National Water-Quality Assessment Program

Prepared in cooperation with The Academy of Natural Sciences,

Patrick Center for Environmental Research

Development and Application of Indices to Assess 

the Condition of Benthic Algal Communities in 

U.S. Streams and Rivers

By Marina Potapova and Daren M. Carlisle

Open File Report 2011–1126

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia K. McNutt, Director

U.S. Geological Survey, Reston, Virginia: 2011 

For additional information about this report write to:

Marina Potapova 

The Academy of Natural Sciences 

Patrick Center for Environmental Research 

1900 Benjamin Franklin Parkway 

Philadelphia, PA 19103 

Daren Carlisle

U.S. Geological Survey, MS 413

National Water-Quality Assessment Program

12201 Sunrise Valley Drive

Reston, VA 20192

To order this and other USGS information products, visit http://store.usgs.gov

For an overview of USGS information products, including maps, imagery, and publications, 

visit http://www.usgs.gov/pubprod

For more information on the USGS—the Federal source for science about the Earth,

its natural and living resources, natural hazards, and the environment, visit

http://www.usgs.gov or call 1-888-ASK-USGS

Any use of trade, product, or firm names is for descriptive purposes only and does not imply 

endorsement by the U.S. Government.

Although this report is in the public domain, permission must be secured from the individual 

copyright owners to reproduce any copyrighted materials contained within this report.

Suggested citation:

Potapova, Marina, and Carlisle, D.M., 2011, Development and application of indices to 

assess the condition of benthic algal communities in U.S. streams and rivers: U.S. Geological 

Survey Open File Report 2011–1126, 40 p. 
iii

Contents

Abstract ............................................................................................................................................. 1 

Introduction ....................................................................................................................................... 2 

Purpose and Scope....................................................................................................................... 3 

General Approach ......................................................................................................................... 4 

Methods of Index Development......................................................................................................... 4 

Datasets ........................................................................................................................................ 4 

Algal Metrics.................................................................................................................................. 6 

Traditional Development of Multi-Metric Indices............................................................................ 7 

Alternative Processes for Selecting Metrics ................................................................................13

Application of Indices to Assess Algal Community Condition at NAWQA Sampling Sites...............14

Major Findings................................................................................................................................. 14

Algal Taxa Indicative of Reference and Disturbed Sites..............................................................15

Evaluation of Alternative Metrics ................................................................................................. 15

Multi-Metric Indices and Logistic Regression Models ..................................................................16

Assessment of Algal Communities by Region and Land-Use Setting..........................................17

Discussion of Index Performance and Metrics ................................................................................17

Metrics Selected for MMIs ...........................................................................................................17

Condition of Algal Communities...................................................................................................19

Use of Diatom MMIs and Individual Metrics ................................................................................20

Summary.........................................................................................................................................21

Acknowledgments...........................................................................................................................21

References Cited ............................................................................................................................21

Appendixes

1. Algal metrics commonly included in a multi-metric index ......................................................25

2. Algal taxa associated with reference or disturbed sites ........................................................33

Figures 

1. Map of the United States showing five regions and 1,071 sampling sites............................ 3

2. Values of the disturbance index in sampling sites grouped by regions and by the 

category “reference” or “disturbed.”...................................................................................... 6

3. Values of multi-metric diatom indices in sites in the “Reference” group or grouped by 

land use settings ................................................................................................................18

Tables 

1. The number of reference and disturbed sites in National Water-Quality Assessment 

Program datasets used for this study, by region and nationally. .......................................... 5

2. Metrics ranked by their ability to differentiate between reference and disturbed sites 

based on an examination of boxplots................................................................................... 8

3. Multi-metric indices and logistic regression model summary................................................ 9

4. Land-cover criteria for classifying National Water-Quality Assessment Program sites

into major land-use settings ..............................................................................................14
iv

5. Spearman correlations between regional and national diatom metrics developed in this 

study. Diatoms associated with reference or disturbed sites and associated with several 

stressor-specific diatom metrics are shown. ......................................................................16
1 

Development and Application of Indices to Assess 

the Condition of Benthic Algal Communities in 

U.S. Streams and Rivers

By Marina Potapova1 and Daren M. Carlisle2

Abstract

Multi-metric indices (MMIs) are a measure of a combination of characteristics of 

biological communities and are used as indicators of water quality and ecological health. 

Although MMIs for algal communities have been developed for specific regions of the 

United States, none of the indices have national applicability. The MMIs described in this 

report were developed by the National Water-Quality Assessment Program of the 

U.S. Geological Survey to assess the overall health of benthic algal communities in 

U.S. streams and rivers within five geographic regions that encompass the conterminous 

United States. 

The traditional procedure for developing MMIs (also referred to as indices of 

biological integrity) is to select individual metrics that, separately, can distinguish 

between undisturbed sites (selected for this study as reference sites) and predetermined 

disturbed sites. The metrics are then combined into a single index. In addition to 

traditional approaches for selecting individual metrics, the current study used stepwise 

logistic regressions to select sets of metrics that best predicted whether sites were in an 

undisturbed or a disturbed condition. Multi-metric indices and logistic regression models 

were developed for five regions of the United States using calibration datasets and were 

evaluated using independent validation datasets. Applying the regional MMIs to 

validation sites, the percentage of sites correctly classified as “reference” or “disturbed” 

ranged from 66 to 92 percent. Most often, only two or three metrics, typically 

percentages of individual organisms belonging to diatom taxa indicative of reference or 

disturbed calibration sites, were needed to distinguish between reference and disturbed 

validation sites. The classification accuracy of these indices did not improve when nondiatom metrics based on the autecological characteristics of algal taxa were included. The 

autecological metrics that were used to differentiate reference and disturbed sites were 

based on the assignment of the same diatom taxa to various ecological categories and 

therefore were redundant. Individual autecological metrics that measure the effect of 

specific stressors are needed, however, to identify potential causes of impairment. The 

regional MMIs developed in this study were applied at National Water-Quality 

Assessment Program sampling sites to assess the overall biological health of algal 

 

1 The Academy of Natural Sciences, Patrick Center for Environmental Research. 2 U.S. Geological Survey, National Water-Quality Assessment Program.
2 

communities. The assessment of algal communities in urban and agricultural land-use 

settings indicated increased stress to ecological health when compared to communities in 

other land-use settings.

Introduction

Algal-based assessments of water quality commonly rely on various metrics based 

on the autecology and relative abundance of individual taxa comprising the community. 

Autecological metrics are usually based on a documented understanding of the 

associations among varying concentrations of individual water chemistry variables (such 

as dissolved organic matter or orthophosphate) as they relate to the distribution of algal 

species (Sládeček, 1986; Steinberg and Schiefele, 1988; Kelly and Whitton, 1995). In a 

few instances, autecological metrics have been developed on the basis of an 

understanding of the relationship of algal species distributions to multiple water-quality 

measures (Lange-Bertalot, 1979; Prygiel and others, 1996). In addition to the question of 

which taxon or community characteristics are the best indicators of specific chemical 

stressors, there is a need to assess overall biological condition, which distinguishes 

impaired communities from healthy biological communities. This assessment usually 

compares various characteristics of undisturbed communities to those of communities 

known to be impaired. 

Using a multi-metric index (MMI) to combine information about several 

characteristics of a biological community (and potentially the ecosystem) is one way to 

assess biological condition. Multi-metric indices (also known as indices of biological 

integrity) have been developed for fish (Karr, 1981), macroinvertebrates (Kerans and 

Karr, 1994; Barbour and others, 1996; Fore and others, 1996), and algae (Hill and others, 

2000; Fore, 2002; Fore and Grafe, 2002; Wang and others, 2005). The principal concept 

of the MMI is that individual metrics represent “measures of condition in individuals, 

populations, communities, ecosystems, and landscapes” (Karr and Chu, 1999) and 

therefore provide an integrated assessment when combined into a single index. The 

standard approach for developing MMIs involves evaluating different metrics 

representing the different levels of ecological organization; the ultimate goal is to select 

metrics that represent as many levels as feasible. For instance, the original MMI 

developed by Karr (1981) included metrics indicative of fish community structure and 

composition, trophic structure, reproductive function, population abundances, and 

individual condition (health). Attempts have been made to implement the same concept 

with algal MMIs, including indicators of richness of algal genera, community 

composition, measures of biomass, and alkaline phosphatase activity (Hill and others, 

2000). Additional indicators that have been proposed for algal MMIs include 

tolerance/intolerance, autecological guilds, morphological guilds, and individual 

condition (Fore and Grafe, 2002) and indicators based on similarity measures (Wang and 

others, 2005). The ecological attributes represented by individual metrics within MMIs 

have varied considerably among these studies. However, most of the metrics selected for 

MMIs are limited to attributes of community composition (Fore and Grafe, 2002; Wang 

and others, 2005). This apparent inconsistency is partly due to the absence of some 

measures, such as the condition of individuals or community productivity, and to 

differences in study objectives. Most importantly, no MMIs have been developed for 

algal communities that are applicable across a large geographic scale, such as that which 
3 

were represented by U.S. Geological Survey National Water-Quality Assessment 

(NAWQA) Program studies throughout the conterminous United States. 

Purpose and Scope

This report presents the research and findings of a NAWQA Program study, the 

primary goal of which was to develop an MMI that could be used to assess the condition 

of algal communities across diverse land-use settings of the United States. Specifically, 

this study had two objectives. The first was to evaluate the efficacy of alternative metrics 

for use in MMIs that distinguish between stream benthic algal communities in 

undisturbed (reference) and disturbed watersheds in five regions of the conterminous 

United States. Several metrics based on the relative abundance of algal taxa associated 

with undisturbed and disturbed watersheds were developed specifically for this study 

(appendix 1). The second objective was to apply these MMIs to assess the condition of 

algal communities in streams and rivers sampled by the NAWQA Program across 

varying land-use settings and geographic regions of the United States (fig. 1). 

Figure 1. Map of the United States showing five regions and 1,071 sampling sites. White circles 

are reference sites, black circles are disturbed sites, and grey circles are unclassified sites.
4 

General Approach

The general approach of this study was to apply several commonly used methods 

for assessing the condition of biological communities to benthic algal data collected by 

the NAWQA Program. Methods applied include traditional approaches as well as a 

statistical approach for selecting metrics that constitute the MMI. A distinct design 

feature of the NAWQA Program study was the targeting of selected sites in key land-use 

settings. 

Methods of Index Development

Benthic algal data were collected by the NAWQA Program using nationally 

consistent methods. Undisturbed sites (the reference sites) and disturbed sites were 

identified among the sampling sites within each of five major regions of the 

conterminous United States. These sites were used to develop and evaluate MMIs to 

assess the condition of benthic algal communities. 

Datasets

The datasets used in this study consisted of counts of benthic algae collected by 

the NAWQA Program at 1,071 river sites throughout the conterminous United States 

between 1993 and 2004 (fig. 1). Data were stratified into five aggregated regions of 

U.S. Environmental Protection Agency level-2 ecoregions, as described by Potapova and 

Charles (2007). The influence of spatial factors on algal community structure in U.S. 

streams and rivers and the need to develop algal indicators on a regional rather than 

national or continental scale were described previously by Potapova and Charles (2002). 

Algal samples used in this study were collected primarily from hard substrates using the 

methods of Porter and others (1993) and Moulton and others (2002). For sites where 

multiple samples in space or time had been collected, a single sample was randomly 

selected for this study. Diatoms were identified and enumerated from permanent slides at 

1000X power and non-diatom algae were enumerated using Palmer-Maloney counting 

chambers at 400X power. Identifications and enumeration were done at the Patrick 

Center for Environmental Research (The Academy of Natural Sciences, Philadelphia, 

Pa.) using the methods described in Charles and others (2002). 

The development of MMIs requires a set of sites classified a priori as reference 

quality and another set of sites classified a priori as disturbed, and that these 

classifications be based on information independent of biological data (Karr and Chu, 

1999). For this study, sites were classified within each of the five regions into two 

contrasting groups: minimally disturbed (reference) and disturbed. The assignment of 

sites to the reference category was based on several criteria, including the professional 

judgment of local biologists, land cover data, and the evaluation of maps and imagery 

(Carlisle and Meador, 2007; Carlisle and others, 2008). Reference sites in some 

geographic areas (such as the midwestern United States in the Central and Western 

Plains) were considered “least disturbed” or “best attainable” (Stoddard and others, 

2006). Disturbed sites were identified on the basis of an index of anthropogenic 

disturbance derived from 11 geospatial indicators of human activities (Falcone and 

others, 2009). These indicators included housing unit density (GeoLytics, 2001), 

percentage of urban land cover (Price and others, 2006), road density (GeoLytics, 2001), 
5 

pesticide use (U.S. Geological Survey, 2007), nitrogen and phosphorus applications from 

fertilizers and manure (Ruddy and others, 2006), total dam storage (U.S. Army Corps of 

Engineers, 2006), percentage of streams that are in the category “canal/ditch/pipeline” 

(Horizon Systems Corporation, 2006), density of pollution dischargers (U.S. 

