PRIMARY RESEARCH PAPER

Assessing the condition of the Missouri, Ohio, and UpperMississippi rivers (USA) using diatom-based indicators

Amy R. Kireta • Euan D. Reavie •

Gerald V. Sgro • Ted R. Angradi •

David W. Bolgrien • Terri M. Jicha • Brian H. Hill

Received: 8 November 2011 / Revised: 6 February 2012 / Accepted: 6 March 2012 / Published online: 23 March 2012

� Springer Science+Business Media B.V. 2012

Abstract Diatom-based indicators were developed to

assess environmental conditions in the Missouri, Ohio,

and Upper Mississippi rivers. Disturbance gradients,

comprising the first two principal components derived

from a suite of stressor variables, included a trophic

gradient (Trophic) and a gradient reflecting agriculture

and other development activities (Ag/Dev). Diatom-

based indicators were developed by creating models

using weighted average calibration and regression-

based transfer functions to relate planktonic and

periphytic diatom species assemblages to each distur-

bance gradient. The most predictive disturbance models

combined phytoplankton and periphyton assemblages

into a single bioindicator model (observed versus

inferred: Trophic r2boot ¼ 0:56; Ag/Dev r2

boot ¼ 0:70).

The geographic applicability of bioindicators was

assessed by limiting sample geographical range during

model calibrations. Geographic scale was limited by

creating bioindicators using samples from: (a) each

river, and (b) combined Mississippi/Missouri samples

excluding Ohio River sites which were chemically

unique. Indicator performance decreased with geo-

graphically restrictive models, therefore river basin-

wide models, developed across all three rivers, is

recommended. The most effective diatom-based dis-

turbance bioindicators for this great river ecosystem

could be applied using phytoplankton, periphyton, or

combined assemblages to infer both trophic and

agriculture/development disturbances.

Keywords Diatoms � Great rivers � Monitoring �Transfer functions

Introduction

The use of diatoms as river condition monitors is well

documented (Round, 1991; Rott, 1991; Whitton &

Kelly, 1995; Stevenson et al., 2010). River water

quality conditions have been assessed using diatom

metrics in Austria (Rott et al., 2003), Japan (Watanabe

Electronic supplementary material The online version ofthis article (doi:10.1007/s10750-012-1067-3) containssupplementary material, which is available to authorized users.

Handling editor: Jasmine Saros

A. R. Kireta (&) � E. D. Reavie

Center for Water and the Environment, Natural Resources

Research Institute, University of Minnesota Duluth, 1900

East Camp Street, Ely, MN 55731, USA

e-mail: akireta@d.umn.edu

G. V. Sgro

Department of Biology, John Carroll University, 20700

North Park Boulevard, University Heights, OH 44118,

USA

T. R. Angradi � D. W. Bolgrien � T. M. Jicha � B. H. Hill

Office of Research and Development, National Health and

Environmental Effects Research Laboratory, Mid-

Continent Ecology Division, US Environmental

Protection Agency, 6201 Congdon Boulevard, Duluth,

MN 55804, USA

123

Hydrobiologia (2012) 691:171–188

DOI 10.1007/s10750-012-1067-3

et al., 1988), France (Prygiel & Coste, 1993), Spain

(Delgado et al., 2010), England & Scotland (Kelly &

Whitton, 1995), and Australia (Dela-Cruz et al., 2006).

Researchers have long used diatoms to monitor

pollution of lotic systems in the United States as well

(Patrick, 1949; Williams, 1964), with recent regional

assessments of western (Stevenson et al., 2008) and

mid-Appalachian streams (Hill et al., 2000), and

eastern (Charles et al., 2006) and continental-scale

rivers (Potapova & Charles, 2003, 2007).

However, the characterization of large river condi-

tions is challenging. United States great rivers (defined

by Angradi et al., 2009b as having a mean discharge

C3,000 m3 s-1 or a basin area C1,000,000 km2) have

been modified for transport, irrigation, flood control

and hydropower; a set of extraneous pressures not

often experienced by wadable rivers and streams.

Large rivers have a scarcity of reference conditions

(Seegert, 2000; Graf, 2001; Smith et al., 2003) and are

challenging to properly sample (Johnson et al., 1995;

Seegert, 2000). The span of large rivers and the

uncertainty of administrative responsibility makes

local monitoring logistically complicated which may

also explain why states and tribes generally expend

more effort on monitoring streams and smaller rivers

(McDonald et al., 2004). A comprehensive study of

river restoration research by Bernhardt et al. (2005)

found that under 10% of 37,000 studies focused on

monitoring with only a small percentage of those

publically sharing monitoring results.

Opinion is mixed as to whether phytoplankton

(Vannote et al., 1980) or periphyton (Round, 1991;

Kelly et al., 1998) are better suited to for ecological

assessments in rivers. Recent studies have investigated

the effectiveness of phytoplankton versus phytoplank-

ton indicators using the entire algal assemblage

(Reavie et al., 2010) and have suggested future testing

of both groups during diatom-based indicator devel-

opment (Potapova & Charles, 2007). Preliminary

analyses of great river phytoplankton and periphyton

diatoms suggested unique responsiveness of each

assemblage to disturbance measures (Kireta et al.,

2011). In addition to determining whether an assem-

blage in a moving water body could be related to

landscape influences, testing relationships of both

phytoplankton and periphyton assemblages with

watershed stressors allows investigation of whether

diatoms collected from discrete habitats differentially

integrate stressor information. For example, attached

algae (periphyton) would be expected to provide a

more localized assessment than phytoplankton, which

flows downriver and may reflect geographically

broader scale conditions (Stevenson et al., 2010).

Furthermore, investigation of both groups tests which

diatom habitat should be preferentially sampled in a

system.

Previous ecological assessments have refined dia-

tom indicators by testing the importance of geographic

variation to indicator performance. Geographical

variability has been found in diatom species dispersal

(Stevenson, 1997; Biggs, 1996) and species response

to environmental conditions, both of which could

decrease indicator potential of broad-based metrics

(Kelly et al., 1998; Soininen & Niemela, 2002;

Charles et al., 2006). Charles et al. (2006) showed

the importance of accounting for natural variation in

geography, chemistry, and physical characteristics

when applying diatom indicators. Potapova & Charles

(2007) suggested tailoring indicators to the regions to

be assessed. However, other large scale studies with

wide geographic variability have found system-wide

indicators most appropriate (Kireta et al., 2007;

Stevenson et al., 2008).

This study evaluates diatoms as bioindicators of

human disturbance as part of a broader Environmental

Protection Agency program which focused on the

large river system of the Missouri, Ohio, and Upper

Mississippi rivers; the Environmental Monitoring and

Assessment Program for Great River Ecosystems

(EMAP-GRE). The three rivers comprise one of the

largest freshwater drainage basins in the world, with

the Ohio and Missouri joining the Mississippi River to

form a combined watershed draining *40% of the

continental United States. EMAP-GRE objectives

were to provide spatially unbiased estimates of mid-

continent great river conditions (the physical, chem-

ical, and biological properties), assess the current

condition of selected great river resources (e.g., water

quality, fish) and evaluate environmental indicators

(e.g., algae and other organism-based indices). An-

gradi et al. (2008) provided the first step in accom-

plishing this by defining a gradient of condition from

reference to highly disturbed using surrogate stressor

variables such as proximity and intensity of point

source polluters, agriculture, and flood plain develop-

ment. This report describes the first comprehensive

diatom-based disturbance indicators developed for

this entire river system. The newly developed

172 Hydrobiologia (2012) 691:171–188

123

indicator is designed to monitor a suite of human

impacts and is, to our knowledge, the first attempt to

calibrate landscape disturbance variables during dia-

tom inference model development. The effectiveness

of phytoplankton, periphyton, and combined diatom

assemblages was tested during integrated disturbance

indicator development because initial testing showed

both diatom habitats showed potential as great river

bioindicators (Kireta et al., 2011). Diatom indicators

were further evaluated to determine the appropriate-

ness of limiting indicator calibrations to geographi-

cally smaller regions (e.g., for a specific river). Our

goal was to develop diatom indicators to assess human

disturbance in the great rivers and in doing so create a

monitoring tool or tools that could be used to assess

and manage conditions throughout the ecosystem.

