### Exploration of Methods for Char acterizing Effects of Chemical S tressors to Aquatic

Plants

November 1 , 2010

Laura Dobbins 1



### Michael Lewis 2



### Sujatha Sankula 3



### Glen Thursby 4



### United States Environmental Protection Agency (USEPA)

1 

### Office of Water, Office of Science and Technology, Washington, D C

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, RIAquatic 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 D erivation (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. N on

- 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

................................ ........... 40Aquatic 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

EC

x 

### 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

HC

x 

### Hazardous concentration threshold for x% of
organisms

IC 50 

### Inhibition concentration for 50% of organisms

LC 50 

### 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 DistributionAquatic 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

Insect icide, 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 characteri ze 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

associate d 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. T he first

involves the va riability associated with toxicity study endpoints. While there are standardized testing methodologies from regulatory agencies and standard writing

orga nizations , the endpoints available for aquatic plants expos ed to the same chemical
can vary ( e.g ., test duration, effect type, summary statistic may differ in each tes t)

. 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 pape r describes methods that may be considered to
develop ALSVs for aquatic plant communities using available toxicity data . These plant

ALSVs are consider ed separately from animal ALSVs. A plant ALSV may simply be
based on the lowest available aquatic plant t oxicity 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 represe nted 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 demonstrate s that using a specified lower confidence interval f or the plant species sensitivity distribution (SSD) can provide a plant ALSV

. A lthough this paper broadly describes approaches for deriving
aquatic plant ALSVs similar to those under consideration for aquatic animals, i t is
expected that these approach es will be further refined as EPA reviews available methods.

2. Introduction

The definition of ―plant‖ varies among taxonomists . S ome 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

- vascularAquatic Plants Page 5 of 56



### aquatic plants, f

ree living algae include groups from four separate kingdoms 1 

### , 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.

A quatic plants have members in eve ry conceivable freshwater and saltwater habitat.
Aquatic plants have various growth habits, such as attached (rooted or other holdfast),

free

- floating, submerged or emerge nt (only partially submerged for part or all of their life
history). Aquatic plants a lso 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

o n 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

metho dologies with varying amounts of data. In 2010, EPA conducted six regionally

based 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 w ater man agement 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

p aper), describes potential additional tools that can be used to estimate toxicity data. Th e
tools paper pr i marily focuses o n methods for estimating toxicity of chemicals to aquatic

animals , as there are limited tools available to estimate toxicity to aqua tic 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 wh ite paper ‖ )

and th is 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 usi ng 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, Sta tes 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 wh at 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 specifi c 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 s everal different approaches currently used by regulatory
agencies to d erive 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 US EPA , 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 T ribes 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 plant s 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 s altwater criterion). A t the time that
the original guidance was written for derivation of ALWQC

, ― procedures for conducting

tests wi th 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 onl y 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 plant s " or " biologically important " endpoint s 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. T he 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 othe r pesticides or even chemical pollutants in g eneral (Lewis 1990, 1995; Wang and Freemark 1995). Table 1
lists a variety of chemical classes for which data ha ve shown plants to be more sensitive

than animals

.

Table 1

. Examples of contaminants found more toxic to algae

than animals a



### .

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

a Adapted 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 atrazine 3 

### . This represents the first time that an ALWQC will

be developed w h ere plant data are used to establish chronic protection limits.

In the draft ALWQC, s eparate freshwater and s altwater FPV s are derived

. The saltwater
FPV is based on the lowest species me an acute value (SMAV) for sago pondweed
( Potamogeton pectinatus 4 

### ). 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, o rigi nal 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 EC 50 

### value s 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 whi ch 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 dat abase (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 Metolac hlor

S tates 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 met olachlor, 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
(US EPA 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 ac ute 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 (US EPA 1995)

. This
methodology is described in the aquati c animal white paper.

C hronic 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.govAquatic Plants Page 9 of 56



### goals of protecting the integrity of the plant community, protecting sensitive plant species
and achieving a 20 th 

### percentile level of protection. The MPCA derived species sensitivity

distributions for each chemical using geometric means of EC 50 

### 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 5 th 

### percentile of the EC 50 

### distribution for aquatic plants (36 μg/L)

. L owering 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 chron ic standard for acetochlor is 3.6 μg/L, which is consistent

with the 20 th 

### percentile of the distribution of EC 50 

### 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 EC 50 

### and MATC 5 

### values 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 EC 20 

### value for all studies. All

EC 20 

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

derive a criterion from the 20 th 

### percentile value 6 

### . 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 m ethod 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 So ciety 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 Cali fornia

- 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 mos t 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 fiv e 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 develo ped regulations (40 CFR Part 158 7 

### )

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 Standa rds for Minnesota.

