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Algae–P relationships, thresholds, and frequency distributions guide nutrient

criterion development

Author(s): R. Jan Stevenson, Brian H. Hill, Alan T. Herlihy, Lester L. Yuan, and Susan B. Norton

Source: Journal of the North American Benthological Society, 27(3):783-799. 2008.

Published By: The Society for Freshwater Science

DOI: http://dx.doi.org/10.1899/07-077.1

URL: http://www.bioone.org/doi/full/10.1899/07-077.1

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J. N. Am. Benthol. Soc., 2008, 27(3):783–799

2008 by The North American Benthological Society DOI: 10.1899/07-077.1

Published online: 5 August 2008

Algae–P relationships, thresholds, and frequency distributions guide

nutrient criterion development

R. Jan Stevenson1

Department of Zoology, Michigan State University, East Lansing, Michigan 48824 USA

Brian H. Hill2

National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division,

US Environmental Protection Agency, Duluth, Minnesota 55804-2595 USA

Alan T. Herlihy3

Department of Fisheries and Wildlife, Oregon State University, c/o US Environmental Protection Agency,

Corvallis, Oregon 97333 USA

Lester L. Yuan4 AND Susan B. Norton5

National Center for Environmental Assessment, Office of Research and Development,

US Environmental Protection Agency, Washington, DC 20460 USA

Abstract. We used complementary information collected using different conceptual approaches to develop

recommendations for a stream nutrient criterion based on responses of algal assemblages to anthropogenic P

enrichment. Benthic algal attributes, water chemistry, physical habitat, and human activities in watersheds

were measured in streams of the Mid-Atlantic Highlands region as part of the Environmental Monitoring

and Assessment Program of the US Environmental Protection Agency. Diatom species composition differed

greatly between low- and high-pH reference streams; therefore, analyses for criterion development were

limited to a subset of 149 well-buffered streams to control for natural variability among streams caused by

pH. Regression models showed that TP concentrations were ;10 lg/L in streams with low levels of human

activities in watersheds and that TP increased with % agriculture and urban land uses in watersheds. The

75th percentile at reference sites was 12 lg TP/L. Chlorophyll a and ash-free dry mass increased and acid

and alkaline phosphatase activities decreased with increasing TP concentration. The number of diatom taxa,

evenness, proportion of expected native taxa, and number of high-P taxa increased with TP concentration in

streams. In contrast, the number of low-P native taxa and % low-P individuals decreased with increasing TP.

Lowess regression and regression tree analysis indicated nonlinear relationships for many diversity indices

and attributes of taxonomic composition with respect to TP. Thresholds in these responses occurred between

10 and 20 lg/L and helped justify recommending a P criterion between 10 and 12 lg TP/L to protect highquality biological conditions in streams of the Mid-Atlantic Highlands.

Key words: algae, biomass, diatoms, frequency distributions, Mid-Atlantic Highlands, nutrients, nutrient criteria, phosphorus, species composition, streams, stressor–response relationship, threshold.

Nutrient enrichment by humans is recognized as a

leading cause of degradation of the ecological integrity

of streams in the US (Vitousek et al. 1997, Carpenter et

al. 1998, Smith et al. 1999, USEPA 2000). Establishment

of nutrient criteria has become the focus of a national

effort to regulate nutrient content in all water body

types in all regions of the US (USEPA 1998). This

national effort has resulted in the production of new

information, syntheses of old information, and devel1 E-mail addresses: rjstev@msu.edu

2 hill.brian@epamail.epa.gov

3 herlihy.alan@epa.gov

4 yuan.lester@epamail.epa.gov

5 norton.susan@epamail.epa.gov

783
opment of new concepts that link the basic and

applied sciences. Questions arose about the use of

different approaches, such as frequency distributions

and stressor–response relationships, recommended by

the USEPA (2000) for development of nutrient criteria

for streams. Our paper addresses use of those

approaches.

Nutrient enrichment of streams caused by human

activities affects algal biomass and species composition. Accrual of benthic algal biomass is limited by low

P and N concentrations in many streams (Francoeur

2001), and algal biomass is positively related to

nutrient concentrations in streams (Biggs 2000, Dodds

et al. 2002, Stevenson et al. 2006). Nutrient concentrations also affect relative abundances of diatom species

in streams (Kelly and Whitton 1995, Pan et al. 1999,

Manoylov and Stevenson 2006).

We often expect algal species richness and evenness

to decrease in streams with increasing pollution, but

nutrients also are a resource stressor. Increasing

resource availability from low to medium levels can

increase diversity because low nutrient concentrations

might constrain algal taxonomic composition to those

species adapted to low nutrient concentrations (Manoylov and Stevenson 2006). Thus, release from nutrient

limitation might allow increases in species richness in

physically disturbed systems such as streams (Biggs

and Smith 2002, Huston 2004). Despite this potential

increase in overall species richness, addition of

nutrients could reduce relative abundance and richness of native species by altering competitive hierarchies or habitat structure (Huston 1994). These

relationships between biodiversity and nutrients have

not been evaluated thoroughly for algae in streams.

Several approaches to establishing environmental

criteria have been recommended, and these approaches provide complementary information (USEPA 2000,

Stevenson et al. 2004). Regression models relating

nutrient concentration and land use in watersheds can

be used to predict natural or best available nutrient

concentrations in streams (Dodds and Oakes 2004).

Documenting the relationships between nutrients and

human activities is important for distinguishing

natural and anthropogenic sources of variability in

nutrients among streams. Frequency distributions of

nutrient concentrations at reference sites, or at sites

meeting other management criteria, such as high

invertebrate biodiversity, can be used to characterize

the variability in nutrients at sites that do meet

management expectations. Stressor–response relationships characterize the progressive degradation in

ecological systems with increasing pollution (stressors,

e.g., nutrients) and help delineate the pollution

concentrations that would support valued ecological

attributes.

Relationships between nutrients and valued ecological attributes are important for documenting effects

on ecosystems that generate support from stakeholders for management actions. For example, water clarity

and algal blooms caused by nutrients are important

concerns for stakeholders. Thus, the stressor–response

approach can be used to establish effects-based criteria

by quantitatively relating nutrient concentrations to

valued ecological attributes (e.g., water clarity) and

determining the specific benefits (e.g., fewer algal

blooms) of protecting or restoring ecosystems at

specific levels of nutrient concentrations. Criteria are

established with this stressor–response approach at

levels of stressors that provide the desired level of

protection for the valued ecological attribute. These

stressor–response relationships might be developed

based on laboratory bioassays or field surveys and are

the foundation of risk assessment (Suter 1993, USEPA

1998). Thresholds (i.e., sudden changes in attributes

along the stressor gradients) can be particularly

valuable for justifying criteria at specific stressor levels

(Muradian 2001). In the development of P criteria for

the Everglades, threshold effects on calcareous algal

mats and species composition of biota were important

for justifying a specific P concentration that would

protect biological integrity (e.g., Stevenson et al. 2002,

King and Richardson 2003). Some evidence for

quantitative relationships between nutrients and algal

biomass has been reported for streams, but few studies

have evaluated thresholds in relationships between

nutrients and algal attributes.

