Exploration of Methods for Characterizing Effects of Chemical Stressors to Aquatic 

Plants 

November 1, 2010

Laura Dobbins1

Michael Lewis2

Sujatha Sankula3

Glen Thursby4

United States Environmental Protection Agency (USEPA)

1 Office of Water, Office of Science and Technology, Washington, DC

2 Office of Research and Development, National Health & Environmental 

Effects Research Laboratory, Gulf Ecology Division, Gulf Breeze, FL

3 Office of Pesticide Programs, Environmental Fate and Effects Division, 

Washington, DC

4 Office of Research and Development, National Health & Environmental 

Effects Research Laboratory, Atlantic Ecology Division, Narragansett, RI
Aquatic Plants Page 2 of 56

Table of Contents

1. Executive Summary........................................................................................................ 4

2. Introduction..................................................................................................................... 4

3. Existing methodologies to evaluate plant effects............................................................ 6

3.1. 1985 Guidelines Method for Deriving ALWQC (OW)........................................... 6

3.2. Draft Atrazine Aquatic Life Criteria........................................................................ 8

3.3. Minnesota Standards for Acetochlor and Metolachlor............................................ 8

3.4 University of California-Davis Methodology........................................................... 9

3.5. Ecological Effects Characterization and Benchmark Derivation (OPP) ................. 9

3.6. Current International Methods for Incorporating Aquatic Plant Data into Aquatic 

Life Guidelines.............................................................................................................. 11

4. Uncertainties Associated with Aquatic Plant Toxicity Data......................................... 12

4.1. Variability Associated with Endpoints .................................................................. 12

4.2. Relative Sensitivity of Test Species to other Aquatic Plants................................. 14

5. Relative Sensitivity of Standard Suite of Aquatic Plant Test Species.......................... 16

5.1. Ratios of OPP standard species endpoints to non-standard species endpoints...... 17

5.1.1. Non-vascular species ...................................................................................... 17

5.1.2. Vascular species.............................................................................................. 21

5.2. Species sensitivity distributions............................................................................. 25

6. Potential approach for deriving plant ALSV ................................................................ 29

7. Conclusions................................................................................................................... 30

8. References..................................................................................................................... 31

Appendix A. Vascular plant (VP) data used in creating ratios comparing Lemna to other 

VPs.................................................................................................................................... 36

Appendix B. Data for Species Sensitivity Distribution plots ........................................... 40
Aquatic Plants Page 3 of 56

List of Acronyms

ALSV Aquatic Life Screening Value

ALWQC Aquatic Life Water Quality Criteria

APHA American Public Health Association

ASTM American Society for Testing and Materials

CWA Clean Water Act

ECx Effective Concentration for x% of response

EF Extrapolation Factor

EPA Environmental Protection Agency

EU European Union

FIFRA Federal Insecticide, Fungicide and Rodenticide Act

FAV Final Acute Value

FCV Final Chronic Value

FPV Final Plant Value

GMAV Genus Mean Acute Value

HCx Hazardous concentration threshold for x% of 

organisms

IC50 Inhibition concentration for 50% of organisms

LC50 Lethal Concentration for 50% of organisms

LOC Level of Concern

MATC Maximum Allowable Toxicant Concentration

MPCA Minnesota Pollution Control Agency

NOAEC No Observed Adverse Effect Concentration

NOEC No Observed Effect Concentration

NPDES National Pollutant Discharge Elimination System

OECD Organization for Economic Cooperation and 

Development

OPP Office of Pesticide Programs

OW Office of Water

SETAC Society of Environmental Toxicology and Chemistry

SMAV Species Mean Acute Value

SSD Species Sensitivity Distribution
Aquatic Plants Page 4 of 56

1. Executive Summary

The Environmental Protection Agency (EPA) assesses the impact of pesticides on aquatic 

plants based on the requirements of the Clean Water Act (CWA) and the Federal 

Insecticide, Fungicide, and Rodenticide Act (FIFRA). The goal of this project is to build 

on the existing approaches used by the Office of Pesticide Programs (OPP) and the Office 

of Water (OW) and to explore tools which both program offices may use to characterize 

effects to aquatic plant communities with varying amounts of data.

This paper summarizes several different approaches that are currently being used by the 

EPA, states and regulatory agencies in other countries which account for the uncertainty 

associated with limited datasets for aquatic plants and the extent to which available data 

are representative of the entire plant community. These methods may be used to 

characterize potential adverse effects of pesticides on aquatic plant communities. 

There are two major uncertainties associated with aquatic plant toxicity data that should 

be considered when characterizing effects of chemicals on aquatic plants. The first 

involves the variability associated with toxicity study endpoints. While there are 

standardized testing methodologies from regulatory agencies and standard writing 

organizations, the endpoints available for aquatic plants exposed to the same chemical 

can vary (e.g., test duration, effect type, summary statistic may differ in each test). The 

second uncertainty involves the unknown sensitivity of test species to a chemical stressor 

relative to other aquatic plant species.

The term ―aquatic life screening value‖ (ALSV) is introduced here to represent 

community-level benchmarks. This paper describes methods that may be considered to 

develop ALSVs for aquatic plant communities using available toxicity data. These plant 

ALSVs are considered separately from animal ALSVs. A plant ALSV may simply be 

based on the lowest available aquatic plant toxicity data. 

In cases where there is evidence to suggest that the available toxicity data are not 

representative (i.e., based on an understanding of the general sensitivity of plants to 

particular modes of action and whether sensitive species are represented in the dataset) of 

the most sensitive plant species that are expected to be impacted, extrapolation factors

(EFs) could be applied to available data to derive the plant ALSV. Extrapolation factors 

are set (default) values that are applied to available toxicity data to account for various 

sources of uncertainty in extrapolating from individual species toxicity data to assessment 

endpoints. When a larger dataset is available, this paper also demonstrates that using a 

specified lower confidence interval for the plant species sensitivity distribution (SSD) can 

provide a plant ALSV. Although this paper broadly describes approaches for deriving

aquatic plant ALSVs similar to those under consideration for aquatic animals, it is 

expected that these approaches will be further refined as EPA reviews available methods.

2. Introduction

The definition of ―plant‖ varies among taxonomists. Some taxonomists reserve the term 

―plant‖ to represent those organisms in the Kingdom Plantae. All vascular plants and 

some non-vascular aquatic plants are in the Kingdom Plantae. Among the non-vascular 
Aquatic Plants Page 5 of 56

aquatic plants, free living algae include groups from four separate kingdoms1, including 

Bacteria (e.g., cyanobacteria); Protozoa (e.g., euglenoids and dinoflagellates), Chromista 

(e.g., diatoms) and Plantae (e.g., green algae). For the purpose of this white paper, the 

term ―plant‖ includes all photosynthetic organisms that contain chlorophyll a—which has 

members in each of the above four Kingdoms. 

Aquatic plants have members in every conceivable freshwater and saltwater habitat. 

Aquatic plants have various growth habits, such as attached (rooted or other holdfast), 

free-floating, submerged or emergent (only partially submerged for part or all of their life 

history). Aquatic plants also form the base of most aquatic food chains, are important 

habitat components of aquatic ecosystems and are functionally important in carbon 

assimilation and oxygen evolution. 

The Environmental Protection Agency (EPA) assesses the impact of pesticides on aquatic 

plants based on the requirements of the Clean Water Act (CWA) and the Federal 

Insecticide, Fungicide, and Rodenticide Act (FIFRA). The goal of this project is to build 

on the existing approaches used by the Office of Pesticide Programs (OPP) and the Office 

of Water (OW) to explore common approaches that both programs may use to 

characterize effects to aquatic communities using the best available scientific tools and

methodologies with varying amounts of data. In 2010, EPA conducted six regionallybased public meetings and drafted three white papers describing additional tools and 

approaches that may be used to augment the ability of the EPA, and states, local and 

tribal water management agencies to derive taxa specific and cross-taxa (community) 

benchmark values for chemicals such as pesticides. One paper, entitled "Predicting the 

Toxicity of Chemicals to Aquatic Animal Species" (hereafter referred to as the tools white 

paper), describes potential additional tools that can be used to estimate toxicity data. The 

tools paper primarily focuses on methods for estimating toxicity of chemicals to aquatic 

animals, as there are limited tools available to estimate toxicity to aquatic plants. Two 

other papers describe methodologies to estimate community effects, one on aquatic 

animals entitled "Exploration of Methods for Characterizing Effects of Chemical 

Stressors to Aquatic Animals" (hereafter referred to as the ―aquatic animal white paper‖) 

and this paper on aquatic plants. The term ―aquatic life screening value‖ (ALSV) is 

introduced here to represent community-level benchmarks. This paper describes methods 

that may be considered to develop ALSVs for aquatic plant communities using available 

toxicity data for aquatic plants. These plant ALSVs are considered separately from 

animal ALSVs, which are described in the aquatic animal white paper. 

This white paper describes various methodologies used to evaluate effects of chemicals 

on aquatic plant communities. It explores uncertainties associated with available toxicity 

tests and their relationships to aquatic plant assessment endpoints. The paper compares 

the relative sensitivities of the test species for which acceptable toxicity data are 

available. Finally, this paper provides a conceptual approach that may be used to 

integrate chemical-specific data, tools and methods (i.e., extrapolation factors and 

sensitivity distributions) for deriving plant ALSVs that may be used by EPA, States and 

 

1 Note the arrangement of ―life‖ into taxonomic categories has been in a state of flux ever since Linnaeus 

first introduced Animale and Vegetabile. The main point here is not to definitively support one particular 

scheme or another, but to point out that what we traditionally refer to as plants is an extremely diverse 

group. Kingdom titles used above are based on Cavalier-Smith (2004).
Aquatic Plants Page 6 of 56

Tribes to interpret aquatic ecological risks associated with chemical exposure information 

(e.g., monitoring data). The tools and methods discussed in this paper are intended to 

compensate for limited data in describing potential effects of specific chemicals on 

aquatic plant communities and would provide regulators with a means of deriving 

advisory values that will ensure the protection of the aquatic environment. 

3. Existing methodologies to evaluate plant effects

As described below, there are several different approaches currently used by regulatory 

agencies to derive measures of effect for aquatic plants based on available data. Measures 

of effect, such as toxicity test results from a laboratory study, are used to quantitatively 

represent assessment endpoints. An assessment endpoint is ―an explicit expression of the 

environmental value to be protected.‖2

3.1. 1985 Guidelines Method for Deriving ALWQC (OW)

The USEPA, as stated in the CWA, is tasked with establishing criteria values for various 

pollutants found in the waters of the United States. These criteria serve as guidance for 

States and Tribes to use in developing their water quality standards. The current OW

methodology for deriving criteria is outlined in the Guidelines for Deriving Numerical 

National Water Quality Criteria for the Protection of Aquatic Organisms and Their Uses

(termed: ―the 1985 Guidelines‖; USEPA 1985). These OW guidelines focus on deriving 

criteria that are based on animal toxicity data, and data for plants have been considered 

more essential recently than in the past. In these guidelines, an acute criterion is derived 

using acute toxicity data for animals. A chronic criterion is derived using the most 

sensitive of the final chronic value (FCV) for animals or the final plant value (FPV). 

The 1985 Guidelines provide limited guidance on deriving the FPV. The OW minimum 

data requirements for the FPV are results of one acceptable test with a freshwater alga

(non-vascular plant) or vascular plant (for the freshwater criterion) and one acceptable 

test with a saltwater alga or vascular plant (for the saltwater criterion). At the time that 

the original guidance was written for derivation of ALWQC, ―procedures for conducting 

tests with plants and interpreting the results of such tests [were] not well developed‖

(USEPA 1985). Because of this, a definitive set of minimum data requirements

(including specific genera or families) is not currently required for aquatic plants in OW. 

The only additional specification is if plants are expected to be more sensitive than 

animals. In this case, the ―results with a plant in another phylum (division) should also be 

available‖; however, the second test result is not required.

In addition, the types of test procedures are vaguely described in the 1985 Guidelines. ―A 

plant value is the result of a 96-hr test conducted with an alga or a chronic test 

conducted with an aquatic vascular plant.‖ The guidelines describe the calculation of the 

FPV as a number that ―should be obtained by selecting the lowest result from an 

 

2

http://www.epa.gov/OCEPATERMS/
Aquatic Plants Page 7 of 56

acceptable test with an important aquatic plant species in which the concentrations of 

test material were measured and the endpoint was biologically important.‖ The 

definitions, however, of "important aquatic plants" or "biologically important" endpoints

are not given. 

Plant toxicity data available for use in a criterion derivation are frequently absent, and no 

criteria to date have been published that use plant data in the calculation. The guidelines 

state that a criterion that is protective of aquatic animals will ―probably protect aquatic 

plants.‖ This assumption, however, clearly has not held true for herbicides (such as 

atrazine, acetochlor and metolachlor) and cannot be assumed for other pesticides or even 

chemical pollutants in general (Lewis 1990, 1995; Wang and Freemark 1995). Table 1

lists a variety of chemical classes for which data have shown plants to be more sensitive 

than animals.

Table 1. Examples of contaminants found more toxic to algae 

than animalsa. 

Trace Metals Wastewaters

Cadmium Paper Mill effluent

Copper Textile effluent

Nickel Oil Refinery effluent

Zinc

Alcohols Organics

Butanol Acridine

Diethylene glycol Acrylates

Heptanol Chloramine

Hexanol Chloroacetaldehyde

Isooctanol Chloronapthalene

Octanol 4-Chlorophenol

Proporgyl alcohol Dibenzofuran

Pesticides (*Herbicides) 1,3- Dichlorpropene

Aldrin Dinitrotoluene

Atrazine* Nitrobenzene

Chlordane 4-Nitrophenol

2,4D* Organotin

Dieldrin Phenol

Diquat* Potassium chlorate

Endrin Potassium dichromate

Glyphosate* Sodium fluoride

Tebuthiuron* Sodium tetraborate

Surfactants 2,4,6-Trinitrophenol

Ditallow dimethyl ammonium chloride

Trimethyl ammonium chloride

Sodium dodecyl sulfate

aAdapted from review table in Lewis (1995).
Aquatic Plants Page 8 of 56

3.2. Draft Atrazine Aquatic Life Criteria

The Office of Water is currently deriving an Aquatic Life Water Quality Criteria 

(ALWQC) for the herbicide atrazine3. This represents the first time that an ALWQC will 

be developed where plant data are used to establish chronic protection limits. 

