EPA Publication 841-R-22-001
National Lakes Assessment 2017:
S Technical Support Document
U.S. Environmental Protection Agency
Office of Wetlands, Oceans and Watersheds
Office of Research and Development
Washington, DC 20460
Version 1.1
October 2024
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NLA 2017 Technical Support Document: Version 1.1
Version History
Version
Date
Revisions or Comments
1.0
June2022
Final document published with survey report
1.1
October 2024
Updated to address errors identified Chapter 8, Section 8.4: Stressor
Extent, Relative Risk and Attributable Risk. Corrections to this section
aligned the calculations with the analytical approach used in the NLA
2017 report.
Suggested citation for this document is: U.S. Environmental Protection Agency. 2024. National Lakes
Assessment 2017: Technical Support Document, Version 1.1. EPA 841-R-22-001. U.S. Environmental
Protection Agency, Office of Water and Office of Research and Development.
https://www.epa.gov/national-aquatic-resource-survevs/national-lakes-assessment-2017-technical-support-
document
NLA Website: https://www.epa.gov/national-aquatic-resource-surveys/nla
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NLA 2017 Technical Support Document: Version 1.1
Table of Contents
Chapter 1: Project Overview 1
1.1 Overview 1
1.2 Objectives of the National Lakes Assessment 1
1.3 Considerations for the NLA 2017 TSD and public report 2
Chapter 2: Survey Design and Population Estimates 3
2.1 Description of sample design 3
2.1.1 Stratification 3
2.1.2 Unequal probability categories 3
2.1.3 Panels 4
2.1.4 Expected sample size 4
2.2 Sample frame summary 10
2.3 Survey design implementation and analysis 11
2.4 Estimated number of the NLA lakes and implications for reporting 11
2.5 Literature cited 14
Chapter 3: Defining Reference Sites and Condition 15
3.1 Background information 15
3.2 Pre-sampling screening (hand-picked sites only) 16
3.3 Post-sampling screening for biological reference condition 17
3.4 Literature cited 20
Chapter 4: Benthic Macroinvertebrates 23
4.1 Background information 23
4.2 Data preparation 23
4.2.1 Standardizing counts 23
4.2.2 Autecological characteristics 23
4.2.3 Tolerance values 24
4.2.4 Functional feeding group and habitat preferences 24
4.2.5 Taxonomic resolution 24
4.3 Multimetric index development 25
4.3.1 Data set 25
4.3.2 Low macroinvertebrate numbers 25
4.3.3 Ecoregion classification 25
4.3.4 Metric screening 25
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4.3.5 All Subsets MMI selection 26
4.3.6 Setting MMI benchmarks 30
4.4 Literature cited 31
Chapter 5: Physical Habitat 32
5.1 Background information 32
5.2 Data preparation 33
5.3 Methods 34
5.3.1 NLA Sites used for Expected Condition modeling and precision estimates 34
5.3.2 Field sampling design and methods 34
5.3.3 Classifications 35
5.3.4 Calculation of lake physical habitat metrics 36
5.3.5 Calculation of summary physical habitat condition indices 43
5.3.6 Deriving expected index values under least disturbed conditions 47
5.3.7 Condition Criteria for Nearshore Lake Physical habitat 49
5.4 Least disturbed reference distributions and regressions (from sections 5.3.6 and 5.3.7) 51
5.4.1 Disturbance within least disturbed reference sites 51
5.4.2 Null Model Results for RVegQ, LitCvrQ, and LitRipCvQ: 52
5.4.3 O/E Model Results for RVegQ, LitCvrQ, and LitRipCvQ: 52
5.4.4 Null Model Results for Lake Drawdown and Level Fluctuations: 53
5.5 Precision of physical habitat indicators 54
5.6 Physical habitat index responses to anthropogenic disturbance 55
5.7 Discussion 56
5.8 Literature cited 58
Chapter 6: Water Chemistry 79
6.1 Background information 79
6.2 Chemical condition benchmarks 79
6.2.1 Acidity 79
6.2.2 Dissolved Oxygen 79
6.2.3 Trophic State 80
6.2.4Total nitrogen, total phosphorus, chlorophyll a, and turbidity 80
6.2.5 Atrazine and Microcystins 83
6.3 Literature cited 85
Chapter 7: Zooplankton 86
7.1 Background information 86
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7.2 Methods 87
7.2.1 Field methods 87
7.2.1 Laboratory methods 89
7.3 Data preparation 89
7.3.1 Data quality assurance 89
7.3.2 Master taxa list 90
7.3.3 Aggregations and rarefaction of count data 90
7.4 Zooplankton MMI development 91
7.4.1 Regionalization 91
7.4.2 Least and most disturbed sites 92
7.4.3 Least disturbed sites: calibration versus validation 93
7.4.4 Candidate metrics 93
7.4.5 Final metric selection 94
7.4.6 Metric scoring 95
7.5 Zooplankton MMI metric composition and performance 96
7.5.1 Coastal Plains MMI 96
7.5.2 Eastern Highlands MMI 97
7.5.3 Plains MMI 99
7.5.4 Upper Midwest MMI 101
7.5.5 Western Mountains MMI 101
7.6Zooplankton MMI performance 105
7.6.1 Calibration versus validation sites 105
7.6.2 Precision of MMIs based on least disturbed sites 105
7.6.3 Responsiveness, redundancy, and repeatability of zooplankton MMIs 105
7.6.4 Responsiveness to a generalized stressor gradient 108
7.6.5 Effect of natural drivers and tow length on MMI scores 108
7.7 Thresholds for assigning ecological condition 113
7.7.1 NLA 2012 113
7.7.2 NLA 2017 116
7.8 Discussion 117
7.9 Literature cited 120
Chapter 8: From Analysis to Results 124
8.1 Background information 124
8.2 Population estimates 124
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8.2.1 Subpopulations 124
8.3 Lake extent estimates 127
8.4 Stressor extent, relative risk and attributable risk 127
8.4.1 Stressor extent 128
8.4.2 Relative risk and attributable risk 128
8.4.3 Considerations when calculating and interpreting relative risk and attributable risk 130
8.5 NLA 2017 change analysis 132
8.5.1 Background information 132
8.5.2 Data preparation 132
8.5.3 Methods 132
8.6 Literature cited 133
Chapter 9: Quality Assurance Summary 134
Appendix A: Lake Physical Habitat Expected Condition Models 136
Appendix B: Survey Design and Estimated Extent Summary for NLA 2007, 2012 and 2017 162
Appendix C: NLA 2017 Indicator Benchmark Summary 165
Appendix D: List of Candidate Metrics for Zooplankton 172
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List of Figures
Figure 2.1. NLA 2017 sample frame, target population, and sampled lakes.
Figure 3.1. Nine aggregate ecoregions used for reference site classification
13
17
Figure 4.1. Box and whisker plots showing discrimination between least disturbed reference (R) and
most disturbed (T) sites by biological ecoregion in the NLA 2007-2012 data used to develop the
MMI. Boxes show the interquartile range and the whiskers show the 5th and 95th percentiles
Figure 5.1. Field sampling design with 10 near-shore stations at which data were collected to
characterize near shore lake riparian and littoral physical habitat in the 2007 and 2012 National
Figure 5.2. Near-shore anthropogenic disturbance (RDis_IX) in NLA0712 regions, ordered by their
median Reference site RDis. Upper plot: Least disturbed reference sites. Lower plot: all sites.
Unweighted sample statistics are shown; box midline and lower and upper ends show median
and 25th and 75th percentile values, respectively; whiskers show maximum and minimum
observations within 1.5 times the interquartile range above/below box ends; circles show
outliers 74
Figure 5.3. Near-shore anthropogenic disturbance in NLA0712 least disturbed reference sites (median
RDisJX), ordered by aggregated region according to the same median level of near-shore
disturbance 75
Figure 5.4. LogSD's for Null-Model and regression-based O/E model for Near-shore RVegQ, LitCvrQ, and
LitRipCvrQ in the set of least disturbed lakes and reservoirs ( 76
Figure 5.5. Contrasts in key NLA physical habitat index values among least disturbed reference (R),
intermediate (S), and most disturbed (T) lakes in the contiguous 48 states of the U.S. based on
combined NLA 2007 and 2012 data 77
Figure 5.6. Contrasts in key NLA physical habitat index values among least disturbed reference (R),
intermediat (S), and most disturbed (T) lakes in the contiguous 48 states of the U.S. shown
separately for the NLA 2007 and 2012 surveys 78
Figure 6.1. Box and whisker plot of Total Phosphorus in GIS screened, outlier removed, 2007-2017
nutrient reference sites by ecoregion 82
Figure 6.2. Box and whisker plot of Total Nitrogen in GIS screened, outlier removed, 2007-2017
nutrient reference sites by ecoregion 82
Figure 7.1 Five aggregated bio-regions used to develop zooplankton MMIs for the 2012 National Lake
Assessment (CPL=Coastal Plains; EHIGH=Eastern Highlands, PLAINS= Plains, UMW=Upper
Midwest, and WMTNS=Western Mountains). Solid dots indicate least disturbed sites used for
developing the zooplankton MMI. White circles indicate least disturbed sites that we excluded
because of atypical samples (too few taxa or number of individuals collected) 92
Figure 7.2. Distribution of six component metrics of the zooplankton MMI for the Coastal Plains bio-
region in least disturbed (LD) versus most disturbed (MD) sites. Dots indicate the 5th and 95th
percentiles 97
Figure 7.3 Distribution of six component metrics of the zooplankton MMI for the Eastern Highlands bio-
region in least disturbed (LD) versus most disturbed (MD) sites. Dots indicate the 5th and 95th
percentiles 98
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Lakes Assessment (NLA) surveys
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Figure 7.4. Distribution of six component metrics of the zooplankton MMI for the Plains bio-region in
least disturbed (LD) versus most disturbed (MD) sites. Dots indicate the 5th and 95th
percentiles 100
Figure 7.5. Distribution of six component metrics of the zooplankton MMI for the Upper Midwest bio-
region in least disturbed (LD) versus most disturbed (MD) sites. Dots indicate the 5th and 95th
percentiles 103
Figure 7.6. Distribution of six component metrics of the zooplankton MMI for the Western Mountains
bio-region in least (LD) disturbed versus most disturbed (MD) sites. Dots indicate the 5th and
95th percentiles 104
Figure 7.7. Distribution of zooplankton MMI scores in-calibration vs. validation sites for five bio-
regions. Sample sizes are in parentheses. Dots indicate the 5th and 95th percentiles 106
Figure 7.8 Distribution of zooplankton MMI scores in least-disturbed (LD) vs. most disturbed (MD) sites
for five bio-regions. Sample sizes are in parentheses. Dots indicate the 5th and 95th percentiles.
107
Figure 7.9. Linear regression of NLA 2012 Zooplankton MMI scores vs. first axis score from principal
components analysis (PCA) based on chemical, habitat, and visual assessment stressor variables
used to screen least- and most disturbed sites 109
Figure 7.10. NLA 2012 Zooplankton MMI scores of human-made (shaded boxes) versus natural lakes
(unshaded boxes) for least disturbed sites in five bio-regions. See Figure 7-1 for bio-region
codes. Sample sizes for each type are in parentheses. Dots indicate 5th and 95th percentiles.. 109
Figure 7.11. Zooplankton MMI scores versus lake size class within least disturbed lakes of the NLA
2012. Sample sizes are in parentheses. Dashed lines are mean values. Dots indicate the 5th and
95th percentiles 110
Figure 7.12.Zooplankton MMI scores versus site depth for least disturbed sites 114
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List of Tables
Table 2-1. National Lakes Assessment 2017 Initial Design 6
Table 2-2. Actual number of sites sampled for NLA 2017 by design categories, including state
intensification sites that were used in the national condition estimate analyses 8
Table 3-1. Least disturbed reference screening filter thresholds for NLA 2017. If a lake exceeded any
one of the thresholds it was not considered as a least disturbed reference site for that ecoregion. Three
filters were applied universally across all ecoregions, 1) ANC < 25 ueq/L and DOC < 5 mg/L, 2)
HifPany_Circa_syn& > 0.9, and 3) no excessive lake drawdown (see Table 3-3) 18
Table 3-2. Most disturbed site screening thresholds for NLA 2017. If a lake exceeded any one of the
thresholds it was considered a most disturbed site for that ecoregion. One screen was applied
universally across all ecoregions, ANC < 0 ueq/L and DOC < 5 mg/L 19
Table 3-3. Dichotomous key for defining NLA lakes likely impacted by anthropogenic drawdown 20
Table 4-1. Final NLA biological ecoregion benthic MMI metrics and their floor/ceiling values for MMI
scoring 27
Table 4-2. Benthic MMI statistics for the NLA 2007-2012 data used to develop the MMI 29
Table 4-3. Macroinvertebrate MMI benchmarks using 2007-2017 reference site data 30
Table 5-1. NLA reference sites from combined 2007 & 2012 surveys 64
Table 5-2. Assignment of riparian vegetation cover complexity, littoral cover complexity, and littoral-
riparian habitat complexity index variants by aggregated ecoregion 64
Table 5-3. Summary of regression models used in estimating lake-specific expected values of Lake
Physical Habitat variables RVegQx, LitCvrQx and LitRipCvrQx under least disturbed conditions 65
Table 5-4. Null Model Geometric Means (gMean), geometric Standard Deviations (gSD), 5th percentiles,
and 25th percentiles of habitat index values in least disturbed reference lakes in the aggregated
ecoregions of the NLA 66
Table 5-5. O/E Physical Habitat Model means (LogMean, gMean), standard deviations (LogSD, gSD),
and percentiles of the distribution of habitat index O/E values for least disturbed reference lakes in the
aggregated ecoregions of the NLA 67
Table 5-6. Empirical 75th and 95th percentiles of the distribution of vertical and horizontal drawdown.68
Table 5-7. Precision of the key NLA Physical Habitat indices used as the primary physical habitat
condition measures in the NLA 70
Table 5-8. Association of NLA-2012 Physical Habitat Indices with high and low anthropogenic
disturbance stress classes (RT_NLA12 = R and T), defined as least disturbed and most disturbed within
NLA regions 71
Table 5-9. Association of NLA 2007 and 2012 Physical Habitat Indices with high and low anthropogenic
disturbance stress classes (RT_NLA12 = R and T), defined as least disturbed and most disturbed within
NLA regions 72
Table 6-1. Trophic State Classification used in NLA 80
Table 7-1. Hypothesized responses of zooplankton assemblages to disturbance 88
Table 7-2. COMPONENT METRICS OF THE ZOOPLANKTON MMI FOR THE COASTAL PLAINS BIO-REGION.
96
Table 7-3. COMPONENT METRICS OF THE ZOOPLANKTON MMI FOR THE EASTERN HIGHLAND BIO-
REGION 98
Table 7-4. COMPONENT METRICS OF THE ZOOPLANKTON MMI FOR THE PLAINS BIO-REGION 100
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Table 7-5. COMPONENT METRICS OF THE ZOOPLANKTON MMI FOR THE UPPER MIDWEST BIO-REGION.
103
Table 7-6. COMPONENT METRICS OF THE ZOOPLANKTON MMI FOR THE WESTERN MOUNTAINS BIO-
REGION 104
Table 7-7. RESULTS OF INDEPENDENT ASSESSMENT AND PRECISION TESTS OF NLA 2012
ZOOPLANKTON MMIs BASED ON LEAST DISTURBED SITES 106
Table 7-8. RESULTS OF RESPONSIVENESS, REDUNDANCY, AND REPEATABILITY TESTS FOR NLA 2012
ZOOPLANKTON MMIs 107
Table 7-9. Component metrics of the zooplankton multimetric indices (MMIs) used for NLA 2017. ...Ill
Table 7-10. LINEAR REGRESSION STATISTICS OF ZOOPLANKTON MMI SCORES VERSUS PCA-BASED
DISTURBANCE SCORE FOR EACH BIO-REGION 115
Table 7-11. ECOLOGICAL CONDITION BENCHMARKS FOR ZOOPLANKTON MMI SCORES (NLA 2012
ONLY) BASED ON THE DISTRIBUTION OF LEAST DISTURBED SITES IN FIVE BIO-REGIONS 115
Table 7-12 Ecological condition benchmarks for NLA 2017 zooplankton MMI scores based on the
distribution of least disturbed sites in five aggregated ecoregions (bio-regions) 116
Table 8-1. Extent estimates for response and stressor categories 129
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Chapter 1: Project Overview
1.1 Overview
This document, the National Lakes Assessment 2017: Technical Support Document, accompanies the
National Lakes Assessment: The Third Collaborative Survey of Lakes in the United States and related on-
line materials. The National Lakes Assessment (NLA) is a collaboration among the U.S. Environmental
Protection Agency (EPA), states, tribes, and other partners. It is part of the National Aquatic Resource
Surveys (NARS) program design to conduct national scale assessments of aquatic resources. The NLA
2017 provides condition assessment results at national and regional scales of the ecological and
recreational condition of lakes. This assessment was accomplished by collecting and analyzing data
from across the conterminous United States.
The National Lakes Assessment: The Third Collaborative Survey of Lakes in the United States (the public
report) is not a technical document, but rather a report geared toward a broad, public audience. It
provides national-scale assessments and compares the condition of lakes to those from the earlier
NLAs (2007 and 2012) conducted by EPA and its partners. You can find results for regional scales and
comparisons between natural lakes and reservoirs using our interactive dashboard at
https://nationallakesassessment.epa.gov/. This document serves as a technical reference to support
findings presented in the public report and on-line.
1.2 Objectives of the National Lakes Assessment
The objective of the NLA is to characterize aspects of the biological, chemical, physical, and
recreational condition of the nation's lakes throughout the conterminous United States. It employs a
statistically valid probability design stratified to allow estimates of the condition of lakes on a national
and regional scale.
The NLA is designed to answer the following questions about lakes across the United States.
1. What is the current biological, chemical, physical, and recreational condition of lakes?
a. What is the extent of degradation among lakes?
b. Is degradation widespread (e.g., national) or localized (e.g., regional)?
2. Is the proportion of lakes in the poor condition getting better, worse, or staying the same over
time?
3. Which environmental stressors are most strongly associated with degraded biological condition in
lakes?
A variety of chemical, physical, and biological data were collected and developed into indicators to
address the NLA questions. For each of these indicators, this Technical Report focuses on the
conceptual basis, methods, and procedures used for the NLA. The information described in this
Technical Report was developed through the efforts and cooperation of NLA scientists from EPA,
technical experts, and participating cooperators from states, tribes, and academia. While this Technical
Report serves as a comprehensive summary of the NLA procedures, it is not intended to present an in-
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depth report of the design, site evaluation process, field sampling, NLA results, or additional data
analysis results. Please see the following documents for additional details on these aspects of the
project.
National Lakes Assessment 2017: Quality Assurance Project Plan (EPA 841-B-16-
003)(hereafter referred to as the NLA 2017 QAPP)
National Lakes Assessment 2017: Site Evaluation Guidelines (EPA 841-B-16-001) (hereafter
referred to as the NLA 2017 SEG)
National Lakes Assessment 2017: Field Operations Manual (EPA 841-B-16-001) (hereafter
referred to as the NLA 2017 FOM)
National Lakes Assessment 2017: Laboratory Operations Manual (EPA 841-B-16-004)
(hereafter referred to as the NLA 2017 LOM)
1.3 Considerations for the NLA 2017 TSD and public report
The EPA is working to stabilize benchmarks and data analyses across the NARS program to facilitate
change and trend analyses. In NLA 2017, most aspects of the survey remained the same including the
field methods, laboratory analyses, target population, benchmark selection process and data analyses.
Changes since the NLA 2012 that are discussed in this document include:
Updated sample frame that uses NHDPIus HR for 1-5 ha lakes (see Chapter 2);
Survey results (population estimates) in 2017 represent the percentage of lakes relative to the
full target population, rather than to the portion of the target population that could be sampled
as in NLA 2007 and 2012 (see Chapters 2 and 8).
New reference lakes were identified and added to the reference pool that revised some
regional benchmark values (see Chapters 3, 4, 6 and 7);
Modifications to the lake drawdown calculations and results (see Chapter 5); and
Clarifications to the NLA 2012 zooplankton biological index and the NLA 2017 modifications
(see Chapter 7).
For purposes of identifying change and trends, 2007 and 2012 results were recalculated based on the
updated 2017 benchmarks (see Appendix C) and the estimates are for the entire target population.
Given the above modifications, direct comparisons should not be made between the NLA 2017 results
and those reported in earlier surveys as this will produce erroneous information.
Finally, the NLA 2017 public report and data dashboard use the lake condition classes of good, fair and
poor condition. In NLA 2012, lake condition classes included least disturbed (good), moderately
disturbed (fair) and most disturbed (poor) condition. This document uses the good/fair/poor
terminology for condition class estimates consistent with the public report. Least, moderate, and most
disturbed are also used in this document to describe anthropogenic disturbance pressure categories
for index and model development (see Chapters 4, 5, and 7).
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Chapter 2: Survey Design and Population Estimates
The NLA was designed to assess the condition of the population of lakes, reservoirs, and ponds in the
conterminous United States. The NLA design allows characterization of lakes at national and regional
scales using chemical, physical and biological indicators. It is not intended to represent the condition of
individual lakes. The statistical design also accounts for the distribution of lakes across the country -
some areas have fewer lakes than others - so that even in areas of the country where there are few
sample sites regional and national results still apply to the broader target population.
This chapter provides details on the NLA survey design, sample frame, analyses and estimated extent
of the NLA lake population. Modifications to the survey design in 2017 are noted throughout the
chapter and are summarized in Appendix B: Survey Design Summary and Population Estimates for NLA
2007, 2012, and 2017.
2.1 Des \ x x x sample design
The target population for the NLA includes all lakes, reservoirs, and ponds within the 48 contiguous
United States greater than 1 hectare (ha) in surface area that are permanent waterbodies, at least 1
meter deep, and have a minimum 0.1 ha of open water. In additon, lakes are required to have a
minimum residence time of one week. The word "lake" in the remainder of this document includes
lakes, reservoirs and ponds. The Great Lakes, Great Salt Lakes and lakes that are tidally influenced are
excluded; as are those used for aquaculture, disposal-tailings, sewage treatment, evaporation, or other
unspecified disposal use.
To select sites for the NLA, EPA statisticians used a Generalized Random Tessellation Stratified (GRTS)
(Stevens and Olsen 2004; Olsen et al. 2012) survey design for a finite resource with stratification and
unequal probability of selection.
2.1.1 Stratification
The overall NLA survey design was stratified by state. The number of lakes in NLA 2017 first allocated
approximately 100 lakes to each of the nine aggregated ecoregions. Then the number of lakes in each
state was determined by proportionally allocating the number of lakes to the state based on the
proportion of lakes in each aggregated ecoregion within the state. For states that wanted to conduct
state-scale surveys, the survey design included additional lakes for that purpose. These additional lakes
from state-scale surveys are only included in the NLA 2017 report when the state used the NLA 2017
field protocols and indicators for all lakes.
2.1.2 Unequal probability categories
Within each stratum, i.e., state, the survey design used unequal probability categories based on lake
area: 1 to 4 ha, 4 to 10 ha, 10 to 20 ha, 20 to 50 ha and greater than 50 ha. Approximately, an equal
number of lakes were allocated to each category. This ensures that larger lakes are included in the
study.
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2.1.3 Panels
The survey design incorporates lakes sampled in NLA 2007 and NLA 2012 as well as selecting new lakes.
This improves the ability of the survey design to estimate change in condition in NLA 2017 from the
condition in NLA 2007 and NLA 2012. In addition, the survey design includes 96 lakes that are sampled
twice in NLA 2017, providing information on measurement variability. These requirements result in five
panels of sites:
NLA17_07RVT2 - Panel of lakes originally sampled twice in NLA 2007, were also sampled in NLA
2012 and will be sampled twice in NLA 2017. Note that these lakes will be sampled twice again
in NLA 2022.
NLA17_07RVT - Panel of lakes originally sampled once in NLA 2007, were also sampled in NLA
2012 and will be sampled once in NLA 2017. Note that these lakes will be sampled again in
NLA 2022.
NLA17_12RVT2 - Panel of lakes originally sampled twice in NLA 2012 and will be sampled
twice in NLA 2017. Note that these lakes will be sampled twice again in NLA 2022 and in NLA
2027.
NLA17_12RVT - Panel of lakes originally sampled once in NLA 2012 and will be sampled once
in NLA 2017. Note that these lakes will be sampled again in NLA 2022 and in NLA 2027.
NLA17_17 - Panel of new lakes to be sampled once in NLA 2017. Note that these lakes will be
sampled again in NLA 2022 and in NLA 2027.
If every lake in these panels are evaluated to be in the target population and can be sampled, then no
additional lakes are required. When this is not the case, additional lakes were provided for each of the
panels. These "over sample" panels are:
NLA17_07RVT_OverSamp - Over sample lakes to be used as replacements for NLA17_07RVT
lakes when they cannot be sampled for any reason.
NLA17_12RVT2_OverSamp - Over sample lakes to be used as replacements for NLA17_07RVT
lakes when they cannot be sampled for any reason.
N LA 17_R VT_0 ve rSa m p - Over sample lakes to be used as replacements for NLA17_07RVT
lakes when they cannot be sampled for any reason.
NLA17_17_OverSamp - Over sample lakes to be used as replacements for NLA17_07RVT lakes
when they cannot be sampled for any reason.
Note that no lakes were available for NLA17_07RVT2 over sample lakes.
2.1.4 Expected sample size
The NLA 2017 design include 904 lakes to be sampled with 96 of the lakes to be sampled twice for a
total of 1000 lake visits. The 904 lakes consisted of three sets of lakes. The first set of 226 lakes were
originally sampled in NLA 2007, resampled in NLA 2012 and resampled again in NLA 2017. Of these, 43
lakes were expected to be sampled twice in NLA 2017. The second set of 218 lakes were originally
sampled in NLA 2012 and were expected to be resampled in NLA 2017. Of these, 53 lakes were
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sampled twice in NLA 2017. The third set of 460 new lakes were sampled for the first time in NLA 2017.
Table 2-lprovides a summary of the survey design and the number of sites by state.
For the NLA17_17 new lake design, the expected number of lakes in each of the five lake area
categories was approximately 90 lakes. Based on NLA experience with lake evaluations in 2007 and
2012, an adjustment was made to achieve approximately 90 target and sampled lakes in each
category. The number of lakes expected in the five categories were multiplied by 8, 4, 3, 2 and 2. That
is, 720, 360, 270, 180 and 180 lakes for 1 to 4ha, 4 to 10 ha, 10 to 20 ha, 20 to 50 ha and >50 ha
categories. The first 90 of these were then designated to be "base" and the remaining designated at
"Over Sample." Additional over sample lakes were selected to provide sufficient lakes for states who
implement a state-level design and for new lake panel in the NLA 2022 design.
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able 2-1,
National Lakes Assessment 2017 Initia
1 Design,
NLA 2007 Lakes Revisited in
NLA 2012 Lakes Revisited in
New
Total
Lakes
Total
Total
Total
NLA 2017
NLA 2017
Lakes in
Number
Sampled
Number
Over
Lakes
NLA 2017
of Lakes
Twice in
of Lake
Sample
Selecte
State
NLA17_07RVT
NLA17_07RVT2
NLA17_12RVT
NLA17_12RVT2
NLA17_17
to be
NLA2017
Visits
in Initia
Sampled
Sampled Twice
Sampled
Sampled Twice
Sampled
Sampled
2012
Design
Once
Once
Once
AL
2
1
1
1
3
8
2
10
538
546
AR
1
1
1
1
4
8
2
10
466
474
AZ
4
1
1
1
6
13
2
15
833
846
CA
3
1
7
1
12
24
2
26
1694
1718
CO
7
1
2
1
12
23
2
25
1650
1673
CT
3
1
0
1
5
10
2
12
932
942
DE
2
1
1
1
2
7
2
9
427
434
FL
5
1
1
1
8
16
2
18
1259
1275
GA
3
1
0
1
6
11
2
13
744
755
IA
3
1
2
1
8
15
2
17
1183
1198
ID
4
1
6
1
12
24
2
26
1864
1888
IL
2
2
7
13
2
15
715
728
IN
8
1
3
1
14
27
2
29
2357
2384
KS
3
1
2
1
8
15
2
17
945
960
KY
1
1
1
1
5
9
2
11
542
551
LA
2
1
3
1
7
14
2
16
1188
1202
MA
2
1
1
1
5
10
2
12
1006
1016
MD
1
1
1
1
4
8
2
10
276
284
ME
5
1
5
1
12
24
2
26
1758
1782
Ml
9
1
8
1
19
38
2
40
3355
3393
MN
11
1
8
1
21
42
2
44
3311
3353
MO
4
1
2
1
9
17
2
19
1396
1413
MS
4
1
1
1
7
14
2
16
932
946
MT
8
0
5
2
16
31
2
33
2655
2686
NC
2
1
2
1
7
13
2
15
880
893
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NLA 2017 Technical Support Document Version 1.1
NLA 2007 Lakes Revisited in
NLA 2012 Lakes Revisited in
New
Total
Lakes
Total
Total
Total
NLA 2017
NLA 2017
Lakes in
Number
Sampled
Number
Over
Lakes
NLA 2017
of Lakes
Twice in
of Lake
Sample
Selecte
State
NLA17_07RVT
NLA17_07RVT2
NLA17_12RVT
NLA17_12RVT2
NLA17_17
to be
NLA2017
Visits
in Initia
Sampled
Sampled Twice
Sampled
Sampled Twice
Sampled
Sampled
2012
Design
Once
Once
Once
ND
6
1
13
1
21
42
2
44
3484
3526
NE
5
1
7
1
14
28
2
30
2260
2288
NH
3
1
0
1
6
11
2
13
965
976
NJ
2
1
1
1
6
11
2
13
996
1007
NM
2
2
7
13
2
15
598
611
NV
3
1
2
1
8
15
2
17
684
699
NY
1
1
2
1
5
10
2
12
719
729
OH
1
1
5
1
8
16
2
18
1285
1301
OK
10
1
3
1
15
30
2
32
2308
2338
OR
6
1
6
1
15
29
2
31
1632
1661
PA
4
1
2
1
8
16
2
18
1433
1449
Rl
2
0
4
8
2
10
278
286
SC
1
1
5
9
2
11
641
650
SD
8
1
11
1
22
43
2
45
3422
3465
TN
1
1
1
1
5
9
2
11
587
596
TX
6
1
12
1
21
41
2
43
3340
3381
UT
4
1
5
1
11
22
2
24
2030
2052
VA
4
1
4
1
11
21
2
23
1478
1499
VT
1
1
2
1
5
10
2
12
538
548
WA
5
1
8
1
16
31
2
33
2043
2074
Wl
6
1
6
1
14
28
2
30
1992
2020
WV
1
1
1
1
4
8
2
10
453
461
WY
2
1
5
1
10
19
2
21
1506
1525
Total
183
43
165
53
460
904
96
1000
67578
68482
Total by Panel Year 226 218 460
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Table 2^2, Actual number of sites sampled for NLA 2017 by design categories, including state intensification sites that were used in the national condition
estimate analyses,
NLA07 Lakes Sampled NLA17 NLA12 Lakes Sampled in NLA17
New Lakes in NLA17
Totals
State
NLA17_0
7RVT
NLA17_07
RVT Over
NLA17
07RV
NLA17_
12RVT
NLA17_
12RVT O
NLA17_12
RVT2
NLA17_12R
VT2 Over
NLA17
17
NLA17_17_
OverSamp
Lakes
Sampled
Total Site
Visits in
Samp
T2
verSamp
Samp
in 2017
2017
AL
2
1
1
1
1
2
8
10
AR
1
1
1
1
4
8
10
AZ
4
1
1
1
1
5
13
15
CA
3
1
7
1
2
10
24
26
CO
7
1
2
1
3
9
23
25
CT
3
1
1
2
3
10
12
DE
2
1
1
1
1
1
7
9
FL
5
1
1
1
2
6
16
18
GA
3
1
1
1
5
11
13
IA
3
1
2
1
8
15
17
ID
4
1
4
2 1
1
13
26
28
IL
2
2
6
1
13
15
IN
7
2
3
1
1
36
50
52
KS
3
1
2
1
2
6
15
17
KY
1
1
1
1
1
4
9
11
LA
1
1
1
2
1 1
7
14
16
MA
2
1
1
1
1
4
10
12
MD
1
1
1
1
4
8
10
ME
4
1
1
3
2 1
7
5
24
26
Ml
9
1
8
1
8
23
50
52
MN
11
1
7
2
19
10
50
52
MO
4
1
2
1
3
6
17
19
MS
4
1
1
1
3 1
2
1
14
16
MT
8
5
1
1
5
11
31
33
NC
1
1
1
1
1 1
4
3
13
15
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NLA07 Lakes Sampled NLA17
NLA12 Lakes Sampled in NLA17
New Lakes in NLA17
Totals
State
NLA17_0
NLA17_07
NLA17
NLA17_
NLA17_
NLA17_12
NLA17_12R
NLA17
NLA17_17_
Lakes
Total Site
7RVT
RVT_Over
_07RV
12RVT
12RVT_0
RVT2
VT2_Over
_17
OverSamp
Sampled
Visits in
Samp
T2
verSamp
Samp
in 2017
2017
ND
6
1
13
1
10
23
54
56
NE
4
1
1
4
3
1
2
12
28
30
NH
3
1
1
1
5
11
13
NJ
2
1
1
1
4
2
11
13
NM
2
2
7
13
15
NV
2
2
1
1
1
8
15
17
NY
1
1
2
1
1
4
10
12
OH
1
1
3
2
1
3
5
16
18
OK
10
1
3
1
3
12
30
32
OR
6
1
6
1
3
32
49
51
PA
4
1
2
1
4
4
16
18
Rl
2
2
3
1
8
10
SC
1
1
5
9
11
SD
8
1
9
2
1
8
14
43
45
TN
1
1
1
1
2
3
9
11
TX
6
1
10
2
1
6
15
41
43
UT
4
1
4
1
1
3
8
22
24
VA
4
1
4
1
6
5
21
23
VT
1
1
2
1
2
3
10
12
WA
5
1
8
1
8
8
31
33
Wl
6
1
6
1
9
29
52
54
WV
1
1
1
1
4
8
10
WY
1
1
1
5
1
5
5
19
21
Total
175
12
40
147
25
48
1
156
401
1005
1101
Total by Panel
227
221
557
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2.2 Sample frame summary
The sample frame was derived from the National Hydrography Dataset (NHD). Sample frames
for NLA 2007 and 2012 designs were based on NHDPIus and are documented as part of those
designs. Note that updates to the sample frame were made based on lake evaluations from
those surveys. That updated sample frame was combined with NHD High Resolution lakes with
lake areas from 1 to 5 ha. This was done to rectify the known deficiency in NHDPIus for small
lakes due to the 1:100,000 scale mapping. Lakes that were in NHD High Res that were also in
NHDPIus were eliminated. The NLA 2017 sample frame preserves the lake polygons from prior
surveys while improving the coverage for small lakes.
Once the initial shapefile that included all lake objects in NHD was prepared additional
attributes were created to identify lakes included in the sample frame and other properties
used to construct the survey design. First, lakes that were less than or equal to 1 hectare were
excluded. In addition, lakes were excluded based on NHDPIus codes.
Lakes included were:
Lake/Pond
Lake/Pond: Hydrographic Category = Perennial
Lake/Pond: Hydrographic Category = Perennial; Stage = Average Water Elevation
Lake/Pond: Hydrographic Category = Perennial; Stage = Date of Photography
Lake/Pond: Hydrographic Category = Perennial; Stage = Normal Pool
Lake/Pond: Hydrographic Category = Perennial; Stage = Spillway Elevation
Lakes excluded were:
Estuary
Playa
Swamp/Marsh
Lake/Pond: Hydrographic Category = Intermittent
Lake/Pond: Hydrographic Category = Intermittent; Stage = Date of Photography
Lake/Pond: Hydrographic Category = Intermittent; Stage = High Water Elevation
Reservoir
Reservoir: Construction Material = Earthen
Reservoir: Construction Material = Non-earthen
Reservoir: Reservoir Type = Aquaculture
Reservoir: Reservoir Type = Cooling Pond
Reservoir: Reservoir Type = Disposal
Reservoir: Reservoir Type = Disposal; Construction Material = Earthen
Reservoir: Reservoir Type = Evaporator
Reservoir: Reservoir Type = Evaporator; Construction Material = Earthen
Reservoir: Reservoir Type = Tailings Pond
Reservoir: Reservoir Type = Tailings Pond; Construction Material = Earthen
Reservoir: Reservoir Type = Water Storage
Reservoir: Reservoir Type = Water Storage; Construction Material = Earthen; Hyd*
Reservoir: Reservoir Type = Water Storage; Construction Material = Non-earthen
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Reservoir: Reservoir Type = Water Storage; Hydrographic Category = Perennial
Reservoir; Reservoir Type = Treatment"
Next lakes were excluded that were evaluated during the NLA 2007 and were identified as lakes
that did not meet definition of a lake for NLA 2017. These were lakes with evaluation codes of
Lake_Saline, Lake_Shallow, Lake_Special_Purpose, Lake_Vegetated, Non_Target, or Not_Lake".
2.3 Survey design implementation and analysis
Field crews evaluated lakes from the NLA survey design using a variety of techniques including
aerial photo interpretations, GIS analyses, local knowledge, etc. to identify locations that did
not meet the definition of a lake for NLA. Crews also dropped lakes from sampling during field
reconnaissance if they were a non-target type or could not be assessed due to accessibility
issues (landowner permission, too dangerous to access, etc.). Dropped lakes were
systematically replaced from a pool of replacement ("over sample") lakes from the survey
design. This process is implemented to maintain the integrity of the survey design and to
sample lakes consistent with the original number planned in different categories.
Any statistical analysis of NLA data must incorporate information about its survey design and
implementation. The statistical analysis accounts for the stratification and unequal probability
selection by using the survey design weights. The initial survey design weights are adjusted to
account for the change in sample size due to the use of over sample lakes within the strata and
unequal probability categories. The adjusted weight represents the number of lakes that each
evaluated lake represents. The sum of all adjusted weights for lakes evaluated equals the
number of lakes in the sample frame. The subset of the lakes that are evaluated as target lakes
and sampled is used to estimate the "sampled population" of lakes by using the adjusted
weights. Not all lakes evaluated as target lakes could be sampled. To account for these lakes, a
second weight adjustment is completed that enables the lakes that are target lakes and
sampled to be used to estimate the "target population" of lakes.
The statistical estimates for the NLA population estimates were completed using lake weights
(see the NLA 2017 Site Information - Data file at https://www.epa.gov/national-aquatic-
resource-surveys/data-national-aquatic-resource-surveys.) and the R package 'spsurvey'
(Kincaid and Olsen 2013) which implements the methods described by Diaz-Ramos et al. (1996).
Population estimates were determined at the national level and for several subpopulations
described in Chapter 8.
2.4 Estimated number of the NLA lakes and implica. x ' ting
The number of lakes in the NLA 2017 target population is not known and must be estimated
based on the lake evaluation conducted during the implementation of the survey design. The
survey design selects lakes for evaluation from the sample frame which is a subset of lake
objects in NHDPIusv2 and 1-5 ha lakes in NHD HiRes. The NHD information may be termed the
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source of the sample frame. Note that the subset is selected such that all lake objects in the
sample frame is expected to include all lake objects that are in the target population and may
include lake objects that the lake evaluation determines are not in the target population. An
assumption is that the sample frame does include all lakes in the target population. The
number of lake objects in the NHD source for the sample frame is 586,678 and the subset that
are in the sample frame is 465,901. The estimated extent (number of lakes) in the target
population is 224,916 and the estimated extent in the sampled population is 109,701. The lake
evaluation categorizes the lake objects in the sample as non-target, target-not-sampled, target-
sampled and unknown. The target-not-sampled and target-sampled categories are used to
estimate the extent of the target population. Since not all lakes that are target lakes can be
sampled, the target-sampled lakes are used to estimate the extent of the sampled population.
The sampled population conceptually is all the target lakes that could have been sampled if
they were selected. The difference between the target population and sampled population is
due to "non-response" for target lakes that could not be sampled. Unknown lakes are those
with insufficient information to categorize the lake as target or non-target.
The initial survey design results in a survey weight for each lake that is based on the assumption
that only lakes selected to be sampled are evaluated and sampled. Since some lakes selected to
be sampled turn out to be non-target lakes or target lakes that cannot be sampled, additional
lakes must be evaluated to achieve the sample size required for each state. The initial weights
are adjusted for the survey design as implemented, i.e., the additional lakes evaluated. This
initial lake weight adjustment results in weights that may be used to estimate the extent of the
lake population and the characteristics of the sampled population. The sampled population is
conceptually all the lakes that are target lakes that could have been sampled if they were
selected. In 2007 and 2012, these weights for the design as implemented were used for
population estimates. The sampled population estimates lead to inappropriate assumptions
about the survey results (e.g., the assumption that target lakes that could not be sampled are
missing completely at random). For 2017, EPA determined it was more appropriate to do a
second weight adjustment so that the weights reflect the complete target population. This
weight adjustment accounts for the "non-response", i.e., target lakes that could not be
sampled. The weight adjustment assumes that target lakes that could not be sampled are
missing at random within the weight adjustment categories based on the combination of state
and lake area categories. See Appendix B for a summary of the NLA survey design
characteristics and estimated extent for all three surveys.
Figure 2.1 shows the known and estimated number of lakes a) in the source of the sample
frame (NHDPIus, NHD Plus HR for 1-5 ha lakes), b) the sample frame and c) the target
population. Part a shows that of the 586,678 lake objects in NHD, only 465,901 comprise the
sample frame. Part b shows the estimated number of lakes identified as target (224,916), non-
target (237,695), and unknown (3,290). Part c shows that of the estimated target population
and the number of sampled lakes (1,005). Note that to estimate the target population requires
assumptions to be made about the target lakes in the sample that could not be sampled. It is
assumed that within a state, that lakes in the same aggregated ecoregion and lake area
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category that could not be sampled would have characteristics similar to those lakes that could
be sampled.
Step 1: EPA found 586,678 lake objects in the
National Hydrography Dataset (NHD) and
identified those lakes that met eligibility
criteria for inclusion in the sample frame. EPA
excluded lakes that are tidally influenced and
those used for aquaculture, disposal tailing,
sewage treatment, evaporation, or other
unspecified disposal use.
Step 2: Of the 465,901 lake objects in the
sample frame, EPA excluded 237,695 lakes
that were non-target by the sampling crews
and 3,290 lakes that had an unknown
status. EPA used the following sampling
criteria to determine eligibility:
Surface area > 1 hectare
Depth > 1 meter
Open water > 0.1 hectare
Step 3: EPA teams collected data from a random
sample of the remaining 224,916 lakes in the target
population. Percentages and confidence intervals
reported for a given indicator are relative to the
target population.
Example: If EPA estimates that between 10% and 20% of
lakes are in the "most disturbed" condition for an
indicator nationally, EPA is confident that between
22,492 and 44,983 lakes nationwide are in this condition.
465/901 lakes in the sample frame
120,777 lakes excluded from the
sample frame
3,290 lakes had an unknown status
224,916 met the sampling criteria (target population)
237,695 lakes did not meet sampling criteria
^^^1,005 lakes were randomly sampled
223,911 unsampled lakes
Step 1
Step 2
Step 3
Figure 2.1. NLA 2017 sample frame, target population, arid sampled lakes.
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2.5 Literature cited
Diaz-Ramos, S., D. L. Stevens Jr, and A. R. Olsen. 1996. EMAP Statistical Methods Manual. US
Environmental Protection Agency, Office of Research and Development, NHEERL-
Western Ecology Division, Corvallis, Oregon.
Kincaid, T. M. and A. R. Olsen. 2013. spsurvey: Spatial Survey Design and Analysis. R package
version 2.6.
Olsen, A. R., T. M. Kincaid and Q. Payton (2012). Spatially balanced survey designs for natural
resources. Design and Analysis of Long-Term Ecological Monitoring Studies. R. A. Gitzen,
J. J. Millspaugh, A. B. Cooper and D. S. Licht. Cambridge, UK, Cambridge University Press:
126-150.
Omernik, J. M. 1987. Ecoregions of the Conterminous United States. Annals of the Association
of American Geographers. 77:118-125.
R Core Team. 2013. R: A language and environment for statistical computing. R Foundation for
Statistical Computing, Vienna, Austria. http://www.R-project.org.
Stevens, D. L., Jr., and A. R. Olsen. 2003. Variance estimation for spatially balanced samples of
environmental resources. Environmetrics. 14:593-610.
Stevens, D. L., Jr., and A. R. Olsen. 2004. Spatially-balanced sampling of natural resources.
Journal of American Statistical Association. 99:262-278.
USEPA. 2011. Level III Ecoregions of the Continental United States (revision of Omernik, 1987).
US Environmental Protection Agency, National Health and Environmental Effects
Laboratory Western Ecology Division. Corvallis, Oregon.
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Chapter 3: Defining Reference Sites and Condition
3.1 Background information
NLA analysts used two types of benchmarks for determining condition estimates (good, fair,
poor; above/below benchmark, etc) in the NLA public report. For trophic status, recreational
indicator microcystin, dissolved oxygen, and atrazine, analysts used fixed, nationally consistent
benchmarks that are discussed in Chapter 6 of this document. The second approach was to
establish regionally consistent reference-based benchmarks.
Reference sites are those locations that display the best available (or least-disturbed) chemical,
physical, and biological habitat condition given the current state of the landscape. To identify
these sites, data from proposed sites were compared to a definition of what is least disturbed
by human activities. To reflect the natural variability of the U.S., the definition of what is least
disturbed varies by ecological region (ecoregion). The approach used in the NLA for developing
benchmarks using reference conditions is consistent with current science, EPA guidance, state
practice, and established protocols for ecological assessment (Bailey et al., 2004; Barbour et.al.,
1999; Carter and Resh, 2013; Hughes, 1995; Reynoldson et.al., 1997; Stoddard et.al., 2006; and
USEPA, 2011).
EPA's approach for establishing reference conditions in the NLA is a well-documented,
systematic process that screens sites using chemical and physical data to identify the least
disturbed sites within each ecological region. The application of percentiles for selecting
thresholds is also consistent with established guidance and practice within the scientific
community and state programs (Arizona DEQ, 2012; Vermont DEC, 2016; USEPA Case Studies).
The specific approaches used in the NLA have been used in various water quality surveys since
the early 1990s and in the scientific literature since the mid-1990s (US EPA, 1998; Barbour et
al., 1999; Gerritsen, 1995; Stoddard et al., 2006; and Herlihy et al., 2008). The reference-based
approach is used by many organizations for defining thresholds for assessing water quality.
Related to nutrients, EPA's guidance for development of nutrient criteria includes identification
of reference reaches considered to be the least impacted systems of the ecological region and
recommends the 75th percentile of the nutrient reference condition distribution for selecting a
criterion (USEPA 2000). Detailed information on the regionally consistent approach is presented
below. A summary of all benchmarks used to generate the condition estimates in the public
report can be found in Appendix C.
In refining benchmarks for NLA 2017, some 2012 benchmark values were revised; therefore,
direct comparisons should not be made between 2017 results and those reported in 2007 and
2012 as this will produce erroneous results. For purposes of identifying change in this
document and the public report, 2007 and 2012 results were recalculated based on new 2017
benchmarks.
To assess ecological condition, it is standard scientific practice to compare measurements to
reference condition. The NLA approach for identifying reference sites is more inclusive than
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some approaches that restrict reference sites to only those with no or minimal human
modification; or historical, pre-industrial or pre-Columbian conditions. Because of this,
reference sites for this analysis are more accurately described as "least disturbed sites." Least
disturbed sites contain the best available chemical, physical, and biological habitat conditions
given the current state of the landscape - or "the best of what's left" (Stoddard et al. 2006).
Benchmarks were based on the distribution or range of values found for each indicator at the
reference sites (or sites with the best available conditions given today's state of the landscape)
in each of nine major ecoregions. A total of four sets of reference sites were developed for use
in establishing reference condition for the NLA results: one for the benthic macroinvertebrate
indicator, one for the zooplankton indicator, one for the nutrient indicators, and one for the
physical habitat indicators. This section describes the selection of the biological reference sites,
which also form the basis for all the nutrient and habitat reference sites.
