major taxonomic groups are derived
from the total taxa metric, but the elimination of taxa from a naturally diverse system can be readily
detected. Subsets of "total" taxa richness are also used to accentuate key indicator groupings of
organisms. Diversity or variety of taxa within these groups are good indications of the ability of the
ecosystem to support varied taxa. Certain indices that focus on a pair-wise site comparison are also
included in this richness category.
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DRAFT REVISION—July 25,1997
Table 7-1. Examples of metric suites used for analysis of macroinvertebrate assemblages (modified from Barbour et
al. 1995).
RBP"
B-IBI
Alternative
Benthic Metrics
ICP RBPb RBPC ID OR WA TN< ORr SCP MBI" BCI'
X
X
X
X
Richness Measures
Total No. Taxa XX XXX
*% Change in Taxa X X
Richness
No. EPTTaxa XX XX
No. Ephemeroptera Taxa X
No. Trichoptera Taxa X
No. Plecoptera Taxa
No. Pteronarcys Species
*Missing Taxa (EPT) X
No. Diptera Taxa X
No. of Chironomidae Taxa X
Composition Measures
Ratio EPT/Chiron. Abund. XXX
% EPT • X
% Ephemeroptera X
% Plecoptera
% Trichoptera X
% Chironomidae
% Tribe Tanytarsini X
% Diptera
% Other Diptera and X
Non insects
% Corbicula
% Oligochaeta
Indicator Assemblage XXX
Index
*Quantitative Similarity X X
Index (Taxa)
*Common Taxa Index X
Pinkham-Pearson X
X
X
X
X
X
X
X
X
X
X
X
. X
X
X
X
X
X
X
X
X
X
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DRAFT REVISION-JuIy 25,1997
RBP"
B-IBI
Alternative
Benthic Metrics
ICP RBP" RBPC ID OR WA TIN' ORr SCI" MBI" BCr
"Community Loss Index
Jaccard Similarity Index
Shannon-Weiner Index
Index of Community
Integrity
Tolerance/Intolerance
Measures
No. of Intolerant Taxa
No. Intol. Snail and Mussel
Species
No. of Sediment Intolerant
Taxa
% Tolerant Organisms
% Sediment Tolerant
Organisms
% Dominant Taxon
% 2 Dominant Taxa
% 3 Dominant Taxa
*5 Dom. Taxa in Common
Hilsenhoff Biotic Index
Florida Index
Chandler Biotic Score
Biotic Condition Index
"Indicator Groups
Ratio Hydropsychidae/
Trichoptera
Total Abundance (Density)
Feeding Measures
No. of Predator Taxa
X
X
X
X
X
XXX
X
X X
X
XXX
X X
X
X
X
X
X X
X X X X
X
X
X
X
X
X
X
X
X
X
X
% Omnivores and
Scavengers
% Ind. Gatherers and
Filterers
% Gatherers
% Filterers
X.
X
X
XX XX
-
7-16
Chapter 7: Benthic Macroinvertebrate Protocols
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DRAFT REVISION—July 25,1997
Alternative
Benthic Metrics
% Grazers and Scrapers
RBP" B-IBI
ICI1 RBPb RBPC ID OR WA TNe ORf SCI£ MBI" BCI'
XX X
Ratio Scrapers/Filterers XXX
Ratio Scrapers/(Scrapers + X
Filterers)
% Strict Predators X
% Shredders X ' X X
*% Similarity Functional XX
Feeding Groups (QSI)
Life Cycle Measures
% Multivoltine X
% Univoltine X
*Metric depends on pair-wise comparison between 2 sites (one is a reference).
"Invertebrate Community Index, Ohio EPA (1987), DeShon (1995).
bRapid Bioassessment Protocols, Barbour et al. (1992) revised from Plafkin et al. (1989).
cRapid Bioassessment Protocols for Arkansas, Shackleford (1988).
dRapid Bioassessment Protocols, Hayslip (1993), ID = Idaho, OR = Oregon, WA = Washington.
(Note: these metrics in ID, OR, and WA are currently under evaluation).
'Benthic Index of Biotic Integrity for the Tennessee Valley, Kerans and Karr (1994).
'Benthic Index of Biotic Integrity for southwestern Oregon, Fore et al. (1996).
sStream Condition Index for Florida, Barbour et al. (1996b).
hMultimetric Benthic Index for Wyoming, Barbour et al. (1994).
'Benthic Comparison Index for Mid-Atlantic Coastal Plains Streams, MACS (1996).
Rapid Bioassessment Protocols for Use in Streams and Rivers 7-17
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DRAFT REVISION—July 25,1997
Table 7-2. Definitions of potential benthic metrics and expected direction of metric response to increasing
perturbation (compiled from Kerans and Karr 1994, Fore et al. 1996, and Barbour et al. 1996b).
Expected
response to
increasing
Category Metric Definition perturbation
Richness measures
Composition
measures
Total No. taxa
*% Change in Taxa Richness
No. EPT taxa
No. Ephemeroptera Taxa
No. Trichoptera Taxa
No. Plecoptera Taxa
No. Pteronarcys Species
*Missing Taxa (EPT)
No. Coleoptera taxa
No. Diptera Taxa
No. Chironomidae taxa
No. Orthocladiinae taxa
No. Tanytarsini taxa
No. Crustacea + Mollusca taxa
Ratio EPT/Chiron. Abund.
% EPT
Measures the overall variety of the Decrease
macroinvertebrate assemblage
Percent of change in number of taxa Increase
between reference and station of
comparison
Number of taxa in the insect orders Decrease
Ephemeroptera (mayflies), Plecoptera
(stoneflies), and Trichoptera (caddisflies)
Number of mayfly taxa (usually genus or Decrease
species level)
Number of caddisfly taxa (usually genus or Decrease
species level)
Number of stonefly taxa (usually genus of Decrease
species level)
The presence or absence of a long-lived Decrease
stonefly genus (2-3 year life cycle)
Measures the loss of EPT taxa from an Increase
upstream site to a downstream site. Based
on a pair-wise site comparison.
Number of beetle taxa (adult or larval) Decrease
Number of "true" fly taxa, which includes Decrease
midges
Number of taxa of chironomid (midge) Decrease
larvae
Number of taxa in the midge subfamily Decrease
Orthocladiinae
Number of taxa in the midge tribe Decrease
Tanytarsini
Sum of the number of calcium-dependent Decrease
taxa
A ratio of 2 indicator groups as a simple Decrease
measure of compositional balance. Is more
robust when EPT + Chironomidae is the
denominator.
Percent of the composite of mayfly, Decrease
stonefly, and caddisfly larvae
% Ephemeroptera
% Plecoptera
% Trichoptera
Percent of mayfly nymphs
Percent of stonefly nymphs
Percent of caddisfly larvae
Decrease
Decrease
Decrease
7-18
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DRAFT REVISION—July 25,1997
Category
Metric
Definition
Expected
response to
increasing
perturbation
% Odonata
% Coleoptera
% Diptera
% Chironomidae
% Orthocladiinae to chironomids
% Tanytarsini to chironomids
% Tribe Tanytarsini
% Diptera
% Other Diptera and Noninsects
% Crustacea + Mollusca
% Gastropoda
% Pelecypoda
% Corbicula
% Amphipoda
% Isopoda
% Oligochaeta
* Indicator Assemblage Index
*Quantitative Similarity Index
(Taxa)
*Common Taxa Index
Pinkham-Pearson Community
Similarity Index
*Community Loss Index
Jaccard Similarity Index
Percent of dragonfly and damselfly nymphs Increase
Percent of beetle larvae and aquatic adults Decrease
Percent of all "true" fly larvae Increase
Percent of midge larvae Increase
Percent of chironomids in the subfamily Increase
Orthocladiinae •
Percent of chironomids in the tribe Decrease
Tanytarsini
Percent of Tanytarisinid midges to total Decrease
fauna
Percent of "true" fly larvae Increase
Composite of those organisms generally Increase
considered to be tolerant to a wide range of
environmental conditions
Percent of individuals classed as Decrease
crustaceans plus molluscs
Percent of snails Decrease
Percent of bivalves Decrease
Percent of asiatic clam in the benthic Increase
assemblage
Percent of amphipods Decrease
Percent of isopods Increase
Percent of aquatic worms Variable
Comparison of a ratio of EPT abundance Decrease
and chironomid + oligochaete abundance.
Based on a pair-wise site comparison.
Pair-wise site comparison of number of Decrease
taxa between reference and station of
comparison
Comparison of the taxa in common at 2 Decrease
sites
Incorporates abundance and compositional Decrease
information. Based on a pair-wise site
comparison.
Measures the loss of benthic taxa between Increase
reference and the station of comparison
Measures the degree of similarity in Decrease
taxonomic composition between 2 stations
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DRAFT REVISION—July 25,1997
Category
Metric
Definition
Expected
response to
increasing
perturbation
Shannon-Wiener Index
Tolerance/Intolerance No. of Intolerant Taxa
Measures
No. Intol. Snail and Mussel
Species
% Tolerant Organisms
% Sediment Tolerant Organisms
% Dominant Taxon
*5 Dominant Taxa in Common
Hilsenhoff Biotic Index
Florida Index
Biotic Condition Index
% Hydropsychidae to Trichoptera
% Baetidae to Ephemeroptera
Density
Feeding measures No. of scraper + piercer taxa
No. of predator Taxa
% omnivores and scavengers
% ind. gatherers and filterers
Incorporates both richness and evenness in Decrease
a measure of general diversity and
composition
Taxa richness of those organisms Decrease
considered to be sensitive to perturbation
Number of species of molluscs generally Decrease
thought to be pollution intolerant
Percent of macrobenthos considered to be Increase
tolerant of various types of perturbation
Percent of infaunal macrobenthos tolerant Increase
of perturbation
Measures the dominance of the single most Increase
abundant taxon. Can be calculated as
dominant 2, 3,4, or 5 taxa.
Pair-wise site comparison of dominant Decrease
components of benthos between reference
and station of comparison
Uses tolerance values to weight abundance Increase
in an estimate of overall pollution.
Originally designed to evaluate organic
pollution
Weighted sum of intolerant taxa, which are Decrease
classed as 1 (least tolerant) or 2 (intolerant).
Florida Index = 2 X Class 1 taxa + Class 2
taxa.
Tolerance classification based on nonpoint Decrease
source impact of sedimentation and
velocity alteration
Relative abundance of pollution tolerant Increase
caddisflies (metric could also be regarded
as a composition measure)
Relative abundance of pollution tolerant Increase
mayflies (metric could also be regarded as a
composition measure)
Abundance corrected to number per rrr Variable
Number of taxa feeding upon living plant Decrease
material either by scraping periphyton or
by piercing macrophytes
Number of taxa that feed upon other Variable
organisms or themselves in some instances
Percent of generalists in feeding strategies Increase
Percent of collector feeders of CPOM and Variable
FPOM
7-20
Chapter 7: Benthic Macroinvertebrate Protocols
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DRAFT REVISION—July 28,1997
Category Metric
% gatherers
% filterers
Definition
Percent of the macrobenthos that "gather"
Percent of the macrobenthos that filter
Expected
response to
increasing
perturbation
Variable
Variable
% predators
% grazers and scrapers
Ratio scrapers/filterers
% shredders
*% similarity functional feeding
groups (QSI)
Life Cycle Measures % multivoltine
% univoltine
FPOM from either the water column or
sediment
Percent of the predator functional feeding Variable
group. Can be made restrictive to exclude
omnivores
Percent of the macrobenthos that scrape or Decrease
graze upon periphyton
Ratio of specialist to generalist feeding Decrease
strategies. Is more robust when scrapers
& filterers is the denominator.
Percent of the macrobenthos that "shreds" Decrease
leaf litter
Pair-wise site comparison of percent of Decrease
functional feeding group composition
between reference and station of
comparison
Percent of organisms having short (several Increase
per year) life cycle
Percent of organisms relatively long-lived Decrease
(life cycles of 1 or more years)
*Metric depends on pair-wise comparison between 2 sites (one is a reference).
Composition measures can be characterized by several classes of information. Identity is the
knowledge of individual taxa and associated ecological patterns and environmental requirements
(Barbour et al. 1995). Key taxa (i.e., those that are of special interest or ecologically important)
provide information that is important to the condition of the targeted assemblage. The presence of
exotics or nuisance species may be an important aspect of biotic interactions that relates to both
identity and sensitivity. Measures of composition (or relative abundance) provide information on the
make-up of the assemblage and the relative contribution of the populations to the total fauna (Table
7-2). Relative abundance is used rather than absolute abundance, because the relative contribution of
individuals to the total fauna - a reflection of interactive principles - is more informative than
abundance data on populations without a knowledge of the interaction among taxa (Plafkin et al.
1989, Barbour et al. 1995). The premise is that a healthy and stable assemblage will be relatively
consistent in its proportional representation, though individual abundances may vary in magnitude.
Percentage of the dominant taxon is a simple measure of redundancy (Plafkin et al. 1989). A high
level of redundancy is equated with the dominance of a pollution tolerant organism and a lowered
diversity. Several diversity indices, which are a measure of information content and incorporate both
richness and evenness in their formulas, may function as viable metrics in some cases, but are
usually redundant with taxa richness and % dominance (Barbour et al. 1996b).
Rapid Bioassessment Protocols for Use in Streams and Rivers
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DRAFT REVISION—July 28,1997
Tolerance/Intolerance measures are intended to be representative of relative sensitivity to
perturbation and may include numbers of pollution-tolerant and -intolerant taxa or percent
composition (Barbour et al. 1995). Tolerance is generally non-specific to the type of stressor.
However, some metrics such as the Hilsenhoff Biotic Index (HBI) (Hilsenhoff 1987, 1988) are
oriented toward detection of organic pollution; the Biotic Condition Index (Winget and Mangum
1979) is useful for evaluating sedimentation. The Florida Index (Ross and Jones 1979) is a weighted
sum of intolerant taxa (insects and crustaceans) found at a site (Beck 1965) and functions similarly to
the HBI (Hilsenhoff 1987) used in other parts of the country. The tolerance/intolerance measures
can be independent of taxonomy or can be specifically tailored to taxa that are associated with
pollution tolerances. For example, both the percent of Hydropsychidae to total Trichoptera and
percent Baetidae to total Ephemeroptera are estimates of evenness within these insect orders that
generally are considered to be sensitive to pollution. As these families (i.e., Hydropsychidae and
Baetidae) increase in relative abundance, effects of pollution (usually organic) also increase.
Density (number of individuals per some unit of area) is a universal measure used in all kinds of
biological studies. Density can be classified with the trophic measures because it is an element of
production; however, it is difficult to interpret because it requires careful quantification and is not
monotonic in its response (i.e., density can either decrease or increase in response to pollution) and is
usually linked to tolerance measures.
Feeding measures or trophic dynamics encompass functional feeding groups, and provide
information on the balance of feeding strategies (food acquisition and morphology) in the benthic
assemblage. Examples involve the feeding orientation of scrapers, shredders, gatherers, filterers, and
predators. Trophic dynamics (food types) are also included here and include the relative abundance
of herbivores, carnivores, omnivores, and detritivores. Without relatively stable food dynamics, an
imbalance in functional feeding groups will result and reflect stressed conditions. Trophic metrics
are surrogates of complex processes such as trophic interaction, production, and food source
availability (Karr et al. 1986, Cummins et al. 1989, Plafkin et al. 1989). Specialized feeders, such as
scrapers, piercers, and shredders, are the more sensitive organisms and are thought to be well
represented in healthy streams. Generalists, such as collectors and filterers, have a broader range of
acceptable food materials than specialists (Cummins and Klug 1979), and thus are more tolerant to
pollution that might alter availability of certain food. However, filter feeders are also thought to be
sensitive in low-gradient streams (Wallace et al. 1977).
7.5 BIOLOGICAL RECONNAISSANCE (BioRecon) OR PROBLEM
IDENTIFICATION SURVEY
The use of biological survey techniques can serve as a screening tool for problem identification
and/or prioritizing sites for further assessment, monitoring, or protection. The application of
biological surveys in site reconnaissance is intended to be expedient, and, as such, requires an
experienced and well-trained biologist. Expediency in this technique is to minimize time spent in the
laboratory and with analysis. The "turn-around" time from the biosurvey to an interpretation of
findings is intended to be relatively short. The BioRecon is useful in discriminating obviously
impaired and non-impaired areas from potentially affected areas requiring further investigation. Use
of the BioRecon allows rapid screening of a large number of sites. Areas identified for further study
7-22 Chapter 7: Benthic Macroinvertebrate Protocols
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DRAFT REVISION—July 28,1997
FIELD EQUIPMENT/SUPPLIES NEEDED FOR BENTHIC
MACROINVERTEBRATE SAMPLING
—BIORECON
Standard D-frame dip net, 500 jU opening mesh, 0.3 meter
width (~ 1.0 ft frame width)
sieve bucket, with 500 p. opening mesh
95% ethanol
sample containers
sample container labels
forceps
field data sheets', pencils, clipboard
first aid kit
waders (chest-high or hip boots), rubber gloves (arm-
length)
camera
Global Positioning Satellite (GPS) Unit
* It is helpful to copy fieldsheets onto "Rite in the Rain" i
use in wet weather conditions
) paper for
can then either be evaluated
using more rigorous
bioassessment methods for
benthic macroinvertebrates
and/or other assemblages, or
ambient toxicity methods.
Because the BioRecon involves
limited data generation, its
effectiveness depends largely on
the experience of the
professional biologist performing
the assessment. The professional
biologist should have assessment
experience, a knowledge of
aquatic ecology, and basic
expertise in benthic
macroinvertebrate taxonomy.
The BioRecon presented here is
refined and standardized from
the original RBPI (Plafkin et al. 1989), and is based on the technique developed by Florida DEP
(1996), from which the approach derives its name. This biosurvey approach is based on a
multihabitat approach similar to the more rigorous technique discussed in Section 7.2. The most
productive habitats, i.e., those that contain the greatest diversity and abundance of
macroinvertebrates, are sampled in the BioRecon. As a general rule, impairment is judged by
richness measures, thereby emphasizing the presence or absence of indicator taxa. Biological
attributes such as the relative abundance of certain taxa may be less useful than richness measures in
the BioRecon approach, because samples are processed more quickly and in a less standardized
manner.
7.5.1 Sampling, Processing, and Analysis Procedures
1. A 100 m reach representative of the characteristics of the stream should be selected. For the
BioRecon, it is unlikely that the alternative reach designation approach, i.e., x times the
stream width, will improve the resolution beyond a standard 100-m reach. Whenever
possible, the area should be at least 100 meters upstream from any road or bridge crossing to
minimize its effect on stream velocity, depth and overall habitat quality. There should be no
major tributaries discharging to the stream in the study area.
2. Before sampling, complete the "Physical Characterization/Water Quality Field Data Sheet"
(Appendix A-l, Form 1) to document site description, weather conditions, and land use.
After sampling, review this information for accuracy and completeness.
3. The major habitat types (see 7.2.1 for habitat descriptions) that are represented in the reach
are to be sampled for macroinvertebrates. A minimum of 1 jab (or kick) is to be taken in
each habitat. More than one jab may be desired in those habitats that are predominant.
Habitat types contributing less than five percent of the stable habitat in the stream reach
should not be sampled. In this case, allocate the remaining jabs proportionately among the
Rapid Bioassessment Protocols for Use in Streams and Rivers
7-23
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DRAFT REVISION—July 28,1997
predominant substrates. The number of jabs taken in each habitat type should be recorded
on the field data sheet.
4. Sampling begins at the downstream end of the reach and proceeds upstream. A total of four
jabs or kicks will be taken over the length of the reach; a single jab consists of forcefully
thrusting the net into a productive habitat for a linear distance of 0.5 m. A kick is a
stationary sampling accomplished by positioning the net and disturbing the substrate for a
distance of 0.5 m upstream of the net.
5. The jabs or kicks collected from the multiple habitats will be composited into a sieve bucket
to obtain a single homogeneous sample. If clogging occurs, discard the material in the net
and redo that portion of the sample in the same habitat type but in a different location.
Remove large debris after rinsing and inspecting it for organisms; place any organisms found
into the sieve bucket.
6. Return to the bank with the sampled material for sorting and organism identifications.
Alternatively, the material can be preserved in alcohol and returned to the laboratory for
processing (see Step 7 in Section 7.1.1 for instructions).
7. Transfer the sample from the sieve bucket (or sample jar, if in laboratory) to a white enamel
or plastic pan. A second, smaller, white pan may be used for the actual sorting. Place small
aliquots of the detritus plus organisms in the smaller pan diluted with a minimal amount of
site water (or tap water). Scan the detritus and water for organisms. When an organism is
found, examine it with a hard lens, determine its identity to the lowest possible level (usually
family or genus), and record it on the Preliminary Assessment Score Sheet (PASS)
(Appendix A-3, Form 4) in the column labeled "tally". Place representatives of each taxon
in a vial, properly labeled and containing alcohol.
8. If field identifications are conducted, verify in the lab and make appropriate changes for
misidentifications.
9. Analysis is done by determining the value of each metric and comparing to a predetermined
value for the associated stream class. These value thresholds should be sufficiently
conservative so that "good" conditions or non-impairment is verified. Sites with metric
values below the threshold(s) are considered "suspect" of impairment and may warrant
further investigation. These simple calculations can be done directly on the PASS sheet.
QUALITY CONTROL (QC)
1. Sample labels must be properly completed, including the sample identification code date, stream name,
sampling location, and collector's name and placed into the sample container. The outside of the
container should be labeled with the same information. Chain-of-custody forms, if needed, must
include the same information as the sample container labels.
2. After sampling has been completed at a given site, all nets, pans, etc. that have come in contact with
the sample will be rinsed thoroughly, examined carefully, and picked free of organisms or debris. Any
additional organisms found should be placed into the sample containers. The equipment should be
examined again prior to use at the next sampling site.
3. A second biologist familiar with the recognition and taxonomy of the organisms should check the
sample to ensure all taxa are encountered and documented.
7-24 Chapter 7: Benthic Macroinvertebrate Protocols
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DRAFT REVISION—July 28,1997
7.6 TAXONOMIC REFERENCES FOR MACROINVERTEBRATES
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Allen, R.K. and G.F. Edmunds. 196 la. A revision of the genus Ephemerella (Ephemeroptera:
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34:161-173.
Allen, R.K. and G.F. Edmunds. 1961b. A revision of the genus Ephemerella (Ephemeroptera:
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54:603-612.
Allen, R.K. and G.F. Edmunds. 1962. A revision of the genus Ephemerella (Ephemeroptera:
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Entomological Society of America 3:583-600.
Allen, R.K. and G.F. Edmunds. 1962. A revision of the genus Ephemerella (Ephemeroptera:
Ephemerellidae). V. The subgenus Drunella in North America. Miscellaneous Publications of the
Entomological Society of America 3:147-179.
Allen, R.K. and G.F. Edmunds. 1963a. A revision of the genus Ephemerella (Ephemeroptera:
Ephemerellidae). VI. The subgenus Seratella in North America. Annals of the Entomological
Society of America 56:583-600.
Allen, R.K. and G.F. Edmunds. 1965. A revision of the genus Ephemerella (Ephemeroptera:
Ephemerellidae). VIII. The subgenus Ephemerella in North America. Miscellaneous Publications
of the Entomological Society of America 4:243-282.
Allen, R.K. 1978. The nymphs of North and Central American Leptohyphes. Entomological
Society of America 71(4):537-558.
Allen, R.K. and G.F. Edmunds. 1959. A revision of the genus Ephemerella (Ephemeroptera:
Ephemerellidae). I. The subgenus Timpanoga. The Canadian Entomologist 91:51-58.
Anderson, N.H. 1976, The distribution and biology of the Oregon Trichoptera. Oregon Agricultural
Experimental Station Technical Bulletin 134:1-152.
Barr, C.B. and J.B. Chapin. 1988. The Aquatic Dryopoidea of Louisiana (Coleoptera:Psepheniae,
Dryopidae, Elmidae). Tulane Studies in Zoology and Botany 26:89-164.
Bauman, R.W. 1975. Revision of the Stonefly Family Nemouridae (Plecoptera): A Study of the
World Fauna at the Generic Level. Smithsonian Contributions to Zoology 211. 74 pp.
Baumann, R.W., A.R. Gaufin, and R.F. Surdick. 1977. The stoneflies (Plecoptera) of the Rocky
Mountains. Memoirs of the American Entomological Society 31:1-208.
Beck, W.M. and E.G. Beck. 1964. New Chironomidae from Florida. Florida Entomologist.
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Beck, W.M., Jr. and E.G. Beck. 966. Chironomidae (Diptera) of Florida. I. Pentaneurini
(Tanypodinae). Bulletin of the Florida State Museum 10: 305-379.
Beck, W.M., Jr. and E.G. Beck. 1970. The immature stages of some Chironomini (Chironomidae).
Quarterly Journal of the Academy of Biological Science 33:29-42.
Beck, E.G. and W.M. Beck, Jr. 1969. Chironomidae (Diptera) of Florida. III. The Harnischia
complex (Chironomidae). Bulletin of the Florida State Museum of Biological Sciences 13:277-313.
Beck, E.G. 1962. Five new Chironomidae (Diptera) from Florida. Florida Entomologist 45:89-92.
Bednarik, a.F. and W.P. McCafferty. 1979. Biosystematic revision of the genus Stenonema
(Ephemeroptera:Heptageniidae). Canadian Bulletin of Fisheries and Aquatic Sciences 201:1-73.
Bergman, E. A. and W.L. Hilsenhoff. 1978. Baetis (Ephemeroptera:Baetidae) of Wisconsin. The
Great Lakes Entomologist 11:125-35.
Berner, L. 1977. Distributional patterns of southeastern mayflies (Ephemeroptera). Bulletin of the
Florida State Museum of Biological Sciences 22:1-55.
Bemer, L. 1956. The genus neoephemera in North America (Ephemeroptera:Neoephemeridae).
Entomological Society of America 49:33-42.
Berner, L. 1975. The Mayfly Family Leptophlebiidae in the Southeastern United States. The
Florida Entomologist 58:137-156.
Berner, L. and M.L. Pescador. 1988. The Mayflies of Florida. University Presses of Florida. Pp. 415.
Boesel, M.W. and R.W. Winner. 1980. Corynoneurinae of Northeastern United States, with a key to
adults and observations on their occurrence in Ohio (Diptera:Chironomidae). Journal Kansas
Entomology Society 53:501-508.
Boesel, M.W. 1983. A review of the genus Cricotopus in Ohio, with a key to adults of species in the
northeastern United States (Diptera:Chironomidae). Ohio Journal of Science 83:74-90.
Boesel, M.W. 1.985. A brief review of the genus Polypedilum in Ohio, with keys to the known
stages of species occuring in Northeastern United States (Diptera:Chironomidae). Ohio Journal of
Science 85:245-262.
Boesel, M.W. 1972. The early stages of Ablabesmyia annulata (Say) (Diptera:Chironomidae). Ohio
Journal of Science 72:170-173.
Boesel, M.W. 1974. Observations on the Coelotanypodini of the northeastern states, with keys to
the known stages (Diptera: Chironomidae: Tanypodinae). Journal Kansas Entomology Society
47:417-432.
Brigham, A.R., W.U. Brigham, and A. Gnilka (eds.). 1982. Aquatic insects and Oligochaetes of
North and South Carolina. Midwest Aquatic Enterprises, Mahomet, IL.
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DRAFT REVISION—July 28,1997
Brinkhurst, R.O. and B.G.M. Jamieson. 1971. Aquatic Oligochaeta of the World. Univ. Toronto
Press, 860 pp.
Brinkhurst, R.O. 1986. Guide to the freshwater microdrile Oligochaetes of North America. Canada
Special Publications Fisheries Aquatic Science 84:1-259.
Brirtain, J.E. 1982. Biology of Mayflies. Annual Review of Entomology 27:119-147.
Brown, H.P. and D.S. White. 1978. Notes on Separation and Identification of North American
Riffle Beetles (Coleoptera:Dryopoidea:Elmidae). Entomological News 89:1-13.
Brown, H.P. 1972. Aquatic dryopoid beetles (Coleoptera) of the United States. Biota of freshwater
ecosystems identification manual no. 6. Water Pollution Control Research Series, EPA, Washington,
D.C.
Brown, H.P. 1976. Aquatic dryopoid beetles (Coleoptera) of the United States. USEPA. Water
Pollution Control Research Series 18050 ELD04/72.
Brown, H.P. 1987. Biology of riffle beetles. Annual Review of Entomology 32:253-273.
Burch, J.B. 1982. Freshwater snails (Mollusca: Gastropoda) of North America. EPA-600/3-82-026.
USEPA, Office of Research and Development, Cincinnati, Ohio.
Burch, J.B. 1972. Freshwater sphaeriacean clams (Mollusca: Pelecypoda) of North America. EPA
Biota of freshwater ecosystems identification manual No. 3. Water Pollution Control Research
Series, EPA, Washington, DC.
Caldwell, B.A. 1986. Description of the immature stages and adult female of Unniella multivirga
Saether (Diptera: Chironomidae) with comments on phylogeny. Aquatic Insects 8:217-222.
Caldwell, B.A. 1984. Two new species and records of other chironomids from Georgia (Diptera:
Chironomidae) with some observations on ecology. Georgia Journal of Science 42:81-96.
Caldwell, B.A. and A.R. Soponis. 1982. Hudsonimyia parrishi, a new species of Tanypodinae
(Diptera: Chironomidae) from Georgia. Florida Entomologist 65:506-513.
Caldwell, B.A. 1985. Paracricotopus millrockensis, a new species of Orthocladiinae (Diptera:
Chironomidae) from the southeastern United States. Brimleyana 11:161-168.
Carle, F.L. 1978. A New Species ofAmeletus (Ephemeroptera:Siphlonuriae) from Western
Virginia. Entomological Society of America 71:581-584.
Carle, F.L. and P.A. Lewis. 1978. A new species ofStenonema (Ephemeroptera:Heptageniidae)
from Eastern North America. Annals of the Entomological Society of America 71:285-288.
Clark, W. 1996. Literature pertaining to the identification and distribution of aquatic
macroinvertebrates of the Western U.S. with emphasis on Idaho. Idaho Department of Health and
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Stark, B.P. and C.H. Nelson. 1994. Systematics, phytogeny, and zoogeography of the genus
Yoraperla (Plecoptera: Peltoperliae). Entomologica Scandinavica 25:241-273.
Stewart, K.W. and B.P. Stark. 1988. Nymphs of North American stonefly genera (Plecoptera).
Thomas Say Foundation Series, Entomological Society of America 12:1-460.
Stewart, K.W. and B.P. Stark. 1984. Nymphs of North American Perlodinae genera (Plecoptera:
erlodidae). The Great Basin Naturalist 44(3):373-415.
Stimpson, K.S., D.J. Klemm and J.K. Hiltunen. 1982. A guide to the freshwater Tubificidae
(Annelida: Clitellata: Oligochaeta) of North America. EPA-600/3-82-033, 61 pp.
Sublette, J.E. 1964. Chironomidae (Diptera) of Louisiana I. Systematics and immature stages of
some lentic chironomids of West-central Louisiana. Tulane Studies in Zoology 11:109-150.
Szczytko, S.W. and K.W. Stewart. 1979. The genus Isoperla of western North America;
holomorphology and Systematics, and a new stonefly genus Cascadoperla. Memoirs of the American
Entomological Society 32:1-120.
Thompson, F. G. 1983. An identification manual of the freshwater snails of Florida. Florida State
Museum, Gainesville, Florida.
Thorp, J.H. and A.P. Covich (editors). 1991. Ecology and Classification of North American
Freshwater Invertebrates. Academic Press, New York, New York.
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Traver, J.R. 1933. Mayflies of North Carolina Part III. The Heptageniinae. Journal of the Elisha
Mitchell Scientific Society 48:141-206.
Traver, J.R. 1937. Notes on mayflies of the Southeastern states (Ephemeroptera). Journal of the
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genus Neophylax (Trichoptera: Limnephilidae). Annals of the Entomological Society 80:62-73.
Waltz, R.D., W.P. McCafferty, and J.H. Kennedy. 1985. Barbaetis: a new genus of eastern nearctic
Mayflies (Ephemeroptera: Baetidae). The Great Lakes Entomologist: 161-165.
Rapid Bioassessment Protocols for Use in Streams and Rivers 7-37
-------
DRAFT REVISION—July 28,1997
Waltz, R.D. & W.P. McCafferty. 1987. Systematics of Pseudodoeon, Acentrella, Baetiella, and
Liebebiella, new genus (Ephemeroptera: Baetidae). Journal of New York Entomology Society.
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Ross (Trichoptera: Rhyacophilidae). Journal of the Georgia Entomological Society 16:269-271.
Wetzel, M.J. 1987. Limnodrilus tortilipenis, a new North American species of freshwater
Tubificidae (Annelida:Clitellata:Oligochaeta). Proceedings of the Biological Society of Washington
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White, D S. 1978. A revision of the nearctic Optioservus (Coleoptera: Elmidae), with descriptions
of new species. Systematic Entomology 3:59-74
Wiederholm, T. (editor). 1983. Chironomidae of the holarctic region. Keys and diagnoses, Part 1,
Larvae. Entomologica Scandinavica Supplement no. 19, 1-457.
Wiederholm, T. (editor). 1986. Chironomidae of the Holartic region. Keys and diagnoses. Part 2.
Pupae. Entomologica Scandinavica Supplement 28: 1-482.
Wiggins, G.B. and J.S. Richardson. 1989. Biosystematics of Eocosmoecus, a new Nearctic
caddisfly genus (Trichoptera: Limnephilidae: dicosmoecinae). Journal of the North American
Benthological Society 8:355-369.
Wiggins, G.B. 1977. Larvae of the North American caddisfly genera (Trichoptera). University of
Toronto Press, Toronto, Canada.
Wiggins, G.B. 1965. Additions and revisions to the genera of North American caddisflies of the
family Brachycentridae with special reference to the larval stages (Trichoptera). Canadian
Entomologist 97:1089-1106.
Wiggins, G.B. 1995. Larvae of the North American caddisfly genera (Trichoptera), 2nd ed.
University of Toronto Press, Toronto, Canada.
Wiggins, G.B. and J.S. Richardson. 1982. Revision and synopsis of the caddisfly genus
Dicosmoecus (Trichoptera: Limnephilidae: Dicosmoecinae). Aquatic Insects 4:181-217.
Wold, J.L. 1974. Systematics of the genus Rhyacophila (Trichoptera: Rhyacophilidae).
Unpublished Master's Thesis, Oregon State University, Corvallis, Oregon.
Wolf, W.G. and J.F. Matta. 1981. Notes on nomenclature and classification ofHydroporus
subgenera with the description of a new genus of Hydroporinia (Coleoptera: Dytiscidae). Pan-Pacific
Entomologist 57:149-175.
Yamamoto, T. and G.B. Wiggins. 1964. A comparative study of the North American species in the
caddisfly genus Mystacides (Trichoptera: Leptoceridae). Canadian Journal of Zoology 42:1105-1210.
Young, F. N. 1954. The water beetles of Florida. University of Florida Press, Gainesville, Florida.
7-38 Chapter 7: Benthic Macroinvertebrate Protocols
-------
DRAFT REVISION—July 28,1997
FISH PROTOCOLS
Monitoring of the fish assemblage is an integral component of many water quality management
programs, and its importance is reflected in the aquatic life use-support designations of many states.
Narrative expressions such as "maintaining coldwater fisheries", "fishable" or "fish propagation" are
prevalent in state standards. Assessments of the fish assemblage must measure the overall structure
and function of the ichthyofaunal community to adequately evaluate biological integrity and protect
surface water resource quality. Fish bioassessment data quality and comparability are assured
through the utilization of qualified fisheries professionals and consistent methods.
The Rapid Bioassessment Protocol (RBP) for fish is directly comparable to RBP V in Plafkin et al.
(1989), and the principal evaluation mechanism utilizes the technical framework of the Index of
Biotic Integrity (IBI), a fish assemblage assessment approach developed by Karr (1981). The IBI
incorporates the zoogeographic, ecosystem, community and population aspects of the fish
assemblage into a single ecologically-based index. Calculation and interpretation of the IBI involves
a sequence of activities including: fish sample collection; data tabulation; and regional modification
and calibration of metrics and expectation values. This concept has provided the overall multimetric
index framework for rapid bioassessment in this document. A more detailed description of this
approach for fish is presented in Karr et al. (1986) and Ohio Environmental Protection Agency
(EPA) (1987). Regional modification and applications are described in Leonard and Orth (1986),
Moyle et al. (1986), Hughes and Gammon (1987), Wade and Stalcup (1987), Miller et al. (1988),
Steedman (1988), Simon (1991), Lyons (1992), and Simon and Lyons (1995).
The RBP for fish involves careful, standardized field collection, species identification and
enumeration, and analyses using aggregated biological attributes or quantification of the numbers
(and in some cases biomass, see Section 8.3.3, Metric 13) of key species. The role of experienced
fisheries scientists in the adaptation and application of the RBP and the taxonomic identification of
fishes cannot be overemphasized. The fish RBP survey yields an objective discrete measure of the
condition of the fish assemblage that usually can be completed onsite by qualified fish biologists
(however, difficult species identifications will require laboratory confirmation). Data provided by
the fish RBP can serve to assess use attainment, develop biological criteria, prioritize sites for further
evaluation, provide a reproducible impact assessment, and assess status and trends of the fish
assemblage.
Fish collection procedures must focus on a multihabitat approach, allowing the sampling of habitats
in relative proportion to their local availability. Each sample reach should contain riffle, run and
pool habitat, when available. Whenever possible, the reach should be sampled sufficiently upstream
of any bridge or road crossing to minimize the hydrological effects on overall habitat quality.
Wadeability and accessability may ultimately govern the exact placement of the sample reach. A
habitat assessment is performed and physical/chemical parameters measured concurrently with fish
sampling to document and characterize available habitat specifics within the sample reach (see
Chapter 5: Habitat Assessment and Physicochemical Characterization).
Rapid Bioassessment Protocols for Use in Streams and Rivers 8-1
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DRAFT REVISION-^Fulv 28,1997
8.1 FISH COLLECTION PROCEDURES: ELECTROFISHING
All fish sampling gear types are generally considered selective to some degree; however,
electrofishing has proven to be the most comprehensive and effective single method for collecting
stream fishes. Pulsed DC (direct current) electrofishing is the method of choice to obtain a
representative sample of the fish assemblage at each sampling station. However, electrofishing in
any form has been banned from certain salmonid spawning streams in the northwest. The accurate
identification of each fish collected is essential, and species-level identification is required. Field
identifications are acceptable; however, voucher specimens must be retained for laboratory
verification, particularly if there is any doubt about the correct identity of the specimen. Because the
collection methods used are not consistently effective for young-of-the-year fish and because their
inclusion may seasonally skew bioassessment results, fish less than 20 millimeters total length will
not be identified or included in standard samples.
ELECTROFISHING CONFIGURATION AND FIELD TEAM ORGANIZATION
All field team members must be trained or briefed in the electrofishing safety precautions and unit operation
procedures identified by the electrofishing unit manufacturer. Each team member must be insulated from
the water and the electrodes; therefore, chest waders and rubber gloves are required. Electrode and dip net
handles must be constructed of insulating materials (e.g., woods, fiberglass). Electrofishers/electrodes must
be equipped with functional safety switches (as installed by virtually all electrofisher manufacturers). Field
team members must not reach into the water unless the electrodes have been removed from the water or the
electrofisher has been disengaged.
It is recommended that at least 2 fish collection team members be certified in CPR (cardiopulmonary
resuscitation). Many options exist for electrofisher configuration and field team organization; however,
procedures will always involve pulsed DC electrofishing and a minimum 2-person team for sampling
streams and wadeable rivers. Examples include:
• Backpack electrofisher with two hand-held electrodes mounted on fiberglass poles, one positive (anode)
and one negative (cathode). One crew member, identified as the electrofisher unit operator, carries the
backpack unit and manipulates both the anode and cathode poles. The anode may be fitted with a net
ring (and shallow net) to allow the unit operator to net specimens. The remaining 1 or 2 team members
net fish with dip nets and are responsible for specimen transport and care in buckets or livewells.
• Backpack electrofisher with one hand-held anode pole and a trailing or floating cathode. The
electrofisher unit operator manipulates the anode with one hand, and has a second hand free for use of a
dip net. The remaining 1 or 2 team members also aid in the netting of specimens, and in addition are
responsible for specimen transport in buckets or livewells.
• Tote barge electrofisher with two hand-held anode poles and a trailing/floating cathode (recommended
for large streams and wadeable rivers). Two team members are each equipped with an anode pole and a
dip net. Each is responsible for electrofishing and the netting of specimens. The remaining team
member will follow, pushing or pulling the barge through the sample reach. A livewell is maintained
within the sampling reach but outside the area of electric current.
