United States
Environmental
Protection Agency
Office of Water
Office of Research and Development
Washington, DC 20460
EPA 841-B-23-001
February 2023
National Wetland Condition Assessment
2016 Technical Support Document
9 the Nat^
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EPA-841-B-23-001
National Wetland Condition Assessment
2016 Technical Support Document
US Environmental Protection Agency
Office of Water
Office of Research and Development
Washington, DC
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Notice
The National Wetland Condition Assessment: 2016 Technical Support Document (EPA-841-B-23-001)
details methods and analysis approaches used in the 2016 National Wetland Condition Assessment
(NWCA) conducted by the United States Environmental Protection Agency (USEPA) and partner
organizations. This document supports the NWCA results presented in National Wetland Condition
Assessment: The Second Collaborative Survey of Wetlands in the United States (EPA-841-R-23-001).
The information in the Technical Support Document has been funded wholly or in part by the US
Environmental Protection Agency. 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:
US Environmental Protection Agency. 2023. National Wetland Condition Assessment: 2016
Technical Support Document. EPA-841-B-23-001. US Environmental Protection Agency,
Washington, DC.
Companion documents for the NWCA are:
National Wetland Condition Assessment 2016: Quality Assurance Project Plan (EPA-843-R-15-008)
National Wetland Condition Assessment 2016: Site Evaluation Guidelines (EPA-843-R-15-010)
National Wetland Condition Assessment 2016: Field Operations Manual (EPA-843-R-15-007)
National Wetland Condition Assessment 2016: Laboratory Operations Manual (EPA-843-R-15-009)
National Wetland Condition Assessment: The Second Collaborative Survey of Wetlands in the United
States (EPA-841-R-23-001)
If you decide to print the document, please use double-side printingto minimize ecological impact.
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Acknowledgements
The EPA Office of Water (OW) and Office of Research and Development (ORD) would like to thank the
many people who contributed to the 2016 National Wetland Condition Assessment. Without the
collaborative efforts and support by state and tribal environmental agencies, federal agencies,
universities, and other organizations, this groundbreaking assessment of wetlands would not have been
possible. In addition, the survey could not have been done without the innumerable field biologists, soil
scientists, taxonomists, statisticians, and data analysts, as well as program administrators, EPA regional
coordinators, project managers, quality control officers, and reviewers. To the many hundreds of
participants, EPA expresses its profound thanks and gratitude.
State and Tribal Agency Partners
Alabama Department of Environmental Management
Arizona Department of Environmental Quality
California State Water Resources Control Board
Colorado Natural Heritage Program
Confederated Tribes of the Umatilla Indian Reservation
Delaware Department of Natural Resources and
Environmental Control
Florida Department of Environmental Protection
Georgia Department of Natural Resources
Idaho Department of Environmental Quality
Illinois Environmental Protection Agency
Illinois Natural History Survey
Indiana Department of Environmental Management
Iowa Department of Natural Resources
Kansas Department of Health and the Environment
Kansas Water Office
Kentucky Division of Water
Leech Lake Band of Ojibwe, Division of Resource
Management
Louisiana Department of Wildlife and Fisheries
Maine Department of Environmental Protection
Maine Natural Areas Program
Maryland Department of the Environment
Massachusetts Department of Environmental Protection
Michigan Department of Environment, Great Lakes, and
Energy
Minnesota Pollution Control Agency
Missouri Department of Natural Resources
Montana Natural Heritage Program
Navajo Environmental Protection Agency
Nebraska Game and Parks Commission
New Hampshire Department of Environmental Services
New Jersey Department of Environmental Protection
New Mexico Environmental Department
New Mexico Natural Heritage Program
New York Natural Heritage Program
Nevada Division of Environmental Protection
North Carolina Department of Environment and Natural
Resources
North Dakota Department of Health
Ohio Environmental Protection Agency
Oklahoma Conservation Commission
Oregon Department of Environmental Quality
Oregon Division of State Lands
Pennsylvania Department of Environmental Protection
Quinault Indian Nation
South Carolina Department of Health and Environment
Control
Tennessee Department of Conservation and Environment
Texas Commission on Environmental Quality
Utah Department of Environmental Quality
Utah Geological Survey
Vermont Department of Environmental Conservation
Virginia Department of Environmental Quality
Washington State Department of Ecology
West Virginia Department of Environment Protection
Wisconsin Department of Natural Resources
Wyoming Department of Environmental Quality
Wyoming Natural Diversity Database
Federal Partners
National Park Service
U.S. Army Corps of Engineers
U.S. Department of Agriculture, Natural Resource
Conservation Service
U.S. Fish and Wildlife Service
U.S. Forest Service
U.S. Geological Survey
U.S. EPA Office of Research and Development
U.S. EPA Office of Water
U.S. EPA Regions 1-10
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Additional Collaborators
ALS Global, Middletown
Avanti
Crow Insight
Eastern Kentucky University
EnviroScience
General Dynamics Information Technology
Great Lakes Environmental Center
Kenyon College
Midwest Biodiversity Institute
Moss Landing Marine Laboratories New England
Interstate Water Pollution Control Commission
Nicholls State University
North Dakota State University
Oregon State University
PG Environmental
Riparia at Pennsylvania State University
Southern California Coastal Water Research Project
University of Central Missouri
University of Florida
University of Houston - Clear Lake
University of Illinois
University of Montana
University of Nebraska-Lincoln
University of Wyoming
Virginia Institute of Marine Sciences
We gratefully acknowledge valuable reviews of the technical report from study partners and an external
peer review panel of wetland science experts.
USEPA's Office of Water provided support throughout the analysis process, especially Gregg Serenbetz,
the NWCA lead, Sarah Lehmann, and Danielle Grunzke from the Watershed Restoration, Assessment, and
Protection Division in the Office of Wetlands, Oceans and Watersheds.
Members of the primary Data Management and Analysis Team for the 2016 NWCA were Amanda Nahlik,
Teresa Magee, Karen Blocksom, Mary Kentula, Anett Trebitz, Tony Olsen, Tom Kincaid, and Marc Weber
from USEPA Office of Research and Development; Alan Herlihy from Oregon State University and USEPA
Office of Water. Steve Paulsen and Dave Peck of USEPA Office of Research and Development provided
valuable insight and advice.
Authors of the 2016 Technical Support Document were: Amanda Nahlik (Chapters 1, 2, 3, 4, 5, 6, 11, 12,
13, 15) Teresa Magee (Chapters 2, 6, 7, 8, 9, 10, 15), Karen Blocksom (Chapters 4, 7, 8, 9, 13), Mary
Kentula (Chapters 4, 6), Tony Olsen (Chapter 2), and Anett Trebitz (Chapter 13) (USEPA Office of Research
and Development), Alan Herlihy (Chapters 6, 11, 12, 13, 15) (Oregon State University and USEPA Office of
Water); Gregg Serenbetz (Chapter 3, 14) and Danielle Grunzke (Chapter 14) (USEPA Office of Water).
Key assistance in acquisition of plant species trait information or in taxonomic standardization of plant
species names was provided by Kendall Harris and Macy Carr (Oak Ridge Institute for Science and
Education fellows at USEPA Office of Water), Siobhan Fennessy (Kenyon College), Gerry Moore (US
Department of Agriculture, Natural Resources Conservation Service), Karen Blocksom (USEPA Office of
Research and Development) and Gregg Serenbetz (USEPA Office of Water).
US Department of Agriculture Natural Resource Conservation Service soil scientists provided invaluable
support to NWCA by training field crews, giving technical assistance with field activities, reviewing field
data, and conducting laboratory analysis on soil samples. Lenore Vasilas, Ann Rossi-Gill, Steve Monteith,
Rich Ferguson, PattyJones, Michelle Etmund, Scarlett Bailey, Larry Arnold, Mike Pearson, Leander Brown,
Aaron Friend, Phil King, David Kingsbury, Aaron Miller, Daniel Ufnar, Jennifer Wood, DougWysocki,
Michael Robotham and many other NRCS soil scientists contributed their time, energy, and expertise in
support of NWCA.
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Table of Contents
Notice i
Acknowledgements iii
Table of Contents v
List of Figures xi
List of Tables xv
Acronym List xxi
Foreword 23
Chapter 1: Overview of Analysis 25
Chapter 2: Survey Design 27
2.1 Description of the NWCA Wetland Type Population 27
2.2 Sample Frame, Survey Design, and Site Selection 27
2.2.1 Sample frame 27
2.2.2 Survey design 29
2.2.2.1 Two-step survey design to select new probability sites 29
2.2.2.2 Survey design to select resampled sites 30
2.2.3 Site Selection 31
2.2.4 Number of Sites Expected to be Sampled 31
2.2.5 State-Requested Modifications to the Survey Design 33
2.3 Wetland Area in the NWCA Sample Frame 33
2.4 Survey Analysis 36
2.5 Estimated Wetland Extent of the NWCA Wetland Population and Implications for Reporting 36
2.6 Literature Cited 37
Chapter 3: Selection of Handpicked Sites 39
3.1 Pre-Sampling Selection of Handpicked Sites 39
3.1.1 Initial Screen 39
3.1.2 Basic Screen 39
3.1.3 Landscape Screen 41
3.1.4 Distribution of Handpicked Sites 41
3.1.5 Replacement of Handpicked Sites Not Sampleable 41
3.1.6 Results 42
3.2 Literature Cited 43
Chapter 4: Data Preparation 44
4.1 Key Personnel 44
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4.2 Data Entry and Review 45
4.2.1 Field Data 45
4.2.1.1 Electronic Field Forms 46
4.2.1.2 Paper Field Forms 46
4.2.1.3 Field Form Validation 46
4.2.2 Laboratory Data 46
4.3 Quality Assurance Checks 47
4.3.1 Verification of Points 47
4.3.2 Confirmation of Coordinates Associated with the Sites Sampled 47
4.3.3 Data Checks 48
4.4 Literature Cited 48
Chapter 5: Subpopulations 50
5.1 Literature Cited 50
Chapter 6: Assigning Disturbance Class 57
6.1 Sites Used to Establish the Disturbance Gradient 58
6.2 Establishing a Disturbance Gradient 59
6.2.1 Indices and Metrics 59
6.2.2 Setting Least-Disturbed Thresholds 60
6.2.3 Setting Most-Disturbed Thresholds 60
6.2.4 Classifying Disturbance at Each Site for each Sampling Visit 60
6.3 Human-Mediated Physical Alteration Screens and Thresholds 61
6.4 Chemical Screens and Thresholds 62
6.5 Abiotic Disturbance Class Assignments 64
6.6 Biological Screen and Threshold 65
6.7 Final Disturbance Class Assignments 66
6.8 Literature Cited 68
6.9 Appendix A: Illustrative Guide to Assigning Disturbance Class in Six Steps 70
Chapter 7: Vegetation Analysis Overview, Data Acquisition, and Preparation 76
7.1 Background 76
7.2 Overview of Vegetation Analysis Process 77
7.3 Vegetation Data Collection 79
7.3.1 Field Sampling 79
7.3.2 Identification of Unknown Plant Species 82
7.4 Data Preparation - Parameter Names, Legal Values, and Data Validation 82
7.4.1 Description of Vegetation Field Data Tables 82
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7.4.2 Data Validation 83
7.5 Nomenclatural Standardization 85
7.5.1 Nomenclature Reconciliation Methods 85
7.5.2 Nomenclature Standardization Results and Documentation 88
7.6 Species Traits - Life History: Growth-habit, Duration, and Plant Category 89
7.6.1 Growth-Habit 89
7.6.2 Duration 89
7.6.3 Plant Categories 90
7.7 Species Traits - Wetland Indicator Status 91
7.7.1 Wetland Indicator Status Assignment Process 92
7.8 Species Traits - Native Status 94
7.9 Species Traits - Coefficients of Conservatism 96
7.9.1 Compilation of Existing State and Regional C-Value Lists from Across the Conterminous US... 97
7.9.2 Assigning Existing C-values to Taxon-Region Pairs Observed in the NWCA Surveys 100
7.9.3 Defining C-values for NWCA Taxon-Region Pairs Where None Were Available 100
7.9.4 Final NWCA C-value Trait Table 102
7.10 Literature Cited 102
7.11 Appendix B: Vegetation Field Data Forms 106
7.12 Appendix C: Parameter Names for Field Collected Vegetation Data Ill
7.13 Appendix D: Existing Coefficient of Conservatism Lists included in the Compiled C-value Lists
(unpublished draft) assembled by NWCA 115
Chapter 8: Vegetation Analyses and Candidate Metric Evaluation Prerequisite to Multimetric Index
Development 123
8.1 Overview 123
8.2 Anthropogenic Disturbance 125
8.3 Considering Regional and Wetland Type Differences 125
8.4 Calculating Candidate Metrics 130
8.5 Evaluating Candidate Vegetation Metrics 131
8.5.1 Range Tests 132
8.5.2 Repeatability (S:N) 132
8.5.3 Responsiveness 133
8.5.4 Redundancy 133
8.5.5 Application of Metric Screening Criteria 134
8.6 Metric Screening Results 134
8.7 Literature Cited 139
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8.8 Appendix E: NWCA 2016 Candidate Vegetation Metrics 141
Chapter 9: Vegetation Multimetric Indices and Wetland Condition 174
9.1 Overview - Vegetation Multimetric Index (VMMI) 174
9.2 Calibration and Validation Data 175
9.3 Developing Vegetation Multimetric Indices (VMMIs) - Methods 176
9.3.1 Step 1 - Metric Scoring 176
9.3.2 Step 2 - Generating and Screening Candidate VMMIs 176
9.3.3 Step 3 - Determining Ecological Condition Thresholds Based on VMMI Values 178
9.4 Final VMMIs - Results 178
9.4.1 VMMI Description, Metric scoring, and VMMI Calculation 179
9.4.2 VMMI Performance 182
9.4.3 Condition Thresholds for the Wetland Group VMMIs 188
9.5 Literature Cited 190
Chapter 10: Nonnative Plant Indicator (NNPI) 191
10.1 Background 191
10.2 Data Collection 192
10.3 Data Preparation 192
10.4 Nonnative Plant Indicator Overview 192
10.5 NNPI Condition Threshold Definition 193
10.6 Literature Cited 195
Chapter 11: Human-Mediated Physical Alterations 198
11.1 Data Collection 198
11.2 Development of Physical Alteration Indices 199
11.3 Scoring Each of the Six Physical Alteration Indices 203
11.4 Physical Alteration Screen Scoring (PALT_ANY and PALT_SUM) 205
11.4.1 PALT_ANY 205
11.4.2 PALT_SUM 205
11.5 Physical Alteration Stressor Condition Thresholds 205
11.6 Literature Cited 207
Chapter 12: Soil Heavy Metals 208
12.1 Data Collection 208
12.2 Development of Heavy Metal Background Concentrations 211
12.3 Calculation of Enrichment Factor (EF) Values and the Heavy Metal Index (HMI) 212
12.3.1 Enrichment Factor (EF) 212
12.3.2 Heavy Metal Index (HMI) 213
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12.4 Soil Heavy Metal Stressor Condition Thresholds 213
12.5 Literature Cited 214
Chapter 13: Water Chemistry 216
13.1 Data Collection 216
13.2 Data Validation 217
13.3 Establishing a Disturbance Gradient for Sites Sampled for Water Chemistry 219
13.3.1 Development of Physical-Alterations-Possibly-Affecting-Chemicals (CALT) Indices 220
13.3.2 Screens and Thresholds for Sites Sampled for Water Chemistry 223
13.3.3 Evaluation of the Disturbance Gradient for Sites Sampled for Water Chemistry 225
13.4 TN and TP Stressor Condition Thresholds 227
13.5 Literature Cited 229
Chapter 14: Microcystins 231
14.1 Data Collection and Analysis 231
14.2 Application of EPA Recommended Criterion for Microcystins 231
14.3 Literature Cited 232
Chapter 15: Condition Extents, Change in Condition Extents, and Relative and Attributable Risk 234
15.1 Condition Extent Estimates 235
15.1.1 Wetland Condition Extent Estimates 236
15.1.2 Nonnative Plant Indicator (NNPI) Condition Extent Estimates 237
15.1.3 Stressor Condition Extent Estimates 238
15.2 Change in Condition Extent from 2011 to 2016 241
15.2.1 Data Preparation 241
15.2.2 Change analysis 241
15.3 Relative Extent, and Relative and Attributable Risk 242
15.3.1 Relative Extent 242
15.3.2 Relative Risk 243
15.3.2.1 Example Calculation of Relative Risk 244
15.3.2.2 Considerations When Calculating and Interpreting Relative Risk 245
15.3.2.3 Application of Relative Risk to the NWCA 245
15.3.3 Attributable Risk 246
15.3.3.1 Considerations When Interpreting Attributable Risk 247
15.4 Where to Find the Summary of NWCA Results 247
15.5 Literature Cited 247
Glossary 250
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List of Figures
Figure 1-1. Annotated analysis flow chart indicating the chapter number (abbreviated as "CHP") in which
details may be found 26
Figure 2-1. Regions captured in the Twelve NWCA Reporting Groups (RPTGRP_12) subpopulations.
Wetland Group classifications (i.e., EH, EW, PRLH, PRLW (see Table 2-2 for descriptions)) are
site-specific and cannot be represented on this map as sites have not been selected at this
point in the survey design development 30
Figure 3-1. Example of map created using ArcGIS software to evaluate candidate handpicked sites.
Information from an assessment of the aerial imagery was recorded for basic and landscape
screening criteria 40
Figure 3-2. Map of the conterminous US showing distribution of handpicked sites (triangles) in relation
to probability sites (circles) sampled in the 2016 NWCA 42
Figure 6-1. Diagram of the disturbance gradient used in the NWCA with three classes of disturbance... 57
Figure 6-2. A visual summary of how rules for assigning abiotic disturbance classes based on the physical
and chemical screens are applied to a site, where L = "least disturbed", I = "intermediate
disturbed", M = "most disturbed", and ? = "unknown". Note that the physical and chemical
screens were evaluated together to determine the abiotic disturbance class assignment for a
site 64
Figure 6-3. Map of sampled sites and their final disturbance class (REF_NWCA) assignments 67
Figure 7-1. Overview of vegetation data preparation and analysis steps used in assessing NWCA
wetlands 78
Figure 7-2. Standard NWCA Assessment Area (AA) (shaded circular area) and standard layout of
Vegetation Plots 80
Figure 7-3. Diagram of a Vegetation Plot illustrating plot boundaries and positions of nested quadrats. 80
Figure 7-4. Overview of vegetation data collection protocol for the 2016 NWCA (USEPA 2016c) 81
Figure 7-5. Process for screening and reconciling names of plant taxa observed in the NWCA. Dark blue
boxes = steps completed using R code, light blue boxes = steps requiring botanical review,
purple boxes = type of name resolution applied, and the dark blue central box = final name
resolution 87
Figure 7-6. Distribution of native status among taxon-state pairs presented as percentages 95
Figure 7-7. Text box outlining C-value selection decision tree when multiple C-values were available for
one taxon-region pair 99
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Figure 8-1. Distribution of probability and hand-picked sites sampled in the 2011 and 2016 NWCA
surveys within Six Reporting Units (RPT_UNIT_6). TDL = coastal areas where tidally-
influenced estuarine wetlands occur. Inland wetlands are mapped within five geographic
regions 124
Figure 8-2. Six Reporting Units and four Wetland Groups: TDL = coastal areas where tidally-influenced
estuarine wetlands occur. Inland wetlands are mapped within 5 NWCA Aggregated
Ecoregions 126
Figure 8-3. Detrended correspondence analysis for NWCA 2011 and 2016 sampled sites. Sites are color-
and symbol-coded by RPTJJNIT12. Blue and TDL = Tidally-influenced, estuarine wetland
sites. Other codes and colors = Inland wetland sites by geographic region. Open symbols =
herbaceous (H) wetlands. Filled symbols = woody (W) wetlands. Note: Among the unique
1985 sampled sites, 208 were resampled sites (sampled in 2011 and 2016), (Section 6.1), and
for these resampled sites the data from 2011 visit were used in this DCA 128
Figure 9-1. Criteria for setting VMMI thresholds for good, fair, and poor condition categories based on
VMMI values observed for least-disturbed sites (REF_NWCA = L). Box plot whiskers: lower =
the 25th percentile -1.5 X IQR (interquartile range), upper = the 75th percentile + 1.5 X IQR.
178
Figure 9-2. Comparison of VMMI Estuarine Herbaceous wetlands (VMMI-EH) for least-disturbed and
most-disturbed sites. Top graph: Compares VMMI values for least- and most-disturbed EH
sites in the calibration and validation data sets. Bottom graph: VMMI values for all least- and
most-disturbed sampled EH sites. Box plots: box is interquartile (IQR) range, line in box is the
median, and whiskers represent most extreme point a distance of no more than 1.5 x IQR
from the box. Values beyond this distance are outliers. Numbers below each box plot
represent number of the least-disturbed or most-disturbed sites sampled 184
Figure 9-3. Comparison of VMMI Estuarine Woody wetlands (VMMI-EW) for least-disturbed and most-
disturbed sites. Top graph: Compares VMMI values for least- and most-disturbed EW sites in
the calibration and validation data sets. Bottom graph: VMMI values for all least- and most-
disturbed sampled EW sites. Box plots: box is interquartile (IQR) range, line in box is the
median, and whiskers represent most extreme point a distance of no more than 1.5 x IQR
from the box. Values beyond this distance are outliers. Numbers below each box plot
represent number of the least-disturbed or most-disturbed sites sampled 185
Figure 9-4. Comparison of VMMI Inland herbaceous wetlands (VMMI-PRLH) for least-disturbed and
most-disturbed sites. Top graph: Compares VMMI values for least- and most-disturbed PRLH
sites in the calibration and validation data sets. Bottom graph: VMMI values for all least- and
most-disturbed sampled PRLH sites. Box plots: box is interquartile (IQR) range, line in box is
the median, and whiskers represent most extreme point a distance of no more than 1.5 x
IQR from the box. Values beyond this distance are outliers. Numbers below each box plot
represent number of the least-disturbed or most-disturbed sites sampled 186
Figure 9-5. Comparison of VMMI Inland woody wetlands (VMMI-PRLW) for least-disturbed and most-
disturbed sites. Top graph: Compares VMMI values for least- and most-disturbed PRLW sites
in the calibration and validation data sets. Bottom graph: VMMI values for all least- and
most-disturbed sampled PRLW sites. Box plots: box is interquartile (IQR) range, line in box is
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the median, and whiskers represent most extreme point a distance of no more than 1.5 x
IQR from the box. Values beyond this distance are outliers. Numbers below each box plot
represent number of the least-disturbed or most-disturbed sites sampled 187
Figure 9-6. Comparison of VMMI values for Inland Woody wetlands (VMMI-PRLW) for least-disturbed
and most-disturbed sites by ecoregions. Top graph: VMMI-PRLW values by Five NWCA
Aggregated Ecoregions (ICP, EMU, PLN, ARW, WVM, (NWCA_EC05) see map in Figure 6-2 for
definitions) Bottom graph: VMMI-PRLW values for more mesic (OTHER) vs. more arid
(PLN_ARIDW) regional groups (OTHER = ICP, EMU, WVM; PLN_ARIDW = ARW & WVM). Box
plots: box is interquartile (IQR) range, line in box is the median, and whiskers represent most
extreme point a distance of no more than 1.5 x IQR from the box. Values beyond this
distance are outliers. Numbers below each box plot represent number of the least-disturbed
or most-disturbed sites sampled 189
Figure 11-1. The entire AA was evaluated using the H-l Form and 13 buffer plots were evaluated using
the B-l Form 199
Figure 11-2. 2016 NWCA Physical Alteration Metric Scoring, with the points assigned to each
observation located in the respective area (either the AA or buffer plot). Note that
observations in the center buffer plot (within the AA) also received 25 points 204
Figure 12-1. Comparison of heavy metal concentrations (ppm) for 12 heavy metals measured in
resampled sites, with the 2011 uppermost horizon within the top 10 cm that had soil
chemistry data on the x-axis and the 2016 Standardized Depth Soil Core that was collected
from the surface to a depth of 10 cm on the y-axis. The correlation statistics and the
significance are reported as R and p-value (a = 0.05) in the upper left corner of each plot. NS
= Not Significant 210
Figure 12-2. Illustration of the 75th percentiles of soil heavy metal concentrations of sites that passed the
Physical Alteration screens (i.e., deemed to be candidate least-disturbed sites), used to set
expected background concentrations for soil heavy metals. Note that this method is
conducted for each of the 12 heavy metals evaluated and by each of five regions in
RPT_UNIT_5 211
Figure 13-1. Box and whisker plots showing differences between least-disturbed (blue) and most-
disturbed (red) (unique 2011 and 2016 Visit 1) sites among five regions (RPT_UNIT_5) for a)
total nitrogen (TN) and b) total phosphorus (TP). TDL = Tidal Saline, ICP = Inland Coastal
Plains, EMU = Eastern Mountains & Upper Midwest, PLN = Plains, and WST = West 226
Figure 13-2. Good stressor condition and poor stressor condition threshold-setting using the 75th and
95th percentiles of total nitrogen (TN) or total phosphorus (TP) concentrations among least-
disturbed sites sampled for water chemistry 228
Figure 15-1. The 2016 NWCA national extent estimates for wetland condition based on the Vegetation
Multimetric Indices (VMMIs). Wetland condition extent is presented for each condition
category by percent of the resource (i.e., percent of target wetland area for the Nation).
Error bars represent 95% confidence intervals as calculated by the R package spsurvey
(Kincaid and Olsen 2019) 237
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Figure 15-2. The 2016 NWCA national extent estimates for Nonnative Plant Indicator (NNPI) condition.
NNPI condition extent is presented for each condition category by percent of the resource
(i.e., percent of target wetland area for the Nation). Error bars represent 95% confidence
intervals as calculated by the R package spsurvey (Kincaid and Olsen 2019) 238
Figure 15-3. The 2016 NWCA national extent estimates for 11 indicators of stressor condition. Stressor
condition extent is presented for each condition category by percent of the resource (i.e.,
percent of target wetland area for the Nation). Error bars represent 95% confidence intervals
as calculated by the R package spsurvey (Kincaid and Olsen 2019). Stressor abbreviations are
defined in Section 15.1.3 240
Figure 15-4. The 2016 NWCA relative extent of wetlands with stressors in poor condition, and the
relative risk and attributable risk of poor a) VMMI condition or b) NNPI condition when
stressor condition is poor as calculated by the R package spsurvey (Kincaid and Olsen 2019).
Note that the microcystins results were excluded due to low values. For relative risk, values
below the dashed line (i.e., a relative risk < 1) signifies that there is no association between
the stressor and VMMI or NNPI condition 243
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List of Tables
Table 2-1. USFWS S&T Wetland Class Codes with crosswalk to NWCA Wetland Types 28
Table 2-2. Crosswalk between regions and Wetland Groups, and the Twelve NWCA Reporting Groups
(RPTGRP_12) subpopulations 30
Table 2-3. Number of sites expected to be sampled, reported by state and Twelve NWCA Reporting
Groups (RPTGRP_12) 32
Table 2-4. Wetland area (acres) in the NWCA sample frame reported by state and Twelve NWCA
Reporting Groups (RPTGRP_12) 34
Table 2-5. Total estimated areal extents for the total target NWCA population, the sampled area extents,
and non-assessed area extents for the nation and by Twelve NWCA Reporting Groups
(RPTGRP_12). Results are reported as millions of acres or percent (%) of total estimated
NWCA wetland area for the nation or by RPTGRP_12.1 The number of sites in each group is
provided as n 37
Table 3-1. Distribution of 90 handpicked sites sampled in 2016 by Five NWCA Aggregated Ecoregions
and the NWCA Wetland Group. Note: All estuarine sites and sites in the Coastal Plains
ecoregion were eliminated because an adequate number of least-disturbed sites for this
region and Wetland Group were identified in the 2011 NWCA 42
Table 4-1. The 2016 NWCA Analysis Team and roles. All people listed are USEPA except as noted 45
Table 5-1. Subpopulation information, including the parameter name that is used in the database, all the
potential subpopulations included in each subpopulation group, and a description of each
subpopulation group 51
Table 6-1. The number of Visit 1 (VI) probability and handpicked sites sampled in 2011 and 2016, with
their totals. Additionally, the numbers of resampled sites are reported in paratheses to
indicate that these are subtracted from the subtotals above. The total number of unique
probability and handpicked sites are reported with the final number of Index Visit sites (in
the red cell) used in the establishment of the NWCA disturbance gradient. Note that this
table does not include the 96 Visit 2 sites sampled in 2011 and 94 Visit 2 sites sampled in
2016, which are only used to calculate Signal-to-Noise ratios for some indicators/metrics (see
Chapter 8: for details) 58
Table 6-2. Indices and metrics used in the 2016 NWCA to establish the disturbance gradient. Final
indices and metrics for which thresholds were created are in uppercase, bold type 59
Table 6-3. a) Least-disturbed thresholds and b) most-disturbed thresholds for the two physical alteration
screens and the number of sites that passed the screens (i.e., are considered candidate "least
disturbed" or "most disturbed") presented for Five Reporting Units (RPT_UNIT_5) 62
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Table 6-4.
a) Least-disturbed thresholds and b) most-disturbed thresholds for the two chemical screens
and the number of sites that passed the screens (i.e., are considered abiotic "least disturbed"
or "most disturbed") presented for Five Reporting Units (RPT_UNIT_5) 63
Table 6-5. n-sites of abiotic disturbance class assignments (REF_NWCA_ABIOTIC) reported by region
(RPT_UNIT_5) for Visit 1, Index Visit 2011 and 2016 sites 65
Table 6-6. The least-disturbed threshold for the biological screen, and the number of sites passing the
screen (and thus, are assigned final "least-disturbed" status as indicated in REF_NWCA) for
the Five Reporting Units (RPT_UNIT_5) 66
Table 6-7. n-sites within final disturbance class assignments (REF_NWCA) reported by region
(RPT_UNIT_5) for Visit 1, Index Visit 2011 and 2016 sites. Note that two sites (one from TDL
and another from ICP) were dropped due to insufficient vegetation data and assigned as
"unknown" 66
Table 7-1. Growth-habit categories, for species observed in the 2011 and 2016 NWCAs and used in
analysis, with a crosswalk to PLANTS database growth-habit designations. Capitalized
Growth-habit Category Names are used in calculation of Growth-habit metrics (see Section
8.8: Appendix E) 90
Table 7-2. Duration categories used in the NWCA analyses and a crosswalk to PLANTS database duration
designations for NWCA observed species. Capitalized Duration Category Codes are used in
calculation of Duration Metrics (see Section 8.8: Appendix E) 90
Table 7-3. Wetland Indicator Status (WIS) definitions. OBL, FACW, FAC, FACU and UPL defined by Lichvar
2016. NOL and UND defined by NWCA. These seven WIS Categories are used in calculating
Hydrophytic Status Metrics (Section 6.8: Appendix E). The Numeric Ecological Value
(ECOIND2) for each indicator status (UPL to OBL) is used in calculating indices describing the
hydrophytic status of the vegetation at each sampled site 91
Table 7-4. Wetland regions within which wetland indicator status for individual plant species are
defined, and a crosswalk between USACE codes and NWCA codes for these regions is
provided 91
Table 7-5. Definition of state-level native status designations for NWCA taxon-state pairs 94
Table 8-1. Numbers of unique NWCA 2011 and 2016 sampled sites. NWCA_REF (Disturbance): L = Least,
I = Intermediate, M = Most, ? = Undetermined. Revisit = site sampled twice in same field
season 124
Table 8-2. Numbers of unique NWCA 2011 and NWCA 2016 sampled sites by RPT_UNIT12 (RPT_UNIT_6
x WETCLS_GRP). RPT_UNIT_6 is defined in Figure 6-2 and Table 6-3. WETCLS_GRP is defined
in Table 6-4. REF_NWCA (Disturbance): L = Least, I = Intermediate, M = Most, ? =
undetermined. Revisit = site sampled twice in same field season 126
Table 8-3. Numbers of unique NWCA 2011 and 2016 sampled sites by six reporting units (RPT_UNIT_6).
REF_NWCA (Disturbance): L = Least, I = Intermediate, M = Most, ? = undetermined. Revisit =
site sampled twice in same field season. Tidal (TDL) = tidally-influenced estuarine wetlands
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occurring in near coastal areas. The other five groups represent inland wetlands within five
ecoregional areas. See Table 8-4 for description of include wetland types 127
Table 8-4. Numbers of unique NWCA 2011 and 2016 sampled sites by Wetland Groups (WETCLS_GRP).
REF_NWCA (Disturbance): L = Least, I = Intermediate, M = Most, ? = undetermined. Revisit =
site sampled twice in same field season. EH and EW are tidally-influenced estuarine
wetlands. PRLH and PRLW are inland wetlands 127
Table 8-5. Metric Groups and component Metric Types for characterizing vegetation condition 131
Table 8-6. Metrics (n = 40) that passed screening criteria for the Estuarine Herbaceous (EH) wetland
subpopulation. Kruskal-Wallis statistics: Chi square and p-value. Metrics defined in Section
8.8 (Appendix E) 134
Table 8-7. Metrics (n = 21) that passed screening criteria for the Estuarine Woody (EW) wetland
subpopulation. Kruskal-Wallis statistics: Chi square and p-value. Metrics defined in Section
8.8 (Appendix E) 135
Table 8-8. Metrics (n = 42) that passed screening criteria for the Inland Herbaceous (PRLH) wetland
subpopulation. Kruskal-Wallis statistics: Chi square and p-value. Metrics defined in Section
8.8 (Appendix E) 136
Table 8-9. Metrics (n = 47) that passed screening criteria for the Inland Woody (PRLW) wetland
subpopulation. Kruskal-Wallis statistics: Chi square and p-value. Metrics defined in Section
8.8 (Appendix E) 137
Table 9-1. Metrics included in each of the four NWCA 2016 Vegetation Multimetric Indices (VMMIs). See
Section 8.8:, Appendix E for formulas for calculation of these metrics 180
Table 9-2. VMMI-EH metrics: floor and ceiling values, disturbance response, and interpolation formula
for scoring individual metrics. Final scores for each metric decrease with disturbance 180
Table 9-3. VMMI-EW metrics: floor and ceiling values, disturbance response, and interpolation formula
for scoring individual metrics. Final scores for each metric decrease with disturbance 181
Table 9-4. VMMI-PRLH metrics: floor and ceiling values, disturbance response, and interpolation formula
for scoring individual metrics. Final scores for each metric decrease with disturbance 181
Table 9-5. VMMI-PRLW metrics: floor and ceiling values, disturbance response, and interpolation
formula for scoring individual metrics. Final scores for each metric decrease with
disturbance 182
Table 9-6. Summary statistics for the final four VMMIs: EH - Estuarine Herbaceous, EW -Estuarine
Woody, PRLH - Inland Herbaceous, PRLW - Inland Woody. Statistics calculated based on
VMMI values for sampled sites and revisit sites from the calibration data set for the relevant
VMMI group 183
Table 9-7. VMMI value thresholds indicating good, fair, and poor ecological condition based on least-
disturbed sites in each Wetland Group (WETCLS_GRP). Sites with VMMI values from the 5th
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up to the 25th percentile for least-disturbed (REF_NWCA) sites are considered in fair
condition 188
Table 9-8. Two-sample unequal variances t-tests comparing VMMI value means for all sampled least-
and most-disturbed sites for each Wetland Group VMMI 188
Table 10-1. Definition of metrics used in the NNPI 192
Table 10-2. Condition Threshold Exceedance Values for each of the metrics informing the Nonnative
Plant Indicator (NNPI): Relative Cover of Nonnative Species (XRCOV_AC), Nonnative Richness
(TOTN_AC), and Relative Frequency of Nonnative Species (RFREQ_AC) 194
Table 11-1. Six indices of human-mediated physical alterations and the 48 metrics crosswalked from
items on the 2011 and 2016 H-l Hydrology or B-l Buffer Forms. Note that the write-in
"others" are numerous and not all are included in this table 200
Table 12-1. Table of NARS chemistry flag codes and their definitions 209
Table 12-2. Heavy metal background concentrations (ppm) for wetlands in five regions (RPT_UNIT_5) of
the United States 212
Table 12-3. Interpretation of Enrichment Factor (EF) results 213
Table 13-1. Water chemistry analytes measured in the laboratory, with their associated units and a
summary of methods 217
Table 13-2. Variability and repeatability of water chemistry analytes measured in the 2016 NWCA,
including below-detection rates for all 2016 NWCA sites (Visit 1 and Visit 2, probability and
handpicked), cross-visit correlations based on the 61 revisit sites, and Signal-to-Noise ratios
(S:N) for all sites and inland (freshwater) sites 219
Table 13-3. The number of Visit 1 (VI) probability and handpicked sites sampled for water chemistry in
2011 and 2016, with their totals. Additionally, the numbers of resampled sites with water
chemistry data are reported in paratheses to indicate that these are subtracted from the
subtotals above. The total number of unique probability and handpicked sites with water
chemistry data are reported with the final number of Index Visit sites (in the red cell) used in
the establishment of the water chemistry disturbance gradient. Note that this table does not
include the 51 Visit 2 sites with water chemistry sampled in 2011 and 64 Visit 2 sites with
water chemistry sampled in 2016, which are only used to calculate Signal-to-Noise ratios. 220
Table 13-4. Subset of Physical Alteration metrics (defined in Chapter 11:, Section 11.2) assigned to the
Physical-Alterations-Possibly-Affecting-Nutrient (CALT_NUT), Physical-Alterations-Possibly-
Affecting-Suspended Sediments (CALT_SED), and Physical-Alterations-Possibly-Affecting-
Salinity (CALT_SAL) indices. "X" indicates that the Physical Alteration metric was included the
CALT index. Note that not all Physical Alteration metrics were assigned to a CALT index.
Write-in "others" from the H-l and B-l Forms were not considered and are therefore
excluded from the list of Form Items 221
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Table 13-5. Six water chemistry screens and their least-disturbed and most-disturbed thresholds used to
assign disturbance class to each site sampled for water chemistry 224
Table 13-6. n-sites sampled for water chemistry, presented by disturbance class assignments
(unpublished) reported by region (RPT_UNIT_5) for Visit 1, Index Visit 2011 and 2016 sites.
224
Table 13-7. Final total nitrogen (TN) and total phosphorus (TP) thresholds and relevant information for
developing those thresholds, including the number of least-disturbed sites with water
chemistry on which threshold percentiles are based (see Section 13.3 for details), the high
outlier cut-off, and the number of outlier sites 228
Table 15-1. Example contingency table for relative risk that reports the proportion of stream length
associated with good and poor condition (as indicated by Fish Index of Biotic Integrity, IBI)
and good and poor stressor condition (as indicated by stream water total nitrogen
concentration, TN). Results are hypothetical 244
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Acronym List
AA
Assessment Area
AR
Attributable Risk
BPJ
Best Professional Judgement
CCs
Coefficients of Conservatism
CDF
Cumulative Distribution Function
C-value
Coefficients of Conservatism
EF
Enrichment Factor
FQAI
Floristic Quality Assessment Index
GIS
Geographic Information System
GRTS
Generalized Random Tessellation Stratified (in relation to the survey design)
HGM
Hydrogeomorphic Class
HMI
Heavy Metal Index
IM
Information Management
IQR
Interquartile Ranges
MDL
Minimum Detection Limit
Mean C
Mean Coefficients of Conservatism
NARS
USEPA National Aquatic Resource Surveys
NFQD
National Floristic Quality Database
NPS
US National Park Service
NNPI
Nonnative Plant Indicator
NRCS
Natural Resources Conservation Service
NWCA
USEPA National Wetland Condition Assessment
NWPL
National Wetland Plant List
ORD
USEPA Office of Research and Development
OW
USEPA Office of Water
PQL
Practical Quantitation Limit
OA
Quality Assurance
RR
Relative Risk
S&T
USFWS Status and Trends
S:N
SignaLNoise (i.e., signal to noise ratio)
UID
Unique Identification
US
United States
USACE
US Army Corps of Engineers
USDA
US Department of Agriculture
USEPA
US Environmental Protection Agency
USFWS
US Fish and Wildlife Service
VMMI
Vegetation Multimetric Index
WD
USEPA Office of Water, Wetland Division
WIS
Wetland Indicator Status
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Foreword
The National Wetland Condition Assessment (NWCA) is a collaboration among the USEPA, State, Tribal,
and Federal partners. It is part of the National Aquatic Resource Survey (NARS) program, a broad effort to
conduct national scale assessments of aquatic resources to generate statistically valid and
environmentally relevant reports on the condition of the nation's aquatic resources every five years. The
2016 NWCA is the second survey of wetland condition and indicators of stress likely affecting condition
that is applicable to national and regional scales. With results from both the 2011 and 2016 NWCA,
changes in wetland condition can also begin to be assessed.
The goals of the NWCA are to:
Produce a national report describing the ecological condition of the nation's wetlands and
anthropogenic stressors commonly associated with poor condition;
Collaborate with states and tribes in developing complementary monitoring tools, analytical
approaches, and data management technology to aid wetland protection and restoration
programs; and
Advance the science of wetland monitoring and assessment to support wetland management
needs.
This document, the National Wetland Condition Assessment: 2016 Technical Support Document,
accompanies the National Wetland Condition Assessment: The Second Collaborative Survey of Wetlands in
the United States (referred to as the "Web Report"). The Web Report describes the background and main
findings of the 2016 NWCA. The Technical Support Document supports the findings presented in the
Public Report by describing the development of the survey design and the scientific methods used to
collect, evaluate, and analyze data collected for the 2016 NWCA.
The Technical Support Document includes information on the target population, sample frame, and site
selection underlying the 2016 NWCA survey design. The report provides a synthesis of data preparation
and management processes, including field and laboratory data entry, review, and several quality
assurance checks used in 2016 NWCA analysis. The NWCA evaluates the ecological condition of and
potential stress to wetlands along a gradient of disturbance, based on comparison to sites designated as
"least-disturbed" (or "reference"). The Technical Support Document provides a thorough overview of the
development and application of this approach.
A variety of biological, chemical, and physical data were collected and developed into several indicators of
ecological condition or stress to wetlands that inform the population estimate results of the 2016 NWCA.
For each of these indicators the Technical Support Document provides background and underlying
rationale, evaluation of candidates, and development of the final indicators chosen for the NWCA,
including defining threshold categories for condition and disturbance in order to evaluate and compare
data.
The information described in the Technical Support Document was developed through the efforts and
cooperation of NWCA scientists from USEPA, technical experts and participating cooperators from
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academia and state and tribal wetland programs. While this Technical Support Document provides a
comprehensive summary of NWCA procedures, including design, sampling, and analysis of data, it is not
intended to present an in-depth report of data analysis results.
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Chapter 1: Overview of Analysis
The analysis for the 2016 National Wetland Condition Assessment (NWCA) involved a number of
interrelated tasks composed of multiple steps. This brief overview of the entire process provides a
context for the details of each of the major tasks described in this document.
Figure 1-1, which can be found on the following page, illustrates the analysis process, beginning with field
sampling probability and handpicked sites (left side of chart) and concluding with the population
estimates of wetland condition extent, stressor condition extent, and relative and attributable risk (right
side of chart) for the NWCA target population in the conterminous US. The components of each of the
major tasks are indicated by text boxes and include the chapter number in which details may be found.
The key elements of the analysis outlined in the flowchart are:
1) Field sampling using protocol from the 2016 Field Operations Manual (USEPA 2016) results in
data acquisition for probability (Chapter 2:) and handpicked sites (Chapter 3:). This data is
prepared, and quality assurance continues throughout all of the analyses resulting in the
production of the data tables used by the analysts (Chapter 4:).
2) Metrics and indices used to develop disturbance and stressor thresholds are calculated for each
site, including Human-Mediated Physical Disturbances (Chapter 11:), Soil Heavy Metals (Chapter
12:), Percent Relative Cover of Nonnative Plant Species (Section 6.6), Nonnative Plant Index
(NNPI) (Chapter 10:), Water Chemistry (Chapter 13:), and Microcystins (Chapter 14:).
3) Three types of data are used to develop disturbance gradient thresholds and categorize each site
as least-, intermediate-, or most-disturbed (Chapter 6:). These data types include physical data
(human-mediated physical alterations), chemical data (soil heavy metals), and biological data
(percent relative cover of nonnative plant species).
4) Five types of data are used to develop stressor thresholds, including human-mediated physical
alterations, soil heavy metals, Nonnative Plant Index (NNPI), water chemistry, and microcystins,
found at the end of their individual chapters (Chapter 10: through Chapter 14:).
5) To develop Vegetation Multimetric Indices (VMMIs), first a vegetation analysis approach is
identified and data acquisition and preparation (Chapter 7:) is conducted, followed by
prerequisite analyses to vegetation indicator development (Chapter 8:). Using least- and most-
disturbed sites, VMMIs are developed and thresholds for good, fair, and poor wetland condition
are established (Chapter 9:).
6) Finally, site weights and only probability sites are used to calculate results for the wetland
population (Chapter 15:) and various subpopulations (Chapter 5:). Results include wetland
condition extent, stressor condition extent, change in both wetland condition extent and stressor
condition extent from the 2011 NWCA and the 2016 NWCA, and relative and attributable risk.
Final results are published using the online dashboard at
https://wetlandassessment.epa.gov/dashboard.
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REPORTING (CHP 15)
and NWCA Public Reporting
DATA
PREPARATION
(CHP 4)
prepare & QA
data before use
SURVEY
DESIGN
(CHP 2)
Probability
sites (PROB)
HANDPICKED
SITES
(CHP 3)
handpicked
sites (HAND)
*
collect data
in the field
using protocol
from the Field
Operations
Manual
for each site, calculate metrics and indices,
including:
NONNATIVE PLANT
INDICATOR (NNPI) (CHP 10)
WATER CHEMISTRY (CHP 13)
MICROCYSTES (CHP 14)
HUMAN-MEDIATED
PHYSICAL ALTERATIONS (CHP 11)1
SOIL HEAVY METALS (CHP 1211-
PERCENT RELATIVE COVER OF
NONNATIVE PLANTS (SECT 6.6)
STRESSOR
THRESHOLDS
(CHP 10-14)
DISTURBANCE
THRESHOLDS
(CHP 6)
least disturbed i
intermediate
disturbed
most disturbed I
H
develop vegetation
multimetric indices
VEGETATION
ANALYSIS
OVERVIEW-,
DATA
ACQUISITION,
PREP (CHP 7)
~
PREREQUISITE
ANALYSES TO
VEGETATION
INDICATOR
DEVELOPMENT
(CHP 8)
~
Ķ M VEGETATION
I MULTIMETRIC
INDICES &
WETLAND
I r CONDITION
Ķ J (CHP 9)
Đ
r * _ site weights from
i VV g " probabiliy design
only probability sites
used for population
estimates:
STRESSOR
CONDITION
EXTENT & CHANGE
good
fair
poor
very poor
t:
NNPI only
RELATIVE &
ATTRIBUTABLE
RISK
WETLAND
CONDITION
EXTENT & CHANGE
good
fair
poor
SUBPOPULATIONS
(CHP 5)
Figure 1-1. Annotated analysis flow chart indicating the chapter number (abbreviated as "CHP") in which details may be found.
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Chapter 2: Survey Design
NWCA was designed to assess the ecological condition of broad groups or populations of wetlands, rather
than as individual wetlands or wetlands across individual states. The NWCA design allows characterization
of wetlands at national and regional scales using indicators of ecological condition and stress. It is not
intended to represent the condition of individual wetlands. The statistical design also accounts for the
distribution of wetlands across the country - some areas have fewer wetlands than others - so that, even
in areas of the country where there are few sample sites, regional and national results still apply to the
broader target population. Olsen et al. (2019) provide an overview of the NWCA design from the 2011
NWCA survey, the concepts of which are largely the same for the 2016 NWCA design.
2.1 Description of the NWCA Wetland Type Population
The target population for the NWCA included all wetlands of the conterminous United States (US) not
currently in crop production, including tidal and nontidal wetted areas with rooted vegetation and, when
present, shallow open water less than one meter in depth. A wetland's status under state or federal
regulatory programs did not factor into this definition. Wetland attributes are assumed to vary
continuously across a wetland.
2.2 Sample Frame, Survey Design, and Site Selection
Probability sites that were sampled as part of the NWCA were selected using a sample frame on which
the survey design was based. The following sections provide details about how the sample frame and
survey design were developed, and how sites were selected.
2.2.1 Sample frame
The foundation of the survey design is a sample frame, or the geographic data layers that identify
locations and boundaries of all wetlands that meet the definition of the target population. The sample
frame for the 2016 NWCA was developed using two different geographic data layers: US Fish & Wildlife
Service (USFWS) National Wetland Status and Trends (S&T) (Dahl and Bergeson 2009, Dahl 2011) and
USFWS National Wetland Inventory (NWI) (USFWS 2014).
A sample frame obtained from the USFWS was utilized by the NWCA to gain aerial imagery interpretation
of land cover types and to identify wetlands. The USFWS sample frame was created for the S&T program,
which surveys approximately 5,000 4-mi2 plots (i.e., 2-mile by 2-mile plots) every five to ten years to
assess the extent (including gains and losses) in wetland area within the conterminous US. The S&T
sample frame is stratified by state and physiographic region (i.e., each plot is associated with a state and
physiographic region) and may result in a plot being subdivided on state and physiographic region
boundaries. The entirety of the 5,000 4-mi2 S&T plots were considered when developing the NWCA
sample frame.
The 2011 NWCA relied solely on S&T plots for the sample frame, resulting in too few sites in the western
US and the occurrence of multiple sites within the same S&T plots. Consequently, the 2016 NWCA sample
frame was expanded to include wetland polygons from the NWI in addition to the S&T plots from the
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2005 S&T survey. The NWI is the most complete spatial information on wetlands in the US (Horvath et al.
2017) and consists of millions of polygons across the contiguous 48 states, representing Cowardin
wetland classes (Cowardin et al. 1979). The numerous Cowardin wetland classes found in the NWI
polygons were consolidated and cross walked to the S&T Class Codes1 and finally to NWCA Wetland
Types (Table 2-1) by the NWCA Analysis Team to provide a consistent terminology for the NWCA sample
frame. A wall-to-wall 4-mi2 national-grid across the contiguous 48 states was overlayed on the NWI
polygons to select 4-mi2 NWI plots for inclusion in the NWCA sample frame.
Table 2-1. USFWS S&T Wetland Class Codes with crosswalk to NWCA Wetland Types.
S&T Code
NWCA Wetland Type
Description of wetlands included in each NWCA Wetland Type
E2EM
EH
Estuarine intertidal (E) emergent (H = herbaceous)
E2SS
EW
Estuarine intertidal (E) forested and shrub (W= woody)
PEM
PRL-EM
Emergent wetlands (EM) in palustrine, shallow riverine, or shallow
lacustrine littoral settings (PRL)
PSS
PRL-SS
Shrub-dominated wetlands (SS) in palustrine, shallow riverine, or shallow
lacustrine littoral settings (PRL)
PFO
PRL-FO
Forested wetlands in palustrine (FO), shallow riverine, or shallow
lacustrine littoral settings (PRL)
Farmed wetlands (f) in palustrine, shallow riverine, or shallow lacustrine
Pf
PRL-f
littoral settings (PRL); only the subset that was previously farmed, but not
currently in crop production
PUBPAB*
PRL-UBAB
Open-water ponds and aquatic bed wetlands
*PUBPAB covered S&T Wetland Classes: PAB (Palustrine Aquatic Bed), PUBn (Palustrine Unconsolidated Bottom,
natural), PUBa (aquaculture), PUBf (agriculture use), PUBi (industrial), PUBu (PBU urban).
1 Note that the S&T Class Codes for the NWCA Wetland Types often encompass more kinds of wetlands than the
code might suggest. For example, E2SS includes both estuarine intertidal shrub and forested wetlands. Palustrine
codes (e.g., PEM and others) reflect palustrine wetlands, and also riverine and lacustrine wetlands with < 1 m water
depth. Palustrine farmed (Pf) and Palustrine Unconsolidated Bottom (PUBPAB) wetlands with non-natural modifiers
were retained in the NWCA frame to allow evaluation of whether they met NWCA Wetland Type criteria; those that
did not were identified as non-target during site evaluation.
Two major S&T wetland categories, Marine Intertidal (Ml, near shore coastal waters) and Estuarine Intertidal
Unconsolidated Shore (E1UB, beaches, bars, and mudflats), were not included in the NWCA because they fall
outside the NWCA target population, i.e., typically occurring in deeper water (> lm deep) or unlikely to contain
rooted wetland vegetation. Other S&T Categories not meeting NWCA criteria or that were not wetlands were also
excluded: Estuarine Intertidal Aquatic Bed (E2AB) or Unconsolidated Shore (E2US), Marine Subtidal (M2), deep-
water Lacustrine (LAC, lakes and reservoirs) and Riverine (RIV, river systems), Palustrine Unconsolidated Shore
(PUS), Upland Agriculture (UA), Upland Urban (UB), Upland Forest Plantations (UFP), Upland Rural Development
(URD), and Other Uplands (UO).
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Several attributes were added to the wetland polygons in the 4-mi2 plots in the sample frame either for
their use in the survey design or survey analyses:
States (PSTL_CODE),
EPA Regions (EPA_REG),
Omernik Level III Ecoregion (Omernik 1987),
Three Aggregated Ecoregions (AG_EC03)
Nine Aggregated Ecoregions (AG_EC09), and
USFWS S&T Wetland Classes (Table 2-1, WETCLS_EVAL).
For more details about each attribute and for descriptions of the capitalized alphanumeric codes2 in
parentheses after each attribute, see Chapter 5:.
2.2.2 Survey design
The 2016 NWCA survey design is a combination of a) a set of new probability sites selected from the
sample frame described in the previous section (2.2.1 Sample frame) and b) a subset of probability sites
resampled from the 2011 NWCA survey. The NWCA uses a Generalized Random Tessellation Stratified
(GRTS) survey design to select sites, which provides spatially distributed samples, and thus, are more
likely to be representative of the population than other common spatial survey designs (Stevens and
Olsen 2004, Olsen et al. 2012).
2.2.2.1 Two-step survey design to select new probability sites
The initial step in selecting new probability sites is to apply a GRTS survey design to select a subset of 4-
mi2-sample-frame-plots from all S&T plots and NWI plots within the 4-mi2 national-grid described in
Section 2.2.1. This survey design was stratified by state, resulting in the selection of 50 to 400 plots for
each state depending on its area. This provided an initial set of 9,100 4-mi2-sample-frame-plots and their
component wetland polygons [the number of which may range from zero to many].
In the second step, a GRTS survey design for an area resource (i.e., the area of the NWCA wetland
population across the US) was applied to all wetland polygons identified from the initial step. This survey
design was stratified by state with unequal probability of selection based on geographic regions and
Wetland Groups (WETCLS_GRP) see Table 5-1 in Chapter 5:) within each state. The combination of these
regions and Wetland Groups are represented by the subpopulations of the Twelve NWCA Reporting
Groups (RPTGRP_12, Table 2-2 and Figure 2-1).
2 Note that the capitalized alphanumeric codes in parentheses (i.e., also found in Table 5-1) following each attribute
are analogous to those used in the design but not exactly the same, as the design information was gleaned from
spatial information and not data directly collected in the field. For example, the S&T Class Code may have been
updated for a site if the field crews arrived at a site and determined that the S&T Class differed on the ground from
that expected based on the spatial data.
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Table 2-2. Crosswalk between regions and Wetland Groups, and the Twelve NWCA Reporting Groups (RPTGRP_12)
subpopulations.
RPTGRP_12 Region Description
RPTGRP_12 Wetland Group Description
RPTGRP 12 Code
All Estuarine (ALL)
Estuarine Herbaceous (EH)
ALL-E H
Estuarine Woody (EW)
ALL-EW
CPL-PRLH
Coastal Plains (CPL)
Palustrine, Riverine, and Lacustrine Herbaceous (PRLH)
Palustrine, Riverine, and Lacustrine Woody (PRLW)
CPL-PRLW
Eastern Mountains & Upper
Midwest (EMU)
Palustrine, Riverine, and Lacustrine Herbaceous (PRLH)
EMU-PRLH
Palustrine, Riverine, and Lacustrine Woody (PRLW)
EMU-PRLW
Interior Plains (IPL)
Palustrine, Riverine, and Lacustrine Herbaceous (PRLH)
IPL-PRLH
Palustrine, Riverine, and Lacustrine Woody (PRLW)
IPL-PRLW
Western Valleys & Mountains
Palustrine, Riverine, and Lacustrine Herbaceous (PRLH)
WMT-PRLH
(WMT)
Palustrine, Riverine, and Lacustrine Woody (PRLW)
WMT-PRLW
Xeric West (XER)
Palustrine, Riverine, and Lacustrine Herbaceous (PRLH)
XER-PRLH
Palustrine, Riverine, and Lacustrine Woody (PRLW)
XER-PRLW
ALL
Coastal Plains (CPL)
Eastern Mountains and Upper Midwest (EMU)
Interior Plains (IPL)
Ķ Xeric West (XER)
I Western Mountains & Valleys (WMT)
Ķ All Estuarine (ALL-E)
Figure 2-1. Regions captured in the Twelve NWCA Reporting Groups (RPTGRP_12) subpopulations. Wetland Group
classifications (i.e., EH, EW, PRLH, PRLW (see Table 2-2 for descriptions)) are site-specific and cannot be
represented on this map as sites have not been selected at this point in the survey design development.
2.2.2.2 Survey design to select resampled sites
Resampte sites are probability sites that were originally sampled in the field in the previous NWCA survey
(i.e., 2011) and selected to be sampled again in the current survey (i.e., 2016). The resample design
included 239 sites sampled in the 2011 NWCA. For other NARS, approximately 50% of the sites sampled
iri a survey are made up of resample sites; however, the 2011 NWCA design limitations led to a decision
to reduce the number of resample sites. Limitations were related to lack of sites in the west, multiple
sites within the same 4-miz-sample-frame-plot, and the sensitivity of wetland ecosystems to damage
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caused by repeat sampling events. The survey design selected sites that were spatially-balanced across
the 48 states with unequal inclusion probabilities defined so that the number of sites in the NWCA
categories based on five geographic regions and four Wetland Groups (Table 2-2) were:
In addition, 96 of these resampled sites (i.e., the first two sites on the site list per state represented the
resample group) were intended to be sampled twice in 2016 field season (i.e., revisit sites- a site
sampled twice within the same year to assess within-season-variability in the collected data). The second
visit to a revisit site is not treated as a probability visit in analysis (i.e., it is excluded from extent
estimates).
2.2.3 Site Selection
Site selection was completed using the R package 'spsurvey' (Kincaid and Olsen 2019, R Core Team 2019).
To select sites using the survey design, four panels were included from which sets points (i.e., site
coordinates selected by the survey design) were to be sampled in the listed order (USEPA 2016a, b). The
panels (in order) were:
1. Basell_RVT2: identifies sites from NWCA 2011 that are to be visited twice within the 2016
season (i.e., both a resample and a revisit site),
2. Basell: identifies sites from NWCA 2011 to be visited once as a resample site in 2016,
3. Basel6: identifies new sites to be visited once, and
4. Basel6_OverSamp: identifies sites available to be used as replacement sites.
The sites were ordered in reverse hierarchical order to ensure that the final set of sites evaluated satisfied
the requirements for a probability survey design (Stevens and Olsen 2004). Sites were sampled based on
this order. All sites - from the first one on the list through the last site sampled from the list - were
evaluated and, hence, included in the study.
To make certain that a sufficient number of sites were available for sampling, a panel of additional sites
was selected as oversampie sites to provide replacements for any sites that were either not part of the
target population or could not be sampled (i.e., permission to sample was not provided by the landowner,
or access was not possible due to safety or other access issues). Note that no oversampie sites from 2011
were included. If any site from 2011 could not be sampled, and all available sites from 2011 were
evaluated and sampled, then the next oversampie site was the next available new site in
Basel6_OverSamp.
2.2.4 Number of Sites Expected to be Sampled
The expected sample size was 904 probability sites for the conterminous 48 states made up of 239
resampled sites from NWCA 2011 and 665 new probability sites. Each state was expected to revisit two
sites within the field season, adding 96 revisits. Therefore, 1,000 site visits (i.e., sampling events) were
expected for the 2016 NWCA. The minimum expected number of sites to be sampled in a state was seven
(Vermont and West Virginia), with two of these sites revisited, for a total of nine site visits. The maximum
number of sites for a state was 61 (Florida) (Table 2-3). Additional sites were sampled in some states with
the objective of enabling a state-level assessment.
ALL_EH = 33
ALL_EW = 18
CPL_PRLH =16
CPL PRLW = 25
EMU_PRLH = 24
EMU_PRLW = 25
IPL_PRLH = 29
IPL PRLW = 18
WMT_PRLH = 21
WMT_PRLW = 15
XER_PRLH = 9
XER PRLW =6
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Table 2-3. Number of sites expected to be sampled, reported by state and Twelve NWCA Reporting Groups
RPTGRP_12).
z
X
i
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AL
2
2
2
2
0
0
0
7
2
0
0
0
17
AR
0
0
3
2
0
0
0
5
2
0
0
0
12
AZ
0
0
0
0
0
2
3
0
0
0
2
10
17
CA
2
2
0
0
0
10
14
0
0
0
7
14
49
CO
0
0
0
0
2
9
4
0
0
2
9
2
28
CT
2
2
0
2
0
0
0
0
2
0
0
0
8
DE
2
2
2
2
0
0
0
2
2
0
0
0
12
FL
6
19
21
0
0
0
0
15
0
0
0
0
61
GA
5
2
4
2
0
0
0
10
2
0
0
0
25
IA
0
0
0
2
3
0
0
0
2
5
0
0
12
ID
0
0
0
0
0
7
5
0
0
0
6
5
23
IL
0
0
2
2
3
0
0
2
2
9
0
0
20
IN
0
0
0
2
2
0
0
0
2
5
0
0
11
KS
0
0
0
2
4
0
0
0
2
2
0
0
10
KY
0
0
2
2
2
0
0
2
2
3
0
0
13
LA
18
2
8
0
0
0
0
12
0
0
0
0
40
MA
2
2
2
2
0
0
0
2
2
0
0
0
12
MD
3
2
2
2
0
0
0
2
2
0
0
0
13
ME
2
2
0
2
0
0
0
0
3
0
0
0
9
Ml
0
0
0
4
2
0
0
0
6
5
0
0
17
MN
0
0
0
10
5
0
0
0
7
4
0
0
26
MO
0
0
2
2
3
0
0
2
2
7
0
0
18
MS
2
2
4
0
0
0
0
9
0
0
0
0
17
MT
0
0
0
0
5
6
2
0
0
2
5
2
22
NC
4
2
2
2
0
0
0
9
2
0
0
0
21
ND
0
0
0
0
14
0
0
0
0
2
0
0
16
NE
0
0
0
0
5
0
0
0
0
3
0
0
8
NH
2
2
0
2
0
0
0
0
2
0
0
0
8
NJ
3
2
2
2
0
0
0
3
2
0
0
0
14
NM
0
0
0
0
2
3
3
0
0
2
2
3
15
NV
0
0
0
0
0
2
5
0
0
0
2
10
19
NY
2
2
2
3
0
0
0
2
3
0
0
0
14
OH
0
0
0
2
2
0
0
0
2
3
0
0
9
OK
0
0
2
2
3
0
0
2
2
6
0
0
17
OR
2
2
0
0
0
13
6
0
0
0
10
5
38
PA
0
0
2
2
0
0
0
2
2
0
0
0
8
Rl
2
2
2
2
0
0
0
2
2
0
0
0
12
SC
5
2
3
2
0
0
0
7
2
0
0
0
21
SD
0
0
0
0
11
2
0
0
0
2
2
0
17
TN
0
0
2
2
0
0
0
3
2
0
0
0
9
TX
4
2
9
0
7
2
2
5
0
5
0
2
38
UT
0
0
0
0
0
3
18
0
0
0
2
3
26
VA
3
2
2
2
0
0
0
3
2
0
0
0
14
VT
0
0
0
2
0
0
0
0
5
0
0
0
7
WA
2
2
0
0
0
7
3
0
0
0
13
3
30
Wl
0
0
0
5
3
0
0
0
5
6
0
0
19
WV
0
0
0
2
0
0
0
0
5
0
0
0
7
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National Wetland Condition Assessment: 2016 Technical Support Document
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I
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DC
I
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s
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DC
I
LU
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LU
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DC
Q-
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2
S
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LU
X
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LU
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2
S
o
1-
WY
0
0
0
0
2
5
5
0
0
2
5
6
25
Total
75
59
82
74
80
71
70
108
80
75
65
65
904
2.2.5 State-Requested Modifications to the Survey Design
Two states Kentucky and North Dakota intensified their state designs to do a state-level assessment. They
used the same survey design as planned for NWCA 2016 but increased the sample size of the over sample
sites to ensure a sufficient number of sites were available.
Minnesota elected to modify the survey design for their state because of the availability of additional
wetland mapping information. In 2006, Minnesota developed a Comprehensive Wetland Assessment,
Monitoring, and Mapping Strategy (CWAMMS). One of the primary outcomes of the CWAMMS was the
development of statewide random surveys under the Wetland Status and Trends Monitoring Program
(WSTMP), to begin assessing the status and trends of wetland quantity and quality in Minnesota (Kloiber
2010). The wetland quantity survey, implemented by the Minnesota Department of Natural Resources,
was modeled after the USFWS S&T program (Dahl 2006, 2011). The WSTMP survey design was the basis
for the Minnesota NWCA design.
The WSTMP design contains 1-mi2 grid cells for Minnesota (and requires that at least 25% of grid cell be
within state of Minnesota) where the grid matches the USFWS S&T 4-mi2 grid boundaries. Each 4-mi2 grid
cell was subdivided into four 1-mi2 grid cells. An equal-probability GRTS survey design was used to select
4,740 1-mi2 plots. All wetland habitats within these plots were delineated using aerial imagery obtained in
years 2009, 2010, and 2011. Where portions of some 1-mi2 plots fell outside of state boundaries, only the
portion occurring within the state was photo-interpreted and mapped. Therefore, the total area of the
sample frame extent was less than 4,740 mi2. S&T Class Codes for the NWCA Wetland Types (Table 2-1)
were PEM, PSS, PFO, Pf, and PUBPAB. The next step was to select 150 sample sites using a GRTS equal-
probability survey design from the delineated wetland polygons. The 26 Minnesota sites required for
NWCA 2016 were two sites from NWCA 2011 to be sampled twice in 2016, five sites from NWCA 2011 to
be sampled once in 2016 and 19 new sites to be sampled once for NWCA 2016. These sites were
identified by the panels Basell_MN_NWCA_RVT2, Basell_MN_NWCA, and Basel6. Additional panels
identified the remaining sites to be sampled as part of Minnesota's state-level design as well as over
sample sites to be used when the base site could not be sampled. An additional 150 sites were selected
for use if any of the initial 150 sites could not be sampled, using the same process described in Section
2.2.4.
2.3 Wetland Area in the NWCA Sample Frame
Using the NWCA sample frame, the total area of the contiguous US is estimated to be approximately 2
billion acres, with approximately 157 million total acres designated as wetlands. Of the wetland acres,
106,672,330 acres are included in the NWCA sample frame. The wetland area included in the NWCA 2016
sample frame is provided in Table 2-4 summarized by state and reporting domain.
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Table 2-4. Wetland area (acres) in the NWCA sample frame reported by state and Twelve NWCA Reporting Groups (RPTGRP_12).
State
ALL_EH
ALL_EW
CPL_PRLH
EMU_PRLH
IPL_PRLH
WMT_PRLH
XER_PRLH
CPL_PRLW
EMU_PRLW
IPL_PRLW
WMT_PRLW
XER_PRLW
Total
AL
26,385
2,186
180,070
103,255
0
0
0
2,755,276
378,189
0
0
0
3,445,361
AR
0
0
231,161
98,872
0
0
0
1,759,186
69,859
0
0
0
2,159,078
AZ
0
0
0
0
0
36,867
209,993
0
0
0
6,567
182,220
435,647
CA
60,084
248
0
0
0
597,822
1,642,708
0
0
0
134,368
273,360
2,708,590
CO
0
0
0
0
207,116
478,456
326,590
0
0
58,792
190,905
17,011
1,278,870
CT
12,413
158
0
54,529
0
0
0
0
149,454
0
0
0
216,554
DE
73,297
723
12,771
330
0
0
0
166,846
1,172
0
0
0
255,139
FL
465,483
660,844
3,708,175
0
0
0
0
6,580,196
0
0
0
0
11,414,698
GA
350,854
6,554
408,670
98,042
0
0
0
4,113,984
327,607
0
0
0
5,305,711
IA
0
0
0
19,896
359,588
0
0
0
28,228
335,915
0
0
743,627
ID
0
0
0
0
0
354,303
408,060
0
0
0
113,369
71,580
947,312
IL
0
0
4,323
8,800
357,681
0
0
15,433
33,540
758,742
0
0
1,178,519
IN
0
0
0
105,896
196,380
0
0
0
151,874
402,331
0
0
856,481
KS
0
0
0
639
468,307
0
0
0
1,135
75,189
0
0
545,270
KY
0
0
15,591
100,591
41,930
0
0
76,219
61,144
153,353
0
0
448,828
LA
1,683,190
11,853
1,337,689
0
0
0
0
5,185,023
0
0
0
0
8,217,755
MA
47,991
1,017
7,752
116,985
0
0
0
22,988
334,309
0
0
0
531,042
MD
205,114
19,296
39,412
17,558
0
0
0
370,216
26,850
0
0
0
678,446
ME
24,241
115
0
286,830
0
0
0
0
1,743,150
0
0
0
2,054,336
Ml
0
0
0
742,967
86,306
0
0
0
5,421,291
370,157
0
0
6,620,721
MN
0
0
0
2,555,414
765,580
0
0
0
6,942,990
251,113
0
0
10,515,097
MO
0
0
99,502
123,264
362,516
0
0
115,033
109,742
527,460
0
0
1,337,517
MS
53,557
1,171
388,813
0
0
0
0
3,588,651
0
0
0
0
4,032,192
MT
0
0
0
0
806,574
312,709
208
0
0
38,536
88,087
232
1,246,346
NC
225,299
15,782
165,226
84,240
0
0
0
3,367,410
202,760
0
0
0
4,060,717
ND
0
0
0
0
2,814,048
0
0
0
0
34,237
0
0
2,848,285
NE
0
0
0
0
746,646
0
0
0
0
110,704
0
0
857,350
NH
5,947
2
0
69,899
0
0
0
0
208,812
0
0
0
284,660
NJ
200,584
1,626
57,357
40,703
0
0
0
486,735
134,746
0
0
0
921,751
NM
0
0
0
0
145,213
111,785
253,325
0
0
6,825
6,564
37,878
561,590
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State
X
LU
J
<
ALL_EW
CPL_PRLH
EMU_PRLH
IPL_PRLH
WMT_PRLH
XER_PRLH
CPL_PRLW
EMU_PRLW
IPL_PRLW
WMT_PRLW
XER_PRLW
Total
NV
0
0
0
0
0
1,359
540,770
0
0
0
600
189,888
732,617
NY
27,498
1,084
3,573
423,371
0
0
0
7,801
1,444,673
0
0
0
1,908,000
OH
0
0
0
142,350
120,615
0
0
0
276,273
119,558
0
0
658,796
OK
0
0
39,309
71,974
444,034
0
0
107,473
116,152
446,583
0
0
1,225,525
OR
14,563
173
0
0
0
802,918
650,867
0
0
0
199,674
74,912
1,743,107
PA
0
0
2,134
137,184
0
0
0
2,614
296,379
0
0
0
438,311
Rl
3,579
76
282
7,485
0
0
0
104
54,933
0
0
0
66,459
SC
343,224
4,395
264,397
45,285
0
0
0
2,873,956
111,063
0
0
0
3,642,320
SD
0
0
0
0
2,033,837
3,140
0
0
0
44,637
243
0
2,081,857
TN
0
0
69,986
98,590
0
0
0
590,908
140,130
0
0
0
899,614
TX
295,910
3,287
1,371,288
0
1,134,629
659
92,946
1,717,953
0
338,293
0
20,622
4,975,587
UT
0
0
0
0
0
127,509
2,227,873
0
0
0
22,212
36,328
2,413,922
VA
181,909
7,457
103,162
99,129
0
0
0
674,148
243,042
0
0
0
1,308,847
VT
0
0
0
73,069
0
0
0
0
181,237
0
0
0
254,306
WA
24,035
131
0
0
0
340,142
151,237
0
0
0
285,714
24,843
826,102
Wl
0
0
0
883,021
435,491
0
0
0
3,800,364
491,010
0
0
5,609,886
WV
0
0
0
45,125
0
0
0
0
24,749
0
0
0
69,874
WY
0
0
0
0
130,143
250,495
538,390
0
0
7,837
97,632
85,210
1,109,707
Total
4,325,159
738,178
8,510,644
6,655,292
11,656,635
3,418,163
7,042,968
34,578,153
23,015,848
4,571,273
1,145,934
1,014,083
106,672,330
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2.4 Survey Analysis
Any statistical analysis of data must incorporate information about the monitoring survey design. When
estimates of characteristics for the entire target population are computed, called population estimates
(discussed in Chapter 15), the statistical analysis must account for any stratification or unequal probability
selection in the design. The statistical analysis of the NWCA population estimates were completed using
the R package 'spsurvey' (Kincaid and Olsen 2019), which implements the methods described by Diaz-
Ramos et al. (1996).
2.5 Estimated Wetland Extent of the NWCA Wetland Population and
Implications for Reporting
Using a site evaluation process (USEPA 2016b), points selected by the NWCA survey design were
screened using aerial photo interpretations and GIS analyses to eliminate locations not suitable for NWCA
sampling (e.g., non-NWCA wetland types, wetlands converted to non-wetland land). Sites could also be
eliminated during field reconnaissance if they were a non-target type or could not be assessed due to
accessibility issues. Dropped sites were systematically replaced from a pool of replacement sites (i.e.,
oversample panel discussed in Section 2.2.3) from the survey design.
Eliminated sites affect how the final population results are estimated and reported. Accounting for non-
NWCA wetland types (e.g., wetlands in active crop production, deeper water ponds, mudflats), there
were an estimated 95.7 million acres of wetlands in the population across the conterminous US.
Throughout this report, wetland area as percentages is relative to the 95.7 million acres.
Table 2-5 illustrates the distribution of estimated extents of the 1) total NWCA wetland population, 2) the
sampled area (based on sampled probability sites), and 3) non-assessed area (based on probability sites
that could not be assessed) for the nation (conterminous US) and within the Twelve NWCA Reporting
Groups (RPTGRP_12).
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Table 2-5. Total estimated areal extents for the total target NWCA population, the sampled area extents, and non-
assessed area extents for the nation and by Twelve NWCA Reporting Groups (RPTGRP_12). Results are reported as
millions of acres or percent (%) of total estimated NWCA wetland area for the nation or by RPTGRP_12.1 The
number of sites in each group is provided as n.