Environmental Protection Agency, 2006), density of mining operations (U.S. Geological 

Survey, 2006), and percentage of mining/transitional land cover (Price and others, 2006). 

Within each region, sites were ranked according to the disturbance index calculated from 

their contributing watersheds. Generally, sites within the highest quartile of disturbance 

within each region were classified as disturbed, although this threshold varied slightly 

among regions in order to achieve adequate numbers of reference and disturbed sites. In 

the Western Mountains region, all sites with a disturbance score greater than zero were 

classified as disturbed. In the Central and Western Plains region and in the Southeastern 

Plains region, sites with disturbance scores equal to or greater than 8 were classified as 

disturbed. Finally, sites in the Glaciated North and Eastern Highlands regions with 

disturbance scores greater than or equal to 4 were classified as disturbed. All other nonreference sites were not classified as disturbed (fig. 1), and therefore not used for the 

development of MMIs, but were later assessed for their degree of impairment. Less than 

5 percent of the sites classified as reference sites, based on best professional judgment, 

had watershed disturbance index scores equal to or greater than the threshold value. 

These reference sites were removed from the reference category for the development of 

metrics and indices. All reference and disturbed sites within each of the five geographic 

regions were randomly subdivided into a calibration dataset (75 percent of the sites) or a 

validation dataset (25 percent of the sites). For each region, 5 to 10 MMIs with different 

combinations of metrics were developed with the calibration data and evaluated with 

validation data. The number of reference and disturbed sites in the calibration and 

validation datasets for each of the five regions is shown in table 1. The distribution of the 

disturbance index values for reference and disturbed sites in each region is shown in 

figure 2. 

Table 1. The number of reference and disturbed sites in National Water-Quality Assessment 

Program datasets used for this study, by region and nationally.

[Subsets of the sites (in parentheses) are calibration (first number) and validation (second number) sites]

Region Number of reference sites Number of Total number of sites

disturbed sites

Glaciated North 55 (42:13) 47 (35:12) 146

Eastern Highlands 64 (48:16) 54 (41:13) 156

Southeastern Plains 72 (54:18) 61 (46:15) 311

Central and Western Plains 54 (41:13) 51 (38:13) 400

Western Mountains 31 (24:7) 13 (9:4) 58

National total 276 (209:67) 226 (169:57) 1,071
6 

Figure 2. Values of the disturbance index in sampling sites grouped by regions and by the 

category “reference” or “disturbed.” The box represents interquartile range, which contains 50 

percent of the observations; the whiskers are lines that extend to the highest and lowest numbers 

excluding outliers, which are values outside 1.5 times the box length. The horizontal line across a 

box is the median. The number of streams in each category is provided above each box.

Algal Metrics 

One hundred twenty-four published and commonly used algal metrics were 

evaluated for inclusion in MMIs. One hundred metrics from existing literature sources 

were calculated and can be grouped into eight general categories (appendix 1), including 

total algal abundance, broad taxa groups, diversity measures, growth habit of diatoms, 

nutrient status indicators, organic pollution indicators, water ionic content indicators, and 

diatoms indicating general impairment. In addition, 24 metrics of general disturbance 

were developed in this study; these represented the relative abundance of diatoms and the 

relative cell density, cell density, and biovolume of non-diatom algae associated with 

reference and disturbed sites using the methods described below. 

The 24 metrics developed in this study were based on the Indicator Species 

Analysis (ISA) method described by Dufrêne and Legendre (1997). The ISA method was 

used to identify species associated with reference and disturbed sites based on prior 

classification of watershed disturbance. Separate ISAs contrasting reference and 

disturbed sites were conducted with calibration data for each region; an additional ISA 

contrasted reference and disturbed calibration sites nationwide because study researchers 
7 

expected that indicator species may be scale dependent. The nonclassified sites were not 

used in this analysis. Indicator values were calculated as products of the average relative 

frequency and relative abundance of each species in a group (that is, reference versus 

disturbed). Taxon indicator abilities identified by ISA can be biased for common taxa, so 

an adjustment was made by carrying out a series of Canonical Correspondence Analyses 

(CCAs) with a single constraining environmental variable “reference “or “disturbed” site 

for each of the six datasets (five regional and one national). If only one constraining 

variable is used, the CCA species scores along the first axis are proportional to weighted 

averages of the species in relation to that variable. In this analysis, the constraining 

variable was binary (reference versus disturbed sites), so the species at the extremes of 

the axis are those found predominantly in reference or disturbed sites. The taxa with 

unusual (in the lower or upper quartile) scores along the first CCA axis and found in at 

least three sites in regional datasets and in six sites in the national dataset were added to 

the lists of indicator taxa. Both ISA and CCA were performed using the PC-ORD/4 

(MjM Software, Gleneden Beach, Oreg.). All metrics expressed as percentages of 

individual organisms were based either on a percentage of valves (for diatoms) or cells 

(for non-diatom algae) in the laboratory count. 

Traditional Development of Multi-Metric Indices

The procedure for the development of MMIs is described in detail in Karr (1981), 

Barbour and others (1996), and Stoddard and others (2006). Approaches specific to algae 

are described by Fore and Grafe (2002) and Wang and others (2005). The methods used 

by Wang and others (2005) for selecting metrics for the MMI and scoring the resulting 

index were followed in this study. The ability of each metric to differentiate between 

reference and disturbed sites was evaluated by the Mann-Whitney nonparametric test, and 

only those metrics that showed significant differences (P<0.05) between reference and 

disturbed sites were retained. Those retained metrics with median values of zero at 

reference or disturbed sites were excluded. The remaining metrics were then assigned a 

score based on the distinctness of the distributions of values at reference compared to 

disturbed sites (table 2) (Barbour and others, 1996; Wang and others, 2005). The highest 

score, a “3,” was assigned to metrics that had nonoverlapping distributions between 

reference and disturbed sites. A score of “2” was assigned to metrics that had overlapping 

interquartile ranges, but both medians were outside of this range. A score of “1” was 

assigned when one of the two medians was inside the overlapping portion of the 

interquartile range. The lowest score of “0” was given when both medians were inside the 

overlapping portion of the range. Metrics that decreased with disturbance (for example, 

the median of disturbed sites is lower than the median of reference sites) were scaled by 

dividing each value by the 90th percentile of the reference sites. Metrics that increased 

with disturbance were scaled by dividing the metric value by the 90th percentile of the 

disturbed sites and subtracting this number from one (Wang and others, 2005). The MMI 

was calculated as the sum of all scaled selected metrics multiplied by 10 and divided by 

the number of component metrics. 
8 

Table 2. Metrics ranked by their ability to differentiate between reference and disturbed sites 

based on an examination of boxplots.

[A score of 3 indicates no overlap of the interquartile ranges; a score of 2 indicates interquartile ranges 

overlapped, but both medians were outside of the overlap area. The metric’s names are given in appendix 1. 

Underlined metrics were selected for inclusion in indices; --, indicates metric did not differentiate between 

reference and disturbed sites in that region.

Metric 

abbreviation*

Glaciated North Eastern 

Highlands

Southeastern 

Plains

Central and 

Western Plains

Western 

Mountains

Score per region

DiaR

DiaD

Dia

SAR

TPh_r

TPl_r

TP_r

TPh

TPl

TP

TNh_r

TNl_r

TN_r

TNh

TNl

TN

CHLl

CHLh

SULFl

SULFh

CONDh

EUTR

EUTRandH

BF

O_h

MOTILE

DiaR_n

DiaD_n

Dia_n

O_l

FB

A_MESO

Bt

AP

SAD

Nhf

SP-OL

MESOTR

SAD_n

CD

CP

O_vl

3

3

3

3

3

3

3

3

3

3

3

3

3

3

3

3

3

3

3

3

3

3

3

3

3

3

--

--

--

--

--

--

--

--

--

--

--

--

--

--

--

--

3

3

3

2

--

--

2

--

2

2

--

3

--

--

2

--

3

3

2

--

--

2

2

2

--

2

3

3

3

2

2

2

2

2

2

--

--

--

--

--

--

--

2

3

3

--

--

--

--

--

--

--

--

--

2

--

--

--

2

2

--

2

2

--

--

2

2

--

2

2

2

--

--

--

--

--

--

2

2

--

--

--

--

--

3

3

3

1

--

--

--

2

--

--

--

--

--

2

--

--

--

2

--

--

--

--

2

2

--

--

2

2

2

--

--

2

--

--

--

2

--

2

1

--

--

--

3

3

3

3

--

--

--

--

2

2

--

--

--

--

2

--

--

--

--

--

--

--

--

2

2

--

3

--

--

--

--

2

--

--

--

--

--

--

--

3

3

2

*Abbreviations are given for metrics included in the models presented in table 3. 
9 

The threshold for classifying validation sites as “reference” or “disturbed” was the 

average of the 75th percentile of the disturbed calibration sites and the 25th percentile of 

the reference calibration sites for each region/regression model, which is a threshold 

commonly used in assessments with MMIs (Wang and others, 2005). The ability of the 

MMIs to distinguish between reference and disturbed conditions was estimated by 

Correct Classification Rate (CCR), which is the percentage of correctly classified sites, 

and also by sensitivity (percentage of correctly classified disturbed sites, or true positive 

rate, CCRd) and specificity (percentage of correctly classified reference sites, CCRr). 

Correct classification rates were calculated for both calibration and validation datasets.

For each region, 5 to 10 MMIs with different combinations of metrics were 

developed with the calibration data and evaluated with validation data. The MMIs 

included the single best (highest score) metric from each of the eight categories discussed 

previously, the best metrics from one or more categories, or other combinations of 

metrics from one or more categories (table 3). 

Table 3. Multi-metric indices and logistic regression model summary.

[CCR, overall correct classification rate; CCRr, correct classification rate of reference sites; CCRd, 

correct classification rate of disturbed sites. The formulas are given to calculate the values of multimetric indices (see Methods section, “Traditional MMI Development”). The values below threshold 

indicate impairment of algal communities. %, percent]

Region/ Metrics and Threshold Calibration set Validation set

 Index or formula of the 

model index/model

CCR CCRr CCRd CCR CCRr CCRd

Glaciated North

NAWQA (1 + DiaR_GN/92 4.03 70/77 39/42 31/35 23/25 12/13 11/12

diatom - DiaD_GN/90)*5 (91%) (92%)

metrics, 

single 

category

NAWQA (3 + 4.89 69/77 40/42 29/35 22/25 12/13 10/12

diatom DiaR_GN)/92 - (90%) (88%)

metrics, DiaD_GN/90 + 

various TP/0.93 + 

categories TN/0.91 - 

CHLh/73 - 

SULFh/80)*1.67

NAWQA (1 + DiaR_GN/92 4.23 71/77 39/42 32/35 23/25 12/13 11/12

diatoms and - DiaD_GN/90 + (92%) (92%)

non-diatom SAR_GN/99.7)*3

metrics .33

NAWQA (3 + DiaR_GN/92 4.89 69/77 39/42 30/35 23/25 12/13 11/12

and non- - DiaD_GN/90 + (90%) (92%)

NAWQA SAR_GN/99.7 + 

metrics TP/0.93 + 

TN/0.91 - 

CHLh/73 - 

SULFh/80)*1.43
10

Table 3. Multi-metric indices and logistic regression model summary.—Continued

[CCR, overall correct classification rate; CCRr, correct classification rate of reference sites; CCRd, 

correct classification rate of disturbed sites. The formulas are given to calculate the values of multimetric indices (see Methods section, “Traditional MMI Development”). The values below threshold 

indicate impairment of algal communities. %, percent]

Region/ Metrics and Threshold Calibration set Validation set

 Index or formula of the 

model index/model

CCR CCRr CCRd CCR CCRr CCRd

Glaciated North—continued

Non- (3 - 5.48 68/77 37/42 31/35 19/25 10/13 9/12

NAWQA EUTRandH/85 - (88%) (76%)

diatom BF/42 + O_h/66 - 

metrics MOTILE/46)*2.5

Logistic Dia_GN 74/77 40/42 34/35 23/25 12/13 11/12

regression (96%) (92%)

