National Wetland Condition Assessment
2011 Draft Technical Report
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EPA-843-R-15-006
NATIONAL WETLAND CONDITION ASSESSMENT
2011 Draft Technical Report
US Environmental Protection Agency
Off ice of Water
Office of Research and Development
Washington, DC
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Notice
Methods described in the National Wetland Condition Assessment: 2011 Technical Report are to be used
specifically in work relating to the National Wetland Condition Assessment (NWCA). Mention of trade
names or commercial products in the document does not constitute endorsement or recommendation
for use.
This document is a preliminary draft. It has not been formally released by the U.S. Environmental
Protection Agency and should not be construed to represent Agency policy. It is being circulated for
comments on its technical merit. Do not quote or cite.
The suggested citation for this document is:
US Environmental Protection Agency. In Review. National Wetland Condition Assessment: 2011
Technical Report. EPA-843-R-15-006. US Environmental Protection Agency, Washington, DC.
Companion documents for the NWCA are:
National Wetland Condition Assessment: Quality Assurance Project Plan (EPA-843-R-10-003)
National Wetland Condition Assessment: Site Evaluation Guidelines (EPA-843-R-10-004)
National Wetland Condition Assessment: Field Operations Manual (EPA-843-R-10-001)
National Wetland Condition Assessment: Laboratory Operations Manual (EPA-843-R-10-002)
National Wetland Condition Assessment 2011: A Collaborative Survey of the Nation's Wetlands
(EPA-843-R-15-005) (In Review)
If you decide to print the document, please use double-side printing to minimize ecological impact.
2011 NWCA Technical Report DISCUSSION DRAFT
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Table of Contents
Notice i
Table of Contents iii
List of Figures xi
List of Tables xvii
Acknowledgements xxiii
Acronym List xxv
Important NWCA Terms xxvii
Foreword xxxi
Conceptual Background 1
NWCA Goals 1
Relationship between the NWCA and USFWS Status and Trends Program 1
Relationship between Field Sampling and Reporting 2
Literature Cited 2
Chapter 1: Survey Design 3
1.1 Description of the NWCA Wetland Type Population 3
1.2 Survey Design and Site Selection 3
1.2.1 Site Visits 4
1.2.2 State-Requested Modifications to the Survey Design 5
1.2.2.1 Wisconsin 5
1.2.2.2 Ohio 5
1.2.2.3 Minnesota 5
1.3 Sample Frame Summary 6
1.4 Site Selection Summary 7
1.5 Survey Analysis 9
1.6 Estimated Wetland Extent of the NWCA Wetland Type Population and Implications for Reporting 9
1.7 Literature Cited 11
Chapter 2: Overview of Analysis 13
Chapter 3: Data Preparation and Management 15
3.1 Introduction 15
3.2 Key Personnel 17
3.3 Data Entry and Review 18
3.3.1 Field Data 18
3.3.2 Laboratory Data 18
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3.4 Quality Assurance Checks 19
3.4.1 Verification of Points from the 2011 NWCA Design 19
3.4.2 Confirmation of Coordinates Associated with the Sites Sampled 19
3.4.3 Legal Value and Range Checks 20
3.5 Literature Cited 20
Chapter 4: Selection of Reference Sites and Definition of Disturbance Gradient 21
4.1 Background Information 21
4.2 Pre-Sampling Selection of Handpicked Sites 22
4.2.1 Pre-Screen 24
4.2.2 Basic Screens 24
4.2.3 Landscape Screens 26
4.2.4 Distribution of Sites by Wetland Type and Ecoregion 30
4.2.5 Replacement of Sites NotSampleable 30
4.2.6 Results 31
4.3 Overview of the Post-Sampling Evaluation of Site Disturbance 32
4.4 Reporting Groups 32
4.5 Selecting Reference Sites and Defining the Disturbance Gradient 37
4.5.1 Overview of Approach 37
4.5.2 Indices of Disturbance Buffer and AA 37
4.5.3 Indices of Hydrologic Disturbance in the AA 39
4.5.4 Index of Disturbance Indicated by Soil Chemistry 39
4.5.5 Metric of Plant Disturbance in the AA 44
4.5.6 Assignment of Sites along a Disturbance Gradient 44
4.5.7 Least and Most Disturbed Site Distribution 48
4.5.8 A Research Tool 51
4.6 Literature Cited 51
Chapter 5: Vegetation Indicators - Background, Analysis Approach Overview, Data Acquisition and
Preparation 55
5.1 Background 55
5.2 Overview of Vegetation Analysis Process 56
5.3 Vegetation Data Collection 59
5.3.1 Field Sampling 59
5.3.2 Identification of Unknown Plant Species 62
5.4 Data Preparation - Parameter Names, Legal Values or Ranges, and Data Validation 62
5.4.1 Description of Vegetation Field Data Tables 62
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5.4.2 Data Validation 63
5.5 Nomenclatural Standardization 64
5.5.1 Nomenclature Reconciliation Methods 64
5.5.2 Nomenclature Standardization Results and Documentation 67
5.6 Species Traits - Life History: Growth Habit, Duration, and Plant Category 67
5.6.1 Growth Habit 68
5.6.2 Duration 68
5.6.3 Plant Categories 69
5.7 Species Traits - Wetland Indicator Status 69
5.8 Species Traits - Native status 70
5.9 Species Traits - Coefficients of Conservatism 72
5.9.1 Creating a Database ofC-Valuesfor the Conterminous United States 72
5.9.1.1 Gathering Existing Lists of Coefficients of Conservatism 73
5.9.1.2 Developing and Compiling the National Floristic Quality Database (NFQD) 73
5.9.2 Assigning C-values to Plant Taxa Observed in the NWCA 74
5.9.2.1Taxonomic Reconciliation 75
5.9.2.2 Standardization of C-values for NWCA Taxa-State Pairs 75
5.9.2.3 Assigning C-values for NWCA Taxa-State Pairs 75
5.9.2.3.1 Species-State Pair Assignments 76
5.9.2.3.2 Genus-State Pair Assignments 77
5.9.2.4 Final NWCA C-value Assignments and Use 77
5.10 Literature Cited 78
5.11 Appendix A: Vegetation Field Data Forms 82
5.12 Appendix B: Parameter Names for Field Collected Vegetation Data 86
5.13 Appendix C: Sources of C-values in the National Floristic Quality Database 91
Chapter 6: Candidate Vegetation Metrics of Condition or Stress 95
6.1 Background 95
6.2 Developing and Calculating Candidate Metrics 95
6.3 Accounting for Regional and Wetland Type Differences 97
6.4 Calibration and Validation Data 99
6.5 Evaluating Candidate Vegetation Metrics 100
6.5.1 Range Tests 101
6.5.2 Repeatability 101
6.5.3 Responsiveness 102
6.5.4 Redundancy 102
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6.6 Metric Screening Results 103
6.7 Literature Cited 104
6.8 Appendix D: NWCA Candidate Vegetation Metrics Evaluated in 2011 106
Chapter 7: Wetland Condition - Vegetation Multimetric Index 137
7.1 Background - Vegetation Multimetric Index Development Approach 137
7.1.1 Wetland Condition Assessment in the NWCA 139
7.2 Developing the Vegetation Multimetric Index (VMMI) - Methods 139
7.3 Final National VMMI - Results 143
7.4 Thresholds for Good, Fair, Poor Wetland Condition 146
7.5 Ecological Condition Extent Estimates 147
7.6 Literature Cited 149
7.7 Appendix E: Cumulative Distribution Function Graphs for VMMI 150
Chapters: Indicators of Stress 155
8.1 Background Information 155
8.2 Selection of Indicators of Stress 156
8.2.1 Conceptual Model Overview 156
8.2.2 Choosing the Type of Data Used for Indicators of Stress 158
8.3 Physical Indicators of Stress 158
8.3.1 Defining Physical Indicators of Stress 158
8.3.2 Data Collection 159
8.3.3 Data Preparation 159
8.3.3.1 Decision-process for assigning form items to stressor categories 159
8.3.4 Index Development 161
8.3.4.1 Buffer Index 162
8.3.4.2 Hydrology Index 162
8.3.5 Stressor-Level Threshold Definition 162
8.3.5.1 Low Stressor-Level Threshold 163
8.3.5.2 High Stressor-Level Threshold 163
8.3.5.3 Applying Stressor-Level Thresholds to Indicators of Stress 163
8.4 Chemical Indicators of Stress 163
8.4.1 Defining Chemical Indicators of Stress 163
8.4.2 Sample Collection and Analysis 163
8.4.3 Data Preparation 164
8.4.4 Indicator Development 164
8.4.4.1 Heavy Metal Index (HMI) 164
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8.4.4.2 Soil Phosphorus Concentration 165
8.4.5 Stressor-Level Threshold Definition 165
8.4.5.1 Heavy Metal Index (HMI) Stressor-Level Thresholds 165
8.4.5.2 Soil Phosphorus Concentration Stressor-Level Thresholds 165
8.5 Biological Indicator of Stress 167
8.5.1 Defining a Biological Indicator of Stress 167
8.5.2 Data Collection 167
8.5.3 Data Preparation 167
8.5.4 Indicator Development 168
8.5.5 Stressor-Level Threshold Definition 169
8.6 Stressor Extent Estimates 170
8.7 Literature Cited 171
8.8 Appendix F: Example Buffer Form (B-l) 174
8.9 Appendix G: Example Hydrology Form (H-l) 175
Chapter 9: Transition from Analysis to Results 177
9.1 Introduction 177
9.2 Population Estimates 178
9.2.1 Wetland Condition Extent Estimates 178
9.2.2 Stressor Extent Estimates 179
9.3 Relative and Attributable Risk 180
9.3.1 Relative Risk 180
9.3.1.1 Example Calculation of Relative Risk 181
9.3.1.2 Considerations When Calculating and Interpreting Relative Risk 182
9.3.1.3 Application of Relative Risk to the NWCA 182
9.3.2 Attributable Risk 183
9.3.2.1 Considerations When Interpreting Attributable Risk 184
9.4 Appropriate Use of Nonnative Plant Stressor Indicator (NPSI) 184
9.5 Where to Find the Summary of NWCA Results 185
9.6 Literature Cited 185
Chapter 10: Research Feature - Microcystins 189
10.1 Background Information 189
10.2 Methods 190
10.2.1 Method 1 (Salinity < 3.5 PPT PSU) 190
10.2.2 Method 2 (Salinity > 3.5 PPT PSU) 190
10.3 Results 191
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10.4 Discussion 193
10.5 Literature Cited 194
Chapter 11: Research Feature-Water Chemistry 197
11.1 Background 197
11.2 Methods 197
11.2.1 Sample Collection and Laboratory Analysis 197
11.2.2 Data Handling 199
11.2.1 Graphical and Statistical Analysis 200
11.3 Results 202
11.3.1 Data Set Overview 202
11.3.2 Repeatability of Water Chemistry Data 203
11.3.3 Broad Patterns in Water Chemistry 204
11.3.4 Relationships of Water Chemistry to Anthropogenic Setting 210
11.3.5 Water Chemistry Patterns at Regional and National Scale - Scaling Up to Wetland Population
215
11.4 Discussion 216
11.5 Literature Cited 217
Chapter 12: Research Feature - USA-Rapid Assessment Method (USA-RAM) 219
12.1 Background Information 219
12.1.1 Tenets of USA-RAM 220
12.1.2 Structure of USA-RAM 221
12.1.2.1 Section A: Assessment of Condition and Stress in the Buffer Zone 222
12.1.2.1.1 Metricl: Percent ofAA Having Buffer 222
12.1.2.1.2 Metric 2: Buffer Width 223
12.1.2.1.3 Metric 3: Stressor to the Buffer Zone 224
12.1.2.2 Section B: Assessment of Wetland Condition in the AA 224
12.1.2.2.1 Metric 4: Topographic Complexity 224
12.1.2.2.2 Metrics: Patch Mosaic Complexity 224
12.1.2.2.3 Metric 6: Vertical Complexity. 224
12.1.2.2.4 Metric 7: Plant Community Complexity 224
12.1.2.3 Section C: Assessment of Stress in the AA 225
12.1.2.3.1 Metric 8: Stressors to Water Chemistry 225
12.1.2.3.2 Metric 9: Stressors to Hydroperiod 225
12.1.2.3.3 Metric 10: Stressors to Habitat/Substrate 225
12.1.2.3.4 Metric 11: The Cover of Invasive Species 225
32.12.3.5 Metric 12: Stressors to the Vegetation Community 226
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12.2 Data Preparation 226
12.3 Data Analysis 228
12.3.1 Overview 228
12.3.2 Data Analysis Steps 228
12.3.2.1 Distribution of Metric Data 228
12.3.2.2 Scoring USA-RAM Metrics 228
12.3.2.3 Procedures to Calculate the AA Condition Index, AA Stressor Index, Buffer Index, and Site Index
229
12.3.2.4 Reporting Groups 231
12.3.2.5 Testing USA-RAM Performance 231
12.4 Results and Discussion 232
12.4.1 Overview 232
12.4.2 Efficacy of the Site Index 232
12.4.3 Efficacy of the Buffer Index, AA Condition Index, and AA Stressor Index 236
12.4.4 Meaning of the Stressor Metrics 236
12.4.4.1 Ranking Stressors 237
12.4.4.2 Links between Stressor Metrics and Condition Metrics 238
12.4.5 Sample Frame Effects 239
12.4.6 Habitat Assessment with USA-RAM 240
12.4.7 Verification with Level 3 Vegetation Data 240
12.5 Literature Cited 241
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List of Figures
Figure 1-1. Estimated wetland area included in the NWCA Wetland Type Population, the proportion of
the population that was assessed (for which inference of results can be made), and the
proportion not assessed 10
Figure 2-1. The 2011 National Wetland Condition Assessment Analysis Pathway, which illustrates the
major components of the analysis and this report 14
Figure 3-1. The major components of the 2011 National Wetland Condition Assessment Analysis
Pathway discussed in this chapter (i.e., data preparation and management). A full-page,
unhighlighted version of this figure may be found on page 14 of this report 15
Figure 3-2. Flowchart of the data preparation and analysis used in the NWCA and other National Aquatic
Resource Surveys (NARS) 16
Figure 4-1. The major components of the 2011 National Wetland Condition Assessment Analysis
Pathway discussed in this chapter (i.e., the selection of reference sites and development of
the disturbance gradient). A full-page, unhighlighted version of this figure may be found on
page 14 of this report 21
Figure 4-2. Flowchart presenting the process resulting in the 150 hand-picked sites sampled in the 2011
NWCA. The green boxes are the components of the selection process. The blue boxes are the
sources of the sites considered. The orange box lists the collaborations with partners
conducting wetland assessments who recommended sites. The numbers with each arrow are
the number of sites considered at that point of the process. Black numbers are BPJ sites;
orange, non-screened sites. The number of sites from each non-screened source is listed in
parenthesis following the source. BPJ = Best Professional Judgment; REMAP = USEPA
Regional Environmental Monitoring and Assessment Program 23
Figure 4-3. Example of a candidate site that met the criteria of the Basic Screen. Yellow dot is the center
of the assessed area. PEM = Palustrine Emergent wetland; PFO =Palustrine Forested wetland
PUB = Palustrine Unconsolidated Bottom wetland; NWI = USFWS National Wetland Inventory
25
Figure 4-4. Example of photo interpretation used in Step 1. The yellow dot is the AA Center within the 1-
km radius area evaluated. Agricultural development (yellow polygons) comprised >25% of
the area for a score of 3 27
Figure 4-5. Example of photo interpretation used in Step 1. The yellow dot is the AA Center within the 1-
km radius area evaluated. Industrial development (orange polygons in A) comprised <10%for
a score of 1. A US Geologic Survey Topographic map (B) was used to interpret and
corroborate the presence of gravel pits found in A 27
Figure 4-6. Example of photo interpretation used in Step 2. The yellow dot is the AA Center within the 1-
km radius area evaluated. The site received a score of 3 due to the presence of paved roads.
28
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Figure 4-7. Example of photo interpretation and scoring used in Step 3. The yellow dot is the AA Center
within the 1-km radius area evaluated. The nearest disturbance was the presence of the
paved road 140 m from the AA Center so the site received a score of 2 for distance to the
nearest road and 2 for the distance to the first edge of human disturbance 29
Figure 4-8. Summary of the scoring for the site in Figure 4-3 through Figure 4-7. The yellow dot is the AA
Center within the 1-km radius area evaluated. The area marked by the circles with a radius of
140m and 200m were assessed in Step 3. The aggregate score of all disturbances for this
candidate site was 11 as indicated by the summary of the scores for each factor evaluated
displayed along the bottom of the photo 30
Figure 4-9. Map of the conterminous US showing distribution of handpicked sites (yellow) in relation to
probability sites (dark red) sampled in the 2011 NWCA. The Nine Aggregated Ecoregions are
based on combinations of Level III Ecoregions (Omernik 1987, USEPA 2011a) and are used in
other NARS assessments 31
Figure 4-10. Ordinations of species composition relative to the seven NWCA Wetland Types and the
Nine Aggregated Ecoregions (ECO_9) resulted in similar, intergrading groups. For definitions
of acronyms in the keys to the figures, see Table 4-4 and Figure 4-9 (ECO_9) and Table 1-1
(NWCA Wetland Types) 34
Figure 4-11. The four NWCA Aggregated Ecoregions that are based on combinations of Omernik's Level
III Ecoregions (Omernik 1987; USEPA 2011a) 35
Figure 4-12. Proximity weights assigned to the 13 plots evaluated as part of the Buffer Protocol 38
Figure 4-13. Example soil pit designating where soil chemistry samples were collected within the layer.
42
Figure 4-14. Examples of frequency histograms of soil metal concentrations used to set thresholds
(designated by the red line and detailed in Table 4-8). Published values are primarily from
Alloway (2013), and natural breaks in the data were considered 43
Figure 4-15. Diagram of the disturbance gradient used in the NWCA with categories of disturbance.
Least disturbed according to the definition of a reference site used in the NWCA and NARS
(Stoddard et al. 2006) 44
Figure 4-16. Illustration of the distribution of NWCA sites by disturbance category across the
conterminous US 48
Figure 4-17. Illustration of the distribution of NWCA sites by disturbance category in the eastern US....49
Figure 4-18. Illustration of the distribution of NWCA sites by disturbance category in the upper Midwest
area of the US 49
Figure 4-19. Illustration of the distribution of NWCA sites by disturbance category in the Gulf Coastal
Plains of the US 50
Figure 4-20. The NWCA disturbance gradient with the minimally disturbed category 51
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Figure 5-1. The 2011 National Wetland Condition Assessment Analysis Pathway. The orange outlined
box on left of diagram highlights the data preparation activities. Some prerequisite analysis
steps involve the use of validated data and the least and most disturbed site designations.
Green outlined and filled boxes represent the analysis path for the development of
vegetation indicators of condition. Development of the vegetation indicator of wetland stress
follows the stressor analysis path indicated by the purple open and filled boxes 57
Figure 5-2. Overview of data preparation and analysis steps for evaluating vegetation condition in the
2011 NWCA 58
Figure 5-3. Standard NWCA Assessment Area (AA) (shaded circular area) and standard layout of
Vegetation Plots 60
Figure 5-4. Detail of Vegetation Plot illustrating plot boundaries and positions of nested quadrats 60
Figure 5-5. Overview of vegetation data collection protocol for the 2011 NWCA (USEPA 2011a) 61
Figure 5-6. 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 66
Figure 5-7. Percentage site occurrences of individual plant taxa observed in the 2011 NWCA by native
status categories (see Table 5-4 for definitions) across 1138 probability and not-probability
sampled sites of native status 71
Figure 5-8. States with complete or partial published or unpublished lists of Coefficients of Conservatism
(C-values) that were included in the National Floristic Quality Database (NFQD) and used to
inform C-Value assignment for NWCA taxa-state pairs 73
Figure 5-9. 2011 NWCA sampled sites plotted on Nine Aggregated Ecoregions used by other NARS. Inset
shows status of available C-values 76
Figure 7-1. The major components of the 2011 National Wetland Condition Assessment Analysis
Pathway that pertain to evaluating wetland condition are highlighted. A full-page,
unhighlighted version of this figure may be found on page 14 of this report 137
Figure 7-2. Comparison of National VMMI values for calibration and validation data. For each boxplot,
the box is the interquartile (IQR) range, line in the box is the median, and each of the
whiskers represent the most extreme point a distance of no more than 1.5 x IQR from the
box. Values beyond this distance are considered outliers 144
Figure 7-3. NWCA National VMMI values for least and most disturbed sites by NWCA Reporting Group.
See Table 6-4 for definition of Reporting Groups. For each boxplot, the box is the
interquartile (IQR) range, line in the box is the median, and each of the whiskers represent
the most extreme point a distance of no more than 1.5 x IQR from the box. Values beyond
this distance are considered outliers. Numbers are number of sampled least and most
disturbed sites (probability and not-probability) for each Reporting Group 145
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Figure 7-4. Criteria for setting VMMI thresholds for good, fair, and poor condition classes based on
VMMI values observed for Least Disturbed (Reference) Sites 146
Figure 7-5. NWCA Analysis Pathway section where VMMI condition thresholds for each Reporting Group
(see Table 7-5) are used to generate estimates of wetland area in good, fair, and poor
ecological condition. A full-page, unhighlighted version of this figure may be found on page
14 of this report 147
Figure 7-6. Cumulative Distribution Function (CDF) of condition extent estimates, with confidence limits,
of wetland condition (VMMI) across the conterminous United States. Blue lines illustrate how
to read graph 148
Figure 8-1. The major components of the 2011 National Wetland Condition Assessment Analysis
Pathway discussed in this chapter (i.e., stressor definition and quantification, and stressor-
level threshold definition, which enable stressor extent estimates). A full-page, unhighlighted
version of this figure may be found on page 14 of this report 155
Figure 8-2. Conceptual model of how specific data collected as part of the 2011 NWCA (red and yellow
boxes containing bulleted lists) are used to estimate Stressor Extent Estimates (purple box)
and, ultimately, Relative & Attributable Risk (teal box). Grey, dashed arrows indicate that a
cause-and-effect relationship is expected to exist among the data, but these relationships
were not explicitly quantified as part of the 2011 NWCA data analysis. Black arrows represent
the explicit information flow (e.g., data represented in one box were used in the calculations
represented by the following box). The arrow with the black circle containing a red "w"
indicates that site weights from the probability design were used to calculate Stressor Extent
Estimates 157
Figure 8-3. Weights assigned to the 13 plots evaluated as part of the Buffer Protocol 162
Figure 8-4. Conceptual model of how the 75th and 95th percentiles of reference site soil phosphorus
concentrations are used to determine high and low stressor-level thresholds 166
Figure 8-5. The connection from stressor threshold definition (described in the preceding sections) to
reporting stressor extent estimates within the 2011 National Wetland Condition Assessment
Analysis Pathway 170
Figure 9-1. The major components of the 2011 National Wetland Condition Assessment Analysis
Pathway discussed in this chapter (i.e., wetland condition and stressor extent estimates, and
relative and attributable risk). A full-page, unhighlighted version of this figure may be found
on page 14 of this report 177
Figure 9-2. An example of how wetland condition extent estimates (based on the Vegetation MMI) are
reported. In this example, wetland condition extent is presented by percent of the resource
(i.e., percent of total wetland area for the Nation or region) in the left half of the figure, and
by wetland acres in the right half of the figure 179
Figure 9-3. An example of how stressor extent estimates are reported using vegetation alteration
stressor data. In this example, stressor extent is presented by percent of the resource (i.e.,
percent of total wetland area for the Nation or region) 180
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Figure 9-4. An example of how relative risk is reported in the NWCA. In this example, stressor extent
estimates (for the high stress level) are presented (left) with relative risk for each indicator of
stress (right). Note that large stressor extent does not necessarily translate to high relative
risk (or visa versa) 183
Figure 9-5. An example of how attributable risk (right panel) is reported in the NWCA 184
Figure 10-1. National Microcystin Occurrence for 2011 National Wetland Condition Assessment 192
Figure 10-2. Percent of Wetland Acres as a Function of World Health Organization Relative Probability of
Adverse Recreational Human Health Risks Based on Microcystin Concentration 193
Figure 11-1. Relationship between laboratory and field measured COND (left) and PH (right) for the sites
at which both lab and field measurements were made. The longer, dashed line in both plots
is the 1:1 line; the shorter solid line is the linear regression 200
Figure 11-2. Bi-plots of water chemistry values as measured at Visit 1 (x-axis) vs. Visit 2 (y-axis). Long
dashed lines are 1:1 lines , shorter solid lines are linear regressions, and the plotted symbol
show the number of weeks elapsed between sample 1 and sample 2 (all values greater than
9 weeks are coded as "9"). The pH slope is ~ 1:1 after removal of the circled outlier 204
Figure 11-3. Dot plots showing distribution of COND by geographic reporting unit, with bottom panel
showing sites classified as fresh-water (COND<833) and top panel showing brackish sites
(COND>833) - note difference in scales between the two. Sites with unusual COND for their
ecoregion are labeled with the US state (2-letter code) in which they are found. The 10 sites
excluded from analyses examining ecoregion and NWCA Reporting Groups are indicated with
red symbols and text 206
Figure 11-4. Box plots showing distribution of TN, TP, and CHLA by geographic reporting unit (left-hand
panels) and by HGM type (right-hand panels). Note log-scale on vertical axes. Nutrient levels
are higher in Interior Plains wetlands than all others and CHLA levels are higher in tidal
wetlands than others (shaded boxes), but differences among other categories are not strong.
Plots by geographic reporting unit exclude 10 sites with unusual conductivity 207
Figure 11-5. Scatterplot showing relationship among TN, TP, and CHLA for all sites. The correlation (in
loglO transformed units) of TN to TP is 0.78, and that of CHLA to TN and TP is 0.63 and 0.65
respectively 208
Figure 11-6. Plots showing TP and TN data distribution by geographic reporting unit. Plots for TP and
CHLA include vertical lines show divisions between trophic state categories commonly used
in classifying lakes (divisions at 10, 35 and 100 ug/L TP and 2.6, 7.3, and 56 ug/L CHLA). Plots
exclude 10 sites with unusual conductivity 208
Figure 11-7. Box plot showing difference in pH, TN, TP, and CHLA between wetlands classified as having
woody or herbaceous type vegetation 209
Figure 11-8. Box plots showing N:P ratios by geographic reporting unit (right) and HGM type (left). Boxes
do not represent the full data distribution, as sites with N:P ratio > 100 have been excluded
to focus on the region where the presumptive limiting nutrient switches from nitrogen
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(below 16) to phosphorus (abovelS; horizontal line). Herbaceous wetlands tend to have
lower N:P ratios than woody-vegetation wetland, but wetlands in almost all geographic
reporting units and HGM types span the range from N-limited to P-limited. Plots by
geographic reporting unit exclude 10 sites with unusual conductivity 210
Figure 11-9. Plots showing Pearson strength of correlation between water chemistry and five
anthropogenic stressor variables for five geographic reporting units (woody and herbaceous
combined). Note that the vertical axis is scaled the same in all 3 graphs but does not extend
as far for TP and CHLA as it does for COND. Plots exclude 10 sites with unusual conductivity.
213
Figure 11-10. Box plots showing distribution of three anthropogenic stressor variables within geographic
reporting unit & vegetation type combinations ("H" and "W" refer to herbaceous and woody,
respectively) 215
Figure 12-1. Conceptual diagram showing the relationship between stressors, buffers and condition. The
effect of a stressor that originates outside a wetland is diminished as it passes through the
buffer area that adjoins it 220
Figure 12-2. Overview of data preparation and analysis steps to describe condition and stress based on
USA-RAM 227
Figure 12-3. Map NWCA sites in portions of Louisiana, Mississippi and Alabama. Sites marked with an
outer white circle were missing plant data for Metric 7 (not all 65 of these sites are
distinguishable in this figure due to overlapping markers). Least disturbed sites are green;
intermediate disturbed sites are white, and the sites designated by NCWA as most disturbed
are red 228
Figure 12-4. Box-plots of the USA-RAM Site Index scores for the least-disturbed and most-disturbed AAs
(as independently defined by the NCWA Analysis Team) for the 10 NWCA Reporting Groups.
233
Figure 12-5. Box-plots for Buffer Index, AA Condition Index, AA Stressor Index scores for the least-
disturbed and most-disturbed sites for the 10 NWCA Reporting Groups. Note high Stressor
Index values indicate greater stress. This figure continues on the next page 234
Figure 12-6. Cumulative frequency distribution of USA-RAM Site Index scores. The possible range of
scores is 0-225. While sites at the top end of the condition gradient appear well represented,
sites at the low end of the range (< 50) are lacking 239
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List of Tables
Table 1-1. Definition of NWCA Wetland Types.
Table 1-2. Sample frame wetland area from the US Fish and Wildlife 2005 National Wetland Status &
Trends plots. Wetland area (in acres) is reported by state and S&T Wetland Categories that
represent the NWCA Wetland Types. See Table 1-1 for definitions of the acronyms and
descriptions of included wetland types 6
Table 1-3. Number of sites planned to be sampled. Number of sites is reported by state and S&T
Wetland Categories that represent the NWCA Wetland Types. See Table 1-1 for definitions of
the acronyms and descriptions of included wetland types 8
Table 1-4. Total estimated areal extents for the NWCA Wetland Type population, the inference
population extents (based on sampled probability sites (n)), and non-assessed area extents
(based on probability sites (n) that could not be assessed) for the nation and within
subpopulations represented by four major geographic regions. Results are reported as
millions of acres or % of total estimated NWCA wetland area for the nation or by region 11
Table 3-1. The 2011 Analysis Team and roles. All people listed are USEPA except as noted 17
Table 4-1. Scoring associated with the level of anthropogenic impact within the 1-km radius buffer
around a site 26
Table 4-2. Scoring associated with the presence of roads and trails within the 1-km radius buffer around
a site 28
Table 4-3. Scoring associated with the distance from disturbance within the 1-km radius buffer around a
site 29
Table 4-4. Distribution of 150 handpicked sites sampled in 2011 by Nine Aggregated Ecoregions and
NWCA Wetland Types. Acronyms for the Nine Aggregated Ecoregions (in parentheses) are
used in tables and figures in this chapter. See Table 1-1 for definitions of acronyms and
description of characteristics for NWCA Wetland Types 31
Table 4-5. Matrix showing the four NWCA Aggregated Ecoregions (Figure 4-11) and the four NWCA
Aggregated Wetland Types combined into 10 NWCA Reporting Groups. Note that estuarine
wetland types are not reported by ecoregions due to insufficient samples. Acronyms for the
NWCA Aggregated Ecoregions, NWCA Aggregated Wetland Types, and the 10 Reporting
Groups are in parentheses following their names. Red text gives the number of sites
sampled, i.e., the sum of the number of sites NWCA probability designs (i.e., the national
assessment and some state intensifications) and from not-probability designs (i.e., the
handpicked sites and some state intensifications) 36
Table 4-6. Six disturbance indices generated from the Buffer Protocol data 39
Table 4-7. Two indices generated from the Hydrology Protocol data 39
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Table 4-8. Summary of the characteristics of the heavy metals considered for use in the stressor index
based on soil chemistry. Natural backgrounds are based on Alloway (2013). Percent of sites
exceeding the thresholds is based on data from Visit 1 42
Table 4-9. The types and numbers of sites sampled from probability and not-probability survey designs
used in the establishment of the NWCA disturbance gradient 45
Table 4-10. Threshold values for sites to be categorized as least disturbed by Reporting Group for the
Buffer Indices. If any single threshold was exceeded at a site, the site was not considered
least disturbed. Numbers in red are thresholds relaxed to achieve about 20% of the sites in
the Group as least disturbed. An index score of 0 indicates disturbance not present. See
Table 4-5 for definitions of Reporting Group acronyms 45
Table 4-11. Threshold values for sites to be categorized as least disturbed by Reporting Group for the
Hydrology and Soil Chemistry Indices, and the relative cover of alien plant species metric. If
any single threshold was exceeded at a site, the site was not considered least disturbed.
Numbers in red are thresholds relaxed to achieve about 20% of the sites in the Group as least
disturbed. A Hydrology or Soil Chemistry Index score of 0 indicates disturbance not present.
See Table 4-5 for definitions of Reporting Group acronyms 45
Table 4-12. Results of screening for least disturbed. See Table 4-5 for definitions of Reporting Group
acronyms. Key to Font color for Reporting Group: Green = Not relaxed; Black = Relaxed; Red
= Most Relaxed 46
Table 4-13. Threshold values for sites to be categorized as most disturbed by Reporting Group for the
Buffer Indices. If any single threshold was exceeded at a site, the site was considered most
disturbed. See Table 4-5 for definitions of Reporting Group acronyms 47
Table 4-14. Threshold values for sites to be categorized as most disturbed by Reporting Group for the
Hydrology and Soil Chemistry Indices and the relative cover of alien plant species metric. If
any single threshold was exceeded at a site, the site was considered most disturbed. See
Table 4-5 for definitions of Reporting Group acronyms 47
Table 4-15. Number and percent of sites in the most and intermediate disturbance categories by NWCA
Reporting Group. See Table 4-5 for definitions of Reporting Group acronyms 47
Table 4-16. Percent of the 1138 sites screened in each disturbance category by NWCA Wetland Types.
Numbers are rounded and may not add to 100 percent. See Table 1-1 for descriptions of the
NWCA Wetland Types, which include PRL (Palustrine, Riverine, and Lacustrine) and E
(Estuarine) wetlands 50
Table 4-17. Percent of the 1138 sites screened in each disturbance category by Hydrogeomorphic (HGM)
Class (Brinson 1993). Numbers are rounded and may not add to 100 percent 50
Table 5-1. Growth habit categories used in NWCA analysis with a crosswalk to PLANTS database growth
habit designations observed across the 2011 NWCA species list. Capitalized Growth Habit
Category Names are used in descriptions of Growth Habit metrics in Section 6.8, Appendix D.
68
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Table 5-2. Duration categories used in the NWCA analyses and a crosswalk to PLANTS database duration
designations observed across the 2011 NWCA species list. Capitalized Duration Category
Codes (listed in parentheses) are used in descriptions of Duration Metrics in Section 6.8,
Appendix D 68
Table 5-3. Descriptions of Wetland Indicator Status (WIS) ratings (from Lichvar 2013). WIS Category
Codes (listed in parentheses) are used in descriptions of Hydrophytic Status Metrics in
Section 6.8, Appendix D. Numeric Ecological Value for each indicator status used in
calculating some metrics 69
Table 5-4. Definition of state-level native status designations for NWCA taxa-state pairs 70
Table 6-1. Metric Groups and component Metric Types for characterizing vegetation condition 96
Table 6-2. Distribution of 1138 NWCA sampled sites (probability and not-probability) by 2011 NWCA
Aggregated Ecoregions. n = numbers of sites 98
Table 6-3. Distribution of 1138 NWCA sampled sites (probability and not-probability) by NWCA
Aggregated Wetland Types, n = numbers of sites. Code PRL is pronounced 'pearl' 98
Table 6-4. Distribution of 1138 NWCA of sampled sites (probability and not-probability) and 96 revisited
sites across the conterminous United States and by NWCA Reporting Groups, n = numbers of
sites 98
Table 6-5. Distribution of sites in calibration and validation data sets for all sites, by disturbance type,
and by Aggregated Wetland Type. Total n = 1138 100
Table 6-6. List of vegetation condition metrics that passed all screening tests described in Section 6.5 for
at least one evaluation Site Group. For metric descriptions see Section 6.8, Appendix D... 103
Table 6-7. List of vegetation stress metrics that passed all screening tests described in Section 6.5 for at
least one evaluation Site Group. For metric descriptions see Section 6.8, Appendix D 104
Table 7-1. NWCA Site Groups for which potential VMMIs were developed and evaluated using
Traditional (adapted from Stoddard et al. (2008)) or Permutation (adapted from Van Sickle
(2010)) approaches. Site Groups resulting in the most robust VMMIs are denoted by stars
(*), the National VMMI having the overall best performance 140
Table 7-2. Four metrics included in the final NWCA Vegetation Multimetric Index (VMMI). Description of
calculation methods for these metrics can be found in Section 6.8, Appendix D. Note that
metric scoring is reversed for metrics that increase with disturbance 143
Table 7-3. Floor and ceiling values for scoring final VMMI metrics based on range of values in the
calibration set 143
Table 7-4. Summary statistics for the National VMMI. Statistics for wetland type groups are calculated
based on the National VMMI values for all sites in a particular group 144
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Table 7-5. Thresholds for Vegetation Multimetric Index (VMMI) values to delineate good, fair, and poor
ecological condition for sites in each of the NWCA Reporting Groups. Sites with VMMI values
that fall from the 5th up to the 25th percentile for least disturbed (reference) sites are
considered in fair condition 146
Table 8-1. Physical indicators of stress, their descriptions, and form items (i.e., from the H-l Hydrology
or B-l Buffer Forms) assigned to each indicator 161
Table 8-2. Threshold definition and physical application to indicators of stress 163
Table 8-3. Threshold definition for the Heavy Metal Index (HMI) 165
Table 8-4. Stressor-level threshold definition for soil phosphorus concentration 166
Table 8-5. Nonnative Plant Stressor Indicator (NPSI) Stressor-Level Threshold Exceedance Values for
each of the three component nonnative species metrics: Relative Cover of Nonnative Species
(XRCOV_AC), Nonnative Richness (TOTN_AC), and Relative Frequency of Nonnative Species
(RFREQ_AC) 169
Table 9-1. 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 low and high stress levels (as indicated by stream water total nitrogen concentration,
TN). Results are hypothetical 181
Table 11-1. Statistics concerning frequency with which water samples were or were not obtained across
various NWCA reporting units. Percent of sites without water samples is also broken out by
herbaceous and woody type wetlands within the estuarine and geographically-based
reporting units 202
Table 11-2. Median and range (in parentheses) for water chemistry analytes across the data set as a
whole and for geographic reporting unit and wetland type subdivisions. Number of sites is
given in parentheses after each reporting unit (sample size for some analytes is slightly lower
due to missing values). "BD" denotes values below the most frequently applicable laboratory
detection limit for CHLA (0.5 u.g/L), NH3 (0.004 mg/L), NOX (0.02 mg/L), and TP (4.0 u.g/L).
Note that some COND values seem inappropriate for their site type (e.g., <1000 in
estuarine/tidal; >10,000 in non-estuarine/non-tidal) but statistics reported here are for all
sites regardless of COND values 205
Table 11-3. Correlation matrix for log-10 conductivity vs. anthropogenic stressor variables for various
wetland groups ("H" vs. "W" refer to herbaceous and woody in the geographic reporting unit
x vegetation type combinations). Correlation coefficients (positive or negative) with
magnitude >0.3 are in bold underline. Stressor variable BlH_all is over the 100 m buffer
assessed by the field crew, all other stressor variables are over a 1000 m radius circle. The
non-estuarine groups omit sites having conductivity suggestive of marine influence 211
Table 11-4. Correlation matrix for log-10 TN vs. anthropogenic stressor variables for various wetland
groups ("H" vs. "W" refer to herbaceous and woody in the geographic x vegetation type
combinations). Correlation coefficients (positive or negative) with magnitude >0.3 are in bold
underline; those of this magnitude but not in the expected direction (positive or negative)
xx 2011 NWCA Technical Report DISCUSSION DRAFT
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are additionally in brackets. Stressor variable BlH_all is over the 100 m buffer assessed by
the field crew, all other stressor variables are over a 1000 m radius circle. The non-estuarine
groups omit sites having conductivity suggestive of marine influence 211
Table 11-5. Correlation matrix for log-10 TP vs. anthropogenic stressor variables for various wetland
groups ("H" vs. "W" refer to herbaceous and woody in the geographic x vegetation type
combinations). Correlation coefficients (positive or negative) with magnitude >0.3 are in bold
underline; those of this magnitude but not in the expected direction (positive or negative)
are additionally in brackets. Stressor variable BlH_all is over the 100 m buffer assessed by
the field crew, all other stressor variables are over a 1000 m radius circle. The non-estuarine
groups omit sites having conductivity suggestive of marine influence 212
Table 11-6. Correlation matrix for log-10 CHLA vs. anthropogenic stressor variables for various wetland
groups ("H" vs. "W" refer to herbaceous and woody in the geographic x vegetation type
combinations). Correlation coefficients (positive or negative) with magnitude >0.3 are in bold
underline; those of this magnitude but not in the expected direction (positive or negative)
are additionally in brackets. Stressor variable BlH_all is over the 100 m buffer assessed by
the field crew, all other stressor variables are over a 1000 m radius circle. The non-estuarine
groups omit sites having conductivity suggestive of marine influence 212
Table 12-1. USA-RAM Attributes and Metrics of wetland condition and stress 221
Table 12-2. Guidelines for Assessing Stressor Severity 222
Table 12-3. Buffer Land Cover Criteria. To qualify as buffer, a land cover must meet all four of the listed
criteria 223
Table 12-4. List of land covers classes and whether they count as buffer land cover or are non-buffer
land covers. Land cover classes based on the Anderson Land Cover Class system 223
Table 12-5. The upper and lower sections of the table show data thresholds separating the four
categories of condition or stress for each Metric. Higher scores for the stressor Metrics
indicate greater stress, except for Metric 3, for which higher scores indicate lesser stress; this
was done to facilitate calculation of the Buffer Index (see text for details) 230
Table 12-6. A summary of the method for calculating USA-RAM scores 231
Table 12-7. Summary of Reporting Regions to Aggregated Ecoregions and wetland types 231
Table 12-8. Total stressor counts recorded in the buffer and the AA for each NWCA Reporting Group,
and the total stressors recorded for each of the individual stressor Metrics (M) in the AA for
the Least-Disturbed AAs. Highlighted cells indicate the highest stressor count recorded for
each Reporting Group. Because Metric 11, Cover of Invasive Species, is not based on a count
of stressor indicators, it is not shown in this table 237
Table 12-9. Total stressor counts recorded in the buffer and the AA for each NWCA Reporting Group,
and the total stressors recorded for each of the individual stressor Metrics (M) in the AA for
the Most-Disturbed AAs. Highlighted cells indicate the highest stressor count recorded for
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each Reporting Group. Because Metric 11, Cover of Invasive Species, is not based on a count
of stressor indicators, it is not shown in this table 238
Table 12-10. Ranking of the stressor indicators that were observed most frequently and least frequently,
which are assumed to have the greatest and least impact, respectively, across the US. Metric
11, Cover of Invasive Species, is not included since it is not based on a count of stressor
indicators 238
Table 12-11. Correlation coefficients for regression between USA-RAM Site Index values and the Level 3
NWCA Floristic Quality Assessment Index (FQAI) and mean Coefficients of Conservatism
(Mean C) for each Reporting Group. Highlighted cells show correlations > 0.40 241
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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 2011 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
Alaska Department of Environmental Conservation
Alabama Department of Environmental
Management
Arizona Department of Environmental Quality
California State Water Resources Control Board
California State University System - Moss Landing
Marine Laboratories
Colorado Natural Heritage Program
Delaware Department of Natural Resources and
Environmental Control
Fort Peck Tribe
Georgia Department of Natural Resources
Georgia Environmental Protection Division
Idaho Department of Environmental Quality
Illinois Natural History Survey
Iowa Department of Natural Resources
Kansas Alliance for Wetlands and Streams
Kansas Department of Health and the Environment
Kansas Water Office
Kentucky Division of Water Resources
Leech Lake Band of Ojibwe, Division of Resource
Management
Maine Department of Environmental Quality
Maryland Department of the Environment
Michigan Department of Environmental Quality
Minnesota Pollution Control Agency
Missouri Department of Natural Resources
Montana Natural Heritage Program
Navajo EPA
Nebraska Game and Parks Commission
New Hampshire Department of Environmental
Services
New Jersey Natural Heritage Program
New Mexico Natural Heritage Program
New York Natural Heritage Program
North Carolina Department of Environment and
Natural Resources
Ohio EPA
Oklahoma Conservation Commission
Oregon Department of Environmental Quality
South Carolina Department of Health and
Environment Control
Utah Division of Water Quality
Vermont Department of Environmental
Conservation
Virginia Department of Environmental Quality
Washington State Department of Ecology
West Virginia Department of Environment
Protection
Wind River Environmental Quality Commission
Wisconsin Department of Natural Resources
Federal Partners
U.S. Army Corps of Engineers
U.S. Department of Agriculture, Forest Service
U.S. Department of Agriculture, Natural Resource
Conservation Service
U.S. Department of Interior, Fish and Wildlife Service
U.S. Department of Interior, National Park Service
U.S. EPA Office of Research and Development
U.S. EPA Off ice of Water
U.S. EPA Regions 1-10
U.S. Geological Survey
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2011 NWCA Technical Report
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Other Partners and Collaborators
Battelle Memorial Institute
BioLogics
California State University System - Moss Landing Marine Laboratories
Center for Plains Conservation and Biodiversity
EcoAnalysts
Great Lakes Environmental Center
Midwestern Biodiversity Institute
North Dakota State University
Pennsylvania State University
San Francisco Estuarine Institute
Tetra Tech, Inc.
University of Central Missouri
University of Florida
University of Houston - Clear Lake
University of Nebraska-Lincoln
Virginia Institute of Marine Sciences
Wells National Estuarine Reserve
USEPA's Office of Water provided support throughout the analysis process, especially Gregg Serenbetz,
the NWCA lead, and Chris Faulkner from Wetland Division, Regina Poeske from Region 3 while on detail
to Wetland Division, and Sarah Lehman from the Assessment & Watershed Protection Division.
The primary Data Management and Analysis Team for the 2011 NWCA included Mary Kentula, Teresa
Magee, Karen Blocksom, Tony Olsen, Steve Paulsen, Tom Kinkaid, Marc Weber, Dave Peck, Anett
Trebitz, Brian Hill, and Janet Nestlerode from USEPA Office of Research and Development; Amanda
Nahlik and Siobhan Fennessy from Kenyon College; Alan Herlihy from Oregon State University and
USEPA Office of Water Wetland Division; Josh Collins from San Francisco Estuary Institute; Gregg
Lomnickyfrom Dynamac Corporation; Marlys Cappaertfrom SRA International, Inc; and Keith Loftin
from U.S. Geological Survey.
Chapters 1 - 9 of this Technical Report support the core indicators of ecological condition and stress for
the NWCA. Authors for these nine chapters included Mary Kentula and Teresa Magee from EPA Office of
Research and Development, Amanda Nahlik from Kenyon College; Alan Herlihy from Oregon State
University and USEPA Office of Water; and Gregg Lomnicky from Dynamac. Chapters 10 - 12 describe
research indicators for the NWCA, and contributors were Keith Loftin from US Geological Survey, Annett
Trebitz and Janet Nestlerode from USEPA Office of Research and Development, Siobhan Fennessy from
Kenyon College, and Josh Collins from San Francisco Estuary Institute.
Key assistance in acquisition of plant species trait information or in taxonomic standardization of plant
species names was provided by Natalie Allen and Rachel Sullivan during their stints as ORISE fellows at
USEPA Office of Water Wetland Division, Nicole Kirchner while at Kenyon College, and Gerry Moore of
the PLANTS database at the US Department of Agriculture, Natural Resources Conservation Service.
xxiv 2011 NWCA Technical Report DISCUSSION DRAFT
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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
ECO_9 Nine Aggregated Ecoregions used by the USEPA NARS program
FQAI Floristic Quality Assessment Index
GIS Geographic Information System
GRTS Generalized Random Tessellation Stratified
HGM Hydrogeomorphic Class
HMI Heavy Metal Index
ICP-MS Inductively Coupled Plasma Mass Spectrometer
IM Information Management
IQR Interquartile Ranges
MDL Minimum Detection Limit
Mean C Mean Coefficients of Conservatism
MMI Multimetric Index
NARS USEPA National Aquatic Resource Surveys
NFQD National Floristic Quality Database
NPS US National Park Service
NPSI Nonnative Plant Stressor 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
Pr Probability
QA Quality Assurance
REMAP USEPA Regional Environmental Monitoring and Assessment Program
RR Relative Risk
S&T USFWS Status and Trends
S:N SignahNoise (i.e., signal to noise ratio)
UID Unique Identification
US United States
USAGE 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
WED USEPA Office of Research and Development, National Health and Environmental Effects
Laboratory, Western Ecology Division
WIS Wetland Indicator Status
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Important NWCA Terms
USFWSS&T Wetland Categories- wetland types, often expressed as codes, specifically surveyed by US
Fish and Wildlife Service to quantify status and decadal trends in national wetland area
NWCA Wetland Types - seven wetland types included in the NWCA Survey, which represent a subset of
USFWSS&T Categories1
Target 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
Sample frame - a list of all members of the target population from which the sample is drawn, which, in
the case of the NWCA, is all the NWCA Wetland Types in the USFWS Status and Trends mapped plots
Probability sites - sites defined by the NWCA sample draw (i.e., NWCA design sites) and some state
intensifications using the same design as NWCA
Not-probability sites- sites not defined by the NWCA sample draw but sampled, including handpicked
sites and some state intensifications
Inference population -final wetland area represented by sampled probability sites; ultimately used by
the NWCA for reporting condition and stressor extent
NWCA Aggregated Wetland Types -four wetland types based on combined NWCA Wetland Types
Nine Aggregated Ecoregions - nine ecoregions in the conterminous US that are based on combinations
of USEPA Level III Ecoregions used in previous NARS2'3
NWCA Aggregated Ecoregions - four ecoregions in the conterminous US that are based on
combinations of Nine Aggregated Ecoregions
NWCA Reporting Groups-ten groups that represent combined NWCA Aggregated Ecoregions and
NWCA Aggregated Wetland Types
1 NOTE: There is a discrepancy with how these seven NWCA Wetland Types are named on the 2011 NWCA field
forms; NWCA Wetland Types are designated as 'Status & Trends Categories' on Form PV-1, 'FWS Status and Trends
Class' on Form AA-2, and 'Predominant S & TClass' on Form V-3.
2 Omernik JM (1987) Ecoregions of the conterminous United States. Annals of the Association of American
Geographers 77: 118-125
3 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, OR
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Reference - sites that represent least disturbed ecological condition4 and the associated functional
capacity typical of a given wetland type in a particular landscape setting (e.g., ecoregion, watershed)
Disturbance Class - classes reflecting the gradient of anthropogenic disturbance across all sampled
wetland sites, and used for Multimetric Index (MMI) 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 habitat 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
• Minimally Disturbed - a Disturbance Class used to describe sites with zero observable human
disturbance, with the exception of up to 5% alien plant species cover
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
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
Buffer-the area (representing a prescribed measurement area) surrounding the Assessment Area
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
Index - a combination of metrics used to generate a single score to describe a particular property
(condition or stress in the case of the NWCA) for a site
Native Status - state level designations of plant taxa nativity for the NWCA, designations include:
• Native - plant taxa native to a specific state
• Introduced - plant taxa introduced from outside the conterminous US
• Adventive - plant taxa native to some areas or states of the conterminous US, but introduced in
the location of occurrence
• Alien - combination of introduced and adventive taxa
• Cryptogenic - plant taxa with both native and introduced genotypes, varieties, or subspecies
• Undetermined- plants identified to growth form or family, or genera with native and alien
species
• Nonnative - combination of alien and cryptogenic taxa
4 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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Taxon-location pair - A particular plant taxon occurring at a particular location:
• X-region pairs - where X can 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-state pairs - where X 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-site pairs- where X can 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-plot pairs - where X 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
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
Condition Class - 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 classes
Stressor-Level Class - describes the ecological stress to wetlands associated with physical, chemical, and
biological indicators of stress as 'Low', 'Moderate', or 'High' (and 'Very High' for Nonnative Plant
Stressor Indicator, only)
Stressor 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
Relative Risk (RR) -the probability (i.e., risk or likelihood) of having poor condition when the magnitude
of a stressor is high relative to when the magnitude of a Stressor is low
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 eliminated5
5 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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Foreword
This document, the National Wetland Condition Assessment: 2011 Technical Report, accompanies the
National Wetland Condition Assessment 2011: A Collaborative Survey of the Nation's Wetlands. The
National Wetland Condition Assessment (NWCA) is a collaboration among the USEPA, and State, Tribal,
and other Federal partners. It is part of the National Aquatic Resource Survey (NARS) program, a broad
effort to conduct national scale assessments of aquatic resources. The NWCA provides the first survey at
national and regional scales of the ecological condition of wetlands and indicators of stress likely
affecting condition. This was accomplished by analyzing data collected across the conterminous US.
National Wetland Condition Assessment 2011: A Collaborative Survey of the Nation's Wetlands (referred
to as the "Public Report") is not a technical document, but rather a report geared toward Congress and a
broad, public audience, that describes the background and main findings of the 2011 NWCA. The
National Wetland Condition Assessment: 2011 Technical Report is a supplemental document that serves
as the technical reference to support the findings presented in the Public Report. The Technical Report is
organized into chapters and appendices that describe the development of the survey design and the
scientific methods used to collect, evaluate, and analyze data collected for the 2011 NWCA. Chapters 1
through 9 provide the key technical information supporting the Public Report.
The technical document includes information on the target population, sample frame, and site selection
underlying the 2011 NWCA survey design. The report also provides a synthesis of data preparation and
management processes, including field and laboratory data entry and review, as well as several quality
assurance checks employed for the 2011 NWCA. The NWCA evaluates the ecological condition of and
potential stress to wetlands along a gradient of disturbance, based on the comparison to sites
designated as least-disturbed or reference. The Technical Report provides a thorough overview of the
development 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 2011
NWCA. For each of these indicators the Technical Report 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.
In addition to the key technical information described in the previous paragraphs, the Technical Report
provides information about data that were collected during the 2011 NWCA but which are not all
included in the population estimates presented in the Public Report. These include data collected and
analyzed for microcystins (Chapter 10), water chemistry (Chapter 11) and the USA Rapid Assessment
Method (USA-RAM; Chapter 12). The structure of these final three chapters is analogous to a white
paper. Although water chemistry and USA-RAM were not included in the Public Report, estimates for
extent of microcystins in wetlands were reported.
The information described in the National Wetland Condition Assessment: 2011 Technical Report was
developed through the efforts and cooperation of NWCA scientists from EPA, technical experts and
participating cooperators from academia and state and tribal wetland programs. While this Technical
Report serves as a comprehensive summary of the NWCA procedures, including information regarding
procedures, design, sampling, and analysis of data, it is not intended to present an in-depth report of
data analysis results.
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xxxii 2011NWCA Technical Report DISCUSSION DRAFT
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i Conceptual Background
2
3 This section briefly describes key concepts related to the goals of the NWCA, the survey design, and
4 reporting of results, each of which is important to the analysis and interpretation of the 2011 NWCA
5 data. These concepts tie together the components of the NWCA - from survey design through reporting
6 the results. They will also be incorporated into future assessments to assure the consistency necessary
7 for reporting on status and trends in wetland condition and in the patterns in indicators of stress.
8
9 NWCA Goals
10
11 The National Wetland Condition Assessment (NWCA) is one of the US Environmental Protection
12 Agency's National Aquatic Resource Surveys (NARS). The purpose of NARS is to generate statistically-
13 valid and environmentally relevant reports on the condition of the nation's aquatic resources every five
14 years. The goals of the NWCA are to:
15
16 • Produce a national report describing the ecological condition of the nation's wetlands and
17 anthropogenic stressors commonly associated with poor condition;
18
19 • Collaborate with states and tribes in developing complementary monitoring tools, analytical
20 approaches, and data management technology to aid wetland protection and restoration
21 programs; and
22
23 • Advance the science of wetland monitoring and assessment to support wetland management
24 needs.
25
26 Relationship between the NWCA and USFWS Status and Trends Program
27
28 The NWCA was designed to complement the US Fish and Wildlife Service's National Wetland Status and
29 Trends Program (S&T). The S&T reports on wetland quantity, while the NWCA reports on the quality of
30 the nation's wetlands (see Chapter 1).
31
32 Estimates of wetland area for the S&T and NWCA were based on samples drawn from the same digital
33 map created by S&T from 2005 aerial photography (see Chapter 1). However, the wetlands sampled as
34 part of NWCA, i.e., the "target population," are a subset of the wetland categories sampled by S&T. The
35 NWCA samples tidal and nontidal wetlands of the conterminous US, including farmed wetlands not
36 currently in crop production. The wetlands must have rooted vegetation and, when present, open water
37 less than one meter deep. Consequently, the S&T Program's estimate of the wetland area in the
38 conterminous US in 2009 was 110.1 million acres (Dahl 2011), while the 2011 NWCA estimated the area
39 of the target population as 94.9 million acres. Thus, the 2011 NWCA target population was
40 approximately 84% of the wetland area reported by S&T for 2009. For more information on the
41 relationship between what was sampled in the 2011 NWCA and by S&T see Chapter 1, especially Table
42 1-1 which relates NWCA Wetland Types to the wetland categories found on the S&T digital maps.
43
44 The seven NWCA wetland types used in the 2011 survey design were combined into four for analysis
45 and reporting. Similarly, the nine ecoregions used in the 2011 survey design were combined into four.
46 Aggregations of the wetland types and ecoregions used in the 2011 NWCA survey design were necessary
2011 NWCA Technical Report DISCUSSION DRAFT
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47 to ensure adequate sample sizes for analysis and provided unique, descriptive names for NWCA
48 reporting (see Section 4.4 for details).
49
50
51 Relationship between Field Sampling and Reporting
52
53 NWCA data and samples are collected in an Assessment Area (AA) and its associated 100-m buffer. The
54 AA represents the location defined by the coordinates (hereafter, called the point) generated by the
55 sample draw from the survey design (see Chapter 1). The NWCA field sampling protocols are designed
56 to support the assessment of the ecological condition of the wetland area at the point (USEPA 2011).
57 Collecting data and samples within a consistent wetland area (i.e., the AA) - regardless of the size of the
58 individual wetland in which the point resides - is an important distinction from sampling individual
59 wetlands. Sampling points that represent a percentage of the area of the entire target population
60 assumes that condition can change spatially, especially in a large wetland, and can result in a wetland
61 having more than one point. It also allows for reporting the results as wetland area and as a percentage
62 of the entire target population.
63
64 The AA is established using an ecological (not jurisdictional) definition of a wetland. It must contain the
65 point, can range from 0.1 to O.Sha in size, and can encompass one or more of the wetland types used in
66 the design (see Table 1-1). The area of the AA was chosen to be large enough to accurately characterize
67 the wetland area at the point using rapid or comprehensive assessment methods (e.g., see Wardrop et
68 al. 2007a, b) but is small enough for a team of four people to typically complete sampling in one day
69 (e.g., see Kentula and Cline 2004, Fennessy et al. 2008).
70
71
72 Literature Cited
73
74 Kentula ME and Cline SP (2004) Riparian Investigations in the John Day and Lower Deschutes Basins of
75 Eastern Oregon: Amendment to Quality Assurance Project Plan. Corvallis, Oregon, U.S. Environmental
76 Protection Agency, National Health and Environmental Effects Research Laboratory, Western Ecology
77 Division, 69 pp.
78
79 Dahl, TE (2011) Status and trends of wetlands in the conterminous United States 2004 to 2009.
80 Washington, DC, US Department of the Interior, Fish and Wildlife Service, 108 pp.
81
82 Fennessy MS, Jacobs A, Kentula ME, Sifneos J, Rokosh A (2008) Field testing rapid assessment methods
83 for use in wetland monitoring programs. Report to USEPA for Grant X7-83158301-0. Gambier, OH,
84 Kenyon College.
85
86 USEPA (2011) National Wetland Condition Assessment: Field Operations Manual. Washington, DC, U.S.
87 Environmental Protection Agency.
88
89 Wardrop, DH, Kentula ME, Jensen SF, Stevens, Jr. DL, Hychka KC, Brooks RP (2007a) Assessment of
90 wetlands in the Upper Juniata watershed in Pennsylvania, USA, using the hydrogeomorphic approach.
91 Wetlands 27: 432-445.
92
93 Wardrop DH, Kentula ME, Stevens DL, Jensen SF, Brooks RP (2007b) Assessment of wetland condition:
94 an example from the Upper Juniata Watershed in Pennsylvania, USA. Wetlands 27: 416-430.
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95 Chapter 1: Survey Design
96
97 NWCA was designed to assess the ecological condition of broad groups or populations of wetlands,
98 rather than as individual wetlands or wetlands across individual states. The NWCA design allows
99 characterization of wetlands at national and regional scales using indicators of ecological condition and
100 stress. It is not intended to represent the condition of individual wetlands.
101
102
103 1.1 Description of the NWCA Wetland Type Population
104
105 The target population for the NWCA included all wetlands of the conterminous United States (US) not
106 currently in crop production, including tidal and nontidal wetted areas with rooted vegetation and,
107 when present, shallow open water less than 1 meter in depth. A wetland's jurisdictional status under
108 state or federal regulatory programs did not factor into this definition. Wetland attributes are assumed
109 to vary continuously across a wetland.
110
in 1.2 Survey Design and Site Selection
112
113 The selection of the sites was completed in two steps. Since a consistent national digital map of all
114 wetlands in the conterminous US was not available, and the US Fish & Wildlife Service (USFWS) conducts
115 the National Wetland Status and Trends (S&T) survey every five years, the approximately 5,000 4-square
116 mile plots from S&T were used to identify wetlands in the first step. The S&T survey is an area frame
117 design stratified by state and physiographic region (Dahl and Bergeson 2009; Dahl 2011). This step
118 results in the aerial imagery interpretation of land cover types focused on S&T Wetland Categories
119 within each 2-mile by 2-mile plot selected (S&T sample size is 5,048 plots).
120
121 In the next step, a Generalized Random Tessellation Stratified (GRTS) survey design (Stevens and Olsen
122 1999; Stevens and Olsen 2004) for an area resource was applied to the S&T wetland polygons. This step
123 was stratified by state with unequal probability of selection by seven NWCA Wetland Types based on a
124 subset of the S&T Wetland Categories (Table 1-1).
125
126 Table 1-1. Definition of NWCA Wetland Types.
USFWS S&T Wetland _ . . , ....... . „,,„„,„,., . . T
Description of wetlands included in each NWCA Wetland Type
Category Codes
E2EM Estuarine intertidal emergent
E2SS Estuarine intertidal forested and shrub
Emergent wetlands in palustrine, shallow riverine, or shallow lacustrine littoral
settings
Shrub-dominated wetlands in palustrine, shallow riverine, or shallow lacustrine
littoral settings
Forested wetlands in palustrine, shallow riverine, or shallow lacustrine littoral
settings
Farmed wetlands in palustrine, shallow riverine, or shallow lacustrine littoral
settings; subset that was previously farmed, but not currently in crop production
PUBPAB* Open-water ponds and aquatic bed wetlands
*PUBPAB is comprised of S&T Wetland Categories: PAB (Palustrine Aquatic Bed), PUBn (Palustrine
Unconsolidated Bottom, natural characteristics), PUBa (aquaculture), PUBf (agriculture use), PUBi
(industrial), and PUBu (PBU urban).
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127
128 Note that the S&T Category Codes for the NWCA Wetland Types often encompass more kinds of
129 wetlands than the code might suggest. For example, E2SS includes both estuarine intertidal shrub and
130 forested wetlands. Palustrine codes (e.g., PEM and others) reflect palustrine wetlands, and also riverine
131 and lacustrine wetlands with < 1 m water depth. Palustrine farmed and Palustrine Unconsolidated
132 Bottom wetlands with non-natural modifiers were retained in the NWCA frame to allow evaluation of
133 whether they met NWCA Wetland Type criteria; those that did not were identified as non-target during
134 site evaluation.
135
136 Two major S&T wetland categories, Marine Intertidal (Ml, near shore coastal waters) and Estuarine
137 Intertidal Unconsolidated Shore (E1UB, beaches, bars, and mudflats), were not included in the NWCA
138 because they fall outside the NWCA Wetland Type population; i.e., they typically occur in deeper water
139 (> 1m deep) or are unlikely to contain rooted wetland vegetation. Other S&T Categories not meeting the
140 NWCA criteria or that were not wetlands were also excluded: Estuarine Intertidal Aquatic Bed (E2AB) or
141 Unconsolidated Shore (E2US), Marine Subtidal (M2), deep-water Lacustrine (LAC, lakes and reservoirs)
142 and Riverine (RIV, river systems), Palustrine Unconsolidated Shore (PUS), Upland Agriculture (UA),
143 Upland Urban (UB), Upland Forest Plantations (UFP), Upland Rural Development (URD), and Other
144 Uplands (UO).
145
146 The expected sample size was 900 sites for the conterminous 48 states. Allocation of sites by state and
147 wetland type categories was completed by solving a quadratic programming problem that minimized
148 the sum of the squared deviations of the expected sample size minus proportional allocation of sites by
149 wetland type based on state area within each wetland type subject to constraints that:
150
151 • The expected sample sizes across conterminous US by wetland type were:
152 o E2EM = 128
153 o E2SS = 127
154 o PEM = 129
155 o PSS = 129
156 o PFO =129
157 o Pi =129
158 o PUBPAB = 129
159 • The minimum number of sites for a state was 8;
160 • The maximum number of sites within a state for E2EM or E2SS was 13 (coastal states);
161 • The maximum number of sites within a state for PEM, PSS, PFO, Pf, or PUBPAB was 10; and,
162 • The minimum number of sites was greater than or equal to zero for each wetland type and state
163 combination.
164
165 This approach ensured that the sample size for the seven NWCA Wetland Types was sufficient for
166 national reporting, each state received a minimum number of sites (which also improved the national
167 spatial balance of the sites) and otherwise proportionally allocated the sites by area within a wetland
168 type. Site selection was completed using the R package 'spsurvey' (Kincaid and Olsen 2013).
169
170 1.2.1 Site Visits
171 The total number of site visits planned was 996 allocated to 900 unique sites with 96 sites to be revisited
172 (two per state). To ensure a sufficient number of sites were available for sampling, an additional 900
173 sites were selected as an oversample to provide replacements for any sites that were either not part of
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174 the target population or could not be sampled (i.e., permission to sample was not provided by the
175 landowner, or access was not possible due to safety or other access issues). A total of 1800 sites were
176 selected for potential sampling. To ensure that the final set of sites evaluated satisfied the requirements
177 for a probability survey design, the sites were ordered in reverse hierarchical order (Stevens and Olsen
178 2004). Sites were sampled based on this order, and all sites from the first one in the list through the last
179 site sampled in the list were evaluated and, hence, included in the study.
180
181 1.2.2 State-Requested Modifications to the Survey Design
182 Three states elected to modify the survey design for their state because of the availability of additional
183 wetland mapping information. The state modifications replaced the above survey design for their state.
184
185
186 Wisconsin chose to intensively study the Southeastern Plains Till region in addition to the sites sampled
187 for the national estimates as part of the NWCA. This was accomplished by the USFWS S&T team
188 selecting additional 4-square mile plots within the study region. For the NWCA survey, the Wisconsin
189 state stratum was replaced by a new design that included two strata - the Southeastern Plains Till
190 region and the rest of the state. The sites selected under the national NWCA design were used for the
191 rest of Wisconsin state region, and a new GRTS unequal-probability survey design of 50 sites were
192 selected for the Southeastern Plains Till region. Unequal-probability selection categories were the five
193 wetland types PEM, PSS, PFO, Pf, and PUBPAB.
194
195 Ohio
196 Ohio decided to base their survey design on a current digital map of wetlands in Ohio. A sample of size
197 50 was selected using a GRTS unequal-probability survey design. The unequal-probability categories
198 were the five wetland types PEM, PSS, PFO, Pf, and PUBPAB.
199
200 1.2,2.3
201 In 2006, Minnesota developed a Comprehensive Wetland Assessment, Monitoring, and Mapping
202 Strategy (CWAMMS). One of the primary outcomes of the CWAMMS was the development of statewide
203 random surveys under the Wetland Status and Trends Monitoring Program (WSTMP), to begin assessing
204 the status and trends of wetland quantity and quality in Minnesota (Kloiber 2010). The wetland quantity
205 survey, implemented by the Minnesota Department of Natural Resources, was modeled after the
206 USFWS S&T program (Dahl 2006, 2011). The WSTMP survey design was the basis for the Minnesota
207 NWCA design.
208
209 The WSTMP design contains 1-square mile grid cells for Minnesota (and requires that at least 25% of
210 grid cell be within state of Minnesota) where the grid matches the USFWS S&T 4-square mile grid
211 boundaries. Each 4-square mile grid cell was subdivided into four 1-square mile grid cells. An equal-
212 probability GRTS survey design was used to select 4,740 1-square mile plots assigned to panels 1
213 through 3 of the WSTMP design. All wetland habitats within these plots were delineated using aerial
214 imagery obtained in years 2006, 2007, and 2008 (panels 1, 2, and 3, respectively). Where portions of
215 some 1-square mile plots fell outside of state boundaries, only the portion occurring within the state
216 was photo-interpreted and mapped. Therefore, the total area of the sample frame extent was less than
217 4,740 square-miles. NWCA Wetland Types were PEM, PSS, PFO, Pf, and PUBPAB. The next step was to
218 select 150 sample sites using a GRTS equal-probability survey design from the delineated wetland
219 polygons. The 22 Minnesota sites required for the NWCA were the first 22 sites that were sampled when
220 ordered by their site identification. An additional 150 sites were selected for use if any of the initial 150
221 sites could not be sampled, using the same process described in Section 1.2.1.
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222
223
224
225
226
227
228
229
230
231
232
233
234
235
1.3 Sample Frame Summary
The NWCA sample frame (with the exception of Minnesota and Ohio, see Sections 1.2.2.2 and 1.2.2.3)
was the USFWS 2005 National Wetland Status and Trends survey, obtained through collaboration with
the USFWS. This sample frame consisted of all S&T polygons mapped based on 2005 remote sensing
information for a 5,048 2-mile by 2-mile plots across the 48 states. Additional attributes added to the
sample frame are state, EPA Region, USEPA Level III Ecoregions (Omernik 1987; USEPA 2011a) and Three
Major Regions and Nine Aggregated Ecoregions (those used in the Wadeable Stream Assessment; USEPA
2006). Seven NWCA Wetland Types were used: E2EM, E2SS, PEM, PSS, PFO, Pf, and PUBPAB (See Table
1-1 for definitions). The wetland area from the USFWS S&T 2005 plot imagery is provided in Table 1-2.
Table 1-2. Sample frame wetland area from the US Fish and Wildlife 2005 National Wetland Status & Trends plots.
Wetland area (in acres) is reported by state and S&T Wetland Categories that represent the NWCA Wetland Types.
See Table 1-1 for definitions of the acronyms and descriptions of included wetland types.
State
E2EM
E2SS
PEM
PSS
PFO
Pf
PUBPAB
Total
37,829
155,327
153
30,159
460
2,869
6,078
413,263
140,298
2,937
4,026
4,321
6,200
589
2,720
555,308
3,957
12,350
30,317
39,618
73,764
2,117 1,681
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236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
State E2EM E2SS PEM PSS PFO Pf
OR 69 0 1,808 272 143 0.37
PA 00 305 568 1,729 0.25
Rl 243 0 60 252 561 0
SC 22,418 217 5,060 6,521 56,211 0.57
SD 0 0 12 567 116 251 290
TN 00 243 176 5,820 489
TX 34,122 56 17,357 5,341 9,467 26,912
UT 00 836 149 15 0
VA 9,010 603 800 648 4,523 6.35
VT 00 1,301 843 1,522 0
WA 1,032 1.38 2,784 1,636 2,835 84
Wl 00 5,999 12,961 17,436 83
WY 00 2,234 779 44 0
Sum 360,758 50,819 295,132 220,869 810,492 249,298
The sample frame areas (acres) for Ohio were:
• 110,403.7 for PEM,
• 17,658.2 for Pf,
• 309,671.2 for PFO,
• 87,158.8 for PSS,
• 63,602.5 for PUBPAB, and
• 588,494.5 total acres in the GIS layer for the state.
The sample frame areas (acres) from Minnesota phase 1 plots were:
• 244,236.6 for PFO,
• 128,787.8 for PSS,
• 175,446.9 for PEM,
• 30,283. 3 for PABPUB,
• 7,698.2 for Pf, and
• 586,453.1 total in the plots.
1.4 Site Selection Summary
PUBPAB Total
79 2,371
375 2,977
46 1,162
1,808 92,235
596 7,325
2,689 95,944
4.45 1,005
417 16,007
211 3,878
546 8,918
732 37,211
87 3,145
55,526 2,042,894
Table 1-3 shows the number of sites planned to be sampled for the NWCA by state and NWCA Wetland
Types (subset of S&T wetland categories). The maximum number of sites
and the minimum number of sites for a state was 8 (Vermont). Additional
states with the objective of enabling a state-level assessment.
for a state was 69 (Louisiana)
sites were sampled in some
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261
262
263
Table 1-3. Number of sites planned to be sampled. Number of sites is reported by state and S&T Wetland
Categories that represent the NWCA Wetland Types. See Table 1-1 for definitions of the acronyms and
descriptions of included wetland types.
State
E2EM
E2SS
PEM
PSS
PFO
Pf
PUBPAB
Total
264
265
266
267
Total
130
122
135
128
136
121
128
900
The number of sites selected for Ohio was 10, 11, 10, 12, and 6 for PEM, Pf, PFO, PSS, and PUBPAB,
respectively. Only the first 11 sites were included in the NWCA.
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268 The number of sites selected for Minnesota was 41, 30, 63, 7 and 9 for PFO, PSS, PEM, PABPUB, and Pf,
269 respectively. Only the first 11 sites were included in the NWCA.
270
271
272 1.5 Survey Analysis
273
274 Any statistical analysis of data must incorporate information about the monitoring survey design. In
275 particular, when estimates of characteristics for the entire target population are computed, called
276 population estimates (discussed in Chapter 9, Section 9.2), the statistical analysis must account for any
277 stratification or unequal probability selection in the design. The statistical estimates for the NWCA
278 population estimates were completed using the R package 'spsurvey' (Kincaid and Olsen 2013) which
279 implements the methods described by Diaz-Ramos et al. (1996).
280
281
282 1.6 Estimated Wetland Extent of the NWCA Wetland Type Population and
283 Implications for Reporting
284
285 Sites from the NWCA survey design were screened using aerial photo interpretations and CIS
286 analyses to eliminate locations not suitable for NWCA sampling (e.g., non NWCA wetland types,
287 wetlands converted to non-wetland land cover due to development). Sites could also be eliminated
288 during field reconnaissance if they were a non-target type or could not be assessed due to
289 accessibility issues. Dropped sites were systematically replaced from a pool of replacement sites
290 from the random design.
291 The treatment of sites eliminated from sampling affects how the final population results are estimated
292 and reported. Taking into account the sites identified as non NWCA wetland types (e.g., wetlands in
293 active crop production, deeper water ponds, mudflats), it was estimated there were 94.9 million acres of
294 wetlands in the NWCA wetland type population across the conterminous US. The area represented by
295 sites that were part of the target population, but not sampled because of accessibility issues, is excluded
296 from the assessment of condition and stress. Sites which had access issues cannot be assumed to be
297 randomly distributed. For example, there may be a bias in land-ownership for sites where access was
298 denied, or sites which were inaccessible may often occur in areas with limited disturbance. As a result,
299 the final acreage represented by the probability sites sampled and reported by the NWCA, i.e., the
300 inference population, was 62.2 million acres or approximately 65% of the target population of NWCA
301 Wetland Types. Throughout this report, wetland area as percentages are relative to the 62.2 million
302 acres.
303
304 Figure 1-1 provides the distribution of the NWCA probability sites that were part of the NWCA wetland
305 type population and the estimated acres and percent of wetland area the sites represent. The inference
306 population is represented by 967 probability sites. The non-assessed component of the population is
307 represented by sites 1) where access was denied (n = 429), 2) inaccessible due to safety considerations
308 or remote location (n = 126 sites), and 3) with various other (n = 122) constraints (e.g., too close to
309 another NWCA sampling point, sampling area crossing HGM boundaries, assessment area too small).
310
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311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
2011 NWCA Target Population
Estimated Wetland Area = 94,913,706 acres
2,847,132 acres
n = 122
448,141 acres
n = 126
233 acres
n=429
Assessed - Inference Population
Not Assessed - Access Denied
Not Assessed - Inaccessible
mtmt Not Assessed - Other
Figure 1-1. Estimated wetland area included in the NWCA Wetland Type Population, the proportion of the
population that was assessed (for which inference of results can be made), and the proportion not assessed.