The safety of all personnel and the quality of the data is assured through the adequate education,
training, and experience of all members of the fish collection team. At least one biologist with
training and experience in electrofishing techniques and fish taxonomy must be involved in each
sampling event. Laboratory analyses are conducted and/or supervised by a fisheries professional
8-2 Chapter 8: Fish Protocols
-------
DRAFT REVISION—July 29,1997
trained in fish taxonomy. Quality assurance and quality control must be a continuous process in
fisheries monitoring and assessment, and must include all program aspects (i.e., field sampling, habitat
measurement, laboratory processing, and data recording).
Pram unit Electroshocker
Backpack Electroshocker
8.1.1 Field Sampling Procedures
1. A representative
stream reach (see
Alternatives for
Stream Reach
Designation, next
page) is selected and
measured such that
primary physical
habitat characteristics
of the stream are
included within the
reach (e.g., riffle, run
and pool habitats,
when available). The
sample reach should
be located away from
the influences of
major tributaries and
bridge/road crossings
(e.g., sufficiently
upstream to decrease
influences on overall
habitat quality). The
exact location (i.e.,
latitude and
FIELD EQUIPMENT/SUPPLES NEEDED FOR FISH
SAMPLING—ELECTROFISHING
backpack or tote barge-mounted electrofisher
dip nets
block nets (i.e., seines)
arm-length insulated waterproof gloves
chest waders (equipped with wading cleats, when necessary)
polarized sunglasses
buckets/livewells
jars for voucher/reference specimens
waterproof jar labels
10% buffered formalin (formaldehyde solution)
measuring board (500 mm minimum, with 1 mm increments)2
balance (gram scale)6
tape measure (100 m minimum)
Fish Sampling Field Data Sheet'
Applicable topographic maps and/or Global Positioning System
Unit
Copies of field protocols
pencils, clipboard
First aid kit
Global Positioning Satellite (GPS) Unit
3 Needed only if program/study requires length frequency information
b Needed only if total biomass and/or the Index of Weil-Being are
included in the assessment process (see Section 8.3.3. Metric 13).
c It is helpful to copy fieldsheets onto "Rite in the Rain"® paper for use
in wet weather conditions.
Rapid Bioassessment Protocols for Use in Streams and Rivers
s-j
-------
DRAFT REVISION—July 28,1997
longitude) of the downstream limit of the reach must be recorded on each field data sheet.
A habitat assessment and physical/ chemical characterization of water quality should'be
performed within the same sampling reach (see Chapter 5: Habitat Assessment and
Physicochemical Characterization).
2. Collection via electrofishing
begins at a shallow riffle, or
other physical barrier at the
downstream limit of the sample
reach, and terminates at a similar
barrier at the upstream end of the
reach. In the absence of physical
barriers, block nets should be set
at the upstream and downstream
ends of the reach prior to the
initiation of any sampling
activities.
3. Fish collection procedures
commence at the downstream
barrier. A minimum 2-person
fisheries crew proceeds to
electrofish in an upstream
direction using a side-to-side or
bank-to-bank sweeping
technique to maximize area
coverage. All wadeable habitats
within the reach are sampled via
a single pass, which terminates at
the upstream barrier. Fish are
held in livewells (or buckets) for
subsequent identification and
enumeration.
4. Sampling efficiency is
dependent, at least in part, on the
field team's ability to see and net
the stunned fish. Therefore, each team member should wear polarized sunglasses, and
sampling is conducted only during periods of optimal water clarity and flow.
5. Fish (except young-of-the-year) collected within the sample reach must be identified to
species (or subspecies). Specimens that cannot be identified with certainty in the field are
preserved in a 10 percent formalin solution and stored in labeled jars for subsequent
laboratory identification. A representative voucher collection must be retained for
unidentified specimens, very small specimens, new locality records, and/or region. In
addition to the unidentified specimen jar, a voucher collection of a subsample of each
species identified in the field should be preserved and labeled for subsequent laboratory
verification, if necessary. Obviously, species of special concern (e.g., threatened,
endangered) should be noted and released immediately on site. Labels should contain (at a
minimum) location data (verbal description and coordinates), date, collectors' names, and
ALTERNATIVES FOR STREAM REACH
DESIGNATION
The collection of a representative sample of the fish
assemblage is essential, and the appropriate sampling
station length for obtaining that sample is best determined
by conducting pilot studies. Alternatives for the
designation of stream sampling reaches include:
• Fixed-distance designation—A standard length
of stream, e.g., a 150-200-meter reach (Ohio EPA
1987), 100-meter reach (Massachusetts DEP
1995) may be used to obtain a representative
sample. Conceptually, this approach should
provide a mixture of habitats in the reach and
provide, at a minimum, duplicate physical and
structural elements such as riffle/pool sequences.
• Proportional-distance designation— A standard
number of stream channel "widths" may be used
to measure the stream study reach, e.g., 40 times
the stream width is defined by Environmental
Monitoring & Assessment Program (EMAP) for
sampling (Klemm and Lazorchak 1995). This
approach allows variation in the length of the
reach based on the size of the stream. Application
of the proportional-distance approach in large
streams or wadeable rivers may require the
establishment of sampling program time and/or
distance maxima (e.g., no more than 3 hours of
electrofishing or 500-meter reach per sampling
site, [Klemm et al. 1993]).
8-4
Chapter 8: Fish Protocols
-------
DRAFT REVISION—July 28,1997
sample identification code and/or station numbers for the particular sampling site. Young-
of-the-year fish less than 20 millimeters (total length) are not identified or included in the
sam'ple, and are released on site. Specimens that can be identified in the field are counted,
examined for external anomalies (i.e., deformities, eroded fins, lesions, and tumors), and
recorded on field data sheets. Example "Fish Sampling Field Data Sheets" are provided in
Appendix A-4, Form 1. Space is available for optional fish length and weight
measurements, should a particular program/study require length frequency or biomass data.
However, these data are not
required for the standard
multimetric assessment. Space is
allotted on the field data sheets for
the optional inclusion of
measurements (nearest millimeter
total length) and weights (nearest
gram) for a subsample (to a
maximum 25 specimens) of each
species. Following the data
recording phase of the procedure,
specimens that have been
identified and processed in the
field are released on site to
minimize mortality.
6. The data collection phase includes
the completion of the top portion
of the "Fish Sampling Field Data
Sheet" (Appendix A-4, Form 1),
which duplicates selected
information from the
physical/chemical field sheet.
Information regarding the sample
collection procedures must also be
recorded. This includes method of
fish capture, start time, ending
time, duration of sampling,
maximum and mean stream
widths. The percentage of each
habitat type in the reach is
estimated and documented on the
data sheet. Comments should
include sampling conditions, e.g.,
visibility, flow, difficult access to
stream, or anything that may prove
to be valuable information to
consider for future sampling
events or by personnel unfamiliar
with the site.
QUALITY CONTROL (QC) IN THE FIELD
1. Quality control must be a continuous process in fish
bioassessment that includes all program aspects,
from field collection and preservation to habitat
assessment, sample processing, and data recording.
Field validation should be conduced at selected sites
and will involve the collection of a duplicate sample
taken from an adjacent reach upstream of the initial
sampling site. The adjacent reach should be similar
to the initial site with respect to habitat and stressors.
Sampling QC data should be evaluated following the
first year of sampling in order to determine a level of
acceptable variability and the appropriate
duplication frequency.
2. Field identifications of fish must be conducted by
qualified/trained fish taxonomists, familiar with
local and regional ichthyofauna. Questionable
records are prevented by: a) requiring the presence
of at least one experienced/trained fish taxonomist
on every field effort, and b) preserving selected
specimens (e.g., Klemm and Lazorchak 1995
recommend as subsample of a maximum 25
specimens of each species) and those that cannot by
readily identified in the field for laboratory
verification and/or examination by a second
qualified fish taxonomist. Specimens must be
properly preserved and labeled (refer to field
sampling procedure no. 5). When needed, chain-of-
custody forms must be initiated following sample
preservation, and must include the same information
as the sample container labels.
3. All field equipment must be in good operating
condition, and a plan of routine inspection,
maintenance, and/or calibration must be developed
to ensure consistency and quality of field data. Field
data must be complete and legible, and should be
entered on standardized field data forms. While in
the field, the field team should possess sufficient
copies of standardized field data forms and chains-
of-custody for all anticipated sampling sites, as well
as copies of all applicable Standard Operating
Procedures (SOPs).
Rapid Bioassessment Protocols for Use in Streams and Rivers
8-5
-------
DRAFT REVISION—July 28,1997
8.2 LABORATORY IDENTIFICATION AND VERIFICATION
Questionable fish records are prevented by preserving specimens (that cannot be readily identified in
the field) for laboratory examination and/or a voucher collection for laboratory verification.
Specimens must be properly preserved (e.g., 10 percent formalin) and labeled. Labels should contain
(at a minimum) site location data (i.e., verbal description and site coordinates), collection date,
collector's names, and sample identification code and/or station number. All samples received in the
laboratory should be tracked by using a sample log-in procedure similar to that described for
macroinvertebrate samples (see Chapter 7). Laboratory fisheries professionals must be capable of
identifying fish to the lowest possible taxonomic level (i.e., species or subspecies) and should have
access to suitable regional taxonomic references (see Section 8.4) to aid in the identification process.
Laboratories that do not typically identify fish, or trained fisheries professionals that have difficulty
identifying a particular specimen or group offish, should contact a taxonomic specialist (i.e., a
recognized authority for that particular taxonomic group). Taxonomic nomenclature must be kept
consistent and current. Common and scientific names of fishes from the United States and Canada
are listed in Robins et al. (1991).
8.3 DESCRIPTION OF FISH METRICS
(1.) REGIONAL MODIFICATION (2.) SAMPLE COLLECTION
AND CALIBRATION AND DATA TABULATION
fish fauna
Selection of *a
«'<•(*)
tlplmg
San
nplm
CO
g of loca
fob
INTERPRETATION
Rating of IB
metric*
Karr et al. (1986) provided a consistent theoretical
framework for analyzing fish assemblage data. The
IBI is an aggregation of 12 biological metrics that are
based on the fish assemblage's taxonomic and
trophic composition and the abundance and condition
of fish. Such multiple-parameter indices are
necessary for making objective evaluations of
complex systems. The IBI was designed to evaluate
the quality of small Midwestern streams but has been
modified for use in many regions (e.g., eastern and
western United States, Canada, France) and in
different ecosystems (e.g., rivers, impoundments,
lakes, and estuaries).
The metrics attempt to quantify an ichthyologist's
best professional judgment of the quality of the fish
community. The IBI utilizes professional judgment,
but in a prescribed manner, and it includes
quantitative standards for discriminating the
condition of the fish assemblage. Judgment is
involved in choosing the most appropriate population
or community element that is representative of each
metric and in setting the scoring criteria. This
process can be easily and clearly modified, as
opposed to judgments that occur after results are
calculated. Each metric is scored against criteria
based on expectations developed from appropriate
regional reference sites. Metric values approximating, deviating slightly from, or deviating greatly
from values occurring at the reference sites are scored as 5, 3, or 1, respectively. The scores of the
12 metrics are added for each station to give an IBI ranging from a maximum of 60 (excellent) to a
minimum 12 (very poor). Trophic and tolerance classifications of selected fish species are listed in
Sequence of activities involved in calculating
and interpreting the Index of Biotic Integrity
8-6
Chapter 8: Fish Protocols
-------
DRAFT REVISION-July 28,1997
Appendix C. Additional classifications can be derived from information in State and regional fish
texts, by objectively assessing a large statewide database, or by contacting authors/originators of
regional IBI programs or pilot studies. Use of the IBI by water resource agencies may result in
further modifications. Many modifications have occurred (Miller et al. 1988) without changing the
IBI's basic theoretical foundations.
The IBI serves as an integrated analysis because individual metrics may differ in their relative
sensitivity to various levels of biological condition. A description and brief rationale for each of the
12 IBI metrics is outlined below. The original metrics described by Karr (1981) for Illinois streams
are followed by substitutes used in or proposed for different geographic regions and stream sizes.
Because of zoogeographic differences, different families or species are evaluated in different
regions, with regional substitutes occupying the same general habitat or niche. The source for each
substitute is footnoted below. Table 8-1 presents an overview of the IBI metric alternatives and their
sources for various areas of the United States and Canada.
8.3.1 Species Richness and Composition Metrics
These metrics assess the species richness component of diversity and the health of the major
taxonomic groups and habitat guilds of fishes.
Two of the metrics assess community
composition in terms of tolerant or intolerant
species.
Metric 1. Total number offish species
Substitutes (Table 8-1): Total number of
native fish species and salmonid age classes.
This number decreases with increased
degradation; hybrids and introduced species
are not included. In coldwater streams
supporting few fish species, the age classes of
the species found represent the suitability of
the system for spawning and rearing. The
number of species is strongly affected by
stream size at most small warmwater stream
sites, but not at large river sites (Karr et al.
1986, Ohio EPA 1987).
EXAMPLES OF SOURCES FOR METRIC
ALTERNATIVES
Karr etal. (1986)
Leonard and Orth (1986)
Moyle etal. (1986)
Fausch and Schrader (1987)
Hughes and Gammon (1987)
Ohio EPA (1987)
Miller etal. (1988)
Steedman(1988)
Simon (1991)
Lyons(1992)
Harbour etal. (1995)
Simon and Lyons (1995)
Hall etal. (1996)
Lyons etal. (1996)
Roth etal. (1997)
Metric 2. Number and identity of darter
species Substitutes (Table 8-1): Number and identity of sculpin species, benthic insectivore species,
salmonid yearlings (individuals); number of sculpins (individuals); percent round-bodied suckers,
sculpin and darter species.
Rapid Bioassessment Protocols for Use in Streams and Rivers
8-7
-------
DRAFT REVISION—July 28,1997
Table 8-1. Fish IBI metrics used in various regions of North America/
s -5
Alternative IBI Metrics
5
o.
1
c
U
•8
s
o
8
o
E
en
i5 -2
S S
2 C3
E *
a .§
— c
U
"5
U
2 H
O
U
O TT T-
1. Total Number of Species
# native fish species
# salmonid age classes'1
1. Number of Darter Species
# sculpin species
# benthic insectivore species
# darter and sculpin species
# darter, sculpin, and madtom species
# salmonid yearlings (individuals)"
% round-bodied suckers
# sculpins (individuals)
# benthic species
3. Number of Sunfish Species
# cyprinid species
# water column species
# sunfish and trout species
# salmonid species
# headwater species
% headwater species
4. Number of Sucker Species
# adult trout species'1
# minnow species
# sucker and catfish species
5. Number of Intolerant Species
# sensitive species
# amphibian species
presence of brook trout
XXX
X X
XXX
X
X X
X
X
X X
XX X X
X
XXX
X
XXX X
X X
XX
Chapter 8: Fish Protocols
-------
DRAFT REVISION—July 28,1997
Alternative IBI Metrics
JB
2
•o
Midwestern Un
c
ra
Central Appala
1
Sacramento-Sa
c
C3
IColorado Front
o
00
o
6
v
u
^o
5
u
Ohio I Icadwatc
"C
n
CD
c
o
Ontario
c
Q
a
c
"ra
E
Wisconsin-Wai
o
§
Wisconsin-Col
1
Maryland Coas
_
Maryland Non-
% stenothermal cool and cold water species
% of salmonid ind. as brook trout
6. % Green Sunfish X
% common carp
% white sucker
% tolerant species
% creek chub X
% dace species
% eastern mudminnow
7. % Omnivores X
% yearling salmonids*1
% generalist feeders X
% generalists, omnivores, and invertivores
8. % Insectivorous Cyprinids X
% insectivores
% specialized insectivores X
# juvenile trout X
% insectivorous species
9. % Top Carnivores X
% catchable salmonids
% catchable trout X
% pioneering species
Density catchable wild trout X
10. Number of Individuals (or catch per effort) .X X X
Density of individuals
% abundance of dominant species
Biomass (per nr)
X
X
X
X X
X X X X
X X
X X
X
X
X X'
X
X
X X
X1
Rapid Bioassessment Protocols for Use in Streams and Rivers
8-9
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DRAFT REVISION-July 28,1997
11. %H>
Alternative IBI Metrics
/brids
rt
•o
Midwestern UniU
X
c
•—
Central Appalach
c
'5
o-
o
Sacramento-San
to
ra
Colorado Front R
Western Oregon
Ohio
VI
0
Ohio Headwater
c
n
CO
c
ID
Z
X
Ontario
=
•*•
Central Com Bel
15
^
Wisconsin-Wami
ra
Wisconsin-Coldv^
c
™
Ma/yland Coasta
T3
Maryland Non-Ti
% introduced species X X
% simple lithophills X XX X
# simple lithophills species X
% native species X
% native wild individuals X
% silt-intolerant spawners X
12. % Diseased Individuals (deformities, eroded XX XXXXXXXX XX
Note: X = metric used in region. Many of these variations are applicable elsewhere.
a Taken from Karr et al. (1986), Leonard and Orth (1986), Moyle et al. (1986), Fausch and Schrader (1987), Hughes and Gammon
(1987), Ohio EPA (1987), Miller et al. (1988a), Steedman (1988), Simon (1991), Lyons (1992), Barbour et al. (1995), Simon and
Lyons (1995), Hall et al. (1996), Lyons et al. (1996), Roth et al. (1997).
b Metric suggested by Moyle et al. (1986) or Hughes and Gammon (1987) as a provisional replacement metric in small western
•salmonid streams.
c Boat sampling methods only (i.e., larger streams/rivers).
d Excluding individuals of tolerant species.
e Non-coastal Plain streams only.
f Coastal Plain streams only.
These species are sensitive to degradation resulting from siltation and benthic oxygen depletion
because they feed and reproduce in benthic habitats (Kuehne and Barbour 1983, Ohio EPA 1987).
Many smaller species live within the rubble interstices, are weak swimmers, and spend their entire
lives in an area of 100-400 m2 (Matthews 1986, Hill and Grossman 1987). Darters are appropriate in
most Mississippi Basin streams; sculpins and yearling trout occupy the same niche in western
streams. Benthic insectivores and sculpins or darters are used in small Atlantic slope streams that
have few sculpins or darters, and round-bodied suckers are suitable in large midwestern rivers.
MetricS. Number and identity of sunfish species. Substitutes (Table 8-1): Number and identity
of cyprinid species, water column species, salmonid species, headwater species, and sunfish and
trout species.
These pool species decrease with increased degradation of pools and instream cover (Gammon et al.
1981, Angermeier 1983, Platts et al. 1983). Most of these fishes feed on drifting and surface
invertebrates and are active swimmers. The sunfishes arid salmonids are important sport species.
The sunfish metric works for most Mississippi Basin streams, but where sunfish are absent or rare,
other groups are used. Cyprinid species are used in coolwater western streams; water column species
occupy the same niche in northeastern streams; salmonids are suitable in coldwater streams;
headwater species serve for midwestern headwater streams; and trout and sunfish species are used in
southern Ontario streams. Karr et al. (1986) and Ohio EPA (1987) found the number of sunfish
species to be dependent on stream size in small streams, but Ohio EPA (1987) found no relationship
8-10 Chapter 8: Fish Protocols
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DRAFT REVISION—July 28,1997
between stream size and sunfish species in medium to large streams, nor between stream size and
headwater species in small streams.
Metric 4. Number and identity of sucker species. Substitutes (Table 8-1): Number of adult trout
species, number of minnow species, and number of suckers and catfish.
These species are sensitive to physical and chemical habitat degradation and commonly comprise
most of the fish biomass in streams. All but the minnows are longlived species and provide a
multiyear integration of physicochemical conditions. Suckers are common in medium and large
streams; minnows dominate small streams in the Mississippi Basin; and trout occupy the same niche
in coldwater streams. The richness of these species is a function of stream size in small and medium
sized streams, but not in large rivers.
Metrics. Number and identity of intolerant species. Substitutes (Table 8-1): Number and
identity of sensitive species, amphibian species, and presence of brook trout.
This metric distinguishes high and moderate quality sites using species that are intolerant of various
chemical and physical perturbations. Intolerant species are typically the first species to disappear
following a disturbance. Species classified as intolerant or sensitive should only represent the 5-10
percent most susceptible species, otherwise this becomes a less discriminating metric. Candidate
species are determined by examining regional ichthyological books for species that were once
widespread but have become restricted to only the highest quality streams. Ohio EPA (1987) uses
number of sensitive species (which includes highly intolerant and moderately intolerant species) for
headwater sites because highly intolerant species are generally not expected in such habitats. Moyle
(1976) suggested using amphibians in northern California streams because of their sensitivity to
silvicultural impacts. This also may be a promising metric in Appalachian streams which may
naturally support few fish species. Steedman (1988) found that the presence of brook trout had the
greatest correlation with IBI score in Ontario streams. The number of sensitive and intolerant
species increases with stream size in small and medium sized streams but is unaffected by size of
large rivers.
Metric 6. Proportion of individuals as green sunfish. Substitutes (Table 8-1): Proportion of
individuals as common carp, white sucker, tolerant species, creek chub, and dace.
This metric is the reverse of Metric 5. It distinguishes low from moderate quality waters. These
species show increased distribution or abundance despite the historical degradation of surface waters,
and they shift from incidental to dominant in disturbed sites. Green sunfish are appropriate in small
midwestem streams; creek chubs were suggested for central Appalachian streams; common carp
were suitable for a coolwater Oregon river; white suckers were selected in the northeast and
Colorado where green sunfish are rare to absent: and dace (Rhinichthys species) were used in
southern Ontario. To avoid weighting the metric on a single species, Karr et al. (1986) and Ohio
EPA (1987) suggest using a small number of highly tolerant species (e.g., alternative Metric 6
—percent abundance of tolerant species).
8.3.2 Trophic Composition Metrics
;
These three metrics assess the quality of the energy base and trophic dynamics of the community.
Traditional process studies, such as community production and respiration, are time consuming to
conduct and the results are equivocal; distinctly different situations can yield similar results. The
Rapid Bioassessment Protocols for Use in Streams and Rivers 8-11
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DRAFT REVISION—July 28,1997
trophic composition metrics offer a means to evaluate the shift toward more generalized foraging
that typically occurs with increased degradation of the physicochemical habitat.
Metric 7. Proportion of individuals as omnivores. Substitutes (Table 8-1): Proportion of
individuals as yearlings.
The percent of omnivores in the community increases as the physical and chemical habitat
deteriorates. Omnivores are defined as species that consistently feed on substantial proportions of
plant and animal material. Ohio EPA (1987) excludes sensitive filter feeding species such as
paddlefish and lamprey ammocoetes and opportunistic feeders like channel catfish. Where
omnivorous species are nonexistent, such as in trout streams, the proportion of the community
composed of yearlings, which initially feed omnivorously, may be substituted.
Metric 8. Proportion of individuals as insectivorous cyprinids. Substitutes (Table 8-1):
Proportion of individuals as insectivores, specialized insectivores, insectivorous species, and number
of juvenile trout.
Insectivores or invertivores are the dominant trophic guild of most North American surface waters.
As the invertebrate food source decreases in abundance and diversity due to physicochemical habitat
deterioration, there is a shift from insectivorous to omnivorous fish species. Generalized
insectivores and opportunistic species, such as blacknose dace and creek chub were excluded from
this metric by Ohio EPA (1987). This metric evaluates the midrange of biological condition, i.e.,
low to moderate condition.
Metric 9. Proportion of individuals as top carnivores. Substitutes (Table 8-1): Proportion of
individuals as'catchable salmonids, catchable wild trout, and pioneering species.
The top carnivore metric discriminates between systems with high and moderate integrity. Top
carnivores are species that feed, as adults, predominantly on fish, other vertebrates, or crayfish.
Occasional piscivores, such as creek chub and channel catfish, are not included. In trout streams,
where true piscivores are uncommon, the percent of large salmonids is substituted for percent
piscivores. These species often represent popular sport fish such as bass, pike, walleye, and trout.
Pioneering species are used by Ohio EPA (1987) in headwater streams typically lacking piscivores.
8.3.3 Fish Abundance and Condition Metrics
The last 3 metrics indirectly evaluate population recruitment, mortality, condition, and abundance.
Typically, these parameters vary continuously and are time consuming to estimate accurately.
Instead of such direct estimates, the final results of the population parameters are evaluated. Indirect
estimation is less variable and much more rapidly determined.
Metric 10. Number of individuals in sample. Substitutes (Table 8-1): Density of individuals.
This metric evaluates population abundance and varies with region and stream size for small streams.
It is expressed as catch per unit effort, either by area, distance, or time sampled. Generally sites with
lower integrity support fewer individuals, but in some nutrient poor regions, enrichment increases the
number of individuals. Steedman (1988) addressed this situation by scoring catch per minute of
sampling greater than 25 as a three, and less than 4 as a one. Unusually low numbers generally
indicate toxicity, making this metric most useful at the low end of the biological integrity scale.
8-12 Chapter 8: Fish Protocols
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DRAFT REVISION—July 28,1997
Hughes and Gammon (1987) suggest that in larger streams, where sizes offish may vary in orders of
magnitude, total fish biomass may be an appropriate substitute or additional metric.
Metric 11. Proportion of individuals as hybrids. Substitutes (Table 8-1): Proportion of
individuals as introduced species, simple lithophils, and number of simple lithophilic species.
This metric is an estimate of reproductive isolation or the suitability of the habitat for reproduction.
Generally as environmental degradation increases the percent of hybrids and introduced species also
increases, but the proportion of simple lithophils decreases. However, minnow hybrids are found in
some high quality streams, hybrids are often absent from highly impacted sites, and hybridization is
rare and difficult for many to detect. Thus, Ohio EPA (1987) substitutes simple lithophils for
hybrids. Simple lithophils spawn where their eggs can develop in the interstices of sand, gravel, and
cobble substrates without parental care. Hughes and Gammon (1987) and Miller et al. (1988)
propose using percent introduced
individuals. This metric is a direct
measure of the loss of species segregation
between midwestem and western fishes
that existed before the introduction of
midwestem species to western rivers.
Metric 12. Proportion of individuals
with disease, tumors, fin damage, and
skeletal anomalies
This metric depicts the health and
condition of individual fish. These
conditions occur infrequently or are
absent from minimally impacted
reference sites but occur frequently
below point sources and in areas where
toxic chemicals are concentrated. They
•are excellent measures of the subacute
effects of chemical pollution and the
aesthetic value of game and nongame
fish.
Metric 13. Total fish biomass
(optional).
Hughes and Gammon (1987) suggest that
in larger rivers where sizes of fish may
vary in orders of magnitude this
additional metric may be appropriate.
Gammon (1976, 1980) and Ohio EPA
(1987) developed an Index of Well-Being
(Iwb) and Modified Index of Weil-Being
(Mlwb), respectively, based upon both
fish abundance and biomass measures.
The combination of diversity and
biomass measures is a useful tool for
THE INDEX OF WELL-BEING (IWB)
The Iwb (Gammon 1976, 1980, Hughes and
Gammon 1987) incorporates two abundance and two
diversity measures in an approximately equal
fashion, thereby representing fish assemblage quality
more realistically than a single diversity or
abundance measure. The Iwb is calculated using the
formula:
Iwb = 0.51nN+0.5 InB+H +H
N B
where
N = number of individuals caught per unit
distance sampled
B = biomass of individuals caught per unit
_ distance
H = Shannon diversity index, calculated as:
_ n. n
H = -S— In (—)
N N
where
n; = relative number or weight of the ith species
N = total number or weight of the sample
THE MODIFIED INDEX OF WELL-BEING
(MIWB)
The Mlwb (Ohio EPA 1987) retains the same
formula as the Iwb; however, highly tolerant species,
hybrids, and exotic species are eliminated from the
abundance (i.e., number and biomass) components of
the formula. This modification increases the
sensitivity of the index to a wider array of
environmental disturbances.
Rapid Bioassessment Protocols for Use in Streams and Rivers
8-13
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DRAFT REVISION—July 28,1997
assessing fish communities in larger rivers (Yoder and Rankin 1995). Ohio EPA (1987) found that
the additional collection of biomass data (i.e., in addition to abundance information needed for the
IBI) required to calculate the Mlwb does not represent a significant expenditure of time, providing
that subsampling techniques are applied (see Field Sampling Procedures 8.1.1).
Because the IBI is an adaptable index, the choice of metrics and scoring criteria is best developed on
a regional basis through use of available publications (Karr el al. 1986, Ohio EPA 1987, Miller et al.
1988, Steedman 1988; Simon 1991, Lyons 1992, Simon and Lyons 1995, Hall et al. 1996, Lyons et
al. 1996, Roth et al. 1997). Several steps are common to all regions. The fish species must be listed
and assigned to trophic and tolerance guilds. Scoring criteria are developed through use of high
quality historical data and data from minimally-impaired regional reference sites. This has been
done for much of the country, but continued refinements are expected as more fish community
ecology data become available.
8.4 Taxonomic References for Fish
Anderson, W.D. 1964. Fishes of some South Carolina coastal plain streams. Quarterly Journal of
the Florida Academy of Science 27:31-54.
Bailey, R.M. 1956. A revised list of the fishes of Iowa with keys for identification. Iowa State
Conservation Commission, Des Moines, Iowa.
Bailey, R.M. and M.O. Allum. 1962. Fishes of South Dakota. Miscellaneous Publications of the
Museum of Zoology, University of Michigan, No. 119, 131pp.
Baxter, G.T. and J.R. Simon. 1970. Wyoming fishes. Wyoming Game and Fish Department.
Bulletin No> 4, Cheyenne, Wyoming.
Becker, G.C. 1983. Fishes of Wisconsin. University of Wisconsin Press, Madison, Wisconsin.
Behnke, R.J. 1992. Native trout of western North America. American Fisheries Society Monograph
6. American Fisheries Society. Bethesda, Maryland.
Bond, C.E. 1973. Keys to Oregon freshwater fishes. Technical Bulletin 58:1-42. Oregon State
University Agricultural Experimental Station, Corvallis, Oregon.
Brown, C.J.D. 1971. Fishes of Montana. Montana State University, Bozeman, Montana.
Clay, W.M. 1975. The fishes of Kentucky. Kentucky Department of Fish and Wildlife Resources,
Frankford, Kentucky.
Cook, F.A. 1959. Freshwater fishes of Mississippi. Mississippi Game and Fish Commission,
Jackson, Mississippi.
Cooper, E.L. 1983. Fishes of Pennsylvania and the northeastern United States. Pennsylvania State
Press, University Park, Pennsylvania.
Cross, F.B. and J.T. Collins. 1995. Fishes of Kansas. University of Kansas Press. Lawrence,
Kansas.
8-14 Chapter 8: Fish Protocols
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DRAFT REVISION—July 28,1997
Dahlberg, M.D. and D.C. Scott. 1971. The freshwater fishes of Georgia. Bulletin of the Georgia
Academy of Science 19:1 -64.
Douglas, N.H. 1974. Freshwater fishes of Louisiana. Claitors Publishing Division, Baton Rouge,
Louisiana.
Eddy, S. and J.C. Underhill. 1974. Northern fishes, with special reference to the Upper Mississippi
Valley. University of Minnesota Press, Minneapolis, Minnesota.
Etnier, D.A. and W.C. Stames. 1993. The fishes of Tennessee. University of Tennessee Press,
Knoxville, Tennessee.
Everhart, W.H. 1966. Fishes of Maine. Third edition. Maine Department of Inland Fisheries and
Game, Augusta, Maine.
Everhart, W.H. and W.R. Seaman. 1971. Fishes of Colorado. Colorado Game, Fish, and Parks
Division, Denver, Colorado.
Hankinson, T.L. 1929. Fishes of North Dakota. Papers of the Michigan Academy of Science, Arts,
and Letters 10:439-460.
Hubbs, C. 1972. A checklist of Texas freshwater fishes. Texas Parks and Wildlife Department
Technical Service 11:1-11.
Hubbs, C.L. and K.F. Lagler. 1964. Fishes of the Great Lakes region. University of Michigan
Press, Ann Arbor, Michigan.
Jenkins, R.E. and N.M. Burkhead. 1994. The freshwater fishes of Virginia. American Fisheries
Society. Bethesda, Maryland.
Kuehne, R.A. and R.W. Barbour. 1983. The American darters. University of Kentucky Press,
Lexington, Kentucky.
La Rivers, I. 1994. Fishes and fisheries of Nevada. University of Nevada Press. Reno, Nevada.
Lee, D.S., C.R. Gilbert, C.H. Hocutt, R.E. Jenkins, D.E. McAllister, and J.R. Stauffer, Jr. 1980.
Atlas of North American freshwater fishes. North Carolina Museum of Natural History, Raleigh,
North Carolina.
Lee, D.S., S.P. Platania, C.R. Gilbert, R. Franz, and A. Norden. 1981. A revised list of the
freshwater fishes of Maryland and Delaware. Proceedings of the Southeastern Fishes Council 3:1-
10.
Loyacano, H.A. 1975. A list of freshwater fishes of South Carolina. Bulletin No. 580. South
Carolina Agricultural Experiment Station.
McPhail, J.D. and C.C. Lindsey. 1970. Freshwater fishes of northeastern Canada and Alaska.
Bulletin No. 173. Fisheries Research Board of Canada.
Rapid Bioassessment Protocols for Use in Streams and Rivers 8-15
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DRAFT REVISION—July 28,1997
Menhinick, E.F. 1991. The freshwater fishes of North Carolina. University of North Carolina,
Charlotte, North Carolina.
Miller, R.J. and H.W. Robinson. 1973. The fishes of Oklahoma. Oklahoma State University Press,
Stillwater, Oklahoma.
Minckley, W.L. 1973. Fishes of Arizona. Arizona Game and Fish Department, Phoenix, Arizona.
Morris, J.L. and L. Witt. 1972. The fishes of Nebraska. Nebraska Game and Parks Commission,
Lincoln, Nebraska.
Morrow, I.E. 1980. The freshwater fishes of Alaska. Alaska Northwest Publishing Company,
Anchorage, Alaska.
Moyle, P.B. 1976. Inland fishes of California. University of California Press, Berkeley, California.
Mugford, P.S. 1969. Illustrated manual of Massachusetts freshwater fish. Massachusetts Division
of Fish and Game, Boston, Massachusetts.
Page,L.M. 1983. Handbook of darters. TFH Publishing, Neptune, New Jersey.
Page, L.M. and B.M. Burr. 1991. A field guide to freshwater fishes. Houghton Miffiin Company,
Boston, Massachusetts.
Pflieger, W.L. 1975. The fishes of Missouri. Missouri Department of Conservation, Columbia,
Missouri.
Robison, H.W. and T.M. Buchanan. 1988. The fishes of Arkansas. University of Arkansas Press,
Fayetteville, Arkansas.
Rohde, F.C., R.G. Arndt, D.G. Lindquist, and J.F. Parnell. 1994. Freshwater fishes of the Carolinas,
Virginia, Maryland, and Delaware. University of North Carolina Press. Chapel Hill, North Carolina.
Scarola, J.F. 1973. Freshwater fishes of New Hampshire. New Hampshire Fish and Game
Department, Concord, New Hampshire.
Scott, W.B. and E.J. Grossman. 1973. Freshwater fishes of Canada. Bulletin No. 1984. Fisheries
Res. Board Canada.
Sigler, W.F. and R.R. Miller. 1963. Fishes of Utah. Utah Game and Fish Department. Salt Lake
City, Utah.
Sigler, W.F., and J.W. Sigler. 1996. Fishes of Utah: A natural history. University of Utah Press,
Ogden, Utah-
Simon, T.P., J.O. Whitaker, J. Castrale, and S.A. Minton. 1992. Checklist of the vertebrates of
Indiana. Proceedings of the Indiana Academy of Science.
f.
Simpson, J.C. and R.L. Wallace. 1982. Fishes of Idaho. The University of Idaho Press, Moscow,
Idaho.
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DRAFT REVISION—July 28,1997
Smith, C.L. 1985. Inland fishes of New York. New York State Department of Environmental
Conservation', Albany, New York.
Smith, P.W. 1979. The fishes of Illinois. Illinois State Natural History Survey. University of
Illinois Press, Urbana, Illinois.
Smith-Vaniz, W.F. 1987. Freshwater fishes of Alabama. Auburn University Agricultural
Experiment Station, Auburn, Alabama.
Stauffer, J.R., J.M. Boltz, and L.R. White. 1995. The fishes of West Virginia. Academy of Natural
Sciences of Philadelphia.
Stiles, E.W. 1978. Vertebrates of New Jersey. Edmund W. Stiles Publishers, Somerset, New
Jersey.
Sublette, J.E., M.D. Hatch, and M. Sublette. 1990. The fishes of New Mexico. University of New
Mexico Press, Albuquerque, New Mexico.
Tomelleri, J.R. and M.E. Eberle. 1990. Fishes of the central United States. University Press of
Kansas, Lawrence, Kansas.
Trautman, M.B. 1981. The fishes of Ohio. Ohio State University Press, Columbus, Ohio.
Whitworth, W.R., P.L. Berrien, and W.T. Keller. 1968. Freshwater fishes of Connecticut. Bulletin
No. 101. State Geological and Natural History Survey of Connecticut.
Wydoski, R.S. and R.R. Whitney. 1979. Inland fishes of Washington. University of Washington
Press.
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DRAFT REVISION-July 28,1997
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8-18 Chapter 8: Fish Protocols
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DRAFT REVISION—July 29,1997
BIOLOGICAL DATA ANALYSIS
States are faced with the challenge of not only developing tools that are both appropriate and cost-
effective (Barbour 1997), but also the ability to translate scientific data for making sound
management decisions regarding the water resource. The approach to analysis of biological (and
other ecological) data should be relatively straightforward to facilitate a translation for management
application. This is not meant to reduce the rigor of data analysis but to ensure its place in making
crucial decisions regarding the protection, mitigation, and management of the nation's aquatic
resources. In fact, biological monitoring should combine biological insight with statistical power
(Karr 1997). Karr and Chu (1997) state that a knowledge of regional biology and natural history
(and not a search for statistical relationships and significance) should drive both sampling design and
analytical protocol.
Two approaches in data analysis
have been debated in scientific
circles (Norris 1995, Gerritsen
1995) over the past few years —
the multimetric approach as
implemented by most U.S. state
water resource agencies (Davis et
al. 1996), and a multivariate
approach advocated by water
resource agencies in Europe and
Australia (Norris and Georges
1993). The contrast and
similarity of these 2 approaches
are illustrated by Figure 9-1 in a
4-stage generic process of
bioassessment development. The
2 more common multivariate
approaches used as an
international level are the
Benthic Assessment of Sediment
(BEAST) used in Canada, the
River Invertebrate Prediction and
Classification System
(RIVPACS) used in England and
its derivation, the Australian
River Assessment System
(AusRivAS) used in Australia.
•z Collection of data on invertebrate assemblages and habitat charactcrist
± at a range of reference sites
Q
« r""
= Multimetric M
" Sites arc a priori grouped
•j based on their
£ gco/phys/chcm attributes;
H * fin a classification confirmed
.2 with species composition
e
groups using clu
methods based c
similarity of their
compositio
cs
ultivariate
d into
icnng
n tie
species
? T 7 ' \
^ Based on aggregate
information of core
biological metrics from
-, each site class
"
j
1
T
c Based on comparison of
c test and reference-site
£ group using pcrccntile
£ distributions of scores of
•* * additive metrics
H
BEAST A
Based on a subset of sites B
with the highest
probability using a
discriminant mode
1
f
BEAST A
Based on comparison of Ba
test site and reference-site
group in taxa ordination c
space using probability we
ellipses constructed
ased on all sites but
weighted by the
probability of site
occurrence
T
cd on the probability
of expected taxa
ccurrcncc from all
ighted reference site
groups
Figure 9-1. Comparison of the developmental process for the
multimetric and multivariate approaches to biological data
analysis (modified from Reynoldson, Rosenberg, and Resh,
unpublished data).
In the first stage, data collection is conducted at a range of reference sites (and non-reference or test
sites) regardless of the approach. The differentiation of site classes into more homogeneous groups
or classes may be based initially on a priori physicochemical or biogeographical attributes, or solely
on a posterior analysis of biology (Stage 2 — Figure 9-1). Analysts who use multimetric indices
Rapid Bioassessment Protocols for Use in Streams and Rivers
9-1
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DRAFT REVISION^JuIy 25,1997
tend to use a priori classification; and analysts who use one of the multivariate approaches tend to
use a posteriori, multivariate classification. However, there is no reason a priori classification could
not be used with multivariate assessments.