Target NWCA
Other
Wetland
Sampled
Access Denied
Inaccessible
Non-Assessed
Population
millions acres
millions acres
millions acres
millions acres
RPTGRP 12
millions acres
(% area)
(% area)
(% area)
(% area)
Nation
95.7
52.9 (55%)
35.6 (37%)
5.1 (5%)
2.1 (2%)
n = 967
n = 1171
n = 281
n = 227
ALL_EH
4.6
3.4 (71%)
0.8 (18%)
0.5 (10%)
<0.1 (1%)
n = 133
n =48
n =40
n =4
ALL_EW
1.1
0.6 (52%)
0.2 (15%)
0.4 (32%)
<0.1 (1%)
n = 29
n = 82
n = 82
n = 12
CPL_PRLH
10.2
6.4 (63%)
2.6 (25%)
1.1 (11%)
<0.1 (<1%)
n = 222
n = 136
n = 38
n =4
CPL_PRLW
33.7
17.5 (52%)
13.8 (41%)
2.0 (6%)
0.4 (1%)
n = 143
n = 160
n = 36
n = 7
EMU_PRLH
4.4
2.9 (65%)
1.1 (25%)
<0.1 (1%)
0.4 (9%)
n =43
n = 57
n = 3
n = 16
EMU_PRLW
22.6
16.3 (72%)
5.6 (25%)
0.6 (3%)
0.2 (1%)
n = 105
n = 79
n = 5
n = 13
IPL_PRLH
9.0
4.4 (49%)
4.1 (46%)
<0.1 (1%)
0.4 (4%)
n = 81
n = 167
n =4
n =49
IPL_PRLW
4.0
2.1 (54%)
1.6 (41%)
0.1 (2%)
0.1 (4%)
n = 96
n = 138
n = 7
n = 19
WMT_PRLH
2.5
0.8 (31%)
1.5 (59%)
0.1 (5%)
0.1 (4%)
n = 73
n = 80
n = 13
n = 16
WMT_PRLW
1.2
0.6 (51%)
0.3 (23%)
0.2 (13%)
0.2 (12%)
n = 51
n = 75
n = 13
n = 24
XER_PRLH
5.5
1.4(26%)
0.2 (69%)
0.1 (2%)
0.2 (3%)
n = 85
n = 75
n = 11
n = 31
XER_PRLW
0.6
0.4 (48%)
0.2 (35%)
<0.1 (4%)
0.1 (13%)
n =43
n = 74
n = 24
n = 31
1Numbers in table may not add to totals due to rounding.
2.6 Literature Cited
Cowardin LM, Carter V, Golet FC, LaRoe ET (1979) Classification of wetlands and deepwater habitats of
the United States. U. S. Department of the Interior, Fish and Wildlife Service, Washington, D.C.
Dahl TE (2006) Status and trends of wetlands in the conterminous United States 1998 to 2004. US
Department of Interior, Fish and Wildlife Service, Washington, DC
Dahl TE (2011) Status and trends of wetlands in the conterminous United States 2004 to 2009. US
Department of Interior, Fish and Wildlife Service, Washington, DC
Dahl TE, Bergeson MT (2009) Technical procedures for conducting status and trends of the Nation's
wetlands. US Fish and Wildlife Service, Division of Habitat and Resource Conservation, Washington, DC
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Diaz-Ramos S, Stevens DL, Jr, Olsen AR (1996) EMAP Statistical Methods Manual. US Environmental
Protection Agency, Office of Research and Development, NHEERL-Western Ecology Division, Corvallis,
Oregon
Horvath EK, Christensen JR, Mehaffey MH, Neale AC (2017) Building a potential wetland restoration
indicator for the contiguous United States. Ecological indicators 83: 463-473.
Kincaid TM, Olsen AR (2019) spsurvey: Spatial Survey Design and Analysis. R package version 4.1.
Kloiber, SM (2010) Status and trends of wetlands in Minnesota: wetland quantity baseline. Minnesota
Department of Natural Resources, St Paul, Minnesota
Olsen AR, Kincaid TM, Kentula ME, Weber MH (2019) Survey design to assess condition of wetlands in the
United States. Environmental Monitoring and Assessment 191 (SI): 268, doi: 10.1007/sl0661-019-7322-
6.
Olsen AR, Kincaid TM, Payton Q (2012) Spatially balanced survey designs for natural resources. In: Gitzen
RA, Millspaugh JJ, Cooper AB, Licht DS (Eds.) Design and Analysis of Long-Term Ecological Monitoring
Studies. Cambridge, UK, Cambridge University Press: 126-150.
Omernik JM (1987) Ecoregions of the Conterminous United States. Annals of the Association of American
Geographers 77: 118-125
R Core Team (2019) R: A language and environment for statistical computing. R Foundation for Statistical
Computing, Vienna, Austria. http://www.R-project.org
Stevens DL, Jr., Olsen AR (2003) Variance estimation for spatially balanced samples of environmental
resources. Environmetrics 14: 593-610
Stevens DL, Jr., Olsen AR (2004) Spatially-balanced sampling of natural resources. Journal of American
Statistical Association 99: 262-278
USEPA (2011) Level III Ecoregions of the Continental United States (revision of Omernik, 1987). US
Environmental Protection Agency, National Health and Environmental Effects Laboratory-Western
Ecology Division. Corvallis, Oregon
USEPA (2016a) National Wetland Condition Assessment 2016: Field Operations Manual. US
Environmental Protection Agency, Washington DC. EPA-843-R-15-007.
USEPA (2016b) National Wetland Condition Assessment 2016: Site Evaluation Guidelines. US
Environmental Protection Agency, Washington DC. EPA-843-R-15-010.
USFWS (2014) National Wetlands Inventory, US Department of the Interior, Fish and Wildlife Service,
Washington, D.C. https://www.fws.gov/wetlands/Data/Metadata.html
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Chapter 3: Selection of Handpicked Sites
In addition to the probability sites identified by the survey design, handpicked sites are identified by
states, tribes, and other partners. These handpicked sites are suggested based on the expectation that
they are minimally disturbed and can be used as least-disturbed (or "reference") sites, although this is not
always the case (Herlihy 2008, 2019). The suggested handpicked sites were evaluated prior to the field
sampling using a screening process to eliminate those that are not likely to meet the criteria for the
NWCA.
3.1 Pre-Sampling Selection of Handpicked Sites
Candidate handpicked sites came from three sources:
1) Best Professional Judgment (BPJ) sites recommended by state, tribal, and federal entities with
responsibilities for wetlands;
2) Designated least-disturbed sites from other NARS with associated wetlands; and,
3) In-the-field replacements for sites from sources above that were determined not sampleable due
to access, permitting, or other constraints.
BPJ sites and least-disturbed sites designated from other NARS underwent the following screening
process.
3.1.1 Initial Screen
The initial screening step eliminated candidate handpicked sites not likely to meet the criteria for NWCA
sampling and to reduce the number of sites to a reasonable size for a manual evaluation employing
analysis of maps and aerial photos. Information provided by the person who suggested each site was
considered, and included wetland size and type, as well as data supporting whether a site was least
disturbed, e.g., scores from a Floristic Quality Assessment Index (FQAI) or Landscape Development Index
(LDI). Wetlands eliminated were typically small, rare types. In cases where many sites were submitted by
an entity, those ranking lower than others, given the data submitted, were eliminated from further
consideration.
All estuarine sites and sites in the Coastal Plains (see Figure 2-1 in Chapter 2:) were eliminated because an
adequate number of least-disturbed sites for this region and Wetland Group were identified in the 2011
NWCA.
3.1.2 Basic Screen
Candidate handpicked sites passing the initial screen were mapped in ArcGIS (exemplified in Figure 3-1).
Maps of each site with recent aerial imagery were assessed to determine if:
The wetland at the site would support the establishment of a sampleable assessment area
o The wetland was in the target population for NWCA
o The wetland was equal or greater than 0.1 ha and at least 20-m wide
o Less than 10% of the area
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Ķ Contained water greater than 1 m deep,
Ķ Had conditions that were unsafe or would make effective sampling impossible
(e.g., likely unstable substrate), and/or
Ķ Was upland
o No hydrogeomorphic boundaries were crossed;
The site was accessible with moderate effort; and,
The site was greater than 1km away from a probability site.
If all these criteria were met, the sites were assessed for evidence of visual landscape disturbance.
NWCA 2016 Handpicked Ref Candidates
Site MNW11-185
$/ N
Ū/ N
1 km): Ū/ N
Basic Screening Information
Sampleable AA can be established:
Site is accessible with moderate effort:
Not co-located with a probability site (<=
Visual Disturbance Information
N (none), Min (minimum), Mod+(mod or high)
Hydrofigic modifications:
Agriculture or forestry:
Residents I, urban, or commercial:
Industrial - oil. gas, mining, etc:
Road networks:
/ Min / Mod-t-
{pjl/ Min / Mod*
{UjMin / Mod+
Min / Mod+
none pved lo
(onpvtl pved hi
Notes: Exceptional sites from 2011 MN intensification
Latitude (DD): 47.705471
Longitude (DD): -94.223595
NWCA Wetland Type: PEM
Ecoregion: UMW
Ownership: Public
Name (if applicable):
Figure 3-1, Example of map created using ArcGIS software to evaluate candidate handpicked sites. Information
from an assessment of the aerial imagery was recorded for basic and landscape screening criteria.
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3.1.3 Landscape Screen
For candidate handpicked sites that passed the basic screen, aerial photos Figure 3-1 were used to
evaluate the presence of anthropogenic impact within buffers defined by 500m- and lkm-radius circles
centered on the likely location of the Assessment Area (AA) that would be used during field sampling.
First, the images were evaluated to determine the level of impact from the following types of
anthropogenic activities within the 500m- and lkm-radius buffer:
Hydrologic modifications (e.g., linear features that would indicate the presence of ditches, dams,
or levees);
Agricultural development (e.g., farm structures, row crops, horticultural fields, pastures) or
forestry activities (e.g., rows of trees, tree stumps and debris, logging roads, tree regeneration);
Residential, urban, or commercial development (e.g., houses, retail malls, commercial buildings,
parking lots); and,
Industrial development (oil and gas structures, mines, gravel pits, industrial facilities).
For each category of activity, the levels were noted as "none", "minimal" (the activity impacted less than
25% of the area), or "moderate and above" (the activity impacted more 25% or more of the area).
Next, the images were evaluated to determine the presence of road networks within the 500m- and lkm-
radius buffer. Road networks were categorized as "none", "unpaved only", "paved-low" (paved roads
impacted less than 25% of the area), or "paved-high" (paved roads impacted 25% or more of the area).
Sites with no impacts from anthropogenic activities and road networks were prioritized for sampling. Sites
with minimal impacts from anthropogenic activities and road networks ("unpaved", "paved-low") were
retained for potential use in regions with few non-disturbed candidate sites. Sites with "moderate" or
greater disturbance in the 500-m buffer were rejected outright.
3.1.4 Distribution of Handpicked Sites
Sites prioritized for sampling and retained for potential use were evaluated to assure, to the greatest
extent possible, adequate distribution across the regions and Wetland Groups likely to be used for
analysis and reporting. Site selection and distribution was also influenced by the availability of field crews
to sample handpicked sites in certain areas of the country. For example, EPA staffed regional field crews
were limited to sampling sites within their respective EPA Regions.
3.1.5 Replacement of Handpicked Sites Not Sampleable
At times, it was necessary to replace sites during the reconnaissance checks performed before sampling
or at the time of sampling. Sites were replaced during reconnaissance due to access issues, but also
because the Field Crew Leader acquired additional information that either 1) eliminated the site as a
candidate for use as "least-disturbed" (e.g., presence of invasive species) or 2) documented there was a
better, more appropriate candidate least-disturbed site. Sites were replaced at time of sampling primarily
due to access issues (e.g., too difficult to get to the exact location, last minute refusals by property
managers).
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3.1.6 Results
In the end, 90 handpicked sites (10 of which were sampled in 2011 and again (i.e., resampled) in 2016)
were selected through this screening process and sampled. Table 3-1 lists the final distribution of the
handpicked sites by the Five NWCA Aggregated Ecoregions and Wetland Group.
Table 3-1. Distribution of 90 handpicked sites sampled in 2016 by Five NWCA Aggregated Ecoregions and the
NWCA Wetland Group. Note: All estuarine sites and sites in the Coastal Plains ecoregion were eliminated because
an adequate number of least-disturbed sites for this region and Wetland Group were identified in the 2011 NWCA.
Five NWCA Aggregated Ecoregions
PRLH
PRLW
Total
Coastal Plains (CPL)
0
0
0
Eastern Mountains & Upper Midwest (EMU)
24
26
50
Interior Plains (IPL)
16
7
22
Western Valleys & Mountains (WMT)
8
3
11
Xeric West (XER)
5
1
6
Sum
46
34
90
, Coastal Plains (CPL)
Eastern Mountains & Upper Midwest (EMU)
Interior Plains (IPL)
Xeric West (XER)
I Western Mountains (WMT)
2016 Probability Sites
a 2016 Handpicked Sites
Figure 3-2. Map of the conterminous US showing distribution of handpicked sites (triangles) in relation to
probability sites (circles) sampled in the 2016 NWCA.
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3.2 Literature Cited
Herlihy AT, Kentula ME, Magee TK, Lomnicky GA, Nahlik AM, Serenbetz G (2019) Striving for consistency
in the National Wetland Condition Assessment: developing a reference condition approach for assessing
wetlands at a continental scale. Environmental Monitoring and Assessment 191 (SI): 327, doi:
10.1007/sl0661-019-7325-3
Herlihy AT, Paulsen SG, Van Sickle J, Stoddard JL, Hawkins CP, Yuan LL (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
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Chapter 4: Data Preparation
The tasks to produce the datasets used in the analysis are described in this chapter. The data checking
steps described, here, were designed to catch many errors. Other errors were found and corrected during
analysis using processes documented in the chapters following this.
The master database for the 2016 NWCA includes:
1) Raw data collected by Field Crews and from laboratory processing of samples collected in the
field (USEPA 2016a, b).
2) Data documenting and characterizing the NWCA sites from the survey design.
3) Field and lab raw data, site information, and ancillary data combined for use in specific analyses.
4) Metrics calculated from raw data from the field forms and the laboratory results.
4.1 Key Personnel
USEPA Office of Water (OW), Office of Wetlands, Oceans and Watersheds, Watershed Restoration,
Assessment, and Protection Division (WRAPD) provided overall leadership for the 2016 NWCA. Gregg
Serenbetz led the team in WRAPD and coordinated and fostered cooperation with the Analysis Team.
Personnel from the Office of Research and Development, Center for Public Health and Environmental
Assessment (CPHEA), Pacific Ecological Systems Division (PESD) were responsible for data entry, quality
assurance, and preparation of datasets for analysis with input from the Indicator Leads.
Mary E. Kentula, Amanda M. Nahlik, and Teresa K. Magee are the primary contacts at PESD for the 2016
NWCA. Together, they provided oversight and coordination of the various components at PESD and their
interactions with Office of Water.
Karen Blocksom deals with all aspects of the management of the data for the NARS surveys, e.g., finding,
correcting, and documenting errors, designing formats for the specific datasets needed for the various
analyses, and programming required for data management and analyses. She is the primary R
programmer and data manager for NARS, including the NWCA.
The Information Management Team (a.k.a., NARS IM) performs data entry and checks, makes and
documents corrections to the database, and creates various data sets for analysis for the NARS
assessments. The NARS IM for the 2016 NWCA is a group of people on contract to USEPA who are located
at PESD.
The NWCA Analysis Team was composed of the Indicator Leads, the scientists working with them on the
analysis, and the scientists conducting work that supported multiple analyses. Table 4-1 lists the
members of the Analysis Team and their roles.
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Table 4-1. The 2016 NWCA Analysis Team and roles. All people listed are USEPA except as noted.
Core Analyses
Leads
Associates
Survey Design and Population Extent
Anthony R. Olsen
Thomas M. Kinkaid, Michel Dumelle
Selection of Handpicked Sites
Gregg Serenbetz
Alan T. Herlihy, Ann Rossi-Gill, Sarah
Lehmann
Data QAand Management
Karen Blocksom
NARS IM contractors"
Subpopulations
Amanda M. Nahlik
Karen Blocksom
Disturbance Gradient
Amanda M. Nahlik
Teresa K. Magee, Alan T. Herlihy, Karen
Blocksom, Mary E. Kentula, Anett S. Trebitz
Landscape Metrics
Amanda M. Nahlik
Marc Weber
Population Estimates
Amanda M. Nahlik
Steven G. Paulsen, Thomas M. Kincaid
Indicators
Leads
Associates
Vegetation Multimetric Indices
Teresa K. Magee
Karen Blocksom, Amanda M. Nahlik, Alan T.
Herlihy, Steven G. Paulsen, Mary E. Kentula
Nonnative Plant Indicator
Teresa K. Magee
Karen Blocksom, Amanda M. Nahlik, Alan T.
Herlihy
Human-Mediated Physical Alterations
Amanda M. Nahlik
Karen Blocksom, Alan T. Herlihy, Teresa K.
Magee, Mary E. Kentula, Steven G. Paulsen
Soil Heavy Metals
Amanda M. Nahlik
Alan T. Herlihy, Karen Blocksom
Water Chemistry
Anett S. Trebitz
Alan T. Herlihy
Microcystins
Danielle Grunzke
N/A
Research Indicators and Topics
Leads
Associates
Soil and Water Stable Isotopes
Amanda M. Nahlik
J. Renee Brooks
Carbon Storage in Wetland Soils
Amanda M. Nahlik
M. Siobhan Fennessy*, Karen Blocksom,
Michael Dumelle
"General Dynamics Information Technology, Inc. (GDIT); *Kenyon College
4.2 Data Entry and Review
4.2.1 Field Data
The 2016 NWCA field forms were available in two formats: electronic or paper. While use of paper forms
has been the traditional method of collecting field data (i.e., in the 2011 NWCA), an NWCA app was
developed for the 2016 survey. Because the NWCA app was new, Field Crews were allowed to opt
between paper field forms and the electronic field forms. While a few Field Crews exclusively used
electronic field forms, most Field Crews chose to use paper field forms. The same information was
collected on both electronic and paper field forms.
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4.2.1.1 Electronic Field Forms
Electronic field forms are advantageous over paper forms because logic checks and completeness checks
can be programmed into them. Electronic field forms in the 2016 NWCA app were available through the
App Store by NARS IM. After collecting data for a site, Field Crews submitted electronic field forms as
.json files via email directly to NARS IM. These .json file were then parsed into different data types and
imported into the appropriate data tables. A PDF file showing the data received in the format of paper
field forms was sent back to the crew for review.
4.2.1.2 Paper Field Forms
Paper field forms for the 2016 NWCA were created in TeleForm software. This form development
software uses optical character recognition/intelligent character recognition technology along with
operator verification to capture data from paper field forms.
The Field Crews mailed packets of completed paper field forms directly to the data management center
at PESD. Form packets were logged and checked for quality and completeness. Field Crews were
immediately contacted if the field forms were incomplete or if there were questions regarding data
written on the forms. Each page was scanned and evaluated by the scanning software. Because the paper
forms were designed in TeleForm, the evaluation process was coded to flag restricted input. For
example, a data field may have an allowable numerical range, or a specified list of expected values. Any
data entries not meeting the criteria were marked by the software as potential errors. The operator
reviewed the marked entries by comparing the entered value to that on the paper form and making
corrections to mis-scanned data.
4.2.1.3 Field Form Validation
Both electronic and paper field forms were subjected to visual checks; the entered data was reviewed in
tabular form. On a daily basis, the data were reviewed for logical errors, for example:
Did Sample ID numbers meet sequential expectations?
If there were flags on a data form, was an associated comment recorded by the Field Crew?
Were there form images for each sheet?
Do the samples in the samples table match the samples in the tracking tables?
Once the phase of verification described above was complete, the data were further scrutinized via
programmatic validation and logic checks in R.
4.2.2 Laboratory Data
Laboratory results were submitted to USEPA WRAPD staff, who checked the data for completeness and
obvious errors. Then the data files were transferred to NARS IM for incorporation into the master NWCA
database.
The water chemistry data produced by Consolidated Safety Services (CSS) located at PESD was handled by
a different process. CSS checks their results based on the approved Quality Assurance Project Plan and
the data files are transferred from CSS to the NARS IM through the Work Assignment Contract Officer
Representative (COR).
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4.3 Quality Assurance Checks
There were three types of Quality Assurance (OA) checks completed before datasets were assembled for
analysis:
1) Verification of the fate of every sample point from the 2016 NWCA design;
2) Confirmation of longitudes and latitudes associated with the sites sampled; and
3) Data checks.
4.3.1 Verification of Points
Estimates of the wetland area falling into a particular condition category are based on the weight from
the survey design used to select the points to be sampled. For examples of how this has been done for
other surveys see Stevens and Jensen (2007) and Olsen and Peck (2008), or for an example of how this
was done for the 2011 NWCA, see USEPA (2016c) and Olsen et al. (2019). Chapter 2: provides specific
details of the NWCA survey design, and Chapter 15: discusses how estimates for the 2016 NWCA wetland
area were made.
In the NWCA survey design, the weight indicates the wetland area in the NWCA target population
represented by a point from the sample draw. After the assessment is conducted, the weights were
adjusted to account for additional sites (i.e., the oversample points) evaluated when primary sites could
not be sampled (e.g., due to denial of access, being non-target).
All points in the design were reviewed to confirm which were sampled, and if not, why not. Three sources
were used:
1) Information compiled during the desktop evaluation of sites (see the NWCA 2016Site Evaluation
Guidelines (USEPA 2016d)), and documented by state and contractor field crews in spreadsheet
submissions to EPA during and after the 2016 field season,
2) Information recorded on Form PV-1 during a field evaluation performed prior to sampling (see
the NWCA 2016Site Evaluation Guidelines (USEPA 2016d)), and
3) Information recorded on Form PV-1 at the time of sampling (see Chapter 3 in the NWCA 2016
Field Operations Manual (USEPA 2016a)).
Results from this evaluation were added to the database containing site information data from the NWCA
survey design and for the handpicked sites.
4.3.2 Confirmation of Coordinates Associated with the Sites Sampled
Longitudes and latitudes are taken at various key locations associated with field sampling (e.g., the
location of the point from the design). These coordinates are especially important if a point needs to be
relocated or shifted to accommodate sampling protocols (see Chapter 3 in the NWCA 2016 Field
Operations Manual (USEPA 2016a)). The coordinates are used to:
Verify the relationship between the point coordinates from the design and those of the sampled
Assessment Area (AA) that represents the point;
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Tie the field data to landscape data from GIS layers; and
Relocate the site and key locations of the field sampling protocol (e.g., the AA center, vegetation
plots) for resampling in future surveys.
Point coordinates from the design and the field were compared. The locations of points from the field
that were more than 60m from the corresponding design coordinates, i.e., that exceeded protocol
guideline (see Section 4.2 in the NWCA 2016Site Evaluation Guidelines (USEPA 2016d)), were flagged.
4.3.3 Data Checks
The first step in this series of checks was to assure all sites with data from a second field sampling (i.e.,
Visit 2, which is also known as the Quality Assurance Visit) had a corresponding initial sampling (i.e., Visit
1). Next, for all data types, computer code was written to generate a list of missing data, and checks were
performed to identify why they were missing (e.g., part of the sampling was not completed by the Field
Crew, data sheet(s) not scanned, etc.). Additional computer code was written to generate a list of data
not meeting a series of legal value and range tests. These tests were to confirm that:
Data type was correct,
Data fell within the valid range or legal value, and
Units reported (especially for laboratory results) matched those expected.
Results of the checks were converted to Excel spreadsheets. Each potential error was evaluated by the IT
Team or the Indicator Lead using the original forms submitted by the Field Crew. A description of the
error and recommended resolution were recorded in the spreadsheet for each type of data and
incorporated into the master NWCA database. The Indicator Lead who would be the primary user of the
data was consulted in cases where the resolution of the issue could affect the results of the analysis.
4.4 Literature Cited
Olsen AR, Kincaid TM, Kentula ME, Weber MH (2019) Survey design to assess condition of wetlands in the
United States. Environmental Monitoring and Assessment 191 (SI): 268, doi: 10.1007/sl0661-019-7322-
6.
Olsen AR, Peck DV (2008) Survey design and extent estimates for the Wadeable Streams Assessment.
Journal of the North American Bethological Society 27: 822-836
Stevens DL, Jr., Jensen SF (2007) Sample design, implementation, and analysis for wetland assessment.
Wetlands 27: 515-523
USEPA (2016a) National Wetland Condition Assessment 2016: Field Operations Manual. US
Environmental Protection Agency, Washington DC. EPA-843-R-15-007.
USEPA (2016b) National Wetland Condition Assessment 2016: Laboratory Operations Manual. US
Environmental Protection Agency, Washington DC. EPA-843-R-15-009.
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USEPA (2016c) National Wetland Condition Assessment 2011 Technical Report. US Environmental
Protection Agency, Washington DC. EPA-843-R-15-006.
USEPA (2016d) National Wetland Condition Assessment 2016: Site Evaluation Guidelines. US
Environmental Protection Agency, Washington DC. EPA-843-R-15-010.
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Chapter 5: Subpopulations
The conterminous United States is the broadest scale at which the NWCA results are reported. However,
the diversity in the Nation's landscape makes it important to assess aquatic resources in the appropriate
geographic setting. Regional variation in species composition, environmental conditions, and human-
caused disturbance often necessitates a finer scale, i.e., sub-national, to:
Define quantitative criteria and thresholds for least-disturbed sites and most-disturbed sites;
Define thresholds for categories of wetland condition and stressors, and
Report wetland condition extent and stressor condition extent.
These tasks and the need for sub-national, geographic reporting units are inherent to all NARS
assessments.
USEPA's Environmental Monitoring and Assessment Program (EMAP) recommends as a general rule that,
absent information on the variability in the target population, 50 sites per subpopulation should be
assessed to increase the likelihood that the sample will be sufficient to make population estimates. For
example, the EPA Level III Ecoregions (Omernik 1987, USEPA 2011a) of the US were aggregated into nine
regions for the Wadeable Streams and National Lakes Assessments (USEPA 2006, 2009) to assure an
adequate number of sites per subpopulation. For the 2011 NWCA, both regions and Wetland Groups
were used to report the results (USEPA 2016b). For the 2016 NWCA, subpopulations for primary
reporting and for further investigations were developed (Table 5-1, found on the pages following this
chapter). We use and discuss several different subpopulation groups throughout the text in this Technical
Support Document.
5.1 Literature Cited
Omernik JM (1987) Ecoregions of the conterminous United States. Annals of the Association of American
Geographers 77: 118-125
USEPA (2006) Wadeable Streams Assessment 2000-2004: A Collaborative Survey of the Nation's Streams.
EPA-841-B-06-002. US Environmental Protection Agency, Office of Water and Office of Research and
Development, Washington, DC
USEPA (2009) National Lakes Assessment 2007: A Collaborative Survey of the Nation's Lakes. EPA-841-R-
09-001. US Environmental Protection Agency, Office of Water and Office of Research and Development,
Washington, DC
USEPA (2011a) Level III Ecoregions of the Continental United States (revision of Omernik, 1987). US
Environmental Protection Agency, National Health and Environmental Effects Laboratory-Western
Ecology Division, Corvallis, OR
USEPA (2016b) National Wetland Condition Assessment 2011: A Collaborative Survey of the Nation's
Wetlands. US Environmental Protection Agency, Washington DC. EPA-843-R-15-005.
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Table 5-1. Subpopulation information, including the parameter name that is used in the database, all the potential subpopulations included in each
subpopulation group, and a description of each subpopulation group.
Subpopulation Group
Parameter Name
Subpopulations
Description
Three Aggregated
Ecoregions
AG_EC03
EHIGH | PLNLOW | WMTNS
Codes for the aggregation of the Omernik Level III Ecoregions into three regions
(using 2015 boundaries); Eastern Highlands (EHIGH), Plains and Lowlands
(PLNLOW), Western Mountains (WMTNS).
Nine Aggregated
Ecoregions
AG_EC09
CPL | NAP | NPL|SAP | SPL|TPL|
UMW | WMT | XER
Codes for the aggregation of the Omernik Level III Ecoregions into nine regions
(using 2015 boundaries); Coastal Plains (CPL), Northern Appalachians (NAP),
Northern Plains (NPL), Southern Appalachians (SAP), Southern Plains (SPL),
Temperate Plains (TPL), Upper Midwest (UMW), Western Mountains (WMT), and
Xeric West (XER). For a visual, see the AG_EC09 tab in this workbook.
USFWSS&T Coastal
Regions
COAST_REG
Great Lakes Region | Gulf Coast
Region | North East Coast Region |
Not Coast | Pacific Coast Region |
South East Coast Region
US Fish and Wildlife Service Status and Trends Coastal Regions, including Great
Lakes Region, Gulf Coast Region, North East Coast Region, Pacific Coast Region,
and South East Coast Region. Sites that are not in a coastal region are designated
'Not Coast1.
EPA Regions
EPA_REG
Region_01 | Region_02 |
Region_03 | Region_04 |
Region_05 | Region_06 |
Region_07 | Region_08 |
Region_09 | Region_10
EPA Regions, responsible for the execution of programs within several states and
territories: Region 1 serving CT, ME, MA, NH, Rl, and VT, Region 2 serving NJ, NY,
Puerto Rico, and the US Virgin Islands, Region 3 serving DE, DC, MD, PA, VA, WV
and 7 federally recognized tribes, Region 4 serving AL, FL, GA, KY, MS, NC, SC, and
TN, Region 5 serving IL, IN, Ml, MN, OH, and Wl, Region 6 serving AR, LA, NM, OK,
and TX, Region 7 serving IA, KS, MO, and NE, Region 8 serving CO, MT, ND, SD, UT,
and WY, Region 9 serving AZ, CA, HI, NV, American Samoa, Commonwealth of the
Northern Mariana Islands, Federated States of Micronesia, Guam, Marshall
Islands, and Republic of Palau, and Region 10 serving AK, ID, OR, WA and 271
native tribes. For a visual, see the EPA_REGION tab in this workbook.
Federal Lands
FED_NONFED
FEDERAL | NON_FEDERAL
Using OWN_NARS, distinguishes Federal from Non-federal lands, with Federal
land comprised of 'BLM1, 'BOR1, 'DOD1, 'FWS1, 'NOAA1, 1NPS1, 'Other Fed', and
' US FS'.
Inland versus Tidal
HYD_CLS
INLAND | TIDAL
Distinguishes tidal saline sites (HYD_CLS = TIDAL) from inland sites (HYD_CLS =
INLAND) combining information from the Aggregated S&T Class (WETCLS_GRP).
Specifically, EH + EW = TIDAL, and PRLH + PRLW = INLAND.
Major USGS Hydrologic
Basins
MAJ_BAS_NM
Arkansas-White-Red Region |
California Region | Great Basin
Region | Great Lakes Region |
Lower Colorado Region | Lower
Mississippi Region | Mid-Atlantic
Region | Missouri Region | New
England Region | Ohio-Tennessee
Region | Pacific Northwest Region |
Rio Grande-Texas-Gulf Region |
Souris-Red-Rainy Region | South-
Major US Geological Survey (USGS) hydrologic basins derived from NHD+ names,
with NHD+ codes in parenthesis: Arkansas-White-Red Region, California Region,
Great Basin Region, Great Lakes Region, Lower Colorado Region, Lower Mississippi
Region, Mid-Atlantic Region, Missouri Region, New England Region, Ohio-
Tennessee Region, Pacific Northwest Region, Rio Grande-Texas-Gulf Region,
Souris-Red-Rainy Region, South-Atlantic Region, Upper Colorado Region, Upper
Mississippi Region.
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Subpopulation Group
Parameter Name
Subpopulations
Description
Atlantic Region | Upper Colorado
Region | Upper Mississippi Region
Mississippi Basin
MISS_BASIN
MISSISS1 PPI_BAS 1N |
NOT_M ISSISS1 PPI_BAS 1N
Designates whether a site is in the Mississippi Basin, which includes USGS
hydrologic basins (from MAJ_BAS_NM): Arkansas-White-Red Region, Lower
Mississippi Region, Missouri Region, Ohio-Tennessee Region, Upper Mississippi
Region.
USEPA National Estuary
Program
NEP_NAT
NEP | Not_NEP
Designates whether a site is in a USEPA National Estuary Program (NEP)
watershed. Does not include Chesapeake Bay.
Four NWCA Aggregated
Ecoregions
NWCA_EC04
CPL | EMU | IPL | W
Omernik Level III Ecoregions aggregated into Four NWCA Aggregated Ecoregions:
Coastal Plains (CPL), Eastern Mountains & Upper Midwest (EMU), Interior Plains
(IPL), and West (W). Note that inland and tidal saline sites are not distinguished.
For a visual, see the NWCA_EC04 tab in this workbook.
Five NWCA Aggregated
Ecoregions
NWCA_EC05
CPL | EMU | IPL | WMT | XER
Omernik Level III Ecoregions aggregated into Five NWCA Aggregated Ecoregions:
Coastal Plains (CPL), Eastern Mountains & Upper Midwest (EMU), Interior Plains
(IPL), Western Valleys & Mountains (WMT), and Xeric West (XER). Note that inland
and tidal saline sites are not distinguished. For a visual, see the NWCA_EC05 tab
in this workbook.
Four NWCA Aggregated
Ecoregions x Inland
versus Tidal
NWCA_EC04_HYD
CPL-INLAND | CPL-TIDAL | EMU-
INLAND | EMU-TIDAL | IPL-INLAND
| W-INLAND | W-TIDAL
Omernik Level III Ecoregions aggregated into Four NWCA Aggregated Ecoregions
(NWCA_EC04) and distinguished by inland sites (HYD_CLS = INLAND) or tidal
saline sites (HYD_CLS = TIDAL): Coastal Plains Inland (CPL-INLAND), Coastal Plains
Tidal (CPL-TIDAL), Eastern Mountains & Upper Midwest Inland (EMU-INLAND),
Eastern Mountains & Upper Midwest Tidal (EMU-TIDAL), Interior Plains Inland
(IPL-INLAND), West Inland (W-INLAND), West Tidal (W-TIDAL). Note that there are
no Interior Plains Tidal sites, thus there is no 1PL-TI DAL subpopulation.
Five NWCA Aggregated
Ecoregions x Inland
versus Tidal
NWCA_EC05_HYD
CPL-INLAND | CPL-TIDAL | EMU-
INLAND | EMU-TIDAL | IPL-INLAND
| WMT-IN LAND | WMT-TIDAL |
XER-INLAND | XER-TIDAL
Omernik Level III Ecoregions aggregated into Five NWCA Aggregated Ecoregions
(NWCA_EC05) and distinguished by inland sites (HYD_CLS = INLAND) or tidal
saline sites (HYD_CLS = TIDAL): Coastal Plains Inland (CPL-INLAND), Coastal Plains
Tidal (CPL-TIDAL), Eastern Mountains & Upper Midwest Inland (EMU-INLAND),
Eastern Mountains & Upper Midwest Tidal (EMU-TIDAL), Interior Plains Inland
(IPL-INLAND), Western Valleys & Mountains Inland (WMT-INLAND), Western
Valleys & Mountains Tidal (WMT-TIDAL), Xeric West Inland (XER-INLAND), and
Xeric West Tidal (XER-TIDAL). Note that there are no Interior Plains Tidal sites,
thus there is no 1 PL-TI DAL subpopulation.
Land Ownership
OWN_NARS
BLM | BOR | City | County | DOD |
FWS | NGO | NOAA | Non-Federal |
NPS | Other Fed | Regional | State |
Tribal | USFS
Designates land ownership: Bureau of Land Management (BLM), Bureau of
Reclamation (BOR), City, County, Department of Defense (DOD), Fish and Wildlife
Survey (FWS), Non Governmental Organizations (NGO), National Oceanic and
Atmospheric Administration (NOAA), National Park Service (NPS), other federal
(Other Fed), Regional, State, Tribal, and US Forest Service (USFS) lands. Non
Federal lands are designated 'Non-Federal1.
States
PSTL_CODE
AL | AR | AZ | CA | CO | CT | DE |
FL | GA | IA | ID | IL | IN | KS | KY |
US State: Alabama (AL), Arizona (AZ), Arkansas (AR), California (CA), Colorado
(CO), Connecticut (CT), Delaware (DE), Florida (FL), Georgia (GA), Idaho (ID),
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Subpopulation Group
Parameter Name
Subpopulations
Description
LA | MA | MD | ME | Ml | MN |
MO | MS | MT | NC | ND | NE | NH
| NJ | NM | NV | NY | OH | OK | OR
| PA | Rl | SC | SD | TN | TX | UT |
VA | VT | WA | Wl | WV | WY
Illinois (IL), Indiana (IN), Iowa (IA), Kansas (KS), Kentucky (KY), Louisiana (LA),
Maine (ME), Maryland (MD), Massachusetts (MA), Michigan (Ml), Minnesota
(MN), Mississippi (MS), Missouri (MO), Montana (MT), Nebraska (NE), Nevada
(NV), New Hampshire (NH), NewJersey (NJ), New Mexico (NM), New York (NY),
North Carolina (NC), North Dakota (ND), Ohio (OH), Oklahoma (OK), Oregon (OR),
Pennsylvania (PA), Rhode Island (Rl), South Carolina (SC), South Dakota (SD),
Tennessee (TN), Texas (TX), Utah (UT), Vermont (VT), Virginia (VA), Washington
(WA), West Virginia (WV), Wisconsin (Wl), Wyoming (WY)
Ten NWCA Reporting
Groups
RPTGRP_10
ALL-EH | ALL-EW | CPL-PRLH | CPL-
PRLW | EMU-PRLH | EMU-PRLW |
IPL-PRLH | IPL-PRLW | W-PRLH | W-
PRLW
Ten NWCA Reporting Groups used for the NWCA analysis that combines Four
NWCA Aggregated Ecoregions (NWCA_EC04) and Aggregated S&T Classes
(NWCA_WET_GRP): All Estuarine Herbaceous (ALL-EH), All Estuarine Woody (ALL-
EW), Coastal Plains Palustrine, Riverine, and Lacustrine Herbaceous (CPL-PRLH),
Coastal Plains Palustrine, Riverine, and Lacustrine Woody (CPL-PRLW), Eastern
Mountains & Upper Midwest Palustrine, Riverine, and Lacustrine Herbaceous
(EMU-PRLH), Eastern Mountains & Upper Midwest Palustrine, Riverine, and
Lacustrine Woody (EMU-PRLW), Interior Plains Palustrine, Riverine, and Lacustrine
Herbaceous (IPL-PRLH), Interior Plains Palustrine, Riverine, and Lacustrine Woody
(IPL-PRLW), West Palustrine, Riverine, and Lacustrine Herbaceous (W-PRLH), West
Palustrine, Riverine, and Lacustrine Woody (W-PRLW). Note that estuarine sites
(ALL-EH and ALL-EW) are combined for the contiguous US.
Twelve NWCA Reporting
Groups
RPTGRP_12
ALL-EH | ALL-EW | CPL-PRLH | CPL-
PRLW | EMU-PRLH | EMU-PRLW |
IPL-PRLH | IPL-PRLW | WMT-PRLH |
WMT-PRLW | XER-PRLH | XER-
PRLW
Twelve NWCA Reporting Groups used for the NWCA analysis that combines Five
NWCA Aggregated Ecoregions (NWCA_EC05) and Aggregated S&T Classes
(NWCA_WET_GRP): All Estuarine Herbaceous (ALL-EH), All Estuarine Woody (ALL-
EW), Coastal Plains Palustrine, Riverine, and Lacustrine Herbaceous (CPL-PRLH),
Coastal Plains Palustrine, Riverine, and Lacustrine Woody (CPL-PRLW), Eastern
Mountains & Upper Midwest Palustrine, Riverine, and Lacustrine Herbaceous
(EMU-PRLH), Eastern Mountains & Upper Midwest Palustrine, Riverine, and
Lacustrine Woody (EMU-PRLW), Interior Plains Palustrine, Riverine, and Lacustrine
Herbaceous (IPL-PRLH), Interior Plains Palustrine, Riverine, and Lacustrine Woody
(IPL-PRLW), Western Valleys & Mountains Palustrine, Riverine, and Lacustrine
Herbaceous (WMT-PRLH), Western Valleys & Mountains Palustrine, Riverine, and
Lacustrine Woody (WMT-PRLW), Xeric West Palustrine, Riverine, and Lacustrine
Herbaceous (XER-PRLH), Xeric West Palustrine, Riverine, and Lacustrine Woody
(XER-PRLW). Note that estuarine sites (ALL-EH and ALL-EW) are combined for the
contiguous US.
Ten Reporting Units
RPTJJNIT
ARW | ATL | GFC | GPL | ICP | NCE
| PAC | SAP | TPL | WVM
Ten Reporting Units created using a combination of information from AG_EC09
and WETCLS_GRP to distinguish regions of inland sites from regions of tidal saline
sites: Atlantic Coast (ATL), Arid West (ARW), Gulf & Florida Coasts (GFC), Great
Plains (GPL), Inland Coastal Plains (ICP), North Central East (NCE), Pacific Coast
(PAC), Southern Appalachians (SAP), Temperate Plains (TPL), and Western Valleys
& Mountains (WVM). Note that the inland region names have been changed to
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Subpopulation Group
Parameter Name
Subpopulations
Description
distinguish regions with similar boundaries but combine inland and tidal saline site
(e.g., CPL from NWCA_EC04 and NWCA_EC05 includes ATL, IPL, and GPL sites
from RPTJJNIT). For a visual, see the RPTJJNIT tab in this workbook. The DATA
CROSSWALK tab in this workbook explains how the units were derived (and how
they refer to other parameters).
Five Reporting Units
RPT_UNIT_5
EMU | ICP | PLN | TDL | WST
Five Reporting Units for reporting that uses tidal saline wetlands as a distinct
region from the Four Aggregated NWCA Ecoregions (NWCA_EC04), and created
by collapsing the Ten Reporting Units (RPTJJNIT): Eastern Mountains & Upper
Midwest (EMU = NCE + SAP), Inland Coastal Plains (ICP), Plains (PLN = GPL + TPL),
Tidal Saline (TDL = ATL + GFC + PAC), West (WST = ARW + WVM).
Six Reporting Units
RPT_UNIT_6
ARW | EMU | ICP | PLN | TDL |
WVM
Six Reporting Units for reporting that uses tidal saline wetlands as a distinct region
from the Five Aggregated NWCA Ecoregions (NWCA_EC05), and created by
collapsing the Ten Reporting Units (RPTJJNIT): Arid West (ARW), Eastern
Mountains & Upper Midwest (EMU = NCE + SAP), Inland Coastal Plains (ICP),
Plains (PLN = GPL + TPL), Tidal Saline (TDL = ATL + GFC + PAC), Western Valleys &
Mountains (WVM).
12-Ecoregion x Wetland
Group Reporting Units
RPTJJNIT12
ARW-H | ARW-W | EMU-H | EMU-
W | ICP-H | ICP-W | PLN-H | PLN-W
| TDL-H | TDL-W | WVM-H | WVM-
W
Twelve reporting units derived from the combination of Six Reporting Units
(RPT_UNIT_6) and Wetland Groups (WETCLS_GRP): Arid West Herbaceous (ARW-
H), Arid West Woody (ARW-W), Eastern Mountains & Upper Midwest Herbaceous
(EMU-H), Eastern Mountains & Upper Midwest Woody (EMU-W), Inland Coastal
Plains Herbaceous (ICP-H), Inland Coastal Plains Woody (ICP-W), Plains
Herbaceous (PLN-H), Plains Woody (PLN-W), Tidal Saline Herbaceous (TDL-H),
Tidal Saline Woody (TDL-W), Western Valleys & Mountains Herbaceous (WVM-H),
Western Valleys & Mountains Woody (WVM-W).
Twenty Reporting Units
RPTJJNIT20
ARW-H | ARW-W | ATL-H | ATL-W |
GFC-H | GFC-W | GPL-H | GPL-W |
ICP-H | ICP-W | NCE-H | NCE-W |
PAC-H | PAC-W | SAP-H | SAP-W |
TPL-H | TPL-W | WVM-H | WVM-W
Twenty Reporting Units created using a combination of information from
AG_EC09 and WETCLS_GRP to distinguish regions of inland sites from regions of
tidal saline sites, and WETCLS_GRP to distinguish herbaceous (H) from woody (W)
dominated sites: Atlantic Coast Herbaceous (ATL-H), Atlantic Coast Woody (ATL-
W), Arid West Herbaceous (ARW-H), Arid West Woody (ARW-W), Gulf & Florida
Coasts Herbaceous (GFC-H), Gulf & Florida Coasts Woody (GFC-W), Great Plains
Herbaceous (GPL-H), Great Plains Woody (GPL-W), Inland Coastal Plains
Herbaceous (ICP-H), Inland Coastal Plains Woody (ICP-W), North Central East
Herbaceous (NCE-H), North Central East Woody (NCE-W), Pacific Coast
Herbaceous (PAC-H), Pacific Coast Woody (PAC-W), Southern Appalachians
Herbaceous (SAP-H), Southern Appalachians Woody (SAP-W), Temperate Plains
Herbaceous (TPL-H), Temperate Plains Woody (TPL-W), Western Valleys &
Mountains Herbaceous (WVM-H), and Western Valleys & Mountains Woody
(WVM-W). Note that the inland region names have been changed to distinguish
regions with similar boundaries but combine inland and tidal saline site (e.g., CPL
from NWCA_EC04 and NWCA_EC05 includes ATL, IPL, and GPL sites from
RPT_UNIT). For a visual, see the RPT_UNIT tab in this workbook. The DATA
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Subpopulation Group
Parameter Name
Subpopulations
Description
CROSSWALK tab in this workbook explains how the units were derived (and how
they refer to other parameters).
USFWSS&T Wetland
Classes
WETCLS_EVAL
E2EM | E2SS | NONE | PEM | PF |
PFO | PSS | PUBPAB
US Fish and Wildlife Service Status and Trends wetland class designated in the
field on Form AA-2 on date of sampling. If evaluated in field but not sampled, then
wetland class is assigned from field visit. If site only evaluated in office, then
wetland class assigned at that time. If no site evaluation information on wetland
class, then wetland class assigned wetland class used for the survey design. Hand-
picked sites should be assigned during field sampling. Wetland classes use FWS
S&T classes: Estuarine Intertidal Emergent (E2EM), Estuarine Intertidal
Forest/Shrub (E2SS), Palustrine Emergent (PEM), Palustrine Farmed (PF),
Palustrine Forested (PFO), Palustrine Shrub (PSS), and Palustrine Unconsolidated
Bottom/Aquatic Bed (PUBPAB). See Reference Card AA-3, Side A in the 2011 and
2016 NWCA Field Operations Manuals for details. NONE only applies to non-
sampled sites.
Wetland Groups
WETCLS_GRP
EH | EW | PRLH | PRLW
Aggregated US Fish and Wildlife Service Status and Trends wetland class based on
the design that combines wetland type and dominant vegetation type for
reporting: Estuarine Herbaceous (EH), Estuarine Woody (EW), Palustrine, Riverine,
and Lacustrine Herbaceous (PRLH), and Palustrine, Riverine, and Lacustrine
Woody (PRLW).
Hydrogeomorphically-
Altered
WETCLS_ALT
HGM_ALTERED |
HGM_NOT_ALTERED
Using WETCLS_HGM2, distinguishes HGM altered sites from not altered sites
using QAed and validated values (HGM_CLASS_VALID and HGM_SUBCLASS_VALID
in tbIASSESSMENT) designated in the field on Form AA-2 on date of sampling.
HGM_ALTERED includes 'DEPRESSION_ALT', 'FLATS_ALT', 'LACUSTRINE_ALT',
1RIVERINE_ALT', 'SLOPE_ALT', and 'TIDAL_ALT' while HGM_NOT_ALTERED includes
'DEPRESSION1, 'FLATS', 'LACUSTRINE', 'RIVERINE', 'SLOPE', and 'TIDAL'.
Hydrogeomorphic Classes
WETCLS_HGM
DEPRESSION | FLATS | LACUSTRINE
| RIVERINE | SLOPE | TIDAL
Hydrogeomorphic (HGM) class from QAed and validated values
(HGM_CLASS_VALID in tbIASSESSMENT) designated in the field on Form AA-2 on
date of sampling, including depression (DEPRESSION), flats (FLATS), lacustrine
fringe (LACUSTRINE), riverine (RIVERINE), slope (SLOPE), and tidal (TIDAL) wetland
classes. See Reference Card AA-3, Side B in the 2011 and 2016 NWCA Field
Operation Manuals for details.
Hydrogeomorphic Classes
Distinguishing Natural
versus Altered
WETCLS_HGM2
DEPRESSION | DEPRESSION_ALT |
FLATS | FLATS_ALT | LACUSTRINE |
LACUSTRINE_ALT | RIVERINE |
RIVERINE_ALT | SLOPE | SLOPE_ALT
| TIDAL | TIDAL_ALT
Hydrogeomorphic (HGM) classes, separated into natural and altered HGM
subclasses from QAed and validated values (HGM_CLASS_VALID and
HGM_SUBCLASS_VALID in tbIASSESSMENT) designated in the field on Form AA-2
on date of sampling. Unaltered HGM classes include depression (DEPRESSION),
flats (FLATS), lacustrine fringe (LACUSTRINE), riverine (RIVERINE), slope (SLOPE),
and tidal (TIDAL). Altered HGM subclasses are indicated by an appended '_ALT'
and include DEPRESSION_ALT (includes subclasses 'Closed, Human Impounded',
'Closed, Human Excavated', 'Closed, Human Excavated and Impounded', 'Open,
Human Impounded', 'Open, Human Excavated', 'Open, Human Excavated and
Impounded', FLATS_ALT (includes subclass 'Human Altered'), LACUSTRINE_ALT
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Subpopulation Group
Parameter Name
Subpopulations
Description
(includes subclass 'Artificially Flooded1), RIVERINE_ALT (includes subclass 'Human
Altered'), SLOPE_ALT (includes subclass 'Human Altered'), and TIDAL_ALT
(includes subclass 'Human Altered'). See Reference Card AA-3, Side B in the 2011
and 2016 NWCA Field Operations Manuals for details. Note that '_ALT' indicates
the historic HGM class that should be at the site, denoting that it has been altered
(e.g., a site that was historically FLATS but excavated into a depression would be
designated as FLATS_ALT).
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Chapter 6: Assigning Disturbance Class
Anthropogenic disturbances to wetlands vary in impacts and intensities across different regions of the
United States (USEPA 2016a,b, Lornnicky et al. 2019). Following the practice of previous NARS
assessments (e.g., USEPA 2006, 2008, 2009, 2016a), the NWCA uses a quantitative definition of
disturbance using physical, chemical, and biological data collected at wetland sites sampled as part of the
NWCA. These data reflect a continuous gradient of anthropogenic disturbance - ranging from no
observable or measurable anthropogenic impacts to highly altered wetland sites. Wetland sites that fall
along this continuous disturbance gradient are assigned to one of three disturbance classes: "least
disturbed", "intermediate disturbed", or "most disturbed" (Figure 6-1, USEPA 2016a). Thus, thresholds
that delineate the boundaries of each disturbance class must be set.
y
y03s \^6<
, 1 y 1 V
,SX
Disturbance Gradient
Reference Sites
Figure 6-1. Diagram of the disturbance gradient used in the NWCA with three classes of disturbance.
Because pristine conditions are uncommon or absent in most places, the NWCA uses the characteristics
found in least-disturbed sites as "reference". Least-disturbed sites are those with the best available
physical, chemical, and biological condition given the current status of the landscape in which the site is
located (Stoddard et al. 2006). Least-disturbed status for the NWCA is defined using a set of explicit
quantitative criteria for specific disturbance indicators. It is expected that these least-disturbed sites will
represent good ecological condition (Karr 1991, Dale and Beyeler 2001, Stoddard et al. 2006, 2008),
although this may not always be the case given that "least disturbed" in some areas of the country still
has considerable disturbance.
The planning for the NWCA assumes:
The survey design provides a representative sample of the target population;
Least-disturbed sites reflect the functional capacity and delivery of services typical of a given
wetland type in a particular landscape setting (e.g., ecoregion, watershed); and,
Thresholds developed from data collected on-site and used to define disturbance classes provide
benchmarks against which to compare assessment results.
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Least- and most-disturbed sites are needed in the development of condition indicators - both for the
evaluation of candidate metrics (Chapter 8:) that may reflect ecological condition and for the
development of Vegetation Multi metric Indices (VMMIs) (Chapter 9:). Specifically, least-disturbed sites
are used in setting thresholds for good, fair, and poor condition based on VMMI values (Magee et al.
2019a, Herlihy et al. 2019).
This chapter documents the complex process for 1) developing quantitative definitions of site-level
anthropogenic disturbance based on physical, chemical, and biological data, 2) establishing least- and
most-disturbed thresholds, and 3) assigning sampled sites to least-, intermediate-, and most-disturbed
classes. The process for calculating indices and metrics and for assigning disturbance class is summarized
in An Illustrative Guide to Assigning Disturbance Class in Six Steps found in Section 6.9, Appendix A.
6.1 Sites Used to Establish the Disturbance Gradient
Data from a total of 1,987 unique probability and handpicked sites across both the 2011 NWCA and the
2016 NWCA (Table 6-1) were used in a screening process to establish a disturbance gradient. The
sampling events at these 1,987 unique sites are referred to as index Visits, as they include only the first
sampling visit (i.e., Visit 1) and only the 2016 site data (i.e., not the 2011 site data) if a site from 2011 was
also sampled in 2016. In other words, if the same site was sampled in both 2011 and 2016, the most
recent Visit 1 was used as the Index Visit. For the 208 resampiedsites, we chose to use the 2016 data
over the 2011 data because of improvements in the field protocols for collecting disturbance information,
and because using data associated with the most recent survey is standard across other NARS. The
probability sites were either from the NWCA design or a related probability design produced by NARS for
a state intensification (Chapter 2:). The handpicked sites included those identified for and sampled in the
2016 survey (Chapter 3:) and the handpicked sites sampled in the 2011 survey (USEPA 2016a).
Table 6-1. The number of Visit 1 (VI) probability and handpicked sites sampled in 2011 and 2016, with their totals.
Additionally, the numbers of resampied sites are reported in paratheses to indicate that these are subtracted from
the subtotals above. The total number of unique probability and handpicked sites are reported with the final
number of Index Visit sites (in the red cell) used in the establishment of the NWCA disturbance gradient. Note that
this table does not include the 96 Visit 2 sites sampled in 2011 and 94 Visit 2 sites sampled in 2016, which are only
used to calculate Signal-to-Noise ratios for some indicators/metrics (see Chapter 8: for details).
VI PROBABILITY
HANDPICKED
SURVEY YEAR
(n-sites)
(n-sites)
TOTAL
2011 NWCA
967
171
1138
2016 NWCA
967
90
1057
SUBTOTAL
1934
261
2195
2011 Sites Resampied in 2016
(207)
(1)
(208)
TOTAL UNIQUE SITES
1727
260
1987
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6.2 Establishing a Disturbance Gradient
The general steps in the process of establishing a disturbance gradient are:
Develop indices or metrics that reflect anthropogenic disturbance,
Set thresholds for "least disturbed" for each index or metric,
Set thresholds for "most disturbed" for each index or metric, and
Use a screening process to define each site as "least", "intermediate", or "most disturbed"
(Herlihy et al. 2008, 2019).
To develop the disturbance gradient for the 2016 NWCA, a stepwise process was used in which sites were
first screened using physical indices, then by chemical indices, and finally through a biological metric.
Methods for calculating the indices and metrics used in screening are explicitly discussed in Chapter 11:
and Chapter 12:. The general process for setting thresholds and assigning disturbance classes are
described in the following sections.
6.2.1 Indices and Metrics
Physical, chemical, and biological data collected in the field and laboratory were evaluated for use in
screening sites to establish the disturbance gradient. Indices and metrics were chosen based on evidence
of a strong association with anthropogenic stress and on the robustness of the data. The indices and
metrics used in the 2016 NWCA are described in Table 6-2.
Table 6-2. Indices and metrics used in the 2016 NWCA to establish the disturbance gradient. Final indices and
metrics for which thresholds were created are in uppercase, bold type.
Screen Type Data Type Indices and Metrics Reference
Physical
Human-
Mediated
Physical
Alterations
Vegetation Removal (PALT_VEGRMV)
Vegetation Replacement (PALT_VEGREP)
Water Addition/Subtraction (PALT_WADSUB)
Water Obstruction (PALT_WOBSTR)
Soil Hardening (PALT_SOHARD)
Surface Modification (PALT_SOMODF)
PALT_ANY
PALT_SUM
Chapter 11
Chemical
Soil
Chemistry
Enrichment Factor (EF)}ŧ EF_MAX
Heavy Metal Index (HMI)
Chapter 12
Biological
Vegetation
Relative Percent Cover of Nonnative (alien and cryptogenic)
Plant Species (XRCOV_AC)
Section 6.6
Physical and chemical indices were used to define least- and most-disturbed sites based primarily on
abiotic characteristics under the variable name REF_NWCA_ABIOTIC. The biological metric was used to
further screen the least-disturbed sites designated in REF_NWCA_ABIOTIC, resulting in some of these
sites being rejected from least-disturbed status. The resulting final disturbance class designations are
found under the variable name REF_NWCA.
Although water chemistry is a part of the NWCA field protocol, only 56% and 65% of the wetlands in 2011
and 2016, respectively, sampled across both Visit 1 and Visit 2 had sufficient surface water to collect and
analyze. In addition, wetland hydroperiod- especially during the growing season when NWCA sampling
occurred - can greatly influence water chemistry (e.g., nutrients can become highly concentrated during
drawdowns) and introduce bias into the types of wetlands sampled for water chemistry (see Kentula et al.
2020). Thus, water chemistry was excluded from the generation of the disturbance gradient. However,
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the water chemistry analyses, including how disturbance classes were assigned to just the wetland sites
sampled for water chemistry, are presented in a stand-alone chapter of this report (Chapter 13:).
Additionally, while we were able to gather landscape data (e.g., land use within a 1-km buffer of the AA)
using GIS layers, we opted not to use these data to screen sites for the disturbance gradient. This was for
two reasons: 1) the GIS layers are less precise than the data we were able to gather in the field, and 2) it
is possible that wetlands in good condition exist in what is considered an "impacted" landscape.
Therefore, we used only information directly measured by Field Crews on the ground to establish the
disturbance gradient.
6.2.2 Setting Least-Disturbed Thresholds
For each of the indices and metrics in Table 6-2, a least-disturbed threshold was set. Physical and
chemical thresholds were set independently by the subpopulation group Five Reporting Units
(RPT_UNIT_5), as the extent of human disturbance can vary greatly among regions. Following the
definition of least-disturbed as the best-available sites (Stoddard et al. 2006), thresholds for "least
disturbed" in ubiquitously impacted regions may be greater than those for "intermediate disturbed" or
even "most disturbed" in regions that have greater amounts of intact area. Initially, physical and chemical
thresholds were set to zero human disturbance in all regions. However, if a subpopulation (i.e., region)
did not have a sufficient number of least-disturbed sites with these stringent thresholds, the thresholds
were relaxed so that approximately 15-25% of the sites in the subpopulation passed the screens to obtain
a sufficient number of least-disturbed sites for data analysis. The set of least-disturbed sites identified
using the physical and chemical screens were further screened using a biological metric, and any sites
that exceeded 10% relative cover of nonnative plants were rejected from least-disturbed status.
6.2.3 Setting Most-Disturbed Thresholds
Most-disturbed sites were defined using a screening process in the same manner as for least-disturbed
sites. The same physical and chemical measures of disturbance were used, and thresholds for most
disturbed were set for each measure. If any single threshold for any measure was exceeded, the site was
considered a most-disturbed site. As "most disturbed" is a relative definition, our objective was to define
approximately 20-30% of the sites in a subpopulation as "most disturbed", and thresholds were set
accordingly.
6.2.4 Classifying Disturbance at Each Site for each Sampling Visit
Finally, disturbance status was assigned to each site for each of its sampling visits (i.e., Visit 1, Visit 2, and
both 2011 and 2016 visits for resampled sites). Disturbance status was assigned by screening each site
visit to test for exceedance of least- and most-disturbed thresholds. Sites were first screened using the
physical and chemical indices and metrics. If a site exceeded the most-disturbed thresholds, it was
considered most-disturbed. If any single physical or chemical threshold was exceeded at a site, it was not
considered "least-disturbed". Sites identified as least-disturbed based on this screen were further
screened using the biological metric. Thus, the final set of least-disturbed sites were those that were
below the thresholds for all physical, chemical, and biological measures. Sites not falling into either least-
or most-disturbed categories were classified as having intermediate disturbance.
The following sections provide details about the data used to develop thresholds for each index or metric
in Table 6-2 and the thresholds used for defining least- and most-disturbed sites.
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6.3 Human-Mediated Physical Alteration Screens and Thresholds
Human-Mediated Physical Alteration scores were calculated for each site using methods described in
Chapter 11: and summarized in Section 6.9, Appendix A: Steps 1 and 2. Thresholds were developed for
Five Reporting Units (RPT_UNIT_5, see Table 5-1 in Chapter 5:), which include the subpopulations Tidal
Saline (TDL), Inland Coastal Plains (I CP), Eastern Mountains & Upper Midwest (EMU), Plains (PLN), and
West (WST). Two screens that integrate scores from all six physical alteration indices (VEGRMV, VEGRPL,
WADSUB, WOBSTR, SOHARD, and SOMODF, see Table 6-2) were applied to each site using the thresholds
described in Table 6-3:
PALT_ANY - For any given site, the PALT_ANY screen for "least disturbed" was applied by
considering each of the six physical alteration indices individually. If the score forany one index
(i.e., the maximum score among all six indices) was greater than a threshold, the site was no
longer considered "least disturbed". The threshold varies by subpopulation, ranging from 0 to <
20, meaning that a least-disturbed site may have (up to) a few observed physical alterations in
the buffer plots, but no observations of physical alterations in the AA (see Chapter 11:, Figure
11-2).
PALT_SUM - The PALT_SUM screen for "least disturbed" was developed to capture instances
where there were multiple observed physical alterations at a site, but those observances were
spread across multiple indices and, therefore, may have passed the PALT_ANY screen despite
moderate to high levels of overall disturbance. For any given site, the PALT_SUM screen was
applied by considering the sum of the scores from all six physical alteration indices. If the sum of
scores for all six indices was greater than a threshold, the site was no longer considered "least
disturbed". Like PALT_ANY, the threshold varies by subpopulation, ranging from 0 to < 40,
meaning that there were no or few observations of physical alterations regardless of index in the
AA or buffer.
Sites may pass the PALT_ANY screen and fail the PALT_SUM screen if there are several observations in
buffer plots within different physical alteration categories. Sites ultimately classified as "least disturbed"
had to pass both the PALT_ANY and the PALT_SUM screens (in addition to other chemical and biological
screens described in the following sections of this Technical Support Document). The least-disturbed
thresholds and the number of sites that passed the physical alteration screens (and were considered
candidate least-disturbed sites) are presented in Table 6-3a.
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Table 6-3. a) Least-disturbed thresholds and b) most-disturbed thresholds for the two physical alteration screens
and the number of sites that passed the screens (i.e., are considered candidate "least disturbed" or "most
disturbed") presented for Five Reporting Units (RPT_UNIT_5).
gj Physical
Screens for
Least-Disturbed
Sites
Tidal Saline
(TDL)
Inland Coastal
Plains (ICP)
Eastern
Mountains &
Upper Midwest
(EMU)
Plains (PLN)
West (WST)
PALT ANY
0
0
0
10
20
PALT SUM
0
0
0
10
40
n-sites
200
100
117
100
83
Physical
' Screens for
Most-Disturbed
Sites
Tidal Saline
(TDL)
Inland Coastal
Plains (ICP)
Eastern
Mountains &
Upper Midwest
(EMU)
Plains (PLN)
West (WST)
PALT ANY
30
50
40
50
70
PALT SUM
60
100
80
100
140
n-sites
87
95
61
84
87
The most-disturbed sites on the disturbance gradient were defined using a screening process in the same
manner as for least-disturbed sites. Thresholds for "most disturbed" were set for PALT_ANY and
PALT_SUM. If any single threshold for any measure was exceeded, the site was considered a most-
disturbed site. As "most disturbed" is a relative definition, our objective was to classify approximately 20-
30% of the sites in a subpopulation as "most disturbed", and thresholds were set accordingly. The most-
disturbed thresholds and the number of sites considered candidate "most disturbed" are presented in
Table 6-3b.
Sites that did not meet "least disturbed" or "most disturbed" threshold criteria were classified as
"intermediate disturbed".
For some sites, data were not collected at all on the H-l Form and/or B-l Form, or an insufficient number
of buffer plots (<5) were sampled. In these cases, the sites could not be evaluated using the physical
screens (i.e., PALT_ANY and PALT_SUM) and were categorized as "unknown" (coded as "?") for their
physical screen disturbance class.
6.4 Chemical Screens and Thresholds
Two chemical screens were used as the second set of screens (with the first set being the physical screens
discussed in the previous section) to assign abiotic disturbance class (REF_NWCA_ABIOTIC) to each site.
These screens are 1) the Heavy Metal Index (HMI) and 2) the Maximum Enrichment Factor (EF_MAX), the
calculations for which are detailed in Chapter 12: and summarized in Section 6.9, Appendix A: Steps 3-5.
In brief, the Enrichment Factor (EF) is calculated for each of 12 heavy metals at each site to capture the
degree to which soils are enriched. Using the EF information, the HMI is calculated, which indicates the
number of heavy metals with moderate enrichment or greater (EF > 3). Finally, the EF_MAX is calculated,
indicating the highest degree to which a site was contaminated by any of the 12 heavy metals.
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Sites ultimately assigned as abiotic "least disturbed" had to pass the PALT_ANY and the PALT_SUM screens
and the HMI and EF_MAX screens. National thresholds for "least disturbed" were used for both the HMI
and EF_MAX and were:
HMI < 1
EF_MAX < 5
In other words, regardless of the region in which a site was located, for a site to be considered "least
disturbed", only one heavy metal EF could be equal to or above three, and the EF of any heavy metal had
to be less than five. Although national thresholds were used for the HMI and EF_MAX screens, region-
specific heavy metal background concentrations were used in the EF calculation (specifically, as the
denominator) (see Chapter 12: and Section 6.9, Appendix A: Step 4 for details). The chemical screen
thresholds for "least disturbed" and the number of sites that passed the chemical screens (i.e., were
considered abiotic "least disturbed" (REF_NWCA_ABIOTIC)) are presented in Table 6-4a.
Table 6-4. a) Least-disturbed thresholds and b) most-disturbed thresholds for the two chemical screens and the
number of sites that passed the screens (i.e., are considered abiotic "least disturbed" or "most disturbed")
presented for Five Reporting Units (RPT_UNIT_5).
Chemical
' Screens for
Least-Disturbed
Sites
Tidal Saline
(TDL)
Inland Coastal
Plains (ICP)
Eastern
Mountains &
Upper Midwest
(EMU)
Plains (PLN)
West (WST)
HMI
< 1
< 1
< 1
< 1
< 1
EF MAX
<5
<5
<5
<5
<5
n-sites
180
96
105
98
68
Chemical
' Screens for
Most-Disturbed
Sites
Tidal Saline
(TDL)
Inland Coastal
Plains (ICP)
Eastern
Mountains &
Upper Midwest
(EMU)
Plains (PLN)
West (WST)
HMI
> 1
> 1
> 1
> 1
> 1
EF MAX
> 10
> 10
> 10
> 10
> 10
n-sites
109
105
72
88
101
The most-disturbed sites on the disturbance gradient were defined using a screening process in the same
manner as for least-disturbed sites. National thresholds for most disturbed were set for the HMI and
EF_MAX and were:
HMI > 1
EF_MAX > 10
If any threshold of either chemical screen (i.e., HMI or EF_MAX) was exceeded, the site was considered
"most-disturbed". In other words, regardless of the region in which a site was located, any more than one
heavy metal EF equal to or above three or an EF of any heavy metal greater than five resulted in a site
regarded as "most disturbed". As "most disturbed" is a relative definition, our objective was to define
approximately 20-30% of the sites in a subpopulation as most disturbed, and thresholds were set
accordingly. In particular, the EF_MAX was set above ten to equate a level of severe enrichment with a
most-disturbed site. The most-disturbed thresholds and the number of sites considered "abiotic most
disturbed" are presented in Table 6-4b.
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It is important to note that the thresholds established for heavy metals do not reflect toxicity thresholds.
These thresholds are indicators of human disturbance.
For some sites, soil chemistry samples were not collected. In these cases, the sites could not be evaluated
using the chemical screens (i.e., HMI and EF_MAX) and were categorized as "unknown" for their chemical
screen disturbance class.
6.5 Abiotic Disturbance Class Assignments
Physical and chemical screens were combined to assign sites to abiotic disturbance classes of "least
disturbed", "intermediate disturbed", "most disturbed", and "unknown", coded in the data as
REF_NWCA_ABIOTIC. In general, the highest disturbance class between the physical and chemical screens
is used to assign the abiotic disturbance class. If physical alteration data were missing from a site, the
abiotic disturbance class was assigned as "unknown". If soil chemistry data were missing from a site, the
abiotic disturbance class was set to that of the physical screen disturbance class3. The application of rules
used to assign abiotic disturbance classes is illustrated in Figure 6-2.
For any single site:
Physical Screen
Disturbance Class
Chemical Screen
Disturbance Class
Abiotic
Disturbance Class
Figure 6-2. A visual summary of how rules for assigning abiotic disturbance classes based on the physical and
chemical screens are applied to a site, where L = "least disturbed", I = "intermediate disturbed", M = "most
disturbed", and ? = "unknown". Note that the physical and chemical screens were evaluated together to
determine the abiotic disturbance class assignment for a site.
A summary of the number of sites within each abiotic disturbance class are reported by region
(RPT_UNIT_5) in Table 6-5.
3 The decision to use the physical screen disturbance level instead of assigning "unknown" when soil chemistry data
were missing from a site was based on the low prevalence of sites with "intermediate disturbance" or "high
disturbance" assignments based on the chemical screens alone.
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Table 6-5. n-sites of abiotic disturbance class assignments (REF_NWCA_ABIOTIC) reported by region (RPT_UNIT_5)
for Visit 1, Index Visit 2011 and 2016 sites
Least
Intermediate
Most
Regional
Region
Disturbed (L)
Disturbed (1)
Disturbed (M)
Unknown (?)
Totals
Tidal Saline (TDL)
180
170
109
3
462
Inland Coastal Plains (ICP)
96
207
105
4
412
E. Mts & Upper Midwest (EMU)
105
172
72
1
350
Plains (PLN)
98
163
88
2
351
West (WST)
68
242
101
1
412
National Totals
547
954
475
11
1987
6.6 Biological Screen and Threshold
Many sites designated as "least disturbed" using the physical and chemical screens had high relative
cover of nonnative plants, and such sites do not reflect natural vegetation conditions (Sala et al. 1996,
Lesica 1997, Vitousek et al. 1997, Ehrenfeld 2003, Dukes and Mooney 2004, Magee et al. 2010, 2019b).
Consequently, the set of abiotic least-disturbed sites (REF_NWCA_ABIOTIC == L) were screened with a
biological screen, resulting in a new set of final least-disturbed sites.
The biological screen was comprised of a single metric - the relative percent cover of nonnative (alien
and cryptogenic) plants species (XRCOV_AC), summarized in Section 6.9, Appendix A: Step 6. Relative
percent cover of nonnative plant species (XRCOV_AC) is calculated as the relative cover of alien and
cryptogenic species across the five sampled 100-m2 vegetation plots4 as a percentage of total plant cover,
or:
a cover of all alien + cryptogenic taxa across 5 plots \
- - 1 *100
cover of all individual taxa across 5 plots #
The final set of least-disturbed sites for the NWCA (see the REF_NWCA variable) had to pass the
PALT_ANY, PALT_SUM, HMI, and EF_MAX least-disturbed screens and the XRCOV_AC least-disturbed
screen. The national threshold used for "least disturbed" was:
XRCOV AC < 10%
In other words, regardless of region, for a site to be considered "least disturbed", nonnative plants had to
make up less than 10% of the total vegetation cover. The biological screen threshold and the number of
sites that passed this screen (i.e., assigned "least-disturbed" status) are presented in Table 6-6.
4 Data describing the abundance (percent cover) of all vascular species were collected in five 100-m2 vegetation
plots systematically distributed within each NWCA Assessment Area according to the Vegetation Protocol (USEPA
2011b, USEPA 2016c). Data collection methods are summarized in Section 7.3. In addition, each individual plant
taxon-state pair identified in NWCA 2011 and 2016 was assigned to a native status category: native, introduced,
adventive, cryptogenic, or unknown (see Chapter 1\, Section 7.8 and Table 7-5).
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Table 6-6. The least-disturbed threshold for the biological screen, and the number of sites passing the screen (and
thus, are assigned final "least-disturbed" status as indicated in REF_NWCA) for the Five Reporting Units
(RPT_UNIT_5).
Biological
Screen for
Least-Disturbed
Sites
Tidal Saline
(TDL)
Inland Coastal
Plains (ICP)
Eastern
Mountains &
Upper Midwest
(EMU)
Plains (PLN)
West (WST)
XRCOV AC
< 10%
< 10%
< 10%
< 10%
< 10%
n-sites
149
86
101
53
50
Contrary to the methods used for physical and chemical disturbance gradient screens, the biological
screen was not used to designate most-disturbed sites. Instead, abiotic least-disturbed sites (based on the
physical and chemical screens) that were rejected using the biological screen were reassigned as
intermediate-disturbed sites. Thus, the set of least- and intermediate-disturbed sites are different for
REF_NWCA_ABIOTIC and REF_NWCA. However, the set of most-disturbed sites are the same.
6.7 Final Disturbance Class Assignments
The final disturbance class site assignments, which include "least disturbed", "intermediate disturbed",
and "most disturbed", are recorded as the variable, REF_NWCA, and was used for evaluation of
vegetation candidate metrics and for VMMI development based on data from NWCA 2011 and 2016 (see
Chapter 8: and Chapter 9:).
A summary of final disturbance designations (REF_NWCA) reporting the number of sites within each
disturbance class by region (RPT_UNIT_5) is provided in Table 6-7 and mapped in Figure 6-3.
Table 6-7. n-sites within final disturbance class assignments (REF_NWCA) reported by region (RPT_UNIT_5) for
Visit 1, Index Visit 2011 and 2016 sites. Note that two sites (one from TDL and another from ICP) were dropped
due to insufficient vegetation data and assigned as "unknown".
Least
Intermediate
Most
Regional
Region
Disturbed (L)
Disturbed (1)
Disturbed (M)
Unknown (?)
Totals
Tidal Saline (TDL)
149
201
108
4
462
Inland Coastal Plains (ICP)
86
216
105
5
412
E. Mts & Upper Midwest (EMU)
101
176
72
1
350
Plains (PLN)
53
208
88
2
351
West (WST)
50
260
101
1
412
National Totals
439
1061
474
13
1987
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a 2011 Least-Disturbed Sites
2011 Intermediate-Disturbed Sites
~ 2011 Most-Disturbed Sites
2016 Least-Disturbed Sites
2016 Intermediate-Disturbed Sites
2016 Most-Disturbed Sites
Figure 6-3. Map of sampled sites and their final disturbance class (REF_NWCA) assignments.
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6.8 Literature Cited
Dale VH, Beyeler SC (2001) Challenges in the development and use of ecological indicators. Ecological
Indicators 1: 3-10.
Dukes JS, Mooney HA (2004) Disruption of ecosystem processes in western North America by invasive
species. Revista Chilena de Historia Natural 77: 411-437.
Ehrenfeld JG (2003) Effects of exotic plant invasions on soil nutrient cycling processes. Ecosystems 6: 503-
523.
Herlihy AT, Paulsen SG, Van Sickle J, Stoddard JL, Hawkins CP, Yuan LL (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
Herlihy AT, Paulsen SG, Kentula ME, Magee TE, NahlikAM, Lomnicky GA (2019) Assessing the relative and
attributable risk of stressors to wetland condition across the conterminous United States. Environmental
Monitoring and Assessment 191 (SI): 320. DOI: 10.1007/sl0661-019-7313-7.
Karr JR (1991) Biological integrity: A long-neglected aspect of water resource management. Ecological
Applications 1: 66-84.
Kentula ME, NahlikAM, Paulsen SG, Magee TK (2020) Wetland assessment: Beyond the traditional water
quality perspective. In: Summers JK (Ed.) Water Quality: Science, Assessments and Policy. IntechOpen,
DOI: 10.5772/intechopen.92583.