3.2 Pre-sampling screening (hand-picked sites only)
In addition to the probability set of lakes, a smaller set of sites were hand selected a priori for
sampling. We were trying to ensure that we captured samples from additional least disturbed
lakes. Potential hand-picked sites were identified as high-quality sites by EPA, states, tribes, and
federal partners. When data were available, these potential sites were compared to water
quality screens. When data were not available, sites underwent a high-level visual screen. The
screen was used to minimize human disturbance around potential lakes (Herlihy et al., 2013).
We identified 91 hand-picked lakes for sampling following this coarse screening process. The
hand-picked sites were sampled during the 2017 index period using NLA sampling protocols,
samples were processed and analyzed with the same analytical methods as the probability site
samples, and then both the hand-picked sites and the probability sites were subjected to the
post-sample screening process (Section 3.3). Regardless of whether sites were probability-
based or hand-selected, only those that met the final screening criteria for the appropriate
indicator (i.e., benthic macroinvertebrates, zooplankton, nutrients, and physical habitat) were
used in developing reference conditions. Reference site classification and screening was done
using the nine aggregate NARS ecoregions (Figure 3-1).
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Ecoregions used In National Aquatic Resource Surveys
yj1 rcitftn" ii'i. nm^i I ฆฆ ii i-fl Xhc
^ Snlwr nm
Figure 3.1. Nine aggregate ecoregions used for reference site classification.
3.3 Post-sampling screening for biological reference condition
To maximize the number of reference sites available for data analysis, hand-selected and
probability-based sampled in either NLA 2007, 2012 or 2017 were considered potential
reference lakes. Analysts used the chemical and physical data collected at each site to
determine whether any given site was in least disturbed condition for its aggregate ecoregion
following the approach described by Herlihy et al. (2008). The nine aggregate NARS ecoregions
were used for the ecoregion classification although in some cases these ecoregions were
further combined or lake types (natural vs. human-made) within an ecoregion were treated
differently (Figure 3-1). In the NLA, screening values were established for twelve chemical and
physical parameters to screen for biological reference sites (Table 3-1). If measurements at a
site exceeded the screening value for any one stressor, it was dropped from reference
consideration. Given that expectations of least disturbed condition vary across regions, the
criteria values for exclusion varied by ecoregion as well.
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Details on the calculation and naming of the shoreline habitat disturbance metrics is given in
the physical habitat chapter (Section 5.3). Scoring of the disturbances on the visual assessment
form for agricultural, residential, and industrial disturbance were simply done by summing the
number of checked off disturbances on the form weighting for the noted level of disturbance.
Low disturbance was weighted as 1 point, medium disturbances were weighted as 3 points, and
high disturbances were weighted as 5 points. Fire was not summed in with the industrial
disturbances as it could be an entirely natural disturbance.
All selected lake reference sites were also screened for excessive lake drawdown that was likely
anthropogenic. Evidence of both horizontal and vertical lake level fluctuations were recorded
by field crews. The square root of lake surface area was used as a surrogate for lake diameter
and was used to scale horizontal exposure of littoral lake bottom. Similarly, lake maximum
depth was used to scale vertical lake fluctuations. In addition, the drawdown criterion was
relaxed for lakes with elevated levels of lakeshore disturbance, as indexed by HiiALL_syn >
0.75. A step by step key to defining NLA lakes impacted by drawdown is provided in Ta
Table 3-1. Least disturbed reference screening filter thresholds for NLA 2017, If a lake exceeded any one of the
thresholds it was not considered as a least disturbed reference site for that ecoregion. Three filters were applied
universally across all ecoregions, 1) ANC < 25 ueq/L and DOC < 5 mg/L, 2) HifPany_Circa_syn& > 0,9, and 3) no
excessive lake drawdown (see Table 3-3),
Aggregate
TP
TN
CI
S04
Turbidity
Hii-
Hii-
Assessment5
Ecoregion
(ug/L)
(ug/L)
(ueq/L)
(ueq/L)
(NTU)
NonAg&
Ag&
(Ag/Res/Ind)
WMT
>30ฎ
>400
>100*
>200
>3
>0.6
>0
> 5/5/5
XER
>100
>1000
>500
>1000
>5
>1.5
>0.2
> 5/5/5
NPL
>150
>2000
>1000
>5
>1.5
>0.5
> 10/6/6
SPL
>150*
>2000*
>1000
>5
>1.5
>0.5
> 10/6/6
TPL
>120
>2000
>1000
>5000
>5.5
>1.7
>0.15
> 9/9/9
UMW
>40
>1200
>200
>200
>5
>0.6
>0
> 5/5/5
CPL
>50
>1200
>1000
>400
>5
>1.0
>0
> 6/10/6
SAP
>35
>800
>125
>300
>5
>0.9
>0
> 6/6/6
NAP
>30
>600
>100*
>300
>5
>0.6
>0
> 6/6/6
metric not used for screening
& HiiNonAg_syn, HiiAg_syn, and HifPany_Circa_syn are lakeshore physical habitat disturbance indices
(see Section 5.3.4.6).
$ Assessment filters are based on indices of agricultural, residential, and industrial disturbance calculated from
observations on the visual assessment form.
* No nutrient (TP, TN) or Turbidity filters applied in Sand Hills in SPL (Omernik Level III Ecoregion 44)
# No Chloride filter applied in Coastal Ecoregions in NAP (ecoregions 59,82), XER (ecoregion 6), and WMT
(ecoregions 1,2,8)
@ No TP filter used in volcanic ecoregions in WMT (ecoregions 4,5,9,77)
In addition to selecting least disturbed reference sites, analysts also determined most disturbed
sites for each ecoregion. These sites were used primarily in developing biotic MMIs that would
be used in the biological assessment of the nation's lakes and in testing the strength of
association of other indicators to anthropogenic stress. Similar to the reference lake selection
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process, thresholds were used to determine which lakes were to be considered most disturbed
in each ecoregion (Table 3-2). If any site exceeded the most disturbed threshold for any one of
these screening criteria, then the site was classified as most disturbed.
Note that the NLA did not use data on land-use in the watersheds for the final reference site
screeningsites in agricultural areas (for example) may well be considered least disturbed,
provided that their chemical and physical conditions are among the least disturbed for the
region. Additionally, the NLA did not use data from the biological assemblages themselves to
define biological reference sites because the reference sites are being used to assess biological
condition and to use biological data to then define reference would constitute circular
reasoning.
Note that additional screening and refinement for macroinvertebrates, zooplankton, physical
habitat, and nutrient reference sites are described subsequently in their respective chapters.
Table 3-2, Most disturbed site screening thresholds for NLA 2017, If a lake exceeded any one of the thresholds it
was considered a most disturbed site for that ecoregion. One screen was applied universally across all ecoregions,
AIMC < 0 ueq/L and DOC < 5 mg/L.
Aggregate
TP
TN
CI
S04
Turbidity
Hii-
Hii-
Assessment5
Ecoregion
(ug/L)
(ug/L)
(ueq/L)
(ueq/L)
(NTU)
NonAg&
Ag&
(Ag/Res/Ind)
WMT
>150ฎ
>1500
>1500*
>1500
>10
>2.5
>0.9
> 15/15/15
XER
>400
>4000
>25
>3.5
>1.0
> 15/15/15
NPL
>400
>4000
>50
>3.5
>1.2
> 15/15/15
SPL
>400*
>4000*
>50
>3.5
>1.2
> 15/15/15
TPL
>500
>5000
>5000
>20,000
>50
>4.0
>1.2
> 15/18/15
UMW
>200
>2500
>2500
>2500
>20
>3.5
>0.9
> 15/15/15
CPL
>200
>3000
>5000
>2500
>30
>3.5
>1.0
> 15/15/15
SAP
>150
>2500
>1500
>1500
>20
>3.5
>0.9
> 15/15/15
NAP
>150
>2500
>1500*
>1500
>20
>3.5
>0.9
> 15/15/15
metric not used for screening
& HiiNonAg_syn and HiiAg_syn are lakeshore physical habitat disturbance indices (see Section 5.3.4.6)
$ Assessment filters are based on indices of agricultural, residential, and industrial disturbance calculated from
observations on the visual assessment form.
* No nutrient (TP, TN) or Turbidity filters applied in Sand Hills in SPL (Omernik Level III Ecoregion 44)
# No Chloride filter applied in Coastal Ecoregions in NAP (ecoregions 59,82), XER (ecoregion 6), and WMT
(ecoregions 1,2,8)
@ No TP filter used in volcanic ecoregions in WMT (ecoregions 4,5,9,77)
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Table 3-3, Dichotomous key for defining NLA lakes likely impacted by anthropogenic drawdown.
Based on field observations of horizontal lake level fluctuations (AH), vertical lake level
fluctuations (AV), and human lakeshore disturbance (physical habitat summary metric
HiiAII_syn).
1. AH < 10 m AND AV < 2 m
Yes - LAKE OK
No - go to 2
2. AH > 10 m and AV > 2 m
Yes - Lake Drawdown, Not Reference
No - go to 3
3. AV > 2 m and AV/Maximum Lake Depth > 10%
Yes - Lake Drawdown, Not Reference
No - go to 4
4. AH < 10 m
Yes - LAKE OK
No - go to 5
5. AH/sqrt(Lakearea) > 5%
Yes - Lake Drawdown, Not Reference
No - go to 6
6. Lake Disturbed, HiiAII_syn > 0.75
Yes - Lake Drawdown, Not Reference
No - LAKE OK
3.41 ted
Arizona Department of Environmental Quality. 2012. Implementation Procedures for the
Narrative Biocriteria Standard, Final draft July 2012,
http://www.azdeq.gov/environ/water/standards/download/draft_bio.pdf
Bailey, R.C., R.H. Norris, and T.B. Reynoldson. 2004. Bioassessment of freshwater ecosystems:
using the reference condition approach. Kluwer Academic Publishers, New York.
Barbour, M.T., J. Gerritsen, B.D. Snyder, and J.B. Stribling. 1999. Rapid bioassessment protocols
for use in streams and wadeable rivers: periphyton, benthic macroinvertebrates and
fish, second edition. EPA 841-B-99-002. United States Environmental Protection Agency;
Office of Water. Washington, D.C.
Carter, J.L., and Resh, V.H. 2013, Analytical approaches used in stream benthic
macroinvertebrate biomonitoring programs of State agencies in the United States: U.S.
Geological Survey Open-File Report 2013-1129, 50 p.,
http://pubs.usgs.gov/of/2013/1129/
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Gerritsen, J. 1995. Additive biological indices for resource management. J. North Am. Benthol.
Soc. 14(3):451-457.
Herlihy, A. T., S. G. Paulsen, J. Van Sickle, J. L. Stoddard, C. P. Hawkins, and L. L. Yuan. 2008.
Striving for consistency in a national assessment: the challenges of applying a reference
condition approach at a continental scale. Journal of the North American Benthological
Society 27:860-877.
Herlihy, A. T., J. B. Sobota, T. C. McDonnell, T. J. Sullivan, S. Lehmann, and E. Tarquinio. 2013. An
a priori process for selecting candidate reference lakes for a national survey.
Freshwater Science 32:385-396. doi: 10.1899/11-081.1.
Hughes, R.M. 1995. Defining acceptable biological status by comparing with reference
conditions. Chapter 4 in Biological assessment and criteria: tools for water resource
planning and decision making, W.S. Davis and T.P. Simon, eds. (pp. 31 - 47). CRC Press,
Boca Raton.
Reynoldson, TB., R.H. Norris, V.H. Resh, K.E. Day, and D.M. Rosenberg. 1997. The reference
condition: a comparison of multimetric and multivariate approaches to assess water-
quality impairment using benthic macroinvertebrates. Journal of the North American
Benthological Society 16:833-852.
Stoddard, J. L., D. P. Larsen, C. P. Hawkins, R. K. Johnson, and R. H. Norris. 2006. Setting
expectations for the ecological condition of running waters: the concept of reference
condition. Ecological Applications 16:1267-1276.
USEPA, Case Studies- Setting Ecologically-Based Water Quality Goals Ohio's Tiered Aquatic Life
Use Designations Turn 20 Years Old (website). United States Environmental Protection
Agency, Office of Water, Office of Science and Technology.
http://water.epa.gov/scitech/swguidance/standards/criteria/aqlife/biocriteria/aquaticlif
eohio.cfm
USEPA. 1998. Lake and Reservoir Bioassessment and Biocriteria. Technical Guidance Document.
USEPA, Office of Wetlands, Oceans and Watershed and Office of Science and
Technology. EPA-841-B98-007. https://www.epa.gov/sites/default/files/2019-
03/documents/la ke-reservoir-tech-guidance-doc-1998.pdf
USEPA 2000. Nutreint Criteria Technical Guidance Manual. Lakes and Reservoirs. First Edition.
EPA-822-B00-001. US EPA, Office of Water, Office of Science and Technology.
https://www.epa.gov/nutrient-policy-data/nutrient-criteria-development-document-
lakes-and-reservoirs
USEPA. 2011. A Primer on Using Biological Assessments to Support Water Quality Management.
USEPA, Office of Science and Tehcnology. EPA 810-R-11-01.
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https://www.epa.gov/sites/default/files/2018-10/documents/primer-using-biological-
assessments.pdf
Vermont Department of Environmental Conservation. 2016. Nutrient Criteria for Vermont's
Inland Lakes and Wadable Streams. Technical Support Document. Vermont Department
of Environmental Conservation, Watershed Management Division.
https://dec.vermont.gov/sites/dec/files/wsm/Laws-Regulations-Rules/2016_12_22-
Nutrient_criteria_technical_support_document.pdf
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Chapter 4: Benthic Macroinvertebrates
4.1 Background information
The taxonomic composition and relative abundance of different taxa that make up the littoral
macroinvertebrate assemblage present in a lake can be used to assess how human activities
affect ecological condition. Two principal types of ecological assessment tools to assess
condition based on macroinvertebrate assemblages are currently prevalent: multimetric indices
and predictive models of taxa richness. The purpose of these indicators is to present the
complex community taxonomic data represented within an assemblage in a way that is
understandable and informative to resource managers and the public. For NLA 2012, we
developed a multimetric index of macroinvertebrate condition using 2007 and 2012 NLA data
as described in Section 4.3. The same MMI was used for the 2017 macroinvertebrate
assessment.
Multimetric indicators have been used in the U.S. to assess condition based on fish and
macroinvertebrate assemblage data (e.g., Karr and Chu, 2000; Barbour et al., 1999; Barbour et
al., 1995). The multimetric approach involves summarizing various assemblage attributes (e.g.,
composition, tolerance to disturbance, trophic and habitat preferences) as individual "metrics"
or measures of the biological community. Candidate metrics are then evaluated for various
aspects of performance and a subset of the best performing metrics are then combined into an
index, referred to as a multimetric index or MMI.
4.2 Data preparation
4.2.1 Standardizing counts
The number of individuals counted in a sample was standardized to a constant number to
provide an adequate number of individuals that was the same for the most samples and that
could be used for multimetric index development. A subsampling technique involving random
sampling without replacement was used to extract, from the dataset, a true "fixed count" of
300 individuals from the total number of individuals enumerated for a sample (target lab count
was 500 individuals). Samples that did not contain at least 300 individuals were used in the
assessment because low counts can indicate a response to one or more stressors.
4.2.2 Autecological characteristics
Autecological characteristics refer to specific ecological requirements or preferences of a taxon
for habitat preference, feeding behavior, and tolerance to human disturbance. These
characteristics are prerequisites for identifying and calculating many metrics. A number of
state/regional organizations and research centers have developed autecological characteristics
for benthic macroinvertebrates in their region. For the NLA, a consistent "national" list of
characteristics that consolidated and reconciled any discrepancies among the regional lists was
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needed before certain biological metrics could be developed and calibrated and an MMI could
be constructed. The same autecological information used in WSA and NRSA was used in NLA.
Members of the data analysis group pulled together autecological information from five
existing sources: the EPA Rapid Bioassessment Protocols document, the National Ambient
Water Quality Assessment (NAWQA) national and northwest lists, the Utah State University list,
and the EMAP Mid-Atlantic Highlands (MAHA) and Mid-Atlantic Integrated Assessment (MAIA)
list. These five were chosen because they were thought to be the most independent of each
other and the most inclusive. A single national-level list was developed based on the following
decision rules for tolerance values, functional feeding group and habitat preferences, and
taxonomic resolution.
4.2.3 Tolerance values
Tolerance value assignments followed the convention for macroinvertebrates, ranging between
0 (least tolerant or most sensitive) and 10 (most tolerant). For each taxon, tolerance values
from all five sources were reviewed and a final assignment made according to the following
rules:
1. If values from different lists were all <3 (sensitive), final value = mean;
2. If values from different lists were all >3 and <7 (facultative), final value = mean;
3. If values from different lists were all >7 (tolerant), final value = mean;
4. If values from different lists spanned sensitive, facultative, and tolerant categories,
best professional judgment was used, along with alternative sources of information
(if available) to assign a final tolerance value; and
5. Tolerance values of 0 to <3 were considered "sensitive" or "intolerant." Tolerance
values >7 to 10 were considered "tolerant," and values in between were considered
"facultative."
4.2.4 Functional feeding group and habitat preferences
In many cases, there was agreement among the five data sources. When discrepancies in
functional feeding group (FFG) or habitat preference ("habit") assignments among the five
primary data sources were identified, a final assignment was made based on the most
prevalent assignment. In cases where there was no prevalent assignment, the workgroup
examined why disagreements existed, flagged the taxon, and used best professional judgment
to make the final assignment.
4.2.5 Taxonomic resolution
Taxonomic resolution is an important factor in the development of multimetric indices.
Maintaining consistent taxonomic resolution for specific taxa across sites helps ensure that
differences between sites are due to environmental factors and not an artifact of taxa
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identifications. For most taxa identified, the taxonomic resolution was to the generic level,
however the following groups had higher-level hierarchical taxonomic resolution: oligochaetes,
mites, polychaetes were rolled up to family, ceratopogonids were rolled up to subfamily.
4.3 Multimet , . development
4.3.1 Data set
The NLA macroinvertebrate 300 fixed count data were used to calculate the community metrics
used in the MMI. A best ecoregional MMI was developed by scoring and summing the six
metrics that performed best in each ecoregion. The NLA macroinvertebrate MMI was
developed using the combined the NLA 2007 and 2012 benthic metric files which were both
calculated with common autecology and taxonomic resolution. All reference sites were defined
using the NLA definitions described in Section 3 based on nine aggregate ecoregion criteria.
Reference sites that had less than 250 individuals were not used as reference for MMI
development. Altogether, there were 2330 site visits (samples) in the data used to develop the
MMI; 1132 from 2007 and 1198 from 2012. There were 1789 unique sites. Some sites were
sampled twice in their respective years and some sites were sampled in both 2007 and 2012.
4.3.2 Low macroinvertebrate numbers
Many samples had a very low number of individuals. Examination of these low number sites did
not suggest that this was primarily due to impairment. We think that it is related to field
collection and lake bottom substrate composition. Samples with low bug numbers will have
poor MMI scores because of the strong relationship between sample count and taxa richness.
We decided that samples with less than 100 individuals were not sufficiently sampled and we
would not assess them. They were removed from the process of MMI development and MMI
scores for them will be set to missing values. These are identified as "not assessed" for
macroinvertebrates in the NLA. In the NLA 2017 data, 60 of the 1191 samples had < 100
individuals.
4.3.3 Ecoregion classification
For the NLA macroinvertebrate MMI development, the nine national aggregate ecoregions
(Figure 3-1) were consolidated into five aggregate biological ecoregions by combining some
ecoregions together. Specifically, that consisted of making an Eastern Highlands (EHIGH) region
by combining the SAP and NAP, a PLAINS ecoregion by combining the TPL, SPL, and NPL, and a
Western ecoregion (WMTNS) by combing the WMT and XER regions. The CPL and UMW remain
their own ecoregions. MMIs were developed independently for each of these 5 biological
ecoregions.
4.3.4 Metric screening
All 126 calculated benthic metrics were screened for both signal:noise (S:N) and discrimination
of least disturbed reference sites from most disturbed sites (F-test). S:N ratios were calculated
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for each metric nationally and within each biological ecoregion using the visit 1 versus visit 2
variance within year as the noise and among site variance as the signal. For calculating F-tests,
and all subsequent MMI development, we only used one visit per site (index visit). The first
sample visit of the year with valid data was used. For sites with valid samples in both years, the
2012 first visit data were used (samples with less than 100 bugs were not considered valid
data). F-tests were run on just the least disturbed reference (R) versus the most disturbed (T)
sites.
Metrics had to pass both F and S:N screens in order to remain in consideration for inclusion in
the final MMI. Metrics had to have S:N > 1.5 either nationally or within their ecoregion in order
to pass. For the F-test, only metrics that had F-values > 4.0 passed. From this screening, 35
metrics from CPL, 42 from EHIGH, 44 from UMW, 29 from PLAINS, and 50 from WMTNS passed
and were considered for the all subsets MMI selection.
4.3.5 All Subsets MMI selection
Passing metrics were assigned to one of the six basic metric classes used to assemble the MMI
as done in the NARS stream MMI (Stoddard et al., 2008). An all subsets procedure was used to
assemble all possible combinations of MMIs using the six metric class framework. There were
8,960 combinations of metrics in the CPL, 12,096 in the EHIGH, 36,855 in the UMW, 3360 in the
PLAINS, and 65,280 in the WMTNs. For each possible MMI combination, the MMI S:N, F-test,
metric correlations, and IQR box delta (separation between least and most disturbed) were
calculated. For correlations, both the mean and maximum correlation among the six metrics
were calculated. IQR box delta or separation is the difference between the 25th percentile of
reference sites and the 75th percentile of most disturbed sites. Thus, positive box deltas
indicate separation between the least and most disturbed boxes, negative values indicate
overlap in the IQRs (boxes of box and whisker plot) of the least and most disturbed sites.
To pick the best MMI from the all subsets results, all MMI candidates were first screened for
S:N and maximum metric correlation. Only MMIs that had max correlation < 0.7 and S:N > 3
were considered. MMIs that passed this screen were evaluated for both box delta and F-value
with the goal of picking the MMI that had the best combination of those two values. These two
measures are highly correlated. To do this objectively, we ran a PCA on box delta and F-value
and selected the MMI that had the highest PCA factor 1 score. The intent was to optimize and
pick the model with the best combination of F-value and separation. The six metrics that make
up the final (best) MMI are shown in Table 4-1.
Each of the six selected metrics were scored on a 0-10 scale by interpolating metrics between a
floor and ceiling value. The six metric 0-10 point scaled scores were then summed and
normalized to a 0-100 scale by multiplying by 10/6 to calculate the final MMI. Details of
this process are described in Stoddard et al. (2008) for the NARS stream MMI but the NLA
process is the same. The final metrics used in each ecoregion, metric direction, and floor and
ceiling values are summarized in Table 4-1. Scoring equations are different depending on if the
metric responds positively (high values good) or negatively (high values bad) with disturbance.
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For positive metrics, values above the ceiling get 10 points, and values below the floor get 0
points. For negative metrics, values above the ceiling get 0 points, and values below the floor
get 10 points. The interpolation equations for scoring the 0-10 points for metrics between the
floor and ceiling values are:
Positive Metrics: Metric Points = 10*((metric value-floor)/(ceiling-floor)); and
Negative Metrics: Metric Points = 10 * (1 - ((metric value-floor)/(ceiling-floor))).
For positive metrics, floor values are set at the 5th percentile of all samples in the ecoregion,
ceiling values are the 95th percentile of reference sites in the ecoregion. Negative metric
floor/ceilings are calculated the opposite way. Statistics for the final MMI in each ecoregion are
shown in Table 4-2. The overall S:N of the MMI based on visit 1 vs. 2 revisits nationally across
both years was 3.56. Box plots showing the R versus T discrimination of the final MMIs are
shown in Figure 4-1.
; 4-1, Fi ' . nlogical ecoregion benthic M :rics and their floor/ceiling values for
MMI scoring.
Ecoregion
Metric Class
Metric name*
Direction
Floor
Value
Ceiling
Value
Coastal Plains
Composition
NOINPTAX
Negative
21.88
55.17
Coastal Plains
Diversity
CHIRDOM3PIND
Negative
38.57
96.08
Coastal Plains
Feeding Group
PREDRICH
Positive
6.00
23.0
Coastal Plains
Habit
SPWLRICH
Positive
5.00
15.0
Coastal Plains
Richness
EPT_RICH
Positive
1.00
8.00
Coastal Plains
Tolerance
NTOLPIND
Positive
6.33
64.33
E. Highlands
Composition
NOINPTAX
Negative
13.79
48.72
E. Highlands
Diversity
CHIRDOM3PIND
Negative
39.87
85.94
E. Highlands
Feeding Group
COGARICH
Positive
8.00
27.0
E. Highlands
Habit
CLNGRICH
Positive
3.00
12.0
E. Highlands
Richness
EPOTRICH
Positive
2.00
14.0
E. Highlands
Tolerance
TL23RICH
Positive
1.00
9.00
Plains
Composition
DIPTPTAX
Negative
16.67
60.00
Plains
Diversity
HPRIME
Positive
0.65
3.17
Plains
Feeding Group
PREDRICH
Positive
2.00
19.0
Plains
Habit
CLMBPTAX
Positive
10.0
33.33
Plains
Richness
EPOTRICH
Positive
0
10.0
Plains
Tolerance
TL23PIND
Positive
0
19.67
Upper Midwest
Composition
NOINPIND
Negative
5.33
89.0
Upper Midwest
Diversity
CHIRDOM3PIND
Negative
36.51
87.91
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Ecoregion
Metric Class
Metric name*
Direction
Floor
Value
Ceiling
Value
Upper Midwest
Feeding Group
SHRDPIND
Negative
2.67
50.67
Upper Midwest
Habit
CLNGRICH
Positive
3.00
14.0
Upper Midwest
Richness
CRUSRICH
Negative
0
3.00
Upper Midwest
Tolerance
TL23PTAX
Positive
2.17
23.81
Western Mts.
Composition
ODONPIND
Negative
0
17.33
Western Mts.
Diversity
CHIRDOM5PIND
Positive
7.33
98.25
Western Mts.
Feeding Group
SCRPRICH
Negative
0
5.00
Western Mts.
Habit
CLNGRICH
Positive
1.00
8.00
Western Mts.
Richness
TRICRICH
Positive
0
4.00
Western Mts.
Tolerance
TL23PTAX
Positive
0
21.43
*Metric Names
NOINPTAX=% Non-Insect Taxa (Non-Insect Taxa Richness /Total Taxa Richness*100)
DIPTPTAX = % Diptera Taxa (Diptera Taxa Richness / Total Taxa Richness*100)
NOINPIND = % Non-Insect Individuals
ODONPIND = % Odonata Individuals
CHIRDOM3PIND = % Chironomid Individuals in Top 3 most abundant Chironomid Taxa
CHIRDOM5PIND = % Chironomid Individuals in Top 5 most abundant Chironomid Taxa
HPRIME = Shannon Diversity Index
PREDRICH = Predator Taxa Richness
COGARICH = Collector-Gatherer Taxa Richness
SHRDPIND = % Shredder Individuals
SCRPRICH = Scraper Taxa Richness
SPWLRICH = Sprawler Taxa Richness
CLNGRICH = Clinger Taxa Richness
CLMBPTAX = % Climber Taxa (Climber Taxa Richness/Total Taxa Richness *100)
EPT_RICH = Ephemeroptera + Plecoptera + Trichoptera Taxa Richness
EPOTRICH = Ephemeroptera + Plecoptera + Trichoptera + Odonata Taxa Richness
CRUSRICH = Crustacean Taxa Richness
TRICRICH = Trichoptera Taxa Richness
NTOLPIND = % Individuals with pollutant tolerance values < 6
TL23RICH = Taxa Richness of taxa with pollutant tolerance values >2.0 and < 4.0
TL23PIND = % Individuals with pollutant tolerance values > 2.0 and < 4.0
TL23PTAX = % Taxa with pollutant tolerance values > 2.0 and < 4.0
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Table 4-2. Benthic MMI statistics for the NLA 2007-2012 data used to develop the MMI.
Ecoregion
F-test
Box Delta
Max Corr.
Mean Corr.
S:N
Coastal Plains
54.7
12.7
0.45
0.17
3.45
E. Highlands
69.0
1.85
0.50
0.26
3.12
Plains
36.2
-2.26
0.68
0.41
3.35
Upper Midwest
64.5
10.4
0.57
0.24
3.00
Western Mts.
88.9
4.46
0.48
0.16
3.66
F-test=F-score for difference between least disturbed (reference) and most disturbed site means; Box
Delta=Separation difference between Reference Q1 and most disturbed Q3 in MMI units; Corr=Pearson correlation
among six MMI metrics; S:N = Ecoregional within year S:N ratio.
1
0 ^ i i i i i
CPL-R CPL-T EHIGH-R EHIGH-T PLAINS-R PLAINS-T UMW-R UMW-T WMTNS-R WMTNS-T
Figure 4.1. Box and whisker pi
Ecoregion - Least Disturbed /Most
Disturbed
:erence (R) and most
disturbed (T) sites by biological ecoregion in tne imla zuuz-zuiz data used to develop the MMI. Boxes show the
interquartile range and the whiskers show the 5th and 95th percentiles
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4.3.6 Setting MM! benchmarks
Previous large-scale assessments have converted MMI scores into classes of assemblage
condition by comparing those scores to the distribution of scores observed at least disturbed
reference sites. See Section 3.3 for information on selecting reference sites. If a site's MMI
score was less than the 5th percentile of the reference distribution, it was classified as in most
disturbed condition; scores between the 5th and 25th percentile were classified as moderately
disturbed and scores in the 25th percentile or higher were classified as least disturbed.
For calculating the benchmarks used in the NLA 2017 public report, we used all NLA reference
sites sampled from 2007-2017 to maximize sample sizes used to calculate percentiles. When a
site was sampled multiple times, only the first visit to the most recent year of sampling was
used to calculate percentiles so sites were not double-counted. Also, only reference sites with
at least 250 individuals were used. Before calculating benchmarks, a 1.5*IQR outlier analysis
was done on the reference site MMIs to remove outliers. No sites were dropped as outliers in
this process leaving 416 reference sites for calculating reference site percentiles to use as
benchmarks. The resulting adjusted MMI benchmark values for the condition classes in each
ecoregion are given in Table 4-3.
Table 4-3, Macroinvertebrate MMI benchmarks using 2007-2017 reference site data
Ecoregion
#of Ref Sites
Least Disturbed
25th Percentile Benchmark
Most Disturbed
5th Percentile Benchmark
Coastal Plains
29
>51.8
<40.4
East. Highlands
105
>44.5
<31.4
Plains
84
>39.5
<26.6
Upper Midwest
76
>51.4
<37.2
Western Mountains
122
>47.6
<32.6
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4.4 Literature cited
Barbour, M. T., J. Gerritsen, B. D. Snyder, and J. B. Stribling. 1999. Rapid bioassessment
protocols for use in streams and wadeable rivers. EPA 841/B-99/002. Office of Water,
US Environmental Protection Agency, Washington, DC.
Barbour, M. T., J. B. Stribling, and J. R. Karr. 1995. Multimetric approach for establishing
biocriteria and measuring biological condition. Pages 63-77 in W. S. Davis and T. P.
Simon (editors). Biological assessment and criteria: tools for water resource planning
and decision making. Lewis Publishers, Boca Raton, Florida.
Herlihy, A. T., S. G. Paulsen, J. Van Sickle, J. L. Stoddard, C. P. Hawkins, and L. L. Yuan. 2008.
Striving for consistency in a national assessment: the challenges of applying a reference
condition approach at a continental scale. Journal of the North American Benthological
Society 27:860-877.
Karr, J. R., and E. W. Chu. 2000. Sustaining living rivers. Hydrobiologia 422/423:1-14.
Stoddard, J. L., A. T. Herlihy, D. V. Peck, R. M. Hughes, T. R. Whittier, and E. Tarquinio. 2008. A
process for creating multi-metric indices for large scale aquatic surveys. Journal of the
North American Benthological Society 27:878-891.
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Chapter 5: Physical Habitat
5.1 Background information
Near-shore physical habitat structure in lakes has only recently been addressed by the U.S.
Environmental Protection Agency (EPA) in its National Aquatic Resource Surveys (NARS)
monitoring efforts (e.g., USEPA 2009, Kaufmann et al. 2014a,b,c). Like human activities, aquatic
and riparian biota are concentrated near lakeshores, making near-shore physical habitat
ecologically important, but exposed and vulnerable to anthropogenic perturbation (Schindler
and Scheuerell 2002, Strayer and Findlay 2010, Hampton et al. 2011). Littoral and riparian zones
are positioned at the land-water interface and tend to be more structurally complex and
biologically diverse than either pelagic areas or upland terrestrial environments (Polis et al.
1997, Strayer and Findlay 2010). This complexity promotes interchange of water, nutrients, and
biota between the aquatic and terrestrial compartments of lake ecosystems (Benson and
Magnuson 1992, Polis et al. 1997, Palmer et al. 2000, Zohary and Ostrovsky 2011). Structural
complexity and variety of cover elements in littoral areas provide diverse opportunities for
supporting assemblages of aquatic organisms (Strayer and Finlay 2010; Kovalenko et al 2012),
while intact riparian vegetation and wetlands surrounding lakes increase near-shore physical
habitat complexity (e.g., Christensen et al. 1996, Francis and Schindler 2006) and buffer lakes
from the influence of upland land use activities (Carpenter and Cottingham 1997, Strayer and
Findlay 2010). Human activities on or near lakeshores can directly or indirectly degrade littoral
and riparian habitat (Francis and Schindler 2006). Increased sedimentation, loss of native plant
growth, alteration of native plant communities, loss of physical habitat structure, and changes
in littoral cover and substrate are all commonly associated with lakeshore human activities
(Christensen et al. 1996, Engel and Pederson 1998, Whittier et al. 2002, Francis and Schindler
2006, Merrell et al. 2009). Such reductions in physical habitat structural complexity can
deleteriously affect fish (Wagner et al. 2006, Taillon and Fox 2004, Whittier et al. 1997, 2002,
Ha I liwel I 2007, Jennings et al. 1999, Wagner et al. 2006), aquatic macroinvertebrates (Brauns et
al. 2007), and birds (Kaufmann et al. 2014b).
The EPA developed standardized, rapid field methods to quantify physical habitat structure and
near-shore anthropogenic disturbances (Kaufmann and Whittier 1997) and piloted them in the
Northeastern U.S. (Larsen and Christie 1993, Whittier et al. 2002b, Kaufmann et al. 2014b).
These methods were modified (USEPA 2007a, Kaufmann et al. 2014a) and applied in 2007 for
the first U.S. national survey of lake physical habitat condition (US EPA 2009, Kaufmann et al.
2014c). The EPA's lake physical habitat methods were once again modified to explicitly assess
habitat structure in exposed drawdown zones (USEPA 2012) and applied in the NLA 2012 survey
as part of the EPA's second national survey of the ecological condition of lakes in the United
States (USEPA 2016). The NLA 2012 field method modifications were structured so that we
were able to duplicate all the lake habitat condition indices that were used in the previous
(2007) national assessment. We calculated habitat metrics and indices described by Kaufmann
et al. 2014a,c) to quantify the variety, structural complexity, and magnitude of areal cover from
physical habitat elements within the near shore zones of lakes in the NLA 2012 survey. For the
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NLA 2017 physical habitat condition we used the same expected condition models that we used
for the 2012 Assessment, with the exception of lake drawdown that is discussed in more detail
below.
Our objectives in this chapter are to describe how we calculated physical habitat indices based
on near-shore physical habitat data collected in the NLA survey, and how we derived physical
habitat condition benchmarks relative to least disturbed conditions. We only briefly describe
the NLA field methods and data reduction procedures, which are published elsewhere (USEPA
2012; Kaufmann 2014a, USEPA 2017). Finally, we evaluate the precision of NLA's key indices of
physical habitat condition and examine their association with anthropogenic disturbances.
5.2 Data preparation
We took the following eight steps to assess physical habitat condition in U.S. lakes based on the
NLA 2017 national probability sample of lakes and reservoirs. For the NLA 2017 physical habitat
condition we used exactly the same expected condition models that we used for the 2012
Assessment, which were derived using combined NLA 2007 and NLA 2012 data, including
reference sites defined based on NLA 2012 screening criteria. [But see notes on
accommodating missing horizontal and vertical lake drawdown measurements.]
1) Field crews made measurements and observations of near-shore physical habitat structure
and human activities on a national probability sample of lakes and reservoirs (described by
USEPA 2016, and Kaufmann et al. 2014a);
2) Classified survey lakes by aggregated ecoregion (ECOWSA9_2015), and by their relative
levels of anthropogenic disturbance within those ecoregions (RT_NLA12_2015).
3) Calculated a set of physical habitat metrics as described by Kaufmann et al. (2014a) for NLA
2007, but adapted calculations to adjust for the NLA 2012's field method change that
assessed riparian vegetation cover, littoral cover, and human disturbance in the drawdown
zone separate from those above the typical high water mark or inundated by water in the
littoral zone;
4) Calculated multimetric indices of lakeshore anthropogenic disturbance and nearshore
physical habitat cover and structure as described by Kaufmann et al. (2014c) for NLA 2007,
and assigned variants of these indices according to aggregated Ecoregions
(ECOWSA9_2015); also defined a new indicator of lake drawdown;
5) Estimated lake-specific expected ("E") values for physical habitat indices from region-
specific regression models of factors predicting physical habitat in the combined set of least
disturbed lakes from the NLA 2007 and 2012 surveys. Our modeling approach is very similar
to that employed by Kaufmann et al. (2014c) in the Western Mountain and Xeric ecoregions
for the NLA 2007 report;
6) Set criteria for low, medium and high lakeshore anthropogenic disturbance (good, fair,
poor) based on professional judgement; good, fair, and poor littoral and riparian physical
habitat condition based on deviation from the central tendency of observed/expected (O/E)
values within the group of least disturbed lakes; and small, medium, and large lake
drawdown based on percentiles of the indicator values themselves in least disturbed lakes.
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7) Examined the precision of NLA 2012 key physical habitat indicators.
8) Examined the association between NLA 2012 physical habitat indicators and anthropogenic
disturbances, comparing the regional distributions of habitat condition in least disturbed
reference lakes with those in most disturbed lakes.
5.3 Methods
5.3.1 NLA Sites used for Expected Condition modeling and precision estimates
The NLA field sampling effort targeted all lakes and reservoirs in the 48 conterminous U.S. with
surface areas >1 ha and depths greater than 1 m. Field data were collected between May and
October of each survey year. See Chapter 2 of this document for additional details on the study
area and site selection. To model expected condition for all three NLA surveys ('07, '12, '17), we
used physical habitat data collected in the 2007 and 2012 survey years. These data included
data from 2268 lakes and reservoirs, 1156 in 2007, and 1112 in 2012. Probability and hand-
selected lakes from both 2012 and 2007 were used to develop expected physical habitat
condition models and distributions of O/E values in least-disturbed lakes. Random subsets of 90
probability lakes from NLA 2007 and 88 from NLA 2012 were visited twice during their
respective summer sampling periods to estimate the precision of NLA indicators, including the
habitat measurements and indices (Kaufmann et al. 2014a).
5.3.2 Field sampling design and methods
Our lake physical habitat field methods (USEPA 2007a, USEPA 2012, USEPA 2017, Kaufmann et
al. 2014a) produced information concerning 7 dimensions of near-shore physical habitat: 1)
water depth and surface characteristics, 2) substrate size and type, 3) aquatic macrophyte
cover and structure, 4) littoral cover for biota, 5) riparian vegetation cover and structure, 6)
near-shore anthropogenic disturbances, and 7) bank characteristics that indicate lake level
fluctuations and terrestrial-aquatic interactions. At each lake, field crews characterized these 7
components of near-shore physical habitat at 10 equidistant stations along the shoreline. Each
station included a littoral plot (10m x 15m) abutting the shoreline, a riparian plot (15m x 15m)
extending landward from the typical high-water mark, and in a 15m wide drawdown zone plot
that extended a variable distance landward, depending on the amount of lake level drop
compared with typical high water levels (Figure 5-1). Littoral depth was measured 10 m off-
shore at each station. Metrics and indices were calculated for the variable-width drawdown
zone plots, the 15m x 15m riparian plots and the 10m x 15m littoral plots. To match the riparian
and near-shore human disturbance indices to those used in the previous (NLA 2007)
assessment, we used information from riparian and drawdown plots along with drawdown
horizontal extent information. These index values are equivalent to the 2007 index values that
were directly calculated from observation the near-shore zone extending from the lake water's
edge 15m outward. See Kaufmann et al. (2014a) for further description of field methods, our
approach for calculating whole-lake physical habitat metrics, and a detailed assessment of
habitat metric precision.
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5.3.3 Classifications
5.3.3.1 Ecoregions
We report findings nationally, and by 9 aggregated Omernik (1987) level III ecoregions (Paulsen
et al. 2008) including the Northern Appalachians (NAP), Southern Appalachians (SAP), Coastal
Plains (CPL), Upper Midwest (UMW), Temperate Plains (TPL), Northern Plains (NPL), Southern
Plains (SPL), Western Mountains (WMT), and Xeric West (XER) (Figure 3-1). We used ecoregions
as a first-level classification for defining and evaluating near-shore riparian and littoral
condition indicators (RVegQ, LitCvrQ, and LitRipCvrQ) and their variants (e.g., RVegQ_2,
LitCvrQ_b, LitRipQ_2d). Ecoregions are useful predictors of many characteristics of landform,
geology, climate, hydrology, and potential natural vegetation (Omernik 1987, Paulsen et al.
2008) that influence physical habitat in lakes (Kaufmann et al. 2014c). Kaufmann et al. (2014c)
used a multivariate classification of lake characteristics including lake chemistry and depth to
assign variants of LitCvrQ, suggesting that such classifications would capture aspects of in-lake
habitat cover complexity better than would ecoregions. We reexamined the 2007 data and
found no substantial difference in assignment of LitCvrQ variants according to Ecoregion
(WSAEC09) versus multivariate cluster analysis (CLUSB). For some aspects of habitat index
development, we grouped ecoregions into broader ecoregions. The grouping included the
Eastern Highlands (EHIGH = NAP + SAP), the Plains and Lowlands (PLNLOW = CPL + UMW + TPL
+ NPL + SPL), Central Plains (CENPL = TPL+ NPL+SPL), and the West (WMT + XER).
5.3.3.2 Anthropogenic disturbance and least disturbed reference site screening
We used region-specific screening based on water chemistry, near-shore human influences, and
evidence of anthropogenic lake drawdown in NLA survey lakes, 1109 from NLA 2012 and 1101
from NLA 2007, to classify all NLA lakes according to their level of anthropogenic disturbance
(low, medium, high), as described in Chapter 3. Lakes meeting low-disturbance screening
criteria served as least disturbed reference sites for best-available condition. Low-disturbance
stress (least disturbed) lakes within each Ecoregion were identified on the basis of chemical
variables (total phosphorus, total nitrogen, chloride, sulfate, acid neutralizing capacity,
dissolved organic carbon, and dissolved oxygen in the epilimnion) and direct observations of
anthropogenic disturbances along the lake margin (proportion of lakeshore with non-
agricultural influences, proportion of lakeshore with agricultural influences, and the relative
extent and intensity of human influences of all types together). For each aggregated ecoregion,
a threshold value representing least disturbed conditions was established as a "pass/fail"
criterion for each parameter (Table 3-1). Thresholds were values that would be very unlikely to
be exceeded in least disturbed lakes within each region and varied by lake type to account for
regional variations in water chemistry and littoral-riparian human activities (Herlihy et al. 2013).
A lake was considered least disturbed if it passed the screening test for all parameters, and we
identified 214 least disturbed lakes from NLA 2012 and 168 from NLA 2007. We used the 2012
survey data for the 44 lakes from NLA 2007 that were again sampled in NLA 2012, and still
passed the reference screening, so 124 NLA 2007 lakes remained in the reference set (Table
5-1). Lakes that were not classified as least disturbed were provisionally considered
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intermediate in disturbance. The intermediate disturbance lakes were then screened with a set
of high-disturbance thresholds applied to the same variables (Table 3-2) Lakes that exceeded
one or more of the high disturbance thresholds were considered most disturbed. To avoid
circularity in defining physical habitat alteration, we did not use any of the physical habitat
cover complexity indices or their subcomponent metrics in defining lake disturbance classes.
Our screening process identified 382 least disturbed, 1309 intermediate, and 519 most
disturbed lake visits. Of the 338 least disturbed lakes that did not overlap survey years, 190
were in the WMT, NAP, and UMW aggregated ecoregions (Table 5-1). Even with relaxed
disturbance screening criteria, it was more difficult to find least disturbed lakes in some other
ecoregions. Respectively, only 11, 20, and 23 least disturbed lakes were identified in the NPL,
XER, and TPL ecoregions. To increase the useable sample size for estimating expected lake
condition, we grouped least disturbed lakes from the NPL, SPL, TPL into the Central Plains
(CENPL), and the WMT and XER into the West (for some models). Because of insufficient
numbers of least disturbed lakes relative to the large amount of lake variability within
ecoregions, we needed all available reference lakes for modeling expected conditions, so were
unable to use totally independent subsets of lakes for developing and validating those models.
5.3.4 Calculation of lake physical habitat metrics
5.3.4.1 Names of habitat metrics
Our variable names are those from the publicly-available NLA 2007,2012 and 2017 datasets
released by the U.S. EPA (https://www.epa.gov/national-aquatic-resource-surveys/data-
national-aquatic-resource-surveys). The first several letters in the NLA variable names denote
the category and type of metric. The initial letters "hi..." identify human influence metrics. The
initial letters "hifp..." specify human influence frequency of presence metrics and "hii..." specify
indices of aggregated or summed human influences. Riparian vegetation mean presence
metrics begin with "rvfp ..."and mean riparian vegetation cover metrics begin with "rvfc...",
whereas "rvi..." denotes riparian vegetation cover sums (e.g., two types of woody cover). The
initial letters "fc..." and "am..." indicate, respectively, fish cover and aquatic macrophyte
metrics. These letters followed by "...fp...", "..fc...", or "..i.." indicate, respectively mean
frequency of presence among stations, mean areal cover, and indices created by summing
various metrics. Littoral bottom and exposed shoreline substrate metrics, respectively, are
identified by "bs..." and "ss...". The summary habitat indices described by Kaufmann et al.
(2014c), and used to define habitat condition in the NLA (RVegQ, LitCvrQ, and LitRipCvQ) all end
in the upper case Q, and the NLA summary human disturbance index is RDis_IX (Riparian
Disturbance Intensity and eXtent). Kaufmann et al. (2014a) describe in detail the definitions and
calculation of NLA physical habitat metrics and quantify their precision.
Many of the physical habitat metrics for NLA 2012 are additionally identified by the suffixes
_rip, _lit, and _DD (e.g., rviWoody_rip, rviWoody_DD, fciNaturalJit, fciNatural_DD),
designating that the habitat observations or measurements were from, respectively, the set of
riparian, littoral, or drawdown plots (Figure 5-1).
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5.3.4.2 Drawdown Zone Apportioning to match NLA 2007 Riparian and Human
Disturbance metrics:
NLA 2012 retained the measures of "bathtub ring" height and horizontal extent exactly as done
in NLA 2007 to quantify lake drawdown and seasonal lake level fluctuations. However, the near-
shore plot designs of the two surveys differ. In NLA 2007, the 15m x 15m riparian plots abutted
the shoreline. Consequently, exposed littoral bottom may comprise 0 to 100% of NLA 2007
plots, depending upon the extent of drawdown. Near-shore habitat was accurately depicted in
the NLA 2007 data, but because cover and disturbances were not separately assessed in the
drawdown zone, there was no accurate way to separately assess changes in habitat condition
attributable to drawdown (vs. riparian vegetation removal, for example). The NLA 2012 field
methods have separate measures of vegetation and human disturbances for the riparian and
drawdown zone plots, and separate fish cover estimates in littoral and drawdown zone plots.