Methods

Field sampling

Samples were collected during the summers of 2004

and 2005 from the Missouri (from Fort Peck Dam in

Montana to the confluence with the Mississippi River

near St. Louis, Missouri, excluding the six main stem

reservoirs in North and South Dakota, *2,900 km),

Ohio (from the confluence of the Allegheny and

Monongahela rivers at Pittsburgh Pennsylvania to the

confluence with the Mississippi at Cairo, Illinois,

*1,560 km), and Upper Mississippi (from Minneap-

olis-St. Paul, Minnesota to the confluence with the

Ohio River) rivers. The survey design included a GIS-

based sample frame (using river center lines from the

National Hydrography Dataset (NHD) http://

nhd.usgs.gov/index.html) and random site selection

using probability survey, applying spatial balance for

site dispersal and representativeness (Stevenson,

1997; McDonald et al., 2004; Schweiger et al., 2004).

State-scale (Upper Mississippi and Missouri rivers)

and reach scale-assessments (Ohio River) were

explicitly included (Angradi, 2006; Angradi et al.,

2008). A detailed explanation of the EMAP-GRE

study area can be found in Angradi et al. (2009a). A

site in this study refers to the discrete location where

periphyton and/or phytoplankton were sampled.

Composite phytoplankton samples were collected by

diaphragm pump from three depth-integrated, cross-

channel transect locations, while composite

periphyton samples were brushed (rock/wood) or

scooped (sand/silt) from 11 adjacent littoral locations.

Detailed diatom sample collection is described by

Reavie et al. (2010) and Kireta et al. (2011). One

hundred seventy-four phytoplankton (PH) and 184

periphyton (PE) samples were considered in analyses.

A total of 224 sites were sampled with phytoplankton

and periphyton samples both analyzed at 134 of those

sites which are hereafter referred to as overlapping

sites. Samples were collected by river for each diatom

assemblage as follows: Missouri PH = 72, PE = 87;

Ohio PH = 19, PE = 24; Mississippi PH = 83,

PE = 73.

Water chemistry measures (Table 1) were compos-

ited using the same method and locations as the

phytoplankton samples. The complete set of variables

collected for this study can be found in Online

Resource 1 and Kireta et al. (2011), with detailed

methods in Angradi (2006) and Reavie et al. (2010).

Integrated landscape variables were constructed from a

geographical information system-based (GIS) dataset

incorporating a diverse suite of potential river stressors

from human activities adjacent to each sample location

(Table 1, see Angradi, 2006 for details).

Diatom preparation

Organic material was digested from diatom samples

using 30% hydrogen peroxide in a heated water bath.

Subsamples of concentrated diatom valves were dried

on coverslips and fixed on slides using highly refrac-

tive mountants (refractive index: Hyrax *1.65?, or

Pleurax *1.7?). Details of diatom preparation can be

found in Kireta et al. (2011). Three hundred valves per

sample, a number used in other large scale river

studies (Kelly et al., 2008a, b) were counted along

transects using one slide per sample. Valve counts of

at least 100, a number which has been shown to reveal

diatom community patterns (Bate and Newall, 2002),

were used from samples with very sparse diatom

assemblages (e.g., Ohio River phytoplankton). Diatom

valves were identified to the lowest practical resolu-

tion, usually species, using light microscopy and oil-

immersion lenses at 1,0009 or higher magnification.

The following references were used to identify

diatoms: Patrick & Reimer (1966a, b), Camburn

et al. (1984–1986), Krammer & Lange-Bertalot

(1986–1991), Cumming et al. (1995), Reavie & Smol

(1998), Reichardt (1999), Stoermer et al. (1999),

Hydrobiologia (2012) 691:171–188 173

123

Camburn & Charles (2000), and Mann et al. (2004).

Consistency between taxonomists at the University of

Minnesota Duluth and John Carroll University labs

was maintained through workshops. Indistinct speci-

mens were grouped into genera categories while

specimens difficult to distinguish from other species

were grouped in combined species categories. Taxa

counts for each sample were converted to percent

relative abundance for indicator development based

on preliminary investigations and recommendations

(Kireta et al., 2011). The taxonomic dataset was

refined to eliminate rare diatoms for a total of 392 taxa

(Online Resource 2) of the 1,539 identified. Non-rare

taxa occurred in at least one sample at greater than five

percent relative abundance or in at least five samples

with at least one sample having greater than one

percent relative abundance.

Outlier removal

Even though the EMAP study design used pseudo-

random stratified sampling to capture environmental

gradients in the rivers (Angradi et al., 2009b) it is

normal for training sets such as this to have outlying

sites based on their diatom assemblages and/or

transient or unique local water quality measures

(e.g., Birks et al., 1990; Ponader et al., 2007). Birks

et al. (1990) explained outlier ‘‘rogue’’ samples to be

atypical observations sometimes found in large heter-

ogeneous datasets, perhaps due to unusual assem-

blages or poor relationships with included

environmental variables. Such outliers can skew and

weaken overall relationships of diatoms with environ-

mental variables and are often excluded from final

analyses in ecological studies (e.g., Hall & Smol,

1992; Winter & Duthie, 2000; Ponader et al., 2007).

Outlier sites for this study were those with extreme

values of species assemblages and/or water chemistry

measures (falling outside 95% confidence intervals) as

determined using sample scores from detrended

correspondence analysis (DCA) and principal compo-

nents analysis (PCA), respectively, using CANOCO

4.5 software (ter Braak & Smilauer, 2002; see Kireta

et al., 2011 for complete details). Sample sites

identified as either phytoplankton or periphyton out-

liers were eliminated from both diatom datasets.

Table 1 Water chemistry and landscape disturbance measures, codes, and transformations applied to approximate normality

Code Measure Transformation

Water chemistry

Al Aluminum (ppb) Log(X ? 1)

ANC Acid neutralizing capacity (mg/l) No transformation

COND Average conductivity by site (lS/cm) Log(X ? 1)

DO_BOT Average bottom dissolved oxygen reading across site (mg/L) Log(X ? 1)

TN Total nitrogen (ppb) Square root (X ? 1)

ORTHOP Orthophosphate (ppb) Square root (X ? 1)

Si Silica (ppm) No transformation

TOC Total organic carbon (ppm) Log(X ? 1)

TSS Total suspended solids (mg/l) Log(X ? 1)

Landscape disturbance

10nAGALLa Percent agriculture local (10 km network; NLCD 2001 classes 81–82) Arcsine

50nAGALL Percent agriculture (50 km network; NLCD 2001 classes 81–82) No transformation

10nDEVALLa Percent developed local (10 km network; NLCD 2001 classes 21–24) Log

50nDEVALL Percent developed (50 km network; NLCD 2001 classes 21–24) Log

RipMajDisa Major pollution dischargers in riparian zone (count) Square root

10nMajDis Major pollution dischargers in 10 km network (count) 4th Root

50nMajDisa Major pollution dischargers in 50 km network (count) Log

HIDWAll Human disturbance (index from 0–5.1) Log

UPURBDIST Distance to nearest upriver urban area (km) Square root

a Landscape disturbance variables were included only in landscape CCAs to investigate spatial scale relationships of watershed

variables to diatom assemblages; all other variables were included in the overall disturbance gradient

174 Hydrobiologia (2012) 691:171–188

123

Thirteen sites were considered outliers, seven of which

were from overlapping sites (PH = 9, PE = 5, one

site identified by both datasets). Outliers were

included in creation of the disturbance gradients to

capture the full range of the measured environmental

variables, as this analysis alone did not involve

diatoms. Subsequent analyses relating diatoms to

environmental variables (i.e., CCA and transfer func-

tions) removed outlying samples since they could

skew ordinations and calibration models, thus provid-

ing a sub-par assessment of an ecosystem. Upon

removal of outliers, there was a total of 211 unique

sampling sites, of which 127 were overlapping sites

that included both phytoplankton and periphyton

samples.

Selection of water chemistry and landscape

stressor data

Environmental variables were transformed as neces-

sary to approximate normality (Table 1). The initial

73 water quality and landscape stressor variables (61

water chemistry and 12 watershed variables; for

complete list see Kireta et al., 2011 or Online Resource

1) were reduced to lessen redundancy and facilitate

interpretations of relationships with species data.

Variables were selected using an average linkage

hierarchical cluster analysis based on a Euclidean

distance matrix using the R software package (R

Development Core Team, 2009). One variable was

chosen from each similarity cluster. Variables con-

sidered to be highly correlated (r [ 0.6) were elimi-

nated and we retained variables that were likely

related to diatom variation using a priori knowledge of

stressor variables of interest and/or variables that are

known to be determinants of diatom distributions.