Table 1-4 illustrates the distribution of estimated extents of thel) total population of NWCA wetland
types, 2) the inference population (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 four major
geographic regions. Some differences were evident among the NWCA regions in the percent of the total
estimated area of NWCA wetland types for which results can be inferred. The percent of the total
estimated NWCA wetland area in particular region that was represented by the inference area was
greatest in the Eastern Mountains & Upper Midwest region (80%), but least in the West (40%), and
intermediate in the Coastal Plains (63%) and the Interior Plains (62%). These differences were related to
varying levels of land-owner denial of access and physical accessibility across the regions.
10
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326
327
328
329
330
Table 1-4. Total estimated areal extents for the NWCA Wetland Type population, the inference population extents
(based on sampled probability sites (n)), and non-assessed area extents (based on probability sites (n) that could
not be assessed) for the nation and within subpopulations represented by four major geographic regions. Results
are reported as millions of acres or % of total estimated NWCA wetland area for the nation or by region.
Estimated
Total NWCA
Wetland Area
NWCA Region1 millions acres
Inference Area
millions acres
(% area)
Access Denied
millions acres
(% area)
Inaccessible
millions acres
(% area)
Other
Non-Assessed
millions acres
(% area)
Nation
Coastal Plain
Eastern Mtns
& Upper MidW
Interior Plains
West
62.2 (65%)
n = 967
30.9 (63%)
n=513
24.7
12.3
9.2
19.9 (80%)
n=152
7.7 (62%)
n=156
3.6 (40%)
n=146
23.5 (25%)
n = 429
12.7 (26%)
n = 165
3.5 (14%)
n = 42
3.6 (29%)
n = 119
3.7 (40%)
n = 103
6.4 (7%)
n = 126
4.2 (9%)
n = 86
0.9 (4%)
n = 6
1.7 (1%)
n = 6
1.2 (13%)
n = 28
2.8 (3%)
n = 122
0.9 (2%)
n = 105
0.4 (2%)
n - 17
8.4 (7%)
n = 55
0.7 (7%)
n = 31
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
Chapter 4, Section 4.4 and Figure 4-11 for definition of NWCA regions.
1.7 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
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
Kincaid TM, Olsen AR (2013) spsurvey: Spatial Survey Design and Analysis. R package version 2.6.
Kloiber, SM (2010) Status and trends of wetlands in Minnesota: wetland quantity baseline. Minnesota
Department of Natural Resources, St Paul, Minnesota
Omernik JM (1987) Ecoregions of the Conterminous United States. Annals of the Association of
American Geographers 77: 118-125
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
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360 R Core Team (2013) R: A language and environment for statistical computing. R Foundation for
361 Statistical Computing, Vienna, Austria. http://www.R-project.org
362
363 Stevens DL, Jr., Olsen AR (1999) Spatially restricted surveys over time for aquatic resources. Journal of
364 Agricultural, Biological, and Environmental Statistics 4: 415-428
365
366 Stevens DL, Jr., Olsen AR (2003) Variance estimation for spatially balanced samples of environmental
367 resources. Environmetrics 14: 593-610
368
369 Stevens DL, Jr., Olsen AR (2004) Spatially-balanced sampling of natural resources. Journal of American
370 Statistical Association 99: 262-278
371
372
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Chapter 2: Overview of Analysis
The
analysis for the 2011 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 Chapters 3 through 9.
Figure 2-1 illustrates the analysis process, represented as the 2011 National Wetland Condition
Assessment Analysis Pathway, beginning with data acquisition (left side of chart) and concluding with
the
and
population estimates for the wetland resource of ecological condition, stressor extent, and relative
attributable risk for the NWCA target population in the conterminous US (right side of chart). The
components of each of the major tasks are indicated in the chart by color:
The
Orange = data acquisition, preparation, and quality assurance (Chapter 3);
Black & Yellow = selection of reference sites and definition of disturbance gradient (Chapter 4);
Green = ecological condition analysis using the vegetation indicator (Chapters 5, 6, and 7); and
Blue = development of indicators of stress (Chapter 8); and,
Teal = calculation of population estimates of ecological condition, stressor extent, and relative
and attributable risk (Chapter 9).
four key elements of the analysis outlined in the Analysis Pathway flowchart (Figure 2-1) are:
1) Data acquisition and quality assurance continues throughout all of the analyses, beginning with
a major effort resulting in the production of the data tables used by the analysts;
2) Data collected at probability (from the assessment design) and not-probability (from other
sources, e.g., handpicked) sites are used in reference site selection and index development for
condition and stressors. Only data from probability sites are used to generate the population
estimates for assessment results;
3) Reference Site Selection (yellow box) involves the definition of a disturbance gradient, which
requires setting disturbance thresholds; and
4) Reference sites are used in the development of the Vegetation Multimetric Index (VMMI) and to
set condition class thresholds for the VMMI (i.e., Good, Fair, Poor classes) and stressor-level
class thresholds for some indicators of stress (i.e., Low, Moderate, High stressor-level classes).
13 2011 NWCA Technical Report DISCUSSION DRAFT
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Data acquisition and
QA continues
through analysis
ONLY probability sites are
used to generate the
population estimates
= site weights
from
probability
design
410
411
412
Figure 2-1. The 2011 National Wetland Condition Assessment Analysis Pathway, which illustrates the major components of the analysis and this report.
14
2011 NWCA Technical Report
DISCUSSION DRAFT
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413
414
Chapters: Data Preparation and Management
Data acquisition and
QA continues
through analysis
INLY probability sites ar
used to generate the
population estimates
Bll.ll V SlltS
siressor
quantification
NOT PROBAttlLI IV SlTLS
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
Figure 3-1. The major components of the 2011 National Wetland Condition Assessment Analysis Pathway
discussed in this chapter (i.e., data preparation and management). A full-page, unhighlighted version of this figure
may be found on page 14 of this report.
3.1 Introduction
This chapter:
• Documents data entry, preparation, and management, and
• Presents procedures used to conduct standard quality assurance checks.
Figure 3-1 presents the Analysis Pathway leading to the results reported for the 2011 National Wetland
Condition Assessment (NWCA). The highlighted area indicates the part of the pathway presented in this
chapter.
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 presenting each phase of the analysis (i.e.,
Chapters 4 through 9).
The master database for the 2011 NWCA includes:
1) Raw data collected by Field Crews and from laboratory processing of samples collected in the
field (USEPA 2011a; b), represented by boxes for field and lab data (top four boxes, left side of
Figure 3-2).
2) Data documenting and characterizing the NWCA sites from the survey design and other ancillary
information represented by the three boxes on the bottom left of Figure 3-2.
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.
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2011 NWCA Technical Report
DISCUSSION DRAFT
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NWCA Data Preparation and Analysis
Non-bio
field data
Biological
field data
Scientist/
R programmer
I O.A leads
I Indicator lead
f J R programmer
Data Gatekeeper
Not automated
Partly automated
Automated
Condition
Results
o
Population
estimates
far Condition
and Risk
Reporting
444
445 Figure 3-2. Flowchart of the data preparation and analysis used in the NWCA and other National Aquatic Resource Surveys (NARS).
446
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DISCUSSION DRAFT
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447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
3.2 Key Personnel
USEPA Office of Water (OW), Wetlands Division (WD) provided overall leadership for the 2011 NWCA.
Gregg Serenbetz led the team in Wetlands Division and coordinated and fostered cooperation with the
Analysis Team. Personnel from the Office of Research and Development, National Health and
Environmental Effects Laboratory, Western Ecology Division (WED) were responsible for data entry,
quality assurance, and preparation of datasets for analysis with input from the Indicator Leads as
illustrated in Figure 3-2.
Mary E. Kentula was the primary contact at WED for the 2011 NWCA. She provided oversight and
coordination of the various components at WED and their interactions with Wetlands Division. She
served as one of the Data Gatekeepers and Quality Assurance (QA) leads.
Karen Blocksom has extensive experience with the data management and analysis with other National
Aquatic Resource Surveys (NARS). She deals with all aspects of the management of the data for an
assessment, e.g., finding, correcting and documenting errors, designing formats for the specific datasets
needed for the various analyses, programming required for data management and analyses. She served
as one of the Data Gatekeepers and QA leads and was the primary R programmer.
Information Management Team (IM Team) performs data entry and checks, makes and documents
corrections to the database, and creates various data sets for analysis for the NARS assessments. The IM
Team for the 2011 NWCA is a group of people on contract to USEPA who are located at WED, and led by
Marlys Cappaert of SRA International, Inc.
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 3-1 lists the
members of the Analysis Team and their roles.
Table 3-1. The 2011 Analysis Team and roles. All people listed are USEPA except as noted.
Reporting Topics Leads Associates
Extent and Description of the Resource
Wetland Condition - Vegetation
Stressor Extent and Risk
Gregg Serenbetz
Teresa K. Magee
Mary E. Kentula
Anthony R. Olsen, Thomas M. Kinkaid
Karen Blocksom, M. Siobhan Fennessy*
Alan T. Herlihy, Gregg A. Lomnicky*, Teresa
K. Magee, Amanda M. Nahlik*
Research Indicators and Topics
Leads
Associates
Algae
Algal Toxins
Ecosystem Services
Sediment Enzymes
Water Chemistry
Chris Faulkner
Keith A. Loftin%
Amanda M. Nahlik*
Brian H. Hill
Battelle Memorial Institute
Mary E. Kentula
Anett S. Trebitz
USA-RAM
Gregg Serenbetz
Janet A. Nestlerode
M. Siobhan Fennessy* and Josh Collins®
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Table 3-1 continued
Work Supporting Multiple Analyses
Leads
Associates
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
Data QAand Management
Karen Blocksom,
Mary Kentula
IM Team"
Development of Disturbance Gradient Mary E. Kentula
Landscape data
Population estimates
Gregg Serenbetz
Gregg Serenbetz
Karen Blocksom, Alan T. Herlihy, Gregg A.
Lomnicky, Teresa K. Magee, Amanda M.
Nahlik*, Marc Weber
Marc Weber, Horizon Systems
Steven G. Paulsen, Thomas M. Kincaid
*Kenyon College; *Dynamac Corporation; %US Geological Survey; @San Francisco Estuary Institute; ASRA
International, Inc.
3.3 Data Entry and Review
3.3.1 Field Data
Field forms for the 2011 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 field forms directly to the data management center at
WED. Form packets were logged and checked for quality and completeness. Field Crews were
immediately contacted if the form packets were incomplete or if there were questions regarding data
written on the forms. Then each page was scanned and evaluated by the scanning software. Because the
forms were designed in TeleForm™, the evaluation process was coded to flag restricted input. For
example, a 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. This was followed by a visual check whereby the operator reviewed the entered data
in tabular form. Finally, 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 checks described in Section 3.4.
3.3.2 Laboratory Data
Laboratory results were submitted to USEPA Wetland Division staff, who checked the data for
completeness and obvious errors. Then the data files were transferred to the IM Team for incorporation
into the master NWCA database.
The water chemistry data produced by Dynamac Corporation located at WED was handled by a different
process. Dynamac checks their results based on the approved Quality Assurance Project Plan and the
18
2011 NWCA Technical Report
DISCUSSION DRAFT
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513 data files are transferred from Dynamac to the IM Team through the Work Assignment Contract Officer
514 Representative.
515
516
517 3.4 Quality Assurance Checks
518
519 There were three types of Quality Assurance (QA) checks completed before datasets were assembled for
520 analysis:
521 1) Verification of the fate of every sample point from the 2011 NWCA design;
522 2) Confirmation of longitudes and latitudes associated with the sites sampled; and
523 3) Legal value and range checks.
524
525 3.4.1 Verification of Points from the 2011 NWCA Design
526 Estimates of the wetland area falling into a particular condition class are based on the weight from the
527 survey design used to select the points to be sampled. For examples of how this has been done for other
528 surveys see Stevens and Jensen (2007) and Olsen and Peck (2008). Chapter 1 provides specific details of
529 the NWCA survey design, and Chapter 9 discusses how estimates for the 2011 NWCA wetland area were
530 made.
531
532 In the NWCA survey design, the weight indicates the wetland area in the NWCA target population
533 represented by a point from the sample draw. After the assessment is conducted, the weights were
534 adjusted to account for additional sites (i.e., the oversample points) evaluated when primary sites could
535 not be sampled (e.g., due to denial of access, being non-target).
536
537 All points in the design were reviewed to confirm which were sampled, and if not, why not. Three
538 sources were used:
539 1) Information compiled during the desktop evaluation of sites (see Section 2.0 in the NWCA Site
540 Evaluation Guidelines (USEPA 2011c)), and documented by state and contractor field crews in
541 spreadsheet submissions to EPA during and after the 2011 field season,
542 2) Information recorded on Form PV-1 during a field evaluation performed prior to sampling (see
543 Section 3.0 in the NWCA Site Evaluation Guidelines (USEPA 2011c)), and
544 3) Information recorded on Form PV-1 at the time of sampling (see Chapter 3 in the NWCA Field
545 Operations Manual (USEPA 2011a)).
546
547 Results from this evaluation were added to the database containing site information data from the
548 NWCA survey design and for the not-probability sites.
549
550 3.4.2 Confirmation of Coordinates Associated with the Sites Sampled
551 Longitudes and latitudes are taken at various key locations associated with field sampling (e.g., the
552 location of the point from the design). These coordinates are especially important if a point needs to be
553 relocated or shifted to accommodate sampling protocols (see Chapter 3 in the NWCA Field Operations
554 Manual (USEPA 2011a)). The coordinates are used to:
555 • Verify the relationship between the point coordinates from the design and those of the sampled
556 Assessment AA (AA) that represents the point (see Chapter 3 in the NWCA Field Operations
557 Manual (USEPA 2011a));
558 • Tie the field data to landscape data from GIS layers; and
559 • Relocate the site and key locations of the field sampling protocol (e.g., the AA center, vegetation
560 plots) for resampling in future surveys.
19 2011 NWCA Technical Report DISCUSSION DRAFT
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561
562 Point coordinates from the design and the field were compared. The locations of points from the field
563 that were more than 60m from the corresponding design coordinates, i.e., that exceeded protocol
564 guideline (see Section 4.2 in the NWCA Site Evaluation Guidelines (USEPA 2011c)), were flagged. There
565 were 25 sites that required further evaluation. All were determined to meet design standards because in
566 some cases permission to move the point beyond 60m was obtained, recording errors made by the Field
567 Crew were identified and corrected, or the distance exceeding 60m from the sample point was
568 determined to be negligible.
569
570 3.4.3 Legal Value and Range Checks
571 The first step in this series of checks was to assure all sites with data from a second field sampling (i.e.,
572 Visit 2 or Quality Assurance Visit) had a corresponding initial sampling (i.e., Visit 1). Next, for all data
573 types, computer code was written to generate a list of missing data, and checks were performed to
574 identify why they were missing (e.g., part of the sampling was not completed by the Field Crew, data
575 sheet(s) not scanned, etc.). Additional computer code was written to generate a list of data not meeting
576 a series of legal value and range tests. These tests were to confirm that:
577 • Data type was correct,
578 • Data fell within the valid range or legal value, and
579 • Units reported (especially for laboratory results) matched those expected.
580
581 Results of the checks were converted to Excel spreadsheets. Each potential error was evaluated by the
582 Data Gatekeeper or the Indicator Lead using the original forms submitted by the Field Crew. A
583 description of the error and recommended resolution were recorded in the spreadsheet for each type of
584 data and incorporated into the master NWCA database. The Indicator Lead who would be the primary
585 user of the data was consulted in cases where the resolution of the issue could affect the results of the
586 analysis.
587
588
589 3.5 Literature Cited
590
591 Olsen AR, Peck DV (2008) Survey design and extent estimates for the Wadeable Streams Assessment.
592 Journal of the North American Bethological Society 27: 822-836
593
594 Stevens DL, Jr., Jensen SF (2007) Sample design, implementation, and analysis for wetland assessment.
595 Wetlands 27: 515-523
596
597 USEPA (2011a) National Wetland Condition Assessment: Field Operations Manual. US Environmental
598 Protection Agency, Washington, DC
599
600 USEPA (2011b) National Wetland Condition Assessment: Laboratory Operations Manual. U. S
601 Environmental Protection Agency, Washington, DC
602
603 USEPA (2011c) National Wetland Condition Assessment: Site Evaluation Guidelines. US Environmental
604 Protection Agency, Washington, DC
605
20 2011 NWCA Technical Report DISCUSSION DRAFT
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606
607
608
Chapter 4: Selection of Reference Sites and Definition of Disturbance
Gradient
Data acquisition and
QA continues
through analysis
ONLY probability sites are
used to generate the
population estimates
3ILITY SITES
stressor
quantification
slressor
threshold
definition
609
610
611
612
613
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616
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618
619
620
621
622
623
624
625
626
627
628
629
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631
632
633
634
635
636
Figure 4-1. The major components of the 2011 National Wetland Condition Assessment Analysis Pathway
discussed in this chapter (i.e., the selection of reference sites and development of the disturbance gradient). A full-
page, unhighlighted version of this figure may be found on page 14 of this report.
4.1 Background Information
The USEPA National Aquatic Resource Survey (NARS) assessments, including the National Wetland
Condition Assessment (NWCA), evaluate the ecological condition of, and potential stress to, aquatic
resources based on biotic, chemical, and physical characteristics along a gradient of disturbance. In
NARS, development of a quantitative definition of disturbance begins with the identification of the end
of the gradient in reference condition. Because pristine conditions are uncommon or absent in most
places, the 2011 NWCA followed the practice of previous NARS assessments and defined reference
condition as least-disturbed (USEPA 2006, 2008, 2009).
Least-disturbed is defined as those sites 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 was defined using a set of explicit quantitative criteria for specific
disturbance indicators, to which all reference sites must adhere. It is expected that these least disturbed
reference sites will typically represent good ecological condition (see Chapter 7) and low stress (see
Section 8.6) (Karr 1991; Dale and Beyeler 2001; Stoddard et al. 2006; Stoddard et al. 2008).
This chapter documents the process for:
• Developing a quantitative definition of site-level disturbance based on the NWCA definition of
reference condition,
• Defining a disturbance gradient, and
• Assigning sites sampled in 2011 to categories of disturbance.
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DISCUSSION DRAFT
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637
638 How this process fits into the overall analysis process is highlighted in Figure 4-1.
639
640 The planning for the 2011 NWCA assumed:
641 • Reference sites represent least disturbed ecological condition and the associated functional
642 capacity and delivery of services typical of a given wetland type in a particular landscape setting
643 (e.g., ecoregion, watershed);
644 • The survey design provides a representative sample of the target population; and,
645 • Wetlands in least disturbed condition provide a benchmark against which to compare
646 assessment results through the establishment of a disturbance gradient defined using data
647 collected on-site during the 2011 assessment.
648
649 Least disturbed wetland sites sampled in 2011 were selected from three sources:
650 1) Handpicked sites selected pre-sampling,
651 2) Probability sites from the 2011 NWCA probability design, and
652 3) State intensifications that used NWCA protocols to sample sites representing the NWCA
653 Wetland Types.
654
655 A two-step selection processes was used. An initial pool of potential reference sites were picked prior to
656 the 2011 field sampling (see Section 4.2); the final set of least disturbed sites were chosen after
657 sampling based on data collected in the field (see Section 4.3).
658
659
660 4.2 Pre-Sampling Selection of Handpicked Sites
661
662 A group of sites were evaluated prior to the field sampling in 2011 to identify 150 handpicked sites likely
663 to be in least disturbed or reference condition. The candidate handpicked sites came from three
664 sources:
665
666 1) Best Professional Judgment (BPJ) sites recommended by the following entities with
667 responsibilities for wetlands (Figure 4-2):
668 • States
669 • Tribes
670 • National Estuarine Research Reserve System
671 • National Park Service
672 • US Fish and Wildlife National Refuge System
673 • US Forest Service
674 • Other USEPA NARS reference sites with associated wetlands;
675
676 2) Collaborations with partner organizations conducting wetland assessments (Figure 4-2); and,
677
678 3) In-the-field replacements for screened and un-screened sites determined not sampleable, e.g.,
679 access issues (see Section 4.2.5).
680
22 2011 NWCA Technical Report DISCUSSION DRAFT
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Selection Process for NWCA Handpicked Sites
Screened Sites
Non-Screened Sites
BPJ Sites
(states, tribes, federal
agency recommendations)
1264
Pre-Screen
Basic & Landscape Screens
Distribute by
Ecoregion/Type
107
107
Gulf of Mexico
PilotRcf Sites (10)
Rocky Mtn REMAP
RefSites(6)
NPS Specified
Sites (23)
State/Tribe Hand-
picked Base Site
Replacements (4)
43
Replacements
(not screened)
Sampled (150)
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
Figure 4-2. Flowchart presenting the process resulting in the 150 hand-picked sites sampled in the 2011 NWCA.
The green boxes are the components of the selection process. The blue boxes are the sources of the sites
considered. The orange box lists the collaborations with partners conducting wetland assessments who
recommended sites. The numbers with each arrow are the number of sites considered at that point of the process.
Black numbers are BPJ sites; orange, non-screened sites. The number of sites from each non-screened source is
listed in parenthesis following the source. BPJ = Best Professional Judgment; REMAP = USEPA Regional
Environmental Monitoring and Assessment Program.
The handpicked sites were divided into two groups—screened and unscreened. The screened sites were
recommended by a number of sources whose definition of reference either was not consistent with the
definition of least disturbed used in NARS or was not given, hence the use of the term Best Professional
Judgement (BPJ) in Figure 4-2. The unscreened sites came from sources from which there was sufficient
information to proceed without the screening.
The pre-sampling selection of the BPJ handpicked sites had five components (Figure 4-2):
1) The Pre-Screen was used to eliminate BPJ sites unlikely to meet the desired characteristics and
to reduce the number of sites needing manual evaluation;
2) The Basic Screen assured that a BPJ site was part of the target population, then determined if
the site was accessible, a minimum distance from a probability site, and sampleable;
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DISCUSSION DRAFT
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704 3) The Landscape Screen was a three-step evaluation to eliminate BPJ sites likely to have an
705 undesirable level of impact due to stressors that could be identified using aerial photography;
706
707 4) The sites passing the screening process and those not screened were evaluated to assure, to the
708 greatest extent possible, the 150 handpicked sites selected for sampling in 2011 were
709 distributed across the NWCA Wetland Types and the Nine Aggregated Ecoregions used by other
710 NARS (i.e., combined from Level III Ecoregions; Omernik 1987; USEPA 2011a); and
711
712 5) Replacement of handpicked sites not meeting the desired characteristics, with difficult or unsafe
713 access or site conditions, or for which access was denied by property owner.
714
715 Details of the process for selecting the 150 handpicked sites sampled in the 2011 NWCA are described in
716 Sections 4.2.1 through 4.2.6.
717
718 4.2.1 Pre-Screen
719 The pre-screen step reviewed 1,264 BPJ sites to eliminate those not likely to meet the criteria for NWCA
720 sampling and to reduce the number of sites to a reasonable size for a manual evaluation employing
721 analysis of maps and aerial photos. Information provided by the person who suggested each site was
722 considered, and included wetland size and type, as well as data supporting whether a site was least
723 disturbed, e.g., scores from a Floristic Quality Assessment Index or Landscape Development Index.
724 Wetlands eliminated were typically small, rare types. In cases where a number of sites were submitted
725 by an entity, those ranking lower than others, given the data submitted, were eliminated from further
726 consideration.
727
728 All BPJ sites in the West and Xeric ecoregions (from the Nine Aggregated Ecoregions) were eliminated
729 because it was anticipated there would be an adequate number of least disturbed sites in these regions,
730 particularly with the sites from collaborations with partner organizations in the area, e.g., the Rocky
731 Mountain Assessment funded through USEPA's Regional Environmental Monitoring and Assessment
732 Program (REMAP) (e.g., Figure 4-2).
733
734 4.2.2 Basic Screens
735 Readily available information (e.g., aerial photos, maps, local contacts (e.g., Figure 4-3)) was used to
736 determine if:
737 • The wetland at the site was part of NWCA target population, i.e.,
738 o Tidal and nontidal wetlands of the conterminous US, including farmed wetlands not
739 currently in crop production. The wetlands have rooted vegetation and, when present,
740 open water less than 1m deep;
741 o The site is described by the source or other supporting information as containing one or
742 more of the US Fish and Wildlife Service's (USFWS) Status and Trends (S&T) Wetland
743 Categories in NWCA the target population (hereafter NWCA Wetland Types; see Chapter
744 1, Section 1.2 for details);
745
746 • The site was accessible (within 10km of a road or trail);
747
748 • The site was >lkm away from a probability site; and
749
750
24 2011 NWCA Technical Report DISCUSSION DRAFT
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751
752
753
754
755
756
757
758
759
760
761
762
• The site could contain a sampleable Assessment Area (AA) (see USEPA (2011a)), i.e.,
o The wetland is > O.lha and at least 20m wide (to accommodate the vegetation plots)
o < 10% of the area
* Contains water >lm deep,
• Has conditions that are unsafe or would make effective sampling impossible
(e.g., likely unstable substrate), and/or
• Is upland
o No hydrogeomorphic boundaries are crossed.
If all these criteria were met, the BPJ site was retained and the Landscape Screens were performed.
763
764
765
766
767
768
Figure 4-3. Example of a candidate site that met the criteria of the Basic Screen. Yellow dot is the center of the
assessed area. PEM = Palustrine Emergent wetland; PFO =Palustrine Forested wetland PUB = Palustrine
Unconsolidated Bottom wetland; NWI = USFWS National Wetland Inventory
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2011 NWCA Technical Report
DISCUSSION DRAFT
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769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
4.2.3 Landscape Screens
GIS land cover data and aerial photos were used to evaluate the presence of anthropogenic impact
within a circular buffer defined by a 1-km radius centered on the likely location of the Assessment Area
(AA) that would be used during field sampling. Coordinates for the AA Center were provided by those
recommending the BPJ site. The location could be shifted within the 1-km buffer during screening to
decrease the amount of anthropogenic disturbances within the circular area being evaluated and, thus,
keeping the site in consideration as least disturbed.
STEP 1: Evaluate the 1-km radius buffer around a site for presence of anthropogenic impact, specifically:
• Hydrologic modifications (e.g., linear features that would indicate the presence of ditches, dams,
or levees);
• Forestry activities (e.g., rows of trees, tree stumps and debris, logging roads, tree regeneration);
• Agricultural development (e.g., farm structures, row crops, horticultural fields, pastures);
• Recreational development (e.g., campsites visible on aerials or indicated on the topographic
maps, public docks, location in a state or national recreation area or park);
• Residential and urban development (e.g., houses, retail malls, commercial buildings, parking
lots); and,
• Industrial development (oil and gas structures, mines, gravel pits, industrial facilities).
The level of impact was scored using the scale in Table 4-1. Examples of photo interpretation based on
this scoring are illustrated in Figure 4-4 and Figure 4-5.
Table 4-1. Scoring associated with the level of anthropogenic impact within the 1-km radius buffer around a site.
Score Impact Anthropogenic Impact
No visual evidence
Disturbance feature is present, but only appears to impact a small (<10%)
portion of the 1-km radius buffer
Disturbance feature appears to impact 10-25% of the 1-km radius buffer
Disturbance feature appears to impact >25% of the 1-km radius buffer
798
26
2011 NWCA Technical Report
DISCUSSION DRAFT
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799
800
801
802
803
804
805
806
807
808
809
Figure 4-4. Example of photo interpretation used in Step 1. The yellow dot is the AA Center within the 1-km radius
area evaluated. Agricultural development (yellow polygons) comprised >25% of the area for a score of 3.
Figure 4-5. Example of photo interpretation used in Step 1. The yellow dot is the AA Center within the 1-km radius
area evaluated. Industrial development (orange polygons in A) comprised <10% for a score of 1. A US Geologic
Survey Topographic map (B) was used to interpret and corroborate the presence of gravel pits found in A.
27
2011 NWCA Technical Report
DISCUSSION DRAFT
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810
811
812
813
814
815
STEP 2: Search for the presence/absence of roads and trails within the 1-km radius buffer. Score the
level of impact using the scale in Table 4-2. Figure 4-6 illustrates an example application of the scoring
procedure.
Table 4-2. Scoring associated with the presence of roads and trails within the 1-km radius buffer around a site.
Score
Impact
Presence of Roads
0
1
None
Low
Moderate
High
No visual evidence
Visual evidence of trails only
Visual evidence of non-paved roads only
Visual evidence of paved roads
816
817
818
819
820
821
Figure 4-6. Example of photo interpretation used in Step 2. The yellow dot is the AA Center within the 1-km radius
area evaluated. The site received a score of 3 due to the presence of paved roads.
28
2011 NWCA Technical Report
DISCUSSION DRAFT
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822
823
824
825
826
827
828
829
830
STEP 3: Determine the distance from the center of the candidate AA to the following disturbances:
• Ditches or channels created by humans,
• Edge of human disturbance identified in Step 1, and
• Paved or non-paved roads and trails identified in Step 2.
Score the level of impact for each disturbance using the scale in Table 4-3 (also see Figure 4-7).
Table 4-3. Scoring associated with the distance from disturbance within the 1-km radius buffer around a site.
Score Impact Distance to Disturbance
None
Low
Moderate
High
> 1 km
200 m - 1 km
140 m - 200 m
< 140m
831
832
833
834
835
836
837
838
Figure 4-7. Example of photo interpretation and scoring used in Step 3. The yellow dot is the AA Center within the
1-km radius area evaluated. The nearest disturbance was the presence of the paved road 140 m from the AA
Center so the site received a score of 2 for distance to the nearest road and 2 for the distance to the first edge of
human disturbance.
29
2011 NWCA Technical Report
DISCUSSION DRAFT
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839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
Figure 4-8. Summary of the scoring for the site in Figure 4-3 through Figure 4-7. The yellow dot is the AA Center
within the 1-km radius area evaluated. The area marked by the circles with a radius of 140m and 200m were
assessed in Step 3. The aggregate score of all disturbances for this candidate site was 11 as indicated by the
summary of the scores for each factor evaluated displayed along the bottom of the photo.
A total of the scores from Steps 1 through 3 of less than or equal to 11 was needed to keep a BPJ site on
the list for further evaluation and potential sampling in 2011 (Figure 4-8). Thus, the example site in
Figure 4-8 would have been retained as a potential reference site.
4.2.4 Distribution of Sites by Wetland Type and Ecoregion
The BPJ sites passing the screening process and those not screened were evaluated to assure, to the
greatest extent possible, the 150 handpicked sites selected for sampling in 2011 were distributed across
the NWCA Wetland Types and the Nine Aggregated Ecoregions (Omernik 1987; USEPA 2011a) used by
NARS. All non-screened sites were retained, while some BPJ sites were eliminated to get the desired
number and distribution of handpicked sites (Figure 4-2).
4.2.5 Replacement of 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 reference (e.g., presence of invasive species) or (2) documented there was a better,
more appropriate candidate reference 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).
30
2011 NWCA Technical Report
DISCUSSION DRAFT
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863
864
865
866
867
868
869
870
871
872
4.2.6 Results
Table 4-4 lists the final distribution of the handpicked sites by the Nine Aggregated Ecoregions used in
previous NARS, and the NWCA Wetland Types (Table 1-1). The NWCA target population is composed of
seven NWCA Wetland Types, which are a subset of wetland categories used in the USFWS Status and
Trends reporting (Dahl 2006). Figure 4-9 shows the distribution of the handpicked sites in relation to the
probability sites by Nine Aggregated Ecoregions.
Table 4-4. Distribution of 150 handpicked sites sampled in 2011 by Nine Aggregated Ecoregions and NWCA
Wetland Types. Acronyms for the Nine Aggregated Ecoregions (in parentheses) are used in tables and figures in
this chapter. See Table 1-1 for definitions of acronyms and description of characteristics for NWCA Wetland Types.
Nine Aggregated Ecoregions
E2EM
E2SS
PEM
PFO
PSS PUBPAB Pf Total
Coastal Plain (CLP)
Northern Appalachians (NAP)
Northern Plains (NPL)
Southern Appalachians (SAP)
Southern Plains (SPL)
Temperate Plains (TPL)
Upper Midwest (UMW)
Western Mountains (WMT)
Xeric (XER)
14
0
0
0
0
0
0
0
0
4
0
0
0
0
0
0
0
0
8
5
5
1
5
8
8
3
0
23
10
0
5
1
3
8
0
0
3
14
3
1
1
0
5
4
0
2
0
0
0
0
3
3
0
0
0
0
0
0
0
0
0
0
0
54
29
8
7
7
14
24
7
0
Sum
14
43
50
31
150
873
874
875
876
877
878
Aggregated Ecoregions
Coastal Plains
Northern Appalachians
Northern Plains
Southern Appalachians
Southern Ptains
Temperate Ptains
Upper Midwest
| Western Mountains
Xeric
Sites Sampled for NWCA V
O HaiBpiaea Sites
• Probability Sites *
Figure 4-9. Map of the conterminous US showing distribution of handpicked sites (yellow) in relation to probability
sites (dark red) sampled in the 2011 NWCA. The Nine Aggregated Ecoregions are based on combinations of Level III
Ecoregions (Omernik 1987, USEPA2011a) and are used in other NARS assessments.
31
2011 NWCA Technical Report
DISCUSSION DRAFT
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879
880 4.3 Overview of the Post-Sampling Evaluation of Site Disturbance
881
882 Post-sampling site evaluation was conducted using the 2011 NWCA sample data to develop quantitative
883 definitions of reference and disturbance. All sample sites were categorized by these definitions for use
884 in the ecological condition analyses (see Chapter 7) and in determination of stressor extent (Chapter 9).
885 Post-sampling site evaluation involved:
886
887 • Defining groups for reporting on ecological condition and stressor status (Section 4.4),
888
889 • Establishing a disturbance gradient (Section 4.5), and
890
891 • Defining disturbance category thresholds (Section 4.5).
892
893 The general approach followed the process used by Herlihy et al. (2008) for defining reporting groups
894 and least disturbed reference sites in the National Wadeable Streams Assessment (USEPA 2006).
895
896
897 4.4 Reporting Groups
898
899 The conterminous United States is the broadest-scale at which the 2011 NWCA results are reported.
900 However, the diversity in the Nation's landscape makes it important to assess aquatic resources in the
901 appropriate geographic setting. Regional variation in species composition, environmental conditions,
902 and human-caused disturbance often necessitates a finer scale, i.e., sub-national, to:
903
904 • Define quantitative criteria for least disturbed and most disturbed condition;
905
906 • Develop indicators for reporting on ecological condition and stressor extent; and
907
908 • Define thresholds for categories of ecological condition and disturbance.
909
910 These tasks and the need for sub-national, geographic reporting units are inherent to all NARS
911 assessments. In some previous NARS, the Nine Aggregated Ecoregions (Figure 4-9) have been used as
912 the geographic basis for reporting units in assessments.
913 USEPA's Environmental Monitoring and Assessment Program (EMAP) recommends as a general rule
914 that, absent any information on the variability in the target population, 50 sites per reporting unit should
915 be assessed to increase the likelihood that the sample will be sufficient to make population estimates6.
916 For example, the EPA Level III Ecoregions (Omernik 1987, USEPA 2011a) of the US were aggregated for
917 the Wadeable Streams and National Lakes Assessments (USEPA 2006, 2009) to assure an adequate
918 number of sites per reporting unit.
919
920 The structure of the NWCA required the use of both ecoregions and wetland types to create Reporting
921 Groups. The combination of the Nine Aggregated Ecoregions (ECO_9, see Figure 4-9) and the seven
6 See www.epa.gov/nheerl/arm/surdesignfaqs.htm for information on sample size and other monitoring design
issues.
32 2011 NWCA Technical Report DISCUSSION DRAFT
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922 NWCA Wetland Types (see Table 1-1 for definitions) resulted in 56 potential groups for analysis.
923 Examination of the distribution of all sampled NWCA sites across the 56 potential groups determined
924 further aggregation was needed because most groups included fewer than 50 sampled sites and 16
925 groups had no sites. The next step was to use vegetation data to suggest aggregations, as vegetation is
926 the primary NWCA indicator of ecological condition.
927
928 A series of ordinations were performed to evaluate the relationships between plant species
929 composition, NWCA Wetland Type (see Table 1-1 for definitions), and ECO_9. Key ordinations included
930 1) all sampled sites, 2) all estuarine wetland sites (E2EM - emergent, E2SS - shrub or forested), 3) all
931 woody (PSS - shrub or PFO forested) palustrine or shallow riverine or lacustrine wetland sites, and 4) all
932 herbaceous (PEM-emergent, PUBPAB-open-water ponds and aquatic bed, Pf-farmed wetlands not
933 in current crop production) palustrine or shallow riverine or lacustrine wetland sites. Ordinations were
934 based on site-level species composition and abundance for all observed taxa and results were plotted
935 with wetland type or ECO_9 overlain as symbol types. Ordinations for subsets of sites by wetland type
936 groups were conducted using Nonmetric Multidimensional Scaling (NMS) (R Statistical Software, version
937 3.1.1, 'Vegan: metaMDS', R Core Team 2014). The dataset for all sampled sites was so large and complex
938 that it was difficult to obtain a stable solution using NMS, thus, when all sites were evaluated,
939 Detrended Correspondence Analysis (DCA) was used for the ordinations (PC-ORD, Version 6.20, McCune
940 and Mefford 2011).
941
942 The ordinations resulted in similar, intergrading groups, which, when viewed together, suggested an
943 interaction between NWCA Wetland Type and ECO_9. An example overview of these patterns is
944 provided by DCA ordinations with overlays of symbols for ECO_9 and the NWCA Wetland Types in Figure
945 4-10. Next, ordinations were performed to evaluate whether there were advantages to using a
946 regionalization created for wetlands. Specifically, the US Army Corps of Engineers regions associated
947 with the national wetland plant list (Lichvar et al. 2012) were compared with ECO_9. The boundaries of
948 regions for both geographic groups and the analysis results were very similar, so the ECO_9 were chosen
949 for NWCA reporting to maintain consistency with the other NARS assessments.
950
951 The vegetation patterns from the ordination analyses, along with sample sizes within each of the 56
952 potential groups were used to inform aggregation of:
953
954 • The ECO_9 into four NWCA Aggregated Ecoregions (Figure 4-11), and
955
956 • The seven NWCA Wetland Types into four NWCA Aggregated Wetland Types (Table 4-5).
957
958 Indicator species analyses (R Statistical Software, version 3.1.1, 'indicspecies, version 1.7.4' using multi-
959 level pattern analysis, R Core Team 2014) were conducted for various combinations of the four NWCA
960 Aggregated Ecoregions and the four NWCA Aggregated Wetland Types to identify native and nonnative
961 species that uniquely indicated particular regions and wetland types or that overlapped between
962 specific groupings. Detailed presentation of the ordination and classification results for the 2011 NWCA
963 data is beyond the scope of this report; however, the results were used, along with sample size
964 limitations, to develop 10 Reporting Groups for the NWCA based on the combination of the four NWCA
965 Aggregated Ecoregions and the four NWCA Aggregated Target Wetland Types (Table 4-5). These
966 aggregations produced adequate sample sizes to allow reference site selection and analyses supporting
967 indicator development within each Reporting Group.
968
33 2011 NWCA Technical Report DISCUSSION DRAFT
-------
969
970
971
972
NWCA Target Wetland Types
$$3i
*-flli
NARS Aggregated Ecoregions
Ecoregions
Figure 4-10. Ordinations of species composition relative to the seven NWCA Wetland Types and the Nine
Aggregated Ecoregions (ECO_9) resulted in similar, intergrading groups. For definitions of acronyms in the keys to
the figures, see Table 4-4 and Figure 4-9 (ECO_9) and Table 1-1 (NWCA Wetland Types).
34
2011 NWCA Technical Report
DISCUSSION DRAFT
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973
974
975
976
NWCA Aggregated Ecoregions
Coastal Plains
Eastern Mountains and Upper Midwest
Interior Plains
West
Figure 4-11. The four NWCA Aggregated Ecoregions that are based on combinations of Omernik's Level III Ecoregions (Omernik 1987; USEPA 2011a).
35
2011 NWCA Technical Report
DISCUSSION DRAFT
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977
978
979
980
981
982
983
Table 4-5. Matrix showing the four NWCA Aggregated Ecoregions (Figure 4-11) and the four NWCA Aggregated Wetland Types combined into 10 NWCA
Reporting Groups. Note that estuarine wetland types are not reported by ecoregions due to insufficient samples. Acronyms for the NWCA Aggregated
Ecoregions, NWCA Aggregated Wetland Types, and the 10 Reporting Groups are in parentheses following their names. Red text gives the number of sites
sampled, i.e., the sum of the number of sites NWCA probability designs (i.e., the national assessment and some state intensifications) and from not-probability
designs (i.e., the handpicked sites and some state intensifications).
NWCA Aggregated
Ecoregions
I
Aggreg
Wetland T
,trine, Riverine,
id Lacustrine
aceous(PRLH)
.ggregates PEM, Pf, PUBPAB
Coastal Plains (CPL)
Some os Coastal Plains (CPL) in
Nine Aggregated Ecoregions;
includes Eastern and
Gulf Coastal Plains
Eastern Mountains &
Upper Midwest (EMU)
Aggregates Northern
Appalachains (NAP), Southern
Appalochains and Piedmont (SAP),
and Upper Midwest (UMV)
Interior Plains (IPL)
Aggregates Temperate Plains (TPL),
Northern Plains (NPL), and
Southern Plains (SPL)
West (W)
Aggregates Western
Mountains (WMT), and Xeric (XER)
1. Coastal Plains
Herbaceous
(CPL-PRLH)
72 Sites Sampled
3. Eastern Mountains &
Upper Midwest
Herbaceous (EMU-PRLH)
73 Sites Sampled
5. Interior Plains
Herbaceous
(IPL-PRLH)
138 Sites Sampled
1. West
Herbaceous
(W-PRLH)
67 Sites Sampled
Palustrine, Rive
and Lacustrir
Woody (PRLV
Aggregates PFO, PSS
2. Coastal Plains
Woody
(CPL-PRLW)
189 Sites Sampled
4. Eastern Mountains &
Upper Midwest
Woody (EMU-PRLW)
127Sites Sampled
6. Interior Plains
Woody
(IPL-PRLW)
52 Sites Sampled
8. West
Woody
(W-PRLW)
75 Sites Sampled
Estuarine
Herbaceous (EH)
Includes E2EM
9. Estuarine
Herbaceous
(ALL-EH)
272 Sites Sampled
Estuarine
Woody (EW)
dudes E2SS
10. Estuarine
Woody
(ALL-EW)
73 Sites Sampled
Note: The Estuarine reporting group
encompasses estuarine wetlands in all
ecoregions (hence, the prefix "ALL").
However, estuarine wetlands only
occur in CPL, EMU, and W ecoregions.
There are no estuarine wetlands in IPL.
36
2011 NWCA Technical Report
DISCUSSION DRAFT
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984 4.5 Selecting Reference Sites and Defining the Disturbance Gradient
985
986 Data from least disturbed reference sites are needed to set thresholds and to anchor the disturbance
987 gradient. A disturbance gradient is needed in the development of condition (Chapter 7) and stressor
988 indicators (Chapter 8) to evaluate how well metrics and versions of a particular index, e.g., a multi-
989 metric index (MMI), distinguish between least and most disturbed sites.
990
991 Data from the first sampling visit for NWCA probability and not-probability sites were used in a
992 screening process to establish a disturbance gradient. The probability sites were either from the national
993 assessment or a related probability design produced by NARS for a state intensification. The not-
994 probability sites were handpicked sites (see Section 4.2) or from a state intensification that did not have
995 a probability design produced by NARS but used the same target population, protocols, data forms, and
996 index period as the 2011 NWCA (USEPA 2011b).
997
998 4.5.1 Overview of Approach
999 The steps in the process of establishing a disturbance gradient are:
1000 • Develop indices or metrics for each category of disturbance data, as needed,
1001 • Set thresholds for least and most disturbed for each disturbance index or metric, and
1002 • Establish the ends of the gradient.
1003
1004 Data collected in the field and laboratory were evaluated for use in screening sites to establish the
1005 disturbance gradient. Screens were chosen based on evidence of a strong association with
1006 anthropogenic stress and on the robustness of the data. Four categories of disturbance were used as
1007 screens:
1008 • Disturbance in the Buffer and AA (six indices developed),
1009 • Hydrologic alteration in the AA (two indices developed),
1010 • Soil chemistry in the AA (one index developed), and
1011 • Relative cover of alien plant species in the AA (one metric developed).
1012
1013 Although water chemistry was part of the NWCA field protocol, only 56% of the wetlands sampled had
1014 sufficient surface water to collect and analyze. For this reason, and because wetland hydroperiod-
1015 especially during the growing season when NWCA sampling occurred - can greatly influence water
1016 chemistry (e.g., nutrients can become highly concentrated during drawdowns), water chemistry was
1017 excluded from the generation of the disturbance gradient. However, water chemistry was retained as a
1018 research indicator and specific results are discussed in Chapter 11 of this report.
1019
1020 Finally, while we were able to gather landscape data (e.g., land use within a 1-km buffer of the AA) using
1021 GIS layers, we opted not to use these data to screen sites. This was for two reasons: 1) the GIS layers are
1022 less precise than the data we were able to gather in the field, and 2) it is possible that wetlands in good
1023 condition exist in what is considered an "impacted" landscape. Therefore, we used only information
1024 directly measured by Field Crews on the ground to establish the disturbance gradient.
1025
1026 4.5.2 Indices of Disturbance Buffer and AA
1027 Development of indices of disturbance in the Buffer and AA was based on data collected from 13
1028 lOmXIOm plots (12 in the buffer; 1 in the center of the AA). Data were recorded on Form B-l within
1029 100m from the edge of the AA using the Buffer Protocol (USEPA 2011b).
1030
37 2011 NWCA Technical Report DISCUSSION DRAFT
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1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
Database files (hereafter Buffer database) derived from scanned data forms were cross checked with
approximately 200 of the original forms to ensure data integrity. No errors were identified in this
subsample of the translation from paper to electronic data.
The Buffer database was used to develop metrics and indices to describe disturbance. R Statistical
Software, version 3.1.1 (R Core Team 2014) was used to develop program code and make calculations
for these metrics and indices. The metrics were reviewed using a number of screens including range
tests (e.g., within acceptable ranges for the data being entered), normality, and skew. Additional checks
were conducted to see if the data fit expectations based on other NARS assessments and the degree of
disturbance by location. For a limited number of sites, buffer and AA metrics were hand-calculated to
determine concurrence with computer calculations made using R code.
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
entered, quality assurance review was critical to identifying and resolving any errors to ensure high
quality data.
Initially, the disturbance information hand-written by the Field Crews in the "Other" category on the
data form was not included in the analysis. Upon examination it was noted the entries comprised a
diverse set of anthropogenic and natural disturbance with many single occurrences survey-wide that did
not fit neatly into the categories listed on Form B-l. In an effort to include all data collected by the
Crews, these data were reclassified to fit into the most appropriate disturbance metric so all data
collected in the field were included in the disturbance indices and site disturbance classification.
Results from the stressor tallies were proximity-weighted by plot (Figure 4-12: Kaufmann et al. 2014).
The score for the five disturbance indices was calculated as the proximity-weighted sum of the average
number of observations per plot. The score for the summary index (i.e., B1H_ALL) was the sum of the
scores of the other five indices (Table 4-6).
1062
1063
0.23 0.44 • 1.0 *1.0 B1.0 •0.441.0
23
Figure 4-12. Proximity weights assigned to the 13 plots evaluated as part of the Buffer Protocol.
38
2011 NWCA Technical Report
DISCUSSION DRAFT
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1064 Table 4-6. Six disturbance indices generated from the Buffer Protocol data.
Index Code
Stressor
Disturbance Index with Buffer Variables Used
B1H_RESURB Residential and Urban
Stressors
I[Pasture/Hay, Range, Row crops, Fallow field, Nursery, Dairy,
Orchard, CAFO, Rural residential, Gravel pit, Irrigation]
Z[Road (gravel, two lane, four lane), Parking lot/Pavement, Golf
course, Lawn/Park, Suburban Residential, Urban/Multifamily,
Landfill, Dumping, Trash]
B1HJND
B1H HYD
B1H HAB
Industrial Stressors
Hydrologic
Modifications
Habitat Modifications
Z[Oil drilling, Gas well, Mine (surface, underground), Military)]
Z[Ditches/Channelization, Dike/Dam/Road/Railroad Bed, Water level
control structure, Excavation, Fill, Fresh sediment, Soil loss/Root
exposure, Wall/Riprap, Inlets, Outlets, Pipes (effluent/stormwater),
Impervious surface input (sheetflow)]
Z[Forest clear cut & Selective cut, Tree plantation, Canopy herbivory,
Shrub layer browsed, Highly grazed grasses, Recently burned forest,
Recently burned grassland, Herbicide use, Mowing/ Shrub cutting,
Trails, Soil compaction, Off road vehicle damage, Soil erosion]
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
B1H ALL
Summary
Z[B1H_AGR, B1H_RESURB, B1HJND, B1H_HYD, B1H_HAB]
4.5.3 Indices of Hydrologic Disturbance in the AA
The data used for the development of indices of hydrologic disturbance were collected from the entire
AA and recorded on Form H-l using the Hydrology Protocol (USEPA 2011b).
As with the Buffer database, a portion of the Hydrology database was cross checked with field forms to
determine if scanning software correctly noted bubbles marked on the form. No scanning errors were
found. Metrics derived from the raw data were checked for appropriate range, and metrics were hand
calculated to confirm the R computer code was correctly calculating metric and index scores. As with the
buffer data, validated data sets were developed and converted to a matrix format from the files used in
data storage. This was done so subsequent recalculation of the metrics and indices would correspond to
the verified calculations regardless of database software and statistical package used. When no stressor
was identified as present in a plot at a site, zeros were entered into the matrix file, as appropriate.
Two indices were developed based on the best professional judgment of the analysts as to the relative
impact of the types of hydrologic alterations documented by Field Crews. The score for each of the
indices was calculated by summing the number of hydrologic Stressors observed at each AA (Table 4-7).
Table 4-7. Two indices generated from the Hydrology Protocol data.
Index Code
Stressor
Disturbance Index with Hydrology Variables Used
1084
1085
1086
1087
HDIS HIGH
HDIS MED
High Impact Hydrologic
Disturbances
Moderate Impact
Hydrologic
Disturbances
Z[Damming features (dikes, berms, dams, railroad bed, roads),
Impervious surfaces (road, concrete, asphalt), Pumps, Pipes,
Culverts, Ditches, Excavation, Field tiling]
Z[Shallow channels (animal trampling, vehicle ruts), Recent
sedimentation]
4.5.4 Index of Disturbance Indicated by Soil Chemistry
There were three steps in developing an index of disturbance based on soil chemistry. First, the soil data
were evaluated to determine the best samples collected at each site to use for the evaluation of soil
39
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
1088 chemistry. Next, we examined which soil chemistry parameters might effectively reflect anthropogenic
1089 stress. Once these two determinations were made, an index was developed.
1090
1091 The index reflecting disturbance indicated by soil chemistry was developed based on data collected from
1092 one of four soil pits dug at each site and chosen by the Field Crew to represent the entire AA according
1093 to the Soils Protocol in the NWCA Field Operations Manual (USEPA 2011b). Soil samples were shipped to
1094 the Kellogg Soil Survey Laboratory for analysis following the procedures in the NWCA Laboratory
1095 Operations Manual (USEPA 2011c). The Kellogg Laboratory is located in Lincoln, Nebraska, and is part of
1096 the Natural Resources Conservation Service (NRCS) of the US Department of Agriculture.
1097
1098 Soil chemistry data returned from NRCS were merged with soil profile data collected by Field Crews
1099 from the representative pit (i.e., the only pit from which soil was analyzed for chemistry). The soil
1100 chemistry database, consisting of soil layers from the representative pits and associated soil chemistry
1101 for sites sampled, was thoroughly inspected for quality assurance. Using both manual screening and
1102 customized R code, potential data errors were identified. Whenever large quantities of data are
1103 collected, it is not surprising for some errors related to data or sample collection, recording, sample
1104 analysis, or data entry to occasionally occur. Therefore, the NWCA established a number of cross-checks
1105 in the data collection and processing procedures within the protocols and field forms, to allow
1106 identification and resolution of potential errors. Once the data were entered, quality assurance review
1107 was critical to identifying and resolving any errors potentially impacting data quality.
1108
1109 For the soils data, errors were primarily associated with two variables, Depth (recorded on the Soil
1110 Profile form) and Bulk Density (collected in the field and measured by NRCS), and included the following:
1111
1112 • Final Pit Depth was shallower than called for in the protocol (often because of field conditions
1113 that prohibited digging or sampling beyond a certain depth);
1114
1115 • Depth (i.e., layer depth) was not reported;
1116
1117 • Depth for all layers of the representative pit was recorded incorrectly;
1118
1119 • Value recorded for Depth failed logic checks;
1120
1121 • Errors occurred in scanning data from the field forms into an electronic format;
1122
1123 • All soil chemistry data was missing for a described layer, presumably because a soil sample could
1124 not be collected;
1125
1126 • Bulk Density value was inconsistent with the layer position (i.e., the bulk density was much
1127 lighter or heavier than surrounding layers);
1128
1129 • Bulk Density was outside the valid range of 0.06-2.53 g/cc; and
1130
1131 • Core volume for bulk density collection recorded in the field failed logic checks.
1132
1133 Errors that could be resolved by inspecting the original field data forms were corrected in an annotated
1134 soil chemistry database, with detailed notes of how the error was corrected. If the error could not be
40 2011 NWCA Technical Report DISCUSSION DRAFT
-------
1135 resolved, the associated data were removed from the database (resulting in an "NA" in place of the
1136 value) or flagged if the datum was suspect but could not be identified as being absolutely incorrect.
1137
1138 NRCS performed internal quality assurance on soil chemistry data. Some soil chemistry data returned by
1139 NRCS was flagged if it was below the practical quantitation limit (PQL) or minimum detection limit (MDL)
1140 of the equipment using to analyze the samples. Aside from identifying which samples were below limits,
1141 the flags also specified the limits for each analyte. Values below the MDL were changed to half the
1142 specified MDL in the soil chemistry database.
1143 To develop an index of disturbance based on soil chemistry, the data were evaluated to determine the
1144 best of the numerous soil samples and the related chemistry to use.
1145
1146 Soil chemistry data were generated for each soil layer greater than 8 cm in thickness at the
1147 representative soil pit. Deciding on which soil layer(s) to use proved to be difficult because:
1148
1149 • The number of soil layers at each site differed (ranging from 1 to 9 layers).
1150
1151 • Soil layers varied in thickness (ranging from 1 to 170 cm).
1152
1153 • Nearly one-quarter of the described soil layers (948 of 4444) were less than 8 cm thick and,
1154 therefore, not sampled for soil chemistry as directed in the NWCA Soils Protocol (USEPA 2011b).
1155
1156 • The first layer, containing the most biologically active soil and most indicative of recent human
1157 impacts, was not sampled at nearly one-third of the sites for soil chemistry because Layer 1 was
1158 less than 8 cm thick (347 of 1082 sites). Even though the NWCA Soils Protocol (USEPA 2011b)
1159 directed crews to combine Layer 1 and Layer 2 when Layer 1 was less than 8 cm thick, this was
1160 not done for every site.
1161
1162 • The soil at 60 wetland sites was not sampled due to site constraints (e.g., deep water,
1163 unconsolidated soils, and shallow bedrock).
1164
1165 Based on the Soils Protocol in the NWCA Field Operations Manual, Field Crews were instructed to collect
1166 soil samples from boundary to boundary of the horizon of each layer regardless of layer thickness
1167 (Figure 4-13). Examination of the data showed that every site with soils data had at least one layer with
1168 soil chemistry measured within 50 cm of the surface. Because the upper part of the soil is the most
1169 biologically active and most indicative of human impacts in and around the AA, soil chemistry collected
1170 from the uppermost layer within 10 cm of the soil surface was used. By making the decision to use data
1171 associated with the uppermost layer, 97% of the sites sampled in the 2011 NWCA and soils most likely to
1172 reflect anthropogenic stressors are represented in the data used in the analysis.
1173
1174 Next, we considered the types of soil chemistry parameters to use. Parameters with natural
1175 concentrations spanning wide ranges that would overlap with anthropogenic signals were dropped from
1176 further consideration, (e.g., nitrogen, phosphorus, and sulfur species) for describing the disturbance
1177 gradient. While we expected bulk density to reflect anthropogenic impacts, problems in collecting and
1178 analyzing the samples resulted in an incomplete database that precluded its use in reference screening.
1179 Heavy metal concentrations were the best candidates for use because many have specific background
1180 ranges, above which anthropogenic impacts are indicated. The signal to noise ratio was examined for
1181 each candidate heavy metal measured at the same site during Visit 1 and Visit 2 (Kaufmann et al. 1999;
1182 Stoddard et al. 2008). Metals with high signal to noise ratios remained candidates for use in reference
41 2011 NWCA Technical Report DISCUSSION DRAFT
-------
1183
1184
1185
1186
1187
1188
1189
1190
1191
determination. Ultimately, 12 metals were chosen to develop a heavy metal index. These heavy metals,
their primary anthropogenic associations, and their signal to noise ratios are reported in Table 4-8.
Soil chemistry
sample
LAYER
THICKNESS
SOIL CHEMISTRY
DEPTH
Layer 1
Layer 2
Layer not
sampled
(< 8 cm}
Layer 4
Example Soil Pit
10 cm
0 cm
27 cm
3 cm
14 cm
10cm
NA
40 cm
Figure 4-13. Example soil pit designating where soil chemistry samples were collected within the layer.
Table 4-8. Summary of the characteristics of the heavy metals considered for use in the stressor index based on
soil chemistry. Natural backgrounds are based on Alloway (2013). Percent of sites exceeding the thresholds is
based on data from Visit 1.
Metal
Primary Anthropogenic
Associations
SignahNoise
Natural
Background
(mg/kg)
Screening
Threshold
(mg/kg)
% Sites
Exceeding
Threshold
1192
1193
1194
1195
Silver (Ag)
Cadmium (Cd)
Cobalt (Co)
Chromium (Cr)
Copper(Cu)
Nickel (Ni)
I pad (PM
Industry
Agriculture
Industry
Industry
Agriculture / Industry / Roads
Industry / Agriculture
Roads/ Industry
9.66
16.5
Antimony (Sb)
Tin (Sn)
Vanadium (V)
Tungsten (W)
Zinc (Zn)
Industry
Industry / Agriculture
Industry/ Roads
Industry / Agriculture
Industry/Agriculture
1.38
10 -150
150
6.6
The heavy metal index was created and scored as the sum of the number of metals present at any given
site with concentrations above a set threshold based on published values. To set the threshold for a
metal, natural background concentrations (ranges or means) in terrestrial soils in, or as close to the US
42
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
as possible, were determined, primarily from Alloway (2013) (Table 4-8) and compared to distributions
in the data (Figure 4-14). This resulted in establishment of the following thresholds of human
disturbance for NWCA:
• Ag, Cd, Cu, V, W, Zn: used the maximum of the natural range concentration;
• Co, Cr, Ni, Sb: halved the maximum of the natural range concentration;
• Pb: Doubled the mean natural concentration; and
• Sn: Used lOxthe minimum of the natural range concentration.
It is important to note that the thresholds established for heavy metals do not reflect toxicity thresholds.
These thresholds are indicators of human disturbance. The screening threshold established for each
heavy metal is reported in Table 4-8. Most metal concentrations seldom exceeded the set thresholds in
the NWCA sites (Figure 4-14).
1210
1211
1212
1213
1214
1215
o
N
20
40
60
80
100
120
140
160
180
200
250
300
350
400
500
750
Threshold = 150
0 100 200 300
Number of Sites
400
100 200 300 400
Number of Sites
500
o>
o
.a
o
O
0
5
10
15
20
25
30
35
40
45
50
55
60
Threshold = 25
5
03
0)
5
10
15
20
25
30
35
40
45
50
55
60
65
70
80
90
100
150
200
Threshold = 35
100 200 300 400
Number of Sites
500
0 50 100 150
Number of Sites
200
Figure 4-14. Examples of frequency histograms of soil metal concentrations used to set thresholds (designated by
the red line and detailed in Table 4-8). Published values are primarily from Alloway (2013), and natural breaks in
the data were considered.
43
2011 NWCA Technical Report
DISCUSSION DRAFT
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1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
4.5.5 Metric of Plant Disturbance in the AA
Alien plant species are recognized as important descriptors of disturbance and stress to wetlands (Mack
and Kentula 2010; Magee et al. 2010). First, the presence and abundance of alien plant species are often
positively related to human mediated disturbance (Lozon and Maclsaac 1997; Magee et al. 1999; Mack
et al. 2000; Magee et al. 2008; Ringold et al. 2008), making them useful disturbance indicators. In
addition, alien plant species can act as direct stressors to ecological condition by competing with or
displacing native plant species or communities, or by altering ecosystem structure and processes (Sala et
al. 1996; Lesica 1997; Vitousek et al. 1997; Ehrenfeld 2003; Dukes and Mooney 2004; Magee et al.
2010).
Consequently, we used a simple metric describing relative cover of alien plant species as one of the
screens for determining the relative position of NWCA sites along a disturbance gradient, and to inform
the determination of least and most disturbed conditions for the NWCA. 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).
Data collection methods are summarized in Chapter 5, Section 5.3. Mean relative cover of alien species
is defined as a percentage of the total cover of all species observed in the five 100-m2 vegetation plots
sampled in the AA. The specific calculation method for this metric can be found in Chapter 6, Section 6.8
(Appendix D) by referencing the metric name (XRCOV_ALIENSPP).
For the NWCA, alien plant species are defined as species that are either introduced to the conterminous
United States or are adventive to the location of occurrence. Adventive species are native to some parts
of the conterminous US, but introduced to the location of the particular NWCA site on which they were
observed. Concepts describing native status categories and the procedures for determining native status
for individual species are described in detail in Chapter 5, Section 5.8.
4.5.6 Assignment of Sites along a Disturbance Gradient
Sites were screened using threshold criteria for the nine disturbance indices and plant disturbance
metric to assign each site to a place along a gradient of three categories of disturbance - least,
intermediate, and most (Figure 4-15). Two types of NWCA sites were used in the screening: probability
and not-probability (Table 4-9). The combination of both types of sites resulted in 1,138 sites being
screened for level of disturbance.
Disturbance Gradient
Reference Sites
Figure 4-15. Diagram of the disturbance gradient used in the NWCA with categories of disturbance. Least disturbed
according to the definition of a reference site used in the NWCA and NARS (Stoddard et al. 2006).
44
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
1253
1254
Table 4-9. The types and numbers of sites sampled from probability and not-probability survey designs used in the
establishment of the NWCA disturbance gradient.
NWCA PROBABILITY
NWCA NOT-PROBABILITY
876 sites - NWCA probability design
91 sites - State intensifications
150 sites - Handpicked
21 sites - Intensifications
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
TOTAL = 967
TOTAL = 171
A filtering process was used to define least disturbed reference sites (Herlihy et al. 2008). Nine indices
and a plant metric were generated from the NWCA data that captured a wide variety of wetland
disturbances (Table 4-10 and Table 4-11). For each of these ten measures of disturbance, a least
disturbed threshold was set and every site screened to test for exceedance. If any single disturbance
threshold was exceeded at a site, it was not considered a least disturbed reference site. Thus, the least
disturbed reference sites were those that were below the thresholds for all ten measures.
Table 4-10. Threshold values for sites to be categorized as least disturbed by Reporting Group for the Buffer
Indices. If any single threshold was exceeded at a site, the site was not considered least disturbed. Numbers in red
are thresholds relaxed to achieve about 20% of the sites in the Group as least disturbed. An index score of 0
indicates disturbance not present. See Table 4-5 for definitions of Reporting Group acronyms.
Reporting
Group
B1H_AGR
(Agriculture)
B1H_RESURB
(Residential/
Urban)
B1H_HYD
(Hydrology)
B1HJND
(Industry)
B1H_HAB
(Habitat)
B1H_ALL
(Summary)
ALL-EW
ALL-EH
EMU-PRLW
EMU-PRLH
CPL-PRLW
CPL-PRLH
IPL-PRLW
IPL-PRLH
W-PRLW
W-PRLH
1267
1268
1269
1270
1271
1272
Table 4-11. Threshold values for sites to be categorized as least disturbed by Reporting Group for the Hydrology
and Soil Chemistry Indices, and the relative cover of alien plant species metric. If any single threshold was
exceeded at a site, the site was not considered least disturbed. Numbers in red are thresholds relaxed to achieve
about 20% of the sites in the Group as least disturbed. A Hydrology or Soil Chemistry Index score of 0 indicates
disturbance not present. See Table 4-5 for definitions of Reporting Group acronyms.
Reporting
Group
Hydrology
High Impact
Hydrology
Moderate Impact
Soil Chemistry
Heavy Metal Index
Relative Cover of
Alien Plant Species
ALL-EW
ALL-EH
EMU-PRLW
EMU-PRLH
CPL-PRLW
CPL-PRLH
IPL-PRLW
IPL-PRLH
W-PRLW
W-PRLH
1273
45
2011 NWCA Technical Report
DISCUSSION DRAFT
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1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
Thresholds were set independently for all ten NWCA Reporting Groups (see Table 4-5) as the extent of
human disturbance can vary greatly among regions and wetland types. Initially, thresholds were set to
zero human disturbance with the exception of a 5% alien plant species cover threshold. These
thresholds became the definition of a minimally disturbed reference site (Stoddard et al. 2006). If a
Reporting Group had a sufficient number of sites not exceeding these thresholds, as was the case in four
of the Reporting Groups, then these zero thresholds were used to define reference sites. In the other six
Reporting Groups, we had to relax our thresholds to obtain a sufficient number of reference sites for
data analysis. Thresholds were relaxed so that approximately 15-25% of the sites in the Reporting Group
passed the filters and these sites were used as the least disturbed reference sites for that Reporting
Group. The nine indices and plant metric and their least disturbed thresholds in each of the ten NWCA
Reporting Groups are shown in Table 4-10 and Table 4-11. The number of least disturbed sites by
Reporting Group is listed in Table 4-12.
Table 4-12. Results of screening for least disturbed. See Table 4-5 for definitions of Reporting Group acronyms. Key
to Font color for Reporting Group: Green = Not relaxed; Black = Relaxed; Red = Most Relaxed.
Reporting
Group
Total Number of
Sites Screened
Number of Least
Disturbed Sites
Percent Least Disturbed
Sites
ALL-EW
ALL-EH
EMU-PRLW
EMU-PRLH
CPL-PRLW
CPL-PRLH
IPL-PRLW
IPL-PRLH
W-PRLW
W-PRLH
23%
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
Totals
1138
277
24%*
* Percent of all sites screened, i.e., 1138
Most disturbed sites on the disturbance gradient were defined using a filtering process in the same
manner as for least disturbed sites. The same ten measures of disturbance were used and thresholds for
most disturbed were set for each of the measures. 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 Reporting Group as most disturbed and
thresholds were set accordingly. Measures and their most disturbed thresholds in each of the ten NWCA
Reporting Groups are shown in Table 4-13 and Table 4-14. The total number of most disturbed sites by
Reporting Group is listed in Table 4-15.
Finally, we classified the sites not falling in to either least or most disturbed into the intermediate
disturbance category. Table 4-15 also lists the total number of intermediate disturbed sites by Reporting
Group.
46
2011 NWCA Technical Report
DISCUSSION DRAFT
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1305
1306
1307
Table 4-13. Threshold values for sites to be categorized as most disturbed by Reporting Group for the Buffer
Indices. If any single threshold was exceeded at a site, the site was considered most disturbed. See Table 4-5 for
definitions of Reporting Group acronyms.
Reporting
Group
B1H_AGR
(Agriculture)
B1H_RESURB
(Residential/
Urban)
B1H_HYD
(Hydrology)
B1HJND
(Industry)
B1H_HAB
(Habitat)
B1H_ALL
(Summary)
ALL-EW
ALL-EH
EMU-PRLW
EMU-PRLH
CPL-PRLW
CPL-PRLH
IPL-PRLW
IPL-PRLH
W-PRLW
W-PRLH
>0.25
>0.25
>0.50
>0.60
>0.50
>1.00
>0.60
>1.20
>0.80
>1.50
>0.75
>0.75
>1.00
>1.00
>1.00
>1.50
>1.00
>1.80
>1.00
>2.50
1308
1309
1310
1311
Table 4-14. Threshold values for sites to be categorized as most disturbed by Reporting Group for the Hydrology
and Soil Chemistry Indices and the relative cover of alien plant species metric. If any single threshold was exceeded
at a site, the site was considered most disturbed. See Table 4-5 for definitions of Reporting Group acronyms.
Reporting
Group
Hydrology
High Impact
Hydrology
Moderate Impact
Soil Chemistry
Heavy Metal Index
Relative Cover of
Alien Plant Species
1312
1313
1314
ALL-EW
ALL-EH
EMU-PRLW
EMU-PRLH
CPL-PRLW
CPL-PRLH
IPL-PRLW
IPL-PRLH
W-PRL'
W-PRLH
>50%
>50%
>50%
>50%
>50%
>50%
>50%
>50%
>50%
>50%
Table 4-15. Number and percent of sites in the most and intermediate disturbance categories by NWCA Reporting
Group. See Table 4-5 for definitions of Reporting Group acronyms.
Reporting
Group
Number of Sites
Screened
Number of
Most Disturbed
Sites
Percent Most
Disturbed
Number of
Intermediate
Disturbed Sites
Percent
Intermediate
Disturbed Sites
-EW
ALL-EH
EMU-PRLW
EMU-PRLH
CPL-PRLW
CPL-PRLH
IPL-PRLW
IPL-PRLH
W-PRLW
W-PRLH
Totals
1138
331
29%*
530
47%*
1315
1316
* Percent of all sites screened, i.e., 1138
47
2011 NWCA Technical Report
DISCUSSION DRAFT
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1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
4.5.7 Least and Most Disturbed Site Distribution
As wetlands are not uniformly distributed across the US, sample sites in the NWCA are also not
distributed uniformly (see Figure 4-16). In general, the distribution of least disturbed reference sites and
most disturbed sites are spread out reasonably well across the NWCA sample. There is a tendency in
some regions to have more least-disturbed reference sites in relatively undisturbed places (e.g.,
northern New England versus southern New England (Figure 4-17)) but in others (Great Plains (Figure
4-18), Gulf Coastal Plain (Figure 4-19), Western Mountains) they are very well distributed across the
area. Unfortunately, some skew in distribution cannot be completely avoided at this scale of analysis.
In terms of NWCA Wetland Types (Table 4-16), it is not surprising that palustrine farmed (Pf) wetlands
have a larger proportion of disturbed sites than the other types, whereas estuarine (E2EM and E2SS) and
palustrine unconsolidated bottom/aquatic bed (PUBPAB) types tended to have fewer disturbed sites
than the other types. The distribution of least and most disturbed sites across HGM classes (Table 4-17)
was similar among the classes. Tidal and fringe wetlands tended to be a bit less disturbed than the other
classes.
Disturbance Classes
• Least Disturbed (L)
• Intermediate Disturbed (I)
• Most Disturbed (M)
NWCA Aggregated Ecoregions
| | Coastal Plains
^ Eastern Mountains and Upper Midwest
| Interior Plains
I I West
1333
1334
1335
1336
Figure 4-16. Illustration of the distribution of NWCA sites by disturbance category across the conterminous US.
48
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
Disturbance Classes
* Least Disturbed (L)
• Intermediate Disturbed ([)
• Most Disturbed (M)
NWCA Aggregated Ecoreglons
___—,-^f f | | Coastal Plains
'''& \*\' I 1 Eastern Mountains and Upper Midwest
2fe\i£-*
1337
1338 Figure 4-17. Illustration of the distribution of NWCA sites by disturbance category in the eastern US.
1339
1340
1341
1342
1343
r x-:-
n
fr
Disturbance Classes
• Least Disturbed (L)
• Intermediate Disturbed (1)
• Most Disturbed (M)
NWCA Aggregated Ecoregions
| | Eastern Mountains and Upper Midwest
1 1 interior Plains
| Wes,
Figure 4-18. Illustration of the distribution of NWCA sites by disturbance category in the upper Midwest area of the
US.
49
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
1344
1345
1346
1347
1348
1349
1350
Disturbance Classes
• Least Disturbed (L)
• Intermediate Disturbed (I)
• Most Disturbed (M)
NWC A Aggregated Ecoregions
| | Coastal Plains
^] Eastern Mountains and Upper Midwest
1 Interior Plains
1 West
Figure 4-19. Illustration of the distribution of NWCA sites by disturbance category in the Gulf Coastal Plains of the
US.
Table 4-16. Percent of the 1138 sites screened in each disturbance category by NWCA Wetland Types. Numbers
are rounded and may not add to 100 percent. See Table 1-1 for descriptions of the NWCA Wetland Types, which
include PRL (Palustrine, Riverine, and Lacustrine) and E (Estuarine) wetlands.
NWCA Target
% Least
% Intermediate
% Most
PEM
21
48
32
1351
1352
1353
1354
PSS
PUBPAB
E2EM
E2SS
Table 4-17. Percent of the 1138 sites screened in each disturbance category by Hydrogeomorphic (HGM) Class
(Brinson 1993). Numbers are rounded and may not add to 100 percent.
HGM Class
% Least
Intermediate
% Most
Depression
Flats
Fringe
Riverine
Slope
Tidal
35
48
41
51
43
35
28
18
28
33
30
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2011 NWCA Technical Report
DISCUSSION DRAFT
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1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
4.5.8 A Research Tool
Examination of the least disturbed sites revealed that a number of the sites met the definition of
minimally disturbed. Minimally disturbed was defined by Stoddard et al. (2006) as the absence of
significant human disturbance. Minimally disturbed sites were identified by setting the thresholds for
the nine disturbance indices and plant disturbance metric to zero, i.e., indicating that none of the
indicators of stress considered in Table 4-10 and Table 4-11 were present in the AA and buffer of the
sites being screened. This resulted in a gradient with four disturbance categories Figure 4-20. Of the
original 277 least disturbed sites (Table 4-12) 170 are minimally disturbed. Comparisons of the
characteristics of minimally and least disturbed sites will be informative to future NWCA analyses and to
management and policy decisions.
"
**
Disturbance Gradient
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
Figure 4-20. The NWCA disturbance gradient with the minimally disturbed category.
4.6 Literature Cited
Alloway BJ (ed) (2013) Heavy metals in soils: trace metals and metalloids in soils and their
bioavailabilaity. Springer, New York, NY
Brinson MM (1993) A Hydrogeomorphic Classification for Wetlands. US Army Corps of Engineers,
Waterways Experiment Station, Vicksburg, MS, p 79
Dale, V. H. and S. C. Beyeler (2001). "Challenges in the development and use of ecological indicators.
Ecological Indicators 1: 3-10.
Dahl TE (2006) Status and Trends of Wetlands in the Conterminous United States 1998 to 2004. US
Department of the Interior, Fish and Wildlife Service, Washington, DC, p 112
51
2011 NWCA Technical Report
DISCUSSION DRAFT
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1385 Dukes JS, Mooney HA (2004) Disruption of ecosystem processes in western North America by invasive
1386 species. Revista Chilena de Historia Natural 77: 411-437
1387
1388 Ehrenfeld JG (2003) Effects of exotic plant invasions on soil nutrient cycling processes. Ecosystems 6:
1389 503-523
1390
1391 Herlihy AT, Paulsen SG, Van Sickle J, Stoddard JL, Hawkins CP, Yuan LL (2008) Striving for consistency in a
1392 national assessment: the challenges of applying a reference-condition approach at a continental scale.