These field plot changes improve the separation of lake level changes and drawdown from
other stressors in a diagnosis of likely causes of poor nearshore habitat condition in NLA 2012.
We used cover and human disturbance tally data from the riparian and drawdown plots to
calculate cover estimates or disturbance tallies simulating the set of ten 15m x 15m near-shore
plots abutting the shoreline, as had been used in the NLA 2007 field methods. We calculated
RCsyn, as a synthetic estimate of cover in the 15m band around the shoreline by summing the
areal covers in the drawdown and riparian plots, after weighting each by the proportion of the
15m band that was, respectively, within the drawdown zone or not within the drawdown zone:
RCsyn ~ (Rpdraw X RCdraw) + (Rprip X RCrip) (Eq 1)
where:
RCsyn = Calculated cover in 15 x 15 m shoreline PHab plot, synthesizing metric values equivalent
to those used in NLA 2007, which represent the riparian condition in the 15m near-
shore band adjacent to the wetted edge of the lake.
Rpdraw and Rprip are the proportions of the 15x15m shoreline PHab plot that are, respectively,
occupied by the drawdown zone and the riparian zone above the high water mark.
[NOTE for NLA-2017 ONLY: There were a large number of missing measurements of
horizontal and vertical drawdown in the 2017 survey. The field protocol directs field
crews to NOT establish a drawdown plot when horizontal drawdown is <=lm. For 2017
we assumed drawdown was
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Rpdraw = (Horizontal Distance to high water)/(15m) = {bfxHorizDist/15m), and Rpdraw= 1.0 if
bfxHorizDist> 15 m.
RpriP= (1 - Rpdraw) by definition because RpriP+ Rpdraw= 1.0
Redraw and Rcnp are, respectively, the areal cover of vegetation in the drawdown and riparian
zones; Rcnp could be single cover type (e.g., canopy layer, or barren ground), or could
be a sum of cover types (e.g., sum of woody cover in 3 layers).
Calculated Rcsynfor a hypothetical lake with a mean horizontal drawdown of 10m (est. by
bfxHorizDist), and 100% canopy cover above the high water mark, but 0% cover in the
drawdown zone is as follows:
Rpdraw = 10/15 = 0.67
Rpnp =(1.0-0.67) = 0.33
Drawdown Canopy cover: RCdraw = 0%
Riparian Canopy cover: RcriP = 100%
RCsyn = (0.67 x 0%) + (0.33 x 100%) = 33%
The loss or gain in near-shore riparian habitat cover resulting from lake drawdown or natural
lake level declines can be estimated by the difference in cover between the riparian cover
above the high water mark (RcriP) and that within 15 m of the lakeshore (Rcsyn)-
We conducted a volunteer Drawdown Pilot Survey in 2011 to determine whether modification
of the NLA 2007 field protocols could be made without jeopardizing our ability to track changes
or trends in riparian habitat overtime (Anne Rogers 2012 NALMS; Kaufmann et al. Jan 9, 2012
webinar presentation to NLA steering committee and states). NLA 2007 and NLA 2012 field
protocols were applied simultaneously at 210 stations on 21 lakes spread over a range of
drawdown conditions in the states of Texas, Wisconsin, Washington, Oregon, Wyoming, North
Dakota, and Colorado. Kaufmann et al. (2012 webinar) demonstrated that 2007 metric values
for lakeshore vegetation and human disturbances were calculated accurately from the new
(2012) protocol, preserving ability to track changes/trends. The regressions predicting the
measured values of key physical habitat metric values from the NLA 2007 protocol from values
calculated by Eq 1 were virtually 1:1 lines with intercepts very close to 0.0, slopes very close to
1.0, and R2 between 0.87 and 0.94. The drawdown pilot analysis also showed that there was
virtually no difference in whole-lake metric values obtained by applying Eq 1 at each station,
versus applying it once per lake based on values of drawdown extent and cover averaged over
the 10 riparian and drawdown plots on each lake. The drawdown pilot results also
demonstrated that adding separate determinations of habitat cover elements in the drawdown
zone was logistically feasible and resulted in very minor increases in field time.
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5.3.4.3 Drawdown Zone Apportioning to Estimate littoral habitat changes due to
drawdown:
We used a calculation similar to Eq 1 to simulate the amount of littoral cover that would be
present if, hypothetical^, the amount of lake drawdown was zero:
LCsim = (Lpdraw X l-Cdraw) + (LpiitX LClit) (Eq 2)
where:
LcSim = Calculated littoral cover simulating the amount of real or potential cover in a 10 x 15 m
littoral plot abutting the high-water mark, ie., simulating littoral cover that might be
present if there were no drawdown.
Lpdraw and Lpnt are the estimated proportions of a hypothetical 10m x 15m littoral PHab plot
abutting the highwater mark that are, respectively, occupied by the drawdown zone
(dry) and the littoral zone (wet). [NOTE for NLA-2017 ONLY: There were a large number
of missing measurements of horizontal and vertical drawdown in the 2017 survey. The
field protocol directs field crews to NOT establish a drawdown plot when horizontal
drawdown is <=lm. For 2017 we assumed drawdown was 10 m.
Lpnt= (1 - Lpdraw) by definition because LpriP+ Lpdraw= 1.0
Lent and LCdraw are, respectively, the areal cover of fish habitat elements in the littoral plot, and
exposed (dry) in the drawdown zone, Lc could be single cover type (e.g,,fcfcSnags) or
could be a sum of cover types (e.g., sum of non-anthropogenic cover types: fcfcNatural).
Calculated Lcs-,m for a hypothetical lake with a mean horizontal drawdown of 10m and 100%
Snag cover in the drawdown zone (dry and exposed), but 0% Snag cover in the littoral
(wet) zone is as follows:
Lpdraw= 10/10 = 1.00
Lpnt = (1.00-1.00) = 0
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Drawdown Snag cover: LCdraw= 100%
Littoral Snag cover: Lcnt= 0%
Lcsim = (1.00 x 100%) + (0 x 0%) = 100%
The loss or gain in littoral habitat cover resulting from lake drawdown or natural lake level
declines can be estimated as the difference between the littoral cover simulated for zero
drawdown conditions (LcSim) the observed cover actually existing in the littoral at the time of
sampling {Lent).
5.3.4.4 Use of Variable suffixes in this document:
Riparian cover or human disturbance metrics calculated by Eq 1 are synthetic values that match
the 2007 metrics, and are designated by the suffixes_sy/7 (e.g., rviWoody_syn and hiiAII_syn) in
the EPA database. For simplicity, we will drop the suffixes on riparian vegetation and human
disturbance metrics in the remainder of this document, and it is understood that we are using
the synthesized variables when no suffix is present (*_syn), and NOT the drawdown zone
(*_DD), or riparian plot (*_r/p) versions of those variables.
Littoral cover metrics designated with the suffix_//t are based on field observations that are
conceptually and procedurally identical to those used in NLA 2007. For simplicity, we will drop
the suffixes on littoral cover metrics in the remainder of this documnet. and it is understood
that we are using the innudated littoral plot version of those variables when no suffix is
present f* lit), and NOT the drawdown zone (*_DD) or zero-drawdown simulated values
(*_s/m) versions of those variables. Littoral cover metrics calculated using Eq 2 simulate littoral
cover that would be present in the near-shore littoral area if the amount of drawdown were
zero, and are designated by the suffix_s/m (eg.,fciNatural_sim).
5.3.4.5 Near-shore disturbance metrics
We calculated extent of shoreline disturbance around the lakeshore (hifpAnyCirca) as the
proportion of stations at which crews recorded the presence of at least one of the 12
anthropogenic disturbance types as described by Kaufmann et al. (2014a). We calculated the
disturbance intensity metric hiiAII as the sum of the 12 separate proximity-weighted means for
all shoreline disturbance types observed at the 10 shoreline stations (Kaufmann et al. 2014a).
We also calculated subsets of total disturbance intensity by summing metrics for defined
groups of disturbance types. For example, hiiAg sums the proximity-weighted presence metrics
for row crop, orchard, and pasture; hiiNonAg sums the proximity-weighted presence metrics for
the remaining 9 non-agricultural disturbance metrics: 1) buildings, 2) commercial
developments, 3) parks or human-made beaches, 4) docks or boats, 5) seawalls, dikes, or
revetments, 6) trash or landfill, 7) roads or railroads, 8) power lines, and 9) lawns.
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5.3.4.6 Riparian vegetation metrics
Field data consisted of visual areal cover % class assignments of the vegetation type and areal
cover for each of 3 layers: canopy (>5 m high), mid-layer (0.5-5 m high), and ground cover (<0.5
m high). Crews estimated large (diameter at breast height [DBH] > 0.3 m) and small (DBH < 0.3
m) diameter tree cover separately in the canopy and mid-layer, distinguished woody from
herbaceous vegetation in the mid-layer and ground cover, and distinguished barren ground
from vegetation inundated by water in the ground layer. To characterize riparian vegetation in
the near-shore zone of the lake, we converted field cover class observations to mean cover
estimates for all the types and combinations of vegetation data (Kaufmann et al. 2014a). We
assigned cover class arithmetic midpoint values to each plot's cover-class observations (i.e.,
absent = 0%, sparse (>0-10%) = 5%, moderate (>10-40%) = 25%, heavy (>40-75%) = 57.5%, and
very heavy (>75-100%) = 87.5%), and then calculated lakeshore vegetation cover as the average
of those cover values across all 10 plots. Metrics for combined cover types (e.g., sum of woody
vegetation in 3 layers) were calculated by summing means for the single-types (see Kaufmann
et al. 1999, 2014a). Metrics describing the proportion of each lakeshore with presence (rather
than cover) of particular features were calculated as the mean of presence (0 or 1) over the 10
riparian plots.
5.3.4.7 Littoral cover and aquatic macrophyte metrics
The NLA survey crews made observations of the areal cover attributable to 8 littoral cover types
within each of the 10 littoral plots: rock ledges, boulders, brush, inundated live trees, snags,
overhanging vegetation, aquatic macrophytes, and human structures. Additionally, field crews
made separate visual estimates of areal cover for emergent, floating, and submerged aquatic
macrophytes within each of the 10 littoral plots. They used the same % cover classes for these
observations as used for riparian vegetation. Metrics describing the mean cover (and mean
presence) of littoral physical habitat features and aquatic macrophytes were calculated from
these cover class observations as described above for riparian vegetation. Metrics for combined
cover types (e.g. sum of natural types fish cover, floating and emergent aquatic macrophyte
cover) were calculated by summing means for single types.
5.3.4.8 Littoral and shoreline substrate metrics
NLA field crews visually estimated the percent areal cover of 8 substrate types (bedrock,
boulder, cobble, gravel, sand, silt/clay/muck, woody debris, and organic detritus) at each of the
10 near-shore stations (Figure 5-1). These estimates were made separately for the 1 m
shoreline band above the lake margin and for the lake bottom within the littoral plot. In cases
where the bottom substrate could not be observed directly, crews viewed the bottom through
a viewing tube, felt the substrate with a 3 m PVC sounding tube, or observed sediments
adhering to the boat anchor as it was retrieved from the bottom. Cover classes were the same
as for riparian vegetation. We calculated metrics describing the lake-wide mean cover of near-
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shore littoral and shoreline substrate in each size category by averaging the cover estimates at
each station, based on the cover class midpoint approach described above.
We adapted the approach of Faustini and Kaufmann (2007) and Kaufmann et al. (2009) for
estimating geometric mean and variance of substrate diameters from systematic pebble-
counts. In this approach (Kaufmann et al. 2014a), we assigned the geometric mean between
the upper and lower diameter bound of each size class for each cover observation before
calculating the cover-weighted mean size index. We calculated the geometric mean diameters
(Dgm) of littoral and shoreline substrate (bsxLdia and ssxLdia) as follows:
Dgm=Antilog{Sum/{P/{[logio(D/u)+logio(D// )]/2}}}, (Eq. 3)
where:
P,=areal cover proportion for diameter class /';
D/u=diameter (mm) at upper limit of diameter class /';
Dn =diameter (mm) at lower limit of diameter class /';
Sum, =summation across diameter classes; and
Nominal size class midpoint diameters of 5660 and 0.0077 mm were set, respectively, for the
largest (bedrock and hardpan) and smallest (silt, clay, and muck) diameter classes.
Our calculations are identical to those of Faustini and Kaufmann (2007), except that here the
percent cover estimates used to weight diameters were the mean values of 10 visual cover
estimates rather than areal streambed cover determinations derived from the pebble-count
percentages for individual particles in each diameter class.
5.3.4.9 Littoral depth, Lake level fluctuations, bank and water surface characteristics
Field crews measured littoral depth, estimated water level fluctuations and bank heights, and,
and observed water surface and bottom sediment color and odor at each of the 10 nearshore
stations (Figure 5-1). SONAR, sounding lines, or sounding tubes were used to measure lake
depth 10 m offshore. NLA field crews used hand-held levels, survey rods, and laser rangefinders
(rather than unaided visual estimates) to measure vertical and lateral (horizontal) lake level
fluctuation. Field indications of short to medium term fluctuation, drawdown and/or declines in
lake levels were based on measurement of the vertical height and horizontal extent of exposed
lake bottom ("Bathtub Ring") field evidence.
Crews recorded the presence of surface films or scums, algal mats, oil slicks, and sediment color
and odor. They visually estimated the bank angle in the 1 m-wide shoreline band and the
vertical and lateral range in lake level fluctuations, based on high and low water marks. We
calculated whole lake metrics for mean littoral depth and water level fluctuations as arithmetic
averages (sixDepth, bfxVertHeight and bfxHorizDist) and standard deviations of the measured
values at the 10 stations. For bank angle classes and qualitative observations of water surface
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condition and sediment color and odor, we calculated the proportion of stations having
observations in each class.
5.3.5 Calculation of summary physical habitat condition indices
We calculated 4 multimetric indices of physical habitat condition and an index of lake
drawdown:
RDis_IX: Lakeshore Anthropogenic Disturbance Index (Intensity and Extent),
RVegQ: Riparian Vegetation Cover Complexity Index,
LitCvrQ: Littoral Cover Complexity Index,
LitRipCvQ: Littoral-Riparian Habitat Complexity Index, and
Drawdown Index: based on bfxVertHeight and bfxHorizDist
5.3.5.1 Lakeshore Anthropogenic Disturbance Index (RDis_IX)
This index was calculated as:
RDis_IX = (Disturbance Intensity + Disturbance Extent)/2; (Eq 4)
where :
disturbance intensity was represented by separate sums of the mean proximity-weighted tallies
of near-shore agricultural and non agricultural disturbance types and extent was expressed as
the proportion of the shore with presence of any type of disturbance.
hiiNonAg = Proximity-weighted mean disturbance tally (mean among stations) of up to 9
types of non-agricultural activities.
hiiAg = Proximity-weighted mean tally of up to 3 types of agriculture-related activities
(mean among stations).
hifpAnyCirca = Proportion of the 10 shoreline stations with at least 1 of the 12 types of
human activities present within their 10 x 15 m littoral plots, drawdown plots, or within
15m of the lake shore in their 15 x 15 m riparian plots.
Field procedures classified only 3 types of agricultural disturbances, versus 9 types of non-
agricultural disturbances, limiting the potential ranges to 0-3 for hiiAg and 0-9 for hiiNonAg. In
the combined NLA 2007 and 2012 surveys, the observed ranges of these variables also differed:
hiiAg ranged from 0 to 1.55, whereas hiiNonAg had an observed range almost 5 times as great
(0 to 7.125). To avoid under-representing agricultural disturbances and over-representing non-
agricultural disturbances in the index, we weighted the disturbance intensity tallies for
agricultural land use by a factor of 5 in Equation 2. This weighting factor (ratio of observed
RDis IX =
(Eq 5)
2
where:
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ranges in non-agricultural to agricultural disturbance types) effectively scales agricultural land-
uses equal in disturbance potential to those for non-agricultural land uses. We scaled the final
index from 0 to 1, where 0 indicates absence of any anthropogenic disturbances and 1 is the
theoretical maximum approached as a limit at extremely high disturbance. We applied a single
formulation of the disturbance index RDis_IX throughout the NLA survey in the U.S.
5.3.5.2 Riparian Vegetation Cover Complexity Index (RVegQ)
This index is based on visual estimates of vegetation cover and structure in three vegetation
layers at the 10 near-shore riparian plots along the lake shore. The cover metrics were
calculated for the variable-width drawdown zone plots (metrics with suffix "_DD") and the 15m
x 15m riparian plots (with suffix "_rip"). For the NLA 2012 report, we used areal cover
information from both types of plots along with drawdown horizontal extent information to
calculate RVegQ estimates matching those for the previous report, which are for the near-
shore zone extending from the lake water's edge 15m outward (see Eq. 1). Because the
potential vegetation cover differs among regions, we calculated three variants of the Riparian
Vegetation Cover-Complexity Index (RVegQ^2, RVegQ^.7, or RVegQ_8) for application to
different aggregated ecoregions (Table 5-2). The region-specific formulations reduce the
among-region variation in index values in least disturbed lakes and reduce ambiguity in their
response to anthropogenic disturbances. If component metrics had potential maximum values
>1, their ranges were scaled to range from 0 to 1 by dividing by their respective maximum
values based on the NLA 2007 data (see Table 3 in Kaufmann et al. 2014a). Each variant of the
final index was calculated as the mean of its component metric values. Index values range from
0 (indicating no vegetative cover at any station) to 1 (40 to 100 % cover in multiple layers at all
stations).
rrviWoody
2.5
RVegQ _ 2 =
RVegQ_7 =
RVegQ _ 8 =
+ rvfcGndlnundated
V
rviLowWood
1.75
+ rvfcGndlnundated
rrviWoody
V 2.5 .
+ rvfpCanBig + rvfcGndlnundated + ssiNATBedBld
(Eq 6)
(Eq 7)
(Eq 8)
where:
rviWoody = Sum of the mean areal cover of woody vegetation in 3 layers: canopy (large and
small diameter trees), understory, and ground layers (rvfcCanBig + rvfcCanSmall +
rvfcUndWoody + rvfcGndWoody).
rviLowWood = Sum of mean areal cover of woody vegetation in the understory and ground
cover layers (rvfcUndWoody + rvfcGndWoody).
rvfcGndlnundated = Mean areal cover of inundated terrestrial or wetland vegetation in the
ground cover layer.
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rvfpCanBig = Proportion of stations with large diameter (>0.3 m dbh) trees present.
ssiNATBedBId = Sum of mean areal cover of naturally-occurring bedrock and boulders
(ssfcBedrock + sfcBoulders), and where the value of ssiNATBedBId was set to 0 in lakes
that have a substantial amount of human-built seawalls and revetments (i.e., hipwWalls
>0.10).
We used RVegQ_2 for mesic ecoregions with maximum elevations <2,000 m (NAP, SAP, UMW,
CPL) where tree vegetation can be expected in relatively undisturbed locations (Table 5-2).
RVegQ_2 sums the woody cover in three lakeside vegetation layers (rviWoody) and includes
inundated groundcover vegetation (rvfcGndlnundated) as a positive characteristic.
We used RVegQ_7 for Central Plains ecoregions (NPL, SPL and TPL). Whereas perennial woody
groundcover and shrubs can be expected on undisturbed lake shorelines throughout the
Central Plains (West and Ruark 2004), the presence or absence of large trees (>5m high) along
lake margins in this region has ambiguous meaning without floristic information (Johnson 2002,
Barker and Whitman 1988, Huddle et al. 2011). RVegQ_7 accommodates lack of tree canopy in
least disturbed lakes by summing only the lower 2 layers of woody vegetation (rviLowWood)
and includes inundated ground cover vegetation as a positive characteristic.
We used RVegQ_8 for the West (WMT, XER), where climate ranges from wet to arid, and where
lakeshores may have the potential to grow large diameter riparian trees but may lack vegetated
lake shorelines at high elevations, or where rock precludes vegetation (Table 5-2). RVegQ_8
sums the woody cover in 3 lakeside vegetation layers and includes inundated groundcover
vegetation as a positive characteristic; it also includes the proportional presence of large
diameter trees around the lakeshore as a positive characteristic. RVegQ_8 includes natural rock
as an undisturbed riparian cover type to avoid penalizing relatively undisturbed lakes in arid
areas or at high elevations above timberline. For lakes where there is a substantial extent or
abundance of constructed seawalls, dikes, or revetments along the shoreline, the substrate
metric was set at 0.
5.3.5.3 Littoral Cover Complexity Index (LitCvrQ)
This index was based on the station-averages for visual estimates of the areal cover of 10 types
of littoral features, including aquatic macrophytes but excluding human structures, within each
of the 10 littoral plots (see Kaufmann et al. 2014a). Note that littoral metrics used to calculate
LitCvrQ are those with the suffix , which match exactly the NLA 2007 littoral cover metrics
having no suffix. We calculated 3 variants, for application in different ecoregions (Table 5-2).
Each variant of the index was calculated as the mean of its component metric scores, so index
values range from 0 (no cover present at any station) to 1 (very heavy cover at all 10 stations).
Component metrics with potential maximum values >1 were scaled from 0-1 by dividing by
their respective maximum values in the NLA 2007 dataset.
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fciNatural+
LitCvrQ _b =
LitCvrQ _c =
LitCvrQ d =
fcfcSnag
0.2875 ,
fciNatural +
fcfcSnag (amfcFltEmg
v
0.2875
1 515
r SomeNatCvr
v is .
fcfcSnag (amfcFltEmg
0.2875
1 515
(Eq 9)
(Eq 10)
(Eq 11)
where:
fciNatural = summed areal cover of non-anthropogenic fish cover elements (fcfcBoulders +
fcfcBrush + fcfcLedges + fcfcLivetrees + fcfcOverhang + fcfcSnag + fcfcAquatic).
SomeNatCvr = summed cover of natural fish cover elements excluding snags and aquatic
macrophytes (fcfcBoulders + fcfcBrush + fcfcLedges +fcfcLivetrees + fcfcOverhang).
amfcFltEmg = summed cover of emergent plus floating aquatic macrophytes (amfcEmergent +
amfcFloating).
fcfcAquatic = total cover of aquatic macrophytes of any type.
All three variants of LitCvrQ include an expression of the summed cover of naturally occurring
fish or macroinvertebrate cover elements. Snag cover is recognized as a particularly important
element of littoral habitat complexity (Francis and Schindler 2006, Christensen et al. 1996,
Miranda et al. 2010). Therefore, we included snags as a separate contributing cover component
in all three variants of the index, and divided cover metrics by their maximum values in the NLA
2007 data to make the weightings of snag cover equal to those of the other two littoral cover
sums. For LitCvrQ^c and LitCvrQ_d, we increased the emphasis on emergent and floating-leaf
aquatic macrophytes relative to other littoral components in response to their reported
importance as cover and their sensitivity to human disturbances in many lake types and regions
(Radomski and Geoman 2001, Jennings et al. 2003, Merrell et al. 2009, Beck et al. 2013).
We used LitCvrQ^b for lakes in the CPL, which includes many generally shallow, warm, low
conductivity lakes. We used LitCvrQ_c for lakes in the SAP, which are all reservoirs, where
disturbed sites commonly have substantial erosion of clay-rich upland soils, large water level
fluctuations, and bare-soil shorelines. These conditions generate abiotic turbidity that
suppresses submerged macrophytes, thereby diminishing the association of abundant
submerged aquatic macrophytes with anthropogenic nutrient inputs that is typically seen in
other regions. LitCvrQ^c emphasizes floating and emergent aquatic macrophytes in addition to
snags, but still includes submerged aquatic macrophytes along with other aquatic macrophytes
and cover types in fciNatural. LitCvrQ_d excludes submerged aquatic macrophytes, and we
used it in the remaining ecoregions (NAP, TPL, NPL, SPL, WMT, and XER), where submerged
aquatic macrophytes provide valuable cover, but high submerged cover is frequently associated
with anthropogenic eutrophication (Hatzenbeler et al. 2004, Merrell et al. 2009).
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5.3.5.4 Littoral-Riparian Habitat Complexity Index (LitRipCvrQ)
We averaged the lake values of the littoral cover complexity and riparian vegetation cover
complexity indices to calculate the littoral-riparian habitat complexity index LitRipCvrQ:
T.. _ _ (RVegQ n + LitCvrQ x)
LitRipCvrQ = = ; (Eq 12)
where:
RVegQ_n = variant of the riparian vegetation cover complexity index (n=2, 7 or 8, depending on
ecoregion, Table 5-2.
LitCvrQ_x = variant of littoral cover-complexity index (x = b, c, or d, depending on ecoregion,
Table 5-2.
5.3.5.5 Lake Level Drawdown Index (combined use of bfxVertHeight and bfxHorizDist)
We used the mean lake values estimating Lake Level Vertical Fluctuation (bfxVertHeight) in
combination with Lake Level Horizontal Fluctuation (bfxHorizDist) to characterize lake
drawdown and natural lake level declines. These metrics are, respectively, the height (meters)
measured from the present lake level to high water, and the horizontal (lateral) distance in
meters from the lake shore to the high water mark in meters. NLA field crews made these
determinations based on the extent and location of vegetation intolerant to frequent or
prolonged inundation, location of flotsom deposits ("trash racks"), evidence of wave action,
and exposed lake bottom. The lake bottom exposure measured by these methods characterizes
seasonal lake level declines and fluctuations on timescales shorter than that required for
disintegration of flotsom at the high water mark, or encroachment of perennial terrestrial
vegetation onto the exposed lake bottom area. In most regions, these measurements should be
adequate to document trends in lake level declines attributable to climate change, water
withdrawals, and reservoir management over a decadal timescale. However, more rigorous
tracking of such trends over longer timescales would require that field crews measure lake
levels in relation to established permanent (monumented) reference elevations and/or staff
gauges at sample lakes.
5.3.6 Deriving expected index values under least disturbed conditions
We based expectations for bfxVertHeight and bfxHorizDist on "Null Models": the expected
value and its dispersion are represented by the central tendency and distribution of these
variables in regional sets of least disturbed reference sites. In the CENPL and WEST,
expectations were set separately for natural lakes versus human-made reservoirs.
We used lake-specific predictive regression models to estimate physical habitat expectations
for RVegQ, LitCvrQ, and LitRipCvrQ under least disturbed condition (Table 5-3). We compared
the performance of these regression models with null models (Table 5-4), for which
expectations were simply the mean of logio-transformed physical habitat index scores among
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least disturbed lakes from each ecoregion. Our motivation for using lake-specific models of
expected ("E") condition was to reduce the variance in physical habitat condition indices (in this
case O/E values of RVegQ, LitCvrQ, and LitRipCvrQ) among least disturbed reference lakes. Air
temperature, precipitation, soils and lithology can vary greatly across ecoregions, resulting in
corresponding variations in potential natural vegetation among least disturbed lakes. In turn,
that variation results in differences in the amount and complexity of littoral cover, especially for
those elements derived from riparian vegetation. We derived lake-specific expected values by
modeling the influence of important non-anthropogenic environmental factors in relatively
undisturbed lakes, an approach analogous to that used to predict least disturbed conditions for
multimetric fish assemblage indices (Esselman et al. 2013, Pont et al. 2006, 2009).
For calculating lake-specific expected (E) values of RVegQ, LitCvrQ, and LitRipCvrQ under least
disturbed condition, we conducted the multiple linear regression (MLR) modeling in 7
aggregated ecoregions (Table 5-3 and Appendix A). These models were based on least
disturbed lakes from the combined NLA 2007 and 2012 surveys within each region (Table 5-1).
The lake habitat index MLRs employed one to four predictors from among the following:
Latitude, Longitude, Elevation, ElevXLatitude, ElevXLongitude, Lake surface area, Lake origin
(human-made reservoir or natural lake), near-shore anthropogenic disturbance of all types
(RDis_IX), and near-shore anthropogenic agricultural disturbance (hiiAg). Latitude, longitude,
elevation, and ecoregion are surrogates for temperature, precipitation, soil, and other
characteristics that influence potential natural vegetation and littoral cover. Field
measurements of bfxVertHeight and bfxHorizDist were good predictors of riparian and littoral
cover in most of the regions. However, we chose not to use these indicators of level fluctuation
and drawdown to predict expected condition because their use would confound interpretations
and obscure the effects of drawdown on habitat condition. We also did not use lake depth
measurements (like maximum depth or littoral mean depth), because of their association with
lake level change. Similarly, survey year was a good predictor of lake physical habitat metrics in
regions where there were marked differences in the amount of lake drawdown between
surveys. We chose not to use survey year as a predictor of expected condition because it would
confound analysis of temporal trends and change between surveys.
Ideally, calculations of expected cover and complexity would be based only on minimally-
disturbed lakes. However, the least disturbed lakes in most regions include sometimes
substantial disturbances, necessitating inclusion of near-shore disturbance predictors in our
models if they were associated with variance in the habitat indices. The use of RDis_IX or hiiAg
as predictors was supported by the data for all three habitat indicators in the NPL, CPL and
CENPL, and the littoral cover indicator in the SAP (Table 5-3). For predicting expected LitCvrQ
and LitRipCvrQ in the NAP, we had to combine least disturbed with moderately disturbed lakes
and reservoirs (RT_NLA12_2015 = R or S) to span lake size and elevation gradients affecting
riparian vegetation and littoral cover in that region. The weak association of human disturbance
with habitat indices would not have warranted including RDis_IX as a predictor within NAP least
disturbed sites alone (RT_NLA12_2015=R). However, the human disturbance gradient
introduced by including moderately disturbed NAP lakes (RT_NLA12_2015=S), and the effect of
that disturbance on littoral habitat in the NAP made it necessary to include RDis_IX as a
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predictor. Inclusion of RDis_IX or hiiAg as predictors of expected lake habitat index values was
not supported by the data for lakes and reservoirs in the UMW, WMT, and XER. As in most of
the other regions, lake level fluctuation indicators were good predictors of riparian and littoral
cover in the UMW and WEST, but were not used as predictors for reasons we stated in the
previous paragraph.
For regions where RDis_IX or hiiAg were used in modeling expected habitat condition, we set
the value of these variables in the predictive MLR equation to the minimum value observed in
the region before calculating expected values of RVegQ, LitCvrQ, and LitRipCvrQ. In all regions
and subregions there were sites with RDisJX and hiiAg values of 0 (See Appendix A). Setting the
reference expected lake habitat index values slightly higher in this way results in the central
tendency for reference site 0/E to be less than 1.0.
5.3.7 Condition Criteria for Nearshore Lake Physical habitat
For the lakeshore anthropogenic disturbance index RDisJX, we used uniform criteria for all
lakes. For RVegQ, LitCvrQ, and LitRipCvQ we set condition criteria based on the distribution of
O/E values of these indices observed in least disturbed lakes. For bfxVertHeight and
bfxHorizDist, we set condition criteria based on the distribution of the metric values themselves
in least disturbed lakes (Null model).
5.3.7.1 Condition Criteria for Lakeshore Anthropogenic Disturbance Intensity and Extent
Because RDisJX is a direct measure of human activities, we based criteria for high, medium,
and low levels of disturbance on judgment:
Good (Low Disturbance): RDisJX <0.20
Fair (Medium Disturbance): RDisJX >0.20 but < 0.75
Poor (High Disturbance): RDisJX>0.75
Lakes with RDisJX <0.20 have very low levels of lake and near-lake disturbance, typically having
anthropogenic disturbance on <8% of their shorelines. Those with RDisJX >0.75 have very high
levels of disturbance, typically having human activities evident on 100% of their shorelines. For
perspective, <21% of the 2364 sample site visits in the combined NLA 2007 and 2012 surveys
had RDisJX <0.20, and <21% had RDisJX >0.75. Most of the reference sites in the WMT, UMW,
and NAP regions have RDisJX <0.20, most of those in SAP, SAP, XER, TPL, and CPL have RDisJX
<0.40, most NAP reference sites have RDisJX between 0.40 and 0.6, and no reference sites
have RDisJX >0.70 (Figure 5-3).
5.3.7.2 Condition Criteria for RVegQ, LitCvrQ, and LitRipCvQ
We calculated physical habitat index observed/expected (O/E) values of RVegQ^OE,
LitCvrQ^OE, and LitRipCvQ_OE for each sample lake by dividing the observed index value at
each lake by the lake-specific expected value derived from regressions in Table 5-3 and
Appendix A. The calculated O/E values of the habitat metrics for each lake express the degree
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of deviation of that lake from an estimate of its expected value under least disturbed
conditions. No model perfectly predicts expected indicator values (E-values) in lakes under least
disturbed conditions, and field measurements of indicator values ("0" values) include error and
temporal variation. Consequently, 0/E values of these indices among reference lakes have a
dispersion (variance) that decreases with the performance of predictive models (i.e., how
precisely does the model predict reference condition?), and with the precision of the habitat
indicator measurements (i.e., how well do the field methods measure observed condition?). We
set condition criteria for RVegQ, LitCvrQ, and LitRipCvQ with reference to the distributions of
these indices among least disturbed lakes within each of the 7 merged ecoregions Table 5-5.
The small number of lakes meeting our low-disturbance criteria in most regions precluded
obtaining reliable percentiles of RVegQ, LitCvrQ, and LitRipCvQ directly from the least disturbed
lake distributions. Consequently, for all regions, we used the central tendency and variance of
index 0/E values in least disturbed lakes values to model their distributions and to estimate
percentiles (Snedecor and Cochran 1980). The logio-transformed 0/E values in the least
disturbed lakes had symmetrical, approximately normal distributions. We calculated means and
standard deviations of logio-transformed 0/E values (Table 5-5, columns 3 and 4), and
estimated the 5th and 25th percentiles (Table 5-5, columns 7 and 8) based on the log-normal
approximation of the index distributions in least disturbed lakes within each ecoregion. Because
the means and SD's are all log values, a range of + 1SD would be calculated, for example, by
multiplying and dividing the geometric mean by the geometric SD (see Table 5-5 legend for
details, including handling of the log-transformation constant).
Lakes with 0/E values (MLR model) that are >25th percentile for least disturbed lakes within
their regions were considered to have habitat in good condition (i.e., similar to that in the
population of least disturbed lakes of the region). Similarly, lakes with index or 0/E values <5th
percentile of least disturbed lakes were considered to have poor habitat quality (i.e., they have
significantly lower cover and complexity than observed within the sub-population of least
disturbed lakes of the region). Those with index or 0/E values between the 5th and 25th
percentiles of least disturbed lakes were scored as fair condition.
We emphasize that our designations of good, fair and poor are relative to the least disturbed
sites available in each ecoregion. We define good condition as habitat quality not
distinguishable from the distribution of habitat in least disturbed sites; and poor condition as
habitat quality that is not likely to be found within the distribution of least disturbed sites of the
ecoregion. Our designations of poor condition do not indicate impaired water body status.
Conversely, our designations of good condition mean that habitat is similar to the least
disturbed sites available in a region, which does not mean pristine, only the best available,
which can be relatively disturbed in extensively and most disturbed regions.
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5.3.7.3 Condition Criteria for Lake Drawdown
We based our assessment of Lake Drawdown condition on null models of the expected amount
of drawdown in least disturbed lakes. Specifically, we examined the empirical distributions of
the metrics quantifying vertical and horizontal lake level fluctuations {bfxVertHeight and
bfxHorizDist) in least disturbed lakes within aggregated ecoregions, sometimes stratified by lake
origin (natural lakes versus human-made reservoirs). We used separate null models for the
NAP, SAP, UMW, and CPL regions. For the CENPL (TPL+SPL+NPL) and the West (WMT+XER), we
used separate null models for natural lakes versus human-made reservoirs. Vertical and
horizontal drawdown were considered small if they were <75th percentile of their respective
reference distributions; large if >95th percentile, and medium if in-between (Table 5-6). Overall
lake drawdown condition was considered small if both vertical and horizontal drawdown were
small; medium if one or both were medium (but not large); and large if vertical, horizontal or
both were large.
NOTE for NLA 2017 ONLY:
In several hundred NLA-2017 sample lakes, field crews did not measure horizontal or vertical
drawdown in cases where they did not establish drawdown zone cover plots. In these cases, we
assumed that missing horizontal drawdown values were
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5.4.2 Null Model Results for RVegQ, LitCvrQ, and LitRipCvQ:
Geometric means for RVegQ, LitCvrQ, and LitRipCvQ in least disturbed lakes differed among
regions (Table 5-4), but these unsealed null model values are not directly comparable because
the habitat index formulations differed among regions. The RVegQ, LitCvrQ, and LitRipCvQ null-
model logSD's and geometric SD's (Columns 4 and 6 of Table 5-4) were calculated from log-
transformed variables, and therefore are expressions of the proportional variance among least
disturbed lakes of each region. Whether scaled (divided by the mean) or not, they are directly
comparable as measures of model precision among regions with different geometric means, or
between null and MLR modeling approaches.
Comparing indicators, the precision in modeling least disturbed condition using null models was
generally better (smaller SDs) for LitRipCvQ than for RVegQ or LitCvrQ, and null models for
RVegQ were generally more precise than for LitCvrQ (Table 5-4, columns 4 and 6). The most
obvious differences, however, were among regions, and the differences were associated with
the level of disturbance in the reference sites. We ordered the seven NLA lake habitat modeling
ecoregions according to increasing reference site median RDisJX for examining variance in the
other lake habitat indicators (Figure 5-3). The regions with the greatest amount of disturbance
in their reference sites (the CENPL, including NPL, SPL, TPL, the CPL, and the XER) generally had
higher within-reference site variance all three lake habitat indices, with the exception of low
variance in all three indicators within reference sites of the relatively high-disturbance CPL
reference sites (Figure 5-4). The precision in modeling least disturbed condition using null
models was generally best in the UMW and NAP (i.e., lowest gSDs). The smaller the SD of index
values (or O/E values) among least disturbed lakes, the easier it is to confidently distinguish
disturbed lakes from least disturbed lakes. The null model SD's serve as an upper bound for the
variance of the indicators among regional reference sites, and are analogous to the RMSE's of
the regressions in Table 5-3. Removing the variance attributed to the predictors reduces the
unexplained variance among reference sites.
5.4.3 O/E Model Results for RVegQ, LitCvrQ, and LitRipCvQ:
The LogSD's of RVegQ^OE, LitCvrQ^OE, and LitRipCvQ_OE among reference sites (Table 5-5,
column 4) were consistently, and in some cases substantially, lower than those for null models
in their respective regions, as evidenced by comparing open circles and black dots plotted in
Figure 5-4. The CPL, CENPL, XER and WMT showed the largest reduction of reference site
variance compared with corresponding null models, denoting improvement in O/E model
performance over null models. As for the null models, however, O/E models in regions with
relatively disturbed reference sites had higher reference site variance (the expected condition
models were less precise). Again, with the exception of the CPL, regions with more disturbance
in their reference sites still had higher SD's than those in regions with less disturbance.
Conversely, the four regions with the lowest level of human disturbance in their reference sites
(WMT, UMW, NAP, and SAP) also had the lowest O/E model variance among their reference
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sites. These results reinforce the idea that human disturbances are likely responsible for a large
amount of the variance in lake physical habitat structure in reference sites within the disturbed
regions. Therefore, further effort to capture this variance by modeling only non-anthropogenic
("natural") controls would not likely be successful in reducing the variance in O/E values among
reference sites.
Except for regions where O/E models incorporated human disturbance variables (NAP, CPL,
CENPL and LitCvr_OE in SAP), the central tendency of reference site O/E values (Table 5-5,
column 6) was very close to 1 (0.98 to 1.01). This is to be expected. Where E-Models contained
human disturbance predictors, reference O/E values regained the variance modeled out when
observed values were divided by expected values determined with human disturbance
predictors (RDis_IXor hiiAg) set to regional minimum values. If human disturbances decrease
the observed value, the mean O/E will be <1. Accordingly, reference site mean O/E values for
MLR Models in the NAP, CPL, and CPL (and LitCvr_OE in SAP) ranged from 0.79 to 0.91. We
regressed the reference O/E values against the RDis_IX or hiiAg values to obtain y-intercepts for
expected O/E for the minimum disturbance observed in these regions. These are shown in the
Table 5-5 rows with "oe Yint" subscripted after their Ecoregion designation. For example the
NAPoEYint row is the result of this final adjustment on reference O/E results from the NAPMLRModei
row.
Anthropogenic disturbance among reference sites tends to increase the variance in O/E values
within regions, even after the minimum disturbance adjustment. There is a strong relationship
between the LogSDs of null and adjusted O/E models for lake habitat among reference lakes
and the regional level of near-shore anthropogenic disturbance in reference sites (Figure 5-4).
Our modeling improves these models, but it is likely that disturbances other than those
captured by RDis_IXcontribute to the uncertainty in predicting habitat characteristics in
minimally-disturbed lakes. These results reinforce the idea that human disturbances are likely
responsible for a large amount of the variance in lake physical habitat structure among least
disturbed reference sites in the disturbed regions. Therefore, further effort to capture this
variance by modeling only non-anthropogenic ("natural") controls would not likely be
successful in reducing the variance in O/E values among reference sites.
5.4.4 Null Model Results for Lake Drawdov ' Level Fluctuations:
Least disturbed reference lakes and reservoirs in the NAP, SAP and UMW experienced less
drawdown and level fluctuation than those in the CPL, CENPL, and WEST; particularly in
comparison with marked drawdown observed in human-made reservoirs of the CENPL and
WEST (Table 5-6). Not surprisingly, least disturbed natural lakes in the CENPL and WEST also
experienced less drawdown and level fluctuation than their human-constructed counterparts.
As a result, the criteria for assessing substantial drawdown in lakes of the Appalachians and
UMW were much smaller than those for lakes (and particularly reservoirs) in the CENPL and
WEST.
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5.5 Precision of physical habitat indicators
In our synoptic survey context, o2iake is the signal of interest, and o2rep is noise variance; we
define their ratio as S/N. The methods we used to quantify precision, the precision of NLA lake
physical habitat metrics and key habitat condition indices, and the implications of varying
precision levels for monitoring and assessment, are comprehensively evaluated by Kaufmann et
al. (1999, 2014a). Here we summarize findings for key physical habitat indicators based on the
NLA 2012 survey data, which is a good representation of precision for NLA 2017, based on
Kaufmann et al. (2014a) and the NLA 2012 Technical Support Document (USEPA 2017b).
The key NLA physical habitat indices had moderate to high S/N (2.2 - 11.0) over the entire NLA
2012 survey (Table 5-7). Compared with the other composite indices, the human disturbance
index RDis_IX and horizontal drawdown index had the highest S/N (9.1-11), whereas the littoral
cover O/E index had the lowest S/N (2.2). The advantage of S/N as a precision measure is its
relevance to many types of statistical analysis and detecting differences in subpopulation
means (Zar 1999). High noise in habitat descriptions relative to the signal (i.e., low signal: noise
ratio, S/N) diminishes statistical power to detect differences among lakes or groups of lakes.
Imprecise data limit the ability to detect temporal trends (Larsen et al. 2001, 2004). Noise
variance also limits the maximum amount of variance that can be explained by models such as
multiple linear regression (Van Sickle et al. 2005, Kaufmann and Hughes 2006). By reducing the
ability to quantify associations between variables (Allen et al. 1999, Kaufmann et al. 1999),
imprecision compromises the usefulness of habitat data for discerning likely controls on biota
and diagnosing probable causes of impairment. The adverse effects of noise variance on these
types of analysis are negligible when S/N >10; becoming minor as S/N decreases to 6, increasing
to moderate as S/N decreases to 2, and finally becoming severely limiting as S/N approaches 0
(Paulsen et al. 1991, Kaufmann et al. 1999). At S/N=0, all the metric variance observed among
lakes in the survey can be attributed to measurement "noise". Based on these guidelines, the
effects of imprecision are minor for all the indicators except for the Littoral Cover index, for
which the effects are minor-to-moderate.
Kaufmann et al. (2014a) explain that the S/N ratio may not always be a good measure of the
potential of a given metric to discern ecologically important differences among sites. For
example, a metric may easily discriminate between sparse and abundant littoral cover for fish,
but S/N for the metric would be low in a region where littoral cover does not vary greatly
among lakes. In cases where the signal variance (o2iake) observed in a regional survey reflects a
large range of habitat alteration or a large range in natural habitat conditions, S/N would be a
good measure of the precision of a metric relative to what we want it to measure. However, in
random surveys or in relatively homogeneous regions, o2iake and consequently S/N, may be less
than would be calculated for a set of sites specifically chosen to span the full range of habitat
conditions occurring in a region. To evaluate the potential usefulness of metrics, Kaufmann et
al. (2014a) suggested that an alternate measure of relative precision, orep divided by its
potential or observed range (Rgpot or Rg0bs) offers additional insight. The minimum detectable
difference in means between 2 lakes (or between two times in one lake) is given by Dmin =
1.96arep(2n)1/2 = 2.77arep , using a 2-sided Z-test with a = 0.05 (Zar 1999). Thus, to detect any
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specified difference between 2 lakes in a metric relative to its potential or observed range [Rgpot
or Rg0bs, the standardized within-lake standard deviation, orep/Rg, cannot exceed (Dm/n/
Rg)/2.71. By the criteria in Kaufmann et al. (2014a - Table 2), the key NLA physical habitat
indices were precise or moderately precise, with orep/Rg0bs between 0.052 - 0.107 (Table 5-7).
Depending on the index, they have the potential to discern differences between single lakes (or
one lake at two different times) that are between l/3rd and l/8th the magnitude of the
observed ranges of these indices.
5.6 Physical habitat index responses to anthropogenic clistu > x x e
In the U.S. as a whole, RVegQ_OE, LitCvrQ^OE, and LitRipCvQ_OE were significantly higher
(p<0.0001) in least disturbed lakes (RT_NLA12_2015=R) than in most-disturbed lakes
(RT_NLA12_2015=T) (Table 5-8, Figure 5-5). The differences were substantial for RVegQ_OE,
and LitRipCvQ_OE, and discrimination was good (no or nearly no overlap in interquartile
ranges). For LitCvrQ^OE, there was an overlap of approximately one-third of the interquartile
range. RDisJX was a major screening variable used to disqualify potential reference sites, so it
is not surprising that the entire range of RDisJX among reference sites had very little overlap
with that for most disturbed sites. Note that a site with very low RDisJX could be classified as
most-disturbed on the basis of many other variables, but the converse is not true because
reference sites must all have low RDisJX. Like RDisJX, both vertical and horizontal drawdown
were significantly lower (p<0.0001) in least disturbed lakes than in most-disturbed lakes (Table
5-8, Figure 5-5). Except for lake drawdown, contrasts were very similar for the NLA 2007 and
2012 surveys (Figure 5-6). Although the t test between reference and most disturbed lakes was
similar in both years, the positive relationship between disturbance and in lake level drawdown
was much less evident in the drier year (2007) than in 2012. In 2012 fewer than 5% of reference
lakes showed any drawdown at all, whereas 75 to 95 % of reference lakes showed drawdown in
2007 - with a lot of overlap in the inter-quartile ranges of reference and most disturbed sites.
RVegQ_OE, LitCvrQ^OE, and LitRipCvQ_OE in sub-sets and sub-regions of the U.S. universally
showed the same pattern of response as the nation, with the mean of reference sites
significantly greater than those for most-disturbed sites (Table 5-9). Discrimination was
generally greater for RVegQ_OE and LitRipCvQ_OE than for LitCvrQ_OE or the drawdown
indices. Discrimination of these 3 indices was somewhat greater for natural lakes than for
reservoirs, but good in both. RVegQ_OE was strongly and clearly associated with disturbance
(RT_NLA12) in all regions and years except for NPL, and SPL in the NLA 2007 survey year.
LitCvrQ_OE was strongly related to disturbance class in the CPL and NPL, moderately related to
disturbance in the NAP, TPL (2012), SPL, and XER; and associations were with disturbance were
weakest in the SAP, WMT, and TPL (2007). LitRipCvQ_OE was strongly and clearly associated
with disturbance (RT_NLA12) in all regions and both years.
