National Rivers and Streams Assessment 2013-2014 Technical Support Document
EPA 841-R-22-005
National Rivers and Streams
Assessment 20182019 Technical
Support Document
U.S. Environmental Protection Agency
Office of Water: Office of Wetlands, Oceans and Watersheds
Office of Research and Development
Washington, DC 20460
Original: November 2023
Updated: September 2024
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National Rivers and Streams Assessment 2013-2014 Technical Support Document
The National Rivers and Streams Assessment 2018-2019 Technical Support Document details methods and
analysis approaches used in the National Rivers and Streams Assessment 2018-2019 conducted by the
United States Environmental Protection Agency (USEPA) and partner organizations. This document
supports the results presented in National Rivers and Streams Assessment: The Third Collaborative Survey (EPA-
841-R-22-004).
The information in the Technical Support Document has been funded wholly or in part by the USEPA.
This technical report has been subjected to review by the USEPA Office of Water and approved for
publication. Approval does not signify that the contents reflect the views of the Agency, nor does
mention of trade names or commercial products constitute endorsement or recommendation for use.
The suggested citation for this document is:
U.S. Environmental Protection Agency. 2023 (Updated 2024). National Rivers and Streams Assessment
2018-2019 Technical Support Document. Version 1.1 EPA 841-R-22-005. Office of Water and Office of
Research and Development. Washington, D.C. https://www.epa.gov/national-aquatic-resource-
survevs/nrsa
If you decide to print the document, please use double-side printing to minimize ecological impact.
Version History:
Version 1.1 September 2024
Changes occurred to section 9.4 on PFAS fish tissue analysis and results and section 9.5 on calculations
of PFAS fish tissue screening levels for human health protection. These changes were related to updates
in analysis and screening levels for PFAS in fish tissue.
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Contents
LIST OF FIGURES 7
LIST OF TABLES 8
LIST OF ACRONYMS 10
1 INTRODUCTION 11
1.1 ADDITIONAL RESOURCES FOR SURVEY OPERATIONS 11
2 QUALITY ASSURANCE 12
2.1 INTRODUCTION 12
2.2 SURVEY DESIGN 13
2.2.1 STATISICAL DESIGN 13
2.2.2 COMPLETENESS 14
2.2.3 COMPARABILITY 14
2.3 QUALITY ASSURANCE IN FIELD OPERATIONS 14
2.3.1 FIELD METHOD PILOT TESTING 14
2.3.2 TRAINING OF FIELD TRAINERS AND ASSISTANCE VISITORS 14
2.3.3 FIELD CREW TRAINING 15
2.3.4 FIELD ASSISTANCE VISITS 15
2.3.5 REVISITS OF SELECTED FIELD SITES 15
2.3.6 EVALUATION OF FISH IDENTIFICATIONS 16
2.4 LABORATORY QU ALITY ASSURANCE AND QU ALITY CONTROL 16
2.4.1 BASIC CAPABILITIES 16
2.4.2 BENTHIC MACRO INVERTEBRATE IDENTIFICATIONS 16
2.4.3 CHEMICAL ANALYSES 17
2.5 DATA MANAGEMENT AND REVIEW 17
2.6 MAIN REPORT 18
2.7 LITERATURE CITED 18
3 SELECTION OF PROBABILITY SITES 20
3.1 OBJECTIVES 20
3.2 TARGET POPULATION 20
3.3 SAMPLE FRAME 21
3.4 SURVEY" DESIGN 22
3.4.1 RESAMPLE DESIGN 22
3.4.2 NEW SITE DESIGN 23
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3.4.3 OVERSAMPLE AND SITE REPLACEMENT 23
3.5 EVALUATION PROCESS 25
3.6 IMPLEMENTATION OF THE DESIGN 25
3.7 STATISTICAL ANALYSIS 26
3.8 LITERATURE CITED 26
4 SELECTION OF SITES TO ESTABLISH REFERENCE CONDITIONS 27
4.1 BACKGROUND AND UPDATES 27
4.2 SOURCES OF REFERENCE SITES 28
4.3 CHEMICAL AND PHYSICAL SCREENS 30
4.4 GEOSPATIAL SCREENS 31
4.5 ESTABLISHING BENCHMARKS 32
4.6 LITERATURE CITED 32
5 BENTHIC MACRO INVERTEBRATES 36
5.1 OVERVIEW 36
5.2 DATA PREPARATION 37
5.2.1 STANDARDIZING COUNTS 37
5.2.2 AUTECOLOGICAL CHARACTERISTICS 37
5.3 MULTIMETRIC INDEX DEVELOPMENT 38
5.3.1 REGIONAL MULTIMETRIC DEVELOPMENT 38
5.3.2 MODELING OF MMI BENCHMARKS 40
5.4 LITERATURE CITED 41
6 FISH ASSEMBLAGE 43
6.1 BACKGROUND 43
6.1.1 MULTIMETRIC INDICATOR FOR NRSA 2018-19 43
6.1.2 REGIONALIZATION 43
6.2 METHODS 43
6.2.1 FIELD METHODS 43
6.2.2 COUNTING, TAXONOMY, AND AUTECOLOGY 44
6.3 FISH MULTIMETRIC INDEX DEVELOPMENT 45
6.3.1 LEAST-DISTURBED REFERENCE SITES FOR FISH 46
6.3.2 CANDIDATE METRICS 46
6.3.3 ADJUSTMENT OF METRIC RESPONSE FOR WATERSHED AREA 47
6.3.4 SELECTION OF FINAL CANDIDATE METRICS 48
6.3.5 METRIC SCORING 48
6.3.6 SELECTION OF FINAL FISH MMIS 49
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6.4 FISH MMI PERFORMANCE 63
6.5 SITES WITH LOW FISH ABUNDANCE 70
6.6 BENCHMARKS FOR ASSIGNING ECOLOGICAL CONDITION 71
6.7 DISCUSSION 73
6.8 LITERATURE CITED 74
APPENDIX 6.A COMPARISON OF MODEL-BASED AND TRADITIONAL FISH
MULTIMETRIC INDICES FOR NRSA 2008-09 78
APPENDIX 6.B CANDIDATE METRICS CONSIDERED FOR FISH MMI
DEVELOPMENT 85
7 WATER CHEMISTRY ANALYSES 91
7.1 ACIDITY AND SALINITY BENCHMARKS 91
7.2 TOTAL PHOSPHORUS AND TOTAL NITROGEN BENCHMARKS 91
7.2.1 SELECTING AN APPROACH 91
7.2.2 APPLYING THE REFERENCE-BASED APPROACH TO NRSA 92
7.3 SIGNAL TO NOISE 93
7.4 LITERATURE CITED 93
8 PHYSICAL HABITAT ASSESSMENT 94
8.1 METHODS 95
8.1.1 PHYSICAL HABITAT SAMPLING AND DATA PROCESSING 95
8.1.2 QUANTIFYING THE PRECISION OF PHYSICAL HABITAT INDICATORS
97
8.2 PHYSICAL HABITAT CONDITION INDICATORS 97
8.2.1 RELATIVE BED STABILITY AND EXCESS FINES 97
8.2.2 RIPARIAN VEGETATION 100
8.2.3 INSTREAM HABITAT COVER COMPLEXITY 101
8.2.4 RIPARIAN HUMAN DISTURBANCES 102
8.3 ESTIMATING REFERENCE CONDITION FOR PHYSICAL HABITAT 103
8.3.1 REFERENCE SITE SCREENING AND ANTHROPOGENIC DISTURBANCE
CLASSIFICATIONS 103
8.3.2 MODELING EXPECTED REFERENCE VALUES OF THE INDICATORS 104
8.3.3 REFERENCE-SITE O/E MODELS WITH DISTURBANCE ADJUSTMENT 105
8.4 RESPONSE OF THE PHYSICAL HABITAT INDICATORS TO HUMAN
DISTURBANCE 107
8.5 LITERATURE CITED 109
APPENDIX 8.A 131
9 HUMAN HEALTH FISH TISSUE INDICATOR 150
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9.1 FIELD FISH COLLECTION 150
9.1.1 WHOLE FISH SAMPLES FOR HOMOGENIZED FILLET AN ALYSIS 150
9.1.2 FISH TISSUE PLUGS 151
9.2 MERCURY AN ALYSIS AND FISH TISSUE CRITERION FOR HUMAN
HEALTH 152
9.3 PCB ANALYSIS AND FISH TISSUE SCREENING LEVELS TO PROTECT
HUMAN HEALTH 153
9.4 PFAS ANALYSIS AND RESULTS 154
9.5 CALCULATION OF FISH TISSUE SCREENING LEVELS FOR HUMAN
HEALTH PROTECTION 157
9.6 LITERATURE CITED 158
10 ENTERCOCCI INDICATOR 160
10.1 FIELD COLLECTION 160
10.2 LAB METHODS 160
10.3 APPLICATION OF BENCHMARKS 161
10.3.1 CALIBRATION 161
10.3.2 BENCHMARKS 161
10.4 LITERATURE CITED 161
11 ALGAL TOXINS 163
11.1 FIELD METHODS 163
11.2 ALGAL TOXIN AN ALYSIS AND APPLICATION OF BENCHMARKS 163
11.3 LITERATURE CITED 164
12 FROM ANALYSIS TO RESULTS 165
12.1 CONDITION CLASSES 165
12.2 STRESSOR EXTENT, RELATIVE RISK, AND ATTRIBUTABLE RISK 165
12.2.1 STRESSOR EXTENT 166
12.2.2 RELATIVE RISK AND ATTRIBUTABLE RISK 166
12.2.3 RELATIVE RISK 167
12.2.4 ATTRIBUTABLE RISK 168
12.3 CHANGE ANAYLSES 169
12.3.1 DATA PREPARATION 169
12.3.2 ANALYSIS 169
12.4 LITERATURE CITED 169
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LIST OF FIGURES
FIGURE 6-1. AGGREGATED OMERNIK ECOREGIONS USED TO DEVELOP TRADITIONAL FISH MMIS
FOR NRSA2018-19. A SEPARATE FISH MMI WAS DEVELOPED FOR EACH OF THE NINE
AGGREGATED ECOREGIONS. NAP=NORTHERN APPALACHIANS, SAP=SOUTHERN
APPALACHIANS, CPL=COASTAL PLAINS, TPL=TEMPERATE PLAINS, UMW=UPPER MIDWEST,
SPL=SOUTHERN PLAINS, NPL=NORTHERNPLAINS, XER=XERIC WEST, WMT=WESTERN
MOUNTAINS 45
FIGURE 6-2. BOXPLOTS COMPARING REGIONAL FISH MMI SCORES OF LEAST-DISTURBED SITES TO
MOST- DISTURBED SITES. WHISKERS INDICATE 10TH AND 90TH PERCENTILES. POINTS
INDICATE 5TH AND 95TH PERCENTILES 66
FIGURE 6-3. REGIONAL FISH MMI SCORES VERSUS STRAHLER ORDER CATEGORY (LEAST-
DISTURBED SITES) 68
FIGURE 6-4. REGIONAL FISH MMI SCORES VERSUS FISH SAMPLING PROTOCOL (LEAST-DISTURBED
SITES) 69
FIGURE 6-5. REGIONAL FISH MMI SCORES VERSUS STREAM TEMPERATURE CLASS (LEAST-
DISTURBED SITES). TEMPERATURE BASED ON MODELED MEAN SUMMER STREAM
TEMPERATURE (MSST) 70
FIGURE 6-6. RELATIONSHIP BETWEEN NUMBER OF FISH COLLECTED, REDUCED HABITAT
VOLUME, AND SMALL WATERSHED SIZE AT LEAST-DISTURBED SITES. FISH ARE NOT LIKELY
TO BE FOUND IN STREAMS WITH A WATERSHED AREA OF < 2 KM2. THE SCALES OF TOTAL
NUMBER OF FISH COLLECTED AND WATERSHED AREA AXES HAVE BEEN TRUNCATED FOR
CLARITY 72
FIGURE 8-1. SAMPLE SITES FOR NRSA 2008-09 AND NRSA 2013-14 122
FIGURE 8-2. RIPARIAN DISTURBANCE (W1_HALL) IN COMBINED NRSA 2008-09 AND 2013-14 SAMPLE
SITES IN 9 AGGREGATE ECOREGIONS OF THE CONTERMINOUS U.S. BOXPLOTS SHOW 5, 25,
MEDIAN, 75, AND 95 PERCENTILES OFTHE UNWEIGHTED SAMPLE DISTRIBUTIONS (NOT
POPULATION ESTIMATES). A. BOATABLE SITES; B. WADEABLE SITES 123
FIGURE 8-3. RIPARIAN DISTURBANCE (W1_HALL) IN COMBINED NRSA 2008-09 AND 2013-14 SAMPLE
SITES IN 9 AGGREGATE ECOREGIONS OF THE CONTERMINOUS U.S., CONTRASTING
DISTRIBUTIONS IN LEAST-, MODERATELY-, AND MOST- DISTURBED SITES WITHIN EACH
AGGREGATED ECOREGION. BOXPLOTS SHOW 5TH, 25TH, MEDIAN, 75TH, AND 95TH
PERCENTILES OF THE UNWEIGHTED \SAMPLE DISTRIBUTIONS (NOT POPULATION
ESTIMATES). A. BOATABLE SITES; B. WADEABLE SITES 124
FIGURE 8-4. LOG RELATIVE BED STABILITY (LRBSJJSE) AND LOG10 GEOMETRIC MEAN BED
SURFACE SUBSTRATE DIAMETER (LSUB_DMM) IN COMBINED NRSA 2008-09 AND 2013-14
SAMPLE SITES IN 9 AGGREGATE ECOREGIONS OFTHE CONTERMINOUS U.S. BOXPLOTS SHOW
5, 25, MEDIAN, 75, AND 95 PERCENTILES OF THE UNWEIGHTED SAMPLEDISTRIBUTIONS
(NOT POPULATION ESTIMATES). A. BOATABLE SITES; B. WADEABLE SITES 125
FIGURE 8-5. OBSERVED/EXPECTED RELATIVE BED STABILITY (LOE_LRBS_USE) IN COMBINED
NRSA 2008-09 AND 2013-14 SAMPLE SITES IN 9 AGGREGATE ECOREGIONS OF THE
CONTERMINOUS U.S., CONTRASTING DISTRIBUTIONS IN LEAST-, MODERATELY-, AND MOST-
DISTURBED SITES WITHIN EACH AGGREGATED ECOREGION. BOXPLOTS SHOW 5, 25,
MED IAN,75, AND 95 PERCENTILES OF THE UNWEIGHTED SAMPLE DISTRIBUTIONS (NOT
POPULATION ESTIMATES). A. BOATABLE SITES; B. WADEABLE SITES 126
FIGURE 8-6. RIPARIAN VEGETATION COVER COMPLEXITY (LPT01 _XCMGW) IN COMBINED NRSA
2008-09 AND 2013-14 SAMPLE SITES IN 9 AGGREGATE ECOREGIONS OF THE CONTERMINOUS
U.S. BOXPLOTS SHOW 5, 25, MEDIAN, 75, AND 95 PERCENTILES OF THE UNWEIGHTED
SAMPLE DISTRIBUTIONS (NOT POPULATION ESTIMATES). A. BOATABLE SITES; B. WADEABLE
SITES 127
FIGURE 8-7. OBSERVED/EXPECTED RIPARIAN VEGETATION COVER COMPLEXITY
(LOE_XCMGW'_USE) IN COMBINED NRSA 2008-09 AND 2013-14 SAMPLE SITES IN 9 AGGREGATE
ECOREGIONS OF THE CONTERMINOUS U.S., CONTRASTING DISTRIBUTIONS IN LEAST-,
MODERATELY-, AND MOST-DISTURBED SITES WITHIN EACH AGGREGATED ECOREGION.
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BOXPLOTS SHOW 5, 25, MEDIAN, 75, AND 95 PERCENTILES OF THE UNWEIGHTED
SAMPLE DISTRIBUTIONS (NOT POPULATION ESTIMATES). A. BOATABLE SITES; B. WADEABLE
SITES 128
FIGURE 8-8. INSTREAM HABITAT COMPLEXITY (LPT01 _XFC_NAT) IN COMBINED NRSA 2008-09 AND
2013-14 SAMPLE SITES IN 9 AGGREGATE ECOREGIONS OF THE CONTERMINOUS U.S. BOXPLOTS
SHOW 5, 25, MEDIAN, 75, AND95 PERCENTILES OF THE UNWEIGHTED SAMPLE
DISTRIBUTIONS (NOT POPULATION ESTIMATES). A. BOATABLE SITES; B. WADEABLE SITES. ..129
FIGURE 8-9. OBSERVED/EXPECTED INSTREAM HABITAT COMPLEXITY (LOE_XFC_NAT_USE) IN
COMBINED NRSA 2008-09 AND 2013-14 SAMPLE SITES IN 9 AGGREGATE ECOREGIONS OF THE
CONTERMINOUS U.S., CONTRASTING DISTRIBUTIONS IN LEAST-, MODERATELY-, AND MOST-
DISTURBED SITES WITHIN EACH AGGREGATED ECOREGION. BOXPLOTS SHOW 5, 25,
MEDIAN, 75, AND 95 PERCENTILES OF THE UNWEIGHTED SAMPLE DISTRIBUTIONS (NOT
POPULATION ESTIMATES). A. BOATABLE SITES; B. WADEABLE SITES 130
LIST OF TABLES
TABLE 3-1. BASE PANELS AND OVERSAMPLE REPLACEMENT CATEGORIES 24
TABLE 3-2. RECOMMENDED CODES FOR EVALUATING SITES 25
TABLE 3-3. EVALUATION STATUS OF DROPPED SITES 25
TABLE 4-1. INITIAL SET OF SITES AVAILABLE FOR USE IN THE NRSA 30
TABLE 4-2. CRITERIA FOR EIGHT CHEMICAL AND PHYSICAL HABITAT FILTERS USED TO IDENTIFY THE
LEAST-DISTURBED REFERENCE SITES FOR BENTHIC MACROINVERTEBRATE AND FISH INDICATORS FOR
EACH OF THE NINE AGGREGATE ECOREGIONS FOR NRSA. A SITE MUST PASS ALL EIGHT FILTERS TO BE
CONSIDERED A LEAST-DISTURBED REFERENCE SITE 34
TABLE 4-3. CRITERIA FOR EIGHT CHEMICAL AND PHYSICAL HABITAT FILTERS USED TO IDENTIFY THE MOST-
DISTURBEDA SITES FOR EACH OF THE NINE AGGREGATE ECOREGIONS FOR NRSA. A SITE NEEDED TO
PASS ONE OF THE EIGHT FILTERS TO BE CONSIDERED A MOST-DISTURBED SITE 35
TABLE 5-1. SIX BENTHIC COMMUNITY METRICS, SCORING DIRECTION, AND FLOOR AND CEILING VALUES
USED IN CALCULATING THE NRSA AND WSA MMI IN EACH OF THE NINE AGGREGATE ECOREGIONS... 39
TABLE 5-2. MMI-DISTURBANCE REGRESSION MODEL STATISTICS USED FOR SETTING BENCHMARKS 41
TABLE 5-3. BENCHMARK VALUES FOR THE NINE REGIONAL BENTHIC MMIS 41
TABLE 6-1. CRITERIA USED TO SELECT LEAST-DISTURBED SITES FOR USE IN DEVELOPING THE REGIONAL
NRSA FISH Mi l 11 ML I RIC INDICES (MMIS) BASED ON 2008-09 AND 2013-14 DATA 47
TABLE 6-2. NUMBER OF FINAL CANDIDATE FISH MULTIMETRIC INDICES (MMIS) CALCULATED FROM THE
FINAL SET OF PASSED METRICS, BEFORE AND AFTER SCREENING FOR MAXIMUM PAIRWISE
CORRELATION AMONG METRICS AND S:N RATIO 50
TABLE 6-3. REGRESSION EQUATIONS FOR ADJUSTING METRICS FOR WATERSHED AREA. LWSAREA_NEW IS
THE LOGIO-TRANSFORMED VALUE OF WATERSHED AREA IN KM2. ONLY METRICS THAT WERE
INCLUDED IN THE FINAL SUITE OF METRICS USED TO CONSTRUCT ONE OF THE NINE REGIONAL FISH
MMIS ARE PRESENTED 50
TABLE 6-4. PERFORMANCE INFORMATION OF METRICS USED TO CONSTRUCT THE FISH MMI FOR THE
COASTAL PLAIN AGGREGATED ECOREGION. COLUMN NAME IS THE FIELD NAME IN THE NRSA
DATABASE 53
TABLE 6-5. PERFORMANCE INFORMATION OF METRICS USED TO CONSTRUCT THE FISH MMI FOR THE
NORTHERN APPALACHIANS AGGREGATED ECOREGION. COLUMN NAME IS THE FIELD NAME IN THE
NRSA DATABASE 54
TABLE 6-6. PERFORMANCE INFORMATION OF METRICS USED TO CONSTRUCT THE FISH MMI FOR THE
NORTHERN PLAINS AGGREGATED ECOREGION. COLUMN NAME IS THE FIELD NAMES IN THE NRSA
DATABASE 56
TABLE 6-7. PERFORMANCE INFORMATION OF METRICS USED TO CONSTRUCT THE FISH MMI FOR THE
SOUTHERN APPALACHIANS AGGREGATED ECOREGION. COLUMN NAME IS THE FIELD NAMES IN THE
NRSA DATABASE 57
TABLE 6-8. PERFORMANCE INFORMATION OF METRICS USED TO CONSTRUCT THE FISH MMI FOR THE
SOUTHERN PLAINS AGGREGATED ECOREGION. COLUMN NAME IS THE FIELD NAMES IN THE NRSA
DATABASE 58
TABLE 6-9. PERFORMANCE INFORMATION OF METRICS USED TO CONSTRUCT THE FISH MMI FOR THE
TEMPERATE PLAINS AGGREGATED ECOREGION. COLUMN NAME IS THE FIELD NAMES IN THE NRSA
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DATABASE 60
TABLE 6-10. PERFORMANCE INFORMATION OF METRICS USED TO CONSTRUCT THE FISH MMI FOR THE UPPER
MIDWEST AGGREGATED ECOREGION. COLUMN NAME IS THE FIELD NAMES IN THE NRSA DATABASE. 61
TABLE 6-11. PERFORMANCE INFORMATION OF METRICS USED TO CONSTRUCT THE FISH MMI FOR THE
WESTERN MOUNTAINS AGGREGATED ECOREGION. COLUMN NAME IS THE FIELD NAMES IN THE NRSA
DATABASE 62
TABLE 6-12. PERFORMANCE INFORMATION OF METRICS USED TO CONSTRUCT THE FISH MMI FOR THE XERIC
WEST AGGREGATED ECOREGION. COLUMN NAME IS THE FIELD NAME IN THE NRSA DATABASE 64
TABLE 6-13. PERFORMANCE STATISTICS FOR THE NINE REGIONAL FISH MMIS 65
TABLE 6-14. DETERMINING THE MINIMUM DRAINAGE AREA EXPECTED TO RELIABLY SUPPORT THE
PRESENCEOF FISH (ADAPTED FROM MCCORMICK ET AL. (2001)). VARIABLE NAMES ARE FROM THE NRSA
DATABASE. SCORES FOR EACH METRIC BETWEEN THE UPPER AND LOWER CRITERIA WERE ESTIMATED
BY LINEAR INTERPOLATION 71
TABLE 6-15. BENCHMARKS FOR ASSIGNING ECOLOGICAL CONDITION BASED ON THE DISTRIBUTION OF
REGIONAL FISH MMI SCORES IN LEAST-DISTURBED SITES SAMPLED IN NRSA 2008-09 OR NRSA 2013-14,
ADJUSTED USING THE HINDCASTING APPROACH OF HERT,THY ET AL. (2008). AGGREGATED
ECOREGIONS ARE SHOWN IN FIGURE 6-2. SAMPLE SIZES ARE IN PARENTHESES 73
TABLE 7-1. NUTRIENT AND SALINITY CATEGORY BENCHMARKS FOR NRSA ASSESSMENT 93
TABLE 8-1. METRICS USED TO CHARACTERIZE THE GENERAL ATTRIBUTES OF STREAM/RIVER PHYSICAL
HABITAT 114
TABLE 8-2. SAMPLING REVISIT PRECISION (REPEATABILITY7) OF THE FOUR PHYSICAL HABITAT CONDITION
INDICATORS. REPEAT VISITS WITHIN THE SUMMER SAMPLING SEASON WERE USED TO CALCULATE
RMSREP, which is essentially thestandard deviation of repeat sampling pairs to the
SAME STREAM OR RIVER REACH. DIVIDING THE SQUARE OF THERMSREP INTO THE VARIANCE AMONG
SITES GIVES THE S:N VARIANCE RATIO. (SEE KAUFMANN ET AL., 1999 FOR ANOVAMETHODS TO
CALCULATE RMSREP AND S:N, WHERE RMSREP IS EQUAL TO THEIR RMSE.) 115
TABLE 8-3. ESTIMATED NUMBER OF YEARS TO DETECT TRENDS IN HABITAT ATTRIBUTES. NUMBER OF YEARS
REQUIRED FOR A50-SITE MONITORING NETWORK TO DETECT 1% AND 2% PER YEAR TRENDS IN
HABITAT ATTRIBUTES WITH 80% LIKELIHOOD (BETA, OR POWER) AND ALPHA = 0.05, IF SPECIFIED
TRENDS OCCUR, AND SITES ARE VISITED EACH YEAR. DATA WERE TAKEN FROM LARSEN ET AL. (2004),A
OR CALCULATED USING THE SAME DATA AND ANALYTICAL PROCEDURES USED IN THAT
PUBLICATIONS 116
TABLE 8-4. ANTHROPOGENIC DISTURBANCE SCREENING CRITERIA 116
TABLE 8-5. NRSA BOATABLE AND WADEABLE LEAST-DISTURBED REFERENCE SITES FROM COMBINED 2008-09
& 2013-14 SURVEYS, SELECTED USING CONSISTENT CRITERIA LISTED IN TABLE 8-4. NUMBERS OF
REFERENCE SITES IDENTIFIED FROM THE 2008-09 AND 2013-14 SURVEYS ARE PARENTHESIZED AND
SEPARATED BY A SLASH (/) 117
TABLE 8-6. SUMMARY OF REGRESSION MODELS USED IN ESTIMATING SITE-SPECIFIC EXPECTED VALUES OF
LOG10 RELATIVE BED STABILITY" (LRBS_G0S) UNDER LEAST-DISTURBED REFERENCE CONDITIONS. SEE
APPENDIX 8.A FOR MODELDETAILS 118
TABLE 8-7. SUMMARY OF REGRESSION MODELS USED IN ESTIMATING SITE-SPECIFIC EXPECTED VALUES OF
RIPARIAN VEGETATION COVER AND STRUCTURE (LOG10[0.01+XCMGW|) UNDER LEAST-DISTURBED
REFERENCE CONDITIONS. SEE APPENDIX 8.A FOR MODEL DETAILS 119
TABLE 8-8. SUMMARY OF REGRESSION MODELS USED IN ESTIMATING SITE-SPECIFIC EXPECTED VALUES OF
INSTREAM HABITAT COVER COMPLEXITY" (LOG10[0.01+XFC_NAT|) UNDER LEAST-DISTURBED
REFERENCE CONDITIONS. SEE APPENDIX 8.A FOR MODEL DETAILS 120
TABLE 8-9. RESPONSIVENESS TO LEVELS OF HUMAN DISTURBANCE 121
TABLE 9-1. RECOMMENDED TARGET SPECIES AND ALTERNATE SPECIES FOR FISH TISSUE INDICATOR
SAMPLE COLLECTION 152
TABLE 9-2. NRSA 2018-19 FISH FILLET TISSUE COMPOSITE SAMPLE SUMMARY DATA 155
TABLE 12-1. EXTENT ESTIMATES FOR RESPONSE AND STRESSOR CATEGORIES 167
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LIST OF ACRONYMS
ANC
Acid Neutralizing Capacity
CCE
Calibrator Cell Equivalent
CPL
Coastal Plain ecoregion
DII
Dam Influence Index
DOC
Dissolved Organic Carbon
EMAP
EPA's Environmental Monitoring and Assessment Program
EPA
U.S. Environmental Protection Agency
FFG
Functional feeding group
FMMI
Fish Multimetric Index
HUC
Hydrologic Unit Codes
IBI
Index of Bio tic Integrity
IQR
Interquartile Range
Km
kilometers
MAHA
Mid-Atlantic Highlands Assessment
MAIA
Mid-Atlantic Integrated Assessment
NAP
Northern Appalachians ecoregion
NARS
National Aquatic Resource Surveys
NAWQA
National Ambient Water Quality Assessment
NLCD
National Land Cover Dataset
MAHA
Mid-Atlantic Highlands
NPL
Northern Plains ecoregion
NRSA
National Rivers and Streams Assessment
O/E
Ratio of Observed to Expected
OTU
Operational Taxonomic Unit
PCA
Principal Component Analysis
QA
Quality Assurance
QC
Quality Control
RBS
Relative Bed Stability
RF
Random Forest
RMSE
Root Mean Squared Error-
S:N
Signal to Noise (SignalNoise) ratio
SAP
Southern Appalachians ecoregion
SD
Standard Deviation
SPL
Southern Plains ecoregion
TPL
Temperate Plains ecoregion
UMW
Upper Midwest ecoregion
WMT
Western Mountains ecoregion
WSA
Wadeable Streams Assessment
XER
Xeric ecoregion
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i Introduction
National Rivers and Streams Assessment: The Third Collaborative Survey is the third in a series of National
Rivers and Streams Assessment (NRSA) reports that utilize a randomized statistical survey design to
assess the quality of the nation's perennial rivers and streams. The NRSA is one of the National
Aquatic Resource Surveys (NARS), a set of collaborative programs between EPA, states, and tribes
designed to assess the quality of the nation's waters using a statistical survey design. The survey data
underlying this NRSA report were collected in the summers of 2018 and 2019; as such, the findings
presented in the report show a snapshot in time.The key goals of the NRSA report are to describe
the ecological and recreational quality of the nation's perennial river and stream resources, how
those conditions are changing, and the key stressors affecting those waters. Clean Water Act (CWA)
Sections 104(a) and (b) collectively grant the Administrator authority to investigate and report on
water quality across the country. NARS data also inform and benefit the national water quality
inventory report that EPA prepares for Congress pursuant to CWA Section 305(b)(2).
This technical support document provides information about the analytical approaches used for the
NRSA 2018-19. National results from NRSA are included in the National Rivers and Streams
Assessment: The Third Collaborative Survey report and results for subpopulations, including EPA regions
and ecological regions, are presented in the online data dashboard
(https://riverstreamassessment.epa.gov/dashboard).
1.1 ADDITIONAL RESOURCES FOR SURVEY OPERATIONS
A series of protocols were used to ensure consistency throughout the survey operations. The
following documents provide the field sampling methods, laboratory procedures, quality measures,
and site selection for the NRSA 2018-19. Data from the survey are available to download at
https: / /www.epa.gov/national-aquatic-resource-surveys / data-national-aquatic-resource-surveys.
U.S. EPA. 2018. National Rivers and Streams Assessment: Field Operations Manual.
EPA-841-B-12-009a and EPA-841-B-12-009b. Washington, D.C.
U.S. EPA. 2018. National Rivers and Streams Assessment: Laboratory Operations
MethodsManual. EPA 841-B-12-010. Washington, D.C.
U.S. EPA. 2018. National Rivers and Streams Assessment: Quality Assurance Project Plan.
EPA 841-B-12-007. Washington, D.C.
U.S. EPA. 2012. National Rivers and Streams Assessment: Site Evaluation Guidelines.
EPA841 -B-l2-008. Washington, D.C.
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2 Quality Assurance
EPA implemented and assessed the quality of its operations and data throughout the NRSA 2018-19
survey. This chapter documents the NRSA's adherence to the requirements of EPA's quality system
implemented by the Office of Water (OW) as explained in the introduction section below. The
following sections describe the quality aspects of the statistical design, field operations, laboratory
assessments, data management, and report writing.
2.1 INTRODUCTION
The EPA quality system incorporates a national consensus standard for quality systems authorized
bythe American National Standards Institute (ANSI) and developed by the American Society for
Quality Control (ASQC), ANSI/ASQC E4-2004, Quality Systems for Environmental Data and Technology
Programs Requirements ivith Guidance for Use. EPA Order CIO 2105.0, dated May 5, 2000, requires all
component organizations to participate in an agency-wide quality system. The EPA Order also
requires quality assurance project plans or "equivalent documents" for all projects and tasks
involvingenvironmental data.
In accordance with the EPA Order, the OW's developed the Office of Water Quality Management
Plan (QMP; USEPA 2021) to describe OW's quality system that applies to all water programs and
activities, including the NRSA, collecting or using environmental data. As required by the EPA
Order and OW QMP, the NRSA team developed and abided by its QAPP throughout the survey.
One significant challenge encountered was application of the quality control procedures for
periphyton. As a result, EPA did not include periphyton in the NRSA 2018-19 reportand continues
to work with the United States Geological Survey (USGS) and other experts to improve periphyton
(specifically diatom) taxonomy through development of tools and training materials. The NRSA
QAPP contains elements of the overall project management, data quality objectives, measurement
and data acquisition, and information management. The QAPP also deals with the dataintegration
necessary between the Wadeable Streams Assessment (WSA), the NRSA, and EPA's Environmental
Monitoring and Assessment Program (EMAP) Western Pilot Study (2001-2004) to create a
comprehensive report on the status of the nation's rivers and streams.
The following companion documents to the QAPP present detailed procedures for implementing
the field and lab work for the NRSA 2018-19 survey:
National Rivers and Streams Assessment 2018-19: Site Evaluation Guidelines EPA 841-B-17-
002
National Rivers and Streams Assessment 2018-19: Field Operations Manual (Wadeable
and Boatable) (FOM), EPA-841-B-17-003a and EPA-841-B-17-003b
National Rivers and Streams Assessment: Laboratory Operations Manual (LOM), EPA
841-B-17-0004
The four documents together address all aspects of the NRSA's data acquisition and evaluation. The
LOM also lists measurement quality objectives (MQOs) which were used to evaluate the level of
quality attainment for individual survey metrics. Every person involved in the NRSA was
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responsiblefor abiding by the QAPP and adhering to the procedures specified in its companion
documents. NRSA participants were instructed and/or trained in the requirements applicable to the
person's role in the survey. For example, field crews attended a combined classroom and hands-on
training in field procedures. Laboratory personnel provided appropriate SOPs and certifications; and
attended calls to discuss implementation of the lab procedures.
2.2 SURVEY DESIGN
The NRSA's survey design was based upon statistical concepts that are well accepted by the
scientific community. As described in the following sections, the survey design objectives were met
by requirements of the statistical design, completeness of implementing the design, and consistency
with established procedures.
2.2.1 STATISICAL DESIGN
There is a large body of statistical literature dealing with sample survey designs which addresses the
challenge of making statements about many by sampling the few (Kish 1965). Sample surveys have
been used in a variety of fields (e.g., monthly labor estimates) to determine the status of populations
of interest, especially if the population is too numerous to census or if it is unnecessary to census the
population to reach the desired level of precision for describing the population's status. In natural
resource fields, probability sampling surveys have been consistently used to estimate the conditions
of the entire population. For example, the National Agricultural Statistics Survey (NASS) conducted
by the U.S. Department of Agriculture and the Forest Inventory Analysis (FIAT) conducted by the
U.S. Forest Service (Bickford et al., 1963, Hazard and Law 1989) have both used probability-based
sampling concepts to monitor and estimate the condition and productivity of agricultural and forest
resources from a commodity perspective. The sampling design strategy for NRSA is based on the
fundamental requirement for a probability sample of an explicitly defined resource population,
where the sample is constrained to reflect the spatial dispersion of the population. This design has
been documented in peer reviewed literature (Stevens 1994, Stevens and Olsen 1999). By applying
the statistical concepts of this design, the survey was able to meet the following overarching data
quality objectives:
In the conterminous U.S., estimate the proportion of perennial river and stream length (+ 5
percent) in good/fair/poor condition (or above/below criteria, etc.) for selected indicators
with 95 percent confidence based on NRSA benchmarks1.
For each of the aggregated Omernik Level III Ecoregions, estimate the proportion of
perennial river and stream length (+15 percent) in good/fair/poor condition (or
above/below criteria, etc.) for selected indicators with 95 percent confidence based on
NRSA benchmarks1.
1 The NRSA assessment benchmarks have no legal effect and are not equivalent to individual state water quality
standards. NRSA condition categories also may not correspond to the categories states and tribes use when they assess
water quality relative to their specific water quality standards under the Clean Water Act. For example, a rating of poor
condition under NRSA does not necessarily mean a site is "impaired" as defined by state and tribal water quality
standards assessment protocols.
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2.2.2 COMPLETENESS
To ensure that the implementation of the NRSA 2018-19 sample design resulted in adequate
measurements, the survey included completeness requirements for field sampling and laboratory
analyses. The QAPP requires that valid data for individual indicators must be acquired from a
minimum number of sampling locations to make subpopulation estimates with a specified level of
confidence or sampling precision. As the starting place for selecting field sites, EPA used the
National Hydrography Dataset (NHD; https:/ /www.usgs.gov/core-science-systems/ngp/national-
hydrographv) as the frame representing streams and rivers in the US because it was the most
complete source of stream hydrology available at the national scale. The data completeness
requirements were achieved, and sites where data for an indicator could not be collected were
classified as "Not Assessed" in the population estimates.
2.2.3 COMPARABILITY
Comparability is defined as the confidence with which one data set can be compared to another
(Stanley and Verner, 1985; Smith et al., 1988). For all indicators, NRSA ensured comparability by
using standardized sampling procedures, sampling equipment, and analytical methodologies by all
sampling crews and laboratories. For all measurements, reporting units and format are specified,
incorporated into standardized data recording forms, and securely transferred into a centralized
information management system. Because NRSA 2018-19 used the same or comparable methods to
collect data in EMAP West and WSA studies, the data also can be compared across the studies. The
following sections on field and laboratory operations describe additional measures to ensure
consistency in NRSA.
2.3 QUALITY ASSURANCE IN FIELD OPERATIONS
The requirements and methods presented in the Field Operations Manuals (FOM) ensured that
quality objectives were attainable and survey activities were manageable. As described below, NRSA
tested its FOM, trained crews using the FOM, visited crews during the field season, and confirmed
fish specimen identifications.
2.3.1 FIELD METHOD PILOT TESTING
Representatives from the NRSA team, logistics and data management contractors, and state partners
tested sampling methods, paper and electronic field forms, and equipment described in the FOM.
The test run assessed the accuracy and clarity of the FOM's instructions for executing the
procedures and quality steps. The test run also evaluated sampling logistics, sample preparation, and
sample shipping instructions. As a result of lessons learned during the test run, NRSA staff
correctedand improved the FOM prior to field crew training.
2.3.2 TRAINING OF FIELD TRAINERS AND ASSISTANCE VISITORS
Before training field crews, members of the NRSA team, oversight staff, contractor trainers, and
other experts tested the training materials during a 3-day period that included classroom and hands
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on training sessions. This "train-the-trainer" event serves two primary purposes. First, the event is
designed to make sure that all trainers understand the methods and are providing consistent
instruction to field crews. Second, it provides another opportunity to ensure that the field
documents and forms are clear and accurate. During this training event, the attendees tested the
materials to ensure that the instructions were correct and easy to execute and practiced actually
training the methods. The training materials included the FOM, iOS App forms, and PowerPoint
presentations. As a result of the training, practice training sessions and expert discussions, NRSA
staff revised and improved training materials, the FOM and QRG before the field crew trainings
began.
2.3.3 FIELD CREW TRAINING
To ensure consistency across field crews, all field crews were required to attend a 4-day training
session in 2018 prior to visiting any field site. In 2019 field crews attended either a 2 or 3-day
training to demonstrate their ability to perform the field methods properly. At a minimum, the field
crew leader and the fish taxonomist from each crew were required to attend each year. NRSA
trainers led regional field crew training sessions consisting of classroom and field-based lessons. The
training included sessions on conducting site reconnaissance, recording field observations and in situ
data, collecting field samples, preparing, packing and shipping sample containers, and use of the
standardized field forms. The field crew leaders were taught to review every form and verify that all
hand-entered data were complete and correct.
2.3.4 FIELD ASSISTANCE VISITS
To further assist the crews in correctly implementing the field procedures and quality steps, a trained
NRSA team member or contractor visited every NRSA field crew during the field season. These
visits, known as assistance visits (AV), provided an opportunity to observe field crews in the normal
course of a field day, assist in correctly applying the procedures, and document the crew's adherence
to sampling procedures. A total of 223 AVs were completed in the summers of 2018 and 2019. If
circumstances were noted where a field crew was not conducting a procedure properly, the observer
recorded the deficiency, reviewed the appropriate procedure with field team, and assisted the field
crew until the procedure was completed correctly.
2.3.5 REVISITS OF SELECTED FIELD SITES
To evaluate within-year sampling variability, the NRSA design called for crews to revisit 10 percent
of the sites selected in the design. These sites were sampled twice in the NRSA index period during a
single year (visit 1 and visit 2). Useful metrics and indicators tend to have high repeatability, that is
among site variability will be greater than sampling variability based on repeat sampling at a subset of
sites. To quantify repeatability, NARS uses Signal:Noise (S:N), or the ratio of variance associated
with sampling site (signal) to the variance associated with repeated visits to the same site (noise)
(Kaufmann et al., 1999). All sites are included in the signal, whereas only revisit sites contribute to
the noise component.
Metrics with high S:N are more likely to show consistent responses to human caused disturbance,
and S:N values < 1 indicate that sampling a site twice yields as much or more metric variability as
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sampling two different sites (Stoddard et al., 2008). The S:N values were used by analysts in the
process of selecting metrics and evaluating indicators.
2.3.6 EVALUATION OF FISH IDENTIFICATIONS
To ensure consistent naming conventions, field taxonomist and laboratory taxonomist were required
to use commonly accepted taxonomic references to identify fish vouchers. To evaluate their
identifications, field taxonomists were required to send fish vouchers from one or more site visits to
expert ichthyologists for a second, independent identification. The laboratory taxonomists were able
to determine the taxa for 1,293 vouchers which came from 10 percent of the sites where fish were
collected for NRSA 2018-19. Overall, there was 79 percent agreement between the field taxonomist
and laboratory taxonomists.
2.4 LABORATORY QUALITY ASSURANCE AND QUALITY CONTROL
The NRSA laboratories used standard methods and/or followed the requirements (e.g.,
performance-based objectives) in the Laboratory Operations Manual (LOM). The QAPP identified
the overall quality requirements and the LOM provided methods that could be used to achieve the
quality requirements. If a laboratory chose a different method, it still had to meet the QA
requirements as described below.
2.4.1 BASIC CAPABILITIES
All laboratories were required to submit documentation of their analytical capabilities prior to
analyzing any NRSA 2018-19 sample. NRSA team members reviewed documentation to ensure that
the laboratories could meet required measurement quality objectives (MQOs; e.g., reporting limits,
detection limits). National Environmental Laboratory Accreditation Conference certification,
satisfactory participation in round-robin, or other usual and customary types of evaluations were
considered acceptable capabilities documentation.
2.4.2 BENTHIC MACROINVERTEBRATE IDENTIFICATIONS
For benthic macroinvertebrate taxonomy, laboratories were required to use the same taxa lists,
conduct regular internal QC checks, and participate in an independent quality check. All
participating laboratories identified organisms using the most appropriate technical literature that
was accepted by the taxonomic discipline and reflected the accepted nomenclature at the time of the
survey. The Integrated Taxonomic Information System (ITIS, https://www.itis.gov/) were also
used to verify nomenclatural validity and reporting.
Taxonomic accuracy is evaluated by comparing identifications of the same organisms by primary
and secondary, independent laboratories. Each primary laboratory provided organisms from 10
percent of its samples, or at least three samples if they had fewer than 10 samples, to a secondary
laboratory for an independent evaluation. EPA, supported by an expert contractor, assessed the
primary and secondary identifications, and then held reconciliation calls to allow the taxonomists to
discuss organisms that were identified differently. As part of this process, recommendations and
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corrective actions were identified to address inaccurate taxonomic identification; and measurement
objectives were calculated to ensure the data were of sufficient quality for the NRSA.
Of the 2,186 benthic macroinvertebrate samples, the secondary laboratory identified organisms in
204 samples. The mean percent taxonomic disagreement (PTD) between laboratories was 9.1
percent for both 2018 and 2019 (better than the NRSA measurement objective of 15 percent as
identified in the QAPP). The overall percent difference in enumeration (PDE) was 3.1 and 0.9
percent for 2018 and 2019, respectively (better than the NRSA measurement objective of 5 percent
as identified in the QAPP).
Even when the measurement objectives were met, laboratories implemented recommendations and
corrective steps for the QC samples and all other samples with the same organisms. If, for example,
it was evident that empty mollusk shells were being identified and recorded in one or more of the
QC samples, the laboratories needed to verify that they had not counted empty mollusk shells in
their other samples.
2.4.3 CHEMICAL ANALYSES
For quality assurance of chemical analyses, laboratories used QC samples which are similar in
composition to samples being measured. They provide estimates of precision and bias that are
applicable to sample measurements. To ensure the ongoing quality of data during analyses, every
batch of water samples was required to include QA samples to verify the precision and accuracy of
the equipment, reagent quality, and other quality measures. These checks were completed by
analyzing blanks or samples spiked with known or unknown quantities of reference materials,
duplicate analyses of the same samples, blank analyses, or other appropriate evaluations. The
laboratories reported QA results along with each batch of sample results. In addition, laboratories
reported holding times. Holding time requirements for analyses ensure analytical results are
representative of conditions at the time of sampling. The NARS team reviewed the data and noted
any quality failures. The data analysts used the information about quality to determine whether to
include or exclude data in the evaluations. As described in the next section, the consolidated NRSA
database was further evaluated for quality issues.
2.5 DATA MANAGEMENT AND REVIEW
Information management (IM) is integral to all aspects of the NRSA from initial selection of
sampling sites through dissemination and reporting of final, validated data. Quality measures
implemented for the IM system are aimed at preventing corruption of data at the time of their initial
incorporation into the system and maintaining the integrity of data and information after
incorporation into the system.
Reconnaissance, field observation and laboratory analysis data were transferred from NRSA survey
participants and collected and managed by the NARS IM center. Data and information were
managed using a tiered approach. First, all data transferred from a field team or laboratory were
physically organized (e.g., system folders) and stored in their original state. Next, NARS IM created a
synthesized and standardized version of the data to populate a database that represented the primary
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source for all subsequent data requests, uses and needs. All samples were tracked from collection to
the laboratory.
The IM staff applied an iterative process in reviewing the database for completeness, transcription
errors, formatting compatibility, consistency issues and other quality control-related topics. This
first-line data review was performed primarily by NARS IM in consultation with the NRSA QA
team. A second-phase data quality review consisted of evaluating the quality of data based on MQOs
as described in the QAPP. This QA review was performed by the NRSA QA team using a variety of
qualitative and quantitative analytical and visualization approaches. Data that met the MQOs were
used without restriction. Data that did not meet the MQOs were qualified and further evaluated to
determine the extent to which quality control results deviated from the target MQOs. Minor
deviations, such as the field latitude and longitude did not fall on the mapped flow line, were noted
and qualified but did not prevent data from being used in analyses. Major deviations were also noted
and qualified, but data were excluded from the analyses. An example of a major deviation was
insufficient fish assemblage sampling; when this occurred, the fish multimetric index was not
calculated for a given site. Data not used for analyses because of quality control concerns account
for a subset of the missing data for each indicator analysis and add to the uncertainty in condition
estimates.
