United States
Environmental Protection
Agency
Office of Water
4503F
Washington, DC 20460
EPA 841 -B-99-002
July 1999
oEPA
Rapid Bioassessment
Protocols for Use in Wadeable
Streams and Rivers
Periphyton, Benthic
Macroinvertebrates, and Fish
Second Edition
aw
StMstef

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Internet Address (URL) * http://www.epa.gov
Recycled/Recyclable • Printed with Vegetable Oil Based Inks on Recycled Paper (Minimum 30% Postconsumer)

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EPA 841-B-99-002
Rapid Bioassessment Protocols
For Use in Streams and Wadeable Rivers:
Periphyton, Benthic Macroinvertebrates, and Fish
Second Edition

http://www.epa.gov/OWOW/raonitoring/techmon.htmI
By:	Project Officer:
Michael T. Barbour	Chris Faulkner
Jeroen Gerritsen	Office of Water
Blaine D. Snyder	USEPA
James B. Stribling	401 M Street, NW
Washington, DC 20460

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Notice
This document has been reviewed and approved in accordance with U.S. Environmental Protection
Agency policy. Mention of trade names or commercial products does not constitute endorsement or
recommendation for use.
Appropriate Citation:
Barbour, M.T., J. Gerritsen, B.D. Snyder, and J.B. Stribling.
1999. Rapid Bioassessment Protocols for Use in Streams and
Wadeable Rivers: Periphyton, Benthic Macroinvertebrates and
Fish, Second Edition. EPA 841-B-99-002. U.S. Environmental
Protection Agency; Office of Water; Washington, D.C.
This entire document, including data forms and other appendices, can be downloaded from the
website of the USEPA Office of Wetlands, Oceans, and Watersheds:
http://wvrvv.epa.gov/OWOW/monitoring/techmon.html

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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 Wadeable Rivers: Periphyton, Benthic
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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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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 Rapid Bioassessment
Protocols 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. Principal authors of
this revision are Michael T. Barbour, James (Sam) Stribling, Jeroen Gerritsen, and Blaine D. Snyder.
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
USEPA 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. Dr. Vincent Resh also provided a critical review that helped strengthen the
RBP approach. While not directly involved with the development of the RBPs, Dr. James Kan-
provided the framework (Index of Biotic Integrity) and theoretical underpinnings for "re-inventing"
bioassessment for water resource investigations. Since 1989, extensive use and application of the
IBI and 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 United States. Editorial and production support, report design,
and HTML formatting were provided by a team of Tetra Tech staff — Brenda Fowler, Michael
Bowman, Erik Leppo, James Kwon, Amanda Richardson, Christiana Daley, and Abby Markowitz.
Technical assistance and critical review was provided by Dr. Jerry Diamond of Tetra Tech.
A Technical Experts Panel was convened by the USEPA to provide an in-depth review and
recommendations for revisions to this document. This group of esteemed scientists provided not
only useful comments, but assisted in revising sections of the document. In particular, Drs. Jan
Stevenson and Loren Bahls revised the periphyton chapter; and Dr. Phil Kaufmann provided
assistance on the habitat chapter. The Technical Experts Panel included:
Dr. Reese Voshell, Virginia Tech University (Chair)
Dr. Loren Bahls, University of Montana
Dr. David Halliwell, Aquatic Resources Conservation Systems
Dr. James Karr, University of Washington
Dr. Phil Kaufmann, Oregon State University
Dr. Billie Kerans, Montana State University
Dr. Jan Stevenson, University of Louisville
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Dr. Charles Hawkins (Utah State University) and Dr. Vincent Resh (University of California,
Berkeley) served as outside readers.
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.
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Table of Contents
FOREWORD 	i
DEDICATION 	 ii
ACKNOWLEDGMENTS 	iii
LIST OF FIGURES AND TABLES	ix
LIST OF ACRONYMS 	 xii
1.	THE CONCEPT OF RAPID BIOASSESSMENT		 1-1
1.1	PURPOSE OF THE DOCUMENT	1-1
1.2	HISTORY OF THE RAPID 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 	2-5
2.3.1	CWA Section 305(b)—Water Quality Assessment	2-5
2.3.2	CWA Section 319— Nonpoint Source Assessment	2-5
2.3.3	Watershed Protection Approach	2-6
2.3.4	CWA Section 303(d)—The TMDL Process 	2-6
2.3.5	CWA Section 402—NPDES Permits and Individual Control Strategies	2-7
2.3.6	Ecological Risk Assessment	2-8
2.3.7	USEPA Water Quality Criteria and Standards	2-8
3.	ELEMENTS OF BIOMONITORING	3-1
3.1	BIOSURVEYS, BIOASSAYS, 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 	3-4
3.4	THE REGIONAL REFERENCE CONCEPT	3-5
3.5	STATION SITING 	3-7
3.6	DATA MANAGEMENT AND ANALYSIS 	3-8
3.7	TECHNICAL ISSUES FOR SAMPLING THE PERIPHYTON ASSEMBLAGE . . 3-10
3.7.1 Seasonality	3-10
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3.7.2 Sampling Methodology		3-10
3.8	TECHNICAL ISSUES FOR SAMPLING THE BENTHIC
MACROINVERTEBRATE ASSEMBLAGE		3-11
3.8.1	Seasonality for Benthic Collections (adapted from Gibson et al.1996) ....	3-11
3.8.2	Benthic Sampling Methodology		3-12
3.9	TECHNICAL ISSUES FOR THE SURVEY OF THE FISH ASSEMBLAGE		3-14
3.9.1	Seasonality for Fish Collections				3-14
3.9.2	Fish Sampling Methodology				3-14
3.9.2.1	Advantages and Disadvantages of Electrofishing 		3-14
3.9.2.2	Advantages and Disadvantages of Seining		3-15
3.10	SAMPLING REPRESENTATIVE HABITAT 				3-16
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	 4-11
4.6	APPLICATION OF THE PBMS		 4-13
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	S ite Location/Map		5-2
5.1.4	Stream Characterization		5-2
5.1.5	Watershed Features		5-3
5.1.6	Riparian Vegetation 				5-3
5.1.7	Instream Features 		5-3
5.1.8	Large Woody Debris			5-4
5.1.9	Aquatic Vegetation		5-5
5.1.10	Water Quality				5-5
5.1.11	Sediment/Substrate		5-5
5.2	A VISUAL-BASED HABITAT ASSESSMENT 				5-5
5.3	ADDITIONS OF QUANTITATIVE MEASURES TO THE HABITAT
ASSESSMENT 		 5-31
6.	PERIPHYTON PROTOCOLS 		 6-1
By R. Jan Stevenson, University of Louisville, and Loren L. Bahls, University of Montana
6.1 STANDARD LABORATORY-BASED APPROACH	 6-2
6.1.1 Field Sampling Procedures: Natural Substrates	 6-2
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6.1.1.1	Multihabitat Sampling 	6-2
6.1.1.2	Single Habitat Sampling	6-4
6.1.2	Field Sampling Procedures: Artificial Substrates 	6-5
6.1.3	Assessing Relative Abundances of Algal Taxa: Both "Soft" (Non-Diatom)
Algae and Diatoms	6-6
6.1.3.1	"Soft" (Non-Diatom) Algae Relative Abundance and Taxa Richness . .. 6-7
6.1.3.2	Diatom Relative Abundances and Taxa Richness 	6-7
6.1.3.3	Calculating Species Relative Abundances and Taxa Richness	6-8
6.1.3.4	Alternative Preparation Techniques	6-8
6.1.4	Metrics Based on Species Composition		6-10
6.1.5	Determining Periphyton Biomass	6-15
6.1.5.1	Chlorophyll a 		6-16
6.1.5.2	Ash-Free Dry Mass 	6-16
6.1.5.3	Area-Specific Cell Densities and Biovolumes	6-16
6.1.5.4	Biomass Metrics	6-17
6.2 FIELD-BASED RAPID PERIPHYTON SURVEY	6-17
6.3	TAXONOMIC REFERENCES FOR PERIPHYTON	6-19
6.4	AUTECOLOGICAL REFERENCES FOR PERIPHYTON 	6-21
7.	BENTHIC MACROINVERTEBRATE 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 DP NET	7-5
7.2.1	Habitat Types 	7-6
7.2.2	Field Sampling Procedures for Multihabitat	7-7
7.3	LABORATORY PROCESSING FOR MACROINVERTEBRATE SAMPLES 	7-9
7.3.1	Subsampling and Sorting 	7-9
7.3.2	Identification of Macroinvertebrates	7-12
7.4	BENTHIC METRICS 		7-13
7.5	BIOLOGICAL RECONNAISSANCE (BioRecon) OR PROBLEM IDENTIFICATION
SURVEY 	7-18
7.5.1 Sampling, Processing, and Analysis Procedures 	7-19
7.6	TAXONOMIC REFERENCES FOR MACROINVERTEBRATES	7-20
8.	FISH PROTOCOLS	8-1
8.1	FISH COLLECTION PROCEDURES: ELECTROFISHENG	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	8-8
8.3.2	Trophic Composition Metrics	8-12
8.3.3	Fish Abundance and Condition Metrics	;	8-12
8.4	TAXONOMIC REFERENCES FOR FISH 		8-14
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9.	BIOLOGICAL DATA ANALYSIS							9-1
9.1	THE MULTIMETRIC APPROACH			9-3
9.1.1	Metric Selection, Calibration, and Aggregation into an Index			9-3
9.1.2	Assessment of Biological Condition					9-13
9.2	DISCRIMINANT MODEL INDEX		9-14
9.3	RIVER INVERTEBRATE PREDICTION AND CLASSIFICATION SCHEME
(RTVPACS)		9-15
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			10-9
11.	LITERATURE CITED 			 11-1
APPENDIX A: SAMPLE DATA FORMS FOR THE PROTOCOLS 	A-1
APPENDIX B: TOLERANCE, FUNCTIONAL FEEDING GROUP, AND
HABIT/BEHAVIOR DESIGNATIONS FOR BENTHOS	B-l
APPENDIX C: TOLERANCE AND TROPHIC GUILDS OF SELECTED FISH
SPECIES	C-l
APPENDIX D: SURVEY APPROACH FOR COMPILATION OF HISTORICAL
DATA						 . D-l
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List of Figures And Tables
FIGURES
Figure 3-1 Example of the relationship of data tables in a typical relational database	3-9
Figure 3-2 Example input or lookup form in a typical relational database	3-10
Figure 4-1 Flow chart summarizing the steps necessary to quantify performance characteristics
of a bioassessment method (modified from Diamond et al. 1996)	 4-7
Figure 4-2 Comparison of the discriminatory ability of the SCI between Florida's Peninsula
and Panhandle Bioregions	4-13
Figure 8-1 Sequence of activities involved in calculating and interpreting the Index of Biotic
Integrity (adapted from Karr et al. 1986)	 8-7
Figure 9-1 Comparison of the developmental process for the multimetric and multivariate
approaches to biological data analysis (patterned after ideas based on Reynoldson,
Rosenberg, and Resh, unpublished data)	9-2
Figure 9-2 Process for developing assessment thresholds (modified from Paulsen et al. [1991]
and Barbour et al. [1995]) 	9-4
Figure 9-3 Species richness versus stream size (taken from Fausch et al. 1984) 	 9-5
Figure 9-4 Results of multivariate ordination on benthic macroinvertebrate data from "least
impaired" streams from Maryland, using nonmetric multidimensional scaling
(NMDS) of Bray-Curtis dissimilarity coefficients		 9-5
Figure 9-5 An example of a metric that illustrates classification of reference stream sites in
Florida into bioregions	9-6
Figure 9-6 Example of discrimination, using the EPT index, between reference and stressed sites
in Rocky Mountain streams, Wyoming	9-8
Figure 9-7 Basis of metric scores using the 95th percentile as a standard	9-10
Figure 9-8 Discriminatory power analysis of the Wyoming Benthic Index of Biotic Integrity. 5-11
Figure 10-1 Cumulative frequency diagrams (CFD) for the IBI (upper) and the ICI (lower)
comparing the pre-1988 and post-1988 status on a statewide basis from Ohio. In each
case, estimated attainable level of future performance is indicated. The Warm Water
Habitat (WWH) and Exceptional Warm Water Habitat (EWH) biological thresholds
are given for each index	10-2
Figure 10-2 Relationship between the condition of the biological community and physical
habitat	10-3
Figure 10-3 Data from a study of streams in Florida's Panhandle 	10-3
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Figure 10-4
Figure 10-5
Figure 10-6
Figure 10-7
Figure 10-8
Figure 10-9
Figure 10-10
Figure 10-11
Figure 10-12
Figure 10-13
Figure 10-14
Comparison of integrated assessment (habitat, fish, and benthos) among stream
sites in Pennsylvania. Station 16 is a reference site. (Taken from Snyder et al.
1998)	 10-4
Use of multidimensional scaling on benthic data to ascertain stream
classification. The first and second axes refer to the dimensions of combinations of
data used to measure similarity (Taken from Barbour et al. 1996b)	 10-5
Example of a cluster dendrogram, illustrating similarities and clustering of sites
(x-axis) using biological data	 10-5
Results of the benthic assessment of streams in the Mattaponi Creek watershed of
southern Prince George's County, Maryland. Percent of streams in each ecological
condition category. (Taken from Stribling et al. 1996b)	 10-6
The population of values of the IBI in reference sites within each of the
ecoregions of Ohio. Contributed by Ohio EPA	 10-6
Spatial and temporal trend of Ohio's Invertebrate Community Index. The Scioto
River - Columbus to Circleville. Contributed by Ohio EPA	 10-7
Cumulative distribution of macroinvertebrate index scores. 21% of sites scored at
or below 60. The median index score is 75, where the cumulative frequency is
50%	 10-7
Biological assessment of sites in the Middle Rockies, showing mean and standard
deviation of repeated measures and the assessment threshold (dashed line)	 10-8
Integration of data from habitat, fish, and benthic assemblages 	 10-8
The response of the benthic macroinvertebrate assemblage (ICI) to various types
of impacts. (Provided by Ohio EPA)	 10-8
Guidance for Florida Ecosummary - A one-page bioassessment report. (Contributed
by Florida DEP) 	 10-10
TABLES
Table 2-1 Chronology of USEPA bioassessment guidance (relevant to streams and rivers). . .. 2-2
Table 4-1 Progression of a generic bioassessment field and laboratory method with associated
examples of performance characteristics	 4-3
Table 4-2 Translation of some performance characteristics, derived for laboratory analytical
systems, to biological laboratory systems (taken from Diamond et al. 1996)	 4-5
Table 4-3 Suggested arithmetic expressions for deriving performance characteristics that can be
compared between 2 or more methods. In all cases, x = mean value, X = test site value,
s = standard deviation. Subscripts are as follows: capital letter refers to site class (A or
B); numeral refers to method 1 or 2; and lower case letter refers to reference (r) or test
site (t) (modified from Diamond et al. 1996)	 4-10
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Table 5-1 Components of EMAP physical habitat protocol 		 5-32
Table 5-2 Example of habitat metrics that can be calculated from the EMAP physical habitat
data	5-33
Table 6-1 Summary of collection techniques for periphyton from wadeable streams (adapted
from Kentucky DEP 1993, Bahls 1993) 		 6-3
Table 6-2 Environmental definitions of autecological classification systems for algae (as modified
or referenced by Lowe 1974). Definitions for classes are given if no subclass is
indicated	6-15
Table 7-1 Definitions of best candidate benthic metrics and predicted direction of metric response
to increasing perturbation (compiled from DeShon 1995, Barbour et al. 1996b, Fore et al.
1996, Smith and Voshell 1997)	 7-15
Table 7-2 Definitions of additional potential benthic metrics and predicted direction of metric
response to increasing perturbation	7-16
Table 8-1 Fish IBI metrics used in various regions of North America	8-9
Table 9-1 Some potential metrics for periphyton, benthic macroinvertebrates, and fish that could
be considered for streams. Redundancy can be evaluated during the calibration phase
to eliminate overlapping metrics	9-7
Table 9-2 Statistics of repeated samples in Wyoming and the detectable difference (effect size)
at 0.05 significance level. The index is on a 100 point scale (taken from Stribling et
al. 1999)	 9-13
Table 9-3 Maine's water quality classification system for rivers and streams, with associated
biological standards (taken from Davies et al. 1993) 	 9-15
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The Concept of Rapid
Bioassessment
1.1 PURPOSE OF THE DOCUMENT
Biological assessment is an
evaluation of the condition of a
waterbody using biological surveys
and other direct measurements of the
resident biota in surface waters.
The primary purpose of this document is to describe 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 for bioassessment nor is it intended to be used
as a rigid protocol without regional modifications. Instead,
they provide options for agencies or groups that wish to
implement rapid biological assessment and monitoring
techniques. This guidance, therefore, is intended to provide basic, cost-effective biological methods
for states, tribes, and local agencies 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 is 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 nonpoint-source evaluations, use attainability analyses, and trend monitoring, as well as
initial screening.
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1.2 HISTORY OF THE RAPID BIO ASSESSMENT 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 and the extensive miles of un-assessed
stream miles in the United States. It was also recognized that the biological data needed to make
informed decisions relevant to the Nation's waters were greatly lacking across the country. It was
further 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 for biological surveys
•	Provisions for multiple site investigations in a field season
•	Quick turn-around of results for management decisions
•	Scientific reports 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 macroinvertebrate protocols. The second phase involved the addition of
analogous protocols pertinent to the assessment of fish assemblages.
The benthic macroinvertebrate 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 USEPA personnel on an earlier
version of the RBPs, a set of fish protocols was also included. 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. During the same time period as the development of the RBPs,
Ohio EPA developed precedent-setting biological criteria using the IBI and Index of Well Being
(TVVB), as well as a benthic macroinvertebrate index, called the Invertebrate Community Index (ICI),
and published methods and supporting documentation (Ohio EPA 1987). A substantial database on
their use for site-specific fish and benthic macroinvertebrate assessments exists, and has been
published (DeShon 1995, Yoder 1995, Yoder and Rankin 1995a,b). In the intervening years since
1989, several other states have followed suit with similar methods (Davis et al. 1996).
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A workgroup of State and USEPA Regional biologists (listed below) was formed in the late 1980's 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.
USEPA
James Plafkin1, Assessment and Watershed Protection Division (AWPD), USEPA
Michael Bilged, USEPA Region I
Michael Bastian2, USEPA Region VI
William Wuerthele, USEPA Region VIII
Evan Hornig2, USEPA Region X
STATES
Brenda Sayles, Michigan DNR
John Howland2, 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 the AWPD
of USEPA, 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 serves as an update to USEPA'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. A section on conducting
performance-based evaluations, i.e., determining the precision and sensitivity of methods, to enable
sharing of comparable data despite certain methodological differences has 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 upon the original RBPs (Plafkin et al. 1989) as well
as numerous other sources that detail relevant modifications. This document is a compilation of the
basic approaches to conducting rapid bioassessment in streams and wadeable rivers and focuses on
deceased
no longer with state agency or USEPA department relevant to water resource assessments of
ecosystem health.
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the periphyton, benthic macroirivertebrates, and fish assemblages and assessing the quality of the
physical habitat structure.
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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 3 components of ecological integrity, ambient water
chemistry may be the most difficult to characterize because of the complex array of possible
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
A substantial scientific foundation was required before the USEPA could endorse a bioassessment
approach that was applicable on a national basis and that served the purpose of addressing impacts to
surface waters from multiple stressors (see Stribling et al. 1996a). Dr. James Karr is credited for his
innovative thinking and research in the mid-1970's and early 1980's that provided the formula for
developing bioassessment strategies to address issues mandated by the Clean Water Act. The
USEPA convened a few key workshops and conferences during a period from the mid-1970's to mid-
1980's to provide an initial forum to discuss aspects of the role of biological indicators and
assessment to the integrity of surface water. These workshops and conferences were attended by
National scientific authorities who contributed immensely to the current bioassessment approaches
advocated by the USEPA. The early RBPs benefitted from these activities, which fostered attention
to biological assessment approaches. The RBPs embraced the multimetric approach described in the
IBI (see Karr 1981, Karr et al. 1986) and facilitated the implementation of bioassessment into
monitoring programs across the country.
Since the publication of the original RBPs in 1989, U.S. Environmental Protection Agency (USEPA)
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 was facilitated by key scientific researchers who argued that bioassessment was crucial to the
underpinnings of the Clean Water Act. The work of these researchers that led to these USEPA
documents resulted in the national trend of adapting biological assessment and monitoring
approaches for detecting problems, evaluating Best Management Practices (BMPs) for mitigation of
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nonpoint source impacts, and monitoring ecological health over time. The chronology of the crucial
USEPA guidance, since the mid-1980's, relevant to bioassessment in streams and rivers is presented
in Table 2-1. (See Chapter 11 [Literature Cited] for EPA document numbers.)
Tabic 2-1. Chronology of USEPA bioassessment guidance (relevant to streams and rivers).
Year
Document Title
Relationship to Bioassessment
Citation
1987
Surface Water Monitoring: A Framework for
Change
USEPA calls for efficacious methods to assess and
determine the ecological health of the nation's
surface waters.
USEPA
1987
1988
Proceedings of the First National Workshop on
Biological Criteria (Lincolnwood, Illinois)
USEPA brings together agency biologists and
"basic" researchers to establish a framework for the
initial development of biological criteria and
associated biosurvey methods.
USEPA
1988
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 USEPA (1987), which
are to provide biological data on a national scale to
address the goals of the Clean Water Act.
Plafkin et
al. 1989
19S9
Regionalization as a Tool for Managing
Environmental Resources
USEPA 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
USEPA 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.
USEPA
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.
USEPA
1990b
1990
Macroinvertebrate Field and Laboratory Methods
for Evaluating the Biological Integrity of Surface
Waters
This USEPA 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.
USEPA
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.
USEPA
1991b
1991
Technical Support Document for Water
Quality-Based Toxics Control
USEPA 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.
USEPA
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.
USEPA
199 Id
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Table 2-1. Chronology of USEPA bioassessment guidance (relevant to streams and rivers) (Continued).
Year
Document Title
Relationship to Bioassessment
Citation
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.
USEPA
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.
USEPA
1991 f
.1991
Design Report for EMAP, the Environmental
Monitoring and Assessment Program
USEPA'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 USEPA 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
USEPA focuses its EMAP program on streams and
wadeable 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
USEPA describes the value and application of
bioassessment to the TMDL process.
USEPA
1994a
1994
Report of the Interagency Biological Methods
Workshop
Summary and results of workshop designed to
coordinate monitoring methods among multiple
objectives and states. [Sponsored by the USGS]
Gurtz and
Muir 1994
1995
Generic Quality Assurance Project Plan Guidance
for Programs Using Community Level Biological
Assessment in Wadeable Streams and Rivers
USEPA develops guidance for quality assurance
and quality control for biological survey programs.
USEPA
1995a
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
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Table 2-1. Chronology of USEPA bioassessment guidance (relevant to streams and rivers) (Continued).
Year
Document Title
Relationship to Bioassessment
Citation
1996
Biological Assessment Methods, Biocriteria, and
Biological Indicators: Bibliography of Selected
Technical, Policy, and Regulatory Literature
USEPA compiles a comprehensive literature survey
of pertinent research papers and studies for
biological assessment methods. This document is
expanded and updated from USEPA 1991b.
Stribling
et al.
1996a
19 96
Summary of State Biological Assessment
Programs for Wadeable Streams and Rivers
The status of bioassessment and biocriteria
programs in state water resource programs is
summarized in this document, providing an update
of USEPA 1991a.
Davis et
al. 1996
1996
Biological Criteria: Technical Guidance for
Streams and Small Rivers
Technical guidance for development of biocriteria
for streams and wadeable rivers is provided as a
follow-up to the Program Guidance (USEPA
1990b). This technical guidance serves as a
framework for developing guidance for other
surface water types.
Gibson et
al. 1996
1996
The Volunteer Monitor's Guide to Quality
Assurance Project Plans
USEPA develops guidance for quality assurance for
citizen monitoring programs.
USEPA
1996a
1996
Nonpoint Source Monitoring and Evaluation
Guide
USEPA 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.
USEPA
1996b
1996
Biological Criteria: Technical Guidance for
Survey Design and Statistical Evaluation of
Biosurvey Data
USEPA describes and define different statistical
approaches for biological data analysis and
development of biocriteria.
Reckhow
and
Warren-
Hicks
1996
1997
Estuarine/Near Coastal Marine Waters
Bioassessment and Biocriteria Technical
Guidance
USEPA provides technical guidance on biological
assessment methods and biocriteria development for
estuarine and near coastal waters.
USEPA
1997a
1997
Volunteer Stream Monitoring: A Methods
Manual
USEPA 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.
USEPA
1997b
1997
Guidelines for Preparation of Comprehensive
State Water Quality Assessments (305[b] reports)
USEPA provides guidelines for states for preparing
305(b) reports to Congress.
USEPA
1997c
1997
Biological Monitoring and Assessment: Using
Multimetric Indexes Effectively
An explanation of the value, use, and scientific
principles associated with using a multimetric
approach to bioassessment is provided by Drs. Karr
and Chu.
Karr and
Chu 1999
1998
Lake and Reservoir Bioassessment and
Biocriteria Technical Guidance Document
USEPA provides technical guidance on biological
assessment methods and biocriteria development for
lakes and reservoirs.
USEPA
1998
1998
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 1995 Methods Manual
for EM AP.
Lazorchak
et al. 1998
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2.3 PROGRAMMATIC APPLICATIONS OF BIOLOGICAL DATA
States (and tribes to a certain extent) are responsible for identifying water quality problems,
especially 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 wadeable streams and
rivers through existing state programs.
2.3.1	CWA Section 305(b)—Water Quality Assessment
Section 305(b) establishes a process for reporting information about the quality of the Nation's water
resources (USEPA 1997c, USEPA 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 to USEPA. This information is
compiled into a biennial National Water Quality Inventory report to Congress.
Use of biological assessment in section 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
(USEPA 1980). Improved coverage of biological integrity issues, based on monitoring protocols
with clear bioassessment endpoints, will make the section 305(b) reports more accessible and
meaningful to many segments of the public.
Biological monitoring provides data that augment several of the section 305(b) reporting
requirements. In particular, the following assessment activities and reporting requirements are
enhanced through the use of 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	CWA Section 319—Nonpoint Source Assessment
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 (NPS) pollution. Under this
program, states are asked to assess their NPS pollution problems and submit these assessments to
USEPA. The assessments include a list of "navigable waters within the state which, without
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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, USEPA has been promoting the Watershed Protection Approach (WPA) as a framework
for meeting the Nation's remaining water resource challenges (USEPA 1994c). USEPA'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.
USEPA has 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
aquatic communities.
2.3.4	CWA Section 303(d)—The TMDL Process
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 (USEPA 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.
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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 (USEPA 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.
Biological assessments and criteria address the cumulative impacts of all stressors, especially habitat
degradation, and chemical contamination, which result in a loss of biological diversity. 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 (USEPA 1997c) 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 CWA Section 402—NPDES Permits and Individual Control Strategies
All point 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 (USEPA 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. In addition, stormwater
may be discrete or diffuse, but is also covered by NPDES permitting regulations.
USEPA does not recommend the use of biological survey data as the basis for deriving an effluent
limit for an NPDES permit (USEPA 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. Where
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appropriate, biological assessment can be used within the NPDES process (USEPA 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 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) (USEPA
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 (USEPA 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.
2.3.6	Ecological Risk Assessment
Risk assessment is a scientific process that includes stressor identification, receptor characterization
and endpoint selection, stress-response assessment, and risk characterization (USEPA 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.
In early 1997, a report prepared by a Presidential/Congressional Commission on risk enlarged the
context of risk to include ecological as well as public health risks (Karr and Chu 1997).
Biological monitoring is the essential foundation of ecological risk assessment because it measures
present biological conditions — not just chemical contamination — and provides the means to
compare them with the conditions expected in the absence of humans (Karr and Chu 1997). 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 and should use such results for critical
management decisions.
2.3.7	USEPA 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 USEPA. The states have primary responsibility for setting,
reviewing, revising, and enforcing water quality standards. USEPA 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 relevant water quality
standards regulations (40 CFR Part 131). USEPA 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
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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.
Biological criteria for aquatic systems provide 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 a minimally
impaired aquatic community (USEPA 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 fiilfill a function missing from USEPA's traditionally chemical-
oriented approach to pollution control and abatement (USEPA 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 1 class of aquatic life into 2 or more subclasses. For example, Ohio has
established an exceptional warmwater use class to include all unique waters (i.e., not representative
of regional streams and different from their standard warmwater class).
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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 ecological data, such as chemical and biological (toxicity) testing is helpful to identify the
causative agent, its source, and to implement appropriate mitigation (USEPA 1991c). Integrating
information from these data types as well as from habitat assessments, hydrological investigations,
and knowledge of land use is helpful to provide a comprehensive diagnostic assessment of impacts
from the 5 principal factors (see Karr et al. 1986, Karr 1991, Gibson et al. 1996 for description of
water quality, habitat structure, energy source, flow regime, and biotic interaction factors).
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.
•	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:
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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
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 helpful to confirm the
point sources and develop appropriate discharge limits. Impacts due to factors other than chemical
contamination will require different ecological data.
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 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 techniques presented in this document focus on the evaluation of water quality (physicochemical
constituents), habitat parameters, and analysis of the periphyton, benthic macroinvertebrate, 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) (Kan- 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).
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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, benthic macroinvertebrates, and fish in a biomonitoring
program are presented in this section. References for this list are Cairns and Dickson (1971),
American Public Health Association et al. (1971), Patrick (1973), Rodgers et al. (1979), Weitzel
(1979), Karr (1981), USEPA (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.
•	Degraded conditions can often be detected by an experienced biologist with only a
cursory examination of the benthic macroinvertebrate assemblage. Macro-
invertebrates 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
minimal detrimental effect on the resident biota.
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Benthic macroinvertebrates serve as a primary food source for fish, including many
recreationally and commercially important species.
•	Benthic macroinvertebrates are abundant in most streams. Many small streams (1 st
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 assemblage 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 most 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 standaids) are typically characterized in terms of
fisheries (coldwater, coolwater, warmwater, sport, forage). Monitoring fish provides
direct evaluation of "fishability" and "fish propagation", which emphasizes the
importance of fish to anglers and commercial fishermen.
•	Fish account for nearly half of the endangered vertebrate species and subspecies in
the United States (Warren and Burr 1994).
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 suid surrounding land, particularly the
catchment of the site under investigation. All of the habitat parameters evaluated are related to
overall aquatic life use and are a potential source of limitation to the aquatic biota.
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The alteration of the physical structure of the habitat is one of 5 major factors from human activities
described by Karr (Karr et al. 1986, Karr 1991) that degrade aquatic resources. 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 REGIONAL REFERENCE CONCEPT
The issue of reference conditions is critical to the interpretation of biological surveys. Barbour et al.
(1996a) describe 2 types of reference conditions that are currently used in biological surveys: site-
specific and regional reference. The former typically consists of measurements of conditions
upstream of a point source discharge or from a "paired" watershed. Regional reference conditions,
on the other hand, consist of measurements from a population of relatively unimpaired sites within a
relatively homogeneous region and habitat type, and therefore are not site-specific.
The reference condition establishes the basis for making comparisons and for detecting use
impairment; it should be applicable to an individual waterbody, such as a stream segment, but also to
similar waterbodies on a regional scale (Gibson et al. 1996).
Although both site-specific and ecoregional references represent conditions without the influence of
a particular discharge, the 2 types of references may not yield equivalent measurements (Barbour et
al. 1996a). While site-specific reference conditions represented by the upstream, downstream, or
paired-site approach are desirable, they are limited in their usefulness. Hughes (1995) points out
three problems with site-specific reference conditions: (1) because they typically lack any broad
study design, site-specific reference conditions possess limited capacity for extrapolation—they
have only site-specific value; (2) usually site-specific reference conditions allow limited variance
estimates; there are too few sites for robust variance evaluations because each site of concern is
typically represented by one-to-three reference sites; the result could be an incorrect assessment if
the upstream site has especially good or especially poor habitat or chemical quality; and (3) they
involve a substantial assessment effort when considered on a statewide basis.
The advantages of measuring upstream reference conditions are these: (1) if carefully selected, the
habitat quality is often similar to that measured downstream of a discharge, thereby reducing
complications in interpretation arising from habitat differences, and (2) impairments due to upstream
influences from other point and nonpoint sources are already factored into the reference condition
(Barbour et al. 1996a). New York DEC has found that an upstream-downstream approach aids in
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diagnosing cause-and-effect to specific discharges and increase precision (Bode and Novak 1995).
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.
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). Waterbodies reflect the lands they drain (Omernik 1987, Hunsaker and
Levinc 1995) and it is assumed that similar lands should produce similar waterbodies. This
ecoregional approach provides robust and ecologically-meaningful regional maps that are based on
an examination of several mapped land variables. 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.
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
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 of fish communities approximate ecoregional boundaries as
defined a priori by Omernik (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).
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). In addition, descriptors at a smaller scale may be needed to characterize
streams within regions or classes. For example, even though a given stream segment is classified
within a subecoregion or other type of stream class, 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 regional reference streams, nor will all conditions (i.e.,
deciduous, coniferous, open) be present in all streams. 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. For example, Ohio EPA (1987) considered
aquatic systems with unique (i.e., unusual for the ecoregion) natural characteristics to be a separate
aquatic life use designation (exceptional warmwater aquatic life use) on a statewide basis.
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Although the final rapid bioassessment guidance should be generally applicable to all regions 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 regional reference 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), regional
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 regional criteria may be the only means of discerning use impairment
or assessing impact. Additional investigation will be needed to: delineate areas (classes of
streams)that differ significantly in their innate biological potential; locate reference sites within each
stream class that fully support aquatic life uses; develop biological criteria (e.g., define optimal
values for the metrics) using data generated from each of the assemblages.
3.5 STATION SITING
Site selection for assessment and monitoring can either be "targeted", i.e., relevant to special studies
that focus on potential problems, or "probabilistic", which provides information of the overall status
or condition of the watershed, basin, or region. In a probabilistic or random 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. Selecting sites randomly provides
an unbiased assessment of the condition of the waterbody at a scale above the individual site or
stream. Thus, an agency can address questions at multiple scales. Studies for 305(b) status and
trends assessments are best done with a probabilistic design.
Most studies conducted by state water quality agencies for identification of problems and sensitive
waters are done with a targeted design. In this case, sampling sites are selected based on known
existing problems, knowledge of upcoming events that will adversely affect the waterbody such as a
development or deforestation; or installation of BMPs or habitat restoration that are intended to
improve waterbody quality. This method provides assessments of individual sites or stream reaches.
Studies for aquatic life use determination and those related to TMDLs can be done with a random
(watershed or higher level) or targeted (site-specific) design.
To meaningfully evaluate biological condition in a targeted design, sampling locations must be
similar enough to have similar biological expectations, which, in turn, 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 sample stations.
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 because these areas will have habitat more typical of the larger waterbody
(Karretal. 1986).
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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 region 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.
3.6 DATA MANAGEMENT AND ANALYSIS
USEPA is developing 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 obtainable 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 (RDMS). Most relational database software is designed for the
Windows operating system and offer menu driven interfaces and ranges of toolbars that provide
quick access to many routine database tasks. Automated tools help users quickly create forms for
data input and lookup, tables, reports, and complex queries about the data. The USEPA is
developing a multiuser relational database management system that can transfer sampling data to
STORET. This relational database management system is EDAS (Ecological Data Application
System) and allows the user to input, compile, and analyze complex ecological data to make
assessments of ecosystem condition. EDAS includes tools to format sampling data so it may be
loaded into STORET as a batch file. These batch files are formatted as flat ASCII text and can be
loaded (transferred) electronically to STORET. This will eliminate the need to key sample data into
STORET.
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By using tables and queries as established in EDAS, a user can enter, manipulate, and print data.
The metrics used in most bioassessments can be calculated with simple queries that have already
been created for the user. New queries may be created so additional metrics can be 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 function 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.
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Table design is the foundation of the relational database, such as EDAS (Figure 3-1), 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 "ChemSamps", "HabSamps", and "BenSamps" to the
"Stations" table. The chemical parameters and habitat parameters table act as reference tables and
contain descriptive data (e.g., measurement units, detection limits). This method of storing data is
more efficient than spreadsheets, because it eliminates a lot of redundant data. Master Taxa tables
are 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 3-2) 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. Reports can
be generated in a variety of styles, and data can be exported to other databases or spreadsheet
programs.
Rapid Bioassessment Protocols for Use in Streams and Wadeahle Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
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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 sampling 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 to characterize
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the natural community. Advantages of artificial and natural substrates are summarized below (Cairns
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.
•	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.
•	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 state 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 Seasonality for Benthic Collections (adapted from Gibson et al. 1996)
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. Such indexing
makes the best use of the biological data. However, resident assemblages integrate stress effects
Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
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over the course of the year, and their seasonal cycles of abundance and taxa composition are fairly
predictable within the limits of interannual variability.
Many programs have found that a single index period p rovides 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. Although
a single index period would not likely be adequate for assessing the effects of catastrophic events,
such as spill, 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 3 factors that reflect
efforts to:
1.	minimize year-to-year variability resulting from natural events,
2.	maximize gear efficiency, and
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
asscmblage(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 or low 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 (Cairns 1982) relative to the use of natural
substrates are presented below.
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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.
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 tetrieve 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.
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Macroinvertebrates, and Fish, Second Edition	3-13
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•	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, Cairns and Kaesler 1971). The Ohio Environmental
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 substantial 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, electrofishing equipment 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).
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Chapter 3: Elements ofBiomonitoring
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•	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.
•	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.
•	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.
Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
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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 sampling of
representative habitat
Composite sampling is the norm for RBP investigations to characterize the reach, rather than
individual small replicates. However, 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. Replication is strongly
encouraged for precision evaluation of the methods.
When sampling wadeable 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. Although some deep water or wetland species may be undersampled, the
data should be adequate for the objective of bioassessment.
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Chapter 3; Elements of Biomonitoring
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PERFORMANCE-BASED METHODS
SYSTEM (PBMS)
Determining the performance characteristics of individual methods enables agencies to share data to
a certain extent by providing an estimate of the level of confidence in assessments from one method
to the next. The purpose of this chapter is to provide a framework for measuring the performance
characteristics of various methods. 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). This chapter is
best assimilated if the reader is familiar with data analysis for bioassessment. Therefore, the reader
may wish to review Chapter 9 on data analysis before reading this PBMS material. Specific quality
assurance aspects of the methods are included in the assemblage chapters.
Regardless of the type of data being collected, field methods share one important feature in
common—they cannot tell whether the information collected is an accurate portrayal of the system
of interest (Intergovernmental Task Force on Monitoring Water Quality [HTM] 1995a). Properties
of a given field sample can be known, but research questions typically relate to much larger spatial
and temporal scales. It is possible to know, with some accuracy, properties or characteristics of a
given sample taken from the field; but typically, research questions relate to 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 information that is representative of the field
conditions at the time of sampling.
An accurate assessment of stream 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, the accuracy of macroinvertebrate assessments
in the field cannot be objectively verified. For example, it isn't possible to "spike" a stream with a
known species assemblage 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 assemblage present, or the trends in status of the
assemblage over time may be misinterpreted. Even with considerable convergence in methods used
in the U.S. by states and other agencies (Southerland and Stribling 1995, Davis et al. 1996), direct
sharing of data among agencies may cause problems because of the uncertainty associated with
unfamiliar methods, misapplication of familiar methods, or varied data analyses and interpretation
Water quality management programs have different reasons for doing bioassessments which may not
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. 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. Most water resource agencies in the U.S. have
developed standard operating procedures (SOPs). These SOPs would be adhered to throughout
statewide or regional areas to provide comparable assessments within each program. The Rapid
(Diamond et al. 1996).
4.1 APPROACHES FOR ACQUIRING COMPARABLE
BIOASSESSMENT DATA
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Bioassessment Protocols (RBPs) developed by Plafkin et al. (1989) and refined in this document 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).
The second approach to acquiring comparable data from different organizations, is to encourage the
documentation of performance characteristics (e.g., precision, sensitivity) for all methods and to 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 (USEPA 1984, 1986, Klemm et al. 1990, USEPA 1995a, 1997c). It is difficult
to make an informed decision regarding which methods to use if data quality characteristics are
unavailable. The successful introduction of the PBMS concept in laboratory chemistry, and more
recently in laboratory toxicity testing (USEPA 1990c, American Society of Testing and Materials
[ASTM] 1995), recommends adapting such a system for biological monitoring and assessment.
If different methods are similar with respect to the quality of data each produces, then results of an
assessment from those methods may be used interchangeably or together. 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, Cuffiiey 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; and
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Chapter 4: Performance-Based Methods System (PBMS)
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4. the method must be effective over the prescribed range of 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 the
individual metrics that make up the score (Diamond et al. 1996).
Several performance characteristics must be characterized for a
given method to utilize a PBMS approach. These characteristics
include method precision, bias, performance range, interferences,
and sensitivity (detection limit). These characteristics, 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 derive a reference method for a given
analyte. Many of these performance characteristics 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: (1) with respect to the collection method itself and, (2) 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 to the assessment.
Table 4-1. Progression of a generic bioassessment field and laboratory method with associated examples
of performance characteristics.	
Step
Procedure
Examples of Performance Characteristics
1
Sampling
device
Precision—repeatability in a habitat.
Bias—exclusion of certain taxa (mesh size).
Performance range—different efficiency in various habitat types or substrates.
Interferences—matrix or physical limitations (current velocity, water depth).
2
Sampling
method
Precision—variable metrics or measures among replicate samples at a site.
Bias—exclusion of certain taxa (mesh size) or habitats.
Performance range—limitations in certain habitats or substrates.
Interferences—high river flows, training of personnel.
			
PERFORMANCE
CHARACTERISTICS
• Precision
Bias
Performance range
Interferences
Sensitivity
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Table 4-1. Progression of a generic bioassessment field and laboratory method with associated
examples of performance characteristics. (Continued)
Step
Procedure
Examples of Performance Characteristics
3
Field sample
processing
(subsampling,
sample
transfer,
preservation)
Precision—variable metrics among splits of subsamples.
Bias— efficiency of locating small organisms.
Performance range—sample preservation and holding time.
Interferences—Weather conditions.
Additional characteristics:
Accuracy—of sample transfer process and labeling.
4
Laboratory
sample
processing
(sieving,
sorting)
Precision—split samples.
Bias—sorting certain taxonomic groups or organism size.
Performance range—sorting method depending on sample matrix (detritus, mud).
Interferences—distractions; equipment.
Additional characteristics:
Accuracy—sorting method; lab equipment.
5
Taxonomic
enumeration
Precision—split samples.
Bias—counts and identifications for certain taxonomic groups.
Performance range—dependent on taxonomic group and (or) density.
Interferences—appropriateness of taxonomic keys.
Sensitivity— level of taxonomy related to type of stressor
Additional characteristics:
Accuracy—identification and counts.
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 characteristics used in analytical chemistry and how these might be translated to
biological methods.
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 then compare their results with those previously considered acceptable. Although field
crews and 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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Chapter 4: Performance-Based Methods System (PBMS)
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Table 4-2. Translation of some performance characteristics, derived for laboratory analytical systems, to
biological laboratory systems (taken from Diamond et al. 1996).
Performance
Characteristics
Analytical Chemical Methods
Biological Methods
Precision
Replicate samples
Multiple taxonomists identifying 1 sample;
split sample for sorting, identification,
enumeration; replicate samples within sites;
duplicate reaches
Bias
Matrix-spiked samples; standard reference
materials; performance evaluation samples
Taxonomic reference samples; "spiked"
organism samples
Performance
range
Standard reference materials at various
concentrations; evaluation of spiked
samples by using different matrices
Efficiency of field sorting procedures under
different sample conditions (mud, detritus,
sand, low light)
Interferences
Occurrence of chemical reactions involved
in procedure; spiked samples; procedural
blanks; contamination
Excessive detrital material or mud in
sample; identification of young life stages;
taxonomic uncertainty
Sensitivity
Standards; instrument calibration
Organism-spiked samples; standard level of
identification
Accuracy
Performance standards; procedural blanks
Confirmation of identification, percentage
of "missed" specimens
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
BIO ASSESSMENT METHODS
Two fundamental requirements for a biological assessment are: (1) that the sample taken and
analyzed is representative of the site or the assemblage of interest and, (2) that the data obtained are
an accurate reflection of the sample. The latter requirement is ensured using proper quality control
(QC) in the laboratory including the types of performance characteristics summarized in Table 4-2.
The first 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 et al. 1989).
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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 relevant to the "accuracy" of data derived from different
methods (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 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, a PBMS framework has
the advantage of forcing scientists to focus on the ever -important issue: what type of sampling
program and data quality are needed to answer the question at hand?
A second 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 National Water Quality Inventory, (USEPA 1997c)
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 meet those minimum data
requirements, thus increasing the amount of usable information that can be shared. 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 for each
bioassessment study. Once data quality characteristics for the method are established, limited
quality control (QC) sampling and analysis should supplement the required sampling for each
bioassessment study to ensure that data quality characteristics of the method are met (USEPA
1995a). The additional effort and expense of such QC are negligible in relation to the potential
environmental cost of producing data of poor or unknown quality.
The first step is to define precision of the collection method, also known as "measurement error".
This is accomplished by replicate sampling within sites (see Hannaford and Resh 1995). The
samples collected are processed and analyzed separately and their metrics compared to obtain a more
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Step 1
Sample "replicate" reaches or sub-reaches within
sites, using different trained personnel. Repeat
for different site classes (stream size, habitat,
ecoregion).


~
Step 2
Sample at least 5 reference sites in the same site
class (habitat type, stream size, ecoregion).



Step 3
Sample processing and organism identification




Step 4
Compute measures/metrics for each site.




Step 5
Compute precision of each measure among sites.


>
r
Step 6
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.




Step 7
Compare data precision, bias, and method
sensitivity for each site class.