7 Code of Federal Regulations. 2010. http://ecfr.gpoaccess.gov/cgi/t/text/text

idx?c=ecfr&sid=1a3c043b5425ffaa607dd0f14f9bddbb&tpl=/ecfrbrowse/Title40/40cfr158_main_02.tplAquatic 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. 660 8 

### describes the
plant protection data requ irements 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 pes ticide is applied)

. As with animal testing
requirements, OPP relies on a tiered approach for examining effects to aquatic plants.

The E cological E ffects T est G uidelines (Sub

- p art G of 40 CFR Part 158 ) describe three
testing tiers for assessing the effects of pesticides on non

- target aquatic plants. The first

t ier as sesses the effect on plant growth resulting from a single test concentration
equivalent to the e stimated environmental concentration resulting from the maximum

label ed 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 d oe s not provide a definitive no observable adverse effects
concentration ( NOAEC )

, Tier II tests are required. Tier II tests involve multip le test
concentrations and are intended to generate EC 50 

### and NOAEC values for the test species

which exhibit detrimental e ffects in the T ier 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 hav e 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 non

vascular aquatic plants, the surrogate species generally are Pseudokirchneriella
subcapitata (formerly Selenastrum capricornutum; green alga), Anabaena flos

- aquae ( cyanobacterium), Navicula pelliculosa ( pe nnate freshwater diatom) and Skeletonema

costatum ( centric marine diatom). L emna 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 tes ting of aquatic non

- vascular and vascular plants. Guideline s
850.5400 9 

### and 850.4400 10 

### describe the test procedures and conditions for micro algae and
aquatic vascular plants, respectively . Test guideline 850.4450 11 

### pertains to the test
procedures and conditions for field, microcosm, and mesocosm studies that cover a
broader range of plant ty pes and study duration s (tests typically continue for the entire
life cycle of the test plant)

. I f data are available for other , non

- standard, non

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

those result s 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/text

idx?c=ecfr&sid=1a 3c043b5425ffaa607dd0f14f9bddbb&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/publicatio ns/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.pdfAquatic Plants Page 11 of 56



### OP P’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 EC 50 

### value of the four non

- vascular plants ( no distinction i s
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 (f rond number at Day 0 , 3, 5, and 7 of exposure ), growth rate, mortality, biomass,

measurements at the end of the 7

- da y test

. Again, OPP typically selects the most sensitive

EC 50 

### 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 toxi city data (considering registrant

- submitted studies and the
scientific literature) from OPP’s ecological risk assessments of specific pesticides. For

non

- vascular and vascular plants, b enchmarks are calculated by multiplying each lowest

EC 50 

### value by the p lant 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 u sed 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 M ethod s for Incorporating Aquatic Plant Data

into A quatic L ife G uidelines

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 compou nds. For

instance, the province of Quebec does no t 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 alga l 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 w ill reduce the extrapolation
factor ( E F ), when an SSD is not used (European Commission 2003)

. The E Fs are used in
the risk assessment process to account for uncertainty in the data.

In other countries, t he type of chemical being assessed for guideline de rivation 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.htmAquatic 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 incl ude plant toxicity values along with animal values in their SSD s 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 EC 50 

### (CCME 2007, European Commission 2003) . For most countries using E F s

, they are applied to the lowest toxicity value, whether it be
plant or animal. E Fs vary depending on the amount of toxicity data that are available and
depending o n whether an acute or chronic guideline is being derived. For chronic toxicity

values, Canada and the EU apply an E F of 10 to the lowest toxicity value (plant or
animal) to derive their regulatory endpoint . However, the required E F 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 t est 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. Thes e 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 D evelopment (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 Elimin ation 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. Plan t 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 activitie s). 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 notAquatic Plants Page 13 of 56



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

established between the biochemical meas urement 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, cytochro me 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

- traditiona l 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

EC 50 

### value for reproduction in the marine red alga Ceramium strictum when exposed to phenol is 5,000 μg/L, while the growth EC 50 