2.6 MAIN REPORT
The main report provides a summary of the findings of each of the data analyses and EPA's
interpretation of them. The main report was extensively reviewed in-house by the NRSA team,its
partners, and other EPA experts. Because previous reports using the same analytical procedures
were reviewed through an Independent External Review process, it was determined that a letter
review was not required for the main report. Note that EPA did conduct a letter peer review of the
NRSA nutrient benchmark setting process in 2021. EPA used the comments from the states and
EPA's Office of Research and Development to refine the main report and improve the clarity of
documentation in this technical support document (TSD). Comments on the nutrient benchmark
setting process were used to improve and clarify information in this TSD.
2.7 LITERATURE CITED
American National Standards Institute and American Society for Quality Control
(ANSI/ASQC).2004. Quality Systems for Environmental Data Collection and
Environmental Technology Programs: Collection and Evaluation of Environmental Data.
E4-2004. Milwaukee, WI.
Bickford, C.A., C.E. Mayer, and K.D. Water. 1963. An Efficient Sampling Design for Forest
Inventory: The Northeast Forest Resurvey. Journal of Forestry. 61: 826-833.
Kaufmann, P.R., P. Levine, E.G. Robison, C. Seeliger, and D.V. Peck. 1999. Quantifying Physical
Habitat in Wadable Streams. EPA/620/R_99/003. U.S. Environmental Protection Agency,
Washington, DC.
Kish, L. 1965. Survey Sampling. John Wiley & Sons. New York. 643 pp.
Smith, F., S. Kulkarni, L.E. Myers, and M.J. Messner. 1988. Evaluating and presenting quality
assurance data. Pages 157-68 in L.H. Keith, ed. ACS Professional Reference Book.
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Principles of Environmental Sampling. American Chemical Society, Washington, D.C.
Stanley, T.W., and S.S. Verner. 1986. The U.S. Environmental Protections Agency's quality
assurance program, pp. 12-19 In: J.K. Taylor and T.W. Stanley (Eds.). Quality Assurance
for Environmental Measurements. ASTM STP 867, American Society for Testing and
Materials, Philadelphia, Pennsylvania.
Stevens Jr., D.L. 1994. Implementation of a National Monitoring Program. Journal Environmental
Management 42:1-29.
Stevens Jr., D.L., and A.R. Olsen. 1999. Spatially restricted surveys over time for aquatic resources.
Journal of Agricultural, Biological, and Environmental Statistics 4:415-428.
Stoddard, J.L., A.T. Herlihy, D.V. Peck, R.M. Hughes, T.R. Whittier, and E. Tarquinio. 2008 A
process for creating multimetric indices for large-scale aquatic surveys. Journal of North
American Benthological Society 27: 878-891.
Stribling, J.B., S.R. Moulton, and G.T. Lester. 2003. Determining the quality of taxonomic data.
Journal of the North American Benthological Society 22(4) :621-631.
USEPA. September 2021. Office of Water Quality Management Plan. EPA EPA-821-F-21-004.
Revision 15. U.S. Environmental Protection Agency, Office of Water. Washington, DC.
USEPA. February 2009. National Rivers and Streams Assessment: Site Evaluation Guidelines.
EPA-841-B- 07-008. U.S. Environmental Protection Agency, Washington, DC.
USEPA. April 2009. National Rivers and Streams Assessment: Field Operations Manual. EPA-841-
B-07-9. U.S. Environmental Protection Agency, Washington, DC.
USEPA. November 2009. National Rivers and Streams Assessment: Laboratory Methods Manual.
EPA-841- B-07-010. U.S. Environmental Protection Agency, Washington, DC.
USEPA. December 2010. National Rivers and Streams Assessment: Integrated Quality Assurance
Project Plan, Final Document. EPA-841-B-07-007. U.S. Environmental Protection Agency,
Office of Water and Office of Research and Development, Washington, DC.
USEPA. May 2000. Order CIO 2105.0, Policy and Program Requirements for the Mandatory
Agency-wide Quality System. U.S. Environmental Protection Agency, Washington, DC.
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3 Selection of Probability Sites
Using a statistical survey design, 1,808 sites were selected at random to represent the quality of the
larger population (1.5 million miles) of perennial rivers and streams across the conterminous United
States, from large rivers to small headwater streams. Sites were selected using a random sampling
technique that uses a probability-based design described in this chapter. The following sections
describe the statistical objectives, target population, sample frame, survey design, evaluation, and
statistical analysis.
3.1 OBJECTIVES
The data quality objects, or design requirements, for the National Rivers and Streams Assessment
2018-19 were:
to estimate the proportion of rivers and streams with a margin of error of + 5% in the
conterminous U.S. in good/fair/poor condition (or above/below criteria, etc.) for selected
indicators with 95 percent confidence based on NRSA benchmarks,
to estimate the proportion of rivers and streams with a margin of error of + 15% in each of
nine ecological reporting regions in good/fair/poor condition (or above/below criteria, etc.)
for selected indicators with95 percent confidence based on NRSA benchmarks.
to estimate the change in proportion of river and streams in the conterminous U.S. between
2008-09, 2013-14 and 2018-19 in good/fair/poor condition (or above/below criteria, etc.)
for selected measures based on NRSA benchmarks. Change estimates should have a margin
of error of + 15% at 95% confidence.
to estimate the change in proportion of river and streams in the conterminous U.S. between
2008-09, 2013-14 and 2018-19 in each of nine ecological reporting regions in
good/fair/poor condition (or above/below criteria, etc.) for selected measures based on
NRSA benchmarks. Change estimates should have a margin of error of + 15% at 95%
confidence.
accomplish the above while ensuring that the minimum sample size for a state will be 20 and
maximum will be 75.
Revisit 10% of the sites for variance component estimation and quality assurance.
3.2 TARGET POPULATION
The target population consisted of all streams and rivers within the 48 contiguous states that
had flowing water during the study index period (i.e., beginning of June through end of September
formost regions). This included major rivers and small streams. Sites had to have > 50% of the
reach length with standing water and sites were to be sampled during base flow conditions. Sites
with water in less than 50% of the reach length were dropped and replaced. The target population
excludes tidal rivers and streams up to head of salt (defined as < 0.5 ppt for this study), as well as
run-of-the-river ponds and reservoirs with greater than 7-day residence time.
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3.3 SAMPLE FRAME
The sample frame, used to represent the target population, was derived from the medium resolution
National Hydrography Dataset (NHD-Plus) V2. Attributes from NHD-Plus and additional
attributes added to the sample frame that are used in the survey design are:
MajorRiver: rivers identified as major rivers or additional rivers in the book: Rivers of North
America (Benke and Cushing 2005);
Strahler order;
Strahler category where categories are RiversMajor (5th order and higher), RiversOther (5th
order and higher), LargeStreams (3rd,4th order), and SmallStreams (1st, 2nd order);
BorderRiver: rivers and streams that occur on state and country boundaries. Each reach is
identified by two-state postal codes such as MO:IL for the portion of the Mississippi River
that forms the boundary between Missouri and Illinois. A border river/stream is assigned to
one of the two states for the survey design;
Ecological Reporting Region: Nine aggregated Omernik ecoregions used for reporting;
Omernik and North American ecoregions Levels I, II, III and IV;
Postal code (state);
Urban and non-urban rivers and streams; and
Landownership as non-federal, Forest Service, BLM, Tribal Land, US Fish and Wildlife
Service, US National Park Service, and Department of Defense.
The urban/non-urban attribute was created by intersecting a modified version of the Census Bureau
national urban boundary GIS coverage with NHD-Plus. The Census Bureau's boundaries were
buffered 100 meters to include a majority of stream features intersecting and coincident with urban
areas. Where this buffer did not completely gather all the river features within the urban areas (rivers
intersecting cities are excluded from the Census Bureau's urban areas), the NHD-Plus river area
(polygon) features were clipped at a three-kilometer buffer around the urban areas and combined
with the buffered urban area to create the modified urban database. If a stream or river segment was
within this boundary, it is designated as "Urban"; otherwise, it is designated as "NonUrban".
FCODE is directly from NHD-Plus and is used to identify which segments in NHD were included
in the sample frame. The FCODEs are a numeric identifier of the channel type. The attribute
Frame07 identifies each segment as either "Include" or "Exclude." Frame07 was created so that
segments included in the sample frame could be easily identified. All segments chosen to be sampled
were evaluated in the field prior to sampling to ensure they met the target population of NRSA (i.e.,
perennial rivers and streams). Sites that were not perennial were not sampled but were instead
replaced by the next perennial segment in the list. FCODE values included in the GIS shapefile:
FCODEs Included in 2018-19 sample frame:
33600 Canal/Ditch
42801 Pipeline: Pipeline Type = Aqueduct; Relationship to Surface = At or Near
46000 Stream/River
46006 Stream/River (Perennial)
58000 Artificial Path (removed from dataset if coded through Lake/Pond and Reservoirs)
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FCODEs Excluded in 2018-19 sample frame
33400 Connector
46003 Stream/River (Intermittent)
42800 Pipeline
42802 Pipeline: Pipeline Type = Aqueduct; Relationship to Surface = Elevated
42803 Pipeline: Pipeline Type = Aqueduct; Relationship to Surface = Underground
42804 Pipeline: Pipeline Type = Aqueduct; Relationship to Surface = Underwater
42806 Pipeline: Pipeline Type = General Case; Relationship to Surface = Elevated
4280 Pipeline: Pipeline Type = General Case; Relationship to Surface = Underground
42809 Pipeline: Pipeline Type = Penstock; Relationship to Surface = At or Near
42811 Pipeline: Pipeline Type = Penstock; Relationship to Surface = Underground
42813 Pipeline: Pipeline Type = Siphon
56600 Coastline
58000 Artificial Path if coded through Lake/Pond and Reservoirs
3.4 SURVEY DESIGN
The survey design consists of two separate designs to address the dual objectives of (1) estimating
current status and (2) estimating change in status for all flowing waters:
Resample design applied to NRSA 2008-09 and NRSA 2013-14 sites
New site design for NRSA 2018-19.
Five basic panels are used for NRSA 2018-19:
NRS18_08TS3R2: sites from NRSA 2008-09 that were sampled twice in 2008-09 and then
sampled twice again in 2013-14 (a few exceptions). TS3 designates that the site will have
been sampled in all three NRSA surveys. R2 designates a site that will be sampled twice in
2018-19.
NRS18_08TS3: sites from NRSA 2008-09 that were sampled once in 2008-09 and sampled
again in 2013-14. TS3 designates that the site will have been sampled in all three NRSA
surveys.
NRS18_13TS2R2: sites from NRSA 2013-14 that were sampled twice in 2013-14. TS2
designates that the site will have been sampled in two NRSA surveys. R2 designates a site
that will be sampled twice in 2018-19.
NRS18_13TS2: sites from NRSA 2013-14 that were sampled once in 2013-14 and will be
sampled again in 2018-19. TS2 designates that the site will have been sampled in two NRSA
surveys.
NRS18_18: new sites selected for NRSA 2018-19 that will be sampled once in 2018-19.
3.4.1 RESAMPLE DESIGN
The Resample survey design is a subsample of the NRSA 2008-09 sites and NRSA 2013-14 sites that
were target and sampled in NRSA 2008-09 and NRSA 2013-14. The major objective for this design
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is change estimation, although all sites sampled in 2013-14 will be used when change is estimated.
The resample design has four panels:
NRS18_08TS3R2 96 sites (two per state) from NRSA 2008-09 sites that were sampled
twice in 2008-09 and that were also sampled twice in 2013-14 and will be sampled twice in
2018-19. In each state one site is a stream (Strahler order 1-4) and one site is a river (Strahler
order 5-10). Note that Arizona sites visited twice are both rivers since no streams were
available that were visited twice in prior surveys.
NRS18_08TS3 377 sites that were sampled once in 2008-09, once in 2013-14 and will be
sampled once in 2018-19. Approximately 50% of sites in each state will be streams and 50%
will be rivers. Sample size for each state is based on sample size used in 2013-14
proportional to achieve 408 sites.
NRS18_13TS2R2 - 96 sites (two per state) from NRSA 2013-14 sites that were sampled
twice in 2013-14 and will be sampled twice in 2018-19. In each state one site is a stream
(Strahler order 1-4) and one site is a river (Strahler order 5-10). Note that Vermont sites
visited twice are both streams since no rivers were available that were visited twice in prior
surveys.
NRS18_13TS2 414 sites that were sampled once in 2013-14 and will be sampled once in
2018-19. Approximately 25% of sites in each state will be Small Streams (lst-2nd), Large
Streams (3rd-4th), Rivers Major (5th+) and Rivers Other (5th+). Sample size for each state is
based on sample size used in 2013-14 proportional to achieve 408 sites.
This results in 983 unique sites in the Resample Design. Allocation of sites to NARS aggregated
ecoregions is proportional to the number sampled in the prior surveys.
3.4.2 NEW SITE DESIGN
The NRSA 2018-19 new site survey design is a new survey design where the expected sample sizes
are based on the nine ecological reporting regions and four categories of Rivers Major (5th and
greater), Rivers Other (5th and greater), Large Streams (Strahler order 3rd, 4th), and Small Streams
(Strahler order 1st, 2nd). Allocation of number of sites to states is proportional to stream length.
The New Site Design is explicitly stratified by state. Unequal probability categories are 36
combinations of NARS nine aggregated ecoregions and four Strahler order categories (SS small
streams (lst-2nd), LS large streams (3rd-4th), RM major rivers (5th+) and RO other rivers
(5th+). In addition, a minimum of 20 sites (Resample and New) was guaranteed in each state and a
maximum of 75 sites (Resample and New) for a state.
Final site distribution: First each state was assigned one site for each unequal probability category of
streams and rivers that occur in the state. This allocates 414 sites in the New Site Design. Next the
remaining 411 sites were allocated to the states proportional to their stream and river length.
3.4.3 OVERSAMPLE AND SITE REPLACEMENT
Site replacement is based on the 2018-19 panel variable NRS18_PNL. Five basic panels are used for
NRSA 2018-19 (Table 3-1):
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NRS18_08TS3R2: sites from NRSA 2008-09 that were sampled twice in 2008-09 and then
sampled twice again in 2013-14 (a few exceptions). TS3 designates that the site will have been
sampled in all three NRSA surveys. R2 designates a site that will be sampled twice in 2018-19.
NRS18_08TS3: sites from NRSA 2008-09 that were sampled once in 2008-09 and sampled again
in 2013-14. TS3 designates that the site will have been sampled in all three NRSA surveys.
NRS18_13TS2R2: sites from NRSA 2013-14 that were sampled twice in 2013-14. TS2 designates
that the site will have been sampled in two NRSA surveys. R2 designates a site that will be
sampled twice in 2018-19.
NRS18_13TS2: sites from NRSA 2013-14 that were sampled once in 2013-14 and will be
sampled again in 2018-19. TS2 designates that the site will have been sampled in two NRSA
surveys.
NRS18_18: new sites selected for NRSA 2018-19 that will be sampled once in 2018-19.
Table 3-1. Base Panels and Oversample replacement categories
NRSA 2018-19 panel
Base sites within 2018-19 panel
Over sample sites within 2018-
19 panel that will be used as
replacement sites within the panel
NRS18_08TS3R2
NRS 18_08TS3R2_BaseStream
NRS 18_08TS3R2_OverStream
NRS18_08TS3R2
NRS 18_08TS3R2_BaseRiver
NRS 18_08TS3R2_OverRiver
NRS18_08TS3
NRS 18_08TS3_BaseStream
NRS 18_08TS3_OverStream
NRS18_08TS3
NRS 18_08TS3_BaseRiver
NRS 18_08TS3_OverRiver
NRS 18 13TS2R2
NRS18_13TS2R2_Bas eStream
NRS 18_13TS2R2_OverStream
NRS 18 13TS2R2
NRS18_13TS2R2_Bas eRiver
NRS 18_13TS2R2_OverRiver
NRS18_13TS2
NRS 18 13TS2_BaseSS
NRS 18 13TS2_OverSS
NRS18_13TS2
NRS 18 13TS2_Bas eLS
NRS 18_13TS2_OverLS
NRS18_13TS2
NRS18_13TS2_BaseRO
NRS 18_13TS2_OverRO
NRS18_13TS2
NRS 18 13TS2_Bas eRM
NRS 18_13TS2_OverRM
NRS18_18
NRS 18_18_BaseSS_XXX
NRS 18_18_BaseSS_XXX
NRS18_18
NRS18_18_BaseLS_XXX
NRS18_18_BaseLS_XXX
NRS18_18
NRS18_18_BaseRO_XXX
NRS18_18_BaseRO_XXX
NRS18_18
NRS18_18_BaseRM_XXX
NRS18_18_BaseRM_XXX
XXX designates one of the nine aggregated ecoregions: CPL, NAP, NPL, SAP, SPL, TPL, UMW,
WMT, or XER. Sites within each state and above six categories are provided in sitelD order, and the
replacement must be in sitelD order within the panel. Panels with "R2" are sites that will be sampled
twice in 2018-19. If no over sample sites are available, or all over sample sites have been used, for an
"R2" panel, then the next site in sitelD order within the same basic panel is used. For example, if no
over sample site is available in panel NRS18_08TS3R2_BaseStream, then use first site in panel
NRS18 08TS3 BaseStream.
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3.5 EVALUATION PROCESS
The survey design weights in the design file assumed that the survey design was implemented as
designed. To achieve the planned sample size, we replaced sites that could not be sampled with
oversamples as described above. Because some sites were replaced, the original survey design
weights are no longer correct and EPA statisticians had to adjust the weights. This weight
adjustment process required the statisticians knowing what happened to each site in the base design
and the oversample sites (e.g., was the site sampled or dropped and if dropped why).
EvalStatus (evaluation status) was initially set to "NotEval" to indicate that the site had yet to be
evaluated for sampling. When a site was evaluated for sampling, then the EvalStatus for the site was
changed. Recommended codes are provided in Table 3-2.
Table 3-2. Recommended Codes for Evaluating Sites
EvalStatus
Code
Name
Meaning
TS
Target Sampled
Site was a member of the target population and was sampled
LD
Landowner Denial
Landowner denied access to the site
PB
Physical Barrier
Physical barrier prevented access to the site
NT
Non-Target
Site was not a member of the target population
NN
Not Needed
Site was a member of the oversample and was not evaluated
forsampling
Other codes
Other codes were often useful. For example, rather than
use NT, die status may include specific codes indicating
why thesite was non-target.
3.6 IMPLEMENTATION OF THE DESIGN
For NRSA 2018-19, 5,129 design sites were evaluated. Of these 1,909 were evaluated as target and
sampled, with 188 sites sampled twice. The remaining sites were dropped and replaced for various
reasons (Table 3-3). The margin of error for national estimates was +/- 3% and for ecoregion
estimates was +/- 15% with 95% confidence. For the difference analysis, estimates had a margin of
error of + /- 5% at the national level and +/- 18% at the ecoregional level with 95% confidence. A
minimum of 20 sites were sampled in each state.
Table 3-3. Evaluation Status of Dropped Sites
Category
Number of sites dropped
Impounded
28
Inaccessible
410
Landowner_NoAccess
1045
MapError
51
NonPerennial
729
N onT arget_Other
23
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Tidal
340
Wetland
33
3.7 STATISTICAL ANALYSIS
Any statistical analysis of the data must incorporate information about the monitoring survey design.
For NRSA, when estimates of characteristics for the entire target population are computed, the
statistical analysis must account for the stratifications and unequal probability selection in the design.
Procedures for doing this are available from the Aquatic Resource Monitoring Web page
(https://archive.epa.gov/nheerl/arm/web/html/index.html). A statistical analysis library of
functions to do common population estimates in the statistical software environment R is available
from the webpage. In the NRSA 2018-19 Site Information data file, the adjusted weights used to
calculate national condition estimates are in the column "WGT_EXT_SP"
3.8 LITERATURE CITED
Benke, A.C., and C.E. Cushing. 2005. Rivers of North America. Elsevier Academic Press,
Burlington, MA. 1168 pp.
Diaz-Ramos, S., D. L. Stevens, Jr, and A.R. Olsen. 1996. EMAP Statistical Methods Manual.
EPA/620/R-96/002, U.S. Environmental Protection Agency, Office of Research and
Development, NHEERL-Western Ecology Division, Corvallis, Oregon.
Horn, C.R., and W.M. Grayman. 1993 Water-quality modeling with EPA reach file system. Journal
of Water Resources Planning and Management, 119, 262-74.
Stevens, D.L., Jr. 1997. Variable density grid-based sampling designs for continuous spatial
populations. Environmetrics, 8:167-95.
Stevens, D.L., Jr., and A.R. Olsen. 1999. Spatially restricted surveys over time for aquatic resources.
Journal of Agricultural, Biological, and Environmental Statistics, 4:415-428
Stevens, D.L., Jr., and A.R. Olsen. 2003. Variance estimation for spatially balanced samples of
environmental resources. Environmetrics 14:593-610.
Stevens, D.L., Jr., and A.R. Olsen. 2004. Spatially-balanced sampling of natural resources in the
presence of frame imperfections. Journal of American Statistical Association: 99:262-278.
Strahler, A.N. 1957. Quantitative Analysis of Watershed Geomorphology. Trans. Am. Geophys.
Un. 38, 913-920.
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4 Selection of Sites to Establish Reference
Conditions
One way to assess current quality is to compare data to a benchmark. For a number of indicators,
the NARS assessments apply a reference approach for setting benchmarks. For NRSA, the reference
approach is one in which least-disturbed sites in ecological regions are used to establish a reference
distribution from which benchmarks for assessing quality at other sites are identified. The least-
disturbed condition approach attempts to capture the best available chemical, physical and biological
habitat conditions given the current state of the landscape (Stoddard et al., 2006). The NRSA
reference sites and distribution do not represent pre-Columbian or "pristine" conditions.
The approach described in this chapter was used to select metrics for benthic macroinvertebrate and
fish multimetric indices (MMI); and to define the ecoregion-specific benchmarks used in the NARS
analyses. This approach was modified for water chemistry and physical habitat analysis. The process
for identifying the final set of reference sites for each of the indicators that use them for setting
benchmarks is described in each of the indicator chapters: see Chapter 5, Chapter 6, Chapter 7, and
Chapter 8 for additional details.
This chapter describes the methodology used to select the reference sites including background and
updates to the approach, the sources of candidate reference sites; and the chemical, physical screens,
and geospatial screens used for assessing the quality of the benthic macroinvertebrate assemblage. It
also describes how analysts used the reference approach to establish benchmarks.
4.1 Background and Updates
The NRSA approach is based on guidance and research for applying the reference approach to
assess streams in terms of biological characteristics (i.e., biocriteria) and nutrient concentrations (US
EPA 1996, USEPA 2000, Herlihy and Sifenos 2008, Herlihy et al., 2008, Stoddard et al., 2008). The
analysis conducted for NRSA builds off the 2004 Wadeable Streams Assessment (WSA; USEPA,
2006), a nationwide assessment that preceded NARS. For the WSA, scientists applied the reference-
based approach at an aggregated level III ecoregional scale. As described below, the NRSA analysis
updates previously published screening criteria (i.e., Herlihy et al., 2008)2 for identifying reference
sites used in setting benchmarks and developing metrics/indices in aggregated ecoregions.
The NRSA 2008-09 analysis used the reference site data from WSA as well as new reference site
data from additional hand-picked and probability sites sampled during NRSA 2008-09. Adding sites
from NRSA was necessary so that non-wadeable streams and rivers would be included in the
reference selection process (WSA included only wadeable streams). After the addition of the NRSA
2008-09 sites, including non-wadeable systems, the analysts reviewed and ultimately updated some
2 Although some of the supporting literature for the nutrient reference-based approach used nutrient ecoregions, the
WSA and subsequent NRSA reference approach is applied at aggregated level III ecoregions for nutrients as well as
other indicators. As a result, the nutrient screening criteria and benchmarks could not be used directly.
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of the reference screening criteria3 originally used for WSA. These updates to the original WSA
screening values are shown in red in Table 4-2. For the 2008-09 analysis, benchmarks were updated
after inclusion of the additional rivers and stream reference sites. Additionally, the fish MMIs and
physical habitat indicators were updated using the additional reference sites (for the benthic
macroinvertebrates the WSA MMIs were still used).
For the NRSA 2013-14 analysis, potential additional reference sites were identified by filtering the
2013-14 sample for disturbance using the same process described in this chapter. A comparison of
existing NRSA 2008-09 benchmarks was made against the benchmarks calculated by adding the new
NRSA 2013-14 reference site data. After analyzing the revised benchmarks, the analysts determined
that the differences compared to the NRSA 2008-09 benchmarks were large enough for the fish
MMI and three of the four physical habitat indicators to warrant revisions to the benchmarks for
these indicators. For other indicators (i.e., benthic macroinvertebrate MMI, nutrients), the analysts
determined that the differences did not warrant revisions to the benchmarks. For these indicators,
the existing NRSA 2008-09 benchmarks were applied.
For the 2018-19 assessment, EPA did not to update the benchmarks for any of the reference-based
indicators, thus establishing a consistent baseline against which to measure condition, changes and
trends.
4.2 SOURCES OF REFERENCE SITES
The fish, macroinvertebrate, and physical reference sites used in the NRSA came from four major
activities:
1. We used sites sampled during the NRSA. These included both sites selected from the
probability sample and sites hand-picked by best professional judgment that were sampled and
analyzed using NRSA methods as part of the NRSA (number of sites shown in Table 4-1,
"NRSA-Screened" column).
2. In addition to the sites sampled in the NRSA, we obtained data for potential reference site
from USGS' National Water-Quality Assessment Program (NAWQA), EPA Region 7, the
State of Wisconsin, and the State of Oklahoma (number of sites shown in Table 4-1, "NRSA-
External" column). These data included fish and macroinvertebrate assemblage data as well as
physical and chemical habitat data.
3. Benthic macroinvertebrate reference site data also came from the 1,655 wadeable
stream sites available for use in the EPA WSA. In the WSA, reference sites were
obtained from two different approaches: first by screening the WSA survey data for
physical and chemical criteria in the same manner described Section 4.3 (number of
sites shown in Table 4-1, "WSA-Screened" column), and second from
macroinvertebrate data provided by other agencies, universities, or states from sites
3 Screening criteria for selecting least-disturbed reference conditions can be developed iteratively with the goal of
establishing the least amount of ambient human disturbance (Stoddard et al 2006) while maintaining sufficient reference
sites for setting benchmarks.
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that were deemed to be suitable as reference sites by best professional judgment
(number of sites shown in Table 4-1, "WSA-External" column). These sites either
were sampled with the same methodology as the WSA or had field and lab protocols
with enough similarities that the data analysis group determined that the data were
comparable. The reference sites from this second approach were only used in
developing an MMI for benthic macroinvertebrate samples, not for setting the
benchmarks.
The WSA reference site screening process and data sources are described in detail in
Herlihy et al. (2008). In Table 4-1, the first two data columns summarize the number
of available WSA benthic macroinvertebrate reference sites by ecoregion.
4. We also included additional reference site data for fish from stream and river sites used by
Herlihy et al. (2006) in a national analysis of fish assemblage data. The screening process
used to define reference sites is described in Herlihy et al. (2006) and defined in detail in
Appendix 1 of that document. The Herlihy et al. (2006) study only used the first two years
of data from EMAP (Environmental Monitoring and Assessment Program)-West. For
NRSA, reference fish data from the last three years of EMAP-West were also available and
were included. Final numbers of reference sites and screening used to refine the fish
reference population are outlined in Chapter 6.
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Table 4-1. Initial set of sites available for use in the NRSA
WSA Activities
NRSA Activities
WSA
WSA
NRSA
NRSA
Ecoregion
External
Screened
External
Screened
Total
Northern Appalachians (NAP)
114
27
2
37
180
Southern Appalachians (SAP)
370
35
22
38
465
Coastal Plain (CPL)
112
15
3
46
176
Upper Midwest (UMW)
68
12
38
30
148
Temperate Plains (TPL)
124
38
50
22
234
Northern Plains (NPL)
10
18
3
47
78
Southern Plains (SPL)
56
21
51
34
162
Western Mountains (WMT)
335
129
4
40
508
Xeric Region (XER)
132
39
2
33
206
Total
1,321
334
175
327
2,157
4.3 CHEMICAL AND PHYSICAL SCREENS
To select reference sites from those compiled as described in Section 4.2, we first used chemical and
physical data collected at each site (e.g., nutrients, turbidity, acidity, riparian condition) to determine
whether the site is in least-disturbed condition for its ecoregion. In the NRSA, eight physical and
chemical parameters were used to screen for reference sites, total nitrogen (total N), total
phosphorus (total P), chloride, sulfate, acid neutralizing capacity, turbidity, percent fine substrate,
and riparian disturbance index. If a site exceeded the screening value identified in Table 4-2 for any
one stressor it was dropped from reference consideration. As described in Section 4.2, some
screening criteria were updated from those used in WSA.
Given that expectations of least-disturbed condition vary across ecoregions, the criteria values for
exclusion varied by ecoregion. The nine aggregate level III ecoregions developed for the WSA
assessment were used to regionalize reference conditions. Ecoregional specific screening criteria in
the Western Mountains ecoregion was broken into three finer-scale ecoregion subgroups for
screening to match EMAP-West's use of a somewhat finer spatial scale.
As noted in Section 4.2, in addition to the sites sampled in the NRSA, we obtained possible
reference site external data from four other agencies. Data from these external surveys were
screened for physical and chemical criteria using the same criteria used for NRSA sample sites in
Table 4-2 using whatever screening data were available in each survey.
All sites in the NRSA (both probability and hand-picked, boatable and wadeable) and the added
external data that passed all criteria were considered to be candidate reference sites for the NRSA
assessment. The number of sites by ecoregion used in the screening of biological reference sites are
summarized in Table 4-1. These reference sites include both fish and macroinvertebrate data. The
NRSA did not use data on the biological assemblages themselves for any screening as these are the
primary components of the stream and river ecosystems being evaluated, and to use them would
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constitute circular reasoning.
Note that the Rapid Bioassessment Protocol (RBP) physical habitat score was used as a filter in
WSA but was not available in the NRSA data to use as a screen. The six ecoregions in the top half of
the table were used in WSA and reported in Herlihy et al. (2008); the ecoregions in the bottom half
of the table were screened using criteria developed in EMAP-West.
Sites were also screened using the criteria in Table 4-3 to identify most disturbed sites that could be
used to test responsiveness in method and indicator development.
4.4 GEOSPATIAL SCREENS
As a final screen, all sites that passed the chemical and physical screens were then screened using
three additional landscape-GIS screening criteria. These screens included a dam influence index,
urbanization influence, and agricultural influence.
The dam influence index (DII) was used to assess the influence of upstream dams and the largest
reservoir on the current list of potential reference sites. The complete watershed was assessed for
any of the sites with a watershed boundary with a maximum distance of less than 200 km upstream
of the sampling point. Any site that had a watershed with a distance greater than 200 km upstream
of the sample point, had a wedge-shaped area assessed until 200 km upstream was reached. A cut-
off distance of 200 km upstream was used because it is unlikely land use activities occurring greater
than 200 km upstream will directly influence a given sample reach downstream. For example, a
sample reach on the lower Mississippi is more likely to be influenced by a dam located near the
sample reach than a dam located in Montana, even though the Missouri River occurring within
Montana is part of the upstream watershed of the lower Mississippi. For all watersheds (i.e., full
watersheds up to 200 km upstream of a sample reach), a calculation of the volume of the largest
reservoir, the number of dams, and an index that weighted the maximum reservoir volume within
the watershed or wedge by its proximity to the sample point was conducted. Each upstream
reservoir was inversely weighted by its upstreamflow distance from the sample point as:
r Dflow \
Wj = e De folding)
where DflOWis the flow distance to the sample site, and Defoidmgis an e-folding value that determines
the rate at which the weight exponentially decreases (here 100 km). DII equals the largest distance-
weighted volume within the watershed:
DII= max(w; * DO
where Di = reservoir volume (km3). The criteria for dropping a potential reference site was a DII
value equal to or greater than one.
Percent urbanization and agricultural influence were assessed within a 1 km2 area around the mid-
point of the sampled stream segment. To conduct this analysis a 1 km2 radius buffer around the
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mid-point was overlaid onto the National Land Cover Database 2006 (USGS 2011) to calculate the
percentage of urban land cover and percent row crop, as defined by the NLCD. The criteria used
for dropping a potential reference site were if it had greater than a) 5% urban land cover or b) 15%
agricultural (row crop) land cover. The land cover percentages used for consistent screening of near-
reach human influence were based on best professional judgement. While other options for
factoring in urbanization and agricultural influences could have been used, such as assessing urban
land cover and percent row crops at the watershed scale, the analysts chose the 1 km2 area to focus
on proximal land use conditions.
4.5 ESTABLISHING BENCHMARKS
To assess sites using the reference condition approach, we compared information from the
probability sites with characteristics observed at least-disturbed sites (reference condition) by
establishing benchmarks identified from the reference distribution. As noted above, the approach
used in NRSA draws on guidance and research for applying the reference approach to assess streams
in terms of biological characteristics (i.e., biocriteria) and nutrient concentrations (US EPA 1996,
USEPA 2000, Herlihy and Sifenos 2008, Herlihy et al., 2008, Stoddard et al., 2008).
Using this approach, NRSA used the 5th/25th or the 75th/95th percentiles from each of nine aggregate
ecoregional reference distribution to define benchmarks for several indicators that delineate
condition between good, fair, and poor4 (Hughes et al., 1986; USEPA 1996) (see Chapter 5, Chapter
6, Chapter 7, and Chapter 8 for additional details). As noted in Chapter 2, the benchmarks described
in this document are not equivalent to state or tribal water quality standards. Instead, they provide a
means of interpreting the results in terms of least-disturbed sites in the region. For the biological and
nutrient data, the percentiles that are selected can be interpreted in terms of a site's probability of
being similar to least-disturbed reference condition. For example, if a site's biological index score is
less than the 5th percentile of the reference condition index scores, then the probability that
biological condition at the site is similar to reference is less than 5%. The physical habitat analysis,
while using regional reference sites, applied other statistical analyses and models then set condition
benchmarks (good, fair, and poor) based on the model results (see Chapter 8 for further details).
4.6 LITERATURE CITED
Herlihy, A.T and J. Sifneos. (2008). Developing Nutrient Criteria and Classification Schemes for
Wadeable Streams in the Conterminous US. Journal of The North American Benthological
Society - J N AMERBENTHOL SOC. 27. 932-948. 10.1899/08-041.1.
Herlihy, A.T., R.M. Hughes, and J.C. Sifneos. 2006. National clusters of fish species assemblages in
the conterminous United States and their relationship to existing landscape classification
schemes, pp. 87-112. In: Hughes, R.M., L. Wang, and P.W. Seelbach (Eds.). Influences of
Landscapes on Stream Habitats and Biological Assemblages. American Fisheries Society
Symposium 48, Bethesda, Maryland.
Herlihy, A.T., S.G. Paulsen, J. Van Sickle, J.L. Stoddard, C.P. Hawkins, and L.L. Yuan. 2008.
4 The 5th/25th percentiles were used when higher indicator values are better, such as with MMIs. The 75th/95th
percentiles were used when higher indicator values are worse, such as with nutrient concentrations.
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Striving for consistency in a national assessment: the challenges of applying a reference
condition approach at a continental scale, journal of the North American Benthological Society
27:860-877.
Hughes, R.M., D.P. Larsen, and J.M. Omernik. 1986. Regional reference sites: A method for
assessing stream potentials. Environmental Management. 10:629-635.
USEPA. 1996. Biological Criteria Technical Guidance for Streams and Small Rivers. EPA 822-B-
96-001. US Environmental Protection Agency, Office of Water, Office of Science and
Technology, Washington, DC.
USEPA. 2006. Wadeable Streams Assessment: a collaborative survey of the Nation's streams. EPA/641/B-
06/002, U.S. Environmental Protection Agency, Washington, D.C.
USGS. 2011. National Land Cover Database (NLCD) 2006 Land Cover Conterminous United
States: U.S. Geological Survey data release.
Yuan, L.L, C.P. Hawkins, and J. Van Sickle. 2008. Effects of regionalization decisions on an O/E
index for the US national assessment. Journal of the North American Benthological Society 27:892-
905.
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Table 4-2. Criteria for eight chemical and physical habitat filters used to identify the least-disturbed reference sites for benthic
macroinvertebrate and fish indicators for each of the nine aggregate ecoregions for NRSA. A site must pass all eight filters to be
considered a least-disturbed reference site.
Filter criterion
NAP
SAP
CPL
UMW
TPL
SPL
NPL
XER
WMT-SW6
WMT-
SRocke
WMT-
Nrock/Pacifice
Total P (ng/L)
<20
<20
<75
<50
<100
<150
<150
<50
<50
<25
<25
Total N (|ig/L)
<750
<750
>2500
<1000
<3000
<4500
<4500
<1500
<750
<750
<750
Cl~~ (|ieq/L)
<250a
<200
<300
<2000
<1000
<1000
<1000
<300
<200
<200a
SO42- (|ieq/L)
<250
<400
<400
<200
<200
ANC (|ieq/L) +
DOC (mg/L)b
>50 + >5
>50 + >5
- + >5
>50 + >5
- + >5
- + >5
- + >5
- ฆ -
>50 + >5
>50 + >5
>50 + >5
Turbidity (NTU)
<5
<5
<10
<5
<50
<50
<50
<25
<5
<5
<5
Riparian Disturbance
Indexc
<2
<2
<2
<2
<2
<2
<2
<1.5
<0.5/<1.5d
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Table 4-3. Criteria for eight chemical and physical habitat filters used to identify the most-disturbedฎ sites for each of the nine
aggregate ecoregions for NRSA. A site needed to pass one of the eight filters to be considered a most-disturbed site.
Filter criterion
NAP
SAP
CPL
UMW
TPL
SPL
NPL
XER
WMT-SW6
WMT-
SRocke
WMT-
Nrock/Pacifice
Total P (ng/L)
>100
>100
>250
>150
>500
>500
>500
>150
>150
>100
>100
Total N (no/1 ;\
>3500
>3500
>8000
>5000
>15000
>10000
>10000
>5000
>1500
>1500
>1500
Cl~~ (lK'(]/ 1.)
>10000
>1000
>2000
>5000
>5000
>5000
>5000
>1000
>1000
>1000
SO42- ((-ieq/L)
>1000
>1000
:
>2000
>1000
>1000
ANC (neq/L) +
<0 + <5
<0 + <5
<0 + <5
<0 + <5
<0 + <5
<0 + <5
<0 + <5
<0 + <5
<0 + <5
<0 + <5
<0 + <5
DOC (mg/L)b
Turbidity (NTU)
>10
>20
>50
>30
>100
>100
>100
>75
>10
>10
>10
Riparian Disturbance
>4
>4
>4
>4
>4
>3
>3
>3
>3
>3
>3
Indexc
% fine substrate
>75
>75
>95
>90
>100
>99
>99
>90
>50
>50
>50
a A set of most-disturbed sites in each ecoregion is needed to test metric and MMI responsiveness in discriminating between most- and least-disturbed sites. The
criteria in Table 4.3 are the screening factors used to identify a set of most-disturbed sites in each ecoregion as reported in Stoddard et al. (2008). All screening
criteria are based on baseflow conditions.
indicates that filter was not used in that ecoregion.
ANC = acid neutralizing capacity, DOC = dissolved organic carbon
b Filter was specific for inorganic acidity; site had to exceed both criteria to fail
c Riparian disturbance index variable name is W'1_HALL in physical habitat database (see Chapter 7).
e To match screening criteria to what was done in the EMAP-West component of WSA, the Western Mountains ecoregion was divided into three subgroups: SW =
Southwestern Mountains (Omernik level III codes 8 and 23, Southern California Mts., and Arizona/New Mexico Mts.), SRock = Southern Rockies (Omernik 19
and 21, Southern Rockies and Wasatch/Uintas), and NRock/Pacific = Northern Rockies and Pacific Mountains (all other WMT level Illecoregions)
35
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5 Benthic Macroinvertebrates
Benthic macroinvertebrates were collected using a D-frame net with 500 [im mesh openings at 11
transects equally distributed along the targeted reach. Samples were composited from the 11
transects and the material was field preserved with -95% ethanol. Organisms were enumerated and
identified to the lowest possible taxonomic level (generally genus) using specified standard keys and
references (see the NRSA 2018-19 Field Operations Manual and Laboratory Operations Manual for
additional details). Benthic macroinvertebrate counts, metrics, and multimetric index condition from
NRSA are available to download from the NARS datawebpage - https://www.epa.gov/national-
aquatic-resource-surveys/data-national-aquatic-resource-surveys.
The taxonomic composition and relative abundance of different taxa that make up the benthic
macroinvertebrate assemblage present in a stream have been used extensively in North America,
Europe, and Australia to assess how human activities affect ecological condition (Barbour et al.,
1995,1999; Karr and Chu 1999). As explained in general terms in the NRSA 2008-09 Technical
Report (USEPA 2016; see Section 5.2) two principal types of ecological assessment tools to assess
conditionbased on benthic macroinvertebrates are currently prevalent: multimetric indices and
predictive models of taxa richness. The purpose of these indicators is to present the complex
community taxonomic data represented within an assemblage in a way that is understandable and
informative to resource managers and the public. The following sections provide an overview of the
approaches used to develop an indicator based on benthic macroinvertebrate assemblages, followed
by details regarding data preparation and the process used to arrive at a final indicator. The same
analyses and benchmarks were used in NRSA 2008-09, NRSA 2013-14, and 2018-19.
5.1 OVERVIEW
Multimetric indicators have been used in the U.S. to assess stream condition based on fish and
macroinvertebrate assemblage data (e.g., Karr and Chu, 1999; Barbour et al., 1999; Barbour et al.,
1995). The multimetric approach involves summarizing various assemblage attributes (e.g.,
composition, tolerance to disturbance, trophic and habitat preferences) as individual "metrics" or
measures of the biological community. Candidate metrics are then evaluated for various aspects of
performance and a subset of the best performing metrics are then combined into an index, referred
to as a multimetric index or MMI. For NRSA 2018-19, NRSA 2013-14 and NRSA 2008-09, the
benthic macroinvertebrate MMI developed in the WSA was used to generate the population
estimates used in the assessment. The WSA MMI is detailed in Stoddard et al. (2008).
The predictive model approach was initially developed in Europe and Australia, and is becoming
more prevalent within the U.S. The approach estimates the expected taxonomic composition of an
assemblage in the absence of human stressors (Hawkins et al.., 2000; Wright, 2000), using a set of
"least-disturbed" sites and other variables related natural gradients (such as elevation, stream size,
stream gradient, latitude, longitude). The resulting models are then used to estimate the expected
taxa composition (expressed as taxa richness) at each stream site sampled. The number of
expected taxa observed at a site is compared to the total number of expected taxa as an
observed:expected ratio (O/E index). Departures from a ratio of 1.0 indicate that the taxonomic
composition in a stream sample differs from that expected under less disturbed conditions.
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5.2 DATA PREPARATION
5.2.1 STANDARDIZING COUNTS
The number of individuals in a sample was standardized to a constant number to provide an
adequate number of individuals that was the same for the most samples and that could be used for
the multimetric index development. A subsampling technique involving random sampling without
replacement was used to extract a true "fixed count" of 300 individuals from the total number of
individuals enumerated for a sample (target lab count was 500 individuals). Samples that did not
contain at least 300 individuals were used in the assessment because low counts can indicate a
response to one or more stressors. Only those sites with at least 250 individuals, however, were used
as least-disturbed reference sites.
5.2.2 AUTECOLOGICAL CHARACTERISTICS
Autecological characteristics refer to specific ecological requirements or preferences of a taxon for
habitat preference, feeding behavior, and tolerance to human disturbance. These characteristics are
prerequisites for identifying and calculating many metrics. A number of state/regional organizations
and research centers have developed autecological characteristics for benthic macroinvertebrates in
their region. For the WSA and NRSA, a consistent "national" list of characteristics that consolidated
and reconciled any discrepancies among the regional lists was needed before certain biological
metrics could be developed and calibrated and an MMI could be constructed. The same
autecological information used in WSA was used in NRSA 2008-09, 2013-14, and 2018-19.
Members of the data analysis group pulled together autecological information from five existing
sources: (1) the EPA Rapid Bioassessment Protocols document; (2) the USGS National Ambient
Water Quality Assessment (NAWQA) national and northwest lists; (3) the Utah State University list;
(4) the EMAP Mid-Atlantic Highlands (MAHA); and (5) the EMAP Mid-Atlantic Integrated
Assessment (MAIA) list. These five were chosen because they were thought to be the most
independent of each other and the most inclusive. A single national-level list was developed based
on the decision rules described in the following sections.
5.2.2.1 TOLERANCE VALUES
Tolerance value assignments followed the convention for macroinvertebrates, ranging between 0
(least tolerant or most sensitive) and 10 (most tolerant). Foreach taxon, tolerance values from all five
sources were reviewed and a final assignment made according to the following rules:
If values from different lists were all <3 (sensitive), final value = mean.
If values from different lists were all >3 and <7 (facultative), final value = mean.
If values from different lists were all >7 (tolerant), final value = mean.
If values from different lists spanned sensitive, facultative, and tolerant categories, best
professional judgment was used, along with alternative sources of information (if available)
to assign a final tolerance value.
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Tolerance values of 0 to <3 were considered "sensitive" or "intolerant." Tolerance values >7 to 10
were considered "tolerant," and values in between were considered "facultative."
5.2.2.2 FUNCTIONAL FEEDING GROUP AND HABITAT PREFERENCES
In many cases, there was agreement among the five data sources identified in Section 5.2.3. When
discrepancies in functional feeding group (FFG) or habitat preference ("habit") assignments among
the five primary data sources were identified, a final assignment was made based on the most
prevalent assignment. In cases where there was no prevalent assignment, the workgroup examined
why disagreements existed, flagged the taxon, and used best professional judgment to make the final
assignment.
5.3 MULTIMETRIC INDEX DEVELOPMENT
5.3.1 REGIONAL MULTIMETRIC DEVELOPMENT
The same autecology and taxonomic resolution used in WSA was applied to the NRSA
macroinvertebrate 300 fixed count data to calculate the community metrics used to calculate the
MMI. In the WSA, a best ecoregional MMI was developed by summing the six metrics that
performed best in that ecoregion (the national aggregate nine ecoregions). Each of the six metrics
was scored on a 010 scale by interpolating metrics between a floor and ceiling value. The six metric
0-10 point scaled scores were then summed and normalized to a 0100 scale by multiplying by
100/60 to calculate the final MMI. Details of this process are described in Stoddard et al. (2008).
The final metrics used in each ecoregion, metric direction, and floor and ceiling values are
summarized in Table 5-1. Scoring equations are different depending on if the metric responds
positively (high values good) or negatively (high values bad) with disturbance. For positive metrics,
values above the ceiling get 10 points, and values below the floor get 0 points. For negative metrics,
values above the ceiling get 0 points, and values below the floor get 10 points. The interpolation
equations for scoring the 0-10 points for metrics between the floor and ceiling values are:
Positive Metrics: Metric Points = 10 * ((metric value-floor)/(ceiling-floor))
Negative Metrics: Metric Points = 10 * (1 - ((metric value-floor)/(ceiling-floor))).
The MMI used in the NRSA report is identical to the WSA MMI in terms of metrics and scoring.