1393 Journal of the North American Benthological Society 27: 860-877
1394
1395 Karr, J. R. (1991). "Biological integrity: A long-neglected aspect of water resource management."
1396 Ecological Applications 1: 66-84.
1397
1398 Kaufmann PR, Levine P, Robinson EG, Seeliger C, Peck DV (1999) Quantifying Physical Habitat in
1399 Wadeable Streams. US Environmental Protection Agency, Washington, DC, p 149
1400
1401 Kaufmann PR, Peck DV, Paulsen SG, Seeliger CW, Hughes RM, Whittier TR, Kamman NC (2014) Lakeshore
1402 and littoral physical habitat structure in a national lakes assessment. Lake and Reservoir Management
1403 30: 192-215
1404
1405 Lesica P (1997) Spread of Phalaris arundinacea adversely impacts the endangered plant Howellia
1406 aquatilis. Great Basin Naturalist 57: 366-368
1407
1408 Lichvar RW, Melvin NC, Butterwick ML, Kirchner WN (2012) National Wetland Plant List: Indicator Rating
1409 Definitions. US Army Corps of Engineers, Engineer Research and Development Center, Cold Regions
1410 Research and Engineering Laboratory, Hanover, NH
1411
1412 Lozon JD, Maclsaac HJ (1997) Biological invasions: are they dependent on disturbance? Environmental
1413 Review 4: 131-144
1414
1415 Mack JJ, Kentula ME (2010) Metric Similarity in Vegetation-Based Wetland Assessment Methods. US
1416 Environmental Protection Agency, Office of Research and Development, Washington, DC
1417
1418 Mack RN, Simberloff D, Londsdale WM, Evans H, Clout M, Bazzaz FA (2000) Biotic invasions: causes,
1419 epidemiology, global consequences, and control. Ecological Applications 10: 689-710
1420
1421 Magee T, Ringold P, Bollman M (2008) Alien species importance in native vegetation along wadeable
1422 streams, John Day River basin, Oregon, USA. Plant Ecology 195: 287-307
1423
1424 Magee TK, Ernst TL, Kentula ME, Dwire KA (1999) Floristic comparison of freshwater wetlands in an
1425 urbanizing environment. Wetlands 19: 517-534
1426
1427 Magee TK, Ringold PL, Bollman MA, Ernst T, L. (2010) Index of Alien Impact: a method for evaluating
1428 potential ecological impact of alien plant species. Environmental Management 45: 759-778
1429
1430 McCune B and Mefford MJ (2011). PC-ORD. Multivariate Analysis of Ecological Data. Version 6.20
1431 MjM Software, Gleneden Beach, Oregon, USA
1432
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1433 Omernik JM (1987) Ecoregions of the conterminous United States. Annals of the Association of American
1434 Geographers 77: 118-125
1435
1436 R Core Team (2014) R: A language and environment for statistical computing. R Foundation for
1437 Statistical Computing, Vienna, Austria. (http://www.R-project.org/)
1438
1439 Ringold PL, Magee TK, Peck DV (2008) Twelve invasive plant taxa in US western riparian ecosystems.
1440 Journal of the North American Bethological Society 27: 949-966
1441
1442 Sala A, Smith SD, Devitt DA (1996) Water use by Tamarix ramosissima and associated phreatophytes.
1443 Ecological Applications 6: 888-898
1444
1445 Stoddard JL, Herlihy AT, Peck DV, Hughes RM, Whittier TR, Tarquinio E (2008) A process for creating
1446 multimetric indices for large-scale aquatic surveys. Journal of the North American Bethological Society
1447 27:878-891
1448
1449 Stoddard JL, Larsen DP, Hawkins CP, Johnson PK, Norris RH (2006) Setting expectations for the ecological
1450 condition of streams: the concept of reference condition. Ecological Applications 16: 1267-1276
1451
1452 USEPA (2006) Wadeable Streams Assessment: A Collaborative Survey of the Nation's Streams. US
1453 Environmental Protection Agency, Office of Water and Office of Research and Development,
1454 Washington, DC
1455
1456 USEPA (2008) Ecological Research Program Multi-Year Plan FY2008-2014: February 2008 Review Draft.
1457 US Environmental Protection Agency, Office of Research and Development, Washington, DC
1458
1459 USEPA (2009) National Lakes Assessment: A Collaborative Survey of the Nation's Lakes. US
1460 Environmental Protection Agency, Office of Water and Office of Research and Development,
1461 Washington, DC
1462
1463 USEPA (2011a) Level III Ecoregions of the Continental United States (revision of Omernik, 1987). US
1464 Environmental Protection Agency, National Health and Environmental Effects Laboratory-Western
1465 Ecology Division, Corvallis, OR
1466
1467 USEPA (2011b) National Wetland Condition Assessment: Field Operations Manual. US Environmental
1468 Protection Agency, Washington, DC
1469
1470 USEPA (2011c) National Wetland Condition Assessment: Laboratory Operations Manual. US
1471 Environmental Protection Agency, Washington, DC
1472
1473 Vitousek PM, D'Antonio CM, Loope LL, Rejmanek M, Westbrooks R (1997) Introduced species: a
1474 significant component of human-caused global change. New Zealand Journal of Ecology 86: 33212-
1475 33218
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Chapter 5: Vegetation Indicators - Background, Analysis Approach
Overview, Data Acquisition and Preparation
5.1 Background
The status of natural vegetation has been
increasingly and effectively used as an
indicator of ecological condition in wetlands
(Mack and Kentula 2010). 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).
Data describing plant species composition (species identity, presence, and abundance) and vegetation
structure (horizontal and vertical) were collected in the 2011 NWCA (see Section 5.3). Such data are
powerful, robust, relatively easy to gather and can be summarized into myriad candidate metrics or
indices of ecological condition (USEPA 2002; Mack and Kentula 2010; USEPA 2011a). In addition to
reflecting ecological condition, some plant species or 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).
Vegetation metrics or indices that distinguish least from most disturbed sites are increasingly used for:
1) Documenting baseline ecological condition,
2) Assessing trends in condition over time,
3) Identifying stressors to condition and predictors of condition decline.
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1526
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1573
Two kinds of condition indicators and one indicator of stress were considered for use in the NWCA:
Vegetation Indicators of Condition
• A Vegetation Multi-Metric Index (VMMI) is comprised of several metrics describing different
components or functional traits of the vegetation (see Section 7.2) that together reflect overall
wetland condition. Candidate metrics of vegetation condition are evaluated for utility in
distinguishing least disturbed sites from those that are most disturbed. The most effective
metrics are then combined into a VMMI as an indicator of wetland condition. VMMIs that
combine a suite of vegetation metrics (representing aspects of plant communities, vegetation
structure, and functional or life history guilds) have been developed for several states and
regions within the United States and elsewhere (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). The multimetric index approach has also been
widely used for other biological assemblages (e.g., fish, birds, periphyton, macroinvertebrates)
and forms the cornerstone of the USEPA National Aquatic Resource Surveys (NARS) (e.g., USEPA
2006; 2009). Condition assessment approaches based on biotic assemblages assume that when
species composition and abundance are similar to reference (or least disturbed) conditions,
ecological integrity is also maintained (Karr 1991; Dale and Beyeler 2001).
• Floristic Quality (FQ) indices can be stand-alone indicators of condition or used as a component
of a VMMI (see Section 7.2). Floristic quality describes the complement of plant species
occurring at a site, and is based on summarization of species-specific, 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 2006;
Miller and Wardrop 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.
Vegetation Indicator of Stress
• Nonnative Plant Stressor Indicator (NPSI) incorporates attributes of richness, occurrence, and
abundance for nonnative plant species (see Section 5.8), and is used to assess extent of
potential stress to wetlands (see Chapter 8, Section 8.5).
5.2 Overview of Vegetation Analysis Process
As the primary biotic indicator for the NWCA, vegetation is a major component of the analysis pathway
(Figure 5-1). Data acquisition, preparation, and quality assurance are covered in this chapter (orange
outlined box in Figure 5-1). Chapter 6 provides detail on prerequisite analysis steps that use validated
data and least and most disturbed site designations for candidate metric development and generation
of data tables for analysis. Development of the NWCA VMMI (green open and filled boxes in Figure 5-1)
is described in Chapter 7, and creation of the Nonnative Plant Stressor Indicator (purple open and filled
boxes in Figure 5-1) is outlined in Section 8.5. Note, both the VMMI and the NPSI are based on
vegetation data, consequently, the NPSI is used only for Stressor extent estimates (purple filled box),
and not for determining relative and attributable risk (teal filled box), which combine information from
the VMMI and a particular Stressor indicator (see Section 9.4).
56
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DISCUSSION DRAFT
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Data acquisition and
QA continues
through analysis
ONLY probabiliy sites are
used to generate the
population estimates
data acquisition
data preparation
data QA
w) = site weights
from
probability
design
1574
1575
1576
1577
1578
1579
1580
Figure 5-1. The 2011 National Wetland Condition Assessment Analysis Pathway. The orange outlined box on left of diagram highlights the data preparation
activities. Some prerequisite analysis steps involve the use of validated data and the least and most disturbed site designations. Green outlined and filled boxes
represent the analysis path for the development of vegetation indicators of condition. Development of the vegetation indicator of wetland stress follows the
stressor analysis path indicated by the purple open and filled boxes.
57
2011 NWCA Technical Report
DISCUSSION DRAFT
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1581 Evaluating vegetation in the NWCA included three primary components, each with several major
1582 analysis steps (Figure 5-2). These three components were necessarily completed in sequence beginning
1583 with data preparation and acquisition, then moving on to prerequisite steps needed before indicator
1584 development could begin. The final stage of analysis was describing wetland condition and stress as
1585 indicated by vegetation. This involved development of vegetation indices of wetland condition and a
1586 nonnative plant indicator of wetland stress, followed by calculation of wetland extent estimates for
1587 condition or stress classes.
1588
1589
1590
1591
1592
1593
1594
1595
1596
1597
1598
1599
1600
1601
1602
1603
1604
1605
1606
Data Acquisition and Preparation
Acquire, Enter,
and Validate
Field & Lab Data
rerequisite Steps for Indicator
>evelopment
Standardize
Plant Species
Taxonomy
Acquire/Develop
Species Trait
Characteristics
Characterize
Vegetation
Identify Least
and Most
Disturbed Sites
Description of Ecological
Condition and Stress
Calculate and
Screen Candidate
Metrics
Develop Vegetation
Index of Condition
and Estimate
Wetland Extent in
Good, Fair, Poor
Condition
Develop Nonnative
Plant Indicator of
Ecological Stress and
Estimate Wetland
Extent with Low,
Moderate, High, and
Very High Stress
Figure 5-2. Overview of data preparation and analysis steps for evaluating vegetation condition in the 2011 NWCA.
Key elements for each of the three analysis components and the Chapters or Sections in which they are
discussed are listed in the following text.
Data Acquisition and Preparation
• Collect field data (Section 5.3)
• Enter and validate raw data (Section 5.3.2)
o Scon field data into raw data tables
o Merge laboratory identifications of unknown plant species into vegetation raw data tables
o Range and legal value checks
o Logic checks
• Standardize plant species taxonomy (Section 5.5)
• Acquire plant species trait information needed to summarize raw plant species data and develop
candidate vegetation metrics. Trait or autecology information was gathered or developed under
six major categories:
58
2011 NWCA Technical Report
DISCUSSION DRAFT
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1607 o Growth habit, Duration, Plant category (Section 5.6)
1608 o Wetland Indicator Status (Section 5.7)
1609 o Native status (Section 5.8)
1610 o Coefficients of Conservatism (Section 5.9)
1611
1612 Prerequisite Steps for Indicator Development
1613 • Characterize vegetation to help identify appropriate groups of sites for which to report results
1614 for the NWCA (Chapter 4)
1615 • Define disturbance gradients and identify least and most disturbed sites within NWCA Reporting
1616 Groups(Chapter 4)
1617 • Develop candidate metrics of vegetation condition or stress (Section 5.12)
1618 o Develop and calculate candidate metrics from raw vegetation data and species trait
1619 information
1620 o Develop an analysis data set including metric values for all NWCA sampled sites
1621
1622 Description of Ecological Condition and Stress
1623 • Evaluate candidate metrics for utility as indicators of vegetation condition or stress (Chapter 7)
1624 • Develop a vegetation index or indices that describe wetland condition (Chapter 7)
1625 • Calculate extent estimates for wetlands in good, fair, and poor condition (Chapter 9 and
1626 National Wetland Condition Assessment 2011: A Collaborative Survey of the Nation's Wetlands
1627 (\JSEPA In Review))
1628 • Develop plant stressor indicator based on alien and cryptogenic plant species (Chapter 8)
1629 • Calculate extent estimates for wetlands with low, moderate, high, and very high stress (Chapter
1630 9 and National Wetland Condition Assessment 2011: A Collaborative Survey of the Nation's
1631 Wetlands (USEPA In Review))
1632
1633
1634 5.3 Vegetation Data Collection
1635
1636 The Vegetation Protocols for the NWCA are described in detail in the NWCA Field Operations Manual
1637 (USEPA 2011a), and were designed to address the survey objectives, while meeting logistics constraints
1638 of completion in one sampling day per site by a four-person Field Crew. Development of vegetation
1639 sampling methods for the NWCA was informed by numerous existing vegetation sampling methods that
1640 have been applied to wetlands (e.g., Lee et al. 2008; Mack 2007; Magee et al. 1993; Peet et al. 1998;
1641 Rocchio 2007) and by extensive discussions and workshops with the many wetland scientists and
1642 managers who were NWCA partners. An overview of NWCA field sampling and plant specimen
1643 identification protocols follows in the next two subsections.
1644
1645 5.3.1 Field Sampling
1646 Vegetation data for the NWCA were collected during the peak growing season when most plants are in
1647 flower or fruit to optimize species identification and characterization of species abundance. At each
1648 NWCA sample point location (see Chapter 1 for details about the survey design), data were gathered in
1649 five 100-m2 Vegetation (Veg) Plots. The five Veg Plots were placed systematically in a 1/£ hectare
1650 Assessment Area (AA) at each site. The standard AA and Veg Plot layout is illustrated in Figure 5-3 and
1651 the configuration of each plot is shown in Figure 5-4. Alternate configurations for AA shape and layout
1652 of the plots were used when necessary as determined by rules related to specific site conditions (USEPA
1653 2011). A flowchart describing the vegetation data collection protocol is provided in Figure 5-5.
59 2011 NWCA Technical Report DISCUSSION DRAFT
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1654
1655
1656
Figure 5-3. Standard NWCA Assessment Area (AA) (shaded circular area) and standard layout of Vegetation Plots.
1657
1658
1659
100-m2Veg Plot
— 10m
~* 3.16m
f=i 1.00m
1m2
10m2
NE Quadrat Nest
10m2
\
1m2
SW Quadrat Nest
Figure 5-4. Detail of Vegetation Plot illustrating plot boundaries and positions of nested quadrats.
60
2011 NWCA Technical Report
DISCUSSION DRAFT
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Collecting NWCA Vegetation Data
J
Establish five 100-m2 (lOXlOm) Veg Plots at the locations
Select Veg Plot Layout configuration appropriate for _^ designated for the selected Veg Plot Layout.
the Assessment Area (AA).
Collect the following data for each Veg Plot.
1. Plant species presence in nested 1-m2 and 10-m2 quadrats
in the SWand NE corners of the 100-m2 Veg Plot, and across
the 100-m2 Veg Plot.
2. Percent cover estimates for all individual vascular
plant species across the entire 100-m2 Veg Plot and
identification of the primary height class in which
each species occurs.
3. Predominant NWCA Target Wetland Type for
the 100-m2 Veg Plot.
I
\ 5, Percent cover estimates across the 100-m2 Veg Plot
4. Percent cover estimates across the 100-m2 Veg Plot for for non-vascular groups (ground bryophytes, ground
Vascular Vegetation Strata: a) submerged aquatic jf lichens, arboreal bryophytes and lichens, filamentous
vegetation, b) floating aquatic vegetation, c), lianas, vines
and epiphytes, djall other vascular vegetation by height
class.
J
or mat forming algae, and macroalgae).
6. Data describing ground surface
attributes across the 100-m2 Veg Plot.
r
7. Across the 100-m2 Veg Plot:
•Percent cover estimates of live tree species by height class.
"^ 'Counts of live trees by species and diameter class.
•Counts of standing dead trees and snags by diameter class.
Plant Specimen Collection, Processing, and Shipping
Collect specimens for all unknown
plant species.
Randomly select and collect five known plant species for
each AA for Quality Assurance check.
Dry specimens; then ship or deliver to designated herbarium or laboratory, enclosing the appropriate plant
specimen tracking information in the shipping box.
1660
1661
1662
Figure 5-5. Overview of vegetation data collection protocol for the 2011 NWCA (USEPA 2011a).
61
2011 NWCA Technical Report
DISCUSSION DRAFT
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1663 5.3.2 Identification of Unknown Plant Species
1664 Plant species, observed across the five sampled Veg Plots at each site, which could not be identified by
1665 the botanist in the field, were collected for later identification. Specimen collection, labeling, specimen
1666 preservation (pressing and drying), shipping or delivering dried specimens to a designated laboratory or
1667 herbarium, and specimen tracking were completed according to standard protocols described in the
1668 NWCA Field Operations Manual (USEPA 2011a).
1669
1670 Identification of unknown plant taxa was guided by protocols in the NWCA Laboratory Operations
1671 Manual (USEPA 2011b). Unknown plant specimens from each Field Crew were identified at a specific
1672 designated regional laboratory or herbarium (hereafter, lab) by a lab botanist. As quality control for the
1673 identification process, ten percent of the lab identifications for unknowns were independently verified
1674 by another botanist at the lab. Lab botanists maintained a detailed spreadsheet that included for each
1675 unknown specimen collected in the field: the collection number and pseudonym from the field
1676 collection, the location of collection (plot and site number), date of sampling, the name assigned during
1677 lab identification based on a regional flora, and any notes related to the identification. The identification
1678 spreadsheets were forwarded to the NWCA Data Management and Analysis Teams. The Analysis Team
1679 reviewed the lab identification spreadsheets and addressed any recording errors. The validated
1680 identifications were integrated with the NWCA raw data tables for plants, replacing the pseudonyms
1681 recorded by the Field Crews with the corresponding scientific name (see Section 5.4.2).
1682
1683
1684 5.4 Data Preparation - Parameter Names, Legal Values or Ranges, and Data
less Validation
1686
1687 5.4.1 Description of Vegetation Field Data Tables
1688 The data from the completed vegetation field forms were electronically scanned into several predefined
1689 long format, raw data tables in the NWCA database. A separate table was created for each of the three
1690 primary vegetation data forms:
1691
1692 • tbIPLANT table - data originated from Form V-2: NWCA Vascular Species Presence and Cover
1693 • tbIVEGTYPE table - data originated from Form V-3: NWCA Vegetation Types (Front) and NWCA
1694 Ground Surface Attributes (Back)
1695 • tbITREE table - data originated from Form V-4: NWCA Snag and Tree Counts and Tree Cover
1696
1697 Examples of the three field forms can be found in Section 5.11, Appendix A.
1698
1699 Form V-2 data describe vascular plant species identity, presence, cover, and height for each observed
1700 taxon and were collected in each 100-m2Veg Plot. Taxa typically represent species or lower level (e.g.,
1701 subspecies, variety) classification, but occasionally individual taxa were identified only to genus, family
1702 or growth form. For convenience, in this report, vascular plant taxa are generally referred to as species
1703 even though in some cases lower or higher taxonomic levels are reflected. Form V-2 data used in
1704 candidate metric development for the 2011 NWCA included taxon name (SPECIES), presence, and
1705 percent cover (COVER).
1706
1707 Other species level data were collected using Form V-2, but were reserved for further research and not
1708 incorporated in the analysis of condition for the 2011. These other data included predominant height for
1709 each species across each plot, and presence of individual species at different spatial scales within the
62 2011 NWCA Technical Report DISCUSSION DRAFT
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1710 plot (i.e., within the quadrats (S = 1-m2 quadrat, M = 10-m2 quadrat) nested in the corners of plot and
1711 the within the overall plot (L = 100-m2 plot), see Section 5.3.1). The former can reflect vegetation
1712 structure and volume by species or guild groups. The latter address fine scale diversity patterns.
1713
1714 Form V-3 data encompass descriptors of wetland type, structure of vascular vegetation, non-vascular
1715 groups, and ground surface attributes which are each sampled in the five 100-m2 Veg Plots. All these
1716 data were used in developing candidate metrics.
1717
1718 Form V-4 data include counts by diameter class of dead trees/snags, as well as cover by height classes
1719 and by diameter classes for individual tree species in each 100-m2 Veg Plot. Tree data were used in
1720 candidate metric development.
1721
1722 Parameter names and legal values or ranges for the field collected vegetation data are listed in Section
1723 5.12, Appendix B. The quality of all the vegetation field data was carefully examined during data
1724 validation.
1725
1726 5.4.2 Data Validation
1727 Whenever large quantities of data are collected, it is not surprising for errors related to data or sample
1728 collection, recording, sample analysis, or data entry to occasionally occur. Therefore, the NWCA
1729 established a number of cross-checks in the data collection and processing procedures, within the
1730 protocols and field forms, to allow identification and resolution of potential errors. Once the data were
1731 entered, quality assurance (QA) review was critical to identifying and resolving any errors to ensure high
1732 quality data. Verification and update of the scanned vegetation data involved several QA steps
1733 conducted by members of the information management team and the Vegetation Analysis Team. Some
1734 checks required manual evaluation of the paper forms or data; others involved the use of specific R
1735 Code written to identify records with specific kinds of potential errors.
1736
1737 Information Management Team:
1738 • Verified that the data from the Vegetation Forms scanned properly
1739 • Where possible, verified spelling of plant species name with USDA PLANTS database
1740 • Conducted quality assurance checks for valid ranges and legal values for all data
1741
1742 Vegetation Analysis Team:
1743 • Updated names for unknown taxa based on plant specimen identification (see Section 5.3.2)
1744 • Reviewed and resolved all instances of missing, out of range or non-legal values identified by
1745 the IM Team:
1746 o Review of the field forms often indicated a scanning or recording error that was readily
1747 resolved and the data updated
1748 o Where no resolution was apparent the data were flagged and the error described
1749 • Resolved species name spelling errors or use of alternative names as part of the nomenclatural
1750 standardization (see Section 5.5)
1751 • Conducted logic checks and data type specific checks to identify:
1752 o Recording errors
1753 o Instances of plant species recorded multiple times at one site
1754 • Determined the cause of each instance of deviation revealed by logic checks
1755 o Resolved these issues manually or used R code to effect updates
1756 o Where no resolution was apparent the data were flagged and the error described
63 2011 NWCA Technical Report DISCUSSION DRAFT
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1758
1759
1760
1761
1762
1763
1764
1768
1769
1770
1771
1772
1773
1774
1775
1776
1777
1778
The vast majority of concerns identified by these QA 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.
5.5 Nomenclatural Standardization
During 2011 field sampling, approximately 140 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 taxa-
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 2013) was selected 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 5.5.1
was used for all three data types. Section 5.5.2 provides a brief
description of the procedures for taxonomic review and
documentation of name assignments that were used for data from
Form V-2. The documentation process for the tree data (Form V-4)
and the lab identifications were similar, but tailored to the structures
of these data.
Nomenclatural standardization was a complex undertaking, and in this
section we provide a basic overview of the methods and process used
for the 2011 NWCA.
1767
5.5.1 Nomenclature Reconciliation Methods
We developed a method to reconcile names for NWCA observed plant taxa, at each location of their
occurrence, to the PLANTS nomenclatural database. First, we identified the steps required to ensure
accurate name reconciliation (Figure 5-6) and refined the process in collaboration with taxonomists at
the PLANTS database program (hereafter, PLANTS). A series of automated filters, paralleling
components in this figure, were developed using code written for R software (R Core Team 2014) to
compare recorded names for NWCA observations to PLANTS accepted names and identify names and
records that required further evaluation by a botanist. In Figure 5-6, medium blue boxes reflect steps
completed using automated filters, light blue boxes represent steps that required review by a botanist,
purple boxes indicate the type of name resolution applied, and the dark blue central box reflects the
final name resolution.
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2011 NWCA Technical Report
DISCUSSION DRAFT
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1779 Step 1: Identify NWCA name-location pairs directly matching PLANTS accepted names
1780
1781 A large proportion of the plant name-plot pairs recorded in the NWCA could be directly matched to
1782 PLANTS accepted names. These included records where:
1783 1) The original NWCA name was the same as the accepted PLANTS name and there were no
1784 synonyms for the name.
1785 2) The original NWCA name pointed to one or more synonyms that all pointed to the same, single
1786 accepted PLANTS name.
1787
1788 Step 2: Identify NWCA name-location pairs needing botanical review to reconcile to PLANTS accepted
1789 names
1790
1791 Even though most NWCA names could be directly matched to PLANTS nomenclature in Step 1, a large
1792 number required botanical review to select the correct PLANTS accepted name. There were three
1793 primary types of name issues which necessitated further botanical review:
1794
1795 1) Unmatched Names- no PLANTS accepted name or synonym matched a particular NWCA name-
1796 plot pair. Common reasons for unmatched names were misspelling or mis-scanning of the
1797 record, or use of an abbreviation or common name. Rarely, the taxon represented a name or
1798 taxon not included in the PLANTS database.
1799 2) Same Name with Different Authorities (shorthand terminology = Multiple Authorities) - refers
1800 to a NWCA name which pointed to synonyms with exactly the same genus and species epithets,
1801 but which had different botanical authorities for the name.
1802 3) Species Concept Unclear - NWCA binomial name was contained in multiple potential PLANTS
1803 accepted names or multiple synonym names that point to multiple possible PLANTS accepted
1804 names.
1805
1806 Step 3: Review name-plot pairs identified in Step 2 and determine correct name assignment
1807
1808 The set of names and records identified as requiring further evaluation were reviewed by the NWCA
1809 lead botanist/ecologist, using a general stepwise procedure for nomenclatural determination:
1810 1) Identify and correct spelling errors or abbreviated names.
1811 2) Identify all synonyms and accepted PLANTS name(s) that could apply to each ambiguous taxa-
1812 plot pair name.
1813 3) Compare geographic distribution of potential synonyms and accepted PLANTS names with
1814 location of the observed NWCA taxon.
1815 4) Review field records and notes from the NWCA Field Crew regarding the observed NWCA taxon.
1816 5) Review the species concept for the taxon based on flora(s) used by field botanist, as well as
1817 other pertinent taxonomic resources and databases.
1818
1819 Items 1 - 4 in the list above allowed determination of the PLANTS nomenclature accepted name for the
1820 majority of taxa-plot pairs that needed botanical review. For taxa where the appropriate PLANTS
1821 accepted name could not be definitively resolved using these procedures, a taxonomist at the PLANTS
1822 database was consulted for final name determination. This consultation involved discussions between
1823 the NWCA lead botanist/ecologist and the PLANTS taxonomist to review floras, historical records, and
1824 floristic/taxonomic databases pertinent to each taxon-location pair considered. In a few cases, the
1825 PLANTS taxonomist consulted with other botanists with specific expertise regarding a particular
1826 taxonomic group (e.g., species, genus, family) to resolve a naming issue.
65 2011 NWCA Technical Report DISCUSSION DRAFT
-------
START HERE:
NWCA Names - Original names
assigned to plants in the field during
data collection
Pseudonyms associated with
unknown plant specimens
'Hold out names for plants with collections until
name assignment from plant id labs
No Match to PLANTS
All Othec Names
'Compare NWCA names to names in
USDA PLANTS checklist of accepted
names and synonyms
•Is NWCA name a PLANTS synonym or a PLANTS
accepted name that has synonyms?
kfor & cor
ellings or
canned names
NWCA name matches
PLANTS accepted
nameandno
synonyms exist
nts to one or more synonyms
the same PLANTS accepted
Life form name or
pseudonym for
unidentified
* Evaluate names to
determine information
available (binomial, genus,
family, pseudonym). Examine
flags & comments and floras
used by field crews to aid in
name determination
Sato?*-. _ -
Final NWCA Species
Names
•Find any NWCA species binomial
occurring in the PLANTS checklist
multiple times (e.g., same binomial
with different taxonomic authorities)
Same name occurs more than
once with diffajtnt authors
Mame
vifTTone set
of authors
•Flag NWCA name if it is a
binomial contained in
multiple accepted names or
in multiple synonyms pointing
to multiple accepted names.
Flagiec|
\
+Species name is complete
and valid, but not in USDA
PLANTS (e.g., recently
described taxon, newly
encountered introduced
species). Flag with
nomendatural source.
Species
binomial, genus
only, or family
only
.-cies Concept Issue: Identify
rrect version of name using
ration of site, flora(s) used by
TW, or other species concept
ormation
'Species Concept Issues: Review flagged f
-ames and synonyms to determine correct species
incepts to match with accepted PLANTS names.
se geographic and distribution information and
•eflora used for identification to aid
'Reconcilesynonyms to
Accepted USDA PLANTS
I
I
1827
1828
1829
1830
Figure 5-6. 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.
66
2011 NWCA Technical Report
DISCUSSION DRAFT
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1831 5.5.2 Nomenclature Standardization Results and Documentation
1832 A standard approach for organizing, resolving, and documenting the name reconciliations, for plant
1833 name-plot pairs needing review, was developed and applied. Specific NWCA species records (including
1834 name, cover value, and other data), along with information from the PLANTS database, were exported
1835 into an Excel Workbook. This gathered key information in one location to facilitate review of the
1836 taxonomy and to highlight when other information was needed. Important NWCA data elements
1837 included in the Excel Workbook were NWCA SITEJD and DID, state, county, a list of the floras used by
1838 the Field Crew collecting the data for a particular site, and a link to the scanned field form image. Access
1839 to the scanned field form allowed easy viewing of any notes Field Crews may have made in relation to a
1840 particular species, as well as a view of other taxa present at a site. Critical information from the PLANTS
1841 database included synonyms and accepted names that could potentially correspond to the specific
1842 taxon-plot pairs.
1843
1844 The Excel Workbook format included separate spreadsheet tabs for reviewing unresolved names in
1845 three categories: Unmatched Names, Multiple Authorities, and Species Concept Issues (see Step 2 in
1846 Section 5.5.1, for definitions). For each taxon-plot pair to be evaluated (rows in spreadsheets), the
1847 associated columns (NWCA data and taxonomic information from the PLANTS database) informed name
1848 resolution. An instruction page accompanying the Excel Workbook described the associated data
1849 included in each of the spreadsheets and the ways this information might aid in name determination.
1850 During the review process, the rationale for the final assignment of the correct PLANTS accepted name
1851 for each name-plot pair was documented by specifying a reason code and, where needed, providing
1852 narrative notes and citations of floras or databases.
1853
1854 Once the NWCA name-plot pairs were reconciled to the PLANTS nomenclature, the accepted PLANTS
1855 names for each NWCA record was applied to the active NWCA data. The original names as recorded by
1856 the Field Crew or lab identifications were retained as inactive data. Following taxonomic
1857 standardization, the master list of plants observed in the 2011 NWCA across the conterminous United
1858 States included:
1859 • 3,640 unique taxa which were distributed as:
1860 o 12,970 unique taxa-state pairs
1861 o 32,363 unique taxa-site pairs
1862 o 171,475 unique taxa-plot pairs
1863
1864 The majority of the NWCA taxa were identified to the species or subspecies/varietal level, with a small
1865 number identified only to the genus, family, or growth form level.
1866
1867
1868 5.6 Species Traits - Life History: Growth Habit, Duration, and Plant Category
1869
1870 Life history guilds can provide important ecological information about wetlands and have proven to be
1871 useful components in metrics describing vegetation condition in other studies. Traits reflecting species
1872 life history based on growth habit, duration, and plant category for all vascular taxa observed in the
1873 NWCA were downloaded from the PLANTS database (USDA-NRCS 2012). This trait information was used
1874 in combination with data describing presence, frequency, and cover for individual species to develop
1875 candidate metrics that reflected the distribution of life history traits across each sampled site. These
1876 candidate metrics serve as descriptors of richness and abundance for all species, native species only, or
1877 for nonnative species only, within specific life history groups (see Appendix D (Section 6.8)).
67 2011 NWCA Technical Report DISCUSSION DRAFT
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1878
1879
1880
1881
1882
1883
1884
1885
1886
1887
1888
1889
1890
1891
1892
1893
1894
5.6.1 Growth Habit
The primary growth habit types describing plant species
observed in the 2011 NWCA include forb/herb, graminoids,
subshrub, shrub, tree, and vine. However in the PLANTS
database, individual species were frequently identified as
spanning more than one of these growth habit types. As a
result, many additional combined categories are implicit
across the growth habit descriptors for specific taxa. This
creates a diversity of growth habit categories, many of which
represent only a few taxa. To facilitate data analysis, we
merged some multiple type groups from the PLANTS
database into larger categories for the NWCA data analysis
(Table 5-1).
Table 5-1. Growth habit categories used in NWCA analysis with a crosswalk to PLANTS database growth habit
designations observed across the 2011 NWCA species list. Capitalized Growth Habit Category Names are used in
descriptions of Growth Habit metrics in Section 6.8, Appendix D.
NWCA Growth Habit Category
Groupings for Metric Calculation
PLANTS Database Growth Habit 'Designations' for NWCA Observed
Species
1895
1896
1897
1898
1899
1900
1901
1902
1903
1904
1905
GRAMINOID
FORB
SUBSHRUB-FORB
SUBSHRUB-SHRUB
SHRUB
TREE-SHRUB
TREE
VINE
VINE-SHRUB
'Graminoid'; 'Subshrub, shrub, graminoid'
'Forb/herb'; 'Forb/herb, shrub'; 'Forb/herb, shrub, subshrub'; 'Forb/herb,
subshrub'
'Subshrub, forb/herb'; 'Subshrub, shrub, forb/herb'
'Subshrub'; 'Subshrub, shrub'; 'Shrub, subshrub'
'Shrub'; 'Shrub, tree'; Tree, subshrub, shrub'
Tree, shrub'; Tree, shrub, vine'
Tree'
'Vine'; 'Vine, forb/herb'; 'Subshrub, forb/herb, vine'; 'Forb/herb, vine'
'Vine, shrub'; 'Vine, subshrub'; 'Subshrub, vine'; 'Shrub, vine'; 'Shrub,
forb/herb, subshrub, vine'; 'Shrub, subshrub, vine'
5.6.2 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 for the NWCA data analysis (Table 5-2).
Table 5-2. Duration categories used in the NWCA analyses and a crosswalk to PLANTS database duration
designations observed across the 2011 NWCA species list. Capitalized Duration Category Codes (listed in
parentheses) are used in descriptions of Duration Metrics in Section 6.8, Appendix D.
NWCA Duration Category Groupings for
Metric Calculation
PLANTS Database Duration 'Designations' for NWCA Observed
Species
Annual (ANNUAL)
Annual-Biennial (ANN_BIEN)
Annual-Perennial (ANN_PEREN)
Perennial (PERENNIAL)
'Annual'
'Annual, biennial'; 'Biennial'
'Annual, biennial, perennial'; 'Annual, perennial'; 'Perennial,
annual'; 'Biennial, perennial'
'Perennial'
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5.6.3 Plant Categories
1907
1908
1909
1910
1912
1913
1914
Several major plant categories were considered in
summarizing raw data to develop guild-based
candidate metrics. Categories assigned for
individual NWCA taxa from designations provided
in the PLANTS database were:
• Dicots
• Monocots
• Gymnosperms
• Ferns
• Horsetails
• Lycopods
5.7 Species Traits - Wetland Indicator Status
The hydrophytic status of the plant species occurring in
wetlands can be useful indicators of ecological condition.
However, the specific values reflecting good condition will vary
with the normal hydrology of each wetland type. Wetland
Indicator Status for each observed NWCA species was obtained
from the US Army Corps of Engineers (USAGE) 2013 update of
the National Wetland Plant List (NWPL) (Lichvar 2013), via the
PLANTS database (USDA-NRCS 2013). Wetland Indicator Status
was downloaded from the PLANTS database because it
reconciles species taxonomy for the NWPL to PLANTS
nomenclature, which is the NWCA standard.
Wetland Indicator Status ratings are defined in Table 5-3. WIS
status for each species is regionally specific based on USAGE
Wetland Regions (USAGE 2014). Upland (UPL) status includes all
NWCA observed taxa not listed in the NWPL.
1911
Table 5-3. Descriptions of Wetland Indicator Status (WIS) ratings (from Lichvar 2013). WIS Category Codes (listed in
parentheses) are used in descriptions of Hydrophytic Status Metrics in Section 6.8, Appendix D. Numeric Ecological
Value for each indicator status used in calculating some metrics.
Wetland Indicator Status Designation
Qualitative Description
Numeric
Ecological
Value
Obligate (OBL)
Facultative Wetland
(FACW)
Facultative (FAC)
Facultative Upland (FACU)
Upland (UPL)
Hydrophyte Almost always occur in wetland
Hydrophyte Usually occur in wetlands, but may occur in
non-wetlands
Hydrophyte Occur in wetlands and non-wetlands
Nonhydrophyte Usually occur in non-wetlands, but may
occur in wetlands
Nonhydrophyte Almost never occur in wetlands
1
2
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1915
1916
1917
1918
1919
1920
1921
1922
1923
1924
1925
1926
1927
1928
1929
1930
1931
1932
1933
1934
1935
1936
1937
1938
1939
1940
1941
1942
1943
1944
1945
1946
1947
1948
1949
1950
1951
1952
Candidate metrics that were calculated to represent particular hydro-logic indicator status or hydrologic
indices based on species composition are described in Appendix D (Section 6.8). These metrics
represent various descriptors of richness and abundance for all species or for native species only for
specific hydrophytic groups.
5.8 Species Traits - Native status
The proportion or abundance of native
vs. nonnative flora at a given location
can help inform assessment of
ecological condition and stress (see
Section 5.1, Chapter 6, and Chapter 8,
Section 8.5). 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. For the NWCA,
state-level native status was
determined for the approximately
13,000 taxa-state pairs observed
across 1138 sampled wetlands in the
conterminous United States. This was
a challenging task across the scale of
the NWCA for several reasons. 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 and the understanding of original species
distributions can be ambiguous. 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 have alien and native
components (e.g., genotypes, lower taxonomic levels).
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 5-4).
Table 5-4. Definition of state-level native status designations for NWCA taxa-state pairs.
Native Status
Codes
Native Status Designations
Native to a specific state
Introduced from outside the United States
Adventive: Native to some areas or states of the United States, but introduced the location of
Introduced + Adventive
Cryptogenic: Both native and introduced genotypes, varieties, or subspecies
Undetermined: Growth forms, families, genera with native and alien species
ALIEN
CRYP
UNO
1953
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2011 NWCA Technical Report
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1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
Using these definitions to determine state-level native status for each of the NWCA taxa-state pairs, we
reviewed existing native status designations of all NWCA taxa-state pairs from a variety of taxonomic
and ecological sources:
1) The PLANTS Database (USDA-NRCS 2013): Native status in the Lower 48 (conterminous United
States Floristic Region)
2) Other Floristic Databases (state and national levels)
3) State and Regional Floras and Checklists
4) Consultation with the PLANTS nomenclatural team
Items 1 through 3 above included approximately 85 floristic sources that were used in the primary
review. A bibliography is retained with the NWCA native status review database. Additional taxonomic
sources were consulted as needed.
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 5.5)
and where needed, on all of its synonyms. Many native status determinations were clear-cut, but others
were more complex and required more 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 numerous resources describing species distributions and first collections
to help inform difficult native status designations.
Native Status determinations were made for all species-state pairs, and wherever possible for genus-
state pairs. Family- and growth form-state pairs were designated as 'Undetermined'. The distribution of
native status groups based on site occurrences of individual taxa across the 1138 sites sampled in the
2011 NWCA is illustrated in Figure 5-7. Native status was used in conjunction with validated field
collected vegetation data and with other species trait information to calculate numerous candidate
metrics, which are described in Appendix D (Section 6.8).
Taxa Site Occurrences
WNAT HINTR UADV UCRYP UUND
Figure 5-7. Percentage site occurrences of individual plant taxa observed in the 2011 NWCA by native status
categories (see Table 5-4 for definitions) across 1138 probability and not-probability sampled sites of native status.
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2011 NWCA Technical Report
DISCUSSION DRAFT
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1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
5.9 Species Traits - Coefficients of Conservatism
Coefficients of Conservatism (C-values, also called
CCs) describe the tendency of individual plant species
to occur in disturbed versus near pristine conditions.
They are state or regionally specific and scaled from 0
to 10.
• A C-value of 0 or 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, alien taxa were assigned a C-
value of 0.
C-values are the primary building blocks of 1) floristic
quality indices (see Section 5.1), and 2) metrics describing sensitivity or tolerance of plant species to
disturbance. Sensitivity and tolerance 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 high C-value taxa, whereas, tolerance may be based on presence or
abundance of low C-value taxa.
We investigated several floristic quality indices as descriptors of condition for the NWCA, including
versions of the Floristic Quality Assessment Index (FQAI) and of Mean Coefficient of Conservatism (Mean
C). Metrics describing sensitivity and tolerance to disturbance were screened as potential components
of the Vegetation MMI. See Appendix D (Section 6.8) for lists of metrics based on C-values and for
details of their calculation and evaluation.
Unfortunately, C-values for individual plant species were not available for all states or regions, nor were
existing C-value lists compiled together in a readily accessible format. Thus, to use this powerful trait in
the NWCA, it first was necessary to obtain or develop state-level C-values for all plant taxa observed
during the 2011 NWCA. This required the:
• Creation of a database of existing C-value lists from the conterminous US that included state-
specific C-values for individual plant species,
• Assignment of existing C-values to each taxon-state pair observed in the NWCA, and
• Identification of NWCA taxa-state pairs lacking existing C-values and development of C-values
for these taxa-state pairs.
5.9.1 Creating a Database of C-Values for the Conterminous United States
The first step was to develop a National Floristic Quality Database (NFQD, unpublished) which collects
together the C-value lists, existing in 2014, that represented individual states or regions within the
conterminous United States. Creating the NFQD involved a large collaborative effort to locate existing C-
value lists, and then compile the lists into a single database with uniform formats. The NWCA gratefully
acknowledges the existing body of work on C-values and the numerous partners who contributed new
or updated C-value lists (Section 5.13, Appendix C).
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2011 NWCA Technical Report
DISCUSSION DRAFT
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2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
2051
2052
2053
2054
2055
2056
5.9.1.1 Gathering Existing Lists of Coefficients of Conservatism
First, a literature search was conducted to gather all state and regional C-value lists published through
2013. In addition, state agencies and other researchers involved in floristic assessment were contacted
to request access to unpublished lists of C-values. The map in Figure 5-8 illustrates the states for which
C-value lists were obtained, and the states for which no C-values existed at the time the NFQD was
compiled. In states where C-value lists are indicated on the map, the existing lists may represent all or
part of a state's area and the entire flora or the wetland flora only. Most C-value lists were developed
for individual states, but some states are represented by regional lists. Section 5.13, Appendix C
provides citations for the C-value lists included in the NFQD. The complete database contains records for
over 115,000 taxa-state pairs from the state and regional C-value lists (a taxa-state pair refers to a
specific plant taxon in a specific state).
Coefficients of Conservatism
None
Complete - Includes entire
flora or wetland flora only
Partial C overage
Figure 5-8. States with complete or partial published or unpublished lists of Coefficients of Conservatism (C-values)
that were included in the National Floristic Quality Database (NFQD) and used to inform C-Value assignment for
NWCA taxa-state pairs.
5.9.1.2 Developing and Compiling the National Floristic Quality Database (NFQD)
All available C-value lists were incorporated into the National Floristic Quality Database (NFQD), which
was built using a relational database management system called 4th Dimension (4D) version 12.4. All
records in the database are arranged in trait tables comprised of fields that are linked by taxonomy and
geographic location. Each record includes the taxon name, C-value, the location where the list was
originally developed (state or region), along with a variety of other ancillary information.
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2011 NWCA Technical Report
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2057
2058
2059
2060
2061
2062
2063
2064
2065
2066
2067
2068
2069
2070
2071
2072
2073
2074
2075
2076
2077
2078
2079
2080
2081
2082
2083
2084
2085
2086
2087
2088
2089
2090
2091
2092
2093
2094
2095
2096
2097
2098
2099
2100
2101
2102
Several important considerations apply to the development and use of the NFQD:
1) Diverse approaches to list organization, data formats, and field names were used across the
original C-value lists, so it was necessary to standardize data formats and field names to allow
the separate lists to be imported into one database.
2) During the compilation of the NFQD, C-value updates or additions were often required as new
data became available from states actively updating existing C-values or developing C-values for
the first time.
3) Items 1 and 2 and the complexity of the merger of data from numerous C-value lists required
detailed quality assurance steps and validation cross-checks to ensure the C-values were
accurately imported.
4) The component C-value lists within the NFQD used diverse taxonomic nomenclatures. The C-
value lists typically referenced scientific names for plant taxa using local or regional taxonomic
sources appropriate to each state; but these were not necessarily consistent between states or
with the USDA PLANTS nomenclatural database (USDA-NRCS 2013). Thus, whenever using the
NFQD to consider geographic scales that span multiple states or regions, it is imperative to
reconcile the nomenclature to one taxonomic standard. For example, for the NWCA it was
necessary to examine the NFQD for all possible synonyms of the NWCA taxa-state pairs and
reconcile the taxonomy for pertinent C-values to PLANTS nomenclature, the NWCA standard,
before the C-values could be assigned to the NWCA taxa-state pairs (see Section 5.9.2.1).
5) States did not treat alien plant species uniformly. Some included nonnative species in their C-
value lists 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
alien 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. Consequently, to use C-
values across multiple states or regions the manner in which C-values are assigned to alien taxa-
state pairs had to be standardized.
5.9.2 Assigning C-values to Plant Taxa Observed in the NWCA
There were approximately 13,000 taxa-state pairs recorded by the NWCA Field Crews, including 3640
taxa observed across the 1138 sites sampled in the conterminous United States. C-value records for
each of these taxa-state pairs were exported from the full National Floristic Quality Database into a
separate table for use in developing C-value assignments for the NWCA. C-value assignments for the
NWCA taxa-state pairs involved several steps:
• Identification and taxonomic standardization of taxa-state pairs from the NFQD that
corresponded to NWCA taxa-state pairs,
• Standardization of C-value formats to whole numbers,
• Standardization of C-value scoring for alien plant species,
• Assignment of existing C-values to NWCA taxa-state pairs, and
• Development of C-values for NWCA taxa-state pairs that lacked existing values.
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2103 5.9.2.1 Taxonomic Reconciliation
2104 C-value records in the NFQD that were matches to the PLANTS accepted name (USDA-NRCS 2013), or to
2105 all possible synonyms of the accepted name, for each NWCA taxa-pair were exported to a table for
2106 making NWCA C-value assignments. The taxonomy of this subset of C-value records was reconciled with
2107 the PLANTS database accepted names. This standardization process was completed using nomenclatural
2108 reconciliation procedures similar to those described in Section 5.5. Many taxa-state pairs in the NFQD
2109 could be directly matched to the NWCA taxa-state pairs. However, there were approximately 390 taxa-
2110 state pairs with synonymy issues that required botanical review to determine how to apply the C-values
2111 for these synonyms to the correct accepted PLANTS names of the relevant NWCA taxa-state pairs. For
2112 example, Aster macrophyllus is a synonym for the PLANTS accepted name Eurybia macrophylla.
2113 Consequently, the C-value recorded for Aster macrophyllus in the Michigan C-value list was applied to
2114 the NWCA taxa-state pair represented by Eurybia macrophylla and occurring in Michigan.
2115
2116 5.9.2.2 Standardization of C-values for NWCA Taxa-State Pairs
2117 The methods and formats used for presentation of C-values between states and regions varied. To
2118 standardize the meaning of C-values, a 'Final C-value' field was created for the NWCA taxa-state pairs.
2119 The original C-value for each these records was also retained in the NFQD database. The Final C-values
2120 for the NWCA reflected the following modifications:
2121
2122 • C-values expressed as decimals were rounded to the nearest integer; for example, a C-value of
2123 5.5 or higher was rounded to 6.
2124 • Native status for the NWCA taxa-state pairs was determined using procedures discussed in
2125 Section 5.8. For purposes of C-value assignments, all alien taxa (introduced + adventive species)
2126 were assigned a value of 0.
2127 • All taxa without C-value assignments were designated 'DA' (unassigned) in the 'Final C-value'
2128 field to identify NWCA taxa-state pairs that still required development of C-values.
2129
2130 5.9.2.3 Assigning C-values for NWCA Taxa-State Pairs
2131 C-values were assigned to approximately 10,300 NWCA taxa-state pairs, including both species-state
2132 and genus-state pairs, based on the existing state and regional C-values included in the NFQD. This left
2133 approximately 2,700 taxa-state pairs for which C-values were needed. This remaining set of taxa-state
2134 pairs was represented by two groups: taxa occurring in states for which C-values have not yet been
2135 developed and taxa representing higher level taxonomic categories (genera, families, and growth form)
2136 without C-values. These 2,700 taxa-state pairs included approximately:
2137 • 250 identified only to family or growth form and were designated as undetermined for C-value
2138 • 1,050 identified only to genus
2139 • 1,400 identified to species
2140
2141 For the NWCA taxa-state pairs where C-values were unavailable, it was necessary to develop methods
2142 for assigning them. There were several important criteria for this effort; it had to:
2143 • be rigorous and repeatable,
2144 • account for ecoregional differences in C-values for species, and
2145 • be possible to complete relatively rapidly.
2146
2147 This C-value development process had two major components (described in the following subsections),
2148 one for the species-state pairs and one for the genus-state pairs lacking C-values.
2149
75 2011 NWCA Technical Report DISCUSSION DRAFT
-------
2150
2151
2152
2153
2154
2155
2156
2157
2158
2159
2160
2161
2162
2163
2164
2165
2166
2167
2168
2169
2170
2171
2172
5.9.2.3.1 Species-State Pair Assignments
C-value Assignment: The approximately 1400 NWCA species-state pairs lacking C-values were evaluated
to determine whether an appropriate C-value could be assigned to each of them using an ecoregional
extrapolation approach. C-values for the same species from neighboring states with similar ecological
conditions were evaluated for application to each species-state pair without a C-value. This was done by
overlaying the locations of the NWCA field sites with a map of the nine NARS Aggregated Ecoregions
(see Figure 5-9) to determine ecologically similar states based on presence in the same ecoregion and
geographic proximity to states where C-values were missing. Ecologically similar states were queried, in
order of distance from the target state, for a matching species record. If a C-value existed in ecologically
similar neighboring states for a given species, the C-value from the nearest state was assigned to the
species-state pair for which no existing C-value was available. If no ecologically supported C-value could
be assigned, then a taxa-state pair received a value of 'Undetermined'.
Example: Notice that in Texas most 2011 NWCA sites occurred along the Gulf Coast, so for a particular
Texas species lacking a C-value, Louisiana might have served as an ecologically similar neighboring state
(i.e., in the same NARS Aggregated Ecoregion) with an existing C-value for that species. If the species in
question had no counterpart in Louisiana, but a C-value was available from the Florida Coast Plain, that
C-value would have been applied to the Texas species.
2011 NWCA -1138 Sites Sampled
Nine Aggregated Ecoregions
Coastal Plain Southern Appalachians
Northern Appalachians Southern Plains
Northern Plains Temperate Plains
Upper Midwest
Western Mountains
Xerte
Figure 5-9. 2011 NWCA sampled sites plotted on Nine Aggregated Ecoregions used by other NARS. Inset shows
status of available C-values.
76
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
2173 Quality Assessment ofC-value Assignment Procedures: To provide a quantifiable assessment of the C-
2174 value assignments made by extrapolation from adjacent states, another botanist independently
2175 assigned C-values to a subsample of 302 taxa-state pairs using a similar procedure, but considered two
2176 or three neighboring states in the C-value assignment. Differences between the C-values obtained by
2177 the two groups of botanists were calculated for each species-state pair. The absolute value of the mean
2178 difference in C-value assignments for all species was 0.6, thus, on average, the assignments were within
2179 0.6 of one another. The low degree of variability observed between the two independent assignments of
2180 C-value scores indicates strong repeatability of the C-value assignment procedure.
2181
2182 C-value Assignments in California: California presented a special case in C-value assignment because
2183 the proximity of neighboring states with existing C-value lists was limited. C-values for California taxa-
2184 state pairs were drawn primarily from Washington, Oregon, and Colorado. This approach still left 54
2185 species with no C-value from an ecoregionally-similar state. A large proportion of these 54 species were
2186 uncommon or endemic to California, though a few were introduced or weedy. The NWCA vegetation
2187 team assigned preliminary C-values for these 54 species based on review of floristic distribution maps,
2188 habitat descriptions, and ecological information from a variety of databases and floras.
2189
2190 5.9.2.3.2 Genus-State Pair Assignments
2191 During the field surveys for the NWCA, crews were occasionally able to identify a plant specimen only to
2192 the genus level. Many of the state lists contained C-values for genera and these were applied to the
2193 pertinent NWCA genus-state pairs from the NFQD (see Section 5.9.2.3). In other situations C-values
2194 were not available for genera. This issue was addressed in several ways. First, for states with C-value
2195 lists, a genus-state pair C-value was assigned as the median of C-values for all species of that genus. For
2196 each NWCA genus-state pair from states without a C-value list, C-values were assigned using a variation
2197 of the procedures for assigning C-values to species-state pairs as described above (Section 5.9.2.3.1),
2198 assuming there was a genus C-value from an appropriate neighboring state. For the remaining genus-
2199 state pairs, a median C-value was calculated using the existing species records for that genus in the
2200 nearest-neighbor state. The median score became the final C-value assignment for the genus-state pair.
2201 The C-value was considered 'Undetermined' when an assignment could not be made with ecological
2202 confidence.
2203
2204 and Use
2205 The final NWCA C-value assignments incorporated into the NFQD for taxa-state pairs observed in the
2206 2011 NWCA, and which were used in the NWCA vegetation analysis included approximately:
2207 • 11,600 species state pairs with C-values
2208 • 1000 genus-state pairs with C-values
2209 • 370 taxa-state pairs lacking C-values
2210 o 260 of these representing family-or growth form-state pairs
2211 o 110 representing species- or genus-state pairs for which no determination could be
2212 made
2213
2214 The NWCA C-values were used in calculation of floristic quality indices (e.g., variations of FQAI and Mean
2215 C) and metrics describing sensitivity and tolerance to disturbance. See Section 6.8, Appendix D for a list
2216 of specific metrics. For taxa-state pairs lacking C-values, the NWCA adopted the standard practice of
2217 excluding these taxa from calculations of metrics of floristic quality and of disturbance sensitivity or
2218 tolerance. The 370 taxa-state pairs lacking C-values represented a very small proportion of NWCA taxa
2219 observed across all sites (i.e., ~ 2%), and where these taxa occurred, they typically had low abundance
2220 (e.g., most < 1% absolute cover), so their exclusion was expected to have little impact on metric values.
77 2011 NWCA Technical Report DISCUSSION DRAFT
-------
2221
2222 5,10
2223
2224 Bourdaghs M, Johnston CA, Regal RR (2006) Properties and performance of the Floristic Quality Index in
2225 Great Lakes Coastal Wetlands. Wetlands 26: 718-735
2226
2227 Bourdaghs M (2014) Rapid Floristic Quality Assessment Manual, wqbwm2-02b. Minnesota Pollution
2228 Control Agency (MPCA), Saint Paul, Minnesota
2229
2230 Bried JT, Jog SK, Matthews JW (2013) Floristic quality assessment signals human disturbance over
2231 natural variability in a wetland system. Ecological Indicators 34: 260-267
2232
2233 Cohen MJ, Carstenn S, Lane CR (2004) Floristic quality indicies for biotic assessment of depressional
2234 marsh condition in Florida. Ecological Applications 14: 784-794
2235
2236 Euliss NH, Mushet DM (2011) A multi-year comparison of IPCI scores for Prairie Pothole Wetlands:
2237 Implications of temporal and spatial variation. Wetlands 31: 713-723
2238
2239 Dale, V. H. and S. C. Beyeler (2001). "Challenges in the development and use of ecological indicators."
2240 Ecological Indicators 1: 3-10.
2241
2242 DeKeyser ES, Kirby DR, Ell MJ (2003) An index of plant community integrity: Development of the
2243 methodology for assessing prairie wetland plant communities. Ecological Indicators 3: 119-133
2244
2245 Deimeke E, Cohen MJ, Reiss KC (2013) Temporal stability of vegetation indicators of wetland condition.
2246 Ecological Indicators 34: 69-75
2247
2248 Galatowitsch S, Whited D, Tester J (1999) Development of community metrics to evaluate recovery of
2249 Minnesota wetlands. Journal of Aquatic Ecosystem Stress and Recovery (Formerly Journal of Aquatic
2250 Ecosystem Health) 6: 217-234
2251
2252 Genet J (2012) Status and Trends of Wetlands in Minnesota: Depressional Wetland Quality Baseline.
2253 Minnesota Pollution Control Agency, Saint Paul, Minnesota
2254
2255 Gara B (2013) The Vegetation Index of Biotic Integrity "Floristic Quality" (VIBI-FQ). Ohio EPA Technical
2256 Report WET/2013-2. Ohio Environmental Protection Agency, Wetland Ecology Group, Division of Surface
2257 Water, Columbus, Ohio
2258
2259 Karr, J. R. (1991). "Biological integrity: A long-neglected aspect of water resource management."
2260 Ecological Applications 1: 66-84.
2261
2262 Lee MT, Peet RK, Roberts SD, Wentworth TR (2008) CVS-EEP protocol for recording vegetation: All levels
2263 of plot sampling. Version 2008. The Carolina Vegetation Survey (CVS, http://cvs.bio.unc.edu) and the
2264 North Carolina Ecosystem Enhancement Program (MMl//wwwjTceej3JTet)
2265
2266 Lichvar RW (2013) The National Wetland Plant List: 2013 wetland ratings. Phytoneuron 2013-49: 1-241
2267
78 2011NWCA Technical Report DISCUSSION DRAFT
-------
2268 Lopez RD, Fennessy MS (2002) Testing the floristic quality assessment index as an indicator of wetland
2269 condition. Ecological Applications 12: 487-497
2270
2271 Mack JJ (2007) Integrated Wetland Assessment Program. Part 9: Field Manual for the Vegetation Index
2272 of Biotic Integrity for Wetlands, v. 1.4. Ohio EPA Technical Report WET/2004-9. Ohio Environmental
2273 Protection Agency, Wetland Ecology Group, Division of Surface Water, Columbus, Ohio
2274
2275 Mack JJ, Kentula ME (2010) Metric Similarity in Vegetation-based Wetland Assessment Methods.
2276 EPA/600/R-10/140. US Environmental Protection Agency, Office of Research and Development,
2277 Washington, DC
2278
2279 Magee TK, Gwin SE, Gibson RG, Holland CC, Honea JE, Shaffer PW, Sifneos JC, Kentula ME (1993)
2280 Research Plan and Methods Manual for the Oregon Wetlands Study. EPA/600/R-93/072. Environmental
2281 Protection Agency, Environmental Research Laboratory, Corvallis, Oregon
2282
2283 Magee TK, Ringold PL, Bollman MA (2008) Alien species importance in native vegetation along wadeable
2284 streams, John Day River basin, Oregon, USA. Plant Ecology 195: 287-307
2285
2286 Magee TK, Ringold PL, Bollman MA, Ernst TL (2010) Index of Alien Impact (IAI):A method for evaluating
2287 alien plant species in native ecosystems. Environmental Management 45: 759-778
2288
2289 Mclntyre S, Lavorel S (1994) Predicting richness of native rare, and exotic plants in response to habitat
2290 and disturbance variables across variegated landscape. Conservation Biology 8(2): 521-531
2291
2292 Mclntyre S, Lavorel S, Landsberg J, Forbes TDA (1999) Disturbance response in vegetation - towards a
2293 global perspective on functional traits. Journal of Vegetation Science 10: 621-630
2294
2295 Milburn SA, Bourdaghs M, Husveth JJ (2007) Floristic Quality Assessment for Minnesota Wetlands.
2296 Minnesota Pollution Control Agency, St. Paul, Minnesota
2297
2298 Miller SJ, Wardrop DH (2006) Adapting the floristic quality assessment index to indicate anthropogenic
2299 disturbance in central Pennsylvania wetlands. Ecological Indicators 6: 313-326
2300
2301 Miller SJ, Wardrop DH, Mahaney WM, Brooks RP (2006) A plant-based index of biological integrity (IBI)
2302 for headwater wetlands in central Pennsylvania. Ecological Indicators 6: 290-312
2303
2304 Mitsch WJ, Gosselink JG (2007) Wetlands. John Wiley & Sons, Hoboken, NJ
2305
2306 Peet RK, Wentworth TR, White PS (1998) A flexible, multipurpose method for recording vegetation
2307 composition and structure. Castena 63: 262-274
2308
2309 Quetier F, Thebault A, Lavorel S (2007) Plant traits in a state and transition framework as markers of
2310 ecosystem response to land-use change. Ecological Monographs 77: 33-52
2311
2312 R Core Team (2014) R: A language and environment for statistical computing. R Foundation for
2313 Statistical Computing, Vienna, Austria. (M|jx//WQ^^
2314
79 2011NWCA Technical Report DISCUSSION DRAFT
-------
2315 Reiss KC (2006) Florida Wetland Condition Index for depressional forested wetlands. Ecological
2316 Indicators 6: 337-352
2317
2318 Ringold PL, Magee TK, Peck DV (2008) Twelve invasive plant taxa in in US western riparian ecosystems.
2319 Journal of North American Benthological Society 27: 949-966
2320
2321 Rocchio J (2007) Assessing Ecological Condition of Headwater Wetlands in the Southern Rocky
2322 Mountains Using a Vegetation Index of Biotic Integrity (Version 1.0). Colorado State University, Colorado
2323 Natural Heritage Program, Fort Collins, Colorado
2324
2325 Rooney R, Bayley S, Rooney RC, Bayley SE (2012) Development and testing of an index of biotic integrity
2326 based on submersed and floating vegetation and its application to assess reclamation wetlands in
2327 Alberta's oil sands area, Canada. Environmental Monitoring and Assessment 184: 749- 761
2328
2329 Swink F, Wilhelm G ( 1979) Plants of the Chicago region: A Checklist of the Vascular Flora of the Chicago
2330 Region,with Keys, Notes on Local Distribution, Ecology, and Taxonomy, and a System for Evaluation of
2331 Plant Communities. Morton Arboretum, Lisle, Illinois
2332
2333 Tiner RW (1999) Wetland Indicators: A Guide to Wetland Identification, Delineation, Classification, and
2334 Mapping. Lewis Publishers, Boca Raton, FL, USA
2335
2336 USAGE (2014) National Wetland Plant List, version 3.2, US Army Corps of Engineers
2337 (http://wetland_plants.usace.army.mil/)
2338
2339 USDA-NRCS (2012) The PLANTS Database (http://plants.usda.gov, 23 January 2012) National Plant Data
2340 Team, Greensboro, NC 27401-4901 USA
2341
2342 USDA-NRCS (2013) The PLANTS Database (http://plants.usda.gov, October-November 2013) National
2343 Plant Data Team, Greensboro, NC 27401-4901 USA
2344
2345 USEPA (2002) Methods for Evaluating Wetland Condition: #10 Using Vegetation to Assess Environmental
2346 Conditions in Wetlands. Office of Water, US Environmental Protection Agency, Washington, DC
2347
2348 USEPA (2006) Wadeable Streams Assessment: A Collaborative Survey of the Nation's Streams. EPA 841-
2349 B-06-002. US Environmental Protection Agency, Washington, DC
2350
2351 USEPA (2009) National Lakes Assessment: A Collaborative Survey of the Nation's Lakes. EPA 841-R-09-
2352 001. US Environmental Protection Agency, Office of Water and Office of Research and Development,
2353 Washington, DC
2354
2355 USEPA (2011a) National Wetland Condition Assessment: Field Operations Manual. EPA/843/R10/001. US
2356 Environmental Protection Agency, Washington, DC
2357
2358 USEPA (2011b) National Wetland Condition Assessment: Labortory Operations Manual. EPA-843-R-10-
2359 002. US Environmental Protection Agency, Washington, DC
2360
80 2011NWCA Technical Report DISCUSSION DRAFT
-------
2361 Veselka W, Rentch JS, Grafton WN, Kordek WS, Anderson JT (2010) Using Two Classification Schemes to
2362 Develop Vegetation Indices of Biological Integrity for Wetlands in West Virginia, USA. Environmental
2363 Monitoring and Assessment 170: 555-569
2364
2365 Wilhelm G, Ladd D (1988) Natural Area Assessment inthe Chicago region. In: Transactions 53rd North
2366 American Wildlife and Natural Resources Conference, Louisville, Kentucky. Wildlife Management
2367 Institute,Washington, DC, pp 361-375
2368
2369 Wilson M, Bayley S, Rooney R (2013) A plant-based index of biological integrity in permanent marsh
2370 wetlands yields consistent scores in dry and wet years Aquatic Conservation-Marine and Freshwater
2371 Ecosystems 23: 698-709
2372
81 2011NWCA Technical Report DISCUSSION DRAFT
-------
2373 5.11 Appendix A: Vegetation Field Data Forms
| FORM V-2a: NWCA VASCULAR SPECIES PRESENCE AND COVER (Front) *,.,.»«„ n,,in,,
Site ID: NWCA11- Date: / / 2 0 1 1 Page 1
|
of
1
Instructions:
1. General: Print us:ng Au CA^ITAI LETTERS. Write as neatly as poss bie, keeping al 1 marks within data f eids or workspace areas.
2. Species Name: Lsl yinomial name or pseudonym for each plant species observed in Ihe Veg P DtsiSee tne I\WCA FOM lor Pseudonym alignment ru es).
3. Presence Data: For each species occurring inacjuodrotnt-st/SlVyrrVC corners ofVcgPfotf, record the smallest quadrat/plot size in which it occurs by filling in the -appropriate bubble (S (small) = 1-m1 quadrat,
M (medium) = 10n quadrat.) If a species does not occur in a particular nest, but occurs in the 100-mZ Veg Plot, fill in the L (large) bubble for that nest.
4. Predominant Height Class: For each species observed, note tspredom nant ne ght across each 100-m' Veg Pot by recording tne appropriate heigrit class code (defined be .w).
5. Cover Data: Estimate cover across each 100-rnJVeg P:ot (0 to 100%; See NWCA-FOM) for each spec eso sserved arid record in the Cover data field . If necessary, jse Lnegiay workspace to make preliminary cover
estimates fof eacn specie's in each of Ihe fojr quarlers of Ihc Vc-g P ol, arid then co'Tiyinc arc mina-y estimates loocjlain tola cover fo- the species in IT. Veg Plot and record in lie Cover data field.
6. Collect Specimens and Assign Collection Numbers: Fo- each UrtKnowri Species {U) or designated dual ly Assurance (QA) specimen co lected fill in Ihe af);>-o;i/ ,alp bubble in the Compile if Collecting co urrtn. Once
coilc-t:ted, ass:gn collection n jmbtvs, bc-grnnirtg v/th 1, corisrcjlivr y n order ol obsc-rval'on or col eclion-
O Fill bubble to confirm empty data fields for a species in a particular plot mean 1) for presence or height clas^ s|j oies iiot present, or 2) for % Cover fields, cover = 0 zero.
Complete
If
Collecting
u
as
o
0
0
0
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
©
Coll
#
Height Classes (except E, which may occur in any verticdl stratum):
1 = <0.5m, 2 = >0.5-2m, 3 = >2-5m, 4= >5-15m, 5 = >15-30m, 6 = >30r , ant, * = nana, vine or epiphyte species
Species Name or Pseudonym
Plot 1
SW
§
§
8
§
Q
f)
8
I
$
1
NE
§
B
8
8
0
£
§
8
Ht.
rhjvv
_
%
Cover
Work
Space
Plot 2
SW
§
§
i
Q
8
§
§
§
§
O
8
NE
§
^
1
3
|
§
1
§
§
O
8
Ht
n
-------
f FORM V-3: NWCA VEGETATION TYPES (Front) R«,i™edb
Site ID: NWCA11- Date: / / 2 0
v(talltall. f
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.
COVER DATA CELLS:
O Confirm that empty cells equal zero by filling in this bubble
Predominant S & T Class
If plot is Pr - Palustnne Farmed (not currently In production) fill Pf bubble AND indicate
predominant S & T class eech plot r/ould be if never cropped.
If not Pf - Palustrine Farmed select the predominant S & T class for each plot.
E2EM - Estuarine Intertidal Emergent PSS - Palustrine Scrub Shrub
E2SS - Estuarine Intertidal Scrub/Shrub/Forested PFO - Palustrine Forested
PEM - Palustrine Emergent PUBPAB- Palustrine
(see Reference Card AA-3, Side A for definitions) UnconsdidatedBottom/Aquatic Bed
% Cover Vascular Vegetation Strata
COVER OF SUBMERGED AQUATIC VEGETATION (rooted in ssdiment, most
plant cover submerged or floating 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 HEIGHTCLASSES:
>30m tall: e.g., very tall trees (0 - 100%)
CATEGORICAL DATA:
Q Confirm a filled data bubble indicates Yes and an unfli ?d
bubble Indicates No by filling In this bubble
Plotl
Off
Q E2EM
Q E2ES
O PEM
Q PSS
O PUBPAB
Plotl
Plot 2
OP'
O EZEM
Q E2SS
O PEM
O PSS
O PUBPAB
Plot 2
Plots
O pf
Q E2EM
O E2-SS
O PES
O PUBPAB
Plot ,
1
MS to 30m tall: e.g., tall trees (0 -100%) I
>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 sap.itos: tall
emergent/terrestrial herbaceous species (0-1 00%)
< 0.5m tall: e.g., low emergentrterrestrial; herbaceous .pecu " low shrubs;
tree seedlinqs (0 - 100%)
% Cover and Categorical Data for Non-Vas ;ula Ta*a
COVER OF BRYOPHYTES (mosses and llverwou * gtv Mng on ground
surfaces, logs, rocks, etc.) (0 - 100%)
Fill bubble if Bryophytes arecHmlr" ed b> ^pbagnumoi other
peat-forming mosses
COVER OF LICHENS growing on ground surfaces, logs, rocks, etc. (0 - 100%)
COVER OF ARBOREAL EPIPHvTiJ Cr(Y« 'HYTES AND LICHENS (see NWCA-
FOIVI for cover estimation pr •• edun • fr this group! (0 - 100%)
COVER OF FILAMENTO! , OR h .IT FORMING ALGAE (0-100%)
COVER OF MACRO ,LGAE (freshwater species/seaweeds) (0 - 100%):
When Mar1 :m • present, fill in all bubbles that apply for each Veg Plot:
Alga*, •'ccur as vtrack (detached, debris, stranded)
Algae is attachedfllvlng
,i 'ae Status Unknown (Can't determine whether algae
is wrack or attachedfliving)
Flag Comments Flag
Plotl
O
Plot 2
O
Plots
O
Plot 4
DPI
O E2EM
Q E2SP
"'ot4
Plot 4
O
Pic, 5
c
x" EZEM
O ?ss
3 P. i
f PSS
r PFO
O PUBPAB
Plots
Plots
O
."' 1
Flag
Flag
O
O
O
O
O
O
O
O
O
O
O
O
O
O
O
Comments
Flag codes: K = No measurement made, U = Suspect measurement., F1.F2. etc. = misc. flags assigned by each field crew. ^_
^^B Explain all flags in comment section. 4741595533 ^^fc
NWCA Vegetation Types 03/10/2011 ^^
2375
83
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
• FORM V-3: NWCA GROUND SURFACE ATTRIBUTES (Back) „.„,. „,,. A
Site ID: NWCA11- DatB: / / 2 0 1 1
W
Instructions: For each ground surface attribute carefully record the requested data.
1. Wider Cover- Estimate total percent of Veg Plot area covered by water, then estimate cover For each subcategory of water. The sum of cowers for water
subcategoriGS should equal the total water cover. Where loafing/submerged and emergent vegetation occur together, classify water type based on vegetation type
with greatest cover; if cover is equal classify as water and emergent vegetation.
2. Water Depth - Measure water depth with marked 1-m PVC pote or ruler at 3 locations representing the water level range across the plot.
3. Utter - Estimate total cover of litter, identify predominant types (all types with ^25% cover), or if total litter is < 25% cover indicate primary litter type, measure litter
depth in SMU and NE most corners of Veg Plot in center of 1-nr quadrat.
4. Bareground - Estimate cover for exposed a) soil/sediment, b) gravel/cobble, c) rock. (The sum of a+b+c <1QQ%).
5, Dead Woody Material Cover- Estimate cover (0 to 100%) for each category of dead woody material.
COVER DATA CELLS:
O Confirm that empty cells equal zero by filling In this bubble
CATEGORICAL DATA:
O Confirm a filled data bubble Indicates presp^e a; H an
unfilled bubble Indicates absence by lillin , in , 's babble
Water Cover Plot 1
1) Total Cover of Water (percent of Veg Plot area with water = a+b+c < 100%)
a) % Veg Plot area with water and no vegetation
b) % Veg Plot area with water and floating/submerged aquatic vegetation
c) % Veg Plot area with water and emergent vegetation
Water Depth {make 3 depth measurements in a Veg Plot within
a 10 minute period)
Minimum Depth |cm)
Predominant Depth (cm)
Plotl
Plot 2
Plot 3
"lo^
Maximum Depth (cm)
Time of Day (24 hour clock)
Cover of Bareground = a*b+c <100% Plot 1
a) Exposed soil/sediment
b) Exposed grave I /cobble (-2rrnm to 25cm)
c) Exposed rock (>25cm)
Vegetative Litter Plot 1
Total Cover Vegetative Liner (0-100%)
Predominant (>25% cover) or Primary Lltt> . tyj. 5cm diameter) (0-100%)
Cover
Flac?
)f D, wied Ine Woody debris (<5cm diameter) (0-100%)
OTQE
OFOD
OCQN
Plotl
Plot 2
Plot 2
OTQE
OFOD
OcQN
Plot 2
Plot 3
Plot 3
Plot 3
OTQE
OFQD
OcOw
Plot 3
Plot *
Plot 4
Plot 4
Plot 4
OTQE
OFOD
OCQN
Plot 4
"•lots
Plots
Plots
PlotS
OTOE
OFOD
OCQN
Plots
Flag
Flag
Flag
Flag
Flag
Comments
Flag codes: K = No measurement made, U = Suspect measurement., F1,F2, etc. = misc. nags assigned by each Held crew.
^k Explain all flags In comment section. 1184195537 ^fc
NWCA Ground Surface Attributes 03/10/2011
2376
84
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
FORM V-4a: NWCA SNAG AND TREE COUNTS AND TREE COVER (Front)
Site ID: NWCA11- Date: / / 2 0 1 1
R»vi»w.dty (Initirt):.
"
Instructions for Recording Data:
1, Fill out Header Information.
2. If Live Trees or Snags are Absent from a Veg Plot, fill in the appropriate bubble in the Tree or Snag
Absence field.
3. If either Live Trees or Snags are Present in a Veg Plot, collect data across the entire 100-m'' area of
each Veg Plot.
4. Standing Dead 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
6. Cover of trees in height classes: Record species names or pseudonyms for each tree species.
Ensure pseudonyms match those used on Form V-2. Record the percent cover (0-100%) for each tree
species for each of the following height classes: < 0 5m, 0.5 to 2m, > 2 to 5m, > 5 to 15m, >15to 30m,
>30m.
7. Live Trees: Count trees =• 5cm DBH 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: If needed, for smaller DBH classes when many trees or snags are prest. t,
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 or tree spec'es 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.
Tally
format"
Plot*
1 04
2 O 5
1 04
2 O5
r:
n
Live Tree Species Name/Pseudonym
Tree Cover by leigh "lass
O Fill in bubble to confirm i ^t empty data cells equal zero.
TREES OR SN-iGS A 1SENCE: Fill in all that apply:
LTA=Live Trees Abt -it. OTA=Dead Trees/Snags Absent
Plotl
OLTA
O DTA
Plotl
OL-,
J T/.
Plots
O LTA
O DTA
Plot 4
OLTA
O DTA
Plots
OLTA
ODTA
"jnd ,ig Dead Tree/Snag Counts by DBH Class
(White box = data field, Gray box = tally workspace)
Flag
Tree Counts by DBH Class
(White box = data field, Gray box = tally workspace) |DBH = diameter breast height)
Flag
i i O I
I 2 O 5
1 0*
2 O 5
I 1 O 4
12 OS
1 04
2 O i
Flag codes: K = No IVK v erneni 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.
NWCA Snag & Tree Counts (Front) 03/10/2011
2377
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DISCUSSION DRAFT
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2378
2379
5.12 Appendix B: Parameter Names for Field Collected Vegetation Data
PARAMETER
NAME
DESCRIPTION
RESULT
VALID RANGE/
LEGAL VALUES
riw/i
Plant Species Data: Cover, presence, and height data for each vascular plant species observed in each of five
100-m2 (lOxlOm) Veg Plots. Presence of each species in four component nested quadrats for each Veg Plot.
SPECIES
Scientific Name for each
Typically the genus and species
Taxon name
species (taxon) encountered in name. In some cases: lower
the Veg Plot. Scientific names
reconciled to USDA_PLANTS
nomenclature. Unknowns are
named using growth form
codes.
taxonomic levels (e.g., subspecies,
varieties) or higher taxonomic
levels (e.g., genus, family, growth
form)
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- S, M, or L
m2 quadrat, or L = entire 100-m2
Veg Plot
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- S, M, or L
m2 quadrat, or entire L = 100-m2
Veg Plot
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 species 0-100%
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%.