realistic measure of the method precision and
consistency. Repeated samples within sites
estimate the precision of the entire method,
comprising variability due to several sources
including small-scale spatial variability
within a site; operator consistency and bias;
and laboratory consistency. Finally, it is
desirable to 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. This is known as sampling error,
referring to the sample (of sites) drawn from
a subpopulation (sites in a region). 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 1995a; Hornig et al.
1995; Barbour et al. 1996b), some USEPA
programs (Gibson et al. 1996), and the U.S.
Geological Survey (USGS) National Water
Quality Assessment Program (Cuffney et al. 1993b, Gurtz 1994). Evaluation of metric or score
variability among replicate reference sites can result in improved data precision and choices of
stream classification. 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 classification (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 a better
classification scheme to measure sensitivity to impairment.
Figure 4-1. Flow chart summarizing the steps
necessary to quantify performance characteristics of a
bioassessment method (modified from Diamond et ai.
1996).
If multiple reference sites are sampled in different site classes (where the sampling method is judged
to be appropriate), several important method performance characteristics can be 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
classes; (3) range of classes over which a given method yields similar precision and "accuracy"; (4)
potential interferences to a given method that are related to specific class characteristics and
qualities; and (5) bias of a given metric, method, or both, owing to differences in classes (Diamond
et al. 1996).
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A study by Barbour et al. (1996b) for Florida streams, illustrates the importance of documenting
method performance characteristics using multiple reference sites in different site classes. Using the
same method at all sites, fewer taxa were observed in reference sites from the Florida Peninsula (one
site class) compared to the Florida Panhandle (another site class), resulting in much lower reference
values for taxa richness metrics in the Peninsula. Although metric precision was similar among
reference sites in each site class, method sensitivity (i.e., the ability of a metric to discern a
difference between reference and stressed sites) was 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
minimally impaired — 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 site classes and incorporate them into their DQOs. The state in this case can
also use the method performance results to identify those site classes for which the biological
indicator (index, metric, or other measurement endpoint) may not be naturally sensitive to
impairment; i.e., the fauna is naturally species-poor and thus less likely to reflect impacts from
stressors. 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, sites with
known levels of impairment or analogous standards by which to create a calibration curve for a given
bioassessment method are lacking. 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 site class (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
while maintaining biological relevance. Given the often wide variation of natural geomorphic
conditions and landscape ecology, even within supposedly "uniform" site classes (Corkum 1989,
Hughes 1995), it is desirable to examine 10 or more reference sites (Yoder and Rankin 1995a,
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 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 target assemblage), are needed to define performance
characteristics. An alternative approach might be to use bootstrap resampling of fewer sites to
evaluate the nature of variation of these samples (Fore et al. 1996).
A range of "known" stressed sites within a site class is sampled to test the performance
characteristics of a given method. It is important that stressed sites meet the following criteria: (1)
they belong to the same site class as the reference sites examined; (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 severe.
The first criterion is necessary to reduce potential interferences owing to class differences between
the test and reference sites. Thus, the condition of the reference site will have high probability of
serving as a true blank as discussed earlier. For example, it is clearly inappropriate to use high
gradient mountain streams as references for assessing plains streams.
The second 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
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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 third criterion, that the site is not obviously 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 little 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 Figure 4-1 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
Figure 4-1. 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 analyses (Norris 1995).
Method comparability is based on 2 factors: (1) the relative magnitude of the coefficients of
variation in measurements within and among site classes, and (2) the relative percent differences in
measurements between reference and test sites. It is important to emphasize that comparability is not
based 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, detection of a systematic relationship among indices or
the same measures among methods is advised. 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.
Figure 4-1 summarizes a suggested test design, and Table 4-3 summarizes recommended analyses
for documenting both the performance characteristics of a given method, and the degree of data
comparability between 2 or more methods. The process outlined in Figure 4-1 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).
The following performance characteristics are quantified for each bioassessment method and
compared: (1) the within-class coefficient of variation for a given metric score or index by
examining reference-site data for each site class separately (e.g., CVAlr and CVBIr\ Fig. 4-1); (2)
difference or bias in precision related to site class for a given metric or index (by comparing
reference site coefficient of variation from each class: CVA]JCVB]r\ Table 4-3); and (3) estimates of
method sensitivity or discriminatory power, by comparing test site data with reference site data
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Tabic 4-3. Suggested arithmetic expressions for deriving performance characteristics that can be
compared between 2 or more methods. In all cases, x = mean value, X = test site value, s = standard
deviation. Subscripts are as follows: capital letter refers to site class (A or B); numeral refers to method
1 or 2; and lower case letter refers to reference (r) or test site (t) (modified from Diamond et al. 1996).
Performance Characteristic
Parameters for Quantifying Method
Comparability
Desired
Outcome
Relative precision of metric or index within
a site class
CVAlr and CVA2r
CVBU and CVB2r
Low values
Relative precision of metric or index
between sites (population of samples at a
site) or site classes (population of sites)
SLl
cr„
cvA2r
cr.„
High ratio
Relative sensitivity or "detection limit" of
metric or index within a site class.
Comparison of those values between
methods reveals the most sensitive method
XAlr XAU
°Air
Xtllr ^Bit
XA2r ^A2t
A2r
*~ -Y
B2r B2t
High ratio
Relative sensitivity of metric or index
between site classes
XAJr Alt
XA2r %A2t
XBlr ^Bit
XB2r XB2t
High ratio
within each site class as a function of reference site variability (Table 4-3), e.g.,
XAJr~^Alt
A method that yields a smaller difference between test and reference sites in relation to the reference
site variability measured (Table 4-3) 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).
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. 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 some volunteer monitoring methods) (Barbour et al. 1996a) can meet prescribed DQOs with
less cost and effort. In this case, the data requirements dictate high priority for method sensitivity or
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discriminatory power (detection if impaired sites), 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
suboptimal habitat. A method with relatively low precision (high variance) among 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
determined. 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 for the types 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 site class than the reference method (e.g., CVAlr >
CVAIr 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 the state'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 composed the Florida stream
condition index (SCI). For the sake of brevity, the characterization process and results for the SCI in
the summer index period and the Peninsula and Northeast bioregions are summarized. The same
process was used for other bioregions in the state and in the winter index period.
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Performance Criteria Characteristics of Florida SO (see Figure 4-1 for process)
Characterize Measurement Error (Method Precision Within a Site)—A total of 7 sites in
the Peninsula bioregion were subjected to multiple sampling (adjacent reaches). The DEP
observed a mean SCI = 28.4 and a CV (within a stream) = 6.8%. These data suggest low
measurement error associated with the method and the index score. 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.
Characterize Sampling Error (Method Precision on a Population of Reference Sites)—A
total of 56 reference sites were sampled in the Peninsula bioregion (Step 1, Figure 4-1). The
SCI score could range from a minimum of 7 to a theoretical maximum of 31 based on the
component metric scores. However, in the Peninsula, reference site SCI scores generally
ranged between 21 and 31. A mean SCI score of 27.6 was observed with a CV of 12.0%.
Determine Method and Index Sensitivity—Distribution of SCI scores of the 56 reference
sites showed that the 5lh percentile was a score of 20. Thus, 95% of Peninsula reference sites
had a score >20. Accuracy of the method, using known stressed sites, indicated that
approximately 80% of the test sites had SCI scores £ 20 (Fig. 4-2). In other words, a
stressed site would be assessed as impaired 80% of the time using the, collection method in
the Peninsula bioregion in the summer, and an impairment criterion of the 5th percentile of
reference sites. The criterion could also be raised to, say, the 25lh percentile of reference
sites, which would increase accuracy of correctly classifying stressed sites to approximately
90%, but would decrease accuracy of correctly assessing unimpaired sites to 75%.
Determination of Method Bias and Relative Sensitivity in Different Site Classes—A
comparative analysis of precision, sensitivity, and ultimately bias, can be performed for the
Florida DEP method and the SCI index outlined in Table 4-3. For example, the mean SCI
score in the Panhandle bioregion, during the same summer index period, was 26.3 with a CV
= 12.8% based on 16 reference sites. Comparing this CV to the one reported for the
Peninsula in the previous step, it is apparent that the precision of this method in the
Panhandle was similar to that observed in the Peninsula bioregion.
The 5* percentile of the Panhandle reference sites was an SCI score of 17, such that actual
sensitivity of the method in the Panhandle was slightly lower than in the Peninsula bioregion
(Figure 4-2). An impaired site would be assessed as such only 50% of the time in the
Panhandle bioregion in the summer as opposed to 80% of the time in the Peninsula bioregion
during the same index period. Part of the difference in accuracy of the method among the 2
bioregions can be attributed to differences in sample size. Data from only 4 "known"
impaired sites were available in the Panhandle bioregion while the Peninsula bioregion had
data from 12 impaired sites. The above analyses show, however, that there may be
differences in method performance between the 2 regions (probably attributable to large
habitat differences between the regions) which should be further explored using data from
additional "known" stressed sites, if available.
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35
30
25
20
15
O
£
m
£ 10
o
£
Peninsula Stream Sites (Summer)
Reference
Impaired
Non-Outlier Max
Non-Outlier Min
75%
25%
Median
Outliers
Panhandle Stream Sites (Summer)
Reference
Impaired
Non-Outlier Max
Non-Outlier Min
75%
25%
Median
Outliers
Figure 4-2. Comparison of the discriminatory ability of the SCI between Florida's Peninsula and
Panhandle Bioregions. Percentiles used (not x, sd) to depict relationship.
4.6 APPLICATION OF THE PBMS
The PBMS approach is intended to provide information regarding the confidence of an assessment,
given a particular method. By having some measure of confidence in the endpoint and the
subsequent decision pertinent to the condition of the water resource, assessment and monitoring
programs are greatly strengthened. Three primary questions can be identified that enable agencies to
ascertain the value and scientific validity of using information derived from different methods. Use
of PBMS is necessary for these questions to be answered.
Question 1 —How rigorous must a method be to accurately detect impairment?
The analyses of Ohio EPA (1992) reveal that the power and ability of a bioassessment technique to
accurately portray biological community performance and ecological integrity, and to discriminate
even finer levels of aquatic life use impairments, are directly related to the data dimensions (i.e.,
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ecological complexity, environmental accuracy, discriminatory power) produced by each (Barbour et
al. 1996b). For example, a technique that includes the identification of macroinvertebrate taxa to
genus and species will produce a higher attainment of data dimensions than a technique that is
limited to family-level taxonomy. In general, this leads to a greater discrimination of the biological
condition of sites.
Some states use one method for screening assessments and a second method for more intensive and
confirmatory assessments. Florida DEP uses a BioRecon (see description in Chapter 7) to conduct
statewide screening for their watershed-based monitoring. A more rigorous method based on a
multihabitat sampling (see Chapter 7) is used for targeted surveys related to identified or suspected
problem areas. North Carolina Water Quality Division (WQD) has a rapid EPT index (cumulative
number of species of Ephemeroptera, Plecoptera, Trichoptera) to conduct screening assessments.
Their more intensive method is used to monitor biological condition on a broader basis.
Use of various methods having differing levels of rigor can be examined with estimates of precision
and sensitivity. These performance characteristics will help agencies make informed decisions of
how resulting data can be used in assessing condition.
Question 2 — How can data derived from different methods be compared to locate additional
reference sites?
Many agencies are increasingly confronted with the issue of locating appropriate reference sites from
which to develop impairment/unimpairment thresholds. In some instances, sites outside of
jurisdictional boundaries are needed to refine the reference condition. As watershed-based
monitoring becomes implemented throughout the U.S., jurisdictional boundaries may become
impediments to effective monitoring. County governments, tribal associations, local environmental
interest groups, and state water resource agencies are all examples of entities that would benefit from
collaborative efforts to identify common reference sites.
In most instances, all of the various agencies conducting monitoring and assessment will be using
different methods. A knowledge of the precision and sensitivity of the methods will allow for an
agency to decide whether the characterization of a site as reference or minimally impaired by a
second agency or other entity fits the necessary criteria to be included as an additional reference site.
Question 3 — How can data from different methods be combined or integrated for increasing a
database for assessment?
The question of combining data for a comprehensive assessment is most often asked by states and
tribes that want to increase the spatial coverage of an assessment beyond their own limited datasets.
From a national or regional perspective, the ability to combine datasets is desirable to make
judgements on the condition of the water resource at a higher geographical scale. Ideally, each
dataset will have been collected with the same methods.
This question is the most difficult to answer even with a knowledge of the precision and sensitivity.
Widely divergent methodologies having highly divergent performance characteristics are not likely
to be appropriate for combining under any circumstances. The risk of committing error in judgement
of biological condition from a combined dataset of this sort would be too high.
Divergent methodologies with similar or nearly identical performance characteristics are plausible
candidates for combining data at metric or index levels. However, a calibration of the methods is
necessary to ensure that extrapolations of data from one method to the other is scientifically valid.
The best fit for a calibrated model is a 1:1 ratio for each metric and index. Realistically, the
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calibration will be on a less-than-perfect relationship; extrapolations may be via range of values
rather than absolute numbers. Thus, combining datasets from dissimilar methods may be valuable
for characterizing severe impairment or sites of excellent condition. However, sites with slight to
moderate impairment might not be detected with a high level of confidence.
For example, a 6-state collaborative study was conducted on Mid-Atlantic coastal plain streams to
determine whether a combined reference condition could be established (Maxted et al. in review). In
this study, a single method was applied to all sites in the coastal plain in all 6 states (New Jersey,
Delaware, Maryland, Virginia, North Carolina, and South Carolina). The results indicated that two
Bioregions exist for the coastal plain ecoregion—the northern portion, including coastal plain
streams in New Jersey, Delaware, and Maryland; and the southern portion that includes Virginia,
North Carolina, and South Carolina. In most situations, agencies have databases from well-
established methods that differ in specific ways. The ability to combine unlike datasets has
historically been a problem for scientific investigations. The usual practice has been to aggregate the
data to the least common denominator and discard data that do not fit the criteria.
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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 general, habitat and
biological diversity in rivers are closely linked (Raven et al. 1998). 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 an altered habitat structure is considered one of the major stressors of aquatic
systems (Karr et al. 1986). 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 an integrated picture of
several of the factors influencing the biological condition of a stream system. These assessments
are not as comprehensive as needed to adequately identify all causes of impact. However,
additional investigation into hydrological modification of water courses and drainage patterns
can be conducted, once impairment is noted.
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 collected for selected chemical analysis. 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-
Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
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1, 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.
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 interpre tation.
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. Examples are
glacial, montane, swamp, and bog. As the size of the stream or river increases, a mixture of
origins of tributaries is likely.
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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.
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	Riparian Vegetation
An acceptable riparian zone includes a buffer strip of a minimum of 18 m (Barton et al. 1985)
from the stream on either side. The acceptable width of the riparian zone may also be variable
depending on the size of the stream. Streams over 4 m in width may require larger riparian
zones. The vegetation within the riparian zone is documented here as the dominant type and
species, if known.
5.1.7	Instream Features
Instream features are measured or evaluated in the sampling reach and catchment as appropriate.
Estimated Reach Length: Measure or estimate the length of the sampling reach. This
information is important if reaches of variable length are surveyed and assessed.
Estimated Stream Width (in meters, m): Estimate the distance from bank to bank at a transect
representative of the stream width in the reach. If variable widths, use an average to find that
which is representative for the given reach.
Sampling Reach Area (m2): Multiply the sampling reach length by the stream width to obtain a
calculated surface area.
Estimated Stream Depth (m): Estimate the vertical distance from water surface to stream
bottom at a representative depth (use instream habitat feature that is most common in reach) to
obtain average depth.
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.
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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.
High Water Mark (m): Estimate the vertical distance from the bankfull margin of 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.
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.
Channelized: Indicate whether or not the area around the sampling reach or station is
channelized (e.g., straightening of stream, bridge abutments and road crossings, diversions, etc.).
Dam Present: Indicate the presence or absence of a dam upstream in the catchment or
downstream of the sampling reach or station. If a dam is present, include specific information
relating to alteration of flow.
5.1.8 Large Woody Debris
Large Woody Debris (LWD) density, defined and measured as described below, has been used in
regional surveys (Shields et al. 1995) and intensive studies of degraded and restored streams
(Shields et al. 1998). The method was developed for sand or sand-and-gravel bed streams in the
Southeastern U.S. that are wadeable at baseflow, with water widths between 1 and 30 m (Cooper
and Testa 1999).
Cooper and Testa's (1999) procedure involves measurements based on visual estimates taken by
a wading observer. Only woody debris actually in contact with stream water is counted. Each
woody debris formation with a surface area in the plane of the water surface >0.25 m2 is
recorded. The estimated length and width of each formation is recorded on a form or marked
directly onto a stream reach drawing. Estimates are made to the nearest 0.5 m , and formations
with length or width less than 0.5 m are not counted. Recorded length is maximum width in the
direction perpendicular to the length. Maximum actual length and width of a limb, log, or
accumulation are not considered.
If only a portion of the log/limb is in contact with the water, only that portion in contact is
measured. Root wads and logs/limbs in the water margin are counted if they contact the water,
and are arbitrarily given a width of 0.5 m Lone individual limbs and logs are included in the
determination if their diameter is 10 cm or larger (Keller and Swanson 1979, Ward and Aumen
1986). Accumulations of smaller limbs and logs are included if the formation total length or
width is 0.5 m or larger. Standing trees and stumps within the stream are also recorded if their
length and width exceed 0.5 m.
The length and width of each LWD formation are then multiplied, and the resulting products are
summed to give the aquatic habitat area directly influenced. This area is then divided by the
water surface area (km2) within the sampled reach (obtained by multiplying the average water
surface width by reach length) to obtain LWD density. Density values of 103 to 104 m2/km2 have
been reported for channelized and incised streams and on the order of 10s m2/km2 for non-incised
streams (Shields et al. 1995 and 1998). This density is not an expression of the volume of LWD,
but rather a measure of LWD influence on velocity, depth, and cover.
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5.1.9 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.
5.1.10	Water Quality
Temperature (°C), Conductivity or "Specific Conductance" (fiohms), Dissolved Oxygen
(|ig/L), pH, Turbidity: Measure and record values for each of the water quality parameters
indicated, using the appropriate 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.
5.1.11	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 7
substrate/particle types listed that are present over the sampling reach.
Organic Substrate Components: Indicate relative abundance of each of the 3 substrate types
listed.
5.2 A VISUAL-BASED 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
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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 during storm events.
Further, he identifies 8 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
properly 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 flow energy are:
•	sinuosity
•	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 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 approaches
for evaluating habitat structure have been developed. In the first, 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
	
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 System (GPS) Unit
•	It is helpful to copy field sheets onto water-resistant
paper for use in wet weather conditions
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Chapter 5: Habitat Assessment and Physicochemical Parameters
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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). In this document, the more rapid visual-based
approach is described. A cursory overview of the more quantitative approaches to characterizing
the physical structure of the habitat is provided.
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.
The Environmental Agency of Great Britain (Environment Agency of England and Wales,
Scottish Environment Protection Agency, and Environment and Heritage Service of Northern
Ireland) have developed a River Habitat Survey (RHS) for characterizing the quality of their
streams and rivers (Raven et al. 1998). The approach used in Great Britain is similar to the
visual-based habitat assessment used in the US in that scores are assigned to ranges of conditions
of various habitat parameters.
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 (Hannaford et al. 1997)
•	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
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involves rating the 10 parameters as optimal, suboptimal, marginal, or poor based on the criteria
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 Workgroup (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, Kauftnann and Robison 1997,
Meader et al. 1993). However, holistic and rapid 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 particulate 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 (optimal, suboptimal, marginal, and poor). Refer to
Appendix A-l, Forms 2 and 3, for a complete field assessment guide.
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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 [e.g., 40 x stream wetted width]) 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.	It is best for the investigators to obtain a close look at the habitat features to make an adequate
assessment. 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.
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 a team of 2 or more biologists, if possible, to
come to a consensus on determination of quality. Those parameters to be evaluated on a scale greater
than a sampling reach require traversing the stream corridor to the extent deemed necessary to assess
the habitat feature. As a general rule-of-thumb, use 2 lengths of the sampling reach to assess these
parameters.
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.
Some text modifications may be needed on a regional basis.
3.	Periodic checks of assessment results are completed using pictures of the sampling reach and
	discussions among the biologists in the agency.	
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Parameters to be evaluated in sampling reach:
J EPIFAUNAL SUBSTRATE/AVAILABLE COVER
high and low Includes the relative quantity and variety of natural structures in the
gradient streams 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 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 suitable for
colonization.
Selected Wesche et al. 1985, Pearsons et al. 1992, Gorman 1988, Rankin 1991,
References 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 et al. 1982,
Beechie and Sibley 1997.
Habitat
Condition Category
Parameter
Optimal
Suboptimal
Marginal
Poor
1. Eplfaunal
Substrate/
Available Cover
(high and low
gradient)
Greater than 70% (50%
for low gradient streams)
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).
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).
20-40% (10-30% for low'
gradient streams) mix of
stable habitat; habitat
availability less than
desirable; substrate
frequently disturbed or
removed.
Less than 20% (10% for
low gradient streams)
stable habitat; lack of
habitat is obvious;
substrate unstable or
lacking.
SCORE
20 19 18 17 16
15 14 13 12 11
10 9 8 7 6
5 4 3 2 1 0
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la. Epifaunal Substrate/Available Cover—High Gradient
Poor Range
Optimal Range
lb. Epifaunal Substrate/Available Cover—Low Gradient

Optimal Range (Mary Kay Corazalia, U. of Minn) Poor Range
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2a
EMBEDDEDNESS
high gradient Refers to the extent to which rocks (gravel, cobble, and boulders) and
streams 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 Ball 1982, Osborne et al. 1991, Barbour and Stribling 1991, Platts et al.
References 1983, MacDonald et al. 1991, Rankin 1991, Reice 1980, Clements 1987,
Benke et al. 1984, Hawkins et al. 1982, Burton and Harvey 1990.
Habitat
Parameter
Condition Category
Optimal
Suboptimal
Marginal
Poor
2.a Embeddedness
Oitgli gradient)
SCOPE
Gravel, cobble, and
boulder particles are 0-
25% surrounded by fine
sediment. Layering of
cobble provides diversity of
niche space.
Gravel, cobble, and
boulder particles are 25-
50% surrounded by fine
sediment.
Gravel, cobble, and
boulder particles are 50-
75% sunounded by fine
sediment.
Gravel, cobble, and
boulder particles are more
than 75% surrounded by
fine sediment.
20 19 18 17 16
15 14 13 12 11
10 9 8 7 6
5 4 3 2 1 0
2a. Embeddedness—High Gradient
Optimal Range	(WiiiiamTaft,MiDNR) Poor Range	(wunam Taft, midnr)
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2b
POOL SUBSTRATE CHARACTERIZATION
low gradient Evaluates the type and condition of bottom substrates found in pools.
streams 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 Beschta and Platts 1986, U.S. EPA 1983.
References
Habitat
Parameter
Condition Category
Optimal
Suboptimal
Marginal
Poor
2b. Pool Substrate
Characterization
(low gradient)
SCORE
Mixture of substrate
materials, with gravel and
firm sand prevalent; root
mats and submerged
vegetation common.
Mixture of soft sand,
mud, or clay; mud may be
dominant; some root mats
and submerged vegetation
present.
All mud or clay or sand
bottom; little or no root
mat; no submerged
vegetation.
Hard-pan clay or
bedrock; no root mat or
submerged vegetation.
20 19 18 17 16
15 14 13 12 11
10 9 8 7 6
5 4 3 2 1 0
2b. Pool Substrate Characterization—Low Gradient
Optimal Range
(Mary Kay Corazalla, U. of Minn.)
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3a
VELOCITY/DEPTH COMBINATIONS
high gradient Patterns of velocity and depth are included for high-gradient streams
streams under this parameter as an important feature of habitat diversity. The best
streams in most high-gradient regions will have all 4 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 4 patterns
relates to the stream's ability to provide and maintain a stable aquatic
environment.
Selected Ball 1982, Brown and Brussock 1991, Gore and Judy 1981, Oswood and
References Barber 1982.
Habitat
Parameter
Condition Category
Optimal
Suboptimal
Marginal
Poor
3». Velocity/
Depth Regimes
(high gradient)
f>CORI>
All 4 velocity/depth
regimes present (slow-
deep, slow-shallow, fast-
deep, fast-shallow)..
(slow is <0.3 m/s, deep is
>0.5 m)
Only 3 of the 4 regimes
present (if fast-shallow is
missing, score lower than
if missing other regimes).
Only 2 of the 4 habitat
regimes present (if fast-
shallow or slow-shallow
are missing, score low).
Dominated by 1 velocity/
depth regime (usually
slow-deep).
20 19 18 17 16
15 14 13 12 11
10 9 8 7 6
5 4 3 2 1 0
3a. Velocity/Depth Regimes—High Gradient
Optimal Range (Mary Kay Corazolla, U. of Minn.)	Poor Range	(William Taft, Ml DNR)
(arrows emphasize different velocity/depth regimes)
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3b
POOL VARIABILITY
low gradient Rates the overall mixture of pool types found in streams, according to
streams size and depth. The 4 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 Beschta and Platts 1986, USEPA 1983.
References
Habitat
Parameter
Condition Category
Optimal
Suboptimal
Marginal
Poor
3b. Pool
Variability
(low gradient)
SCORE
Even mix of large-
shallow, large-deep,
small-shallow, small-
deep pools present.
Majority of pools large-
deep; very few shallow.
Shallow pools much more
prevalent than deep pools.
Majority of pools small-
shallow or pools absent.
20 19 18 17 16
15 14 13 12 11
10 9 ' 8 7 6
5 4 3 2 1 0
3b. Pool Variability—Low Gradient
Optimal Range	(Peggy Morgan,FLDEP) Poor Range	(WilliamTaft.MIDNR)
Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
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SEDIMENT DEPOSITION
high and low Measures the amount of sediment that has accumulated in pools and the
gradient streams 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 MacDonald et al. 1991, Platts et al. 1983, Ball 1982, Armour et al. 1991,
References Barbour and Stribling 1991, Rosgen 1985.
Habitat

Condition
Category

Parameter
Optimal
Suboptimal
Marginal
Poor
4. Sediment
Deposition
(high and low
gradient)
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.
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.
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.
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.
SCORE
20 19 18 17 16
15 14 13 12 11
10 9 8 7 6
5 4 3 2 1 0
5-16
Chapter 5: Habitat Assessment and Physicochemical Parameters
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4a.
Sediment Deposition—High Gradient
Optimal Range	Poor Range
(arrow pointing to sediment deposition)
4b. Sediment Deposition—Low Gradient
Poor Range
(arrows pointing to sediment deposition)
Optimal Range
Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
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g CHANNEL FLOW STATUS
high and low The degree to which the channel is filled with water. The flow status will
gradient streams 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 is used for surveys
or the timing of sampling is inconsistent among sites or annual
periodicity.
Selected Rankin 1991, Rosgen 1985, Hupp and Simon 1986, MacDonald et al.
References 1991, Ball 1982, Hicks et al. 1991.
Habitat
Parameter
Condition Category
Optimal
Suboptimal
Marginal
Poor
5. Channel Flow
Status
(high and tow
gradient)
SCORE
Water reaches base of
both lower banks, and
minimal amount of
channel substrate is
exposed.
Water fills >75% of the
available channel; or
<25% of channel
substrate is exposed.
Water fills 25-75% of the
available channel, and/or
riffle substrates are
mostly exposed.
Very little water in
channel and mostly
present as standing pools.
20 19 18 17 16
15 14 13 12 U
10 9 8 7 6
5 4 3 2 1 0
5-18
Chapter 5: Habitat Assessment and Physicochemical Parameters
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5a. Channel Flow Status—High Gradient
Poor Range
(arrow showing that water is not reaching both banks; leaving
much of channel uncovered)

Optimal Range
Optimal Range
5b. Channel Flow Status—Low Gradient
Poor Range
(James Stahl, IN DEM)
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Parameters to be evaluated broader than sampling reach:
CHANNEL ALTERATION
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.
Barbour and Stribling 1991, Simon 1989a, b, Simon and Hupp 1987,
Hupp and Simon 1986, Hupp 1992, Rosgen 1985, Rankin 1991,
MacDonald et al. 1991.


Condition
Category

Parameter
Optimal
Suboptimal
Marginal
Poor
6. Channel
Alteration
(high and low
gradient)
Channelization or
dredging absent or
minimal; stream with
normal pattern.
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.
Channelization may be
extensive; embankments
or shoring structures
present on both banks;
and 40 to 80% of stream
reach channelized and
disrupted.
Banks shored with
gabion or cement; over
80% of the stream reach
channelized and
disrupted. Instream
habitat greatly altered or
removed entirely.
SCORE
20 19 18 17 16
15 14 13 12 11
10 9 8 7 6
5 4 3 2 1 0
5-20	Chapter 5: Habitat Assessment and Physicochemical Parameters
high and low
gradient streams
Selected
References
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6a. Channel Alteration—High Gradient
Optimal Range
Poor Range
(arrows emphasizing large-scale channel
alterations)
6b. Channel Alteration—Low Gradient
Optimal Range
Poor Range
(John Maxted, DE DNREC)
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7a
FREQUENCY OF RIFFLES (OR BENDS)
high gradient Is a way to measure the sequence of riffles and thus the heterogeneity
streams 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 high gradient
streams where distinct riffles are uncommon, a run/bend ratio can be used
as a measure of meandering or sinuosity (see 7b). 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
excessive erosion and flooding and provides refiigia 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 should 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. 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 Hupp and Simon 1991, Brussock and Brown 1991, Platts et al. 1983,
References Rankin 1991, Rosgen 1985, 1994, 1996, Osborne and Hendricks 1983,
Hughes and Omernik 1983, Cushman 1985, Bain andBoltz 1989,
Gislason 1985, Hawkins et al. 1982, Statzner et al. 1988.
Habitat
Parameter
Condition Category
Optimal
Suboptimal
Marginal
Poor
7». Frequency of
Riffles (or bends)
(high gradient)
SCORE
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.
Occurrence of riffles
infrequent; distance
between riffles divided
by the width of the
stream is between 7 to
15.
Occasional riffle or bend;
bottom contours provide
some habitat; distance
between riffles divided
by the width of the
stream is between 15 to
25.
Generally all flat water
or shallow riffles; poor
habitat; distance between
riffles divided by the
width of the stream is a
ratio of >25.
20 19 18 17 16
15 14 13 12 11
10 9 8 7 6
5 4 3 2 1 0
5-22
Chapter 5: Habitat Assessment and Physicochemical Parameters
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7a. Frequency of Riffles (or bends)—High Gradient
Poor Range
Optimal Range
(anows showing frequency of riffles and
bends)
7b
CHANNEL SINUOSITY
low gradient Evaluates the meandering or sinuosity of the stream. A high degree of
streams 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 excessive
erosion and flooding and provides refugia for benthic invertebrates and
fish during storm events. To gain an appreciation of this parameter in low
gradient 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 "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 Hupp and Simon 1991, Brussock and Brown 1991, Platts et al. 1983,
References 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.
Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
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Condition Category
Parameter
Optimal
Suboptimal
Marginal
Poor
7b. Channel
Sinuosity
(tow gradient)
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.)
The bends in the stream
increase the stream
length 2 to 3 times longer
than if it was in a straight
line.
The bends in the stream
increase the stream
length 1 to 2 times longer
than if it was in a straight
line.
Channel straight;
waterway has been
channelized for a long
distance.
SCORE
20 19 18 17 16
15 14 13 12 11
10 9 8 7 6
5 4 3 2 1 0
7b. Channel Sinuosity—Low Gradient
Optimal Range
Poor Range
5-24
Chapter 5: Habitat Assessment and Physicochemical Parameters
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8
BANK STABILITY (condition of banks)
high and low Measures whether the stream banks are eroded (or have the potential for
gradient streams 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. Each bank is evaluated separately and the
cumulative score (right and left) is used for this parameter.
Selected Ball 1982, MacDonald et al. 1991, Armour et al. 1991, Barbour and
References Stribling 1991, Hupp and Simon 1986, 1991, Simon 1989a, Hupp 1992,
Hicks et al. 1991, Osborne et al. 1991, Rosgen 1994, 1996.
Habitat
Condition Category
Parameter
Optimal
Suboptimal
Marginal
Poor
8. Bank Stability
(score each bank)
Note: determine
left or right side
by facing
downstream
Banks stable; evidence
of erosion or bank failure
absent or minimal; little
potential for future
problems. <5% of bank
affected.
Moderately stable;
infrequent, small areas of
erosion mostly healed
over. 5-30% of bank in
reach has areas of
erosion.
Moderately unstable; 30-
60% of bank in reach has
areas of erosion; high
erosion potential during
floods.
Unstable; many eroded
areas; "raw" areas
frequent along straight
sections and bends;
obvious bank sloughing;
60-100% of bank has
erosional scars.
(high and low
gradient)




SCORE (LB)
Left Bank 10 9
8 7 6
5 4 3
2 1 0
SCORE fRB>
Right Bank 10 9
t-
00
5 4 3
2 1 . 0
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8a. Bank Stability (condition of banks)—High Gradient
Optimal Range	Poor Range	(MD save Our streams)
(arrow pointing io stable streambanks)	(arrow highlighting unstable streambanks)
8b. Bank Stability (condition of banks)—Low Gradient
5-26	Chapter 5: Habitat Assessment and Physicochemical Parameters
Optimal Range
(Peggy Morgan, FLDEP) Poor Range
(arrow highlighting unstable streambanks)
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{J BANK VEGETATIVE PROTECTION
high and low Measures the amount of vegetative protection afforded to the stream bank
gradient streams 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 native
vegetation for the region and stream type (i.e., shrubs, trees, etc.). In
some regions, the introduction of exotics has virtually replaced all native
vegetation. The value of exotic vegetation to the quality of the habitat
structure and contribution to the stream ecosystem must be considered in
this parameter. 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. Each bank is evaluated separately and
the cumulative score (right and left) is used for this parameter.
Selected Platts et al. 1983, Hupp and Simon 1986, 1991, Simon and Hupp 1987,
References 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.

Condition Category
Parameter
Optimal
Suboptimal
Marginal
Poor
9. Vegetative
Protection (score
each bank)
Note: determine
left or right side
by facing
downstream.
(high and low
gradient)
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.
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.
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.
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.
SCORE (LB)
Left Bank 10 . 9
8 7 6
5 4.3
2 1 0
SCORE fRB)
Rieht Batik 10 9
\C>
00
5 4 3
2 . 1 0
Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
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9a. Bank Vegetative Protection—High Gradient
Optimal Range	Poor Range
(arrow pointing to streambank with high level of vegetative	(arrow pointing to streambank wiih almost no vegetative cover)
cover)
9b. Bank Vegetative Protection—Low Gradient
Optimal Range	(Pegs? Morgan, FLDEP Poor Range	(MD Save Our Streams)
(arrow pointing to channelized streambank with no vegetative
cover)
5-28
Chapter 5: Habitat Assessment and Physicochemical Parameters
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10
RIPARIAN VEGETATIVE ZONE WIDTH
high and low Measures the width of natural vegetation from the edge of the stream
gradient streams 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. Conversely, 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 altering the riparian zone and may be
given relatively high scores. For variable size streams, the specified
width of a desirable riparian zone may also be variable and may be best
determined by some multiple of stream width (e.g., 4 x wetted stream
width). Each bank is evaluated separately and the cumulative score (right
and left) is used for this parameter.
Selected Barton et al. 1985, Naiman et al. 1993, Hupp 1992, Gregory et al. 1991,
References Platts et al. 1983, Rankin 1991, Barbour and Stribling 1991, Bauer and
Burton 1993.

Condition Category
Parameter
Optimal
Suboptimal
Marginal
Poor
10. Riparian
Vegetative Zone
Width (score each
bank riparian
zone)
Width of riparian zone
>18 meters; human
activities (i.e., parking
lots, roadbeds, clear-cuts,
lawns, or crops) have not
impacted zone.
Width of riparian zone
12-18 meters; human
activities have impacted
zone only minimally.
Width of riparian zone 6-
12 meters; human
activities have impacted
zone a great deal.
Width of riparian zone
<6 meters: little or no
riparian vegetation due
to human activities.
(high and low
gradient)




SCORE (LB)
Left Bank 10 9
8 7 6
. 5 4 .3
2 1 0
SCORE (RBI
Richt Bank 10 9
8 7 6
5 4 3
2 1 0
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10a. Riparian Vegetative Zone Width—High Gradient
Optimal Range
(arrow pointing out an undisturbed riparian zone)
Poor Range
(arrow pointing out lack of riparian zone)
10b. Riparian Vegetative Zone Width—Low Gradient
Optimal Range
(arrow emphasizing an undisturbed riparian zone)
Poor Range (MD Save Our Streams)
(an-ow emphasizing lack of riparian zone)
5-30	Chapter 5: Habitat Assessment and Physicochemical Parameters
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5.3 ADDITIONS OF QUANTITATIVE MEASURES TO THE
HABITAT ASSESSMENT
Kaufmann (1993) identified 7 general physical habitat attributes important in influencing stream
ecology. These include:
•	channel dimensions
•	channel gradient
•	channel substrate size and type
•	habitat complexity and cover
•	riparian vegetation cover and structure
•	anthropogenic alterations
•	channel-riparian interaction.
All of these attributes vary naturally, as do biological characteristics; thus expectations differ
even in the absence of anthropogenic disturbances. Within a given physiographic-climatic
region, stream drainage area and overall stream gradient are likely to be strong natural
determinants of many aspects of stream habitat, because of their influence on discharge, flood
stage, and stream power (the product of discharge times gradient). In addition, all of these
attributes may be directly or indirectly altered by anthropogenic activities.
In Section 5.2, an approach is described whereby habitat quality is interpreted directly in the
field by biologists while sampling the stream reach. This Level 1 approach is observational and
requires only one person (although a team approach is recommended) and takes about 15 to 20
minutes per stream reach. This approach more quickly yields a habitat quality assessment.
However, it depends upon the knowledge and experience of the field biologist to make the
proper interpretation of observed of both the natural expectations (potentials) and the biological
consequences (quality) that can be attributed to the observed physical attributes. Hannaford et
al. (1997) found that training in habitat assessment was necessary to reduce the subjectivity in a
visual-based approach. The authors also stated that training on different types of streams may be
necessary to adequately prepare investigators.
The second conceptual approach described here confines observations to habitat characteristics
themselves (whether they are quantitative or qualitative), then later ascribing quality scoring to
these measurements as part of the data analysis process. Typically, this second type of habitat
assessment approach employs more quantitative data collection, as exemplified by field methods
described by Kaufmann and Robison (1997) for EMAP, Simonson et al. (1994), Meador et al.
(1993) for NAWQA, and others cited by Gurtz and Muir (1994). These field approaches
typically define a reach length proportional to stream width and employ transect measurements
that are systematically spaced (Simonson et al. 1994, Kaufmann and Robison 1997) or spaced by
judgement to be representative (Meador et al. 1993). They usually include measurement of
substrate, channel and bank dimensions, riparian canopy cover, discharge, gradient, sinuosity, in-
channel cover features, and counts of large woody debris and riparian human disturbances. They
may employ systematic visual estimates of substrate embeddedness, fish cover features, habitat
types, and riparian vegetation structure. The time commitment in the field to these more
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quantitative habitat assessment methods is usually 1.5 to 3 hours with a crew of two people.
Because of the greater amount of data collected, they also require more time for data
summarization, analysis, and interpretation. On the other hand, the more quantitative methods
and less ambiguous field parameters result in considerably greater precision. The USEPA
applied both quantitative and visual-based (RBPs) methods in a stream survey undertaken over 4
years in the mid-Atlantic region of the Appalachian Mountains. An earlier version of the RBP
techniques were applied on 301 streams with repeat visits to 29 streams; signal-to-noise ratios
varied from 0.1 to 3.0 for the twelve RBP metrics and averaged (1.1 for the RBP total habitat
quality score). The quantitative methods produced a higher level of precision; signal-to-noise
ratios were typically between 10 and 50, and sometimes in excess of 100 for quantitative
measurements of channel morphology, substrate, and canopy densiometer measurements made
on a random subset of 186 streams with 27 repeat visits in the same survey. Similarly, semi-
quantitative estimates of fish cover and riparian human disturbance estimates obtained from
multiple, systematic visual observations of otherwise measurable features had signal:noise ratios
from 5 to 50. Many riparian vegetation cover and structure metrics were moderately precise
(signal:noise ranging from 2 to 30). Commonly used flow dependent measures (e.g., riffle/pool
and width/depth ratios), and some visual riparian cover estimates were less precise, with
signal:noise ratios more in the range of those observed for metrics of the EPA's RBP habitat
score (<2).
The USEPA's EMAP habitat assessment field methods are presented as an option for a second
level (II) of habitat assessment. These methods have been applied in numerous streams
throughout the Mid-Atlantic region, the Midwest, Colorado, California, and the Pacific
Northwest. Table 5-1 is a summary of these field methods; more detail is presented in the field
manual by Kaufmann and Robison (1997).
Table 5-1. Components of EMAP physical habitat protocol.
Component
Description
1. Thalweg
Profile
Measure maximum depth, classify habitat, determine presence of soft/small sediment
at 10-15 equally spaced intervals between each of 11 channel cross-sections (100-150
along entire reach). Measure wetted width at 11 channel cross-sections and mid-way
between cross-sections (21 measurements).
2. Woody
Debris
Between each of the channel cross sections, tally large woody debris numbers within
and above the bankfull channel according to size classes.
3. Channel
and
Riparian
Cross-
Sections
At 11 cross-section stations placed at equal intervals along reach length:
•	Measure: channel cross section dimensions, bank height, undercut, angle
(with rod and clinometer); gradient (clinometer), sinuosity (compass backsite),
riparian canopy cover (densiometer).
•	Visually Estimate*: substrate size class and embeddedness; areal cover class
and type (e.g., woody) of riparian vegetation in Canopy, Mid-Layer and
Ground Cover; areal cover class of fish concealment features, aquatic
macrophytes and filamentous algae.
•	Observe & Record*: human disturbances and their proximity to the channel.
4. Discharge
In medium and large streams (defines later) measure water depth and velocity @ 0.6
depth (with electromagnetic or impeller-type flow meter) at 15 to 20 equally spaced
intervals across one carefully chosen channel cross-section. In very small streams,
measure discharge with a portable weir or time the filling of a bucket.
* Substrate size class and embeddedness are estimated, and depth is measured for 55 particles taken at 5 equally-spaced points on
each of 11 cross-sections. The cross-section is defined by laying the surveyor's rod or tape to span the wetted channel. Woody
debris is tallied over the distance between each cross-section and the next cross-section upstream. Riparian vegetation and
human disturbances are observed 5 m upstream and 5 m downstream from the cross section station. They extend shoreward 10
m from left and right banks. Fish cover types, aquatic macrophytes, and algae are observed within channel 5 m upstream and 5
m downstream from the cross section stations. TTiese boundaries for visual observations are estimated by eye.
5-32
Chapter 5: Habitat Assessment and Physicochemical Parameters
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Table 5-2 lists the physical habitat metrics that can be derived from applying these field
methods. Once these habitat metrics are calculated from the available physical habitat data, an
assessment would be obtained from comparing these metric values to those of known reference
sites. A strong deviation from the reference expectations would indicate a habitat alteration of
the particular parameter. The close connectivity of the various attributes would most likely
result in an impact on multiple metrics if habitat alteration was occurring. The actual process for
interpreting a habitat assessment using this approach is still under development.
Table 5-2. Example of habitat metrics that can be calculated from the EMAP physical habitat data.
Channel mean width and depth
Channel volume and Residual Pool volume
Mean channel slope and sinuosity
Channel incision, bankfull dimensions, and bank characteristics
Substrate mean diameter, % fines, % embeddedness
Substrate stability
Fish concealment features (areal cover of various types, e.g., undercut banks, brush)
Large woody debris (volume and number of pieces per 100 m)
Channel habitat types (e.g., % of reach composed of pools, riffles, etc.)
Canopy cover
Riparian vegetation structure and complexity
Riparian disturbance measure (proximity-weighted tally of human disturbances)
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6
Benthic algae (periphyton or phytobenthos) are primary producers and an important foundation of
many stream food webs. These organisms also stabilize substrata and serve as habitat for many other
organisms. Because benthic algal assemblages are attached to substrate, their characteristics are
affected by physical, chemical, and biological disturbances that occur in the stream reach during the
time in which the assemblage developed.
Diatoms in particular are useful ecological indicators because they are found in abundance in most
lotic ecosystems. Diatoms and many other algae can be identified to species by experienced
algologists. The great numbers of species provide multiple, sensitive indicators of environmental
change and the specific conditions of their habitat. Diatom species are differentially adapted to a
wide range of ecological conditions.
Periphyton indices of biotic integrity have been developed and tested in several regions (Kentucky
Department of Environmental Protection 1993, Hill 1997). Since the ecological tolerances for many
species are known (see section 6.1.4), changes in community composition can be used to diagnose
the environmental stressors affecting ecological health, as well as to assess biotic integrity
(Stevenson 1998, Stevenson and Pan 1999).
Periphyton protocols may be used by themselves, but they are most effective when used with one or
more of the other assemblages and protocols. They should be used with habitat and benthic
macroinvertebrate assessments particularly because of the close relation between periphyton and
these elements of stream ecosystems.
Presently, few states have developed protocols for periphyton assessment. Montana, Kentucky, and
Oklahoma have developed periphyton bioassessment programs. Others states are exploring the
possibility of developing periphyton programs. Algae have been widely used to monitor water
quality in rivers of Europe, where many different approaches have been used for sampling and data
analysis (see reviews in Whitton and Rott 1996, Whitton et al. 1991). The protocols presented here
are a composite of the techniques used in Kentucky, Montana, and Oklahoma (Bahls 1993, Kentucky
Department of Environmental Protection 1993, Oklahoma Conservation Commission 1993).
Two Rapid Bioassessment Protocols for periphyton are presented. These protocols are meant to
provide examples of methods that can be used. Other methods are available and should be
considered based on the objectives of the assessment program, resources available for study,
numbers of streams sampled, hypothesized stressors, and the physical habitat of the streams studied.
Examples of other methods are presented in textboxes throughout the chapter.
The first protocol (6.1) is a standard approach in which species composition and/or biomass of a
sampled assemblage is assessed in the laboratory. The second protocol (6.2) is a field-based rapid
survey of periphyton biomass and coarse-level taxonomic composition (e.g., diatoms, filamentous
greens, blue-green algae) and requires little taxonomic expertise. The two protocols can be used
together. The first protocol has the advantage of providing much more accuracy in assessing biotic
integrity and in diagnosing causes of impairment than the second protocol, but it requires more effort
than the second protocol. Additionally, the first protocol provides the option of sampling the natural
substrate of the stream or placing artificial substrates for colonization.
PERIPHYTON PROTOCOLS
By R. Jan Stevenson, University of Louisville, and
Loren L. Bahls, University of Montana
Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
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6.1
STANDARD LABORATORY-BASED APPROACH
6.1.1 Field Sampling Procedures: Natural Substrates
Periphyton samples should be collected during periods of stable stream flow. High flows can scour
the stream bed, flushing the periphyton downstream. Recolonization of substrates will be faster after
less severe floods and in streams with nutrient enrichment. Peterson and Stevenson (1990)
recommend a three-week delay following high, bottom-scouring stream flows to allow for
recolonization and succession to a mature periphyton community. However, recovery after high
discharge can be as rapid as 7 days if severe scouring of substrata did not occur (Stevenson 1990).
Two sampling approaches are described for natural substrate sampling. Multihabitat sampling best
characterizes the benthic algae in the reach, but results may not be sensitive to subtle water quality
changes because of habitat variability between reaches. Species composition of assemblages from a
single habitat should reflect water quality differences among streams more precisely than
multi-habitat sampling, but impacts in other habitats in the reach may be missed.
The length of stream sampled depends upon the objectives of the project, budget, and expected
results. Multihabitat sampling should be conducted at the reach scale (30-40 stream widths) to
ensure sampling the diversity of habitats that occur in the stream. Ideally, single habitat sampling
should also be conducted at the reach scale. A shorter length of stream can probably be sampled for
single habitat samples than multihabitat samples because the chosen single habitat (e.g., riffles) is
usually common within the study streams.
6.1.1.1 Multihabitat Sampling
The following procedures for
multihabitat sampling of algae
have been adapted from the
Kentucky and Montana protocols
(Kentucky DEP 1993, Bahls
1993). These procedures are
recommended when subsequent
laboratory assessments of species
composition of algal assemblages
will be performed.
1. Establish the reach for
multihabitat sampling as per
the macroinvertebrate
protocols (Chapter 7). In
most cases, the reach
required for periphyton
sampling will be the same
size as the reach required for
macroinvertebrate or fish
sampling (30-40 stream
widths) so that as many algal habitats can be sampled as is practical.
FIELD EQUIPMENT FOR PERIPHYTON
SAMPLING-NATURAL SUBSTRATES
•	stainless steel teaspoon, toothbrush, or similar brushing and
scraping tools
•	section of PVC pipe (3" diameter or larger) fitted with a rubber
collar at one end
•	field notebook or field forms*; pens and pencils
•	white plastic or enamel pan
•	petri dish and spatula (for collecting soft sediment)
•	forceps, suction bulb, and disposable pipettes
•	squeeze bottle with distilled water
•	sample containers (125 ml wide-mouth jars)
•	sample container labels
•	preservative [Lugol's solution, 4% buffered formalin, "M3"
fixative, or 2% glutaraldehyde (APHA 1995)]
•	first aid kit
•	cooler with ice
•	During wet weather conditions, waterproof paper is useful or
copies of field forms can be stored in a metal storage box
(attached to a clip-board).
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2.	Before sampling, complete the physical/chemical field sheet (see Chapter 5; Appendix A-l,
Form 1) and the periphyton field data sheet (Appendix A-2, Form 1). Visual estimates or
quantitative transect-based assessments can be used to determine the percent coverage of each
substrate type and the estimated relative abundance of macrophytes, macroscopic filamentous
algae, diatoms and other microscopic algal accumulations (periphyton), and other biota (see
section 6.2).
3.	Collect algae from all available substrates and habitats. The objective is to collect a single
composite sample that is representative of the periphyton assemblage present in the reach.
Sample all substrates (Table 6-1) and habitats (riffles, runs, shallow pools, nearshore areas)
roughly in proportion to their areal coverage in the reach. Within a stream reach, light, depth,
substrate, and current velocity can affect species composition of periphyton assemblages.
Changes in species composition of algae among habitats are often evident as changes in color
and texture of the periphyton. Small amounts (about 5 mL or less) of subsample from each
habitat are usually sufficient. Pick specimens of macroalgae by hand in proportion to their
relative abundance in the reach. Combine all samples into a common container.
Table 6-1. Summary of collection techniques for periphyton from wadeable streams (adapted from
Kentucky PEP 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 masses
Place a portion of the plant in a sample container
with some water. Shake it vigorously and rub it
gently to remove algae. Remove plant from sample
container.
Large substrates (not removable): boulders, bedrock,
logs, trees, roots
Place PVC pipe with a neoprene collar at one end on
die substrate so that the collar is sealed against the
substrate. Dislodge algae in the pipe with a
toothbrush, nail brush, or scraper. Remove algae
from pipe with pipette.
Loose sediments: sand, silt, fine particulate organic
matter, clay
Invert petri dish over sediments. Trap sediments in
petri dish by inserting spatula under dish. Remove
sediments from stream and rinse into sampling
container. Algal samples from depositional habitats
can also be collected with spoons, forceps, or
pipette.
4.	Place all samples into a single water-tight, unbreakable, wide-mouth container. A composite
sample measuring four ounces (ca. 125 ml) is sufficient (Bahls 1993). Add recommended
amount of Lugol's (IKI) solution, "M3" fixative, buffered 4% formalin, 2% glutaraldehyde, or
other preservative (APHA 1995).
5.	Place a permanent label on the outside of the sample container with the following information:
waterbody name, location, station number, date, name of collector, and type of preservative.
Record this information and relevant ecological information in a field notebook or on the
periphyton field data sheet (Appendix A-2, Form 1). Place another label with the same
Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
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information inside the sample container. (Caution! Lugol's solution and other iodine-based
preservatives will turn paper labels black.)
6.	After sampling, review the recorded information on all labels and forms for accuracy and
completeness.
7.	Examine all brushing and scraping tools for residues. Rub them clean and rinse them in distilled
water before sampling the next site and before putting them away.
8.	Transport samples back to the laboratory in a cooler with ice (keep them cold and dark) and store
preserved samples in the dark until they are processed. Be sure to stow samples in a way so that
transport and shifting does not allow samples to leak. When preserved, check preservative every
few weeks and replenish as necessary until taxonomic evaluation is completed.
9.	Log in all incoming samples (Appendix A-2, Form 2). At a minimum, record sample
identification code, date, stream name, sampling location, collector's name, sampling method,
and area sampled (if it was determined).
6.1.1.2 Single Habitat Sampling
Variability due to differences in
habitat between streams may be
reduced by collecting periphyton from
a single substrate/habitat combination
that characterizes the study reach
(Rosen 1995). For comparability of
results, the same substrate/habitat
combination should be sampled in all
reference and test streams. Single
habitat sampling should be used when
biomass of periphyton will be
assessed.
1.	Define the sampling reach. The
area sampled for single habitat
sampling can be smaller than the
area used for multihabitat
sampling. Valuable results have
been achieved in past projects by
sampling just one riffle or pool.
2.	Before sampling, complete the
physical/chemical field sheet (see
Chapter 5; Appendix A-l, Form
1) and the periphyton field data
sheet (Appendix A-2, Form 1). Complete habitat assessments as in multihabitat sampling so that
the relative importance of the habitats sampled can be characterized.
3.	The recommended substrate/habitat combination is cobble obtained from riffles and runs with
current velocities of 10-50 cm/sec. Samples from this habitat are often easier to analyze than
from slow current habitats because they contain less silt. These habitats are common in many
	
CHLOROPHYLL a SUBSAMPLING (OPTIONAL)
1.	Chlorophyll a subsamples should be taken as soon as
possible (< 12 hours after sampling). Generally, if
chlorophyll subsamples can not be taken in the lab on the
day of collection, subsample in the field,
2.	Homogenize samples. In the field, shake vigorously. In
the lab, use a tissue homogenizer.
3.	Record the initial volume of sample on the periphyton
sample log form.
4.	Stir the sample on a magnetic stirrer and subsample.
When subsampling, take at least two aliquots from the
sample for each chlorophyll sample (two aliquots
provides a more representative subsample than one).
Record the subsample volume for chlorophyll a on the
periphyton sample log form.
5.	Concentrate the chlorophyll subsample on a glass fiber
filter (e.g., Whatman® GFC or equivalent).
6.	Fold the filter and wrap with aluminum to exclude light.
7.	Store the filter in a cold cooler (not in water) and
eventually in a freezer.
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Chapter 6: Periphyton Protocols
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streams. In low gradient streams where riffles are rare, algae on snags or in depositional habitats
can be collected. Shifting sand is not recommended as a targeted substrate because the species
composition on sand is limited due to the small size and unstable nature of the substratum.
Phytoplankton should be considered as an alternative to periphyton in large, low gradient
streams.
4.	Collect several subsamples from the same substrate/habitat combination and composite them
into a single container. Three or more subsamples should be collected from each reach or study
stream.
5.	The area sampled should always be determined if biomass (e.g., chlorophyll) per unit area is to
be measured.
6.	If you plan to assay samples for chlorophyll a, do not preserve samples until they have been
subsampled (see textbox entitled "Chlorophyll a Subsampling").
7.	Store, transport, process, and log in samples as in steps 4-9 in section 6.1.1.1.
6.1.2 Field Sampling Procedures:
Artificial Substrates
Most monitoring groups prefer sampling natural
substrates whenever possible to reduce field time
and improve ecological applicability of
information. However, 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 habitats. Both natural and artificial
substrates are useful in monitoring and assessing
waterbody conditions, 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).
Although glass microslides are preferred, a variety
of artificial substrates have been used with success
(see #2 below and textbox on p 6-6).
1.	Microslides should be thoroughly cleaned
before placing in periphytometers (e.g.,
Patrick et al. 1954). Rinse slides in acetone
and clean with Kimwipes®.
2.	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
QUALITY CONTROL (QC)
IN THE FIELD
1.	Sample labels must be accurately and
thoroughly completed, including the sample
identification code, date, stream name,
sampling location, and collector's name.
The outside and any inside labels of the
container should contain the same
information. Chain of custody and sample
log forms must include the same
information as the sample container labels.
Caution! Lugol's solution and iodine-based
preservatives will turn paper labels black.
2.	After sampling has been completed at a
given site, all brushes, suction and scraping
devices that have come in contact with the
sample should be rubbed clean and rinsed
thoroughly in distilled water. The
equipment should be examined again prior
to use at the next sampling site, and rinsed
again if necessary.
3.	After sampling, review the recorded
information on all labels and forms for
accuracy and completeness.
4.	Collect and analyze one replicate sample
from 10% of the sites to evaluate precision
or repeatability of sampling technique,
collection team, sample analysis, and
taxonomy.
Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
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weeks for periphyton recruitment and colonization.
3.	Replicate a minimum of 3 periphytometers at each site to account for spatial variability. The
total number should depend upon the study design and hypotheses tested. Samples can either be
composited or analyzed individually.
4.	Attach periphytometers to rebars pounded into the stream bottom or to other stable structures.
Periphytometers should be hidden from view to minimize disturbance or vandalism. Avoid the
main channel of floatable, recreational streams. Each periphytometer should be oriented with the
shield directed upstream.
5.	If flooding or a similar scouring event
occurs during incubation, allow
waterbody to equilibrate and reset
periphytometers with clean slides.
6.	After the incubation period (2-4 weeks),
collect substrates. Remove algae using
rubber spatulas, toothbrushes and razor
blades. You can tell when all algae have
been removed from substrates by a
change from smooth, mucilaginous feel
(even when no visible algae are present)
to a non-slimy or rough texture.
7.	Store, transport, process, and log in
samples as in steps 4-9 in section 6.1.1.1.
8.	One advantage of using artificial
substrates is that containers (e.g.,
whirl-pack bags or sample jars) can be
purchased that will hold the substrates so
that substrates need not be scraped in the field. Different substrates can be designated for
microscopic analysis and chlorophyll assay. Then algae and substrates can be placed in
sampling containers and preserved for later processing and microscopic analysis or placed in a
cooler on ice for later chlorophyll a analysis. Laboratory sample processing is preferred; so if
travel and holding time are less than 12 hours, it is not necessary to split samples before
returning to the lab.
6.1.3 Assessing Relative Abundances of Algal Taxa: Both "Soft" (Non-Diatom)
Algae and Diatoms
The Methods summarized here are a modified version of those used by Kentucky (Kentucky DEP
1993), Florida (Florida DEP 1996), and Montana (Bahls 1993). For more detail or for alternative
methods, see Standard Methods for the Examination of Water and Wastewater (APHA 1995).
Many algae are readily identifiable to species level by trained personnel who have a good library of
literature on algal taxonomy (see section 6.3). All algae can not be identified to species because: the
growth forms of some algal species are morphologically indistinguishable with the light microscope
(e.g., zoospores of many green algae); the species has not been described previously; or the species is
not in the laboratory's literature. Consistency in identifications within a laboratory and program is
very important, because most bioassessment are based on contrasts between reference and test sites.
Accuracy of identifications becomes most important when using autecological information from
FIELD EQUIPMENT/SUPPLIES NEEDED FOR
PERIPHYTON SAMPLING-
ARTIFICIAL SUBSTRATES
•	periphytometer (frame to hold artificial substrata)
•	microslides or other suitable substratum (e.g.,
clay tiles, sanded Plexiglass® plates, or wooden
or acrylic dowels)
•	sledge hammer and rebars
•	toothbrush, razor blade, or other scraping tools
•	water bottle with distilled water
•	white plastic or enamel pan
•	aluminium foil
•	sample containers
•	sample container labels
•	field notebook (waterproof)
•	preservative [Lugol's solution, 4% buffered
formalin, "M3" fixative, or 2% glutaraldehyde
(APHA 1995)]
•	cooler with ice
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Chapter 6: Periphyton Protocols
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other studies. Quality assurance techniques are designed to ensure "internal consistency" and also
improve comparisons with information in other algal assessment and monitoring programs.
6.1.3.1	"Soft" (Non-Diatom) Algae Relative Abundance and Taxa Richness
1.	Homogenize algal samples with a tissue homogenizer or blender.
2.	Thoroughly mix the homogenized sample and pipette into a Palmer counting cell (see textbox
for alternative methods). Algal suspensions that produce between 10 and 20 cells in a field
provide good densities for counting and identifying cells. Lower densities slow counting. Dilute
samples if cells overlap too much for counting.
3.	Fill in the top portion of the benchsheet for "soft" algae (Appendix A-2, Form 3) with enough
information from the sample label and other sources to uniquely identify the sample.
4.	Identify and count 300 algal "cell units" to the lowest possible taxonomic level at 400X
magnification with the use of the references in Section 6.3.
•	Distinguishing cells of coenocytic algae (e.g., Vaucheria) and small filaments of blue-green
algae is a problem in cell counts. "Cell units" can be defined for these algae as 10mm
sections of the thallus or filament.
•	For diatoms, only count live diatoms and do not identify to lower taxonomic levels if a
subsequent count of cleaned diatoms is to be undertaken (See section 6.1.3.2).
•	Record numbers of cells or cell units observed for each taxon on a benchsheet.
•	Make taxonomic notes and drawings on benchsheets of important specimens.
5.	Optional - To better determine non-diatom taxa richness, continue counting until you have not
observed any new taxa for 100 cell units or about three minutes of observation.
6.1.3.2	Diatom Relative Abundances and Taxa Richness
1.	Subsample at least 5-10 mL of concentrated preserved sample while vigorously shaking the
sample (or using magnetic stirrer). Oxidize (clean) samples for diatom analysis (APHA 1995,
see textbox entitled "Oxidation Methods for Cleaning Diatoms").
2.	Mount diatoms in Naphrax® or another high refractive index medium to make permanent slides.
Label slides with same information as on the sample container label.
3.	Fill in the top portion of the bench sheet for diatom counts (Appendix A-2, Form 4) with enough
information from the sample label to uniquely identify the sample.
4.	Identify and count diatom valves to the lowest possible taxonomic level, which should be species
and perhaps variety level, under oil immersion at 1000X magnification with the use of the
references in Section 6.3. At minimum, count 600 valves (300 cells) and at least until 10 valves
of 10 species have been observed. Be careful to distinguish and count both valves of intact
frustules. The 10 valves of 10 species rule ensures relatively precise estimates of relative
abundances of the dominant taxa when one or two taxa are highly dominant. Six hundred valve
counts were chosen to conform with methods used in other national bioassessment programs
(Porter et al. 1993). Record numbers of valves observed for each taxon on the bench sheet.
Make taxonomic notes and drawings on benchsheets and record stage coordinates of important
specimens.
Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
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5. Optional - To estimate total diatom taxa richness, continue counting until you have not observed
any new species for 100 specimens or about three minutes of observation.
6.1.3.3	Calculating Species Relative Abundances and Taxa Richness
1.	Relative abundances of "soft" algae are determined by dividing the number of cells (cell units)
counted for each taxon by the total number of cells counted (e.g., 300). Enter this information on
Appendix A-2, Form 3.
2.	Relative abundances of diatoms have to be corrected for the number of live diatoms observed in
the count of all algae. Therefore, determine the relative abundances of diatom species in the
algal assemblage by dividing the number of valves counted for each species by the total number
of valves counted (e.g., 600); then multiply the relative abundance of each diatom taxon in the
diatom count by the relative abundance of live diatoms in the count of all algae. Enter this
information on Appendix A-2, Form 4. Some analysts prefer to treat diatom and soft algal
species composition separately. In this case, determine the relative abundances of diatom
species in the algal assemblage by dividing the number of valves counted for each species by the
total number of valves counted (e.g., 600).
3.	Total taxa richness can be estimated by adding the number of "soft" algal taxa and diatom taxa.
6.1.3.4	Alternative Preparation Techniques
Palmer counting cells are excellent for identifying and counting soft-algae in most species
assemblages. When samples have many very small blue-green algae or a few, relatively important
large cells, other slide preparation techniques may be useful to increase magnification and sample
size, respectively. Because accurate diatom identification is not possible in Palmer cells, we have
recommended counting cleaned diatoms in special mounts. However, if the taxonomy of algae in
samples is well known, preparation and counting time can be reduced by mounting algae in syrup. In
syrup, both soft algae and diatoms can be identified, but resolution of morphological details of
diatoms is not as great as in mounts of diatoms in resins (e.g., Naphrax®).
Assemblages with many small cells: We recommend a simple wet mount procedure when samples
contain many small algae so samples can be observed at 1000X. A small volume of water under the
coverglass prevents movement of cells when adjusting focus and using oil immersion. These
preparations usually last several days if properly sealed (see below).
Wet mounts:
1.	Clean coverglasses and place on flat surface.
2.	Pipette 1.0 mL of algal suspension onto the coverglass.
3.	Dry the algal suspension on the coverglass. For convenience, the evaporation of water can be
increased on a slide-warmer or slowed by drying the sample in a vapor chamber (as simple as a
cake pan or aluminum foil hood placed over samples).
4.	As soon as the algal suspension dries, invert the coverglass into the 0.G2 mL of distilled water on
a microscope slide.
5.	Seal the water under the microscope slide with fingernail polish or polyurethane varnish.
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Chapter 6: Periphyton Protocols
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Assemblages with a few large cells;
Sedgewiek-Rafter counting
chambers, which are large modified
microscope slides with 1.0 mL wells,
increase sample size. Counts in
Sedgewick-Rafter counting cells
should be done after counts in Palmer
cells or wet mounts so that the
relation between sample proportions
with the two methods can be
determined. While keeping track of
the proportion of sample observed,
identify and count large algae in
transects at 200X or 100X
magnification in the counting cell.
Syrup mounts:
1.	Prepare Taft's syrup medium
(TSM) by mixing 30 mL of clear
corn syrup (e.g., Karo's® Corn
Syrup) with 7 mL of
formaldehyde and 63 mL of
distilled water. Dilute a 10 mL
proportion of this 100% TSM
with 90 mL of distilled water to
make 10% TSM.
2.	Place 0.2 mL of 10% TSM on
coverglass.
3.	Place 1.0 mL of algal suspension
on coverglass. Consider using
several dilutions.
4.	Let dry for 24 hours.
Alternatively, dry on slide
warmer on low setting. Do not
overdry or cells will plasmolyze.
5.	Place another ~ 1.0 mL of 10%
TSM on cover glass and dry
(overnight or 4 hours on a slide
warmer). Apply 10% TSM
quickly to avoid patchy
resuspension of the original layer
of TSM and algae.
6.	Invert coverglass onto
microscope slide; place slide on
hot plate to warm the slide and
syrup. Do not boil, just warm.
Press coverglass gently in place
	