Based on NRSA revisit data, the MMI had a S:N ratio of 2.8 and a pooled standard deviation of 10.0
(out of 0100).
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Table 5-1. Six benthic community metrics, scoring direction, and floor and ceiling values
used in calculating the NRSA and WSA MMI in each of the nine aggregate ecoregions.
Ecoregion
Direction
Metric
Floor
Ceiling
Negative
Non-Insect % Individuals
0.70
73.0
CPL
Positive
Shannon Diversity
1.62
3.31
Positive
Shredder Taxa Richness
1
9
Positive
Clinger % Taxa Richness
14.3
54.8
Positive
EPT Taxa Richness
1
17
Negative
Tolerant % Taxa Richness
5.56
50.0
Positive
EPT % Taxa Richness
9.52
57.6
NAP
Negative
% Individuals in Top 5 Taxa
37.2
76.2
Positive
Scraper Taxa Richness
3
12
Positive
Clinger % Taxa Richness
28.6
70.0
Positive
EPT Taxa Richness
3
24
Positive
PTV 0-5.9 % Taxa Richness
+6.2
86.1
Positive
EPT % Taxa Richness
3.85
50.0
NPL
Positive
Shannon Diversity
1.10
3.07
Positive
Scraper Taxa Richness
1
6
Negative
Burrower % Taxa Richness
6.45
35.3
Positive
Ephemeroptera Taxa Richness
0
7
Positive
PTV 0-5.9 Taxa Richness
+
28
Positive
Ephemeroptera % Taxa Richness
5.41
28.6
SAP
Positive
Shannon Diversity
2.05
3.44
Positive
Scraper Taxa Richness
3
12
Negative
Burrower % Taxa Richness
3.45
25.0
Positive
EPT Taxa Richness
5
25
Negative
Tolerant % Taxa Richness
2.44
27.6
Positive
EPT % Individuals
0.67
66.0
5PL
Positive
Shannon Diversity
1.16
3.27
Positive
Scraper Taxa Richness
1
8
Negative
Burrower % Taxa Richness
5.0
36.1
Positive
EPT Taxa Richness
1
16
Positive
Intolerant Taxa Richness
1
8
Positive
EPT % Individuals
0.67
80.3
TPL
Positive
Shannon Diversity
1.41
3.17
Positive
Scraper Taxa Richness
1
9
Positive
Clinger Taxa Richness
3
20
Positive
Ephemeroptera Taxa Richness
1
11
Negative
PTV 8-9.9 % Taxa Richness
+.35
33.3
Negative
Chironomid % Taxa Richness
11.2
50.8
UMW
Positive
Shannon Diversity
2.01
3.56
Positive
Shredder Taxa Richness
3
10
Negative
Burrower % Taxa Richness
3.77
28.6
Positive
EPT Taxa Richness
+
22
Negative
PTV 8-9.9 %Taxa Richness
2.51
29.5
Positive
EPT % Taxa Richness
18.5
62.9
WMT
Negative
% Individuals in Top 5 Taxa
+0.6
82.3
Positive
Scraper Taxa Richness
1
8
Positive
Clinger % Taxa Richness
27.0
69.6
Positive
EPT Taxa Richness
6
23
Negative
Tolerant %Taxa Richness
2.27
25
Negative
Non-Insect % Individuals
3.33
36.0
XER
Negative
% Individuals in Top 5 Taxa
+4.7
92.3
Positive
Scraper Taxa Richness
0
7
Positive
Clinger % Taxa Richness
15.8
65.8
Positive
EPT Taxa Richness
1
18
Negative
Tolerant % Taxa Richness
3.57
36.4
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5.3.2 MODELING OF MMI BENCHMARKS
Previous large-scale assessments have converted MMI scores into classes of assemblage quality by
comparing those scores to the distribution of scores observed at least-disturbed reference sites. If a
site's MMI score was less than the 5th percentile of the reference distribution, it was classified as
"poor" quality; scores between the 5th and 25th percentile were classified as "fair"; and scores in the 25th
percentile or higher were classified as "good." This approach assumes that the distribution of MMI
scores at reference sites reflects an approximately equal, minimum level of human disturbance across
those sites. But this assumption did not appear to be valid for some of the nine WSA regions,which
was confirmed by state and regional parties at meetings to review the draft results.
For the WSA, the project team performed a principal components analysis (PCA) of the physical
habitat and water chemistry variables (Total P, Total N, pH, Chloride, Sulfate, Turbidity, %Fine
Substrate, Riparian Disturbance Index) that had originally been used to screen for biological
reference sites as described in Chapter 4. The first principal component (Factor 1) of this PCA well
represented a generalized gradient of human disturbance. MMI scores at the reference sites,
however, were weakly, but significantly, related to this disturbance gradient in some of the aggregate
ecoregions. Thus, MMI reference distributions from these regions may be biased downward because
they include somewhat disturbed sites which may have lower MMI scores. As part of the WSA,
Herlihy et al. (2008) developed a process that used this PCA disturbance gradient to reduce the
effects of disturbance on benchmark values within the reference site population. The process uses
multiple regression modeling to develop adjusted benchmarks analogous to the 5th and 25th
percentiles of reference sites in each ecoregion based on the slope of the MMI-disturbance
relationship in each ecoregion.
These adjusted benchmarks were used in the WSA but were based on a small sample size of
reference sites. To increase the sample size used in the regression model, the benchmark adjustment
process was rerun for NRSA using the original WSA reference sites plus the additional NRSA
reference sites identified in Chapter 4. As in the WSA analysis and other benchmark setting, we used
a 1.5*interquartile range (IQR) outlier screening test in each ecoregion to drop MMI outliers from
the analysis (sites with values outside the range of Q1-1.5TQR or Q3+1.5TQR were dropped).
This removed 6 sites from the analysis (all low; 3 in WMT, and 3 in XER). There were a grand total
of 647 least-disturbed reference sites used for the benchmark regression adjustment modeling and
the resulting regression statistics for each ecoregion are shown in Table 5-2. The process for
calculating these adjusted benchmarks and fitting the regression model is detailed in Herlihy et al.
(2008). Briefly, the process involves setting the goal for disturbance to the 25th percentile of the
Factor 1 disturbance score for reference sites in each ecoregion. The ecoregion MMI value at that
goal is predicted from the MMI-disturbance regression as:
MMIpred = (GOAL * SLOPE) + INTERCEPT
Then the percentiles to be used as the adjusted benchmarks are calculated assuming there is a
normal distribution around this predicted mean using the RMSE of the regression model as the
standard error,
Good-Fair 25th benchmark = MMIpred - 0.675 * RMSE
Fair-Poor 5th benchmark = MMIpred - 1.650 * RMSE
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The resulting adjusted MMI benchmark values for the condition classes in each ecoregion used in
the NRSA report are given in Table 5-3.
Table 5-2. MMI-Disturbance Regression Model Statistics Used for Setting Benchmarks
Ecoregion
Number of
Reference
Sites
Factor 1
Goal*
Regression
RMSE
Regression
Slope
Regression
Intercept
CPL
32
-0.1501
14.55
0
64.74
NAP
56
-0.5247
14.55
-7.257
61.06
NPL
65
0.8723
14.55
-14.95
79.66
SAP
64
-0.5531
14.55
-7.257
50.78
SPL
43
0.7637
14.55
-7.257
50.84
TPL
49
1.045
14.55
-7.257
57.75
UMW
39
-0.1138
14.55
0
46.74
WMT
209
-1.326
14.55
-7.257
50.27
XER
90
-0.4628
14.55
-7.257
63.44
* The 25th percentile of Factor 1 score was the "goal" on the PCA factor 1 disturbance
gradient for hindcasting ecoregional benchmarks.
Table 5-3. Benchmark Values for the Nine Regional Benthic MMIs.
Ecoregion
Good Benchmark
Poor Benchmark
CPL
>54.9
<40.7
NAP
>55.0
<40.9
NPL
>56.8
<42.6
SAP
>45.0
<30.8
SPL
>35.5
<21.3
TPL
>40.3
<26.2
UMW
>36.9
<22.7
WMT
>50.1
<35.9
XER
>57.0
<42.8
*Any site with an MMI score that was not "gooc
or "poor" was considered "fair."
5.4 LITERATURE CITED
Barbour, M.T., J. Gerritsen, G.E. Griffith, R. Frydenborg, E. McCarron, J.S. White, and M.L.
Bastian. 1996. A framework for biological criteria for Florida streams using benthic
macroinvertebrates. Journal of the North American Benthological Society 15:185-211.
Barbour, M.T., J. Gerritsen, B.D. Snyder, and J.B. Stribling. 1999. Rapid bioassessment protocols
for use in streams andwadeable rivers: periphyton, benthic macroinvertebrates, and fish.
EPA 841/B- 99/002, Office of Water. US Environmental Protection Agency, Washington,
DC.
Hawkins, C.P., R.H. Norris, J.N. Hogue, and J.W. Feminella. 2000. Development and Evaluation
of Predictive Models for Measuring the Biological Integrity of Streams. Ecological
41
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Applications 10(5): 1456-1477.
Herlihy, A.T., S.G. Paulsen, J. Van Sickle, J.L. Stoddard, C.P. Hawkins, and L.L. Yuan. 2008.
Striving for consistency in a national assessment: the challenges of applying a reference
condition approach at a continental scale. Journal of the North American Benthological
Society 27:860-877.
Karr, J. R., and E.W. Chu. 2000. Sustaining living rivers. Hydrobiologia 422/423:1-14.
Stoddard, J.L., A.T. Herlihy, D.V. Peck, R.M. Hughes, T.R. Whittier, and E. Tarquinio. 2008. A
process for creating multi-metric indices for large scale aquatic surveys. Journal of the North
American Benthological Society 27:878-891.
USEPA. 2016. National Rivers and Streams Assessment 2008-2009. Technical Report EPA 841- R-
16-008. U.S. Environmental Protection Agency, Office of Water and Office of Research
and Development, Washington, DC.
Wright, J.F., 2000. An introduction to RIVPACS. In: Wright, J.F., D.W. Sutcliffe, and M.T. Furse
(Eds.). Assessing the Biological Quality of Fresh Waters: RIVPACS and Other Techniques.
Freshwater Biological Association, Ambleside, UK, pp. 1-24.
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6 Fish Assemblage
6.1 BACKGROUND
Fish assemblages in streams and rivers offer several unique advantages to assess ecological quality,
based on their mobility, longevity, trophic relationships, and socioeconomic importance (Barbour et
al., 1999, Roset et al., 2007). For fish assemblages, assessing ecological quality has generally been
based on developing and using multimetric indices (MMIs), which are derivations of the original
Index of Biotic Integrity (IBI) developed by Karr (Karr 1981). There are numerous examples of
MMIs developed for fish assemblages in smaller streams (e.g., McCormick et al., 2001, Hughes et al,.
2004, Bramblett et al., 2005, Roset et al.. 2007) as well as for larger rivers (Lyons et al.. 2001, Emery
et al., 2003, Mebane et al., 2003, Pearson et al., 2011).
6.1.1 MULTIMETRIC INDICATOR FOR NRSA 2018-19
For the NRSA 2008-09, we developed fish MMIs using predictive models of metric response (e.g.,
Oberdorff et al., 2002, Tejerina-Garro et al., 2006, Pont et al., 2007, Pont et al., 2009). This approach
essentially provided an estimate of expected quality (in terms of metric values) at individual sites,
rather than using a set of regional least-disturbed reference sites to define expected values for a
particular metric. For the NRSA 2013-14, we constructed MMIs using a more traditional approach
that used regional sets of reference sites to define expected conditions for metrics (e.g., Stoddard et
al. ,2008), and adjusted metrics for watershed area using linear regression if the effect was large
enough Details of the development and evaluation of the predictive model based MMIs can be
found in the technical support documents for the NRSA 2008-09 and 2013-14 ( USEPA 2016,
2020).
For the NRSA 2018-19, we used the same MMIs as were used for the 2013-14 assessment. We have
retained the details regarding the development and evaluation of these MMIs here for convenience.
6.1.2 REGIONALIZATION
We developed separate traditional fish MMIs for each of the nine NARS reporting regions for the
NRSA 2013-14 (Figure 6-1).
6.2 METHODS
6.2.1 FIELD METHODS
Collection methods for fish are described in the NRSA 2018-19 field operations manuals (USEPA
2017a, b). Collection methods used for the NRSA 2018-19 were essentially unchanged from those
used for the previous NRSA studies (USEPA 2009, USEPA 2018a, b). These minor changes
included text changes to help clarify sampling procedures or field forms. Three variants of the basic
sampling protocol (using electrofishing) were used depending on the width of the stream and if it
was wadeable. For streams less than 12.5 m wide, a reach length equal to 40 channel widths was
sampled for fish. For larger streams (> 12.5 m wide), a minimum reach length of 500 m or 20
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channel widths was sampled (whichever was longer). If 500 individuals were not collected after
sampling the minimum reach length, sampling continued until 500 individuals were collected (or a
reach length equal to 40 channel widths was sampled). Larger wadeable streams were sampled using
backpack or barge electro fishing units; non-wadeable rivers were sampled using raft or boat
electrofishing systems.
For the NRSA 2018-19, 2,110 site visits were initially available for collecting fish. These included
2,039 visits to 1,851 probability sites and to 71 hand-picked sites (including one revisit) that were
evaluated as potential least-disturbed reference sites (see Section 4.2). There were 188 revisits to a
subset of the 1,851 probability sites (either within a single year or across the two years of sampling).
Fish sampling was attempted at 1,775 site visits (including 175 revisits). A sufficient sample (based
on length of reach sampled for fish and the number of individuals collected) was obtained at 1,726
site visits (including 172 revisits). Conditions prevented a sufficient sample from being collected at
55 site visits (including three revisits). Of the sites sampled for fish, no fish were collected at 68 site
visits (including one revisit). Seining only was conducted at 29 site visits (including two revisits). No
fish data were obtained from 305 site visits (including 12 revisits), due to collection permit
restrictions (183 site visits, including 8 revisits), equipment failure (19 site visits, including one
revisit), site conditions (92 site visits, including three revisits), loss of data after collection (3 site
visits), or other reasons (8 site visits).
6.2.2 COUNTING, TAXONOMY, AND AUTECOLOGY
Fish were tallied and identified in the field, then released alive unless used for fish tissue or
vouchers. Voucher specimens were collected if field identification could not be accomplished.
Voucher samples of all species collected were also prepared at 10% of sites for each field
taxonomist. Voucher samples were sent to an independent taxonomist to evaluate the taxonomic
proficiency of each field taxonomist. All names submitted on field data forms were reviewed and
revised when necessary to create a listing of nationally consistent common and scientific names.
Where possible, taxonomic names (common and scientific) were based on Nelson et al. (2004) and
Page et al. (2013). The online database FishBase (http://www.fishbase.org) served as a secondary
source of taxonomic names. In rare cases, a journal article of a newly described species was used.
Collection maps for each taxon were prepared and compared to published maps in Page and Burr
(2011) or alternative web publications for a few rare endemic species. For the 2018-2019 NRSA, 101
new taxa names were added to the 631 unique taxa names from the NRSA 2018-2019 (excluding
unknowns, hybrids, and amphibians). Amphibians were not used in the fish MMIs but were retained
in the database for potential use by other users of NRSA data.
Each taxon was characterized for several different autecological traits, based on available sources of
published information (e.g., McCormick et al., 2001, Goldstein and Meador 2004, Whittier et al.,
2007b, Frimpong and Angermeier 2009). Traits included habitat guilds (lotic habitat and
temperature), trophic guild, reproductive guild, migration strategy, and tolerance to human
disturbance. A file of all fish taxa and their associated autecological assignments is available on the
NRSA website.
Assignments of native status were based primarily on shapefiles of individual species distribution
from NatureServe (http://www.natureserve.org). Alternative sources included the USGS
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Nonindigenous Species database (http://nas.er.usgs.gov), FishBase, published maps in Page and
Burr (2011), and relevant state fish publications (if available).
Because fish collected at a site cannot always be confidently identified to species, there is a risk of
inflating the number of species actually collected. For each sample, we reviewed the list of taxa to
determine whether they were represented at more than one level of resolution. For example, if an
"Unknown Catostomus" was collected, and it was the only representative of the genus at the site, we
assigned it as a distinct taxon. If any other species of the genus were collected, then we considered
the unknown as not distinct. We used only the number of distinct taxa in the sample to calculate any
metrics based on species richness.
Figure 6-1. Aggregated Omernik ecoregions used to develop traditional fish MMIs for NRSA2018-
19. A separate fish MMI was developed for each of the nine aggregated ecoregions.
NAP=Northern Appalachians, SAP=Southern Appalachians, CPL=Coastal Plains,
TPLTemperate Plains, UMW= Upper Midwest, SPL= Southern Plains, NPL=NorthernPlains,
XER=Xeric West, WMT=Western Mountains.
6.3 FISH MULTIMETRIC INDEX DEVELOPMENT
We used a consistent process to develop a multimetric index for fish for each of the nine aggregated
ecoregions. We used the sites from the MRSA 2008-09 to develop and evaluate the fish MMIs, then
calculated fish MMI scores for the MRSA 2018-19 data. We evaluated each metric for its
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responsiveness to disturbance, i.e., its ability to discern between least-disturbed and most-disturbed
sites (following Stoddard et al., 2008). We then selected metrics representing different dimensions of
assemblage structure or function to include in the fish MMI based on responsiveness and lack of
correlation with other metrics, following Whittier et al. (2007b) and Stoddard et al. (2008).
6.3.1 LEAST-DISTURBED REFERENCE SITES FOR FISH
We modified the base list of least-disturbed reference sites (Chapter 4) determined for NRSA to
eliminate additional fish samples that might not be representative of least-disturbed conditions (i.e.,
excluded sites where < 25 fish were caught or had >50% non-native individuals) (Table 6-1). The
final set of least-disturbed reference sites are identified in the NRSA database (variable
RT_NRSA_FISH=R). No new least-disturbed sites for fish were identified for the 2018-19 NRSA.
To validate the fish MMIs and their component metrics, we identified a random subset of least-
disturbed sites (validation sites) within each aggregated ecoregion and excluded them from fish MMI
development. We set aside 29 validation sites in the Eastern Highlands (NAP=16, SAP=13), 66 sites
in the Plains and Lowlands (CPL=10, NPL=16, SPL=13, TPL=14, UMW=13), and 23 sites in the
West region (WMT=13, XER=10). We expected the distribution of fish MMI scores calculated for
the validation sites would be similar to the distribution of fish MMI scores calculated for the
calibration sites that were used to develop the fish MMIs.
6.3.2 CANDIDATE METRICS
We calculated 162 candidate metrics (Appendix 7.B) representing the following dimensions of fish
assemblage structure and function (following Stoddard et al., 2008):
Nonnative species (ALIEN) based on presence in 8-digit USGS Hydrologic Units
Taxonomic composition (COMP)
Species richness (RICH)
Habitat guild (HABIT)
Life history/migratory strategy (LIFE)
Reproductive guild (REPRO)
Trophic guild (TROPH)
Tolerance (TOLER) to anthropogenic disturbance
The codes (in uppercase) for each category are used in the NRSA database to identify metric
categories. For nearly all metrics, we derived three variants based on all taxa in the sample and for
only native taxa in the sample: one based on distinct taxa richness, one based on the percent of
individuals in the sample, and one based on the percent of distinct taxa in the sample (potentially
yielding 6 different variants). For some trophic metrics, additional variants were derived using only
taxa that were not considered tolerant to disturbance. We included only those tolerance metrics
based on sensitive and tolerant taxa, because the "intermediate tolerance" assignments included taxa
with unknown tolerance.
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6.3.3 ADJUSTMENT OF METRIC RESPONSE FOR WATERSHED AREA
We used the set of least-disturbed reference sites in each aggregated ecoregion to evaluate whether
metrics should be adjusted for stream size. Many studies have shown that some metrics (especially
those based on species richness) vary naturally with stream size (e.g., Fausch et al., 1984, Simon and
Lyons 1995, McCormick et al., 2001). We used watershed area (in km2) as our measure of stream
size and compared the metric response to watershed area (transformed using loglO) using linear
regression. We used an R2 value >0.10 (following the rationale of Hawkins et al., 2010a and Vander
Laan and Hawkins 2014) in deciding whether to use the model-adjusted responses for a particular
metric. For metrics requiring adjustment, we used the residual values from the regression as the
adjusted metric response (Stoddard et al., 2008).
Table 6-1. Criteria used to select least-disturbed sites for use in developing the regional
NRSA fish multimetric indices (MMIs) based on 2008-09 and 2013-14 data.
Criteria
Start with the base set of NRSA least-disturbed reference sites
Keep sites with fish samples
Drop sites where seining was the only sampling method
Drop sites with insufficient sampling
Wadeable: Reach length sampled was less than 20 channel widths and less than
500 individuals were collected
Large Wadeable: Reach length sampled was less than 500 m and less than 500
individuals were
collected
Boatable: Reach length sampled was less than 20 channel widths
sampled
Drop sites with sufficient sampling where less than 30 individuals were collected
Drop sites with sufficient sampling where nonnative individuals comprised >50% of total
number of individuals collected
Drop non
-wadeable sites hand-selected from the EMAP-Western Pilot Study that were
sampled for fish. These sites were sampled using a much larger reach length (100 channel
widths) than the reach length used for NRSA (40 channel widths).
Final Number of Least-Disturbed Reference Sites
Calibration Sites
Validation Sites
Total
Northern
NAP
43
16
59
Appalachians
Southern
SAP
72
13
85
Appalachians
Coastal Plains
CPL
27
10
37
Northern Plains
NPL
33
16
49
Southern Plains
SPL
34
13
47
Temperate
TPL
31
14
45
Plains
Upper Midwest
UMW
48
13
61
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Western
Mountains
WMT
77
13
90
Xeric West
XER
30
10
40
Total
395
118
513
6.3.4 SELECTION OF FINAL CANDIDATE METRICS
We reduced the number of candidate metrics using a series of screening procedures, following
Stoddard et al. (2008). The original (i.e., prior to any adjustment for watershed area) metric response
values were evaluated for range. To evaluate repeatability, we calculated S:N for each metric
following Kaufmann et al. (1999), to compare the variance observed at revisit sites (within the index
period) with the total variance observed across all sites. For adjusted metrics, the S:N value was
calculated after adjusting for watershed area to remove the effects of natural variability from the
"signal", as suggested by Esselman et al. (2013). For both original and adjusted metrics, the mean
response values of the set of least-disturbed reference sites and the set of most-disturbed sites were
compared with two-sample t-tests (assuming unequal variances). Stoddard et al. (2008) present the
advantages of using t values over other statistics as an indicator of metric responsiveness to
disturbance. A candidate metric was not generally considered further if it met any of the following
conditions:
A richness metric (NTAX) had a range < 4
A percentage metrics (PTAX, PIND) had a range < 10%, or had a 90th percentile value=0
A metric had a S:N value < 1.25
A metric had an absolute value of t < 1.73
The set of least-disturbed validation sites was significantly different (p < 0.05) from the set
of least-disturbed calibration sites (two sample t-test)
Exceptions were made if there were no metrics in a category that passed all the screens. In these
cases, we chose the metric with the best t value to include in the final set of candidate metrics.
Metrics that passed these screens were then sorted by metric category and t-value. In cases where
the "native only" variant was similar in t-value to the "all species" variant, only one was retained
(usually the all species variant unless there was a sizable difference in the S:N value, and then both
variants were retained in the final list of candidate metrics).
6.3.5 METRIC SCORING
We rescaled response values for each of the final suite of metrics to a score ranging between 0 and
10. For "positive" metrics (those having higher values in least-disturbed sites) we used the 5th
percentile of all sites to set the "floor" (below which a score of 0 was assigned), and the 95th
percentile of least-disturbed sites to set the "ceiling" (above which a score of 10 was assigned)
following Stoddard et al. (2008) and as described by Blocksom (2003). For "negative" metrics
(where values were higher in the more disturbed sites), the floor was set at the 5th percentile of
least-disturbed sites, and the ceiling was set at the 95th percentile of all sites. We assigned a score to
response values between the floor and ceiling using linear interpolation.
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We summed the metric scores for each site to derive the fish MMI score. We then multiplied the
fish MMI score by (10/number of metrics) to rescale the score to range between 0 and 100.
6.3.6 SELECTION OF FINAL FISH MMIS
For each of the nine aggregated ecoregions, we used the final list of candidate metrics, and
calculated thousands of candidate fish MMIs based on all possible combinations of the eight metrics
(one from each category), as recommended by Van Sickle (2010). This approach allowed us to
evaluate not only the maximum pairwise correlation among a suite of metrics comprising a fish
MMI, but also the mean pairwise correlation of the suite itself. Indices having low mean correlations
among pairs of metrics may perform better than an index containing component metrics selected to
minimize redundancy based on a maximum allowable correlation coefficient (Van Sickle 2010).
For each candidate fish MMI, we determined:
1. The F value based on comparing the set of least-disturbed vs. the set
of more highly disturbed sites. We derived a /-value as VF.
2. The difference between the 25th percentile of the set of least-disturbed sites and
the 75th percentile of the set of more highly disturbed sites. This value
(SEPDIFF) is an estimate of the degree of overlap of the respective boxplots,
which has been used to evaluate metric andindex performance (Barbour et al.,
1996).
To select the "best" fish MMI from the large number of potential candidates, we excluded any
candidate fish MMIs that had a maximum pairwise correlation of >0.7, or which had a S:N ratio of
<2.5 (Table 6-2). We input the t values and the SEPDIFF values for the remaining candidate fish
MMIs into a principal components analysis. We selected the candidate fish MMI that had the
highest score for the first PCA axis for further evaluation. Combining the values for t and SEPDIFF
into a single PCA axis score provided a simple, objective, and repeatable way to select a fish MMI
that had optimal responsiveness to anthropogenic alteration.
We examined the performance of the component metrics across the range of stream sizes sampled
for NRSA. The potential exists for bias in the fish MMI due to different fish species pools being
available for larger rivers versus smaller streams. Differences across the size range might also result
from the different sampling protocols that were used (wadeable, large wadeable, and boatable). We
used the set of least-disturbed sites to examine patterns in metric response values across Strahler
stream order categories. If one of the component metrics in the "best" fish MMI identified for an
aggregated ecoregion showed a noticeable pattern of either increasing or decreasing response with
Strahler order based on examining boxplots of least-disturbed sites across stream orders, we selected
the fish MMI with the next highest PCA axis score.
Table 6-3 presents the regression equations used to adjust metrics that were included in each of the
nine regional fish MMIs. The number of adjusted metrics included in a final suite of eight metrics
ranged from two (Southern Plains) to six (Northern Plains). For two aggregated ecoregions (Coastal
Plain and Temperate Plains), the ALIEN metric performed better after adjusting for watershed area.
While it is expected that many richness-based metrics would require adjustment, there are a fair
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number of proportional metrics (based on either individuals or taxa) that performed better after
adjustment. This may be due to NRSA including a wider range of stream sizes than many other
MMI development efforts that are based on a smaller set of streams (either smaller or larger).
Table 6-2. Number of final candidate fish multimetric indices (MMIs) calculated from the
final set of passed metrics, before and after screening for maximum pairwise correlation
among metrics and S:N ratio.
Number of
Number of
Candidate Fish
Aggregated Ecoregion
Candidate fish
MMIs remaining
MMIs calculated
after screening
Northern Appalachians (NAP)
33,264
9,472
Southern Appalachians (SAP)
36,288
21,976
Coastal Plains (CPL)
9,072
1,494
Southern Plains (SPL)
8,064
2,084
Northern Plains (NPL)
27,648
5,092
Temperate Plains (TPL)
21,600
3,115
Upper Midwest (UMW)
90,720
25,692
Western Mountains (WMT)
84,000
7,120
Xeric West (XER)
32,400
13,220
Table 6-3. Regression equations for adjusting metrics for watershed area. LWSAREA NEW
is the loglO-transformed value of watershed area in km2. Only metrics that were included in
the final suite of metrics used to construct one of the nine regional fish MMIs are presented.
Coastal Plain Aggregated Ecoregion (CPL)
ALIENPIND_WS=ALIENP IND-(-0.219734+(0.178533 kLWSAREA_NEW));
LQTPIND_WS=LOTPIND-(83.680193+(-5.644243 kLWSAREA_NEW));
LITHPIND_WS=LITHPIND-(90.591166+(-21.2575tLWSAREA_NEW));
NAT_TOTLNTAX_WS=NAT_TOTLNTAX-(l 0.929299+(2.873952 kLWSAREA_NEW));
TOLRNTAX_WS=TOLRNTAX-(l .831029+(1.559498 kLWSAREA_NEW));
Northern Appalachians Aggregated Ecoregion (NAP)
LITHPTAX_WS=LITHPTAX- (91.493806+ (-9.389 536*LWS ARE A_NEW));
NTOLPTAX_WS=NTOLPTAX-(83.244125+(-5.594874 kLWSAREA_NEW));
TOLRNTAX_WS=TQLRNTAX-(-0.072385+(1.002947tLWSAREA_NEW));
Northern Plains Aggregated Ecoregion (NPL)
LOTNTAX_WS=LOTNTAX-(Q.878392+(l .759049 kLWSAREA_NEW));
MIGRNTAX_WS=MIGRNTAX- (0.438798+(0.39651 *LWSAREA_NEW));
LITHPIND_WS=LITHPIND- (81.213041 + (-13.064343* LWSAREA_NEW));
NTOLPTAX_WS=NTOLPTAX-(121.656224+(-18.471843 kLWSAREA_NEW));
NAT_INTLPIND_WS=NAT_INTLPIND-(84.560234+(-21.788603 kLWSAREA_NEW));
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NAT_CARNNT AX_WS=NAT_CARNNTAX-(-l .380617+(0.928968 kLWSAREA_NEW));
Southern Appalachians Aggregated Ecoregion (SAP)
NAT_CENTNTAX_WS=NAT_CENTNT AX-(-0.017051 +(0.776488* LWSAREA_NEW));
NAT_LITHPIND_WS=NAT_LITHPIND-(85.390153+(-10.818128 kLWSAREA_NEW));
INVPIND_WS=INVPIND-(26.04262+(11.423482tLWSAREA_NEW));
Southern Plains Aggregated Ecoregion (SPL)
CYPRPTAX_WS=CYPRPTAX-(45.705777+(-9.448293 kLWSAREA_NEW));
NATJ\4IGRPTAX_WS=NAT_MIGRPTAX-(-0.604356+(0.532868tLWSAREA_NEW));
Temperate Plains Aggregated Ecoregion (TPL)
ALIENNTAX_WS=ALIENNTAX-(-0.22423+(0.200411tLWSAREA_NEW));
NAT_ICTAPIND_WS=NAT_ICTAPIND-(-Q. 189542+ (0.816572* LWSAREA_NEW));
NAT_NTOLNTAXJ^S=NAT_NTOLNTAX-(1.946393+(2.1Q7837tLWSAREA_NEW));
CARNNTAXJtyS=CARNNTAX-(-0.005878+(1.292597tLWSAREA_NEW));
Upper Midwest Aggregated Ecoregion (UMW)
INTLLOTNTAX_WS=INTLLOTNTAX-(l .09723+(0.659379 kLWSAREA_NEW));
NTOLNTAX_WS=NTOLNTAX-(2.216995+(2.870941 kLWSAREA_NEW));
TOLRNTAX_WS=TQLRNTAX-(0.398305+(1.755202*LWSAREA_NEW));
Western Mountains Aggregated Ecoregion (WMT)
INTLLOTPTAX_W S=INTLLOTPTAX- (110.962575+(-21.540681 *LWSAREA_NEW));
NAT_MIGRPTAX_WS=NAT_MIGRPTAX-(90.991326+(-15.318296*LWSAREA_NEW));
\AT_T()T].\T\X_\VS=\\T_K )TJ AT AX-(0.748128+ (1.104128* LW S ARE A_NE W));
Xeric West Aggregated Ecoregion (XER)
MIGRPTAX_WS=MIGRPTAX-(93.412006+(-20.33135* LWSAREA_NEW));
LITHNTAXJ^S=LITHNTAX-(-0.265844+ (1.369981*LWSAREA_NEW));
TOLRNTAX_WS=TOLRNTAX-(-Q.142977+ (0.094138* LWSAREA_NEW));
BENTINVPTAX_WS=BENTINVPTAX-(-5.705387+(9.987192tLWSAREA_NEW));
The following subsections provide information on the performance of each of the metrics that were
used to construct a regional fish MMI. The information includes the floor and ceiling values that
were used to develop a score for each metric (Section 6.3.5).
6.3.6.1 METRIC PERFORMANCE AND SCORING: COASTAL PLAIN AGGREGATED
ECOREGION
Table 6-4_presents the performance and scoring information for the eight metrics that were used to
construct the fish MMI for the Coastal Plain aggregated ecoregion (CPL). The final suite included
two negative metrics (the alien and tolerance metrics), and five metrics that were adjusted for
watershed area (Table 6-3). Absolute values of t ranged from 2.05 to 5.04, with only two metrics
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having a t-value > 4. Signal to noise ratios ranged from 0.6 to 61.7. The life history metric (percent
of migratory taxa that were intolerant to disturbance) had a low S:N ratio, but it was the best-
performing of any of the life history metrics in this aggregated ecoregion.
6.3.6.2 METRIC PERFORMANCE AND SCORING: NOR THERN APPALACHIANS
AGGREGATED ECOREGION
Table 6-5 presents the performance and scoring information for the eight metrics that were used to
construct the fish MMI for the Northern Appalachians aggregated ecoregion (NAP). The final suite
included three negative metrics (the alien, tolerance, and trophic metrics), and three metrics that
were adjusted for watershed area (Table 6-3). Absolute values of t ranged from 2.40 to 8.39, with
five metrics having a t-value > 4. Signal to noise ratios ranged from 1.9 to 180. The trophic metric
(number of invertivore taxa) did not respond as we expected; it is a negative metric in this fish MMI,
indicating that there were more invertivore species in the set of most-disturbed sites than in the set
of least-disturbed sites. However, fish MMIs that included trophic metrics that responded as
expected did not perform as well as the fish MMI constructed using the metrics in Table 6-5.
The INVNTAX metric was the most responsive trophic metric based on the t-value (Table 6-5)
and had a higher S:N ratio than other trophic metrics with similar t-values.
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Table 6-4. Performance information of metrics used to construct the fish MMI for the Coastal Plain aggregated ecoregion.
Column name is the field name in the NRSA database.
Scoring Information
Metric
t-
value*
Signal:
Noise
Direction
of
Category
Column Name
Description
Value4
Response
Floor
Ceiling
% nonnative individuals (adjusted for
Alien
ALIENPIND_WS
watershed area)
-2.88
15.8
NEG
-0.49
14.28
Composition
RBCATONTAX
Number of round-bodied sucker taxa
5.04
2.7
POS
0
3.00
% Lotic individuals (adjusted for watershed
Habitat''
LOTPIND_WS
area)
3.65
7.4
POS
-73.80
30.66
% of taxa that are migratory and intolerant
Life History
INTLMIGRPTAX
to disturbance
2.30
0.6
POS
0
5.88
Reproductive"
LITHPIND_WS
% lithophil individuals (adjusted for
watershed area)
4.71
61.7
POS
-81.39
33.83
Number of native taxa (adjusted for
Richness
NAT_TOTLNTAX_WS
watershed area)
2.60
6.8
POS
-15.49
7.21
Number of tolerant taxa (adjusted for
Tolerance
TOLRNTAX_WS
watershed area)
-2.05
11.8
NEG
-3.60
7.12
Trophic
INVPTAX
% of taxa that are invertivores
3.90
7.1
POS
9.09
68.75
'' Based on comparisons of mean values of least-disturbed and most-disturbed sites (validation sites have been excluded).
h Based on variability among sites vs. variability within sites.
'' Direction: POS=Positive metric (mean value for least-disturbed sites is greater than mean value for most-disturbed sites).
NEG=negative metric (mean value for most-disturbed sites is greater than the mean value for least-disturbed sites). For positive
metrics, the floor=5th percentile of all sites (a metric value < floor is assigned a score of 0), and the ceiling=95th percentile of least-
disturbed sites (a metric value
> ceiling is assigned a score of 10). For negative metrics, the floor=5th percentile of least-disturbed sites (a metric value < floor is assigned
a score of 10), and the ceiling=95th percentile of all sites (a metric value > ceiling is assigned a score of 0).
d Habitat metrics: Lotic: Occupies flowing water habitats; Rheophils occupy fast water habitats.
f Reproductive metrics: Lithophils require clean substrate for spawning.
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Table 6-5. Performance information of metrics used to construct the fish MMI for the Northern Appalachians aggregated
ecoregion. Column name is the field name in the NRSA database.
Scoring Information17
Metric
t-
value"
Signal:
Noise
Direction
of
Category
Column Name
Description
Value6
Response
Floor
Ceiling
Alien
ALIENNTAX
Number of nonnative taxa
-4.02
1.9
NEG
0
4
Composition
SALMNTAX
Number of taxa in family Salmonidae
6.06
5.4
POS
0
2
Habitat''
NAT_RHEOPIND
% individuals that are native and rheophils
6.37
10.2
POS
0
100
Life History
INTLMIGRPIND
% individuals that are migratory and
intolerant to disturbance
2.40
180
POS
0
8
Reproductive"
LITHPTAX_WS
% of taxa that are lithophils (adjusted for
watershed area)
7.46
14.2
POS
-55.684
23.496
% of taxa that are not tolerant (adjusted
Richness
NTOLPTAX_WS
for watershed area)
3.55
3.8
POS
-39.539
22.490
Number of tolerant taxa (adjusted for
Tolerance
TOLRNTAX_WS
watershed area)
-8.39
3.1
NEG
-1.611
6.853
Trophic
INVNTAX
Number of taxa that are invertivores
-5.54
4.0
NEG
0
9
'' Based on comparison of mean values of least-disturbed and most disturbed sites (validation sites have been excluded).
h Based on variability among sites vs. variability within sites (validation sites have been excluded).
'' Direction: POS=Positive metric (mean value for least-disturbed sites is greater than mean value for most-disturbed sites).
NEG=negative metric (mean value for most-disturbed sites is greater than the mean value for least-disturbed sites). For positive
metrics, the floor= 5th percentile of all sites (a metric value < floor is assigned a score of 0), and the ceiling=95th percentile of least-
disturbed sites (a metric value
> ceiling is assigned a score of 10). For negative metrics, the floor= 5th percentile of least-disturbed sites (a metric value < floor is assigned
a score of 10), and the ceiling=95th percentile of all sites (a metric value > ceiling is assigned a score of 0).
d Habitat metrics: Lotic species occupy flowing water habitats; Rheophils occupy fast water habitats.
f Reproductive metrics: Lithophils require clean substrate for spawning.
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6.3.6.3 METRIC PERFORMANCE AND SCORING: NORTHERN PLAINS AGGREGATED
ECOREGION
Table 6-6 presents the performance and scoring information for the eight metrics that were used to
construct the fish MMI for the Northern Plains aggregated ecoregion (NPL). The final suite
included two negative metrics (the alien and composition metrics), and six metrics that were adjusted
for watershed area (Table 6-3). Absolute values of t ranged from 1.24 to 4.59, with only one metric
having a t-value > 4. Signal to noise ratios ranged from 0.4 to 332. The most responsive alien metric
(number of nonnative taxa, a negative metric) did not meet the criteria for responsiveness or
repeatability (Section 6.3.4). Other alien metrics did not show much response to disturbance,
suggesting that the set of least-disturbed sites in this aggregated ecoregion were similar in terms of
the number of nonnative taxa, the percent of nonnative individuals, and the percent of nonnative
taxa to the set of most-disturbed sites. Because nonnative species typically represent a direct stressor
to native fish communities, native/nonnative metrics are commonly used by fishery biologists in
assessing fish community health (e.g., Simon and Lyons 1995, McCormick et al., 2000, Hughes et al.,
2004, Bramblett et al., 2005, Whittier et al., 2007b). The low values for responsiveness of metrics
based on nonnative species or individuals has been observed in other studies (McCormick et al.,
2000, Hughes et al., 2004, Bramblett et al., 2005, Whittier et al., 2007b). The number of nonnative
taxa metric was included to represent the alien metric category, even though its influence was
negligible on the overall MMI for the ecoregion.
6.3.6.4 METRIC PERFORMANCE AND SCORING: SOUTHERN APPALACHIANS
AGGREGATED ECOREGION
Table 6-7 presents the performance and scoring information for the eight metrics that were used to
construct the fish MMI for the Southern Appalachians aggregated ecoregion (SAP). The final suite
included three negative metrics (the composition, life history, and tolerance metrics), and three
metrics that were adjusted for watershed area (Table 6-3). Absolute values of t ranged from
0.58 to 12.15, with seven metrics having a t-value > 4. Signal to noise ratios ranged from 1.5 to
23.4. The best alien metric, percent of taxa that are native, was not very responsive. However, the
percent of native taxa is considered to have a positive influence on a fish assemblage, and thus was
included as the alien metric in the regional fish MMI.
6.3.6.5 METRIC PERFORMANCE AND SCORING: SOUTHERN PLAINS AGGREGATED
ECOREGION
Table 6-8 presents the performance and scoring information for the eight metrics that were used to
construct the fish MMI for the Southern Plains aggregated ecoregion (SPL). The final suite included
three negative metrics (the composition, life history, and tolerance metrics), and two metrics that
were adjusted for watershed area (Table 6-3). Absolute values of t ranged from 0.61 to 4.26, with
only one metric having a t-value > 4. Signal to noise ratios ranged from 4.6 to 113.4. The most
responsive metrics in three categories (composition, life history, and tolerance) did not meet the
criteria for responsiveness (Section 6.3.4).
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Table 6-6. Performance information of metrics used to construct the fish MMI for the Northern Plains aggregated ecoregion.
Column name is the field names in the NRSA database.
Metric
Category
Column Name
Description
t-
value*
Signal:
Noise
Value6
Scoring Information17
Direction
of
Response
Floor
Ceiling
Alien
ALIENNTAX
Number of nonnative taxa
1.24
-0.4
NEG
0
2
Composition
NAT_CYPRPIND
% individuals that are native and within
the family Cyprinidae
-2.54
2.3
NEG
0
100
Habitat''
LOTNTAX_WS
Number of lotic taxa (adjusted for
watershed area)
4.59
1.5
POS
-5.045
4.352
Life History
MIGRNTAX_WS
Number of migratory taxa (adjusted for
watershed area)
2.69
0.7
POS
-1.907
1.579
Reproductive"
LITHPIND_WS
% individuals that are lithophils (adjusted
for watershed area)
3.14
6.1
POS
-52.180
53.848
Richness
NTOLPTAX_WS
% taxa that are not tolerant (adjusted for
watershed area)
2.52
6.3
POS
-66.112
29.110
Tolerance
NAT_INTLPIND_WS
% individuals that are native and
intolerant (adjusted for watershed area)
1.82
332.4
POS
-42.369
62.153
Trophic
NAT_CARNNTAX_WS
Number of taxa that are native and
carnivores (adjusted for watershed area)
3.81
1.3
POS
-2.091
1.960
'' Based on comparison of mean values of least-disturbed and most disturbed sites (validation sites have been excluded).
h Based on variability among sites vs. variability within sites (validation sites have been excluded).
'' Direction: POS=Positive metric (mean value for least-disturbed sites is greater than mean value for most-disturbed sites).
NEG=negative metric (mean value for most-disturbed sites is greater than the mean value for least-disturbed sites). For positive
metrics, the floor= 5th percentile of all sites (a metric value < floor is assigned a score of 0), and the ceiling=95th percentile of least-
disturbed sites (a metric value
> ceiling is assigned a score of 10). For negative metrics, the floor= 5th percentile of least-disturbed sites (a metric value < floor is
assigneda score of 10), and the ceiling=95th percentile of all sites (a metric value > ceiling is assigned a score of 0).
d Habitat metrics: Lotic species occupy flowing water habitats; Rheophils occupy fast water habitats.
f Reproductive metrics: Lithophils require clean substrate for spawning.
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Table 6-7. Performance information of metrics used to construct the fish MMI for the Southern Appalachians aggregated
ecoregion. Column name is the field names in the NRSA database.
Scoring Information17
Metric
t-
value*
Signal:
Noise
Direction
of
Category
Column Name
Description
Value6
Response
Floor
Ceiling
Alien
\\T_PT\X
% of taxa that are native
-0.58
23.4
POS
80
100
Number of taxa within the family
Centrarchidae (adjusted for watershed
Composition
NAT_CENTNTAX_WS
area)
-4.30
1.5
NEG
-1.535
3.620
% of taxa that are native, benthic and not
Habitat''
NAT_NTOLBENTPTAX
tolerant
9.19
3.9
POS
0
66.670
Number of taxa that are native and
Life History
NAT_MIGRNTAX
migratory
-5.42
2.2
NEG
0
4
% of individuals that are native and
Reproductive"
NAT_LITHPIND_WS
lithophils (adjusted for watershed area)
5.85
7.7
POS
-56.528
28.448
Richness
NTOLPTAX
% of taxa that are not tolerant
8.72
8.7
POS
31.820
100
Tolerance
TOLRPTAX
% of taxa that are tolerant
-12.15
9.7
NEG
0
66.670
% of individuals that are invertivores
Trophic
INVPIND_WS
(adjusted for watershed area)
5.01
8.1
POS
-60.259
38.399
'' Based on comparison of mean values of least-disturbed and most disturbed sites (validation sites have been excluded).
h Based on variability among sites vs. variability within sites (validation sites have been excluded).
'' Direction: POS=Positive metric (mean value for least-disturbed sites is greater than mean value for most-disturbed sites).
NEG=negative metric (mean value for most-disturbed sites is greater than the mean value for least-disturbed sites). For positive
metrics, the floor= 5th percentile of all sites (a metric value < floor is assigned a score of 0), and the ceiling=95th percentile of least-
disturbed sites (a metric value
> ceiling is assigned a score of 10). For negative metrics, the floor= 5th percentile of least-disturbed sites (a metric value < floor is assigned
a score of 10), and the ceiling=95th percentile of all sites (a metric value > ceiling is assigned a score of 0).
d Habitat metrics: Lotic species occupy flowing water habitats; Rheophils occupy fast water habitats.
f Reproductive metrics: Lithophils require clean substrate for spawning.
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Table 6-8. Performance information of metrics used to construct the fish MMI for the Southern Plains aggregated ecoregion.
Column name is the field names in the NRSA database.
Scoring Information
Metric
t-
value*
Signal:
Noise
Direction
of
Category
Column Name
Description
Value4
Response
Floor
Ceiling
Alien
NAT_PIND
% of individuals that are native
1.98
113.4
POS
67.610
100
Composition
CYPRPTAX_WS
% of taxa within the family Cyprinidae
(adjusted for watershed area)
-1.71
4.8
NEG
-16.787
62.614
Habitat''
RHEOPIND
% of individuals that are rheophils
3.49
51.3
POS
0
92.710
Life History
NAT_MIGRPTAX_WS
% of taxa that are native and migratory
(adjusted for watershed area)
-0.61
6.3
NEG
-1.326
31.490
Reproductive"
LITHNTAX
Number of taxa that are lithophils
4.26
6.9
POS
0
6
Number of taxa that are native and not
Richness
NAT_NTOLNTAX
tolerant
1.85
4.6
POS
1
8
Tolerance
TOLRNTAX
Number of tolerant taxa
-1.54
14.4
NEG
2
14
Trophic
HERBPTAX
% of taxa that are herbivores
3.96
19.0
POS
0
25
'' Based on comparison of mean values of least-disturbed and most disturbed sites (validation sites have been excluded).
h Based on variability among sites vs. variability within sites (validation sites have been excluded).