The development of the reference condition from the range of reference sites, illustrated as Stage 3,
is formulated by a suite of biological metrics in the multimetric approach; whereas, the species
composition data are the basis for models used in the multivariate approach. However, both
multivariate techniques differ in their probability models. Once the reference condition is
established, which serves as a benchmark for assessment, the final stage becomes the basis for the
assessment and monitoring program. In this fourth and final stage (Figure 9-1), the multimetric
approach uses established percentiles of the population distribution of the reference sites for the
metrics to discriminate between impaired and unimpaired conditions. Where a dose/response
relationship can be established from sites having a gradient of conditions (reference sites unknown),
an upper percentile of the metric is used to partition metric values into condition ranges. The
BEAST multivariate technique uses a probability model based on taxa ordination space and the "best
fit" of the test site(s) to-the probability ellipses constructed around the reference site classes. The
AusRivAS/RIVPACS model calculates the probability of expected taxa occurrence from the
weighted reference site groups.
In this chapter, both the multimetric and multivariate approaches to biological data analysis are
discussed. Discussion of the multivariate approach is restricted to the AusRivAS/RIVPACS
technique. Details on the BEAST technique can be found in Reynoldson et al. 1995.
9.1 THE MULTIMETRIC APPROACH
Performing data analysis for the Rapid Bioassessment Protocols (RBPs) or any other multimetric
approach typically involves 2 phases: (1) Selection and calibration of the metrics and subsequent
aggregation into an index according to homogenous site classes; and (2) assessment of biological
condition at sites and judgment of impairment. The first phase is a developmental process and is
only necessary as biological programs are being implemented. This process is essentially the
characterizing of reference conditions that will form the basis for assessment. It is well-documented
(Davis and Simon 1995, Gibson et al. 1996, Barbour et al. 1996b) and is summarized here.
Developing the framework for reference conditions (i.e., background or natural conditions) is a
process that is applicable to non-biological monitoring as well (Karr 1993, Barbour et al. 1996a).
The actual assessment of biological condition is ongoing and becomes cost-effective once Phase 1
has been completed, and the thresholds for determining the attainment or non-attainment
(impairment) of reference conditions have been established. The thresholds of the metrics and/or
index, or some other means of aggregating the biological information are "key" to performing the
assessment. The establishment of reference conditions (through actual sites or other means) is
crucial to the determination of thresholds. It is possible that reference conditions (and resultant
thresholds) will need to be established on a seasonal basis to accommodate year-round sampling and
assessment.
The 2 phases in data analysis are discussed separately in the following section. The reader is referred
to supporting documentation cited throughout for more in-depth discussion of the concept of
multimetric assessment.
9-2 Chapter 9: Multimetric Data Analysis
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DRAFT REVISION—July 25,1997
9.1.1 Metric Selection, Calibration, And Aggregation Into an Index
The development of biological indicators as part of a bioassessment program and as a framework for
biocriteria is an iterative process wherein the site classifications and metric selections are revisited at
various stages of the analysis. However, once this process has been completed and the various
technical issues have been addressed, continued monitoring becomes cost-effective. The conceptual
process for proceeding from measurements to indicators to assessment of condition was described in
Barbour et al. (1995) and Gibson et al. (1996) and is illustrated in Figure 9-2. Using a basic
framework of stream class (to partition natural variability), metrics are evaluated for scientific
validity. The core metrics represent various attributes of the targeted aquatic assemblage can be
either aggregated into an index or retained as individual measures. The selection and confirmation
of robust measures for use as indicators in the developmental process for a bioassessment program is
outlined below.
Phase-1 Development
1. Stream Classification —The natural
variability is partitioned into appropriate
homogeneous bioregions for reference
conditions
2. Metric Identification —Those
metrics or attributes that are
ecologically relevant to assemblage
and zoogeography are identified
3. Metric Calibration Core
metrics are sensitive to pollution
and are informative of the
ecological relationships of the
assemblage to specific stressors
or cumulative impacts
4. Index Development—Core
metrics, whose values vary in scale,
are transformed to dimensionless
• numbers for aggregation
5. Threshold Establishment—The
threshold for discriminating between
impaired and unimpaired is
determined to provide a basis for
assessment
Phase II Site Assessment
Assessment and monitoring now
based on ecologically determined
framework.
Figure 9-2. Process for developing assessment thresholds (modified from Paulsen et
al. [1991] and Barbour et al. [1995]).
Rapid Bioassessment Protocols for Use in Streams and Rivers
9-3
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DRAFT REVISION—July 25,1997
Step 1. Classify the Stream Resource
Classification is the
partitioning of
natural variability
into groups or classes
of stream sites that
are relatively
homogeneous with
regard to physical,
chemical, and
biological attributes.
Classification provides a meaningful way to evaluate natural differences
among streams, and ecoregions are the prime example of a site
classification framework (Omernik 1995). However, classification
variables can be at a finer scale than ecoregions or subecoregions.
Elevation has been determined to be an important classification variable in
montane regions of the country (Barbour et al. 1992, 1994, Spindler 1996).
Spindler (1996) found that benthic data adhered more closely to elevation
than to ecoregions. Ohio Environmental Protection Agency (EPA) (1987)
found that stream size (or drainage area) was a covariate to stream classes
of ecoregions. The number offish species found in streams increased with
size of stream (Figure 9-3).
30-
20-
IT
io-i
I
l
4
StnssmQtisr
Sites are initially classified according to
distinctive geographic, physical, or chemical
attributes. Refinement and confirmation of
the site classes is accomplished using the
biological data. Classification of sites is best
done with reference sites that represent the
most natural condition of the region. Criteria
can be established for candidate reference
sites that are based on land use, physical
habitat, and water chemistry. These criteria
are independent of biological measures, so
that a reference condition of expected
biological attributes can be established. An
example of criteria for reference sites is taken
from Roth et al. (1997) in which reference
sites for Maryland streams were to meet all
12 criteria.
pH > 6; if blackwater stream, then pH
< 6 and DOC > 8 mg/1
ANC ;> 50 ueq/1
DO > 4ppm
nitrate < 300 ueq/1
urban land use < 20% of catchment area
forest land use > 25% of catchment area
remoteness rating: optimal or suboptimal
aesthetics rating: optimal or suboptimal
insrream habitat rating: optimal or suboptimal
riparian buffer width > 15 m
no channelization
no point source discharges
Classification is used to determine whether the sampled sites should be placed into specific groups
that will minimize variance within groups and maximize variance among groups. As an example, 3
ecoregionally based delineations (bioregions) were effective at partitioning the variability among
reference sites in Florida (Figure 9-4).
Figure 9-3. Species richness versus stream size (taken
from Fausch et al. 1984).
9-4
Chapter 9: Multimetric Data Analysis
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DRAFT REVISION—July 25,1997
Components of Step 1 include:
• Identify classification alternatives. Use
physical and chemical parameters that are
minimally influenced by human activity to
identify classes.
• Identify candidate reference sites that meet the
criteria of most "natural" conditions of region.
Summer 1993
X
•20- —
a 16
III —
«. 12 ° i
O • _^_
0> 8 : . .. .
a ' o —
p . .
5 4 _ =
Z _ i
^ :
Panhandle Peninsula Northeast
Non-Outll.f UH
Non-Outlier Mln
T?%
25%
o Midiin
Figure 9-4. Classification of reference stream sites
in Florida into bioregions.
• Test alternative classification schemes of
subecoregion, stream type, elevation, etc.,
using multiple biological characteristics (non-
metric data) including species composition as well as metrics. Several multivariate classification
and ordination methods, and univariate descriptions and tests, can assist in this process
(Reckhow and Warren-Hicks 1996, Gerritsen 1995, 1996, Barbour et al. 1996b).
• Evaluate classification alternatives and determine best distinction into groups or classes.
• Confirm resource classification based on biological data to determine which set of site classes
adequately partitions variability.
Step 2. Identify Potential Measures For Each Assemblage
A metric is a characteristic of the
biota that changes in some
predictable way with increased
human influence.
The purpose of using multiple metrics to assess biological
condition is to maximize the information available regarding the
elements and processes of aquatic communities. Metrics allow
the investigator to use meaningful indicator attributes in
assessing the status of communities in response to perturbation.
The definition of a metric is a characteristic of the biota that
changes in some predictable way with increased human influence (Barbour et al. 1995). For a metric
to be useful, it must have the following technical attributes: (1) ecologically relevant to the
biological assemblage or community under study and to the specified program objectives; (2)
sensitive to stressors and provides a response that can be discriminated from natural variation.
All metrics that have ecological relevancy to the assemblage under study and that will be indicative
of the targeted stressors are potential metrics for consideration. From this "universe" of metrics,
some will be eliminated because of insufficient data or because the range in data is not sufficient for
discrimination between natural variability and anthropogenic effects. This step is to identify the
candidate metrics that warrant further analysis, that is those that are most informative.
All potential measures that are relevant to the ecology of the streams within the region or state need
to be evaluated. To ensure that various aspects of the elements and processes of the aquatic
assemblage are addressed, representative metrics from each of 4 primary categories should be
selected: (1) richness measures for diversity or variety of the assemblage; (2) composition measures
for identity and dominance; (3) tolerance measures that represent sensitivity to perturbation; and (4)
trophic measures for information on feeding strategies and guilds. Karr and Chu (1997) suggests that
measures of individual health be used to supplement other metrics. Karr has expanded this concept
to include metrics that are reflective of landscape level attributes, thus providing a more
comprehensive multimetric approach to ecological assessment (Karr et al. 1997). See Tables 9-1,9-
Rapid Bioassessment Protocols for Use in Streams and Rivers
9-5
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DRAFT REVISION—July 25,1997
2, and 9-3 for potential metrics for periphyton, benthic macroinvertebrates, and fish. Metrics that
have been useful for these 3 assemblages are summarized in Chapters 6, 7, and 8.
Components of Step 2 include:
• Review all metrics calculated to eliminate those that have too many zero values to calculate the
metric at a large enough proportion of sites.
• Use descriptive statistics (central tendency, range, distribution, outliers) to characterize metric
performance within the population of sites within each site class.
• Eliminate metrics that have too high variability in the reference site population that they can not
discriminate among sites of different condition (this step often eliminates more than 50% of
potential metrics). The potential for each measure is based on possessing enough information
and a specific range of variability to discriminate among site classes and condition.
Table 9-1. Some potential periphyton metrics that could be considered for streams.
Redundancy of metrics can be evaluated during analysis phase.
Richness Measures
• Total no. of taxa
• Margalef Richness
• Mehinick Richness
• No. of common
nondiatom genera
Composition Measures
• % contribution of most
dominant taxon
• Hills Nl
• Hills N2
• Modified Hill
Evenness
• Cairns Sequential
Comparison Index
• Diatom (Shannon)
Diversity Index
Tolerance Measures
• Diatom Index - No. of
Intolerant Species
• % Most Tolerant
Group
-------
DRAFT REVISION—July 25,1997
Table 9-3. Some potential fish metrics that could be considered for streams. Redundancy of
metrics can be evaluated during analysis phase.
Richness Measures
• Total no. of native fish
species
• No. and identity of
darter species
• No. and identity of
sunfish species
• No. and identity of
sucker species
• No. and identity of
sculpin species
• No. and identity of
minnow species
Composition Measures
• % carp
• % white sucker
• % round-bodied
suckers
• *% catchable (>=20
cm) salmonids
• *No. of salmonid age
classes
• *No. or % of yearling
salmonids
• % pioneering species
• % introduced
Tolerance Measures
• No. and identity of
intolerant species
• No. of individuals in
sample
• Catch per unit effort
• % of individuals as
green sunfish
• % of individuals as
tolerant species
• % of individuals as
hybrids
• % of individuals with
disease, tumors, fin
damage, and skeletal
anomalies
• Number of fish per unit
of sampling effort
related to drainage area
• % representation of
reproductive guilds
• % simple lithophils (or
lithophilic species)
Trophic Measures
• % omnivores
• % insectivorous
cyprinids
• % insectivores
• % specialized
invertebrate feeders
• • % benthic insectivores
• % top carnivores
• % representation of
feeding guilds
* Omitting stocked salmonids
Step 3. Select Robust Measures
Core metrics are those remaining following initial candidate metric screening that will discriminate
between good and poor quality ecological conditions. It is important to understand the effects of
various stressors on the behavior of specific metrics. Metrics that are responsive to specific
pollutants or stressors, where the response is well-characterized, can be most useful as a diagnostic
tool. Core metrics should be selected to represent diverse aspects of structure, composition,
individual health, or processes of the aquatic biota. Together they form the foundation for a sound,
integrated analysis of the biotic condition to judge attainment of biological criteria.
The ability of a metric to discriminate
between "known" reference conditions
and "known" impaired conditions
(determined by non-biological
judgment criteria) is crucial to selecting
core metrics for future assessments.
Discriminatory ability of metrics can be evaluated by
comparing the distribution of each metric at a set of
reference sites with the distribution of metrics from a set
of "known" impaired sites (determined by non-biological
judgement criteria) within each site class. If there is
minimal or no overlap between the distributions, then the
metric can be considered to be a strong discriminator
between reference and impaired conditions (Figure 9-5).
As was done with candidate reference sites (see Step 1), criteria are established to identify a
population of "known" impaired sites based on physical and chemical measures of degradation. An
example set of criteria established for Maryland streams for which failure of any resulted in a
judgment of impairment for testing discrimanatory power (Roth et al. 1997) are as follows:
Rapid Bioassessment Protocols for Use in Streams and Rivers
9-7
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DRAFT REVISION-^July 25,1997
• pH < 5 and ANC < 0 ueq/1 (except for
blackwater streams, DOC > 8 mg/1)
• DO < 2 ppm
• nitrate > 500 ueq/1 and DO < 3 ppm
• instream habitat rating poor and urban
land use > 50% of catchment area
• instream habitat rating poor and bank
stability rating poor
• instream habitat rating poor and channel
alteration rating poor
Middle Rockies - Central Ecoregion, Wyoming
Benthic Metrics
30
26'
22
10
i ~~ Win-Max
2 ' . ~ 25%-75%
Reference Impaired ° M«ian value
Figure 9-5. Example of discrimination between
Step 3 can be separated into 2 elements that reference and impaired sites.
correspond to discrimination of core metrics
(element 1) and determination of
biological/physicochemical associations (element 2). Components of these elements include:
Element 1 Select core measures that are best for discriminating degraded condition
• Good (reference) designations of stream sites should be based on land use, physical and
chemical quality, and habitat quality.
• Poor (impaired) designations of stream sites for testing discriminations are also based on
judgement criteria involving land use, physical and chemical and quality, and habitat quality.
• Determine which biological metrics best discriminate between the reference sites and sites
with identified anthropogenic stressors.
• Those metrics having the strongest discriminatory power will provide the most confidence in
assessing biological condition.
Element 2 Determine the associations/linkages between candidate biological and
physicochemical measures
• Plot relationship of metric values against habitat condition and other measured stressors.
• If desired, multivariate ordination models may be used to elucidate gradients of metrics and
stressors.
• Monotonic relationships between metrics and stressors allow the use of extreme values
(highest or lowest) as reference condition.
• Some metrics may not always be monotonic. For example, taxa richness values may exceed
the reference at intermediate levels of nutrient enrichment.
• Multiple metrics should be selected to provide a strong and predictable relationship with
stream condition.
9-8 Chapter 9: Multimetric Data Analysis
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DRAFT REVISION—July 25,1997
Step 4. Determine the best aggregation of core measures for indicating status and change in
condition
An index provides
a means of
integrating
information from a
composite of the
various measures
of biological
attributes.
The purpose of an index is to provide a means of integrating information
from the various measures of biological attributes (or metrics). Metrics vary
in their scale; they are integers, percentages, or dimensionless numbers. Prior
to developing an integrated index for assessing biological condition, it is
necessary to standardize core metrics via a transformation to unitless scores.
The standardization assumes that each metric has the same value and
importance (i.e., they are
weighted the same), and
that a 50% change in one
metric is of equal value to
change in another.
-rel
kn
METRIC
VALUE
M!N
0
erence sites
own
5
25*
3
1
STRIBUT1ON OF SCORING
REFERENCE
SITES
*re1
un
METRIC
VALUE
a
2.
WIN
erence
known
95ft
DISTRIBUTION
OF ALL SITES
sites
5
I
I
3
. I
i
1 !
i
SCORING
.0
A
I
Figure 9-6. Basis of metric scores for
bioassessment.
assessment as a 50%
Where possible, the
scoring criterion for each metric is based on the
distribution of values in reference streams; for
example, the 25th percentile (lower quartile) of
reference expectations is commonly used (Figure
9-6). In this case, a conservative approach is
taken, and it is assumed that 25 percent of sites in
the reference database may be below the
expectations for a particular metric. For those metrics whose values increase in response to
perturbation (see Table 7-2 for examples of reverse metrics for benthic macroinvertebrates) any
value below the upper quartile (75th percentile) of the reference distribution receives the highest
score. Thus, using the appropriate quartile as the threshold, a score of 5 represents the maximum
value of the reference population; a score of 3 represents a lower condition; and a score of 1
represents the greatest degradation.
If reference sites can not be defined, then a composite of all sites representing a gradient of
conditions is used. This situation is analogous to a determination of a dose/response relationship and
depends on the ability of incorporating both reference and non-reference sites. In this case, an upper
percentile, such as the 95th (Figure 9-6), can be used to determine scores.
Aggregation of metric scores simplifies management and decision making so that a single index
value is used to determine whether action is needed. Biological condition of waterbodies is judged
based on the summed index value (Karr et al. 1986). If the index value is above a criterion, then the
stream is judged as "optimal" or "excellent" in condition. The exact nature of the action needed
(e.g., restoration, mitigation, pollution enforcement) is not determined by the index value, but by
analyses of the component metrics, in addition to the raw data. Therefore, the index is not the sole
determinant of impairment and diagnostics, but when used in concert with the component
information, strengthens the assessment (Barbour et al. 1996a).
Components of Step 4 include:
• Determine scoring criteria for each metric (within each site class) from the appropriate
percentile of the reference distribution (Figure 9-6). If the metric is associated with a
significant covariate such as watershed size, a scatterplot of the metric and covariate (Figure
Rapid Bioassessment Protocols for Use in Streams and Rivers
9-9
-------
DRAFT REVISION—July 25,1997
9-3) and a moving estimate of the appropriate percentile, are used to determine scoring
criteria as a function of the covariate (e.g., Plafkin et al. 1989).
• Test the ability of the final index to discriminatebetween populations of reference and
anthropogenically affected (impaired) sites; generally, indices (aggregate of metrics)
discriminate better than individual metrics (e.g., total taxa is generally a weak metric
because of inconsistency in taxonomic resolution) (Figure 9-7). Those sites that are mis-
classified with regard to "reference" and "impaired" can be identified and evaluated for re-
assignment.
Step 5. Index thresholds for assessment and biocriteria
The multimetric index for a site is a summation of the scores of the metrics and has a finite range
within each stream class and index period depending on the maximum possible scores of the metrics
(Barbour et al. 1996c). This range can be subdivided into any number of categories corresponding to
various levels of impairment. Because the metrics are normalized to reference conditions and
expectations for the stream classes, any decision on subdivision should reflect the distribution of the
scores for the reference sites. For example, a quadrisection of the Florida SCI range (aggregation of
metric scores) within each stream class and index period provides 4 ordinal rating categories for
assessment of impairment (Barbour et al. 1996c, Figure 9-8).
Biocriteria are based on thresholds
determined to differentiate impaired from
non-impaired conditions. While these
thresholds may be subjective, the
performance of the a priori selected reference
sites will ultimately verify the
appropriateness of the threshold.
The 4 rating categories are used to assess the
condition of both reference and non-reference sites.
Most of the reference sites should be rated as good or
very good in biological condition, which would be as
expected. However, a few reference sites may be
given the rating as poor sporadically among the
collection dates. If a "reference" site consistently
receives a poor rating, then the site should be re-
evaluated as to its proper assignment.
An understanding of variability is necessary to ensure that sites that are near to the threshold are
rated with known precision (discussed in more detail in Chapter 4). To account for variance
associated with measurement error in an assessment, replication is required. The first step is to
estimate the standard deviation of repeated measures of streams. The standard deviation is
calculated as the root mean square error (RMSE) of an analysis of variance (ANOVA), where the
sites are treatments in the ANOVA.
As an example, this question was tested for the Florida SCI score in the Peninsula stream class. This
study showed that an SCI score below the criterion by 4 points is significant at the p<0.05 level, from
a single sample (Table 9-4). What if we sample a single site with no replication and find that it is
two points below the biocriterion? The rightmost column (Table 9-4) shows that a triplicate sample
is required to demonstrate significance of a 2-point difference. These conclusions make 3
assumptions:
• measurement error is normally distributed,
• measurement error is not affected by subecoregion or impairment, and
• the sample standard deviation of repeated measures is an unbiased and precise estimate of
population measurement error.
9-10
Chapter 9: Multimetric Data Analysis
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DRAFT REVISION-July 28,1997
30
£
c 25
0
ream Condil
N>
O
W 13
10
30
X
w 1S
10
Panhandle Stream Sites (Summer)
G
P .
0
Reference impaired
Peninsula Stream Sites (Summer)
VG
T"
G
VP
Reference mpaired
Northeast Stream Sites (Summer)
VG
--••. I
G
••:¥".
VP
Reference Impaired
30
Outliers
35
30
0)
c
c 25
o
•o
5 20
E
-------
DRAFT REVISION-^July 25,1997
sampled sequentially (sequential replicates) to reduce the uncertainty, and could be sampled in
subsequent years to determine if there is a trend in the site's score.
Components of Step 5 include:
• The range in possible scores for each stream class is the minimum number of metrics (if a
score of 1 is assigned to greatest level of degradation) to the maximum aggregate of scores.
Quadrisect or trisect this range, depending on how many biological condition categories are
desired.
• Evaluate the validity of these biological condition categories by comparing the index scores
of the reference sites to those categories. If reference sites are not rated as good or very
good, then some adjustment in either the biological condition designations or the listing of
reference sites may be necessary.
• Test for confidence in multimetric analysis to determine biological condition for sites that
fall within close proximity to threshold. Calculate precision and sensitivity values to
determine repeatability and detectable differences that will be important in the confidence
level of the assessment.
9.1.2 Assessment of Biological Condition
Once the framework for bioassessment is in place, conducting bioassessments becomes relatively
straightforward. Either a targeted design that focuses on site-specific problems or a probabilistic
design, which is random and appropriate for 305(b) monitoring, can be done efficiently. Routine
monitoring of reference sites should be based on a random selection procedure, which will allow cost
efficiencies in sampling while monitoring the status of the reference condition of a state's streams.
Potential reference sites of each stream class would be randomly selected for sampling, so that an
unbiased estimate of reference condition can be developed. A randomized subset of reference sites
can be re-sampled at some regular interval (e.g., a 4 yr cycle to provide information on trends in
reference sites.
This reduction in monitoring reference sites allows more investment of time into assessing other
stream reaches and problem sites. Through use of Geographical Information System (GIS) and
station location codes, assessment sites throughout the state can be randomly selected for sampling
as is being done for the reference sites. This procedure will provide a statistically valid means of
estimating attainment of aquatic life use for the state's 305(b) reporting. In addition, the multimetric
index will be helpful for targeted sampling at specific problem areas and judging biological
condition with a procedure that has been calibrated regionally (Barbour et al. 1996c). To evaluate
possible influences on the biological condition of sites, relationships among total bioassessment
scores and physicochemical variables can be investigated. These relationships may indicate the
influence of particular categories of stressors on the biological condition of individual sites. For
example, a strong negative correlation between total bioassessment score and embeddedness would
suggest that siltation from nonpoint sources could be affecting the biological condition at a site.
Considerations relevant to assessment and diagnostics of biological condition are as follows:
• Evaluate the relationship of biological response signatures such as functional attributes
(reproduction, feeding group responses, etc.) to specific stressors.
Rapid Bioassessment Protocols for Use in Streams and Rivers 9-13
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DRAFT REVISION—July 25,1997
• Hold physical habitat relationships constant and look for associations with other physical
stressors (e.g., hydrological modification, streambed stability), chemical stressors (e.g.,
point-source discharges or pesticide application to cropland), biological stressors (i.e.,
exotics), and landscape measures (e.g., impervious surface, Thematic mapper land use
classes, human population census information, landscape ecology parameter of dominance,
contagion, fractal dimension).
• Explore the relationship between historical change in biota and change in landscape (e.g.,
use available historical data from the state or region).
9.2 RIVER INVERTEBRATE PREDICTION AND CLASSIFICATION
SCHEME (RIVPACS)
RIVPACS and its derivative, AusRivAS (Australian Rivers Assessment System) are empirical
(statisitcal) models that predict the aquatic macroinvertebrate fauna that would be expected to occur
at a site in the absence of environmental stress (Simpson et al. 1996). The AusRivAS models predict
the invertebrate communities that would be expected to occur at test sites in the absence of impact.
A comparison of the invertebrates predicted to occur at the test sites with those actually collected
provides a measure of biological impairment at the tested sites. The predicted taxa list also provides
a "target" invertebrate community to measure the success of any remediation measures taken to
rectify identified impacts. The type of taxa predicted by the AusRivAS models may also provide
clues as to the type of impact a test site is experiencing. This information can be used to facilitate
further investigations e.g., the absence of predicted Leptophlebiidae may indicate an impact on a
stream from trace metal input.
These models are the primary ecological assessment analysis techniques for Great Britian (Wright et
al. 1993) and Australia (Norris 1995). These models are based on a stepwise progression of
multivariate analyses and have been developed for several regions of counties and various habitat
types found in lotic systems. Regional models of AusRivAS model, in particular, have been
developed for the Australian states and territories (Simpson et al. 1996), and has been applied to lotic
systems in California (Hawkins and Norris 1997). Users of these models claim rapid turn around of
results is possible and output can be tailored for a range of users including community groups,
managers, and ecologists. These attributes make RIVPACS and AusRivAS likely candidate analysis
techniques for rapid bioassessment programs.
Discussion of RIVPACS and AusRivAS is taken from the Australian River Assessment System
National River Health Program Predictive Model Manual by Simpson et al. (1996). As is the case
with the multimetric approach, a more thorough treatment of the RIVPACS/AusRivAS models can
be obtained by referring to the citations of the supporting documentation provided in this discussion.
Two phases of the multivariate model (AusRivAS) that summarizes the development of the
predictive system and the reporting are discussed here.
9.2.1 Building Predictive Models Using AusRivAS
Although the same procedures are used to build all AusRivAS models, each model is tailored to
specific regions (or states) to provide the most accurate predictions for the season and habitat
sampled. The lotic habitats for which these models have been applied include the edge/backwater,
main channel, riffle, pool, and macrophyte stands. The multihabitat sampling techniques used in
many RBP programs have not yet been tested with a RIVPACS model. The models can be
9-14 Chapter 9: Multimetric Data Analysis
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DRAFT REVISION—July 25,1997
constructed for a single season, or data from several seasons may be combined to provide more
robust predictions. To date, the RIVPACS/AusRivAS models have only been developed for the
benthic assemblage. Building a predictive model of AusRivAS is separated here into 5 steps.
Step 1. Selecting Reference Sites and Collecting Data
As with any bioassessment approach, "reference sites" are considered to be those that are minimally
affected by anthropogenic impacts. "Test sites" are sites being tested by the model for biological
impairment. These sites may have known or suspected impacts, be selected for a regional
assessment, or be reference sites selected for periodic calibration of a model. Unlike the multimetric
approach, "known impaired sites" are not required for calibration. It is important that all stream and
river types which may be encountered at test sites have been sampled at sites considered to be
equivalent to reference condition. This ensures test sites will be compared against reference
conditions that the test sites could be expected to have in the absence of impact. The test sites must
also have been sampled in a season or seasons that correspond with the reference site sampling used
to construct the model. For example, if a riffle habitat spring/autumn model is used, riffle samples
from the test site must also have been collected in spring and autumn.
A standard set of habitat variables, established by the water resource agency, should be collected at
each reference and test site. These specific variables are discussed more in step 3. All the variables
chosen MUST be collected at ALL sites because the multivariate analysis used in the AusRivAS
models will not allow missing data. If a habitat variable is-missed at a site, either that variable must
be selected from the entire data set or that site must be excluded from the analysis. Alternatively,
extrapolations using data from similar sites, means from previous years or a return to the site may be
used to fill in the missing data.
The collected invertebrates are identified to family level with the exception of Oligochaeta (Class),
Acarina (Order), Collembola (Order), Turbellaria (Order), and Chironomidae (Sub-family).
Invertebrate identifications may be taken past family level if desired but at present the AusRivAS
models only use family level data. The models only use presence/absence data for predictions. As
such, abundance data will be converted to presence/absence data by AusRivAS when used in any
calculations.
Step 2. Classifying the Invertebrate Assemblage
This step consists of classifying reference sites into groups, which have similar invertebrate
composition (based on family level presence/absence data). A number of classification methods can
be used for form the reference site groupings. The agglomerative clustering technique, flexible
Unweighted Pair-Group arithMetic Averaging (UPGMA) recommended by Belbin (1994) is the most
commonly used technique. The flexible component refers to the techniques ability to distort
classification space to optimize clustering. The Bray-Curtis association measure is used on the
recommendation of Faith et al. (1987) as a robust measure of association for cluster analysis and
ordination. The classifications are viewed as dendrograms (Figure 9-9) allowing the fusion level
which divides sites into groups to be selected. If adequate site discrimination is not achieved using
UPGMA then either the divisive classification technique TWINSPAN or the non-hierarchical
technique ALOC can be used as alternative classification techniques. Based on the
recommendations of Wright et al. (1993) groups should contain not less than 5 sites. Small
classification groups are either deleted from further analysis or those sites are amalgamated with
another group of appropriate reference sites. Groups containing less than 5 sites can result from poor
representation of a particular type of reference site in the initial sampling, problems with the initial
Rapid Bioassessment Protocols for Use in Streams and Rivers 9-15
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DRAFT REVISION-^July 25,1997
Figure 9-9. Example of a cluster dendrogram, illustrating
similarities and clustering of sites (x-axis) using biological data.
sampling or degradation of sites
in some manner resulting in loss
of taxa indicative of reference
conditions.
Step 3. Choosing
Habitat Predictor Variables
The AusRivAS models use
habitat features (predictor
variables) from a site to predict
which taxa should occur at that
site in the absence of
environmental stress. Habitat
variables which may be affected
by impact cannot be used as
predictor variables. Variables
such as turbidity, dissolved
oxygen and phosphorus concentration are often effected by anthropogenic impacts and would
provide spurious predictions if used to predict the membership of test sites to the reference site
groups. In contrast, habitat features such as altitude, distance from source and substrate composition
often make good predictor variables because they are rarely affected by impacts.
A stepwise Multiple Discriminant Function Analysis (MDFA) is used to select the predictor habitat
variables from the reference site groups. This procedure selects a subset of habitat variables, which
best discriminate between the groups of sites formed from the faunal classifications. The stepwise
procedure includes habitat variables one at a time, selecting at each step the variables that give the
best discrimination of the groups. At eac'h step of the analysis, the significance of variables included
is checked and variables that are no longer significant are removed. The. significance level for
variables to enter and be retained by the Stepwise MDFA are both set at 0.05. For alternative
analyses, see Simpson et al. (1996).
The subset of habitat variables obtained from the stepwise MDFA are used as predictor variables for
the AusRivAS model being constructed. The predictor variables and the reference site invertebrate
classification form the foundation of AusRivAS, allowing predictions to be made of which taxa
should be found at new sites.
Step 4. Developing Indices
The AusRivAS predictive system only considers taxa that were calculated to have a probability of
50% or greater of occurring at a test site. The British RIVPACS model (Wright et al. 1993) uses
both the 0.5 and 0.75 probability levels. These are the actual taxa "predicted" to occur at a test site
with the probability of finding them at any one sampling occasion. The sum of the probabilities of
occurrence for these taxa gives the number of taxa "expected" (E) to be found at a test site.
In addition to calculating the expected number of taxa at a test site, AusRivAS also calculates the
expected SIGNAL score for a site. SIGNAL (Chessman 1995) is a system that assigns a grade to
each invertebrate family based on its sensitivity to pollution. The sum of all grades at a site are then
divided by the number of families to give an average SIGNAL score for the site. A grade of 10
represents high sensitivity to pollution, while a grade of 1 represents high tolerance to pollution.
9-16
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DRAFT REVISION—July 25,1997
For each site the taxa probabilities of occurrence are multiplied by the SIGNAL grade for each
taxon. Similar to the expected number of taxa, this calculation is only done for taxa which have a
probability of occurrence > 0.5. The expected SIGNAL score for a site is then calculated by
summing the weighted SIGNAL grades and dividing the total by the number of expected families
(Table 9-5).
Table 9-5. An example of the calculation of the expected number of taxa and the expected
SIGNAL score (taken from Simpson et al. 1996).
Family
Aeshnidae
Baetidae
Hydridae
Isostictidae
Kokiriidae
Leptoceridae
Notonectidae
Scirtidae
SIGNAL
Grade
6
5
4
7
10
8
3
9
Taxa Probability of
Occurrence
0.4
0.8
0.1
0.9
0.8
0.9
0.7
0.5
Expected No. of Taxa =
4.6
Weighted SIGNAL Grade
taxon probability <-0.5
4
taxon probability < 0.5
6.3
8
7.2
2.1
4.5
Expected SIGNAL
Score = 6.98
AusRivAS compares both the expected (E) number of taxa and the expected SIGNAL score against
what taxa were actually observed (O) at a test site. This provides two complementary indices which
provide a measure of biological impairment at a test site. These are:
O/E Families This is the ratio of the number of invertebrate families observed at a site to
- the number of families expected at that site.
O/E SIGNAL This is the ratio of the observed SIGNAL score for a site to the expected
SIGNAL score. The observed SIGNAL score for a site is simply the sum of
the SIGNAL grades of the "predicted" taxa that were collected at a site
divided by the number of "predicted" taxa.
The values of both indices can range from a minimum of 0 (indicating that none of the families
expected at a site were actually found at that site) to a theoretical maximum of 1.0, indicating a
perfect match between the families expected and those that were found. In practice, this maximum
can exceed 1.0 indicating that more families were found at that site than were predicted by the
model. This can indicate an unusually diverse site, but could also indicate mild enrichment by
organic pollution where the added nutrients have allowed families not normally found in that site to
establish. Conversely, an unimpacted, high-quality site may score an index value less than 110
because of chance exclusions of families during sampling.
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DRAFT REVISION—July 25,1997
The reasons for using two indices is because they have slightly different emphases which can
provide additional diagnostic information for the end user. In most situations, both indices are likely
to agree. O/E Families should be sensitive to a wide variety of impacts provided these impacts result
in the loss of macroinvertebrate taxa from the site. Thus, this index should not only detect loss of
families because of deteriorated water quality, but also families lost because of physical habitat
degradation as well. O/E SIGNAL weights the families by their sensitivity to water pollution. Most
of the data used to derive the SIGNAL grades has come from studies where the pollutants are mostly
organic effluents (e.g., treated or untreated sewage). Accordingly, O/E SIGNAL could detect
situations where water pollution has resulted in the loss of only a few, but very sensitive, families. In
addition O/E SIGNAL averages the contribution of the families found to the final value of the index.
Thus, this index is potentially less sensitive to variations in sampling effort than O/E Families.
However, there are disadvantages as well as advantages of rising either index (Table 9-6).
Table 9-6. Advantages and disadvantages of using O/E Families and O/E SIGNAL (taken from
Simpson et al. 1996).
O/E Families
O/E SIGNAL
Advantages
Simple to understand.
Should reflect a wide variety
of impacts including habitat
degradation as well as
diminished water quality.
Takes sensitivity of taxa to
pollution into account;
therefore emphasizes water
quality effects on fauna.
Because of averaging, this
index is less sensitive to
variations in sampling effort.
Disadvantages
Can be sensitive to sampling
effort.
Higher variability amongst
reference sites compared with
O/E SIGNAL.
Some feel grades need more
testing with wider range of
known impacts.
Some situations where O/E
SIGNAL remains close to
reference whereas O/E
Families shows substantial
impairment.
Step 5. Using a "Banding" Scheme for Assessment
Similar to interpretation of multimetric index scores, both O/E indices can be divided into "bands" or
ordinal rankings representing different levels of biological condition. The width of the bands is
based on the distribution of index values for the reference sites in a particular model. The width of
the reference band, labeled A in Table 9-7, is centered on the value 1.0 and includes the central 80%
of the reference sites. A site whose index value exceeds the upper bound of these values (i.e., the
index value is greater than the 90* percentile of the reference sites) is judged to be richer than the
reference condition and is allocated to "Band X". A site whose index value falls below the lower
bound (i.e., the index value is smaller than the lower 10th percentile of the reference sites) is judged
to have fewer families and/or a lower SIGNAL score than expected and is allocated to one of the
lower bands according to its value. The width of Bands B and C are the same as for Band A, the
9-18
Chapter 9: Multimetric Data Analysis
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DRAFT REVISION—July 25,1997
reference band. Band D may be narrower than these bands depending on the variability in the index
values of the reference sites in the model. In most cases, sites falling in Band D on either index will
be severely impaired and have few of the families expected at the site.
Table 9-7. Division of the indices into bands or categories for reporting (taken from Simpson
et al. 1996). The names of the bands refer to the relationship of the index value to the reference
condition (Band A). Under comments, for each index, the verbal interpretation of the Band is
stated first, followed by likely causes as dot-points.
X
A
B
C
D
Richer than
reference
Reference
Below
reference
Well below
reference
Impoverished
Band Label O/E Families
More families found than
expected. Potential
biodiversity "hot-spot"
Mild organic enrichment
Index value within range of
central 80% of reference sites
Fewer families than expected
Potential impact either on
water quality or habitat
quality or both resulting in a
loss of families
Many fewer families than
expected
Loss of families due to
substantial impacts on water
and/or habitat quality
Few of the expected families
remain
Severe impact
O/E SIGNAL
Greater SIGNAL value than
expected.
Potential biodiversity "hot-spot"
Differential loss of pollution-
tolerant taxa (potential impact
unrelated to water quality)
Index value within range of
central 80% of reference sites
Lower SIGNAL value than
expected
Differential loss of pollution-
sensitive families
Potential impact on water quality
Much lower SIGNAL value than
expected
Most expected families that are
sensitive to pollution have been
lost
Substantial impact on water
quality
Very low SIGNAL value
Only hardy, pollution-tolerant
families remain
In many cases the values of the indices will allocate a site to the same band. Occasionally, a site
may be allocated to one band on the basis of O/E Families and to another band (either higher or
lower) based on the value of O/E SIGNAL. The potential interpretations of these mis-matches may
provide valuable diagnostic information, as the data are reviewed in the interpretive stage.
Rapid Bioassessment Protocols for Use in Streams and Rivers
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DRAFT REVISION—July 25,1997
9.2.2 Reporting of AusRivAS Results
For a given test site, the most comprehensive assessment of its biological condition will be based on
data collected from two habitats in two seasons. This should yield the most comprehensive list of
families found at the site and, in general, the fairest assessment of the status of the site relative to the
reference conditions. Under this scenario, there are potentially 8 index values that need to be
synthesized for final reporting. If resources or time only allow one sampling occasion, then the
seasonally most appropriate model should be used.
Assessments of several sites simultaneously should all use the same basis for comparison; mixing
assessments based on different seasonal models or mixtures of single and two-habitat -data should be
discouraged. In some circumstances only one habitat will be assessed either because the other
habitat was not present or because the investigation is targeted at only one habitat (such as the habitat
deemed most susceptible to the potential impact, for example). However, the fact that only one
habitat has been employed in the assessment should be made explicit.
Indices are calculated for each habitat separately, and banded according to the appropriate model for
that habitat. If the indices within a particular habitat allocate the site to different bands, then
reconciliation of the different index values from the same habitat must be done. First, review the
faunal and environmental data for the site with the collector(s) of those data to ensure that data were
entered correctly and to determine if any unusual circumstances were encountered during collection.
Summary data for the site, including the taxa that were found and those that were expected but not
found, should be inspected, and the collector of the data should contribute any additional information
that might assist interpretation. Additional information might include the following:
a. Low overall abundance or difficulty in collecting the required number of
invertebrates. This can lead to underestimation of either index.
b. The proximity of tributaries (even small tributaries) to the test site. It is possible that
invertebrates may be washed in from these tributaries and be recorded as part of the
observed fauna for the site when in reality these families are unlikely to be resident
at this site. This can lead to overestimation of either index.
c. Recent unusual flow events (spates or low flows) that might affect invertebrate
composition or abundance. Usually unusual flow events reduce the numbers of
invertebrates encountered, and severe floods and droughts can depress both indices.
d. Details of the operators who conducted the sampling and identification, and any
information that might have affected the quality of the data collected. Severe
weather conditions while live-sorting, or accidents while processing in the lab can
bias the families that are picked.
Second, if the first step indicates problematic faunal data, then there should be no post hoc alteration
or "correction" of the faunal data beyond data-entry (typographic) errors. The integrity of the data is
paramount, and no post hoc alterations (such as deletions of families that were not "supposed" to be
present) can be tolerated. The options in order of preference are:
a. Re-sampling and re-assessment is the obvious choice if time and resources permit
re-sampling.
b. If this is not possible, then draw a conclusion of "no reliable assessment possible".