Certain landscape stressor variables were collected

at various spatial scales which integrated stressor

information within varying distances from each sam-

ple location: percent agriculture at 10 and 50 km

network scales; percent development at 10 and 50 km

network scales; and origins of point source pollution in

the riparian zone and at 10 km, and 50 km network

scales. These landscape variables have been used in

other studies to test relationships of watershed distur-

bances with water quality and biology in the great

rivers (Angradi et al., 2009b; Reavie et al., 2010;

Kireta et al., 2011). Landscape disturbance measures

were initially kept at various scales to test differential

influences on phytoplankton and periphyton assem-

blages, but were further reduced to eliminate redun-

dancies in refined analyses. Watershed disturbance

measures were reduced by selecting agriculture and

development stressor data at the 50 km scale (50nA-

GALL and 50nDEVALL, respectively) which pre-

liminary analyses showed contributed more to derived

environmental gradients than other scales, and by

selecting point source polluter data at the 10 km scale

(10nMajDis), which was less correlated with other

variables.

Relationships between selected environmental

variables and species assemblages were determined

by performing a constrained canonical correspon-

dence analysis (CCA) for each of the selected

variables using R software with the contributed vegan

library (Oksanen et al., 2009; R Development Core

Team, 2009). An analysis of variance (ANOVA) was

performed on each CCA to ensure each chosen

environmental measure was significantly related to

diatom species distribution (P = 0.05). Comparisons

of constrained with unconstrained eigenvalues were

used as another way to determine the explanatory

power of the chosen environmental variables on the

diatom assemblage (Birks, 2010), although it is often

applied to one environmental variable at a time

(Ponader et al., 2008). Ultimately, a total of 18

environmental measures were selected for analyses, 9

each for water chemistry and landscape disturbance

variables (Table 1).

Diatom relationships with water chemistry

and landscape stressors

CCA, applied using the R software package with the

vegan library, was used to determine how species

assemblages responded to the selected water chemis-

try and landscape stressors (Oksanen et al., 2009; R

Development Core Team, 2009). We aimed to take

advantage of diatom life strategies (i.e., periphyton

and phytoplankton) to investigate the specialization of

stressor integrations as well as spatial scale of the

diatom response variable with the environmental

condition. Analyses were performed separately for

phytoplankton and periphyton assemblages from

overlapping sites (n = 127) in order to directly

compare species relationships with: (a) water chem-

istry variables combined with the reduced set of

landscape variables and (b) landscape disturbance

Hydrobiologia (2012) 691:171–188 175

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variables alone including the broader watershed

dataset with measures at various scales (Table 1).

Aluminum measures were excluded from analyses as

there was no variation in included samples.

Significances of CCA axes (P = 0.05) were deter-

mined using ANOVA-like permutation tests with 500

iterations (anova.cca function in the R package vegan;

Oksanen et al., 2009). Variance inflation factors

(VIFs) were examined to assess correlations among

variables, considering VIFs less than ten, generally

considered a rule of thumb although at times perhaps

overly conservative, unrelated enough for inclusion in

ordination (e.g., see O’Brien, 2007). The proportion of

variation in the species data explained by the envi-

ronmental variables (Var) was used as a quantitative

measure of diatom-environmental relationships for

periphyton and phytoplankton.

Disturbance gradients

Disturbance gradients were created using the selected

environmental data from all sampled sites (n = 224)

to capture the full range of environmental condition in

the great rivers. A total of 14 water quality and

landscape stressor variables were chosen (Table 1).

Integrated disturbance gradients (i.e., axes) were

derived from PCA using the software package R with

the vegan library (Oksanen et al., 2009; R Develop-

ment Core Team, 2009). PCA was performed on the

correlation matrix of environmental data, with scaling

focused on inter-variable correlations. The number of

PCA axes that uniquely explained interpretable var-

iation in the environmental data was determined using

the broken-stick method (Jackson, 1993). A scree plot

successively ranked the eigenvalues for each axis and

chosen axes (i.e., components) had eigenvalues

greater than those of a broken-stick distribution, which

randomly plots variance among components. Explan-

atory axes were considered integrated disturbance

gradients. Disturbance values were verified by spot

checking site placements on the PCA plot with

measured environmental data to ensure classifications

were accurate.

Bioindicator development

Bioindicators were developed to infer integrated

disturbance using diatom assemblages. C2 software

(Juggins, 2003) was used to develop disturbance

inference models (also called transfer functions) using

weighted averaging (WA) to calculate optima and

tolerances for each common diatom taxon to the

integrated disturbance gradients. Based on prelimin-

ary evaluations (Kireta et al., 2011), taxa with

effective occurrences of less than two (Hill, 1973)

were not considered in model development. Model

validation was performed using bootstrapping, an

internal cross-validation technique, with 1000 itera-

tions (Birks, 1995). Model strengths were determined

by comparing observed to diatom-inferred variables

with the bootstrap squared correlation coefficient

(r2boot), the root mean square error of prediction

(RMSEP) and bias of the observed-inferred relation-

ship. RMSEP and maximum bias data were divided by

the range of model input data to standardize compar-

isons between models developed using different

subsets of samples. Models with higher r2boot values

and lower RMSEP and bias values were considered

more robust (sensu Birks, 1998). Although there are no

clear threshold criteria for evaluating transfer function

performance, models with r2boot [ 0:5 were considered

predictive as is consistent with previous studies (e.g.,

Dixit & Smol, 1994; Denys, 2004; Kelly et al., 2008b).

Several parameters were evaluated during model

development to optimize indicator performance: dia-

tom assemblage habitat, modeling technique, species

data format, geographical scale, and independent

reconstructions. Each parameter is described below.

Diatom habitat

For simplicity, phytoplankton and periphyton assem-

blages will hereafter also be referred to by the

general habitat from which they were sampled.

Phytoplankton and periphyton were compared by

testing bioindicator models comprising species from

each assemblage and combined species from both

diatom habitats to determine the most appropriate

species group for indicator development. Relative

abundance data for each taxon were divided in half

and added together for the combined PH and PE

assemblage dataset to directly compare to datasets

from sites with only one sampled assemblage.

Diatom habitat models were created using samples

from overlapping sites (i.e., both periphyton and

phytoplankton were analyzed from those sites),

which enabled direct comparison of assemblage

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123

ability to infer the same suite of measured

disturbances.

Modeling technique

WA (Hall & Smol, 1992) and weighted average partial

least squares (WAPLS; ter Braak & Juggins, 1993)

modeling techniques were evaluated. WAPLS adjusts

the species optima developed using WA with addi-

tional calculations on errors and may offer superior

models for a given dataset (ter Braak & Juggins,

1993).

Species data format

Species transformations were tested to optimize

bioindicator predictability. Raw relative abundance

input data was tested as well as transformations giving

greater weight to rarer taxa and/or minimizing the

effects of abundant taxa (i.e., log10 and square root).

Geographical scale

We tested spatial scale of model development to

determine if broader or more regional datasets created

the most reliable great river diatom indicators. The

collected biological (diatom) and environmental

(water chemistry) data were used to delineate geo-

graphic spatial scale by examining sample dendro-

grams created using a group average hierarchical

algorithm to identify sites that differed from major

clusters (NCSS software; Hintze, 1998). Clusters and

prospective outliers were further examined by viewing

samples on maps and ordination plots created for

species (DCA), water quality (PCA), and combined

data (CCA) using CanoDraw with CANOCO 4.5

software (ter Braak & Smilauer, 2002, results not

shown). Various cluster analysis cutoffs were exam-

ined on plots to determine sensible geographic and

physicochemical clustering of samples. Analyses

included all sites for each habitat (PH = 174,

PE = 184) and the full suite of water chemistry

measures (PH = 60, P = 61). The original species

dataset was tested (PH = 231, PE = 247), as was a

smaller species dataset (PH = 95, PE = 126) which

attempted to simplify interpretations of diatom distri-

butions by further eliminating uncommon taxa occur-

ring at\5% relative abundance.

Independent reconstructions

Although bootstrapping provides an estimate of model

power and probable error, additional model tests were

performed to independently test disturbance predic-

tions from diatoms to determine the most appropriate

calibration set for application of new species data. Test

sample subsets representing 20% of sites (n = 26)

were selected randomly at set intervals along each

disturbance gradient and were omitted from bioindi-

cator model development. Training sets (101 remain-

ing samples) were used to calibrate diatom-based

disturbance models for each stressor gradient. Train-

ing sets were calibrated using species transformations

deemed most effective for respective datasets during

initial bioindicator model development (i.e., before

exclusion of test sites). Disturbance values were

independently reconstructed for test sets of diatom

samples using the associated bioindicator models. In

addition, the ability of the combined assemblage-

based training set to infer disturbance using diatoms

from individual diatom habitats was tested by recon-

structing disturbances for each phytoplankton and

periphyton assemblage test set using the combined

assemblage model. Simple linear regressions were

used to compare values of reconstructed disturbance

with measured disturbance from the test sample

locations.

Results

Diatom relationships to water chemistry

and landscape stressors

Several environmental variables had strong relation-

ships to patterns in the diatom assemblages (Fig. 1).