Fergus et al. (2020) examined differences in lake hydrologic variables between the 2007 and
2012 surveys, providing insight on the sensitivity of lake levels and water balance parameters to
inter-annual climate conditions. Between-year variation in water-level decline was greater on
natural lakes than human-made lakes, suggesting that natural lakes are more responsive to
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changes in weather. They reported less vertical drawdown in natural lakes in 2012 (a cooler,
wetter weather year) compared to 2007, whereas large drawdown persisted on human-made
lakes, particularly in western regions. Dam and outlet structures can significantly alter lake and
stream hydrology and potentially mask effects from climate or weather. Fergus et al. (2020,
2021) suggested, based on the 2007- 2012 changes in evaporative concentration and water
levels and an index of the potential for anthropogenic hydro-alteration, that water levels in
natural lakes levels may be more responsive to temperature and precipitation in a given year,
whereas water levels in human-made lakes may be more strongly influenced by water
management and indirectly by weather conditions, particularly in western U.S. regions. Fergus
et al. (2021) also showed evidence that in the wetter eastern regions of the U.S., water
management infrastructure is used to stabilize lake and reservoir water levels, whereas water
management for irrigation, hydropower, and water supply in the drier regions leads to greater
level fluctuation and drawdown.
5.7 Discussion
The NLA and other lake survey and monitoring efforts increasingly rely upon biological
assemblage data to define lake condition. Information concerning the multiple dimensions of
physical and chemical habitat is necessary to interpret this biological information and
meaningfully assess ecological condition. The controlling influence of littoral structure and
complexity on lake biota has been long recognized, and recent research highlights the roles of
habitat structural components like littoral woody debris in providing refuges from predation
and affecting nutrient cycling and littoral production. NLA field crews characterized lake depth,
water surface characteristics, bank morphology and evidence of lake level fluctuations, littoral
and shoreline substrate, fish concealment features, aquatic macrophytes, riparian vegetation
cover and structure, and human land use activities. These littoral and riparian physical habitat
measurements and visual observations were made in a randomized array of 10 near-shore
littoral-riparian plots systematically spaced along the shoreline of each sample lake. Metrics
describing a rich variety of lake characteristics were calculated from this raw data, and many of
these were determined with moderate precision in the national dataset. For the NLA, we
summarize this information with four integrative measures of lake condition, and one measure
of lake drawdown and lake level fluctuation: RDis_IX, incorporating measures of the extent and
intensity of near-shore human land and water use activities; RVegQ, incorporating the structure
and cover in three layers of riparian vegetation, including inundated vegetation; LitCvrQ, a
combined biotic cover complexity measure including large woody snags, brush, overhanging
vegetation, aquatic macrophytes, boulders, and rock ledges; and LitRipCvrQ, which combines
RipVegQ and LitCvrQ. The measure of lake level drawdown incorporates both horizontal and
vertical fluctuation, comparing them to the regional mean values observed in least disturbed
lakes and reservoirs.
We modeled expected values of RVegQ, LitCvrQ, and LitRipCvrQ and their divergence from
reference conditions in least disturbed lakes using regression-based O/E models. The precision
of these O/E indices was moderate to high and showed good discrimination between least
disturbed and most disturbed lakes nationally, and within ecoregions. These results show that,
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compared with least disturbed reference lakes, those with moderate or high human
disturbances in the same region have reduced cover and extent of multi-layered riparian
vegetation or natural wetlands. In addition, those with moderate or high disturbance generally
also have reduced snag, brush and emergent aquatic macrophyte cover. These results
complement the results of the NLA 2012 public report and those of Kaufmann et al. 2014b,
2014c), confirming our general expectation that near-shore wetland and multi-layered riparian
vegetation and abundant, complex fish concealment features foster native fish,
macroinvertebrate, zooplankton, and avian assemblage integrity, whereas extensive and
intensive shoreline human activities that reduce natural riparian vegetation and reduce littoral
cover complexity are detrimental to these biotic assemblages.
We believe that the metrics and indices derived from the NLA physical habitat field approach
and the O/E indices expressing their divergence from least disturbed reference conditions
describe ecologically-relevant characteristics of lake habitat with sufficient precision to evaluate
near-shore lake habitat structure in national, state, and ecoregional assessments. Their
association with gradients of human disturbance demonstrates that they also describe lake
attributes that are vulnerable to anthropogenic degradation and potential for productive
restoration through lake and land management.
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Strayer, D. L., and S. E. G. Findlay. 2010. Ecology of freshwater shore zones. Aquatic Science
72:127-163.
TailIon, D., and M. Fox. 2004. The influence of residential and cottage development on littoral
zone fish communities in a mesotrophic north temperate lake. Environmental Biology of
Fishes 71: 275-285.
USEPA. 2007a. Survey of the Nation's Lakes. Field Operations Manual. EPA 841-B-07-004. U.S.
Environmental Protection Agency, Washington, DC.
USEPA. 2009. National Lakes Assessment: A Collaborative Survey of the Nation's Lakes. EPA
841-R-09-001. U.S. Environmental Protection Agency, Office of Water and Office of
Research and Development, Washington, D.C.
USEPA. 2010. National Lakes Assessment: Technical Appendix. EPA 841-R-09-001a. U.S.
Environmental Protection Agency, Office of Water and Office of Research and
Development, Washington, D.C. 63pp.
USEPA. 2012. 2012 National Lakes Assessment: Field Operations Manual. EPA 841-B-11-003.
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USEPA. 2016. National Lakes Assessment 2012: A Collaborative Survey of Lakes in the United
States. EPA 841-R-16-113. U.S. Environmental Protection Agency, Washington, DC.
WagnerT, Jubar AK, Bremigan MT. 2006. Can habitat alteration and spring angling explain
largemouth bass nest success? Transactions of the American Fisheries Society 135:843-
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USEPA. 2017a. 2017 National Lakes Assessment: Field Operations Manual. EPA 841-B-16-001.
U.S. Environmental Protection Agency, Washington, DC.
USEPA. 2017b. National Lakes Assessment 2012: Techical Report. EPA 841-R-16-114. U.S.
Environmental Protection Agency, Washington, D.C.
West, E., and G. Ruark. 2004. Historical evidence of riparian forests in the Great Plains and how
that knowledge can aid with restoration and management. USDA Forest Service / UNL
Faculty Publications. Paper 7. Digital Commons at University of Nebraska - Lincoln.
http://digitalcommons.unl.edU/usdafsfacpub/7.
Whittier, T. R., D. B. Ha I liwel I, and S. G. Paulsen. 1997. Cyprinid distributions in northeast U.S.A.
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62
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Whittier, T. R., S. G. Paulsen, D. P. Larsen, S. A. Peterson, A. T. Herlihy, and P. R. Kaufmann.
2002. Indicators of ecological stress and their extent in the population of Northeastern
lakes: a regional-scale assessment. Bioscience 52(3):235-247.
Whittier, T. R., S. G. Paulsen, D. P. Larsen, S. A. Peterson, A. T. Herlihy, and P. R. Kaufmann.
2002b. Indicators of ecological stress and their extent in the population of northeastern
lakes: a regional-scale assessment. Bioscience 52:235-247.
Zohary, T., and I. Ostrovsky. 2011. Ecological impacts of excessive water level fluctuations in
stratified freshwater lakes. Inland Waters 1:47-59.
Zar, J.H. 1999. Biostatistical Analysis, 4th ed. Prentice-Hall, Inc. New Jersey, USA.
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Table 5-1, NLA reference sites from combined 2007 & 2012 surveys.
Selected using consistent criteria (Alan Herlihy's RT_NLA12_2015, choosing 2012 visit for sites sampled in both
years). Bold font indicates grouping of reference sites used for modeling expected values for RVegQ, LitCvrQ, and
LitRipCvrQ.
ECQ9 ECOp5 Total 2007 2012
NAP APPAL 67 23 44
SAP. _AP PAL 31 14 17
APPAL (98) (37) (61)
CPL _CP L 28 5 23
U M W _u M W_ 49 18 31
TPL CENPL 23 7 16
NPL CENPL 11 3 8
SPL _CENPL _35 21 14
CENPL (69) (31) (38)
WMT WEST 74 29 45
XER_ WESJ 20 _4 16
WEST (94) (33) (61)
Totals for lower 48 states 338 124 214
Table 5-2. Assignment of riparian vegetation cover complexity, littoral cover complexity, and littoral-riparian
habitat complexity index variants by aggregated ecoregion.
Littoral-Riparian
Habitat Complexity
Index
(LitRipCvrQ)
Aggregated
Omernik
Ecoregion
Riparian Vegetation
Cover Complexity Index
(RVegQ)
Littoral Cover
Complexity Index
( LitCvrQ)
CPL
SAP
NAP, UMW
TPL, NPL, SPL
WMT, XER
RVegCL2
RVegCL2
RVegCL2
RVegCi.7
RVegCL8
LitCvrQ_b
LitCvrQ_c
LitCvrQ_d
LitCvrQ_d
LitCvrQ_d
LitRipCvrQ_2b
LitRipCvrQ_2c
LitRipCvrQ_2d
LitRipCvrQ_7d
LitRipCvrQ_8d
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Table 5-3. Summary of regression models used in estimating lake-specific expected values of Lake Physical Habitat
variables RVegQx, LitCvrQx and LitRipCvrQx under least disturbed conditions.
See Appendix A for model details,
REGION v = RVeaQ v = LitCvrQ v = LitRipCvrQ
NAP Ly* =f(Lat, Lon, LkOrig, RDisIX,) Ly =f(L_LkArea, RDisIX)
(R2=23%, RMSE=0.162L*
(R2= 12% RMSE=0.281L)
Ly =f(Lat, Lon, LkOrig, RDisIX)
(R2=24%, RMSE=0.168L)
SAP Ly =f(Lon)
(R2=16%, RMSE=0.119L)
Ly =f(ElevXLon, RDisIX)
(R2=19%, RMSE=0.267L)
Ly =f(Lon, ElevXLon, Elev)
(R2=31%, RMSE= 0.148L)
CPL y =f(ElevXLat, RDisIX)
(R2=39%, RMSE=0 .0896)
y =f(L_Elev, RDisIX)
(R2=25%, RMSE= 0.174)
y =f( L_Elev, RDisIX)
(R2=44%, RMSE=0.093)
UMW Ly = (mean LRVegQ)
(R2=0%, RMSE=0.153L)
Ly = (mean LitCvrQ)
(R2=0%, RMSE=0.199L)
Ly = (mean LitRipCvrQ)
(R2=0%, RMSE=0 .115L)
CENPL Ly =f{hiiAg)
(R2=15%, RMSE=0.318L)
Ly =f(LkOrig, hiiAg)
(R2=9%, RMSE=0.276L)
Ly =f(hiiAg)
(R2=15%, RMSE=0.233L)
WMT Ly =f(Lat, Elev, L_LkArea, LkOrigin) Ly =f(Lat, Elev, L_LkArea, LkOrigin) Ly =f(Lat, Elev, L_LkArea, LkOrigin)
(R2=28%, RMSE=0.167L) (R2=16%, RMSE=0.244L) (R2=29%, RMSE=0.145L)
XER Ly =f(Lat, Elev)
(R2=24%, RMSE=0.284L)
Ly =f(Lat, Elev)
(R2=16%, RMSE=0.290L)
Ly =f( Lat, Elev)
(R2=21%, RMSE=0.265L)
*Ly refers to Logio-transformed lake habitat metric values.
**L refers to RMSE's that are in Logio units (e.g., 0.162L
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Table 5-4, Null Model Geometric Means (gMean), geometric Standard Deviations (gSD), 5th percentiles, and 25th
percentiles of habitat index values in least disturbed reference lakes in the aggregated ecoregions of the NLA,
The gMeans and gSDs are antilogs of mean and SD of logio-transformed index values (LogMean and LogSD), Bold,
italicized text identifies minimum LogSD and gSD values, i.e., the most precise models for each index. Bold,
underlined text marks the least precise models, gSDs calculated from log-transformed variables are expressions of
the proportional variance of these distributions, so are directly comparable among regions with different gMeans,
A range of+lLogSD is equivalent to multiplying and dividing the gMean by the gSD, For example, the gMean +1
gSD for the riparian vegetation cover complexity index in least disturbed NAP lakes translates to a range of RVegQ
from 0,182 to 0,338: the geometric mean habitat index value of 0,2482 multiplied and divided by 1,363, The 5th
and 25th percentiles were estimated, respectively, as the mean of log-transformed index values minus 1,65 and
0,67 times the SD of log-transformed habitat index values (see Table 5-2 for the variant of each index used). All
percentiles are expressed in the units of the habitat indices, i.e., as antilogs of log-transformed values, (Note that
the constant 0,01 is subtracted from all antilogs because it was added when O/E values were log-transformed).
Aggregated
Refo7i2
Refo7i2
Refo7i2
Refo7i2
Refo7i2
Refo7i2
eco region
Index
LogMean
LogSD
gMean
gSD
est 5th%
est 25th %
Riparian Veaetation Cover Complexity:
NAP null
RVegQ
-0.5881
0.1345
0.2482
1.363
0.1449
0.1998
SAP null
RVegQ
-0.6111
0.1277
0.2348
1.342
0.1407
0.1911
UMWnull
RVegQ
-0.6130
0.1533
0.2338
1.423
0.1262
0.1824
CPLnull
RVegQ
-0.6645
0.2810
0.2065
1.910
0.0644
0.1304
CENPLnull
RVegQ
-0.8346
0.3427
0.1364
2.201
0.0298
0.0760
TPLnull
RVegQ
-0.7295
0.3129
0.1764
2.055
0.0468
0.1050
NPLnull
RVegQ
-1.1352
0.2500
0.0632
1.778
0.0183
0.0398
SPLnull
RVegQ
-0.8093
0.3402
0.1451
2.189
0.0326
0.0817
WMTnull
RVegQ
-0.5900
0.1922
0.2470
1.557
0.1138
0.1811
XERnull
RVegQ
-0.8301
0.3070
0.1379
2.028
0.0360
0.0821
Littoral Cover Complexity:
NAPnull
LitCvrQ
-0.8174
0.2418
0.1423
1.745
0.0508
0.9049
SAPnull
LitCvrQ
-0.6469
0.2873
0.2155
1.938
0.0657
0.1347
UMWnull
LitCvrQ
-0.8756
0.1994
0.1232
1.583
0.0524
0.0879
CPLnull
LitCvrQ
-0.4883
0.2331
0.3049
1.710
0.1240
0.2167
CENPLnull
LitCvrQ
-1.0164
0.2880
0.0863
1.941
0.0222
0.0518
TPLnull
LitCvrQ
-0.9927
0.3190
0.0917
2.084
0.0203
0.0522
NPLnull
LitCvrQ
-0.9974
0.2116
0.0906
1.628
0.0350
0.0626
SPLnull
LitCvrQ
-1.0389
0.2929
0.0814
1.963
0.0200
0.0482
WMTnull
LitCvrQ
-1.0162
0.2578
0.0863
1.811
0.0262
0.0547
XERnull
LitCvrQ
-1.1457
0.2990
0.0615
1.991
0.0130
0.0351
Littoral-Riparian Habitat Complexity:
NAPnull
LitRipCvrQ
-0.6740
0.1404
0.2018
1.382
0.1143
0.1606
SAP null
LitRipCvrQ
-0.6069
0.1690
0.2372
1.476
0.1201
0.1805
UMWnull
LitRipCvrQ
-0.7083
0.1149
0.1857
1.303
0.1165
0.1541
CPLnull
LitRipCvrQ
-0.5391
0.1687
0.2796
1.475
0.1422
0.2128
CENPLnull
LitRipCvrQ
-0.8820
0.2508
0.1212
1.782
0.0406
0.0791
TPLnull
LitRipCvrQ
-0.8230
0.2813
0.1403
1.911
0.0416
0.0874
NPLnull
LitRipCvrQ
-1.0442
0.1887
0.0803
1.544
0.0341
0.0575
SPLnull
LitRipCvrQ
-0.8698
0.2305
0.1902
1.700
0.0462
0.0846
WMTnull
LitRipCvrQ
-0.7369
0.1677
0.1733
1.471
0.0869
0.1315
XERnull
LitRipCvrQ
-0.9455
0.2818
0.1034
1.913
0.0289
0.0634
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Table 5-5, 0/E Physical Habitat Model means (LogMean, gMean), standard deviations (LogSD, gSD), and percentiles
of the distribution of habitat index O/E values for least disturbed reference lakes in the aggregated ecoregions of
the NLA,
See Table 5-3 for the variant of each index used. The gMean and gSD are antilogs of mean and SD of logio-
transformed index values (LogMean and LogSD), Percentiles were estimated, respectively, as the log-transformed
index O/E value of 0,0 (see text) minus 1,65 and 0,67 times the SD of log-transformed habitat index values. Bold,
italicized text identifies minimum SD values, i.e., the most precise models for each index. Bold, underlined text
marks the least precise models, gSDs calculated from log-transformed variables are expressions of the proportional
variance of these distributions, so are directly comparable among regions with different geometric means, A range
of +1SD is calculated by multiplying and dividing the gMean by the gSD, For example, the LogMean + 1 LogSD for
the riparian vegetation cover complexity O/E index in least disturbed lakes of the NAP (0,04276 + 0,1255)
translates to a range of O/E values from 0,78 to 1,31: the geometric mean habitat index O/E value of 1,00 (antilog
of+0,04276 = 1,10 minus log-transform constant 0,10) multiplied and divided by 1,34, the antilog of 0,1255, All
percentiles expressed as antilogs of log-transformed values minus constant 0,10, We based physical habitat
condition criteria based on the distribution of O/E index values in least disturbed lakes within each region. The 5th
and 25th percentiles, respectively, were set as the upper bounds for poor and fair condition.
Aggregated
ecoregion
Index
Ref 0/E Ref 0/E Ref O/E Ref O/E
LogMean LogSD gMean gSD
Ref O/E
5th %tile
Ref O/E
25th
%ti
le
NAP mlr Model >gQ_OE
NAPoe
Yint
SAP mlr Model RVegQ_OE
UMWmlr Model RVegQ_OE
CPL MLR Model
CPLoe
Yint
CENPLmlr
Mode
i CENPLoe
Yint
(-0.00811) (0.1255) (0.88) (l.34)
+0.0427 0.1255 1.00 1.34
RVegQ_OE
RVegQ_OE
+0.0422
6
+0.0428
(-0.0617)
-0.00067 0.2129
0.1105
0.1442
(0.2113)
WMTmlr Model RVegQ_OE
XERmlr Model RVegQ_OE
(-
0.0
279
9)
+0.0468
8
+0.0429
0
+0.0419
9
(0.3165)
0.2928
1.00
1.00
(0.87)
0.90
(0.84)
1.01
1.29
1.39
(1.63)
1.63
(207)
1.96
0.5850 0.8092
0.6244 0.8295
0.5381 0.7835
0.3449 0.6191
0.2663 0.6091
0.1535
0.2656
1.00
1.00
1.42
1.84
0.5162
0.3016
0.7711
0.6312
NAP mlr Model LitCvrQ_OE
NAPoe
Yint
SAP mlr Model LitCvrQ^OE
SAPoe
Yint
(+0.04502)
+0.0466
5
(-0.05093)
+0.0428
7
(0.2330)
0.2330
(0.2500)
0.2440
UMWmlr Model LitCvrQ^OE
+0.0442 0.1954
2
(1.01)
1.01
(0.79)
1.00
1.00
(1.71)
1.71
(1.78)
1.75
1.57
0.3594 0.6772
0.3368
0.4245
0.6575
0.7152
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Ref O/E
25th
Aggregated
Ref 0/E
Ref 0/E
Ref O/E
Ref O/E
Ref O/E
%ti
ecoregion
Index
LogMean
LogSD
gMean
gSD
5th %tile
le
CPL MLR Model
LitCvrQ_OE
(-0.03310)
(0.1909)
(0.83)
(1.55)
CPLoe
// //
-
0.1940
0.88
1.56
0.3704
0.6288
Yint
0.0
07
43
CENPLmlr
LitCvrQ_OE
(+0.00495)
(0.2870)
(o.9l)
(1.94)
Model
// //
+0.0275
0.2839
0.97
1.92
0.2624
0.5876
CENPLoe
2
Yint
WMTmLR Model
LitCvrQ_OE
+0.0377
n
0.2528
0.99
1.79
0.3174
0.6385
XERmLR Model
LitCvrQ_OE
u
+0.0345
1
0.2983
0.98
1.99
0.2486
0.5834
NAP MLR Model
LitRipCvrQ-
(+0.00344)
(0.132l)
(o.9l)
(1.36)
NAPoe
_OE
+0.0423
0.1321
1.00
1.36
0.5672
0.7990
Yint
// //
0
SAP MLR Model
LitRipCvrQ-
+0.0432
0.1329
1.00
1.36
0.5667
0.7999
_OE
6
UMWmLR Model
LitRipCvrQ-
+0.0419
0.1110
1.00
1.29
0.6252
0.8296
_OE
9
CPL MLR Model
LitRipCvrQ-
(-0.0248)
(0.1230)
(0.84)
(1.33)
CPLoe
_OE
+0.0161
0.1234
0.94
1.33
0.5494
0.7580
Yint
// //
5
CENPLmlr
LitRipCvrQ-
(-0.0121)
(0.2413)
(0.87)
(1.74)
Model
_OE
+0.0430
0.2246
1.00
1.68
0.3703
0.6808
i CENPLoe
// //
3
Yint
WMTmLR Model
LitRipCvrQ-
+0.0420
0.1366
1.00
1.37
0.5556
0.7922
_OE
0
XERmLR Model
LitRipCvrQ-
+0.0401
0.2552
1.00
1.80
0.3159
0.6398
_OE
2
Table 5-6, Empirical 75th and 35th percentiles of the distribution of vertical and horizontal drawdown.
As interpreted from indicators of lake level fluctuation (bfxVertHeight and bfxHorizDist) at least disturbed
reference lakes sampled by NLA in 2007 and 2012, We used the 75th and 95th percentiles to define the boundaries
between small, medium and large magnitude of drawdown.
Number of Reference Lakes
(2007+2008)
Vertical Drawdown (m)
(bfxVertHeight)
Horizontal Drawdown (m)
(bfxHorizDist)
Lake
Ecogion . .
Origin
t 4. 1 a. , Human-
Total Natural
made
median 75th% 95th%
median 75th% 95th%
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NLA 2017 Technical Support Document Version 1.1
NAP
All
67
54
13
0.000
0.12
0.470
0.00
0.25
1.65
SAP
All
31
0
31
0.000
0.20
0.760
0.00
0.20
2.15
UMW
All
49
49
0
0.000
0.11
0.50
0.00
0.51
2.65
CPL
All
28
5
23
0.000
0.03
1.00
0.00
0.10
4.00
CENPL
Natural
29
29
0
0.000
0.06
0.28
0.00
0.10
2.85
U 11
Human-
made
39/40
0
39/40
0.010
0.36
1.20
0.21
1.55
14.63
WEST
Natural
69
69
0
0.021
0.33
1.00
0.00
0.64
9.43
U 11
Human-
made
25
0
25
0.232
1.05
2.00
0.27
4.39
11.37
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Table 5-7, Precision of the key NLA Physical Habitat indices used as the primary physical habitat condition
measures in the NLA,
Precision expressed as: 1) the pooled standard deviation of repeat visits (orep), 2) precision relative to potential or
observed range (orep/Rgpot and 0rep/RgPot), and 3) the signal: noise ratio, where signal is among-lakes variance and
noise is within-lake variance during the same year and season (S/N = a2[ake/a2rep). Analysis was based on NLA field
measurements on a summer probability sample of 1203 lakes in the 48 conterminous U.S. states, with repeat
sampling on a random subset of 88 of those lakes during the summer of 2012, Six of the sample lakes showed very
large changes in water level, which affected the littoral and riparian indicator values. We excluded these 6 lakes in
this analysis, except for values within perentheses, RDisJX is the Near-shore human disturbance index, RVegQc is
the Riparian vegetation cover & structure index, Log(RVegQc3OE) is the log-transformed O/E index for Riparian
vegetation cover & structure, LitCvrQcis the Littoral cover complexity index, Log(LitCvrQc3OE is the log-
transformed O/E index for Littoral cover complexity, LitRipCvrQc is the Littoral-riparian habitat complexity index,
LogfLitRipCvrQcaOE) is the log-transformed O/E index for Littoral-riparian habitat complexity, L_VertDD =
Logio(Vertical drawdown +0.1m), and L_HorizDD = Logi0(Horizontal drawdown + 1m).
NLA PHab Indices
Orep
RQobs
Orep/R Qobs
S/N
RDisJX
0.098
0.0-+0.950
0.103
9.1
L_ RVegQc
0.144
-2.0--0.266
0.083
6.6
L_RVegQc3OE
0.130
-1.0-+0.666
0.078
5.0
L_LitCvrQc
0.190
-2.0-+0.0266
0.094
3.4
L_LitCvrQc30E
0.188
-1.0-+0.759
0.107
2.2
L_LitRipCvrQc
0.134
-2.0--0.135
0.072
5.6
L_LitRipCvrQc30E
0.122
-1.0-+0.681
0.073
4.1
L_ VertDD
0.193
(0.266)
-1.0-+1.654
0.073 (0.100)
5.9 (2.7)
L_HorizDD
0.148
(0.283)
0.0-+2.873
0.052 (0.099)
11.0(3.8)
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Table 5-8, Association of NLA-2012 Physical Habitat Indices with high and low anthropogenic disturbance stress
classes (RT_NLA12 = R and T), defined as least disturbed and most disturbed within NLA regions.
The t-values test the null hypothesis that the mean value of the habitat index in Reference sites minus the mean in
most disturbed sites was zero in the NLA 2012 survey. Positive Rvalues indicate that habitat index values are
greater in least disturbed sites; negative values indicate higher index values in disturbed sites. See Figure 5-6 for
box and whisker plots by NLA regions, presented separately for the NLA 2012 and 2007 surveys.
NLA Physical Habitat Indices
tRT
PRT>I tRT 1
RDis_IX- Near-shore human disturbance index
-25*
<0.0001*
L_RVegQc - Riparian vegetation cover & structure index
13
<0.0001
L_RVegQc30E - O/E index for Riparian vegetation cover & structure
14
<0.0001
L_LitCvrQc - Littoral cover complexity index
8.3
<0.0001
L_LitCvrQc30E- O/E index for Littoral cover complexity
9.3
<0.0001
L_LitRipCvrQc-Littoral-riparian habitat complexity index
13
<0.0001
L_LitRipCvrQc30E - O/E index for Littoral-riparian habitat complexity
14
<0.0001
L_VertDD - Logio(Vertical drawdown +0.1m)
-4.3*
<0.0001*
L_HorizDD- Logio(Horizontal drawdown +1.0m)
-4.7*
<0.0001*
* Note that RDis_IX was one of the screening variables used to define least disturbed reference
sites (RT_NLA12=R) and most disturbed sites (RT_NLA12=T), and was a very influential. The
drawdown variables bfxVertHeight and bfxHorizDist were also used in the screening process,
but had only a minor influence on the definition of sites.
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Table 5-9, Association of NLA 2007 and 2012 Physical Habitat Indices with high and low anthropogenic disturbance
stress classes (RT_NLA12 = R and T), defined as least disturbed and most disturbed within NLA regions.
The t-values test the null hypothesis that the mean value of the habitat index in Reference sites minus the mean in
most disturbed sites was zero in the Domain specified in column 1, Positive values indicate that habitat index
values are greater in least disturbed sites; negative values indicate higher index values in disturbed sites. See
Figure 5-6 for box and whisker plots by NLA regions, presented separately for the NLA 2012 and 2007 surveys.
DOMAIN
L_RVegOE
L LitCvrOE
L_LitRipCvrOE
L HorizDD
National
07&12
ig****
12****
ig****
_7 7****
National 07&12
Natural
Human-
14****
g g****
14****
-3.5***
made
12****
g g****
12****
-6 o****
National 2007
12****
-j 2****
12****
_g 2****
2012
14****
g 2****
1^****
_4 7****
APPAL 2007
g 4****
3.0***
4 4****
+1.9
2012
g 4****
^ 1****
4 1****
-3 2***
NAP 2007
4 Q***
2.4**
4 1***
+1.1
2012
2 g***
2 g***
4 2****
-2.4*
SAP 2007
4 g****
1.1
2.9**
-0.2
2012
g 2****
1.4
3.3**
-2.4*
CENPL 2007
4 4****
2.5**
^ Q****
_4 Q****
2012
g 2****
^ 5****
g 4****
-0.6
TPL 2007
4 Q***
0.3
2.9**
-1.2
2012
3.6***
3.3**
2 7***
0.6
NPL 2007
1.3
4.6***
4 g***
i****
2012
2.4*
2.4*
2.2*
+1.6*
SPL 2007
1.4
2.1*
2 2**
-1.2
2012
g Q****
4 4****
gi****
-2.2*
CPL 2007
4 5***
1.4
4 g****
-1.3
2012
3.6***
4 2****
^ 4****
-0.5
UMW 2007
g 5****
g 2****
-j 2****
+4 4****
2012
g i****
3.3***
g 5****
-0.5
WEST 2007
g y****
2 ^***
-j y****
_g i****
2012
g 2****
2 2***
7 2****
2****
WMT 2007
g 2****
1.6*
^ 4****
7****
2012
g y****
2.3*
g Q****
g****
XER 2007
g 2****
3.5***
^ g****
-4 g****
2012
4 5****
2.0*
3.6**
-1.4
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Near-Shore Station NLA-2007:
< i5m >
A
ฃ
Riparian
i/i
rH
V
lm Shoreline band
A
-10m
Littoral
--15m -
Littoral-Riparian Plot
Near-Shore Station NLA-2012:
15 m
A
Variable-
width
{
Riparian
zone
Drawdown
zone
Littoral
zone
>e-
15 m
1m Shore zone
}
10 m
t
Observation station
Figure 5.1. Field sampling design with 10 near-shore stations at which data were collected to characterize near shore
lake riparian and littoral physical habitat in the 2007 and 2012 National Lakes Assessment (NLA) surveys.
The 10 stations were systematically spaced around the shore of the lake from random starting point. Insert shows
riparian plot, shoreline band, littoral plot, and (for NLA 2012 only) drawdown zone plot located at each station.
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X
I
Ln
Q
cc
CD
'lo
M
CD
CC
X
I
Ln
Q
cc
IS)
(V
'lo
i i i i i i i I r
1_WMT 2-UMW 3-NAP 4-SAP 5-SPL 6-XER 7-TPL 8-CPL 9-NPL
DRankEcoRef
1 WMT 2-UMW
3-NAP
4-SAP
5-SPL
6-XER
7-TPL
8-CPL
9-NPL
Figure 5.2. Near-shore anthropogenic disturbance (RDisJX) in NLA0712 regions, ordered by their median
Reference site RDis. Upper plot: Least disturbed reference sites. Lower plot: all sites. Unweighted sample statistics
are shown; box midline and lower and upper ends show median and 25th and 75th percentile values, respectively;
whiskers show maximum and minimum observations within 1.5 times the interquartile range above/below box
ends; circles show outliers.
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C
T3
CD
E
CD
+->
to
4
CD
or
RDislXjmed
0.3-
0.25-
0.2-
0.15-
0.1-
0.05-
o-
i i i i i i i
1-WMT 2-UMW 3-NAP 4-SAP 5-XER 6-CENPL 7-CPL
RegionDRk
Figure 5.3. Near-shore anthropogenic disturbance in NLA0712 least disturbed reference sites (median RDisJX),
ordered by aggregated region according to the same median level of near-shore disturbance.
The NLA EC09 regions NPL, SPL, and TPL are combed into the Central Plains (CENPL) region.
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Log(RVegQ):
RVegLSDrNul
RVegLSDrOEaj
0.35-
O
-0.35
0.3-
0.25-
o
o
-0.3
-0.25
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Q
-0.2
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9
-0.15
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9
o
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-0.05
0-
-0
1-WMT 2-UMW 3-NAP 4-SAP 5-XER 6-CENPL 7-CPL
RegionDRk
IQ RVegLSDrNul | | % RVegLSDrOEaj |
Log(LitCvrQ):
LtCvLSDrNul
LtCvLSDrOE
0.35-
-0.35
0.3-
o
$
-0.3
0.25-
w
-0.25
0.2-
-0.2
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-0.15
0.1-
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-0
1-WMT 2-UMW 3-NAP 4-SAP 5-XER 6-CENPL 7-CPL
RegionDRk
IQ LtCvLSDrNuT| 9 LtCvLSDrpj]
Log(LitRipCvrQ):
LtRpCvLSDrNul LtRpCvLSDrOEaj
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0.25-
o
-0.25
0.2-
-0.2
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1-WMT 2-UMW 3-NAP 4-SAP 5-XER 6-CENPL 7-CPL
RegionDRk
| O LtRpCvLSDrNul | # LtRpCvLSDrOEaj |
Figure 5.4. LogSD's for Null-Model and regression-based Q/E model for Near-shore RVegQ,
LitCvrQ, and LitRipCvrQ in the set of least disturbed lakes and reservoirs (
Table 5-1) sampled in the combined NLA 2007 and 2012 surveys.
X-axis shows the 7 modeling regions ordered by increasing median RDisJX in the reference sites. The NLA EC09
regions NPL, SPL, and TPL are combed into the Central Plains (CENPL) region. Low variance among reference sites
denotes greater precision in estimating expected reference condition, The smaller variance in regression-based
O/E models (black dots) illustrate their greater precision compared with null models (open circles) for a given
indicator and region.
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10-"
0.5-
0.5-
uo
LU
o
co
o
O
o
0.0-
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8
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Figure 5.5. Contrasts in key NU\ physical habitat index values among least disturbed reference (R),
intermediate (S), and most disturbed (T) lakes in the contiguous 48 states of the U.S. based on combined NLA
2007 and 2012 data.
Unweighted sample statistics are shown; box midline and lower and upper ends show median and 25th and
75th percentile values, respectively; whiskers show maximum and minimum observations within 1.5 times the
interquartile range above/below box ends; circles show outliers. See Table 5-9 for t and p values for the
differences between means for least disturbed reference (R) and most disturbed (T) sites.
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RDisJX L1_RVegQc30E15
Figure 5.6. Contrasts in key NLA physical habitat index values among least disturbed reference (R), intermediat (S),
and most disturbed (T) lakes in the contiguous 48 states of the U.S. shown separately for the NLA 2007 and 2012
surveys.
Unweighted sample statistics are shown; box midline and lower and upper ends show median and 25th and 75th
percentile values, respectively; whiskers show maximum and minimum observations within 1.5 times the
interquartile range above/below box ends; circles show outliers. See Table 5-9 for t and p values for the
differences between means for reference (R) and most disturbed (T) sites.
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Chapter 6: Water Chemistry
6.1 Background information
The NLA public report summarizes water quality stressor data collected at the deepest part of
each study lake (up to 50 m). Field sampling included a depth profile and a 0-2 m depth
integrated water sample. Variables analyzed for the NLA 2017 report include: total nitrogen
(TN), total phosphorus (TP), chlorophyll a (CHLA), turbidity, acidity, dissolved oxygen, atrazine
and microcystin. Acidity, dissolved oxygen, trophic state class, atrazine and microcystin
benchmarks were based on established criteria and applied consistently across the nation.
Good, fair and poor condition classes were established for TP, TN, CHLA, and turbidity using the
same percentile of reference sites approach that was used throughout the NLA (Herlihy and
Sifneos, 2013). Benchmarks, however, were recalculated to include additional nutrient
reference sites sampled in 2017. This increased the number of nutrient reference sites available
in each ecoregion allowing for better estimation of the percentiles used to calculate the
benchmarks. Separate benchmarks were established for each of the nine ecoregions reported
on in NLA. As a result of benchmark refinement, 2017 benchmark values were revised;
therefore, direct comparisons should not be made between 2012 condition class results and
those reported in 2017.
6.2 Chemi."x! v ; o ^ 'v >chmarks
6.2.1 Acidity
For setting acidity classes, concentrations of acid neutralizing capacity (ANC) and dissolved
organic carbon (DOC) were analyzed following the scheme developed by Herlihy et al. (1991).
Sites with acid neutralizing capacity (ANC) > 50 ueq/L were considered to be non-acidic and
least disturbed (good condition class) for acidification. Sites with ANC < 50 |aeq/L and DOC
values > 6 mg/L were classified as naturally acidic due to organic acids (also good condition
class). Sites with ANC < 0 |aeq/L and DOC values < 6 mg/L were classified as acidic due to either
acidic deposition or acid mine drainage and considered most disturbed or poor condition class.
Sites with ANC between 0 and 50 |aeq/L and DOC < 6 mg/L were considered acid-influenced but
not currently acidic. These low ANC sites typically become acidic during high flow events
(episodic acidity) and were considered moderately disturbed (fair condition class).
6.2.2 Dissolved Oxygen
Depth profiles of dissolved oxygen were collected at the deepest location of the lake. Surface
water dissolved oxygen was calculated by removing all duplicate depth observations and taking
the mean of all dissolved oxygen values between 0 and 2 meters depth, inclusive. If the lake
was shallower than 2 m depth, the entire depth profile was used. Surface water dissolved
oxygen was classified into three classes, good (>5 mg/L), fair (3-5 mg/L), and poor (<3 mg/L).
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Dissolved oxygen benchmarks of 5 mg/L and 3 mg/L represent US EPA's dissolved oxygen water
qualtiy criteria recommendations for a warmwater daily minimum for early life stages and
other life stages, respectively (USEPA 1986).
6.2.3 Trophic State
Lakes have long been classified according to their trophic state. By the dictionary, "trophic" is
defined as of or relating to nutrition. A eutrophic lake has high nutrients and high algal and/or
macrophyte plant growth. An oligotrophic lake has low nutrient concentrations and low plant
growth. Mesotrophic lakes fall somewhere in between eutrophic and oligotrophic lakes and
hypereutrophic lakes have very high nutrients and plant growth. Lake trophic state is typically
determined by a wide variety of natural factors that control nutrient supply, climate, and basin
morphometry. Trophic state can be defined based on a number of different nutrient or plant
biomass variables. For NLA, trophic state was defined using specific numeric criteria for
concentrations of CHLA (Table 6-1). The same trophic state classification was used for all
ecoregions.
T rophic State Classifi used in NLA
\n;il> le
(Hiuiiimphic
\1CMปll\ปplllC
1 Miimplnc
11> pciviili\2 and <7
>7 and <30
>30
6.2.4 Total nitrogen, total phosphorus, chlorophyll a, and turbidity
TN, TP, CHLA, and turbidity were classified into good, fair and poor condition classes based on
percentiles of the nutrient reference site distribution (Herlihy and Sifneos, 2008, 2013).
Because nutrients (TN, TP) were used to select biological reference sites, the biological
reference sites could not be used as is for nutrient reference lakes due to circularity. During the
development of nutrient reference sites, we compiled all sampled sites in NLA 2007, 2012, and
2017 as was done for the biological reference condition process. All sites were then passed
through the NLA biological reference screening process for their ecoregion as described in
section 3.4 with one exception. To avoid complete circularity, TP and TN thresholds were
removed as screening variables in the screening process.
After this initial screening, there remained a fairly strong disturbance signal in the reference
sites as evidenced by looking at relationships with GIS landscape stressor variables in particular,
% Agriculture and % Developed. In order to remove this disturbance signal, an additional GIS
stressor screen was added to the process to remove from the nutrient reference site pool those
sites that failed the filtering for these two metrics. For watershed % agriculture, ecoregional
criteria were used: >10% for NAP, WMT, and XER lakes; >25% for NPL, SAP, SPL, and UMW
lakes; >40% for CPL lakes; and >50% for TPL lakes. For watershed % developed, a >10% criterion
was used for all ecoregions but the CPL where a >15% filtering criterion was used.
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For calculating the nutrient condition class benchmarks used in the NLA 2017 public report, we
used all NLA nutrient reference sites sampled from 2007-2017 (Table 6-2). When a site was
sampled multiple times, only the first visit to the most recent year of sampling was used to
calculate percentiles so reference sites were not double-counted. Before calculating
benchmarks, a 1.5*IQR outlier analysis was done on the reference site concentrations to
remove outliers. Separate thresholds were calculated for each of the nine NARS ecoregions (Fig.
3-1), and just in the Southern Plains, for natural and manmade lakes separately. Thresholds
were determined for TP, TN, CHLA, and turbidity. The cutoff between good and fair condition
class was set at the 75th percentile (Q3) of reference lakes, and the cutoff between fair and
poor condition class was set at the 95th percentile (P95) of reference lakes (Table 6-3).
Table 6-2, Number of unique nutrient reference sites used to calculate nutrient benchmarks (2007-2017 data).
Ecoregion
Number of Nutrient Reference
Sites
CPL
33
NAP
88
NPL
16
SAP
41
SPL-manmade
24
SPL-natural
20
TPL
26
UMW
87
WMT
142
XER
32
Total
509
There was a very large difference in the absolute concentrations of TP and TN among
ecoregions in the nutrient reference sites (Figure 6-1 and Figure 6-2). Looking at the data, it is
also evident why the natural lakes in the SPL need their own benchmark versus human-made
SPL lakes. Table 6-3 reports the 75th and 95th percentile-based benchmarks used to define the
good, fair and poor condition classes for TP, TN, CHLA, and turbidity for each of the ecoregions
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CPL NAP NPL SAP SPLman SPLnat TPL
NLA Ecoregion
UMW WMT
XER
Figure 6.1. Box and whisker plot of Total Phosphorus in GIS screened, outlier removed, 2007-2017 nutrient
reference sites by ecoregion. Boxes are the interquartile range, whiskers are 5th/95th percentiles.
ฆIOOOOOt
O)
3,
c
d)
O)
2
g
10000
1000
100
101
CPL
NAP
NPL
SAP SPLman SPLnat TPL UMW WMT XER
NLA Ecoregion
Figure 6.2. Box and whisker plot of Total Nitrogen in GIS screened, outlier removed, 2007-2017 nutrient reference
sites by ecoregion. Boxes are the interquartile range, whiskers are 5th/95th percentiles.
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Table 6-3, NLA 2017 good, fair, and poor benchmarks (75th/95th percentiles) for TP, TN, CHLA, and turbidity
condition classes.
TP (Hg/L)
TP (Hg/L)
TN (Hg/L)
TN (ng/L)
75th
95th
75th
95th
Ecoregion
Good-fair
Fair-poor
Good-fair
Fair-poor
CPL
43.0
59.5
659
923
NAP
16.0
27.9
428
655
NPL
63.0
82.0
849
1,620
SAP
18.0
33.0
266
409
SPL-
30.0
43.0
650
830
manmade
SPL-natural
486
839
7,840
11,100
TPL
38.4
57.5
865
1,350
UMW
24.8
40.0
766
926
WMT
23.4
43.0
253
429
XER
44.0
84.8
605
954
CHLA (|ag/L)
CHLA (|ag/L)
Turbidity (NTU)
Turbidity (NTU)
75th
95th
75th
95th
Ecoregion
Good-fair
Fair-poor
Good-fair
Fair-poor
CPL
12.7
28.0
3.42
4.15
NAP
4.52
8.43
1.30
2.52
NPL
10.9
19.3
3.08
4.46
SAP
5.54
13.1
2.83
4.21
SPL-
8.97
12.6
3.32
4.67
manmade
SPL-natural
118
219
71.3
86.4
TPL
13.9
19.8
3.64
4.23
UMW
7.43
14.6
2.18
3.32
WMT
1.86
3.86
0.910
1.60
XER
5.92
9.00
2.97
4.84
6.2.5 Atrazine and Microcystins
Samples for atrazine and microcystins were collected from a 0-2 m vertically integrated water
column sample at the open-water site. Measured concentrations for both parameters were
compared to nationally consistent benchmarks to estimate ecological risk for atrazine and
recreational risk for microcystins. The NLA also reports on the percentage of lakes with
detections of these indicators and changes in detection over time. Detection is defined as a
value greater than the minimum detection limit (MDL). When the MDL changed between
surveys, the greatest MDL for all surveys was used to determine detection.
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Atrazine water chemistry analyses were added to the NLA in 2012. The NLA atrazine benchmark
is the EPA's aquatic plant concentration equivalent level of concern (CE-LOC) used in the EPA's
atrazine ecological exposure monitoring program. This benchmark ensures that atrazine levels
will not cause significant changes in aquatic plant community structure, function and
productivity (US EPA Atrazine website). In NLA 2012, the EPA used a proposed CE-LOC of 4 ppb
for atrazine risk results. In NLA 2017, this value was updated to the current CE-LOC of 3.4 ppb.
To report on the percentage of lakes with atrazine detections, a consistent detection value
needed to be selected. The MDL was equal to 0.046 ppb for most samples in NLA 2012 and 0.03
ppb for most samples in NLA 2017. Therefore, detection results presented in the public report
and data dashboard present the percentage of lakes with measured values greater than 0.046
ppb for both NLA 2012 and NLA 2017.
Microcystins have been measured and reported on in all NLAs. In NLA 2007 and 2012,
concentrations were compared to the World Health Organization's cyanobacteria risk
benchmarks that allowed for the presentation of low, moderate and high human health risk
conditions categories (low risk < 10 ppb, moderate risk 10-< 20 ppb and high risk >20 ppb). In
NLA 2017, microcystins risk results were determined by a comparison to the EPA's recreational
water quality criteria and swimming advisory recommendation of 8 ppb (US EPA 2019).
Microcystins risk results identify the percentage of lakes at or below the benchmark and above
the benchmark. The microcystins detection results were determined using the MDL of 0.1 ppb,
which was consistent in all surveys. The detection results presented in the public report and
data dashboard represent the percentage of lakes with measured values greater than 0.1 ppb.
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6.3 Literature cited
Herlihy, A. T., P. R. Kaufmann, and M. E. Mitch. 1991. Chemical characteristics of streams in the
Eastern United States: II. Sources of acidity in acidic and low ANC streams. Water
Resources Research 27:629-642.
Herlihy, A. T., and J. C. Sifneos. 2008. Developing nutrient criteria and classification schemes for
wadeable streams in the conterminous USA. Journal of the North American
Benthological Society 27:932-948.
Herlihy, A. T., N. C. Kamman, J. C. Sifneos, D. Charles, M. D. Enache, and R. J. Stevenson. 2013.
Using multiple approaches to develop nutrient criteria for lakes in the conterminous
USA. Freshwater Science 32:367-384. doi: 10.1899/11-097.
US EPA. 1986. Quality Criteria for Water ("Gold Book"). EPA 440/5-86-001
US EPA. 2019. Recommended human health recreational ambient water quality criteria or
swimming advisories for microcystins and cylindrospermopsin. US EPA Office of Water.
EPA 822-R-19-001.
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Chapter 7: Zooplankton
7.1 Background information
Zooplankton assemblages have several attributes that make them potentially useful for
assessing the ecological condition of lakes (Stemberger and Lazorchak 1994, Jeppesen et al.
2011). Zooplankton are typically the dominant pelagic consumer in lakes (in terms of both
biomass and numbers (Larsen and Christie 1993). Taxa richness tends to be high in nearly all
lakes. Zooplankton species or guild structure can respond to abiotic stressors such as
eutrophication and acidification, and possibly climate change. Zooplankton occupy an
intermediate level in the overall food web of lakes, and thus can respond to stress responses
from within lower (e.g., phytoplankton) or higher trophic levels (e.g., fish). Zooplankton taxa
demonstrate a range of life history strategies and patterns (e.g., parthenogenesis, resting eggs)
that can be related to environmental stress, both natural and anthropogenic.
The use of zooplankton assemblages in the context of bioassessment appears to be limited,
with many studies focused mainly on taxa richness and taxonomic composition changes in
response to disturbance. Gannon and Stemberger (1978) discussed the potential of using
zooplankton communities to help determine trophic state in lakes, primarily through the use of
"indicator species" that were associated with either oligotrophic or eutrophic conditions.
Sprules and Holtby (1979) and Sprules (1980) examined the utility of using metrics related to
body size and feeding ecology of zooplankton to evaluate lake condition. Duggan et al. (2001,
2002) investigated the potential for developing bioindicators of trophic state using rotifer
assemblages. Dodson et al. (2005) concluded that zooplankton assemblages are indirectly
associated with land use through effects on riparian vegetation and lake characteristics such as
typology and water chemistry. Dodson et al. (2009) examined changes in zooplankton
community structure within a set of lakes in northern Wisconsin in relation to a variety of
within-lake and watershed level characteristics (including human disturbance in the riparian
zone). Stemberger and Lazorchak (1994) calculated 14 metrics based on taxonomy, body size,
life history stage, and trophic guild in 19 lakes in the Northeastern USA representing a gradient
of human disturbance, lake type, and land use. Stemberger and Miller (1998) discussed
expected changes in zooplankton assemblage trophic structure and species composition in
response to changes in the N:P ratio that might result from increased anthropogenic
disturbance.