Lesica P (1997) Spread of Phalaris arundinacea adversely impacts the endangered plant Howellia aquatilis.
Great Basin Naturalist 57: 366-368.
Lomnicky GA, Herlihy AT, Kaufmann PR (2019) Quantifying the extent of human disturbance activities and
anthropogenic stressors in wetlands across the conterminous United States - results from the National
Wetland Condition Assessment. Environmental Monitoring and Assessment 191 (SI): 324, doi:
10.1007/s 10661-019-7314-6.
Magee TK, Blocksom KA, Fennessy MS (2019a) A national-scale vegetation multimetric index (VMMI) as
an indicator of wetland condition across the conterminous United States. Environmental Monitoring and
Assessment 191 (SI): 322, doi: 10.1007/sl0661-019-7324-4.
Magee TK, Blocksom KA, Herlihy AT, &. NahlikAM (2019b) Characterizing nonnative plants in wetlands
across the conterminous United States. Environmental Monitoring and Assessment 191 (SI): 344, doi:
10.1007/s 10661-019-7317-3. https://link.springer.com/article/10.1007/sl0661-019-7317-3
Magee TK, Ringold PL, Bollman MA, Ernst T (2010) Index of Alien Impact: a method for evaluating
potential ecological impact of alien plant species. Environmental Management 45: 759-778.
Sala A, Smith SD, Devitt DA (1996) Water use by Tamarix ramosissima and associated phreatophytes.
Ecological Applications 6: 888-898.
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Stoddard JL, Larsen DP, Hawkins CP, Johnson PK, Norris RH (2006) Setting expectations for the ecological
condition of streams: the concept of reference condition. Ecological Applications 16: 1267-1276.
Stoddard JL, Herlihy AT, Peck DV, Hughes RM, Whittier TR, Tarquinio E (2008) A process for creating
multimetric indices for large-scale aquatic surveys. Journal of the North American Bethological Society 27:
878-891.
USEPA (2006) Wadeable Streams Assessment: A Collaborative Survey of the Nation's Streams. US
Environmental Protection Agency, Office of Water and Office of Research and Development, Washington,
DC.
USEPA (2008) Ecological Research Program Multi-Year Plan FY2008-2014: February 2008 Review Draft. US
Environmental Protection Agency, Office of Research and Development, Washington, DC.
USEPA (2009) National Lakes Assessment 2007: A Collaborative Survey of the Nation's Lakes. EPA-841-R-
09-001. US Environmental Protection Agency, Office of Water and Office of Research and Development,
Washington, DC.
USEPA (2016a) National Wetland Condition Assessment 2011 Technical Report. US Environmental
Protection Agency, Washington DC. EPA-843-R-15-006.
USEPA (2016b) National Wetland Condition Assessment 2011: A Collaborative Survey of the Nation's
Wetlands. US Environmental Protection Agency, Washington DC. EPA-843-R-15-005.
Vitousek PM, D'Antonio CM, Loope LL, Rejmanek M, Westbrooks R (1997) Introduced species: a
significant component of human-caused global change. New Zealand Journal of Ecology 86: 33212-33218.
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6.9 Appendix A: Illustrative Guide to Assigning Disturbance Class in Six Steps
CALCULATE PHYSICAL ALTERATION INDICES
For each Visit 1, Index Visit probability and handpicked site, calculate a score for each of six Physical Alteration indices (VEGRMV,
VEGRPL, WADSUB, WOBSTR, SOHARD, and SOMODF). Evaluate the highest score among all six Physical Alteration indices to
determine PALT_ANY and the total score among all six Physical Aleration indices to determine PALT_SUM.
For each of the six Physical Alteration indices (VEGRMV, VEGRPL, WADSUB,
WOBSTR, SOHARD, SOMODF), sum the score of the observed metrics (i.e., eight
metrics in each of the six indices) in the AA and buffer plots.
Where, VEGRMV
VEGRPL
WADSUB
WOBSTR
SOHARD
SOMODF
Vegetation Removal
Vegetation Replacement
Water Addition/Subtraction
Water Obstruction
Soil Hardening
Surface Modification
1
Calculate PALT ANY and PALT SUM.
Where, PALT_ANY=
The maximum score among all six
Physical Alteration indices.
Where, PALT_SUM=
The total score by summing the scores of
all six Physical Alteration indices.
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SCREEN SITES THROUGH PHYSICAL ALTERATION INDICES
Screen sites using least-disturbed and most-disturbed Physical Alteration thresholds in five regions using Physical Alteration indices:
PALT_ANY (VEGRMV, VEGRPL, WADSUB, WOBSTR, SOHARD, SOMODF) and PALT_SUM. All Visit 1 sites (probability and
handpicked) sampled in 2011 and 2016 are evaluated. Sites that pass the screens remain candidates for least- or most-disturbed sites.
PHYSICAL ALTERATION THRESHOLDS
LEflST-DISf IIBBIB I ICR TBI I MOST-DISTURBED I ICR I TBI
PALT_ANY 20 10 0 0 0 PALT_ANY 70 50 | 40 50 | 30
PALT_SUM 40 i 0 0 i 0 PALT_SUM 140 : 100 i 80 100 : 60
n-sites 83 | 100 117 i 100 200 n-sites 87 84 G1 95 87
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ESTIMATE SOIL HEAVT METAL BACKGROUNDS
Using the candidate least-disturbed sites that passed the Physical Alteration screens in Steps 1 and 2, calculate the 75th percentile of
the concentration of each of 12 heavy metals (Ag, Cd, Co, Cr, Cu, Ni, Pb, Sb, Sn, V, W, Zn) within each of Five Reporting Units
(RPT_UNIT_5). Note: heavy metal concentration graphs illustrate how 75th percentiles are determined and do not show actual results.
westtwsTi
1 1
Analysis of
Heavy Metal Background
Site Distribution
n+7 |
n+6
n+5_
O
O
jS n+3
Percentiles:
95th
r?5tfr-
T
50th
25th
5th
CANDIDATE LEAST- / MOST-DISTURBED SITES
L.
Piains (PLN)
Analysis of
Heavy Metal Background
Site Distribution
n+7
n+6
n+5
O
O
2 n+3
o
2
^ n+2
1
Percentiles:
95th
-rWth
I
50th
Analysis of
Heavy Metal Background
Site Distribution
n+7 |
Percentiles:
O
o
2 n+3
0)
5
I
95th
Mfe
50th
25th
5th
n+7
o
(0
i_
n+5
c
0)
c
n+4
o
o
co
n+3
0
>.
>
n+2
0)
T
n+'l
0
Inland Coastal
Plains (ICP)
i
Analysis of
Heavy Metal Background
Site Distribution
Percentiles:
I
75th
50th
T
25th
5th
L.
Tidal Saline
(TDL)
nr
Analysis of
Heavy Metal Background
Site Distribution
n+7 Ķ
n+6
n+5
O
O
2 n+3
>
o
X
Percentiles:
I
,75th
1
25th
HEAVY METAL BACKGROUND CONCENTDATIONS (ppm)
75th Percentile
(ppm)
Ag
Cd
Co
Cr
Cu
Ni
Pb
Sb
Sn
v
w
Zn
WST
0.19
0.46
8.99
39.7
28.5
22.6
24.3
0.47
1.46
65.4
0.19
81.7
PLN
0.17
0.55
9.17
38.8
19.5
23.3
26.4
0.34
1.45
65.6
0.04
97.2
EMU
0.15
0.82
5.17
22.9
15.2
13.8
37.4
0.40
1.41
33.9
0.18
61.7
ICP
0.09
0.26
8.06
39.4
14.2
18.3
24.6
0.31
1.47
52.9
0.05
64.6
TDL
0.15
0.15
7.30
538
17.2
21 4
25.1
0.29
1.69
75.8
0.06
73.0
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CALCULATE ENRICHMENT FACTORS ANO HEAVY METAL INDEX
For each site, calculate an Enrichment Factor, or EF, (Chen et al. 2007) for each of the 12 heavy metals using the regional heavy metal
background estimated from Step 3. Then, combine the 12 EFs into a Heavy Metal Index (HMI) and calculate the maximum
EF (EF_MAX) for each site, which are used in combination as the chemical screen in abiotic reference site selection (Step 5).
Enrichment Factor = EF
For each site, EF_AG
EF_CD
EF_CO
EF_CR
EF_CU
EF_NI
EF_PB
EF_SB
EF_SN
EF_V
EF_W
EF ZN
-(
Observed heavy metal concentration at a site
Regional 75th percentile heavy metal background
)
Where, EF < 1
no enrichment
EF < 3
minor enrichment
EF = 3-5
moderate enrichment
EF = 5-10
moderately severe enrichment
EF = 10-25
severe enrichment
EF = 25-50
very severe enrichment
EF > 50
extremely severe enrichment
^number of heavy metals with EF > 3 = Heavy Metal Index = HMI
Where, the maximum of the HMI can be 12 for any
site (i.e., if all 12 heavy metal EFs are
equal to or greater than 3, indicating
moderate enrichment or greater depending
on the EF values).
Maximum Enrichment Factor = EF_MAX = maximum value of the 12 heavy metal EFs
Chen, C.W., C.M. Kao, C.F. Chen, & C.D. Dong (2007) Distribution and accumulation of heavy metals in the sediments of Kaohsiung Harbor, Taiwan. Chemosphere 66(8): 1431-1440.
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SCREEN SITES THROUGH HEAVY METAL INDICES
Using the sites that passed the Physical Alteration screens (i.e., candidate least-disturbed / most-disturbed sites), rescreen the sites using
the heavy metal screens (HMI and EF_MAX) in Five Reporting Units (RPT_UNIT_5). Sites that pass the screens are the final abiotic
least- or most-disturbed sites (i.e., sites passed all the physical and chemical screens).
U
&
westmvsT)
Plains (PIN! Eastern Mountains inland Coastal
a Upper Midwest (EMII] Plains (ICP)
Tidal Saline
(TDD
>
I
u
w
HEAVY METAL THRESHOLDS
LEAST-DISTURBED
WST
ICP
TDI
HMI
< 1
< 1
< 1
< 1
< 1
EF_MAX
< 5
< 5
<5
< 5
< 5
n-sites
68
98
105
96
180
MOST-DISTURRED
WST j
ICP
TDI
HMI
> 1
> 1
> 1
> 1
> 1
EF_MAX
> 10
> 10
> 10
> 10
> 10
n-sites
101
88
72
105
109
74
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u
s
u
ŦS
to
0.
yd
M
CALCULATE RELATIVE PERCENT COVER OF NONNATIVES & SCREEN
Calculate the relative percent cover of normative (alien and cryptogenic) plant species, XRCOV_AC, of each site. Then, screen only the
abiotic least-disturbed sites (i.e., sites that passed all the physical and chemical screens) using a national biological screen with a threshold
of XRCOV_AC < 10%. Any site that does not pass the biological screen is classified as "intermediate disturbed".
For each site, calculate the relative percent cover of normative (alien and cryptogenic)
plant species (XRCOV AC):
XRCOV AC =
(I
cover of all alien + cryptogenic taxa across 5 plots
cover of all individual taxa across 5 plots
)
100
C3
C/D
CD
&
bhi
H
C/D
*
WestlWSTl
4 A
O
Eastern Mountains Inland Coastal Tidal Saline
Plains IICP1 (TDD
xr: ^
M
(A
t/i
oĢ
i
LJ
NATIONAL THRESHOLD:
XRCOV AC <10%
Sites that pass the biological screen are designated as final least-disturbed, or "reference", sites.
Sites that do not pass are classified as "intermediate-disturbed".
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Chapter 7: Vegetation Analysis Overview, Data Acquisition, and
Preparation
7.1 Background
The status of natural vegetation has been
increasingly and effectively used as an
indicator of ecological condition in wetlands
(Mack and Kentula 2010, USEPA 2016a,
Magee et al. 2019a). In wetland ecosystems,
vegetation provides biodiversity, primary
productivity, habitat for organisms in other
trophic levels, and contributes to energy,
nutrient, and sediment or soil dynamics
(Mitsch and Gosselink 2007, Tiner 1999).
Wetland vegetation both responds to and
influences hydrology, water chemistry, soils,
and other components of the biophysical
habitat of wetlands. Because plants respond
directly to physical, chemical, and biological
conditions at multiple temporal and spatial
scales, they can be excellent indicators of
ecological condition or stress (Mclntyre and
Lavorel 1994, Mclntyre et al. 1999). For
example, wetland plant species 1) represent
diverse adaptations, ecological tolerances,
and life history strategies, and 2) integrate
environmental conditions, species
interactions, and human-caused disturbance. As a result, many human-mediated disturbances are
reflected in shifts in the presence or abundance of particular plant species, plant functional groups
(Quetier et al. 2007), plant communities (Galatowitsch et al. 1999, DeKeyser et al. 2003), and vegetation
structural elements (Mack 2007). In addition, some vegetation metrics are likely to be more prominently
expressed in particular wetland types, and some wetland types may be more likely than others be
subjected to higher anthropogenic disturbance levels or to be less resilient to this disturbance (USEPA
2016b, Magee et al 2019a).
Data describing plant species composition (species identity, presence, and abundance) and vegetation
structure were collected in the 2011 and 2016 NWCA Surveys. Such data are powerful, robust, relatively
easy to gather and can be summarized into myriad candidate metrics that may be related to ecological
condition (USEPA 2002, Mack and Kentula 2010, Magee et al. 2019a). In addition to reflecting ecological
condition, some plant species groups can be indicators of stress to wetlands. Nonnative plant species, in
particular, are recognized as indicators of declining ecological condition, or as stressors to ecological
condition (Magee et al. 2008, Ringold 2008, Magee et al. 2010, Magee et al. 2019b).
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Vegetation Multimetric Indices (VMMI) and a Nonnative Plant Indicator of Stress (NNPI) were used to aid
in evaluating wetland condition based on the 2016 NWCA and changes in condition observed between
the 2011 and 2016 NWCAs.
Vegetation Multimetric indices (VMM!) of Condition
Background: VMMIs include several metrics describing different aspects of the observed vegetation that
together can reflect wetland condition in relation to least-disturbed wetland sites. In developing VMMIs,
individual candidate vegetation metrics are evaluated for their utility in distinguishing least disturbed sites
from those that are most disturbed. Several of most effective metrics are then selected and combined
into a VMMI as an indicator of wetland condition. VMMIs commonly include a suite of vegetation metrics
(representing aspects of plant communities, vegetation structure, and functional or life history guilds)
(e.g., DeKeyser et al. 2003, Miller et al. 2006, Reiss 2006, Rocchio 2007, Veselka et al. 2010, Euliss and
Mushet 2011, Genet 2012, Rooney et al. 2012, Deimeke et al. 2013, Wilson et al. 2013).
NWCA VMMIs:
NWCA 2011\ A four-metric VMMI that is applicable across the national-scale of the conterminous
US was developed and employed in assessing wetland condition based on data from the 2011
NWCA (USEPA 2016a, USEPA 2016b, Magee et al. 2019a).
NWCA 2016\ For the NWCA 2016 analysis, the combined number of wetland sites sampled in the
2011 and 2016 NWCAs provided sufficient data to allow development of separate VMMIs for four
major Wetland Groups: Estuarine Herbaceous, Estuarine Woody, Inland Herbaceous, and Inland
Woody.
Nonnative Plant indicator of Stress (NNPIJ
The NNPI was developed for the 2011 NWCA (USEPA 2016a, USEPA 2016b, Magee et al. 2019b) and this
indicator is also used for the 2016 NWCA analyses. The NNPI incorporates attributes of richness,
occurrence, and abundance for nonnative (alien and cryptogenic) plant species and can be used to assess
the extent of potential stress to wetlands from nonnative plants (see Chapter 10). In addition to
describing stress to a wetland, the NNPI can also be viewed as an indicator of vegetation condition.
7.2 Overview of Vegetation Analysis Process
As the primary biotic indicator of wetland condition for the NWCA, vegetation is a major component of
the NWCA analysis pathway (see Figure 1-1). Evaluating vegetation in the NWCA included three
sequential phases, each with several major analysis steps (Figure 7-1). First, data acquisition and
preparation are covered in this chapter. Chapter 8 describes prerequisite steps for vegetation indicator
development, including candidate metric calculation and evaluation. Development of the 2016 NWCA
VMMIs is described in Chapter 9, and the Nonnative Plant Indicator is summarized in Chapter 10.
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Acquire and
Validate Field &
Lab Data
Standardize
Plant Taxonomy
Acquire/Develop
Species Trait
Characteristics
Identify Least
and Most
Disturbed Sites
Evaluate Plant
Species
Composition
Calculate and
Screen Candidate
Metrics
3. Describing Condition and Stress
Develop Vegetation
Multimetric Indices
(VMMIs)
Estimate Wetland
Area in Good, Fair,
Poor Condition
based on VMMIs
Calculate Nonnative
Plant Indicator (NNPI)
Estimate Wetland Area
in Good, Fair, Poor, or
Very Poor Condition in
Relation to Stress from
Nonnative Plants
Figure 7-1. Overview of vegetation data preparation and analysis steps used in assessing NWCA wetlands.
The three analysis elements depicted in Figure 7-1, their included steps, and the Sections or Chapters in
which they are discussed are listed below:
1. Data Acquisition and Preparation
Collect field data (Section 7.3)
Validate raw data (Section 7.4)
Standardize plant species taxonomy (Section 7.5)
Acquire or develop plant species trait information used in development of candidate vegetation
metrics (Sections 7.6 - 7.9)
2. Steps Prerequisite to indica tor Development
Define disturbance gradients by identifying least- and most-disturbed sampled sites (Section 8.2
and Chapter 6)
Evaluate plant species composition in relation to ecoregion and wetland type to maximize
homogeneity within groups of sites for analysis and potential VMMI development (Section 8.3)
Use raw vegetation data (Section 7.12 Appendix C) and species trait information (Sections 7.6 -
7.9) to calculate candidate vegetation metrics (Section 8.4)
Evaluate candidate vegetation metrics for potential utility for use in VMMI development (Section
8.5).
3. Description of Ecological Condition and Stress
For each of four major Wetland Groups, develop a Vegetation Multimetric Index that reflects
wetland condition along an anthropogenic disturbance gradient (Chapter 9).
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Describe how VMMIs are used to estimate wetland area in good, fair, and poor condition across
the conterminous US and within various wetland subpopulations (Chapter 15:),
Calculate Nonnative Plant Indicator of stress (NNPI) (Chapter 10)
Describe how NNPI is used to estimate wetland area, across the conterminous US and within
various wetland subpopulations, that has good, fair, poor, or very poor condition in relation to
stress from nonnative plants (Chapter 15:).
7.3 Vegetation Data Collection
The Vegetation Protocols for the NWCA were designed to address the survey objectives, while meeting
logistics constraints of completion in one sampling day per site by a four-person Field Crew. The sampling
protocols are detailed in the NWCA 2016 Field Operations Manual (FOM) (USEPA 2016c), which has
updates and additions compared to the 2011 FOM (USEPA 2011a). A brief overview of the standardized
NWCA field sampling and plant data collection protocols, and identification protocols for unknown plants
represented by collected specimens, is provided in the following two subsections.
7.3.1 Field Sampling
To facilitate consistency and quality in vegetation data collection, Field Crews were provided with:
Standardized training in vegetation sampling protocols prior to beginning sampling; and
An Assistance Visit from NWCA experts to a sample site to answer any crew questions about
protocol implementation, generally during the first week of field sampling.
Vegetation data for the NWCA were collected during the peak growing season when most plants were in
flower or fruit to optimize species identification and characterization of species abundance. At each
NWCA sample point location, data were gathered in five 100-m2 Vegetation (Veg) Plots.
The five Veg Plots were placed systematically in a >2 hectare Assessment Area (AA) at each site.
In each plot vegetation data were collected across the entire 100-m2 plot and also in smaller
nested quadrats within each plot.
Alternate configurations for AA shape and plot locations were used only, when necessary, as
determined by rules related to specific site conditions (USEPA 2016c).
Standard AA and Veg Plot layouts are illustrated in Figure 7-2, the configuration of each plot is
shown in Figure 7-3.
Key activities of the vegetation sampling protocol, and the data collected in each step are
provided in the flowchart in Figure 7-4.
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Figure 7-2. Standard NWCA Assessment Area (AA) (shaded circular area) and standard layout of Vegetation Plots.
100-m2 Veg Plot
10m
3.16m
1.00m
1m2
10m2
/
NE Quadrat Nest
10m2
1m2
\
SW Quadrat Nest
Figure 7-3. Diagram of a Vegetation Plot illustrating plot boundaries and positions of nested quadrats.
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Figure 7-4. Overview of vegetation data collection protocol for the 2016 NWCA (USEPA 2016c).
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7.3.2 Identification of Unknown Plant Species
Plant species observed in the Veg Plots at each site that could not be identified in the field, were collected
for later identification. Specimen collection, labeling, specimen preservation (pressing and drying),
shipping or delivering dried specimens to a designated laboratory or herbarium, and specimen tracking
were completed according to standard protocols described in the NWCA 2016 Field Operations Manual
(USEPA 2016c).
Identification of unknown plant taxa was guided by protocols in the NWCA 2016 Laboratory Operations
Manual (USEPA 2016d). Unknown plant specimens from each Field Crew were identified at a specific
designated regional laboratory or herbarium (hereafter, lab) by one or more lab botanists. As quality
control for the identification process, ten percent of the lab identifications for unknowns were
independently verified by another botanist at the lab. Lab botanists maintained a detailed spreadsheet
that included for each unknown specimen collected in the field: the collection number and pseudonym
from the field collection, the location of collection (plot and site number), date of sampling, the name
assigned during lab identification based on a regional flora, and any notes related to the identification.
The lab botanists also reviewed quality assurance (QA) plant voucher specimens (5 per site) collected by
the field crew to confirm identifications of these species by the field botanists and to calculate percent
taxonomic agreement between lab and field botanists.
All identifications of unknown and QA vouchers were recorded in the 2016 NWCA Plant ID Lab
Spreadsheets (an Excel workbook). This Excel workbook includes instructions for required information to
be recorded for each specimen, as well as user information tabs that provide quick reference lists and
instructions for recording data on the Unknown and QA voucher spreadsheets. For example, a list of
growth-habit codes as well as floras and field guides are included for quick reference while other tabs
provide examples and specific instructions on how to fill out the various data fields of the Excel
spreadsheets for the QA voucher and Unknown specimen spreadsheets.
The identification spreadsheets were forwarded to the NWCA Data Management and Analysis Teams. The
Vegetation Analysis Team reviewed the identification spreadsheets submitted by the labs and
standardized nomenclature to the USDA-NRCS PLANTS database (USDA, NRCS 2019-2020). The validated
identifications of unknown taxa were integrated with the NWCA raw plant data tables, replacing the
pseudonyms recorded by the Field Crews for unknowns with their accepted scientific names (see Section
7.4.2).
7.4 Data Preparation - Parameter Names, Legal Values, and Data Validation
7.4.1 Description of Vegetation Field Data Tables
The data from the completed vegetation field forms were electronically scanned into several predefined
long format, raw data tables in the NWCA database. A separate table was created for each of the three
primary vegetation data forms:
tbIPLANT - data from Form V-2: NWCA 2016 Vascular Species Presence and Cover
tbIVEGTYPE table - data from Form V-3: NWCA 2016 Vegetation Types (Front) and NWCA 2016
Ground Surface Attributes (Back), and from Form VI (predominant wetland type section).
tbITREE table - data from Form V-4: NWCA 2016 Snag and Tree Counts and Tree Cover
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Examples of the NWCA 2016 vegetation field forms can be found in Section 7.11, Appendix B.
Form V-l data describe the vegetation plot layout at each site and the wetland type observed in each of
the five 100-m2Veg Plots.
Form /-2data describe vascular plant species identity, presence, cover, and height for each observed
taxon and were collected in each 100-m2 Veg Plot. Taxa typically represent species or lower level (e.g.,
subspecies, variety) classification, but occasionally individual taxa were identified only to genus, family, or
growth form. For convenience, in this report, vascular plant taxa are generally referred to as species even
though in some cases lower or higher taxonomic levels are reflected. Form V-2 data used in candidate
metric development for the 2016 NWCA analyses included taxon name, presence, and percent cover
(COVER).
Other species level data were collected using Form V-2 but were reserved for further research and not
incorporated in the analysis of condition for the 2016 NWCA. These other data included predominant
height for each species across each plot, and presence of individual species at different spatial scales, i.e.,
within the S = 1-m2 quadrats, M = 10-m2 quadrats) nested in the two corners of plot and within the
overall plot (L = 100-m2 plot), see Section 7.3.1). The former can reflect vegetation structure and, when
used with cover, volume by species or guild groups. The latter address fine scale diversity patterns.
Form V-3 data encompass descriptors of the structure of vascular vegetation, non-vascular groups
present, and ground surface attributes which are each sampled in the five 100-m2 Veg Plots. All these
data were used in developing candidate metrics.
Form V-4data include counts by diameter class of dead trees/snags, as well as cover by height classes and
by diameter classes for individual tree species in each 100-m2 Veg Plot. Tree data were used in candidate
metric development.
Parameter names and legal values or ranges for the field collected vegetation data are listed in Section
7.12, Appendix C. The quality of all the vegetation field data was carefully examined during data
validation.
7.4.2 Data Validation
Whenever large quantities of data are collected, it is not surprising for errors related to data or sample
collection, recording, sample analysis, or data entry to occasionally occur. Therefore, a series of quality
assurance (OA) review checks were conducted to identify and resolve any errors to ensure high quality
data. The NWCA established numerous cross-checks in the data collection and processing procedures,
within the protocols and field forms, to help limit potential errors during data collection. Verification and
update of the scanned vegetation data involved several OA steps conducted by members of the
Information Management Team and the Vegetation Analysis Team. Some checks required manual
evaluation of the paper forms or data scanned into the databases; others involved the use of specific R
Code written to identify records with specific kinds of potential errors. Tasks conducted by the
Information Management Team and the Vegetation Analysis Team are listed below.
Information Management Team:
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Verified that the data from the Vegetation Forms scanned properly
Where possible, verified spelling of plant species name with USDA PLANTS database
Conducted quality assurance checks for valid ranges and legal values for all data
Vegetation Analysis Team:
Updated the names for unknown taxa at each site based on plant specimen identification (see
Section 7.3.2)
Conducted nomenclatural resolution, correcting species name spelling errors and resolving taxon
names that were recorded as synonyms to accepted names of the USDA PLANTS database (see
Section 7.5)
Reviewed and resolved all instances of missing, out of range or non-legal values identified by the
IM Team:
o Review of the field forms often indicated a scanning or recording error that was readily
resolved and the data updated
o Where no resolution was apparent the data were flagged, and the error described
Conducted logic checks and data type specific checks using R code to identify:
o Missing data (e.g., checking that if a certain type of data is present, another specific type
must also have a value)
o Recording errors (e.g., data recorded in a form workspace, rather than in the data field)
o Incongruities in values among related data
o Instances of individual plant species recorded multiple times at one site (i.e., multiple
data rows for the same species at one site which may have resulted when an unknown
was identified and was the same taxon as one already recorded)
Determined the cause of each instance of a potential error revealed by logic checks
o Resolved these issues and provided a brief explanation of the issue and resolution in
tracking spreadsheets
For all these data the relevant updates to the database were implemented using R-code, and a
brief explanation of the resolution was included with each of these records in the database
For situations, where no resolution was apparent the data were flagged, and the errors described
The vast majority of concerns identified by these OA screenings were readily resolved allowing accurate
updates to the data. For the instances where specific issues could not be corrected, the data were flagged
with restrictions for use. Where corrections were needed, all original data values were retained as
inactive records in the NWCA database.
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7.5 Nomenclatural Standardization
Across the 2011 and 2016 field sampling seasons,
approximately 170 regional floras and field guides
were used by Field Crews for identification of
plants. Thus, a wide range of taxonomies were
applied to the occurrences of taxon-site pairs
observed across the United States. Consequently, a
critical step in data preparation was
standardization of plant nomenclature to ensure
that each taxonomic entity was called by the same
name throughout the NWCA study area. The
PLANTS nomenclatural database (USDA-NRCS
2020) was used as the national standard for
taxonomy for the NWCA.
In the NWCA, plant species names originated from
raw data records collected using Form V-2: NWCA
Vascular Species Presence and Cover, Form V 4:
NWCA Snag and Tree Counts and Tree Cover, and
from lab identifications of unknown taxa that were
collected in the field. The process for reconciliation
of nomenclature outlined in Section 7.5.1 was used
for all three data types. Section 7.5.2 provides a
brief description of procedures for taxonomic
review and documentation of name assignments
that were used for data from Form V-2. The
documentation process for tree data (Form V-4)
and the lab identifications of unknown plants were
similar but tailored to structures of these data.
Nomenclatural standardization was a complex
undertaking, and in this section, we provide an
overview of the process used for NWCA 2016.
7.5.1 Nomenclature Reconciliation Methods
We reconciled names for the 2016 NWCA observed plant taxa, at each location of their occurrence, to
the PLANTS nomenclatural database (hereafter, PLANTS) (USDA-NRCS 2020) using the methods (Figure
7-5) we developed for the 2011 NWCA (USEPA 2016a). For species where PLANTS accepted names had
changed between the 2011 NWCA analysis and 2020, we also updated nomenclature for these 2011
observations to maintain consistency in plant names for analyses that use data from both surveys. A
series of automated filters based on the components in Figure 7-5 were employed via R code, written
using R software (R Core Team 2018-2019), to link recorded names for NWCA observations to PLANTS
accepted names and to identify names and records 1) that matched accepted PLANTS names and 2) those
that required further evaluation by a botanist to resolve nomenclature.
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Step 1: Identify NWCA name-location pairs directly matching PLANTS accepted names
A large proportion of the plant name-site pairs recorded in the NWCA could be directly matched to
PLANTS accepted names. These included records where:
1) The original NWCA name was the same as the accepted PLANTS name and there were no
synonyms for the name.
2) The original NWCA name pointed to one or more synonyms that all pointed to the same, single
accepted PLANTS name.
Step 2: identify NWCA name-location pairs needing botanical review to reconcile to PLANTS accepted
names
Even though most NWCA names could be directly matched to PLANTS nomenclature in Step 1, a large
number required botanical review to select the correct PLANTS accepted name. There were three
primary types of name issues which necessitated further botanical review:
1) Unmatched Names- no PLANTS accepted name or synonym matched a particular NWCA name-
site pair. Common reasons for unmatched names were misspelling or mis-scanning of the record
or use of an abbreviation or common name. Rarely, the taxon represented a name or taxon not
included in the PLANTS database.
2) Same Name with Different Authorities (shorthand terminology = Multiple Authorities) - refers to
an NWCA name which pointed to synonyms with exactly the same genus and species epithets,
but which had different botanical authorities for the name.
3) Species Concept Unclear - NWCA binomial name was contained in multiple potential PLANTS
accepted names or multiple synonym names that point to multiple possible PLANTS accepted
names.
Step 3: Review name-site pairs identified in Step 2 and determine correct name assignment
The set of names and records identified as requiring further evaluation were reviewed by the NWCA lead
botanist/ecologist, using a general stepwise procedure for nomenclatural determination:
1) Identify and correct spelling errors or abbreviated names.
2) Identify all synonyms and accepted PLANTS name(s) that could apply to each ambiguous taxon-
site pair name.
3) Compare geographic distribution of potential synonyms and accepted PLANTS names with
location of the observed NWCA taxon.
4) Review field records and notes from the NWCA Field Crew regarding the observed NWCA taxon.
5) Review the species concept for the taxon based on flora(s) used by field botanist, as well as other
pertinent taxonomic resources and floristic databases.
The procedures in Step 3 allowed determination of the PLANTS nomenclature accepted name for the
majority of taxon-site pairs that needed botanical review. For taxa where the appropriate PLANTS
accepted name could not be definitively resolved using these procedures, a taxonomist at the PLANTS
database was consulted for assistance with final name determination. This consultation involved
discussions between the NWCA lead botanist/ecologist and the PLANTS taxonomist to review floras,
historical records, and floristic/taxonomic databases pertinent to each taxon-location pair considered. In
a few cases, the PLANTS taxonomist consulted with other botanists across the US with specific expertise
regarding a particular taxonomic group (e.g., species, genus, family) to resolve a naming issue.
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Figure '7-5. Process for screening and reconciling names of plant taxa observed in the NWCA. Dark blue boxes = steps completed using R code, light blue boxes
= steps requiring botanical review, purple boxes = type of name resolution applied, and the dark blue central box = final name resolution.
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7.5.2 Nomenclature Standardization Results and Documentation
For the 2011 NWCA, we developed and applied a standard approach for organizing, resolving, and
documenting the name reconciliations for plant name-site pairs needing review (USEPA 2016a). We used
the same general procedure to reconcile plant nomenclature for the 2016 NWCA data to the PLANTS
database. The NWCA 2011 plant data were also reviewed at this time to identify and update any plant
names that were no longer congruent with the current PLANTS database nomenclature.
Specific NWCA species records (including name, cover value, and other data), along with information
from the PLANTS database, were exported into an Excel Workbook for Nomenclature Resolution. This
workbook gathered key information in one location to facilitate review of the taxonomy and to highlight
when other information was needed. Important NWCA data elements included in the Excel Workbook
were NWCA SITEJD and UID, state, county, latitude and longitude, wetland type, a list of the floras used
by the Field Crew at a particular site, and a link to the scanned field form image. Access to the scanned
field form allowed easy viewing of any notes Field Crews may have made in relation to a particular
species, as well as a view of other taxa present at a site. Critical information from the PLANTS database
included synonyms and accepted names that could potentially correspond to the specific taxon-site pairs.
Various other location pertinent floristic resources and databases were also used when needed by the
NWCA botanist/ecologist in resolving name issues.
The Excel Nomenclature Workbook includes separate spreadsheet tabs for reviewing unresolved names
in three categories: Unmatched Names, Multiple Authorities, and Species Concept Issues (see Step 2 in
Section 7.5.1, for definitions). For each taxon-site pair to be evaluated (rows in spreadsheets) listed, the
associated columns (e.g., NWCA data, taxonomic and distributional information from the PLANTS
database, and other information) informed name resolution. An instruction page in the Workbook
described the associated data included in each of the spreadsheets and the ways this information could
aid in name determination. During nomenclatural review, the rationale for assignment of the correct
PLANTS accepted name to each name-site pair in the NWCA data was documented by specifying a reason
code and, where needed, providing narrative notes and citations of taxonomic sources.
Following taxonomic standardization, the master list of plants observed in the 2011 and/or 2016 NWCAs
included: 5,045 taxa that occurred as 21,359 taxon-state pairs and 73,119 taxon-site pairs. The majority
of taxa observed in the NWCA were identified to the species, subspecies, or varietal level (n = 4,586, 23 of
these were hybrids). The remaining taxa in the list represented identifications made at higher taxonomic
levels, e.g., genus, family.
Once nomenclature for the NWCA name-site pairs was resolved, the appropriate accepted PLANTS name
was applied to each NWCA record. The original names recorded by the Field Crew or lab identifications
were retained as inactive data. Names (NWCA_NAME) and symbols (ACCEPTED_SYMBOL) for the 5,045
taxa are listed in the plant taxa file (nwca_2016_plant_taxa.csv5). The NWCA_NAME typically reflects an
accepted scientific name from the PLANTS nomenclature and ACCEPTED_SYMBOL typically reflects the
PLANTS accepted symbol. In a few cases (20 taxa), where an appropriate taxonomic concept was not
available in PLANTS, we determined the name from other sources and assigned an ACCEPTED_SYMBOL
preceded by the number 1. For these 20 taxa, the complete name with authorities for the relevant taxon
can be found in the SCI_NAME_AUTH column. Taxa that were identifiable only to growth form were
5 .csv files referenced throughout the vegetation chapters are available to download from the USEPA NARS website
(https://www.epa.gov/national-aquatic-resource-surveys/data-national-aquatic-resource-surveys).
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assigned an NWCA_NAME representing one of 35 detailed standardized growth habit/category
designations, each of which were connoted by an ACCEI'I LI) SYMBOL preceded by the number "2".
7.6 Species Traits - Life History: Growth-habit, Duration, and Plant Category
Traits reflecting species life history based on growth-habit, duration, and plant category for all vascular
taxa observed in the NWCA were downloaded from the PLANTS database (USDA-NRCS 2020). This trait
information was used directly or summarized to reduce the number of classes in each life history group.
Life history designations for each taxon observed in the 2011 and 2016 NWCAs are included in the plant
taxa file {nwca_2016_plant_taxa.csv). Life history information was used in combination with presence,
frequency, and cover data for individual species to develop candidate metrics to summarize the
distribution and importance of life history traits across each sampled site (see Section 8.8: Appendix E).
7.6.1 Growth-Habit
Primary growth-habit types for
the plant taxa observed in the
2011 and 2016 NWCAs were
based on growth-habit
designations in the PLANTS
database (USDA-NRCS 2019-
2020).
In the PLANTS database,
individual species were
frequently identified as spanning
multiple growth-habit types. This
results in numerous combined
growth-habit categories, each
often representing few taxa. To
facilitate data analysis, we
merged some of multiple
growth-habit groups from the
PLANTS database into larger
categories for the NWCA data
analysis (Table 7-1).
7.6.2 Duration
Duration or longevity for plants is described by annual, biennial, and perennial life cycles. Some individual
species may exhibit different durations depending on growing conditions. Consequently, in addition to
the individual duration classes, a variety of mixed duration categories occur in the PLANTS trait database.
To facilitate data analysis, we merged some multiple type groups from the PLANTS database into larger
categories (Table 7-2).
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Table 7-1. Growth-habit categories, for species observed in the 2011 and 2016 NWCAs and used in analysis, with a
crosswalk to PLANTS database growth-habit designations. Capitalized Growth-habit Category Names are used in
calculation of Growth-habit metrics (see Section 8.8: Appendix E).
NWCA Growth-habit
PLANTS Database Growth-habit 'Designations' for NWCA Observed Species included in
NWCA Growth-habit Category
GRAMINOID
'Graminoid'; 'Subshrub, shrub, graminoid'; "Graminoid, shrub, subshrub"; 'Graminoid,
shrub, vine; subshrub'; 'Graminoid, shrub'; "Subshrub, shrub, graminoid'
FORB
'Forb/herb'; 'Forb/herb, shrub'; 'Forb/herb, shrub, subshrub'; 'Forb/herb, subshrub';
'Forb/herb, subshrub, shrub'
SUBSHRUB-FORB
'Subshrub, forb/herb'; 'Subshrub, forb/herb, shrub'; 'Subshrub, shrub, forb/herb'
SUBSHRUB-SHRUB
'Subshrub, shrub'; 'Shrub, forb/herb, subshrub'; 'Shrub, subshrub'; 'Subshrub'
SHRUB
'Subshrub, forb/herb, shrub, tree'; 'Shrub, tree'; 'Shrub', 'Tree, subshrub, shrub'
TREE-SHRUB
'Tree, shrub'; 'Tree, shrub, subshrub'; 'Tree, shrub, vine'
TREE
'Tree'
VINE
'Vine'; 'Vine, forb/herb'; 'Subshrub, forb/herb, vine'; 'Forb/herb, vine'; 'Vine,
herbaceous'; 'Vine, forb/herb'; 'Vine, forb/herb, subshrub'
VINE-SHRUB
'Vine, shrub1; 'Vine, subshrub'; 'Subshrub, vine'; 'Shrub, vine'; 'Shrub, forb/herb,
subshrub, vine'; 'Shrub, subshrub, vine'
NWCA Growth-habit
NWCA Growth-habit Category Combinations
HERB
GRAMINOID + FORB
SHRUB-COMB
SUBSHRUB-SHRUB + SHRUB
TREE-COMB
TREE-SHRUB + TREE
VINE-ALL
VINE + VINE-SHRUB
Table 7-2. Duration categories used in the NWCA analyses and a crosswalk to PLANTS database duration
designations for NWCA observed species. Capitalized Duration Category Codes are used in calculation of Duration
Metrics (see Section 8.8: Appendix E).
NWCA Duration
Categories
PLANTS Database Duration 'Designations' for NWCA Observed Species
ANNUAL
'Annual'
ANN_BIEN
(Annual-Biennial)
'Annual, biennial'; 'Biennial'; 'Biennial, an'
ANN_PEREN
(Annual-Perennial)
'Annual, biennial, perennial'; 'Annual, perennial'; 'Annual, perennial biennial'; 'Biennial,
perennial'; 'Biennial, perennial, an';
PERENNIAL
'Perennial'; 'perennial, an'; 'Perennial, annual'; 'Perennial, annual, biennial'; 'Perennial,
biennial'; 'Perennial, biennial, an'; Perennial, biennial, annual'
7.6.3 Plant Categories
Several major plant categories were considered in summarizing raw data to develop guild-based
candidate metrics. The categories assigned for individual NWCA vascular taxa based on PLANTS
database categories were:
Dicot
Monocot
Gymnosperm
Fern
Horsetail
Lycopod
Quillwort
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7.7 Species I raits - Wetland Indicator Status
Hydrophytic status for plants observed in the NWCA surveys was defined
using Wetland Indicator Status (WIS) (Table 7-3). WIS values for
individual species vary across 7 Wetland Regions (Table 7-4). A WIS
category was assigned for each Laxon wetland region pair observed in
2011 or 2016 (see nwca_2016_plant_wis.csv). Most of the NWCA WIS
assignments originated from the National Wetland Plant List (NWPL)
(USACE 2016-2018). However, the NWPL lacked WIS values for some
NWCA taxon wetland region pairs. NWCA evaluated this subset of taxon-
wetland region pairs and assigned WIS values (Table 7-3) as appropriate:
1) UPL to OBL, 2) NOL (not on the NWPL and too little information to
assign UPL to OBL, but often occurring in moist locations), or 3) UNO
(undetermined due to limited information).
Table 7-3. Wetland Indicator Status (WIS) definitions. OBL, FACW, FAC, FACU and UPL defined by Lichvar 2016.
NOL and UND defined by NWCA. These seven WIS Categories are used in calculating Hydrophytic Status Metrics
(Section 6.8: Appendix E). The Numeric Ecological Value (ECOIND2) for each indicator status (UPL to OBL) is used in
calculating indices describing the hydrophytic status of the vegetation at each sampled site.
Wetland Indicator Status
(WIS)
Qualitative Description
Numeric Ecological
Value (ECOIND2)
OBL - Obligate
Almost always occur in wetland
5
FACW - Facultative Wetland
Usually occur in wetlands, but may occur in non-wetlands
4
FAC - Facultative
Occur in wetlands and non-wetlands
3
FACU - Facultative Upland
Usually occur in non-wetlands, but may occur in wetlands
2
UPL- Upland
Almost never occur in wetlands
1
NOL - Not on National
Wetland Plant List
Not on NWPL, but observed in NWCA wetlands under wet
or moist conditions
UND - Undetermined
Wetland status is undetermined
Table 7-4. Wetland regions within which wetland indicator status for individual plant species are defined, and a
crosswalk between USACE codes and NWCA codes for these regions is provided.
Wetland Regions Map [US Army Corps of Engineers,
National Wetland Plant List Map (USACE 2016-2018)]
Wetland Region
USACE/NWPL
Wetland
Region Code
IMWCA
Wetland
Region Code
(C0E_REG_l D)
Atlantic and Gulf Coastal
Plain
AGCP
CSTL_PLAIN
Arid West
AW
ARID W
NONE
MW
Eastern Mountains and
AW GP
Piedmont
EMP
E MTNS
EMP
Western Mountains,
AGCP
Valleys, and Coast
WMVC
W MTS
Great Plains
GP
GT PLAINS
Northcentral and
Northeast
NCNE
NE
Midwest
MW
MIDWEST
91
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7.7.1 Wetland Indicator Status Assignment Process
All taxon-wetland region pairs observed (n = 9,584) in the 2011 or 2016 NWCA Surveys were assigned a
wetland indicator status (WIS) category (Table 7-3): OBL - obligate (n = 2088), FACW - facultative
wetland (n = 2,011), FAC - facultative (n =1,622 ), FACU - facultative upland (n = 1,794), UPL - upland (n =
1,409), NOL- not on NWPL list but considered by NWCA to occur in wetlands some of the time (n = 94),
or UND - undetermined (n = 566). The process used for making these category assignments is outlined in
Steps 1 through 3 below. Step 4 explains how the origin of the WIS value for each NWCA taxon-wetland
region pair was documented.
Step 1: WIS available directly from NWPL for 6631 species-region pairs- Where available, the WIS
category from the National Wetland Plant List (NWPL) (USACE 2016-2018) was assigned to the
corresponding NWCA taxon-wetland region pair. Assignments were made based on nomenclatural and
wetland region matches between the NWPL and observed NWCA taxa. The NWPL provides taxon names
as binomials (genus and species) only. WIS values were assigned to all NWCA names that were binomials
and direct matches to the NWPL names. Some NWCA names represented lower taxonomic levels (e.g.,
subspecies or varieties). For NWCA names with subspecies or variety designations where the genus and
species name matched a binomial on the NWPL, the NWPL WIS category for that binomial was assigned
to the NWCA taxon.
Step2\ WIS assigned from multiple sources for 1875species-region pairs- Each NWCA taxon-wetland
region pair representing a taxonomic level of species, subspecies, variety, or hybrid and not assigned a
WIS category in Stepl was evaluated to determine whether a WIS category could be assigned. This was a
two-step process:
Step 2a - First each of these NWCA taxon-wetland region pairs was evaluated to see if it was a
synonym for a binomial included on the NWPL. If so, the taxon was assigned an NWPL WIS
category following the procedures in Step 1 (n =554).
Step 2b - The taxon-wetland region pairs in the species and lower taxonomic level group that
were not synonyms for taxa on the NWPL list (n = 1,321), were reviewed using a variety of
sources of ecological information (e.g., primary floras, distributional databases, and expertise of
the NWCA vegetation analysis team) to determine if a WIS category might reasonably be
assigned. Based on this review, each of these taxon-wetland region pairs was assigned a WIS
category with the majority assigned to the UPL and NOL categories:
o OBL (n = 19)
o FACW (n = 11)
o FAC (n = 15)
o FACU (n = 13)
o UPL (n = 1,160)
o NOL (n = 94)
o UND (n = 9)
Step 3: WIS assigned for 1,078 higher level taxon-wetland region pairs- Finally 1,078 taxon-wetland
region pairs that were identified only to growth form, family, or genus, or that were nonvascular plants
were assigned a WIS category. The few nonvascular taxon-wetland region pairs (n = 18) included in the
NWCA taxa list were classified as UND. Most NWCA taxon-wetland region pairs identified only to family or
growth habit were assigned an undetermined (UND, n = 202) WIS. Aquatic growth form-region pairs were
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assigned OBL (n = 9) status. Genus-level taxon-wetland region pairs (n = 849) were evaluated as to
whether species in those genera for a given wetland region tended to predominantly occur in wetlands or
uplands. Genera that had species that were predominantly a particular WIS category were assigned that
category; those for which species spanned a wide range of categories were assigned UND:
OBL (n = 129)
FACW (n = 161)
FAC (n = 128)
FACU (n = 71)
UPL (n = 23)
UND (n = 337)
Step 4: Documentation of WIS Value Origin for9,584 observed NWCA taxon-wetland region pairs- In
addition to the WIS assignment for each of the 9,584 NWCA taxon-wetland region pairs, a source or
reason for each assignment was included in the WIS_SOURCE column of the wetland indicator status trait
table (nwca_2016_plant_wis.csv) to provide documentation of its origin. WIS_SOURCE codes, definitions,
and included WIS categories are provided below:
NWPL: WIS value directly from NWPL [OBL, FACW, FAC, FACU, UPL]
NWPL-NOMEN: WIS value from NWPL synonym of NWCA_NAME [OBL, FACW, FAC, FACU, UPL]
NWPL-UPL: no WIS value listed on NWPL, but likely UPL based on habitat descriptions [UPL]
NWPL-ADJREG: WIS value from NWPL for the same taxon from an adjacent wetland region [OBL,
FACW, FAC, FACU]
NWCA-WIS: NWCA assigned WIS value based on other wetland indicator information or habitat
descriptions from floras pertinent to region [OBL, FACW, FAC, FACU]
NWCA-EPIPAR: Taxon-wetland region pair is an epiphyte or parasite that occurs on wetland
species [NOL]
NWCA-MOIST: Habitat descriptions indicate that taxon-wetland region pair often occurs under
moist to wet conditions [NOL]
NWCA-GENUS: UPL- OBL assignment based on predominant wetland status for species in a genus
for a wetland region, or if too little information was available or a wide range of WIS values were
present in the genus it was assigned UND. [OBL, FACW, FAC, FACU, UPL, UND]
NWCA-NI: Insufficient information for a WIS assignment for taxon-wetland region pair [UND]
NONVASCULAR: 18 nonvascular taxa that were included non NWCA taxa list [UND]
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7.8 Species ! raits - Native Status
The number, proportion, or abundance
of native vs. nonnative flora at a given
location can help inform assessment of
ecological condition and stress (Magee
et al. 2019b). To calculate metrics
describing native and nonnative
components of the flora, it was first
necessary to determine the native status
of the vascular plant taxa observed in the
NWCA (USEPA 2016a, Magee et al.
2019b). Here, the state-level native
status was determined for the
approximately 21,360 taxon-state pairs
observed in the 2011 or 2016 NWCA
surveys across various states of the
conterminous US.
Assigning state-level native status for such a large number of taxon-state pairs across the scale of the
NWCA was a demanding task. First, there is currently no comprehensive national standard for native
status of plant species at the local or state level. Next, existing native status designations can be
ambiguous, and the understanding of indigenous species distributions is incomplete. In addition, defining
the concepts for native and nonnative is not always straightforward. Nonnative species may originate
from other countries or continents. Some species are native in one part of the United States, but
nonnative in another. Other taxa may both have alien and native components (e.g., genotypes,
subspecies, varieties, or hybrids).
Consequently, our first step in determining native status for the observed taxa-state pairs was to define
several concepts describing native status for the NWCA (Table 7-5).
Table 7-5. Definition of state-level native status designations for NWCA taxon-state pairs.
Native Status Designations
Native (NAT): Indigenous to specific states in the conterminous US
Alien (ALIEN): Introduced + Adventive
Introduced (INTR): Indigenous outside of, and not native in, the conterminous US
Adventive (ADV): Native to some areas or states of the conterminous US, but introduced in the
location of occurrence
Cryptogenic (CRYP): Includes both Native and Alien genotypes, varieties, or subspecies
Undetermined (UND): Taxa identified at level of growth form, most families, or genera with both native
and alien species
Definitions from Magee et al. 2019b
Note: NWCA defines nonnative plants to include both alien and cryptogenic taxa (Magee et al. 2019b)
Cryptogenic species include taxa with both introduced (often aggressive) and native (generally less
prevalent) genotypes, varieties, or subspecies. Many cryptogenic species are invasive or act as ecosystem
engineers (Magee et al. 2019b), so we grouped them with alien species and considered them nonnative
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for the purpose of indicating ecological stress. For example, see the Nonnative Plant Indicator (NNPI,
Chapter 10:) and metrics ending in AC (Section 8.8, Appendix E).
Using the definitions from Table 7-5 to determine state-level native status for each of the NWCA taxon-
state pairs, we reviewed existing native status designations for all NWCA taxon-state pairs from a variety
of taxonomic and ecological sources:
1) Floristic Databases (state and national levels)
2) State and Regional Floras and Checklists
3) PLANTS Database (USDA, NRCS 2020): Native status and species distribution (conterminous US)
4) Consultation with the PLANTS nomenclatural team
Items 1 through 3 above were used in the primary review of native status for the NWCA taxon-state pairs
and included numerous floristic sources (> 85). Final NWCA native status assignments for individual
taxon-state pairs were based on the body of evidence from relevant reviewed sources. The native status
review process was conducted by the NWCA Lead Ecologist/Botanist and another member of the
Vegetation Analysis Team with strong botanical expertise. One key element of the review was to search
native status designations based on the NWCA accepted name (see Section 7.5) and where needed, on
its synonyms. Many native status determinations were clear-cut, but others were more complex and
required extensive review of distributions and floristic sources. For taxa with particularly complex origins,
the nomenclature team at the PLANTS Database provided input based on their expertise and access to
resources describing species distributions and first collections to inform native status designations.
Native Status determinations for NWCA observed taxa were made for all species-state pairs, and
wherever possible for taxa identified only to genus-state pairs. Family- and growth form-state pairs were
designated 'Undetermined'. The native status designations are compiled in the native status trait table
(.nwca_2106_plant_native_status.csv). The approximately 21,360 taxon-state pairs were distributed as
Native = 17,403, Introduced = 2,195, Adventive = 99, Cryptogenic = 297, and Undetermined = 1,354. The
distribution of native status among taxon-state pairs are presented as percentages in Figure 7-6.
PERCENT OF TAXON-STATE PAIRS
Ķ NAT BUND Ķ INTR ADV Ķ CRYP
ADV CRYP
<1% 1%
Figure 7-6. Distribution of native status among taxon-state pairs presented as percentages.
Native status was used in conjunction with validated field collected vegetation data and with other
species trait information to calculate numerous candidate metrics (Section 8.8: Appendix E).
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7.9 Species I raits - Coefficients of Conservatism
Coefficients of Conservatism [C-v alues, also called
CCs) describe the tendency of individual plant
species to occur in disturbed versus near pristine
conditions. C-values for individual species are state
or regionally specific and scaled from 0 to 10.
A C-value of 1 indicates a widespread
generalist species that thrives under
disturbed conditions.
A C-value of 10 indicates a species that
occurs in specific habitats that are
minimally disturbed (i.e., largely
unaltered).
For the NWCA, nonnative taxa were
assigned a C-value of 0.
C-values are the primary building blocks of 1)
floristic quality indices and 2) metrics describing
vegetation sensitivity or tolerance to disturbance. Coefficients of Conservatism (C-values) for individual
plant taxa in particular locations reflect a taxon's response to anthropogenic disturbance and its habitat
specificity. C-values are applied to taxa by state, region, or habitat, so the C-value for a particular species
often varies by location. Typically, C-values are assigned by panels of expert botanists/ecologists and have
proven to be powerful tools in describing vegetation condition.
Floristic Quality (I Q) indices can be stand-alone indicators of condition or used as a component of a
VMMI (e.g., see Section 9.4). Floristic quality describes the complement of plant species occurring at a
site, and is based on summarization of species-specific, state or regional Coefficients of Conservatism that
rank the responsiveness of each species to disturbance (Swink and Wilhelm 1979, Wilhelm and Ladd
1988). FQ indices have proven utility as indicators of wetland condition in many regions of the US (e.g.,
Lopez and Fennessy 2002, Cohen et al. 2004, Bourdaghs et al. 2006, Miller and Ward top 2006, Milburn et
al. 2007, Bried et al. 2013, Gara 2013, Bourdaghs 2014). Several kinds of FQ indices have been used to
describe wetland condition; the two most common are Mean Coefficient of Conservatism (Mean C) and
the Floristic Quality Assessment Index (FQAI). Both can be based on species presence only or weighted by
species abundance.
Sensitivity and tolerance to disturbance are often key attribute categories used in MMIs for other
biological assemblages and for some wetland VMMIs. For plants, sensitivity can be described based on
presence or abundance of taxa with high C-values, whereas tolerance may be based on presence or
abundance of taxa with low C-values.
See Appendix E Metrics (Section 8.8) for description of metrics based on C-values and for details of their
calculation. Several versions of Floristic Quality Assessment Index (FQAI) and of Mean Coefficient of
Conservatism (Mean C) were investigated in NWCA analyses as potential metrics for inclusion in one or
more Vegetation VMMIs. Metrics describing sensitivity and tolerance to disturbance were also screened
as of potential VMMI components.
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C-values for individual plant species were not universally available for all states or regions, nor for all taxa
observed across the 2011 and 2016 NWCAs. In addition, existing state or regional C-value lists were not
compiled together in a readily accessible format. Thus, to use C-values as a plant trait and calculate C-
value based metrics for the NWCA, it first was necessary to obtain or develop state or regional C-values
for the plant taxa observed during the 2011 or 2016 NWCAs. A unique C-value was needed for each
observed NWCA taxon-region pair representing a specific plant taxon in either a specific state or a specific
region within a state.
This required:
Step 1 - Compiling and standardizing existing State and Regional C-Value Lists from across the
Conterminous US (Section 7.9.1)
Step 2 - Assigning existing C-values, where available, to each taxon-region pair observed in the
2011 and 2016 NWCA surveys (Section 7.9.2)
Step 3 - Developing C-values for each NWCA taxon-region pair observed in the 2011 and 2016
NWCA surveys for which there was no existing C-value (Section 7.9.3)
Step 4 - Finalizing NWCA C-value trait table (Section 7.9.4)
The final C-value assignments for the taxon-region pairs observed in the 2011 and 2016 NWCAs are
located in the NWCA C-value Trait Table (nwca_2016_plant_cvalues.csv) on the NWCA website.
7.9.1 Compilation of Existing State and Regional C-Value Lists from Across the
Conterminous US
An initial compilation of C-value lists (unpublished) was developed for the 2011 NWCA and is described in
the 2011 NWCA Technical Report (USEPA 2016a). C-value coverage for the western states was sparse;
consequently, USEPA convened an expert panel to assign C-values to NWCA taxon-state pairs observed in
the 2011 and 2016 NWCAs and occurring in Arizona, California, Idaho, New Mexico, Nevada, Oregon,
Texas, and Utah (Fennessy & Great Lakes Environmental Center Inc., 2019, unpublished). These two sets
of C-value lists served as the starting point for an updated, more extensive compilation of C-value lists.
For the 2016 NWCA analysis, the NWCA vegetation analysis team developed a standardized compilation
of C-value lists available at the end of 2019, and applicable to plant taxa occurring in specific individual
states or regions across the conterminous US. This unpublished compilation is hereon referred to as the
Compiled C-value Lists (unpublished draft) or the CCL. Citations for the individual C-value lists included in
the CCL are provided in Appendix D (Section 7.13).
The CCL ultimately contained C-values for over 124,000 taxon-region pairs, which were standardized for
potential use with observed NWCA taxon-region pairs. The 124,000+ taxon-region pairs in the CCL were
recorded under the parameter name C-VALUE_NWCA_USE and accompanying each of these taxon-region
pair C-values was the source abbreviation (see Appendix D, Section 7.13) for the specific C-Value List from
which it originated.
Because diverse approaches to list organization, data formats, and taxonomy were used across the
various C-value lists, it was necessary to standardize a variety of elements in the CCL. This standardization
was reflected in the C-values listed under C-VALUE_NWCA_USE so they could later be applied to
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observed NWCA taxon-region pairs. The original C-value for each these records was also retained in the
CCL.
C-values in the C-VALUE_NWCA_USE field of the CCL were standardized as indicated in the bullet points
below:
Standardization of nomenclature - The component C-value lists within the CCL used diverse
taxonomic nomenclatures, with scientific names for plant taxa derived from state or regional
floristic resources. To ensure that each taxonomic entity was referred to by the same name and
compatible with NWCA accepted names (see Section 7.5), taxonomy for the Compiled C-value
Lists was standardized, wherever possible, to the PLANTS database (USDA, NRCS 2019)
nomenclature. For each taxon-region pair, both the PLANTS name and the name or names from
the original C-value list were included in the CCL. When two or more synonyms for a single taxon-
region pair were subsumed under a single PLANTS name, a decision tree (see B in the text box in
Figure 7-7) was used to select among the C-values for the synonyms to apply for the NWCA
taxon-region pair based on the PLANTS name.
Selecting C-value when multiple values were available for a taxon-region pair - In the CCL, there
were sometimes multiple C-values lists available for the same state or region. Consequently,
there could potentially be more than one C-value available for the same taxon in that
state/region. Where this occurred, a decision-tree (see A in the text box in Figure 7-7) was used
to choose the most update-to-date or most rigorous/comprehensive list source from which to
select the C-value for a specific taxon-region pair.
Standardization of C-value formats -The methods and formats used for presentation of C-values
varied among the state and regional lists, with C-values sometimes expressed as whole numbers
ranging from 0 to 10 and sometimes as decimal numbers, e.g., 2.1, 6.7. Consequently, NWCA
standardized all C-values as whole numbers between 0 and 10. C-values originally expressed as
decimals were rounded to the nearest integer; for example, a C-value of 5.5 or higher was
rounded to 6.
Standardization of C-value scoring for nonnative plant species - States and regional C-value lists
did not treat alien plant species uniformly. Some included nonnative species and others did not.
Among those that did, the methods used to assign C-values for alien species were not
standardized. For example, many states assigned a C-value of zero to all nonnative taxa, but
occasionally alien taxa were ranked on a gradient of invasiveness using a range of negative
integers for C-values to indicate increasing potential impact. To address this issue, NWCA
standardized C-values for taxon-region pairs indicated as nonnative species by the CCL to zero.
Native taxon-region pairs listed in the CCL without C-values - were designated undetermined
('UND').
Finally, we note that the specific criteria for C-value assignment varies somewhat across different state or
regional lists, and this is likely to introduce some variability in C-values for taxon-region pairs listed in the
CCL that is not strictly related to taxon responses to disturbance or natural conditions. However, floristic
quality metrics calculated from C-values tend to be robust to many sources of noise.
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Decision Tree for selecting C-value for potential use in the NWCA (C-VALUE_NWCA_USE)) when more than
one C-value existed for a taxon-region pair in the Compiled C-value Lists (unpublished)
Definitions:
C-VALUE_NWCA_USE - C-value for a taxon-region pair that has been standardized for potential use with
observed NWCA taxon-region pairs
2011NWCA_CVALUE - interim C-values used in 2011 NWCA analysis for observed taxon-region pairs
(nwca2011_planttaxa_cc_natstat.csv on NWCA website)
SOURCE 1- for a state or region: 1) only one list exists, or 2) the oldest list if two lists exist
SOURCE2 - for a state or region: Where two C-value lists exist for a state or region, the most recently
completed one.
A. Decision points for selection of taxon-region pair for which the accepted PLANTS name in the CCL relates
to only one taxon name from a C-value list(s) for a state or region
1. Are there multiple C-values for the taxa-location pair?
a. NO-> Use the available C-value
b. YES -> 2.
2. How many and which sources are there?
a. Only one source is available (2011NWCA_CVALUE1 or Source l2 or Source 23) -> Use the
available C-value
b. 2011NWCA_CValue and Source 1 -> 3.
c. Source 1 and Source 2-> 4.
d. 2011NWCA_CValue, Source 1 and Source 2 -> 5.
3. Are 2011NWCA_CValue and Source 1 equal?
a. NO -> Prioritize Source 1 where available. Use 2011NWCA_CValue when there is no Source 1
value, or Source 1 has no value for a specific taxon.
b. YES-> Use matching C-value
4. How do Source 1 and Source 2 compare?
a. Source 1 and Source 2 are equal -> Use matching -value
b. Source 1 and Source 2 differ by only one value -> Use Source 2
c. Source 1 and Source 2 differ by more than one value -> Review & decision by NWCA lead
botanist
d. Source 1 and Source 2 disagree on Nativity -> Review & decision by NWCA lead botanist
5. 2011NWCA_CValue, Source 1 and Source 2 are all available
a. Use Source 1 or Source2-> 4
B. Decision points where the accepted PLANTS name is applied to two to several names from an original C-
value list (synonyms of the PLANTS name)1
1. If C-values among synonyms for the accepted name differ by 2 values or less -> Choose the higher C-
value as the C-VALUE_NWCA_USE
2. If C-values among synonyms differ by more than 2 values -> Review and C-value decision by NWCA
lead botanist
3. If there is a difference in native status between listed synonym names -> Review and C-value decision
by NWCA lead botanist
1Note: The steps listed in B were completed for all taxon-region pairs observed in the 2011 and 2016 NWCAs
that occurred in the CCL, but due to time limitations may not have been completed for all records in the CCL
Figure 7-7. Text box outlining C-value selection decision tree when multiple C-values were available for one taxon-
region pair
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7.9.2 Assigning Existing C-values to Taxon-Region Pairs Observed in the NWCA Surveys
Each available C-value list included in the CCL was assigned a geography (GEOG) which reflected the areas
to which that list was applicable, typically this was an individual state, or an EPA Level III Ecoregion
(USEPA 2013) falling within part of one or more adjacent states. To facilitate assigning C-values from the
Compiled C-value Lists (CCL) to observed NWCA taxa, each NWCA site sampled was assigned to one of 84
NWCA C-value Regions (see NWCA_CREG16, in the 2011 and 2016 site information files). Individual
NWCA C-value Regions were defined as one of the following: an entire individual state, the portion of a
state described by a specific Level III Ecoregion, or, in one case, the portions of a state falling on the east
vs. west side of a mountain divide. Thus, a given NWCA C-value Region could represent an entire state or
a part of state.
Existing standardized C-values (see Section 7.9.1) were assigned to NWCA taxon-region pairs (where
region = NWCA C-value Region) with C-values for a particular region selected from one or more applicable
lists of the CCL. Often both a regional and a state C-value list were pertinent to a particular NWCA C-value
Region. In some instances, C-value coverage was incomplete for an NWCA C-value Region. When this was
the case, C-values were considered from nearby geographies, i.e., adjacent or nearby states with the
same or similar Level III Ecoregions. For each NWCA C-value Region, the NWCA lead botanist/ecologist
prioritized the applicable CCL lists in order of best geographic/ecoregional fit and availability of C-values.
R-code was developed to assign C-values from the CCL to taxa in each NWCA C-value Region based on the
prioritized order of the specific applicable C-value lists. Each NWCA C-value Region had two to four CCL
geographies, from which C-values could be drawn. The CCL geographies (and their accompanying C-value
lists) that were applicable to each NWCA C-value Region were given Priority 1, Priority 2, Priority 3, or
Priority 4 for order of use. C-values were assigned from the CCL to the 2011 and 2016 NWCA taxon-region
pairs in each NWCA C-value region using the following order:
C-values were assigned first from the Priority 1 List.
If no C-value for a taxon-region pair was available in the Priority 1 List, then C-value was assigned
from the Priority 2 List.
This process was continued sequentially through lists of subsequent priority levels until all
available C-values from the CCL for relevant NWCA taxon-region pairs were assigned.
Using this approach, existing C-values from the CCL were assigned to nearly 22,000 NWCA taxon-region
pairs. Of these taxon-region pairs, most were species or lower taxonomic-levels (i.e., subspecies, variety,
hybrid). However, the CCL also included C-values for some genera, and, where available, they were
applied to NWCA genus-region pairs.
7.9.3 Defining C-values for NWCA Taxon-Region Pairs Where None Were Available
After applying existing C-values from the CCL, a set of 2,245 NWCA taxon-region pairs still lacked C-values.
These taxon-region pairs fell into three groups:
Group 1 - 802 identified to species or lower taxonomic levels (e.g., species, subspecies, variety,
or hybrid), hereafter species-region pairs
Group 2 - 872 identified to only to genus
Group 3-571 identified to only to high-level taxonomic categories (e.g., subfamily, family,
growth form, or a few nonvascular taxa)
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Group 1 - C-value Assignment for Species-Region Pairs-The 802 NWCA species-region pairs lacking C-
values were evaluated to determine whether an existing C-value from a proximate geography that was
not previously identified in the priority C-value lists for a particular NWCA C-value region might be
available for use. Using this ecoregion extrapolation approach, the following steps were used in
identifying C-values for these 802 NWCA species-region pairs:
If a relevant C-value in an adjacent state and the same Level III ecoregion was available, it was
applied to the NWCA species-region pair.
If no such value was available, but a C-value from a nearby state and similar ecoregion was
available, that C-value was selected.
If multiple C-values from nearby geographies might apply, these were reviewed by the NWCA
botanist/ecologist and the highest value (if there were 2 potential C-values) or the median value
(if there were 3 or more potential C-values) was selected.
If the NWCA species-region pair was considered introduced or adventive by NWCA, a C-value of 0
was assigned.
If no C-value could be assigned, the NWCA species-region pair was assigned UND.
In all cases, the list in the CCL from which the C-value, assigned to a NWCA species-region pair, originated
was noted (see Appendix D for source list abbreviations) in the final trait table. Where the C-value was
derived from the median of several C-value source lists or was otherwise assigned by the NWCA
botanist/ecologist, the C-value source was noted as NWCA16.
Using the above process, C-values were selected for 661 of the 802 NWCA species-region pairs that were
not assigned values in the initial prioritization from the CCL.
Group 2 - Genus-Region Pair Assignments-The 872 NWCA genus-region pairs that were not initially
assigned C-values from the CCL were evaluated in a two-step process to see if C-values could be
developed. First, a tentative C-value was assigned based on the median C-value for species in the genus
and occurring in the NWCA C-Region and also appearing on the priority 1, or priority 1 and 2 C lists in the
CCL. Note, some C-lists include the flora of a state, but others include only a subset of the flora (e.g.,
sometimes only wetland species). The NWCA botanist/ecologist then reviewed these tentative genus-
region C-values and accepted or rejected them using BPJ supported by information for the genus in the
NWCA C-value region, e.g.:
The number of C-values and species in the genus that the median C-value represented.
How well was the genus represented on the C-value lists applicable to the NWCA C-value region?
To address this question, PLANTS database maps or relevant floras were consulted to evaluate
distribution of taxa in genus in the NWCA C-value region.
Were nonnative species included in the genus, and if so, how many, and are they typically
widespread invaders?
Based on this review decide whether to accept median C-value or assign as undetermined.
Record notes on decisions.
Using the above process, median C-values were selected for 665 of the 872 NWCA genus-region pairs not
assigned values in the initial prioritization from the CCL.
Group 3-High-Level Taxa or Growth Forms-The 571 taxon-region pairs in this group were assigned
undetermined C-value (UND).
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7.9.4 Final NWCA C-value Trait Table
The last step in finalizing the NWCA C-value Trait Table was ensuring that all taxa designated as
introduced or adventive by the NWCA (see Section 7.8) received a C-value of zero.
The final C-value assignments for the taxon-region pairs observed in the 2011 and 2016 NWCAs are
located in the NWCA C-value Trait Table (nwca_2016_plant_cvalues.csv) on the NWCA website. The
source list from the CCL from which each NWCA taxon-region pair C-value originated is also noted in this
table and designated by abbreviations defined in Section 7.13, Appendix D. C-values or UND status
defined by NWCA are denoted by NWCA16 as the C-value source.
The NWCA C-value Trait Table includes C-values specific to 24,206 NWCA taxon-region pairs:
21,479 species-region pairs with C-values ranging from 0 to 10 (here species includes: species,
subspecies, varieties, or hybrids)
1832 genus-region pairs with C-values ranging from 0 to 10
895 taxon-region pairs where C-value remained undetermined (UND)
o 571 of these were family level or higher, taxa identified only to growth form, and a
handful of nonvascular taxa
o 183 of these were genus-region pairs
o 141 of these were species-region pairs
The NWCA C-values were used in calculation of floristic quality indices (e.g., variations of FQAI and Mean
C) and metrics describing sensitivity and tolerance to disturbance. See Section 8.8, Appendix E Metrics for
a list of specific metrics. The NWCA adopted the standard practice of excluding taxon-region pairs with C-
values = UND taxa from calculations of metrics of floristic quality and of disturbance sensitivity or
tolerance. The NWCA taxon-region pairs with C-values = UND represented a very small proportion of
NWCA taxa observed across all sites, and where these occurred, they typically had low abundance, so
their exclusion was expected to have little impact on metric values.
7.10 Literature Cited
Bourdaghs M, Johnston CA, Regal RR (2006) Properties and performance of the Floristic Quality Index in
Great Lakes Coastal Wetlands. Wetlands 26: 718-735
Bourdaghs M (2014) Rapid Floristic Quality Assessment Manual, wqbwm2-02b. Minnesota Pollution
Control Agency (MPCA), Saint Paul, Minnesota
Bried JT, Jog SK, Matthews JW (2013) Floristic quality assessment signals human disturbance over natural
variability in a wetland system. Ecological Indicators 34: 260-267
Cohen MJ, Carstenn S, Lane CR (2004) Floristic quality indicies for biotic assessment of depressional
marsh condition in Florida. Ecological Applications 14: 784-794
Euliss NH, Mushet DM (2011) A multi-year comparison of IPCI scores for Prairie Pothole Wetlands:
Implications of temporal and spatial variation. Wetlands 31: 713-723
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DeKeyser ES, Kirby DR, Ell MJ (2003) An index of plant community integrity: Development of the
methodology for assessing prairie wetland plant communities. Ecological Indicators 3: 119-133
Deimeke E, Cohen MJ, Reiss KC (2013) Temporal stability of vegetation indicators of wetland condition.
Ecological Indicators 34: 69-75
Galatowitsch S, Whited D, Tester J (1999) Development of community metrics to evaluate recovery of
Minnesota wetlands. Journal of Aquatic Ecosystem Stress and Recovery (Formerly Journal of Aquatic
Ecosystem Health) 6: 217-234
Genet J (2012) Status and Trends of Wetlands in Minnesota: Depressional Wetland Quality Baseline.
Minnesota Pollution Control Agency, Saint Paul, Minnesota
Gara B (2013) The Vegetation Index of Biotic Integrity "Floristic Quality" (VIBI-FQ). Ohio EPA Technical
Report WET/2013-2. Ohio Environmental Protection Agency, Wetland Ecology Group, Division of Surface
Water, Columbus, Ohio
Lichvar RW, Banks DL, Kirchner WN, and. Melvin NC (2016) The National Wetland Plant List: 2016 wetland
ratings. Phytoneuron 2016-30: 1-17. Published 28 April 2016. ISSN 2153 733X
http://www.phytoneuron.net/
Lopez RD, Fennessy MS (2002) Testing the floristic quality assessment index as an indicator of wetland
condition. Ecological Applications 12: 487-497
Mack JJ (2007) Integrated Wetland Assessment Program. Part 9: Field Manual for the Vegetation Index of
Biotic Integrity for Wetlands, v. 1.4. Ohio EPA Technical Report WET/2004-9. Ohio Environmental
Protection Agency, Wetland Ecology Group, Division of Surface Water, Columbus, Ohio
MackJJ, Kentula ME (2010) Metric Similarity in Vegetation-based Wetland Assessment Methods.
EPA/600/R-10/140. US Environmental Protection Agency, Office of Research and Development,
Washington, DC
Magee TK, Ringold PL, Bollman MA (2008) Alien species importance in native vegetation along wadeable
streams, John Day River basin, Oregon, USA. Plant Ecology 195: 287-307
Magee TK, Ringold PL, Bollman MA, Ernst TL (2010) Index of Alien Impact (IAI):A method for evaluating
alien plant species in native ecosystems. Environmental Management 45: 759-778
Magee TK, Blocksom KA, Fennessy MS (2019a) A national-scale vegetation multimetric index (VMMI) as
an indicator of wetland condition across the conterminous United States. Environmental Monitoring and
Assessment 191 (SI): 322, doi: 10.1007/sl0661-019-7324-4.
https://link.springer.com/article/10.1007/sl0661-019-7324-4
Magee TK, Blocksom KA, Herlihy AT, &. Nahlik AM (2019b) Characterizing nonnative plants in wetlands
across the conterminous United States. Environmental Monitoring and Assessment 191 (SI): 344, doi:
10.1007/s 10661-019-7317-3. https://link.springer.com/article/10.1007/sl0661-019-7317-3
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Mclntyre S, Lavorel S (1994) Predicting richness of native rare, and exotic plants in response to habitat
and disturbance variables across variegated landscape. Conservation Biology 8(2): 521-531
Mclntyre S, Lavorel S, Landsberg J, Forbes TDA (1999) Disturbance response in vegetation -- towards a
global perspective on functional traits. Journal of Vegetation Science 10: 621-630
Milburn SA, Bourdaghs M, Husveth JJ (2007) Floristic Quality Assessment for Minnesota Wetlands.