CCA eigenvalues (Fig. 1) were similarly high for each

diatom assemblage as they were in preliminary DCAs

(PH: axis 1 = 0.559, axis 2 = 0.541; PE: axis

1 = 0.559, axis 2 = 0.434, results not shown), indi-

cating chosen variables characterized a large portion

of the explainable variation in the diatom data. The

chosen variables have already been determined

important to diatom assemblage distributions, but it

is informative to examine the relationship of the

combination of variables used in the overall and

landscape based CCAs. Based on explained varia-

tion (Fig. 1) and eigenvalue ratios, phytoplankton

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assemblages were more strongly related to the chosen

water chemistry and landscape disturbance variables

than periphyton assemblages (ratios for combined

water quality and landscape: PH = 0.303,

PE = 0.202; ratios for landscape disturbance only:

PH = 0.145, PE = 0.109). Further, along with vari-

ance explained, the ratio of constrained to uncon-

strained CCA eigenvalues showed water chemistry

variables explaining more variation in the diatom

assemblages than landscape stressors. Selected vari-

ables in the ordination combining water chemistry and

landscape disturbance measures (Fig. 1a) were largely

unrelated, with VIFs of less than 8.5 for all variables

shown and less than 4 for most. Some environmental

variables were differentially important in explaining

species distributions for each habitat. For example,

total nitrogen contributed more to phytoplankton

gradients whereas total suspended solids contributed

more to periphyton explanatory gradients. There were

also variables, such as acid neutralizing capacity and

total organic carbon, which were similarly important

to both diatom habitats.

Landscape disturbance variables alone explained

55–59% the explainable variation in diatom assem-

blages than when combined with water chemistry

(Fig. 1b). Landscape disturbance variables shown in

ordinations were also largely unrelated, with VIFs less

than 4 for all variables. Agricultural disturbance

within 50 km was most explanatory to both diatom

assemblages, with periphyton also strongly related to

urban proximity. The diatom habitats were affected by

landscape stressors at different geographical scales in

some instances. For example, on the primary explan-

atory axis, point source pollution sources appeared

more important at a 50 km scale for phytoplankton

and a 10 km scale for periphyton.

-1.0 -0.5 0.0 0.5

-0.5

0.0

0.5

TSS

ORTHOP

COND

TOC

Si

ANC

50nAGALL

50nDEVALL

10nMajDis

TN

-1.0 -0.5 0.0 0.5

-0.5

0.0

0.5

TSS

ORTHOP

COND

TOC

Si

ANC

50nAGALL

50nDEVALL

UPURBDIST

TN

-0.4 0.0 0.4

-0.4

0.0

0.4

10nAGALL

50nAGALL

50nDEVALL

RipMajDis10nMajDisUPURBDIST

HIDWAll

0.0 0.4

-0.8

-0.4

0.0

10nAGALL

50nAGALL

50nDEVALL

RipMajDis

UPURBDIST

HIDWAll

10nMajDis

10nDEVALL50nMajDis

HIDWAllDO_BOT

HIDWAll

UPURBDIST

DO_BOT

10nMajDis

50nMajDis

10nDEVALL

0.672

0.47

1

Var= 0.232

0.422

0.27

3

0.24

0.16

2

0.309

0.27

1

Var= 0.127 Var= 0.099

Var= 0.168

PeriphytonPhytoplanktona

b

Fig. 1 Canonical correspondence analysis sample plots of

phytoplankton and periphyton combined water chemistry and

landscape disturbance (a) and for landscape disturbance

variables alone (b). The eigenvalues for each axis are listed in

the bottom left corners, while the proportions of explained

diatom variation captured by the environmental variables (Var)

are listed in the bottom right corners. Vector labels match those

in Table 1

178 Hydrobiologia (2012) 691:171–188

123

Disturbance gradients

The broken-stick method determined that the first two

axes were significant; therefore, two disturbance

gradients were derived from the PCA of environmen-

tal variables (Fig. 2). The two disturbance gradients

(principal components) characterized 23% and 22%

explained variance for combined water chemistry and

landscape disturbance for the first two axes, respec-

tively. The first disturbance gradient (PC1) was a

combination of nutrient variables (largest eigenvec-

tors were as follows: ORTHOP = 1.61, TN = 1.58,

Si = 1.37, TOC = 1.34, ANC = 0.94, and

TSS = 0.92) representing site eutrophication at lower

PC1 scores (Fig. 2a). The second disturbance gradient

(PC2) separated highly agricultural sites (largest

positive eigenvectors: COND = 1.57, 50nA-

GALL = 1.41, ANC = 1.40, UPURBDIST = 1.20,

and TSS = 1.14) which typically had higher conduc-

tivity and acid neutralizing capacity with highly

urbanized sites (largest negative eigenvectors:

10nMajDIS = 1.08, 50nDEVALL = 0.93, and

TOC = 0.53). For simplicity, these gradients are

hereinafter referred to as Trophic and Ag/Dev for

PC1 and PC2, respectively. The Trophic gradient was

subjectively named as such since it was largely defined

by water chemistry variables associated with eutro-

phication, while the Ag/Dev gradient clearly delin-

eated agricultural from urbanized sites.

A two-dimensional disturbance plot classified sites

using best professional judgment according to cate-

gories of human influences (Fig. 2b). This allowed a

functional grouping of samples based on water quality

and overall stressor relationships. Trophic gradient

categories of mesotrophic, eutrophic, and hypereu-

trophic, approximately grouped sites with total phos-

phorus values of\30 lg/l, 30–100 lg/l, and[100 lg/l,

respectively (Heiskary & Wilson, 2008). Levels of

disturbance on the Ag/Dev gradient approximately

grouped sites with the highest levels of agricultural,

urban development, and combined watershed

stressors.

Disturbance indicator development

For brevity, results from these model permutations

focus on model parameters with the best performance.

Selected best disturbance model configurations are

in Table 2. The broadest model including both

periphyton and phytoplankton assemblages (All) had

the highest predictability for both Trophic (r2boot ¼ 0:56)

and Ag/Dev disturbances (r2boot ¼ 0:70), with stron-

gest indicator predictions for the latter (Fig. 3).

Fig. 2 Principal components analysis plots illustrating the

derivation of disturbance gradients. The upper diagram shows

water chemistry and landscape variables used to create gradients

based on dominant environmental variables on those axes (a).

The first axis represents a trophic gradient, while the second axis

represents a gradient of agriculture/development impacts. The

lower left corner lists eigenvalues while the right corner

indicates the % explained variance for each axis in character-

izing the disturbance variables (Var). The lower diagram shows

PCA sample scores plotted relative to the disturbance axes (b).

The plot is subdivided based on characteristics of the sample

locations. Symbols represent sites from each river: diamondsMissouri, circles Mississippi, squares Ohio

Hydrobiologia (2012) 691:171–188 179

123

The bioindicator model developed from overlapping

sites using combined diatom habitats (All overlap)

was a stronger predictor of disturbance (Trophic:

r2boot ¼ 0:49; Ag/Dev: r2

boot ¼ 0:65) than either indi-

vidual diatom habitat. Periphyton species had weaker

Trophic inferences (r2boot ¼ 0:35), but performed well

for Ag/Dev disturbance inferences (r2boot ¼ 0:55).

Phytoplankton species models performed slightly

better than periphyton for both disturbances gradients

(Trophic: r2boot ¼ 0:40; Ag/Dev: r2

boot ¼ 0:57).

Investigations to geographically limit model cali-

bration regions revealed complicated species data

which did not allow for clearly defined groups of

samples. Thus water chemistry was used to categorize

samples to determine grouping for regional models. A

total of 48 sites with dissimilar water quality to the

majority were considered outliers in attempts to

delineate geographic range (PH = 27, PE = 31, ten

from overlapping sites; Fig. 4). Ohio River samples

were clearly distinct based on water quality, with all

separating in the cluster analysis from the larger group

comprising Mississippi and Missouri samples. How-

ever, twelve potential outlier sites were located in the

Missouri and Mississippi rivers and were decidedly

kept with the larger cluster (i.e., not included as

geographical outliers) as there was no clear way to

delineate further geographic separations. Therefore,

models were created for a) combined Missouri and

Mississippi samples, based on the unique water

chemistry properties in the Ohio River, and; b) each

river: the Ohio, Mississippi, and Missouri to test

further limitation of spatial scale on indicator devel-

opment (Table 2).

Model performances from individual rivers were

generally weaker than geographically larger models

(Fig. 5). However, Trophic model performance was

greatest using only Missouri samples (r2boot ¼ 0:65),

with samples from combined Missouri/Mississippi

sites also producing predictive eutrophication infer-

ences (r2boot ¼ 0:55). The Missouri/Mississippi model

for Ag/Dev also had strong performance

(r2boot ¼ 0:53), making it the only regionally limited

model able to infer both disturbance gradients.