### value is 10,000 μg/L (Eklund 1998, Bruno
and Eklund 2003) . Therefore, more se nsitive 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 (NO A EC), EC 50 

### or some other EC

x 

### level. When an EC endpoint is selected

other than EC 50 

### it is often chosen in an attempt to provide a substitute for an NO A EC
( e.g ., EC 20 

### ). This is usually done to eliminate some of the concerns about using
hypothesis testing in establishing no effect conc entrations 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 e ffects were not statistically
significant; 2 ) the NOAEC is heavily influenced by the test concentrations that were

c hosen 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 b etween 4 and 7 d ; d urations for
toxicity tests conducted with rooted vascular plants (freshwater) have ranged from 1 hr to
6 wk (Lewis and Wang 1999) ; t est durations with seagrasses have been conducted
between 2 hr and 42 d (Lewis and Devereux 2007) and be tween 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 ALWQ C). 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 differe nt. 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 beenAquatic Plants Page 14 of 56



### appropriate. While Haber recognized that C x t = k [wher e 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 candid ates 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 w eeks (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 EC 50 

### values for non

- vascular plant s and 7

- d EC 50 

### 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 ., EC 50 

### ) 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 database 13 

### , 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 sin gle 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 smal l number of tests (1,0 72 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 t oxicity 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 recei ves 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

ex posed 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, s ince 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 pathways 14 

### . For example, Lemna sp ecies 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 submerge d phase is C3 (Cronk and Fennessy 2001).

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

groups, s o 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 compoun d in CO 2 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).Aquatic Plants Page 16 of 56



### species. The typical vascular plant test spe cies, 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 benchmar k 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 test s 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 aquati c 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 EC 50 

### data for algae that h ad 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 endpoi nts 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 durati ons 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 analy sis 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.

A quatic phytotoxicity information available to the Agency is primar ily 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 taxonomi cally 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 ecologically

relevant 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 community 15



### .

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 s ubmitted by
registrants to OPP often includes both EC 50 

### 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 pl ant 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

- stan dard 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 a quatic 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

represent s a benchmark based on a data set containing a much larger numbe r of data
values representing more species of the aquatic plant community. In this latter respect the

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

would need to be restrict ed 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 benchmar k 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 reaso nably 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 det ermined for OPP’s minimum required species

. This is done separately for
microalgae (non

- vascular) and aquatic vascular plants. Second, they consider the
representative ness of a species sensitivity distribution (SSD) using a minimal data set

from a distribu tion based on a more diverse data set

5.1. Ratios of OPP standard species endpoints to non

- standard species
endpoints

T he 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 o f having less than these data. As described in the evaluation below,

d ata 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 su mmary 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 mi croalgal 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 EC 50 

### 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 EC 50 

### had to be available for at least

one of the OPP stand ard species and at least one non

- standard algal species. For each

chemical included, the EC 50 

### value for each non

- standard species was divided by the

lowest EC 50 

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

chemicals included in th e 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 chemical 16



### . 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 cumulativ e frequency distribution for the EC 50 

### 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 O PP 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
specie s, 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

o f 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 EC 50 

### values for non

- vascular plants were calculated a



### .

Chemical Most sensitive OPP species N

2,4

- D a cid Skeletonema costatum 4

2,4

- D butoxyethanol e ster Anabaena flos

- aquae 4

Ametryn Pseudokirchneriella subcapitata b 18

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

a The denominator of the ratio was the lowest EC 50 from the available standard species. The most
sensitive of these latter species is also listed along with the total number of available EC 50 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 capricornutumAquatic Plants Page 20 of 56



### Table 3. List of the non

- standard algal species used to calculate the EC 50 

### ratios with the most sensitive OPP standard species a



### .

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

a The 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 EC 50 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 EC 50 

### 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 lit erature 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 EC 50 

### 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 L ande et al . 2009)

data were available for both species of Lemna , and the geometric mean of the EC 50 

### values

were used to establish the EC 50 

### ratios 17 

### . 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.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 . 2004 a ,b), they were treated as a non

standard species for the purpose of this ev aluation 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 EC 50 

### values for some me asure 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 EC 50 

### values for different measures of
individual plant growth

— for example, leaf area, wet or dry weight, stem length, or
relative growth rate. See Appendi x A for a list of all of the data used.