Eastern Highlands

NAWQA (1 + DiaR_EH/86 3.90 79/89 42/48 37/41 19/29 10/16 9/13

diatom - DiaD_EH/76)*5 (89%) (66%)

metrics, 

single 

category

NAWQA (3 + DiaR_EH/86 4.96 69/89 38/48 31/41 17/29 10/16 7/13

diatom - DiaD_EH/76 + (78%) (59%)

metrics, CHLl/97 - 

various CHLh/51 - 

categories SULFh/65 + 

TN_EH/0.88 + 

TP_EH/0.90)*1.4

3

NAWQA (2+ DiaR_EH/86 4.43 75/89 40/48 35/41 23/29 12/16 11/13

diatoms and - DiaD_EH/76 + (84%) (79%)

non-diatom SAR_EH/94.1 - 

metrics SAD_EH/71)*2.5

NAWQA (7 + DiaR_EH/86 5.84 69/89 37/48 32/41 20/29 11/16 9/13

and non- - DiaD_EH/76 - (78%) (69%)

NAWQA MOTILE/54 - 

metrics O_l/34 - Bt/33 - 

FB/93 - AP/34 - 

EUTRandH/83)*1

.25

Non- (6 - MOTILE/54 - 6.66 67/89 36/48 31/41 20/29 11/16 9/13

NAWQA O_l/34 - Bt/33 - (75%) (69%)

metrics only FB/93 - AP/34 - 

EUTRandH/83)*1

.67

Logistic DiaD_EH 74/89 41/48 33/41 18/29 10/16 8/13

regression (83%) (62%)
11

Table 3. Multi-metric indices and logistic regression model summary.—Continued

[CCR, overall correct classification rate; CCRr, correct classification rate of reference sites; CCRd, 

correct classification rate of disturbed sites. The formulas are given to calculate the values of multimetric indices (see Methods section, “Traditional MMI Development”). The values below threshold 

indicate impairment of algal communities. %, percent]

Region/ Metrics and Threshold Calibration set Validation set

 Index or formula of the 

model index/model

CCR CCRr CCRd CCR CCRr CCRd

Southeastern Plains

NAWQA (1 + DiaR_SP)/98 4.45 81/10 44/54 37/46 27/33 14/18 13/15

diatom - DiaD_SP/79)*5 0 (82%)

metrics, (81%)

single 

category

NAWQA (2 + DiaR_SP/98 7.20 77/10 42/54 35/46 25/33 14/18 11/15

diatom - DiaD_SP/79 - 0 (76%)

metrics, CHL2/58 + (77%)

various TN_SP/0.98)*2.5

categories

NAWQA No non-diatom 

diatom and metric had score 

non-diatom above 0

metrics

NAWQA (4 + DiaR_SP/98 5.40 77/10 42/54 35/46 25/33 13/18 12/15

and non- - DiaD_SP/79 - 0 (76%)

NAWQA Nhf/51 - BF/30 - (77%)

metrics EUTRandH/79 + 

O_h/77 + 

SP_OL/50)*1.43

Non- (3 - Nhf/51 - 72/10 40/54 32/46 23/22 13/18 11/15

NAWQA BF/30 - 0 (70%)

metrics only EUTRandH/79 + (72%)

O_h/77 + 

SP_OL/50)*2

Logistic Dia_SP 79/10 40/54 39/46 24/33 11/18 13/15

regression 0 (73%)

(79%)

Central and Western Plains

NAWQA (1 + 3.58 70/79 37/41 33/38 22/26 10/13 12/13

diatom DiaR_WCP/79 - (89%) (85%)

metrics, DiaD_WCP/84)*

single 5 

category
12

Table 3. Multi-metric indices and logistic regression model summary.—Continued

[CCR, overall correct classification rate; CCRr, correct classification rate of reference sites; CCRd, 

correct classification rate of disturbed sites. The formulas are given to calculate the values of multimetric indices (see Methods section, “Traditional MMI Development”). The values below threshold 

indicate impairment of algal communities. %, percent]

Region/ Metrics and Threshold Calibration set Validation set

 Index or formula of the 

model index/model

CCR CCRr CCRd CCR CCRr CCRd

Central and Western Plains—continued

NAWQA (4 + 3.80 64/79 34/41 30/38 21/26 10/13 11/13

diatom DiaR_WCP/79 - (81%) (81%)

metrics, DiaD_WCP/84 - 

various CHLh/83 - 

categories TNh/90 - 

TPh/91)*2

NAWQA (2 + 4.43 70/79 37/41 33/38 21/26 12/13 9/13

diatom and DiaR_WCP/79 - (89%) (81%)

non-diatom DiaD_WCP/84 + 

metrics SAR_WCP/94.3 - 

SAD/53.2)*2.5

NAWQA (5 + 4.66 62/79 32/41 30/38 22/26 10/13 12/13

and non- DiaR_WCP/79 - (78%) (85%)

NAWQA DiaD_WCP/84 - 

metrics Nhf/54 - BF/61 - 

A_MESO /52 - 

EUTRandH /91 + 

MESOTR/15)*1.

43

Non- (4 - Nhf/54 - 5.12 55/79 28/41 27/38 20/26 10/13 10/13

NAWQA BF/61 – (70%) (77%)

metrics only A_MESO/52 - 

EUTRandH/91 + 

MESOTR/15)*2

Logistic Dia_WCP 69/79 35/41 34/38 23/26 11/13 12/13

regression (87%) (88%)

Western Mountains

NAWQA (1+DiaR_WM/86 5.25 30/33 23/24 7/9 5/11 3/7 2/4

diatom - (91%) (45%)

metrics only DiaD_WM/71)*5

NAWQA (1 + 4.46 25/33 18/24 7/9 6/11 4/7 2/4

diatom DiaR_WM/86 - (76%) (55%)

metrics,vario DiaD_WM/71 + 

us categories TPl/93)*3.33

NAWQA (2 + 5.99 29/35 22/24 7/9 7/11 4/7 3/4

metrics DiaR_WM/86 - (83%) (64%)

DiaD_WM/71 - 

SAD_WM/71)*3.

33
13

Table 3. Multi-metric indices and logistic regression model summary.—Continued

 [CCR, overall correct classification rate; CCRr, correct classification rate of reference sites; CCRd, 

correct classification rate of disturbed sites. The formulas are given to calculate the values of multimetric indices (see Methods section, “Traditional MMI Development”). The values below threshold 

indicate impairment of algal communities. %, percent]

Region/ Metrics and Threshold Calibration set Validation set

 Index or formula of the 

model index/model

CCR CCRr CCRd CCR CCRr CCRd

Western Mountains

NAWQA (4 + 6.70 29/33 21/24 8/9 8/11 6/7 2/4

and non- DiaR_WM/86 - (89%) (73%)

NAWQA DiaD_WM/71 + 

metrics TPl/93 - O_vl/10 

- A_MESO/68 - 

CP/0.22)*1.67

Non- (4 - O_vl/10 - 9.05 23/33 17/24 6/9 7/11 5/7 2/4

NAWQA A_MESO/68 - (70%) (64%)

metrics only CP/0.22 - 

BF/18)*2.5

Logistic DiaD_WM 31/33 24/24 7/9 7/11 6/7 1/4

regression (94%) (64%)

Alternative Processes for Selecting Metrics

In addition to developing a traditional MMI, other approaches for selecting 

indicators were explored. Logistic regression was used as an exploratory tool for 

determining combinations of metrics that could distinguish between reference and 

disturbed sites in each of the five regions. This technique is convenient for identifying 

combinations of independent variables that provide the best prediction of a nominal 

dependent variable (Zuur and others, 2007). The dependent variable in this study was the 

classification of a site as reference or disturbed, and all 124 candidate metrics were 

considered as possible independent variables. Unlike in the MMI development, no 

metrics were excluded from consideration based on preliminary tests. The rationale for 

using a logistic regression was that any metric might be included in the model if it 

increased the model’s predictive power, even if the metric did not distinguish between 

reference and disturbed sites when considered individually. The number of metrics was 

thus comparable to the number of observations in regional datasets, which can lead to 

spurious correlations and overfitting of the models. Thus, in the case of several highly 

correlated variables, the variable that causes the effect might not be the one selected for 

the model; the variable may have happened by chance to have had a somewhat higher 

correlation with the dependent variable. We did not search, however, for causative 

relationships, and knew beforehand that many metrics were highly correlated. This 

analysis was for exploratory purposes, and therefore a large number of candidate 

independent variables were included. Logistic regression was carried out with SPSS 

statistical software using forward selection, and F values for variable inclusion and 

exclusion were set at 0.10. The models were developed using only the calibration sites, 
14

and their predictive power was evaluated as the number of correctly classified sites after 

applying the models to the validation datasets. 

Application of Indices to Assess Algal Community Condition at 

NAWQA Sampling Sites

The second goal of this study was to use the MMIs to assess the condition of algal 

communities at 1,071 sites sampled by the NAWQA Program (fig. 1). A distinct design 

feature of the NAWQA Program was the targeting of selected sites in key land-use and 

environmental settings. Accordingly, all non-reference sites were classified on the basis 

of land cover (Nakagaki and others, 2007) in their contributing watersheds using the 

criteria presented in table 4. Sites classified as “agricultural” were in watersheds 

dominated by agricultural land cover. Sites classified as “urban” were in watersheds with 

substantial urban land cover relative to agricultural or other land uses. The criteria by 

which basins were classified as “less developed” (table 4) were less stringent than the 

criteria used to classifiy sites as “reference” in this study, so sites classified as less

developed represent an intermediate level of disturbance between reference sites and 

other land-use classifications (urban, agricultural, and mixed). In addition, sites classified 

as less developed may also be influenced by land use in close proximity to the sampling 

site even if most of the watershed is undeveloped. Sites classified as “mixed” are 

influenced by a mixture of urban and agricultural land uses. 

Table 4. Land-cover criteria for classifying National Water-Quality Assessment Program sites 

into major land-use settings (from Gilliom and others, 2006).

[>, greater than; ≤, less than or equal to; %, percent]

Land-use classification Watershed land-cover critieria

Agricultural > 50% agricultural land and ≤ 5% urban land

Urban >25% urban land and ≤ 25% agricultural land

Less developed ≤ 5% urban land and ≤ 25% agricultural land

Mixed use All other combinations of urban, agricultural, and undeveloped land

Site values of MMIs were rescaled to facilitate interregional comparisons of algal 

condition by dividing the original MMI value at each site by the mean MMI value of the 

reference sites in the respective region (for example, Hawkins, 2006). This rescaling 

assumes that the expected MMI value at each non-reference site is the average of the 

reference site MMI values within the same region. The ratio of the observed MMI value 

and the expected MMI value can therefore be interpreted as a measure of disturbance, 

where ratios less than (<) or greater than (>) 1 indicate a substantial deviation of the MMI 

score at a site from the regional mean reference condition. All comparisons among landuse settings were made using an analysis of variance followed by multiple comparison 

tests. 

Major Findings

Multi-metric indices based on different approaches to metric selection were

evaluated for their ability to differentiate between reference and disturbed sites in 

independent, validation datasets. The best-performing MMI was applied to all sites and 
15

algal communities in streams influenced by varying land uses, which are assessed across 

the United States.

Algal Taxa Indicative of Reference and Disturbed Sites

A total of 1,179 algal taxa were identified at 1,071 sites; 829 of these were 

diatoms and 350 were non-diatoms. The 300 algal taxa identified by ISA and CCA as 

indicative of reference or disturbed sites in the five U.S. regions are listed in appendix 2. 

Most diatom species found to be indicative of disturbed sites in most regions (Amphora 

pediculus, Cyclotella meneghiniana, Navicula gregaria, Navicula minima, Nitzschia 

amphibia, Nitzschia inconspicua, Planothidium lanceolatum, Rhoicosphenia abbreviata, 

and Sellaphora seminulum) are known to increase in abundance with increasing nutrients, 

organic matter, and general pollution. Species indicative of reference sites in most 

regions (Achnanthidium deflexum, Cymbella affinis, Encyonema minutum, Encyonopsis

microcephala, Gomphonema angustatum, Staurosirella pinnata) are those known to 

decrease in abundance with increasing organic and chemical nutrients. In contrast to the 

266 diatom species found to be indicative of reference or disturbed sites, only 34 nondiatom taxa (or morphological groups) were found to be possible indicators of reference 

or disturbed sites (appendix 2). 

Evaluation of Alternative Metrics

Few of the algal metrics examined were distinctly different between reference 

sites and disturbed sites. In most of the regional datasets, relatively few of the 124 

metrics that passed the screening and Mann-Whitney tests had nonoverlapping 

interquartile ranges (a score of “3” in table 2). There were 26 such metrics in the 

Glaciated North but only 2 to 9 such metrics in the other regions. The metric that most 

often differentiated reference sites from disturbed sites in the validation data was the 

percentage of diatoms indicative of reference and disturbed sites within the calibration 

data. Other metrics with discriminatory ability included diatom metrics developed 

previously from the NAWQA dataset (for example, Potapova and Charles, 2007), such as 

percentage of diatoms indicative of nutrient enrichment and ionic strength. To increase 

the number of candidate metrics in the four regions other than the Glaciated North, 

metrics with scores of “2” and “1” were also considered (table 2).