Disturbance predictions for independent samples

(model verification)

All models were able to predict disturbance for

independent sets of diatom samples (Fig. 6). Recon-

structed values for Ag/Dev disturbances were more

Table 2 Descriptions and abbreviations of diatom-based disturbance models

Code Sample locales n Model configuration

(Trophic: Ag/Dev)

Species

transformation

Overall models

All All rivers 211 WAPLS C2: WAPLS C3 Log10

All overlap Sites from all rivers with

both PH and PE analyses

127 WAPLS C2: WA None: log10

PE All rivers 127 WA Square root

PH All rivers 127 WA Log10

Regional models

MO/MI Missouri and Mississippi 178 WAPLS C2: WA None: log10

MO Missouri 87 WA Square root: log10

MI Mississippi 91 WA Square root: log10

OH Ohio 33 WA None: square root

Model names, site location, and sample numbers are described in the first three columns, respectively. The top four (overall) models

were developed over the entire basin, while the bottom models were developed for limited geographical regions. The last two

columns indicate model information for each stressor gradient with a colon separating descriptions for each of the two disturbance

variables (eutrophication disturbance: agriculture/development). Colon absence indicates identical parameters were chosen for each

disturbance gradient. Model configuration indicates whether transfer functions were created using weighted averaging (WA) or

weighted averaging partial least squares techniques (WAPLS), for which the chosen component is listed (C2 = second component;

C3 = third component). The species transformation column lists whether log or square root transformations improved model

performance, with none indicating that raw relative abundance species data contributed to the strongest models

180 Hydrobiologia (2012) 691:171–188

123

accurate than those for Trophic disturbances, which

verified initial model results (Fig. 3). Both disturbance

gradients were reconstructed reliably from diatom

assemblages in independent test samples.

Periphyton-inferred Trophic disturbance was the

only instance in which test sample inferences were

predicted equally well using a training set derived

from a single diatom assemblage (PE only r2 = 0.47)

as those predicted using a training set derived from

combined assemblages (PE all r2 = 0.46, Fig. 6). In

most cases, test sample disturbances were more

realistically reconstructed using models derived using

combined diatom assemblages (all models). This was

true whether test sample disturbance reconstructions

were made applying combined (all), phytoplankton

(PH all), or periphyton (PE all) assemblages to training

sets derived from combined assemblages.

Test sets of Mississippi and Missouri sites were

evaluated using training sets developed across the

entire basin (n = 175) and excluding Ohio River

samples (n = 142) to investigate the applicability of

the Missouri/Mississippi model (Table 3). Distur-

bance reconstructions for the independent sites were

strongest when Ohio River samples were included in

model development (Fig. 7). This was true for both

disturbance gradients, with a marked improvement in

Ag/Dev inferences.

Diatom model application

We believe that presenting disturbance data in terms of

biological condition more effectively illustrates the

condition of an ecosystem than simply showing

landscape pressures or water quality measures. Dia-

tom-inferred disturbances across the great river eco-

system are illustrated in Fig. 8 using values from the

region-wide model created from combined phyto-

plankton and periphyton assemblages (All, Table 2).

As expected, eutrophication increased downriver in

the Missouri River, which was the river characterized

by the most agricultural disturbance and least urban

development, seen almost exclusively in major cities

in Missouri (Fig. 8, inset). The Mississippi River had

consistently high eutrophication and fluctuating land-

scape disturbance, with high development disturbance

at the upper sampling area near Minneapolis/St. Paul,

Minnesota which decreased downstream in places and

shifted to agricultural disturbance pressures in others.

Overall, the Mississippi River was in the mid-range of

agriculture and urbanization disturbance compared to

the other two rivers. The Ohio River was the most

developmentally disturbed river, most notably in the

upper Ohio Valley with areas of decreased develop-

mental disturbance downstream. The Ohio River was

the least nutrient enriched overall, with no discernable

downstream trend. These findings reflect what we

know about anthropogenic disturbance in these rivers,

but more importantly show that there are biological

responses to these measures. While there is some

pseudoreplication of samples in the development of

reconstructions in Fig. 8, it provides an example of

0

0.2

0.4

0.6

0.8r2

boot

0

0.5

1

RM

SE

P

0

0.25

0.5

All All overlap PE PH

% m

ax b

ias

Diatom-Inferred Model

Fig. 3 Diatom-based model performance statistics for eutro-

phication (black) and agriculture/development (white) distur-

bance gradients. Diatom-inferred models were created using

diatom data from both assemblages for all sites (All), and from

overlapping sites where both phytoplankton and periphyton

were collected using the following diatom datasets: periphyton

(PE), phytoplankton (PH), and combined assemblages (All

overlap). The All model included more sites than the other

models since it also included sites sampled in only one diatom

habitat. The All overlap model was created to directly compare

performance of species from individual diatom habitat models

with a combined assemblage model. Model strength is indicated

by r2boot and standardized error is represented by root mean

square error of prediction (RMSEP) and percent maximum bias

(% max bias) in model reconstructions

Hydrobiologia (2012) 691:171–188 181

123

how this model might be applied. Online Resource 2

lists the calibrated disturbance values, the expected

range of variation, and the weighted average

calculations for each diatom taxa, which could be

applied to a new set of species data to infer

disturbances.

Discussion

The purpose of developing a diatom inference model

that includes landscape disturbance is not necessarily

to predict stressors, which could perhaps more easily

be studied using GIS or other techniques, but to

provide evidence that the biological organisms living

in a water body are linked with the stressors occurring

Fig. 4 Map of great river

states and sampling

locations. Inset shows map

of the continental United

States outlining regional

great river sampling

locations. Black diamondsindicate sites considered

water chemistry outliers by

cluster analysis

0

0.2

0.4

0.6

0.8

r2bo

ot

0

0.4

0.8

1.2

RM

SE

P

0

0.2

0.4

0.6

All MO/MI MO MI OH

% m

ax b

ias

Diatom-Inferred Geographic Model

Fig. 5 Diatom-inferred model performance for eutrophication

(black) and agriculture/development (white) disturbance models

created from the overall model (All), combined Missouri and

Mississippi samples (MO/MI) and each individual river:

Missouri (MO), Mississippi (MI), and Ohio (OH). Model

strength is indicated by r2boot, while standardized error is

represented by root mean square error of prediction (RMSEP)

and percent maximum bias (% max bias) in model

reconstructions

0

0.2

0.4

0.6

0.8

PE only PE all PH only PH all all

r2

Independently Reconstructed Disturbance

Fig. 6 Comparisons of observed versus reconstructed distur-

bances on an independent set of samples (i.e., excluded from

model development). Simple linear regression was used to

derive squared correlation coefficients for integrated eutrophi-

cation (black) and agriculture/development (white) gradient

models. Reconstructed disturbance was derived from models

constructed and tested using phytoplankton and periphyton

assemblages (PH only, PE only) and from combined-assem-

blage calibration models made with periphyton (PE all),

phytoplankton (PH all), and the combined assemblage test set

(all)

182 Hydrobiologia (2012) 691:171–188

123

in the surrounding watershed. This is why we included

landscape stressor variables in our disturbance gradi-

ent although diatoms were more related to water

quality variables; to provide a broader context and

understanding of factors influencing river condition.

Unlike more traditional diatom inference models

directly linking water quality measures to changes in

assemblages, which would be expected, showing a

strong and replicable relationship with anthropogenic

activities allows further comprehension of the extent

that humans are altering the river environment,

including our biological response variables. We were

able to use these integrative diatom bioindicators to

investigate the relationships between stressors and

diatom habitat as well as the scale of geographic

influence.

Phytoplankton and periphyton are known to be

influenced by many environmental variables (Hodgkiss

& Law, 1985) and can integrate environmental condi-

tions at various spatial scales (Stevenson et al., 2010).

Great river periphyton and phytoplankton were differ-

entially related to human disturbances as was found

previously (Reavie et al., 2010; Kireta et al., 2011). For

example, phytoplankton assemblages were more related

to total nitrogen and point source polluters, while

periphyton were more related to suspended solids and

urban proximity. Diatom assemblages also varied in

terms of diatom/watershed stressor scale relationships.