Form V-3: NWCA Vegetation Types (Front) and Ground Surface Attributes (Back
Vegetation Type Data: Observations from each of five 100-m2 (lOxlOm) Veg Plots
Predominant Status & Trends Category
PAL_FARMED Palustrine farmed (Pf) Class
dominating Veg Plot
If Pf present, PF where present
PF
SANDT CLASS
FWS Status Trends Class
dominating Veg Plot
One S&T Category: E2EM -
Estuarine Intertidal Emergent,
E2SS - Estuarine Shrub/Forested,
PEM - Palustrine, Lacustrine, or
Riverine Emergent, PSS-
Palustrine, Lacustrine, or Riverine
Scrub/Shrub, PFO - Palustrine,
Lacustrine, or Riverine Forested,
PUBPAB - Palustrine, Lacustrine,
or Riverine Unconsolidated
Bottom
E2EM, E2SS,
PEM, PSS, PFO,
or PUBPAB
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PARAMETER
NAME
DESCRIPTION
RESULT
VALID RANGE/
LEGAL VALUES
i Cover Vascular Vegetation Strata
SUBMERGED AQ
% Cover Submerged Aquatic 0-100 % Cover
Vegetation
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 Categorical Data for Non-Vascular Taxa
BRYOPHYTES
PEAT_MOSS
LICHENS
ARBOREAL
ALGAE
MACROALGAE
% Cover of Bryophytes
growing on ground surfaces,
logs, rocks, etc.
Bryophytes dominated by
Sphagnum or other peat
forming moss
% Cover of Lichens growing on
ground surfaces, logs, rocks,
etc.
% Cover of Arboreal
Bryophytes and Lichens
% Cover of filamentous or mat
forming algae
% Cover of macroalgae
(freshwater
species/seaweeds)
0-100 % Cover
Y (yes), if present
0-100 % Cover
0-100 % Cover
0-100 % Cover
0-100 % Cover
0-100%
Yes/No
0-100%
0-100%
0-100%
0-100%
WRACK
Macroalgae occurs wrack
(detached, debris, stranded)
Y (yes), if present
Yes/No
ATTACHED
Macroalgae is attached/living Y (yes), if present
Yes/No
UNK ALGAE
Macroalgae status unknown
(can't determine whether
wrack or living)
Y (yes), if present
Ground Surface Attributes
Yes/No
Water Cover and Depth
TOTAL WATER
Total cover of water (percent
of Veg Plot area with water =
a+b+c < 100%)
% Cover
0-100%
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PARAMETER
NAME
WATER_NOVEG
WATER_AQVEG
WATER_EMERGVEG
MINIMUM_DEPTH
PREDOMINANT_DEPT
H
MAXIMUM_DEPTH
TIME
DESCRIPTION
a) % Veg Plot area with water
and no vegetation
b) % Veg Plot area with water
and floating/submerged
aquatic vegetation
c) % Veg Plot area with water
and emergent and/or woody
vegetation
Minimum water depth
Predominant water depth
Maximum water depth
Time water depth
measurements were made
RESULT
% Cover
% Cover
% Cover
depth in cm
depth in cm
depth in cm
time on 24 hour clock
VALID RANGE/
LEGAL VALUES
0-100%, <
TOTAL_WATER
0-100%, <
TOTAL_WATER
0-100%, <
TOTAL_WATER
Investigate if
>100 cm
Investigate if
>100 cm
Investigate if
>100 cm
500 to 2100
(investigate if
outside this
range)
Barea round and Litter
Total cover of bareground = a + b + c < 100%
EXPOSED_SOIL
EXPOSED_GRAVEL
EXPOSED_ROCK
TOTAL_LITTER
a) Cover exposed
soil/sediment
b) Cover exposed
gravel/cobble (~2mm to
25cm)
c) Cover exposed rock
(>25cm)
Total cover of litter
% Cover
% Cover
% Cover
% Cover
Predominant Litter Types (>25% cover) or Primary Litter type (if all litter < 25%):
UTTER_THATCH
UTTER_FORB
LITTER_CONIFER
LITTER_DECID
UTTER_BROADLEAF
LITTER_NONE
LITTER_DEPTH_SW
LITTER_DEPTH_NE
WD_FINE
Thatch (dead graminoid (e.g.,
grasses, sedges, rushes)
leaves, rhizomes, or other
material))
Forb litter
Conifer litter
Deciduous litter
Broadleaf evergreen litter
No litter
Litter depth (cm) in center of
1-m2 quadrat at SW corner of
Veg Plot
Litter depth (cm) in center of
1-m2 quadrat at NE corner of
Veg Plot
Cover of fine woody debris
(<5cm diameter)
If present, THATCH
If present, FORB
If present, CONIFER
If present, DECID
If present, BROADLEAF
If litter absent, NONE
depth in cm
depth in cm
% Cover
< 100%
< 100%
< 100%
< 100%
THATCH
FORB
CONIFER
DECID
BROADLEAF
NONE
Investigate if
>100 cm
Investigate if
>100 cm
0-100%
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PARAMETER
NAME
WD_COARSE
DESCRIPTION
Cover of coarse woody debris
(>5cm diameter)
RESULT
% Cover
Form V-4a and V-4b: NWCA Snag and Tree Counts and Tree Cover
Tree species (cover and counts) and Snag (counts) data for each of 5 100-m2 (lOxlOm)Veg Pk
Snag Data
XXTHIN_SNAG
XTHIN_SNAG
THIN_SNAG
JR_SNAG
THICK_SNAG
XTHICK_SNAG
Dead trees/snags 5 to 10 cm
DBH (diameter breast height)
Counts of dead trees/snags 11
to 25cm DBH
Counts of dead trees/snags 26
to 50cm DBH
Counts of dead trees/snags 51
to 75cm DBH
Counts of dead trees/snags
76 to 100cm DBH
Counts of dead trees/snags
101 to 200 cm DBH
Counts
Counts
Counts
Counts
Counts
Counts
VALID RANGE/
LEGAL VALUES
0-100%
Investigate if >
200
Investigate if >
200
Investigate if >
200
Investigate if >
200
Investigate if >
200
Investigate if >
200
Tree Data
Tree Species Name
TREE_SPECIES
Scientific Name for each tree
species (taxon) encountered
in the Veg Plot. All scientific
names reconciled to
USDA_PLANTS nomenclature.
Unknowns are named using
growth form codes.
Typically the genus and species
name. In some cases: lower
taxonomic levels (e.g., subspecies,
varieties) or higher taxonomic
levels (e.g., genus, family, growth
form group)
Taxon name
Tree Species Cover by Height Class
VSMALL_TREE
SMALL_TREE
LMED_TREE
HMED_TREE
TALL_TREE
VTALL_TREE
XXTHIN_TREE
XTHIN_TREE
For each tree species, cover of
trees < 0.5m tall
For each tree species, cover of
trees 0.5m to 2m tall
For each tree species, cover of
trees >2 to 5m tall
For each tree species, cover of
trees > 5m to 15m tall
For each tree species, cover of
trees > 15m to 30m tall
For each tree species, cover of
trees > 30m tall
For each tree species, counts
of trees 5 to 10cm DBH
(diameter breast height)
For each tree species, counts
of trees 11 to 25cm DBH
0-100 % Cover
0-100 % Cover
0-100 % Cover
0-100 % Cover
0-100 % Cover
0-100 % Cover
Counts
Counts
0-100%
0-100%
0-100%
0-100%
0-100%
0-100%
Investigate if >
200
Investigate if >
100
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2011 NWCA Technical Report
DISCUSSION DRAFT
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PARAMETER
NAME
THIN_TREE
JR_TREE
THICK_TREE
XTHICK_TREE
XXTHICK_TREE
DESCRIPTION
For each tree species, counts
of trees 26 to 50cm DBH
For each tree species, counts
of trees 51 to 75cm DBH
For each tree species, counts
of trees 76 to 100cm DBH
For each tree species, counts
of trees 101 to 200 cm DBH
For each tree species, counts
of trees > 200 cm DBH
RESULT
Counts
Counts
Counts
Counts
Counts
VALID RANGE/
LEGAL VALUES
Investigate if >
50
Investigate if >
20
Investigate if >
10
Investigate if >
5
Investigate if >
5
2380
2381
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DISCUSSION DRAFT
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2382
2383
5.13 Appendix C: Sources of C-values in the National Floristic Quality Database
State
AL
AK
AZ
AR
CA
CO
CT
DE
FL
GA
IA
ID
Source of C-values used in National Floristic Quality Database
Gianopulos, K. 2014. Coefficient of Conservatism Database Development for Wetland Plants in
the Southeast United States. North Carolina Dept. of Environment and Natural Resources,
Division of Water Resources. Wetlands Branch. Report to the EPA, Region 4.
Wo State or regional CC list available.
No State or regional CC list available.
No State or regional CC list available.
No State or regional CC list available.
Rocchio, J. 2007. Floristic Quality Assessment Indices of Colorado Plant Communities. Colorado
ram, Colorado State University. Natural Heritage Program, Fort Collins, CO.
New England Interstate Water Pollution Control Commission. 2011. Coefficients of
Conservatism for the Vascular Flora of New York and New England (unpublished).
http://www.neiwpcc.org/nebawwg/necocscores.asp.
McAvoy, W. A. 2012. 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
Gianopulos, K. 2014. Coefficient of Conservatism Database Development for Wetland Plants in
the Southeast United States. North Carolina Dept. of Environment and Natural Resources,
Division of Water Resources. Wetlands Branch. Report to the EPA, Region 4.
Lane, C. R., M. T. Brown, M. Murray-Hudson, and M. B. Vivas. 2003. The Wetland Condition
Index (WCI): Biological Indicators for Isolated Depressional Herbaceous Wetlands in Florida. A
report to the Florida Department of Environmental Protection. HT Odum Center for Wetlands,
University of Florida, Gainesville, Florida, USA.
Mortellaro, S., M. Barry, G. Gann, J. Zahina, S. Channon, C. Hilsenbeck, D. Scofield, G. Wilder,
and G. Wilhelm. 2012. Coefficients of Conservatism Values and the Floristic Quality Index for
the Vascular Plants of South Florida. Southeastern Naturalist 11: 1-62.
Reiss, K. C. and M. T. Brown. 2005a. The Florida Wetland Condition Index (FWCI): Developing
Biological Indicators for Isolated Depressional Forested Wetlands. A report to the Florida
Department of Environmental Protection. HT Odum Center for Wetlands, University of Florida,
Gainesville, Florida, USA.
Reiss, K. C. and M. T. Brown. 2005b. Pilot Study - The Florida Wetland Condition Index (FWCI):
Preliminary Development of Biological Indicators for Forested Strand and Floodplain Wetlands.
A report to the Florida Department of Environmental Protection. HT Odum Center for
Wetlands, University of Florida, Gainesville, Florida, USA.
Gianopulos, K. 2014. Coefficient of Conservatism Database Development for Wetland Plants in
the Southeast United States. North Carolina Dept. of Environment and Natural Resources,
Division of Water Resources. Wetlands Branch. Report to the EPA, Region 4.
Zomlefer, W. B., L. Chafin, J. R. Carter, & D. E. Giannasi. 2013. Coefficient of conservatism
rankings for the flora of Georgia: Wetland indicator species. Southeastern Naturalist 12: 790-
808.
Brudvig, L A., C. M. Mabry, J. R. Miller, and T. A. Walker. 2007. Evaluation of Central North
American Prairie Management Based on Species Diversity, Life Form, and Individual Species
Metrics. Conservation Biology 21: 864-874.
Wo State or regional CC list available.
91
2011 NWCA Technical Report
DISCUSSION DRAFT
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State
IL
IN
KS
KY
LA
ME
MD
MA
Ml
MN
MC
MO
MT
NE
NV
Source of C-values used in National Floristic Quality Database
Taft, J. B., G. S. Wilhelm, D. M. Ladd, and L A. Masters. 2003. Floristic Quality Assessment for
Vegetation in Illinois a Method for Assessing Vegetation Integrity. Reprinted with the
permission of the Illinois Native Plant Society.
Rothrock, P. E. 2004. Floristic Quality Assessment in Indiana: The concept, use, and
development of coefficients of conservatism. EPA Wetland Program Development Grant,
Taylor University.
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/
White, D., M. Shea., D. Ladd, and M. Evans. 1997. Kentucky Coefficients of Conservatism. The
Kentucky State Nature Preserves Commission, the Kentucky Chapter of The Nature
Conservancy, the Missouri Chapter of The Nature Conservancy, and the Kentucky State Nature
Preserves Commission.
Gianopulos, K. 2014. Coefficient of Conservatism Database Development for Wetland Plants in
the Southeast United States. North Carolina Dept. of Environment and Natural Resources,
Division of Water Resources. Wetlands Branch. Report to the EPA, Region 4.
Cretini, K. F., J. M. Visser, K. W. Krauss, and G. D. Steyer. 2012. Development and use of a
floristic quality index for coastal Louisiana marshes. Environmental Monitoring Assessment
184:2389-2403.
New England Interstate Water Pollution Control Commission. 2011. Coefficients of
Conservatism for the Vascular Flora of New York and New England (unpublished).
http://www.neiwpcc.org/nebawwg/necocscores.asp.
Sarah J. Chamberlain and Hannah M. Ingram. 2012. Developing coefficients of conservatism to
advance floristic quality assessment in the Mid-Atlantic region. J. Torrey Bot. Soc. 139: 416-
427.
New England Interstate Water Pollution Control Commission. 2011. Coefficients of
Conservatism for the Vascular Flora of New York and New England (unpublished).
http://www.neiwpcc.org/nebawwg/necocscores.asp.
Herman, K. D., L. A. Masters, M. R. Penskar, A. A. Reznicek, G. S. Wilhelm, W. W. Brodovich,
and K. P. Gardiner. 2001. Floristic Quality Assessment with Wetland Categories and Examples
of Computer Applications for the State of Michigan. Revised, 2nd edition. MDNR.
Milburn, S. A., M. Bourdaghs, and J. J. Husveth. Floristic Quality Assessment for Minnesota
Wetlands. Minnesota Pollution Control Agency, St. Paul, Minn.
Herman, B. D., J. D. Madsen, and G. N. Ervin. 2006. Development of Coefficients of
Conservatism for Wetland Vascular Flora of North and Central Mississippi. GeoResources
Institute Report 4001.
Gianopulos, K. 2014. Coefficient of Conservatism Database Development for Wetland Plants in
the Southeast United States. North Carolina Dept. of Environment and Natural Resources,
Division of Water Resources. Wetlands Branch. Report to the EPA, Region 4.
Ladd, D. M. 1993. Coefficients of Conservatism for Missouri vascular flora. The Nature
Conservancy, St. Louis, MO. 53 p.
Jones, W. M. 2005. A vegetation index of biotic integrity for small-order streams in
southwestern Montana and a Floristic Quality Assessment for western Montana wetlands.
Report to the Montana Department of Environmental Quality and US Environmental Protection
Agency, Montana Natural Heritage Program.
Rolfsmeier, S. and G. Steinauer. 2003. Vascular Plants of Nebraska (Version 1 -July 2003).
Nebraska Game and Parks Commission, Lincoln, NE 57 pp.
Wo State or regional CC list available.
92
2011 NWCA Technical Report
DISCUSSION DRAFT
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State
Source of C-values used in National Floristic Quality Database
New England Interstate Water Pollution Control Commission. 2011. Coefficients of
NH Conservatism for the Vascular Flora of New York and New England (unpublished).
http://www.neiwpcc.org/nebawwg/necocscores.asp.
NJ
Kelly, L, K. Anderson, K. S. Walz, and D. B. Snyder. 2013. New Jersey Floristic Quality
Assessment: Coefficients of Conservatism for Vascular Taxa. New Jersey Department of
Environmental Protection, State Forestry Services, Office of Natural Lands Management,
Natural Heritage Program, Trenton, NJ.
NM
Wo State or regional CC list available.
New England Interstate Water Pollution Control Commission. 2011. Coefficients of
NY Conservatism for the Vascular Flora of New York and New England (unpublished).
http://www.neiwpcc.org/nebawwg/necocscores.asp.
Gianopulos, K. 2014. Coefficient of Conservatism Database Development for Wetland Plants in
NC the Southeast United States. North Carolina Dept. of Environment and Natural Resources,
Division of Water Resources. Wetlands Branch. Report to the EPA, Region 4.
ND
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/ITR—
2001-0001, 32 p.
Andreas, B. K., J. J. Mack, and J. S. McCormac. 2004. Floristic Quality Assessment Index (FQAI)
OH for vascular plants and mosses for the State of Ohio. Ohio Environmental Protection Agency,
Division of Surface Water, Wetland Ecology Group, Columbus, OH, 219 p.
Ewing, A. K. and B. Hoagland. 2012. Development of Floristic Quality Index Approaches for
OK Wetland Plant Communities in Oklahoma. USEPA Final Report, FY 2010,104(b)(3), CD-OOF074,
Project 2.
OR
Magee, T.K. and M. A. Bollman. Unpublished. Coefficients of Conservatism for 538 species
observed in riparian areas associated with randomly selected 1-km long stream reaches
including 36 sites distributed across the John Day River Basin (eastern Oregon) and 4 sites in
the Oregon Cascade Range.
Chamberlain, S. J. and H. M. Ingram. 2012. Developing coefficients of conservatism to advance
PA floristic quality assessment in the Mid-Atlantic region. Journal of the Torrey Botanical Society
139: 416-427.
New England Interstate Water Pollution Control Commission. 2011. Coefficients of
Rl Conservatism for the Vascular Flora of New York and New England (unpublished).
http://www.neiwpcc.org/nebawwg/necocscores.asp.
Gianopulos, K. 2014. Coefficient of Conservatism Database Development for Wetland Plants in
SC the Southeast United States. North Carolina Dept. of Environment and Natural Resources,
Division of Water Resources. Wetlands Branch. Report to the EPA, Region 4.
SD
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/ITR—
2001-0001, 32 p.
TN
Willis, K. and L Estes. 2013. Floristic Quality Assessment for Tennessee Vascular Plants, and
Application to Barrens Environments. Manuscript in Preparation.
Gianopulos, K. 2014. Coefficient of Conservatism Database Development for Wetland Plants in
the Southeast United States. North Carolina Dept. of Environment and Natural Resources,
Division of Water Resources. Wetlands Branch. Report to the EPA, Region 4.
TX
No State or regional CC list available.
UT
No State or regional CC list available.
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State
VT
VA
WA
WV
Wl
WY
Source of C-values used in National Floristic Quality Database
New England Interstate Water Pollution Control Commission. 2011. Coefficients of
Conservatism for the Vascular Flora of New York and New England (unpublished).
http://www.neiwpcc.org/nebawwg/necocscores.asp.
Virginia Department of Environmental Quality. Office of Wetlands & Water Protection. 2005.
Determining Coefficient of Conservatism Values (C-Values) for Vascular Plants Frequently
Encountered in Tidal and Nontidal Wetlands in Virginia
Rocchio, F. J. 2013. Western Washington Floristic Quality Assessment. Natural Heritage
Program Report Number 2013-03. Natural Heritage Program, Washington Department of
Natural Resources. Olympia, Washington.
http://wwwl.dnr.wa. gov/nhp/refdesk/communities/fqa/fqa_report.pdf
Rentch, J. S. and J. T. Anderson. 2006. A floristic quality index for West Virginia wetland and
riparian plant communities. West Virginia Agricultural and Forestry Experiment Station
Bulletin. Bulletin 2967. 67pp
Bernthal, T. W. 2003. Development of a Floristic Quality Assessment methodology for
Wisconsin. Report to the USEPA (Region V). Wisconsin Department of Natural Resources,
Madison, Wl. Note the appendix containing the C values is listed in a separate website.
Wo State or regional CC list available.
2384
2385
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2386
2387
2388
2389
Chapter 6: Candidate Vegetation Metrics of Condition or Stress
2391
2392
2393
2394
2395
2396
2397
2398
2399
2400
2401
2402
2403
2404
2405
2406
2407
2408
2409
6.1 Background
Potential vegetation indicators of ecological
condition or stress in wetlands were identified
during the planning effort for NWCA. The
indicator selection process included extensive
literature review, several workshops involving
many wetland experts who provided
recommendations of indicators based on
evaluations of utility and cost-effectiveness, and a
final workshop including states, tribes, and other
NWCA partners to allow review of and consensus
on selection of Metric Groups to be evaluated in
the NWCA. Several major Vegetation Metric
Groups (Table 6-1) were recognized as
ecologically important and/or commonly used as
indicators in wetland assessments.
The NWCA Vegetation Field Protocol (see Section
5.3) was designed to collect data to inform the
development of candidate metrics within these
Metric Groups. Validated vegetation field data
(see Sections 5.3.2 and 5.5), along with species
trait information, (see Sections 5.6 through 5.9)
were used to develop candidate metrics.
2390
In this chapter, we focus on development and evaluation of candidate vegetation metrics that describe
wetland ecological condition or stress. Both metric evaluation (this chapter) and the development of the
NWCA VMMI (Chapter 7) require 1) accounting for natural, regional, and wetland type variability, and 2)
the use of calibration and validation data. The NWCA divided the data from sampled sites into two
groups, one data set for calibration of metrics and potential VMMI(s) and one for validation of results.
The first application of accounting for variability and first use of calibration and validation data occurs
here, so both topics are discussed in subsections this chapter and we refer back to them, as needed,
from Chapter 7 "Wetland Condition -Vegetation Multimetric Index". All analyses for metric
development, calculation, and evaluation were conducted using R Statistical Software, version 3.1.1 (R
Core Team 2014).
6.2 Developing and Calculating Candidate Metrics
Each Metric Group listed in Table 6-1 is comprised of a variety of major metric types, and for each
metric type, several-to-many specific candidate metrics for describing ecological condition or stress
were calculated. Most of the metric types include versions of metrics that incorporate all species, only
native species, or only nonnative species. Vegetation metrics based on all species or on native species
only were considered potential descriptors of wetland condition (n = 405). Metrics based on only
95
2011 NWCA Technical Report
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2410 nonnative species were viewed as indicators of wetland stress (n = 126). For the NWCA, nonnative plant
2411 species were defined as both alien and cryptogenic species (see Section 5.8 and Chapter 8, Section 8.5).
2412 Candidate condition metrics were used in developing the Vegetation Multimetric Index (VMMI, see
2413 Chapter 7), whereas, candidate vegetation stressors were used in the development of the Nonnative
2414 Plant Stressor Indicator (NPSI) (see Chapter 8, Section 8.5).
2415
2416 Numerous metrics were developed and evaluated because the NWCA was the first attempt to develop
2417 vegetation indices reflecting ecological condition or stress at the scale of the conterminous US. The
2418 NWCA candidate metric set included metrics that were likely to have broad applicability across regions
2419 and wetland types, as well as, metrics that were expected to have more restricted utility for specific
2420 wetland types. The 531 candidate metrics developed and calculated for the NWCA are described in
2421 Section 6.8, Appendix D, which lists: names and short descriptions of the metrics, how each was
2422 calculated, the field data and species trait groups on which each metric is based, and whether the metric
2423 is intended to describe ecological condition or stress.
2424
2425 Table 6-1. Metric Groups and component Metric Types for characterizing vegetation condition.
Metric Groups Major Metric Types for each Indicator/Metric Group (Most types include versions
of metrics based on all, native, or nonnative species)
Taxa Composition
Floristic Quality
Richness, diversity, frequency, cover, importance of vascular plant species,
genera, families, etc.
Mean Coefficient of Conservatism, Floristic Quality Assessment Index (presence
based and frequency and cover weighted versions )
ilerance and Sensitivity to Richness and abundance of sensitive, insensitive, tolerant, highly tolerant species
Disturbance
Hydrophytic Status
Richness and abundance by Wetland Indicator Status; Wetland Indices
'
Life History
Vegetation Structure
Richness and abundance by growth habit type, duration/longevity category,
vascular plant category (e.g., ferns, dicots, etc.)
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, types of water, litter, bare ground
Woody Debris and Snags Frequency, cover, importance for woody debris, counts for snags
Trees
Richness, counts, or frequency, cover or importance by height or diameter classes
2426
2427
2428
2429
2430
2431
2432
Development of each metric necessitated specification of required validated field data and trait
information, the data tables within the NWCA database where relevant data were located, and a general
formula for metric calculation. Autecological traits for each vascular plant and tree species were merged
with cover data based on geographic region where necessary, resulting in site-specific traits associated
with cover information. This information was used to develop R code to calculate each metric.
96
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2433 The accuracy of metric calculations was checked in several ways. First, five NWCA sampled sites
2434 representing highly divergent species richness, species composition, and wetland types were selected
2435 for checking accuracy of the R code computations, and to ensure that the R code was calculating the
2436 metrics as intended. For these five sites, formulas for calculating the metrics were developed in Excel
2437 and results were compared to the values for the metrics resulting from the R-code. In addition, all
2438 metrics for these five sites were recalculated by hand to verify that they reflected the concepts intended
2439 by the Vegetation Analysis Team. Any discrepancies observed were resolved in the R code.
2440
2441 Next, code was developed independently in SAS (v.9.2, SAS Institute Inc., Gary, NC) to calculate all
2442 vegetation metrics for all sampled sites. The results of the SAS-based calculations were compared to the
2443 results obtained from the R Code as a quality assurance check on the accuracy of computations.
2444 Comparison of both sets of code showed no differences in any calculated values. Following completion
2445 of these quality assurance procedures, the resulting 531 candidate metrics, calculated for all sampled
2446 sites, were compiled in the vegetation metric data set that was used in analyses to assess wetland
2447 condition or stress based on vegetation properties.
2448
2449
2450 6.3 Accounting for Regional and Wetland Type Differences
2451
2452 Ecoregional variation in species composition, environmental conditions, and human-caused disturbance
2453 may be great at the scale of the conterminous United States. In addition, wetland type interacts with
2454 these sources of variability. All these sources of variation have implications for the definition of least
2455 disturbed or reference sites. In addition, this variation can influence or obscure the response of
2456 candidate metrics to human-caused disturbance. To account for physical and biotic diversity, finer scales
2457 (sub-national) or modeling approaches are often needed to facilitate development of effective VMMI(s)
2458 and to define thresholds for good, fair, and poor classes of ecological condition (Hawkins et al. 2010;
2459 Pont et al. 2009; Stoddard et al. 2008; USEPA 2006).
2460
2461 For the NWCA, we employed a series of site groupings to account for this variation and inform candidate
2462 metric evaluation and VMMI development:
2463 • All wetlands - National Scale
2464 • 4 Aggregated Ecoregions
2465 • 4 Aggregated Wetland Types
2466 • 10 Aggregated Ecoregion x Aggregated Wetland Type Groups (Reporting Groups)
2467
2468 Rationale for, and a description of, these Site Groups are provided in Chapter 4, along with the
2469 procedures defining least (reference) and most disturbed sites.
2470
2471 All 1138 sites (probability and not-probability sites) sampled in in the 2011 NWCA (see Chapter 4, Table
2472 4-9) were used in candidate metric evaluation and for developing the NWCA VMMI. The distribution of
2473 the 1138 sites (total number of sites and the numbers of sampled sites identified as least, intermediate,
2474 and most disturbed) for each of three major NWCA Site Groups are listed in Table 6-2 through Table 6-4.
2475 In Table 6-4, in addition to numbers of sampled sites for the 10 NWCA Reporting Groups, the total
2476 sample sizes across the conterminous US are provided, along with the number of Revisit Sites that were
2477 sampled, once during the index visit (primary sampling event, Visit 1) and again during the sampling
2478 season (Visit 2) to quantify within-year sampling variability.
2479
97 2011 NWCA Technical Report DISCUSSION DRAFT
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2480
2481
2482
2483
2484
2485
2486
2487
Table 6-2. Distribution of 1138 NWCA sampled sites (probability and not-probability) by 2011 NWCA Aggregated
Ecoregions. n = numbers of sites.
Code
CPL
EMU
I PL
W
Aggregated Ecoregions
(NWCA_ECO4)
Coastal Plains
Eastern Mtns & Upper Midwest
Interior Plains
West
n Total
567
214
190
167
n Least
Disturbed
n Intermediate
Disturbance
n Most
Disturbed
167
39
38
33
252
116
96
65
148
59
56
69
Table 6-3. Distribution of 1138 NWCA sampled sites (probability and not-probability) by NWCA Aggregated
Wetland Types, n = numbers of sites. Code PRL is pronounced 'pearl'.
Code
EW
PRLH
PRLW
Aggregated Wetland Types
(NWCA_WET_GRP)
Estuarine Herbaceous (emergent)
Estuarine Woody (shrub or forest)
Palustrine, Riverine and Lacustrine
Herbaceous (emergent, ponds,
previously farmed)
Palustrine, Riverine and Lacustrine
Woody (shrub or forest)
n Total
272
73
358
435
n Least
Disturbed
n Intermediate
Disturbance
n Most
Disturbed
100
16
86
90
38
169
232
19
117
Table 6-4. Distribution of 1138 NWCA of sampled sites (probability and not-probability) and 96 revisited sites
across the conterminous United States and by NWCA Reporting Groups, n = numbers of sites.
Code
Reporting Groups (Ecoregion by
Wetland Type, ECO_X_WETGRP)
n Total
n Least
Disturbed
n Intermediate
Disturbance
n Most
Disturbed
n Revisit
Sites
NATIONAL
Conterminous US
1138
277
529
332
96
ALL-EH*
ALL-EW*
CPL-PRLH
CPL-PRLW
EMU-PRLH
EMU-
PRLW
IPL-PRLH
All - Estuarine Herbaceous
All - Estuarine Woody
Coastal Plain - Palustrine,
Riverine, and Lacustrine
Herbaceous
Coastal Plain - Palustrine,
Riverine, and Lacustrine Woody
Eastern Mountains & Upper
Midwest- Palustrine, Riverine,
and Lacustrine Herbaceous
Eastern Mountains & Upper
Midwest- Palustrine, Riverine,
and Lacustrine Woody
Interior Plains- Palustrine,
Riverine, and Lacustrine
Herbaceous
272
73
72
189
73
127
138
100
16
90
38
36
16
33
82
19
20
55
24
18
3
11
10
26
70
42
Interior Plains- Palustrine,
Riverine, and Lacustrine Woody
West- Palustrine, Riverine, and
Lacustrine Herbaceous
West- Palustrine, Riverine, and
Lacustrine Woody
52
75
67
12
26
17
30
14
28
16
30
21
16
*The Estuarine Reporting Groups span all coastal areas of the conterminous United States
2488
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2011 NWCA Technical Report
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2489
2490
2491
2492
2493
2494
2495
2496
2497
2498
2499
2500
2501
2502
2503
2504
2505
2506
2507
2508
2509
2510
2511
2512
2513
2514
2515
2516
2517
2518
2519
2520
2521
2522
2523
2524
2525
2526
2527
2528
2529
2530
2531
2532
2533
2534
6.4 Calibration and Validation Data
The NWCA marks the first use of vegetation in a NARS
assessment across the conterminous United States,
and the first time a wetland VMMI has been
developed at this scale. The sampled sites for the
2011 NWCA spanned wide geographic and ecological
diversity, as well as many wetland types. For these
reasons, a large number of candidate vegetation
metrics of condition (n=405) were evaluated for
potential effectiveness and possible inclusion in
national or reporting group VMMI(s). In addition, 126
metrics based on nonnative plant taxa were
evaluated for consideration for use in the Nonnative
Plant Stressor Indicator (NPSI).
The NWCA VMMI development approach (Chapter 7)
examines many potential VMMI versions; evaluating
1) numerous VMMIs constructed from randomly
selected sets of 4, 6, 8, or 10 metrics, or 2) all possible
VMMI combinations based on a particular number of
metrics (Van Sickle 2010). The many permutations of
potential VMMIs could result in selection of a VMMI
well fit to the 2011 NWCA data, but which might not
reflect conditions from future NWCA Surveys or other
wetland data sets. Thus, to help ensure that the final
VMMI would be widely applicable and not over-fitted
to specific data collected in 2011, we divided the
vegetation data into validation (20% of sampled sites)
and calibration (80% of sampled sites) data sets Table
6-5.
The 20% of sampled sites included in the validation data were randomly selected from least,
intermediate, and most disturbed categories to encompass the entire range of the disturbance gradient
observed in the NWCA. The random selection of the validation sites was also stratified by All Estuarine
(EH + EW), PRLH, and PRLW wetlands to span the range of Aggregated Wetland Types. These validation
data were reserved to evaluate the consistency and robustness of each potential VMMI.
The 80% of sampled sites comprising the calibration data were used to examine the efficacy of each
candidate metric across all wetlands (national scale) and across wetlands within each of three wetland
type groups (see Section 6.5). Calibration data were also used to score condition metrics on a 0-10
continuous scale within each NWCA Site Group for which a potential VMMI was developed (see Section
7.2). The resulting metric scoring was applied to the corresponding validation data. A robust potential
VMMI developed using this metric scoring should similarly distinguish least from most disturbed for
both the calibration and validation data (see Section 7.3).
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2011 NWCA Technical Report
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2535 Table 6-5. Distribution of sites in calibration and validation data sets for all sites, by disturbance type, and by
2536 Aggregated Wetland Type. Total n = 1138.
Site Type
All
Disturbance Class
Least Disturbed (Reference)
Intermediately Disturbed
Most Disturbed
Aggregated Wetland Type
E - Estuarine
1PRLH - Palustrine, riverine, lacustrine herbaceous
PRLW - Palustrine, riverine, lacustrine woody
Calibration Data
n = 911
n = 222
n=423
n = 266
n = 276
n = 286
n = 349
Validation
n=227
n = 55
n = 106
n = 66
n = 69
n = 72
n = 86
Data
2537
2538
2539 6.5 Evaluating Candidate Vegetation Metrics
2540
2541 The performance of NWCA candidate vegetation metrics describing ecological condition or stress
2542 (Section 6.8, Appendix D) was evaluated for potential utility in the VMMI(s) or the NPSI using the
2543 calibration data set (see Section 6.4). A series of screening criteria have commonly been employed by
2544 NARS for evaluating metrics considered in index development (Stoddard et al. 2008; Pont et al. 2009).
2545 The NWCA metric screening approach was adapted and expanded from these standard methods and
2546 applied to both candidate condition and stress metrics. An overview of the metric criteria included in
2547 the screening approach is listed below.
2548
2549 Metric Screening Criteria:
2550 • Range - Sufficient range to permit signal detection
2551 o Total range, skewness, % values identical
2552 • Repeatability - Among site variability (signal) > sampling variability (noise)
2553 o Signai.Noise > 4
2554 • Responsiveness - Distinguish least (reference) from most disturbed sites
2555 o Kruskal-Wallis test (p < 0.05)
2556 o Ranking of box-plot separation of least and most disturbed sites
2557 • Redundancy - Metrics included in a MMI should not be strongly correlated
2558 o Considered when assembling the VMMI (r < |0.75 |)
2559
2560 These screening criteria were applied across all sites nationally and for three wetland type Site Groups.
2561 To be retained for further consideration each metric had to pass all screening criteria for at least one of
2562 the Site Groups:
2563 • All Wetlands-Conterminous US
2564 • All Estuarine Wetlands (EH + EW)
2565 • Palustrine, Riverine, and Lacustrine Herbaceous Wetlands (PRLH)
2566 • Palustrine, Riverine, and Lacustrine Woody Wetlands (PRLW)
2567
2568 For a subset of plant stressor metrics, the responsiveness criterion was given less weight compared to
2569 the range and repeatability criteria in evaluating metric utility. One of the criteria used in defining least
2570 disturbed (reference) sites was based on a metric describing relative alien cover (Chapter 4). Relative
2571 alien cover was also incorporated in some potential stressor metrics, so for these particular metrics,
2572 responsiveness was given limited consideration to avoid circularity.
100 2011 NWCA Technical Report DISCUSSION DRAFT
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2573
2574 Prior to beginning metric screening, we examined histograms of the distributions of values for the 531
2575 vegetation metrics. Most were strongly non-normal; consequently, nonparametric statistical (e.g.,
2576 Kruskal-Wallis test) approaches were used in the screening analyses. Specific tests or evaluation criteria
2577 were developed for each screening test and are detailed under the subheadings below. R code was
2578 written to implement the screening tests, and results for all metrics were exported from R into a multi-
2579 page Excel Workbook for review.
2580
2581 6.5.1 Range Tests
2582 Metrics with limited range, too many zero values, or highly skewed distributions have been shown to
2583 generally be poor indicators of ecological condition. We used two tests to define sufficient (PASS),
2584 marginal (PASS-), and insufficient (FAIL) range for metric values.
2585
2586 • Test 1 - Identifies metrics with large proportion of 0 values or highly skewed distributions:
2587 o If the 75th percentile = 0, i.e., more than 75% of values are zero, then FAIL
2588 o // the 75th percentile = the minimum OR the 25th percentile = max (indicating 75% of
2589 values identical), then FAIL (ensures that a majority of values are not the same as the
2590 minimum or maximum to help eliminate variables that are highly skewed and mostly a
2591 single non-zero value;
2592 o If the median=0, then PASS-
2593
2594 • Test 2 - Identifies metrics with very narrow ranges
2595 o // the metric is a percent variable and (max-25th percentile) < 15%, then FAIL
2596 o If the metric is not a percent variable and (max-25th) < (max/3), then FAIL
2597
2598 If either Test 1 or 2 resulted in a FAIL, the final assignment for the metric was FAIL. If the first two
2599 screens in Test 1 resulted in a PASS, but the third screen a PASS-, the result was PASS-. To pass the range
2600 screen, each metric had to receive a PASS or PASS-.
2601
2602 6.5.2 Repeatability
2603 Useful metrics tend to have high repeatability, that is among site variability will be greater than
2604 sampling variability based on repeat sampling at a subset of sites (see Table 6-3, revisit sites). To
2605 quantify repeatability, NARS uses SignahNoise (S:N) or the ratio of variance associated with sampling site
2606 (signal) to the variance associated with repeated visits to the same site (noise) (Kaufmann et al. 1999).
2607 All sites are included in the signal, whereas only revisit sites contribute to the noise component. Metrics
2608 with high S:N are more likely to show consistent responses to human caused disturbance, and S:N values
2609 < 1 indicate that sampling a site twice yields as much or more metric variability as sampling two
2610 different sites (Stoddard et al. 2008).
2611
2612 In other NARS, S:N thresholds for retention of metrics have been set to reflect the variability in the
2613 assemblages being sampled, e.g., S:N > 4 or 5 for fish metrics, 2 for macroinvertebrate metrics (Stoddard
2614 et al. 2008). In the NWCA, because we had such a large number of metrics to evaluate, we set an initial
2615 criterion of S:N > 4. In practice, however, the observed S:N values for the vegetation metrics were much
2616 higher, so we ultimately set the metric retention criterion to S:N > 10, or > 5 if metric type was as yet
2617 unrepresented in the suite of metrics passing all selection criteria. For the NWCA, S:N for individual
2618 metrics was calculated using the R package "Ime4" (version 1.1-7, Bates et al. 2014). Each metric was
101 2011 NWCA Technical Report DISCUSSION DRAFT
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2619 used as a response variable with SITEJD (a site identifier) as the main factor in a random effects model.
2620 Then the variance components from the resulting model were used to calculate S:N.
2621
2622 6.5.3 Responsiveness
2623 The most fundamental test of the efficacy of a candidate metric is its capacity to discriminate degraded
2624 from relatively undisturbed ecosystems. Responsive candidate metrics effectively distinguish least
2625 disturbed (reference) from most disturbed sites (Stoddard et al. 2008). In the NWCA, the ability to
2626 differentiate least from most disturbed sites was evaluated based on p-values and Chi-values from a
2627 Kruskal-Wallis test (large sample approximation). The assessment of the discriminatory capability of
2628 individual metrics was also supported by ranking the separation of least and most disturbed sites based
2629 on boxplot comparisons, where the degree of overlap of medians and interquartile ranges (IQRs)
2630 between least and most disturbed sites provides a signal of the metric responsiveness (Klemm et al.
2631 2002).
2632
2633 R code was developed to automate a process to simulate comparison of boxplots for least and most
2634 disturbed sites, for each vegetation metric, and to rank the separation levels. Using the approach
2635 developed by Barbour et al. (1996) and outlined in Klemm et al. (2002), the medians and IQRs of the
2636 least and most disturbed sites were compared, and metrics were scored as follows:
2637
2638 • Score of 0 (lowest discriminatory power) - Complete overlap of each group's IQRs with the
2639 median of the other group
2640
2641 • Score of 1 - Only one median was overlapping with the IQRs of the other group
2642
2643 • Score of 2 - Neither median overlapped with the IQR of the other group, but the IQRs
2644 overlapped
2645
2646 • Score of 3 (highest discriminatory power) - IQRs did not overlap.
2647
2648 Metric responsiveness was evaluated using three acceptance thresholds:
2649
2650 1) Kruskal-Wallis p< 0.05
2651
2652 2) Chi-squared value from Kruskal-Wallis test >10, or >5 if metric type was as yet unrepresented in
2653 the suite of metrics passing all selection criteria
2654
2655 3) A boxplot separation score of 1, 2, or 3, unless metric type was unrepresented then a 0 value
2656 was permitted.
2657
2658 Among metrics passing the responsiveness screen, the Kruskal-Wallis p-values were often much lower
2659 and Chi-squared values were often much higher than acceptance thresholds. The boxplot separation
2660 scores for passing metrics were ranged from 1 to 3, with 2 being the most common value.
2661
2662 6.5.4 Redundancy
2663 It is generally agreed that metrics included in a MMI should not be strongly correlated, and r < |0.75 |is
2664 often a cut off point for metrics included in the same MMI (e.g., Stoddard et al. 2008; Pont et al 2009;
2665 Van Sickle 2010). Redundancy screening was primarily handled during the process of VMMI
102 2011 NWCA Technical Report DISCUSSION DRAFT
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2666
2667
2668
2669
2670
2671
2672
2673
2674
2675
2676
2677
2678
2679
2680
2681
2682
2683
2684
2685
2686
2687
development; metrics were screened to ensure that none of the metrics included in a particular
candidate VMMI had correlations greater than this threshold (see Section 7.2).
In addition, during metric screening, a subset of ~50 metrics that passed the range, repeatability, and
responsiveness tests, but which conveyed very similar information to other metrics were dropped,
particularly if they were not strong performers. This typically included metrics that were very similar
(absolute versus relative cover for trait based metrics) or which contained nested information, e.g.,
stressor metrics, such as, introduced versus alien (introduced + adventive) species. In such cases, the
metric which performed best on screening tests was selected. Where screening results were similar, the
metric that was most ecologically meaningful or easiest to collect or calculate was selected.
6.6 Metric Screening Results
Candidate condition and stress metrics, based on vegetation, that passed all screening tests for at least
one of the evaluation Site Groups (all wetlands, estuarine wetlands (EH + ES), PLRH wetlands, or PRLW
wetlands) were retained for consideration in further analyses. Condition metrics (Table 6-6) were used
in VMMI development (see Section 7.2). Stress metrics (Table 6-7) were considered as potential
components of the Nonnative Plant Stressor Indicator (see Chapters, Section 8.5).
Table 6-6. List of vegetation condition metrics that passed all screening tests described in Section 6.5 for at least
one evaluation Site Group. For metric descriptions see Section 6.8, Appendix D.
N_PEAT_MOSS_DOM
XCOV_BRYOPHYTES
IMPJJCHENS
XCOV_BAREGD
XDETPHJJTTER
TOTN SNAGS
Vegetation Condition Metrics that Passed Evaluation Screens
All Native Species Life History
TOTN_NATSPP
PCTN_NATSPP
XRCOV_NATSPP
RFRECLNATSPP
RIMP_NATSPP
XBCDIST NATSPP
Floristic Quality
XC_NAT
XC_ALL
XC_COV_NAT
XC_COV_ALL
FQAI_NAT
FQAI_ALL
FQAI_COV_NAT
FQAI_COV_ALL
Sensitivity or Tolerance
N_SEN
N_TOL
N_HTOL
PCTN_SEN
PCTN_TOL
XRCOV_SEN
XRCOV_TOL
XRCOV HTOL
PCTNJDBL
PCTN_FACW
PCTN_FAC
WETIND_COV_ALL
PCTN_GRAMINOID_NAT
XRCOV_GRAMINOID_NAT
PCTN_MONOCOTS_NAT
XRCOV_MONOCOTS_NAT
PCTN_HERB_NAT
XRCOV_HERB_NAT
N_VINE
N_SHRUB_COMB_NAT
PCTN_S H RU B_CO M B_N AT
XRCOV_SHRUB_COMB_NAT
N_TREE_UPPER
IMP_TREE_UPPER
PCTN_GYMNOSPERM
PCTN_ANNUAL
PCTN PERENNIAL NAT
2688
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2689
2690
2691
Table 6-7. List of vegetation stress metrics that passed all screening tests described in Section 6.5 for at least one
evaluation Site Group. For metric descriptions see Section 6.8, Appendix D.
2692
2693
2694
2695
2696
2697
2698
2699
2700
2701
2702
2703
2704
2705
2706
2707
2708
2709
2710
2711
2712
2713
2714
2715
Vegetation Stress Metrics that Passed Evaluation Scree.
All Normative Species Normative Species by Life History Groups
TOTN_ALIENSPP
XN_ALIENSPP
PCTN_ALIENSPP
RFRECLALIENSPP
XABCOV_ALIENSPP
RIMP_ALIENSPP
TOTN_AC
XN_AC
PCTN_AC
RFRECLAC
XABCOV_AC
XRCOV_AC
RIMP_AC
TOTN_AC
XN_AC
PCTN_AC
RFRECLAC
XABCOV_AC
XRCOV_AC
RIMP AC
XRCOV_OBLFACW_AC
XRCOV_FORB_AC
N_HERB_AC
PCTN_HERB_AC
XRCOV_HERB_AC
N_GRAMINOID_AC
PCTN_GRAMINOID_AC
XRCOV_OBLFACW_AC
N_MONOCOTS_AC
PCTN_MONOCOTS_AC
XRCOV_MONOCOTS_AC
N_DICOTS_AC
PCTN_DICOTS_AC
XRCOV_DICOTS_ALIEN
XRCOV_DICOTS_AC
PCTN_PERENNIAL_AC
XRCOV PERENNIAL AC
6.7 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).
Hawkins CP, Cao Y, Roper B (2010) Method of predicting reference condition biota affects the
performance and interpretation of ecological indices. Freshwater Biology 55: 1066-1085
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, Fulk FA, Herlihy AT, Kaufmann PR, Corimer SM (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
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
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2716
2717 Stoddard JL, Herlihy AT, Peck DV, Hughes RM, Whittier TR, Tarquinio E (2008) A process for creating
2718 multimetric indices for large-scale aquatic surveys. Journal of North American Benthological Society 27:
2719 878-891
2720
2721 R Core Team (2014) R: A language and environment for statistical computing. R Foundation for
2722 Statistical Computing, Vienna, Austria. (http://www.R-project.org/)
2723
2724 USEPA (2006) Wadeable Streams Assessment: A Collaborative Survey of the Nation's Streams. EPA 841-
2725 B-06-002. US Environmental Protection Agency, Washington, DC
2726
2727 Van Sickle J (2010) Correlated metrics yield multimetric indices with inferior performance. Transactions
2728 of the American Fisheries Society 139: 1802-1817
2729
2730
105 2011NWCA Technical Report DISCUSSION DRAFT
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2763
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2765
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2767
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6.8 Appendix D: NWCA Candidate Vegetation Metrics Evaluated in 2011
READ THIS: Key Information for Reading and Using This Appendix
• Important: This Appendix is intended only as a descriptive overview of the NWCA Candidate Vegetation
Metrics. Exact methods/formulas for calculations and 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, then 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 fewer than 5 vegetation plots were 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 corresponds to the metric name in the NWCA vegetation metrics data set.
• DESCRIPTION column provides 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 text in the DESCRIPTION column,
is provided. PARAMETER NAMES representing raw data that are included in calculations are
highlighted in BLUE and are defined in Section 5.12, Appendix B.
• Some calculated metrics listed in the METRIC NAME column are, in turn, used as components of
other calculated metrics.
• 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)
• Wetland Indicator Status is applied to taxa-region pairs based on species values for the National
Wetland Plant List Regions (see Section 5.7).
• Native status designations are applied to taxa-site pairs based on state-level native status for each
species (see Section 5.8).
• Coefficients of Conservatism (CCs, aka C-values) are applied to taxa-site pairs based on state
specific C-values for each species (see Section 5.9).
• METRIC TYPE column indicates whether the candidate metric describes ecological condition or stress.
• Metrics of the National Vegetation Multimetric Index (VMM) are highlighted in blue bold font
• Metrics included in the Nonnative Plant Stressor Indicator (NPSI) are highlighted in red bold font
106
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2785
METRIC NAME METRIC DESCRIPTION
CALCULATION (listed in White
Metric Row),
SPECIES TRAIT TYPE (if applicable,
indicated in Colored Banners)
METRIC TYPE
(C = condition,
S = stress)
SECTIONS 1 - 5
SECTION 1
Section 1.1
Metrics based on field data: FORM V-2 - NWCA VASCULAR
SPECIES PRESENCE AND COVER
TAXA COMPOSITION (RICHNESS,
FREQUENCY, COVER, DIVERSITY)
All Species/Taxonomic Groups
TOTN_SPP Richness-Total number of unique Count unique species across all
species across all 100-m2 plots plots
XN_SPP Mean number of species across all
100-m2 plots
MEDN_SPP Median number of species across all
100-m2 P|ot_s
SDN_SPP Standard deviation in number of
species across all 100-m2 plots
TOTN_GEN Total number of unique genera Count unique genera across all
across all 100-m2 plots plots
XN_GEN Mean number of unique genera
across all 100-m2 plots
MED_NGEN Median number of genera across all
ipo-m2 plots
SDN_GEN Standard deviation in number of
genera across 100-m2 plots
TOTN_FAM Total number of families across Count unique families observed
100-m2 plots across all plots
XN_FAM Mean number of families across
100-m2 P|ot_s
MEDN_FAM Median number of families across
100-m2 P|ot_s
SDN_FAM Standard deviation in number of
families across 100-m2 plots
XTOTABCOV Mean total absolute cover summed E COVER of all individual taxa
(summary data across all species across 100-m2 across 5 plots/5 plots
used in plots
calculation of
other metrics)
H_ALL Shannon-Wiener Diversity Index -
All species ^--i
s = number of species observed,/= j C
species i, p = proportion of
individuals (relative cover)
belonging to species /
J_ALL Evenness (Pielou) - All species
_ H<
S= number of species observed * - -
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METRIC NAME METRIC DESCRIPTION
D ALL
CALCULATION (listed in White
Metric Row),
SPECIES TRAIT TYPE (if applicable,
indicated in Colored Banners)
METRIC TYPE
(C = condition,
S = stress)
Simpson Diversity Index - All species
s = number of species observed, / =
species /, p = proportion of
individuals (relative cover)
belonging to species/
Within Assessment Area
dissimilarity based on species
composition = Mean of between-
plot Bray-Cutis (BC) Distance
(Dissimilarity) based on all species.
XBCDIST SPP
Calculate between-plot Bray Curtis
Distance for all plot pairs based on
species and plot level cover
values. Calculate mean of these
values to get mean within AA
distance:
BCih ~
SECTIONS 1.2 -
1.3
Section 1.2
TOTN_NATSPP
XN_NATSPP
MEDN_NATSPP
SDN_NATSPP
PCTN_NATSPP
RFRECLNATSPP
XABCOV_
NATSPP
XRCOV_NATSPP
RIMP_NATSPP
H_NAT
J_NAT
NATIVE STATUS
Native (NAT) Species/Taxonomic
Groups
Native Richness: Total number of
unique native species across all 100-
m2 plots
Mean number of native species
across 100-m2 plots
Median number of native species
across 100-m2 plots
Standard deviation in number of
native species across 100-m2 plots
Percent richness of native species
observed across 100-m2 plots
Relative frequency of occurrence
for native species as a percent of
total frequency (sum of all species)
Mean total absolute cover of native
species across 100-m2 plots
Mean relative cover of native
species across 100-m2 plots as a
percentage of total cover
Mean relative importance of all
native species
Shannon-Wiener Diversity Index -
Native species only
Evenness (Pielou) - Native species
only
Trait Information = Native Status
(see Table 5-4)
Count unique native (NAT) species
across all plots
(TOTN_NATSPP/TOTN_SPP) x 100
2 Frequencies of all (NAT
species/J Frequencies of all
species) x 100; Frequency for
individual species = % of 100-m2
plots in which it occurs.
I COVER of all individual native
(NAT) taxa across 5 plots/5 plots
(XABCOV_NATSPP/XTOTABCOV) x
100
(RFRECLNATSPP +
XRCOV_NATSPP)/2
See H_ALL
See J_ALL
C
C
C
C
C
C
C
C
C, Used in
VMM!
C
C
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METRIC NAME METRIC DESCRIPTION
D_NAT Simpson Diversity Index- Native
species only
' ................... within"AA"diss]imiia7ityba^ed"oin
native species only composition =
Mean of between plot Bray-Cutis
Distance (Dissimilarity) based on
native species only
CALCULATION (listed in White
Metric Row),
SPECIES TRAIT TYPE (if applicable,
indicated in Colored Banners)
See D_NAT
METRIC TYPE
(C = condition,
S = stress)
NATSPP
Section 1.3 Introduced (INTR), Adventive
(ADV), ALI EN ( I NTR + ADV),
Cryptogenic (CRYP)
Trait Information = Native Status
(see Ta ble 5-4)
TOTN_INTRSPP Introduced Richness: Total number Count unique introduced (INTR)
of unique introduced species species across all plots S
across all 100-m2 plots
XNJNTRSPP Mean number of introduced species
across 100-m2 plots
MEDNJNTRSPP Median number of introduced
species across 100-m2 plots
SDNJNTRSPP Standard deviation in number of
introduced species across 100-m2 S
plots
"PCTNJNTRSPP .......... PercInTrkhnlssTnTroduced'species [fbTNlTNTRSPP/TOTN^SPPl "x ~100 ................. ""
observed across 100-m2 plots
RFREQJNTRSPP Relative frequency of occurrence (£ Frequencies of all introduced
for introduced species as a percent (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 aN E COVER of all individual INTR taxa
INTRSPP introduced species across 100-m2 across 5 plots/5 plots S
plots
"xRcbvjNTRSPP Mlan7eiativ"ecover"ofaiiTNTR .............. [xABcbvjNTR?Pp7xTOTABCOVrx""
species across 100-m2 plots as a 100 S
percentage of total cover
RIMPJNTRSPP Mean relative importance of all (RFREQJNTRSPP +
introduced species XRCOVJNTRSPPJ/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
MEDN_ADVSPP Median number of adventive
species across 100-m2 plots
SDN_ADVSPP Standard deviation in number of
adventive species across 100-m2 S
plots
PCTN_ADVSPP Percent richness adventive species (TOTN_ADVSPP/TOTN_SPP) x 100
observed across all 100-m2 plots
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METRIC NAME METRIC DESCRIPTION
CALCULATION (listed in White
Metric Row),
SPECIES TRAIT TYPE (if applicable,
indicated in Colored Banners)
METRIC TYPE
(C = condition,
S = stress)
RFREQ_ADVSPP Relative frequency of adventive (2 Frequencies of all adventive
species occurrence across 100-m2 (ADV) species/2 Frequencies of all
plots species) x 100; Frequency for S
individual species = % of 100-m2
plots in which it occurs.
XABc6v_ Mean total absolute cover of aN E COVER of all individual ADV taxa
ADVSPP ADV species across 100-m2 plots .across 5 plots/5 plots
"XRCOVIADVSPP Mlan7eiativ"ecover"ofaii"ADV [xABCOvlADVS>"p7xTOTATcOV)"x
species or lowest taxonomic unit 100
across 100-m2 plots as a percentage
of total cover
RIMP_ADVSPP Mean relative importance of all (RFRECLADVSPP+
adventive species xR?9_Y_-ADYS_PP)/2
"TOTN^AUENSPP Aiyen"RTch7elT:"fot7l7umbe7of foTN^ADV^PP^'foTNUNTRSPP
unique alien (INTR +ADV) species S
across 100-m2 plots
TOLrENSPP Me^7un^"ofa^]ii^RTADV)" ""
species across 100-m2 plots
"MEDi\TALIENsipp" Me^ia77ul^r"c^7lieinliNTR"+ ""
ADV) species across 100-m2 plots
SDN_ALIENSPP Standard deviation in number of
alien (INTR +ADV) species
"PCTNIAUENSPP PercInTrkhne'sraNen'spe'cTes" (TbTOuFNlpp7f6TN™sTp)"x ""
across 100-m2 plots 100
RFRECL Relative frequency of alien (INTR+ (I Frequencies of all ALIEN
ALIENSPP ADV) species occurrence across species/2 Frequencies of all
100-m2 plots species) x 100; Frequency for S
individual species = % of 100-m2
plots in which it occurs.
XABCOV_ Mean total absolute cover of ALIEN E COVER of all individual ALIEN
ALIENSPP (INTR+ADV) species across 100-m2 taxa across 5 plots/5 plots S
plots
"XRCOVI Mla77eiatTve"cover"ofaii"ALIEN [xABravlAUEN?Pp7xf6TABCCJvy
ALIENSPP (INTR+ADV) species across 100-m2 x 100 S
plots as a percentage of total cover
"' iatTviymportance"ofaTl [RFRio3ALIENSPP+" ""
(INTR + ADV) species xR?9y_-Ay_EJ^SP_PJ/_2_
H_ALIEN Shannon-Wiener Diversity Index See H_ALL S
J_ALIEN Evenness (Pielou) SeeJ_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
MEDN_CRYPSPP Median number of cryptogenic
species across 100-m2 plots
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METRIC NAME METRIC DESCRIPTION
CALCULATION (listed in White
Metric Row),
SPECIES TRAIT TYPE (if applicable,
indicated in Colored Banners)
METRIC TYPE
(C = condition,
S = stress)
SDN_CRYPSPP Standard deviation in number of
cryptogenic species across 100-m2 S
plots
TCTN"1"CRYPSPP" PercInTrychneTs'c'ryptog'enic [fbTNl"cRYS"pp7TbfN3"pP)"x"lb"d """
species across 100-m2 plots
RFREQ_CRYPSPP Relative frequency of cryptogenic (£ Frequencies of all cryptogenic
species occurrence across 100-m2 (CRYP) species/2 Frequencies of
plots all species) x 100; Frequency for S
individual species = % of 100-m2
plots in which it occurs.
XABCOV_ Mean total absolute cover of aN E COVER of all CRYP taxa across 5
CRYPSPP CRYP species across 100-m2 plots P|ots/5_ P_l°ts
"xRcbvl'cRYPSPp" Mlan7eiatTvI7o7er"ofaii"cRYP [xABravlc'RyplpP/XTOTABcbvj
species across 100-m2 plots as a x 100 S
percentage of total cover
"RTMP'I'CRYPSPP Mlan7eiatTviymportance"ofaTl [RFRECL.CRYPSPP+" """
CRYP species xR?9_Y_-9_RYPSPP)/2
" AC: Richness:: totaTnumberof f6fNlcRYPSPP + ----------------
unique alien and cryptogenic TOTN ALIENSPP '
«nn ?! NPSI
species across 100-rrr plots
Me^7un^"ofAc"(AUENTciRYP) """
species across 100-m2 plots
um^r"ofAc"(ALIEN7 """
) species across 100-m2 plots
SDN_AC Standard deviation number of AC
(ALIEN + CRYP) species across 100- S
m2 plots
"pcTl\LAC PercInTRrchnIssAc"species"(AUEN" [fbTNl'cRYPSPP+'TofN-" ""
+ CRYP) across 100-m2 plots ALIENSPP/TOTN_SPP) x 100
RFRECLAC Relative frequency of alien and (£ Frequencies of all ALIEN +
cryptogenic species occurrence in CRYP species/^ Frequencies of all
flora based on five 100-m2 plots species) x 100; Frequency for ' .
INI PS I
individual species = % of 100-m2
plots in which it occurs.
XABCOV_AC Mean total absolute cover of all AC E COVER of all ALIEN + CRYP taxa
(ALIEN + CRYP) species across 100- across 5 plots/5 plots S
m2 plots
"XRCOVTAC" Mean relative cover of all AC (XABCOV^
(ALIEN + CRYP) species across 100- S, Used in
m2 plots as a percentage of total NPSI
cover
"rn'MPlAC Mla77eiatTvI7mportance"ofaTrAC (RTRTcLAT^XRCOV^Aq/i" """
LA.!rl^ +CRYP) species
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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METRIC NAME
Section 2
METRIC DESCRIPTION
ORISTIC QUA
CALCULATION (listed in White
Metric Row),
SPECIES TRAIT TYPE (if applicable,
indicated in Colored Banners)
METRIC TYPE
(C = condition,
S = stress)
mrormaiion =
Coefficients of Conservatism
(see Section 5.9); Native
Status (see Table 5-4)
Equation 1 General formula for Mean C
CQ•- coefficient of conservatism for n _ (X^ }/AT
each unique species/at sitey, N = \L^^^ij/l 1- V j
number of species at site./'
Equation 2 General formula for FQAI _
CQ•- coefficient of conservatism for ^ ~ , ,
each unique species /at s\tej, N = / ,(,(,iJ-J /V .•
number of species at site./
Equation 3 For weighted Mean C or FQAI wCCii = D C^C^
Replace CO/ with wCC,/. where p,y = *U y
relative frequency or relative cover
XC_NAT Mean Coefficient of Conservatism Equation 1
with native species only
XC_ALL Mean Coefficient of Conservatism Equation 1
with all species
XC_FREQ_NAT Relative frequency-weighted Mean Equation 1, Equation 3
Coefficient of Conservatism with C
native species only
XC_FREQ_AII Relative frequency-weighted Mean Equation 1, Equation 3
Coefficient of Conservatism with all C
species only
XC_COV_NAT Relative cover-weighted Mean Equation 1, Equation 3
Coefficient of Conservatism with C
native species only
XC_COV_AII Relative cover-weighted Mean Equation 1, Equation 3
Coefficient of Conservatism with all C
species
FQAI_NAT Floristic Quality Index with native Equation 2
species only
FQAI_ALL Floristic Quality Index with all Equation 2 C, Used in
species VMM!
FQAI_FREQ_NAT Proportional frequency-weighted Equation 2, Equation 3
Floristic Quality Assessment Index C
with native species only
FQAI_FREQ_ALL Proportional frequency-weighted Equation 2, Equation 3
Floristic Quality Assessment Index C
with all species only
FQAI_COV_NAT Proportional cover-weighted Equation 2, Equation 3
Floristic Quality Assessment Index C
with native species only
FQAI_COV_ALL Proportional cover-weighted Equation 2, Equation 3
Floristic Quality Assessment Index C
with all species
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-------
METRIC NAME METRIC DESCRIPTION
Section 3 STRESS
TOLERANCE/SENSITIVITY
CALCULATION (listed in White
Metric Row),
SPECIES TRAIT TYPE (if applicable,
indicated in Colored Banners)
Trait Information =
Coefficients of Conservatism
(Section 5.9)
METRIC TYPE
(C = condition,
S = stress)
N_HSEN
N_SEN
NJSEN
N_TOL
N_HTOL
PCTNJHSEN
PCTN_SEN
PCTNJSEN
PCTN_TOL
PCTN_HTOL
XABCOV_HSEN
XABCOV_SEN
XABCOV_ISEN
XABCOV_TOL
XABCOV_HTOL
XRCOV_HSEN
XRCOV_SEN
XRCOV_ISEN
XRCOV_TOL
XRCOV_HTOL
Number (Richness) Highly Sensitive
Species; C-value >= 9
Number (Richness) Sensitive
Species; C -value >= 7
Number (Richness) Intermediate
Sensitivity Species; C-value = 5 to 6
Number (Richness) Tolerant
Species; C -value <= 4
Number (Richness) Highly Tolerant
Species; C-value <= 2
Percent Richness Highly Sensitive
Species; C-value >= 9
Percent Richness Sensitive Species;
C-value >= 7
Percent Richness Intermediate
Sensitivity Species; C-value = 5 to 6
Percent Richness Tolerant Species;
C-value <= 4
Percent Richness Highly Tolerant
Species; C-value <= 2
Absolute Mean Cover Highly
Sensitive Species; C-value >= 9
Absolute Mean Cover Sensitive
Species; C-value >= 7
Absolute Mean Cover Intermediate
Sensitivity Species; C-value= 5 to 6
Absolute Mean Cover Tolerant
Species; C-value <= 4
Absolute Mean Cover Highly
Tolerant Species; C-value <= 2
Relative Mean Cover Highly
Sensitive Species; C >= 9
Relative Mean Cover Sensitive
Species; C-value >= 7
Relative Mean Cover Intermediate
Sensitivity Species; C-value = 5 to 6
Relative Mean Cover Tolerant
Species; C-value <= 4
Relative Mean Cover Highly
Tolerant Species; C-value <= 2
Count unique species that meet
criterion across 100-m2 plots
Count unique species that meet
criterion across 100-m2 plots
Count unique species that meet
criterion across 100-m2 plots
Count unique species that meet
criterion across 100-m2 plots
Count unique species that meet
criterion across 100-m2 plots
(N_HSEN/TOTN_SPP) x 100
(N_SEN/TOTN_SPP)xlOO
(N_ISEN/TOTN_SPP) x 100
(N_TOL/TOTN_SPP) x 100
(N_HTOL/TOTN_SPP) x 100
E COVER of species with C-value
>= 9 across 5 plots/5 plots
E COVER of species with C-value
>= 7 across 5 plots/5 plots
E COVER of species with C-value =
5 or 6 across 5 plots/5 plots
E COVER of species with C-value
<= 4 across 5 plots/5 plots
E COVER of species with C-value
<= 2 across 5 plots/5 plots
(XABCOV_HSEN/XTOTABCOV) x
100
(XABCOV_SEN/XTOTABCOV) x 100
(XABCOVJSEN/XTOTABCOV) x
100
(XABCOV_TOL/XTOTABCOV) x 100
(XABCOV_HTOL/XTOTABCOV) x
100
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
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METRIC NAME METRIC DESCRIPTION
SECTION 4 HYDROPHYTIC STA.
Obligate ( ), Facult;
(fft W), Facultative (FAC),
Facultative Upland (fft :U), Upland
CALCULATION (listed in White
Metric Row),
SPECIES TRAIT TYPE (if applicable,
indicated in Colored Banners)
rait Information = Wetla
Indicator Status (WIS) from
National Wetland Plant List
(Table 5-3); Native Status
(Table 5-4)
METRIC TYPE
(C = condition,
S = stress)
NJDBL
N_FACW
N_FAC
N_FACU
N_UPL
PCTNJDBL
PCTN_FACW
PCTN_FAC
PCTN_FACU
PCTN_UPL
XABCOV_OBL
XABCOV_FACW
XABCOV_FAC
XABCOV_FACU
XABCOV_UPL
XRCOV_OBL
XRCOV_FACW
XRCOV_FAC
XRCOV_FACU
XRCOV_UPL
Richness (number) of Obligate
species
Richness (number) of Facultative
Wetland species
Richness (number) of Facultative
species
Richness (number) of Facultative
Upland species
Richness (number) of UPL species =
UPL
Percent richness of Obligate species
Percent richness of Facultative
Wetland species
Percent richness of Facultative
species
Percent richness of Facultative
Upland species
Percent richness of UPL (= UPL + NL)
species
Mean Absolute Cover of Obligate
species
Mean Absolute Cover of Facultative
Wetland species
Mean Absolute Cover of Facultative
species
Mean Absolute Cover of Facultative
Upland species
Mean Absolute Cover of UPL
species
Mean Relative Cover of Obligate
species
Mean Relative Cover of Facultative
Wetland species
Mean Relative Cover of Facultative
species
Mean Relative Cover of Facultative
Upland species
Mean Relative Cover of UPL (= UPL
+ NL) species
Count unique OBL species across
100-m2 plots
Count unique FACW species
across 100-m2 plots
Count unique FACU species across
100-m2 plots
Count unique FAC species across
100-m2 plots
Count unique UPL species across
100-m2 plots
(N_OBL/TOTN_SPP) x 100
(N_FACW/TOTN_SPP) x 100
(N_FAC/TOTN_SPP) x 100
(N_FACU/TOTN_SPP) x 100
(N_UPL/TOTN_SPP)xlOO
E COVER of OBL species across 5
plots/5 plots
E COVER of FACW species across 5
plots/5 plots
E COVER of FAC species across 5
plots/5 plots
E COVER of FACU species across 5
plots/5 plots
E COVER of UPL species across 5
plots/5 plots
(XABCOV_OBL/XTOTABCOV) x 100
(XABCOV_FACW/XTOTABCOV) x
100
(XABCOV_FAC/XTOTABCOV) x 100
(XABCOV_FACU/XTOTABCOV) x
100
(XABCOV_UPL/XTOTABCOV) x 100
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
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METRIC NAME METRIC DESCRIPTION
WETIND_COV Wetland Index, Cover Weighted - all
species
lij= Importance Value = Mean
absolute cover species / in site/ E: =
Ecological score for species based
on WIS (OBL = 1, FACW = 2, FAC = 3,
FACU=4, UPL = 5)
CALCULATION (listed in White
Metric Row),
SPECIES TRAIT TYPE (if applicable,
indicated in Colored Banners)
WI =
METRIC TYPE
(C = condition,
S = stress)
WETIND_FREQ Wetland Index, Frequency
Weighted - all species
l:j= 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 =
WETIND_
COV NAT
Wetland Index, Cover Weighted -
native species only
lij= Importance Value = Mean
absolute cover for species / in site/
E: = Ecological score for species
based on WIS (OBL = 1, FACW = 2,
FAC = 3, FACU =4, UPL = 5)
P
£= 1
WETIND_ Wetland Index, Frequency
FREQ_NAT Weighted - native species only
lij= Importance Value = Frequency
for species / in site/ E: = Ecological
score for species based on WIS (OBL
= 1, FACW = 2, FAC = 3, FACU = 4,
UPL = 5)
WI
N OBLFACW AC
Number of Alien + Cryptogenic
Obligate and facultative wetland
species
Mean Absolute Cover of Alien +
Cryptogenic Obligate and
Facultative Wetland species
Count unique ALIEN and CRYP OBL
and FACW species across 100-m2
plots
XABCOV_
OBLFACW AC
E COVER of ALIEN and CRYP OBL
and FACW species across 5 plots/5
plots
""" .................