The objectives of our research were to relate changes

in stream benthic algae to anthropogenic increases in

total P (TP) concentrations and to develop recommendations for TP criteria. We chose TP rather than total N

(TN) or both TP and TN to limit complexity of the

results and because experiments, nutrient ratios, and

correlations among attributes indicate that P commonly regulates algal responses in streams of the study

region (e.g., Pan et al. 1999, Francoeur 2001). First, we

establish relationships between TP concentrations in

streams and human activity in watersheds to predict

natural TP concentrations in streams (predictive

models). We also use frequency distributions of TP

concentrations at sites with low levels of human

activities to characterize natural TP concentrations.

Then we relate TP to changes in algal biomass, enzyme

activity, diversity, and taxonomic composition in these

streams to confirm that TP is correlated with changes

in valued ecological attributes, to characterize magnitudes of effects, and to identify thresholds in algae–TP

relationships. We identify candidate concentrations for

784 R. J. STEVENSON ET AL. [Volume 27
TP criteria (benchmarks) using predictive models of

natural conditions, frequency distributions, stressor–

response relationships, and thresholds. Last, we

compare these benchmarks and discuss transferability

among regions.

Methods

Sample collection and analysis

From 1993 to 1998, 1185 periphyton samples were

collected in 607 streams throughout the Mid-Atlantic

Highlands (MAH) region as part of the Environmental

Monitoring and Assessment Program (EMAP) of the

US Environmental Protection Agency (EPA) (Fig. 1).

This region covers Maryland, Pennsylvania, West

Virginia, Virginia, Delaware, and parts of New York

and North Carolina. It includes 7 ecoregions (Omernik

1987) and 5 major watersheds (Alleghany, Kanawha,

Monongahela, Potomac, and Susquehanna). Periphyton was collected by scraping and brushing organic

material from a known area of natural substrate at a

random location on each of 9 transects in a stream

reach (Pan et al. 1996, Hill et al. 2000; see Lazorchak et

al. 1998 for details of all methods). Samples from riffles

and pools were separated into 2 composite samples

from a stream if both habitats existed in the reach. The

volume of each sample was measured in the field, and

the sample was then split into 4 subsamples for assays

of chlorophyll a (chl a), ash-free dry mass (AFDM),

diatom taxonomic composition, and acid and alkaline

phosphatase activity. Chl a was measured with a

spectrophotometer using standard methods (APHA

FIG. 1. Sites in the Mid-Atlantic Highlands region of the US, including Pennsylvania, West Virginia, Maryland, Virginia, and

parts of North Carolina and New York, where periphyton samples were collected during 1993 to 1998.

2008] ALGAE AND NUTRIENT CRITERIA 785
1992) including acidification to determine the phaeophytin degradation portion of uncorrected chl a

estimates. AFDM was assayed by drying and combusting samples according to standard methods

(APHA 1992). Diatom taxonomic composition was

determined by identifying and counting 500 diatom

valves at 10003 after cleaning and mounting diatoms

on slides in a high refractive index mounting medium

(HYRAXt or NAPHRAXt; The Biology Shop, Hazelbrook, New South Wales, Australia; http://www.pnc.

com.au/;biology). Acid and alkaline phosphatases in

periphyton subsamples were assayed with standard

methods and using nitrophenyl phosphate as a

substrate (Sayler et al. 1979).

Water-chemistry, physical-habitat, and riparian characteristics in watersheds also were assessed at most

sites. A list of sample preparation and preservation

protocols for water chemistry can be found in Pan et al.

(1996). Water-chemistry characteristics, such as nutrients, Cl, pH, SO4, and many other variables, were

analyzed with procedures outlined in USEPA (1987).

TP and TN were analyzed spectrophotometrically after

persulfate digestion. Soluble reactive P was not

measured, but NO3 and NH4 were. Physical-habitat

characteristics measured in streams included thalweg

depth, mean wetted width, mean reach cross section,

slope, residual pool area, substrate size, and embeddedness (Kaufmann et al. 1999). Land use and land cover

were characterized for topographically delineated

watersheds with US Geological Survey landuse and

land-cover digital maps and Thematic Mapper satellite

imagery (Herlihy et al. 1998, Hill et al. 2000).

Data analysis

We downloaded the data from the EMAP web site

(www.epa.gov/emap). Diatom data consisted of cell

densities. We calculated diatom relative abundances

(% of diatoms counted) by dividing cell densities of

each species by sums of cell densities for all diatom

taxa in the same sample and multiplying by 100. We

restricted analyses of relationships among nutrients,

diatoms, and human activities to samples collected

from riffles during 1993 to 1996, when most samples

came from 1st- to 3rd-order streams in the MAH. The

1997 to 1998 samples came from both wadeable and

nonwadeable streams in the Coastal Plain and

Piedmont ecoregions of the Mid-Atlantic states. Some

streams in the database had been sampled more than

once during the year and in more than one year. In

those cases, we used the first sample from the first year

in which the stream was sampled. Data from the

streams sampled during 1997 to 1998 were used to

develop diatom autecological information (see Effectsbased TP benchmarks below).

Stream classification.—The 1st step in data analysis

was to classify MAH streams based on similarity in

diatom taxonomic composition among reference sites

so that we could limit analyses to streams in which we

expected similar natural (or reference) conditions and

similar responses to watershed disturbance. We chose

reference sites initially for consistency with other

analyses of MAH streams based on EMAP data. We

used selection criteria that were assumed to eliminate

sites with evidence of stressors from mining, agriculture, and urban development (criteria and assumptions are described in Waite et al. 2000). These criteria

were .50 leq/L acid neutralizing capacity (ANC),

,100 leq Cl/L, ,20 lg TP/L, ,750 lg TN/L, and

,400 leq SO4/L. We included species in the cluster

analysis if their relative abundances averaged .1% at

the reference sites where they were observed. Twoway indicator species analysis (TWINSPAN, www.

canodraw.com/wintwins.htm; Hill et al. 1975) was

used to group sites with similar diatom assemblages

because this analysis provided groups of sites that

could be related to environmental factors in past

studies (e.g., Pan et al. 2000).