Some associations were intuitive. For example, point

source polluters, quantified adjacent to each periphyton

sampling location, were related at a more local scale to

periphyton than to phytoplankton assemblages, which

are considered to integrate conditions at a larger spatial

scale (Stevenson et al., 2010). Other diatom/stressor

relationships, such as developmental disturbance relat-

ing to phytoplankton at a more local scale, were difficult

to explain. We did not propose conclusions for the

Table 3 Descriptions of models developed to independently reconstruct disturbance

Code Species, n Sites, n Reconstructed

test sites, nModel

configuration

Species

transformation

Species testing

All 324:317 101 26 WAPLS Log10

PE all 324:317 101 26 WAPLS Square root

PH all 324:317 101 26 WAPLS Log10

PE only 205:208 101 26 WA Square root

PH only 228:229 101 26 WA Log10

Geographical testing

Basin 362:358 175 36 WAPLS None: log10

MO/MI only 346:341 142 36 WA Log10

The top five categories represent models created to test species assemblage type. Models created using combined phytoplankton and

periphyton training sets (models labeled all) were used to reconstruct disturbance for test samples using the following diatom

assemblages: phytoplankton (PH all), periphyton (PE all), and combined assemblages (all). Individual assemblages of phytoplankton

(PH only) and periphyton (PE only) were also used to calibrate training sets and reconstruct test site disturbances. The final two

models tested regional model application by investigating reconstructed disturbance predictions for Missouri and Mississippi test

samples using training sets calibrated basin-wide (Basin) and calibrated with Missouri and Mississippi sites (MO/MI only). The

number of included variables (n) is denoted for diatom taxa, calibration sites, and independently reconstructed test sites. Transfer

function configurations (model configuration) were created using weighted averaging (WA) or weighted averaging partial least

squares (WAPLS) techniques. Chosen species transformations are listed in the last column. Values separated with a colon first show

models for Trophic followed by Ag/Dev disturbances

0

0.2

0.4

0.6

Trophic Ag/Dev

r2

Fig. 7 Linear regression comparisons of observed versus

independently reconstructed disturbances for Missouri and

Mississippi test sites using basin-wide training sets (white)

and training sets developed using only Missouri and Mississippi

samples (black) for each disturbance gradient

Hydrobiologia (2012) 691:171–188 183

123

complicated relationships between diatom assemblages

and specific stressors but used these investigations to

develop the most applicable integrated indicators.

Initial testing showed each diatom habitat assem-

blage separately predicted disturbance equally well for

agriculture and developmental disturbances, while

phytoplankton-based models appeared to have slightly

stronger eutrophication predictions. However, when

comparing reconstructed eutrophication for a subset of

samples, predictions made with the phytoplankton-

derived model were weaker than other independent

predictions. It may be that phytoplankton assemblages

had relatively weak relationships with nutrients. This

could be related to the potential drawbacks of using

one-time measures to describe water quality condition

which may have been unrepresentative of conditions

experienced by the plankton (Detenbeck et al., 1996;

Bradshaw et al., 2002).

Models developed from combined phytoplankton

and periphyton assemblages produced the most pre-

dictive indicators, even when applied to assemblages

from only one diatom habitat. These findings suggest

combined assemblage calibration sets are preferable in

this ecosystem, regardless of the assemblage that will

be selected for future reconstructions. Combined

assemblages were likely better able to assess distur-

bance due to the higher number of taxa included for

species-specific calibrations. This is consistent with

Potapova & Charles (2007) finding that included

entrained planktonic species may have supported

metric development of periphyton indicators by

increasing the overall number of indicator species.

The use of combined assemblage training sets appar-

ently overcame weak environmental relationships of a

single assemblage, allowing realistic disturbance

assessments of independent sites using phytoplankton,

periphyton, or combined assemblages.

Geographical differences were clearly apparent

within our sample region, supporting testing of

regional models. River sites tended to group together

along stressor gradients (Fig. 2b). Missouri samples

(diamonds) were largely defined by the Trophic

gradient, while Ohio sites (squares) followed the Ag/

Dev gradient. Upper Mississippi sites (circles) were

defined by both gradients having high agricultural and

developmental disturbance as well as eutrophication.

Nevertheless, a geographically broad spatial mod-

eling approach was most appropriate for great river

diatom indicators. The poorer performance for most

regional models, suggested limited geographical scale

Fig. 8 Eutrophication, agricultural, and watershed develop-

ment disturbances for each sampling site throughout the great

river region as inferred from diatom indicators. Symbols have

been slightly offset from geographical location to visualize each

disturbance category. Symbol sizes indicate increasing

disturbance condition for each disturbance category: darkcircles eutrophication, medium colored diamonds agriculture,

light inverted triangles development. Inset shows values for the

entire region with the black box indicating the zoomed in region

shown on the larger map

184 Hydrobiologia (2012) 691:171–188

123

was not useful in refining indicators. This is consistent

with Potapova & Charles’ (2007) finding that a higher

number of indicator taxa in a broadly developed model

improved inference predictability. Models developed

specifically for each river generally had poorer

predictability, likely due to poorer characterization

of the environmental gradient and fewer diatom data

for species calibration. The basin-wide model pro-

duced stronger independently reconstructed distur-

bance for a regional subset of samples, indicating that

broader models are suitable for use in smaller regional

reconstructions. Sites from a different region (i.e., the

Ohio River) apparently strengthened local disturbance

predictions for Missouri and Mississippi samples. This

could also be due to a lack of characterization of some

specific sites within a river reach. For example, highly

urbanized sites may have been more phycologically

similar to comparable sites on other rivers than they

were to rural sites on the same river.

Not surprisingly, observed versus inferred relation-

ships for independent datasets had relatively low r2

values compared to evaluations using cross-validation

due to smaller inferred sample sizes and reduced

likelihood of pseudoreplication in the training sets.

However, independent testing indicated that environ-

mental conditions could be assessed by applying these

great river diatom tools to recent collections of

diatoms, such as those from an ongoing monitoring

program. For instance, scores from new samples may

be plotted relative to the zones delineated in Fig. 2b to

visualize relationships to, and changes along, distur-

bance gradients. Re-sampling of sites could be used to

assess improvement or degradation of areas or discrete

locales. Further, diatoms have the unique advantage of

providing retrospective data from fossil assemblages,

and one could apply these diatom indicators in a

paleolimnological context to assemblages collected

from dated sediment cores (e.g., from oxbows, bays, or

backwaters, Reavie & Edlund, 2010).

Conclusion

The ability of diatom assemblages to track stressors in

great rivers is apparent, and our models provide a

means to evaluate whether there are biological

responses to stress in lower levels of the food chain.

Phytoplankton and periphyton diatom assemblages

can be used to infer human disturbances representing

agriculture and landscape development modifications

and, to a lesser extent, eutrophication of the water

column. Each assemblage had varying stressor

responses and when combined provided more com-

prehensive disturbance characterizations. Diatom

indicators were most robust when developed at the

whole river basin scale, providing more accurate

disturbance assessments than geographically smaller,

regional models. Indicators developed in this study

may be used as tools for future monitoring of the Ohio,

Missouri, and Upper Mississippi rivers. The integra-

tive diatom bioindicators meet EMAP-GRE objectives

of estimating the physical, chemical, and biological

conditions of the rivers while providing a tool that

could be used for long-term monitoring and paleolim-

nological applications.

Acknowledgments Special thanks to Adam Heathcote and

Steve Juggins for statistical support and suggestions. We would

like to thank all of our EMAP-GRE colleagues for their

contributions including: field crews for sample collection and

field measures, the EPA Mid-Continent Ecology Division lab in

Duluth, Minnesota for chemical analyses, and K. Kennedy and

L. Allinger for slide preparations. This study was supported by a

grant to E. Reavie from the US Environmental Protection

Agency under cooperative agreement CR-83272401. This

document has not been subjected to the Agency’s required

peer and policy review and therefore does not necessarily reflect

the view of the Agency, and no official endorsements should be

inferred. This is contribution number 534 of the Center for

Water and the Environment, Natural Resources Research

Institute, University of Minnesota Duluth.

References

Angradi, T. R. (ed.), 2006. Environmental monitoring and

assessment program: great river ecosystems field opera-

tions manual. EPA/620/R-06/002. US Environmental

Protection Agency, Office of Research and Development,

Washington, DC: 217 pp [available on internet at

www.epa.gov/emap/greatriver/]. Accessed 7 May 2011.

Angradi, T. R., D. W. Bolgrien, T. M. Jicha, M. S. Pearson, B.

H. Hill, D. L. Taylor, E. W. Schweiger, L. Shepard, A.

R. Batterman, M. F. Moffett, C. M. Elonen & L.

E. Anderson, 2008. A bioassessment approach for mid-

continent great rivers: the Upper Mississippi, Missouri, and

Ohio (USA). Environmental Monitoring Assessment 152:

425–442.

Angradi, T. R., D. W. Bolgrien, T. M. Jicha, M. S. Pearson, D.