This is the most conservative approach. Diagnostic information can still be
presented, explained and qualified, but no allocation should be made to a band. The
reasons that no reliable assessment could be made should be made clear and explicit.
9-20 Chapter 9: Multimethc Data Analysis
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DRAFT REVISION^July 25,1997
Third, if the first step indicates no problems with the original data, then allocate the site to the lower
of the two bands for Bands A, B, C and D; if the mis-match between the bands is between Band A
and Band X, allocate the site to Band X. This is the most precautionary approach. For index values
that indicate the site is below reference conditions, the final allocation will indicate that families or
SIGNAL sensitivities are below reference conditions or worse. If one index indicates above
reference conditions, allocation to Band X should require further evidence to determine whether the
site is richer than reference because of higher biodiversity or a mild impact such as mild organic
enrichment.
In many cases two bandings will be available from a given test site: two index values will have been
computed for each of the two habitats and for each habitat the indices combined to allocate the
habitat to a band. Where the banding from both habitats allocate the site to the same band, then that
is the final band allocation for the site. Where there is a mismatch in the band allocation from the
two habitats, then allocate the site to the lower of the two bands for Bands A, B, C and D; if the
mismatch between the bands is between Band A and Band X, allocate the site to Band X.
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9-22 Chapter 9: Multimetric Data Analysis
-------
DRAFT REVISION—July 25,1997
DATA INTEGRATION AND
REPORTING
Water resource management will require a change in management strategies and policies for
effective environmental improvements to continue (Courtemanch 1995). A shift from technology-
based management to water resource management requires a commensurate shift from measurement
of pollutant loadings to a measurement of ecosystem health. Biological assessment, focusing on
population and community level response, addresses impact rather than only discharger performance
(Courtemanch 1995). The translation of biological data into a coherent report that adequately
conveys the message of the assessment to managers, who may not be biologists, to make informed
decisions regarding the water resource is an important process. First, the data must be summarized
and integrated, then clearly explained and presented. The use of a multimerric index provides a
convenient, yet technically sound method for summarizing complex biological data for each
assemblage (Karr et al. 1986, Plafkin et al. 1989). The procedures for developing the Multimetric
Index for each assemblage is described in Chapter 9. The index itself is only an aggregation of
contributory biological information and should not be used exclusive of its component metrics and
data (Yoder 1991, Barbour et al. 1996a). However, the index and its component metrics serve as
effective tools to communicate biological status of a water resource.
10.1 DATA INTEGRATION
Once indices and values are obtained for each assemblage, the question is how to interpret all of the
results, particularly if the findings are varied and suggest a contradiction in assessment among the
assemblages? Also, how are habitat data used to evaluate relationships with the biological data?
These questions are 2 of the most important that will be addressed in this chapter. The integration of
chemical and toxicological data with the biological is not treated in depth here. It is briefly
described in Chapter 3 and discussed in more detail elsewhere (Jackson 1992, U.S. EPA 1997).
10.1.1 Data Integration of Assemblages
The value of surveying more than 1 assemblage is to obtain a more complete assessment of
biological condition. The different assemblages respond differently to certain srressors or to
recovery from restoration activities. For instance, Ohio Environmental Protection Agency (EPA)
found that the fish assemblage recovered quicker to restoration of the Scioto River from cumulative
srressors (i.e., impoundments, combined sewer overflows, wastewater treatment plants, urbanization)
than did the benthic macroinvertebrate assemblage (Yoder and Rankin 1995). Although significant
improvement was observed in the condition of both assemblages in the river from 1980 to 1991, the
benthic assemblage was still impaired in several reaches of the river; whereas, the fish assemblage
met Ohio's warm water habitat criterion in 1991 for many of the same reaches. The use of both
assemblages enhanced the assessment of trend analysis of Ohio EPA for the Scioto River.
Mount et al. (1984) found that benthic and fish assemblages responded differently to the same inputs
in the Ottawa River in Ohio. Benthic diversity and abundance responded negatively to organic
loading from a wastewater treatment plant and exhibited no observable response to chemical input
from industrial effluent. Fish exhibited no response to the organic inputs and a negative response to
metal concentrations in the water. U.S. EPA advises that more than 1 assemblage be incorporated
Rapid Bioassessment Protocols for Use in Streams and Rivers 10-1
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DRAFT REVISION-^July 23,1997
into biocriteria programs whenever practical to more fully assess the occurrence of multiple stressors
and seasonal variation in the intensity of the stressors (Gibson et al. 1996).
Integration of information from each assemblage
should be done such that the results complement
and supplement the assessment of the site. Trend
analysis (monitoring changes over time) is useful
to illustrate differences in response of the
assemblages (Figure 10-1). In this example of the
Scioto River (Figure 10-1), the improvement in
the condition of both the fish Index of Biotic
Integrity (IBI) and benthic macroinvertebrate
Index of Community Integrity (ICI) assemblages
can be seen over time (1980 and 1991) and over a
length of the river (River Mile [KM] 140 to 90)
(Yoder 1995).
Scloto River: Columbuc to Circlcvlll*
o
140
130
120 110
RIVER MILE
100
Figure 10-1. Spatial and temporal trend of Ohio's
Invertebrate Community Index. (Contributed by
Ohio EPA).
Different biological attributes and indices can
also be illustrated side-by-side to highlight
differences and similarities in the biological
results. Oftentimes, the occurrence of differences in the results are useful for diagnosing cause-and-
effect.
10.1.2
Relationship Between Habitat and Biological Condition
Basic to maintaining diverse, functional aquatic communities in surface waters is the preservation of
the natural physical habitat of these ecosystems (Rankin 1995). Habitat quality is an essential
measurement in any biological survey because aquatic fauna often have very specific habitat
requirements independent of water-quality composition (Barbour et al. 1996a). Diagnostic
evaluations are enhanced when habitat assessment is incorporated into the interpretation (U.S. EPA
1990b).
Assuming that water quality remains constant, the relationship between habitat quality (as defined by
site-specific factors, riparian quality, and upstream land use) and biological condition can be
predictable, as illustrated in Figure 10-2. On the X-axis, habitat is shown to vary in quality from
poor (nonsupporting of an acceptable biological condition) to good (comparable to the reference
condition). Biological condition, on the Y-axis,
varies from poor (severely impaired) to good
(unimpaired). Interpretation of the relationship
as depicted by the graph can be summarized by
seven points relating to specific areas of the
curve.
1) The upper right-hand corner of the curve
(area 1) is the ideal situation where
optimal habitat quality and biological
condition occur. Some variability in
habitat quality is possible without
affecting the condition of the biological
community.
Good i i
Biological
Condition
Area?
Areas
Habitat Condition
Figure 10-2. Relationship between the condition
of the biological community and physical habitat.
10-2
Chapter JO: Data Integration and Reporting
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DRAFT REVISION—July 23,1997
2) However, in the midsectional part of the curve (area 2), the decrease in biological condition
is proportional to a decrease in habitat quality.
3) In the lower left-hand portion (area 3) of the curve, habitat quality is poor, and further
degradation may result in relatively little difference in biological condition. Communities in
this region of the curve are pollution tolerant, are opportunistic, thrive in areas of reduced
competition, and are able to withstand highly variable conditions (Barbour and Stribling
1991).
4) Area 4 is where occasional occurrences of a biological condition exceed that predicted by the
quality of the habitat, which may be due to nutrient enrichment or change in energy source,
both of which may stimulate diversity (Plafkin et al. 1989).
5) Distinguishing between habitat effects and other stressors will be difficult in area 5. The use
of response signatures are useful for diagnosing cause-and-effect in this portion of the graph.
6) Perhaps the most important area of the graph is the lower right-hand corner (area 6) where
degraded biological condition can be attributed to something other than habitat quality
(Barbour et al. 1996a).
7) The upper left-hand corner (area 7) is where optimal biological condition is not possible in a
severely degraded habitat (Barbour et al. 1996a).
The actual determination of these possible
outcomes is supported by a reference database
adequate to defining the expected relationship
between habitat quality and biological condition.
A relationship between biology and habitat
should be substantiated with a large database
sufficient to develop confidence intervals around
a regression line. Rankin (1995) found that
Ohio's visual-based habitat assessment approach,
called the Qualitative Habitat Evaluation Index
(QHEI), explained most of the variation in the
IBI for the fish assemblage. However, Rankin
also pointed out that covariate relationships
between aggregate riparian quality and land use
of certain subbasins could be used to partition
natural variability. In one example, Rankin
illustrated how high-quality oases of habitat
structure in otherwise habitat-degraded stream
reaches may harbor sensitive species, thus
masking the effects of habitat alteration.
Figure 10-3. Comparison of integrated assessment
(habitat, fish, and benthos) among stream sites in
Pennsylvania. Station 16 is a reference site.
(Taken from Snyder et al. 1996).
When multiple data types (i.e., habitat, assemblages, chemical, etc.) are available, sun ray plots may
be used to display the results for assessment. As an example, the assessments of habitat,
macroinvertebrates and fish are integrated for evaluation of .the condition of individual stream sites
in a Pennsylvania watershed (Snyder et al. 1996). The assessment scores for each of the triad data
types are presented as a percentage of reference condition (Figure 10-3). The area enclosed by each
sun ray plot can be measured to provide a comparison of the biological and habitat condition among
Rapid Bioassessment Protocols for Use in Streams and Rivers
10-3
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DRAFT REVISION—July 25,1997
the sites of interest (Snyder et al, 1996). Using this technique aids in the determination of extent of
impairment and which ecological components are most affected. This technique has not been widely
used for biological data.
10.2 REPORTING
Reporting of results and recommendations can take on a multitude of formats and is specifically
designed for varying objectives and uses relevant to an agency's program. However, assessment
reports are being read by a broader audience than historically was the case. Reports are used by
Water Resource Mangers who may not be biologists or other type of scientists and by the
environmentally conscious public. Communicating the condition of biological systems, and the
consequence of human activities to those systems, is the ultimate purpose of biological monitoring
(Karr and Chul997). The format for presentation of data is becoming increasingly important to
convey the appropriate findings of an ecological assessment. Effective communication can
transform biological monitoring from a scientific exercise into an effective tool for environmental
decision making (Karr and Chu 1997).
10.2.1
Graphical Display
Graphical displays are the basis for effective illustration of scientific information. Graphs reveal
better than strictly statistical tools, patterns of biological response, including "outliers", which may
convey unique information that can help diagnose particular problems or traits of a site (Karr and
Chu 1997). Examples of the more useful graphical techniques are as follows:
1) Line graphs—used to illustrate temporal or spatial trends that are contiguous. Assumes that
linkage between points is linear (Figure 10-4).
.1.1
"E 0.9
a
•a
c
£ 0.7
in
o
o> 0.3
I 0.1
1234567
Number of Composited Surbers
Figure 10-4. Standardized values for EPT
abundance from 7 Wyoming streams as a function
of the number of Surber samples composited at
each site. (Taken from Diamond et al. 1996).
10-4
Chapter 10: Data Integration and Reporting
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DRAFT REVISION—July 23,1997
2)
Bar charts—used to display magnitude of values for discrete entities. Can be used to
illustrate deviation from a value of central tendency (Figure 10-5).
OHIO
Bioassay/Biosurvey Comparison
emuem MmngZone NoTonoty Overai
(•Goofl
j • Fair
! B None
3)
Figure 10-5. Comparison of bioassay and
biosurvey results in Ohio streams. (Taken from
Barbour et al. 1996c).
Box-and-whisker plots—used to illustrate population attributes (percentiles) and provides
some sense of variability (Figure 10-6).
Index of Biotic Integrity
(Ohio Reference Sites)
60
50.
40
S 30.
20
10.
0
Range
HELP IP EOLP WAP ECBP
ECOREGIONS
Figure 10-6. The population of values of the IBI
in reference sites within each of the ecoregions of
Ohio. (Contributed by Ohio EPA).
Rapid Bioassessment Protocols for Use in Streams and Rivers
10-5
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DRAFT REVISION—July 23,1997
4)
Bivariate scatter plots—used for comparing the scatter or clustering of points given 2
dimensions. Can be used to develop regression lines or to incorporate 3 factors (3-
dimensional) (Figure 10-7).
Multidimensional Scaling
Florida Reference Sites
• Panhandle
'' Peninsula
A Northeast
First Axis
5)
Figure 10-7. Use of multidimensional scaling on
benthic data to ascertain stream classification.
(Taken from Barbour et al. 1996b).
Sun Ray plots—used to compare more than 2 endpoints or data types. Most effective when
reference condition is incorporated into axes or comparison (Figure 10-8).
% HABITAT
% FISH IBI
% BENTHIC
MACROINVERTEBRATE IBI
Figure 10-8. Integration of data from habitat,
fish, and benthic assemblages.
10-6
Chapter 10: Data Integration and Reporting
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DRAFT REVISION—July 23,1997
6) Pie charts—used to illustrate proportional representation of the whole by its component
parts. Can be sized according to magnitude or density (Figure 10-9).
Ecological Condition - % of streams having some level of
biological impairment
Very Good 36.4%
Good 36.4%
Poor 27.3%
Figure 10-9. Results of the benthic assessment of
streams in the Mattaponi Creek watershed of
southern Prince George's County, Maryland.
(Taken from Stribling et al. 1996b).
10.2.2
Report Format
Two report formats are recommended for summarizing the ecological assessment. Each of these is
intended to highlight the scientific process and to emphasize the attainment of the study objectives
and judging the condition of the site. The first format is structured as a report to be submitted to
management for facilitating decisions regarding the resource and to be widely disseminated to the
general public. The second format is patterned after that of peer-reviewed journals and is for the
purpose of informing a more technical audience.
The Ecosummary is an example of the first format for a report that conveys the information and
results of the study and is simple in format, which allows for a "quick" and effective documentation
of results for making informed decisions. An executive summary format is appropriate to present the
"bottom line" assessment for the Ecosummary, which will be read by agency managers and decision-
makers. Technical appendices or supplemental documentation should either accompany the report or
be available to support the scientific integrity of the study.
These Ecosummaries may be only 2 to 4 pages in length for use in reading and dissemination. Color
graphics may be added to enhance the presentation or findings. An example of an Ecosummary
format used by Florida Department of Environmental Protection (DEP) is illustrated in Figure 10-10.
This 2-page report highlights the findings on the first page along with the purpose of the study and
significance of the findings. A summary of the ecological data in the form of bar charts and tables is
provided on the second page. Because this study follows prescribed methods and procedures, all of
this documentation is not included in the report but is included in agency Standard Operating
Procedures (SOPs).
Rapid Bioassessment Protocols for Use in Streams and Rivers
10-7
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DRAFT REVISION-July 23,1997
standard heading
report title
assessment type1
& date sampled
standard header
text for BioRecon2'
variable text -
site photo •
standard header-
variable text-
locale map-
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impacted sv*am vriu. habitats and
ms«rofcveit*brot* species Mat diHer from
unimpactcd S^VWBC in ft* cam* art*, but his water
quality *t5*K«*t» to support » marglnaly IMaUiy
peftublkMortnvtitatHittt. lh**i(t l*toc>t*d.n a
ratidofttial srt» wtn • vaetnltot MI one (id*. Both
buitct *rt Untd w«h BruaUM Ptppcr. SeaBcratf
cyprvst art *!•« praMnt. Tht undaratory on ft*
banks is jr»« and poiian ivy, Th« lublxt eondiU
of ttitd (S3%». wti a fi*»n»gt, teaf mati, and
about 10% exposed rooMAuidareut banks. Flewwai
tvidcnt but riow (0.1 «rV«oc>.Ctrowd Crtak tvanly
ptt*«*< Ut* »tonct>n thmheM far a hatthy
ttrtam.Thiri wnr *ioMi>ca difl*r«nt taxa
(mintmum thretltoW * 18), «Vc mxyfta* or
caddisfteii (mnjotuni • 4), snd a Fteftoa Indtix
•cor* of iivca (nwumim - 10) W»««rchwnhtry
kidicattd Hi it pM w»* nthar tow (5.8) and
ditteJvtdorygBn was <14 part* p«r mNllen.
below Ih* C1M> H standard of & pptn.
Basin Characteristics
Ttiadr.un«0«baM tar5boudCr*«k
6t/^ tnohidas Mibufban and Mmlnirai fttrttntial
mat and
-------
DRAFT REVISION-July 28,1997
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patterns in stream ecosystems in Oregon. Canadian Journal of Fisheries and Aquatic Sciences 45:1264-
1278.
Wilhm, J.L. and T.C. Doris. 1968. Biological parameters for water quality criteria. Bioscience 18:477-
481.
Winget, R.N. and F.A. Mangum. 1979. Biotic condition index: integrated biological, physical, and
chemical stream parameters for management. Intermountain Region, U.S. Department of Agriculture,
Forest Service, Ogden, Utah.
Wright, J.F., M.T. Furse, and P.D. Armitage. 1993. RIVPACS: A technique for evaluating the
biological quality of rivers in the UK. European Water Pollution Control 3(4):15-25.
Yoder, C.O. 1991.The integrated biosurvey as a tool for evaluation of aquatic life use attainment and
impairment in Ohio surface waters. Pages 110-122 in George Gibson, editor. Biological criteria:
research and regulation, proceedings of a symposium. U.S. Environmental Protection Agency, Office of
Water, Washington, D.C. EPA-440-5-91-005.
Yoder, C.O. 1995. Policy issues and management applications for biological criteria, pages 327-343 in
W.S. Davis and T.P. Simon (editors). Biological assessment and criteria: tools for water resource
planning and decision making. Lewis Publishers, Boca Raton, Florida.
Yoder, C.O. and E.T. Rankin. 1995. Biological response signatures and the area of degradation value:
new tools for interpreting multimetric data. Pages 263-286 in W.S. Davis and T.P Simon (editors).
Biological assessment and criteria: tools for water resource planning and decision making. Lewis
Publishers, Boca Raton, Florida.
Rapid Bioassessment Protocols for Use in Streams and Rivers 11-19
-------
DRAFT REVISION-July 29,1997
This Page Intentionally Left Blank
11-20 Chapter 11: Literature Cited
-------
DRAFT REVISION-^Iuly 28,1997
APPENDIX A:
SAMPLE DATA FORMS FOR THE PROTOCOLS
Rapid Bioassessment Protocols for Use in Streams and Rivers A-l
-------
DRAFT REVISION—July 28,1997
APPENDIX A-1:
Habitat Assessment and Physicochemical Characterization Field
Data Sheets
A-2 Appendix A-1: Habitat Assessment and Physicochemical Characterization Field Data Sheets - Form 1
-------
DRAFT REVISION—July 30,1997
PHYSICAL CHARACTERIZATIONAVATER QUALITY FIELD DATA SHEET
(FRONT)
STREAM NAME
STATION # RIVERMILE
LAT LONG
STORET #
INVESTIGATORS
FORM COMPLETED BY
LOCATION
STREAM CLASS
RIVER BASIN
AGENCY
DATE REASON FOR SURVEY
TIME AM PM
WEATHER
CONDITIONS
Now
Q
Q
Q
Past 24 hours
Q
Q~
storm (heavy
rain)
rain (steady
rain)
showers
(intermittent)
_%cloud cover
"clear/sunny
Has there been a heavy rain in the last 7 days?
Q Yes Q No
Air Temperature_
Other
°C
SITE LOCATION/MAP
Draw a map of the site and indicate the areas sampled (or attach a photgraph)
STREAM
CHARACTERIZATION
Stream Subsystem
Q Perennial Q Intermittent Q Tidal
Stream Origin
Q Glacial
Q Non-glacial montane
Q Swamp and bog
Stream Type
Q Coldwater Q Warmwater
Q Spring-fed
Q Mixture of origins
Q Other
Catchment Area
km:
Rapid Bioassessment Protocols for Use in Streams and Rivers
A-3
-------
DRAFT REVISION—July 28,1997
PHYSICAL CHARACTERIZATION/WATER QUALITY FIELD DATA SHEET
(BACK)
WATERSHED
FEATURES
Predominant Surrounding Landuse
Q Forest Q Commercial
Q Field/Pasture Q Industrial
Q Agricultural Q Other
Q Residential
Local Watershed NFS Pollution
Q No evidence Q Some potential sources
Q Obvious sources
Local Watershed Erosion
Q None Q Moderate Q Heavy
RIPARIAN
VEGETATION
(18 meter buffer)
Indicate the dominant type and record the dominant species present
Q Trees Q Shrubs Q Grasses Q Herbaceous
dominant species present
INSTREAM
FEATURES
Estimated Stream Width m
Estimated Stream Depth m
Surface Velocity m/sec
(at thalweg)
High Water Mark
Estimated Reach Length
Canopy Cover
Q Partly open Q Partly shaded
Q Shaded
Proportion of Reach Represented by Stream
Morphology Types
Q Riffle % Q Run %
Q Pool %
Channelized Q Yes Q No
Dam Present Q Yes Q No
AQUATIC
VEGETATION
Indicate the dominant type and record the dominant species present
Q Rooted emergent Q Rooted submergent Q Rooted floating
Q Floating Algae Q Attached Algae
dominant species present
Q Free Floating
Portion of the reach with aquatic vegetation
WATER QUALITY
Temperature_
Specific Conductance_
Dissolved Oxygen
pH
Turbidity
Water Odors
Q Normal/None Q Sewase
Q Petroleum Q Chemical
Q Fishy Q Other
Water Surface Oils
Q Slick O Sheen Q Globs Q Flecks
Q None Q Other
WQ Instrument Used
Turbidity (if not measured)
Q Clear Q Slightly turbid Q Turbid
Q Opaque Q Stained Q Other_
SEDIMENT/
SUBSTRATE
Odors
Q Normal
Q Chemical
Q Other
Deposits
Q Sewage Q Petroleum Q Sludge Q Sawdust Q Paper fiber Q Sand
Q Anaerobic QNone Q Relict shells Q Other
Looking at stones which are not deeply
embedded, are the undersides black in color?
Q Profuse Q Yes QNo'
Oils
Q Absent Q Slight Q Moderate
INORGANIC SUBSTRATE COMPONENTS
(should add up to 100%)
ORGANIC SUBSTRATE COMPONENTS
(does not necessarily add up to 100%)
Substrate
Type
Diameter
% Composition in
Sampling Reach
Substrate
Type
Characteristic
% Composition in
Sampling Area
Bedrock
Detritus
Boulder
> 256 mm (10")
sticks, wood, coarse plant
materials (CPOM)
Cobble
64-256 mm (2.5"-10")
Muck-Mud
Gravel
2-64mm(0.1"-2.5")
black, very fine organic
(FPOM)
Sand
0.06-2mm (gritty)
Marl
grey, shell fragments
Silt
0.004-0.06 mm
Clay
< 0.004 mm (slick)
A-4 Appendix A-J: Habitat Assessment and Physicochemical Characterization Field Data Sheets - Form 1
-------
DRAFT REVISION—July 28,1997
HABITAT ASSESSMENT FIELD DATA SHEET—HIGH GRADIENT STREAMS (FRONT)
STREAM NAME
STATION # RIVERMILE
LAT LONG
STORET #
LOCATION
STREAM CLASS
RIVER BASIN
AGENCY
INVESTIGATORS
FORM COMPLETED BY
DATE REASON FOR SURVEY
TIME AM PM
U
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Habitat
Parameter
l.Epifaunal
Substrate/
Available Cover
SCORE
2. Embeddedness
SCORE
3. Velocity/Depth
Regime
SCORE
4. Sediment
Deposition
SCORE
S. Channel Flow
Status
SCORE
Condition Category-
Optimal
Greater than 70% of
substrate favorable for
epifaunal colonization
and fish cover; mix of
snags, submerged logs,
undercut banks, cobble
or other stable habitat
and at stage to allow full
colonization potential
(i.e., logs/snags that are
not new fall and not
transient).
20 19 18 17 16
Gravel, cobble, and
boulder particles are 0-
25% surrounded by fine
sediment. Layering of
cobble provides diversity
of niche space.
20 19 18 17 16
All four velocity/depth
regimes present (slow-
deep, slow-shallow, fast-
deep, fast-shallow).
(Sow is < 0.3 m/s, deep
is > 0.5 m.)
20 19 18 17 16
Little or no enlargement
of islands or point bars
and less than 5% (<20%
for low-gradient streams)
of the bottom affected by
sediment deposition.
20 19 18 17 16
Water reaches base of
both lower banks, and
minimal amount of
channel substrate is
exposed.
20 19 18 17 16
Suboptimal
40-70% mix of stable
habitat; well-suited for
full colonization
potential; adequate
habitat for maintenance
of populations; presence
of additional substrate in
the form of newfall, but
not yet prepared for
colonization (may rate at
high end of scale).
15 14 13 12 11
Gravel, cobble, and
boulder particles are 25-
50% surrounded by fine
sediment.
15 14 13 12 11
Only 3 of the 4 regimes
present (if fast-shallow is
missing, score lower
than if missing other
regimes).
15 14 13 12 11
Some new increase in
bar formation, mostly
from gravel, sand or fine
sediment;
5-30% .(20-50% for low-
gradient) of the bottom
affected; slight
deposition in pools.
15 14 13 12 11
Water fills >75% of the
available channel; or
<25% of channel
substrate is exposed.
15 14 13 12 11
Marginal
20-40% mix of stable
habitat; habitat
availability less than
desirable; substrate
frequently disturbed or
removed.
10 9 8 7 6
Gravel, cobble, and
boulder particles are 50-
75% surrounded by fine
sediment.
10 9 8 7 6
Only 2 of the 4 habitat
regimes present (if fast-
shallow or slow-shallow
are missing, score low).
10 9 8 7 6
Moderate deposition of
new gravel, sand or fine
sediment on old and new
bars; 30-50% (50-80%
for low-gradient) of the
bottom affected;
sediment deposits at
obstructions,
constrictions, and bends;
moderate deposition of
pools prevalent.
10 9 8 7 6
Water fills 25-75% of
the available channel,
and/or riffle substrates
are mostly exposed.
10 9 8 7 6
Poor
Less than 20% stable
habitat; lack of habitat is
obvious; substrate
unstable or lacking.
543210
Gravel, cobble, and
boulder particles are
more than 75%
surrounded by fine
sediment.
543210
Dominated by 1
velocity/ depth regime
(usually slow-deep).
543210
Heavy deposits of fine
material, increased bar
development; more than
50% (80% for low-
gradient) of the bottom
changing frequently;
pools almost absent due
to substantial sediment
deposition.
543210
Very little water in
channel and mostly
present as standing
pools.
543210
Rapid Bioassessment Protocols for Use in Streams and Rivers
A-5
-------
DRAFT REVISION—July 28,1997
HABITAT ASSESSMENT FIELD DATA SHEET—HIGH GRADIENT STREAMS (BACK)
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a.
Habitat
Parameter
6. Channel
Alteration
SCORE
7. Frequency of
Riffles (or bends)
SCORE
8. Bank Stability
(score each bank)
Note: determine left
or right side by
facing downstream.
SCORE (LB)
SCORE _ (RB)
9. Vegetative
Protection (score
each bank)
SCORE (LB)
SCORE (RB)
10. Riparian
Vegetative Zone
Width (score each
bank riparian zone)
SCORE (LB)
SCORE (RB)
Condition Category
Optimal
Channelization or
dredging absent or
minimal; stream with
normal pattern.
20 19 18 17 16
Occurrence of riffles
relatively frequent; ratio
of distance between
riffles divided by width
of the stream <7:1
(generally 5 to 7);
variety of habitat is key.
In streams where riffles
are continuous,
placement of boulders or
other large, natural
obstruction is important.
20 19 18 17 16
Banks stable; evidence
of erosion or bank
failure absent or
minimal; little potential
for future problems.
<5% of bank affected.
Left Bank 10 9
Right Bank 10 9
More than 90% of the
streambank surfaces and
immediate riparian zone
covered by native
vegetation, including
trees, understory shrubs,
or non woody
macrophytes; vegetative
disruption through
grazing or mowing
minimal or not evident;
almost all plants allowed
to grow naturally.
Left Bank 10 9
Right Bank 10 9
Width of riparian zone
>18 meters; human
activities (i.e., parking
lots, roadbeds, clear-
cuts, lawns, or crops)
have not impacted zone.
Left Bank 10 9
Right Bank 10 9
Suboptimal
Some channelization
present, usually in areas
of bridge abutments;
evidence of past
channelization, i.e.,
dredging, (greater than
past 20 yr) may be
present, but recent
channelization is not
present.
15 14 13 12 11
Occurrence of riffles
infrequent; distance
between riffles divided
by the width of the
stream is between 7 to
15.
15 14 13 12 11
Moderately stable;
infrequent, small areas of
erosion mostly healed
over. 5-30% of bank in
reach has areas of
erosion.
876
876
70-90% of the
streambank surfaces
covered by native
vegetation, but one class
of plants is not well-
represented; disruption
evident but not affecting
full plant growth
potential to any great
extent; more than one-
half of the potential plant
stubble height
remaining.
876
876
Width of riparian zone
12-18 meters; human
activities have impacted
zone only minimally.
876
876
Marginal
Channelization may be
extensive; embankments
or shoring structures
present on both banks;
and 40 to 80% of stream
reach channelized and
disrupted.
10 9 8 7 6
Occasional riffle or
bend; bottom contours
provide some habitat; .
distance between riffles
divided by the width of
the stream is between 1 5
to 25.
10 9 8 7 6
Moderately unstable; 30-
60% of bank in reach has
areas of erosion; high
erosion potential during
floods.
543
543
50-70% of the
streambank surfaces
covered by vegetation;
disruption obvious;
patches of bare soil or
closely cropped
vegetation common; less
than one-half of the
potential plant stubble
height remaining.
543
543
Width of riparian zone
6-12 meters; human
activities have impacted
zone a great deal.
543
5 4 3
Poor
Banks shored with
gabion or cement; over
80% of the stream reach
channelized and
disrupted. Instream
habitat greatly altered or
removed entirely.
5 .4 3 2 I 0
Generally all flat water
or shallow riffles; poor
habitat; distance between
riffles divided by the
width of the stream is a
ratio of >25.
543210
Unstable; many eroded
areas; "raw" areas
frequent along straight
sections and bends;
obvious bank sloughing;
60- 100% of bank has
erosional scars.
2 1 0
2 1 0
Less than 50% of the
streambank surfaces
covered by vegetation;
disruption of streambank
vegetation is very high;
vegetation has been
removed to
5 centimeters or less in
average stubble height.
2 1 0
2 1 0
Width of riparian zone
<6 meters: little or no
riparian vegetation due
to human activities.
2 1 0
2 1 0
Total Score
A-6 Appendix A-]: Habitat Assessment and Physicochemical Characterization Field Data Sheets - Form 1
-------
DRAFT REVISION—July 28,1997
HABITAT ASSESSMENT FIELD DATA SHEET—LOW GRADIENT STREAMS (FRONT)
STREAM NAME
STATION # RIVERMILE
LAT LONG
STORET #
LOCATION
STREAM CLASS
RIVER BASIN
AGENCY
INVESTIGATORS
FORM COMPLETED BY
DATE REASON FOR SURVEY
TIME AM PM
n sampling reach
Parameters to be evaluated i
Habitat
Parameter
1. Epifaunal
Substrate/
Available Cover
SCORE
2. Pool Substrate
Characterization
SCORE
3. Pool Variability
SCORE
4. Sediment
Deposition
SCORE
5. Channel Flow-
Status
SCORE
Condition Category
Optimal
Greater than 50% of
substrate favorable for
epifaunal colonization
and fish cover; mix of
snags, submerged logs,
undercut banks, cobble
or other stable habitat
and at stage to allow full
colonization potential
(i.e., logs/snags that are
not new fall and not
transient).
20 19 18 17 16
Mixture of substrate
materials, with gravel
and firm sand prevalent;
root mats and submerged
vegetation common.
20 19 18 17 16
Even mix of large-
shallow, large-deep,
small-shallow, small-
deep pools present.
20 19 18 17 16
Little or no enlargement
of islands or point bars
and less than 5% <20%
for low-gradient streams)
of the bottom affected by
sediment deposition.
20 19 18 17 16
Water reaches base of
both lower banks, and
minimal amount of
channel substrate is
exposed.
20 19 18 17 16
Suboptimal
30-50% mix of stable
habitat; well-suited for
full colonization
potential; adequate
habitat for maintenance
of populations; presence
of additional substrate in
the form of newfall, but
not yet prepared for
colonization (may rate at
high end of scale).
15 14 13 12 11
Mixture of soft sand,
mud, or clay; mud may
be dominant; some root
mats and submerged
vegetation present.
15 14 13 12 1!
Majority of pools large-
deep; very few shallow.
15 14 13 12 11
Some new increase in
bar formation, mostly
from gravel, sand or fine
sediment;
5-30% (20-50% for low-
gradient) of the bottom
affected; slight
deposition in pools.
15 14 13 12 11
Water fills >75% of the
available channel; or
<25% of channel
substrate is exposed.
15 14 13 12 11
Marginal
10-30% mix of stable
habitat; habitat
availability less than
desirable; substrate
frequently disturbed or
removed.
10 9 8 7 6
All mud or clay or sand
bottom; little or no root
mat; no submerged
vegetation.
10 9 8 7 6
Shallow pools much
more prevalent than deep
pools.
10 9 8 7 6
Moderate deposition of
new gravel, sand or fine
sediment on old and new
bars; 30-50% (50-80%
for low-gradient) of the
bottom affected;
sediment deposits at
obstructions,
constrictions, and bends;
moderate deposition of
pools prevalent.
10 9 8 7 6
Water fills 25-75% of the
available channel, and/or
riffle substrates are
mostly exposed.
10 9 8 7 6
Poor
Less than 10% stable
habitat; lack of habitat is
obvious; substrate
unstable or lacking.
543210
Hard-pan clay or
bedrock; no root mat or
vegetation.
543210
Majority of pools small-
shallow or pools absent.
543210
Heavy deposits of fine
material, increased bar
development; more than
50% (80% for low-
gradient) of the bottom
changing frequently;
pools almost absent due
to substantial sediment
deposition.
543210
Very little water in
channel and mostly
present as standing
pools.
543210
Rapid Bioassessment Protocols for Use in Streams and Rivers
A-7
-------
DRAFT REVISION—July 28,1997
HABITAT ASSESSMENT FIELD DATA SHEET—LOW GRADIENT STREAMS (BACK)
.=
u
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E
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Habitat
Parameter
6. Channel
Alteration
SCORE
7. Channel
Sinuosity
SCORE
8. Bank Stability
(score each bank)
SCORE (LB)
SCORE (RB)
9. Vegetative
Protection (score
each bank)
Note: determine
left or right side by
facing downstream.
SCORE _ (LB)
SCORE (RB)
10. Riparian
Vegetative Zone
Width (score each
bank riparian zone)
SCORE (LB)
SCORE (RB)
Condition Category
Optimal
Channelization or
dredging absent or
minimal; stream with
normal pattern.
20 19 18 17 16
The bends in the stream
increase the stream
length 3 to 4 times
longer than if it was in a
straight line. (Note -
channel braiding is
considered normal in
coastal plains and other
low-lying areas. This
parameter is not easily
rated in these areas.
20 19 18 17 16
Banks stable; evidence
of erosion or bank failure
absent or minimal; little
potential for future
problems. <5%ofbank
affected.
Left Bank 10 9
Right Bank 10 9
More than 90% of the
streambank surfaces and
immediate riparian zone
covered by native
vegetation, including
trees, understory shrubs,
or nonwoody
macrophytes; vegetative
disruption through
grazing or mowing
minimal or not evident;
almost all plants allowed
to grow naturally.
Left Bank 10
Right Bank 10
Width of riparian zone
>18 meters; human
activities (i.e., parking
lots, roadbeds, clear-cuts,
lawns, or crops) have not
impacted zone.
Left Bank 10 9
Right Bank 10 9
Suboptimal
Some channelization
present, usually in areas
of bridge abutments;
evidence of past
channelization, i.e.,
dredging, (greater than
past 20 yr) may be
present, but recent
channelization is not
present.
15 14 13 12 11
The bends in the stream
increase the stream
length 2 to 3 times
longer than if it was in a
straight line.
15 14 13 12 11
Moderately stable;
infrequent, small areas of
erosion mostly healed
over. 5-30% of bank in
reach has areas of
erosion.
876
876
70-90% of the
streambank surfaces
covered by native
vegetation, but one class
of plants is not well-
represented; disruption
evident but not affecting
full plant growth
potential to any great
extent; more than one-
half of the potential plant
stubble height
remaining.
876
8 7 6 .
Width of riparian zone
12-18 meters; human
activities have impacted
zone only minimally.
876
8 76
Marginal
Channelization may be
extensive; embankments
or shoring structures
present on both banks;
and 40 to 80% of stream
reach channelized and
disrupted.
10 9 8 7 6
The bends in the stream
increase the stream
length 2 to 1 times
longer than if it was in a
straight line.
10 9 87 6
Moderately unstable; 30-
60% of bank in reach has
areas of erosion; high
erosion potential during
floods.
543
543
50-70% of the
streambank surfaces
covered by vegetation;
disruption obvious;
patches of bare soil or
closely cropped
vegetation common; less
than one-half of the
potential plant stubble
height remaining.
543
543
Width of riparian zone 6-
12 meters; human
activities have impacted
zone a great deal.
543
5 43
Poor
Banks shored with
gabion or cement; over
80% of the stream reach
channelized and
disrupted. Instream
habitat greatly altered or
removed entirely.
543210
Channel straight;
waterway has been
channelized for a long
distance.
5 43 2 10
Unstable; many eroded
areas; "raw" areas
frequent along straight
sections and bends;
obvious bank sloughing;
60-1 00% of bank has
erosional scars.
2 1 0
2 1 0
Less than 50% of the
streambank surfaces
covered by vegetation;
disruption of streambank
vegetation is very high;
vegetation has been
removed to
5 centimeters or less in
average stubble height.
2 1 0
2 1 0
Width of riparian zone
<6 meters: little or no
riparian vegetation due
to human activities.
2 1 0
2 1 0
Total Score
A-8 Appendix A-l: Habitat Assessment and Physicochemical Characterization Field Data Sheets - Form /
-------
DRAFT REVISION—July 28,1997
APPENDIX A-2:
Periphyton Field and Laboratory Data Sheets
Rapid Bioassessment Protocols for Use in Streams and Rivers A-9
-------
DRAFT REVISION—July 29,1997
PERIPHYTON FIELD DATA SHEET
STREAM NAME LOCATION
STATION # R1VERMILE STREAM CLASS
LAT LONG RIVER BASIN
STORET # AGENCY
INVESTIGATORS LOT NUMBER
FORM COMPLETED BY DATE REASON FOR SURVEY
TIME AM PM
HABITAT TYPES
SAMPLE
COLLECTION
GENERAL
COMMENTS
Indicate the percentage of each habitat type present
Q Sand-Silt-Mud-Muck % Q Gravel-Cobble % Q Bedrock %
Q Small Woody Debris % Q Large Woody Debris % Q Plants, Roots %
Gear used Q suction device Q bar clamp sample Q scraping Q Other
How were the samples collected? Q wading Q from bank Q from boat
If natural habitat collections, indicate the number of samples taken in each habitat type.
Q Sand-Silt-Mud-Muck % Q Gravel-Cobble % Q Bedrock %
Q Small Woodv Debris % Q Large Woody Debris % Q Plants, Roots %
QUALITATIVE LISTING OF AQUATIC BIOTA
Indicate estimated abundance: 0 = Absent/Not Observed, 1 = Rare, 2 = Common, 3= Abundant, 4 = Dominant
Periphyton
Filamentous Algae
Macrophytes
0 1
0 1
0 1
234
234
234
Slimes
Macroinvertebrates
Fish
0 1
0 1
0 1
234
234
234
A-10
Appendix A-2: Periphyton Field and Laboratory Data Sheets - Form 2
-------
page of
o'
R
t!
3
§
8
-f
(-1
O
a
o,
PERIPHYTON SAMPLE LOG-IN SHEET
Rate
Collected
Collected
By
Number of
Containers
Preservation
Station #
Stream Name and Location
Date Received
by Lab
Lot Number
Date of Completion
sorting
mounting
identification
o
I
ve
vo
-4
Serial Code Example: B075400I(1)
B = Benthos (F = f:ish; P = Periphyton)
0754 = project number
001 = sample number
(I) = lot number (e.g., winter 1996 =1; summer 1996 = 2)
-------
DRAFT REVISION-July 29,1997
PERIPHYTON LABORATORY BENCH SHEET (FRONT)
Page.
of
STREAM NAME
STATION #
LAT
RIVERMILE
LONG
STORET #
COLLECTED BY
DATE
LOCATION
STREAM CLASS
RIVER BASIN
AGENCY
LOT#
SUBSAMPLE TARGET FOR DIATOMS Q 300 Q400 Q 500 Q Other
Enter Family and/or Genus and Species name on blank line.