'' Direction: POS=Positive metric (mean value for least-disturbed sites is greater than mean value for most-disturbed sites).
NEG=negative metric (mean value for most-disturbed sites is greater than the mean value for least-disturbed sites). For positive
metrics, the floor= 5th percentile of all sites (a metric value < floor is assigned a score of 0), and the ceiling=95th percentile of least-
disturbed sites (a metric value
> ceiling is assigned a score of 10). For negative metrics, the floor=5th percentile of least-disturbed sites (a metric value < floor is assigned
a score of 10), and the ceiling=95th percentile of all sites (a metric value > ceiling is assigned a score of 0).
d Habitat metrics: Lotic species occupy flowing water habitats; Rheophils occupy fast water habitats.
f Reproductive metrics: Lithophils require clean substrate for spawning.
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6.3.6.6 METRIC PERFORMANCE AND SCORING: TEMPERATE PLAINS AGGREGATED
ECOREGION
Table 6-9 presents the performance and scoring information for the eight metrics that were used to
construct the fish MMI for the Temperate Plains aggregated ecoregion (TPL). The final suite
included three negative metrics (the alien, composition, and life history metrics), and four metrics
that were adjusted for watershed area (Table 6-3). Absolute values of t ranged from 1.69 to 6.96,
with three metrics having a t-value > 4. Signal to noise ratios ranged from 1.2 to 12.6. The most
responsive metric in the life history category did not quite meet the criteria for responsiveness
(Section 6.3.4). The life history metric (number of taxa that are migratory and intolerant) also did
not respond as we expected; it is a negative metric in this fish MMI, indicating that there were more
intolerant migratory species in the set of most-disturbed sites than in the set of least-disturbed sites.
6.3.6.7 METRIC PERFORMANCE AND SCORING: UPPER MIDWEST AGGREGATED
ECOREGION
Table 6-10 presents the performance and scoring information for the eight metrics that were used
to construct the fish MMI for the Upper Midwest aggregated ecoregion (UMW). The final suite
included only one negative metric (the tolerance metric), and three metrics that were adjusted for
watershed area (Table 6-3). Absolute values of t ranged from 0.22 to 5.91, with four metrics having
a t-value > 4. Signal to noise ratios ranged from 2.3 to 12.7. The best alien metric, percent of taxa
that are native, was not very responsive. However, the percent of native taxa is considered to have a
positive influence on a fish assemblage, and thus was included as the alien metric in the regional fish
MMI
6.3.6.8 METRIC PERFORMANCE AND SCORING: WESTERN MOUNTAINS
AGGREGATED ECOREGION
Table 6-11 presents the performance and scoring information for the eight metrics that were used
to construct the fish MMI for the Western Mountains aggregated ecoregion (WMT). The final suite
included three negative metrics (the composition, tolerance, and trophic metrics), and three metrics
that were adjusted for watershed area (Table 6-3). Absolute values of t ranged from 1.45 to 5.56,
with five metrics having a t-value > 4. Signal to noise ratios ranged from 1.3 to 23.2. The most
responsive reproductive metric (% of taxa that are lithophils) did not quite meet the criteria for
responsiveness (Section 6.3.4).
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Table 6-9. Performance information of metrics used to construct the fish MMI for the Temperate Plains aggregated ecoregion.
Column name is the field names in the NRSA database.
Scoring Information
Metric
t-
value"
Signal:
Noise
Direction
of
Category
Column Name
Description
Value4
Response
Floor
Ceiling
Number of nonnative taxa (adjusted for
Alien
ALIENNTAX_WS
watershed area)
-4.12
2.1
NEG
-0.298
2.045
% of individuals that are native and within
Composition
NAT_ICTAPIND_WS
the family Ictaluridae (adjusted for
watershed area)
-2.94
12.6
NEG
-1.940
17.204
Habitat''
RHEONTAX
Number of taxa that are rheophils
5.02
1.7
POS
0
4
Life History
INTLMIGRNTAX
Number of taxa that are migratory and
intolerant
-1.69
1.2
NEG
0
1
Reproductive"
LITHPIND
% of individuals that are lithophils
2.93
1.4
POS
0
97.520
Number of taxa that are native and not
Rchness
NAT_NT OLNTAX_W S
tolerant (adjusted for watershed area)
6.96
2.7
POS
-9.403
4.824
Tolerance
INTLPTAX
% of taxa that are intolerant
3.19
4.2
POS
0
50
Number of taxa that are carnivores
Trophic
CARNNTAX_WS
(adjusted for watershed area)
3.40
2.5
POS
-3.761
2.235
'' Based on comparison of mean values of least-disturbed and most disturbed sites (validation sites have been excluded).
h Based on variability among sites vs. variability within sites (validation sites have been excluded).
'' Direction: POS=Positive metric (mean value for least-disturbed sites is greater than mean value for most-disturbed sites).
NEG=negative metric (mean value for most-disturbed sites is greater than the mean value for least-disturbed sites). For positive
metrics, the floor= 5th percentile of all sites (a metric value < floor is assigned a score of 0), and the ceiling=95th percentile of least-
disturbed sites (a metric value
> ceiling is assigned a score of 10). For negative metrics, the floor=5th percentile of least-disturbed sites (a metric value < floor is assigned
a score of 10), and the ceiling=95th percentile of all sites (a metric value > ceiling is assigned a score of 0).
d Habitat metrics: Lotic species occupy flowing water habitats; Rheophils occupy fast water habitats.
f Reproductive metrics: Lithophils require clean substrate for spawning.
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Table 6-10. Performance information of metrics used to construct the fish MMI for the Upper Midwest aggregated ecoregion.
Column name is the field names in the NRSA database.
Scoring Information17
Metric
t-
value*
Signal:
Noise
Direction
of
Category
Column Name
Description
Value6
Response
Floor
Ceiling
Alien
WTJ'TAX
% of taxa that are native
-0.22
4.8
POS
85.710
100
Number of taxa within the family
Composition
CYPRNTAX
Cyprinidae
2.19
3.6
POS
0
9
Number of taxa that are lotic and
Habitat''
INTLLOTNTAX_WS
intolerant (adjusted for watershed area)
5.91
2.9
POS
-3.287
2.110
Life History
INTLMIGRPTAX
% of taxa that are migratory and intolerant
3.04
2.3
POS
0
13.330
Reproductive"
LITHPIND
% of individuals that are lithophils
4.22
12.7
POS
0
95.350
Number of taxa that are not tolerant
Richness
NTOLNTAX_WS
(adjusted for watershed area)
4.54
8.2
POS
-8.389
6.445
Number of tolerant taxa (adjusted for
Tolerance
TOLRNTAX_WS
watershed area)
-3.02
3.1
NEC
-3.785
5.549
% of taxa that are invertivores and
Trophic
INTLINVPTAX
intolerant
5.40
7.5
POS
0
33.330
'' Based on the comparison of mean values of least-disturbed and most disturbed sites (validation sites have been excluded).
h Based on variability among sites vs. variability within sites (validation sites have been excluded).
'' Direction: POS=Positive metric (mean value for least-disturbed sites is greater than mean value for most-disturbed sites).
NEG=negative metric (mean value for most-disturbed sites is greater than the mean value for least-disturbed sites). For positive
metrics, the floor= 5th percentile of all sites (a metric value < floor is assigned a score of 0), and the ceiling=95th percentile of least-
disturbed sites (a metric value
> ceiling is assigned a score of 10). For negative metrics, the floor=5th percentile of least-disturbed sites (a metric value < floor is
assigneda score of 10), and the ceiling=95th percentile of all sites (a metric value > ceiling is assigned a score of 0).
d Habitat metrics: Lotic species occupy flowing water habitats; Rheophils occupy fast water habitats.
f Reproductive metrics: Lithophils require clean substrate for spawning.
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Table 6-11. Performance information of metrics used to construct the fish MMI for the Western Mountains aggregated
ecoregion. Column name is the field names in the NRSA database.
Scoring Information
Metric
t-
value"
Signal:
Noise
Direction
of
Category
Column Name
Description
Value4
Response
Floor
Ceiling
Alien
NAT_PIND
% of individuals that are native
5.56
10.5
POS
0
100
NAT_CATOPIND
% of individuals that are native and within
-4.20
15.2
NEG
0.000
68
Composition
the family Catastomidae
INTLLOTPTAX WS
% of taxa that are lotic and intolerant
4.23
7.3
POS
-72.045
27.826
Habitat''
(adjusted for watershed area)
NAT_MIGRPTAX_WS
% of taxa that are native and migratory
2.10
23.2
POS
-74.290
40.433
Life History
(adjusted for watershed area)
Reproductive"
LITHPTAX
% of taxa that are lithophils
1.45
16.6
POS
25.000
100
NAT_TOTLNTAX_WS
Number of native taxa (adjusted for
2.39
1.5
POS
-3.009
3.272
Richness
watershed area)
Tolerance
TOLRNTAX
Number of tolerant taxa
-4.71
1.3
NEG
0
2
Trophic
NAT_HERBPTAX
% of taxa that are native and herbivores
-4.20
8.2
NEG
0
33.330
'' Based on the comparison of mean values of least-disturbed and most disturbed sites (validation sites have been excluded).
h Based on variability among sites vs. variability within sites (validation sites have been excluded).
'' Direction: POS=Positive metric (mean value for least-disturbed sites is greater than mean value for most-disturbed sites).
NEG=negative metric (mean value for most-disturbed sites is greater than the mean value for least-disturbed sites). For positive
metrics, the floor= 5th percentile of all sites (a metric value < floor is assigned a score of 0), and the ceiling=95th percentile of least-
disturbed sites (a metric value
> ceiling is assigned a score of 10). For negative metrics, the floor=5th percentile of least-disturbed sites (a metric value < floor is
assigneda score of 10), and the ceiling=95th percentile of all sites (a metric value > ceiling is assigned a score of 0).
d Habitat metrics: Lotic species occupy flowing water habitats; Rheophils occupy fast water habitats.
f Reproductive metrics: Lithophils require clean substrate for spawning.
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6.3.6.9 METRIC PERFORMANCE AND SCORING: XERIC WEST AGGREGATED
ECOREGION
Table 6-12 presents the performance and scoring information for the eight metrics that were used
to construct the fish MMI for the Xeric West aggregated ecoregion (XER). The final suite included
two negative metrics (the composition and tolerance metrics), and four metrics that were adjusted
for watershed area (Table 6-3). Absolute values of t ranged from 1.45 to 5.56, with five metrics
having a t-value > 4. Signal to noise ratios ranged from 3.4 to 21.8.
6.4 FISH MMI PERFORMANCE
We evaluated several aspects of performance of the nine regional fish MMIs (Table 6-13). We
compared the fish MMI scores from a set of validation least-disturbed sites to those of the set of
calibration least-disturbed sites to confirm that the models were behaving as anticipated. For all nine
regional fish MMIs, the mean values of the validation sites and sites used to evaluate the metrics and
construct the fish MMIs were not significantly different (two-sample t-test).
We evaluated the responsiveness of the regional fish MMIs to disturbance using two measures: 1) t-
tests to compare the fish MMI scores for the set of least-disturbed sites to those for the set of more
highly disturbed sites (Stoddard et al., 2008), and 2) the difference between the 25th percentile of
least-disturbed sites and the 75th percentile of the set of most-disturbed sites. Boxplots are
presented in Figure 6-2. The results of t-tests (two sample tests assuming unequal variances) and
the percentile differences are presented in Table 6-13. The t-values ranged from 5.71 for the
Northern Plains to 15.38 for the Northern Appalachians. The percentile differences were all positive
(i.e., the boxes did not overlap), and ranged from 0.75 for the Western Mountains to 22.4 for the
Northern Appalachians.
We estimated precision of the fish MMIs by calculating the standard deviation of standardized fish
MMI scores (dividing each value by the mean) from all least-disturbed sites. Precision values greater
than zero provide an indication of the remaining disturbance signal left in the set of least-disturbed
sites, plus measurement error. Precision values ranged from 0.10 in the Xeric West aggregated
ecoregion to 0.28 in the Northern Plains and the Upper Midwest aggregated ecoregions. Precision
values between 0.10 and 0.25 are comparable to values obtained for other predictive models of taxa
loss (Hawkins et al., 2010a).
We evaluated the repeatability of the regional fish MMIs using a set of sites that were visited at least
twice during the course of the NRSA 2008-09 project, typically two times in a single year (Kaufmann
et al., 1999, Stoddard et al., 2008). We used a general linear model (PROC GLM, SAS v. 9.12) to
obtain estimates of among-site and within-site (from repeat visits) variability. PROC GLM was used
because of the highly unbalanced design (only a small subset of sites had repeat visits). We used a
nested model (sites within year) where both site and year were random effects.
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Table 6-12. Performance information of metrics used to construct the fish MMI for the Xeric West aggregated ecoregion.
Column name is the field name in the NRSA database.
Scoring Information
Metric
t-
value*
Signal:
Noise
Direction
of
Category
Column Name
Description
Value4
Response
Floor
Ceiling
Alien
NAT_PIND
% of individuals that are native
7.75
5.4
POS
0
100
CENTPTAX
% of taxa that are within the family
-4.12
3.4
NEG
0
25.000
Composition
Centrarchidae
Habitat''
RHEOPIND
% of individuals that are rheophils
5.03
13.1
POS
0
100
MIGRPTAX_WS
% of taxa that are migratory (adjusted for
1.79
7.9
POS
-64.832
38.279
Life History
watershed area)
LITHNTAX_WS
Number of taxa that are lithophils
8.39
6.4
POS
-6.202
1.649
Reproductive"
(adjusted for watershed area)
Richness
NTOLPTAX
% of taxa that are not tolerant
8.02
8.9
POS
0
100
TOLRNTAX_WS
Number of taxa that are tolerant (adjusted
-8.56
3.7
NEG
-0.129
4.670
Tolerance
for watershed area)
BENTINVPTAX WS
% of taxa that are benthic invertivores
6.43
21.8
POS
-48.740
23.306
Trophic
(adjusted for watershed area)
'' Based on the comparison of mean values of least-disturbed and most disturbed sites (validation sites have been excluded).
h Based on variability among sites vs. variability within sites (validation sites have been excluded).
'' Direction: POS=Positive metric (mean value for least-disturbed sites is greater than mean value for most-disturbed sites).
NEG=negative metric (mean value for most-disturbed sites is greater than the mean value for least-disturbed sites). For positive
metrics, the floor= 5th percentile of all sites (a metric value < floor is assigned a score of 0), and the ceiling=95th percentile of least-
disturbed sites (a metric value
> ceiling is assigned a score of 10). For negative metrics, the floor=5th percentile of least-disturbed sites (a metric value < floor is assigned
a score of 10), and the ceiling=95th percentile of all sites (a metric value > ceiling is assigned a score of 0).
d Habitat metrics: Lotic species occupy flowing water habitats; Rheophils occupy fast water habitats.
f Reproductive metrics: Lithophils require clean substrate for spawning.
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Table 6-13. Performance statistics for the nine regional fish MMIs.
Coastal
Northern
Northern
Southern
Southern
Temperate
Upper
Western
Xeric
Performance
Plain
Appalachians
Plains
Appalachians
Plains
Plains
Midwest
Mountains
West
Characteristic
Fish
Fish
Fish
Fish
Fish
Fish
Fish
Fish
Fish
MMI
MMI
MMI
MMI
MMI
MMI
MMI
MMI
MMI
Validation least-
f=-1.22
/=1.00
/= 1.12
t= 0.92
/=- 0.02
/=0.41
/= 1.40
/=0.43
/=0.86
disturbed sites vs.
least-disturbed
sites used in MMI
development
Least-disturbed
t=10.3
t-15.38
t-5.71
t-14.7
1=8.07
t=9.76
t= 7.45
/=7.74
/= 11.42
sites vs. most-
disturbed sites
Difference
+6.2
+22.4
+ 1.6
+8.8
+2.4
+8.5
+ 1.42
+0.75
+9.5
between 25th
percentile of least-
disturbed sites
and 75th percentile
of most-disturbed
sites
Model precision
0.17
0.22
0.28
0.17
0.14
0.15
0.28
0.0.11
0.10
(SD of least-
disturbed sites)
Repeatability
6.6
71.2
4.4
6.5
13.5
6.2
4.3
29.1
9.8
(S:N)
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COASTAL PLAIN (CPL)
WESTERN MOUNTAINS (WMT)
XERIC WEST (XER)
SOUTHERN PLAINS (SPL)
NORTHERN APPALACHIANS (NAP)
100
SOUTHERN APPALACHIANS (SAP)
100
g ao
0
co 60
2
5 40
1
I FAST MOST
DISTURBED DISIUHSEO
Figure 6-2. Boxplots comparing regional fish MMI scores of least-disturbed sites to most-
disturbed sites. Whiskers indicate 10th and 90th percentiles. Points indicate 5th and 95th
percentiles.
We: estimated repeatability by deriving a S:N ratio as (F - l)/c, where. F is the F-statistic from the
\\( )Y and c is a coefficient in the equation used to estimate the expected mean square. If all sites
had repeat visits, c would equal 2 (Kaufmann et al., 1999). If no sites had repeat visits, c would equal
1. 'S:N ratios ranged from 4.3 m the Upper Midwest aggregated ecoregion to 71.2 in the Northern
Appalachians aggregated ecoregion. High values of S:N need to be interpreted in the context of die
number of repeat visit sites included in the analysis. Artificially high values of S:N can result if there
are a small number of repeat visit sites that have litde (or no) variance in fish MMI scores among
them.
We examined the performance of the fish MMIs across the range of stream sizes sampled for
NRSA. The potential exists for bias in the fish MMI due to different fish species pools being
available for larger rivers versus smaller streams. Differences across the size range might result from
the different sampling protocols used according to river or stream size (wadeable, large wadeable,
and boatable). We used the set of least-disturbed sites to examine patterns in fish MMI scores across
three size categories based on Strahler Order (Figure 6-3). In most aggregated ecoregions, there is
little difference between the distribution of fish MMI scores among stream size classes. In die
Northern Appalachians aggregated ecoregion, fish MMI scores at the least-disturbed sites that are
5th order or larger are significantly different from fish MMI scores in least-disturbed sites that are:
first or second order (one-way ANOVA with Tukey multiple comparisons test, p < 0.05).
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We examined the potential effect of the three different fish sampling protocols for streams of
different sizes (Figure 6-4). The distribution of fish MMI scores in least-disturbed sites were similar
among the three protocols for most of the nine aggregated ecoregions. In the Northern
Appalachians, this appears to be a tendency for fish MMI scores for least-disturbed sites sampled
using the boatable protocol to be lower than fish MMI scores for least-disturbed sites sampled using
either the wadeable or large wadeable protocols, but the difference is not significantly different (one-
way ANOVA with Tukey multiple comparisons test). In the Northern Plains aggregated ecoregion,
fish MMI scores for least-disturbed sites sampled with the boatable protocol are significantly higher
than fish MMI scores at least-disturbed sites sampled using either the wadeable or large wadeable
protocol (one-way ANOVA with Tukey multiple comparisons test, p < 0.05). The effect of
sampling protocol in the Upper Midwest and Xeric West aggregated ecoregions are difficult to
evaluate, as there was only one least-disturbed site sampled each using the large wadeable and
boatable protocols.
The NRSA includes streams of different temperature regimes as well as a broad range of stream
sizes. We used the predicted summer (July-August) daily stream temperatures (ฐC) based on
reference condition USGS stream temperature stations (Hill et al.) to estimate the mean summer
stream temperature (MSST). We classified the set of least-disturbed streams in each aggregated
ecoregion as either cold water (MSST < 17 ฐC), cool water (MSST between 17 and 20 ฐC) or warm
water (MSST > 20 ฐC). Figure 6-5 shows the distribution of fish MMI scores among the three
temperature classes for each aggregated ecoregion. The Coastal Plains aggregated ecoregion did not
have any least-disturbed sites that were classified as either cold or cool water. The Southern Plains
aggregated ecoregion did not have any least-disturbed sites classified as cold water (and only two
sites classified as cool water), and the Temperate Plains aggregated ecoregion only had one least-
disturbed site classified as cold water (and only two sites classified as cool water). The Upper
Midwest aggregated ecoregion only had three least-disturbed sites classified as warm water. The
Western Mountains aggregated ecoregion had only one least-disturbed site classified as warm water.
In the Northern Appalachians aggregated ecoregion, fish MMI scores for warm water sites are
significantly lower than fish MMI scores for either cool water or cold water sites (one-way ANOVA
with Tukey multiple comparisons test, p < 0.001). In the Northern Plains aggregated ecoregion, fish
MMI scores for warm water and cool water sites are significantly lower than fish MMI scores at cold
water sites (one-way ANOVA with Tukey multiple comparisons test, p < 0.001). In the Xeric West
aggregated ecoregion, fish MMI scores for warm water sites are significantly lower than fish MMI
scores for either cool water or cold water sites (one-way ANOVA with Tukey multiple comparisons
test, p < 0.01).
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COASTAL PLAINS (CPL)
Strahler Order
SOUTHERN APPALACHIANS (SAP)
Strahler Order
UPPER MIDWEST (UMW)
100
80 +
60 -f
40
20 +
0
tT J>
Strahler Order
Figure 6-3. Regional fish MM
NORTHERN APPALACHIANS (NAP)
100
80
60 - J
40 -
20 -E
0
1
f lif t?"
s* ^ 4?
Strahler Order
SOUTHERN PLAINS (SPL)
80
60
40
20 +
0
Strahler Order
WESTERN MOUNTAINS (WMT)
100
ฃ 80 -ฆ
o
0
-------
100
ฃ 80 -f
o
o
ซ 60 -ฃ
ฃ
S 40-f
ฆC
to
iZ 20
0
COASTAL PLAINS (CPL)
,S>
? <*
FISH PROTOCOL
NORTHERN APPALACHIANS (NAP)
100
80
ซ 60
ฃ
E 40
.c
ID
E 20
0
^ #3^
_L
: 1 i
1 Q
4
J?
r\*
FISH PROTOCOL
SOUTHERN APPALACHIANS (SAP)
120
o 100 -f
0 :
ฐ 80 ฆ
cn ;
1 60 --
s :
C 40
'
u- 20
J?
FISH PROTOCOL
SOUTHERN PLAINS (SPL)
FISH PROTOCOL
UPPER MIDWEST (UMW)
FISH PROTOCOL
WESTERN MOUNTAINS (WMT)
100
ฃ 80
o
0
60
1
ฃ 40
iZ 20
0
j ^^3 r
& &
<>v ซ>v
-------
COASTAL PLAINS (CPL)
100
ฃ 80
o
u
ซ 60
S
2 40
>
il 20
0
cPV
TEMPERATURE CLASS (MSST)
SOUTHERN APPALACHIANS (SAP)
120
100
80
60
40
20 ฆ;
0
J?
1 I
c-PV ^
TEMPERATURE CLASS(MSST)
UPPER MIDWEST (UMW)
100
t
j
*
TB j
^ ^
^ f ./
TEMPERATURE CLASS (MSST)
Figure 6-5. Regional fish MM
NORTHERN APPALACHIANS (NAP)
100
80
60
40
20
0
&
TEMPERATURE CLASS(MSST)
SOUTHERN PLAINS (SPL)
100
TEMPERATURE CLASS (MSST)
WESTERN MOUNTAINS (WMT)
TEMPERATURE CLASS (MSST)
NORTHERN PLAINS (NPL)
100
ฃ 80
o
u
w 60
ฃ
S 40
ฆe
in
il 20
0
&
&
9^ 0^
TEMPERATURE CLASS (MSST)
TEMPERATE PLAINS (TPL)
100
o
80
o
o
ซ
60
ฃ
40
ฃ
V)
LL
20
0
o
i3
.o
<
J
&
o
. 60
E
2 40 ฆฆ
.c
V)
il 20 ;
0 --
&
TEMPERATURE CLASS (MSST)
scores versus stream temperature class (least-disturbed
sites). Temperature based on modeled mean summer stream temperature (MSST).
6.5 SITES WITH LOW FISH ABUNDANCE
The target population for NRSA includes small perennial headwater streams. Some very small
streams may not contain fish even in the absence of human disturbance. We followed the approach
described by McCormick et al. (2001) and used least-disturbed sites to estimate a drainage area
below which the probability was high that no fish would be present (Table 6-14). This approach
uses the relationship between a set of four physical habitat variables that characterize habitat volume
and the number of fish collected. This relationship defines a habitat volume value below which
nearly all sites sampled were devoid of fish. Then this habitat volume value is related to watershed
area to determine the drainage area below which streams are expected to be naturally fishless.
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Figure 6-6_shows the results of this analysis. The value for the habitat volume index below which
almost all sites were Ashless is 0.41. When habitat volume is plotted against watershed area, this
value corresponds to a watershed area of approximately 2 km2. For sites with watershed areas less
than 2 km2 where no fish were collected, we do not report the fish MMI score. Otherwise, we
assign a fish MMI score of zero to sites with no fish collected.
6.6 BENCHMARKS FOR ASSIGNING ECOLOGICAL CONDITION
For the NRSA, ecological condition is based on the deviation from least-disturbed condition
(Stoddard et al., 2006, Hawkins et al., 2010b). Within each of the nine aggregated ecoregions regions,
benchmarks for defining "good" condition and "poor" condition are based on the distribution of
fish MMI scores in least-disturbed sites. For the NRSA 2018-2919, we used the same benchmarks
as were used for the NRSA 2013-20142019 (Table 6-15).
Benchmarks were set following the same process used for benthic macroinvertebrate condition (see
Section 5.3.2). We combined the least-disturbed sites identified for the NRSA 2008-09 and the
NRSA 2013-14 to develop benchmarks that were then applied to the fish MMI scores from both
assessments. We used a single visit per site and used the latest visit if a least-disturbed site was
sampled in 2008-09 and resampled in 2013-14. We attempted to adjust for differences in the quality
of least-disturbed sites across the nine aggregated ecoregions by applying the "hindcasting"
approach described in Section 5.3.2 and by Herlihy et al. (2008), and the NRSA 2008-09 technical
report (USEPA 2016).
Table 6-14. Determining the minimum drainage area expected to reliably support the
presenceof fish (adapted from McCormick et al. (2001)). Variable names are from the NRSA
database. Scores for each metric between the upper and lower criteria were estimated by
linear interpolation.
SET OF SITES
Use least-disturbed sites only (RT_NRSA_FISH="R") to minimize effects of human disturbance
HABITAT VOLUME INDEX
Percent of support reach length that is dry (PCT_DRS)
If PCT_DR < 1%, score = 1. If PCT-DR > 20%, then score = 0.
Logio[(mean wetted width x mean thalweg depth) +0.001] (LXWXD)
If LXWXD > 1, score=l. If LXWXD < -1.4, then score = 0.
Residual pool depth (RP100)
If RP100 > 20, then score=l. If RP100 < 0, then score = 0.
Mean wetted width
If XWIDTH > 6, then score = 1. If XWIDTH = 0, then score = 0.
HABITAT VOLUME INDEX = (PCT_DR score + LXWXD score + RP100 score + XWIDTH
score)/4
PLOT NUMBER OF FISH COLLECTED (TOTLNIND) VS. HABITAT VOLUME INDEX
(QVOLX)
Value for QVOLX below which most sites have no fish = 0.41
PLOT HABITAT VOLUME INDEX VS. WATERSHED AREA (WSAREA_KM2)
QVOLX = 0.41 corresponds to a watershed area of ~ 2 km2
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1000
0.0 0.2 0.4 o.e o.a
Habitat Volume Index
1.0
1.0
0.8 --
x
n 0.4
n
0.2
: ซv ....
: '-'a./; .
/ # ซ *
t*
fir*/.
>-
0.41 =2 km2
*
0.0
_! i I I I I I I I I I I I I I I I i l_
10
20
30
40
50
Watershed Area (krrr)
Figure 6-6. Relationship between number of fish collected, reduced habitat volume, and
small watershed size at least-disturbed sites. Fish are not likely to be fovind in streams with
a watershed area of < 2 km2. The scales of total number of fish collected and watershed area
axes have been truncated for clarity.
The benchmarks for assigning "good" condition range between > 39.8 for the Upper Midwest to >
76.8 for the Xeric West. The benchmarks for assigning "poor" condition range from < 29.3 in the
Upper Midwest to <65.4 in the Western Mountains. The hmdcasting approach results in the
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benchmarks in each aggregated ecoregion differing by 10.5. Note that even though the fish MMI for
the Upper Midwest has lower benchmarks than the other aggregated ecoregions, the fish MMI still
performs well (Table 6-13; Figure 6-2).
6.7 DISCUSSION
We developed and evaluated regional fish MMIs based on approaches that have evolved from our
experience with other regional-scale assessment efforts (e.g., McCormick et al., 2001, Whittier et al.,
2007b, Stoddard et al., 2008, Van Sickle 2010). Using this approach, we constructed a fish MMI that
was responsive to disturbance and repeatable for each of the nine aggregated ecoregions (Table 6-13
and Figure 6-2). Our evaluation approach focuses on selecting metrics and fish MMIs that
maximize responsiveness to disturbance and have adequate values for other performance criteria
such as repeatability. In all nine aggregated ecoregions, the fish MMIs tend to be more responsive to
disturbance and repeatable than any of their component metrics (Table 6.4 through Table 6.13).
We calculate candidate metrics based on the percent of taxa, which are not commonly considered
for fish. For each of the nine regional fish MMIs, one or more of the final metrics is based on the
percent of taxa (Table 6.4 through Table 6.12). We examine the relationship between watershed area
and metric response for all candidate metrics (not just richness metrics) and adjust the metric
response value when the coefficient of determination (R2) for the linear regression is > 0.10. The
fish MMIs for eight of the nine aggregated ecoregions have at least one metric that is not a richness
metric where the adjusted metric performs better than the unadjusted metric (Table 6-3).
The ability to calculate large numbers of candidate MMIs from a set of metrics that met all our
evaluation criteria is an improvement over stepwise selection of metrics based on correlations with
metrics already selected (Stoddard et al., 2008, Van Sickle 2010). This approach provides the
opportunity to evaluate MMIs based on suites of metrics that might not otherwise be considered
and helps to ensure the best-performing MMI is selected. Incorporating the difference between the
25th percentile of least-disturbed and 75th percentile of more disturbed sites (Table 6-13) and the
F- score (or t-value) provides a quick and reproducible way of selecting a final fish MMI from the
tens of thousands of candidate fish MMIs that can be generated (Table 6-2). However, within each
aggregated ecoregion, there may be several alternative fish MMIs with similar performance (i.e., a
slightly lower PCA axis score, t-value, and S:N ratio) to the fish MMI we selected as the final.
Table 6-15. Benchmarks for assigning ecological condition based on the distribution of regional fish
MMI scores in least-disturbed sites sampled in NRSA 2008-09 or NRSA 2013-14, adjusted using the
hindcasting approach of Herlihy et al. (2008). Aggregated ecoregions are shown in Figure 6-2.
Sample sizes are in parentheses.
Aggregated Ecoregion
Good/Fair
Fair/Poor
Eastern Highlands
Northern Appalachians (60)
> 57.6
< 47.1
Southern Appalachian (94)
> 60.3
< 49.8
Plains and Lowlands
Coastal Plains (39)
> 57.3
< 46.8
Northern Plains (42)
> 46.3
< 35.8
Southern Plains (43)
> 50.2
< 39.7
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Temperate Plains (28)
> 58.0
< 47.5
Upper Midwest (28)
> 39.8
< 29.3
West
Western Mountains (70)
> 75.9
< 65.4
Xeric West (25)
> 76.8
< 63.7
We did note some potential influence of stream size, sampling protocol, and temperature regime on
fish MMI scores in least-disturbed sites in some aggregated ecoregions (Figure 6.4 through Figure
6.6). These patterns were less evident in the original fish MMIs we developed for the three climatic
regions (Figure 6-1; USEPA 2016). The fish MMIs in these aggregated ecoregions still performed
well despite these influences (Figure 6-3, Table 6-13). At the scale of our aggregated ecoregions,
small sample sizes and, in some cases, a limited geographic range of some classes of least-disturbed
sites, make developing separate MMIs for different types of streams (e.g., larger streams or warm
water streams) impractical.
We can consider several future refinements to the NRSA fish MMI development process as data are
acquired from future rounds of NRSA. At present, we cannot develop fish MMIs for those relatively
few NRSA sites that are sampled by seining. These sites tend to be confined to certain geographic
areas. Once we have acquired seining data from enough sites, we may be able to construct a fish
MMI that performs well and is compatible with the fish MMIs developed based on electrofishing
data. An increased pool of least-disturbed sites in each of the nine aggregated ecoregions would
allow for a more rigorous evaluation of the potential influence of factors such as stream size,
protocol, and temperature regime. For larger streams, a national-scale index might be feasible given
the advances in available techniques used to construct and evaluate MMIs. We have the data to
construct numeric tolerance values for individual fish species based on a national-scale data, which
would expand upon previous efforts (Meador and Carlisle 2007, Whittier et al., 2007a) and provide a
tool with broad applicability to bioassessment activities. The fish MMIs we developed are tailored to
respond to a general measure of disturbance, rather than being comprised of metrics that are
responsive to different types of specific stressors. Examining the relationships between metrics and
individual stressors would improve the interpretability of the fish MMI and the resultant estimates of
risk that are produced as part of the overall assessment in NRSA.
Fish counts, metrics, and multimetric index condition from NRSA are available to download from
the NARS datawebpage - https://www.epa.gov/national-aquatic-resource-surveys/data-national-
aquatic- resource-surveys.
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Lyons, J., R.R. Piette, and K.W. Niermeyer. 2001. Development, validation, and application of a
fish- based index of biotic integrity for Wisconsin's large warmwater rivers. Transactions of
the American Fisheries Society 130:1077-1094.
McCormick, F.H., R.M. Hughes, P.R. Kaufmann, D.V. Peck, J.L. Stoddard, and A.T. Herlihy.
2001. Development of an index of biotic integrity for the Mid-Atlantic Highlands region.
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Mebane, C.A., T.R. Maret, and R.M. Hughes. 2003. An Index of Biological Integrity (IBI) for
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Pearson, M.S., T.R. Angradi, D.W. Bolgrien, T.M. Jicha, D.L. Taylor, M.F. Moffett, and B.H. Hill.
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APPENDIX 6.A COMPARISON OF MODEL-BASED AND TRADITIONAL FISH
MULTIMETRIC INDICES FOR NRSA 2008-09
We used the data from the NRSA 2008-09 study to compare the performance of fish MMIs
developed with predictive models (random forests) to adjust metric responses for natural variability
to the performance of fish MMIs developed with a more traditional approach, where metric
responses are adjusted for watershed area using linear regression (Sections 6.1.1 and 6.1.2). The
development and evaluation process for both approaches is essentially the same; the objective being
that the fish MMIs are the best representation of fish ecology that is responsive to disturbance
(Sections 6.4 through 6.6). Both fish MMIs are developed using the same sets of least-disturbed
(LD) and most disturbed (MD) sites. Both fish MMIs are comprised of eight metrics andinclude a
combination of adjusted and unadjusted metrics. Condition classes were developed for both fish
MMIs in the same manner (Section 6.7).
We evaluated four aspects of the performance of the fish MMIs: responsiveness to disturbance,
precision, repeatability, and sensitivity.
6.A.1 Responsiveness to Disturbance and Precision
Figure 6.A.1 compares scores for both types of fish MMIs in least-disturbed and most disturbed
sites in each of the nine aggregated ecoregions. The model-based fish MMI was developed for the
three large climatic regions (Figure 6-1), but the fish MMI scores are broken down for each of the
nine aggregated ecoregions. In general, the distributions of least-disturbed and most disturbed sites
are similar for both types of fish MMIs.
We used a /-test between least-disturbed and most disturbed sites as our performance test for
responsiveness to disturbance (Figure 6.A.2). Sample sizes of least-disturbed and most disturbed
sites within each ecoregion were similar if not identical for both types of fish MMI. For both typesof
fish MMI, differences between mean values of least-disturbed and most disturbed sites were highly
significant in all aggregated ecoregions (p < 0.0001). The traditional fish MMIs had higher values for
tin all but one aggregated ecoregion (Xeric West).
We used the standard deviation of fish MMI scores in least-disturbed sites, after adjusting scores by
dividing by the mean value, as our performance test for precision. Precision is expected to be zero if
our adjustments have accounted for natural variability. Precision values greater than zero represent
any disturbance signal remaining after adjustment as well as measurement error. Neither approach to
constructing the fish MMI completely adjusted for natural variability (Figure 6.A.2), but the amount
of unexplained variability in both types of fish MMIs did not impact the ability of the fish MMIs to
be responsive to disturbance (e.g., in the Northern Appalachians aggregated ecoregion, the
traditional fish MMI was comparatively imprecise, but was very responsive to disturbance). The
model-based fish MMIs tended to be slightly more precise than the traditional MMIs. Both types of
fish MMIs were comparatively imprecise in the aggregated ecoregions that were included in the
Plains and Lowlands climatic region.
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SEPA
RF= model-based
Trad=Traditional
II = LP
r~] = md
Least Disturbed (LD) Sites vs.
Most Disturbed (MD) Sites
SAP
100
CD
8 75
)
5 50
2
ง 25
u.
0
RF
Trad
Figure 6.A.I. Distribution of fish MMI scores in least-disturbed vs. most-disturbed sites in
NRSA 2008-09. For each aggregated ecoregion (see Figure 6.2), the left-hand pair of
boxplots are for the model-based fish MMI (RF), and the right-hand pair are for the
"traditional" fish MMI (Trad). Gray boxes=least-disturbed sites and red boxes=most-
disturbed sites.
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*>EPA
Responsiveness and
Precision
Responsiveness (f-value)
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* Modeled FMMIs slightly more precise
Traditional FMMIs have higher t- ' [MMIsin Plainsand Lowlandsecoregions
have poorest precision
values Still a fair amount of unexplained variability
All are significant at P < 0.0001 for in both types of FMMIs, but it seems to have
both FMMIs imPact on responsiveness
NRSA Steering Committee Webinar
Figure 6.A.2. Comparison of two types of fish MMI scores for responsiveness to
disturbance and precision in nine aggregated ecoregions (see Figure 6-2). Y-axis=
Traditional fish MMI score; X-axis=model-based fish MMI score; line is a 1:1 line. Colors
coincide with regions on map inset (vio1et=Eastern Highlands, brown= Plains and
Lowlands, blue=Western Mountains and Xeric).
6.A.2 Repeatability and Sensitivity
We evaluated repeatability of the fish MM Is by calculating a S:N ratio (S:N; see Section 6.4), which
compared the variance among sites to variance within sites (from repeat visits). We estimated the
sensitivity of the fish MMIs based on the proportion of most disturbed sites that were significantly
different (using an interval test) from the set of least-disturbed sites. The interval test is more
conservative than simply looking at the number of most disturbed sites that are below a single
percentile value (e.g., the 5th percentile) of the least-disturbed sites.
Figure 6.A.3_shows the results of the comparisons for repeatability and sensitivity. S:N ratios for
both types of fish MMIs are adequate for use in NRSA, The model-based fish MMIs tend to have
slightly higher values of S:M than the traditional fish MMIs. In some aggregated ecoregions (e.g., the
Northern Appalachians Or the Upper Midwest), there is a small number of sites with repeat visits. If
80
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there is little or no variability in the fish MMI scores at these sites between visits, it will result in a
very high estimate of S:N that is mostly a function of small sample size.
Sensitivity values are similar for both types of fish MMIs and are nearly identical for those
aggregated ecoregions in the Plains and Lowlands climatic regions. The low values (< 40% for all
but one aggregated ecoregion) reflect the variability present in both the least-disturbed and most
disturbed sites at the scale of the aggregated ecoregions. The lowest sensitivity was seen in the
Northern Plains aggregated ecoregion, while the greatest sensitivity was observed in the Xeric West
aggregated ecoregion.
6.A.3 Correlation of Fish MMI Scores
We looked at how similar the traditional fish MMI scores were to the model-based fish MMI scores
at all sites. For each aggregated ecoregion, we calculated the Pearson correlation coefficient, and
calculated the geometric mean functional regression (GMFR) because each fish MMI is measured
with error. We used a single index visit for each site and excluded sites where no fish were collected.
Correlation coefficients are > 0.7 for all but the Northern Plains aggregated ecoregion (Figure
6.A.4). The GFMR analysis indicates that for two aggregated ecoregions (the Upper Midwest and
the Western Mountains), the two fish MMIs are identical (slope=l, intercept=0). In the Xeric West
aggregated ecoregion, the traditional fish MMI scores are consistently higher by a small amount than
the model-based fish MMI scores (slope=l, intercept > 0). For the remaining aggregated ecoregions,
slopes are >1 except in the Southern Plains aggregated ecoregion.
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oEPA
Repeatability
and Sensitivity
80
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80
Modeled FMMIs slightly more
repeatable
Affected by number of repeat visits
(little or no within site variance would
inflate the S:N valueO
Sensitivity is similar for PLNLOW
regions
Traditional MMI is slightly more
sensitive in EHIGH, RF slightly better
in WMTNS regions
7/12/2017 NRSA Steering Committee Webirsar 19
Figure 6.A.3. Comparison of two types of fish MMI scores for repeatability and sensitivity in
nine aggregated ecoregions (see Figure 6.2). Y-axis=Traditional fish MMI score; X-
axis=model-based fish MMI score; line is a 1:1 line. Colors coincide with regions on map
inset (violet=Eastern Highlands, bro\vn=Plains and Lowlands, blue=Western Mountains
and Xeric).
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SEPA FMMI Scores Are Correlated
Figure 6.A.4. Comparison of two types of fish MMI scores in nine aggregated ecoregions (see
Figure 6.2). Points are index visits only, and siteswhere no fish were collected are excluded. Y-
axis =Traditional fish MMI score; X-axis=model-based fish MMI score. Solid black line: 1:1 line;
Dashed Blue line: Regression line based on geometric mean functional regression.
6.A.4 Population Estimates
The survey design for NRSA allows us to make inferences from the set of sampled sites to a much
larger target population. We wanted to know if the two types of fish M Mis would yield different
estimates of biological condition for the target population. We assigned condition classes for each
type of fish MMI in each aggregated ecoregion using the approach described in Section 6.5.
Condition class is assigned for each oi" the nine aggregated ecoregions based on the deviation from
the set of LD sites. For the four aggregated ecoregions in the Eastern Highlands and Western
Mountains climatic regions, the two types of fish MMIs yield similar estimates of the percent of
stream length in both good and poor condition (Figure 6.A.5); the largest differences in length are
in the Xeric West aggregated ecoregion. In the Plains and Lowlands climatic region, the two types of
fish MMIs yield similar estimates of the percent of stream length in good and poor condition for all
aggregated ecoregions except for the Coastal Plains, where the traditional fish MMI produces a
smaller percent of stream length in good condition and a larger percent of stream length in poor
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condition compared to the model-based fish M M I. One or both types of fish M M Is in the Northern
Plains and the Upper Midwest aggregated ecoregions had some performance issues, yet the
condition class estimates for the sampled target population were similar.
Based on our evaluations, both types of fish MMIs generally have similar performance and provide
similar estimates of biological condition for the samples target population in each aggregated
ecoregion. Scores for the two types of fish MMIs are well correlated despite differences in
component metrics and the scale at which metric adjustments are made (climatic region for the
model-based fish MM1 and aggregated ecoregion for the traditional fish MMI). The quality of least-
disturbed sites may be less similar among the five aggregated ecoregions that are included in the
Plains and Lowlands climatic region than the aggregated ecoregions that are included m either the
Eastern Highlands or Western Mountains climatic regions.
We calculated the traditional fish MMI scores for all sites in the NRSA 2008-09 and the NRSA
2013-14. The scale of traditional fish MMI development (i.e., the nine aggregated ecoregions) is
consistent with the MMI developed for the benthic macroinvertebrate assemblage in NRSA. We did
not use any least-disturbed sites identified in NRSA 2013-14 to develop the fish MMIs, but we did
pool the least-disturbed sites from both studies to estimate the benchmark values to assign biological
condition.
v>EPA
Extent Estimates are Similar
SEPA ^
Coastal Plains Estimates are
Less Similar
Eastern Highlands (EHIGH)
NAP SAP
EC09 Subpopulation
Western Mountains and Xeric (WEST)
WMT XER
EC09 Subpopulation
GOOD= similar to LD sites POOR= different from LD sites
Est. stream length (km) 189,488
509,319
Coastal (CPL), Southern (SPL), and Northern (NPL) Plains
ฃ 70
CPL SPL NPL
ECQ9 Subpopulation
Temperate Plains (TPL) and Upper Midwest (UMW)
TPL UMW
ECQ9 Subpopulation
1=1 RF GOOD TR GOOD ฆ=ง RF POOR 1=1 7R POOR
CPL
SPL
NPL TPL
UMW
Est. stream length (km) 284,065 58,853 43,432 371,316 148,951
Figure 6.A.5. Biological condition in nine aggregated ecoregions (EC09) (see Figure 6-2) for
the NRSA 2008-09 based on two types of fish MMIs. Left panel shows aggregated ecoregions
within the Eastern Highlands and Western Mountains climatic regions. Right panel shows
aggregated ecoregions within the Plains and Lowlands climatic regions. Bars represent the
percent of the length of the sampled target population inferred from the set of sampled sites;
error bars are 95% confidence intervals. RF=model-based fish MMI; TR=traditional fish
MMI. Good=similar to least-disturbed sites; Poor= different from least-disturbed sites (see
Section 6.5 and Table 6-15). The total estimated length of the sampled target population for
each aggregated ecoregion is shown in the table.
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APPENDIX 6.B CANDIDATE METRICS CONSIDERED FOR FISH MMI
DEVELOPMENT
Table 6.B.1 presents the candidate metrics that were evaluated for potential inclusions in the
regional fish MMIs. Metric classes represent different attributes of fish assemblage structure or
function. Some metrics are combinations of two different metric classes. Composition metrics
generally focus on the family level of taxonomic resolution. Round-bodied suckers include the
following genera: Catostomus, Cycleptus, Erimyzon, Hypentelium, Minytrema, Moxostoma,
Pantosteus, and Thoburnia. Migratory species include both diadromous and anadromous species.
"Not tolerant" metrics include all species not classified as tolerant (and thus include intolerant
species, moderately tolerant species, and species that have no tolerance category assigned). We use
"intolerant" here in the same context as others use "sensitive."
Table 6.B.I. List of candidate metrics.
METRIC VARIABLE
METRIC CLASS
NAME
DESCRIPTION
ALIEN
ALIENNLAX
No. Non-native species
ALIEN
ALIENPIND
% Non-native individuals
ALIEN
ALIENPLAX
% Non-native taxa
ALIEN
NAT_PIND
% Native individuals
ALIEN
\\T_PT\X
% Native taxa
COMPOSITION
CATONTAX
No. Catostomid species
COMPOSITION
CALOPIND
% Catostomid individuals
COMPOSITION
CATOPTAX
% Catostomid taxa
COMPOSITION
NAT_CATONTAX
No. Native catostomid species
COMPOSITION
NAT_CATOPIND
% Native catostomid
individuals
COMPOSITION
NAL_CALOPLAX
% Native catostomid taxa
COMPOSITION
RBCATONTAX
No. Round-bodied catostomid
species
COMPOSITION
RBCATOPIND
% Round-bodied catostomid
individuals
COMPOSITION
RBCATOPTAX
% Round-bodied catostomid
taxa
COMPOSITION
NAT_RBCATONTAX
No. Native round-bodied
catostomid species
COMPOSITION
NAT_RBCATOPIND
% Native round-bodied
catostomid individuals
COMPOSITION
NAT_RBCATOPTAX
% Native round-bodied
catostomid taxa
COMPOSITION
CENLNLAX
No. Centrarchid species (excl.