More recently, there have been attempts to develop indices of biotic condition in lakes using
plankton assemblages, following two approaches. The multimetric approach pioneered by Karr
(e.g., Karr 1981, Karr 1991) has been implemented successfully for other assemblages (e.g., fish,
benthic invertebrates) in streams. Kane et al. (2009) combined zooplankton and phytoplankton
metrics from Lake Erie into a single multimetric index (MMI), the Planktonic Index of Biotic
Integrity, to reflect the response of the plankton to eutrophication. The second approach
(predictive model approach) compares the observed taxa collected at each site to the list of
taxa expected at that site under least disturbed conditions by means of an Observed/Expected
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index (0/E, e.g., Wright 1995, Hawkins et al. 2000, Hawkins 2006, Hawkins et al. 2010). The
predictive modelling approach has been used successfully for other assemblages, principally
benthic invertebrates, but also fish, in streams. The 2007 National Lake Assessment (NLA 2007)
used an O/E model that combined zooplankton and phytoplankton assemblages to assess
ecological condition of lakes in the conterminous US (Yuan et al. 2008, USEPA 2009). Table 7-1
summarizes current knowledge regarding the hypothesized responses of zooplankton
assemblages to different types of disturbance.
For NLA 2012, we decided to develop a MM I for pelagic zooplankton assemblages to assess
biological condition in lakes. We followed the approach described by Stoddard et al. (2008) to
screen candidate metrics for possible inclusion in an MMI. We then computed a large number
of MMIs based on all possible combinations of the metrics that passed the screening process,
following Van Sickle (2010), and selected the MMI that showed the best combination of
responsiveness to disturbance, repeatability, and low redundancy among component metrics.
For NLA 2017, we used the same MMIs to assess lake condition. This chapter provides
corrections and clarifications to the 2012 technical report that we identified since its
publication and describes any modifications that we implemented for the NLA 2017 analyses.
7.2 Methods
7.2.1 Field methods
Sample collection procedures for zooplankton are described in the NLA 2017 FOM (USEPA
2017a). Field crews collected two samples at the index site (deepest area of a lake or the
midpoint of a reservoir) of each lake. The crew collected a "Coarse" sample (ZOCN) using a 1-m
long, 30-cm diameter plankton net having a mesh size of 150 |am. The crew collected a "Fine"
sample (ZOFN) using a 1-m long net with a reducing collar (20-cm diameter) with a mesh size of
50 |am. The total tow length for each net was 5 m, with the number of tows being dependent
on the site depth. At lakes deeper than 6 m, a single 5 m vertical tow was done. At lakes
between 4 and 6 m deep, two 2.5-m vertical tows were done. At lakes between 2 to 3 m deep,
five 1-m vertical tows were done. At lakes less than 2 m deep, ten 0.5 m vertical tows were
collected. Results from pilot studies suggested that a total tow length of 5 m would provide
sufficient numbers of taxa and organisms to develop the MMI from nearly all lakes.
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Table 7-1. Hypothesized responses of zooplankton assemblages to disturbance
Hypothesized
Type of disturbance
Assemblage component or metric
response
References
Catchment development
Biomass of small cladocerans
Increase
Gelinas and Pinel-Alloul
(2008), Beaver et al. (2014)
Catchment development
Abundance of small daphnids and
cladocerans
Increase
Gelinas and Pinel-Alloul
(2008), Dodson et al. (2009),
Van Egeren et al. (2011),
Beaver et al. (2014)
Nutrients; Agricultural
Species richness
Decrease
Gannon and Stemberger
land use; riparian buffer
(1978), Dodson etal. (2005)
presence
Nutrients, land use
Large-sized species richness (e.g.,
Daphnia spp., calanoid copepods)
Decrease
Stemberger and Lazorchak
(1994)
Nutrients, land use
Small-sized species richness (e.g.,
Ceriodaphnia, rotifers)
Increase
Stemberger and Lazorchak
(1994)
Nutrients
Proportion of calanoid copepod
taxa
Decrease
Jeppesen et al. (2000), Du et
al. (2015)
Nutrients
Proportion of cyclopoid copepod
taxa
Increase
Jeppesen et al. (2000), Du et
al. (2015)
Nutrients
Ratio of calanoid copepods to
(cyclopoid copepods + cladocerans)
Decrease
Gannon and Stemberger
(1978), Kane et al. (2009)
Nutrients
Mean size
Decrease
Gannon and Stemberger
(1978)
Nutrients
Total biomass
Increase
Gannon and Stemberger
(1978)
Nutrients
Proportion of cladoceran biomass
Decrease
Jeppesen et al. (2000), Du et
al. (2015)
Nutrients
Relative abundance of calanoid
copepods
Decrease
Brooks (1969), Gannon and
Stemberger (1978)
Nutrients
Relative abundance of cyclopoid
copepods and small-bodied
cladocerans
Increase
Brooks (1969), Attayde and
Bozelli (1998)
Nutrients
Omnivorous taxa richness,
abundance, or biomass
Increase
Stemberger and Lazorchak
(1994), Stemberger et al.
(2001)
Nutrients (total P)
Biomass of rotifers and cyclopoid
copepods
Increase
Du et al. (2015)
Nutrients (total P)
Biomass of cladocerans and
cyclopoid copepods
Decrease
Du et al. (2015)
Nutrients, chlorophyll a,
Rotifer assemblage composition
Change
Duggan et al. (2001), (2002)
Secchi transparency,
temperature, dissolved
oxygen
Decrease in acid
Abundance of large-bodied
Decrease
Tessier and Horwitz (1990)
neutralization
zooplankton
capacity/calcium
concentrations
Invasive species
Native species richness, abundance,
or biomass
Decrease
Kane et al. (2009)
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7.2.1 Laboratory methods
Laboratory methods for zooplankton samples are described in the NLA 2012 laboratory
operations manual (USEPA 2012b). For both the ZOCN and ZOFN samples, the objective was to
subsample a sufficient volume to enumerate and identify at least 400 individuals. In the ZOCN
samples, only cladocerans and copepods (including copepedids) were enumerated. In the ZOFN
samples, only "small" taxa were enumerated (cladocerans < 0.2 mm long, copepods < 0.6 mm
long, rotifers, and nauplii). Veligers were not enumerated in the ZOFN sample. Individuals were
identified to species where possible. A "Large/Rare" search of the entire subsample was done
to identify larger taxa (e.g., Chaoborus, Leptodora, Mysidae, Ostracoda, and Hydracarina). In
2012, only the presence of these taxa in the subsample was noted (i.e., they were not
enumerated). In 2017, some laboratories did record the number of organisms encountered in
the Large/Rare search.
Besides the number of individuals enumerated in the subsample (abundance), we estimated
the volume of water sampled by the tow using the tow length and the radius of the net mouth
for the sample. We used this tow volume to estimate density (no. individuals/L) of each taxon:
Density =
Sample Vol. (mL)
Vol. Counted (mL)
A
x Abundance
Tow Vol. (L)
The biomass (mg dry mass/L) of each taxon in a sample was estimated by measuring the length
of 20 individuals (if possible). Length was converted to a biomass factor (mg dry
mass/individual) based on published length-weight relationships (Dumont et al. 1975, McCauley
1984, Lawrence et al. 1987). Biomass was then calculated as:
Biomass = Density (Indiv./ L) x Biomass Factor (mg / Indiv.)
In 2012, one laboratory did not estimate biomass for their samples. For these samples, we
estimated biomass as the mean biomass of a taxon from samples collected from surrounding
states or used a national mean (all samples collected that included the taxon) if the regional
sample size was too small. In 2017, one laboratory processed all zooplankton samples and
provided quantitative biomass data.
7.3 Data preparation
7.3.1 Data quality assurance
We reviewed field data to correct recording errors and, when possible, to fill in missing values,
especially for critical variables like tow length. We reviewed the raw count files from each
laboratory to correct spelling errors in taxon names, and to make the taxonomy consistent
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across laboratories (using the national lab taxonomy as the standard for all labs). We used
range checks on count, density, and biomass estimates to identify outliers, and corrected them
if they were due to recording errors. The number of errors discovered in the NLA 2017 data was
substantially less than what was found in 2012.
7.3.2 Master taxa list
We developed a master taxa list that included all taxa identified in the ZOFN and ZOCN samples.
The master taxa list included taxonomic information (e.g., phylum, class, order, suborder,
family, subfamily, genus, species, and subspecies. Autecological information for each taxon
included feeding guild (Predator, Omnivore, or Herbivore), Cladocera size class (LARGE vs.
SMALL), based on data from Stemberger and Lazorchak (1994) and the Northeastern Lakes
Survey (Whittier et al. 2002), and a size class variable (NET_SZECLS_NEW) based on whether a
taxon was collected in the ZOCN samples vs. only in the ZOFN samples. Additional attributes for
a limited number of taxa that are included in the list but were not used include trophic
assignments from Sprules and Holtby (1979), and some trait information from Barnett et al.
(2007, 2013).
The laboratory identified 535 unique taxa in the NLA 2012 ZOCN and ZOFN samples
(variable=TAXANAME). We combined some of these unique taxa using a different variable
(TARGET_TAXON), which resulted in 481 unique taxon names as used in metric calculations.
We also had some information regarding non-native zooplankton taxa based on the USGS
Nonindigenous Aquatic Species (NAS) database (Fuller and Neilson 2015). Bosmina coregoni (or
Eubosmina coregoni), Daphnia lumholtzi, and Sinocalanus doerri were considered to be
introduced to North America. Eutymora affinis was considered to be introduced to inland
waters of the US. Pseudodiaptomus forbesi has been introduced into San Francisco Bay, and so
we considered it to be non-native if collected from nearby lakes. Arctodiaptomus dorsalis has
been introduced into lakes in Arizona, Hawaii, and Indiana.
For NLA 2017, we updated the master taxa list from NLA 2012 to add new taxa and associated
autecological information that were identified in the coarse and fine net samples collected in
2017. The NLA 2017 taxa list for zooplankton contains 580 unique names for the variable
TARGET_TAXON, which are used for metric calculations. This is an increase of 99 taxa from
those included in the taxa list for NLA 2012.
7.3.3 Aggregations and rarefaction of count data
We aggregated some values of TARGET_TAXON within a given ZOCN or ZOCN sample. We
combined copepodites and nauplii with adults of the same taxon if both were present in a
sample. If a species and a lower level taxon (i.e., subspecies, variety, or form) were both
present in a single sample, we aggregated the count data to the species level.
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After aggregating at the sample level, we combined the results for each ZOCN and ZOFN sample
to create a separate site-level count file. We assumed that individuals collected in the ZOCN
samples that were also present in the ZOFN sample represented smaller individuals that passed
through the coarse-mesh net, and so we added the counts from the two samples together.
Because not all zooplankton individuals in a sample can be confidently identified to species,
there is a risk of overestimating taxa richness. For each sample, we reviewed the list of taxa to
determine whether they were represented at more than one level of resolution. For example, if
a "Daphnia sp." was collected, and it was the only representative of the genus in the sample (or
at the site), we assigned it as distinct. If any other members of the genus were collected, then
we considered the unknown as not distinct. We used only the number of distinct taxa in the
sample to calculate any metrics based on species richness. We calculated distinct taxa for both
the sample-level aggregated count file and the site-level count file. Taxa that were identified
(but not enumerated) during the Large/Rare search were included in calculating richness
metrics.
Even with a fixed count subsampling approach, taxonomic richness and metrics can be
influenced by the number of individuals enumerated in a subsample (Stoddard et al. 2008). We
created an additional count file to use for metric calculation by subjecting the sample-level
aggregated count data to a rarefaction procedure to randomly select 300 individuals per
sample (for those samples that had > 300 individuals enumerated and identified). We repeated
the sample level aggregation of taxa on the 300-count file; thus, the resultant site-level count
file typically had a total count of 600 individuals. We did not calculate density on the 300-count
files but did calculate biomass.
7.4 Zooplankton MMI development
7.4.1 Regionalization
We divided the conterminous US into five "bio-regions" based on nine aggregated Omernik
Level III ecoregions (Omernik 1987, Stoddard 2004, Herlihy et al. 2008, Omernik and Griffith
2014) that were developed for use on NARS reporting Figure 7.1). We combined the Northern
and Southern Appalachian regions (NAP, SAP) into a single bio region (Eastern Highlands,
EHIGH). We combined the three "plains" regions (Northern, Southern, and Temperate [NPL,
SPL, and TPL]) into a single bio-region (PLAINS). In the western US, we combined the Xeric and
Western Mountains regions (XER, WMT) into a single "Western Mountains" bio-region
(WMTNS). Despite relatively small sample sizes of least disturbed sites, we kept the Coastal
Plains (CPL) and Upper Midwest (UMW) as separate bio-regions. These are the same regions as
are used for the NLA benthic macroinvertebrate MMI.
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Figure 7.1 Five aggregated bio-regions used to develop zooplankton MMIs for the 2012 National Lake Assessment
(CPL=Coastal Plains; EHIGH=Eastern Highlands, PLAINS= Plains, UMW=Upper Midwest, and WMTNS=Western
Mountains). Solid dots indicate least disturbed sites used for developing the zooplankton MMI. White circles
indicate least disturbed sites that we excluded because of atypical samples (too few taxa or number of individuals
collected).
7.4.2 Least and most disturbed sites
For NLA 2012, we used the same list of sites for the zooplankton MMI as those selected for
benthic macroinvertebrates (RT_NLA12; see Section 3.3). We identified two least disturbed
sites that appeared to have abnormal zooplankton samples and excluded them from the list of
least-disturbed sites.
For NLA 2017, we combined least disturbed sites sampled in 2017 with those from the NLA
2012 assessment. We retained only one visit per site by excluding revisits (VISIT_NO=2) and
using the 2017 visit for sites from 2012 that were resampled in 2017. In addition, we identified
three situations where we felt that the zooplankton samples from least disturbed sites were
not representative of the existing assemblage, and excluded these sites from developing
condition class benchmarks:
1. Sites where at least one of the net samples were hugely dominated by unidentified
copepod individuals (which included nauplii and immature copepodites).
2. Sites where no rotifers were collected.
3. Sites where less than 100 individuals were collected in either the coarse or fine net
sample.
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The first two of these situations were used in the NLA 2012 assessment. The third situation was
added for the NLA 2017 assessment. Out of 343 least disturbed lakes, we identified 21 sites (15
from 2012 and only six from 2017) where the coarse sample had less than 100 individuals
counted. At two of these sites (both from 2012), the fine net sample also had less than 100
individuals counted.
7.4.3 Least disturbed sites: calibration versus validation
As an independent check on the MMI developed for each bio-region, we set aside a small
number of least disturbed sites as "validation" and did not include them in any MMI or metric
evaluations or performance testing. We used revisit sites (typically VISIT_NO=2) as validation
sites because they are not used in any metric or MMI testing. We then supplemented the list of
revisit sites in each region by randomly selecting sites from the list of least disturbed sites.
Where possible, we withheld ~10% of the least disturbed sites in each bio-region as validation
sites, leaving at least 15 least disturbed sites available for developing and evaluating metrics
and MMIs. For the CPL and UMW bio-regions, the small number of least disturbed sites
prevented setting aside 10% of the site for validation. Numbers of validation sites were as
follows: CPL (8), EHIGH (16), PLAINS (14), UMW (10), and WMTNS (18).
7.4.4 Candidate metrics
We used the count data file and the master taxa list file to calculate candidate metrics. We
assigned candidate metrics to one of six metric categories, with each category reflecting a
different attribute of assemblage structure or ecological function.
The Abundance category included metrics based on abundance, density, or biomass. We
calculated these metrics separately for the ZOFN samples, the ZOCN samples, and for the
combined samples. Within the combined sample, we also calculated abundance metrics
separately for the net-based size classes (COARSE vs. FINE).
The Richness category included metrics based on taxa richness and metrics related to taxa
diversity or dominance. Richness metrics included total distinct taxa richness, number of
genera, and number of families. We calculated these metrics separately for the ZOCN, ZOFN,
and combined sample. We calculated diversity and dominance metrics for the combined
sample based on abundance, density, and biomass. Diversity metrics included Shannon-Weiner
and Simpson indices, and Hurlbert's Probability of Interspecific Encounter (PIE, Hurlbert 1971,
Jeppesen et al. 2000). For each combined net sample, we developed dominance metrics based
on the percent of individuals represented in the most dominant taxon and represented in the
three and five most dominant taxa.
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We assigned separate categories for each of the three principal taxonomic components of the
zooplankton assemblage: Cladoceran, Copepod, and Rotifer. Metrics in these three categories
included abundance and richness metrics calculated separately for each taxonomic group. For
copepods, we also calculate the ratio of calanoids to the sum of cladocerans and cyclopoids,
following Gannon and Stemberger (1978) and Kane et al. (2009).
The sixth metric category was trophic guild. We identified three major guilds, herbivores,
omnivores, and predators. Each taxon was assigned to a trophic guild based on information
from the Northeast Lakes Survey (Stemberger and Lazorchak 1994, Stemberger et al. 2001). We
calculated metrics using both the entire sample and for the 300-count rarefied samples. Metrics
derived from the rarefied sample have "300" in the variable name.
For many metrics, we could calculate six different variants: the number of distinct taxa
(metr/'c_NTAX), total biomass (metric_BIO), density (metr/'c_DEN), percent of individuals
(metr/'c_PIND), percent of total biomass (metric_PB\0) and percent of total density
(metr/'c_PDEN). We did not calculate density-based metrics for the 300-count rarefied samples.
Each variant was calculated based using all the individuals in the sample, and for just the native
individuals in the sample. We calculated a total of 374 candidate metrics for the whole sample
count data, and an additional 272 metrics from the 300-count rarefied sample data.
7.4.5 Final metric selection
We subjected all of the candidate metrics to five screening procedures, following Stoddard et
al. (2008). The first was a range test. We excluded richness metrics (metr/'c_NTAX) with a range
of <4 from further consideration. We excluded metrics based on biomass (metric_BIO), density
(metr/'c_DEN), diversity metrics, and zooplankton ratio if the 90th percentile (P90) was 0. We
excluded percentage metrics (metric_PTAX, metr/'c_PBIO, metr/'c_PDEN) if the 75th percentile
(P75) was <10%.
The second screen was a signal to noise (S:N) test, following Kaufmann et al. (1999). We
compared the total variance observed across all sites (signal) against the variance observed for
sites that were sampled twice in the same index period (noise). We excluded metrics that had
S:N values < 1.25.
The third screen was for responsiveness to disturbance. For each metric, we calculated the t-
statistic for each metric comparing values for the set of least disturbed sites with those for the
set of most disturbed sites. We considered metrics having 1t\ values < 1.73 as non-responsive
to disturbance.
The fourth screen was to determine if metrics required adjustment for lake size. We generated
plots of linear regressions of each metric with lake area (AREA_HA) to determine if the metric
response changed with increasing lake size. For all metrics, the upper 95% prediction interval at
the minimum response value overlapped the lower 95% prediction interval at the maximum
response value, indicating there was no significant effect of lake size on the metric response.
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For each bio-region, we used the set of candidate metrics that had passed the four screens
describe above to develop candidate MMIs. We constrained the MMIs to contain at least one
metric from each of the six metric categories (abundance, richness, crustacean, copepod,
rotifer, and trophic). If no metrics within a category passed all of the screens, we selected one
or more metrics that had the highest tvalues and had S:N values near 1 (if possible). Values of
S:N <1 indicate that that variation within a site is equal to or greater than the variation among
sites, so the metric cannot discriminate among sites.
Finally, we evaluated the redundancy among candidate metrics using correlation analysis.
Historically, we have evaluated redundancy based on the establishing a maximum allowable
correlation coefficient (r) between two metrics (e.g., r >0.7; Stoddard et al. 2008)). Van Sickle
(2010) demonstrated that MMIs containing a suite of metrics that have a low average
correlation among them perform better that simply using a maximum threshold value of r to
reduce redundancy within the suite of metrics. We included correlations in the procedure
below, computing correlations among metrics for each candidate MMI, rather that evaluating
individual input metrics within a category and choosing only non-redundant metrics to include
in a final MMI, as described by Stoddard et al. (2008).
Candidate metrics that we considered for inclusion into an MMI for each of the five bio-regions
are listed inAppendix D: List of Candidate Metrics for Zooplankton. For each bio-region, we
computed MMIs from all possible combinations of candidate metrics from the six categories.
We evaluated each MMI for responsiveness (ttest of least disturbed vs. most disturbed sites)
and repeatability (S:N). For each bio-region, we selected MMI that had a combination of high t
value, a reasonable value for S:N, low mean r among the suite of metrics, and, when possible, a
maximum value of r for the suite of metrics that was <0.7.
7.4.6 Metric scoring
We followed the approach described by Stoddard et al. (2008) to transform metric responses
into a metric score that ranged between 0 and 10 (Blocksom 2003). For positive metrics (i.e., t
>0), we used the 5th percentile of all sites in the bio-region as the "floor" value, and the 95th
percentile of the set of least disturbed sites as the "ceiling" value. For negative metrics (i.e., t
<0), we used the 5th percentile of least disturbed sites in the bio-region as the "floor" value, and
the 95th percentile of all sites as the "ceiling" value. When metric response values were less
than the floor value, we assigned a score of 0. When metric response values were greater than
the ceiling, we assigned a score of 10. We estimated scores for response values that were
between the floor and ceiling values by linear interpolation.
We calculated the final MMI score for each bio-region by summing the six component metric
scores, and then multiplying by 10/6. This resulted in an MMI score that ranged between 0 and
100.
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7.5 Zooplankton MMI metric composition and performance
See Appendix D: List of Candidate Metrics for Zooplankton for metric descriptions.
7.5.1 Coastal Plains MMI
The component metrics for the Coastal Plains MMI are presented in Table 7-2. Information
related to the performance of the Coastal Plains MMI are presented in section 7.6. Figure 7.2.
compares the distributions of the six metrics in least disturbed vs. most disturbed sites. Three
metrics are "negative" metrics (t <0) values, indicating that the response is greater in most
disturbed sites compared to least disturbed sites. No abundance or cladoceran metrics passed
both the responsiveness and repeatability screens. The abundance metric (FINE_BIO [biomass
of smaller-sized taxa]) had a t value and an S:N value that were just below the screening
criterion. The cladoceran metric (SIDID_PIND [percent of individuals of the cladoceran family
Sididae]) had an S:N value that was below the screening criterion.
The abundance metric (FINE_BIO), the cladoceran metric (SIDID_PIND), the richness metric
(FAM300_NAT_NTAX), and the trophic metric (OMNI_PTAX) responded as expected to
disturbance as expected (Figure 7.2; Table 7-1). The copepod metric (DOM1_300_COPE_PBIO)
and the rotifer metric (COLLO_PBIO) decreased in response to disturbance (Figure 7-2).
Declines in the proportion of total biomass contributed by either dominant copepods or a
subgroup of rotifers might be expected if the total richness and abundance total biomass of
cyclopoid copepods and rotifers increased with disturbance (Table 7-1).
Table 7-2, COMPONENT METRICS OF THE ZOOPLANKTON MMI FOR THE COASTAL PLAINS BIO-REGION,
Evaluations for responsiveness (t-value) and signahnoise (S:N) based on index visits and do not include least
disturbed "validation" sites. Negative values for t indicate response is greater in most disturbed sites vs. least
disturbed sites. Metrics having values marked with an asterisk were among the best performing metric of that
category but failed one or more evaluation screens. Floor and ceiling values are used to derive a score for the
metric.
Metric Type
Metric Variable Name (floor, ceiling)
t value
S:N (bio-region)
Abundance/Size
FINE_BIO (2.913623, 173.279784)
-1.67*
1.2*
Cladoceran
SIDID_PIND (0, 24.88)
-1.80
0.5*
Copepod
DOM1_3(DO_COPE_PBIO (45.90, 100)
+1.16*
1.9
Richness/Diversity
FAM300_NAT_NTAX (5, 15)
+2.72
2.0
Rotifer
COLLO_PBIO (0, 5.90)
+1.85
7.2
Trophic
OMNI_PTAX (10.53, 47.06)
-3.35
4.3
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ABUNDANCE
CLADOCERAN
O
m
400
300
j 200
100
0
100
80
t 60
g'
Q 40
in
20
0
:
100
80
60
40
20
0
COPEPOD
-i
LD MD
LD MD
LD MD
RICHNESS
ROTIFER
20
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10
10
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30
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LD MD
LD MD
LD MD
Figure 7.2. Distribution of six component metrics of the zooplankton MMI for the Coastal Plains bio-region in least
disturbed (LD) versus most disturbed (MD) sites. Dots indicate the 5th and 95th percentiles.
7.5.2 Eastern Highlands MMI
The component metrics for the Eastern Highlands MMI are presented in Table 7-3. Information
related to the performance of the Eastern Highlands MMI are presented in section 7.6. Figure
7.3 compares the distributions of the six metrics in least disturbed vs. most disturbed sites. The
suite of metrics includes both positive (2) and negative (4) metrics. No richness metrics passed
the screens for responsiveness or repeatability. The richness metric (ZOCN300_FAM_NTAX) had
a t value (1.64) just below the screening criterion, while the S:N value (0.3) was well below the
screening criterion.
The cladoceran metric (SMCLAD_PBIO), the richness metric (COARSE_NAT_PTAX ),the rotifer
metric (ROT_PBIO), and the trophic metric (OMNI300_PTAX) responded as expected to
increased disturbance (Figure 7.3; Table 7-1). The abundance metric (ZOCN_DEN) and the
copepod metric (COPE_NAT_DEN) both increased in response to disturbance (Error! Reference
source not found.)- An increase in cyclopoid copepods expected with increased disturbance
(Table 7-1) would help to explain the observed response in both of these metrics.
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Table 7-3, COMPONENT METRICS OF THE ZOOPLANKTON MMI FOR THE EASTERN HIGHLAND BIO-REGION,
Evaluations for responsiveness (t-value) and signahnoise (S:N) based on index visits and do not include least
disturbed "validation" sites. Negative values for t indicate response is greater in most disturbed sites vs. least
disturbed sites. Floor and ceiling values are used to derive a score for the metric, SeeAppendix D: List of Candidate
Metrics for Zooplanktonfor metric descriptions.
Metric Type
Metric Variable Name (floor, ceiling)
t value
S:N (bio-region)
Abundance/Size
ZOCN_DEN (0.216450905,259.3045469)
-1.89
2.2
Cladoceran
SMCLAD_PBIO (0, 57.31)
-2.91
1.3
Copepod
COPE_NAT_DEN (8.8236,398.397)
-1.69
1.5
Richness/Diversity
COARSE_NAT_PTAX (22.22,57.14)
+1.71*
0.2*
Rotifer
ROT_PBIO (0.79,86.39)
-1.94
1.2*
Trophic
OMNI300_PTAX (12.50, 44.44)
-2.48
1.8
ABUNDANCE
CLADOCERAN
COPEPOD
250
200
q 150
O
o
N
100
50
0
-T-
80 -
o
CO
Q-J 60 -
Q
<
I AC! _
o
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7.5.3 Plains MMI
The component metrics for the Plains MMI are presented in Table 7-4. Information related to
the performance of the Plains MMI are presented in section 7.6. Figure 7.4 compares the
distributions of the six metrics in least disturbed vs. most disturbed sites. The MMI was
comprised of two negative and four positive metrics. All metrics passed the screening criteria
for both responsiveness and repeatability.
The copepod (COPE_RATIO_300_BIO), richness (FAM300_NAT_TAX), and the trophic
(COPE_HERB_PDEN) metrics responded as expected to increased disturbance (Figure 7.4; Table
7-1). The abundance (FINE300_NAT_PBIO), cladoceran (SMCLAD_NAT_PIND), and the rotifer
(ROT_NTAX) metrics all decreased with response to increased disturbance. If herbivorous
cyclopoid copepods are becoming more dominant in terms of richness, abundance, and
biomass, that may result in a decline in the relative biomass of individuals collected in the fine-
mesh net (principally rotifers), a decline in the relative abundance of smaller cladocerans, and a
decline in rotifer taxa richness.
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Table 7-4. COMPONENT METRICS OF THE ZOOPLANKTON MMI FOR THE PLAINS BIO-REGION.
Evaluations for responsiveness (t-value) and signaknoise (S:N) based on index visits and do not include least
disturbed "validation" sites. Negative values for t indicate response is greater in most disturbed sites vs. least
disturbed sites. Floor and ceiling values are used to derive a score for the metric.
Metric Type
Metric Variable Name (floor, ceiling)
t value
S:N (bio-region)
Abundance/Size
FINE300_NAT_PBIO (0.66, 85.12)
+1.89
5.8
Cladoceran
SMCLAD_NAT_PIND (0, 49.03)
+3.11
1.8
Copepod
COPE_RATIO_300_BIO (0, 62.81)
+2.41
3.0
Richness/Diversity
FAM300_NAT_NTAX (5, 15)
+2.20
2.6
Rotifer
ROT_NTAX (3, 17)
+2.63
1.7
Trophic
COPE_HERB_PDEN (0, 29.93)
-2.45
9.1
ABUNDANCE
CLADOCERAN
COPEPOD
100
o
80
m
Q.
i-1
<
60
z
O
40
CO
LU
LL
20
0
I
60
Q 50
40
30
20
O
oj 10
80
I 60
40
I
ID
o- 20
O
O
:
LD MD
LD MD
LD
MD
RICHNESS
ROTIFER
TROPHIC
16
12
8
4
0
20
15
10
5
0
30
20
w
Q
0.
m'
a.
LU
x
LU 10
Q.
O
O
LD MD
LD MD
LD MD
Figure 7.4. Distribution of six component metrics of the zooplankton MMI for the Plains bio-region in least
disturbed (LD) versus most disturbed (MD) sites. Dots indicate the 5th and 95th percentiles.
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7.5.4 Upper Midwest MM!
The component metrics for the Upper Midwest MMI are presented in Table 7-5. Information
related to the performance of the Upper Midwest MMI are presented in section 7.6. Figure 7.5
compares the distributions of the six metrics in least disturbed vs. most disturbed sites. The
MMI is composed of four negative and two positive metrics. No abundance metrics passed the
screen for responsiveness. The abundance metric (ZOCN_NAT_PDEN [the percent of total
density represented by native individuals in the coarse net sample]) had a t-value that is below
the screening criteria for responsiveness. Repeatability (S:N values) of the metrics in this bio-
region are higher than in other bio-regions, but interpretation of the S:N values is constrained
somewhat by a limited number of revisit samples (5).
Only three of the six metrics responded to disturbance as expected (Figure 7.5Error! Reference
source not found.; Table 7-1). The abundance metric (TOTL_NAT_PIND) showed a slight
decrease with disturbance, indicating the effect of non-native taxa in this bio-region. The rotifer
metric (DOMl_ROT_PBIO) indicates a reduction in species richness (i.e., increased dominance
by one or a few taxa) with increased disturbance. The trophic metric (COPE_HERB300_PBIO)
indicates an increase in herbivorous taxa (possibly cyclopoid copepods) with increased
disturbance. The cladoceran metric (BOSM300_NAT_PTAX) was expected to increase with
increased disturbance, but the response may reflect a larger increase in the taxa richness of
other forms of smaller zooplankton (e.g., cyclopoid copepods). The copepod metric
(CALAN300_NAT_BIO) indicates an increase in larger forms of zooplankton. Such a response
might occur if the least disturbed population of lakes is dominated by oligotrophic lakes that do
not support large populations of zooplankton. The richness metric (FINE_PTAX) decreased in
response to disturbance. This response may be similar to that observed for the cladoceran
metric, where other forms of smaller zooplankton (e.g., cyclopoid copepods) increase in
taxonomic richness compared to rotifers, which are the dominant taxa collected in the fine-
mesh net.
7.5.5 Western Mountains MMI
The component metrics for the Western Mountains MMI are presented in Table 7-6.
Information related to the performance of the Western mountains MMI are presented in
Section 7.6. Figure 7.6 compares the distributions of the six metrics in least disturbed vs. most
disturbed sites. The MMI is composed of three negative and three positive metrics. No richness
metrics passed the screen for responsiveness. The richness metric (ZOFN300_NTAX [Number of
distinct taxa in the 300-count rarefied sample from the fine net sample]) had a t value that was
below our acceptance criteria for responsiveness.
The abundance (COARSE300_NAT_PBIO), cladoceran (LGCLAD300_NAT_PTAX), richness
(ZOFN300_NTAX), rotifer (PLOIMA_PTAX), and trophic (COPE_OMNI_PTAX) metrics responded
as expected to increased disturbance (Figure 7.6, Table 7-1). The copepod metric
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(COPE300_BIO) would respond as expected to disturbance if the increase in biomass was due
primarily to smaller forms (e.g., cyclopoid copepods).
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Table 7-5, COMPONENT METRICS OFTHE ZOOPLANKTON MMI FOR THE UPPER MIDWEST BIO-REGION,
Evaluations for responsiveness (t-value) and signahnoise (S:N) based on index visits and do not include least
disturbed "validation" sites. Negative values for t indicate response is greater in most disturbed sites vs. least
disturbed sites. Metrics having values marked with an asterisk were the best performing metric of that category
but failed one or more evaluation screens. Floor and ceiling values are used to derive a score for the metric.
Metric Type
Metric Variable Name (floor, ceiling)
t value
S:N (bio-region)
Abundance/Size
TOTL_NAT_PIND (96.75, 100)
+1.47*
Noise=0
Cladoceran
BOSM300_NAT_PTAX (0, 12.5)
+2.72
1.3
Copepod
CALAN300_NAT_BIO (0,65.037544)
-2.17
9.9
Richness/Diversity
FINE_PTAX (37.50, 77.78
+1.87
1.4
Rotifer
DOMl_ROT_PBIO (25.30, 93.60)
-2.46
3.5
Trophic
COPE_HERB300_PBIO (0.19, 59.42)
-1.99
5.1
100
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E
-1
< 80
I
70
60
100
80
60
40
20
0
ABUNDANCE
CLADOCERAN
T
LD MD
RICHNESS
20
x
<
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Table 7-6. COMPONENT METRICS OF THE ZOOPLANKTON MMI FOR THE WESTERN MOUNTAINS BIO-REGION.
Evaluations for responsiveness (t-value) and signal:noise (S:N) based on index visits and do not include least
disturbed "validation" sites. Negative values for t indicate response is greater in most disturbed sites vs. least
disturbed sites. Metrics having values marked with an asterisk were the best performing metric of that category
but failed one or more evaluation screens. Floor and ceiling values are used to derive a score for the metric.
Metric Type
Metric Variable Name (floor, ceiling)
t value
S:N (bio-region)
Abundance/Size
COARSE300_NAT_PBIO (10.94, 99.26)
+1.89
5.6
Cladoceran
LGCLAD300_NAT_PTAX (0, 29.285)
+2.53
2.0
Copepod
COPE300_BIO (0.073928, 149.035677)
-2.76
2.0
Richness/Diversity
ZOFN300_NTAX (3, 15)
-1.69*
1.9
Rotifer
PLOIMA_PTAX (20, 70.835)
+0.49*
4.3
Trophic
COPE_OMNI_PTAX (0, 22.22)
-2.46
1.5
ABUNDANCE
CLADOCERAN
COPEPOD
m
o.
LU
<
O
o
40
30
20
Q
<
O
a
10
400
300
O
m
o'
8 200
w
Q.
o
ฐ 100
LD MD
LD MD
LD MD
RICHNESS
ROTIFER
TROPHIC
20
g 15
10
5
0
30
X
<
Q-! 20
m 10
O
o
LD MD
LD MD
LD
MD
Figure 7.6. Distribution of six component metrics of the zooplankton MMI for the Western Mountains bio-region in
least (LD) disturbed versus most disturbed (MD) sites. Dots indicate the 5th and 95th percentiles.
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7.6 Zooplankton MMI performance
We evaluated each of the five regional MMIs in several ways.
7.6.1 Calibration versus validation sites
To provide an independent assessment of MMI performance, we compared the distribution of
MMI scores between the set of validation sites (which we did not use in MMI development) and
the calibration sites using a t-test. The null hypothesis was that the mean values of the two
groups would be equal. Mean values of the two groups were not significantly different (p <
0.05) for any bio-region (Table 7-7). Error! Reference source not found, shows the distribution
of MMI scores between the calibration and validation sites in the five bio-regions.
7.6.2 Precision of MMIs based on least disturbed sites
We evaluated the precision of the regional MMIs using the sets of least disturbed calibration
sites, following Van Sickle (2010). We rescaled the MMI scores in each bio-region by dividing
each site score by the mean MMI score, which resulted in a mean rescaled MMI score of 1. We
calculated the standard deviation of the rescaled MMI scores (Table 7-7). The smaller the
standard deviation, the more precise the index is, and the better the ability to detect sites that
are not in least disturbed condition. Standard deviations were generally small except for the
Plains, where site MT-104 had a large influence.
7.6.3 Responsiveness, redundancy, and repeatability of zooplankton MMIs
We compared the MMI scores from the set of least disturbed sites to the set of most disturbed
sites (excluding the validation sites) using a t-test. We calculated the S:N values using the set of
revisit sites within each bio-region (again excluding the validation sites). Table 7-8 presents the
results of these tests, along with the maximum and average correlations observed for the
component metrics. The t values for responsiveness are comparable to MMIs developed for
other resource types and assemblages (e.g., benthic invertebrates). Figure 7.8 Distribution of
zooplankton MMI scores in least-disturbed (LD) vs. most disturbed (MD) sites for five bio-
regions. Sample sizes are in parentheses. Dots indicate the 5th and 95th percentiles. Figure
7.8Error! Reference source not found, shows the distribution of MMI scores between least-
and most disturbed sites in the five bio-regions. Signal:Noise values are comparable to other
MMIs that have been developed for other assemblages. The S:N value for the UMW bio-region
is constrained by the small number of revisit sites (5) available. When MMI scores from all bio-
regions are considered, the national-level estimate of S:N is 7.0.
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Table 7-7. RESULTS OF INDEPENDENT ASSESSMENT AND PRECISION TESTS OF NLA 2012 ZOOPLANKTON MMIs
BASED ON LEAST DISTURBED SITES.
None of the t-values were significant at p = 0.05. Standard deviations were calculated using only calibration sites.
Calibration vs. Validation
Standard Deviation
Sites
of Standardized
Regional MMI
(t-value)
MMI scores
Coastal Plains (CPL)
0.85
0.187
Eastern Highlands (EHIGH)
-1.23
0.119
Plains (PLAINS)
1.21
0.237
Upper Midwest (UMW)
0.94
0.112
Western Mountains (WMTNS)
0.42
0.117
100
^^ ^ ^ ^ V* ^ ^
BIO-REGION x LD CLASS
Figure 7.7. Distribution of zooplankton MMI scores in-calibration vs. validation sites for five bio-regions. Sample
sizes are in parentheses. Dots indicate the 5th and 95th percentiles.
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Table 7-8. RESULTS OF RESPONSIVENESS, REDUNDANCY, AND REPEATABILITY TESTS FOR NLA 2012 ZOOPLANKTON
MMIs.
* For the Upper Midwest (UMW) MMI, the abundance metric scores in all least-disturbed sites were identical.
Values in parentheses are correlation coefficients with the abundance metric coefficients set to missing.
Redundancy
Responsiveness
Redundancy
(Mean pairwise
Repeatability
t-test of Least
(Maximum pairwise
correlation among
Signal: Noise ratio
disturbed vs. Most
correlation among
component
based on revisit
Bio-Region
disturbed Sites
component metrics)
metrics)
sites
Coastal Plains
(CPL)
4.11
0.55
0.28
2.7
Eastern Highlands
(EHIGH)
5.09
0.43
0.17
2.5
Plains (PLAINS)
5.49
0.57
0.20
3.6
Upper Midwest
(UMW)
5.78
1.0 (0.61)*
0.50 (0.20)*
18.0
Western
Mountains
(WMTNS)
6.28
0.561
0.20
3.6
100
Least Disturbed (LD) vs. Most Disturbed (MD)
(Index visits only. For LD: Calibration sites only)
\^ ^ ^ ^ # # v ^ ? .>? ,>?
BIO-REGION x DISTURBANCE CLASS
Figure 7.8 Distribution of zooplankton MMI scores in least-disturbed (LD) vs. most disturbed (MD) sites for five bio-
regions. Sample sizes are in parentheses. Dots indicate the 5th and 95th percentiles.
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7.6.4 Responsiveness to a generalized stressor gradient
We performed an additional evaluation of the MMIs for responsiveness to disturbance. We
performed principal components analysis (PCA) on the set of chemical, physical habitat, and
visual assessment stressor variables used to screen for least disturbed and most disturbed sites.
Chemical stressor variables included chloride, sulfate, turbidity, and acid neutralizing capacity
(CL, S04, TURB, and ANC, respectively). Habitat stressor variables (Kaufmann et al. 2014; see
Chapter 5 for descriptions and calculations) included shoreline disturbance due to non-
agricultural activities (hiiNonAg), shoreline disturbance due to agricultural activities (hiiAg_Syn),
and the proportion of shoreline stations with at least one type of disturbance present in either
the littoral zone or shoreline plots (hifpAnyCirca_syn). Stressor variables from the visual
assessment included the intensity of observed types of agricultural activities (AGR_SCORE),
intensity of observed types of residential activities (RES_SCORE), and intensity of observed
types of commercial and industrial activities, excluding evidence of fire (IND_NOFIRE). We
transformed the chemical variables (logio[x+l]) and standardized all variables to mean=0 and
variance=l. The first PCA axis explained 38% of the total variance, and the highest variable
loadings were for the chemical and agricultural-related habitat variables. The second PCA axis
explained an additional 18% of the total variance, and the highest variable loadings were for
the non- agricultural habitat variables and the intensity of residential activities. Linear
regression of the MMI score versus the PCA axis 1 scores yielded an r2 of 0.42 (r= 0.65) for PCA
axis 1 (Figure 7-9), and 0.006 for PCA axis 2 scores. These results indicate the zooplankton MMI
is principally responsive to nutrient conditions resulting from agricultural disturbance, and less
responsive to other types of habitat disturbance.
7.6.5 Effect of natural drivers and tow length on MMI scores
The set of lakes sampled for the NLA 2012 included both natural and human-made lakes and
included a wide range of sizes (as estimated by lake area as represented in NHD). In addition,
the sampling protocol did not include a vertical tow through the entire water column. Any one
of these factors might produce a bias in the MMI scores that would require assessing ecological
condition separately for one or more of these groups of lakes (natural vs. human-made, small
vs. large lakes, or shallow versus deeper lakes). We use the set of least disturbed sites
(calibration and validation) to evaluate the potential differences in MMI scores in these groups
of lakes.
7.6.5.1 Lake origin
We compared the distributions of MMI scores in least disturbed natural lakes vs. human-made
reservoirs for each of the five bio-regions (Figure 7.10). The distributions are similar within each
bio-region except the WMTNS, where human-made lakes appear to have much lower MMI
scores than natural lakes. In the Coastal Plains, human-made lakes have higher MMI values than
natural lakes, but interpretation is constrained by the small number of least disturbed natural
lakes (n=3). In the WMTNS, the sample size for least disturbed human-made lakes is relatively
small (n=16) and is influenced to some extent by the presence of outliers with low MMI scores
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(Figure 7.10). We did not feel the observed differences were large enough to treat MMI scores
from lakes and reservoirs differently in terms of settings condition benchmarks.
MMI= -11.94(PCA Axisl Score) + 55.97 (R2=0.42)
-4 -2 0 2 4 6
PCA Axis 1 Score
Figure 7.9. Linear regression of NLA 2012 Zooplankton MMI scores vs. first axis score from principal components
analysis (PCA) based on chemical, habitat, and visual assessment stressor variables used to screen least- and most
disturbed sites.
Least Disturbed Sites
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7.6.5.2 Lake size
We examined the set of least disturbed sites for evidence of difference in MMI scores due to
lake size (Figure 7.11). We noted earlier than we did not have to calibrate individual metrics for
lake size (Section 7.4.5). Distributions of MMI scores were similar in median values and ranges
for all size classes except for the largest (> 500 ha), which had a similar median but a wider
range.
100
ฃ 80
o
0
(/>
1 60
ฃ
O
ฃ 40
ฃ
re
Q.
ง 20
N
0
Figure 7.11. Zooplankton MMI scores versus lake size class within least disturbed lakes of the NLA 2012. Sample
sizes are in parentheses. Dashed lines are mean values. Dots indicate the 5th and 95th percentiles.
7.6.5.3 Site depth
Least Disturbed Sites
# ^ #
vO* * ^
K. kO kO m m
1 "? J? ^
n -7
We had some concerns that the 5-m tow length used to collect zooplankton samples might be
less effective in deeper lakes, where larger taxa may migrate to deeper waters during the day
to avoid fish predation, and thus be underrepresented in the samples. We examined MMI
scores in least disturbed sites as they related to the depth of the index site where samples were
collected (Figure 7-12). There was no apparent pattern in relation to site depth, and the
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distribution of MMI scores was similar for least-disturbed lakes that were < 6 m deep (the
maximum depth where the tow length encompassed the entire water column), and for lakes >
6 m deep (where part of the water column would not be subject to sampling).
7.6.5.4 Component metrics used in zooplankton MMIs for NLA 2017
Table 7-9 summarizes the component metrics for each of the five zooplankton MMIs used in NLA
2017. There were no changes or modifications from those used in the NLA 2012.
Table 7-3, Component metrics of the zooplankton multimetric indices (MMIs) used for NLA 2017,
Metric Category
Metric Description
Direction
of
Response"
Metric Variable Name
Coastal Plains MMI
Abundance/Biomass/Density
Biomass of fine mesh net (50 urn) taxa
INC
FIN E_BIO
Cladoceran
% of total individuals that are within
the cladoceran family Sididae
INC
SIDID_PIND
Copepod
% of biomass in dominant copepod
taxon (300 count subsamples)
DEC
DOM1_300_COPE_BIO
Richness/Diversity
Number of native families (300 count
subsamples)
DEC
FAM300_NAT_NTAX
Rotifer
% of total biomass within the rotifer
order Collothecaceae
DEC
COLLO_PBIO
Trophic
% of taxa that are omnivorous
INC
OMNI_PTAX
Eastern Highlands MMI
Abundance/Biomass/Density
Density of individuals collected in
coarse mesh net (150-nm)
INC
ZOCN_DEN
Cladoceran
% Biomass represented by small
cladoceran individuals
INC
SMCLAD_PBIO
Copepod
Density represented by native copepod
individuals
INC
COPE_NAT_DEN
Richness/Diversity
% of taxa that are larger-sized and
native
DEC
COARSE_NAT_PTAX
Rotifer
Percent total biomass from rotifers
INC
ROT_PBIO
Trophic
Percent of taxa that are omnivorous
(300-count subsamples)
INC
OMNI300_PTAX
Plains MMI
Abundance/Biomass/Density
% of biomass represented in individuals
of smaller-sized native taxa (300-count
subsamples)
DEC
FINE300_NAT_PBIO
Cladoceran
% of native individuals within the
suborder Cladocera that are "small"
DEC
SMCLAD_NAT_PBIO
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Metric Category
Metric Description
Direction
of
Response"
Metric Variable Name
(coarse and fine net samples
combined)
Copepod
Ratio of Calanoids to
(Cladocera+Cyclopoids) based on
biomass (300-count subsamples).