Unlike the data for algae, the EC 50 

### 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 EC 50 

### values that we re more sensitive compared to those for Lemna, the
majority of the EC 50 

### value s were within a factor of 10 of the Lemna EC 50 

### value. In fact,
only the EC 50 

### 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 no tably, 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 r ecent 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 va scular plant. A dicot is desirable in part because many herbicides target broadleaf ―weeds‖ (dicots). T he data analyses
suggest that if resources are limited, th en 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. Chemica ls selected from the open scientific

literature for which ratios of EC 50 

### values for vascular

plants were calculated a



### .

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

a The denominator of the ratio was the EC 50 from the available Lemna species. N = the total number of available EC 50 values for non

- OPP species
for each chemical. Note, if multiple EC 50 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 EC 50 

### r atios with

Lemna a



### .

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 maritima b monocot 1

Spirodela polyrhiza monocot 4

Zostera marina b monocot 1

Azolla japonica fern 1

Salvinia natans fern 1

Total 58

a N = the total number of available EC 50 values for each species. Note, if multiple EC 50 values for a given species were available,
then a separate ratio was calculated for each.

b Marine macrophytesAquatic Plants Page 25 of 56



### Figure 3. Cumulative frequency plot of the 58 aquatic vascular plant EC 50 

### 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

separate ly . This is one reason why the analyses in the last section were done with the dataAquatic 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 SS D approaches separated the dat a into freshwater and saltwater; acetochlor and metolachlor
only used freshwater data and the draft atrazine document created separate distributions
for each medium. The analyses describe d below combines all data from all taxo nomic
groups and both freshwater and saltwater.

One of the underly ing 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. H owever, 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 EC 50 

### 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 ex pect 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 i s there no consensus on what constitutes a mini mum 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 fo r 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 collect ed 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 plan ts were downloaded from the

USEPA’s ECOTOX database 18 

### 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 OP P Ecotoxicity

Datab ase. All data were used ―as is‖ ( i.e ., original references were not checked ) . These
compounds were selected because a reasonably large number of EC 50 

### values were
available (LC 50 

### and IC 50 

### values also were included). It also should be note d 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 EC 50 

### for a
compound. SSDs for all six compounds are plotted in Figure 4. To d emonstrate 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/ecotoxAquatic Plants Page 27 of 56



### other included just the data from the OPP standard species. Equation 1 19 

### was fit to each of the ―full‖ and ―partial‖ data sets using Microsoft Excel’s solver routine. HC 5 

### 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 = EC 50 

### value,

X

m 

### = median EC 50 

### value, and

S = shape factor (slope)

Table 6. Comparison of calculated plants values using three different approaches.
All values are μg/L.

Calculated HC 5 b FAV 5 th

percentile d OPP data e

Compound N a Full

data set Partial

Data c Full

data set Partial

Data c Lowest

EC 50 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 fo r 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 w ould be used for deriving an OPP benchmark. For instance, the actual benchmark for atrazine is

1 μg/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 t hat 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 ., EC 05 or EC 20 ).Aquatic Plants Page 28 of 56



### Figure 4. Species sensitivity distributions for aquatic plants using species mean EC 50 

### 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.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 HC 10 

### of the

distribution. The HC 5 

### 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

evaluated 21 

### . 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 HC 5 

### 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 ind icated
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 phytotoxicit y and its calculation excludes animal toxicity

data which are used for the animal ALSV calculation alone

.

Di fferent 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 a ction 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 disc ussed 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 numbe r 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 en hance 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 wh ite 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 ., HC 20 ).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 reviewe d

, 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 ALS V. This includes
considering the desired duration of exposure, level of effect ( e.g ., EC 50 

### ) 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 ., HC

x 

### ) if an SSD approach is used.

In addition to the above, an approach for determining appropriate E Fs 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 po ints (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 EC 50 

### values already exist. This will include non

pesticide data in order to maximize the number of chemicals represented. The non

pesticide 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 a s SSDs in this document, EFs could be developed that could be us ed to account for when
only the standard set of OPP data are available. W hen more data than the standard OPP

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

some of the non

- OPP standard species data to the OPP stan dard 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 aquati c 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, f uture analyses will explore the different sources of un certainty 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 distribution s . This paper also explores
uncertainties associated with aquatic plant toxicity data , including the sensitivities of
typical test species relative to ot her aquatic plants for which test species are surrogates.
Based upon methodologies us ed by other countries and by U.S. regulatory agencies , and
the analyses of relative sensitivities of the standard OPP test species, plant communityAquatic Plants Page 31 of 56



### benchmark values can lo gically be derived

. The amount of data available for plant
species should be considered when calculat ing a plant ALSV . For pesticide s , data are
routinely available for the standard five OPP test species. It may be possible to use the

lowest of the availab le 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 th e sensitivity of more vulnerable aquatic plant species

. Possible

a pproach es 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 a nd to make the best use of available data

.