Microptems spp.)
COMPOSITION
CENLPIND
% Centrarchid individuals (excl.
Microptems spp.)
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METRIC
METRIC VARIABLE
DESCRIPTION
CLASS
NAME
COMPOSITION
CENTPTAX
% Centrarchid taxa (excl.
Microptenis spp.)
COMPOSITION
NAT_CENTNTAX
No. Native centrarchid species
(excl. Microptenis spp.)
COMPOSITION
NAT_CENTPIND
% native centrarchid individuals (excl.
Microptenis
spp.)
COMPOSITION
NAT_CENTPTAX
% Native centrarchid taxa (excl.
Microptenis spp.)
COMPOSITION
CYPRNTAX
No. Cyprinid species (excluding
all carps and goldfish)
COMPOSITION
CYPRPIND
% Cyprinid individuals (excluding all
carps and goldfish)
COMPOSITION
CYPRPTAX
% Cyprinid individuals (excluding all
carps and goldfish)
COMPOSITION
NAT_CYPRNT AX
No. Native cyprinid species (excluding all
carps and goldfish)
COMPOSITION
NAT_CYPRPIND
% Native cyprinid individuals (excluding all
carps and goldfish)
COMPOSITION
NAT_CYPRPT AX
% Native cyprinid individuals (excluding all
carps and goldfish)
COMPOSITION
ICTANTAX
No. Ictalurid species
COMPOSITION
ICTAPIND
% Ictalurid individuals
COMPOSITION
ICTAPTAX
% Ictalurid taxa
COMPOSITION
NATJCTANTAX
No. Native ictalurid species
COMPOSITION
NAT_ICTAPIND
% Native ictalurid individuals
COMPOSITION
NAT.ICTAPTAX
% Native ictalurid taxa
COMPOSITION
SALMNTAX
No. Salmonid species
COMPOSITION
SALMPIND
% Salmonid individuals
COMPOSITION
SALMPTAX
% Salmonid taxa
COMPOSITION
NAT_S ALMNT AX
No. Native salmonid species
COMPOSITION
NAT_SALMPIND
% Native salmonid individuals
COMPOSITION
NAT_SALMPTAX
% Native salmonid taxa
HABITAT
COLDNTAX
No. Coldwater species
HABITAT
COLDPIND
% Coldwater individuals
HABITAT
COLDPTAX
% Coldwater taxa
HABITAT
NAT_COLDNTAX
No. Native coldwater species
HABITAT
NAT_COLDPIND
% Native coldwater individuals
HABITAT
NAT_COLDPTAX
% Native coldwater taxa
HABITAT
LOTNTAX
No. Lotic species
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METRIC CLASS
METRIC VARIABLE
NAME
DESCRIPTION
HABITAT
LOTPIND
% Lotic individuals
HABITAT
LOTPTAX
% Lotic taxa
HABITAT
NAT_LOTNT AX
No. Native lotic species
HABITAT
NAT_LOTPIND
% Native lotic individuals
HABITAT
NAT_LOTPTAX
% Native lotic taxa
HABITAT
NAT_NTOLBENTNTAX
No. of Native not tolerant
benthic species (BPJ based
tolerance assignments)
HABITAT
NAT_NTOLBENTPIND
% Native not tolerant benthic
individuals (BPJ based tolerance
assignments)
HABITAT
NAT_NTOLBENTPTAX
% Native not tolerant benthictaxa
(BPJ based tolerance assignments)
HABITAT
RHEONTAX
No. Rheophilic species
HABITAT
RHEOPIND
% Rheophilic individuals
HABITAT
RHEOPTAX
% Rheophilic taxa
HABITAT
NAT_RHEONTAX
No. Native rheophilic species
HABITAT
NAT_RHEOPIND
% Native rheophilic individuals
HABITAT
NAT_RHEOPT AX
% Native rheophilic taxa
HABITAT
NTOLBENTNTAX
No. Not tolerant benthic
species
HABITAT
NTOLBENTPIND
% Not tolerant benthic species
HABITAT
NTOLBENTPTAX
% not tolerant benthic taxa
HABITAT (TOLERANCE)
INTLLOTNTAX
No. Intolerant lotic species
HABITAT (TOLERANCE)
INTLLOTPIND
% Intolerant lotic individuals
HABITAT (TOLERANCE)
INTLLOTPTAX
% Intolerant lotic taxa
HABITAT (TOLERANCE)
NAT_INTLLOTNTAX
No. Native intolerant lotic species
HABITAT (TOLERANCE)
NAT_INTLLOTPIND
% Native intolerant lotic
individuals
HABITAT (TOLERANCE)
NAT_INTLLOTPTAX
% Native intolerant lotic taxa
HABITAT (TOLERANCE)
INTLRHEONTAX
No. Intolerant rheophilic
species
HABITAT (TOLERANCE)
INTLRHEOPIND
% Intolerant rheophilic
individuals
HABITAT (TOLERANCE)
INTLRHEOPTAX
% Intolerant rheophilic taxa
HABITAT (TOLERANCE)
NAT_INTLRHEONTAX
No. Native intolerant
rheophilic species
HABITAT (TOLERANCE)
NATJNTLRHEOPIND
% Native intolerant rheophilic
individuals
HABITAT (TOLERANCE)
NAT_INTLRHEOPTAX
% Native intolerant rheophilic
taxa
LIFE HISTORY
(MIGRATION STRATEGY)
MIGRNTAX
No. Migratory species
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METRIC CLASS
METRIC VARIABLE
NAME
DESCRIPTION
LIFE HISTORY
(MIGRATION STRATEGY)
MIGRPIND
% Migratory individuals
LIFE HISTORY
(MIGRATION STRATEGY)
MIGRPTAX
% Migratory taxa
LIFE HISTORY
(MIGRATION STRATEGY)
NAT_MIGRNTAX
No. Native migratory species
LIFE HISTORY
(MIGRATION STRATEGY)
NAT_MIGRPIND
% Native migratory individuals
LIFE HISTORY
(MIGRATION STRATEGY)
NAT_MIGRPTAX
% Native migratory taxa
LIFE HISTORY (TOLERANCE)
INTLMIGRNTAX
No. Intolerant migratory species
LIFE HISTORY (TOLERANCE)
INTLMIGRPIND
% Intolerant migratory
individuals
LIFE HISTORY (TOLERANCE)
INTLMIGRPTAX
% Intolerant migratory taxa
LIFE HISTORY (TOLERANCE)
NATJNTLMIGRNTAX
No. Native intolerant migratory
species
LIFE HISTORY
(TOLERANCE)
NAT_INTLMIGRPIND
% Native intolerant migratory
individuals
LIFE HISTORY (TOLERANCE)
NATJNTLMIGRPTAX
% Native intolerant migratory
taxa
REPRODUCTIVE
LITHNTAX
No. Lithophilic spawner
species
REPRODUCTIVE
LITHPIND
% Lithophilic spawner
individuals
REPRODUCTIVE
LITHPTAX
% Lithophilic spawner taxa
REPRODUCTIVE
NAT_LITHNT AX
No. Native lithophilic spawner
species
REPRODUCTIVE
NAT_LITHPIND
% Native lithophilic spawner
individuals
REPRODUCTIVE
NAT_LITHPTAX
% Native lithophilic spawner taxa
RICHNESS
TOTLNTAX
Total no. distinct species
collected
RICHNESS
NAT_T OTLNT AX
No. Native distinct species
collected
RICHNESS
NTOLNTAX
No. Not tolerant species
RICHNESS
NTOLPIND
% Not tolerant individuals
RICHNESS
NTOLPTAX
% Not tolerant taxa
RICHNESS
NAT_NTOLNTAX
No. Native not tolerant species
RICHNESS
NAT_NTOLPIND
% Native not tolerant
individuals
RICHNESS
NAT_NTOLPTAX
% Native not tolerant taxa
TOLERANCE
INTLNTAX
No. Intolerant species (BPJ-
based tolerance assignments)
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METRIC VARIABLE
METRIC CLASS
NAME
DESCRIPTION
TOLERANCE
INTLPIND
% Intolerant individuals (BPJ-
based tolerance assignments)
TOLERANCE
INTLPTAX
% Intolerant taxa (BPJ-based
tolerance assignments)
TOLERANCE
NATJNTLNTAX
No. Native intolerant species(BPJ-
based tolerance
assignments)
TOLERANCE
NATJNTLPIND
% Native intolerant individuals (BPJ-
based tolerance
assignments)
TOLERANCE
NATJNTLPT AX
% Native intolerant taxa (BPJ-
based tolerance assignments)
TOLERANCE
NAT_TOLRNTAX
No. Native tolerant species(BPJ-
based tolerance assignments)
TOLERANCE
NAT_T OLRPIND
% Native tolerant individuals (BPJ-
based tolerance assignments)
TOLERANCE
NAT_T OLRPT AX
% Native tolerant taxa (BPJ-
based tolerance assignments)
TOLERANCE
TOLRNTAX
No. Tolerant species (BPJ-
based tolerance assignments)
TOLERANCE
TOLRPIND
% Tolerant individuals (BPJ-based
tolerance assignments)
TOLERANCE
TOLRPTAX
% Tolerant taxa (BPJ-based
tolerance assignments)
TROPHIC
CARNNTAX
No. Carnivore species
TROPHIC
CARNPIND
% Carnivore individuals
TROPHIC
CARNPTAX
% Carnivore taxa
TROPHIC
NAT_CARNNTAX
No. Native carnivore species
TROPHIC
NAT_CARNPIND
% Native carnivore individuals
TROPHIC
NAT_CARNPT AX
% Native carnivore taxa
TROPHIC
NT OLCARNNT AX
No. Not tolerant carnivore species
TROPHIC
NT OLCARNPIND
% Not tolerant carnivore individuals
TROPHIC
NTOLCARNPTAX
% Not tolerant carnivore taxa
TROPHIC
NAT_NTOLCARNNTAX
No. Native not tolerant
carnivore species
TROPHIC
NAT_NTOLCARNPIND
% Native not tolerant carnivore
individuals
TROPHIC
NAT_NTOLCARNPTAX
% Native not tolerant carnivore taxa
TROPHIC
HERBNTAX
No. Herbivore species
TROPHIC
HERBPIND
% Herbivore individuals
TROPHIC
HERBPTAX
% Herbivore taxa
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METRIC VARIABLE
METRIC CLASS
NAME
DESCRIPTION
TROPHIC
NAT_HERBNTAX
No. Native herbivore species
TROPHIC
NAT_HERBPIND
% Native herbivore individuals
TROPHIC
NAT_HERBPTAX
% Native herbivore taxa
TROPHIC
INYNTAX
No. Invertivore species
TROPHIC
INVPIND
% Invertivore individuals
TROPHIC
INVPTAX
% Invertivore taxa
TROPHIC
NAT_INVNTAX
No. Native invertivore species
TROPHIC
NAT_INVPIND
% Native invertivore
individuals
TROPHIC
NAT_INVPTAX
% Native invertivore taxa
TROPHIC
NTOLINVNTAX
No. Not tolerant invertivore
species
TROPHIC
NT OLINVPIND
% Not tolerant invertivore
individuals
TROPHIC
NTOLINVPTAX
% Not tolerant invertivore taxa
TROPHIC
NAT_NTOLINVNTAX
No. Native not tolerant
invertivore species
TROPHIC
NAT_NTOLINVPIND
% Native not tolerant
invertivore individuals
TROPHIC
NAT_NTOLINVPTAX
% Native not tolerant
invertivore taxa
TROPHIC
OMNINTAX
No. Omnivore species
TROPHIC
OMNIPIND
% Omnivore individuals
TROPHIC
OMNIPTAX
% Omnivore taxa
TROPHIC
NAT_OMNINTAX
No. Native omnivore species
TROPHIC
NAT_OMNIPIND
% Native omnivore individuals
TROPHIC
NAT_OMNIPTAX
% Native omnivore taxa
TROPHIC (HABITAT)
BENTINYNTAX
No. Benthic invertivore species
TROPHIC (HABITAT)
BENTINVPIND
% Benthic invertivore individuals
TROPHIC (HABITAT)
BENTIN VPT AX
% benthic invertivore taxa
TROPHIC (HABITAT)
nat_bentinvntax
No. Native benthic invertivore
species
TROPHIC (HABITAT)
nat_bentinvpind
% Native benthic invertivore
individuals
TROPHIC (HABITAT)
nat_bentinvptax
% Native benthic invertivore taxa
TROPHIC (HABITAT)
INTLINVNTAX
No. Intolerant invertivore species
TROPHIC (HABITAT)
INTLINVPIND
% Intolerant invertivore species
TROPHIC (HABITAT)
INTLIN VPT AX
% Intolerant invertivore taxa
TROPHIC (HABITAT)
NAT_INTLINVNTAX
No. Native intolerant
invertivore species
TROPHIC (HABITAT)
NAT_INTLINVPIND
% Native intolerant invertivore
species
TROPHIC (HABITAT)
NATJNTLINVPTAX
% Native intolerant invertivore taxa
90
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7 Water Chemistry Analyses
Water samples were collected as a grab sample either at the midpoint of the reach in wadeable
systems or at the upper most point of the sample reach in boatable systems (see NRSA 2018-19
Field Operations Manual and Laboratory Operations Manual for additional details). The main report
presents assessments for four chemical stressors: total nitrogen (TN), total phosphorus (TP), acidity,
and salinity. These benchmark values and class definitions were identical to those used in the NRSA
2008-09. Water chemistry data, including additional parameters not assessed in the report, are
available to download from the NARS datawebpage - https: / /www.epa.gov/national-aquatic-
resource-surveys/data-national-aquatic-resource-surveys.
7.1 ACIDITY AND SALINITY BENCHMARKS
For acidity, criteria values were determined based on values derived during the National Acid
Precipitation Assessment Program (Baker et al. ,1990; Kaufmann et al., 1991). Sites with acid
neutralizing capacity (ANC) less than zero were considered acidic. Acidic sites with dissolved
organic carbon (DOC) greater than 10 mg/L were classified as organically acidic (natural). Acidic
sites with DOC less than 10 and sulfate less than 300 (j,eq/L were classified as acidic deposition
impacted, while those with sulfate above 300 (j,eq/L were considered acid mine drainage impacted.
Sites with ANC between 0 and 25 (j,eq/L and DOC less than 10 mg/L were considered acidic-
deposition-influenced but not currently acidic. These low ANC sites typically become acidic during
high flow events (episodic acidity).
Salinity data values were divided into good, fair, or poor classes. Salinity classes were defined by
specific conductance using ecoregional specific values (Table 7-1).
7.2 TOTAL PHOSPHORUS AND TOTAL NITROGEN BENCHMARKS
The process for setting the good/fair/poor benchmark for nutrients (TP, TN) in NRSA was derived
from EPA's approach for the development of regional nutrient criteria (USEPA 1998). Implicit in
this approach is the recognition that excess nutrients are a major cause of water quality impairment
in the United States. The approach also acknowledges that because of diverse geology, climate, and
geomorphology, a single national nutrient criteria level for all types of water bodies is inappropriate.
7.2.1 SELECTING AN APPROACH
Various approaches have been used to develop nutrient benchmarks such as reference sites, best
professional judgment (BPJ), paleolimnological analysis, use of historical data, and dose-response
modeling. In evaluating possible approaches for NRSA, analysts determined that three of these
approaches (BPJ, historical data from undisturbed sites, and paleolimnological approaches) did not
work for NRSA. Best professional judgment was considered too subjective, there is little if any of
the needed historical data available from rivers and streams prior to human disturbance, and
paleolimnological approaches were not deemed applicable for NRSA due to the
erosional/depositional nature of rivers and streams.
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NRSA analysts also investigated other approaches to determine their efficacy for developing nutrient
benchmarks. For example, dose-response models, both watershed disturbance-nutrient (see Herlihy and
Sifneos, 2008; Herlihy et al., 2010) and nutrient-chlorophyl\ were examined and deemed insufficient due
to the nation-wide extent of the NRSA sampling effort for rivers and streams; and the fact that
many other factors besides nutrients (e.g., light, substrate) control biological responses in rivers and
streams. Additionally, dose-response modeling does not eliminate the need to define "good" or
undisturbed condition for the response variable to implement the model. Finally, breakpoint analysis
was evaluated. The analysis did not show clear breakpoints at the ecoregional scales assessed and
reported on by NRSA. As a result, this approach was not considered appropriate for NRSA.
Analysts determined that the reference approach lends itself to the scale of NRSA data and provides
a means of establishing least-disturbed conditions which can be used for setting benchmarks.
As a result of the evaluation of multiple approaches and the pros/cons identified, analysts selected
the reference site approach as the process to set good/fair/poor condition for the NRSA nutrient
analyses. Following the same basic approach as that used for biological data, regionally relevant
nutrient benchmarks were calculated from conditions observed at the population of least-disturbed
NRSA reference, except for nutrients not being used in the reference site screening criteria.
7.2.2 APPLYING THE REFERENCE-BASED APPROACH TO NRSA
Total nitrogen and phosphorus concentrations were classified as "good", "fair," or "poor" using a
method similar to that described in Chapter 4. The benchmarks have been updated since the Herlihy
and Sifenos 2008 paper as a result of adding additional sites to the reference (least-disturbed) set of
sites, categorizing them based on the 9 aggregated ecoregions used in NRSA rather than nutrient
ecoregions, and the expansion of the analysis to include non-wadeable systems in addition to
wadeable ones.
For nutrients, the value at (and below) the 75th percentile of the reference distribution was used for
each ecoregion to define the least-disturbed condition class (goodfair boundary). The 95th
percentile (and above) of the reference distribution in each ecoregion defines the most disturbed
condition class (fair-poor boundary) Table 7-1.
A set of "nutrient reference sites" was defined for this analysis using both WSA and NRSA data. All
available WSA and NRSA sample sites were screened for water chemical and physical habitat
disturbances using the process described in Chapter 4 with the exception that total phosphorus and
total nitrogen values were not used as screens to avoid circularity in defining nutrient benchmarks.
Sites with screening values exceeding criteria for the remaining parameters in Table 4-2 were
excluded as nutrient reference sites.
To adjust the process after the removal of the nutrient screens, we incorporated screens for land
cover disturbance. A single national criterion was used to exclude sites that had watershed
%Urban LULC (Land Use Land Cover) >10%, watershed road density > 3 km/km2, and watershed
population density >100 people/km2. For watershed %Agriculture LULC screening, ecoregional
specific criteria were used as screens; NAP, WMT, XER (>10%), CPL, NPL, SAP, SPL, UMW
(>25%), TPL (>50%). Before calculating ecoregional nutrient reference site percentiles, outliers
(values outside 1.5 times the interquartile range above and below the quartiles) were removed.
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Ecoregion
Salinity as
Conductivity
(|iS/cm)
Good-Fair
Salinity as
Conductivity
(|iS/cm)
Fair-Poor
Total N
(Hg/L)
Good-Fair
Total N
(Hg/L)
Fair-Poor
Total P
(Hg/L)
Good-Fair
Total P
(Hg/L)
Fair-Poor
CPL
500
1000
624
1081
55.9
103
NAP
500
1000
345
482
17.1
32.6
SAP
500
1000
240
456
14.8
24.4
UMW
500
1000
583
1024
36.3
49.9
TPL
1000
2000
700
1274
88.6
143
NPL
1000
2000
575
937
64.0
107
SPL
1000
2000
581
1069
55.8
127
WMT
500
1000
139
249
17.7
41.0
XER
500
1000
285
529
52.0
95.9
Table 7-1. Nutrient and Salinity Category Benchmarks for NRSA Assessment.
7.3 SIGNAL TO NOISE
To examine within-year variability of water chemistry data, analysts used the revisit sites from the
Wadeable Streams Assessment, NRSA 2008-09, and NRSA 2013-14 to calculate S:N estimates for
the national dataset. The results were a S:N ratio of 12.3 for total nitrogen, 10.2 for total
phosphorus, 31.2 for conductivity, and 39.2 for ANC.
7.4 LITERATURE CITED
Baker, L.A., P.R. Kaufmann, A.T. Herlihy, and J.M. Eilers. 1990. Current Status of Surface Water
Acid-Base Chemistry. State of Science/Technology Report 9. National Acid Precipitation
Assessment Program, Washington, D.C., 650 pp.
Herlihy, A.T., and J.C. Sifneos. 2008. Developing nutrient criteria and classification schemes for
wadeable streams in the conterminous US. Journal of the North American Benthological Society
27:932-948.
Kaufmann, P.R., A.T. Herlihy, M.E. Mitch, J.J. Messer, and W.S. Overton. 1991. Chemical
characteristics of streams in the Eastern United States: I. Synoptic survey design, acid-base
status and regional chemical patterns. Water Resources Research 27:611-627.
USEPA. 2000. Nutrient Criteria Technical Guidance Manual for Rivers and Streams. EPA 822-B-
00-002. US. Environmental Protection Agency, Office of Water, Office of Science and
Technology, Washington, DC.
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8 Physical Habitat Assessment
An assessment of river and stream (fluvial) physical habitat (PHab) condition is a major component
of the National Rivers and Streams Assessment (NRSA). Of many possible general and specific
fluvial habitat indicators measured in the NRSA surveys, the assessment team chose streambed
stability and excess fine sediments, instream habitat cover complexity, riparian vegetation, and
riparian human disturbances for the 2013-14 assessment. These four indicators have been used in
earlier U.S. Environmental Protection Agency (USEPA) national assessments, are important
nationwide, can be reliably and economically measured, and their reference conditions and degree of
anthropogenic alteration can be interpreted with reasonable confidence (Paulsen etal., 2008).
In the broadest sense, fluvial habitat includes all physical, chemical, and biological attributes that
influence or sustain organisms within streams or rivers. We use the term physical habitat to refer to
the structural attributes of habitat. NRSA made field measurements aimed at quantifying eight
general attributes of physical habitat condition, including direct measures of human disturbance.
Habitat Volume / Stream Siz e
Habitat Complexity and Cover for Aquatic Biota
Streambed Particle Size
Bed Stability and Hydraulic Conditions
Channel-Riparian and Floodplain Interaction
Hydrologic Regime
Riparian Vegetation Cover and Structure
Riparian Disturbance
These attributes were previously identified during EPA's 1992 national stream monitoring workshop
(Kaufmann 1993) as those essential for evaluating physical habitat in regional monitoring and
assessments. They are typically incorporated in some fashion in regional habitat survey protocols
(Platts et al., 1983, Fitzpatrick et al., 1998, Lazorchak et al., 1998, Peck et al. 2006, USEPA 2004) and
were applied in the NRSA 2008-09 assessment (USEPA 2016), the National Wadeable Streams
Assessment (WSA: USEPA 2006) and the Western Rivers and Streams Pilot (EMAP-W) surveys
conducted between 2000 and 2005 (Stoddard et al., 2005a, b).
The major habitat metrics used in those past assessments and considered in NRSA are listed and
defined in Table 8-1. Some measures of these attributes are useful measures of habitat condition in
their own right (e.g., channel incision as a measure of channel-riparian interaction); others are
important controls on ecological processes and biota (e.g., bed substrate size), still others are
important in the computation of more complex habitat condition metrics (e.g., bankfull depth is
used to calculate Relative Bed Stability [RBi]). Like biological characteristics, most habitat attributes
vary according to their geomorphic and ecological setting. Even direct measures of riparian human
activities and disturbances are strongly influenced by their geomorphic setting. And even within a
region, differences in precipitation and stream drainage area channel gradient (slope) lead to
variation in many aspects of stream habitat. Those geoclimatic factors influence discharge, flood
stage, stream power (the product of discharge times gradient), bed shear stress (proportional to the
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product of depth and slope), and riparian vegetation. However, all eight of the major habitat
attributes can be directly or indirectly altered by anthropogenic activities.
NRSA follows the precedent of EMAP-W and WSA in reporting the condition of fluvial physical
habitat condition on the basis of four habitat indicators that are important nationwide, can be
reliably and economically measured, and their reference condition under minimal anthropogenic
disturbance can be interpreted with reasonable confidence. These are: relative bed stability (RBS) as
an indicator of bed sedimentation or hydrologic alteration, the areal cover and variety of fish
concealment features as a measure of in-stream habitat complexity, riparian vegetation cover and
structure as an indicator of riparian vegetation condition, and a proximity-weighted tally of
streamside human activities as an indicator of riparian human disturbances (Paulsen et al., 2008).
In this document, we describe the approach taken by NRSA in 2013-14 (which applied to the NRSA
18-19 data) for assessing physical habitat condition in rivers and streams based on the four above-
mentioned indicators. We revisited the screening of reference sites, consistently defining a set of
reference sites from the combined 2013-14 and 2008-09 NRSA surveys, thereby increasing the
number of sample sites available for modeling expected condition, and for evaluating precision and
responsiveness. We recalculated PHab condition assignments in all previous surveys using the
current NRSA 2013-14 assessment procedures described here for our estimates of change or trends
in PHab. We also examined the rationale, importance, and measurement precision of each of the
four indicators, including the analytical approach for estimating reference conditions for each.
Reference conditions for each indicator were interpreted as their expected value in sites having the
least amount of anthropogenic disturbance within appropriately stratified regions. In most cases, we
also refine the expected values as a function of geoclimatic controlling factors within regions.
Finally, we examine patterns of association between physical habitat indicators and anthropogenic
disturbance by contrasting habitat indicator values in least-, moderately-, and most-disturbed sites
nationally and within regions.
Physical habitat metrics and condition assessment data from NRSA are available to download from
the NARS datawebpage - https: / /www.epa.gov/national-aquatic-resource-surveys /data-national-
aquatic-resource-surveys
8.1 METHODS
8.1.1 PHYSICAL HABITAT SAMPLING AND DATA PROCESSING
Sample sites visited in NRSA are shown in Figure 8-1. In the wadeable streams sampled in NRSA,
field crews took measurements while wading the length of each sample reach (Peck et al., 2006); in
non-wadeable rivers, these measurements were made from boats (Hughes and Peck 2008). Physical
habitat data were collected from longitudinal profiles and from 11 cross-sectional transects and
streamside riparian plots evenly spaced along each sampled stream reach (USEPA 2007, 2013a, b).
The length of each sampling reach was defined proportional to the wetted channel width, and
measurements were placed systematically along that length to represent the entire reach. Sample
reach lengths were 40 times the wetted channel-width (ChW) long in wadeable streams and rivers,
with a minimum reach length of 150 m for channels less than 3.5 m wide. In non-wadeable
(boatable) rivers, reach lengths were also set to 40 ChW with a maximum length of 2,000 m.
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Thalweg (maximum) depth measurements (in the deepest part of channel), habitat classification, and
mid-channel substrate observations were made at tightly spaced intervals; whereas channel cross-
sections and shoreline-riparian stations for measuring or observing substrate, fish cover
(concealment features), large woody debris, bank characteristics and riparian vegetation structure
were spaced further apart. Thalweg depth was measured at points evenly spaced every 0.4 ChW
along these reaches to give profiles consisting of 100 measurements (150 in streams <2.5 m wide).
The tightly spaced depth measures allow calculation of indices of channel structural complexity,
objective classification of channel units such as pools, and quantification of residual pool depth,
pool volume, and total stream volume. Channel slope and sinuosity on non-wadeable rivers were
estimated from l:24,000-scale digital topographic maps.
In wadeable streams and rivers, wetted width was measured, and substrate size and embeddedness
were evaluated using a modified Wolman pebble count of 105 particles spaced systematically along
21 equally spaced cross-sections (Faustini and Kaufmann 2007), in which individual particles were
classified visually into seven size-classes plus bedrock, hardpan and other (e.g., organic material).
The numbers of pieces of large woody debris in the bankfull channel were tallied in 12 size classes (3
length by 4 width classes) along the entire length of sample reaches. Channel incision and the
dimensions of the wetted and bankfull stream channel were measured at 11 equally-spaced transects.
Bank characteristics and areal cover of fish concealment features were visually assessed in 10 m long
instream plots centered on transects, while riparian vegetation structure, presence of large (legacy)
riparian trees, non-native (alien) riparian plants, and evidence of human disturbances
(presence/absence and proximity) in 11 categories were visually assessed on adjacent 10 m x 10 m
riparian plots on both banks. In addition, channel gradient (slope) in wadeable streams was
measured to provide information necessary for calculating residual pool depth and RBS. In wadeable
streams, crews used laser or hydrostatic levels, but if necessary, were allowed to use hand-held
clinometers in channels with slopes >2.5%. Compass bearings between stations were obtained for
calculating channel sinuosity. Channel constraint and evidence of debris torrents and major floods
were assessed over the whole reach after the other components were completed. Discharge was
measured by the velocity-area method at the time of sampling, or by other approximations if that
method was not practicable (Peck et al., 2006; USEPA 2007, 2013a, b).
In boatable rivers, NRSA field crews measured the longitudinal thalweg depth profile (approximated
at mid-channel) using 7.5 m telescoping survey rods or SONAR. At the same time, crews tallied
snags and off-channel habitats, classified main channel habitat types, and characterized mid-channel
substrate by probing the bottom. At 11 littoral/riparian plots (each 10 m wide x 20 m long) spaced
systematically and alternating sides along the river sample reach, field crews measured channel
wetted width, bankfull channel dimensions, incision, channel constraint. They assessed near-shore,
shoreline, and riparian physical habitat characteristics by measuring or observing littoral depths,
riparian canopy cover, substrate, large woody debris, fish cover, bank characteristics, riparian
vegetation structure, presence of large ("legacy") riparian trees, non-native riparian plants, and
evidence of human activities. As was the case for wadeable streams, NRSA 2013-14 did not assess
presence of large (legacy) trees and non-native (alien) riparian plants in boatable rivers, as had been
done in previous surveys. After all the thalweg and littoral/riparian measurements and observations
were completed, the crews estimated the extent and type of channel constraint (see USEPA 2007,
2013a, b). Channel slope and sinuosity on all boatable rivers were estimated from l:24,000-scale
digital topographic maps.
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See Kaufmann et al. (1999) for calculations of reach-scale summary metrics from field data,
including mean channel dimensions, residual pool depth, bed particle size distribution, wood
volume, riparian vegetation cover and complexity, and proximity-weighted indices of riparian human
disturbances. See Faustini and Kaufmann (2007) for details on the calculation of geometric mean
streambed particle diameter, Kaufmann et al. (2008, 2009) for calculation of bed shear stress and
relative bed stability (modified since published by Kaufmann et al., 1999), and Kaufmann and
Faustini (2012) for demonstrating the utility of EMAP and NRSA channel morphologic data to
estimate transient storage and hydraulic retention in wadeable streams.
8.1.2 QUANTIFYING THE PRECISION OF PHYSICAL HABITAT INDICATORS
The absolute and relative precision of the physical habitat condition metrics used in NRSA are
shown in Table 8-2, based on data from 4,193 sites (2,113 from NRSA 2008-09 and 2,080 from
NRSA 2013-14) and repeat visits to a random subset of 388 of those sites (197 and 191 revisits in
the two surveys). The RMSrep expresses the precision or replicability of field measurements,
quantifying the average variation in a measured value between same-season site revisits, pooled
across all sites where measurements were repeated. We calculated RMSrep as the root-mean-square
error of repeat visits during the same year, equivalent to the pooled standard deviation of repeat
visits relative to their site means, as discussed Kaufmann et al. (1999) and Stoddard et al. (2005a).
S:N is the ratio of variance among streams ("signal") to that for repeat visits to the same stream
("noise") as described by Kaufmann et al. (1999).
The ability of a monitoring program to detect trends is sensitive to the spatial and temporal variation
in the target indicators as well as the design choices for the network of sites and the timing and
frequency of sampling. Sufficient temporal sampling of sites was not available to estimate all relevant
components of variance for the entire U.S. However, Larsen et al. (2004) examined the survey
sampling variance components for a number of the EMAP-NARS physical habitat variables,
including some of interest in this chapter (residual depth, canopy cover, fine sediment, and in-
channel large wood). Their analysis was based on evaluation on six Pacific Northwest surveys that
included 392 stream reaches and 200 repeat visits. These surveys were conducted in Oregon and
Washington from 1993 to 1999. Most were from one to three years in duration, but one survey
lasted six years. They modeled the likelihood of detecting a 12% per year trend in the selected
physical habitat characteristics, if such a trend occurs, as a function of the duration of a survey. To
calculate the number of years required to detect the defined trends in a monitoring network with a
set number of sites, they set the detection probability at >80% with <5% probability of incorrectly
asserting a trend if one is not present. We used the same survey data sets to duplicate their analysis
for several variables not included in the Larsen et al. (2004) publication, including log transformed
relative bed stability (LKBS_BWrS) and riparian vegetation cover complexity (XCMGW, the
combined cover of three layers of riparian woody vegetation); the results of that trend detection
potential is summarized in Table 8-3.
8.2 PHYSICAL HABITAT CONDITION INDICATORS
8.2.1 RELATIVE BED STABILITY AND EXCESS FINES
Streambed characteristics (e.g., bedrock, cobbles, silt) are often cited as major controls on the
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species composition of macroinvertebrate, periphyton, and fish assemblages in streams (e.g., Hynes
1970, Cummins 1974, Platts et al., 1983, Barbour et al., 1999, Bryce et al., 2008, 2010). Along with
bedform (e.g., riffles and pools), streambed particle size influences the hydraulic roughness and
consequently the range of water velocities in a stream channel. It also influences the size range of
interstices that provide living space and cover for macroinvertebrates and smaller vertebrates.
Accumulations of fine substrate particles (excess fine sediments) fill the interstices of coarser bed
materials, reducing habitat space and its availability for benthic fish and macroinvertebrates
(Hawkins et al., 1983, Platts et al., 1983, Rinne 1988). In addition, these fine particles impede
circulation of oxygenated water into hyporheic habitats reducing egg-to-emergence survival and
growth of juvenile salmonids (Suttle et al., 2004). Streambed characteristics are often sensitive
indicators of the effects of human activities on streams (MacDonald et al., 1991, Barbour et al, 1999,
Kaufmann et al., 2009). Decreases in the mean particle size and increases in streambed fine
sediments can destabilize stream channels (Wilcock 1997, 1998) and may indicate increases in the
rates of upland erosion and sediment supply (Lisle 1982, Dietrich et al., 1989).
"Unsealed" measures of surficial streambed particle size, such as percent fines or D50, can be useful
descriptors of streambed conditions. In a given stream, increases in percent fines or decreases in
D50 may result from anthropogenic increases in bank and hillslope erosion. However, a great deal of
the variation in bed particle size among streams is natural: the result of differences in stream or river
size, slope, and basin lithology. The power of streams to transport progressively larger sediment
particles increases in direct proportion to the product of flow depth and slope. All else being equal,
steep streams tend to have coarser beds than similar size streams on gentle slopes. Similarly, the
larger of two streams flowing at the same slope will tend to have coarser bed material, because its
deeper flow has more power to scour and transport fine particles downstream (Leopold et al., 1964,
Morisawa 1968). For these reasons, we "scale" bed particle size metrics, expressing bed particle size
in each stream as a deviation from that expected as a result of its size, power, and landscape setting
(Kaufmann et al., 1999, 2008, 2009).
The scaled median streambed particle size is expressed as RBS, calculated as the ratio of the
geometric mean diameter, Dgm, divided by Dcbf, the critical diameter (maximum mobile diameter) at
bankfull flow (Gordon et al., 1992), where Dgm is based on systematic streambed particle sampling
("pebble counts") and Dcbf is based on the estimated streambed shear stress calculated from slope,
channel dimensions, and hydraulic roughness during bankfull flow conditions.
RBS is a measure of habitat stability for aquatic organisms as well as an indication of the potential
for economic risk to streamside property and structures from stream channel movement. In many
regions of the U.S., we may also be able to use RBS to infer whether sediment supply is augmented
by upslope or bank erosion from anthropogenic or other disturbances, because it can indicate the
degree of departure from a balance between sediment supply and transport. In interpreting RBS on a
regional scale, Kaufmann et al. (1999, 2009) argued that, over time, streams and rivers adjust
sediment transport to match supply from natural weathering and delivery mechanisms driven by the
natural disturbance regime, so that RBS in appropriately stratified regional reference sites should
tend towards a range characteristic of the climate, lithology, and natural disturbance regime. Values
of the RBS index either substantially lower (finer, more unstable streambeds) or higher (coarser,
more stable streambeds) than those expected based on the range found in least-disturbed reference
sites within an ecoregion are considered to be indicators of ecological stress.
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Excess fine sediments can destabilize streambeds when the supply of sediments from the landscape
exceeds the ability of the stream to move them downstream. This imbalance can result from
numerous human uses of the landscape, including agriculture, road building, construction, and
grazing. Lower-than-expected streambed stability may result either from high inputs of fine
sediments (from erosion) or increases in flood magnitude or frequency (hydrologic alteration).
When low RBS results from fine sediment inputs, stressful ecological conditions result from fine
sediments filling in the habitat spaces between stream cobbles and boulders (Bryce et al., 2008,
2010). Instability (low RBS) resulting from hydrologic alteration can be a precursor to channel
incision and arroyo formation (Kaufmann et al., 2009). Perhaps less well recognized, streams that
have higher than expected streambed stability can also be considered stressedvery high bed
stability is typified by hard, armored streambeds, such as those often found below dams where fine
sediment flows are interrupted, or within channels where banks are highly altered. Values of RBS
higher than reference expectations can indicate anthropogenic coarsening or armoring of
streambeds, but streams containing substantial amounts of bedrock may also have very high RBS,
and at this time it is difficult to determine the role of human alteration in stream coarsening on a
national scale. For this reason, NRSA reported only on the "low end" of RBS relative to reference
conditions, generally indicating streambed excess fine sediments or augmented stormflows
associated with human disturbance of stream drainages and riparian zones.
8.2.1.1 PRECISION OF SEDIMENT AND BED STABILITY MEASUREMENTS
The geometric mean bed particle diameter (Dgm) and RBS, respectively, varied over 6 and 9 orders
of magnitude in the NRSA surveys. Because of this wide variation and the fact that both exhibit
repeat-visit variation that is proportional to their magnitude at individual streams, it is useful and
necessary to log transform these variables (LSUB_DMM and LRBS^gOS). The RMSrep of
LSUB_DMM in the two combined NRSA surveys was 0.39, but the wadeable stream "pebble
count" procedure was more precise (RMSrep=0.25) than the bottom-probing procedure applied in
boatable rivers (RMSrep=0.51). For NRSA's wadeable streams, this precision for LSUB-DMMwas
similar to that reported by Faustini and Kaufmann (2007) for EMAP-W (0.21). For a Dgm -
mm, the log-based RMSrep of 0.25 translates to an asymmetrical 1SD error bound of 0.56y to l.lSy
mm, and for a log-based RMSrep of 0.51, a 1SD error bound of 0.31 >' to 3.24y mm.
The RMSrep of LRBS^OS in NRSA wadeable and boatable sites was 0.52, approximately 6% of its
observed range, but less precise (surprisingly) than that for EMAP-W (RMSrep = 0.365). The log-
based RMSrep of 0.52 for NRSA LRBS^OS translates to an asymmetrical error bound of O^Oy to
3.3 y around an untransformed RBS value of"/' (Table 8-2). Compared with the high S:N ratio for
LSUB_DMM in NRSA wadeable+boatable waters (S:N=10.9), relative precision for LRBS^gOS was
lower (S:N=4.2), reflecting the reduction in total variance that results from "modeled out" a large
component of natural variability by scaling for channel gradient, water depth, and channel
roughness. Nevertheless, the moderate relative precision of LRJ3S_g08 is easily adequate to make it a
useful variable in regional and national assessments (Kaufmann et al., 1999, 2008, Faustini and
Kaufmann 2007). The transformation of the unsealed geometric mean bed particle diameter Dgm to
the ratio R3S by dividing by the critical diameter reduced the within-region variation by accounting
for some natural controlling factors. As a result, we feel that the scaled variable helps to reveal
alteration of bed particle size and mobility from anthropogenic erosion and sedimentation
(Kaufmann et al., 2008, 2009).
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We have examined the components of variability of LRBS based on earlier surveys and modeled its
potential utility in trend detection in the Pacific Northwest region of the U.S. with the same data and
procedures as used by Larsen et al. (2004), in which all methods were the same as used in EMAP-W
and WSA except that bed substrate mean diameter data used by Larsen et al. was determined based
on 55, rather than 105 particles. (NRSA data differed from data used in that analysis by using laser
levels rather than hand-held clinometers to measure wadeable stream slopes <2.5%) That analysis
showed that a 50-site monitoring program could detect a subtle trend in LRBS_BWZ5 of 2% per year
within 8 years, if sites were visited every year (Table 8-3).
8.2.2 RIPARIAN VEGETATION
8.2.2.1 QUANTIFYING RIPARIAN VEGETATION COVER COMPLEXITY
The importance of riparian vegetation to channel structure, cover, shading, inputs of nutrients and
large wood, and as a wildlife corridor and buffer against anthropogenic disturbance is well
recognized (Naiman et al., 1988, Gregory et al., 1991). Riparian vegetation not only moderates
stream temperatures through shading, but also increases bank stability and the potential for inputs of
coarse and fine particulate organic material. Organic inputs from riparian vegetation become food
for stream organisms and provide structure that creates and maintains complex channel habitat.
The presence of a complex, multi-layered vegetation corridor along streams and rivers is an indicator
of how well the stream network is buffered against sources of stress in the watershed. Intact riparian
areas can help reduce nutrient and sediment runoff from the surrounding landscape, prevent
streambank erosion, provide shade to reduce water temperature, and provide leaf litter and large
wood that serve as food and habitat for stream organisms (Gregory et al., 1991). The presence of
large, mature canopy trees in the riparian corridor reflects its longevity, whereas the presence of
smaller woody vegetation typically indicates that riparian vegetation is reproducing and suggests the
potential for future sustainability of the riparian corridor (Kaufmann and Hughes 2006).
NRSA evaluated the cover and complexity of riparian vegetation based on the metric XCMG W,
which is calculated from visual estimates made by field crews of the areal cover and type of
vegetation in three layers: the ground layer (<0.5 m), mid-layer (0.5-5.0 m) and upper layer (>5.0 m).
The separate measures of large and small diameter trees, woody and non-woody mid-layer
vegetation, and woody and non-woody ground cover are all visual estimates of areal cover.
XCMGW sums the cover of woody vegetation over these three vegetation layers, expressing both the
abundance of vegetation cover and its structural complexity. Its theoretical maximum is 3.0 if there
is 100% cover in each of the three vegetation layers. XCMGI-Fgives an indication of the longevity
and sustainability of perennial vegetation in the riparian corridor (Kaufmann et al., 1999, Kaufmann
and Hughes 2006).
8.2.2.2 PRECISION OF RIPARIAN VEGETATION INDEX
XCMGI-Franged from 0 to 2.6 (260% cover), with RMSrep of Log(0.01+XCMGI-F) = 0.148 (Table
8-2), meaning that an XCMGWvalue of 10% at a single stream site has a +1.0 RMSrep error bound
of 7% to 14%. Its S:N ratio was 8.45, indicating very good potential for discerning differences
among sites. We examined the components of variability of XCMGW and modeled its potential
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utility in trend detection in the Pacific Northwest region of the U.S. with the same data and
procedures used by Larsen et al. (2004). Based on that analysis, a 50-site monitoring program could
detect a trend in XCMGW oi 2% per year within 8 years if sites were visited every year (Table 8-3).
8.2.3 INSTREAM HABITAT COVER COMPLEXITY
Although the precise mechanisms are not completely understood, the most diverse fish and
macroinvertebrate assemblages are usually found in streams that have complex mixtures of habitat
features: large wood, boulders, undercut banks, tree roots, etc. (see Kovalenko et al., 2011). When
other needs are met, complex habitat with abundant cover should generally support greater
biodiversity than simple habitats that lack cover (Gorman and Karr 1978, Benson and Magnuson
1992). Human use of streams and riparian areas often results in the simplification of this habitat,
with potential effects on biotic integrity (Kovalenko et al., 2011). For this assessment, we use a
measure (XFC_NAT in Kaufmann et al., 1999) that sums the amount of instream habitat consisting
of undercut banks, boulders, large pieces of wood, brush, and cover from overhanging vegetation
within a meter of the water surface, all of which were estimated visually by NRSA field crews.
8.2.3.1 QUANTIFYING INSTREAM HABITAT COMPLEXITY
Habitat complexity is difficult to quantify; it could be quantified or approximated by a wide variety
of measures. The NRSA Physical Habitat protocols provide estimates for nearly all the following
components of complexity identified during EPA's 1992 stream monitoring workshop (Kaufmann
1993):
Habitat type and distribution (e.g., Bisson et al., 1982, O'Neill and Abrahams 1984, Frissell
et al., 1986, Hankin and Reeves 1988, Hawkins et al., 1993, Montgomery and Buffmgton
1993, 1997, 1998).
Large wood count and size (e.g., Harmon et al., 1986, Robison and Beschta 1989, Peck et
al. 2006).
In-channel cover: percentage areal cover of fish concealment features, including undercut
banks, overhanging vegetation, large wood, boulders (Hankin and Reeves 1988,
Kaufmann and Whittier 1997, Peck et al., 2006).
Residual pools, channel complexity, hydraulic roughness (e.g., Kaufmann 1987a, b, Lisle
1987, Stack and Beschta, 1989; Lisle and Hilton 1992, Robison and Kaufmann 1994,
Kaufmann et al., 1999, Kaufmann et al., 2008, Kiem et al., 2002; Kaufmann et al., 2011).
Width and depth variance, bank sinuosity (Kaufmann 1987a, Moore and Gregory 1988,
Kaufmann et al., 1999, Madej 1999, 2001, Kaufmann et al., 2008, Mossop and Bradford
2006, Pearsons and Temple 2007, 2010, Kaufmann and Faustini 2012).
Residual depth is a measure of habitat volume, but also serves as one of the indicators of channel
habitat complexity, particularly when expressed as a deviation from reference expectations, including
the influences of basin size. A stream with more complex bottom profile will have greater residual
depth than one with similar drainage area, discharge and slope, but lacking that complexity
(Kaufmann 1987a). Conversely, between two streams of equal discharge and slope, the one with
greater residual depth (i.e., larger, more abundant residual pools) will have greater variation in cross-
sectional area, slope, and substrate size. A related measure of the complexity of channel morphology
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is the coefficient of variation in thalweg depth, calculated entirely from the thalweg depth profile
(SDDEPTH / XDEPTH). The thalweg profile is a systematic survey of depth in the stream channel
along the path of maximum depth (i.e., the thalweg). In addition to measures of channel
morphometric complexity, NRSA physical habitat protocols measure in-channel large wood
(sometimes called "large woody debris" or simply "LWD"), and several estimates of the areal cover
of various types of fish and macroinvertebrate "cover" or concealment features. The large wood
metrics include counts of wood pieces per 100 m of bankfull channel and estimates of large wood
volume in the sample reach expressed in cubic meters of wood per square meter of bankfull
channel. The "fish cover" variables are visual estimates of the areal cover of single or combined
types of habitat features.