Minnesota Pollution Control Agency, St. Paul, Minnesota
Miller SJ, Wardrop DH (2006) Adapting the floristic quality assessment index to indicate anthropogenic
disturbance in central Pennsylvania wetlands. Ecological Indicators 6: 313-326
Miller SJ, Wardrop DH, Mahaney WM, Brooks RP (2006) A plant-based index of biological integrity (IBI) for
headwater wetlands in central Pennsylvania. Ecological Indicators 6: 290-312
Mitsch WJ, Gosselink JG (2007) Wetlands. John Wiley & Sons, Hoboken, NJ
Quetier F, Thebault A, Lavorel S (2007) Plant traits in a state and transition framework as markers of
ecosystem response to land-use change. Ecological Monographs 77: 33-52
R Core Team (2018-2019) R: A language and environment for statistical computing. R Foundation for
Statistical Computing, Vienna, Austria. (http://www.R-project.org/)
Reiss KC (2006) Florida Wetland Condition Index for depressional forested wetlands. Ecological Indicators
6: 337-352
Ringold PL, Magee TK, Peck DV (2008) Twelve invasive plant taxa in in US western riparian ecosystems.
Journal of North American Benthological Society 27: 949-966
Rocchio J (2007) Assessing Ecological Condition of Headwater Wetlands in the Southern Rocky Mountains
Using a Vegetation Index of Biotic Integrity (Version 1.0). Colorado State University, Colorado Natural
Heritage Program, Fort Collins, Colorado
Rooney R, Bayley S, Rooney RC, Bayley SE (2012) Development and testing of an index of biotic integrity
based on submersed and floating vegetation and its application to assess reclamation wetlands in
Alberta's oil sands area, Canada. Environmental Monitoring and Assessment 184: 749- 761
Swink F, Wilhelm G ( 1979) Plants of the Chicago region: A Checklist of the Vascular Flora of the Chicago
Region,with Keys, Notes on Local Distribution, Ecology, and Taxonomy, and a System for Evaluation of
Plant Communities. Morton Arboretum, Lisle, Illinois
Tiner RW (1999) Wetland Indicators: A Guide to Wetland Identification, Delineation, Classification, and
Mapping. Lewis Publishers, Boca Raton, FL, USA
USACE (2016-2018) National Wetland Plant List, version 3.3. and 2018 NWPL Speice Update. US Army
Corps of Engineers Engineer Research and Development Center, Cold Regions Research and Engineering
Laboratory, Hanover, NH
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http://wetland-plants.usace.army.mil/ [Accessed February 2020, downloaded: National_2016v3.xlsx and
2018_NWPL_Update_Species.xlsx]
USDA, NRCS. (2020). The PLANTS Database (http://plants.usda.gov). National Plant Data Team,
Greensboro, NC USA)
USEPA (2002) Methods for Evaluating Wetland Condition: #10 Using Vegetation to Assess Environmental
Conditions in Wetlands. Office of Water, US Environmental Protection Agency, Washington, DC
USEPA (2006) Wadeable Streams Assessment: A Collaborative Survey of the Nation's Streams. EPA 841-B-
06-002. US Environmental Protection Agency, Washington, DC
USEPA (2011a) National Wetland Condition Assessment: Field Operations Manual. EPA-843-R10-001. US
Environmental Protection Agency, Washington, DC
USEPA (2013) Level III ecoregions of the continental United States: Corvallis, Oregon, US Environmental
Protection Agency - National Health and Environmental Effects Research Laboratory, map scale
1:7,500,000, https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states.
USEPA (2016a) National Wetland Condition Assessment: 2011 Technical Report. EPA-843-R-15-006. .US
Environmental Protection Agency, Washington, DC. https://www.epa.gov/national-aquatic-resource-
surveys/national-wetland-condition-assessment-2011-results
USEPA (2016b) National Wetland Condition Assessment 2011: A Collaborative Survey of the Nation's
Wetlands. EPA-843-R-15-005. US Environmental Protection Agency, Washington, DC.
https://www.epa.gov/national-aquatic-resource-surveys/national-wetland-condition-assessment-2011-
results.
USEPA (2016c) National Wetland Condition Assessment 2016: Field Operations Manual. EPA-843-R-15-
007. US Environmental Protection Agency, Washington D.C.
USEPA (2016d). National Wetland Condition Assessment 2016: Laboratory Operations Manual. EPA-843-
R-15-009. US Environmental Protection Agency, Office of Water, Washington, DC.
Veselka W, Rentch JS, Grafton WN, Kordek WS, Anderson JT (2010) Using Two Classification Schemes to
Develop Vegetation Indices of Biological Integrity for Wetlands in West Virginia, USA. Environmental
Monitoring and Assessment 170: 555-569
Wilhelm G, Ladd D (1988) Natural Area Assessment inthe Chicago region. In: Transactions 53rd North
American Wildlife and Natural Resources Conference, Louisville, Kentucky. Wildlife Management
Institute,Washington, DC, pp 361-375
Wilson M, Bayley S, Rooney R (2013) A plant-based index of biological integrity in permanent marsh
wetlands yields consistent scores in dry and wet years Aquatic Conservation-Marine and Freshwater
Ecosystems 23: 698-709
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7.11 Appendix B: Vegetation Field Data Forms
FORM V-1: NWCA 2016 VEGETATION PLOT ESTABLISHMENT (Front) <ŧy(ao:
Site ID: NWCA16- Date: / / 2 0 1 6
Vegetation Plot Layout - Fill in the bubble for the Vegetation Plot Layout configuration used in this AA (see Reference
Card V-2 for descriptions of plot layout configurations).
O Standard Veg Plot Layout -1/2 ha Circular AA (Veg Plots on 2 axes, cardinal directions from AA Center)
Alternate Veg Plot Layouts
O Wide Polygon AA Veg Plot Layout -1/2 ha Polygon AA with width and length >30m (Veg Plots on 2 axes)
O Narrow Polygon AA Veg Plot Layout - 1/2-ha Polygon AA jĢ 30m wide (Veg Plots on 1 axis)
O Wetland Boundary AA Veg Plot Layout - AA <1/2-ha polygon equal to wetland boundary (Veg Plots distributed)
For alternate or obstacle vegetation plot layouts only: Coordinates for plot comer closest to the AA center.
o Obstacle Veg Plot Layout Used
(Fill bubble if obstacles prevent placement of
plot(s) as designated by the selected Veg Plot Layout).
Obstacle Type (mark all that apply):
O Deep Water
O Tide Channel
O Safety Hazard
Other:
Plot
Plot 1
Plot 2
Plot 3
Plot 4
Plot 5
Latitude North
(Decimal Degrees)
Longitude West
(Decimal Degrees)
Add Veg Plot locations to the annotated aerial photo. Number Veg Plots 1 through 5 using guidelines on Reference Card V-2.
If needed, elaborate on plot layout or make notes about unique features or gradients in the vegetation or environment in the
notes section on the back of this form.
Predominant NWCA Target Wetland Type
Plotl
Plot 2
Plot 3
Plot 4
Plot 5
Definitions:
EH - Estuarine Emergent
EW - Estuarine Shrub/Forest
PRL-EM - Palustrine, Riverine, and Lacustrine Emergent
PRL-SS - Palustrine, Riverine, and Lacustrine Scrub/Shrub
PRL-FO - Palustrine, Riverine, and Lacustrine Forested
PRL-UBAB - Palustrine, Riverine, and Lacustrine Unconsolidated
PRL-f - Palustrine, Riverine, and Lacustrine Farmed (not actively farmed)
(See Reference Card AA-3, Side A for wetland type descriptions)
O EH
O EW
O PRL-EM
O PRL-SS
O PRL-FO
O PRL-UBAB
O PRL-f
O EH
O EW
O PRL-EM
O PRL-SS
O PRL-FO
o PRL-UBAB
O PRL-f
O EH
O EW
O PRL-EM
O PRL-SS
O PRL-FO
O PRL-UBAB
O PRL-f
O EH
O EW
O PRL-EM
O PRL-SS
O PRL-FO
O PRL-UBAB
O PRL-f
O EH
O EW
O PRL-EM
O PRL-SS
O PRL-FO
O PRL-UBAB
O PRL-f
Plant Species Nomenclature: Record citations for Floras/Field Guides/Databases used for plant identification
2.-
03/24/2016 V-1 NWCA 2016 Vegetation Plot Establishment
4543346166
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FORM V-2: NWCA 2016 VASCULAR SPECIES PRESENCE AND COVER (Front)
Reviewed by (initial):^
Site ID: NWCA16-
Date:
I
I 2 0 16
Page 1 0f
Instructions:
1. General: Print using ALL CAPITAL LETTERS. Write as neatly as possible, keeping all marks within data fields or workspace areas.
2. Species Name: List scientific name or pseudonym for each plant species observed in the Veg Plots {See the NWCA FOM for Pseudonym assignment rules).
3. Presence Data: For each species occurring in a quadrat nest (SW or NE corners of Veg Plot), record the smallest quadrat/plot size in which it occurs by filling in the appropriate bubble (S (small) = 1-rrrquadrat, M (medium)
= 10-nf quadrat.) If a species does not occur in a particular nest (SW or NE), but occurs in the 100-nf Veg Plot, fill in the W (whole plot) bubble for that comer.
4. Predominant Height Class: For each species observed, note its predominant height across each 100-m2 Veg Plot by recording the appropriate height class code (defined below).
5. Cover Data: Estimate cover across each 100-m2 Veg Plot {0 to 100%; See NWCA-FOM) for each species observed and record in the Cover data field. If necessary, use the gray workspace to make preliminary cover estimates for
each species in each of the four quarters of the Veg Plot, and then combine preliminary estimates to obtain total cover for the species in the Veg Plot and record in the Cover data field.
6. Collect Specimens and Assign Collection Numbers: Follow procedures in NWCA FOM for specimen collection. Record the collection number for each specimen in the Coll # column. Label Unknown Specimens consecutively
prefaced with the letter U, e.g., Ill, U2, U3, etc. Label Quality Assurance (QA) Voucher Specimens consecutively prefaced with the letter Q, e.g., Ql, Q2, Q3, Q4, or Q5.
Total number of plots sampled:
of 5
If less than 5 plots sampled, flag here & explain in
comments
IMPORTANT: Empty data cells or bubbles indicate absence or zero.
Complete
if
Collected
Height Classes (except E, which may occur in any vertical stratum):
1 = <0.5111, 2 = >0.5-201, 3 = >2-5m, 4= >5-15m, 5 = >15-30m, 6 = >30(11, and E = liana, vine or epiphyte species
Plot 1
Plot 2
Species Name or Pseudonym
Work
Space
Work
Space
Plot 3
Plot 4
Work
Space
Work
Space
Plot 5
Work
Space
Flag
o o
o o
oo
oo
Flag codes: K = No measurement made, U = Suspect measurement. F1,F2, etc. = misc. flags assigned by each field crew. Explain all flags in comment section on back side of form.
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Ģ FORM V-3:NWCA 2016 VEGETATION TYPES (Front) R,v,by(ii,iaii: %
Site ID: Date: / /
1 1 1 1 1 1 1 1 1 1
Instructions:
1. Estimate the cover for each Vascular Vegetation Stratum.
2. Estimate cover and collect categorical data for Non-Vascular Taxonomic Groups.
3. Cover can range from 0 -100% for each of the following groups: submerged aquatic vegetation, floating aquatic vegetation, lianas, vines, and
epiphytes, each height class of other vascular vegetation and each Non-Vascular Group.
Total number of plots sampled: of 5
If less than 5 plots sampled, flag here & explain in comments
IMPORTANT: Empty data cells or bubbles indicate absence
or zero.
% Cover Vascular Vegetation Strata
Plot 1
Plot 2
Plot 3
Plot 4
Plot5
Flag
COVER OF SUBMERGED AQUATIC VEGETATION (rooted in sediment, most
plant cover submerqed or floatinq on water) (0 -100%)
COVER OF FLOATING AQUATIC VEGETATION (not rooted in sediment) (0 -100%)
COVER OF LIANAS, VINES AND EPIPHYTES IN ANY HEIGHT CLASS (0-100%)
COVER FOR ALL OTHER VASCULAR VEGETATION FOR EACH OF THE
FOLLOWING HEIGHT CLASSES:
>30m tall: e.g., very tall trees (0 -100%)
>15 to 30m tall: e.g., tall trees (0 -100%)
>5 to 15m tall: e.g., very tall shrubs; short to mid-sized trees (0-100%)
>2 to 5m tall: e.g., tall shrubs; tree saplings (0 -100%)
0.5 to 2m tall: e.g., medium height shrubs; tree seedlings and saplings; tall
aquatic emergent/terrestrial herbaceous species (0 -100%)
< 0.5m tall: e.g., low aquatic emergent/terrestrial herbaceous species; low
shrubs; tree seedlings (0 -100%)
% Cover and Categorical Data for Non-Vascular Taxa
Plot 1
Plot 2
Plot 3
Plot 4
Plot 5
Flag
COVER OF BRYOPHYTES (mosses and liverworts) growing on ground
surfaces, logs, rocks, etc.) (0 -100%)
Fill bubble if Bryophytes are dominated by Sphagnum or other
peat-forming mosses
o
o
o
o
o
COVER OF LICHENS growing on ground surfaces, logs, rocks, etc. (0 -100%)
ABUNDANCE OF ARBOREAL EPIPHYTIC BRYOPHYTES AND LICHENS
NONE: Absent.
SPARSE: Less than 1/3 of woody surface area covered.
COMMON: 1/3 to 3/4 of woody surface area covered.
ABUNDANT: >3/4 of woody surface area covered, epiphytes often draping or pendant.
O None
O Sparse
O Common
O Abundant
O None
O Sparse
Q Common
O Abundant
O None
O Sparse
0 Common
O Abundant
O None
O Sparse
O Common
O Abundant
O None
O Sparse
O Common
O Abundant
COVER OF FILAMENTOUS OR MAT FORMING ALGAE (0 -100%)
COVER OF MACROALGAE (freshwater species/seaweeds, living or wrack)
(0-100%):
Flag
Comments
Flag
Comments
Flag codes: K = No measurement made, U = Suspect measurement, F1,F2, etc. = misc. flags assigned by each field crew. Explain all flags in comment section.
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Site ID:
FORM V-3: NWCA 2016 GROUND SURFACE ATTRIBUTES (Back) Reviewedby(lnltial,: Ģ
Date: j j
Instructions: For each eround surface attribute carefully record the reauested data.
1. Water Cover - Estimate total percent of Veg Plot area covered by water.
2. Water Depth - Measure water depth with marked PVC pole or ruler to represent the predominant water level across the Veg Plot.
3. Litter - Estimate total cover of litter. Identify the predominant type. Measure litter depth in SW and NE most corners of Veg Plot in center of 1-m2
quadrat.
4. Bare ground - Estimate cover for exposed a) soil/sediment, b) gravel/cobble, c) rock. (The sum of a+b+c <100%).
5. Dead Woody Material Cover - Estimate cover (0 to 100%) for each category of dead woody material.
Total number of plots sampled: of 5
If less than 5 plots sampled, flag here & explain in comments
IMPORTANT: Empty data cells or bubbles indicate absence
or zero.
Water Cover
Plotl
Plot 2
Plot 3
Plot 4
Plot5
Flag
Total Cover of Water (0-100%)
Water Depth
Plotl
Plot 2
Plot 3
Plot 4
Plots
Flag
Predominant Depth (cm)
Time of Day (24 hour clock)
Cover of Bare ground = a+b+c <100%
Plotl
Plot 2
Plot 3
Plot 4
Plot 5
Flag
a) Exposed soil/sediment
b) Exposed gravel/cobble (~2mm to 25cm)
c) Exposed rock (>25cm)
Vegetative Litter
Plotl
Plot 2
Plot 3
Plot 4
Plots
Flag
Total Cover Vegetative Litter (0-100%)
Predominant Litter type (Select one per plot)
G = Graminoid (e.g., grasses, sedges, rushes) C= Coniferous Tree
F = Forb D = Deciduous Tree
R = Fern E = Broadleaf Evergreen Tree
O GO C
OFOD
OrOe
OgOc
OF OD
OrOe
O gO c
OFOD
OrOe
O gO c
OFOD
OrOe
OgOc
OFOD
OrOe
Litter Depth (cm) in center of 1-m2 quadrat at SW Veg Plot corner
Litter Depth (cm) in center of 1-m2 quadrat at NE Veg Plot corner
Cover of Downed Dead Woody Material (angle of incline <45°)
Plotl
Plot 2
Plot 3
Plot 4
Plots
Flag
Cover of Downed Coarse Woody debris (>5cm diameter) (0-100%)
Cover of Downed Fine Woody debris (<5cm diameter) (0-100%)
Flag
Comments
"lag codes: K = No measurement made, U = Suspect measurement, F1,F2, etc. = misc. flags assigned by each field crew. Explain all flags in comment section.
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g FORM V-4: NWCA 2016 SNAG AND TREE COUNTS AND TREE COVER R.iew,dby(initiai|: |
Site ID: NWCA16- Date: / / 2 0 1 6 Page 1 of
Total number of plots sampled: , _ If less than 5 plots sampled,
If no trees were observed in plots, this counts as sampled. 0 flag here & explain in comments
IMPORTANT: Empty data cells equal zero
nstructions for Recording Data:
1. Fill out Header Information.
2. If either Live Trees or Snags are Present in a Veg Plot, collect data across the entire 100-m2 area of
each Veg Plot.
3. Small {<5cm DBH) Standing Dead Trees/Snags: Rapidly estimate approximate number.
4. Standing Dead {>5cm DBH) Trees and Snags {angle of incline > 45°): Count snags > 5cm DBH by
diameter class and record the total number of snags for each DBH class in the white data column for
the appropriate Veg Plot.
5. For Each Live Tree Species: Use one row for each plot in which each tree species is found. Be sure to
indicate the Veg Plot number in the Plot # column next to each species name. Record species names
or pseudonyms for each tree species. Ensure pseudonyms match those used on Form V-2.
6. Cover of trees in height classes: Record the percent cover (0-100%) for each tree species for each
height class. (All trees, no minimum DBH).
7. Live Trees (>5cm DBH): Count trees in each Veg Plot by species in DBH classes and record the total
number of trees for each diameter class in the white data column.
8. Counting Trees or Snags (>5cm DBH): If needed, for smaller DBH classes when many trees or snags
are present, a running tally* of the numbers of all snags, or for each tree species, in each DBH class
can be recorded in the gray shaded workspace in the DBH columns. Once all the snags ortree species
are tallied for a plot, record the total number for each species in each DBH class in the white data field
for each DBH column.
Estimate small standing dead trees/snags (<5cm DBH)
Plotl
Plot 2
Plot 3
Plot 4
Plot 5
O None
O Few(1-10)
O Common(11-20
O Many(>20)
O None
O Few(1-10)
O Common(11-20)
O Many(>20)
3 None
O Few(1-10)
Common(11-20)
O Many(>20)
O None
O Few(1-10)
O Common(11-20)
O Many(>20)
O None
O Few(1-10)
O Common(11-20)
O Many(>20)
Standing Dead Tree/Snag Counts by DBH Class
(White box = data field, Gray box = tally workspace)
Plot
5 to 10cm
11 to 25cm
26 to 50cm
51 to 75cm
76 to
100cm
101 to
200cm
Flag
1
2
3
4
*Tally
format
i 2 3 4 mm 5 m m e m m i mm e mm s
X X_* i1 Ģ3
5
Plot#
Live Tree Species Name/Pseudonym
Tree Cover by Height Class
Tree Counts by DBH Class
(White box = data field, Gray box = tally workspace) (DBH = diameter breast height)
<0.5m
>0.5-
2m
>2-5m
>5-
15m
>15-
30m
>30m
5 to 10cm
11 to 25cm
26 to 50cm
51 to 75cm
76 to
100cm
101 to
200cm
>200
cm
Flag
03
01 O 4
O 2 o 5
O 3
oo
r-CN <0
OOO
O 1 04
O 2 O 5
O 3
O 1 04
O 2 O 5
03
OOO
UNJ-i
OO
Flag codes:K = No measurement made, U = Suspect measurement, F1, F2, etc = misc.flags assigned by each field crew. Explain all flags in comment section on the continuation page.
H 03/24/2016 V-4 NWCA2016 Snag & Tree Counts (Front) 2766277681 ^
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7.12 Appendix C: Parameter Names for Field Collected Vegetation Data
PARAMETER
NAME
DESCRIPTION
RESULT
VALID RANGE/
LEGAL VALUES
Form V-l: NWCA 2016 Vegetation Plot Establishment
Plot Predominant Wetland Type Data: Observations from each of five 100-m2 (10x10m) Veg Plots
WETLAND TYPE
NWCA Target Wetland Type
dominating Veg Plot
One Category: EH - Estuarine Intertidal
Emergent, EW - Estuarine
Shrub/Forested, PRL-EM -Palustrine,
Lacustrine, or Riverine Emergent, PRL-
SS - Palustrine, Lacustrine, or Riverine
Scrub/Shrub, PRL-FO - Palustrine,
Lacustrine, or Riverine Forested, PRL-
UBAB - Palustrine, Lacustrine, or
Riverine Unconsolidated Bottom, PRL-f
- Palustrine, Lacustrine, or Riverine
previously farmed (not currently
actively farmed)
EH, EW,
PRLEM,
PRLSS,
PRLFO,
PRLUBAB, or
PRLF
Form V-2a and V-2b: NWCA Vascular Species Presence and Cover
Plant Species Data: Cover, presence, and height data for each vascular plant species observed in each of five 100-
m2 (10x10m) Veg Plots. Presence of each species in four component nested quadrats for each Veg Plot.
SPECIES
Scientific Name for each
species (taxon) encountered in
the Veg Plot.
Typically, the binomial genus and
species name. In some cases: lower
taxonomic levels (e.g., subspecies,
varieties) or higher taxonomic levels
(e.g., genus, family, growth form) or
pseudonyms for unknowns
Taxon name
SW
For each species present, the
smallest scale at which it is first
observed: l-m2or 10-m2
quadrat in SW corner or in
larger 100-m2 Veg Plot
One of: S = 1-m2 quadrat, M = 10-m2
quadrat, or W = the whole 100-m2
Veg Plot
S, M, orW
NE
For each species present, the
smallest scale at which it is first
observed: l-m2 or 10-m2
quadrat in NE corner or in
larger 100-m2 Veg Plot
One of: S = 1-m2 quadrat, M = 10-m2
quadrat, or W = the whole 100-m2
Veg Plot
S, M, orW
HEIGHT
Predominant height class for
each species present across a
Veg Plot
One Height Class: 1 = < 0.5m, 2 = >
0.5m-2m, 3 = > 2-5m, 4 = > 5-15m, 5
= > 15-30m, 6 = > 30m, or E = Liana,
vine, or epiphyte species
1, 2,3, 4, 5, 6, or
E
COVER
Percent cover of each species
across a Veg Plot
Cover value for each individual
species present is estimated as a
direct percentage of the spatial area
of the plot overlain by that species
and can range from 0 to 100%.
0-100%
Form V-3: NWCA Vegetation Types (Front) and Ground Surface Attributes (Back)
% Cover Vascular Vegetation Strata
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PARAMETER
NAME
DESCRIPTION
RESULT
VALID RANGE/
LEGAL VALUES
SUBMERGED_AQ
% Cover Submerged Aquatic
Vegetation
0-100 % Cover
0-100%
FLOATING_AQ
% Cover Floating Aquatic
Vegetation
0-100 % Cover
0-100%
LIANAS
% Cover Lianas, vines, and vascular
epiphytes
0-100 % Cover
0-100%
Cover for other vascular vegetation in height classes indicated below:
VTALL_VEG
% Cover Vegetation > 30m tall
0-100 % Cover
0-100%
TALL_VEG
% Cover Vegetation > 15m to 30m
tall
0-100 % Cover
0-100%
HMED_VEG
% Cover Vegetation > 5m to 15m
tall
0-100 % Cover
0-100%
MED_VEG
% Cover Vegetation >2m to 5 tall
0-100 % Cover
0-100%
SMALL_VEG
% Cover Vegetation 0.5 to 2m tall
0-100 % Cover
0-100%
VSMALL_VEG
% Cover Vegetation < 0.5m tall
0-100 % Cover
0-100%
% Cover and Cateaorical Data for Non-Vascular Taxa
BRYOPHYTES
% Cover of Bryophytes growing on
ground surfaces, logs, rocks, etc.
0-100 % Cover
0-100%
PEAT_MOSS
Bryophytes dominated by
Sphagnum or other peat forming
moss
Y (yes) if present
Y
LICHENS
% Cover of Lichens growing on
ground surfaces, logs, rocks, etc.
0-100 % Cover
0-100%
ARBOREAL
Abundance of Arboreal Bryophytes
Categorical classes: ABUNDANT,
ABUNDANT,
and Lichens
COMMON, SPARSE, NONE
COMMON,
SPARSE, or
NONE
ALGAE
% Cover of filamentous or mat
forming algae
0-100 % Cover
0-100%
MACROALGAE
% Cover of macroalgae (freshwater
species/seaweeds)
0-100 % Cover
0-100%
Water Cover and Depth
TOTAL_WATER
Total percent cover of water
across Veg Plot area
% Cover
0-100%
PREDOMINANT_D
Predominant water depth
depth in cm
Investigate if
EPTH
>200 cm
TIME
Time water depth measurements
were made
time on 24 hour clock
500 to 2100
(investigate if
outside this
range)
Bare around and Litter
Cover of bare ground = a + b + c < 100%
EXPOSED_SOIL
a) Cover exposed soil/sediment
% Cover
< 100%
EXPOSED_GRAVEL
b) Cover exposed gravel/cobble
(~2mm to 25cm)
% Cover
< 100%
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PARAMETER
NAME
DESCRIPTION
RESULT
VALID RANGE/
LEGAL VALUES
EXPOSED_ROCK
c) Cover exposed rock (>25cm)
% Cover
< 100%
Vegetation Litter
TOTAL_UTTER
Total cover of vegetation litter
% Cover
< 100%
PREDOMINANT_UTT
ER
Predominant litter type
G=Graminoid (e.g., grasses, sedges,
rushes), F=Forb, R=Fern,
C=Coniferous Tree/shrub,
D=Deciduous Tree/shrub,
E=Broadleaf Evergreen Tree/shrub
CONIFEROUS,
DECIDUOUS,
GRAMINOID,
FORB, FERN,
BROADLEAF
DEPTH_SW
Litter depth (cm) in center of 1-
m2 quadrat at SW corner of Veg
Plot
depth in cm
Investigate if
>100 cm
DEPTH_NE
Litter depth (cm) in center of 1-
m2 quadrat at NE corner of Veg
Plot
depth in cm
Investigate if
>100 cm
Cover of Downed Dead Woody Material (angle of incline < 45°)
WD_FINE
Cover of fine woody debris
(<5cm diameter)
% Cover
0-100%
WD_COARSE
Cover of coarse woody debris
(> 5cm diameter)
% Cover
0-100%
Form V-4a and V-4b: NWCA Snag and Tree Counts and Tree Cover
Standina Dead trees/snaps (<5cm DBH)
STANDING
Estimate of small standing
trees/snags on plot
Abundance Class: None (0), Few (1-
10), Common (11-20), Many (>20)
NONE, FEW,
COMMON,
MANY
Standina Dead Tree/Snaa Counts by DBH Class
XXTHIN_SNAG
Dead trees/snags 5 to 10 cm
DBH (diameter breast height)
Counts
Investigate if >
200
XTHIN_SNAG
Counts of dead trees/snags 11
to 25cm DBH
Counts
Investigate if >
200
THIN_SNAG
Counts of dead trees/snags 26
to 50cm DBH
Counts
Investigate if >
100
JR_SNAG
Counts of dead trees/snags 51
to 75cm DBH
Counts
Investigate if >
20
THICK_SNAG
Counts of dead trees/snags 76
to 100cm DBH
Counts
Investigate if >
20
XTHICK_SNAG
Counts of dead trees/snags 101
to 200 cm DBH
Counts
Investigate if >
20
Tree Data
Tree Species Name
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PARAMETER
NAME
DESCRIPTION
RESULT
VALID RANGE/
LEGAL VALUES
TREE_SPECIES
Scientific Name for each tree
species (taxon) encountered in
the Veg Plot.
Typically, the binomial genus and
species name. In some cases: lower
taxonomic levels (e.g., subspecies,
varieties) or higher taxonomic levels
(e.g., genus, family, growth form) or
pseudonyms for unknowns
Taxon Name
Tree Species Cover by Height Class
VSMALL_TREE
For each tree species, cover of
trees < 0.5m tall
0-100 % Cover
0-100%
SMALL_TREE
For each tree species, cover of
trees 0.5m to 2m tall
0-100 % Cover
0-100%
LMED_TREE
For each tree species, cover of
trees > 2 to 5m tall
0-100 % Cover
0-100%
HMED_TREE
For each tree species, cover of
trees > 5m to 15m tall
0-100 % Cover
0-100%
TALL_TREE
For each tree species, cover of
trees > 15m to 30m tall
0-100 % Cover
0-100%
VTALL_TREE
For each tree species, cover of
trees > 30m tall
0-100 % Cover
0-100%
Tree Species Counts by DBH Class
XXTHIN_TREE
For each tree species, counts of
trees 5 to 10 cm DBH (diameter
breast height)
Counts
Investigate if >
200
XTHIN_TREE
For each tree species, counts of
trees 11 to 25cm DBH
Counts
Investigate if >
100
THIN_TREE
For each tree species, counts of
trees 26 to 50cm DBH
Counts
Investigate if >
50
JR_TREE
For each tree species, counts of
trees 51 to 75cm DBH
Counts
Investigate if >
20
THICK_TREE
For each tree species, counts of
trees 76 to 100cm DBH
Counts
Investigate if >
10
XTHICK_TREE
For each tree species, counts of
trees 101 to 200 cm DBH
Counts
Investigate if >
5
XXTHICK_TREE
For each tree species, counts of
trees > 200 cm DBH
Counts
Investigate if >
5
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7.13 Appendix D: Existing Coefficient of Conservatism Lists included in the
Compiled C-value Lists {unpublished draft) assembled by NWCA
State or Region
Source
Abbreviation
Coefficient of Conservatism Lists included in the Compilation of Existing C-
values
All 48
Individual
Conterminous
United States
NWCA11
USEPA (2016) US Environmental Protection Agency. National Aquatic
Resource Surveys. National Wetland Condition Assessment 2011 (NWCA
2011 Plant CC and Native Status Values - Data (CSV) and NWCA 2011 Plant
CC and Native Status Values - Metadata (TXT)). [Includes (for observed plant
species) state-level trait information for: C-Values, Native Status
Designations, and Disturbance Sensitivity Categories], Available from USEPA
website: https://www.epa.gov/national-aquatic-resource-surveys/data-
national-aquatic-resource-surveys.
AZ
EPA19_AZ
Fennessy, M. S., & Great Lakes Environmental Center, Inc (2019, unpub.).
Project to assign C-values to western states for use in the USEPA National
Wetland Condition Assessment (NWCA): C-values for taxon-state pairs
observed in AZ, CA, ID, NV, NM, OR, TX, UT during the 2011 and 2016 NWCA
Surveys. Funded by USEPA Office of Wetlands, Oceans, and Watersheds to
the Great Lakes Environmental Center, Traverse City, Ml. EP-C-16-
008: Task Order #08. Unpublished Report and Excel File
(NWCA_C_Values Western States_ll-6-2018_Draft) Submitted to USEPA.
CA
EPA19_CA
Fennessy, M. S., & Great Lakes Environmental Center, Inc (2019). Project to
assign C-values to western states for use in the USEPA National Wetland
Condition Assessment (NWCA): C-values for taxon-state pairs observed in
AZ, CA, ID, NV, NM, OR, TX, UT during the 2011 and 2016 NWCA Surveys.
Funded by USEPA Office of Wetlands, Oceans, and Watersheds to the Great
Lakes Environmental Center, Traverse City, Ml. EP-C-16-008: Task Order #08.
Unpublished Report and Excel File (NWCA_C_Values Western States_ll-6-
2018_Draft) Submitted to USEPA.
CO
ROCC.07
Rocchio, J. (2007). Floristic quality assessment indices for Colorado plant
communities. Fort Collins, Colorado: Colorado Natural Heritage Program,
Colorado State University.
a
NEIW13_CT
New England Interstate Water Pollution Control Commission (NEIWPCC).
(2013) Northeast Regional Floristic Quality Assessment. Current URL (27
August 2019): https://neiwpcc.org/our-programs/wetlands-aquatic-
species/nebawwg/nqa/. Individual State CoC lists: Connecticut.
DE
MCAV12
McAvoy, W.A. (2011) The Flora of Delaware Online Database. Delaware
Division of Fish and Wildlife, Natural Heritage and Endangered Species
Program, Smyrna, Delaware, http://www.wra.udel.edu/de-flora. Current
URL (28 August 2019): http://www.wrc.udel.edu/de-flora/
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State or Region
Source
Abbreviation
Coefficient of Conservatism Lists included in the Compilation of Existing C-
values
FL (Source 1)
LANE03
Lane, C.R., Brown, M., Murray-Hudson, M. and Vivas, M. B. (2003) The
Wetland Condition Index (WCI): Biological indicators for Isolated
Depressional Herbaceous Wetlands in Florida, Report Submitted to the
Florida Department of Environmental Protection under Contract #WM-683
FL (Source 2)
REIS05
A) Reiss, K.C.& Brown, M.T. (2005a) The Florida Wetland Condition Index
(FWCI): Developing Biological Indicators for Isolated Depressional Forested
Wetlands. B) Reiss, K.C.& Brown, M.T. (2005) Pilot Study-The Florida
Wetland Condition Index (FWCI): Preliminary Development of Biological
Indicators for Forested Strand and Floodplain Wetlands. Report Submitted to
the Florida Department of Environmental Protection Under Contract #WM-
683
FL_South
MORT09
Mortellaro, S., Barry, M., Gann, G., Zahina, J., Channon, S., Hilsenbeck, C.,
Scofield, D., Wilder, G., & Wilhelm, G. (2009). Coefficients of Conservatism
Values and the Floristic Quality Index for the Vascular Plants of South
Florida. Southeastern Naturalist, ll(mo3), 1-62, 62.
GA
ZOML13
Zomlefer, W. B., Chafing. L.G., Carter, J.R. and Giannasi, D.E. (2013)
Coefficient of Conservatism Rankings for the Flora of Georgia: Wetland
Indicator Species. Southeastern Naturalist 12:790-808.
IA
DROBOl
Drobney, P.D., Wilhelm, G.S., Horton, D., Leoschke, M., Lewis, D., Pearson, J.,
Roosa, D., and Smith, D. (2001) Floristic quality assessment for the state of
Iowa. Unpublished report.
ID
EPA19JD
Fennessy, M. S., & Great Lakes Environmental Center, Inc (2019). Project to
assign C-values to western states for use in the USEPA National Wetland
Condition Assessment (NWCA): C-values for taxon-state pairs observed in
AZ, CA, ID, NV, NM, OR, TX, UT during the 2011 and 2016 NWCA Surveys.
Funded by USEPA Office of Wetlands, Oceans, and Watersheds to the Great
Lakes Environmental Center, Traverse City, Ml. EP-C-16-008: Task Order #08.
Unpublished Report and Excel File (NWCA_C_Values Western States_ll-6-
2018_Draft) Submitted to USEPA.
IL
TAFT03
Taft, J.B., Wilhelm, G.S., & Masters, LA. (2003) Floristic quality assessment
for vegetation in Illinois a method for assessing vegetation integrity. Illinois
Native Plant Society
IN
ROTH19
Rothrock, P.E. (2019) The Floristic quality assessment of Indiana concepts,
use and development of coefficients of conservatism. Final Report for ARN
A305-4-53 EPA Wetland Program Development Report Grant CD975586-01
2022
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116
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State or Region
Source
Abbreviation
Coefficient of Conservatism Lists included in the Compilation of Existing C-
values
IN
ROTH04
Rothrock, P.E. (2004) The Floristic quality assessment of Indiana concepts,
use and development of coefficients of conservatism. Final Report for ARN
A305-4-53 EPA Wetland Program Development Report Grant CD975586-01.
KS
FREE12
Freeman, C. C. (2012) Coefficients of Conservatism for Kansas Vascular
Plants (2012) and Selected Life History Attributes. Kansas Biological Survey,
University of Kansas, http://ksnhi.ku.edu/media/ksnhi/public-data-
resources/Coefficients%20of%20Conservatism%20for%20Kansas%20Vascula
r%20Plants%20%282012%29.pdf
KY
WHIT97
White, D., Shea, M., Ladd, D. and Evans, M. (1997) Floristic quality
assessment for Kentucky. The Kentucky Chapter of The Nature Conservancy,
Kentucky State Nature Preserves Commission, The Missouri Chapter of The
Nature Conservancy
LA
CRET12
Cretini, K.F., Visser, J.M., Krauss, K.W., & Steyer, G.D. (2012) Development
and use of a floristic quality index for coastal Louisiana marshes.
Environmental Monitoring and Assessment. 184:2389-2403. List included as
supplement to paper. An updated list with a few added species was provided
to Nicole Kirchner in July 2012.
MA
NEIW13_MA
New England Interstate Water Pollution Control Commission (NEIWPCC).
(2013). Northeast Regional Floristic Quality Assessment. Current URL (27
August 2019): https://neiwpcc.org/our-programs/wetlands-aquatic-
species/nebawwg/nqa/. Individual State CoC lists: Massachusetts.
ME
NEIW13_ME
New England Interstate Water Pollution Control Commission (NEIWPCC).
(2013). Northeast Regional Floristic Quality Assessment. Current URL (27
August 2019): https://neiwpcc.org/our-programs/wetlands-aquatic-
species/nebawwg/nqa/. Individual State CoC lists: Maine.
ME
MENA14
Maine Natural Areas Program. (2014) Coefficient of Conservatism Scores for
Maine. Maine Natural Areas Program, Augusta, Maine, USA. Current URL (8-
28-2019: https://www.maine.gov/dacf/mnap/features/coc.htm
Ml
REZN14
Reznicek, A.A., Penskar, M.R., Walters, B.S. and Slaughter, B.S. (2014)
Michigan Floristic Quality Assessment Database. Herbarium, University of
Michigan, Ann Arbor, Ml and Michigan Natural Features Inventory, Michigan
State University, Lansing, Ml. (http://michiganflora.net/home.aspx)
Ml
HERM01
Herman, K.D., Masters, L.A., Penskar, M.R., Reznicek, A.A., Wilhelm, G.S.,
Brodovich, W.W. and Gardiner, K.P. (2001) Floristic quality assessment with
wetland categories and examples of computer applications for the state of
Michigan. 2nd Edition. Michigan Dept. of Natural Resources, Lansing, Ml. 19
pp. + appendices.
2022
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State or Region
Source
Abbreviation
Coefficient of Conservatism Lists included in the Compilation of Existing C-
values
http://www.michigandnr.com/publications/pdfs/HuntingWildlifeHabitat/FQ
A_text.pdf
MN
MILB07
Milburn, S. A., Bourdaghs, M. and Husveth (2007) Floristic Quality
Assessment for Minnesota Wetlands. Minnesota Pollution Control Agency,
St. Paul, Minn. Accessed at www.pca.state.mn.us/water/biomonitoring/bio-
wetlands.html
MO
LADD93
Ladd, D. (1993) Coefficients of conservatism for the Missouri vascular flora: a
database of the flora of missouri with species conservatism coefficients,
wetness ratings, physiognomy, standardized acronyms, and common names.
Missouri chapter of the Nature conservancy.
MO
LADD15
Ladd, D. and Thomas, J.R. (2015) Ecological checklist of the Missouri flora for
Floristic Quality Assessment. Phytoneuron. 2015-12:1-274. Published 12
February 2015. ISSN 2153 733X
MS
HERM06
Herman, B.D., Madsen, J. D. and Ervin, G.D. (2006) Development of
coefficients of conservatism for wetland vascular flora of north and central
Mississippi. Mississippi State University, GeoResources Institute Report 4001
(Water Resources)
MT
PIPP15_16
Pipp, A. (2015) Coefficient of Conservatism Rankings for the Flora of
Montana: Part 1.
Report to the Montana Department of Environmental Quality, Helena,
Montana. Prepared by
the Montana Natural Heritage Program, Helena, Montana. 73 pp
MT
PIPP15_16
Pipp, A. (2016) Coefficient of Conservatism Rankings for the Flora of
Montana: Part II.
Report to the Montana Department of Environmental Quality, Helena,
Montana. Prepared by
the Montana Natural Heritage Program, Helena, Montana. 75 pp.
MT
JONE05
Jones, W.M. (2005) A vegetation index of biotic integrity for small order
streams in southwestern Montana and floristic quality assessment for
western Montana wetlands. Report to the Montana Department of
Environmental Quality and US Environmental Protection Agency, Montana
Natural Heritage program, Helena Montana. 29 pp. plus appendices.
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State or Region
Source
Abbreviation
Coefficient of Conservatism Lists included in the Compilation of Existing C-
values
ND
TNGP01
The Northern Great Plains Floristic Quality Assessment Panel. (2001)
Coefficients of conservatism for the vascular flora of the Dakotas and
adjacent grasslands: US Geological Survey, Biological Resources Division,
Information and Technology Report USGS/ BRD/ITR2001-0001, 32 p.
NE
ROLF11
Rolfsmeier, S. & Steinauer, G. (2003, 2011) Vascular plants of Nebraska
(Version 1 -July 2003). Nebraska Game and Parks Commission. Lincoln, NE 57
pp. List was updated in 2011 and forwarded via email from G. Steinaur to
Nicole Kirchner in 2011.
NH
NEIW13_NH
New England Interstate Water Pollution Control Commission (NEIWPCC).
(2013) Northeast Regional Floristic Quality Assessment. Current URL (27
August 2019): https://neiwpcc.org/our-programs/wetlands-aquatic-
species/nebawwg/nqa/. Individual State CoC lists: New Hampshire.
NJ
WALZ17
Walz, K. S., Kelly, L. and Anderson, K. (2017) Floristic Quality Assessment
Index for Vascular Plants of New Jersey: Coefficient of Conservancy (CoC)
Values for Species and Genera. New Jersey Department of Environmental
Protection, New Jersey Forest Service, Office of Natural Lands Management,
Trenton, NJ, 08625. Submitted to United States Environmental Protection
Agency, Region 2, for State Wetlands Protection Development Grant, Section
104(B)(3); CFDA No. 66.461, CD97225809.
NJ
KELL13
Kelly, L, Anderson, K. & Walz, K.S. (2013) New Jersey floristic quality
assessment: coefficients of conservatism for vascular taxa
NM
EPA19_NM
Fennessy, M. S., & Great Lakes Environmental Center, Inc (2019). Project to
assign C-values to western states for use in the USEPA National Wetland
Condition Assessment (NWCA): C-values for taxon-state pairs observed in
AZ, CA, ID, NV, NM, OR, TX, UT during the 2011 and 2016 NWCA Surveys.
Funded by USEPA Office of Wetlands, Oceans, and Watersheds to the Great
Lakes Environmental Center, Traverse City, Ml. EP-C-16-008: Task Order #08.
Unpublished Report and Excel File (NWCA_C_Values Western States_ll-6-
2018_Draft) Submitted to USEPA.
NV
EPA19_NV
Fennessy, M. S., & Great Lakes Environmental Center, Inc (2019). Project to
assign C-values to western states for use in the USEPA National Wetland
Condition Assessment (NWCA): C-values for taxon-state pairs observed in
AZ, CA, ID, NV, NM, OR, TX, UT during the 2011 and 2016 NWCA Surveys.
Funded by USEPA Office of Wetlands, Oceans, and Watersheds to the Great
Lakes Environmental Center, Traverse City, Ml. EP-C-16-008: Task Order #08.
Unpublished Report and Excel File (NWCA_C_Values Western States_ll-6-
2018_Draft) Submitted to USEPA.
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State or Region
Source
Abbreviation
Coefficient of Conservatism Lists included in the Compilation of Existing C-
values
NY
NEIW13_NY
New England Interstate Water Pollution Control Commission (NEIWPCC)
(2013) Northeast Regional Floristic Quality Assessment. Current URL (27
August 2019): https://neiwpcc.org/our-programs/wetlands-aquatic-
species/nebawwg/nqa/. Individual State CoC lists: New York.
OH
ANDR04
Andreas, B.K., J.J. Mack, and J.S. McCormac (2004) Floristic quality
assessment index (FQAI) for vascular plants and mosses for the State of
Ohio. Ohio Environmental Protection Agency, Division of Surface Water,
Wetland Ecology Group, Columbus, OH. 219 pp.
OK
EWIN12
Ewing, A.K., and Hoagland, B. (2012) Development of floristic quality index
approaches for wetland plant communities in Oklahoma. USEPA Final
Report, FY201,104(b)(3), CD-00F074, Project 2.
OR
EPA19_OR
Fennessy, M. S., & Great Lakes Environmental Center, Inc (2019). Project to
assign C-values to western states for use in the USEPA National Wetland
Condition Assessment (NWCA): C-values for taxon-state pairs observed in
AZ, CA, ID, NV, NM, OR, TX, UT during the 2011 and 2016 NWCA Surveys.
Funded by USEPA Office of Wetlands, Oceans, and Watersheds to the Great
Lakes Environmental Center, Traverse City, Ml. EP-C-16-008: Task Order #08.
Unpublished Report and Excel File (NWCA_C_Values Western States_ll-6-
2018_Draft) Submitted to USEPA.
OR
MAGE01
Magee, T.K. and Bollman, M.A. (2013, unpublished). C-values for ~500
Streamside plant species in eastern Oregon.
Rl
NEIW13_RI
New England Interstate Water Pollution Control Commission (NEIWPCC).
(2013) Northeast Regional Floristic Quality Assessment. Current URL (27
August 2019): https://neiwpcc.org/our-programs/wetlands-aquatic-
species/nebawwg/nqa/. Individual State CoC lists: Rhode Island.
SD
TNGP01
The Northern Great Plains Floristic Quality Assessment Panel, (2001)
Coefficients of conservatism for the vascular flora of the Dakotas and
adjacent grasslands: US Geological Survey, Biological Resources Division,
Information and Technology Report USGS/ BRD/ITR2001-0001, 32 p.
TN
TN_CC
Compiled from:l) Willis, K. and Estes, L. unpub. 2013. Floristic Quality
Assessment for Tennesse Vascular Plants, 2) Gianopulos, K. (2014)
Coefficient of Conservatism Database Development for Wetland Plants
Occurring in the Southeast United States: Summary Document. North
Carolina Dept. of Environment and Natural Resources, Division of Water
Resources. See: USEPA (2016) National Wetland Condition Assessment: 2011
Technical Report. EPA-843-R-15-006. Section 5.9 Species Traits-Coefficients
of Conservatism.
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State or Region
Source
Abbreviation
Coefficient of Conservatism Lists included in the Compilation of Existing C-
values
TX
EPA19_TX
Fennessy, M. S., & Great Lakes Environmental Center, Inc (2019). Project to
assign C-values to western states for use in the USEPA National Wetland
Condition Assessment (NWCA): C-values for taxon-state pairs observed in
AZ, CA, ID, NV, NM, OR, TX, UT during the 2011 and 2016 NWCA Surveys.
Funded by USEPA Office of Wetlands, Oceans, and Watersheds to the Great
Lakes Environmental Center, Traverse City, Ml. EP-C-16-008: Task Order #08.
Unpublished Report and Excel File (NWCA_C_Values Western States_ll-6-
2018_Draft) Submitted to USEPA.
UT
EPA19_UT
Fennessy, M. S., & Great Lakes Environmental Center, Inc (2019). Project to
assign C-values to western states for use in the USEPA National Wetland
Condition Assessment (NWCA): C-values for taxon-state pairs observed in
AZ, CA, ID, NV, NM, OR, TX, UT during the 2011 and 2016 NWCA Surveys.
Funded by USEPA Office of Wetlands, Oceans, and Watersheds to the Great
Lakes Environmental Center, Traverse City, Ml. EP-C-16-008: Task Order #08.
Unpublished Report and Excel File (NWCA_C_Values Western States_ll-6-
2018_Draft) Submitted to USEPA.
VA
VDEP05
Virginia Department of Environmental Quality (2005) Determining
coefficient of conservatism values (C-Values) for vascular plants frequently
encountered in tidal and nontidal wetlands in Virginia. Report prepared for
US Environmental Protection Agency-Region III. Wetlands Program
Development Grant #CD983380-01
VT
NEIW13_VT
New England Interstate Water Pollution Control Commission (NEIWPCC).
(2013) Northeast Regional Floristic Quality Assessment. Current URL (27
August 2019): https://neiwpcc.org/our-programs/wetlands-aquatic-
species/nebawwg/nqa/. Individual State CoC lists: Vermont.
WA
ROCC13
Roccio, F.J. & Crawford, R.C. (2013) Floristic quality assessment for
Washington vegetation. Washington Natural Heritage Program Washington
Department of Natural Resources. Natural Heritage Report 2013-03. USEPA
Wetland Program Development Grant Assistance Agreements: 1) CD-
00J26301 and CD-00J49101
Wl
BERN03
Bernthal, T.W. (2003) Development of a Floristic Quality Assessment
methodology for Wisconsin. Report to the USEPA (Region V). Wisconsin
Department of Natural Resources: Bureau of Fisheries Management and
Habitat Protection. USEPA Wetland Grant # CD975115-01-0
Wl
CHUN17
Chung-Gibson, M., Bernthal T., Doyle K., Wetter, M., Haber, E. (2017).
Wisconsin Department of Natural Resources, Water Quality Bureau. From
WDNR_FQA_Calculator_vl.5.17. Nomenclature from Wisconsin State
Herbarium, University of Wisconsin-Madison (2016). COFC values from
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State or Region
Source
Abbreviation
Coefficient of Conservatism Lists included in the Compilation of Existing C-
values
Bernthal, TW. Development of a Floristic Quality Assessment Methodology
for Wisconsin. Wisconsin Department of Natural Resources, 2003. Note that
regions differ only in Wetland Indicator Status.
Wl
PARK14
Parker, E.C., Curran, M., Waechter, Z.S. and Grosskopf, E.A. (2014) Wisconsin
FQA (Floristic Quality Assessment) Databases for Midwest and Northcentral-
Northeast Regions for Universal FQA Calculator.
WV
RENT06
Rentch, J.S.& Anderson, J.T. (2006) A floristic quality index for West Virginia
wetland and riparian plant communities. Division of Forestry and Natural
Resources, West Virginia University. US Department of Agriculture CREES,
Award No. 2004-38874-02133.
WV
WVHP15
West Virginia Natural Heritage Program (2015) Coefficients of Conservatism
for the Vascular Flora of West Virginia. Wildlife Diversity Unit, West Virginia
Division of Natural Resources, Elkins, West Virginia, USA.
WY
WASH15
Washkoviak L, Heidel, B, and Jones, G (2017). Floristic Quality Assessment
for Wyoming Flora: Developing Coefficients of Conservatism. Prepared for
the US Army Corps of Engineers. The Wyoming Natural Diversity Database,
Laramie, Wyoming. 13 pp. plus appendices.
Mid-Atlantic
(Mid_Atl)
Region
CHAM12
Chamberlin J, Ingram H (2012) Developing coefficients of conservatism to
advance floristic quality assessment in the Mid-Atlantic region.
Northeast
(NEngl) Region
FABE18
Faber-Langendoen, D. (2018) Northeast Regional Floristic Quality
Assessment Tools for Wetland Assessments. NatureServe, Arlington VA
Southeast
(SEast) Region
GIAN14
Gianopulos, K. (2014) Coefficient of Conservatism Database Development for
Wetland Plants Occurring in the Southeast United States: Summary
Document. North Carolina Dept. of Environment and Natural Resources,
Division of Water Resources.
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Chapter 8: Vegetation Analyses and Candidate Metric Evaluation
Prerequisite to Multimetric Index Development
8.1 Overview
In the 2011 NWCA, a national-scale Vegetation
Multimetric Index (VMMl) was developed with
thresholds for good, fair, and poor based on VMMI
values observed in least-disturbed sites (USEPA 2016a,
Magee et al. 2019a). However, with the additional data
from the 2016 survey, it was possible to consider
developing more specific VMMIs, e.g., for broad wetland
groups or broad geographic regions.
Therefore, we used data from both the 2011 and 2016
NWCA surveys (Figure 8-1) to develop updated VMMIs.
For sites that had repeat sampling events, the data from
the Index Visit ( see Section 6.1) to that site were used
for developing the disturbance gradient (Chapter 6:) and
for developing the VMMIs (this chapter and Chapter 9:).
1,987 unique sites were used in setting the disturbance
gradient (see Table 6-1); however, at two of these sites,
vegetation data were not collected.
Consequently, the Index Visit data from 1985 NWCA
sites where vegetation was sampled in 2011 or 2016
(Table 8-1) were used in calculating and evaluating
candidate vegetation metrics (Sections 8.4 and 8.5) and
developing four 'Wetland Group VMMIs (Chapter 9:).
Several initial analysis steps were needed to support development of the NWCA VMMIs:
Step 1: Definition of anthropogenic disturbance gradients by identifying least- and most-disturbed
sites (Section 8.2 and Chapter 6:).
Step 2: Consideration of sample sizes and variability in species composition across regions and
wetland types to determine potential scales (e.g., national, wetland type, ecoregion) for metric
evaluation and VMMI development (Section 8.3).
Step 3: Calculation (Section 8.4) of candidate vegetation metrics.
Step 4: Evaluation of candidate vegetation metrics (Section 8.5) for use in VMMI development.
In addition to the Index Visit data, where unique sites also had a sampling revisit (Visit 2) during the same
field season, these revisit data were compared to data of the Index Visit to calculate signal:noise (S:N)
ratios, which were used in aspects of metric (Section 8.5.2) and VMMI (Section 9.3.2) screening.
Analyses were completed with R Statistical Software, ver. 3.6.1 (R Core Team 2019), except detrended
correspondence analysis for which PC-ORD, ver. 7.8 (McCune and Mefford 2018) was used.
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Table 8-1. Numbers of unique NWCA 2011 and 2016 sampled sites. NWCA_REF (Disturbance): L = Least, I =
Intermediate, M = Most, ? = Undetermined. Revisit = site sampled twice in same field season,
n Numbers of Unique Sites by Type
ALL SITES
Total
L
1
M
?
Revisit
Calibration
Validation
1985
439
1061
474
11
104
1587
398
NWCARPT UNIT 6
r^o.
Inland Coastal Plains (ICP)
Eastern Mountains and Upper Midwest (EMU)
Plains (PLN)
Arid West (ARW)
Western Valleys & Mountains (WVM)
Tidal (TDL)
2011 Probability Sites
2011 Handpicked Sites
2016 Probability Sites
2016 Handpicked Sites
Figure 8-1. Distribution of probability and hand-picked sites sampled in the 2011 and 2016 NWCA surveys within
Six Reporting Units (RPT_UNIT_6). TDL = coastal areas where tidally-influenced estuarine wetlands occur. Inland
wetlands are mapped within five geographic regions.
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8.2 Anthropogenic Disturbance
Both the evaluation of candidate metrics for utility in reflecting ecological condition and the development
of VMMIs require least- and most-disturbed sites to anchor the ends of an anthropogenic disturbance
gradient (USEPA 2016a, Magee et al. 2019a). In addition, least-disturbed sites are used in setting
thresholds for good, fair, and poor condition based on VMMI values (Magee et al. 2019a, Herlihy et al.
2019).
The multi-step process for screening and assigning least-disturbed, intermediate-disturbed, or most-
disturbed status to NWCA sites is detailed in Chapter 6: and summarized in Appendix A: Illustrative
Guide to Assigning Disturbance Class in Six Steps. In brief, a stepwise process was used in which sites
were first screened for abiotic disturbance using physical indices (Section 6.3), then by chemical indices
(Section 6.4) to assign abiotic disturbance classes. Least-disturbed sites passing the physical and chemical
screens (Section 6.5), were further screened with a biological metric (XRCOV_AC, (Section 6.6), the
relative percent cover of nonnative (alien and cryptogenic, Table 7-5) plants. The final set of least-,
intermediate-, and most-disturbed sites (REF_NWCA) was used for evaluation of vegetation candidate
metrics and for VMMI development based on the Index Visit data from 1985 unique NWCA sites where
vegetation was sampled in 2011 or 2016 (Table 8-1).
8.3 Considering Regional and Wetland Type Differences
To account for physical and biotic diversity across the national scale, finer scales are often needed to
facilitate development of the most effective MMIs (Stoddard et al. 2008, USEPA 2006, Herlihy et al.
2019). Plant species composition in wetlands varies widely across the conterminous United States, both
with environmental conditions and wetland type (Herlihy et al. 2019, USEPA 2016a, Magee et al. 2019a).
We evaluated a series of potential subpopulation groups (Table 5-1) in an effort to minimize natural
variation, while maintaining sample sizes sufficient to inform candidate metric evaluation and VMMI
development. To identify scales relevant for VMMI development based on the plant data from NWCA
2011 and NWCA 2016 sampled sites, we examined the following groupings listed from finer to coarser
scale:
RPT_UNIT12 (Table 8-2, Figure 8-2): 12 subpopulations based on combining region (RPT_UNIT_6)
and wetland group (WETCLS_GRP)
RPT_UNIT_6 (Table 8-3): six subpopulations including tidally-infIueneed estuarine wetlands in
coastal areas and inland wetlands in 5 aggregated ecoregions
WETCLS_GRP (Table 8-4): four subpopulations describing broad Wetland Groups
Table 8-2 through Table 8-4 include, for each subpopulation: 1) the total number of unique sampled sites;
2) the numbers of sites identified as "least disturbed", "intermediate disturbed", and "most disturbed"; 3)
the number of revisit sites (sites sampled twice during the same sampling season to quantify within-year
sampling variability); and 4) the number of calibration and validation sites used in analyses. Ordination
analysis of the plant species data was used to evaluate how species composition (presence and
abundance) varied in relation to these broad ecoregional and wetland group subpopulations (Figure 8-3).
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Table 8-2. Numbers of unique NWCA 2011 and NWCA 2016 sampled sites by RPT_UNIT12 (RPT_UNIT_6 x
WETCLS_GRP). RPT_UNIT_6 is defined in Figure 6-2 and Table 6-3. WETCLS_GRP is defined in Table 6-4.
REF_NWCA (Disturbance): L = Least, I = Intermediate, M = Most, ? = undetermined. Revisit = site sampled twice in
same field season.
RPT_UNIT12
(RPT_UNIT_6x
WETCLS_GRP)
RPT_GRP_12*
n Numbers of Sites by Type
Total
L
1
M
?
Revisit
Calibration
Validation
TDL-H
ALL-EH
374
134
158
81
1
18
298
76
TDL-W
ALL-EW
87
15
43
27
2
4
70
17
ICP-H
CPL-PRLH
104
21
48
34
1
3
83
21
ICP-W
CPL-PRLW
307
65
168
71
3
11
247
60
EMU-H
EMU-PRLH
116
29
61
26
0
11
90
26
EMU-W
EMU-PRLW
234
72
115
46
1
17
181
53
PLIM-H
IPL-PRLH
210
19
124
65
2
15
169
41
PLN-W
IPL-PRLW
141
34
84
23
0
4
121
20
ARW-H
XER-PRLH
109
7
70
32
0
2
86
23
ARW-W
XER-PRLW
59
3
43
13
0
3
40
19
WVM-H
WMT-PRLH
113
20
63
30
0
8
94
19
WVM-W
WMT-PRLW
131
20
84
26
1
8
108
23
*Note: membership of sites in subpopulations of RPT_UNIT12 and of RPT_GRP_12 is the same. RPT_UNIT12 codes
were created to allow matching with codes in RPT_UNIT_6 and WETCLS_GRP.
Eastern Mountains and Upper Midwest (EMU)
Plains (PLN)
Ķ Arid West (ARW)
I Western Valleys & Mountains (WVM)
Ķ Tidal (TDL)
WETCLS_GRP
A Estuarine Herbaceous (EH)
a Estuarine Woody (EW)
0 Palustrine, Riverine, and Lacustrine Herbaceous (PRLH)
Palustrine, Riverine, and Lacustrine Woody (PRLW)
Figure 8-2. Six Reporting Units and four Wetland Groups: TDL = coastal areas where tidally-influenced estuarine
wetlands occur. Inland wetlands are mapped within 5 NWCA Aggregated Ecoregions.
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Table 8-3. Numbers of unique NWCA 2011 and 2016 sampled sites by six reporting units (RPT_UNIT_6).
REF_NWCA (Disturbance): L = Least, I = Intermediate, M = Most, ? = undetermined. Revisit = site sampled twice in
same field season. Tidal (TDL) = tidally-influenced estuarine wetlands occurring in near coastal areas. The other five
groups represent inland wetlands within five ecoregional areas. See Table 8-4 for description of include wetland
types.
RPT_UNIT_6
n Numbers of Sites by Type
Total
L
1
M
?
Revisit
Calibration
Validation
TDL
Tidal
461
149
201
108
3
22
368
93
ICP
Inland Coastal Plains
411
86
216
105
4
14
330
81
EMU
Eastern Mtns & Upper
Midwest
350
101
176
72
1
28
271
79
PLN
Interior Plains
351
53
208
88
2
19
290
61
ARW
Arid West
168
10
113
45
0
5
126
42
WVM
Western Valley & Mountains
244
40
147
56
1
16
202
42
Table 8-4. Numbers of unique NWCA 2011 and 2016 sampled sites by Wetland Groups (WETCLS_GRP). REF_NWCA
(Disturbance): L = Least, I = Intermediate, M = Most, ? = undetermined. Revisit = site sampled twice in same field
season. EH and EW are tidally-influenced estuarine wetlands. PRLH and PRLW are inland wetlands.
WETCLS_GRP1 (Wetland Groups)
n Numbers of Sites by Type
Total
L
1
M
?
Revisit
Calibration
Validation
EH
Estuarine Herbaceous
374
134
158
81
1
18
298
76
EW
Estuarine Woody
87
15
43
27
2
4
70
17
PRLH
Palustrine, Riverine or
Lacustrine Herbaceous
654
96
366
187
3
39
522
130
PRLW
Palustrine, Riverine or
Lacustrine Woody
872
194
494
179
5
43
697
175
1Wetland types included in each WETCLS_GRP category listed above are defined below
WETCLS GRP
Description of wetland types included
NWCA
Wetland
Type
USFWS
Status &
Trends Code
Estuarine
EH
Estuarine intertidal (E) emergent (H = herbaceous)
EH
E2EM
EW
Estuarine intertidal (E) forested and shrub (W=
woody)
EW
E2SS
Inland
PRLH
Emergent wetlands (EM) in palustrine, shallow
riverine, or shallow lacustrine littoral settings (PRL)
PRL-EM
PEM
Farmed wetlands (f) in palustrine, shallow riverine,
or shallow lacustrine littoral settings (PRL); only
subset previously farmed, but not currently in crop
production
PRL-f
Pf
Open-water ponds and aquatic bed wetlands
PRL-UBAB2
PUBPAB2
PRLW
Shrub-dominated wetlands (SS) in palustrine,
shallow riverine, or shallow lacustrine littoral
settings (PRL)
PRL-SS
PSS
Forested wetlands in palustrine (FO), shallow
riverine, or shallow lacustrine littoral settings (PRL)
PRL-FO
PFO
2PUBPAB covered S&T Wetland Categories: PAB (Palustrine Aquatic Bed), PUBn (Palustrine Unconsolidated Bottom, natural),
PUBa (aquaculture), PUBf (agriculture use), PUBi (industrial), PUBu (PBU urban).
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Ideally, for VMMI development, each subpopulation or analysis group would have at least 100 total sites,
with 30 of these meeting least-disturbed criteria. Not all of these potential analysis groups (Table 8-2
through Table 8-4) had the recommended number of total sites or of least-disturbed sites for VMMI
development. For example, among the RPTJJNIT12 categories (Table 8-2) the tidally-infIuenced Estuarine
Woody wetlands (TDL-W) and the inland Arid West woody wetlands (ARW-PRLW) each had fewer than
100 sites, and several region-Wetland Group combinations had fewer than 30 least-disturbed sites. At the
coarser scale of RPT_UNIT_6 subpopulations, only 10 least-disturbed sites were available for the Arid
West (ARW) (Table 8-3). In the WETCLS_GRP classification (Table 8-4), there were only 87 Estuarine
Woody (EW) wetland sites and only 15 of these were least-disturbed.
Ordination using detrended correspondence analysis (DCA) (McCune and Mefford 2018) illustrated how
species composition, based on species identity and abundance (estimated as percent cover), at the site-
level varied in relation to broad wetland type and ecoregional subpopulations. The DCA was based on the
percent cover of 4,798 observed taxa (native and nonnative) observed in one or more of the sampled
sites and was run with down-weighting of uncommon taxa and axis rescaling (segments = 30).
Eigenvalues for axes 1 and 2 were 0.949 and 0.803, respectively, with a Monte Carlo randomization test
(999 permutations) having p = 0.0001 for both axes. Total variance in the species data was 113.3. The
ordination (Figure 8-3) was plotted using raw site scores and unrotated axes (McCune and Mefford 2018),
with sites coded to represent the 12-Ecoregion x Wetland Group (RPTJJNIT12) subpopulations.
1200 Ķ
1000 Ķ
800 Ķ
(N
U)
X
<
600 Ķ
400 Ķ
200 Ķ
*
. * iĢ.
* ^
RPTJJNIT12
+ ARW-H
+ ARW-W
EMU-H
a EMU-W
v ICP-H
t ICP-W
PLN-H
~ PLN-W
oTDL-H
TDL-W
~ WVM-H
Ķ WVM-W
0 Ķ
200
400
600
800
Axis 1
1000
1200
1400
1600
Figure 8-3. Detrended correspondence analysis for NWCA 2011 and 2016 sampled sites. Sites are color- and
symbol-coded by RPT_UNIT12. Blue and TDL = Tidally-influenced, estuarine wetland sites. Other codes and colors =
Inland wetland sites by geographic region. Open symbols = herbaceous (H) wetlands. Filled symbols = woody (W)
wetlands. Note: Among the unique 1985 sampled sites, 208 were resampled sites (sampled in 2011 and 2016),
(Section 6.1), and for these resampled sites the data from 2011 visit were used in this DCA.
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DCA axis gradient length reflects the standard deviations (SD) in species composition along an axis; and
sites with scores that differ by more than 4 SD are expected to have no species in common (McCune and
Grace 2002, Jongman et al. 1995). Gradient length for Axis 1 was 14.9 and for Axis 2 was 11.8. This means
that from one edge of Axis 1 to the other (i.e., moving from left to right across the ordination), there are
3.7 complete turnovers in species composition. Similarly, for Axis 2 (i.e., moving from top to bottom of
the ordination), there are nearly 3 turnovers in species composition. This level of beta diversity is not
surprising given the geographic scope of the study area (conterminous US) and the diversity of wetland
plant communities that are represented in each 12-Ecoregion x Wetland Group subpopulation.
The ordination plot (Figure 8-3) shows distinct to intergrading groups of sites associated with wetland
subpopulations along gradients described by the axes. Tidal (EH, EW) vs. inland (PRLH, PRLW) wetlands
separate distinctly along Axis 1. Inland herbaceous and woody wetlands tend to separate within
ecoregional groups along both Axes 1 and 2. Axis 2 appears to be related to longitude, with sites from the
western half of the US (e.g., WVM, ARW, PLNS) tendingto occur in the top half of the ordination and
those from the eastern half (EMU, ICP) occurring in the bottom half. Within the ecoregional groups the
woody sites separate more distinctly than the herbaceous sites, and woody wetlands tend to be
distributed along the outer edges/portions of the ordination by their ecoregional groups. The woody
(PRLW) wetlands in the PLNS tend to intermix at the interface between EMU-PRLW and ICP-PRLW Some
Inland Herbaceous wetlands (PRLH in the WMV, ARW, and PLNS), tend to intermix in the center and
upper right of the ordination and to intergrade more among regions than do the woody wetlands.
Intermixing of these herbaceous Wetland Groups may be related in part to the presence of widespread
native species and to nonnative species with wide ecological amplitude (Magee et al 2019b).
The ordination of these 12-Ecoregion x Wetland Type (RPT_UNIT12) subpopulations was useful in
describing variation in wetland vegetation at a continental scale, with wetland type (WETCLS_GRP)
appearing to be primary and ecoregion (RPT_UNIT_6) to be secondary drivers of species composition.
Given these patterns and the available sample sizes for least-disturbed sites in the various classifications
(Table 8-1 through Table 8-4), we evaluated metrics (Section 8.5) and developed candidate VMMIs
(Section 9.3) at the national scale, and for subpopulations in WETCLS_GRP and in RPT_UNIT_6:
National scale - all sampled wetlands (Table 8-1)
Five subpopulations based on RPT_UNIT_6 groups (Figure 8-2 and Table 8-3):
o TDL-tidally-influenced estuarine wetlands in coastal areas
o Inland wetlands in Four NWCA Aggregated Ecoregions
Ķ ICP - Inland Coastal Plains
Ķ EMU - Eastern Mountains and Upper Midwest
Ķ PLNS_ARW- Plains (PLN) and Arid West (ARW); note the PLN and ARW groups
were combined because there were few least-disturbed sites in ARW
Ķ WVM - Western Mountains and Valleys
Four Wetland Group subpopulations (WETCLS_GRP) (Table 8-4)
Candidate VMMIs for all these groups were developed and evaluate to identify which might have the
most robust performance.
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8.4 Calculating Candidate Metrics
Validated vegetation data (see Sections 7.4 and
7,5), along with species trait information, (see
Sections 7.6 through 7.9) were used to calculate
numerous candidate metrics representing several
major Metric Groups (Table 8-5). These
ecologically important Metric Groups and their
component metrics are commonly recognized as
potential indicators of condition or stress (USEPA
2016a, Magee et al 2019).
The Metric Groups listed in Table 8-5 are
comprised of a variety of broad metric types, and
for each metric type, several-to-many specific
candidate metrics with potential relationships to
ecological condition or stress were calculated.
NWCA candidate vegetation metrics included
descriptors that were likely to have broad
applicability across regions and wetland types, as
well as metrics expected to have more restricted
utility for specific broad wetland groups. Section
8.8, Appendix E lists: 1) the name and a short
description of each metric, 2) how each metric was calculated, 3) the field data and species trait groups
on which each metric is based, and 4) whether the metric is used primarily to describe ecological
condition or stress in the NWCA.
The metric information specified in Section 8.8; Appendix E was used in updating R code to calculate 556
candidate vegetation metrics for each sampled site. The original, accuracy-tested, R code that was
developed for metric calculation for the 2011 survey (USEPA 2016a) was updated, here, for the joint
analysis of the 2011 and 2016 data. The calculated metrics can be found on the NWCA website
(.nwca_2016_ veg metrics, cs v).
Most of the metric types described in Table 8-5 include versions of metrics that incorporate all species,
only native species, or only normative species. Vegetation metrics based on all species or on only native
species were considered as potential descriptors of wetland condition (n = 426). Metrics based on only
nonnative species (alien and cryptogenic species, see Section 7.8) (n = 130) were viewed as indicators of
wetland stress (USEPA 2016a). Only the former group of metrics was considered in VMMI development.
The 426 candidate condition metrics were used in developing candidate VMMIs (see Chapter 9:). In
previous work, the Nonnative Plant Indicator (NNPI) was developed based on data from the 2011 NWCA
(Magee et al. 2019a). Here, the NNPI was applied in analysis of the combined 2011 and 2016 data
(Chapter 10:). The NNPI uses exceedance values for three nonnative plant metrics to assign categorical
classes (good, fair, poor, and very poor) to describe wetland condition in relation to impact from
nonnative plants.
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Table 8-5. Metric Groups and component Metric Types for characterizing vegetation condition.
Metric Groups
Major Metric Types for each Metric Group
Taxa Composition3
Richness, diversity, frequency, cover, importance of vascular plant species, genera,
families, etc.
Floristic Quality3
Mean Coefficient of Conservatism, Floristic Quality Assessment Index (versions
based on species presence or frequency and cover-weighted versions)
Tolerance and Sensitivity
to Disturbance
Richness and abundance of sensitive, insensitive, tolerant, highly tolerant species
Hydrophytic Status3
Richness and abundance by Wetland Indicator Status; Wetland Indices
Life History3
Richness and abundance by growth-habit type, duration/longevity category,
vascular plant category (e.g., ferns, dicots, etc.)
Vegetation Structure
Frequency, cover, importance, diversity, by structural (height) vegetation groups
Nonvascular
Frequency, cover, importance for ground or arboreal bryophytes or lichens, algae
Ground Surface Attributes
Frequency, cover, importance, depth of water, litter, bare ground
Woody Debris and Snags
Frequency, cover, importance for woody debris, counts for snags
Trees3
Richness, counts, or frequency, cover or importance by height or diameter classes
individual metrics in a group often included versions based on all species or native species only. Note: All
importance metrics combine frequency and cover.
Only a small number of the calculated metrics were ultimately incorporated in NWCA vegetation indices
(VMM Is, Chapter9:) or (NNPI, Chapter 10:, Magee et al. 2019b). However, many of the other metrics are
expected to be useful in describing other characteristics of wetlands or for addressing ecological
questions related to diversity, structure, functional traits, or relationships to environmental conditions or
ecological processes. For example, the nonnative plant metrics (n =130) are likely to inform questions
related to the impacts of nonnative plants, which can 1) reflect condition of the vegetation, 2) be
indicators of anthropogenic disturbance, or 3) behave as direct stressors to vegetation and ecosystem
properties (e.g., Kuebbing et al. 2015, Magee et al. 2008, 2010, 2019b, Pysek et al. 2020, Riccardi et al.
2020, Ruaro et al 2020, Simberloff 2011).
8.5 Evaluating Candidate Vegetation Metrics
Data from all 1,985 unique 2011 and 2016 sampled sites were used to evaluate 426 individual NWCA
candidate vegetation metrics of condition for their potential utility in development of candidate VMMI(s).
The NWCA metric screening approach was adapted and expanded for wetlands (Magee et al. 2019a) from
metric evaluation methods used in other NARS (e.g., Stoddard et al. 2008, Pont et al. 2009, VanSickle
2010). Most of the wetland vegetation metrics were strongly non-normal (Magee et al. 2019a, USEPA
2016a); consequently, nonparametric statistical (e.g., Kruskal-Wallis test) approaches were used in the
screening analyses where appropriate. Specific criteria for range, repeatability, responsiveness, and
redundancy were defined. R code was written to implement these screening tests.
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8.5.1 Range Tests
Metrics with limited range, too many zero values, or highly skewed distributions have been shown to
generally be poor indicators of ecological condition. Thus, sufficient range in values to permit signal
detection is important. We used two tests to define sufficient (PASS), marginal (PASS-), and insufficient
(FAIL) range for metric values.
Test 1 - Identifies metrics with large proportion of 0 values or highly skewed distributions:
o If the 75th percentile = 0, i.e., more than 75% of values are 0, then FAIL
o If the 75th percentile = the minimum OR the 25th percentile = max (indicating 75% of
values identical), then FAIL (ensures that a majority of values are not the same as the
minimum or maximum to help eliminate variables that are highly skewed and mostly a
single non-zero value)
o If the median = 0, then PASS-
Test 2 - Identifies metrics with very narrow ranges
o If the metric is a percent variable and (max-25th percentile) < 15%, then FAIL
o If the metric is not a percent variable and (max-25th) < (max/3), then FAIL
If either Test 1 or 2 resulted in a FAIL, the final assignment for the metric was FAIL. If the first two screens
in Test 1 resulted in a PASS, but the third screen a PASS-, the result was PASS-. To pass the range screen,
each metric had to receive a PASS or PASS-.