A
Chrysophyta
Chlorophyta
Pyrophyta
Igae
No. of Cells
TI
TCR
Cyanobacteria .
Rhodophyta
Other
Algae
No. of Cells
TI
TCR
Taxonomic certainty rating (TCR) l-5:l=most certain, 5=least cenam. If rating is 3-5, give reason.
TI = Taxonomists initials; No. of Cells for filamentous algae is an estimate of relative biomass.
Total No. Algal cells
Total No. Taxa
A-12
Appendix A-2: Periphyton Field and Laboratory Data Sheets - Form 2
-------
DRAFT REVISION-^fuly 29,1997
PERIPHYTON LABORATORY BENCH SHEET (BACK)
TAXONOMY
ID
Date
Explain TCR ratings of 3-5:
Other Comments (e.g. condition of algae)
QC: DYES QNO
QC Checker _
Algal recognition Q pass Q fail
Verification complete Q YES Q NO
General Comments (use this space to add additional comments):
Rapid Bioassessment Protocols for Use in Streams and Rivers
A-13
-------
DRAFT REVISION-^TuIy 29,1997
Appendix A-3:
Benthic Macroinvertebrate Field and Laboratory Data SheetsO
A-14 Appendix A-3: Benthic Macroinvertebrate Field and Laboratory Data Sheets - Form 2
-------
DRAFT REVISION—July 29,1997
BENTHIC MACROINVERTEBRATE FIELD DATA SHEET
STREAM NAME
STATION # RJVERMILE
LAT LONG
STORET #
INVESTIGATORS
FORM COMPLETED BY
LOCATION
STREAM CLASS
RIVER BASIN
AGENCY
DATE
TIME AM PM
LOT NUMBER
REASON FOR SURVEY
HABITAT TYPES
SAMPLE
COLLECTION
GENERAL
COMMENTS
Indicate the percentage of each habitat type present
Q Riffles % Q Stream Banks % Q Snags % Q Banks %
Q Submerged Macr
ophytes % Q Other ( ) %
Gear used Q D-frame Q kick-net Q Other
How were the samples collected? Q wading Q from bank 01 from boat
Indicate the number of jabs/kicks taken in each habitat type.
Q Riffles Q Stream Banks Q Snags Q Banks
Q Submerged Macr
ophytes Q Other ( )
QUALITATIVE LISTING OF AQUATIC BIOTA
Indicate estimated abundance: 0 = Absent/Not Observed, 1 = Rare, 2 = Common, 3= Abundant, 4 = Dominant
Periphyton
Filamentous Algae
Macrophytes
0
0
0
234
234
234
Slimes
Macroinvertebrates
Fish
0
0
0
1234
1234
1234
FIELD OBSERVATIONS OF MACROBENTHOS
Indicate estimated abundance: 0 = Absent/Not Observed, 1 = Rare (1-3 organisms), 2 = Common (3-9
organisms), 3= Abundant (>10 organisms), 4 = Dominant (>50 organisms)
Porifera
Hydrozoa
Platyhelminthes
Turbellaria
Hirudinea
Oligochaeta
Isopoda
Amphipoda
Decapoda
Gastropoda
Bivalvia
0 1
0 1
0 1
0 1
0 1
0 1
0 1
0 1
0 1
0 1
0 1
234
234
234
234
234
23-4
234
234
234
234
234
Anisoptera
Zygoptera
Hemiptera
Coleoptera
Lepidoptera
Sialidae
Corydalidae
Tipulidae
Empididae
Simuliidae
Tabinidae
Culcidae
0 1
0 1
0 1
0 1
0 1
0 1
0 1
0 1
0 1
0 1
0 1
0 1
234
234
234
234
234
234
234
234
234
234
234
2 3 4
Chironomidae 01234
Ephemeroptera 01234
Trichoptera 01234
Other 01234
Rapid Bioassessment Protocols for Use in Streams and Rivers
A-15
-------
page of
:§
§
to
§
-------
DRAFT REVISION—July 29,1997
BENTHIC MACROINVERTEBRATE LABORATORY BENCH SHEET (FRONT)
page of
STREAM NAME
STATION # RIVERMILE
LAT LONG
STORET #
COLLECTED BY DATE
TAXONOMIST DATE
LOCATION
STREAM CLASS
RIVER BASIN
AGENCY
LOT#
SUBS AMPLE TARGET Q 100
Q 200 Q 300 Q Other
Enter Family and/or Genus and Species name on blank line.
Organisms
Oligochaeta
Hirudinea
Isopoda
Amphipoda
Decapoda
Ephemeroptera
Plecoptera
Trichoptera
riemiptera
No.
LS
TI
TCR
Organisms
Megaloptera
Coleoptera
Diptera
Gastropoda
Pelecypoda
Other
No.
LS
TI
TCR
Taxonomic certainty rating (TCR) l-5:l=most certain, 5=least certain. If rating is 3-5, give reason (e.g., missing gills). LS= life stage:
= immature; P = pupa; A = adult TI = Taxonomists initials
Total No. Organisms
Total No. Taxa
Rapid Bioassessment Protocols for Use in Streams and Rivers
A-17
-------
DRAFT REVISION—July 29,1997
BENTHIC MACROINVERTEBRATE LABORATORY BENCH SHEET (BACK)
SUBSAMPLING/SORTING
INFORMATION
Sorter
Date
Number of grids picked:
Time expenditure
No. of organisms
Indicate the presence of large or obviously abundant organisms:
QC:
# organisms
recovered by
checker
QYES QNO QC Checker.
# organisms
originally sorted
% sorting
efficiency
X 100
2 90%, sample passes
<90%, sample fails, action taken
TAXONOMY
ID
Date
Explain TCR ratings of 3-5:
Other Comments (e.g. condition of specimens):
QC:
Q YES QNO QC Checker.
Organism recognition
Verification complete
Q pass
QYES
Qfail
QNO
General Comments (use this space to add additional comments):
A-18 Appendix A-3: Benthic Macroinvertebrate Field and Laboratory Data Sheets - Form 2
-------
DRAFT REVISION—July 29,1997
PRELIMINARY ASSESSMENT SCORE SHEET (PASS)
Page.
of
STREAM NAME
STATION #
LAT
RJVERMILE
LONG
STORET #
COLLECTED BY
HABITATS: Q COBBLE
DATE
LOCATION
STREAM CLASS
RIVER BASIN
AGENCY
LOT#
QSHOREZONE Q SNAGS
NUMBER OF SWEEPS
Q VEGETATION
Enter Family and/or Genus and Species name on blank line.
Organisms
Oligochaeta
Hirudinea
Isopoda
Amphipoda
Decapoda
Ephemeroptera
Plecoptera
Trichoptera
Hemiptera
No.
LS
TI
TCR
Organisms
Megaloptera
Coleoptera
Diptera
Gastropoda
Pelecypoda
Other
Taxonomic cert:
certain. If rating
stage: I = immat
initials
No.
in ty rating (TCR) l-5:l=most
is 3-5, give reason (e.g., missir
ure' P — pupa' A ~ adult TI — "
LS
certain
TI
TCR
, 5=least
g gills). LS=life
"axonomists
Total No. Taxa
EPT Taxa
Tolerance Index '
Site Value
Target Threshold
If 2 or more metrics are * target threshold, site is
HEALTHY
If less than 2 metrics are within target range, site is
SUSPECTED IMPAIRED
Rapid Bioassessment Protocols for Use in Streams and Rivers
A-19
-------
DRAFT REVISION—July 29,1997
Appendix A-4:
Fish Field and Laboratory Data Sheets
A-20 Appendix A-4: Fish Field and Laboratory Data Sheets - Form 1
-------
DRAFT REVISION—July 29,1997
FISH SAMPLING FIELD DATA SHEET (FRONT)
page
of
STREAM NAME
STATION # RIVERMILE
LAT LONG
STORET #
GEAR
FORM COMPLETED BY
LOCATION
STREAM CLASS
RIVER BASIN
AGENCY
INVESTIGATORS
DATE REASON FOR SURVEY
TIME AM PM
SAMPLE
COLLECTION
HABITAT TYPES
GENERAL
COMMENTS
How were the fish captun
Block nets used? Q YI
Sampling Duration Start
Stream width (in meters)
:d? Q back pack Q tote barge Q other
:s QNO
time End time Duration
Max Mean
Indicate the percentage of each habitat type present
Q Riffles % Q Stream Banks % Q Snags % Q Banks %
Q Submerged Macrophytes
% Q Other ( ) %
SPECIES
TOTAL
(COUNT)
OPTIONAL: LENGTH (mm)AVEIGHT (g)
(25 SPECIMEN MAX SUBSAMPLE)
ANOMALIES'
M
" ^ * "H '
'^Xvrf- '•*-
Rapid Bioassessment Protocols far Use in Streams and Rivers
A-21
-------
DRAFT REVISION-^Iuly 29,1997
SPECIES
TOTAL
(COUNT)
OPTIONAL: LENGTH (mm)AVEIGHT (g)
(25 SPECIMEN MAX SUBSAMPLE)
ANOMALIES'
,
** *"
' ANOMALY CODES:
D = deformities; E = eroded fins; F = fungus; L = lesions; M = multiple DELT anomalies;
S = emaciated; Z = other
A-22
Appendix A-4: Fish Field and Laboratory Data Sheets - Form J
-------
DRAFT REVISION-^July 29,1997
FISH BIORECON: FISH ASSEMBLAGE QUESTIONNAIRE
STREAM NAME
STATION #
LAT
RIVERMILE
LONG
COUNTY/STATE
LOCATION
STREAM CLASS
RIVER BASIN
AGENCY
WATERBODY SIZE (please circle one) <1 cfs 1-10 cfs >10cfs
QUESTIONNAIRE COMPLETED BY (name, address, phone#)
DATE
Introduction
This questionnaire is part of an effort to assess the biological health or integrity of our flowing
waters. Our principle focus is to evaluate the biotic health of the designated waterbody by assessing
its fish community. You were selected to participate in the study because of your expertise in fish
biology and your knowledge of the waterbody identified in this questionnaire.
First, please complete the header information in the box above. Then, using the scale below, please
circle the rank (at left) corresponding to the explanation (at right) that best describes your impression
of the condition of the waterbody. Please complete all statements.
If you cannot complete this form, check here [ ] and return it.
If you are unable to complete the questionnaire, but are aware of someone who is familiar with the
waterbody, please indicate their name, address, and phone number in the space provided below.
(Answer questions 1-4 using the scale below)
5 Species composition, age, classes, and trophic structure comparable to non (or minimally) impaired
sites of similar waterbody in that ecoregion.
4 Species richness somewhat reduced by loss of some intolerant species; young of the year of top
carnivores rare; less than optimal abundances, age distributions, and trophic structure for waterbody
size and ecoregion.
3 Intolerant species absent; considerably fewer species and individuals than expected for that waterbody
size and ecoregion, older age classes of top carnivores rare; trophic structure skewed toward omnivory.
2 Dominated by highly tolerant species, omnivores, and habitat generalists; top carnivores rare or absent;
older
Rapid Bioassessment Protocols for Use in Streams and Rivers A-23
-------
>
NJ
§
a
R
Cr-
o
I
I
i
page of
FISH SAMPLE LOG-IN SHEET
Date
Collected
Collected
By
Number of
Containers
Preservation
Station #
Stream Name and Location
Date
Received by
Lab
Lot Number
Date of Completion
sorting
mounting
identification
I
•~
Serial Code Example: B075400I(1)
B = Benthos (F = Fish; P = Periphyton) 0754 = project number
001 = sample number
(1) = lot number (e.g..winter 1996=1; summer 1996 = 2)
-------
DRAFT REVISION-^July 29,1997
APPENDIX B:
TOLERANCE AND FUNCTIONAL FEEDING
GROUP ASSIGNMENTS FOR BENTHOS
(Information organized alphabetically by
genus, rather than phytogenetically)
Rapid Bioassessment Protocols for Use in Streams and Rivers B-l
-------
DRAFT REVISION—July 29,1997
Sciname
Abedus
Abedus immaculatus
Ablabesmyia
Ablabesmyia (karelia) grp.
Ablabesmyia annulata
Ablabesmyia aspera
Ablabesmyia cinctipes
Ablabesmyia hauberi
Ablabesmyia idei
Ablabesmyia janta
Ablabesmyia mallochi
Ablabesmyia peleensis
Ablabesmyia rhamphe
Ablabesmyia rhamphe grp.
Ablabesmyia sp. b epler
Acamptocladius
Acari
Acariformes
Acentrella
Acentrella insignificans
Acentrella turbida
Acentria
Acerpenna
Acerpenna pygmaeus
Acroneuria
Acroneuria abnormis
Acroneuria arenosa
Acroneuria mela
Acroneuria perplexa
Aedes
Aeolosoma
Aeschnidae
Aeshnidae
Agabetes acuductus
Agabus
Flaindx
0
0
0
0
0
1
0
0
0
2
1
2
2
FL FFGrp
PR
PR
OM
OM
OM
OM
OM
OM
OM
OM
OM
Idaho TV Idaho FFG | MACS TV i MACS FFG Sourcelist
FL
FL
18 icg FL
i : FL
! FL
!FL
i FL
i FL
OM f |
OM | I
2 JOM
0 OM j
0
0
1
1
1
1
IFL
IFL
[FL
FL
IFL
FL
FL
In ICG IID
SH
OM
PR
PR
PR
PR
1 PR'
0
0
0
0
0
Agapetus
Agarodes 1 0
Agarodes libalis JO
Agasicles 1 0
Agasicles hygrophila |o
Agathon |
Agnetina j
Agnetina annulipes 1 1
Agnetina capitata ! 1
Agraylea
Albia iO
Allocapnia i
Allocosmoecus partitus
Allognosta
Allonais 0
Allonais inaequalis
Alloperla
Alluaudomyia
0
0
11
PR !
4 CG
4
4
[1
PR |3
PR
PR
SH
8
0
0
i
i
PR
PR
8
0
CG
CG
SH
ID
IP IMACS
4
u
0
8
eg llD
HD
IID
i Fi-
sh
P
MACS
FL
FL
FL
IFL
FL
FL
fc IFL
IFL
3 IP
PR
PR
SC
SC
UN
5
2
|3
SC
|8 CG
CG
CG
1
PR
PR
•
P
P
sh
MACS
FL
FL
FL
ID
FL
FL
FL
FL
ID
MACS
FL
FL
ID
FL
MACS
ID
ID
FL
FL
ID
FL
B-2
Appendix B: Tolerance and Functional Feeding Group Assignments for Benthos
-------
DRAFT REVISION—July 29,1997
Sciname
Alolanypus
Ambrysus
Ameletus
Ameletus connectus
McDunnough
Ameletus cooki
Ameletus similor McDunnough
Ameletus sparsatus McDunnough
Ameletus validus
Ameletus velox
Ametropus
Amiocentrus
Amiocentrus aspilus
Amnicola
Amnicola dalli
Amnicola dalli johnsoni
Amphiagrion
Amphicosmoecus
Amphinemura
Amphinemura delosa
Amphinemura nigritta
Amphipoda
Amphizoa
Ampumixis dispar
Anachytarsus
Anagapetus
Anax
Anax junius
Anax longipes
Anchytarsus
Ancryronyx
Ancylidae
Ancyronyx
Ancyronyx variegatus
Anisocentropus
Anisocentropus pyraloides
Anisogammarus
Annellida
Anodonta
Anodonta couperiana
Anodonta nuttalliana idahoensis
Anopheles
Anopsilana
Antillocladius
Antocha
Antocha monticola Alexander
Apatania
Apataniinae
Apedilum
Apedilum elachista _|
Aphylla williamsoni
Apsectrotanypus
jFlaindx
io
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
FL FFGrp
sc
sc
sc
CG
PR
PR
PR'
SC
OM
OM
SH
SH
CF
CF
CF
CG
PR
PR
| Idaho TV
111
Io
0
0
0
0
11
0
11
1
2
8
5
11
2
4
1
4
11
0
8
6
4
5
8
8 i
3
3
1
1
Idaho FFG
PR
CG
CG
CG
CG
CG
CG
CG
CG
CG
CG
SC
PR
SH
SH
CG
PR
CG
SH
SC
PR
SC
CG
CG
CF
CF
CG
CG
SC
SC
MACS TV
5
6
MACS FFG
P
fc
I Sourcelist
FL
ID
ID
IID
ID
ID
IID
IID
ilD
ID
ilD
ID
FL
FL
FL
ID
ID
FL
FL
FL
FL
ID
ID
ID
ID
FL
FL
FL
MD
MD
FL
FL
FL
FL
FL
ID
ID
FL
FL
ID
FL
FL
FL
FL
ID
ID
ID
FL
FL
FL
FL
Rapid Bioassessment Protocols for Use in Streams and Rivers
B-3
-------
DRAFT REVISION—July 28,1997
Sciname iFlaindx
Apsectrotanypus johnsoni 0
Arachnida 10
Arctopsyche
Arctopsyche californica Ling
Arctopsyche grandis
Arctopsychinae
Argia
Argia apicalis
Argia fumipennis
Argia moesta
Argia sedula
Argia tibialis
Arigomphus
Arigomphus pallidus
Arrenurus
Arrenurus apetiolatus
Arrenurus bicaudatus
Arrenurus hovus
Arrenurus problecornis
Arrenurus zapus
Asellidea
Asellus
Asellus occidentalis
Asheum beckae
Astacidae
Atherix
1
1
1
1
1
1
2
0
0
0
0
FL FFGrp
PR
PR
PR
PR
PR
PR
PR
PR
PR
PR
PR
PR
0 PR
0 PR
2
0
0
CG
CG
Idaho TV | Idaho FFG MACS TV MACS FFG Sourcelist
1 iCF
2 CF
2
CF
2 ICF
7
PR
J
6
8
8
8
0 JPR 2
Atherix lantha 0 |PR !
Atherix variagata
Atherix variegata 10
Atractelmis
Atractides
Atrichopogon
Attaneuria ruralis
Attenella
Attenella attenuata
Attenella delantala
Attenella margarita Needham
12
PR
U
0 PR
CG
CG
CG
SC
PR
PR
CG
FL
FL
ID
ID
IID
ID
6 ip |FL
i !FL
FL
i FL
I FL
FL
MD
FL
FL
FL
FL
FL
FL
FL
ilD
I
i
2
P
0 JCG 6 | PR
1
0
|3 CG
Aulodrilus americanus !0 CG
Aulodrilus limnobius |0 CG
Aulodrilus pigueti 0 ICG
Axarus 0 CG
Baetidae 0 OM
Baetis
Baetis alachua
Baetis armillatus
Baetis australis
Baetis bicaudatus Dodds
Baetis diphetorhageni
Baetis ephippiatus
Baetis flavistriga
0
0
0
OM
OM
OM
0 OM
0
OM
Baetis frondalis i 0 I OM
Baetis insignificans McDunnough
Baetis intercalaris 0 OM
3 iCG
1
CG
i
4 JCG
5 JCG
2
11
11
6
CG
UN
UN
CG
i
i
i
I
\
!
4
6
eg
eg
FL
ID
FL
FL
FL
FL
ID
FL
ID
FL
FL
FL
ID
FL
ID
ID
FL
FL
FL
FL
FL
FL
FL
FL
FL
ID
ID
FL
ID
FL
ID
FL
B-4
Appendix B: Tolerance and Functional Feeding Group Assignments for Benthos
-------
DRAFT REVISION-^Iuly 28,1997
Sciname Flaindx jFLFFGrp i Idaho TV
Baetis Intermedius Dodds
Baetis propinquus
Baetis propinquus (Walsh)
Baetis punctiventris
Baetis pygmaeus
Baetis tricaudatus Dodds
Baetisca
Baetisca becki
Baetisca obesa
Baetisca rogersi
Bagous
Bagous carinatus
Balanus
Balanus ebumeus
Barbaetis
Basiaeschna
Basiaeschna Janata
Batracobdella
Idaho FFG
le ICG
o |OM
0
0
0
0
0
0
0
0
0
0
0
0
Batracobdella paludosa |0
Batracobdella phalera j 0
Beardius sp. a epler 1 0
Beardius truncatus 1 0
Beloneuria
Belostoma
Belostoma flumineum
Belostoma lutarium
Belostoma testaceum
Belostomatidae
Beraea
0
0
0
0
0
0
Berosus |o
Berosus peregrinus |0
Berosus striatus |0
Bezzia |0
Bibiocephala
OM
IOM
IOM
OM
OM
OM
SH
SH
CF
CF
PR
PA
PA
5 CG
MACS TV I MACS FFG
Sourcelist
ID
FL
; |ID
; iFL
5 ICG
11
IFL
ilD
4 eg !FL
FL
IFL
' !
CG
Is PR
PR
PR
PR
PR I
PR
5
6
1
Bidessus 0 j
Bittacomorpha • I
Bivalvia 0 CF i
Bledius
Blepharicera
Blephariceridae
Boreochlus
Boreoheptagyia
Bourletiella
Jourletiella spinata
J11
•
IFL
!FL
JFL
FL
FL
IID
|MD
FL
I
PR |
CG
SC
6
'
PR
io jsc \
io sc
|6 CG
|6 CG
0 iCG
0
Boyeria 1 1 PR
Boyeria vinosa
Brachidontes exustus
Brachycentridae
1
0
PR
i i
CF
M JCF
Brachycentrus 1
Brachycentrus americanus 1
Jrachycentrus chelatus 1
Brachycentrus numerosus 1
11
1
CF
CF
i
P
FL
FL
FL
FL
FL
ID
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
ID
FL
MD
FL
ID
ID
ID
ID
ID
FL
FL
FL
FL
FL
ID
FL
FL
FL
FL
Rapid Bioassessment Protocols for Use in Streams and Rivers
B-5
-------
DRAFT REVISION—July 28,1997
Sciname
Brachycentrus occidentalis
Brachycera
Brachycercus
Brachycercus maculatus
Brachycercus prudens
Brachymesia gravida
Brachyvatus
Branchiobdellida
Branchiobdellidae
Branchiura sowerbyi
Bratislavia
Bratislavia bilongata
Bratislavia unidentata
Brillia
Brillia flavifrons
Brillia par
Brillia retifinis
Brundiniella
Brychius
Brychius homii Cr
Byssanodonta
Byssanodonta cubensis
Caecidotea
Caecidotea communis
Caecidotea racovitzai australis
Caenidae
Caenis
Caenis arnica
Caenis diminuata
Caenis diminuta
Caenis hilaris
Calamoceratidae
Calaparyphus
Calineuria
Calineuria califonica
Callibaetis
Flaindx FL FFGrp I Idaho TV ildahoFFG I MACS TV I MACS FFG |Sourcelist
ll JCF llD
!l1 JUN ID
0 JCG FL
0 iCG
0
0
0
0
0
0
0
|3
PR
CG
CG
CG
o ICG
0
1
0
0
0
0
8
0
0
0
0
0
0
0
Callibaetis floridanus 10
Callibaetis pretiosus
Callicorixa
Calliperla
Caloparyphus
0
Calopterygidae 1 0
Calopteryx 1 1
Calopteryx dimidiata 1
Calopteryx maculata 1 1
Cambaridae
Cambarus
0
Camelobaetidius I
Campeloma !o
Campeloma floridense :0
Campeloma geniculum :0
Campeloma limum ;0
OM
CF
CF
CG
CG
OM
OM
OM
OM
OM
CG
CG
CG
6
5
5
! IFL
CG |
CG
ID
FL
FL
FL
FL
! !FL
IFL
SH
SH
5 ISH
6
11
5
8
6
7
7
PR
SC
mh
CG
CG
CG
i' FL
IFL
5 |sh ID
FL
FL
ID
ilD
I ID
! ID
6
IFL
FL
FL
I ' IlD
! FL
! IFL
CG |7 icg |FL
!
3 | PR
1
9
. 111
PR
PR
PR
2
7
6
PR
PR
I IFL
IFL
FL
FL
FL
7 icg MACS
! IlD
i IID
CG |g eg IFL
i 'FL
i
PR i
PR
CG
PR
i
5 |p
6 |p
SC
11
SC
UN
6 |cg
SC
SC
FL
ID
ID
ID
FL
FL
FL
FL
FL
MD
ID
FL
FL
IFL
FL
B-6
Appendix B: Tolerance and Functional Feeding Group Assignments for Benthos
-------
DRAFT REVISION-^Fuly 28,1997
Sciname
Canaceidae
Capnia
Capniidae
Carabidae
Cardiocladius
Cascadoperta
Cassidinidea ovalis
Caudatella
Caudatella cascadia
Caudatella edmundsi Allen
Caudatella heterocaudata
McDunnough
Caudatella hystrix Traver
Cecidomyiidae
Celina
Celina contiger
Cenocorixa
Cenocorixa bifida
Cenocorixa bifuda hungerfordi
Centrolimnesia
Centroptilum
Centroptilum hobbsi
Centroptilum viridocularis
Ceraclea
Ceratopogon
Ceratopogonidae
Ceratopogoninae
Ceratopsyche
Cernotina
Cernotina spicata
Chaetarthria
Chaetocladius
Chaetogaster diaphanus
Chaoboridae
Chaoborus
Chaoborus punctipennis
Chauliodes I
Chauliodes pectinicornis I
Chauliodes rastricornis I
Chelifera 1
Chelonariidae !
Chelonarium 1
Cheumatopsyche
Cheumatopsyche campyla
Cheumatopsyche enonis j
Cheumatopsyche pettiti
Chimarra
Chironomidae
Chironominae
Chironomini
Chironomus
Chironomus decorus
Flaindx
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
1
0
0
o i
(FLFFGrp
Isc
PR
CG
PR
PR
PR
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OM
OM
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PR
PR
PR
PR
PR
PR
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li
1
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6
6
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6 i
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6
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Idaho FFG
SH
SH
PR
PR
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SC
PR
pr
PR
CG
CG
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CF
CF
CF
CF
CF
CG
CG
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11
5
2
3
6
5
4
10
! MACS FFG
i
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P
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i
Sourcelist
!FL
JID
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JR.
IID
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ID
ID
ID
ID
ID
FL
FL
FL
ID
ID
ID
FL
FL
FL
FL
FL
MACS
FL
ID
FL
FL
FL
FL
ID
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
ID
ID
ID
FL
FL
MD
ID
FL
FL
Rapid Bioassessment Protocols for Use in Streams and Rivers
B-7
-------
DRAFT REVISION-July 28,1997
Sciname
Chironomus riparius
Chironomus stigmaterus
Chlaenius
Chloroperlidae
Choroterpes
Choroterpes hubbelli
Chromagrion
Chrysogaster
Chrysomelidae
Chrysops
Chyranda
Chyranda centralis
Cincinnatia
Cincinnatia floridana
Cinygma
Cinygma integrum Eaton
Cinygmula
Cirolanidae
Claassenia
Claassenia saboulosa (Banks)
Cladocera
Cladopelma
Cladotanytarsus
Clatnrosperchon
Cleptelmis
Cleptelmis omata
Climacea
Climacea areolaris
Climacia
Climacia areolaris
Clinocera
Clinotanypus
Clioperla
Flaindx JFLFFGrp i Idaho TV Idaho FFG j MACS TV I MACS FFG Sourcelist
0
0
0
0
0
OM |
OM
OM
SH
CG
|
0
0
SC
SC
0 CG
i
0 CG
0
FG
0 | PR
0
0
0
0
1
11
6
11
11
1
FL
iFL
PR I
CG
PR
SH
MD
ID
ID
FL
CG |7
SH
1 JSH
4
2
4
SC
SC
SC
3 PR
3 PR
8 |CF
7
4
4
CG
CG
CG
| i
P
ID
MD
FL
FL
ID
IlD
IFL
IFL
ilD
llD
|
ID
FL
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7
7
0 OM 6 PR
2 I PR
Clioperta clio 1
Cloeon
0
Cloeon rubropictum |0
Clostocea
Cnidaria
Coelotanypus
Coelotanypus concinnus
Coelotanypus scapularis
Coelotanypus tricolor
Coenagrionidae
Coleoptera
0
0
0
0
0
0
0
I
OM
OM
H
PR |
SH .
PR I
PR
PR
PR 9
Collembola JO
11
10
Conchapelopia lo PR 6
I
Constempellina |0
Copelatus 0
Copelatus caelatipennis 0
Copepoda !
Coptotomus 1 0
Coptotomus interrogatus 10
PR
PR
6
eg
fc
ID
FL
FL
FL
IlD
IlD
8 |p
FL
FL
FL
FL
FL
FL
IMD
FL
FL
FL
IlD
| iFL
I
i
PR |9
PR
CG
PR
CG
\8 CG
PR
PR
11
6
5
PR
FL
FL
FL
FL
P FL
FL
IFL
P FL
FL
P iFL
FL
ID
FL
FL
B-8
Appendix B: Tolerance and Functional Feeding Group Assignments for Benthos
-------
DRAFT REVISION—July 28,1997
Sciname
Corbicula
Corbicula fluminea
Corbicula manilensis
Cordulegaster
Cordulegaster maculata
Cordulegastridae
Corduliidae
Corethrella
Corisella
Corixidae
Corophium
Corophium lacustre
Corticacarus delicatus
Corydalidae
Corydalus
Corydalus cornuta
Corydalus cornutus
Corynoneura
Corynoneura celeripes
Corynoneura sp. b epler
Corynoneura taris
Corynothrix
Coryphaeschna ingens
Crangonyx
Crangonyx richmondensis
Crenitis
Cricotopus
Cricotopus bicinctus
Cricotopus bicinctus Meigen
Cricotopus bicinctus grp.
Cricotopus festivellus
Cricotopus isocladius
Cricotopus nostococladius
Cricotopus or orthocladius
Cricotopus politus
Cricotopus silvestris grp.
Cricotopus tremulus
Cricotopus trifascia Panzer
Cricotopus trifascia grp.
Cricotopus/Orthocladius
Crustacea
Cryptochia
Cryptochironomus
Flaindx |FLFFGrp i Idaho TV
0
CF j
o ICF !
0 CF
o IPR
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
0
PR
PR
PR
0
Idaho FFG I MACS TV | MACS FFG ISourcelist
IFL
|FL
PR 13
I
11 IPR
|11
CF
CF
PR
PR
PR
10
8
0
CG |7
CG
CG
CG
0 iPR
2
2
2
2
OM
OM
5
PR
UN
PR
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!
5 PR
OM 7 JSH
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7 ISH
2 |OM
J7 !SH
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JFL
IFL
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7
7 SH |
7 SH
0
2 OM i
2 LOM _
|7 SH
|7 SH I
2 OM
0
Cryptochironomus fulvus lo
Cryptotendipes
Crysomelidae
Culex
Culicidae
|8 CG
10
SH
PR |8 IPR
PR
0 CG
0
Culicoides |o
Culoptila cantha
Cultus
Cura
i
CF |8 JCG
OM
0
SC
2 IPR
8
6
8
10
eg IFL
FL
ID
eg IFL
FL
ID
FL
IID
ID
ID
FL
IFL
FL
IID
ID
IFL
IMD
p
eg
fc
ID
ID
FL
FL
FL
ID
MACS
FL
P IFL
ID
ID
MD
Rapid Bioassessment Protocols for Use in Streams and Rivers
B-9
-------
DRAFT REVISION—July 28,1997
Sciname
Curculionidae
Cyathura polita
Cybister
Cyclopoida
Cylloepus
Cymatia
Cymbiodyta
Cyphon
Cymellus
Cymellus fratemus
Dannella simplex
Daphnia
Oashyhelea
Dasyhelea
Decapoda
Deinocerites
Demicryptochironomus
Denopelopia atria
Derallus
Derallus altus
Dero
Dero botrytis
Dero digitata
Dero flabelliger
Dero furcata
Dero lodeni
Dero nivea
Flaindx
0
0
0
0
0
0
FL FFGrp
SH
CG
PR
Idaho TV jldahoFFG I MACS TV MACS FFG ISourcelist
11
SH
FL
FL
i FL
8 |CF ID
11 UN | ID
CF
0 ICF
0 .
0
0
0
0
0
0
0
8
8
OM
CF
CG
OM
OM
o ICG
0
0
0
0
CG
CG
CG
CG
o ICG
0
Deroobtusa 10
Dero pectinata
Dero trifida
Dero vaga
Deronectes
Deronectes striatellus LeConte
Derovatellus
Desmona
Desmopachria
Despaxia
Despaxia augusta
Deuterophlebia
Deuterophlebia nielsoni Kennedy
Deuterophlebiidae
Diamesa
Diamesinae
Diaptomus pribilofensis
0
0
0
0
0
0
Dibolocelus ovatus 0
Dibusa
Dicosmoecinae
Dicosmoecus !
Dicosmoecus atripes
Dicosmoecus gilvipes |
Dicranota
Dicrotendipes |0
CG
8
UN ilD
. FL
' 7 sc FL
CF
-
SH
I
CG
CG
CG
CG
5
PR
5 PR
FL
FL
FL
ilD
6 leg
8
'
eg
MACS
FL
FL
FL
FL
FL
IFL
10 eg
FL
FL
FL
FL
FL
FL
FL
FL
FL
i
1 JSH |
PR
0 SH
o ISH
io Isc
11 |SC
in sc
5
111
111
11
1
CG
CG I
UN
|
MH
SC
1 ISH
FG
1 PR
2 |SC
3 JPR |
8
CG J8
FL
FL
FL
FL
ID
FL
ID
FL
ID
ID
ID
ID
ID
ID
ID
ID
FL
ID
ID
ID
ID
ID
ID
eg IFL
B-10
Appendix B: Tolerance and Functional Feeding Group Assignments for Benthos
-------
DRAFT REVISION^TuIy 28,1997
Sciname [Flaindx FL FFGrp (Idaho TV i Idaho FFG 1 MACS TV | MACS FFG ISourcelist
Dicrotendipes leucoscelis
Dicrotendipes lobus
Dicrotendipes modestus
Dicrotendipes neomodestus
Dicrotendipes nervosus
Dicrotendipes simpsoni
Dicrotendipes sp. a epler
Dicrotendipes thanatogratus
Dicrotendipes tritomus
Didymops transversa
Dina
Dineutus
Dineutus carolinus
Dineutus ciliatus
Dineutus discolor
Dineutus emarginatus
Dineutus serrulatus
Diphetor
Diphetor hageni
Diplectrona
Diplectrona modesta
Diplocladius
Diploperla
Diptera
Disoncha
Diura knowltoni
Dixa
Dixella
Dixidae
Djalmabatista
Djalmabatista pulcher
Doddsia occidentalis
0 IFG
o IFG !
0
0
0
0
0
0
0
FG ! j 1
FG
FG
FG
FG
FG
FG
o IPR
0
0
0
0
0
\8
0
0
0
0
0
0
0
0
Dohmiphora 0
Dolania americana 0
Dolichopodidae
Dolichopus
Dolophilodes
Donacia
Doroneuria
Doroneuria baumanni
Doroneuria theodora
Dromogomphus
Dromogomphus armatus
Dromogomphus spinosus
Drunella
Drunella coloradensis Dodds
Drunella coloradensis/flavilinea
Drunella doddsi Needham
Drunella flavilinea McDunnough
Drunella grandis Eaton
Drunella pelosa Mayo
Drunella spinifera Needham
0
0
0
0
0
CF
CF
5
5
! !
!
PR
PR
CG
0 ICF
7
UN
|11 JSH
PR
PR
2
1
11
1
2
I
14
i
SC
CG
CG
CG
SC
PR
h CG
SH 11
11
11
1
PR
PR
PR
MH
4
FL
FL
IFL
FL
FL
IFL
JFL
!FL
IFL
IFL
!ID
P IFL
FL '
FL
FL
FL
IFL
1
IMD
IID
IFL
IFL
!MD
IMD
IFL
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PR
PR
PR
1 |0 PR
0 ]PR
IO PR
JO ISC
11 SC
1
IO
iO
CG
SC
PR
ID
ID
FL
FL
ID
FL
FL
ID
FL
FL
ID
FL
ID
FL
ID
ID
ID
FL
FL
FL
ID
ID
ID
ID
ID
ID
ho
IID
Rapid Bioassessment Protocols for Use in Streams and Rivers
B-ll
-------
DRAFT REVISION—July 28,1997
Sciname |Flaindx FL FFGrp | Idaho TV j Idaho FFG 1 MACS TV
Dmnella spinifera/grandis
Dryopidae
Dryops
Dubiraphia
Oubiraphia bivittata
Dubiraphia giullianii
Oubiraphia quadrinotata
Dubiraphia vittata
Dugesia
Dugesia tigrina
Dugesia tigrina (Girard)
Dytiscidae
Ecclisocosmoecus scylla
Ecclisomyia
Eccoptura
Eccoptura xanthenes
Eclipidrilus
Eclipidrilus palustris
Ectopria
Ectopria nervosa
Ectoprocta
Edotea montosa
Einfeldia
Einfeldia austini
Einfeldia natchitocheae
Elephantomyia
Elimia
Elimia atheami
Elimia curvicostata
Elimia floridensis
Elliptic
Elliptic buckleyi
Elmidae
Elodes
Empididae
Enallagma
Enallagma cardenium
Enallagma daecki
Enallagma divagans
Enallagma dubium
Enallagma pallidum
Enallagma pollutum
Enallagma signatum
Enallagma vesperum
Enallagma weewa
i
i
0
0
o |OM
0
0
0
OM
OM
OM
0 |PR
0 PR
0
1
0
0
0
0
0
0
0
0
0
PR
CG
CG
SC
SC
CG
0
5
4
6
4
PR
SH
CG
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MACS FFG ISourcelist
IID
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JFL
6
OM |
| [
4 OM
5
0
2
CG |8
CG
CG |
I
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11
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SH
CG
CG
SH
1 Isc !
1 ISC
1 ISC I
0 CF
0 CF
0 ;OM U CG
0 • I
o OM :e >PR
0 PR :9 PR
0 'PR
0 PR
0 PR
0 PR
0 :PR
0 iPR
0 PR
o ;PR
0 PR
Enchytraeidae JO ICG
Endochironomus 0 10 SH
Endochironomus nigricans
2
Endochironomus subtendens 0
8
5
2
sc |FL
IFL
IID
FL
JFL
FL
FL
IID
FL
IID
SC
SC
8 |p
I
!
I
ID
MD
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
ID
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
10 jcg FL
10
Endotribelos JO CG
sh
Endotribelos hesperium 0 'CG ;
FL
FL
FL
FL'
FL
Enochrus 0 ' . i JFL
B-12
Appendix B: Tolerance and Functional Feeding Group Assignments for Benthos
-------
DRAFT REVISION—July 28,1997
Sciname
Enochrus ochraceus
Entocytheridae
Entomobryidae
Eocosmoecus
Eocosmoecus schmidi (Wiggins)
Epeorus
Epeorus albertae
Epeorus deceptivus
Epeorus grandis
Epeorus iron
Epeorus ironopis
Epeorus longimanus
Ephemera
Ephemerella
Ephemerella alleni Jensen and
Edmunds
Ephemerella aurivillii (Bengtsson)
Ephemerella inermis Eaton
Ephemerella infrequens
McDunnough
Ephemerella infrequens/inermis
Ephemerella lacustris
Ephemerella rotunda
Ephemerella trilineata
Ephemerellidae
Ephemeridae
Ephemeroptera
Ephoron
Ephydridae
Epiaeschna heros
Epicordulia princeps
Epicordulia regina
Epitheca
Epitheca cynosura
Epitheca princeps
Epitheca princeps regina
Epitheca sepia
Eriocera
Erioptera
Erpobdellidae
Erythemis
Erythemis simplicicollis
Erythrodiplax
Erythrodiplax minuscula
Eubranchiopoda
Eubrianax edwardsi (Le Conte)
Eubrianix
Eucapnopsis brevicauda
Eucorethra
i
Eukiefferiella ]
Eukiefferiella brehmi
Flaindx
0
0
0
0
0
0
0
0
0
0
0
0 !
0. 1
0 !
0
0
0
0
0
0
o ;
0
1
FL FFGrp Idaho TV
11
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LH
|11
0
0
0
0
0
1
0
|4
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1
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\B
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PA 8
PR
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.8
'8 I
Idaho FFG
UN
CG
ISH
SH
sc
sc
sc
sc
sc
sc
sc
CG
CG
CG
CG
SH
CG
CG
CG
CG
CG
CG
CG
CG
CG
PR
CF
SC
sc
SH
PR
CG
CG
j MACS TV
4
MACS FFG
P
ISourcelist
FL
IID
ID
ID
ID
FL
ID
ID
ID
ID
ID
ID
ID
ID
ID
ID
ID
ID
ID
ID
FL
FL
FL
ID
FL
ID
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
ID
ID
ID
ID
ID
FL
ID
Rapid Bioassessment Protocols for Use in Streams and Rivers
B-13
-------
DRAFT REVISION—July 28,1997
Scirlame
Eukiefferiella brevicalcar
Eukiefferiella claripennis
Eukiefferiella claripennis grp.