OXIDATION (CLEANING) METHODS FOR DIATOMS
Concentrated Acid Oxidation:
1.	Place a 5-10 mL subsample of preserved algal sample in
a beaker.
2.	Under a fume hood, add enough concentrated nitric or
sulfuric acid to produce a strong exothermic reaction.
Usually equal parts of sample and acid will produce such
a reaction.
(Caution! With some preservatives and samples from
hard water, adding concentrated acid will produce a
violent exothermic reaction. Use a fume hood, safety
glasses, and protective clothing. Separate the sample
beakers by a few inches to prevent
cross-contamination of samples in the event of
overflow.)
3.	Allow the sample to oxidize overnight.
4.	Fill the beaker with distilled water.
5.	Wait 1 hour for each centimeter of water depth in the
beaker.
6.	Siphon off the supernatant and refill the beaker with
distilled water. Siphon from the center of the water
column to avoid siphoning light algae that have adsorbed
onto the sides and surface of the water column.
7.	Repeat steps 4 through 6 until all color is removed and
the sample becomes clear or has a circumneutral pH.
Hydrogen Peroxide/Potassium Dichromate Oxidation:
1.	Prepare samples as in step 1 above, but use 50% H2Oz
instead of concentrated acid.
2.	Allow the sample to oxidize overnight, then add a
microspatula of potassium dichromate.
(Caution! This will cause a violent exothermic
reaction. Use a fume hood, safety glasses, and
protective clothing. Separate the sample beakers by a
few inches to prevent cross-contamination in the event
of overflow.)
3.	When the sample color changes from purple to yellow
and boiling stops, fill the beaker with distilled water.
4.	Wait 4 hours, siphon off the supernatant, and refill the
beaker with distilled water. Siphon from the center of
the water column to avoid siphoning light algae that have
adsorbed onto the sides and surface of the water column.
5.	Repeat step 4 until all color is removed and the sample
becomes clear.
Rapid Bioassessment Protocols for Use in Streams and WadeabJe Rivers: Periphyton, Benthic
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with forceps, being careful to keep all syrup under the coverglass. The syrup should spread
under coverglass.
7. Remove the slide from the hotplate. Cooling should partially seal the coverglass to the slide.
8. More permanently seal the syrup under slides by painting fingernail polish around the edge of
the cover glass and onto the microscope slide.
Note: Preserve color of chloroplasts by
keeping samples in dark.
Special Note: If slides get too warm in
storage, syrup will loose viscosity and
become runny. Algae and medium may then
escape containment under coverglass. Store
slides in a horizontal position,
6.1.4 Metrics Based on Species
Composition
The periphyton metrics presented here are
used by several states and environmental
assessment programs throughout the US and
Europe (e.g., Kentucky DEP 1993, Bahls
1993, Florida DEP 1996, Whitton et al. 1991,
Whitton and Kelly 1995). Each of these
metrics should be tested for response to
human alterations of streams in the region in
which they are used (see Chapter 9,
Biological Data Analysis). In many cases,
diatom and soft algal metrics have been
determined separately because changes in
small abundant cyanobacteria (blue-green
algae) can numerically overwhelm metrics
based on relative abundance and because
green algae with large cells (e.g.,
Cladophora) may not have appropriate
weight. However, attempts should be made
to integrate diatoms and soft algae in as many
metrics as possible, especially in cases such
as species and generic richness when great
variability in relative abundance is not an
issue.
Many metrics can be calculated based on
presence/absence data or on relative
abundances of taxa. For example, percent
Pollution Tolerant Diatoms can be calculated
as the sum of relative abundances of
pollution tolerant taxa in an assemblage or as
the number of species that are tolerant to
pollution in an assemblage. Percent
COSTS AND BENEFITS OF SIMPLER
ANALYSES
We recommend that all algae (soft and diatom)
be identified and counted. Information may be
lost if soft algae are not identified and counted
because some impacts may selectively affect
soft algae. Most of the species (and thus
information) in a sample will be diatoms. Costs
of both analyses are not that great.
Costs can be reduced by only counting diatoms
or soft algae. Since diatoms are usually the most
species-rich group of algae in samples and most
metrics are based on differences in taxonomie
composition, we recommend that diatoms be
counted. In addition, permanently preserved
and readily archived microslides of diatoms can
serve as a historic reference of ecological
conditions.
In general, identifying algae to species is
recommended for two reasons: (1) to better
characterize differences between assemblages
that may occur at the species level and (2)
because large differences in ecological
preferences do exist among algal species within
the same genus.
However, substantial information can be gained
by identifying algae just to the genus level.
Whereas identifying algae only to genus may
loose valuable ecological information, costs of
analyses can be reduced, especially for
inexperienced analysts.
If implementing a new program and only an
inexperienced analyst is available for the job,
identifying diatom genera in assemblages can
provide valuable characterizations of biotic
integrity and environmental conditions.
As analysts get more experience counting, the
taxonomie level of their analyses should
improve. The cost of an experienced analyst
counting and identifying algae to species is not
much greater than analysis to genus.
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Chapter 6: Periphyton Protocols
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community similarity can be calculated as presented below, which quantifies the percent of
organisms in two assemblages that are the same. Alternatively, it can be calculated as the percent of
species that are the same by making all relative abundances greater than 0 equal to 1. The following
metrics can also be calculated with presence/absence data instead of species relative abundances: %
sensitive taxa, % motile taxa, % acidobiontic, % alkalibiontic, % halobiontic, % saprobiontic, %
eutrophic, simple autecological indices, and change in inferred ecological conditions. Although we
may find that metrics based on species relative abundances are more sensitive to environmental
change, metrics based on presence/absence data may be more appropriate when developing metrics
with multihabitat samples and proportional sampling of habitats is difficult. In the latter case,
presence/absence of species should remain the same, even if relative abundance of taxa differs with
biases in multihabitat sampling.
The metrics have been divided into two groups which may be helpful in developing an Index of
Biotic Integrity (IBI). Metrics in the first group are less diagnostic than the second group of metrics.
Metrics in the first group (species and generic richness, Shannon diversity, etc.) generally
characterize biotic integrity ("natural balance in flora and fauna...." as in Karr and Dudley 1981)
without specifically diagnosing ecological conditions and causes of impairment. The second group
of metrics more specifically diagnoses causes of impaired biotic integrity. Metrics from both groups
could be included in an IBI to make a hierarchically diagnostic IBI. Alternatively, an IBI could be
constructed from only metrics of biotic integrity so that inference of biotic integrity and diagnosis of
impairment are independent (Stevenson and Pan 1999).
Autecological information about many algal species and genera has been reported in the literature.
This information comes in several forms. In some cases, qualitative descriptions of the ecological
conditions in which species were observed were reported in early studies of diatoms. Following the
development of the saprobic index by Kolkwitz and Marsson (1908), several categorical
classification systems (e.g., halobian spectrum, pH spectrum) were developed to describe the
ecological preferences and tolerances of species (see Lowe 1974 for a review). Most recently, the
ecological optima and tolerances of species for specific environmental conditions have been
quantified by using weighted average regression approaches (see ter Braak and van Dam 1989 for a
review). We have compiled a list of references for this information in Section 6.4. These references
will be valuable for developing many of the metrics below.
Metrics of Biotic Integrity
1.	Species richness is an estimate of the number of algal species (diatoms, soft algae, or both)
in a sample. High species richness is assumed to indicate high biotic integrity because many
species are adapted to the conditions present in the habitat. Species richness is predicted to
decrease with increasing pollution because many species are stressed. However, many
habitats may be naturally stressed by low nutrients, low light, or other factors. Slight
increases in nutrient enrichment can increase species richness in headwater and naturally
unproductive, nutrient-poor streams (Bahls et al. 1992).
2.	Total Number of Genera (Generic richness) should be highest in reference sites and lowest
in impacted sites where sensitive genera become stressed. Total number of genera (diatoms,
soft algae, or both) may provide a more robust measure of diversity than species richness,
because numerous closely related species are within some genera and may artificially inflate
richness estimates.
3.	Total Number of Divisions represented by all taxa should be highest in sites with good
water quality and high biotic integrity.
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4.	Shannon Diversity (for diatoms). The Shannon Index is a function of 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 and complexly with
water pollution. Stevenson (1984) suggests that changes in species diversity, rather than the
diversity value, may be useful indicators of changes in water quality. 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. Under certain conditions Shannon diversity values may underestimate water
quality 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 Beeson1, personal communication).
5.	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 algal bioassessment
because it shows community similarities based on relative abundances, and in doing so,
gives more weight to dominant taxa than rare ones. Percent similarity can be used to
compare control and test sites, or average community of a group of control or reference sites
with a test site. Percent community similarity values range from 0 (no similarity) to 100%.
The formula for calculating percent community similarity is:
PSc = lOO-jE^Jaj-bJ = S-=1min(ai,bi)
where:
a,	= percentage of species i in sample A
b,	= percentage of species i in sample B
6. Pollution Tolerance Index for Diatoms. The pollution tolerance index (PTI) for algae
resembles the Hilsenhoff biotic index for macroinvertebrates (Hilsenhoff 1987). Lange-
Bertalot (1979) distinguishes 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 parvulum) to 3 for relatively sensitive species. Relative tolerance for
taxa can be found in Lange-Bertalot (1979) and in many of the references listed in section
6.4. Thus, Lange-Bertalot's PTI varies from 1 for most polluted to 3 for least polluted
waters when using the following equation:
N
where:
n, = number of cells counted for species i
tj = tolerance value of species i
N = total number of cells counted
'David Beeson is a phycologist with Schafer & Associates, Inc.
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In some cases, the range of values for tolerances has been increased, thereby producing a
corresponding increase in the range of PTI values.
7.	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 many other metrics to
underestimate water quality.
8.	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 (Bahls, unpublished data).
9.	Percent live diatoms was proposed by Hill (1997) as a metric to indicate the health of the
diatom assemblage. Low percent live diatoms could be due to heavy sedimentation and/or
relatively old algal assemblages with high algal biomass on substrates.
Diagnostic Metrics that Infer Ecological Conditions
The ecological preferences of many diatoms and other algae have been recorded in the literature.
Using relative abundances of algal species in the sample and their preferences for specific habitat
conditions, metrics can be calculated to indicate the environment stressors in a habitat. These
metrics can more specifically infer environmental stressors than the general pollution tolerance
index.
10.	Percent Aberrant Diatoms is the percent of diatoms in a sample that have anomalies in
striae patterns or frustule shape (e.g, long cells that are bent or cells with indentations). This
metric has been positively correlated to heavy metal contamination in streams (McFarland et
al. 1997).
11.	Percent Motile Diatoms. The percent motile diatoms is a siltation index, expressed as the
relative abundance of Navicula + Nitzschia + Surirella. 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 amount and frequency of siltation.
Relative abundances of Gyrosigma, Cylindrotheca, and other motile diatoms may also be
added to this metric.
12.	Simple Diagnostic Metrics can infer the environmental stressor based on the autecology of
individual species in the habitats. For example, if acid mine drainage was impairing stream
conditions, then we would expect to find more acidobiontic taxa in samples. Calculate a
simple diagnostic metric as the sum of the percent relative abundances (range 0-100%) of
species that have environmental optima in extreme environmental conditions. For example
(see Table 6-2):
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% acidobiontic + % acidophilic
% alkalibiontic + % alkaliphilic
% halophilic
% mesosaprobic + % oligosaprobic + % saprophitic
% eutrophic
13.	Inferred Ecological Conditions with Simple Autecological Indices (SAI) - The ecological
preferences for diatoms are commonly recorded in the literature. Using the standard
ecological categories compiled by Lowe (1974, Table 6-2), the ecological preferences for
different diatom species can be characterized along an environmental (stressor) gradient.
For example, pH preferences for many taxa are known. These preferences (©,) can be
ranked from 1-5 (e.g., acidobiontic, acidophilic, indifferent, alkaliphilic, alkalibiontic, Table
6-2) and can be used in the following equation to infer environmental conditions (EC) and
effect on the periphyton assemblage.
SAIec —21 0jpi
14.	Inferred Ecological Conditions with Weighted Average Indices are based on the specific
ecological optima (pf) for algae, which are being reported more and more commonly in
recent publications (see Pan and Stevenson 1996). Caution should be exercised, because we
do not know how transferable these optima are among regions and habitats. Using the
following equation, the ecological conditions (EC) in a habitat can be inferred more
accurately by using the optimum environmental conditions (P;) and relative abundances (p,)
for taxa in the habitat (ter Braak and van Dam 1989, Pan et al., 1996) than if only the
ecological categorization were used (as above for the SAI). Optimum environmental
conditions are those in which the highest relative abundances of a taxon are observed. These
can be determined from the literature or from past surveys of taxa and environmental
conditions in the study area (see ter Braak and van Dam 1989). In a pH example, the
specific pH in a habitat can be inferred if we know the pH optima (H) of taxa in the habitat,
and use the following general equation:
WAIec = SpjPi
and modify for inferring pH:
WAIpH = S ifiPi
15. Impairment of Ecological Conditions can be inferred with algal assemblages by
calculating the deviation (AEC) between inferred environmental conditions at a test site and at
a reference site.
Compare inferred ecological conditions at the test site to the expected ecological conditions (ECex) of
regional reference sites by using either simple autecological indices (SAIEC) or weighted average
indices (WAIEC):
Aec = |SAIec-ECJ
Aec = |WAIec-ECJ
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Table 6-2. Environmental definitions of autecological classification systems for algae (as modified or
referenced by Lowe 1974). Definitions for classes are given if no subclass is indicated.	.
Classification System/
Ecological Parameter
Class
Subclass
Conditions of Highest Relative
Abundances
pH Spectrum
Acidobiontic

Below 5.5 pH
Acidophilic

Above 5.5 and below 7 pH
Indifferent

Around 7 pH
Alakaliphilic

Above 7 and below 8.5 pH
Alkalibiontic

Above 8.5 pH
Nutrient Spectrum - based on
P and N concentrations
Eutrophic

High nutrient conditions
Mesotrophic

Moderate nutrient conditions
Oligotrophic

Low nutrient conditions
Dystrophic

High humic (DOC) conditions
Halobion Spectrum - based
on chloride concentrations or
conductivity
Polyhalobous

Salt concentrations > 40,000 mg/L
Euhalobous

Marine forms: 30,000-40,000 mg/L
Mesohalobous
Alpha range
Brackish water forms: 10,000-30,000 mg/L
Mesohalobous
Beta range
Brackish water forms: 500-10,000 mg/L
Oligohalobous
Halophilous
Freshwater - stimulated by some salt
Oligohalobous
Indifferent
Freshwater - tolerates some salt
Oligohalobous
Halophobic
Freshwater - does not tolerate small
amounts of salt
Saprobien System - based on
organic pollution
Polysaprobic

Characteristic of zone of degradation and
putrefication, oxygen usually absent or low
in concentration
Mesosaprobic
Alpha range
Zone of organic load oxidation — N as
amino acids

Beta range
Zone of organic load oxidation — N as
ammonia
Oligosaprobic

Zone in which oxidation of organics
complete, but high nutrient concentrations
persist
Saprophilic

Usually in polluted waters, but also in clean
waters
Saproxenous

Usually in clean waters, but also found in
polluted waters
Saprophobic

Only found in unpolluted waters
6.1.5 Determining Periphyton Biomass
Measurement of periphyton biomass is common in many studies and may be especially important in
studies that address nutrient enrichment or toxicity. In many cases, however, sampling benthic algae
misses peak biomass, which may best indicate nutrient problems and potential for nuisance algal
growths (Biggs 1996, Stevenson 1996).
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Macroinvertebrates, and Fish, Second Edition
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Biomass measurements can be made with samples collected from natural or artificial substrates. To
quantify algal biomass (chl a, ash-free dry mass, cell density, biovolume cm"2), the area of the
substrate sampled must be determined. Two national stream assessment programs sample and assess
area-specific cell density and biovolume (USGS-NAWQA, Porter et al. 1993; and EMAP, Klemm
and Lazorchak 1994). These programs estimate algal biomass in habitats and reaches by collecting
composite samples separately from riffle and pool habitats.
Periphyton biomass can be estimated with chl
~,	ash-free dry mass (AFDM), cell densities,
and biovolume, usually per cm2 (Stevenson
1996). Each of these measures estimates a
different component of periphyton biomass
(see Stevenson 1996 for discussion).
~.1.5.1	Chlorophyll a
Chlorophyll a ranges from 0.5 to 2% of total
algal biomass (APHA 1995), and this ratio
varies with taxonomy, light, and nutrients. A
detailed description of chlorophyll a analysis
is beyond the scope of this chapter. Standard
methods (APHA 1995, USEPA 1992) are
readily available. The analysis is relatively
simple and involves:
1.	extracting chlorophyll a in acetone;
2.	measuring chlorophyll concentration in the
extract with a spectrophotometer or
fluorometer; and
3.	calculating chlorophyll density on
substrates by determining the proportion
of original sample that was assessed for
chlorophyll.
6.1.5.2	Ash-Free Dry Mass
Ash-free dry mass is a measurement of the organic matter in samples, and includes biomass of
bacteria, fungi, small fauna, and detritus in samples. A detailed description of analysis is beyond the
scope of this chapter, but standard methods (APHA 1995, USEPA 1995) are readily available. The
analysis is relatively simple and measures the difference in mass of a sample after drying and after
incinerating organic matter in the sample. We recommend using AFDM versus dry mass to measure
periphyton biomass because silt can account for a substantial proportion of dry mass in some
samples. Ash mass in samples can be used to infer the amount of silt or other inorganic matter in
samples.
6.1.5.3	Area-Specific Cell Densities and Biovolumes
Cell densities (cells cm'2) are determined by dividing the numbers of cells counted by the proportion
of sample counted and the area from which samples were collected. Cell biovolumes (mm3
biovolume cm'2) are determined by summing the products of cell density and biovolume of each
	
LABORATORY EQUIPMENT FOR
PERIPHYTON ANALYSIS
compound microscope with 10X or 15X
oculars and 20X, 40X and 100X (oil)
objectives
tally counter (for species proportional count)
microscope slides and coverglasses
•	immersion oil, lens paper and absorbent tissues
tissue homogenizer or blender
•	magnetic stirrer and stir bar
forceps
•	hot plate
•	fume hood
•	squeeze bottle with distilled water
•	oxidation reagents (HN03, H2SO„ K2Cr207,
HA)
•	200-500 ml beakers
•	safety glasses and protective clothing
drying oven for AFDM
muffle furnace for AFDM
•	aluminum weighing pans for AFDM
spectrophotometer or fluorometer for chl a
•	centrifuge for chl a
graduated test tubes for chl a
acetone for chl a
•	MgC03 for chl a
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Chapter 6: Periphyton Protocols
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species counted (see Lowe and Pan 1996) and dividing that sum by the proportion of sample counted
and the area from which samples were collected.
6.1.S.4 Biomass Metrics
High algal biomass can indicate
eutrophication, but high algal
biomass can also accumulate in less
productive habitats after long
periods of stable flow. Low algal
biomass may be due to toxic
conditions, but could be due to a
recent storm event and spate or
naturally heavy grazing. Thus,
interpretation of biomass results is
ambiguous and is the reason that
major emphasis has not been placed
on quantifying algal biomass for
RBP. However, nuisance levels of
algal biomass (e.g., > 10 ng chl a
cm"2, > 5 mg AFDM cm"2, > 40%
cover by macroalgae; see review by
Biggs 1996) do indicate nutrient or
organic enrichment. If repeated
measurements of biomass can be
made, then the mean and maximum
benthic chl a could be used to
define trophic status of streams.
Dodds et al. (1998) have proposed
guidelines in which the
oligotrophic-mesotrophic boundary
is a mean benthic chl a of 2 pg cm"2
or a maximum benthic chl a of 7 fig
cm"2 and the mesotrophic-eutrophic
boundary is a mean of 6 pg chl a
cm"2 and a maximum of 20 |ig chl a
cm"2.
6.2 FIELD-BASED RAPID
PERIPHYTON SURVEY
Semi-quantitative assessments of
benthic algal biomass and
taxonomic composition can be
made rapidly with a viewing bucket
marked with a grid and a biomass
scoring system. 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 requires no laboratory
QUALITY CONTROL IN THE LABORATORY
1.	Upon delivery of samples to the laboratory, complete
entries on periphyton sample log-in forms (Appendix 2,
Form 2).
2.	Maintain a voucher collection of all samples and diatom
slides. They should be accurately and completely labeled,
preserved, and stored in the laboratory for future
reference. Specimens on diatom slides should be clearly
circled with a diamond or ink marker to facilitate location.
A record of the voucher specimens should be maintained.
Photographs of specimens improve "in-house" QA.
3.	For every QA/QC sample (replicate sample in every 10th
stream), assess relative abundances and taxa richness in
replicate wet mounts and a replicate diatom slide to assess
variation in metrics due to variability in sampling within
reaches (habitats), sample preparation, and analytical
variability.
4.	QA/QC samples should be counted by another taxonomist
to assess taxonomic precision and bias, if possible.
5.	Common algal taxa should be the same for the two wet
mount replicates. The percent community similarity index
(Whittaker 1952) (see Section 6.5.1) calculated from
proportional counts of the two replicate diatom slides
should exceed 75%.
6.	If it is not possible to get another taxonomist in the lab to
QA/QC samples, an outside taxonomist should be
consulted on a periodic basis to spot-check and verify
taxonomic identifications in wet mounts and diatom slides.
All common genera in the wet mount and all major species
on the diatom slide (>3% relative abundance) should be
identified similarly by both analysts (synonyms are
acceptable). Any differences in identification should be
reconciled and bench sheets should be corrected,
7.	A library of basic taxonomic literature is an essential aid in
the identification of algae and should be maintained and
updated as needed in the laboratory (see taxonomic
references for periphyton in Section 6.5), Taxonomists
should participate in periodic training to ensure accurate
identifications
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processing, but hand picked samples can be returned to the laboratory to quickly verify
identification. It is a technique developed by Stevenson and Rier2.
1.	Fill in top of Rapid Periphyton Survey
(RPS) Field Sheet, Appendix A-2,
Form 5.
2.	Establish at least 3 transects across the
habitat being sampled (preferably riffles
or runs in the reach in which benthic
algal accumulation is readily observed
and characterized).
3.	Select 3 locations along each transect
(e.g., stratified random locations on
right, middle, and left bank).
4.	Characterize algae in each selected
location by immersing the bucket with
50-dot grid (7 x 7 + 1) in the water.
•	First, characterize macroalgal
biomass.
•	Observe the bottom of the
stream through the bottom of
the viewing bucket and count
the number of dots that occur over macroalgae (e.g., Cladophora or Spirogyra) under
which substrates cannot be seen. Record that number and the kind of macroalgae under
the dots on RPS field sheet.
•	Measure and record the maximum length of the macroalgae.
•	If two or more types of macroalgae are present, count the dots, measure, and record
information for each type of macroalgae separately.
•	Second, characterize microalgal cover.
•	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 (usually diatoms and blue-green algae) and estimate the thickness
(density) of microalgae under each dot using the following thickness scale:
0	- substrate rough with no visual evidence of microalgae
0.5 - substrate slimy, but no visual accumulation of microalgae is evident
1	- a thin layer of microalgae is visually 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
Mat thickness can be measured with a ruler.
•	Record the number of dots that are over each of the specific thickness ranks separately
for diatoms, blue-green algae, or other microalgae.
5.	Statistically characterize density of algae on substrate by determining:
•	total number of grid points (dots) evaluated at the site (Dt);
•	number of grid points (dots) over macroalgae (Dm)
2 S.T. Rier is a graduate student at the University of Louisville.
	
FIELD EQUIPMENT FOR RAPID
PERIPHYTON SURVEY
viewing bucket with 50-dot grid [Make the
viewing bucket by cutting a hole in bottom of
large (a0.5 m diameter) plastic bucket, but leave
a small ridge around the edge. Attach a piece of
clear acrylic sheet to the bottom of the bucket
with small screws and silicon caulk. The latter
makes water tight seal so that no water enters the
bucket when it is partially submerged.
Periphyton can be clearly viewed by looking
down through the bucket when it is partially
submerged in the stream. Mark 50 dots in a 7 x 7
grid on the top surface of the acrylic sheet with a
waterproof black marker. Add another dot .
outside the 7 x 7 grid to make the 50 dot grid.]
meter stick
pencil
• Rapid Periphyton Survey Field Sheet
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•	total number of grid points (dots) over suitable substrate for microalgae at the site (dj;
•	number of grid points over microalga of different thickness ranks for each type of microalga
(d();
•	average percent cover of the habitat by each type of macroalgae (i.e., 100X Dm/D,);
•	maximum length of each type of macroalgae;
•	mean density (i.e., thickness rank) of each type of macroalgae on suitable substrate (i.e.,
Sdjr/d,); maximum density of each type of microalgae on suitable substrate.
6. QA/QC between observers and calibration between algal biomass (chl a, AFDM, cell density and
biovolume cm2 and taxonomic composition) can be developed by collecting samples that have
specific microalgal rankings and assaying the periphyton.
6.3 TAXONOMIC REFERENCES FOR PERIPHYTON
A great wealth of taxonomic literature is available for algae. Below is a subset of that literature. It is
a list of taxonomic references that are useful for most of the United States and are either in English,
are important because no English treatment of the group is adequate, or are valuable for the good
illustrations.
Camburn, K.E., R.L. Lowe, and D.L. Stoneburner. 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.
Cox, E. J. 1996. Identification offreshwater diatoms from live material. Chapman & Hall, London.
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 oscillator iaceae. Monograph 15. Academy of
Natural Sciences, Philadelphia. Fulton Press, Lancaster, Pennsylvania.
Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition	6-19
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Hohn, M.H. and J. Hellerman. 1963. The taxonomy and structure of diatom populations from three
North American rivers using three sampling methods. Transaction of the American Microscopal
Society 82:250-329.
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. Academy of Natural Sciences, Philadelphia, Pennsylvania.
Patrick, R. and C.W. Reimer. 1975. The Diatoms of the United States. Vol. 2, Parti. Monograph
No. 13. Academy of Natural Sciences, 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. Croasdale, 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. Croasdale, 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., H.T. Croasdale, and W.C. Vinyard. 1981. A synopsis of North American desmids.
Part II. Desmidaceae: Placodermae. Section 3. Univ. Nebraska Press, Lincoln, Nebraska.
Prescott, G.W. 1978. How to know the freshwater algae. 3rd Edition. 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.
6-20
Chapter 6: Periphyton Protocols
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VanLandingham, S. L. 1982. Guide to the identification, environmental requirements and pollution
tolerance of freshwater blue-green algae (Cyanophyta). EPA-600/3-82-073.
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.
6.4 AUTECOLOGICAL REFERENCES FOR PERIPHYTON
Beaver, J. 1981 .Apparent ecological characteristics of some common freshwater diatoms. Ontario
Ministry of the Environment. Rexdale, Ontario, Canada.
Cholnoky, B. J. 1968. Okologie der Diatomeen in Binnegewassem. Cramer, Lehre.
Fabri, R. and L. Leclercq. 1984. Etude ecologique des rivieres du nord du massif Ardennais
(Belgique): flore et vegetation de diatomeees et physico-chimie des eaux. 1. Station scientifique des
Hautes Fagnes, Robertville. 379 pp.
Fjerdingstad, E. 1950. The microflora of the River Molleaa with special reference to the relation of
benthic algae to pollution. Folia Limnologica Scandanavica 5, 1-123.
Hustedt, F. 1938-39. Systamatische und okologische Untersuchungen liber die Diatomeen-Flora von
Java, Bali und Sumatra nach dem Material deter Deutschen Limnologischen Sunda-Expedition.
Allgemeiner Teil. I. Ubersicht tiber das Untersuchungsmaterial und Charakterisktik der
Diatomeenflora der einzelnen Gebiete. II. Die Diatomeen flora der untersuchten Gesassertypen. III.
Die Okologische Faktoren und ihr Einfluss auf die Diatomeenflora. Archiv fiir Hydrobiologie,
Supplement Band, 15:638-790(1938); 16:1-155 (1938); 16:274-394(1939).
Hustedt, F. 1957. Die Diatomeenflora des Flusssystems der Weser im Gebiet der Hansestadt
Bremen. Abhandlungen naturwissenschaftlichen. Verein zu Bremen, Bd. 34, Heft 3, S. 181-440, 1
Taf.
Lange-Bertalot, H. 1978. Diatomeen-Differentialarten anstelle von Leitformen: ein geeigneteres
Kriterium der Gewasserbelastung. Archiv fur Hydrobiologie Supplement 51, 393-427.
Lange-Bertalot, H. 1979. Pollution tolerance of diatoms as a criterion for water quality estimation.
Nova Hedwigia 64, 285-304.
LeCointe C., M. Coste, and J. Prygiel. 1993. "OMNIDIA" software for taxonomy, calculation of
diatom indices and inventories management. Hydrobiologia 269/270: 509-513.
Lowe, R. L. 1974. Environmental Requirements and Pollution Tolerance of Freshwater Diatoms. US
Environmental Protection Agency, EPA-670/4-74-005. Cincinnati, Ohio, USA.
Palmer, C. M. 1969. A composite rating of algae tolerating organic pollution. Journal ofPhycology
5, 78-82.
Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
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Rott, E., G. Hofmann, K. Pall, P. Pfister, and E. Pipp. 1997. Indikationslisten fur Aufwuchsalgen in
Osterreichischen Fliessgewassern. Teil 1: Saprobielle Indikation. Wasserwirtschaftskataster.
Bundesminsterium fur Land- und Forstwirtschaft. Stubenring 1, 1010 Wein, Austria.
Sl&decek, V. 1973. System of water quality from the biological point of view. Archiv fur
Hydrobiologie und Ergebnisse Limnologie 7, 1-218.
Van Dam, H., Mertenes, A., and Sinkeldam, J. 1994. A coded checklist and ecological indicator
values of freshwater diatoms from the Netherlands. Netherlands Journal of Aquatic Ecology 28,
117-33.
Vanlandingham, S. L. 1982. Guide to the identification, environmental requirement and pollution
tolerance of freshwater blue-green algae (Cyanophyta). U. S. Environmental Protection Agency.
EPA-600/3-82-073.
Watanabe, T., Asai, K., Houki, A. Tanaka, S., and Hizuka, T. 1986. Saprophilous and eurysaprobic
diatom taxa to organic water pollution and diatom assemblage index (DAIpo). Diatom 2:23-73.
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7BENTHIC 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 the Florida Department of Environmental Protection
(DEP) were pioneers in this effort. These 2 groups (MACS and FLDEP) 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 (USEPA) for their Environmental Monitoring and Assessment
Program (EMAP) program and the United States Geological Survey (USGS) for their National Water
STANDARD BENTHIC MACROINVERTEBRATE SAMPLING GEAR TYPES FOR STREAMS
(assumes standard mesh size of 500 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 lm (Note — Depths of lm 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 mJ. 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 Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
7-1
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Quality Assessment Program (NAWQA) program 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 HI. 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
Rectangular Dipnet
Hess sampler
(Mary Kay Corazalla, Univ. of Minnesota)
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Chapter 7: Benthic Macroinvertebrate Protocols
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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 of 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 the amount of cobble substrate expected for the region.
7.1.1 Field Sampling Procedures for Single Habitat
1.	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.
2.	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.
3.	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 System (GPS) for latitude and longitude
determination taken at the furthest downstream point of the sampling reach.
	
FIELD EQUIPMENT/SUPPLIES NEEDED FOR BENTHIC
MACROINVERTEBRATE SAMPLING
—SINGLE HABITAT APPROACH
standard kick-net, 500 ^ opening mesh, 1.0 meter width
•	sieve bucket, with 500 jj. 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 System (GPS) Unit
•	It is helpful to copy fieldsheets onto water-resistant paper for use
in wet weather conditions
Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
7-3
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4.	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.
5.	Sampling begins at the downstream ei
of the reach and proceeds upstream.
Using a 1 m 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 scrape the underlying bed.
Larger substrate particles should be picked up and rubbed by hand to remove attached
organisms. If different gear is used (e.g., a D-frame or rectangular net), a composite is
obtained from numerous kicks (See Section 7.2).
6.	The jabs or kicks collected from different locations in the cobble substrate will be
composited to obtain a single homogeneous sample. After ever}' 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. [Note —
an alternative is to keep the samples from different habitats separated as done in EMAP
(Klemm and Lazorchak 1995).]
7.	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 than 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.
	
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 and
Lazorchak 1995). This approach allows
variation in the length of the reach based on
the size of the stream.
7-4
Chapter 7: Benthic Macroinvertebrate Protocols
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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;
walking 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 should 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 the sampling
technique or the 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 silty sediments.
Therefore, a method suitable to
sampling a variety of habitat
types is desired in these cases.
The method that 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
by Florida DEP (1996) and
Massachusetts 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 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
			
FIELD EQUIPMENT/SUPPLIES NEEDED FOR BENTHIC
MACROINVERTEBRATE SAMPLING
-MULTI-HABITAT APPROACH
•	standard D-frame dip net, 500 jj. opening mesh, 0.3 m width
(~ 1.0 ft frame width)
•	sieve bucket, with 500 // 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 System (GPS) Unit
" It is helpful to copy fieldsheets onto water-resistant paper for use
in wet weather conditions
Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
7-5
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sampling of approximately 3.1m2 of habitat. For example, if the habitat in the sampling reach is
50% snags, then 50% or 10 jabs should be taken in that habitat. An organism-based subsample
(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 listed 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.
7-6
Chapter 7: Benthic Macroinvertebrate Protocols
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7.2.2 Field Sampling Procedures for Multihabitat
1.	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.
2.	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.
3.	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. If available, use hand-held GPS for latitude and longitude determination taken at the
furthest downstream point of the sampling reach.
4.	Different types of habitat are to be sampled in approximate 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.
5.	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.
6.	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
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.
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 and
Lazorchak 1995). This approach allows
variation in the length of the reach based on
the size of the stream.
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7.	Transfer the sample from the net to sample containers) 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 should 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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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
include subsampling, sorting, and identification of organisms.
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
Subsampling benthic samples is not a
requirement, and in fact, is frowned
upon by certain scientists.
Courtemanch (1996) provides an
argument against subsampling, or to use a volume-based procedure if samples are to be subsampled.
Vinson and Hawkins (1996) and Barbour and Gerritsen (1996) provide arguments for a fixed-count
method, which is the preferred subsampling technique for RBPs.
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 200-
organism subsample, but it could be usedfor any subs ample size (100, 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 (im-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.
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
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3.	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.
4.	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 200 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 200 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
200 organisms ± 20% are found. If picking through the entire next grid is likely to result in a
subsample of greater than 240 organisms, then that grid may be subsampled in the same
manner as before to decrease the likelihood of exceeding 240 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.
SUBSAMPLE PROCEDURE MODIFICATIONS
Subsampling procedures developed by Hilsenhoff
(1987) and modified by Plafkin et al. (1989) were
used in the original RBPII and RBP HI 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.
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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 4 primary steps in assessing the PCE of a suite of alternative subsampling strategies:
Step 1: For each subsampling strategy (i.e., 100-, 200-, 300- organism level, or other) 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 coefficient of variation (CV).
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 ((3; 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:
("Xc)min
PCE, = 		-2HL
(n,Xc,)
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 200-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.
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7.	Midge (Chironomidae) larvae and pupae 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 done consistently among samples. In the original
RBPs, two levels of identification were suggested — family (RBP II) and genus/species (RBP EH)
(Plafkin et al. 1989). Genus/species provides more accurate information on ecological/
environmental relationships and sensitivity to impairment. Family level provides 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
available to other taxonomists should be used. Unnamed species (i.e., species A, B, 1, or 2) may be
ecologically informative, but may be inconsistently handled among taxonomists and will, thus,
contribute to variability 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
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 the taxonomist's initials) are added to the vials of
specimens by the taxonomist. (Note that individual specimens may be extracted from the
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sample to be included in a reference collection or to be verified by a second 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 used to keep track
of the cumulative count. Also, record the life stage of the organisms, the taxonomist's
initials and the Taxonomic Certainty Rating (TCR) as a measure of confidence.
4.	Use 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 the back of the bench 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 1st 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.
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, 1996b, Hayslip 1993, Smith and Voshell 1997), 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
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developed, they are typically appropriate over wide geographic areas with minor modification
(Barbour et al. 1995).
The process for testing the efficacy and calibrating the metrics is described in Chapter 9. While the
candidate metrics described here are ecologically sound, they may require testing on a regional basis.
Those metrics that are most effective are those that have a response across a range of human
influence (Fore et al. 1996, Karr and Chu 1999). Resh and Jackson (1993) tested the ability of 20
benthic metrics used in 30 different assessment protocols to discriminate between impaired and
minimally impaired sites in California. The most effective measures, from their study, were the
richness measures, 2 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 measures evaluating chironomid larvae
deformities have recently been advocated (Lenat 1993).
Four studies that were published from 1995 through 1997 serve as a basis for the most appropriate
candidates for metrics, because the metrics were tested in detail in these studies (DeShon 1995,
Barbour et al. 1996b, Fore et al. 1996, Smith and Voshell 1997). These metrics have been evaluated
for the ability to distinguish impairment and are recommended as the most likely to be useful in
other regions of the country (Table 7-1). Other metrics that are currently in use in various states are
listed in Table 7-2 and may be applicable for testing as alternatives or additions to the list in Table
7-1.
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 EBI (Kerans et al. 1992, Kerans and Karr, 1994), and RBP's (Plafkin et al.
1989, Barbour et al. 1996b). 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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Table 7-1. Definitions of best candidate benthic metrics and predicted direction of metric response to
increasing perturbation (compiled from DeShon 1995, Barbour et al. 1996b, Fore et al. 1996, Smith and
Voshell 1997).	
Category
Metric
Definition
Predicted
response to
increasing
perturbation
Richness measures
Total No. taxa
Measures the overall variety of the
macroinvertebrate assemblage
Decrease

No. EPT taxa
Number of taxa in the insect orders
Ephemeroptera (mayflies), Plecoptera
(stoneflies), and Trichoptera (caddisflies)
Decrease

No. Ephemeroptera Taxa
Number of mayfly taxa (usually genus or
species level)
Decrease

No. Plecoptera Taxa
Number of stonefly taxa (usually genus of
species level)
Decrease

No. TrichopteraTaxa
Number of caddisfly taxa (usually genus
or species level)
Decrease
Composition
measures
% EPT
Percent of the composite of mayfly,
stonefly, and caddisfly larvae
Decrease

% Ephemeroptera
Percent of mayfly nymphs
Decrease
Tolerance/Intolerance
measures
No. of Intolerant Taxa
Taxa richness of those organisms
considered to be sensitive to perturbation
Decrease

% Tolerant Organisms
Percent of macrobenthos considered to be
tolerant of various types of perturbation
Increase

% Dominant Taxon
Measures the dominance of the single
most abundant taxon. Can be calculated
as dominant 2, 3, 4, or 5 taxa.
Increase
Feeding measures
% Filterers
Percent of the macrobenthos that filter
FPOM from either the water column or
sediment
Variable

% Grazers and Scrapers
Percent of the macrobenthos that scrape or
graze upon periphyton
Decrease
Habit measures
Number of Clinger Taxa
Number of taxa of insects
Decrease

% dingers
Percent of insects having fixed retreats or
adaptations for attachment to surfaces-in
flowing water.
Decrease
Composition measures can be characterized by several classes of information, i.e., the identity, key-
taxa, and relative abundance. 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 exotic or nuisance species may be an important aspect of
biotic interactions that relate 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, rather than absolute, abundance is used
because the relative contribution of individuals to the total fauna (a reflection of interactive
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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 measures 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).
Table 7-2. Definitions of additional potential benthic metrics and predicted direction of metric response
to increasing perturbation. 			
Category
Metric
Definition
Predicted
response to
increasing
perturbation
References
Richness
measures
No. Pteronarcys
species
The presence or absence of a long-lived stonefly
genus (2-3 year life cycle)
Decrease
Fore et al.
1996

No. Diptera taxa
Number of "true" fly taxa, which includes
midges
Decrease
DeShon 1995

No. Chironomidae
taxa
Number of taxa of chironomid (midge) larvae
Decrease
Hayslip 1993,
Barbour et al.
1996b
Composition
measures
% Plecoptera
Percent of stonefly nymphs
Decrease
Barbour et al.
1994

% Trichoptera
Percent of caddisfly larvae
Decrease
DeShon 1995

% Diptera
Percent of all "true" fly larvae
Increase
Barbour et al.
1996b

% Chironomidae
Percent of midge larvae
Increase
Barbour et al.
1994

% Tribe
Tanytarsini
Percent of Tanytarisinid midges to total fauna
Decrease
DeShon 1995

% Other Diptera
and noninsects
Composite of those organisms generally
considered to be tolerant to a wide range of
environmental conditions
Increase
DeShon 1995

% Corbiciila
Percent of asiatic clam in the benthic assemblage
Increase
Kerans and
Karr 1994

% Oligochaeta
Percent of aquatic worms
Variable
Kerans and
Karr 1994
Tolerance/
Intolerance
No. Intol. Snail and
Mussel species
Number of species of molluscs generally thought
to be pollution intolerant
Decrease
Kerans and
Karr 1994
measures
% Sediment
Tolerant organisms
Percent of infaunal macrobenthos tolerant of
perturbation
Increase
Fore et al.
1996