NRSA required a general summary metric as a holistic indicator of many aspects of habitat
complexity, so NRSA used the metric XFC_NAT, summing the areal cover from large wood, brush,
overhanging vegetation, live trees and roots, boulders, rock ledges, and undercut banks in the wetted
stream channel. Habitat complexity and the abundance of particular types of habitat features differ
naturally with stream size, slope, lithology, flow regime, and potential natural vegetation. For
example, boulder cover will not occur naturally in streams draining deep deposits of loess or
alluvium that do not contain large rocks. Similarly, large wood will not be found naturally in streams
located in regions where riparian or upland trees do not grow naturally. Though the index
XFC_NAT partially overcomes these differences by summing divergent types of cover, we set
stream-specific expectations for habitat complexity metrics in NRSA based on region-specific
reference sites and further refined them as a function of geoclimatic controls.
8.2.3.2 PRECISION OF HABITAT COMPLEXITY MEASURES
The instream habitat complexity index XFC_NAT ranged from 0 to 2.3, or 0% to 230% in NRSA
(2008-09 and 2013-14 combined), expressing the combined areal cover of the five cover elements
contributing to its sum. The RMSrep of Log(0.01+XFC_IV^4T) was 0.21, meaning that an
XFC_NAT value of 10% cover at a single stream site has a +1.0 RMSrep error bound of 6% to 16%
(Table 8-2). S:N was relatively low for this indicator (2.27), though higher in wadeable streams
(2.76) than in boatable rivers (1.66). Despite its relatively low S:N, the RMSrep for LXFC_NATwas
9% of its observed range. It was retained as a habitat complexity indicator because it contains
biologically relevant information not available in other metrics, shows moderate responsiveness to
human disturbances, and has precision adequate to discern relatively large differences in habitat
complexity.
8.2.4 RIPARIAN HUMAN DISTURBANCES
Agriculture, roads, buildings, and other evidence of human activities in or near stream and river
channels may exert stress on aquatic ecosystems and may also serve as indicators of overall
anthropogenic stress. EPA's 1992 stream monitoring workshop recommended field assessment of
the frequency and extent of both in-channel and near-channel human activities and disturbances
(Kaufmann 1993). The vulnerability of the stream network to potentially detrimental human
activities increases with the proximity of those activities to the streams themselves. NRSA follows
Stoddard et al. (2005b) and U.S. EPA (2006) in using a direct measure of riparian human disturbance
that tallies 11 specific forms of human activities and disturbances (walls, dikes, revetments or dams;
buildings; pavement or cleared lots; roads or railroads; influent or effluent pipes; landfills or trash;
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parks or lawns; row crop agriculture; pasture or rangeland; logging; and mining) at 22 separate
locations along the stream reach, and weights them according to how close to the channel they are
observed (IV1 _HA J J, in Kaufmann et al., 1999). Observations within the stream or on its banks are
weighted by 1.5, those within the 10 x 10 m plots are weighted by 1.0, and those visible beyond the
plots are weighted by 0.5. The index WZ1_HA1 J, ranged from 0 (no observed disturbance) to ~7
(e.g., equivalent to four or 5 types of disturbance observed in the stream, throughout the reach; or
seven types observed within all 22 riparian plots bounding the stream reach). Although direct human
activities certainly affect riparian vegetation complexity and layering measured by the Riparian
Vegetation Index (previous paragraph), the Riparian Disturbance Index is more encompassing, and
differs by being a direct measure of observable human activities that are presently or potentially
detrimental to streams.
8.2.4.1 PRECISION OF RIPARIAN DISTURBANCE INDICATORS
The proximity-weighted human disturbance indicator IV1 _HA T J, ranged from 0 to 8.3 in NRSA,
and its precision was proportional to the level of disturbance. The RMSrep of log{0.1 + W1_HAJ J,)
was 0.178 (Table 8-2), meaning that a W1 _HAJ J, value of 1.0 at a single stream site has a +1.0
RMSrep error bound of 0.66 to 1.51. The relative precision of Log(0.1 + W1 _HAJ J,) was moderate
(S:N=5.46), indicating good potential for discerning differences among sites.
8.3 ESTIMATING REFERENCE CONDITION FOR PHYSICAL HABITAT
8.3.1 REFERENCE SITE SCREENING AND ANTHROPOGENIC DISTURBANCE
CLASSIFICATIONS
As part of the routine application of its field and GIS protocols, NRSA (2008-09 and 2013-14
combined) obtained various measures of human disturbance associated with each site and its
catchment. Following a similar approach as described in Chapter 4 and Herlihy et al. (2008),
indicators of local scale human disturbance and water chemistry (Chloride, Total Phosphorus, Total
Nitrogen, Sulfate, and Turbidity) were used to screen probability and hand-picked sites and
designate them as least- moderately-, and most-disturbed, relative to other sites within each of the
nine aggregate ecoregions used in NRSA. To avoid circularity, we did not use any field measures of
sediment, in-channel habitat complexity, or riparian vegetation to screen least-disturbed sites used to
estimate reference condition for excess streambed fining, instream fish cover, and riparian
vegetation. Nor did we use such measures in defining levels of disturbance to use in examining the
associations of these habitat metrics with human disturbances. We did, however, use field
observations of the level and proximity of streamside human activities (1T7-HA J J,, W1 _HAG,
W1H_CROP, and W1 H_UVA T J) in screening reference sites and defining levels of disturbance for
evaluating indicator responsiveness (Table 8-4). In this chapter, the designation "R" refers to least-
disturbed ("reference") sites; "S" to moderately-disturbed sites, and "T" to the most-disturbed sites
within each of the nine aggregate ecoregions discussed herein. We defined these site disturbance
categories independent of the habitat indicators we evaluate in this report (other than riparian
human disturbances), allowing an assessment of fluvial habitat response to a gradient of human
activities and disturbances. We also used sub-basin row crop and urban land use percentages, and
the density of dams and impoundments to reject potential reference sites.
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Screening the NRSA 2008-09 and 2013-14 survey sites by the disturbance variables in Table 8-4
yielded 708 reference sites, 349 from the first survey and 359 from the second (Table 8-5). Fewer
reference sites were identified for boatable (281) than for wadeable (427) streams and rivers; except
in several regions, reference sites were approximately evenly distributed between the two surveys.
Notably, only 2 boatable reference sites were identified in the SPL (both in the 2008-09 survey), and
only 7 boatable and 7 wadeable reference sites were identified in the SAP. Interestingly, more
reference sites were identified in the 2013-14 survey of the CENPL and Western regions than in the
2008-09 survey of those regions. The opposite was true of the Appalachians.
8.3.2 MODELING EXPECTED REFERENCE VALUES OF THE INDICATORS
8.3.2.1 MODELING APPROACHES
In the following paragraphs, we describe the conceptual basis for modeling the expected range of
values for the each of the physical habitat indicators under least-disturbed (reference) condition. The
details of these models are presented in Table 8-6Table 8-8, and with more detail in Appendix
8.A. For riparian human activities, we applied uniform criteria based on professional judgement and
literature to assign high, medium and low disturbance to individual sample sites across the entire
U.S. For the other three PHab indicators, we assigned habitat condition based on the distribution of
PHab metric values within the combined set of NRSA reference sites, employing several types of
modeling:
NULL MODELS based expected least-disturbance values and their distribution on the mean and
SD of the indicator metric (e.g., ERBS^gOS, XCMGW, or XFC_Naf) in the set of reference sites
representing least-disturbed condition within resource types (e.g., wadeable and boatable) in their
respective regions (ECOmaP) or aggregations of those regions (e.g., Central Plains = CENPL =
NPL+SPL+TPL). For example, in NAP boatable sites, ERBS^gOS null model condition classes were
defined based on normal approximations of the 5th and 25th percentiles of the actual reference
distributions. The definition of "Poor" condition was set for those sites with ERBS^gOS < the
reference mean E~RBS_g08 minus 1.65(SDref). Sites in "Good" condition with respect to this
indicator were those with ERBS^gOS > the reference mean ERBS^gOS minus 0.67(SDref). As for
RBS_g08, we log-transformed XCMGW and XFC_Nat to approximate statistical normality in
distributions (e.g., ERBS^gOS = LogiofRBJ^O#], EPffJI_XCMG\V = Logio[0.01+XCMGI-F], and
EP/01 _XFC_Nat = Logio[0.01+XFC_M?/|).
REFERENCE-SITE OBSERVED/EXPECTED (O/E) MODELS: In cases where reference
sites were sufficiently numerous and spanned a representative range of the natural controlling
variables, we applied Multiple Linear Regression (MLR) to regional reference sites (only) in order to
factor out the influence of natural controlling factors on habitat separate from the influences of
anthropogenic disturbances. These MLR models estimate site-specific expected values of habitat
metrics under least-disturbed conditions, given their geoclimatic and geomorphic setting (e.g.,
ecoregion, latitude, longitude, drainage area, channel width, slope, elevation, and soil erodibility). If
there were less than 22 reference sites in a region, or we determined that reference sites may not
fully encompass the geoclimatic variables controlling a habitat metric, we combined regions with
similar controlling factors in the modelling. The variables made available to MLR were EAT_DD83,
EON_DD83, E_A.reaWSkm2_nse, EI El T_PE_nse, EXSlope_/ise, EXWidth_nse, and KFCT_WS_//se.
We then calculated observed/expected (O/E) values of the habitat metrics for every site within the
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modelled region, including non-reference sites. We set expectations of the O/E values based on the
mean and SD of the O/E values in the regional reference distribution, and set Good, Fair, and Poor
condition determinations based on normal approximation of log-transformed O/E values as
described for the LRBS null model in the previous paragraph.
8.3.3 REFERENCE-SITE O/E MODELS WITH DISTURBANCE ADJUSTMENT
In cases where reference sites were sufficiently numerous and spanned a representative range of the
natural controlling variables, but had substantial anthropogenic disturbances that influenced the
habitat metric response variable, we included riparian and basin disturbance variable(s) as predictors
in the Reference Site MLRs. As with the Reference Site models with no adjustment, we combined
regions with similar controlling factors in the modelling, where the number and representativeness
of reference sites were inadequate in a given region. Besides the geoclimatic and geomorphic
variables listed in the previous paragraph, we considered the following disturbance variables in these
MLRs: W1_H.aU, W1_HNOAG, W1_HAG, W1H_Crvp, DAM_dii, AG_1 KMCircle,
URB_1KMCircle, RDDEN_WrS_/ise, PCT_AG_]VS_nse, and AGm_X_KFcf (interaction of basin %
crop agriculture with soil erodibility factor). Site-specific expected ("E") values of the habitat metric
were then calculated by setting the anthropogenic disturbance metric values to the lowest value
observed ("O") among reference sites in the modelled region. Because we had already modeled-out
disturbance to some extent in our calculation of E values, the distributions of O/E in reference sites
did not necessarily have a mean of 1/1 (Log=0), although means were very close to 1/1. We then
calculated O/E values of the habitat metrics for every site within the modelled region, including
non-reference sites. We set expectations of the O/E values based on the mean and SD ofthe
distributions of Log/0(O/E) values in regional reference sites, analogous to that described for
reference site regressions in the previous paragraph, and set Good, Fair, and Poor condition
determinations based on normal approximation of log-transformed O/E values analogous to that
described for the LRBS null model above.
"ALL-SITES" O/E MODELS: In cases where reference sites were generally disturbed and where
the number and distribution of minimally-disturbed reference sites were insufficient to accurately
quantify geoclimatic influences on a given habitat metric, we employed "All-Sites" O/E models.
We used two steps to calculate reference expected values. The first step was to calculate expected
values from MLRs that employed all sites (not just reference sites) in the model region; and
considered both geoclimatic and anthropogenic predictors. Site-specific expected ("E") values of the
habitat metric were then calculated using the MLR equation with the anthropogenic disturbance
metric values set to their lowest value observed ("O") in the modelled region. We then calculated
O/E values of the habitat metrics for every site within the modelled region. In the second step, we
examined the distribution of O/E values in reference sites and their association with anthropogenic
disturbance within the region. In cases where reference site O/E values showed no association with
disturbance, we based reference expectations on the mean and SD of the distributions of
Log zo(0/E) values in these regional reference sites, analogous to that described for unadjusted
regression site regressions in the previous paragraph. We then set Good, Fair, and Poor condition
determinations based on normal approximation of log-transformed O/E values analogous to that
described for the LRBS null model in the previous paragraph. In cases where reference site O/E
values were still associated with anthropogenic disturbance, our second step included regressing the
Log zo(0/E) values against anthropogenic disturbance variables to determine expected O/E values
under least-disturbed conditions. We then set the anthropogenic disturbance variables in the MLR to
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their regional minimum values, effectively choosing the y-intercept of these equations as the central
tendency for expected reference condition. We set expectations of the O/E values based on the y-
intercept and regression RMSE of Log/o(0/E) values in regional reference sites, analogous to that
described for unadjusted reference site regressions in the previous paragraph, and set Good, Fair,
and Poor condition determinations based on normal approximation of log-transformed O/E values
analogous to that described for the LRBS null model above.
8.3.3.1 BED SEDIMENT CONDITION MODELING
We used reference site null models to estimate expected reference values of Logio Relative Bed
Stability (LRBS^g08) in boatable rivers and streams in 5 of the 9 ecoregions (NAP, SAP, CPL,
WMT, and XER). RMSE's for these null models ranged from 0.365 in the WMT to 1.539 in the
NAP. Modeling for boatable sites in the 4 remaining regions were MLR models with R2 ranging from
18% to 56%, RMSE from 0.365 to 1.539, and included one to three predictors. Predictors were
primarily drainage area (LAm), channel width ('LXWJdtb), and extent of agricultural land use in the
contributing drainages, or within a 1 km radius of the sample sites on these rivers (Table 8-6, and
more detail in Appendix 8.A). For boatable rivers in the NPL, SPL, and TPL, we employed All-
Sites MLR models that incorporated similar predictors as those used in the reference site MLRs.
For wadeable streams in all except the Central Plains regions (NPL, SPL, and TPL), we used
reference site MLRs to estimate LoglO Relative Bed Stability (LRBS^gOS) in least-disturbed sites.
These MLRs most commonly included a basin or stream size variable (LAm or LXWJdtb), slope
(LXSlope), and usually a site-scale or basin measure of human land use intensity. In the NPL, SPL,
and TPL, we employed All-Sites MLR models typically incorporating Lat and/or Lon with LAm,
Elevation or Slope and one or more variables representing the intensity of human land use activityin
the drainage basin, vicinity, or near the banks of the sample reaches. MLR model R2 values ranged
from 20% to 41%, and RMSE ranged from 0.430 to 0.990. The reference site models had lto 3
predictors and the All-Sites models had 4 to 5 predictors.
8.3.3.2 RIPARIAN VEGETATION COVER & STR UCTURE CONDITION MODELING
Reference site null models were employed for estimating expected reference condition for Riparian
Vegetation Cover & Structure (LPt01 _XCMGW) only for boatable rivers in the TPL and WMT
(Table 8-7 with greater detail in Appendix 8.A). All-Sites MLR models were used for boatable
rivers in the combined NPL and SPL and for wadeable streams in the NPL. The boatable All-Sites
MLR incorporated Lat., Lon, site-level agriculture (IJ-7_HAG), basin road density (RDDEN_m), and
% of agricultural land use in the drainage basin (PCT_AG_m). The NPL wadeable stream All-Sites
model was similar, incorporating Lat, Lon, LXSlope, LXWJdtb, site-level agriculture (W1 _HAG),
basin road density (RDDEN_m), and PCT_AG_m. Expected condition models for boatable or
wadeable streams in all the remaining ecoregions were reference site regression modelswith 1 to 4
geoclimatic predictors including Lat or Lon, along with LAm, LXWJdtb, LXSlope, or Elev. Most of
these MLRs also included one or more variables representing the intensity of human land use
activity in the drainage basin, vicinity, or near the banks of the sample reaches. Model R2 was 1% for
CPL wadeable streams, and 14% to 40% elsewhere. The precision of these reference site MLRs and
All-Sites models (RMSE 0.119 to 0.487) was generally greater (smaller RMSE) for these riparian
vegetation models than for the LRBS models.
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8.3.3.3 IN STREAM HABITAT COVER & COMPLEXITY CONDITION MODELING
Reference site null models were employed for estimating expected reference condition for Instream
Habitat Cover Complexity (LPt01 _XFC_Nat) only in the CPL, where we used separate null models
for wadeable and boatable sites (Table 8-8 with greater detail in Appendix 8.A). All the remaining
expected condition models were reference site regression models incorporating 1 to 5 predictors,
with R2 ranging from 7% to 53% and RMSE's from 0.175-0.335, somewhat less precise (larger) than
those for riparian vegetation condition. These expected condition MLRs typically included 1 to 3
predictors from the set of geoclimatic variables including Eat, Eon, EAm, UiWidth, EXSlope, or
Elev. Except for NAP and UMW wadeable stream MLRs and the XER boatable river model, all the
other instream habitat condition MLRs also included one or more variables representing the
intensity of human land use activity in the drainage basin, vicinity, or near the banks of the sample
reaches.
8.3.3.4 RIPARIAN HUMAN DISTURBANCE INDICATOR CONDITION
DETERMINATION
For the riparian human disturbance indicator, we did not base condition benchmarks on the
reference distributions or expected condition MLRs, as was done for bed sediments, riparian
vegetation condition and habitat complexity. Instead, we set these classes using uniform judgement-
based criteria for all regions. W1 _Hall, the database variable name for this indicator, is a direct
measure of human disturbance "pressure," unlike the other habitat indicators, which are actually
measures of habitat response to human disturbance pressures. It is very difficult to define reference
sites without screening sites based on W1 _Hall. For this reason, we took this different approach for
setting riparian disturbance benchmarks, defining low disturbance sites as those with W1_Hall
<0.33 and high riparian disturbance sites as those with W1_H^//>1.5; we applied these same
benchmarks in all ecoregions. A value of 1.5 for a stream means, for example, that at 22 locations
along the stream the field crews found an average of one of 11 types of human disturbance within
the stream or its immediate banks. A value of 0.33 means that, on average, one type of human
disturbance was observed at one-third of the 22 riparian plots along a sample stream or river.
8.4 RESPONSE OF THE PHYSICAL HABITAT INDICATORS TO HUMAN
DISTURBANCE
Riparian human disturbance (W1_Ha/l) values between 0 and 3 were found in all regions and in both
boatable and wadeable sites (Figure 8-2). Among regional reference sites, UMW boatable and
wadeable reference sites and WMT wadeable reference sites had the lowest riparian disturbance
(Figure 8-3). Very high values of Wl_Hall were found in all regions with the exception of wadeable
streams in the UMW (note tradition of riparian buffer protection that is visible from the air), and
steep gradients of W1 _Hallwere found across the three disturbance classes in all regions (Figure
8-3). Because the field-obtained measures of riparian disturbance used in the NRSA are themselves
direct indicators of human disturbance, and were used to screen reference sites, we did not do t-tests
to quantify the strength of relationship between and general disturbance class in Table
8-9. However, we do illustrate the relationship of W1_Hallto the human disturbance gradient in
Figure 8-3 to compare the relative magnitudes of W1_Hallamong least-, moderately-, and most-
disturbed streams in the various regions of the U.S.
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We quantified the responsiveness of NRSA physical habitat condition metrics to levels of human
disturbance by the t-values (trt) of the difference between mean of the indicator Logio(0/E) values
in least-disturbed reference sites (prk.3RRT_NRSsl1314=R) minus the mean for the most-disturbed
sites (those screened as prk.RRT_NRSA1314=T). Throughout the text, figures, and tables, we
indicate the order of magnitude of p-values of these comparisons by the number of asterisks
following the t value. For example, trt = +2.34* indicates that the mean Logio(0/E) in reference sites
exceeds that in the most-disturbed sites by 2.34 log units, and = p~ 0.1. Multiple asterisks denote the
magnitude of p values (* = p~ 0.1; ** = p~ 0.01; *** = p~ 0.001; and **** = p~0.0001).
Regional differences in bed substrate texture do not necessarily indicate anthropogenic
sedimentation. In other words, there are streams and rivers that are naturally fine-bedded.
Examination of the distribution of the Logio of geometric mean bed surface substrate diameter
('LSUB_dmm) shows that the wadeable streams clearly separate into fine-bedded and coarse-bedded
regions (Figure 8-4). Wadeable streams in CPL, UMW, and Central Plains (NPL, SPL, TPL) are
largely low gradient streams, and median bed sediments with LSUB_dmm <0 (<1 mm) which is sand
or finer (Figure 8-4b). A similar, but less distinct pattern is seen in boatable rivers, but NPL and
XER rivers are relatively more coarse-bedded than expected from the pattern in wadeable streams.
These patterns are driven largely by the slope and lithology of these sites. Patterns in the distribution
of LRBS^gOS (=LKBS_/tse) show less difference among regions, and a number of the fine-bedded
regions have similar bed stability as those found in coarser regions (Figure 8-4a and Figure 8-4b).
Once scaled as an O/E variable (LOE_LRBS_/tse) to adjust for natural controls on bed material size
and more clearly reflect anthropogenic influences, LKBS showed modest to strong negative
response to human disturbance for combined boatable and wadeable sites in most regions and
aggregations of regions, as illustrated by trt values ranging from +3.38*** to + 12.84****, showing
substantial and statistically significant differences between means of least-disturbed minus most-
disturbed sites (Table 8-9). The strength of associations of instream sediments with human
disturbance (Table 8-9 and Figure 8-5) tended to be similar and relatively strong for both boatable
and wadeable rivers and streams (trt= +2.24** to + 11.32****). We observed moderate to strong
declines LRBS with disturbance in all regions, the strongest associations were in the UMW boatable
sites (trt = 6.59 ****), the Western Rivers (trt = 3.96***), and in EHIGH, CENPL, and WEST
wadeable sites (trt= 4.12**** to 8.25****).
Riparian vegetation cover (LPt01 _XCMGIV) adjacent to both wadeable and boatable rivers and
streams was markedly lower in the NPL than in any other region (Figure 8-6). By contrast, riparian
vegetation cover for both types of waters was consistently higher in the CPL, NAP, and SAP, with
the other regions having moderately high median values of riparian cover. Once scaled as an O/E
variable to adjust for natural geoclimatic controls (LOE_XCMGW_/tse), riparian vegetation cover
complexity showed modest to strong negative response to human disturbance for combined
boatable and wadeable sites in most regions and aggregations of regions, as illustrated by trt values
from +2.95*** to +14.17****), showing substantial and statistically significant differences between
means of least-disturbed minus most-disturbed sites (Table 8-9). Compared with the similar
response of sediment to disturbance in boatable and wadeable sites, the association between riparian
vegetation and disturbance was much stronger for wadeable sites (trt= +4.06**** to +13.46****)
than for boatable sites (trt = -0.13 to +3.44***) sites (Table 8-9 and Figure 8-7). Among boatable
rivers, riparian vegetation cover complexity was moderately correlated with the disturbance levels
only in the Coastal Plain (trt = 2.99***) and West (trt = 2.24**), and relatively weakly associated
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elsewhere (tn 0.13ns to 1.73*). Among wadeable streams, however, riparian vegetation was strongly
correlated with disturbance in all regions (trt= 4.06**** in the UMW to 7.35**** in CENPL). Note
of course that expected riparian vegetation cover complexity is much higher in the CPL and
EHIGH, for example, than in the CENPL.
Instream habitat cover complexity (LPt01 _XFC_NAT) in boatable and wadeable rivers and streams
was markedly lower in the NPL than in any other region (Figure 8-8). In wadeable streams, the
Central Plains ecoregions (NPL, SPL, and TPL) had markedly lower instream cover complexity than
the other regions. Boatable and wadeable rivers and streams in the SAP, CPL, and NAP, and
wadeable rivers and streams in the WMT had generally higher instream habitat cover complexity
than the other regions (Figure 8-8). We scaled instream cover complexity as an O/E variable
(.LOE_XFC_NAT_//se) to adjust for geoclimatic influences on instream cover, we examined the
associations between instream cover and anthropogenic influences (Table 8-9 and Figure 8-9)-
Except for the weak response in the Upper Midwest (trt = + 1.08*), the instream habitat complexity
indicator showed moderate response to human disturbance, with trt values ranging from +2.30** to
+6.62**** for combined boatable and wadeable sites (Table 8-9).
However, as was the case for the riparian vegetation indicators, associations were in most cases
much stronger for wadeable (trt = +1.73* to 8.16****), than for boatable sites (Figure 8-9), where
most regional associations of instream habitat complexity to human disturbance levels were non-
significant, with low or negative t values (-1.78* to +0.91*). Among wadeable sites, however, the
associations of instream habitat complexity with disturbance ranged from weak in the EHIGH and
UMW (trt = 1.73* and 2.02**) to very strong in the WEST (trt = 8.16 and p < 0.0001). Note that
expected instream habitat complexity is generally higher in the CPL and upland regions (EHIGH
and WEST) than for the CENPL and UMW.
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Table 8-1. Metrics used to characterize the general attributes of stream/river physical habitat.
Habitat Volume:
UIP100 = log(RP100) = Log of Mean Residual Depth (cm)
Scaled Habitat Volume:
LDVRP100 = log(RP100) log(PredictedRP100) = Deviation in Mean Residual Depth from expected value
Habitat Complexity:
CVDPTH = SDDEPTH / XDEPTH = Coefficient of Thalweg Depth Variation
C1WM100 = Number of Large Woody Debris pieces/ 100m of channel.
EV1W_MSQ log [Volume of Large Woody Debris per m2 of bankfull channel area (m3/m2)].
XFC_NAT = Areal Cover of Woody Debris, Brush, Undercut Banks, Overhanging Vegetation, plus Boulders andRock Ledges.
XFC_NORK = Areal Cover of Woody Debris, Brush, Undercut Banks, Overhanging Veg.
XFC_A.QM Areal Cover of Aquatic Macrophytes
XFC_AL,G Areal Cover of Filamentous Algae detectable by the unaided eye.
Streambed Particle Size:
ESUB_dmm log [Streambed surface particle Dgm mm] log of geometric mean diameter of bed surfacesediments in
millimeters.
PCT_FN = % Streambed Silt & Finer
PCT_SAFN = % Streambed Sand & Finer
XEMBED = % Substrate Embedded by Sand and Fines
Scaled Streambed Particle Size:
DPCT_FN = Deviation of PCT_FN from expected value ("excess Fines")
DPCT_SF = Deviation of PCT_SAFN from expected value ("excess Sand+Fines")
DE VLSUB = Deviation of LSUB_DMM from expected value (Streambed Fining Index)
Relative Bed Stability:
LRBS- logio of diameter ratio: Geometric mean bed particle diameter / Critical (mobile) diameter at bankfull flowstage.
(LRBS_bw5: see Kaufmann et al. 1999; LRBS_g08: see Kaufmann et al. 2008, 2009).
Floodplain Interaction:
LSINU Log(SINU) Log(Channel Sinuosity).
11NCIS_H log(XINC_H - XBKF_H + 0.1) Log of Incision from terrace to bankfull ht (m).
LBFWDRAT = log{BKF_W/ BKF_H+(XDEPTH/100)} = log (Bankfull Width/Depth Ratio)
U5FXWRAT = log(BKF_W / XWIDTH)= log (Bankfull Width / Wetted Width) (an index of streamside floodinundation
potential)
Hydrologic Regime:
LQSLTR_RAT = log{ (Qsp+0.0000001)/L,TROFF_M} =log{low flow /annual mean runoff} (~ an inverse index of
"droughtiness",
where: Qsp - Fkw_mps/WSAKEAKM= (flow_cfs/35.315)/ WSAKEAKM
LBFXDRAT =log{(XBKF_H+ (XDEPTH/100) / (XDEPTH/100)}= log(ratio of bankfull depth / wetted depth), a
morphometric index of "flashiness".
Riparian
Vegetation:
XCDENMID: % Canopy Density measured midstream.
XCMG = Riparian Canopy+Mid-+Ground Layer Vegetation (areal cover proportion)
XCMGW = Riparian Canopv+Mid+Ground Laver Woodv Vep.(areal cover proportion)
Riparian
Habitat Alteration:
QR1=(QRVEG1 *QRVEG2*QRDIST1where:
^ ^ if XCMGW <=2.00 then QR Veg1 =. 1 + (0.9 (XCMGW / 2.00));
if XCMGW>2.00 thenQR~Veg1=1;
ฃ>RVeg2=.l+(0.9(XCDENBK/ 100))-, andฃ>RDIST1=1/(1+W1_HALL)
Riparian
Human Disturbances:
W1_HAG = Riparian & near-Stream Agriculture all types (proximity-weighted tally)
W1H_ROAD = Riparian & near-Stream Roads (proximity-weighted tally)
W1H_CROP = Riparian & near-Stream Row Crop Agriculture (proximity-weighted tally)
W1H_WALL = Riparian & near-Stream Walls, Dikes, Revetment (proximity-weighted tally)
W1 _HAT J, = Proximity-weighted Index of Human Disturbances of All Types
QRDIST1 = 1/ (1+ W1 _HA /",/",) = Proximity-weighted Inverse Index of Human Disturbances of All Types
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Table 8-2. Sampling revisit precision (repeatability) of the four physical habitat condition indicators.
Repeat visits within the summer sampling season were used to calculate RMSrep, which is essentially the
standard deviation of repeat sampling pairs to the same stream or river reach. Dividing the square of the
RMSrep into the variance among sites gives the S:N variance ratio. (See Kaufmann et alv 1999 for ANOVA
methods to calculate RMSrep and S:N, where RMSrep is equal to their RMSE.)
Metric
Group
Sites fn>
mean
Repeat
pairs (n)
RMSren
S:N
LRBS_g08
All Sites
4058
-0.938
375
0.519
4.17
All
(0809 / 1314)
(2032 / 2025)
(-0.942 / -0.933)
(191 / 184)
(0.482 / 0.556)
(5.13 / 3.42)
Boatable
1484
-0.661
178
0.479
6.70
Wade able
2573
-1.104
197
0.553
2.58
EHIGH
1075
-0.541
134
0.539
3.65
PLNLOW
2060
-1.242
164
0.514
3.89
WMTNS
921
-0.740
77
0.493
4.07
L_xcmgw
All Sites
4193
-0.252
388
0.148
8.45
All
(0809 / 1314)
(2112/ 2080)
(-0.286 / -0.218)
(197 / 191)
(0.146/ 0.150)
(9.38 / 7.46)
Boatable
1599
-0.154
187
0.144
4.70
Wade able
2593
-0.315
201
0.151
10.08
EHIGH
1100
-0.051
138
0.083
8.05
PLNLOW
2158
-0.341
173
0.188
6.72
WMTNS
933
-0.293
77
0.135
7.79
L_xฃ_nat
All Sites
4193
-0.603
388
0.214
2.27
All
(0809 / 1314)
(2112/ 2080)
(-0.590 / -0.617)
(197 / 191)
(0.240 / 0.184)
(1.87 / 2.99)
Boatable
1599
-0.626
187
0.220
1.66
Wade able
2593
-0.589
201
0.209
2.76
EHIGH
1100
-0.494
138
0.200
1.57
PLNLOW
2158
-0.670
173
0.227
2.24
WMTNS
933
-0.584
77
0.211
2.28
L_Wl_Hall
All Sites
4193
-0.129
388
0.178
5.46
All
(0809 / 1314)
(2112/ 2080)
(-0.152/ -0.106)
(197 / 191)
(0.186/ 0.170)
(5.18 / 5.76)
Boatable
1599
-0.091
187
0.137
9.03
Wade able
2593
-0.154
201
0.210
3.89
EHIGH
1100
-0.078
138
0.181
5.15
PLNLOW
2158
-0.151
173
0.168
5.85
WMTNS
933
-0.142
77
0.196
5.10
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Table 8-3. Estimated number of years to detect trends in habitat attributes. Number of years required for a
50-site monitoring network to detect 1% and 2% per year trends in habitat attributes with 80% likelihood
(beta, or power) and alpha = 0.05, if specified trends occur, and sites are visited each year. Data were
taken from Larsen et al. (2004),a or calculated using the same data and analytical procedures used in that
publication.13
Variable
Description
1% trend
2% trend
SDDEPTH1'
(Std. Deviation of Thalweg Depth)
13 years
8 years
LRP100'
(logfMean Residual Depth])
20
12
PCT_SAFN'
(% Sand + Silt)
21
13
XEMBEiy
(% Embeddedness)
20
12
LRBS_BWy
(logfRel. Bed Stability])
12
8
U'l Wr_MSO'
(logfLarge Wood Volume/m2])
27
17
XCMGW*
(3-Layer Riparian Woody Veg Areal Cover)
12
8
XCDENMID'
(Canopy Density measured midstream)
13
8
Table 8-4. Anthropogenic disturbance screening criteria.
Criteria used to characterize least-disturbed reference (R), moderately-disturbed (S), and most-disturbed (T) sample reaches for developing
physical habitat condition criteria. In addition to the tabulated criteria, potential reference sites were rejected if DAM_DII > 1, or
URB_1 KMCIRCLE > 5%, or AG_1KMCIRCLE > 15%.
Values > than those before the slash (/) are EXCLUSION criteria for least-disturbed reference sites.
Values > those after slash are INCLUSION criteria for most-disturbed sites.
W, B, and G refer to Wadeable, Boatable, and Great River sites.
Region
PTL
NTL
CI
SQ4
Turb
W1 HALL
W1 HAG
Wadeable
W1H CROP
Wadeable
Wll I WALL
Wadeable
NAP
20/100
750/3500
250/10000
250/1000
5/10
2.0/4.0
0.1/0.4
0.05/0.10
0.2/0.4
SAP
20/100
750/3500
200/1000
400/1000
5/20
2.0/4.0
0.1/0.4
0.05/0.10
0.2/0.4
UMW
50/150
1000/5000
300/2000
400/2000
5/30
2.0/4.0
0.15/1.4
0.1/0.4
0.2/0.4
CPL
75/250
2500/8000
999999/
999999
600/4000
10/50
2.0/4.0
0.15/1.4
0.05/ 0.4
0.2/0.4
TPL
100/500
3000/15000
2000/5000
999999/
999999
50/100
2.0/4.0
0.67/1.4
0.25/0.48
0.4/0.6
NPL&
SPL
150/500
4500/10000
1000/5000
999999/
999999
50/100
2.0/3.0
1.0/1.4
0.15/ 0.25
0.2/0.4
WMT:
Southwest
50/100
750/1500
300/1000
99999/
99999
5/10
W:0.5/3.0
B,G:1.5/3.0
0.25/1.4
0.10/0.25
0.2/0.4
S.Rockies
25/100
750/1500
200/1000
200/1000
5/10
W: 1.0/3.0
B,G:1.5/3.0
0.3/1.4
0.1/0.25
0.2/0.4
N.Rockies
& Pacific
25/100
750/1500
200/1000
200/1000
5/10
W:0.5/3.0
B,G:1.5/3.0
0.3/1.4
0.10/0.25
0.2/0.4
XER
50/150
1500/5000
1000/5000
999999/
999999
25/75
1.5/3.0
0.6/1.4
0.15/0.25
0.2/0.4
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Table 8-5. NRSA boatable and wadeable least-disturbed reference sites from combined 2008-09 & 2013-14
surveys, selected using consistent criteria listed in Table 8-4. Numbers of reference sites identified from
the 2008-09 and 2013-14 surveys are parenthesized and separated by a slash (/).
EC09
ECOp5
Total
Boatable
Wadeable
NAP
SAP
APPAL
APPAL
88 (45/43)
54 (40/14)
47 (24/23)
22 (15/7)
41 (21/20)
32 (25/7)
CPL
CPL
103 (55/48)
52 (25/27)
51 (30/21)
UMW
UMW
79 (40/39)
36 (18/18)
43 (22/21)
TPL
NPL
SPL
CENPL
CENPL
CENPL
83 (44/39)
85 (29/56)
44 (23/21)
22 (12/10)
33 (11/22)
2(2/0)
61 (32/29)
52 (18/34)
42 (21/21)
WMT
XER
WESL
WESL
112 (47/65)
60 (26/34)
43 (16/27)
24 (6/18)
69 (31/38)
36 (20/16)
Totals for lower 48 states
708 (349/359)
281 (129/152)
427 (220/207)
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Table 8-6. Summary of regression models used in estimating site-specific expected values of LoglO
Relative Bed Stability (LRBS_g08) under least-disturbed reference conditions. See Appendix 8.A for model
details.
REGION/Realm
NAP/Boatable
ExpRef ERBS_g08 (mean)xEF-47
NAP /Wade able
ExpRef ERBS^g08 = f(LAws, W1_HAG)REF-4i, where W1 _HAG=0
SAP / Boatable
ExpRef ERBS_g08 (mean)REF-22
SAP/Wade able
ExpRef ERBS^g08 f(EAws, W1 _Hall)REF-3:
where W1 JHall0
CPL/Boatable
ExpRef ERBS_g08 (mean)REF^2
CPL/Wadeable
ExpRef ERBS^g08 f(ESlope, EWidth, W1 _Ha/l)REF-51 where W1 JHall0
UMW /Boatable
ExpRef ERBS^g08 (Eat, W1_Hall)^EF..36 where W1 _Hall0
UMW /Wadeable
ExpRef ERBS_g0 8 (LSlope, W1 _Hall)zEF-43 where W1 JHall0
NPL/Boatable
Exp ERBS_g08 f(EAws, ESlope, [AGws-x-KFct])allsi >
ExpRef (ERBS_g08/Exp ERBS_g08) f(PCT_AG_WS)REF.28
NPL/Wade able
Exp ERBS^g08 f(Elev, ESlope, EWidth, WIJELall, W1 Crop)Ai 1-114
where W1 JH.aU, W1 _Crop [AGws-x-KFct]) 0
ExpRef (ERBS_g08 / Exp ERBS_g08) f(W1 _Ha/l)REF.^ 1
where W1 Half=0
(R2=0%, RMSE=1.539)
(R2=22%, RMSE=0.525)
(R2=0%, RMSE=0.704)
(R2=28%, RMSE=0.691)
(R2=0%, RMSE=1.331)
(R2=35%, RMSE=0 .736)
(R2= 18%, RMSE=1.259)
(R2=41%, RMSE=0.925)
where AGws-x-KFct 0
where PCT AG WS 0
(R2=56%, RMSE=0.610)
(R2=23%, RMSE=0.512)
(R2=39%, RMSE=0.837)
(R2=3%, RMSE=0.839)
where AG_ IKMCirle 0
SPL+TPL/Boatable
Exp ERBS_J>08 ffEAws, AG_ 1 KMCircle)REF-4 7 ,spl+tpl+npl)
SPL/Wadeable
Exp ERBS_g08 f(Eat, EAws, ESlope, W1 _HAG, AG 1 KMCircle)at 1.797
where W1 _HAG, AGJl KMCircle 0
ExpRjf (ERBS_g08/Exp ERBS_g08) f(W1 HJSfOAG, Dam_cki, RdDenjws, PCT_AG_ws)ref-42 (R2=26%, RMSE=0.990)
where W1 HJSfOAG, Dam_dii, RdDen_ws, PCT_AG_ws 0
TPL/Wadeable
Exp ERBS_g08 f(Eat, Eon, ESlope)aj 1 ..u?
ExpRef (ERBS_g08/ Exp ERBS_g08)
f(W1 HJSfOAG, W1 H_Crop, AG_1 KMCircle, PCT_AG_WS, AgWS-x-KFct)^F58
where W1HJNOAG,W1H_Crop, AG_1 KMCircle, PCT_AG_WS, AgWS-x-KFct = 0
(R2=18%, RMSE= 1.139)
(R2=35%, RMSE=0.952)
(R2=20%, RMSE=0.976)
(R2=26%, RMSE=0.990)
WMT/Boatable
ExpRjfERBS_g0 8 (mean)^p-43
WMT /Wadeable
ExpRef ERBS_g0 8 f(ESlope, EWidth)^F.69,
XER/Boatable
ExpRef ERBS_g08 (mean)xEF-24
XER/Wadeable
ExpRef ERBS8 = f(EWidth)ref-36,
(R2=0%, RMSE=0.365)
(R2=27%, RMSE=0.430)
(R2=0%, RMSE=0.985)
(R2=23%, RMSE=0.794)
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Table 8-7. Summary of regression models used in estimating site-specific expected values of Riparian
Vegetation Cover and Structure (Logl0[0.01+XCMGW]) under least-disturbed reference conditions. See
Appendix 8.A for model details.
REGION/Realm
NAP/Boatable
E.xpRef E_XCAIG\F =f(Lat,, AGJKMGrcle, PCT_AGJFS, Ag\FS-x-KFc-t)f^7 (R2=40%, RMSE=0.156)
where AGJKMCm-le, PGT_AGJFS,Ag\FS-x-KFct=Q
NAP/W adeable
E.xpRef E_XCMG\F =f(EAws, EWidth, \F1 _Hall)fEF-4t, where \F1 _Ha11=0
where W'1 HAG=0
SAP/Boatable
ExpRef E_XCAIG\F =f(W1 _HAG)sef-22,
SAP/W adeable
E.xpRef L_XCMG\F = f(LAws, ELEJ ', \F1 _Hall)fEF-j2,where \F1 _Hall=0
CPL/Boatable
E.xpRefL_XCMGW =f(Eon, LAws, JF1 _HAG)rbf-j2, where \F1 _HAG=0
CPL/Wadeable
E.xpRef L_XCMGW =f(Loป)sEF-n
UMW/Boatable
ExpRef E_XCMG\l f(Eat, EAws, I Slope, / .If idlhj ฆ ฆ - . / ; ;
UMW/Wadeable
ExpRef E_XCMG\F =f{ESlope, EWidth, \F1 _Hall)fEF-4j, where \F1_Hall=0
(R2=24%, RMSE=0.121)
(R17%, RMSE=0.141)
(R2=32%, RMSE=0.141)
(R2=26%, RMSE=0.119)
(R2= 1%, RMSE=0 .152)
(R2=34%, RMSE=0.373)
(R2=33%, RMSE=0.130)
NPL+SPL /Boatable
Exp E_XCMGJF =f(Lat, Eon, \F1_HAG, RDDEN_ws, PCT_AG_ws)^249pifl+spl+tpl) (R2=25%, RMSE=0.362)
where \F1_HAG, RDDEN_ws, PCT_AG_ws = 0
ExpRef (L_XCMG\F/Exp L_XCMGW~) =/(PCT_AG_WS)rbf-2s, where PCT_AG_\FS =0 (R2=31%, RMSE=0.324)
TPL/Boatable
ExpRef L_XCMG\F = (meat,^22 (R2= 0%, RMSE=0.159)
NPL/Wadeable
Exp L_XCAIG1F =f(Lat, Eon, ESlope, EWidth, \F1_HAG, PCT_AG_ws)_^922 /nfl+spl+tfl) (R2=31%, RMSE=0.487)
where W1_HAG, PCT_AG_ivs = 0
ExpRef (E_XCMG\F/Exp E_XCMG\F) =f(Damm_dii, PCT_AG_ws, AgWs-x-KFct)BEF-i 52 /nfl+spl+tfl) (R2=14%, RMSE=0.386)
where Damm_dii, PCT_AG_ws, AgWs-x-KFct = 0
SPL+TPL/Wadeable
ExpRef L_XCMGN f(Eo/i, ET ET , AG_1 KA'IGrcle, PGT_AG_ws, AGws-x-KFcty^BF-u?(Sfl+tfl+umifj R2=40ฐ'o, RMSE=0.267)
where AG_1 KAlCircle, PCT_AG_ws, AGws-x-KFct = 0
WMT/Boatable
E.xpRef E_XCAIGW = (mean) ; ฆ ฆ
WMT/Wadeable
ExpRef E_XCMGW =f(LAws, ELE1LSlope,)BnF^s,
(R2= 0%, RMSE=0.262)
(R2=20%, RMSE=0.153)
XER/Boatable
E.xpRef E_XCMG\F =f(W1_HNOAG, \F1 _HAG)ref-24, where W~f_HNOAG, W~1_HAG = 0 (R2=29%, RMSE=0.153)
XER/Wadeable
E.xpRef E_XCMGW =f(EAws, ESlope, EWidth)BEF-j6,
(R2=23%, RMSE=0.253)
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Table 8-8. Summary of regression models used in estimating site-specific expected values of Instream
Habitat Cover Complexity (Logl0[0.01+XFC_NAT]) under least-disturbed reference conditions. See
Appendix 8.A for model details.
REGION/Realm
NAP/Boatable
ExpRefL_XFC_NAT = f/Loti, LAws, LWidth, \F1H_Cropkef-4?, where \F1H_Crop = 0 (R==34%, RMSE=0.319)
NAP/W adeable
ExpRef L_XFC_NAT =f(L\Fidth)BEF-4i (R2= 7%, RMSE=0.285)
SAP/Boatable
ExpRef L_XFC_NAT =f(Lat, \F1 _HallfEF-22, where \F1_Hall = 0 (R2=53%, RMSE=0.175)
SAP/W adeable
ExpRefL_XFC_NAT =f(Lat, ELEJ ', \F1 _HAG)ฅ:Ef-32, where \F1_HAG =0 (R==42%, RMSE=0.310)
CPL/Boatable
ExpRef L_XFC_NAT = (mean)sEF-52 (R2= 0%, RMSE=0.235)
CPL/Wadeable
ExpRef L_XFC_NAT = {mean)KEF-si (R2= 0%, RMSE=0.298)
UMW/Boatable
ExpRefL_XFC_NAT =f(Lon, W'1 _HAG)rbf-^, where \F1_HAG =0 (R==23%, RMSE=0.316)
UMW/Wadeable
ExpRef L_XFC_NAT =f(LAws, UFidth)i:EF-43, (R2= 7%, RMSE=0.290)
NPL+SPL+TPL/Boatable
Exp L_XFC_NAT =f(Lat, Lo/i, LAws, ET ET , /](:_!K\l( -/.rriey ฆ ฆ ฆ / . . (R-=34ฐ'o, RMSE=0.323)
where AG_1 KJvlCirtie = 0
NPL+SPL+TPL/Wadeable
ExpL_XFC_NAT =f(Lon, LAws, ELEJ ', AG_1 KMCirde, URB_1 KMCmle)wF-i52 avl+sfl+vl) (R2=17%, RMSE=0.335)
whereAG_1 KA'ICircle, URB_1 KNlCircle = 0
WMT/Boatable
ExpRef L_XFC_NAT =/(LWidth, W1H_Crop, RDDEN_ws)rbf^, (R2= 24%, RMSE=0.230)
where W1H_Crop, RDDEN_ws = 0
WMT/Wadeable
ExpRef L_XFC_NAT =f(Lat, Lot,, LAws, \F1_HAG, RDDEN_ws)rbf^s, (R2=35%, RMSE=0.217)
where W1 _HAG, RDDEN_ws = 0
XER/Boatable
ExpRefL_XFC_NAT=f(ELEJ ', LW7M)mF-2j, (R2=13%, RMSE=0.310)
XER/Wadeable
ExpRefL_XFC_NAT =/(Lon, LSlope, W1 H_Crop)fEF-36, where W1H_Crop = 0 (R2=27%, RMSE=0.242)
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Table 8-9. Responsiveness to levels of human disturbance.