Eukiefferiella devonica
Eukiefferiella gracei
Eukiefferiella pseudomontana
Euparyphus
Eupera cubensis
Eurylophella
Eurylophella dons
Eurylophella temporalis
Eurylophella trilineata
Exosphaeroma
Farula
Ferrissia
Ferrissia hendersoni
Ferrissia rivularis Allen &
Cheatum
Fittkauimyia serta
Fluminicola
Fontelicella
Forcipomyia
Forcipomyiinae
Fossaria
Frisonia picticeps
Frontipoda
Gammaridae
Gammanjs
Gammarus lacustris Sars
Gammarus tigrinus
Gastropoda
Geayia
Gelastocoridae
Gelastocoris
Georthocladius
Geranomyia
Gerridae
Gerris
Gerris buenoi
Gerris remigis
Gloiobdella elongata
Glossiphonia complanta
(Linnaeus)
Glossiphonia heteroclita
Glossiphoniidae
Glossophonia
Glossoscolecidae
Glossosoma
Glossosoma alascense Banks
Glossosoma intermedium
Glossosoma montana Ross
Glossosoma oregonense Ling
Flaindx
1
1
0 _j
0
0
0
0
0
0
0
0
0
0
FLFFGrp j Idaho TV
CF
CG
CG
CG
8
8
8
8
8
11
CG
CG
SC
SC
SC
PR
2 ICG
2
2
0
0
0
0 _,
0
0
0
0
CG
CG
SC
SC
PR
11
6
11
5
8
6
8
Idaho FFG MACS TV i MACS FFG ISourcelist
CG
FL
CG ID
CG
CG
CG
CG
•
FL
(ID
IID
1
IT • jsc
SC
SC
SC
7
I
SC
SC
1
PR
SC
2 IPR
4
OM
4 ]OM
7 JSC
SC
!
I
I
11 PR
11
PR
5
PR
11 (PR
5
5
8
I
PR
PR
PR
ID
ID
FL
FL
FL
FL
FL
FL
ID
FL
FL
ID
FL
ID
ID
FL
ID
ID
ID
FL
FL
FL
ID
FL
FL
FL
IID
3 Jsh
\8 PR
I
10 IPR
0
0
0
0
0
ho
SC
SC
SC
SC
SC
eg
ID
FL
FL
FL
ID
ID
ID
FL
ID
FL
ID
ID
MACS
FL
ID
ID
IID
IID
B-14
Appendix B: Tolerance and Functional Feeding Group Assignments for Benthos
-------
DRAFT REVISION—July 28,1997
Scinatne
Glossosoma penitum Banks
Glossosoma sp 1
Glossosoma sp 2
Glossosoma wenatchee Ross and
Spencer
Glossosomatidae
Glutops
Glyphopsyche
Glyptotendipes
Glyptotendipes meridionalis
Glyptotendipes paripes
Flaindx
0
0
0
Glyptotendipes seminole |o
Glyptotendipes sp. b epler .
Glyptotendipes sp. f epler
Glyptotendipes sp. g epler
Goeldichironomus
Goeldichironomus amazonicus
Goeldichironomus carus
Goeldichironomus fluctuans
0
0
0
FL FFGrp
Idaho TV j Idaho FFG
0
4
0
0
0
3
SC
MACS TV
SC
SC
SC !
SC i
PR
1 JMH
I
0 CG
0 JOG
o ICG
0
Goeldichironomus holoprasinus |o
Goeldichironomus natans
Goera
Goera archaon
Goera cea
Goerinae
Gomphaeschna furcillata
Gomphidae
Gomphurus dilatatus
Gomphus '
Gomphus dilatatus
0
CG
CG
CG
0
0
2
2
2
Gomphus geminatus 1 2
Gomphus lividus
Gomphus minutus
Gomphus pallidus
Gonidea
Gonidea angulata
2
2
2
Gonielmis iO
Gonielmis dietrichi 0
Goniobasis
Gonomyia
Grammotaulius
Grandidierella bonnieroides
Graptocorixa
Grensia
Gumaga
Guttipelopia guttipennis
Gymnometriocnemus
Gymnopais
Gyraulus
Gyraulus parvus
Gyretes
Gyretes iri color
Gyrinidae
0
0
PR
PR
PR
1
1
.110
8
I MACS FFG Sourcelist
ID
IID
ID
IID
ID
ID
ID
fc I Fl-
og
!
I
SC
SC
1 |SC
1
PR •
PR |
PR
PR
PR
PR
OM
OM
CG
PR
I
1
FL
PL'
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
MD
ID
ID
ID
IFL
P
I
5
I
4 CF
8 CF
5 ICG
11
I
UN
4 ISH
|11
PR
6 ISH
3 SH
PR
!
I 111
0
0
0
0
SC
SC |8 SC
SC
7
I
0 i \5 PR
FL
FL
P IFL
FL
FL
FL
FL
FL
ID
ID
FL
FL
MD
ID
ID
FL
IID
eg
ID
ID
FL
MACS
ID
FL
FL
FL
FL
FL
Rapid Bioassessment Protocols for Use in Streams and Rivers
B-15
-------
DRAFT REVTSION-^JuIy 28,1997
Sciname
Gyrinus
Gyrinus lugens
Gyrinus pachysomus
Haber
Haber speciosus
Habrophlebia
Habrophlebia vibrans
Habrophlebiodes brunneipennis
Haemonais waldvogeli
Haemopsis
Haemopsis marmorata (Say)
Hagenius brevistylus
Halacaridae
Haliplidae
Haliplius
Flaindx
0
0
0
0
0
0
0
0
0
0
Haliplus 10
Haploperla
Hargeria rapax
Hamischia
Hayesomyia
Hebetancylus excentricus
Hebrus
Heleniella
Helichus
Helichus basalis
Helichus fastigiatus
Helichus lithophilus
Helichus striatus LeConte
Helichus striatus foveatus
Helicopsyche
Helicopsyche borealis
Helicopsychidae
Helisoma
Helius
0
0
0
0
0
FL FFGrp
Idaho TV | Idaho FFG
5
i
OM
CG
PR
CF
CG
PR
SC
PR
I
0
0
0
0
0
0
SC
Helobata . 0 OM
Helobata striata ' lo OM
Helobdella 0
Helobdella elongata
Helobdella fusca
Helobdella stagnalis
Helobdella stagnalis (L.)
Helobdella triserialis
Helochares
Helodidae
0
0
PA
PR
PA
0 PR
0
0
0
Helopelopia JO
Helophorus 10
Helopicus bogaloosa 1
Hemerodromia 0
Hemerodromia/Chelifera
Hemiptera |0
Heptagenia
Heptagenia criddlei McDunnough
PA
OM
PR
SH
PR
MACS TV MACS FFG
4 p
Sourcelist
FL
FL
FL
FL
•
I
10 IPR
10
7
7
6
5
JFL
IMD
FL
FL
!
-------
DRAFT REVISION—July 28,1997
Sciname
Heptagenia elegantula
Heptagenia flavescens
Heptagenia simpliciodes
McDunnough
Heptagenia/Nixe
Heptageniidae
Hesperoconopa
Hesperocorixa
Hesperoperla
Hesperoperta pacifica (Banks)
Hesperophylax
Hetaerina
Hetaerina americana
Hetaerina titia
Heterelmis
Heterlimnius
Heteriimnius corpulentus
Heteroplectron californicum
Heterotrissocladius
Heterotrissocladius subpilosus
Hexagenia
Hexagenia bilineata
Hexagenia limbata
Hexagenia munda orlando
Hexatoma
Himalopsyche
Hirudinea
Hirudinidae
Hobsonia florida
Homophylax
Homoplectra
Homoptera
Hudsonimyia
Hyalella
Hyalella azteca
Hyallela
Hyallela azteca
Hydaticus
Hydatophylax
Hydra
Hydra americana
Hydra carina
Hydra chna
Hydra chnidae
Hydraena
Hydraena pennsylvanica
Hydraenidae
Hydrobaenus
Hydrobates
Hydrobiidae
Hydrobiomorpha
Hydrobiomorpha castus !
iFlaindx
io
0
0
1
1
0
0
0
0
0
o
0
0
0
0
0
0
0
0
0 !
0
0
o !
0
0
0
0
0
FL FFGrp
OM
OM
PR
PR
PR
CG
CG
CG
CG
CG
PR
PR
CG
PR
OM
PR
I
PR
SC
Idaho TV
4
4
4
4
1
11
11
1
5
4
4
1
0
2
11
10
7
0
8
8
8
5
1
5
8
5
5
8 !
i
11
Idaho FFG
SC
SC
SC
SC
CG
UN
PR
PR
SH
CG
CG
SH
CG
PR
PR
PR
PR
SH
MH
"
CG
CG
PR
SH
PR
PR
PR
PR
SC
SC
MACS TV
6
2
8
MACS FFG ISourcelist
;iD
FL
ID
IID
FL
ID
ID
ID
IID
ilD
!FL
!FL
FL
FL
ilD
ilD
ID
iFL
IID
eg !FL
FL
iFL
FL
P IFL
!ID
iFL
FL
IFL
IID
IMD
IID
FL
IMD
FL
HD
ilD
!FL
ID
IID
FL
IID
FL
iFL
FL
FL
FL
sc ID
FL
FL
FL
FL
Rapid Bioassessment Protocols for Use in Streams and Rivers
B-17
-------
DRAFT REVISION-July 28,1997
Sciname
Hydrobius
Hydrobius tumidus
Hydrocanthus
Hydrocanthus iricolor
Hydrocanthus oblongus
Hydrochus
Hydrodroma
Hydrometra
Hydrometra wileyae
Hydrophilidae
Hydrophilus
Hydroporus
Hydroporus or hygrotus
Hydroporus pilatei
Hydropsyche
Hydropsyche californica
Hydropsyche decalda
Hydropsyche elissoma
Hydropsyche mississippiensis
Hydropsyche occidentalis
Hydropsyche oslari
Hydropsyche spama
Hydropsychidae
Hydroptila
Hydroptila ajax
Hydroptila arctia
Hydroptila argosa
Hydroptilidae
Hydrovatus
Hydrovatus pustulatus
Hydrovatus pustulatus
compressus
Hygrobates
Hygrobates occidentalis
Hygrobatidae
Hygrotus
Hylogomphus geminatus
Hymenoptera
Hypogastrura •
Idotea
llybius
llyodrilus templetoni
Imania
Incertus
Insecta
Ironodes
Ironopsis grandis
Ironoquia
Ischnura
Ischnura hastata
Ischnura posita
Ischnura ramburi
Flaindx
0
0
0
0
0
\o_
0
0
0
0
0
0
0
1
1
1
1
1
0
1
0
0
0
0
0
0
2
0
0
FLFFGrp I Idaho TV
OM |8
OM
OM
OM
SH
PR
PR
PR
OM
PR
PR
CF
CF
CF
CF
CF
11
5
5
4
4
4
4
CF |4
6
|6
|6
|6
PR
PR
PR
PR
PR
PR
CG
0 CG
.
0
CG
4
11
Idaho FFG
PR
SH
PR
PR
CF
CF
CF
CF
CF
SC
SC
SC
SC
UN
PR
8 JPR
8
11
8
11
5
11
4
3
PR
MACS TV i MACS FFG Sourcelist
FL
FL
j
I
FL
FL
FL
FL
IFL
FL
FL
IFL
MD
5 -IP IFL
4
FL
FL
fc FL
llD
FL
FL
IFL
4 Ifc
6
p
ID
ID
FL
FL
FL
i ID
(ID
ID
FL
IFL
FL
FL
IFL
PR |
I
PA
SC
CG
UN
ID
iio
FL
j
5 jp
SC
SC
0 JPR |9 PR
0 PR
0 PR
0 PR
9
P
FL
FL
FL
FL
MACS
FL
ID
ID
ID
ID
ID
MD
FL
FL
FL
FL
B-18
Appendix B: Tolerance and Functional Feeding Group Assignments for Benthos
-------
DRAFT REVISION-^Iuly 28,1997
Sciname
Isogenus
Isonychia
Isonychia arida
Isonychia sayi
Isonychia sicca
Isoperla
Isoperla fulva
Isoperla fusca
Isoperla orata
Isoperla pinta Prison
Isopoda
Isotomidae
Isotomurus
Isotomurus palustris
Juga
Kathroperla perdita
Kiefferulus
Kiefferulus dux
Koenikea
Koenikea angulata
Koenikea aphrasta
Koenikea elaphra
Flaindx
0
0
FLFFGrp Idaho TV | Idaho FFG MACS TV j MACS FFG Sourcelist
|2
0
0
1 JOM
|
1
0
0
0
0
0
0
0
0
0
o
Koenikea spinipes carella 0
Kogotus
Kogotus/Rickera
Krendowskia
Krenopelopia
0
OM
2
2
2
2
CG |8
OM
CG
CG
CG
7
1
CG
I
2
|2
i
PR
2
IID
fc !FL
JFL
I FL
PR |2
PR
PR
PR
CG
FL
p IFL
IID
! |ID
!
sc !
CG
10
i
PR i
PR
FL
(ID
!FL
IFL
IFL
IFL
IID
IID
eg
!
Krenopelopia hudsoni JO JPR | 1
Labrudinea
Labrundinia 1 0
PR
Labrundinia becki 0 PR !
Labrundinia johannseni
Labrundinia maculata
Labrundinia neopilosella
1 PR
0 JPR
1 PR
Labrundinia pilosella |1 !PR
Labrundinia sp. 4 epler |0 I PR
.abrundinia sp. a epler
.abrundinia virescens
o IPR
1
Laccobius 0
Laccodytes 0
Laccophilus JO
PR
PR
PR \5
Laccophilus fasciatus 0 PR
Laccophilus fasciatus rufus
Laccophilus gentilis
Laccophilus proximus
0 PR \
0 PR
0 PR
Laccophilus schwarzi |0 |PR
Laccomis difformis
Lacobius
0
11
Laeonereis culveri 0 OM
!
I
PR
5
PR
Laevapex 0 SC ! I
Laevapex diaphanus 0
SC i
Laevapex fuscus : 0 I SC <
FL
FL
FL
FL
FL
FL
FL
ID
ID
FL
IMD
FL
MD
FL
FL
FL
FL
FL
FL
FL
IFL
FL
FL
SFL
P FL
FL
IFL
FL
:FL
FL
FL
IID
FL
IFL
FL
IFL
.aevapex peninsulae 0 SC I FL
Rapid Bioassessment Protocols for Use in Streams and Rivers
B-19
-------
DRAFT REVISION—July 28,1997
Sciname Flaindx FL FFGrp
Lampsilis teres
Lanthus
Lanx
Lara
Lara avara
Larsia
Larsia bemeri
Larsia decolorata
Larsia indistincta
Lauterborniella agrayloides
Lebertia
Lebertia sp. 1 pluchino
Lebertia sp. 4 pluchino
Lebertiidae
Lepidoptera
Lepidostoma
Lepidostoma cinereum
Lepidostoma quercina
Lepidostomatidae
Leptoceridae
Leptochelia rapax
Leptohyphes dolani
0
0
0
2
0
0
0
0
0
0
0
0
0
0
Leptophlebia 0
Leptophlebia bradleyi 0
Leptophlebia intermedia 0
Leptophlebiidae 0
Leptoxis
Lestes I
Lethocerus
Leucorrhinia
Leucotrichia
Leucrocuta
Leuctra
Leuctridae
1
Libellula 0
Libellula auripennis 0
Libellula incesta 0
Libellula semifasciata 0
Libellula vibrans 0
Libellulidae 0
Limacidae
Limnephilidae !
Limnephilinae
Limnephilus
Limnesia 0
Limnochares ! 0
Limnodrilus jO
Limnodrilus angustipenis 0
Limnodrilus hoffmeisteri |0
Limnodrilus profundicola lO
Limnodrilus udekemianus |0
Limnophila |0
Limnophora ! 0
CF
PR
PR
PR
PR
CG
PR
PR
PR
SH
SH
OM
OM
OM
OM
SH
PR
PR
PR
Idaho TV Idaho FFG MACS TV |MACS FFG ISourcelist
6
4
4
6
8
8
6
1
3
1
3
4
2
2
9
11
6
0
I FL
IMD
CG I ID
SH ! JID
SH ID
PR |6
j
PR
!
PR
SH
SH
SH
SH
SH
CG
CG
CG
PR
PR
SC
SH
9 PR
P FL
FL
IFL
!FL
IFL
P iFL
IFL
FL
'ID
SC JFL
1 |sh FL
0
8
!
PR
PR
PR 9
|4
PR
PR
CG
CG
CG
CG
CG
PR
PR
4
5
4
11
PR
SH
SH
9
4
SH j
IID
IID
IID
FL
IFL
IFL
FL
FL
FL
iFL
MD
ID
IID
MD
IID
MD
sh FL
IID
P IFL
FL
FL
FL
FL
P FL
MACS
sh ID
ID
ID
iFL
i FL
PR
PR
FL
FL
IFL
FL
FL
FL
FL
B-20
Appendix B: Tolerance and Functional Feeding Group Assignments for Benthos
-------
DRAFT REVISION-^July 28,1997
Sciname iFlaindx FL FFGrp | Idaho TV | Idaho FFG MACS TV | MACS FFG jSourcelist
Limnophyes 0 CG 8
Limnophytes I
CG |8
Limnoporus 11 I PR
Limonia
0 |SH 6
Liodessus lu I PR
Lioplax pilsbryi
Lipogomphus
Lirceus
Littoridinops
Littoridinops monroensis
Lopescladius
Lumbricidae
Lumbricina
Lumbriculidae
Lumbriculus
Lumbriculus incxinstans
Lumbriculus variegatus
Lymnaea
Lymnaeidae
type
type diversa
Macrobdella dltetra
Macrobrachium
Macrobrachium acanthurus
Macromia
Macromia georgiana
Macromia georgina
Macromia taeniolata
Macromiidae
Macronema
Macronema Carolina
Macronychus
Macronychus glabratus
Macropelopia
Macrostemum
Macrostemum Carolina
0 ISC
0
o ICG
0 SC
0 SC
0 CG
6
o !
! IB
0
0
0
0
0
0
0
0
0
0
1
1
1
1
0
CG
CG
CG
CG
SC
SC
SC
PR
PR
PR
PR
PR
1 ICF
0
0
0
1
OM
8
8
6
SH I
CG
CG
CG
SC
SC
eg IFL
JMD
I ID
IFL
FL
IFL
FL
8 jcg iFL
I FL
I
10
eg
FL
FL
FL
IID
FL
FL
FL
I IFL
! ' ID
6
SC IFL
!
FL
FL
FL
FL
I FL
2
I
I
! is
3 CF
OM
6 JPR
CF ;
CF
Malenka 2 SH .
Manophylax J11 ISC
Margaritifera
Margaritifera margaritifera fal
|4 -|CF
la
Marisa cornuarietis JO
Maruina
Mayatrichia
Vlayatrichia ayama
1
o sc Is
CF
SC
P IFL
FL
FL
iFL
P
I
3
FL
ID
FL
FL
FL
ID
fc IFL
IFL
IID
IID
ID
I
SC
0 SC i
Megaleuctra |0 |SH l
ID
FL
ID
IFL
FL
IID
Megarcys 2 PR
Megistocera 0 I i I
Melanoides 0 SC
Melanoides tuberculata 0 SC ;
Melyridae i >11 PR
Menetus . > \
Meringodixa ! : J2 CG
Meropelopia '0 PR • 7
ID
FL
FL
FL
UD
IMD
IID
IFL
Rapid Bioassessment Protocols for Use in Streams and Rivers
B-21
-------
DRAFT REVISION-^Tuly 28,1997
Sciname i Flaindx
Merragata
Merragata brunnea
Merragata hebroides
Mesosmittia
0
0
0
0
Mesovelia 1 0
Mesovelia cryptophila JO
Mesovelia mulsanti
Mesoveliidae
Metacnephia
Metriocnemus
Metrobates
Metrobates hesperius
Miathyria marcella
Micrasema
Micrasema bactro
Micrasema rusticum
Micrasema wataga
Microclloepus
Microcylloepus
Microcylloepus pusillus
Microcylloepus pusillus foveatus
Microcylloepus pusillus lodingi
Microcylloepus similis
Micromenetus
Micromenetus dilatatus
Micromenetus dilatatus avus
Micromenetus floridensis
Micropsectra
Microtendipes
Microtendipes pedellus
Microtendipes pedellus grp.
Microtendipes rydalensis
Microtendipes rydalensis grp.
Microvelia
Microvelia hinei
Microvelia pulchella
Mideopsis
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
FL FFGrp
PR
PR
PR
PR
PR
PR
PR
OM
PR
Idaho TV | Idaho FFG MACS TV j MACS FFG i Sourcelist
I i
11
FL
FL
IFL
!FL
PR |p FL
! FL
6
PR |
PR
OM
OM
OM
OM
OM
OM
SC
sc
SC
sc
CG
1
1
4
2
4
2
7
FG le
FG I
FG
FG
FG
o |PR
0
0
0
Molanna 0
Molanna tryphena
Molluska
Molophilus
Monodiamesa
Monopelopia
0
Monopelopia boliekae 10
Mooreobdella
Mooreobdella tetragon
Moselia infuscata
Moselyana
Munna reynoldsi
Munroessa
Munroessa gyralis
Muscidae
Muscomorpha
0
0
PR
PR
11
PR
|
11
17
6
PR
PR 11
PR :
0
u
o ICG
iFL
IFL
CF i !
MH
MH
CG
CG
SC
'
2
sh
i
CG |
CG |7
CF
PR
SC
6
6
CG
PR
PR
SH i
CG \
I
o |SH
0
SH
|6
!
PR
ID
FL
FL
FL
FL'
FL
ID
FL
FL
ID
FL
FL
ID
FL
ilD
eg
FL
FL
FL
FL
FL
FL
IFL
P
FL
FL
FL
FL
IFL
SC
FL
FL
FL
FL
ID
MD
ID
ID
FL
IFL
IFL
ID
ID
IFL
IFL
FL
ID
MACS
B-22
Appendix B: Tolerance and Functional Feeding Group Assignments for Benthos
-------
DRAFT REVISION—July 28,1997
Sciname Flaindx
FLFFGrp Idaho TV Idaho FFG MACS TV
Musculium 0 CF
Mycetophila
Mycetophilidae :
Mysidacea 0
5
j
Mysidopsis 10 |CF
Mysis
Mystacides
Mytilopsis leucophaeata
Mytilopsis(mollusca)
Myzobdella lugubris
Naididae
Nais behningi
Nais communis
Nais elinguis
Nais pardalis
Nais pseudobtusa
Nais simplex
Nais variabilis
Nais(animal)
Namamyia
Nannothemis belia
Nanocladius
Nanocladius distinctus
Nanocladius rectinervis
Narpus
Narpus concolor
Nasiaeschna pentacantha
Natarsia
Natarsia sp. a roback
Naucoridae
Neargyractis
Neavipeda
Neaviperia forcipata (Neave)
Neaviperla/Suwallia
Nectopsyche
Nectopsyche Candida
Nectopsyche exquisita
Nectopsyche gracilis
Nectopsyche halia
Nectopsyche lahontanensis
Nectopsyche pavida
viectopsyche stigmatica
Nehalennia
vlehalennia intergricollis
Nematoda
Nematomorpha
0
0
0
0
0
0
0
0
0
0
0
0
I
CF
CF
PR
CG
CG
CG
CG
CG
CG
CG
o ICG
o ICG
0
0
0
0
PR
CG
CG
CG
4
11
1
3
CG 14
CG
OM
CG
I
I
4 ICG
J4 ICG
0 JPR 1
0
0
0
2
2
2
2
0
0
0
Nemertea 10
Nemotelus 0
PR
PR
5 PR
SH
J11 iPR
MACS FFG Sourcelist
fc FL
I | MACS
I MACS
IFL
IFL
eg
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
ilD
! FL
3 ' kg FL
8
FL
IFL
IlD
IlD
FL
P IFL
IFL
1 PR
1 PR
OM 3
OM I
OM
is
UN
3
SC
3 SC
3 SC
OM
3
PR
PR
5
11
Nemoura
Nemouridae
^Jeoclypeodytes
SC
PA
PA
2 |SH
11 PR
HD
IFL
IlD
ID
(ID
sn IFL
IFL
IFL
ID
ID
IlD
Neoephemera lo CG I
FL
ID
FL
FL
FL
ID
FL
FL
MD
ID
ID
FL
Rapid Bioassessment Protocols for Use in Streams and Rivers
B-23
-------
DRAFT REVISION—July 28,1997
Sciname
Neoephemera compressa
Neoephemera youngi
Neogerris
Neogerris hesione
Neohermes
Neoleprea
Neoperta
Neoperta clymene
Neophylax
Neophylax occidentalis
Neophylax rickeri
Neophylax splendens
Neoplea
Neoplea striola
Neothremma
Neothremma alicia
Neotrichia
Neotrichia halia
Nephelopsis obscura
Nepldae
Nereidae
Nereis
Nereis succinea
Neritina reclivata
Nerophllus
Nerophilus califomicus
Neurnania
Neumania distlncta
Neurecllpsis
Neureclipsis crepuscularis
Neurocordulia
Neurocordulia alabamensis
Neurocordulia molesta
Neurocordulia obsoleta
Neurocordulia virginiensis
Nigronia
Nigronia fasciatus
Migronia serricornis
Nilobezzia
Nllotanypus
Nilotanypus fimbriatus
Nilothauma
Nilothauma bicorne
Nimbocera
Nimbocera limnetica
Nixe
Nixe criddlei
Nixe simplicioides
Noctuidae
Noteridae
Notogillia wetherbyi
Notomicrus
Notonecta
Flaindx
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
2
2
2
2
2
0
0
0
0
0
0
0
0
0
0
o
o
0
FL FFGrp
CG
CG
PR
PR
PI
PI
sc
PR ^
OM
OM
SC
PR
PR
PR
PR
PR
PR
PR
PR
PR
PR
PR
PR
CG
CG
FG
SH
OM
SC
.
Idaho TV
3
3
3
3
0
0
11
4
11
0
0
6
6
4
2
2
11
11
Idaho FFG
SC
SC
SC
SC
SC
SC
SC
SH
PR
.
OM
OM
PR
CF
SC
SH
SH
PR
PR
MACS TV
7
2
MACS FFG
fc
Sourcelist
IFL
FL
FL
FL
FL
FL
LFL
FL
ID
ID
ID
ID
FL
FL
ID
ID
FL
ID
ID
FL
FL
FL
FL
FL
ID
ID
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
ID
FL
ID
ID
ID
FL
FL
FL
FL
ID
B-24
Appendix B: Tolerance and Functional Feeding Group Assignments for Benthos
-------
DRAFT REVISION—July 28,1997
Sciname Flaindx
Notonectidae i
Nyctiophylax ! 0
Nyctiophylax moestus Banks
Ochrotrichia
Ochrotrichia
Ochthebius
Ochthebius sculptus
Octogomphus
Odonata
Odontomesa
Odontomyia
Odontyomyia
Oecetis
Oecetis avara
Oecetis cinerascens
Oecetis georgia
Oecetis inconspicua
Oecetis inconspicua cmplx.
Oecetis noctuma
Oecetis parva
Oecetis persimilis
Oemopteryx
0
0
0
0
2
2
2
2
FLFFGrp i Idaho TV Idaho FFG | MACS TV j MACS FFG Isourcelist
J11 PR
5 CF
IlD
5 iPR
FL
ID
OM 4 PR JFL
OM U ICG i
11 JUN i
5 PR i
1 PR
PR 11 PR
4 CG
CG 17
5 CG
8 PR 8
I
2
2
2
2
2
i
I
I .
Oligochaeta |o CG 5 ICG
Oligophlebodes
Oligoplectrum
Oliveridia
Onocosmoecus
Onocusmoecus unicolor
Ophiogomphus
Opistocystidae
Optioservus
Optioservus castanipennis
Optioservus divergens
Optioservus quadrimaculatus
Optioservus seriatus
Orconectes
Ordobrevia
Ordobrevia nubrifera
Oreodytes
Oreodytes congruus
Oreogeton
Oreothalia
Oribatei
!i jsc
!1 CG
FL
ID
ID
IlD
!FL
ID
eg !FL
ID
p IFL
IFL
IFL
iFL
IFL
IFL
FL
FL
IFL
i6 CG
h ISH I
2 'SH i
|1 ;PR
0
0 4 SC
MD
FL
ID
ID
IlD
ID
IlD
ilD
4 |sc
FL
FL
l4 SC ilD
i4 SC
4 SC
4 SC
IlD
JID
4 UN
'4 CG |
:5 PR
5 'PR
5 PA
6 !PR
0
Ormosia 3 CG
Orohermes i 0 PR
Oromosia 1
ID
MD
IlD
I
3
Oroperla 2 PR i
Orthemis 0 'PR
Orthemis ferruginea 0 iPR !
Orthocladiinae lo CG
Orthocladiinae gen. f epler 0 :
ID
ID
ID
ID
ID
FL
IlD
eg
ID
MACS
ID
IFL
FL
FL
FL
Orthocladiini 6 CG [ID
Rapid Bioassessment Protocols for Use in Streams and Rivers
B-25
-------
DRAFT REVISION-^TuIy 28,1997
Scirwme Flaindx iFLFFGrp
Orthocladinae
Orthocladius 0 ICG
Orthocladius annectens lo !CG
Orthocladius complex
Orthocladius eudactylocladius
Orthocladius euorthocladius
Idaho TV '
6
i |6
j
I
Orthocladius lignicola 0 JCG
Orthocladius pogonocladius
Orthotrichia
Ostracoda
Ostrocera
Ostrocerca
Oulimnius
Oulimnius latiusculus
Oxus
Oxycera
Oxyethira
Oxythira
Pachydiplax longipennis
Pacifastacus
Pacifastacus cambilii
Pacifastacus connectens
Pacifastacus leniusculus
Pagastia
Pagastiella
Pagastiella orophila
Palaemonetes
6
6
6
0 |PI \6
0
0
0
0
1
0
0
o
0
Palaemonetes kadiakensis 1 0
Palaemonetes paludosus
Palaemonidae
Palmacorixa
Palmacorixia
Palpomyia
Palpomyia tibialis
Palpomyia/bezzia grp.
Paracapnia
Parachaetocladius
Parachironomus
Parachironomus carinatus
Parachironomus directus
Parachironomus hirtalatus
Parachironomus monochromus
Parachironomus pectinatellae
Parachironomus schneideri
Parachironomus sp. a epler
Parachironomus sublettei
Paracladopelma
Paracladopelma nereis
Paracladopelma undine
2
0
0
0
PR
OM
PR
CG
CG
8
11
3
6
Idaho FFG I MACS TV MACS FFG ISourcelist
I iMD
CG le
CG
CG
CG
eg iFL
IFL
ID
IID
IID
FL
CG I !ID
SC FL
CG
MH
MH
3
OM |
6 ISH
6
6
1
SH
SH
CG
IID
IMD
iMD
!FL
!FL
IFL
FL
P
FL
ID
FL
IID
|ID
ilD
I
! I
6
o !
! h
o !CG
0
o
0
0
o
0
0
0
0
0
0
0
Paracymus 1 0
Paradixa
Paragnetina
Paragnetina fumosa
CG
CG
CG
OM
6
PR
SH
CG
5
2
I
I
5 PR
I 2 CG
1 !PR
1 iPR
P
ID
ID
FL
FL
FL
FL
FL
FL
MACS
MD
FL
IFL
iFL
IID
eg
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
IFL
I
I
FL
FL
ID
IFL
FL
B-26
Appendix B: Tolerance and Functional Feeding Group Assignments for Benthos
-------
DRAFT REVISION—July 28,1997
Scirrame 1 Flaindx
Paragnetina kansensis 1
Parakiefferiella !0
Parakiefferiella sp. a epler 0
Parakiefferiella sp. b epler iO
Parakiefferiella sp. d epler
0
Paralauterbomiella 0
Paralauterbomiella nigrohalterale |0
Paraleptophlebia
Paraleptophlebia bicomuta
Paraleptophlebia debilis (Walker)
Paraleptophlebia heteronea
Paraleptophlebia memorialis
(Eaton)
Paraleptophlebia volitans
Paraleuctra
Paraleuctra occidentalis
Paramerina
Parametriocnemus
Parametriocnemus lundbecki
Paranais litoralis
Paraperla
Paraperla frontalis (Banks)
Paraphaenocladius
Paraponyx
Parapoynx
Parapsyche
Parapsyche almota
Parapsyche elsis Milne
Parasitengona
Paratanytarsus
^aratanytarsus sp. a epler
Paratanytarsus sp. b epler
Paratendipes
Paratendipes albimanus
Paratendipes subaequalis
Paratrichocladius
Daravelia
Paravelia brachialis
Parorthocladius
•'aychomyiidae
Pedicia
-"edomoecus sierra
0
FLFFGrp i Idaho TV
PR
Idaho FFG | MACS TV MACS FFG ISourcelist
i
i
CG |6 ICG i4
o/~ I '
CG i
CG
CG
OM H
4
111
0 )OM
0
0
2
4
0
0
PR |e
CG
o ICG
0
0
0
0
0
0
0
0
0
0
0
Pelecorhynchidae !
Pelecypoda ! 0
Pelocoris 0
Pelocoris femoratus 1 0
Pelonomus
Pelonomus obscurus
Peltodytes
0
0
0
Peltodytes duodecimpuntatus 1 0
Peltodytes floridensis |0
'eltodytes lengi ! 0
Peltodytes muticus ! 0
CG
5
1
0
\5
SH
SH
FG
FG
FG
CG
CG
CG
PR
PR
CF
CG
CG
CG
CG
CG
SH
SH
PR
CG
PR
PR
CG
!
1 PR
8
1
4
FL
leg FL
;FL
:FL
FL
eg iFL
iFL
eg IFL
ID
ID
ilD
ID
FL
llD
IlD
P
5 |cg
4
5
3 PR
1 I PR I
11
6
8
UN
CG
CG
6 CG
6 |CG
6 ICG
6 PR
0 ISC
3 JPR
8
PR
PR
j
CF
6
eg
sh
FL
FL
FL
FL
ID
ID
ID
FL
FL
ID
IlD
ID
|ID
eg iFL
FL
FL
8 leg JFL
I FL
17
11 [SH
•
5
IFL
ilD
iFL
FL
ID
IlD
ID
IlD
IlD
IFL
P
sh
FL
FL
FL
FL
FL
FL
FL
FL
FL
Rapid Bioassessment Protocols for Use in Streams and Rivers
B-27
-------
DRAFT REVISION—July 28,1997
Sciname Flaindx ipLFFGrp
Peltodytes oppositus 1 0
Peltodytes sexmaculatus |0
Peltoperla
Peltoperlidae
Pentacora 1
Pentaneura
Pentaneura inconspicua
Pentaneura inculta
Pentaneurini
Percymoorensis
Pericoma
Perithemis
Perithemis seminola
Perithemis seminole
Perithemis tenera
Periesta
Perlesta placida
Perlidae
Perlinella
Perlinella drymo
Perlinella ephyre
Perlinodes
Perlinodes aurea
Periodidae
Periomyia
Petrophila
Phaenopsectra
Phaenopsectra obediens
Phaenopsectra obediens grp.
Phaenopsectra punctipes grp.
Phasganophora
Phasganophora capitata
Philobdella
Philopotamidae
Philorus
Phrygaenidae
Phychodidae
Phylocentropus
Physa
Physella
Physella cubensis
Physella hendersoni
Physella heterostropha
Physidae
Phytobius
Pictetiella
Pictetiella expansa
Piersigiidae
Pilaria
Piona
Piscicola
Piscicola salmositica
0
1
1
0
0
0
0
1
1
1
1
1
PR
PR
PR
PR
PR
PR
PR
OM
OM
PR
PR
PR
1 I PR
i
i
0
0
OM
OM
o IOM
0
1
OM
PR
Idaho TV I Idaho FFG | MACS TV I MACS FFG Sourcelist
! ! iFL
! ! FL
2 !SH
11 iPR
6 PR
6 PR
10 !PR
4 CG
iMD
I ID
i llD
FL
!FL
FL
ID
ilD
4 icg IlD
4
• !
i
i
1
1
11
2
2
0
5
7
PR
PR
PR
PR
PR
SH
SC
SC
1 | PR
0
0
0
0
0
0
0
0
0
0
Pisidiidae • 0
CF
SC
SC
SC
SC
SC
SC
PR
PR
3
1
10
8
8
8
1
P FL
IFL
iFL
FL
FL
P
FL
FL
FL
IFL
IFL
!ID
IlD
2 |p | ID
7
CF i3
SC i
U
ID
ID
SC
FL
FL
FL
FL
FL
FL
FL
fc I ID
IlD
sh I MACS
CG |
5
SC
SC
SC
11 ISH
2 PR
2 PR
8 iPR
3 UN
10 IPR
r !PR
CF
s ICG
8
fc
SC
I
7
ID
FL
FL
FL
FL
FL
FL
FL
IlD
ID
ID
ID
P IFL
IP IFL
ID
ID
FL
B-28
Appendix B: Tolerance and Functional Feeding Group Assignments for Benthos
-------
DRAFT REVISION—July 28,1997
Sciname
Pisidium
Pisidium casertanum
Pisidium compressum
Pisidium dubium
Pisidium idahoense
Pisidium punctiferum
Placobdella
Placobdella papillifera
Placobdella parasitica
Placobdella translucens
Planariidae
Planorbella
Planorbella duryi
Planorbella scalaris
Planorbella trivolvis intertexta
Planorbidae
Planoribae
Plathemis lydia
Platycentropus
Platypeza
Plecoptera
Pleidae
Plumatella repens
Plumiperla
Podmosta
Podura
Podura aquatica
Polyceles
Polycelis coronata (Girard)
Polycentropidae
Polycentropodidae
Polycentropus
Polydora ligni
Polymitarcidae
Polypedilum
Polypedilum aviceps
-'olypedilum convictum
Polypedilum convictum grp.
Polypedilum fallax
Polypedilum halterale
Polypedilum halteraie grp.
Polypedilum illinoense
Polypedilum illinoense grp.
Polypedilum laetum
Polypedilum pentapedilum
3olypedilum scalaenum
Polypedilum scalaenum grp.
Polypedilum sp. a epler
Polypedilum sp. c epler
Polypedilum trigonum
Polypedilum tritum
Polyplectropus
iFlaindx
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
!
0
0
0
0
0
1
2
ij
2
2
0
0
0
0
0
0
0
0
JFLFFGrp
CF
CF
CF
CF
PA
PA
PA
PA
SC
SC
SC
SC
SC
PR
PR
FG
;
I
I
i
Idaho TV
8
8
8
18
6
1
7
11
11
2
11
6
1
6
6
2
6
6
HdahoFFG
ICF
!sc
CF
CF
PR
OM
SC
PR
PR J
SH
CG
CG
OM
CF
PR
CG
SH
SH
i
I MACS TV
8
6
5
6
(MACS FFG
Ifc
I
SC
-
fc
sh
Sourcelist
FL
FL
ID
FL
ID
FL
FL
FL
FL
FL
ID
FL
FL
FL
FL
FL
MACS
FL
MD
MACS
FL
FL
FL
ID
ID
ID
FL
ID
ID
ID
FL
FL
FL
ID
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
ID
FL
FL
FL
FL
FL
FL
FL
Rapid Bioassessment Protocols for Use in Streams and Rivers
B-29
-------
DRAFT REVISION^July 28,1997
Sciname I Flaindx
Pomacea paludosa 0
Porifera ' 10
Potamopyrgus
Potamyia
0
FL FFGrp
sc
CF
CF
Potamyia flava 0 |CF
Potimirim potimirim
Potomopyrgus antipodarium
Potthastia
Potthastia gaedii
Potthastia longimana
Prionocera
Prionocyphon
Prionoxystus
Pristina
Pristina aequiseta
Pristina breviseta
Pristina foreli
Pristina leidyi
Pristina longisoma
Pristina osbomi
Pristina plumaseta
Pristina sima
Pristina synclites
Pristinella
Pristinella jenkinae
Pristinella longisoma •
Pristinella osborni
Probezzia
Probopyris floridensis
Probopyus pandalicola
Procambarus
Procambarus alleni
Procambarus fallax
Procambarus pygmaeus
Procambarus spiculifer
Procladius
Procladius bellus
Procladius bellus var. 1 epler
Procladius bellus var. 2 epler
Procloeon
Procloeon rubropictum
Procloeon sp. a pescador
Procloeon viridocularis
Prodiamesa
Progomphus
Progomphus obscurus
Promenetus
Promentus
Promoresia
0
0
0
0
0
0
0
0
0
0
Idaho TV | Idaho FFG
11
10
CF
SC
I
OM |
OM
CG
CG
CG
CG
CG
CG
o ICG
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
2
CG
CG
CG
CG
CG
CG
CG
CG
CG
PR
PR
2 IPR
2
0
0
0
0
PR
6
2
3
5
CG
CG
UN
UN
I
9
OM
OM
OM
OM
1 |PR
1 |PR
3
16
o IOM
Promoresia elegans |0 |OM
Prosimulium I
Prostoia
3
2
Prostoia besametsa | 2
MACS TV I MACS FFG Isourcelist
I iFL
iFL
|ID
IFL
I
6 ip
PR
CG
CG
CF
SH
SH
g
2
p
sc
FL
FL
ID
FL
ID
FL
ID
FL
ID
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
MACS
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
ID
FL
FL
MD
ID
FL
FL
ID
ID
llD
B-30
Appendix B: Tolerance and Functional Feeding Group Assignments for Benthos
-------
DRAFT REVISION—July 28,1997
Sciname Flaindx JFLFFGrp Idaho TV
Prostoma rubrum |0
Protoplasa
Protoplasa fitchii Osten Sacken
Protoptila
Protoptila coloma
Protoptila tenebrosa
Protzia californensis
Psammory elides
Psammoryctides convolutus
Psectrocladius
Psectrocladius allopsectroclad
Psectrocladius elatus
Psectrocladius limbatellus
Psectrocladius sordidellus
Psectrotanypus
5
Idaho FFG | MACS TV I MACS FFG
CG
Sourcelist
FL
ilD
1 UN
1 SC
i h sc
h Isc
0
0
1
1
Psectrotanypus dyari 1 0
Psephenidae I
Psephenus
Psephenus falli
Pseudochironomus
Pseudocloeon
Pseudocloeon bimaculatum
Pseudocloeon parvulum
Pseudocloeon punctiventris
Pseudodiamesa
0
0
0
0
0
Pseudogoera |
Pseudolimnophila j
Pseudorthocladius
Pseudosmittia
Pseudosuccinea
0
0
0
Pseudosuccinea columella lo
Psilometriocnemus
Psilotreta
Psilotretra
Psychoda j 0
Is IPR
CG
OM
OM
CG
OM
OM
OM
OM
8
8
8
8
10
4
4
4
5
4
6
1
CG 0
CG
SC
SC
CG
Psychoda altemata 0 CG
CG
!
i
8 |sh
CG I
CG
CG
PR
10
SC
SC
SC
CG
SC
CG
OM
|2
CG
I
" I
0
0
ID
ID
ID
ID
ID
FL
FL
FL
ID
FL
ID
ID
P IlD
FL
ID
ID
ID
FL
FL
FL
FL
FL
IlD
P
eg
ID
MACS
FL
FL
IFL
FL
MD
sc (MACS.