Hilsenhoff Biotic
Index
Uses tolerance values to weight abundance in an
estimate of overall pollution. Originally
designed to evaluate organic pollution
Increase
Barbour et al.
1992,	Hayslip
1993,	Kerans
and Karr 1994
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Table 7-2. Definitions of additional potential benthic metrics and predicted direction of metric response
to increasing perturbation (continued).
Category
Metric
Definition
Predicted
response to
increasing
perturbation
References
Tolerance/
Intolerance
measures
(continued)
Florida Index
Weighted sum of intolerant taxa, which are
classed as 1 (least tolerant) or 2 (intolerant).
Florida Index = 2 X Class 1 taxa + Class 2 taxa
Decrease
Barbour et al.
1996b
% Hydropsychidae
to Trichoptera
Relative abundance of pollution tolerant
caddisflies (metric could also be regarded as a
composition measure)
Increase
Barbour et al.
1992, Hayslip
1993
Feeding
measures
% Omnivores and
Scavengers
Percent of generalists in feeding strategies
Increase
Kerans and
Karr 1994
% Ind. Gatherers
and Filterers
Percent of collector feeders of CPOM and FPOM
Variable
Kerans and
Karr 1994
% Gatherers
Percent of the macrobenthos that "gather"
Variable
Barbour et al.
1996b
% Predators
Percent of the predator functional feeding group.
Can be made restrictive to exclude omnivores
Variable
Kerans and
Karr 1994
% Shredders
Percent of the macrobenthos that "shreds" leaf
litter
Decrease
Barbour et al.
1992, Hayslip
1993
Life cycle
measures
% Multivoltine
Percent of organisms having short (several per
year) life cycle
Increase
Barbour et al.
1994
% Univoltine
Percent of organisms relatively long-lived (life
cycles of 1 or more years)
Decrease
Barbour et al.
1994
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.
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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, reflecting 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). The usefulness of functional feeding
measures for benthic macroinvertebrates has not been well demonstrated. Difficulties with the
proper assignment to functional feeding groups has contributed to the inability to consider these
reliable metrics (Karr and Chu 1997).
Habit measures are those that denote the mode of existence among the benthic macroinvertebrates.
Morphological adaptation among the macroinvertebrate distinguishes the various mechanisms for
maintaining position and moving about in the aquatic environment (Merritt et al. 1996). Habit
categories include movement and positioning mechanisms such as skaters, planktonic, divers,
swimmers, dingers, sprawlers, climbers, burrowers. Merritt et al. (1996) provide an overview of the
habit of aquatic insects, which are the primary organisms used in these measures. Habit measures
have been found to be more robust than functional feeding groups in some instances (Fore et al.
1996).
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 "tum-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 can then either be evaluated using more rigorous
FIELD EQUIPMENT/SUPPLIES NEEDED FOR BENTHIC
MACROINVERTEBRATE SAMPLING
—BIORECON
•	standard D-frame dip net, 500 jj. opening mesh, 0.3 meter
width 1.0 ft frame width)
•	sieve bucket, with 500 n 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 System (GPS) Unit
" It is helpful to copy fieldsheets onto water-resistant paper for use
in wet weather conditions
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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) represented in the reach are to be
sampled for macroinvertebrates. A total of 4 jabs or kicks will be taken over the length of
the reach. A minimum of 1 jab (or kick) is to be taken in each habitat. More than 1 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. Thus, 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.
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.
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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.
7.6 TAXONOMIC REFERENCES FOR MACROINVERTEBRATES
The following references are provided as a list of taxonomic references currently being used around
the United States for identification of benthic macroinvertebrates. Any of these references cited in
the text of this document will also be found in Chapter 11 (Literature Cited).
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. 1965. A revision of the genus Ephemerella (Ephemeroptera:
Ephemerellidae). Vm. The subgenus Ephemerella in North America. Miscellaneous Publications
of the Entomological Society of America 4:243-282.
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Chapter 7: Benthic Macroinvertebrate Protocols
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Allen, R.K. and G.F. Edmunds. 1963. 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. 1963. A revision of the genus Ephemerella (Ephemeroptera:
Ephemerellidae). VII. The subgenus Eurylophella. Canadian Entomologist 95:597-623.
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 3:583-600.
Allen, R.K. and G.F. Edmunds. 1961. A revision of the genus Ephemerella (Ephemeroptera:
Ephemerellidae). III. The subgenus Attenuatella. Journal of the Kansas Entomological Society
34:161-173.
Allen, R.K. and G.F. Edmunds. 1961. A revision of the genus Ephemerella (Ephemeroptera:
Ephemerellidae). II. The subgenus Caudatella. Annals of the Entomological Society ofAmerica
54:603-612.
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.
Baumann, 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, E.C. 1962. Five new Chironomidae (Diptera) from Florida. Florida Entomologist 45:89-92.
Beck, W.M., Jr. and E.C. Beck. 1970. The immature stages of some Chironomini (Chironomidae).
Quarterly Journal of the Academy of Biological Science 33:29-42.
Beck, E.C. 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, W.M. and E.C. Beck. 1964. New Chironomidae from Florida. Florida Entomologist.
47:201-207.
Beck, W.M., Jr. and E.C. Beck. 1966.' Chironomidae (Diptera) of Florida. I. Pentaneurini
(Tanypodinae). Bulletin of the Florida State Museum 10: 305-379.
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.
Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
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Bemer, L. 1977. Distributional patterns of southeastern mayflies (Ephemeroptera). Bulletin of the
Florida State Museum of Biological Sciences 22:1-55.
Berner, L. 1975. The Mayfly Family Leptophlebiidae in the Southeastern United States. The
Florida Entomologist 58:137-156.
Bemer, L. 1956. The genus neoephemera in North America (Ephemeroptera:Neoephemeridae).
Entomological Society of America 49:33-42.
Bemer, L. and M.L. Pescador. 1988. The Mayflies of Florida, University Presses of Florida. Pp. 415.
Boesel, M.W. 1985. A brief review of the genus Polypedilum in Ohio, with keys to the known
stages of species oceuring in Northeastern United States (Diptera:Chironomidae). Ohio Journal of
Science 85:245-262.
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. 1974. Observations on the Coelotanypodini of the northeastern states, with keys to
the known stages (Diptera: Chironomidae: Tanypodinae), Journal of the Kansas Entomology Society
47:417-432.
Boesel, M.W. 1972. The early stages of Ablabesmyia annulata (Say) (Diptera:Chironomidae), Ohio
Journal of Science 72:170-173.
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 of the Kansas
Entomology Society 53:501-508.
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.
Brinkhurst, R.O. 1986. Guide to the freshwater microdrile Oligochaetes of North America. Canada
Special Publications Fisheries Aquatic Science 84:1-259.
Brinkhurst, R.O. and B.G.M. Jamieson. 1971. Aquatic Oligochaeta of the World. Univ. Toronto
Press, 860 pp.
Brittain, J.E. 1982. Biology of Mayflies. Annual Review of Entomology 27:119-147.
Brown, H.P. 1987. Biology of riffle beetles. Annual Review of Entomology 32:253-273.
Brown, H.P. 1976. Aquatic dryopoid beetles (Coleoptera) of the United States. USEPA. Water
Pollution Control Research Series 18050 ELD04/72.
Brown, H.P. 1972. Aquatic dryopoid beetles (Coleoptera) of the United Statef. Biota of freshwater
ecosystems identification manual no. 6. Water Pollution Control Research Series, EPA, Washington,
D.C.
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.
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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. 1985. Paracricotopus millrockensis, a new species of Orthocladiinae (Diptera:
Chironomidae) from the southeastern United States. Brimleyana 11:161-168.
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. Hudsonimyiaparrishi, a new species of Tanypodinae
(Diptera: Chironomidae) from Georgia. Florida Entomologist 65:506-513.
Carle, F.L. 1978. A New Species of Ameletus (Ephemeroptera:Siphlonuriae) from Western
Virginia. Entomological Society of America 71:581-584.
Carle, F.L. and P.A. Lewis. 1978. A new species of Stenonema (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
Welfare, Division of Environmental Quality, Boise, Idaho.
Cranston, P.S. 1982. A key to the larvae of the British Orthocladiinae (Chironomidae). Freshwater
Biological Association Scientific Publication No. 45:1-152.
Cranston, P.S. and D.D. Judd. 1987. Metriocnemus (Diptera: Chironomidae)-an ecological survey
and description of a new species. Journal New York Entomology Society 95:534-546.
Cummins, K. W. and M. A. Wilzbach. 1985. Field Procedures for Analysis of Functional Feeding
Groups of Stream Macroinvertebrates. Contribution 1611. Appalachian Environmental Laboratory,
University of Maryland, Frostburg, Maryland.
Davis, J.R. 1982. New records of aquatic Oligochaeta from Texas, with observations on their
ecological characteristics. Hydrobiologia 96:15-29.
Edmunds, G.F. and R.K. Allen. 1964. The Rocky Mountain species of Epeorus (Iron) Eaton
(Ephemeroptera: Heptageniidae). Journal of the Kansas Entomological Society. 37:275-288.
Edmunds, G.F., Jr., S.L. Jensen, and L. Berner. 1976. The mayflies of North and Central America.
University of Minnesota Press, Minneapolis.
Epler, J.H. 1988. Biosystematics of the genus Dicrotendipes Kieffer, 1913 (Diptera: Chironomidae:
Chironominae) of the world. Memoirs of the American Entomology Society 36:1-214.
Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition	7-23
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Epler, J.H. 1987. Revision of the nearctic Dicrotendipes Kieffer, 1913 (Diptera: Chironomidae).
Evolutionary Monographs: 1-101.
Etnier, D.A. and G.A. Schuster. 1979. An annotated list of Trichoptera (Caddisflies) of Tennessee.
Journal of the Tennessee Academy of Science 54:15-22.
Faulkner, G.M. and D.C. Tarter. 1977. Mayflies, or Ephemeroptera, of West Virginia with
emphasis on the nymphal stage. Entomological News 88:202-206.
Ferrington, L.C. 1987. Collection and identification offloating exuviae of Chironomidae for use in
studies of surface water quality. SOP No. FW 130A. U.S. Environmental Protection Agency, Region
VII, Kansas City, Kansas.
Flint, O.S. 1984. The genus Brachycentrus in North America, with a proposed phylogeny of the
genera of Brachycentridae (Trichoptera). Smithsonian Contributions to Zoology.
Hint, O.S. Jr. 1964. Notes on some nearctic Psychomyiidae with special reference to their larvae
(Trichoptera). Proceeding of the United States National Museum 115:467-481.
Flint, O.S. Jr. 1962. The immature stages of Paleagapetus celsus Ross (Trichoptera: Hydroptilidae).
Bulletin of the Brooklyn Entomological Society LVTI:40-44.
Flint, O.S. Jr. 1962, Larvae of the caddis fly genus Rhyacophila in Eastern North America
(Trichoptera: Rhyacophilidae). Proceedings of the United States National Museum 113:465-493.
Flint, O.S. Jr. 1960. Taxonomy and biology of nearctic limnephilid larvae (Trichoptera), with
special reference to species in Eastern United States. Entomologicia Americana XL: 1-117.
Flowers, R.W. 1980. Two new genera of nearctic Heptageniidae (Ephemeroptera). The Florida
Entomologist. 63:296-307.
Flowers, R.W. and W.L. Hilsenhoff. 1975. Heptageniidae (Ephemeroptera) of Wisconsin. The
Great Lakes Entomologist 8:201 -218.
Floyd, M.A. 1995. Larvae of the caddisfly genus Oecetis (Trichoptera: Leptocerida) in North
America. Bulletin of the Ohio Biological Survey.
Fullington, K.E. and K.W. Stewart. 1980. Nymphs of the stonefly genus Taeniopteryx (Plecoptera:
Taeniopterygidae) of North America. Journal of the Kansas Entomological Society 53(2);237-259.
Givens, D.R. and S.D. Smith. 1980. A synopsis of the western Arctopsychinae (Trichoptera:
Hydropsychidae). Melanderia 35:1-24.
Grodhaus, G. 1987. Endochironmus Kieffer, Tribelos Townes, Synendotendipes, n. ge., and
Endotribelos, n. gen. (Diptera: Chironomidae) of the nearctic region. Journal of the Kansas
Entomological Society 60:167-247.
Hamilton, A.L. and O.A. Saether. 1969. A classification of the nearctic Chironomidae. Journal of
the Fisheries Research Board of Canada Technical Report 124:1-42.
Hatch, M.H. 1965. The beetles of the Pacific Northwest, Part IV, Macrodactyles, Palpicornes, and
Heteromera. University of Washington Publications in Biology, Volume 16.
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Hatch, M.H. 1953. The beetles of the Pacific Northwest, Part I, Introduction and Adephaga.
University of Washington Publications in Biology, Volume 16.
Hilsenhoff, W.L. 1973. Notes on Dubiraphia (Coleoptera: Elmidae) with descriptions of five new
species. Annals of the Entomolgical Society of America 66:55-61.
Hitchcock, S.W. 1974. Guide to the insects of Connecticut: Part VII. The Plecoptera or stoneflies
of Connecticut. State Geological and Natural History Survey of Connecticut Bulletin 107:191 -211.
Hobbs, H.H., Jr. 1981. The crayfishes of Georgia. Smithsonian Contribution in Zoology 318:1-549.
Hobbs, H.H., Jr. 1972. Crayfishes (Astacidae) of North and Middle America. Biota of freshwater
ecosystems identification manual no. 9. Water Pollution Control Research Series, E.P.A.,
Washington, D.C.
Holsinger, J.R. 1972. The freshwater amphipod crustaceans (Gammaridae) of North America.
Biota of freshwater ecosystems identification manual no. 5. Water Pollution Control Research
Series, E.P.A., Washington, D.C.
Hudson, P. A. 1971. The Chironomidae (Diptera) of South Dakota. Proceedings of the South Dakota
Academy of Sciences 50:155-174.
Hudson, P.A:, D.R. Lenat, B.A. Caldwell, and D. Smith. 1990. Chironomidae of the southeastern
United States: a checklist of species and notes on biology, distribution, and habitat. Fish and Wildlife
Research: 7:1-46.
Hudson, P.L., J.C.Morse, and J.R. Voshell. 1981. Larva and pupa of Cernotina spicata. Annals of
the Entomological Society of America 74:516-519
Jackson, G.A. 1977. Nearctic and palaearctic Paracladopelma Hamisch and Saetheria n.ge.
(Diptera:Chironomidae). Journal of the Fisheries Research Board of Canada 34:1321-1359.
Jensen, S.L. 1966. The mayflies of Idaho. Unpublished Master's Thesis, University of Utah.
Kenk, R. 1972. Freshwater planarians (Turbellaria) of North America. Biota of freshwater
ecosystems identification manual no. 1. Water Pollution Control Research Series, U.S.
Environmental Protection Agency, Washington, D.C.
Kirchner, R.F. and B.C. Kondratieff. 1985. The nymph of Hansonoperla appalachia Nelson
(Plecoptera: Perlidae). Proceedings of the Entomological Society of Washington. 87(3):593-596.
Kirchner, R.F. and P.P. Harper. 1983. The nymph of Bolotoperla rossi (Frison) (Plecoptera:
Taeniopterygidae: Brachypterinae). Journal of the Kansas Entomological Society 56(3): 411-414.
Kirk, V.M. 1970. A list of beetles of South Carolina, Part 2-Mountain, Piedmont, and Southern
Coastal Plain. South Carolina Agricultural Experiment Station Technical Bulletin 1038:1-117.
Kirk, V.M. 1969. A list of beetles of South Carolina, Part 1-Northern Coastal Plain. South Carolina
Agricultural Experiment Station Technical Bulletin 1033:1-124.
Klemm, D.J. 1982. Leeches (Annelida: Hirudinea) of North America. EPA-600/3-82-025. Office of
Research and Development, Cincinnati, Ohio.
Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
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Klemm, D.J. 1972. Freshwater leeches (Annelida: Hirudinea) of North America. Biota of
freshwater ecosystems identification manual no. 8. Water Pollution Control Research Series, U.S.
Environmental Protection Agency, Washington, D.C.
Kondratieff, B.C. 1981. Seasonal distributions of mayflies (Ephemeroptera) in two piedmont rivers
in Virginia. Entomological News 92:189-195.
Kondratieff, B.C. and R.F. Kirchner. 1984. New species of Taeniopteryx (Plecoptera:
Taeniopterygidae) from South Carolina. Annals of the Entomological Society of America 77(6):733-
736.
Kondratieff, B.C. and R.F. Kirchner. 1982. Taeniopteryx nelsoni, a new species of winter stonefly
from Virginia (Plecoptera: Taeniopterygidae). Journal of the Kansas Entomological Society 55(1): 1-
7.
Kondratieff, B.C. and J.R. Voshell, Jr. 1984. The north and Central American species of Isonychia
(Epnemeroptera: Oligoneuriidae). Transactions of the American Entomological Society.
110:129-244.
Kondratieff, B.C. and J.R. Voshell, Jr. 1983. A checklist of mayflies (Ephemeroptera) of Virginia,
with a review of pertinent taxonomic literature. University of Georgia Entomology Society 18:213-
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Kondratieff, B.C, R.F. Kirchner and K.W. Stewart. 1988. A review of Perlinella Banks (Plecoptera:
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Loden, M.S. 1978. A revision of the genus Psammoryctides (Oligochaeta: Tubificidae) in North
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Merritt, R.W., D.H. Ross, and B.V. Perterson. 1978. Larval ecology of some lower Michigan
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Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
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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 presented in this document, is directly comparable
to RBP V in Plafkin et al. (1989). 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 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 (1992a), Simon and Lyons (1995), Lyons et al.
(1996), and Simon (1999).
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. Although the fish survey can usually be completed in the field by
qualified fish biologists, 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 evaluate status and trends
of the fish assemblage.
Fish collection procedures must focus on a multihabitat approach — sampling habitats in relative
proportion to their local representation (as determined during site reconnaissance). 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 Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
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8.1 FISH COLLECTION PROCEDURES: ELECTROFISHING
All fish sampling gear types are generally considered selective to some degree; however,
electro fishing has proven to be the most comprehensive and effective single method for collecting
stream fishes. Pulsed DC (direct current) electroflshing is the method of choice to obtain a
representative sample of the fish assemblage at each sampling station. However, electroflshing in
any form has been banned from certain salmonid spawning streams in the northwest. As with any
fish sampling method, the proper scientific collection permit(s) must be obtained before
commencement of any electroflshing activities. The accurate identification of each fish collected is
essential, and species-level identification is required (including hybrids in some cases, see Section
8.3.3, Metric 11). 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 (see Section 8.2). 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 in electroflshing 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 electroflshing and a minimum 2-person team for sampling
streams and wadeable rivers. Examples include:
•	Backpack electrofisher with 2 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 1 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 I 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 (pramunit) electrofisher with 2 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 barge and/or 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 1 biologist with training
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Chapter 8: Fish Protocols
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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 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).


Tote barge (pram unit) Electrofishing
ffs si
X. W 1
' > -i a »•
m w

Backpack Electrofishing
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/SUPPLIES NEEDED FOR FISH
SAMPLING—ELECTROFISHING
appropriate scientific collection permit(s)
backpack or tote barge-mounted electrofisher
dip nets
block nets (i.e., seines)
elbow-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)0
balance (gram scale)b
tape measure (100 m minimum)
fish Sampling Field Data Sheet0
applicable topographic maps
copies of field protocols
pencils, clipboard
first aid kit
Global Positioning System (GPS) Unit
Needed only if program/study requires length frequency
information
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).
It is helpful to copy fieldsheets onto water-resistant paper for use in
wet weather conditions.
Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
8-3
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longitude) of the downstream
limit of the reach must be
recorded on each field data sheet.
(If a Global Positioning System
unit is used to provide location
information, the accuracy or
design confidence of the unit
should be noted.) 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 eleetrofish 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 water clarity and 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.	All fish (greater than 20 millimeters total length) 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% formalin solution and stored in labeled jars for subsequent
laboratory identification (see Section 8.2). A representative voucher collection must be
retained for unidentified specimens, very small specimens, new locality records, and/or a
particular 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'
					
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 (Lyons 1992b, Simonson et
al. 1994, Simonson and Lyons 1995). 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]).
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Chapter 8: Fish Protocols
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names, and 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 sample, 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. An example of a "Fish Sampling Field Data
Sheet" is 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. Although fish length and
weight measurements are optional,
recording a range of lengths for
species encountered may be a
useful routine measure. 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
QUALITY CONTROL (QC) IN THE FIELD
1. Quality control must be a continuous process in
fish bioassessment and should include 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.
3.
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 a subsample of a maximum 25
voucher 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 (see Section 8.2).
Specimens must be properly preserved and labeled
(refer to Section 8.1.1, number 5). When needed,
chain-of-custody forms must be initiated following
sample preservation, and must include the same
information as the sample container labels.
All field equipment must be in good operating
condition, and a plan for 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
and/or digital recorders. 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 Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
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to be valuable information to consider for future sampling events or by personnel unfamiliar
with the site.
8.2	LABORATORY IDENTIFICATION AND VERIFICATION
Fish records of questionable quality 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% formalin for tissue fixing and 70%
ethanol for long-term storage) and labeled
(using museum-grade archival labels/paper,
and formalin/alcohol-proof pen or pencil).
Labels should contain (at a minimum) site
location data (i.e., verbal description and site
coordinates), collection date, collector's
names, species identification (for fishes
identified in the field), species totals, and
sample identification code and/or station
number. All samples received in the
laboratory should be tracked using a sample
log-in procedure (Appendix A-4, Form 2).
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 of fish, 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 etal. (1991).
8.3	DESCRIPTION OF FISH
METRICS
Through the IBI, 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 warmwater streams but has been modified for use in many regions (e.g., eastern and
QUALITY CONTROL (QC) FOR TAXONOMY
1.	A representative voucher collection must be
retained for unidentified specimens, small
specimens, and new locality records. In addition,
a second voucher jar should be retained for a
subsample of each species identified in the field
(e.g., Klemm and Lazorchak 1995 recommend a
subsample of 25 voucher specimens of each
species). The vouchers must be properly
preserved, labeled, and stored in the laboratory
for future reference (see Section 8.2).
2.	Voucher collections should be verified by a
second qualified fish taxonomist, i.e., a
professional other than the taxonomist
responsible for the original field identifications.
The word "validated" and the name of the
taxonomist that validated the identification
should be added to each voucher label.
Specimens sent from the laboratory to taxonomic
specialists should be recorded in a "Taxonomy
Validation Notebook" (see Chapter 7), noting the
label information and date sent. Upon return of
the specimens, the date received and findings
should also be recorded in the notebook (and the
voucher label), along with the name of the person
who performed the validation.
3.	Information on samples completed (through the
identification/validation process) will be tracked
in a "Sample Log" notebook, to track the
progress of each sample (Appendix A-4, Form 2).
Sample log entries will be updated as each step is
completed (e.g., receipt, identification, validation,
archive).
4.	A library of taxonomic literature is essential for
the aid and support of identification/verification
activities, and must be maintained (and updated
as needed) in the laboratory. A list of selected
taxonomic references is provided in Section 8.4.
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Chapter 8: Fish Protocols
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western United States, Canada, France) and in different ecosystems (e.g., rivers, impoundments,
lakes, and estuaries).
The metrics attempt to quantify a biologist's best professional judgment (BPJ) of the quality of the
fish assemblage. The IBI utilizes professional judgment, but in a prescribed manner, and it includes
quantitative standards for discriminating the condition of the fish assemblage (Figure 8-1). BPJ is
involved in choosing both the most appropriate population or assemblage 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 of 12 (very
poor). Trophic and tolerance classifications of selected fish species are listed in 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
(1.) REGIONAL MODIFICATION AND
CALIBRATION
(2.) SAMPLE COLLECTION AND
DATA TABULATION




Identification of regional fish
fauna



Selection of sampling site(s)





Assignment of trophic guild
and tolerance



Sampling of local fish
community

4jg!r

4|U

Evaluation of metric suitability



Listing of species and tabulation
of numbers of individuals

4H-

4SU

Development of expectation
(reference) values and metric
ratings



Summarization of fisheries
information for IBI metrics

(3.) COMPUTATION AND
INTERPRETATION
Rating of IBI metrics

Calculation of total IBI score
45-
Assignment of integrity class
Interpretation of !BI
Figure 8-1. Sequence of activities involved in calculating and interpreting the Index of Biotic
Integrity (adapted from Karr et al. 1986).
Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
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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
resident taxonomic groupings and habitat guilds
of fishes. Two of the metrics assess
assemblage composition in terms of tolerant or
intolerant species.
Metric 1. Total number of fish species
Substitutes (Table 8-1): Total number of
resident 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).
Metric 2. Number and identity of darter species Substitutes (Table 8-1): Number and identity of
sculpin species, benthic insectivore species, salmonid juveniles (individuals); number of sculpins
(individuals); percent round-bodied suckers, sculpin and darter species.
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 m* (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.
Metric 3. 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.
EXAMPLES OF SOURCES FOR METRIC
ALTERNATIVES
Karret al. (1986)
Leonard and Orth (1986)
Moyle et al. (1986)
Fausch and Schrader (1987)
Hughes and Gammon (1987)
Ohio EPA (1987)
Miller etal. (1988)
Steedman (1988)
Simon (1991)
Lyons (1992a)
Barbour et al. (1995)
Simon and Lyons (1995)
Hall et al. (1996)
Lyons et al. (1996)
Roth et al. (1997)
Simon (1999)
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Chapter 8: Fish Protocols
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Table 8-1. Fish IBI metrics used in various regions of North America.3
Alternative IBI Metrics
Midwestern United States
Central Appalachians
Sacramento-San Joaquin
Colorado Front Range
Western Oregon
Ohio
Ohio Headwater Sites
Northeastern United States
Ontario
Central Corn Belt Plain
Wisconsin-Warmwater
Wisconsin-Coldwater
Maryland Coastal Plain
Maryland Non-Tidal
1. Total Number of Species
#native fish species
# salmonid age classes'
XXXX X X XX
XXX X X
X X
2. Number of Darter Species
#	sculpin species
#	benthic insectivore species
ft darter and sculpin species
#	darter, sculpin, and madtom species
#	salmonid juveniles (individuals)'
% round-bodied suckers
#	sculpins (individuals)
#	benthic species
XXXX XX
X
X
X
X
XX X
Xc
X
X X
3. Number ofSunfish Species
if cyprinid species
#	water column species
#	sunfish and trout species
#	salmonid species
#	headwater species
% headwater species '
X XX XX
X
X
X
X X
X
X X
4. Number of Sucker Species
#	adult trout species"
if minnow species
#	sucker and catfish species
X XXX XX
X X
XX X
X
5. Number of Intolerant Species
#	sensitive species
#	amphibian species
presence of brook trout
% stenothermal cool and cold water species
% of salmonid ind. as brook trout
X XXX X XXXX
X X
X
X
X
X
6. % Green Sunfish
% common carp
% white sucker
% tolerant species
% creek chub
% dace species
% eastern mudminnow
X
X
X X
XX X X X X X
X
X
X
Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
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Table 8-1. Fish IBI metrics used in various regions of North America."
Alternative IBI Metrics
Midwestern United States
Central Appalachians
Sacramento-San Joaquin
Colorado Front Range
Western Oregon
Ohio
Ohio Headwater Sites
Northeastern United States
Ontario
Central Corn Belt Plain
Wisconsin-V/armwater
Wisconsin-Coidwater
Maryland Coastal Plain
Maryland Non-Tidal
7. % Omnivores
% gcncralist feeders
% generalises, omnivores, and invertivores
X X XXXXXX
X
X
8. % Insectivorous Cyprinids
% insectivores
% specialized insectivores
# juvenile trout
% insectivorous species
X X
X X XX XX'
X X
X
X X
9. % Top Carnivores
% catchable salmonids
% catchable trout
% pioneering species
Density catchable wild trout
X X X X X X X
X
X
XXX
X
10. Number of Individuals (or catch per effort)
Density of individuals
% abundance of dominant species
Bionuss (per m1)
X X X X X XJ XJ XXX1 X
X X
X X
xr
U.% Hybrids
% introduced species
% simple lilhophills
U simple lilhophills species
% native species
% native wild individuals
H silt-intolerant spawners
X x
X X
X XX X
X
X
X
X
12. % Diseased Individuals (deformities, eroded
11ns, lesions, and tumors)
XX XXXXXXXX XX
Note: X " metric used in region. Many of these variations are applicable elsewhere.
a Taken from Karret al. (1986), Leonard and Orth (1986), Moyle et al. (1986), Fausch and Schrader (1987), Hughes and Gammon
(1987), Ohio EPA (1987), Miller et al. (1988), Steedman (1988), Simon (1991), Lyons (1992a), 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 pool species decrease with increased degradation of pools and instream cover (Gammon et al.
1981, Angermeier 1987, Platts et al. 1983). Most of these fishes feed on drifting and surface
invertebrates and are active swimmers. The sunfishes and salmonids are important sport species.
The sunflsh 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
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Chapter 8: Fish Protocols
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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
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 (e.g., non-wadeable) rivers.
Metric 5. 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 (e.g., non-wadeable) 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
midwestern 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).
Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
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8.3.2 Trophic Composition Metrics
These three metrics assess the quality of the energy base and trophic dynamics of the fish
assemblage. 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 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 generalist feeders.
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. In areas where
few species fit the true definition of omnivore, the proportion of generalized feeders may be
substituted (Leonard and Orth 1986).
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.
Invertivores, primarily insectivores, are the dominant trophic guild of most North American surface
waters. As the invertebrate food source decreases in abundance and diversity due to habitat
degradation (e.g., anthropogenic stressors), 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.
Pioneering species predominate in unstable environments that have been affected by temporal
desiccation or anthropogenic stressors, and are the first to reinvade sections of headwater streams
following periods of desiccation.
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 detailed population attributes or estimates, general population parameters are
evaluated. Indirect estimation is less variable and much more rapidly determined.
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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 3, and
less than 4 as a 1. Unusually low
numbers generally indicate toxicity,
making this metric most useful at the low
end of the biological integrity scale.
Hughes and Gammon (1987) suggest that
in larger streams, where sizes of fish 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 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 midwestern and western fishes that existed before
the introduction of midwestern 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
	
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 = 0.5InN +0.5 lnB+HN+HB
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.
H = -2— 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.
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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 (e.g., non-wadeable) 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 Well-Being
(Mlwb), respectively, based upon both fish abundance and biomass measures. The combination of
diversity and biomass measures is a useful tool for assessing fish assemblages in larger rivers (Yoder
and Rankin 1995b). 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 et al. 1986, Ohio EPA 1987, Miller et al.
1988, Steedman 1988; Simon 1991, Lyons 1992a, Simon and Lyons 1995, Hall et al. 1996, Lyons et
al. 1996, Roth et al. 1997, Simon 1999). 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 ecological
data become available for the fish community.
8.4 TAXONOMIC REFERENCES FOR FISH
The following references are provided as a list of taxonomic references currently being used around
the United States for identification of fish. Any of these references cited in the text of this document
will also be found in Chapter 11 (Literature Cited).
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.
Baxter, G.T. and M.D. Stone. 1995. Fishes of Wyoming. Wyoming Game and Fish Department.
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.
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Chapter 8: Fish Protocols
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Bond, C.E. 1994. Keys to Oregon freshwater fishes. Oregon State University. 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.
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.
Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benihic
Macroinvertebrates, and Fish, Second Edition
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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.
Markle, D.F., D.L. Hill, and C.E. Bond. 1996. Sculpin identification workshop and working guide to
freshwater sculpins of Oregon and adjacent areas. Oregon State University. Corvallis, Oregon.
McPhail, J.D. and C.C. Lindsey. 1970. Freshwater fishes of northeastern Canada and Alaska.
Bulletin No. 173. Fisheries Research Board of Canada.
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, J.E. 1980. The freshwater fishes of Alaska. Alaska Northwest Publishing Company,
Anchorage, Alaska.
Moyle, P.B. 1976. Inlandfishes 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. Afield guide to freshwater fishes. Houghton Mifflin Company,
Boston, Massachusetts.
Pflieger, W.L. 1975. The fishes ofMissouri. 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. Amdt, D.G. Lindquist, and J.F. Pamell. 1994. Freshwater fishes of the Carolinas,
Virginia, Maryland, and Delaware. University of North Carolina Press. Chapel Hill, North
Carolina.
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Chapter 8: Fish Protocols
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Scarola, J.F. 1973. Freshwater fishes of New Hampshire. New Hampshire Fish and Game
Department, Concord, New Hampshire.
Scott, W.B. and E.J. Crossman. 1973. Freshwater fishes of Canada. Bulletin No. 1984. Fisheries
Research Board of 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.
Simpson, J.C. and R.L. Wallace. 1982. Fishes of Idaho. The University of Idaho Press, Moscow,
Idaho.
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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Macroinvertebrates, and Fish, Second Edition
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Chapter 8: Fish Protocols
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9 —
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 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 1987). Karr and Chu (1999) state that a knowledge of regional biology and natural history (not
a search for statistical relationships and significance) should drive both sampling design and
analytical protocol.
A framework for bioassessment can be either an a priori or a posteriori approach to classifying sites
and establishing reference condition. To provide a broad comparison of the 2 approaches, it is
assumed that candidate reference sites are available from a wide distribution of streams. 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 posteriori analysis of biology (Stage 2 as illustrated in Figure 9-1). Analysts who use multimetric
indices 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, and vice-versa.
Two data analysis strategies have been debated in scientific circles (Norris 1995, Gerritsen 1995)
over the past few years — the multimetric approach as implemented by most water resource agencies
in the United States (Davis et al. 1996), and a multivariate approach advocated by several water
resource agencies in Europe and Australia (Wright et al. 1993, Norris and Georges 1993). The
contrast and similarity of these 2 approaches are illustrated by Figure 9-1 in a 5-stage generic process
of bioassessment development. While there are many forms of multivariate analyses, the 2 most
common multivariate approaches are the Benthic Assessment of Sediment (BEAST) used in parts of
Canada, the River Invertebrate Prediction and Classification System (RIVPACS) used in parts of
England and its derivation, the Australian River Assessment System (AusRivAS) used in Australia.
The development of the reference condition from the range of reference sites (Figure 9-1, Stage 4), 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 fifth 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 minimally impaired 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
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a priori
classification
Candidate classes
identified from prior
knowledge and
hypothesis
DO
a posteriori
classification
Classes resulting from
. data distribution
Assign test sites to
confirmed site classes
Aggregate information
of core biological
metrics for each site
class
Multlmetrlc
Develop discriminant
model to predict cluster
groups, using
non-biological data
(i.e., goo/phys/chem)
Classify sites into groups
using clustering methods
based on the similarity of
their species composition
Compare test and
reference site groups
using distribution of
scores of additive
metrics
Multimetric
Compare ratio of
observed/expected taxa
of test and reference
group sites
RIVPACS/AusRivAs
Site-specific reference
condition of aggregate
species composition of
clusters weighted by
probability of
membership
RIVPACS/AusRivAs
Collection of data on invertebrate assemblages
and habitat characteristics at a
range of reference and test sites
Test and confirm classification
with univariate or multivariate
methods (clustering, similarity
analysis, ordination, MANOVA)
on species composition
Figure 9-1. Comparison of the developmental process for the multimetric and multivariate approaches to
biological data analysis (patterned after ideas based on Reynoldson, Rosenberg, and Resh, unpublished
data).
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Chapter 9: Multimetric Data Analysis
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and the "best fit" of the test site(s) to the probability ellipses constructed around the reference site
classes (Reynoldson et al. 1995). The AusRivAS/RIVPACS model calculates the probability of
expected taxa occurrence from the weighted reference site groups.
The bioassessment program in Maine is an example of a state that uses a multivariate analysis in the
form of discriminant function models and applies these models to a variety of metrics. Decisions are
made with regard to attainment (or non-attainment) of designated aquatic life uses. The approach
used by Maine is based on characteristics of both the multivariate and multimetric approach. In this
chapter, only the multimetric approach to biological data analysis is discussed in detail. Discussion
of multivariate approaches is restricted to the overview of the discriminant function model used by
Maine and the AusRivAS/RTVPACS technique.
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 (i.e., physical and chemical) 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 attainment or non-attainment (impairment)
have been established. The establishment of reference conditions (through actual sites or other
means) is crucial to the determination of metric and index thresholds. These thresholds are essential
elements in performing the assessment. 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. If data are available, a dose/response relationship between specific or cumulative
stressors and biological condition will provide information on a gradient response, which can be a
powerful means of determining impairment thresholds.
The 2 phases in data analysis for the multimetric approach are discussed separately in the following
section. The reader is referred to supporting documentation cited throughout for more in-depth
discussion of the concepts of multimetric assessment.
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 where the site classification 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 is illustrated in
Figure 9-2 (Paulsen et al. 1991; Barbour et al., 1995; Gibson et al., 1996).
Index development outlined in this section requires a stream classification framework to partition
natural variability and in which metrics are evaluated for scientific validity. The core metrics
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representing various attributes of the targeted aquatic assemblage can be either aggregated into an
index or retained as individual measures.
Step 1. Classify the Stream Resource
Site classification provides a framework for organizing and
interpreting natural variability among streams; ecoregions
are a principal example of a classification framework
(Omernik 1995). However, classification variables can be
at a coarser or finer scale than ecoregions or subecoregions,
such as elevation and drainage area. Elevation was
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 EPA (1987) found that stream size (or
drainage area) was a covariate and not a determinant of stream classes. The number of fish species
increased with stream size (Figure 9-3).
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 is best accomplished with reference sites that reflect the most natural and
representative condition of the region. Candidate reference sites that are based on minimally
degraded physical habitat and water chemistry are used as the basis for stream classification.
1. Stream Classification—The
biological data are used to group
reference sites Into homogeneous
classes
2. Metric Identification —Those
candidate attributes that are
ecologically relevant to assemblage
and zoogeography are Identified
3. Metric Calibration—Core metrics
are those that 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 (biocriterion) of the index
for discriminating between impaired
and unimpaired is determined to
provide a basis for assessment
Partitioning of Entire Vtetar Rssoixca

X
Stream Class 1

Stream Class 2

Stream Class N

Identification of Biological Attributes
T
Metric 1 Vtalue

Metric 2 N&lue

Metric N \&iue
Evaluation and Celebration
Biological
Indicators
Core Metric

Core Metric

Core Metrie

Biocntena
Relative to
Stream
Class
Index Score
Figure 9-2. Process for developing assessment thresholds (modified from Paulsen et
al. [1991] and Barbour et al. [1995]). Dotted lines indicate use of individual metric
information to aid in the evaluation of biological condition and cause of impairment.
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Chapter 9: Multimetric Data Analysis
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Quantitative criteria for reference sites aid in
a consistent framework for selection. An
example of quantitative criteria for
identifying reference sites in a statewide
study for Maryland (Roth et al., 1997) is
presented below (a reference site must meet
all 12 criteria):
1.	pH z 6; if blackwater stream, then pH
< 6 and DOC a 8 mg/1
2.	ANC 2 50 neq/1
3.	DO * 4 ppm
4.	nitrate <, 300 jj.eq/1
5.	urban land use ^ 20% of catchment
area
6.	forest land use 2 25% of catchment Figure 9-3. Species richness versus stream size (taken
area	from Fausch et al, 1984).
7.	remoteness rating: optimal or suboptimal
8.	aesthetics rating: optimal or suboptimal
9.	instream habitat rating: optimal or suboptimal
10.	riparian buffer width a 15 m
11.	no channelization
12.	no point source discharges
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 (Figure 9-
4). 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 9-5).
Components of Step 1 include:
•	Identify classification alternatives. Use physical and chemical parameters that are minimally
influenced by human activity to identify classes for testing.
•	Identify candidate reference sites that meet the criteria of most "natural" conditions of region.
•	Test alternative classification schemes of subecoregion, stream type, elevation, etc., using
multiple metric and non-metric biological characteristics including measures such as species
composition and EPT taxa (Figure 9-5). Several multivariate classification and ordination
Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
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methods, and univariate
descriptions and tests, can
assist in this process
(Reckhow and Warren-
Hicks 1996, Gerritsen
1995, 1996, Barbour etal.
1996b).
• Evaluate classification
alternatives and determine
best distinction into
groups or classes using
biological data. By
confirming resource
classification based on
biological data, site
classes are identified that
adequately partition
variability.
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o •
° Non-coastal Plain
¦1.6 -1.2 -0.8 -0.4 0.0 0.4 0.8 1.2 * Coastal Plain
Dimension 1
Figure 9-4. Results of multivariate ordination on benthic
macroinvertebrate data from "least impaired" streams from
Maryland, using nonmetric multidimensional scaling (NMDS) of
Bray-Curtis dissimilarity coefficients.
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.
Metrics allow the investigator to use meaningful indicator
attributes in assessing the status of assemblages and 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. The purpose of using multiple
metrics to assess biological condition is to aggregate and convey the information available regarding
the elements and processes of aquatic communities.
All metrics that have ecological relevance to the assemblage under study and that respond to the
targeted stressors are potential metrics for testing. From this "universe" of metrics, some will be
eliminated because of insufficient data or because the range of values is not sufficient for
discrimination between natural variability and anthropogenic effects. This step is to identify the
candidate metrics that are most informative, and
therefore, warrant further analysis.
The potential measures that are relevant to the
ecology of streams within the region or state
should be selected to ensure that various aspects
of the elements and processes of the aquatic
assemblage are addressed. Representative
metrics should be selected from each of 4
primary categories: (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 or
Summvr 1993

28
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Figure 9-5. An example of a metric that illustrates
classification of reference stream sites in Florida
into bioregions.
9-6
Chapter 9: Multimetric Data Analysis
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habit measures for information on feeding strategies and guilds. Karr and Chu (1999) suggest 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. 1987). See Table 9-1 for
potential metrics that have been useful for periphyton, benthic macroinvertebrates, and fish are
summarized in Chapters 6, 7, and 8, respectively.
Components of Step 2 include:
•	Review value ranges of potential metrics, and eliminate those that have too many zero values
in the population of reference sites 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 reference sites of 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. The potential for each measure is based
on possessing enough information and a specific range of variability to discriminate among
site classes and biological condition.
Step 3. Select Robust Measures
Core metrics are those 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, are most useful as a diagnostic tool. Core metrics are those that 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.
Discriminatory ability of biological 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" stressed sites (defined by physical and
chemical characteristics) 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 9-6).
As was done with candidate reference sites (see Step 1), criteria are established to identify a
population of "known" stressed sites based on physical and chemical measures of degradation. An
example set of criteria established for Maryland streams for which failure indicated a stressed site for
testing discriminatory power (Roth et al. 1997) is as follows:
•	pH < 5 and ANC <; 0 p.eq/1 (except for blackwater streams, DOC > 8 mg/1)
•	DO < 2 ppm
•	nitrate > 500 jxM/1 and DO < 3 ppm
•	instream habitat rating poor and urban land use > 50% of catchment area
	
The ability of a biological metric to
discriminate between "known"
reference conditions and "known"
stressed conditions (defined by
physical and chemical characteristics)
is crucial in the selection of core
metrics for future assessments.
Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
9-7
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instream habitat rating poor and bank stability rating poor
instream habitat rating poor and channel alteration rating poor
Table 9-1. Some potential metrics for periphyton, benthic macroinvertebrates, and fish that could be
considered for streams. Redundancy can be evaluated during the calibration phase to eliminate
overlapping metrics.

Richness Measures
Composition
Measures
Tolerance Measures
Trophic/Habit
Measures
Periphyton
•	Total no. of taxa
•	No. of common
nondiatom taxa
•	No. of diatom taxa
•	% community
similarity
•	% live diatoms
•	Diatom (Shannon)
diversity index
•	% tolerant diatoms
•	% sensitive taxa
•	% aberrant diatoms
•	% acidobiontic
•	% alkalibiontic
•	% halobiontic
•	% motile taxa
•	Chlorophyll a
•	% saprobiontic
•	% eutrophic
Benthic
Macroinvertebrate
•	No. Total taxa
•	No. EPT taxa
•	No. Ephemeroptera
taxa
•	No. Plecoptera taxa
•	No. Trichoptera taxa
•	% EPT
•	% Ephemeroptera
•	% Chironomidae
•	No. Intolerant Taxa
•	% Tolerant
Organisms
•	Hilsenhoff Biotic
Index (HBI)
•	% Dominant Taxon
•	No. Clinger taxa
•	% dingers
•	% Filterers
•	% Scrapers
JS
•	Total no. of native
fish species
•	No. and identity of
darter species
No. ana identity of
sunfish species
•	No. and identity of
sucker species
•	% pioneering
species
•	Number of fish
per unit of
sampling effort
related to drainage
area
•	No. and identity of
intolerant species
•	% of individuals as
tolerant species
•	% of individuals as
hybrids
•	% of individuals with
disease, tumors, fin
damage, and skeletal
anomalies
•	% omnivores
•	% insectivores
•	% top carnivores
Step 3 can be separated into 2 elements that correspond to discrimination of core metrics (element 1)
and determination of biological/physicochemical associations (element 2). Components of these
elements include:
Element I 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 (stressed) designations of stream sites for testing impairment 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.
9-8
Chapter 9: Multimetric Data Analysis
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• Those metrics having the
strongest discriminatory power
will provide the most
confidence in assessing
biological condition of
unknown sites.
Element 2
Determine the
associations/linkages
between candidate
biological and
physicochemical
measures
26
22
Reference
Stressed
Mln-Max
I I 25%-75%
° Median value
Plot relationship of metric
values against various stressor
categories, e.g., chemical
concentrations, habitat
condition and other measured stressors.
Figure 9-6. Example of discrimination, using the EPT index,
between reference and stressed sites in Rocky Mountain
streams, Wyoming.
•	If desired, multivariate ordination models may be used to elucidate gradients of response of
metrics to 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, total biomass and 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.
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 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 assessment as a 50% change in another.
Where possible, the scoring criterion for each metric is based on the distribution of values in the
population of sites, which include reference streams; for example, the 95th percentile of the data
distribution is commonly used (Figure 9-7) to eliminate extreme outliers. From this upper percentile,
the range of the metric values can be standardized as a percentage of the 95th percentile value, or
other (e.g., trisected or quadrisected), to provide a range of scores. Those values that are closest to
the 95th percentile would receive higher scores, and those having a greater deviation from this
percentile would have lower scores. For those metrics whose values increase in response to
Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
9-9
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perturbation (see Table 7-2 for examples of "reverse" metrics for benthic macroinvertebrates) the 5th
percentile is used to remove outliers and to form a basis for scoring.
Alternative methods for scoring metrics, as illustrated in Figure 9-7, are currently in use in various
parts of the US for multimetric indexes. A "trisection" of the scoring range has been well-
documented (Karr et al. 1986, Ohio EPA 1987, Fore et al. 1996, Barbour et al. 1996b). A
"quadrisection" of the range has been found to be useful for benthic assemblages (DeShon 1995,
Maxted et al. in press). More recent studies are finding that a standardization of all metrics as
percentages of the 95lh percentile value yields the most sensitive index, because information of the
component metrics is retained (Hughes et al. 1998). Unpublished data from statewide databases for
Idaho, Wyoming, Arizona, and West Virginia, are supportive of this third alternative for scoring
metrics. Ideally, 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.
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 and integrated with other ecological
information. 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 data distribution (Figure 9-7). If the metric is associated with a significant
covariate such as watershed size, a scatterplot of the metric and covariate (Figure 9-3) and a
moving estimate of the appropriate percentile, are used to determine scoring criteria as a
function of the covariate (e.g., Fausch et al. 1984, Plafkin et al. 1989).
Test the ability of the final index to discriminate between populations of reference and
anthropogenically affected (stressed) sites (Figure 9-8). 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).
Those sites that are
misclassified with regard
to "reference" and
"stressed" can be
identified and evaluated
for reassignment.
Step 5. Index thresholds for
assessment and biocriteria
The multimetric index value for a
site is a summation of the scores
of the metrics and has a finite
range within each stream class and
- *
o
• - - "T"- 95th percentile
X
observed value
All
Sites
Trisection
Quadrisection
Percentage
of standard
Scoring Methods
Figure 9-7. Basis of metric scores using the 95,h percentile as a
standard.
9-10
Chapter 9: Multimetric Data Analysis
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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, division of the Wyoming benthic IBI range
(aggregation of metric scores) within each stream class
provides 5 ordinal rating categories for assessment of impairment (Stribling et al. 1999, Figure 9-8).
The 5 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 fair or poor rating, then the site
should be re-evaluated as to its proper assignment.
	
Biocriteria axe 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.
©
i_
o
o
CO
X
o
~a
c
100
90
80
70
60
50
40
30
20
10
0
100
90
80
70
60
50
40
30
20
10
0
Very Good
Good
-r£j .
Fair
Poor
Very Poor
8
Very Good
I			1 Good
Fair —|—
....a	
Poor
Very Poor
Reference	Stressed
Rockies
Reference	Stressed
Black Hills
Very Good
Good
j Fair
~

Poor







Very Poor
Very Good
Good

Fair
~




Poor
Very Poor
Reference	Stressed
Plains
Reference	. Stressed
Basin
Figure 9-8. Discriminatory power analysis of the Wyoming Benthic Index of Biotic
Integrity. The population of stressed sites was determined a priori. The 25
-------
Putative reference sites may be rated "poor" for several reasons:
•	Natural variability — owing to seasonal, spatial, and random biological events, any reference
site may score below the reference population 10th percentile. If due to natural variability, a
low score should occur 10% of the time or less.
•	Impairment — stressors that were not detected in previous sampling or surveys may occur at
a "reference" site; for example, episodic non-point-source pollution or historical contamination
may be present at a site.
•	Non-representative site — reference sites are intended to be representative of their class. If
there are no anthropogenic stressors, yet a "reference" site consistently scores outside the
range of the rest of the reference population the site may be a special or unique case, or it may
have been misclassified and actually belong to another class of streams.
An understanding of variability is necessary to ensure that sites that are near 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, the question of precision was tested for the Wyoming Benthic D3I scores in the stream
classes. This study showed that the 95% confidence interval (CI) around a single sample is ±8 points,
on a scale of 100 (Table 9-2). What if a single site was sampled with no replication and found to be
points below the biocriterion? The rightmost column (Table 9-2) shows that a triplicate sample is
required for a 95% CI less than 5 points. 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.
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.
Pentasect, 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 and known stressed 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.
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Chapter 9: Multimetric Data Analysis
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Table 9-2. Statistics of repeated samples in Wyoming and the detectable difference (effect size) at 0.10
significance level. The index is on a 100 point scale (taken from Stribling et al. 1999).
Metric
Standard Deviation
for Repeated
Measures
Approx.
Mean3
Approx.
Coefficient of
Variation (%)
Detectable Differences (p = 0.10)
Single
Sample
Duplicate
Samples
Triplicate
Samples
Total Taxa
4.1
35.9
11.5
7 taxa
5 taxa
5 taxa
Ephemeroptera
taxa
0.9
6.8
13.3
2 taxa
1 taxa
1 taxa
Plecoptera taxa
1.0
4.8
21.2
2 taxa
1 taxa
1 taxa
Trichoptera taxa
1.1
6.9
15.3
2 taxa
1 taxa
1 taxa
% non-insects
3.8
8.9
42.9
6.3 %
4.4 %
4.3 %
% diptera
(non-chironomid)
1.3
5.1
25.0
2.1 %
1.5%
1.4%
HBI
0.27
3.43
7.85
0.44 units
0.31 units
0.26 units
% 5 dominant taxa
4.3
64.2
6.7
7.1 %
5.0 %
4.1 %
% scrapers
4.8
25.5
18.9
7.9 %
5.6 %
4.6 %
Index
2.0
70.0
2.9
3.3 units
2.3 units
1.9 units
a: Mean of 25 replicated sites; population means may differ.
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 probability-based
design, which has a component of randomness and is appropriate for 305(b), area-wide, and
watershed 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 resampled at some regular interval (e.g., a
4 year cycle) to provide information on trends in reference sites.
A reduced effort 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.
•	Hold physical habitat relationships constant and look for associations with other
physical stressors (e.g., hydrologic modification, streambed stability), chemical
Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
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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 DISCRIMINANT MODEL INDEX
Discriminant analysis may be used to develop a model that will divide, or discriminate, observations
among two or more predetermined classes. Output of discriminant analysis is a function that is a
linear combination of the input variables, and that obtains the maximum separation (discrimination)
among the defined classes. The model may then be used to determine class membership of new
observations. Thus, given a set of unaffected reference sites, and a set of degraded sites (due to
toxicity, low DO, or habitat degradation), a discriminant function model can identify variables that
will discriminate reference from degraded sites.
Developing biocriteria with a discriminant model requires a training data set to develop the
discriminant model, and a confirmation data set to test the model. The training and confirmation data
may be from the same biosurvey, randomly divided into two, or they may be two consecutive years of
survey data, etc. All sites in each data set are identified by degradation class (e.g., reference vs
stressed) or by designated aquatic life use class. To avoid circularity, identification of reference and
stressed, or of designated use classes, should be made from non-biological information such as quality
of the riparian zone and other habitat features; presence of known discharges and nonpoint sources,
extent of impervious surface in the watershed, extent of land use practices, etc.
One or more discriminant function models are developed from the training set, to predict class
membership from biological data. After development, the model is applied to the confirmation data
set to determine its performance: The test determines how well the model can assign sites to classes,
using independent data that were not used to develop the model. More information on discriminant
analysis is in any textbook on multivariate statistics (e.g., Ludwig and Reynolds 1988, Jongman et al.
1987, Johnson and Wichern 1992).
An example of this approach is the hierarchical decision-making technique used by Maine DEP. It
begins with statistical models (linear discriminant analysis) to make an initial prediction of the
classification of an unknown sample by comparing it to characteristics of each class identified in the
baseline database (Davies et al. 1993). The output from analysis by the primary statistical model is a
list of probabilities of membership for each of four groups designated as classes A, B, C, and
nonattainment (NA) of Class C (Table 9-3). Subsequent models are designed to distinguish between a
given class and any higher classes as one group, and any lower classes as a second group.
One or more discriminant models to predict class membership are developed from the training set.
The purpose of the discriminant analysis here is not to test the classification (the classification is
administrative rather than scientific), but to assign test sites to one of the classes.
Stream biologists from Maine DEP assigned a training set of streams to four life use classes. In
operational assessment, sites are evaluated with the two-step hierarchical models. The first stage
linear discriminant model is applied to estimate the probability of membership of sites into one of the
four classes (A, B, C, or NA). Second, the series of two-way models are applied to distinguish the
membership between a given class and any higher classes, as one group. The model uses 31
quantitative measures of community structure, including the Hilsenhoff Biotic Index, Generic Species
9-14
Chapter 9: Multimetric Data Analysis
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Richness, EPT, and EP values. Monitored test sites are then assigned to one of the four classes based
on the probability of that result, and uncertainty is expressed for intermediate sites. The classification
can be the basis for management action if a site has gone down in class, or for reclassification to a
higher class if the site has improved.
Table 9-3. Maine's water quality classification system for rivers and streams, with associated biological
standards (taken from Davies et al. 1993).			
Aquatic
Life Use
Class
Management
Biological Standard
Discriminant
Class
AA
High quality water for recreation and
ecological interests. No discharges or
impoundments permitted.
Habitat natural and free flowing.
Aquatic life as naturally occurs.
A
A
High quality water with limited human
interference. Discharges restricted to
noncontact process water or highly
treated wastewater equal to or better
than the receiving water.
Impoundments allowed.
Habitat natural. Aquatic life as
naturally occurs.
A and AA are
indistinguish-
able because
biota are "as
naturally
occurs."
B
Good quality water. Discharge of well
treated effluent with ample dilution
permitted.
Habitat minimally impaired. Ambient
water quality sufficient to support life
stages of all indigenous aquatic species.
Only nondetrimental changes in
community composition allowed.
B
€
Lowest water quality. Maintains the
interim goals of the Federal Water
Quality Act (fishable/swimmable).
Discharge of well-treated effluent
permitted.
Ambient water quality sufficient to
support life stages of all indigenous fish
species. Change in community
composition may occur but structure
and function of the community must be
maintained.
C
NA


Not attaining
Class C
Maine biocriteria thus establish a direct relationship between management objectives (the three
aquatic life use classes and nonattainment) and biological measurements. The relationship is
immediately viable for management and enforcement as long as the aquatic life use classes remain the
same. If the classes are redefined, a complete reassignment of streams and a review of the calibration
procedure would be necessary. This approach is detailed by Davies et al. (1993).
See Maine DEP's website for more information
http://www.state.me.us/dep/blwq/biohompg.htm
9.3 RIVER INVERTEBRATE PREDICTION AND CLASSIFICATION
SCHEME (RIVPACS)
RIVPACS and its derivative, AusRivAS (Australian Rivers Assessment System) are empirical
(statistical) 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
Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
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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 Britain (Wright et
al. 1993) and Australia (Norris 1995). The models are based on a stepwise progression of
multivariate and univariate analyses and have been developed for several regions and various habitat
types found in lotic systems. Regional applications of the AusRivAS model, in particular, have been
developed for the Australian states and territories (Simpson et al. 1996), and for streams in the Sierra
and Cascade mountain ranges 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.
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 stream 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
constructed for a single season, or data from several seasons may be combined to provide more robust
predictions. To date the RTVPACS/AusRivAs models have only been developed for the benthic
assemblage. Discussion of RTVPACS 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.
The reader is directed to the AusRivAS website for more specific information and guidance
regarding these multivariate techniques.
http://ausrivas.canberra.edu.au/ausrivas
9-16
Chapter 9: Multimetric Data Analysis
-------
10
Data Integration and
Reporting
Human impacts on the biological integrity of water resources are complex and cumulative (Karr
1998). Karr (1998) states that human actions jeopardize the biological integrity of water resources
by altering one or more of five principal factors — physical habitat, seasonal flow of water, the food
base of the system, interactions within the stream biota, and chemical quality of the water. These
factors can be addressed in environmental management by shifting our focus from technology-based
to water resource-based management strategies. This change in focus requires a commensurate shift
from the measurement of pollutant loadings to a measurement of ecosystem health. Biological
assessment addresses ecosystem health and cumulative impacts by concentrating on population and
community level response rather than on discharger performance (Courtemanch 1995).
The translation of biological data into a report that adequately conveys the message of the
assessment is a critical process. It is important to identify the intended audience(s) for the report and
to bear in mind that users of the report will likely include groups (i. e. managers, elected officials,
communities) who are not biologists. Reports must be coherent and easily understood in order for
people to make informed decisions regarding the water resource. First, the data must be summarized
and integrated, then clearly explained and presented. The use of a multimetric 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.
Once indices and values are obtained for each assemblage, the question becomes 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 among the most important that will be addressed in this chapter. The integration
of chemical and toxicological data with biological data is not treated in depth here. It is briefly
described in Chapter 3 and discussed in more detail elsewhere (Jackson 1992, USEPA 1997c).
10.1.1 Data Integration of Assemblages
USEPA advises incorporating more than 1 assemblage into biocriteria programs whenever practical.
Surveying multiple assemblages provides a more complete assessment of biological condition since
the various assemblages respond differently to certain stressors and restoration activities. For
instance, Ohio EPA found, in a study of the Scioto River, that fish responded (recovered) more
quickly than did benthos to restoration activities aimed at reducing the effects of cumulative impacts
(i.e., impoundments, combined sewer overflows, wastewater treatment plants, urbanization) (Yoder
and Rankin 1995a). 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
Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition	10-1
10.1 DATA INTEGRATION
-------
for many of the same reaches. The use
of both assemblages enhanced the
agency's assessment of trend analysis
for the Scioto River.
In addition, using more than 1
assemblage allows programs to more
fully assess the occurrence of multiple
stressors and seasonal variation in the
intensity of the stressors (Gibson et al.
1996). 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.
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 fish Index of Biotic Integrity
JACKSON SOUTHERLY
WHITER
PIKEWWTP WOTP
EWH Cnterkm
{ICI=46i
„ STREET CSO
WWH Criterion
(1CI=3«)
C3 30
mpoun
de?m
60
50
40
30
JACKSON
WKITT1EH PKEWWTP ~nlmmYWWrp
¦ STREET CSO	SOUTHERLY WWTP
-V
, /

i
3
EV.'HCSeiion:;;
(IBM8)
V
2 0 r
12 r
»•
impounded

/

WWH Crtaion 1
(IBW2)
1980
O- - 1991
-3	 1994
140
130
120 110
RIVER MILE
100
90
Figure 10-1. Cumulative frequency diagrams (CFD) for the
IBI (upper) and the ICI (lower) comparing the pre-1988 and
post-1988 status on a statewide basis from Ohio. In each
case, estimated attainable level of future performance is
indicated. The Warm Water Habitat (WWH) and
Exceptional Warm Water Habitat (EWH) biological
thresholds are given for each index.
(IBI) and the 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 [RM] 140 to 90) (Yoder 1995a).
Biological attributes and indices can also be illustrated side-by-side to highlight differences and
similarities in the results. Oftentimes, differences in the results are useful for diagnosing cause-and-
effect.
10.1.2 Relationship Between Habitat and Biological Condition
Historically, non-chemical impacts to biotic systems have not been a major focus of the nation's
water quality agencies. Yet there is clear evidence that habitat alteration is a primary cause of
degraded aquatic resources (USEPA 1997c). Habitat degradation occurs as a result of hydrological
flow modification, alteration of the system's energy base, or direct impact on the physical habitat
structure. Preservation of an ecosystem's natural physical habitat is a fundamental requirement in
maintaining diverse, functional aquatic communities in surface waters (Rankin 1995). Habitat
quality is an essential measurement in any biological survey because aquatic fauna often have very
10-2
Chapter 10: Data Integration and Reporting
-------
specific habitat requirements independent of
water-quality composition (Barbour et al. 1996a).
Diagnostic evaluations are enhanced when
assessment of the habitat, flow regime, and
energy base are incorporated into the
interpretation of the biological condition (USEPA
1990b).
The relationship between habitat quality (as
defined by site-specific factors, riparian quality,
and upstream land use) and biological condition
can be graphed, as illustrated in Figure 10-2 to
enhance data interpretation. On the X-axis,
habitat is shown to vary in quality from 30 points,
which is poor (nonsupporting of an acceptable
biological condition) to 85 points, which is good (comparable to the reference condition). Biological
condition, represented by the fish IBI on the Y-axis, varies from 10 points (severely impaired) to 60
points (excellent). Interpretation of the relationship between habitat and biology as depicted by
Figure 10-2 can be summarized by 4 points relating to specific areas of the graph.
1.	The upper right-hand corner of the curve is the ideal situation where optimal habitat quality
and biological condition occur.
2.	The decrease in biological condition is proportional to a decrease in habitat quality.
3.	Perhaps the most important area of the graph is the lower right-hand corner where degraded
biological condition can be attributed to something other than habitat quality (Barbour et al.
1996a).
4.	The upper left-hand corner is where optimal biological condition is not possible in a severely
degraded habitat (Barbour et al. 1996a).
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 patches of habitat structure in
otherwise habitat-degraded stream reaches may
harbor sensitive species, thus masking the
effects of habitat alteration.
An informative approach to evaluating affects
from specific or cumulative stressors is to
ascertain a gradient response of the aquatic
community using a bivariate scatter plot. In one example provided by Florida DEP, a gradient
response of the EPT taxa indicated a strong relationship to nitrogen in the stream (Figure 10-3).
90—
/¦¦¦/
I
/ *
1 "
y//* * **
| ..-J

0
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o
o
yyr' *•. *
•§ M"

£
' '' '

i i i i i i i i
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Habttat Quality
Figure 10-2. Relationship between the condition
of the biological community and physical habitat.