8.5.2 Repeatability (S:N)
Useful metrics tend to have high repeatability, that is, the among-site variability will be greater than
within-year sampling variability based on repeat sampling during the same field season at a subset of sites
(see Table 8-1 through Table 8-4, revisit sites). To quantify repeatability, NARS uses Signal-to-NoiseRatio
(S:N), that is, the ratio of variance associated with a 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 sampling two different sites (Stoddard et al. 2008).
In the NWCA, we set an initial criterion of S:N > 4 (Magee et al. 2019a). In practice, however, the
observed S:N values for the vegetation metrics were much higher, so we ultimately set the metric
retention criterion to S:N > 10, or > 5 if metric type was as yet unrepresented in the suite of metrics
passing all selection criteria. For the NWCA, S:N for individual metrics was calculated using the R package
"Ime4" (version 1.1-7, Bates et al. 2014). Each metric was used as a response variable with SITEJD (a site
identifier) as the main factor in a random effects model. Then the variance components from the
resulting model were used to calculate S:N.
Note, that among the analysis groups for which metric screening was conducted (Section 8.5.5), two
subpopulations had < 5 revisit sites (ARW, EW). For these, two groups S:N values were given little
consideration compared to other screening criteria.
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8.5.3 Responsiveness
The most fundamental test of the efficacy of a candidate metric is its capacity to discriminate degraded
from relatively undisturbed ecosystems. Responsive candidate metrics effectively distinguish least-
disturbed from most-disturbed sites (Stoddard et al. 2008). In the NWCA, the ability to differentiate least-
from most-disturbed sites was evaluated based on p-values and Chi-squared values from a Kruskal-Wallis
test (large sample approximation). The assessment of the discriminatory capability of individual metrics
was also supported by ranking the separation of least- and most-disturbed sites based on box plot
comparisons, where the degree of overlap of medians and interquartile ranges (IQRs) between least- and
most-disturbed sites provides a signal of the metric responsiveness (Klemm et al. 2002).
We developed R code to automate a process to simulate comparison of box plots for least and most
disturbed sites, for each vegetation metric, and to rank the separation levels. Using the approach
developed by Barbour et al. (1996) and outlined in Klemm et al. (2002), the medians and IQRs of the least
and most disturbed sites were compared, and metrics were scored as follows:
Score of 0 (lowest discriminatory power) - Complete overlap of each group's IQRs with the
median of the other group
Score of 1 - Only one median was overlapping with the IQRs of the other group
Score of 2 - Neither median overlapped with the IQR of the other group, but the IQRs
overlapped
Score of 3 (highest discriminatory power) - IQRs did not overlap
Metric responsiveness was evaluated using three acceptance thresholds:
Kruskal-Wallis p < 0.05
Chi-square value from Kruskal-Wallis test >10, or >5 if metric type was as yet unrepresented in
the suite of metrics passing all selection criteria
Box plot separation score > 0
o A zero-value box plot did not disqualify if the metric passed the other screens and was
not represented in the suite of metrics passing all other selection criteria
o Higher box plot separation scores received greater preference (3 > 2 > 1) in selecting
among related metrics
Among metrics passing the responsiveness screen, the Kruskal-Wallis p-values were often much lower
and Chi-square values were often much higher than acceptance thresholds. In some cases where other
screening criteria were high, a metric with Chi-square < 5 might be retained.
8.5.4 Redundancy
Step 1- During metric screening, a subset of metrics that passed the range, repeatability, and
responsiveness tests, but which conveyed information similar to other metrics, were dropped. Dropped
metrics typically included those that were very similar (e.g., absolute versus relative cover for trait-based
metrics) or individual metrics that were also represented as a component of another metric. In such
cases, the metric that was considered most ecologically meaningful, performed best on screening tests,
or was easiest to collect or calculate was selected.
Step 2- Additional redundancy screening was handled during the process of VMM I development. It is
generally agreed that metrics included in a MMI should not be strongly correlated, and r < 0.75 is often a
cut off point for correlation among metrics included in the same MMI (e.g., Stoddard et al. 2008, Pont et
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al 2009, Van Sickle 2010). Candidate VMMIs were screened to ensure that correlations among their
component metrics were less than this threshold. If this threshold was exceeded the candidate VMMI
was disqualified (see Section 9.3).
8.5.5 Application of Metric Screening Criteria
Screening criteria were applied nationally and to subpopulations of the RPT_UNIT_6 or WETCLS_GRP
subpopulation groups, that is to:
All Wetlands - Conterminous US (Table 8-1)
RPT_UNIT_6 subpopulations: TDL, ICP, EMU, PLN-ARW, WVM (Table 8-3)
WETCLS_GRP subpopulations: EH, EW, PRLH, PRLW (Table 8-4)
The metrics passing screening tests (range, repeatability, responsiveness criteria, and Step 1 of the
redundancy criteria) for a given subpopulation were retained for consideration in VMMI development.
8.6 Metric Screening Results
Candidate vegetation metrics that passed screening tests (Section 8.5) for the national scale, for five
subpopulations based on RPT_UNIT_6, or the subpopulations of WETCLS_GRP were retained for further
analysis. Passing metrics for each subpopulation were used in developing potential VMMIs for that
subpopulation. In the VMMI development process (described in Chapter 9:), four final VMMIs were
ultimately selected as the best performing, one for each WETCLS_GRP subpopulation: Estuarine
Herbaceous (EH), Estuarine Woody (EW), Inland Herbaceous (PRLH), and Inland Woody (PRW). These
Wetland Group VMMIs were used for population estimates of condition for the 2016 survey and for
change analysis between 2011 and 2016. Therefore, in this section we report metric screening results only
for the Wetland Group subpopulations (Table 8-6 through Table 8-9).
Table 8-6. Metrics (n = 40) that passed screening criteria for the Estuarine Herbaceous (EH) wetland
subpopulation. Kruskal-Wallis statistics: Chi square and p-value. Metrics defined in Section 8.8 (Appendix E).
Estuarine Herbaceous
Range
S:N
Chi
P
Box
Metric Type
Wetland (EH) Metrics
Test
Ratio
Square
Value
plot
Score
TOTN_SPP
PASS
26.18
41.15
0.0000
2
All or Native Species
TOTN_FAM
PASS
24.3
34.87
0.0000
2
All or Native Species
H_ALL
PASS
47.24
29.66
0.0000
2
All or Native Species
XBCDIST_SPP
PASS
21.6
26.99
0.0000
2
All or Native Species
TOTN_NATSPP
PASS
29.6
30.05
0.0000
1
All or Native Species
PCTN_NATSPP
PASS
18.26
59.64
0.0000
1
All or Native Species
RFREQJMATSPP
PASS
27.6
64.49
0.0000
1
All or Native Species
H_NAT
PASS
18.98
25.23
0.0000
2
All or Native Species
XBCDIST_NATSPP
PASS
16.39
27.64
0.0000
2
All or Native Species
XC_NAT
PASS
57.23
18.79
0.0000
1
Floristic Quality
XC_ALL
PASS
60.34
40.06
0.0000
2
Floristic Quality
FQAI_COV_NAT
PASS
11.87
45.56
0.0000
2
Floristic Quality
FQAI_COV_ALL
PASS
14.06
61.41
0.0000
2
Tolerance
N_TOL
PASS
25.31
43.8
0.0000
2
Tolerance
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Estuarine Herbaceous
Range
S:N
Chi
P
Box
Metric Type
Wetland (EH) Metrics
Test
Ratio
Square
Value
plot
Score
PCTN_SEN
PASS
38.06
39.08
0.0000
2
Tolerance
PCTN_TOL
PASS
59.19
49.27
0.0000
2
Tolerance
XRCOV_SEN
PASS
115.37
46.9
0.0000
2
Tolerance
XRCOV_TOL
PASS
56.52
57.21
0.0000
2
Tolerance
XRCOV_HTOL
PASS-
21.98
56.07
0.0000
1
Tolerance
PCTN_OBL
PASS
14.82
41.74
0.0000
2
Hydrophytic Status
PCTN_OBL_FACW
PASS
37.3
35.26
0.0000
1
Hydrophytic Status
XRCOV_OBL
PASS
67.43
27.68
0.0000
1
Hydrophytic Status
WETIN D_COV_ALL
PASS
39.45
31.28
0.0000
2
Hydrophytic Status
WETIN D2_COV_ALL
PASS-
39.45
31.28
0.0000
2
Hydrophytic Status
N_FORB
PASS
22.15
55.93
0.0000
2
Growth Habit
XRCOV_FORB
PASS
79.94
41.43
0.0000
2
Growth Habit
PCTN_GRAMINOID_NAT
PASS
8.04
37.81
0.0000
2
Growth Habit
XRCOV_GRAMINOID_NAT
PASS
35.55
46.85
0.0000
2
Growth Habit
N_HERB
PASS
24.94
48.72
0.0000
2
Growth Habit
XRCOV_HERB_NAT
PASS
22.74
24.26
0.0000
2
Growth Habit
N_ANNUAL
PASS-
3.08
42.3
0.0000
1
Duration
N_PERENNIAL
PASS
20
35.05
0.0000
1
Duration
N_PERENNIAL_NAT
PASS
20.44
26.05
0.0000
1
Duration
PCTN_PERENNIAL_NAT
PASS
8.27
62.92
0.0000
2
Duration
N_DICOT
PASS
23.02
39.67
0.0000
2
Category
N_MONOCOT
PASS
13.63
27.1
0.0000
2
Category
PCTN_MONOCOTS_NAT
PASS
7.61
37.05
0.0000
2
Category
XRCOV_DICOT
PASS
44.16
28.45
0.0000
2
Category
XRCOV_MONOCOT
PASS
40.53
28.13
0.0000
2
Category
XRCOV_MONOCOTS_NAT
PASS
36.39
45.87
0.0000
2
Category
Table 8-7. Metrics (n = 21) that passed screening criteria for the Estuarine Woody (EW) wetland subpopulation.
Kruskal-Wallis statistics: Chi square and p-value. Metrics defined in Section 8.8 (Appendix E).
Estuarine Woody Wetland
Range
S:N
Chi
p Value
Box plot
Metric Type
(EW) Metrics
Test
Ratio
Square
Score
XTOTABCOV
PASS
2.74
4.14
0.0419
2
All or Native Species
PCTN_NATSPP
PASS
9.15
7.44
0.0064
2
All or Native Species
RIMP_NATSPP
PASS-
122.29
8.97
0.0027
2
All or Native Species
FQAI_ALL
PASS
17.73
3.57
0.0587
1
Floristic Quality
PCTNJSEN
PASS
6.26
4.2
0.0405
2
Tolerance
PCTN_HTOL
PASS
22.33
4.47
0.0345
1
Tolerance
PCTN_GRAMINOID_NAT
PASS
10.68
8.38
0.0038
2
Graminoid
XABCOV_GRAMINOID
PASS
61.84
5.21
0.0225
2
Graminoid
XABCOV_GRAMINOID_NAT
PASS
115.16
7.34
0.0068
2
Graminoid
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Estuarine Woody Wetland
(EW) Metrics
Range
Test
S:N
Ratio
Chi
Square
p Value
Box plot
Score
Metric Type
XRCOV_GRAMINOID
PASS
72.74
4.07
0.0436
2
Graminoid
XRCOV_GRAMINOID_NAT
PASS
44.94
5.85
0.0156
2
Graminoid
XCOV_WD_FINE
PASS
40.88
4.69
0.0303
2
Woody
XRCOV_SHRUB_COMB
PASS
80.27
3.63
0.0568
1
Woody
XRCOV_SHRUB_COMB_NAT
PASS
80.76
4.04
0.0445
1
Woody
PCTN_DICOT
PASS
13.93
4.1
0.0429
2
Dicots
XRCOV_DICOT
PASS
74.9
4.31
0.0378
1
Dicots
XRCOV_DICOTS_NAT
PASS
70.83
3.58
0.0584
1
Dicots
PCTN_MONOCOT
PASS
8.86
5.91
0.015
2
Monocots
PCTN_MONOCOTS_NAT
PASS
7.94
9.77
0.0018
3
Monocots
XABCOV_MONOCOT
PASS
67.64
7.62
0.0058
3
Monocots
XRCOV_MONOCOTS_NAT
PASS
49.56
8.34
0.0039
3
Monocots
Table 8-8. Metrics (n = 42) that passed screening criteria for the Inland Herbaceous (PRLH) wetland subpopulation.
Kruskal-Wallis statistics: Chi square and p-value. Metrics defined in Section 8.8 (Appendix E).
Inland Herbaceous Wetland
(PRLH) Metrics
Range
Test
S:N
Ratio
Chi
Square
p Value
Box
plot
Score
Metric Type
PCTN_NATSPP
PASS
8.14
79.11
0.0000
3
Native Species
RFREQJMATSPP
PASS
10.61
82.2
0.0000
3
Native Species
XRCOV_NATSPP
PASS
8.37
86.26
0.0000
3
Native Species
RIMP_NATSPP
PASS
11.84
92.83
0.0000
3
Native Species
XC_NAT
PASS
23.06
62.01
0.0000
2
Floristic Quality
XC_ALL
PASS
37.2
94.31
0.0000
3
Floristic Quality
XC_COV_ALL
PASS
28.62
45.66
0.0000
2
Floristic Quality
FQAI_NAT
PASS
30.81
30.74
0.0000
2
Floristic Quality
FQAI_ALL
PASS
38.13
40.38
0.0000
2
Floristic Quality
FQAI_COV_ALL
PASS
14.7
69.77
0.0000
2
Floristic Quality
N_SEN
PASS
25.5
38.99
0.0000
1
Sensitive
PCTN_SEN
PASS
18.17
53.79
0.0000
2
Sensitive
PCTNJSEN
PASS
9.09
24.95
0.0000
2
Sensitive
XRCOV_SEN
PASS
24.21
37.16
0.0000
1
Sensitive
XRCOV_ISEN
PASS
7.32
24.08
0.0000
1
Sensitive
N_TOL
PASS
8.85
35.94
0.0000
1
Tolerant
N_HTOL
PASS
6.51
58.06
0.0000
2
Tolerant
PCTN_TOL
PASS
17.36
66.16
0.0000
2
Tolerant
PCTN_HTOL
PASS
18.1
71.48
0.0000
3
Tolerant
XRCOV_TOL
PASS
8.68
57.34
0.0000
2
Tolerant
XRCOV_HTOL
PASS
12.83
66.44
0.0000
2
Tolerant
PCTN_FAC_FACU
PASS
11.77
47.19
0.0000
2
Hydrophytic Status
PCTN_OBL_FACW
PASS
20.25
47.64
0.0000
2
Hydrophytic Status
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Inland Herbaceous Wetland
(PRLH) Metrics
Range
Test
S:N
Ratio
Chi
Square
p Value
Box
plot
Score
Metric Type
PCTN_OBL_FACW_FAC
PASS
9.95
33.08
0.0000
2
Hydrophytic Status
XRCOV_OBL
PASS
23.08
51.97
0.0000
2
Hydrophytic Status
XRCOV_FAC_FACU
PASS
9.23
42.47
0.0000
2
Hydrophytic Status
XRCOV_OBL_FACW
PASS
25.31
40.06
0.0000
2
Hydrophytic Status
WETIN D2_COV_ALL
PASS-
30.75
51.2
0.0000
2
Hydrophytic Status
WETIN D2_COV_N AT
PASS-
15.29
30.7
0.0000
1
Hydrophytic Status
PCTN_FORB_NAT
PASS
6.7
28.73
0.0000
2
Herbaceous
XRCOV_GRAMINOID_NAT
PASS
17.3
12.02
0.0005
0
Herbaceous
PCTN_HERB_NAT
PASS
6.63
30.75
0.0000
1
Herbaceous
XRCOV_HERB_NAT
PASS
8.98
41.62
0.0000
2
Herbaceous
PCTN_SHRUB_COMB
PASS
19.96
17.36
0.0000
1
Shrub
PCTN_SHRUB_COMB_NAT
PASS
14.52
16.81
0.0000
1
Shrub
XRCOV_SHRUB_COMB
PASS
24.64
10.57
0.0011
0
Shrub
XRCOV_SHRUB_COMB_NAT
PASS
24.57
9.87
0.0017
0
Shrub
PCTN_ANNUAL
PASS
7.8
14.51
0.0001
0
Category
PCTN_PERENNIAL
PASS
11.06
24.6
0.0000
2
Category
PCTN_PERENNIAL_NAT
PASS
13.41
60.92
0.0000
2
Category
XRCOV_PERENNIAL_NAT
PASS
10.8
62.87
0.0000
2
Category
XRCOV_MONOCOTS_NAT
PASS
9.04
18.53
0.0000
1
Category
Table 8-9. Metrics (n = 47) that passed screening criteria for the Inland Woody (PRLW) wetland subpopulation.
Kruskal-Wallis statistics: Chi square and p-value. Metrics defined in Section 8.8 (Appendix E).
Inland Woody Wetland
(PRLW) Metrics
Range
Test
S:N
Ratio
Chi
Square
p Value
Box
plot
Score
Metric Type
PCTN_NATSPP
PASS
7.11
51.39
0.0000
2
Native Species
RFREQJMATSPP
PASS
12.63
56.12
0.0000
2
Native Species
XRCOV_NATSPP
PASS-
18.29
65.53
0.0000
2
Native Species
RIMP_NATSPP
PASS
20.14
64.77
0.0000
2
Native Species
XC_NAT
PASS
49.34
27.37
0.0000
1
Floristic Quality
XC_ALL
PASS
62.91
47.61
0.0000
2
Floristic Quality
FQAI_COV_ALL
PASS
49.62
30.81
0.0000
0
Floristic Quality
PCTN_SEN
PASS
34.53
37.56
0.0000
1
Tolerance
PCTN_TOL
PASS
39.68
32.01
0.0000
1
Tolerance
PCTN_HTOL
PASS
28.94
35.93
0.0000
1
Tolerance
XRCOV_HTOL
PASS
25.05
37.11
0.0000
1
Tolerance
PCTN_FAC_FACU
PASS
13.71
12.09
0.0005
0
Hydrophytic Status
PCTN_OBL_FACW
PASS
16.23
18.3
0.0000
0
Hydrophytic Status
XRCOV_UPL
PASS
35.16
11.68
0.0006
0
Hydrophytic Status
XRCOV_FAC_FACU
PASS
20.36
11.47
0.0007
0
Hydrophytic Status
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Inland Woody Wetland
Range
S:N
Chi
p Value
Box
Metric Type
(PRLW) Metrics
Test
Ratio
Square
plot
Score
XRCOV_OBL_FACW
PASS
18.13
15.56
0.0001
0 Hydrophytic Status
WETIN D2_COV_ALL
PASS
24.24
14.54
0.0001
0 Hydrophytic Status
PCTN_HERB
PASS
24.74
6.38
0.0115
0
Vine
PCTN_VINE_ALL
PASS
16.29
7.5
0.0062
0
Vine
XRCOV_VINE_ALL
PASS
28.36
9.92
0.0016
0
Vine
XRCOV_VINE_ALL_NAT
PASS
30.92
8.26
0.0041
0
Vine
PCTN_SHRUB_COMB
PASS
32.05
18.1
0.0000
0
Shrub
PCTN_SHRUB_COMB_NAT
PASS
11.11
10.44
0.0012
0
Shrub
XRCOV_SHRUB_COMB_NAT
PASS
41.98
26.03
0.0000
0
Shrub
PCTN_TREE_COMB
PASS
25.43
8.25
0.0041
0
Tree
XRCOV_TREE_COMB_NAT
PASS
20.59
5.72
0.0168
0
Tree
XRCOV_GYMNOSPERM
PASS
24.45
20.89
0.0000
1
Tree
IMP_TREE_GROUND
PASS
2.12
7.15
0.0075
0
Tree
IMP_TREE_UPPER
PASS
6.23
7.01
0.0081
0
Tree
TOTN_TREES
PASS
10.42
11.63
0.0007
0
Tree
TOTN_MID
PASS
11.59
7.88
0.005
0
Tree
TOTN_SMALL
PASS
9.24
10.81
0.001
0
Tree
TOTN_SNAGS
PASS
11.96
20.25
0.0000
0
Tree
XN_SNAGS
PASS
11.94
20.39
0.0000
0
Tree
PCTN_PERENNIAL
PASS
7.99
38.6
0.0000
2
Duration
PCTN_PERENNIAL_NAT
PASS
9.29
56.34
0.0000
2
Duration
XRCOV_ANNUAL
PASS
29.94
11.47
0.0007
0
Duration
XRCOV_ANNUAL_NAT
PASS
31.48
11.77
0.0006
0
Duration
XRCOV_PERENNIAL_NAT
PASS
24.97
56.17
0.0000
1
Duration
XRCOV_MONOCOTS_NAT
PASS
14.99
8.07
0.0045
0
Duration
PCTN_FERN
PASS
15.36
10.11
0.0015
0
Non-seed Plants
PCTN_FERNS_NAT
PASS
14.95
10.86
0.001
0
Non-seed Plants
XRCOV_FERN
PASS
14.56
7.86
0.005
0
Non-seed Plants
freclbryophytes
PASS
2.42
29.2
0.0000
1
Non-seed Plants
IMP_BRYOPHYTES
PASS
5.88
26.81
0.0000
0
Non-seed Plants
XCOV_LICHENS
PASS
4.26
33.36
0.0000
1
Non-seed Plants
IMP_LICHENS
PASS
5.53
13.91
0.0002
0
Non-seed Plants
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8.7 Literature Cited
Barbour MT, Gerritsen J, Griffth GE, Frydenborg R, McCarron E, White JS, Bastian ML (1996) A framework
for biological criteria for Florida streams using benthic macroinvertebrates. Journal of North American
Benthological Society 15: 185-211
Bates D, Maechler M, Bolker B and Walker S (2014) Ime4: Linear mixed-effects models using Eigen and S4.
R package version 1.1-7 (http://CRAN.R-project.org/packageHme4).
Herlihy AT, Kentula ME, Magee TK, Lomnicky GA, Nahlik AM, Serenbetz G (2019) Striving for consistency
in the National Wetland Condition Assessment: developing a reference condition approach for assessing
wetlands at a continental scale. Environmental Monitoring and Assessment 191 (1):327.
doi: 10.1007/s 10661-019-7325-3
Jongman RHG, Ter Braak CJF, van Tongeren OFR (eds) (1995) Data Analysis in Community and Landscape
Ecology. Cambridge University Press, Cambridge, United Kingdom
Kaufmann PR, Levine P, Robison EG, Seeliger C, Peck DV (1999) Quantifying Physical Habitat in Wadable
Streams. EPA/620/R_99/003. US Environmental Protection Agency, Washington, DC
Klemm DJ, Blocksom KA, Thoeney WT, FulkFA, Herlihy AT, Kaufmann PR, CorimerSM (2002) Methods
development and use of macroinvertebrates as indicators of ecological conditions for streams in the Mid-
Atlantic Highlands Region. Environmetal Monitioring and Assessment 78: 169-212
Kuebbing SE, Classen AT, Sanders NJ, Simberloff D (2015) Above- and below-ground effects of plant
diversity depend on species origin: an experimental test with multiple invaders. New Phytologist 208
(3):727-735. doi:10.1111/nph.13488
Magee TK, Ringold PL, Bollman MA (2008) Alien species importance in native vegetation along wadeable
streams, John Day River basin, Oregon, USA. Plant Ecology 195:287-307. doi:10.1007/sll258-007-9330-9
Magee TK, Ringold PL, Bollman MA, Ernst TL (2010) Index of Alien Impact (IAI): A method for evaluating
alien plant species in native ecosystems. Environmental Management 45:759-778. doi:10.1007/s00267-
010-9426-1
Magee TK, Blocksom KA, Fennessy MS (2019a) A national-scale vegetation multimetric index (VMMI) as
an indicator of wetland condition across the conterminous United States. Environmental Monitoring and
Assessment 191 (SI): 322, doi: 10.1007/sl0661-019-7324-4.
https://link.springer.com/article/10.1007/sl0661-019-7324-4
Magee TK, Blocksom KA, Herlihy AT, &. Nahlik AM (2019b) Characterizing nonnative plants in wetlands
across the conterminous United States. Environmental Monitoring and Assessment 191 (SI): 344, doi:
10.1007/sl0661-019-7317-3. https://link.springer.com/article/10.1007/sl0661-019-7317-3
McCune, B and Mefford, MJ (2018) PC-ORD. Multivariate Analysis of Ecological Data.Version 7.08
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McCune B, Grace JB (2002) Analysis of Ecological Communities. Gleneden Beach, OR: MjM Software
Design
Pont D, Hughes RM, Whittier TR, Schumtz S (2009) A predictive index of biotic integrity model for aquatic-
vertebrate assemblages of western US streams. Transactions of the American Fisheries Society 138: 292-
305
Pysek P, Hulme P, Simberloff D, Bacher S, Blackburn T, Carlton J, Foxcroft L, Genovesi P, Jeschke J, Kuhn I,
Liebhold A, Mandrak N, Meyerson L, Pauchard A, Pergl J, Roy H, Richardson D (2020) Scientists' warning
on invasive alien species. Biological Reviews. doi:10.1111/brv.12627
R Core Team (2019) R: A language and environment for statistical computing. Version 3.6.1. R Foundation
for Statistical Computing, Vienna, Austria. (http://www.R-project.org/)
Ricciardi A, lacarella JC, Aldridge DC, Blackburn TM, Carlton JT, Catford JA, Dick JTA, Hulme PE, Jeschke
JM, Liebhold AM, Lockwood JL, Maclsaac HJ, Meyerson LA, Pysek P, Richardson DM, Ruiz GM, Simberloff
D, Vila M, Wardle DA (2020) Four priority areas to advance invasion science in the face of rapid
environmental change. Environmental Reviews:l-23. doi:10.1139/er-2020-0088
Ruaro R, Gubiani EA, Thomaz SM, Mormul RP (2020) Nonnative invasive species are overlooked in
biological integrity assessments. Biological Invasions. doi:10.1007/sl0530-020-02357-8
Simberloff D (2011) How common are invasion-induced ecosystem impacts? Biological Invasions 13
(5):1255-1268. doi:10.1007/sl0530-011-9956-3
Stoddard JL, Herlihy AT, Peck DV, Hughes RM, Whittier TR, Tarquinio E (2008) A process for creating
multimetric indices for large-scale aquatic surveys. Journal of North American Benthological Society 27:
878-891
USEPA (2006) Wadeable Streams Assessment: A Collaborative Survey of the Nation's Streams. EPA 841-B-
06-002. US Environmental Protection Agency, Washington, DC
USEPA (2016a) National Wetland Condition Assessment: 2011 Technical Report. EPA-843-R-15-006. .US
Environmental Protection Agency, Washington, DC. https://www.epa.gov/national-aquatic-resource-
surveys/national-wetland-condition-assessment-2011-results
Van Sickle J (2010) Correlated metrics yield multimetric indices with inferior performance. Transactions of
the American Fisheries Society 139: 1802-1817
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8.8 Appendix E: NWCA 2016 Candidate Vegetation Metrics6
READ THIS: Key Information for Reading and Using This Appendix
Important: This Appendix is a descriptive overview of Candidate Vegetation Metrics. Exact methods/formulas
for calculations, specific field data, and trait information used for each metric were defined in the Vegetation
Metric R Code.
Unless otherwise indicated, vegetation metrics are summarized to site level. Metrics are calculated based on
data from five 100-m2 plots in the Assessment Area (AA) for the site (or if fewer than 5 plots were sampled,
the total number plots sampled). In the metric descriptions or formulas provided in this appendix, the phrase
'five 100-m2 plots' can be assumed to mean the 5 plots in the AA or the total number of plots sampled if less
than 5. Rarely were fewer than 5 vegetation plots sampled at the AA.
The term 'Species' as typically used in this appendix refers to taxonomic species or lowest identifiable
taxonomic unit (e.g., variety, genus, family, growth-habit).
BLACK BANNER with column headings is repeated at the top of each page.
GRAY BANNER, heading each major group of metrics, lists the NWCA Field Data Form from which the
validated field data that is used in metrics originated.
COLORED BANNERS, under each major metric group, provide section and subsection headings for sets of
metrics that describe related ecological components.
METRIC NAME column the metric name used in the NWCA vegetation metrics data set.
DESCRIPTION column gives narrative description of each metric.
CALCULATION/TRAIT INFORMATION column provides:
o In white metric rows:
* A general formula for calculation of the metric, if not evident in the DESCRIPTION column, is provided.
PARAMETER NAMES representing raw data included in calculations are highlighted in GRAY-BLUE and
re defined in Section 5.12, Appendix C.
Ķ Some calculated metrics listed in the METRIC NAME column are, in turn, used as components of other
calculated metrics.
Ķ Some calculated metrics use species trait information to aggregate species level data. Where traits are
used, trait names are indicated in the calculation column using GREEN font.
o In colored banner rows defining metric sets- General categories of species trait information used in
calculating a particular series of metrics are listed, if applicable. Codes for specific traits are indicated in
GREEN font. For metrics that use species traits, trait designations are applied as follows:
* Growth-habit, Duration, and Taxonomic Category are applied by species (see Section 5.6)
Ķ Wetland Indicator Status is applied to taxon-wetland region pairs (see Section 5.7)
Ķ Native status designations for taxon-site pairs are based on state-level status (see Section 5.8)
Ķ Coefficients of Conservatism (CCs, aka C-values) are applied to taxon-site pairs based on state or
regional specific C-values for each species (see Section 5.9)
METRIC TYPE column indicates whether the candidate metric is to reflect ecological condition or stress.
METRICS INCLUDED IN NWCA VEGETATION INDICES are indicated in the METRIC TYPE column in bold color-
coded font: the four 2016 Wetland Type Vegetation Multimetric Indices (VMMIs) in light blue (EH), dark blue
(EW), purple (PRLH), forest green (PRLW), respectively; the Nonnative Plant Indicator (NNPI) in red; and the
previously used 2011 National (VMMI) in "ose.
6 Most metrics developed for analysis of the 2011 NWCA vegetation data (USEPA 2016a) were considered here. A few (n = 11)
metrics were dropped because the 2016 field protocols were simplified and requisite data for those specific metrics were
unavailable for 2016 data. Also, several new metrics that described additional characteristics of hydrophytic vegetation (n =16),
vines (n = 12), and summaries of tree counts by three major size (dbh) ranges (n = 6) were added.
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METRIC NAME
METRIC DESCRIPTION
CALCULATION (listed in Metric
Row),
SPECIES TRAIT TYPE (indicated in
Banner if applicable)
METRIC TYPE
(C = condition,
S = stress)
SECTIONS 1 - 5
SECTION 1
Metrics based on field data: FORM V-2 - NWCA 2016 VASCULAR
SPECIES PRESENCE AND COVER
TAX A COMPOSITION (RICHNESS,
FREQUENCY, COVER, DIVERSITY)
Section 1.1
All Species/Taxonomic Groups
TOTN_SPP
Richness - Total number of unique
species across all 100-m2 plots
Count unique species across all
plots
C
XN_SPP
Mean number of species across all
100-m2 plots
C
MEDN_SPP
Median number of species across all
100-m2 plots
C
SDN_SPP
Standard deviation in number of
species across all 100-m2 plots
C
TOTN_GEN
Total number of unique genera
across all 100-m2 plots
Count unique genera across all
plots
C
XN_GEN
Mean number of unique genera
across all 100-m2 plots
C
MEDN_GEN
Median number of genera across all
100-m2 plots
C
SDN_GEN
Standard deviation in number of
genera across 100-m2 plots
C
TOTN_FAM
Total number of families across
100-m2 plots
Count unique families observed
across all plots
C
XN_FAM
Mean number of families across
100-m2 plots
C
MEDN_FAM
Median number of families across
100-m2 plots
C
SDN_FAM
Standard deviation in number of
families across 100-m2 plots
C
XTOTABCOV
(summary data
used in calculation
of other metrics)
Mean total absolute cover summed
across all species across 100-m2
plots
S COVER of II individual taxa
across 5 plots/5 plots
H_ALL
Shannon-Wiener Diversity Index -
All species
s = number of species observed, / =
species i, p = proportion of
individuals (relative cover)
belonging to species /
3
H* =
Ģ
C
J_ALL
Evenness (Pielou) - All species
H"
' ~ his
S = number of species observed
C
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CALCULATION (listed in Metric
Row),
SPECIES TRAIT TYPE (indicated in
Banner if applicable)
METRIC TYPE
METRIC NAME
METRIC DESCRIPTION
(C = condition,
S = stress)
D_ALL
Simpson Diversity Index - All species
3
s = number of species observed, / =
D = 1 "Xrf
Q
species i, p = proportion of
individuals (relative cover)
belonging to species /
XBCDIST_SPP
Within Assessment Area
dissimilarity based on species
composition = Mean of between-
plot Bray-Cutis (BC) Distance
Calculate between-plot Bray Curtis
Distance for all plot pairs based on
species and plot level cover
values. Calculate mean of these
(Dissimilarity) based on all species.
values to get mean within AA
distance:
2 V* MlN(atpahJ)
BCih = 1 J
Zj=iaU+ ^j=iahj
C
SECTIONS 1.2 -1.3
NATIVE STATUS
Trait Information = Native Status
(see Table 5-5)
Section 1.2
Native ( JA ) Species/Taxonomic
Groups
TOTN_NATSPP
Native Richness: Total number of
Count unique native (NAT) species
unique native species across all 100-
across all plots
C
m2 plots
XN_NATSPP
Mean number of native species
across 100-m2 plots
C
MEDN_NATSPP
Median number of native species
across 100-m2 plots
C
SDN_NATSPP
Standard deviation in number of
native species across 100-m2 plots
C
PCTN_NATSPP
Percent richness of native species
(TOTN_NATSPP/TOTN_SPP) x 100
C,
observed across 100-m2 plots
in EH-WMI,
EW-VMMI
RFRECLNATSPP
Relative frequency of occurrence
2 Frequencies of all (NAT
for native species as a percent of
species/2 Frequencies of all
C, in
PRLW-VMMI
total frequency (sum of all species)
species) x 100; Frequency for
individual species = % of 100-m2
plots in which it occurs.
XABCOV
Mean total absolute cover of native
2 COVER of all individual native
c
NATSPP
species across 100-m2 plots
(NAT) taxa across 5 plots/5 plots
L
XRCOV_NATSPP
Mean relative cover of native
(XABCOV_NATSPP/XTOTABCOV) x
C, in
species across 100-m2 plots as a
percentage of total cover
100
PRLH-VMMI,
PRLW-VMMI
RIMP_NATSPP
Mean relative importance of all
(RFRECLNATSPP +
c,
Ķ, Ķ
native species
XRCOV_NATSPP)/2
in EW-VMMI,
2011 National
VMMI
H_NAT
Shannon-Wiener Diversity Index-
Native species only
See H_ALL
c
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CALCULATION (listed in Metric
Row),
SPECIES TRAIT TYPE (indicated in
Banner if applicable)
METRIC TYPE
METRIC NAME
METRIC DESCRIPTION
(C = condition,
S = stress)
J_NAT
Evenness (Pielou) - Native species
only
See J_ALL
C
D_NAT
Simpson Diversity Index - Native
species only
See D_NAT
C
XBCDIST_
Within AA dissimilarity based on
See XBCDIST_SPP
NATSPP
native species only composition =
Mean of between plot Bray-Cutis
Distance (Dissimilarity) based on
native species only
C
Section 1.3
Introduced ( MTR), Adventive
( ), \LIEN (INTR + ADV),
Cryptogenic (CRY )
Trait Information = Native Status
(see Table 5-5)
TOTNJNTRSPP
Introduced Richness: Total number
Count unique introduced (INTR)
of unique introduced species across
species across all plots
S
all 100-m2 plots
XNJNTRSPP
Mean number of introduced species
across 100-m2 plots
S
MEDNJNTRSPP
Median number of introduced
species across 100-m2 plots
S
SDNJNTRSPP
Standard deviation in number of
introduced species across 100-m2
plots
S
PCTNJNTRSPP
Percent richness introduced species
observed across 100-m2 plots
(TOTN_INTRSPP/TOTN_SPP) x 100
S
rfreqjntrspp
Relative frequency of occurrence
for introduced species as a percent
(2 Frequencies of all introduced
(INTR) species/2 Frequencies of all
of total frequency (sum of all
species) x 100; Frequency for
S
species)
individual species = % of 100-m2
plots in which it occurs.
XABCOV_
Mean total absolute cover of all
S COVER of all individual INTR
INTRSPP
introduced species across 100-m2
plots
taxa across 5 plots/5 plots
S
XRCOV_INTRSPP
Mean relative cover of all INTR
(XABCOVJNTRSPP/XTOTABCOV) x
species across 100-m2 plots as a
100
S
percentage of total cover
RIMPJNTRSPP
Mean relative importance of all
(RFRECLINTRSPP+
c
introduced species
XRCOV_INTRSPP)/2
TOTN_ADVSPP
Adventive Richness: Total number
Count unique adventive (ADV)
of adventive species across 100-m2
species across all plots
s
plots
XN_ADVSPP
Mean number of adventive species
across 100-m2 plots
s
MEDN_ADVSPP
Median number of adventive
species across 100-m2 plots
s
SDN_ADVSPP
Standard deviation in number of
adventive species across 100-m2
plots
s
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CALCULATION (listed in Metric
Row),
SPECIES TRAIT TYPE (indicated in
Banner if applicable)
METRIC TYPE
METRIC NAME
METRIC DESCRIPTION
(C = condition,
S = stress)
PCTN_ADVSPP
Percent richness adventive species
observed across all 100-m2 plots
(TOTN_ADVSPP/TOTN_SPP) x 100
S
rfrecladvspp
Relative frequency of adventive
species occurrence across 100-m2
(2 Frequencies of all adventive
(ADV) species/2 Frequencies of all
plots
species) x 100; Frequency for
individual species = % of 100-m2
plots in which it occurs.
S
XABCOV
Mean total absolute cover of all
S COVER of all individual ADV taxa
C
ADVSPP
ADV species across 100-m2 plots
across 5 plots/5 plots
o
XRCOV_ADVSPP
Mean relative cover of all ADV
(XABCOV_ADVSPP/XTOTABCOV) x
species or lowest taxonomic unit
100
c
across 100-m2 plots as a percentage
J
of total cover
RIMP_ADVSPP
Mean relative importance of all
(RFRECLADVSPP +
c
adventive species
XRCOV_ADVSPP)/2
J
TOTN_ALIENSPP
Alien Richness: Total number of
unique alien (INTR + ADV) species
across 100-m2 plots
TOTN_ADVSPP + TOTNJNTRSPP
s
XN_ALIENSPP
Mean number of alien (INTR + ADV)
species across 100-m2 plots
s
MEDN_ALIENSPP
Median number of alien (INTR +
ADV) species across 100-m2 plots
s
SDN_ALIENSPP
Standard deviation in number of
alien (INTR + ADV) species
s
PCTN_ALIENSPP
Percent richness alien species
(TOTN_ALIENSPP/TOTN_SPP) x
c
across 100-m2 plots
100
RFRECL
Relative frequency of alien (INTR +
(2 Frequencies of all ALIEN
AUENSPP
ADV) species occurrence across
species/2 Frequencies of all
100-m2 plots
species) x 100; Frequency for
individual species = % of 100-m2
plots in which it occurs.
s
XABCOV_
Mean total absolute cover of ALIEN
S COVER of all individual ALIEN
AUENSPP
(INTR + ADV) species across 100-m2
plots
taxa across 5 plots/5 plots
s
XRCOV_
Mean relative cover of all ALIEN
(XABCOV_ALIENSPP/XTOTABCOV)
AUENSPP
(INTR + ADV) species across 100-m2
plots as a percentage of total cover
x 100
s
RIMP_ALIENSPP
Mean relative importance of all
(RFRECLALIENSPP +
c
ALIEN (INTR + ADV) species
XRCOV_ALIENSPP)/2
J
H_AUEN
Shannon-Wiener Diversity Index
See H_ALL
s
J_ALIEN
Evenness (Pielou)
See J_ALL
s
D_ALIEN
Simpson Diversity Index
See D_NAT
s
TOTN_CRYPSPP
Cryptogenic Richness: Total number
Count unique cryptogenic (CRYP)
of unique cryptogenic species
species across all plots
s
across 100-m2 plots
XN_CRYPSPP
Mean number of cryptogenic
species across 100-m2 plots
s
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CALCULATION (listed in Metric
Row),
SPECIES TRAIT TYPE (indicated in
Banner if applicable)
METRIC TYPE
METRIC NAME
METRIC DESCRIPTION
(C = condition,
S = stress)
MEDN_CRYPSPP
Median number of cryptogenic
species across 100-m2 plots
S
SDN_CRYPSPP
Standard deviation in number of
cryptogenic species across 100-m2
plots
S
PCTN_CRYPSPP
Percent richness cryptogenic
species across 100-m2 plots
(TOTN_CRYPSPP/TOTN_SPP) x 100
S
RFRECLCRYPSPP
Relative frequency of cryptogenic
species occurrence across 100-m2
(2 Frequencies of all cryptogenic
(CRYP) species/2 Frequencies of
plots
all species) x 100; Frequency for
individual species = % of 100-m2
plots in which it occurs.
S
XABCOV_
Mean total absolute cover of all
S COVER of all CRYP taxa across 5
C
CRYPSPP
CRYP species across 100-m2 plots
plots/5 plots
o
XRCOV_CRYPSPP
Mean relative cover of all CRYP
(XABCOV_CRYPSPP/XTOTABCOV)
species across 100-m2 plots as a
x 100
s
percentage of total cover
RIMP_CRYPSPP
Mean relative importance of all
(RFRECLCRYPSPP+
c
CRYP species
XRCOV_CRYPSPP)/2
TOTN_AC
AC Richness: Total number of
TOTN_CRYPSPP + TOTN_ALIENSPP
S, Used in
NNPI
unique alien and cryptogenic
species across 100-m2 plots
XN_AC
Mean number of AC (ALIEN + CRYP)
species across 100-m2 plots
S
MEDN_AC
Median number of AC (ALIEN +
CRYP) species across 100-m2 plots
S
SDN_AC
Standard deviation number of AC
(ALIEN + CRYP) species across 100-
m2 plots
s
PCTN AC
Percent Richness AC species (ALIEN
(TOTN_CRYPSPP + TOTN-
c
+ CRYP) across 100-m2 plots
ALIENSPP/TOTN_SPP) x 100
J
RFRECLAC
Relative frequency of alien and
(2 Frequencies of all ALIEN + CRYP
cryptogenic species occurrence in
species/2 Frequencies of all
S, Used in
NNPI
flora based on five 100-m2 plots
species) x 100; Frequency for
individual species = % of 100-m2
plots in which it occurs.
XABCOV_AC
Mean total absolute cover of all AC
S COVER of all ALIEN + CRYP taxa
(ALIEN + CRYP) species across 100-
across 5 plots/5 plots
S
m2 plots
XRCOV_AC
Mean relative cover of all AC (ALIEN
(XABCOV_AC/XTOTABCOV) x 100
S, Used in
NNPI
+ CRYP) species across 100-m2 plots
as a percentage of total cover
RIMP_AC
Mean relative importance of all AC
(ALIEN + CRYP) species
(RFRECLAC + XRCOV_AC)/2
S
H_AC
Shannon-Weiner Diversity Index
See H_ALL
S
J_AC
Evenness (Pielou)
See J_ALL
s
D_AC
Simpson Diversity Index
See D_NAT
s
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CALCULATION (listed in Metric
Row),
SPECIES TRAIT TYPE (indicated in
Banner if applicable)
METRIC TYPE
METRIC NAME
METRIC DESCRIPTION
(C = condition,
S = stress)
Section 2
FLORISTIC QUALITY
Trait Information =
Coefficients of Conservatism
(see Section 5.9); Native
Status (see Table 5-5)
Equation 1
General formula for Mean C
c = fccc,)/Nj
CGy - coefficient of conservatism for
each unique species / at site j, N =
number of species at site j
Equation 2
General formula for FQAI
CC,y - coefficient of conservatism for
each unique species / at site j, N =
number of species at site j
FOAI =
ICC,
Equation 3
For weighted Mean C or FQAI
Replace CGy with wCGy, where p,y =
relative frequency or relative cover
wCCij = PjjCC
J V
XC_NAT
Mean Coefficient of Conservatism
with native species only
Equation 1
C
XC_ALL
Mean Coefficient of Conservatism
with all species
Equation 1
C, in PRLW-
VMMI
xc_freclnat
Relative frequency-weighted Mean
Coefficient of Conservatism with
native species only
Equation 1, Equation 3
C
xc_freclaii
Relative frequency-weighted Mean
Coefficient of Conservatism with all
species only
Equation 1, Equation 3
c
XC_COV_NAT
Relative cover-weighted Mean
Coefficient of Conservatism with
native species only
Equation 1, Equation 3
c
XC_COV_AII
Relative cover-weighted Mean
Coefficient of Conservatism with all
species
Equation 1, Equation 3
c
FQAI_NAT
Floristic Quality Index with native
species only
Equation 2
c
FQAI_ALL
Floristic Quality Index with all
Equation 2
C, in PRLH-
species
VMMI, 2011
National
VMMI
FQAI_FRECLNAT
Proportional frequency-weighted
Floristic Quality Assessment Index
with native species only
Equation 2, Equation 3
C
FQAI_FRECLALL
Proportional frequency-weighted
Floristic Quality Assessment Index
with all species only
Equation 2, Equation 3
C
FQAI_COV_NAT
Proportional cover-weighted
Floristic Quality Assessment Index
with native species only
Equation 2, Equation 3
c
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CALCULATION (listed in Metric
Row),
SPECIES TRAIT TYPE (indicated in
Banner if applicable)
METRIC TYPE
METRIC NAME
METRIC DESCRIPTION
(C = condition,
S = stress)
FQAI_COV_ALL
Proportional cover-weighted
Floristic Quality Assessment Index
with all species
Equation 2, Equation 3
C
Section 3
STRESS
TOLERANCE/SENSITIVITY
Trait Information =
Coefficients of Conservatism
(Section 5.9)
N_HSEN
Number (Richness) Highly Sensitive
Count unique species that meet
r
Species; C-value >= 9
criterion across 100-m2 plots
N_SEN
Number (Richness) Sensitive
Count unique species that meet
r
Species; C -value >= 7
criterion across 100-m2 plots
NJSEN
Number (Richness) Intermediate
Count unique species that meet
r
Sensitivity Species; C-value = 5 to 6
criterion across 100-m2 plots
N_TOL
Number (Richness) Tolerant
Count unique species that meet
C, in
Species; C -value <= 4
criterion across 100-m2 plots
PRLH-VMMI,
2011 National
VMMI
N_HTOL
Number (Richness) Highly Tolerant
Count unique species that meet
r
Species; C-value <= 2
criterion across 100-m2 plots
PCTN_HSEN
Percent Richness Highly Sensitive
Species; C-value >= 9
(N_HSEN/TOTN_SPP) x 100
c
PCTN_SEN
Percent Richness Sensitive Species;
C-value >= 7
(N_SEN/TOTN_SPP) x 100
c
PCTN_ISEN
Percent Richness Intermediate
Sensitivity Species; C-value = 5 to 6
(N_ISEN/TOTN_SPP) x 100
C,
in EW-VMMI
PCTN_TOL
Percent Richness Tolerant Species;
C-value <= 4
(N_TOL/TOTN_SPP) x 100
C
PCTN_HTOL
Percent Richness Highly Tolerant
Species; C-value <= 2
(N_HTO L/TOTN_S P P) x 100
c
XABCOV_HSEN
Absolute Mean Cover Highly
S COVER of species with C-value
r
Sensitive Species; C-value >= 9
>= 9 across 5 plots/5 plots
L,
XABCOV_SEN
Absolute Mean Cover Sensitive
S COVER of species with C-value
r
Species; C-value >= 7
>= 7 across 5 plots/5 plots
L,
XABCOV_ISEN
Absolute Mean Cover Intermediate
S COVER of species with C-value =
r
Sensitivity Species; C-value= 5 to 6
5 or 6 across 5 plots/5 plots
L,
XABCOV_TOL
Absolute Mean Cover Tolerant
S COVER of species with C-value
r
Species; C-value <= 4
<= 4 across 5 plots/5 plots
L,
XABCOV_HTOL
Absolute Mean Cover Highly
S COVER of species with C-value
r
Tolerant Species; C-value <= 2
<= 2 across 5 plots/5 plots
L,
XRCOV_HSEN
Relative Mean Cover Highly
(XABCOV_HSEN/XTOTABCOV) x
r
Sensitive Species; C >= 9
100
XRCOV_SEN
Relative Mean Cover Sensitive
Species; C-value >= 7
(XABCOV_S E N/XTOTABCOV) x 100
c,
in EH-VMMI
XRCOV_ISEN
Relative Mean Cover Intermediate
(XABCOVJSEN/XTOTABCOV) x
r
Sensitivity Species; C-value = 5 to 6
100
XRCOV_TOL
Relative Mean Cover Tolerant
Species; C-value <= 4
(XABCOV_TOL/XTOTABCOV) x 100
c
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CALCULATION (listed in Metric
Row),
SPECIES TRAIT TYPE (indicated in
Banner if applicable)
METRIC TYPE
METRIC NAME
METRIC DESCRIPTION
(C = condition,
S = stress)
XRCOV_HTOL
Relative Mean Cover Highly
(XABCOV_HTOL/XTOTABCOV) x
c,
Tolerant Species; C-value <= 2
100
in EH-VMMI
SECTION 4
HYDROPHYTIC
CHARACTERISTICS OF
VEGETATION
Trait Information = Wetland
Indicator Status (WIS): Obligate
( 'Bl), Facultative Wetland
( OiCV ), Facultative (FAC),
Facultative Upland ( ACL ), Upland
( PI) (Table 5-3); Native Status
(Table 5-5)
N_OBL
Richness (number) of Obligate
Count unique OBL species across
r
species
100-m2 plots
N_FACW
Richness (number) of Facultative
Count unique FACW species
r
Wetland species
across 100-m2 plots
N_FAC
Richness (number) of Facultative
Count unique FACU species across
r
species
100-m2 plots
N_FACU
Richness (number) of Facultative
Count unique FAC species across
r
Upland species
100-m2 plots
N_UPL
Richness (number) of UPL species =
Count unique UPL species across
r
UPL
100-m2 plots
N_OBL_FACW
Richness (number) of Obligate +
Count unique OBL+ FACW species
r
Facultative Wetland species
across 100-m2 plots
N_OBL_FACW_FAC
Richness (number) of Obligate +
Count unique OBL+ FACW + FAC
r
Facultative Wetland species
species across 100-m2 plots
L,
N_FAC_FACU
Richness (number) of Facultative +
Count unique FAC + FACU species
r
Facultative Upland species
across 100-m2 plots
PCTN_OBL
Percent richness of Obligate species
(N_OBL/TOTN_SPP) x 100
c
PCTN_FACW
Percent richness of Facultative
Wetland species
(N_FACW/TOTN_SPP) x 100
c
PCTN_FAC
Percent richness of Facultative
species
(N_FAC/TOTN_SPP) x 100
c
PCTN_FACU
Percent richness of Facultative
Upland species
(N_FACU/TOTN_SPP) x100
c
PCTN_UPL
Percent richness of UPL (= UPL+ NL)
species
(N_UPL/TOTN_SPP) x 100
c
PCTN_OBL_FACW
Percent richness (number) of
(N_OBL_FACW/TOTN_SPP) x 100
C, in
PRLH-VMMI
Obligate + Facultative Wetland
species
PCTN_OBL_FACW_F
Percent richness (number) of
(N_OBL_FACW_FAC/TOTN_SPP) x
AC
Obligate + Facultative Wetland
species
100
C
PCTN_FAC_FACU
Percent richness (number) of
Facultative + Facultative Upland
species
(N_FAC_FACU/TOTN_SPP) x 100
C
XABCOV_OBL
Mean Absolute Cover of Obligate
S COVER of OBL species across 5
r
species
plots/5 plots
L,
XABCOV_FACW
Mean Absolute Cover of Facultative
S COVER of FACW species across
Wetland species
5 plots/5 plots
L
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CALCULATION (listed in Metric
Row),
SPECIES TRAIT TYPE (indicated in
Banner if applicable)
METRIC TYPE
METRIC NAME
METRIC DESCRIPTION
(C = condition,
S = stress)
XABCOV_FAC
Mean Absolute Cover of Facultative
S COVER of FAC species across 5
c
species
plots/5 plots
XABCOV_FACU
Mean Absolute Cover of Facultative
S COVER of FACU species across 5
r
Upland species
plots/5 plots
L,
XABCOV_UPL
Mean Absolute Cover of UPL
S COVER of UPL species across 5
r
species
plots/5 plots
XABCOV
Mean Absolute Cover of Obligate +
S COVER of OBL and FACW
c
_OBL_FACW
Facultative Wetland species
species across 5 plots/5 plots
L
XABCOV
Mean Absolute Cover of Obligate +
S COVER of OBL, FACW, and FAC
c
_OBL_FACW_FAC
Facultative Wetland species
species across 5 plots/5 plots
L
XABCOV FAC FACU
Mean Absolute Cover of Facultative
S COVER of FAC and FACU species
c
+ Facultative Upland species
across 5 plots/5 plots
L
XRCOV_OBL
Mean Relative Cover of Obligate
species
(XABCOV_OBL/XTOTABCOV) x 100
c
XRCOV_FACW
Mean Relative Cover of Facultative
(XABCOV_FACW/XTOTABCOV) x
r
Wetland species
100
XRCOV_FAC
Mean Relative Cover of Facultative
species
(XABCOV_FAC/XTOTABCOV) x 100
c
XRCOV_FACU
Mean Relative Cover of Facultative
(XABCOV_FACU/XTOTABCOV) x
r
Upland species
100
XRCOV_UPL
Mean Relative Cover of UPL (= UPL)
species
(XABCOV_UPL/XTOTABCOV) x 100
c
XRCOV OBL FACW
Mean Relative Cover of Obligate +
(XABCOV _OBL_FACW
r
Facultative Wetland species
/XTOTABCOV) x 100
L,
XRCOV_OBL_FACW
Mean Relative Cover of Obligate +
(XABCOV _OBL_FACW_FAC/
_FAC
Facultative Wetland + Facultative
species
XTOTABCOV) x 100
c
XRCOV_FAC_FACU
Mean Relative Cover of Obligate +
(XABCOV _FAC_FACU/
Facultative Wetland + Facultative
XTOTABCOV) x 100
c
species
WETIND_COV_ Wetland Index, Cover Weighted - all
ALL species
lij = Importance Value = Mean
absolute cover species / in site j. Ei =
Ecological score for species based
on WIS (OBL = 1, FACW = 2, FAC = 3,
FACU = 4, UPL = 5)
WI
WETIND_FREQ_
ALL
Wetland Index, Frequency
Weighted - all species
lij = Importance Value = Frequency
for species / in site j. Ei = Ecological
score for species based on WIS (OBL
= 1, FACW = 2, FAC = 3, FACU = 4,
UPL = 5)
WI
i= 1
Ģ= 1
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METRIC NAME
WETIND_
COV NAT
METRIC DESCRIPTION
Wetland Index, Cover Weighted -
native species only
lij = Importance Value = Mean
absolute cover for species / in site j.
Ei = Ecological score for species
based on WIS (OBL = 1, FACW = 2,
FAC = 3, FACU = 4, UPL = 5)
CALCULATION (listed in Metric
Row),
SPECIES TRAIT TYPE (indicated in
Banner if applicable)
WI
Ģ= 1
i= 1
WETIND_ Wetland Index, Frequency
FREQ_NAT Weighted - native species only
lij = Importance Value = Frequency
for species / in site j. Ei = Ecological
score for species based on WIS (OBL
= 1, FACW = 2, FAC = 3, FACU = 4,
UPL = 5)
WI
= I E-
i= 1
WETIND2_COV_ Wetland Index, Cover Weighted - all
ALL species
lij = Importance Value = Mean
absolute cover species / in site j. Ei =
Ecological score for species based
on WIS (OBL = 5, FACW = 4, FAC = 3,
FACU =2, UPL = 1)
""= ZwZ'ŧ
Ģ= 1 ' i= 1
WETIND2_FRECL
ALL
Wetland Index, Frequency
Weighted - all species
lij = Importance Value = Frequency
for species / in site j. Ei = Ecological
score for species based on WIS (OBL
= 5, FACW = 4, FAC = 3, FACU =2,
UPL = 1)
f if
M"=XWl'Ŧ
Ģ= 1
:=1
WETIND2_
COV NAT
Wetland Index, Cover Weighted -
native species only
lij = Importance Value = Mean
absolute cover for species / in site j.