The diatom metrics that were indicative of reference and disturbed sites in this 

study were also correlated with several chemical-specific metrics developed by other 

authors (table 5). Metrics for nutrient enrichment, organic pollution, and ionic strength 

were strongly (that is, absolute value Spearman rank (|rho|) > 0.7) correlated with 

percentages of diatom species associated with disturbed or reference sites. In contrast, 

previously developed algal metrics indicative of ionic strength (Potapova and Charles,

2003), trophic state (Van Dam and others, 1994), and dissolved oxygen (Van Dam and 

others, 1994) were weakly correlated (that is, |rho| < 0.6) with metrics developed in this 

study.
16

Table 5. Spearman correlations between regional and national diatom metrics developed in this 

study. Diatoms associated with reference or disturbed sites and associated with several stressorspecific diatom metrics are shown.

[All correlations except one are significant at the 0.01 level; *, correlation significant at the 0.05 

level. Regional metrics were calculated for corresponding sites. TP, total phosphorus; TN, total 

nitrogen] 

Stressor-specific diatom Metrics developed in this study

metrics Source Diatoms associated with Diatoms associated with 

reference sites disturbed sites

Regional National Regional National 

Brackish-fresh Van Dam and -0.21 -0.61 0.51 0.61

others, 1994

High conductivity Potapova and -0.23 -0.66 0.57 0.64

Charles, 2003

Low conductivity Potapova and 0.18 0.46 -0.42 -0.50

Charles, 2003

Eutraphentic+ Van Dam and -0.33 -0.70 0.62 0.73

hypereutraphentic others, 1994

Oligo+ oligo- Van Dam and 0.28 0.27 -0.27 -0.28

mesotraphentic others, 1994

National low/high ratio TP Potapova and 0.39 0.84 -0.62 -0.72

indicators Charles, 2007

National low/high ratio TN Potapova and 0.42 0.87 -0.70 -0.81

indicators Charles, 2007

High oxygen-requiring Van Dam and 0.46 0.79 -0.56 -0.63

taxa others, 1994

Low oxygen-tolerant taxa Van Dam and -0.07* -0.35 0.27 0.33

others, 1994

Sum of α-mesosaprobic to Van Dam and -0.36 -0.53 0.39 0.54

polysaprobic others, 1994

Multi-Metric Indices and Logistic Regression Models

In all five regions, the metrics developed in this study were most able to 

distinguish between reference and disturbed validation sites and were therefore selected 

for MMIs (table 5). Moreover, in all datasets except for the Western Mountains, the MMI 

with the most accurate classification was based on these metrics only. In the Southeastern 

Plains and Central and Western Plains, the MMIs with the highest CCR (82 percent and 

88 percent, respectively) were those based on only two diatom metrics developed in this 

study: the regional lists of the diatom taxa associated with the reference sites and with the 

disturbed sites. The addition of existing diatom metrics or non-diatom metrics lowered 

the predictive power of the MMIs in these two regions. In the Glaciated North, the best 

MMIs were those including either diatom only or diatom and non-diatom metrics 

representing percentages of algae associated with reference and disturbed sites, and the 

CCR was exceptionally high (92 percent). In the Eastern Highlands, the MMI with the 

highest CCR for the validation dataset (79 percent) was based on diatom and non-diatom 

metrics representing percentages of algae associated with reference and disturbed sites. 

Only in the Western Mountains region did the most accurate MMI (CCR=73 percent) 

include metrics derived both from this and other studies. For the Western Mountains, 
17

inclusion of other metrics, such as proportion of diatoms known to be associated with 

organic pollution and dissolved oxygen concentration, helped to increase the 

classification accuracy of the index.

Logistic regression models and MMI-classified reference and disturbed sites with 

similar accuracy (60 to 90 percent) are shown in table 3. In all regions the models that 

had the highest CCR in validation datasets had only one predictor metric. In the Glaciated 

North, Southeastern Plains, and Central and Western Plains, the most accurate metric was 

the ratio of diatoms associated with reference sites to diatoms associated with disturbed 

sites. In the Eastern Highlands and Western Mountains, the most accurate metric was the 

percentage of diatoms associated with disturbed sites.

Assessment of Algal Communities by Region and Land-Use Setting

The condition of algal communities varied substantially by region and land-use 

setting (fig. 3). In all regions, algal communities in agricultural, urban, and mixed-use

watersheds had a reduced biological condition relative to reference sites. Algal 

community condition in urban watersheds of the Central and Western Plains was lower 

than that of all other land-use settings. In contrast, the biological condition of algal 

communities in less-developed watersheds was generally similar to that of the reference 

sites, except in the Glaciated North, where the number of sites was low (n=7). 

Discussion of Index Performance and Metrics

The performance of MMIs developed in this study was similar at a national scale 

to MMIs developed by others at smaller regional scales. Most algal MMIs, including the 

indices developed in this study, do not incorporate metrics truly reflecting different 

aspects of the communities as envisioned by early developers of MMIs. Although the 

overall MMI developed in this study has limited application to other studies, the 

individual metrics identified herein provide useful insight into the development of future 

MMIs to assess algal community condition.

Metrics Selected for MMIs

The relatively high classification accuracy of MMIs developed in this study (66 to 

92 percent of the validation sites were correctly classified as reference or disturbed sites) 

is comparable to the index of biotic integrity developed by Wang and others (2005) for a 

much more homogeneous dataset of the Interior Plateau ecoregion, where a CCR of 80 

percent was achieved. Notably, the small number of calibration sites in the Western 

Mountains probably diminished the clarity with which indicator species could be 

identified in that region. 
18

Figure 3. Values of multi-metric diatom indices in sites in the “Reference” group or grouped by 

land use settings. The box represents interquartile range, which contains 50 percent of values; the 

whiskers are lines that extend to the highest and lowest numbers excluding outliers, which are 

values outside 1.5 times the box length; the horizontal line across each box is the median. Boxes 

with the same superscript were not significantly different (P<0.05) in multiple comparison tests 

(bonferroni correction). The number of streams in each land-use category is provided above each 

box. Rsq.= adjusted r-squared value from anovas.
19

The original concept of combining different metrics into an index stems from the 

assumption that these metrics represent different structural and functional aspects of 

communities and ecosystems (Karr, 1981). For example, the major components of fish 

and macroinvertebrate indices of biotic integrity are usually some measures of 

community diversity and composition related to pollution tolerance and functional groups 

(Barbour and others, 1996; Fore and others, 2002). The metrics selected for algal MMIs 

in this study seem to represent different aspects of the community, but in fact these 

metrics are often highly redundant. Various diatom metrics related to trophic status, 

growth habit, organic pollution, and general pollution tolerance are actually based on 

similar lists of species compiled by others (Van Dam and others, 1994). They are usually 

correlated (table 2) because there is a uniform set of species known to favor eutrophic, 

silty, and otherwise anthropogenically disturbed waters—or conversely, oligotrophic, 

clear and undisturbed waters. For instance, 63 diatom taxa found in NAWQA samples 

were used to calculate the metric “percent brackish-water diatoms,” while 43 of those 

were also classified as “eutraphentic diatoms” (both metrics are based on Van Dam and 

others, 1994). Similarly, 18 out of 37 “brackish,” 51 out of 69 “alpha-mesosaprobic,” and 

15 out of 20 “polysaprobic-alpha-mesosaprobic” diatom taxa were also considered 

“eutraphentic.” Further, 51 out of 69 taxa were also “eutraphentic.” Combining various 

metrics into a single MMI essentially creates a new list of species that most accurately 

distinguish between impaired and non-impaired sites in a certain area. The same list can 

be more easily created by determining directly which species are commonly associated 

with either disturbed sites or reference sites.

Most algal MMIs, including the indices developed in this study, do not 

incorporate metrics truly reflecting different aspects of the communities as envisioned by 

Karr (1981). Hill and others (2000) did include metrics representing several distinct 

aspects of the community, such as measures of the diversity, productivity, biomass, and 

species composition, in their “periphyton index of biotic integrity,” but the metric 

selection for this index was based on their expected responses rather than on their ability 

to distinguish between reference and disturbed sites. The diversity of algal communities 

usually increases at intermediate levels of watershed disturbance, and can be quite low in 

undisturbed sites as well as in heavily disturbed sites. For this reason, diversity measures 

are not considered reliable indicators of impairment. The percentage of malformed 

diatoms is rarely recorded during routine algal counts because in most areas malformed 

diatoms are rarely observed (but see Fore and Grafe, 2002). 

Condition of Algal Communities

The MMIs developed in this study showed distinct differences in the condition of 

algal communities among four classified land uses (table 4). This result is not remarkable 

given that the index used to identify disturbed sites was partially based on land-cover 

characteristics. Pronounced alteration of algal communities in urban and agricultural 

streams was obvious in all five regions. Similar trends of degradation of algal 

communities with increased human activities were observed in other assessments using 

MMIs (Fore and Grafe, 2002; Wang and others, 2005). A unique aspect of this study, 

however, is that the condition of algal communities was assessed at a national spatial 

scale. 
20

Use of Diatom MMIs and Individual Metrics

The MMIs developed in this study were intended to assess the condition of algal

communities at sites sampled by the NAWQA Program, but with several caveats—the 

MMIs may be of use in other algal assessments. First, the MMIs developed in this study 

are limited to diatoms. Although metrics representing other algal groups were considered 

(such as percentages of specimens from certain ecological categories, total algal cell 

density and biovolume, and abundance of several algal divisions), they generally added 

little additional information to the assessment; that is, the predictive ability of MMIs

improved by a negligible amount when diatom-based metrics were already included. 

Identification to species level is often difficult or impossible for non-diatom algae when 

using the traditional methodology based on light microscopy, and this might in part

explain the lower success of non-diatom metrics. Because assessments of other algal 

groups are important to managers (for example, evaluating nuisance algae), new methods 

of characterizing non-diatom algal communities should be developed for environmental 

monitoring. 

An additional caveat in using the MMIs developed in this study is the varying 

quality and relatively limited number of reference sites sampled in some regions. The 

difficulty in locating and sampling minimally disturbed watersheds in the Central and 

Western Plains region, for instance, means that the reference sites in this region may have 

experienced substantial anthropogenic disturbance. Such an unavoidable limitation 

should not, however, preclude the use of the MMIs or predictive models to assess 

impairment. It has been recognized (such as by Fore and Grafe, 2002) that quantifying 

human disturbance requires regional approaches; therefore, the assessment of impairment 

should also be region-specific (Stoddard and others, 2006). A more serious concern is the 

relatively small number of reference sites sampled in regions such as the Western 

Mountains. Reliable assessments depend on a set of reference sites that are 

environmentally similar to sites being evaluated. MMIs developed for regions that are 

environmentally heterogenous over small spatial scales (for example, elevation gradients 

in the Western Mountains region) require many more reference sites than were available 

for this study.

A final caveat for the use of these MMIs concerns the thresholds. The thresholds 

for MMI scoring used in this study are entirely dependent on the distribution of MMI 

values at NAWQA sites within each region. Assessments of sites sampled as part of other 

local or regional studies may or may not be appropriate, depending largely on their 

environmental similarity to sites sampled by NAWQA. 

In addition to the MMIs, the individual metrics developed in this study may be 

reliable for assessing general water-quality conditions. Because NAWQA sampling was 

targeted toward streams influenced by intensive land use, diatom metrics identified in this 

study that are indicative of disturbed conditions are probably a reliable indicator of

generally poor water quality. In contrast, diatom metrics that were indicative of 

undisturbed conditions are probably indicative of minimal human disturbance within 

most regions. Finally, metrics developed in many previous studies are known to be 

reliable indicators of specific chemical stressors and should continue to provide useful 

information regarding possible causes of algal community disturbance. 
21

Summary

The National Water-Quality Assessment (NAWQA) Program of the U.S. 

Geological Survey developed multi-metric indices for use in assessing the condition of 

benthic algal communities in U.S. streams and rivers that encompass five geographic 

regions across the conterminous United States. The indices were developed by evaluating 

whether 124 alternative metrics of algal communities were able to discriminate between 

streams in disturbed and undisturbed watersheds. The study assessed the condition of 

algal communities at 1,071 NAWQA Program sampling sites. Algal communities in 

agricultural, urban, and mixed use settings in all regions had a reduced biological 

condition relative to undisturbed reference sites. The algal data and algal taxa developed 

for this study are available for use in the development of additional multi-metric indices.

Acknowledgments

This report was prepared as part of a cooperative research agreement between the 

Academy of Natural Sciences Patrick Center for Environmental Research and the U.S. 