25
•



20



3
16
•
• m
• m
• • • •




• • • •• m
• •

Q_
UJ
10




• •••«•
• •



• mm • • • m
• • •



• • • • • •
• • •
• M • «
m •• • • •


0





Total KJeldah! Nitrogen {mg/I}
Figure 10-3. Data from a study of streams in
Florida's Panhandle.
Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
10-3
-------
When multiple data types (i.e., habitat,
biological, chemical, etc.) are available, sun ray
plots may be used to display the assessment
results. As an example, the assessments of
habitat, macroinvertebrates and fish are
integrated for evaluating of the condition of
individual stream sites in a Pennsylvania
watershed (Snyder et al. 1998). The assessment
scores for each of the triad data types are
presented as a percentage of reference condition
(Figure 10-4). The area enclosed by each sun
ray plot can be measured to provide a
comparison of the biological and habitat
condition among the sites of interest (Snyder et
al. 1998). This technique helps determine the
extent of impairment and also which ecological
components are most affected.
10.2 REPORTING
Historically, reports containing assessment results and recommendations for further action have been
designed to address objectives and data uses relevant to the specific monitoring program.
Increasingly, however, assessment reports are designed to reach a broader, non-scientific audience
including water resource managers and the environmentally conscious public. Communicating the
condition of biological systems, and the impact of human activities on those systems, is the ultimate
purpose of biological monitoring (Karr and Chu 1999). Reporting style and format has become an
important component in effectively communicating the findings of ecological assessments to diverse
audiences. As pointed out by Karr and Chu (1999), effective communication can transform
biological monitoring from a scientific exercise into a powerful tool for environmental decision
making.
10.2.1 Graphical Display
Graphical displays are a fundamental tool for illustrating scientific information. Graphs
reveal—more effectively than do strictly statistical tools—patterns of biological response. Patterns
include "outliers," which may convey unique information that can help diagnose particular problems
or reveal specific traits of a site (Karr and Chu 1999). Examples of some of the most useful
graphical techniques are presented for specific biological program objectives:
Station

Station
8
Station
7
Station
Figure 10-4. Comparison of integrated assessment
(habitat, fish, and benthos) among stream sites in
Pennsylvania. Station 16 is a reference site.
(Taken from Snyder et al. 1998).
10-4
Chapter 10: Data Integration and Reporting
-------
1.
Stream classification — a graph should illustrate the distinction between and among site
classes or groups. Two common graphical displays are bivariate scatter plots (used in non-
metric multidimensional scaling) and cluster dendrograms.
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
10-5).
n D
D n O
O
Q
'Q ~~
o
e^1 - ~
~
~
~ Q
O V
•
Panhandle
~
Peninsula
A
Northeast
First Axis
Figure 10-5. Use of multidimensional scaling on benthic data to ascertain
stream classification. The first and second axes refer to the dimensions of
combinations of data used to measure similarity (Taken from Barbour et al.
1996b).
Cluster
dendrogram—used to
illustrate the
similarities and
dissimilarities of sites
in support of classes
(Figure 10-6).
1.0
0.9

5
D)
re
c
0.8
0.7
0.6
0.5
0.4
0.3
fl
II
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Figure 10-6. Example of a cluster dendrogram, illustrating similarities and
clustering of sites (x-axis) using biological data.
Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
10-5
-------
2. Problem Identification and Status of Water Resource — The status of the condition of water
resources requires consolidating information from many samples and can be illustrated in
several ways*.
Pie charts—used to
illustrate proportional
representation of the
whole by its
component parts. Can
be sized according to
magnitude or density
(Figure 10-7)
VteryGood 36.4%
Good 36.4%
Fbor 27.3%
Figure 10-7. Results of the benthic assessment of streams in the Mattaponi
Creek watershed of southern Prince George's County, Maryland. Percent of
streams in each ecological condition category. (Taken from Stribling et al.
1996b).
Box-and- whisker
plots— used to
illustrate population
attributes (via
percentile
distribution) and
provides some sense
of variability (Figure
10-8).

75%
Median.'
—
Range
HELP IP EOLP WAP ECBP
ECOREGIONS
Figure 10-8. The population of values of the IBI in reference sites within each
of the ecoregions of Ohio. (Contributed by Ohio EPA).
10-6	Chapter 10: Data Integration and Reporting
-------
3.
Trend monitoring and assessment — Monitoring over a temporal or spatial scale requires a
graphical display depicting trends, which may show improvement, degradation, or no
change.
Line graphs—used to
illustrate temporal or
spatial trends that are
contiguous. Assumes
that linkage between
points is linear
(Figure 10-9).
Scioto River: Columbus to Circleville
60
JACKSON
• WHITTIER PIKE WWTP,
IfiTREET CSO ,
SOUTHERLY WWTP
EWH Criterion.;
(ICI=46) -
50
40
WWH Criterion -
(ICI=36)_;
30
20
" 1980
. 1991
10
Impounded
0
130
120
110
100
90
140
RIVER MILE
Figure 10-9. Spatial and temporal trend of Ohio's Invertebrate Community
Index. The Scioto River - Columbus to Circleville. (Contributed by Ohio
EPA).
Cumulative
frequency
diagram—illustrates
an ordered
accumulation of
observations from
lowest to highest
value that allows
one to determine
status of resource at
any given level
(Figure 10-10),
Cumulative Distribution of Index Scores
1.0
0.9
0.8
0.2
0.1
0.0
20
30
40 50
Maorolnvertebrate Index Score
60
100
0
Figure 10-10. Cumulative distribution of macroinvertebrate index
scores. 21% of sites scored at or below 60. The median index score is
75, where the cumulative frequency is 50%.
Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroin vertebrates, and Fish, Second Edition
10-7
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4. A determination of cause-and-effect — illustrating the source of impairment may not be a
straightforward process. However,
certain graphs lend themselves to
showing comparative results in
diagnosing problems.
Bar charts — used to display magnitude
of values for discrete entities. Can be
used to illustrate deviation from a value
of central tendency (Figure 10-11).

85
80
75 :
:«7o -
- 85
60
,55 :
-
SO
i
&
c c
r'#K
WM, , V J. <*
Figure 10-11. Biological assessment of sites in the
Middle Rockies, showing mean and standard
deviation of repeated measures and the assessment
threshold (dashed line).
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 10-12).
•/. HABITAT
% FISH 1BI
% BENTHIC
MACROINVERTEBRATE IBI
Figure 10-12. Integration of data from habitat,
fish, and benthic assemblages.
Box-and-whiskerplots— used to
illustrate population attributes (via
percentile distribution). Distinction
among plots illustrates degree of
similarity/differences (Figure 10-13).
Complex Muni. CSO Chan. Agrlc. Flow CSO Llvo-
Toile Conv. Cettv. Mod, HPS Altar. Toxic $tack
n*>?2 0=275 (fc>22 n*10 n-63 n*23 ns=24 n<*11
IMPACT TYPES
Figure 10-13. The response of the benthic
macroinvertebrate assemblage (ICI) to various
types of impacts (provided by Ohio EPA).
10-8
Chapter 10: Data Integration and Reporting
-------
10.2.2 Report Format
Two basic formats are recommended for reporting ecological assessments. Each of these formats is
intended to highlight the scientific process, focus on study objectives, and judge the condition of the
assessed sites. The first format is a summary report, targeted for use by managers in making
decisions regarding the resource. This report format can also be an invaluable public information
tool. The second report format is patterned after that of peer-reviewed journals and is primarily
designed for informing a more technical audience.
The Ecosummary is an example of the first report format. It has an uncomplicated style and conveys
various information including study results. The simplicity of this format quickly and effectively
documents results and assists a non-technical audience in making informed decisions. An executive
summary format is appropriate. 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 are generally between 1-4 pages in length and lend themselves to quick and
easy 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 10-14. This 1-page report highlights the purpose of the study as well as the
results and significance of the findings. A summary of the ecological data in the form of bar charts
and tables may be provided on subsequent pages. 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).
The second format for reporting is a scientific report, which is structured similarly to a peer-
reviewed journal. The report should be peer-reviewed by non-agency scientists to validate its
scientific credibility. An abstract or executive summary should be prepared to highlight the essential
findings. As in a peer-reviewed journal article, the methods and results are presented succinctly and
clearly. The introductory text should outline the objectives and purpose of the study. A discussion
of the results should include supporting literature to add credence to the findings, particularly if there
is a discussion of suspected cause of impairment. Preparation of a report using this format will
require more time than the Ecosummary. However, this report format is more inclusive of
supportive information and will be more important in litigious situations.
Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
10-9
-------
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Macroinvertebrates, and Fish, Second Edition
11-1
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U.S. Environmental Protection Agency (U.S. EPA). 1992. Framework for ecological risk assessment.
U.S. Environmental Protection Agency, Washington, D.C. EPA/630/R-92/001.
U.S. Environmental Protection Agency (U.S. EPA). 1994a. Watershed protection: TMDL Note #2
bioassessment and TMDLs. U.S. Environmental Protection Agency, Office of Water, Washington, D.C.
EPA841-K-94-005a..
U.S. Environmental Protection Agency (U.S. EPA). 1994b. National water quality inventory: 1992
report to Congress. U.S. Environmental Protection Agency, Office of Water, Washington, D.C. EPA
841-R-94-001.
U.S. Environmental Protection Agency (U.S. EPA). 1994c. The watershed protection approach 1993/94
supplement to 1992 annual report draft. U.S. Environmental Protection Agency, Office of Water,
Washington, D.C.
Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
11-19
-------
U.S. Environmental Protection Agency (U.S. EPA). 1994d. Guidance on implementation of biological
criteria. Draft. U.S. Environmental Protection Agency, Office of Science and Technology, Washington,
D.C. January 13.
U. S. Environmental Protection Agency (U.S. EPA). 1995a. Generic quality assurance project plan
guidance for programs using community-level biological assessment in streams and wadeable rivers.
U.S. Environmental Protection Agency, Office of Water, Washington, D.C. EPA 841-B-95-004.
U.S. Environmental Protection Agency (U.S. EPA). 1995b. Guidelines for preparation of the 1996 State
Water Quality Assessments (305[b] Reports). Office of Water, U.S. Environmental Protection Agency,
Washington, D.C. EPA 841-B-95-001.
U.S. Environmental Protection Agency (U.S. EPA). 1996a. The volunteer monitor's guide to quality
assurance project plans. U.S. Environmental Protection Agency, Office of Wetlands, Oceans, and
Watersheds, Washington, D.C. EPA 841-B-96-003.
U.S. Environmental Protection Agency (U.S. EPA). 1996b. Nonpoint source monitoring and evaluation
guide. U.S. Environmental Protection Agency, Office of Water, Washington, D.C.
U.S. Environmental Protection Agency (U.S.EPA). 1996c. Level III ecoregions of the continental
United States. U.S. Environmental Protection Agency, National Health and Environmental Effects
Research Laboratory, Corvallis, Oregon.
U.S. Environmental Protection Agency (U.S. EPA). 1997a. Estuarine and coastal marine waters
bioassessment and biocriteria technical guidance. U.S. Environmental Protection Agency, Office of
Water, Washington, D.C. EPA-822-B-97-001.
U.S. Environmental Protection Agency (U.S. EPA). 1997b. Volunteer stream monitoring: A methods
manual. U.S. Environmental Protection Agency, Office of Water, Washington, D.C. EPA 841-B-97-
003.
U.S. Environmental Protection Agency (U.S. EPA). 1997c. Guidelines for the preparation of the
Comprehensive State Water Quality Assessments (305[bj reports). U.S. Environmental Protection
Agency, Office of Water, Washington, D.C. EPA-841-B-97-002A.
U.S. Environmental Protection Agency (U.S. EPA). 1998. Lake and reservoir bioassessment and
biocriteria technical guidance document. U.S. Environmental Protection Agency, Office of Water,
Washington, D.C. EPA-841-B-98-007.
VanLandingham, S. L. 1982. Guide to the identification, environmental requirements and pollution
tolerance of freshwater blue-green algae (Cyanophyta). EPA-600/3-82-073.
Vinson, M.R. and C.P. Hawkins. 1996. Effects of sampling area and subsampling procedure on
comparisons of taxa richness among streams. Journal of the North American Benthological Society
15(3):392-399.
Volstad, J.H., D.H. Heimbuch, M.T. Southerland, P.T. Jacobson, J.A. Chaillou, S.B. Weisberg, H.T.
Wilson. 1995. Maryland Biological Stream Survey; The 1993 pilot study. Maryland Department of
Natural Resources, Chesapeake Bay Research and Monitoring Division, Annapolis Maryland.
Wade, D.C. and S.B. Stalcup. 1987. Assessment of the Sport Fishery Potential for the Bear Creek
Floatway: Biological Integrity of Representative Sites, 1986. Tennessee Valley Authority, Muscle
Shoals, Alabama. Report No. TVA/ONRED/AWR-87/30.
11-20
Chapter 11: Literature Cited
-------
Wallace, J.B., J.W. Grubaugh, and M.R. Whiles. 1996. Biotic indices and stream ecosystem processes:
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Wallace,J.B., J.R. Webster, and W.R. Woodall. 1977. The role of filter feeders in flowing waters.
Archiv fur Hydrobiologie 79:506-532.
Ward, G.M. and N.G. Aumen. 1986. Woody debris as a source of fine particulate organic matter in
coniferous forest stream ecosystems. Canadian Journal of Fisheries and Aquatic Sciences. 43:1635-
1642.
Warren, C.E. 1979. Toward Classification and Rationale for Watershed Management and Stream
Protection. U.S. Environmental Protection Agency, Corvallis, Oregon. EPA-600/3-79-059.
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imperiled fauna. Fisheries 19(1):6-18.
Weber, C.I. (editor). 1973. Biological field and laboratory methods for measuring the quality of surface
water and effluents. U.S. Environmental Protection Agency, Office of Research and Development,
Cincinnati, Ohio. EPA 670-4-73-001.
Weitzel, R. L. 1979. Periphyton measurements and applications. In R. L. Weitzel (editor). Methods
and measurements of periphyton communities: A review. Special Technical Publication 690. American
Society for Testing and Materials.
Wesche, T.A., C.M. Goertler, C. B. Frye. 1985. Importance and evaluation of instream and riparian
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Conference Riparian Ecosystems and their Management: Reconciling conflicting uses. U.S. Department
of Agriculture Forest Service, General Technical Report TM-120. Tucson, Arizona.
Whittaker, R.H. 1952. A study of summer foliage insect communities in the Great Smoky Mountains.
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Whittaker, R.H. and C.W. Fairbanks. 1958. A study of plankton copepod communities in the Columbia
basin, Southeastern Washington. Ecology 39:46-65.
Whittier, T.R., R.M. Hughes, and D.P. Larsen. 1988. Correspondence between ecoregions and spatial
patterns in stream ecosystems in Oregon. Canadian Journal of Fisheries and Aquatic Sciences 45:1264-
1278.
Whitton, B. A., and Kelly, M. G. 1995. Use of algae and other plants for monitoring rivers. Australian
Journal of Ecology 20, 45-56.
Whitton, B. A. and E. Rott. 1996. Use of algae for monitoring rivers II. E. Rott, Publisher, Institut ftir
Botanik, Universitat Innsbruck, Innsbruck, Austria
Whitton, B. A., Rott, E., and Friedrich, G., ed. 1991. Use of Algae for Monitoring Rivers. E. Rott,
Publisher, Institut fur Botanik, Universitat Innsbruck, Innsbruck, Austria
Wilhm, J.L. and T.C. Doris. 1968. Biological parameters for water quality criteria. Bioscience 18:477-
481.
Rapid BioassessmentProtocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
11-21
-------
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. 1995a. Biological criteria program development and implementation in
Ohio. Pages 109-144 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. 1995b. 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.
11-22
Chapter 11: Literature Cited
-------
Appendix A:
Sample Data Forms for the Protocols
Rapid Bioassessment Protocols For Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
A-l
-------
This Page Intentionally Left Blank
A-2	Appendix A-J: Habitat Assessment and Physicochemical Characterization Field Data Sheets
-------
APPENDIX A-l:
Habitat Assessment and Physicochemical Characterization Field
Data Sheets
Form 1: Physical Characterization/Water Quality Field Data Sheet
Form 2: Habitat Assessment Field Data Sheet - High Gradient Streams
Form 3: Habitat Assessment Field Data Sheet - Low Gradient Streams
Rapid Bioassessment Protocols For Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
A-3
-------
This Page Intentionally Left Blank
A-4	Appendix A-J: Habitat Assessment and Physicochemical Characterization Field Data Sheets
-------
PHYSICAL CHARACTERIZATION/WATER QUALITY FIELD DATA SHEET
(FRONT)
STREAM NAME
LOCATION
STATION # RTVERMILE
STREAM CLASS
LAT LONG
RIVER BASIN
STORET #
AGENCY
INVESTIGATORS
FORM COMPLETED BY
DATE
TIME AM PM
REASON FOR SURVEY
WEATHER
CONDITIONS
Now Past 24 Has there been a heavy rain in the last 7 days?
hours ~ Yes ~ No
~	storm (heavy rain) ~
~	rain (steady rain) ~ ^lr Temperature	 C
~	showers (intermittent) ~ other
	%Q %cIoud cover ~	%
~	clear/sunny ~
SITE LOCATION/MAP
Draw a map of the site and indicate the areas sampled (or attach a photograph)
STREAM
CHARACTERIZATION
Stream Subsystem Stream Type
~	Perennial ~ Intermittent ~ Tidal ~ Coldwater O Warmwater
Stream Origin Catchment Area km2
~	Glacial ~ Spring-fed
~	Non-glacial montane ~ Mixture of origins
~	Swamp and boe ~ Other
Rapid Bioassessment Protocols For Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition - Fortn 1
A-5
-------
PHYSICAL CHARACTERIZATION/WATER QUALITY FIELD DATA SHEET
(BACK)
WATERSHED
FEATURES
Predominant Surrounding Landuse
~	Forest ~ Commercial
~	Field/Pasture ~ Industrial
Q Agricultural ~ Other
~	Residential
Local Watershed NPS Pollution
~	No evidence O Some potential sources
~	Obvious sources
Local Watershed Erosion
~	None ~ Moderate ~ Heavy
RIPARIAN
VEGETATION
(18 meter buffer)
Indicate the dominant type and record the dominant species present
~ Trees Q Shrubs ~ Grasses ~ Herbaceous
dominant species present


INSTREAM
FEATURES
Estimated Reach Lentrth m
Estimated Stream Width m
Sampling Reach Area m1
Area in km1 (m'xlOOO) knr
Estimated Stream Depth m
Canopy Cover
~	Partly open O Partly shaded Q Shaded
Hieh Water Mark m
Proportion of Reach Represented by Stream
Morphology Types
~	Riffle % ~ Run %
~	Pool %

Surface Velocity m/sec
(at thalweg)
Channelized ~ Yes ~ No
Dam Present ~ Yes ~ No
LARGE WOODY
DEBRIS
LWD m1

Density of LWD mVknr CI, WD/ reach area)
AQUATIC
VEGETATION
Indicate the dominant type and record the dominant species present
~	Rooted emergent ~ Rooted submergent ~ Rooted floating ~ Free floating
~	Floating Algae ~ Attached Algae

dominant snecics present


Portion of the reach with aquatic vegetation
%
WATER QUALITY
Temperature u C
Specific Conductance
Dissolved Oxveen
nH
Turbidity
WO Instrument Used
Water Odors
~	Normal/None ~ Sewage
~	Petroleum ~ Chemical
~	Fishv ~ Other
Water Surface Oils
~	Slick ~ Sheen ~ Globs ~ Flecks
~	None ~ Other
Turbidity (if not measured)
~	Clear ~ Slightly turbid ~ Turbid
~	Opaque Q Stained Q Other
SEDIMENT/
SUBSTRATE
Odors
O Normal ~ Sewage O Petroleum
~	Chemical ~ Anaerobic Q None
~	Other
Deposits
~ Sludge ~ Sawdust ~ Paper fiber ~ Sand
O Relict shells ~ Other

Oils
~ Absent ~ Slight ~ Moderate ~ Profuse
Looking at stones which are not deeply
embedded, arc the undersides black in color?
~ Yes ~ No
INORGANIC SUBSTRATE COMPONENTS
(should add up to 100%)
ORGANIC SUBSTRATE COMPONENTS
(docs not necessarily add up to 100%)
Substrate
Type
Diameter
% Composition in
Sampling Reach
Substrate
Type
Characteristic
% Composition in
Sampling Area
Bedrock


Detritus
sticks, wood, coarse plant
materials (CPOM)

Boulder
>256 mm (10")

Cobble
64-256 mm (2.5"-10")

Muck-Mud
black, very fine organic
(FPOM)

Gravel
2-64 mm (0.1 "-2.5")

Sand
0.06-2mm (gritty)

Marl
grey, shell fragments

Silt
0.004-0.06 mm

Cl*y
< 0,004 mm (slick)

A-6 Appendix A-l: Habitat Assessment and Physicochemical Characterization Field Data Sheets - Form I
-------
HABITAT ASSESSMENT FIELD DATA SHEET—HIGH GRADIENT STREAMS (FRONT)
STREAM NAME
LOCATION
STATION # RIVERMILE
STREAM CLASS
LAT LONG
RIVER BASIN
STORET #
AGENCY
INVESTIGATORS
FORM COMPLETED BY
DATE
TIME AM PM
REASON FOR SURVEY

Habitat
Parameter
Condition Category

Optimal
Suboptimal
Marginal
Poor

1. Epifaunal
Substrate/
Available Cover
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).
40-70% mix of stable
habitat; well-suited for
full colonization
potential; adequate
nabitat 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).
20-40% mix of stable
habitat; habitat
availability less than
desirable; substrate
frequently disturbed or
removed.
Less than 20% stable
habitat; lack of habitat is
obvious; substrate
unstable or lacking.
4B
SCORE
*20 19 V8g«;7 '"'W
15 H
io'..9
%% % 0'
«
u
60
G
"a.
E
ca
tfi
2. Embeddedness
Gravel, cobble, and
boulder particles are 0-
25% surrounded by fine
sediment. Layering of
cobble provides diversity
of niche space.
Gravel, cobble, and
boulder particles are 25-
50% surrounded by fine
sediment.
Gravel, cobble, and
boulder particles are 50-
75% surrounded by fine
sediment.
Gravel, cobble, and
boulder particles are
more than 75%
surrounded by fine
sediment.
TJ
SCORE
ITlliffl
gl5 14''9|g|g2. Ill
*fofe,9 6

a
>
4)
-©
2
M
k.
3. Velocity/Depth
Regime
All four velocity/depth
regimes present (slow-
deep, slow-shallow, fast-
deep, fast-shallow).
(Slow is < 0.3 m/s, deep
is > 0.5 m.)
Only 3 of the 4 regimes
present (if fast-shallow is
missing, score lower
than if missing other
regimes).
Only 2 of the 4 habitat
regimes present (if fast-
shallow or slow-shallow
are missing, score low).
Dominated by 1
velocity/ depth regime
(usually slow-deep).
£
SCORE
feo.: Sfe d1?
lS-^13JSfel'3-. 12 it*
10 9 5^,8 ~~7 6-.
% mM- lit®
2
«
ft.
4. Sediment
Deposition
Little or no enlargement
of islands or point bars
and less than 5% of the
bottom affected by
sediment deposition.
Some new increase in
bar formation, mostly
from gravel, sand or fine
sediment; 5-30% of the
bottom affected; slight
deposition in pools.
Moderate deposition of
new gravel, sand or fine
sediment on old and new
bare; 30-50% of the
bottom affected;
sediment deposits at
obstructions,
constrictions, and bends;
moderate deposition of
pools prevalent.
Heavy deposits of fine
material, increased bar
development; more than
50% of the bottom
changing frequently;
pools almost absent due
to substantial sediment
deposition.

SCORE

»15§*^ ,i3*$5Sj&t
.. iS^x
%, I*,'®

S. Channel Flow
Status
Water reaches base of
both lower banks, and
minimal amount of
channel substrate is
exposed.
Water fills >75% of the
available channel; or
<25% of channel
substrate is exposed.
Water fills 25-75% of
the available channel,
and/or riffle substrates
are mostly exposed.
Very little water in
channel and mostly
present as standing
pools.

SCORE
=20
12 :n'e
ItOfe 9 Tgj^. /?*¦ ^

Rapid Bioassessment Protocols For Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition - Form 2
A-7
-------
HABITAT ASSESSMENT FIELD DATA SHEET—HIGH GRADIENT STREAMS (BACK)

Habitat
Parameter
Condition Category

Optimal
Suboptimal
Marginal
Poor

6. Channel
Alteration
Channelization or
dredging absent or
minimal; stream with
normal pattern.
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.
Channelization may be
extensive; embankments
or shoring structures
present on both banks;
and 40 to 80% of stream
reach channelized and
disrupted.
Banks shored with
gabion or cement; over
80% of the stream reach
channelized and
disrupted. Instream
habitat greatly altered or
removed entirely.

SCORE
20 19 18 17 16
15 14 13 12 11
10 9 8 7 6
5 4 3 2 10
¦8
2
w
a
tL
E
a
e
7. Frequency of
Riffles (or bends)
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.
Occurrence of riffles
infrequent; distance
between riffles divided
by the width of the
stream is between 7 to
15.
Occasional riffle or
bend; bottom contours
provide some habitat;
distance between riffles
divided by the width of
the stream is between 15
to 25.
Generally all flat water
or shallow riffles; poor
habitat; distance between
riffles divided by the
width of the stream is a
ratio of >25.
SCORE
20 19 18 17 16
15 14 13 12 11
10 9 8 7 6
5 4 3 2 1 0
1
u
CI
*Q
2
ja
1
rt
3
8. Bank Stability
(score each bank)
Hole: determine left
or right side by
facing downstream.
Banks stable; evidence
of erosion or bank
failure absent or
minimal; little potential
for future problems.
<5% of bank affected.
Moderately stable;
infrequent, small areas of
erosion mostly healed
over. 5-30% of bank in
reach has areas of
erosion.
Moderately unstable; 30-
60% of bank in reach has
areas of erosion; high
erosion potential during
floods.
Unstable; many eroded
areas; "raw" areas
frequent along straight
sections and bends;
obvious bank sloughing;
60-100% of bank has
erosional scars.
£
SCORE	(LB)
Left Bank 10 9
8 7 6
5 4 3
2 I 0
4)
Xl
O
SCORE	(RB)
Right Bank 10 9
8 7 6
5 4 3
2 1 0
s
V
E
e
(9
Dm
9. Vegetative
Protection (score
each bank)
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.
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.
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.
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.

SCORE	(LB)
Left Bank 10 9
8 7 6
5 4 3
2 10

SCORE (RB)
Right Bank 10 9
8 7 6
5 4 3
2 10

10. Riparian
Vegetative Zone
Width (score each
bank riparian zone)
Width of riparian zone
>18 meters; human
activities (i.e., parking
lots, roadbeds, clear-
cuts, lawns, or crops)
have not impacted zone.
Width of riparian zone
12-18 meters; human
activities have impacted
zone only minimally.
Width of riparian zone
6-12 meters; human
activities have impacted
zone a great deal.
Width of riparian zone
<6 meters; little or no
riparian vegetation due
to human activities.

SCORE	(LB)
Left Bank 10 9
8 7 6
5 4 3
2 I 0

SCORE (RB)
Right Bank 10 9
8 7 6
5 4 3
2 1 0 ,
Total Score
A-8 Appendix A-l: Habitat Assessment and Physicochemical Characterization Field Data Sheets - Form 2
-------
HABITAT ASSESSMENT FIELD DATA SHEET—LOW GRADIENT STREAMS (FRONT)
STREAM NAME
LOCATION
STATION # RIVERMILE
STREAM CLASS
LAT LONG
RIVER BASIN
STORET #
AGENCY
INVESTIGATORS
FORM COMPLETED BY
DATE
TIME AM PM
REASON FOR SURVEY

Habitat
Parameter
Condition Category

Optimal
Suboptimal
Marginal
Poor

1. Epifaunal
Substrate/
Available Cover
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).
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).
10-30% mix of stable
habitat; habitat
availability less than
desirable; substrate
frequently disturbed or
removed.
Less than 10% stable
habitat; lack of habitat is
obvious; substrate
unstable or lacking.
Jm
u
3
SCORE
17'tS
us -mrnmrrn

MWMmmt
OS
.5
*5.
S
«
ffi
e
2. Pool Substrate
Characterization
Mixture of substrate
materials, with gravel
and firm sand prevalent;
root mats and submerged
vegetation common.
Mixture of soft sand,
mud, or clay; mud may
be dominant; some root
mats and submerged
vegetation present.
All mud or clay or sand
bottom; little or no root
mat; no submerged
vegetation.
Hard-pan clay or
bedrock; no root mat or
vegetation.
i
SCORE
rn 16-;
ilii&r.
tiir-ilii 7fi
1*11 'ft 'l lk M
>

.Q
e
3. Pool Variability
Even mix of large-
shallow, large-deep,
small-shallow, small-
deep pools present.
Majority of pools large-
deep; very few shallow.
Shallow pools much
more prevalent than deep
pools.
Majority of pools small-
shallow or pools absent.
M
fc.
6>
SCORE

15 ::®3:vl2vlg
ill ' ^ life

E
«
>-
«
0.
4. Sediment
Deposition
Little or no enlargement
of islands or point bars
and less than <20% of
the bottom affected by
sediment deposition.
Some new increase in
bar formation, mostly
from gravel, sand or fine
sediment; 20-50% of the
bottom affected; slight
deposition in pools.
Moderate deposition of
new gravel, sand or fine
sediment on old and new
bars; 50-80% of the
bottom affected;
sediment deposits at
obstructions,
constrictions, and bends;
moderate deposition of
pools prevalent.
Heavy deposits of fine
material, increased bar
development; more than
80% of the bottom
changing frequently;
pools almost absent due
to substantial sediment
deposition.

SCORE
Is. -lit':18 life16'




5. Channel Flow
Status
Water reaches base of
both lower banks, and
minimal amount of
channel substrate is
exposed.
Water fills >75% of the
available channel; or
<25% of channel
substrate is exposed.
Water fills 25-75% of the
available channel, and/or
riffle substrates are
mostly exposed.
Very little water in
channel and mostly
present as standing
pools.

SCORE

|I5. i4-fj§Pi|i;i..

Ittito-i
Rapid Bioassessment Protocols For Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition - Form 3
A-9
-------
HABITAT ASSESSMENT FIELD DATA SHEET—LOW GRADIENT STREAMS (BACK)
Parameters to be evaluated broader than sampling reach
Habitat
Parameter
Condition Category
Optimal
Suboptimal
Marginal
Poor
6. Channel
Alteration
SCORE
Channelization or
dredging absent or
minimal; stream with
normal pattern.
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.
Channelization may be
extensive; embankments
or shoring structures
present on both banks;
and 40 to 80% of stream
reach channelized and
disrupted.
Banks shored with
gabion or cement; over
80% of the stream reach
channelized and
disrupted. Instream
habitat greatly altered or
removed entirely.
20 19 18 17 16
15 14 13 12 11
10 9 8 7 6
5 .4 3 -2 !.' 0.
7. Channel
Sinuosity
SCORE
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.)
The bends in the stream
increase the stream
length 1 to 2 times
longer than if it was in a
straight line.
The bends in the stream
increase the stream
length 1 to 2 times
longer than if it was in a
straight line.
Channel straight;
waterway has been
channelized for a long
distance.
20 19 18 17 16
15 14 13 12 11
10 9 8 7 6
5 4 3 2 1 0.
8. Bank Stability
(score each bank)
SCORE	(LB)
SCORE	(RB)
Banks stable; evidence
of erosion or bank failure
absent or minimal; little
potential for future
problems. <5% of bank
affected.
Moderately stable;
infrequent, small areas of
erosion mostly healed
over. 5-30% of bank in
reach has areas of
erosion.
Moderately unstable; 30-
60% of bank in reach has
areas of erosion; high
erosion potential during
floods.
Unstable; many eroded
areas; "raw" areas
frequent along straight
sections and bends;
obvious bank sloughing;
60-I00%ofbankhas
erosional scars.
Left Bank 10 9
8 7 6
5 4 3 ¦ :
2, 1 0 >
Right Bank 10 9
8 7 6
5 4 3
. . 2' 1 0
9. Vegetative
Protection (score
each bank)
Note: determine
left or right side by
facing downstream.
SCORE	(LB)
SCORE	(RB)
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.
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.
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.
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.
Left Bank 10 9
8 7 6
5 4 3
2 1 . 0
Right Bank 10 9
8 7 6
5 4 3
2 1 0 .
10. Riparian
Vegetative Zone
Width (score each
bank riparian zone)
SCORE	(LB)
SCORE	(RB)
Width of riparian zone
>18 meters; human
activities (i.e., parking
lots, roadbeds, clear-cuts,
lawns, or crops) have not
impacted zone.
Width of riparian zone
12-18 meters; human
activities have impacted
zone only minimally.
Width of riparian zone 6-
12 meters; human
activities have impacted
zone a great deal.
Width of riparian zone
<6 meters: little or no
riparian vegetation due
to human activities.
Left Bank 10 9
8 7 6
5 4 , .. . 3 : '
2 1 .c 0.v.
Right Bank 10 9
8 7 6
5 " 4 3
2 1 -0
Total Score
A-l 0 Appendix A-l: Habitat Assessment and Physicochemical Characterization Field Data Sheets - Form 3
-------
APPENDIX A-2:
Periphyton Field and Laboratory Data Sheets
Form 1: Periphyton Field Data Sheet
Form 2: Periphyton Sample Log-In Sheet
Form 3: Periphyton Soft Algae Laboratory Bench Sheet (front and back)
Form 4; Periphyton Diatom Laboratory Bench Sheet (front and back)
Form 5: Rapid Periphyton Survey Field Sheet
Rapid Bioassessment Protocols For Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroin vertebrates, and Fish, Second Edition	A-l 1
-------
This Page Intentionally Left Blank
A-12
Appendix A-2: Periphyton Field and Laboratory Data Sheets
-------
PERIPHYTON FIELD DATA SHEET
STREAM NAME
LOCATION
STATION # RIVERMILE
STREAM CLASS
LAT LONG
RIVER BASIN
STORET#
AGENCY
INVESTIGATORS
LOT NUMBER
FORM COMPLETED BY
DATE
TIME AM PM
REASON FOR SURVEY
HABITAT TYPES
Indicate the percentage of each habitat type present
~	Sand-Silt-Mud-Muck % ~ Gravel-Cobble % ~ Bedrock %
~	Small Woodv Debris % ~ Large Woodv Debris % ~ Plants. Roots %
~	Riffle % QRun % ~ Pool %
~	Canopv %
SAMPLE
COLLECTION
Gear used ~ suction device ~ bar clamo sample ~ scraping ~ Other
How were the samples collected? ~ wading ~ from bank ~ from boat
If natural habitat collections, indicate the number of samples taken in each habitat type.
~	Sand-Silt-Mud-Muck % ~ Gravel-Cobble % ~ Bedrock %
~	Small Woodv Debris % ~ Large Woodv Debris % ~ Plants, Roots %
GENERAL
COMMENTS

QUALITATIVE LISTING OF AQUATIC BIOTA
Indicate estimated abundance: 0 = Absent/Not Observed, 1 = Rare (<5%), 2 = Common (5% - 30%),
3= Abundant (30% - 70%), 4 = Dominant (>70%)
Periphyton
0
1 2
3
4
Slimes
0
1 2
3
4
Filamentous Algae
0
1 2
3
4
Macroinvertebrates
0
1 2
3
4
Macroohvtes
0
1 2
3
4
Fish
0
1 2
3
4
Rapid Bioassessment Protocols For Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition - Form 1
A-13
-------
This Page Intentionally Left Blank
A-14
Appendix A-2: Periphyton Field and Laboratory Data Sheets
-------
page of
PERIPHYTON 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
~—»
Ul
Serial Code Example: P0754001(l)
P = Periphyton (B = Benthos, F = Fish) ¦ 0754 = project number ¦ 001 = sample number ¦ (1) = lot number (e.g., winter 1996 =1; summer 1996 = 2)
-------
This Page Intentionally Left Blank
A-16
Appendix A-2: Periphyton Field and Laboratory Data Sheets
-------
PERIPHYTON SOFT ALGAE LABORATORY BENCH SHEET (FRONT)
page	of
STREAM NAME
LOCATION
STATION #
RIVERMILE

STREAM CLASS
LAT
LONG

RIVER BASIN
STORET U
LOT#

AGENCY
COLLECTORS INITIALS
DATE

TAXONOMISTS INITIALS
DATE
SUBSAMPLE TARGET FOR SOFT ALGAE
~ 300
~ 400 ~ 500 ~ Other	

TAXA NAME
TALLY
CODE
# OF
CELLS
TCR




































































































Taxonomic certainty ratings (TCR) can be determined for each taxa or for the laboratory as a whole. The TCR scale is 1-5, with: 1 =
most certain and 5 = least certain. If rating is 3-5, give reason. The number of cells for filamentous algae is an estimate of relative
biomass.
Total No. Algal cells 	 Total No. Taxa 	
Rapid Bioassessment Protocols For Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition - Form 3	A-17
-------
PERIPHYTON SOFT ALGAE LABORATORY BENCH SHEET (BACK)
STREAM IDENTIFICATION CODE
DATE COUNTED
COUNTED TRANSECT LENGTH
COUNTED TRANSECT WIDTH
SIZEOFCOVERGLASS
TOTAL SAMPLE VOLUME
VOLUME OF SAMPLE ON COVERGLASS
SAMPLE DILUTION FACTOR
PROPORTION OF SAMPLE COUNTED
AREA OF SUBSTRATE SAMPLED
TOTAL NUMBER OF CELLS COUNTED
TOTAL ASSEMBLAGE CELL DENSITY
TAXONOMY
Explain TCR ratings of 3-5:
ID

Due
Other Comments (e.g. condition of nJgac):

OC: ~ YES QNO OC Checker

Algal recognition Q pass Q fail

Verification complete ~ YES ~ NO
General Comments (use this space to add additional comments):
A-18
Appendix A-2: Periphyton Field and Laboratory Data Sheets - Form 3
-------
PERIPHYTON DIATOM LABORATORY BENCH SHEET (FRONT)
page	of
STREAM NAME
LOCATION
STATION #
RIVERMILE
STREAM CLASS
LAT
LONG
RIVER BASIN
STORET #
LOT#
AGENCY
COLLECTORS INITIALS
DATE
TAXONOMISTS INITIALS
DATE
SUBSAMPLE TARGET FOR DIATOM ~ 300 ~ 400
~ 600 ~ Other

TAXA NAME
TALLY (# of valves)
CODE
# OF
CELLS
TCR




































































































Taxonomic certainty ratings (TCR) can be determined for each taxa or for the laboratory as a whole. The TCR scale is 1-5, with: 1 -
most certain and 5 = least certain. If rating is 3-5, give reason. The number of cells for filamentous algae is an estimate of relative
biomass.
Total No. Algal cells 	 Total No. Taxa 	
Rapid Bioassessment Protocols For Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macro invertebrates, and Fish, Second Edition - Form 4	A-19
-------
PERIPHYTON DIATOM LABORATORY BENCH SHEET (BACK)
TAXONOMY
Explain TCR ratings of 3-5:

ID


D
Other Comments (e.g. condition of algae):

QC: ~ YES ~ NO
OC Checker

Alga) recognition
~ pass ~ foil

Verification complete
~ YES ~ NO
General Comments (use this space to add additional comments):
A-20
Appendix A-2: Periphyton Field and Laboratory Data Sheets - Form 4
-------
RAPID PERIPHYTON SURVEY FIELD SHEET
STREAM NAME
LOCATION
STATION #
RIVERMILE
STREAM CLASS
LAT
LONG
RIVER BASIN
STORET #
LOT#
AGENCY
COLLECTORS INITIALS
DATE
TAXONOMISTS INITIALS
DATE
Macroalga #1 Maximum Length
Macroalga #2 Maximum Length
ASSESSED BY
GRID AREA
ID MACROALGA#!
ID MACROALGA #2
ID MICROALGA #1
ID MICROALGA #2
TRANSECT/
VIEW#
# DOTS IN
GRID AREA
MACROALGA
#1 DOTS
COVERED
MACROALGA
#2 DOTS
COVERED
# DOTS
MICROALGA
SUBSTRATE
MICROALGA #1
DOTS COVERED BY
THICKNESS RANK
0 0.5 1
MICROALGA #2
DOTS COVERED BY
THICKNESS RANK
0 0.5 1
TOTAL # DOTS AT SITE
I
to
General Comments:
-------
This Page Intentionally Left Blank
A-22
Appendix A-2: Periphyton Field and Laboratory Data Sheets
-------
APPENDIX A-3:
Beiithic Macroinvertebrate Field and Laboratory Data Sheets
Form 1: Benthic Macroinvertebrate Field Data Sheet
Form 2; Benthic Macroinvertebrate Sample Log-In Sheet
Form 3: Benthic Macroinvertebrate Laboratory Bench Sheet
Form 4: Preliminary Assessment Score Sheet (Pass)
Rapid Bioassessment Protocols For Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
A-23
-------
This Page Intentionally Left Blank
A-24
Appendix A-3: Benthic Macroinvertebrate Field and Laboratory Data Sheets
-------
BENTHIC MACROINVERTEBRATE FIELD DATA SHEET
STREAM NAME
LOCATION
STATION # RIVERMILE
STREAM CLASS
LAT LONG
RIVER BASIN
STORET #
AGENCY
INVESTIGATORS
LOT NUMBER
FORM COMPLETED BY
DATE
TIME AM PM
REASON FOR SURVEY
HABITAT TYPES
Indicate the percentage of each habitat type present
~	Cobble % ~ Snags % ~ Vegetated Banks % ~ Sand %
~	Submerged Macrophvtes % ~ Other ( ) %
SAMPLE
COLLECTION
Gear used ~ D-frame ~ kick-net ~ Other
How were the samples collected? ~ wading ~ from bank ~ from boat
Indicate the number of jabs/kicks taken in each habitat type.
~	Cobble ~ Snags ~ Vegetated Banks ~ Sand
~	Submerged Macroohvtes ~ Other ( )
GENERAL
COMMENTS

QUALITATIVE LISTING OF AQUATIC BIOTA
Indicate estimated abundance: 0 = Absent/Not Observed, 1 = Rare, 2 = Common, 3= Abundant, 4 = Dominant
Periphyton
0 1
2
3
4
Slimes
0 1
2
3
4
Filamentous Algae
0 I
2
3
4
Macroinvertebrates
0 1
2
3
4
Macrophvtes
0 1
2
3
4
Fish
0 1
2
3
4
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
0
2
3
4
Anisoptera
0
2
3
4
Chironomidae
0 1
2
3
4
Hydrozoa
0
2
3
4
Zygoptera
0
2
3
4
Ephemeroptera
0 1
2
3
4
Platyhelminthes
0
2
3
4
Hemiptera
0
2
3
4
Trichoptera
0 1
2
3
4
Turbellaria
0
2
3
4
Coleoptera
0
2
3
4
Other
0 1
2
3
4
Hirudinea
0
2
3
4
Lepidoptera
0
2
3
4





Oligochaeta
0
2
3
4
Sialidae
0
2
3
4





Isopoda
0
2
3
4
Corydalidae
0
2
3
4





Amphipoda
0
2
3
4
Tipulidae
0
2
3
4





Decapoda
0
2
3
4
Empididae
0
2
3
4





Gastropoda
0
2
3
4
Simuliidae
0
2
3
4





Bivalvia
0
2
3
4
Tabinidae
Culcidae
0
0
2
2
3
3
4
4





Rapid Bioassessment Protocols For Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition - Form 1
A-25
-------
This Page Intentionally Left Blank
A-26
Appendix A-3: Benthic Macroinvertebrate Field and Laboratory Data Sheets - Form J
-------
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page of
BENTHIC MACROINYERTEBRATE SAMPLE LOG-IN SHEET
Date
Collected
Collected
By
Number of
Containers
Preservation
Station
it
Stream Name and Location
Date
Received by
Lab
Lot Number
Date of Completion
sorting
mounting
identification



























































































































































































>
K>
Serial Code Example: B0754001(l)
B = Benthos (F = Fish; P = Periphyton) ¦ 0754 = project number ¦ 001 = sample number ¦ (l) = lot number (e.g., winter 1996 =1; summer 1996 = 2)
-------
This Page Intentionally Left Blank
A-28
Appendix AS: Benthic Macroihvertebrate Field and Laboratory Data Sheets
-------
BENTHIC MACROINVERTEBRATE LABORATORY BENCH SHEET (FRONT)
page of
STREAM NAME
LOCATION
STATION #
RIVERMILE
STREAM CLASS
LAT
LONG
RIVER BASIN
STORET #
AGENCY
COLLECTED BY
DATE
LOT #
TAXONOMIST
DATE
SUBSAMPLE TARGET ~ 100 Q200 Q300 ~ Other	
Enter Family and/or Genus and Species name on blank line.
Organisms
No.
LS
TI
TCR
Organisms
No.
LS
TI
TCR
Oligochaeta





Megaloptera















Hirudinea





Coleoptera















Isopoda




















Amphipoda





Diptera
















Decapoda




















Ephemeroptera















Gastropoda































Pelecypoda





Plecoptera

























Other

























Trichoptera




























































Hemiptera






























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:
I = immature; P = pupa; A = adult TI = Taxonomists initials
Total No. Organisms 		Total No. Taxa 	
Rapid Bioassessment Protocols For Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition - Form 3
A-29
-------
BENTHIC MACROINVERTEBRATE LABORATORY BENCH SHEET (BACK)
SUBSAMPLING/SORTING
INFORMATION
Sorter
Number of grids picked:
Time expenditure No. of organisms
Indicate the presence of large or obviously abundant organisms:
Date

OC: ~ YES ~ NO OC Checker
/ # organisms "V
# organisms M recovered by # organisms Tk % sorting
originally sorted M checker originally sorted ¦ efficiency
I - J
V /
a90%, sample passes
<90%, sample fails, action taken
TAXONOMY
ID
Explain TCR ratings of 3-5:
Other Comments (e.g. condition of specimens):
Date

OC: ~ YES ~ NO OC Checker
Organism recognition ~ pass ~ fail
Verification complete ~ YES ~ NO
General Comments (use this space to add additional comments):
A-30
Appendix A-3: Benthic Macroinvertebrate Field and Laboratory Data Sheets - Form 3
-------
PRELIMINARY ASSESSMENT SCORE SHEET
(PASS)
		page	of
STREAM NAME
LOCATION
STATION H
RTVERMiLE

STREAM CLASS
LAT
LONG

RIVER BASIN
STORET #
AGENCY
COLLECTED BY
DATE

LOT#
NUMBER OF SWEEPS
HABITATS: ~ COBBLE
~ SHOREZONE
Q SNAGS
~ VEGETATION
Enter Family and/or Genus and Species name on blank line.
Organisms
No.
LS
TI
TCR
Organisms
No.
LS
TI
TCR
Oligochaeta





Megaloptera















Hirudinea





Coleoptera















Isopoda




















Amphipoda





Diptera















Decapoda




















Epbemeroptera















Gastropoda






























Pelecypoda





Plecoptera

























Other

























Trichoptera













































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: I = immature; P = pupa; A = adult TI = Taxonomists
initials





Hemiptera











Site Value
Target Threshold
If 2 or more metrics are a target threshold, site is
HEALTHY
Total No. Taxa


EPT Taxa


If less than 2 metrics are within target range, site is
SUSPECTED IMPAIRED
Tolerance Index


Rapid Bioassessment Protocols For Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition - Form 4
A-31
-------
This Page Left Intentionally Blank
A-32
Appendix A-3: Benthic Macroinvertebrate Field and Laboratory Data Sheets
-------
Appendix A-4:
Fish Field and Laboratory Data Sheets
Form 1: Fish Sampling Field Data Sheet
Form 2: Fish Sample Log-In Sheet
Rapid Bioassessment Protocols For Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
A-33
-------
This Page Intentionally Left Blank
A-34
Appendix A-4: Fish Field and Laboratory Data Sheets
-------
FISH SAMPLING FIELD DATA SHEET (FRONT)
		EM®	
STREAM NAME
LOCATION
STATION # RIVERMILE
STREAM CLASS
LAT LONG
RIVER BASIN
STORET#
AGENCY
GEAR
INVESTIGATORS
FORM COMPLETED BY
DATE
TIME AM PM
REASON FOR SURVEY
SAMPLE
COLLECTION
How were the fish captured? ~ back pack ~ tote barge ~ other
Block nets used? ~ YES ~ NO
Sampling Duration Start time End time Duration
Stream width (in meters) Max Mean
HABITAT TYPES
Indicate the percentage of each habitat type present
~	Riffles % ~ Pools % ~ Runs % ~ Snags %
~	Submerged Macrophytes % ~ Other ( ) %
GENERAL
COMMENTS

SPECIES
TOTAL
(COUNT)
OPTIONAL: LENGTH (mm)AVEIGHT (g)
(25 SPECIMEN MAX SUBSAMPLE)
ANOMALIES*
D
E
F
L
M
S
T
z















r*. ^





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-r 1 \ '






























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Rapid Bioassessment Protocols For Use in Streams and Wadeable Rivers: Peripkyton, Benthic
Macroinvertebrates, and Fish, Second Edition - Form I
A-35
-------
FISH SAMPLING FIELD DATA SHEET (BACK)
SPECIES
TOTAL
(COUNT)
OPTIONAL: LENGTH (mm)/WEIGHT (g)
(25 SPECIMEN MAX SUBSAMPLE)
ANOMALIES*
D
E
F
L
M
S
T
Z















r"




































f
5*





. . i






























-



















































































































































•










































¦,















ANOMALY CODES: D * deformities; E = eroded fins; F ~ fungus; L = lesions; M = multiple DELT anomalies; S = emaciated; Z = other
A-3<5	Appendix A-4: Fish Field and Laboratory Data Sheets - Form 1
-------
mm	2L
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






































































































































































































Serial Code Example: F0754001(l)
F = Fish (B = Benthos; P = Periphyton) ¦ 0754 = project number ¦ 001 = sample number ¦ (1) = lot number (e.g., winter 1996 =1; summer 1996 = 2)
-------
This Page Intentionally Left Blank
i
A-38
Appendix A-4: Fish Field and Laboratory Data Sheets
-------
Appendix B:
Regional Tolerance Values,
Functional Feeding Groups and
Habit/Behavior Assignments for
Benthic Macroinvertebrates
Rapid Bioassessment Protocols for Use in Streams and Wadeahle Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
B-l
-------
This Page Intentionally Left Blank
Appendix B: Regional Tolerance Values, Functional Feeding Groups,
B-2	and Habit/Behavior Assignments for Benthic Macroinvertebrates
-------
Appendix B
Appendix B is a list of selected benthic macroinvertebrates of the United States in phylogenetic order.
Included are the Taxonomic Serial Number (TSN) and the Parent Taxonomic Serial Number for each of
the taxa listed according to the Integrated Taxonomic Information System (ITIS). The ITIS generates a
national taxonomic list that is constantly updated and currently posted on the World Wide Web at
. If you are viewing this document electronically, this page is linked to the ITIS web
site.
This Appendix displays regional tolerance values, primary and secondary functional feeding group
information, and primary and secondaiy habit designations for selected benthic macroinvertebrates. In
an effort to provide regionally accurate tolerance information, lists included in this Appendix were taken
from the following states (and workgroup): Idaho (Northwest), Ohio' (Midwest), North Carolina
(Southeast), Wisconsin (Upper Midwest), and the MACS workgroup (Mid-Atlantic Coastal Streams).
Tolerance values are on a 0 to 10 scale, 0 representing the tolerance value of an extremely sensitive
organism and 10 for a tolerant organism. For functional feeding group and habit/behavior assignments,
primary and secondary designations are listed, if both are known. Each characterization is based on the
organisms' larval qualities, except a group of beetles (listed as 'adult') that are aquatic as adults. The
following are lists of the abbreviations used in this appendix.
Sources For Benthic Tolerance, Functional Feeding Group, and Habit/Behavior
Designations(a)
ID= Idaho DEP (Northwest)
OH= Ohio EPA (Midwest)
NC = North Carolina DEM (Southeast)
WI = Wisconsin DNR (Upper Midwest)
MACS= Mid-Atlantic Coastal Streams Workgroup (NJ DEP, DE DNREC, MD DNR, VA
DEC, NC DEM, SC DHES)
(a) Habit/Behavior information is primarily based on Merritt and Cummins (1996) and
pertains to insect larval forms (except for Dryopidae adults) and is mostly at genus level.
'Ohio traditionally uses an inverted 60-point scale compared to the other states in this list. In order to
be comparable to the other listed states, the Ohio values were converted to a 0-10 scale as discussed above.
Functional Feeding Designations
PA=parasite
PR=predator
OM=omnivore
GC=gatherer/collector
FC=filter/collector
SC=scraper
SH=shredder
PI=piercer
Habit/Behavior designations
cn=clinger
cb=climber
sp=sprawler
bu=burrower
sw=swimmer
dv=diver
sk=skater
Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
B-3
-------
This Page Intentionally Left Blank
Appendix B: Regional Tolerance Values, Functional Feeding Groups,
B-4	and Habit/Behavior Assignments for Benthic Macroinvertebrates
-------
Regional Tolerance Values, Functional Feeding Groups, and Habit/Behavior
Assignments for Benthic Macroinvertebrates
Parent
TSN
TSN
Scientific Name
R
i
-5
3 O
<8 5.
n>
Upper Midwest g.
(WI) g
Midwest 2.
(OH) |
ce Valu
3
JZ
Id
z o
	 „
Mid-Atlantic
(MACS)
Func
Feedinj
t9
e
•c
Cu
tional
Group
I?
c
o
O
CD
Ha
Beh
b
e
ait/
ivior
c
o
8
-------



Regional Tolerance Values










Functional
Habit/




1
"O
s



Feeding Group
Behavior
Parent
TSN
TSN
Scientific Name
Southeast
(NC)
M
4)
5 =
V>
a
£
x:
O
'O
c
*5 oo
< CJ
1
is
T3
c
£•
is
c




2S
I§

•c
o.
8
(/>
X
Q.
o
QJ
81381
81385
Corbicula





FC



81385
81387
Corbicula fluminea
6.3

3.2


FC



81385
81386
Corbicula manilensis





FC



81333
81335
Mytilopsis leucophaeata





FC



80384
81388
Pisidiidae



8

GC




81389
Sphaeriidae



8
8
FC



81388
81436
Eupera










205642
Byssanodonta cubensis (= Eupera)





FC



81436
81438
Eupera cubensis





FC



81388
81427
Musculium




5
FC



81427
81430
Musculium lacustre




5
FC





Byssanodonta (= Eupera)