We used linear discriminant analyses and descriptive statistics (SYSTAT 10.0; Systat Software, Chicago,

Illinois) to determine the natural ecological variables

that could best predict membership of sites in different

TWINSPAN groups. The 1st TWINSPAN analysis

indicated that the person counting samples had a

significant effect on membership of sites in TWINSPAN groups. Therefore, we reviewed the list of

diatom taxa to determine the probable differences in

how analysts had identified taxa. We combined groups

of taxa with similar morphology, such as varieties

within the same species and groups of small taxa.

After lumping taxa according to the preceding criteria,

we analyzed the data again; then environmental

factors that often affect diatom species composition

became most important for distinguishing groups of

reference sites. Subsequent discriminant analyses with

forward and backward selection identified environmental variables that best distinguished membership

of sites in TWINSPAN groups. We chose a group of

streams with the largest number of sites sampled

during the 1993 to 1996 field seasons for all analyses of

relationships among nutrients, algae, and human

activities. We chose this level of classification because

finer levels of classification (more small groups) did

not improve relationships between metrics and TP

concentration.

TP benchmarks for best available conditions.—Benchmarks is a term used in ecotoxicological literature to

786 R. J. STEVENSON ET AL. [Volume 27
refer to specific levels of variables that can be justified,

for one reason or another, as candidates for criteria

(Setzer and Kimmel 2003). The actual criterion is then

selected from the benchmarks. We determined benchmarks related to best available TP conditions using 2

different methods. First, we used linear regression to

relate TP to the percentages of watersheds altered by

human activities (% WA) (SYSTAT). We calculated %

WA as the sum of % agricultural, urban, and mining

land uses. We used the resulting model to document

that human activities affected TP concentration and to

predict TP concentration when human activities were

lowest in the region, i.e., when % WA was 0.

Preliminary exploration of the data indicated that no

more variation in TP could be explained by separating

% agricultural, urban, and mining land uses in the

regression model than by using % WA. We set the 1st

benchmark for TP criteria at the 75% confidence

interval (CI) for predicted TP concentration when %

WA was 0. We chose the 75% CI rather than the 90th and

95th to balance Type I and Type II errors in the same

way that selection of the 75th percentile of frequency

distributions balances these errors (see below).

We used the 75th percentile of frequency distributions

of TP concentrations for reference sites as the 2nd

benchmark for TP criteria because of precedent and

USEPA recommendations (Dodds et al. 1998, USEPA

2000). Different definitions of reference condition, such

as best professional judgment for conditions supporting high biological diversity of aquatic invertebrates or

sites that actually support biological conditions that are

deemed acceptable, have been used. After the preliminary stream classification step (see Stream classification

above), we modified our definition of reference

condition for nutrient criteria development. We used

0% WA as a conservative starting point for selecting

reference sites because these conditions existed in the

region. The number of streams with 0% WA was very

low, so we repeated evaluations of TP frequency

distributions with the maximum % WA set at successively higher and higher levels (% WA 0, 1, 2, 3, 4, 5, 6,

8, 10, 15, 20, 30, 40%). We then plotted the median and

75th percentile TP concentrations of the TP frequency

distributions as a function % WA in the 13 sets of

streams. We used the largest set of streams in which the

median and 75th percentile of TP concentrations did not

increase above natural (i.e., concentrations at 0% WA)

to define the maximum % WA for reference sites and to

determine the 75th percentile of the TP concentrations

in reference condition. We used the same statistical

analysis for TN and Cl (Cl is an indicator of human

activities in watersheds) to evaluate corresponding

changes in other chemical conditions as human

activities increased in watersheds.

Effects-based TP benchmarks.—We calculated stressor–

response relationships with 11 algal attributes (described below) to determine specific effects of specific

TP concentrations. We selected the 11 algal attributes

because they were indicators related to valued

ecological attributes, such as intrinsic values, nuisance

problems, or periphyton function.

We included 2 biomass indicators, chl a and ash-free

dry mass (AFDM) per unit area, as algal attributes

because they are 2 common measures of benthic algal

biomass in streams, and they were provided in the

EPA database. Alkaline and acid phosphatase activities, indicators of P demand by microbes in aquatic

ecosystems, also were provided in the EPA database.

We calculated 4 diversity indicators with species data:

the number of taxa observed in the 500-valve count,

evenness (Hurlbert 1971), proportion of expected

native taxa (see definition below), and the proportion

of expected native taxa able to live in low P

concentrations (low-P native taxa). Last, we calculated

3 measures of taxonomic composition: Bray–Curtis

(BC) similarity of species composition to reference

condition, proportion of taxa able to live in low P

concentrations (low-P taxa), and proportion of taxa

requiring high P (high-P taxa).

We defined native taxa as the taxa occurring at

reference sites. Many of these taxa are commonly

found at sites around the US where % WA is low; thus,

they probably existed in North American streams prior

to major alterations of the landscape by humans. We

identified native taxa originally from a randomly

selected subset consisting of 1⁄2 of the reference sites

(subset 1). We measured the proportion of native taxa

at a test (nonreference) site as the ratio of the observed

number of subset-1 native taxa at the test site to the

expected number of native taxa at a subset-1 reference

site. The observed number of subset-1 native taxa at a

test site was the number of native taxa at the site that

had been observed in subset-1 reference sites. The

expected number of native taxa at a test site was the

average number of native taxa at subset-1 reference

sites. We used the native taxa observed in the other 1⁄2

of the reference sites (subset 2) to measure the

proportion of native taxa at a subset-1 reference site

as the ratio of the observed number of subset-2 native

taxa at the subset-1 reference site to the average

number of native taxa at subset-2 reference sites. The

proportion of native taxa varies from 0 to .1.0. Values

are ;1.0 at high-quality sites, ,1.0 when native taxa

have been lost, and .1.0 when more native species are

present than would be expected at reference sites

(thus, more native taxa than naturally would occur at a

site). This metric is the same as the ratio of observed to

expected species number used in River InVertebrate

2008] ALGAE AND NUTRIENT CRITERIA 787
Prediction And Classification System (RIVPACS)

models and invertebrate assessment (Wright et al.

1993, Hawkins et al. 2000), but in this case, we studied

only one class of streams. RIVPACS models are usually

used for many classes of streams simultaneously.