In the draft ALWQC, separate freshwater and saltwater FPVs are derived. The saltwater 

FPV is based on the lowest species mean acute value (SMAV) for sago pondweed 

(Potamogeton pectinatus4). For the freshwater FPV, a unique method is being considered 

that uses a large number of laboratory toxicity studies and experimental ecosystem 

studies (Erickson 2010). In this method, original data were used to create dose-response 

curves for each species based on a common endpoint. Rather than using a species 

sensitivity distribution (SSD) consisting of EC50 values alone, the method results in a 

distribution of dose response curves for individual plant species. The experimental 

designs of the mesocosm studies varied in concentrations tested, duration of exposures 

and effect level observed. The data from these experiments provide information on 

effects to actual plant communities which can be used to determine what magnitude of 

effect on the species composition of the aquatic plant community. It should be noted that 

the methods used for the atrazine ALWQC are unique to atrazine because they rely upon 

a large aquatic plant toxicity database (that includes both single species toxicity testing as 

well as numerous mesocosm studies) that is available for this chemical. 

3.3. Minnesota Standards for Acetochlor and Metolachlor

States have explored how aquatic plant toxicity data could be used to develop water 

quality standards since herbicides are frequently detected in US surface waters and 

aquatic plants are an obvious susceptible taxonomic group for these chemical stressors.

To derive the state water quality standards for acetochlor and metolachlor, the Minnesota 

Pollution Control Agency (MPCA) analyzed available aquatic plant and animal toxicity 

data for each herbicide. As might have been expected, aquatic plants were more sensitive 

than animals to both herbicides. Consistent with the Great Lakes Initiative (GLI) Tier II 

(USEPA 1995) approach, the acute regulatory endpoints for these compounds were 

calculated using the available animal data and plant data were considered to represent 

chronic effects. 

The minimum data requirements for the acute criterion (eight animal families) were not 

available to derive the acute value using the 1985 guidelines, thus the acute values for 

both herbicides were derived using the GLI Tier II methodology (USEPA 1995). This 

methodology is described in the aquatic animal white paper. 

Chronic standards for acetochlor and metolachlor were developed using only plant data

from the OPP database and the open literature. In deriving these criteria, the MPCA had 

 

3

June 23, 2009 draft document prepared by Great Lakes Environmental Center and University of 

Wisconsin-Superior.

4 Now called Stuckenia pectinatus. www.plants.usda.gov
Aquatic Plants Page 9 of 56

goals of protecting the integrity of the plant community, protecting sensitive plant species 

and achieving a 20th percentile level of protection. The MPCA derived species sensitivity 

distributions for each chemical using geometric means of EC50 values for the same 

species (if more than one value was available for a species). The Minnesota chronic

standard for metolachlor is 23 μg/L to protect coon’s tail (Ceratophyllum demersum), a 

resident aquatic vascular plant. This value is lower than the 5th percentile of the EC50

distribution for aquatic plants (36 μg/L). Lowering the standard to protect an important 

species, such as the coon’s tail is similar to the provision set out in the 1985 guidelines 

that allows for lowering the criterion to protect commercially or recreationally important 

species. The Minnesota chronic standard for acetochlor is 3.6 μg/L, which is consistent 

with the 20th percentile of the distribution of EC50 values for aquatic plants. 

After the above standards were proposed, some reviewers commented on some 

challenges faced by MPCA in deriving the chronic acetochlor criterion. One criticism of 

the criterion was the separation of EC50 and MATC5values into separate distributions. In 

a review of MPCA’s methodologies, an independent researcher used the dose-response 

raw data from the individual plant studies to calculate an EC20 value for all studies. All 

EC20 values were plotted in one distribution, increasing the number of studies used to 

derive a criterion from the 20th percentile value6. Also included in the review of MPCA’s 

methodology, was a standardization of the measured endpoint and exposure duration. 

While MPCA used a variety of growth endpoints (e.g., dry weight, frond number, cell 

density) and exposure durations, the reviewer’s method limited data to include only four 

day algae studies measuring cell density, four or seven day duckweed studies measuring 

frond number and seven day rooted macrophyte studies measuring dry weight. This is 

consistent with the recommendation from the Society of Environmental Toxicology and 

Chemistry (SETAC) Europe Workshop on Aquatic Macrophyte Risk Assessment for 

Pesticides that, where practical, SSDs should be created using similar protocols and 

endpoints (Maltby et al. 2010). 

3.4 University of California-Davis Methodology 

In the UC-Davis Methodology (TenBrook et al. 2010), herbicides must be evaluated 

using data from algae or vascular plants. Only chronic criteria are derived for plants. For 

herbicides, and for pesticides where plants are the most sensitive taxa, plant-only SSDs 

should be used, provided there are reliable and relevant studies for five different plant 

species. If there are fewer than five plant species, the criterion is determined as the lowest 

plant NOEC with a relevant endpoint, similar to the 1985 Guidelines final plant value.

3.5. Ecological Effects Characterization and Benchmark Derivation (OPP)

OPP, under FIFRA, has the authority to require data in support of the registration of a 

pesticide product. Accordingly, OPP has developed regulations (40 CFR Part 1587) 

 

5 MATC =Maximum Allowable Toxic Concentration. It is calculated as the geometric mean of the lowest 

observable effect concentration and the no observed effect concentration.

6 Giddings, JM. 2007. Review of Plant-Based Acetochlor Class 2 Water Quality Standards for Minnesota. 

7

Code of Federal Regulations. 2010. http://ecfr.gpoaccess.gov/cgi/t/text/textidx?c=ecfr&sid=1a3c043b5425ffaa607dd0f14f9bddbb&tpl=/ecfrbrowse/Title40/40cfr158_main_02.tpl 
Aquatic Plants Page 10 of 56

which specify the types and amount of information that pesticide companies must 

routinely submit to EPA to support product registration. Section 158.6608describes the 

plant protection data requirements and specifies the type and amount of data the Agency 

needs to characterize the effects of a pesticide on aquatic plants and is based on proposed 

or existing use(s) (how and where the pesticide is applied). As with animal testing 

requirements, OPP relies on a tiered approach for examining effects to aquatic plants. 

The Ecological Effects Test Guidelines (Sub-part G of 40 CFR Part 158) describe three 

testing tiers for assessing the effects of pesticides on non-target aquatic plants. The first 

tier assesses the effect on plant growth resulting from a single test concentration 

equivalent to the estimated environmental concentration resulting from the maximum 

labeled application rate or a limit concentration. At minimum, Tier I tests are required for 

all pesticides. If Tier I testing shows growth reduction or visual phytotoxicity of >50% 

inhibition, or the Tier I test does not provide a definitive no observable adverse effects 

concentration (NOAEC), Tier II tests are required. Tier II tests involve multiple test 

concentrations and are intended to generate EC50 and NOAEC values for the test species 

which exhibit detrimental effects in the Tier I testing. While Tier I and II tests are 

laboratory tests, Tier III tests are field studies which are designed to evaluate adverse 

effects during critical stages of development on sensitive native plant communities. To

date, Tier III aquatic plant tests have been submitted to OPP for only a few compounds 

(e.g., diquat, irgarol).

OPP uses a screening approach to assess plant sensitivity to pesticides, which relies on a 

suite of toxicity tests performed on a specified set of surrogate species. For aquatic plants, 

these categories are based on the presence or absence of a vascular system. For nonvascular aquatic plants, the surrogate species generally are Pseudokirchneriella 

subcapitata (formerly Selenastrum capricornutum; green alga), Anabaena flos-aquae

(cyanobacterium), Navicula pelliculosa (pennate freshwater diatom) and Skeletonema 

costatum (centric marine diatom). Lemna species (usually either L. gibba or L. minor) 

serve as surrogates for aquatic vascular plant species (USEPA 2004). Specific guidelines 

are available for the tiered testing of aquatic non-vascular and vascular plants. Guidelines

850.54009and 850.440010 describe the test procedures and conditions for microalgae and 

aquatic vascular plants, respectively. Test guideline 850.445011 pertains to the test 

procedures and conditions for field, microcosm, and mesocosm studies that cover a 

broader range of plant types and study durations (tests typically continue for the entire 

life cycle of the test plant). If data are available for other, non-standard, non-vascular or 

vascular species (e.g., from the scientific literature) that show greater sensitivity than 

those results from the standard test species, OPP uses data for the more sensitive species

in risk assessments provided those studies meet acceptability criteria. Endpoints suitable 

for quantitative use for all species are used in the risk characterization. 

 

8

http://ecfr.gpoaccess.gov/cgi/t/text/textidx?c=ecfr&sid=1a3c043b5425ffaa607dd0f14f9bddbb&rgn=div8&view=text&node=40:23.0.1.1.9.7.1.2&idno=40

9

http://www.epa.gov/ocspp/pubs/frs/publications/OPPTS_Harmonized/850_Ecological_Effects_Test_Guidelines/Drafts/850-

5400.pdf

10 http://www.epa.gov/ocspp/pubs/frs/publications/OPPTS_Harmonized/850_Ecological_Effects_Test_Guidelines/Drafts/850-

4400.pdf

11 http://www.epa.gov/ocspp/pubs/frs/publications/OPPTS_Harmonized/850_Ecological_Effects_Test_Guidelines/Drafts/850-

4450.pdf
Aquatic Plants Page 11 of 56

OPP’s measurement endpoints for aquatic non-vascular plants focus primarily on algal 

growth rates and biomass (vegetative reproduction) measurements based on 4-day (96-hr)

tests. The most sensitive EC50 value of the four non-vascular plants (no distinction is 

made between freshwater and marine plants) is typically used to characterize effects to 

aquatic plants and in risk assessment. Measurements on vascular plants typically include 

growth (frond number at Day 0, 3, 5, and 7 of exposure), growth rate, mortality, biomass, 

measurements at the end of the 7-day test. Again, OPP typically selects the most sensitive 

EC50 for the vascular aquatic plant endpoint. 

The OPP benchmarks were developed in response to recommendations and input from 

stakeholders, who were concerned about potential effects of pesticides with no existing 

ALQWC. OPP developed a webpage of non-regulatory taxa-specific aquatic toxicity 

endpoints referred to as ―OPP Aquatic Benchmarks‖12. These Benchmarks are based on 

the most sensitive toxicity data (considering registrant-submitted studies and the 

scientific literature) from OPP’s ecological risk assessments of specific pesticides. For 

non-vascular and vascular plants, benchmarks are calculated by multiplying each lowest 

EC50 value by the plant Level of Concern (LOC = 1.0), which is based on OPP’s 

ecological risk assessment process. LOCs are the Agency’s interpretative policy and are 

used to analyze potential risk to non-target organisms and the need to consider regulatory 

action. LOCs are used to indicate when a pesticide use (as directed on the label) has the 

potential to cause adverse effects on non-target organisms (USEPA 2004).

3.6. Current International Methods for Incorporating Aquatic Plant Data 

into Aquatic Life Guidelines

Throughout the world, aquatic life guidelines for contaminants are derived using different 

methodologies and data requirements. The use of plant data is not always required, and in 

some cases plant data are only required for herbicides or phytotoxic compounds. For 

instance, the province of Quebec does not require plant data (MENVIQ 1990), while the 

Canadian Water Quality Guidelines for the Protection of Aquatic Life Protocol (CCME 

2007) requires plant data for their two highest tier guidelines, but not the lowest tier 

guideline. The European Union (EU) requires algal data as part of their ―base set‖ of test 

values, and the algal data are incorporated when the species sensitivity distribution (SSD)

approach is used to derive the guideline, while plant data will reduce the extrapolation 

factor (EF), when an SSD is not used (European Commission 2003). The EFs are used in 

the risk assessment process to account for uncertainty in the data. 

In other countries, the type of chemical being assessed for guideline derivation may play 

a role in the amount of plant data required. Canada requires only one plant study for the 

two highest tier guidelines, unless plants are the most sensitive (e.g., with herbicides), in 

which case three plant toxicity values are required. For the lowest tier guideline, no plant

data are required unless the plants are the most sensitive organisms (CCME 2007). The 

EU also requires additional plant studies when the stressor of concern is an herbicide or 

plant growth regulator (European Commission 2003). 

 

12 OPP Aquatic Benchmark Table. Available online at:

http://www.epa.gov/oppefed1/ecorisk_ders/aquatic_life_benchmark.htm
Aquatic Plants Page 12 of 56

When both plant and animal data are required in deriving a guideline for a contaminant, 

most countries assess the plant toxicity values alongside the toxicity values for animals. 

Canada and the EU include plant toxicity values along with animal values in their SSDs

when deriving their highest tier guidelines. Canada generally considers most standardized 

plant tests to be chronic data, while the EU considers algal data to be chronic if the 

endpoint is a NOEC rather than an EC50 (CCME 2007, European Commission 2003). For 

most countries using EFs, they are applied to the lowest toxicity value, whether it be 

plant or animal. EFs vary depending on the amount of toxicity data that are available and 

depending on whether an acute or chronic guideline is being derived. For chronic toxicity 

values, Canada and the EU apply an EF of 10 to the lowest toxicity value (plant or 

animal) to derive their regulatory endpoint. However, the required EF is higher in EU if 

plant data were not available in the analysis. 

4. Uncertainties Associated with Aquatic Plant Toxicity Data

There are two major uncertainties associated with aquatic plant toxicity data that should 

be considered when characterizing effects of chemicals on aquatic plants. The first 

involves the variability associated with endpoints resulting from toxicity studies. The 

second involves the unknown sensitivity of test species to a chemical stressor relative to 

other untested aquatic plant species. These uncertainties are described below.