I |MD
FL
I
Psychodidae 0 icG 10
Psychoglypha
Psychoglypha bella
Psychoglypha subborealis
:>sychomyia
3sychomyia lumina
Pteronarcella
Pteronarcella badia
Pteronarcella regularis
'teronarcyidae
I
1
J2
|2
0
2
\2
0
0
0
\ lo
Pteronarcys 1 |SH 0
Pteronarcys califomica
Pteronarcys dorsata 1
CG I
CG
CG
CG
SC
SC
SH
SH
SH
SH
SH
JO iSH
SH |
Pteronarcys dorsata (Say) , i 0
Pteronarcys princeps
Ptiliidae
0
Ill
SH I
SH
FL
IFL
IlD
ID
ID
FL
ID
ID
ID
ID
ID
FL
ID
FL
ID
ID
UN ! ||D
Rapid Bioassessment Protocols for Use in Streams and Rivers
B-31
-------
DRAFT REVISION-^July 28,1997
Sciname
Ptilodactyla
Ptilostomis
Ptychoptera
Ptychopteridae
Pycnopsyche
Pycnopsyche guttifer
Pycnopsyche scabripennis
Pyralidae
Pyrgophorus platyrachis
Pyrgulopsis idahoensis
Pyroderces
Pyrogophorus platyrachis
Pyrrhalta
Quistradrilus multisetosus
Radix
Ramphocorixa
Ranatra
Ranatra australis
Ranatra buenoi
Ranatra drakei
Ranatra fusca
Ranatra kirkaldyi
Ranatra nigra
Rhabdomastix
Rhagorelia distincta
Rhagovelia .
Rhagovelia choreutes
Rhagovelia obesa
Rhamphomyia
Rhapinema dacryon
Rheocricotopus
Rheocricotopus robacki
Rheocricotopus tuberculatus
Rheopelopia
Rheosmittia
Rheotanytarsus
Rheotanytarsus distinctissimus
Rheotanytarsus distinctissimus
grp-
Rheotanytarsus exiguus
Rheotanytarsus exiguus grp.
Rheumatobates
Rheumatobates palosi
Rheumatobates tenuipes
Rhithrogena
Rhithrogena hageni Eaton
Rhithrogena morrisoni/hageni
Rhithrogena robusta Dodds
Rhithropanopeus harrisii
Rhizelmis
Rhyacodrilus sodalis
Rhyacophila
Rhyacophila acropedes Banks
Flaindx
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
1
1
0
0
0
0
0
FL FFGrp
SH
SH
SH
SH
SC
sc
PR
PR
PR
PR
PR
PR
PR
PR
PR
PR
SC
PR
CG
CF
CF
CF
CF
CF
Idaho TV
7
7
5
8
5
8
11
6
6
6
0
0
0
0
1
10
0
1
Idaho FFG
CG
CG
SH
SC
UN
PR
PR
PR
CG
CF
SC
CG
SC
CG
SC
CG
PR
PR
MACS TV
5
5
4
6
6
6
MACS FFG
sh
sh
sh
P
eg
fc
P
Sourcelist
MACS
FL
ID
ID
FL
FL
FL
FL
FL
ID
ID
FL
FL
FL
MD
FL
FL
FL
FL
FL
FL
FL
FL
ID
ID
FL
FL
FL
ID
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
ID
ID
ID
ID
FL
ID
ID
FL
ID
B-32
Appendix B: Tolerance and Functional Feeding Group Assignments for Benthos
-------
DRAFT REVISION—July 28,1997
Sciname
Rhyacophila acropedes/vao
Rhyacophila alberta Banks
Rhyacophila angelita Banks
Rhyacophila arnaudi Denning
Rhyacophila betteni Ling
Rhyacophila bifila Banks
Rhyacophila blarina Ross
Rhyacophila brunnea Banks
Rhyacophila Carolina
Rhyacophila coloradensis Banks
Rhyacophila grandis
Rhyacophila hyalinata Banks
Rhyacophila iranda
Rhyacophila narvae Navas
Rhyacophila nevadensis Banks
Rhyacophila oreia group
Rhyacophila pellisa
Rhyacophila rayneri
Rhyacophila rotunda Banks
Rhyacophila sibirica
Rhyacophila torva
Rhyacophila trissemani
Rhyacophila tucula Ross
Rhyacophila vaccua Milne
Rhyacophila vaefes group
Rhyacophila vaeter group
Rhyacophila vagrita Milne
Rhyacophila valuma
Rhyacophila valuma/pellisa
Rhyacophila velora Denning
Rhyacophila vepulsa Milne
Rhyacophila verrula Milne
Rhyacophila visor Milne
Rhyacophila vofixa Milne
Rhyacophilidae
^hynchocoela
Rickera
Rickera sorpta (Needham &
Claassen)
Robackia
Robackia claviger
Robackia demeijerei
SIALIDAE
Saetheria
Saetheria hirta
Saldidae
Saldula
Salpingidae
Sciaridae
Sciomyzidae i
Scirtes
Scirtidae 1
Flaindx
0
0
0
0
0
0 I
0
0
0
0
0
0
FLFFGrp 1 Idaho TV
to
10
0
io
0
0
0
io
j
io
1
0
0
0
1
0
0
0
0
Io
1
11
i°
11
11
Io
h
1
1
o
Io
h
h
IO
111
2
CG
CG
CG I
i
CG
CG
ho
no
11
111
11
Idaho FFG
PR
[PR
(PR
PR
PR
PR
PR
PR
PR
PR
PR
PR
PR
PR
PR
PR
pr
PR
PR
PR
UN
PR
PR
PR
PR
PR
PR
PR
UN
MH
PR
PR
PR
PR
PR
PR
PR
UN
PR
I MACS TV
!
!
MACS FFG
i
I
I
Sourcelist
ID
ID
ID
ID
ID
ID
ID
ID
FL
ID
ID
ID
ID
ID
ID
ID
ID
ID
ID
ID
FL
ID
ID
ID
ID
ID
ID
ID
ID
ID
ID
ID
ID
ID
ID
FL
ID
ID
FL
FL
FL
MD
FL
FL
FL
ID
ID
ID
FL
FL
FL
Rapid Bioassessment Protocols for Use in Streams and Rivers
B-33
-------
DRAFT REVISION—July 28,1997
Sciname
Sepedon
Sericostomatidae
Flaindx FL FFGrp
Serratella
Serratella deficiens
Serratella teresa Traver
Serratella tibialis McDunnough
Serromyia
Setodes
Setvena
Setvena bradleyi (Smith)
Shipsa
Sialis
Sialis americana
Sialis iola
Sialis mohri
Sigara
Sigara alternata
Sigara washingtonensis
Silvius
Simuliidae
Simulium
Simulium bivattatum
Simulium slossonae
Simulium vittatum
Siphlonuridae
Siphlonurus
Siphloplectron
0
0
0
0
0
0
1
1
1
Sisyra JO
Skwala
Slavina
Slavina appendiculata
Smittia
Soliperla
Somatochlora
Somatochlora linearis
Somatogyrus walkerianus
Soyedina
Sparganophilidae
Specaria josinae
Sperchon
Sperchon pseudoplumifer
Sperchonidae
Sperchonopsis
Sperchopsis
Sperchopsis tessellatus
0
0
0
0
0
0
0
0
0
Idaho TV
11
Idaho FFG | MACS TV! MACS FFG iSourcelist
PR
[ID
11 JSH llD
2 CG |2 sc | ID
FL
11 ICG
2 CG
OM !
PR
PR
PR
PR
CF
CF
CF
PI
CG
CG
CG
PR
PR
SC
CG
PR
0 PR
0
0
Sphaeriidae 10
Sphaeriidae(mollusca)
Sphaerium
Sphaerium patella
Sphaerium striatinum
Sphaerium striatum
Sphaeroma
0
0
0
0
Sphaeroma destructor 10
OM
OM
CF
CF
CF
CF
CG
CG
2
2
4
11
11
8
IID
6
PR
PR
PR
UN
UN
CG
11 | PR
6
6
6
6
CF
CF
CF
CF
7 JCG
7
2
2
9
CG
PR
SH
PR
I
2 SH
8
8
5
8
PR
PR
PR
IID
P
i
4 P
I
9
P
MACS
FL
ID
ID
MD
FL
FL '
FL
FL
ID
ID
ID
\ IID
6
fc
FL
FL
ID
FL
ID
ID
ID
2 p MACS
IFL
1 IP
ID
FL
FL
FL
ID
FL
FL
FL
IID
5 eg
I
CF Is jfc
8 CG
8
CF
8 ICF
I
FL
FL
FL
ID
ID
FL
FL
FL
FL
FL
FL
ID
FL
ID
FL
FL
B-34
Appendix B: Tolerance and Functional Feeding Group Assignments for Benthos
-------
DRAFT REVISION—July 28,1997
Sciname iFlaindx
Sphaeroma terebrans |0
FLFFGrp i Idaho TV Idaho FFG I MACS TV I MACS FFG Sourcelist
CG ! ! FL
Sphaeromicis j
Spilochlamys lo
Spilochlamys conica
Spionidae
Spirosperma
Spirosperma ferox
Spongilla
Spongilla aspinosa
Spongillidae
Stactobiella
Stagnicola
Stagnicola/Fossaria
Staphylinidae
Stegopterna
Stelechomyia
Stelechomyia perpulchra
Stempellina
Stempellinella
Stenacron
Stenacron floridense
Stenacron interpunctatum
Stenelmis
Stenelmis antennalis
Stenelmis convexula
Stenelmis crenata
Stenelmis fuscata
Stenelmis humerosa
Stenelmis hungerfordi
Stenelmis sinuata
Stenelmis vittipennis
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
sc
sc
! MD
iFL
FG
CG i
CG i
CF
CF
CF
CG
CG
CG
FG
OM
OM
OM
OM
OM
0 OM
0
0
0
OM
FL
I
I
I
2 ISH
10
11
8
2
4
SC
sc
PR
7
7
I
CG
CG
7 |SC
I
I
OM
OM
0 iOM
0 iOM
0
Stenochironomus 1 1
Stenonema
Stenonema exiguum
Stenonema integrum
Stenonema mexicanum integrum
Stenonema smithae
Stenus
Stephensoniana trivandrana
Stictochironomus
0
1
OM
i
OM !
OM
OM
2 OM
2
1
0
0
0
Stictochironomus devinctus 1 1
Stilobezzia 0
Stilocladius 0
Stratiomyidae
Stratiomys
Strophopteryx
Stygonectes
Stylaria lacustris
Stylogomphus
Stylurus
Stylurus ivae
0
0
OM
2 iSC
!
I
4
4
5
5
4
OM
CG !
OM
OM
CG i
CG 8 |CG
FG
iFL
FL
FL
FL
FL
iFL
tlD
sc I ID
ID '
IFL
iMD
eg IFL
IFL
IFL
eg IFL
sc
FL
!FL
sc
FL
FL
FL
FL
!FL
FL
FL
FL
sh
sc
'
o ICG j
2 PR
2 PR
Subletta ! 6 CF
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
IFL
FL
FL
FL
FL
MD
MD
FL
IMD
FL
IFL
IID
Rapid Bioassessment Protocols for Use in Streams and Rivers
B-35
-------
DRAFT REVISION—July 28,1997
Sciname
Suphis inflatus
Flaindx
0
Suphisellus JO
Suphisellus floridanus 10
Suphisellus insularis
Suphisellus puncticollis
Suwallia
Suwallia/Neaviperla
Sweltsa
Symbiocladius
Sympetrum
Sympetrum ambiguum
Symposiocladius
Sympotthastia
0
0
0
Synclita obliteralis 0
Synorthocladius
Syrphidae
TORTRICIDAE
Tabanidae
Tabanus
Tachopteryx
Taenionema
Taenionema pallidum
Taeniopterygidae
Taeniopteryx
Taeniopteryx burksi
Taeniopteryx lita
Talitridae
Tallaperla cornelia
Talloperla
Tanaidacea
Tanais cavolinii (part)
Tanyderidae
Tanypodinae
Tanypus
Tanypus carinatus
0
0
0
1
1
1
0
1
0
0
FL FFGrp
OM
OM
OM
OM
PR
SH
CG
PR
PR
Idaho TV | Idaho FFG
|
1
MACS TV ! MACS FFG ISourcelist
i IFL
! !FL
1
1
1
1
6
2
2
10
8
5
10
2
12
PR
PR
PR
PA
CG
CG
CG
PR
PR
PR
SC
SC
4
5
\2 SH
OM i J2
OM !
OM i
8
i
!
••
FG !
in
CG
: i
'
0 OM ! 10
0 OM i ! j
Tanypus neopunctipennis |o iOM !
Tanypus punctipennis |o !OM ;
Tanypus stellatus 0 JOM
Tanytarsini
Tanytarsus
Tanytarsus glabrescens
Tanytarsus guerlus
Tanytarsus sp. a epler
Tanytarsus sp. b epler
Tanytarsus sp. c epler
'5 'CF
0 FG '6 iCF
o IFG
o IFG
0 !FG :
6
0 iFG 1 !
0 'FG i i
Tanytarsus sp. d epler 0 ; FG
Tanytarsus sp. e epler
Tanytarsus sp. f epler
0 iFG
0 iFG
i
Tanytarsus sp. g epler !0 ;FG
Tanytarsus sp. j epler |o FG I
Tanytarsus sp. k epler • iO !FG '• \
Tanytarsus sp. 1 epler !0 FG i
Tanytarsus sp. m epler |0 |FG i j
FL
iFL
IFL
ilD
ID
ilD
ilD
p iMACS
IFL
!MD
ID
FL
P
sh
P
fc
FL
ID
MD
FL
FL
ID
ID
ID
ID
FL
FL
FL
FL
FL
MD
FL
FL
ID
MACS
FL
FL
FL
FL
FL
ID
FL
FL
!FL
FL
FL
FL
FL
FL
FL
!FL
IFL.
;FL
!FL
FL
B-36
Appendix B: Tolerance and Functional Feeding Group Assignments for Benthos
-------
DRAFT REVISION—July 28,1997
Sciname
Tanytarsus sp. o epler
Tanytarsus sp. p epler
Tanytarsus sp. r epler
Tanytarsus sp. s epler
Tanytarsus sp. t epler
Tanytarsus sp. u epler
Tanytarsus sp. v epler
Tanytarsus sp. w epler
Taphromysis bowmani
Telebasis byersi
Telmatoscopus albipunctatus
Tenagobia
Tetragoneuria
Tetragoneuria cynosura
Thaumalea
Thaumalea einora
Thaumalea fusca
Thaumaleidae
Thermonectus basillaris
Theromyzon
Thiaridae
Thienemanniella
Thienemanniella fusca
Thienemanniella similis
Thienemanniella sp. a epler
Thienemanniella xena
Thienemannimyia
Thienemannimyia grp.
Thienemanniola
Thinopinus
Timpanoga hecuba
Tinodes
Tipula
Tipulidae
Tipulidae ormosia
Tipulide
Torrenticola
Toxorhynchites
Tramea Carolina
Trepobates
Trepobates pictus
Triaenodes
Flaindx
0
0
0
FLFFGrp (Idaho TV
FG
FG
FG
o |FG
0 |FG
0
0
0
0
FG
FG
FG
CF
Idaho FFG MACS TV I MACS FFG iSourcelist
o IPR I
o
0
0
0
PR
PR
PR
o Isc
1 ICG
8
11
11
11
11
10
1 JCG
1 iCG
1
FL
FL
FL
! FL
I
UN !
OM
•
OM
OM
OM
PR
I
CG !
1 CG i
|6 IPR
0 iPR
!6 CG
0
6
7 iCG
2 sc
0 ''-4 SH
4
IFL
JFL
IFL
FL
FL
IFL
IFL
ilD
FL
FL
ilD
IlD
ID
ID
IFL
eg
ID
FL
FL
IFL
FL
FL
IFL
ID
IFL
ilD
FL
IIP
IlD
sh
0 IOM 3 SH '
4 iOM
0 !PR
0 PR
0 PR
0 PR 10 PR
0 -PR
0 :SH 6 MH
Triaenodes abus |0 SH
Triaenodes flavescens
Triaenodes florida
Triaenodes Ignitus
Triaenodes ochraceus
Triaenodes pema
Triaenodes tardus
0 SH
0 :SH
0 fSH
0 iSH
0 SH
3
6
sh
FL
FL
ID
MACS
IFL
IFL
IFL
sh
I
0 SH
Tribelos id 'CG ! J5
Tribelosatrum |0 CG
Tribelos fuscicornis JO CG ; i
eg
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL.
FL
IFL
Rapid Bioassessment Protocols for Use in Streams and Rivers
B-37
-------
DRAFT REVISION-^Tuly 28,1997
Sciname
Tribelos jucundus
Trichocorixa
Trichocorixa calva
Trichocorixa sexcincta
Flaindx |FLFFGrp
0
0
0
0
Trichoptera |fl
Tricladida
Tricorythidae
Tricorythodes
Tricorythodes albilineatus
Tricorythodes minutus
Trissopelopia
Triznaka
Trochopus (heteroptera)
Trombidiforrnes
Tropistemus
Tropistemus blatchleyi
Tropistemus lateralis
Tropistemus lateralis nimbatus
Tropistemus striolatus
Tubifex
Tubifex tubifex
0
CG
Idaho TV | Idaho FFG i MACS TV I MACS FFG Sourcelist
OM 5
o ICG
2
0
0
0
0
0
0
0
0
Tubificidae Jo
Turbellaria
Tvetenia
Tvetenia bavarica
Tvetenia discoloripes
0
0 .
Tvetenia discoloripes grp. lo
Twinnia
Tyloderma capitale
Uenoidae
Ulomorpha
Unid.Heptageniidae
Unionicola
Unionidae
Unniella
Unniella multivirga
Urnatella gracilis
Uromunna reynoldsi
Uvarus
Valvata
Valvatidae
Vejdovskyella
0
0
0
0
0
0
0
CG
CG
CG
PR
CG
CG
PR
4
4
5
4
11
1
5
10
IFL
p iFL
IFL
! FL
i [
CG
CG
CG 4 eg
CG
PR
PR
PR
FL
ID
FL
FL
IFL
I
I
10 |p
CG
10
4
5
5
5
6
0
CF |s
CG
CG
0 CG
0
CG
PR
CG
10
5
CG I
CG
j
CF
SC i
CF
4
|4 .
|
\8 SC
|11 SC
0 !CG
Vejdovskyella comata |o
Veliidae ' lo
Visoka
Visoka cataractae
Viviparidae
Viviparus
Viviparus georgianus
Vorticifex
Vorticifex effusa
Wandesia
CG
o Isc
0 SC
11
1
6
SC
SH j
SC
j
0 SC
8 JSC
6 iSC
11 |UN
ID
ID
ID
FL
FL
FL
FL
FL
r IFL
IFL
eg
eg
ID
FL
FL
FL
FL
ID
IID
IFL
SC
eg
ID
FL
ID
FL
MACS
FL
FL
FL
FL
FL
FL
FL
ID
ID
FL
FL
FL
ID
ID
FL
FL
iFL
IID
! HD
IID
B-38
Appendix B: Tolerance and Functional Feeding Group Assignments for Benthos
-------
DRAFT REVISION—July 28,1997
Scindme JFIaindx IFLFFGrp | Idaho TV Idaho FFG i MACS TV | MACS FFG jSourcelist
Wiedemannia i 6 PR
Wormaldia
Wormalidia gabriella
Xenochironomus
Xenochironomus xenolabis
Xestochironomus
i
0
0
0
0
Xestochironomus subletti |o
Xylotopus 0
Xylotopus par 1 1
Yoraperla
Yoraperla brevis
Yoraperla mariana
Yugus
Zaitzevia
Zaitzevia milleri
Zaitzevia parvula j
Zalutschia !0
Zapada
Zapada cinctipes
Zapada columbiana
Zapada frigida
Zapada oregonensis
Zavrelia
Zavrelielia
0
Zavreliella marmorata |o
Zavrelimyia |o
Zavrelimyia sinu&sa
Zoniagrion
Zygoptera
acutus
aeneolus
americanum
anceps
annulipes
anomala
antennalis
antennuatum
argus
atrum
attenuatus
aviceps
azteca
barbipes
bavarica
bergi
betteni
bicinctus
bicolor
jrevistyus
carinatus
carolinensis
casertanum
0
0
3 CF
3 ISC
llD
FL
ilD
PR j PI-
PR . j i FL
OM
OM
I
i
12
2
SH
SH
2 SH
2
4
!4
k
CG
2
2
2
2
2
8
PR [8
PR
PR
9
PR
CG
CG
CG
IFL
7
SH
SH
SH
SH
SH
CG
PR
PR
i
8
9
4
3
6
2
5
8
2
!5
6
|6
8
10
5
6
6
7
4
1
10
10
L FL
!FL
FL
IP
IlD
ilD
IlD
IlD
IlD
IlD
sh IFL
ID
ID
ID
IlD
IlD
ID
JFL
P
sh
P
sh
sc
P
FL
FL
FL
ID
FL
MACS
MACS
MACS
MACS
MACS
MACS
sc I MACS
P
sh
eg
sh
eg
fc
eg
sh
fc
MACS
MACS
MACS
MACS
MACS
MACS
MACS
MACS
MACS
MACS
eg I MACS
sh I MACS
p JMACS
p I MACS
eg [MACS
8 fc
MACS
Rapid Bioassessment Protocols for Use in Streams and Rivers
B-39
-------
DRAFT REVISION-^Iuly 28,1997
Sciname |plaindx iFLFFGrp
cestum
chamberlain!
circumstriatus
columella
communis
compressum
convictum
comutus
crassicomus
crenata
cultriger
curvisetosus
cylindrica
decisum
decora ta
deficiens
digitata
dilatatus
diminuta
discolor
discoloripes
diversa
duodecimpunctatus
fallax
fallax
fasciatus
fasciatus
1
fastiditus
ferox
flavifrons
fluminea
fluminea
forbesi
fossularis
frondalis
frontalis
fuscicome
fuscus
georgia
glabratus
graecense
grana
halterale
heteroclita
hoffmeisteri
holopracinus
humerosus
illinoense
inconspicua
inopterus
intercalaris
irrorata
Janata
Idaho TV Idaho FFG I MACS TV | MACS FFG
4 eg
Sourcelist
MACS
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6 !sc
6 eg
8 eg
MACS
MACS
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s Ifc (MACS
6 jsh iMACS
5 p IMACS
3 eg MACS
5
17
10
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8
sc IMACS
eg MACS
eg IMACS
fc IMACS
sc
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6 jsh iMACS
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sc MACS
10 eg
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eg IMACS
sc ' MACS
6 sh IMACS
8 Ip
6
|6
eg
MACS
MACS
eg (MACS
5 |p IMACS
6 p IMACS
B-40
Appendix B: Tolerance and Functional Feeding Group Assignments for Benthos
-------
DRAFT REVISION—July 28,1997
Sciname
janta
jenningsi
johnsoni
jonesi
jucundum
kadiakensis
kirkaldyi
lacustris
lacustris
latiusculus
lengi
lillejborgi
limbata
limnobius
limosa
limosus
lividus
longimana
longipennis
lycorias
macafferti
macdunnoughi
maculata
mallochi
marginatus
markeli
mela
modesta
modestum
modestus
moestus
monochromus
multilineata
multisetosus
neopilosella
neopunctipennis
nervosus
nigrior
numerosus
Daludosus
papillifera
par
parvus
pauciseta
pedellus-gr.
peleensis
pentacantha
persimilis
phalera
sictus
pigueti
pilosella
pluriseta ;
Flaindx
FL FFGrp
Idaho TV
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1
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fc
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p
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MACS
MACS
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MACS
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MACS
MACS
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MACS
MACS
MACS
MACS
MACS
MACS
MACS
MACS
MACS
MACS
MACS
MACS
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MACS
MACS
MACS
MACS
MACS
MACS
MACS
MACS
MACS
MACS
MACS
MACS
MACS
MACS
Rapid Bioassessment Protocols for Use in Streams and Rivers
B-41
-------
DRAFT REVISION—July 28,1997
Sciname
propinquus
pulcher
punctate
punctatum
punctipes
pusillus
pyraloides
racovitzai
rasticornis
rivuli
robacki
rydalensis-gr.
scalaenum
securis
sedula
serratus
serricornis
sexmaculata
simplicicollis
simpsoni
spama
striatinum
tardella
tardus
tibialis
tigrina
transversa
tremulus
tritum
tuberosum
udekemianus
uhleri
undine
Flaindx
variabilis !
variegate
variegatus
venustum
vinosa
violacea
vittatum
waldvogeli
walkeri
walked
xenolabis
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7 leg MACS
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7 sc MACS
3 isc MACS
J2 Ish IMACS
|6
i
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r
4
MACS
p IMACS
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6 eg . iMACS
6 fc MACS
6 sh {MACS .
5 Ifc IMACS
8 p [MACS
4 eg JMACS
0 |p JMACS
5 sh
10
P
MACS
MACS
1 10 sc IMACS
6 fc MACS
8
fc
2 isc
6
6
7
4
7
6
4
10
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7
10
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P
eg
P
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sh
fc
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MACS
MACS
MACS
MACS
MACS
MACS
MACS
MACS
eg MACS
P
P
MACS
MACS
eg IMACS
6 eg
8
5
2
6
7
8
8
eg
fc
p
p
fc
eg
MACS
MACS
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MACS
MACS
MACS
MACS
fc IMACS
7 Isc
0 Ip
MACS
MACS
B-42
Appendix B: Tolerance and Functional Feeding Group Assignments for Benthos
-------
DRAFT REVISION-^July 29,1997
APPENDIX C:
TOLERANCE AND TROPHIC GUILDS OF
SELECTED FISH SPECIES
Rapid Bioassessment Protocols for Use in Streams and Rivers C-l
-------
DRAFT REVISION-^July 29,1997
A Checklist of Index of Biotic Integrity Designations for Fishes of the United States
Common Name
Ohio lamprey
Chestnut lamprey
Northern brook lamprey
Southern brook lamprey
Mountain brook
lamprey
Silver lamprey
Least brook lamprey
American brook
lamprey
River lamprey
Kem brook lamprey
Arctic lamprey
Pit-Klamath brook
lamprey
Vancouver lamprey
Miller Lake lamprey
Western brook lamprey
Klamath lamprey
Pacific lamprey
Sea lamprey
Sturgeons v*- X-,1 ' ^ ' -'
Shortnose sturgeon
Lake sturgeon
Green sturgeon
Scientific Name
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Appendix C: Tolerance and Trophic Guilds of Selected Fish Species
-------
DRAFT REVISION-^July 29,1997
Common Name
Scientific Name
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Atlantic sturgeon
Acipenser oxyrhynchus
White sturgeon
Acipenser iransmontanus
Pallid sturgeon
Scaphirhynchus albus
Shovelnose sturgeon
Scaphirhynchus platorynchus
IN
Paddlefish
Polyodon spathula
FI
Spotted gar
Lepisosteus oculatus
Longnose gar
Lepisosteus osseus
TC
Shortnose gar
Lepisosteus platostomus
P -
TC
Florida gar
Lepisosteus platyrhincus
Alligator gar
Lepisosteus spatula
P
Bowfm
Amia calva
p _
TC
Goldeye
Hiodon alosoides
IN
Mooneye
Hiodon tergisus
IN
American eel
Anguilla rostrata
C -
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GF
4
Blueback herring
,4/osa aestivalis
Alabama shad
Alosa alabamae
Skipjack herring
Alosa chrysochloris
TC
Hickory shad
Alosa mediocris
Alewife
Alosa pseudoharengus
IN
FI
American shad
Alosa sapidissima
Rapid Bioassessment Protocols for Use in Streams and Rivers
C-3
-------
DRAFT REVISION-July 29,1997
Common Name
Finescale menhaden
Gulf menhaden
Yellowfin menhaden
Atlantic menhaden
Atlantic herring
Pacific herring
Gizzard shad
Threadfin shad
Round herring
False pilchard
Redear sardine
Scaled sardine
Flatiron herring
Dwarf herring
Little-eye herring
Shorthand herring
Deepbody thread
herring
Middling thread herring
Atlantic thread herring
Spanish sardine
Orangespot sardine
Pacific sardine
Scientific Name
Brevoortia gunleri
Brevoortia patronus
Brevoortia smith!
Brevoortia tyrannus
Clupea harengus
Clupea pallasi
Dorosoma cepedianum
Dorosoma petenense
Etrumeus teres
Harengula clupeola
Harengula humeralis
Harengula jaguana
Harengula thrissina
Jenkinsia lamprotaenia
Jenkinsia majua
Jenkinsia slolifera
Opisthonema libertate
Opisthonema medirastre
Opisthonema oglinum
Sardinella aurita
Sardinella brasiliensis
Sardinops sagax
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C-4
Appendix C: Tolerance and Trophic Guilds of Selected Fish Species
-------
DRAFT REVISION—July 29,1997
Common Name
T^A^S&i^^W^^^^^^*
Key anchovy
Deepbody anchovy
Cuban anchovy
Slough anchovy
Striped anchovy
Bigeye anchovy
Dusky anchovy
Bay anchovy
Longnose anchovy
Flat anchovy
Anchoveta
Silver anchovy
Northern anchovy
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Longfm dace
Central stoneroller
Largescale stoneroller
Mexican stoneroller
Bluefin stoneroller
Goldfish
Redside dace
Rosyside dace
Lake chub
Grass carp
Scientific Name
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Anchoa cayorum
Anchoa compressa
Anchoa cubana
Anchoa delicatissima
Anchoa hepsetus
Anchoa lamprotaenia
Anchoa lyolepis
Anchoa mitchilli
Anchoa nasuta
Anchoviella perfasciala
Cetengraulis mysticetus
Engraulis eurystole
Engraulis mordax
Acrocheilus alutaceus
Agosia chrysogaster
Campostoma anomalum
Campostoma oligolepis
Campostoma ornatum
Campostoma pauciradii
Carassius auratus
Clinostomus elongatus
Clinoslomus funduloides
Couesius plumbeus
Ctenopharyngodon idella
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Rapid Bioassessment Protocols for Use in Streams and Rivers
C-5
-------
DRAFT REVISION—July 29,1997
Common Name
Satinfin shiner
Blue shiner
Ocmulgee shiner
Alabama shiner
Bluestripe shiner
Bluntface shiner
Greenfin shiner
Beautiful shiner
Whitetail shiner
Tallapoosa shiner
Thicklip chub
Bannerfin shiner
Plateau shiner
Red shiner
Spotfin chub
Whitefin shiner
Proserpine shiner
Fieryblack shiner
Spotfin shiner
Tricolor shiner
Blacktail shiner
Steelcolor shiner
Altamaha shiner
Santee chub
Common carp
Scientific Name
Cyprinella analostana
Cyprinella caerulea
Cyprinella callisema
Cyprinella callistia
Cyprinella callitaenia
Cyprinella camura
Cyprinella chloristia
Cyprinella formosa
Cyprinella galactura
Cyprinella gibbsi
Cyprinella labrosa
Cyprinella leedsi
Cyprinella lepida
Cyprinella lulrensis
Cyprinella monacha
Cyprinella nivea
Cyprinella proserpina
Cyprinella pyrrhomelas
Cyprinella spiloplera
Cyprinella irichroistia
Cyprinella vcnusla
Cyprinella whipplei
Cyprinella xaenura
Cyprinella -anema
Cyprinus carpio
Midwestern United States (Karr ct al. 1986)'
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C-6
Appendix C: Tolerance and Trophic Guilds of Selected Fish Species
-------
DRAFT REVISION—July 29,1997
Common Name
Devils River minnow
Roundnose minnow
Desert dace
Slender chub
Streamline chub
Ozark chub
Blotched chub
Gravel chub
Tonguetied minnow
Cutlips minnow
Alvord chub
Utah chub
Tui chub
Borax Lake chub
Blue chub
Leatherside chub
Thicktail chub
Humpback chub
Sonora chub
Bonytail
Gila chub
Chihuahua chub
Arroyo chub
Rio Grande chub
Yaqui chub
Roundtail chub
Scientific Name
Dionda diaboli
Dionda episcopa
Eremichthys acros
Erimystax cahni
Erimystax dissimilis
Erimystax harryi
Erimystax. insignis
Erimystax x-punctatus
Exoglossum laurae
Exoglossum maxillingua
Gila alvordensis
Gila alraria
Gila bicolor
Gila boraxobius
Gila coerulen
Gila copei
Gila crassicauda
Gila cypha
Gila dilaenia
Gila elegans
Gila intermedia
Gila nigrescens
Gila orculti
Gila pandora
Gila purpurea
Gila robusia
Midwestern United States (Karr et al. 1986)'
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Rapid Bioassessment Protocols for Use in Streams and Rivers
C-7
-------
DRAFT REVISION—July 29,1997
Common Name
Flame chub
California roach
Rio Grande silvery
minnow
Western silvery
minnow
Brassy minnow
Cypress minnow
Mississippi silvery
minnow
Plains minnow
Eastern silvery minnow
Silver carp
Bighead carp
Least chub
Hitch
White River spinedace
Pahranagat spinedace
Virgin spinedace
Little Colorado
spinedace
Ide
White shiner
Cardinal shiner
Crescent shiner
Striped shiner
Warpaint shiner
Common shiner
Scientific Name
Hemitremia flammea
Hesperoleucus symmetricus
Hybognathus amarus
Hybognathus argyritis
Hybognathus hankinsoni
Hybognathus hayi
Hybognathus nuchalis
Hybognathus placitus
Hybognathus regius
Hypophthalmichthys molitrix
Hypophthalmichthys nobilis
lotichthys phlegethontis
Lavinia exilicauda
Lepidomeda albivallis
Lepidomeda altivelis
Lepidomeda mollispinis
Lepidomeda vittata
Leuciscus idus
Luxilus albeolus
Luxilus cardinalis
Luxilus cerasinus
Luxilus chrysocephalus
Luxilus coccogenis
Luxilus cornutus
Midwestern United States (Karr et al. 1986)'
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Appendix C: Tolerance and Trophic Guilds of Selected Fish Species
-------
DRAFT REVISION-^July 29,1997
Common Name
Duskystripe shiner
Bleeding shiner
Bandfm shiner
Rosefin shiner
Blacktip shiner
Pretty shiner
Ribbon shiner
Mountain shiner
Cherryfm shiner
Ouachita shiner
Redfin shiner
Speckled chub
Sturgeon chub
Sicklefm chub
Silver chub
Pearl dace
Spikedace
Moapa dace
Peamouth
Hardhead
Redspot chub
Homyhead chub
Redtail chub
Bluehead chub
River chub
Bigmouth chub
Scientific Name
Luxilus pilsbryi
Luxilus zonatus
Luxilus zonistius
Lythrurus ardens
Lythrurus alrapiculus
Lythrurus bellus
Lythrurus fumeus
Lythrurus lints
Lythrurus roseipinnis
Lythrurus snelsoni
Lythrurus umbratilis
Macrhybopsis aestivalis
Macrhybopsis gelida
Macrhybopsis meeki
Macrhybopsis storeriana
Margariscus margarita
Medafulgida
Moapa coriacea
Mylocheilus caurinus
Mylopharodon conocephalus
Nocomis asper
Nocomis biguttatus
Nocomis effusus
Nocomis leptocephalus
Nocomis micropogon
Nocomis platyrhynchus
Midwestern United States (Karr ct al. 1986)'
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Rapid Bioassessment Protocols for Use in Streams and Rivers
C-9
-------
DRAFT REVISION—July 29,1997
Common Name
Bull chub
Golden shiner
Whitemouth shiner
Highfin shiner
Texas shiner
Bigeye chub
Orangefin shiner
Pallid shiner
Comely shiner
Pugnose shiner
Popeye shiner
Burrhead shiner
Emerald shiner
Blackspot shiner
Rough shiner
Red River shiner
Bridle shiner
River shiner
Bigeye shiner
Tamaulipas shiner
Silverjaw minnow
Smalleye shiner
Ghost shiner
Cahaba shiner
Silverside shiner
Scientific Name
Nocomis raneyi
Notemigonus crysoleucas
Notropis alborus
Notropis altipinnis
Notropis amabilis
Notropis amblops
Notropis ammophilus
Notropis amnis
Notropis amoenus
Notropis anogenus
Notropis ariommus
Notropis asperifrons
Notropis atherinoides
Notropis atrocaudalis
Notropis baileyi
Notropis bairdi
Notropis bifrenatus
Notropis blennius
Notropis hoops
Notropis braytoni
Notropis buccatus
Notropis buccula
Notropis buchanani
Notropis cahabae
Notropis Candidas
Midwestern United States (Karr et al. 1986)'
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C-10
Appendix C: Tolerance and Trophic Guilds of Selected Fish Species
-------
DRAFT REVISION—July 29,1997
Common Name
Ironcolor shiner
Chihuahua shiner
Redlip shiner
Greenhead shiner
Rainbow, shiner
Dusky shiner
Bigmouth shiner
Fluvial shiner
Broadstripe shiner
Arkansas River shiner
Wedgespot shiner
Redeye chub
Blackchin shiner
Blacknose shiner
Bluehead shiner
Spottail shiner
Sailfin shiner
Highscale shiner
Highback chub
Rio Grande shiner
Tennessee shiner
Lined chub
Longnose shiner
Yellovvfin shiner
Taillight shiner
Scientific Name
Notropis chalybaeus
Notropis chihuahua
Notropis chiliticus
Notropis chlorocephalus
Notropis chrosomus
Notropis cummingsae
Notropis dorsalis
Notropis edwardraneyi
Notropis euryzonus
Notropis girardi
Notropis greenei
Notropis harperi
•Notropis heterodon
Notropis heterotepis
Notropis hubbsi
Notropis hudsonius
Notropis hypselopterus
Notropis hypsilepis
Notropis hypsinotus
Notropis jemezan us
Notropis leuciodus
Notropis lineapunctatus
Notropis longirostris
Notropis lutipinnis
Notropis maculatus
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Rapid Bioassessment Protocols for Use in Streams and Rivers
C-ll
-------
DRAFT REVISION—July 29,1997
Common Name
Cape Fear shiner
Blackmouth shiner
Ozark minnow
Phantom shiner
Kiamichi shiner
Sharpnose shiner
Ozark shiner
Peppered shiner
Coastal shiner
Silver shiner
Chub shiner
Swallowtail shiner
Rosyface shiner
Rosyface chub
Saffron shiner
Bedrock shiner
Sabine shiner
New River shiner
Sandbar shiner
Roughhead shiner
Silverband shiner
Flagfm shiner
Bluntnose shiner
Mirror shiner
Silverstripe shiner
Sand shiner
Scientific Name
Notropis mekistocholas
Notropis melanostomus
Notropis nubilus
Notropis area
Notropis ortenburgeri
Notropis oxyrhynchus
Notropis ozarcanus
Notropis perpallidus
Notropis pelersoni
Notropis photogenis
Notropis potteri
Notropis procne
Notropis rubellus
Notropis rubescens
Notropis rubricroceus
Notropis rupestris
Notropis sabinae
Notropis scabriceps
Notropis scepticus
Notropis semperasper
Notropis shumardi
Notropis signipinnis
Notropis simus
Notropis spectrunculus
Notropis stilbius
Notropis stramineus
Midwestern United States (Karr ct al. 1986)'
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C-12
Appendix C: Tolerance and Trophic Guilds of Selected Fish Species
-------
DRAFT REVISION-July 29,1997
Common Name
Telescope shiner
Weed shiner
Topeka shiner
Skygazer shiner
Mimic shiner
Bluenose shiner
Channel shiner
Clear chub
Coosa shiner
Pugnose minnow
Oregon chub
Sacramento blackfish
Riffle minnow
Fatlips minnow
Suckermouth minnow
Kanawha minnow
Stargazing minnow
Blackside dace
Northern redbelly dace
Southern redbelly dace
Finescale dace
Mountain redbelly dace
Tennessee dace
Bluntnose minnow
Fathead minnow
Slim minnow
Scientific Name
Notropis telescopus
Notropis texanus
Notropis topeka
Notropis uranoscopus
Notropis volucellus
Notropis welaka
Notropis wickliffl
Notropis winchelli
Notropis xaenocephalus
Opsopoeodus emiliae
Oregonichthys crameri
Orthodon microlepidotus
Phenacobius catostomus
Phenacobius crassilabrum
Phenacobius mirabilis
Phenacobius leretulus
Phenacobius uranops
Phoxinus cumberlandensis
Phoxinus eos
Phoxinus erythrogasler
Phoxinus neogaeus
Phoxinus areas
Phoxinus tennesseensis
Pimephales notatus
Pimephales promelas
Pimephales tenellus
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C-13
-------
DRAFT REVISION—July 29,1997
Common Name
Bullhead minnow
Woundfin
Flathead chub
Clear Lake splittail
Splittail
Sacramento squawfish
Colorado squawfish
Northern squawfish
Umpqua squawfish
Relict dace
Blacknose dace
Longnose dace
Loach minnow
Las Vegas dace
Umpqua dace
Leopard dace
Speckled dace
Bitterling
Redside shiner
Lahontan redside
Rudd
Creek chub
Fallfish
Sandhills chub
Dixie chub
Tench
Scientific Name •
Pimephales vigilax
Plagopterus argentissimus
Platygobio gracilis
Pogonichthys ciscoides
Pogonichthys macrolepidotus
Ptychocheilus grandis
Ptychocheilus lucius
Ptychocheilus oregonensis
Ptychocheilus umpquae
Relictus solitarius
Rhinichthys atratulus
Rhinichthys cataractae
Rhinichthys cobitis
Rhinichthys deaconi
Rhinichthys evermanni
Rhinichthys falcatus
Rhinichthys osculus
Rhodeus sericeus
Richardsonius balteatus
Richardsonius egregius
Scardinius erythrophthalmus
Semotilus atromaculatus
Semotilus corporalis
Semotilus lumbee
Semotilus thoreauianus
Tinea tinea
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C-14
Appendix C: Tolerance and Trophic Guilds of Selected Fish Species
-------
DRAFT REVISION-^TuIy 29,1997
Common Name
River carpsucker
Quillback
Highfin carpsucker
Utah sucker
Yaqui sucker
Longnose sucker
Desert sucker
Bridgelip sucker
White sucker
Bluehead sucker
Owens sucker
Sonora sucker
Flannelmouth sucker
Largescale sucker
Modoc sucker
Sacramento sucker
Mountain sucker
Rio Grande sucker
Klamath smallscale
sucker
Santa Ana sucker
Klamath largescale
sucker
Tahoe sucker
Warner sucker
Shortnose sucker
Scientific Name
Carpiodes carpio
Carpiodes cyprinus
Carpiodes velifer
Catostomus ardens
Catostomus bernardini
Catostomus catostomus
Catostomus clarki
Catostomus columbianus
Catostomus commersoni
Catostomus discobolus
Catostomus fumeiventris
Catostomus insignis
Catostomus latipinnis
Catostomus macrocheilus
Catostomus microps
Catostomus occidentalis
Catostomus platyrhynchus
Catostomus plebeius
Catostomus rimiculus
Catostomus santaanae
Catostomus snyderi
Catostomus tnhoensis
Catostomus warnerensis
Chasmistes brevirostris
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-------
DRAFT REVISION—July 29,1997
Common Name
Cui-ui
June sucker
Snake River sucker
Blue sucker
Lost River sucker
Creek chubsucker
Lake chubsucker
Sharpfin chubsucker
Alabama hog sucker
Northern hog sucker
Roanoke hog sucker
Smallmouth buffalo
Bigmouth buffalo
Black buffalo
Harelip sucker
Spotted sucker
Silver redhorse
Bigeyejumprock
Blackfin sucker
West Mexican redhorse
River redhorse
Black jumprock
Gray redhorse
Black redhorse
Golden redhorse
Scientific Name
Chasmistes cujus
Chasmistes Hants
Chasmistes muriei
Cycleptus elongatus
Deltistes Iwcatus
Erimyzon oblongus
Erimyzon sucetta
Erimyzon tenuis
Hypentelium etowanum
Hypentelium nigricans
Hypentelium roanokense
Ictiobus bubalus
Ictiobus cyprinellus
Ictiobus niger
Lagochila lacera
Minytrema melanops
Moxostoma anisurum
Moxostoma ahommum
Moxostoma atripinne
Moxostoma austrinum
Moxostoma carinatum
Moxostoma cervinum
Moxostoma congestum
Moxostoma duquesnei
Moxostoma erythrurum
Midwestern United States (Karr et al. 1986)'
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C-16
Appendix C: Tolerance and Trophic Guilds of Selected Fish Species
-------
DRAFT REVISION-^July 29,1997
Common Name
Rustyside sucker
Copper redhorse
Greater jumprock
Shorthead redhorse
V-lip redhorse
Blacktail redhorse
Torrent sucker .