We calculated similarity of taxonomic composition

to reference condition with a BC similarity index (i.e., 1

 BC dissimilarity; Legendre and Legendre 1998). For

each site, we calculated BC similarity between the site

and each reference site and used the mean of these BC

similarities to indicate the similarity of the site to

reference condition. BC similarity to reference condition ranges from 0.0 (no individuals of the same

species are found in the compared assemblages) to 1.0

(relative abundances of all taxa at 2 sites are the same).

We used relative abundances of diatoms and TP

concentrations in streams during the 1997 to 1998

EMAP sampling seasons to determine TP autecologies

for the diatoms in the 1993 to 1996 data set to maintain

independence in analyses. We used weighted average

regression (CALIBRATE, version 0.6t; Juggins and ter

Braak 1992) to determine species TP optima. We

transformed TP optima into 6 categorical values so

that these data could be combined with another

database of categorical autecological characterizations

(van Dam et al. 1994). Values for TP optima ranged

from 1 for species with the lowest TP optima to 6 for

species with the highest TP optima. We defined lowand high-P taxa as taxa with TP autecologies in the 2

lowest or 2 highest TP optimum categories, respectively. We calculated % low- and % high-P individuals

by adding the relative abundances of all low- or high-P

taxa, respectively, in a sample. We also calculated %

low-P native taxa by adding the relative abundances of

those low-P taxa that had been characterized as native

in a sample.

We used linear regression, lowess regression, and

regression tree analysis to relate TP concentration to

algal attributes to determine effects-based TP benchmarks. We used the 3 regression methods as complementary approaches (Fig. 2A–D). Regression tree and

lowess techniques order observations along the stressFIG. 2. Approaches to development of stressor criteria when potential responses of valued ecological attributes to stressors (e.g.,

total P [TP]) are nonlinear with assimilative capacity for increases at low levels the stressor (A, B), nonlinear with strong sensitivity

to changes at low levels of the stressor (C), and linear (D). A stressor criterion is established at a level of a stressor that protects the

valued ecological attribute. Arrows indicate TP criteria justified on the basis of the form of the stressor–response relationship.

Shaded areas indicate the range of TP criteria that could be acceptable. Acceptable ranges vary as a function of the linearity of the

stressor–response relationship and the type of nonlinear relationship.

788 R. J. STEVENSON ET AL. [Volume 27
or gradient. Both methods provide analyses of the

stressor–response pattern without the a priori assumption of a specific nonlinear pattern.

Regression tree analysis can identify precise changepoints (thresholds) at specific stressor levels where the

mean response values above and below the stressor

level are significantly different (e.g., higher end of the

shaded box in Fig. 2A). The analysis produces a

regression tree in which clusters of observations

grouped by stressor levels have different mean values.

Lowess regression is a locally weighted regression

based on sets of similar stressor values (Fig. 2B, C).

We did regression tree analysis with 1000 bootstrap

regression tree simulations with the rpart and bootstrap routines in the statistical program R (R Core

Development Team 2006) (King and Richardson 2003,

Lougheed et al. 2007). We determined the probability

that a threshold would be observed by chance and the

range of TP concentrations within which the probability of observing a threshold was 0.10 to 0.90. We did

lowess regression with S-PLUS (version 6; Insightful

Corporation, Seattle, Washington; tension 1⁄4 0.5),

plotted the data with SYSTAT, and determined lowess

thresholds as rules illustrated in Fig. 2 and described in

the next paragraph. The likelihood that observed

thresholds could occur by chance was evaluated by

comparing the variance explained (r

2

) by regression

tree, lowess, and linear models in outputs from

SYSTAT and S-PLUS, which in all cases was equivalent

to the regression sum of squares divided by the total

sum of squares. We assumed relationships were

nonlinear if we observed a narrow range for TP

concentrations within which the probability of observing a regression tree threshold was 0.10 to 0.90 or we

observed greater r

2 for lowess and regression tree

models than for linear models.

Lowess regression and regression tree analysis

delineate thresholds or change-points in nonlinear

responses of attributes to TP (Fig. 2A–C). We define

thresholds as either a nonlinear response or the nutrient

concentration at which the response occurs. Thresholds

provide justification for narrowing the range of possible

stressor levels that should be considered for criteria.

Stressor criteria should be established at stressor levels

below thresholds in nonlinear responses that demonstrate assimilative capacity (the ability of the system to

resist change in the valued ecological attribute up to

some level of the stressor) (Fig. 2A, B). Setting criteria

below thresholds in responses demonstrating assimilative capacity provides a margin of safety to protect

valued attributes, which is a requirement for setting

total maximum daily loads (US Clean Water Act Section

303(d)(1)(c); USEPA 1999). Negative exponential responses with saturation levels (Fig. 2C) call for

protecting attributes, processes, or condition before

they are completely lost. Setting criteria at stressor

levels below the saturation level (upper threshold)

reduces risk of losing those attributes. The range of

possible stressor criteria is widest with linear responses

(Fig. 2D) when little rationale exists for specific criteria

based on degree of loss of valued attributes with

incremental increases in stressors. When responses are

linear, criteria need to be established using other

decision processes that are not evaluated here.

Results

Diatom taxonomic composition in reference streams

indicated that factors associated with conductivity and

alkalinity had strong effects on expected taxonomic

composition that might interfere with our ability to use

a 1-class reference-condition approach for setting TP

criteria (i.e., species composition varied along a

confounding natural gradient). One hundred nine taxa

had an average relative abundance .1.0% in samples

from a set of 123 reference sites. Two groups of sites

were identified by TWINSPAN. One group was

characterized by frequent occurrence of diatoms in

the genus Eunotia, which are commonly found in lowalkalinity waters (van Dam et al. 1994). The other

group was dominated by taxa common in alkaline

waters. Discriminant functions (based on both forward

and backward selection) identified dissolved organic C

(DOC), conductivity, alkalinity, Si, and SO4 as important factors affecting species composition in reference

streams. Therefore, we used only well-buffered

streams in the following analyses to control for natural

variability among streams caused by pH. We defined

well-buffered streams as those streams that showed

little evidence of effects of mining and that met criteria

for 2 discriminant functions based on forward and

backward selection: ANC .50 and SO4 ,400 leq/L

(Herlihy et al. 1991). More streams in the data set were

classified as well buffered than as poorly buffered. The

TP benchmarks that are developed with these analyses

would only apply to well-buffered streams.

Most molar N:P ratios in the well-buffered streams

were greater than the 16:1 Redfield ratio, and these

N:P values suggested that algae in most MAH streams

would be P-limited if nutrient concentrations were low

(Redfield 1958). Ratios ranged from 5 to 456 (median 1⁄4

73, mean 1⁄4 102, SD 1⁄4 81). The 25th and 75th percentiles

of the N:P distribution were 42 and 140.