Ei = Ecological score for species
based on WIS (OBL = 5, FACW = 4,
FAC = 3, FACU =2, UPL= 1)
WI
=zWz,<'
i= 1
i= 1
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CALCULATION (listed in Metric
Row),
SPECIES TRAIT TYPE (indicated in
Banner if applicable)
METRIC TYPE
METRIC NAME
METRIC DESCRIPTION
(C = condition,
S = stress)
WETIND2_
Wetland Index, Frequency
freclnat
Weighted - native species only
lij = Importance Value = Frequency
p i p
for species / in site j. Ei = Ecological
C
score for species based on WIS (OBL
= 5, FACW = 4, FAC = 3, FACU =2,
UPL = 1)
N OBLFACW AC
Number of Alien + Cryptogenic
Count unique ALIEN and CRYP OBL
Obligate and facultative wetland
and FACW species across 100-m2
S
species
plots
XABCOV_
Mean Absolute Cover of Alien +
S COVER of ALIEN and CRYP OBL
OBLFACW_AC
Cryptogenic Obligate and
and FACW species across 5 plots/5
Facultative Wetland species
plots
S
XRCOV
Mean Relative Cover of Alien +
(XABCOV_OBLFACW_AC/
OBLFACW_AC
Cryptogenic Obligate and
Facultative Wetland species
XTOTABCOV) x 100
S
SECTION 5
LIFE HISTORY
SECTION 5.1
GROWTH-HABIT
Trait Information = Growth-habit
(Table 5-1); Native Status (Table
5-5)
N_GRAMINOID
Graminoid richness
Count unique GRAMINOID species
across 100-m2 plots
C
N_GRAMINOID_
Native Graminoid richness
Count unique native (NAT)
NAT
GRAMINOID species across 100-
m2 plots
C
N_GRAMINOID_
Alien and cryptogenic Graminoid
Count unique ALIEN and CRYP
AC
richness
GRAMINOID species across 100-
m2 plots
S
N_FORB
Forb richness
Count unique FORB species across
100-m2 plots
C
N_FORB_NAT
Native Forb richness
Count unique native (NAT) FORB
species across 100-m2 plots
C
N_FORB_AC
Alien and cryptogenic Forb richness
Count unique ALIEN and CRYP
FORB species across 100-m2 plots
S
N_HERB
Herbaceous plant (FORB +
GRAMINOID) species richness
N_FORB+ N_GRAMINOID
C
N_HERB_NAT
Native Herbaceous species richness
N_FORB_NAT+
N_GRAMINOID_NAT
C
N_HERB_AC
Alien and cryptogenic Herbaceous
richness
N_FORB_AC+ N_GRAMINOID_AC
S
N_SSHRUB_
Subshrub-forb richness
Count unique SUBSHRUB-FORB
r
FORB
species across 100-m2 plots
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CALCULATION (listed in Metric
Row),
SPECIES TRAIT TYPE (indicated in
Banner if applicable)
METRIC TYPE
METRIC NAME
METRIC DESCRIPTION
(C = condition,
S = stress)
N_SSHRUB_
Subshrub-shrub richness
Count unique SUBSHRUB-SHRUB
c
SHRUB
species across 100-m2 plots
N_SHRUB
Shrub richness
Count unique SHRUB species
across 100-m2 plots
c
N SHRUB
Combined Shrub growth-habits
N SHRUB+N SSHRUB SHRUB +
r
COMB
richness
N SSHRUB-FORB
L,
N SHRUB
Native richness of Combined Shrub
Count unique native (NAT)
COMB_NAT
growth-habits richness
SHRUB_COMB species across 100-
m2 plots
c
N_SHRUB_
Alien and cryptogenic richness for
Count unique ALIEN and CRYP
COMB_AC
Combined Shrub growth-habits
SHRUB_COMB species across 100-
m2 plots
s
N_TREE_SHRUB
Tree-Shrub richness
Count unique TREE-SHRUB species
across 100-m2 plots
c
N_TREE
Tree richness
Count unique TREE species across
100-m2 plots
c
N_TREE_COMB
Combined Tree and Tree-Shrub
richness
N_TREE_SHRUB + N_TREE
c
N TREE
Combined Tree and Tree-Shrub
Count unique native (NAT)
COMB_NAT
richness
TREE_COMB species across 100-
m2 plots
c
N TREE
Combined Tree and Tree-Shrub
Count unique ALIEN and CRYP
COMB_AC
richness
TREE_COMB species across 100-
m2 plots
s
N_VINE
Vine richness
Count unique VINE species across
100-m2 plots
c
N_VINE_NAT
Vine richness
Count unique native (NAT) VINE
species across 100-m2 plots
c
N_VINE_AC
Vine richness
Count unique ALIEN and CRYP
VINE species across 100-m2 plots
s
N_VINE_SHRUB
Vine-Shrub richness
Count unique a VINE-SHRUB
species across 100-m2 plots
c
N_VINE_
Native Vine-Shrub richness
Count unique native (NAT) VINE-
SHRUB_NAT
SHRUB species across 100-m2
plots
c
N_VINE_
Alien and cryptogenic Vine-Shrub
Count unique ALIEN and CRYP
SHRUB_AC
richness
VINE-SHRUB species across 100-
m2 plots
s
N_VINE_ALL
Vine-All richness
Count unique a VINE_ALL species
across 100-m2 plots
c
N_VINE_ALL_NAT
Native Vine-All richness
Count unique native (NAT)
VINE_ALL species across 100-m2
plots
c
N_VINE_ALL_AC
Alien and cryptogenic Vine-Shrub
Count unique ALIEN and CRYP
richness
VINE_ALL species across 100-m2
plots
s
PCTN_
Graminoid percent richness
(N_GRAMINOID/TOTN_SPP) x 100
r
GRAMINOID
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CALCULATION (listed in Metric
Row),
SPECIES TRAIT TYPE (indicated in
Banner if applicable)
METRIC TYPE
METRIC NAME
METRIC DESCRIPTION
(C = condition,
S = stress)
PCTN_
Native Graminoid percent richness
(N_GRAMINOID_NAT/TOTN_SPP)
c
GRAMINOID NAT
x 100
PCTN_
Graminoid percent richness
(N_GRAM 1 NO 1 D_AC/TOTN_SPP) x
C
GRAMINOID AC
100
J
PCTN_FORB
Forb percent richness
(N_FORB/TOTN_SPP) x 100
c
PCTN_FORB_
Native Forb percent richness
(N_FORB_NAT/TOTN_SPP) x 100
r
NAT
PCTN_FORB_AC
Alien and cryptogenic Forb percent
richness
(N_FORB_AC/TOTN_SPP) x 100
s
PCTN_HERB
Percent Herbaceous (FORB +
GRAMINOID) richness
(N_HERB/TOTN_SPP) x 100
c
PCTN HERB
Percent native Herbaceous richness
(N_HERB_NAT/TOTN_SPP) x 100
r
NAT
L,
PCTN_HERB_
Percent alien and cryptogenic
(N_HERB_AC/TOTN_SPP) x 100
c
AC
Herbaceous richness
j
PCTN_SSHRUB_
Subshrub-Forb percent richness
(N_SSHRUB_FORB/TOTN_SPP) x
r
FORB
100
L,
PCTN_SSHRUB_
Subshrub-Shrub percent richness
(N_SSHRUB/TOTN_SPP) x 100
r
SHRUB
PCTN_SHRUB
Shrub percent richness
(N_SHRUB/TOTN_SPP) x 100
C
PCTN_SHRUB_
Combined Shrub richness
(N_S H RU B_COM B/TOTN_S P P) x
r
COMB
100
PCTN SHRUB
Percent native richness of
(N_S H RU B_COMB_NAT/TOTN_S P
r
COMB NAT
Combined Shrub growth-habits
P) x 100
L,
PCTN_SHRUB_
Percent alien and cryptogenic
(N_SHRUB_COMB_AC/TOTN_SPP)
COMB_AC
richness for Combined Shrub
growth-habits
x 100
s
PCTN_TREE_
Tree-Shrub percent richness
(N_TREE_SHRUB/TOTN_SPP) x
r
SHRUB
100
PCTN_TREE
Tree percent richness
(N_TREE/TOTN_SPP) x 100
c
PCTN_TREE_
Combined Tree and Tree-Shrub
(N_TREE_COMB/TOTN_SPP) x 100
r
COMB
percent richness
PCTN_TREE_
Combined Tree and Tree-Shrub
(N_TREE_COMB_NAT/TOTN_SPP)
r
COMB NAT
percent richness
x 100
PCTN TREE
Combined Tree and Tree-Shrub
(N_TRE E_CO M B_AC/TOTN_S P P) x
c
COMB AC
percent richness
100
j
PCTN_VINE
Vine percent richness
(N_VINE/TOTN_SPP) x 100
c
PCTN_VINE_NAT
Native Vine percent richness
(N_VINE_NAT/TOTN_SPP) x 100
c
PCTN_VINE_AC
Alien and cryptogenic Vine percent
richness
(N_VINE_AC/TOTN_SPP) x 100
s
PCTN_VINE_
Vine-Shrub percent richness
(N_VINE_SHRUB/TOTN_SPP) x 100
r
SHRUB
PCTN_VINE_
Native Vine-Shrub percent richness
(N_VINE_SHRUB_NAT/TOTN_SPP)
c
SHRUB_NAT
x 100
PCTN_VINE_
Alien and Cryptogenic Vine-Shrub
(N_VINE_SHRUB_AC/TOTN_SPP) x
C
SHRUB_AC
percent richness
100
3
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CALCULATION (listed in Metric
Row),
SPECIES TRAIT TYPE (indicated in
Banner if applicable)
METRIC TYPE
METRIC NAME
METRIC DESCRIPTION
(C = condition,
S = stress)
PCTN_VINE_ALL
All-Vine percent richness
(N_VINE_ALL/TOTN_SPP) x 100
C
PCTN_VI N E_ALL_N A
All-Vine native percent richness
(N_VINE_ALL_NAT/TOTN_SPP) x
r
T
100
PCTN_VI N E_ALL_AC
All-Vine alien and cryptogenic
(N_VINE_ALL_AC/TOTN_SPP) x
c
percent richness
100
j
XABCOV_
Mean absolute Graminoid cover
S COVER of GRAMINOID species
c
GRAMINOID
across 5 plots/5 plots
L
XABCOV_
Mean absolute native Graminoid
S COVER of GRAMINOID NAT
c
GRAMINOID_NAT
cover
species across 5 plots/5 plots
L
XABCOV_
Mean absolute alien and
S COVER of GRAMINOID ALIEN
GRAMINOID_AC
cryptogenic Graminoid cover
and CRYP species across 5 plots/5
plots
s
XABCOV_FORB
Mean absolute FORB cover
S COVER of FORB species across 5
plots/5 plots
c
XABCOV_FORB_
Mean absolute native FORB cover
S COVER of NAT FORB species
c
NAT
across 5 plots/5 plots
L
XABCOV_FORB_
Mean absolute alien and
S COVER of ALIEN and CRYP FORB
c
AC
cryptogenic FORB cover
species across 5 plots/5 plots
o
XABCOV HERB
Mean absolute Herbaceous species
XABCOV FORB +
c
cover (FORB + GRAMINOID)
XABCOV GRAMINOID
L
XABCOV HERB
Mean absolute native Herbaceous
XABCOV FORB NAT +
r
NAT
cover
XABCOV GRAMINOID NAT
L,
XABCOV HERB
Mean relative Herbaceous alien and
XABCOV FORB AC +
c
AC
cryptogenic cover
XABCOV GRAMINOID AC
j
XABCOV_
Mean absolute Subshrub-Forb
S COVER of SUBSHRUB-FORB
r
SSHRUB_FORB
cover
species across 5 plots/5 plots
L,
XABCOV
Mean absolute Subshrub-Shrub
S COVER SUBSHRUB-SHRUB
c
SSHRUB_SHRUB
cover
species across 5 plots/5 plots
L
XABCOV_SHRUB
Mean absolute Shrub cover
S COVER of SHRUB species across
5 plots/5 plots
c
XABCOV_
Combined Shrub growth-habits
S COVER of SHRUB_COMB species
r
SHRUB_COMB
absolute cover
across 5 plots/5 plots
L,
XABCOV SHRUB
Mean absolute native Combined
S COVER of NAT SHRUB-COMB
c
COMB_NAT
Shrub growth-habits cover
species across 5 plots/5 plots
L
XABCOV_SHRUB_
Mean absolute alien and
S COVER of ALIEN and CRYP
COMB_AC
cryptogenic Combined Shrub
growth-habits cover
SHRUB_COMB species across 5
plots/5 plots
s
XABCOV TREE
Mean absolute Tree-Shrub cover
S COVER of TREE-SHRUB species
c
SHRUB
across 5 plots/5 plots
L
XABCOV_TREE
Mean absolute Tree cover
S COVER of TREE species across 5
plots/5 plots
c
XABCOV_TREE_
Combined Tree and Tree-Shrub
S COVER of TREE_COMB species
c
COMB
absolute cover
across 5 plots/5 plots
L
XABCOV_TREE_
Combined native Tree and Tree-
S COVER of NAT TREE_COMB
c
COMB_NAT
Shrub absolute cover
species across 5 plots/5 plots
L
2022
National Wetland Condition Assessment: 2016 Technical Support Document
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CALCULATION (listed in Metric
Row),
SPECIES TRAIT TYPE (indicated in
Banner if applicable)
METRIC TYPE
METRIC NAME
METRIC DESCRIPTION
(C = condition,
S = stress)
XABCOV_TREE_
Combined alien and cryptogenic
S COVER of ALIEN and CRYP
COMB_AC
Tree and Tree-Shrub absolute cover
TREE_COMB species across 5
plots/5 plots
S
XABCOV_VINE
Mean absolute Vine cover
S COVER of VINE species across 5
plots/5 plots
C
XABCOV_VINE_
Mean native absolute Vine cover
S COVER of NAT VINE species
c
NAT
across 5 plots/5 plots
L
XABCOV_VINE_
Mean alien and cryptogenic
S COVER of ALIEN and CRYP VINE
c
AC
absolute Vine cover
species across 5 plots/5 plots
o
XABCOV_VINE_
Mean absolute Vine-Shrub cover
S COVER of VINE-SHRUB species
r
SHRUB
across 5 plots/5 plots
L,
XABCOV_VINE_
Mean absolute native Vine-Shrub
S COVER of NAT VINE-SHRUB
c
SHRUB_NAT
cover
species across 5 plots/5 plots
L
XABCOV_VINE_
Mean absolute alien and
S COVER of ALIEN and CRYP VINE-
SHRUB_AC
cryptogenic Vine-Shrub cover
SHRUB species across 5 plots/5
plots
s
XABCOV_VINE_
Mean absolute Vine-ALL cover
S COVER of VINE-ALL species
c
ALL
across 5 plots/5 plots
L
XABCOV_VINE_
Mean absolute native Vine-ALL
S COVER of NAT VINE-ALL species
c
ALL_NAT
cover
across 5 plots/5 plots
L
XABCOV_VINE_
Mean absolute alien and
S COVER of ALIEN and CRYP VINE-
c
ALL_AC
cryptogenic Vine-ALL cover
ALL species across 5 plots/5 plots
o
XRCOV_
Mean relative Graminoid cover
(XABCOV_GRAMINOID/
C,
GRAMINOID
XTOTABCOV) x 100
in EW-VMMI
XRCOV
Mean relative native Graminoid
(XABCOV_GRAMINOID_NAT/
c
GRAMINOID NAT
cover
XTOTABCOV) x 100
L
XRCOV_
Mean relative alien and cryptogenic
(XABCOV_GRAMINOID_AC/
c
GRAMINOID AC
Graminoid cover
XTOTABCOV) x 100
j
XRCOV_FORB
Mean relative Forb cover
(XABCOV_FORB/XTOTABCOV) x
100
C,
in EH-VMMI
XRCOV
Mean relative native Forb cover
(XABCOV_FORB_NAT/
c
FORB NAT
XTOTABCOV) x 100
XRCOV_FORB_AC
Mean relative alien and cryptogenic
(XABCOV_FORB_AC/XTOTABCOV)
r
Forb cover
x 100
XRCOV_HERB
Mean relative Herbaceous (FORB +
(XABCOV_HERB/XTOTABCOV) x
r
GRAMINOID) cover
100
XRCOV
Mean relative native Herbaceous
(XABCOV_HERB_NAT/
r
HERB NAT
cover
XTOTABCOV) x 100
L,
XRCOV_HERB_AC
Mean relative alien and cryptogenic
(XABCOV_H ERB_AC/XTOTABCOV)
C
Herbaceous cover
x 100
3
XRCOV_SSHRUB_
Mean relative Subshrub-Forb cover
(XABCOV_SSHRUB_FORB/
r
FORB
XTOTABCOV) x 100
L,
XRCOV_SSHRUB_
Mean relative Subshrub-Shrub
(XABCOV_SS H RU B_S H RU B/
c
SHRUB
cover
XTOTABCOV) x 100
L
XRCOV_SHRUB
Mean relative Shrub cover
(XABCOV_SHRUB/XTOTABCOV) x
100
c
2022
National Wetland Condition Assessment: 2016 Technical Support Document
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CALCULATION (listed in Metric
Row),
SPECIES TRAIT TYPE (indicated in
Banner if applicable)
METRIC TYPE
METRIC NAME
METRIC DESCRIPTION
(C = condition,
S = stress)
XRCOV SHRUB
Mean relative Combined Shrub
(XABCOV_SHRU B_COMB/
c
COMB
growth-habits cover
XTOTABCOV) x 100
L
XRCOV SHRUB
Mean relative native Combined
(XABCOV_S H RU B_COM B_N AT/
c
COMB NAT
Shrub growth-habits cover
XTOTABCOV) x 100
L
XRCOV_SHRUB_
Mean relative alien and cryptogenic
(XABCOV_SHRU B_COM B_AC/
COMB_AC
Combined Shrub growth-habits
cover
XTOTABCOV) x 100
s
XRCOV TREE
Mean relative Tree-Shrub cover
(XABCOV_TRE E_S H RU B/
r
SHRUB
XTOTABCOV) x 100
L,
XRCOV_TREE
Mean relative Tree cover
(XABCOV_TREE/XTOTABCOV) x
100
c
XRCOV TREE
Mean relative Combined Tree and
(XABCOV_TRE E_CO M B/
c
COMB
Tree-Shrub cover
XTOTABCOV) x 100
L
XRCOV TREE
Mean relative Combined Tree and
(XABCOV_TREE_COMB_NAT/
c
COMB NAT
Tree-Shrub cover
XTOTABCOV) x 100
L
XRCOV TREE
Mean relative Combined Tree and
(XABCOV_TREE_COMB_AC/
c
COMB AC
Tree-Shrub cover
XTOTABCOV) x 100
j
XRCOV_VINE
Mean relative Vine cover
(XABCOV_VINE/XTOTABCOV) x
100
c
XRCOV_VINE_
Mean native relative Vine cover
(XABCOV_VI N E_N AT/XTOTABCOV)
r
NAT
x 100
XRCOV_VINE_
Mean alien and cryptogenic relative
(XABCOV_VI N E_AC/XTOTABCOV)
c
AC
Vine cover
x 100
o
XRCOV VINE
Mean relative Vine-Shrub cover
(XABCOV_VI N E_S H RU B/
c
SHRUB
XTOTABCOV) x 100
L
XRCOV VINE
Mean native relative Vine-Shrub
(XABCOV_VI N E_S H RU B_N AT/
r
SHRUB NAT
cover
XTOTABCOV) x 100
L,
XRCOV_VINE_
Mean alien and cryptogenic relative
(XABCOV_VI N E_S H RU B_AC/
C
SHRUB AC
Vine-Shrub cover
XTOTABCOV) x 100
J
XRCOV VINE
Mean relative Vine-ALL cover
(XABCOV_VINE_ALL/
c
ALL
XTOTABCOV) x 100
L
XRCOV VINE
Mean native relative Vine-ALL cover
(XABCOV_VI N E_ALL_N AT/
c
ALL NAT
XTOTABCOV) x 100
L
XRCOV_VINE_
Mean alien and cryptogenic relative
(XABCOV_VI N E_ALL_AC/
c
ALL AC
Vine-ALL cover
XTOTABCOV) x 100
j
Section 5.2
DURATION
Trait Information = Duration
(Table 5-2); Native Status (Table
5-5)
N_ANNUAL
Annual species richness
Count unique ANNUAL species
across 100-m2 plots
C,
In EH-VMMI
N_ANNUAL_NAT
Native Annual richness
Count unique NAT ANNUAL
species across 100-m2 plots
C
N_ANNUAL_AC
Alien and cryptogenic Annual
Count unique ALIEN and CRYP
richness
ANNUAL species across 100-m2
plots
S
N_ANN_BIEN
Annual-Biennial richness
Count unique ANN_BIEN species
across 100-m2 plots
c
157
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National Wetland Condition Assessment: 2016 Technical Support Document
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CALCULATION (listed in Metric
Row),
SPECIES TRAIT TYPE (indicated in
Banner if applicable)
METRIC TYPE
METRIC NAME
METRIC DESCRIPTION
(C = condition,
S = stress)
N_ANN_
Native Annual-Biennial richness
Count unique NAT ANN_BIEN
c
BIEN NAT
species across 100-m2 plots
N_ANN_
Alien and cryptogenic Annual-
Count unique ALIEN and CRYP
BIEN_AC
Biennial richness
ANN_BIEN species across 100-m2
plots
s
N_ANN_PEREN
Annual-Perennial richness
Count unique ANN_PEREN species
across 100-m2 plots
c
N_ANN_
Native Annual-Perennial richness
Count unique NAT ANN_PEREN
r
PEREN NAT
species across 100-m2 plots
N_ANN_
Alien and cryptogenic Annual-
Count unique ALIEN and CRYP
PEREN_AC
Perennial richness
ANN_PEREN species across 100-
m2 plots
S
N_PERENNIAL
Perennial richness
Count unique PERENNIAL species
across 100-m2 plots
C
N_PERENNIAL_
Native Perennial richness
Count unique NAT PERENNIAL
r
NAT
species across 100-m2 plots
N_PERENNIAL_AC
Alien and cryptogenic Perennial
Count unique ALIEN and CRYP
richness
PERENNIAL species across 100-m2
plots
S
PCTN_ANNUAL
Percent Annual richness
(N_ANNUAL/TOTN_SPP) x 100
C
PCTN ANNUAL
Percent native Annual richness
(N_ANNUAL_NAT/TOTN_SPP) x
r
NAT
100
L,
PCTN_ANNUAL_
Percent alien and cryptogenic
(N_ANNUAL_AC/TOTN_SPP) x 100
c
AC
Annual richness
j
PCTN_ANN_BIEN
Percent Annual-Biennial richness
(N_ANN_BIEN/TOTN_SPP) x 100
c
PCTN ANN
Percent native Annual-Biennial
(N_ANN_BIEN_NAT/TOTN_SPP) x
c
BIEN NAT
richness
100
L
PCTN_ANN_
Percent alien and cryptogenic
(N_ANN_BIEN_AC/TOTN_SPP) x
c
BIEN AC
Annual-Biennial richness
100
3
PCTN_ANN_
Percent Annual-Perennial richness
(N_ANN_PEREN/TOTN_SPP) x 100
r
PEREN
PCTN ANN
Percent native Annual-Perennial
(N_ANN_PEREN_NAT/TOTN_SPP)
r
PEREN NAT
richness
x 100
L,
PCTN_ANN_
Percent alien and cryptogenic
(N_ANN_PEREN_AC/TOTN_SPP) x
C
PEREN AC
Annual-Perennial richness
100
J
PCTN_PERENNIAL
Percent Perennial richness
(N_PERENNIAL/TOTN_SPP) x 100
c
PCTN
Percent native Perennial richness
(N_PERENNIAL_NAT/TOTN_SPP) x
c
PERENNIAL NAT
100
L
PCTN_
Percent alien and cryptogenic
(N_PERENNIAL_AC/TOTN_SPP) x
c
PERENNIAL AC
Perennial richness
100
j
XABCOV_
Mean absolute Annual cover
S COVER of ANNUAL species
c
ANNUAL
across 5 plots/5 plots
L
XABCOV_
Mean absolute native Annual cover
S COVER of NAT ANNUAL species
c
ANNUAL_NAT
across 5 plots/5 plots
L
XABCOV_
Mean absolute alien and
S COVER of ALIEN and CRYP
ANNUAL_AC
cryptogenic Annual cover
ANNUAL species across 5 plots/5
plots
s
158
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National Wetland Condition Assessment: 2016 Technical Support Document
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CALCULATION (listed in Metric
Row),
SPECIES TRAIT TYPE (indicated in
Banner if applicable)
METRIC TYPE
METRIC NAME
METRIC DESCRIPTION
(C = condition,
S = stress)
XABCOV_ANN_
Mean absolute Annual-Biennial
S COVER of ANN_BIEN species
c
BIEN
cover
across 5 plots/5 plots
L
XABCOV_ANN_
Mean absolute native Annual-
S COVER of NAT ANN BIEN
c
BIEN_NAT
Biennial cover
species across 5 plots/5 plots
L
XABCOV_ANN_
Mean absolute alien and
S COVER of ALIEN and CRYP
BIEN_AC
cryptogenic Annual-Biennial cover
ANN_BIEN species across 5 plots/5
plots
s
XABCOV_ANN_
Mean absolute Annual-Perennial
S COVER of ANN_PEREN species
c
PEREN
cover
across 5 plots/5 plots
L
XABCOV_ANN_
Mean absolute native Annual-
S COVER of NAT ANN PEREN
c
PEREN_NAT
Perennial cover
species across 5 plots/5 plots
L
XABCOV_ANN_
Mean absolute alien and
S COVER of ALIEN and CRYP
PEREN_AC
cryptogenic Annual-Perennial cover
ANN_PEREN species across 5
plots/5 plots
s
XABCOV_
Mean absolute Perennial cover
S COVER of PERENNIAL species
c
PERENNIAL
across 5 plots/5 plots
L
XABCOV_
Mean absolute native Perennial
S COVER of NAT PERENNIAL
c
PEREN NIAL_NAT
cover
species across 5 plots/5 plots
L
XABCOV_
Mean absolute alien and
S COVER of ALIEN and CRYP
PEREN NIAL_AC
cryptogenic Perennial cover
PERENNIAL species across 5
plots/5 plots
s
XRCOV_ANNUAL
Mean relative annual cover
(XABCOV_ANNUAL/XTOTABCOV) x
100
c
XRCOV ANNUAL
Mean relative native Annual cover
(XABCOV_ANNUAL_NAT/
r
NAT
XTOTABCOV) x 100
L,
XRCOV_ANNUAL_
Mean relative alien and cryptogenic
(XABCOV_ANNUAL_AC/
C
AC
Annual cover
XTOTABCOV) x 100
J
XRCOV ANN
Mean relative Annual-Biennial
(XABCOV_AN N_BI E N/
r
BIEN
cover
XTOTABCOV) x 100
L,
XRCOV ANN
Mean relative native Annual-
(XABCOV_AN N_BIE N_N AT/
c
BIEN NAT
Biennial cover
XTOTABCOV) x 100
L
XRCOV_ANN_
Mean relative alien and cryptogenic
(XABCOV_AN N_BIE N_AC/
c
BIEN AC
Annual-Biennial cover
XTOTABCOV) x 100
j
XRCOV ANN
Mean relative Annual-Perennial
(XABCOV_ANN_PEREN/
c
PEREN
cover
XTOTABCOV) x 100
L
XRCOV ANN
Mean relative native Annual-
(XABCOV_AN N_PE RE N_NAT/
r
PEREN NAT
Perennial cover
XTOTABCOV) x 100
L,
XRCOV_ANN_
Mean relative alien and cryptogenic
(XABCOV_AN N_P E RE N_AC/
C
PEREN AC
Annual-Perennial cover
XTOTABCOV) x 100
j
XRCOV_
Mean relative Perennial cover
(XABCOV_PE RE N NIAL/
r
PERENNIAL
XTOTABCOV) x 100
L,
XRCOV_
Mean relative native Perennial
(XABCOV_PE RE N N IAL_N AT/
c
PEREN NIAL_NAT
cover
XTOTABCOV) x 100
L
XRCOV_
Mean relative alien and cryptogenic
(XABCOV_PE RE N N IAL_AC/
c
PEREN NIAL_AC
Perennial cover
XTOTABCOV) x 100
j
2022
National Wetland Condition Assessment: 2016 Technical Support Document
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CALCULATION (listed in Metric
Row),
SPECIES TRAIT TYPE (indicated in
Banner if applicable)
METRIC TYPE
METRIC NAME
METRIC DESCRIPTION
(C = condition,
S = stress)
Section 5.3
PLANT CATEGORY
Trait Information = Plant
Category (See Section 5.6.3);
Native Status (Table 5-5)
N_DICOT
Dicot richness
Count unique DICOT species
across 100-m2 plots
C
N_DICOTS_NAT
Native Dicot richness
Count unique NAT DICOT species
across 100-m2 plots
C
N_DICOTS_AUEN
Alien Dicot richness
Count unique ALIEN DICOT species
across 100-m2 plots
S
N_DICOTS_CRYP
Cryptogenic Dicot richness
Count unique CRYP DICOT species
across 100-m2 plots
C
N_DICOTS_AC
Alien and Cryptogenic richness
N_DICOT_ALIEN + N_DICOT_CRYP
S
N_FERN
Fern richness
Count unique FERN species across
100-m2 plots
C
N_FERNS_NAT
Native Fern richness
Count unique native FERN species
across 100-m2 plots
C
N_FERNS_INTR
Introduced FERN species richness
Count unique introduced FERN
species across 100-m2 plots
S
N_GYMNOSPERM
Gymnosperm richness
Count unique GYMNOSPERM
species across 100-m2 plots
C
N_LYCOPOD
Lycopod richness
Count unique LYCOPOD species
across 100-m2 plots
C
N_HORSETAIL
Horsetail richness
Count unique HORSETAIL species
across 100-m2 plots
C
N_MONOCOT
Monocot richness
Count unique MONOCOT species
across 100-m2 plots
C
N_MONOCOTS_
Native Monocot richness
Count unique NAT MONOCOT
r
NAT
species across 100-m2 plots
N_MONOCOTS_
Alien Monocot richness
Count unique ALIEN MONOCOT
c
ALIEN
species across 100-m2 plots
j
N_MONOCOTS_
Cryptogenic Monocot richness
Count unique CRYP MONOCOT
c
CRYP
species across 100-m2 plots
J
N MONOCOTS
Alien and cryptogenic Monocot
N MONOCOT ALIEN +
c
AC
richness
N MONOCOT CRYP
o
PCTN_DICOT
Dicot percent richness
(N_DICOTS/TOTN_SPP) x 100
c
PCTN_DICOTS_
Native Dicot percent richness
(N_DICOTS_NAT/TOTN_SPP) x 100
r
NAT
PCTN_DICOTS_
Alien Dicot percent richness
(N_D 1 COTS_ALI E N/TOTN_S P P) x
c
ALIEN
100
J
PCTN_DICOTS_
Cryptogenic Dicot percent richness
(N_DICOTS_CRYP/TOTN_SPP) x
c
CRYP
100
PCTN_DICOTS_AC
Alien and cryptogenic Dicot percent
richness
(N_D 1 COTS_AC/TOTN_S P P) x 100
S
PCTN_FERN
Fern percent richness
(N_FERNS/TOTN_SPP) x 100
C
PCTN_FERNS_
Native Ferns percent richness
(N_FERNS_NAT/TOTN_SPP) x 100
r
NAT
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CALCULATION (listed in Metric
Row),
SPECIES TRAIT TYPE (indicated in
Banner if applicable)
METRIC TYPE
METRIC NAME
METRIC DESCRIPTION
(C = condition,
S = stress)
PCTN_FERNS_
Introduced Fern percent richness
(N_FERNS_INTR/TOTN_SPP) x 100
C
INTR
J
PCTN_
GYMNOSPERM Percent Richness
(N_GYNOSPERM/TOTN_SPP) x 100
c
GYMNOSPERM
PCTN_LYCOPOD
Lycopod percent richness
(N_LYCOPOD/TOTN_SPP) x 100
c
PCTN_HORSETAIL
Horsetail percent richness
(N_HORSETAIL/TOTN_SPP) x 100
c
PCTN_
Monocot percent richness
(N_MONOCOTS/TOTN_SPP) x 100
C,
MONOCOT
in EW-VMMI
PCTN_
Native Monocot percent richness
(N_MONOCOTS_NAT/TOTN_SPP)
r
MONOCOTS NAT
x 100
PCTN_
Alien Monocot percent richness
(N_MONOCOTS_ALIEN/
MONOCOTS
TOTN_SPP) x 100
s
ALIEN
PCTN_
Cryptogenic Monocot percent
(N_MONOCOTS_CRYP/TOTN_SPP)
MONOCOTS
richness
x 100
s
CRYP
PCTN_
Alien and cryptogenic monocot
(N_MONOCOTS_AC/TOTN_SPP) x
c
MONOCOTS AC
percent richness
100
J
XABCOV_DICOT
Mean absolute cover Dicots
S COVER of DICOT species across
5 plots/5 plots
c
XABCOV_
Mean absolute cover native Dicots
S COVER of NAT DICOT species
c
DICOTS_NAT
across 5 plots/5 plots
L
XABCOV_
Mean absolute cover Alien Dicots
S COVER of ALIEN DICOT species
c
DICOTS_ALIEN
across 5 plots/5 plots
o
XABCOV_
Mean absolute cover cryptogenic
S COVER of CRYP DICOT species
c
DICOTS_CRYP
Dicots
across 5 plots/5 plots
o
XABCOV
Mean absolute cover of alien and
XABCOV DICOTS ALIEN +
c
DICOTS AC
cryptogenic Dicots
XABCOV DICOTS CRYP
J
XABCOV_FERN
Mean absolute cover of Ferns
S COVER of FERN species across 5
plots/5 plots
c
XABCOV_FERNS_
Mean absolute cover of native
S COVER of NAT FERN species
r
NAT
Ferns
across 5 plots/5 plots
L,
XABCOV_FERNS_
Mean absolute cover of introduced
S COVER ol ntroduced INTR FERN
c
INTR
Ferns
species across 5 plots/5 plots
j
XABCOV_
Mean absolute cover of
S COVER of GYMNOSPERM
r
GYMNOSPERM
Gymnosperms
species across 5 plots/5 plots
L,
XABCOV_
Mean absolute cover of Lycopods
S COVER of LYCOPOD species
r
LYCOPOD
across 5 plots/5 plots
L,
XABCOV_
Mean absolute cover of Horsetails
S COVER of HORSETAIL species
c
HORSETAIL
across 5 plots/5 plots
L
XABCOV_
Mean absolute cover of Monocots
S COVER of MONOCOT species
c
MONOCOT
across 5 plots/5 plots
L
XABCOV_
Mean absolute cover of native
S COVER of NAT MONOCOT
r
MONOCOTS_NAT
Monocots
species across 5 plots/5 plots
L
XABCOV_
Mean absolute cover of alien
S COVER of ALIEN MONOCOT
MONOCOTS_
Monocots
species across 5 plots/5 plots
s
ALIEN
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CALCULATION (listed in Metric
Row),
SPECIES TRAIT TYPE (indicated in
Banner if applicable)
METRIC TYPE
METRIC NAME
METRIC DESCRIPTION
(C = condition,
S = stress)
XABCOV_
Mean absolute cover of cryptogenic
S COVER of CRYP MONOCOT
MONOCOTS_
Monocots
species across 5 plots/5 plots
S
CRYP
XABCOV
Mean absolute cover of alien and
XABCOV MONOCOTS ALIEN +
C
MONOCOTS AC
cryptogenic Monocots
XABCOV MONOCOTS CRYP
J
XRCOV_DICOT
Mean relative cover Dicots
(XABCOV_DICOTS/XTOTABCOV) x
100
c
XRCOV DICOTS
Mean relative cover native Dicots
(XABCOV_DICOTS_NAT/
r
NAT
XTOTABCOV) x 100
L,
XRCOV DICOTS
Mean relative cover alien Dicots
(XABCOV_DICOTS_ALIEN/
c
ALIEN
XTOTABCOV) x 100
o
XRCOV_DICOTS_
Mean relative cover cryptogenic
(XABCOV_DICOTS_CRYP/
c
CRYP
Dicots
XTOTABCOV) x 100
J
XRCOV DICOTS
Mean relative cover of alien and
(XABCOV_DICOTS_AC/
c
AC
cryptogenic Dicots
XTOTABCOV) x 100
o
XRCOV_FERN
Mean relative cover of Ferns
(XABCOV_FERNS/
XTOTABCOV) x 100
c
XRCOV FERNS
Mean relative cover of native Ferns
(XABCOV_FERNS_NAT/
r
NAT
XTOTABCOV) x 100
L,
XRCOV FERNS
Mean relative cover of introduced
(XABCOV_FERNS_INTR/
c
INTR
Ferns
XTOTABCOV) x 100
j
XRCOV_
Mean relative cover of
(XABCOV_GYMNOSPERMS/
r
GYMNOSPERM
Gymnosperms
XTOTABCOV) x 100
XRCOV_LYCOPOD
Mean relative cover of Lycopods
(XABCOV_LYCOPODS/
XTOTABCOV) x 100
c
XRCOV_
Mean relative cover of Horsetails
(XABCOV_HORSETAILS/
r
HORSETAIL
XTOTABCOV) x 100
XRCOV_
Mean relative cover of Monocots
(XABCOV_MONOCOTS/
r
MONOCOT
XTOTABCOV) x 100
XRCOV
Mean relative cover of native
(XABCOV_MONOCOTS_NAT/
C, in EH-
MONOCOTS_NAT
Monocots
XTOTABCOV) x 100
VMMI, PRLW-
Ķ,
VMMI, 2011
National
VMMI
XRCOV_
Mean relative cover of alien
(XABCOV_MONOCOTS_ALIEN/
MONOCOTS_
Monocots
XTOTABCOV) x 100
S
ALIEN
XRCOV_
Mean relative cover of cryptogenic
(XABCOV_MONOCOTS_CRYP/
MONOCOTS_
Monocots
XTOTABCOV) x 100
S
CRYP
XRCOV_
Mean relative cover of alien and
(XABCOV_MONOCOTS_AC/
c
MONOCOTS_AC
cryptogenic Monocots
XTOTABCOV) x 100
o
Sections 6 - 8 METRICS BASED ON FIELD DATA FROM FORM V-l: NWCA 2016
VEGETATION PLOT ESTABLISHMENT AND FORM V-3: NWCA 2016
VEGETATION TYPES (FRONT) AND NWCA 2016 GROUND SURFACE
ATTRIBUTES (BACK)
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CALCULATION (listed in Metric
Row),
SPECIES TRAIT TYPE (indicated in
Banner if applicable)
METRIC TYPE
METRIC NAME
METRIC DESCRIPTION
(C = condition,
S = stress)
SECTION 6
WETLAND TYPE HETEROGENEITY
BASED ON PLOT-LEVEL NWCA
WETLAND TYPES (designated as
'Predominant NWCA Wetland
Type' on Form V-l)
N SANDT
Number of unique NWCA Wetland
Count number of unique NWCA
c
Types (WETLAND_TYPE) in AA
WETLAND_TYPE across the 5 plots
DOM_SANDT
Dominant NWCA
WETLAND_TYPI (s) in AA
Select dominant NWCA
WETLAND_TYPE: Most frequent
(greatest number of plots), or in
case of ties, the two most
frequent hyphenated
c
D_SANDT
Simpson's Diversity - Heterogeneity
of NWCA A/ETLAND_TYPI s in AA
s = number of S&T classes present, /
= class i, p = proportion of S&T
Classes belonging to class /
D = t-Jp?
i
c
H_SANDT
Shannon-Wiener - Heterogeneity of
NWCA l/ETLAND_TYPE s in AA
s = number of S&T classes present, /
= class i, p = proportion of S&T
Classes belonging to class /
s
H' =
i
c
J_SANDT
Pielou Evenness - Heterogeneity of
H'
' ~ liiS
NWCA l/ETLAND_TYPE s in AA
c
S = number of S&T classes observed
SECTION 7
VEGETATION STRUCTURE/TYPES
SECTION 7.1
Vascular Strata
N_VASC_STRATA
Number of unique Vascular
Count number of unique vascular
Vegetation Strata across AA
vegetation strata across the 5
plots
c
XN_VASC_
Mean number of vascular
r
STRATA
vegetation strata across plots
RG_VASC_
Range in number of vascular
Maximum - minimum number of
STRATA
vegetation strata found in all 100-
m2 plots
vegetation strata across five 100-
m2 plots
c
XTOTCOV_VASC_
Mean total cover of all vascular
(S cover for all vascular strata
r
STRATA
strata
across all 100-m2 plots)/5 plots
L
FRECL
Frequency Submerged Aquatic
(# of 100-m2 plots in which
SUBMERGED_AQ
Vegetation
SUBMERGED_AQ occurs/5 plots) x
100
c
freclfloating_
Frequency Floating Aquatic
(# of 100-m2 plots in which
AQ
Vegetation
FLOATING_AQoccurs/5 plots) x
100
c
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CALCULATION (listed in Metric
Row),
SPECIES TRAIT TYPE (indicated in
Banner if applicable)
METRIC TYPE
METRIC NAME
METRIC DESCRIPTION
(C = condition,
S = stress)
FRECLUANAS
Frequency Lianas, vines, and
(# of 100-m2 plots in which
r
vascular epiphytes
LIANAS occi s/5 plots) x 100
FRECLVTALL_VEG
Frequency Vegetation > 30m tall
(# of 100-m2 plots in which
VTALL_VEG occurs/5 plots) x 100
c
frecltall_veg
Frequency Vegetation > 15m to
(# of 100-m2 plots in which
r
30m tall
TALL_VEG occurs/5 plots) x 100
freclhmed_
Frequency Vegetation > 5m to 15m
(# of 100-m2 plots in which
r
VEG
tall
HMED_VEG occurs/5 plots) x 100
freclmed_veg
Frequency Vegetation >2m to 5 tall
(# of 100-m2 plots in which
MED_VEG occurs/5 plots) x 100
C
FREQ_SMALL_
Frequency Vegetation 0.5 to 2m tall
(# of 100-m2 plots in which
r
VEG
SMALL_VEG occurs/5 plots) x 100
FRECLVSMALL_
Frequency Vegetation < 0.5m tall
(# of 100-m2 plots in which
VEG
VSMALL_VEG occ rs/5 plots) x
100
C
XCO V_
Mean absolute cover Submerged
S cover of UBMERGED_AQ
r
SUBMERGED_AQ
Aquatic Vegetation
across 5 plots/5 plots
XCO V_
Mean absolute cover Floating
S cover of LOATING AC across 5
r
FLOATING_AQ
Aquatic Vegetation
plots/5 plots
L,
XCOV_LIANAS
Mean absolute cover Lianas, vines,
S cover of IANA! across 5 plots/5
c
and vascular epiphytes
plots
L
XCOV_VTALL_
Mean absolute cover Vegetation >
S cover of 'TALL VE< across 5
c
VEG
30m tall
plots/5 plots
L
XCOV_TALL_VEG
Mean absolute cover Vegetation >
S cover of ALL VEG across 5
c
15m to 30m tall
plots/5 plots
L
XCOV_HMED_
Mean absolute cover Vegetation >
S cover of IMED VEG across 5
c
VEG
5m to 15m tall
plots/5 plots
L
XCOV_MED_VEG
Mean absolute cover Vegetation
S cover of /IED VEG across 5
c
>2m to 5 tall
plots/5 plots
L
XCOV_SMALL_
Mean absolute cover Vegetation 0.5
S cover of MALL VEG across 5
c
VEG
to 2m tall
plots/5 plots
L
XCOV VSMALL
Mean absolute cover Vegetation <
Jcover of VSMALL_VE( across 5
c
VEG
0.5m tall
plots/5 plots
L
IMP_
Importance Submerged Aquatic
(FRECLSUBMERGED_AQ +
r
SUBMERGED_AQ
Vegetation
XCOV_S U BM E RG E D_AQ)/2
IMP_FLOATING_
Importance Floating Aquatic
(freclfloating_aq+
r
AQ
Vegetation
XCOV_FLOATING_AQ)/2
IMP_LIANAS
Importance Lianas, vines, and
vascular epiphytes
(FRECLUANAS + XCOV_LIANAS)/2
C
IMP_VTALL_VEG
Importance Vegetation > 30m tall
(FRECLVTALL_VEG +
XCOV_VTALL_VEG)/2
C
IMP_TALL_VEG
Importance Vegetation > 15m to
(frecltall_veg +
r
30m tall
XCOV_TALL_VEG)/2
IMP_HMED_VEG
Importance Vegetation > 5m to
(freclhmed_veg +
r
15m tall
XCOV_HMED_VEG )/2
IMP_MED_VEG
Importance Vegetation >2m to 5 tall
(FREQJVIED_VEG +
XCOV_MED_VEG)/2
C
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CALCULATION (listed in Metric
Row),
SPECIES TRAIT TYPE (indicated in
Banner if applicable)
METRIC TYPE
METRIC NAME
METRIC DESCRIPTION
(C = condition,
S = stress)
IMP_SMALL_VEG
Importance Vegetation 0.5 to 2m
(FRECLSMALL_VEG +
r
tall
XCOV_SMALL_VEG)/2
IMP_VSMALL_
Importance Vegetation < 0.5m tall
(FRECLVSMALL_VEG +
r
VEG
XCOV_VSMALL_VEG)/2
XRCOV_
Relative mean cover Submerged
(XCOV_S U B M E RG E D_AQ/
r
SUBMERGED_AQ
Aquatic Vegetation
XTOTCOV_VASC_STRATA) x 100
XRCOV_
Relative mean cover Floating
(XCOV_F LO ATI N G_AQ/
r
FLOATING_AQ
Aquatic Vegetation
XTOTCOV_VASC_STRATA) x 100
XRCOV LIANAS
Relative cover Lianas, Vines, and
(XCOV_LIANAS/
r
Vascular Epiphytes
XTOTCOV_VASC_STRATA) x 100
L,
XRCOV_VTALL_
Relative cover Vegetation > 30m tall
(XCOV_VTALL_VEG/
r
VEG
XTOTCOV_VASC_STRATA) x 100
L,
XRCOV_TALL_
Relative cover Vegetation > 15m to
(XCOV_TALL_VEG/
r
VEG
30m tall
XTOTCOV_VASC_STRATA) x 100
XRCOV_HMED_
Relative cover Vegetation > 5m to
(XCOV_HMED_VEG/
r
VEG
15m tall
XTOTCOV_VASC_STRATA) x 100
XRCOV_MED_
Relative cover Vegetation >2m to 5
(XCOV_MED_VEG/
r
VEG
tall
XTOTCOV_VASC_STRATA) x 100
XRCOV_SMALL_
Relative cover Vegetation 0.5 to 2m
(XCOV_SMALL_VEG/
r
VEG
tall
XTOTCOV_VASC_STRATA) x 100
XRCOV_VS MALL_
Relative cover Vegetation < 0.5m
(XCOV_VSMALLJ
r
VEG
tall
XTOTCOV_VASC_STRATA) x 100
D_VAS C_ST RATA
Simpson's Diversity - Heterogeneity
of Vertical Vascular Structure in AA
based on occurrence and relative
cover of all strata in all plots
s = number of veg strata observed, /
= veg stratum /', p = relative cover
belonging to veg stratum /
3
D =
I
c
H_VASC_STRATA
Shannon-Wiener - Heterogeneity of
Vertical Vascular Structure in AA
based on occurrence and relative
3
H' = ~ V Pi In Pi
cover of all strata in all plots
s '
I
c
s = number of veg strata observed, /
= veg stratum /', p = relative cover
belonging to veg stratum /
J _V AS C_ST RATA
Pielou Evenness - Heterogeneity of
Vertical Vascular Structure in AA
H'
Ķ! ~\n5
based on occurrence and relative
r
cover of all strata in all plots
5=number of strata observed
Section 7.2
Non-Vascular Groups
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CALCULATION (listed in Metric METRIC TYpE
SPECIES TRAIT TYPE (indicated in 'C_ ^ond'tlon'
METRIC NAME METRIC DESCRIPTION Banner if applicable) - s ress)
N_PEAT_MOSS_
DOM
Number of plots where bryophytes
are dominated by Sphagnum or
other peat forming moss
Count number of plots where
PEAT_MOSS = Y
C
freclpeat_
MOSS_DOM
Frequency of plots where
bryophytes are dominated by
Sphagnum or other peat forming
moss
(N_PEAT_MOSS_DOM/5 plots) x
100
C
FRECL
BRYOPHYTES
Frequency of bryophytes growing
on ground surfaces, logs, rocks, etc.
(# of 100-m2 plots in which
BRYOPHYTES occur/5 plots) x 100
C
FREQJJCHENS
Frequency of lichens growing on
ground surfaces, logs, rocks, etc.
(# of 100-m2 plots in which
LICHENS occur/5 plots) x 100
C
freclarboreal
Frequency of arboreal Bryophytes
and Lichens
(# of 100-m2 plots in which
ARBOREA occur/5 plots) x 100
C
freclalgae
Frequency of filamentous or mat
forming algae
(# of 100-m2 plots in which ALGAE
occurs/5 plots) x 100
C
FRECL
MACROALGAE
Macroalgae (freshwater
species/seaweeds)
(# of 100-m2 plots in which
MACROALGA occurs/5 plots) x
100
C
XCO V_
BRYOPHYTES
Mean absolute cover bryophytes
growing on ground surfaces, logs,
rocks, etc.
S cover of IRYOPHYTE across 5
plots/5 plots
C
XCOV_LICHENS
Mean absolute cover lichens
growing on ground surfaces, logs,
rocks, etc.
S cover of ICHENS across 5
plots/5 plots
C
XCOV_ARBOREAL
Mean absolute cover arboreal
Bryophytes and Lichens
2 cover of lRBOREAL across 5
plots/5 plots
C
XCOV_ALGAE
Mean absolute cover filamentous or
mat forming algae
2 cover of iLGAE across 5 plots/5
plots
C
XCO V_
MACROALGAE
Mean absolute cover macroalgae
(freshwater species/seaweeds)
2 cover of flACROALGAE across 5
plots/5 plots
C
IMP_
BRYOPHYTES
Bryophytes growing on ground
surfaces, logs, rocks, etc.
(FRECLBRYOPHYTES +
XCOV_BRYOPHYTES)/2
C
IMP_LICHENS
Lichens growing on ground
surfaces, logs, rocks, etc.
(FRECLUCHENS +
XCOV_LICHENS)/2
C
IMP_ARBOREAL
Arboreal Bryophytes and Lichens
(FRECLARBOREAL +
XCOV_ARBOREAL)/2
C
IMP_ALGAE
Filamentous or mat forming algae
(FRECLALGAE + XCOV_ALGAE)/2
C
IMP_
MACROALGAE
Macroalgae (freshwater
species/seaweeds)
(FREQJVIACROALGAE +
XCOV_MACROALGAE)/2
C
Section 8
Ground Surface Attributes
Section 8.1
Water Cover and Depth
XH20_DEPTH
Mean Predominant water depth in
plots where water occurs
I 'REDOMINANT_DEPTH across
plots where standing water
occurs/number of plots where
standing water occurs
C
2022
National Wetland Condition Assessment: 2016 Technical Support Document
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CALCULATION (listed in Metric
Row),
SPECIES TRAIT TYPE (indicated in
Banner if applicable)
METRIC TYPE
METRIC NAME
METRIC DESCRIPTION
(C = condition,
S = stress)
XH20_DEPTH_AA
Mean Predominant water depth
PREDOMINANT_DEPTh across
across AA
plots all sampled 100-m2 plots/5
plots
C
FRECLH20
Frequency of occurrence of water
(# of 100-m2 plots in which
across 100-m2 plots
TOTAL_WATER occurs/5 plots) x
100
C
MIN_C0V_H20
Minimum cover of water
Lowest value for TOTAL_WATER
across five 100-m2 plots
C
MAX_C0V_H20
Maximum cover of water
Highest value for TOTAL_WATER
across five 100-m2 plots
C
XC0V_H20
Mean total cover of water (mean
S cover of TOTAL WATER acr< ss
percent of Veg Plot area with
5 plots/5 plots
C
water)
IMP_H20
Importance total cover of water
across Veg Plot area
(FREQJH20 + XC0V_H20)/2
C
Section 8.2
Bare ground and Vegetation Litter
UTTER_TYPE
Predominant litter type
PREDOMINANTJ.ITTER:
CONIFEROUS, DECIDUOUS,
GRAMINOID, FORB, FERN,
BROADLEAF
C
XDEPTH UTTER
Mean depth of litter across all 1-m2
Sum DEPTH SW and DEPTH NE
quadrats in AA
for all 1-m2 quadrats/total number
of sampled quadrats in AA (usually
10)
C
MEDDEPTH
Median depth of litter across all 1-
Median DEPTH SW and
LITTER
m2 quadrats in AA
DEPTH_NE for all 1-m2
quadrats/total number of sampled
quadrats in AA (usually 10)
C
freqjjtter
Frequency of litter
(# of 100-m2 plots in which
TOTAL_LITTER >0/5 plots) x 100
C
freclbaregd
Frequency of bare ground
(# of 100-m2 plots in which any
one of EXPOSED_SOIL;
EXPOSED_GRAVEL;
EXPOSED_ROCK occurs/5 plots) x
100
C
FRECLEXPOSED_
Frequency exposed soil/sediment
(# of 100-m2 plots in which
SOIL
EXPOSED_SOIL occurs/5 plots) x
100
C
FRECLEXPOSED_
Frequency exposed gravel/cobble
(# of 100-m2 plots in which
GRAVEL
(~2mm to 25cm)
EXPOSED_GRAVEL occurs/5 plots)
x 100
C
FRECLEXPOSED_
Frequency exposed rock (> 25cm)
(# of 100-m2 plots in which
ROCK
EXPOSED_ROCK occurs/5 plots) x
100
C
FRECLWD_FINE
Frequency of fine woody debris (<
(# of 100-m2 plots in which
r
5cm diameter)
WD_FINE occurs/5 plots) x 100
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CALCULATION (listed in Metric
Row),
SPECIES TRAIT TYPE (indicated in
Banner if applicable)
METRIC TYPE
METRIC NAME
METRIC DESCRIPTION
(C = condition,
S = stress)
FRECLWD_
Frequency of coarse woody debris
(# of 100-m2 plots in which
r
COARSE
(> 5cm diameter)
WD_COARSE occurs/5 plots) x 100
XCOV_UTTER
Mean Cover of litter
S cover of "OTALJ.ITTER aci ss 5
plots/5 plots
c
XCOV_BAREGD
Mean cover of bare ground
S cover of :XPOSED_SOIL +
EXPOSED_GRAVEL +
EXPOSED_ROCI across 5 plots/5
plots
c
XCOV_EXPOSED_
Mean Cover exposed soil/sediment
S cover of iXPOSED SOIL across 5
r
SOIL
plots/5 plots
L,
XCOV_EXPOSED_
Mean Cover exposed gravel/cobble
S cover of iXPOSED GRAVEL
c
GRAVEL
(~2mm to 25cm)
across 5 plots/5 plots
L
XCOV_EXPOS E D_
c) Cover exposed rock (> 25cm)
S cover of iXPOSED ROCK aci ss
r
ROCK
5 plots/5 plots
L,
XCOV_WD_FI N E
Mean Cover of fine woody debris (<
S cover of WD FINE across 5
c,
5cm diameter)
plots/5 plots
in EW-VMMI
XCOV WD
Mean Cover of coarse woody debris
S cover of WD COARS across 5
r
COARSE
(> 5cm diameter)
plots/5 plots
L
IMP_LITTER
Importance of litter
(FREQJJTTER + XCOV_LITTER)/2
c
IMP_BAREGD
Importance of bare ground
(FRECLBAREGD +
XCOV_BAREGD)/2
c
IMP_EXPOSED_
Importance exposed soil/sediment
(freclexposed_soil +
r
SOIL
XCOV_EXPOSED_SOIL)/2
IMP_EXPOSED_
Importance exposed gravel/cobble
(FRCLEXPOSED_GRAVEL +
r
GRAVEL
(~2mm to 25cm)
XCOV_EXPOS E D_G RAVE L)/2
IMP_EXPOSED_
Importance exposed rock (> 25cm)
(freclexposed_rock +
r
ROCK
XCOV_EXPOS E D_ROC K)/2
IMP_WD_FINE
Importance of fine woody debris (<
(FREQJA/D_FINE +
r
5cm diameter)
XCOV_WD_FINE)/2
IMP_WD_
Importance of coarse woody debris
(FREQJA/D_COARSE+
r
COARSE
(> 5cm diameter)
XCOV_WD_COARSE)/2
L,
SECTIONS 9 -11
METRICS BASED ON RAW DATA FROM FORM V-4: NWCA 2016
SNAG AND TREE COUNTS AND TREE COVER
Snag and tree metrics are calculated as means/100-m2 plots to represent
AA, unless specified as totals across AA (from all 5 100m2). Snag and tree
metrics were not placed on a per hectare basis because the AA and
sampled plots do not necessarily represent homogenous patches and
many wetlands are not forested but may have occasional trees. Basal
area was not calculated because diameters were estimated in classes.
SECTION 9
DEAD/SNAG COUNT METRICS -
Based on data from FORM V-4
(Snag/standing dead tree section)
TOTN_XXTHIN_
Total Number Dead tree or snags 5
I number of KXTHIN_SNAGS
SNAG
to 10 cm DBH (diameter breast
height)
across of all 100-m2 plots
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CALCULATION (listed in Metric
Row),
SPECIES TRAIT TYPE (indicated in
Banner if applicable)
METRIC TYPE
METRIC NAME
METRIC DESCRIPTION
(C = condition,
S = stress)
TOTN XTHIN
Total number of dead trees or snags
2 number of XTHIN_SNAGS across
r
SNAG
11 to 25cm DBH
of all 100-m2 plots
L
TOTN THIN
Total number of dead trees or snags
2 number of THIN_SNAGS across
r
SNAG
26 to 50cm DBH
of all 100-m2 plots
L
TOTN JR
Total number of dead trees or snags
2 number of R_SNAGS across of
r
SNAG
51 to 75cm DBH
all 100-m2 plots
L,
TOTN THICK
Total number of dead trees or snags
2 number of THICK_SNAGS across
r
SNAG
76 to 100cm DBH
of all 100-m2 plots
L,
TOTN XTHICK
Total number of dead trees or snags
I number of 2 to
Count unique tree species (taxa) in
5m tall
LMED_TREE height class across all
5 plots
C
N_HMED_TREE
Richness tree species, trees > 5m to
Count unique tree species (taxa) in
15m tall
HMED_TREE height class across all
5 plots
C
N_TALL_TREE
Richness tree species, trees > 15m
Count unique tree species (taxa) in
to 30m tall
TALL_TRE height class across all 5
plots
C
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CALCULATION (listed in Metric
Row),
SPECIES TRAIT TYPE (indicated in
Banner if applicable)
METRIC TYPE
METRIC NAME
METRIC DESCRIPTION
(C = condition,
S = stress)
N_VTALL_TREE
Richness tree species, trees > 30m
Count unique tree species (taxa) in
tall
VT_TREE height class across all 5
plots
C
N_TREE_
Richness tree species in ground
Count unique tree species (taxa) in
GROUND
layer (e.g., seedlings, saplings),
GROUND LAYER ( SMALL_TREE
r
trees < 2m
and SMALL_TREE height classes)
across all 5 plots
N_TREE_MID
Richness tree species in subcanopy
Count unique tree species (taxa) in
layer, trees 2m to 15m tall
MID LAYER (,MED_TREE and
HMED_TREE hei| ht classes) across
all 5 plots
C
N_TREE_UPPER
Richness tree species in subcanopy
Count unique tree species (taxa) in
layer, trees > 15m
UPPER LAYER (TALL_TREE and
VTALL_TREE height classes) across
all 5 plots
C
PCTN_TREE_
Percent richness of tree species
(N_TREE_GROUND/N_TREESPP) x
GROUND
found in ground layer (e.g.,
seedlings, saplings), trees < 2m
100
C
PCTN_TREE_MID
Percent richness of tree species
found in subcanopy layer, trees 2m
to 15m tall
(N_TREE_MID/N_TREESPP) x 100
C
PCTN_TREE_
Percent richness of tree species
(N_TREE_UPPER/N_TREESPP) x
UPPER
found in subcanopy layer, trees >
15m
100
C
FRECLVSMALL_
Frequency (proportion of plots) of
(Number of 100-m2 plots in which
TREE
VSMALL trees, trees < 0.5m tall
any species of k/SMALL trees
occurs/5 plots) x 100
C
FREQ_SMALL_
Frequency (proportion of plots) of
(Number of 100-m2 plots in which
TREE
SMALL trees, trees 0.5m to 2m tall
any species of SMALL trees
occurs/5 plots) x 100
C
FREQJ-MED_
Frequency (proportion of plots) of
(Number of 100-m2 plots in which
TREE
LMED trees, trees > 2 to 5m tall
any species of LMED trees
occurs/5 plots) x 100
C
freclhmed_
Frequency (proportion of plots) of
(Number of 100-m2 plots in which
TREE
HMED, trees > 5m to 15m tall
any species of HMED trees
occurs/5 plots) x 100
C
freqjtall_tree
Frequency (proportion of plots) of
(Number of 100-m2 plots in which
TALL trees, trees > 15m to 30m tall
any species of TALL tree: occurs/5
plots) x 100
C
FRECLVTALL_
Frequency (proportion of plots) of
(Number of 100-m2 plots in which
TREE
Frequency of individual, trees >
30m tall
any species of i/TALL trees
occurs/5 plots) x 100
C
freqjtree_
Frequency (proportion of plots) of
(Number of 100-m2 plots in which
GROUND
ground layer trees < 2m
any species of GROUND LAYER
( MALL or SMALL) trees
occurs/5 plots) x 100
C
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CALCULATION (listed in Metric
Row),
SPECIES TRAIT TYPE (indicated in
Banner if applicable)
METRIC TYPE
METRIC NAME
METRIC DESCRIPTION
(C = condition,
S = stress)
FREQJTREEJVIID
Frequency (proportion of plots) of
(Number of 100-m2 plots in which
subcanopy, trees 2m to 15m tall
any species of MID LAYER ( k/IED
or HMED) trees occurs/5 plots) x
100
C
FREQJ"REE_
Frequency (proportion of plots) of
(Number of 100-m2 plots in which
UPPER
CANOPY trees, trees >15m
any species of UPPER LAYER
(LMED or HMED) trees occurs/5
plots) x 100
C
XCOV VSMALL
Mean absolute cover VSMALL trees,
2 of cover for all tree species in
TREE
trees < 0.5m tall
VSMAL1 height class across all
plots/5 plots
C
XCOV SMALL
Mean absolute cover SMALL trees,
2 of cover for all tree species in
TREE
trees 0.5m to 2m tall
SMAL height class across all
plots/5 plots
C
XCOV LMED
Mean absolute cover LMED trees,
2 of cover for all tree species in
TREE
trees > 2 to 5m tall
LMED height class across all
plots/5 plots
C
XCOV HMED
Mean absolute cover HMED trees,
2 of cover for all tree species in
TREE_
trees > 5m to 15m tall
HMEC height class across all
plots/5 plots
C
XCOV TALL TREE
Mean absolute cover TALL trees,
2 of cover for all tree species in
trees > 15m to 30m tall
TALI height class across all plots/5
plots
C
XCOV VTALL
Mean absolute cover VTALL trees,
2 of cover for all tree species in
TREE_
trees > 30m tall
VTALL height class across all
plots/5 plots
C
XCOV TREE
Mean absolute cover trees in
2 of cover for all tree species in
GROUND
ground layer (e.g., seedlings,
GROUND LAYER (VSMALL_TREE
r
saplings), trees < 2m
and SMALL_TREE height classes)
across all plots/5 plots
XCOV TREE MID
Mean absolute cover trees in MID
2 of cover for all tree species in
layer, trees 2m to 15m tall
MID LAYER (LMED_TREE and
HMED_TREE height classes) across
all plots/5 plots
c
XCOV TREE
Mean absolute cover trees in
2 of cover for all tree species in
UPPER
UPPER layer, trees >15m
UPPER LAYER (TALL_TREE and
VTALL_TREE height classes) across
all plots/5 plots
c
IMP_VSMALL_
Importance of VSMALL trees, trees
(FREQ_VSMALL_TREE +
r
TREE
< 0.5m tall
XCOV_VSMALL_TREE)/2
IMP_SMALL_TREE
Importance of SMALL trees, trees
(FRECLSMALL_TREE +
r
0.5m to 2m tall
XCOV_SMALL_TREE)/2
IMP_LMED_TREE
Importance of LMED trees,trees > 2
(FREQJ_MED_TREE +
r
to 5m tall
XCOV_LMED_TREE)/2
IMP_HMED_TREE
Importance of HMED trees, trees >
(freclhmed_tree +
r
5m to 15m tall
XCOV_HMED_TREE)/2
IMP_TALL_TREE
Importance of TALL trees, trees >
(FREQJ"ALL_TREE +
r
15m to 30m tall
XCOV_TALL_TREE)/2
L,
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CALCULATION (listed in Metric
Row),
SPECIES TRAIT TYPE (indicated in
Banner if applicable)
METRIC TYPE
METRIC NAME
METRIC DESCRIPTION
(C = condition,
S = stress)
IMP_VTALL_TREE
Importance of VTALL trees, trees >
(FRECLVTALL_TREE +
c
30m tall
XCOV_VTALL_TREE)/2
IMP_TREE_GROUN
Importance of trees in GROUND
(FREQJ"REE_GOUND +
D
layer (e.g., seedlings, saplings),
trees < 2m
XCOV_TRE E_G ROU N D)/2
c
IMP_TREE_MID
Importance of trees in MID layer,
(FREQJ"REE_MID +
r
trees 2m-15m tall
XCOV_TRE E_M 1 D)/2
IMP_TREE_UPPER
Importance of trees in UPPER layer,
(FREQJ"REE_UPPER +
r
trees > 15m
XCOV_TREE_UPPER)/2
SECTION 10.2
TREE COUNT METRICS
TOTN XXTHIN
Total number of tree stems in
2 number of tree stems in
TREE
XXTHIN class, trees 5 to 10cm DBH
(diameter breast height)
XXTHIN_TREE cla s across all
species and across all 100-m2 plots
c
TOTN XTHIN
Total number of tree stems in
2 number of tree stems in
TREE
XTHIN class, trees 11 to 25cm DBH
XTHIN_TREE class across all
species and across 100-m2 plots
c
TOTN THIN
Total number of tree stems in THIN
2 number of tree stems in
TREE
class, trees 26 to 50cm DBH
THIN_TREE class across all species
and across all 100-m2 plots
c
TOTN JR TREE
Total number of tree stems in JR
2 number of tree stems in
class, of trees 51 to 75cm DBH
JR_TREE cla s across all species
and across all 100-m2 plots
c
TOTN THICK
Total number of tree stems in THICK
2 number of tree stems in
TREE
class, trees 76 to 100cm DBH
THICK_TREE class across all
species and across all 100-m2 plots
c
TOTN XTHICK
Total number of tree stems in
2 number of tree stems in
TREE
XTHICK class, trees 101 to 200cm
DBH
XTHICK_TREE cl s across all
species and across all 100-m2 plots
c
TOTN XXTHICK
Total number of tree stems in
2 number of tree stems in
TREE
XXTHICK class, of trees > 200cm
DBH
XXTHICK_TREE cl; s across all
species and across all 100-m2 plots
c
TOTN TREES
Total number of tree stems across
2 number of tree stems across all
all classes DBH
size classes, across all species, and
across all 100-m2 plots
c
TOTN LARGE
Total number of tree stems > 76cm
TOTN THICK TREE +
DBH
TOTN_XTHICK_TREE +
TOTN XXTHICK TREE
c
TOTN MID
Total number of tree stems 26 to
TOTN THIN TREE +
r
75cm DBH
TOTN JR TREE
L,
TOTN SMALL
Total number of tree stems 5 to
TOTN XX THIN TREE +
r
25cm DBH
TOTN_XTHIN_TREE
L
XN_XXTHIN_
Mean number of tree stems in
TOTN_XXTHIN_TREES/5 plots
TREE
XXTHIN class, trees 5 to 10 cm DBH
(diameter breast height)
c
XN_XTHIN_TREE
Mean number of tree stems in
XTHIN class, trees 11 to 25cm DBH
TOTN_XTHIN_TREES/5 plots
c
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CALCULATION (listed in Metric
Row),
SPECIES TRAIT TYPE (indicated in
Banner if applicable)
METRIC TYPE
METRIC NAME
METRIC DESCRIPTION
(C = condition,
S = stress)
XN_THIN_TREE
Mean number of tree stems in THIN
class, trees 26 to 50cm DBH
TOTN_THIN_TREES/5 plots
C
XN_JR_TREE
Mean number of tree stems in JR
class, of trees 51 to 75cm DBH
TOTN_JR_TREES/5 plots
C
XN_THICK_TREE
Mean number of tree stems in
THICK class, trees 76 to 100cm DBH
TOTN_THICK_TREES/5 plots
C
XN_XTHICK_
Mean number of tree stems in
TOTN_XTHICK_TREES/5 plots
TREE
XTHICK class, trees 101 to 200 cm
DBH
C
XN XXTHICK
Mean number of tree stems in
TOTN_XXTHICK_TREES/5 plots
TREE
XXTHICK class, of trees > 200 cm
DBH
C
XN_TREES
Mean number of tree stems across
all classes DBH
TOTN_TREES/5 plots
C
XN LARGE
Mean number of tree stems > 76cm
XN THICK TREE +
DBH
XN_XTHICK_TREE +
XN_XXTHICK_TREE
C
XN_MID
Mean number of tree stems 26 to
75cm DBH
XN_THIN_TREE + XN_JR_TREE
C
XN_SMALL
Mean number of tree stems 5 to
XN_XX_THIN_TREE +
c
25cm DBH
XN_XTHIN_TREE
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Chapter 9: Vegetation Multimetric Indices and Wetland Condition
9.1 Overview - Vegetation
Multimetric Index (VMMI)
Multimetric indices (MMIs) of
ecological condition based on
biological assemblages (e.g.,
wetland vegetation, fish, birds,
periphyton, macroinvertebrates)
are cornerstones of the USEPA
National Aquatic Resource Surveys
(NARS). For MMIs, good and poor
condition are defined relative to
characteristics of the biota in least-
disturbed sites. This chapter
describes the process of
Vegetation Multimetric Index
/I/MM',/development and the
development of thresholds (also
known as benchmarks) for good,
fair, and poor condition based on
VMMI values observed at least-
disturbed sites. Figure 1-1 in the
Analysis Overview illustrates how
the VMMI fits into the NWCA Analysis Pathway: 1) steps supporting VMMI development (see Chapter
6:,Chapter 7:, and Chapter 8: for details), 2) VMMI development and the determination of condition
thresholds based on VMMI values (this chapter), and 3) the use of VMMI values, condition thresholds,
and site weights in estimating wetland area in good, fair, or poor ecological condition (see Chapter 15:).
Previously, a national-scale VMMI, based on four broadly applicable metrics, was developed for the 2011
NWCA (USEPA 2016a, USEPA 2016b, Magee et al. 2019a). However, the availability of the added data
from the 2016 survey made it possible to develop more specific, finer-scale VMMIs. Using vegetation data
from the 1,985 unique NWCA sites sampled in 2011 or 2016 and methods detailed in Magee et al. (2019),
numerous candidate VMMIs were generated for the following site groups:
National scale - all sampled wetlands (Table 8-1)
Five subpopulations based on RPT_UNIT_6 groups (Figure 8-2, Table 8-3): tidally-infIueneed
Estuarine Wetlands in coastal areas (TDL) and Inland Wetlands in Five NWCA Aggregated
Ecoregions (ICP, EMU, PLNS-ARW, and WVM)
Four broad 'Wetland Group subpopulations (WETCLS_GRP (Table 8-4))
Characteristics of these groups are discussed in Section 8.3. Candidate vegetation metrics that passed
several screening tests (Section 8.5) for each site group were used in VMMI development. The methods
for VMMI development (Section 9.3) detailed in this chapter were applied to all the above VMMI site
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groups or subpopulations. Metrics that passed screening for the national-scale, for the five
subpopulations based on RPT_UNIT_6, or for the four of WETCLS_GRP subpopulations, were scored
(standardized) (Section 9.3.1) and used in developing numerous candidate VMMIs by subpopulation
(Section 9.3.2). Evaluation of performance criteria (Section 9.3.2) for the candidate VMMIs for each of
these groups, indicated the WETCLS_GRP subpopulations had the strongest performance. Based on these
results, four final VMMIs were ultimately selected, one for each Wetland Group: Estuarine Herbaceous
(VMMI-EH), Estuarine Woody (VMMI-EW), Inland Herbaceous (VMMI-PRLH), and Inland Woody (VMM-
PRLW). Thresholds for good, fair, and poor condition (Section 9.3.3) were established for each Wetland
Group VMMI. The Wetland Group VMMIs and their condition thresholds were used to calculate
population estimates of condition (Section 15.1) for the 2016 survey and for change analysis between
2011 and 2016 (see Chapter 15: and USEPA 2022). Consequently, in the results sections of this chapter
(Sections 9.4 and 9.4.3), we discuss only these final four VMMIs.
The R-code for VMMI development and threshold assignment was developed using Statistical Software,
ver. 3.6.1 (R Core Team 2019).
9.2 Calibration and Validation Data
During the NWCA VMMI development process, numerous candidate vegetation metrics (n = 426, Section
8.8, Appendix E) were examined for potential utility in indicating condition and hundreds of thousands
potential VMMIs were generated and evaluated (Section 9.3). To aid in developing the strongest final
VMMIs and avoid over-fitting them to specific data collected in 2011 and 2016, vegetation data were
divided into calibration (80% of sampled sites, n = 1,587) and validation (20% of sampled sites, n = 398)
data sets. Numbers of calibration and validation sites in various subpopulations are listed in Table 8-1
through Table 8-4. Metric scoring and VMMI development were conducted using the calibration data and
the validation data were used to confirm the performance of the most promising candidate VMMIs.
The 20% of sampled sites included in the validation data were randomly selected from the total number
of sampled sites and reserved to evaluate the consistency of candidate VMMIs. To encompass the range
of disturbance and wetland types in the NWCA, sites for the validation data set were designated by
stratified-random selection based on disturbance class (least-, intermediate-, and most-disturbed) and
four Wetland Groups (WETCLS_GRP).
The 80% of sampled sites comprising the calibration data were used to score candidate metrics on a 0 to
10 continuous scale (Sections 9.3.1, 9.4). Candidate metrics that passed screening tests (Section 8.5) were
scored within the NWCA subpopulations used for metric screening and development of potential VMMIs.
The resulting metric scoring was applied to the corresponding validation data. A robust potential VMMI
based on calibration data metric scoring is expected to similarly distinguish least-disturbed from most-
disturbed sites for both calibration and validation data (VanSickle 2010, Magee et al. 2019a), and we
evaluated this ability using box-and-whisker plots (see Section 9.4)
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9.3 Developing Vegetation Multimetric Indices (VMMIs) - Methods
Using procedures that were developed for the 2011 NWCA (USEPA 2016b, Magee et al 2019), numerous
candidate VMMIs were generated and evaluated for the national scale, for subpopulations of
RPT_UNIT_6 (Table 8-3, Figure 8-2), and for subpopulations of WETCLS_GRP (Table 8-4). These groups
were selected in an effort to minimize within group variability and maintain a sufficient number of least-
disturbed sites within each group to allow VMMI development (see Section 8.3). Methods for candidate
VMMI generation and evaluation are summarized in this section and any differences from Magee et al.
2019 are incorporated in this summary.
9.3.1 Step 1 - Metric Scoring
Candidate metrics must be standardized to the same scale before they can be used as components of a
VMMI. Metrics that passed screening tests for a given subpopulation were standardized on a 0 to 10
continuous scale using the calibration data. The metrics were scored based on interpolation of metric
values between the 5th (floor) and 95th (ceiling) percentiles across all calibration sites (Blocksom 2003).
The direction of each metric was determined by the direction of the difference between the mean of the
least-disturbed sites and the mean of the most-disturbed sites. If the difference was positive, better
condition is associated with higher metric values, and if negative, the reverse is true. For metrics
decreasing with increasing disturbance, the ceiling was scored as 10 and the floor as zero. Conversely, for
metrics that increased with increasing disturbance, the floor was scored as 10 and the ceiling as zero.
Scores were truncated to 0 or 10 if observed values fell outside the floor to ceiling range. The resulting
metric scoring was applied to the corresponding validation (see Section 9.2) data. A robust potential
VMMI developed using this metric scoring should similarly distinguish least-disturbed from most-
disturbed sites for both the calibration and validation data.
9.3.2 Step 2 - Generating and Screening Candidate VMMIs
Determining the optimal set of metrics for inclusion in a Vegetation Multimetric Index (VMMI) is a
complex process. In analyses based on the 2011 NWCA vegetation data, USEPA (2016b) found that a
random approach for selecting sets of metrics to include in candidate VMMIs (adapted from VanSickle
2010) ultimately produced more robust VMMIs than did expert selection of sets of individual metrics that
were maximally responsive. Accordingly, Magee et al. (2019) refined this approach to build a national-
scale wetland VMMI using the 2011 NWCA vegetation data. The methods of Magee et al. (2019) were
applied to the vegetation data from 1,985 unique NWCA sites sampled in 2011 or 2016 to develop a set
of finer-scale VMMIs, based on NWCA subpopulations, for describing wetland condition across the
conterminous US. To this end, the calibration data set (n = 1,587 sites) was used to generate and evaluate
numerous candidate VMMIs.
Candidate VMMIs were developed based on all sites in the calibration data set for several NWCA
subpopulations using the final set of scored metrics applicable to that subpopulation. All candidate
metrics, passing metric screens for a particular subpopulation, were used in generating the random
metric combinations for the candidate VMMIs. Each potential VMMI was calculated and placed on a 100-
point scale using the formula:
VMMI = I metric scores x 10/number of metrics
Magee et al. (2019) found that when developing VMMIs across numerous wetland types and large scales,
candidate VMMIs with between 4 and 6 metrics better distinguished least- and most-disturbed sites than
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those with 7 to 10 metrics. Consequently, here, we considered candidate VMMIs based on 4, 5, or 6
randomly selected metrics for the national scale, the five RPT_UNIT_6 subpopulations and the four
WETCLS_GRP subpopulations. In addition, for the Inland Herbaceous (PRLH) and Inland Woody (PRLW)
Wetland Groups, candidate VMMIs based on 7 randomly selected metrics were also considered because
a somewhat larger number of metrics passed screening tests for these two groups. 100,000 candidate
VMMIs were generated for each metric number applicable to the national scale and each of the six
RPT_UNIT_6 subpopulations, and the four WETCLS_GRP subpopulations.
The resulting candidate VMMIs were evaluated using a series of performance criteria to determine which
VMMIs were most effective. Performance statistics for evaluating the candidate VMMIs included
measures of redundancy, sensitivity, repeatability, and precision (Magee et al. 2019). To avoid metric
redundancy, only candidate VMMIs with maximum and mean Pearson correlations among component
metrics of < 0.75 and < 0.5, respectively, were retained for further review. Sensitivity of each VMMI was
evaluated using an interval test, (Kilgour et al. 1998), alpha = 0.05, to determine the percentage of most-
disturbed sites with VMMI values that were significantly less than the fifth percentile of the distribution of
VMMI values for least-disturbed sites (Van Sickle 2010). Repeatability for each candidate VMMI was
assessed using a SignakNoise (S:N) ratio (Kaufmann et al. 1999, Section 8.5.2) calculated based on data
from the primary sampling visits for calibration sites and repeat sampling visits (i.e., revisits) for a subset
of these primary visit sites (see Table 8-1 through Table 8-4 for site numbers by subpopulations). The
standard deviation (SD) of VMMI values among the least-disturbed sites was used to describe precision of
the VMMI.
To identify the most effective candidate VMMIs for each subpopulation, we first arranged all relevant
VMMIs that passed the correlation filter in order of increasing correlation and decreasing sensitivity.
Typically, the VMMIs with the lowest correlations were also the most sensitive. Next, for the most
sensitive VMMIs in each subpopulation set (up to several hundred), those with the lowest mean and
maximum correlation among component metrics were identified. Among these, the VMMIs with the
highest S:N and smallest SD were identified. The resulting reduced set of candidate VMMIs was further
evaluated by collectively considering redundancy, sensitivity, S:N, SD, and the ecological content of
component metrics to identify 9 to 12 highest performing VMMIs in each VMMI subpopulation. Finally,
for each subpopulation, box-and-whisker plots were created for the set of 9 to 12 most promising VMMIs
to evaluate their robustness and responsiveness by comparing how well each VMMI distinguished 1) the
least- and most-disturbed sites for calibration data vs. validation data and 2) least- and most-disturbed
sites from data for all sampled sites in the pertinent subpopulation.
Consideration of the performance criteria and the box plots for the best candidate VMMIs informed the
selection of the four final VMMIs for use in condition assessment for NWCA 2016. The four VMMIs were
based on the Wetland Group subpopulations (Section 9.4): Estuarine Herbaceous (VMMI-EH), Estuarine
Woody (VMMI-EW), Inland Herbaceous (VMMI-PRLH), and Inland Woody (VMMI-PRW). Thresholds for
good, fair, and poor ecological condition for each of these VMMIs were set usingthe distribution of
VMMI values for subpopulation relevant least-disturbed sites. Lastly, for the four selected VMMIs, a final
evaluation of VMMI responsiveness was conducted using two sample t-tests (Welsh t-test to account for
unequal variances and sample size, Welsh 1947) to compare mean VMMI values between all sampled
least- and most-disturbed sites occurring within each Wetland Group.
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9.3.3 Step 3 - Determining Ecological Condition Thresholds Based on VMMI Values
Thresholds for good, fair, and poor ecological condition were determined only for the final four Wetland
Group VMMIs: Estuarine Herbaceous (VMMI-EH), Estuarine Woody (VMMI-EW), Inland Herbaceous
(VMMI-PRLH), and Inland Woody (VMMI-PRLW). Prior to setting condition thresholds for each of these
VMMIs, the relevant set of least-disturbed sites were evaluated for outlier VMMI values, and values
below the 25th percentile - 1.5*IQR (interquartile range) for a VMMI group were excluded in setting
thresholds. Ecological condition categories (good, fair, and poor) were defined based on the distribution
of VMMI values observed in least-disturbed sites in a particular Wetland Group, following the percentile
approach described in Paulsen et al. (2008). Good condition was defined by VMMI values greater than or
equal to the 25th percentile, fair condition ranged from the 5th up to the 25th percentile, and poor
condition was delimited as less than the 5th percentile of the least-disturbed sites (Figure 9-1).
Least-Disturbed Site Distribution
100
0)
o
o
CO
CO
o
'ĶH
<1>
0
Q.
>ŧ
1
Percentiles:
95th
75th
50th
25th-
Good Condition
Fair Condition
5th
Poor Condition
Figure 9-1. Criteria for setting VMMI thresholds for good, fair, and poor condition categories based on VMMI
values observed for least-disturbed sites (REF_NWCA = L). Box plot whiskers: lower = the 25th percentile -1.5 X IQR
(interquartile range), upper = the 75th percentile + 1.5 X IQR.
Once the condition thresholds were established, each sampled site was assigned a condition category
(good, fair, or poor) based on the Wetland Group threshold applicable to the site and the site's observed
VMMI value.
9.4 Final VMMIs - Results
Usingthe VMMI development process outlined in Section 9.3, four of the candidate VMMIs were selected
for use in estimating wetland area in good, fair, and poor condition based on the 2016 NWCA and for
estimating areal changes in wetland condition between NWCA 2011 and 2016. The four final VMMIs
represented subpopulations of WETCLS_GRP: Estuarine Herbaceous (EH), Estuarine Woody (EW), Inland
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Herbaceous (PRLH), and Inland Woody (PRLW). These four VMM Is had stronger performance than the
highest-performing national-scale VMMI or the best VMMIs for subpopulations of RPT_UNIT_6.
Results describing the four final VMMIs for the NWCA 2016 analysis are organized under three headings:
VMMI Description, Metric Scoring, and VMMI Calculation (Section 9.4.1)
VMMI Performance (Section 9.4.2)
VMMI Condition Thresholds (Section 9.4.3)
9.4.1 VMMI Description, Metric scoring, and VMMI Calculation
An overview of the Wetland Group VMMIs (VMMI-EH (Estuarine Herbaceous, VMMI-EW (Estuarine
Woody), VMMI-PRLH (Inland Herbaceous), and VMMI-PRLW (Inland Woody)) is provided in Table 9-1,
which lists the name and a brief description of all individual metrics included in each VMMI. Methods for
calculating the metrics comprising each VMMI can be found in Section 8.8:, Appendix E; metrics included
in the four NWCA 2016 Wetland Group VMMIs is indicated in the METRIC TYPE column of Appendix in
bold, color-coded font:
VMMI-EH in light blue,
VMMI-EW in dark blue,
VMMI-PRLH in purple, and
VMMI-PRLW in forest green.
Note that the Appendix E descriptions/formulas for how to calculate individual metrics may contain
names of other metrics listed in Appendix E or parameter names (Section 7.12:, Appendix C) that refer to
specific field collected data. For metrics that include information using species traits (e.g., growth habit,
duration, plant categories, wetland indicator status, native status, and coefficients of conservatism), it
may be useful to refer to the relevant section in Chapter 7: (Sections 7.6 through 7.9).
The NWCA metric scoring process (see Section 9.3), standardizes all individual metrics on a continuous
scale from 0 to 10, with higher values reflecting less disturbed conditions. Scoring of the metrics
comprising each VMMI was based on the metric values from the calibration data sites for that particular
VMMI site group (Table 8-4) and was applied to all sampled sites7 evaluated for that group. For scoring
the individual metrics that make up each VMMI (VMMI-EH, Table 9-2; VMMI-EW, Table 9-3; VMMI-PRLH,
Table 9-4; VMMI-PRLW, Table 9-5), the following information is provided 1) the direction of each metric's
response to disturbance based on observed metric values, 2) the metric floor (5th percentile) and ceiling
(95th percentile) values, and 3) the formula for metric scoring. VMMI-EH, VMMI-EW, and VMMI-PRLH
include one or metrics where observed values increase in response to disturbance. For metrics that
increase in response to disturbance, scoring is reversed so that the standardized metric scores will always
reflect less disturbance with higher values. The metric scoring reflected in Table 9-2 through Table 9-5
was used in to calculate VMMI values (scaled from 0 to 100) for each site based on the relevant VMMI for
the site (nwca_2016_veg_mmi.csv). The equations for VMMI-EH, VMMI-EW, VMMI-PRLH, and VMMI-
PRLW are presented immediately below the relevant scoring table.
7 All sampled sites include all Index Visits (probability and handpicked) and all site visits for sites sampled more than
once (i.e., revisit and resample events).
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Table 9-1. Metrics included in each of the four NWCA 2016 Vegetation Multimetric Indices (VMMIs). See Section
8.8:, Appendix E for formulas for calculation of these metrics.
VMMI
Metric Name
Description
EH
XRCOV_HTOL
Relative cover highly tolerant species (C-value <= 2)
(Estuarine Herbaceous) XRCOV_MONOCOTS_
.NAT
Relative cover native monocots
XRCOV SEN
Relative cover sensitive species (C-value >= 7)
XRCOV FORB
Relative cover forbs
N_ANNUAL
Annual species richness
PCTN NATSPP
Percent richness native species
EW
PCTN MONOCOT
Monocot percent richness
(Estuarine Woody)
XRCOV_GRAMINOID
Relative cover graminoids
RIMP_NATSPP
Relative importance native species
XCOV_WD_FINE
Mean Cover of fine woody debris (< 5cm diameter)
PCTN NATSPP
Percent richness native species
PCTNJSEN
Percent richness intermediately sensitive species (C-
value = 5 or 6)
PRLH
PCTN OBL FACW
Percent richness Obligate + Facultative Wetland
(Inland Herbaceous)
species
FQAI_ALL
Floristic quality index based on all species
XRCOV NATSPP
Relative cover native species
N TOL
Richness tolerant species (C-value < = 4)
PRLW
XRCOV MONOCOTS
NAT
Relative cover native monocots
(Inland Woody)
XC_ALL
Mean coefficient of conservatism based on all species
XRCOV NATSPP
Relative cover native species
rfreclnatspp
Relative frequency native species
Table 9-2. VMMI-EH metrics: floor and ceiling values, disturbance response, and interpolation formula for scorin
individual metrics. Final scores for each metric decrease with disturbance.
VMMI-EH Metrics
Unscored response Floor Ceiling Scoring formula (Observed = metric
to Disturbance
value at a given site)
XRCOV HTOL
Increases3
0
84.57 (84.57 - Observed)/(84.57 - 0)*10
XRCOV MONOCOTS
NAT Decreases
0.29 100 (Observed-0.29)/(100-0.29)* 10
XRCOV_SEN
Decreases
0
100 (Observed-0)/( 100-0)* 10
XRCOV FORB
Increases3
0
69.37 (69.37 - Observed)/(69.37 - 0)*10
N ANNUAL
Increases3
0
2 (2 - Observed)/(2 - 0)*10
PCTN_NATSPP
Decreases
62.96 100 (Observed-62.96)/(100-62.96)*10
Note: Scoring based on EH calibration data (n= 298 sites) and applied to all EH data (n = 374 sites).
aScoring is reversed for metrics that increase with disturbance. Scores truncated to 0 or 10 if observed values
fell outside the floor to ceiling range. Metrics are defined in Table 9-1.
The Estuarine Herbaceous VMMI (VMMI-EH) was calculated for each site on a continuous 0 to 100 scale:
VMMI EH = (XRC0V_HT0L_SC + XRC0V_M0N0C0TS_NAT_SC + XRCOV_SEN_SC
10
+ XRC0V_F0RB_SC + N_ANNUAL_SC + PCTN_NATSPP_SC) *
6
where, the ' SC' suffix is the scored value for a metric.
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Table 9-3. VMMI-EW metrics: floor and ceiling values, disturbance response, and interpolation formula for scoring
individual metrics. Final scores for each metric decrease with disturbance.
VMMI-EW Metrics Unscored response Floor Ceiling Scoring formula (Observed = metric
to Disturbance value at a given site)
PCTN MONOCOT Decreases
0 55.68 (Observed-0)/(55.68-0)*10
XRCOV_GRAMINOID Decreases 0 90.36 (Observed-0)/(90.36-0)* 10
RIMP NATSPP Decreases
68.56 100 (Observed-68.56)/(100-68.56)* 10
XCOV_WD_FINE Increases3 0 13.85 (13.85 - Observed)/(13.85-0)*10
PCTN_NATSPP Decreases 66.98 100 (Observed - 66.98)/( 100 - 66.98)* 10
PCTNJSEN Decreases 7.57 45.45 (Observed - 7.57)/(45.45 - 7.57)*10
Note: Scoring based on EW calibration data (n = 70 sites) and applied to all EW data (n = 87 sites).
aScoring is reversed for metrics that increase with disturbance. Scores truncated to 0 or 10 if observed values
fell outside the floor to ceiling range. Metrics are defined in Table 9-1.
The Estuarine Woody VMMI (VMMI-EW) was calculated for each site on a continuous 0 to 100 scale:
VMM I EW = (PCTN_M0N0C0T _SC + XRCOV_GRAMINOID_SC + RIMP_NATSPP_SC
10
+ XCOV_WD_FINE_SC + PCTN_NATSPP_SC + PCTN_ISEN_SC) *
6
where, the ' SC' suffix is the scored value for a metric.
Table 9-4. VMMI-PRLH metrics: floor and ceiling values, disturbance response, and interpolation formula for
scoring individual metrics. Final scores for each metric decrease with disturbance.
VMMI-PRLH Metrics Unscored response Floor Ceiling Scoring formula (Observed = metric value
to Disturbance at a given site)
PCTN_OBL_FACW Decreases 17.21 100 (Observed -17.21)/( 100 -17.21)* 10
FQAI_ALL Decreases 4.90 35.77 (Observed-4.90)/(35.77-4.90)*10
XRCOV NATSPP Decreases
12.42 100 (Observed - 12.42)/(100 - 12.42)* 10
N_TOL Increases3 3 41 (41 - Observed)/(41 - 3)*10
Note: Scoring based on PRLH calibration data (n = 522 sites) and applied to all PRLH data (n = 654 sites).
aScoring is reversed for metrics that increase with disturbance. Scores truncated to 0 or 10 if observed values
fell outside the floor to ceiling range. Metrics are defined in Table 9-1.
The Inland Herbaceous VMMI (VMMI-PRLH) was calculated for each site on a continuous 0 to 100 scale:
VMMI PRLH = (PCTN_OBL_FACW_SC + FQAI_ALL_SC + XRCOV_NATSPP_SC
10
+ N TOL SC) *
4
where, the '_SC' suffix is the scored value for a metric.
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Table 9-5. VMMI-PRLW metrics: floor and ceiling values, disturbance response, and interpolation formula for
scoring individual metrics. Final scores for each metric decrease with disturbance.
VMMI-PRLW Metrics Unscored response Floor Ceiling Scoring formula (Observed = metric
to Disturbance value at a given site)
XRCOV MONOCOTS NAT
Decreases
0.17
48.41
(Observed - 0.17)/(48.41 - 0.17)* 10
XC ALL
Decreases
2.52
6.19
(Observed - 2.52)/(6.19 - 2.52)*10
XRCOV_NATSPP
Decreases
53.04
100
(Observed - 53.04)/(100 - 53.04)*10
RFREQJMATSPP Decreases 62.83 100 (Observed - 62.83)/(100 - 62.83)*10
Note: Scoring based on PRLW calibration data (n = 697 sites) and applied to all PRLW data (n = 872 sites).
aScoring is reversed for metrics that increase with disturbance. Scores truncated to 0 or 10 if observed
values fell outside the floor to ceiling range. Metrics are defined in Table 9-1.
The Inland woody VMMI (VMMI-PRLW) was calculated for each site on a continuous 0 to 100 scale:
VMMI PRLW = (XRC0V_M0N0COTS_NAT_SC + XC_ALL_SC + XRCOV_NATSPP_SC
10
+ RFREQ_NATSPP_SC) *
where, the '_SC' suffix is the scored value for a metric.
9.4.2 VMMI Performance
Descriptive statistics - Descriptive statistics for the Wetland Group VMMIs (EH, EW, PRLH, PRLW) are
summarized in Table 9-6. The high S:N values for the EH, PRLH, and PRLW VMMIs reflect consistency in
the VMMI across repeat samplings. However, the S:N value for the EW VMMI is not very meaningful
because only two revisit sites (i.e., a second sampling visit to a site during the same year as the first
sampling visit) were available. Low mean correlations among metrics in VMMI indicate low redundancy
among metrics. Sensitivity, or the percentage of most-disturbed sites distinguished from least-disturbed
sites, based on the conservative Kilgour test (VanSickle 2010), varies by wetland type group. The observed
sensitivity values were comparatively high for MMIs (see Magee et al. 2019). VMMI-PRLW had the lowest
separation of least- and most-disturbed sites, a pattern that may be influenced by the diversity of specific
wetland community types and structural types within the PRLW group.