Geological Survey National Water-Quality Assessment Program. The authors 

acknowledge Yangdong Pan, Portland State University, and Wade Bryant and James 

Coles, U.S. Geological Survey, for thoughtful reviews that improved the clarity of the

report.

References Cited

Bahls, L.L., 1993, Periphyton bioassessment methods for Montana streams: Helena, 

Montana, Montana Department of Health and Environmental Sciences, accessed 

February 2, 2011, from http://www.archive.org/details/periphytonbioass00bahl.

Barbour, M.T., Gerritsen, J., Griffith, G.E., Frydenborg, R., McCarron, E., White, J.S., 

and Bastian, M.L., 1996, A framework for biological criteria for Florida streams using 

benthic macroinvertebtrates: Journal of the North American Benthological Society, v. 

15, no. 2, p. 185–211.

Carlisle, D.M., and Meador, M.R., 2007, A biological assessment of streams in the 

eastern United States using a predictive model for macroinvertebrate assemblages:

Journal of the American Water Resources Association, v. 43, no. 5, p. 1194–1207.

Carlisle, D.M., Hawkins, C.P., Meador, M.R., Potapova, Mariana, and Falcone, James,

2008, Biological assessments of Appalachian streams based on predictive models for 

fish, macroinvertebrate, and diatom assemblages: Journal of the North American 

Benthological Society, v. 27, no. 1, p. 16–37.

Charles, D.F., Knowles, Candia, and Davis, R.S., eds., 2002, Protocols for the analysis of 

algal samples collected as part of the U.S. Geological Survey National Water-Quality 

Assessment Program: Academy of Natural Sciences Report No. 02–06, 124 p.

Dufrêne, Marc, and Legendre, Pierre, 1997, Species assemblages and indicator species—

The need for a flexible asymmetrical approach: Ecological Monographs, v. 67, no. 3, p. 

345–366.
22

Falcone, J.A., Carlisle, D.M., and Weber, L.C., 2010. Quantifying human disturbance in 

watersheds—Variable selection and performance of a GIS-based disturbance index for 

predicting the biological condition of perennial streams: Ecological Indicators, v. 10, 

no. 2, p. 264–273.

Fore, L.S., 2002, Response of diatom assemblages to human disturbance—Development 

and testing of a multimetric index for the Mid-Atlantic region (USA), in Simon, T. P. 

(ed.), Biological response signatures—Indicator patterns using aquatic communities:

Boca Raton, Fla., CRC Press, p. 445–480.

Fore, L.S., Karr, J.R., and Wisseman, R.W., 1996, Assessing invertebrate responses to 

human activities—Evaluating alternative approaches: Journal of the North American 

Benthological Society, v. 15, no. 2, p. 212–231.

Fore, L.S., and Grafe, Cynthia, 2002, Using diatoms to assess the biological condition of 

large rivers in Idaho (U.S.A.): Freshwater Biology, v. 47, no. 10, p. 2015–2037.

GeoLytics, 2001, Census 2000 and street 2000: East Brunswick, N.J., GeoLytics, Inc., 2 

CD-ROMs.

Gilliom, R.J., Barbash, J.E., Crawford, C.G., Hamilton, P.A., Martin, J.D., Nakagaki, 

Naomi, Nowell, L.H., Scott, J.C., Stackelberg, P.E., Thelin, G.P., and Woolock, D.M., 

2006, The quality of our nation’s waters—Pesticides in the nation’s streams and ground

water, 1992–2001: U.S. Geological Survey Circular 1291, 172 p. (Also available at

http://pubs.usgs.gov/circ/2005/1291/.)

Hawkins, C.P., 2006, Taxonomic completeness as a measure of biological integrity. 

Ecological Applications, v. 16, p. 1277-1294.

Hill, B.H., Herlihy, A.T., Kaufmann, P.R., Stevenson, R.J., McCorkick, F.H., and Burch 

Johnson, C., 2000, Use of periphyton assemblage data as an index of biotic integrity: 

Journal of the North American Benthological Society, v. 19, no. 1, p. 50–67.

Horizon Systems Corporation, 2006, National Hydrography Dataset Plus (NHDPlus) 

Home: Horizon Systems Corporation, accessed February 9, 2011, at 

http://www.horizon-systems.com/nhdplus/.

Karr, J.R., 1981, Assessment of biotic integrity using fish communities: Fisheries, v. 6,

no. 6, p. 21–27.

Karr, J.R., and Chu, E.W., 1999, Restoring life in running waters—Better biological 

monitoring: Washington, D.C., Island Press, 206 p. 

Kelly, M.G., and Whitton, B.A., 1995, The trophic diatom index—A new index for 

monitoring eutrophication in rivers: Journal of Applied Phycology, v. 7, no. 4, p. 433–

444. 

Kerans, B.L., and Karr, J.R., 1994, A benthic index of biotic integrity (B–IBI) for rivers 

of the Tennessee Valley: Ecological Applications, v. 4, p. 768–785.

Kentucky Division of Water, 1993, Methods for assessing biological integrity of surface 

waters: Frankfort, Kentucky, Kentucky Department of Environmental Protection. 

(Available from the Kentucky Department of Environmental Protection, 14 Reilly 

Road, Frankfort, Kentucky 40601 USA.) 

Lange-Bertalot, H., 1979, Pollution tolerance of diatoms as a criterion for water quality 

estimation: Nova Hedwigia, v. 64, p. 285–304.
23

Moulton, S.R., II, Kennen, J.G., Goldstein, R.M., and Hambrook, J.A., 2002, Revised 

protocols for sampling algal, invertebrate, and fish communities as part of the National 

Water-Quality Assessment Program: U.S. Geological Survey Open-File Report 02–

150, 87 p. (Available at http://pubs.usgs.gov/of/2002/ofr-02-150/index.html.)

Nakagaki, N., Price, C.P., Falcone, J.A., Hitt, K.J., and Ruddy, B.C., 2007, Enhanced 

National Land Cover Data 1992 (NLCDe 92), Edition 1.0, [digital map], accessed 

December 1, 2007, at http://water.usgs.gov/lookup/getspatial?nlcde92.

Porter, S.D., Cuffney, T.F., Gurtz, M.E., Meador, M.R., 1993, Methods for collecting 

algal samples as part of the National Water-Quality Assessment Program: U.S. 

Geological Survey Open-File Report 93–409. (This report has been superseded by U.S. 

Geological Survey Open-File Report 02–150, http://pubs.usgs.gov/of/2002/ofr-02-

150/index.html.) 

Potapova, Marina, and Charles, D.F., 2003, Distribution of benthic diatoms in U.S. rivers 

in relation to conductivity and ionic composition: Freshwater Biology, v. 48, no. 8, p.

1311–1328.

Potapova, Marina, and Charles, D.F., 2007, Diatom metrics for monitoring eutrophication 

in rivers of the United States: Ecological Indicators, v. 7, p. 48–70.

Price, C.V., Nakagaki, Naomi, Hitt, K.J., and Clawges, R.M., 2006, Enhanced historical 

land-use and land-cover data sets of the U.S. Geological Survey: U.S. Geological 

Survey Data Series 240, with digital maps. (Available at 

http://pubs.usgs.gov/ds/2006/240.)

Prygiel, J., Lévéque, L., and Iserentant, R., 1996, Un nouvel indice diatomique pratique 

pour l’évaluation de la qualité des eaux en réseau de surveilliance: Revue des Sciences 

de l’Eau, v. 9, no. 1, p. 97–113.

Ruddy, B.C., Lorenz, D.L., and Mueller, D.K., 2006, County-level estimates of nutrient 

inputs to the land surface of the conterminous United States, 1982–2001: U.S. 

Geological Survey Scientific Investigations Report 2006–5012, 17 p. (Available online 

from http://pubs.usgs.gov/sir/2006/5012/.)

Sládeček, V., 1986, Diatoms as indicators of organic pollution: Acta Hydrochimica et 

Hydrobiologica, v. 14, no. 5, p. 555–566.

Steinberg, C., and Schiefele, S., 1988, Indication of trophy and pollution in running 

waters: Zeitschrift Fur Wasser-Abwasser-Forschung, v. 21, p. 227–234.

Stoddard, J.L., Larsen, D.P., Hawkins, C.P., Johnson, R.K., and Norris, R.H., 2006, 

Setting expectations for the ecological condition of running waters—The concept of 

reference conditions: Ecological Applications, v. 16, p. 1267–1276.

U.S. Army Corps of Engineers, 2006, National Inventory of Dams: U.S. Army Corps of 

Engineers, accessed in July 2006 at 

http://crunch.tec.army.mil/nidpublic/webpages/nid.cfm.

U.S. Geological Survey, 2006, National atlas home page: U.S. Geological Survey,

accessed in November 2006 at http://www-atlas.usgs.gov.

U.S. Geological Survey, 2007, Grids of agricultural pesticide use in the conterminous 

United States, 1997: U.S. Geological Survey Web page, accessed in June 2007 at 

http://water.usgs.gov/GIS/metadata/usgswrd/XML/agpest97grd.xml.

Van Dam, Herman, Mertens, Adrienne, and Sinkeldam, Jos, 1994, A coded checklist and 

ecological indicator values of freshwater diatoms from the Netherlands: Aquatic 

Ecology, v. 28, no. 1, p. 117–133.
24

Wang, Yi-Kuang, Stevenson, R.J., and Metzmeier, Lythia, 2005, Development and 

evaluation of a diatom-based Index of Biotic Integrity for the Interior Plateau 

Ecoregion, USA: Journal of the North American Benthological Society, v. 24, no. 4, p. 

990–1008.

Zuur, Alain, Ieno, E.N., and Smith, G.M., 2007, Analysing ecological data (Statistics for 

biology and health): New York, Springer, 672 p. 
25

Appendix 1. Algal metrics commonly included in a multi-metric index. A total of 124 metrics are grouped into 8 categories. Abbreviations: TN, 

total nitrogen; TP, total phosphorous; μS/cm, microsiemens per centimeter; mg/L, milligrams per liter; %, percent; <, less than; >, greater than.

General category Most common algal metrics Algal metric Source

(total number of metrics abbreviation

per category) (if used in this report) 

Total algal abundance (2) Total algal biovolume

Total algal cell density CD

Broad taxa groups (28) Red algae, absolute and relative biovolume 

or cell density 

Green algae, absolute and relative 

biovolume or cell density

Diatoms, absolute and relative biovolume 

or cell density

Cyanobacteria, absolute and relative 

biovolume or cell density

Xanthophytes, absolute and relative 

biovolume or cell density

Euglenophytes, absolute and relative 

biovolume or cell density

Cryptophytes, absolute and relative 

biovolume or cell density

Diversity measures (6) Number of all algal species or diatom 

species only

Percent of the valves in the diatom count 

made by the dominant diatom taxon and by 

the ten dominant diatom taxa

Shannon-Wiener diversity index based on 

proportions of cell densities of all algal 

taxa and diatoms only

Growth habit of diatoms (2) Ratio of centric to pennate diatoms CP

Motile diatoms MOTILE Bahls 1993

Nutrient status indicators Relative abundance of oligotraphentic Van Dam and others (1994)

(24) diatoms

Relative abundance of oligo- Van Dam and others (1994)

mesotraphentic diatoms

Relative abundance of oligotraphentic and Van Dam and others (1994)

oligo-mesotraphentic diatoms
26

Appendix 1. Algal metrics commonly included in a multi-metric index. A total of 124 metrics are grouped into 8 categories. Abbreviations: TN, 

total nitrogen; TP, total phosphorous; μS/cm, microsiemens per centimeter; mg/L, milligrams per liter; %, percent; <, less than; >, greater than.—

Continued.

General category Most common algal metrics Algal metric Source

(total number of metrics abbreviation

per category) (if used in this report)

Nutrient status indicators Relative abundance of mesotraphentic MESOTR Van Dam and others (1994)

(24)—continued diatoms

Relative abundance of meso-eutraphentic Van Dam and others (1994)

diatoms

Relative abundance of eutraphentic EUTR Van Dam and others (1994)

diatoms

Relative abundance of hypereutraphentic Van Dam and others (1994)

diatoms

Relative abundance of eutraphentic and EUTR and H Van Dam and others (1994)

hypereutraphentic diatoms

Relative abundance of nitrogen-autotrophic Van Dam and others (1994)

diatoms tolerating very small 

concentrations of organic nitrogen

Relative abundance of nitrogen-autotrophic Van Dam and others (1994)

diatoms tolerating elevated concentrations 

of organic nitrogen

Relative abundance of facultatively Nhf Van Dam and others (1994)

nitrogen-heterotrophic diatoms needing 

periodically elevated concentrations of 

organic nitrogen

Relative abundance of obligately nitrogen- Van Dam and others (1994)

heterotrophic diatoms needing 

continuously elevated concentrations of 

organic nitrogen

Relative abundance of diatoms–indicators TPl Potapova and Charles (2007)

of low total phosphorus at national scale

Relative abundance of diatoms–indicators TPl_r Potapova and Charles (2007)

of low total phosphorus at regional scale

Relative abundance of diatoms–indicators TPh Potapova and Charles (2007)

of high total phosphorus at national scale
27

Appendix 1. Algal metrics commonly included in a multi-metric index. A total of 124 metrics are grouped into 8 categories. Abbreviations: TN, 

total nitrogen; TP, total phosphorous; μS/cm, microsiemens per centimeter; mg/L, milligrams per liter; %, percent; <, less than; >, greater than.—

Continued.