4.1. Variability Associated with Endpoints

A variety of aquatic phytotoxicity test methods (laboratory and in situ) have been 

described in the scientific literature during the last 40 years for various single species of 

freshwater and saltwater algae (mostly microalgae), vascular plants, periphyton and 

phytoplankton assemblages. These include the laboratory methods described by standard 

writing organizations and regulatory agencies such as American Public Health

Association (APHA Standard Methods for the Examination of Water and Wastewater), 

Organization for Economic Cooperation and Development (OECD Guidelines for 

Testing of Chemicals), American Society for Testing and Materials (ATSM Standards on 

Biological Effects and Environmental Fate) and USEPA (850 series – OCSPP 

Harmonized Test Guidelines and National Pollution Discharge Elimination System 

[NPDES] Short-term Methods for Estimating the Chronic Toxicity of Effluents and 

Receiving Waters).

Despite the availability of standard methods, the measurement endpoints available for 

aquatic plants exposed to the same chemical can vary. Plant sublethal effects extend to a 

variety of attributes which include biomass (e.g., cell count, growth rate, pigment 

content), activity (e.g., fluorescence, oxygen evolution, ethylene production) and 

biochemical attributes (e.g., ATP level, enzyme activities). In practice, the receptor 

attributes most often used by OPP and OW are either some aspect of growth or growth 

rate. Biochemical effects can be early warning indicators of environmental stressors as 

toxicity in aquatic plants is first manifested at the biochemical level before effects are 

evident at the whole-organism level. Activity or biochemical attributes are generally not 
Aquatic Plants Page 13 of 56

routinely considered by the EPA, unless a peer reviewed quantitative relationship can be 

established between the biochemical measurement endpoint and the Agency’s apical 

assessment endpoints. The sublethal biochemical effects that are commonly reported in 

aquatic plant studies in response to stressors include changes in carbon fixation, plant 

pigments, carbohydrate content, cytochrome F, ethylene/ethane, oxidative enzyme 

activity and protein concentration, enzyme levels, antioxidant levels, formation of stress 

proteins, chlorophyll fluorescence and lipid peroxidation. These effects often may be 

more sensitive, but their environmental relevance, relationship with more standard 

endpoints, and link to whole plant effects is not known and/or poorly characterized. Like 

the situation with animals, the connection between these three desired assessment 

endpoints and some of the non-traditional measurement endpoints has not been well 

established. Reproduction endpoints with aquatic plants have generally been restricted to 

vegetative reproduction. Sexual reproduction has received much less attention, but may 

be a more sensitive plant endpoint as compared to vegetative growth. For instance, the 

EC50 value for reproduction in the marine red alga Ceramium strictum when exposed to 

phenol is 5,000 μg/L, while the growth EC50 value is 10,000 μg/L (Eklund 1998, Bruno 

and Eklund 2003). Therefore, more sensitive endpoints may not always be captured in 

typical studies that evaluate vegetative growth. 

Plant summary statistics are often expressed as either a no observable adverse effect 

concentration (NOAEC), EC50 or some other ECx level. When an EC endpoint is selected 

other than EC50 it is often chosen in an attempt to provide a substitute for an NOAEC 

(e.g., EC20). This is usually done to eliminate some of the concerns about using 

hypothesis testing in establishing no effect concentrations when the study has a 

regression-based design and inadequate replication to support hypothesis testing. 

Concerns with the NOAEC include the following: 1) the potential for a biologically 

significant effect at a test concentration where observed effects were not statistically 

significant; 2) the NOAEC is heavily influenced by the test concentrations that were 

chosen for the test; and 3) confidence intervals cannot be derived (Crane and Newman 

2000). 

The exposure durations of standardized tests often differ, which brings up the 

consideration of whether plant tests of different exposure durations are appropriate to 

combine in deriving screening values or ALWQC. Most microalgal tests have exposure 

durations of either 72 or 96 h; Lemna often range between 4 and 7 d; durations for 

toxicity tests conducted with rooted vascular plants (freshwater) have ranged from 1 hr to 

6 wk (Lewis and Wang 1999); test durations with seagrasses have been conducted 

between 2 hr and 42 d (Lewis and Devereux 2007) and between 1 and 26 wk for 

mangroves (Lewis et al. in review). If growth rate is the receptor attribute of interest then 

data from tests using different exposure durations could probably be combined (as is 

being proposed for a portion of the draft atrazine ALWQC). However, if pesticide 

exposure concentrations are not measured (which is often the case with plant tests), then 

care should be taken when including data from widely differing exposure durations—the 

actual exposure concentrations could be vastly different. Rentz and Hanson (2009) 

merged data from multiple exposure durations from tests with aquatic macrophytes by 

adjusting them all to the same duration. They used Haber’s rule which may not have been 
Aquatic Plants Page 14 of 56

appropriate. While Haber recognized that C x t = k [where C = concentration, t = time, k 

= constant] was applicable only under certain conditions, many toxicologists have used 

this rule to analyze experimental data whether or not their chemicals, biological 

endpoints, and exposure scenarios were suitable candidates for the rule. Haber studied the 

acute lethality of war gases and his exposure durations were on the order of a few 

minutes to several hours. There is no indication that he advocated his rule held for all 

toxins and for exposures ranging from days to weeks (Miller et al. 2000). 

OPP’s guidance suggests using the most sensitive measurement endpoint as long as it can 

be related to the assessment endpoints of survival, growth or reproduction. This generally 

includes 96-h EC50 values for non-vascular plants and 7-d EC50 values for vascular plants 

based on biomass or growth rate. When considering available data for aquatic plants 

exposed to a chemical stressor, an attempt should be made to make the endpoints as 

consistent as possible. For instance, endpoints with similar durations and measures of 

effect (e.g., EC50) should be considered together. This will ensure that the variability in 

responses to stressors can be attributed to differences in species responses, rather than 

variability in endpoints.

4.2. Relative Sensitivity of Test Species to other Aquatic Plants

The ECOTOX database13, which is maintained by the EPA’s Office of Research and 

Development Mid-Continental Ecology Division (ORD/MED) is a source of open 

literature ecological effects data on single chemicals for aquatic and terrestrial plants and 

animals. Based on the results of 2008 query of ECOTOX, approximately 23,000 studies 

were conducted with aquatic plants and of these, the majority (86%; 19,700 entries) were 

with freshwater species. The majority of the aquatic plant studies (18,700) were 

conducted with microalgae. While 2,120 were conducted with duckweeds, 570 with 

submerged grasses, 4 tests with mangroves and about 1,670 tests with other vascular 

plants (Figure 1). Only a relatively small number of tests (1,072 studies) have been 

conducted on pesticides – the majority of plant studies were conducted with metals or 

other toxicants such as high production volume chemicals. It should be noted that the 

ECOTOX database contains aquatic plant toxicity data submitted to OPP by pesticide 

registrants.

 

13 Available online at: http://cfpub.epa.gov/ecotox/
Aquatic Plants Page 15 of 56

Figure 1. Summary of available aquatic plant data from USEPA’s ECOTOX 

database as of 2008. Other VP means Other Vascular Plants

As indicated previously, OPP typically receives aquatic plant toxicity data for macroalgae 

and Lemna sp. The relative sensitivities of these test species to other aquatic plants are

generally unknown. This can be attributed to limited data for other species of plants 

exposed to pesticides (Figure 1). Section 5 below explores the relative sensitivities of 

OPP’s typical test species with other test species that may be available for the same 

chemical.

Differences in sensitivities of test species to chemicals may be attributed to the mode of 

action of the chemical and a plant’s physiology. For instance, since herbicides often 

target various aspects of the photosynthetic pathway, there could be differences in 

response between vascular plants that have C3 vs. those with C4 pathways14. For 

example, Lemna species are C3 plants (Longstreth 1989), many saltmarsh grasses (e.g., 

species of Spartina and Distichlis) are C4 plants, other saltmarsh plants (e.g, species of 

Salicornia and Scirpus) are C3 (Drake 1989). In some species of amphibious freshwater 

plants (e.g., the sedges Elocharis vivipara and E. baldwinii), the emergent phase exhibits 

C4 photosynthesis while the submerged phase is C3 (Cronk and Fennessy 2001). 

In addition, there is a wide variety of photosynthetic pigments among the various algal 

groups, so type of accessory pigments (and thus different pigment synthesis pathways) 

also might make a difference in sensitivity. Although the analysis presented below in this 

paper did not show any obvious pattern in plant sensitivity relative to major taxonomic 

groups (e.g., diatoms vs. green algae).

Also aquatic plants have highly diverse habits (i.e., floating, submerged and emergent 

species) which may need to be considered as the route of exposure varies among these 

 

14 C3 and C4 refer to the first stable carbon compound in CO2 fixation. In C3 plants this compound is 3-

phosphoglyceric acid (a 3 carbon compound) and in C4 plants it is oxaloacetic acid (a 4 carbon compound).

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

Total Number of Tests

4
Aquatic Plants Page 16 of 56

species. The typical vascular plant test species, Lemna, is a floating plant, with roots 

suspended in the water column. This route of exposure (i.e., via the suspended roots and 

undersurface portion of leaves) may not accurately characterize submerged or emergent 

aquatic plant species, as those that are rooted in the sediment. There currently is a test 

protocol for the rooted freshwater macrophyte Myriophyllum spp. undergoing evaluation 

for standardization in Europe (Maltby et al. 2010). 

5. Relative Sensitivity of Standard Suite of Aquatic Plant Test Species

In an ideal situation, the sensitivities of a wide variety of aquatic plant groups would be 

available, represented using a standard set of study durations and measurement endpoints 

for any chemical for which a plant community toxicity benchmark is desired. 

Unfortunately, these data sets do not exist for the majority of pesticides of interest. When 

only minimal data sets are available they usually represent standardized test results 

because the tests were usually run for regulatory purposes. In practice, however, data 

from any ―reasonable‖ test procedure are often included in aquatic plant sensitivity 

distributions (such as in the acetochlor and metolochlor standards in Minnesota). The 

need to characterize the range of sensitivities in the aquatic plant community may 

outweigh the desire for comparable test conditions. For example, recent state standards 

for aquatic plants derived for acetochlor and metolachlor by the Minnesota Pollution 

Control Agency included freshwater EC50 data for algae that had exposure durations 

ranging from 3 to 21 days and measurement endpoints that included, among others, 

chlorophyll, abundance, biomass and growth rate. Aquatic vascular plant data for these 

compounds included exposure durations from 4 to 70 days for endpoints including 

growth, abundance and dry weight. In addition, the draft atrazine document currently 

being developed by EPA’s Office of Water includes data for freshwater and saltwater 

algae with exposure durations ranging from 2 to 10 days, and durations of 7 to 35 days 

for aquatic vascular plants. Endpoints used in the atrazine analyses include population 

growth rate (Lemna spp.), photosynthesis, biomass, chlorophyll, etc. Chronic toxicity

data for OW’s ALWQC can include results derived for different durations since early life 

stage, partial life cycle, and full life cycle data are equally acceptable. In addition, 

survival, growth and reproduction endpoints are all acceptable. Combining different

exposure durations and endpoints is not unique to hazard analysis for aquatic plants, 

however, it should be emphasized that it is not ideal either. Future examination of the 

uncertainty associated with combining durations and endpoints is warranted. 

Aquatic phytotoxicity information available to the Agency is primarily that for short-term 

growth effects for a few single species of easily cultured microalgae and a single floating 

macrophyte (duckweed). This database may not be sufficiently comprehensive to capture 

the variability of chemical sensitivities for taxonomically diverse aquatic flora inhabiting 

different freshwater and saltwater ecosystems. It has been assumed that this 

predominately freshwater algal-duckweed toxicity data base can serve as an ecologicallyrelevant surrogate for the sensitivities of the many types of non-vascular and vascular 

plants, freshwater and marine. Its ability to serve this purpose, however, has not been 

adequately addressed by the scientific community15. 

 

15 Although recently a SETAC advisory group has been formed (Aquatic Macrophyte Ecotoxicology 

Group) that will address the issue as related to aquatic vascular plants.
Aquatic Plants Page 17 of 56

The data describing aquatic plant sensitivity to pesticides which is typically submitted by 

registrants to OPP often includes both EC50 and NOEC values. Submitted data from 

registrants can include those from standardized tests for the four species of microalgae 

and duckweed (described above in Section 3.5), as well as other aquatic plant species. 

For convenience, in this paper the standard tests species are referred to collectively as the 

―OPP standard species‖ and all others as ―non-standard species‖. It is important to note 

that, when available, OPP assessments also include non-standard test species data from 

the open literature that meet quality standards (which is often the case for most 

herbicides). The microalgal data are intended to represent non-vascular aquatic plants, 

and the duckweed data are intended to represent vascular aquatic plants. If these are the 

only data available, one of the obvious key issues is the extent to which these required 

data are representative of the sensitivity of communities of aquatic plants. Another way to 

view the issue is to consider how well a benchmark derived using a minimum data set 

represents a benchmark based on a data set containing a much larger number of data 

values representing more species of the aquatic plant community. In this latter respect the 

focus is not on the actual relative sensitivity of various species; otherwise, the effort

would need to be restricted to comparisons of paired values using the same duration of 

exposure and the same measurement endpoint. Rather, the focus here is the relevance of 

the conclusion for a benchmark derived based largely on a minimal data set as compared 

to the conclusion about that benchmark if a more diverse database had been available. In 

this respect it is necessary to make comparisons between toxicity data points that are 

derived from any reasonably acceptable endpoint—no matter what the relative exposure 

durations or actual measured endpoints. The analyses below explore this issue in two 

ways. First, they consider how likely is it to find phytotoxicity values that are less than 

those values determined for OPP’s minimum required species. This is done separately for 

microalgae (non-vascular) and aquatic vascular plants. Second, they consider the

representativeness of a species sensitivity distribution (SSD) using a minimal data set 

from a distribution based on a more diverse data set

5.1. Ratios of OPP standard species endpoints to non-standard species 

endpoints

The OPP standard test species are selected as the likely minimum data set since any 

pesticide registered or re-registered in the United States should have these data 

(especially if a phytotoxic compound is involved). Therefore, not much is to be gained by 

addressing the issue of having less than these data. As described in the evaluation below, 

data were normalized by dividing by the data point for the most sensitive OPP standard 

species in order to be able to combine data from all compounds in one analysis. 