Box plot comparisons of calibration and validation data by VMMI - For all four VMMIs, comparison of
VMMI values between calibration and validation data showed similar distributions and satisfactory
discrimination between least- and most-disturbed sites (top graph in Figure 9-2 (VMMI-EH), Figure 9-3
(VMMI-EW), Figure 9-4 (VMMI-PRLH), and Figure 9-5 (VMMI-PRLW)). Similar results between calibration
and validation data sets indicate consistent behavior for the VMMIs across different data sets, suggesting
robustness of VMMI performance for wetland data collected in future years. Sample sizes in the
validation data were very small for the Estuarine Woody VMMI (EW), so in this case differences in VMMI
values for least-disturbed sites between calibration and validation data may not be meaningful.
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Box plot comparisons of least- and most-disturbed sites by VMM I - For each of the Wetland Group VMM Is
(bottom graphic in Figure 9-2 (VMMI-EH), Figure 9-3 (VMMI-EW), Figure 9-4 (VMMI-PRLH), and Figure 9-5
(VMMI-PRLW)), box plots comparing the VMMI value distributions showed clear separation for the set of
least- and most-disturbed unique sites sampled in 2011 and 2016. These figures illustrate no overlap in
VMMI values between the 25th percentile of the least-disturbed sites and the 75th percentile of most-
disturbed sites for VMMI-EH, VMMI-EW and VMMI-PRLH. The separation between least- vs. most-
disturbed sites is somewhat less distinct for the Inland Woody VMMI (PRLW), with slight overlap of the
25th percentile of the least-disturbed sites and the 75th percentile of most-disturbed sites. However, this
response was improved by separating the PRLW sites into two groups, arid vs. mesic, for setting condition
thresholds (see Section 9.4.3 and Figure 9-6).
Table 9-6. Summary statistics for the final four VMMIs: EH - Estuarine Herbaceous, EW -Estuarine Woody, PRLH -
Inland Herbaceous, PRLW - Inland Woody. Statistics calculated based on VMMI values for sampled sites and revisit
sites from the calibration data set for the relevant VMMI group.
VMMI
n-sites by
Mean
SD1
S:N2
Max r
Mean r
Sensitivity
n = calibration
disturbance
VMMI
VMMI
n = revisit
among
among
(%)
data sites3
class
(L sites)
(L sites)
sites4
metrics
metrics
EH
n=298
L=107, 1=126,
M=65, ? = 0
92.37
10.37
35.53
n = 16
0.63
0.39
61.53
EW
n=70
L=12, 1=34,
M=22, ? =2
75.52
7.46
32.12
n = 2
0.73
0.14
72.73
PRLH
n=522
L=77, 1=293,
M=150, ?=2
78.27
11.46
16.82
n = 29
0.39
0.17
50.67
PRLW
n=697
L=155, 1=395,
M=143, ?=4
68.95
12.48
17.30
n = 36
0.73
0.37
32.17
VMMIs defined in Section 9.4.1. L = least disturbed sites, I = intermediately disturbed sites, M=most
disturbed sites, ?=undetermined disturbance, 1SD =standard deviation, 2S:N = signal/noise (For each VMMI,
S:N is based on the 3sampled sites and the 4revisit sites from calibration data set), r = Pearson correlation.
Sensitivity = Percent M sites with VMMI values significantly less than the fifth percentile of the distribution of
VMMI values for L sites based on an interval test, alpha = 0.05 (Kilgour et al. 1998, Van Sickle 2010).
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Estuarine Herbaceous
100
x
0
TJ
C
75
| 50
I ŧ
03
CD
(U
>
Calibration
Validation
Eą] Least Disturbed ^1 Most Disturbed
Figure 9-2. Comparison of VMMI Estuarine Herbaceous wetlands (VMMI-EH) for least-disturbed and most-
disturbed sites. Top graph: Compares VMMI values for least- and most-disturbed EH sites in the calibration and
validation data sets. Bottom graph: VMMI values for all least- and most-disturbed sampled EH sites. Box plots: box
is interquartile (IQR) range, line in box is the median, and whiskers represent most extreme point a distance of no
more than 1.5 x IQR from the box. Values beyond this distance are outliers. Numbers below each box plot
represent number of the least-disturbed or most-disturbed sites sampled.
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Estuarine Woody
100
x
0)
~o
c
75
g 50
iŧ
CD
Ķ*Ķ
01
CS)
OJ
>
Calibration
Validation
$ Least Disturbed Most Disturbed
Estuarine Woody
100
x
Least Disturbed
Most Disturbed
Figure 9-3. Comparison of VMMI Estuarine Woody wetlands (VMMI-EW) for least-disturbed and most-disturbed
sites. Top graph: Compares VMMI values for least- and most-disturbed EW sites in the calibration and validation
data sets. Bottom graph: VMMI values for all least- and most-disturbed sampled EW sites. Box plots: box is
interquartile (IQR) range, line in box is the median, and whiskers represent most extreme point a distance of no
more than 1.5 x IQR from the box. Values beyond this distance are outliers. Numbers below each box plot
represent number of the least-disturbed or most-disturbed sites sampled.
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Inland Herbaceous
100
x
a>
*o
c
a>
E
75
50
'ĶS 25
41
(D
O)
a)
>
Calibration
Validation
$ Least Disturbed ^ Most Disturbed
Inland Herbaceous
100
x
CD
U
c
o
'u
QJ
C
o
75
50
iS 25
0)
D)
03
>
Least Disturbed
Most Disturbed
Figure 9-4. Comparison of VMMI Inland herbaceous wetlands (VMMI-PRLH) for least-disturbed and most-disturbed
sites. Top graph: Compares VMMI values for least- and most-disturbed PRLH sites in the calibration and validation
data sets. Bottom graph: VMMI values for all least- and most-disturbed sampled PRLH sites. Box plots: box is
interquartile (IQR) range, line in box is the median, and whiskers represent most extreme point a distance of no
more than 1.5 x IQR from the box. Values beyond this distance are outliers. Numbers below each box plot
represent number of the least-disturbed or most-disturbed sites sampled.
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Inland Woody
100
x
0)
O
c
75
oj 50
E
c
o
§
0)
o>
0)
>
25
Calibration
Validation
$ Least Disturbed Most Disturbed
Inland Woody
100
> 75
x
a>
Ķa
c
50
H 25
0)
a>
a)
>
Least Disturbed
Most Disturbed
Figure 9-5. Comparison of VMMI Inland woody wetlands (VMMI-PRLW) for least-disturbed and most-disturbed
sites. Top graph: Compares VMMI values for least- and most-disturbed PRLW sites in the calibration and validation
data sets. Bottom graph: VMMI values for all least- and most-disturbed sampled PRLW sites. Box plots: box is
interquartile (IQR) range, line in box is the median, and whiskers represent most extreme point a distance of no
more than 1.5 x IQR from the box. Values beyond this distance are outliers. Numbers below each box plot
represent number of the least-disturbed or most-disturbed sites sampled.
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9.4.3 Condition Thresholds for the Wetland Group VMMIs
Wetland condition thresholds for each the four final VMMIs (Table 9-7) were based on the distribution of
VMMI scores in least-disturbed sites (see Section 9.3.3):
Good = VMMI scores > 25th percentile of least-disturbed sites
Fair = VMMI scores from the 5th up to the 25th percentile of least-disturbed sites
Poor = VMMI scores < 5th percentile of least-disturbed sites
Least-disturbed sites in a Wetland Group with VMMI values below the 25th percentile - 1.5*IQR
(interquartile range) were considered outliers and not used in setting condition thresholds
Note that the VMMI-PRLW values in least-disturbed PRLW sites varied widely by ecoregion (Figure 9-6,
top graph). As a result, two sets of thresholds were developed for the VMMI-PRLW, one set for sites in
more mesic regions (PRLWother) and one set for sites in more arid regions (PRLWplnarw) (Table 9-7, Figure
9-6, bottom graph). A final evaluation of responsiveness (two sample unequal variance t-tests) for each of
the four NWCA VMMIs and the two VMMI-PRLW threshold groups showed significantly different mean
VMMI values between all sampled least- and most-disturbed sites (Table 9-8).
Each sampled site was assigned a condition category (good, fair, or poor) based on the site's observed
VMMI value and the Wetland Group VMMI and condition thresholds applicable to the site
(.nwca_2016_ veg_mmi. csv).
Table 9-7. VMMI value thresholds indicating good, fair, and poor ecological condition based on least-disturbed
sites in each Wetland Group (WETCLS_GRP). Sites with VMMI values from the 5th up to the 25th percentile for least-
disturbed (REF_NWCA) sites are considered in fair condition.
NWCA VMMIs
(n = least-
disturbed sites)
Poor Condition
Good Condition
Description (Wetland Type and Site Groups)
(VMMI < 5th
Percentile Least-
(VMMI > 25th
Percentile Least-
Disturbed Sites)
Disturbed Sites)
VMMI-EH
(n =134)
Tidal - Estuarine Herbaceous [ALL]
73.6
86.4
VMM-EW
(n = 15)
Tidal - Estuarine Woody [ALL]
64.6
69.8
VMM-PRLH
Inland (Palustrine, Riverine, or Lacustrine)
Herbaceous [ALL]
63.8
74.2
VMMI-PRLW
Inland (Palustrine, Riverine, or Lacustrine) Woody
PRLWother
(n = 157)
Inland (Palustrine, Riverine, or Lacustrine) Woody
[EMU, ICP, WVM]
53.7
65.5
PRLWplnarw
(n = 37)
Inland (Palustrine, Riverine, or Lacustrine) Woody
[PLN, ARW]
43.7
49.9
Table 9-8. Two-sample unequal variances t-tests comparing VMMI value means for all sampled least- and most-
disturbed sites for each Wetland Group VMMI.
VMMI
t statistic
p value
Degrees of freedom (df)
VMMI-EH
9.89
< < 0.001
105.4
VMMI-EW
5.64
< < 0.001
39.4
VMMI-PRLH
15.38
< < 0.001
256.7
VMMI-PRLW
9.52
< < 0.001
276.0
PRLWother
8.06
< < 0.001
215.0
PRLWplnarw
6.90
< < 0.001
58.0
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Inland Woody
100-
x
0)
"O
c
75
15 50
E
c
o
25
eTB
65 71
\G?
72 46
34 23
3 13
0^
20 26
$ Least Disturbed ^ Most Disturbed
Inland Woody
-100
x
0)
TJ
C
75
a3 50
E
25
0)
CD
O)
>
$ Least Disturbed ^ Most Disturbed
Figure 9-6. Comparison of VMMI values for Inland Woody wetlands (VMMI-PRLW) for least-disturbed and most-
disturbed sites by ecoregions. Top graph: VMMI-PRLW values by Five NWCA Aggregated Ecoregions (ICP, EMU,
PLN, ARW( WVM, (NWCA_EC05) see map in Figure 6-2 for definitions) Bottom graph: VMMI-PRLW values for
more mesic (OTHER) vs. more arid (PLN_ARIDW) regional groups (OTHER = ICP, EMU, WVM; PLN_ARIDW = ARW &
WVM). Box plots: box is interquartile (IQR) range, line in box is the median, and whiskers represent most extreme
point a distance of no more than 1,5 x IQR from the box. Values beyond this distance are outliers. Numbers below
each box plot represent number of the least-disturbed or most-disturbed sites sampled.
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9.5 Literature Cited
Blocksom KA (2003) A performance comparison of metric scoring methods for a multimetric index for
Mid-Atlantic Highlands streams. Environmental Management 31: 670-682
Kaufmann PR, Levine P, Robison EG, Seeliger C, Peck DV (1999) Quantifying Physical Habitat in Wadable
Streams. EPA/620/R_99/003. US Environmental Protection Agency, Washington, DC
Kilgour BW, Sommers KM, Matthews DE (1998) Using the normal range as a criterion for ecological
significance in environmental monitioring and assessment. Ecoscience 5: 542-550
Kincaid TM, Olsen AR, Weber MH (2019). spsurvey: Spatial Survey Design and Analysis. R package version
4.1.0.
Magee TK, Blocksom KA, Fennessy MS (2019) A national-scale vegetation multimetric index (VMMI) as an
indicator of wetland condition across the conterminous United States. Environmental Monitoring and
Assessment 191 (SI): 322, doi: 10.1007/sl0661-019-7324-4.
https://link.springer.com/article/10.1007/sl0661-019-7324-4
Paulsen SG, Mayio A, Peck DV, Stoddard JL, Tarquinio E, Holdsworth SM, Sickle JV, Yuan LL, Hawkins CP,
Herlihy AT, Kaufmann PR, Barbour MT, Larsen DP, Olsen AR (2008) Conditions of stream ecosystems in
the US: an overview of the first national assessment. Journal of the North American Benthological Society
27(4), 812-821
R Core Team (2019) R: A language and environment for statistical computing. Version 3.6.1. R Foundation
for Statistical Computing, Vienna, Austria. (http://www.R-project.org/)
Van Sickle J (2010) Correlated metrics yield multimetric indices with inferior performance. Transactions of
the American Fisheries Society 139: 1802-1817
USEPA (2016a) National Wetland Condition Assessment 2011: A Collaborative Survey of the Nation's
Wetlands. EPA-843-R-15-005. US Environmental Protection Agency, Office of Water, Washington, DC
USEPA (2016b) National Wetland Condition Assessment: 2011 Technical Report. EPA-843-R-15-006. .US
Environmental Protection Agency, Washington, DC. https://www.epa.gov/national-aquatic-resource-
surveys/national-wetland-condition-assessment-2011-results
USEPA (2022) National Wetland Condition Assessment 2016: The Second Collaborative Survey of Wetlands
in the United States. EPA-XXX-R-XX-XXX. US Environmental Protection Agency, Office of Water,
Washington, DC
Welch BL (1947). "The generalization of "Student's" problem when several different population variances
are involved". Biometrika. 34 (1-2): 28-35
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Chapter 10: Nonnative Plant Indicator (NNPI)
10.1 Background
Nonnative plant species are widely recognized as important biological indicators of ecological stress on
wetland condition (Mack and Kentula 2010, Magee et al. 2010). They can 1) reflect ecological condition of
the 'natural' vegetation, 2) be indicators of anthropogenic disturbance, or 3) behave as direct stressors to
vegetation and ecosystem properties (e.g., Kuebbing et al. 2015, Magee et al. 2008, 2010, 2019, Pysek et
al. 2020, Riccardi et al. 2020, Ruaro et al 2020, Simberloff 2011). Presence and abundance of nonnative
plants are often positively related to human mediated disturbance (Lozon and Maclsaac 1997, Mack et al.
2000, Magee 1999, Magee et al. 2008, Ringold et al. 2008). In addition, nonnative plants can act as direct
stressors to ecological condition by competing with or displacing native plant species or communities,
altering vegetation structure, or by altering ecosystem structure and processes (Vitousek et al. 1997,
Dukes and Mooney 2004). Numerous direct and indirect effects of nonindigenous plants on native
vegetation and other ecosystem components demonstrate their role as potential stressors and indicators
of lowered ecological condition.
For example, nonnative plant species have been linked to:
increased risk of local extinction or population declines for many rare, native plant species
(Randall 1996, Lesica 1997, Seabloom etal. 2006),
changes in species composition within and among plant community types, and to local and
regional floristic homogenization (McKinney 2004, Rooney et al. 2004, Magee et al. 2008),
alteration of fire regimes (Dwire and Kauffman 2003, Brooks et al. 2004),
alteration of geomorphic and hydrologic processes (Rowantree 1991, Sala et al. 1996), and
alteration of carbon storage patterns (Farnsworth and Meyerson 2003, Bradley et al. 2006),
nutrient cycling, and composition of soil biota (Belnap and Phillips 2001, Ehrenfeld 2003).
Major ecological changes like these negatively influence the intactness or integrity of natural ecosystems
(Angermeier and Karr 1994, Dale and Beyeler 2001) and can lead to losses of ecosystem services (Dukes
and Mooney 1999, Dale et al. 2000, Hooper et al. 2005, Meyerson and Mooney 2007).
Recall from Section 7.8 (Species Traits - Native Status) and from Magee et al. 2019 that NWCA defines
nonnative plants to include both alien and cryptogenic taxa. Concepts describing native status categories
used by the NWCA, including alien and cryptogenic, are described in brief here and in Table 7-5. First,
Native plant taxa are defined as indigenous to specific states in the conterminous US. Introduced taxa are
indigenous outside of, and not native, in any of conterminous US. Adventive taxa are native to some parts
of the conterminous US but introduced to the location of occurrence. We use the term A/iento include
both introduced and adventive taxa. Cryptogenic species include taxa that have both introduced (often
aggressive) and native (generally less prevalent) genotypes, varieties, or subspecies. Because many
cryptogenic species are invasive or act as ecosystem engineers (Magee et al. 2019), we grouped them
with alien species and considered them nonnative for the purpose of indicating ecological stress.
The Nonnative Plant Indicator (NNPIf was developed as a categorical descriptor of stress to ecological
condition for the 2011 NWCA (Magee et al 2019, USEPA 2016a) and was also used in the NWCA 2016
8 In the NWCA 2011 Technical Report (USEPA 2016a), the NNPI was referred to as the "Nonnative Plant Stressor
Indicator" (NPSI) - a name that is no longer used.
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analysis. Magee et a I. (2019) detailed the development of the Nonnative Plant Indicator (NNPI) and,
based on the 2011 NWCA, reported on relationships of the NNPI to disturbance and environmental
conditions and on the 2011 extent of wetland area in different NNPI condition categories.
In the following subsections, data collection, data preparation, description of the NNPI, and condition
category threshold definitions are described.
10.2 Data Collection
Nonnative plant data were collected as part of the standard Vegetation Protocol (USEPA 2016a). An
overview of vegetation field and laboratory methods is provided in Chapter 7, Section 7.3.
10.3 Data Preparation
Preparation and validation of raw data for nonnative plant species are described in Chapter 7, Sections
7.4 and 7.5. Definition of the native status categories used in the NWCA and the procedures for
determining state-level native status for the individual species observed in the 2011 and 2016 NWCAs are
provided in Chapter 7, Section 7.8. Numerous metrics summarizing different attributes of nonnative
species (e.g., all alien and cryptogenic species, or subgroups of these species based on life history traits)
were calculated and are described in Chapter 8, Sections 8.4 and 8.8 (Appendix E).
10.4 Nonnative Plant Indicator Overview
The categorical NNPI was based on three straightforward continuous metrics (Table 10-1) that reflect
different potential impacts of nonnative plants, and which can be readily calculated from field
observations.
Table 10-1. Definition of metrics used in the NNPI.
Metric Name Calculation3 Range
VC - Relative Cover of
(2 Absolute cover nonnative species,/Ģ Absolute
0 to 100%
Nonnative Species
cover all species,) x 100; where for each unique
species /': Absolute Cover = 0-100%
TOTN_AC - Richness of Nonnative
Number of unique nonnative species observed at a
Number of unique
Species
site
nonnative species
RFREQ_AC - Relative Frequency of Ģ Frequency nonnative species,/Ģ Frequency all 0 to 100%
Nonnative Species species,) x 100; where for each unique species /':
Frequency = 0-100%, calculated as the percent of
Veg Plots in which it occurred.
Calculation of metrics based on data collected in the five 100-m2 vegetation plots sampled at each site.
Additional information about these metrics can be found in Chapter 8, Section 8.8 (Appendix E) by
referencing the metric names indicated in red font in the list above and highlighted in red and bolded in
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the appendix. The "_AC" suffix in the metric names refers to combined alien and cryptogenic species that
together are considered nonnative by the NWCA.
Each of the three metrics consider all nonnative species at a location but, taken together, integrate
different avenues of impact to ecological condition. Relative Nonnative Cover(0 to 100%) reflects
preemption of space and resources and is often associated with changes in plant community composition
(species identity, richness, and abundance) and vegetation structure (horizontal or vertical), or with
alteration of ecosystem processes (e.g., hydrology, nutrient cycling, fire regime). Greater Richness of
Nonnative Species (number of unique nonnative species) increases the risk that individual nonnative taxa
are or may become invasive or act as ecosystem engineers that negatively alter biotic or abiotic
properties. Increasing Relative Nonnative Frequency^) to 100%) across a site reflects increasing numbers
of loci from which nonnatives could compete with native species, expand in cover, or spread to new
locations. Of the three metrics, relative nonnative cover is likely to represent the greatest potential
negative effect on ecological condition. The other two metrics provide additional pathways of impact that
may have synergistic relationships with relative nonnative cover, potentially increasing the amount
overall stress related to nonnative plants.
The three metrics of the NNPI are used together in a decision matrix to assign a condition category
reflecting potential stress from nonnative species to each site. Four condition categories (good, fair, poor,
or very poor) were defined9. Assignment of the condition category for each site is based exceedance
values for each of the three metrics; see the following section (Section 8.5) for details.
10.5 NNPI Condition Threshold Definition
NNPI condition thresholds were developed to:
reflect wetland condition as an additional indicator to the VMMI (Chapter 9:) and
indicate stressor condition related to nonnative plants.
The same thresholds were used for both of these purposes. Details of how the NNPI is used in final
reporting for wetland condition and stressor condition are discussed in Chapter 15:.
The three NNPI metrics (nonnative relative cover, nonnative richness, and nonnative relative frequency),
were used together in a decision matrix to assign each sampled site to a condition category (good, fair,
poor, or very poor) based on exceedance values for each of the metrics (see Table 10-2, below, and
Magee et al. 2019). The overall NNPI status for each site was determined by the lowest condition
category observed across the three NNPI metrics.
Exceedance values for the four condition categories for each metric were developed by Magee et al.
(2019) using best professional judgement, considering diverse wetland community types and changes in
plant community composition and structure with varying levels of nonnative cover, frequency, or
richness. Exceedance values for the four condition categories (Table 10-2) reflect the strong influence of
9 In previous work (USEPA 2016a, Magee et al. 2019), the NNPI categories were described in relation to potential
stress (i.e., low, moderate, high, or very high). However, to better align with other USEPA National Aquatic Resource
Surveys, the NNPI categories were renamed to reflect condition (good, fair, poor, or very poor). Now, good
condition is equivalent to the previously defined low stressor-level, and very poor condition is equivalent to the
formerly described very high stressor-level.
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nonnative relative cover, with the values for nonnative richness and nonnative relative frequency set to
consider these two metrics as additional sources of ecological stress.
Table 10-2. Condition Threshold Exceedance Values for each of the metrics informing the Nonnative Plant Indicator
(NNPI): Relative Cover of Nonnative Species (XRCOV_AC), Nonnative Richness (TOTN_AC), and Relative Frequency
of Nonnative Species (RFREQ^AC).
Condition Category*
XRCOV_AC
TOTN_AC
RFRECLAC
Good
<1
<5
<10
Fair
>1-15
>5-10
>10-30
Poor
>15-40
>10-15
>30-60
Very Poor
>40
>15
>60
* Exceedance of a threshold value for a particular condition category for any one of the three metrics moves the
metric condition to next lower (better to worse) category. The NNPI condition for a site is based on the lowest
observed condition category among the metrics.
The approach for designating the NNPI condition category for each site integrates information from three
different pathways from which nonnative species may influence ecological condition. To see how the
exceedance thresholds work, consider the two hypothetical examples of nonnative species results that
are outlined below.
Hypothetical Site 1 (NNPI Condition Category = Poor) has:
XRCOV_AC = 7% ^ Fair Condition
TOTN_AC = 14 nonnative species ^ Poor Condition
RFREQ_AC = 52% ^ Poor Condition
Hypothetical site 1 has nonnative relative cover of 7%, placing the site in the fair condition category.
However, this site also has nonnative richness of 14 species and relative frequency of 52%, which reflect
poor condition for both metrics. Thus, the site would be assigned to the NNPI poor condition category.
Even though relative nonnative cover is not extensive at this hypothetical site, the number of individual
nonnative species and their frequency of occurrence might indicate shifting community composition and
strong risk for expansion of nonnative cover.
Hypothetical Site 2 {NNPI Condition Category = Very Poor) has:
XRCOV_AC = 80% ^ Very Poor Condition
TOTN_AC = 1 nonnative species ^ Good Condition
RFREQ_AC = 59% ^ Poor Condition
Next, consider hypothetical site 2 with 80% nonnative relative cover indicating very poor condition,
nonnative richness of 1 indicating good condition, and nonnative relative frequency of 59% indicating
poor condition. Here, the overall NNPI condition category would be very poor. Even though there is only
one nonnative species present at the site, it occupies 80% of the total vegetation cover and nearly 60% of
all species occurrences across the sampled area of the vegetation plots are nonnative.
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10.6 Literature Cited
Angermeier PL, Karr JR (1994) Biological integrity versus biological diversity as policy directives. Bioscience
44:690-697
Belnap J, Phillips SL (2001) Soil biota in an ungrazed grassland: Response to annual grass (Bromus
tectorum) invasion. Ecological Applications 11: 1261-1275
Bradley BA, Houghton RA, Mustard JF, Hamburg SP (2006) Invasive grass reduces aboveground carbon
stocks in shrublands of the Western US. Global Change Biology 12: 1815-1822
Brooks ML, D'Antonio CM, Richardson DM, Grace JB, Keeley JE, DiTomaso JM, Hobbs RJ, Pellant M, Pyke D
(2004) Effects of Invasive Alien Plants on Fire Regimes. Bioscience 54: 677-688
Dale VH, Beyeler SC (2001) Challenges in the development and use of ecological indicators. Ecological
Indicators 1: 3-10
Dale VH, Brown SC, Haeuber RA, Hobbs NT, Huntly N, Naiman RJ, Riebsame WE, Turner MG, Valone TJ
(2000) Ecological principles and guidelines for managing the use of land. Ecological Applications 10: 639-
670
Dukes JS, Mooney HA (1999) Does global change increase the success of biological invaders? Trends in
Ecology and Evolution 14: 135-139
Dukes JS, Mooney HA (2004) Disruption of ecosystem processes in western North America by invasive
species. Revista Chilena de Historia Natural 77: 411-437
Dwire KA, Kauffman JB (2003) Fire and riparian ecosystems in landscapes of the western USA. Forest
Ecology and Management 178: 61-74
Ehrenfeld JG (2003) Effects of exotic plant invasions on soil nutrient cycling processes. Ecosystems 6: 503-
523
Farnsworth EJ, Meyerson LA (2003) Comparative ecophysiology of four wetland plant species along a
continuum of invasiveness. Wetlands 23: 750-762
Hooper DU, Chapin FS, III, Ewel JJ, Hector A, Inchausti P, Lavorel S, Lawton JH, Lodge DM, Loreau M,
Naeem S, Schmid B, Setala H, Symstad AJ, Vandermeer J, Wardle DA (2005) Effects of biodiversity on
ecosystem functioning: A consensus of current knowledge. Ecological Monographs 75: 3-35
Kuebbing SE, Classen AT, Sanders NJ, Simberloff D (2015) Above- and below-ground effects of plant
diversity depend on species origin: an experimental test with multiple invaders. New Phytologist 208
(3):727-735. doi:10.1111/nph.13488
Lesica P (1997) Spread of Phalaris arundinacea adversely impacts the endangered plant Howellia
aquatilis. Great Basin Naturalist 57: 366-368
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Lozon JD, Maclsaac HJ (1997) Biological invasions: Are they dependent on disturbance? Environmental
Reviews 5: 131-144
Mack JJ, Kentula ME (2010) Metric Similarity in Vegetation-Based Wetland Assessment Methods.
EPA/600/R-10/140. US Environmental Protection Agency, Office of Research and Development,
Washington, DC
Mack RN, Simberloff D, Lonsdale MS, Evans H, Clout M, Bazzaz FA (2000) Biotic invasions: Causes,
epidemiology, global consequences, and control. Ecological Applications 10: 698-710
Magee TK, Ernst TL, Kentula ME, Dwire KA (1999) Floristic comparison of freshwater wetlands in an
urbanizing environment. Wetlands 19: 517-534
Magee TK, Ringold PL, Bollman MA (2008) Alien species importance in native vegetation along wadeable
streams, John Day River basin, Oregon, USA. Plant Ecology 195: 287-307
Magee TK, Ringold PL, Bollman MA, Ernst TL (2010) Index of Alien Impact (IAI): A method for evaluating
alien plant species in native ecosystems. Environmental Management 45: 759-778
Magee TK, Blocksom KA, Herlihy AT, &. Nahlik AM (2019) Characterizing nonnative plants in wetlands
across the conterminous United States. Environmental Monitoring and Assessment 191 (SI): 344, doi:
10.1007/s 10661-019-7317-3. https://link.springer.com/article/10.1007/sl0661-019-7317-3
McKinney ML (2004) Do exotics homogenize or differentiate communities? Roles of sampling and exotic
species richness. Biological Invasions 6: 495-504
Meyerson LA, Mooney HA (2007) Invasive alien species in an era of globalization. Frontiers in Ecology and
the Environments: 199-208
Pysek P, Hulme P, Simberloff D, Bacher S, Blackburn T, Carlton J, Foxcroft L, Genovesi P, Jeschke J, Kuhn I,
Liebhold A, Mandrak N, Meyerson L, Pauchard A, Pergl J, Roy H, Richardson D (2020) Scientists' warning
on invasive alien species. Biological Reviews. doi:10.1111/brv.12627
Randall JM (1996) Weed control for the preservation of biological diversity. Weed Technology 10: 370-
383
Ricciardi A, lacarella JC, Aldridge DC, Blackburn TM, Carlton JT, Catford JA, Dick JTA, Hulme PE, Jeschke
JM, Liebhold AM, Lockwood JL, Maclsaac HJ, Meyerson LA, Pysek P, Richardson DM, Ruiz GM, Simberloff
D, Vila M, Wardle DA (2020) Four priority areas to advance invasion science in the face of rapid
environmental change. Environmental Reviews:l-23. doi:10.1139/er-2020-0088
Ringold PL, Magee TK, Peck DV (2008) Twelve invasive plant taxa in in US western riparian ecosystems.
Journal of North American Benthological Society 27: 949-966
Rooney TP, Wiegmann SM, Rogers DA, Waller DM (2004) Biotic impoverishment and homogenization in
unfragmented forest understory communities. Conservation Biology 18: 787-798
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Rowantree K (1991) An assessment of the potential impact of alien invasive vegetation on the
geomorphology of river channels in South Africa. South African Journal of Aquatic Science 17: 28-43
Sala A, Smith SD, Devitt DA (1996) Water use by Tamarix ramosissima and associated phreatophytes.
Ecological Applications 6: 888-898
Seabloom E, Williams J, Slayback D, Stoms D, Viers J, Dobson A (2006) Human impacts, plant invasion, and
imperiled plant species in California. Ecological Applications 16: 1338-1350
Simberloff D (2011) How common are invasion-induced ecosystem impacts? Biological Invasions 13
(5):1255-1268. doi:10.1007/sl0530-011-9956-3
USEPA (2016a) National Wetland Condition Assessment: 2011 Technical Report. EPA-843-R-15-006. .US
Environmental Protection Agency, Washington, DC. https://www.epa.gov/national-aquatic-resource-
surveys/national-wetland-condition-assessment-2011-results
USEPA (2016b) National Wetland Condition Assessment 2016: Field Operations Manual. EPA-843-R-15-
007. US Environmental Protection Agency, Washington D.C.
USEPA (2016c). National Wetland Condition Assessment 2016: Laboratory Operations Manual. EPA-843-
R-15-009. US Environmental Protection Agency, Office of Water, Washington, DC.
Vitousek PM, D'Antonio CM, Loope LL, Rejmanek M, Westbrooks R (1997) Introduced species: A
significant component of human-caused global change. New Zealand Journal of Ecology 21: 1-16
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Chapter 11: Human-Mediated Physical Alterations
Physical indicators of disturbance to a wetland site are one of the key categories of data (in addition to
chemical and biological indicators) used in wetland assessment for the NWCA. Six Human-Mediated
Physical Alteration (hereon, "Physical Alteration") indices were developed in addition to two indicators
that integrate scores from all six Physical Alteration indices. Thresholds associated with the six indices and
the two metrics were used for:
assigning disturbance class and
indicating stressor condition.
Physical Alteration thresholds used to assign disturbance class are discussed broadly in Chapter 6: (and
specifically in Section 6.3), while thresholds used to indicate stressor condition are provided in Section
11.5 at the end of this chapter. Note that the disturbance class thresholds differ from the stressor
condition thresholds. The methods used to develop the six Physical Alteration indices are discussed in the
following subsections of this chapter.
11.1 Data Collection
In both the 2011 and 2016 NWCAs, two separate protocols (and, thus, two forms) were used in the field
to collect data pertaining to physical disturbances (Figure 11-1, USEPA 2011a, 2016a):
The Hydrology field protocol and corresponding H-l Form instructed crews to identify and record
the presence of a set of stressors (hereon referred to as "items") within the Assessment Area
(AA).
The Buffer field protocol and corresponding B-l Form instructed crews to identify and record the
presence of a set of items within six categories in 13 plots: one plot at the center of AA and
twelve 100-m2 plots located along transects outside of the AA (four plots at each of the cardinal
directions at 40m, 85m, and 130m from the AA center).
On both the H-l and B-l Forms, field crews were instructed to use the "other" bubbles to identify
and describe observations of disturbances that were not adequately captured in the provided lists.
Although the H-l and B-l Forms changed slightly between 2011 and 2016, the protocols and the
majority of items on the forms remained the same.
Logic checks of the data from the field forms identified potential issues that were resolved by the
NWCA Technical Analysis Team. For example, unless the PLOT_NOT_SAMPLED bubble was filled for
the plot on the B-l Form to indicate that it was not evaluated, buffer plots that had no filled bubbles
were assumed to have no observed disturbances. In addition, the NWCA Technical Analysis Team
evaluated all "other" write-ins on both the H-l and B-l Forms and determined whether the
observation was valid (i.e., a disturbance). Any write-in that was unclear (i.e., not well-described, for
example, "woody debris") or considered a natural disturbance was excluded from the analysis. For
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example, "earthworms", "gopher activity", and "beaver run" were considered to be natural and not
reflective of anthropogenic disturbance to the site, and, therefore, were not used in the analysis.
Figure 11-1. The entire AA was evaluated using the H-l Form and 13 buffer plots were evaluated using the B-l
Form.
11.2 Development of Physical Alteration Indices
Six physical indices of disturbance were developed from the data collected from the H-l and B-l Forms
(USEPA 2011a, 2016a) and include Vegetation Removal (VEGRMV), Vegetation Replacement (VEGRPL),
Water Addition/Subtraction (WADSUB), Flow Obstruction (WOBSTR), Soil Hardening (SOHARD), and
Surface Modification (SOMODF) (Table 11-1). These indices are collectively referred to as "Human-
Mediated Physical Alterations" and indicate human impacts to the three components that define
wetlands: vegetation (Vegetation Removal, Vegetation Replacement), hydrology (Water
Addition/Subtraction, Flow Obstruction), and soils (Soil Hardening, Surface Modification). Each of the six
indices is composed of eight Physical Alteration metrics. To build the metrics, we started with the H-l and
B-l Forms and combined, simplified, and reorganized all the listed items and the relevant "other" items
into 48 Physical Alteration metrics10. Items that were repeated on the H-l and B-l Forms were only
counted once in the AA (if observed on both forms) to eliminate double-counting.
10 These metric categories are used to bridge analyses between 2011/2016 and 2021, where the Physical Alteration
Form and field protocol is used for the first time.
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Table 11-1. Six indices of human-mediated physical alterations and the 48 metrics crosswalked from items on the 2011 and 2016 H-l Hydrology or B-l Buffer
Forms. Note that the write-in "others" are numerous and not all are included in this table.
Physical
Alteration
Index
Physical Alteration Metrics
H-l Hydrology Form Items Included
B-l Buffer Form Items Included
Forest Clear Cut
Forest Clear Cut
"5
Forest Selective Cut
Forest Selective Cut
o
a >
<*Ķ 5
Vegetation Damage from Insects
Tree Canopy Herbivory (insect)
Herbicide/Pesticide Use
Herbicide/Pesticide Use
C 0Ģ
O 13
Shrub/Tree Browsing
Shrub Layer Browsed (wild or domestic)
UJ
IS >
Grass/Forb Grazing
Highly Grazed Grasses (overall <3" high)
a
Ķto
Mowing/Pruning/Clearing
Other: "Right of Way"
Mowing/Shrub Cutting
>
Human-Altered Fire Regime
N/A
Recently Burned Forest (canopy)
Recently Burned Grassland (blackened)
Abandoned Crop Field/Historical
Fallow Field (old - grass, shrubs, trees)
Cultivation
Other: "Historic Cultivation"
4-ŧ
c
a>
E
Recent Fallow/Resting Crop Field
Fallow Field (recent - resting row crop field)
Lawn/Park/Cemetery /Golf Course
N/A
Golf Course
u
rr
Lawn/Park
a. a.
ai q:
DC U
c UJ
o >
Other: "Garden", "Landscape"
Silviculture/Tree
N/A
Orchard/Nursery
Plantation/Orchard/Nursery
Silviculture/Tree Plantation
a>
Active Row or Field Crop
Row Crops - Tilling
ai
>
Range (passively managed)
Range
Pasture (actively managed)
Pasture/Hay
Nonnative Pest Plants
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Table 11-1 (continued)
Physical
Alteration
Index
Physical Alteration Metrics
H-l Hydrology Form Items Included
B-l Buffer Form Items Included
Ditch/Channelized Stream
Culverts & Ditching: Ditches
Ditches, Channelization
(human-made)
Culverts & Ditching: Channelized Streams
Inlets, Outlets
Culvert (corrugated pipe, arch,
Culverts & Ditching: Corrugated Pipe
N/A
box)
Culverts & Ditching: Box
C
O
Point Source/Pipe (effluent,
Pipes: Sewer Outfall
Point Source/Pipe (effluent or stormwater)
IS
sewer, storm water)
Pipes: Standpipe Outflow
-Q _
Tile Drainage/Drain Tiles
Field Drainage Tiling
Drain Tiling
3 CO
JC. D
Irrigation
Pumps: Irrigation
Irrigation
s=
5 <
Water Withdrawal Pump
Pumps: Other
N/A
s 1
Pumps: Water Supply
Impervious Surface Input
N/A
Impervious Surface Input (sheetflow)
ai
(sheetflow)
5
Human-mediated Shallow
Channels (ruts)
Shallow Channels: Vehicle Ruts
Shallow Channels: Abandoned Road
Shallow Channels: Eroded Foot Paths
Shallow Channels: Trails
Shallow Channels: Animal Trampling
N/A
Dike/Berm/Levee
Damming Features: Dikes
Damming Features: Berms
Dam (human-made or beaver-
Damming Features: Dams
Dike/Dam/Road/RR Bed (impedeflow)
modified structure)
Wall/Riprap
Wall/Riprap
0
1 H
Trash/Soil/Gravel/Spoil/Organic
N/A
Fill/Spoil Banks
Debris Heap (human-made)
W CO
O
Road/Railroad/Walkway (raised
Damming Features: Roads (all types)
N/A
St
_o
bed)
Damming Features: Railroad Bed
Water Level Control Structure
N/A
Water Level Control Structure
Other: "tide gates"
Pond/Retention Basin/Quarry
N/A
Other: "Wastewater Lagoon", "Stocked Pond", "Created Pond"
(human-made)
Silvicultural/Agricu Itural
Other: "Pine Plantation Bedding"
N/A
Mounding of Soil
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Table 11-1 (continued)
Physical
Alteration
Index
Physical Alteration Metrics
H-l Hydrology Form Items Included
B-l Buffer Form Items Included
Soil Hardening
(SOHARD)
Oil/Gas/Utility
Wells/Drilling/Pipeline
Oil/Gas Wells/Drilling
Other: "Gas Pipeline"
Soil
Compaction/Pugging/Wallows
N/A
Confined Animal Feeding
Dairy (on 2011 B-l Form only)
Livestock or Domesticated Animals (on 2016 B-l Form Only)
Soil Compaction (animal or human)
Non-Paved Trail
Impervious Surfaces: Compacted non-paved (on 2016
H-l Form only)
Trails
Vehicle Rut/Off-Road Vehicle
Damage
N/A
Off road Vehicle Damage
Other: "Vehicle Ruts"
Unpaved Road (gravel, aggregate,
dirt, sand)
N/A
Road (paved or unpaved) (on 2016 B-l Form only)
Road - Gravel (on 2011 B-l Form only)
Paved Road (asphalt, concrete,
chip & seal)
Impervious Surfaces: Roads (on 2011 H-l Form only)
Road - Two Lane (on 2011 B-l Form only)
Road - Four Lane (on 2011 B-l Form only)
Other Impervious Surface
(building, parking lot, drive)
Impervious Surfaces: Asphalt
Impervious Surfaces: Concrete
Parking Lot/Pavement
Rural Residential
Suburban Residential
Urban/Multifamily
Other: "General Structure"
Piling/Utility Pole/RR Track
(fence, dock, boardwalk)
N/A
Power Line
Other: "Boardwalk", "Fence pilings"
Surface Modification
(SOMODF)
Conspicuous Trash/Dumping
Dumping
Trash
Soil/Gravel/Spoil/Organic Debris
Heap (human-made)
N/A
Other: "Slash", 'Trees", "Wood Pilings"
Landfill (active or historic)
Landfill
Excavation/Dredging
Excavation/Dredging
Excavation, Dredging
Gravel Pit
Mine (surface/underground)
Mine (surface/underground)
Soil Deposition/Sedimentation
Recent Sedimentation
Freshly Deposited Sediment (unvegetated)
Soil Erosion/Oxidation/
Subsidence (human-mediated)
N/A
Soil Erosion/Deposition (from wind, water, or overuse)
Soil Loss/Root Exposure
Soil Tilling/Plowing/
Disking/Harrowing
N/A
Other: "Soil Tilling"
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11.3 Scoring Each of the Six Physical Alteration Indices
For each site, each of the six Physical Alteration indices (i.e., VEGRMV, VEGRPL, WADSUB, WOBSTR,
SOHARD, and SOMODF) was scored using a proximity-weighted scheme (illustrated in Figure 11-2), with
observations in the AA receiving the highest scores and observations in the furthest buffer plots from the
AA receiving the lowest scores. The following steps describe the methods for calculating the site score for
any one of the six Physical Alteration indices:
1. First, each of the six Physical Alteration indices for the AA was scored using data from the H-l
Form and only the center buffer plot of the B-l Form. Each metric with observed items11 in the
AA scored 25 points. For each of the six indices, the total points of the metrics in the AA were
summed as PALTaa so that the highest score any one index could receive in the AA was 200 points
(i.e., 8 metrics x 25 points).
2. Next, the 12 buffer plots outside the AA were scored using proximity-weighting (Kaufmann et al.
2014), with each metric with observed items in the inner-ring plots scoring 4 points, middle-ring
plots scoring 2 points, and outer-ring plots scoring 1 point. For each of the six indices, the total
points of the metrics with observed items were summed for the sampled plots (PALTbuffer).
Maximum scores were: 32 points in an inner-ring buffer plot (i.e., 8 metrics x 4 points), 16 points
in a middle-ring buffer plot (i.e., 8 metrics x 2 points), and 8 points in an outer-ring buffer plot
(i.e., 8 metrics x 1 point).
If any of the buffer plots were not sampled in the field, the points were redistributed among the
number of sampled plots within the same ring; for example, if only two of four plots were
sampled in the inner ring, each metric with observed items would be scored as 8 points (i.e.,
instead of the 4 points used when all four plots were sampled).
3. Finally, the total for each of the six physical indices (VEGRMV, VEGRPL, WADSUB, WOBSTR,
SOHARD, and SOMODF) was calculated. The calculation for determining an overall site (PALTSite)
score for any one of the six physical alteration indices is the sum of the PALT scores for the AA
and buffer, i.e. PALTaa + PALTbuffer = PALTsite.
Note that Field crews may have observed multiple items on the H-l or B-l Forms pertaining to a single
metric. Even if multiple items associated with a metric were observed, the metric was scored only once.
For example, if a field crew marked observations for items "Gravel Pit" and "Excavation, Dredging" on the
B-l Form for the same inner-ring buffer plot, the metric "Excavation/Dredging" only received one score of
4 points. This example, in which "Gravel Pit" and "Excavation, Dredging" are essentially the same
disturbance, also illustrates how the metrics reduce double-counting (as opposed to scoring each
observed item).
11 Recall from Table 11-1 that multiple items from the H-l and B-l Forms may be included under any given metric,
yet the metric receives only one score even if multiple items associated with that metric are observed.
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2016 NWCA Physical Alteration Metric Scoring
Scale:
40 m
i 1
Buffer Area
o Point
Ķ
Inner Ring Buffer Plots
Numbers are
IS! Assessment Area
Ķ
Middle Ring Buffer Plots
the points per
Buffer Area
Ķ
Outer Ring Buffer Plots
observed metric
Six Physical Alteration indices representing Vegetation Removal, Vegetation Replacement, Water
Addition/Substraction, Water Obstruction, Soil Hardening, Surface Modification, each with 8
metrics (i.e., checkboxes from 2016 NWCA B-1 and H-1 forms), are evaluated in the Assessment
Area and in 12 Buffer Plots. Metric scoring is based on proximity to the Point, with a maximum
score of 424 per index for the site. Scoring is as follows:
Points per
Number
Distance from
Maximum Index Score
Location
Observed Metric
of Plots
the Point
Plot Area
per Plot
per Location
Assessment Area
25
1
0-40 m
5000 m2(0.5 ha)
200
200
Inner Ring Buffer
4
4
40-50 m
100 m2
32
128
Middle Ring Buffer
2
4
85-95 m
100 m2
16
64
Outer Ring Buffer
1
4
130-140 m
100 m2
8
32
Figure 11-2. 2016 NWCA Physical Alteration Metric Scoring, with the points assigned to each observation located
in the respective area (either the AA or buffer plot). Note that observations in the center buffer plot (within the
AA) also received 25 points.
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11.4 Physical Alteration Screen Scoring (PALT_ANY and PALT_SUM)
Two Physical Alteration screens that integrate scores from all six Physical Alteration indices are calculated
for each site: PALT_ANY and PALT_SUM. Both of these screens are used to set thresholds assigning
disturbance class (Section 6.3), and PALT_SUM (in addition to scores for each index as described in the
previous section) are used to set thresholds for indicating stressor condition.
11.4.1 PALT_ANY
PALT_ANY indicates the maximum degree of Human-Mediated Physical Alterations for any index and is
calculated as the maximum Physical Alteration index score among all six Physical Alteration index scores
for a site.
For any one index at a sampled site, there are only 8 metrics that can be scored within 13 locations (the
AA and 12 buffer plots); therefore, the maximum PALT_ANY score for the site is 424 points:
25 points * 1 AA *8 metrics
+ 4 points * 4 inner ring buffer plots * 8 metrics
+ 2 points * 4 middle ring buffer plots * 8 metrics
+ 1 point* 4 outer ring plots * 8 metrics
424 maximum points total per index
However, it is implausible that every single metric within an index would occur at the same time in the AA
and in all buffer plots. The observed maximum PALT_ANY score was 149 considering all unique sites (i.e.,
Index Visit, probability and handpicked sites) from 2011 and 2016.
11.4.2 PALT_SUM
PALT_SUM is a secondary screen that indicates the cumulative amount of Human-Mediated Physical
Alterations among all indices. It is calculated as the sum of all six Physical Alteration index scores for a
site. This screen was developed to detect instances, e.g., where several metric items were observed, but
the observations are dispersed across several Physical Alteration indices (i.e., no one index has a
particularly high score). Thus, a site may pass the threshold for the PALT_ANY screen and fail the
threshold for the PALT_SUM screen (but not vice versa).
With 424 total points per index, and six indices, the highest possible PALT_SUM score for a site is 2,544.
However, it is implausible that every single metric within an index would occur at the same time in the AA
and in all buffer plots, much less across all indices. The observed maximum PALT_SUM score was 396
considering all unique sites from 2011 and 2016.
11.5 Physical Alteration Stressor Condition Thresholds
Like other National Aquatic Resource Survey (NARS) assessments, the NWCA data was used to identify
connections between the presence of indicators of stress and ecological condition. Anthropogenic
stressors act to degrade ecological condition, and consequently, evaluation of indicators of stress is an
important component of an assessment method (Fennessy et al. 2007). Using physical, chemical, and
human-health indicators of stress, the NWCA analysis examined a variety of stressor data to detect
factors likely affecting wetland condition. The use of stressor data is consistent with current approaches
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to assess wetlands and recognizes the connection between the presence of stressors and wetland
condition. For example, rapid assessment methods have been developed which use only stressors as
indicators of condition (e.g., the Delaware Rapid Assessment Method (Jacobs 2007)) and models
comprising an HGM assessment (a Level 3, intensive assessment) use stressors as variables (e.g.,
Whigham et al. 2007, Wardrop et al. 2007). The data sources for the indicators of stressor condition used
in the NWCA analysis were primarily from field observations and soil and water chemistry samples
collected from the Assessment Area (AA) and its buffer at each sampled site.
Seven physical indicators of stressor condition are reported for the 2016 NWCA:
Vegetation Removal (VEGRMV),
Vegetation Replacement (VEGRPL),
Water Addition/Subtraction (WADSUB),
Flow Obstruction (WOBSTR),
Soil Hardening (SOHARD),
Surface Modification (SOMODF), and
Physical Alterations (PALT_SUM).
In contrast to the Disturbance Gradient, six individual Physical Alteration indices are used instead of the
PALT_ANY screen to indicate stressor condition. The reasoning for this decision to use the six individual
indices was to provide condition extent and relative and attributable risk associated with each of these
specific indicators.
For each of the seven Physical Alteration indicators, each site was assigned to "good", "fair", or "poor"
stressor condition based on thresholds for each indicator. The same national thresholds were used for all
seven indicators, with sites scoring:
0 points assigned to good stressor condition,
>50 points assigned to poor stressor condition, and
>0 and < 50 points (i.e., everything between good and poor) assigned to fair stressor condition.
These thresholds were chosen based on common sense for the good condition threshold (i.e., the
expectation for a good condition site is to have no observed physical alterations) and best professional
judgement for the poor condition threshold. For any one of the seven indicators of stressor condition, a
site assigned to poor stressor condition for that indicator, for example, may have:
two or more observed physical alteration metrics in the AA (scored 25 points each);
one observed physical alteration metric in the AA (scored 25 points) and two observed metrics in
each of half of the buffer plots (i.e., two metrics in two inner-ring buffer plots for 4 points each,
two metrics in two middle-ring buffer plots for 2 points each, and two metrics in two outer-ring
buffer plots for 1 point each); or
two observed physical alteration metrics in each of the 12 buffer plots.
An explanation of how stressor condition extent estimates, and relative and attributable risk are
calculated for each indicator of stressor condition is discussed explicitly in.
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11.6 Literature Cited
Fennessy MS, Jacobs AD, Kentula ME (2007) An evaluation of rapid methods for assessing the ecological
condition of wetlands. Wetlands 27: 543-560
Jacobs AD (2007) Delaware Rapid Assessment Procedure Version 4.1. Delaware Department of Natural
Resources and Environmental Control, Dover, DE
USEPA (2011a) National Wetland Condition Assessment: Field Operations Manual. EPA-843-R10-001. US
Environmental Protection Agency, Washington, DC
USEPA (2016a) National Wetland Condition Assessment 2016: Field Operations Manual. EPA-843-R-15-
007. US Environmental Protection Agency, Washington DC
Wardrop DH, Kentula ME, Jensen SF, Stevens Jr. DL, Hychka KC, Brooks RP (2007) Assessment of wetlands
in the Upper Juniata watershed in Pennsylvania, USA using the hydrogeomorphic approach. Wetlands 27:
432-445
Whigham DF, Jacobs AD, Weller DE, Jordan TE, Kentula ME, Jensen SF, Stevens Jr. DL (2007) Combining
HGM and EMAP procedures to assess wetlands at the watershed scale - Status of flats and non-tidal
riverine wetlands in the Nanticoke River Watershed, Delaware and Maryland (USA). Wetlands 27: 462-
478
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Chapter 12: Soil Heavy Metals
Chemical indicators of disturbance to a wetland site are one of the key categories of data (in addition to
physical and biological indicators). Soil heavy metals were established as the main chemical indicator of
disturbance for the 2011 NWCA (USEPA 2016a) and have been clearly associated with anthropogenic
disturbance (Alloway 2013, Nahlik et al. 2019). For the 2011 NWCA, natural background concentrations
were established using published values (in Alloway 2013) for terrestrial soils in, or as close to the US as
possible. For the 2016 NWCA, in part because of the larger number of sites and associated data available
after two surveys, the NWCA Analysis Team made the decision to update the heavy metal natural
background concentrations so they reflect the wetland soils in the NWCA sample population. Using these
updated natural background concentrations, a Heavy Metal Index (HMI) and an Enrichment Factor (EF)
metric based on soil heavy metal concentrations were developed. Thresholds associated with the HMI
and EF were used for:
assigning disturbance class and
indicating stressor condition.
Soil heavy metal thresholds used to assign disturbance class are discussed broadly in Chapter 6: (and
specifically in Section 6.4), while thresholds used to indicate stressor condition are provided in Section
12.4 at the end of this chapter. Note that the disturbance class thresholds differ from the stressor
condition thresholds. The methods used to develop the HMI and the EF are discussed in the following
subsections.
12.1 Data Collection
The Heavy Metal Index (HMI) and Enrichment Factors (EFs) are based on observational data and physical
samples collected from soil pits excavated each site according to the Soils Protocol in the NWCA Field
Operations Manual (USEPA 2011a, USEPA 2016b). Briefly, field crews excavated a soil pit with a maximum
depth of 125 cm in 2011 and of 100 cm in 2016. For each soil horizon, field crews described the soil
colors, characteristics, and soil type of each horizon. Additionally, field crews collected a bulk soil sample
(approximately 1.5 L) from boundary to boundary of the horizon and between one (in 2011) and three
(2016) bulk density samples from the top of each horizon. In 2016, field crews also collected a
Standardized Depth Soil Core (SDSC) from 0 cm (i.e., surface) to 10 cm deep. Each of these samples were
shipped to the US Department of Agriculture Natural Resources Conservation Service (NRCS) Kellogg Soil
Survey Laboratory in Lincoln, Nebraska for analysis following the procedures in the NWCA Laboratory
Operations Manual (USEPA 2011b).
Soil chemistry data returned from NRCS were merged with soil profile data (i.e., observational data)
collected by Field Crews. The resulting soil chemistry database was thoroughly inspected for quality
assurance. Using both manual screening and customized R code, potential data errors were identified.
Whenever large quantities of data are collected, it is not surprising for some errors related to data or
sample collection, recording, sample analysis, or data entry to occasionally occur. Therefore, the NWCA
established a number of cross-checks in the data collection and processing procedures within the
protocols and field forms, to allow identification and resolution of potential errors. Once the data were
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entered, quality assurance review was critical to identifying and resolving any errors potentially impacting
data quality.
Errors that could be resolved by inspecting the original field data forms were corrected in an annotated
soil chemistry database, with detailed notes of how the error was corrected. If the error could not be
resolved, the associated data were removed from the database (resulting in an "NA" in place of the value)
or flagged if the datum was suspect but could not be identified as being absolutely incorrect.
NRCS performed internal quality assurance on soil chemistry data. Some soil chemistry data returned by
NRCS was flagged, e.g., if it was below the practical quantitation limit (PQL) or minimum detection limit
(MDL) of the equipment using to analyze the samples. Table 12-1) provides information about the
meaning of the flags in the data.
In 2016, all values below the MDL were flagged by the NRCS lab as ND. These, in turn, were all changed
from 0 value to "NA" in the data files. All values above MDL but below PQL, flagged "L", were retained as
the same value the lab provided. For some analytes, the lab reported "0" values due to how values were
rounded, and these were retained in the data files. Note that there are some "0" values in the data files
that do not have an "L" or "ND" flag associated with them. These are values above the PQL but that still
round to zero because of rounding format used by NRCS. Values remaining in the database (particularly
from 2011) below the MDL were changed to half the specified MDL in the soil chemistry database.
Table 12-1. Table of NARS chemistry flag codes and their definitions.
NARS Flag Code
Definition
L
Result is below the practical quantitation limit (PQL)
ND
Result is below the method detection limit (MDL)
NA1
Not applicable when % carbon > 20
NA2
Not applicable from pretest
N
Insufficient sample for analysis
NF
No 2-20 mm fragments in sample
NAL
2-20 mm fragments present but not analyzed
In 2011, the Heavy Metal Index was developed using the uppermost horizon within the top 10 cm that
had soil chemistry data12. Most sites, approximately 97% of those sampled, had soil chemistry data that
began within the top 10 cm, although, the thickness of the horizon varied among sites. To address these
consistency issues, field crews collected a Standardized Depth Soil Core (SDSC) from the surface to 10-cm
deep at the soil pit of each site in 2016. A comparison of soil heavy metal concentrations from the
resampled sites (i.e., sites sampled both in 2011 and 2016) showed that 2011 data from the uppermost
horizon were, in most cases, highly correlated with 2016 data from the 10-cm deep SDSC (Figure 12-1).
Therefore, we used the uppermost horizon within the top 10 cm that had soil chemistry data for 2011
data and the SDSC for 2016 to develop heavy metal background concentrations and to calculate the
Enrichment Factors and the Heavy Metal Index. If data associated with the SDSC for 2016 were missing,
data associated with the uppermost horizon within the top 10 cm that had soil chemistry were
substituted.
12 In 2011, soil chemistry data were only generated for each soil layer greater than 8 cm in thickness, and nearly
one-quarter of the described soil layers (948 of 4444) were less than 8 cm thick and not sampled for soil chemistry.
Furthermore, the first layer, containing the most biologically active soil and most indicative of recent human
impacts, was not sampled at nearly one-third of the sites for soil chemistry because Layer 1 was less than 8 cm thick
(347 of 1082 sites).
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Heavy Metal Concentration (ppm) in 2011 Uppermost Horizon with
Soil Chemistry Data
Figure 12-1. Comparison of heavy metal concentrations (ppm) for 12 heavy metals measured in resampled sites,
with the 2011 uppermost horizon within the top 10 cm that had soil chemistry data on the x-axis and the 2016
Standardized Depth Soil Core that was collected from the surface to a depth of 10 cm on the y-axis. The correlation
statistics and the significance are reported as R and p-value (a = 0.05) in the upper left corner of each plot. NS =
Not Significant
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12.2 Development of Heavy Metal Background Concentrations
For the first NWCA conducted in 2011, natural background concentrations of 12 heavy metals (silver (Ag),
cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), nickel (Ni), lead (Pb), antimony (Sb), tin (Sn),
vanadium (V), tungsten (W), and zinc (Zn) that have known associations with anthropogenic activities
were established using published values (in Alloway 2013) for terrestrial soils in, or as close to the US as
possible (USEPA 2016a, Nahlik et al. 2019). In part because of the larger number of sites and associated
data available after two surveys, the NWCA Analysis Team made the decision to update the heavy metal
natural background concentrations of each of these 12 heavy metals so that they reflected the wetland
soils in the NWCA sample population. To do this, only Visit 1, Index Visit sites from 2011 and 2016 that
passed the both the PALT_ANY (see Section 11.4.1) and PALT_SUM (see Section 11.4.2) Physical
Alteration screens (i.e., candidate least-disturbed sites, see Chapter 6: Section 6.3) were used. Next, for
each heavy metal, the distributions of heavy metal concentrations were evaluated by region
(RPT_UNIT_5). Heavy metal background concentrations were set using the 75th percentiles of soil the
concentrations found in candidate least-disturbed sites (Figure 12-2). This method of using the 75th
percentile for setting thresholds is a common method used in the USEPA National Aquatic Resource
Surveys (NARS) as described in Herlihy et al. (2008, 2013) and USEPA (2016a). The heavy metal
background concentrations (ppm) are presented in Table 12-2.
Physical Alteration
Least-Disturbed Site Distribution
E
Q.
Q.
Ģ
O
"J
ra
+-ŧ
c
CD
O
c
o
o
+Ķ>
a>
>ŧ
>
re
a>
Percentiles:
95th
75th Expected Backgi
Concentration
50th
25th
5th
c
ound
Figure 12-2. Illustration of the 75th percentiles of soil heavy metal concentrations of sites that passed the Physical
Alteration screens (i.e., deemed to be candidate least-disturbed sites), used to set expected background
concentrations for soil heavy metals. Note that this method is conducted for each of the 12 heavy metals
evaluated and by each of five regions in RPT_UNIT_5.
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Table 12-2. Heavy metal background concentrations (ppm) for wetlands in five regions (RPT_UNIT_5) of the United
States.
Eastern Mts &
Tidal Saline
Inland Coastal
Upper Midwest
(TDL)
Plains (ICP)
(EMU)
Plains (PLN)
West (WST)
Silver (Ag)
0.15
0.09
0.15
0.17
0.19
Cadmium (Cd)
0.15
0.26
0.82
0.55
0.46
Cobalt (Co)
7.30
8.06
5.17
9.17
8.99
Chromium (Cr)
53.8
39.4
22.9
38.8
39.7
Copper(Cu)
17.2
14.2
15.2
19.5
28.5
Nickel (Ni)
21.4
18.3
13.8
23.3
22.6
Lead (Pb)
25.1
24.6
37.4
26.4
24.3
Antimony (Sb)
0.29
0.31
0.40
0.34
0.47
Tin (Sn)
1.69
1.47
1.41
1.45
1.46
Vanadium (V)
75.8
52.9
33.9
65.6
65.4
Tungsten (W)
0.06
0.05
0.18
0.04
0.19
Zinc (Zn)
73.0
64.6
61.7
97.2
81.7
12.3 Calculation of Enrichment Factor (EF) Values and the Heavy Metal Index
(HMI)
Enrichment factor (EF) values and the Heavy Metal Index (HMI) are calculated for each site based on the
heavy metal background concentrations. Both the HMI and the maximum EF value across all 12 heavy
metals (EF_MAX) are used to set thresholds assigning disturbance class (Section 6.4Chemical Screens and
Thresholds) and for indicating stressor condition (discussed in the following Section 12.4).
12.3.1 Enrichment Factor (EF)
The Heavy Metal Index calculation used for the 2011 NWCA (USEPA 2016a, Nahlik et al. 2019) was
improved and updated for the 2016 NWCA by incorporating Enrichment Factors (EFs). EFs capture the
degree to which soils are enriched with heavy metals and, for each metal, are calculated as:
Enrichment Factor = EF
Ķ(
Observed heavy metal concentration at a site \
Regional 75th percentile heavy metal background]
This calculation is similar to that reported by Chen et al. (2007); however, unlike the methods reported in
Chen et al. 2007, heavy metal concentrations were not normalized to the textural characteristics of the
soils. Due to the wide range of wetland types and soil types sampled in the NWCA, the background
concentrations estimated for wetlands (Table 12-2) were used as the denominators in the EF calculations.
To interpret the results, the same enrichment factor scale reported by Chen et al. (2007) was used and
are reported in the following table (Table 12-3):
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Table 12-3. Interpretation of Enrichment Factor (EF) results
Enrichment Factor (EF)
Interpretation
EF <1
No enrichment
EF <3
Minor enrichment
EF = 3-5
Moderate enrichment
EF = 5-10
Moderately severe enrichment
EF = 10-25
Severe enrichment
EF = 25-50
Very severe enrichment
EF > 50
Extremely severe enrichment
12.3.2 Heavy Metal Index (HMI)
Next, the revised Heavy Metal Index (HMI) was calculated based on the number of soil heavy metals with
EFs greater or equal to three, indicating moderate enrichment or greater, depending on the EF values.
The HMI is calculated as:
^^number of heavy metals with EF > 3 = Heavy Metal Index = HMI
where the maximum the HMI can be for any site is 12 (i.e., if all 12 heavy metal EFs are equal to or
greater than 3).
EF_MAX indicates the highest degree to which a site was contaminated by any of the heavy metals and is
calculated for each site as:
Maximum Enrichment Factor = EF MAX = maximum value of the 12 heavy metal EFs
The EF_MAX detects sites that have at least one heavy metal in high concentrations above the expected
background. This indicator is important, as some sites have only one principal contaminant, so the HMI can
be low even though one or more heavy metals are severely enriched, which indicate stress to the wetland.
12.4 Soil Heavy Metal Stressor Condition Thresholds
Like other National Aquatic Resource Survey (NARS) assessments, the NWCA data was used to identify
connections between the presence of indicators of stress and ecological condition. Anthropogenic
stressors act to degrade ecological condition, and consequently, evaluation of indicators of stress is an
important component of an assessment method (Fennessy et al. 2007). Using physical, chemical, and
human-health indicators of stress, the NWCA analysis examined a variety of stressor data to detect
factors likely affecting wetland condition. The use of stressor data is consistent with current approaches
to assess wetlands and recognizes the connection between the presence of stressors and wetland
condition. For example, rapid assessment methods have been developed which use only stressors as
indicators of condition (e.g., the Delaware Rapid Assessment Method (Jacobs 2007)) and models
comprising an HGM assessment (a Level 3, intensive assessment) use stressors as variables (e.g.,
Whigham et al. 2007, Wardrop et al. 2007).The data sources for the indicators of stressor condition used
in the NWCA analysis were primarily from field observations and soil and water chemistry samples
collected from the Assessment Area (AA) and its buffer at each sampled site.
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One soil heavy metal indicator of stressor condition is reported for the 2016 NWCA. This chemical
indicator considers both the HMI score and the EF_MAX score at a site to assign a site to a "good", "fair",
or "poor" stressor condition. National thresholds were used for the soil heavy metal indicator, with sites
with:
HMI < 1 and EF_MAX < 5 assigned to good stressor condition,
HMI >3 or EF_MAX > 10 assigned to poor stressor condition, and
HMI = 2-3 or EF_MAX = 6-10 (i.e., everything between good and poor) assigned to fair stressor
condition.
These thresholds were chosen based on best professional judgement. A site assigned to good heavy
metal stressor condition can have no more than one heavy metal (of the 12 included in the HMI) that is
more than moderately enriched. A site assigned to poor soil heavy metal stressor condition, for example,
may have:
more than three heavy metals that are more than moderately enriched, or
at least one heavy metal that is severely (or very severely, or extremely severely) enriched.
An explanation of how stressor condition extent estimates, and relative and attributable risk are
calculated for the soil heavy metal indicator of stressor condition is discussed explicitly in Chapter 15.
12.5 Literature Cited
Alloway BJ (ed) (2013) Heavy metals in soils: trace metals and metalloids in soils and their bioavailabilaity.
Springer, New York, NY
Chen C, Kao C, Chen C, Dong C (2007) Distribution and accumulation of heavy metals in the sediments of
Kaohsiung Harbor, Taiwan. Chemosphere 66: 1431-1440
Fennessy MS, Jacobs AD, Kentula ME (2007) An evaluation of rapid methods for assessing the ecological
condition of wetlands. Wetlands 27: 543-560
Jacobs AD (2007) Delaware Rapid Assessment Procedure Version 4.1. Delaware Department of Natural
Resources and Environmental Control, Dover, DE
Nahlik AM, Blocksom KA, Herlihy AT, Kentula ME, Magee TK, Paulsen SG (2019) Use of national-scale data
to examine human-mediated additions of heavy metals to wetland soils of the US. Environmental
Monitoring and Assessment 191 (SI): 336. DOI: 10.1007/sl0661-019-7315-5
USEPA (2011a) National Wetland Condition Assessment: Field Operations Manual. EPA-843-R10-001. US
Environmental Protection Agency, Washington, DC
USEPA (2011b) National Wetland Condition Assessment: Laboratory Operations Manual. EPA-843-R10-
002. US Environmental Protection Agency, Washington, DC
USEPA (2016a) National Wetland Condition Assessment 2011 Technical Report. EPA-843-R-15-006. US
Environmental Protection Agency, Washington DC
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USEPA (2016b) National Wetland Condition Assessment 2016: Field Operations Manual. EPA-843-R-15-
007. US Environmental Protection Agency, Washington DC
Wardrop DH, Kentula ME, Jensen SF, Stevens Jr. DL, Hychka KC, Brooks RP (2007) Assessment of wetlands
in the Upper Juniata watershed in Pennsylvania, USA using the hydrogeomorphic approach. Wetlands 27:
432-445
Whigham DF, Jacobs AD, Weller DE, Jordan TE, Kentula ME, Jensen SF, Stevens Jr. DL (2007) Combining
HGM and EMAP procedures to assess wetlands at the watershed scale - Status of flats and non-tidal
riverine wetlands in the Nanticoke River Watershed, Delaware and Maryland (USA). Wetlands 27: 462-
478
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Chapter 13: Water Chemistry
Chemical indicators of disturbance to a wetland site are one of the key categories of data (in addition to
physical and biological indicators). In the 2011 NWCA, water chemistry was introduced as a research
indicator - in part due to the fact that only a subset of the NWCA sites were able to be sampled for water
chemistry. Here, we present two water chemistry parameters, total nitrogen (TN) and total phosphorus
(TP) concentrations, as core indicators of stressor condition. In order to use TN and TP as indicators of
stressor condition, several distinct procedures needed to be completed to provide the basis for setting TN
and TP stressor condition thresholds and include:
Describing the population of wetlands sampled for water chemistry (Section 13.3);
Developing Physical-Alterations-Possibly-Affecting-Chemicals (CALT) indices for use in screening
sites to establish the disturbance gradient for sites sampled for water chemistry;
Using two CALT indices and four landscape metrics and their associated disturbance thresholds to
screen sites and assign disturbance classes (i.e., "least disturbed", "intermediate disturbed", and
"most disturbed"); and, finally,
Calculating "good", "fair", and "poor" thresholds for stressor condition using the 75th and 95th
percentiles of TN and TP concentrations among least-disturbed sites sampled for water
chemistry.
These steps are discussed in this chapter so that, ultimately, the extent of TN and TP stressor conditions
may be reported for wetlands sampled for water chemistry in the final 2016 NWCA Report.
13.1 Data Collection
Water chemistry samples were collected at all wetlands having sufficient sampleable surface water within
the 0.5 ha assessment area (AA) during the sampling visit. Because surface water was required to be
within the AA, not all sites yielded a water sample - even when surface water was present elsewhere in
the wetland. Furthermore, some wetlands lacked surface water entirely during the sampling visit. Sixty-
four percent of probability and handpicked sites across both Visit 1 and Visit 2 yielded a water sample in
2016. The percentage of 2016 sites with water chemistry samples is approximately 10% higher than in the
2011 NWCA, largely attributed to the removal of the 2011 sampling location water-depth-minimum of 15
cm.
Laboratory analyses were conducted per methods detailed in 2016 Field Operations Manual (USEPA
2016a) and in Table 13-1. In summary, water chemistry sampling consisted of using a dipper to fill 1) a 1L
bottle that was filtered on-site for later chlorophyll-a analysis, and 2) a 1L cubitainer for laboratory
analysis of other water chemistry parameters. The chlorophyll filters and cubitainers were chilled
immediately and express-shipped to the USEPA Pacific Ecological Systems Division (PESD) in Corvallis,
Oregon for analyses.
In addition to the analytes measured in the lab (Table 13-1), conductivity and pH were measured in the
field at some sites (at the field crew's discretion). While most analytes were measured in both the 2011
and 2016 surveys, four analytes were added to the 2016 analysis: turbidity, DOC, chloride, and sulfate.
Chloride and sulfate, important indicators of anthropogenic disturbance (e.g., water softeners, fertilizers,
and road salt for chloride (Herlihy et al. 1998) and mine influences for sulfate (Herlihy et al. 1990)), were
only measured in freshwater samples. There is no expectation that chloride and sulfate concentrations in
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saltwater would be informative of anthropogenic impacts (i.e., concentrations would reflect the saltwater
influence).
Table 13-1. Water chemistry analytes measured in the laboratory, with their associated units and a summary of
methods.
Analyte
Units
Summary of Method
Conductivity
|iS/cm at 25°C
Electrolytic
pH (laboratory)
Standard (Std) Units
Automated with autotitrator and combination pH electrode
or manual electrolytic analysis
Turbidity
Nephelometric Turbidity
Units (NTU)
Automated nephelometric analysis ormanual turbidmetric
analysis (high turbidity samples)
Dissolved Organic
Carbon (DOC)
mg-C/L
UV promoted persulfate oxidation to CO2 with infrared
detection
Ammonia (NH3)
mg-N/L
FIA automated colorimetric (with use of salicylate,
dichloroisocyan urate)
Nitrate-Nitrite (NO3-NO2)
mg-N/L
Ion chromatography (freshwater samples) or FIA
automated colorimetric (cadmium reduction for brackish or
freshwater samples)
Total Nitrogen (TN)
mg/L
Persulfate digestion followed by FIA automated
colorimetric analysis
Total Phosphorus (TP)
mg-P/L
Persulfate digestion followed by FIA automated
colorimetric analysis
Sulfate (SO4)
mg-SO^L
Ion Chromatography (freshwater samples only)
Chloride (CI)
mg-CI/L
Ion Chromatography (freshwater samples only)
Chlorophyll-a
M-g/L
90% acetone exraction followed by fluorometry analysis
13.2 Data Validation
Data validation refers to the process of checking for completeness and repeatability the data, which
begins upon receiving the data from participating laboratories through the assembly of data into results
files. Validation is especially important for water chemistry data because samples were processed by
multiple state and regional laboratories across the US. Data validation was completed for all water
chemistry parameters using completeness-checking, repeatability-checking, and evaluation of cross-visit
repeatability. Details about how each of these methods were applied to the data are discussed in the
following paragraphs.
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Completeness-checking refers to checking and addressing any missing values or any data values that
should be set to missing because of documented collection or analysis concerns. Water chemistry
analytes whose values were flagged as being below the laboratory's minimum detection limit (MDL) were
generally assigned a value of half that detection limit. Per Hornung and Reed (1990), the practice of using
half the MDL more accurately preserves the data distribution properties than alternatives, such as setting
below-detection values to zero. An exception to this general rule was made for certain chlorophyll-a and
ammonia samples. These few samples had high detection limits due to either 1) the amount of water
filtered in the field or 2) the amount of dilution that occurred in the laboratory before analysis to get the
sample within instrument range. To avoid over-inferring concentration values these samples that were
poorly characterized by such high detection limits, flagged samples with MDLs above 2.0 |ag/L for
chlorophyll-a and 0.03 mg-N/L for ammonia were set to "missing" (i.e., "NA" in the database).
Data for TN and TP were complete across the dataset (i.e., no missing values), as were data for ammonia,
nitrate/nitrite, conductivity, pH, and turbidity. Chloride and sulfate data, which are associated exclusively
with freshwater, were not analyzed (i.e., missing) from 55 saltwater sites identified by high conductivity
levels. Several DOC values were missing because one laboratory erroneously analyzed total organic
carbon (TOC). Chlorophyll-a values were laboratory-reported as "missing" from four sites due to problems
with filter type or filter volume, and an additional 27 sites were set to "missing" due to flagged samples
with MDLs above 2.0 |ag/L. While the dataset started as complete, 26 ammonia values were set to
"missing" due to flagged samples with MDLs above 0.03 mg-N/L, and five cases were missing because the
ammonia concentration of the sample exceeded that of TN (indicating measurement error).
Repeatability-checking included the comparison of analyte values between Visit 1 and Visit 2 (for the
approximately 10% of sites where a second visit was done), and comparison of any field measurements
for conductivity and pH to the corresponding laboratory measurements. The field versus laboratory
comparisons revealed several cases of conductivity being recorded in the wrong units in the field (e.g.,
milliSiemens rather than microSiemens per centimeter), likely because of limitations on the field meter
display. Once these were corrected, the Pearson correlation between field-measured and lab-measured
conductivity was extremely high (r = 0.99), confirming that conductivity is consistent between laboratory
and field measurements. On the other hand, there are consistent differences in laboratory-measured pH
and field-measured pH (r = 0.72) - likely driven by varying degrees of carbon dioxide (C02) saturation.