FC



81427
81434
Musculium securis




5
FC



81427
81428
Musculium transversum









81388
81400
Pisidium
6.8

4.6
8
8
FC



81400
81405
Pisidium casertanum



8

SC



81400

Pisidium lilljborgi




8
FC



81400
81406
Pisidium comprcssum



8

FC



81400
81402
Pisidium dubium





FC



81400
81408
Pisidium fallax



8

FC



81400
81403
Pisidium idahoense



8

FC



81400
81424
Pisidium punctatum



8

FC



81400
81425
Pisidium punctiferum





FC



81400
81420
Pisidium walked



8

FC



81388
81391
Sphaerium
7.7

4.7
6

GC
FC


81391
81395
Sphaerium patella



8

FC



81391
81398
Sphaerium striatinum





FC



69458
69459
Gastropoda



7

SC



76437
76568
Ancylidae



6

SC



76568
76569
Ferrissia
6.9

5.2
6
7
SC



76569
76573
Ferrissia hendersoni





SC



76569
76572
Ferrissia rivularis





SC



76569
76575
Ferrissia walked




7
SC



76585
76586
Hebetancylus excentricus





SC



76568
76576
Laevapex





SC



76576
76578
Laevapex diaphanus





SC



76576
76577
Laevapex fuscus
7.3

6.7


SC



76576
76579
Laevapex peninsulae





SC



76476
76477
Lanx



6

GC



76437
76483
Lymnaeidae


6.9
6
6
SC



76483
L76497
Fossaria


2.6
8

SC



76483
76484
Lymnaea



8

SC



76483
76528
Pscudosuccinea





SC



76528
76529
Pseudosuccinea columella
7.2




SC



76483
76525
Radix









76483
76534
Stagnicola
8


10
7
SC



B-6
Appendix B: Regional Tolerance Values, Functional Feeding Groups,
and Habit/Behavior Assignments for Benthic Macroinvertebrates
-------
Parent
TSN
TSN
Scientific Name
Southeast
(NC)
30
egional
W
O
*
2
Her
Toleran
to
U
Is
ss
ce Valu
v>
II
Mid-Atlantic "
(MACS)
Func
Feeding
•c
CL
tional
Group
£•
CB
•o
§
Ha
Beh
b
-------
Parent
TSN
TSN
Scientific Name
Southeast
50
o
Upper Midwest
(WI) g
Toleran
8
is
SS
ce Valu
-------



Regional Tolerance Values










Functional
Feeding Group
Habit/
Behavior
Parent
TSN
TSN
Scientific Name
Southeast
(NC)
•f
"O
§
8.c
at
Midwest
(OH)
Northwest
(ID)
Mid-Atlantic
(MACS)
! primary
secondary
primary
secondary
68898
555636
Dero botiytis





GC



68898
68904
Dero digitata





GC



68898
68902
Dero flabelliger





GC



68898
68912
Dero furcata





GC



68898
68924
Dero lodeni





GC



68898
68900
Dero nivea





GC



68898
68907
Dero obtusa





GC



68898
68923
Dero pectinata





GC



68898
68903
Dero trifida





GC



68898
68915
Dero vaga





GC



69003
69004
Haemonais waldvogeli





GC




68946
Nais
9.1








68946
68949
Nais behningi





GC



68946
68950
Nais communis/





GC



68946
68952
Nais elinguis ~~





GC



68946
68954
Nais pardalis





GC



68946
68956
Nais pseudobtusa





GC



68946
68957
Nais simplex





GC



68946
68959
Nais variabilis





GC



68862
68863
Paranais litoralis





GC



68854
68876
Pristina
9.9




GC



68876
68879
Pristina aequiseta





GC



68876
68880
Pristina breviseta





GC



68876
68881
Pristina foreli





GC



68876
68894
Pristina leidyi





GC



68876
68893
Pristina longisoma





GC



68876
68887
Pristina osborni





GC



68876
68891
Pristina plumaseta





GC



68876
68878
Pristina sima





GC



68876
68895
Pristina synclites





GC



68854
69024
Pristinella





GC



69024
69030
Pristinella jenkinae





GC



69024
69025
Pristinella longisoma





GC



69024
69026
Pristinella osborni





GC



68854
68855
Slavina





GC



68855
68856
Slavina appendiculata
7.1




GC



68984
68985
Specaria josinae





GC



69017
69018
Stephensoniana trivandrana





GC



68871
68873
Stylaria fossularis




8
GC



68871
68872
Stylaria lacustris
8.5




GC



68854
69009
Vejdovskyella





GC



69009
69010
Vejdovskyella comata





GC



68509
69041
Opistocystidae









68509
68585
Tubificidae



10
10
GC




68588
Peloscolex
8.8








68679
68683
Aulodrilus amen'canus





GC



Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
B-9
-------
Parent
TSN
TSN
Scientific Name
R
(A
C3
O
3 U
C9
Upper Midwest —
(WI) g
Midwest 2.
(OH) |
n
Northwest
(ID) g.
Mid-Atlantic
(MACS)
Func
Feedin,
f
a.
O g 1
secondary 3 » |
_ . 	¦§ 	1
Ha
Beh<
X
CL
< s:
secondary °
68679
68682
Aulodrilus limnobius
5.2




GC



68679
68680
Aulodrilus pigueti
4.7




GC



68679
68684
Aulodrilus pluriseta




8
GC



68619
68621
Branchiura sowerbyi
8.4




GC



68585
68745
Haber









68745
68746
Haber speciosus
2.8








68660
68662
Ilyodrilus templetoni
9.4




GC



68808
68809
Isochaetidcs curvisetosus
7.2




GC



68808
68810
Isochaetides freyi
7.6








68585
68638
Lirrwodrilus
9.6




GC



68638
68653
Limnodrilus angustipenis





GC



68638
68652
Linmodrilus cervix
10








68638
68639
Limnodrilus hoffmeisteri
9.8




GC



68638
68649
Limnodrilus profundicola





GC



68638
68644
Limnodrilus udekemianus
9.7




GC



68780
68610
Spirosperma fcrox





GC



6S780
68781
Spirosperma nikoiskyi
7.7








68585
68751
Psammoryctides









68751
68752
Psammoryctides convolutus





GC



68793
68794
Quistradrilus multisetosus




10
GC



68839
68844
Rhyacodrilus sodalis



10

GC



68585
68780
Spirosperma





GC



68780
68782
Spirosperma carolinensis




10
GC



68585
68622
Tubifex



10

GC



68622
68623
Tubifex tubifex
10




GC



68439
68440
Lumbriculidae
7.3


8

GC



68440
68473
Eclipidrilus




8




68473
68476
Eclipidrilus palustris





GC



68440
68441
Lumbriculus





GC



68441
68447
Lumbriculus inconstans





GC



68441
68444
Lumbriculus variegata





GC



68422
69290
Hirudinea



10

PR



69406
69407
Hirudinidae



7

PR



69407
69408
Hacmopsis



10

PR



69408
69412
Haemopsis marmorata





PR



69418
69421
Macrobdella ditetra









69407
69430
Percymoorensis



10

PR



69407
69423
Philobdella









69437
69438
Erpobdellidae



8

PR



69438
69439
Dina



8

PR



69438
69449
Moorcobdella
7.8




PR



69449
69454
Mooreobdclla tetragon
9.7




PR



69455
69456
Nephelopsis obscura





PR



69295
69357
Glossiphoniidae



8

PR



69388
69389
Alboglossiphonia heteroclita





PR



69380
69390
Qlossiphonia heteroclita









B-10
Appendix B: Regional Tolerance Values, Functional Feeding Groups,
and Habit/Behavior Assignments for Benthic Macroinvertebrates
-------
Parent
TSN
TSN
Scientific Name
Southeast
	 _ 73
n
Upper Midwest "3.
(Wl) g
Midwest 2.
(OH) |
ce Valu
to
fl
;s
o
¦a
c
a ^
5= on
•o <•
Func
Feeding
b
a
E
•c
O.
ional
Group
&
¦3
o
s
1/)
Ha
Behi
•e
o.
secondary °
69357
69358
Batracobdella





PA



69358
69359
Batracobdella paludosa





PA



69357
69380
Glossiphonia





PR



555637
555638
Desserobdella phalera





PR



69380
69381
Glossiphonia complanata





PR



69357
69396
Helobdella



6

PA
PR



204822
Gloiobdella elongata





PR



69396
69397
Helobdella elongata
9.9




PR



69396
69401
Helobdella fusca





PA



69396
69398
Helobdella stagnalis
6.7




PR



69396
69399
Helobdella triserialis
8.9




PA



69357
69363
Placobdella



6

PR



69363
69367
Placobdella multilineata





PR



69363
69364
Placobdella papillifera
9




PA



69363
69365
Placobdella parasitica
6.6




PA




69374
Batracobdella phalera
7.1








69363
69372
Placobdella translucens





PA



69357
69375
Theromyzon



10

PR



69315
69316
Myzobdella lugubris





PR



69296
69304
Piscicola



10

PR



69304
69309
Piscicola salmositica



7

PR





Acari





PR





Acariformes





PR





Corticacarus delicatus



8

PR



83538
83544
Oribatei











Parasitengona











Protzia califomensis



8

PR



82754
82769
Trombidiformes









82862
82864
Arrenurus





PR



82864
82907
Arrenurus apetiolatus





PR



82864
82953
Arrenurus bicaudatus





PR



82864
205790
Arrenurus hovus





PR



82864
205791
Arrenurus problecomis





PR



82864
205792
Arrenurus zapus





PR



83434
83435
Albia





PR



83176
83177
Clathrosperchon





PR



82770
82771
Halacaridae









82770
83122
Hydrachnidae









83122
83123
Hydrachna





PR



83224
83225
Hydrodroma





PR



82770
83281
Hygrobatidae



8

PR



83281
83282
Atractides





PR



83281
83297
Hygrobates





PR



83297
83310
Hygrobates occidentalis



8

PR



83499
83500
Geayia









83499
83502
Krendowskia









Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
B-ll
-------
Parent
TSN
TSN
Scientific Name
Southeast
S3
figional
-------
Parent
TSN
TSN
Scientific Name
Southeast
(NC)
.. 50
egionai
CO
£
*o
s
<5 ^
a ~
Midwest 2.
(OH) 3
3
ce Valut
<8
s
o Q
2 c.
Mid-Atlantic "
(MACS)
Func
Feeding
ir
£
'C
O.
tional
Group
&
•o
P
o
o
8
Ha
Beh;
b
CO
E
*n
o.
5. |
secondary °
97490
97498
Procambarus alleni









97490
97514
Procambarus fallax









97490
97555
Procambarus pygmaeus









97490
97566
Procambarus spiculifer









89802
93294
Amphipoda



4

GC



93584
93589
Corophium





FC



93589
93594
Corophium lacustre





FC



93641
93642
Grandidierella bonnieroides





GC



95080
95081
Crangonyx
8



4
GC



95081
95088
Crangonyx richmondensis





OM



95081
193517
Crangonyx serratus
8.1




GC



93295
93745
Gammaridae





GC



93745
93747
Anisogammarus



4

GC




97160
Argis
8.7
8







93745
93773
Gammarus



4

OM



93773
93780
Gammarus fasciatus
6.9



6
GC



93773
93789
Gammarus lacustris





OM



93773
93781
Gammarus tigrinus





GC




93862
Stygonectes









93947
93949
Synurella chamberlaini





GC



94022
94025
Hyalella



8

GC



94025
94026
Hyalella azteca
7.9
8


8
GC



93295
95032
Talitridae



8

GC



89802
92120
Isopoda



8

GC



92148
92149
Cyathura polita





GC



92650
92657
Asellidae





GC



92657
92658
Asellus
9.4
8

8

GC



92658
92659
Asellus occidentals



8

GC



92657
92686
Caecidotea



8
6
GC



92686

Caecidotea attenuatus




6




92686

Caecidotea communis



6

GC



92686
92701
Caecidotea forbesi




6




92686
92692
Caecidotea racovitzai




6




92692
92695
Caecidotea racovitzai australis





GC



92657
92666
Lirceus
7.7



8
GC




92977
Munna reynoldsi





GC



92973
92976
Uromunna reynoldsi





GC



93207
93209
Probopyris floridensis





GC



93132
93133
Probopyus pandalicola





GC



92224
92225
Cirolanidae





GC



92225
541967
Anopsilana





GC



92345
92348
Cassidinidea ovalis





GC



92283
92301
Exosphaeroma





GC



92283
92337
Sphaeroma





GC



92337
92338
Sphaeroma destructor





GC



92337
92342
Sphaeroma terebrans





GC



Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
B-13
-------



Regional Tolerance Values










Functional
Habit/




¦5



Feeding Group
Behavior
Parent
TSN
TSN
Scientific Name
Southeast
(NC)
Upper Midwe
(WI)
Midwest
(OH)
Northwest
(ID)
Mid-Atlantic
(MACS)
primary
secondary
primary
03
"O
c
o
U
0>
206378
206379
Oniscus asellus









92623
92624
Edotea montosa





GC



92564
92588
Idotea





GC



89802
89807
Mysidacea









89856
90138
Mysidopsis





FC



89856
90041
Mysis









90275
90277
Taphromysis bowmani





FC



89802
91061
Tanaidacea





FG




92068
Hargeria rapax





FC



92026
92067
Leptochelia rapax










91502
Tanais cavolinii (part)










91396
Tanais cavolinii (part)










91400
Tanais cavolinii (part)










91519
Tanais cavolinii (part)









83677
85257
Copepoda



8

GC



83677
84195
Ostracoda



8

GC



83767
83832
Cladocera



8

FC



83872
83873
Daphnia



8

FC



89599
89600
Balanus





FC



89600
89621
Balanus eburneus





FC



85780
85801
Diaptomus pribilofensis









85257
88530
Cyclopoida



8

FC



84409
84763
Entocytheridae









82697
99208
Insecta









99209
992^7
Collembola



10

GC



99239
99240
Podura





GC



99240
99241
Podura aquatica









99917
99918
Hypogastrura





GC



99238
99245
Isotomidae





OM



99245
99246
Isotomurus





GC



99246
99247
lsotomurus palustris





GC



99238
99643
Entomobryidae





GC



100257
100258
Sminthuridae









100258
100402
Bourletiella





GC



100402
100436
Bourletiella spinata









100500
100502
Ephcmeroptcra





GC





Polymitarcidae



2

GC



101569
101570
Ephoron



2

GC

bu

101570
101572
Ephoron leukon
1.5
2







101459
I0I467
Cacnidae



7

GC



101467
101468
Brachycercus
3.5
3



GC



101468
101475
Brachycercus maculatus





GC



101468
101477
Brachycercus prudens



3

GC



101467
101478
Caenis
7.6
7
3.1
7
7
GC

sp
cb
101478
101480
Caenis arnica





OM



101478
101488
Caenis latipcnnis



7

GC
SC


B-14
Appendix B: Regional Tolerance Values, Functional Feeding Groups,
and Habit/Behavior Assignments for Benthic Macroinvertebrates
-------
Parent
TSN
TSN
Scientific Name
Southeast
^C>
50
CD
Upper Midwest "2.
(WI) g
Midwest SL
(OH) |
ce Valu
C/V
ll
	
(n
Mid-Atlantic
(MACS)
Func
Feedin
b
CO
E
¦c
o.
tional
> Group
CO
•a
c
o
o
o
-------



Regional Tolerance Values










Functional
Habit/




Upper Midwest
(WI)



Feeding Group
Behavior
Parent
TSN
TSN
Scientific Name
Southeast
(NC)
Midwest
(OH)
Northwest
(ID)
Mid-Atlantic
(MACS)
primary
secondary
primary
secondary
100755
100873
Ccntroptilum
6.3
2
2.7
2
2
GC



100873
100884
Ccntroptilum hobbsi





OM



100873
100897
Ccntroptilum viridocularis





OM



100755
100756
Cloeon
7.4
4
3.5


OM

sw
cn
100756
100758
Cloeon rubropictum





OM





Diphetor



5

GC

sw
cn


Diphetor hageni


2.3
5

GC





Fallccon quilleri





GC




100794
Heterocloeon
3.6




SC

sw
cn


Labiobaetis



6

QC

sw
cn


Labiobaetis frondalis











Labiobaetis propinquus



6

GC




100899
Paracloeodes
8.7




SC




206622
Procloeon





OM
GC
sw
cn
206622
206617
Procloeon rubropictum





OM



206622
206623
Procloeon viridocularis





OM '



100755
100771
Pscudocloeon
4.4
4
1.7
4

SC



100771
100776
Pseudocloeon bimaculatum





OM



100771
100783
Pscudocloeon parvulum





OM



100771
100784
Pseudocloeon punctiventris





OM '





Ametropodidae









101073
101074
Ametropus





GC

bu

100503
100504
Heptageniidae



4

SC



100504
100598
Cinygma



4

SC

cn

100598
100600
Cinygma integrum





SC



100504
100557
Cinygmula



4

SC

cn

100557
100570
Cinygmula subaequalis
0








100504
100626
Epeorus
1.2
0

0

SC

cn

100626

Epeorus iron



0

SC



100626

Epeorus ironopis



1

SC



100626
100629
Epeorus albertac



0

SC



100626
100632
Epeorus deceptivus



0

SC



100626
100651
Epeorus dispar
1







•
100626
100635
Epeorus grandis



0

SC



100626
100637
Epeorus longimanus



0

SC



100626
100642
Epeorus pleuralis
2








100626
100645
Epeorus rubidus
1.4








100627
100636
Ironopsis grandis



3

SC



100504
100602
Heptagenia
2.8
3

4

SC

cn
sw
100602
100694
Heptagenia criddlci





SC



100602
100608
Heptagenia diabasia


1.9






100602
100604
Heptagenia elegantula



4

SC



100602
100610
Heptagenia flavescens





OM



100602
100612
Heptagenia julia
0.5








100602
100616
Heptagenia marginalis
2.5








100602
100619
Heptagenia pulla
2.3








B-16
Appendix B: Regional Tolerance Values, Functional Feeding Groups,
and Habit/Behavior Assignments for Benthic Macroinvertebrates
-------
Parent
TSN
TSN
Scientific Name
Southeast
50
egional
•Si
£
T3
s
«3 <	t
Q. —^
St
Toleran
VI
1 —
3 as
!> S
ce Valu
CD
S
o Q
Z C.
Mid-Atlantic
(MACS)
Func
Feedin;
is
e
•c
o.
tional
Group
5a
•u
c
o
o
a>
Ha
Beh
Ss
£
*c
o.
i -¦
secondary °
100602
100620
Heptagenia simpliciodes





SC



100504
100666
Ironodes



4

sc

cn

100504
100676
Leucrocuta
0
1
2.4
1

SC
GC
cn

100676

Leucrocuta aphrodite
2.5
1







100676
100677
Leucrocuta hebe


2.7






100676
100679
Leucrocuta maculipennis


2.1






100504
100692
Nixe



4

sc
GC
cn

100692

Nixe simplicioides



2

SH



100692
100693
Nixe criddlei



2

SH



100692
100705
Nixe perfida


5.1






100504
100572
Rhithrogena
0.4
0

0

SC

cn

100572
100577
Rhithrogena arnica
0








100572
100579
Rhithrogena exilis
0








100572
100595
Rhithrogena fuscifrons
0








100572
100583
Rhithrogena hageni





GC



100572
100575
Rhithrogena morrisoni





SC



100572
100589
Rhithrogena robusta





GC



100504
100713
Stenacron


3.1

4
SC

cn

100713
100735
Stenacron Carolina
1.7








100713
100739
Stenacron floridense





OM



100713
100714
Stenacron interpunctatum
7.1
7



OM



100713
100736
Stenacron pallidum
2.9








100504
100507
Stenonema



2
4
SC

cn

100507
100513
Stenonema carlsoni
2.1








100507
100514
Stenonema exiguum


1.9


OM



100507
100516
Stenonema femoratum
7.5
5
3.1






100507
100521
Stenonema integrum
5.5
4



OM



100507
100527
Stenonema ithaca
4.1








100507

Stenonema lenati
2.3








100507
100530
Stenonema mediopunctatum
1.7
3
1.9






100507
100531
Stenonema meririvulanum
0.3








100507
206616
Stenonema mexicanum integrum


2.6


OM



100507
100532
Stenonema modestum
5.8
1



SC



100507
100536
Stenonema pudicum
2.1








100507
100509
Stenonema pulchellum


2.3






100507
100541
Stenonema smithae





OM



100507
100542
Stenonema terminatum
4.5
4
2.3






100507
100548
Stenonema vicarium
1
2
2.3






100503
100951
Siphlonuridae



7

GC




100953
Siphlonurus
2.6
7

7

GC

sw
cb
100953
100955
Siphlonurus occidentalis



7

GC
SC




Acanthametropodidae









100951
100996
Ameletus



0

GC

sw
cb
100996
101019
Ameletus celer



0

GC
SC


100996
101009
Ameletus lineatus
2.1
0







100996
101012
Ameletus similior





GC



Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
B-17
-------



Regional Tolerance Values






Upper Midwest
(Wl)



Functional
Feeding Group
Habit/
Behavior
Parent
TSN
TSN
Scientific Name
Southeast
(NC)
Midwest
(OH)
Northwest
(ID)
1 Mid-Atlantic
(MACS)
primary
secondary
la
s
'C
ex
secondary
100996
101005
Ameletus connectus





GC



100996
101006
Ameletus cooki



0

GC



100996
101013
Ameletus sparsatus





GC



100996
101002
Ameletus validus





GC



100996
101003
Ameletus velox



0

GC



101094
101232
Ephcmcrellidac



1

GC



101232
101338
Attend la



3

GC



101338
101340
Attenella attenuata
2.6
3







101338
101345
Attenella delantala



3

GC



101338
101343
Attenella margarita





GC



101232
101347
Caudatella



1

GC

cn

101347

Caudatella cascadia



1

GC



101347

Caudatella edmundsi





SC



101347
101351
Caudatella hcterocaudata





GC



101347
101348
Caudatella hystrix





SC





Caurinella



0

GC





Caurinetla idahoensis



0

GC



101232
101365
Drunella



0

PR

cn
sp
101365

Drunella allegheniensis
1.3








101365
101389
Drunella coloradensis





PR



101365

Drunella conestce
0








101365
101366
Drunella comutella
0








101365
101368
Drunella doddsi





SC



101365
101392
Drunella flavilinea





SC



10136S
101370
Drunella grandis





GC



101365
185972
Drunella lata
0.1








101365

Drunella pelosa





SC



101365
101385
Drunella spinifera





PR



101365
185974
Drunella tuberculata
0.2








101365
185973
Drunella walkcri
1








101365

Drunella \vayah
0








101232
101233
Ephemerella


2.9
1

GC

cn
sw
101233
101251
Ephemerella alleni





GC



101233
101255
Ephemerella aurivillii





GC



101233
101259
Ephemerella bemeri
0








101233
101262
Ephemerella catawba
4
1







101233
101280
Ephemerella hispida
0.6








101233
101239
Ephemerella inermis





SH



101233
101240
Ephemerella infrequens



,

GC



101233
101282
Ephemerella invaria
2.2
1







101233
101285
Ephemerella lacustris



1

GC



101233
101291
Ephemerella needhami
0
2







101233
101296
Ephemerella rotunda
2.8




OM



101233
101299
Ephemerella septentrionalis
2








101233
101305
Ephemerella trilineata





OM



101232
101324
Eurylophella


2.1

4
SC

cn
sp
B-18
Appendix B: Regional Tolerance Values, Functional Feeding Groups,
and Habit/Behavior Assignments for Benthic Macroinvertebrates
-------
Parent
TSN
TSN
Scientific Name
Southeast
. . . . ... 	 . 50
1 £
Upper Midwest  ^
72 K
SS
ce Value
-------



Regional Tolerance Values











Functional
Habit/




Upper Midwest
(WI)



Feeding Group
Behavior
Parent
TSN
TSN
Scientific Name
Southeast
(NC)
Midwest
(OH)
Northwest
(ID)
Mid-Atlantic
(MACS)
primary
secondary
primary
secondary
101494

Baetisca bemeri
0.6








101494
101499
Baetisca Carolina
3.6
5







101494
101503
Baetisca gibbera
1.4








101494
101495
Baetisca obesa





OM



101494
101506
Baetisca rogersi





OM





Metretopodidae











Siphloplectron
3.1
2


2
PR

sw
cn


Isonychiidae









101029
101041
Isonychia
3.8
2
1.9

2
FC

sw
cn
101041
101069
Isonychia arida









101041
101060
Isonychia sayi









101041
101062
Isonychia sicca








. . _


Neocphemeridae









101460
101461
Neoephemera





GC

sp
cn
101461
101463
Neocphemera compressa





GC



101461
101464
Neoephemera purpurea
2.1








101461
101465
Neoephemera youngi





GC



101523
101524
Dolania americana







bu



Anthopotamus


3.2







101510
Pota man thus
1.6
4







109215
109216
Coleoptora





PR



111952
111953
Amphizoa



1

PR

cn

109226
109234
Carabidae



4

PR



109234
111436
Chlaenius









109226
111963
Dytiscidae



5

¦ PR



112072
112073
Agabetes acuductus





PR



111963
111966
Agabus



8
5
PR

sw
dv
111963
112319
Bidessonotus







sw
cb
111963
112322
Bidessus









111963
112362
Brachyvatus







sw
cb
111963
112136
Celina




5
PR

sw
dv
112136
112142
Celina contiger





PR




112379
Colymbetes



5

PR

sw
dv
111963
112561
Copelatus
9.1



5
PR

sw ¦
dv
112561
112567
Copelatus caelatipennis





PR



111963
112371
Coptotomus
9




PR

sw
dv
112371
112375
Coptotomus interrogates





PR



111963
112364
Cybister





PR

sw
dv
111963
112153
Deronectes



5

PR

sw

112153

Deronectes striatellus





PR



111963
112159
Derovatellus







sw
cb
111963
112145
Desmopachria



5

PR

sw
cb

112118
Dytiscus



5

PR

sw
dv
111963
112172
Hydaticus



5

PR

sw
dv
111963
112390
Hydroporus
8.9

4.1
5
5
PR

sw
cb
112390
112423
Hydroporus mellitus
1.8








B-20
Appendix B: Regional Tolerance Values, Functional Feeding Groups,
and Habit/Behavior Assignments for Benthic Macroinvertebrates
-------
Parent
TSN
TSN
Scientific Name
Southeast 1
_ 50 1
h>
Upper Midwest "E.
(WI) g
Toleran
V>
ft>
•§ X
S&
ce Valu

s
fa
z &
Mid-Atlantic
(MACS)
t?
primary g. 21
3'3
tional
Group
b
fJ
*o
c
o
o
4>
Ha
Beh
£
•E
CL
-------



Regional Tolerance Values










Functional
Habit/




C/5



Feeding Group
Behavior
Parent
TSN
TSN
Scientific Name
Southeast
(NC)
1
IE
Midwest
(OH)
Northwest
(ID)
Mid-Atlantic
(MACS)
primary
1
secondary
i
primary
secondary
111923
111930
Peltodytes oppositus









111923
111932
Peltodytes sexmaculatus









109226
112606
Noteridae





PR

cb

112606
112623
Hydrocanthus
6.9








112623
112626
Hydrocanthus iricolor





OM



112623
112624
Hydrocanthus oblongus





OM

bun

112606
112621
Notomicrus









112636
193587
Suphis inflatus







cb

112606
112607
Suphisellus





OM



112607
112614
Suphisellus floridanus





OM



112607
112613
Suphisellus gibbulus









112607
193586
Suphisellus insularis





OM



112607
112610
Suphisellus puncticollis





OM




112745
Hydroscapha



7

sc



112736
112737
Sphaeriidae



8
8
FC



114496
114509
Chrysomelidne





SH

cn

114509
114613
Agasicles









114613
114614
Agasicles hygrophila





SH

cn

114509
114615
Disonycha





SH

cn

114509
114510
Donacia





SH

cn

114509
114546
Pyrrhalta









113844
113869
Melyridae





PR



114654
114666
Curculionidae





SH

cn
cb
114666
114667
Anchytarsus





SH



114667
114668
Anchytarsus bicolor
3.8




SH

sp
cn

114037
Lutrochus









114037
114038
Lutrochus laticeps


2.9




cn

114666
114779
Bagous





SH



114779

Bagous carinatus





SH

cn
cb
114666
114676
Phytobius





SH




114679
Stenopelmus





SH



206639
206640
Tyloderma capitale









113918
113923
Helodidae (= Scirtidae)










113924
Scirtidae







cb

113923
113948
Cyphon




7
SC

cb
sp
113923
113969
Elodcs







cb
sp
113923
113925
Prionocyphon







cb

113923
113929
Scirtes









113998
114278
Chelonariidae









114278
114279
Chclonarium lecontei









113998
113999
Dryopidae (adult)





SH

cb

113999
114025
Dryops (adult)







cn

113999
114006
Helichus (adult)
5.4
5
3.2

5
SH



114006
114011
Helichus basalis (adult)









114006
114013
Helichus fastigiatus (adult)









114006
114009
Helichus lithophilus (adult)









B-22
Appendix B: Regional Tolerance Values, Functional Feeding Groups,
and Habit/Behavior Assignments for Benthic Macroinvertebrates
-------



Regional Tolerance Values










Functional
Habit/




Upper Midwest
(WI)



Feeding Group
Behavior
Parent
TSN
TSN
Scientific Name
Southeast
(NC)
Midwest
(OH)
Northwest
(ID)
Mid-Atlantic
(MACS)
primary
secondary
primary
secondary
114006
114017
Helichus striatus (adult)



5

SH



114017
114019
Helichus striatus foveatus (adult)



5

SH

cb

113999
114001
Pelonomus (adult)









114001
114004
Pelonomus obscurus (adult)









113998
114093
Elmidae



4

GC

cn
bu

114196
Ampumixis



4

GC
SC
cn
bu
114196
114197
Ampumixis dispar



4

GC

cn
sp
114093
114193
Ancyronyx





OM



114193
114194
Ancyronyx variegatus
6.9
6
4


OM

cn

114093
114251
Atractelmis



4

GC

cn

114093
114164
Cleptelmis



4

GC



114164
114166
Cleptelmis addenda



4

GC
SC
cn

114164
114165
Cleptelmis ornata



4

GC

cn

114093
114208
Cylloepus



4

GC
SC
cn
cb
114093
114126
Dubiraphia
6.4
6
4.7
4
6
GC
SC


114126
114129
Dubiraphia bivittata


3.1


OM



114126

Dubiraphia giullianii



6

SC



114126
114130
Dubiraphia quadrinotata


3.2


OM



114126
114131
Dubiraphia vittata





OM

cn
cb
114093
114216
Gonielmis



5

GC



114216
114217
Gonielmis dietrichi





OM

cn

114093
114237
Heterelmis



4

GC

cn

114093
114167
Heterlimnius



4

GC



114167
114169
Heterlimnius corpulentus



4

GC

cn
bu
114167
114168
Heterlimnius koebelei



4

GC
SC
cn

114093
114137
Lara



4

SH



114137
114139
Lara avara



4

SH

cn

114093
114212
Macronychus





OM



114212
114213
Macronychus glabratus
4.7
4
2.9


OM

cn
cb
114093
114146
Microcylloepus



4

GC
SC


114146
114147
Microcylloepus pusillus
2.1
3

2

GC



114147
114151
Microcylloepus pusillus lodingi





OM



114146
114160
Microcylloepus similis



2

GC

cn

114093
114142
Narpus



4

GC



114142
114144
Narpus concolor



4

GC

cn

114093
114177
Optioservus
2.7
4
3.6
4
4
SC



114177
193732
Optioservus castanipennis



4

SC



114177
114178
Optioservus divergens



4

SC



114177
114190
Optioservus fastiditus


1.9
4
4
SC



114177
114180
Optioservus quadrimaculatus



4

SC



114177
114181
Optioservus seriatus



4

SC

cn

114093
114235
Ordobrevia



4





114235

Ordobrevia nubrifera



4

GC

cn

114093
114244
Oulimnius



4

SC



114244
114245
Oulimnius latiusculus
1.8






cn

114093
114229
Promoresia




2
SC



Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphytort, Benthic
Macroinvertebrates, and Fish, Second Edition
B-23
-------
Parent
TSN
TSN
Scientific Name
Southeast
(NC)
50
.egional
(A
-------



Regional Tolerance Values











Functional
Habit/




Upper Midwest
(WI)



Feeding Group
Behavior
Parent
TSN
TSN
Scientific Name
Southeast
(NC)
Midwest
(OH)
Northwest
(ID)
Mid-Atlantic
(MACS)
primary
secondary
primary
secondary
112811
113162
Helobata





OM



113162
113165
Helobata striata





OM



112811
113150
Helochares





OM



112811
113106
Helophorus
7.9




SH

sw
dv
112811
113244
Hydrobiomorpha









113244
113245
Hydrobiomorpha castus







cb
cn
112811
113196
Hydrobius



8

PR



113196
113200
Hydrobius tumidus





OM

cb

112811
113166
Hydrochus





SH

sw
dv
112811
113204
Hydrophilus









112811
112858
Laccobius
8

1.9


PR



112811
112909
Paracymus



5

PR
OM
cn

112811
112931
Sperchopsis



5
5
PR
CG


112931
112932
Sperchopsis tessellatus
6.5




OM

cb

112811
112938
Tropistemus
9.8


5
10
PR



112938
112951
Tropistemus blatchleyi









112938
112944
Tropistemus lateralis









112944
112946
Tropistemus lateralis nimbatus









112938
193660
Tropistemus striolatus









113264
113805
Ptiliidae









113264
113265
Staphylinidae



8

PR

cn

113265
113304
Bledius





PR

sk

113265
113576
Stenus







bu

113265
113440
Thinopinus









114413
114429
Salpingidae









109215
152741
Hymenoptera



8

PA



109215
117232
Lepidoptera



6

SH
SC


117294
117318
Noctuidae





SH

bu

117915
117952
Pyroderces



5





117639
117641
Pyralidae



5

SH

cb

117641
117741
Acentria



1

SH

cb

117641
117672
Munroessa





SH



117672
117677
Munroessa gyralis





SH

cb

117641
117756
Neargyractis





SH

cb
sw
117641
117642
Paraponyx




5
SH

cn

117641
117682
Petrophila


2.7
5

SC

cb
sw
117654
117656
Synclita obliteralis





SH



117906
117909
Prionoxystus



5





117854
117856
Tortricidae









109215
115000
Megaloptera









115000
115023
Corydalidae



0

PR

cn
cb
115023
115024
Chauliodes





PR



115024
115027
Chauliodes pectinicomis





PR



115024
115025
Chauliodes rastricomis





PR

cn
cb
115023
115033
Corydalus





PR



115033
115034
Corydalus comutus
5.6
6
2.4


PR

cn
cb
Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition	B-25
-------
Parent
TSN
TSN
Scientific Name
Southeast
(NQ
-------



Regional Tolerance Values










Functional
Habit/




t/i



Feeding Group
Behavior
Parent
TSN
TSN
Scientific Name
Southeast
(NC)
=s
•o
1
,~s
fx p
5c
Midwest
(OH)
Northwest
(ID)
Mid-Atlantic
(MACS)
primary
secondary
primary
secondary
116046
116050
Neophlax mitchelli
0








116046
116065
Neophylax occidentalis



3

sc



116046
116057
Neophlax oligius
2.6








116046
116052
Neophlax omatus
1.6








116046
116054
Neophylax rickeri



3

sc

cn

116046
116063
Neophylax splendens



3

sc



115933
116388
Neothremma



0

sc

cn

116388
116389
Neothremma alicia



0

sc

sp

115933
116039
Oligophlebodes



1

sc





Sericostriata



0

sc





Sericostriata surdickae



0

sc



115095
117120
Glossosomatidae



0

sc

cn

117120
117121
Agapetus
0


0

sc



117120
117154
Anagapetus



0

sc

cn

115236
115238
Culoptila cantha



0

sc

cn

117120
117159
Glossosoma
1.5


0

sc



117159
117165
Glossosoma penitus





sc



117159
117167
Glossosoma alascense





sc



117159
117162
Glossosoma intermedium



0

sc



117159
117160
Glossosoma montana





sc



117159
117202
Glossosoma oregonense





sc



117159
117220
Glossosoma wenatchee





sc



115246
115247
Matrioptila jeanae
0








115096
115221
Protoptila
2.8
1

1

sc



115221
183768
Protoptila coloma



1

sc



115221
115232
Protoptila tenebrosa



1

sc

sp

115095
117015
Helicopsychidae



3

sc

cn

117015
117016
Helicopsyche



3

sc



117016
117020
Helicopsyche borealis
0
3
1.8
3

sc



115095
115398
Hydropsychidae



4
4
FC





Hydropsychidae











Arctopsychinae



2

FC

cn

115398
115529
Arctopsyche



1

FC



115529
115538
Arctopsyche califomica



2

FC
OM


115529
115530
Arctopsyche grandis



2

FC

cn

115529
115533
Arctopsyche irrorata
0










Hydropsychinae





FC



115398
115570
Ceratopsyche





FC

cn

115570
115596
Ceratopsyche alhedra
0
3







115570

Ceratopsyche bifida
1








115570
115577
Ceratopsyche bronta
2.7
5







115570

Ceratopsyche macleodi
0.9








115570
115580
Ceratopsyche morosa
3.2
2
1.8






115570
115586
Ceratopsyche slossonae
0
4
2






115570
115589
Ceratopsyche spama
3.2
1
3.2






115570

Ceratopsyche ventura
0








Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
B-27
-------



Regional Tolerance Values










Functional
Habit/




t/3



Feeding Group
Behavior
Parent
TSN
TSN
Scientific Name
Southeast
(NC)
Upper Midwe
(WI)
Midwest
(OH)
Northwest
(ID)
Mid-Atlantic
(MACS)
prrmary
secondary
primary
secondary
115398
115408
Cheumatopsyche
6.6
5
2.9
5
5
FC



115408
115409
Cheumatopsyche campyla



6

FC



115408
115441
Cheumatopsyche enonis



6

FC



115408
115426
Cheumatopsyche pettiti



6

FC

cn

115393
115399
Diplectrona



0

FC



115399
115402
Diplectrona modes ta
2.2




FC

cn

115398
115618
Homoplectra







cn

115398
115453
Hydropsyche



4
4
FC



115453
115456
Hydropsyche aetata


2.6






115453
115454
Hydropsyche betteni
8.1
6


4
FC



115453
115458
Hydropsyche bidens


2.5






115453
115455
Hydropsyche caiifomica



4

FC



115453
115462
Hydropsyche decalda
4.1




FC



115453
115463
Hydropsyche demora
1.8








115453
115465
Hydropsyche dicantha


3.5






115453
115488
Hydropsyche elissoma





FC ;



115453
115468
Hydropsyche frisoni


1.8






115453
115469
Hydropsyche hageni
0








115453
115471
Hydropsyche incommoda
5
7







115453
115474
Hydropsyche mississippiensis





FC



115453
115513
Hydropsyche occidentalis



4

FC



115453
115485
Hydropsyche orris


2.6






115453
115490
Hydropsyche oslari



4

FC



115453
115477
Hydropsyche phalerata
3.7
1







115453
206641
Hydropsyche rossi
4.9








115453
115480
Hydropsyche scalaris
3
2







115453
115481
Hydropsyche simulans


2.4






115453
115527
Hydropsyche spama




4
FC

cn

115453
115484
Hydropsyche venularis
5.3

2.9






115453
115482
Hydropsyche valanis


3






115398
115603
Macrostemum
3.6
3


3
FC



115603
115608
Macrostemum Carolina





FC

cn

115603
115606
Macrostemum zebratum


1.8






115398
115556
Parapsyche



1

PR



115556
115563
Parapsyche almota



3

PR



115556
115559
Parapsyche cardis
0








1155S6
115560
Parapsyche elsis



1

PR

cn

115398
115551
Potamyia





FC



115551
115552
Potamyia flava


2.5


FC



115095
115629
Hydroptilidae



4

:

cb

115629
115635
Agraylea


5.7
8



cn

115629
115826
Dibusa







cn

115826
115827
Dibusa angata


2.6






115629
115641
Hydroptila
6.2
6
3.2
6
6
SC
PR


115641
115643
Hydroptila ajax



6

sc



115641
115695
Hydroptila arctia



6

SC



B-28
Appendix B: Regional Tolerance Values, Functional Feeding Groups,
and Habit/Behavior Assignments for Benthic Macroinvertebrates
-------
Parent
TSN
TSN
Scientific Name
Southeast
(NC)
50
a
Upper Midwest <2-
(WI) g
1
Midwest 2.1
(OH) |
3 1
n
Northwest JL
(ID) g.
! Mid-Atlantic
(MACS)
Func
Feedin
£
E
•e
o.
tional
; Group
1
C
t/i
Ha
Beh
i?
B
'C
o.
bit/
avior
c
o
o
V
t/i
115641
115696
Hydroptila argosa



6

SC

cn

115629
115630
Leucotrichia



6

SC

cn

115630
115631
Leucotrichia pictipes
4.3
2







115629
115811
Mayatrichia



6

SC



115811
115812
Mayatrichia ayama





SC

cn

115629
115833
Neotrichia


3.6


SC



115833

Neotrichia halia



4

SH

cn

115629
115714
Ochrotrichia
7.2


4

GC

cn

115629
115714
Ochrotrichia



4

GC

cb

115629
115828
Orthotrichia



6

SC

cn

115629
115779
Oxyethira


5.2






115629
115817
Stactobiella



2

SH

cb
sp


Limnephiloidea









115095
116793
Lepidostomatidae



3

SH



116793
116794
Lepidostoma
1
1

1
1
SH



116794
116888
Lepidostoma cmereum



3

SH



116794
116870
Lepidostoma quercinum



1

SH

sp
cb
115095
116547
Leptoceridae



4

GC

cb
sw
116547
116684
Ceraclea


2.6
5
3
GC

cn
sp
116684
116696
Ceraclea ancylus
2.5
3







116684

Ceraclea flava
0








116684
116725
Ceraclea maculata
6.4

3.6






116684

Ceraclea transversa
2.7








116547
116598
Mystacides



4
4
GC



116598
116599
Mystacides sepulchralis
3.5
4







116547
116651
Nectopsyche


2.4
3
3
SH



116651
116661
Nectopsyche Candida
3.8




OM



116651
116663
Nectopsyche diarina


3.2






116651
116659
Nectopsyche exquisita
4.2
3



OM



116651
116662
Nectopsyche gracilis



3

SC



116651
116660
Nectopsyche pavida
4.2

2.1


OM



116651

Nectopsyche halia



3

SC



116651

Nectopsyche lahontanensis



3

SC

sp
cb
116651

Nectopsyche stigmatica



3

SC

sp
cb
116547
116607
Oecetis
5.7
8
3
8
8
PR



116607

Oecetis parva









116607
116608
Oecetis avara









116607
116609
Oecetis cinerascens









116607
116643
Oecetis georgia




8




116607
116613
Oecetis inconspicua




8




116607
116631
Oecetis nocturna







sp
cn
116607
116636
Oecetis persimilis




8


sw
cb
116547
116548
Setodes
0.9
2



OM



116547
116565
Triaenodes



6
6




116565
206642
Triaenodes abus
4.3




SH



116565
116569
Triaenodes flavescens





SH



Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
B-29
-------



Regional Tolerance Values










Functional
Habit/




-------



Regional Tolerance Values










Functional
Habit/




8
S
T3
is



Feeding Group
Behavior
Parent
TSN
TSN
Scientific Name
IS)
a
O
Midwest
(OH)
S
•e
o
•e
c
~ 00
1
PJ
"O
c

TD
C




it
la
2 £
•E
O.
$
'C
o.
o
(O

115989
Pseudostenophylax



i

SH



115933
115974
Psychoglypha



i

GC



115974
115977
Psychoglypha bella



2

GC

sp
cb
115974
115981
Psychoglypha subborealis



2

GC



115933
116409
Pycnopsyche
2.3
4
3.3

4
SH



116409
116413
Pycnopsyche gentilis
0.8








116409
116414
Pycnopsyche guttifer
2.7




SH

sp
cn
116409
116416
Pycnopsyche lepida
2.5








116409
116417
Pycnopsyche scabripennis
4




SH




116473
Molannidae









116473
116474
Molanna




6
SC

sp

116474
116478
Molanna blenda
3.9

4






116474
116479
Molanna tryphena







sp


116496
Odontoceridae









116496
116520
Namamyia



0

OM
GC


116496
116522
Nerophilus



0

OM

sp

116522
116523
Nerophilus califomicus



0

OM

sp

116496
116527
Pseudogoera



0

OM
PR


116496
116497
Psilotreta
0
0


0
SC



116497
116498
Psilotreta frontalis







cn

115095
115257
Philopotamidae



3
3
FC

cn

115257
115273
Chimarra
2.8
4


4
FC

cn


115278
Chimarra aterrima


1.9







115276
ChimaiTa obscura


3.4






115257
115319
Dolophilodes
1


1

GC



115257
115258
Wormaldia
0.4


3

FC



115258
115261
Wormaldia gabriella





SC



115095
115867
Phryganeidae





SH

cb


115892
Phryganea



4

OM



115867
115868
Ptilostomis
6.7
5


5
SH

cn



Goerinae



1

SC



115933
116423
Goera
0.3






sn

116423
116431
Goera archaon



1

SC

sb

115933
116298
Goeracea



0

SC

sp



Goereilla





SH



115095
117043
Polycentropodidae





FC

cn

115334
115373
Ccmotina





PR

cn

115373
115375
Cemotina spicata





PR



117043
117091
Cyrnellus





FC

cn

117091
117092
Cyrnellus fratemus
7.4
8
4


FC



117043
117095
Neureclipsis
4.4
7
2.7

7
FC

cn

117095
117098
Ncureclipsis crepuscularis









117043
117104
Nyctiophylax
0.9
5
2.5
5

FC

cn


117112
Nyctiophylax moestus
2.6


5
5
PR





Paranyctiophylax









117043
117044
Polycentropus
3.5
6
3.4
6
5
PR
FC
cn

Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
B-31
-------



Regional Tolerance Values










Functional
Habit/




2



Feeding Group
Behavior
Parent
TSN
TSN
Scientific Name
Southeast
(NC)
s
•o
 „	„
Midwest
(OH)
vt
%
o
•e
c
¦52 M
< u
1
la
•0
c
53
£
*a
c



it
la
1 &
¦g.
S
(/)
*c
0.
8
u

115334
115361
Phylocentropus
5.6
4


5
FC

cn

115334
115395
Polyplectropus









115095
115334
Psychomyiidae





GC



115334
115391
Lypc





SC

bu

115391
115392
Lype divcrsa
4.3
2
2.8


SC



115334
115335
Psychomyia



2

SC



115335
115341
Psychomyia flavida
3.3
2
1.9






115335
115346
Psychomyia lumina



2

SC



115335
115344
Psychomyia nomada
2








115334
115350
Tinodes



2

SC



115095
115096
Rhyacophilidae



0

PR

cn

115096
115243
Himalopsyche





PR '



115096
115097
Rhyacophila



0

PR



115097
115098
Rhyacophila acropcdes



1

PR



115097
115160
Rhyacophila acutiloba
0








115097
115163
Rhyacophila alberta





PR



115097
115099
Rhyacophila angelita





PR



115097
115165
Rhyacophila arnaudi





PR



115097
115146
Rhyacophila atrata
0








115097
115101
Rhyacophila betteni





PR



115097
115102
Rhyacophila bifila





PR



115097
115153
Rhyacophila blarina





PR



115097
115151
Rhyacophila brunnea





PR



115097
115131
Rhyacophila Carolina
0








115097
115156
Rhyacophila coloradensis





PR



115097
115133
Rhyacophila fuscula
2
0







115097
115105
Rhyacophila grandis



1

PR



115097
115159
Rhyacophila hyalinata





PR



115097
115177
Rhyacophila iranda



0

PR



115097
115134
Rhyacophila ledra
3.4








115097
115147
Rhyacophila minor
0








115097
115155
Rhyacophila narvae





PR



115097
115111
Rhyacophila nevadensis



1

PR



115097
115138
Rhyacophila nigrita
0




,



115097
115208
Rhyacophila oreia





PR



115097
115114
Rhyacophila pellisa



0

PR



115097
115116
Rhyacophila rayncri



0

PR



115097
115187
Rhyacophila robusta









115097
115117
Rhyacophila rotunda





PR



115097

Rliyacophila sibirica



0

PR



115097
115144
Rhyacophila torva
1.8








115097

Rhyacophila trissemani



1

PR



115097
115189
Rhyacophila tucula









115097
115120
Rhyacophila vaccua





PR



115097
115191
Rhyacophila vaefes



I

PR



115097

Rhyacophila vaeter



1

PR



B-32
Appendix B: Regional Tolerance Values, Functional Feeding Groups,
and Habit/Behavior Assignments for Benthic Macroinvertebrates
-------
Parent
TSN
TSN
Scientific Name
Southeast
50
Gt
Upper Midwest "E.
(WI) g
Toleran
V)
"2 ®
sS
¦ ' ¦—	 		 fi
Northwest ^
(ID) g.
1 ¦¦ " ¦ ¦ IW
Mid-Atlantic
(MACS)
Func
Feeding
b
«
B
•c
a.
tional
Group
OS
"O
a
o
u
U
Ha
Beh
'C
Q.
bit/
avior
£
CO
-o
c
o
o
o
(/>
115097
115152
Rhyacophila vagrita





PR



115097
115121
Rhyacophila valuma



1

PR



115097
115123
Rhyacophila velora



1

PR



115097
115124
Rhyacophila vepulsa









115097
115125
Rhyacophila verrula









115097
115195
Rhyacophila visor



1

PR

cn

115097
115197
Rhyacophila vofixa



0

PR



115097
115148
Rhyacophila vuphipes
0








115095
116982
Sericostomatidac





SH



116982
116983
Agarodes







sp

116983
116991
Agarodes libalis
0
3







117012
117013
Fattigia pele
1.1








116982
117003
Gumaga



3

SH



100900
103358
Hemiptera





PR

cb
SW
103358
103683
Belostomatidae





PR



103683
103717
Abedus





PR

cb
SW
103717
103739
Abedus immaculatus





PR



103683
103684
Belostoma
9.8




PR



103684
103689
Bclostoma flumineum





PR



103684
103687
Belostoma lutarium





PR

cb
SW
103684
103688
Belostoma testaceum





PR



103683
103699
Lethocerus





PR

sw

103358
103364
Corixidae
9


10
5
PR

sw

103364
103514
Callicorixa





PR



103364
103501
Cenocorixa





PR

sw

103501
103504
Cenocorixa bifida



3

PR

sw

103364
103484
Corisella





PR

sw

103364
103525
Cymatia



8

PI

sw
cb
103364
103547
Graptocorixa





PR

sw

103364
103444
Hesperocorixa







sw

103364
103491
Palmacorixa




5
PR

sw
cb
103364
103365
Ramphocorixa









103364
103369
Sigara




9
PR



103369
103370
Sigara alternata







sw

103369
103398
Sigara washingtonensis



8

GC

sw
cb
103364
181192
Tenagobia



8





103364
103423
Trichocorixa




5
PR



103423
103424
Trichocorixa calva









103423
103429
Trichocorixa sexcincta







sp

103358
103768
Gelastocoridae





PR



103768
103769
Gelastocoris





PR

sk

103358
103801
Gerridae



5

PR



103801
103829
Gerris





PR



103829
103842
Gerris buenoi



5

PR

sk

103829
103841
Gerris remigis



5

PR

sk

103801
103872
Limnoporus





PR



Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
B-33
-------



Regional Tolerance Values











Functional
Habit/




Ui



Feeding Group
Behavior
Parent
TSN
TSN
Scientific Name
Southeast
(NC)
*o
is
tj
Midwest
(OH)
Hi
1)
?
o
'S3
~ e/3
1
s?
*13
C

&
"O
e



St
Is
s a
s §.
•e
o.
8
'C
rc.
£
103801
103857
Metrobates





PR

sk

103857
103859
Metrobates hesperius





PR



103801
103881
Neogerris





PR

sk

103881
103882
Neogerris hesione





PR



103801
103802
Rheumatobates





PR



103802
103807
Rheumatobates palosi







sk

103802
103804
Rheumatobates tenuipes









103801
103811
Trepobates



10

PR

cb
bu
103811
103815
Trepobates pictus





PR

cb
bu
103964
103965
Hebrus





PR

sk
cb
103964
103986
Lipogomphus





PR



103964
103983
Menagata





PR



103983
103984
Merragata brunnea





PR

sk

103983
103985
MetTagata hebroides





PR



103938
103939
Hydro metra





PR



103939
103944
Hydrometra wileyae





PR

sk
cb
103358
103953
Mesoveliidae





PR



103953
103954
Mesovelia





PR



103954
103955
Mesovelia cryptophila





PR



103954
103956
Mesovelia mulsanti





PR

cn
sw
103358
103613
Naucoridae



5

PR

cb
sw
103613
103614
Ambrysus





PR



103613
103665
Pelocoris




7
PR



103665
103667
Pelocoris femoratus





PR

cb

103358
103747
Nepidae





PR



103747
103748
Ranatra
7.5




PR



103748
103749
Ranatra australis





PR



103748
103750
Ranatra buenoi





PR



103748
103761
Ranatra drakei





PR



103748
103755
Ranatra fusca





PR



103748
103751
Ranatra kirkaldyi





PR



103748
103754
Ranatra nigra





PR

sw
cb
103358
103557
Notonectidae





PR



103557
103558
Notonecta





PR



103558
103573
Notonecta irrorata





PR



103558
103575
Notonecta uhleri





PR

sw
cb
103358
103602
Pleidae





PR



103602
103603
Neoplea





PI



103603
103604
Neoplea striola





PI

cb

103358
104063
Saldidae



10

PR



104063
104069
Pentacora





PR



104063
104140
Saldula



10

PR

sk

103358
103885
Veliidae









103885
103900
Microvelia




6
PR



103900
103908
Microvelia hinei





PR



103900
103910
Microvelia pulchella





PR



B-34
Appendix B: Regional Tolerance Values, Functional Feeding Groups,
and Habit/Behavior Assignments for Benthic Macroinvertebrates
-------



Regional Tolerance Values











Functional
Habit/




«S



Feeding Group
Behavior
Parent
TSN
TSN
Scientific Name
Southeast
(NC)
Upper Midwe
(WI)
Midwest
(OH)
Northwest
(ID)
Mid-Atlantic
(MACS)
primary
secondary
&
g
secondary
103885
103923
Paravelia





PR

sk

103923
103924
Paravelia brachialis





PR



103885
103886
Rhagovelia




6
PR



103886
103894
Rhagovelia choreutes





PR



103886
103895
Rhagovelia disticta





PR

sk

103886
103887
Rhagovelia obesa





PR




103935
Trochopus





PR



100500
102467
Plecoptera





PR

cn

102468
102643
Capniidae



1
1
SH

sp
cn
102643
102644
Allocapnia
2.8
' 3


3
SH

sp
cn
102643
102688
Capnia



1

SH



102785
102786
Eucapnopsis brevicauda



1

SH

sp
cn
102788
102804
Paracapnia



1

SH

sp
cn
102804
102805
Paracapnia angulata
0.2
1







102468
102840
Leuctridae



0

SH



102840
102841
Despaxia



0

SH

cn

102841
102842
Despaxia augusta



0

SH

sp
cn
102840
102844
Leuctra
0.7



0
SH

sp
cn
102840
102877
Megaleuctra



0

SH

sp
cn
102909
102910
Moselia infuscata



0

SH



102840
102887
Paraleuctra



0

SH

sp
cn
102887
102890
Paraleuctra occidentalis



0

SH



103202
103239
Perlomyia



0

SH

sp
cn
102468
102517
Nemouridae



2

SH



102517
102540
Amphinemura
3.4
3

2

SH



102540
102541
Amphinemura delosa







sp
cn
102540
102542
Amphinemura nigritta







sp
cn
102517
102567
Malenka



2

SH

sp
cn
102517
102526
Nemoura







sp
cn
102517
102632
Ostrocera







sp
cn
102517
102622
Ostrocerca







sp
cn
102517
102605
Podmosta



2

SH



102517
102584
Prostoia
6.1
2

2

SH

sp
cn
102584
102585
Prostoia besametsa



2

SH

sp
cn
102517
102640
Shipsa







sp
cn
102640
102641
Shipsa rotunda
0.3
2







102517
102556
Soyedina



2

SH



102517
102614
Visoka





SC

sp
cn
102614
102615
Visoka cataractae



1

SH



102517
102591
Zapada



2

SH



102591
102594
Zapada cinctipes



2

SH



102591
102596
Zapada columbiana



2

SH



102591
102601
Zapada frigida



2

SH



102591
102597
Zapada oregonensis



2

SH

cn
sp
102468
102488
Peltoperlidae



2

SH

cn
sp
102488
102489
Peltoperla







cn
sp
Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
B-35
-------
Parent
TSN
TSN
Scientific Name
Southeast
(NC)
50
O
Upper Midwest tE.
(W!) g
¦""J
Midwest 2.
(OH) |
ce Valu
U)
s
ts
z =-
Mid-Atlantic
(MACS)
Func
Feeding
b
C3
E
'g.
O §
secondary o »
¦o
Ha
Beh
*
•n
Cl
secondary §
102994
103142
Soliperla



2

SH



102488
102500
Tallaperla
1.4






cn
sp
102500
102505
Tallaperla Cornelia









102488
102510
Yorapcrla



2

SH



102510

Yorapcrla mariana



2

SH



102510
102512
Yoraperla brevis



2

SH

cn
sp
102468
102470
Pteronarcidae





SH



102470
102485
Pteronarcella



0

SH



102485
102486
Pteronarcella badia



0

SH

cn
sp
102485
102487
Pteronarcella regularis



0

SH



102470
102471
Pteronarcys
1.7

2.2
0

SH



102471
102473
Pteronarcys califomica



0

SH



102471
102478
Pteronarcys dorsata
1.8




SH



102471
102484
Pteronarcys princcps



0

SH

sp
cn
102468
102788
Taeniopterygidae



2

SH

sp
cn
102838
102839
Doddsia occidentalis



2

SC

sp
cn
102788
102830
Oemopteryx







sp
cn
102788
102808
Strophopteryx
2.5
3







102788
102816
Tacnionema



2

SC

sp
cn
102816
102827
Taenionema pallidum



2

SC



102788
102789
Taeniopteryx
6.3
2


2
SH



102789
102791
Taeniopteryx burksi
5.8




OM



102789
102792
Taeniopteryx lita





OM

cn

102789
102795
Taeniopteryx mctequi
1.4








102912
103202
Chloroperlidae



1

PR

cn


103236
Kathroperla



0

PR



103236
103237
Knthroperla perdita



1

GC

cn



Chloroperlinae



1

PR



103202
103203
Alloperla
1.4


1

PR

cn

103202
103260
Haploperla







cn

103260
103263
Haploperla brevis
1.3
1







103202
103303
Neaviperla





PR

cn

103303
103304
Neaviperla forcipata



1

PR

cn

103202
103233
Paraperla



1

PR

cn

103233
103234
Paraperla frontalis





PR



103202
103305
Plumiperla





PR

cn

103202
103254
Suwallia
0


1

PR

cn

103202
103273
Sweltsa
0


1

PR



103202
103308
Triznaka



1

PR

cn

102912
102914
Perlidae



1
1
PR



102914
102917
Acroneuria




0
PR



102917
102919
Acroneuria abnormis
2.2
0



PR



102917
102920
Acroneuria arenosa
2.2




PR



102917
102922
Acroneuria carolinensis
0

2.3






102917
102923
Acroneuria evoluta


2.8






102917
102925
Acroneuria intemata


2.2






B-36
Appendix B: Regional Tolerance Values, Functional Feeding Groups,
and Habit/Behavior Assignments for Benthic Macroinvertebrates
-------



Regional Tolerance Values










Functional
Habit/




Upper Midwest
(WI)



Feeding Group
Behavior
Parent
TSN
TSN
Scientific Name
Southeast
(NC)
Midwest
(OH)
Northwest
(ID)
Mid-Atlantic
(MACS)
primary
secondary
1
1
! primary
secondary
102917
102918
Acroneuria lycorias
1.5

2.4


PR



102917
102926
Acroneuria mela
0.9




PR

cn

102917
102927
Acroneuria perplexa





PR

cn

102914
102975
Agnetina


1.8

2
PR

cn

102975
102983
Agnetina annulipes
0



2


cn

102975
102979
Agnetina capitata





PR

cn

102975
102984
Agnetina flavescens
0








102954
102955
Attaneuria ruralis





PR

cn

102914
102934
Beloneuria
0


3

PR

cn

102914
102985
Calineuria



3

PR

cn

102985
102986
Calineuria califomica



1

PR

cn

102994
103121
Doroneuria



1

PR

cn

103121
103123
Doroneuria baumanni



1

PR

cn

103121
103122
Doroneuria theodora



1

PR

cn

102914
102930
Claassenia



3

PR

cn

102930
102932
Claassenia sabulosa



3

PR

cn

102914
102939
Eccoptura







cn

102939
102940
Eccoptura xanthenes
4.1






cn

102914
102971
Hesperoperla





PR

cn

102971
102972
Hesperoperla pacifica



1

PR

cn

102914
102942
Neoperla
1.6
1
3.1


PR

cn

102942
102944
Neoperla clymene





PR



102914
102962
Paragnetina





PR



102962
102965
Paragnetina fumosa
3.5




PR



102962
102970
Paragnetina ichusa
0








102962
102966
Paragnetina immarginata
1.7








102962
102967
Paragnetina kansensis
2




PR

cn
sp
102962
102968
Paragnetina media.