5.1.1. Non-vascular species

To explore the uncertainty related to the relative sensitivities of algae, studies that tested 

various algal species were evaluated using OPP’s Ecotoxicity Database of available 

toxicity data submitted as part of the pesticide registration process. This summary 

database includes test results from both the OPP standard species and non-standard 

species. Although this may not include all available aquatic plant pesticide toxicity data, 

 
Aquatic Plants Page 18 of 56

it provides a comparison of the relative sensitivity of the standard OPP microalgal species 

with other species of non-vascular aquatic plants for which toxicity data are available. A 

subset of the microalgal data was used to evaluate this relative sensitivity. Only EC50

values were used, but exposure durations ranged from 2 to 10 days. The durations for the 

OPP standard test species were usually 4 or 5 days. The measurement endpoints used 

were not listed in the database.

For a pesticide to be included in the evaluation, an EC50 had to be available for at least 

one of the OPP standard species and at least one non-standard algal species. For each 

chemical included, the EC50 value for each non-standard species was divided by the 

lowest EC50 from the data available for OPP standard species. Table 2 lists the 17 

chemicals included in the evaluation along with the most sensitive OPP standard species 

for each chemical. All but two of the chemicals were herbicides. There was no single 

species that was consistently the most sensitive to the range of compounds tested. The 

total number of non-standard species ratios also is included for each chemical16. Table 3

lists the non-standard species for which data were included and the total number of values 

included in the evaluation for each species. There were 30 species (20 marine and 10 

freshwater) listed from 122 tests. Most of the species represented were either green algae 

or diatoms (10 species each). Data for four of the algal species (all marine macroalgae) 

came from Di Landa et al. (2009), and are not included in OPP’s database.

The cumulative frequency distribution for the EC50 ratios represented by the species in 

Table 3 is graphed in Figure 2. Only one ratio is less than 1.0, the ratio of 0.92 for 

Isochrysis galbana exposed to atrazine for 5 days, indicating that the most sensitive of 

the OPP standard species results may be a sufficient screening value to represent the 

expected toxicity of a pesticide to saltwater and freshwater algae. Although this 

conclusion seems to be based on a large database, it is dominated by four non-standard 

species, Chlorococcum sp. (a freshwater green alga), Dunaliella tertiolecta (a saltwater 

green alga), Isochrysis galbana (a saltwater golden brown alga) and Phaeodactylum 

tricornutum (a saltwater pennate diatom). There is no apparent pattern to the sensitivity 

of these species relative to that for the OPP standard species.

 

16 If more than one test was available for a non-standard species, then each was included as a separate ratio.
Aquatic Plants Page 19 of 56

Table 2. Chemicals selected from the OPP Ecotoxicity Database from which 

ratios of EC50 values for non-vascular plants were calculateda.

Chemical Most sensitive OPP species N

2,4-D acid Skeletonema costatum 4

2,4-D butoxyethanol ester Anabaena flos-aquae 4

Ametryn Pseudokirchneriella subcapitatab18

Atrazine Skeletonema costatum 25

Captan Skeletonema costatum 5

Dichlobenil Navicula pelliculosa 4

Diquat dibromide Pseudokirchneriella subcapitata 4

Diuron Pseudokirchneriella subcapitata 15

Endothall dipotassium salt Anabaena flos-aquae 4

Fluometuron Anabaena flos-aquae 1

Irgarol Navicula pelliculosa 12

Paraquat dichloride Anabaena flos-aquae 4

Pentachlorophenol Skeletonema costatum 2

Prometon Pseudokirchneriella subcapitata 8

Simazine Navicula pelliculosa 4

Triallate Pseudokirchneriella subcapitata 4

Trifluralin Navicula pelliculosa 4

Total 122

aThe denominator of the ratio was the lowest EC50 from the available standard species. The most 

sensitive of these latter species is also listed along with the total number of available EC50 values 

for non-standard species. All chemicals except captan (fungicide) and irgarol (microbiocide) are 

herbicides. N is the number test results for non-standard species.

b

Formerly Selenastrum capricornutum
Aquatic Plants Page 20 of 56

Table 3. List of the non-standard algal species used to calculate the EC50

ratios with the most sensitive OPP standard speciesa.

Species N Aquatic Plant Type Medium

Chaetoceros gracilis 1 Diatom Saltwater

Chlamydomonas sp. 2 Green Freshwater

Chlorella sp. 3 Green Freshwater

Chlorella pyrenoidosa 1 Green Freshwater

Chlorococcum sp. 16 Green Freshwater

Closterium ehrenbergii 1 Desmid Freshwater

Cyclotella nana 1 Diatom Saltwater

Dunaliella tertiolecta 21 Green Saltwater

Eisenia bicyclis 1 Macroalga-Kelp* Saltwater

Entermorpha intestinalis 1 Macroalga-Green* Saltwater

Gracilaria tenistipitata 1 Macroalga-Red* Saltwater

Isochrysis galbana 20 Haptophyte Saltwater

Microcystis aeruginosa 1 Cyanobacteria Freshwater

Navicula incerta 3 Diatom Saltwater

Neochloris sp. 3 Green Freshwater

Nitzschia closterium 3 Diatom Saltwater

Nitzschia palea 1 Diatom Saltwater

Pavlova gyrans 2 Chrysophyte Saltwater

Pavlova lutheri 5 Chrysophyte Saltwater

Phaeodactylum tricornutum 15 Diatom Saltwater

Platymonas sp. 3 Green Freshwater

Porphyra yezoensis 1 Macroalga-Red* Saltwater

Porphyridium cruentum 4 Red Saltwater

Scenedesmus costatum 1 Green Freshwater

Scenedesmus subspicatus 3 Green Freshwater

Stauroneis amphoroides 2 Diatom Saltwater

Tetraselmis suecica 1 Green Saltwater

Thalassiosira fluviatilis 3 Diatom Saltwater

Thalassiosira guillardii 1 Diatom Saltwater

Thalassiosira pseudonana 1 Diatom Saltwater

Total 122

aThe macroalgae with an asterisk were included from Di Landa et al. 2009. All other 

species are from OPP’s Ecotoxicity Database of test results submitted during the 

registration process. Note, if multiple EC50 values for a given species were available, then a 

separate ratio was calculated for each.
Aquatic Plants Page 21 of 56

Figure 2. Cumulative frequency plot of the 122 algal EC50 ratios calculated with the 

most sensitive OPP standard species.

5.1.2. Vascular species

OPP’s Ecotoxicity Database has very little data for aquatic vascular species other than 

those for the freshwater genus Lemna. Therefore, to do a similar sensitivity evaluation as 

was performed for algae, additional data were selected from the published literature 

within which data for various aquatic vascular plants and at least one species of Lemna

also were included. Data were restricted in this manner to minimize differences in test 

conditions between the two EC50 values used for each ratio. This should not be 

considered either an exhaustive search of the literature, or necessarily the optimal way to 

select data. It is merely one way to begin the process of evaluating the relative sensitivity 

of Lemna. Data were included from seven different publications (Aida et al., 2006; 

Cedergreen et al. 2004a,b; Fairchild et al. 1998; Forney and Davis 1981; Hanson and 

Solomon 2004, and Lande et al. 2009). Lemna minor and L. gibba were the species 

usually represented in OPP data registration packages. In one case (Di Lande et al. 2009) 

data were available for both species of Lemna, and the geometric mean of the EC50 values 

were used to establish the EC50 ratios17. In another case, only data for L. perpusilla were 

 

17 In this instance, by using the Lemna geometric mean, a genus-level value is used, while other 

comparisons are on the species-level. Using the geometric mean is reflective of how OW typically utilizes 

multiple species in one genus, while OPP would use the lowest species value. This difference will be 

explored in future analyses.

Algal EC50 Divided by Lowest "OPP" Microalgal EC50

0.0

0.2

0.4

0.6

0.8

1.0

0.1 1 10 100 1000 10000 100000

EC50 Ratio

Cumulative Frequency

Freshwater

Saltwater

Seaweeds

Red macroalga

Green macroalga

Brown macroalga

Red macroalga
Aquatic Plants Page 22 of 56

available (Forney and Davis 1981) and they were used because of the plant’s similarity in 

habitat to the two more commonly used species. However, when data for L. trisculca

were included in a publication (Cedergreen et al. 2004a,b), they were treated as a nonstandard species for the purpose of this evaluation because it is a submerged species of 

Lemna. Data were available from 11 chemicals and 19 different non-standard aquatic 

vascular plants, representing 58 separate toxicity tests (Tables 4 and 5). Eight of the 

species were dicots, nine were monocots, and two were aquatic ferns. Exposure durations 

were between 4 and 14 days for Lemna (one test using L. perpusilla lasted 28 days) and 

endpoints were EC50 values for some measure of population growth rate (i.e., either 

number of fronds or relative growth rate). Exposure durations for other aquatic vascular 

plants ranged from 14 to 28 days. Endpoints were EC50 values for different measures of 

individual plant growth—for example, leaf area, wet or dry weight, stem length, or 

relative growth rate. See Appendix A for a list of all of the data used.

Unlike the data for algae, the EC50 values for aquatic vascular plants are not consistently 

greater than those for Lemna spp. tests. Lemna was the most sensitive species for only 

one chemical – metribuzin. There was no pattern to the position of the relative sensitivity 

among the non-standard species whether compared by either species or chemical tested 

(Figure 3). Of the EC50 values that were more sensitive compared to those for Lemna, the 

majority of the EC50 values were within a factor of 10 of the Lemna EC50 value. In fact, 

only the EC50 for the floating fern Salvinia natans tested using bensulfuron methyl was 

outside this factor of 10. A 10-fold extrapolation factor applied to Lemna data has been 

suggested by Rentz and Hanson (2009) to be more protective of other macrophytes. A 

factor of 10 also is used by the European Union and Canada in their lower-tiered 

assessments; however, this factor is applied to the lowest value whether plant or animal 

(these methods, along with methods employed by other countries are briefly summarized 

in Section 3.5). The use of the factor of 10 is not being advocated in this document, but 

its use will be considered as an option after further data analysis. As with the microalgal 

data, the Lemna analysis should be interpreted with caution given the limited number of 

plant groups represented. Most notably, data for near coastal marine species are 

particularly sparse, (e.g., saltmarsh species, seagrasses and mangroves). 

The concern about the representativeness of Lemna effects data for other groups of 

aquatic vascular plants was a common issue at a recent European workshop held in the 

Netherlands in 2008 (Maltby et al. 2010). A standardized test procedure using species of 

Myriophyllum and synthetic sediment is being evaluated to fill the above needs for 

European risk assessments (Maltby et al. 2010). Myriophyllum was selected because it is 

a dicot (Lemna is a monocot) and is a rooted aquatic vascular plant. A dicot is desirable

in part because many herbicides target broadleaf ―weeds‖ (dicots). The data analyses 

suggest that if resources are limited, then the most significant data to add to a minimal 

data set would be for additional aquatic vascular plants. There is more uncertainty 

associated with the sensitivity of this portion of the plant community than for the 

microalgal portion.
Aquatic Plants Page 23 of 56

Table 4. Chemicals selected from the open scientific 

literature for which ratios of EC50 values for vascular 

plants were calculateda.

Chemical N

Alachlor 4

Atrazine 4

Bensulfuron methyl 2

Chlorodifluoroacetic acid 2

Dichloroacetic acid 2

Metolachlor 4

Metribuzin 7

Metsulfuron-methyl 11

Monochloroacetic acid 2

Terbuthylazine 13

Trichloroacetic acid 2

Trifluoroacetic acid 2

Irgarol 3

Total 58

aThe denominator of the ratio was the EC50 from the available Lemna

species. N = the total number of available EC50 values for non-OPP species 

for each chemical. Note, if multiple EC50 values for a given species were 

available, then a separate ratio was calculated for each.
Aquatic Plants Page 24 of 56

Table 5. List of the non-standard vascular aquatic 

plant species used to calculate the EC50 ratios with 

Lemnaa.

Species Group N

Batrachium trichophyllum dicot 1

Berula erecta dicot 1

Caboma caroliniana dicot 1

Ceratophyllum demersum dicot 6

Ceratophyllum submersum dicot 3

Myriophyllum heterophyllum dicot 4

Myriophyllum sibiricum dicot 2

Myriophyllum spicatum dicot 8

Callitriche platycarpa monocot 2

Elodea canadensis monocot 9

Lemna trisulca monocot 2

Najas sp. monocot 4

Potamogeton crispus monocot 3

Potamogeton pectinatus monocot 1

Ruppia maritimab monocot 1

Spirodela polyrhiza monocot 4

Zostera marinab monocot 1

Azolla japonica fern 1

Salvinia natans fern 1

Total 58

aN = the total number of available EC50 values for each species. 

Note, if multiple EC50 values for a given species were available, 

then a separate ratio was calculated for each. 

bMarine macrophytes
Aquatic Plants Page 25 of 56

Figure 3. Cumulative frequency plot of the 58 aquatic vascular plant EC50 ratios 

with Lemna. Both plots have same data; the top is labeled by species and the bottom 

is labeled by chemical.