TP

TP concentrations ranged from 1 to 103 lg/L (Fig.

3A, B). The median and 25th percentile of TP

concentrations at all sites were 14 and 10 lg/L,

respectively (Fig. 3A). TP concentrations in streams

2008] ALGAE AND NUTRIENT CRITERIA 789
were positively related to % WA (log10[TP] 1⁄4 2.265 þ

0.017[% WA]; r

2 1⁄4 0.134, p , 0.001). TP concentrations

increased from a predicted mean of 9.6 lg TP/L at 0%

WA to as high as 52.7 at 100% WA. The standard error

(SE) of the y-intercept was 0.089; thus, the 75% and

90% CIs for predicted mean TP at 0% WA were 10.7

and 11.5 lg/L for streams with 0% WA.

The median and 75th percentiles of the TP distributions at reference sites with 0% WA (n 1⁄4 16) were 10

and 22 lg/L, respectively (Fig. 3B). The number of

sites with 0% WA was low. Therefore, we increased the

maximum % WA in the definition of reference

condition to 3% WA to increase sample sizes. The

75th percentile of the TP distribution was 12 lg/L at

3% WA (Fig. 4A), and did not increase above 12 lg/L

until the maximum % WA for reference sites .10% (n

1⁄4 44). The 75th percentile of the TP distribution

increased much more rapidly than median TP concentration as we increased the maximum % WA in a

sample set above 10% (Fig. 4A). The median and 75th

percentiles of the TN (Fig. 4B) and Cl (Fig. 4C)

distributions in streams also increased with % WA (p

, 0.001). We did not observe lags in responses of TN

and Cl concentrations to increasing % WA in lowdisturbance watersheds (Fig. 4B, C).

Relationships of algal attributes to TP

Chl a and AFDM.—Ten of 11 algal attributes (all

except chl a) were significantly linearly related to TP,

but the explanatory value (r

2

) of many of these models

was low (Table 1). Benthic algal chl a and AFDM were

highly variable among streams and ranged from ,5

mg chl a/m2 and 3 g AFDM/m2 to .100 mg chl a/m2

and 50 g AFDM/m2 (Fig. 5A, B). In linear models, chl a

and AFDM were only weakly related to TP concentrations. Among nonlinear models, regression tree

models for ch a and AFDM were statistically signifiFIG. 3. Frequency distributions of total P (TP) concentrations at all sites (A) and sites in reference condition (B) in

streams in the Mid-Atlantic Highlands region. Reference

condition was defined as 0% watershed alteration by human

land use. Arrows mark the 25th percentile of all sites in panel

A and 75th percentile of reference sites in panel B. Numbers

above the arrows are the TP associated with the percentile.

FIG. 4. Medians and 75th percentiles of frequency distributions of total P (TP) (A), total N (TN) (B), and Cl (C)

concentrations when reference sites were defined with

increasing levels of % watershed alteration (% WA) by

human land use.

790 R. J. STEVENSON ET AL. [Volume 27
cant, but lowess models for chl a and AFDM were not

statistically significant (Table 1). In linear models, acidand alkaline-phosphatase activities were significantly

negatively related to TP concentrations in streams

(Table 1, Fig. 5C, D). The regression-tree and lowess

models of the relationship of acid phosphatase with TP

were statistically significant, and the regression-tree

model of the relationship between alkaline phosphatase and TP was statistically significant (Table 1).

Taxonomic composition and diversity.—Three hundred

forty-five diatom taxa were observed in 193 wellbuffered streams. On average, Achnanthidium minutisTABLE 1. Regression statistics indicating the relationships between algal assemblage attributes and total P (TP) concentrations in

streams of the Mid-Atlantic Highlands ecoregion. a is the y-intercept and b is the regression coefficient in a regression of the form y1⁄4

aþb(x), N is the number of streams in the analysis, p is the attained significance (2-tailed t-test) of b, SE is the standard error of a or b.

r

2 is the variance explained by linear, regression tree, and lowess regression. r2 values in bold indicate p , 0.05 for the regression.

Algal attribute

Linear regression parameters Regression

tree r

2 Lowess r2 a SE(a) b SE(b) p r N 2

ln(chlorophyll a) 2.898 0.327 0.196 0.113 0.087 0.023 0.069 0.054 127

ln(algal ash-free dry mass) 1.487 0.277 0.358 0.124 0.027 0.040 0.068 0.084 124

ln(acid phosphatase activity) 8.812 0.337 0.289 0.116 0.014 0.054 0.168 0.099 112

ln(alkaline phosphatase activity) 7.632 0.384 0.368 0.131 0.006 0.064 0.134 0.079 116

Number of observed taxa 15.727 3.079 4.255 1.074 ,0.001 0.097 0.143 0.147 148

Evenness 0.488 0.036 0.05 0.012 ,0.001 0.100 0.136 0.112 148

Proportion of native taxa 0.674 0.088 0.105 0.031 ,0.001 0.075 0.151 0.129 148

Proportion low-P native taxa 1.119 0.082 0.075 0.028 0.009 0.045 0.060 0.063 148

Taxonomic similarity to reference 0.488 0.029 0.056 0.014 ,0.001 0.167 0.237 0.101 148

% low-P individuals 56.593 4.942 7.823 1.724 ,0.001 0.124 0.250 0.138 148

ln(% high-P individuals) 1.518 0.351 1.044 0.122 ,0.001 0.333 0.315 0.407 140

FIG. 5. Algal biomass measured as chlorophyll a (chl a) (A) and ash-free dry mass (AFDM) (B) and acid (C) and alkaline (D)

phosphatase activity (nmoles activity g1 AFDM h1

) in Mid-Atlantic Highlands streams as a function of total P (TP) concentration.

Solid lines indicate statistically significant predicted linear regressions (p , 0.05). Dashed lines indicate the cumulative probability

of a change-point determined by bootstrapped regression tree analyses (see Effects-based TP benchmarks for details).

2008] ALGAE AND NUTRIENT CRITERIA 791
simum (Ku ̈ tzing) Czarnecki and Achnanthes biasolettiana

(Ku ̈ tzing) Grunow constituted 30 and 20%, respectively, of assemblages in streams with TP ,10 lg/L (n1⁄436

streams). One hundred forty taxa were observed in

these low-TP streams, and only 7 taxa had an average

regional relative abundance .2%. On average, A.

minutissimum and A. biasolettiana constituted 14 and

11%, respectively, of assemblages in streams with TP

.30 lg/L (n 1⁄4 35 streams). However, 245 taxa were

observed in these higher-TP streams, and 12 taxa had

average regional abundances .2%.