General category Most common algal metrics Algal metric Source

(total number of metrics abbreviation

per category) (if used in this report)

Nutrient status indicators Relative abundance of diatoms–indicators TPh_r Potapova and Charles (2007)

(24)—continued of high total phosphorus at regional scale

Ratio of low to sum of high and low TP TP Potapova and Charles (2007)

diatom indicators at national scale

Ratio of low to sum of high and low TP TP_r Potapova and Charles (2007)

diatom indicators at regional scale

Relative abundance of diatoms–indicators TNl Potapova and Charles (2007)

of low total nitrogen at national scale

Relative abundance of diatoms–indicators TNl_r Potapova and Charles (2007)

of low total nitrogen at regional scale

Relative abundance of diatoms–indicators TNh Potapova and Charles (2007)

of high total nitrogen at national scale

Relative abundance of diatoms–indicators TNh_r Potapova and Charles (2007)

of high total nitrogen at regional scale

Ratio of low to sum of high and low TN TN Potapova and Charles (2007)

diatom indicators at national scale

Ratio of low to sum of high and low TN TN_r Potapova and Charles (2007)

diatom indicators at regional scale

Organic pollution indicators Relative abundance of oligosaprobic SP-OL Van Dam and others (1994)

(11) diatoms

Relative abundance of β-mesosaprobic Van Dam and others (1994)

diatoms

Relative abundance of α-mesosaprobic A_MESO Van Dam and others (1994)

diatoms

Relative abundance of α -meso- to AP Van Dam and others (1994)

polysaprobic diatoms

Relative abundance of polysaprobic Van Dam and others (1994)

diatoms
28

Appendix 1. Algal metrics commonly included in a multi-metric index. A total of 124 metrics are grouped into 8 categories. Abbreviations: TN, 

total nitrogen; TP, total phosphorous; μS/cm, microsiemens per centimeter; mg/L, milligrams per liter; %, percent; <, less than; >, greater than.—

Continued.

General category Most common algal metrics Algal metric Source

(total number of metrics abbreviation

per category) (if used in this report)

Organic pollution indicators Sum of relative abundances of α- Van Dam and others (1994)

(11) —continued mesosaprobic, α -meso- to polysaprobic, 

and polysaprobic diatoms

Relative abundance of diatoms requiring O_h Van Dam and others (1994)

high dissolved oxygen (100% saturation) 

Relative abundance of diatoms requiring Van Dam and others (1994)

fairly high dissolved oxygen (above 75% 

saturation) 

Relative abundance of diatoms requiring Van Dam and others (1994)

moderate dissolved oxygen (above 50% 

saturation) 

Relative abundance of diatoms tolerating O_l Van Dam and others (1994)

low dissolved oxygen (30% saturation) 

Relative abundance of diatoms tolerating O_vl Van Dam and others (1994)

very low dissolved oxygen (about 10% 

saturation)

Water ionic content Relative abundance of acidobiontic Van Dam and others (1994)

indicators (15) diatoms (pH optima <5.5) 

Relative abundance of acidophilous Van Dam and others (1994)

diatoms (mainly occurring at pH <7) 

Relative abundance of circumneutral Van Dam and others (1994)

diatoms (mainly occurring at pH about 7) 

Relative abundance of alkalibiontic Van Dam and others (1994)

diatoms (exclusively occurring at pH>7) 

Relative abundance of alkaliphilous Van Dam and others (1994)

diatoms (mainly occurring at pH >7) 

Relative abundance of diatoms indicating Van Dam and others (1994)

fresh water

Relative abundance of diatoms indicating FB Van Dam and others (1994)

fresh to brackish water
29

Appendix 1. Algal metrics commonly included in a multi-metric index. A total of 124 metrics are grouped into 8 categories. Abbreviations: TN, 

total nitrogen; TP, total phosphorous; μS/cm, microsiemens per centimeter; mg/L, milligrams per liter; %, percent; <, less than; >, greater than.—

Continued.

General category Most common algal metrics Algal metric Source

(total number of metrics abbreviation

per category) (if used in this report)

Water ionic content Relative abundance of diatoms indicating BF Van Dam and others (1994)

indicators (15)—continued brackish to fresh water

Relative abundance of diatoms indicating Van Dam and others (1994)

brackish water

Relative abundance of diatoms with Potapova and Charles (2003)

conductivity optima below 200 μS/cm

Relative abundance of diatoms with CONDh Potapova and Charles (2003)

conductivity optima above 400 μS/cm

Relative abundance of diatoms with CHLl Potapova and Charles (2003)

chloride optima below 8 mg/L

Relative abundance of diatoms with CHLh Potapova and Charles (2003)

chloride optima above 17 mg/L

Relative abundance of diatoms with sulfate SULFl Potapova and Charles (2003)

optima below 8 mg/L

Relative abundance of diatoms with sulfate SULFh Potapova and Charles (2003)

optima above 24 mg/L

Diatoms indicating general Relative abundance of most sensitive Kentucky Division of Water (1993)

impairment (36) diatoms

Relative abundance of sensitive diatoms Kentucky Division of Water (1993)

Relative abundance of tolerant diatoms Kentucky Division of Water (1993)

Relative abundance of most tolerant Kentucky Division of Water (1993)

diatoms 

Relative abundance of very tolerant Lange_Bertalot (1997)

diatoms 

Relative abundance of tolerant diatoms, 2a Lange_Bertalot (1997)

category

Relative abundance of tolerant diatoms, 2b Lange_Bertalot (1997)

category

Relative abundance of less tolerant Lange_Bertalot (1997)

diatoms, 3a category
30

Appendix 1. Algal metrics commonly included in a multi-metric index. A total of 124 metrics are grouped into 8 categories. Abbreviations: TN, 

total nitrogen; TP, total phosphorous; μS/cm, microsiemens per centimeter; mg/L, milligrams per liter; %, percent; <, less than; >, greater than.—

Continued.

General category Most common algal metrics Algal metric Source

(total number of metrics abbreviation

per category) (if used in this report)

Diatoms indicating general Relative abundance of less tolerant Lange_Bertalot (1997)

impairment (36)—continued diatoms, 3b category

Relative abundance of most tolerant Bt Bahls (1993)

diatoms

Relative abundance of less tolerant diatoms Bahls (1993)

Relative abundance of sensitive diatoms Bahls (1993)

Relative abundance of diatoms associated DiaR This study

with reference sites at regional scale

Relative abundance of diatoms associated DiaD This study

with disturbed sites at regional scale

Ratio: DiaR/(DiaR+DiaD) Dia This study

Relative abundance of diatoms associated DiaR_n This study

with reference sites at national scale

Relative abundance of diatoms associated DiaD_n This study

with disturbed sites at national scale

Ratio: DiaR_n/(DiaR_n+DiaD_n) Dia_n This study

Relative density of non-diatom algae SAR This study

associated with reference sites at regional 

scale

Relative density of non-diatom algae SAD This study

associated with disturbed sites at regional 

scale

Ratio: SAR/(SAR+SAD) This study

Relative density of non-diatom algae This study

associated with reference sites at national 

scale

Relative density of non-diatom algae SAD_n This study

associated with disturbed sites at national 

scale
31

Appendix 1. Algal metrics commonly included in a multi-metric index. A total of 124 metrics are grouped into 8 categories. Abbreviations: TN, 

total nitrogen; TP, total phosphorous; μS/cm, microsiemens per centimeter; mg/L, milligrams per liter; %, percent; <, less than; >, greater than.—

Continued.

General category Most common algal metrics Algal metric Source

(total number of metrics abbreviation

per category) (if used in this report)

Diatoms indicating general Ratio: SAR_n /( SAR_n + SAD_n) This study

impairment (36)—continued

Cell density of non-diatom algae associated This study

with reference sites at regional scale

Cell density of non-diatom algae associated This study

with disturbed sites at regional scale

Ratio: SARcd/(SARcd+SADcd) This study

Cell density of non-diatom algae associated This study

with reference sites at national scale

Cell density of non-diatom algae associated This study

with disturbed sites at regional and national 

scale

Ratio: SARcd_n/(SARcd_n+SADcd_n) This study

Biovolume of non-diatom algae associated This study

with reference sites at regional scale

Biovolume of non-diatom algae associated This study

with disturbed sites at regional scale

Ratio: SARb/(SARb+SADb) This study

Biovolume of non-diatom algae associated This study

with reference sites at national scale

Biovolume of non-diatom algae associated This study

with disturbed sites at national scale

Ratio: SARb_n/(SARb_n+SADb_n) This study
32

Appendix 2. Algal taxa associated with reference (+) or disturbed (-) sites. Taxa were included 

in this list as a result of two analyses. First, indicator species analysis (Dufrêne and Legendre, 

1997) determined species indicator values in groups of reference or disturbed sites. Taxa with 

indicator values greater than 5 (P<0.1) are listed here and their indicator values are shown. 

Second, the canonical correspondence analysis of species relative abundance data and a single 

constraining variable “reference” or “disturbed” site was carried out. The taxa with extreme (upper 

or lower quartile) scores along the first Canonical Correspondence Analyses axis were included in 

the list and marked by asterisks (*). All analyses were carried out using the five regional datasets 

and the national dataset. WM, Western Mountains; CWP, Central and Western Plains; GN, 

Glaciated North; EH, Eastern Highlands; SP, Southeastern Plains; NAT, national dataset. 

Species or morphological category WM CWP GN EH SP NAT

Diatoms, followed by source(s)

Achnanthes conspicua Mayer

Achnanthes lemmermannii Hustedt

-19

+20*

-38 -14 -20* -21*

+*

Achnanthes subhudsonis var. kraeuselii (Cholnoky) Cholnoky

Achnanthidium deflexum (Reimer) Kingston

Achnanthidium exiguum (Grunow) Czarnecki

Achnanthidium minutissimum (Kützing) Czarnecki

Achnanthidium rivulare Potapova et Ponader

Adlafia minuscula (Grunow) Lange-Bertalot

Amphora inariensis Krammer

Amphora montana Krasske

Amphora ovalis (Kützing) Kützing

Amphora pediculus (Kützing) Grunow

Amphora veneta Kützing

Aulacoseira ambigua (Grunow) Simonsen

Aulacoseira granulata (Ehrenberg) Simonsen

Aulacoseira italica (Ehrenberg) Simonsen

Aulacoseira muzzanensis (Meister) Krammer

-8*

+24

+51

-50*

-33*

+10

-16

-12

-50

-11

+45*

+77

+39

-30

-78

-*

-* 

+55

+26*

+*

-23*

-20*

-* 

-65

-* 

+55

-26*

-12

+29

+54

+18

-22*

-10

-10*

-51*

-11*

-*

Bacillaria paradoxa Gmelin

Biremis circumtexta (Meister ex Hustedt) Lange-Bertalot et 

Witkowski

-22*

-*

-*

-*

Brachysira brebissonii Ross

Brachysira microcephala (Grunow) Compère

Caloneis amphisbaena (Bory) Cleve

Caloneis bacillum (Grunow) Cleve

Caloneis hyalina Hustedt

Caloneis silicula (Ehrenberg) Cleve

Chamaepinnularia mediocris (Krasske) Lange-Bertalot

Cocconeis fluviatilis Wallace

+34

-*

-39*

+*

-30

+18

+31

+28*

+11*

+9*

-*

+6

+12

Cocconeis neodiminuta Krammer -14*

Cocconeis neothumensis Krammer +30 +8

Cocconeis pediculus Ehrenberg

Cocconeis placentula Ehrenberg

Cocconeis placentula var. pseudolineata Geitler

Craticula molestiformis (Hustedt) Lange-Bertalot

Ctenophora pulchella (Ralfs ex Kützing) Williams et Round

Ctenophora pulchella var. lacerata (Hustedt) Bukhtiyarova

Cyclostephanos dubius (Frick) Round

Cyclotella atomus Hustedt

+42

+69

+*

+35

-*

-10

-11

-17*

-24*

-50

-13*

-*

-*

-7*

-*
33

Appendix 2. Algal taxa associated with reference (+) or disturbed (-) sites. Taxa were included 

in this list as a result of two analyses. First, indicator species analysis (Dufrêne and Legendre, 

1997) determined species indicator values in groups of reference or disturbed sites. Taxa with 

indicator values greater than 5 (P<0.1) are listed here and their indicator values are shown. 