5.2. Species sensitivity distributions

For OPP’s risk assessments, data for non-vascular and vascular plants are used

separately. This is one reason why the analyses in the last section were done with the data 

Ceratophyllum

Berula

Myriophyllum

Elodea

Elodea

Elodea

Myriophyllum

Potamogeton

Elodea

Myriophyllum

Spirodela

Lemna

Elodea

Callitriche

Myriophyllum

Elodea

Elodea

Lemna

Spirodela

Spriodela

Spriodela

Ceratophyllum

Myriophyllum

Callitriche

Caboma

Potamogeton

Najas

Potamogeton

Ceratophyllum

Potamogeton

Myriophyllum

Najas

Ceratophyllum

Myriophyllum

Elodea

Najas

Azolla

Myriophyllum

Zostera

Myriophyllum

Ceratophyllum

Batrachium

Ceratophyllum

Myriophyllum

Myriophyllum

Ruppia

Myriophyllum

Myriophyllum

Najas

Myriophyllum

Ceratophyllum

Elodea

Myriophyllum

Myriophyllum

Ceratophyllum

Myriophyllum

Ceratophyllum

Salvinia

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.01 0.10 1.00 10.00 100.00

Lemna EC50 Ratio Cumulative Rank

dicot

monocot

fern

metsulfuron-methyl

metsulfuron-methyl

metolachlor

metsulfuron-methyl

metolachlor

alachlor

alachlor

terbutylazine

metribuzin

metribuzin

terbutylazine

metsulfuron-methyl

metsulfuron-methyl

terbutylazine

metsulfuron-methyl

terbutylazine

terbutylazine

terbutylazine

terbutylazine

metsulfuron-methyl

metsulfuron-methyl

terbutylazine

atrazine

terbutylazine

metribuzin

metsulfuron-methyl

alachlor

iragrol

metsulfuron-methyl

terbutylazine

chlorodifluoroacetic acid

metolachlor

terbutylazine

chlorodifluoroacetic acid

metribuzin

metribuzin

bensulfruon methyl

terbutylazine

iragrol

metribuzin

terbutylazine

metsulfuron-methyl

metribuzin

trifluoroacetic acid

monochloroacetic acid

iragrol

dichloroacetic acid

monochloroacetic acid

atrazine

trifluoroacetic acid

atrazine

atrazine

trichloroacetic acid

trichloroacetic acid

metolachlor

dichloroacetic acid

alachlor

bensulfruon methyl

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.01 0.10 1.00 10.00 100.00

Lemna EC50 Ratio Cumulative Rank

dicot

monocot

fern
Aquatic Plants Page 26 of 56

segregated into these two groups. Another approach is to combine all data for both 

groups into one assessment. This is what was done for the species sensitivity distribution 

used for the earlier mentioned acetochlor, metolachlor and atrazine. These SSD 

approaches separated the data into freshwater and saltwater; acetochlor and metolachlor 

only used freshwater data and the draft atrazine document created separate distributions 

for each medium. The analyses described below combines all data from all taxonomic 

groups and both freshwater and saltwater. 

One of the underlying issues related to determining an appropriate toxicity benchmark (or 

criterion) for aquatic plants is that there is not a consensus (or, unlike aquatic animals, 

even a historical precedence) for what constitutes a minimum data set which would 

represent the range of sensitivities for a given plant community. However, a number of 

existing data sets can be found in which numerous data points exist for a variety of 

species representing many niches in the aquatic plant community. For example there are 

44 species for which diuron EC50 values exist, 38 for irgarol, 31 for pentachlorophenol, 

25 for atrazine, 21 for metolachlor and 13 for diquat. Each of these data sets contains a 

variety of both non-vascular and vascular plant data, and it is reasonable to expect that 

these data sets are equal to or greater than some as yet undetermined ―representative 

minimum data set‖. Each of these data sets also contains data for all of the OPP ―standard 

species‖. 

Not only is there no consensus on what constitutes a minimum data set of species, there is 

also no consensus on what the appropriate durations of exposure are or what the most 

appropriate measurement endpoints are —with the possible exception of microalgae. The 

few times that a large data set has been used for setting plant values (e.g., acetochlor, 

metolachlor, atrazine) a variety of durations and measurement endpoints have been used. 

This is largely because most of the plant data used in these SSDs came from studies 

whose goals were not regulatory in nature—data collected for a variety of reasons, by its 

nature would have a variety of durations and endpoints. A variety of endpoints are 

included in the sample SSD data sets. The data are listed in Appendix B.

Both freshwater and saltwater data for aquatic plants were downloaded from the 

USEPA’s ECOTOX database18 in May 2010 for four of the herbicides and 

pentachlorophenol (a wood preservative). The data for atrazine came from the June 2009 

draft water quality criteria document, supplemented with data from the OPP Ecotoxicity 

Database. All data were used ―as is‖ (i.e., original references were not checked). These 

compounds were selected because a reasonably large number of EC50 values were 

available (LC50 and IC50 values also were included). It also should be noted that many of 

these plotted values are based on unmeasured (nominal) exposure concentrations. For all 

compounds, the geometric mean was calculated for species with more than one EC50 for a 

compound. SSDs for all six compounds are plotted in Figure 4. To demonstrate how a 

benchmark derived using only the OPP standard test species compares to a benchmark 

value derived using a larger, more diverse data set, two separate species sensitivity 

distributions were plotted for each chemical. One SSD included all of the data and the 

 

18 http://cfpub.epa.gov/ecotox
Aquatic Plants Page 27 of 56

other included just the data from the OPP standard species. Equation 119 was fit to each 

of the ―full‖ and ―partial‖ data sets using Microsoft Excel’s solver routine. HC5 values 

were calculated using the resulting fitted equations, as well as the method OW typically 

uses to develop final acute values for animals (USEPA 1985)20. These data are shown in 

Table 6.

Equation 1

Where:

C = EC50 value,

Xm = median EC50 value, and

S = shape factor (slope)

Table 6. Comparison of calculated plants values using three different approaches. 

All values are μg/L.

Calculated HC5

b FAV 5th

percentiled OPP datae

Compound N

a Full 

data set

Partial 

Datac

Full 

data set

Partial 

Datac

Lowest 

EC50

Species

Atrazine 25 10.37 9.69 13.79 50.52 50.5 Lemna gibba

Diquat 13 0.647 1.154 0.491 4.07 5.1 Lemna minor

Diuron 44 1.68 4.04 2.84 13.89 8.0 Pseudokirchneriella 

subcapitata

Irgarol 38 0.097 0.032 0.107 0.415 0.1 Navicula pelliculosa

Metolachlor 21 12.47 6.02 38.88 29.69 34.2 Pseudokirchneriella 

subcapitata

Pentachlorophenol 31 15.26 8.30 39.69 49.11 35.3 Skeletonema costatum

a Number of species mean plant values

b Calculated using the fitted Equation 1to all of the SSD data

c Only the data for the OPP standard species are included

d Calculated using the four most sensitive values—the Final Acute Value method from USEPA 1985

e Using only the data for the OPP standard species. If lower acceptable test values are available to OPP, those 

species would be used for deriving an OPP benchmark. For instance, the actual benchmark for atrazine is 

1μg/L. 

 

19 There are a variety of possible sigmoid equations that could be used. This one is shown only as one 

example for the purpose of demonstration.

20 OW’s 1985 guidelines’ procedure for FAV calculation is essentially a regression using the four most 

sensitive genus mean values (species means used for FVP). The calculated value represents the cumulative 

probability of 0.05 of all the test values. Note that in other countries (as well as for the Minnesota 

calculations for acetochlor and metolachlor) SSDs are used by fitting a sigmoid curve to the data and 

calculating a percentile effect concentration (e.g., EC05 or EC20). 
Aquatic Plants Page 28 of 56

Figure 4. Species sensitivity distributions for aquatic plants using species mean EC50

values. See Appendix B for data. Closed circles represent all of the available data. 

Open circles are the subsets of the data containing only the data for the OPP 

standard species.

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

10 100 1000 10000

Cummulative Probability

ug/L

Atrazine

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0.1 1 10 100 1000 10000

Cummulative Frequency

ug/L

Diquat

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1 10 100 1000 10000 100000

Cummulative Frequency

ug/L

Diuron

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.01 0.10 1.00 10.00 100.00

Cummulative Frequency

ug/L

Irgarol

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

10 100 1000 10000 100000

Cummulative Frequency

ug/L

Metolachlor

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

10 100 1000 10000 100000

Cummulative Frequency

ug/L

Pentachlorophenol
Aquatic Plants Page 29 of 56

The information in Table 6 demonstrates that a different benchmark value is derived if 

more data are available and if different approaches are used. It should be noted that for 

most of the example chemicals, the lowest OPP value falls nearly at the HC10 of the 

distribution. The HC5 approach with the full data set are the only derived values that use 

all of the available data, and is often the approach preferred when plant data are 

evaluated21. While the fitting of a curve to the full data set will probably result in the best 

estimate of the ―true‖ benchmark for a given compound, the method used above (i.e., 

Equation 1) should not be taken as the best. Likewise, the selection of the HC5 as the 

point of interest is presented here only as one example. In addition, these data suggest 

that an factor may be needed in order to extrapolate from the minimal data set (e.g., data 

from only the OPP standard species). 

6. Potential approach for deriving plant ALSV

The amount of aquatic plant toxicity data available varies among pesticides. As indicated 

above, toxicity data are required for pesticides; however, data are often available in the 

scientific literature, particularly for herbicides (e.g., atrazine, metolachlor). In cases 

where plants are expected to be more sensitive than animals (i.e., for herbicides), it may 

be necessary to derive a plant ALSV with which to compare against the animal ALSV.

The plant ALSV is based on phytotoxicity and its calculation excludes animal toxicity 

data which are used for the animal ALSV calculation alone. 

Different approaches for deriving this value may be used, depending upon availability of 

data. The plant ALSV may be represented by the lowest single toxicity value for aquatic 

plants. In cases where there is evidence to suggest that the available toxicity data are not 

representative of the most sensitive plant species which are expected to be impacted, 

extrapolation factors may be applied to available data to derive the plant ALSV. In 

deciding whether to use an extrapolation factor, the chemical’s mode of action on aquatic 

plants and whether the available test species are likely to be impacted should be 

considered. If a chemical has a large data set (e.g., atrazine, metolachlor), it may be 

possible to use a SSD to derive a plant ALSV. Additionally, as discussed in the animal 

white paper, predictive tools for estimating toxicity values may be available which could 

allow further development of plant SSDs when combined with existing data. As 

discussed in the tools white paper though, there are a limited number of predictive tools 

at this time for estimating the toxicity of chemicals to aquatic plants. Where such tools 

are available, they may only predict for a limited number of aquatic plants, e.g., 

freshwater algae alone. However, efforts are underway to enhance the predictive 

capabilities of [quantitative] structure activity relationship ([Q]SAR) models through 

inclusion of plant toxicity data in model training sets. Use of other predictive methods 

such as analogs and read-across (discussed in the tools white paper) may provide the user 

with an understanding of the extent existing data for the chemical in question are 

reflective of plant sensitivity for similarly structured chemicals. This information could 

provide a rationale for using existing data alone, reliance on an extrapolation factor, or 

 

21 By ―approach preferred‖ we mean that fitting a sigmoid curve to the full data set is often the preferred 

approach. Different equations may be used, as well as different percentiles (e.g., HC20). 
Aquatic Plants Page 30 of 56

possibly using these analog/read-across and/or [Q]SAR estimates as a means of 

populating SSDs which would in turn be used for developing a plant ALSV.

It is expected that once the methods described here are reviewed, the approach for 

deriving plant ALSVs will be revised to be more specific and to incorporate the methods 

determined to have the greatest utility. In order to refine this approach, it will be 

necessary to define the endpoints used in deriving a plant ALSV. This includes 

considering the desired duration of exposure, level of effect (e.g., EC50) and measurement 

endpoint (e.g., growth rate) that are used to define the toxicity data for the ALSV. Also, it 

will be necessary to define the assessment endpoint, i.e., survival, growth or 

reproduction, which the ALSV is intended to represent. Finally, it will be necessary to 

define the community effect level (e.g., HCx) if an SSD approach is used.

In addition to the above, an approach for determining appropriate EFs for when different 

amounts of plant data are available can also be undertaken. The minimum amount of data 

considered will be the data for the OPP ―standard species‖. This is because any new or 

reregistered pesticide should have a minimum of five data points (four microalgal points 

and one for Lemna sp.). The approach will consist of finding as many data sets as 

possible for which a large number of EC50 values already exist. This will include nonpesticide data in order to maximize the number of chemicals represented. The nonpesticide data sets also will need to contain data that would meet the OPP data 

requirements for aquatic plants. With enough additional data sets like the six presented as 

SSDs in this document, EFs could be developed that could be used to account for when 

only the standard set of OPP data are available. When more data than the standard OPP 

data set are available, one approach would be to return to the data sets and start adding 

some of the non-OPP standard species data to the OPP standard set and observing how 

the ―partial‖ SSD equation changes. It will get closer to matching the equation for the 

―full‖ plot as more data are added. This can be done for all possible combinations of 

adding data in groups of 1, 2, 3, etc. Using the ―full‖ SSD to represent the best 

benchmark, recommendations could be made on how the EF should be altered as the 

number of data points increases. Based on the plots presented above in Figures 2 and 3, 

the analysis could concentrate on adding back data for aquatic vascular plants. This may 

also shed some light on how many and/or what species reduce the difference between the 

full and partial curves the quickest. In relation to the derivation of SSDs and EFs, future 

analyses will explore the different sources of uncertainty in plant data, including those 

described above. 

7. Conclusions

This white paper presents several methods used to characterize the effects of stressors on 

aquatic plant communities. These methods include use of the most sensitive empirical 

toxicity data, extrapolation factors, and sensitivity distributions. This paper also explores 

uncertainties associated with aquatic plant toxicity data, including the sensitivities of 

typical test species relative to other aquatic plants for which test species are surrogates. 