2.1






103202
103251
Perlesta
0

4.5
5

PR

cn

103251
103253
Perlesta placida
4.9
5



OM



103202
103244
Perlinella





PR



103244
103246
Perlinella drymo
0
1



PR

cn

103244
103248
Perlinella ephyre





PR

cn

102912
102994
Perlodidae



2
2
PR

cn
sp
102994
103155
Calliperla



2

PR

cn
sp
102994
103157 -
Cascadoperla



2

PR



102994
103118
Clioperla







cn

103118
103119
Clioperla clio
4.8
1





cn

102994
103137
Cultus



2

PR

cn

103137
103139
Cultus decisus
1.6








102994
103166
Diploperla
2






cn

103166
103167
Diploperla duplicata
2.7








103166
103169
Diploperla morgani
1.5









103094
Diura



2

PR



103094
103096
Diura knowltoni



2

SC

cn

103171
103172
Frisonia picticeps



2

PR

cn

Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
B-37
-------
Parent
TSN
TSN
Scientific Name
Southeast
(NC)
_
in
Upper Midwest "G.
(WI) |
Toleran
1	—
"2 S3
2	2.
ce Valu
i
II
Mid-Atlantic "
(MACS)
Ftmc
Feeding
'C
o.
O g
secondary 3 s.
•o
Ha
Behi
1
•c
CL
3. |
secondary °
102994
103084
Helopicus







cn

103084
103087
Helopicus bogaloosa
0






cn

103084
103085
Helopicus subvan'ans
0.8









103124
Isogenoides



2

PR



103124

Isogenoidcs hansoni
0








102994
103070
Isogenus



2

PR



102994
102995
Isoperia



2
2
PR



102995
103012
Isoperla bilineata
5.5








102995
103021
Isoperia dicala
2.2
2







102995
103004
Isoperla fulva



2

PR



10299S
103029
Isoperia fusca



2

PR



102995
103020
Isoperla holochlora
0








102995
103007
Isoperla mormona



2

PR



102995
103017
Isoperla namata
1.8








102995
103018
Isoperla orata
0




OM



102995
103009
Isoperla pinta



2

PR



102995
103019
Isoperla similis
0.7








102995
103035
Isoperla slossonae
2.6







-
102995
103036
Isoperla transmarina
5.6








102994
103149
Kogotus



2

PR

cn

103174
103175
Malirekus hastatus
1.4








102994
103110
Megarcys



2

PR

cn

102994
103180
Oroperla



2

PR

cn

102994
103134
Perlinodes





PR

cn

103134
103135
Perlinodes aureus



2

PR

cn

102994
103186
Pictetiella



2

PR

cn

103186
103188
Pictetiella expansa



2

PR

cn

103099
103100
Remenus bilobatus
0.3








102994
103189
Rickcra





PR

cn

103189
103190
Rickera sorpta



2

PR

cn

102994
103193
Setvcna



2

PR

cn

103193
103194
Setvena bradleyi



2

PR

cn

102994
103102
Skwala



2

PR



102994
103197
Yugus



2

PR

cn
sp
103197
103200
Yugus arinus
0








103197
103198
Yugus bulbosus
0








100500
101593
Odonata





PR

cb

101595
101596
Aeshnidae



3

PR




101602
Aeshna



5

PR



101596
101597
Anax



8
5
PR



101597
101598
Anax junius





PR

cb
sp
101597
101599'
Anax longipes





PR

cb
sp
101596
101648
Basiaeschna







cb
sp
101648
101649
Basiaeschna janata
7.7
6



PR



101596
101645
Boyeria





PR

cb

101645
101646
Boyeria grafiana
6.3








B-38
Appendix B: Regional Tolerance Values, Functional Feeding Groups,
and Habit/Behavior Assignments for Benthic Macroinvertebrates
-------



Regional Tolerance Values










Functional
Habit/




tz



Feeding Group
Behavior
Parent
TSN
TSN
Scientific Name
Southeast
(NC)
Upper Midwt
(WI)
Midwest
(OH)
Northwest
(ID)
Mid-Atlantic
(MACS)
primary
secondary
primary
secondary
101645
101647
Boyeria vinosa
6.3
2
3.5


PR

cb
sp
101639
101640
Coryphaeschna ingens





PR

cb
cn
101637
101638
Epiaeschna heros





PR

cb
cn
101634
101635
Gomphaeschna furcillata





PR



101653
101654
Nasiaeschna pentacantha
8




PR

bu

101595
101664
Gomphidae



1

PR

bu

101715
101716
Aphylla williamsoni





PR



101664
101770
Arigomphus







bu

101770
101771
Arigomphus pallidus





PR



101664
101730
Dromogomphus
6.3




PR



101730
101731
Dromogomphus armatus





PR



101730
101732
Dromogomphus spinosus





PR

bu


101725
Erpetogomphus



4

PR



101777
101780
Gomphurus dilatatus
6.2
5
2.5 "


PR



101664
101665
Gomphus




5
PR



101665
101677
Gomphus dilatatus





PR



101665
101668
Gomphus geminatus





PR



101665
101685
Gomphus lividus




5
PR



101665
101686
Gomphus minutus





PR



101665
101689
Gomphus pallidus





PR

sp

101665
101694
Gomphus spiniceps
4.9








101734
101735
Hagenius brevistylus
4
1



PR

bu

101791
206625
Hylogomphus geminatus





PR

bu

101664
101766
Lanthus
2.7






bu

101664
101736
Octogomphus



1

PR

bu

101664
101738
Ophiogomphus
6.2
I

1

PR

bu

101664
101718
Progomphus





PR

bu

101718
101720
Progomphus obscurus
8.7




PR

bu

101664
101761
Stylogomphus







bu

101761
101762
Stylogomphus albistylus
4.8








101664
206626
Stylurus





PR

sp

206626
206627
Stylurus ivae





PR




101594
Anisoptera





PR



101659
101660
Tachopteryx



10

PR

bu

102025
102026
Cordulegastridae





PR

bu

102026
102027
Cordulegaster
6.1
3

0
3
PR



102027
102031
Cordulegaster maculata





PR

sp

101796
102020
Corduliidae



2
5
PR

cb
sp
101851
101852
Didymops transversa





PR

cb
sp

101862
Epicordulia
5.6








101862
101863
Epicordulia princeps





PR

sp

101862
101864
Epicordulia regina





PR

sp

101797
101918
Macromia
6.7
2


2
PR

sp

101918
101920
Macromia georgiana





PR

sp

101918
101924
Macromia georgina





PR

cb
cn
101918
101922
Macromia taeniolata





PR



Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton,
Macroinvertebrates, and Fish, Second Edition
Benthic
B-39
-------
Parent
TSN
TSN
Scientific Name
Southeast
(NC)
ft
Upper Midwest g.
(WI) g
H
Midwest £.
(OH) |
ce Valu<
tf)
la
z c-
Mid-Atlantic
(MACS).
Func
Feedinj
1
•n
a.
ional
Group
&
T3
1
5!
Ha
Beh:
b
¦c
o.
S. |
secondary °
101797
101934
Neurocordulia
5.8




PR



101934
101938
Neurocordulia alabamensis





PR



101934
101936
Neurocordulia molesta
3.3
5



PR



101934
101939
Neurocordulia obsoleta
5.4
0



PR

sp

101934
101935
Neurocordulia virginiensis
1.6




PR

sp

101797
101947
Somatochlora
8.9
1

9
1
PR

cb
sp
101947
101949
Somatochlora linearis





PR



102026
102035
Epitheca




4
PR



102035
206629
Epitheca princeps





PR



102035

Epitheca sepia





PR



206629
206631
Epitheca princeps regina





PR

cb
sp
102035
185986
Epitheca cynosura





PR



101797
101994
Tetragoneuria
8.5




PR



101994
101996
Tetragoneuria cynosura





PR

sp

1017%
101797
Libellulidae



9
9
PR

sp

101830
101831
Brachymesia gravida





PR

sp

101797
101865
Erythemis





PR

cb

101865
101866
Erythemis simplicicollis
7.7




PR

cb

101797
101870
Erythrodiplax





PR

cb

101870
101872
Erythrodiplax minuscula





PR .

sp

101797
101885
Leucorrhinia









101797
101893
Libellula
9.8
9

9
8
PR



101893
101901
Libel lula auripennis





PR



101893
101900
Libellula incesta





PR



101893
101903
Libellula semifasciata





PR

sp

101893
101904
Libellula vibrans





PR

sp
cb
102009
102010
Miathyria marcella





PR

sp

101932
101933
Nannothemis bella





PR

sp

101797
101945
Orthemis





PR

sp

101945
101946
Orthemis ferruginea





PR

sp

101798
101799
Pachydiplax longipennis
9.6




PR

sp

101797
101803
Perithemis
10



4
PR

sp

101803
101805
Perithemis seminola





PR

sp

101803
101804
Perithemis tenera





PR

sp
cb
101808
101809
Plathemis lydia
10
8
8.2


PR



101797
101976
Sympetrum
7.3
10


4
PR

sp

101976
101977
Sympetrom ambiguum





PR



101818
101820
Tramea Carolina





PR



100500
102042
Zygoptcra





PR

cb

102042
102043
Calopterygidae




5
PR

cb

102043
102052
Calopteryx
8.3
5
3.7
6
6
PR

cb

102052
102054
Calopteryx dimidiata





PR

cb
cn
102052
102055
Calopteryx maculata





PR



102043
102048
Hetaerina
6.2
6
2.8


PR



102048
102050
Hetaerina americana





PR



102048
102049
Hetaerina titia





PR

cb
sw
B-40
Appendix B: Regional Tolerance Values, Functional Feeding Groups,
and Habit/Behavior Assignments for Benthic Macroinvertebrates
-------
Parent
TSN
TSN
Scientific Name
Southeast
n
Upper Midwest <2.
(WI) |
Toleran
(A
O
> ^
sa
ce Valu
W
P
A
II
Mid-Atlantic
(MACS)
Func
Feedin;
£•
•c
a.
tional
Group
£
X3
C
o
O
flj
Ha
Beh
*c
CL
s.|
secondary °
102042
102077
Coenagrionidae


6.1
9
9
PR

cb

102077
102093
Amphiagrion



5

PR

cn
cb
102077
102139
Argia


5.1
7
6
PR



102139
102140
Argia apicalis





PR



102139
102143
Argia fumipennis





PR



102139
102146
Argia moesta





PR



102139
102147
Argia sedula





PR



102139
102148
Argia tibialis





PR

cb

102139
102154
Argia violacea





PR

cb

102077
102133
Chromagrion



6

PR

cb

102077
102102
Enallagma
9
9

9
8
PR

cb

102102
102103
Enallagma antennuatus





PR

cb

102102
102104
Enallagma cardenium





PR

cb

102102
102106
Enallagma daecki





PR

cb

102102
102108
Enallagma divagans





PR

cb

102102
102110
Enallagma dubium





PR

cb

102102
181184
Enallagma pallidum





PR

cb

102102
102114
Enallagma pollutum





PR

cb

102102
102115
Enallagma signatum





PR

cb

102102
102119
Enallagma vesperum





PR

cb

102102
102120
Enallagma weewa





PR

cb

102077
102078
Ischnura
9.4
9

9
9
PR

cb

102078
206632
Ischnura hastata





PR



102078
102082
Ischnura posita





PR

cb

102078
102084
Ischnura ramburi





PR

cb

102077
102135
Nehalennia





PR

cb

102135
102136
Nehalennia intergricollis





PR

cb

102096
102099
Telebasis byersi





PR



102077
102100
Zoniagrion



9

PR



102058
102061
Lestes



9

PR

cb

109215
118831
Diptera



7





121226
121227
Blephariceridae



0

SC



121229
121230
Agathon



0

SC

cn

121229
121250
Bibiocephala



0

SC



121229
121255
Blepharicera
0.2
0

0

SC

sp
bu
121229
121278
Philorus



0

SC

sp
cb
125808
127076
Ceratopogonidae


5.7
-------



Regional Tolerance Values










Functional
Habit/




Upper Midwest
(WI)



Feeding Group
Behavior
Parent
TSN
TSN
Scientific Name
Southeast
(NC)
Midwest
(OH)
Northwest
(ID)
Mid-Atlantic
(MACS)
primary
secondary
primary
secondary
127339
127340
Culicoides
6.5
10


10
PR
GC
bu

127683
127720
Nilobezzia





PR



127774
127859
Palpomyia



6

PR
GC
bu

127859
127905
Palpomyia tibialis







bu

127683
127729
Probezzia




6
PR

bu

127526
127614
SeiTOmyia




6
PR

bu

127683
127761
Sphaeromias





PR
GC


127526
127619
Stilobczzia





PR

sp
sw
I25S08
125886
Chaoboridae





PR



125892
125904
Chaoborus





PR



125904
125923
Chaoborus punctipennis
8.5
8



PR



125887
12588S
Eucorethra



7

PR



125808
127917
Chironomidae



6

OC

bu

127917
127994
Tanypodinae



7

PR

bu

127995
127996
Clinotanypus




8
PR :



127996
127998
Clinotanypus pinguis
9.8
8
7.5






127995
128010
Coelotanypus
6.2




PR



128010
128012
Coelotanypus concinnus
7.7




PR .



128010
128016
Coelotanypus scapularis





PR

bu

12S010
128013
Coelotanypus tricolor





PR

bu


128020
Macropelopiini





PR



127995
206646
Alotanypus









128020
128021
Apsectrotanypus





PR

bu

128021
128024
Apsectrotanypus johnsoni
0




PR



128020
128026
Bnindiniella



6

PR

sp

128026
128028
Brundiniella eumorpha
3.8








206647
206648
Fittkauimyia serla







sp
bu
128020
128034
Macropelopia



6

PR



128020
128048
Pscctrotanypus


8.1
10
10
PR ¦

sp

128048
128056
Psectrotanypus dyari
10
10
8.6






128270
128271
Djalmabatista





PR

sp

128271
128272
Djalmabatista pulcher





PR



128270
128277
Procladius
9.3
9
6.5
9
9
PR
GC
sp

128277
128285
Procladius bellus





PR



128069
128070
Natarsia
10
8
5.9

8
PR

sp

128070
128071
Natarsia baltimorcus


5.6






127994
128078
Pentaneurini



6

PR



128078
128079
Ablabestnyia


5.2

8
GC
PR


128079
128081
Ablabesmyia annulata


4.1


OM



128079
128083
Ablabesmyia aspera





OM



128079
128087
Ablabesmyia cinctipes





OM



128079
128089
Ablabesmyia hauberi





OM



128079
128090
Ablabesmyia idei





OM



128079
128093
Ablabesmyia janta
7.1

4.9


OM



128079
128097
Ablabesmyia mallochi
7.6
8
5


OM



12S079
128113
Ablabesmyia peleensis
4.6




OM

sp

B-42
Appendix B: Regional Tolerance Values, Functional Feeding Groups,
and Habit/Behavior Assignments for Benthic Macro invertebrates
-------



Regional Tolerance Values










Functional
Habit/




CO



Feeding Group
Behavior
Parent
TSN
TSN
Scientific Name
Southeast
(NC)
Upper Midwe
(WI)
Midwest
(OH)
Northwest
(ID)
Mid-Atlantic
(MACS)
primary
secondary
primary
secondary
128079
128121
Ablabesmyia rhamphe





OM



128078
128130
Conchapelopia
8.7
6
4.3
6
6
PR





Denopelopia atria









128161
128162
Guttipelopia guttipennis





PR




128237
Hayesomyia





PR

sp

128237
128249
Hayesomyia senata


4.6







128131
Helopelopia


3.9

6
PR

sp

128078
128167
Hudsonimyia





PR



128078
128170
Kienopclopia





PR

sp

128170
128171
Krenopelopia hudsoni





PR



128078
128173
Labrundinia


3.8


PR



128173
128174
Labrundinia becki





PR



128173
128175
Labrundinia johannseni





PR



128173
128176
Labrundinia maculata





PR



128173
128177
Labrundinia neopilosella




7
PR



128173
128178
Labrundinia pilosella
6
7
3.1


PR

sp

128173
128182
Labrundinia virescens
4.5




PR



128078
128183
Larsia
8.3
6
4.3
6
6
PR



128183
128184
Larsia bemeri





PR



128183
128186
Larsia decolorata





PR



128183
128189
Larsia indistincta





PR

sp


128132
Meropelopia


2.7

7




128078
128199
Monopelopia



6

PR

sp

128199
128200
Monopelopia boliekae





PR



128078
128202
Nilotanypus
4
6

6

PR

sp

128202
128203
Nilotanypus fimbriatus


2.8


PR



128078
128207
Paramerina
2.8


6
4
PR

sp

128207
128208
Paramerina anomala









128207
128209
Paramerina fragilis


4.7






128078
128215
Pentaneura
4.6
6

6

PR
GC


128215
128216
Pentaneura inconspicua


4.9


PR

sp

128215
128218
Pentaneura inculta





PR

sp

128078
128226
Rheopelopia





PR

sp

128226
128229
Rheopelopia paramaculipennis


2.9







128234
Telopelopia okoboji


4






128078
128236
Thienemannimyia



6
6
PR

sp

128078
128251
Trissopelopia





PR



128078
128259
Zavrelimyia
9.3
8
4.1
8
8
PR

sp

128259
128262
Zavrelimyia sinuosa


,


PR



128323
128324
Tanypus
9.6
10
8.8

10
PR
GC


128324
128329
Tanypus neopunctipennis


7.5


OM



128324
128335
Tanypus carinatus





OM



128324
128333
Tanypus puncdpennis





OM

sp

128324
128336
Tanypus stellatus





OM



127953
127954
Boreochlus



6

GC
SC


127917
128341
Diamesinae





GC

sp

Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
B-43
-------
Parent
TSN
TSN
Scientific Name
Southeast
(NC)
50
O
Upper Midwest *2.
m) g
Toleran
V5
£ c-
"H X
2 2-
ce Valu
U
J
la
2 C,
¦ <9
Mid-Atlantic
(MACS)
Func
Fecdinj
1
o.
tional
Group
•o
c
Ha
Beh
•1"
a
, I"1
secondary "
128342
128343
Boreoheptagyia



6

GC




128351
Diamesini



2

GC



128351
128355
Diamcsa
7.7
8

5

GC
SC
sp

128351
128401
Pagastia
2.2
1

1

GC



128351
128408
Potthastia



2

OM
GC


128408
128409
Potthastia gaedii
2


6

GC

sp

128408
128412
Potthastia longimana
7.4


2

GC

sp

128351
128416
Pseudodiamesa



6

GC

sp

128351
128426
Sympotthastia
5.7
2

2

GC
SC
sp

128437
128440
Monodiamesa



7

GC

bu
sp
128437
128446
Odontomcsa



4

GC



128446
128447
Odontomesa fulva
5.9
4







128437
128452
Prodiamcsa



3

GC

sp

128452
128454
Prodiamesa oiivacea
7.9
3







125808
128457
Orthocladiinae



5

GC

bu

128457
128563
Corynoncura
6.2
7
3.5
7
7
GC



128563
128565
Corynoneura celeripes


2.3


GC

sp

128563
128567
Corynoncura lobata


3.3






128563
128570
Corynoneura taris





GC



128457
129182
Thienemanniella
6
6
3.7
6
6
GC



129182
129193
Thienemanniella fusca





GC



129182
129189
Thienemanniella similis


2.4


GC



129182
129190
Thienemanniella xena


3.6


GC





Orthocladiini



6

GC



128457
128460
Acamptocladius





GC

bu
sp
128457
128470
Antillocladius









128457
128477
Brillia
5.2
5

5
5
SH
GC


128477
128478
Brillia flavifrons



5

SH



128477
128487
Brillia par







bu
cn
128477
128482
Brillia reti finis



5

SH

sp

128457
128511
Cardiocladius
6.2
5

5

PR

cn
bu
128511
128515
Cardiocladius obscurus


2.2






128457
128520
Chaetocladius



6

GC



128457
128575
Cricotopus

7
4.3
7
7
SH .
GC


128575
128583
Cricotopus bicinctus
8.7

6.7

7
OM



128575
128594
Cricotopus festivellus



7

SH



128575
128610
Cricotopus infuscatus
9








128575

Cricotopus Isocladius



7

SH



128575

Cricotopus Nostococladius



7

SH



128575
128640
Cricotopus politus





OM



128575

Cricotopus sylvestris
10




OM



128575
128651
Cricotopus tremulus



7
7
SH

sp

128575
128659
Cricotopus trifascia



7

OM



128575
128664
Cricotopus varipes
8.1








128575
128666
Cricotopus vieniensis
4.8

4.2






128457
128670
Diplocladius





GC

sp

B-44
Appendix B: Regional Tolerance Values, Functional Feeding Groups,
and Habit/Behavior Assignments for Benthic Macroinvertebrates
-------
Parent
TSN
TSN
Scientific Name
Southeast
50
Upper Midwest tS.
(WI) |
Toleran

Mid-Atlantic
(MACS)
Func
Feedinj
1
•E
O.
tional
Group
£
c
o
O
4>
-------
Parent
TSN
TSN
Scientific Name
Southeast
(NC)
a
Upper Midwest <5.
(Wl) |
H
Midwest 2.
(OH) |
ce Valu
1
¦ ¦ ¦¦ TS
Mid-Atlantic
(MACS)
Func
Feedinj
&
B
'&
tional
'Group
1
I
Ha
Beh
¦n
a.
3it/
ivior
1
c
o
g
128457
129011
Pnrorthocladius



6

GC



128457
129018
Psectrocladius
3.8
8
5.7
8
8
GC
SH


129018
129027
Psectrocladius elatus





OM



129018
129031
Psectrocladius Iimbatellus



8

GC

sp

129018
129051
Psectrocladius sordidcllus



8

GC .



123457
129052
Pscudorthocladius
0
0

0
0
GC

sp

128457 i 129071
Pseudosmittin





GC

sp

128457 j 129083
Psilometriocnemus





GC



128457 129086
Rheocricotopus


4.9
6
6
GC
SH


129086 j 129101
Rhcocricotopus pauciseta




6
GC



129086
129102
Rheocricotopus robacki
7.7
6
3.8






129086
129105
Rheocricotopus tuberculatus
6.8






bu

128457
129107
Rheosmittia





GC



128457
129110
Smittia





GC



128457
129152
Stilocladius





GC

sp

128457
129156
Symbiocladius



6

PA




128877
Symposiocladius







sp

128877
128915
Symposiocladius lignicola
5.4








128457 j 129161
Synorthocladius
4.7
2

2

GC
SC


129161 129162
Synorthocladius semiviiens


2.5






128457 | 129197
Tvetenia

5

5
5
gc ;



129197 129205
Tvetenia bavarica
4


5

GC :



129197 189327
Tvetenia discoloripes
3.9


5

GC



128457 I 129206
Unniella




4
GC

bu

129206
129207
Unniella multivirga
0




GC



128457
129208
Xylotopus
6.6
2





bu

129208
129209
Xylotopus par




2




128457
129213
Zalutschia




7
SH



128457
129228
Chironominae



6

GC



129228
129229
Chironomini



6

GC




206655
Apedilum









206655
129618
Apcdilum elachista







sp
bu
129231
129234
Asheum beckae





GC



129229
129236
Axarus





GC



129229
206657
Beardius







bu

206657
206658
Bcardius truncatus









129229
129254
Chironomus
9.8
10
8.1
10
10
GC
SH


129254
129280
Chironomus decorus





OM



129254
129313
Chironomus riparius





OM

bu

129254
129322
Chironomus stigmaterus





OM

sp
bu
129229
129350
Ciadopclma
2.5
9


7
GC ,



129229
129368
Cryptochironomus


4.9
8
8
PR ;

sp

129368
129370
Cryptochironomus blarina
8
8



j



129368 j 129376
Cryptochironomus fulvus
6.7
8



PR

bu

129229 | 129394
Cryptotendipes
6.1
6
4.2

6
GC

bu

129229 i 129421
Dcmicryptochironomus
2.1



8
GC



B-46
Appendix B: Regional Tolerance Values, Functional Feeding Groups,
and Habit/Behavior Assignments for Benthic Macroinvertebrates
-------
Parent
TSN
TSN
Scientific Name
Southeast
50
Q
Upper Midwest <5.
(WI) g
Toleran
M
-------



Regional Tolerance Values










Functional
Habit/




ts



Feeding Group
Behavior
Parent
TSN
TSN
Scientific Name
Southeast
(NC)
Upper Midwe
(wo
Midwest
(OH)
Northwest
(ID)
Mid-Atlantic
(MACS)
primary
secondary
•n
a.
secondary
129564
129565
Parachironomus abortivus


8






129564
129569
Parachironomus carinatus


5.3






129564
129573
Parachironomus dircctus


7.9






129564
129579
Parachironomus frcquens


3.8






129564
129595
Parachironomus hirtalatus









t29564
129581
Parachironomus monochromus
7.9








129564
129583
Parachironomus pectinatellae


3.7






129564
129587
Parachironomus schneideri







sp

129564
129588
Parachironomus sublettei









129229
129597
Paracladopelma
6.4
7



GC



129597
129608
Paracladopelma nereis
1.8




GC

c n

129597
129612
Paracladopelma undine
5.2




GC



129229
129616
Paralauterbomiella




8
GC

bu

129616
129619
Paralauterbomiella nigrohalterale









129229
129623
Paratendipes
5.3
8
5.7
8
8
GC



129623
129624
Paratendipes albimanus


4.3


GC

cn

129623
129632
Paratendipes subaequalis





GC
_ .


129229
129637
Phaenopsectra
6.8
7

7
7
SC
GC


129637
129642
Phaenopsectra flavipes
8.5

5.7






129637
129647
Phaenopsectra obediens





OM

cb
cn
129637
129652
Phaenopsectra punctipes


3.5


SC



129229
129657
Polypedilum



6
6
SH
GC


129657

Polypedilum Pentapcdilum



6

SH



129657
129725
Polypedilum angulum
5.6








129657
129666
Polypedilum aviceps
4

1.9






129657
129726
Polypedilum bergi




6
SH '



129657
129671
Polypedilum convictum
5.3

3.6






129657
129676
Polypedilum fallax
6.7








129657
129684
Polypedilum halterale
7.2








129657
129686
Polypedilum illinoense
9.2

6.9






129657
129692
Polypedilum laetum









129657
129698
Polypedilum Ontario


2.6






129657
129708
Polypedilum scalaenum
8.7








129657
129718
Polypedilum trigonum







bu

129657
129719
Polypedilum tritum









129229
129730
Robackia





GC



129730
129731
Robackia claviger
2.4




GC

bu

129730
129733
Robackia demeijerei
4.3


7

GC



129229
129735
Sactheria





GC

wood

129735
129736
Saetheria hirta





GC



129735
129737
Sactheria tylus
8.1
4







129229
129743
Stelechomyia




7
GC

bu

129743
129744
Stelechomyia perpulchra
4.6




GC

bu

129229
129746
Stenochironomus
6.4
5
3.6

5
SH
GC


129229
129785
Stictochironomus
6.7
9
4


OM
GC
bu

129785
129790
Sticlochironomus devinctus





OM



B-48
Appendix B: Regional Tolerance Values, Functional Feeding Groups,
and Habit/Behavior Assignments for Benthic Macroinvertebrates
-------
Parent
TSN
TSN
Scientific Name
Southeast 1
50 1
1 °
1 Upper Midwest C-
(WI) g
Toleran
¦s
•§ X
S S
ce Valu

cS
T3
C
o
o
o
Ha
Beh
•c
o.
<1
secondary °
129229
129820
Tribelos
6.6
5


5
GC



129820
206656
Tribelos atrum





GC



129820
129823
Tribelos fuscicorne


5.1


GC

bu

129820
129827
Tribelos jucundus


5.6


GC



129229
129837
Xenochironomus





PR



129837
129838
Xenochironomus xenolabis
7
0



PR



129229
129842
Xestochironomus





OM



129842
129844
Xestochironomus subletti





OM



129872
130040
Zavreliella







bu

130040
189328
Zavreliella marrnorata









129850
129851
Pseudochironomus
4.2
5
4.7
5

GC



129228
129872
Tanytarsini



6

FC



129872
129873
Cladotanytarsus
3.7
7
4.4
7
7
GC
FC
cb
sp
129872
129884
Constempellina



6

GC



129872
129890
Micropsectra
1.4
7
3.5
7
7
GC



129872
129932
Nimbocera



6

FC

sp

129932
206659
Nimbocera limnetica





FG



129872
129935
Paratanytarsus
7.7
6
4.2
6
6
GC

cn

129935

Paratanytarsus inopterus




6
GC



129872
129952
Rheotanytarsus
6.4
6
3.3
6
6
FC



129952
129955
Rheotanytarsus distinctissimus





FC

cb
sp
129952
129955
Rheotanytarsus distinctissimus





FC

cb
sp
129952
129957
Rheotanytarsus exiguus





FC



129872
129962
Stempellina
2
2

2

GC

cb
cn
129872
129969
Stempellinella
5.3
4
2.6
4
4
GC



129872
129975
Sublettea



6

FC



129975
129976
Sublettea coffmani
1.7

2.2






129872
129978
Tanytarsus
6.7
6
3.5
6
6
FC
GC


129978
130030
Tanytarsus glabrescens





FG

cb
sp
129978
129997
Tanytarsus guerlus





FG





Thienemanniola



6

GC



129872
130038
Zavrelia
2.7


8

GC

sw

125875
125877
Corethrella







sw

125808
125930
Culicidae



8

GC

sw

126233
126234
Aedes




8
FC



125955
125956
Anopheles
9.1



6
FC



126233
126455
Culex
10



8
FC



126233
126518
Deinocerites





FC



125931
125932
Toxorhynchites





PR



121226
121286
Deuterophlcbiidae





SC



121286
121287
Deuterophlebia



0

SC

sw
cb
121287
121290
Deuterophlebia nielsoni





SC



125808
125809
Dixidae



1
1
GC



125809
125810
Dixa
2.8


1

GC



125809
125854
Dixelia





GC



125809
125873
Meringodixa



2

GC

bu

Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
B-49
-------
Parent
TSN
TSN
Scientific Name
R
tn
CQ
U
3 U
£5-
Upper Midwest <5.
(Wl) g
¦}
Midwest 2.
(OH) §
	 o
Northwest ™
(ID) |.
Mid-Atlantic
(MACS)
Func
Feedin;
&
•G
o.
tional
Group
£•
a
•o
c
o
o
0>
(/)
Ha
Bch:
&
cs
E
'C
Q.
secondary §
125350
125351
Psychodidae



10

GC



125391
125392
Maruina



1

SC



125391
125514
Pericoma


5.6
4
4
GC



125391
125468
Psychoda
9.9

3.7
10

GC



125468
125469
Psychoda alternate





GC

bu

125399
125400
Telmatoscopus albipunctatus









t25762
125763
Ptychopteridae



7

GC



125764
125765
Bittacomorplia









125785
125786
Ptychoptera



7

GC



125808
126640
Simuliidae



6

FC

cn


126658
Cnephia mutata
4

5






126648
126674
Gymnopais





SC

cn

126648
126687
Metacnephia



6

FC




126642
Parasimulium





FC



126648
126703
Prasimulium
2.6


3

FC



126703
126736
Prosimulium mixtum
3.3
3




1


126773
126774
Simulium
4.4

4.8
6
6
FC



126774
126790
Simulium bivittatum



6

FC



126774
126832
Simulium jcnningsi




6
FC



126774
126834
Simulium jonesi




6
FC



126774
126841
Simulium meridionale



6

FC



126774
126870
Simulium rivuli




6
FC



126774
126873
Simulium slossonae





FC



126774
126883
Simulium tuberosum




6
FC

crt

126774
126892
Simulium venustum
7.4
5


6
FC



126774
126903
Simulium vittatum
8.7
7

6
6
FC



126648
126761
Stcgoptema









126648
126767
Twinnia



6

FC



125762
125799
Tanydcridae










125802
Prolanyderus



1



sp
bu
125799
125800
Protoplasa



5

GC



125800
125801
Protoplasa fitchii
5








125808
126624
Thaumalcidae





OM



126624
126629
Thaumalea





OM



126629
126631
Thaumalea elnora





OM



126629
126632
Thaumalea fusca





OM



118839
118840
Tipulidae



3

SH

bu

118841
118905
Megistocera









118841
119008
Prionocera



4

SH

cn

118841
119037
Tipula
7.7
4
7.2
4
4
SH



119037
119041
Tipulaabdominalis-


4






119037

Tipula ormosia



4

OM



119655
119656
Antocha
4.6
3
2.2
3

GC



119656
119660
Antocha monticola



3

GC




120488
Oyptolabis





SH '
GC
bu

121026
121027
Dicranota
0
3

3

PR



B-50
Appendix B: Regional Tolerance Values, Functional Feeding Groups,
and Habit/Behavior Assignments for Benthic Macroinvertebrates
-------



Regional Tolerance Values











Functional
Habit/




Upper Midwest
(WI)



Feeding Group
Behavior
Parent
TSN
TSN
Scientific Name
Southeast
(NC)
Midwest
(OH)
Northwest
(ID)
! Mid-Atlantic
I (MACS)
primary
secondary
primary
secondary
120030
120076
Elephantomyia





SH

sp
bu
120397
120503
Erioptera



3

GC



120397
120640
Gonomyia





GC

bu
sp
119655
119690
Helius




4
GC

hu
sp
120397
120732
Hesperoconopa



1

GC

bu

120030
120094
Hexatoma
4.7
2
2.3
2
2
PR

bu
sp

120095
Eiiocera





PR

bu
sp
120030
120164
Limnophila



4

PR

bu

119655
119704
Limonia
10
6

6

SH

bu


119706
Geranomyia




3
SH



120397
120758
Molophilus



4

SH

bu

120397
120830
Ormosia
6.5


3

GC

bu

121026
121118
Pedicia



6

PR

bu

120030
120335
Pilaria



7
7
PR



120030
120365
Pseudolimnophila
7.3
2


2
PR



120397
120968
Rhabdomastix



8

PR

sp
bu
120968
120977
Rhabdomastix fascigera



3

GC

bu

120968
120995
Rhabdomastix setigera



3

GC

bu

120030
120387
Ulomorpha









118831
130052
Brachycera









130928
130929
Atherix



2
2
PR



130929
130930
Atherix lantha
2.1
2
3.1


PR



130929
130932
Atherix variegata



2

PR



130741
130914
Pelecorhynchidae



3

PR



130914
130915
Glutops



3

PR



131750
136824
Dolichopodidae
9.7
4

4

PR



137952
137953
Dolichopus







cn

131750
135830
Empididae
8.1
6
3.5
6

PR

sp
bu
136304
136305
Chelifera



6

GC



135844
135849
Clinocera



6

PR



136304
136327
Hemerodromia



6
6
PR



136361
136377
Oreogeton



5

PA



135844
135881
Oreothalia



6

PR



135930
136123
Rhamphomyia



6

PR

sp
bu
135844
135920
Wiedemannia



6

PR



130130
130150
Stratiomyidae



8

GC



130155
130160
Allognosta



7

GC



130408
130409
Caloparyphus



7

GC

sp

130408
130436
Euparyphus





GC



130685
130694
Nemotelus







sp
bu
130483
130573
Odontomyia




7
GC



130408
130461
Oxycera







sp
bu
130483
130627
Stratiomys





FG



130741
130934
Tabanidae



8

PR

sp
bu
131061
131078
Chrysops
7.3
6
4.6

7
GC
PR


131061
131062
Silvius





PR



Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
B-51
-------



Regional Tolerance Values












Functional
Habit/




t/i



Feeding Group
Behavior
Parent
TSN
TSN
Scientific Name
Southeast
(NC)
%
•a
to ^
Midwest
(OH)
CO
%
Mid-Atlantic
(MACS)
f
w
-a
a

secondary



it
o Q
Z w
•c
cx
o
u
¦c
CL
131318
131527
Tabanus
9.7
5

5
5
PR



131750
148316
Canaceidae





SC

bu

131750
146893
Ephydridae



6

GC



131750
150025
Muscidae



6

PR



150729
150730
Limnophora
7




PR



138933
139013
Dohmiphora









131750
144653
Sciomyzidae



6

PR

bu

144770
144898
Sepedon





PR



131750
139621
Syrphidae



10

GC



141029
141049
Chrysogaster










140904
Eri stalis
10

0


GC

bu

B-52
Appendix B: Regional Tolerance Values, Functional Feeding Groups,
and Habit/Behavior Assignments for Benthic Macroinvertebrates
-------
Appendix C:
Tolerance and Trophic Guilds of
Selected Fish Species
Rapid Bioassessment Protocols For Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
C-l
-------
This Page Intentionally Left Blank
C-2
Appendix C: Tolerance and Trophic Guilds of Selected Fish Species
-------
Appendix C
Appendix C is a list of selected fishes of the United States in phylogenetic order. Included are the
Taxonomic Serial Number (TSN) and the Parent Taxonomic Serial Number for each of the species listed
according to the Integrated Taxonomic Information System (ITIS'). The ITIS generates a national
taxonomic list that is constantly updated and currently posted on the World Wide Web at
. If you are viewing this document electronically, this page is linked to the ITIS
web site.
Additionally, this Appendix details trophic and tolerance designations for selected fishes of the United
States. To generate this list, we compiled a consensus rating for each taxon from the literature sources
listed below. Exceptions are listed for each source that does not agree with the consensus of other cited
literature. Exceptions are noted by first listing the designation then the literature source code in
parentheses. The following is a list of the designations and literature sources used in this Appendix.
Trophic Designations
P=Piscivore	F=Filter feeder
H=Herbivore	G=Generalist feeder
0=Omnivore	V=Invertivore
I=Insectivore (including specialized insectivores)
Notes on Trophic Designations
Piscivore—although some investigators separate certain species into subcategories such as parasitic
(e.g., sea lamprey) or top carnivore (e.g., walleye), we have grouped these together aspiscivores for this
list.
Tolerance Designations (relevant to non-specific stressors)
I = Intolerant
M = Intermediate
T = Tolerant
Notes on Tolerance Designations
Intolerant—although some investigators separate certain species into subcategories such as rare
intolerant, special intolerant or common intolerant, we have grouped these together as intolerant for this
list.
Literature Sources For Trophic/Tolerance Designations
(A)	= Midwestern United States (Karr et al. 1986)
(B)	= Ohio (Ohio EPA 1987)
(C)	= Midwestern United States (Plafkin et al. 1989)
(D)	= Central Corn Belt Plain (Simon 1991)
(E)	= Wisconsin Warmwater (Lyons 1992)
(F)	= Maryland Coastal Plain (Hall et al. 1996)
(G)	= Northeastern United States (Halliwell et al. 1999)
Rapid Bioassessment Protocols For Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
C-3
-------
This Page Intentionally Left Blank
C-4
Appendix C; Tolerance and Trophic Guilds of Selected Fish Species
-------
A Checklist of Index of Biotic Integrity Designations for Fishes of the United States
(Nomenclature follows Robins et al.1991)
Parent
TSN
TSN
Common Name
Scientific Name
Trophic
Trophic
Exceptions
I Tolerance 1
Tolerance
Exceptions
"159696
159697
I.ampreys i-
Petr.omyiontidae .';i


~4,

159723
159724
Ohio lamprey
Ichthyomyzon bdellium
P

1
M(G)
159723
159725
Chestnut lamprey
Ichthyomyzon castaneus
P

M

159723
159726
Northern brook lamprey
Ichthyomyzon fossor
F

1

159723
159727
Southern brook lamprey
Ichthyomyzon gagei


I

159723
159728
Mountain brook lamprey
Ichthyomyzon greeleyi
F

I

159723
159730
Silver lamprey
Ichthyomyzon unicuspis
P

M

159700
159705
Least brook lamprey
Lampetra aepyptera
F

M
I(D, G)
159700
159708
American brook lamprey
Lampetra appendix
F

I

159700
159704
River lamprey
Lampetra ayresi




159700
159709
Kern brook lamprey
Lampetra hubbsi




159700
159701
Arctic lamprey
Lampetra japonica




159700
159710
Pit-Klamath brook lamprey
Lampetra lethophaga




159700
201891
Vancouver lamprey
Lampetra macrostoma




159700
159711
Miller Lake lamprey
Lampetra minima




159700
159707
Western brook lamprey
Lampetra richardsoni




159700
201892
Klamath lamprey
Lampetra similis




159700
159713
Pacific lamprey
Lampetra tridentata




159721
159722
Sea lamprey
Petromyzon marinus
P

M

161063
*16106#
Sturgeons
Acipenseridae "


¦ . ;

161065
161069
Shortnosc sturgeon
Acipenser brevirostrum
V

i

161065
161071
Lake sturgeon
Acipenser fulvescens
V
1(E)
M
KG)
161065
161067
Green sturgeon
Acipenser medirostris




161065
161070
Atlantic sturgeon
Acipenser oxyrhynchus
V

I

161065
161068
White sturgeon
Acipenser transmontanus




161080
161081
Pallid sturgeon
Scaphirhynchus albus




161080
161082
Shovelnose sturgeon
Scaphirhynchus platorynchus
I

M

161063
'J61085
Paddlefishes ..
Polyodontidae •

'
*•:

161087
161088
Paddlefish
Polyodon spathula
F

1

161091
161092 -
. "Gars
Lepisosteidae V


' "i

161093
161095
Spotted gar
Lepisosteus oculatus
P

M

161093
161094
Longnose gar
Lepisosteus osseus
P

M

161093
161096
Shortnose gar
Lepisosteus platostomus
P

M

161093
161098
Florida gar
Lepisosteus platyrhincus





161097
Alligator gar
Lepisosteus spatula
P

M

161101
161102
Bowfins
. Amiidae


sfc

161103
161104
Bowfm
Amia calva
P

M
T{G)
161902.
161903
Mooneyes
Hiodontidae . ...




161904
161905
Goldeye
Hiodon alosoides
1

I

161904
161906
Mooneye
Hiodon tergisus
1

I

161124
161125:
Freshwater eels
Anguillidae -'.'v'-1--
d.



161126
161127
American eel
Anguilla rostrata
P
G(F)
M
T(F,G)
,161699
161700
Herrincs
Clupeidae ^ '

'¦nx .• ' AS'-". •
•V.