Responsiveness of NRSA physical habitat condition metrics to levels of human disturbance, as quantified by t-values of the difference
between means of least-disturbed reference sites (prk3RRT_NRSA1314=R) minus most-disturbed sites (those screened as
prkRRT_NRSA1314=T). Values shown in red have a sign contrary to expectations. Order of magnitude of p-values shown by number
of asterisks (e.g., * = p~ 0.1; **** = p~ 0.0001)
Metric
Region
t-value R-T
(Boatable)
t-value R-T
(Wadeable)
t-value R-T
(All sites)
LOE_RBS_g08
USA-48
_|_y 2(5****
+12 84****
CPL
+2.47**
+2.68**
+3.38***
EHIGH (NAP+SAP)
+3.22***
_l_4 -j^****
+4 yQ****
UMW
+6 59****
+2.24**
+5 32****
CENPL (TPL+NPL+SPL)
+2.64**
+6 39****
+6 93****
West (WMT+XER)
+3 96****
+8 25****
+8.98****
LOEXCMGW
USA-48
+3 44 ' - -
+13.46****
+14 17****
CPL
+2 99-f-f-f
+5.69****
+6 32****
EHIGH (NAP+SAP)
+1.73*
+5 61****
+5 25****
UMW
-0.13
+4.06****
+2 95***
CENPL (TPL+NPL+SPL)
+1.43
+7 35****
+7 95****
West (WMT+XER)
+2.24**
+7 16****
+7 35****
LOE_XF C_N at
USA-48
-0.64
+7
+6.62****
CPL
+0.59
+3.64****
+3 52***
EHIGH (NAP+SAP)
+0.91*
+1.73*
+2.30**
UMW
-0.85
+2.02**
+1.08*
CENPL (TPL+NPL+SPL)
+0.56
+3 68****
+2.82**
West (WMT+XER)
-1.78*
+8 16****
+5 37****
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A. NRSA 2008-09 sample sites
B. NRSA 2013-14 sample sites
Figure 8-1. Sample sites for NRSA 2008-09 and NRSA 2013-14.
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A. Boatable
W1 HALL
8
7
6
5
4
3
2
1
0
CPL NAP NPL SAP SPL TPL UMW WMT XER
ECOWSA9 2015
o
0.
O
Q ฐ
i ง
j.ฎ ii
p 1
0
8
0
o g 8 i
jcj
!
<
T
II ^
1
^
4
1
1
=" \
1
1
B. Wadeable
W1JHALL
8-
SAP SPL TPL
ECOWSA9 2015
UMW
Figure 8-2. Riparian Disturbance (Wl_Hall) in combined NRSA 2008-09 and 2013-14
sample sites in 9 aggregate ecoregions of the conterminous U.S. Boxplots show 5th, 25th,
median, 75th, and 95th percentiles ofthe unweighted sample distributions (not
population estimates). A. Boatable sites; B. Wadeable sites.
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A. Beatable
EHIGH
W1_HALL
8-
7-
6
5
4 i
3 a
.8.
11
1
0
I
R
I
S
i
T
prk3RRT...RSA1314
B. Wadeable
EHIGH
W1_HALL
8
5
4
3-
t 9 T
n i- -r4
R S T
prkSRRT... RSA1314
CPL
W1JHALL
8
7
6-
5
4 i
3-
2-
o
o
1
TIT oJb
~T
R
I
S
"T"
T
prk3RRT.. .RSA1314
CPL
W1JHALL
8-
6-
4-
2- o
o
1
n-3
R
o
o
8
"T"
s
I
r~
T
UMW
W1_HALL
8 -
7-
6-
5-
4-
3-
2
H
0
CENPL
W1 HALL
WEST
W1 HALL
o
Q
i
I
R
I
S
T
r
T
prk3RRT... RSA1314
UMW
W1_HALL
8-
prk3RRT... RSA1314
CENPL
W1_HALL
8--
prk3RRT... RSA1314
WEST
W1 HALL
3
1
prk3RRT...RSA1314
prk3RRT...RSA1314
1 1 1
R S T
prk3RRT...RSA1314
prk3RRT...RSA1314
Figure 8-3. Riparian Disturbance (Wl_Hall) in combined NRSA 2008-09 and 2013-14 sample sites in 9
aggregate ecoregions of the conterminous U.S., contrasting distributions in least-, moderately-, and most-
disturbed sites within each aggregated ecoregion. Boxplots show 5th, 25th, median, 75th, and 95th percentiles
of the unweighted \sample distributions (not population estimates). A. Boatable sites; B. Wadeable sites.
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A. Boatable
LRBS_use
4
-2
-6
-7
0
1 i
\
J
i | 1
JL 1
Tr
| i :
1 T ฆ
) ^ -
h f
i \
i i i
h '
ฐ J
| A
:: * 1
o
1 A 0 o
& o-
CPL
LSUBJDMM
8
-7
6
-5
4
-3
2
-1
0
NAP
NPL
SAP SPL TPL
ECOWSA9 2015
UMW WMT
XER
~ LRBS_use HLSUB_DMM
B. Wadeable
LRBS_use LSUB_DMM
4- ฆ- ~ ^ ~-B
3 8 6 ฐ"h7
|i;|;: + t ฃ ฃ I: 1
u ^ $ f $ $
-U | ฃ-
CPL NAP NPL SAP SPL TPL UMW WMT XER
EC0WSA9 2015
~LRBS_use BLSUB_DMM
Figure 8-4. Log Relative Bed Stability (LRBS_use) and LoglO geometric mean bed surface
substrate diameter (LSUBdmm) in combined NRSA 2008-09 and 2013-14 sample sites in 9
aggregate ecoregions ofthe conterminous U.S. Boxplots show 5th, 25th, median, 75th, and 95th
percentiles of the unweighted sampledistribulions (not population estimates). A. Boatable
sites; B. Wadeable sites.
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A. Boatable
prk3RRT.. RSA1314
prk3RRT... RSA1314
prk3RRT... RSA1314
prk3RRT.. RSA1314
R S
prk3RRT... RSA1314
B. Wadeable
EHIGH
tn=+4.12****
LOE_RBS_use
3
UMW
trt= +2.24**
LOE_RBS_use
3
CENPL
trt=+6.39****
LOE_RBS_use
4
WEST
trt= +8.25****
LOE_RBS_use
6-
prk3RRT.. .RSA1314
R S
prk3RRT...RSA1314
prk3RRT...RSA1314
prk3RRT... RSA1314
R S
prk3RRT.. .RSA1314
Figure 8-5. Observed/Expected Relative Bed Stability (LOE_LRBS_use) in combined NRSA 2008-09 and 2013-14
sample sites in 9 aggregate ecoregions of the conterminous U.S., contrasting distributions in least-, moderately-, and
most-disturbed sites within each aggregated ecoregion. Boxplots show 5th, 25th, median,75th, and 95th percentiles of
the unweighted sample distributions (not population estimates). A. Boatable sites; B. Wadeable sites.
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A Boatable
Lpt01_XCMGW
0.
0.
-0.
-1.
-1.
-2.
B. Wadeable
Lpt01_XCMGW
0.5
0.0
-0.5
-1.0
-1.5
-2.0
CPL NAP NPL SAP SPL TPL UMW WMT XER
ECOWSA9_2015
Figure 8-6. Riparian Vegetation Cover Complexity (LPt01_XCMGW) in combined NRSA 2008-09 and 2013-14
sample sites in 9 aggregate ecoregions of the conterminous U.S. Boxplots show 5th, 25th, median, 75th, and 95th
percentiles of the unweighted sample distributions (not population estimates). A. Boatable sites; B. Wadeable sites.
SAP SPL TPL
ECOWSA9 2015
6
_L
?'
8
o
T '
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A. Boatable
EHIGH
trt=+1.73*
LOE XCMGW use
B. Wadeable
EHIGH
trt=+5.61****
LOE_XCMGW_use
0.5-
CPL
tn=+2.99***
LOE XCMGW use
0.5-
0.0
-0.5
-1.0-
-1.5-
-2.0-
UMW
trt= -0.13ns-
LOE XCMGW use
1.0-
CENPL
trt=+1.43ns-
LOE XCMGW use
0.5-
0.5-
0.0-
-0.5
-1.0-
-1.5-
? -20^ , r
R S T
0.0
-0.5
-1.0-
-1.5 -
-2.0-
WEST
trt= +2.24**
LOE XCMGW use
0.5-
o.o H
-0.5-
-1.0-
-1.5-
-2.0 -
S T
prk3RRT...RSA1314 prk3RRT...RSA1314 prk3RRT...RSA1314! prk3RRT...RSA1314
CPL
trt=+5.69****
LOE_XCMGVi/_use
0.5-
0.0
-0.5-
-1.0-
-1.5
-2.0
-2.5 -
UMW
trt= +4.06****
LOE_XCMG',V_use
0.5-
0.0
CENPL
trt=+7.35****
LOE_XCMGW_use
WEST
trt= +7.16****
LOE_XCMGW_use
-0.5
-1.0
-1.5-
-2.0
prk3RRT...RSA1314
prk3RRT...RSA1314
prk3RRT...RSA1314
prk3RRT... RSA1314
Figure 8-7. Observed/Expected Riparian Vegetation Cover Complexity (LOE_XCMGW_use) in combined NRSA
2008-09 and 2013-14 sample sites in 9 aggregate ecoregions of the conterminous U.S., contrasting distributions in
least-, moderately-, and most-disturbed sites within each aggregated ecoregion. Boxplots show 5th, 25th, median,
75th, and 95th percentiles of the unweighted sample distributions (not population estimates). A. Boatable sites; B.
Wadeable sites.
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A. Boatable
Lpt01_XFC_NAT
0.5
0.0
-0.5
-1.0
-1.5
-2.0
CPL NAP NPL SAP SPL TPL UMW WMT XER
ECOWSA9 2015
B. Wadeable
Lpt01_XFC_NAT
0.5
0.0
-0.5
-1.0
-1.5
-2.0
Figure 8-8. Instream Habitat Complexity (LPt01_XFC_NAT) in combined NRSA 2008-09
and 2013-14 sample sites in 9 aggregate ecoregions of the conterminous U.S. Boxplots show
5th, 25th, median, 75th, and95th percentiles of the unweighted sample distributions (not
population estimates). A. Boatable sites; B. Wadeable sites.
" i , T , ? T "
CPL NAP NPL SAP SPL TPL
ECOWSA9 2015
UMW
WMT
XER
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A. Boatable
h
EHIGH
trt=+0.91*
LOE_XFC_NAT_use
1.5-
1.0
0.5
00
-0.5
-1.0
-1.5
-2.0
CPL
trt=+0.59nI
LOE_XFC_NAT_use
1.0
0 5
00
-0.5
-10
-1.5
UMW
trt= -0.85"5
LOE_XFC_NAT_use
1.0
0.5
0.0
-0.5
-1.0
CENPL
trt=+0.56ns
LOE_XFC_NAT_use
1.0
0.5
00
-0.5
-1.0
-1.5
-20
1
WEST
trt= -0.78"'
LOE_XFC_NAT_use
1.5
O '
1.0
R S T
prURRT RSA1314
R S T
prk3RRT. RSA1314
R S T
prk3RRT...RSA1314
R S T
prk3RRT ..RSA1314
R S T
pfk3RRT...RSA1314
B. Wadeable
EHIGH
trt=+1.173*
LOE_XFC_NAT_use
1.0-
0.5
00
-05
-1.0
-1.5
CPL
trt=+3.64****
LOE_XFC_NAT_use
10
0.5-
00
-0.5
-1.0
-1.5
-2.0
UMW
trt= +2.02**
LOE_XFC_NAT_use
0.5-
CENPL
trt=+3.68****
WEST
trt= +8.76****
I
00
-0.5
-10
-1.5
LOE XFC NAT use L0E_XFC NAT use
10
05
00
-0.5
-10
-15
1 I 1
-20
1.0
0.5
0.0
-0.5
-1.0
-1.5
-2.0
R 3 T RST RST
prk3RRT RSA1314 prk3RRT RSA1314 prt3RRT RSA1314
RST RST
prk3RRT ..RSA1314 prk3RRT RSA1314
Figure 8-9. Observed/Expected Instreain Habitat Complexity (LOE_XFC_NAT_use) in combined NRSA 2008-09
and 2013-14 sample sites in 9 aggregate ecoregions of the conterminous U.S., contrasting distributions in least-,
moderately-, and most-disturbed sites within each aggregated ecoregion. Boxplots show 5th, 25th, median, 75th, and
95th percentiles of the unweighted sample distributions (not population estimates). A. Boatable sites; B. Wadeable
sites.
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Appendix 8. A
NRSA 2008-09 & 2013-14 Expected
Condition Models and Condition Criteria
NOTES:
ฆ Uni-Fixed Models are fixed values of a metric that are
uniform across all ecoregions andboth Boatable and
Wadeable "Realms."
ฆ NULL MODELS are based on mean & SD for reference sites
(prk3RRT_NRSAl 314=R) from NRSA0809 and NRSA1314.
ฆ Cond 1 and Cond lb MODELS are MLRs using reference sites
(prk3RRT_NRSA1314=R)from NRSA0809 and NRSA1314.
Cond_l MLRs may have disturbance variable (s) as predictors in
cases where reference sites have anthropogenic disturbance that
influences response variable.
ฆ Cond ID MODELS are "All-Sites" MLRs using all sites (except
Great Rivers) and incorporate disturbance variables as predictors.
We use 2-steps to calculate reference expected values. First step is
to calculate All-Sites Model Expected values then calculate O/E
values by setting disturbance to empirical minimum values for the
ecoregion/realm. Second value is to examine distribution of All-
Sites Model O/E values within the ReferenceSites of the
appropriate ecoregion/realm.
ฆ The expected reference value of the All-Sites Model OE is
calculated from the reference sitedistribution of All-Sites model
O/E values (refOE mean & refSD) or a regression factoring out
disturbance in the reference sites (refOE y-intercept and refRMSE
from disturbance regression) +++ note that there is no requirement
that the disturbance variable be the same asin the All-Sites model
regression in fact it is likely to be a different variable because
the influence of the disturbance variable used in the "All-Sites
Model" has already been accounted for.
Condition Benchmarks for Riparian Human
Disturbances (RDist_COND) based on W1_HALL
We applied uniform condition benchmarks nationwide. The Low (1
Low), Medium (2 Medi), and High (3 High) disturbance levels are
analogous to the Good, Fair, Poor condition classification usedfor the
other indicators.
All Ecoregions and both Boatable and Wadeable sites
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If Wl_Hall<0.33 then RDIST_COND= ฃ1 Low';
If Wl_Hall>=0.33 and Wl_Hall<1.5 then
RDIST_COND='2 Medi';If
Wl_Hall> = 1.5 then RDIST_COND='3
High';
Reference Condition Models for Channel
Bed Sedimentationbased on Relative Bed
Stability (LRBS_use = LRBS_g08)
Coastal Plain
(CPL) Boatable
Sites Cond_Null
(eco9-B n=52)
RfNullMLRB S=
-0.92405
RfNull SDLRB S= 1.33124
Coastal Plain (CPL) Wadeable Sites
Cond_l (eco9-W n=51):
LRBS_use= -1.67044 -0.77290(LXSlope_use)-0.49218(LXWidth_use)
-0.12031(Wl_Hall) R2=0.3497; AdjR2=0.3054; RMSE=0.73642;
n=48/51; p=0.0003;pl<0.0001; p2=0.2637; p3=0.5799
Set Wl_Hall= 0 = minimum in ref sites:
RfEl_LRBS=-l.67044-
0.77290(LXSlope_use) -
0.49218(LXWidth_use)
RfEl_RMSE_LRBS=0.73642
Northern Appalachian (NAP) Boatable Sites
Cond_Null (eco9-B n=47):
RfNullMLRB S= -0.63226
RfNullSD_LRBS= 1.53888
Northern Appalachian (NAP) Wadeable Sites
Cond_l (eco9-W n=41):
LRBS_use= -0.64678 +0.32478(L_AreaWSkm2_use) -8.04380(W1_HAG)
R2= 0.2250; AdjR2=0.1842; RMSE=0.52529; n=41/41; p=0.0079;pl=0.0097;p2=0.1123
Set W1_HAG=0 = minimum in ref sites:
RfEl_LRBS= -0.64678
+0.32478(L_AreaWSkm2_u
se)
RfEl RMSE LRBS=0.5252
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9
Southern Appalachian (SAP) Boatable Sites
Cond_Null (eco9-B n=22):
RfNullM_LRBS= 0.44138;RfNullSD_LRBS=0.70357;
Southern Appalachian (SAP) Wadeable Sites
Cond_l (eco9-W n=32):
LRBS_use= -0.74349 +0.48842(L_AreaWSkm2_use) -0.75562(Wl_Hall)
R2= 0.2835; AdjR2=0.2341; RMSE=0.69081; n=32/32; p=0.0079;pl=0.0026;p2=0.0472
Set Wl_Hall=0 = minimum in ref sites:
RfEl_LRBS= -0.74349
+0.48842(L_AreaWSkm2_u
se)
RfE 1RMSELRB S=0.6908
1
Northern Plains (NPL) Boatable Sites
Cond lD (eco9-B n=51)
"All-Sites Model" Regression on all 51 NPL boatable sites:
LRBS_use= -0.42002
+0.44371(L_AreaWSkm2_use)+1.26686(LXSlope_use) -
0.08698(AGws_X_KFct)
Set AGws_X_KFct = 0 = minimum for the ecoregion:
RfElD_LRBS= -0.42002
+(0.44371 *L_AreaWSkm2_use)
+(1,26686*LXSlope_use)R2=0.5598; AdjR2=0.5311;
RMSE All-Sites model= 0.61027; n=50/51;
p<0.0001;pl=0.0089;p2<0.0001;p3=0.0011
If don't have KFactor the following is very equivalent, as
KFactors are close to 0.35.LRBS use= -0.50236
+0.44371 (L AreaWSkm2_nse)+1.29164(LXSlopejise) -
0.02628(PCT_AG_WS_use)
RfOE 1D LRBS=LRBS use - RfElD LRBS
Regression using only NPL boatable Reference sites (n=28):
RfOElDLRBS = 0.15939 -0.02276(PCT_AG_WS_use)
R2=0.2322; AdjR2=0.2026; RMSE=0.51215; n=28/28; p=0.0094; pl=0.0094
~ Set PCT_AG_WS_use=0 = minimum in ref sites.
RfE OE1D LRBS
= 0.15939
RfE OEID RMSE
_LRBS=0.51215;
Northern Plains (NPL) Wadeable Sites
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Cond_lD (eco9-W n=314)
"All-Sites Model" Regression on all 314 NPL wadeable sites:
LRBS_use= -2.80718 +0.00084015(ELEV_PT_use) -0.70092(LXSlope_use)
+0.64948(LXWidth_use)
-0.20932(W 1_HALL) -0.49739(WlH_Crop)
Set W1HALL and WI Crop = 0, the minima for
the ecoregion: RfElD_LRBS= -2.80718
+0.00084015(ELEV_PT_use) -0.70092(LXSlope_use)
+0.64948(LXWidth_use);
R2 = 0.3854; AdjR2=0.3754; RMSE All-
Sites model =0.83720; n=314;
p<0.0001;pl-
3<0.0001;p4=0.0048;p5=0.0553;
RfOE 1DLRBS=LRBS use - RfElD LRBS;
Regression using only NPL
Wadeable Reference sites (n=52):
RfOElD LRBS = +0.19752 -
0.31987(Wl_Hall);
R2=0.0280; AdjR2=0.0086; RMSE=0.83941; n=51/52; p=0.2356;pl=0.2356
RfEOE 1 DLRB S= 0.19752
RfEOE 1DRMSELRB S= 0.83941
Southern Plains & Temperate Plains (SPL + TPL) Boatable Sites
Cond i (cenpl-B) n=47 ref sites from TPL, SPL, and NPL
LRBS_use= 1.44046 -0.32356*L_AreaWSkm2_use -
0.02377*AG1KMCIRCLE R2=0.1789; Adj R2=0.1416;
RMSE=1.13936; n=47/47; p=0.0131;pl=0.0852;p2=0.0084
Set AG_lKMCircle=0 = minimum in reference sites:
RfEl_LRBS=l.44046 -
0.323 5 6(L_AreaWSkm2_
use)
RfEl_RMSE_LRBS=1.14
939
Southern Plains (SPL) Wadeable Sites
Cond lD (eco9-W) n=301
"All-Sites Model" Regression on all SPL wadeable sites:
LRBS_use= 0.89319 -0.06565(LAT_DD83) -0.09181(L_AreaWSkm2_use) -
0.86897(LXSlope_use)
-0.24209(W 1_HAG) -0.00308(AG_1KMCIRCLE)
-0.02727(AGws_X_KFct)R2=0.3525; Adj
R2=0.3391; RMSE All-Sites=0.95158; n=297/301;
p<0.0001;pl=0.0002;p2=0.0519;p3<0.0001;p4=0.0
155;p5=0.2490;p6=0.0049
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Set W1HAG, AGlKMCircle, AGws_x_KFct = 0 =
minima for SPL wadeable sites: RfE 1D_LRBS= +0.89319 -
0.06565(LAT_DD83) -0.09181(L_AreaWSkm2_use) -
0.86897(LXSlope_use)
RfOElD_LRBS= LRBSuse - RfElDLRBS
Regression on SPL wadeable ref sites:
RfElD_LRBS=
-0.00983 -0.83096(W1_HNOAG) -3.3658(Dam_dii) +0.6857(RdDen_ws_use) -
0.02242(PCT_Ag_ws_use)
R2 = 0.2616; Adj R2=0.1817;
RMSE=0.99030; n=42/42;
p=0.0214;p 1=0.0618;p2=0.2
056;p3=0.0364;p4=0.0338
Set W l_HNOAG, Dam_dii, PCT_Ag_ws_use = 0 = minima for ref sites;
Set RdDen_ws_use = 0 (Ref site mimimum = 0.19) zero leads to more lenient
expected condition.
RfE_OElD_LRBS= -0.00983
RfE_OElD_RMSE_LRBS= 0.99030
Temperate Plains (TPL) Wadeable Sites
Cond_lD (eco9-W) ~ All-Sites Model Regression on all 344 TPL
wadeable sites: LRBS_use= 0.22205 +0.04387(LAT_DD83)
+0.03596(LON_DD83) -0.49057(LXSlope_use)
-0.08247(W1_HAG) -0.01116(AG_1KMCIRCLE);
Set W1HAG and AG1KMCIRCLE = 0 = minima for region:
RfElD_LRBS=0.22205 +0.04387(LAT_DD83)
+0.03596(LON_DD83) -0.49057(LXSlope_use) ;R2=0.1974; Adj
R2=0.1854; RMSE- All-Sites =0.97639; n=342/344;
p<0.000 l;p 1=0.0556;p2=0.0074;p3<0.000 l;p4=0.497 l;p5<0.0001
RfOElD_LRBS= LRBS use - RfElD LRBS
Regression on TPL ref sites:
RfE 1DLRBS= +0.21704 -0.83169(W1_HN0AG) +6.55336(WlH Crop) -
0.0228(Ag_lKmCircle)
-0.05988(PCT_Ag_ws_use)
+0.19465(AgWS_x_KFct)R2 =
0.3279; Adj R2=0.2633;
RMSE=0.93335; n=58
/61 ; p=0.0007;p 1=0.0608;p2=0.0295;p3=0.0065;p4=0.0107;p5=0.0036
Set W1HNOAG, WlH Crop, Ag_lKmCircle , PCT_Ag_ws_use, and
AgWS_x_KFct = 0 = minimafor ref sites;
RfE OE 1 DLRB S=0.217
04 = y-intercept from
above
RfE OE1D RMSE LRB
S=0.93335
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Upper Midwest (UMW) Boatable Sites
Cond i (eco9-B n=36):
LRBS_use= 22.86206 -0.50298(LAT_DD83) -0.92704(W1_HALL)
R2=0.1820; Adj R2=0.1324; RMSE=1.25933; n=36/36; p=0.0363;pl=0.0113;p2=0.2028
Set W1_HALL= 0 = minimum for regional ref sites
RfEl_LRBS=22.86
206-
0.50298(LAT_DD8
3)
RfE 1RMSELRB
S=1.25933;
Upper Midwest (UMW) Wadeable Sites
Cond_l (eco9-W n=43):
LRBSuse = -1.38974 -0.69289(LXSlope_use) -0.26824(W1_HALL)
R2=0.4103; Adj R2=0.3808; RMSE=0.92535; n=43/43; p<0.0001;pl<0.0001;p2=0.5347
Set W1_HALL=0 = minimum for regional ref sites:
RfEl_LRBS= -
1.38974-
0.69289(LXSlope_us
e)
RfElRMSELRBS
=0.92535
Western Mountain (WMT) Boatable Sites
Cond_N (eco9-B n=43):
RfNullM_LRBS= 0.36550RfNullSD_LRBS=0.48996
Western Mountain (WMT) Wadeable Sites
Cond i (eco9-B n=69):
LRBS use = -0.77810 -0.31541(LXSlope_use) +0.48616(LXWidth_use)
R2= 0.2739; Adj R2=0.2516; RMSE=0.42995; n=68/69; p<0.0001;pl=0.0382;p2=0.022333
RfEl_LRBS= -0.77810 -0.31541(LXSlope_use)
+0.48616(LXWidth_use)RfEl_RMSE_LRBS=0.42995
Xeric (XER) Boatable Sites
Cond_N (eco9-B n=24):
RfNullMLRB S=0.08641 RfNull SDLRB S=0.98518
Xeric (XER) Wadeable Sites
Cond_l (eco9-W n=36):
LRBS_use = -2.01510 +1.33328(LXWidth_use)
R2=0.2333; Adj R2=0.2107; RMSE=0.79439; n=36/36 ; p=0.0028;pl=0.0028
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RfEl_LRBS= -
2.01510
+1.33328(LXWidth_u
se)
RfEl_RMSE_LRBS=
0.79439
CONDITION ASSIGNMENTS FOR LRBS_use NULL MODELS:
RfNull25_LRBS=RfNullM_LRB
S-(0.67*RfNullSD_LRBS);
RfNull05_LRBS=RfNullM_LRB
S-(1.65*RfNullSD_LRBS);
RfOENullLRB S=LRB S_us
e-RfNullM_LRBS;
LRB S_C ond_N=XXXX';
if LRBS_use<=RfNull05_LRBS then LRBS_Cond_N='Poor';
if LRBS_use>RfNull05_LRBS and
LRBS_use<=RfNull25_LRBSthen
LRB S_C ond_N='Medi';
if LRBS_use>RfNull25_LRBS then LRBS_Cond_N='Good';
If RfOENull_LRBS=. then
LRBS COND N=XXXX';
If LRBS_use=. then
LRBS COND N=XXXX';
CONDITION ASSIGNMENTS FOR LRBS_use COND_l O/E MODELS:
RfOE 1 LRB S=LRB Suse -RfE 1 LRB S;
RfE 1 25 LRB S=RfE 1 LRB S -
(0.67*RfEl_RMSE_LRBS);
RfEl_05_LRBS=RfEl_LRBS-
(1.65*RfEl_RMSE_LRBS);
LRB S_C ond_ 1=XXXX';
if LRBS_use<=RfEl_05_LRBS then LRBS_Cond_l='Poor';
if LRBS_use>RfEl_05_LRBS and
LRBS_use<=RfE l_25_LRBSthen
LRBS_Cond_l='Medi';
if LRBS_use>RfEl_25_LRBS then LRBS_Cond_l='Good';
If RfEl_LRBS=. then LRBS_COND_l=XXXX';
If LRBS_use=. then LRBS_COND_l=XXXX';
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CONDITION ASSIGNMENTS FOR LRBS_use COND_lD ("All-Sites") O/E MODELS:
*** NOTE RfOE 1DLRBS=LRBSuse-RfE 1DLRBS;
*** We base expectations on the distribution of OE in ref sites;
RfEOE 1D 25 LRBS=RfE OE 1DLRBS-
(0.67* RfEOE 1 D_RMSE_LRB S);
RfEOE 1 D_05_LRBS=RfE_OE 1DLRBS-
(1.65 * RfEOE 1 D_RMSE_LRB S);
if RfOE 1 D LRB S<=RfE_OE 1D 05 LRB S then LRBS_Cond_lD='Poor';
if RfOE 1 DLRB S> RfE_OElD_05_LRBS and RfOE 1 DLRB S<=RfE_OE 1D25LRB S
then LRBS_Cond_lD='Medi';
if RfOE 1 D LRB S> RfE_OElD_25_LRBS then LRBS_Cond_lD='Good';
If RfOElD_LRBS=. then LRBS_COND_ 1D-XXXX';
If LRBS_use=. then LRBS COND 1D=XXXX';
Reference Condition Models for Riparian
Vegetation CoverCondition
based on Logio(0.01+XCMGW)
Coastal Plain (CPL) Boatable Sites
Cond i (eco9-B n=52):
LPtO 1_XCMGW= 0.83657 +0.00658(LON_DD83) -
0.06020(L_AreaWSkm2_use) -0.57160(W1_HAG);
R2=0.2583; AdjR2=0.2121; RMSE=0.11862; n=52/52 ; p=0.0023; pl=0.0570; p2=0.0461;
p3=0.0166
Set W 1_HAG = 0 = minimum for ref sites in region:
RfEl_LXCMGW=0.83657 +0.00658(LON_DD83) -
0.06020(L_AreaWSkm2_use);
RfE 1_RMSE_LXCMGW=0.11862;
Coastal Plain (CPL) Wadeable Sites
Cond_l (eco9-W n=51):
LPtO 1_XCMGW= -0.58185 -0.00700(LON_DD83)
R2= 0.0551; AdjR2= 0.0358; RMSE=0.15238; n=51/51 p=0.0972 pl=0.0972
RfEl LXCMGW=-
0.58185 -
0.00700(LON_DD83)
RfE 1 RMSE LXCMGW
=0.15238
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Northern Appalachian (NAP) Boatable Sites
Cond_l-eco9-B:
LptO 1_XCMGW= 2.51398 -0.05498(LAT_DD83)
-0.00786(AG_1KMCIRCLE) -
0.79370(PCT_AG_WS_use) +2.68820(Agws_X_KFct)R2
= 0.4025; AdjR2=0.3456; RMSE=0.15628; n=47/47
p=0.0002; pl=0.0056; p2=0.0107;p3=0.0005;p4=0.0007
Set AG1KMCIRCLE, PCTAGWSuse and AGws X KFct = 0 = minima for
reference sites:
RfEl_LXCMGW=2.51
398-
0.05498(LAT_DD83)
RfEl RMSE LXCMG
W=0.15628
Northern Appalachian (NAP) Wadeable Sites
Cond_l (eco9-W n=41):
LPt01_XCMGW=0.21141+0.09026(L_AreaWSkm2_use)
-0.30883(LXWidth_use) -0.14456(W1_HALL)
R2 = 0.2411; AdjR2=0.1795; RMSE=0.12059; n= 41/41
p=0.0159;p 1=0.0894;p2=0.0130;p3=0.0293
Set W 1_HALL = 0 = minimum for reference sites:
RfE 1_LXCMGW=0.21141
+0.09026(L_AreaWSkm2_use) -0.30883(LXWidth_use);
RfEl RMSE LXCMGW=0.12059
Southern Appalachian (SAP) Boatable Sites
Cond_l (eco9-B n=22):
LPtO 1_XCMGW= 0.02698 -0.44778(W1_HAG)
R2 = 0.1689; AdjR2=0.1274;RMSE= 0.14138; n= 22/22 p=0.0574; pl=0.0574
Set W 1_HAG = 0 = minimum for reference sites:
RfEl LXCMGW=
0.02698
RfE 1RMSELXC
MGW=0.14138;
Southern Appalachian (SAP) Wadeable Sites
Cond_l (eco9-W n=32):
LPtO 1_XCMGW= -0.14633+0.04120(L_AreaWSkm2_use) +0.00051106(ELEV_PT_use)
-0.16089(W1_HALL);
R2=0.3232; AdjR2=0.2507; RMSE= 0.14090; n= 32/32 ; p=0.0111; pl=0.2142;
p2=0.0028; p3=0.0429
Set W1 HALL = 0 = minimum for reference sites:
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RfEl_LXCMGW= -0.14633 +0.04120(L_AreaWSkm2_use)
+0.00051106(ELEV_PT_use);
RfEl_RMSE_LXCMGW=0.14090;
Northern Plains (NPL) & Southern Plains (SPL) Boatable Sites
Cond lD (CENPL-B n=249): ~ All-Sites Regression on All CENPL Boatable
sites (NPL, SPL, & TPL) LPt01_XCMGW= 1.80926 -0.02245(LAT_DD83)
+0.01036(LON_DD83)
-0.24323(W1_HAG) -
0.11970(RDDEN_WS_use)+0.00306(PCT_AG_WS_
use)R2 = 0.2485; AdjR2=0.2331; RMSE All-Sites=
0.36204; n= 249/249
p<0.0001;pl=0.0685;p2<0.0001;p3=0.0283;p4=0.01
13.
Set W1HAG and PCTAGWSuse = 0 = minimum in NPL+SPL ref sites
Set RDDEN_WS_use, = 0 (minimum for NPL+SPL ref sites = 0.043):
RfE 1 D_LXCMGW= 1.80926 -0.02245*LAT_DD83)+(0.01036*LON_DD83)
Regression on TPL CENPL ref sites:
RfOE 1 D_LXCMGW=LPt0 1 XCMG
W - RfE 1DLXCMGW;
RfEOE 1DLXCMGW = -0.08047 -
0.01773(PCT_AG_WS_use)
R2 = 0.3141; AdjR2=0.2877; RMSE = 0.32423; n= 28/28; p=0.0019;pl=0.0019
Set PCT_AG_WS_use = 0 = min in ref sites
RfE OE 1 D LXCMGW = -0.08047 ;
RfEOE 1 D_RMSE_LXCMGW=0.32423;
Northern Plains (NPL) Wadeable Sites
Cond_lD (CENPL-W n=959) All-Sites Regression on All CENPL
Wadeable sites (NPL, SPL, & TPL) :LPt01_XCMGW= 2.43249 -
0.02325(LAT_DD83) +0.01579(LON_DD83)+0.16417(LXSlope_use)
-0.32696(W 1_HAG) -0.00256(PCT_AG_WS_use)
R2 = 0.3126; AdjR2=0.3088; RMSE- All-Sites =0.48720; n=922/959; p<0.0001;pl-
p4<0.000 l;p5=0.0002
Set W1HAG and PCT AG WS use = 0 = minima in ref sites of NPL (also SPL &
TPL):
RfE 1 D LXCMGW = 2.43249 -0.02325*LAT_DD83) +(0.01579*LON_DD83)
+(0.16417*LXSlope_use)
RfOElD LXCMGW=LPt01 XCMGW - RfE 1 D LXCMGW;
Regression using only CENPL Wadeable Ref sites (155)
RfOElD LXCMGW = " -0.13159 -2.01216(Dam_dii) -0.02708(PCT_AG_WS_use) +
0.08125 (Ag W s_x_KF ct);
R2 =0.1443; AdjR2=0.1270; RMSE=0.38555; n=152/155;
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p<0.0001;p 1=0.0015;p2=0.0006;p3=0.0006
Set Dam dii, PCTAGWSuse & AgWs x KFct = 0 = minima in CENPL and NPL
alone:
RfE_OElD_LXCMGW= -0.13159;
RfEOE 1 D_RMSE_LXCMGW=0.38555;
Southern Plains (SPL) Boatable Sites
see combined NPL & SPL Boatable Sites above
Southern Plains (SPL) & Temperate Plains (TPL) Wadeable Sites
Cond_lb (spl-tpl-umw-W ~ ref sites only n=146):
LPtO 1_XCMGW= 1.25746 +0.01355(LON_DD83) -0.00024404(ELEV_PT_use)
-0.00636(AG_1KMCIRCLE) -0.02587(PCT_AG_WS_use)
+0.08055(AGws_X_KFct) R2=0.3956; AdjR2=0.3735; RMSE=0.26692;
n=143/146 p<0.000l;p 1=0.0088;p2=0.0048;p3=0.0013;p4-5<0.0001
Set AG1KMCIRCLE, PCT_AG_WS_use & AGws_X_KFct = 0 =
minima in both SPL and TPL refsites:
RfElb_LXCMGW= 1.25746 +0.01355(LON_DD83) -
0.00024404(ELEV_PT_use);
RfElb_RMSE_LXCMGW=0.26692;
Temperate
Plains (TPL)
Boatable Sites
Cond_N (eco9-B
n=22):
RfNullMLXCM
GW= -0.08249 ;
RfNullSD LXCMGW = 0.15980 ;
Temperate Plains (TPL) Wadeable Sites
see combined SPL & TPL Wadeable sites above
Upper Midwest (UMW) Boatable Sites
Condlb (SPL + TPL+ UMW Boatable Refsites n=55):
LPt01_XCMGW=l .52755 -0.03762(LAT_DD83) -0.33101(L_AreaWSkm2_use)
+0.17072(LXSlope_use)
+0.82145(LXWidth_use) R2-
Square=0.3354 AdjR2=0.2822;
RMSE=0.37273 n=55/55;
p=0.0003;pl=0.0057;p2=0.0019;
p3=0.0284;p4=0.0003
Expected Ref condition model applied only to UMW
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Boatable sites: RfE 1 b LXCMGW = 1.52755 -
0.03762* LAT_DD83) 0.33101* L_AreaW Skm2_use)
+(0.17072*LXSlope_use
) +(0.82145*LXWidth_use)
RfElb_RMSE_LXCMGW=0.37
273
Upper Midwest (UMW) Wadeable Sites
Cond_l (eco9-W n=43):
LPtO 1_XCMGW= -0.13511 +0.05069(LXSlope_use)+0.17937(LXWidth_use)
-0.06747(W1_HALL)
R2=0.3303 AdjR2=0.2465; RMSE=0.12999 n=43/43 ;
p=0.0028;p 1=0.0115;p2=0.0025;p3=0.2867
Set W1HALL = 0 = minimum for ref sites:
RfE 1 _LXCMGW= -0.13511
+(0.05069*LXSlope_use) +(0.17937*LXWidth_use)
RfE 1_RMSE_LXCMGW= 0.12999
Western Mountains (WMT) Boatable Sites
Cond_N (eco9-B n=43):
RfNullM_LXCMGW= -0.12272
RfNullSD_LXCMGW= 0.26191
Western Mountains (WMT) Wadeable Sites
Cond_l (eco9-W n=69):
LPt01_XCMGW=0.24290 -0.09638(L_AreaWSkm2_use) -0.00007192(ELEV_PT_use) -
. 11520(LXSlope_use)
R2= 0.2037; AdjR2=0.1669; RMSE= 0.15289; n=68/69;
p=0.0019;pl=0.0063;p2=0.0024;p3=0.0425
RfEl_LXCMGW= 0.24290 -0.09638(L_AreaWSkm2_use) -0.00007192(ELEV_PT_use)
0.11520
*LXSlo
pe_use)
RfEIR
MSEL
XCMG
W=0.15
289
Xeric (XER) Boatable Sites
Cond i (eco9-B n=24):
LPtO 1_XCMGW= -0.32820 +0.24638(W1_HNOAG) -0.15614(W1_HAG)
R2=0.2896; AdjR2=0.2220; RMSE=0.15263; n=24/24 ; p=0.0276;pl=0.0273;p2=0.1633;
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Set W1HN0AG (positive beta) and W1HAG (negative beta) = 0 = minima for ref sites;
Note this results in lower ref mean, smaller RMSE, but lower (more lenient) percentile
values than NULL
RfElL
XCMG
W=-
0.32820
RfEIR
MSEL
XCMG
W=0.15
263
Xeric (XER) Wadeable Sites
Cond_l (eco9-W n=36):
LPtO 1_XCMGW= -0.21113 -0.19122(L_AreaWSkm2_use) +0.19148(LXSlope_use)
+0.65498(LXWidth_use)
R2=0.2294; AdjR2=0.1571; RMSE=0.25328; n=36/36;
p=0.0374;p 1=0.0695;p2=0.0730;p3=0.0086
RfE 1LXCMGW = -0.21113-0.19122* L_AreaW Skm2_use) +(0.19148*LXSlope_use)
+(0.654
98*LX
Width_
use);
RfEIR
MSEL
XCMG
W=0.25
328
CONDITION ASSIGNMENTS FOR RIPARIAN VEGETATION COVER NULL
MODELS:
RfNull25_LXCMGW=RfNullM_LXCMGW -
(0.67*RfNullSD_LXCMGW);
RfNull05_LXCMGW=RfNullM_LXCMGW -
(1 65*RfNullSD_LXCMGW);
RfOENullLXCMGW=LPt01 XCMGW-
RfNullMLXCMGW;
LXCMGW Cond N=XXXX';
if LPtO l_XCMGW<=RfNull05_LXCMGW then LXCMGW_Cond_N='Poor';
if LPtO l_XCMGW>RfNull05_LXCMGW and
LPtO l_XCMGW<=RfNull25_LXCMGWthen
LXCMGW_Cond_N='Medi';
if LPtO l_XCMGW>RfNull25_LXCMGW then
LXCMGW_Cond_N='Good';if LPtO 1XCMGW
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=. then LXCMGWCond_N='XXXX';
CONDITION ASSIGNMENTS FOR RIPARIAN VEGETATION COVER COND_l O/E
MODELS:
RfOE 1 _LXCMGW=LPtO lXCMGW-RfE 1 LXCMGW;
RfE 1 25 LXCMGW=RfE 1 LXCMGW-
(0.67*RfEl_RMSE_LXCMGW);
RfE l_05_LXCMGW=RfEl_LXCMGW -
(1.65 *RfE 1RMSELXCMGW);
LXC MGW C ond_ 1 -XXXX';
if LPtO l_XCMGW<=RfE 105LXCMGW then LXCMGW_Cond_l='Poor';
if LPtO l_XCMGW>RfE 105LXCMGW and LPt01_XCMGW<=RfEl_25_LXCMGW
then LXCMGW_Cond_l='Medi';
if LPtO 1 XCMGW>RfE 1 25 LXCMGW then
LXCMGW_Cond_l='Good';If
RfEl_LXCMGW=. then
LXC MGW C ond_ 1=XXXX';
if LPtO 1XCMGW =. then LXCMGW_Cond_l=XXXX';
CONDITION ASSIGNMENTS FOR RIPARIAN VEGETATION COVER COND_lb
O/EMODELS:
RfE lb_25_LXCMGW=RfE lbLXCMGW-
(0.67*RfElb_RMSE_LXCMGW);
RfE lb_05_LXCMGW=RfE lbLXCMGW-
(1 65*RfElb_RMSE_LXCMGW);
LXC MGW C ond_ 1 b=XXXX';
if LPtO l_XCMGW<=RfE lb_05_LXCMGW then LXCMGW_Cond_lb='Poor';
if LPtO l_XCMGW>RfE lb_05_LXCMGW and LPt01_XCMGW<=RfElb_25_LXCMGW
then LXCMGW_C ond_ 1 b='Medi';
if LPtO l_XCMGW>RfElb_25_LXCMGW then
LXCMGW_Cond_lb='Good';If
RfElb_LXCMGW=. then
LXC MGW C ond_ 1 b=XXXX';
if LPtO 1XCMGW =. then LXCMGW_Cond_lb=XXXX';
CONDITION ASSIGNMENTS FOR RIPARIAN VEGETATION COVER COND_lD
("All-Sites") O/E MODELS:
RfE OE1D 25 LXCMGW=RfE OE1D LXCMGW-
(0.67* RfEOE 1DRMSELXCMGW);
RfE OE 1D 05 LXCMGW=RfE OE 1D LXCMGW-
(1.65* RfEOE 1 DRMSELXCMGW);
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LXCMGW Cond 1D=XXXX';
if RfOE 1 D_LXCMGW<=RfE_OE 1D05LXCMGW then
LXCMGW_Cond_lD='Poor';if RfOElD_LXCMGW>
RfE OE 1D05LXCMGW and
RfOE 1D LXCMGW<=RfE OE1D 25 LXCMGW
then LXCMGW_Cond_lD='Medi';
ifRfOElD LXCMGW> RfE_0ElD_25_LXCMGW then LXCMGW_Cond_lD='Good';
If RfE_OElD_LXCMGW=. then LXCMGW_Cond_lD=XXXX';
if RfOE 1D LXCMGW =. then LXCMGW_Cond_lD=XXXX';
Reference Condition Models for
Instream Fish Coverbased on
Logio(0.01+XFC NAT)
Coastal Plain (CPL) Boatable Sites
Cond_N (eco9-B n=52):
RfNullM_LXFC_NAT= -0.57048 ;
RfNullSD LXFC NAT = 0.23527 ;
Coastal Plain (CPL) Wadeable Sites
Cond_N (eco9-B n=51):
RfNull
MLXF
C_NAT
0.39218
RfNullS
DLXF
C_NAT
=0.2982
0;
Northern Appalachian (NAP) Boatable Sites
Cond i (eco9-B n=47):
LPtO 1 _XF C_NAT= -5.46962 -0.06654(LON_DD83) -0.46088(L_AreaWSkm2_use)
+0.92383(LXWidth_use)
-1.05887(WlH_Crop);
R2= 0.3404; AdjR2=0.2776; RMSE=0.31921; n=47;
p=0.0013;pl=0.0047;p2=0.0897;p3=0.0483;p4=0.0082
Set WlH Crop = 0 = minimum in ref sites:
RfE 1 _LXFC NAT = -5.46962 -0.06654(LON_DD83) -0.46088(L_AreaWSkm2_use)
+(0.92383(LXWidt
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h_use);
RfE 1RMSELXF
C_NAT=0.31921;
Northern Appalachian (NAP) Wadeable Sites
Cond_l (eco9-W n=41):
LPtO 1_XFC_NAT= -0.08246 -0.26338(LXWidth_use);
R2=0.0736; AdjR2=0.0499; RMSE=0.28459; n=41; p=0.0862;p 1=0.0862
RfE 1 _LXF CNAT = -
0.08246-
0.26338(LXWidth_use);
RfE 1 RMSELXF CNAT=
0.28459;
Southern Appalachian (SAP) Boatable Sites
Cond_l (eco9-W n=22):
LPtO 1_XFC_NAT= -3.54570+0.07646(LAT_DD83) +0.22940(W1_HALL);
R2= 0.5343; AdjR2=0.4852; RMSE=0.17528; n=22/22; p=0.0007;pl=0.0089;p2=0.0065
Set W1_HALL= 0 ~ note it is a positive association (mimimum in ref sites=0.03; in all
sites=0):
RfE 1 _LXF CNAT = -
3.54570+(0.07646*LAT_D
D83);
RfE 1 RMSE LXFC NAT=
0.17528;
Southern Appalachian (SAP) Wadeable Sites
Cond_l (eco9-W n=32):
LPtO 1 _XF C_NAT= -2.89088 +0.06090(LAT_DD83)
+0.0006263 l(ELEV_PT_use) -7.37514(W1_HAG);
R2=0.4169; AdjR2=0.3544; RMSE=0.31006; n=32/32;
p=0.0015;pl=0.0896;p2=0.0785;p3=0.0041
Set W 1_HAG = 0 = minimum for ref sites:
RfE 1 _LXF CNAT = -2.89088 +(0.06090*LAT_DD83)
+(0.0006263 l*ELEV_PT_use) ;RfEl_RMSE_LXFC_NAT=
0.31006;
CENPL (NPL, SPL, TPL) Northern, Southern & Temperate Plains -- Boatable
Sites
Cond i (CENPL-B n=47):
LPtO 1_XFC_NAT= 2.42961 -0.02335(LAT_DD83) +0.01564(LON_DD83) -
0.11096(L_AreaWSkm2_use)
-0.00934(AG_1KMCIRCLE);
R2=0.3446; AdjR2=0.2822; RMSE=0.32257; n=47/47;
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p=0.0012;p 1=0.1204;p2=0.1228;p3=0.0400;p4=0.0070
Set AG_1KMCIRCLE = 0 = min for CENPL {Minima are 0%,
3.6%, 0.06% for NPL(n=33),SPL(n=2), TPL(n=22)}:
RfE 1 _LXFC NAT = 2.42961 -0.02335*LAT_DD83) +(0.01564*LON_DD83) -
0.11096*
LAreaW
Skm2_use
);
RfElRM
SELXFC
_NAT=
0.32279;
CENPL (NPL, SPL, TPL) Northern, Southern & Temperate Plains --
Wadeable Sites
Cond i (CENPL-W n=155):
LPtO 1_XFC_NAT= -0.20615 +0.00409(LON_DD83) -
0.08735(L_AREAWSkm2_use)+0.00025270(ELEV_PT_use)
-0.00258(AG_1KMCIRCLE)
+0.04332(URB_1KMCIRCLE);R2
=0.1740; AdjR2=0.1457;
RMSE=0.33531; n=152/155;
p<0.000 l;p 1=0.5890;p2=0.0039;p3=0.0154;p4=0.1134;p5=0.0032
Set AG1KMCIRCLE and URB1KMCIRCLE =0 = minima for ref sites each of the 3
regions:
RfE 1 _LXFC NAT = -0.20615 +(0.00409*LON_DD83) -
0.08735*L_AREAWSkm2_use)+(0.00025270*ELEV_PT_use);
RfE 1 RMSELXF C_NAT=0.33531;
Upper Midwest (UMW) Boatable Sites
Cond i (eco9-B n=36)
LPtO 1_XFC_NAT= 3.97716 +0.05232(LON_DD83) -0.20032(W1_HAG);
R2=0.2349 AdjR2=0.1885; RMSE=0.31606; n=36/36 ; p=0.0121;pl=0.0049;p2=0.8532
Set W 1_HAG = 0 = minimum in ref sites:
RfE 1 _LXF CNAT = 3.97716 +(0.05232*LON_DD83);
RfE 1 RMSELXF CNAT = 0.31606;
Upper Midwest (UMW) Wadeable Sites
Cond_l (eco9-W n=43):
LPtO 1_XFC_NAT= -0.48451
+0.17605(L_AreaWSkm2_use) -0.35844(LXWidth_use) ;R2
=0.0740; AdjR2=0.0277; RMSE=0.29010;
n=43/43;p=0.215 l;p 1=0.0818;p2=0.1406
RfEl LXFC NAT=-0.48451
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+(0.17605*L_AreaWSkm2_use) -0.35844*LXWidth_use);
RfEl_RMSE_LXFC_NAT= 0.29010;
Western Mountain (WMT) Boatable Sites
Cond i (eco9-B n=43):
LPtO 1 _XF C_NAT= -1.40552 +0.48649(LXWidth_use)
-5,67454(WlH_Crop) -0.11975(RDDEN_WS_use);
R2=0.2408; AdjR2=0.1824; RMSE=0.23044; n=43/43;
p=0.0124;p 1=0.0175;p2=0.0077;p3=0.0654
Set WlH Crop and RDDENWSuse = 0 = minima for ref sites:
RfE 1 _LXF CNAT = -1.40552 +(0.48649*LXWidth_use)
RfE 1 RMSELXF CNAT = 0.23044;
Western Mountain (WMT) Wadeable Sites
Cond_l (eco9-W n=69):
LPtO 1_XFC_NAT= 1.57993 +0.01058(LAT_DD83) +0.01895(LON_DD83) -
0.08287(L_AreaWSkm2_use)
-11.24156(W1_HAG) -
0.05374(RDDEN_WS_use
) ; R2 =0.3466;
AdjR2=0.2939;
RMSE=0.21669 n=68/69;
p<0.0001;pl=0.1652;p2=0.0013;p3=0.0414;p4=0.0054;p5=0.1064
Set W1HAG and RDDEN_WS_use = 0 = minima for ref sites:
RfE 1 _LXFC NAT = 1.57993 +(0.01058*LAT_DD83) +(0.01895LON_DD83) -
0.08287(L_AreaW
Skm2_use);
RfE IRMSELXF
C_NAT=0.21669;
Xeric (XER) Boatable Sites
Cond i (eco9-B n=24):
LPtO 1_XFC_NAT= -0.03292 -0.00013276(ELEV_PT_use) -
0.42159(LXWidth_use) ; R2 =0.1266; AdjR2=0.1266;
RMSE=0.31024; n=23/24; p=0.2582;pl=0.1323;p2=0.1973
RfE 1 _LXFC NAT = -0.03292 -0.00013276*ELEV_PT_use)
-0.42159*LXWidth_use) ;RfEl_RMSE_LXFC_NAT=
0.31024;
Xeric (XER) Wadeable Sites
Cond_l (eco9-W n=36):
LPtO 1_XFC_NAT= 0.96284
+0.01132(LON_DD83)+0.18104(LXSlope_use) -19.86518(WlH_Crop);
R2=0.2738; AdjR2=0.2057; RMSE=0.24231 n=36/36;
p=0.0155;pl=0.1628;p2=0.043 l;p3=0.0353
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note LXSlope distribution is similar across the range of the other model variables in all
sites;
Set WlH_Crop = 0 = minimum in ref sites:
RfE 1 _LXF CNAT = 0.96284 +(0.01132*LON_DD83)
+(0.18104*LXSlope_use);
RfEl_RMSE_LXFC_NAT=0.24231;
CONDITION ASSIGNMENTS FOR INSTREAM FISH COVER NULL MODELS:
RfNull25_LXF C_NAT=RfNullM_LXFC_NAT -
(0.67*RfNullSD_LXFC_NAT);
RfNull05_LXF C_NAT=RfNullM_LXFC_NAT -
(1 65*RfNullSD_LXFC_NAT);
RfOENull_LXFC_NAT=LPtO 1XFCNAT-
RfNullMLXFCNAT;
LXFC NAT Cond N=XXXX';
if LPtO l_XFC_NAT<=RfNull05_LXFC_NAT then LXFC_NAT_Cond_N='Poor';
if LPtO l_XFC_NAT>RfNull05_LXFC_NAT and
LPt 01 _XF C_NAT <=RfNull25_LXF C_NATthen
LXFC_NAT_Cond_N='Medi';
if LPtO l_XFC_NAT>RfNull25_LXFC_NAT then
LXFC_NAT_Cond_N='Good';If LPtO 1XFCNAT =.