DEC
COPE_RATIO_300_BIO
Richness/Diversity
Total native family richness (300-count
subsamples)
DEC
FAM300_NAT_NTAX
Rotifer
Number of rotifer taxa
DEC
ROT_NTAX
Trophic
% of total density represented by
herbivorous copepods
INC
COPE_HERB_PDEN
Upper Midwest MMI
Abundance/Biomass/Density
% of native individuals
DEC
TOTL_NAT-PIND
Cladoceran
% of native taxa that are within the
cladoceran family Bosminidae (300-
count subsamples)
DEC
BOSM300_NAT_PTAX
Copepod
Biomass of individuals within native
calanoid taxa (300-count subsamples)
INC
CALAN300_NAT_BIO
Richness/Diversity
% of fine mesh net (50 nm) taxa
DEC
FINE_PTAX
Rotifer
Percent of rotifer biomass in dominant
rotifer taxon
INC
DOMl_ROT_PBIO
Trophic
Percent of biomass represented by
herbivorous copepods (300-count
subsamples)
INC
COPE_HERB300_PBIO
Western Mountains MMI
Abundance/Biomass/Density
% biomass of individuals of native
coarse mesh net (150 nm) taxa (300-
count subsamples)
INC
COARSE300_NAT_PBIO
Cladoceran
% of distinct native taxa that are large
cladocerans (300-count subsamples)
INC
LGCLAD300_NAT_PTAX
Copepod
Total biomass of copepod individuals
within the subclass Copepoda (300-
count subsamples)
INC
COPE300_BIO
Richness/Diversity
Number of taxa in the fine net (50-nm)
sample (300-count subsample)
INC
ZOFN300_NTAX
Rotifer
% taxa that are within the rotifer order
Ploima
DEC
PLOIMA_PTAX
Trophic
% taxa that are omnivorous copepods
DEC
COPE_OMNI_PTAX
0 Direction of response to increased disturbance: INC= response increases with increased disturbance,
DEC=response decreases with increased disturbance.
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7.7 Thresholds for assigning ecological condition
7.7.1 NLA 2012
For the NLA 2012, we followed Stoddard et al. (2008) in using the set of least disturbed sites
(including calibration and validation sites) to set ecological condition benchmarks based on the
zooplankton MMI. We used the 25th percentile value to distinguish sites in "good" condition (similar
to least disturbed) from sites in "fair" condition (slightly deviant from least disturbed). We used the
5th percentile value to distinguish sites in "fair" condition from sites in "poor" condition (different
from least disturbed).
Because of varying quality of least disturbed sites within each bio-region, we adjusted the
percentiles using the same process as for the NLA 2012 benthic macroinvertebrate indicator
(Herlihy et al. 2008; see Chapter 6). We performed principal components analysis (PCA) based
on all variables used in the screening of least disturbed sites (TP, TN, CI, S04, Turbidity, physical
habitat disturbance indices, and assessment indices). We transformed values (logio[x] or
logio[x+l]) before analysis. Initially, there were 214 least disturbed sites for zooplankton. We
performed a linear regression of zooplankton MMI score versus the score for the first principal
component. Before calculating benchmarks, we performed a 1.5*IQR outlier analysis on the set
of least disturbed site MMIs to remove outliers. We excluded three sites based on this test (one
each in the CPL EHIGH, and WMTNS), leaving 211 least disturbed sites. Of the 211 least
disturbed sites, 9 sites (8 in WMTNS and 1 in PLAINS) were missing data required for the PCA
analysis, and so do not have principal component scores (mostly missing turbidity in CA). Thus,
there were a total of 202 sites used for the benchmark adjustment statistical analysis.
The best regression model had two different slopes and separate intercepts for each bio-region
(Table 7-10). The pooled model RMSE was 10.86. We used a pooled RMSE (based on all sites) to
provide an adequate sample size for estimating the distribution of MMI scores about the
intercept value for each bio-region. The regression models for the CPL, EHIGH and UMW bio-
regions had no relationship with disturbance and their slopes were set to zero. The slopes for
the PLAINS and WMTNS bio-regions were similar enough that a single value (-6.113) was used
for both. The intercepts were 74.16 in the CPL, 78.75 in the EHIGH, 74.10 in the UMW, 58.32 in
the PLAINS, and 74.39 in the WMTNS. Table 7-11 shows both the raw (unadjusted sample) 5th
and 25th percentiles and the regression model adjusted percentiles that we are using as the
MMI benchmarks. In three bio-regions (CPL, EHIGH, and UMW), the adjustment resulted in as
slight lowering (< 2 points) of the Good/Fair benchmark value. In the PLAINS and WMTNS bio-
regions, the Good/Fair benchmark values were increased (4.6 to 5.6 points). Adjustment
lowered the Fair/Poor benchmark values in the CPL, EHIGH, and UMW bio-regions by 2.7 to 6.7
points. The Fair/Poor benchmark value was increased by 14.5 points in the PLAINS bio-region,
and 3.9 points in the WMTNS bio-region.
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Least Disturbed Sites
a>
o
o
C/5
c
o
+ฆ>
J*
c
re
Q.
O
o
N
100
80
60
40
20
+
+
+
*
T)
. #\-
+
+
+
+
+
+
+
10 20 30 40 50
Index Site Depth (m)
Reference line=6m (maximum tow length=5m)
60
Least Disturbed Sites
<= 6 m
> 6 m
(113) (97)
Depth Class
Figure 7.12.Zooplankton MMI scores versus site depth for least disturbed sites.
Upper panel shows MMI scores versus actual site depth. The reference line of 6 m separates shallower lakes
where the entire water column was sampled and deeper lakes where part of the water column was not
sampled. The lower panel compares distribution of MMI scores in shallow lakes (<6 m; n=113) versus deeper
lakes (> 6 m, n=97). Dots indicate the 5th and 95th percentiles.
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Table 7-10, LINEAR REGRESSION STATISTICS OF ZOOPLANKTON MMI SCORES VERSUS PCA-BASED DISTURBANCE
SCORE FOR EACH BIO-REGION,
Bio-Region
Slope
Intercept
RMSE (Pooled)
Coastal Plains (CPL)
0
64.94
10.01
Eastern Highlands (EHIGH)
0
76.50
10.01
Plains (PLAINS)
-6.143
54.55
10.01
Upper Midwest (UMW)
0
72.49
10.01
Western Mountains
(WMTNS)
-6.143
63.48
10.01
Table 7-11, ECOLOGICAL CONDITION BENCHMARKS FOR ZOOPLANKTON MMI SCORES (NLA 2012 ONLY) BASED ON
THE DISTRIBUTION OF LEAST DISTURBED SITES IN FIVE BIO-REGIONS,
Poor condition indicates a site is different from least disturbed condition. Fair condition indicates a site is
somewhat deviant from least disturbed condition. Good condition indicates a site is similar to least disturbed
condition. Values in bold (adjusted based on the regressions of MMI scores to PCA-based disturbance scores) are
used to assign condition.
Good/Fair Benchmark
Range of MMI
Bio-
(P25)
Fair/Poor Benchmark (P5)
scores in Least
Region
na
Adjusted
Unadjusted
Adjusted
Unadjusted
disturbed Sites
Coastal
22
57.7
59.4
48.4
49.7
38.80 to 94.47
Plains
(CPL)
Eastern
59
57.2
58.0
60.0
57.3
46.37 to 92.62
Highlands
(EHIGH)
Plains
37
42.4
37.8
33.2
17.4
4.42 to 78.57
(PLAINS)
Upper
31
73.3
73.7
56.0
58.0
53.37 to 92.01
Midwest
(UMW
Western
51
69.2
63.6
54.6
53.9
31.24 to 97.94
Mountains
(WMTNS)
0 Number of least disturbed sites remaining after excluding statistical outliers and sites with missing PCA -based
disturbance scores.
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7.7.2 NLA 2017
The process used to develop condition class benchmarks in the NLA 2012 was modified as
follows for the NLA 2017:
1. We excluded within-year revisits (see Section 2.1.3) and used the 2017 visit for sites that
were sampled in both 2012 and 2017.
2. We did not try to adjust the benchmarks for varying quality among regions by using the
"hindcasting" approach described by Herlihy et al. (2008).
Before calculating benchmarks for each of the five bio-regions, we removed outliers based on a
1.5*IQR outlier analysis of the MMI scores in least disturbed sites (Tukey 1977). We used the
25th percentile value to distinguish sites in "good" condition (similar to least disturbed) from
sites in "fair" condition (slightly deviant from least disturbed). We used the 5th percentile value
to distinguish sites in "fair" condition from sites in "poor" condition (different from least
disturbed). The revised benchmark values (Table 7-12) were used to assign condition classes for
the NLA 2017 sites, and to re-assign condition classes for the NLA 2012 sites (so that the change
in condition status could be estimated).
Table 7-12 Ecological condition benchmarks for NLA 2017 zooplankton MMI scores based on the distribution of
least disturbed sites in five aggregated ecoregions (bio-regions).
Poor condition indicates a site is different from least disturbed condition. Fair condition indicates a site is
somewhat different from least disturbed condition. Good condition indicates a site is similar to least disturbed
condition.
Number of
Least Disturbed
Zooplankton
Good-Fair
Fair-Poor
Bio-region
Sites"
Benchmark
Benchmark
Coastal Plains
23
59.42
53.77
Eastern
88
73.595
60.03
Highlands
Plains
61
36.72
28.17
Upper Midwest
61
63.68
52.03
Western Mountains
102
60.78
51.32
0 Based on a single visit per site from the NLA 2012 and the NLA 2017 and after excluding sites where less than 100
individuals were collected in either the coarse or fine net sample, anomalous samples, and statistical outliers.
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7.8 Discussion
We were able to develop regional MMIs for pelagic zooplankton assemblages that were
sufficiently responsive and repeatable to allow us to assess ecological condition for the NLA.
The zooplankton assemblage appears to be responsive principally to disturbance resulting from
increased nutrients and from increases in agricultural-related activity, which is consistent with
previous studies (e.g., Gannon and Stemberger 1978, Stemberger and Lazorchak 1994). We did
not observe a strong response of the zooplankton assemblage to shoreline habitat disturbance,
as has been noted by others (e.g., Stemberger and Lazorchak 1994).
Based on our evaluations, the zooplankton MMIs we developed do not appear to be affected by
lake origin (except possibly in the WMTNS), lake size, or by the use of a restricted tow length
that does not collect individuals which might be occupying waters deeper than 6 m. Presence of
these effects requires dealing with different types or sizes of lakes differently, either in terms of
developing separate MMIs for them, or in setting different benchmark values for them based
on a very small number of least disturbed lakes.
The regional zooplankton MMIs have the following limitations. Samples must be collected using
the same protocols and nets. Individuals were identified to the lowest practical taxon (with
species being the target level). However, total richness metrics did not perform well in terms of
responsiveness or repeatability, so coarser level identification may be possible in the future.
However, coarser-level identification will constrain the development of predictive models
based on taxa richness (O/E models) and would reduce the precision associated with biomass
estimates due to lumping of taxa to coarser levels. Many richness metrics didnot perform well
in the 2012 NLA, but stronger richness signals may be observed in future rounds of the NLA.
Many density- and biomass-based metrics did perform well, thus laboratory analyses will
require the determination of biomass, which increases costs.
In some bio-regions, our requirement for inclusion of at least one metric from each of the six
categories resulted in using metrics that were either not very responsive to disturbance or were
not very repeatable, and, in some bio-regions, including metrics that were most correlated.
Eliminating the poor-performing metrics from the suite of metrics did not appear to improve
the MMI performance, so we retained them for consistency across bio-regions. Moreover, in
those cases where we had a pair of highly correlated metrics, the mean correlation among all
pairs of component metrics was low, so we did not feel the correlation unduly influenced the
performance of the MMI (Van Sickle 2010). Future research might eliminate the requirement of
metric categories and just include the best performing metrics regardless of metric category to
determine if the resulting MMIs prove to be more responsive and repeatable than those
described in this document.
We observed that the responses of some metrics were contradictory to what we expected with
increased disturbance (Table 7-1). However, little information is available, other than
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generalization about taxa richness and assemblage composition, and possibly feeding ecology,
to support or refute the responses we observed in metrics related to density or biomass. We
are not aware of any studies that have conducted an evaluation of an exhaustive list of
candidate zooplankton metrics such as we developed for the NLA; it is possible that there has
not been the incentive to do so up to now. We hope that the success of the initial NLA
zooplankton MMIs will increase interest in the use of zooplankton metrics and indices in lake
bioassessment activities. This would lead to additional information related to responses of
zooplankton assemblages to various types of human disturbance.
We also worked with a limited set of autecological information for the zooplankton taxa that
were collected (essentially taxonomic and coarse-level feeding ecology). Additional information
is available for a limited number of taxa (e.g., Sprules and Holtby 1979, Barnett et al. 2007,
2013, Vogt et al. 2013; Hebert et al. (2016)), but it is uncertain if this information can be
assigned to related taxa. We did not have any information regarding the tolerance of
zooplankton taxa either to specific stressors or to a generalized disturbance variable. Tolerance
values have been developed for large numbers offish taxa as well as benthic invertebrate taxa
(Yuan 2004, Carlisle et al. 2007, Whittier et al. 2007, Meador et al. 2008, Whittier and Van Sickle
2010), and for rotifers in New Zealand (Duggan et al. 2001). Data are available from NLA 2007
that would allow tolerance values to be developed and applied to the NLA zooplankton MMI,
albeit at a coarser taxonomic level than species, and tolerance values derived from NLA 2012
would be available for future assessments.
Finally, it is well known that predation by fish and larger invertebrate predators can affect
zooplankton assemblages. Predation by planktivorous fish can result in smaller-sized taxa
becoming more abundant. The NLA does not collect any detailed information about fish
assemblages, so interpretations of response of metrics or the MMI to increased nutrients may
be confounded with an increase in the number of fish species (including planktivorous species)
that might accompany an increase in nutrients and a shift in the temperature regime from cold
water to warm water.
The primary modifications to the NLA zooplankton MMI indicator implemented for the NLA
2017 were focused on defining the reference distribution for ecological condition benchmark
calculations. Adding a minimum count criterion for excluding least disturbed sites before
calculating ecological condition benchmarks is consistent with what is done for the NLA benthic
macroinvertebrate MMI. We excluded more NLA 2012 sites with this screen than NLA 2017
sites. The observed decrease may have been due to clarifications made in the field NLA 2017
operations manual and during training to help reduce the occurrence of problematic samples.
For sites that were not least disturbed, we did not treat sites with low counts differently, unless
there was evidence that any zooplankton sample was compromised.
We combined least disturbed sites from the NLA 2012 and the NLA 2017 to increase the sample
sizes to provide more robust estimates of the percentiles on which the condition class
benchmarks are based. This is consistent with what has been done for several other NLA
indicators that derive benchmarks based on least disturbed condition. Sample sizes were
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substantially increased in four of the five bio-regions. The sample size for the Coastal Plains
(CPL; n=24) was only increased by two sites over what was available in the NLA 2012.
Finally, we have determined for other indicators and NARS assessments that adjusting the
percentiles used as thresholds for ecological condition class assignments using the approach
described in Herlihy et al. (2008) does not yield benchmarks that are much different from the
unadjusted percentiles for nearly all aggregated ecoregions (or bio-regions). The adjustment
process requires additional time and effort and is more complicated to explain. Having
increased sample sizes of least disturbed sites from combining multiple surveys may be a factor
in the increased comparability of the unadjusted and adjusted percentiles.
Several aspects of the zooplankton MMI development process warrant further work:
1. Evaluating MMIs constructed using the best-performing metrics regardless of their
metric category.
2. Investigating metrics that perform well, but whose response to disturbance appears to
be contrary to our current expectations.
3. Developing better autecological information for zooplankton taxa, especially with
respect to tolerance to environmental stressors.
All of these aspects are still applicable after the NLA 2017 study and could lead to refinements
of the MMI process before the next round of the NLA is implemented in 2022.
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7.9 Literature cited
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Zealand, and their potential use as bioindicators of lake trophic state. Hydrobiologia
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Duggan, I. C., J. D. Green, and R. J. Shiel. 2002. Distribution of rotifer assemblages in North
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Gelinas, M., and B. Pinel-Alloul. 2008. Relating crustacean zooplankton community structure to
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Hawkins, and L. L. Yuan. 2008. Striving for consistency in a national assessment: the
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Hurlbert, S. H. 1971. The non-concept of species diversity: a critique and alternative
parameters. Ecology 52:577-586.
Jeppesen, E., P. Noges, T. Davidson, J. Haberman, T. Noges, K. Blank, T. Lauridsen, M.
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Jeppesen, E., J. Peder Jensen, M. S0ndergaard, T. Lauridsen, and F. Landkildehus. 2000. Trophic
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gradient. Freshwater Biology 45:201-218.
Kane, D. D., S. I. Gordon, M. Munawar, M. N. Charlton, and D. A. Culver. 2009. The Planktonic
Index of Biotic Integrity (P-IBI): An approach for assessing lake ecosystem health.
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Kaufmann, P. R., P. Levine, E. G. Robison, C. Seeliger, and D. V. Peck. 1999. Quantifying physical
habitat in wadeable streams. EPA 620/R-99/003, Office of Research and Development,
US Environmental Protection Agency, Washington, DC.
Kaufmann, P. R., D. V. Peck, S. G. Paulsen, C. W. Seeliger, R. M. Hughes, T. R. Whittier, and N. C.
Kamman. 2014. Lakeshore and littoral physical habitat structure in a national lakes
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Larsen, D. P., and S. J. Christie, editors. 1993. EMAP-Surface Waters: 1991 pilot report. EPA
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Omernik, J. M. 1987. Ecoregions of the conterminous United States. Annals of the Association
of American Geographers 77:118-125.
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Evolution of a Hierarchical Spatial Framework. Environmental Management 54:1249-
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Whittier, T. R., R. M. Hughes, G. A. Lomnicky, and D. V. Peck. 2007. Fish and amphibian
tolerance values and an assemblage tolerance index for streams and rivers in the
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Chapter 8: From Analysis to Results
8.1 Background information
In the NLA 2017 public report, lake condition estimates based on chemical, physical and
biological information are expressed as percentage of lakes or number of lakes; therefore, site
weights from the probability design must be used to generate population estimates along with
the data from the probability sites sampled (1005). Extent estimates for biological indicators
and other measures are used to calculate relative and attributable risk.
8.2 Population estimates
The survey design for the NLA, discussed in Chapter 2 of this document, produces a spatially
balanced sample using the NHD+/NHD+ HR for 1-5 ha lakes as the sample frame. Each lake has
a known probability of being sampled (Stevens and Olsen 1999, Stevens and Olsen 2000,
Stevens and Olsen 2004). A sample weight is assigned to each individual site as the inverse of
the probability of that lake being sampled. Sample weights can be adjusted for different survey
populations (e.g., sampled population or target population; see Chapter 2 and Appendix B) and
are expressed in units of lakes. In 2017, EPA determined it was appropriate to adjust the site
weights used to calculated the populations estimates to represent the percentage of lakes
relative to the target population. Results presented in prior NLAs were relative to the sampled
population.
The probability of a site being sampled was related to lake size class and was stratified by state.
Site weights for the survey were adjusted to account for additional sites (i.e., oversample lakes)
that were evaluated when the primary sites were not sampled (e.g., due to denial of access,
being non-target). These site weights are explicitly used in the calculation of lake condition and
extent estimates, so results can be expressed as estimates of lakes (i.e., numbers of lakes or
percentage of the entire resource) in a particular condition class for the entire conterminous
U.S. For examples of how this has been done for other National Aquatic Resource Survey
(NARS) assessments, see USEPA (2006), Olsen and Peck (2008), and USEPA (2009). It is
important to note that the NLA was not designed to report on individual lakes or states, but to
report at national and regional scales. The NLA 2017 national results are the focus in the public
report. Regional results are also presented for some indicators. All regional scale and
subpopulation results are presented in the interactive dashboard.
8.2.1 Subpopulations
8.2.1.1 Ecoregions
The EPA has defined ecoregions at various scale, ranging from the coarse ecoregions at the
continental scale (Level I) to finer ecoregions that divide the land into smaller units (Level II or
IV). The nine ecoregions used in NLA are aggregations of the Level III ecoregions delineated by
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EPA for the continental U.S. These nine ecoregions include the Northern Appalachians (NAP),
Southern Appalachians (SAP), Coastal Plains (CPL), Upper Midwest (UMW), Temperate Plains
(TPL), Southern Plains (SPL), Northern Plains (NPL), Western Mountains (WMT), and Xeric (XER).
Additional information on the NLA ecoregions is available on the NARS website
(https://www.epa.gov/national-aquatic-resource-surveys/ecoregions-used-national-aquatic-
resource-surveys).
8.2.1.2 Lake origin: natural vs. human-made
The NLA condition estimates can also be explored and analyzed by lake origin. Unfortunately,
there is not a clear dichotomy between natural and human-made lakes. Many naturally existing
lakes are altered hydrologically to widely varying degrees by flow control structures, lake level
augmentation, and other human activities. For NLA analyses, we defined human-made lakes as
only those that are totally artificial, either impounded streams/rivers (reservoirs) or excavated
basins, an adaption of the definition developed by Whittier et al. (2002) during the EMAP lake
surveys. Excavated lakes are formed by flooding of quarries, borrow pits or any other type of
human dug hole and usually lack flowing outlets. Impoundments were originally lotic
waterbodies now turned into lentic waterbodies intentionally by humans. In our definition for
NLA purposes, human-made lakes are those where no lake existed prior to European
settlement. These include millponds, created residential, agricultural, or recreational ponds and
lakes, as well as reservoirs created for flood control, water supply, or hydroelectric production.
Every other type of lake is considered natural, even if the flow or shape is highly altered by
humans.
It was not always easy to assign lake origin to NLA sample lakes. The following information was
used after sampling to determine the classification for each lake:
Lake name (reservoir in name);
Google Earth views (or ArcGIS Explorer Desktop);
Online topographic maps (ArcGIS Explorer Desktop or DeLorme Topo USA);
Field collected data (i.e., assessment form including determination of
Seepage/Drainage/Reservoir and dams; verification form with general comments;
maximum lake depth);
Initial site evaluation/reconnaissance determination;
GNIS waterbody type;
Internet lake history searches; and
Ecoregion location.
The process used to determine lake origin in NLA has evolved based in part on lessons learned
and in part due to advances in technology (e.g., availability of online images and maps and free
apps such as Google Earth and ArcGIS, Desktop Explorer). As a first step, we look for agreement
of the initial reconnaissance with the field crew classification, followed by a quick map or
Google Earth review. When there are discrepancies in this information, a more in depth analysis
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was conducted. No one source of information on lake origin by itself, was definitive. Sources
sometimes give conflicting answers; therefore, we used a weight of evidence approach to make
the classification in difficult cases. General guidelines included the following.
1. Ecoregion location review. The Southern Appalachians have almost no natural lakes, any
lake there classified as natural should be checked. Natural lakes are common in
glaciated ecoregions and less common elsewhere. In NLA in the past, the ratio of
human-made to natural lakes is about 1:1.
2. Google Earth review. Google Earth views are good to get the lay of the land and look for
obvious dams and human activities/roads around the lake. A lake with no roads or
human activity around them are unlikely to be human-made. We examined digital
topographic maps for dams, or other evidence of impoundment such as a substantial
elevation drop from lake surface to the outlet stream, a straight shoreline, or a road
crossing at the outlet.
3. Comparison of the mapped elevation change at the outlet to the maximum lake depth.
If maximum lake depth is greater than any possible dam/elevation change, it's not a
human-made impoundment by our definition.
4. Historical information search. Most named lakes have a surprising amount of
information about them on the internet (note this doesn't work well for very small
lakes, or lakes with no name).
Some common types of lakes were especially problematic when assigning lake origin.
Oxbow/riverine flood plain lakes. Classic oxbow lakes are inherently natural. However, many
old oxbow lakes or lakes in riverine floodplain are highly altered by human activities (e.g.,
road/railroad berms, bridges, dikes) and look very artificial. Unless we could tell that these lakes
were actually created (dug out) by humans, we classified them all as natural.
Wetland complex lakes. A number of lakes are part of wetland complexes. Many of them are in
areas heavily managed by humans (e.g., state and federal wildlife/wetland management areas).
These lakes are often very shallow and augmented to hold more water, and the flows are highly
regulated for purposes of wetland management. Whether these lakes met the NLA definition of
a lake (< 1 m deep and 10,000 m2 of open water) in the past or not is almost impossible to
determine. It's likely that in the past, some of these were what we now call wetlands and were
not NLA target lakes. We have, however, classified all of these types of lakes as natural in that
there was very likely some type of a wetland/waterbody there in the past, pre-human
development.
Augmented natural lakes. Many natural lakes are flow altered by human activity either by
outlet flow control or raising the height of the lake with some kind of dam. The dams on these
lakes are often very apparent when looking through the various sources of lake information,
but we consider these to be natural lakes if a lake basin existed their pre-human settlement. It
can be difficult to determine if a lake basin existed in the past to separate them from what we
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define as human-made impoundments. Dam height (or elevation contours) versus lake depth
was one approach to differentiate the two as well as doing internet history searches.
Irrigation/water district management. Water is often stored and moved around for irrigation or
drinking water, especially in the Xeric West. A number of lakes are a part of water management
districts where water is pumped into and out of them depending on water needs. If the lake
existed in the past, even though the flow now is extremely altered by humans, we called them
natural.
Quarry lakes, borrow pits, and reclaimed strip mine lakes. There are a large number of quarry
or borrow pit lakes and ponds that are created by humans when they dug holes and then the
holes filled with water. Since they are small and often unnamed it can be very hard to
distinguish these from small unnamed natural ponds. Looking at the general landscape, lake
shape, depths and crew notes are the only way to make an educated guess. For lakes within
reclaimed strip mines, topographic maps may provide more information than imagery). If a
major road (especially an interstate highway) is adjacent, road fill was often dug out from
adjacent areas creating the borrow pit. Larger borrow pits and big quarries are sometimes
turned into parks and have historical information.
8.3 Lake extent estimates
The condition of each NLA probability site (i.e., good, fair, poor; above or below benchmark;
detected or not-detected) is determined by the appropriate indicator values and benchmarks
established for that indicator and ecoregion. Next, the site weights from the probability design
are summed across all sites in each condition class to estimate the percentage of lakes
nationally or in other sub populations (e.g., ecoregions, natural vs. manmade lakes, etc) in each
condition class for the target population. The survey design allows calculation of confidence
intervals around these condition estimates and allows for estimates of the whole resource not
just those lakes sampled. Note that only Visit 1 (i.e., the index visit) data and only probability
sites are used in the calculation of extent. Hand-selected sites have a weight of zero. Using this
method, the lakes in a particular condition class is estimated and reported in percentage of
lakes or number of lakes.
8.4 Stressor extent, relative risk and attributable risk
A major goal of the National Aquatic Resource Surveys is to assess the relative importance of
stressors that impact aquatic biota on a national basis. The EPA assesses the influence of
stressors in three ways: stressor extent, relative risk, and population attributable risk. In NLA,
each targeted and sampled lake was classified as being in either Good, Fair, or Poor condition,
separately for each stressor variable and for each biological response variable. From this data,
we estimated the stressor extent (prevalence) of lakes in Poor condition for a specified stressor
variable. We also estimated the relative risk of each stressor for a biological response. Relative
risk is the ratio of the probability of a poor biological condition when the stressor is poor to the
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probability of a poor biological condition when the stressor is not poor (Van Sickle et al. (2006)).
Finally, we estimated the population attributable risk (AR) of each stressor for a biological
response. AR combines RR and stressor extent into a single measure of the overall impact of a
stressor on a biological response, over the entire population of lakes (Van Sickle and Paulsen
(2008)).
8.4.1 Stressor extent
For each particular stressor, the stressor extent (SE) may be reported as the number of lakes,
the proportion of lakes, or the percent of lakes in Good, Fair, Poor, or Not Assessed condition. If
the SE is reported as the proportion of lakes, then it can be interpreted as the probability that a
lake chosen at random from the population will be in Poor condition for the stressor.
Stressor extent in Poor condition is estimated as
(1) SEp, the sum of the sampling weights for sites that are assessed in Poor condition
n
SEp Wpj
i=1
t
(2) SEPp, as the ratio of the sums of the sampling weights for the probability selected sites
that are assessed in Poor condition divided by the sum of the sampling weights of all the
selected sites regardless of condition, i.e.,
v-i Tip
_ li=1 Wpj
p~ iu*t
, or
(3) SERp, the percent of stressor extent in Poor condition (i.e., stressor relative extent)
v-i Tip
SERV == 100 * SEPV = 100 * '-1 v
P P I"=i
where wpi is the weight for the /'th selected site in the Poor condition category, Wj is the weight
for the /'th selected site regardless of condition category, np is the number of selected sites that
are in Poor condition, and n is the total number of sites regardless of their condition category. A
stressor condition category may use other terminology to identify if a site is in poor condition
but generically, we use the term Poor. Note that the extent for a response variable is defined
similarly.
8.4.2 Relative risk and attributable risk
To estimate relative risk and attributable risk, we restrict the sites to those that both the
stressor and response variable assessed as Good, Fair, or Poor (or their equivalents). That is, if a
site is Not Assessed for either the stressor or response variable, it is dropped. Next, for these
sites the condition classes are combined to be either Poor or Not Poor for the stressor and
response variables. For example, Not Poor combines the Good and Fair condition classes. Thus,
each sampled lake was designated as being in either Poor (P) or Not Poor (NP) condition for
each stressor and response variable separately.
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To estimate the relative risk and attributable risk for one stressor (S) and one response (B)
variable, we compiled a 2x2 table (Table 8-1), based on data from all lakes that were included in
the probability sample and that had both the stressor and response variable measured. A
separate table must be compiled for each pair of stressor and response variables.
Table 8-1. Extent estimates for response and stressor categories
Response(B)
Stressor (S)
Not Poor (NP)
Poor(P)
Not Poor (NP)
n7l7l
CL ^ ' Wnni
i=1
nnp
b ^ ' Wnpi
i=1
7lpn
nvv
Poor(P)
C / VVpni
d y wpp j
i=1
i=l
Table entries (a, b, c, d) are the sums of the sampling weights of all sampled lakes that were
found to have each combination of Poor or Not Poor condition for stressor and response. For
example, d = wppi where npp is the number of sites with both the stressor and response
in poor condition and wppi is the weight for the /'th site. Note that the estimates in Table 8-1
may differ from the stressor extent estimates since both the stressor and response variables
must be measured at each site.
ฆ Relative risk
Relative risk (RR) is the ratio of the probability of a Poor biological condition when the stressor
is Poor to the probability of a Poor biological condition when the stressor is Not Poor. That is,
Pr(B = P\S = P)
RR ~ Pr(B = P\S = NP)
Using the simplified notation in Table 8-1, relative risk (RR) is estimated as:
d/{b + d)
est c/(a + c)
A RR = 1.0 indicates there is no association between the stressor and response. That is, a Poor
response condition in a lake is equally likely to occur whether or not the stressor condition is
Poor. A RR > 1.0 indicates that a Poor response condition is more likely to occur when the
stressor is Poor. For example, when the RR is 2.0, the chance that a lake is in Poor biological
(response) condition is twice as likely when the stressor is Poor than when the stressor is Not
Poor. Further details of RR and its interpretation, including estimation of a confidence interval
for RRest, can be found in Van Sickle et al. (2006).
ฆ Attributable risk
Population attributable risk (AR) measures what percent of the extent in Poor condition for a
biological response variable can be attributed causally to the Poor condition of a specific
stressor. AR is based on a scenario in which the stressor in Poor would be entirely eliminated
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from the population of lakes, e.g., by means of restoration activities. That is, all lakes in Poor
condition for the stressor are restored to the Not Poor condition. AR is defined as the
proportional decrease in the extent of Poor biological response condition that would occur if
the stressor were eliminated from the population of lakes. Mathematically, AR is defined as
(Van Sickle and Paulsen (2008))
Pr{B = P) - Pr{B = P\S = NP)
AR ~ Pr(B = P)
We estimated AR as
BEPV c/(a + c)
ARest = p ' -
BEPp
where
(c + d)
BEPv = 7 N jT
(& + b + c + d)
and is the estimated proportion of the biological response that is in Poor condition. We
calculated a confidence interval for ARest following Van Sickle and Paulsen (2008).
An AR can take a value between 0 and 1. A value of 0 indicates either "No association" between
stressor and response, or else a stressor has a zero extent, i.e., is not present in the population.
A strict interpretation of AR in terms of stressor elimination, as described above, requires one
to assume that the stressor-response relation is strongly causal and that stressor effects are
reversible. Van Sickle and Paulsen (2008) discuss the reality of these assumptions, along with
other issues such as interpreting them when multiple, correlated stressors are present, and
using them to express the joint effects of multiple stressors.
However, AR can also be interpreted more informally, as a measure that combines RR and SE
into a single index of the overall, population-level impact of a stressor on a response. Van Sickle
and Paulsen (2008) show that the population attributable risk can be written as
SEPJRR - 1)
AD = 1
1 +SEPp(RR - 1)
This shows that the numerator of AR is the product of the SE of Poor stressor condition and the
"excess" RR, i.e., RR-1, of that stressor. The denominator standardizes this product to yield AR
values between 0 and 1. Thus, a high AR for a stressor indicates that the stressor is widely
prevalent (has a high SE of Poor condition), and the stressor also has a large effect (high RR) in
those lakes where it does have Poor condition.
8.4.3 Considerations when calculating and interpreting relative risk and attributable risk
It is important to understand that contingency tables are created using a categorical, two-by-
two matrix; therefore, only two condition classes / stress levels can be used. There are three
ways in which condition classes / stress levels can be used for contingency tables:
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Good vs. Poor
Good vs. Not-Good
Not-Poor vs. Poor
where, "Not Good" combines fair and poor condition classes, and "Not Poor" combines good
and fair condition classes. In the first bulleted method, "Good vs. Poor" data associated with
the fair condition class is excluded from the analysis. Therefore, the results of the associated
calculation of relative risk are affected by which one of the above combinations is used to make
the contingency tables, and it is crucial that the objectives of the analysis are carefully
considered to help guide this decision. For the NLA, for non-biological condition indicators (e.g.,
nutrients, physical habitat, etc.), a condition / stressor-level contingency table was created,
comparing the Not Poor condition class (i.e., a combination of good condition and fair
condition) to Poor condition class. This decision was made to indicate which stressors policy
makers and managers may want to prioritize for management efforts to improve poor
condition. After creating contingency tables, relative risk for each indicator was calculated.
A second consideration is that relative risk does not model joint effects of correlated stressors.
In other words, each stressor is modeled individually, when in reality, stressors may interact
with one another potentially increasing or decreasing impact on condition. This is an important
consideration when interpreting the results associated with relative risk.
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 aquatic ecosystems - lakes, in the case of the NLA. 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).
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8.5 NLA 2017 change analysis
8.5.1 Background information
One of the objectives of the National Lakes Assessment (NLA) is to track changes over time. The
NLA conducted in 2017 was the third statistically valid survey of the nation's lakes and
reservoirs. Previously, EPA and partners reported on the condition of the nation's natural and
human-made lakes in the 2007 and 2012 National Lakes Assessments. In NLA 2007, lakes 4
hectares and larger were sampled. As discussed earlier in this document, the NLA 2012
expanded the target population to include lakes within a smaller size class category (1-4
hectares) and this remained the same in 2017. Because of this change in design, the change
analysis was conducted on 1) lakes equal to or greater than 4 hectares (2007 to 2012, 2012 to
2017 and 2007 to 2017) and 2) all lakes (2012 to 2017 only). As with other NLA analyses,
differences in the population condition estimates between surveys included both natural and
human-made lakes.
8.5.2 Data preparation
For the large lakes study population, the change analysis included all sites from NLA 2007 (1130
sites), 951 sites from NLA 2012 (excluded 87 lakes from 1-4 hectares in size), and 801 sites from
NLA 2017 (excluded 204 lakes from 1-4 hectares). For the all lakes study population, the change
analysis included all NLA 2012 and 2017 sites. Change estimates between NLA 2007 and NLA
2012 could not be made for some indicators due to differences in methodologies and
indicators, including zooplankton and atrazine; however, change estimates between 2012 and
2017 are available for these parameters. All other indicators were included in the change
analysis.
8.5.3 Methods
Change analysis was conducted using the spsurvey 3.3 package in R (Kincaid and Olsen, 2016).
Within the GRTS (Generalized Random Tessellation Stratified) survey design, change analysis
can be conducted on continuous or categorical response variables (e.g., good, fair, and poor).
The analysis measures the difference between response variables of two survey time periods.
For NLA 2017, the categorical response variables were used to compare changes between NLA
2007 and NLA 2017 and NLA 2012 and NLA 2017. When using categorical response variables,
change is estimated by the difference in category estimates from the two surveys. Category
estimates are defined as the estimated proportion of values in each category, for example
good, fair, and poor. Change between the two years is statistically significant when the resulting
error bars around the change estimate do not cross zero.
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8.6 Literature cited
Kincaid, T. M., and A. R. Olsen. 2016. spsurvey: Spatial Survey Design and Analysis. R package
version 3.3.
Olsen, A. R., and D. V. Peck. 2008. Survey design and extent estimates for the Wadeable
Streams Assessment. Journal of the North American Benthological Society 27:822-836.
Stevens, D. L., Jr, and S. F. Jensen. 2007. Sampling design, implementation, and analysis for
wetland assessment. Wetlands 27:515-523.
Stevens, D. L., Jr, and A. R. Olsen. 1999. Spatially restricted surveys over time for aquatic
resources. Journal of Agricultural, Biological, and Environmental Statistics 4:415-428.
Stevens, D. L., Jr, and A. R. Olsen. 2000. Spatially restricted random sampling designs for design-
based and model based estimation. Pages 609-616 in Accuracy 2000: Proceedings of the
4th International Symposium on Spatial Accuracy Assessment in Natural Resources and
Environmental Sciences. Delft University Press, The Netherlands.
Stevens, D. L., Jr, and A. R. Olsen. 2004. Spatially-balanced sampling of natural resources.
Journal of American Statistical Association 99:262-278.
Van Sickle, J., J. L. Stoddard, S. G. Paulsen, and A. R. Olsen. 2006. Using relative risk to compare
the effects of aquatic stressors at a regional scale. Environmental Management 38:1020-
1030.
Van Sickle, J., and S. G. Paulsen. 2008. Assessing the attributable risks, relative risks, and
regional extents of aquatic stressors. Journal of the North American Benthological
Society 27:920-931.
USEPA. 2006. Wadeable Streams Assessment: A Collaborative Survey of the Nation's Streams.
US Environmental Protection Agency, Office of Water and Office of Research and
Development, Washington, DC.
USEPA. 2009. National Lakes Assessment: A Collaborative Survey of the Nation's Lakes. US
Environmental Protection Agency, Office of Water and Office of Research and
Development, Washington, DC.
Whittier, T.R., Larsen, D.P., Peterson, S.A., and Kinkaid, T.M. 2002. A comparison of
impoundments and natural drainage lakes in the Northeast USA. Hydrobiologia 470:
157-171, doi.org/10.1023/A: 1015688407915
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Chapter 9: Quality Assurance Summary
The NLA has been designed as a statistically valid report on the condition of the Nation's lakes
at multiple scales, i.e., ecoregion (Level II), and national, employing a randomized site selection
process. The NLA is an extension of the EMAP methods for assessing lakes, similar to the 1997
Northeastern Lakes Assessment; therefore, it uses similar EMAP-documented and tested field
methods for site assessment and sample collection as the Northeast Lakes Assessment.
Key elements of the NLA Quality Assurance (QA) program include:
Quality Assurance Project Plan - A Quality Assurance Project Plan (QAPP) was developed and
approved by a QA team consisting of staff from EPA's Office and Wetlands, Oceans and
Watersheds (OWOW) and Office of Environmental Information (OEI) and a Project QA Officer.
All participants in the program signed an agreement to follow the QAPP standards. Compliance
with the QAPP was assessed through standardized field training, site visits, and audits. The
QAPP addresses all levels of the program, from collection of field data and samples and the
laboratory processing of samples to standardized/centralized data management.
Field training and sample collection - EPA provided training sessions throughout the study area
(with at least one instructor in each session) for all field crew members of each field crew team.
All field teams were audited on site within the first few weeks of fieldwork. Adjustments and
corrections were made on the spot for any field team problems. To assure consistency, EPA
supplied standard sample/data collection equipment and site container packages for all random
site, reference site, and repeat site sample collections.
Water chemistry laboratory QA procedures - NLA used three labs for the water chemistry
samples, the national lab and two state labs (e.g., Wisconsin and North Dakota). The Western
Ecology Division (WED) was responsible for QA oversight in implementing the NLA QAPP and
lab standard operating procedures (SOPs) for sample processing.
Zooplankton laboratory QA procedures - NLA used one lab that was audited for adherence to
the NLA QAPP/SOP for benthic sample processing. This included internal quality control (QC)
checks on sorting and identification of zooplankton and the use of the Integrated Taxonomic
Information System for correctly naming species collected, as well as the use of a standardized
data management system.
Benthic macroinvertebrate laboratory QA procedures - NLA used one lab, this lab was audited
for adherence to the NLA QAPP/SOP for benthic macroinvertebrate sample processing. This
included internal quality control (QC) checks on sorting and identification of benthic
macroinvertebrates and the use of the Integrated Taxonomic Information System for correctly
naming species collected, as well as the use of a standardized data management system.
Independent taxonomists were contracted to perform QC analysis of 10% of each lab's samples
(audit samples).
Entry of field data - NLA used a standardized data management structure, i.e., use of the same
standard field forms for data collected and centralized data management. Most field data were
collected electronically using an iPad with the NLA field data mobile application. Following a
review for accuracy and completeness, field crews submitted the electronic forms directly from
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the NLA App to NARS IM, which automated upload to the NLA 2017 SQL database. Paper field
forms were scanned and email to a centralized location for data upload. Internal error checks
were used to confirm data sheets were filled out properly.
Records management - These records include (1) planning documents, such as the NLA QAPP,
SOPs, and assistance agreements and (2) documents in the NLA FOM and LOM, such as data
sheets, lab notebooks, and audit records. These documents are ultimately to be maintained at
EPA. All data will eventually be archived in the water quality portal
(https://www.epa.gov/waterdata/water-quality-data).
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Appendix A: Lake Physical Habitat Expected Condition Models
Table 3 from TSD Chapter. Summary of regression models used in estimating lake-specific
expected values of Lake Physical Habitat variables RVegQx, LitCvrQx and LitRipCvrQx under least
disturbed conditions. Variable definitions and model details on following pages.
REGION v = RVeaQ
v = LitCvrQ
v = LitRipCvrQ
NAP Ly* =f(Lat, Lon, LkOrig, RDisIX,) Ly =f(L_LkArea, RDisIX)
(R2=23%, RMSE=0.162L*
(R2= 12% RMSE=0.281L)
Ly =f(Lat, Lon, LkOrig, RDisIX)
(R2=24%, RMSE=0.168L)
SAP Ly =f(Lon)
(R2=16%, RMSE=0.119L)
Ly =f(ElevXLon, RDisIX)
(R2=19%, RMSE=0.267L)
Ly =f(Lon, ElevXLon, Elev)
(R2=31%, RMSE= 0.148L)
CPL y =f(ElevXLat, RDisIX)
(R2=39%, RMSE=0 .0896)
y =f(L_Elev, RDisIX)
(R2=25%, RMSE= 0.174)
y =f( L_Elev, RDisIX)
(R2=44%, RMSE=0.093)
UMW Ly = (mean LRVegQ)
(R2=0%, RMSE=0.153L)
Ly = (mean LitCvrQ)
(R2=0%, RMSE=0.199L)
Ly = (mean LitRipCvrQ)
(R2=0%, RMSE=0 .115L)
CENPL Ly =f{hiiAg)
(R2=15%, RMSE=0.318L)
Ly =f(LkOrig, hiiAg)
(R2=9%, RMSE=0.276L)
Ly =f(hiiAg)
(R2=15%, RMSE=0.233L)
WMT Ly =f(Lat, Elev, L_LkArea, LkOrigin) Ly =f(Lat, Elev, L_LkArea, LkOrigin) Ly =f(Lat, Elev, L_LkArea, LkOrigin)
(R2=28%, RMSE=0.167L) (R2=16%, RMSE=0.244L) (R2=29%, RMSE=0.145L)
XER Ly =f(Lat, Elev)
(R2=24%, RMSE=0.284L)
Ly =f(Lat, Elev)
(R2=16%, RMSE=0.290L)
Ly =f( Lat, Elev)
(R2=21%, RMSE=0.265L)
*Ly refers to Logio-transformed lake habitat metric values.
**L refers to RMSE's that are in Logio units (e.g., 0.162L)
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VARIABLE DEFINITIONS
On following pages variables are defined as follows:
Observed Habitat Indicator values are: (in the TSD text, these are abbreviated as RVegQ,
LitCvrQ. and LitRipCvrQ)
RVegQcl5, LitCvrQcl5, LitRipCvrQclS
L_RVegQcl5 = Log10(RVegQcl5 +0.01)
L_LitCvrQcl5 = Logio (LitCvrQcl5 +0.01)
L_LitRipCvrQcl5 = Logio (LitRipCvrQclS +0.01)
Expected Condition Regression Models have the form (in the TSD text. Expected condition
variables are abbreviated as RVegQX, LitCvrQX, and LitRipCvrQX):
L_RVegQc3xl5 = f(predictors) or RVegQc3xl5 = f(predictors)
L_LitCvrQc3xl5 = f(predictors) or LitCvrQc3xl5 = f(predictors)
L_LitRipCvrQc3xl5 = f(predictors) or LitRipCvrQc3xl5 = f(predictors)
Observed/Expected Condition Variables are defined as follows (in the TSD text. O/E variables
are abbreviated as RVegQ OE, LitCvrQ OE, and LitRipCvrQ OE)\
RVegQc30E15= (RVegQcl5/RVegQc3xl5) and Ll_RVegQc30E15 = Log10(RVegQc3OE15 +0.1)
LitCvrQc30E15= (LitCvrQcl5/LitCvrQc3xl5) and Ll_LitCvrQc30E15 = Logio(LitCvrQc30E15
+0.1)
LitRipCvrQc30E15= (LitRipCvrQcl5/LitRipCvrQc3xl5) and Ll_LitRipCvrQc30E15 =
Logio (LitRipCvrQc30E15 +0.1)
Predictors defined from variables in prk datafile NLA12 pc.nla lakeinfo all 20150415 are as
follows:
LATdd_use = LAT_DD_N83 = latitude in decimal degrees
LONdd_use = LON_DD_N83 = longitude in decimal degrees
ELEV_use = ELEVATION = lake surface elevation (meters above mean sea level)
L_ELEV_use = Log10(ELEV_use)
LkArea_km2 = LAKEAREA = lake surface area (km2)
L_LkAreakm2 = Logio(LkArea_km2)
Lake_Origin_use = LAKE_ORIGIN (with values: 'NATURAL' or 'MAN-MADE')
Reservoir = an indicator variable of Lake Origin, where
If Lake_Origin_use = 'MAN-MADE' then Reservoir= 1;
If Lake_Origin_use = 'NATURAL' then Reservoir= 0;
Field human disturbance variables:
RDis_IX index of near-shore human disturbance intensity and extent (see TSD text equation
5)
hiiAg proximity-weighted mean tally of up to 3 near-shore agricultural disturbances
(mean among stations
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NAP Expected PHab Reference Condition Models:
L_RVegQc3xl5 = 2.34593-(0.03705* LATdd_use)+(0.01723*LONdd_use)-(0.07954* Reservoir)
-(0.31865 *RDisJX);
Note: Reservoir = 0 for natural lakes, 1 for man-made reservoirs.