Based upon methodologies used by other countries and by U.S. regulatory agencies, and 

the analyses of relative sensitivities of the standard OPP test species, plant community 
Aquatic Plants Page 31 of 56

benchmark values can logically be derived. The amount of data available for plant 

species should be considered when calculating a plant ALSV. For pesticides, data are 

routinely available for the standard five OPP test species. It may be possible to use the 

lowest of the available data to represent the plant ALSV (either alone or with 

extrapolation factors). When additional data are available in addition to the standard OPP 

test species, SSDs have proven effective for deriving toxicity values which are 

considered reflective of the sensitivity of more vulnerable aquatic plant species. Possible 

approaches for deriving plant ALSVs are broadly described which are similar to those 

discussed in the aquatic animal white paper. These approaches are intended to account 

for uncertainties and to make the best use of available data.

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Appendix A. Vascular plant (VP) data used in creating ratios comparing Lemna to other VPs

Species Chemical Duration (d) Endpoint EC50 (μg/L) Reference

Ceratophyllum demersum alachlor 14 increase in wet wt 85 Farichild et al. 1998

Elodea canadensis alachlor 14 increase in wet wt > 3000 Farichild et al. 1998

Lemna minor alachlor 4 # fronds 482 Farichild et al. 1998

Myriophyllum heterophyllum alachlor 14 increase in wet wt > 3000 Farichild et al. 1998

Najas sp. alachlor 14 increase in wet wt 584 Farichild et al. 1998

Ceratophyllum demersum atrazine 14 increase in wet wt 22 Farichild et al. 1998

Elodea canadensis atrazine 14 increase in wet wt 21 Farichild et al. 1998

Lemna minor atrazine 4 # fronds 92 Farichild et al. 1998

Myriophyllum heterophyllum atrazine 14 increase in wet wt 132 Farichild et al. 1998

Najas sp. atrazine 14 increase in wet wt 24 Farichild et al. 1998

Azolla japonica bensulfruon methyl 12 relative growth rate 5 Aida et al. 2006

Lemna minor bensulfruon methyl 12 relative growth rate 10 Aida et al. 2006

Salvinia natans bensulfruon methyl 12 relative growth rate 0.54 Aida et al. 2006

Lemna gibba chlorodifluoroacetic acid 7 frond # 176.4 Hanson & Solomon 2004a

Myriophyllum sibiricum chlorodifluoroacetic acid 14 root length 161.4 Hanson & Solomon 2004a

Myriophyllum spicatum chlorodifluoroacetic acid 14 root length 105.3 Hanson & Solomon 2004a

Lemna gibba dichloroacetic acid 7 frond # 199.2 Hanson & Solomon 2004a

Myriophyllum sibiricum dichloroacetic acid 14 root length 75.3 Hanson & Solomon 2004a

Myriophyllum spicatum dichloroacetic acid 14 root length 37.1 Hanson & Solomon 2004a

Potamogeton pectinatus iragrol 6.10 Di Lande et al. 2009

Ruppia maritima iragrol 1.92 Di Lande et al. 2009
Aquatic Plants Page 37 of 56

Zostera marina iragrol 2.50 Di Lande et al. 2009

Ceratophyllum demersum metolachlor 14 increase in wet wt 70 Farichild et al. 1998

Elodea canadensis metolachlor 14 increase in wet wt 2355 Farichild et al. 1998

Lemna minor metolachlor 4 # fronds 360 Farichild et al. 1998

Myriophyllum heterophyllum metolachlor 14 increase in wet wt > 3000 Farichild et al. 1998

Najas sp. metolachlor 14 increase in wet wt 242 Farichild et al. 1998

Caboma caroliniana metribuzin 21 stem length 22 Forney & Davis 1981

Ceratophyllum demersum metribuzin 14 increase in wet wt 14 Farichild et al. 1998

Elodea canadensis metribuzin 14 increase in wet wt 21 Farichild et al. 1998

Elodea canadensis metribuzin 21 stem length 78 Forney & Davis 1981

Lemna minor metribuzin 4 # fronds 36 Farichild et al. 1998

Lemna perpusilla metribuzin 28 new plants 16 Forney & Davis 1981

Myriophyllum heterophyllum metribuzin 14 increase in wet wt 17 Farichild et al. 1998

Myriophyllum spicatum metribuzin 28 stem length 64 Forney & Davis 1981

Najas sp. metribuzin 14 increase in wet wt 19 Farichild et al. 1998

Batrachium trichophyllum metsulfuron-methyl 14 specific leaf area 0.07 Cedergreen et al. 2004 a

Berula erecta metsulfuron-methyl 14 specific leaf area 3.92 Cedergreen et al. 2004 a

Ceratophyllum demersum metsulfuron-methyl 14 specific leaf area 0.2 Cedergreen et al. 2004 a

Ceratophyllum submersum metsulfuron-methyl 14 specific leaf area 2.21 Cedergreen et al. 2004 a

Elodea canadensis metsulfuron-methyl 14 specific leaf area 0.57 Cedergreen et al. 2004 a

Elodea canadensis metsulfuron-methyl 14 specific leaf area 0.79 Cedergreen et al. 2004 a

Lemna minor metsulfuron-methyl 14 relative growth rate 0.8 Cedergreen et al. 2004 a

Lemna minor metsulfuron-methyl 14 relative growth rate 1.13 Cedergreen et al. 2004 a

Lemna minor metsulfuron-methyl 14 specific leaf area 0.18 Cedergreen et al. 2004 a
Aquatic Plants Page 38 of 56

Lemna minor metsulfuron-methyl 14 specific leaf area 0.1 Cedergreen et al. 2004 a

Lemna trisulca metsulfuron-methyl 14 specific leaf area 0.62 Cedergreen et al. 2004 a

Myriophyllum spicatum metsulfuron-methyl 14 specific leaf area 0.29 Cedergreen et al. 2004 a

Potamogeton crispus metsulfuron-methyl 14 specific leaf area 0.23 Cedergreen et al. 2004 a

Spriodela polyrhiza metsulfuron-methyl 14 specific leaf area 0.32 Cedergreen et al. 2004 a

Spriodela polyrhiza metsulfuron-methyl 14 specific leaf area 0.19 Cedergreen et al. 2004 a

Lemna gibba monochloroacetic acid 7 frond # 17.2 Hanson & Solomon 2004a

Myriophyllum sibiricum monochloroacetic acid 14 root length 5.8 Hanson & Solomon 2004a

Myriophyllum spicatum monochloroacetic acid 14 root length 6.6 Hanson & Solomon 2004a

Callitriche platycarpa terbutylazine 14 dry weight 158 Cedergreen et al. 2004b

Callitriche platycarpa terbutylazine 14 dry weight 119 Cedergreen et al. 2004b

Ceratophyllum demersum terbutylazine 14 dry weight 196 Cedergreen et al. 2004b

Ceratophyllum submersum terbutylazine 14 dry weight 17 Cedergreen et al. 2004b

Ceratophyllum submersum terbutylazine 14 dry weight 69 Cedergreen et al. 2004b

Elodea canadensis terbutylazine 14 dry weight 98 Cedergreen et al. 2004b

Elodea canadensis terbutylazine 14 dry weight 305 Cedergreen et al. 2004b

Lemna minor terbutylazine 14 dry weight 40 Cedergreen et al. 2004b

Lemna minor terbutylazine 14 dry weight 111 Cedergreen et al. 2004b

Lemna trisulca terbutylazine 14 dry weight 254 Cedergreen et al. 2004b

Myriophyllum spicatum terbutylazine 14 dry weight 55 Cedergreen et al. 2004b

Potamogeton crispus terbutylazine 14 dry weight 109 Cedergreen et al. 2004b

Potamogeton crispus terbutylazine 14 dry weight 199 Cedergreen et al. 2004b

Spirodela polyrhiza terbutylazine 14 dry weight 228 Cedergreen et al. 2004b

Spirodela polyrhiza terbutylazine 14 dry weight 146 Cedergreen et al. 2004b
Aquatic Plants Page 39 of 56

Lemna gibba trichloroacetic acid 7 frond # 254.1 Hanson & Solomon 2004a

Myriophyllum sibiricum trichloroacetic acid 14 root length 55.4 Hanson & Solomon 2004a

Myriophyllum spicatum trichloroacetic acid 14 root length 57.1 Hanson & Solomon 2004a

Lemna gibba trifluoroacetic acid 7 frond # 884 Hanson & Solomon 2004a

Myriophyllum sibiricum trifluoroacetic acid 14 root length 340.7 Hanson & Solomon 2004a

Myriophyllum spicatum trifluoroacetic acid 14 root length 222.1 Hanson & Solomon 2004a
Aquatic Plants Page 40 of 56

Appendix B. Data for Species Sensitivity Distribution plots

Atrazine

Species Common name Medium Species group Duration 

(d)

Endpoint EC50/IC50 or 

LC50

a,b (μg/L)

SMPV 

(μg/L)

Anabaena flos-aquae Cyanobacterium FW Non-vascular 5 230a230

Chlamydomonas reinhardtii Green alga FW Non-vascular 4 cell number 51b27.32

Chlamydomonas reinhardtii Green alga FW Non-vascular 4 cell number 51b

Chlamydomonas reinhardtii Green alga FW Non-vascular 7 cell number 21b

Chlamydomonas reinhardtii Green alga FW Non-vascular 10 cell number 10.2b

Chlorella saccharophila Green alga FW Non-vascular 4 cell number 1,300b1,300

Chlorella sp. Green alga SW Non-vascular 3 growth 140b140

Dunaliella tertiolecta Green alga SW Non-vascular 5 180a180

Elodea canadensis Elodea FW Vascular 10 biomass 1200b1,200

Isochrysis galbana Golden/brown alga SW Non-vascular 5 22a22

Lemna gibba Duckweed FW Vascular 7

frond 

production 180b

50.54

Lemna gibba Duckweed FW Vascular 14 frond number 37b

Lemna gibba Duckweed FW Vascular 14 frond biomass 45b

Lemna gibba Duckweed FW Vascular 14 frond biomass 22 b

Lemna gibba Duckweed FW Vascular 14 frond number 50 b

Lemna minor Duckweed FW Vascular 14 biomass 8700 b,*

Lemna minor Duckweed FW Vascular 10 frond number 56 b59.28

Lemna minor Duckweed FW Vascular 10 fresh weight 60 b
Aquatic Plants Page 41 of 56

Lemna minor Duckweed FW Vascular 10 chlorophyll 62 b

Microcystis aeruginosa Cyanobacterium FW Non-vascular 5 129a129

Myriophyllum spicatum Eurasian water milfoil SW Vascular 28 photosynthesis 117 b54.08

Myriophyllum spicatum Eurasian water milfoil SW Vascular 35 biomass 25 b

Navicula incerta Diatom SW Non-vascular 3 460a460

Navicula pelliculosa Diatom FW Non-vascular 5 60a60

Neochloris sp. Green alga SW Non-vascular 3 growth 82 b82

Nitzschia closterium Diatom SW Non-vascular 3 290a290

Pavlova sp. Golden/brown alga SW Non-vascular 4 growth 147 b147

Platymonas sp. Green alga SW Non-vascular 3 100 b100

Porphyridium cruentum Red alga SW Non-vascular 3 growth 79 b79

Potamogeton perfoliatus Redheadgrass pondweed SW Vascular 28 photosynthesis 55 b20.49

Potamogeton perfoliatus Redheadgrass pondweed SW Vascular 35 final biomass 30 b

Pseudanabaena geleata Cyanobacterium FW Non-vascular 4 cell number 14 b14

Pseudokirchneriella subcapitata Green alga FW Non-vascular 4 cell number 4

b

51.04

Pseudokirchneriella subcapitata Green alga FW Non-vascular 4 phaeophytin-a 20 b

Pseudokirchneriella subcapitata Green alga FW Non-vascular 4 chlorophyll-a 150 b

Pseudokirchneriella subcapitata Green alga FW Non-vascular 4 cell number 128.2 b

Pseudokirchneriella subcapitata Green alga FW Non-vascular 4 cell number 130 b

Pseudokirchneriella subcapitata Green alga FW Non-vascular 5 55 b

Pseudokirchneriella subcapitata Green alga FW Non-vascular 4 growth 82 b

Scenedesmus acutus Green alga FW Non-vascular 4 cell number 14 b14

Skeletonema costatum Diatom SW Non-vascular 2 growth 265 b265

Thalassiosira fluviatilis Diatom SW Non-vascular 3 110a110
Aquatic Plants Page 42 of 56

Zostera marina Eelgrass SW Vascular 21 survival 540 b291.6

Zostera marina Eelgrass SW Vascular 21 survival 100 b

Zostera marina Eelgrass SW Vascular 21 survival 365 b

Zostera marina Eelgrass SW Vascular 21 survival 367 b

*Not used in SMPV

aEC50/IC50/LC50 data from OPP database

bEC50/IC50/LC50 data from WQC draft (June 23, 2009)

Diquat

Species Common name Medium Species Group Duration 

(d)

Endpoint EC50/IC50

a

(μg/L)

SMPV 

(μg/L)

Anabaena flos aquae Cyanobacterium FW Non-Vascular 3 growth rate 42 42

Anacystis aeruginosa Cyanobacterium FW Non-Vascular 3 growth rate 65 65

Chlorella vulgaris Green algae FW Non-Vascular 3 growth rate 2940 2940

Cryptomonas ozolini Cryptomonad FW Non-Vascular 3

population change, 

general 35 35

Euglena gracilis Flagellate euglenoid FW Non-Vascular 3 growth rate 2940 2940

Lemna minor Duckweed FW Vascular 7 growth rate 1.5 5.08

Lemna minor Duckweed FW Vascular 7 growth rate 2.7

Lemna minor Duckweed FW Vascular 7 growth rate 3.1

Lemna minor Duckweed FW Vascular 7 relative growth rate 15

Lemna minor Duckweed FW Vascular 4

population change, 

general 18

Lyngbya sp. Cyanobacterium FW Non-Vascular 3 population growth rate 145 145

Myriophyllum sibiricum Water milfoil FW Vascular 14 area 56.2 200.43

Myriophyllum sibiricum Water milfoil FW Vascular 14 number of root 57
Aquatic Plants Page 43 of 56