Indicators of diversity (number of taxa observed and

evenness) increased with TP, unless the taxa considered were low-P native taxa (Table 1, Fig. 6A–D). The

number of diatom taxa observed (Fig. 6A) and the

proportion of native taxa (Fig. 6C) increased significantly with TP in linear, regression tree, and lowess

models, and evenness (Fig. 6B) increased significantly

with TP in linear and regression tree models (Table 1).

However, the proportion of low-P native taxa decreased significantly with increasing TP in linear and

regression tree models (Table 1, Fig. 6D). Linear

models explained only a small part of the variation

in diversity metrics as a function of TP (r

2 1⁄4 0.045–

1.00). Nonlinear models (r

2 1⁄4 0.060–0.151) explained

substantially more variation than linear models for the

number of taxa observed and proportion of native

taxa, but not for evenness and proportion of low-P

native taxa (Table 1). Lowess curves for number of taxa

observed (Fig. 6A) and proportion of native taxa (Fig.

6C) indicated that the greatest change in numbers of

taxa occurred between 8 and 20 lg TP/L.

Models relating changes in species composition to

TP concentrations were more precise than models of

biomass, diversity, and phosphatase activity as a

function of TP (Table 1). Similarity to reference

condition decreased from .0.45 to ;0.25 as TP

increased from 0 to 100 lg/L (Fig. 7A). Percent lowP individuals in assemblages decreased from 40 to 50%

at low TP concentrations to ,30% in high TP

concentrations (Fig. 7B). Percent high-P individuals

increased from close to 0% at low TP concentrations to

.20% at high TP concentrations (Fig. 7C). Linear and

nonlinear models differed in their ability to explain

these variables as a function of TP. The regression tree

model explained more variability in similarity to

reference than did the lowess model, and the linear

model was intermediate between the 2 nonlinear

FIG. 6. Algal diversity measured as number of observed taxa (A), evenness (B), proportion of all native taxa (C), and proportion

of low-P native taxa (D) (see Effects-based TP benchmarks for definitions and details of calculation of metrics) in Mid-Atlantic

Highlands streams as a function of total P (TP) concentration. Solid lines indicate statistically significant predicted linear regressions

(p , 0.05). Linear regressions are replaced by a smoothing line generated by lowess regression if the lowess regression explained

20% more variation in the diversity measure–TP relationship than did linear regression and r

2 of the lowess regression 0.10.

792 R. J. STEVENSON ET AL. [Volume 27
models (Table 1). The regression tree model explained

substantially more variability in % low-P individuals

than did the linear model, but the lowess model was

not statistically significant (Table 1). However, the

lowess model explained substantially more variability

in % high-P individuals than did either the linear or

regression tree models (Table 1). Lowess regression

indicated that similarity to reference conditions and %

high-P individuals changed most rapidly at TP

concentrations between 10 and 35 lg/L (Fig. 7A, C).

TP thresholds.—Most TP benchmarks derived by

regression tree and lowess regression were between 10

and 20 lg/L. Lowess regression detected statistically

significant nonlinear patterns for 5 of the 11 algal

attributes (Table 1). Four of those 5 (number of observed

taxa, proportion of native taxa, similarity to reference, %

high-P individuals) showed threshold patterns with no

response in biological condition when TP was ,10 lg/L

(Figs 6A, B, 7A, C). Percent high-P individuals increased

from 1 to 3% at TP values ,5 lg/L (Fig. 7C), but only 2

streams had very low TP concentrations and very low

percentages of high-P individuals.

The most precise TP change-points derived from

bootstrapped regression tree analyses were for the 4

algal attributes that had threshold patterns with

lowess regression. The bootstrapped thresholds for

number of taxa observed, proportion of native taxa,

and % high-P individuals were 11.5 lg TP/L (Table 2).

However, the bootstrapped threshold for similarity to

reference was 26.5 lg TP/L (Table 2). The similarity-toreference threshold change-point indicates one problem with regression tree analysis: .1 change-point

might be identified in some threshold patterns, and the

change-point identified might be at the higher end of

the range of changing conditions. The change-points

for these 4 variables were the most precise because

these variables had the highest r

2

, greatest number of

observations of the same bootstrapped change-point,

and the lowest deviation in TP between the 10th and

90th percentiles of the frequency distribution of

bootstrapped change-points (Tables 1, 2).

We found some evidence for a threshold in chl a; the

probability of chl a being .10 mg/m2 was greater

when TP was .12.7 lg/L (Fig. 5A). r

2 for the chl a

regression tree analysis was only 0.069 (Table 1), but

the 369 bootstrapped observations of the change-point

at 12.7 increased to 510 if we added bootstrapped

change-points recorded for just one stream with TP

,12.7. Thus, the probability of observing chl a ,10

mg/m2 was low when TP was .12.7 (Fig. 5A). The

relatively wide range in TP between 10th and 90th

percentiles of the bootstrapped change-points was

related to a 2nd change-point at ;21 lg TP/L.

Discussion

Expected condition and responses to P enrichment

The natural TP concentrations in MAH streams

averaged ;10 lg/L, a value similar to those observed

in low-nutrient lakes and wetlands (e.g., McCormick et

al. 1996, Dixit et al. 1999) and in streams in watersheds

FIG. 7. Taxonomic composition of algal assemblages

measured as the similarity of the assemblage at a site to

reference conditions (A), % low-P individuals (B), and %

high-P individuals (C) (see Effects-based TP benchmarks for

definitions and details of calculation of metrics) in MidAtlantic Highlands streams as a function of total P (TP)

concentration. Solid lines indicate statistically significant

predicted linear regressions (p , 0.05). Linear regressions are

replaced by a smoothing line generated by lowess regression

if the lowess regression explained 20% more variation in the

species composition measure–TP relationship than did linear

regression and r

2 of the lowess regression 0.10.

2008] ALGAE AND NUTRIENT CRITERIA 793
with low levels of human land use in other regions of

the US (Stevenson et al. 2006). Regression analysis

predicted that natural TP was 9.6 lg/L and that

median TP concentrations were 10 lg/L when human

disturbance in watersheds was 0% WA. However, this

benchmark might vary among regions despite the fact

that natural nutrient concentrations are ;10 lg/L in

aquatic ecosystems of many regions. Dodds and Oakes

(2004) predicted that variation in natural TP concentrations would range from 20 to .100 lg/L among

ecoregions. Most variability in TP concentrations

among streams in the MAH was associated with

human disturbance in watersheds, as noted elsewhere

(Allan et al. 1997, Dodds and Oakes 2004), but

distinguishing between natural and anthropogenic

sources of P is essential for assessing and managing

human effects on ecosystems.