Rsq=0.2331 RMSE=0.16177 p<.0001 n=166/170;
Sites: All non-overlapping 2007-2012 NAP RT_NLA12=R or S;
Set RDis_IX to zero (14% of 2007-&12 NAP sample sites have RDis_IX=0);
RVegQc3xl5=10**(L_RVegQc3xl5)-0.01;
Applied simple dirty models for LitCvr and LitRipCvr (see powerpoint file of regressions
6/13/14) that better define the influence of lake area but then MUST include RDisJX,
because it is the strongest predictor of any of the 3 PHab indices if RT_NLA12_2015 S or T sites
are included with reference (R) sites;
Adjustment for reference distribution of O/E values:
L_RVegQc30E15= +0.04276 - (0.29150 RDisJX);
Rsq= 0.2026 RMSE=0.14469 p<0.0001 n=166/170;
Sites: All non-overlapping 2007-2012 NAP RT_NLA12=R or S;
Ref O/E distribution based on Y-intercept of adjustment regression, but SD of ref sites only (not
S sites)
L_LitCvrQc3xl5= -0.8598 -(0.08109*L_LkAreakm2) - (0.28562*RDisJX);
Rsq=0.1228 RMSE=0.2808 p<0.0001 n=166/170;
Set RDis_IX to zero (14% of 2007-2012 NAP sample sites have RDis_IX=0);
Sites: All non-overlapping 2007-2012 NAP RT_NLA12_2015=R or S;
LitCvrQc3xl5= 10 * *(L_LitCvrQc3xl5)-0.01;
Adjustment for reference distribution of O/E values:
L_LitCvrQc3OE15= +0.04665 - (0.28240 RDisJX);
Rsq= 0.0592 RMSE=0.26819 p=0.0009 n=166/170;
Sites: All non-overlapping 2007-2012 NAP RT_NLA12=R or S;
Ref O/E distribution based on Y-intercept of adjustment regression, but SD of ref sites only (not
S sites)
L_ Lit Rip CvrQc3xl 5= 2A1606-(0.03964*LATdd_use)+(0.01798*LONdd_use) -(0.08301*
Reservoir)
-{0.34039* RDisJX);
Note: Reservoir = 0 for natural lakes, 1 for man-made reservoirs.
Rsq=0.2407 RMSE=0.16783 p<0.0001 n=166/170;
Set RDisJX to zero (14% of 2007-2012 NAP sample sites have RDis_IX=0);
Sites: All non-overlapping 2007-2012 NAP RT_NLA12_2015=R or S;
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Lit Rip CvrQc3xl 5=10* *(L_ LitRipCvrQc3xl5) -0.01;
Adjustment for reference distribution of O/E values:
L_LitRipCvrQc30E15= +0.04230 - (0.31323 RDisJX);
Rsq= 0.2075 RMSE=0.15095 p<0.0001 n=166/170;
Sites: All non-overlapping 2007-2012 NAP RT_NLA12=R or S;
Ref O/E distribution based on Y-intercept of adjustment regression, but SD of ref sites only (not
S sites).
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SAP -- Expected PHab Condition Models:
L_RVegQc3xl5= 0.24710 +(0.01012* LONdd_use);
Rsq=0.1637 RMSE=0.11878 p=0.0240 n=31/31;
Sites: All non-ovelapping 2007-2012 SAP RT_NLA12_2015=R;
RVegQc3xl5=10**(L_RVegQc3xl5)-0.01;
Ref O/E distribution based on mean and SD of ref sites.
L_LitCvrQc3xl5= -0.66613 -(0.00000410*ElevXLon_use) -(0.51350*RDis_IX);
Rsq=0.1942 RMSE=0.26697 p=0.0487 n=31/31;
Set RDis_IX to zero (2% of 2007-2012 SAP sample sites have RDis_IX=0);
Sites: All non-overlapping 2007-2012 SAP RT_NLA12_2015=R;
LitCvrQc3xl5= 10 * *(L_LitCvrQc3xl5)-0.01;
Adjustment for reference distribution of O/E values:
L_LitCvrQc3OE15= +0.04287 - (0.46211 RDisJX);
Rsq= 0.0790 RMSE=0.24397 p=0.1255 n=31/31;
Sites: All non-overlapping 2007-2012 SAP RT_NLA12=R;
Ref O/E distribution based on Y-intercept and RMSE of adjustment regression.
L_ Lit Rip CvrQc3xl5=1.92708 -(0.000115130* ElevXLon_use) + (0.03141* LONdd_use) -
(0.00923* ELEV_use);
Rsq=0.3083 RMSE=0.14817 p=0.0175 n=31/31;
Sites: All non-overlapping 2007-2012 SAP RT_NLA12_2015=R;
Lit Rip CvrQc3xl 5=10* *(L_ LitRipCvrQc3xl5) -0.01;
Ref O/E distribution based on mean and SD of ref sites.
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CPL Expected PHab Condition Models:
RVegQc3xl5=0.35438 -0.00003019(ElevXLat_use) - 0.15193(RDisJX);
Rsq= 0.3868 RMSE=0.08963 p<0.0001 n=28/28;
Sites: All non-overlapping 2007-2012 CPL RT_NLA12_2015=R;
Set RDis_IX to lowest value in the region (4.4% have RDis_IX=0 in CPL);
Adjustment for reference distribution of O/E values:
L_RVegQc30E15= -0.0006653 - (0.22746 RDisJX);
Rsq= 0.0235 RMSE=0.21279 p=0.4362 n=28/28;
Sites: All non-overlapping 2007-2012 CPL RT_NLA12=R;
Note: Regression keeping one low outlier with very little leverage;
Ref O/E distribution based on Y-intercept and RMSE of adjustment regression.
LitCvrQc3xl5= 0.71804 - (0.19300*L_Elev_use) - (0.12565*RDisJX);
Rsq= 0.2526 RMSE=0.17393 p<0.0001 n=28/28;
Sites: All non-overlapping 2007-2012 CPL RT_NLA12_2015=R;
Set RDisJX to lowest value in the region (0 in CPL);
Adjustment for reference distribution of O/E values:
LJitCvrQc30E15= -0.00743 - (0.09579 RDisJX);
Rsq= 0.0051 RMSE=0.1940 p=0.7178 n=28/28;
Sites: All non-overlapping 2007-2012 CPL RT_NLA12=R;
Ref O/E distribution based on Y-intercept and RMSE of adjustment regression.
Lit Rip CvrQc3xl5= 0.59561 - (0.15322 *L_Elev_use) - (0.14358* RDisJX);
Rsq= 0.4423 RMSE=0.09293 p<0.0001 n=28/28;
Sites: All norepeat 2007-2012 CPL RT_NLA12_2015=R;
Set RDisJX to lowest value in the region (0 in CPL);
Adjustment for reference distribution of O/E values:
LJitRipCvrQc30E15= 0.01615 - (0.15265 RDisJX);
Rsq= 0.0312 RMSE=0.1234 p=0.3685 n=28/28;
Sites: All non-overlapping 2007-2012 CPL RT_NLA12=R;
Ref O/E distribution based on Y-intercept and RMSE of adjustment regression.
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UMW Expected PHab Condition Models:
L_RVegQc3xl5= -0.61298;
****Dropped LON and LkArea - USED geometric (Log mean) NULL MODEL;
Rsq=0 RMSE=0.15333 n=49/50 ;
Sites: All non-overlapping 2007-2012 UMW RT_NLA12_2015=R;
RVegQc3xl5=10**(L_RVegQc3xl5)-0.01;
Ref O/E distribution based on mean and SD of ref sites.
L_LitCvrQc3xl5= -0.87559;
****Dropped survey year - USED geometric (Log mean) NULL MODEL;
Rsq=0 RMSE=0.19944 p=N/A n=49/50;
Sites: All non-overlapping 2007-2012 UMW RT_NLA12_2015=R;
LitCvrQc3xl5= 10 * *(L_LitCvrQc3xl5)-0.01;
Ref O/E distribution based on mean and SD of ref sites.
L_LitRipCvrQc3xl5=-0.70830;
***** Dropped Lake Area - USED geometric (Log mean) NULL MODEL;
Rsq=0 RMSE=0.11487 p=N/A n=49/50;
Sites: All non-overlapping 2007-2012 UMW RT_NLA12_2015=R;
Lit Rip CvrQc3xl 5=10* *(L_ LitRipCvrQc3xl5) -0.01;
LitCvrQc3xl5= 10 * * (L_LitCvrQc3xl5)-0.01;
Ref O/E distribution based on mean and SD of ref sites.
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CENPL fNPL + SPL + TPL) Expected PHab Condition Models:
L_RVegQc3xl5=-0.75460- (0.0.86385*hiiAg);
Rsq=0.1532 RMSE=0.3178 p<0.0009 n=69/71;
Sites: All non-overlapping 2007-2012 CENPL_2015 RT_NLA12_2015=R, Excluding KS-R02SD-101
(Oahi Res) which has inadequate no of transects, but Includes Mound City res KS-R02 with corrected Elevation;
Set hiiAg to lowest value in the region (0)
Note: 2007-2012 NLA sites in CENPL with hiiAg=0 in NPL(>25%) SPL(>50%) TPL(75%)
RVegQc3xl5=10**(L_RVegQc3xl5)-0.01;
Adjustment for reference distribution of O/E values:
L_RVegQc30E15= 0.04688 - (0.80799 hiiAg);
Rsq= 0.1571 RMSE=0.29278 p=0.0007 n=69/71;
Ref O/E distribution based on Y-intercept and RMSE of adjustment regression.
L_LitCvrQc3xl5= -1.03378 + 0.10822*Reservoir -(0.38197*hiiAg);
Note: Reservoir = 0 for natural lakes, 1 for man-made reservoirs.
Rsq=0.0855 RMSE= 0.27579 p<0.0572 n=69/71;
Sites: All non-overlapping 2007-2012 CENPL_2015 RT_NLA12_2015=R
Set hiiAg to lowest value in the region (0)
Note: 2007-2012 NLA sites in CENPL with hiiAg=0 in NPL(>25%) SPL(>50%) TPL(75%)
LitCvrQc3xl5= 10 * *(L_LitCvrQc3xl5)-0.01;
Adjustment for reference distribution of O/E values:
L_LitCvrQc3OE15= 0.02752 - (0.35038 hiiAg);
Rsq= 0.0359 RMSE=0.28386 p=0.1255 n=69/71;
Ref O/E distribution based on Y-intercept and RMSE of adjustment regression.
L_LitRipCvrQc3xl5=-0.82455-(0.61960* hiiAg);
Rsq=0.1471 RMSE=0.23336 p=0.0011 n=69/71;
Sites: All non-overlapping 2007-2012 CENPL_2015 RT_NLA12_2015=R
Set hiiAg to lowest value in the region (0)
Note: 2007-2012 NLA sites in CENPL with hiiAg=0 in NPL(>25%) SPL(>50%) TPL(75%)
Lit Rip CvrQc3xl 5=10* *(L_ LitRipCvrQc3xl5) -0.01;
Adjustment for reference distribution of O/E values:
L_LitRipCvrQc30E15= 0.04303 - (0.59485 hiiAg);
Rsq= 0.1465 RMSE=0.22462 p=0.0012 n=69/71;
Ref O/E distribution based on Y-intercept and RMSE of adjustment regression.
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**** Note: If remove sites East of approximately -95 degrees LON that removes all hiiAg so
association with LON is largely assoc with hiiAg - adopted conservative model without LON.
See dirty models for all three indices with hiiAg alone (prk 3/13/15 SAS EnterpriseGuide
projects) for all three of the above, they all have higher Rsq, similar RMSE, similar intercepts,
similar slopes p<0.0001 n= 669/694 to 673/694.
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WMT Expected PHab Condition Models:
L_RVegQc3xl5= 0.53572-(0.00008953*ELEV_use)-
(0.25957*Reservoir)+(0.07296*L_LkAreakm2)
-(0.01939*LATcfcf_use);
Note: Reservoir = 0 for natural lakes, 1 for man-made reservoirs.
Rsq=0.2825 RMSE=0.16743 p=0.0001 n=74/75;
Sites: All non-overlapping 2007-2012 WMT RT_NLA12_2015=R;
RVegQc3xl5=10**(L_RVegQc3xl5)-0.01;
Ref O/E distribution based on mean and SD of ref sites.
L_LitCvrQc3xl5= -1.10550-(0.00004299* ELEV_use)-
(0.05083* L_LkAreakm2)+(0.00407* LATdd_use)
-(0.18384* Reservoir);
Note: Reservoir = 0 for natural lakes, 1 for man-made reservoirs.
Rsq=0.1555 RMSE=0.24373 p=.0187 n=74/75;
Sites: All non-overlapping 2007-2012 WMT RT_NLA12_2015=R;
LitCvrQc3xl5= 10 * *(L_LitCvrQc3xl5)-0.01;
Ref O/E distribution based on mean and SD of ref sites.
L_LitRipCvrQc3xl5= -0.08802-(0.00006666*ELE\/juse)+(0.04200* L_LkAreakm2)-
(0.01015* LATdd_use)-(0.22650* Reservoir);
Note: Reservoir = 0 for natural lakes, 1 for man-made reservoirs.
Rsq=0.2922 RMSE=0.14513 p<.0001 n=74/75;
Sites: All no-repeat 2007-2012 WMT RT_NLA12_2015=R;
Lit Rip CvrQc3xl 5=10* *(L_ LitRipCvrQc3xl5) -0.01;
Ref O/E distribution based on mean and SD of ref sites.
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XER Expected PHab Condition Models:
L_RVegQc3xl5= 0.44708 -(0.02612 *LATdd_use) -(0.00013249*ELEV_use);
Rsq=0.2365 RMSE=0.28355 p=0.1009 n=20/21;
Sites: All no-repeat 2007-2012 XER RT_NLA12_2015=R;
RVegQc3xl5=10**(L_RVegQc3xl5)-0.01;
Ref O/E distribution based on mean and SD of ref sites.
L_LitCvrQc3xl5=0.08706-(0.02849* LATdd_use)-(0.00003932* ELEV_use);
Rsq=0.1578 RMSE=0.29004 p=0.2322 n=20/21;
Sites: All no-repeat 2007-2012 XER RT_NLA12_2015=R;
*** Note this was 8th best in All Subsets Regression models with <=2 predictors ranked by Cp;
*** Note this was 6th best in All Subsets ranked by Rsq;
*** Consistent model across all the indicators and across full set of sites;
LitCvrQc3xl5= 10 * *(L_LitCvrQc3xl5)-0.01;
Ref O/E distribution based on mean and SD of ref sites.
L_ Lit Rip CvrQc3xl5=0.24931 - (0.02529* LATdd_use)-(0.00010090* ELEV_use);
Rsq=0.2115 RMSE= 0.26455 p=0.1327 n=20/21;
Sites: All no-repeat 2007-2012 XER RT_NLA12_2015=R;
Lit Rip CvrQc3xl 5=10* *(L_ LitRipCvrQc3xl5) -0.01;
Ref O/E distribution based on mean and SD of ref sites.
NOTE 3/13/15 prk: Reexamined models. The p-values (and of course also r2 and RMSE) not
improved by using
single predictors (ELEV_use LATdd_use and ELEVxLatdd_use). The mechanisms and univariate
plots of these single predictors all convincing and support the 3 models above;
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NLA 07,12,17 P.R.Kaufmann April 27, 2020
Log10[Observed/Expected] Lake Habitat Cover & Structural Complexity
Versus Anthropogenic Disturbance Stress (RST-2020) and Year
For 9 Ecoregions (Sample stats, not weighted -%iles: 5/25/50/75/95
w/outliers shown as"+"
Following figures present the O/E values vs RTS for
the three PHafa indicators for the three surveys for
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Riparian Vegetation:
NAP 2007
L1_RVegQc30E15
R
S
2012
L1_RVegQc30E15
T
2017
L1_RVegQc30E15
T
R
~r
S
T
T
Reference/Tra...year NLA data
Reference/Tra...year NLA data
Reference/Tra...year NLA data
Riparian Vegetation:
SAP 2007
L1_RVegQc30E15
R
S
T
2012
L1_RVegQc30E15
T
2017
L1_RVegQc30E15
Reference/Tra...year NLA data
R S
Reference/Tra...year NLA data
R
T
s
T
ReferencaTra...year NLA data
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Riparian Vegetation:
CPL 2007
L1_RVegQc30E15
R
2012
L1_RVegQc30E1S
2017
L1_RVegQc30E15
T
Referenee/Tra...year NLA data
T
Reference/Tra...year NLA data
R
T
s
T
T
Reference/Tra...year NLA data
Riparian Vegetation:
UMW 2007
L1_RVegQc30E15
R
S
2012
L1_RVegQc30E15
2017
L1_RVegQc30E15
T
T
R
T
s
T
ReferencaTra...year NLA data
Reference/Tra...year NLA data
Reference/Tra...year NLA data
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Riparian Vegetation:
TPL
2007
L1_RVegQc30E15
R
S
2012
L1_RVegQc30E1S
2017
L1_RVegQc30E15
T
Referenee/Tra...year NLA data
Reference/Tra...year NLA data
R
T
T
Reference/Tra...year NLA data
Riparian Vegetation:
NPL:
2007
L1_RVegQc30E15
1.0
0.8H
0.6
0.4-
0.2-
0.0
-0.2-|
-0.4
-0.6-|
-0.8
-1.0 H
~T
R
"T"
s
~ir
T
~T"
R
+
2012
L1_RVegQc30E15
1.0-
0.8-
0.6-
0.4-
0.2-
0.0-
-0.2-
-0.4-
-0.6-
-0.8-
-1.0-
"T"
T
2017
L1_RVegQc30E15
1.0
0.8H
0.6
0.4-
0.2-
0.0
-0.2 |
-0.4
-0.6H
-0.8
-1.0H
H
o
R
T
ReferencaTra...year NLA data
Reference/Tra...year NLA data
Reference/Tra...year NLA data
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Riparian Vegetation:
SPL:
2007
L1_RVegQc30E15
1.0
0.8H
0.6
0.4-
0.2-
0.0
-0.2
-0.4
-0.6H
-0.8
-1.0H
+
T
s
-r
T
2012
L1_RVegQc30E15
1.0-
0.8-
0.6
0.4-
0.2-
0.0
-0.2
-0.4
-0.6-
-0.8
-1.0H
~r
R
"T"
"T"
T
2017
L1_RVegQc30E15
1.0
0.8H
0.6
0.4-
0.2-
0.0
-0.2
-0.4
-0.6-
-0.8
-1.0-1
R
+
+
1
+
T
Reference/Tra...year NLA data
Refererice/Tra...year NLA data
Reference/Tra...year NLA data
Riparian Vegetation:
WMT
2007
L1_RVegQc30E15
1.0
0.8H
0.6
0.4-
0.2-
0.0
-0.2 |
-0.4
-0.6 H
-0.8
-1.0 H
T"
R
~r
S
r
T
2012
L1_RVegQc30E15
1.0-
0.8-
0.6-
0.4-
0.2-
o.o-
-0.2-
-0.4
-0.6-
-0.8-
-1.0-
t
+
+
4
+
Reference/Tra...year NLA data
R S T
Reference/Tra...year NLA data
"T"
R
2017
L1_RVegQc30E15
1.0
0.8H
0.6
0.4-
0.2-
0.0
-0.2 H
-0.4
-0.6H
-0.8
-1.0-1
I
+
+
"T"
s
~r
T
ReterencaiTra...year NLA data
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Riparian Vegetation:
XER
i* +
2007
L1_RVegQc30E15
1.0
0.8H
0.6
0.4-
0.2-
0.0
-0.2
-0.4
-0.6H
-0.8
-1.0H
"T"
R
"T"
s
r
T
2012
L1_RVegQc30E15
1.0
0.8H
0.6
0.4-
0.2-
0.0
-0.2
-0.4
-0.6H
-0.8
-1.0H
"T"
R
"T"
s
r
T
2017
L1_RVegQc30E15
1.0
Q.8H
0.6
0.4-
0.2-
0.0
-0.2
-0.4
-0.6H
-0.8
-1 .OH
+
A
|
i
Reference/Tra...year NLA data
Reference/Tra...year NLA data
R S T
Reference/Tra...year NLA data
Littoral Habitat:
NAP 2007
L1_LitCvrQc30E15
R
S
T
2012
L1_LitCvrQc30E15
T
2017
L1_LitCvrQc30E15
R
"T
T
Reference/Tra...year NLA data
Referenc&Tra...year NLA data
Reference/Tra...year NLA data
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Littoral Habitat:
SAP 2007
L1_LitCvrQc30E15
R
S
T
2012
L1_LitCvrQc30E15
T
2017
L1_LitCvrQc30E15
R
T
T
Reference/Tra...year NLA data
Referenc&Tra...year NLA data
Reference/Tra...year NLA data
Littoral Habitat:
CPL 2007
L1_LitCvrQc30E15
R
T
2012
L1_LitCvrQc30E15
T
2017
L1_LitCvrQc30E16
R
"T
T
Reference/Tra...year NLA data
Reference/Tra...year NLA data
Reference/Tra...year NLA data
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Littoral Habitat:
UMW 2007
L1_LitCvrQc30E15
R
S
T
2012
L1_LitCvrQc30E15
T
2017
L1_LitCvrQc30E15
R
T
T
Reference/Tra...year NLA data
Referenc&Tra...year NLA data
Reference/Tra...year NLA data
Littoral Habitat:
TPL 2007
L1_LitCvrQc30E15
R
S
T
2012
L1_LitCvrQc30E15
2017
L1_LitCvrQc30E16
Reference/Tra...year NLA data
R S T
Reference/Tra...year NLA data
r
R S T
Reference/Tra...year NLA data
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Littoral Habitat:
NPL: 2007
L1_LitCvrQc30E15
1.0 -f
0.8-
0.6-
0.4-
0.2-
0.0-
-0.2
-0.4-
-0.6-
-0.8-
-1.0-
R
ll ฆ+ฆ
+
M
+
T
2012
L1_LitCvrQc30E15
i.o -r
0.8-
0.6-
0.4-
0.2-
0.0-
-0.2-
-0.4-
-0.6-
-0.8-
-1.0-
"T"
R
....
+
"T"
s
r
T
2017
L1_LitCvrQc30E15
i.o -r
0.8-
0.6-
0.4-
0.2-
0.0-
-0.2-
-0.4-
-0.6-
-0.8-
-1.0-
"T"
R
r
T
Reference/Tra...year NLA data
Reference/Tra...year NLA data
Reference/Tra...year NLA data
Littoral Habitat:
SPL: 2007
L1_LitCvrQc30E15
2012
L1_LitCvrQc30E15
R S T
Reference/Tra...year NLA data
2017
L1_LitCvrQc30E15
R
"T
T
Reference/Tra...year NLA data
Reference/Tra...year NLA data
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Littoral Habitat:
WMT 2007
L1_LitCvrQc30E15
R
S
2012
L1_LitCvrQc30E15
T
T
2017
L1_LitCvrQc30E15
Reference/Tra...year NLA data
R
T
Referenc&Tra...year NLA data
Reference/Tra...year NLA data
Littoral Habitat:
XER 2007
L1_LitCvrQc30E1S
R
S
T
2012
L1_LitCvrQc30E15
2017
L1_LitCvrQc30E16
R
"T
T
Reference/Tra...year NLA data
Reference/Tra...year NLA data
Reference/Tra...year NLA data
156
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Littoral-Riparian Complexity:
NAP 2007
L1_LitRipCvrQc30E15
2012
L1_LitRipCvrQc30E15
2017
L1_LitRipCvrQc30E15
R
S
T
T
Reference/Tra...year NLA. data
R
T
T
Referenc&Tra...year NLA data
Reterence/Tra...year NLA. data
Littoral-Riparian Complexity:
SAP 2007
L1_LitRipCvrQc30E15
S
T
2012
L1_LitRipCvrQc30E15
T
2017
L1_LitRipCvrQc30E15
R
T
T
Reference/Tra...year NLA data
Referenc&Tra...year NLA data
Reference/Tra...year NLA data
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Littoral-Riparian Complexity:
CPL 2007
L1 _LitRipCvrQc30E 15
1.0-
0.8-
0.6-
0.4-
0.2-
0.0-
-0.2
-0.4-
-0.6-
-0.8-
-1.0-
y 1
t .+...
T
r +-
2012
L1 _LitRipCvrQc30E15
1.0-
0.8-
0.6-
0.4-
0.2-
0.0-
-0.2
-0.4-
-0.6-
-0.8-
-1.0-
4
CQ
"t
1
+
1
ฆT
4
+
+ +
2017
L 1_LitRipCvrQc30E 15
1.0-1
0.8-
0.6-
0.4-
0.2-
0.0-
-0.2-
-0.4-
-0.6-
-0.8-
-1.0-
R
T
Reference/Tra...year NLA data
R S T
Reference/Tra...year NLA data
"T"
R
r
T
Reference/Tra...year NLA data
Littoral-Riparian Complexity:
UMW 2007
L1_LitRipCvrQc30E15
2012
L1_LitRipCvrQc30E15
R
S
T
2017
L1_LitRipCvrQc30E15
T
R
"T
T
Reference/Tra...year NLA data
Reference/Tra...year NLA data
Reference/Tra...year NLA data
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Littoral-Riparian Complexity:
TPL 2007
L1 _LitRipCvrQc30E 15
R
2012
L1 _LitRipCvrQc30E15
2017
L1 _LitRipCvrQc30E 15
S
T
Reference/Tra...year NLA data
Referenc&Tra...year NLA data
R
T
T
Reference/Tra...year NLA data
Littoral-Riparian Complexity:
NPL: 2007
L1_LitRipCvrQc30E15
2012
L1_LitRipCvrQc30E15
2017
L1_LitRipCvrQc30E15
R
S
T
T
Reference/Tra...year NLA data
Reference/Tra...year NLA data
T
R S T
Reference/Tra...year NLA data
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Littoral-Riparian Complexity:
SPL: 2007
L1 _LitRipCvrQc30E 15
2012
L1 _LitRipCvrQc30E15
2017
L1 _LitRipCvrQc30E 15
R
S
T
Reference/Tra...year NLA data
R
T
T
Referenc&Tra...year NLA data
Reference/Tra...year NLA data
Littoral-Riparian Complexity:
WMT 2007
L1_LitRipCvrQc30E15
2012
L1_LitRipCvrQc30E15
2017
L1_LitRipCvrQc30E15
R
S
T
R
"T
T
Reference/Tra...year NLA data
Reference/Tra...year NLA data
Reference/Tra...year NLA data
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Littoral-Riparian Complexity:
XER 2007
L1 _LitRipCvrQc30E 15
2012
L1_LitRipCvrQc30E15
2017
L1 _LitRipCvrQc30E 15
R
S
T
~T~
T
T
R
T
T
Reference/Tra...year NLA data
Referenc&Tra...year NLA data
Reference/Tra...year NLA data
161
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Appendix B: Survey Design and Estimated Extent Summary for NLA 2007, 2012 and 2017
Category
sample frame
sample frame
sample frame
sample frame
survey design
survey design
survey design
Characteristic
sample frame
sample frame
sample frame
sample frame
exclusions
survey design
survey design
restriction
restriction
stratification
Description
source
total number of
lake objects in
source (NHD)
lake objects
included in the
sample frame
lake objects
excluded because
they are not
expected to meet
the target
population
definition
2017
NHDPIus and
NHDPIus HR for 1-5
ha lakes
586,678
465,901
2012 2007
NHDPIus, version 2 NHD
120,777
minimum lakes per
state
maximum lakes per
state
stratification
GRTS with
stratification and
unequal probability
of selection
7
50
by state
378,858
277,886
100,972
GRTS with
stratification and
unequal probability
of selection
7
43
by state and
NLA12 CLS
389,005
123,369
265,636 (of which
233,627 were l-4ha)
GRTS with
stratification and
unequal probability
of selection
7
none
None
162
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Category
survey design
survey design
survey design
survey design
survey design
survey design
survey
implementation
survey
implementation
survey
implementation
survey
implementation
survey
implementation
survey
implementation
estimated extent
estimated extent
estimated extent
Characteristic Description 2017 2012 2007
lake area categories description (ha) (1-4 ], (4-10], (10-20], (1-4 ], (4-10], (10-20], (4-10], (10-20], (20-
(20-50], >50 (20-50], >50 50], (50-100], >100
lake area categories
expected unique
lakes
expected sample
size
expected split
revisits
survey design lakes
sampled
survey design lakes
sampled
survey design lakes
sampled
survey design lakes
sampled
survey design lakes
sampled
survey design lakes
sampled
estimated lake
population
estimated lake
population
estimated lake
population
minimum (ha) 1 ha
total lakes 904
total visits 1,000
new/previously
sampled
number of lakes 96
total lakes samples 1,005
(used in population
estimates)
Size class: 1-4 ha 204
Size class: 4-10 ha 179
Size class: 10-20 ha 192
Size class: 20-50 ha 164
Size class: >50 ha 266
target population 224,916
target unknown 3,290
non-target lakes 237,695
163
1 ha
904
1,000
62/38
96
1,038
87
142
173
225
411
159,652
3,538
114,695
4 ha
909
1,000
NA
91
1130
0
73
162
211
684
68,223
55,146
-------
Category
estimated extent
estimated extent
NLA 2017 Technical Support Document Version 1.1
Characteristic
estimated lake
population
estimated lake
population
Description
sampled
population
NLA report result
representation
2017
109,701
target population
2012
111,818
sampled population
2007
49,546
sampled population
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Appendix C: NLA 2017 Indicator Benchmark Summary
Metric
Category
Indicator
Benchmark Description
National/
Ecoregion
Condition class
Value
Units
General Assessment Notes
Biological
Benthic
Macroinvertebrate
NLA-derived regionally specific
benchmark
Coastal
Plains
Good
>51.8
Sample collected from the lake
bottom at 10 shoreline locations
and composited for each lake.
Organisms were usually identified
to genus and an index was
Biological
Benthic
Macroinvertebrate
NLA-derived regionally specific
benchmark
Eastern
Highlands
Good
>44.5
Biological
Benthic
Macroinvertebrate
NLA-derived regionally specific
benchmark
Plains
Good
>39.5
developed based on life history
characteristics and tolerance to
environmental conditions.
Biological
Benthic
Macroinvertebrate
NLA-derived regionally specific
benchmark
Upper
Midwest
Good
>51.4
Biological
Benthic
Macroinvertebrate
NLA-derived regionally specific
benchmark
Western
Mountains
Good
>47.6
Biological
Benthic
Macroinvertebrate
NLA-derived regionally specific
benchmark
Coastal
Plains
Poor
<44.1
Biological
Benthic
Macroinvertebrate
NLA-derived regionally specific
benchmark
Eastern
Highlands
Poor
<31.4
Biological
Benthic
Macroinvertebrate
NLA-derived regionally specific
benchmark
Plains
Poor
<26.6
Biological
Benthic
Macroinvertebrate
NLA-derived regionally specific
benchmark
Upper
Midwest
Poor
<37.2
Biological
Benthic
Macroinvertebrate
NLA-derived regionally specific
benchmark
Western
Mountains
Poor
<32.6
Biological
Zooplankton
NLA-derived regionally specific
benchmark
Coastal
Plains
Good
>59.42
Sample collected from the water
column at the open-water site.
Organisms were usually identified
Biological
Zooplankton
NLA-derived regionally specific
benchmark
Eastern
Highlands
Good
>73.595
to genus and an index was
developed based on life history
characteristics and tolerance to
environmental conditions.
Biological
Zooplankton
NLA-derived regionally specific
benchmark
Plains
Good
>36.72
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NLA 2017 Technical Support Document Version 1.1
Metric
Category
Indicator
Benchmark Description
National/
Ecoregion
Condition class
Value
Units
General Assessment Notes
Biological
Zooplankton
NLA-derived regionally specific
benchmark
Upper
Midwest
Good
>63.68
Biological
Zooplankton
NLA-derived regionally specific
benchmark
Western
Mountains
Good
>60.78
Biological
Zooplankton
NLA-derived regionally specific
benchmark
Coastal
Plains
Poor
<53.77
Biological
Zooplankton
NLA-derived regionally specific
benchmark
Eastern
Highlands
Poor
<60.03
Biological
Zooplankton
NLA-derived regionally specific
benchmark
Plains
Poor
<28.17
Biological
Zooplankton
NLA-derived regionally specific
benchmark
Upper
Midwest
Poor
<52.03
Biological
Zooplankton
NLA-derived regionally specific
benchmark
Western
Mountains
Poor
<51.32
Biological
Chlorophyll a
NLA-derived regionally specific
benchmark
CPL
Good
<12.7
ug/L
Sample collected from a vertically
integrated water column at the
Biological
Chlorophyll a
NLA-derived regionally specific
benchmark
NAP
Good
<4.52
ug/L
open-water site. Measured
concentrations were compared to
benchmarks.
Biological
Chlorophyll a
NLA-derived regionally specific
benchmark
NPL
Good
<10.9
ug/L
Biological
Chlorophyll a
NLA-derived regionally specific
benchmark
SAP
Good
<5.54
ug/L
Biological
Chlorophyll a
NLA-derived regionally specific
benchmark
SPL-
manmade
Good
<8.97
ug/L
Biological
Chlorophyll a
NLA-derived regionally specific
benchmark
SPL-natural
Good
<118
ug/L
Biological
Chlorophyll a
NLA-derived regionally specific
benchmark
TPL
Good
<13.9
ug/L
Biological
Chlorophyll a
NLA-derived regionally specific
benchmark
UMW
Good
<6.7
ug/L
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Metric
Category
Indicator
Benchmark Description
National/
Ecoregion
Condition class
Value
Units
General Assessment Notes
Biological
Chlorophyll a
NLA-derived regionally specific
benchmark
WMT
Good
<1.83
ug/L
Biological
Chlorophyll a
NLA-derived regionally specific
benchmark
XER
Good
<5.92
ug/L
Biological
Chlorophyll a
NLA-derived regionally specific
benchmark
CPL
Poor
>28
ug/L
Sample collected from a vertically
integrated water column at the
open-water site. Measured
concentrations were compared to
benchmarks.
Biological
Chlorophyll a
NLA-derived regionally specific
benchmark
NAP
Poor
>8.43
ug/L
Biological
Chlorophyll a
NLA-derived regionally specific
benchmark
NPL
Poor
>19.3
ug/L
Biological
Chlorophyll a
NLA-derived regionally specific
benchmark
SAP
Poor
>13.1
ug/L
Biological
Chlorophyll a
NLA-derived regionally specific
benchmark
SPL-
manmade
Poor
>12.6
ug/L
Biological
Chlorophyll a
NLA-derived regionally specific
benchmark
SPL-natural
Poor
>219
ug/L
Biological
Chlorophyll a
NLA-derived regionally specific
benchmark
Temperate
Plains
Poor
>19.8
ug/L
Biological
Chlorophyll a
NLA-derived regionally specific
benchmark
UMW
Poor
>14.6
ug/L
Biological
Chlorophyll a
NLA-derived regionally specific
benchmark
WMT
Poor
>3.86
ug/L
Biological
Chlorophyll a
NLA-derived regionally specific
benchmark
XER
Poor
>9
ug/L
Chemical
Acidity
Nationally consistent, literature
benchmark described in Herlihy
etal. (1991)
National
Good
ANC > 50
ueq/L
ueq/L
ANC (corrected for DOC) measured
from a vertically integrated water
column at the open-water site.
Chemical
Acidity
Nationally consistent, literature
benchmark described in Herlihy
etal. (1991)
National
Poor
ANC < 0
Ueq/L and
DOC values
< 6 mg/L
ueq/L
Measured concentrations were
compared to benchmarks.
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Metric
Category
Indicator
Benchmark Description
National/
Ecoregion
Condition class
Value
Units
General Assessment Notes
Chemical
Atrazine
EPA aquatic plant concentration
equivalent level of concern (CE-
LOC); click here
National
Above
Benchmark =
Poor
> 3.4 ppb
ppb
Sample collected from a vertically
integrated water column sample at
the open-water site. Measured
concentrations were compared to
benchmark.
Chemical
Atrazine
Atrazine minimum detection
level (MDL)
National
Detected
> 0.046
ppb
Chemical
Microcystins
EPA recreational water qualtiy
criteria and swimming advisory
recommendation. US EPA 2019.
EPA 822-R-19-001.
National
Above
Benchmark =
Poor
>8
ppb
Sample collected from a vertically
integrated water column sample at
the open-water site. Measured
concentrations were compared to
benchmark.
Chemical
Microcystins
Microcystin minimum detection
level (MDL)
National
Detected
0.1
ppb
Chemical
Oxygen (Dissolved)
Nationally consistent, literature
benchmark; warmwater daily
minimum for "other life stages";
US EPA 1986. Quality Criteria for
Water ("Gold Book")
National
Good
<= 3 ppm
ppm
Measures were collected from the
in-situ oxygen measure from the
top 2m of the profile at the index
site. The mean of all measurements
between 0 and 2 meters was
compared to the benchmark.
Chemical
Oxygen (Dissolved)
Nationally consistent, literature
benchmark; warmwater daily
minimum for " early life stages";
US EPA 1986. Quality Criteria for
Water ("Gold Book")
National
Poor
>= 5 ppm
ppm
Chemical
Total Nitrogen
NLA-derived regionally specific
benchmark
CPL
Good
<659
ug/L
Sample collected from a vertically
integrated water column at the
open-water site. Measured
concentrations were compared to
benchmarks.
Chemical
Total Nitrogen
NLA-derived regionally specific
benchmark
NAP
Good
<428
ug/L
Chemical
Total Nitrogen
NLA-derived regionally specific
benchmark
NPL
Good
<849
ug/L
Chemical
Total Nitrogen
NLA-derived regionally specific
benchmark
SAP
Good
<266
ug/L
Chemical
Total Nitrogen
NLA-derived regionally specific
benchmark
SPL-
manmade
Good
<650
ug/L
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Metric
Category
Indicator
Benchmark Description
National/
Ecoregion
Condition class
Value
Units
General Assessment Notes
Chemical
Total Nitrogen
NLA-derived regionally specific
benchmark
SPL-natural
Good
<7840
ug/L
Chemical
Total Nitrogen
NLA-derived regionally specific
benchmark
TPL
Good
<865
ug/L
Chemical
Total Nitrogen
NLA-derived regionally specific
benchmark
UMW
Good
<766
ug/L
Chemical
Total Nitrogen
NLA-derived regionally specific
benchmark
WMT
Good
<253
ug/L
Chemical
Total Nitrogen
NLA-derived regionally specific
benchmark
XER
Good
<605
ug/L
Chemical
Total Nitrogen
NLA-derived regionally specific
benchmark
CPL
Poor
>923
ug/L
Sample collected from a vertically
integrated water column at the
open-water site. Measured
concentrations were compared to
benchmarks.
Chemical
Total Nitrogen
NLA-derived regionally specific
benchmark
NAP
Poor
>655
ug/L
Chemical
Total Nitrogen
NLA-derived regionally specific
benchmark
NPL
Poor
>1620
ug/L
Chemical
Total Nitrogen
NLA-derived regionally specific
benchmark
SAP
Poor
>409
ug/L
Chemical
Total Nitrogen
NLA-derived regionally specific
benchmark
SPL-
manmade
Poor
>830
ug/L
Chemical
Total Nitrogen
NLA-derived regionally specific
benchmark
SPL-natural
Poor
>11100
ug/L
Chemical
Total Nitrogen
NLA-derived regionally specific
benchmark
TPL
Poor
>1350
ug/L
Chemical
Total Nitrogen
NLA-derived regionally specific
benchmark
UMW
Poor
>926
ug/L
Chemical
Total Nitrogen
NLA-derived regionally specific
benchmark
WMT
Poor
>429
ug/L
Chemical
Total Nitrogen
NLA-derived regionally specific
benchmark
XER
Poor
>954
ug/L
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Metric
Category
Indicator
Benchmark Description
National/
Ecoregion
Condition class
Value
Units
General Assessment Notes
Chemical
Total Phosphorus
NLA-derived regionally specific
benchmark
CPL
Good
<43
ug/L
Sample collected from a vertically
integrated water column at the
open-water site. Measured
concentrations were compared to
benchmarks.
Chemical
Total Phosphorus
NLA-derived regionally specific
benchmark
NAP
Good
<16
ug/L
Chemical
Total Phosphorus
NLA-derived regionally specific
benchmark
NPL
Good
<63
ug/L
Chemical
Total Phosphorus
NLA-derived regionally specific
benchmark
SAP
Good
<18
ug/L
Chemical
Total Phosphorus
NLA-derived regionally specific
benchmark
SPL-
manmade
Good
<30
ug/L
Chemical
Total Phosphorus
NLA-derived regionally specific
benchmark
SPL-natural
Good
<486
ug/L
Chemical
Total Phosphorus
NLA-derived regionally specific
benchmark
TPL
Good
<38.4
ug/L
Chemical
Total Phosphorus
NLA-derived regionally specific
benchmark
UMW
Good
<24.8
ug/L
Chemical
Total Phosphorus
NLA-derived regionally specific
benchmark
WMT
Good
<23.4
ug/L
Chemical
Total Phosphorus
NLA-derived regionally specific
benchmark
XER
Good
<44
ug/L
Chemical
Total Phosphorus
NLA-derived regionally specific
benchmark
CPL
Poor
>59.5
ug/L
Sample collected from a vertically
integrated water column at the
open-water site. Measured
concentrations were compared to
benchmarks.
Chemical
Total Phosphorus
NLA-derived regionally specific
benchmark
NAP
Poor
>27.9
ug/L
Chemical
Total Phosphorus
NLA-derived regionally specific
benchmark
NPL
Poor
>82
ug/L
Chemical
Total Phosphorus
NLA-derived regionally specific
benchmark
SAP
Poor
>33
ug/L
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Metric
Category
Indicator
Benchmark Description
National/
Ecoregion
Condition class
Value
Units
General Assessment Notes
Chemical
Total Phosphorus
NLA-derived regionally specific
benchmark
SPL-
manmade
Poor
>43
ug/L
Chemical
Total Phosphorus
NLA-derived regionally specific
benchmark
SPL-natural
Poor
>839
ug/L
Chemical
Total Phosphorus
NLA-derived regionally specific
benchmark
TPL
Poor
>57.5
ug/L
Chemical
Total Phosphorus
NLA-derived regionally specific
benchmark
UMW
Poor
>40
ug/L
Chemical
Total Phosphorus
NLA-derived regionally specific
benchmark
WMT
Poor
>43
ug/L
Chemical
Total Phosphorus
NLA-derived regionally specific
benchmark
XER
Poor
>84.8
ug/L
Chemical
Trophic State
Nationally consistent, NLA-
derived benchmark
National
Oligotrophic
<2
ug/L
Sample collected from a vertically
integrated water column at the
open-water site.
Trophic state was based on
measured chlorophyll a
concentrations.
Chemical
Trophic State
Nationally consistent, NLA-
derived benchmark
National
Mesotrophic
>2 and <7
ug/L
Chemical
Trophic State
Nationally consistent, NLA-
derived benchmark
National
Eutrophic
>7 and <30
ug/L
Chemical
Trophic State
Nationally consistent, NLA-
derived benchmark
National
Hypereutrophic
>30
ug/L
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Appendix D: List of Candidate Metrics for Zooplankton
This section provides additional details for the candidate metrics we considered when developing the
MMIs for each bio-region. Tables 7-13 through 7-17 list each metric by its variable name, which of the
six metric categories it was assigned to (see Section 7.4.3), and a description of the metric for the
Coastal Plains, Eastern Highlands, Plains, Upper Midwest, and Western Mountains bio-regions,
respectively. In addition, the responsiveness to disturbance and repeatability of each metric is
provided (t-value for responsiveness, and S:N value for repeatability).
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Table D, 1, List of candidate metrics used to develop the zooplankton MMI for the Coastal Plains bioregion.
t value
(Least disturbed
Mean Value for
Mean Value for
vs.
Metric
Least disturbed
Most disturbed
Most disturbed
SignakNoise
Category
Metric Name
Description
Sites
Sites
Sites)
Value
Abundance/
Biomass of individuals of smaller-sized taxa
Biomass/
(NET_SIZECLS_NEW=FINE; coarse and fine
Density
FINE BIO
net samples combined)
14.7
50.2
-1,67
1.2
Abundance/
Biomass/
Biomass represented by individuals
Density
ZOFN BIO
collected in fine mesh net (50-um)
20.5
67.2
-1.79
1.2
Percent of total individuals that are within
the cladoceran family Sididae (coarse and
Cladoceran
SIDID PIND
fine net samples combined)
2.10
8.18
-1.80
0.4
Total density of individuals within the
copepod order Calanoida (coarse and fine
Copepod
CALAN DEN
net samples combined)
5.6
22.9
--1.30
1.9
Number of families represented by distinct
native taxa (coarse and fine net samples
Richness/Diversity
FAM NAT NTAX
combined)
11.9
9.3
2.66
1.9
Number of families represented by distinct
taxa (coarse and fine net samples
Richness/Diversity
FAM NTAX
combined)
11.9
9.4
2.55
2.0
Number of genera represented by distinct
taxa (coarse and fine net samples
Richness/Diversity
GEN NTAX
combined)
15.4
12.1
2.21
1.5
Number of genera represented by distinct
native taxa (coarse and fine net samples
Richness/Diversity
GEN NAT NTAX
combined)
15.3
11.9
2.29
1.3
Number of families represented by distinct
Richness/Diversity
ZOFN FAM NAT NTAX
native taxa in the fine mesh net (50-um)
7.4
5.4
2.32
1.4
Total density of individuals within the
rotifer order Collothecaceae (coarse and
Rotifer
COLLO BIO
fine net samples combined)
0.22
0.02
1.79
3.3
Percent of total individuals within the
rotifer order Collothecaceae (coarse and
Rotifer
COLLO PIND
fine net samples combined)
2.27
0.32
1.87
2.0
Percent of total biomass within the rotifer
order Collothecaceae (coarse and fine net
Rotifer
COLLO PBIO
samples combined)
1.0
0.15
1.8
7.2
Number of distinct predator taxa (coarse
Trophic
PRED NTAX
and fine net samples combined)
2.5
1.3
2.56
4.6
Percent of distinct taxa that are predators
Trophic
PRED PTAX
(coarse and fine net samples combined)
12.01
6.59
2.71
2.2
Number of distinct herbivore taxa (coarse
Trophic
HERB NTAX
and fine net samples combined)
11.9
8.8
2.22
2.1
173
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t value
(Least disturbed
Mean Value for
Mean Value for
vs.
Metric
Least disturbed
Most disturbed
Most disturbed
SignahNoise
Category
Metric Name
Description
Sites
Sites
Sites)
Value
Percent of distinct taxa that are
omnivorous (coarse and fine net samples
Trophic
OMNI PTAX
combined)
22.03
34.10
-3.35
4.3
Percent of total density represented by
omnivorous individuals (coarse and fine
Trophic
OMNI PDEN
net samples combined)
18.12
39.82
-2.37
1.7
Number of distinct rotifer taxa that are
predators (coarse and fine net samples
Trophic
ROT PRED NTAX
combined)
2.2
1.1
2.50
4.5
Percent of distinct rotifer taxa that are
Trophic
ROT PRED PTAX
predators
10.78
5.64
2.70
1.9
Number of distinct rotifer taxa that are
herbivores (coarse and fine net samples
Trophic
ROT HERB NTAX
combined)
6.8
4.6
2.00
1.8
Biomass represented by rotifer individuals
Trophic
ROT OMNI BIO
that are omnivores
4.8
35.0
-1.76
1.4
Percent of rotifer individuals represented
Trophic
ROT OMNI PIND
by omnivores
13.41
26.55
-1.88
2.0
Percent of distinct rotifer taxa that are
Trophic
ROT OMNI PTAX
omnivorous
17.26
27.95
-3.34
2.6
Percent of rotifer density represented by
Trophic
ROT OMNI PDEN
omnivores
17.82
39.27
-2.36
1.7
Metrics Derived from 300-count Subsam
pies of Coarse and Fine Net Samples
Abundance/
Biomass
Total biomass in 300-count subsample of
Density
ZOFN300 BIO
fine-mesh net sample (50-|im)
11.6
34.6
-1.73
1.0
Percent of distinct taxa in the 300-count
subsamples that are in the family
Bosminidae (coarse and fine net samples
Cladoceran
BOSM300 PTAX
combined)
7.98
4.07
2.94
0.3
Percent of individuals within the
cladoceran family Sididae in 300-count
subsamples (coarse and fine net samples
Cladoceran
SIDID300 PIND
combined)
2.95
9.10
-1.68
0.7
Percent of biomass in dominant copepod
taxon in the 300 count subsamples (coarse
Copepod
DOM1 300 COPE PBIO
and fine net samples combined)
85.21
79.61
0.86
1.9
Number of genera represented by distinct
taxa (coarse and fine net samples
Richness/Diversity
GEN300 NTAX
combined)
14.1
11.1
2.16
1.8
Number of genera represented by distinct
native taxa (coarse and fine net samples
Richness/Diversity
GEN300_NAT_NTAX
combined)
14.1
11.0
2.24
1.5
174
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t value
(Least disturbed
Mean Value for
Mean Value for
vs.