Myriophyllum sibiricum Water milfoil FW Vascular 14 weight 78.2

Myriophyllum sibiricum Water milfoil FW Vascular 14 length 79.7

Myriophyllum sibiricum Water milfoil FW Vascular 14 length 105.7

Myriophyllum sibiricum Water milfoil FW Vascular 14 area 127.7

Myriophyllum sibiricum Water milfoil FW Vascular 14 number of root 155.1

Myriophyllum sibiricum Water milfoil FW Vascular 14 weight 184

Myriophyllum sibiricum Water milfoil FW Vascular 14 abundance 271.3

Myriophyllum sibiricum Water milfoil FW Vascular 14 length 346.2

Myriophyllum sibiricum Water milfoil FW Vascular 14 abundance 365.7

Myriophyllum sibiricum Water milfoil FW Vascular 14 length 403.8

Myriophyllum sibiricum Water milfoil FW Vascular 14 abundance 982.8

Myriophyllum sibiricum Water milfoil FW Vascular 14 length 1610.9

Navicula sp. Diatom FW Non-Vascular 3 growth rate 19 19

Ochromonas danica Diatom FW Non-Vascular 3

population change, 

general 23 23

Pseudokirchneriella subcapitata Green algae FW Non-Vascular 4 abundance 4.9 37.91

Pseudokirchneriella subcapitata Green algae FW Non-Vascular 4 abundance 34.2

Pseudokirchneriella subcapitata Green algae FW Non-Vascular 3 growth rate 73

Pseudokirchneriella subcapitata Green algae FW Non-Vascular 3 population growth rate 80

Pseudokirchneriella subcapitata Green algae FW Non-Vascular 4

population change, 

general 80

Skeletonema costatum Diatom FW Non-Vascular 3 growth rate 2940 2940

Spirodela punctata Large duckweed FW Vascular 14 abundance 0.75 0.75

aData from ECOTOX database
Aquatic Plants Page 44 of 56

Diuron

Species Common Name Species Group Medium Duration 

(d)

Endpoint

EC50/IC50/ 

LC50

a

(μg/L)

SMPV 

(μg/L)

Anabaena doliolum Cyanobacterium Non-vascular FW 12 population growth rate 1000 632.46

Anabaena doliolum Cyanobacterium Non-vascular FW 12 population growth rate 400

Anabaena flos aquae Cyanobacterium Non-vascular FW 5 abundance 38.8 38.8

Anabaena variabilis Cyanobacterium Non-vascular FW 14 population growth rate 5.8 5.8

Ankistrodesmus sp. Green algae Non-vascular FW 14 population growth rate 6 6

Apium nodiflorum European Marshwort Vascular FW 14 growth rate 2.808 2.808

Ceramium tenuicorne Red Algae Non-vascular SW 7 population growth rate 3.4 3.4

Chaetoceros gracilis Diatom Non-vascular SW 3 abundance 36 36

Chara vulgaris Stonewort Non-vascular FW 14 chlorophyll 4.033 4.033

Chlorella pyrenoidosa Green algae Non-vascular FW 4 population growth rate 1.3 6.24

Chlorella pyrenoidosa Green algae Non-vascular FW 14 population growth rate 30

Chlorella sp. Green algae Non-vascular FW 14 population growth rate 40 40

Chlorella vulgaris Green algae Non-vascular FW 4 population growth rate 4.3 4.3

Chlorococcum hypnosporum Green algae Non-vascular FW 4 population growth rate 25 25

Chlorococcum sp. Green algae Non-vascular FW 14 population growth rate 5 5

Chroococcus sp. Cyanobacterium Non-vascular FW 9 abundance 206 270.55

Chroococcus sp. Cyanobacterium Non-vascular FW 9 abundance 218

Chroococcus sp. Cyanobacterium Non-vascular FW 9 abundance 441

Coccolithus huxleyi Coccolithophorid Non-vascular SW 3 abundance 2.26 2.26

Dictyosphaerium pulchellum Green algae Non-vascular FW 14 population growth rate 6 6

Dunaliella tertiolecta Green algae Non-vascular FW 4 population growth rate 4.9 44.60
Aquatic Plants Page 45 of 56

Dunaliella tertiolecta Green algae Non-vascular FW 4 population growth rate 6.9

Dunaliella tertiolecta Green algae Non-vascular FW 4 population growth rate 300

Dunaliella tertiolecta Green algae Non-vascular FW 4 population growth rate 390

Elodea nuttalli Waterweed, ditchmoss Vascular FW 21 biomass 75 13.69

Elodea nuttalli Waterweed, ditchmoss Vascular FW 21 biomass 75

Elodea nuttalli Waterweed, ditchmoss Vascular FW 21 population change, general 2.5

Elodea nuttalli Waterweed, ditchmoss Vascular FW 21 population growth rate 2.5

Entomoneis punctulata Diatom Non-vascular SW 3 population growth rate 24 24

Gracilaria tenuistipitata Red algae Non-vascular SW 4 population growth rate 15 17.32

Gracilaria tenuistipitata Red algae Non-vascular SW 4 population growth rate 20

Hormidium flaccidum Green algae Non-vascular FW 14 population growth rate 500 500

Lemna gibba Inflated duckweed Vascular FW 14 abundance 27.3 27.3

Lemna minor Duckweed Vascular FW 5 abundance 7 13.23

Lemna minor Duckweed Vascular FW 7 population growth rate 25

Lemna perpusilla Duckweed Vascular NR 7 population change, general 15 15

Microcystis sp. Cyanobacterium Non-vascular FW 9 abundance 120 197.95

Microcystis sp. Cyanobacterium Non-vascular FW 9 abundance 162

Microcystis sp. Cyanobacterium Non-vascular FW 9 abundance 399

Myriophyllum spicatum Eurasian watermilfoil Vascular SW 28 biomass 137 56.98

Myriophyllum spicatum Eurasian watermilfoil Vascular SW 35 biomass 137

Myriophyllum spicatum Eurasian watermilfoil Vascular FW 14 growth rate 5

Myriophyllum spicatum Eurasian watermilfoil Vascular SW 28 photosynthesis 80

Myriophyllum spicatum Eurasian watermilfoil Vascular SW 35 photosynthesis 80

Navicula forcipata Diatom Non-vascular FW 4 population growth rate 25 26.46
Aquatic Plants Page 46 of 56

Navicula forcipata Diatom Non-vascular FW 4 population growth rate 28

Navicula pelliculosa Diatom Non-vascular FW 5 abundance 13.7 13.7

Nitzschia closterium Diatom Non-vascular SW 3 population growth rate 17 17

Oscillatoria sp. Cyanobacterium Non-vascular FW 14 population growth rate 40 40

Phaeodactylum tricornutum Diatom Non-vascular SW 10 abundance 10 10

Potamogeton perfoliatus Pondweed Vascular SW 28 biomass 25 42.52

Potamogeton perfoliatus Pondweed Vascular SW 35 biomass 61

Potamogeton perfoliatus Pondweed Vascular SW 28 photosynthesis 45

Potamogeton perfoliatus Pondweed Vascular SW 35 photosynthesis 45

Pseudokirchneriella subcapitata Green algae Non-vascular SW 3 abundance 45 8.814

Pseudokirchneriella subcapitata Green algae Non-vascular FW 4 abundance 0.7

Pseudokirchneriella subcapitata Green algae Non-vascular FW 4 abundance 2.4

Pseudokirchneriella subcapitata Green algae Non-vascular FW 5 abundance 67

Pseudokirchneriella subcapitata Green algae Non-vascular FW 3 population growth rate 10.5

Pyrocystis lunula Dinoflagellate Non-vascular SW 4 abundance 35000 35000

Scenedesmus acutus Green algae Non-vascular FW 14 population growth rate 50 14.30

Scenedesmus acutus acutus Green Algae Non-vascular FW 4 population growth rate 4.09

Scenedesmus quadricauda Green algae Non-vascular FW 4 population growth rate 2.7 11.62

Scenedesmus quadricauda Green algae Non-vascular FW 14 population growth rate 50

Scenedesmus subspicatus Green algae Non-vascular FW 3 population change, general 36 36

Skeletonema costatum Diatom Non-vascular SW 5 abundance 35.9 35.9

Spirodela polyrhiza Large duckweed Vascular NR 7 population change, general 41 41

Spirulina platensis Cyanobacterium Non-vascular FW 14 population growth rate 8.5 8.5

Stichococcus sp. Green algae Non-vascular FW 14 population growth rate 1500 1500
Aquatic Plants Page 47 of 56

Synechococcus sp. Cyanobacterium Non-vascular SW 3 abundance 0.55 14.57

Synechococcus sp. Cyanobacterium Non-vascular FW 9 abundance 2

Synechococcus sp. Cyanobacterium Non-vascular FW 9 abundance 22

Synechococcus sp. Cyanobacterium Non-vascular FW 9 abundance 38

Synechococcus sp. Cyanobacterium Non-vascular FW 9 abundance 714

Ulothrix fimbriata Green algae Non-vascular FW 4 population growth rate 540 540

Zostera marina Eelgrass Vascular SW 10 photosynthesis 3.2 3.2

aData from ECOTOX database

Irgarol

Species Common name Species Group Media 

Type

Duration 

(d) Endpoint EC50/IC50

a

(μg/L)

SMPV 

(μg/L)

Anabaena flosaquae Cyanobacterium Non-vascular FW 5 abundance 1.9 1.9

Apium nodiflorum European Marshwort Vascular FW 14 biomass 0.01328 0.13

Apium nodiflorum European Marshwort Vascular FW 14 relative growth rate 1.177

Asterionella formosa Diatom Non-vascular FW 4 biomass > 253 > 253

Ceramium tenuicorne Red Algae Non-vascular SW 7 population growth rate 0.96 0.96

Chaetoceros gracilis Diatom Non-vascular SW 3 abundance 1.1 1.1

Chara vulgaris Stonewort Non-vascular FW 14 relative growth rate 0.01175 0.01175

Chlamydomonas intermedia Green algae Non-vascular FW 4 biomass 0.5 0.5

Chlorella vulgaris Green algae Non-vascular FW 4 biomass 1.5 0.8857

Chlorella vulgaris Green algae Non-vascular FW 5.3 chlorophyll a 0.523

Chlorococcum sp. Green algae Non-vascular SW 5 abundance 0.42 0.42

Chroococcus minor Cyanobacterium Non-vascular SW 4 population growth rate 7.71 7.71
Aquatic Plants Page 48 of 56

Closterium ehrenbergii Green algae Non-vascular FW 5 population growth rate 2.5 3

Closterium ehrenbergii Green algae Non-vascular FW 5 gamete production 3.6

Coccolithus huxleyi Coccolithophorid Non-vascular SW 3 abundance 0.25 0.3012

Coccolithus huxleyi Coccolithophorid Non-vascular SW 3 population growth rate 0.363

Craticula accomoda Pennate diatom Non-vascular FW 3 biomass 0.455 0.4770

Craticula accomoda Pennate diatom Non-vascular FW 4 biomass 0.5

Dunaliella tertiolecta Green algae Non-vascular FW 4 population growth rate 0.9 0.9793

Dunaliella tertiolecta Green algae Non-vascular FW 4 population growth rate 1.4

Dunaliella tertiolecta Green algae Non-vascular FW 4 population growth rate 0.73

Dunaliella tertiolecta Green algae Non-vascular SW 4 population growth rate 1

Eisenia bicyclis Brown alga Non-vascular SW 7 cell cleavage 2.2 2.717

Eisenia bicyclis Brown alga Non-vascular SW 4 size 5.9

Eisenia bicyclis Brown alga Non-vascular SW 7 size 2

Eisenia bicyclis Brown alga Non-vascular SW 7 size 2.1

Enteromorpha intestinalis Green algae Non-vascular SW 3 photosynthesis 2.5 2.5

Fibrocapsa japonica Algae Non-vascular SW 3 population growth rate 0.479

Gracilaria tenuistipitata Red algae Non-vascular SW 4 population growth rate 2 2

Lemna gibba Inflated duckweed Vascular FW 7 size 11 4.195

Lemna gibba Inflated duckweed Vascular FW 14 abundance 1.6

Lemna minor Duckweed Vascular FW 7 size 8.1 8.1

Myriophyllum spicatum Eurasian watermilfoil Vascular FW 14 relative growth rate 2 2

Myriophyllum verticillatum Whorl-leaf watermilfoil Vascular FW 43 length 2.3 1.965

Myriophyllum verticillatum Whorl-leaf watermilfoil Vascular FW 43 weight 1.1

Myriophyllum verticillatum Whorl-leaf watermilfoil Vascular FW 43 biomass 3
Aquatic Plants Page 49 of 56

Navicula forcipata Diatom Non-vascular FW 4 population growth rate 0.5 0.5916

Navicula forcipata Diatom Non-vascular FW 4 population growth rate 0.7

Navicula pelliculosa Diatom Non-vascular FW 5 abundance 0.1 0.1

Nitzschia sp. Diatom Non-vascular FW 4 biomass 0.8 0.8

Pediastrum duplex Green algae Non-vascular FW 3 biomass 2.4 2.4

Porphyra yezoensis Red algae Non-vascular SW 4 size 0.1 1.206

Porphyra yezoensis Red algae Non-vascular SW 4 size 0.4

Porphyra yezoensis Red algae Non-vascular SW 4 size 1.3

Porphyra yezoensis Red algae Non-vascular SW 4 germination 2.7

Porphyra yezoensis Red algae Non-vascular SW 4 germination 3.6

Porphyra yezoensis Red algae Non-vascular SW 4 germination 6.1

Pseudokirchneriella subcapitata Green algae Non-vascular FW 3 abundance 1.47 3.584

Pseudokirchneriella subcapitata Green algae Non-vascular FW 3 abundance 1.6

Pseudokirchneriella subcapitata Green algae Non-vascular SW 3 abundance 10

Pseudokirchneriella subcapitata Green algae Non-vascular FW 3 abundance 10.8

Pseudokirchneriella subcapitata Green algae Non-vascular FW 5 abundance 1.3

Pseudokirchneriella subcapitata Green algae Non-vascular FW 3 population growth rate 10