Differences in benthic algal biomass, enzyme activity, diatom diversity, and diatom species composition

were related to TP concentrations in MAH streams.

Some of these observations are common in studies of

algal assemblages, but all of these attributes are

seldom measured in the same study and in hundreds

of streams. In general, the precision of relationships

increased with taxonomic resolution; indices based on

species composition were much more precisely related

to TP (r

2 ’ 0.12–0.41) than were assemblage-level

measures of biomass (r

2 ’ 0.02–0.08).

The relationship between algal biomass and nutrient

concentrations in stream water is notoriously complex

(Stevenson 1997, Dodds et al. 1998, Biggs 2000). Time

elapsed since scouring by storms, shading, grazers,

patchiness caused by macroalgal colonization, and

current velocity can affect algal biomass and its

estimation in streams (Stevenson et al. 1996, Kohler

and Wiley 1997, Biggs 2000, Riseng et al. 2004).

Moreover, benthic algae remove nutrients from the

water column (Mulholland 1996), and this removal can

mask the relationship between benthic algae and

limiting nutrients. In our study, use of nonlinear

regression techniques increased r

2 for chl a and AFDM

relationships with TP, but TP explained less variation

in algal biomass in our study than in others (Dodds et

al. 1997, 2002, Stevenson et al. 2006). Diverse hydrogeomorphology of MAH streams, seasonal and spatial

variability in biomass, a single measurement of

nutrient concentrations, and negative effects of biomass on soluble P concentration probably constrained

our ability to discern precise relationships between

biomass and TP.

The activities of both acid and alkaline phosphatase

decreased with increasing TP in our study. We are

unaware of any other large-scale studies in streams

that showed decreases in phosphatase activities as

nutrients increased. However, phosphatase activity is

seldom measured in large-scale surveys. Phosphatase

activity decreased with P enrichment at TP concentrations .10 to 15 lg/L in the Everglades, a large

freshwater marsh (Richardson and Qian 1999). The

relationships between phosphatase activity and TP are

highly variable and are influenced by many factors.

For example, algal biomass could confound our

understanding of phosphatase–TP relationships if high

biomass generated nutrient limitation within the algal

mat. Nutrient limitation might occur if the rates of

nutrient uptake within a mat were higher than the rate

TABLE 2. Total P (TP) concentration (lg/L) benchmarks for nutrient criteria derived from biological responses (attributes) to TP

using bootstrapped regression tree change-points (BCP) and lowess regression. 10th and 90th are the TP concentrations for the 10th

and 90th percentiles of the cumulative frequency distribution of bootstrapped change-points. DLTP is the deviation in ln(TP)

between the 10th and 90th percentiles, BCP is the bootstrapped change-point observed most often, and Boot no. is the number of

bootstrapped observations (of 1000) that identified BCP. High Boot no. and low DLTP indicate more precise BCPs. NA indicates p .

0.05 or r

2 . 0.10 for lowess regression.

Algal attribute

Regression tree

10 Lowess th 90th BCP Boot no. DLTP

ln(chlorophyll a) 8.5 27.8 12.7 369 0.51 NA

ln(algal ash-free dry mass) 7.5 36.5 8.2 261 0.69 NA

ln(acid phosphatase activity) 6.5 41 6.5 225 0.80 NA

ln(alkaline phosphatase activity) 6.5 16.3 6.5 531 0.40 NA

Number of observed taxa 11.5 16.5 11.5 619 0.16 10

Evenness 12.2 26.5 19.5 305 0.34 NA

Proportion of native taxa 9.5 11.5 11.5 709 0.08 10

Proportion low-P native taxa 4.5 34.5 18.5 217 0.88 NA

Species similarity to reference 26.5 26.5 26.5 1000 0.00 9

% low-P individuals 7.5 36.5 18.5 154 0.69 NA

ln(% high-P individuals) 11.5 16.5 11.5 619 0.16 10

794 R. J. STEVENSON ET AL. [Volume 27
of nutrient transport into the mat (Stevenson and

Glover 1993). Nevertheless, in our study, the decrease

in phosphatase activity as TP increases suggests that P

limitation in MAH streams is relieved as TP increases.

The number of observed taxa and evenness increased as TP increased. This result probably reflects a

release of algae from severe P limitation and increased

habitat suitability for high-P taxa. The number of

observed diatom taxa in counts is a poor estimate of

the true number of species in a habitat when only 500

valves (halves of individual diatom cell walls) and a

small fraction of species in samples are observed

(Patrick et al. 1954, Stevenson and Lowe 1986). The

number of observed taxa in such counts is highly

correlated with evenness (Archibald 1972, Stevenson

and Lowe 1986). In our study, the number of observed

taxa also was correlated with evenness because high-P

taxa were added to low-P assemblages and more taxa

had relative abundances .2% as TP increased.

Growth rates of low-P taxa increase more slowly

than growth rates of high-P taxa when P concentrations increase, and this phenomenon might explain the

increase in evenness with increasing P concentrations

in streams at the lower end of the TP range. Shifts from

low- to high-P taxa with increasing TP would result if

growth rates of low-P taxa became saturated at lower

TP concentrations than did growth rates of high-P

taxa, and if low-P taxa had slower maximum growth

rates than high-P taxa. Experimental results confirm

that A. minutissimum and A. biasolettiana, the 2

dominant taxa in low-P MAH streams, have relatively

high growth rates in low-P conditions, but that other

taxa have higher growth rates than do A. minutissimum

and A. biasolettiana in high-P conditions (Manoylov

and Stevenson 2006). Thus, as TP concentrations

increase, species growth rates become more similar,

relative abundances become more even, and more taxa

are observed in a 500-valve count. In MAH streams, %

high-P individuals increased as TP increased. Thus,

invasion of assemblages by diatom species that require

high resource levels seems to be prevented in nearnatural MAH streams by low P availability. If a low-P

habitat cannot sustain high-P taxa, then release from

resource constraints (i.e., addition of P) might increase

taxon numbers. However, estimates of species richness

based on 500-valve counts are not sufficient to test this

hypothesis.

We found some evidence that low-P native species

might be negatively affected by increased TP. The

number of low-P native taxa and % low-P individuals

decreased with increasing TP. The r

2 value of the

relationship between the number of low-P native taxa

and TP was low, but the 25% reduction in % low-P

individuals as TP increased might be ecologically

significant if it indicates a proportional loss of low-P

native taxa. Loss of native taxa as resources increase

could be caused by one of many possible densitydependent biotic interactions, such as competition for

other resources, allellopathy, habitat alteration, or viral

or bacterial attack. The decrease in proportion of

expected low-P native species in samples could

indicate a negative effect of nutrient enrichment on

biodiversity of native microbial taxa.