Second, the canonical correspondence analysis of species relative abundance data and a single 

constraining variable “reference” or “disturbed” site was carried out. The taxa with extreme (upper 

or lower quartile) scores along the first Canonical Correspondence Analyses axis were included in 

the list and marked by asterisks (*). All analyses were carried out using the five regional datasets 

and the national dataset. WM, Western Mountains; CWP, Central and Western Plains; GN, 

Glaciated North; EH, Eastern Highlands; SP, Southeastern Plains; NAT, national dataset.—

Continued.

Species or morphological category WM CWP GN EH SP NAT

Diatoms, followed by source(s)—continued

Cyclotella meneghiniana Kützing

Cyclotella ocellata Pantocsek

Cyclotella pseudostelligera Hustedt

Cyclotella stelligera (Cleve et Grunow) Van Heurck

Cymatopleura solea (Brébisson) Smith

Cymbella affinis Kützing

Cymbella cistula (Ehrenberg) Kirchner

Cymbella cymbiformis Agardh

Cymbella delicatula Kützing

Cymbella hustedtii Krasske

Cymbella mesiana Cholnoky

Cymbella naviculiformis Auerswald ex Héribaud

Cymbella subcuspidata Krammer

Cymbella subturgidula Krammer

Cymbella tumida (Brébisson ex Kützing) Van Heurck

Cymbellonitzschia diluviana Hustedt

Denticula elegans Kützing

Diadesmis confervacea Kützing

Diadesmis contenta (Grunow ex Van Heurck) Mann

Diatoma mesodon (Ehrenberg) Kützing

Diatoma vulgaris Bory

Encyonema auerswaldii Rabenhorst

Encyonema lunatum (Smith) Van Heurck

Encyonema minutum (Hilse) Mann

Encyonema muelleri (Hustedt) Mann

Encyonema prostratum (Berkeley) Kützing

Encyonema silesiacum (Bleisch) Mann

Encyonopsis cesatii (Rabhenhorst) Krammer

Encyonopsis microcephala (Grunow) Krammer

Epithemia reichelti var. 1 ANS OZRK 

Epithemia sorex Kützing

Epithemia turgida (Ehrenberg) Kützing

Eunotia bilunaris (Ehrenberg) Mills

Eunotia exigua (Brébisson ex Kützing) Rabenhorst

Eunotia flexuosa Brébisson ex Kützing

Eunotia formica Ehrenberg

-52*

-22*

+21*

-23

-20*

+13*

+13*

-35

-11*

+23

-10

+10*

+29

+23

+12*

+9

+17*

+11

-50

+54

+14*

+17*

+17*

+30

+*

-17

+71

+14*

+23

-18*

-12*

+51

+23*

+52*

+17*

+10*

+20

+25*

+*

+10*

-15*

+11

+19

+11*

-12*

+27

-15*

+29

+52

+34

+49

+26

+33

+30

-30

-*

+33

+*

+6

+16

+*

+7*

+*

-11*

+*

+7

+10

+46

+*

+16

+9

+7

+14

+9
34

Appendix 2. Algal taxa associated with reference (+) or disturbed (-) sites. Taxa were included 

in this list as a result of two analyses. First, indicator species analysis (Dufrêne and Legendre, 

1997) determined species indicator values in groups of reference or disturbed sites. Taxa with 

indicator values greater than 5 (P<0.1) are listed here and their indicator values are shown. 

Second, the canonical correspondence analysis of species relative abundance data and a single 

constraining variable “reference” or “disturbed” site was carried out. The taxa with extreme (upper 

or lower quartile) scores along the first Canonical Correspondence Analyses axis were included in 

the list and marked by asterisks (*). All analyses were carried out using the five regional datasets 

and the national dataset. WM, Western Mountains; CWP, Central and Western Plains; GN, 

Glaciated North; EH, Eastern Highlands; SP, Southeastern Plains; NAT, national dataset.—

Continued.

Species or morphological category WM CWP GN EH SP NAT

Diatoms, followed by source(s)—continued

Eunotia incisa Smith ex Gregory

Eunotia minor (Kützing) Grunow

Eunotia monodon Ehrenberg

Eunotia muscicola var. tridentula Nörpel et Lange-Bertalot

Eunotia naegelii Migula

Eunotia pectinalis var. undulata (Ralfs) Rabenhorst

Eunotia praerupta Ehrenberg

Eunotia rhomboidea Hustedt

+*

+*

+29

+17

+13*

+11*

+33*

+33

+15*

+26

+*

+*

+9

+10

+*

+7

Eunotia sp. 9 NAWQA EAM 

Eunotia tenella (Grunow) Cleve

Fallacia monoculata (Hustedt) Mann

Fallacia omissa (Hustedt) Mann

Fallacia subhamulata (Grunow) Mann

Fallacia tenera (Hustedt) Mann

Fistulifera pelliculosa (Brébisson ex Kützing) Lange-Bertalot

Fragilaria aff. amphicephala ANS NAWQA EAM 

Fragilaria capucina var. gracilis (Østrup) Hustedt

Fragilaria capucina var. mesolepta Rabenhorst

Fragilaria crotonensis Kitton

Fragilaria nanana Lange-Bertalot

Fragilaria pinnata var. acuminata Mayer

Fragilaria pinnata var. lancettula (Schumann) Hustedt

Fragilaria vaucheriae (Kützing) Petersen

Frustulia amphipleuroides (Grunow) Cleve-Euler

Frustulia crassinervia (Brebisson) Lange-Bertalot et Krammer

Frustulia rhomboides (Ehrenberg) De Toni

Frustulia vulgaris (Thwaites) deToni

Frustulia weinholdii Hustedt

+13*

+13*

+13*

-

+ 

+*

+32

-9 

-* 

-*

-15

-11*

+19*

+40

-12*

+10*

+10 *

-15*

+20*

-10*

+11

+20

-9*

+25

+20

+40

+41

+16

+5*

+*

-*

+10

-8*

+7

+*

+30

+9

+13

+12

Geissleria acceptata (Hustedt) Lange-Bertalot et Metzeltin

Gomphoneis eriense (Grunow) Skvortzow et Meyer

Gomphoneis herculeana (Ehrenberg) Cleve

Gomphonema acuminatum Ehrenberg

Gomphonema angustatum (Kützing) Rabenhorst

Gomphonema angustatum var. intermedia Grunow

Gomphonema apuncto Wallace

Gomphonema gracile Ehrenberg emend Van Heurck

-16*

+23

+17*

+12*

+12* +12*

+30

+*

+*

+*

+21*

+12

+43

+7

+29

+7
35

Appendix 2. Algal taxa associated with reference (+) or disturbed (-) sites. Taxa were included 

in this list as a result of two analyses. First, indicator species analysis (Dufrêne and Legendre, 

1997) determined species indicator values in groups of reference or disturbed sites. Taxa with 

indicator values greater than 5 (P<0.1) are listed here and their indicator values are shown. 

Second, the canonical correspondence analysis of species relative abundance data and a single 

constraining variable “reference” or “disturbed” site was carried out. The taxa with extreme (upper 

or lower quartile) scores along the first Canonical Correspondence Analyses axis were included in 

the list and marked by asterisks (*). All analyses were carried out using the five regional datasets 

and the national dataset. WM, Western Mountains; CWP, Central and Western Plains; GN, 

Glaciated North; EH, Eastern Highlands; SP, Southeastern Plains; NAT, national dataset.—

Continued.

Species or morphological category WM CWP GN EH SP NAT

Diatoms, followed by source(s)—continued

Gomphonema insigne Gregory

Gomphonema intricatum Kützing

Gomphonema kobayasii Kociolek et Kingston

Gomphonema lagenula Kützing

Gomphonema mehleri Camburn

Gomphonema micropus Kützing

Gomphonema minutum (Agardh) Agardh

Gomphonema olivaceoides var. hutchinsoniana Patrick

Gomphonema olivaceum (Lyngbye) Kützing

Gomphonema parvulum (Kützing) Kützing

Gomphonema patrickii Kociolek et Stoermer

Gomphonema pumilum (Grunow) Reichardt et Lange-Bertalot

Gomphonema rhombicum Fricke

Gomphonema sp. 32 NAWQA EAM 

Gomphonema sphaerophorum Ehrenberg

Gyrosigma acuminatum (Kützing) Rabenhorst

Gyrosigma attenuatum (Kützing) Rabenhorst

Gyrosigma nodiferum (Grunow) Reimer

Gyrosigma obtusatum (Sullivant et Wormley) Boyer

Hannaea arcus (Ehrenberg) Patrick

Hantzschia amphioxys (Ehrenberg) Grunow

Hippodonta capitata (Ehrenberg) Lange-Bertalot, Metzeltin et 

Witkowski

-16*

-42

+33

+42*

+17*

+37

-8

-12

+9

+14

+22

+16

-*

-33

-11*

-55

+21

+59

+45

-*

-46

+13

-60

+17*

-17*

-20*

-20*

-* 

-19*

-34

-13*

+12

+20*

-*

-22

+5

+*

+18

+5*

+7

-7*

-9*

+6

-19

Hippodonta lueneburgensis (Grunow) Lange-Bertalot, Metzeltin 

et Witkowski +11*

Karayevia clevei (Grunow) Bukhtiyarova

Karayevia laterostrata (Hantzsch) Bukhtiyarova

Kolbesia ploenensis (Hustedt) Kingston

Lemnicola hungarica (Grunow) Round et Basson

Luticola goeppertiana (Bleisch) Mann

Luticola mutica (Kützing) Mann

Mayamaea atomus (Kützing) Lange-Bertalot

Mayamaea atomus var. permitis (Hustedt) Lange-Bertalot

Melosira varians Agardh

Meridion circulare (Greville) Agardh

Navicula aff. subminuscula ANS NAWQA EAM Manguin

Navicula angusta Grunow

-21*

-55*

+17

+20*

-10

-10

+40

-11*

-17*

-27*

-* 

-34

-* 

-23*

-19*

-*

+16

+17

-*

-12*

-6*

-31

-6*
36

Appendix 2. Algal taxa associated with reference (+) or disturbed (-) sites. Taxa were included 

in this list as a result of two analyses. First, indicator species analysis (Dufrêne and Legendre, 

1997) determined species indicator values in groups of reference or disturbed sites. Taxa with 

indicator values greater than 5 (P<0.1) are listed here and their indicator values are shown. 

Second, the canonical correspondence analysis of species relative abundance data and a single 

constraining variable “reference” or “disturbed” site was carried out. The taxa with extreme (upper 

or lower quartile) scores along the first Canonical Correspondence Analyses axis were included in 

the list and marked by asterisks (*). All analyses were carried out using the five regional datasets 

and the national dataset. WM, Western Mountains; CWP, Central and Western Plains; GN, 

Glaciated North; EH, Eastern Highlands; SP, Southeastern Plains; NAT, national dataset.—

Continued.

Species or morphological category WM CWP GN EH SP NAT

Navicula

Navicula

Diatoms, followed by source(s)—continued

antonii Lange-Bertalot

arvensis Hustedt

+8

+11

-18

Navicula canalis Patrick +8 -* 

Navicula

Navicula

capitatoradiata Germain

caterva Hohn et Hellermann -26*

+31 -48 -21

Navicula

Navicula

Navicula

Navicula

Bertalot

cf. acceptata CODY Hustedt

cf. kriegerii NAWQA KM Krasske

cryptocephala Kützing

cryptotenella Lange-Bertalot ex Krammer et Lange-

-52*

+10*

-15

-51 -49

+11*

+49

-30

-38

Navicula

Navicula

Navicula

erifuga Lange-Bertalot

exilis Kützing

festiva Krasske

-*

-21

-*

+9

-8

Navicula

Navicula

Navicula

germainii Wallace

gregaria Donkin

harderii Hustedt

-19*

-55* -36

-37

-66

-14*

-17

-42 -41*

-*

Navicula

Navicula

Navicula

Navicula

Navicula

ingenua Hustedt

lanceolata (Agardh) Ehrenberg

lateropunctata Wallace

longicephala Hustedt

menisculus Schumann

-16*

+17

+28

-*

-21

-19*

+25

+18

-7*

+7

+

Navicula

Navicula

menisculus var. upsaliensis (Grunow) Grunow

meniscus Schumann

-* 

-18 -*

Navicula minima Grunow -65 -70 -59 -50 -58*

Navicula notha Wallace +45 +14

Navicula

Navicula

Navicula

Navicula

Navicula

Navicula

perminuta Grunow

pseudoventralis Hustedt

recens Lange-Bertalot

reichardtiana Lange-Bertalot

rostellata Kützing

schroeteri var. escambia Patrick

-21*

-31*

-*

-30 -*

-* 

-18

-*

-23

-20*

-10

Navicula

Navicula

Navicula

sp. 47 NAWQA HAMSHER

subminuscula Manguin

submuralis Hustedt

-29*

-30*

-18*

-7*

-17

-6*

Navicula

Navicula

symmetrica Patrick

tantula Hustedt -19*

+19

-13

-23 -10

-9*

Navicula tenelloides Hustedt -22
37

Appendix 2. Algal taxa associated with reference (+) or disturbed (-) sites. Taxa were included 

in this list as a result of two analyses. First, indicator species analysis (Dufrêne and Legendre, 

1997) determined species indicator values in groups of reference or disturbed sites. Taxa with 

indicator values greater than 5 (P<0.1) are listed here and their indicator values are shown. 