Pseudokirchneriella subcapitata Green algae Non-vascular FW 3 population growth rate 2.3

Ruppia maritima Widgeon-grass Vascular SW 28 biomass 1.8872 1.607

Ruppia maritima Widgeon-grass Vascular SW 28 biomass 1.9228

Ruppia maritima Widgeon-grass Vascular SW 28 growth, general 0.8425

Ruppia maritima Widgeon-grass Vascular SW 28 morphology, general 2.008

Ruppia maritima Widgeon-grass Vascular SW 28 vegetative reproduction 1.7484

Scenedesmus acutus Green algae Non-vascular FW 4 biomass 3.3 4.102
Aquatic Plants Page 50 of 56

Scenedesmus acutus Green algae Non-vascular FW 4 biomass 5.1

Scenedesmus subspicatus Green algae Non-vascular FW 3 abundance 2.4 2.4

Skeletonema costatum Diatom Non-vascular SW 5 abundance 0.45 0.3612

Skeletonema costatum Diatom Non-vascular SW 4 population growth rate 0.29

Staurastrum sebaldi Desmid Non-vascular FW 4 biomass 2.5 2.5

Stuckenia pectinata Sago Pondweed Vascular SW 28 biomass 6.1152 6.757

Stuckenia pectinata Sago Pondweed Vascular SW 28 biomass 7.4664

Synechococcus sp. Blue-green algae Non-vascular SW 3 abundance 0.16 0.16

Tetraselmis sp. Green flagellate Non-vascular SW 3 population growth rate 0.1 0.1

Thalassiosira pseudonana Diatom Non-vascular SW 4 population growth rate 0.41 0.41

Thalassiosira weissflogii Diatom Non-vascular SW 3 population growth rate 0.28 0.28

Zostera marina Eelgrass Vascular SW 10 biomass 1.1 1.465

Zostera marina Eelgrass Vascular SW 10 distance 2.6

Zostera marina Eelgrass Vascular SW 10 photosynthesis 1.1

aData from ECOTOX database

Metolachlor

Species Common name Species 

group

Medium Duration 

(d)

Endpoint EC50/IC50

a

(μg/L)

SMPV 

(μg/L)

Anabaena cylindrica Cyanobacterium Non-vascular FW 3 abundance 5000 > 5000

Anabaena flos aquae Cyanobacterium Non-vascular FW 5 abundance 1200 1200

Ceratophyllum demersum Coon-tail Vascular FW 14 biomass 70 70

Chlamydomonas reinhardtii Green algae Non-vascular FW 4 chlorophyll 1138 1138

Chlorella fusca Green algae Non-vascular FW 12 population growth rate 100.61 105.27
Aquatic Plants Page 51 of 56

Chlorella fusca Green algae Non-vascular FW 12 population growth rate 105.03

Chlorella fusca Green algae Non-vascular FW 12 population growth rate 107.3

Chlorella fusca Green algae Non-vascular FW 12 population growth rate 108.3

Chlorella pyrenoidosa Green algae Non-vascular FW 4 population growth rate 12717.2 12717

Chlorella vulgaris Green algae Non-vascular FW 4 population growth rate 18926.1 1960.1

Chlorella vulgaris Green algae Non-vascular FW 4 chlorophyll 203

Elodea canadensis Waterweed Vascular FW 14 biomass 2355 2355

Lemna gibba Inflated duckweed Vascular FW 14 abundance 48 67.42

Lemna gibba Inflated duckweed Vascular FW 7 abundance 304

Lemna gibba Inflated duckweed Vascular FW 5 abundance 21

Lemna minor Duckweed Vascular FW 4 abundance 360 351.4

Lemna minor Duckweed Vascular FW 4 population growth, general 343

Microcystis sp. Cyanobacterium Non-vascular FW 4 chlorophyll 3000 > 3000

Myriophyllum heterophyllum Two-leaf water-milfoil Vascular FW 14 biomass 3000 > 3000

Myriophyllum sibiricum Water milfoil Vascular FW 14 area 579.6 1024.4

Myriophyllum sibiricum Water milfoil Vascular FW 14 growth rate 1535.2

Myriophyllum sibiricum Water milfoil Vascular FW 14 length 670.1

Myriophyllum sibiricum Water milfoil Vascular FW 14 length 1896

Myriophyllum sibiricum Water milfoil Vascular FW 14 number of roots 1684.8

Myriophyllum sibiricum Water milfoil Vascular FW 14 weight 606.7

Najas sp. Water nymph Vascular FW 14 biomass 242 242

Navicula pelliculosa Diatom Non-vascular FW 5 abundance 380 380

Pseudokirchneriella subcapitata Green algae Non-vascular FW 4 abundance 5508.1* 34.16

Pseudokirchneriella subcapitata Green algae Non-vascular FW 4 abundance 50.9
Aquatic Plants Page 52 of 56

Pseudokirchneriella subcapitata Green algae Non-vascular FW 4 abundance 55.5

Pseudokirchneriella subcapitata Green algae Non-vascular FW 5 abundance 10

Pseudokirchneriella subcapitata Green algae Non-vascular FW 4 chlorophyll 84

Pseudokirchneriella subcapitata Green algae Non-vascular FW 4 population growth, general 77

Pseudokirchneriella subcapitata Green algae Non-vascular FW 3 abundance 37.17

Pseudokirchneriella subcapitata Green algae Non-vascular FW 5 abundance 8

Salvinia natans Floating watermoss (fern) Vascular FW 28 growth rate 150 86.60

Salvinia natans Floating watermoss (fern) Vascular FW 28 biomass 50

Scenedesmus quadricauda Green algae Non-vascular FW 4 population growth rate 600 600

Scenedesmus vacuolatus Green algae Non-vascular FW 1 abundance 232 232

Scenedesmus subspicatus Green algae Non-vascular FW 3 abundance 57100 57100

Skeletonema costatum Diatom Non-vascular SW 5 abundance 61 81.91

Skeletonema costatum Diatom Non-vascular SW 5 abundance 110

*Not used in SMPV

aData from ECOTOX database

Pentachlorophenol

Species Common name Species Group Media 

Type

Duration 

(d)

Endpoint EC50

a

(μg/L) SMPV

(μg/L)

Anabaena flos aquae Cyanobacterium Non-vascular FW 5 abundance 50 50

Anabaena inaequalis Cyanobacterium Non-vascular FW 4 abundance 130 130

Callitriche platycarpa Water Starwort Vascular FW 21 relative growth rate 3300 3300

Callitriche platycarpa Water Starwort Vascular FW 21 dry biomass 3300

Chlamydomonas reinhardtii Green Algae Non-vascular FW 10 population change, general 360 327.92
Aquatic Plants Page 53 of 56

Chlamydomonas reinhardtii Green Algae Non-vascular FW 3 population change, general 168

Chlamydomonas reinhardtii Green Algae Non-vascular FW 4 population change, general 405

Chlamydomonas reinhardtii Green Algae Non-vascular FW 7 population change, general 410

Chlamydomonas reinhardtii Green Algae Non-vascular FW 10 population growth rate 360

Chlamydomonas reinhardtii Green Algae Non-vascular FW 3 population growth rate 220

Chlamydomonas reinhardtii Green Algae Non-vascular FW 4 population growth rate 410

Chlamydomonas reinhardtii Green Algae Non-vascular FW 7 population growth rate 410

Chlamydomonas sp. Green Algae Non-vascular SW 4 growth rate 1400 1400

Chlorella emersonii Green Algae Non-vascular FW 20 population growth rate 5000 5000

Chlorella kessleri Green Algae Non-vascular FW 4 abundance 34300 34300

Chlorella pyrenoidosa Green Algae Non-vascular SW 4 growth rate 5500 4134.53

Chlorella pyrenoidosa Green Algae Non-vascular FW 4 growth rate 7000

Chlorella pyrenoidosa Green Algae Non-vascular FW 2 chlorophyll a 2300

Chlorella pyrenoidosa Green Algae Non-vascular FW 6 population growth rate 3300

Chlorella vulgaris Green Algae Non-vascular FW 20 abundance 10030 7737.24

Chlorella vulgaris Green Algae Non-vascular FW 4 growth rate 10300

Chlorella vulgaris Green Algae Non-vascular FW 4 growth rate 10300

Chlorella vulgaris Green Algae Non-vascular FW 20 population growth rate 12000

Chlorella vulgaris Green Algae Non-vascular FW 7 population growth rate 1663

Chlorella vulgaris var. viridis Green Algae Non-vascular NR 2 population growth rate 10120.92

Chlorella vulgaris var. viridis Green Algae Non-vascular NR 3 population growth rate 7723.86

Chlorella zofingiensis Green Algae Non-vascular NR 2 chlorophyll 42.6144 42.61

Dunaliella sp. Green Algae Non-vascular SW 4 growth rate 3600 3600

Dunaliella tertiolecta Green Algae Non-vascular SW 4 abundance 170 170
Aquatic Plants Page 54 of 56

Elodea canadensis Waterweed Vascular FW 21 length 4

Elodea canadensis Waterweed Vascular FW 21 dry biomass 3265 3265

Elodea nuttalli Waterweed, 

Ditchmoss Vascular FW 21 dry weigth 109 333.1096

Elodea nuttalli Waterweed, 

Ditchmoss Vascular FW 21 dry biomass 1018

Lemna gibba Inflated Duckweed Vascular FW 14 abundance 250 413.9593

Lemna gibba Inflated Duckweed Vascular FW 7 reproduction, general 532.68

Lemna gibba Inflated Duckweed Vascular FW 7 vegetative reproduction 532.68

Lemna minor Duckweed Vascular FW 10 photosynthesis 1670 849.38

Lemna minor Duckweed Vascular FW 4 photosynthesis 1940

Lemna minor Duckweed Vascular FW 2 abundance 800

Lemna minor Duckweed Vascular FW 10 population change, general 1250

Lemna minor Duckweed Vascular FW 4 population change, general 610

Lemna minor Duckweed Vascular FW 3 survival 190

Lemna trisulca Duckweed Vascular FW 21 relative growth rate 1282 1457.05

Lemna trisulca Duckweed Vascular FW 21 dry biomass 1656

Macrocystis pyrifera Giant Kelp Non-vascular SW 4 photosynthesis 300 300

Myriophyllum spicatum Eurasian Watermilfoil Vascular FW 21 dry weigth 236 614.30

Myriophyllum spicatum Eurasian Watermilfoil Vascular FW 21 dry biomass 1599

Navicula pelliculosa Diatom Non-vascular FW 5 abundance 124 124

Pavlova sp. Chrysophyte Non-vascular SW 4 growth rate 200 200

Phaeodactylum tricornutum Diatom Non-vascular SW 4 growth rate 3000 3000

Potamogeton crispus Curled Pondweed Vascular FW 21 relative growth rate 338 416

Potamogeton crispus Curled Pondweed Vascular FW 21 dry biomass 512
Aquatic Plants Page 55 of 56

Pseudokirchneriella subcapitata Green Algae Non-vascular FW 3 abundance 240 259.42

Pseudokirchneriella subcapitata Green Algae Non-vascular FW 3 population growth rate 100

Pseudokirchneriella subcapitata Green Algae Non-vascular FW 3 population growth rate 250

Pseudokirchneriella subcapitata Green Algae Non-vascular FW 4 growth rate 110

Pseudokirchneriella subcapitata Green Algae Non-vascular FW 4 growth rate 150

Pseudokirchneriella subcapitata Green Algae Non-vascular FW 4 growth rate 760

Pseudokirchneriella subcapitata Green Algae Non-vascular FW 4 growth rate 420

Pseudokirchneriella subcapitata Green Algae Non-vascular FW 4 growth rate 420

Pseudokirchneriella subcapitata Green Algae Non-vascular FW 2 abundance 410

Pseudokirchneriella subcapitata Green Algae Non-vascular FW 4 abundance 70

Pseudokirchneriella subcapitata Green Algae Non-vascular FW 4 abundance 290

Pseudokirchneriella subcapitata Green Algae Non-vascular FW 4 abundance 290

Pseudokirchneriella subcapitata Green Algae Non-vascular FW 5 abundance 50

Pseudokirchneriella subcapitata Green Algae Non-vascular FW 3 chlorophyll a 412

Pseudokirchneriella subcapitata Green Algae Non-vascular FW 5 chlorophyll a 335

Pseudokirchneriella subcapitata Green Algae Non-vascular FW 7 chlorophyll a 331

Pseudokirchneriella subcapitata Green Algae Non-vascular FW 4 population change, general 310

Pseudokirchneriella subcapitata Green Algae Non-vascular FW 5 population change, general 520

Pseudokirchneriella subcapitata Green Algae Non-vascular FW 4 population growth rate 312

Pseudokirchneriella subcapitata Green Algae Non-vascular FW 5 population growth rate 518

Ranunculus longirostris Longbeak Buttercup Vascular FW 21 length 341 449.30

Ranunculus longirostris Longbeak Buttercup Vascular FW 21 dry biomass 592

Ranunculus peltatus Pond Water Crowfoot Vascular FW 21 dry weight 16 121.46

Ranunculus peltatus Pond Water Crowfoot Vascular FW 21 dry biomass 922
Aquatic Plants Page 56 of 56

Scenedesmus abundans Green Algae Non-vascular FW 4 growth rate 90 90

Scenedesmus quadricauda Green Algae Non-vascular FW 4 growth rate 80 80

Scenedesmus subspicatus Green Algae Non-vascular FW 3 population growth rate 183 183

Skeletonema costatum Diatom Non-vascular SW 4 abundance 80 35.26

Skeletonema costatum Diatom Non-vascular SW 4 abundance 20.3

Skeletonema costatum Diatom Non-vascular SW 5 abundance 27

Thalassiosira pseudonana Diatom Non-vascular SW 4 abundance 179 179

aData from ECOTOX database