Candidate nutrient criteria

Multiple lines of evidence indicate that a nutrient

criterion of 10 to 12 lg/L can be justified for MAH

wadeable streams based on reference conditions and

benthic algal responses to nutrients. Natural concentrations of TP were predicted to be 9.6 lg/L with a

75% CI of 10.7 lg/L at 0% WA. The median and 75th

percentile of TP concentrations at reference sites (WA

, 10%) were 10 and 12 lg/L, respectively.

Increases in TP from 10 to 30 lg/L were associated

with responses of benthic diatom assemblages that

indicated release from nutrient limitation. Nonlinear

regressions showed that some biological variables

differed little among streams when TP was ,10 lg/

L, but differed greatly as TP increased from 10 to 30

lg/L. Observational and experimental studies have

shown that benthic algae in streams respond to

changes in PO4-P or TP between 1 and 30 lg/L (e.g.,

Bothwell 1989, Dodds et al. 1997, Che ́telat et al. 1999,

Rier and Stevenson 2006, Stevenson et al. 2006). Algae

in wetlands and lakes respond to changes in TP at

concentrations as low as 10 lg/L (McCormick et al.

1996, Dixit et al. 1999). Thus, many attributes of

benthic algal assemblages are sensitive to increases in

nutrient concentrations above natural background

levels.

Protection of all MAH streams with a TP criterion of

10 to 12 lg/L probably is not practical. We did not

observe strong evidence for using other TP criteria, but

others have. Extensive, long-lasting nuisance growths

of the filamentous green alga Cladophora that are

unaesthetic and a problem for fishing and recreational

use can be prevented by maintaining an average of 30

lg TP/L (Dodds et al. 1997, Stevenson et al. 2006).

Effect of nutrients on fish assemblages occur when TN

. 0.61 mg/L and TP . 0.06 mg/L (Miltner and Rankin

1998, Wang et al. 2007). Any of these benchmarks (i.e.,

concentrations at which TP causes important ecological

responses) could be used as a TP criterion that would

provide some desired level of protection for streams

and support for management goals corresponding to

tiered aquatic life uses (Davies and Jackson 2006). Within

a region, different nutrient criteria could be identified

2008] ALGAE AND NUTRIENT CRITERIA 795
and applied selectively to streams that have different

management goals. For example, low stressor criteria,

such as 10 lg TP/L, would prevent degradation of

high-quality sites and set targets for restoration that

would correspond to the goals of biological integrity in

the US Clean Water Act. A 10-lg TP/L criterion would

constrain productivity to natural levels, maintain Presource constraints on species membership, and

prevent proliferation of invasive algae that require

high nutrient concentrations. On the other hand, higher

stressor criteria, such as between 30 and 60 lg TP/L,

could be interpreted as protective of ‘‘fish, shellfish,

and wildlife’’ that would correspond to the lower,

interim goal of the US Clean Water Act.

The different approaches for defining nutrient

criteria used in our study provided complementary

information that should aid criterion development.

Predictive models confirmed that human activities

increased TP in streams and estimated the TP

concentration that should be expected when human

activities with watersheds are low. Frequency distributions of TP at sites with low WA characterized

minimally disturbed conditions. Frequency distributions of TP at all sites described the existing range of

TP concentrations within the region and provided an

indication of proportion of streams that would require

restoration for each TP benchmark. Relationships

between variables indicating biological condition and

TP described the valued attributes that could be

supported at successively higher TP concentrations.

Change-points in stressor–response relationships provided important benchmarks along the TP gradient

that justified special attention (as suggested by

Muradian 2001 and as observed in our discussions

with stakeholders).

The degree to which our results can be transferred

from MAH streams to other streams and other types of

aquatic ecosystems will depend on the analytical

approach and the ecosystem. Regional factors, such

as geochemistry, climate, and hydrology, and local

factors, such as stream size and gradient, will affect the

expected natural TP concentrations predicted by

models or the frequency distributions of reference

conditions (e.g., Dodds and Oakes 2004). Regional and

local factors also might affect responses of algae and

invertebrates to increases in TP (Biggs 1995, Riseng et

al. 2004). Characterizations of minimally disturbed

conditions with frequency distributions also will vary

greatly with the extent of human alteration of regions.

Two of our approaches (evaluation of frequency

distributions of reference sites and thresholds in

stressor–response relationships) identified TP benchmarks at 10 to 12 lg TP/L. This co-occurrence

probably is related to the sensitivity of algae to

deviations from natural conditions and to the substantial proportion of MAH streams with low levels of

human disturbance. Had we studied a region with

more extensive alteration of watersheds than MAH,

we would have been able to estimate natural

conditions with predictive models as long as some

watersheds had % WA ’ 0; however, we might not

have had sufficient sample size to characterize central

tendency and variability in natural conditions precisely with frequency distributions. Nevertheless, if

climate, geology, and stream hydrogeomorphology

are sufficiently similar, we expect transferability of

results from regression-based stressor–response approaches and threshold benchmarks. For example,

responses of Cladophora in riffles to P are very similar

in Montana and the Midwest (Dodds et al. 1997,

Stevenson et al. 2006).

In conclusion, multiple analytical approaches can

and should be used when developing nutrient criteria

to provide the diversity of information that justify

criteria to stakeholders and increase the probability of

successful management actions. These approaches

have successfully characterized best available conditions and effects of nutrients on algae. However,

variations in nutrient concentrations and responses

probably are related to natural and anthropogenic

factors in complex ways that call for future work to

characterize how predicted best available condition

and ecological responses to nutrients vary among

regions. In addition, experiments in which nutrients

are manipulated are needed to confirm cause–effect

relationships, especially with nuisance macroalgae in

streams. The complementary use of predictive models,

frequency distributions, and stressor–response relationships provides a sound analytical basis for

criterion development by characterizing best available

conditions and quantifying effects on valued ecological

attributes.

Acknowledgements

The Office of Research and Development of the US

EPA supported the work done by RJS on this paper.

Robert Hughes and members of the US EPA Tiered

Aquatic Life Use Workgroup provided valuable

conversations in the formulation of the concepts

evaluated in this paper. Patti Grace-Jarrett, Kalina

Manoylov, Mark VanderBorgh, and Susanna Decelles

identified and counted algae.

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Received: 13 July 2007

Accepted: 10 June 2008

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