then LXFC_NAT_Cond_N=XXXX';
CONDITION ASSIGNMENTS FOR INSTREAM FISH COVER COND_l O/E
MODELS:
RfE l_25_LXFC_NAT=RfE 1LXFCNAT-
(0.67*RfEl_RMSE_LXFC_NAT);
RfE l_05_LXFC_NAT=RfE 1LXFCNAT-
(1 65*RfEl_RMSE_LXFC_NAT);
RfOE 1 _LXF CNAT=LPtO 1 _XF CNAT -
RfE 1 _LXF CNAT;
if LPt01_XFC_NAT<=RfEl_05_LXFC_NAT then LXFC_NAT_Cond_l='Poor';
if LPtO 1 _XFC_NAT>RfE 1 05 LXFC NAT and
LPtO 1 _XF CNAT <=RfE 1 25LXF CNAT
then LXFC_NAT_Cond_l='Medi';
if LPtO 1 _XFC_NAT>RfE 1 25 LXFC NAT then
LXF CNATC ond_ 1='Good' ;If
RfE 1 _LXF C NAT =. then
LXFC NAT COND 1=XXXX';
If LPtOl XFC NAT =. then LXFC_NAT_Cond_l=XXXX';
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9 Human Health Fish Tissue Indicator
Fish are time-integrating indicators of persistent pollutants, and the bioaccumulation of
contaminants in fish tissue has important human health implications. Contaminants in fish pose
various health risks to human consumers (e.g., cancer risks, and noncancer risks such as
reproductive effects or impacts to neurological development). The NRSA 2018-19 human health
fish tissue indicator consists of collection and analysis of two types of fish composite samples,
including whole fish samples for homogenized fillet analyses and fish fillet plug samples.
Collectively, these samples provide information on the national distribution of selected persistent,
bioaccumulative, and toxic (PBT) chemical residues (specifically, mercury, polychlorinated biphenyls
or PCBs, and per- and polyfluoroalkyl substances or PFAS) in fish species that people might catch
and eat. The whole fish samples for homogenized fillet analyses were collected from a subset of 290
rivers 5th order and greater in size in the conterminous United States (because these rivers are more
likely to contain predator fish commonly consumed by humans) whereas the fish fillet plug samples
were collected from all river and stream sites regardless of river or stream size. Results of analyses of
mercury, PCB, and PFAS fillet tissue concentrations are presented for this indicator.
9.1 FIELD FISH COLLECTION
9.1.1 WHOLE FISH SAMPLES FOR HOMOGENIZED FILLET ANALYSIS
The human health fish tissue indicator field and analysis procedures described below were based on
EPA's National Study of Chemical Residues in Lake Fish Tissue (EPA 2009) and EPA's Guidance for
Assessing Chemical Contaminant Datafor Use in Fish Advisories, Volumes 1-2 (third edition) (EPA 2000).
The NRSA crews collected whole fish samples for the fillet tissue indicator from a subset of rivers 5th
order and greater in size. The fish samples collected for fillet tissue analysis consisted of a composite
of fish specimens (i.e., typically five similarly sized individuals of one target species)5 from each site.
The fish had to be large enough to provide sufficient tissue for analysis and for archiving, when
possible. Additional criteria for each fish composite sample included fish that were:
All of the same species (for each site);
Harvestable size per legal requirements or of consumable size if there were no harvest limits;
and
At least 190 mm in length and of similar size so that the smallest individual in the composite
was no less than 75% of the total length of the largest individual in the composite.
Crews were provided with a recommended list of target and alternate fish species (Table 9-1), but
they could choose an appropriate substitute if none of the recommended fish species were available.
Fish collection data were screened to exclude individual fish specimens with lengths less than 190
mm or composite samples where field crews collected non-target species.
5 Use of composite sampling for screening studies is a cost-effective way to estimate average contaminant concentrations
while also ensuring that there is sufficient fish tissue to analyze for all contaminants of concern.
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To prepare fillet composite samples for chemical analysis, fish composite samples from each site
were scaled and filleted in the laboratory. In filleting individual fish, muscle tissue was removed from
both sides of each fish leaving the skin on and the belly flap attached. Fillets from the individual
specimens that comprised a composite sample were homogenized together before being analyzed
for contaminants.
9.1.2 FISH TISSUE PLUGS
NRSA crews attempted to collect fish for the tissue plug analysis from all river and stream sites
regardless of river or stream size. Two fish tissue plugs for mercury analysis were removed from two
fish of the same species (one plug per fish) from the target list. These fish were collected during the
fish assemblage sample collection effort. A plug tissue sample was collected by inserting a biopsy
punch into a descaled thicker area of dorsal muscle section of a live fish. After collection, antibiotic
salve was placed over the wound and the fish was released.
Crews were provided with a recommended list of target and alternate fish species (Table 9-1), but
they could choose an appropriate substitute if none of the recommended fish species were available.
Suitable alternate species include species that are typically consumed by humans and meet the
minimum size requirements (i.e., at least 190 mm in length).
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Table 9-1. Recommended Target Species and Alternate Species for Fish Tissue Indicator
Sample Collection.
Family Name
Common Name
Scientific Name
Length Guideline
(Estimated
Minimum)
Spotted bass
Micmpterus pmictulatus
~280 mm
Centrarchidae
Largemouth bass
Micmpterus salmoides
~280 mm
Smallmouth bass
Micmpterus dolomieu
~300 mm
Black crappie
Pomoxis nigromaculatus
~330 mm
White crappie
Pomoxis annularis
~330 mm
Ictaluridae
Channel catfish
Ictaluruspunctatus
~300 mm
Blue catfish
Ictalurusfurcatus
~300 mm
Flathead catfish
Pjlodictis olivaris
~300 mm
Percidae
Sauger
Sander canadensis
~380 mm
Cfl
Walleye
Sander lit re us
~380 mm
.ฃ!
u
Yellow perch
Percajlavescens
~330 mm
a
Moronidae
White bass
Morone chiysops
~330 mm
P
bn
H
Esocidae
Northern pike
Esox lucius
-430 mm
Chain pickerel
Esox niger
-430 mm
Brown trout
Salmo trutta
300 mm
Salmonidae
Cutthroat trout
Oncorhjnchus clarkii
300 mm
Rainbow trout
Oncorhjnchus my kiss
300 mm
Brook trout
Salvelinusfontinalis
330 mm
ฃ CO
CS
Oh
Rock bass
Amblonplites rupestris
200 mm
Redbreast sunfish
Eepomis auritus
200 mm
9.2 MERCURY ANALYSIS AND FISH TISSUE CRITERION FOR HUMAN
HEALTH
All fish tissue samples (both homogenized fillet composite tissue and fillet tissue plug samples) were
analyzed for total mercury. The samples were prepared using EPA Method 163 IB, Appendix A
(EPA 2001a) and analyzed using EPA Method 1631E (EPA 2002), which utilizes approximately 1 g
of fillet tissue for analysis. In screening-level studies of fish contamination, EPA guidance
recommends monitoring for total mercury rather than methylmercury (an organic form of mercury)
since most mercury in adult fish is in the toxic form of methylmercury, which will be captured
during an analysis for total mercury. Applying the assumption that all mercury is present in fish
tissue as methylmercury is also protective of human health. The fish tissue criterion used to interpret
mercury concentrations in fillet tissue for human health protection is 0.3 milligrams (mg) of
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methylmercury per kilogram (kg) of tissue (wet weight), or 300 parts per billion (ppb), which is
EPA's fish tissue-based CWA Section 304(a) water quality criterion recommendation for
methylmercury (EPA 2001b).6 For more information on the fish tissue criterion or screening levels
for human health protection, see Section 9.5. This criterion represents the concentration of mercury
that, if exceeded, may adversely impact human health.
Application of this criterion to the fish tissue composite data from this study identifies the number
and percentage of river miles in the sampled population containing fish with mercury tissue
concentrations that are above the recommended mercury fish tissue-based water quality criterion.
Results for the fish fillet composite data are presented for the sampled population of miles of rivers,
which are defined as 5th order or larger, and for the percentage of miles containing fish with
mercury fillet concentrations that are above the criterion. Mercury concentration data from analysis
of homogenized fish fillet composite samples are available to download from the NRSA Fish Tissue
Studies webpage, https:/ /www.epa.gov/fish-tech/national-rivers-and-streams-assessment-fish-
tissue-studies. In addition, summary statistics, including the number of detections, are reported in
Table 9-2.
Results for the fish tissue plugs are presented for all rivers and streams in the NRSA target
population including the unassessed portion where fish tissue plugs could not be collected. To
examine within-year variability, analysts used the revisit sites to calculate a signal: noise (S:N)
estimate for the national mercury in fish tissue plug dataset. For NRSA 2013-14 the result was a S:N
value of 6.35. Mercury concentration data from fish tissue plugs are available to download from the
NARS data webpage, https: / /www.epa.gov/national-aquatic-resource-surveys /data-national-
aquatic-resource-surveys.
9.3 PCB ANALYSIS AND FISH TISSUE SCREENING LEVELS TO PROTECT
HUMAN HEALTH
Fillet tissue samples from 290 whole fish composite samples collected at river sites were analyzed for
PCBs. EPA Method 1668C (EPA 2010) was used to analyze homogenized fillet tissue samples from
each fish composite sample. This method uses approximately 10 g of fillet tissue for analysis and
provides results for the full suite of 209 PCB congeners. The total PCB concentration for each
sample was determined by summing the concentration results for any of the 209 congeners that
were detected, using zero for any congeners that were not detected in the sample.
In the NRSA 2018-19 report, EPA included total PCB results for general fish consumers and for
high-frequency fish consumers, such as subsistence fishers or those who eat several meals of locally
caught river fish per week, which include some recreational fishers or some individuals in
underserved communities. EPA used fish tissue screening levels, expressed as wet-weight
concentrations of total PCBs, to characterize cancer human health risks for general fish consumers
and high-frequency fish consumers. EPA applied a total PCB fish tissue screening level of 12 ppb
(wet weight) for cancer effects among general fish consumers, which is based on a fish consumption
6 Because EPA relies on the CWA Section 304(a) water quality criterion for methylmercury to interpret the mercury
results, EPA is only reporting mercury results for the general population and is not including an additional analysis and
interpretation for general fish consumers or high-frequency consumers.
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rate of 32 grams per day (or one 8-ounce meal of locally caught river fish per week). EPA also
applied a total PCB fish tissue screening level of 2.8 ppb (wet weight) for cancer effects among high-
frequency fish consumers, which is based on a fish consumption rate of 142 grams per day (or four
to five 8-ounce meals of locally caught river fish per week). For more information on the fish tissue
screening levels for human health protection, see Section 9.5.
Application of these screening levels to the PCB fillet tissue composite data identifies the number
and percentage of river miles in the sampled population containing fish with total PCB fillet
concentrations that are above each total PCB fish tissue screening level. Results are presented for
sampled population of the miles of rivers (defined as 5th order or larger) and for the percentage of
river miles containing fish with total PCB fillet concentrations that are above each total PCB fish
tissue screening level to protect human health. PCB concentration data from analysis of
homogenized fish fillet composite samples are available to download from the NRSA Fish Tissue
Studies webpage, https:/ Avww.epa.gov/fish-tech/national-rivers-and-streams-assessment-fish-
tis sue-studies.
9.4 PFAS ANALYSIS AND AND FISH TISSUE SCREENING LEVELS TO
PROTECT HUMAN HEALTH
Fillet tissue samples prepared from 290 whole fish composite samples collected at river sites were
analyzed for 33 per- and polyfluoroalkyl substances (PFAS). At the time when the composite samples
were analyzed for PFAS, there were no standard EPA methods for PFAS analysis of tissue, so the
samples were analyzed by SGS AXYS Analytical Services using a proprietary procedure developed
by their laboratory in Sidney, British Columbia. That procedure, which utilizes approximately 1 g of
fillet tissue for analysis, uses high performance liquid chromatography with tandem mass
spectrometry (HPLC-MS/MS) and applies the technique known as isotope dilution to determine the
concentration of each of the 33 PFAS.
For PFOS, the EPA applied a 0.25 ppb noncancer screening level for general fish consumers to
interpret PFOS concentrations in each fillet tissue composite sample.7 Note that although the EPA
classifies PFOS as likely to be carcinogenic to humans, the agency applied a noncancer screening
level for this analysis. Noncancer health effects from PFOS exposure can occur at lower PFOS
levels than cancer does, so applying a lower screening level to reduce the risk of noncancer health
effects from dietary exposure to PFOS also reduces risks of cancer.
In April 2024, EPA released the National Primary Drinking Water Regulation for six PFAS,
including PFOS, and issued a final Human Health Toxicity Assessment for Perfluorooctane Sulfonic
Acid (PFOS) and Related Salts (EPA 2024). EPA developed an overall RfD for PFOS of 1x107
mg/kg/day. This RfD value was used to derive the PFOS fish tissue screening level mentioned
above. For more information on the fish tissue screening levels for human health, see Section 9.5.
Application of the PFOS noncancer screening level for general fish consumers to the PFOS fillet
7 The calculated screening level represents a fillet tissue concentration slightly below the method detection limit (MDL)
for PFOS for the NRSA 2018-19 (MDL = 0.35 ppb), making it difficult to determine the full extent of exceedances due
to the possibility of some samples containing PFOS concentrations above 0.25 but below 0.35 ppb.
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tissue data identifies the number and percentage of river miles in the sampled population containing
fish with PFOS fillet concentrations that are above the PFOS fish tissue screening level for human
health. Results are presented for the miles of rivers (defined as 5th order or larger) that could be
sampled and for the percentage of river miles containing fish with PFOS fillet concentrations that
are above the PFOS fish tissue screening level to protect human health. PFAS concentration data
from fish fillet tissue composite samples are available to download from the NRSA Fish Tissue
Studies webpage, https:/ /www.epa.gov/fish-tech/national-rivers-and-streams-assessment-fish-
tis sue-studies.
Summary statistics, including the number of detections for mercury, total PCBs, and each of the 33
PFAS are provided in Table 9-2.
Table 9-2. NRSA 2018-19 Fish Fillet Tissue Composite Sample Summary Data.
Chemical
Number of
Detection
Method
Measured
Weighted
Measured
Detections
Frequency
Detection
Minimum
Median
Maximum
(%)
Limit
Concentration
Concentration
Concentration
(MDL)
(PPb)
(PPb) *
(PPb) *
(PPb) *
Mercury
290
100
0.090
9.44
180
1340.00
Total PCBs
290
100
0.00022-
0.00561**
0.171
9.04
1212.00
Perfluorobutanoic acid
18
6
0.551
0.517
-------
Chemical
Number of
Detection
Method
Measured
Weighted
Measured
Detections
Frequency
Detection
Minimum
Median
Maximum
(%)
Limit
Concentration
Concentration
Concentration
(MDL)
(ppb)
(ppb)*
(ppb) *
(ppb)*
Perfluoroheptane sulfonic
acid (PFHpS)
1
<1
0.154
0.162
-------
Chemical
Number of
Detections
Detection
Frequency
(%)
Method
Detection
Limit
(MDL)
(ppb)
Measured
Minimum
Concentration
(ppb)*
Weighted
Median
Concentration
(ppb) *
Measured
Maximum
Concentration
(ppb)*
ll-Chloroeicosafluoro-3-
oxaundecane-l-sulfonic acid
(HCl-PF30UdS)
0
0
0.889
0.00
-------
day (or one 8-ounce meal of locally caught river fish per week), consistent with the U.S. Department
of Agriculture and Department of Health and Human Services' Dietary Guidelines for Americans, 2020-
2025 (USDA and HHS 2020). For the screening level for high-frequency fish consumers (such as
subsistence or recreational fishers or individuals from underserved populations), EPA used a fish
consumption rate of 142 grams per day (or four to five 8-ounce meals of locally caught river fish per
week) which is described in the EPA 2000 Human Health Methodology (USEPA 2000b). Because
the total PCBs screening levels associated with cancer effects were lower than the screening levels
associated with noncancer effects, EPA only evaluated the PCB concentrations in fish samples
against the screening levels associated with cancer effects. This conservative approach is also likely
to be protective against noncancer effects, which may occur at higher levels of total PCB
contamination.
PFAS: For the NRSA 2018-19 report, EPA analyzed fish fillet tissue samples for 33 PFAS
chemicals. PFOS was the most commonly detected of the PFAS in 91 percent of the fish fillet
composite samples so EPA derived PFOS fish tissue screening levels for cancer and non-cancer
effects using the equations found in EPA's Guidancefor Assessing Chemical Contaminant Data for Use in
Fish Advisories. The screening levels represent the concentration of PFOS in fish tissue that should
not be exceeded based on a total consumption-weighted rate of 0.032 kg of fish/day for general fish
consumers. The PFOS screening levels were based on an average adult human body weight default
value of 80 kg9, an RfD of 11 10 7 mg/kg day (for non-cancer effects), a cancer slope factor of 39.5
(mg/kg/d)-1 and a cancer risk level of 10~5 (for cancer effects). Because the PFOS screening level
associated with noncancer effects was lower than the screening level associated with cancer effects,
EPA intended to apply only the screening level associated with noncancer effects. This conservative
approach is also likely to be protective against cancer effects, which may occur at higher levels of
PFOS contamination.
9.6 LITERATURE CITED
EPA. 2000. Guidance for Assessing Chemical Contaminant Data for Use in Fish Advisories,
Volumes 1-2 (Third Edition). EPA 823-B-00-007. U.S. Environmental Protection Agency,
Office of Water, Washington, DC.
EPA. 2001a. Appendix to Method 1631, Total Mercury in Tissue, Sludge, Sediment, and Soil by
Acid Digestion and BrCl Oxidation. EPA-821-R-01-013. January 2001. U.S. Environmental
ProtectionAgency, Office of Water, Washington, DC.
EPA. 2001b. Water Quality Criterion for the Protection of Human Health: Methylmercury. EPA-
823- R-01-001. U.S. Environmental Protection Agency, Office of Water, Washington, DC.
EPA. 2002. Method 1631, Revision E: Mercury in Water by Oxidation, Purge and Trap, and Cold
VaporAtomic Fluorescence Spectrometry. EPA-821-R-02-019. August 2002. U.S.
Environmental Protection Agency, Office of Water, Washington, DC.
EPA. 2009. The National Study of Chemical Residues in Lake Fish Tissue. EPA-823-R-09-006. U.S.
Environmental Protection Agency, Office of Water, Washington, DC.
EPA. 2010. Method 1668C, Chlorinated Biphenyl Congeners in Water, Soil, Sediment, Biosolids,
and Tissue by HRGC/HRMS, April 2010. U.S. Environmental Protection Agency, Office of
9 For PFOS, the reference dose value was based on immune, cardiovascular, and hepatic health effects applicable to the
general population, in addition to developmental effects, so the relevant population is average adults.
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Water,Washington, DC.
EPA. 2024. Final Human Health Toxicity Assessment for Perfluorooctane Sulfonic Acid (PFOS)
and Related Salts. EPA/815/R-24/007. U.S.Environmental Protection Agency, Office of
Water, Washington, DC.
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10 ENTERCOCCI INDICATOR
The EPA has developed and validated a molecular testing method employing quantitative
polymerase chain reaction (qPCR) as a rapid approach for the detection of enterococci in
recreational water. NRSA used this method to estimate the presence and quantity of these fecal
indicator bacteria in the nation's rivers and streams. The statistical threshold value of 1,280 calibrator
cell equivalents (CCE)/100 mL from EPA's 2012 'Recreational Water Quality Criteria document
(RWQC) was then applied to the enterococci data to assess the recreational condition ofirivers and
streams.
10.1 FIELD COLLECTION
To collect enterococci samples, field crews took a water sample for the fecal indicator at the last
transect after all other sampling was completed. Using a pre-sterilized 250 mL bottle, they collected
the sample approximately 1 m off the bank at about 0.3 m (12 inches) below the water. Following
collection, crews placed the sample in a cooler and kept it on ice prior to filtration of two 50 mL
volumes. Samples were filtered and frozen on dry ice within 6 hours of collection. In addition to
collecting the sample, crews looked for signs of disturbance throughout the reach that would
contribute to the presence of fecal contamination to the waterbody.
10.2 LAB METHODS
The sample collections and the laboratory method followed EPA's Enterococcus qPCR Method
1609.1 (USEPA 2015; available on-line at https://www.epa.gov/cwa-methods/other-clean-water-
act-test-methods-microbiological). As with EPA Draft Method A, used in the NRSA 2008-09 study,
Method 1609.1 describes a quantitative polymerase chain reaction (qPCR) procedure for the
detection of DNA from enterococci bacteria in ambient water matrices based on the amplification
and detection of a specific region of the large subunit ribosomal RNA gene (IsrRNA, 23S rRNA)
from these organisms. Both methods use an arithmetic formula (the comparative cycle threshold
(CT) method; Applied Biosystems, 1997) to calculate the ratio of enterococcus IsrRNA gene target
sequence copies (TSC) recovered in total DNA extracts from the water samples relative to those
recovered from similarly prepared extracts of calibrator samples containing a consistent, pre-
determined quantity of Enterococcus cells. Mean estimates of the absolute quantities of TSC
recovered from the calibrator sample extracts were then used to determine the quantities of TSC in
the water samples and then converted to CCE values as described in the section below. To
normalize results for potential differences in DNA recovery, monitor signal inhibition or
fluorescence quenching of the PCR analysis caused by a sample matrix component, or detect
possible technical error, CT measurements of sample processing control (SPC) and internal
amplification control (IAC) target sequences were performed as described in Method 1609.1.
The primary differences between EPA Draft Method A (subsequently published as EPA Method
1611,USEPA 2012a) and Method 1609.1 are that Method 1609.1 includes the IAC assay, an
improved polymerase reagent with greater resistance to inhibitory compounds and allows direct
analyses of undiluted sample DNA extracts. Analyses of diverse river water samples have indicated
no significant difference in the quantitative estimates obtained by the two methods (Sivaganesan et
al., 2014).
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10.3 APPLICATION OF BENCHMARKS
10.3.1 CALIBRATION
Estimates of absolute TSC recoveries from the calibrator samples were determined from standard
curves using EPA-developed plasmid DNA standards of known TSC concentrations as described in
Method 1609.1. Estimates of TSC recovered from the test samples were determined by the
comparative cycle threshold (Ct) method, as also described in Method 1609.1. Before applying the
EPA benchmark to the qPCR data, it was necessary to convert the TSC estimates to CCE values.
The standardized approach developed for this conversion is to assume 15 TSC/CCE (USEPA
2015). This approach allows the CCE values to be directly compared to the EPA RWQC values
(Haugland et al., 2014). A slightly modified approach was employed in the earlier NRSA 2008-09
study toobtain the same conversions of TSC to standardized CCE units.
10.3.2 BENCHMARKS
For the data analysis of the enterococci measurements determined by Method 1609.1, analysts used
a benchmark as defined and outlined in EPA's recommended recreational criteria document for
protecting human health in ambient waters designated for swimming (USEPA 2012b). Enterococci
CCE/100 mL values were compared to the EPA benchmark of 1280 CCE/100 mL10 (USEPA
2012b). Enterococci concentration data are available to download from the NARS datawebpage -
https: / /www.epa.gov/national-aquatic- resource-surveys /data-national-aquatic-resource-surveys.
To examine within-year variability, analysts used the revisit sites to analyze signal to noise of
enterococci concentrations and to analyze condition classes in a 2x2 contingency table. Condition
classes were defined as "above benchmark" and "at or below benchmark" based on the EPA
benchmark value of 1,280 CCE/100 mL. The S:N ratio for concentration values was 0.36. However,
results from the contingency table analysis show that 78% of sites had the same condition class
during both visits (i.e., 64.7% of the 184 revisits that were assessed were at or below benchmark in
both visits and 13.6% were above benchmark in both visits) and 21.7% had mixed classes between
visits.
10.4 LITERATURE CITED
Applied Biosystems (1997) User Bulletin #2. ABI PRISM 7700 Sequence Detection System.
AppliedBiosystems Corporation, Foster City, CA.
Haugland, R.A., S.D. Siefring, M. Varma, A.P. Dufour, K.P. Brenner, T.K. Wade, E. Sams, S.
Cochran, S. Braun, and M. Sivaganensan. 2014. Standardization of enterococci density
estimates by EPAqPCR methods and comparison of beach action value exceedances in
river waters with culturemethods. Journal of Microbiological Methods 105, 59-66.
Sivaganesan, M., S. Siefring, M. Varma, and R.A. Haugland. 2014. Comparison of Enterococc/ts
quantitative polymerase chain reaction analysis results from midwestern U.S. river samples
10 Estimated Illness Rate (NGI): 32/1000 primary contact recreators. See USEPA 2012b for more information on
additionalNGI statistical threshold values for the qPCR method.
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using EPA Method 1611 and Method 1609 PCR reagents. Journal of Microbiological Methods
101:9-17. Corrigendum 115, 166.
USEPA. 2012a. Method 1611: Enterococci in Water by TaqManฎ Quantitative Polymerase Chain
Reaction (qPCR) Assay. EPA-821-R-12-008. Office of Water,Washington, DC.
USEPA. 2012b. Recreational Water Quality Criteria. EPA 820-F-12-058. Washington, D.C.
USEPA. 2015. Method 1609.1: 'Enterococci in water by TaqManฎ quantitative polymerase chain
reaction (qPCR) assay with internal amplification control (IAC)assay. EPA-820-R-15-009.
US Environmental Protection Agency, Office of Water, Washington, D.C.
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11 ALGAL TOXINS
Cyanobacteria are one-celled photosynthetic organisms that normally occur at low levels. Under
eutrophic conditions, cyanobacteria can multiply rapidly. Not all cyanobacterial blooms are toxic,
but some may release toxins, such as microcystins and cylindrospermopsin. Recreational exposure is
typically a result of inhalation, skin contact, or accidental ingestion. When people are exposed to
cyanotoxins, adverse health effects may range from a mild skin rash to serious illness or in rare
circumstances, death. Acute illnesses caused by short-term exposure to cyanobacteria and
cyanotoxins during recreational activities include hay fever-like symptoms, skin rashes, respiratory
and gastrointestinal distress.
Microcystins refers to an entire group of toxins (all of the different congeners, rather than just one
congener). Cyanobacteria can produce one or many different congeners at any one time, including
Microcystin-LR (used in the kit's calibration standards), Microcystin-LA, and Microcystin-RR. The
different letters on the end signify the chemical structure (each one is slightly different) which makes
each congener different.
For the NRSA, both microcystins and cylindrospermopsin were analyzed.
11.1 FIELD METHODS
The algal toxin sample was collected as a grab sample from Transect A (non-wadeable) or the X-
site11 (wadeable) in a flowing portion near the middle of the channel. Water was collected in a 3 L
beaker and then transferred to a 500 mL bottle. The bottle was kept on ice and then stored frozen
until analysis. Both microcystins and cylindrospermopsin were analyzed from the 500 mL bottle.
11.2 ALGAL TOXIN ANALYSIS AND APPLICATION OF BENCHMARKS
The microcystins sample was measured using an enzyme-linked immunosorbent assay (ELISA)
procedure with an Abraxis Microcystins-ADDA Test Kit. For freshwater samples, the procedure's
reporting range is 0.15 [ig/L to 5.0 [ig/L, although, theoretically, the procedure can detect, but not
quantify, microcystins concentrations as low as 0.10 [ig/L. Microcystin concentrations were
evaluated against the EPA recommended criterion and swimming advisory level of 8 (ig/L (USEPA
2019). Microcystin concentration data are available to download from the NARS data webpage -
https: / /www.epa.gov/national-aquatic-resource-surveys / data-national-aquatic-resource-surveys.
The cylindrospermopsin sample was measured using an enzyme-linked immunosorbent assay
(ELISA) procedure with an Abraxis Cylindrospermopsin Test Kit. For freshwater samples, the
procedure's reporting range is 0.02 [ig/L to 2.0 [ig/L, although, theoretically, the procedure can
detect, but not quantify, concentrations as low as 0.04 [ig/L. Cylindrospermopsin concentrations
were evaluated against the EPA recommended criterion and swimming advisory level of 15 (Jg/L
(USEPA 2019). Cylindrospermopsin concentration data are available to download from the NARS
11 The "X-site" is the mid-point of the sampling reach, and it determines the location and extent for the rest of the
sampling reach.
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data webpage - https://www.epa.gov/national-aquatic-resource-surveys/data-national-aquatic-
resource-surveys.
To examine within-year variability, analysts used the revisit sites to calculate a S:N ratio estimate for
the national microcystin dataset. The result was a S:N value of 4.8. For this calculation, non-detect
values were excluded due to the fact that no variance between repeat sites when both were non-
detect may overestimate the S:N.
11.3 LITERATURE CITED
Abraxis, Cylindrospermopsin ELISA Microtiter Plate Enzyme-Linked Immunosorbent Assay for
the Determination of Cylindrospermopsin in Water," Product 520011, UG 21-059 (REV
01), Undated. Retrieved December 2022 from
https://www.goldstandarddiagnostics.us/media/15642/ug-21-059-rev-01-abraxis-
cylindrospermopsin-elisa_522011 .pdf.
Abraxis, "Cylindrospermopsin ELISA Plate 522011," Flowchart. 03FEB2022. Retrieved December
2022 from https://www.goldstandarddiagnostics.us/media/15789/fc-22-043-rev-01-
abraxis-cylindrospermopsin_522011 .pdf.
Abraxis, "Microcystins-ADDA ELISA Microtiter Plate Enzyme-Linked Immunosorbent Assay for
the Congener-Independent* Determination of Microcystins and Nodularins in Water
Samples," Product 520011, UG 21-052 (REV 01), Undated. Retrieved December 2022
from https://www.goldstandarddiagnostics.us/media/ 15635/ug-21-052-rev-01-abraxis-
microcystins-adda-elisa_520011 .pdf.
Abraxis, "Microcystin-ADDA ELISA Plate 520011," Flowchart. 03FEB2022. Retrieved December
2022 from https://www.goldstandarddiagnostics.us/media/15783/fc-22-037-rev-01-
abraxis-microcystin-adda_52001 l.pdf.
James, R., et al., "Environmental Technology Verification Report: Abraxis Microcystin Test Kits:
ADDA ELISA Test Kit; DM ELISA Test Kit; Strip Test Kit," in Environmental
Technology Verification System Center 2010. Retrieved March 2013 from
http: / / nepis.epa.gov/Adobe /PDF /PI 00EL6B.pdf
USEPA. 2019. Recommended Human Health Recreational Ambient Water Quality Criteria or
Swimming Advisories for Microcystins and Cylindrospermopsin. EPA 822-R-l9-001. U.S.
Environmental Protection Agency, Office of Water, Washington, DC.
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12 FROM ANALYSIS TO RESULTS
12.1 CONDITION CLASSES
The NRSA database contained the field and laboratory data for all sampled sites, whether selected as
potential reference sites (i.e., hand-selected sites) or from the statistical design. NRSA analysts
reviewed the raw data for each indicator independently and assigned the values in each dataset to
categories (for example, "above criterion" or "at or below criterion"; good, fair, or poor). To assign
the appropriate condition category, EPA used two broad types of assessment benchmarks for
NRSA 2018-19.
The first type consisted of fixed benchmarks applied nationally based on values in the peer-reviewed
scientific literature, EPA published values, or EPA-derived screening levels. For example, EPA's
recommended water quality criteria were used nationally to classify rivers and streams as above or
below a criterion or benchmark for microcystins, cylindrospermopsin, enterococci, and mercury.
Similarly, EPA fish tissue screening levels, developed using information on human health risk and
fish consumption rates for PCBs, were applied for human health fish fillet tissue indicators. See
Chapters 9, 10, and 11 for additional information.
The second type consisted of NRSA-specific ecoregional benchmarks based on the distribution of
indicator values from a set of river and stream least disturbed (reference) sites. Within each region,
least-disturbed sites (i.e., reference sites described in Chapter 4) provided a benchmark against which
all other sites were compared and classified. The condition classes for each stressor and biological
response were determined from data and observations from the least-disturbed sites in each
ecoregion and the continuous gradient of observed values at all sites.
The resulting condition classes were defined as follows:
Good: Not different from the reference sites
Fair: Somewhat different from the reference sites
Poor: Markedly different from the reference sites
Not Assessed: indicator not available for the site
While the "Not Assessed" category was included in the assessment (for instance, if fish were not
caught at a site or a sample was damaged) for stressor and response extent analyses, these sites were
not utilized in the relative risk or attributable risk analysis.
12.2 STRESSOR EXTENT, RELATIVE RISK, AND ATTRIBUTABLE RISK
A major goal of the National Aquatic Resource Surveys is to assess the relative importance of
stressors that impact aquatic biota on a national basis. EPA assesses the influence of stressors in
three ways: stressor extent, relative risk, and population attributable risk. In NRSA, each targeted
and sampled river and stream reach was classified as being in either Good, Fair, or Poor condition,
separately for each stressor variable and for each biological response variable. From this data, we
estimated the stressor extent (prevalence) of rivers and streams in Poor condition for a specified
stressor variable. We also estimated the relative risk of each stressor for a biological response.
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Relative risk is the ratio of the probability of a poor biological condition when the stressor is poor to
the probability of a poor biological condition when the stressor is not poor (Van Sickle et al. (2006)).
Finally, we estimated the population attributable risk (AR) of each stressor for a biological response.
AR combines RR and stressor extent into a single measure of the overall impact of a stressor on a
biological response, over the entire population of rivers and streams (Van Sickle and Paulsen
(2008)).
12.2.1 STRESSOR EXTENT
For each particular stressor, the stressor extent (SE) may be reported as the number of miles, the
proportion of miles, or the percent of miles in Good, Fair, Poor, or Not Assessed condition. If the SE is
reported as the proportion of miles, then it can be interpreted as the probability that a stream
chosen at random from the population will be in Poor condition for the stressor.
Stressor extent in Poor condition is estimated as
(1) SEp, die sum of the sampling weights for sites that are assessed in Poor condition
SEp
=YJwvi
i=1
(2) SEPp, as die ratio of the sums of die sampling weights for die probability selected sites diat are
assessed in Poor condition divided by die sum of die sampling weights of all die selected sites
regardless of condition, i.e.,
SEP
p iu*t
, or
(3) SERp, the percent of stressor extent in Poor condition (i.e., stressor relative extent)
Z/-1 wvi
SERV == 100 * SEPV = 100 * Jr1 v
P p Z"=1 Wi
where wpj is the weight for the /th selected site in the Poor condition category, Wj is the weight for
the /th selected site regardless of condition category, np is the number of selected sites that are in
Poor condition, and n is the total number of sites regardless of their condition category. A stressor
condition category may use other terminology to identify if a site is in poor condition but generically,
we use the term Poor. Note that the extent for a response variable is defined similarly.
12.2.2 RELATIVE RISK AND ATTRIBUTABLE RISK
To estimate relative risk and attributable risk, we restrict the sites to those that both the stressor and
response variable assessed as Good, Fair, or Poor (or their equivalents). That is, if a site is Not Assessed
for either the stressor or response variable, it is dropped. Next, for these sites the condition classes
are combined to be either Poor or Not Poor for the stressor and response variables. For example, Not
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Poor combines the Good and Fair condition classes. Thus, each sampled river or stream was
designated as being in either Poor (P) or Not Poor (NP) condition for each stressor and response
variable separately.
To estimate the relative risk and attributable risk for one stressor (S) and one response (B) variable,
we compiled a 2x2 table (Table 12-1), based on data from all river and stream sites that were
included in the probability sample and that had both the stressor and response variable measured. A
separate table must be compiled for each pair of stressor and response variables.
Table 12-1. Extent estimates for response and stressor categories
Stressor (S)
Response (B)
Not Poor (NP)
Poor (P)
Not Poor (NP)
nnn
nnp
^ ~ , ^nni
b / Wnpi
i=1
i=1
Poor (P)
71 pn
Y"1
nvv
Y"1
C ~ / Wpni
d } Wppi
i=l
i=l
Table entries (a, b, c, d) are the sums of the sampling weights of all sampled rivers and streams that
were found to have each combination of Poor or Not Poor condition for stressor and response. For
example, d = Zj=i wppi where npp is the number of sites with both the stressor and response in
poor condition and wppj is the weight for the /th site. Note that the estimates in Table 12.1 may
differ from the stressor extent estimates since both the stressor and response variables must be
measured at each site.
12.2.3 RELATIVE RISK
Relative risk (RR) is the ratio of the probability of a Poor biological condition when the stressor is
Poor to the probability of a Poor biological condition when the stressor is Not Poor. That is,
Pr(B = P\S = P)
RR ~ Pr(B = P\S = NP)
Using the simplified notation in Table 12.1. relative risk (RR) is estimated as:
d/(b + d)
est c/(a + c)
A RR =1.0 indicates there is no association between the stressor and response. That is, a Poor
response condition in a river or stream is equally likely to occur whether or not the stressor
condition is Poor. A RR >1.0 indicates that a Poor response condition is more likely to occur when
the stressor is Poor. For example, when the RR is 2.0, the chance that a stream is in Poor biological
(response) condition is twice as likely when the stressor is Poor than when the stressor is Not Poor.
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Further details of RR and its interpretation, including estimation of a confidence interval for RRest,
can be found in Van Sickle et al. (2006).
12.2.4 ATTRIBUTABLE RISK
Population attributable risk (AR) measures what percent of the extent in Poor condition for a
biological response variable can be attributed causally to the Poor condition of a specific stressor. AR
is based on a scenario in which the stressor in Poo/-would be entirely eliminated from the population
of river and streams, e.g., by means of restoration activities. That is, all rivers and streams in Poor
condition for the stressor are restored to the Not Poor condition. AR is defined as the proportional
decrease in the extent of Poor biological response condition that would occur if the stressor were
eliminated from the population of rivers and streams. Mathematically, AR is defined as (Van Sickle
and Paulsen (2008))
Pr(B = P) - Pr(B = P\S = NP)
AR ~ Pr(B = P)
We estimated AR as
BEPV c/(a + c)
ARest = p ' -
BEPp
where
(c + d)
BEPv = 7 N
(& + b + c + d)
and is the estimated proportion of the biological response that is in Poor condition. We calculated a
confidence interval for ARest following Van Sickle and Paulsen (2008).
An AR can take a value between 0 and 1. A value of 0 indicates either "No association" between
stressor and response, or else a stressor has a zero extent, i.e., is not present in the population. A
strict interpretation of AR in terms of stressor elimination, as described above, requires one to
assume that the stressor-response relation is strongly causal and that stressor effects are reversible.
Van Sickle and Paulsen (2008) discuss the reality of these assumptions, along with other issues such
as interpreting them when multiple, correlated stressors are present, and using them to express the
joint effects of multiple stressors.
However, AR can also be interpreted more informally, as a measure that combines RR and SE into a
single index of the overall, population-level impact of a stressor on a response. Van Sickle and
Paulsen (2008) show that the population attributable risk can be written as
SEPp(RR - 1)
AR =
1 +SEPJRR - 1)
This shows that the numerator of AR is the product of the SE of Poor stressor condition and the
"excess" RR, i.e., RR-1, of that stressor. The denominator standardizes this product to yield AR
values between 0 and 1. Thus, a high AR for a stressor indicates that the stressor is widely prevalent
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(has a high SE of Poor condition), and the stressor also has a large effect (high RR) in those river and
stream reaches where it does have Poor condition.
12.3 CHANGE ANAYLSES
One of the objectives of the NRSA is to track changes and trends over time. Previously, EPA and
partners reported on the condition of all rivers and streams for NRSA 2008-09 and 2013-14, and on
the condition of wadeable streams in the Wadeable Streams Assessment (WSA) 2004. The 2018-19
report presents the difference in percentage points of river and stream miles in various condition
categories between NRSA 2013-14 and 2018-19. Additional change comparisons back to 2008-09
can be found in the NRSA data dashboard.
12.3.1 DATA PREPARATION
The survey frame inclusion variables were used to identify sites for change estimation. Only sites
that were included in the survey frame for all surveys were used to calculate change estimates. The
same set of benchmarks and analyses were applied to all applicable datasets (e.g., NRSA 2008-09,
2013-14, 2018-19) in order for results to be directly comparable. Change analysis was not conducted
for cylindrospermopsin because this indicator was not included in earlier surveys.
12.3.2 ANALYSIS
Change analysis was conducted through the use of the spsurvey 5.4.0 package in R (Dumelle et al.,
2022). Within the CRTS (Generalized Random Tessellation Stratified) survey design, change analysis
can be conducted on continuous or categorical variables. Wien using categorical variables, change is
estimated by the difference in category estimates from the two surveys. Category estimates were
defined as the estimated proportion of values in each category (i.e., good, fair, and poor categories).
Change between the two years was statistically significant when the resulting error bars around the
change estimate did not cross zero.
Early trend information, calculated using a linear regression, are also available in the data dashboard
by hovering over the bar graphs in the "Change" section of the dashboard.
12.4 LITERATURE CITED
Dumelle, M., T.M. Kincaid, A.R. Olsen, and M.H. Weber. 2022. Spsurvey: Spatial Survey Design
and Analysis. R package 5.4.0
Van Sickle, J. and S.G. Paulsen. 2008. Assessing the attributable risks, relative risks, and regional
extents of aquatic stressors. Journal of the North American Benthological Society 27:920-931.
Van Sickle, J., J. L. Stoddard, S. P. Paulsen, and A.R. Olsen. 2006. Using relative risk to compares
the effects of aquatic stressors at a regional scale. Environmental Management
38:1020-1030.
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