XRCOV_ Mean Relative Cover of Alien +
OBLFACW_AC Cryptogenic Obligate and
Facultative Wetland species
XTOTABCOV) x 100
115
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
METRIC NAME
SECTION 5
SECTION 5.1
N_GRAMINOID
N_GRAMINOID_
NAT
N_GRAMINOID_
AC
N_FORB
N_FORB_NAT
N_FORB_AC
N_HERB
N_HERB_NAT
N_HERB_AC
N_SSHRUB_
FORB
N_SSHRUB_
SHRUB
N_SHRUB
N_SHRUB_
COMB
N_SHRUB_
COMB_NAT
N_SHRUB_
COMB_AC
N_TREE_SHRUB
N_TREE
N_TREE_COMB
N_TREE_
COMB_NAT
METRIC DESCRIPTION
LIFE HISTORY
GROWTH HABIT
Graminoid richness
Native Graminoid richness
Alien and cryptogenic Graminoid
richness
Forb richness
Native Forb richness
Alien and cryptogenic Forb richness
Herbaceous plant (FORB +
GRAMINOID) species richness
Native Herbaceous species richness
Alien and cryptogenic Herbaceous
richness
Subshrub-forb richness
Subshrub-shrub richness
Shrub richness
Combined Shrub growth habits
richness
Native richness of Combined Shrub
growth habits richness
Alien and cryptogenic richness for
Combined Shrub growth habits
Tree-Shrub richness
Tree richness
Combined Tree and Tree-Shrub
richness
Combined Tree and Tree-Shrub
richness
CALCULATION (listed in White METR|CTYpE
Metric Row),
SPECIES TRAIT TYPE (if applicable,
-------
METRIC NAME
N_TREE_
COMB_AC
N_VINE
N_VINE_NAT
N_VINE_AC
N_VINE_SHRUB
N_VINE_
SHRUB_NAT
N_VINE_
SHRUB_AC
PCTN_
GRAMINOID
PCTN_
GRAMINOID_NAT
PCTN_
GRAMINOID_AC
PCTN_FORB
PCTN_FORB_
NAT
PCTN_FORB_AC
PCTN_HERB
PCTN_HERB_
NAT
PCTN_HERB_
AC
PCTN_SSHRUB_
FORB
PCTN_SSHRUB_
SHRUB
PCTN_SHRUB
PCTN_SHRUB_
COMB
PCTN_SHRUB_
COMB_NAT
PCTN_SHRUB_
COMB_AC
METRIC DESCRIPTION
Combined Tree and Tree-Shrub
richness
Vine richness
Vine richness
Vine richness
Vine-Shrub richness
Native Vine-Shrub richness
Alien and cryptogenic Vine-Shrub
richness
Graminoid percent richness
Native Graminoid percent richness
Graminoid percent richness
Forb percent richness
Native Forb percent richness
Alien and cryptogenic Forb percent
richness
Percent Herbaceous (FORB +
GRAMINOID) richness
Percent native Herbaceous richness
Percent alien and cryptogenic
Herbaceous richness
Subshrub-Forb percent richness
Subshrub-Shrub percent richness
Shrub percent richness
Combined Shrub richness
Percent native richness of
Combined Shrub growth habits
Percent alien and cryptogenic
richness for Combined Shrub
growth habits
CALCULATION (listed in White METR|CTYpE
Metric Row),
SPECIES TRAIT TYPE (if applicable,
-------
METRIC NAME
PCTN_TREE_
SHRUB
PCTN_TREE
PCTN_TREE_
COMB
PCTN_TREE_
COMB NAT
PCTN_TREE_
COMB_AC
PCTN_VINE
PCTN_VINE_NAT
PCTN_VINE_AC
PCTN_VINE_
SHRUB
PCTN_VINE_
SHRUB_NAT
PCTN_VINE_
SHRUB_AC
XABCOV_
GRAMINOID
XABCOV_
GRAMINOID_NAT
XABCOV_
GRAMINOID_AC
XABCOV_FORB
XABCOV_FORB_
NAT
XABCOV_FORB_
AC
XABCOV_HERB
XABCOV_HERB_
NAT
XABCOV_HERB_
AC
XABCOV_
SSHRUB_FORB
XABCOV_
SSHRUB_SHRUB
XABCOV_SHRUB
XABCOV_
SHRUB COMB
METRIC DESCRIPTION
Tree-Shrub percent richness
Tree percent richness
Combined Tree and Tree-Shrub
percent richness
Combined Tree and Tree-Shrub
percent richness
Combined Tree and Tree-Shrub
percent richness
Vine percent richness
Native Vine percent richness
Alien and cryptogenic Vine percent
richness
Vine-Shrub percent richness
Native Vine-Shrub percent richness
Alien and Cryptogenic Vine-Shrub
percent richness
Mean absolute Graminoid cover
Mean absolute native Graminoid
cover
Mean absolute alien and
cryptogenic Graminoid cover
Mean absolute FORB cover
Mean absolute native FORB cover
Mean absolute alien and
cryptogenic FORB cover
Mean absolute Herbaceous species
cover (FORB + GRAMINOID)
Mean absolute native Herbaceous
cover
Mean relative Herbaceous alien and
cryptogenic cover
Mean absolute Subshrub-Forb
cover
Mean absolute Subshrub-Shrub
cover
Mean absolute Shrub cover
Combined Shrub growth habits
absolute cover
CALCULATION (listed in White METR|CTYpE
Metric Row),
SPECIES TRAIT TYPE (if applicable,
-------
METRIC NAME METRIC DESCRIPTION
CALCULATION (listed in White
Metric Row),
SPECIES TRAIT TYPE (if applicable,
indicated in Colored Banners)
METRIC TYPE
(C = condition,
S = stress)
XABCOV_SHRUB_
COMB_NAT
XABCOV_SHRUB_
COMB_AC
XABCOV_TREE_
SHRUB
XABCOV_TREE
XABCOV_TREE_
COMB
XABCOV_TREE_
COMB_NAT
XABCOV_TREE_
COMB_AC
XABCOV_VINE
XABCOV_VINE_
NAT
XABCOV_VINE_
AC
XABCOV_VINE_
SHRUB
XABCOV_VINE_
SHRUB_NAT
XABCOV_VINE_
SHRUB_AC
XRCOV_
GRAMINOID
XRCOV_
GRAMINOID NAT
XRCOV_
GRAMINOID AC
XRCOV_FORB
XRCOV_
FORB NAT
XRCOV_FORB_AC
XRCOV_HERB
XRCOV_
HERB_NAT
XRCOV_HERB_AC
Mean absolute native Combined
Shrub growth habits cover
Mean absolute alien and
cryptogenic Combined Shrub
growth habits cover
Mean absolute Tree-Shrub cover
Mean absolute Tree cover
Combined Tree and Tree-Shrub
absolute cover
Combined native Tree and Tree-
Shrub absolute cover
Combined alien and cryptogenic
Tree and Tree-Shrub absolute cover
Mean absolute Vine cover
Mean native absolute Vine cover
Mean alien and cryptogenic
absolute Vine cover
Mean absolute Vine-Shrub cover
Mean absolute native Vine-Shrub
cover
Mean absolute alien and
cryptogenic Vine-Shrub cover
Mean relative Graminoid cover
Mean relative native Graminoid
cover
Mean relative alien and cryptogenic
Graminoid cover
Mean relative Forb cover
Mean relative native Forb cover
Mean relative alien and cryptogenic
Forb cover
Mean relative Herbaceous (FORB +
GRAMINOID) cover
Mean relative native Herbaceous
cover
Mean relative alien and cryptogenic
Herbaceous cover
E COVER of NAT SHRUB-COMB
species across 5 plots/5 plots
E COVER of ALIEN and CRYP
SHRUB_COMB species across 5
plots/5 plots
E COVER of TREE-SHRUB species
across 5 plots/5 plots
E COVER of TREE species across 5
plots/5 plots
E COVER of TREE_COMB species
across 5 plots/5 plots
E COVER of NATTREE_COMB
species across 5 plots/5 plots
E COVER of ALIEN and CRYP
TREE_COMB species across 5
plots/5 plots
E COVER of VINE species across 5
plots/5 plots
E COVER of NAT VINE species
across 5 plots/5 plots
E COVER of ALIEN and CRYP VINE
species across 5 plots/5 plots
E COVER of VINE-SHRUB species
across 5 plots/5 plots
E COVER of NAT VINE-SHRUB
species across 5 plots/5 plots
E COVER of ALIEN and CRYP VINE-
SHRUB species across 5 plots/5
plots
(XABCOV_GRAMINOID/
XTOTABCOV) x 100
(XABCOV_GRAMINOID_NAT/
XTOTABCOV) x 100
(XABCOV_GRAMINOID_AC/
XTOTABCOV) x 100
(XABCOV_FORB/XTOTABCOV) x
100
(XABCOV_FORB_NAT/
XTOTABCOV) x 100
(XABCOV_FORB_AC/XTOTABCOV)
xlOO
(XABCOV_HERB/XTOTABCOV) x
100
(XABCOV_HERB_NAT/
XTOTABCOV) x 100
(XABCOV_HERB_AC/XTOTABCOV)
xlOO
C
S
C
C
C
C
S
C
C
S
C
C
S
C
C
S
C
C
C
C
C
S
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2011 NWCA Technical Report
DISCUSSION DRAFT
-------
METRIC NAME METRIC DESCRIPTION
CALCULATION (listed in White
Metric Row),
SPECIES TRAIT TYPE (if applicable,
indicated in Colored Banners)
METRIC TYPE
(C = condition,
S = stress)
XRCOV_SSHRUB_
FORB
XRCOV_SSHRUB_
SHRUB
XRCOV_SHRUB
XRCOV_SHRUB_
COMB
XRCOV_SHRUB_
COMB_NAT
XRCOV_SHRUB_
COMB_AC
XRCOV_TREE_
SHRUB
XRCOV_TREE
XRCOV_TREE_
COMB
XRCOV_TREE_
COMB_NAT
XBCOV_TREE_
COMB_AC
XRCOV_VINE
XABCOV_VINE_
NAT
XABCOV_VINE_
AC
XRCOV_VINE_
SHRUB
XRCOV_VINE_
SHRUB NAT
XRCOV_VINE_
SHRUB AC
Mean relative Subshrub-Forb cover
Mean relative Subshrub-Shrub
cover
Mean relative Shrub cover
Mean relative Combined Shrub
growth habits cover
Mean relative native Combined
Shrub growth habits cover
Mean relative alien and cryptogenic
Combined Shrub growth habits
cover
Mean relative Tree-Shrub cover
Mean relative Tree cover
Mean relative Combined Tree and
Tree-Shrub cover
Mean relative Combined Tree and
Tree-Shrub cover
Mean relative Combined Tree and
Tree-Shrub cover
Mean relative Vine cover
Mean native relative Vine cover
Mean alien and cryptogenic relative
Vine cover
Mean relative Vine-Shrub cover
Mean native relative Vine-Shrub
cover
Mean alien and cryptogenic relative
Vine-Shrub cover
(XABCOV_SSHRUB_FORB/
XTOTABCOV) x 100
(XABCOV_SSHRUB_SHRUB/
XTOTABCOV) x 100
(XABCOV_SHRUB/XTOTABCOV) x
100
(XABCOV_SHRUB_COMB/
XTOTABCOV) x 100
(XABCOV_SHRUB_COMB_NAT/
XTOTABCOV) x 100
(XABCOV_S H RU B_CO M B_AC/
XTOTABCOV) x 100
(XABCOV_TREE_SHRUB/
XTOTABCOV) x 100
(XABCOV_TREE/XTOTABCOV) x
100
(XABCOV_TREE_COMB/
XTOTABCOV) x 100
(XABCOV_TREE_COMB_NAT/
XTOTABCOV) x 100
(XABCOV_TREE_COMB_AC/
XTOTABCOV) x 100
(XABCOV_VINE/XTOTABCOV) x
100
(XABCOV_VINE_NAT/XTOTABCOV)
xlOO
(XABCOV_VINE_AC/XTOTABCOV)
xlOO
(XABCOV_VINE_SHRUB/
XTOTABCOV) x 100
(XABCOV_VI N E_S H RU B_N AT/
XTOTABCOV) x 100
(XABCOV_VINE_SHRUB_AC/
XTOTABCOV) x 100
C
C
C
C
C
S
C
C
C
C
S
C
C
S
C
C
S
Section 5.2
DURATION
Trait Information = Duration
(Table 5-2); Native Status (Table
5-4)
N ANNUAL
Annual species richness
Native Annual richness
Count unique ANNUAL species
across 100-m2 plots
Count unique NAT ANNUAL
species across 100-m2 plots
Count unique ALIEN and CRYP
ANNUAL species across 100-m2
plots
C
C
N_ANNUAL_NAT
"N"ANNUAL"AC""
Alien and cryptogenic Annual
richness
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2011 NWCA Technical Report
DISCUSSION DRAFT
-------
METRIC NAME
N_ANN_BIEN
N_ANN_
BIEN NAT
N_ANN_
BIEN_AC
N_ANN_PEREN
N_ANN_
PEREN NAT
N_ANN_
PEREN_AC
N_PERENNIAL
N_PERENNIAL_
NAT
N_PERENNIAL_AC
PCTN_ANNUAL
PCTN_ANNUAL_
NAT
PCTN_ANNUAL_
AC
PCTN_ANN_BIEN
PCTN_ANN_
BIEN NAT
PCTN_ANN_
BIEN AC
PCTN_ANN_
PEREN
PCTN_ANN_
PEREN NAT
PCTN_ANN_
PEREN AC
PCTN_PERENNIAL
PCTN_
PERENNIAL NAT
PCTN_
PERENNIAL_AC
XABCOV_
ANNUAL
XABCOV_
ANNUAL_NAT
METRIC DESCRIPTION
Annual-Biennial richness
Native Annual-Biennial richness
Alien and cryptogenic Annual-
Biennial richness
Annual-Perennial richness
Native Annual-Perennial richness
Alien and cryptogenic Annual-
Perennial richness
Perennial richness
Native Perennial richness
Alien and cryptogenic Perennial
richness
Percent Annual richness
Percent native Annual richness
Percent alien and cryptogenic
Annual richness
Percent Annual-Biennial richness
Percent native Annual-Biennial
richness
Percent alien and cryptogenic
Annual-Biennial richness
Percent Annual-Perennial richness
Percent native Annual-Perennial
richness
Percent alien and cryptogenic
Annual-Perennial richness
Percent Perennial richness
Percent native Perennial richness
Percent alien and cryptogenic
Perennial richness
Mean absolute Annual cover
Mean absolute native Annual cover
CALCULATION (listed in White METR|CTYpE
Metric Row),
SPECIES TRAIT TYPE (if applicable,
-------
METRIC NAME METRIC DESCRIPTION
CALCULATION (listed in White
Metric Row),
SPECIES TRAIT TYPE (if applicable,
indicated in Colored Banners)
METRIC TYPE
(C = condition,
S = stress)
XABCOV_
ANNUAL_AC
XABCOV_ANN_
BIEN
XABCOV_ANN_
BIEN_NAT
XABCOV_ANN_
BIEN_AC
XABCOV_ANN_
PEREN
XABCOV_ANN_
PEREN_NAT
XABCOV_ANN_
PEREN_AC
XABCOV_
PERENNIAL
XABCOV_
PEREN NIAL_NAT
XABCOV_
PEREN NIAL_AC
XRCOV_ANNUAL
XRCOV_ANNUAL_
NAT
XRCOV_ANNUAL_
AC
XRCOV_ANN_
BIEN
XRCOV_ANN_
BIEN_NAT
XRCOV_ANN_
BIEN_AC
XRCOV_ANN_
PEREN
XRCOV_ANN_
PEREN NAT
XRCOV_ANN_
PEREN AC
XRCOV_
PERENNIAL
XRCOV_
PERENNIAL NAT
Mean absolute alien and
cryptogenic Annual cover
Mean absolute Annual-Biennial
cover
Mean absolute native Annual-
Biennial cover
Mean absolute alien and
cryptogenic Annual-Biennial cover
Mean absolute Annual-Perennial
cover
Mean absolute native Annual-
Perennial cover
Mean absolute alien and
cryptogenic Annual-Perennial cover
Mean absolute Perennial cover
Mean absolute native Perennial
cover
Mean absolute alien and
cryptogenic Perennial cover
Mean relative annual cover
Mean relative native Annual cover
Mean relative alien and cryptogenic
Annual cover
Mean relative Annual-Biennial
cover
Mean relative native Annual-
Biennial cover
Mean relative alien and cryptogenic
Annual-Biennial cover
Mean relative Annual-Perennial
cover
Mean relative native Annual-
Perennial cover
Mean relative alien and cryptogenic
Annual-Perennial cover
Mean relative Perennial cover
Mean relative native Perennial
cover
E COVER of ALIEN and CRYP
ANNUAL species across 5 plots/5
plots
E COVER of ANN_BIEN species
across 5 plots/5 plots
E COVER of NAT ANN_BIEN
species across 5 plots/5 plots
E COVER of ALIEN and CRYP
ANN_BIEN species across 5 plots/5
plots
E COVER of ANN_PEREN species
across 5 plots/5 plots
E COVER of NAT ANN_PEREN
species across 5 plots/5 plots
E COVER of ALIEN and CRYP
ANN_PEREN species across 5
plots/5 plots
E COVER of PERENNIAL species
across 5 plots/5 plots
E COVER of NAT PERENNIAL
species across 5 plots/5 plots
E COVER of ALIEN and CRYP
PERENNIAL species across 5
plots/5 plots
(XABCOV_ANNUAL/XTOTABCOV) x
100
(XABCOV_ANNUAL_NAT/
XTOTABCOV) x 100
(XABCOV_ANNUAL_AC/
XTOTABCOV) x 100
(XABCOV_ANN_BIEN/
XTOTABCOV) x 100
(XABCOV_ANN_BIEN_NAT/
XTOTABCOV) x 100
(XABCOV_ANN_BIEN_AC/
XTOTABCOV) x 100
(XABCOV_ANN_PEREN/
XTOTABCOV) x 100
(XABCOV_ANN_PEREN_NAT/
XTOTABCOV) x 100
(XABCOV_AN N_PE RE N_AC/
XTOTABCOV) x 100
(XABCOV_PERENNIAL/
XTOTABCOV) x 100
(XABCOV_PERENNIAL_NAT/
XTOTABCOV) x 100
S
C
C
S
C
C
S
C
C
S
C
C
S
C
C
S
C
C
S
C
C
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DISCUSSION DRAFT
-------
METRIC NAME METRIC DESCRIPTION
XRCOV_ Mean relative alien and cryptogenic
PERENNIAL AC Perennial cover
CALCULATION (listed in White
Metric Row),
SPECIES TRAIT TYPE (if applicable,
indicated in Colored Banners)
(XABCOV_PERENNIAL_AC/
XTOTABCOV) x 100
METRIC TYPE
(C = condition,
S = stress)
Section 5.3
""
PLANT CATEGORY
Trait Information = Plant
Category (See Section 5.6.3);
Native Status (Table 5-4)
N_DICOTS
N_DICOTS_NAT
N_DICOTS_ALIEN
N_DICOTS_CRYP
N_DICOTS_AC
N_FERNS
N_FERNS_NAT
N_FERNS_INTR
N_GYMNOSPERM
N_LYCOPOD
N_HORSETAIL
N_MONOCOT
N_MONOCOTS_
NAT
N_MONOCOTS_
ALIEN
N_MONOCOTS_
CRYP
N_MONOCOTS_
AC
PCTN_DICOTS
PCTN_DICOTS_
NAT
PCTN_DICOTS_
ALIEN
PCTN_DICOTS_
CRYP
PCTN_DICOT_AC
PCTN_FERNS
Dicot richness
Native Dicot richness
Alien Dicot richness
Cryptogenic Dicot richness
Alien and Cryptogenic richness
Fern richness
Native Fern richness
Introduced FERN species richness
Gymnosperm richness
Lycopod richness
Horsetail richness
Monocot richness
Native Monocot richness
Alien Monocot richness
Cryptogenic Monocot richness
Alien and cryptogenic Monocot
richness
Dicot percent richness
Native Dicot percent richness
Alien Dicot percent richness
Cryptogenic Dicot percent richness
Alien and cryptogenic Dicot percent
richness
Fern percent richness
Count unique DICOT species
across 100-m2 plots
Count unique NAT DICOT species
across 100-m2 plots
Count unique ALIEN DICOT species
across 100-m2 plots
Count unique CRYP DICOT species
across 100-m2 plots
N_DICOT_ALIEN + N_DICOT_CRYP
Count unique FERN species across
100-m2 plots
Count unique native FERN species
across 100-m2 plots
Count unique introduced FERN
species across 100-m2 plots
Count unique GYMNOSPERM
species across 100-m2 plots
Count unique LYCOPOD species
across 100-m2 plots
Count unique HORSETAIL species
across 100-m2 plots
Count unique MONOCOT species
across 100-m2 plots
Count unique NAT MONOCOT
species across 100-m2 plots
Count unique ALIEN MONOCOT
species across 100-m2 plots
Count unique CRYP MONOCOT
species across 100-m2 plots
N_MONOCOT_ALIEN +
N MONOCOT CRYP
(N_DICOTS/TOTN_SPP) x 100
(N_DICOTS_NAT/TOTN_SPP) x 100
(N_DICOTS_ALIEN/TOTN_SPP) x
100
(N_DICOTS_CRYP/TOTN_SPP) x
100
(N_DICOTS_AC/TOTN_SPP) x 100
(N_FERNS/TOTN_SPP) x 100
C
C
S
C
S
C
C
S
C
C
C
C
C
S
S
S
C
C
S
S
S
C
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DISCUSSION DRAFT
-------
METRIC NAME METRIC DESCRIPTION
CALCULATION (listed in White
Metric Row),
SPECIES TRAIT TYPE (if applicable,
indicated in Colored Banners)
METRIC TYPE
(C = condition,
S = stress)
PCTN_FERNS_
NAT
PCTN_FERNS_
INTR
PCTN_
GYMNOSPERM
PCTN_LYCOPOD
PCTN_HORSETAIL
PCTN_
MONOCOTS
PCTN_
MONOCOTS_NAT
PCTN_
MONOCOTS_
ALIEN
PCTN_
MONOCOTS_
CRYP
PCTN_
MONOCOTS AC
XABCOV_DICOTS
XABCOV_
DICOTS_NAT
XABCOV_
DICOTS_ALIEN
XABCOV_
DICOTS_CRYP
XABCOV_
DICOTS AC
XABCOV_FERN
XABCOV_FERNS_
NAT
XABCOV_FERNS_
INTR
XABCOV_
GYMNOSPERM
XABCOV_
LYCOPODS
XABCOV_
HORSETAIL
XABCOV_
MONOCOT
XABCOV_
MONOCOTS_NAT
Native Ferns percent richness
Introduced Fern percent richness
GYMNOSPERM Percent Richness
Lycopod percent richness
Horsetail percent richness
Monocot percent richness
Native Monocot percent richness
Alien Monocot percent richness
Cryptogenic Monocot percent
richness
Alien and cryptogenic monocot
percent richness
Mean absolute cover Dicots
Mean absolute cover native Dicots
Mean absolute cover Alien Dicots
Mean absolute cover cryptogenic
Dicots
Mean absolute cover of alien and
cryptogenic Dicots
Mean absolute cover of Ferns
Mean absolute cover of native
Ferns
Mean absolute cover of introduced
Ferns
Mean absolute cover of
Gymnosperms
Mean absolute cover of Lycopods
Mean absolute cover of Horsetails
Mean absolute cover of Monocots
Mean absolute cover of native
Monocots
(N_FERNS_NAT/TOTN_SPP) x 100
(N_FERNS_INTR/TOTN_SPP) x 100
(N_GYNOSPERM/TOTN_SPP) x 100
(N_LYCOPOD/TOTN_SPP) x 100
(N_HORSETAIL/TOTN_SPP) x 100
(N_MONOCOTS/TOTN_SPP) x 100
(N_MONOCOTS_NAT/TOTN_SPP)
xlOO
(N_MONOCOTS_ALIEN/
TOTN_SPP) x 100
(N_MONOCOTS_CRYP/TOTN_SPP)
xlOO
(N_MONOCOTS_AC/TOTN_SPP) x
100
E COVER of DICOT species across
5 plots/5 plots
E COVER of NAT DICOT species
across 5 plots/5 plots
E COVER of ALIEN DICOT species
across 5 plots/5 plots
E COVER of CRYP DICOT species
across 5 plots/5 plots
XABCOV_DICOTS_ALIEN +
XABCOV DICOTS CRYP
E COVER of FERN species across 5
plots/5 plots
E COVER of native FERN species
across 5 plots/5 plots
E COVER of introduced FERN
species across 5 plots/5 plots
E COVER of GYMNOSPERM
species across 5 plots/5 plots
E COVER of LYCOPOD species
across 5 plots/5 plots
E COVER of HORSETAIL species
across 5 plots/5 plots
E COVER of MONOCOT species
across 5 plots/5 plots
E COVER of NAT MONOCOT
species across 5 plots/5 plots
C
S
C
C
C
C
C
S
S
S
C
C
S
S
S
C
C
S
C
C
C
C
C
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METRIC NAME METRIC DESCRIPTION
CALCULATION (listed in White
Metric Row),
SPECIES TRAIT TYPE (if applicable,
indicated in Colored Banners)
METRIC TYPE
(C = condition,
S = stress)
XABCOV_
MONOCOTS_
ALIEN
XABCOV_
MONOCOTS_
CRYP
XABCOV_
MONOCOTS_AC
XRCOV_DICOT
XRCOV_DICOTS_
NAT
XRCOV_DICOTS_
ALIEN
XRCOV_DICOTS_
CRYP
XRCOV_DICOTS_
AC
XRCOV_FERN
XRCOV_FERNS_
NAT
XRCOV_FERNS_
INTR
XRCOV_
GYMNOSPERM
XRCOV_LYCOPOD
XRCOV_
HORSETAIL
XRCOV_
MONOCOT
XRCOV_
MONOCOTS_NAT
XRCOV_
MONOCOTS_
ALIEN
XRCOV_
MONOCOTS_
CRYP
XRCOV_
MONOCOTS_AC
Mean absolute cover of alien
Monocots
Mean absolute cover of cryptogenic
Monocots
Mean absolute cover of alien and
cryptogenic Monocots
Mean relative cover Dicots
Mean relative cover native Dicots
Mean relative cover alien Dicots
Mean relative cover cryptogenic
Dicots
Mean relative cover of alien and
cryptogenic Dicots
Mean relative cover of Ferns
Mean relative cover of native Ferns
Mean relative cover of introduced
Ferns
Mean relative cover of
Gymnosperms
Mean relative cover of Lycopods
Mean relative cover of Horsetails
Mean relative cover of Monocots
Mean relative cover of native
Monocots
Mean relative cover of alien
Monocots
Mean relative cover of cryptogenic
Monocots
Mean relative cover of alien and
cryptogenic Monocots
E COVER of ALIEN MONOCOT
species across 5 plots/5 plots
E COVER of CRYP MONOCOT
species across 5 plots/5 plots
XABCOV_MONOCOTS_ALIEN +
XABCOV_MONOCOTS_CRYP
(XABCOV_DICOTS/XTOTABCOV) x
100
(XABCOV_DICOTS_NAT/
XTOTABCOV) x 100
(XABCOV_DICOTS_ALIEN/
XTOTABCOV) x 100
(XABCOV_DICOTS_CRYP/
XTOTABCOV) x 100
(XABCOV_DICOTS_AC/
XTOTABCOV) x 100
(XABCOV_FERNS/
XTOTABCOV) x 100
(XABCOV_FERNS_NAT/
XTOTABCOV) x 100
(XABCOV_FERNS_INTR/
XTOTABCOV) x 100
(XABCOV_GYMNOSPERMS/
XTOTABCOV) x 100
(XABCOV_LYCOPODS/
XTOTABCOV) x 100
(XABCOV_HORSETAILS/
XTOTABCOV) x 100
(XABCOV_MONOCOTS/
XTOTABCOV) x 100
(XABCOV_MONOCOTS_NAT/
XTOTABCOV) x 100
(XABCOV_MONOCOTS_ALIEN/
XTOTABCOV) x 100
(XABCOV_MONOCOTS_CRYP/
XTOTABCOV) x 100
(XABCOV_MONOCOTS_AC/
XTOTABCOV) x 100
S
S
S
C
C
S
S
S
C
C
S
C
C
C
C
C, Used in
VMM!
S
S
S
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DISCUSSION DRAFT
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METRIC NAME
Sections 6 - 8
SECTION
SECTION 7
METRIC DESCRIPTION
CALCULATION (listed in White
Metric Row),
SPECIES TRAIT TYPE (if applicable,
indicated in Colored Banners)
METRIC TYPE
(C = condition,
S = stress)
METRICS BASED ON FIELD DATA FROM FORM V-3: NWCA
VEGETATION TYPES (FRONT) AND NWCA GROUND SURFACE
ATTRIBUTES I
ETLAND TYPE HETEROGENEI
BASED ON PLOT-LEVEL NWCA
WETLAND TYPES (designated as
'Predominant S &T Class' on Form
N_SANDT Number of unique NWCA Wetland Count number of unique NWCA
Types in AA Wetland Types across the 5 plots
DOM_SANDT Dominant NWCA Wetland Type(s) Select dominant NWCA Wetland
in AA Types: Most frequent (greatest
number of plots), or in case of C
ties, the two most frequent
hyphenated
D_SANDT Simpson's Diversity - Heterogeneity
of NWCA Wetland Types in AA *
D = 1-W c
s = number of S&T classes present, / j'
= class i, p = proportion of S&T
Classes belonging to class /
H_SANDT Shannon-Wiener-Heterogeneity of j
NWCA Wetland Types in AA „, V ,
H = ~ / Pi^Pi
C
s = number of S&T classes present, /
= class i, p = proportion of S&T
Classes belonging to class /
J_SANDT Pielou Evenness - Heterogeneity of H1
NWCA Wetland Types in AA ^ ~ In5
5 = number of S&T classes observed
~
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 C
plots
XN_VASC_ Mean number of vascular
STRATA vegetation strata across plots
RG_VASC_ Range in number of vascular Maximum - minimum number of
STRATA vegetation strata found in all 100- vegetation strata across five 100- C
m2 plots m2 plots
XTOTCOV_VASC_ Mean total cover of all vascular (E cover for all vascular strata
STRATA strata across all 100-m2 plots)/5 plots
FREQ_ Frequency Submerged Aquatic (# of 100-m2 plots in which
SUBMERGED_AQ Vegetation SUBMERGED_AQ occurs/5 plots) x C
100
VEGETATION STRUCTURE/TYPES
126
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DISCUSSION DRAFT
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METRIC NAME METRIC DESCRIPTION
CALCULATION (listed in White
Metric Row),
SPECIES TRAIT TYPE (if applicable,
indicated in Colored Banners)
METRIC TYPE
(C = condition,
S = stress)
FREQ_FLOATING_
AQ
FREQ_LIANAS
FREQ_VTALL_VEG
FREQ_TALL_VEG
FREQ_HMED_
VEG
FREQ_MED_VEG
FREQ_SMALL_
VEG
FREQ_VSMALL_
VEG
XCOV_
SUBMERGED_AQ
XCOV_
FLOATING_AQ
XCOV_LIANAS
XCOV_VTALL_
VEG
XCOV_TALL_VEG
XCOV_HMED_
VEG
XCOV_MED_VEG
XCOV_SMALL_
VEG
XCOV_VSMALL_
VEG
IMP_
SUBMERGED_AQ
IMP_FLOATING_
AQ
IMP_LIANAS
IMP_VTALL_VEG
IMP_TALL_VEG
Frequency Floating Aquatic
Vegetation
Frequency Lianas, vines, and
vascular epiphytes
Frequency Vegetation > 30m tall
Frequency Vegetation > 15m to
30m tall
Frequency Vegetation > 5m to 15m
tall
Frequency Vegetation >2m to 5 tall
Frequency Vegetation 0.5 to 2m tall
Frequency Vegetation < 0.5m tall
Mean absolute cover Submerged
Aquatic Vegetation
Mean absolute cover Floating
Aquatic Vegetation
Mean absolute cover Lianas, vines,
and vascular epiphytes
Mean absolute cover Vegetation >
30m tall
Mean absolute cover Vegetation >
15m to 30m tall
Mean absolute cover Vegetation >
5m to 15m tall
Mean absolute cover Vegetation
>2m to 5 tall
Mean absolute cover Vegetation 0.5
to 2m tall
Mean absolute cover Vegetation <
0.5m tall
Importance Submerged Aquatic
Vegetation
Importance Floating Aquatic
Vegetation
Importance Lianas, vines, and
vascular epiphytes
Importance Vegetation > 30m tall
Importance Vegetation > 15m to
30m tall
(# of 100-m2 plots in which
FLOATING_AQ occurs/5 plots) x
100
(# of 100-m2 plots in which LIANAS
occurs/5 plots) x 100
(# of 100-m2 plots in which
VTALL_VEG occurs/5 plots) x 100
(# of 100-m2 plots in which
TALL_VEG occurs/5 plots) x 100
(# of 100-m2 plots in which
HMED_VEG occurs/5 plots) x 100
(# of 100-m2 plots in which
MED_VEG occurs/5 plots) x 100
(# of 100-m2 plots in which
SMALL_VEG occurs/5 plots) x 100
(# of 100-m2 plots in which
VSMALL_VEG occurs/5 plots) x
100
E cover of SUBMERGED_AQ
across 5 plots/5 plots
E cover of FLOATING_AQ across 5
plots/5 plots
E cover of LIANAS across 5 plots/5
plots
E cover of VTALL_VEG across 5
plots/5 plots
E cover of TALL_VEG across 5
plots/5 plots
E cover of HMED_VEG across 5
plots/5 plots
E cover of MED_VEG across 5
plots/5 plots
E cover of SMALL_VEG across 5
plots/5 plots
Icover of VSMALL_VEG across 5
plots/5 plots
(FREQ_SUBMERGED_AQ +
XCOV_SUBMERGED_AQ)/2
(FREQ_FLOATING_AQ +
XCOV_FLOATING_AQ)/2
(FREQ_LIANAS + XCOV_LIANAS)/2
(FREQ_VTALL_VEG +
XCOV_VTALL_VEG)/2
(FREQ_TALL_VEG +
XCOV_TALL_VEG)/2
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
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2011 NWCA Technical Report
DISCUSSION DRAFT
-------
METRIC NAME METRIC DESCRIPTION
CALCULATION (listed in White
Metric Row),
SPECIES TRAIT TYPE (if applicable,
indicated in Colored Banners)
METRIC TYPE
(C = condition,
S = stress)
IMP_HMED_VEG
IMP_MED_VEG
IMP_SMALL_VEG
IMP_VSMALL_
VEG
RXCOV_
SUBMERGED_AQ
RXCOV_
FLOATING_AQ
RXCOV_LIANAS
RXCOV_VTALL_
VEG
RXCOV_TALL_
VEG
RXCOV_HMED_
VEG
RXCOV_MED_
VEG
RXCOV_SMALL_
VEG
RXCOV_VSMALL_
VEG
D_VASC_STRATA
Importance Vegetation > 5m to
15m tall
Importance Vegetation >2m to 5 tall
Importance Vegetation 0.5 to 2m
tall
Importance Vegetation < 0.5m tall
Relative mean cover Submerged
Aquatic Vegetation
Relative mean cover Floating
Aquatic Vegetation
Relative cover Lianas, Vines, and
Vascular Epiphytes
Relative cover Vegetation > 30m tall
Relative cover Vegetation > 15m to
30m tall
Relative cover Vegetation > 5m to
15m tall
Relative cover Vegetation >2m to 5
tall
Relative cover Vegetation 0.5 to 2m
tall
Relative cover Vegetation < 0.5m
tall
Simpson's Diversity - Heterogeneity
of Vertical Vascular Structure in AA
based on occurrence and relative
cover of all strata in all plots
(FRECLHMED_VEG +
XCOV_HMED_VEG )/2
(FREQJVIED_VEG +
XCOV_MED_VEG)/2
(FRECLSMALL_VEG +
XCOV_SMALL_VEG)/2
(FRECO/SMALL_VEG +
XCOV_VSMALL_VEG)/2
(XCOV_SUBMERGED_AQ/
XTOTCOV_VASC_STRATA) x 100
(XCOV_FLOATING_AQ/
XTOTCOV_VASC_STRATA) x 100
(XCOV_LIANAS/
XTOTCOV_VASC_STRATA) x 100
(XCOV_VTALL_VEG/
XTOTCOV_VASC_STRATA) x 100
(XCOV_TALL_VEG/
XTOTCOV_VASC_STRATA) x 100
(XCOV_HMED_VEG/
XTOTCOV_VASC_STRATA) x 100
(XCOV_MED_VEG/
XTOTCOV_VASC_STRATA) x 100
(XCOV_SMALL_VEG/
XTOTCOV_VASC_STRATA) x 100
(XCOV_VSMALLJ
XTOTCOV_VASC_STRATA) x 100
&
o = i-£Pr
:
C
C
C
C
C
C
C
C
C
C
C
C
C
s = number of veg strata observed, /
= veg stratum /, p = relative cover
belonging to veg stratum /
H VASC STRATA
Shannon-Wiener - 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 /
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2011 NWCA Technical Report
DISCUSSION DRAFT
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METRIC NAME METRIC DESCRIPTION
CALCULATION (listed in White
Metric Row),
SPECIES TRAIT TYPE (if applicable,
indicated in Colored Banners)
METRIC TYPE
(C = condition,
S = stress)
J VASC STRATA
Pielou Evenness - Heterogeneity of
Vertical Vascular Structure in AA
based on occurrence and relative
cover of all strata in all plots
5=number of strata observed
Section 7.2
N_PEAT_MOSS_
DOM
FRECLPEAT_
MOSS_DOM
FRECL
BRYOPHYTES
FREQJ-ICHENS
FRECLARBOREAL
FRECLALGAE
FRECL
MACROALGAE
XCOV_
BRYOPHYTES
XCOV_LICHENS
XCOV_ARBOREAL
XCOV_ALGAE
XCOV_
MACROALGAE
IMP_
BRYOPHYTES
IMPJJCHENS
IMP_ARBOREAL
IMP_ALGAE
IMP_
MACROALGAE
Non-Vascular Groups
Number of plots where bryophytes
are dominated by Sphagnum or
other peat forming moss
Frequency of plots where
bryophytes are dominated by
Sphagnum or other peat forming
moss
Frequency of bryophytes growing
on ground surfaces, logs, rocks, etc.
Frequency of lichens growing on
ground surfaces, logs, rocks, etc.
Frequency of arboreal Bryophytes
and Lichens
Frequency of filamentous or mat
forming algae
Macroalgae (freshwater
species/seaweeds)
Mean absolute cover bryophytes
growing on ground surfaces, logs,
rocks, etc.
Mean absolute cover lichens
growing on ground surfaces, logs,
rocks, etc.
Mean absolute cover arboreal
Bryophytes and Lichens
Mean absolute cover filamentous or
mat forming algae
Mean absolute cover macroalgae
(freshwater species/seaweeds)
Bryophytes growing on ground
surfaces, logs, rocks, etc.
Lichens growing on ground
surfaces, logs, rocks, etc.
Arboreal Bryophytes and Lichens
Filamentous or mat forming algae
Macroalgae (freshwater
species/seaweeds)
Count number of plots where
PEAT_MOSS = Y
(N_PEAT_MOSS_DOM/5 plots) x
100
(# of 100-m2 plots in which
BRYOPHYTES occur/5 plots) x 100
(# of 100-m2 plots in which
LICHENS occur/5 plots) x 100
(# of 100-m2 plots in which
ARBOREAL occur/5 plots) x 100
(# of 100-m2 plots in which ALGAE
occurs/5 plots) x 100
(# of 100-m2 plots in which
MACROALGAE occurs/5 plots) x
100
E cover of BRYOPHYTES across 5
plots/5 plots
E cover of LICHENS across 5
plots/5 plots
I cover of ARBOREAL across 5
plots/5 plots
2 cover of ALGAE across 5 plots/5
plots
I cover of MACROALGAE across 5
plots/5 plots
(FRECLBRYOPHYTES +
XCOV_BRYOPHYTES)/2
(FREQJJCHENS +
XCOV_LICHENS)/2
(FRECLARBOREAL +
XCOV_ARBOREAL)/2
(FRECLALGAE + XCOV_ALGAE)/2
(FRECLMACROALGAE +
XCOV MACROALGAE)/2
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
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DISCUSSION DRAFT
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CALCULATION (listed in White
Metric Row),
SPECIES TRAIT TYPE (if applicable,
METRIC TYPE
(C = condition,
METRIC NAME
Section 8
Section 8.1
MIN_H2O_DEPTH
XH2O_DEPTH
XH2O_DEPTH_AA
MAX_H2O_
DEPTH
FRECLH2O
FRECLH2O_
NOVEG
FRECLH2O_
AQVEG
FRECLH2O_
EMERGVEG
MIN_COV_H2O
MAX_COV_H2O
XCOV_H2O
XCOV_H2O_
NOVEG
XCOV_H2O_
AQVEG
XCOV_H2O_
EMERGVEG
IMP_H2O
IMP_H2O_
NOVEG
METRIC DESCRIPTION
Water Cover and Depth
Minimum water depth
Mean Predominant water depth in
plots where water occurs
Mean Predominant water depth
across AA
Maximum water depth
Frequency of occurrence of water
across 100-m2 plots
Frequency of occurrence of water
and no vegetation
Frequency of occurrence of water
and floating/submerged aquatic
vegetation
Frequency of occurrence of water
and emergent and/or woody
vegetation
Minimum cover of water
Maximum cover of water
Total cover of water (percent of Veg
Plot area with water = a+b+c <
100%)
a) % Veg Plot area with water and
no vegetation
b) % Veg Plot area with water and
floating/submerged aquatic
vegetation
c) % Veg Plot area with water and
emergent and/or woody vegetation
Importance total cover of water
(percent of Veg Plot area with
water = a+b+c < 100%)
Importance a) % Veg Plot area with
water and no vegetation
indicated in Colored Banners)
Lowest value for
MINIMUM_DEPTH across five
100-m2 plots
IPREDOMINANT_DEPTH across
plots where standing water
occurs/number of plots where
standing water occurs
IPREDOMINANT_DEPTH across
plots all sampled 100-m2 plots/5
plots
Highest value for
MAXIMUM_DEPTH across five
100-m2 plots
(# of 100-m2 plots in which
TOTAL_WATER occurs/5 plots) x
100
(# of 100-m2 plots in which
WATER_NOVEG occurs/5 plots) x
100
(# of 100-m2 plots in which
WATER_AQVEG occurs/5 plots) x
100
(# of 100-m2 plots in which
WATER_EMERGVEG occurs/5
plots) x 100
Lowest value for TOTAL_WATER
across five 100-m2 plots
Highest value for TOTAL_WATER
across five 100-m2 plots
E cover of TOTAL_WATER across 5
plots/5 plots
E cover of WATER_AQVEG across
5 plots/5 plots
E cover of WATER_NOVEG across
5 plots/5 plots
E cover of WATER_EMERGVEG
across 5 plots/5 plots
(FRECLH2O + XCOV_H2O)/2
(FRECLH2OJMOVEG +
COV H2O NOVEG)/2
•H^
c
C
c
c
c
c
c
c
c
c
c
c
c
c
c
c
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DISCUSSION DRAFT
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METRIC NAME METRIC DESCRIPTION
IMP_H2O_AQVEG Importance b) % Veg Plot area with
water and floating/submerged
aquatic vegetation
CALCULATION (listed in White
Metric Row),
SPECIES TRAIT TYPE (if applicable,
indicated in Colored Banners)
(FRECLH2O_AQVEG +
XCOV_H2O_AQVEG)/2
METRIC TYPE
(C = condition,
S = stress)
IMP_H2O_ Importance c) % Veg Plot area with
EMERGVEG water and emergent and/or woody
vegetation
(FRECLH2O_EMERGVEG +
XCOV_H2O_EMERGVEG)/2
Section 8.2
Bareground and Litter
N_LITTER_TYPE Number of unique litter types Count the number of unique litter
observed across the five 100-m2 types (LITTER_THATCH,
plots UTTER_FORB, UTTER_CONIFER,
UTTER_DECID,
UTTER_BROADLEAF). Count each
type only once.
"X'DEPT'H'JITTER Mlan"depThoTiitteraao7s"ali"i-m"2" sum'uffERjDEPTHi"for"aTi"i-m2
quadrats in AA quadrats/total number of sampled C
quadrats (usually 10)
MEDDEPTH_ Median depth of litter across aN 1-
LITTER m2 quadrats in AA
FREQJ-ITTER Frequency of litter (#of 100-m2 plots in which
TOTAL_LITTER occurs/5 plots) x C
100
FREQ_BAREGD Frequency of bareground (#of 100-m2 plots in which any
oneofEXPOSED_SOIL;
EXPOS ED_G RAVEL; C
EXPOSED_ROCK occurs/5 plots) x
100
FREQ_EXPOSED_ Frequency exposed soil/sediment (# of 100-m2 plots in which
SOIL EXPOSED_SOIL occurs/5 plots) x C
100
FRQ_EXPOSED_ Frequency exposed gravel/cobble (# of 100-m2plots in which
GRAVEL (~2mmto25cm) EXPOSED_GRAVEL occurs/5 plots) C
x 100
FREQ_EXPOSED_ Frequency exposed rock (> 25cm) (# of 100-m2 plots in which
ROCK EXPOSED_ROCK occurs/5 plots) x C
100
FREQ_WD_FINE Frequency of fine woody debris (< (#of 100-m2 plots in which
5cm diameter) W?-F|NE_ occurs/5 plots) x 100
FREQ_WD_ Frequency of coarse woody debris (#of 100-m2 plots in which
COURSE (> 5cm diameter) W.?-C9A.?1.E. occurs/5 plots) x 100
XCOV_UTTER Mean Cover of litter E cover of TOTALJJTTER across 5
plots/5 plots
XCOV_BAREGD Mean cover of bareground E cover of EXPOSED_SOIL +
EXPOSED_GRAVEL+
EXPOSED_ROCK across 5 plots/5
plots
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DISCUSSION DRAFT
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METRIC NAME METRIC DESCRIPTION
CALCULATION (listed in White
Metric Row),
SPECIES TRAIT TYPE (if applicable,
indicated in Colored Banners)
METRIC TYPE
(C = condition,
S = stress)
XCOV_EXPOSED_
SOIL
XCOV_EXPOSED_
GRAVEL
XCOV_EXPOSED_
ROCK
XCOV_WD_FINE
XCOV_WD_
COARSE
IMP_LITTER
IMP_BAREGD
IMP_EXPOSED_
SOIL
IMP_EXPOSED_
GRAVEL
IMP_EXPOSED_
ROCK
IMP_WD_FINE
IMP_WD_
COARSE
Mean Cover exposed soil/sediment
Mean Cover exposed gravel/cobble
(~2mm to 25cm)
c) Cover exposed rock (> 25cm)
Mean Cover of fine woody debris (<
5cm diameter)
Mean Cover of coarse woody debris
(> 5cm diameter)
Importance of litter
Importance of bare ground
Importance exposed soil/sediment
Importance exposed gravel/cobble
(~2mm to 25cm)
Importance exposed rock (> 25cm)
Importance of fine woody debris (<
5cm diameter)
Importance of coarse woody debris
(>5cm diameter)
E cover of EXPOSED_SOIL across 5
plots/5 plots
E cover of EXPOSED_GRAVEL
across 5 plots/5 plots
E cover of EXPOSED_ROCK across
5 plots/5 plots
E cover of WD_FINE across 5
plots/5 plots
E cover of WD_COARSE across 5
plots/5 plots
(FRECLLITTER + XCOV_LITTER)/2
(FRECLBAREGD +
XCOV_BAREGD)/2
(FRECLEXPOSED_SOIL +
XCOV_EXPOSED_SOIL)/2
(FRCLEXPOSED_GRAVEL +
XCOV_EXPOS E D_G RAVE L)/2
(FRECLEXPOSED_ROCK +
XCO V_EX POS E D_ROC K)/2
(FRECLWD_FINE +
XCOV_WD_FINE)/2
(FRECLWD_COARSE+
XCOV_WD_COARSE)/2
C
C
C
C
C
C
C
C
C
C
C
C
SECTIONS 9 -11
SECTION 9
TOTN_XXTHIN_
SNAG
METRICS BASED ON RAW DATA FROM FORM V-4: NWCA SNAG
AND TREE COUNTS AND TREE COVER
Snag and tree metrics are calculated as means/100-m2plots 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.
DEAD/SNAG COUNT METRIC
Based on data from FORM V-4
section)
Total Number Dead tree or snags 5
to 10 cm DBH (diameter breast
height)
Total number of dead trees or snags
11 to 25cm DBH
Total number of dead trees or snags
26 to 50cm DBH
Total number of dead trees or snags
51 to 75cm DBH
Total number of dead trees or snags
76 to 100cm DBH
InumberofXXTHIN_SNAGS
across of all 100-m2 plots
Y numVeroTxfmN^NAGS "across"
of all 100-m2 plots
I number of THIN_SNAGS across
of aII 100-m2 plots
2 number of JR_SNAGS across of
all 100-m2 plots
"in'umber'of'fHTcK^NAGSacro
of all 100-m2 plots
_
TOTN_XTHIN_
SNAG
C
C
C
C
SNAG
"toTNJFT
SNAG
SNAG
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DISCUSSION DRAFT
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METRIC NAME METRIC DESCRIPTION
TOTN_XTHICK_ Total number of dead trees or snags
SNAG 101 to 200 cm DBH
TOTN_SNAGS Total number of dead trees and
snags
XN_XXTHIN_ Mean Number Dead tree or snags 5
SNAG to 10 cm DBH (diameter breast
height)
XN_XTHIN_SNAG Mean number of dead trees or
snags 11 to 25cm DBH
XN_THIN_SNAG Mean number of dead trees or
snags 26 to 50cm DBH
XN_JR_SNAG Mean number of dead trees or
snags 51 to 75cm DBH
XN_THICK_SNAG Mean number of dead trees or
snags 76 to 100cm DBH
XN_XTHICK_ Mean number of dead trees or
SNAG snags 101 to 200 cm DBH
XN_SNAGS Mean number of dead trees and
snags
CALCULATION (listed in White
Metric Row),
SPECIES TRAIT TYPE (if applicable,
indicated in Colored Banners)
InumberofXTHICK_SNAGS
across of all 100-m2 plots
2 number of all dead trees and
snags across all DBH classes
METRIC TYPE
(C = condition,
S = stress)
C
C
InumberofXXTHIN_SNAG/5
plots
I number of XTHIN_SN_AG/5 plots
I number of THIN_SNAG/5 plots
I number of JR_SNAG/5 plots
C
C
C
C
C
_ of THICK_SNAG/5 plots
I number of XTHICK_SNAG/5
plots
J_ number of dead trees and snags
across all DBH classes/5 plots
C
C
SECTION 10.1
TREE COVER METRICS
N TREESPP
Richness tree species
Count unique tree species (taxa)
across all 5 plots
Count unique tree species (taxa)
in VSMALL_TREE height class
across all 5 plots
Count unique tree species (taxa)
in SMALL_TREE height class across
all 5 plots
Count unique tree species (taxa) in
LMED_TREE height class across all
5 plots
Count unique tree species (taxa) in
HMED_TREE height class across all
5 plots
Count unique tree species (taxa) in
TALL_TREE height class across all 5
plots
Count unique tree species (taxa) in
VT_TREE height class across all 5
plots
Count unique tree species (taxa) in
GROUND LAYER (VSMALL_TREE
and SMALL_TREE height classes)
across all 5 plots
N VSMALL TREE
Richness tree species, trees < 0.5m
tall
N_SMALL_TREE Richness tree species, trees 0.5m to
2m tall
N_LMED_TREE Richness tree species, trees > 2 to
5m tall
N_HMED_TREE Richness tree species, trees > 5m to
15m tall
N_TALL_TREE Richness tree species, trees > 15m
to 30m tall
N_VTALL_TREE Richness tree species, trees > 30m
tall
N_TREE_
GROUND
Richness tree species in ground
layer (e.g., seedlings, saplings),
trees < 2m
133
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
METRIC NAME METRIC DESCRIPTION
CALCULATION (listed in White
Metric Row),
SPECIES TRAIT TYPE (if applicable,
indicated in Colored Banners)
METRIC TYPE
(C = condition,
S = stress)
N_TREE_MID Richness tree species in subcanopy Count unique tree species (taxa) in
layer, trees 2m to 15m tall MID LAYER (LMED_TREE and
HMED_TREE height classes) across
al[ 5 pjots
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
PCTN_TREE_ Percent richness of tree species
GROUND found in ground layer (e.g., (N_TREE_GROUND/N_TREESPP) x C
seedlings, saplings), trees < 2m 100
PCTN_TREE_MID Percent richness of tree species
found in subcanopy layer, trees 2m C
to 15m tall (N.-LREE_MID/N_TREESPP) x 100
PCTN_TREE_ Percent richness of tree species
UPPER found in subcanopy layer, trees > (N_TREE_UPPER/N_TREESPP) x C
15m 100
FREQ_VSMALL_ Frequency (proportion of plots) of (Number of 100-m2 plots in which
TREE VSMALL trees, trees < 0.5m tall any species of VSMALL trees C
occurs/5 plots) x 100
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 C
occurs/5 plots) x 100
FREQ_LMED_ 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 C
occurs/5 plots) x 100
FREQ_HMED_ Frequency (proportion of plots) of (Number of 100-m2 plots in which
TREE HMED, trees > 5m to 15m tall any species of HMED trees C
occurs/5 plots) x 100
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 trees occurs/5 C
plots) x 100
FREQ_VTALL_ Frequency (proportion of plots) of (Number of 100-m2 plots in which
TREE Frequency of individual, trees > any species of VTALL trees C
30m tall occurs/5 plots) x 100
FREQ_TREE_ Frequency (proportion of plots) of (Number of 100-m2 plots in which
GROUND ground layer trees < 2m any species of GROUND LAYER
(VSMALL or SMALL) trees occurs/5
plots) x 100
FREQ_TREE_MID Frequency (proportion of plots) of (Number of 100-m2 plots in which
subcanopy, trees 2m to 15m tall any species of MID LAYER (LMED
or HMED) trees occurs/5 plots) x
100
FREQJTREE_ 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
134
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
METRIC NAME METRIC DESCRIPTION
CALCULATION (listed in White
Metric Row),
SPECIES TRAIT TYPE (if applicable,
indicated in Colored Banners)
METRIC TYPE
(C = condition,
S = stress)
XCOV_VSMALL_
TREE
XCOV_SMALL_
TREE
XCOV_LMED_
TREE
XCOV_HMED_
TREE_
XCOV_TALL_TREE
XCOV_VTALL_
TREE_
XCOV_TREE_
GROUND
XCOV_TREE_MID
XCOV_TREE_
UPPER
IMP_VSMALL_
TREE
IMP_SMALL_TREE
IMP_LMED_TREE
IMP_HMED_TREE
IMP_TALL_TREE
IMP_VTALL_TREE
IMP_TREE_GROU
ND
IMP_TREE_MID
Mean absolute cover VSMALL trees,
trees < 0.5m tall
Mean absolute cover SMALL trees,
trees 0.5m to 2m tall
Mean absolute cover LMED trees,
trees > 2 to 5m tall
Mean absolute cover HMED trees,
trees > 5m to 15m tall
Mean absolute cover TALL trees,
trees > 15m to 30m tall
Mean absolute cover VTALL trees,
trees > 30m tall
Mean absolute cover trees in
ground layer (e.g., seedlings,
saplings), trees < 2m
Mean absolute cover trees in MID
layer, trees 2m to 15m tall
Mean absolute cover trees in
UPPER layer, trees >15m
Importance of VSMALL trees, trees
< 0.5m tall
Importance of SMALL trees, trees
0.5m to 2m tall
Importance of LMED trees ,trees > 2
to 5m tall
Importance of HMED trees, trees >
5m to 15m tall
Importance of TALL trees, trees >
15m to 30m tall
Importance of VTALL trees, trees >
30m tall
Importance of trees in GROUND
layer (e.g., seedlings, saplings),
trees < 2m
Importance of trees in MID layer,
trees 2m-15m tall
2 of cover for all tree species in
VSMALL height class across all
plots/5 plots
2 of cover for all tree species in
SMALL height class across all
plots/5 plots
2 of cover for §JJ tree species in
LMED height class across all
plots/5 plots
2 of cover for aN tree species in
HMED height class across all
plots/5 plots
2 of cover for §H tree species in
TALL height class across all plots/5
plots
2 of cover for §JJ tree species in
VTALL height class across all
plots/5 plots
2 of cover for aN tree species in
GROUND LAYER (VSMALL_TREE
and SMALL_TREE height classes)
across all plots/5 plots
2 of cover for aN tree species in
MID LAYER (LMED_TREE and
HMED_TREE height classes) across
all plots/5 plots
2 of cover for §JJ tree species in
UPPER LAYER (TALL_TREE and
VTALL_TREE height classes) across
all plots/5 plots
(FRECLVSMALL_TREE +
XCOV_VSMALL_TREE)/2
(FRECLSMALL_TREE +
XCOV_SMALL_TREE)/2
(FREQJJV1ED_TREE +
XCOV_LMED_TREE)/2
(FRECLHMED_TREE +
XCOV_HMED_TREE)/2
(FREQJTALL_TREE +
XCOV_TALL_TREE)/2
(FRECLVTALL_TREE +
XCOV_VTALL_TREE)/2
(FREQJTREE_GOUND +
XCOV_TREE_GROUND)/2
(FREQJTREE_MID +
XCOV_TREE_MID)/2
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
135
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
METRIC NAME METRIC DESCRIPTION
IMP TREE UPPER
Importance of trees in UPPER layer,
trees > 15m
CALCULATION (listed in White
Metric Row),
SPECIES TRAIT TYPE (if applicable,
indicated in Colored Banners)
(FREQJTREE_UPPER
XCOV_TREE_UPPER)/2
METRIC TYPE
(C = condition,
S = stress)
SECTION 10.2
TOTN_XXTHIN_
TREES
TOTN_XTHIN_
TREES
TOTN_THIN_
TREES
TOTNJR_TREES
TOTN_THICK_
TREES
TOTN_XTHICK_
TREES
TOTN_XXTHICK_
TREES
TOTN_TREES
XN_XXTHIN_
TREES
XN_XTHIN_TREES
XN_THIN_TREES
XNJR_TREES
XN_THICK_TREES
XN_XTHICK_
TREES
XN_XXTHICK_TRE
ES
XN_TREES
TREE COUNT METRICS
Total number of tree stems in
XXTHIN class, trees 5 to 10 cm DBH
(diameter breast height)
Total number of tree stems in
XTHIN class, trees 11 to 25cm DBH
Total number of tree stems in THIN
class, trees 26 to 50cm DBH
Total number of tree stems in JR
class, of trees 51 to 75cm DBH
Total number of tree stems in THICK
class, trees 76 to 100cm DBH
Total number of tree stems in
XTHICK class, trees 101 to 200 cm
DBH
Total number of tree stems in
XXTHICK class, of trees > 200 cm
DBH
Total number of tree stems across
all classes DBH
Mean number of tree stems in
XXTHIN class, trees 5 to 10 cm DBH
(diameter breast height)
Mean number of tree stems in
XTHIN class, trees 11 to 25cm DBH
Mean number of tree stems in THIN
class, trees 26 to 50cm DBH
Mean number of tree stems in JR
class, of trees 51 to 75cm DBH
Mean number of tree stems in
THICK class, trees 76 to 100cm DBH
Mean number of tree stems in
XTHICK class, trees 101 to 200 cm
DBH
Mean number of tree stems in
XXTHICK class, of trees > 200 cm
DBH
Mean number of tree stems across
all classes DBH
2 number of tree stems in
XXTHIN_TREE class across all
species and across all 100-m2 plots
2 number of tree stems in
XTHIN_TREE class across all
species and across 100-m2 plots
2 number of tree stems in
THIN_TREE class across all species
and across all 100-m2 plots
2 number of tree stems in
JR_TREE class across all species
and across all 100-m2 plots
2 number of tree stems in
THICK_TREE class across all
species and across all 100-m2 plots
2 number of tree stems in
XTHICK_TREE class across all
species and across all 100-m2 plots
2 number of tree stems in
XXTHICK_TREE lass across all
species and across all 100-m2 plots
2 number of tree stems across all
size classes, across all species, and
across all 100-m2 plots
TOTN_XXTHIN_TREES/5 plots
TOTN_XTHIN_TREES/5 plots
TOTN_THIN_TREES/5 plots
TOTN_JR_TREES/5 plots
TOTN_THICK_TREES/5 plots
TOTN_XTHICK_TREES/5 plots
TOTN_XXTHICK_TREES/5 plots
TOTN_TREES/5 plots
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
136
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
2786
2787
Chapter 7: Wetland Condition - Vegetation Multimetric Index
ONLY probability sites are
used to generate the
population estimates
B1I.ITV S1TFS
definition &
quarriifiealion
Itireshold
NOT PROBABILITY SITES
2788
2789
2790
2791
2792
2793
2794
2797
2798
2799
2800
Figure 7-1. The major components of the 2011 National Wetland Condition Assessment Analysis Pathway that
pertain to evaluating wetland condition are highlighted. A full-page, unhighlighted version of this figure may be
found on page 14 of this report.
7.1 Background-Vegetation Multimetric Index Development Approach
2795
Multimetric indices of ecological condition based on
biota have been widely used for other biological
assemblages (e.g., fish, birds, periphyton,
macroinvertebrates, etc.) and are a cornerstone of
USEPA National Aquatic Resource Surveys (NARS). For
MMIs (also known as IBIs - Index of Biotic Integrity),
ecological condition is defined relative to the biota in
least disturbed sites. In this chapter, we focus on the
development of a Vegetation Multimetric Index
(VMMI) as an indicator of wetland condition. Figure
7-1 illustrates the portion of the NWCA Analysis
Pathway that applies to 1) VMMI development, 2)
determination of ecological condition thresholds, and
3) the use of VMMI values, condition thresholds, and
site weights in estimating wetland area in good, fair,
or poor ecological condition.
2796
Several regional or state VMMIs have previously been developed and applied within the United States
(e.g., Mack 2007; Gara and Stapanian 2015; MPCA 2015; see Chapter 5: Section 5.1 for additional
example citations). Existing VMMIs for wetland or riparian systems are comprised of several metrics
describing different components or traits (representing aspects of plant species composition, floristic
137
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
2801 quality, native status, vegetation structure, and functional or life history guilds) of the vegetation.
2802 Candidate metrics of vegetation condition are evaluated for their utility in distinguishing least disturbed
2803 sites from those that are most disturbed. The most effective metrics representing different elements of
2804 vegetation ecology are typically combined into a VMMI reflecting overall ecological condition.
2805
2806 NWCA criteria for an effective VMMI were that it should:
2807 • Accurately reflect ecological condition (i.e., distinguish least disturbed (reference) sites from
2808 most disturbed sites),
2809 • Be parsimonious (i.e., based on a limited number of easy-to-measure metrics that describe
2810 condition in relation to least-disturbed condition), and
2811 • Account for biotic variability that is related to natural environmental gradients or to regional
2812 differences in least-disturbed condition.
2813
2814 Accounting for variability related to natural gradients or regional differences in least-disturbed condition
2815 (see Section 6.3), is particularly critical to VMMI development because the former can influence the
2816 performance of candidate metrics of condition and the latter has implications for setting appropriate
2817 VMMI thresholds for ecological condition classes.
2818
2819 A variety of methods have been used to develop MMIs for vegetation or for other biotic assemblages. In
2820 selecting an approach to use for the NWCA VMMI, three principal methods were explored:
2821
2822 • Stoddard et al. (2008) -Traditional NARS MMI development using reporting groups to account
2823 for environmental and wetland type variation.
2824
2825 • Hawkins et al. (2010) - An approach that uses MMI development criteria similar to the
2826 traditional NARS approach, but which uses multivariate, nonparametric (Random Forests)
2827 modeling to account for environmental and wetland type variation and to inform metric
2828 selection.
2829
2830 • Van Sickle (2010) - An adaptation of the Stoddard et al. (2008) method that evaluates numerous
2831 MMIs based on randomly selected or all possible metric combinations of an optimum or set
2832 number of metrics.
2833
2834 In initial analyses for the NWCA, preliminary VMMIs developed using the Random Forest method
2835 appeared to perform similarly to those developed using the Stoddard approach. However, the Random
2836 Forest approach is complex and can be difficult to communicate to general audiences. Also, it has
2837 received limited testing for wetland systems. Although potentially promising, we considered the
2838 Random Forest approach to need further research before application to VMMI development for
2839 wetlands at the national scale. In addition, the traditional MMI methods result in robust and repeatable
2840 MMIs, allow straightforward communication of results on ecological condition and provide consistency
2841 between the NWCA and other NARS.
2842
2843 Consequently, we developed an approach to generating and evaluating potential VMMIs for the 2011
2844 NWCA (see Section 7.2) that was adapted from the methods of Stoddard et al. (2008) and Van Sickle
2845 (2010). All analyses for VMMI development were conducted using the R software, version 3.1.1 (R Core
2846 Team 2014) using R code written for the NWCA.
2847
138 2011 NWCA Technical Report DISCUSSION DRAFT
-------
2848 7.1.1 Wetland Condition Assessment in the NWCA
2849 Evaluating wetland condition in the NWCA VMMI involved three major components: VMMI
2850 development, threshold determination, and condition estimates. These components are briefly outlined
2851 below along with a listing of the sections of this report where each is discussed:
2852
2853 VMMI Development (Sections 6.3 through 6.5, and Sections 7.2 and 7.3)
2854 • Account for natural gradients across the conterminous US using various NWCA Site Groups (e.g.,
2855 Aggregated Ecoregions, Aggregated Wetland Types, or Reporting Groups) (Section 6.3).
2856 • Divide site level vegetation data into calibration and validation data sets for use in evaluating
2857 candidate vegetation metrics and potential VMMIs (Section 6.4).
2858 • Evaluate candidate metrics to identify those with utility for use in potential VMMIs (Section 6.5).
2859 • Construct and evaluate potential VMMI(s) across all sites (nationally) and within various NWCA
2860 Site Groups, then select the final VMMI(s) for the 2011 NWCA (Sections 7.2 and 7.3).
2861
2862 Threshold Determination (Section 7.4)
2863 • Define threshold values for good, fair, and poor ecological condition for the final VMMI(s), based
2864 on least disturbed sites in each applicable Reporting Group.
2865
2866 Condition Estimates (see Section 7.5 and Chapter 9)
2867 • Use site weights from the survey design, condition thresholds, and VMMI values for each site to
2868 estimate wetland area in good, fair, and poor condition for the Nation, by Aggregated Ecoregion
2869 or Aggregated Wetland Type.
2870
2871
2872 7.2 Developing the Vegetation Multimetric Index (VMMI) - Methods
2873
2874 The NWCA used a two-step process in developing a set of candidate VMMIs. Table 7-1 lists the NWCA
2875 Site Groups for which VMMIs were developed and evaluated using the approaches adapted from
2876 Stoddard et al. (2008) and Van Sickle (2010). First, VMMIs were created within the hierarchy of NWCA
2877 Site Groups (reflecting various aspects of natural and regional variability) using a traditional NARS
2878 approach (Stoddard et al. 2008). We began by generating 10 to 30 potential VMMIs per Site Group. The
2879 potential VMMIs were constructed from combinations of 4 to 12 of the highest performing metrics
2880 (Sections 6.5 and 6.6) representing various metric types, and metrics which were not strongly correlated
2881 with one another (r < 10.751). The set of preliminary VMMIs for each Site Group were then evaluated for
2882 their ability to distinguish least from most disturbed sites based on Kruskal-Wallis tests and boxplot
2883 discrimination. Although, a number of VMMIs that performed adequately were observed for many of
2884 the Site Groups, it was not always clear that the best possible VMMI was obtained because it was
2885 logistically practical to generate only a few VMMIs for comparison in each group. Sites Groups that were
2886 based on wetland types tended to produce the most robust VMMIs.
2887
2888 Consequently, several wetland type Site Groups were evaluated further using an approach developed by
2889 Van Sickle (2010) to evaluate numerous potential MMIs and identify those with the highest
2890 performance. We refer to this method as the MMI Permutation Approach. For each Site Group, many
2891 potential VMMIs were created, including: 1) 5,000 VMMIs based on random combinations of metrics,
2892 for a given number of metrics (4, 6, 8, or 10) selected from the available list of candidate metrics (see
2893 Table 6-6), or 2) all possible VMMIs based on all possible metric combinations for a particular number of
2894 metrics. The VMMIs for each Site Group were evaluated using a series of performance tests.
139 2011 NWCA Technical Report DISCUSSION DRAFT
-------
2895
2896
2897
2898
Table 7-1. NWCA Site Groups for which potential VMMIs were developed and evaluated using Traditional (adapted
from Stoddard et al. (2008)) or Permutation (adapted from Van Sickle (2010)) approaches. Site Groups resulting in
the most robust VMMIs are denoted by stars (*), the National VMMI having the overall best performance.
Site Group Site Group Name
Group Type
ro
c
.0
]M
'-a
E
01
0.
«-§
01 01
00 0.
NATIONAL
EH + EW
CPL-PRLH +
EMU-PRLH
CPL-PRLH
EMU-PRLH
IPL-PRLH +
W-PRLH
IPL-PRLH
PSS
CPL-PRLW
EMU-PRLW
IPL-PRLW
W-PRLW
All Sites
All Sites
All - Estuarine
All - Estuarine Herbaceous
All - Estuarine Woody
All - Palustrine, Riverine, and Lacustrine
Herbaceous
Coastal Plain + Eastern Mountains & Upper
Midwest- Palustrine, Riverine, and Lacustrine
Herbaceous
Combined Aggregated
Wetland Types
Aggregated Wetland
Type/Reporting Group
Aggregated Wetland
Type/Reporting Group
itland Type
Combined Reporting
Groups
Coastal Plain - Palustrine, Riverine, and Lacustrine
Herbaceous
Eastern Mountains & Upper Midwest - Palustrine,
Riverine, and Lacustrine Herbaceous
Interior Plains + West - Palustrine, Riverine, and
Lacustrine Herbaceous
Interior Plains- Palustrine, Riverine, and Lacustrine
Herbaceous
West- Palustrine, Riverine, and Lacustrine
Herbaceous
All - Palustrine, Riverine, and Lacustrine Woody
All - Palustrine Shrub Scrub
Coastal Plain - Palustrine, Riverine, and Lacustrine
Woody
Eastern Mountains & Upper Midwest - Palustrine,
Riverine, and Lacustrine Woody
Interior Plains - Palustrine, Riverine, and Lacustrine
Woody
West- Palustrine, Riverine, and Lacustrine Woody
Coastal Plain
Eastern Mountains & Upper Midwest
'nterior Plain
West
Reporting Group
Reporting Group
Combined Reporting
Groups
Reporting Group
Reporting Group
Aggregated Wetland Type
NWCA Wetland Type
NWCA Wetland Type
Reporting Group
Reporting Group
Reporting Group
Reporting Group
Aggregated Ecoregion
Aggregated Ecoregion
igregated Ecoregion
Aggregated Ecoregion
**
*
*
;•
2899
2900
140
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
2901 Details of the MMI Permutation Approach for constructing and identifying robust VMMIs, from which to
2902 select the final VMMI for the 2011 NWCA are described in the remainder of this section.
2903
2904 For each of the Site Groups listed in the VMMI Permutation column of Table 7-1, the 47 vegetation
2905 condition metrics that passed the screening evaluation (Section 6.6) were further screened to tailor the
2906 candidate metric list to each specific Site Group. As in the initial screening, only calibration data (see
2907 Section 6.4) were used in this second evaluation which retained only metrics that distinguished least
2908 from most disturbed sites based on a Kruskal-Wallis significance level of 0.01 within a given Site Group.
2909
2910 Calibration data were used to score condition metrics on a 0 to 10 continuous scale within each NWCA
2911 Site Group (permutation column, Table 7-1). For each Site Group, the selected metrics were scored
2912 based on interpolation of metric values between the 5th and 95th percentiles across all calibration sites
2913 (Blocksom 2003). For metrics decreasing with increasing disturbance, the 95th percentile was scored as
2914 10 and the 5th as zero. For metrics that increased with increasing disturbance, the 5th percentile was
2915 scored as 10 and the 95th as zero. The resulting metric scoring was applied to the corresponding
2916 validation (see Section 6.4) data. A robust potential VMMI developed using this metric scoring should
2917 similarly distinguish least from most disturbed for both the calibration and validation data.
2918
2919 We adapted the procedure of Van Sickle (2010), in which sets of randomly selected metrics of various
2920 sizes are used to create multimetric indices (VMMIs) to identify the optimal number of metrics and the
2921 best-performing sets of metrics. First, for a given NWCA Site Group, we randomly selected sets of 4, 6, 8,
2922 and 10 metrics from the set of metrics passing screening tests. A random set of 10 metrics was first
2923 selected, and then 8 metrics were randomly selected from that set of 10. The set of 6 metrics was
2924 randomly selected from the 8 metric set, and the set of 4 was randomly selected from the 6 metric set.
2925 We repeated this process 5000 times for the 4, 6, 8, and 10 metric combinations, for a total of 20,000
2926 VMMIs. The VMMI for each randomly selected set of metrics consisted of summing metric scores and
2927 multiplying the result by (10/(number of metrics)) to place the MMI on a 100-point scale.
2928
2929 Based on the initial traditional VMMI runs (traditional column, Table 7-1), we found that none of the
2930 VMMIs constructed from best performing metrics ever had all metric types (Table 6-1) represented.
2931 Also, among these preliminary VMMIs those that encompassed greater numbers of metric types often
2932 did not perform as well as VMMIs with fewer metric types. Consequently, in the VMMI permutation
2933 procedure outlined above, we chose not to parse metrics into different types, but selected randomly
2934 from the full set of metrics.
2935
2936 For each of the 20,000 VMMIs generated for each Site Group by the permutation procedure, we
2937 calculated the maximum and mean Pearson correlations among metrics included in the VMMI as a
2938 gauge of metric redundancy. In an effort to avoid redundant metrics being included in the same VMMI,
2939 we filtered the results of the evaluation tests described below to only examine: 1) VMMIs with
2940 component metrics that had a maximum correlation between any two metrics of < 10.751, and 2) a
2941 mean correlation among metrics of < 10.5 |. In addition, we used data from the Revisit Sites to calculate
2942 the signal-to-noise ratio, as was done for metric evaluation (Section 6.5.2), to measure repeatability of
2943 each VMMI.
2944
2945 We evaluated sensitivity and precision for each generated VMMI. Sensitivity was assessed using an
2946 interval test (Kilgour et al. 1998; Van Sickle 2010), in which intermediate and most disturbed sites were
2947 compared with the reference (least disturbed sites) distribution. The interval test determines for each
2948 non-reference site VMMI score whether it is significantly lower than the 5th percentile of reference sites,
141 2011 NWCA Technical Report DISCUSSION DRAFT
-------
2949 assuming normally distributed scores among reference sites (Van Sickle 2010). This is a conservative
2950 test that accounts for variability around the estimate of the 5th percentile. The percentages of
2951 intermediate and most disturbed sites evaluated as different from reference were then used to assess
2952 sensitivity of each VMMI. We evaluated precision as the standard deviation of MMI scores among
2953 reference sites. This measure may influence the interval test above, with MMIs having less variation
2954 among reference sites tending to result in more non-reference sites being considered outside the
2955 reference range (van Sickle 2010). We examined plots of the number of metrics in a VMMI against the
2956 percentage of non-reference sites evaluated as different from reference and against the standard
2957 deviation of reference sites for patterns to aid in selecting the most appropriate number of metrics for
2958 an MMI for each Site Group examined.
2959
2960 The best performing VMMIs in each Site Group were identified by reviewing the mean and maximum
2961 correlations among metrics within a VMMI, the standard deviation and S:N for each VMMI, and the
2962 percent of most or intermediately disturbed sites that were distinguished from least disturbed sites. The
2963 top 6 to 10 VMMIs from each metric set size (4, 6, 8, and 10) were then plotted as series of boxplots
2964 depicting VMMI values of least and most disturbed sites. Boxplot series for each group included
2965 comparisons of least and most disturbed for (where applicable):
2966
2967 • Calibration versus validation data
2968 • 7 NWCA Wetland Types
2969 • 4 NWCA Aggregated Wetland Types
2970 • 4 NWCA Aggregated Ecoregions
2971 • NWCA Reporting Groups that combine wetland types and ecoregions
2972
2973 Taking all this information together, the best one or two VMMIs were selected for each Site Group
2974 evaluated using the permutation procedure ('Permutation' column, Table 7-1). This set of best VMMIs
2975 was then compared to select the final NWCA VMMI.
2976
2977 After evaluation of many thousands of potential VMMIs, there were 4 top candidates:
2978
2979 • A National VMMI (4 metrics)
2980 • Three separate Wetland Type VMMIs
2981 o Estuarine(EH + EW)VMMI(4or6metrics)
2982 o Palustrine, Riverine, Lacustrine Herbaceous VMMI (4 metrics)
2983 o Palustrine, Riverine, Lacustrine Woody VMMI (8 or 10 metrics)
2984
2985 The most effective VMMI was a national VMMI with four metrics that have wide applicability across
2986 numerous wetland types and regions. The top VMMIs based on NWCA Aggregated Wetland Types
2987 contained metrics similar to the national VMMI and also showed promise, but generally did not perform
2988 as well as the national VMMI. To ensure that the best National VMMI was obtained we reran the
2989 permutation procedures to calculate all possible VMMI combinations based on 4 metrics randomly
2990 selected from the 36 metrics that passed the second metric evaluation (see above).
2991
2992 The performance statistics for the final National VMMI were typically similar to, or better than, the
2993 performance statistics observed for the best VMMIs based on NWCA Aggregated Wetland Types. In
2994 addition, the National VMMI showed the least overlap between least and most disturbed sites for
2995 wetlands in the Interior Plains and West Aggregated Ecoregions.
142 2011 NWCA Technical Report DISCUSSION DRAFT
-------
2996
2997
2998
2999
3000
3001
3002
3003
3004
3005
3006
3007
3008
3009
3010
3011
3012
3013
3014
3015
3016
3017
3018
3019
3020
3021
3022
3023
3024
3025
3026
3027
3028
7.3 Final National VMMI - Results
A national level VMMI, which included four metrics with wide applicability (Table 7-2), was ultimately
selected as having the best overall performance in assessing wetland condition for the 2011 NWCA.
Three of the metrics decrease in value with disturbance and one increases. Calculation methods for
these three metrics can be found in Chapter 6, Section 6.8 Appendix D by referencing the metric names
indicated in parentheses in Table 7-2. These metric names are highlighted in blue and bolded in the
appendix to make them easier to locate.
Table 7-2. Four metrics included in the final NWCA Vegetation Multimetric Index (VMMI). Description of
calculation methods for these metrics can be found in Section 6.8, Appendix D. Note that metric scoring is
reversed for metrics that increase with disturbance.
Metric Name
Metric Description
Response to
Disturbance
Floristic Quality Assessment Index
(FQAI_ALL)
Relative Importance of Native Plants
RIMP_NATSPP)
FNumber of Plant Species Tolerant to
Disturbance (N_TOL)
Relative Cover of Native Monocots
(XRCOV_MONOCOTS_NAT)
Based on all species present at a site
Combines Relative Cover and Relative
Frequency for native species
Tolerance to disturbance defined as C-
value <<
Decreases
Decreases
Increase:
Relative Cover of native monocot species Decreases
Metrics are scored or standardized (see Section 7.2) on a continuous scale from 0 to 10, with higher
values reflecting less disturbed conditions. The floor and ceiling values for scoring each of these metrics
at the national scale are provided in Table 7-3. Recall, that for metrics that decrease with disturbance,
values above ceilings were given a score of 10 and values below the floor a score of 0. For metrics that
increase with disturbance, values below the floor are assigned a 10 and above the ceiling a 0. All other
metric values are interpolated to scores between 0 and 10.
Table 7-3. Floor and ceiling values for scoring final VMMI metrics based on range of values in the calibration set.
Metric
Floor
Ceiling
FQAI_ALL
RIMP_NATSPP
N_TOL
XRCOV MONOCOTS NAT
6.94
44.34
0
0.065
38.59
100
40.0
100
The National VMMI for each site was calculated on a continuous 0 to 100 scale:
VMMI = (FQAI_ALL_SC + RIMP_NATSPP_SC + N_TOL_SC + XRCOV_MONOCOTS_NAT_SC) * —
where, the '_SC' suffix is the scored value for a metric.
Performance results for the National VMMI are summarized in Table 7-4 for the conterminous US, and
three wetland type Site Groups (Estuarine, PRLH, and PRLW). The high S:N values reflect consistency in
the VMMI across repeat samplings. The low maximum and mean correlations among metrics indicate
each metric is contributing unique information about condition. The percentage of most or
intermediately disturbed sites distinguished from least disturbed sites, based on the conservative
Kilgour test, varies by wetland type group. The Palustrine, Riverine, and Lacustrine Herbaceous (PRLH)
group had the lowest separation of least and most disturbed sites. This pattern is likely influenced by
143
2011 NWCA Technical Report
DISCUSSION DRAFT
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3029
3030
3031
3032
3033
3034
higher disturbance levels among reference sites associated with the PRLH type, particularly in the
Interior Plains and West (e.g., see Chapter 4: Table 4-10 and Table 4-11, for relaxed criteria for least-
disturbed status).
Table 7-4. Summary statistics for the National VMMI. Statistics for wetland type groups are calculated based on
the National VMMI values for all sites in a particular group.
Site
Group
n sites by
disturbance
class
Mean
VMMI
(L sites)
SD
VMMI
(L sites)
S:N
VMMI
Max r
among
metrics
Mean r
among
metrics
% M sites
distinguished
from L sites
% I sites
distinguished
from L sites
3035
3036
3037
3038
3039
3040
3041
3042
3043
3044
3045
ALL
n=1138
EH+EW
n=345
I PRLH
n=358
PRLW
n=435
1=277, 1=529,
M=332
L=116, 1=128,
M=101
L=75, 1=169,
M=114
L=86, 1=232,
M=117
67.0
74.3
62.3
61.3
12.2
n=96
6.4
n=21
16.6
n=38
8.0
n=37
20.9
49.9
13.2
20.7
0.40
0.53
0.50
0.53
0.10
0.14
0.21
0.11
42.7
55.5
24.6
43.6
17.0
31.3
17.2
Site Groups defined in Table 7-1. L = least disturbed sites, I = intermediately disturbed sites, M=most disturbed
sites, SD =standard deviation, S:N = Signal:Noise (n=revisit sites), r = Pearson correlation. Percent of sites
significantly different from least-disturbed site distribution based on an interval test with alpha = 0.05 (Kilgour
et al. 1998; Van Sickle 2010).