L. Taylor & B. H. Hill, 2009a. Multispatial-scale variation

in benthic and snag-surface macroinvertebrate assem-

blages in mid-continent USA great rivers. Journal of the

North American Benthological Society 28: 122–141.

Hydrobiologia (2012) 691:171–188 185

123

Angradi, T. R., M. S. Pearson, T. M. Jicha, D. L. Taylor, D.

W. Bolgrien, M. F. Moffett, K. A. Blocksom & B. H. Hill,

2009b. Using stressor gradients to determine reference

expectations for great river fish assemblages. Ecological

Indicators 9: 748–764.

Bate, N. & P. Newall, 2002. Techniques for the use of diatoms in

water quality assessment: how many valves? In Proceed-

ings of the 15th International Diatom Symposium, Perth,

Australia, 28 September–2 October 1998: 153–160.

Bernhardt, E. S., M. A. Palmer, J. D. Allan, G. Alexander, K.

Barnas, S. Brooks, J. Carr, S. Clayton, C. Dahm, J. Folls-

tad-Shah, D. Galat, S. Gloss, P. Goodwin, D. Hart, B.

Hassett, R. Jenkinson, S. Katz, G. M. Kondolf, P. S. Lake,

R. Lave, J. L. Meyer, T. K. O’Donnell, L. Pagano, B.

Powell & E. Sudduth, 2005. Synthesizing U.S. river res-

toration efforts. Science 308: 636–637.

Biggs, B. J. F., 1996. Pattern of benthic algae of streams. In

Stevenson, J. R., M. L. Bothwell & R. L. Lowe (eds), Algal

Ecology: Freshwater Benthic Ecosystems. Academic

Press, San Diego: 31–56.

Birks, H. J. B., 1995. Quantitative paleoenvironmental recon-

structions. In Maddy, D. & J. S. Brew (eds), Statistical

Modelling of Quaternary Science Data, Tech Guide 5.

Quaternary Research Association, Cambridge: 161–254.

Birks, H. J. B., 1998. Numerical tools in paleolimnology—

progress, potentialities and problems. Journal of Paleo-

limnology 20: 307–332.

Birks, H. J. B., 2010. Numerical methods for the analysis of

diatom assemblage data. In Smol, J. P. & E. F. Stoermer

(eds), The Diatoms: Applications for the Environmental

and Earth Sciences, 2nd ed. Cambridge University Press,

New York: 23–54.

Birks, H. J. B., J. M. Line, S. Juggins, A. C. Stevenson & C. J. F.

Ter Braak, 1990. Diatoms and pH reconstruction. Philo-

sophical Transactions of the Royal Society of London B

327: 263–278.

Bradshaw, E. G., N. J. Anderson, J. P. Jensen & E. Jeppesen,

2002. Phosphorus dynamics in Danish lakes and the

implications for diatom ecology and palaeoecology.

Freshwater Biology 47: 1963–1975.

Camburn, K. E. & D. F. Charles, 2000. Diatoms of low-alka-

linity lakes in the northeastern United States. In E.

E. Spamer (ed.), Special publications 18. The Academy of

Natural Sciences of Philadelphia, Philadelphia: 152 pp.

Camburn, K. E., J. C. Kingston & D. F. Charles, 1984–1986.

PIRLA diatom iconograph. PIRLA Unpublished Report

Series 3. Indiana University, Bloomington.

Charles, D. F., F. W. Acker, D. D. Hart, C. W. Reimer & P.

B. Cotter, 2006. Large-scale regional variation in diatom-

water chemistry relationships: rivers of the eastern United

States. Hydrobiologia 561: 27–57.

Cumming, B. F., S. E. Wilson, R. I. Hall & J. P. Smol, 1995.

Diatoms from British Columbia (Canada) lakes and their

relationship to salinity, nutrients, and other limnological

variables. Bibliotheca Diatomologica 31: 1–207.

Dela-Cruz, J., T. Pritchard, G. Gorden & P. Ajani, 2006. The use

of periphytic diatoms as a means of assessing impacts of

point source inorganic nutrient pollution in south-eastern

Australia. Freshwater Biology 51: 951–972.

Delgado, C., I. Pardo & L. Garcıa, 2010. A multimetric diatom

index to assess the ecological status of coastal Galician

rivers (NW Spain). Hydrobiologia 644: 371–384.

Denys, L., 2004. Relation of abundance-weighted averages of

diatom indicator values to measured environmental con-

ditions in standing freshwaters. Ecological Indicators 4:

255–275.

Detenbeck, N. E., D. L. Taylor, A. Lima & C. Hagley, 1996.

Temporal and spatial variability in water quality of wet-

lands in the Minneapolis/St. Paul, MN metropolitan area:

implications for monitoring strategies and designs. Envi-

ronmental Monitoring Assessment 40: 11–40.

Dixit, S. S. & J. P. Smol, 1994. Diatoms as indicators in the

environmental monitoring and assessment program-sur-

face waters (EMAP-SW). Environmental Monitoring and

Assessment 31: 275–306.

Graf, W. L., 2001. Damage control: restoring the physical

integrity of America’s Rivers. Annals of the Association of

American Geographers 91: 1–27.

Hall, R. I. & J. P. Smol, 1992. A weighted-averaging regression

and calibration model for inferring total phosphorus con-

centration from diatoms in British Columbia (Canada)

lakes. Freshwater Biology 7: 417–434.

Heiskary, S. & B. Wilson, 2008. Minnesota’s approach to lake

nutrient criteria development. Lake and Reservoir Man-

agement 24: 282–297.

Hill, M. O., 1973. Diversity and evenness: a unifying notation

and its consequences. Ecology 54: 427–432.

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

H. McCormick & C. B. Johnson, 2000. Use of periphyton

assemblage data as an index of biotic integrity. Journal of

the North American Benthological Society 19: 50–67.

Hintze, J., 1998. NCSS 97. NCSS, Kaysville, UT [available on

internet at www.ncss.com]. Accessed 7 May 2011.

Hodgkiss, I. J. & C. Y. Law, 1985. Relating diatom community

structure and stream water quality using species diversity

indices. Water Pollution Control 84: 134–139.

Jackson, D. A., 1993. Stopping rules in principal components

analysis: a comparison of heuristical and statistical

approaches. Ecology 74: 2204–2214.

Johnson, B. L., T. J. Naimo & W. B. Richardson, 1995. Past

present and future concepts in large river ecology: how

rivers function and how human activities influence river

processes. BioScience 45: 134–141.

Juggins, S., 2003. C2 User Guide. Software for Ecological and

Palaeoecological Data Analysis and Visualization. Uni-

versity of Newcastle, Newcastle upon Tyne.

Kelly, M. G. & B. A. Whitton, 1995. The trophic diatom index: a

new index for monitoring eutrophication in river. Journal

of Applied Phycology 7: 433–444.

Kelly, M. G., A. Cazaubon, E. Coring, A. Dell’Uomo, L. Ector, B.

Goldsmith, H. Guasch, J. Hurlimann, A. Jarlman, B. Kawe-

cka, J. Kwandrans, R. Laugaste, E.-A. Lindstrøm, M. Leitao,

P. Marvan, J. Padisak, E. Pipp, J. Prygiel, E. Rott, S. Sabater,

H. van Dam & J. Vizinet, 1998. Recommendations for the

routine sampling of diatoms for water quality assessments in

Europe. Journal of Applied Phycology 10: 215–224.

Kelly, M. G., S. Juggins, R. Gurthrie, S. Pritchard, J. Jamieson,

B. Rippey, H. Hirst & M. Yallop, 2008a. Assessment of

186 Hydrobiologia (2012) 691:171–188

123

ecological status in U.K. rivers using diatoms. Freshwater

Biology 53: 403–422.

Kelly, M. G., L. King, R. I. Jones, P. A. Barker & B. J. Jamieson,

2008b. Validation of diatoms as proxies for phytobenthos

when assessing ecological status in lakes. Hydrobiologia

610: 125–129.

Kireta, A. R., E. D. Reavie, N. P. Danz, R. P. Axler, G. V. Sgro,

T. N. Brown & T. P. Hollenhorst, 2007. Coastal geomor-

phic and lake variability in the Laurentian Great Lakes:

implications for a diatom-based monitoring tool. Journal of

Great Lakes Research 33: 136–153.

Kireta, A. R., E. D. Reavie, G. V. Sgro, T. R. Angradi, D.

W. Bolgrien, T. M. Jicha & B. H. Hill, 2011. Planktonic

and periphytic diatom as indicators of stress on great rivers

of the United States: testing water quality and disturbance

models. Ecological Indicators 13: 222–231.