Second, the canonical correspondence analysis of species relative abundance data and a single 

constraining variable “reference” or “disturbed” site was carried out. The taxa with extreme (upper 

or lower quartile) scores along the first Canonical Correspondence Analyses axis were included in 

the list and marked by asterisks (*). All analyses were carried out using the five regional datasets 

and the national dataset. WM, Western Mountains; CWP, Central and Western Plains; GN, 

Glaciated North; EH, Eastern Highlands; SP, Southeastern Plains; NAT, national dataset.—

Continued.

Species or morphological category WM CWP GN EH SP NAT

Diatoms, followed by source(s)—continued

Navicula tripunctata (Müller) Bory

Navicula trivialis Lange-Bertalot

Navicula veneta Kützing

Navicula viridula (Kützing) Kützing emend. Van Heurck

Navicula viridulacalcis (Hustedt) Lange-Bertalot

Navicula wallacei Reimer

+37

-20

-69

-38

-21

-56

-28*

-17* *- 

+17

+9*

-36

-19*

-8*

Nitzschia amphibia Grunow

Nitzschia archibaldii Lange-Bertalot

Nitzschia capitellata Hustedt

Nitzschia clausii Hantzsch

-19*

-7 

-52

+22

-8*

-64

+31

-17

-61

-19

-*

-58

+13

-55*

-5*

Nitzschia desertorum Hustedt -9 -*

Nitzschia dissipata (Kützing) Grunow

Nitzschia dissipata var. media (Hantzsch) Grunow

Nitzschia fonticola Grunow

+14

+30

-60

-*

-43 -31

+11*

-39

Nitzschia frustulum (Kützing) Grunow

Nitzschia gracilis Hantzsch ex Rabenhorst

Nitzschia heufleriana Grunow

-41

-11*

+35* +9

Nitzschia inconspicua Grunow

Nitzschia intermedia Hantzsch ex Cleve et Grunow

-64* -37

+23

-48 -49*

+14

-40*

Nitzschia linearis (Agardh ex Smith) Smith

Nitzschia linearis var. tenuis (Smith) Grunow ex Cleve et 

Grunow

-41* -15

-* 

-11

Nitzschia nana Grunow ex Van Heurck -*

Nitzschia palea (Kützing) Smith

Nitzschia palea var. debilis (Kützing) Grunow

Nitzschia palea var. tenuirostris Grunow

Nitzschia paleacea Grunow ex Van Heurck

Nitzschia pusilla Grunow

Nitzschia recta Hantzsch ex Rabenhorst

-35*

-33*

-28*

-38

-31

+12*

+16

-31

-*

-27

-36

+25

-35

-14

Nitzschia reversa Smith -7

Nitzschia sigmoidea (Nitzsch) Ehrenberg

Nitzschia sinuata var. delognei (Grunow) Lange-Bertalot

Nitzschia sinuata var. tabellaria (Grunow) Grunow

Nitzschia sociabilis Hustedt

+25

+12*

-20

-* 

-17

-9

+7

-*

-11

Nitzschia solita Hustedt -*

Nitzschia subtilis Grunow +25

Nupela carolina Potapova et Clason +10
38

Appendix 2. Algal taxa associated with reference (+) or disturbed (-) sites. Taxa were included 

in this list as a result of two analyses. First, indicator species analysis (Dufrêne and Legendre, 

1997) determined species indicator values in groups of reference or disturbed sites. Taxa with 

indicator values greater than 5 (P<0.1) are listed here and their indicator values are shown. 

Second, the canonical correspondence analysis of species relative abundance data and a single 

constraining variable “reference” or “disturbed” site was carried out. The taxa with extreme (upper 

or lower quartile) scores along the first Canonical Correspondence Analyses axis were included in 

the list and marked by asterisks (*). All analyses were carried out using the five regional datasets 

and the national dataset. WM, Western Mountains; CWP, Central and Western Plains; GN, 

Glaciated North; EH, Eastern Highlands; SP, Southeastern Plains; NAT, national dataset.—

Continued.

Species or morphological category WM CWP GN EH SP NAT

Diatoms, followed by source(s)—continued

Nupela sp. 1 ANS NEW JERSEY KCP 

Opephora cf. schwartzii NAWQA EAM (Grunow) Petit

Opephora martyi Héribaud

Pinnularia acidophila Hofmann et Krammer

Pinnularia divergens Smith

Pinnularia gibba Ehrenberg

Pinnularia interrupta Smith

Pinnularia mesogongyla Ehrenberg

Planothidium apiculatum (Patrick) Lange-Bertalot

Planothidium frequentissimum (Lange-Bertalot) Lange-Bertalot

Planothidium lanceolatum (Brébisson ex Kützing) LangeBertalot -48

+22*

+40

-38

+28

-39

-*

+15*

+19

+24

+14

+16

+28*

-33

-54*

-*

+11

+

+6

+*

+8*

-34

Planothidium peragalli Brun et Héribaud

Planothidium robustum (Hustedt) Lange-Bertalot

Pleurosira laevis (Ehrenberg) Compère

Psammothidium grischunum fo. daonensis (Lange-Bertalot) 

Bukhtiyarova et Round

Psammothidium helveticum (Hustedt) Bukhtiyarova et Round

Pseudostaurosira brevistriata (Grunow) Williams et Round

Reimeria sinuata (Gregory) Kociolek et Stoermer

Reimeria sinuata fo. antiqua Kociolek et Stoermer

Rhoicosphenia abbreviata (Agardh) Lange-Bertalot

Rhopalodia gibba (Ehrenberg) Müller

Sellaphora laevissima (Kützing) Mann

Sellaphora pupula (Kützing) Meresckowsky

Sellaphora seminulum (Grunow) Mann

Sellaphora sp. 6 NAWQA EAM

Simonsenia delognei (Grunow) Lange-Bertalot

Stauroneis kriegeri Patrick

Stauroneis smithii Grunow

-15

+45

-55

-8

+12*

+19

+40

+10*

-52

+8

+25

-27

+19

-7

+*

-11

-86

-29

-36

-11*

-* 

-67

-35*

-29

+13*

+9*

-17

-30*

-50*

+20*

-32

-34

+9*

+18

+*

-62*

+*

-31

-9

Stauroneis thermicola (Petersen) Lund

Staurosira construens Ehrenberg

Staurosira construens var. binodis (Ehrenberg) Hamilton

Staurosira construens var. venter (Ehrenberg) Hamilton

Staurosirella leptostauron (Ehrenberg) Williams et Round

Staurosirella pinnata (Ehrenberg) Williams et Round

Stephanodiscus niagarae Ehrenberg

-22

+27

+15

+20

+39

+17*

+28

+37

+23

+53

-11

-22*

+13*

-12

-31

+*

+23

-*
39

Appendix 2. Algal taxa associated with reference (+) or disturbed (-) sites. Taxa were included 

in this list as a result of two analyses. First, indicator species analysis (Dufrêne and Legendre, 

1997) determined species indicator values in groups of reference or disturbed sites. Taxa with 

indicator values greater than 5 (P<0.1) are listed here and their indicator values are shown. 

Second, the canonical correspondence analysis of species relative abundance data and a single 

constraining variable “reference” or “disturbed” site was carried out. The taxa with extreme (upper 

or lower quartile) scores along the first Canonical Correspondence Analyses axis were included in 

the list and marked by asterisks (*). All analyses were carried out using the five regional datasets 

and the national dataset. WM, Western Mountains; CWP, Central and Western Plains; GN, 

Glaciated North; EH, Eastern Highlands; SP, Southeastern Plains; NAT, national dataset.—

Continued.

Species or morphological category WM CWP GN EH SP NAT

Diatoms, followed by source(s)—continued

Stephanodiscus tenuis Hustedt

Surirella angusta Kützing

Surirella biseriata Brébisson

-7

+10*

Surirella brebissonii Krammer et Lange-Bertalot

Surirella brebissonii var. kuetzingii Krammer et Lange-Bertalot

Surirella minuta Brébisson -11*

+15*

-*

-* 

-17*

-*

-6*

-12

Synedra acus Kützing

Synedra delicatissima Smith

Synedra ulna (Nitzsch) Ehrenberg

Tabellaria flocculosa (Roth) Kützing

Tabularia fasciculata (Agardh) Williams et Round

Tabularia tabulata (Agardh) Snoeijs

Thalassiosira weissflogii (Grunow) Fryxell et Hasle

Tryblionella apiculata Gregory

Tryblionella hungarica (Grunow) Mann

Green algae

Ankistrodesmus falcatus (Corda) Ralfs

Oedogonium sp. 

Protoderma viride Kützing

Scenedesmus acuminatus (Lagerheim) Chodat

Scenedesmus acutus Meyen

Scenedesmus ecornis (Ralfs) Chodat

Scenedesmus quadricauda (Turpin) Brébisson

Scenedesmus spinosus Chodat

Unknown Chlorophyte flagellate 

+39

+17*

-10*

-24*

-10*

-10

+17

+12*

-22*

-9*

+12*

+56

+*

-* 

-*

-16

-15

-* 

+*

-*

-19*

-11

-12

+22

+11

+40

-10*

-13

+8

+40

+15

-12

-*

-13

Cyanobacteria

Anabaena sp. 

Blennothrix brebissonii (Kützing ex Gomont) Anagnostidis et 

Komárek

+21*

-20* +31* +17* -35

Calothrix fusca (Kützing) Bornet et Flahault

Calothrix parietina (Nägeli) Thuret

Calothrix sp.

Chroococcus limneticus Lemmermann

-26*

+21*

+20*

-* 

+26*

+43 -19

+*

+10

Heteroleibleinia sp. 

Homoeothrix janthina (Bornet et Flahault) Starmach

Jaaginema pseudogeminatum (Schmidle) Anagnostidis et 

Komárek

-31*

-23*

+30

-17

-24

-

-
40

Appendix 2. Algal taxa associated with reference (+) or disturbed (-) sites. Taxa were included 

in this list as a result of two analyses. First, indicator species analysis (Dufrêne and Legendre, 

1997) determined species indicator values in groups of reference or disturbed sites. Taxa with 

indicator values greater than 5 (P<0.1) are listed here and their indicator values are shown. 

Second, the canonical correspondence analysis of species relative abundance data and a single 

constraining variable “reference” or “disturbed” site was carried out. The taxa with extreme (upper 

or lower quartile) scores along the first Canonical Correspondence Analyses axis were included in 

the list and marked by asterisks (*). All analyses were carried out using the five regional datasets 

and the national dataset. WM, Western Mountains; CWP, Central and Western Plains; GN, 

Glaciated North; EH, Eastern Highlands; SP, Southeastern Plains; NAT, national dataset.—

Continued.

Species or morphological category WM CWP GN EH SP NAT

Cyanobacteria)—continued

Leptolyngbya sp.

Leptolyngbya tenuis (Gomont) Anagnostidis et Komárek

Lyngbya sp. 

Oscillatoria angustissima West et West

Oscillatoria fremyii De Toni

Oscillatoria sp. 1? 

Phormidium granulatum 

Phormidium laetevirens 

-10*

+29*

+*

-*

-8*

-* 

-11*

-11*

+29

+11

+8

-*

-

Planktolyngbya subtilis (Lemmermann) Anagnostidis et 

Komárek -17*

-*

Schizothrix friesii (Agardh) Gomont

Unknown Cyanophyte coccoid (1-3μ) 

Unknown Cyanophyte coccoid (3-5μ) 

Unknown Cyanophyte Oscillatoriales without sheath

Unknown Cyanophyte Oscillatoriales with sheath

Red algae

Unknown Rhodophyte Florideophycidae (chantransia)

Unknown alga

+25*

+63*

+33*

+15*

+15

+34 +30

+28

+23

-11

+32

+38

-34

+14

+29

+20