Parallel to findings from the 2011 survey (USEPA 2016c), laboratory-measured pH values tended to be
higher than those measured in the field for acidic waters (i.e., pH < 7.0), while laboratory-measured pH
values tended to be lower than those measured in the field for alkaline waters (i.e., pH > 7.0).
Cross-visit repeatability can be assessed directly by analyzing the correlation of values between visits to
the same site within the same year (i.e., Visit 1 compared to Visit 2). However, the interpretation of cross-
visit repeatability is affected by the rate at which below-detection (i.e., MDL) values occur for any given
analyte. Abundant data below the MDL (e.g., NH4 and N03) results in the same low below detection limit
values, leaving few data to correlate.
Comparing the variance associated with a sampling site (signal) to the variance associated with repeated
visits to the same site (noise) results in the Signal-to-Noise Ratio (S:N) (Kaufmann et al. 1999, 2014),
which is described in detail in Chapter 8:, Section 8.5.2. All sites are included in the signal, whereas only
revisit sites contribute to the noise component. S:N is a useful for discerning environmentally-significant
patterns for an analyte against the background of its typical variability. Analytes with high S:N are more
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likely to show consistent responses, and S:N values < 1 indicate that sampling a site twice yields as much
or more variability as sampling two different sites (Stoddard et al. 2008).
Considering all sites sampled in the 2016 NWCA, only nitrate-nitrite, chloride, and TN had S:N < 3 (Table
13-2), indicating that these analytes had high within-site variability. Chloride (and to a lesser extent
sulfate), which were not measured in saltwater sites but may have been measured in brackish sites
(discussed previously in Section 13.1), had low S:N for all 2016 sites as a result of high between-visit
variability in estuarine sites. However, when S:N is calculated for inland (i.e., freshwater) sites, the ratio
for both chloride and sulfate increased to > 10.
Table 13-2. Variability and repeatability of water chemistry analytes measured in the 2016 NWCA, including below-
detection rates for all 2016 NWCA sites (Visit 1 and Visit 2, probability and handpicked), cross-visit correlations
based on the 61 revisit sites, and Signal-to-Noise ratios (S:N) for all sites and inland (freshwater) sites.
Analyte
Below-Detection
Rate
Cross-Visit
Pearson
Correlation (r)
S:N
S:N
(All 2016 Sites)
(2016 Inland Sites)
Conductivity
None
0.97
20.9
29.7
pH (laboratory)
NA
0.88
15.9
18.3
Turbidity
0.3%
0.58
40.1
37.6
Dissolved Organic Carbon (DOC)
0.2%
0.87
6.67
7.11
Ammonia (NHs)
37.1%
0.10
3.94
4.30
Nitrate-Nitrite (NO3-NO2)
33.6%
0.28
1.97
3.11
Total Nitrogen (TN)
None
0.39
1.98
1.55
Total Phosphorus (TP)
0.3%
0.85
17.6
14.7
Sulfate (SO4)
2.7%
0.94
3.09
12.9
Chloride (CI)
0.3%
0.99
1.40
10.3
Chlorophyll-a
7.3%
0.62
11.9
13.1
13.3 Establishing a Disturbance Gradient for Sites Sampled for Water
Chemistry
The wetland population represented by water chemistry is a subset of the larger NWCA wetland
population; 56% and 65% of the wetlands in 2011 and 2016, respectively, sampled across both Visit 1 and
Visit 2 had sufficient surface water to collect and analyze. Thus, water chemistry data were excluded from
the generation of the disturbance gradient used to identify abiotic and final least- and most-disturbed
sites (i.e., ABIOTIC_REF_NWCA and REF_NWCA), discussed in Chapter 6:.
However, in order to develop chemical indicators of stressor condition based on TN and TP measured in
the water column (presented later in Section 13.4), it is necessary to create a specially-defined
disturbance gradient for the subset of sites that were sampled for water chemistry. To establish a water
chemistry disturbance gradient, all 1,198 unique probability and handpicked sites across both the 2011
NWCA and the 2016 NWCA that were sampled for water chemistry (Table 13-3) were used. The general
process for setting least-disturbed and most-disturbed thresholds, and for assigning disturbance class is
discussed in Chapter 6:, Section 6.2.2 through Section 6.2.4. Here, the process used for assigning least-
disturbed and most-disturbed water chemistry sites is described, beginning with the development of
indices used to develop least- and most-disturbed thresholds.
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Table 13-3. The number of Visit 1 (VI) probability and handpicked sites sampled for water chemistry in 2011 and
2016, with their totals. Additionally, the numbers of resampled sites with water chemistry data are reported in
paratheses to indicate that these are subtracted from the subtotals above. The total number of unique probability
and handpicked sites with water chemistry data are reported with the final number of Index Visit sites (in the red
cell) used in the establishment of the water chemistry disturbance gradient. Note that this table does not include
the 51 Visit 2 sites with water chemistry sampled in 2011 and 64 Visit 2 sites with water chemistry sampled in
2016, which are only used to calculate Signal-to-Noise ratios.
SURVEY YEAR
VI PROBABILITY
WITH WATER
CHEMISTRY
(n-sites)
HANDPICKED
WITH WATER
CHEMISTRY
(n-sites)
TOTAL
2011 NWCA
531
86
617
2016 NWCA
611
64
675
SUBTOTAL
1142
150
1292
2011 Sites with Water Chemistry Resampled in 2016
(94)
(0)
(94)
TOTAL UNIQUE SITES WITH WATER CHEMISTRY
1048
150
1198
13.3.1 Development of Physical-Alterations-Possibly-Affecting-Chemicals (CALT) Indices
Three indices, collectively referred to as the Physical-Alterations-Possibly-Affecting-Chemicals (CALT)
indices, were developed for use in screening sites to establish the disturbance gradient for sites sampled
for water chemistry:
the CALT_NUT index, alterations thought likely to affect nutrient levels,
the CALT_SED index, alterations thought likely to affect suspended sediment levels, and
the CALT_SAL index, alterations thought likely to affect salinity levels.
These three indices were based on observational data associated with the stressor check lists (hereon
referred to as "Items") on the H-l and B-l Forms (see Table 11-1). Best professional judgement (BPJ) was
used to evaluate if and how each H-l and B-l Form Item might affect nutrients, suspended sediments,
and salinity at a site. Write-in "others" were not considered and were therefore excluded from this
analysis. Based on this evaluation, a subset of the H-l and B-l Form Items were assigned to one, two, or
all three of the Chemical-Response-to-Physical-Alteration indices (Table 13-4). Note that not all metrics
were assigned to one of the CALT indices (hence, "subset").
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Table 13-4. Subset of Physical Alteration metrics (defined in Chapter 11:, Section 11.2) assigned to the Physical-
Alterations-Possibly-Affecting-Nutrient (CALT_NUT), Physical-Alterations-Possibly-Affecting-Suspended Sediments
(CALT_SED), and Physical-Alterations-Possibly-Affecting-Salinity (CALT_SAL) indices. "X" indicates that the Physical
Alteration metric was included the CALT index. Note that not all Physical Alteration metrics were assigned to a
CALT index. Write-in "others" from the H-l and B-l Forms were not considered and are therefore excluded from
the list of Form Items.
Form
Form Items
2016 Parameter Name
Nutrients
(CALT_NUT)
Suspended Sediments
(CALT_SED)
Salinity
(CALT_SAL)
B-l
Forest Clear Cut
HAB CLEAR-CUT
X
X
B-l
Forest Selective Cut
HAB SELECTIVE CUT
X
B-l
Tree Canopy Herbivory (insect)
HAB HERBIVORY
B-l
Herbicide/Pesticide Use
HAB_HERBICIDE_PESTICIDE
B-l
Shrub Layer Browsed (wild or domestic)
HAB SHRUB
B-l
Highly Grazed Grasses (overall <3" high)
HAB GRAZED
X
X
B-l
Mowing/Shrub Cutting
HAB_MOWING
B-l
Recently Burned Forest (canopy)
HAB FOREST BURNED
X
B-l
Recently Burned Grassland (blackened)
HAB_GRASS_BURNED
X
B-l
Fallow Field (old - grass, shrubs, trees)
AGR_FALLOW_OLD
B-l
Fallow Field (recent - resting row crop field)
AGR FALLOW RECENT
X
B-l
Golf Course
RES GOLF
X
X
X
B-l
Lawn/Park
RES_LAWN
X
X
B-l
Orch ard/Nursery
AGR ORCHARD
X
B-l
Silviculture/Tree Plantation
HAB PLANTATION
B-l
Row Crops - Tilling
AG R_ROW
X
X
X
B-l
Range
AGR RANGE
X
B-l
Pasture/Hay
AGR_PASTURE
X
H-l
Culverts & Ditching: Ditches
DITCH_PRESENT
H-l
Culverts & Ditching: Channelized Streams
CHANNELIZED PRESENT
H-l
Culverts & Ditching: Corrugated Pipe
CORR PRESENT
H-l
Culverts & Ditching: Box
BOX PRESENT
H-l
Pipes: Sewer Outfall
SEWER PRESENT
X
X
H-l
Pipes: Standpipe Outflow
STANDPIPE PRESENT
H-l
Field Drainage Tiling
TILING PRESENT
H-l
Pumps: Irrigation
IRRIGATION_PRESENT
X
H-l
Pumps: Other
PUMP OTHER PRESENT
X
H-l
Pumps: Water Supply
WAT SUPPLY PRESENT
H-l
Shallow Channels: Vehicle Ruts
RUTS_PRESENT
X
H-l
Shallow Channels: Abandoned Road
ABANDONED PRESENT
X
H-l
Shallow Channels: Eroded Foot Paths
PATHS_PRESENT
X
H-l
Shallow Channels: Trails
TRAILS PRESENT
X
H-l
Shallow Channels: Animal Trampling
ANTRAMP PRESENT
X
X
B-l
Ditches, Channelization
HYD DITCH
B-l
Inlets, Outlets
HYD INLETS
B-l
Point Source/Pipe (effluent or stormwater)
HYD_PIPE
B-l
Drain Tiling
AGR TILING
X
X
B-l
Irrigation
AGR IRRIGATION
X
X
B-l
Impervious Surface Input (sheetflow)
HYDJMPERVIOUS
X
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Form
Form Items
2016 Parameter Name
Nutrients
(CALT_NUT)
Suspended Sediments
(CALT_SED)
Salinity
(CALT_SAL)
H-l
Damming Features: Dikes
DIKES_PRESENT
H-l
Damming Features: Berms
BERMS PRESENT
H-l
Damming Features: Dams
DAMS PRESENT
H-l
Damming Features: Roads (all types)
ROADS_PRESENT
X
X
H-l
Damming Features: Railroad Bed
RRBED PRESENT
B-l
Dike/Dam/Road/RR Bed (impedeflow)
HYD DDRR
B-l
Wall/Riprap
HYD WALL
B-l
Fill/Spoil Banks
HYD FILL
X
B-l
Water Level Control Structure
HYD_WATER
H-l
Impervious Surfaces: Compacted non-paved (on 2016 H-l Form only)
Impervious Surfaces: Roads (on 2011 H-l Form only)
IMPER_ROADS_PRESENT
X
H-l
Impervious Surfaces: Asphalt
IMPER ASPHALT PRESENT
X
H-l
Impervious Surfaces: Concrete
IMPER CONCRETE PRESENT
X
B-l
Oil/Gas Wells/Drilling
IND OIL GAS
X
X
B-l
Confined Animal Feeding
AGR ANIMAL
X
X
X
B-l
Dairy (on 2011 B-l Form only)
Livestock or Domesticated Animals (on 2016 B-l Form Only)
AGR_DAIRY
X
X
X
B-l
Soil Compaction (animal or human)
HAB SOIL
X
B-l
Trails
HAB TRAILS
X
B-l
Off road Vehicle Damage
HAB_ORV
X
B-l
Road (paved or unpaved) (on 2016 B-l Form only)
Road - Gravel (on 2011 B-l Form only)
Road - Two Lane (on 2011 B-l Form only)
Road - Four Lane (on 2011 B-l Form only)
RES_ROAD
X
B-l
Parking Lot/Pavement
RES LOT
X
X
B-l
Rural Residential
AGR RURAL
X
B-l
Suburban Residential
RES_RES
X
X
B-l
Urban/Multifamily
RES URBAN
X
B-l
Power Line
RES_POWER
H-l
Excavation/Dredgin g
EXCAVATION PRESENT
X
H-l
Recent Sedimentation
SEDIMENT PRESENT
X
B-l
Dumping
RES DUMPING
B-l
Trash
RES_TRASH
B-l
Landfill
RES LANDFILL
X
X
X
B-l
Excavation, Dredging
HYD EXCAVATION
X
B-l
Gravel Pit
AG R_G RAVEL
X
X
B-l
Mine (surface/underground)
IND MINING
X
X
B-l
Freshly Deposited Sediment (unvegetated)
HYD SEDIMENT
X
B-l
Soil Erosion/Deposition (from wind, water, or overuse)
HAB_EROSION
X
B-l
Soil Loss/Root Exposure
HYD_SOIL
X
B-l
Military
IND_MILITARY
X
X
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For each site, each of the three Physical-Alterations-Possibly-Affecting-Chemicals indices (i.e., CALT_NUT,
CALT_SED, and CALT_SAL) was scored by simply tallying the number of B-l and H-l Items observed and
weighting each tally using the same proximity-weighted scheme used for the Physical Alteration indices.
Observations in the AA received the highest scores (25 points for each tally) and observations in the
buffer plots received increasingly lower weighted scores with distance from the AA (inner-ring buffer
plots = 4 points per tally, middle-ring buffer plots = 2 points per tally, and outer-ring buffer plots = 1 point
per tally). Detailed scoring protocol and a scoring illustration can be found in Chapter 11:, Section 11.3
and Figure 11-2.
Site CALT_NUT, CALT_SED, and CALT_SAL scores range from 0 points (no items from H-l or B-l Forms
observed) to almost 300 points, although few sites scored over 100 points.
TP, and to a lesser extent TN were correlated to CALT_NUT and CALT_SAL and these indices were used to
help define the disturbance gradient screen. The third CALT index, the Physical-Alterations-Possibly-
Affecting-Suspended Sediments (CALT_SED) index, was not included as a disturbance gradient screen
(i.e., it was excluded from further use) because analysis revealed that it was not sufficiently related to
nutrients or turbidity. Turbidity, a measure of the degree to which a beam of light passed through a water
sample is attenuated by particulates in that water, is the one NWCA 2016 water chemistry measurement
that might be expected to respond to sediment loading, but the ability to see such a response can be
weakened by 1) the fact that turbidity also reflects the concentration of plankton algae in the water
column, and 2) unless they are derived from very fine-grained sediments (e.g., clays), sediments loaded to
wetlands settle out of the water column rather quickly.
13.3.2 Screens and Thresholds for Sites Sampled for Water Chemistry
Six physical and landscape screens were used to identify least-disturbed, intermediate-disturbed, and
most-disturbed sites sampled for water chemistry. These screens include two Physical-Alterations-
Possibly-Affecting-Chemicals (CALT) indices and four landscape metrics:
the Physical-Alterations-Possibly-Affecting-Nutrients (CALT_NUT) index,
the Physical-Alterations-Possibly-Affecting-Salinity (CALT_SAL) index,
the Percent Agriculture in the 1000-m buffer surrounding the AA,
the Percent Developed in the 1000-m buffer surrounding the AA,
the Percent Agriculture in the HUC-12 in which the AA was located, and
the Percent Developed in the HUC-12 in which the AA was located.
Land cover derived from 30-m resolution 2011 and 2016 rasters (depending on the NWCA collection year
of the data being screened) of the 2016 National Land Cover Database (NLCD, Dewitz 2019) were used to
calculate the extent of agriculture and developed land cover for the 1000-m buffer surrounding the AA
and the US Geological Survey (USGS) 12-digit Hydrologic Unit Code (HUC-12) in which the AA was located.
The extent (percent) of agriculture encompasses Planted/Cultivated Classes and includes NLCD Values 81
and 82.13 The extent (percent) of developed encompasses the Developed Class and includes NLCD Values
13 Value 81 = Pasture/Hay - areas of grasses, legumes, or grass-legume mixtures planted for livestock grazing or
the production of seed or hay crops, typically on a perennial cycle. Pasture/hay vegetation accounts for greater
than 20% of total vegetation. Value 82 = Cultivated Crops - areas used for the production of annual crops, such as
corn, soybeans, vegetables, tobacco, and cotton, and also perennial woody crops such as orchards and vineyards.
Crop vegetation accounts for greater than 20% of total vegetation. This class also includes all land being actively
tilled, (https://www.mrlc.gov/data/legends/national-land-cover-database-class-legend-and-description)
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21, 22, 23, and 24.14 Land cover at the 1000-m buffer scale and at the HUC-12 scale were highly
correlated (r = 0.7) but different enough to filter different sites, so both screens for both scales were used
to help define the disturbance gradient for sites sampled for water chemistry.
National thresholds (i.e., the same thresholds regardless of region) for "least disturbed" and "most
disturbed" were used for all six screens and are reported in Table 13-5. Sites that passed all six screens
were considered "least disturbed" while sites that exceeded any one of the six most-disturbed thresholds
were considered "most disturbed". All other sites were assigned to the intermediate disturbance class.
Table 13-5. Six water chemistry screens and their least-disturbed and most-disturbed thresholds used to assign
disturbance class to each site sampled for water chemistry.
Water Chemistry Screen
Least-Disturbed
Thresholds
Most-Disturbed
Thresholds
Physical-Alterations-Possibly-Affecting-Nutrients (CALT_NUT)
< 5 points
> 50 points
Physical-Alterations-Possibly-Affecting-Salinity (CALT_SAL)
< 5 points
> 50 points
% Agriculture in 1000-m buffer
< 5%
> 50%
% Developed in 1000-m buffer
< 5%
> 50%
% Agriculture in HUC-12
< 5%
> 50%
% Developed in HUC-12
< 5%
> 50%
A summary of the number of sites within each water chemistry disturbance class are reported by region
(RPT_UNIT_5) in Table 13-6. There were 1,198 unique NWCA sites (see Table 13-3) that had measured
water chemistry with roughly equal sample sizes among the five reporting units. However, there were
only six least-disturbed sites in the Plains (PLN). Even though so few sites are not ideal for analysis, the
least-disturbed threshold would have needed to be so severely relaxed to gain the optimal 30-50 least-
disturbed sites for PLN, that least-disturbed and most-disturbed thresholds would have been almost
equivalent. Three sites that lacked CALT scores were assigned as "unknown".
Table 13-6. n-sites sampled for water chemistry, presented by disturbance class assignments (unpublished)
reported by region (RPT_UNIT_5) for Visit 1, Index Visit 2011 and 2016 sites.
Least
Intermediate
Most
Regional
Region
Disturbed (L)
Disturbed (1)
Disturbed (M)
Unknown (?)
Totals
Tidal Saline (TDL)
92
204
32
1
329
Inland Coastal Plains (ICP)
31
117
40
1
189
E. Mts & Upper Midwest (EMU)
59
138
32
1
230
Plains (PLN)
6
65
142
0
213
West (WST)
56
91
90
0
237
14 Value 21 = Developed, Open Space - areas with a mixture of some constructed materials, but mostly vegetation
in the form of lawn grasses. Impervious surfaces account for less than 20% of total cover. These areas most
commonly include large-lot single-family housing units, parks, golf courses, and vegetation planted in developed
settings for recreation, erosion control, or aesthetic purposes. Value 22 = Developed, Low Intensity - areas with a
mixture of constructed materials and vegetation. Impervious surfaces account for 20% to 49% percent of total
cover. These areas most commonly include single-family housing units. Value 23 = Developed, Medium Intensity -
areas with a mixture of constructed materials and vegetation. Impervious surfaces account for 50% to 79% of the
total cover. These areas most commonly include single-family housing units. Value 24 = Developed, High Intensity
- highly developed areas where people reside or work in high numbers. Examples include apartment complexes,
row houses and commercial/industrial. Impervious surfaces account for 80% to 100% of the total cover.
(https://www.mrlc.gov/data/legends/national-land-cover-database-class-legend-and-description)
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National Totals
244
615
336
3
1198
13.3.3 Evaluation of the Disturbance Gradient for Sites Sampled for Water Chemistry
The disturbance gradient for sites sampled for water chemistry was developed to support the
development of TN and TP as indicators of stressor condition. Least-disturbed sites will serve as the
foundation for defining TN and TP stressor condition thresholds (presented in the next section, Section
13.4), thus, it is imperative that least-disturbed sites are distinguished from most-disturbed sites in both
the TN and TP data.
Using LoglO-transformed TP and TN, t-tests performed on national data (i.e., unique 2011 and 2016 Visit
1 sites) showed that distinction of least-disturbed from most-disturbed sites was highly significant (t > 11,
p < 0.001). Figure 13-1 illustrates this distinction among five regions (RPT_UNIT_5). However, statistical
analyses showed that there were no differences between least- and most-disturbed sites in Tidal Saline
(TDL) wetlands, and there were not enough least-disturbed sites in the Plains (PLN) to reach any statistical
conclusions. Significant differences (t = 4 to 8, p < 0.001) were found for the Inland Coastal Plains (ICP),
Eastern Mountains and Upper Midwest (EMU), and West (WST). TP differences were generally stronger
than TN differences (Figure 13-1).
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txo
E
o
ro
QJ
u
c
o
u
10.0-
1.0
0.1
a) Total Nitrogen (TN)
I I Least-Disturbed
|~1 Most-Disturbed
TDL
ICP EMU PLN WST
10000
O
ĶP
fB
QJ
u
c
o
u
Q_
1000
100
10
b) Total Phosphorus (TP)
l~l Least-Disturbed
TDL ICP EMU PLN WST
Figure 13-1. Box and whisker plots showing differences between least-disturbed (blue) and most-disturbed (red)
(unique 2011 and 2016 Visit 1) sites among five regions (RPT_UNIT_5) for a) total nitrogen (TN) and b) total
phosphorus (TP). TDL = Tidal Saline, ICP = Inland Coastal Plains, EMU = Eastern Mountains & Upper Midwest, PLN :
Plains, and WST = West.
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13.4 TN and TP Stressor Condition Thresholds
Like other National Aquatic Resource Survey (NARS) assessments, the NWCA data was used to identify
connections between the presence of indicators of stress and ecological condition. Anthropogenic
stressors act to degrade ecological condition, and consequently, evaluation of indicators of stress is an
important component of an assessment method (Fennessy et al. 2007). Using physical, chemical, and
human-health indicators of stress, the NWCA analysis examined a variety of stressor data to detect
factors likely affecting wetland condition. The use of stressor data is consistent with current approaches
to assess wetlands and recognizes the connection between the presence of stressors and wetland
condition. For example, rapid assessment methods have been developed which use only stressors as
indicators of condition (e.g., the Delaware Rapid Assessment Method (Jacobs 2007)) and models
comprising an HGM assessment (a Level 3, intensive assessment) use stressors as variables (e.g.,
Whigham et al. 2007, Wardrop et al. 2007). The data sources for the indicators of stressor condition used
in the NWCA analysis were primarily from field observations and soil and water chemistry samples
collected from the Assessment Area (AA) and its buffer at each sampled site.
Two water chemistry indicators of stressor condition are reported for the 2016 NWCA: 1) total nitrogen
(TN) and 2) total phosphrous (TP) concentrations measured in the water column of sampled sites with
water. Because TN and TP are highly variable in wetlands depending on the wetland type, hydrology, and
other defining characteristics of wetlands that influence nutrient cycling, there is no concurrence in the
literature about expected "reference" concentrations of TN and TP. Thus, thresholds for water column TN
and TP were developed using the same percentile approach that is used by NARS (e.g., Paulsen et al.
2008, USEPA 2016d).
First, subpopulations for which thresholds should be developed needed to be determined. This was
completed by evaluating concentrations of TN and TP in least-disturbed sites sampled for water chemistry
(defined in the previous Section 13.3 and in Table 13-6) across regional subpopulations (specifically, Five
Reporting Units (RPT_UNIT_5)). The results of these evaluations, illustrated in Figure 13-1, indicated that
there were no significant differences in TN concentrations across the least-disturbed sites among the Five
Reporting Units (i.e., TDL, ICP, EMU, PLN, and WST). However, TP concentrations across the least-
disturbed sites were significantly higher in tidal (TDL) compared to the inland subpopulations (i.e., ICP,
EMU, PLN, and WST), although TP did not differ significantly among the 4 inland reporting units. The
significant differences among these subpopulations warranted separate TN and TP stressor condition
thresholds for inland and tidal subpopulations (i.e., HYD_CLS, see Table 5-1 in Chapter 5:).
Thus, TN and TP thresholds for good stressor condition and poor stressor condition were developed for
inland and tidal subpopulations using the distribution of least-disturbed sites sampled for water
chemistry. After deleting outliers using a 1.5*IQR test (with IQR referring to "interquartile range"),
threshold values were calculated using the 75th and 95th percentiles of TN and TP concentrations among
least-disturbed sites sampled for water chemistry (Figure 13-2). Specifically:
Good stressor condition thresholds were calculated as the 75th percentile of TN and TP
concentrations among least-disturbed sites sampled for water chemistry.
Poor stressor condition thresholds were calculated as the 95th percentile of TN and TP
concentrations among least-disturbed sites sampled for water chemistry.
Sites with TN and TP concentrations higher than the threshold for "good" and lower than the
threshold for "poor" are classified as fair stressor condition.
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Distribution of Least-Disturbed Sites
Sampled for Water Chemistry
(!)
D
Q.
CO
o
.c
a.
TO
-#-ŧ
1°
c
a>
O)
o
+->
Ģ
o
Percentiles:
^MrStressor Con_djtion
Fair Stressor Condition
Good Stress
or Condition
50th
25th
Figure 13-2. Good stressor condition and poor stressor condition threshold-setting using the 75th and 95th
percentiles of total nitrogen (TN) or total phosphorus (TP) concentrations among least-disturbed sites sampled for
water chemistry.
Threshold results for inland sites and tidal sites are shown in Table 13-7. Inland and tidal thresholds are
very similar for TN (approximately 1.2 mg/L for both inland and tidal sites) but very different for TP (98
|ag/L for inland sites and 174 |ag/L for tidal sites). In general, wetlands tend to have higher "natural"
background TN and TP concentrations compared to streams. For comparison, good stressor condition
thresholds for NARS streams in mountainous ecoregions (SAP, NAP, and WMT from the AG_EC09
subpopulation) are approximately 0.15-0.35 mg/L for TN and 15-20 |ag/L for TP, and approximately 0.6-
0.7 mg/L for TN and 50-90 |ag/L for TP in the Plains ecoregions (NPL, SPL, and TPL from the AG_EC09
subpopulation) (USEPA 2016d).
Table 13-7. Final total nitrogen (TN) and total phosphorus (TP) thresholds and relevant information for developing
those thresholds, including the number of least-disturbed sites with water chemistry on which threshold
percentiles are based (see Section 13.3 for details), the high outlier cut-off, and the number of outlier sites.
Total Nitrogen
Inland Sites
Total Nitrogen
Tidal Sites
Total Phosphorus
Inland Sites
Total Phosphorus
Tidal Sites
Number of Least-Disturbed
Sites with Water Chemistry
152
92
152
92
High Outlier Cut-off
3.073 mg/L
2.858 mg/L
240.5 |ig/L
424.0 Mg/L
Number of Outlier Sites
Removed from Analysis
14
10
9
7
Good (75th percentile)
Stressor Condition Threshold
< 1.26 mg/L
< 1.24 mg/L
< 98 ng/L
< 174 Mg/L
Poor (95th percentile)
Stressor Condition Threshold
> 2.04 mg/L
> 2.18 mg/L
> 166 Mg/L
> 358 Mg/L
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13.5 Literature Cited
Dewitz J (2019) National Land Cover Database (NLCD) 2016 Products: U.S. Geological Survey data release,
https://doi.orR/10.5066/P96HHBIE
Fennessy MS, Jacobs AD, Kentula ME (2007) An evaluation of rapid methods for assessing the ecological
condition of wetlands. Wetlands 27: 543-560
Herlihy AT, Kaufmann PR, Mitch ME (1990) Regional estimates of acid mine drainage impact on streams in
the Mid-Atlantic and Southeastern United States. Water, Air, Soil Pollution 50: 91-107
Herlihy AT, Stoddard JL, Burch Johnson C (1998) The relationship between stream chemistry and
watershed land cover data in the Mid-Atlantic Region, US. Water, Air, Soil Pollution 105: 377-386
Hornung RW, Reed LD (1990) Estimation of average concentration in the presence of nondetectable
values. Applied Occupational and Environmental Hygiene 5(1): 46-51
Jacobs AD (2007) Delaware Rapid Assessment Procedure Version 4.1. Delaware Department of Natural
Resources and Environmental Control, Dover, DE
Kaufmann PR, Levine P, Robison EG, Seeliger C, Peck DV (1999) Quantifying Physical Habitat in Wadable
Streams. EPA/620/R_99/003. US Environmental Protection Agency, Washington, DC
Kaufmann PR, Hughes RM, Van Sickle J, Whittier TR, Seeliger CW, Paulsen SG (2014) Lakeshore and littoral
physical habitat structure: a field survey method and its precision. Lake and Reservoir Management 30:
157-176
Paulsen SG, Mayio A, Peck DV, Stoddard JL, Tarquinio E, Holdsworth SM, Sickle JV, Yuan LL, Hawkins CP,
Herlihy AT, Kaufmann PR, Barbour MT, Larsen DP, Olsen AR (2008) Conditions of stream ecosystems in
the US: an overview of the first national assessment. Journal of the North American Benthological Society
27(4), 812-821
Stoddard JL, Herlihy AT, Peck DV, Hughes RM, Whittier TR, Tarquinio E (2008) A process for creating
multimetric indices for large-scale aquatic surveys. Journal of North American Benthological Society 27:
878-891
USEPA (2016a) National Wetland Condition Assessment 2016: Field Operations Manual. EPA-843-R-15-
007. US Environmental Protection Agency, Washington, DC
USEPA (2016b) National Wetland Condition Assessment 2016: Laboratory Operations Manual. EPA-843-R-
15-009. US Environmental Protection Agency, Washington, DC
USEPA (2016c) National Wetland Condition Assessment: 2011 Technical Report. EPA-843-R-15-006. US
Environmental Protection Agency, Washington, DC
USEPA (2016d) National Rivers and Streams Assessment 2008-2009 Technical Report. EPA-841-R-16-008.
US Environmental Protection Agency, Washington, DC
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Wardrop DH, Kentula ME, Jensen SF, Stevens Jr. DL, Hychka KC, Brooks RP (2007) Assessment of wetlands
in the Upper Juniata watershed in Pennsylvania, USA using the hydrogeomorphic approach. Wetlands 27:
432-445
Whigham DF, Jacobs AD, Weller DE, Jordan TE, Kentula ME, Jensen SF, Stevens Jr. DL (2007) Combining
HGM and EMAP procedures to assess wetlands at the watershed scale - Status of flats and non-tidal
riverine wetlands in the Nanticoke River Watershed, Delaware and Maryland (USA). Wetlands 27: 462-
478
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Chapter 14: Microcystins
Microcystins are one group of naturally occurring toxins produced by various cyanobacteria (blue-green
algae) that are common to surface waters (Chorus and Bartram 1999). Microcystins have been detected
nationally in wetlands (USEPA 2016) and are considered to be the most commonly occurring class of
cyanobacteria toxins (cyanotoxins) (Chorus and Bartram 1999). Microcystin exposure risk is typically
elevated when an overabundance of cyanobacteria occurs in surface water causing a cyanobacteria
harmful algal bloom (cyanoHABs). There is concern that changes in weather patterns, human population
expansion, and associated behaviors are leading to perceived increases in occurrence and severity of
cyanoHABs (Paerl and Scott 2010). Three main exposure scenarios are of potential concern regarding
microcystins and wetlands: direct ecological impacts on plants and animals, human consumption of
exposed organisms, and direct human exposure through recreational contact.
Adverse ecological impacts due to microcystin exposure on plants and animals have been summarized in
several sources. Various adverse impacts of microcystins on cellular processes in a variety of aquatic and
terrestrial plants resulting in diminished plant growth and accumulation of microcystins have been
reported (Crush et al. 2008, Corbel et al. 2013, Romero-Oliva et al. 2014). Some macrophytes common to
certain types of wetlands have shown sensitivity to microcystins also. Microcystins have been shown to
inhibit the growth and oxygen production of some wetland macrophytes at concentrations of 1 |ag/L or
less (Rojo et al. 2013). Additionally, illness and mortality due to microcystin exposure has been reported
in wildlife, livestock, companion animals and all trophic levels of freshwater, brackish and marine aquatic
life. Animal illness and mortality has been reported in numerous cases including amphibians, cats, cattle,
chickens, deer, dogs, frogs, horses, muskrat, sheep, turkey, and waterfowl, but the true number of cases
remains unknown since many are not reported or observed (Chorus and Bartram 1999, Landsberg 2002,
Briand et al. 2003, Handeland and 0stensvik 2010, Vareli et al. 2013).
14.1 Data Collection and Analysis
Samples were collected for microcystin analysis from sites with sufficient surface water for sample
collection and shipped to analytical labs following procedures outlined in the NWCA Field Operations
Manual (USEPA 2016a). Samples were lysed by three sequential freeze/thaw cycles and filtered with 0.45
micron HVLP syringe filters (Loftin et al. 2008, Graham et al. 2010). Following the NWCA Laboratory
Operations Manual (USEPA 2016b), samples were analyzed by one of two methods depending on
whether practical salinity units (PSU) were < 3.5 PPT (part per thousand, Method 1) or > 3.5 PPT (Method
2). Samples were stored frozen prior to further extraction (Method 2) and analysis for microcystins by
enzyme-linked immunosorbent assay (Abraxis ADDA kit, Warminster, PA) at -20°C.
14.2 Application of EPA Recommended Criterion for Microcystins
Microcystins concentrations were evaluated against the EPA recommended recreational water quality
criterion and swimming advisory level of 8 ppb (US EPA 2019). Microcystins results identify the
percentage of wetland area at or below the criterion and above the criterion. The microcystins detection
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results were determined using the MDL of 0.1 ppb which was consistent in both surveys. The detection
results presented in the public report and data dashboard represent the percentage of wetland area with
measured values greater than 0.1 ppb.
14.3 Literature Cited
Abraxis Bulletin (R110211) Microcystins in brackish water or seawater sample preparation.
http://www.abraxiskits.com/uploads/products/docfiles/385_MCT-
ADDA%20in%20Seawater%20Sample%20Prep%20%20Bulletin%20R041112.pdf: accessed on 2/20/2015
Briand JF, Jacquet S, Bernard C, Humbert JF (2003) Health hazards for terrestrial vertebrates from toxic
cyanobacteria in surface water ecosystems. Veterinary Research 34: 361-377
Corbel S, Mougin C, Bouaicha N (2013) Review: Cyanobacteria toxins: Mode of actions, fate in aquatic and
soil ecosystems, phytotoxicity and bioaccumulation in agricultural crops. Chemosphere 96: 1-15
Crush JR, Briggs LR, Sprosen JM, Nichols SN (2008) Effect of irrigation with lake water containing
microcystins on microcystin content and growth of ryegrass, clover, rape, and lettuce. Environmental
Toxicology 23: 246-252
Chorus I, Bartram J (Eds.) (1999) Toxic cyanobacteria in water: A guide to their public health
consequences, monitoring, and management. World Health Organization and E&FN Spon Press, London,
UK
Graham JL, Loftin KL, Meyer MT, Ziegler AC (2010) Cyanotoxin mixtures and taste-and-odor compounds in
cyanobacterial blooms from the Midwestern United States. Environmental Science and Technology 44:
7361-7368.
Handeland K, 0stensvik 0 (2010) Microcystin poisoning in roe deer (Capreolus capreolus). Toxicon 56:
1076-1078
Ibelings BW, Chorus I (2007) Accumulation of cyanobacterial toxins in freshwater "seafood" and its
consequences for public health: A review. Environmental Pollution 150: 177-192
LandsbergJH (2002) The effects of harmful algal blooms on aquatic organisms. Reviews in Fisheries
Science 10: 113-390
Loftin KA, Meyer MT, Rubio F, Kamp L, Humphries E, Wherea, E (2008) Comparison of two cell-lysis
procedures for recovery of microcystins in water samples from Silver Lake in Dover, Delaware with
microcystin producing cyanobacterial accumulations. US Geological Survey Open File Report 2008-1341
USEPA (2016a) National Wetland Condition Assessment 2016: Field Operations Manual. US
Environmental Protection Agency, Washington DC. EPA-843-R-15-007.
USEPA (2016b) National Wetland Condition Assessment 2016: Laboratory Operations Manual. US
Environmental Protection Agency, Washington DC. EPA-843-R-15-009.
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US EPA (2019) Recommended human health recreational ambient water quality criteria or swimming
advisories for microcystins and cylindrospermopsin. US EPA Office of Water. EPA 822-R-19-001.
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Chapter 15: Condition Extents, Change in Condition Extents, and
Relative and Attributable Risk
REPORTING (CHP 15)
and NWCA Public Reporting
1
DATA
PREPARATION
(CHP 4)
prepare & QA
data before use
SURVEY
DESIGN
(CHP 2)
Probability
sites (PROB)
HANDPICKED
SITES
(CHP 3)
handpicked
sites (HAND)
collect data
in the field
using protocol
from the Field
Operations
Manual
for each site, calculate metrics and indices,
including:
NONNATIVE PLANT
INDICATOR (NNPI) (CHP 10)
WATER CHEMISTRY (CHP 13)
MICROCYSTES (CHP 14)
HUMAN-MEDIATED
PHYSICAL ALTERATIONS (CHP 11)1
SOIL HEAVY METALS (CHP 12)H
PERCENT RELATIVE COVER OF
NONNATIVE PLANTS (SECT 6.6)
STRESSOR
THRESHOLDS
(CHP 10-14)
H
least disturbed
develop vegetation
multimetric indices
VEGETATION
ANALYSIS
OVERVIEW-,
DATA
ACQUISITION,
PREP (CHP 7)
~
PREREQUISITE
ANALYSES TO
VEGETATION
INDICATOR
DEVELOPMENT
(CHP 8)
~
site weights from
~~ probabiliy design
only probability sites
used for population
estimates:
SUBPOPULATIONS
(CHP 5)
Recap of Figure 1-1. Annotated analysis flow chart indicating the chapter number (abbreviated as "CHP") in which
details may be found.
The information provided in the previous chapters is in tended to provide a solid understanding of how
the 2016 NWCA was designed, conducted, and analyzed. Up to this point in this document, details have
been provided regarding the:
survey design (Chapter 2:),
selection of handpicked sites (Chapter 3:),
preparation of data (Chapter 4:),
definition of subpopulations (Chapter 5:),
establishment of the disturbance gradient (Chapter 6:),
development of the Vegetation Multimetric Indices (VMMIs) (Chapter 7: through Chapter 9:),
development of the Normative Plant Indicator (NNPI) (Chapter 10:), and
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development of physical and chemical indicators used for the disturbance gradient (Chapter 6:)
and stressor condition (Chapter 11: through Chapter 14:).
This chapter will describe how all the above components are used to calculate population estimates,
which include three different types of condition:
wetland condition extent estimates based on the Vegetation Multimetric Indices (VMMIs)
(Section 15.1.1),
Nonnative Plant Indicator (NNPI) condition extent estimates (Section 15.1.2), and
stressor condition extent estimates based on physical and chemical indicators (Section 15.1.3).
Wetland condition, NNPI condition, and stressor condition extent estimates are calculated usingspsurvey:
Spatial Survey Design and Analysis (Kincaid and Olsen 2019) and expressed as wetland area in acres or
percent of the resource; therefore, site weights from the probability design must be used to generate
population estimates along with the data from the probability sites sampled (n-sites = 967). The role of
population estimates and site weights in these calculations is discussed in Section 15.1. Additionally,
methods for calculating and reporting change in wetland condition and stressor condition extent
estimates between the NWCA 2011 and NWCA 2016 (referred to as "change analyses") are discussed in
Section 15.2. Ultimately, relative and attributable risk, discussed in detail in Section 15.3, are used to
calculate the relationship between:
wetland condition and stressors, and
NNPI condition and stressors.
Final results, including:
wetland condition extent estimates,
NNPI condition extent estimates,
stressor condition extent estimates,
change analyses, and
relative and attributable risk
are presented in National Wetland Condition Assessment 2016: A Collaborative Survey of the Nation's
Wetlands (USEPA 2022a) and in the USEPA National Wetland Condition Assessment 2016 Data Dashboard
(2022b), primarily as bar graphs. This document provides guidance on how to interpret these results.
15.1 Condition Extent Estimates
The survey design for the NWCA, discussed in Chapter 2: of this report, produces a spatially-balanced
sample using a combination of two different geographic data layers: US Fish & Wildlife Service (USFWS)
National Wetland Status and Trends (S&T) (Dahl and Bergeson 2009, Dahl 2011) and USFWS National
Wetland Inventory (NWI) (USFWS 2014). Each point (n-probability sites = 967, see Table 6-1) has a known
probability of being sampled (Stevens and Olsen 1999, Stevens and Olsen 2000, Stevens and Olsen 2004,
Olsen et al. 2019), and a sample weight is assigned to each individual site as the inverse of the probability
of that point being sampled. Sample weights are expressed in units of acres.
The probability of a site being sampled, as discussed in Chapter 2:, Section 2.2.3, was stratified by state
with unequal probability of selection based on geographic regions and Wetland Groups (WETCLS_GRP)
see Table 5-1 in Chapter 5:) within each state. Site weights for the survey were adjusted to account for
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additional sites (i.e., oversample points) that were evaluated when the primary sites were not sampled
(e.g., due to denial of access, being non-target). These site weights, designated by the red "W" enclosed
in a circle (i.e., Đ) in the Overview of Analysis figure (Figure 1-1), are explicitly used in the calculation of
wetland condition extent estimates, NNPI condition extent estimates, and stressor condition extent
estimates, so results can be expressed as estimates of wetland area (i.e., numbers of acres or percent of
the entire resource) in a particular condition category (i.e., "good", "fair", "poor", and, for the NNPI only,
"very poor") for the Nation and any of the subpopulations in Table 5-1. In the following sections, the
methods by which estimates are calculated and reported are described for wetland condition extent
(Section 15.1.1), NNPI condition extent (Section ), and stressor condition extent (Section 15.1.3). It is
important to note that the NWCA was not designed to report on individual sites or states, but to report at
national and regional scales (see Chapter 2:).
15.1.1 Wetland Condition Extent Estimates
A Vegetation Multimetric Index (VMMI) summarizes several metrics describing different aspects of
observed vegetation that together can reflect wetland condition in relation to least-disturbed wetland
sites. For the NWCA 2016 analysis, four separate VMMIs were developed, one for each of four Wetland
Groups: Estuarine Flerbaceous, Estuarine Woody, Inland Flerbaceous, and Inland Woody.
Wetland condition extent estimates are based on the four Vegetation Multi metric Indices (VMMIs). Each
NWCA probability site is designated as in good, fair, or poor condition based on its VMMI value and
associated thresholds appropriate to the site (Chapter 7). Next, the site weights from the probability
design are summed across all sites in each condition category to estimate the wetland area in good, fair,
and poor condition for the NWCA target wetland population (see Chapter 2:, Section 2.2.5) nationally and
for the subpopulations reported in Table 5-1. The survey design allows calculation of confidence intervals
around these condition estimates.
Note that only Visit 1 (i.e., the Index Visit) data and only probability sites are used in the calculation of
extent. Flandpicked sites have a weight of zero. Using this method, wetland area in a particular wetland
condition category is estimated and reported in numbers of acres or by percent of the resource (Figure
15-1). The National Wetland Condition Assessment: The Second Collaborative Survey of Wetlands in the
United States (2022a) provides national results, whereas the USEPA National Wetland Condition
Assessment 2016 Data Dashboard (2022b) provides an interactive format for users to explore national
results and results for different subpopulations.
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2016 National Extent Estimates for Wetland Condition
Based on the VMMI
Poor
1 QO/~
34%
47%
Not Assessed <0.5%
0% 20% 40% 60% 80% 100%
Percent (%) of Target Wetland Population
Figure 15-1. The 2016 NWCA national extent estimates for wetland condition based on the Vegetation Multimetric
Indices (VMMIs). Wetland condition extent is presented for each condition category by percent of the resource
(i.e., percent of target wetland area for the Nation). Error bars represent 95% confidence intervals as calculated by
the R package spsurvey (Kincaid and Olsen 2019).
15.1.2 Normative Plant Indicator (NNPI) Condition Extent Estimates
Normative plant species are widely recognized as important biological indicators of lowered ecological
condition. They have numerous direct and indirect effects on native vegetation and other ecosystem
components, properties, and processes. The Nonnative Plant Indicator (NNPI) reflects wetland condition
in relation to stress from nonnative plants (Magee et al. 2019) by incorporating attributes of richness,
occurrence, and abundance for nonnative (alien and cryptogenic) plant species (see Chapter 10:).
NNPI condition extent estimates are based on the designation of each probability site as good, fair, poor,
or very poor condition based on NNPI. Site weights from the probability design are summed across all
sites in each condition category to estimate the wetland area in good, fair, poor, and very poor condition
for the NWCA target wetland population (see Chapter 2:, Section 2.2.5) nationally and for the
subpopulations reported in Table 5-1. The survey design allows calculation of confidence intervals around
these condition estimates.
Note that only Visit 1 (i.e., the Index Visit) data and only probability sites are used in the calculation of
extent. Handpicked sites have a weight of zero. Using this method, wetland area in a particular NNPI
condition category is estimated and reported in numbers of acres or by percent of the resource (Figure
15-2). The National Wetland Condition Assessment: The Second Collaborative Survey of Wetlands in the
United States (2022a) provides national results, whereas the USEPA National Wetland Condition
Assessment 2016 Data Dashboard (2022b) provides an interactive format for users to explore national
results and results for different subpopulations.
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2016 National Extent Estimates for NNPI Condition
Good
Fair
Poor
Very Poor
Not Assessed
0% 20% 40% 60% 80% 100%
Percent (%) of Target Wetland Population
Figure 15-2. The 2016 NWCA national extent estimates for Nonnative Plant Indicator (NNPI) condition. NNPI
condition extent is presented for each condition category by percent of the resource (i.e., percent of target
wetland area for the Nation). Error bars represent 95% confidence intervals as calculated by the R package
spsurvey (Kincaid and Olsen 2019).
15.1.3 Stressor Condition Extent Estimates
Indicators of stressor condition are used as descriptors of the potential impact of anthropogenic activities
on wetland condition. Although indicators of stressor condition do not necessarily imply causation of
ecological decline, they are often associated with impaired condition. For simplicity, they are sometimes
referred to using the shorthand term "stressors". Stressors are used to support analyses that provide four
types of information (i.e., results):
Stressor Condition Extent-an estimate (by percent of the resource or relative ranking of
occurrence) of how spatially common a stressor is based on the population design;
Relative Extent- an estimate of the areal percentage of the wetland population with poor
stressor condition for a particular indicator;
Relative Risk - the probability (i.e., risk or likelihood) of having poor condition when the
stressor condition category is poor relative to when it is good; and,
Attributable Risk-an estimate of the proportion of the population in poor condition that might
be reduced if the effects of a particular stressor were eliminated (Van Sickle and Paulsen 2008).
Eleven indicators of stressor condition are reported for the 2016 NWCA (2022a,b):
Vegetation Removal (VEGRMV),
Vegetation Replacement (VEGRPL),
Water Addition/Subtraction (WADSUB),
Flow Obstruction (WOBSTR),
57%
22%
9%
12%
0%
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Soil Hardening (SOHARD),
Surface Modification (SOMODF), and
Physical Alterations (PALT_SUM)
Soil Heavy Metals (METALS),
Total Nitrogen in the water column (TN),
Total Phosphorus in the water column (TP), and
Microcystins (MICX).
Stressor condition categories are defined at each wetland site as "good", "fair", or "poor", except for
microcystins, which is defined as "at or below benchmark" or "above benchmark". These stressor
condition categories were assigned for multiple physical, chemical, and human-health indicators based on
specific thresholds, as described at the end of each of the individual chapters describing the indicators
(i.e., Chapter 10: through Chapter 14:). To calculate stressor condition extent estimates, site weights
were summed by stressor condition category and applied to the NWCA target wetland population
(Chapter 2:, Section 2.5) nationally and the subpopulations reported in Table 5-1 to estimate wetland
area in the good, fair, and poor stressor condition categories. The National Wetland Condition
Assessment: The Second Collaborative Survey of Wetlands in the United States (2022a) provides national
results, whereas the USEPA National Wetland Condition Assessment 2016 Data Dashboard (2022b)
provides an interactive format for users to explore national results and results for different
subpopulations.
Note that only Visit 1 (i.e., the Index Visit) data and only probability sites are used in the calculation of
extent. Handpicked sites have a weight of zero. Using this method, wetland area in a particular stressor
condition category is estimated and reported in numbers of acres or by percent of the resource (Figure
15-3). Population results for condition based on the 11 stressors are detailed in the National Wetland
Condition Assessment 2016: A Collaborative Survey of the Nation's Wetlands (USEPA 2022a).
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2016 National Extent Estimates for Stressor Condition
Good
Fair
Poor
Not Assessed
Good
Fair
Poor
Not Assessed
Good
Fair
Poor
Not Assessed
Good
Fair
Poor
Not Assessed
Good
Fair
Poor
Not Assessed
3-
2%
2%
B-
a) VEGRMV
2%
3 =
c) WADSUB
Ķ
e) SOHARD
g) PALT_SUM
i 3%
2%
<0.5%
i)TN
3--
1-
3-=
b) VEGRPL
d) WOBSTR
79%
f) SOMODF
h) METALS
j) TP
Percent (%) of Target Wetland Population
>0% 40% 60% 80% 100%
Percent (%) of Target Wetland Population
At or Below
Benchmark
Above
Benchmark
k) MICX
0% 20% 40% 60% 80% 100%
Percent (%) of Target Wetland Population
Figure 15-3. The 2016 NWCA national extent estimates for 11 indicators of stressor condition. Stressor condition
extent is presented for each condition category by percent of the resource (i.e., percent of target wetland area for
the Nation). Error bars represent 95% confidence intervals as calculated by the R package spsurvey (Kincaid and
Olsen 2019). Stressor abbreviations are defined in Section 15.1.3.
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15.2 Change in Condition Extent from 2011 to 2016
One of the objectives of the NWCA is to track changes in the condition of wetlands over time. For the first
cycle of the NWCA, USEPA and partners reported on the condition of all wetlands in the NWCA 2011. For
this second cycle of the NWCA, change analyses were performed to determine the difference in the
condition of the wetland population between 2011 and 2016.
15.2.1 Data Preparation
2011 was the first NWCA survey and, as such, there were improvements that were made to the 2016
NWCA. Updates from 2011 to 2016 were made to the survey design, field protocols, the methods by
which the indicators were calculated, and the thresholds used to assign disturbance classes and condition
categories.
The NWCA Analysis Team made every effort possible to balance the evolution of the survey while keeping
the 2011 and 2016 data comparable. Consequently:
Comparability analyses were conducted using resampled site data when field protocols changed
to ensure that results were not influenced by changes in sampling methods (e.g., Figure 12-1).
All indicators were recalculated for 2011 using the new methods developed for the 2016 analysis
to allow valid comparisons to 2016 results. All comparisons between the first NWCA and this one
should be made using the new information presented in this document, in the National Wetland
Condition Assessment 2016: The Second Collaborative Survey of Wetlands in the United States
(2022a)), and in the interactive dashboard (2022b).
Due to improvements in the sample frame for 2016, survey weights were updated for 2011 in
order to make the population as comparable as possible to 2016.
However, a fundamental change in the survey design between 2011 and 2016 is the incorporation of NWI
digital map data to enable better regional geographic coverage for sites in 2016 (see Chapter 2:). This
affected the distribution and types of wetlands sampled across the nation and regionally, which may
influence comparability in patterns - especially when evaluating change between 2011 and 2016. In
other words, even though the regional allocation of sites improved in 2016, change analyses for those
regions that were not well-covered in 2011 may be impacted.
15.2.2 Change analysis
Change analysis was conducted through the use of the spsurvey package in R (Kincaid and Olsen 2019).
Within the GRTS (Generalized Random Tessellation Stratified) survey design, change analysis can be
conducted on continuous or categorical variables. When 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 (or very poor in the case of
NNPI) categories). Change between the two years was statistically significant when the resulting error
bars around the change estimate did not cross zero.
Change analysis is conducted between two points in time (n =2) and thus can only analyze differences
between two survey time periods. In other words, changes between 2011 and 2016 do not necessarily
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indicate trend or pattern of change. Trends are likely to become clearer after multiple survey years (e.g.,
adding results from 2021 and 2026).
15.3 Relative Extent, and Relative and Attributable Risk
The relationship between condition based on the VMMI or the NNPI and the condition based on the 11
indicators of stress can be described by calculating relative extent, and relative and attributable risk.
15.3.1 Relative Extent
Relative extent shows the percent of the resource estimated to be in a given condition category for an
indicator. Here, the relative extent of poor stressor condition for a given indicator is calculated for
comparison to relative and attributable risk results (as shown in the left panels in Figure 15-4).
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Relative Extent (% of Wetland Acreage in Poor Condition)
Relative Risk
Attributable Risk
0% 20% 40% 60% 80%
0 1 2 3 4 5 0% 20% 40% 60% 80% 100%
Chemical
Indicators
Physical
Indicators
Soil Heavy Metals
Total Nitrogen in Water
Total Phosphorus in Water
Physical Alteration (Sum)
Vegetation Removal
Vegetation Replacement
Water Addition/Subtraction
Flow Obstruction
Soil Hardening
Surface Modification
3-
3~
3
3
Chemical
Indicators
Physical
Indicators
Soil Heavy Metals
Total Nitrogen in Water
Total Phosphorus in Water
Physical Alteration (Sum)
Vegetation Removal
Vegetation Replacement
Water Addition/Subtraction
Flow Obstruction
Soil Hardening
Surface Modification
3-
3-
3
!
3
3
- Increased risk
}
Ķi
3-
3-
3
1
*
3-
Ķ
At or below zero not shown
Figure 15-4. The 2016 NWCA relative extent of wetlands with stressors in poor condition, and the relative risk and
attributable risk of poor a) VMMI condition or b) NNPI condition when stressor condition is poor as calculated by
the R package spsurvey (Kincaid and Olsen 2019). Note that the microcystins results were excluded due to low
values. For relative risk, values below the dashed line (i.e., a relative risk < 1) signifies that there is no association
between the stressor and VMMI or NNPI condition.
15.3.2 Relative Risk
Relative risk is the probability (i.e., risk or likelihood) of having poor wetland condition based on the
VMMI, or poor/very poor NNPI condition, when the stressor condition category is poor relative to when
the stressor condition category is good. Relative risk analysis was derived from medical literature, where
it is used commonly to describe, for example, the risk of having a heart attack based on cholesterol levels.
The fact that relative risk is used so commonly to report human health risks is an advantage because, as a
result, relative risk is an understandable concept to the general public. Applied to the NWCA, a relative
risk analysis can be used to evaluate the relative effect of a stressor on wetland condition based on the
VMMI or NNPI condition. Relative risk analyses are standard for reporting results in NARS assessments
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(e.g., USEPA 2006; USEPA 2009), and examples can be found for lake and stream NARS assessments in the
literature (e.g., Van Sickle et al. 2006; Van Sickle et al. 2008; Van Sickle 2013).
15.3.2.1 Example Calculation of Relative Risk
Risk is calculated using contingency tables and expressed as a probability, which is unitless. Consider the
example two-by-two contingency table15 presented as Table 15-1, which relates stream condition
indicated by Fish Index of Biotic Integrity (IBI) and stress indicated by total nitrogen (TN). The probabilities
in the contingency table are calculated from weighted analysis of the data and reflect the proportion of
the resource, stream length in the case of Table 15-1, which is in each of the four cells of the table. For
wetland analysis, the resource is areal and the probabilities would reflect the proportion of wetland area
in the population in each of the cells.
Table 15-1. Example contingency table for relative risk that reports the proportion of stream length associated
with good and poor condition (as indicated by Fish Index of Biotic Integrity, IBI) and good and poor stressor
condition (as indicated by stream water total nitrogen concentration, TN). Results are hypothetical.
STRESS LEVEL
O
u
TN: Good TN:Poor
Fish IBI: Good
Fish IBI: Poor
0.598 0.275
0.070 0.056
Total
0.668 0.331
Using the hypothetical example data provided in Table 15-1, the risk of a stream having poorf\s\\
condition when the TN stressor condition is poor is calculated as:
0.056
0.331
= 0.169
The risk of a stream having poor condition when the TN stressor condition is good is calculated in the
same manner:
0.070
0.668
= 0.105
By comparing these two results, it is apparent that the risk of a stream having poor condition when the
TN stressor condition is poor (0.169) is greater than when the TN stressor condition is good (0.105). The
relative risk (RR) can then be simply calculated as the ratio of these two probabilities (Pr):
RR =
Pr (Poor condition given Poor stressor condition) 0.169
Pr (Poor condition given Good stressor condition) 0.105
= 1.61
Therefore, in this example, we can conclude that the risk of poor condition is 1.61 times greater in
streams with poor TN stressor condition than in streams with good TN stressor condition.
15 The numbers used in this example are hypothetical and were not measured as part of any USEPA NARS
assessment.
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These calculations are repeated for each appropriate indicator of stress so relative risk can be reported
for each of them. If the stressor has no effect on condition, the relative risk is 1. Confidence intervals are
also used in reporting to express uncertainty in the estimate of relative risk (see Van Sickle et al. 2006).
15.3.2.2 Considerations When Calculating and Interpreting Relative Risk
It is important to understand that contingency tables are created using a categorical, two-by-two matrix;
therefore, only two condition categories can be used. There are multiple methods by which condition
categories can be used for contingency tables.
For wetland condition categories based on the VMMI / condition categories for stressor indicators, three
methods of calculating contingency tables may be considered:
Good vs. Poor / Good vs. Poor,
Good vs. Not-Good / Good vs. Not-Good, or
Not-Poor vs. Poor / Not-Poor vs. Poor*
where, "Not-Good" combines fair and poor condition categories, and "Not-Poor" combines good and fair
condition categories.
For NNPI condition categories / condition categories for stressor indicators, five methods of calculating
contingency tables may be considered:
Good vs. Very Poor/ Good vs. Poor
Good vs. Poor + Very Poor / Good vs. Poor
Good vs. Not-Good / Good vs. Not-Good
Good + Fair vs. Poor + Very Poor / Not-Poor vs. Poor*
Not-Very Poor vs. Very Poor / Not-Poor vs Poor
where, "Not-Good" combines fair, poor, and very poor condition categories, and "Not-Very Poor"
combines good, fair, and poor condition categories.
In the first bulleted method, "Good vs. Poor / Good vs. Poor", for example, data associated with the fair
condition categories are excluded from the analysis. Therefore, the results of the associated calculation of
relative risk are affected by which one of the above combinations is used to make the contingency tables,
and it is crucial that the objectives of the analysis are carefully considered to help guide this decision.
A second consideration is that relative risk does not model joint effects of correlated stressors. In other
words, each stressor is modeled individually, when in reality, stressors may interact with one another
potentially increasing or decreasing impact on condition. This is an important consideration when
interpreting the results associated with relative risk.
The two bold, asterisked (*) methods (one for each the VMMI and the NNPI condition categories) indicate
the method used for the NWCA analysis.
15.3.2.3 Application of Relative Risk to the NWCA
For each site sampled as part of the NWCA:
Wetland condition is assigned as good, fair, or poor using the Vegetation Multimetric Index
(VMMI) thresholds as described in Chapter 9:;
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Nonnative Plant Indicator (NNPI) condition is assigned as good, fair, poor, or very poor, using
exceedance values as described in Chapter 10:; and
Stressor conditions of 11 physical and chemical indicators are assigned as good, fair, or poor
using thresholds as described in Chapter 11: through Chapter 14:.
For each indicator of stressor condition, a contingency table was created, comparing:
the Not-Poor VMMI condition category (i.e., a combination of good and fair wetland conditions)
to Poor condition category, and Not-Poor stressor condition category (i.e., a combination of good
and fair stressor conditions) to Poor stressor condition; and
the combination of Good and Fair NNPI condition categories to Poor and Very Poor NNPI
condition categories, and Not-Poor stressor condition category (i.e., a combination of good and
fair stressor conditions) to Poor stressor condition.
These decisions for the contingency tables were made because the objective of reporting relative risk in
the NWCA is to indicate which stressors policy makers and managers may want to prioritize for
management efforts to improve poor wetland condition. After creating contingency tables, relative risk
for each indicator of stress was calculated. Figure 15-4 provides the relative risk reported for the 2016
NWCA; with stressor extent, relative risk provides an overall picture of the relative importance of
individual stressors on condition.
A relative risk value of 1.0 indicates that there is no association between the stressor and the VMMI or
NNPI, while values greater than 1.0 suggest greater relative risk. For example, if 30% of the population is
in poor condition based on the VMMI or NNPI, but the population is equally divided among sites with
Poor and Not-Poor stressor conditions (15% in each), then the RR = 0.15/0.15 = 1, and there is no
association between condition and the stressor. Conversely, if the 30% in poor condition was observed as
25% in sites with Poor stressor condition and 5% in sites with Not-Poor stressor condition, then the RR =
25/5 = 5.0. The higher the relative risk value for a given stressor, the greater the risk of poor wetland
condition. A relative risk of 5 indicates that we are five times more likely to see a wetland in poor
condition when the stressor is poor than when it is not poor (Herlihy et al. 2019).
15.3.3 Attributable Risk
Attributable risk provides an estimate of the proportion of the resource population (i.e., extent) in poor
condition that might be reduced if the effects of a particular stressor were eliminated. Attributable risk
(AR) combines relative stressor extent with relative risk into a single index using the following formula
(see Van Sickle et al. 2008 for details):
Pr(Extent with Poor Stressor Condition) * (RR 1)
1 + Pr (Extent with Poor Stressor Condition) * (RR 1)
where RR is relative risk and Pr is probability.
Similar to the consideration presented in Section 15.3.1.2, it is critical to define relative extent (i.e.,
percent of the resource) and relative risk in the same way. Therefore, for the NWCA data, the same
categories were used for calculating attributable risk as relative risk (e.g., Not-Poor was compared to
Poor) for VMMI and NNPI condition categories vs. stressor condition categories).
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The ranking of stressors according to attributable risk (e.g., Figure 15-4) represents their relative
magnitude or importance relative to decreased ecological condition and can be used by policy makers
and managers to inform prioritization of actions for specific stressors, geographic area, and/or wetland
type.
15.3.3.1 Considerations When Interpreting Attributable Risk
To appropriately interpret attributable risk, it is important to understand that attributable risk is
associated with the following three major assumptions:
Causality, or that the stressor causes an increased probability of poor condition;
Reversibility, or that if the stressor is eliminated, causal effects will also be eliminated and
damage is reversible; and,
Independence, or that stressors are independent of each other, so that individual stressor effects
can be estimated in isolation from other stressors.
These assumptions should be kept in mind when applying attributable risk results to management
decisions. Attributable risk provides much needed insight into how to prioritize management for the
improvement of our Nation's aquatic ecosystems - wetlands, in the case of the NWCA. While the results
of attributable risk estimates are presented as percent area in poor condition that could be reduced if the
effects of a particular stressor were eliminated, these estimates are meant to serve as general guidance
as to what stressors are affecting condition and to what degree (relative to the other stressors evaluated).
15.4 Where to Find the Summary of NWCA Results
All of the methods presented in this document are the scientific basis for what is reported in National
Wetland Condition Assessment: The Second Collaborative Survey of Wetlands in the United States (USEPA
2022a) and future peer-reviewed manuscripts. National Wetland Condition Assessment: The Second
Collaborative Survey of Wetlands in the United States (USEPA 2022a) report provides an overview of the
important results from the 2016 NWCA. The presentation of results in that document is geared toward
the lay public, environmental managers, and government decision makers.
15.5 Literature Cited
Dahl TE (2006) Status and trends of wetlands in the conterminous United States 1998 to 2004. US
Department of Interior, Fish and Wildlife Service, Washington, DC
Dahl TE (2011) Status and trends of wetlands in the conterminous United States 2004 to 2009. US
Department of Interior, Fish and Wildlife Service, Washington, DC
Dahl TE, Bergeson MT (2009) Technical procedures for conducting status and trends of the Nation's
wetlands. US Fish and Wildlife Service, Division of Habitat and Resource Conservation, Washington, DC
Kincaid TM, Olsen AR (2019) spsurvey: Spatial Survey Design and Analysis. R package version 4.1.
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Herlihy AT, Paulsen SG, Kentula ME, Magee TK, Nahlik AM, Lomnicky GA (2019) Assessing the relative and
attributable risk of stressors to wetland condition across the conterminous United States. Environmental
Monitoring and Assessment 191 (SI): 320, doi: 10.1007/sl0661-019-7313-7
Magee TK, Blocksom KA, Herlihy AT, &. Nahlik AM (2019) Characterizing nonnative plants in wetlands
across the conterminous United States. Environmental Monitoring and Assessment 191 (SI): 344, doi:
10.1007/s 10661-019-7317-3. https://link.springer.com/article/10.1007/sl0661-019-7317-3
Olsen AR, Peck DV (2008) Survey design and extent estimates for the Wadeable Streams Assessment.
Journal of the North American Benthological Society 27: 822-836
Olsen AR, Kincaid TM, Kentula ME, Weber MH (2019) Survey design to assess condition of wetlands in the
United States. Environmental Monitoring and Assessment 191 (SI): 268, doi: 10.1007/sl0661-019-7322-
6.
Stevens DL, Jr, Jensen SF (2007) Sampling design, implementation, and analysis for wetland assessment.
Wetlands 27: 515-523
Stevens DL, Jr, Olsen AR (1999) Spatially restricted surveys over time for aquatic resources. Journal of
Agricultural, Biological, and Environmental Statistics 4: 415-428
Stevens DL, Jr, Olsen AR (2000) Spatially restricted random sampling designs for design-based and model
based estimation. Pages 609-616 in Accuracy 2000: Proceedings of the 4th International Symposium on
Spatial Accuracy Assessment in Natural Resources and Environmental Sciences. Delft University Press, The
Netherlands
Stevens DL, Jr, Olsen AR (2004) Spatially-balanced sampling of natural resources. Journal of American
Statistical Association 99: 262-278
Van Sickle J, Stoddard JL, Paulsen SG, Olsen AR (2006) Using relative risk to compare the effects of aquatic
stressors at a regional scale. Environmental Management 38: 1020-1030
Van Sickle J, Paulsen SG (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 (2013) Estimating the risks of multiple, covarying stressors in the National Lakes Assessment.
Freshwater Science 32: 204-216
USEPA (2006) Wadeable Streams Assessment: A Collaborative Survey of the Nation's Streams. US
Environmental Protection Agency, Office of Water and Office of Research and Development, Washington,
DC
USEPA (2009) National Lakes Assessment: A Collaborative Survey of the Nation's Lakes. US Environmental
Protection Agency, Office of Water and Office of Research and Development, Washington, DC
USEPA (2016a) National Wetland Condition Assessment: 2011 Technical Report. EPA-843-R-15-006. US
Environmental Protection Agency, Washington, DC
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USEPA (2016b) National Wetland Condition Assessment 2011: A Collaborative Survey of the Nation's
Wetlands. EPA-843-R-15-005. US Environmental Protection Agency, Office of Water, Washington, DC
USEPA (2022a) National Wetland Condition Assessment: The Second Collaborative Survey of Wetlands in
the United States. EPA-841-R-23-001. US Environmental Protection Agency, Washington, DC
USEPA (2022b) National Wetland Condition 2016 Data Dashboard. US Environmental Protection Agency,
Washington, DC
USFWS (2014) National Wetlands Inventory, US Department of the Interior, Fish and Wildlife Service,
Washington, D.C. https://www.fws.Rov/wetlands/Data/Metadata.html
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Glossary
Abiotic disturbance class- disturbance class assignments to sites based on only the physical and chemical
disturbance gradient screens (and not the biological disturbance gradient screen); the parameter name
for these abiotic disturbance class assignments is REF_NWCA_ABIOTIC
Assessment Area (AA)-the 0.5 ha area that represents the location defined by the coordinates
generated by the NWCA sample draw, and in which most of the data collection for the NWCA occurs
Attributable Risk-an estimate of the proportion of the population in poor condition that might be
reduced if the effects of a particular stressor were eliminated16
Buffer- the area (representing a prescribed measurement area) surrounding the Assessment Area
Coefficients of Conservatism- (C-values, also called CCs) describe the tendency of individual plant species
to occur in disturbed versus near pristine conditions; C-values for individual species are state or regionally
specific and scaled from 0 to 10
Condition Category- describes the ecological condition of wetlands based on a biological indicator, a
Vegetation Multimetric Index (VMMI); classes include "Good", "Fair", or "Poor"
Condition Extent-estimates of the wetland area in good, fair, and poor condition categories
Contingency table- a two-by-two table that relates condition and stress used to calculate relative risk;
results of the contingency table are expressed as probabilities
Disturbance Class-classes reflecting the gradient of anthropogenic disturbance across all sampled
wetland sites, and used for the Vegetation Multi metric Index (VMMI) development and to set thresholds
for indicators of stress and condition
Least Disturbed- a disturbance class describing sites that represent the best available physical,
chemical, and biological conditions in the current state of the landscape4; used as "reference" for
the NWCA Survey
Most Disturbed- a disturbance class describing sites defined as most disturbed relative to "least
disturbed"; typically representing 20-30% of sites in an NWCA Reporting Group
Intermediately Disturbed- a disturbance class used to describe sites that fall between "least
disturbed" and "most disturbed"
Disturbance gradient- a continuous gradient of anthropogenic disturbance, divided into three
disturbance classes to which wetland sites are assigned
16 Van Sickle J, Paulsen SG (2008) Assessing the attributable risks, relative risks, and regional extents of aquatic
stressors. Journal of the North American Benthological Society 27: 920-931
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Duration- longevity for plants, described by annual, biennial, and perennial life cycles or combinations
thereof (see Table 7-2 for details)
Exceedance value- for the NNPI, the exceedance of a threshold value for a particular condition category
for any one of the three metrics, resulting in the assignment of the metric condition to next lower (better
to worse) category; the NNPI condition for a site is based on the lowest observed condition category
among the metrics
Final disturbance class-disturbance class assignments to sites based on physical, chemical, and for least-
disturbed sites, biological disturbance gradient screens; the parameter name for these final disturbance
class assignments is REF_NWCA
Growth habit- Primary growth-habit types for the plant taxa (see Table 7-1 for details)
Handpickedsites- sampled sites suggested by states, tribes, and other partners based on the expectation
that they are minimally disturbed and can be used as least-disturbed (or "reference") sites
indicator- a metric or index that reflects anthropogenic (human-mediated) disturbance to wetland
condition, vegetation condition, or stressor condition
index- a combination of metrics used to generate a single score to describe a particular property
(disturbance, stressor condition, or wetland condition in the case of the NWCA) for a site
index period- the temporal range when sites were sampled for the 2011 NWCA; the peak growing
season (April through September, depending on state) when most vegetation is in flower or fruit
index Visit- the sampling event used when conducting analyses on the set of unique sites sampled
inference population-final wetland area represented by sampled probability sites; ultimately used by the
NWCA for reporting condition and stressor extent
Metric-an individual measurement or combinations of data types to describe a particular property (e.g.,
soil phosphorus concentration, species richness, species cover by growth form, etc.) for a site
Native Status- state level designations of plant taxa nativity for the NWCA, designations include:
Native-plant taxa indigenous to specific states in the conterminous US
Alien- combination of introduced and adventive taxa
Introduced- plant taxa indigenous outside of, and not native in, the conterminous US
Adventive- plant taxa native to some areas or states of the conterminous US, but introduced in
the location of occurrence
Cryptogenic- plant taxa that includes both Native and Alien genotypes, varieties, or subspecies
Undetermined-taxa identified at level of growth form, most families, or genera with both native
and alien species
Nonnativeplants-for the NWCA, includes both alien and cryptogenic taxa
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Oversamplesites- a panel of additional sites selected by the survey design to provide replacements for
any sites that were either not part of the target population or could not be sampled
Parameter Names- specific code names (usually written in all caps) used to reference data in the official
NARS databases and in the NWCA raw datasets
Points-site coordinates selected by the survey design
Population- see the definition for "Target Population" in this Glossary
Population estimates-estimates of characteristics of the target or inference population of wetlands in
the conterminous US (or smaller reporting groups), usually described in acres or percent total area
Probability sites- sites defined by the NWCA sample draw (i.e., NWCA design sites) and some state
intensifications using the same design as NWCA
Reference-analogous to "least disturbed". Sites that represent least disturbed ecological condition17 and
the associated functional capacity typical of a given wetland type in a particular landscape setting (e.g.,
ecoregion, watershed)
Relative Extent- shows the percent of the resource estimated to be in a given condition category for an
indicator
Relative Risk (RR)-the probability (i.e., risk or likelihood) of having poor condition when the magnitude of
a stressor is high (i.e., poor stressor condition) relative to when the magnitude of a stressor is low (i.e.,
good stressor condition)
Resamplesites- probability sites that were originally sampled in the field in the previous NWCA survey
and selected to be sampled again in the current survey
Resource-the population of the aquatic resource (i.e., wetlands) evaluated in the NWCA
Revisit sites- a site sampled twice within the same year to assess within-season-variability in the
collected data
Sample frame-the geographic data layers that identify locations and boundaries of all wetlands that
meet the definition of the target population
Screen-the method for determining threshold (a.k.a., "cutoff" or "exceedance") values for assigning
disturbance class or condition category
Stressor Condition Extent-an estimate (by percent of the resource or relative ranking of occurrence, or
stressor-level class) of how spatially common a stressor is based on the population design
17 Stoddard JL, Larsen DP, Hawkins CP, Johnson PK, Norris RH (2006) Setting expectations for the ecological
condition of streams: the concept of reference condition. Ecological Applications 16:1267-1276
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Stressor Condition Category- describes the stress to wetlands associated with physical and chemical
indicators as "Good", "Fair", or "Poor"
Subpopu/ations- individual units within a subpopulation group
Subpopulation Group-the descriptive name for a parameter name and set of individual subpopulations
Survey design - the methods by which sites are selected for the survey; in the case of the NWCA, a
Generalized Random Tessellation Stratified (GRTS) survey design is used,which provides spatially-
distributed samples that are more likely to be representative of the population than other common
spatial survey designs
Target population- also called "the population", all wetland area included in the NWCA Wetland Types
and used in the survey design; defined as all tidal and nontidal wetted areas with rooted vegetation and,
when present, shallow open water less than 1 meter in depth, and not currently in crop production,
across the conterminous US
Taxon-iocationpair- A particular plant taxon occurring at a particular location:
X-regionpairs- where Xcan be any particular taxon, species, or name[e.g., one of several
potential taxonomic names) that occurs or was observed in a given region
X-statepairs-where A'can be any particular taxon, species, or name[e.g., one of several
potential taxonomic names) that occurs or was observed in a given state
X-sitepairs- where Xcan be any particular taxon, species, or name[e.g., one of several potential
taxonomic names) that occurs or was observed in a given site
X-piotpairs- where A'can be any particular taxon, species, or name(e.g., one of several potential
taxonomic names) that occurs or was observed in a given plot
Thresholds- similar to "exceedance values" and analogous to "benchmarks", thresholds are specific
values used to delineate boundaries to assign sites to specific disturbance classes or condition categories
Unique sites- each unique site occupies the same coordinates but may have up to four sampling visits
(revisit sites (Visit 1 and Visit 2 in the same year) and resample sites (sites sampled in both 2011 and again
in 2016)
Wetland Indicator Status (WIS)- hydrophytic status for plants designated as one of seven WIS Categories
(see Table 7-3 for details)
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