Smallfin redhorse
Striped jumprock
Greater redhorse
Razorback sucker
Bullbt«dc.tfljh« --
Snail bullhead
White catfish
Black bullhead
Yellow bullhead
Brown bullhead
Flat bullhead
Spotted bullhead
Blue catfish
Headwater catfish
Yaqui catfish
Channel catfish
Ozark madtom
Smoky madtom
Scientific Name
Moxostoma hamiltoni
Moxostoma hubbsi
Moxostoma lachneri
Moxostoma macrolepidotum
Moxostoma pappillosum
Moxostoma poecilurum
Moxostoma rhothoecum
Moxostoma robustum
Moxostoma rupiscartes
Moxostoma valenciennesi
Xyrauchen lexanus
Ameiurus brunneus
Ameiunts catus
Ameiurus melas
Ameiurus natalis
Ameiunts nebulosus
Ameiurus platycephalus
Ameiurus serracanlhus
Ictalurus furcatus
Ictalurus lupus
Ictalurus pricei
Ictalurus punclatus
Noturus albaler
Noturus baileyi
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Rapid Bioassessment Protocols for Use in Streams and Rivers
C-17
-------
DRAFT REVISION—July 29,1997
Common Name
Elegant madtom
Mountain madtom
Slender madtom
Checkered madtom
Yellowfin madtom
Stonecat
Black madtom
Carolina madtom
Orangefin madtom
Tadpole madtom
Least madtom
Margined madtom
Ouachita madtom
Speckled madtom
Brindled madtom
Frecklebelly madtom
Freckled madtom
Brown madtom
Neosho madtom
Pygmy madtom
Northern madtom
Caddo madtom
Scioto madtom
Flathead catfish
Widemouth blindcat
Scientific Name
Noturus elegans
Nolurus eleutherus
Noturus exilis
Noturus flavater
Noturus flavipinnis
Notuntsflavus
Noturus funebris
Noturus furiosus
Noturus gilbert!
Noturus gyrinus
Noturus hildebrandi
Noturus insignis
Noturus lachneri
Noturus leptacanthus
Noturus miurus
Noturus munilus
Noturus nocturnus
Noturus phaeus
Noturus placidus
Nolurus stannuli
Noturus sligmosus
Noturus taylori
. Noturus trautmnni
Pylodictis olivaris
Satan euryslomus
Midwestern United States (Karr ct al. I986)1
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C-18
Appendix C: Tolerance and Trophic Guilds of Selected Fish Species
-------
DRAFT REVISION-July 29,1997
Common Name
Toothless blindcat
Redfin pickerel
Grass pickerel
Northern pike
Muskellunge
Chain pickerel
»«»stsW*^}*IM'^|&SSBaSS!&gf;
Alaska blackfish
Olympic mudminnow
Central mudminnow
Eastern mudminnow
Whitebait smelt
Wakasagi
Pond smelt
Surf smelt
Delta smelt
Capelin
Rainbow smelt
Night smelt
Longfin smelt
Eulachon
Cisco or Lake herring
Arctic cisco
Scientific Name
Trogloglanis pattersoni
58«38"»>ls8|s?$P$8SS§P»e'«&
Esox americanus americanus
Esox americanus
vermiculatus
Esox lucius
Esox masquinongy
Esox niger
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Dallia pectoralis
Novumbra hubbsi
Umbra limi
Umbra pygmaea
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Allosmerus elongalus
Hypomesus nipponensis
Hypomesus olidus
Hypomesus preiiosus
Hypomesus transpacificus
Mallolus villosus
Osmerus mordax
Spirinchus siarksi
Spirinchus lhaleichthys
Thaleichlhys pacificus
Salmonbtae < '•'.,' *. "^ ,. '.
Coregonus nrtedi
Coregonus nutumnalis
Midwestern United States (Karr ct al. 1986)'
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Rapid Bioassessment Protocols for Use in Streams and Rivers
C-19
-------
DRAFT REVISION—July 29,1997
Common Name
Lake whitefish
Bloater
Atlantic whitefish
Deepwater cisco
Kiyi
Bering cisco
Broad whitefish
Blackfin cisco
Humpback whitefish
Shortnose cisco
Least cisco
Shortjaw cisco
Golden trout
Apache trout
Cutthroat trout
Gila trout
Pink salmon
Chum salmon
Coho salmon
Rainbow trout
Sockeye salmon
Chinook salmon
Bear Lake whitefish
Pygmy whitefish
Round whitefish
Bonneville cisco
Scientific Name
Coregonus clupeaformis
Coregonus hoyi
Coregonus huntsmani
Coregonus johannae
Coregonus kiyi
Coregonus laurettae
Coregonus nasus
Coregonus nigripinnis
Coregonus pidschian
Coregonus reighardi
Coregonus sardinella
Coregonus zenithicus
Oncorhynchus aguabonita
Oncorhynchus apache
Oncorhynchus clarki
Oncorhynchus gilae
Oncorhynchus gorbuscha
Oncorhynchus keta
Oncorhynchus kisulch
Oncorhynchus mykiss
Oncorhynchus nerka
Oncorhynchus tshawytscha
Prosopium abyssicola
Prosopium coulteri
Prosopium cylindraceum
Prosopium gemmifer
Midwestern United States (Karr ct al. 1986)'
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C-20
Appendix C: Tolerance and Trophic Guilds of Selected Fish Species
-------
DRAFT REVISION—July 29,1997
Common Name
Bonneville whitefish
Mountain whitefish
Atlantic salmon
Brown trout
Arctic char
Bull trout
Brook trout
Dolly Varden
Lake trout
Inconnu
Arctic grayling
Trout-perch
Sand roller
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Diamond killifish
White River springfish
Railroad Valley
springfish
Leon Springs pupfish
Devils Hole pupfish
Comanche Springs
pupfish
Scientific Name
Prosopium spilonotus
Prosopium williamsoni
Salmo salar
Salmo tnttta
Salvelinus alpinus
Salvelinus confluentus
Salvelinus fontinalis
Salvelinus malma
Salvelinus namaycush
Stenodus leucichthys
Thymallus arcticus
Percopsis omiscomaycus
Percopsis transmonlana
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Aphredoderus sayanus
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Lota lota
Adinia xenica
Crenichthys baileyi
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Cyprinodon diabolis
Cyprinodon elegans
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DRAFT REVISION—July 29,1997
Common Name
Conchos pupfish
Lake Eustis minnow
Desert pupfish
Amargosa pupfish
Pecos pupfish
Owens pupfish
Red River pupfish
Salt Creek pupfish
White Sands pupfish
Sheepshead minnow
Pahrump poolfish
Ash Meadows poolfish
Goldspotted killifish
Whiteline topminnow
Stippled studfish
Northern studfish
Golden topminnow
Banded topminnow
Marsh killifish
Banded killifish
Starhead topminnow
Russetfin topminnow
Broadstripe topminnow
Gulfkillifish
Mummichog
Saltmarsh topminnow
Scientific Name
Cyprinodon eximius
Cyprinodon hubbsi
Cyprinodon macularius
Cyprinodon nevadensis
Cyprinodon pecosensis
Cyprinodon radiosus
Cyprinodon rubrofluviatilis
Cyprinodon salinus
Cyprinodon tularosa
Cyprinodon variegatus
Empetrichthys latos
Empetrichthys merriami
Floridichthys carpio
Fundulus albolineatus
Fundulus bifax
Fundulus catenatus
Fundulus chrysotus
Fundulus cingulatus
Fundulus confluentus
Fundulus diaphanus
Fundulus dispar
Fundulus escambiae
Fundulus euryzonus
Fundulus grandis
Fundulus heteroclitus
Fundulus jenkinsi
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Appendix C: Tolerance and Trophic Guilds of Selected Fish Species
-------
DRAFT REVISION-July 29,1997
Common Name
Barrens topminnow
Lined topminnow
Spotfin killifish
Striped killifish -
Blackstripe topminnow
Bayou topminnow
Blackspotted
topminnow
California killifish
Bayou killifish
Speckled killifish
Plains topminnow
Seminole killifish
Longnose killifish
Southern studfish
Waccamaw killifish
Plains killifish
Flagfish
Pygmy killifish
Bluefin killifish
Rainwater killifish
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Western mosquitofish
Amistad gambusia
Big Bend gambusia
Largespring gambusia
Scientific Name
Fundulus julisia
Fundulus lineolatus
Fundulus luciae
Fundulus majalis
Fundulus notalus
Fundulus notti
Fundulus olivaceus
Fundulus parvipinnis
Fundulus pulvereus
Fundulus rathbuni
Fundulus sciadicus
Fundulus seminolis
Fundulus similis
Fundulus stellifer
Fundulus waccamensis
Fundulus zebrinus
Jordanella floridae
Leplolucania ommata
Lucania goodei
Lucania parva
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Gambusia affinis
Gambusia amistadensis
Gambusia gaigei
Gambusia geiseri
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C-23
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DRAFT REVISION-July 29,1997
Common Name
San Marcos gambusia
Clear Creek gambusia
Eastern mosquitofish
Pecos gambusia
Mangrove gambusia
Blotched gambusia
Least killifish
Amazon molly
Sailfin molly
Shortfin molly
Guppy
Porthole livebearer
Gila topminnow
Green swordtail
Southern platyfish
Variable platyfish
Hardhead silverside
Topsmelt
Jacksmelt
Reef silverside
Brook silverside
California grunion
Rough silverside
Inland silverside
Texas silverside
Scientific Name
Gambusia georgei
Gambusia heterochir
Gambusia holbrooki
Gambusia nobilis
Gambusia rhizophorae
Gambusia senilis
Helerandria formosa
Poecilia formosa
Poecilia latipinna
Poecilia mexicana
Poecilia reticulata
Poeciliopsos gracilis
Poeciliopsis occidentalis
Xiphophorus helleri
Xiphophorus maculalus
Xiphophorus variatus
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Atherinops affinis
Atherinopsis californiensis
Hypoatherina
harringlonensis
Labidesthes sicculus
Leuresthes tenuis
Membras maninica
Menidia beryllina
Menidia clarkhubbsi
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Appendix. C: Tolerance and Trophic Guilds of Selected Fish Species
-------
DRAFT REVISION^Iuly 29,1997
Common Name
Key silverside
Waccamaw silverside
Atlantic silverside
Tidewater silverside
Fourspine stickleback
Tube-snout
Brook stickleback
Threespine stickleback
Blackspotted
stickleback
Ninespine stickleback
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Prickly sculpin
Rough sculpin
Black sculpin
Mottled sculpin
Paiute sculpin
Banded sculpin
Slimy sculpin
Shorthead sculpin
Utah Lake sculpin
Bear Lake sculpin
Potomac sculpin ••
Shoshone sculpin
Scientific Name
Menidia conchorum
Menidia extensa
Menidia menidia
Menidia peninsulae
Apeltes quadracus
Aulorhynchus flavidus
Culaea inconstans
Gasterosteus aculeatus
Gasterosteus wheatlandi
Pungitius pungitius
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Cottus aleuticus
Cottus asper
Cottus asperrimus
Cottus baileyi
Cottus bairdi
Coitus beldingi
Cottus carolinae
Cotlus cognatus
Cottus confusus
Cottus echinatus
Cottus extensus
Cottus girardi
Cottus greenei
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Rapid Bioassessment Protocols for Use in Streams and Rivers
C-25
-------
DRAFT REVISION—July 29,1997
Common Name
Riffle sculpin
Ozark sculpin
Marbled sculpin
Wood River sculpin
Margined sculpin
Reticulate sculpin
Pit sculpin
Klamath Lake sculpin
Pygmy sculpin
Torrent sculpin
Spoonnead sculpin
Slender sculpin
Deepwater sculpin
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White bass
Yellow bass
Striped bass
Wreckfish
Giant sea bass
Blackmouth bass
Keelcheek bass
Scientific Name
Coitus gulosus
Cottus hypselurus
Cottus klamathensis
Cottus leiopomus
Cottus marginatus
Cottus perplexus
Cottus pitensis
Cottus princeps
Cottus pygmaeus
Cottus rhotheus
Cottus ricei
Cottus tenuis
Myoxocephalus thompsoni
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Morone chrysops
Morone mississippiensis
Morone saxatilis
Polyprion americanus
Stereolepis gigas
Synagrops bellus
Synagrops spinosus
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Appendix C: Tolerance and Trophic Guilds of Selected Fish Species
-------
DRAFT REVISION—July 29,1997
Common Name
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Mud sunfish
Shadow bass
Roanoke bass
Ozark bass
Rock bass
Sacramento perch
Flier
Carolina pygmy sunfish
Everglades pygmy
sunfish
Bluebarred pygmy
sunfish
Okefenokee pygmy
sunfish
Banded pygmy sunfish
Blackbanded sunfish
Bluespotted sunfish
Banded sunfish
Redbreast sunfish
Green sunfish
Pumpkinseed
Warmouth
Orangespotted sunfish
Bluegill
Dollar sunfish
Scientific Name
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Acantharchus pomotis
Ambloplites ariommus
Ambloplites cavifrons
Ambloplites constellatus
Ambloplites nipestris
Archoplites interruptus
Centrarchus macropterus
Elassoma boehlkei
Elassoma evergladei
Elassoma okatie
Elassoma okefenokee
Elassoma zonatum
Enneacanthus chaetodon
Enneacanlhus gloriosus
Enneacanthus obesus
Lepomis auritus
Lepomis cyanellus
Lepomis gibbosus
Lepomis gulosus
Lepomis humilis
Lepomis macrochirus
Lepomis marginatus
Midwestern United States (Karr ct al. 1986)'
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Common Name
Longear sunfish .
Redear sunfish
Spotted sunfish
Bantam sunfish
Redeye bass
Smallmouth bass
Suwannee bass
Spotted bass
Largemouth bass
Guadalupe bass
White crappie
Black crappie
s^«s^^*^||?*£J££^*|»|y*S£S»«gig^,y"<
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Florida sand darter
Western sand darter
Southern sand darter
Eastern sand darter
Scaly sand darter
Sharphead darter
Coppercheek darter
Mud darter
Emerald darter
Scientific Name
Lepomis megalotis
Lepomis microlophus
Lepomis punctatus
Lepomis symmetricus
Micropterus coosae
Micropterus dolomieu
Micropterus notius
Micropterus punctulatus
Micropterus salmoides
Micropterus treculi
Pomoxis annularis
Pomoxis nigromaculatus
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Ammocrypta asprella
Ammocrypta beani
Ammocrypta bifascia
Ammocrypta clara
Ammocrypta meridiana
Ammocrypta pellucida
Ammocrypta vivax
Etheostoma acuticeps
Elheostoma aquali
Etheostoma asprigene
Etheostoma baileyi
Midwestern United States (Karr ct al. 1986)'
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C-28
Appendix C: Tolerance and Trophic Guilds of Selected Fish Species
-------
DRAFT REVISION-^July 29,1997
Common Name
Teardrop darter
Splendid darter
Orangefin darter
Greenside darter
Blenny darter
Slackwater darter
Rainbow darter
Bluebreast darter
Greenfin darter
Bluntnose darter
Ashy darter
Creole darter
Carolina darter
Coosa darter
Arkansas darter
Fringed darter
Choctawhatchee darter
Coldwater darter
Black darter
Brown darter
Cherry darter
Arkansas saddled darter
Iowa darter
Fantail darter
Saffron darter
Fountain darter
Scientific Name
Etheostoma barbouri
Etheostoma barrenense
Etheostoma bellum
Etheostoma blennioides
Etheostoma blennius
Etheostoma boschungi
Etheostoma caeruleum
Etheostoma camurum
Etheostoma chlorobranckium
Etheostoma chlorosomum
Etheostoma cinereum
Etheostoma collettei
Etheostoma collis
Etheostoma coosae
Etheostoma cragini
Etheostoma crossoplerum
Etheostoma davisoni
Etheostoma dilrema
Etheostoma duryi
Etheostoma edwini
Etheostoma etnieri
Etheostoma euzonum
Etheostoma exile
Etheostoma flabellare
Etheostoma flavum
Elheostoma fonticola
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C-29
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DRAFT REVISION—July 29,1997
Common Name
Savannah darter
Swamp darter
Slough darter
Rio Grande darter
Harlequin darter
Christmas darter
Turquoise darter
Blueside darter
Greenbreast darter
Yoke darter
Kanawha darter
Stripetail darter
Greenthroat darter
Longfln darter
Redband darter
Brighteye darter
Spotted darter
Pinewoods darter
Smallscale darter
Least darter
Yellowcheek darter
Lollipop darter
Niangua darter
Blackfin darter
Johnny darter
Scientific Name
Etheostoma fricksium
Etheostoma fusiforme
Etheostoma gracile
Etheostoma grahami
Etheostoma histrio
Etheostoma hopkinsi
Etheostoma inscriptum
Etheostoma jessiae
Etheostoma jordani
Etheostoma juliae
Etheostoma kanawhae
Etheostoma kennicotti
Etheostoma lepidum
Etheostoma longimanum
Etheostoma luteovinctum
Etheostoma lynceum
Etheostoma maculaium
Eiheosloma mariae
Etheostoma microlepidum
Etheostoma micropercn
Etheostoma moorei
Etheosloma neoplerum
Etheostoma nianguae
Etheosloma nigripinne
Etheostoma nigrum
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C-30
Appendix C: Tolerance and Trophic Guilds of Selected Fish Species
-------
DRAFT REVISION—July 29,1997
Common Name
Watercress darter
Barcheek darter
Okaloosa darter
Sooty darter
Tessellated darter
Candy darter
Paleback darter
Goldstripe darter
Waccamaw darter
Riverweed darter
Cypress darter
Stippled darter
Firebelly darter
Orangebelly darter
Kentucky darter
Bayou darter
Redline darter
Rock darter
Arrow darter
Bloodfin darter
Maryland darter
Sawcheek darter
Snubnose darter
Slabrock darter
Orangethroat darter
Scientific Name
Etheostoma nuchale
Etheostoma obeyense
Etheostoma okaloosae
Elheosloma olivaceum
Etheostoma olmsledi
Etheostoma osburni
Etheostoma pallididorsum
Etheostoma parvipinne
Etheostoma perlongum
Etheostoma podostemone
Etheostoma proeliare
Etheostoma punclulatum
Etheostoma pyrrhogasler
Etheostoma radiosum
Etheostoma rafinesauei
Etheostoma rubrum
Etheostoma ruftlineatum
Etheostoma rupesire
Etheostoma sagnta
Etheostoma sanguifluum
Elheosloma sellare
Etheostoma serrifer
Etheostoma simoterum
Etheostoma smith:
Etheostoma spectabile
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Rapid Bioassessment Protocols for Use in Streams and Rivers
C-31
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DRAFT REVISION—July 29,1997
Common Name
Spottail darter
Speckled darter
Striated darter
Gulf darter
Swannanoa darter
Missouri saddled darter
Seagreen darter
Tippecanoe darter
Trispot darter
Tuscumbia darter
Variegate darter
Striped darter
Glassy darter
Wounded darter
Boulder darter
Redfin darter
Banded darter
Backwater darter
Bandfln darter
Ruffe
Yellow perch
Amber darter
Tangerine darter
Goldline darter
Blotchside darter
Scientific Name
Etheostoma squamiceps
Etheostoma stigmaeum
Etheostoma striatulum
Etheostoma swaini
Etheostoma swannanoa
Etheostoma tetrazonum
Etheostoma thalassinum
Etheostoma tippecanoe
Etheostoma trisella
Etheostoma tuscumbia
Etheostoma variatum
Etheostoma virgatum
Etheostoma vitreum
Etheostoma vulneratum
Etheostoma wapiti
Etheostoma whipplei
Etheostoma zonale
Etheostoma zonifer
Etheostoma zonistium
Gymnocephalus cernuus
Perca flavescens
Percina antesella
Percina aurantiaca
Percina aurolineata
Percina bunoni
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C-32
Appendix C: Tolerance and Trophic Guilds of Selected Fish Species
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DRAFT REVISION—July 29,1997
Common Name
Logperch
Texas logperch
Channel darter
Piedmont darter
Bluestripe darter
Gilt darter
Appalachia darter
Conasauga logperch
Freckled darter
Longhead darter
Bigscale logperch
Blackside darter
Longnose darter
Blackbanded darter
Stripeback darter
Sharpnose darter
Bronze darter
Leopard darter
Shield darter
Slenderhead darter
Roanoke logperch
Roanoke darter
Dusky darter
River darter
Olive darter
Scientific Name
Percina caprodes
Percina carbonaria
Percina copelandi
Percina crassa
Percina cymatotaenia
Percina evides
Percina gymnocephala
Percina jenkinsi
Percina lenticula
Percina macrocephala
Percina macrolepidd
Percina maculata
Percina nasuta
Percina nigrofasciata
Percina nologramma
Percina oxyrhynchus
Percina palmaris
Percina pantherina
Percina peltata
Percina phoxocephala
Percina rex
Percina roanoka
Percina sciera
Percina shumardi
Percina squamata
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Rapid Bioassessment Protocols for Use in Streams and Rivers
C-33
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DRAFT REVISION—July 29,1997
Common Name
Scientific Name
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Snail darter
Percina tanas:
Stargazing darter
Percina uranidea
Saddleback darter
Percina vigil
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Stizostedion canadense
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Walleye
Stizostedion vitreum
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Freshwater drum
Aplodinotus grunniens
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Blue tilapia
Tilapia aurea
Spotted tilapia
Tilapia mariae
Blackchin tilapia
Tilapia melanotheron
Mozambique tilapia
Tilapia mossambica
Wami tilapia
Tilapia urolepis
Redbelly tilapia
Tilapia zilli
C-34
Appendix C: Tolerance and Trophic Guilds of Selected Fish Species
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DRAFT REVISION-July 29,1997
Common Name
Scientific Name
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Mountain mullet
Agonostomus monticola
Striped mullet
Mugil cephalus
White mullet
Mugil curema
Redeye mullet
Mugil gaimardianus
Fantail mullet
Mugil gyrans
Liza
Mugil liza
Note: Nomenclature follows Robbins et al. 1991
a. Trophic designations: P=piscivore; H=herbivore; O=omnivore; l=insectivore
Tolerance designation: IS=intolerant species
b. Trophic designations: P=piscivore; F=filter feeder; V=invertivore; I=specialist insectivore; O=omnivore; G=generalist; H=herbivore;
C=camivore
Tolerance designations: R=rare intolerant; S=special intolerant; I=common intolerant; M=moderately intolerant; T=highly tolerant;
P=moderately tolerant
c. Trophic designations: P=piscivore; F=filter feeder; lN=invertivore; I=insectivore; O=omnivore; H=herbivore; G=generalist
Tolerance designations: l=intolerant; T=tolerant; ^^intermediate
d. Trophic designations: P=piscivore; F=filter feeder; V=invertivore; I=specialist insectivore; O=omnivore; G=generalist; H=herbivore;
C=camivore; —functional feeding guild behaviorally plastic
Tolerance desigations: R=rare intolerant; S=special intolerant; l=common intolerant; M=moderately intolerant; T=highly tolerant;
P=moderately tolerant; —=tolerance classification moderate
e. Trophic designations: FI=filter feeder (planktivore); GE=generalist feeder; HE=herbivore; IN=insectivore; OM=omnivore;
PA=parasite; TC=top carnivore (piscivore)
Tolerance designations: I=intolerant: T=tolerant
f. Trophic designations: OM=omnivore; IS=insectivore; GF=generalist feeder; SP=specialist insectivore; BI=benthic insectivore;
IC=insectivorous cyprinid; TC=top carnivore; C=camivore; FI=filter feeder; HE=herbivore
Tolerance designations: I=intolerant; T=tolerant; M=moderate tolerance
Rapid Bioassessment Protocols for Use in Streams and Rivers
C-35
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DRAFT REVISION—July 29,1997
This Page Intentionally Left Blank
C-36 Appendix C: Tolerance and Trophic Guilds of Selected Fish Species
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DRAFT REVISION-July 29,1997
APPENDIX D:
SURVEY APPROACH FOR COMPILATION OF
HISTORICAL DATA
Rapid Bioassessment Protocols for Use in Streams and Rivers D-l
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DRAFT REVISION—July 29,1997
QUESTIONNAIRE SURVEY FOR EXISTING BIOSURVEY DATA AND
BIOASSESSMENT INFORMATION
Ecological expertise and knowledge of the aquatic ecosystems of a state can reside in agencies and
academic institutions other than the water resource agency. This expertise and historical knowledge
can be valuable in problem screening, identifying sensitive areas, and prioritizing watershed-based
investigations. Much of this expertise is derived from biological survey data bases that are generally
available for specific surface waters in a state. A systematic method to compile and summarize this
information is valuable to a state water resource agency.
The questionnaire survey approach presented here is modified from Rapid Bioassessment Protocol
(RBP) IV (Plafkin et al. 1989) to be applicable to various types of biological data. The purpose of
this questionnaire survey is to compile and document historical/existing knowledge of stream
physical habitat characteristics and information on the periphyton, macroinvertebrate, and fish
assemblages.
The template questionnaire is divided into 2 major sections: the first portion is modeled after RBP
IV, and serves as a screening assessment; and the second portion is designed to query state program
managers, technical experts, and researchers regarding existing biosurvey and/or bioassessment data.
This approach can provide a qualitative screening assessment (Section 1) of a large number of
waterbodies in a relative short period of time for a low cost. The questionnaire can also prevent a
duplication of effort (e.g., investigating a waterbody that has already been adequately characterized)
by polling the applicable experts for available existing information (Section 2).
The quality of the information obtained from this approach depends on survey design (e.g., number
and location of waterbodies), the questions presented, and the knowledge and cooperation of the
respondents. The potential respondent (e.g., agency chief, program manager, professor) should be
contacted initially by telephone to specifically identify appropriate respondents. To ensure
maximum response, the questionnaire should be sent at times other than the peak of the field season
and/or the beginning or end of the fiscal year. The inclusion of a self-addressed, stamped envelope
should also increase the response rate. A personalized cover letter (including official stationary,
titles, and signatures) should accompany each questionnaire. Telephone contact may be necessary as
a follow-up procedure subsequent to the mailing(s).
Historical data may be limited in coverage and varied in content on a statewide basis, but be more
comprehensive in coverage and content for specific watersheds. A clearly stated purpose of the
survey will greatly facilitate evaluation of data from reaches that are dissimilar in characteristics.
The identification of data gaps will be critical in either case. Regardless of the purpose, minimally
impaired reference reaches may be selected to serve as benchmarks for comparison. The definition
of minimal impairment varies from region to region. However, it includes those waters that are
generally free of point source discharges, channel modifications, and/or diversions, and have diverse
habitats, complex substrates, considerable instream cover and a wide buffer of riparian vegetation.
Selection of specific reaches for consideration (e.g., range and extent) in the questionnaire survey is
ultimately dependent on program objectives and is at the discretion of the surveyor. The
questionnaire approach and the following template form allows considerable flexibility. Results can
be reported as histograms, pie graphs, or box plots.
D-2 Appendix D: Survey Approach for Compilation of Historical Data
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DRAFT REVISION^Tuly 29,1997
Questionnaire design and responses should address, when possible, the:
• extent of waterbody or watershed surveyed
• condition of the periphyton, macroinvertebrate and/or fish assemblage
• quality of available physical habitat
• frequency of occurrence of particular factors/causes limiting the biological condition
• effect of waterbody type and size on the spatial and temporal trends, if known
• likelihood of improvement or degradation based on known land use patterns or
mitigation efforts
Rapid Bioassessment Protocols for Use in Streams and Rivers D-3
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DRAFT REVISION-JuIy 29,1997
BIOASSESSMENT/BIOSURVEY QUESTIONNAIRE
Date of Questionnaire Survey
This questionnaire is part of an effort to assess the biological condition or health of the flowing waters of
this state. Our principle focus is on the biotic health of the designated waterbody as indicated by its
periphyton, macroinvertebrate and/or fish community. You were selected to participate in this survey
because of your expertise in periphyton, macroinvertebrate, and/or fish biology and your knowledge of the
waterbody identified in this questionnaire.
Please examine the entire questionnaire form. If you feel that you cannot complete the form, check here [ ]
and return it. If you are unable to complete the questionnaire but are aware of someone who is familiar with
the waterbody and/or related bioassessments, please identify that person's name, address, and telephone
number in the space provided below:
Contact: . Name
Address
Agency/Institution
Phone Fax_
Email
This questionnaire is divided into two major sections. Section 1 serves as a screening assessment and
Section 2 is a request for existing biosurvey data and/or bioassessment results.
This form addresses the following waterbody:
Waterbody
State: County: Lat./Long.: Waterbody code:_
Ecoregion: Subecoregion: Description of site/reach:
Drainage size: Flow: 10cfs
Description of data set (i.e., years, seasons, type of data, purpose of survey)_
D-4 Appendix D: Survey Approach for Compilation of Historical Data
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DRAFT REVISION—July 29,1997
SECTION 1. SCREENING ASSESSMENT
Using the scale of biological conditions found in the following text box, please circle the rank that best
describes your impression of the condition of the waterbody.
SCALE OF CONDITIONS
Species composition, age classes, and trophic structure comparable to non (or minimally)
impaired waterbodies of similar size in that ecoregion or watershed.
Species richness somewhat reduced by loss of some intolerant species; less than optimal
abundances, age distributions, and trophic structure for waterbody size and ecoregion.
Intolerant species absent; considerably fewer species and individuals than expected for that
waterbody size and ecoregion; trophic structure skewed toward omnivory.
Dominated by highly tolerant species, omnivores, and habitat generalists; top carnivores rare
or absent; older age classes of all but tolerant species rare; diseased fish and anomalies
relatively common for that waterbody size and ecoregion.
Few individuals and species present; mostly tolerant species; diseased fish and anomalies
abundant compared to other similar-sized waterbodies in the ecoregion.
No fish, depauperate macroinvertebrate and/or periphyton assemblages.
(Circle one number using the scale above.)
1. Rank the current conditions of the reach
543210
Rank the conditions of the reach 10 years ago
543210
Given present trends, how will the reach rank 10 years from now?
2 1 0
2.
3.
5 4.3
Describe/comment
4.
If the major human-caused limiting factors were eliminated, how would the reach rank 10 years
from now?
543
Describe/comment
1
0
5.
Decision criteria based on:
n Site-specific reference sites
D Ecoregional reference conditions
n Professional opinion.
D Other (specify)
Rapid Bioassessment Protocols for Use in Streams and Rivers
D-5
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DRAFT REVISION—July 29,1997
If impairment noted (i.e., scale of 1-3 given), complete each subsection below by
checking off the most appropriate limiting factor(s) and probable cause(s). Clarify if
reference is to past or current conditions.
PHYSICOCHEMICAL
(a.) WATER QUALITY
Limiting Factor
n Temperature too high
n Temperature too low
n Turbidity
a Salinity
n Dissolved oxygen
n Gas supersaturation
D pH too acidic
D pH too basic
o Nutrient deficiency
n Nutrient surplus
n Toxic substances
n Other (specify below)
n Not limiting
Probable Cause
D Primarily upstream
n Within reach
Point source discharge
D Industrial
n Municipal
D Combined sewer
d Mining
D Dam release
Nonpoint source discharge
Q Individual sewage
o Urban runoff
D Landfill leachate
n Construction
D Agriculture
n Feedlot
n Grazing
D Silviculture
n Mining
D Natural
d Unknown
d Other (specify below)
(b.) WATER QUANTITY
Limiting Factor
D Below optimum flows
n Above optimum flows
n Loss of flushing flows
n Excessive flow fluctuation
n Other (specify below)
n Not limiting
Probable Cause
D Dam
D Diversion
Watershed conversion
D Agriculture
D Silviculture
n Grazing
D Urbanization
n Mining
D Natural
D Unknown
D Other (specify below)
D-6
Appendix D: Survey Approach for Compilation of Historical Data
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DRAFT REVISION—July 29,1997
BIOLOGICAL/HABITAT
(Check the appropriate categories)
(a.) Limiting Factor
Insufficient instream structure
Insufficient cover
Insufficient sinuosity
Loss of riparian vegetation
Bank failure
Excessive siltation
Insufficient organic detritus
Insufficient woody debris for organic detritus
Frequent scouring flows
Insufficient hard surfaces
Embeddedness
Insufficient light penetration
Toxicity
High water temperature
Altered flow
Overharvest
Underharvest
Fish stocking
Non-native species
Migration barrier
Other (specify)
Not limiting
HABI
PERI
MACR
FISH
Key:
HABI - Habitat
MACR - Macroinvertebrates
PERI - Periphyton
FISH - Fish
Rapid Bioassessment Protocols for Use in Streams and Rivers
D-7
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DRAFT REVISION-^July 29,1997
(b.) Probable Cause
Agriculture
Silviculture
Mining
Grazing
Dam
Diversion
Channelization
Urban encroachment
Snagging
Other channel modifications
Urbanization/impervious surfaces
Land use changes
Bank failure
Point source discharges
Riparian disturbances
Clear cutting
Mining runoff
Stormwater
Fishermen
Aquarists
Agency
Natural
Unknown
Other (specify)
HABI
PERI
MACR
FISH
Key:
HABI - Habitat
MACR - Macroinvertebrates
PERI - Periphyton
FISH - Fish
Appendix D: Survey Approach for Compilation of Historical Data
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DRAFT REVISION—July 29,1997
SUMMARY: ASPECT OF PHYSICOCHEMICAL OR BIOLOGICAL CONDITION AFFECTED
a Water quality
n Water quantity
a Habitat structure
d Periphyton assemblage
D Macroinvertebrate assemblage
D Fish assemblage
o Other (specify)
SECTION 2. AVAILABILITY OF DATA
Please complete this section with applicable response(s) and fill in the blanks with appropriate information
based on your knowledge of available biosurvey and bioassessment information.
Reach characterized by:
a Stream habitat surveys
n Periphyton surveys assemblage n key species n
D Macroinvertebrate surveys assemblage Q key species D
a Fish surveys assemblage n key species a
Sampling gear(s) or methods Sampling frequency (spatial and temporal)
Data analysis/interpretation based on: Electronic file available:
Tabulated data D Format
Graphical data n
Multivariate analyses, n
Multimetric approach, a
Statistical routines include: Metrics include:
Rapid Bioassessment Protocols for Use in Streams and Rivers D-9
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DRAFT REVISION-July 29,1997
List pertinent reports/citations:
List other relevant technical contacts:
Contact.
Address
Phone
Contact.
Address
Phone
Questionnaire completed by:
Signature
D-10
Appendix D: Survey Approach for Compilation of Historical Data
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