161701
161703
Blueback herring
Alosa aestivalis
F

M

161701
161705
Alabama shad
Alosa alabamae


M

161701
161707
Skioiack herring
Alosa chrysochloris
P

M

Rapid Bioassessment Protocols For Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macro invertebrates, and Fish, Second Edition
C-5
-------
Parent
TSN
TSN
Common Name
Scientific Name
1 Trophic
Trophic
Exceptions
| Tolerance |
Tolerance
Exceptions
161701
161704
Hickory shad
Aiosa mediocris




161701
161706
Alewife
Alosa pseadohnrengus
F
V(C)
M

161701
161702
American shad
Atosa sapidissima
V
F(G)
M

161731
161733
Finescale menhaden
Brevoortia gunteri




161731
161734
Gulf menhaden
Brevoortia patronus




161731
161735
Yellowfin menhaden
Brevoortia smithi




161731
161732
Atlantic menhaden
Brevoortia tyrannus




161721
161722
Atlantic herring
Cltipea harengus




161721
551209
Pacific herring
Clupea pallasi




161736
161737
Gizzard shad
Dorosoma cepedianum
O
F(E),H(G)
M
T(G)
161736
161738
Threadfin shad
Dorosoma petenense
O

M

161742
161743
Round herring
Etrumeus teres




161752
161753
False pilchard
Harengida clupeola




161752
161754
Redear sardine
Harenguia humeralis




161752
161755
Scaled sardine
Harenguia jaguana




161752
161757
Flatiron herring
Harenguia thrissina




161758
161759
Dwarfhcrring
Jenkinsia lamprotaenia




161758
161760
Little-eye herring
Jenkinsia majua




161758
161761
Shorthand herring
Jenkinsia stolifera




161747
161750
Deepbody thread herring
Opisthonema libertate




161747
161751
Middling thread herring
Opisthonema medirastre




161747
161748
Atlantic thread herring
Opisthonema oglinum




161762
161763
Spanish sardine
Sardinella aurita




161762
161764
Orangespot sardine
Sardinella brasiliensis




161728
161729
Pacific sardine
Sardinops sagax




161699
161826
Anchovies
Engraulidae




161837
161846
Key anchovy
Anchoa cayorum




161837
161847
Deepbody anchovy
Anchoa compressa




161837
161840
Cuban anchovy
Anchoa cubana




161837
161848
Slough anchovy
Anchoa deiicalissima




161837
161838
Striped anchovy
Anchoa hepsetus




161837
161841
Bigeye anchovy
Anchoa lamprotaenia




161837
161842
Dusky anchovy
Anchoa lyolepis




161837
161839
Bay anchovy
Anchoa mitchilli





161843
Longnose anchovy
Anchoa nasuta




161853
161857
Flat anchovy
Anchoviella perfasciata




161860
161862
Anchoveta
Cetengraulis mysticetus




161827
161830
Silver anchovy
Engraulis eurystole




161827
161828
Northern anchovy
Engraulis mordax




163341
163342
Carps and Minnows
Cypr'uiidae




163530
1C3531
Chiselmouth
Acrocheilus alutaceus
H

M

163532
163533
Longfin dace
Agosia chrysogaster




163507
163508
Central stonerollcr
Campostoma anomalum
H

M
T(G)
163507
163509
Largescale stoneroller
Campostoma oligolepis
H

M

163507
163510
Mexican stoneroller
Campostoma ornatum




163507
163511
Bluefin stoneroller
Campostoma pnuciradii




163349
163350
Goldfish
Carassius auratus
O
G(G)
T

163370
163373
Redside dace
Clinostomus elongatus
I

I

163370
163371
Rosyside dace
Clinostomus funduloides
I

I

163534
163535
Lake chub
Couesius phtmbcus
1
G(G)
M

163536
163537
Grass carp
Ctenooharvngodon idella
H
OfDl
M
TfDl
C-6
Appendix C: Tolerance and Trophic Guilds of Selected Fish Species
-------
Parent
TSN
TSN
Common Name
Scientific Name
Trophic
Trophic
Exceptions
| Tolerance |
Tolerance
Exceptions
163765
163766
Satinfin shiner
Cyprinella analostann
I

i
T(G)
163765
163768
Blue shiner
Cyprinella caerulea




163765
163770
Ocmulfjee shiner
Cyprinella callisema




163765
163772
Alabama shiner
Cyprinella callistia




163765
163774
Bluestripe shiner
Cyprinella callitaenia




163765
163776
Bluntface shiner
Cyprinella camura




163765
163778
Greenfin shiner
Cyprinella chloristia




163765
163780
Beautiful shiner
Cyprinella formosa




163765
163782
Whitetail shiner
Cyprinella galactura




163765
163784
Tallapoosa shiner
Cyprinella gibbsi




163765
163786
Thicklip chub
Cyprinella labrosa




163765
163788
Bannerfin shiner
Cyprinella leedsi




163765
163790
Plateau shiner
Cyprinella lepida




163765
163792
Red shiner
Cyprinella lutrcnsis
O
I(B,C,D)
T
M(C)
163765
163795
Spotfin chub
Cyprinella monacha
I

T

163765
163797
Whitefin shiner
Cyprinella nivea




163765
163799
Proserpine shiner
Cyprinella proserpina




163765
163801
Fieryblack shiner
Cyprinella pyrrhomelas




163765
163803
Spotfin shiner
Cyprinella spiloptera
1

M
T(G)
163765
163806
Tricolor shiner
Cyprinella trichroistia




163765
163809
Blacktail shiner
Cyprinella venusta




163765
163811
Steelcolor shiner
Cyprinella whipplei
I

M
1(A)
163765
163814
Altamaha shiner
Cyprinella xaenura




163765
163817
Santee chub
Cyprinella zaitema




163343
163344
Common carp
Cyprinus carpio
O
G(G)
T

163512
163514
Devils River minnow
Dionda diaboli




163512
163513
Roundnose minnow
Dionda episcopa




163539
163540
Desert dace
Eremichthys acros




163819
163820
Slender chub
Erimyslax cahni




163819
163821
Streamline chub
Erimystax dissimilis
I

1

163819
163822
Ozark chub
Erimystax harryi




163819
163823
Blotched chub
Erimystax insignis




163819
163824
Gravel chub
Erimystax x-punctatus
1

M
I(E,G)
163355
163357
Tonguetied minnow
Exoglossum laurae
1

I
M(G)
163355
163356
Cutlips minnow
Exopjossum maxillininia
I

1

163541
163542
Alvord chub
Gila alvordensis




163541
163543
Utah chub
Gila atraria




163541
163544
Tui chub
Gila bicolor




163541
163547
Borax Lake chub
Gila boraxobius




163541
163548
Blue chub
Gila coendea





163549
Leatherside chub
Gila copei




163541
163550
Thicktail chub
Gila crassicauda




163541
163551
Humpback chub
Gila cypha




163541
163552
Sonora chub
Gila ditaenia




163541
163553
Bonytail
Gila elegans




163541
163560
Gila chub
Gila intermedia




163541
163554
Chihuahua chub
Gila nigrescens




163541
163555
Arroyo chub
Gila orcutti




163541
163556
Rio Grande chub
Gila pandora




163541
163557
Yaoui chub
Gila purourea




Rapid Bioassessment Protocols For Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
C-7
-------
P»rcnt
TSN
TSN
Common Name
Scientific Name
Trophic
Trophic
Exceptions
| Tolerance |
Tolerance
Exceptions
163541
163558
Roundtail chub
Gila robiista




163562
163563
Flame chub
Hemilremia flammea




163564
163565
California roach
Hesperoleucus symmetricus




163358
163365
Rio Grande silvery minnow
Hybognathus amarus




163358
163362
Western silvery minnow
Hybognathus argyritis




163358
163363
Brassy minnow
Hybognathus hankinsoni
0
H(E,G)
M

163358
163364
Cypress minnow
Hybognathus Itayi
0

M

163358
163360
Mississippi silvery minnow
Hybognathus nuchalis
H
0(D)
M
1(A,E)
163358
163361
Plains minnow
Hybognathus placitus




163358
163359
Eastern silvery minnow
Hybognathus regius
H
O(D)
M
KG)
163690
163691
Silver carp
Hypophthalmichthys molitrix
0

T

163690
163692
Bighead carp
Hypophthalmichthys nobilis




163566
163567
Least chub
lotichthys phlegethontis




163568
163569
Hitch
Lavinia exilicauda




163570
163571
White River spinedace
Lepidomeda albivallis




163570
163572
Pahranagat spinedace
Lepidomeda altivelis




163570
163573
Virgin spinedace
Lepidomeda mollispinis




163570
103574
Little Colorado spinedace
Lepidomeda vittaia




163575
163576
Ide
Leuciscus idus




163825
163826
White shiner
Luxilus albeolus




163825
163828
Cardinal shiner
Luxilus cardinalis




163825
163830
Crescent shiner
Luxilus cerasinus




163825
163832
Striped shiner
Luxilus chrysocephalus
I

M
T(G)
163825
163834
Warpaint shiner
Luxilus coccogenis




163825
163836
Common shiner
Luxilus cornutus
1
G(G)
M

163825
163838
Duskystripe shiner
Luxilus pilsbryi




163825
163840
Bleeding shiner
Luxilus zonatus




163825
163843
Bandfln shiner
Luxilus zonistius




163846
163847
Rosefin shiner
Lythrurus ardens
I

M

163846
163849
Blacktip shiner
Lythrurus atrapiculus




163846
163851
Pretty shiner
Lythrurus bellus




163846
163853
Ribbon shiner
Lythrurus fumeus
I

M

163846
163855
Mountain shiner
Lythrurus lirus




163846
163857
Cherryfin shiner
Lythrurus roseipinnis




163846
163859
Ouachita shiner
Lythrurus snelsoni




163846
163861
Redfin shiner
Lythrurus umbratilis
1

M
T(G)
163863
163864
Speckled chub
Macrhybopsis aestivalis
1

1

163S63
163866
Sturgeon chub
Macrhybopsis gelida




163863
163868
Sicklefin chub
Macrhybopsis meeki




163863
163870
Silver chub
Macrhybopsis storeriana
I

M
KG)
163872
163873
Pearl dace
Margariscus margarita
I
G(G)
M

163582
163583
Spikcdace
Meda fulgida




163584
163585
Moapa dace
Moapa coriacea




163520
163521
Peamouth
Mylocheilus caurinus
I

M

163586
163587
Hardhead
Mvlonharodon conocepltalus




C-8
Appendix C: Tolerance and Trophic Guilds of Selected Fish Species
-------
Parent
TSN
TSN
Common Name
Scientific Name
Trophic
Trophic
Exceptions
Tolerance 1
Tolerance
Exceptions
163391
163394
Redspot chub
Nocomis asper




163391
163395
Hornyhead chub
Nocomis biguttatus
1
G(G)
i
M(G)
163391
163396
Rcdtail chub
Nocomis effusus




163391
163393
Bluehead chub
Nocomis leptocephalus




163391
163392
River chub
Nocomis micropogon
I
GfG)
i
M(F,G)
163391
163397
Bipmouth chub
Nocomis platyrhynchus




163391
163398
Bull chub
Nocomis raneyi




163367
163368
Golden shiner
Notemigonus crysoleucas
O
1(B,D),G(F,G)
T

163399
163422
Whitemouth shiner
Notropis alborus




163399
163423
Highfin shiner
Notropis aitipinnis




163399
163410
Texas shiner
Notropis amabilis




163399
163475
Bigeye chub
Notropis amblops
1

I
M(G)
163399
163477
Orangefin shiner
Notropis ammophilus





163411
Pallid shiner
Notropis amnis
1

I

163399
163401
Comely shiner
Notropis amoenus
I

T

163399
163424
Pugnose shiner
Notropis anogenus
I
H(E)
1

163399
163425
Popeye shiner
Notropis ariommus
1

I

163399
163426
Burrhead shiner
Notropis asperifrons




163399
163412
Emerald shiner
Notropis atherinoides
I

M

163399
163413
Blackspot shiner
Notropis atrocaudalis




163399
163427
Rough shiner
Notropis baiieyi




163399
163428
Red River shiner
Notropis bairdi




163399
163402
Bridle shiner
Notropis bifrenatus
I

1

163399
163429
River shiner
Notropis blennius
I

M

163399
163430
Bigeye shiner
Notropis boops
I

I

163399
163431
Tamaulipas shiner
Notropis braytoni




163399
163478
Silverjaw minnow
Notropis buccatus
I

M
T(G)
163399
163432
Smalleye shiner
Notropis buccula




163399
163414
Ghost shiner
Notropis buchanani
I

M
1(E)
163399
163480
Cahaba shiner
Notropis cahabae




163399
163433
Silverside shiner
Notropis candidus




163399
163403
Ironcolor shiner
Notropis chalybaeus
I

I
M(G)
163399
163434
Chihuahua shiner
Notropis chihuahua




163399
163435
Redlip shiner
Notropis chiiiticus




163399
163436
Greenhead shiner
Notropis chlorocephalus




163399
163437
Rainbow shiner
Notropis chrosomus




163399
163438
Dusky shiner
Notropis cummingsae




163399
163439
Bigmouth shiner
Notropis dorsalis
1

M

163399
163440
Fluvial shiner
Notropis edwardraneyi





163441
Broadstripe shiner
Notropis euryzonus




163399
163442
Arkansas River shiner
Notropis girardi




163399
163443
Wedgespot shiner '
Notropis greenei




163399
163444
Redeye chub
Notropis harperi




163399
163445
Blackchin shiner
Notropis heterodon
I

I

Rapid Bioassessment Protocols For Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
C-9
-------
Parent
TSN
TSN
Common Name
Scientific Name
Trophic
Trophic
Exceptions
| Tolerance |
Tolerance
Exceptions
163399
163446
Blacknose shiner
Notropis heterolepis
I

i

163399
163447
Bluehead shiner
Notropis hubbsi




163399
163404
Spottail shiner
Notropis hudsonius
I
G(F)
M
I(A,E,F)

163448
Sailfin shiner
Notropis hypselopterus




163399
163449
Highscale shiner
Notropis hypsilepis




163399
163481
Hishback chub
Notropis hypsinotus




163399
163450
Rio Grande shiner
Notropis jemezanus




163399
163451
Tennessee shiner
Notropis leuciodus




163399
163483
Lined chub
Notropis lineapunctatus




163399
163452
Longnose shiner
Notropis longirostrts




163399
163453
Yellowfin shiner
Notropis lutipinnis




163399
163454
Taillijsht shiner
Notropis maculatus




163399
163455
Cape Fear shiner
Notropis mekistocholas




163399
163485
Blackmouth shiner
Notropis melanostomus




163399
163456
Ozark minnow
Notropis nubilus
H

1

163399
163486
Phantom shiner
Notropis orca




163399
163457
Kiamichi shiner
Notropis ortenburgeri




163399
163415
Sharpnose shiner
Notropis oxyrliynchus




163399
163458
Ozark shiner
Notropis ozarcanus




163399
163459
Peppered shiner
Notropis perpallidus




163399
163460
Coastal shiner
Notropis petersoni




163399
163461
Silver shiner
Notropis photogenis
I

I
T(G)
163399
163416
Chub shiner
Notropis potteri




163399
163407
Swallowtail shiner
Notropis procne
I

I
M(G)
163399
163409
Rosyface shiner
Notropis rubelius
1

I

163399
163487
Rosyfacc chub
Notropis rubescens
I

I

163399
163462
Saffron shiner
Notropis rubricroceus




163399
163490
Bedrock shiner
Notropis rupestris




163399
163463
Sabine shiner
Notropis sabinae




163399
163464
New River shiner
Notropis scabriceps




163399
163465
Sandbar shiner
Notropis scepticus




163399
163466
Roughhead shiner
Notropis scmperasper




163399
163417
Silverband shiner
Notropis shumardi





163467
Flagfin shiner
Notropis signipinnis




163399
163418
Bluntnose shiner
Notropis simus




163399
163468
Mirror shiner
Notropis spectrunculus




163399
163469
Silverstripe shiner
Notropis stilbius




163399
163419
Sand shiner
Notropis stramineus
I
G(O)
M

163399
163470
Telescope shiner
Notropis telescopus




163399
163420
Weed shiner
Notropis texanus
I
H(E)
I

163399
163471
Topeka shiner
Notropis topeka




163399
163472
Skygazer shiner
Notropis uranoscopus




163399
163421
Mimic shiner
Notropis volucellus
1
G(G)
I
M(G)

163473
Bluenose shiner
Notropis welaka




163399
16349]
Channel shiner
Notronis wicklifTi
I

M

I
C-10
Appendix C: Tolerance and Trophic Guilds of Selected Fish Species
-------
Parent
TSN
TSN
Common Name
Scientific Name
Trophic
Trophic
Exceptions
V
u
s
«
u
¦o
Tolerance
Exceptions
163399
163493
Clear chub
Notropis winchelli




163399
163474
Coosa shiner
Notropis xaenocephalus




163875
163876
Pugnose minnow
Opsopoeodus emiliae
I

1

163878
163879
Oregon chub
Oregonichthys crameri




163588
163589
Sacramento blackfish
Orthodon microlepidotus




163501
163503
Riffle minnow
Phenacobius catostomus




163501
163504
Fatlips minnow
Phenacobius crassilabrum




163501
163502
Suckermoutli minnow
Phenacobius mirabilis
I

M

163501
163505
Kanawha minnow
Phenacobius teretulus




163501
163506
¦ Stargazing minnow
Phenacobius uranops




163590
163591
Blackside dace
Phoxinus cumberlandensis




163590
163592
Northern redbelly dace
Phoxinus eos
H
G(G)
M

163590
163593
Southern redbelly dace
Phoxinus erythrogaster
H

M
1(A)
163590
163594
Finescale dace
Phoxinus neogaeus
I
G(G)
M

163590
163595
Mountain redbelly dace
Phoxinus oreas




163590
163598
Tennessee dace
Phoxinus tennesseensis




163515
163516
Bluntnose minnow
Pimephales nolalus
O
0(G)
T

163515
163517
Fathead minnow
Pimephales promelas
O
G(F,G)
T

163515
163519
Slim minnow
Pimephales tenellus




163515
163518
Bullhead minnow
Pimephales vigilax
o

M

163599
163600
Woundfm
Plagopterus argentissimus




163881
163882
Flathead chub
Platygobio gracilis




163601
163602
Clear Lake splittail
Pogonichthys ciscoides




163601
163603
Splittail
Pogonichthys macrolepidotus




163522
163524
Sacramento squawfish
Ptychocheilus grandis




163522
163525
Colorado squawfish
Ptychocheilus lucius




163522
163523
Northern squawfish
Ptychocheilus oregonensis
p

T

163522
163526
Umpqua squawfish
Ptychocheilus umpquae




163604
163605
Relict dace
Relictus solitarius




163381
163382
Blacknose dace
Rhinichlhys atratulus
G
1(A)
T

163381
163384
Longnose dace
Rhinichthys calaraclae
I

I
M(G)
163381
163388
Loach minnow
Rhinichlhys cobitis




163381
163390
Las Vegas dace
Rhinichlhys deaconi




163381
163385
Umpqua dace
Rhinichthys evermanni




163381
163386
Leopard dace
Rhinichthys falcatus
I

M

163381
163387
Speckled dace
Rhinichthys osculus
I

M

163606
163607
Bitterling
Rhodeus sericeus




163527
163528
Redside shiner
Richardsonius balteatus




163527
163529
Lahontan redside
Richardsonius egregius




163612
163613
Rudd
Scardinius erythrophthalmus
o
KG)
T

163374
163376
Creek chub
Semotilus atromaculatus
G
1(A)
T

163374
163375
Fallfish
Semotilus corporalis
G

M

163374
163377
Sandhills chub
Semotilus luntbee




163374
163379
Dixie chub
Semotilus thoreauianus




Rapid Bioassessment Protocols For Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
C-l
1
-------
P«rcnt
TSN
TSN
Common Name
Scientific Name
Trophic
Trophic
Exceptions
1 Tolerance |
Tolerance
Exceptions
163347
163348
Tench
Tinea linen




163341
163892
Suckers
Catostomidae




163916
163919
River carpsucker
Carpiodes enrpio
O

M

163916
163917
Ouillback
Carpiodes cyprimis
O
G(G)
M
T(G) •
163916
163920
HiRhfin carpsucker
Carpiodes velifer
O

I
M(C)
163893
163899
Utah sucker
Catostomus ardens




163893
163900
Yaqui sucker
Catostomus bernardini




163893
163894
Lonjmose sucker
Catostomus catostomus
I

M
KG)
163893
163901
Desert sucker
Catostomus clarki




163893
163897
Bridgelip sucker
Catostomus eolumbianus




163893
163895
White sucker
Catostomus commersoni
O
I(A)G(F,G)
T

163893
163902
Bluehead sucker
Catostomus discobolus




163893
163904
Owens sucker
Catostomus fumeiventris




163893
163905
Sonora sucker
Catostomus insignis




163893
163906
Flannelmouth sucker
Catostomus latipinnis




163893
163896
Largescale sucker
Catostomus macrocheilus
O

T

163893
163907
Modoc sucker
Catostomus microps




163893
163908
Sacramento sucker
Catostomus occidentalis




163893
163909
Mountain sucker
Catostomus platyrhynchus
H

M

163893
163910
Rio Grande sucker
Catostomus plebeius




163893
163911
Klamath smallscale sucker
Catostomus rimiculus




163893
163912
Santa Ana sucker
Catostomus santaanae




163893
163913
Klamath largesoale sucker
Catostomus snyderi




163893
163914
Tahoe sucker
Catostomus tahoensis




163893
163915
Warner sucker
Catostomus warnerensis




163960
163961
Shortnosc sucker
Chasmistes brevirostris




163960
163962
Cul-ui
Chasmistes cuius




163960
163963
June sucker
Chasmistes liorus




163960
163964
Snake River sucker
Chasmistes muriei




163952
163953
Blue sucker
Cycleptus elongatus
I
0(A)
I

163969
163970
Lost River sucker
Deitistes luxatus




163921
163924
Creek chubsucker
Erimyzon oblongus
I
O(F),G(G)
M
T(F),I(G)
163921
163922
Lake chubsucker
Erimyzon sucetta
I

M

163921
163926
Sharpfin chubsucker
Erimyzon tenuis




163948
163950
Alabama hog sucker
Hypentelium etowanum




163948
163949
Northern hog sucker
Hypentelium nigricans
I
G(G)
I
M(B,D,G)
163948
163951
Roanoke hog sucker
Hypentelium roanokense




163954
163955
Smallmouth buffalo
Ictiobus bubalus
I

M
1(E)
163954
163956
Bigmouth buffalo
Ictiobus cyprinellus
I
P(A)
M

163954
163957
Black buffalo
Ictiobus niger
I

M
1(E)
163965
163966
Harelip sucker
Lagochila lacera
1

I

163958
163959
Spotted sucker
Minytrema melanops
I

M
1(A,E)
163927
163933
Silver redhorse
Moxostoma anisurum
1

M


163934
Bigeye jumprock
Moxostoma ariommum





163935
Blackfin sucker
Moxostoma atripinne




C-12
Appendix C: Tolerance and Trophic Guilds of Selected Fish Species
-------
Parent
TSN
TSN
Common Name
Scientific Name
Trophic
Trophic
Exceptions
1 Tolerance 1
Tolerance
Exceptions
163927
163936
River redhorse
Moxostoma carinatum
I

i

163927
163937
Black jumprock
Moxostoma cervinum





163931
Gray redhorse
Moxostoma congestum




163927
163938
Black redhorse
Moxostoma duquesnei
1
G(G)
i

163927
163939
Golden redhorse
Moxostoma erythrurum
I

M
KG)

163940
Rustyside sucker
Moxostoma hamiltoni




163927
163941
Copper redhorse
Moxostoma hubbsi





163942
Greater jumprock
Moxostoma lachneri
I

I

163927
163928
Shorthead redhorse
Moxostoma macrolepidotum
I

M

163927
163943
V-lip redhorse
Moxostoma pappillosum




163927
163932
Blacktail redhorse
Moxostoma poecilurum





163944
Torrent sucker
Moxostoma rhothoecum




163927
163945
Smallfin redhorse
Moxostoma robustum





163946
Striped jumprock
Moxostoma ruptscartes




163927
163947
Greater redhorse
Moxostoma valenciennesi
1

I

163967
163968
Razorback sucker
Xyrauchen texanus




163992
163995
" Bullhead catfishcs,-.
Ictaluridae




164034
164035
Snail bullhead
Ameiurus brunneus




164034
164037
White catfish
Ameiurus catus
I
P(G)
M

164034
164039
Black bullhead
Ameiurus melas
1

M
T(D)
164034
164041
Yellow bullhead
Ameiurus natalis
I
O(F),G(G)
T
M(D)
164034
164043
Brown bullhead
Ameiurus nebulosus
I
G(F,G)
T
M(D)
164034
164045
Flat bullhead
Ameiurus platycephalus




164034
164047
Spotted bullhead
Ameiurus serracanthus




163996
163997
Blue catfish
Ictalurus furcatus
P
1(A)
M

163996
164001
Headwater catfish
Ictalurus lupus




163996
164000
Yaqui catfish
JctaluiMS pricei




163996
163998
Channel catfish
Ictalurus punctatus
P
1(A),G(C)
M

164002
164006
Ozark madtom
Noturus albater




164002
164007
Smoky madtom
Noturus baiteyi




164002
164008
Elegant madtom
Noturus elegans




164002
164009
Mountain madtom
Noturus eleutherus
I

1

164002
164010
Slender madtom
Noturus exilis
I

I

164002
164011
Checkered madtom
Noturus flavater




164002
164012
Yellowfin madtom
Noturus flavipinnis




164002
164013
Stonecat
Noturus flavus
I

I
M(G)
164002
164014
Black madtom
Noturus funebris




164002
164015
Carolina madtom
Noturus furiosus




164002
164016
OranRefin madtom
Noturus gilberti




164002
164003
Tadpole madtom
Noturus gyrimis
I

M
1(A)
164002
164017
Least madtom
Noturus hildebrandi




164002
164004
Margined madtom
Noturus insignis
I

M

164002
164018
Ouachita madtom
Noturus lachneri




164002
164019
Speckled madtom
Noturus leotacanthus




Rapid Bioassessment Protocols For Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
C-13
-------
Tarcnt
TSN
TSN
Common Name
Scientific Name
Trophic
Trophic
Exceptions
| Tolerance |
Tolerance
Exceptions
164002
164020
Brindled madtom
Nolurus miurtts
I

i
M{G)
164002
164021
Frecklebelly madtom
Noturus munitus




164002
164005
Freckled madtom
Noturus nocturnus
1

M
1(D)
164002
164022
Brown madtom
Noturus phaeus




164002
164023
Neosho madtom
Noturus placidus




164002
164024
Pygmy madtom
Noturus stanauli




164002
164025
Northern madtom
Noturus stigmosus
I

I

164002
164026
Caddo madtom
Noturus taylori




164002
164027
Scioto madtom
Noturus trautmani
I

I

164028
164029
Flathead catfish
Pylodictis olivaris
P

M

164030
164031
Widemouth blindcat
Satan eurystomus




164032
164033
Toothless blindcat
Trogloglanis pattersoni




162136
162137
Pikes
Esocidae




162140
162141
Rcdfin pickerel
Esox americanus americanus
P

M

162140
162142
Grass pickerel
Esox americanus vermiculatus
P

M

162138
162139
Northern pike
Esox lucius
P

M
KG)
162138
162144
Muskellunge
Esox masquinongy
P

M
I(E,G)
162138
162143
Chain pickerel
Esox niger
P

M

162136
162146
Mudniinnows
Umbridae




162158
162159
Alaska blackfish
Dallia pectoralis




162160
162161
Olympic mudminnow
Novumbra hubbsi




162147
162153
Central miidminnow
Umbra limi
I
0(A,D),G(G)
T

162147
162148
Eastern mudminnow
Umbra pygmaea
G

T

161930
162028
Smelts
Osmeridae




162052
162053
Whitebait smelt
Allosmcrus elongatus




162029
162033
Wakasagi
Hypomesus nipponensis




162029
162031
Pond smelt
Hypomesus olidus




162029
162030
Surf smelt
Hypomesus pretiosus




162029
162032
Delta smelt
Hypomesus transpacificus




162034
162035
Capelin
Mallotus villosus




162038
162041
Rainbow smelt
Osmerus mordax
V
F(E),G(G)
M
KG)
162047
162048
Night smelt
Spirinchus starksi




162047
162049
Longfin smelt
Spirinchus thaleichthys




162050
162051
Eulachon
Thalcichthys paciftcus




161930
161931
Trouts
Salmonidae




161932
161942
Cisco or Lake herring
Coregonus artedi
F

M
KG)
161932
161933
Arctic cisco
Coregonus autumnalis




161932
161941
Lake whitefish
Coregonus clupeaformis
V
P(C),I(E,G)
M
KG)
161932
161943
Bloater
Coregonus hoyi
F

M

161932
161973
Atlantic whitefish
Coregonus huntsmani




161932
161944
Deepwater cisco
Coregonus johannae
F

M

161932
161945
Kiyi
Coregonus kiyi
F

M

161932
161935
Bering cisco
Coregonus laurettae




161932
161936
Broad whitefish
Coregonus nasus




161932
161946
Blackfin cisco
Coregonus nigrioinnis
F

1

C-14
Appendix C: Tolerance and Trophic Guilds of Selected Fish Species
-------
Parent
TSN
TSN
Common Name
Scientific Name
Trophic
Trophic
Exceptions
Tolerance |
Tolerance
Exceptions
161932
161937
Humpback whitefish
Coregonus pidschian




161932
161947
Shortnose cisco
Coregonus reighardi
F

i

161932
161938
Least cisco
Coregonus sardinella




161932
161948
Shortjaw cisco
Coregonus zenithiais
F

M

161974
161987
Golden trout
Oncorhynchus aguabonita




161974
161981
Apache trout
Oncorhynchus apache




161974
161983
Cutthroat trout
Oncorhynchus clarki
I

I

161974
161985
Gila trout
Oncorhynchus gilae




161974
161975
Pink salmon
Oncorhynchus gorbuschn
P

M

161974
161976
Chum salmon
Oncorhynchus keta




161974
161977
Coho salmon
Oncorhynchus kisutch
P

M

161974
161989
Rainbow trout
Oncorhynchus inykiss
P
1(C)
M
KC.G)
161974
161979
Sockeye salmon
Oncorhynchus nerka




161974
161980
Chinook salmon
Oncorhynchus tshawytscha
P

M

162007
162012
Bear Lake whitefish
Prosopium abyssicoia




162007
162011
Pygmy whitefish
Prosopium coulteri
I

M

162007
162008
Round whitefish
Prosopium cylindraceum
I

M
KG)
162007
162013
Bonneville cisco
Prosopium gemmifer




162007
162010
Bonneville whitefish
Prosopium spilonotus




162007
162009
Mountain whitefish
Prosopium williamsoni
I

I

161994
161996
Atlantic salmon
Salmo salar
P

M
KG)
161994
161997
Brown trout
Salmo trutta
P
1(C)
M
KG)
161999
162001
Arctic char
Salvelinus alpinus
P

I

161999
162004
Bull trout
Salvelinus confluentus




161999
162003
Brook trout
Salvelinus fontinalis
P
1(C)
M
KE,G)
161999
162000
Dolly Varden
Salvelinus malma




161999
162002
Lake trout
Salvelinus namaycush
P

M
KG)
162005
162006
Inconnu
Stenodus leucichthys




162015
162016
Arctic grayling
Thymallus arclicus




164406
164407
Trout-Perches ;
Percopsidae



ty. ¦ ' ri
164408
164409
Trout-perch
Percopsis omiscomaycus
I

M

164408
164410
Sand roller
Percopsis transmontana
I

M

164402 '
164403
•Pirate Perches . ...
¦ Aphredoderidae ;,j.- _ ;


^ •

'164404
164405
Pirate perch
Aphredoderus sayanus
1

M

'164669
164701
Cods . r
Gadidaei
•
".~v- -v_ ' 7

-
164724
164725
Burbot
Lota lota
P

M

165614
165629.
Killifishes
Cyprinodonlidae C?-Mcf* ...
fS!



165681
165682
Diamond killifish
Adinia xenica




165686
165687
White River springfish
Crenichthys baileyi




165686
165688
Railroad Valley springfish
Crenichthys nevadae




165630
165632
Leon Springs pupfish
Cyprinodon bovinus




165630
165633
Devils Hole pupfish
Cyprinodon diabolis




165630
165634
Comanche Springs pupfish
Cyprinodon elegans




165630
165635
Conchos mrafish
Cyprinodon eximius




Rapid Bioassessment Protocols For Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
C-15
-------
Parent
TSN
TSN
Common Name
Scientific Name
Trophic
Trophic
Exceptions
Tolerance
Tolerance
Exceptions
165630
165636
Lake Eustis minnow
Cyprinodon hubbsi




165630
165637
Desert pupfish
Cyprinodon macularius




165630
165638
Amargosa pupfish
Cyprinodon nevadensis




165630
165639
Pecos pupfish
Cyprinodon pecosensis




165630
165640
Owens pupfish
Cyprinodon radiosus




165630
165641
Red River pupfish
Cyprinodon rubrofluviatilis




165630
165642
Salt Creek pupfish
Cyprinodon salinus




165630
165643
White Sands pupfish
Cyprinodon tularosa




165630
165631
Sheepshead minnow
Cyprinodon variegatus




165690
165691
Pahrump poolfish
Empetrichthys latos




165690
165692
Ash Meadows poolfish
Empelrichthys merriami




165684
165685
Goldspotted killifish
Fioridichthys carpio




165644
165659
Whitelinc topminnow
Fundulus albolineatus




165644
165671
Stippled studfish
Fundulus bifax




165644
165660
Northern studfish
Fundulus catenatus
1

1

165644
165652
Golden topminnow
Fundulus chrysotus




165644
165661
Banded topminnow
Fundulus cingulatus




165644
165645
Marsh killifish
Fundulus confluentus




165644
165646
Banded killifish
Fundulus diaphanus
I

T
M(D,F)
165644
165672
Starhead topminnow
Fundulus dispar
I

f

165644
165675
Russetfin topminnow
Fundulus escambiae




165644
165676
Broadstripe topminnow
Fundulus euryzonus




165644
165651
Gulf killifish
Fundulus grandis




165644
165647
Mummichog
Fundulus heteroelitus
G

M
T(G)
165644
165653
Saltmarsh topminnow
Fundulus ienkinsi




165644
165677
Barrens topminnow
Fundulus julisia




165644
165662
Lined topminnow
Fundulus lineolatus




165644
165648
Spotfin killifish
Fundulus luciae




165644
165649
Striped killifish
Fundulus majalis




165644
165663
Blackstripe topminnow
Fundulus notatus
I

M

165644
165664
Bayou topminnow
Fundulus notti




165644
165655
Blackspotted topminnow
Fundulus olivaceus
I

M

165644
165650
California killifish
Fundulus parvipinnis




165644
165656
Bayou killifish
Fundulus pulvereus




165644
165665
Speckled killifish
Fundulus rathbuni




165644
16S666
Plains topminnow
Fundulus sciadicus




165644
165667
Seminole killifish
Fundulus seminolis




165644
165657
Longnose killifish
Fundulus similis




165644
165668
Southern studfish
Fundulus stellifer




165644
165669
Waccamaw killifish
Fundulus waccamensis




165644
165658
Plains killifish
Fundulus zebrinus




165693
165694
Flagfish
Jordanella floridae




165695
165696
Pygmy killifish
Leptolucania ommata




165678
165680
Bluefin killifish
Lucania goodei




165678
165679
Rainwater killifish
Lucania oarva




C-16
Appendix C: Tolerance and Trophic Guilds of Selected Fish Species
-------
Parent
TSN
TSN
Common Name
Scientific Name
Trophic
Trophic
Exceptions
Tolerance 1
Tolerance
Exceptions
16561%
165876
Uvobearcrs
' Poeciliidae . : S




165912
165913
Pike killifish
Belonesox belizanus




165877
165878
Western mosquitofish
Gambusia afflnis
1

M
T(G)
165877
165883
Amistad gambusia
Gambusia amisiadensis




165877
165884
Big Bend gambusia
Gambusia gaigei




165877
165885
Largespring gambusia
Gambusia geiseri




165877
165886
San Marcos gambusia
Gambusia georgei




165877
165887
Clear Creek gambusia
Gambusia lieterochir




165877
165896
Eastern mosquitofish
Gambusia holbrooki
I
G(F)
M
T(G)
165877
165888
Pecos gambusia
Gambusia nobilis




165877
165882
Mangrove gambusia
Gambusia rhizophorae




165877
165889
Blotched gambusia
Gambusia senilis




165914
165915
Least killifish
Heterandria formosa




165897
165899
Amazon molly
Poecilia formosa




165897
165898
Sailfin molly
Poecilia latipinna




165897
165902
Shortfin molly
Poecilia mexicana




165897
165903
Guppy
Poecilia reticulata




165916
165917
Porthole livcbearer
Poeciliopsis gracilis




165916
165918
Gila topminnow
Poeciliopsis occidentalis




165919
165920
Green swordtail
Xiphophorus helleri




165919
165922
Southern platyfish
Xiphophorus maculatus




165919
165925
Variable platyfish
Xiphophorus variatus




1*65973
<165984 '
•Silversides *. :
Atherinidae

SSfc/ ¦" ' W'-Sr


166005
166006
Hardhead silverside
Atherinomorus stipes




165985
165986
Topsmelt
Atherinops afflnis




166011
166012
Jacksmelt
Alherinopsis californiensis




166037
166038
Reef silverside
Hypoatherina harringtonensis




166015
166016
Brook silverside
Labidesthes sicculus
I

M
KG)
166013
166014
California grunion
Leuresthes tenuis




165988
165989
Rough silverside
Membras martinica




165992
165993
Inland silverside
Menidia beryllina




165992
166000
Texas silverside
Menidia clarkiiubbsi




165992
165995
Key silverside
Menidia conchorum




165992
165997
Waccamaw silverside
Menidia extensa




165992
165994
Atlantic silverside
Menidia menidia




165992
165996
Tidewater silverside
Menidia peninsulae




166362
166363
Sticklebacks:.
¦ Gasterosteidae •"




166396
166397
Fourspine stickleback
Apeltes quadracus
I

M

166403
166404
Tube-snout
Aulorhynchus flavidus




166398
166399
Brook stickleback
Culaea inconstans
I

M
KG)
166364
166365
Threespine stickleback
Gasterosteus aculeatus
I

M

166364
166385
Blackspotted stickleback
Gasterosteus wheatlandi




166386
166387
Ninespine stickleback
Pungitius pungitius
I

M

167185'
167196
. Sculnins
Cottidae.. : 'A-




Rapid Bioassessment Protocols For Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
C-17
-------
Parent
TSN
TSN
Common Name
Scientific Name
Trophic
Trophic
Exceptions
Tolerance |
Tolerance
Exceptions
167229
167230
Coastrange sculpin
Coitus aleuticus




167229
167233
Prickly sculpin
Coitus asper
I

M

167229
167235
Rough sculpin
Cottus asperrimus




167229
167236
Black sculpin
Coitus baileyi




167229
167237
Mottled sculpin
Coitus bairdi
I

I
M(C,D,G)
167229
167238
Paiute sculpin
Cottus beldingi
I

I

167229
167239
Banded sculpin
Coitus carolinae
I

M

167229
167232
Slimy sculpin
Cottus cognatus
f

M
I(E,G)
167229
167240
Shorthead sculpin
Coitus confusus




167229
167241
Utah Lake sculpin
Coitus echinalus




167229
167242
Bear Lake sculpin
Coitus extensus




167229
167243
Potomac sculpin
Coitus girardi




167229
167244
Shoshone sculpin
Cottus greenei




167229
167234
Riffle sculpin
Cottus gulosus




167229
167263
Ozark sculpin
Cottus hypselurus




167229
167245
Marbled sculpin
Cottus klamathensis




167229
167246
Wood River sculpin
Cottus leiopomus




167229
167247
Margined sculpin
Coitus marginalus




167229
167248
Reticulate sculpin
Coitus perplexus
I

T

167229
167249
Pit sculpin
Cottus pitensis




167229
167250
Klamath Lake sculpin
Cottus princeps




167229
167251
Pygmy sculpin
Cottus pygmaeus




167229
167252
Torrent sculpin
Cottus rhotheus
I

1

167229
167253
Spoonhead sculpin
Cottus ricei
I

M
1(E)
167229
167254
Slender sculpin
Coitus tenuis




167311
167323
Deepwater sculpin
Myoxocephalus thompsoni
1

M
KB)
' 167641
170315
Temperate Basses
Percichthyidae




167676
167678
While perch
Morone americana
P
KE)
M

167676
167682
White bass
Morone chrysops
P
1(A)
M
T(G)
167676
167683
Yellow bass
Morone mississippiensis
P
1(A)
M

167676
167680
Striped bass
Morone saxatilis
P

M
KG)
167913
167914
Wreckfish
Polyprion americanus




167917
167918
Giant sea bass
Stereolepis gigas




168334
168335
Blackmouth bass
Synagrops bellus




163334
168337
Keelcheek bass
Synagrops spinosus




167641
168093
Sunfishcs
Centrarchidae




168094
168095
Mud sunfish
Acantharchus pomotis
I

M

168096
168099
Shadow bass
Ambloplites ariommus




168096
168098
Roanoke bass
Ambloplites envifrons




168096
168100
Ozark bass
Ambloplites constellatus




168096
168097
Rock bass
Ambloplites rupestris
P
I (A)
M
I(A,E)
168174
168175
Sacramento perch
Archoplites interruptus




168101
168102
Flier
Centrarchus macropterus
I

M

168168
168172
Carolina pygmy sunfish
Elnssoma boehlkei




168163
168169
Everglades nvemv sunfish
Elassoma evereladei




C-18
Appendix C: Tolerance and Trophic Guilds of Selected Fish Species
-------
Parent
TSN
TSN
Common Name
Scientific Name
Trophic
Trophic
Exceptions
1 Tolerance 1
Tolerance
Exceptions
168168
168173
Bluebarred pygmy sunfish
Elassoma okatie




168168
168170
Okefenokee pygmy sunfish
Elassoma okefenokee




168168
168171
Banded pygmy sunfish
Elassoma zonalum
1

M

168107
168108
Blackbanded sunfish
Enneacanthus chaelodon
I

I

168107
168113
Bluespotted sunfish
Enneacanlhus gloriosus
I

M
KG)
168107
168117
Banded sunfish
Enneacanthus obesus
I

M
KG)
168130
168131
Redbreast sunfish
Lepomis auritus
I
G(G)
M

168130
168132
Green sunfish
Lepomis cyanellus
I
V(C)P(A,F),G(G)
T
M(A)
168130
168144
Pumpkinseed
Lepomis gibbosus
I
P(F),G(G)
M


168138
Warmouth
Lepomis gulosus

V(C)
M

168130
168151
Orangespotted sunfish
Lepomis humilis
I

M

168130
168141
Bluegill
Lepomis macrochirus
I
G(G)
M
T(C,G)
168130
168152
Dollar sunfish
Lepomis marginatus




168130
168153
Longear sunfish
Lepomis megalotis
I

I
M(A,C)
168130
168154
Redear sunfish
Lepomis microlophus
I

M

168130
168155
Spotted sunfish
Lepomis punctatus
1

M

168130
168156
Bantam sunfish
Lepomis symmetricus
I

M

168158
168163
Redeye bass
Micropterus coosae




168158
550562
Smallmouth bass
Micropterus dolomieu
P
1(A)
M
1(E)
168158
168164
Suwannee bass
Micropterus notius




168158
168161
Spotted bass
Micropterus punctulatus
P

M

168158
168160
Largemouth bass
Micropterus salmoides
P
KA)
M
T(C)
168158
168162
Guadalupe bass
Micropterus treculi




168165
168166
White crappie
Pomoxis annularis
P
1(A,C),V(C)
M
T(C,G)
168165
168167
Black crappie
Pomoxis nigromaculatus
P
1(A),V(C)
M

167641
168356
Perches
Percidae 5 ¦¦



is-; ^

168512
Crystal darter
Ammocrypta asprella
I

I

168511
168513
Naked sand darter
Ammocrypta beani




168511
168514
Florida sand darter
Ammocrypta bifascia




168511
168515
Western sand darter
Ammocrypta clara
I

I
M(E)
168511
168516
Southern sand darter
Ammocrypta meridiana




168511
168517
Eastern sand darter
Ammocrypta pellucida
I

I

168511
168518
Scaly sand darter
Ammocrypta vivax




168357
168370
Sharphead darter
Etheostoma acuticeps




168357
168371
Coppercheek darter
Etheostoma aquali




168357
168372
Mud darter
Etheostoma asprigene
I

M

168357
168452
Emerald darter
Etheostoma baileyi




168357
168373
Teardrop darter
Etheostoma barbouri




168357
168453
Splendid darter
Etheostoma barrenense




168357
168374
Orangefin darter
Etheostoma bellum




168357
168375
Greenside darter
Etheostoma blennioides
I

M
KG)
168357
168376
Blenny darter
Etheostoma blennius




168357
168377
Slackwater darter
Etheostoma boschungi




168357
168378
Rainbow darter
Etheostoma caeruleum
I

M
KE.G)
Rapid Bioassessment Protocols For Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
C-19
-------
farcnt
TSN
TSN
Common Name
Scientific Name
| Trophic
Trophic
Exceptions
| Tolerance |
Tolerance
Exceptions
168357
168379
Bluebreast darter
Etheostoma camurum
I

i

108357
168380
Green fin darter
Etheostoma chlorobranchium




168357
168365
Bluntnose darter
Etheostoma chiorosomum
1

M

168357
168381
Ashy darter
Etheostoma cinereum




168357
168382
Creole darter
Etheostoma collettei




168357
168383
Carolina darter
Etheostoma collis




168357
168385
Coosa darter
Etheostoma coosae




168357
168386
Arkansas darter
Etheostoma crag in i




168357
168454
Fringed darter
Etheostoma crossopterum




168357
168387
Choctawhatchee darter
Etheostoma davisoni




168357
168388
Coldwater darter
Etheostoma ditrema




168357
168389
Black darter
Etheostoma duryi




168357
168390
Brown darter
Etheostoma edwini




1683S7
168391
Clierry darter
Etheostoma etnieri




168357
168392
Arkansas saddled darter
Etheostoma euzonum




168357
168393
Iowa darter
Etheostoma exile
I

M
1(E)
168357
168394
Fantai! darter
Etheostoma flabellare
I

M

168357
1684S5
Saffron darter
Etheostoma flavum




168357
168395
Fountain darter
Etheostoma fonticola




168357
163396
Savannah darter
Etheostoma fricksium




168357
168358
Swamp darter
Etheostoma fusiforme
I

M
KG)
168357
168366
Slough darter
Etheostoma gracile
I

M

168357
168397
Rio Grande darter
Etheostoma grahami




168357
168398
Harlequin darter
Etheostoma histrio
I

I

168357
168399
Christmas darter
Etheostoma hopkinsi




168357
168400
Turquoise darter
Etheostoma inscriptum




1683S7
168401
Blueside darter
Etheostoma jessiae




168357
168402
Greenbreast darter
Etheostoma jordani




168357
168403
Yoke darter
Etheostoma juliae




168357
168404
Kanawha darter
Etheostoma kanawhae




168357
168405
Stripetail darter
Etheostoma kennicotti
I

M

168357
168367
Green throat darter
Etheostoma lepidum




168357
168406
Longfin darter
Etheostoma longimanum




168357
168407
Redband darter
Etheostoma luteovinctum




168357
168456
Brighteye darter
Etheostoma lynceum




168357
168408
Spotted darter
Etheostoma maculatum
1

I

168357
168409
Pinewoods darter
Etheostoma mariae




168357
168410
Smallscale darter
Etheostoma microlepidum




168357
168411
Least darter
Etheostoma microperca
I

M
1(E)
168357
168412
Yellowcheek darter
Etheostoma moorei




168357
168413
Lollipop darter
Etheostoma neopterum




168357
168414
Niangua darter
Etheostoma nianguae




168357
168458
Blackfin darter
Etheostoma nigripinne




168357
168369
Johnny darter
Etheostoma nigrum
I

M

168357
168415
Watercress darter
Etheostoma nuchale




C-20
Appendix C: Tolerance and Trophic Guilds of Selected Fish Species
-------
Parent
TSN
TSN
Common Name
Scientific Name
Trophic
Trophic
Exceptions
| Tolerance |
Tolerance
Exceptions
168357
168416
Barcheek darter
Etheostoma obeyense




168357
168417
Okaloosa darter
Etheostoma okaloosae




168357
168418
Sooty darter
Etheostoma olivaceum




168357
168360
Tessellated darter
Etheostoma olmstedi
I

M

168357
168419
Candy darter
Etheostoma osburni




168357
168420
Paleback darter
Etheostoma pallididorsum




168357
168421
Goldstripe darter
Etheostoma parvipinne




168357
168422
Waccamaw darter
Etheostoma perlongum




168357
168423
Riverweed darter
Etheostoma podostemone




168357
168424
Cypress darter
Etheostoma proeliare




168357
168425
Stippled darter
Etheostoma punctulatum




168357
168459
Firebelly darter
Etheostoma pyrrhogasler




168357
168426
Orangebelly darter
Etheostoma radiosum




168357
168460
Kentucky darter
Etheostoma raftnesquei




168357
168427
Bayou darter
Etheostoma rubrum




168357
168428
Redline darter
Etheostoma rufilineatum




168357
168429
Rock darter
Etheostoma rupestre




168357
168430
Arrow darter
Etheostoma sagitta




168357
168461
Bloodfin darter
Etheostoma sanguifluum




168357
168361
Maryland darter
Etheostoma sellare




168357
168362
Sawcheek darter
Etheostoma serrifer




168357
168431
Snubnose darter
Etheostoma simoterum




168357
168435
Slabrock darter
Etheostoma smithi




168357
168368
Orangethroat darter
Etheostoma speclabile
I

M

168357
168436
Spottai! darter
Etheostoma squamiceps
I

M

168357
168437
Speckled darter
Etheostoma stigmaeum




168357
168438
Striated darter
Etheostoma striatulum




168357
168439
Gulf darter
Etheostoma swaini




168357
168440
Swannanoa darter
Etheostoma swannanoa




168357
168441
Missouri saddled darter
Etheostoma tetrazonum




168357
168442
Seagreen darter
Etheostoma thalassinum




168357
168443
Tippecanoe darter
Etheostoma tippecanoe
I

I

168357
168444
Trispot darter
Etheostoma trisella




168357
168445
Tuscumbia darter
Etheostoma tuscumbia




168357
168446
Variegate darter
Etheostoma variatum
I

I
M(G)
168357
168447
Striped darter
Etheostoma virgatum




168357
168364
Glassy darter
Etheostoma vitreum
I

I

168357
168463
Wounded darter
Etheostoma vulneratum




168357
168466
Boulder darter
Etheostoma wapiti




168357
168448
Redfin darter
Etheostoma whipplei




168357
168449
Banded darter
Etheostoma zonale
1

I

168357
168450
Backwater darter
Etheostoma zonifer




168357
168467
Bandfin darter
Etheostoma zonistium




168519
168520
Ruffe
Gvmnoceohalus cernttus
I

M

Rapid Bioassessment Protocols For Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
C-21
-------
Parent
TSN
TSN
Common Name
Scientific Name
Trophic
Trophic
Exceptions
| Tolerance |
Tolerance
Exceptions
16846S
168469
Yellow perch
Pcrca flavescens
I
P(A,C,G);G(F,D)
M

168471
168476
Amber darter
Pcrcina antesella




168471
168477
Tangerine darter
Percina auranliaca




168471
168478
Goldline darter
Percina aurolineata




168471
168479
Blotchside darter
Percina burtoni




168471
168472
Logperch
Percina caprodes
I

M

168471
168501
Texas logperch
Percina carbonaria




168471
168480
Channel darter
Percina copelandi
I

I

168471
168481
Piedmont darter
Percina crassa




168471
168482
Bluestripe darter
Percina cymatotaenia




168471
168483
Gilt darter
Percina evictes
1

I

I6847t
168484
Appalochia darter
Percina gymnocephala




168471
168502
Conasauga logperch
Percina jenkinsi




168471
168485
Freckled darter
Percina lenticula




168471
1684S6
Longhead darter
Percina macrocephala
I

I

168471
168487
Bigscale logperch
Percina macrolepida




168471
168488
Blackside darter
Percina maculata
I

M

168471
168489
Longnose darter
Percina nasula




1C8471
168490
Blackbanded darter
Percina nigrofasciata




168471
168473
Stripeback darter
Percina notogramma




168471
201997
Sharpnose darter
Percina oxyrhynchus




168471
168492
Bronze darter
Percina paimaris




163471
168493
Leopard darter
Percina pantherina




168471
168474
Shield darter
Percina peitata
I

I
M(G)
168471
168494
Slenderhead darter
Percina phoxocephala
I

I

168471
168495
Roanoke logperch
Percina rex




168471
168496
Roanoke darter
Percina roanoka




168471
168475
Dusky darter
Percina sciera
I

M

168471
168497
River darter
Percina shumardi
I

M

168471
168498
Olive darter
Percina squamata




168471
168499
Snail darter
Percina tanasi




168471
168500
Stargazing darter
Percina uranidea
I

I

168471
168503
Saddleback darter
Percina vigil
I

M

168505
168509
Sauger
Stizostedion canadense
P

M

168505
168506
Walleye
Stizostedion vitreum
P

M

167641
169237
Drums
Sciaenidae




169363
169364
Freshwater drum
Aplodinotus grtinniens
V
1(E)
M

167641
169770
Cichlids
Cichlidae




169809
169810
Blue tilapia
Tilapia aurea




169809
169811
Spotted tilapia
Tilapia mariae




169809
169812
Blackchin tilapia
Tilapia melanotheron





170017
Mozambique tilapia
Tilapia mossambica




169809
169820
Wami tilapia
Tilapia urolepis




169809
169813
Redbelly tilapia
Tilapia zilli




170332
170333
Mullets
Mueilidae




C-22
Appendix C: Tolerance and Trophic Guilds of Selected Fish Species
-------
Parent
TSN
TSN
Common Name
Scientific Name
Trophic
Trophic
Exceptions
| Tolerance |
Tolerance
Exceptions
170354
170355
Mountain mullet
Agonostomus monticola




170334
170335
Striped mullet
Mugil cephnlus




170334
170336
White mullet
Mugil curema




170334
170337
Redeye mullet
Mugil gaimardianus




170334
170351
Fantail mullet
Mugil gyrans




170334
170338
Liza
Mugil liza




172979
'172980
Soles .
"SoleiHae
4



172981
172982
Hogchoker •
Trinectes maculatus
G

i

Rapid Bioassessment Protocols For Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
C-23
-------
This Page Intentionally Left Blank
C-24
Appendix C: Tolerance and Trophic Guilds of Selected Fish Species
-------
Appendix D:
J
Survey Approach for Compilation of
Historical Data
Rapid Bioassessment Protocols For Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
D-l
-------
This Page Intentionally Left Blank
i
D-2
Appendix D: Survey Approach for Compilation of Historical Data
-------
QUESTIONNAIRE SURVEY FOR EXISTING BIOSURVEY DATA AND
BIO ASSESSMENT 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 the methods outlined in the
original RBPIV (Plafkin et al. 1989) and is 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; 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 low cost qualitative screening assessment (Section 1) of a large number
of waterbodies in a relative short period. 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. As a follow-up to mailings, telephone
contact may be necessary.
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.
Rapid Bioassessment Protocols For Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macro invertebrates, and Fish, Second Edition	D-3
-------
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
D-4
Appendix D; Survey Approach for Compilation of Historical Data
-------
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				.Pax.								
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).
Rapid Bioassessment Protocols For Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macro in vertebrates, and Fish, Second Edition
D-5
-------
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


5 Species composition, age classes, and trophic structure comparable to non (or minimally)
impaired waterbodies of similar size in that ecoregion or watershed.


4 Species richness somewhat reduced by loss of some intolerant species; 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; trophic structure skewed toward omnivory.


2 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.


1 Few individuals and species present; mostly tolerant species; diseased fish and anomalies
abundant compared to other similar-sized waterbodies in the ecoregion.


0 Ho fish, depauperate macroinvertebrate and/or periphyton assemblages.

(Circle one number using the scale above.)

1.
Rank the current conditions of the reach
5 4 3 2 1 0

2.
Rank the conditions of the reach 10 years ago
5 4 3 2 1 0

3.
Given present trends, how will the reach rank 10 years from now?
5 4 3 2 1 0
Describe/comment
1

4.
If the major human-caused limiting factors were eliminated, how would the reach rank 10 years
from now?

5 4 3 2 1 0
r)ftsrrihft/romment
t

5.
Decision criteria based on:
~ Site-specific reference sites ~ Professional opinion
D F.coreginnal reference conditions ~ Other fspecifvt
—-
D-6
Appendix D: Survey Approach for Compilation of Historical Data
-------
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
Probable Cause
~ Temperature too high
~ Primarily upstream
~ Temperature too low
~ Within reach
~ Turbidity
Point source discharge
~ Salinity
~ Industrial
~ Dissolved oxygen
~ Municipal
~ Gas supersaturation
~ Combined sewer
~ pH too acidic
~ Mining
~ pH too basic
~ Dam release
o Nutrient deficiency
Nonpoint source discharge
~ Nutrient surplus
~ Individual sewage
~ Toxic substances
~ Urban runoff
~ Other (specify below)
~ Landfill leachate

~ Construction

~ Agriculture
~ Not limiting
~ Feedlot

~ Grazing

~ Silviculture

~ Mining

~ Natural

~ Unknown

~ Other (specify below)
(b.) WATER QUANTITY
Limiting Factor
Probable Cause
~ Below optimum flows
~ Dam
~ Above optimum flows
~ Diversion
~ Loss of flushing flows
Watershed conversion
~ Excessive flow fluctuation
~ Agriculture
~ Other (specify below)
~ Silviculture

~ Grazing

~ Urbanization
~ Not limiting
~ Mining

~ Natural

~ Unknown

~ Other (specify below)

Rapid Bioassessment Protocols For Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
D-7
-------
BIOLOGICAL/HABITAT
(Check the appropriate categories

(a.) Limiting Factor
HABI
PERI
MACR
FISH
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




Key:
HABI - Habitat	PERI - Periphyton
MACR - Macroinvertebrates FISH - Fish
D-8
Appendix D: Survey Approach for Compilation of Historical Data
-------
(b.) Probable Cause
HABI
PERI
MACR
FISH
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)




Key:
HABI - Habitat	PERI - Periphyton
MACR - Macroinvertebrates FISH - Fish
Rapid Bioassessment Protocols For Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates, and Fish, Second Edition
D-9
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SUMMARY: ASPECT OF PHYSICOCHEMICAL OR BIOLOGICAL CONDITION AFFECTED
~	Water quality
~	Water quantity
~	Habitat structure
~	Periphyton assemblage
~	Macroinvertebrate assemblage
~	Fish assemblage
~	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:

~ Stream habitat surveys

o Periphyton surveys
assemblage ~ key species ~
~ Macroinvertebrate surveys
assemblage ~ key species ~
~ Fish surveys
assemblage ~ key species ~
Sampling gear(s) or methods
Sampling frequency (spatial and temporal)
!				•


Data analysis/interpretation based on:
Electronic file available:
Tabulated data ~
Format
Graphical data ~

Multivariate analyses. ~

Multimetric approach. ~

Statistical routines include:
Metrics include:

			


D-10
Appendix D: Survey Approach for Compilation of Historical Data
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vvEPA
United States
Environmental Protection Agency
(4503F)
Washington, DC 20460
Official Business
Penalty for Private Use
$300
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