Comparison of National VMMI values between calibration and validation data Figure 7-2, show similar
distributions and satisfactory discrimination between least and most disturbed sites. Patterns from this
comparison indicate consistent behavior for the VMMI across different data sets, suggesting potential
for robust performance with data collected in diverse wetlands going forward.
JOO-
50-
| 25
TO
OJ
?
-------
3046
3047
3048
3049
3050
3051
3052
3053
3054
3055
3056
3057
3058
The next step was to see how well the national VMMI described conditions for each NWCA Reporting
Group. We generated boxplots of VMMI values for least and most disturbed sites within the Reporting
Groups (Figure 7-5). There was reasonable separation between least and most disturbed sites for 8 of
the 10 groups. In the Estuarine Herbaceous (EH) wetland group, there was some overlap of the median
for least disturbed sites with upper interquartile of most disturbed sites. However, this was likely due to
wide range in most disturbed sites and the fact that a substantial proportion of the most disturbed sites
had little disturbance (see Chapter 4). The largest overlap occurred in the Interior Plains Palustrine
Herbaceous (IPLH) wetland group, where the 25th percentile of least disturbed sites overlapped with the
75th percentile of the most disturbed sites, and the whisker for least disturbed sites overlapped with the
median of most disturbed sites. This overlap was likely due to human-mediated disturbance patterns in
the Interior Plains and the consequent requirement to relax criteria for least disturbed designation for
that region (see Chapter 4).
100-
•o
_c
o
75
50-
B 25
CD
0>
Estuarine
Coastal
| Eastern Mtns [
West
10082 16 19116 20 37 55J16 23 21 27[25 42 12 13] 17 28 16 21
i i i i
Least Disturbed 4l Most Disturbed
3059
3060
3061
3062
3063
3064
3065
3066
3067
3068
3069
Figure 7-3. NWCA National VMMI values for least and most disturbed sites by NWCA Reporting Group. See Table
6-4 for definition of Reporting Groups. For each boxplot, the box is the interquartile (IQR) range, line in the box is
the median, and each of the whiskers represent the most extreme point a distance of no more than 1.5 x IQR from
the box. Values beyond this distance are considered outliers. Numbers are number of sampled least and most
disturbed sites (probability and not-probability) for each Reporting Group.
VMMI values for least disturbed sites varied widely across groups, particularly for median and range. To
account for this variation across the United States, threshold values for good, fair, and poor condition
were set within Reporting Groups based on the National VMMI values of the least disturbed sites in
each group.
145
2011 NWCA Technical Report
DISCUSSION DRAFT
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3070
3071
3072
3073
3074
3075
3076
3077
3078
3079
3080
3081
3082
3083
3084
3085
7.4 Thresholds for Good, Fair, Poor Wetland Condition
Wetland condition thresholds for each Reporting Group (Table 7-5) were set using NARS conventions
based on the distribution of VMMI Scores in least disturbed (reference) sites (see Figure 7-4, Stoddard
etal. 2006):
• Good = VMMI scores > 25th percentile of reference,
• Fair = VMMI scores from the 5th up to the 25th percentile of reference, and
• Poor = VMMI scores < 5th percentile of reference.
Least Disturbed (Reference)
Site Distribution
Figure 7-4. Criteria for setting VMMI thresholds for good, fair, and poor condition classes based on VMMI values
observed for Least Disturbed (Reference) Sites.
Table 7-5. Thresholds for Vegetation Multimetric Index (VMMI) values to delineate good, fair, and poor ecological
condition for sites in each of the NWCA Reporting Groups. Sites with VMMI values that fall from the 5th up to the
25th percentile for least disturbed (reference) sites are considered in fair condition.
NWCA
Reporting
Group
Description (Ecoregion by Wetland Type)
Poor Condition Good Condition
(VMMI < 5th (VMMI > 25th
Percentile Least Percentile Least
Disturbed Sites) Disturbed Sites)
ALL-EH
ALL-EW
CPL-PRLH
CPL-PRLW
EMU-
PRLH
All- Estuarine Herbaceous
All - Estuarine Woody
Coastal Plain - Palustrine, Riverine, and Lacustrine
Herbaceous
Coastal Plain - Palustrine, Riverine, and Lacustrine Woody
Eastern Mountains & Upper Midwest - Palustrine,
Riverine, and Lacustrine Herbaceous
65.0
56.0
PRLW
IPL-PRLH
IPL-PRLW
W-PRLH
W-PRLW
Riverine, and Lacustrine Woody
Interior Plains- Palustrine, Riverine, and Lacustrine
Herbaceous
Interior Plains- Palustrine, Riverine, and Lacustrine
Woody
West- Palustrine, Riverine, and Lacustrine Herbaceous
West- Palustrine, Riverine, and Lacustrine Woody
55.8
25.3
40.3
30.0
47.9
60.5
36.2
49.4
57.4
54.4
146
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
3086
3087 7.5 Ecological Condition Extent Estimates
3088
3089
3090
3091
3092
3093
3094
3095
3096
3097
3098
3099
3100
3101
3102
3103
3104
NLY probability sites are
used to generate the
population estimates
VMMI
threshold
definition
Figure 7-5. NWCA Analysis Pathway section where VMMI condition thresholds for each Reporting Group (see Table
7-5) are used to generate estimates of wetland area in good, fair, and poor ecological condition. A full-page,
unhighlighted version of this figure may be found on page 14 of this report.
The 2011 NWCA probability sites (n=967) are used to estimate wetland area in particular condition
classes. The thresholds for good, fair, and poor condition based on the Vegetation Multimetric Index
(VMMI) for each Reporting Group (see Table 7-5), are used in conjunction with site weights for the
probability sites from the NWCA survey design (see Chapters 1 and 9) to calculate extent estimates for
wetland condition (Figure 7-5). Site weights reflect the number of acres each site represents across the
total population of NWCA Wetland Types. Each NWCA probability site is assigned good, fair, or poor
ecological condition based on its VMMI value and the Reporting Group thresholds appropriate to the
site. Next, the site weights from the probability design are summed within condition class to estimate
the wetland area in good, fair, and poor condition. The survey design allows calculation of confidence
intervals around these condition estimates.
147
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
3105
3106
3107
3108
3109
3110
3111
3112
3113
3114
3115
3116
3117
3118
3119
3120
3121
3122
3123
3124
3125
3126
3127
3128
3129
Chapter 9, Section 9.2 provides more explanation of population estimates and site weights, as well as
illustrating how to interpret the NWCA condition results summarized as bar charts representing wetland
area (as number of acres or percent area) for each condition class for a specific NWCA Site Group, e.g.,
nationally, by Aggregated Ecoregions, etc. (see Figure 9-2, for example). Complete wetland condition
assessment results, including extent estimates (numbers of acres or percent of wetland area) for
wetland condition classes, are detailed in National Wetland Condition Assessment 2011: A Collaborative
Survey of the Nation's Wetlands (USEPA In Review).
Cumulative Distribution Function (CDF) Graphs (Sokal and Rohlf 1995) can be used, in addition to the bar
graph presentation of results in USEPA (In Review). CDFs illustrate the population extent estimates
(percent wetland area) with confidence intervals (Y-axis) across the continuous range of VMMI values
(X-axis) for particular NWCA groups of sites. Figure 7-6 shows the VMMI CDF for the national scale
results. CDFs are provided by Reporting Groups, NWCA Aggregated Wetland Types, and NWCA
Aggregated Ecoregions in Section 7.7, Appendix E. On each graph, the intersection of a VMMI value
from the X-axis and the percent wetland area from the Y-Axis provides an estimate of the percent of
wetland area with a VMMI score at or below that value. For example, in Figure 7-6, at the national scale
approximately 15% of the wetland area is represented by VMMI values less than 40, and about 58% of
wetland area is estimated to have VMMI values less than 60. Note that at the national scale the
confidence intervals are relatively narrow. Small sample sizes associated with some NWCA Site Groups
can influence the size of confidence intervals.
National
CDF Estimate
95% Confidence Limits
20
40 60
Vegetation MM I
Figure 7-6. Cumulative Distribution Function (CDF) of condition extent estimates, with confidence limits, of
wetland condition (VMMI) across the conterminous United States. Blue lines illustrate how to read graph.
148
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
3130 7.6 Literature Cited
3131
3132 Blocksom KA (2003) A performance comparison of metric scoring methods for a multimetric index for
3133 Mid-Atlantic Highlands streams. Environmental Management 31: 670-682
3134
3135 Gara BD, Stapanian MA (2015) A candidate vegetation index of biological integrity based on species
3136 dominance and habitat fidelity. Ecological Indicators 50: 225-232.
3137
3138 Hawkins CP, Cao Y, Roper B (2010) Method of predicting reference condition biota affects the
3139 performance and interpretation of ecological indices. Freshwater Biology 55: 1066-1085
3140
3141 Kilgour BW, Sommers KM, Matthews DE (1998) Using the normal range as a criterion for ecological
3142 significance in environmental monitioring and assessment. Ecoscience 5: 542-550
3143
3144 Mack, JJ (2007) Developing a wetland IBI with statewide application after multiple testing iterations.
3145 Ecological Indicators 7: 864-881.
3146
3147 MPCA (Minnesota Pollution Control Agency) (2015) Status and trends of wetlands in Minnesota:
3148 Vegetation Quality baseline. Wq-bwm-1-09 Minnesota Pollution Control Agency, St. Paul, MN
3149
3150 R Core Team (2014) R: A language and environment for statistical computing. R Foundation for
3151 Statistical Computing, Vienna, Austria (http://www.R-project.org/)
3152
3153 Sokal RR, Rohlf FJ (1995) Biometry. W.H. Freeman and Company, New York
3154
3155 Stoddard JL, Larsen DP, Hawkins CP, Johnson RK, & Norris RH (2006) Setting expectations for the
3156 ecological condition of streams: The concept of reference condition. Ecological Applications 16: 1267-
3157 1276.
3158
3159 Stoddard JL, Herlihy AT, Peck DV, Hughes RM, Whittier TR, Tarquinio E (2008) A process for creating
3160 multimetric indices for large-scale aquatic surveys. Journal of North American Benthological Society 27:
3161 878-891
3162
3163 Van Sickle J (2010) Correlated metrics yield multimetric indices with inferior performance. Transactions
3164 of the American Fisheries Society 139: 1802-1817
3165
3166 USEPA (In Review) National Wetland Condition Assessment 2011: A Collaborative Survey of the Nation's
3167 Wetlands.EPA-843-R-15-005. US Environmental Protection Agency, Office of Water, Washington, DC
3168
149 2011NWCA Technical Report DISCUSSION DRAFT
-------
3169
3170
3171
3172
3173
3174
7.7 Appendix E: Cumulative Distribution Function Graphs for VMMI
CDF graphs for the population estimates of wetland condition extent based on the Vegetation MMI are
presented by NWCA Reporting Group (blue), Aggregated Wetland Type (green), and Aggregated
Ecoregion (red). The CDF for the national scale is provided in Section 7.5.
NWCA Reporting Group: ALL-EH
8-
s.
— CDF Estimate
. . 95% Confidence Limits
20
40 60
Vegetation MMI
80
NWCA Reporting Group: ALL-EW
&
— CDF Estimate
. . 95% Confidence Limits
20
40 60
Vegetation MMI
80
3175
3176
3177
NWCA Reporting Group: CPL-PRLH
0>
Q_
CDF Estimate
. .. . 95% Confidence Limits
20
40 60
Vegetation MMI
80
OJ
o
0)
Q_
NWCA Reporting Group: CPL-PRLW
— CDF Estimate
. . . 95% Confidence Limits
20
40 60
Vegetation MMI
80
150
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
NWCA Reporting Group: EMU-PRLH
g-
8-
— CDF Estimate
. . . 95% Confidence Limits
1
20
1
40
Vegetation MMI
i
60
80
NWCA Reporting Group: EMU-PRLW
y
s
Q_
— CDF Estimate
. . . 95% Confidence Limits
I
20
40 60
Vegetation MMI
i
80
NWCA Reporting Group: IPL-PRLH
-------
NWCA Aggregated Wetland Type: EH
NWCA Aggregated Wetland Type: EW
CDF Estimate
. ... 95% Confidence Limits
c
i
s.
§
CDF Estimate
.... 96% Confidence Limits
20 40 60
Vegetation MMI
80
20
40 60
Vegetation MMI
so
NWCA Aggregated Wetland Type: PRLH
NWCA Aggregated Wetland Type: PRLW
CDF Estimate
.... 95% Confidence Limits
8-1
s-
CDF Estimate
95% Confidence Limits
20
40 60
Vegetation MMI
80
20
40 60
Vegetation MMI
3180
3181
152
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
3182
3183
C I
8
CD
Q_
NWCA Aggregated Ecoregion: CPL
CDF Estimate
95% Confidence Limits
20
40 60
Vegetation MMI
80
NWCA Aggregated Ecoregion: IPL
— CDF Estimate
... 95% Confidence Limits
20
40 60
Vegetation MMI
80
8-1
I
I
g
Is
°~ §
s-
NWCA Aggregated Ecoregion: EMU
CDF Estimate
.. . . 95% Confidence Limits
20 40 60
Vegetation MMI
80
NWCA Aggregated Ecoregion: W
— CDF Estimate
. . 95% Confidence Limits
20
40 60
Vegetation MMI
80
153
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
3184 This page was intentionally left blank for double-sided printing.
154 2011NWCA Technical Report DISCUSSION DRAFT
-------
3185
3186
Chapters: Indicators of Stress
Data acquisition and
QA continues
through analysis
ONLY probability sites are
used to generate the
population estimates
BILITY SITES
stressor
quantification
threshold
definition
NOT PROBABILITY SITES
3187
3188
3189
3190
3191
3192
3193
3194
3195
3196
3197
3198
3199
3200
3201
3202
3203
3204
3205
3206
3207
3208
3209
3210
3211
3212
3213
3214
3215
Figure 8-1. The major components of the 2011 National Wetland Condition Assessment Analysis Pathway
discussed in this chapter (i.e., stressor definition and quantification, and stressor-level threshold definition, which
enable stressor extent estimates). A full-page, unhighlighted version of this figure may be found on page 14 of this
report.
8.1 Background Information
Like other National Aquatic Resource Survey (NARS) assessments, the NWCA data was collected and
used specifically to identify connections between the presence of indicators of stress and ecological
condition. Indicators of stress 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
biological, chemical, and physical indicators of stress, the NWCA analysis examined a variety of stressor
data to detect factors likely affecting ecological condition. The use of physical, chemical, and biological
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 sources of
the stressor data used in the NWCA analysis were primarily from data collected during field sampling of
a site in the Assessment Area (AA) and its buffer. However, GIS provided other supporting data on land
use, presence of roads, and other characteristics of the landscape in a set area surrounding the point
that were also available to be used as indicators of stress.
Indicators and thresholds are used in different ways throughout the NWCA analysis. For example,
indicators of disturbance and disturbance thresholds are described in Chapter 4, and the Vegetation
Multimetric Index (VMMI), an indicator of condition, and condition thresholds are described in Chapter
7. In this chapter, we discuss indicators of stress and stressor-level thresholds. While some of the
155
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
3216 general methods used to develop indicators and thresholds are similar among specific applications (i.e.,
3217 for disturbance, condition, and stressors), the specific indicators and/or thresholds used for each
3218 application are different.
3219
3220 Indicators of stress are used as descriptors of the potential impact of anthropogenic activities on
3221 wetland condition. Although indicators of stress do not necessarily imply causation of ecological decline,
3222 they are often associated with impaired condition. For simplicity, they are sometimes referred to using
3223 the shorthand term 'stressors'. Indicators of stress are used to support analyses that provide three types
3224 of information (i.e., results), which will be discussed in detail in the following chapter (Chapter 9):
3225
3226 • Stressor Extent -an estimate (by percent of the resource or relative ranking of
3227 occurrence) of how spatially common an indicator of stress is based on the population
3228 design;
3229
3230 • Relative Risk-the probability (i.e., risk or likelihood) of having poor condition when the
3231 stressor-level class is high relative to when it is low; and,
3232
3233 • Attributable Risk-an estimate of the proportion of the population in poor condition
3234 that might be reduced if the effects of a particular stressor were eliminated (Van Sickle
3235 and Paulsen 2008).
3236
3237 Nine indicators of stress were developed for reporting stressor extent, and relative and attributable risk
3238 (Figure 8-1). In this chapter, we focus on documenting:
3239 • The selection process for indicators of stress (Section 8.2);
3240 • Steps to develop indicators of stress for each stressor category (Sections 8.3, 8.4, and 8.5),
3241 including:
3242 o Stressor definition
3243 o Data collection
3244 o Data preparation
3245 o Indicator or index development
3246 o Stressor-level threshold definition
3247 • How stressor indicators are used to report stressor extent estimates (Section 8.6).
3248
3249 Stressor extent is crucial for determination of relative and attributable risk. Discussion and an example
3250 calculation of relative and attributable risk are presented in Chapter 9. The 2011 results for stressor
3251 extent and stressor relative and attributable risk are presented in National Wetland Condition
3252 Assessment 2011: A Collaborative Survey of the Nation's Wetlands (USEPA In Review).
3253
3254
3255 8.2 Selection of Indicators of Stress
3256
3257 8.2.1 Conceptual Model Overview
3258 Because the magnitude of data generated from the NWCA was extensive, there were many potential
3259 indicators of stress from field data and GIS data. A conceptual model was developed to help guide the
3260 selection a few strong indicators of stress from all the possibilities, and to illustrate how data related to
3261 wetland condition estimates, stressor extent estimates, and relative and attributable risk are used
3262 (Figure 8-2). There were two types of stressor data collected as part of the 2011 NWCA; GIS data and
156 2011 NWCA Technical Report DISCUSSION DRAFT
-------
3263
3264
3265
3266
3267
3268
3269
3270
3271
3272
3273
3274
3275
3276
3277
3278
3279
3280
3281
3282
3283
3284
data collected in the field. GIS data represent landscape information, specifically human land uses, that
are posited to affect physical, chemical, and biological properties of wetlands. The NWCA field data were
used as indicators of stress (see Section 8.2.2 for an explanation of why this decision was made). While
the presence and magnitude of these stressors are expected to affect wetland condition, the
relationship between indicators of stress and condition was not explicitly determined as part of the
NWCA analysis. Wetland condition was independently estimated using a vegetation multimetric index
(VMMI) as discussed in Chapter 7. The presence and magnitude of measured indicators of stress at a
wetland site above stressor-specific thresholds in combination with site weights (discussed in detail in
Chapter 9) were used to determine the stressor extent estimates. Finally, both wetland condition
estimates and stressor extent estimates are used to calculate relative and attributable risk of each
indicator of stress as described in the following chapter.
Human Land Uses
(GIS Data)
Indicators of Stress
(NWCA Field Data)
Wetland Condition
Extent Estimates
(NWCA Field Data)
• agriculture
* commercial &
industrial
• forestry
• mining
• rangeland
• recreation
• residential
• roads
fw) = site weights
from
probability
design
• physical
o vegetation removal
o vegetation replacement
o damming
o ditching
o hardening
o filling / erosion
> chemical
o heavy metals
o soil phosphorus
• biological
o nonnative plants
• Vegetation
Multimetric
Index (VMMI)
Relative &
Attributable
Risk
Figure 8-2. Conceptual model of how specific data collected as part of the 2011 NWCA (red and yellow boxes
containing bulleted lists) are used to estimate Stressor Extent Estimates (purple box) and, ultimately, Relative &
Attributable Risk (teal box). Grey, dashed arrows indicate that a cause-and-effect relationship is expected to exist
among the data, but these relationships were not explicitly quantified as part of the 2011 NWCA data analysis.
Black arrows represent the explicit information flow (e.g., data represented in one box were used in the
calculations represented by the following box). The arrow with the black circle containing a red "w" indicates that
site weights from the probability design were used to calculate Stressor Extent Estimates.
157
2011 NWCA Technical Report
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3285 8.2.2 Choosing the Type of Data Used for Indicators of Stress
3286 For reporting, it is highly desirable that indicators of stress be as independent from one another as
3287 possible to avoid redundancy. For example, percent agriculture in the buffer or soil phosphorus
3288 concentrations could be used as an indicator of stress, but not both, because they are often strongly
3289 related and essentially represent the same anthropogenic stress. In other words, it was important to
3290 separate the cause of stress from the impact of the stress. With this simple principle, the human land
3291 uses collected using GIS data were separated from the data collected in the field (Figure 8-2, red and
3292 yellow boxes with bulleted text inside). Therefore, when choosing between the GIS data set and the field
3293 data set, it was determined that field data were more appropriate to use as indicators of stress for this
3294 assessment, as they were based on direct observations of condition at the randomly-selected sample
3295 point. NWCA field data were used to develop indicators of stress, with indicators representing physical,
3296 chemical, and biological categories. Each indicator of stress and the methods by which it was used to
3297 estimate stressor extent are described in detail in subsequent sections of this chapter:
3298
3299 • Physical (Section 8.3)
3300 o Vegetation Removal
3301 o Vegetation Replacement
3302 o Damming
3303 o Ditching
3304 o Hardening
3305 o Filling/Erosion
3306 • Chemical (Section 8.4)
3307 o Heavy Metals
3308 o Soil Phosphorus
3309 • Biological (Section 8.5)
3310 o Nonnative Plants
3311
3312 Although water chemistry was part of the NWCA field protocol, only 56% of the wetlands sampled had
3313 sufficient surface water to collect and analyze. For this reason, and because wetland hydroperiod-
3314 especially during the growing season when NWCA sampling occurred - can greatly influence water
3315 chemistry (e.g., nutrients can become highly concentrated during drawdowns), water chemistry was
3316 excluded from the core NWCA indicators. However, water chemistry was retained as a research
3317 indicator and specific results are discussed in Chapter 11 of this report.
3318
3319
3320 8.3 Physical Indicators of Stress
3321
3322 8.3.1 Defining Physical Indicators of Stress
3323 Physical site information was collected as part of the 2011 NWCA Buffer and Hydrology Protocols. To
3324 consolidate the extensive data into a few, meaningful indicators of stress that could be used for
3325 reporting, nearly all the data collected as part of these protocols was assigned to one of six indicator
3326 categories representing vegetation alterations or hydrologic alterations. In the following subsections,
3327 data collection, data preparation, index development, and stressor-level threshold definition for these
3328 physical indicators of stress are described.
3329
3330
3331
158 2011 NWCA Technical Report DISCUSSION DRAFT
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3332 8.3.2 Data Collection
3333 Physical indicators of stress include vegetation and hydrologic alterations to the wetland sites. These
3334 data were primarily observational and collected by Field Crews using the Buffer and Hydrology Protocols
3335 detailed in the NWCA Field Operations Manual (USEPA 2011a). Data collection was guided by extensive
3336 lists of items (that were marked when an item was observed) on both the Buffer and Hydrology Forms
3337 (Form B-l and Form H-l for buffer and hydrology, respectively; see Section 8.8 and Section 8.9. Field
3338 Crews recorded the presence of physical stressors in 13 proximity-weighted plots located at the center
3339 of the AA and along four 140-m transects aligned with cardinal directions from the AA center for the
3340 Buffer Protocol. Presence/absence of stressors was also recorded within the AA for the Hydrology
3341 Protocol.
3342
3343 8.3.3 Data Preparation
3344 To categorize physical indicators of stress, items from the Buffer Form and Hydrology Form were
3345 assigned to one of six indicators representing vegetation or hydrological alterations: vegetation removal,
3346 vegetation replacement, damming, ditching, hardening, and filling/erosion. Table 8-1 provides a
3347 description and the items from the field forms assigned to each of these six categories. While all the
3348 items from the Hydrology Form were assigned to hydrological alteration indicators (i.e., damming,
3349 ditching, hardening, and filling/erosion), the items from the Buffer Form were split among indicators of
3350 vegetation alteration and hydrological alteration.
3351
3352 Because the AA was established within a designated wetland, regardless of the wetland size, the buffer
3353 was often also in wetland. It is incorrect to assume that the buffer always represents upland. Regardless
3354 of whether the buffer is wetland or upland, anthropogenic disturbances in the buffer indicate that the
3355 point represented by the AA may be disturbed. Furthermore, the NWCA Field Operations Manual
3356 (USEPA 2011a) clearly instructs that a valid AA does not contain more than one hydrogeomorphic (HGM)
3357 class and may have up to 10% of upland or anthropogenic features (e.g., road, culverts, etc.). There were
3358 no restrictions on anthropogenic features in the buffer.
3359
3360 8.3.3.1 Decision-process for assigning form items to stressor categories
3361 Each item from the Buffer and Hydrology Form was assigned to one - and only one - stressor category
3362 based on the dominant type of disturbance (Table 8-1). To consistently and logically assign items from
3363 the Buffer and Hydrology Forms to one of the six stressor categories, several rules were applied:
3364
3365 • Both domesticated animal and mechanical removal of vegetation were considered
3366 anthropomorphic stress and placed in the Vegetation Removal category. Animal-mediated
3367 vegetation removal was a stressor if it was determined to be human influenced (e.g., grazing by
3368 cattle).
3369
3370 • A wholesale change in the natural mix of species native to the area (i.e., lawns, agricultural
3371 fields, gardens, landscaping, orchards, nursery, row crops, etc.) was classified as Vegetation
3372 Replacement.
3373
3374 • Disturbances leading to an artificial increase in the elevation of the water table, including
3375 human-created surface water and evidence of unnatural damming events (e.g., dead pines from
3376 human-influenced flooding), were classified as Damming.
3377
159 2011 NWCA Technical Report DISCUSSION DRAFT
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3378 • Any form of channeling water was considered ditching, including ditches, visual evidence of
3379 drainage tiling, piping and channelization. All were placed in the Ditching category.
3380
3381 • Dumping of material (e.g., soil, rocks, large-scale landfills) and water (e.g., waste water
3382 discharge pipes) were considered in the Filling/Erosion category.
3383
3384 • Any activity leading to surface hardening or compaction was placed in the Hardening category.
3385 This includes roads, trails trampling, animal tracks, and animal pugging.
3386
3387 • Any development (i.e., urban or residential) or stress thought to cause compaction were
3388 categorized as Hardening. Exposed pipelines were included in the Hardening category due to
3389 probable compaction and hardening (due to pads) during installation, maintenance, and
3390 inspections.
3391
3392 • In a single case, a brick wall (checked off as a fence on the form with a note defining it as a brick
3393 wall) was classified as Hardening due to the concrete footing required for stabilization.
3394
3395 Some stressors were more difficult to classify into one of the six categories. For example, if erosion was
3396 determined to likely stem from a human activity (e.g., irrigation, aquaculture), the stressor was placed in
3397 the Filling/Erosion category. Note that in some cases, observations, such as freshly deposited sediment,
3398 could be due to natural causes like storms.
3399
3400 In addition to the listed items on the Buffer and Hydrology Forms, Field Crews could record observations
3401 that were not listed using a write-in option called "Other". The Other items were assigned to stressor
3402 categories according to the same rules as the stressors specifically listed on the Buffer and Hydrology
3403 Forms. However, a number of Other items were dropped from consideration as stressors to include in
3404 the analysis including:
3405
3406 • fences, which were considered as not impacting vegetation or otherwise creating a stress;
3407
3408 • garbage (e.g., wrack, litter, shopping carts), which was deemed insignificant in terms of
3409 affecting the wetland condition;
3410
3411 • herbivory or disturbances associated with insects or native/feral animals (e.g., beaver, elk,
3412 hogs), which were considered natural occurrences; and
3413
3414 • other naturally occurring phenomena (e.g., sand dunes, rivers), which were sometimes listed in
3415 the Other category by Field Crews.
3416
3417 If a listed item could not be readily characterized or determined to be not a stress, it was not
3418 categorized as a stressor. Non-stressor items commonly recorded included, for example, ordinary mean
3419 high water mark, lake levels, and soil cracks.
3420
3421
160 2011NWCA Technical Report DISCUSSION DRAFT
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3422
3423
Table 8-1. Physical indicators of stress, their descriptions, and form items (i.e., from the H-l Hydrology or B-l
Buffer Forms) assigned to each indicator.
Indicator of
Stress
Description
B-l Buffer Form Items
Included
H-l Hydrology Form Items
Included
3424
3425
3426
3427
3428
3429
3430
3431
Vegetation
Replacement
Damming
Ditching
Hardening
Filling/Erosion
any field observation
related to loss, removal, or
damage of wetland
vegetation
any field observation of
altered vegetation within
the site due to
anthropogenic activities
any field observation
related to impounding or
impeding water flow from
or within the site
any field observation
related to draining water
any field observation
related to soil compaction,
including activities and
infrastructure that
primarily result in soil
hardening
any field observation
related to soil erosion or
deposition
gravel pit, oil drilling, gas
wells, underground mine,
forest clear cut, forest
selective cut, tree canopy
herbivory, shrub layer
browsed, highly grazed
grasses, recently burned
forest, recently burned
grassland, herbicide use,
mowing/shrub cutting,
pasture/hay, range
N/A
golf course, lawn/park, row
crops*, fallow field, nursery,
orchard, tree plantation
N/A
dike/dam/road/RR bed, water
level control structure,
wall/riprap
ditches, channelization,
inlets/outlets, point
source/pipe
gravel road, two lane road,
four lane road, parking
lot/pavement, trails, soil
compaction, off road vehicle
damage, confined animal
feeding, dairy, suburban
residential, urban/multifamily,
rural residential, impervious
surface input
excavation/dredging, fill/spoil
banks, freshly deposited
sediment, soil loss/root
exposure, soil erosion,
irrigation, landfill, dumping,
surface mine
dikes, berms, dams, railroad
beds, sewer outfall
irrigation, water supply, field
tiling, standpipe outflow,
corrugated pipe, box culvert,
outflowing ditches
animal trampling, vehicle ruts,
roads, concrete, asphalt
recent sedimentation,
excavation/dredging
*Although actively farmed wetlands did not meet criteria for NWCA Wetland Types, row crops may still have been
present in the buffer surrounding an AA or in small quantities (up to 10%) within the AA.
8.3.4 Index Development
Two indices were developed for the physical indicators of stress - one that applies to the buffer data
and the other that applies to the hydrology data. Depending on whether the indicator was based on
buffer data alone (i.e., vegetation removal and vegetation replacement), or hydrology and buffer data
(i.e., damming, ditching, hardening, and filling/erosion), one or both of the indices were used to score
161
2011 NWCA Technical Report
DISCUSSION DRAFT
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3432
3433
3434
3435
3436
3437
3438
3439
3440
3441
3442
3443
3444
3445
3446
3447
3448
3449
3450
3451
3452
3453
3454
3455
3456
3457
each of the six indicators of stress. We provide a short summary of how each index was calculated in the
following subsections.
8.3 A.I Buffer Index
The stressor observations recorded as part of the Buffer Protocol were proximity-weighted based on the
distance of the plot from the AA. For each indicator of stress and for each wetland site, the Buffer Index
score was calculated as the sum of proximity-weighted stressor observations (assigned to the stressor
category) divided by the total number of plots evaluated (i.e., 13). See Figure 8-3 for the values used in
proximity weighting and Table 8-2 for the thresholds the low stressor-level and high stressor-level
categories.
• 0.23 • 0.44 • 1.0 *1.0 "1.0 B0.44BO
23
Figure 8-3. Weights assigned to the 13 plots evaluated as part of the Buffer Protocol
8.3.4.2 Hydrology Index
Field Crews surveyed the entire AA and recorded all stressor observations as part of the Hydrology
Protocol. For each of the hydrologic alteration indicators of stress and for each wetland site, the
Hydrology Index score was calculated by summing the number of observed stressors (assigned to the
stressor category) at each site. See Table 8-2.
8.3.5 Stressor-Level Threshold Definition
For each of the Buffer and Hydrology Indices, two stressor-level thresholds were defined - one for "low"
and one for "high". Indicators of stress at sites that exceeded the "low stressor-level" threshold but
were under the threshold set for "high stressor-level" were categorized as "moderate".
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2011 NWCA Technical Report
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3458
3459
3460
3461
3462
3463
3464
3465
3466
3467
3468
3469
3470
3471
3472
3473
3474
3475
3476
3477
3478
3479
3480
3481
3482
3483
3484
3485
3486
3487
3488
3489
3490
3491
3492
3493
3494
3495
8.3.5.1 Low Stressor-Level Threshold
The stressor-level threshold for both indices was assigned using strict criteria, i.e., the stressor-level
threshold score was set to zero. In other words, for an indicator of stress at site to be considered low,
there were no observed stressors marked on either the Buffer or Hydrology Form.
8.3.5.2 High Stressor-Level Threshold
The high stressor-level threshold was assigned using best professional judgement, and the stressor-level
threshold differs between Buffer and Hydrology Indices. High stressor-level threshold values were set as
>0.1 for the Buffer Index and >1.0 for the Hydrology Index. A Buffer Index score of >0.1 means that, for
example, at least two stressors were observed in the closest proximity to the AA, or at least six stressors
were observed in the farthest proximity to the AA. On the other hand, Hydrology Index scores are
integers, and a value of >1.0 represents one or more observations of stressors within the AA.
8.3.5.3 Applying Stressor-Level Thresholds to Indicators of Stress
Because the vegetation alteration indicators of stress are based on buffer data alone, index scoring and
the application of the stressor-level threshold is straightforward. Hydrologic alteration indicators of
stress, on the other hand, which combine buffer and hydrology data, have two index scores and a more
complicated application of stressor-level thresholds. For these four indicators of stress (i.e., damming,
ditching, hardening, filling/erosion) at a site, both threshold criteria (buffer and hydrology) had to be
met for a stressor-level to be low, while meeting either buffer or hydrology threshold criteria place a
stressor in the high category (Table 8-2).
Table 8-2. Threshold definition and physical application to indicators of stress.
Stressor Group
Vegetation Alteration
Hydrologic Alteration
Indicators of Stress
Vegetation Replacement
Vegetation Removal
Damming
Ditching
Hardening
Filling/Erosion
Low Stressor-Level
Threshold
Buffer Index = 0
Buffer Index = 0
AND
Hydrology Index = 0
High Stressor-Level
Threshold
Buffer Index > 0.1
Buffer Index > 0.1
OR
Hydrology Index > 1.0
8.4 Chemical Indicators of Stress
8.4.1 Defining Chemical Indicators of Stress
Chemical indicators of stress are associated with the soil chemistry analyses conducted as part of the
Soils Protocol. Although the soil analyses provided extensive data, only the strongest indicators of stress
- heavy metals and soil phosphorus- were used for reporting. In the following subsections, data
collection, data preparation, index development, and stressor-level threshold definition for each
chemical indicator of stress is described.
8.4.2 Sample Collection and Analysis
Chemical indicators of stress include heavy metal and soil phosphorus concentrations in the wetland site
soil. Soil samples were collected by Field Crews from each layer greater than 8 cm thick from one
(Representative Pit) of four soil pits chosen to represent the entire AA according to the Soils Protocol
163
2011 NWCA Technical Report
DISCUSSION DRAFT
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3496 (USEPA 2011a). Soil samples were shipped to the Kellogg Soil Survey Laboratory for analysis following
3497 the procedures in the NWCA Laboratory Operations Manual (USEPA 2011b). The Kellogg Laboratory is
3498 located in Lincoln, Nebraska, and is part of the Natural Resources Conservation Service (NRCS) of the US
3499 Department of Agriculture.
3500
3501 8.4.3 Data Preparation
3502 Soil chemistry data returned from NRCS were merged with soil profile data collected by Field Crews
3503 from the Representative Pit (the only pit from which soil was analyzed for chemistry) by layer. Soil
3504 chemistry data representing the uppermost layer within 10 cm of the soil surface (as described in
3505 Chapter 4, Section 4.5.4) was used to develop chemical indicators of stress. By making the decision to
3506 use data associated with the uppermost layer, 97% of the sites sampled in the 2011 NWCA and soils
3507 most likely to reflect anthropogenic stressors were represented.
3508
3509 8.4.4 Indicator Development
3510 Two chemical indicators of stress were developed - a Heavy Metal Index (HMI) and soil phosphorus
3511 concentrations. Heavy metal concentrations are excellent indicators of stress, as heavy metals often
3512 have specific background ranges above which anthropogenic impacts are indicated. Soil phosphorus can
3513 be an important indicator of anthropogenic impacts (especially agricultural and residential stresses that
3514 result in eutrophication), but concentrations can be highly influenced by soil type, wetland type, region,
3515 and other factors. In the following subsections, we provide a short summary of how these two chemical
3516 indicators of stress were developed.
3517
3518 8.4.4.1 Heavy Metal Index (HMI)
3519 Heavy metals were analyzed from soil samples using a trace element procedure (HNO3 and HCI
3520 extraction) followed by measurement with an inductively coupled plasma mass spectrometer (ICP-MS;
3521 (USEPA 2011b). Twelve heavy metals, with high signal to noise ratios that were closely related to
3522 anthropogenic impacts, and which occurred in consistently measureable quantities were used to
3523 develop an HMI. These 12 metals are:
3524
3525 • Silver (Ag)
3526 • Cadmium (Cd)
3527 • Cobalt (Co)
3528 • Chromium (Cr)
3529 • Copper(Cu)
3530 • Nickle(Ni)
3531 • Lead (Pb)
3532 • Antimony (Sb)
3533 • Tin (Sn)
3534 • Vanadium (V)
3535 • Tungsten (W)
3536 • Zinc (Zn)
3537
3538 The HMI was created and scored as the sum of the number of metals present at any given site with
3539 concentrations above natural background levels based on published values, primarily from Alloway
3540 (2013) and reported in detail in Table 4-8. Summary of the characteristics of the heavy metals
3541 considered for use in the stressor index based on soil chemistry. Natural backgrounds are based on
3542 Alloway (2013). Percent of sites exceeding the thresholds is based on data from Visit 1.
164 2011 NWCA Technical Report DISCUSSION DRAFT
-------
3543
3544 8.4.4.2 So/7 Phosphorus Concentration
3545 Soil phosphorus concentrations were analyzed using four different methods by NRCS; the Olsen P test
3546 (OLSEN_P), the Mehlich III method (MEHLICH_P), ammonium oxalate extraction (P), and trace element
3547 procedure (P_T). It was decided that the concentration results from the trace element procedure, which
3548 uses an HNO3 and HCI extraction and measurement with an ICP-MS (USEPA 2011b), would be used for
3549 the indicator of stress. This procedure extracts a greater proportion of the total phosphorus in the soil
3550 and is less influenced by soil type than the other methods. The value for the measured soil phosphorus
3551 concentration (from the uppermost layer within 10 cm of the soil surface) at each site was used as a
3552 chemical indicator of stress.
3553
3554 8.4.5 Stressor-Level Threshold Definition
3555 For each the HMI and soil phosphorus concentration indicator, two thresholds were defined - one for
3556 "low stressor-level" and one for "high stressor-level". Indicators of stress at sites that exceeded the
3557 "low" threshold but were under the threshold set for "high" were considered "moderate stressor-level".
3558 The threshold definition is described in detail for each chemical indicator of stress in the following
3559 subsections.
3560
3561 8.4.5.1 Heavy Metal Index (HMI) Stressor-Level Thresholds
3562 Stressor-Level thresholds for the HMI were based upon the number of different heavy metals above
3563 background concentrations for each site, with the maximum possible number of observed metals equal
3564 to 12. The low stressor-level threshold for the HMI was assigned using strict criteria, with the threshold
3565 score set at zero. In other words, for an indicator of stress at a site to be considered low stressor-level,
3566 all 12 heavy metals included in the index were at or below background concentrations (Table 8-3). The
3567 high stressor-level threshold, assigned using best professional judgement, was set as 3. Therefore, a site
3568 that had soils with 3 or more heavy metals exceeding background concentrations was considered high
3569 stressor-level. The greatest number of heavy metals determined above background concentrations at
3570 any site was 7.
3571
3572 Table 8-3. Threshold definition for the Heavy Metal Index (HMI).
Indicator of Stress Low Stressor-Level Threshold High Stressor-Level Threshold
... All metals < background 3 or more metals > background
Heavy Metal Index . .
concentrations concentrations
3573
3574 8.4.5.2 So/7 Phosphorus Concentration Stressor-Level Thresholds
3575 Soil phosphorus concentrations can be strongly influenced by soil type, wetland type, region, and other
3576 factors, so determining low and high stressor-level thresholds based upon published ranges or even best
3577 professional judgement is not appropriate. Instead, soil phosphorus concentration stressor-level
3578 thresholds for low and high were set using the 75th and 95th percentiles of soil phosphorus
3579 concentrations observed in reference sites, respectively (Table 8-4). This method is used for lakes and
3580 streams nutrient criteria in USEPA National Aquatic Resource Surveys (NARS) as described in Herlihy et
3581 al. (2008, 2013) and illustrated in Figure 8-4.
3582
3583
165 2011NWCA Technical Report DISCUSSION DRAFT
-------
3584 Table 8-4. Stressor-level threshold definition for soil phosphorus concentration.
Stressor-Level Threshold Groups
Reporting Groups
Included
Low Stressor-Level
Threshold
(mgP/kgsoil)
High Stressor-
Level Threshold
(mgP/kgsoil)
Estuarine
Coastal Plains
Eastern Mountains & Upper Midwest
Interior Plains
West
CPL-PRLH, CPL-PRLW < 582
EMU-PRLH, EMU-PRLW < 914
IPL-PRLH, IPL-PRLW < 1110
W-PRLH, W-PRLW < 1140
3585
3586
3587
3588
3589
3590
3591
3592
3593
3594
3595
3596
3597
3598
2100
Least Disturbed (Reference)
Site Distribution
95th uar-i
75th ^^--
Figure 8-4. Conceptual model of how the 75th and 95th percentiles of reference site soil phosphorus concentrations
are used to determine high and low stressor-level thresholds.
A single national threshold for soil phosphorus was not adequate to capture the regional and geological
variation in concentrations. Therefore, stressor-level thresholds were determined by combining
herbaceous and woody vegetative types for across NWCA Reporting Groups. Table 8-4 presents low and
high stressor-level thresholds for all Estuarine wetland types, and for all PRL wetland types within the
Coastal Plains, Eastern Mountains & Upper Midwest, Interior Plains, and West Aggregated Ecoregions.
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3599 8.5 Biological Indicator of Stress
3600
3601 8.5.1 Defining a Biological Indicator of Stress
3602 The Nonnative Plant Stressor Indicator (NPSI) was developed as a descriptor of stress to ecological
3603 condition for the 2011 NWCA. Vegetation was the principle biological ecosystem component evaluated
3604 in the NWCA (see Chapter 5), and collection of information describing the species-level presence and
3605 abundance of nonnative plants was a major component of the NWCA protocols.
3606
3607 Nonnative plant species are recognized as important biological indicators of ecological stress on wetland
3608 condition (Mack and Kentula 2010; Magee et al. 2010). Their presence and abundance are often
3609 positively related to human mediated disturbance (Lozon and Maclsaac 1997; Mack et al. 2000; Magee
3610 1999; Magee et al. 2008; Ringold et al. 2008). In addition, nonnative plants can act as direct stressors to
3611 ecological condition by competing with or displacing native plant species or communities, or by altering
3612 ecosystem structure and processes (Vitousek et al. 1997; Dukes and Mooney 2004). Numerous direct
3613 and indirect effects of nonindigenous plants on native vegetation and other ecosystem components
3614 demonstrate their role as potential stressors. For example, nonnative plant species have been linked to:
3615
3616 • increased risk of local extinction or population declines for many rare, native plant species
3617 (Randall 1996; Lesica 1997; Seabloom et al. 2006);
3618 • changes in species composition within and among plant community types, and to
3619 homogenization of local and regional floras (McKinney 2004; Rooney et al. 2004; Magee et al.
3620 2008);
3621 • alteration of fire regimes (Dwire and Kauffman 2003; Brooks et al. 2004);
3622 • alteration of geomorphic and hydrologic processes (Rowantree 1991; Sala et al. 1996); and
3623 • alteration of carbon storage patterns (Farnsworth and Meyerson 2003; Bradley et al. 2006);
3624 nutrient cycling, and composition of soil biota (Belnap and Phillips 2001; Ehrenfeld 2003).
3625
3626 Major ecological changes like these negatively influence the intactness or integrity of natural
3627 ecosystems (Angermeier and Karr 1994; Dale and Beyeler 2001), and can lead to losses of ecosystem
3628 services (Dukes and Mooney 1999; Dale et al. 2000; Hooper et al. 2005; Meyerson and Mooney 2007).
3629
3630 For the NWCA, we defined nonnative plants to be comprised of both alien and cryptogenic taxa. Alien
3631 plants include taxa that are either 1) introduced to the conterminous United States, or 2) adventive, that
3632 is, native to some parts of the conterminous United States but introduced to the location of occurrence
3633 on a particular NWCA site. Cryptogenic species include taxa that have both introduced (often aggressive)
3634 and native (generally less prevalent) genotypes, varieties or subspecies. Because many cryptogenic
3635 species are invasive or act as ecosystem engineers, we grouped them with alien species and considered
3636 them nonnative for the purpose of indicating ecological stress.
3637
3638 8.5.2 Data Collection
3639 Nonnative plant data were collected as part of the standard Vegetation Protocol (USEPA 2011a). An
3640 overview of vegetation field and laboratory methods is provided in Chapter 5, Section 5.3.
3641
3642 8.5.3 Data Preparation
3643 Preparation and validation of raw data for nonnative plant species are described in Chapter 5, Section
3644 5.4 and Section 5.5. Definition of the native status categories used in the NWCA and the procedures for
3645 determining state-level native status for the individual species observed in 2011 are provided in Chapter
167 2011 NWCA Technical Report DISCUSSION DRAFT
-------
3646 5, Section 5.8. Numerous metrics summarizing different attributes (e.g., all alien and cryptogenic
3647 species, or subgroups of these species based on life history traits) of nonnative species were calculated
3648 and are described in Chapter 6, Section 6.2 and Section 6.8 Appendix D.
3649
3650 8.5.4 Indicator Development
3651 Approximately 30 of the metrics describing nonnative plants passed initial evaluations for range and
3652 repeatability and were considered as potential indicators of stress. Wetlands sampled across the
3653 conterminous United States as part of the 2011 NWCA spanned an enormous range of diversity and
3654 compositional and structural variability. As a result, nonnative metrics characterizing specific life history
3655 groups (e.g., growth habit, duration, hydrophytic status) were less robust across all NWCA sampled sites
3656 or across sites within Reporting Groups than were metrics based on all nonnative species. Consequently,
3657 metrics that included all nonnative plant species occurring at each site were used in developing the
3658 Nonnative Plant Stressor Indicator (NPSI).
3659
3660 Ultimately, three complementary metrics that describe different avenues of potential impact to
3661 ecological condition were selected for inclusion in the NWCA NPSI. The NPSI integrates:
3662
3663 • Relative Cover of Nonnative Species (XRCOV_AC)
3664 o 0 to 100%
3665 • Richness of Nonnative Species (TOTN_AC)
3666 o Number of unique nonnative species
3667 • Relative Frequency of Nonnative Species (RFREQ_AC)
3668 o 0 to 100%
3669
3670 Calculation methods for these three metrics can be found in Chapter 6, Section 6.8 Appendix D by
3671 referencing the metric names indicated in parentheses in the list above. These metric names are
3672 highlighted in red and bolded in the appendix to make them easier to locate. The '_AC' suffix in the
3673 metric names refers to combined alien and cryptogenic species.
3674
3675 Relative Nonnative Cover reflects preemption of space and resources, changes in species composition,
3676 and alteration of ecosystem processes. Higher values are often associated with greater decreases in
3677 ecological condition. Total Richness of Nonnative Species can be an indicator of potential risk for
3678 ecological impact; greater numbers of individual nonnative taxa increases the risk that one or more may
3679 be or become invasive or ecosystem engineers. Greater Relative Frequency of Nonnative Species
3680 reflects increasing numbers of loci for further nonnative incursions, and a decreasing proportion of the
3681 flora that is native, both of which can lead to decreased resiliency of the vegetation or ecosystem. Of the
3682 three metrics, Relative Nonnative Cover is likely to represent the greatest potential impact to ecological
3683 condition. The other two metrics provide additional pathways of impact that may have synergistic
3684 relationships with Relative Nonnative Cover, potentially increasing the amount overall stress related to
3685 nonnative plants.
3686
3687 The composite NPSI derived from these three metrics was used to assign stressor-level classes reflecting
3688 potential ecological stress from nonnative species to each site. Four stressor-level classes were defined:
3689 low, moderate, high, and very high. Assignment of stressor-level is based on stressor-level threshold
3690 values for each of the three metrics. Stressor-level thresholds are described in the following section.
3691
3692
3693
168 2011 NWCA Technical Report DISCUSSION DRAFT
-------
3694 8.5.5 Stressor-Level Threshold Definition
3695 Designation of the Nonnative Plant Stressor Indicator (NPSI) stressor-level class (low, moderate, high, or
3696 very high) is based on exceedance thresholds for each of the three component metrics (Table 8-5).
3697 Development of these stressor-level exceedance values were based on best professional judgement.
3698
3699 Stressor-level thresholds were assigned to reflect the strong potential influence of Relative Nonnative
3700 Cover, and were set for this metric as though it were a standalone stressor. Stressor-Level thresholds for
3701 Nonnative Richness and Relative Frequency of Nonnative Species were then set to reflect additional
3702 sources of potential stress at a particular level of Relative Nonnative Cover. Exceedance of a threshold
3703 value for a particular stressor-level class for any of the three component metrics (see Table 8-5) moves
3704 the NPSI designation to next higher stressor-level.
3705
3706 Table 8-5. Nonnative Plant Stressor Indicator (NPSI) Stressor—Level Threshold Exceedance Values for each of the
3707 three component nonnative species metrics: Relative Cover of Nonnative Species (XRCOV_AC), Nonnative Richness
3708 (TOTN_AC), and Relative Frequency of Nonnative Species (RFRECLAC).
~Stressor-Level Class* XRCOV_AC TOTN_AC RFRECLAC
Moderate >1-15 >5-10
Very High >40 >15
3709 *Exceedance of a threshold value for a particular stressor-level class for any of the three component metrics
3710 moves the NPSI to next higher stress level.
3711
3712 This approach for designating the NPSI stressor-level for each site integrates information from three
3713 different pathways from which nonnative species may influence ecological condition. To see how the
3714 exceedance thresholds work, consider the two hypothetical examples of nonnative species results that
3715 are outlined below.
3716
3717 Hypothetical Site 1 (Stressor-Level Class = High) has:
3718
3719 • XRCOV_AC = 7% •» Moderate Stressor-Level Class
3720 • TOTN_AC = 14 nonnative species ^ High Stressor-Level Class
3721 • RFREQ_AC = 28% •» Moderate Stressor-Level Class
3722 •
3723 In this case, Relative Nonnative Cover would place the site in the moderate stressor-level; however the
3724 number of unique nonnative species moves the NPSI to the high stressor-level class. Even though
3725 Relative Nonnative Cover is not extensive, the number of individual nonnative species and their
3726 frequency of occurrence could indicate shifting community composition and strong risk for expansion of
3727 nonnative impact.
3728
3729 Hypothetical Site 2 (Stressor-Level Class = Very High) has:
3730
3731 • XRCOV_AC = 80% •» Very High Stressor-Level Class
3732 • TOTN_AC = 1 nonnative species ^ Low Stressor-Level Class
3733 • RFREQ_AC = 59% •» High Stressor-Level Class
3734
3735 Here, the stressor-level class for the NPSI would be very high. Even though there is only 1 nonnative
3736 species present at the site (which could reflect limited stress), it occurs at very high relative cover (e.g., it
169 2011NWCA Technical Report DISCUSSION DRAFT
-------
3737
3738
3739
3740
3741
3742
3743
3744
3745
3746
3747
3748
3749
3750
occupies 80% of the sampled area) and relative frequency of occurrence (e.g., nearly 60% of all species
occurrences across the sampled area are nonnative and represented by this one species).
8.6 Stressor Extent Estimates
Established thresholds for physical, chemical, and biological indicators of stress (defined in the
preceding sections) are used in conjunction with site weights to calculate stressor extent estimates
(Figure 8-5), which are reported in National Wetland Condition Assessment 2011: A Collaborative Survey
of the Nation's Wetlands (USEPA In Review). The following chapter (Chapter 9) will provide a detailed
explanation of how population estimates are used to estimate for wetland condition and stressor extent
(Chapter 9, Section 9.2) and how stressor extent estimates are calculated using the thresholds described
in this chapter (Sections 8.3.5, 8.4.5, and 8.5.5).
3751
3752
3753
3754
3755
Data acquisition and
QA continues
through analysis
ONLY probability sites are
used to generate the
population estimates
stressor
threshold
definition
Stressor
Extent
Estimates
Figure 8-5. The connection from stressor threshold definition (described in the preceding sections) to reporting
stressor extent estimates within the 2011 National Wetland Condition Assessment Analysis Pathway.
170
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
3756 8.7 Literature Cited
3757
3758 Angermeier PL, Karr JR (1994) Biological integrity versus biological diversity as policy directives.
3759 Bioscience 44: 690-697
3760
3761 Alloway BJ (2013) Heavy Metals in Soils: Trace Metals and Metalloids in Soils and Their Bioavailability.
3762 Springer, New York, NY
3763
3764 Belnap J, Phillips SL (2001) Soil biota in an ungrazed grassland: Response to annual grass (Bromus
3765 tectorum) invasion. Ecological Applications 11: 1261-1275
3766
3767 Bradley BA, Houghton RA, Mustard JF, Hamburg SP (2006) Invasive grass reduces aboveground carbon
3768 stocks in shrublands of the Western US. Global Change Biology 12: 1815-1822
3769
3770 Brooks ML, D'Antonio CM, Richardson DM, Grace JB, Keeley JE, DiTomaso JM, Hobbs RJ, Pellant M, Pyke
3771 D (2004) Effects of Invasive Alien Plants on Fire Regimes. BioScience 54: 677-688
3772
3773 Dale VH, Beyeler SC (2001) Challenges in the development and use of ecological indicators. Ecological
3774 Indicators 1: 3-10
3775
3776 Dale VH, Brown SC, Haeuber RA, Hobbs NT, Huntly N, Naiman RJ, Riebsame WE, Turner MG, Valone TJ
3777 (2000) Ecological principles and guidelines for managing the use of land. Ecological Applications 10: 639-
3778 670
3779
3780 Dukes JS, Mooney HA (1999) Does global change increase the success of biological invaders? Trends in
3781 Ecology and Evolution 14: 135-139
3782
3783 Dukes JS, Mooney HA (2004) Disruption of ecosystem processes in western North America by invasive
3784 species. Revista Chilena de Historia Natural 77: 411-437
3785
3786 Dwire KA, Kauffman JB (2003) Fire and riparian ecosystems in landscapes of the western USA. Forest
3787 Ecology and Management 178: 61-74
3788
3789 Ehrenfeld JG (2003) Effects of exotic plant invasions on soil nutrient cycling processes. Ecosystems 6:
3790 503-523
3791
3792 Farnsworth EJ, Meyerson LA (2003) Comparative ecophysiology of four wetland plant species along a
3793 continuum of invasiveness. Wetlands 23: 750-762
3794
3795 Fennessy MS, Jacobs AD, Kentula ME (2007) An evaluation of rapid methods for assessing the ecological
3796 condition of wetlands. Wetlands 27: 543-560
3797
3798 Herlihy AT, Paulsen SG, Van Sickle J, Stoddard JL, Hawkins CP, Yuan LL (2008) Striving for consistency in a
3799 national assessment: The challenges of applying a reference-condition approach at a continental scale.
3800 Journal of the North American Benthological Society 27: 860-877
3801
171 2011NWCA Technical Report DISCUSSION DRAFT
-------
3802 Herlihy AT, Kamman NC, Sifneos JC, Charles D, Enache MD, Stevenson RJ (2013) Using multiple
3803 approaches to develop nutrient criteria for lakes in the conterminous USA. Freshwater Science 32: 367-
3804 384
3805
3806 Hooper DU, Chapin FS, III, Ewel JJ, Hector A, Inchausti P, Lavorel S, Lawton JH, Lodge DM, Loreau M,
3807 Naeem S, Schmid B, Setala H, Symstad AJ, Vandermeer J, Wardle DA (2005) Effects of biodiversity on
3808 ecosystem functioning: A consensus of current knowledge. Ecological Monographs 75: 3-35
3809
3810 Jacobs AD (2007) Delaware Rapid Assessment Procedure Version 4.1. Delaware Department of Natural
3811 Resources and Environmental Control, Dover, DE
3812
3813 Lesica P (1997) Spread of Phalaris arundinacea adversely impacts the endangered plant Howellia
3814 aquatilis. Great Basin Naturalist 57: 366-368
3815
3816 Lozon JD, Maclsaac HJ (1997) Biological invasions: Are they dependent on disturbance? Environmental
3817 Reviews 5: 131-144
3818
3819 Mack JJ, Kentula ME (2010) Metric Similarity in Vegetation-Based Wetland Assessment Methods.
3820 EPA/600/R-10/140. US Environmental Protection Agency, Office of Research and Development,
3821 Washington, DC
3822
3823 Mack RN, Simberloff D, Lonsdale MS, Evans H, Clout M, Bazzaz FA (2000) Biotic invasions: Causes,
3824 epidemiology, global consequences, and control. Ecological Applications 10: 698-710
3825
3826 MageeTK, Ernst TL, Kentula ME, Dwire KA (1999) Floristic comparison of freshwater wetlands in an
3827 urbanizing environment. Wetlands 19: 517-534
3828
3829 Magee TK, Ringold PL, Bollman MA (2008) Alien species importance in native vegetation along wadeable
3830 streams, John Day River basin, Oregon, USA. Plant Ecology 195: 287-307
3831
3832 Magee TK, Ringold PL, Bollman MA, Ernst TL (2010) Index of Alien Impact (IAI): A method for evaluating
3833 alien plant species in native ecosystems. Environmental Management 45: 759-778
3834
3835 McKinney ML (2004) Do exotics homogenize or differentiate communities? Roles of sampling and exotic
3836 species richness. Biological Invasions 6: 495-504
3837
3838 Meyerson LA, Mooney HA (2007) Invasive alien species in an era of globalization. Frontiers in Ecology
3839 and the Environment 5: 199-208
3840
3841 Randall JM (1996) Weed control for the preservation of biological diversity. Weed Technology 10: 370-
3842 383
3843
3844 Ringold PL, Magee TK, Peck DV (2008) Twelve invasive plant taxa in in US western riparian ecosystems.
3845 Journal of North American Benthological Society 27: 949-966
3846
3847 Rooney TP, Wiegmann SM, Rogers DA, Waller DM (2004) Biotic impoverishment and homogenization in
3848 unfragmented forest understory communities. Conservation Biology 18: 787-798
3849
172 2011NWCA Technical Report DISCUSSION DRAFT
-------
3850 Rowantree K (1991) An assessment of the potential impact of alien invasive vegetation on the
3851 geomorphology of river channels in South Africa. South African Journal of Aquatic Science 17: 28-43
3852
3853 Sala A, Smith SD, Devitt DA (1996) Water use by Tamarix ramosissima and associated phreatophytes.
3854 Ecological Applications 6: 888-898
3855
3856 Seabloom E, Williams J, Slayback D, Stoms D, Viers J, Dobson A (2006) Human impacts, plant invasion,
3857 and imperiled plant species in California. Ecological Applications 16: 1338-1350
3858
3859 Van Sickle J, Paulsen SG (2008) Assessing the attributable risks, relative risks, and regional extents of
3860 aquatic stressors. Journal of the North American Benthological Society 27: 920-931
3861
3862 USEPA (2011a) National Wetland Condition Assessment: Field Operations Manual. EPA/843/R-10/001.
3863 US Environmental Protection Agency, Office of Water, Washington, DC
3864
3865 USEPA (2011b) National Wetland Condition Assessment: Laboratory Operations Manual. EPA/843/R-
3866 10/002. US Environmental Protection Agency, Office of Water, Washington, DC, p 256
3867
3868 USEPA (In Review) National Wetland Condition Assessment 2011: A Collaborative Survey of the Nation's
3869 Wetlands.EPA-843-R-15-005. US Environmental Protection Agency, Office of Water, Washington, DC
3870
3871 Vitousek PM, D'Antonio CM, Loope LL, Rejmanek M, Westbrooks R (1997) Introduced species: A
3872 significant component of human-caused global change. New Zealand Journal of Ecology 21: 1-16
3873
3874 Wardrop DH, Kentula ME, Jensen SF, Stevens Jr. DL, Hychka KC, Brooks RP (2007) Assessment of
3875 wetlands in the Upper Juniata watershed in Pennsylvania, USA using the hydrogeomorphic approach.
3876 Wetlands 27: 432-445
3877
3878 Whigham DF, Jacobs AD, Weller DE, Jordan TE, Kentula ME, Jensen SF, Stevens Jr. DL (2007) Combining
3879 HGM and EMAP procedures to assess wetlands at the watershed scale - Status of flats and non-tidal
3880 riverine wetlands in the Nanticoke River Watershed, Delaware and Maryland (USA). Wetlands 27: 462-
3881 478
3882
173 2011NWCA Technical Report DISCUSSION DRAFT
-------
3883 8.8 Appendix F: Example Buffer Form (B-l)
Site ID:
FOIWIB-1: NWCA BUFFER SAMPLE PLOTS (Front) "^MV^MBS^
NWCA11- \~j-^ DATE: f) £ / • [ ^ / 2 0 1 1
S^Pi^SSS^Sfe:|i
^glll;lrf;fiu.bbj,e(§;
'.jpw
|)f^TiW"nqtb^sampjec!;and fia'a r-t-i.
j
-
-
Buffer Natural Cover Strata
FlMrilwbWes for »ll trial «ppry:C»ntn>U type for eoch ptel 0 - Abler* t - Sp>r»(<10%): 2= V.«J»™te( 1 Mint}: 3 • Heavy (40-75%); 4 • Very Heavy 075%)
Suffer
Plotl
BI,T«..(,
Canopy Type: © |
Uafiypo:© 4
0.3m OBK,'
Smell Tr^**^)
Woody Shnjb* Saplnai
: (O.SmAnHIBH)
Vttxriy 3M.-&* 54f*rv*
W.SmhlGH)
Herbs, Forti and
Bare ground
• Littar, duff
: Rock
.,.•_• Water
Vegetation
•
6
0
•
0
i
0
9
0
•
G
0
^
0
0
0
0
0
0
0
G
G
0
0
0
0
•
0
0
©
I Absent Q
> Flag
G
G
0
0
0
0
0
0
i
0
G
G
0
0
•
0
0
0
0
0
Buffer
Plot 2
CxxwTyps:© (3
Le.
Big Tree* (>o.3m OBMJ
Smell Tr«B» (0.3n) OBH)
Woody Shrub*, Sapling
Woody Sttnjtjs, SafSrnyj
HWmHISH)
Herbs. Foibs end
Ors»wa
Bare ground
Utter, duff
Rock
Water
Submerged
VegatalKin
Typ
0
0
9
9
0
*
0
0
f
•
«0 G
0
G
O
0
O
0
0
0
G
O
G
G
G
0
G
0
0
G
0
0
) Absent, ||
) . .Flag
G
G
G
0
0
0
•
G
O
0
G
G
G
0
0
0
0
G
G
G
Buffer
Plot 3
• Blow;
3m.ll. r™>n
Canopy Type: 0 Q
LeafTypa:© (£
.O.toDBH,
(«r).JmOBH>
\-W^Mr^rr,"S[(3H'
..-VM^-'*(5i)rSaH)
Herbs, Forbs and
.' GreSMI
Bare ground
Ufler, duff
' i Rock '
Water
'. •' V«aet»t;cn
f
•
•
•;
G
0
0
•
f
•
G
G
G
0
0
•
•
O
G
0
G
G
G
0
G
0
0
G
0
G
Absent; £
Flag
G
G
G
0
G
0
G
G
0
0
G
G
G
0
•
0
0
G
G
0
Stfssaor Presence/Absonce-Confimi that a flll«jdala bubblo Indicates presence -and an linfliiBd bubble Indicates absence by filling this bubble. (>
.' Residential and Urban Stresaors
Fill bubble If pr***nt- Plot
Road- gravel '
Road -two lane
Road • four lane . ,!3:.,
Parking LoUPavement
Golf Course :
Lawn/Park .-. ;.
Suburban
Residential
LifbarvMultrfamily
Landfill ' . • ' . .•
'Dumping . . •
Trash . . ' •'•• ; '. '
Other:
Other
1
0
0
o
o
o
o
o
0
o
o
o
o
0
2
o
o
o
o
o
o
o
o
o
o
o
o
o
3
o
o
0
o
o
o
o
o
o
o
o
o
o
Flag
i Industrial Development Strossors
Fill bubbtert present- Plot
OH Drilling
.Gas Well s
Mine (surf
ace)
Wins (underground)
Military
Other:
Other:
Other:
1
o
o
o
o
o
o
o
o
2
o
o
o
o
o
o
o
o
3
o
o
o
o
o
o
o
o
Flag
0 Hydrology Stressors j;A' ••>• '••, "•:•.
Fill bubble (f present - Plot
Ditches, Channelization
Dike^Dam/Road/Rft Bed •
llMPfOEFLOWl
Water Level Control Structure
Excavation,
Dredging
Fill/Spoil Banks
Freshly Deposited Sediment >
Soil Loss/Root Exposure
VVall/Rlprap
Inlets, Outlets
Pdfnt Sourea'Pfpe
(EFFLUENT OR StOawwATERl
Impervious surface Input
(SHEETFLOWl '
Other
Other.
i
o
o
o
o
o
o
o
o
o
o
o
o
o
2
O
O
0
o
o
o
o
o
o
o
o
o
o
3
O
O
o
o
o
o
o
o
o
0
o
o
o
Rag
; !' Aarlcultural & Rural StrosBOrs :
nil bubble If present • Plot
Paalure/Hay • ' '
Range .... ' .
Row Crops
FallOW Field (RECENT-HESTINe-
FalroWFIeW (oto • 3R*$s,
ShfiUBS TREES>
Nuraery
Dairy
Orchard '.
Cortflnest Anirjial Feeding
Rural Residential
Gravel Pit
Irrigation
Other:
1
O
o
o
o
o
o
o
0
o
o
o
o
0
2
O
o
o
o
o
o
o
o
o
o
o
o
o
3
o
o
o
o
o
o
o
o
o
o
o
o
o
Flag
•V ' . ' ' •'"'"' '••"'<'•'" '. ' '•-.'• '••'' '' : '.: '"'••' • '- • "•''" •'•'' ' : '-' <'.'•' "''••. :: '•. •:'• ''' '•''. •.".'.'. •.:": ''. '. ": . '. ":.v,
; ^ ' ' / ':• '.' '.;;: V--i ':..-•;-::-: • '.'-: H9 bltAtlVOQO^tlO D iStraSSOrS '' ['•"' .'...'/i-:. •.. , :': .;•,.;:.'.:.>:'...•,••'.'!:. -!:.'.~ 7;^ ':
Fill bubble If prtwnt- Plot
Forest Clear Cut
Forest Selective Cut ;;
Tree Plantation L . .
7ree CatK/py HerDivory
(INSECT)
Shrub Layer Browsed
(WILD OR DOMESTIC)
Highly Grazed Grasses
(OVERALL
-------
3885 8.9 Appendix G: Example Hydrology Form (H-l)
3886
;;: iV FORM H>l: N\TOA A^ESSM
NWCA11-1314 ..'*-:i$Ni
TInwofSampHnBlhhTOin):
.'•. ••? •i>^*>'1"'" 24 nr clock i
r M Tidal Stage: O NA O Incoming Q Outgoing O3I«* Q Flood;'
• • j~*> ill '' ~
fW . H 1 £ V\
Tf>
VHfl'X
Wook prior to sampling:
A
, HlfcV\
"m LO uj
". Identify and Rank Water Sources / Stressors: ; r
Rank the top 3 Water Sources (1 = most influential). Rank Hie top 3 Strossors (1 = most stress) by perceived Influence on the
Stream Inflow (creeks, rivers)
Outflow
Springs (seeps)
Lake
Precipitation (rain, snow)
Groundwntor
2 O3
O1 O2 O3
O2 Os
O:1 • O* O3
•1 Q2 O3
Overbank Flooding
Estuary
Tkjal Channel
-.-Tloi Surge
Other (describe with flag)
o
Q1-.
Oi O2 Q3
Or
Q1 O2 O3
O 1 O 2 O 3
Flag
», U-Su«p«tm«wit»m»n«, F1,F2,«c. «,
•••;.' -••: • : : ' ;;••.•. exptaln»llltag«l<"M«niii«r>l»«etlon- .'if
- NWCA Assessment Area Hydrology. 03/10/2011 ? jt' :-'^>: •' "
9911639020
175
2011 A/l/l/CA Technical Report
DISCUSSION DRAFT
-------
3887 This page was intentionally left blank for double-sided printing.
176 2011NWCA Technical Report DISCUSSION DRAFT
-------
3888
3889
3890
Chapter 9: Transition from Analysis to Results
Data acquisition and
QA continues
through analysis
ONLY probability sites are
used to generate the
population estimates
BILITV SITES
slressor
quantification
stress or
threshold
definition
NWCA
design
sites
98 Stale
inter sification
sites
NOTPROBABILITl i-ULb
3891
3892
3893
3894
3895
3896
3897
3898
3899
3900
3901
3902
3903
3904
3905
3906
3907
3908
3909
3910
3911
3912
3913
3914
3915
3916
3917
3918
Figure 9-1. The major components of the 2011 National Wetland Condition Assessment Analysis Pathway
discussed in this chapter (i.e., wetland condition and stressor extent estimates, and relative and attributable risk).
A full-page, unhighlighted version of this figure may be found on page 14 of this report.
9.1 Introduction
The information provided in the previous chapters is intended to provide a solid understanding of how
the 2011 NWCA was designed, conducted, and data were analyzed. Up to this point in the NWCA
Technical Report, details have been provided on the development of:
• survey design (Chapter 1),
• data acquisition, preparation, and quality assurance (Chapter 3),
• selection of reference sites and definition of disturbance gradient (Chapter 4),
• vegetation indicator development (Chapters 5 through 7),
• definitions associated with wetland condition and condition thresholds (Chapter 7), and
• definitions associated with indicators of stress and stressor-level thresholds (Chapter 8).
This chapter of the NWCA Technical Report will describe how definitions and thresholds associated with
the data (discussed in Chapters 7 and 8) are used to calculate:
• wetland condition extent estimates (Section 9.2.1) and
• stressor extent estimates (Section 9.2.2).
Wetland condition and stressor extent estimates are 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=967). The role of population
177
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
3919 estimates and site weights in these calculations is discussed in
3920 Section 9.2. Ultimately, stressor extent and wetland condition estimates are used to calculate relative
3921 and attributable risk (Figure 9-1), which is discussed in detail in Section 9.3.
3922
3923 The results from the wetland condition estimates, stressor extent estimates, and relative and
3924 attributable risk are presented in National Wetland Condition Assessment 2011: A Collaborative Survey
3925 of the Nation's Wetlands (USEPA In Review) primarily as bar graphs. This NWCA Technical Report
3926 provides guidance on how to interpret the results summarized by USEPA (In Review).
3927
3928
3929 9.2 Population Estimates
3930
3931 The survey design for the NWCA, discussed in Chapter 1 of this report, produces a spatially-balanced
3932 sample using USFWS Status and Trends wetland polygons as the sample frame (Dahl 2006, Dahl and
3933 Bergeson 2009). Each point (n=967) has a known probability of being sampled (Stevens and Olsen 1999,
3934 Stevens and Olsen 2000, Stevens and Olsen 2004), and a sample weight is assigned to each individual
3935 site as the inverse of the probability of that point being sampled. Sample weights are expressed in units
3936 of acres.
3937
3938 The probability of a site being sampled, as discussed in Chapter 1, Section 1.4 "Site Selection
3939 Summary", was stratified by state and wetland type for the NWCA. Site weights for the survey were
3940 adjusted to account for additional sites (i.e., oversample points) that were evaluated when the primary
3941 sites were not sampled (e.g., due to denial of access, being non-target). These site weights, designated
3942 by the red "W" enclosed in a circle (i.e., © ) in the NWCA Analysis Pathway (Figure 9-1), are explicitly
3943 used in the calculation of wetland condition and stressor extent estimates, so results can be expressed
3944 as estimates of wetland area (i.e., numbers of acres or percent of the entire resource) in a particular
3945 condition class or stressor-level for the Nation. For examples of how this has been done for other
3946 National Aquatic Resource Survey (NARS) assessments, see USEPA (2006), Olsen and Peck (2008), and
3947 USEPA (2009). In the following sections, the methods by which estimates are calculated and reported
3948 are described for wetland condition (Section 9.2.1) and stressor extent (Section 9.2.2). It is important to
3949 note that the NWCA was not designed to report on individual sites or states, but to report at national
3950 and regional scales (see Chapter 1).
3951
3952 9.2.1 Wetland Condition Extent Estimates
3953 Wetland condition is defined at each wetland site as "good", "fair", or "poor". These condition classes
3954 were assigned using Vegetation Multimetric Index (VMMI) thresholds, as described in Chapter 7. To
3955 calculate condition extent estimates, site weights were summed by condition class and applied to the
3956 NWCA inference population (i.e., the area) of wetlands across the conterminous US or other Reporting
3957 Groups. Note that only Visit 1 (i.e., the index visit) data and only probability sites are used in this
3958 calculation (not-probability sites have a weight of zero). Using this method, wetland area in a particular
3959 condition class is estimated and reported in numbers of acres, by percent of the resource, or by a
3960 relative ranking of occurrence (Figure 9-2).
3961
178 2011 NWCA Technical Report DISCUSSION DRAFT
-------
Vegetation MM I
Percent Area
Vegetation MMI
Area
National
Coastal
Plains
Eastern Mtn. &
Upper Midw.
Interior
Plains
West
0 20 40 60 80 100 0
Percent Area
I Good I Fair I I Poor
20,000,000
Area
40,000,000
3962
3963
3964
3965
3966
3967
3968
3969
3970
3971
3972
3973
3974
3975
3976
3977
3978
Figure 9-2. An example of how wetland condition extent estimates (based on the Vegetation MMI) are reported. In
this example, wetland condition extent is presented by percent of the resource (i.e., percent of total wetland area
for the Nation or region) in the left half of the figure, and by wetland acres in the right half of the figure.
9.2.2 Stressor Extent Estimates
Stressor extent is an estimate of how spatially common a stressor is. Stressor-level classes is defined at
each wetland site as "low", "moderate", or "high". These stressor-level classes (hereon shortened to
"stressor-levels") were assigned for multiple physical, chemical, and biological indicators of stress based
on specific stressor-level thresholds, as described in Chapter 8. To calculate stressor extent estimates,
site weights were summed by stressor-levels and applied to the population (i.e., the area) of wetlands in
the Nation (or other Reporting Group) to estimate wetland area low, moderate, and high stressor-level
classes. Note that only Visit 1 (i.e., the index visit) data and only probability sites are used in this
calculation. Using this method, wetland area affected by a particular stressor-level is estimated and
reported in numbers of acres, by percent of the resource, or by a relative ranking of occurrence (Figure
9-3).
179
2011 NWCA Technical Report
DISCUSSION DRAFT
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Vegetation Removal
Percent Area
Vegetation Replacement
Percent Area
National
Coastal
Plains
Eastern Mtn. &
Upper Midw.
Interior
Plains
West
0 20 40 60 80 100 0 20 40 60 80 100
Percent Area Percent Area
Lowi I Moderate^^B High
3979
3980
3981
3982
3983
3984
3985
3986
3987
3988
3989
3990
3991
3992
3993
3994
3995
3996
3997
3998
3999
4000
Figure 9-3. An example of how stressor extent estimates are reported using vegetation alteration stressor data. In
this example, stressor extent is presented by percent of the resource (i.e., percent of total wetland area for the
Nation or region).
9.3 Relative and Attributable Risk
The relationship between the extent of stressors and wetland condition can be described by calculating
relative and attributable risk.
9.3.1 Relative Risk
Relative risk is the probability (i.e., risk or likelihood) of having poor ecological condition when the
stressor-level class is high relative to when the stressor-level class is low. 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. Relative risk analyses are standard for reporting results in NARS
assessments (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).