Metric
Least disturbed
Most disturbed
Most disturbed
SignahNoise
Category
Metric Name
Description
Sites
Sites
Sites)
Value
Number of families represented in 300
count subsamples (coarse and fine net
Richness/Diversity
FAM300 NTAX
samples combined)
10.9
8.6
2.61
2.2
Number of native families represented in
300 count subsamples (coarse and fine net
Richness/Diversity
FAM300 NAT NTAX
samples combined)
10.9
8.6
2.72
2.1
Number of distinct native families in 300-
count subsample of fine-mesh net sample
Richness/Diversity
ZOFN300 FAM NAT NTAX
(50-nm)
6.7
4.8
2.49
1.4
Biomass represented by individuals of the
rotifer order Collothecaceae in the 300-
count subsamples (coarse and fine net
Rotifer
COLLO300 BIO
samples combined)
0.08
0.01
1.81
7.0
Percent of biomass within the rotifer order
Collothecaceae in the 300-count
subsamples (coarse and fine net samples
Rotifer
COLLO300 PBIO
combined)
0.96
0.16
1.75
5.9
Number of distinct taxa that are predators
in 300 count subsamples (coarse and fine
Trophic
PRED300 NTAX
net samples combined)
1.7
1.0
1.94
2.7
Biomass of predator individuals in 300
count subsamples (coarse and fine net
Trophic
PRED300 BIO
samples combined)
0.46
0.14
2.45
1.5
Number of distinct taxa that are herbivores
in 300 count subsamples (coarse and fine
Trophic
HERB300 NTAX
net samples combined)
10.9
7.8
2.58
1.8
Percent of omnivorous individuals in 300
count subsamples (coarse and fine net
Trophic
OMNI300 PIND
samples combined)
15.54
28.43
-1.85
1.4
Percent of distinct taxa that are omnivores
in 300 count subsamples (coarse and fine
Trophic
OMNI300 PTAX
net samples combined)
23.75
37.16
-2.91
4.1
Percent of biomass represented by
omnivorous individuals in 300 count
subsamples (coarse and fine net samples
Trophic
OMNI300 PBIO
combined)
27.14
33.99
-0.79
1.2
Number of distinct rotifer taxa that are
predators in 300 count subsamples (coarse
Trophic
ROT PRED300 NTAX
and fine net samples combined)
1.7
1.0
1.940
2.7
Biomass represented by rotifer individuals
that are predators in 300 count
subsamples (coarse and fine net samples
Trophic
ROT_PRED300_BIO
combined)
0.46
0.14
2.45
1.5
175
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Metric
Category
Metric Name
Description
Mean Value for
Least disturbed
Sites
Mean Value for
Most disturbed
Sites
t value
(Least disturbed
vs.
Most disturbed
Sites)
SignahNoise
Value
Trophic
ROT HERB300 NTAX
Number of distinct rotifer taxa that are
herbivores in 300 count subsamples
(coarse and fine net samples combined)
6.0
3.7
2.45
1.4
Trophic
ROT OMNI300 PIND
Percent of rotifer individuals that are
omnivorous in 300 count subsamples
(coarse and fine net samples combined)
12.24
25.10
-2.00
1.9
Trophic
ROT OMNI300 PTAX
Percent of distinct rotifer taxa that are
omnivorous in 300 count subsamples
(coarse and fine net samples combined)
18.35
30.18
-3.06
3.6
176
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Table D, 2, List of candidate metrics used to develop the zooplankton MMI for the Eastern Highlands bio-region
t value
Mean Value for
Mean Value for
(Least disturbed vs.
Metric
Least disturbed
Most disturbed
Most disturbed
SignahNoise
Category
Metric Name
Description
Sites
Sites
Sites)
Value
Abundance/
Density represented by individuals collected in
Biomass/
coarse mesh net (150-um for 2012 samples, 243 um
Density
ZOCN DEN
for 2007 resamples)
12.56848
34.33432549
-1.89
7.1
Abundance/
Density represented by native individuals collected
Biomass/
in coarse mesh net (150-um for 2012 samples, 243
Density
ZOCN NAT DEN
um for 2007 resamples)
12.56848
34.33106863
-1.89
2.1
Abundance/
Density represented by individuals of taxa collected
Biomass/
in coarse mesh net (150-um; coarse and fine net
Density
COARSE DEN
samples combined)
21.26666667
53.84573922
-2.13
2.4
Abundance/
Biomass represented by individuals of taxa
Biomass/
collected in coarse mesh net (150-um; coarse and
Density
COARSE PBIO
fine net samples combined)
68.49155556
56.48058824
1.86
1.7
Abundance/
Density represented by individuals of native larger-
Biomass/
sized taxa (NET_SIZECLS_NEW=COARSE; coarse and
Density
COARSE NAT DEN
fine net samples combined)
21.266666667
53.80877451
-2-12
1.5
Abundance/
Biomass represented by individuals of native larger-
Biomass/
sized taxa (NET_SIZECLS_NEW=COARSE; coarse and
Density
COARSE NAT PBIO
fine net samples combined)
68.491555556
56.44254902
1.86
1.5
Abundance/
Biomass represented by individuals of smaller-sized
Biomass/
taxa (NET_SIZECLS_NEW=FINE; coarse and fine net
Density
FINE PBIO
samples combined)
31.508444444
43.519411765
-1.86
1.7
Density of native individuals within the suborder
Cladoceran
CLAD DEN
Cladocera (coarse and fine net samples combined)
6.813766667
27.71694902
-1.94
1.9
Density of native individuals within the suborder
Cladoceran
CLAD NAT DEN
Cladocera (coarse and fine net samples combined)
6.813766667
27.71382549
-1.94
1.8
Biomass represented by large cladoceran
individuals (SUBORDER=CLADOCERA and
CLADOCEAN SIZE=LARGE; coarse and fine net
Cladoceran
LGCLAD BIO
samples combined)
25.780533111
10.663794725
2.16
1.3
Biomass represented by native large cladoceran
individuals (SUBORDER=CLADOCERA and
CLADOCEAN_SIZE=LARGE; coarse and fine net
Cladoceran
LGCLAD NAT BIO
samples combined)
25.780533111
10.656975706
2.16
1.3
Biomass represented by small cladoceran
individuals (SUBORDER=CLADOCERA and
CLADOCEAN SIZE=SMALL; coarse and fine net
Cladoceran
SMCLAD BIO
samples combined)
2.985147667
31.80179637
-2.37
2.6
Density represented by small cladoceran individuals
(SUBORDER=CLADOCERA and
CLADOCERAN_SIZE=SMALL; coarse and fine net
Cladoceran
SMCLAD DEN
samples combined)
2.476364444
22.86743922
-1.99
2.4
177
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t value
Mean Value for
Mean Value for
(Least disturbed vs.
Metric
Least disturbed
Most disturbed
Most disturbed
SignakNoise
Category
Metric Name
Description
Sites
Sites
Sites)
Value
Percent of small cladoceran individuals
(SUBORDER=CLADOCERA and CLAD-SIZE=SMALL;
Cladoceran
SMCLAD PIND
coarse and fine net samples combined)
9.58
17.42
-2.73
1.6
Percent of total density represented by small
cladoceran individuals (SUBORDER=CLADOCERA
and CLADOCERAN_SIZE=SMALL; coarse and fine net
Cladoceran
SMCLAD PDEN
samples combined)
1.03
3.34
-1.91
19.1
Biomass represented by native small cladoceran
individuals (SUBORDER=CLADOCERA and
CLADOCERAN SIZE=SMALL; coarse and fine net
Cladoceran
SMCLAD NAT BIO
samples combined)
2.985147667
31.79812541
-2.37
2.5
Density represented by native small cladoceran
individuals (SUBORDER=CLADOCERA and
CLADOCERA SIZE=SMALL; coarse and fine net
Cladoceran
SMCLAD NAT DEN
samples combined)
2.476364444
22.86662549
-1.99
2.2
Percent of total density represented by native small
cladoceran individuals (SUBORDER=CLADOCERA
and CLADOCERAN_SIZE=SMALL; coarse and fine net
Cladoceran
SMCLAD NAT PDEN
samples combined)
1.03
3.33
-1.91
19.1
Density of individuals within the family Daphniidae
Cladoceran
DAPHNIID DEN
(coarse and fine net samples combined)
3.223097778
16.27482549
-2.09
2.5
Density of native individuals within the family
Cladoceran
DAPHNIID NAT DEN
Daphniidae (coarse and fine net samples combined)
3.223097778
16.27251961
-2.09
2.5
Density represented by individuals within the
subclass Copepoda (coarse and fine net samples
Copepod
COPE DEN
combined)
81.931315556
139.66798235
-1.74
1.5
Density represented by native individuals within the
subclass Copepoda (coarse and fine net samples
Copepod
COPE NAT DEN
combined)
81.931315556
139.66784314
-1.74
1.5
Number of distinct taxa within the copepod order
Copepod
CALAN NTAX
Calanoida (coarse and fine net samples combined)
1.3
1.1
2.10
2.4
Percent of total density represented by taxa of the
copepod order Calanoida (coarse and fine net
Copepod
CALAN PDEN
samples combined)
3.82
1.64
1.80
35.0
Number of distinct native taxa within the copepod
order Calanoida (coarse and fine net samples
Copepod
CALAN NAT NTAX
combined)
1.3
1.0
2.22
1.3
Percent of total density represented by individuals
of native taxa within the copepod order Calanoida
Copepod
CALAN NAT PDEN
(coarse and fine net samples combined)
3.81
1.64
1,80
35.0
Percent of distinct larger-sized native taxa
(NET SIZECLS NEW=COARSE; coarse and fine net
Richness/Diversity
COARSE_NAT_PTAX
samples combined)
40.65
37.17
1.64
0.3
178
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t value
Mean Value for
Mean Value for
(Least disturbed vs.
Metric
Least disturbed
Most disturbed
Most disturbed
SignahNoise
Category
Metric Name
Description
Sites
Sites
Sites)
Value
Percent total biomass from rotifers (coarse and fine
Rotifer
ROT PBIO
net samples combined)
23.72
34.91
-1.88
1.3
Percent of distinct taxa that are omnivorous (coarse
Trophic
OMNI PTAX
and fine net samples combined)
23.38
27.56
-2.36
1.6
Density of herbivorous cladocerans
(suborder=CLADOCERA; coarse and fine net
CLAD HERB DEN
samples combined)
6.8127244444
27.71694902
-1.94
1.9
Percent density represented by herbivorous
copepods (order=COPEPODA; coarse and fine net
COPE HERB PDEN
samples combined)
4.22
1.92
1.86
20.0
Metrics Derived from 300-count Subsamples of Coarse and Fine Net Samples
Percent of biomass represented by individuals of
taxa collected in coarse mesh net (150-um;
Abundance/
NET_SIZECLS_NEW=COARSE) in 300 count
Biomass/
subsamples (coarse and fine net samples
Density
COARSE300 PBIO
combined)
70.74
58.61
1.96
1.7
Percent of biomass represented by individuals of
native taxa collected in coarse mesh net (150-um;
Abundance/
NET_SIZECLS_NEW=COARSE) in 300 count
Biomass/
subsamples (coarse and fine net samples
Density
COARSE300 NAT PBIO
combined)
70.738666667
58.570196078
1.96
1.5
Percent biomass represented by individuals of
Abundance/
smaller-sized taxa (NET_SIZECLS_NEW=FINE) in
Biomass/
300-count subsamples (coarse and fine net samples
Density
FINE300 PBIO
combined)
29.26
41.39
-1.96
1.7
Biomass represented by large cladoceran
individuals (SUBORDER=CLADOCERA and
CLADOCEAN_SIZE=LARGE) in 300-count subsamples
Cladoceran
LGCLAD300 BIO
(coarse and fine net samples combined)
15.692285844
7.0078742941
2.02
1.4
Biomass represented by native large cladoceran
individuals (SUBORDER=CLADOCERA and
CLADOCEAN_SIZE=LARGE) in 300-count subsamples
Cladoceran
LGCLAD300 NAT BIO
(coarse and fine net samples combined)
15.692285844
7.0031208824
2.02
1.4
Biomass represented by small cladoceran
individuals (SUBORDER=CLADOCERA and
CLADOCEAN_SIZE=SMALL) in 300-count subsamples
Cladoceran
SMCLAD300 BIO
(coarse and fine net samples combined)
1.8545441111
21.410646353
-2.40
2.6
Percent of small cladoceran individuals
(SUBORDER=CLADOCERA and
CLADOCEAN_SIZE=SMALL) in 300-count subsamples
Cladoceran
SMCLAD300 PIND
(coarse and fine net samples combined)
10.90
19.03
-2.72
1.7
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t value
Mean Value for
Mean Value for
(Least disturbed vs.
Metric
Least disturbed
Most disturbed
Most disturbed
SignahNoise
Category
Metric Name
Description
Sites
Sites
Sites)
Value
Percent of biomass represented by small
cladoceran individuals (SUBORDER=CLADOCERA
and CLADOCEAN_SIZE=SMALL) in 300-count
subsamples (coarse and fine net samples
Cladoceran
SMCLAD300 PBIO
combined)
5.50
16.12
-2.82
1.6
Biomass represented by native small cladoceran
individuals (SUBORDER=CLADOCERA and
CLADOCEAN_SIZE=SMALL) in 300-count subsamples
Cladoceran
SMCLAD300 NAT BIO
(coarse and fine net samples combined)
1.8545441111
21.410646353
-2.40
2.5
Percent of native small cladoceran individuals
(SUBORDER=CLADOCERA and
CLADOCEAN_SIZE=SMALL) in 300-count subsamples
Cladoceran
SMCLAD300 NAT PIND
(coarse and fine net samples combined)
10.90
19.03
-2.72
1.4
Number of distinct taxa within the copepod order
Calanoida in 300-count subsamples (coarse and fine
Copepod
CALAN300 NTAX
net samples combined)
1.3
1.0
1.94
2.8
Number of distinct native taxa within the copepod
order Calanoida in 300-count subsamples (coarse
Copepod
CALAN300 NAT NTAX
and fine net samples combined)
1.3
1.0
2.08
1.4
Percent distinct native taxa in 300-count subsample
Richness/Diversity
ZOCN300 NAT PTAX
of coarse net sample (150-um)
100
98.55
1.88
0.1
Number of distinct native taxa in coarse net
Richness/Diversity
ZOCN300 FAM NTAX
samples (150-um) based on 300-count subsample
5.1
4.7
1.47
0.8
Percent biomass from rotifers in 300-count
subsamples (coarse and fine net samples
Rotifer
ROT300 PBIO
combined)
22.26
34.91
-1.89
1.3
Percent of distinct taxa that are omnivorous in 300-
count subsamples (coarse and fine net samples
Trophic
OMNI300_PTAX
combined)
23.31
28.29
-2.60
1.5
180
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Table D, 3, List of candidate metrics used to develop the zooplankton MMI for the Plains bio-region
t value
Mean Value for
Mean Value for
(Least disturbed vs.
Metric
Least disturbed
Most disturbed
Most disturbed
SignahNoise
Category
Metric Name
Description
Sites
Sites
Sites)
Value
Abundance/
Percent of total biomass represented by individuals
Biomass/
collected in coarse mesh net (150-um for 2012
Density
COARSE PBIO
samples, 243 um for 2007 resamples)
57.38
70.00
-1.75
6.3
Abundance/
Percent of total biomass represented by native
Biomass/
individuals collected in coarse mesh net (150-um
Density
COARSE NAT PBIO
for 2012 samples, 243 um for 2007 resamples)
57.38
69.94
-1.74
6.3
Abundance/
Percent of biomass represented by individuals of
Biomass/
smaller-sized taxa (NET_SIZECLS_NEW=FINE; coarse
Density
FINE PBIO
and fine net samples combined)
42.62
30.00
1.75
6.3
Percent of biomass represented by native
Abundance/
individuals of smaller-sized taxa
Biomass/
(NET_SIZECLS_NEW=FINE; coarse and fine net
Density
FINE NAT PBIO
samples combined)
42.62
29.99
1.75
6.2
Percent of total individuals within the suborder
Cladocera that are "small"
(CLADOCERA_SIZE=SMALL; coarse and fine net
Cladoceran
SMCLAD PIND
samples combined)
19.26
9.03
3.09
1.8
Percent of native individuals within the suborder
Cladocera that are "small"
(CLADOCERA_SIZE=SMALL; coarse and fine net
Cladoceran
SMCLAD NAT PIND
samples combined)
19.26
8.94
3.11
1.8
Percent of total biomass represented by native
small cladoceran individuals
(SUBORDER=CLADOCERA and
CLADOCEAN SIZE=SMALL; coarse and fine net
Cladoceran
SMCLAD NAT PBIO
samples combined)
13.35
7.02
1.74
1.4
Copepod
Percent of total individuals within the subclass
COPE PIND
Copepoda (coarse and fine net samples combined)
29.45
41.97
-2.46
1.4
Copepod
Percent of native individuals within the subclass
COPE NAT PIND
Copepoda (coarse and fine net samples combined)
29.45
41.97
-2.46
1.4
Percent of distinct taxa that are within the copepod
order Calanoida (coarse and fine net samples
Copepod
CALAN PTAX
combined)
6.38
10.16
-2.32
2.0
Percent of total density represented by individuals
within the copepod order Calanoida (coarse and
Copepod
CALAN PDEN
fine net samples combined)
1.20
6.52
-2.06
14.1
Percent of total density represented by native
individuals within the copepod order Calanoida
Copepod
CALAN_NAT_PDEN
(coarse and fine net samples combined)
1.20
6.52
-2.06
14.1
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t value
Mean Value for
Mean Value for
(Least disturbed vs.
Metric
Least disturbed
Most disturbed
Most disturbed
SignakNoise
Category
Metric Name
Description
Sites
Sites
Sites)
Value
Ratio of Calanoid to (Cladoccera+Cyclopoids) based
on number of individuals (coarse and fine net
samples combined). Adapted from Kane et al.
(2009) Lake Erie plankton IBI. Calculated as
Copepod
COPE RATIO NIND
CALANOID_NIND/(CLAD_NIND+CYCLOPOID_NIND)
17.435
0.812
1.84
38.9
Ratio of Calanoid to (Cladoccera+Cyclopoids) based
on biomass (coarse and fine net samples
combined). Adapted from Kane et al. (2009) Lake
Erie plankton IBI. Calculated as
Copepod
COPE RATIO BIO
CALANOID BIO/(CLAD BIO+CYCLOPOID BIO)
7.325729723
1.327404241
2.31
4.6
Total distinct taxa richness (coarse and fine net
Richness/Diversity
TOTL NTAX
samples combined)
17.3
14..6
2.27
2.2
Total distinct native taxa richness (coarse and fine
Richness/Diversity
TOTL NAT NTAX
net samples combined)
17.3
14.5
2.34
2.2
Number of genera represented by distinct taxa
Richness/Diversity
GEN NTAX
(coarse and fine net samples combined)
13.8
11.6
2.45
2.2
Number of genera represented by distinct native
Richness/Diversity
GEN NAT NTAX
taxa (coarse and fine net samples combined)
13.8
11.5
2.56
2.2
Number of families represented by distinct taxa
Richness/Diversity
FAM NTAX
(coarse and fine net samples combined)
10.7
9.1
2.32
1.9
Number of families represented by distinct native
Richness/Diversity
FAM NAT NTAX
taxa (coarse and fine net samples combined)
10.7
9.1
2.41
2.2
Number of distinct taxa in fine net sample (ZOFN;
Richness/Diversity
ZOFN NTAX
80-um mesh)
12.4
9.8
2.69
1.7
Number of distinct native taxa in fine net sample
Richness/Diversity
ZOFN NAT NTAX
(ZOFN; 80-um mesh)
12. 4
9.8
2.73
1.7
Number of genera represented by distinct taxa in
Richness/Diversity
ZOFN GEN NTAX
fine net sample (ZOFN; 80-um mesh)
8.1
5.8
3.36
3.8
Number of genera represented by distinct native
Richness/Diversity
ZOFN GEN NAT NTAX
taxa in fine net sample (ZOFN; 80-um mesh)
8.1
5.8
3.42
3.8
Number of families represented by distinct taxa in
Richness/Diversity
ZOFN FAM NTAX
fine net sample (ZOFN; 80-um mesh)
6.6
4.7
3.48
3.0
Number of families represented by distinct native
Richness/Diversity
ZOFN FAM NAT NTAX
taxa in fine net sample (ZOFN; 80-um mesh)
6.6
4.7
3.56
3.0
Number of distinct taxa collected only in the fine-
Richness/Diversity
FINE NTAX
mish net (80-um; NET SIZECLS NEW=FINE)
10.5
8.0
2.61
1.8
Number of distinct native taxa collected only in the
Richness/Diversity
FINE NAT NTAX
fine-mish net (80-um; NET_SIZECLS_NEW=FINE)
10.5
8.0
2.63
1.7
Percent of total biomass represented in top 5 taxa
Richness/Diversity
DOM5 PBIO
(coarse and fine net samples combined)
91.31
94.16
-1.77
2.5
Number of distinct rotifer taxa (coarse and fine net
Rotifer
ROT NTAX
samples combined)
10.5
8.0
2.63
1.7
Percent of total density represented by herbivorous
Trophic
COPE HERB PDEN
copepods (coarse and fine net samples combined)
1.23
6.58
-2.13
13.0
182
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t value
Mean Value for
Mean Value for
(Least disturbed vs.
Metric
Least disturbed
Most disturbed
Most disturbed
SignahNoise
Category
Metric Name
Description
Sites
Sites
Sites)
Value
Metrics Derived from 300-count Subsamples of Coarse and Fine Net Samples
Percent of biomass represented by individuals of
Abundance/
taxa collected in coarse mesh net (150-um) in 300
Biomass/
count subsamples (coarse and fine net samples
Density
COARSE300_PBIO
combined)
59.0316
71.48616279
-1.77
5.2
Percent of biomass represented by native
Abundance/
individuals of taxa collected in coarse mesh net
Biomass/
(150-um) in 300 count subsamples (coarse and fine
Density
CO A RS E300_N AT_P B10
net samples combined)
59.0316
71.42267442
-1.76
5.1
Percent of biomass represented in individuals of
Abundance/
smaller-sized taxa (NET_SIZECLS_NEW=FINE) in the
Biomass/
300-count subsample (coarse and fine mesh
Density
FINE300 PBIO
samples combined)
42.15
28.64
1.89
6.0
Percent of biomass represented in native
individuals of smaller-sized taxa
Abundance/
(NET_SIZECLS_NEW=FINE) in the 300-count
Biomass/
subsample (coarse and fine mesh samples
Density
FINE300_NAT_PBIO
combined)
42.15
28.63
1.90
5.8
Percent of small cladoceran individuals
(SUBORDER=CLADOCERA and
CLADOCEAN_SIZE=SMALL) in 300-count subsamples
Cladoceran
SMCLAD300_PIND
(coarse and fine net samples combined)
19.788
9.848139535
2.97
2.0
Percent of biomass represented by small
cladoceran individuals (SUBORDER=CLADOCERA
and CLADOCEAN_SIZE=SMALL) in 300-count
subsamples (coarse and fine net samples
Cladoceran
SMCLAD300 PBIO
combined)
14.17
7.52
1.74
1.4
Percent of native small cladoceran individuals
(SUBORDER=CLADOCERA and
CLADOCEAN_SIZE=SMALL) in 300-count subsamples
Cladoceran
SMCLAD300 NAT PIND
(coarse and fine net samples combined)
19.788
9.760930233
2.99
2.0
Percent of biomass represented by native small
cladoceran individuals (SUBORDER=CLADOCERA
and CLADOCEAN_SIZE=SMALL) in 300-count
subsamples (coarse and fine net samples
Cladoceran
SMCLAD300_NAT_PBIO
combined)
14.17
7.47
1.76
1.4
Percent of individuals within the subclass Copepoda
in 300-count subsamples (coarse and fine net
Copepod
COPE300_PIND
samples combined)
30.94
43.16
2.42
1.3
Percent of native individuals within the subclass
Copepoda in 300-count subsamples (coarse and
Copepod
COPE300 NAT PIND
fine net samples combined)
30.94
43.16
30.93
1.3
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t value
Mean Value for
Mean Value for
(Least disturbed vs.
Metric
Least disturbed
Most disturbed
Most disturbed
SignakNoise
Category
Metric Name
Description
Sites
Sites
Sites)
Value
Percent of distinct taxa within the copepod order
Calanoida in 300-count subsamples (coarse and fine
Copepod
CALAN300 PTAX
net samples combined)
7.51
11.20
-2.07
4.6
Ratio of Calanoid to (Cladoccera+Cyclopoids) based
on number of individuals in 300-count subsamples
(coarse and fine net samples combined). Adapted
from Kane et al. (2009) Lake Erie plankton IBI.
Calculated as
Copepod
COPE RATIO 300 NIND
CALANOID NIND/(CLAD NIND+CYCLOPOID NIND)
12.675
0.800
1.83
19.6
Ratio of Calanoid to (Cladoccera+Cyclopoids) based
on biomass in 300-count subsamples (coarse and
fine net samples combined). Adapted from Kane et
al. (2009) Lake Erie plankton IBI. Calculated as
Copepod
COPE RATIO 300 BIO
CALANOID BIO/(CLAD BIO+CYCLOPOID BIO)
5.712
1.003
2.41
3.0
Total distinct native taxa richness in 300-count
subsamples (coarse and fine net samples
Richness/Diversity
TOTL300 NAT NTAX
combined)
14.8
12.9
1.76
1.4
Total distinct generic richness in 300-count
subsamples (coarse and fine net samples
Richness/Diversity
GEN300 NTAX
combined)
12.3
10.6
2.03
2.7
Total distinct native generic richness in 300-count
subsamples (coarse and fine net samples
Richness/Diversity
GEN300 NAT NTAX
combined)
12.3
10.5
2.13
2.9
Total distinct family richness in 300-count
subsamples (coarse and fine net samples
Richness/Diversity
FAM300 NTAX
combined)
9.8
8.4
2.11
2.3
Total distinct native family richness in 300-count
subsamples (coarse and fine net samples
Richness/Diversity
FAM300 NAT NTAX
combined)
9.8
8.4
2.22
2.6
Number of distinct genera in 300-count subsample
Richness/Diversity
ZOFN300 GEN NTAX
of fine-mesh net sample (50-^m)
6.8
5.3
2.45
2.7
Number of distinct native genera in 300-count
Richness/Diversity
ZOFN300 GEN NAT NTAX
subsample of fine-mesh net sample (50-^m)
6.8
5.2
2.48
2.9
Number of distinct families in 300-count subsample
Richness/Diversity
ZOFN300 FAM NTAX
of fine-mesh net sample (50-^m)
5.6
4.3
2.74
3.1
Number of distinct native families in 300-count
Richness/Diversity
ZOFN300 FAM NAT NTAX
subsample of fine-mesh net sample (50-^m)
5.6
4.3
2.79
3.1
Percent of biomass represented in top 5 taxa in
300-count subsamples (coarse and fine net samples
Richness/Diversity
DOM5_300_PBIO
combined)
91.38
94.27
-1.78
1.9
Table D, 4, List of candidate metrics used to develop the zooplankton MMI for the Upper Midwest bio-region
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t value
Mean Value for
Mean Value for
(Least disturbed vs.
Metric
Least disturbed
Most disturbed
Most disturbed
SignahNoise
Category
Metric Name
Description
Sites
Sites
Sites)
Value
Abundance/
Biomass/
Percent of native individuals (coarse and fine net
Density
TOTL NAT PIND
samples combined)
100
98.02
1.47
2348
Abundance/
Biomass/
Percent of density represented by native individuals
Density
ZOCN NAT PDEN
in coarse net sample (150-um)
100
95.90
1.52
Noise=0
Number of distinct taxa within the cladoceran
family Daphniidae (coarse and fine net samples
Cladoceran
DAPHNIID NTAX
combined)
1.4
1.8
-1.91
3.1
Density of individuals within the cladoceran family
Bosminidae (coarse and fine net samples
Cladoceran
BOSM DEN
combined)
28.20401905
6.857369231
1.85
2.8
Percent of individuals within the cladoceran family
Bosminidae (coarse and fine net samples
Cladoceran
BOSM PIND
combined)
15.31
8.35
1.85
19.5
Biomass of native individuals within the cladoceran
family Bosminidae (coarse and fine net samples
Cladoceran
BOSM NAT BIO
combined)
16.33606357
3.165346051
1.89
1.8
Density of native individuals within the cladoceran
family Bosminidae (coarse and fine net samples
Cladoceran
BOSM NAT DEN
combined)
28.204019048
5.0981051282
2.01
4.9
Percent of native individuals within the cladoceran
family Bosminidae (coarse and fine net samples
Cladoceran
BOSM NAT PIND
combined)
15.31
6.71
2.29
9.6
Percent of distinct native taxa within the
cladoceran family Bosminidae (coarse and fine net
Cladoceran
BOSM NAT PTAX
samples combined)
5.59
3.96
2.16
1.6
Percent of biomass represented by native
individuals within the cladoceran family Bosminidae
Cladoceran
BOSM NAT PBIO
(coarse and fine net samples combined)
10.01
2.57
2.07
4.9
Shannon Diversity based on the number of
cladoceran individuals (coarse and fine net samples
combined). Calculated as SUM{p(i)*Log[p(i)]},
where p(i) is proportion of individuals of taxon i,
Cladoceran
HPRIME CLAD
and Log= natural logarithm.
0.579
0.772
-1.91
1.3
Biomass of individuals within the copepod order
Copepod
CALAN BIO
Calanoida (coarse and fine net samples combined)
12.010544048
27.035772872
-1.73
12.7
Biomass of native individuals within the copepod
order Calanoida (coarse and fine net samples
Copepod
CALAN NAT BIO
combined)
12.010544048
27.025444897
-1.73
12.8
Percent of distinct native taxa (coarse and fine net
Richness/Diversity
TOTL NAT PTAX
samples combined)
100
98.05
2.65
21.7
Richness/Diversity
Percent of distinct taxa represented by native
ZOCN_NAT_PTAX
individuals in coarse net sample (150-um)
100
95.84
2.59
8.9
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t value
Mean Value for
Mean Value for
(Least disturbed vs.
Metric
Least disturbed
Most disturbed
Most disturbed
SignahNoise
Category
Metric Name
Description
Sites
Sites
Sites)
Value
Richness/Diversity
Percent of distinct larger-sized taxa
(NET_SIZECLS_NEW=COARSE; coarse and fine net
COARSE PTAX
samples combined)
39.74
45.09
-1.89
1.4
Richness/Diversity
Percent of distinct smaller-sized taxa
(NET_SIZECLS_NEW=FINE; coarse and fine net
FINE PTAX
samples combined)
60.26
54.91
-1.89
1.4
Percent of distinct taxa within the phylum Rotifera
Rotifer
ROT PTAX
(coarse and fine net samples combined)
60.26
54.91
1.87
1.4
Density of individuals within the rotifer order
Flosculariaceae (coarse and fine net samples
Rotifer
FLOS DEN
combined)
290.0439619
115.22284872
1.82
7.6
Shannon Diversity based on the number of rotifer
individuals (coarse and fine net samples combined).
Calculated as SUM{p(i)*Log[p(i)]}, where p(i) is
proportion of individuals of taxon i, and Log=
Rotifer
HPRIME ROT
natural logarithm.
1.524
1.264
2.12
1.4
Simpson Diversity based on the number of rotifer
individuals (coarse and fine net samples combined).
Calculated as SUM{p(i)*p(i)} where p(i) is the
Rotifer
SIMPSON ROT
proportion of taxon 1 in the sample.
0.325
0.414
-1.79
2.4
Hurlbert's Probability of Interspecific Encounter
(PIE) based on the number of rotifer individuals
(coarse and fine net samples combined).
Calculated as SUM{p(i)*[N-n(i)/N-l]} where p(i) is
the proportion of taxon 1 in the sample, N is the
total number of rotifer individuals in the sample,
and n(i) is the number of rotifer individuals of taxon
Rotifer
PIE ROT
i in the sample.
0.678
0.590
1.76
2.5
Percent of rotifer individuals in top 3 Rotifer taxa
Rotifer
DOM3 ROT PIND
(coarse and fine net samples combined)
78.89
86.34
-2.35
1.6
Percent of rotifer individuals in top 5 Rotifer taxa
Rotifer
DOM5 ROT PIND
(coarse and fine net samples combined)
91.39
94.46
-1.81
2.6
Percent of rotifer biomass in dominant rotifer taxon
Rotifer
DOM1 ROT PBIO
(coarse and fine net samples combined)
45.30
59.27
-2.46
3.5
Percent of rotifer density in top 3 Rotifer taxa
Rotifer
DOM3 ROT PDEN
(coarse and fine net samples combined)
78.89
86.34
-2.35
1.6
Percent of density in top 5 rotifer taxa (coarse and
Rotifer
DOM5 ROT PDEN
fine net samples combined)
91.39
94.46
-1.81
2.6
Metrics Derived from 300-count Subsamples of Coarse and Fine Net Samples
Number of distinct taxa within the cladoceran
family Daphniidae in 300-count subsamples (coarse
Cladoceran
DAPHNIID300 NTAX
and fine net samples combined)
1.2
1.7
-2.3
3.1
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Mean Value for
Mean Value for
(Least disturbed vs.
Metric
Least disturbed
Most disturbed
Most disturbed
SignahNoise
Category
Metric Name
Description
Sites
Sites
Sites)
Value
Number of distinct native taxa within the
cladoceran family Daphniidae in 300-count
subsamples (coarse and fine net samples
Cladoceran
DAPHNIID300 NAT NTAX
combined)
1.4
1.7
-2.3
3.1
Biomass of native individuals within the cladoceran
family Bosminidae in 300-count subsamples (coarse
Cladoceran
BOSM300 PIND
and fine net samples combined)
16.74
9.15
1.87
15.4
Density of native individuals within the cladoceran
family Bosminidae in 300-count subsamples (coarse
Cladoceran
BOSM300 NAT BIO
and fine net samples combined)
9.9940477143
2.211484641
1.84
2.1
Percent of native individuals within the cladoceran
family Bosminidae in 300-count subsamples (coarse
Cladoceran
BOSM300 NAT PIND
and fine net samples combined)
16.74
7.12
2.42
15.3
Percent of distinct native taxa that are within the
cladoceran family Bosminidae in 300-count
subsamples (coarse and fine net samples
Cladoceran
BOSM300 NAT PTAX
combined)
6.48
4.08
2.73
1.4
Biomass of biomass represented by native
individuals within the cladoceran family Bosminidae
in 300-count subsamples (coarse and fine net
Cladoceran
BOSM300 NAT PBIO
samples combined)
10.56
2.78
211
4.7
Biomass of individuals within the copepod order
Calanoida in 300-count subsamples (coarse and fine
Copepod
CALAN300 BIO
net samples combined)
6.3444415238
17.540568538
-2.17
9.2
Percent of distinct native taxa in 300-count
subsamples (coarse and fine net samples
Richness/Diversity
TOTL300 NAT PTAX
combined)
100
97.87
2.66
8.2
Percent of distinct native taxa in the coarse net
sample (150-um) based on the 300-individual
Richness/Diversity
ZOCN300 NAT PTAX
subsamples
100
95.92
2.76
Noise=0
Percent of distinct taxa represented by the rotifer
order Ploima in 300-count subsamples (coarse and
Rotifer
PLOIMA300 PTAX
fine net samples combined)
48.72
42.16
2.05
9.8
Shannon Diversity based on the number of rotifer
individuals in 300-count subsamples (coarse and
fine net samples combined). Calculated as
SUM{p(i)*Log[p(i)]}, where p(i) is proportion of
Rotifer
HPRIME ROT300
individuals of taxon i, and Log= natural logarithm.
1.515
1.254
2.12
1.4
Simpson Diversity based on the number of rotifer
individuals in 300-count subsamples (coarse and
fine net samples combined). Calculated as
SUM{p(i)*p(i)} where p(i) is the proportion of taxon
Rotifer
SIMPSON_ROT300
1 in the sample.
0.324
0.416
-1.86
2.1
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t value
Mean Value for
Mean Value for
(Least disturbed vs.
Metric
Least disturbed
Most disturbed
Most disturbed
SignahNoise
Category
Metric Name
Description
Sites
Sites
Sites)
Value
Hurlbert's Probability of Interspecific Encounter
(PIE) based on the number of rotifer individuals in
300-count subsamples (coarse and fine net samples
combined). Calculated as SUM{p(i)*[N-n(i)/N-l]}
where p(i) is the proportion of rotifer taxon 1 in the
sample, N is the total number of rotifer individuals
in the sample, and n(i) is the number of individuals
Rotifer
PIE ROT300
of taxon i in the sample.
0.680
0.590
1,78
2.2
Percent of rotifer individuals in dominant rotifer
taxon in 300-count subsamples (coarse and fine net
Rotifer
D0M1 300 ROT PIND
samples combined)
45.70
54.61
-1.74
2.1
Percent of rotifer individuals in top 3 Rotifer taxa in
300-count subsamples (coarse and fine net samples
Rotifer
DOM3 300 ROT PIND
combined)
78.91
86.25
-2.26
1.4
Percent of rotifer individuals in top 5 Rotifer taxa in
300-count subsamples (coarse and fine net samples
Rotifer
DOM5 300 ROT PIND
combined)
91.50
94.71
-1.91
3.7
Percent of rotifer biomass in dominant Rotifer
taxon in 300-count subsamples (coarse and fine net
Rotifer
DOM1 300 ROT PBIO
samples combined)
47.97
58.94
-1.95
2.0
Percent of biomass represented by predator
individuals in 300-count subsamples (coarse and
Trophic
PRED300 PBIO
fine net samples combined)
2.06
0.93
1.86
95.5
Percent of biomass represented by predaceous
rotifer individuals in 300-count subsamples (coarse
Trophic
ROT PRED300 PBIO
and fine net samples combined)
2.06
0.93
1.86
95.5
Percent of biomass represented by herbivorous
Trophic
COPE HERB PBIO
copepods (coarse and fine net samples combined)
16.04
24.53
-1.96
5.0
188
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Table D, 5, List of candidate metrics used to develop the zooplankton MMI for the Western Mountains bio-region
t value
Mean Value for
Mean Value for
(Least disturbed vs.
Metric
Least disturbed
Most disturbed
Most disturbed
SignakNoise
Category
Metric Name
Description
Sites
Sites
Sites)
Value
Percent of distinct native taxa within the
cladoceran family Bosminidae (coarse and fine net
Cladoceran
BOSM NAT PTAX
samples combined)
5.59
3.96
2.16
1.3
Number of distinct taxa within the subclass
Copepod
COPE NTAX
Copepoda (coarse and fine net samples combined)
2.6
3.3
-2.15
1.7
Percent of distinct taxa within the subclass
Copepod
COPE PTAX
Copepoda (coarse and fine net samples combined)
14.33
18.08
-2.29
1.9
Number of distinct native taxa within the subclass
Copepod
COPE NAT NTAX
Copepoda (coarse and fine net samples combined)
2.6
3.3
-2.07
1.7
Percent of distinct native taxa within the subclass
Copepod
COPE NAT PTAX
Copepoda (coarse and fine net samples combined)
14.33
18.00
-2.21
1.9
Total density of individuals within the subclass
Copepod
COPE DEN
Copepoda (coarse and fine net samples combined)
177.8479619
156.08843077
0.3
1.6
Total biomass of individuals within the copepod
order Calanoida (coarse and fine net samples
Copepod
CALAN BIO
combined)
12.010544048
27.035772872
-1.73
4.4
Total biomass of native individuals within the
copepod order Calanoida (coarse and fine net
Copepod
CALAN NAT BIO
samples combined)
12.010544048
27.025444897
-1.73
4.4
Percent of distinct larger-sized taxa
(NET SIZECLS NEW=COARSE; coarse and fine net
Richness/Diversity
COARSE PTAX
samples combined)
39.75
45.09
-1.87
2.3
Percent of distinct taxa collected only in the fine-
mesh net (50-um; NET_SIZECLS_NEW=FINE; coarse
Richness/Diversity
FINE PTAX
and fine net samples combined)
60.25
54.91
1.87
2.3
Simpson Diversity based on the total density
individuals (coarse and fine net samples combined).
Calculated as SUM{p(i)*p(i)} where p(i) is the
Richness/Diversity
SIMPSON DEN
proportion of density of taxon i in the sample.
0.288
0.353
-1.46
1.25
Percent distinct rotifer taxa (coarse and fine net
Rotifer
ROT PTAX
samples combined)
60.26
54.91
1.87
2.5
Percent distinct taxa that are within the rotifer
order Ploima (coarse and fine net samples
Rotifer
PLOIMA PTAX
combined)
48.72
42.00
2.28
4.3
Simpson Diversity based on the number of rotifer
individuals (coarse and fine net samples combined).
Calculated as SUM{p(i)*p(i)} where p(i) is the
Rotifer
SIMPSON ROT
proportion of taxon 1 in the sample.
0.325
0.414
-1.79
1.4
Percent of distinct taxa that are omnivorous
Trophic
COPE OMNI PTAX
copepods (coarse and fine net samples combined)
5.44
8.65
-2.526
1.5
Metrics Derived from 300-count Subsamples of Coarse and Fine Net Samples
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t value
Mean Value for
Mean Value for
(Least disturbed vs.
Metric
Least disturbed
Most disturbed
Most disturbed
SignahNoise
Category
Metric Name
Description
Sites
Sites
Sites)
Value
Abundance/
Total biomass of individuals in 300-count
Biomass/
subsamples (coarse and fine net samples
Density
TOTL300 BIO
combined)
90.072878905
270.55043706
-3.09
1.4
Abundance/
Total biomass of native individuals in 300-count
Biomass/
subsamples (coarse and fine net samples
Density
TOTL300 NAT BIO
combined)
90.072878905
269.19077886
-3.07
1.4
Abundance/
Biomass/
Biomass of individuals in 300-count subsample of
Density
ZOCN300 BIO
coarse net sample (150 um)
81.538501524
226.56640233
-2.68
2.2
Abundance/
Biomass/
Biomass of native individuals in 300-count
Density
ZOCN300 NAT BIO
subsample of coarse net sample (150 um)
81.538501524
225.20674414
-2.65
2.2
Biomass represented by individuals of large-sized
Abundance/
taxa in 300-count subsamples
Biomass/
(NET_SIZE_CLS=COARSE; coarse and fine net
Density
COARSE300 BIO
samples combined)
83.550340952
235.93896061
-2.77
3.0
Biomass represented by native individuals of large-
Abundance/
sized taxa in 300-count subsamples
Biomass/
(NET_SIZE_CLS=COARSE; coarse and fine net
Density
COARSE300 NAT BIO
samples combined)
62.150708119
234.5793024
-2.74
3.1
Percent biomass of native individuals of large-sized
Abundance/
taxa in 300-count subsamples
Biomass/
(NET_SIZE_CLS=COARSE; coarse and fine net
Density
COARSE300 NAT PBIO
samples combined)
85.15
75.20
1.88
5.7
Biomass of individuals within the suborder
Cladocera in 300-count subsamples (coarse and fine
Cladoceran
CLAD300 BIO
net samples combined)
62.150708119
173.03849657
-2.301
2.2
Biomass of native individuals within the suborder
Cladocera in 300-count subsamples (coarse and fine
Cladoceran
CLAD300 NAT BIO
net samples combined)
61.59444164
171.73934691
-2.28
2.2
Biomass represented by large cladoceran
individuals (SUBORDER=CLADOCERA and
CLADOCEAN_SIZE=LARGE) in 300-count subsamples
Cladoceran
LGCLAD300 BIO
(coarse and fine net samples combined)
54.826014262
142.47459983
-1.92
2.2
Percent of large cladoceran individuals
(SUBORDER=CLADOCERA and
CLADOCEAN_SIZE=LARGE) in 300-count subsamples
Cladoceran
LGCLAD300 PIND
(coarse and fine net samples combined)
20.42
14.14
2.22
1.8
Biomass represented by native large cladoceran
individuals (SUBORDER=CLADOCERA and
CLADOCEAN_SIZE=LARGE) in 300-count subsamples
Cladoceran
LGCLAD300 NAT BIO
(coarse and fine net samples combined)
54.826014262
142.37664379
-1.91
2.2
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t value
Mean Value for
Mean Value for
(Least disturbed vs.
Metric
Least disturbed
Most disturbed
Most disturbed
SignakNoise
Category
Metric Name
Description
Sites
Sites
Sites)
Value
Percent of native large cladoceran individuals
(SUBORDER=CLADOCERA and
CLADOCEAN_SIZE=LARGE) in 300-count subsamples
Cladoceran
LGCLAD300 NAT PIND
(coarse and fine net samples combined)
20.41
13.47
2.49
1.8
Percent of distinct native taxa that are large
cladocerans (SUBORDER=CLADOCERA and
CLADOCEAN_SIZE=LARGE) in 300-count subsamples
Cladoceran
LGCLAD300 NAT PTAX
(coarse and fine net samples combined)
16.37
12.90
2.12
2.3
Biomass of individuals within the family Daphniidae
in 300-count subsamples (coarse and fine net
Cladoceran
DAPHNIID300 BIO
samples combined)
54.749187071
150.72825063
-2.08
3.0
Biomass of native individuals within the family
Daphniidae in 300-count subsamples (coarse and
Cladoceran
DAPHNIID300 NAT BIO
fine net samples combined)
54.749187071
150.63029459
-2.08
3.0
Total biomass of individuals within the subclass
Copepoda in 300-count subsamples (coarse and
Copepod
COPE300 BIO
fine net samples combined)
22.109055071
66.786813029
-2.76
2.0
Total biomass of native individuals within the
subclass Copepoda in 300-count subsamples
Copepod
COPE300 NAT BIO
(coarse and fine net samples combined)
22.109055071
66.726304529
-2.75
2.0
Total biomass of individuals within the copepod
order Calanoida in 300-count subsamples (coarse
Copepod
CALAN300 BIO
and fine net samples combined)
14.414470595
36.214300186
-2.00
3.2
Total biomass of native individuals within the
copepod order Calanoida in 300-count subsamples
Copepod
CALAN300 NAT BIO
(coarse and fine net samples combined)
14.414470595
36.153791686
-1.99
3.2
Number of distinct taxa in the 300-count subsample
Richness/Diversity
ZOFN300 NTAX
from the fine net sample (50-um)
7.3
8.4
-1.69
1.9
Simpson diversity based on number of individuals
Richness/Diversity
SIMPSON300 NIND
(coarse and fine net samples combined)
0.307
0.306
0.08
0
Percent of distinct taxa that are within the rotifer
family Asplanchnidae in 300-count subsamples
Rotifer
ASPLAN300 PTAX
(coarse and fine net samples combined)
0.88
2.25
-2.04
1.3
Biomass of herbivorous individuals in 300-count
subsamples (coarse and fine net samples
Trophic
HERB300 BIO
combined)
75.625607619
201.15711961
-2.56
3.1
Percent biomass of herbivorous individuals in 300-
count subsamples (coarse and fine net samples
Trophic
HERB300 PBIO
combined)
76.31
65.36
2.06
3.6
Number of distinct taxa that are omnivorous in 300-
count subsamples (coarse and fine net samples
Trophic
OMNI300 NTAX
combined)
3.0
3.6
-1.94
1.8
191
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t value
Mean Value for
Mean Value for
(Least disturbed vs.
Metric
Least disturbed
Most disturbed
Most disturbed
SignahNoise
Category
Metric Name
Description
Sites
Sites
Sites)
Value
Percent of distinct taxa that are predaceous
cladocerans in 300-count subsamples (coarse and
Trophic
CLAD PRED300 PTAX
fine net samples combined)
0.87
0
2.67
Noise=0
Percent biomass of herbivorous cladoceran
individuals in 300-count subsamples (coarse and
Trophic
CLAD HERB300 BIO
fine net samples combined)
62.140336143
173.03849657
-2.30
2.2
Biomass of omnivorous copepod individuals in 300-
count subsamples (coarse and fine net samples
Trophic
COPE OMNI300 BIO
combined)
4.7491737381
24.176607243
-2.38
2.0
Percent of distinct taxa represented by omnivorous
copepod individuals in 300-count subsamples
Trophic
CO P E_0 M N1300_PTAX
(coarse and fine net samples combined)
8.16
11.5
-2.15
2.1
192
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