Krammer, K. & H. Lange-Bertalot, 1986–1991. Bacillariophy-

ceae. In Ettl, J., J. Gerloff, H. Hyenig & D. Mollenhauer

(eds), Susswasserflora von Mitteleuropa. Fischer, Stuttgart:

1–4.

Mann, D. G., S. M. McDonald, M. M. Bayer, S. J. M. Droop, V.

A. Chepurnov, R. E. Loke, A. Ciobanu & J. M. H. du Buf,

2004. The Sellaphora pupula species complex (Bacillari-

ophyceae): morphometric analysis, ultrastructure and

mating data provide evidence for five new species. Phyc-

ologia 43: 459–482.

McDonald, M., R. Blair, D. Bolgrien, B. Brown, J. Dlugosz, S.

Hale, S. Hedtke, D. Heggem, L. Jackson, K. Jones, B.

Levinson, R. Linthurst, J. Messer, A. Olsen, J. Paul, S.

Paulsen, J. Stoddard, K. Summers & G. Veith, 2004. The

US environmental protection agency’s environmental

monitoring and assessment program. In Wiersma, G. B.

(ed.), Environmental Monitoring. CRC Press, New York:

649–668.

O’Brien, R. M., 2007. A caution regarding rules of thumb for

variance inflation factors. Quality and Quantity 41:

673–690.

Oksanen, J., R. Kindt, P. Legendre, B. O’Hara, G. L. Simpson,

P. Solymos, M. H. H. Stevens & H. Wagner, 2009. vegan:

Community Ecology Package, R package version 1.15-4

[available online at http://CRAN.R-project.org/package=

vegan]. Accessed 7 May 2011.

Patrick, R., 1949. A proposed biological measure of stream

condition based on a survey of the Conestoga basin, Lan-

caster County, Pennsylvania. Proceedings of the Academy

of Natural Sciences Philadelphia 101: 288–341.

Patrick, R. & C. W. Reimer, 1966a. The Diatoms of the United

States Exclusive of Alaska and Hawaii, No. 13, Vol. 1.

Monographs of Natural Sciences of Philadelphia, Sutter

House, PA.

Patrick, R. & C. W. Reimer, 1966b. The Diatoms of the United

States Exclusive of Alaska and Hawaii, Vol. 2. Monographs

of Natural Sciences of Philadelphia, Sutter House, PA.

Ponader, K. C., D. F. Charles & T. J. Belton, 2007. Diatom-

based TP and TN inference models and indices for moni-

toring nutrient enrichment of New Jersey streams. Eco-

logical Indicators 7: 79–93.

Ponader, K. C., D. F. Charles, T. J. Belton & D. M. Winter,

2008. Total phosphorus inference models and indices for

coastal plan streams based on benthic diatom assemblages

from artificial substrates. Hydrobiologia 610: 139–152.

Potapova, M. G. & D. F. Charles, 2003. Distribution of benthic

diatom in U.S. rivers in relation to conductivity and ionic

composition. Freshwater Biology 48: 1311–1328.

Potapova, M. G. & D. F. Charles, 2007. Diatom metrics for

monitoring eutrophication in rivers of the United States.

Ecological Indicators 7: 48–70.

Prygiel, J. & M. Coste, 1993. The assessment of water quality in

the Artois-Picardie water basin (France) by the use of

diatom indices. Hydrobiologia 269(270): 343–349.

R Development Core Team, 2009. R: A Language and Environ-

ment for Statistical Computing. R Foundation for Statistical

computing, Vienna. ISBN 3-9000051-07-0 [available on

internet at http://www.R-project.org]. Accessed 7 May 2011.

Reavie, E. D. & J. P. Smol, 1998. Freshwater diatoms from the

St. Lawrence River. Bibliotheca Diatomologica 43:

224–225.

Reavie, E. D. & M. B. Edlund, 2010. Diatoms as indicators of

environmental change in rivers, fluvial lakes and

impoundments. In Smol, J. P. & E. F. Stoermer (eds), The

Diatoms: Applications for the Environmental and Earth

Sciences, 2nd ed. Cambridge University Press, New York:

86–97.

Reavie, E. D., T. M. Jicha, T. R. Angradi & B. H. Hill, 2010.

Algal assemblages for large river monitoring: comparison

among biovolume, absolute, and relative abundance met-

rics. Ecological Indicators 10: 167–177.

Reichardt, E., 1999. Zur revision der gattung Gomphonema. Die

arten um G. affine/insigne, G. angustatum/micropus, G.acuminatum, sowie gomphonemoide diatomeed aus dem

Oberoligozan in Bohem. In Lange-Bertalot, H. (ed.), Ico-

nographia Diatomologica. Annotated Diatom Micro-

graphs, Vol. 8. Taxonomy. Koeltz Scientific Books,

Konigstein: 203 pp.

Rott, E., 1991. Methodological aspects and perspectives in the

use of periphyton for monitoring and protecting rivers. In

Whitton, B. A., E. Rott & G. Friedrich (eds), Use of Algae

for Monitoring Rivers. Institute of Botany, University of

Innsbruck, Austria: 9–16.

Rott, E., E. Pipp & P. Pfister, 2003. Diatom methods developed

for river quality assessment in Austria and a cross-check

against numerical trophic indication methods used in

Europe. Algological Studies 110: 91–115.

Round, F. E., 1991. Diatoms in river water-monitoring studies.

Journal of Applied Phycology 3: 129–145.

Schweiger, E. W., D. W. Bolgrien, T. R. Angradi & J. R. Kelly,

2004. Environmental monitoring and assessment of a great

river ecosystem: the upper Missouri River pilot. Environ-

mental Monitoring and Assessment 103: 5–20.

Seegert, G., 2000. Considerations regarding development of

index of biotic integrity metrics for large rivers. Environ-

mental Science & Policy 3: S99–S106.

Smith, R. A., R. B. Alexander & G. E. Schwarz, 2003. Natural

background concentrations of nutrients in streams and

rivers of the conterminous United States. Environmental

Science and Technology 37: 3039–3047.

Soininen, J. & P. Niemela, 2002. Inferring the phosphorus levels

of rivers from benthic diatoms using weighted averaging.

Archiv fur Hydrobiologie 154: 1–18.

Stevenson, R. J., 1997. Scale dependent determinants and con-

sequences of benthic algal heterogeneity. Journal of the

North American Benthological Society 16: 248–262.

Hydrobiologia (2012) 691:171–188 187

123

Stevenson, R. J., Y. Pan, K. M. Manoylov, C. A. Parker, D.

P. Larsen & A. T. Herlihy, 2008. Development of diatom

indicators of ecological conditions for streams of the

western US. Journal of the North American Benthological

Society 27: 1000–1016.

Stevenson, R. J., Y. Pan & H. van Dam, 2010. Assessing envi-

ronmental conditions in rivers and streams with diatoms. In

Stoermer, E. F. & J. P. Smol (eds), The Diatoms: Appli-

cations to the Environmental and Earth Sciences, 2nd ed.

Cambridge University Press, Cambridge: 57–85.

Stoermer, E. F., R. G. J. Kreis & N. A. Andresen, 1999.

Checklist of diatoms from the Laurentian Great Lakes.

Journal of Great Lakes Research 25: 515–566.

ter Braak, C. J. F. & S. Juggins, 1993. Weighted averaging

partial least squares regression (WA-PLS): an improved

method for reconstructing environmental variables from

species assemblages. Hydrobiologia 269(270): 485–502.

ter Braak, C. J. F. & P. Smilauer, 2002. CANOCO Reference

Manual and CanoDraw for Windows User’s Guide: Soft-

ware for Canonical Community Ordination (Version 4.5).

Microcomputer Power, Ithaca: 500 pp.

Vannote, R. L., G. W. Minshall, K. W. Cummins, J. R. Sedell &

C. E. Cushing, 1980. The river continuum concept. Cana-

dian Journal of Fisheries and Aquatic Sciences 37:

130–137.

Watanabe, T., K. Asai & A. Houki, 1988. Numerical water

quality monitoring of organic pollution using diatom

assemblages. In Round, F. E. (ed.), Proceedings of the 9th

Diatom Symposium. Biopress Ltd., Bristol: 123–141.

Whitton, B. A. & M. G. Kelly, 1995. Use of algae and other

plants for monitoring rivers. Australian Journal of Ecology

20: 45–56.

Williams, L. G., 1964. Possible relationships between plankton

diatom species numbers and water-quality estimates.

Ecology 45: 809–823.

Winter, J. G. & H. C. Duthie, 2000. Epilithic diatoms as indi-

cators of stream total N and total P concentration. Journal

of the North American Benthological Society 19: 32–49.

188 Hydrobiologia (2012) 691:171–188

123