180
2011 NWCA Technical Report
DISCUSSION DRAFT
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4001
4002
4003
4004
4005
4006
4007
4008
4009
4010
4011
4012
4013
4014
4015
4016
4017
4018
4019
4020
4021
4022
4023
4024
4025
4026
4027
4028
4029
4030
4031
9.3.1.1 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 table7 presented as Table 9-1, 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 9-1, 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 9-1. 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 low and high stress levels (as
indicated by stream water total nitrogen concentration, TN). Results are hypothetical.
STRESS LEVEL
z
o
^ Fish IBI: Good
2 Fish IBI: Poor
u Total
TN: Low
0.598
0.070
0.668
TN: High
0.275
0.056
0.331
Using the hypothetical example data provided in Table 9-1, the risk of a stream having poor fish
condition when the TN stress level is high is calculated as:
0.056
0.331
= 0.169
The risk of a stream having poor condition when the TN stress level is low can also be calculated in the
same manner:
0.070
= 0.105
0.668
Comparing these two results, it is apparent that the risk of a stream having poor condition when the TN
stress level is high (0.169) is greater than when the TN stress level is low (0.105). The relative risk (RR)
can then be simply calculated as the ratio of these two probabilities (Pr):
Pr(Poor condition given High stressor-level) 0.169
Pr(Poor condition given Low stressor-level level} 0.105
Therefore, in this example, we can conclude that the risk of poor condition is 1.61 times greater in
streams with high TN stressor-level than in streams with low TN stressor-level.
7 The numbers used in this example are hypothetical and were not measured as part of any USEPA NARS
assessment.
181
2011 NWCA Technical Report
DISCUSSION DRAFT
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4032 These calculations are repeated for each appropriate8 indicator of stress so relative risk can be reported
4033 for each of them. If the stressor has no effect on condition, the relative risk is 1. Confidence intervals are
4034 also used in reporting to express uncertainty in the estimate of relative risk (see Van Sickle et al. 2006).
4035
4036 9.3.1.2 Considerations When Calculating and Interpreting Relative Risk
4037 It is important to understand that contingency tables are created using a categorical, two-by-two matrix;
4038 therefore, only two condition classes / stress levels can be used. There are three ways in which
4039 condition classes / stress levels can be used for contingency tables:
4040
4041 • Good vs. Poor/Low vs. High,
4042 • Good vs. Not-Good / Low vs. Not Low, or
4043 • Not-Poor vs. Poor / Not High vs. High,
4044
4045 where, "Not Good" combines fair and poor condition classes, "Not Low" combines moderate and high
4046 stressor-levels, "Not Poor" combines good and fair condition classes, and "Not High" combines low and
4047 fair stressor-levels. In the first bulleted method, "Good vs. Poor / Low vs. High", data associated with the
4048 fair condition class and the moderate stressor-level is excluded from the analysis. Therefore, the results
4049 of the associated calculation of relative risk are affected by which one of the above combinations is used
4050 to make the contingency tables, and it is crucial that the objectives of the analysis are carefully
4051 considered to help guide this decision.
4052
4053 A second consideration is that relative risk does not model joint effects of correlated stressors. In other
4054 words, each stressor is modeled individually, when in reality, stressors may interact with one another
4055 potentially increasing or decreasing impact on condition. This is an important consideration when
4056 interpreting the results associated with relative risk.
4057
4058 9.3.1.3 Application of Relative Risk to the NWCA
4059 For the NWCA, wetland condition is defined at each wetland site as good, fair, or poor and assigned
4060 using Vegetation Multimetric Index (VMMI) thresholds, as described in Chapter 7. Stressor-level is
4061 defined at each wetland site as low, moderate, or high using multiple physical, chemical, and biological
4062 indicators of stress and thresholds, as described in Chapter 8. For each indicator of stress (except the
4063 Nonnative Plant Stressor Index (NPSI); see Section 9.4 for details), a wetland condition / stressor-level
4064 contingency table was created, comparing the Not Poor condition class (i.e., a combination of good
4065 condition and fair condition) to Poor condition class, and Not High stressor-level (i.e., a combination of
4066 low and moderate) to High stressor-level. This decision was made because the objective of reporting
4067 relative risk in the NWCA is to indicate which stressors policy makers and managers may want to
4068 prioritize for management efforts to improve poor wetland condition. After creating contingency tables,
4069 relative risk for each indicator of stress was calculated. Figure 9-4 provides an example of how relative
4070 risk is reported for the NWCA; with stressor extent, relative risk provides an overall picture of the
4071 relative importance of individual stressors on condition.
4072
8 In some cases, it may not be appropriate to calculate relative risk for a stressor, for example, when a stressor and
condition index are based on the same type of data. See Section 9.4 for details.
182 2011 NWCA Technical Report DISCUSSION DRAFT
-------
4073
4074
4075
4076
4077
4078
4079
4080
4081
4082
4083
4084
4085
4086
4087
4088
4089
4090
4091
4092
4093
4094
4095
4096
Relative Extent
High Stressor Levels
Relative Risk
Vegetation Removal -
Hardening
Ditching
Damming -
Filling -
Vegetation Replacement -
Soil Phosporus
Heavy Metals
10 15 20 25 30 35 0.0
Percent of Area
High Stressor Levels
0.5 1.0 1.5 2.0
Relative Risk
Figure 9-4. An example of how relative risk is reported in the NWCA. In this example, Stressor extent estimates (for
the high stress level) are presented (left) with relative risk for each indicator of stress (right). Note that large
Stressor extent does not necessarily translate to high relative risk (or visa versa).
9.3.2 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 estimated Stressor extent with relative risk into a single index using the following formula
(see Van Sickle et al. 2008 for details):
AR =
Pr(Extent with High stressor-levels} * (RR - 1)
1 + Pr(Extent with High stressor-levels) * (RR - 1)
Where RR is relative risk and Pr is probability. Similar to the consideration presented in Section 9.3.1.2,
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 (i.e., Not Poor and Not High was compared to Poor and High condition classes and stressor-levels,
respectively).
The ranking of stressors according to attributable risk (e.g., Figure 9-5) 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.
183
2011 NWCA Technical Report
DISCUSSION DRAFT
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4097
4098
4099
4100
4101
4102
4103
4104
4105
4106
4107
4108
4109
4110
4111
4112
4113
4114
4115
4116
4117
4118
4119
4120
4121
4122
4123
4124
4125
4126
4127
Relative Extent
High Stressor Levels
Relative Risk
Attributable Risk
10 15 20 25 30 35 0.0 0.5 1.0 1.5 2,0 2.5 0
Percent of Area
High Stressor Levels
Relative Risk
10 15 20 25 30 35
Attributable Risk
Percent of Area
Figure 9-5. An example of how attributable risk (right panel) is reported in the NWCA.
9.3.2.1 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,
• 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 these 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).
9.4 Appropriate Use of Nonnative Plant Stressor Indicator (NPSI)
The Nonnative Plant Stressor Indicator (NPSI) is a biological descriptor of stress based on data collected
as part of the Vegetation Protocol (see Chapter 8, Section 8.5 for details). Estimates of the extent of
wetland area with low, moderate, high, or very high stress levels for the NPSI were calculated using an
approach that mirrors the extent estimates for other stress indicators (see Section 9.2.2 and Figure 9-3).
NPSI extent estimates are provided in USEPA (In Review). Relative and attributable risk associated with
NPSI are not reported; this is because both the NPSI and the Vegetation Multimetric Index (VMMI) (see
Chapter 7) used to determine wetland condition are based on the NWCA vegetation data. Because
relative and attributable risk specifically relate stressors to condition, and both the NPSI and VMMI are
184
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
4128 based on related data (albeit, not the same data, see Chapters 7 and 8 for details), it is not appropriate
4129 to include NPSI in reporting relative and attributable risk.
4130
4131
4132 9.5 Where to Find the Summary of NWCA Results
4133
4134 All of the methods presented in Chapters 1 through 9 of this NWCA Technical Report are the scientific
4135 basis for what is reported in National Wetland Condition Assessment 2011: A Collaborative Survey of the
4136 Nation's Wetlands (USEPA In Review) and future peer-reviewed manuscripts. This report (USEPA, In
4137 Review) provides an overview of the important results from the 2011 NWCA. The presentation of results
4138 is geared toward the lay public, environmental managers, and government decision makers.
4139
4140
4141 9.6 Literature Cited
4142
4143 Dahl TE (2006) Status and Trends of Wetlands in the Conterminous United States 1998 to 2004, US
4144 Department of the Interior, Fish and Wildlife Service, Washington, DC
4145
4146 Dahl TE, Bergeson MT (2009) Technical procedures for conducting status and trends of the Nation's
4147 wetlands. US Fish and Wildlife Service, Division of Habitat and Resource Conservation, Washington, DC
4148
4149 Olsen AR, Peck DV (2008) Survey design and extent estimates for the Wadeable Streams Assessment.
4150 Journal of the North American Benthological Society 27: 822-836
4151
4152 Stevens DL, Jr, Jensen SF (2007) Sampling design, implementation, and analysis for wetland assessment.
4153 Wetlands 27: 515-523
4154
4155 Stevens DL, Jr, Olsen AR (1999) Spatially restricted surveys over time for aquatic resources. Journal of
4156 Agricultural, Biological, and Environmental Statistics 4: 415-428
4157
4158 Stevens DL, Jr, Olsen AR (2000) Spatially restricted random sampling designs for design-based and model
4159 based estimation. Pages 609-616 in Accuracy 2000: Proceedings of the 4th International Symposium on
4160 Spatial Accuracy Assessment in Natural Resources and Environmental Sciences. Delft University Press,
4161 The Netherlands
4162
4163 Stevens DL, Jr, Olsen AR (2004) Spatially-balanced sampling of natural resources. Journal of American
4164 Statistical Association 99: 262-278
4165
4166 Van Sickle J, Stoddard JL, Paulsen SG, Olsen AR (2006) Using relative risk to compare the effects of
4167 aquatic stressors at a regional scale. Environmental Management 38: 1020-1030
4168
4169 Van Sickle J, Paulsen SG (2008) Assessing the attributable risks, relative risks, and regional extents of
4170 aquatic stressors. Journal of the North American Benthological Society 27: 920-931
4171
4172 Van Sickle J (2013) Estimating the risks of multiple, covarying stressors in the National Lakes Assessment.
4173 Freshwater Science 32: 204-216
4174
185 2011 NWCA Technical Report DISCUSSION DRAFT
-------
4175 USEPA (2006) Wadeable Streams Assessment: A Collaborative Survey of the Nation's Streams. US
4176 Environmental Protection Agency, Office of Water and Office of Research and Development,
4177 Washington, DC
4178
4179 USEPA (2009) National Lakes Assessment: A Collaborative Survey of the Nation's Lakes. US
4180 Environmental Protection Agency, Office of Water and Office of Research and Development,
4181 Washington, DC
4182
4183 USEPA (In Review) National Wetland Condition Assessment 2011: A Collaborative Survey of the Nation's
4184 Wetlands. EPA-843-R-15-005. US Environmental Protection Agency, Office of Water, Washington, DC
4185
186 2011NWCA Technical Report DISCUSSION DRAFT
-------
2011 NATIONAL WETLAND CONDITION ASSESSMENT
Research Features
Chapter 10: Microcystins
Chapter 11: Water Chemistry
Chapter 12: USA-Rapid Assessment Method (USA-RAM)
187 2011NWCA Technical Report DISCUSSION DRAFT
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188 2011NWCA Technical Report DISCUSSION DRAFT
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4186 Chapter 10: Research Feature - Microcystins
4187
4188
4189 10.1 Background Information
4190
4191 Microcystins are one group of naturally occurring toxins produced by various cyanobacteria (blue-green
4192 algae) that are common to surface waters (Chorus and Bartram 1999). Microcystins have been detected
4193 nationally in lakes and reservoirs (Beaver et al. 2014; USEPA 2007) and are considered to be the most
4194 commonly occurring class of cyanobacteria toxins (cyanotoxins) (Chorus and Bartram 1999). Microcystin
4195 exposure risk is typically elevated when an overabundance of cyanobacteria occurs in surface water
4196 cyanobacteria harmful algal bloom (cyanoHABs). There is concern that changes in weather patterns,
4197 human population expansion, and associated behaviors are leading to perceived increases in occurrence
4198 and severity of cyanoHABs (Paerl and Scott 2010). Three main exposures scenarios are of potential
4199 concern regarding microcystins and wetlands: direct ecological impacts on plants and animals, human
4200 consumption of exposed organisms, and direct human exposure through recreational contact.
4201
4202 Adverse ecological impacts due to microcystin exposure on plants and animals have been summarized in
4203 several sources. Various adverse impacts of microcystins on cellular processes in a variety of aquatic and
4204 terrestrial plants resulting in diminished plant growth and accumulation of microcystins have been
4205 reported (Crush et al. 2008; Corbel et al. 2013; Romero-Oliva et al. 2014). Some macrophytes common
4206 to certain types of wetlands have shown sensitivity to microcystins also. Microcystins have been shown
4207 to inhibit the growth and oxygen production of some wetland macrophytes at concentrations of 1 u.g/L
4208 or less (Rojo et al. 2013). Additionally, illness and mortality due to microcystin exposure has been
4209 reported in wildlife, livestock, companion animals and all trophic levels of freshwater, brackish and
4210 marine aquatic life. Animal illness and mortality has been reported in numerous cases including
4211 amphibians, cats, cattle, chickens, deer, dogs, frogs, horses, muskrat, sheep, turkey, and waterfowl, but
4212 the true number of cases remains unknown since many are not reported or observed (Chorus and
4213 Bartram 1999; Landsberg 2002; Briand et al. 2003; Handeland and 0stensvik 2010; Vareli et al. 2013).
4214
4215 While Zhang et al. (2013) reported that mammals (especially humans) are more susceptible to
4216 microcystin poisoning compared to fish, it has been shown that humans should have some measure of
4217 caution for consumption of animals contaminated by microcystins (Ibelings and Chorus 2007; Poste et
4218 al. 2011). Papadimitriou et al. (2012) found measureable microcystins present in in all trophic levels of
4219 an aquatic ecosystem including phytoplankton, zooplankton, freshwater shrimp, crayfish, mussels, frogs,
4220 and fish when total microcystin water column concentrations ranged from non-detect up to
4221 approximately 20 u.g/L for the study year between January and December. Microcystin concentrations
4222 were not found to bioaccumulate and tissue concentrations tended to decrease as trophic level
4223 increased, but concentrations were a function of exposure route and length of exposure. Higher water
4224 column microcystin concentrations did relate to higher tissue concentrations. Microcystin
4225 concentrations were typically greater in organs versus the more commonly eaten muscle tissues. Tissue
4226 concentrations did exceed the World Health Organization (WHO) suggested tolerable daily intake (TDI)
4227 value of 0.04 u.g of microcystin/kg human body weight (Chorus and Bartram 1999; Papadimitriou et al.
4228 2012). Boiling and microwave techniques were evaluated for preparation of different aquatic organisms
4229 contaminated by microcystins typically consumed by humans and found that microcystin concentrations
4230 in tissues can be reduced by 25 to 59% (Gutierrez-Praena et al. 2013). However, microcystins have been
4231 shown to resist degradation at temperatures up to 300°C or after boiling for several hours, and studies
4232 have suggested that water used in boiling instead becomes contaminated with microcystins
189 2011NWCA Technical Report DISCUSSION DRAFT
-------
4233 (Wannemacher et al. 1989; van Apeldoorn et al. 2007; Gutierrez-Praena et al. 2013). Other techniques
4234 such as frying, roasting, or grilling are yet to be evaluated to our knowledge.
4235
4236 Direct human toxicity by microcystin exposure is also of concern during recreation. Microcystins and
4237 associated cyanobacteria have been associated with adverse symptoms in humans ranging in severity
4238 from nausea, diarrhea, weakness, to liver and kidney failure, potentially cancer, and even death in
4239 severe cases (Chorus and Bartram 1999; Giannuzzi et al. 2011; Meneely and Elliott 2013). While there
4240 are currently (as of 2015) no known, documented human fatalities indicating microcystin exposure was
4241 the cause of death in the United States, fatalities have been observed in other countries on occasion
4242 (Chorus and Bartram 1999). Relative probability of adverse recreational health risks for humans due to
4243 microcystin exposure is frequently assessed based on WHO guidance thresholds (Chorus and Bartram
4244 1999), for example:
4245
4246 • Low: < 10 u.g/L
4247 • Moderate: < 20 u.g/L
4248 • High: < 2000 u.g/L
4249 • Very High: > 2000 u.g/L
4250
4251 Many US states have also developed their own guidance thresholds that are usually similar to WHO
4252 guidance (summarized in Graham et al. 2010; Chorus 2012).
4253
4254
4255 10.2 Methods
4256
4257 Samples were collected for microcystin analysis from sites with standing water > 15 cm and included a
4258 composited water and epiphyte sample following procedures outlined in the 2011 NWCA Field
4259 Operations Manual (USEPA 2011). Samples were shipped overnight, frozen from the USEPA National
4260 Health and Environmental Effects Research Laboratory (NHEERL) in Corvallis, Oregon, to the US
4261 Geological Survey's Organic Geochemistry Research Laboratory in Lawrence, Kansas. Samples were lysed
4262 by three sequential freeze/thaw cycles and filtered with 0.45 micron HVLP syringe filters (Loftin et al.
4263 2008; Graham et al. 2010). Samples were then analyzed by one of two methods depending on whether
4264 practical salinity units (PSU) were < 3.5 PPT (part per thousand, Method 1) or > 3.5 PPT (Method 2).
4265 Samples were stored frozen prior to further extraction (Method 2) and analysis for microcystins by
4266 enzyme-linked immunosorbent assay (Abraxis ADDA kit, Warminster, PA) at -20°C.
4267
4268 10.2.1 Method 1 (Salinity < 3.5 PPT PSU)
4269 Lysed and filtered samples with salinity < 3.5 PPT PSU were analyzed as previously reported by the
4270 Abraxis, LLC microcystins/nodularins ADDA enzyme-linked immunosorbent assay (ELISA) kit as described
4271 by Graham et al. (2010) and in National Lakes Assessment: A Collaborative Survey of the Nation's Lakes
4272 (USEPA 2009). No additional sample preparation was needed.
4273
4274 10.2.2 Method 2 (Salinity > 3.5 PPT PSU)
4275 Lysed and filtered samples with salinity > 3.5 PPT PSU were further extracted to remove the elevated
4276 levels of salt and eliminate adverse performance effects on the Abraxis, LLC microcystins/nodularins
4277 ADDA enzyme-linked immunosorbent assay kit. False positives and enhanced recovery were observed if
4278 salt was not removed from samples when salinity was > 3.5 PSU. Samples with salinity above 3.5 PPT
4279 were extracted to remove salt prior to analysis according to procedures provided by Abraxis, LLC
190 2011 NWCA Technical Report DISCUSSION DRAFT
-------
4280 (Warminster, PA, USA, Abraxis Bulletin R110211). Salinity was calculated based on specific conductance
4281 measured at 25°C and barometric pressure (Schemel et al. 2001). All samples were then analyzed by the
4282 Abraxis microcystins/nodularins ADDA ELISA (Graham et al. 2010).
4283
4284 Samples with salinity greater than 3.5 PPT PSD were extracted using the Abraxis Brackish water or
4285 Seawater sample preparation kit for microcystins (Abraxis Bulletin R110211). Extraction cartridges were
4286 assembled by placing approximately 5 mm of glass wool (Abraxis, LLC, Warminster, PA) into a 5 3/4"
4287 Pasteur pipette and loading with approximately 1.5 g of Seawater Sample Clean-up Resin (Abraxis, LLC,
4288 Warminster, PA). One mL of sample was pretreated with 50 ul of Microcystin-ADDA Seawater sample
4289 treatment solution (Abraxis, LLC, Warminster, PA). Sample was loaded onto seawater sample clean-up
4290 resin and allowed to drain by gravity through resin into a glass conical test tube. Remaining sample from
4291 the resin was evacuated using positive air displacement into the conical test tube. The resin retains the
4292 sample salt while allowing microcystins to pass through. Samples were stored frozen (-20°C) until
4293 analysis.
4294
4295 Minimum reporting level (MRL) for microcystins reported by Method 1 (< 3.5 PPT PSD) and Method 2 (>
4296 3.5 PPT PSD) was 0.10 u.g/L and 0.53 u.g/L as microcystin-LR equivalents. Method performance was
4297 evaluated by the use of ELISA Microcystin-LR kit controls, laboratory sample replicates, laboratory
4298 sample spiked replicates, and blanks. Assay performance was deemed acceptable if values were within
4299 28.3% relative standard deviation (RSD) which is equivalent to ± 20% of expected or average values.
4300 Microcystin concentrations were quantitated by a 4-parameter curve fit and high values above the
4301 upper calibration standard were diluted back onto the curve. Dilution corrected concentrations were
4302 reported in those cases.
4303
4304
4305 10.3 Results
4306
4307 Microcystins were detected in 26% (N=591) of all samples with standing water with a maximum
4308 concentration of 21 u.g/L. Figure 10-1 shows national occurrence of microcystins in wetlands as a
4309 function of method used. Of the 591 sampling sites with standing water, 66% (n=391) had a salinity of <
4310 3.5 PPT PSD (microcystins measured by Method 1) and 34% (n=200) had a salinity greater than 3.5 PPT
4311 PSD (Microcystins measured by Method 2). Microcystins were detected more frequently in wetlands
4312 with salinity less than or equal to 3.5 PPT PSD (38%) where concentrations ranged from 0.10 to 13 u.g/L.
4313 However, when microcystins were detected in the samples with salinity greater than 3.5 PPT (1.5% of
4314 sampled sites) the microcystin concentrations ranged from 3.7 to 21 u.g/L. The majority of microcystin
4315 detections (22% of sampled sites) were 0.50 u.g/L or less, but samples exceeded microcystin
4316 concentrations of 1.0 u.g/L in 3.4% of samples. Microcystins were detected in 12% of assessed wetland
4317 area nationally. Within each NWCA reporting ecoregion, microcystins were detected in 9% of wetland
4318 area in the Coastal Plains, 10% of wetland area in the Eastern Mountains & Upper Midwest, 34% of
4319 wetland area in the Interior Plains, and 8% of wetland area in the West.
191 2011 NWCA Technical Report DISCUSSION DRAFT
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4320
4321
4322
4323
4324
4325
4326
4327
4328
4329
4330
4331
4332
Figure 10-1. National Microcystin Occurrence for 2011 National Wetland Condition Assessment.
Samples from this study were categorized using the WHO guidance thresholds for recreational health
risks of human exposure to microcystins. All samples were categorized as having low relative
recreational risk with the exception of two. One sample from a site in the Coastal Plains was categorized
as having moderate relative recreational risk (13 u.g/L) and one sample from a site in the Eastern
Mountains & Upper Midwest was categorized as having high relative recreational risk (21 u.g/L). 38.9%
of wetland area nationally had a low relative risk for recreational purposes, while 0.04% and 0.01% had
moderate and high relative risks, respectively (Figure 10-2). 61.1% of wetland area nationally could not
be assessed for microcystin presence because surface water was not present at the time of sampling.
192
2011 NWCA Technical Report
DISCUSSION DRAFT
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4333
4334
4335
4336
4337
4338
4339
4340
4341
4342
4343
4344
4345
4346
4347
4348
4349
4350
4351
4352
4353
4354
4355
4356
4357
4358
4359
4360
4361
Microcystin Presence
Percent Area
HH 27%
National 1 -•7i2%
|-H61%
I— I— I 31%
Coastal hfL a-
Eastern Mtn. & •, -BfHio%
UpperMidw. | _>««»/
Interior &* 9%
,-1 , • ~^H ^H 1 J^ ™
Plains ; ™— 1
1 1 1 40%
West -» B%
I 1 1 1 52%
•
Microcystin Risk
Percent Area
HH ay%
hH61%
»-(-H 40%
I 1 1 43%
I 1 1 4S1X.
1 1 152%
0 20 40 60 80 100 0 20 40 60 80 100
Percent Area Percent Area
1 1 Not Detected ^H Detected 1 1 Unassessed
1 I Low Ritki i M
^derate Risk^H High Risk|
Figure 10-2. Percent of Wetland Acres as a Function of World Health Organization Relative Probability of Adverse
Recreational Human Health Risks Based on Microcystin Concentration.
10.4 Discussion
In the first national survey of microcystins in wetlands of the United States, results from this study
clearly identified microcystins were present in wetlands nationally. Microcystins were detected in 27%
of the samples collected at sites with standing water > 15 cm, representing 12% of wetland acres
nationally. Wetland resources are used for a variety of human recreational activities, including hunting,
trapping, fishing, and swimming with the extent of use related to opportunity and regional influences.
Samples from most wetland sites were categorized as having low relative human recreational risk based
on WHO guidelines for this study. Two sites had values exceeding the low WHO microcystin human
recreational guideline of 10 u.g/L and one site had a microcystin value that exceeded the moderate WHO
threshold of 20 u.g/L. The highest microcystin concentration of 21 u.g/L occurred in a coastal wetland
with a salinity of 27 PPT PSU. Microcystins were rarely detected above a salinity of 3.4 PPT PSU, but
three of the six highest microcystin concentrations occurred in wetlands with salinities ranging from
11.1 to 38 PPT PSU. While limited information is available regarding the physical, chemical, and
biological controls over cyanotoxin occurrence in wetland settings, there are well known adverse
impacts of microcystins on some macrophytes common to wetlands. Microcystins exceeded 1.0 u.g/L in
3.4% of samples; this microcystin concentration was shown by Rojo et al. (2013) to limit growth and
photosynthetic oxygen production in some charophyte species. Additionally, seedling germination and
macrophyte density were impeded in experiments with microcystin concentrations of 8 to 16 u.g/L in
sediments, where concentrations are more persistent relative to the water column (Rojo et al. 2013).
Concerns regarding microcystin concentration in tissues of wetland organisms consumed by humans
cannot be directly evaluated from the results of this study. However, they cannot be summarily
dismissed even with what may currently be believed to be lower level microcystin concentrations in
193
2011 NWCA Technical Report
DISCUSSION DRAFT
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4362 many of the wetlands in this survey. The WHO developed a microcystin tolerable daily intake (TDI) value
4363 of 0.04 u.g of microcystin-LR/kg body weight as the basis for recreational and consumption guidance
4364 regarding human microcystin exposure. Poste et al (2011) noted that a person weighing 60 kg and eating
4365 100 g of fish daily would not want the edible portions of fish to exceed an available microcystin
4366 concentration of 24 u.g/kg of fish on a wet weight basis. Several cases were summarized by Ibelings and
4367 Chorus (2007) indicating that there were multiple cases where edible portions of various fish, mussels,
4368 crayfish, and shrimp species have exceeded the WHO TDI. More work is needed to better relate ambient
4369 water column microcystin concentrations and exposure duration with potential food web accumulation,
4370 impacts of cooking, microcystin concentrations after consumption, and relationships tied to adverse
4371 human health impacts. Additional research is also needed to understand depuration rates for
4372 microcystin excretion and metabolism compared with water column concentrations which are currently
4373 used to provide risk assessment guidance to the public in many cases.
4374
4375 As this is the first survey of the ecologic condition of the Nation's wetlands, it is not clear yet how
4376 microcystin occurrence might change in time. Changing environmental conditions and anthropogenic
4377 influences exert pressures on complex ecosystems that are sometimes threatened by multiple stressors.
4378 Salinity and nutrients have frequently been considered as the two important variables regarding
4379 phytoplankton succession in wetlands when all other aspects are suitable for phytoplankton life (Lopez-
4380 Flores 2014). Salinity is relevant to cyanobacteria, a form of phytoplankton, since some species are more
4381 tolerant of salt than others and is therefore relevant to what cyanotoxins can be produced. Coastal
4382 wetlands tend to have elevated salinity related to their degree of connectivity to the marine setting.
4383 Elevated salinity in inland wetlands is usually associated with natural processes (such as evaporation,
4384 drought, and geology), but there are also potential anthropogenic sources of salinity such as road salt,
4385 brine spills, and other human activities (Lopez-Flores et al. 2014).
4386
4387
4388 10.5 Literature Cited
4389
4390 Abraxis Bulletin (R110211) Microcystins in brackish water or seawater sample preparation.
4391 http://www.abraxiskits.com/uploads/products/docfiles/385_MCT-
4392 ADDA%20in%20Seawater%20Sample%20Prep%20%20Bulletin%20R041112.pdf: accessed on 2/20/2015
4393
4394 Beaver JR, Manis EE, Loftin KA, Graham JL, Pollard A, Mitchell RM (2014) Land use patterns, ecoregion,
4395 and microcystin relationships in US lakes and reservoirs: A preliminary evaluation. Harmful Algae 36: 57-
4396 62
4397
4398 Briand JF, Jacquet S, Bernard C, Humbert JF (2003) Health hazards for terrestrial vertebrates from toxic
4399 cyanobacteria in surface water ecosystems. Veterinary Research 34: 361-377
4400
4401 Corbel S, Mougin C, BouaVcha N (2013) Review: Cyanobacteria toxins: Mode of actions, fate in aquatic
4402 and soil ecosystems, phytotoxicity and bioaccumulation in agricultural crops. Chemosphere 96: 1-15
4403
4404 Crush JR, Briggs LR, Sprosen JM, Nichols SN (2008) Effect of irrigation with lake water containing
4405 microcystins on microcystin content and growth of ryegrass, clover, rape, and lettuce. Environmental
4406 Toxicology 23: 246-252
4407
194 2011NWCA Technical Report DISCUSSION DRAFT
-------
4408 Chorus I, (Ed) (2012) Current approaches to cyanotoxin risk assessment, risk management and
4409 regulations in different countries. Federal Environment Agency (Umweltbundesamt).
4410 http://www.umweltbundesamt.de/sites/default/files/medien/461/publikationen/4390.pdf: accessed
4411 1/2/2015
4412
4413 Chorus I, Bartram J (Eds.) (1999) Toxic cyanobacteria in water: A guide to their public health
4414 consequences, monitoring, and management. World Health Organization and E&FN Spon Press, London,
4415 UK
4416
4417 Giannuzzi L, Sedan D, Echenique R, Andrinolo D (2011) An acute case of intoxication with cyanobacteria
4418 and cyanotoxins in recreational water in Salto Grande Dam, Argentina. Marine Drugs 9: 2164-2175
4419
4420 Graham JL, Loftin KA, Kamman N (2009) Monitoring recreational freshwaters. Lakeline Summer: 18-24
4421
4422 Graham JL, Loftin KL, Meyer MT, Ziegler AC (2010) Cyanotoxin mixtures and taste-and-odor compounds
4423 in cyanobacterial blooms from the Midwestern United States. Environmental Science and Technology
4424 44:7361-7368.
4425
4426 Gutierrez-Praena D, Jos A, Pichardo S, Moreno IM, Camean AM (2013) Presence and bioaccumulation of
4427 microcystins and cylindrospermopsins in food and the effectiveness of some cooking techniques at
4428 decreasing their concentrations: A review. Food and Chemical Toxicology 53: 139-152
4429
4430 Handeland K, 0stensvik 0 (2010) Microcystin poisoning in roe deer (Capreolus capreolus). Toxicon 56:
4431 1076-1078
4432
4433 Ibelings BW, Chorus I (2007) Accumulation of cyanobacterial toxins in freshwater "seafood" and its
4434 consequences for public health: A review. Environmental Pollution 150: 177-192
4435
4436 Landsberg JH (2002) The effects of harmful algal blooms on aquatic organisms. Reviews in Fisheries
4437 Science 10: 113-390
4438
4439 Loftin KA, Meyer MT, Rubio F, Kamp L, Humphries E, Wherea, E (2008) Comparison of two cell-lysis
4440 procedures for recovery of microcystins in water samples from Silver Lake in Dover, Delaware with
4441 microcystin producing cyanobacterial accumulations. US Geological Survey Open File Report 2008-1341
4442
4443
195 2011NWCA Technical Report DISCUSSION DRAFT
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4444 This page was intentionally left blank for double-sided printing.
196 2011NWCA Technical Report DISCUSSION DRAFT
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4445
4446 Chapter 11: Research Feature - Water Chemistry
4447
4448
4449 11.1 Background
4450
4451 Characterizing water chemistry is an integral part of the assessment of aquatic resources, because the
4452 physical and chemical properties of water directly reflect the geochemical setting and anthropogenic
4453 influences on water bodies. Water chemistry measures can provide context for understanding patterns
4454 of biological productivity and composition, can be sensitive indicators of ecological condition in and of
4455 themselves, can be used to infer potential stressors, and are important in determining the human use
4456 and enjoyment of aquatic ecosystems. Furthermore, having broad-scale, consistently measured water
4457 chemistry data can inform areas of management and regulatory concern such as the development of
4458 nutrient criteria. NARS surveys of lakes, streams, rivers, and coastal waters therefore have made a
4459 practice of allocating substantial resources to measuring water chemistry, and the water chemistry data
4460 play a major role in the resulting condition reports and related scientific analyses (e.g., Herlihy and
4461 Sifneos 2008; Herlihy et al. 2013).
4462
4463 Wetlands, however, differ from lakes, streams, and coastal waters in that standing water is not
4464 necessarily present and its makeup might be less reflective of broad watershed features because of the
4465 great variability among wetlands in hydrologic sources, hydroperiod, landscape connectivity, internal
4466 biogeochemical processing, and geomorphic setting (Carter 1986; Mitsch and Gosselink 2000). Water
4467 chemistry data have played a central role in assessments of wetlands in some parts of the US (e.g., Great
4468 Lakes coastal wetlands - Lougheed et al. 2007; Trebitz et al. 2009), but these reflect only a subset of the
4469 wetland types across the nation. This first ever NARS assessment of wetlands provides an opportunity to
4470 explore the value of water chemistry data in reporting on wetland resources nationwide. Compared to
4471 other NARS surveys, the suite of water chemistry parameters collected in the 2011 NWCA is relatively
4472 small, but the core measurements are consistent with other NARS surveys.
4473
4474 Objectives of the water chemistry data analyses presented here are to examine the extent to which
4475 water chemistry could be sampled across US wetlands, to evaluate the various measurement endpoints
4476 obtained (e.g., variability, repeatability, information content), to present broad patterns in water
4477 chemistry across the nation and relate them to possible classification variables and natural and
4478 anthropogenic drivers, and to generate recommendations concerning further research and protocols for
4479 future NWCA assessments.
4480
4481
4482 11.2 Methods
4483
4484 11.2.1 Sample Collection and Laboratory Analysis
4485 Water chemistry parameters measured or analyzed were chlorophyll-a (CHLA), conductivity (COND),
4486 ammonia (NH3), nitrate and nitrite (NO3 and NO2, abbreviated as NOX hereafter), total nitrogen (TN),
4487 total phosphorus (TP), and pH (PH). Water temperature and dissolved oxygen levels were also measured
4488 at some sites at the option of the states or regions involved; however because they were not
4489 consistently measured across all sites, these parameters are not included in the water chemistry analysis
197 2011 NWCA Technical Report DISCUSSION DRAFT
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4490
4491
4492
4493
4494
4495
4496
4497
4498
4499
4500
4501
4502
4503
4504
4505
4506
4507
4508
4509
4510
4511
4512
4513
4514
4515
4516
4517
4518
4519
4520
4521
4522
4523
4524
4525
4526
4527
4528
4529
4530
4531
4532
4533
4534
4535
presented here. A quantitative measure of water clarity was not made, although water clarity was
qualitatively assessed by noting on field sheets whether water appeared clear, turbid, stained, or milky.
Water samples were collected if surface water of sufficient depth to sink the pole-mounted dipper (~15
cm) was present within the assessment area. Water was dipped from the middle of the inundated area
if possible and away from any inlets or outlets. Crews were requested to collect water samples prior to
11:00 am local time if possible, so as to reduce diurnal changes in water chemistry (e.g., due to
metabolic activity of organisms in water). Dippers and bottles were rinsed with site water before filling
and vegetation and surface debris was gently moved aside if needed. Enough water to fill a one-liter
cubitainer to overflowing (i.e., without air being retained) was collected, and another up to 500 ml
volume of water was filtered with a hand-held vacuum pump on site for later chlorophyll analysis
(Whatman GF/F 0.7 um glass fiber filter). At approximately every tenth site, duplicate water samples
were collected for quality assurance purposes (chlorophyll measures were not ordinarily duplicated).
Cubitainers and chlorophyll filters were placed out of the sun and on ice as soon as possible, and later
express-shipped to the analytical laboratory (generally arriving within 24 to 48 hours of collection).
The bulk of the samples (89%) were analyzed by the WRS laboratory (Willamette Research Station, in
Corvallis OR), however four other laboratories each analyzed some samples:
• GLEC- Great Lakes Environmental Center in Traverse City Ml
• ND - North Dakota Department of Health Laboratory Services, Bismark ND
• USGS - US Geological Survey Laboratory in Denver Colorado
• Wl - Wisconsin State Laboratory of Hygiene in Madison Wl
Briefly, analytical methods used by the WRS lab (and any differences in procedures at other labs) are as
follows:
• CHLA: Filters ground and extracted with acetone and then measured by fluorescence (Wl lab
sonicated samples prior to extraction instead); detection limit 0.5 at WRS lab and ranging from
1.4 to 20 at the ND lab (all samples above detection limits at other labs).
and NO2 (NOX): Determined via ion chromatography for freshwater samples but via
cadmium reduction method on a flow injection analyzer for brackish samples (other labs ran all
samples with the cadmium reduction method); detection limits 0.004 or 0.02 for the WRS lab
(brackish and freshwater respectively), 0.001 mg/L for the GLEC lab, 0.02 for the USGS lab, 0.019
for the Wl lab, and 0.03 for the ND lab.
• NH3: Determined colorimetrically; detection limits 0.004 mg/L for the WRS lab and 0.03 mg/L for
the ND lab (all samples above detection limits at the other labs).
• TN and TP: Determined colorimetrically following persulfate digestion; TP detection limits 4 u.g/L
for all laboratories, all samples above detection limits for TN.
• PH and COND: Measured on an auto-titrater or manually with a YSI or similar meter, no samples
below detection.
198
2011 NWCA Technical Report
DISCUSSION DRAFT
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4536 Results for NH3 and NOX are reported as the concentration of nitrogen (i.e., mg N/L, although hereafter
4537 abbreviated simply as mg/L).
4538
4539 11.2.2 Data Handling
4540 In screening data for use in analyses, we rejected (i.e. set to missing) only measurements affected by
4541 sample loss (e.g., cracked test-tube) or errors in filtration (failure to record filtration volume or wrong
4542 filter medium for some CHLA samples, accidental filtration before analysis of some samples intended for
4543 TN and TP). We decided to accept for analyses samples with hold-time exceedance, minor deviations in
4544 laboratory procedures (e.g., extraction by soaking rather than grinding), or shipping-related issues
4545 (usually a generic "ship flag" indicating delay in transit, a few samples noted as arriving "warm"). There
4546 was also few samples (~15) for which laboratory data were simply missing for all analytes measured on
4547 the unfiltered sample (i.e., lab COND and pH, TN, TP) although present for analytes measured on the
4548 filtered sample (i.e., NH3, NOX). The rate of missing data due to rejection or the laboratory not providing
4549 a value was ~2% for CHLA, COND, NH3, pH, TN, and TP but <0.2% for NOX. A decision to reject all samples
4550 with shipping flags would have eliminated a large portion of the data (~30%).
4551
4552 Nitrogen samples were analyzed for TKN rather than TN at two labs (GLEC and Wl) and the ND lab
4553 analyzed its samples for both TKN and TN. Since TN as computed from TKN + NOX was perfectly
4554 correlated with measured TN for the 44 samples where both TN and TKN were run; we substituted the
4555 value of TKN + NOX for samples where TN was not measured directly. Mass-based ratios of nitrogen to
4556 phosphorus were computed by dividing TN and TP (both expressed in microgram per liter units) by their
4557 respective atomic weights (i.e., N:P = (TN /14.0076)/(TP /30.9738)).
4558
4559 COND and PH were measured only in the field at some sites, only in the laboratory at others, and in both
4560 the lab and field for still others. Conductivity lab and field measurements were fully interchangeable
4561 (Pearson correlation = 0.99, slope essentially 1:1), despite a few outliers that may represent recording
4562 errors (Figure 11-1). On the premise that recording errors were more likely in the field than the lab, we
4563 merged lab- and field-measured COND into a single variable for analyses by retaining the lab value when
4564 available and the field value otherwise. For pH, the slope of lab vs. field measurements was again
4565 essentially 1:1 but the correlation was lower (r = 0.83) and there was a tendency for laboratory values to
4566 lie above field values at lower PH values versus below field values at higher pH values (Figure 11-1). Our
4567 interpretation is that pH is not entirely stable in sample containers but rather changes in ways consistent
4568 with exposure to atmosphere despite care being taken to exclude air. Nevertheless, the difference
4569 between field and lab-measured pH did not seem sufficient to warrant the complication of treating
4570 them separately in statistical analyses, so they were merged into a single pH variable by using the field
4571 measure when available (about 1/3 of the sites) but the lab measure otherwise.
4572
4573 We checked water chemistry data for suspicious values before proceeding to statistical analyses. All
4574 data points passed basic logic checks (e.g., within legitimate ranges for water in the environment,
4575 combined value of NH3 and NOX not exceeding the value for TN). There were a few distributional outliers
4576 but absent information to suggest the measurements were invalid our philosophy was to retain them.
4577 The only data point rejected as an outlier (i.e., replaced with a missing value) was one CHLA value of
4578 2059 u.g/L from a revisit at a site where CHLA at the first visit was only 16 u.g/L. While this second visit
4579 data point was not the highest CHLA value in the dataset, its magnitude seemed excessive given the
4580 modest nutrient levels at this site, and its inclusion weakened the otherwise substantial correlation
4581 among Visit land Visit 2 CHLA.
4582
199 2011NWCA Technical Report DISCUSSION DRAFT
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4583
4584
4585
4586
4587
4588
4589
4590
4591
4592
4593
4594
4595
4596
4597
4598
4599
4600
4601
4602
4603
4604
4605
4606
4607
4608
4609
4610
4611
4612
4613
4614
4615
Analyte values that were below the reported laboratory detection limit (for NOx, NH3, TP, and CHL)
were replaced with a value equal to half the detection limit prior to further analyses (Hornung & Reed
1990; USEPA 2006). Detection limits varied among laboratories and accordingly the value substituted for
below-detection samples also varied.
90000
60000
O 30000
0
10
9
8
7-
5
4
i i i i i i i
30000 60000 90000
corr. = 0.829
COND (Ms/cm) lab
3456789 10
PH lab
Figure 11-1. Relationship between laboratory and field measured COND (left) and PH (right) for the sites at which
both lab and field measurements were made. The longer, dashed line in both plots is the 1:1 line; the shorter solid
line is the linear regression.
11.2.1 Graphical and Statistical Analysis
The main data set analyzed for water chemistry combines sites selected based on the probability design
with hand-picked sites (i.e., all sites sampled in 2011) but examines only data from the first site visit and
the primary water chemistry sample. A duplicate water-quality sample was collected at every 10th site
for QA purposes. Two secondary data sets are also analyzed: one comparing the primary to the
duplicate water chemistry data from first-visit sites where duplicate samples were taken, and one
comparing first-visit to second-visit primary water chemistry data from sites that received two
independent sampling visits. All results concerning water chemistry patterns and conditions stem from
analyses of this main data set; the secondary data sets are used only to evaluate temporal variability and
repeatability.
Site classification variables used in the analyses included:
• four NWCA Aggregated Ecoregions (Coastal Plains, Eastern Mountains & Upper Midwest,
Interior Plains, and West)
• estuarine wetlands
• NWCA Aggregated Wetland Types (woody or herbaceous)
• 10 NWCA Reporting Groups obtained by crossing geographic reporting units with vegetation
type
• HGM categories (depression, flats, lacustrine fringe, riverine, slope, and tidal
The three sites where field crews had not assigned an HGM category were assigned based on a desktop
review of Google Map imagery surrounding the sampling coordinates. Salinity status (freshwater or
200
2011 NWCA Technical Report
DISCUSSION DRAFT
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4616 brackish) was also used as a classification variable; wetlands can be considered freshwater if <0.5 ppt
4617 salt and brackish otherwise (Cowardin et al. 1979). Since salinity was not measured directly in the
4618 NWCA, we used 833 u.S/cm COND as the threshold between fresh and salt (an approximation assuming
4619 COND in u.S/cm x 0.6 = salinity in ppm; precise conversion of conductivity to salinity depends on
4620 temperature, pressure, and component salts; Clesceri et al. 1998). The 16 sites at which conductivity
4621 was not measured were assumed to be freshwater based on location (all from the inland states of
4622 Arkansas or North Dakota).
4623
4624 A suite of potential anthropogenic stressor variables were used in the analyses. Landuse/landcover in
4625 concentric circles of various radii (200 m, 500 m, and 1 km) around the assessment area was
4626 summarized from the 2006 National Land Cover Database, road density data (km/sq km) based on 2010
4627 TIGER road data obtained from the US National Park Service), and population density data (people/sq
4628 mi) compiled from 2010 US census data. Because water chemistry is generally considered a function of
4629 anthropogenic influences over an entire watershed, analyses focused on landuse/landcover, road
4630 density, and population density summarized for the largest, 1 km radius, circle. The NLCD category
4631 combinations used in computing percentage of the total area were: agriculture (pasture/hay + cultivated
4632 crops), developed (combining low, medium, and high-density development plus developed open-space),
4633 forested (combining deciduous, evergreen, and mixed), and wetland/water (combining open water and
4634 woody and emergent herbaceous wetland), as well as the percent impervious value that NLCD tallies
4635 separately from the other categories (i.e., the rest are additive while percent impervious is not).
4636
4637 Potential site disturbance was also classified using a buffer disturbance index (B1H_ALL) that is a
4638 proximity-weighted summary of potential stressors noted in thirteen buffer plots assessed by NWCA
4639 field crews(see Chapter 4, Section 4.5). Sites with a B1H_ALL score of zero were classified as
4640 undisturbed; sites with non-zero B1H_ALL scores were classified as "least disturbed", "intermediate
4641 disturbed", or "most disturbed" based on the distribution of values within their NWCA Reporting Group
4642 (i.e., thresholds for disturbance categories differed among ecoregions and wetland types).
4643
4644 Analyses comparing primary vs. duplicate samples and Visit 1 versus Visit 2 values for each water
4645 chemistry analyte are based primarily on Pearson correlations (for actual water chemistry values) or
4646 spearman rank correlations (to examine relative values). Diagnostics considered include magnitude of
4647 the correlation and degree to which the correlation line corresponds to the 1:1 line (assessed
4648 graphically). We examined whether the time lag between Visit 1 and Visit 2 had any systematic effect on
4649 water chemistry by using the number of weeks elapsed as a plotting symbol in correlation plots and
4650 looking for whether the magnitude of departure from the 1:1 correlation line depended on weeks
4651 elapsed.
4652
4653 Analyses of patterns in primary sample water chemistry data used correlation and regression analyses
4654 for assessing relationship among analytes and to anthropogenic stressor variables. Given the large
4655 sample size even relatively small magnitude correlations can be significant with this dataset, so analyses
4656 focused on relationship magnitude rather than p-value. Differences in water chemistry among site
4657 classification variables were assessed with box plots and ANOVA. Following methods used in other NARS
4658 assessments, assignment of sites into good, fair, or poor categories for various water chemistry analytes
4659 were attempted using the 75th percentile and the 90th percentile of sites classified as least-disturbed
4660 (i.e., the undisturbed and low disturbance sites) as thresholds between good and fair, and fair and poor,
4661 respectively.
4662
201 2011 NWCA Technical Report DISCUSSION DRAFT
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4663
4664
4665
4666
4667
4668
4669
4670
4671
4672
4673
4674
4675
4676
4677
4678
4679
4680
4681
4682
4683
4684
4685
4686
4687
4688
4689
4690
4691
4692
Ranges for COND, CHLA, NH3, NOX, TN, and TP were large enough (several orders of magnitude) to
warrant log transformation. Accordingly, correlation and regression analyses presented for these
parameters are based on loglO-transformed units, and graphical analyses use loglO-transformed axes.
Data medians, min/max values, and percentiles (which are invariant to log-transformation) are however
presented in untransformed units for greater interpretability. Because of using 1/£ the detection limit as a
minimum value, there were no zero values for any of these analytes meaning that log-10 transformation
did not result in any undefined (and therefore missing) values.
For all of the primary data-set analyses that examined relationships by the NWCA Reporting Group or
NWCA Ecoregion Group, sites that were not classified as an Estuarine wetland yet had COND > 3000
u.S/cm were omitted. These sites have water chemistry that appeared unusual for their reporting group
and would skew results were they included in group analysis (not only COND but also higher nutrients
and CHLA). These sites are however included in any overall description of the water chemistry data
(overall means and ranges, correlations among analytes).
11.3 Results
11.3.1 Data Set Overview
A total of 631 of the 1138 sites sampled yielded water chemistry data on the primary visit, with 51 (of
the 96 sites that were revisited) also having water chemistry data collected at a second visit. Water
chemistry data were collected from at least one wetland in all conterminous US states except Kansas
(Alaska and Hawaii were outside the scope of the NWCA). Sample sizes for the primary analysis data set
(i.e., excluding samples from second visits and QA duplicates) ranged from 615 to 630 depending on the
analyte. The distribution of wetland water chemistry samples across the five water chemistry reporting
units (the four NWCA Aggregated Ecoregions plus a separate reporting unit representing Estuarine
wetlands) and six HGM categories is given in Table 11-1.
Table 11-1. Statistics concerning frequency with which water samples were or were not obtained across various
NWCA reporting units. Percent of sites without water samples is also broken out by herbaceous and woody type
wetlands within the estuarine and geographically-based reporting units.
Reporting unit
All sites
Estuarine
Coastal Plains
Eastern Mountains
& Upper Midwest
Interior Plains
West
HGM type
Depressional
Flats
Lacustrine fringe
Riverine
Slope
Tidal
# with water sample
631
220
94
111
116
90
170
54
28
143
26
207
# without water
sample
507
167
125
89
74
52
113
132
21
126
20
92
% without
water
sample
overall
44.5
43.1
57.0
44.5
38.9
36.6
39.9
71.0
42.9
46.8
43.5
30.8
% without
water
sample
herbaceous
33.6
31.9
56.3
28.8
29.7
30.7
-
-
-
-
-
-
% without
water
sample
woody
58.1
52.8
66.8
53.5
63.5
43.2
-
-
-
-
-
-
202
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
4693 The NWCA expected that water would be collected at only a subset of all sites because wetlands do not
4694 always have standing water. Reasons water chemistry was not obtained from the remaining 507 sites
4695 were:
4696
4697 • 411 sites: no standing water
4698 • 94 sites: standing water in the assessment area was not deep enough to meet the sampling
4699 criteria (at least 15 cm deep)
4700 • 2 sites: lost samples
4701
4702 The inability to collect a water sample occurred more than 30% of the time in all wetland type and
4703 geographic reporting unit combinations. Surprisingly, given the generally more arid climate in the
4704 western US compare to the east, the inability to collect a water sample was lowest in the West reporting
4705 unit and highest in the Coastal Plains reporting unit (Table 11-1). Lack of water or of sufficient water was
4706 substantially higher in woody than herbaceous wetlands (58 versus 34 percent overall). This pattern was
4707 found in all NWCA Aggregated Ecoregions and Estuarine wetlands. The inability to collect water was
4708 highest in the flats HGM type at 71% and lowest in the tidal HGM type at 31% with the depressional,
4709 lacustrine fringe, slope, and riverine HGM types intermediate.
4710
4711 The ability to collect a water sample was not generally related to day of year the wetland was sampled,
4712 even though sampling extended from April through October and we might have expected wetlands to
4713 be generally drier later in the year. This held true within the NWCA Aggregated Ecoregions as well.
4714
4715 Whether a water sample could be collected or not did not appear to be related to wetland condition as
4716 measured by the buffer disturbance index, as lack of water or of sufficient water differed little among
4717 disturbance categories. Hydrologic alteration is well documented as a major source of wetland
4718 disturbance and was frequently noted in the field assessment data sheets. However hydrologic
4719 disturbances that tend to increase surface water availability (e.g., impoundment) may balance out
4720 hydrologic disturbances that tend to decrease surface water availability.
4721
4722 11.3.2 Repeatability of Water Chemistry Data
4723 By design, approximately 10% of the NWCA sites were revisited within the 2011 effort (i.e., sampled at
4724 two different points in time), and also approximately 10% of the sites had a duplicate water sample
4725 taken during the site visit (i.e., side-by-side samples from same point in time).
4726
4727 Duplicate water chemistry samples were collected on 99 site visits (89 from Visit 1, 10 from Visit 2) and
4728 were well-spread geographically with duplicates collected in 42 states (Correlations among primary and
4729 duplicate samples were extremely high for all analytes (r = 0.99 for COND, NH3, NOx, PH-LAB, PH-FIELD,
4730 TN, TP). CHLA collection and analyses were duplicated at only 4 sites but here also the correlation was
4731 0.99 (crews were not instructed to duplicate CHLA collection but one crew did so). These results indicate
4732 that variability due to sample collection, handling, or analytical procedures is negligible. Duplicate
4733 samples were always collected from a single location in the wetland; accordingly these results do not
4734 speak to spatial variability in water chemistry within wetlands.
4735
4736 Forty-eight sites have water chemistry data from two points in time as part of the revisit effort. The
4737 number of days between visits ranged from 10 to 133 (mean of 37 days). We coded points in the plots
4738 by the number of weeks elapsed between visits but there was no obvious tendency for larger water
4739 chemistry differences to be associated with longer elapsed times (Figure 11-2). The water chemistry
4740 analytes fall into two groups with regard to temporal stability, with the heavily biologically influenced
203 2011 NWCA Technical Report DISCUSSION DRAFT
-------
4741
4742
4743
4744
4745
4746
4747
4748
4749
4750
4751
4752
4753
4754
4755
4756
4757
4758
4759
4760
4761
4762
4763
4764
4765
4766
4767
analytes (NH3, NOX, and CHLA) being less stable than the less biologically influenced analytes (COND, pH,
TN, TP). OND, TN, TP, and pH all had between-visit Pearson correlations >0.8 and regression-line slopes
close to 1:1 indicating little change between visits. In contrast, NH3, NOX, and CHLA have substantially
lower between-visit correlations and line slopes substantially flatter than 1:1 (i.e., typically lower values
at Visit 2 than Visit 1; Figure 11-2). Spearman correlations among Visit 1 and 2 were very similar to the
Pearson correlations (COND = 0.99, TN = 0.82, TP = 0.88, pH = 0.89, NH3 = 0.53, NOX = 0.47, CHLA = 0.64),
indicating that wetland rank order varied by an amount similar to the water chemistry values
themselves. Stability of wetland rank order is of interest because percentiles of the site distribution
(which depend only on rank order) are commonly used to bin sites into condition categories with
respect to some measurement variable.
11 1 10 100 1 000
0 01 010
Figure 11-2. Bi-plots of water chemistry values as measured at Visit 1 (x-axis) vs. Visit 2 (y-axis). Long dashed lines
are 1:1 lines, shorter solid lines are linear regressions, and the plotted symbol show the number of weeks elapsed
between sample 1 and sample 2 (all values greater than 9 weeks are coded as "9"). The pH slope is ~ 1:1 after
removal of the circled outlier.
11.3.3 Broad Patterns in Water Chemistry
The range in water chemistry across the 2011 NWCA dataset was quite large. Across all sites, pH ranged
from quite acidic to alkaline (3.3 to 10.2), and conductivity ranged from 10 uS/cm to exceeding 73000
uS/cm (Table 11-2). The "flats" HGM type accounted for the largest proportion of the sites with pH<5 (as
would be expected since peat bogs fall into this HGM category), however there were some sites with
pH<5 from every HGM category except slope. The vast majority of sites with brackish conductivity (>833
uS/cm) were of the tidal HGM type, but there were at least one site with COND values well above this
brackish threshold in every HGM category.
204
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
4768 Table 11-2. Median and range (in parentheses) for water chemistry analytes across the data set as a whole and for
4769 geographic reporting unit and wetland type subdivisions. Number of sites is given in parentheses after each
4770 reporting unit (sample size for some analytes is slightly lower due to missing values). "BD" denotes values below
4771 the most frequently applicable laboratory detection limit for CHLA (0.5 u.g/L), NH3 (0.004 mg/L), NOX (0.02 mg/L),
4772 and TP (4.0 u.g/L). Note that some COND values seem inappropriate for their site type (e.g., <1000 in
4773 estuarine/tidal; > 10,000 in non-estuarine/non-tidal) but statistics reported here are for all sites regardless of COND
4774 values.
Reporting unit
All sites
(N=631)
COND
(uS/cm)
572
(10-73660)
(3.
PH
7.19
3-10
.2)
CHLA
(M8/L)
7.5
(BD-2117)
NH3
(mg/L)
0.03
(BD-4.7)
NO*
(mg/L)
BD
(BD-7.8)
TN
iMf/U
1080
(43-700500)
4775
Estuarine
(N=220)
CPL
(N=94)
EMU
(N=lll)
IPL
(N=116)
W
(N=90)
HGM
depressional
(N=171)
flats
(N=54)
lacustrine
fringe (N=28)
riverine
(N=131)
slope
(N=26)
tidal
(N=207)
28785
(60- 73660)
200
(32-10840)
93
(11-1133)
558
(39-3822)
184
(11-21670)
339
(11-21670)
201
(17-40480)
292
(23-3713)
204
(16-18340)
116
(18-3822)
29450
(60-73660)
(3
(3
(3
(5
(3.
(3.
(3
7.6
.5-9.
6.7
.3-9.
6.7
.8-8.
7.6
.7-9.
7.4
6-10
7.2
6-10
6.9
.8-8.
7.3
5)
3)
9)
1)
.2)
.2)
5)
(3.8-8.4)
(3
(5
(3
7.2
.3-8.
7.4
.6-8.
7.6
.5-9.
8)
7)
5)
4776 There were some Estuarine wetland sites
4777
4778
4779
some Non-Estuarine wetland
orackish group.
sites whose
Inspection of the physical
12
(1-1505)
10.5
(BD-49)
3.3
(2-183)
9.5
(BD-2117)
3.0
(BD-1030)
6.4
(BD-2117)
6.4
(BD-633)
10.8
(1.5-309)
3.8
(BD-239)
2.9
(BD-177)
11
(BD-1505)
0.09
(BD-2.33)
0.04
(BD-3.7)
0.02
(BD-0.9)
0.04
(BD-4.7)
0.01
(0.01-1.0)
0.03
(BD-4.7)
0.02
(BD-1.4)
0.3
(0.005-
0.8)
0.3
(BD-3.7)
0.01
(BD-0.6)
0.02
(BD-2.3)
BD
(BD-7.8)
BD
(BD-1.5)
BD
(BD-0.8)
BD
(BD-1.0)
BD
(BD-0.9)
BD
(BD-0.8)
BD
(BD-1.0)
BD
(BD-0.2)
BD
(BD-1.5)
BD
(BD-0.9)
BD
(BD-7.8)
944
(98-23075)
1428
(151-18813)
772
(118-35900)
1985
(309-70050)
421
(43-9313)
1703
(70-70050)
1309
(205-12700)
2145
(155-19675)
806
(43-35900)
394
(78-5131)
933
(98-23075)
TP
(Mf/y
121
(BD-11510)
122
(BD-2481)
132
(7-3140)
44
(BD-3325)
357
(18-11510)
93
(7-3612)
226
(BD-11510)
64
(BD-
1782)
196
(BD-5485)
91
(6-7364)
87
(10-1272)
121
(BD-2481)
with COND values more characteristic of freshwater, as well as
conductivity
fell into ranges more
location of sites within
COND values typical of freshwater systems showed all
of them to
characteristic
of the
the estuarine reporting group with
be locatec
close to a substantial size
205
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
4780
4781
4782
4783
4784
4785
4786
4787
4788
4789
4790
4791
4792
4793
4794
4795
4796
4797
4798
4799
4800
4801
river. Inspection of the physical location of the Non-Estuarine sites with COND values typical of brackish
systems showed that many (notably all with COND>3000 uS/cm) were located in close proximity to an
ocean and might therefore be receiving a marine influence; the COND measure may be indicative of a
stressor influencing those sites. Brackish sites in landlocked states such as North Dakota and South
Dakota had maximum COND of only ~ 1200 u.S/cm (Figure 11-3).
80,000-
-
60.000 -
40,000-
20,000 -
E Q-\
-
*
t
1 o^ /:S
^:NC,LA* ^ / '£
Fl »NC LA T ^' • CA
( m ? ^&^ iCA, NM, WA
_o
<§ 900-1
Q
2
0
o
600-
—
300-
•
t
A
|
* •
* LA „ s .
•LA
— 5
: DE&LA - •
•? ! i
J. . . *
; 1
I • CT - *
•••mi
J i . WA | ~
It ' WA jg i .3.
0 1 i i i i i i
J> 833) - note difference in scales
between the two. Sites with unusual COND for their ecoregion are labeled with the US state (2-letter code) in
which they are found. The 10 sites excluded from analyses examining ecoregion and NWCA Reporting Groups are
indicated with red symbols and text.
Examining relationships of conductivity to anthropogenic setting is of interest for the NWCA.
Conductivity measurements that are significantly higher or lower than what is typical for certain wetland
systems may be a sign of anthropogenic influence. Some sites with very high COND in non-estuarine
geographically-based reporting units also had elevated levels of other water chemistry analytes, which
would potentially skew results when examining water chemistry by reporting unit. Excluding sites having
COND>3000 (highlighted in red in Figure 11-3) caused maximum values in West-herbaceous sites (W-
PRLH) to fall from 9313 to 4508 ug/L for TN; maximum values in Coastal Plains-woody sites (CPL-PRLW)
to fall from 5888 to 4683 u.g/L TN and from 327.2 to 239.5 u.g/L CHLA; and maximum values in Coastal
Plains-herbaceous sites (CPL-PRLH) to fall from 19913 to 5938 u.g/L TN, from 3140 to 1314 u.g/L TP, and
206
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
4802
4803
4804
4805
4806
4807
4808
4809
4810
4811
4812
4813
4814
4815
4816
4817
4818
4819
4820
4821
4822
4823
4824
4825
4826
4827
4828
from 463.2 to 206.7 u.g/L TP. Given their unusual COND and their effect of the distribution of other
analytes, these 10 non-estuarine sites with COND>3000 are excluded from all analyses below that are
specific to geographic reporting units or NWCA Reporting Group, although the sites are included in other
analyses (e.g., across all sites or by HGM type).
Across all sites, there was a 4+ order-of-magnitude range in nutrients and CHLA concentrations, and at
least a 3 order-of-magnitude range in these within any one geographically-based reporting units or HGM
category (Table 11-2, Figure 11-4). Given these large ranges, loglO transformations of these variables
(or logarithmic intervals on the plot scales) are used in presenting all analyses. LoglO TN and TP were
strongly correlated (Pearson r=0.78) and loglO CHLA was correlated to both (Pearson r=0.63 for TN and
0.65 for TP; Figure 11-5). LoglO NH3 was also fairly well correlated with loglO TN (r=0.62); however the
correlation of loglO NOX to TN was weak (only r=0.38), possibly because of the high level of below-
detect values for NOX. No samples were below detection for TN, but 0.5% of samples were below the
detection limit for TP (4 u.g/L), 6% were below the detection limit for CHLA (which varied among
samples), 12% were below detection limits for NH3 (0.004 to 0.03 mg/L, depending on lab), and 54%
were below detection limits for NOX (0.001 to 0.03 mg/L, depending on lab). The high percentage of
below-detection values for NOX and NH3 combined with their greater temporal variability (Figure 11-2)
makes these analytes seem less useful for classifying and comparing sites; accordingly further analyses
focused on TN rather than on NOX and NH3.
Figure 11-4. Box plots showing distribution of TN, TP, and CHLA by geographic reporting unit (left-hand panels) and
by HGM type (right-hand panels). Note log-scale on vertical axes. Nutrient levels are higher in Interior Plains
wetlands than all others and CHLA levels are higher in tidal wetlands than others (shaded boxes), but differences
among other categories are not strong. Plots by geographic reporting unit exclude 10 sites with unusual
conductivity.
207
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
4829
4830
4831
4832
8-
I I I 1
10 100 1,000 10,000
TP (Mg/L)
Figure 11-5. Scatterplot showing relationship among TN, TP, and CHLA for all sites. The correlation (in loglO
transformed units) ofTN toTP is 0.78, and that of CHLA to TN andTP is 0.63 and 0.65 respectively.
4833
4834
4835
4836
4837
'.in
CO
a
"5 o.e-
o
= 0.4-
0.2-
0.0
(0
TN (ug/L)
Interior Plains
E Mts & Up Mid
West
Estuarine
Coastal Plains
10.000
o
= 0.4-
0.2-
o.o-
oligo
trophic
1.000 10.000
0.1 1.0 10.0 100.0 1.000.0
CHLA UGL
Figure 11-6. Plots showing TP and TN data distribution by geographic reporting unit. Plots for TP and CHLA include
vertical lines show divisions between trophic state categories commonly used in classifying lakes (divisions at 10,
35 and 100 ug/L TP and 2.6, 7.3, and 56 ug/L CHLA). Plots exclude 10 sites with unusual conductivity.
208
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
4838
4839
4840
4841
4842
4843
4844
4845
4846
4847
4848
4849
4850
4851
4852
4853
4854
4855
4856
4857
4858
4859
4860
4861
Ranges of nutrient and CHLA levels broadly overlapped among the reporting units (Figure 11-4), yet data
distribution graphs revealed some consistent differences. Distributions of TN and TP in Interior Plains
wetlands were shifted towards higher values relative to the other 4 geographically-based reporting
units, however CHLA distributions in Interior Plains wetlands were similar to the distributions in
Estuarine and Coastal Plains wetlands (Figure 11-6). Eastern Mountains & Upper Midwest wetlands had
somewhat lower TP values and wetlands in the West had lower TN values than the other reporting units,
whereas CHLA levels were lowest in Eastern Mountains & Upper Midwest and West wetlands (Figure
11-6). Wetlands are inherently fairly productive environments so it is not surprising that a large
proportion of the sites had phosphorus values that in lakes would be indicative of eutrophic or hyper-
eutrophic conditions; nevertheless some sites in the Eastern Mountains & Upper Midwest, West, and
Coastal Plains geographical reporting units had TP levels associated with an oligotrophic state in lakes
and a substantial percentage had mesotrophicTP levels (middle panel, Figure 11-6). Afar larger
percentage of sites in each of the geographically-based reporting units have CHLA levels associated with
oligotrophic or mesotrophic conditions in lakes (bottom panel, Figure 11-6) than would be expected
from the TP levels, suggesting that wetlands do not necessarily channel nutrient-fueled productivity to
plankton algae. Levels of pH, TN, TP, and CHLA were typically higher in herbaceous than woody wetlands
(Figure 11-7).
10-
9-
8-
I ?~
6-
5-
4-
s-
4-
1
•— 3-
Q_
h-
0
Tr* '>-
g"
]
T
1
*
*
0
1
herbaceous
*
|Jq
1
1
herbaceous
5-
T ?
3.
z'
1
0)
*
1 i 2
woody
3-
g"
T
-1-
woody
O
*
*
T
1
T -
j
herbaceous
*
*
1
1
1
herbaceous
T
1
1
i
woody
I
1
woody
Figure 11-7. Box plot showing difference in pH, TN, TP, and CHLA between wetlands classified as having woody or
herbaceous type vegetation.
Ratios of TN to TP (N:P ratios, hereafter) varied from a low of 0.4 to a high of 713, and spanned the
range from presumably N-limited (i.e., below Redfield ratio of N:P=16) to presumably P-limited (well
209
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
4862
4863
4864
4865
4866
4867
4868
4869
4870
4871
4872
4873
4874
4875
4876
4877
4878
4879
4880
4881
4882
4883
4884
4885
4886
4887
4888
4889
4890
4891
4892
4893
above N:P=16) in almost all geographical reporting units and HGM types (Figure 11-8). Accordingly, no
single type of nutrient limitation can be inferred from across this broad suite of wetlands, although such
patterns may be present on a finer spatial scale (e.g., Bedford et al. 1999). N:P ratios were typically
lower (i.e., more N-limited) in wetlands with herbaceous than those with woody-vegetation (Figure
11-8). This may be because of nitrogen fixation by some plants characteristic of woody wetlands (e.g.,
alder; Hurd et al. 2001).
100-
80-
o
ro 60-
\_
CL
^ 40-
20-
10
T
* 0 o
* 8
1 :
•
! [[
1 J-.
EtL 9i
o
8 g
*
i
J
T Tl
i 0
*
1
U 1
1 i
100-1
herbaceous
woody
80-
0
0.
Z 40-
20-
J
*
+
$
T
" 3
*
- L
' t
o
i
*
.
1
1
*
*
n lr
9 g
|
!
T
•
ttl
*
1
A
t? if
Coastal Estuarine E Mts & Interior West depress flats lacust riverine slope tidal
Plains Up Mid Plains
fringe
Figure 11-8. Box plots showing N:P ratios by geographic reporting unit (right) and HGM type (left). Boxes do not
represent the full data distribution, as sites with N:P ratio > 100 have been excluded to focus on the region where
the presumptive limiting nutrient switches from nitrogen (below 16) to phosphorus (abovelS; horizontal line).
Herbaceous wetlands tend to have lower N:P ratios than woody-vegetation wetland, but wetlands in almost all
geographic reporting units and HGM types span the range from N-limited to P-limited. Plots by geographic
reporting unit exclude 10 sites with unusual conductivity.
The field-assigned water clarity categories ("clear", "milky", "turbid", or "stained") had no obvious
relationship to any of the laboratory water chemistry analytes. We had expected such relationships
because numerical measures of water clarity such as turbidity and secchi depth are consistently related
to nutrients and planktonic chlorophyll in other water body types, and low pH wetlands are expected to
have water stained with humic substances (i.e., tea-colored). We suspect the lack of relationship is
because the categories did not adequately capture the water clarity and color gradients actually
present.
11.3.4 Relationships of Water Chemistry to Anthropogenic Setting
Relationships of water chemistry to potential measures of anthropogenic stress focused on COND, TN,
TP, and CHLA (all log-10 transformed) and on potential predictor variables B1H_ALL (the field-checklist
based stressor summary over a 100 m buffer zone), and on population density, road density, and
percentages of various NLCD 2006-based categories in the 1000 m area around the sample point.
Correlation matrices arising from these analyses are presented in Table 11-3 through Table 11-6 (one
table per analyte), while the major correlation patterns are depicted in Figure 11-9. Correlations having
magnitude >0.30 are used as a threshold in describing presence of a relationship.
210
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
4894
4895
4896
4897
4898
4899
4900
4901
4902
4903
4904
4905
4906
Table 11-3. Correlation matrix for log-10 conductivity vs. anthropogenic stressor variables for various wetland
groups ("H" vs. "W" refer to herbaceous and woody in the geographic reporting unit x vegetation type
combinations). Correlation coefficients (positive or negative) with magnitude >0.3 are in bold underline. Stressor
variable BlH_all is over the 100 m buffer assessed by the field crew, all other stressor variables are over a 1000 m
radius circle. The non-estuarine groups omit sites having conductivity suggestive of marine influence.
loglO COND
All sites
Estuarine
Non-estuarine
Estuarine H
Estuarine W
Coastal Plains H
Coastal Plains W
E Mts&Upp Mid
E Mts&Upp Mid
Interior Plains H
Interior Plains W
WestH
WestW
B1H_ALL
-022
-0.17
0.18
0.08
0.01
0.27
0.25
0.01
0.22
0.24
0.24
agric
(%)
-0 19
-0.11
0.40
-0.22
0.02
-0.01
-0.06
0.47
0.41
0.06
-0.18
0.30
0.27
devel (%)
0.04
0.04
0.08
0.05
-0.04
0.26
0.12
0.45
0.30
-0.04
0.15
-0.08
0.15
forest
(%)
-0.59
-0.01
-0.61
0.03
-0.29
-0.48
-0.37
-0.52
-0.47
-0.14
-0.48
-0.54
-0.52
wetl. +
water (%)
0.66
0.03
0.09
-0.02
0.31
0.12
0.30
0.03
0.07
0.04
-0.12
0.23
0.17
pop.
dens
(#/mi2)
0.17
0.10
0.03
0.10
0.12
0.25
0.06
0.36
0.30
0.03
-0.46
-0.18
0.21
road dens
(km/km2)
0.02
0.04
0.12
0.07
-0.18
0.01
0.23
0.53
0.38
0.08
0.09
0.28
0.23
imper-
vious
(%)
0.07
0.07
0.03
0.07
0.05
0.24
0.20
0.22
0.27
-0.04
0.08
-0.07
0.15
Table 11-4. Correlation matrix for log-10 TN vs. anthropogenic stressor variables for various wetland groups ("H"
vs. "W" refer to herbaceous and woody in the geographic x vegetation type combinations). Correlation coefficients
(positive or negative) with magnitude >0.3 are in bold underline; those of this magnitude but not in the expected
direction (positive or negative) are additionally in brackets. Stressor variable BlH_all is over the 100 m buffer
assessed by the field crew, all other stressor variables are over a 1000 m radius circle. The non-estuarine groups
omit sites having conductivity suggestive of marine influence.
loglO TN
All sites
Estuarine
Non estuarine
ystuarine H
stuarine W
Coastal Plains H
Coastal Plains W
E Mts&Upp Mid
E Mts&Upp Mid
Interior Plains H
Interior Plains W
WestH
WestW
B1H
-0
-0
0.
-0
0.
0.
0.
-0
o
_ALL
.02
.15
01
.24
25
22
06
.03
06
0.02
f-0.341
0.12
0.21
agric
0.
0.
0.
-0
0.
0.
0.
0.
n
0.
-0
0.
0.
30
20
38
.01
51
26
19
28
?4
07
.19
18
32
devel (%)
0.02
-0.07
0.05
0.01
-0.29
0.03
0.01
0.33
0.22
-0.00
0.27
-0.04
-0.08
forest
-0.43
-0.12
-0.54
-0.14
-0.07
-0.20
-0.37
-0.30
-0.67
0.19
-0.07
-0.38
-0.25
wetl+
water
0.13
0.08
0.20
0.13
-0.06
-0.16
0.13
-0.10
0.50
-0.08
0.30
0.11
0.11
pop.
dens
(#/mi2)
0.02
-0.05
0.05
0.02
r-o.3n
0.05
-0.00
0.31
0.30
0.01
0.12
-0.09
0.26
road dens
(km/km2)
0.00
-0.06
0.00
0.01
-0.26
0.01
-0.06
0.40
0.09
-0.05
-0.10
0.28
0.25
imper-
vious
0.00
-0.06
0.03
0.05
r-o.3oi
0.08
0.06
0.09
0.23
0.08
0.40
-0.01
-0.11
4907
211
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
4908
4909
4910
4911
4912
4913
4914
4915
4916
4917
4918
4919
4920
4921
Table 11-5. Correlation matrix for log-10 TP vs. anthropogenic stressor variables for various wetland groups ("H"
vs. "W" refer to herbaceous and woody in the geographic x vegetation type combinations). Correlation coefficients
(positive or negative) with magnitude >0.3 are in bold underline; those of this magnitude but not in the expected
direction (positive or negative) are additionally in brackets. Stressor variable BlH_all is over the 100 m buffer
assessed by the field crew, all other stressor variables are over a 1000 m radius circle. The non-estuarine groups
omit sites having conductivity suggestive of marine influence.
loglO TP
All sites
Estuarine
Non estuarine
Estuarine H
Estuarine W
Coastal Plains H
Coastal Plain W
E Mts&Upp Mid
E Mts&Upp Mid
Interior Plains H
Interior Plains W
WestH
WestW
B1H_ALL
0.10
-0.06
0.13
-0.12
0.23
0.27
0.22
0.05
0.08
0.07
f-0.351
0.04
0.26
agric
(%)
0.37
0.20
0.43
0.07
0.41
0.47
0.21
0.28
0.33
0.16
-0.12
0.22
0.42
devel (%)
-0.00
-0.10
0.04
0.00
r-o.3oi
-0.07
-0.12
0.33
0.16
0.05
0.21
0.08
forest
(%)
-0.36
0.02
-0.46
0.01
0.07
[037]
-0.19
-0.30
-0.54
0.08
-0.08
-0.22
-0.37
wetl+
water
(%)
-0.01
-0.06
0.01
-0.05
-0.18
-0.45
0.05
-0.10
0.30
-0.07
0.27
-0.05
0.13
pop.
dens
(#/mi2)
-0.02
-0.03
-0.01
0.04
-0.19
-0.11
-0.16
0.31
0.20
-0.03
0.05
0. 12
0.47
road dens
(km/km2)
0.01
-0.05
0.03
0.04
-0.26
0.05
-0.24
0.40
0 12
-0.01
-0.03
0.40
imper-
vious
(%)
-0.01
-0.10
0.02
0.02
-0.27
-0.09
-0.10
0.20
0.15
0.08
0.39
0.21
0.06
Table 11-6. Correlation matrix for log-10 CHLA vs. anthropogenic stressor variables for various wetland groups ("H"
vs. "W" refer to herbaceous and woody in the geographic x vegetation type combinations). Correlation coefficients
(positive or negative) with magnitude >0.3 are in bold underline; those of this magnitude but not in the expected
direction (positive or negative) are additionally in brackets. Stressor variable BlH_all is over the 100 m buffer
assessed by the field crew, all other stressor variables are over a 1000 m radius circle. The non-estuarine groups
omit sites having conductivity suggestive of marine influence.
loglO CHLA
All sites
Estuarine
Non estuarine
Estuarine H
Estuarine W
Coastal Plains H
Coastal Plains W
IE Mts&Upp Mid
E Mts&Upp Mid
Interior Plains H
Interior Plains W
WestH
WestW
B1H_ALL
-0.05
-0.05
0.03
-0.17
0.35
0.45
0.16
0.11
-0.04
-0.14
[-0.49]
-0.19
0.14
agric
(%)
0.13
0.29
0.25
0.12
0.47
0.23
0.21
-0.07
0.20
0.09
-0.13
0.16
0.19
devel (%)
0.01
-0.14
0.06
-0.04
r-0.371
0.02
-0.11
0.16
-0.03
-0.01
0.33
0.45
0.07
forest
(%)
-0.34
0.02
-0.32
0.00
0.05
0.19
-0.08
-0.19
-0.29
0.24
-0.17
-0.27
-0.21
wetl+
water (%)
0.23
-0.00
0.14
0.04
-0.10
-0.31
-0.05
0.16
0.28
-0.01
0.44
0.32
0.04
pop.
dens
(#/mi2)
0.02
-0.13
0.04
-0.02
r-o.4n
0.04
-0.06
0.20
0.01
0.06
-0.02
0.44
0.20
road
dens
(km/km2)
-0.02
-0.12
0.02
-0.03
r-0.351
0.07
-0.15
0.21
-0.14
-0.03
0.00
0.57
0.32
imper-
vious
(%)
0.03
-0.15
0.08
-0.01
r-0.381
0.09
-0.06
0.12
-0.05
0.06
0.37
0.46
0.09
4922
212
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
4923
4924
4925
4926
4927
4928
4929
4930
4931
4932
4933
4934
4935
4936
4937
4938
4939
4940
4941
4942
4943
4944
4945
4946
4947
4948
4949
4950
4951
0.6-1
0.3-
COND
-0.3-
0 B1H_all
x % agric
% developed
* % forest
% impervious
0.3-
TP
-0.3-
CHL
0.3-
-0.3-
Figure 11-9. Plots showing Pearson strength of correlation between water chemistry and five anthropogenic
stressor variables for five geographic reporting units (woody and herbaceous combined). Note that the vertical axis
is scaled the same in all 3 graphs but does not extend as far for TP and CHLA as it does for COND. Plots exclude 10
sites with unusual conductivity.
Across all sites, % forested area was negatively correlated with all four analytes (magnitude > 0.30,
strongest for COND), while % agriculture was positively correlated with TN and TP. Within site
groupings, % forested remained a predictor (negative sign) for water chemistry in all non-estuarine
geographically-based reporting units and for most non-estuarine reporting units x vegetation type
combinations, while % agriculture remained a predictor (positive sign) for all water chemistry analytes
except CHLA. In general, correlation coefficients were higher in magnitude for % forested than for
%agriculture; however % agriculture had higher correlation coefficients than did % forested in estuarine
woody sites. Correlation coefficients were generally lower for CHLA than the other three analytes
examined. Percent forested and % agriculture generally trade off in NLCD-based assessments (i.e.,
increases in one tend to lead to decreases in the other) but because land formerly in forest can also be
converted to urban land-uses and because land can be in a natural state yet not be forested (e.g., in
grassland or in wetland) the relationship is not exactly inverse - hence it of interest to examine which is
the better predictor where.
There were no correlations >0.3 in magnitude for population density, road density, B1H_ALL, or %
developed across all sites, nor across estuarine vs. non-estuarine reporting unit site groupings. However
each of these stressor variables was at times a significant predictor of water chemistry within reporting
units x vegetation type site groupings. For example, % developed land, population density, and road
density all were predictors (positive sign) for COND, TN, and TP in the Eastern Mountains & Upper
Midwest and the West. B1H_ALL was a negative predictor for TN and TP in some reporting units (e.g.,
the Interior Plains) but a positive predictor for TP and CHLA in other (e.g., the Coastal Plains) making it
somewhat hard to interpret. Other predictors also had unexpected signs sometimes, but usually these
were accompanied by low correlation magnitudes.
213
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
4952
4953 Water chemistry, in general, was not well predicted from landuse-landcover variables in estuarine sites.
4954 Within estuaries the herbaceous sites have no correlations >0.30 for any stressor variables and any
4955 water chemistry analytes, and the woody sites have relationships whose direction is often
4956 counterintuitive (e.g., lower analyte levels with higher population density, road density, and percentages
4957 in developed or impervious land); only % agriculture shows the expected positive relationship to TN, TP,
4958 and CHLA. In contrast, water chemistry was much better predicted from landuse-landcover variables in
4959 non-estuarine sites, with many more correlations >0.3 in magnitude and the direction of the relationship
4960 (positive or negative) usually as expected. Water chemistry was more poorly predicted in the Interior
4961 Plains reporting unit than in other non-estuarine site types (Figure 11-9). No predictors with correlation
4962 magnitude >0.3 were found for the herbaceous Interior Plains site for any of the four water chemistry
4963 analytes. At the other extreme, the North-Central east reporting unit is one where nutrients and
4964 conductivity was strongly predicted from stressor variables (however this was not the case for CHLA).
4965
4966 Patterns of which predictors were strong were fairly consistent between COND, TN, and TP but often
4967 quite different for CHLA. For example, there were no correlations >0.3 for CHLA in the North-Central
4968 east despite many such correlations for the other three analytes, and notably fewer significant
4969 correlations for CHLA than the other analytes in West woody sites. CHLA was negatively correlated with
4970 % forested across all sites and across all non-Estuarine groups but had no correlations above 0.3 for any
4971 of the finer NWCA Reporting Group categories. Percent agriculture and/or developed land were
4972 predictors of CHLA in estuarine woody sites (where they also had been predictors of TN and TP), but also
4973 in Interior Plains woody and West herbaceous sites (where they had not been predictors of TN or TP).
4974 Population density and road density were predictors of CHLA in the West but not in the Eastern
4975 Mountains & Upper Midwest - in fact there were no significant predictors of CHLA in the Eastern
4976 Mountains & Upper Midwest even though both TN and TP were related to landuse/landcover there,
4977 suggesting that primary productivity responses are not being channeled to planktonic algae.
4978
4979 Differences in which landuse/landcover variables are correlated with water chemistry in which site
4980 groups are not necessarily because these predictors differ regionally in their ability to affect water
4981 chemistry, but rather that there are differences among regions in whether they have sufficiently high
4982 range that their effects are detectible. This is illustrated by box plots showing the range in
4983 landuse/landcover within site types. The one non-estuarine site type where declining percent forested
4984 was never a predictor for increasing COND was Interior Plains-Herbaceous, where forest levels are low
4985 (naturally) anyway (Figure 11-10, bottom). Population density was most consistently a predictor for
4986 water chemistry in the Eastern Mountains & Upper Midwest, which (aside from the Estuaries) is also
4987 where wetlands have the highest median and range in population density (Figure 11-10, middle).
4988 Agriculture levels are highest in the Interior Plains (Figure 11-10, top) leading one to wonder why %
4989 agriculture was not a predictor variable for water chemistry there; however this reporting unit covers
4990 not only much of the US cornbelt but also the prairies, meaning "agriculture" characterized broadly
4991 ranges from nutrient-intensive row-crop cultivation to much less intensive hay and pasture use; we
4992 suspect that landuse/landcover classifications examined here are insufficient to resolve these or that
4993 finer spatial categorization is necessary.
4994
214 2011 NWCA Technical Report DISCUSSION DRAFT
-------
4995
4996
4997
4998
4999
5000
5001
5002
5003
5004
5005
5006
5007
5008
5009
5010
5011
O)
03
100—
80 —
60-
40-
20-
O
o
n rji T
o
o
100 -
80 -
-o
0)
In 60 -
•2 40 -
SI
20 -
II
1
Eestu. Estu. CPL CPL EMU EMU IPL IPL West West
H WHWHWHWHW
Figure 11-10. Box plots showing distribution of three anthropogenic stressor variables within geographic reporting
unit & vegetation type combinations ("H" and "W" refer to herbaceous and woody, respectively).
11.3.5 Water Chemistry Patterns at Regional and National Scale - Scaling Up to Wetland
Population
NARS reports typically summarize water body condition and stressor data into categories (e.g.,
good/fair/poor) constructed by using percentiles of the reference-site distribution for preselected
reporting units (the NWCA Reporting Groups in the case of 2011 NWCA). Because water samples could
not be collected at all sites as noted earlier, this resulted in even fewer number of least disturbed sites
in each reporting group with water chemistry data. This confounded efforts to use this reference
condition based approach to report on water chemistry parameters. We will continue to explore the
development of meaningful condition or stressor metrics derived from the water chemistry data
collected in NWCA that can be used for national and regional population estimates.
215
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
5012 11.4 Discussion
5013
5014 In addition to being the first ever nationwide survey of the condition of the nation's wetlands, this
5015 survey also served as the first national-scale survey of wetland surface water chemistry. Questions of
5016 interest in analyzing these data included evaluating patterns in the water chemistry data, evaluating
5017 success in and barriers to obtaining water chemistry, and developing recommendations for future
5018 sampling protocols. Despite the challenges of the more limited water chemistry dataset for the NWCA,
5019 the data were valuable to the survey as a whole in understanding broad water chemistry patterns in
5020 across reporting units and in understanding potential stressors. As has been seen in other NARS, we also
5021 found that water chemistry results taken at Visit 1 were relatively stable with results taken at the revisit.
5022
5023 We had wondered whether the more complicated and diverse hydrology of wetlands relative to other
5024 waterbody types (lakes, streams, estuaries) might make wetland water chemistry patterns more difficult
5025 to interpret. We found a very large, multiple orders of magnitude range in TN, TP, CHLA, and nutrient
5026 ratios across wetlands, but also a corroboration of patterns seen in broad surveys of other water body
5027 types including increased nutrient and chlorophyll levels with increasingly agricultural and urbanized
5028 landuse/landcover. Despite the expectation that wetlands would be generally productive environments,
5029 the water chemistry data shows they can span a range from what would qualify as oligotrophic in lakes
5030 and streams to extremely eutrophic.
5031
5032 The geographic reporting units explored in this analyses did not explain variability patterns, suggesting
5033 that other geographic and hydrologic units ought to be examined. Further assessment of water
5034 chemistry predictors including other types and scales of landuse/landcover data and more refined
5035 analyses of field-collected stressor data is also needed. One intriguing finding from this data analysis is
5036 that across geographic reporting units, wetlands dominated by woody rather than herbaceous
5037 vegetation consistently had lower TN, TP, and CHLA - is this because wetlands in different vegetation
5038 types process nutrients differently, is it because landscape changes that increase nutrient loading also
5039 tend to change wetland vegetation types, or is it related to some other interaction? Water chemistry
5040 data from this and future NWCA surveys will enable us to uncover and explore such questions.
5041
5042 The inclusion of water chemistry parameters within the NWCA also provided valuable information to the
5043 survey overall. Water chemistry metrics served as a screening tool to identify sites impacted by potential
5044 stressors that may not have otherwise been detected through other indicators or observed during the
5045 on-site field evaluations. By identifying sites with measures on the extreme ends of the sample
5046 distribution, the Analysis Team was able to investigate those sites further and identify potential stresses
5047 acting on the system that may not have been visible at the time of the site visit. For example, surface
5048 water collected from a non-estuarine site in New Jersey with higher than expected COND value was
5049 determined to have experienced overwash from the coastal surge associated with Hurricane Irene in
5050 August 2011. The water chemistry from this site thus served as a diagnostic tool to identify reasons why
5051 the vegetation community metrics observed deviated from those expected for the wetland type.
5052
5053
216 2011 NWCA Technical Report DISCUSSION DRAFT
-------
5054 11.5 Literature Cited
5055
5056 Bedford B, Walbridge MR, Aldou A (1999) Patterns in nutrient availability and plant diversity of
5057 temperate North American wetlands. Ecology 80: 2151-2169
5058
5059 Carter V (1986) An overview of the hydrologic concerns related to wetlands in the United States.
5060 Canadian Journal of Botany 64: 364-374
5061
5062 Clesceri LS, Greenberg AE, Eaton AD (1998) Standard methods for the examination of water and
5063 wastewater, 20th Edition. American Public Health Association, Washington DC
5064
5065 Cowardin LM, Carter V, Golet FC, LaRoe ET (1979) Classification of wetland and deepwater habitats of
5066 the United States. FWS/OBS-79/31, US Fish and Wildlife Service, Washington DC
5067
5068 Herlihy AT, Sifneos JC (2008) Developing nutrient criteria and classification schemes for wadeable
5069 streams in the conterminous US. Journal of North American Benthological Society 27: 932-948
5070
5071 Herlihy AT, Kamman NC, Sifneos JC, Charles D, Enache MD, Stevenson RJ (2013) Using multiple
5072 approaches to develop nutrient criteria for lakes in the conterminous USA. Freshwater Science 27: 932-
5073 948
5074
5075 Hornung RW, Reed LD (1990) Estimation of average concentration in the presence of nondetectable
5076 values. Applied Occupational and Environmental Hygiene 5: 46-51
5077
5078 Hurd TM, Rynal DJ, Schwintzer CR (2001) Symbiotic N2 fixation of Alnus incana ssp. rugosa in shrub
5079 wetlands of the Adirondack Mountains, New York, USA. Oecologia 126: 94-103
5080
5081 Lougheed VL, Parker CA, Stevenson RJ (2007) Using non-linear responses of multiple taxonomic groups
5082 to establish criteria indicative of wetland biological condition. Wetlands 27: 96-109
5083
5084 Mitsch WJ, Gosselink JG (2000) Wetlands, 3rd Edition. Wiley, New York
5085
5086 Trebitz AS, Brazner JC, Cotter AM, Knuth ML, Morrice JA, Peterson GS, Sierszen ME, Thompson JA, Kelly
5087 JR (2007) Water chemistry in Great Lakes coastal wetlands: basin-wide patterns and response to an
5088 anthropogenic disturbance gradient. Journal of Great Lakes Resources 33: 67-85
5089
5090 USEPA (2006) Data quality assessment: statistical methods for practitioners. US Environmental
5091 Protection Agency, Office of Environmental Information, EPA/240/B-06/003, Washington DC
5092
5093
217 2011NWCA Technical Report DISCUSSION DRAFT
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5094 This page was intentionally left blank for double-sided printing.
5095
218 2011NWCA Technical Report DISCUSSION DRAFT
-------
5096 Chapter 12: Research Feature - USA-Rapid Assessment Method (USA-
5097 RAM)
5098
5099
5100 12.1 Background Information
5101
5102 The increasing pressure that human activities are having on wetland ecosystems (Brinson and Malvarez
5103 2002; Kentula et al. 2004) has generated considerable interest in developing methods designed to assess
5104 the ecological condition or integrity of wetlands. The assessment of wetlands can be approached both
5105 with quantitative biological methods, such as multimetric indexes of ecological condition (MMIs; Karr
5106 and Chu 1999) and by using semi-quantitative, rapid assessment methods (RAMs; e.g., Collins et al.
5107 2008). Rapid methods have benefits such as requiring less time in the field and less taxonomic expertise
5108 than more quantitative methods, leading to cost savings and potentially larger sample sizes. For these
5109 reasons, RAMs have a key role in the implementation of wetland monitoring and assessment programs
5110 and the effective management of the resource (USEPA 2003; Fennessy et al. 2007).
5111
5112 The USA-Rapid Assessment Method (USA-RAM) was developed as an integral component of the suite of
5113 methods used in the 2011 National Wetland Condition Assessment (NWCA). The three primary
5114 objectives of the NWCA are to: (1) report the ecological condition of the nation's wetlands, (2) build
5115 state and tribal capacity for wetland monitoring and assessment, and (3) advance the science of wetland
5116 assessment. USA-RAM helps meet the first objective by providing relatively less expensive, semi-
5117 quantitative measures of overall wetland health that complement the more quantitative and expensive
5118 NWCA methods for assessing particular aspects of wetland condition or stress. USA-RAM helps meet the
5119 second objective by serving as a RAM template for consideration by States and Tribes that do not have
5120 RAMs at this time. To help meet the third objective, USA-RAM provide data that can support an
5121 exploration of the statistical relationships between stress and condition of wetland areas as mediated by
5122 their buffers (Figure 12-1). Buffers are crucial elements that protect wetlands from the effects of human
5123 activities in the landscape context (Lopez and Fennessy 2002).
5124
219 2011 NWCA Technical Report DISCUSSION DRAFT
-------
Stressor
Wetland
Condition
5125
5126 Figure 12-1. Conceptual diagram showing the relationship between stressors, buffers and condition. The effect of a
5127 stressor that originates outside a wetland is diminished as it passes through the buffer area that adjoins it.
5128
5129 12.1.1 Tenets of USA-RAM
5130 USA-RAM was designed through a series of regional field tests involving experts in wetland assessment
5131 from across the conterminous 48 states. An iterative process of field trials and revisions was conducted
5132 over the course of two field seasons based on the following set of ten key guiding principles or tenets.
5133 1) Condition, as assessed using USA-RAM, means the potential of a wetland area to provide high
5134 levels of its intrinsic ecosystem services;
5135 2) Stress, as assessed using USA-RAM, means the combined measures of the abundance, diversity,
5136 and magnitude of common stressors evident within a wetland area or its buffer;
5137 3) Wetland health, as assessed using USA-RAM, means the aggregate assessment of condition and
5138 stress within a wetland area and its buffer;
5139 4) For any wetland class, the condition of a wetland area increases as the physical and biological
5140 structural complexity of the area and its buffer increases, and as the stress in the area and its
5141 buffer decreases, relative to best achievable or least-impacted wetland areas and their buffers;
5142 5) There should be one version of USA-RAM that reflects the full range of form, structure and
5143 stress for all wetland classes and regions throughout the 2011 NWCA, and that can be applied
5144 consistently by all 2011 NWCA field crews;
5145 6) USA-RAM should be based on easily recognized visible indicators of Metrics of condition and
5146 stress that represent universal Attributes of wetland health, namely buffer, hydrology, physical
5147 structure, and biological structure (Fennessy et al. 2007);
5148 7) Rapid means that 2-3 trained practitioners require fewer than 2 hours elapsed time to
5149 successfully apply the entire method in the field to achieve a measure of overall wetland health;
5150 8) Condition and stress should be assessed separately within each wetland area and within its
5151 surrounding buffer;
5152 9) There should be no numerical weighting of any USA-RAM Metrics, Attributes, or Indices of
5153 condition or stress; and,
220
2011 NWCA Technical Report
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5154 10) Any re-scaling of Metric scores for condition or stress, relative to regional differences, should be
5155 done as a post-survey analysis.
5156
5157 12.1.2 Structure of USA-RAM
5158 USA-RAM is designed to assess the overall of a 0.5-ha Assessment Area (AA) and its buffer zone. The
5159 buffer zone is defined as the area within 100m distance from the perimeter of the AA. Ultimately,
5160 Metrics that assess condition and stressor within a wetland area were used to determine its overall
5161 health, as mediated by its buffer. In essence, the effects of a stressor that originate outside a wetland
5162 area are diminished as the stress passes through the buffer, lessening its impact.
5163 USA-RAM recognizes four Attributes of condition and stress: buffer, hydrology, physical structure, and
5164 biological structure (Table 12-1). Each Attribute is assessed using two Metrics, except for the hydrology
5165 Attribute, which is only assessed in terms of its stressors. Hydrological condition was not assessed
5166 directly for three reasons:
5167
5168 1) Since all aspects of wetland condition are affected by hydrology, its condition is represented by
5169 the condition of the other Attributes, such that assessing hydrology directly would essentially be
5170 adding emphasis to the hydrology Attribute in violation of tenet 8 above;
5171
5172 2) A survey of how hydrology is treated in other RAMs revealed that it is usually assessed as the
5173 amount of departure from natural hydrological conditions due to stress, such that it could be
5174 well-represented by stressor indicators; and
5175
5176 3) Early efforts to develop USA-RAM Metrics of hydrological condition concluded with the
5177 recognition that the natural variability of hydrology across wetland classes and regions of the US
5178 was too great to be reasonably represented by a single version of USA-RAM, as stipulated by
5179 tenet 5 above.
5180
5181 An assessment of hydrological stressors is critical, however, to account for human activities that alter
5182 hydrology, and to be better able to interpret the results of the condition assessment.
5183
5184 Table 12-1. USA-RAM Attributes and Metrics of wetland condition and stress.
Attributes Condition Metrics Stress Metrics
Buffer Percent of AA Having Buffer Stress to the Buffer Zone
Buffer Width
Hydrology None Alterations to Hydroperiod
Stress to Water chemistry
Physical Structure Topographic Complexity Habitat/Substrate Alterations
Patch Mosaic Complexity
Biological Structure Vertical Complexity Percent Cover of Invasive Plants
Plant Community Complexity Vegetation Disturbance
5185
5186 USA-RAM is designed to be rapid, taking a crew of 2 or 3 trained practitioners 1 to 1.5 hours to prepare
5187 for a field visit, and another 1.5 to 2 hours to conduct the field assessment.
5188
221 2011NWCA Technical Report DISCUSSION DRAFT
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5189 USA-RAM provides separate scores for stress and condition for each AA and its associated buffer zone.
5190 The Metric scores are derived from standardized "scoring tables" that are used to assign one of four
5191 scores to each Metric of condition or stress. In total, USA-RAM is made up of 12 Metrics, three to assess
5192 the buffer zone, four to assess condition of the AA, and five to assess stress in the AA. Each Metric
5193 consists of a checklist of visible indicators of field conditions, based on reference sites. Narrative
5194 descriptions are provided for each indicator, allowing rapid scoring in the field. The data for each Metric
5195 were used to develop metric scores for the AAs and their buffer zones, and an overall ecological
5196 condition score for each AA (also referred to as the site index score or USA-RAM score).
5197
5198 Stressors are an important component of an assessment because of their effect on condition.
5199 Knowledge of the stressors present in and around a wetland is valuable in determining how condition
5200 might be improved through management actions. All stressor Metrics are scored based on the number
5201 of stressors that are observed (i.e., visibly evident at the time of the assessment), as well as a ranking of
5202 their severity. The severity of a stressor was characterized based on the portion of the zone or AA that
5203 was obviously influenced by the stressor, as indicated in Table 12-2. The total number of stressors (i.e., a
5204 stressor count), regardless of their severity, was also tabulated.
5205
5206 Table 12-2. Guidelines for Assessing Stressor Severity.
Description of Stressor Prevalence Stressor Severity Score
1
Less than one-third of the buffer or AA is influenced by the stressor
(not severe)
Between one-third and two thirds of the buffer 2
(moderately severe)
More than two-thirds of the buffer or AA is influenced by the stressor
(severe)
5207
5208
5209 12.1.2.1 Section A: Assessment of Condition and Stress in the Buffer Zone
5210 There are three Metrics designed to evaluate the extent and condition of the buffer zone, as well as the
5211 kinds and severity of the stressors to which it is subject. In the USA-RAM we define the buffer as the
5212 land immediately adjacent to the AA that is mostly covered with natural vegetation and lacks evidence
5213 of intrusive human activity. The buffer has a maximum width of 100m. It is assumed that the buffer
5214 helps protect the AA by mitigating external stress, including deleterious effects of nearby or adjacent
5215 human land uses. The three buffer Metrics are described in the following subsections.
5216
5217 12.1.2.1.1 Metric 1: Percent of AA Having Buffer
5218 The land area adjacent to the AA only qualifies as buffer if it consists of a land cover type that is capable
5219 of "buffering" the AA by protecting it from stress originating in the landscape outside of the buffer. This
5220 Metric tallies the percent of the AA perimeter that adjoins a qualifying "buffer land cover" as defined in
5221 Table 12-3 and Table 12-4. For the NWCA, land covers that might provide limited buffering under special
5222 circumstances, such as pasture and land managed for ecological functions were not considered to be
5223 buffers because adequate knowledge of such localized circumstances could not be assured.
5224
5225 Metric 1 is completed in two steps. The first is a desktop evaluation at the time of AA planning (USEPA
5226 2010) to determine the land use surrounding each survey point used to locate an AA. The NWCA sample
222 2011 NWCA Technical Report DISCUSSION DRAFT
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5227
5228
5229
5230
5231
5232
5233
5234
5235
5236
5237
5238
5239
5240
5241
5242
5243
5244
5245
5246
5247
5248
5249
5250
5251
5252
point imagery was used in this effort, although other sources of data such as Google Earth could be
used. Once the AA was established, the land area within 100m of the AA boundary was defined as the
buffer zone. For the sake of USA-RAM, this is the maximum area that has the potential to serve as
buffer, depending on its land use. The second step is a field verification of the data derived from the
aerial imagery. The field reconnaissance is used to evaluate the perimeter of the AA and to estimate the
percent of the distance along the perimeter of the AA that adjoins buffer land covers, based on Table
12-3 and Table 12-4.
Table 12-3. Buffer Land Cover Criteria. To qualify as buffer, a land cover must meet all four of the listed criteria.
Buffer Land Cover Criteria
1. Is on the list of "buffer land covers" in Table 2
2. Is at least 5m wide
3. Extends at least 10m along the AA boundary as a contiguous cover patch
4. Is not separated from the AA by a non-buffer cover that is > 5m wide
Table 12-4. List of land covers classes and whether they count as buffer land cover or are non-buffer land covers.
Land cover classes based on the Anderson Land Cover Class system.
Buffer Land Covers
Non-Buffer Land Covers
Open water surfaces of lakes, bays, ponds,
rivers, etc. with <5% plant cover)
Wetlands
Natural vegetation (areas with > 5% cover
of mostly non-impacted vegetation,
including herbaceous, forest, or old fields
undergoing succession,
Permanent ice or snow (year round snow
or ice surfaces with <5% plant cover)
Natural, non-vegetated earth surfaces
(natural rock outcrops, sand, gravel, etc.
with <5% plant cover)
Trails (foot trails, equestrian trails, single-
track bicycle trails, etc.)
Built structures (houses, factories, schools, etc.)
Urban and suburban lawns, including recreational
lawns, sports fields, etc.)
Any active agriculture (orchards, vineyards, row
crops, hay or grain fields, sod farms, feedlots,
recently clear-cut or otherwise severely impacted
forest lands, etc. Includes fallow agricultural fields)
Artificial, non-vegetated land surfaces (parking
lots, feed lots, etc. that support <5% plant cover)
Active mining areas (quarries, strip mines, gravel
pits, etc.)
Any recently burned lands
Roads (including railroads, streets, highways, etc.)
ATV trails
12.1.2.1.2 Metric 2: Buffer Width
The ability of an area to buffer a wetland from external stressors depends on the width of the buffer
that is present. Minimum effective buffer widths can vary depending on the type of stressors present.
However, it is assumed that buffers do not usually need to be wider than 100m. A width of 100m has
become a common definition for the sake of assessment in many programs, and land use in the 100m
buffer has been found to be correlated with wetland condition.
To complete this Metric, four transect lines, each 100m long, are drawn from the AA perimeter on the
site imagery in the four cardinal directions (N, S, E, W). Another four lines are drawn outward from the
AA perimeter in the ordinal directions (NE, SE, SW, NW). Lines are numbered clockwise with North as
"1" as shown. Starting at the AA perimeter, the following procedure is followed.
223
2011 NWCA Technical Report
DISCUSSION DRAFT
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5253 • On each of the eight (8) transect lines, estimate the distance (in increments of 5m) between the
5254 AA perimeter and the point at which the line first intercepts any type of non-buffer land cover
5255 (see Table 12-4 above). This distance equals the buffer width for that transect line.
5256 • Ignore any non-buffer areas that do not cover at least 5m of a line.
5257 To ensure the best possible estimate of buffer width, the buffer area should be ground-checked to
5258 ensure the accuracy of the aerial imagery in the field. If there is a substantial difference between buffer
5259 zone land cover as evident in the aerial imagery and what is observed in the field, the data to indicate
5260 buffer width based on the imagery will have to be corrected, based on the field observations.
5261
5262 12.1.2.1.3 Metric 3: Stressor to the Buffer Zone
5263 This metric is designed to tabulate and characterize the types and severity of stressors that occur within
5264 the 100m buffer zone that can act to reduce the effectiveness of the buffer in protecting the AA from
5265 human activity in the surrounding landscape. For the sake of this Metric, the buffer zone is considered
5266 to be the entire 100m area around the AA, regardless of land use. Stressors that occur in any land use
5267 type, whether or not they count as buffers, have the potential to directly impact the AA. Therefore,
5268 stressors that occur in any land use within 100m of the AA will be tallied using a stressor checklist.
5269
5270 12.1.2.2 Section B: Assessment of Wetland Condition in the AA
5271
5272 12.1.2.2.1 Metric 4: Topographic Complexity
5273 Natural wetlands develop topographic relief due to variations in sediment production or deposition,
5274 erosion or oxidation of sediments, variations in hydroperiod, wildlife activity, etc. Increases in both
5275 micro- and macro-relief represent increases in the surface area of a wetland and therefore can lead to
5276 increased biological and geo-chemical processes at the sediment-water or sediment-air interface. It can
5277 also represent an increase in habitat quantity and diversity through an increase in habitat heterogeneity.
5278
5279 12.1.2.2.2 Metric 5: Patch Mosaic Complexity
5280 This Metric assesses the horizontal structural complexity of the AA (as viewed from above), a
5281 characteristic that is sometimes referred to as interspersion. When viewed from above, most wetlands
5282 are mosaics of different patches of substrate or plant cover. The complexity of the mosaic is made up of
5283 the diversity of the component patches and the degree to which they are interspersed. Within a given
5284 wetland class, the diversity and levels of ecological function of a wetland mosaic are expected to
5285 increase with its overall complexity.
5286
5287 12.1.2.2.3 Metric 6: Vertical Complexity
5288 Metric 6 addresses the vertical structure of the plant community in terms of its component number of
5289 plant strata. Different strata provide different physical and ecological services. For instance, tall
5290 vegetation tends to be more efficient at intercepting and holding rainwater, serving as a source of
5291 allochthonous inputs, and moderating air temperature. Low stature vegetation can shield soils from
5292 intense rainfall while serving as forage for herbivorous game animals. The basic assumption is that more
5293 strata provide a greater amount of niche space and broader ranges in habitat condition, as well as more
5294 kinds and higher levels of material and energy transformations for the wetland as a whole.
5295
5296 12.1.2.2.4 Metric 7: Plant Community Complexity
5297 This metric evaluates the diversity of plant species that dominate the plant strata. Since different
5298 species tend to have different growth patterns and morphometry, an increase in species diversity within
5299 a stratum tends to increase its internal architectural complexity. Within a wetland class, the diversity
224 2011NWCA Technical Report DISCUSSION DRAFT
-------
5300 and levels of ecological function of a wetland are expected to increase with the number and abundance
5301 of different plant species. The basic assumption is that a greater diversity of co-dominant species
5302 translates into a wider variety and higher levels of wetland functions.
5303
5304 12.1.2.3 Section C: Assessment of Stress in the AA
5305 The following Metrics were used to assess stressors within the AA. In general, the effects of stressors on
5306 wetland condition tend to increase as their number, variety, and severity increases, regardless of
5307 wetland type or vegetation community. The severity of a stressor depends on its duration, intensity,
5308 frequency, and proximity. The field indicators of stress tend to integrate across these parameters, such
5309 that they are not assessed independently. In this case, by observing whether the stressor indicators
5310 were obvious and pervasive, or characterized as more moderate, each stressor was evaluated to
5311 determine whether it had a high, medium, or low degree of severity, as indicated in the previous Table
5312 12-2. The total number of stressors, regardless of their severity, was also tabulated. Ultimately data on
5313 stressors offer a diagnostic tool by documenting causes of degradation within the AA. All available
5314 information was used to identify stressors including direct observation of the AA, aerial photos, and
5315 maps.
5316
5317 12.1.2.3.1 Metric 8: Stressors to Water Chemistry
5318 Hydrology has been called the "master variable" that determines the structure, function and ecosystem
5319 services provided by wetlands. In USA-RAM, Hydrology is represented by a Metric for water chemistry
5320 (Metric 8) and quantity (Metric 9). Human activities that degrade water chemistry include discharge
5321 from point sources and watershed activities that result in high sediment loads, nutrient runoff, mine
5322 drainage, excess salts, etc. As stressors accumulate at a site, services such as biodiversity support and
5323 biogeochemical cycling are compromised and downstream aquatic systems can become impaired.
5324
5325 12.1.2.3.2 Metric 9: Stressors to Hydroperiod
5326 The hydroperiod, or the pattern of water level change over time, affects wetland vegetation community
5327 composition and productivity, controls the provision of spawning and nursery grounds for fish and
5328 amphibians, affects migratory waterfowl habitat, and biogeochemical processes. Functions such as
5329 floodwater storage and flood peak reduction are reflected in the hydroperiods of wetlands.
5330
5331 12.1.2.3.3 Metric 10: Stressors to Habitat/Substrate
5332 Some human activities such as grading, cattle grazing, off-road vehicle use, and vegetation control can
5333 severely alter wetland substrates and other parameters of wetland habitats. Some urban wetlands are
5334 severely impacted by dumping of yard debris and other trash. Substrate alterations can cause changes in
5335 soil quality and drainage that subsequently alter wetland plant communities. Severe alterations of
5336 wetland substrates often lead to invasions by non-native vegetation.
5337
5338 12.1.2.3.4 Metric 11: The Cover of Invasive Species
5339 Wetland plants are particularly useful as indicators because they are an easily observed, universal
5340 component of wetland ecosystems, and they integrate across other aspects of wetland condition or
5341 stress that vary more rapidly over time. Plant community composition, including the occurrence of
5342 invasive species, provides clear and robust signals of human disturbance. This Metric is assessed based
5343 on field observations of the percent cover of invasive species in each of the plant strata within the AA.
5344 Local invasive plant species lists or resource agencies were consulted to determine the plant species
5345 within a region of the NWCAthat are considered invasive in wetlands.
5346
5347
225 2011NWCA Technical Report DISCUSSION DRAFT
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5348 12.1.2.3.5 Metric 12: Stressors to the Vegetation Community
5349 This metric accounts for human activities that directly alter the plant community in the AA. Vegetation is
5350 an easily observed component of wetlands that responds predictably to disturbance. As vegetation
5351 communities respond to stressors, important wetland services, such as biodiversity support and water
5352 chemistry improvement, may be affected. Common stressors might include mowing within the AA,
5353 excess herbivory, or various management practices to suppress the risk of wildfires.
5354
5355
5356 12.2 Data Preparation
5357
5358 As described in Chapter 2, all field data, including data for USA-RAM, were collected during field visits
5359 conducted in the 2011 growing season. The USA-RAM was developed by Collins and Fennessy (2011)
5360 based on their experience with other rapid assessment approaches for wetlands (Fennessy et al. 1997;
5361 Mack 2001; Fennessy et al. 2007; Collins et al. 2008), and discussions with regional teams working on
5362 the NWCA. A field manual was written for use by field crews, which included the rationale for each
5363 metric and instructions for completing the field data forms (USEPA 2011).
5364
5365 At each site where the Level 3 intensive data were collected on vegetation, soils, algae, etc., data for the
5366 USA-RAM were also collected. Field crews recorded data using the USA-RAM field data sheets, but did
5367 not score the Metrics during the site visits. The methods and breakpoints used to score the Metrics and
5368 to combine them into the final USA-RAM scores were developed as part of the subsequent NWCA data
5369 analysis effort.
5370
5371 The USA-RAM data were exported for analysis both in a summary form, in which the Metric scores were
5372 compiled, and using the raw data for each indicator that comprised a metric. Both data sets were used
5373 in data analysis.
5374
5375 Data were prepared for analysis using the approach shown in Figure 12-2. Field data were entered by
5376 scanning the field data forms, and the scanned data were validated according to NWCA protocols as
5377 described in Chapter 2. Once all the data were compiled, several quality assurance reviews were
5378 conducted:
5379 • The field data for all AAs were reviewed to ensure that they were complete and had
5380 been compiled accurately. We found only one data value for one AA had been
5381 miscalculated;
5382 • 15 AAs were selected for intensive review. The sites were selected because of suspect
5383 combinations of Metric data; for example, one site that was designated as a reference
5384 site also had a high number of stressors. All data recorded on the forms were checked
5385 against the corresponding data in the scanned data files. We found no errors in the
5386 scanned data; all field data had been recorded correctly.
5387
5388
226 2011 NWCA Technical Report DISCUSSION DRAFT
-------
5389
5390
5391
5392
5393
5394
5395
5396
5397
5398
5399
5400
5401
5402
5403
5404
Data Preparation
Data Entry &
Validation
QA Review of
Sites and
Submetric
Scores
Data for
Verification
Aquired
Data Analysis Steps
Develop
Procedures to
Score Condition
& Stress of
Buffer Zone & AA
Test Against
Reference
Condition
Describing Condition
Calculate Final USA-RAM
Scores to Indicate Overall
Wetland Condition
Evaluate Stressor
Data by Reporting
Group
Figure 12-2. Overview of data preparation and analysis steps to describe condition and stress based on USA-RAM.
In order to prepare the data to score Metric 7 (Plant Community Complexity), the dominant species
recorded in each plant stratum at an AA were compiled into a single list, with each species appearing
only once, regardless of the number of strata in which the species occurred. Species lists were compiled
and the total species count for each site was used in scoring the Metric. Compiling the species list
revealed that 97 sites were missing plant data for Metric 7', despite the fact that these sites had plant
data recorded in other data tables. A map of these sites showed that a large number of them were
concentrated along the Gulf Coast, specifically in Louisiana, Mississippi, and Alabama (Figure 12-3).
Because of their missing data, these 97 sites were eliminated from the analysis. An additional 18 sites
were dropped; six due to other missing data, and 12 sites because they were outliers (defined as data
beyond the 95th and 5th percentiles of the distribution of their respective Metric scores), leaving a total
of 1,119 AAs included in the USA-RAM analysis.
227
2011 NWCA Technical Report
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5405
5406
5407
5408
5409
5410
5411
5412
5413
5414
5415
5416
5417
5418
5419
5420
5421
5422
5423
5424
5425
5426
5427
5428
5429
5430
5431
5432
5433
5434
5435
Figure 12-3. Map NWCA sites in portions of Louisiana, Mississippi and Alabama. Sites marked with an outer white
circle were missing plant data for Metric 7 (not all 65 of these sites are distinguishable in this figure due to
overlapping markers). Least disturbed sites are green; intermediate disturbed sites are white, and the sites
designated by NCWA as most disturbed are red.
12.3 Data Analysis
12.3.1 Overview
The data for each Metric were separated into four categories of condition or stress, and the four
categories were assigned values of 3, 6, 9, and 12, with the high values representing increases in
condition or stress. Each AA was therefore given one of these values for each Metric, termed the Metric
score. The values for the AA Condition Index, AA Stressor Index, and Buffer Index were calculated as the
simple sum of their respective Metric scores, scaled to a maximum of 100 points. The Site Index is a
combination of these three other indices, as explained in Section 12.3.2.3.
12.3.2 Data Analysis Steps
12.3.2.1 Distribution of Metric Data
A frequency histogram was calculated for all the data of each Metric. The histogram for all but one
Metric indicated that the data were reasonably distributed across the full range of condition
represented by all the AAs. However, the data for Metric 1, the percent of the AA perimeter adjoining a
buffer land cover, were very heavily skewed toward high scores. Ninety-two percent of all the AAs had
more than 75% of their perimeter buffered, while only 2% of the AAs had less than 25% of their
perimeter buffered. This indicates that the condition of the AA buffer zones was essentially the same
with regard to Metric 1, which was therefore excluded from further analyses of the USA-RAM data. For a
discussion of the likely causes of the poor performance of this Metric, see Section 12.4.
12.3.2.2 Scoring USA-RAM Metrics
As stated above, the data for each Metric were separated into four categories of condition or stress. For
228
2011 NWCA Technical Report
DISCUSSION DRAFT
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5436 most Metrics, the data were categorized in the field, based on the field indicators of the Metrics. Data
5437 for the other Metrics were not initially categorical. For these Metrics, the four categories corresponded
5438 to either the quartiles of the frequency distributions of the data, or to natural breaks in the frequency
5439 distributions (Table 12-5). The four categories were assigned values of 3, 6, 9, and 12, with the high
5440 values representing increases in condition or stress. Each AA was therefore given one of these values for
5441 each Metric, termed the Metric score.
5442
5443 12.3.2.3 Procedures to Calculate the AA Condition Index, AA Stressor Index, Buffer Index, and Site Index
5444 For each AA, the Buffer Index, AA Condition Index, and AA Stressor Index are each calculated as the sum
5445 of its component Metric scores, which is then divided by its maximum possible sum. The value of each of
5446 these indices for each AA therefore represents the proportion of its maximum possible value. This value
5447 is then scaled to a maximum of 100 points, such that each of these indices has a minimum possible value
5448 of 25 and a maximum possible value of 100. Thus, an index score of 25 indicates that each component
5449 Metrics had the lowest score possible (3 points), while an index score of 100 indicates that each
5450 component Metric had the highest score possible (12 points). This scoring approach ensures that the
5451 index scores are weighted equally, regardless of the number of their component Metrics, as stipulated
5452 in the guiding tenets (Section 12.1.1). The formulas for these three indices are given below:
5453
5454 Buffer Index: ((Metric 2+Metric 3)/24)*100.
5455 Scores for the two Metrics are summed, then divided by the maximum possible sum (i.e., 2
5456 Metrics at 12 maximum points each = 24), then multiplied by 100; the full range of possible
5457 index values is therefore 25 to 100.
5458
5459 AA Condition Index: (Metric 4 + Metric 5 + Metric 6 + Metric 7)/48)*100.
5460 Scores for the four Metrics are summed, then divided by the maximum possible sum (i.e., 4
5461 Metrics at 12 maximum points each = 48), then multiplied by 100; the full range of possible
5462 index values is therefore 25-100.
5463
5464 AA Stressor Index: (Metric 8 + Metric 9 + Metric 10 + Metric 11 + Metric 12)/60)*100.
5465 Scores for the five Metrics are summed, then divided by the maximum possible sum (i.e., 5
5466 Metrics at 12 maximum points each = 60, then multiplied by 100; the full range of possible
5467 index values is therefore 25-100.
5468
5469 The overall Site Index or Wetland Health Index is calculated by summing the Buffer and AA Condition
5470 Indices (since in both cases high Index values indicate good condition), then subtracting a modified AA
5471 Stressor Index (for which high index values are correlated to poor condition), as follows:
5472 Site Index = (Buffer Index + AA Condition Index) + (50 - AA Stressor Index).
5473
5474 The Stressor Index is subtracted from 50 to ensure that the Site Index is positive. Without this
5475 adjustment, AAs having very low values for both the Buffer Index and the AA Condition Index, but having
5476 high values for the AA Stressor Index could have negative values for the Site Index. With this
5477 adjustment, the possible values for the Site Index range from 0 to 225. An overview of the procedure to
5478 calculate USA-RAM scores is shown in Table 12-5.
229 2011NWCA Technical Report DISCUSSION DRAFT
-------
5479
5480
5481
Table 12-5. The upper and lower sections of the table show data thresholds separating the four categories of condition or stress for each Metric. Higher scores
for the stressor Metrics indicate greater stress, except for Metric 3, for which higher scores indicate lesser stress; this was done to facilitate calculation of the
Buffer Index (see text for details).
5482
Condition
Category
Good
Moderately
Good
Moderately
Poor
Poor
Score
12
9
6
3
Buffer Condition
Metric 1
% AA Perimeter
Adjoining Buffer
>75
51-75
26-50
<25
Buffer Condition
Metric 2
Buffer Width
>75
51-75
26-50
<25
AA Condition
Metric 4
Topographic
Complexity
>5
3-4
2
<2
AA Condition
Metric 5
Patch
Complexity
Row 4
Row 3
Row 2
Row 1
AA Condition
Metric 6
Vertical
Complexity
>4
3
2
<2
AA Condition
Metric 7
Plant Community
Complexity
>6
5-6
3-4
<2
Stressor
Category
Very High
Stress
High Stress
Moderate
Stress
Low Stress
Score
12
9
6
3
Buffer Stressor
Metric 3 Buffer
Stressors
(reversed scale)
<2 (low stress)
2
3-4
>5 (high stress)
AA Stressor
Metric 8 Water
chemistry
Stressors
>3
2
1
0
AA Stressor
Metric 9
Hydroperiod
Stressors
>3
2
1
0
AA Stressor
Metric 10
Substrate
Stressors
>3
2
1
0
AA Stressor
Metric 11
Invasive Species
Cover
26-75% and >75%
5-25%
<5%
Absent
AA Stressor
Metric 12
Vegetation
Stressors
>3
2
1
0
5483
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2011 NWCA Technical Report
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5484
5485
5486
5487
5488
5489
5490
5491
5492
5493
5494
12.3.2.4 Reporting Groups
Many factors affect stress and condition for wetlands across the conterminous US. It is assumed that
these factors vary more between wetland classes and ecoregions than within them. Based on this
assumption the NWCA Analysis Team adopted the following reporting groups for the USA-RAM analysis
(Table 12-7). The NWCA Analysis Team also identified the least-disturbed sites (i.e., reference sites) and
the most-disturbed sites, based on a NCWA screening procedure (Chapter 4).
Table 12-6. A summary of the method for calculating USA-RAM scores.
1.
2.
3.
Calculate
Metric Score
Calculate
Buffer and
AA Indices
Calculate Site
Index
Convert the Metric field data to the corresponding numerical scores (i.e., 3, 6, 9, or 12) as
indicated on Table 12-5.
Calculate each Index using its component Metrics:
• Buffer index: ((Metric 2+Metric 3)/24)*100
• AA Condition Index: (Metric 4+ Metrics + Metrics + Metric 7)/48)* 100
• AA Stressor Index: (Metric 8 + Metric 9 + Metric 10 + Metric 11 + Metric
12)/60)*100
Calculate the Site Index:
(Buffer Index Score + Condition Index Score) + (50 - Stressor Index Score)
Table 12-7. Summary of Reporting Regions to Aggregated Ecoregions and wetland types.
Aggregated Ecoregions
CPL (Coastal Plains)
EMU: (Eastern Mountains & Upper Midwest)
IPL (Interior Plains)
W (West)
Aggregated Wetland Types
EH (Estuarine Herbaceous)
EW (Estuarine Woody Shrub or Forest)
PRLH (denoted PH, Palustrine, Riverine, Lacustrine Herbaceous)
PRLW (denoted PW, Palustrine, Riverine, Lacustrine Woody)
5495
Reporting Regions
EH
EW
CPL-PH
CPL-PW
EMU-PH
EMU-PW
IPL-PH
IPL-PW
W-PH
W-PW
Estuarine Herbaceous
Estuarine Woody
Coastal Plain - Palustrine, Riverine, and Lacustrine Herbaceous
Coastal Plain - Palustrine, Riverine, and Lacustrine Woody
Eastern Mountains & Upper Midwest - Palustrine, Riverine, and Lacustrine Herbaceous
Eastern Mountains & Upper Midwest - Palustrine, Riverine, and Lacustrine Woody
Interior Plains - Palustrine, Riverine, and Lacustrine Herbaceous
Interior Plains - Palustrine, Riverine, and Lacustrine Woody
West - Palustrine, Riverine, and Lacustrine Herbaceous
West- Palustrine, Riverine, and Lacustrine Woody
5496
5497
5498
5499
5500
5501
5502
5503
5504
5505
12.3.2.5 Testing USA-RAM Performance
The Metric scores, Buffer Index, AA Condition Index, AA Stressor Index, and Site Index were calculated
for each of the ten NWCA Reporting Groups. The data analysis packages JMP 11.0 (SAS Institute) and R
were used to generate box plots of the indices for the populations of least-disturbed and most-disturbed
sites, as defined by the NWCA Analysis Team. The efficacy of USA-RAM was assessed based on its ability
to distinguish between these two populations of sites.
231
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
5506 12.4 Results and Discussion
5507
5508 12.4.1 Overview
5509 USA-RAM provides a rapid means to evaluate a wetland's overall health, based on visible indicators used
5510 to score common Metrics of stress and condition for standard Assessment Areas (AAs) and their buffer
5511 zones. Stressor Metrics provide details on specific human activities that tend to degrade wetlands. The
5512 condition Metrics reflect wetland form and structure, the complexity of which is linked to the capacity of
5513 wetlands to sustain high levels of their intrinsic ecosystem services, particularly wildlife and biodiversity
5514 support. The Metric scores are used to calculate four components of USA-RAM: the AA Condition Index,
5515 the AA Stressor Index, the Buffer Index, and the total USA-RAM Site Index score of overall ecological
5516 health. Here we report on the performance of USA-RAM in describing the status of the Nation's
5517 wetlands.
5518
5519 12.4.2 Efficacy of the Site Index
5520 The Efficacy of the USA-RAM Site Index was evaluated based on its ability to distinguish between the
5521 least-disturbed AAs and most disturbed AAs for each of the 10 NWCA Reporting Groups. In each case,
5522 the efficacy of the USA-RAM Site Index was high, as indicated in Figure 12-4. For example, for the CPL-
5523 PW, where the interquartile range (25th-75th percentiles) for the least-disturbed sites is well above the
5524 range for the most-disturbed sites. Palustrine herbaceous wetlands in the Interior Plains (IPL-PH)
5525 showed the least difference in Site Index, indicating a narrow range of overall ecological condition for
5526 this group. This ecoregion is one of the most modified by human activities, and herbaceous wetlands are
5527 subject to some of the greatest amount of stressors. This is reflected by the relatively low median Site
5528 Index values (i.e., median values were 135 and 115 for least- and most-disturbed AAs, respectively).
5529 However, in every case, the differences in mean Site Index values were highly significant (p < 0.001;
5530 except for IPL-PH with p <0.002).
5531
5532 The USA-RAM Site Index scores were very high for the least-disturbed woody wetlands in the Coastal
5533 Plains (CPL-PW) and for Estuarine woody wetlands (EW), which had median Site Index values of 189 and
5534 185, respectively. The lowest mean USA-RAM Site Index scores were seen in the palustrine herbaceous
5535 wetlands of the Interior Plains (IPL-PH) and the West (W-PH), which had median values of 135 and 150.
5536 In all of the Aggregated Ecoregions, woody wetlands tended to have greater Site Index values than
5537 herbaceous wetlands (i.e., see the right hand panels in each row of Figure 12-4. This may be due to the
5538 structural characteristics of woody vegetation; woody species are longer lived with more permanent
5539 structure than are herbaceous species, which probably tends to increase their Metric scores for physical
5540 and biological structure, while also increasing the performance of the buffer zone.
5541
232 2011 NWCA Technical Report DISCUSSION DRAFT
-------
•
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175
150
175
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Least Disturbed Most Disturbed
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Most vs. Least
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9 -r
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L^ast Disturbed Most Disturbed
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5542
5543
5544
5545
Figure 12-4. Box-plots of the USA-RAM Site Index scores for the least-disturbed and most-disturbed AAs (as
independently defined by the NCWA Analysis Team) for the 10 NWCA Reporting Groups.
233
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
Group
CPL-PH
CPL-PW
E-H
E-W
EMU-PH
Buffer Index Score
110
100
f m
i =»
S ™
20
10
0
1 ^ T
Q
least Disturbed Most Distorted
110
U»
£ »
| «
- 40
° 30
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110
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90
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Least Disturbed ' Most Drsturfaed
LIO
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• 60
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110
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Least Disturbed Most Disturbed
AA Condition Index Score
-
_
-
° sturbed Most Disturbed
sturbed Most Disturbed
-
° sturbed Most Disturbed
-
sturbed Most Disturbed
-
sturbed Most Disturbed
AA Stressor Index Score
100
90
S -
3 60
a H
a
S i
Least DiEtiMbed Most DistLvbed
100
90
S 80
i! 70
S so
1 50
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T
£ s
least Disturbed Mart Disturbed
110
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| 80
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least Disturbed Most Disturbed
110
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110
100
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S 60
S
«
•• 40
0 Ml
|_ .•"
20
10
5
5 y
Least Disturbed Most Disturbed
5546
5547
5548
5549
Figure 12-5. Box-plots for Buffer Index, AA Condition Index, AA Stressor Index scores for the least-disturbed and
most-disturbed sites for the 10 NWCA Reporting Groups. Note high Stressor Index values indicate greater stress.
This figure continues on the next page.
234
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
Figure 12-5 continued
Group
EMU-PW
IPL-PH
IPL-PW
W-PH
W-PW
Buffer Index Score
100
90
- 30
0
it
i 60
5 5"
= ™
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20
10
0
T
2
Uwst Dirtirbcd Most Dctubcd
100
90
• 80
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AA Condition Index Score
-
-
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-
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-
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sturbed Most Disturbed
-
-
sturbed Most Disturbed
100
90 _^
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AA Stressor Index Score
100
90
* 80
a TO
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g
5 5
Ifast Dirtirbcd Most Dctixbcd
100
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ii 70
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100
lE
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100
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T
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[fsA Disbvbed Most Disturbed
I
Least Disbrbed Most Distarfced
5550
5551
235
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
5552 12.4.3 Efficacy of the Buffer Index, AA Condition Index, and AA Stressor Index
5553 The Efficacy of the Buffer Index, AA Condition Index, and AA Stressor Index was evaluated separately
5554 based on their ability to distinguish between the least-disturbed AAs and most disturbed AAs for each of
5555 the 10 NWCA Reporting Groups (Table 12-5). As described above, higher scores for the AA Stressor
5556 Index indicate more anthropogenic disturbance.
5557
5558 The efficacy of the Buffer Index and AA Stressor Index is high. For most of the Reporting Groups, the
5559 median values for these two indices are significantly different for the most-disturbed sites versus the
5560 least-disturbed AAs, and their interquartile ranges are clearly separate. The high efficacy of these two
5561 indices is likely due to their dependence on easily recognized visible indicators of common stressors that
5562 vary little between wetland types or ecoregions. For example, the evidence of ditching, vegetation
5563 control, and substrate disturbance is relatively obvious and very similar for all wetlands throughout the
5564 conterminous US. This means that many of the Stressor indicators were universally applicable and could
5565 be consistently applied by the different ecoregion teams.
5566
5567 The AA Condition Index did not perform as well as the AA Stressor Index or the Buffer Index. The median
5568 values for the AA Condition Index were similar for the least-disturbed and most-disturbed AAs for most
5569 of the Reporting Groups. There are at least four likely reasons for this. First, while the USA-RAM
5570 Attributes and their component Metrics are universally applicable among wetland types and ecoregions
5571 of the US, the indicators of the Metrics are probably not. Based on the guiding principles or tenets of
5572 USA-RAM (see Section 12.1.1), it consists of a single set of field indicators that does not vary among all
5573 the ecoregions and wetland types of the conterminous 48 states. Other RAMs that consist of similar
5574 Attributes and Metrics either employ a single set of indicators for narrower range of wetland types (e.g.,
5575 ORAM; Mack 2001), or different sets of indicators are employed for very different wetland types (e.g.,
5576 CRAM; Collins et al 2008). The NWCA results suggest that the condition indicators of USA-RAM were not
5577 equally applicable among all the wetland types and ecoregions of the 2011 survey. Second, there is
5578 evidence that the reference conditions defined by the NCWA screening method (Chapter 4) may not
5579 pertain to the AA Condition Index of USA-RAM. The condition Metrics of USA-RAM are designed to
5580 assess the overall structural complexity of an AA, which does not have a clear relationship to the
5581 screening method. AAs defined as least-disturbed or most-disturbed by the screening method can be
5582 structurally very complex. Indeed, high values of the AA Condition Index were calculated across the
5583 range of condition as defined by the screening method (see following Section 12.4.4.2 for further
5584 explanation). Third, linkages between some Stressor Metrics and Condition Metrics can reduce the
5585 efficacy of the condition Metrics. Simply stated, some stressors in a wetland can increase its structural
5586 complexity, such that AAs having high values for the AA Stressor Index (indicating human disturbance)
5587 can also have high values for the AA Condition Index. Fourth, correct application of the condition metrics
5588 can require considerable interpretation subject to practitioner experience. To some degree, the
5589 relatively poor performance of the AA Condition Index was due to inconsistent application of the
5590 condition indicators among the assessment teams.
5591
5592 12.4.4 Meaning of the Stressor Metrics
5593 The stressor metrics are based on easily observable field indicators of stress. They were grouped into
5594 different categories of stress based on the most closely associated aspects of wetland condition, namely
5595 water chemistry, hydroperiod, substrate and habitat, and vegetation.
5596
5597 As stated above, the USA-RAM stressor Metrics were able to differentiate among AAs across the range
5598 of condition as defined by then NCWA screening method. This is reflected in the calculations of the
236 2011 NWCA Technical Report DISCUSSION DRAFT
-------
5599
5600
5601
5602
5603
5604
5605
5606
5607
5608
5609
5610
5611
5612
5613
5614
5615
5616
5617
5618
5619
5620
5621
5622
5623
5624
5625
5626
5627
5628
5629
5630
5631
Buffer Index and AA Stressor Index (Figure 12-5). All of the least-disturbed AAs in some Reporting
Groups (e.g., CPL-PW, E-H, E-W, EMU-PW) had the maximum possible score (100) for the Buffer Index.
Information on stressors also provides a basis for identifying human activities that can be adjusted to
reduce stress and thus improve condition. Table 12-8 and Table 12-9 show the total stressor counts (a
sum of the number of stressor indicators checked in the field) for the least-disturbed and most-
disturbed AAs for each Reporting Group. The sum of all stressors recorded in both the buffer zone and
the AA are also shown. As expected, the total number of stressors recorded is substantially lower for the
least-disturbed AAs than for the most-disturbed AAs, as defined by the NWCA screening method. For
the buffer zone, the largest counts of stressors were recorded for the estuarine herbaceous wetlands (E-
H). For AAs, the largest counts of stressors were recorded for the Coastal Plains palustrine woody (CPL-
PW). The counts for Metric 10, Total Stressors to Substrate, received the highest counts in more than
half of the Reporting Groups. This indicates that substrate disturbance was relatively common,
particularly in the most-disturbed AAs (Table 12-8).
12.4.4.1 Ranking Stressors
To determine which stressors are most common to US wetlands, the stressor indicators (i.e., the
individual stressors that make up each Metric) were ranked according to their frequency of observation
by the assessment teams (Table 12-10). Ranks are shown for the three most common stressor
indicators, which are assumed to have the greatest impact across the US, and the indicator selected
least frequently, which is assumed to have the least impact. Invasive plant species was the most
common stressor recorded in the buffer zone. For both the AAs and their buffer zone, the presence of
ditches and dikes were among the most common stressor indicator noted, cumulatively affecting as
much as 46% of the buffer zone of all wetlands, and 31% of all AAs. Thus, for the NWCA as a whole, the
most widespread stressor indicators are due to activities that alter hydroperiods. It should be noted that
for many AAs, the buffer zone was also wetland, so the presence of ditches and dikes in the buffer zones
can directly impact the AAs. The most common cause of substrate disturbance was over-grazing, both by
native and domestic animals.
Table 12-8. Total stressor counts recorded in the buffer and the AAfor each NWCA Reporting Group, and the total
stressors recorded for each of the individual stressor Metrics (M) in the AA for the Least-Disturbed AAs.
Highlighted cells indicate the highest stressor count recorded for each Reporting Group. Because Metric 11, Cover
of Invasive Species, is not based on a count of stressor indicators, it is not shown in this table.
Reporting
Group
CPL-PH
CPL-PW
E-H
E-W
EMU-PH
EMU-PW
IPL-PH
IPL-PW
W-PH
W-PW
Sum of all
Stressors in
Buffer
11
13
33
2
19
11
46
20
44
25
Sum of all
Stressors in
AA
18
35
44
9
7
11
36
30
40
27
MS
Total Water
chemistry Stressors
inAA
1
6
25
1
1
0
14
3
7
1
M9
Total Hydroperiod
Stressors in AA
2
5
6
2
2
2
6
3
9
2
M10
Total Substrate
Stressors in AA
8
17
10
5
1
7
7
11
15
15
M12
Total Vegetation
Stressors in AA
7
7
3
1
3
2
9
13
9
9
5632
5633
237
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
5634
5635
5636
5637
Table 12-9. Total stressor counts recorded in the buffer and the AAfor each NWCA Reporting Group, and the total
stressors recorded for each of the individual stressor Metrics (M) in the AA for the Most-Disturbed AAs.
Highlighted cells indicate the highest stressor count recorded for each Reporting Group. Because Metric 11, Cover
of Invasive Species, is not based on a count of stressor indicators, it is not shown in this table.
Reporting
Group
CPL-PH
CPL-PW
E-H
E-W
EMU-PH
EMU-PW
IPL-PH
IPL-PW
W-PH
W-PW
Sum of all
Stressors in
Buffer
137
282
337
112
226
182
232
64
224
120
Sum of all
Stressors
inAA
103
229
172
62
120
96
211
65
184
80
MS
Total Water
chemistry Stressors
in AA
16
43
44
9
24
19
56
29
35
15
M9
Total Hydroperiod
Stressors in AA
24
65
88
25
32
24
45
16
70
17
M10
Total Substrate
Stressors in AA
36
75
32
18
38
32
58
14
50
29
M12
Total Vegetation
Stressors in AA
27
46
8
10
26
21
52
6
29
19
5638
5639
5640
5641
5642
Table 12-10. Ranking of the stressor indicators that were observed most frequently and least frequently, which are
assumed to have the greatest and least impact, respectively, across the US. Metric 11, Cover of Invasive Species, is
not included since it is not based on a count of stressor indicators.
Stressor Metric (M)
M3
All Buffer Stressors
MS
Water chemistry
Stressors in AA
M9
Hydroperiod
Stressors in AA
M10
Substrate stressors
inAA
M12
Vegetation Stressors
inAA
Rank of Stressor Indictor and % of NWCA AAs Affected
Most Common
Indicator
Invasive
Species
Algae
Dikes
Grazing by
Native
Species
Grazing
31.7%
9.7%
16.2%
19.2%
10%
2nd Most Common
Indicator
Ditches
Present
Turbidity
Ditches
Grazing by
Domestic
Species
Wildlife
26%
8%
15.0%
12.8%
7.6%
3rd Most Common
Indicator
Dikes
Present
Sediment
Upland
Species
Compaction
Mowing
20.3%
7.2%
9.9%
6.4%
5.5%
Least Common
Indicator
Mining
Septic
Systems
Siphons
Fire Lines
Fire
<0.1%
0.3%
0.5%
0.8%
1%
5643
5644
5645
5646
5647
5648
5649
5650
5651
5652
5653
5654
12.4.4.2 Links between Stressor Metrics and Condition Metrics
As expected, there is a link between the scores for condition and stressor Metrics. For example,
substrate disturbance (stressor Metric 8) can increase topographic complexity (condition Metric 4).
Therefore, AAs having disturbed substrates (i.e., AAs for which stressor indicators for substrate
disturbance were recorded) tended to have high scores for topographic complexity. For example, since
over-grazing acts to increase micro-topographic relief, scores for topographic complexity were high for
AAs where over-grazing was observed. Over-grazing was also the most common indicator of stress to
substrates (see Table 12-10), and was a common stressor indicator among the most-impacted AAs. As a
result of this linkage between over-grazing and micro-topographic relief, plus the association of over-
grazing with the most-impacted AAs, many of these AAs had high scores for topographic complexity.
238
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
5655
5656
5657
5658
5659
5660
5661
5662
5663
5664
5665
5666
5667
5668
5669
5670
5671
5672
5673
5674
5675
5676
5677
5678
5679
5680
5681
5682
5683
5684
5685
5686
5687
Such linkages between the stressor Metrics and condition Metrics contributed to the relative inability of
the AA Condition Index to distinguish between the least-impacted and most-impacted AAs (see section
13.4.3).
12.4.5 Sample Frame Effects
Figure 12-6 shows a plot of the Cumulative Distribution Frequency (CDF) of the Site Index values for all
AAs included in the NWCA. The range of possible Site Index values is 0 to 225. While the high end of the
range is well represented, the low end of the range is not. There are almost no AAs with index values
less than 50. Fully 100% of NWCA AAs had scores greater than 46, and 95% of sites had scores greater
than 85. It should be noted that the CDF is based on the number of AAs, rather than wetland area.
However, it is common that highly disturbed sites tend to be small and fragmented (Lopez and Fennessy
2002; Fennessy et al. 2007a). Therefore, had this CDF been plotted using wetland area, the under-
representation of highly disturbed AAs may have been even more pronounced. A cursory examination of
30 AAs having low values for the AA Condition Index indicated that their encompassing wetlands were
not especially small, relative to the size distribution of intensively mapped wetlands in some ecoregions.
The site selection process seems to have favored larger wetlands. One consequence of this was to
greatly increase the abundance of AAs with intact buffers. This because an AA in a large wetland tends
to be completely surrounded by other areas of the same wetland that qualify as buffer land cover (see
Table 12-4). Nearly all AAs had the full extent of buffers possible; 92% of all AAs were assigned to the
highest-scoring category (75% - 100% cover) for Metric 1 (percent of AA perimeter adjoining a buffer
land cover), while only 2% of the AAs were assigned to the lowest-scoring category. The very low
efficacy of this Metric resulted in its omission from the USA-RAM analysis. The data for Metric 2, mean
buffer width, were similarly distributed, with over 50% of the AAs being assigned to the upper quartile
of possible mean buffer widths, and only 3.5% being assigned to the lower quartile. The systematic bias
of the sample frame against small wetlands clearly reduced the range of the Buffer Index, thus reducing
its ability to differentiate among AAs across the gradient of their condition.
0.8
IS
J3
O
£,
O.
U>
0.6
0.4
3
S
U
0.2
50 100 150
USA RAM Site Index
200
Figure 12-6. Cumulative frequency distribution of USA-RAM Site Index scores. The possible range of scores is 0-
225. While sites at the top end of the condition gradient appear well represented, sites at the low end of the range
(< 50) are lacking.
239
2011 NWCA Technical Report
DISCUSSION DRAFT
-------
5688
5689 12.4.6 Habitat Assessment with USA-RAM
5690 The condition Metrics in the USA-RAM were designed to evaluate the structural complexity of wetlands.
5691 The assumption underlying this design is that the capacity or potential of a wetland to sustain high levels
5692 of its intrinsic ecosystem services increases with its natural structural complexity. The structural
5693 complexity of wetlands has been correlated to a broad variety of their services, including peak flood
5694 reduction, pollutant filtration, chemical processing, biodiversity support, and especially overall habitat
5695 diversity and quality for wildlife (Fennessy et al. 2007; Collins et al. 2008; Stein et al. 2009; Faulkner et al.
5696 2011; Steven and Gramling 2012). USA-RAM can therefore be especially useful for assessing wetlands as
5697 wildlife habitat. For example, the diversity of wetland dependent and riparian bird species has been
5698 linked to indicators of structural diversity metrics in the Ohio Rapid Assessment Method (ORAM),
5699 including those based on microtopography, vegetation communities, and modifications to hydrology
5700 (Stapanian et al. 2003). Many rapid assessment methods use the number of vegetation community
5701 types (including the extent of invasive species) as a proxy for overall community diversity (Mack 2001;
5702 Fennessy et al. 2007). The Montana Wetland Assessment Method is one example that rates structural
5703 diversity using the number of Cowardin vegetation classes present, and relates those to the provision of
5704 wildlife habitat. Food chain support has been assessed relative to vegetation cover and structural
5705 diversity (Burglund 1999). USA-RAM adds the assessment of wetlands as habitat to the NWCA, which
5706 extends the ecosystem services that are evaluated in the survey.
5707
5708 12.4.7 Verification with Level 3 Vegetation Data
5709 USA-RAM provides measures of wetland stress and condition that complement the assessments
5710 provided by more intensive methods (i.e., Level 3 methods). It also provides measures of overall
5711 condition or health, and helps identify human actions that can be taken to reduce stress and otherwise
5712 improve conditions. The Level 3 methods focus on key biotic assemblages and other aspects of stress or
5713 condition, and are essential to quantify relationships between conditions and human actions.
5714
5715 Although USA-RAM and the Level 3 NWCA methods serve different, complementary purposes, some
5716 degree of correlations between their results is expected. Such correlations have two obvious
5717 applications. First, a high degree of correlation can justify replacing some relatively expensive Level 3
5718 assessment with the less expensive USA-RAM. Combinations of rapid and Level 3 assessment can
5719 increase the overall geographic scope or density of assessment per unit of time or cost. Second, the
5720 correlations can be used to identify or verify the ecosystem services that are represented by USA-RAM.
5721 For example, knowing the degree to which USA-RAM correctly characterizes ecological condition as
5722 related to plant community metrics requires regressing the USA-RAM results on the more quantitative
5723 Level 3 measures of plant diversity as it relates to ecological condition. Establishing the relationship
5724 between USA-RAM and Level 3 NWCA data provides confidence on the reliability and defensibility of
5725 USA-RAM. However, caution should be exercised before using correlations between USA-RAM and Level
5726 3 data to calibrate USA-RAM. That is, the correlations should usually not be used to adjust the USA-RAM
5727 Metrics, their indicators, or their scoring tables. The justification for this is that USA-RAM was designed
5728 to assess the overall potential or capacity of a wetland area to provide high levels of all or most of its
5729 intrinsic ecosystem services, and adjusting the method to increase the correlations of its results to any
5730 one or a few services may decrease its correlation to other services.
5731
5732 At the time of this analysis, several Level 3 plant metrics that will be part of the vegetation MMI
5733 development effort for the NWCA were made available for testing against the USA-RAM results. The
5734 Level 3 metrics are based on the Floristic Quality Assessment Index (FQAI) and its component
5735 Coefficients of Conservatism (C-values) (see Chapter 5). Both the FQAI and the mean C-values for
240 2011 NWCA Technical Report DISCUSSION DRAFT
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5736
5737
5738
5739
5740
5741
5742
5743
5744
5745
5746
5747
5748
5749
5750
5751
5752
5753
5754
5755
5756
5757
5758
5759
5760
5761
5762
5763
5764
5765
5766
5767
5768
5769
5770
wetlands have been shown to have a strong linear response to wetland disturbance (Fennessy et al.
1998; Lopez and Fennessy 2002). The FQAI is based on the concept that the ecological condition of a
wetland can be objectively evaluated by examining the degree of conservatism (or tolerance) of the
wetland's plant species. We found statistically significant positive correlations between values of the
USA-RAM Site Index and the Levels 3 floristic metrics, with correlation coefficients ranging from 0.58 to
0.08. In eight cases, the correlation coefficient was greater than 0.4, and in four cases the coefficient
was greater than 0.5 (Table 12-11). The weakest correlation was seen for estuarine herbaceous sites,
which naturally tend to have very low plant diversity. The strongest correlation was seen for EMU-PH
and W-PH. Considering the broad variability in plant species composition and richness among the broad
range of wetland types and ecoregions included in the NWCA, the degree of correlation between the
Level 3 plant metrics and the USA-RAM results strongly suggests that USA-RAM can be used to assess
overall ecological condition and the ecosystem services associated with community structure of
wetlands. Further verification will take place as the final Vegetation MMI data are available.
Table 12-11. Correlation coefficients for regression between USA-RAM Site Index values and the Level 3 NWCA
Floristic Quality Assessment Index (FQAI) and mean Coefficients of Conservatism (Mean C) for each Reporting
Group. Highlighted cells show correlations > 0.40.
NWCA
Reporting
Group
CPL-PH
CPL-PW
E-H
E-W
EMU-PH
EMU-PW
IPL-PH
IPL-PW
W-PH
W-PW
Correlation Coefficients
(all with p < 0.01)
USA-RAM vs. FQAI
0.225
0.360
0.080
0.360
0.580
0.170
0.273
0.254
0.414
0.524
USA-RAM vs. Mean C
0.504
0.432
0.210
0.151
0.470
0.260
0.270
0.425
0.381
0.524
12.5 Literature Cited
Brinson MM, Malvarez A (2002) Temperate freshwater wetlands: types, status and threats.
Environmental Conservation 29:115-133
Burglund J (1999) Montana wetland assessment method. Montana Department of Transportation and
Morrison-Maierle, Inc. Montana Department of Transportation, Helena, Montana
Collins JN, Stein ED, Sutula M, Clark R, Fetscher AE, Grenier L, Grosso C, Wiskind A (2008) California rapid
assessment method (CRAM) for wetlands, v. 5.0. 2. California Wetland Monitoring Workgroup.
http://www.cramwetlands.org/sites/default/files/2013-04-22_CRAM_manual_6.1%20all.pdf
Collins J, Fennessy S (2011) USA Rapid Assessment Method. Final Report to USEPA for the National
Wetland Condition Assessment.
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2011 NWCA Technical Report
DISCUSSION DRAFT
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5771 Cowardin LM, Carter V, Golet FC, LaRoe ET (1979) Classification of wetlands and deepwater habitats of
5772 the United States. Report Number FWS/OBS-79/31. US Fish and Wildlife Service, Washington, DC
5773
5774 De Steven D, Gramling JM (2012) Diverse characteristics of wetlands restored under the Wetlands
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5776
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5780
5781 Fennessy MS, Geho R, Elfritz B, Lopez R (1998) Testing the floristic quality assessment index as an
5782 indicator of riparian wetland disturbance. Ohio Environmental Protection Agency, Wetlands Unit,
5783 Division of Surface Water. Columbus, OH
5784
5785 Fennessy MS, Jacobs A, Kentula ME (2007) An evaluation of rapid methods for assessing the ecological
5786 condition of wetlands. Wetlands 27: 543-560
5787
5788 Fennessy MS, Mack JJ, Sullivan M, Knapp M, Micacchion M (2007a) Assessing wetland ecological
5789 condition in the Cuyahoga River Watershed. Technical Report WET/2007-4. Ohio Environmental
5790 Protection Agency, Wetlands Ecology Unit, Division of Surface Water. Columbus, OH
5791
5792 Lopez R, Fennessy MS (2002) Testing the floristic quality assessment index as an indicator of
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5794
5795 Hawkins CP (2006) Quantifying biological integrity by taxonomic completeness: its utility in regional and
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5797
5798 Karr JR, Chu EW (1999) Restoring Life in Running Waters: Better Biological Monitoring. Island Press,
5799 Washington, DC
5800
5801 Kentula ME, Gwin SE, Pierson SM (2004) Tracking changes in wetlands with urbanization: sixteen years
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5803
5804 Mack JJ (2001) Ohio rapid assessment method for wetlands v. 5.0: User's Manual and Forms. Technical
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5806 Ecology Unit, Columbus, OH
5807
5808 Stapanian M, Waite T, Krzys G, Mack JJ, Micacchion M (2004) Rapid assessment indicator of wetland
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5810
5811 Stein ED, Fetscher A, Clark RP, Wiskind A, Grenier JL, Sutula M, Collins JN, Grosso C (2009) Validation of a
5812 wetland rapid assessment method: use of EPA's level 1-2-3 framework for method testing and
5813 refinement. Wetlands 9: 648-665
5814
5815 Faulkner S, Barrow Jr. W, Keeland B, Walls S, Telesco D (2011) Effects of conservation practices on
5816 wetland ecosystem services in the Mississippi Alluvial Valley. Ecological Applications 21: S31-S48
5817
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5818 USEPA (2003) Elements of a State Water Monitoring and Assessment Program. Technical Report EPA
5819 841-B-03-003. US Environmental Protection Agency, Washington DC
5820
5821 USEPA (2011) National Wetland Condition Assessment: Field Operations Manual. US Environmental
5822 Protection Agency, Washington, DC
5823
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