8

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### Appendix A

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

Species Chemical Duration (d) Endpoint EC 50 (μ g/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 a lachlor 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 a trazine 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 c hlorodifluoroacetic 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 d ichloroacetic 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

. 2009Aquatic 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 m etolachlor 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 m etribuzin 4 # fronds 36 Farichild et al

. 1998

Lemna perpusilla m etribuzin 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 aAquatic 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 m onochloroacetic 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 d ry weight 17 Cedergreen et al

. 2004b

Ceratophyllum submersum terbutylazine 14 d ry weight 69 Cedergreen et al

. 2004b

Elodea canadensis terbutylazine 14 d ry weight 98 Cedergreen et al

. 2004b

Elodea canadensis terbutylazine 14 d ry weight 305 Cedergreen et al

. 2004b

Lemna minor terbutylazine 14 d ry weight 40 Cedergreen et al

. 2004b

Lemna minor terbutylazine 14 d ry weight 111 Cedergreen et al

. 2004b

Lemna trisulca terbutylazine 14 d ry weight 254 Cedergreen et al

. 2004b

Myriophyllum spicatum terbutylazine 14 d ry weight 55 Cedergreen et al

. 2004b

Potamogeton crispus terbutylazine 14 d ry weight 109 Cedergreen et al

. 2004b

Potamogeton crispus terbutylazine 14 d ry weight 199 Cedergreen et al

. 2004b

Spirodela polyrhiza terbutylazine 14 d ry weight 228 Cedergreen et al

. 2004b

Spirodela polyrhiza terbutylazine 14 d ry weight 146 Cedergreen et al

. 2004bAquatic Plants Page 39 of 56

Lemna gibba t richloroacetic 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 t rifluoroacetic 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 2004aAquatic Plants Page 40 of 56



### Appendix B. Data for Species Sensitivity Distribution plots

Atrazine

Species Common name Medium Species group Duration
(d ) Endpoint EC 50 /IC 50 or
LC 50 a ,b (μ g/L) SMPV

(μ g/L)

Anabaena flos

- aquae Cyanobacterium FW Non

- vascular 5 230 a 230

Chlamydomonas reinhardtii Green alga FW Non

- vascular 4 cell number 51 b 27.32

Chlamydomonas reinhardtii Green alga FW Non

- vascular 4 cell number 51 b

Chlamydomonas reinhardtii Green alga FW Non

- vascular 7 cell number 21 b

Chlamydomonas reinhardtii Green alga FW Non

- vascular 10 cell number 10.2 b

Chlorella saccharophila Green alga FW Non

- vascular 4 cell number 1,300 b 1,300

Chlorella sp. Green alga SW Non

- vascular 3 growth 140 b 140

Dunaliella tertiolecta Green alga SW Non

- vascular 5 180 a 180

Elodea canadensis Elodea FW Vascular 10 biomass 1200 b 1,200

Isochrysis galbana Golden/brown alga SW Non

- vascular 5 22 a 22

Lemna gibba Duckweed FW Vascular 7 frond
production 180 b 50.54

Lemna gibba Duckweed FW Vascular 14 frond number 37 b

Lemna gibba Duckweed FW Vascular 14 frond biomass 45 b

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 b 59.28

Lemna minor Duckweed FW Vascular 10 fresh weight 60 bAquatic Plants Page 41 of 56

Lemna minor Duckweed FW Vascular 10 chlorophyll 62 b

Microcystis aeruginosa Cyanobacterium FW Non

- vascular 5 129 a 129

Myriophyllum spicatum Eurasian water milfoil SW Vascular 28 photosynthesis 117 b 54.08

Myriophyllum spicatum Eurasian water milfoil SW Vascular 35 biomass 25 b

Navicula incerta Diatom SW Non

- vascular 3 460 a 460

Navicula pelliculosa Diatom FW Non

- vascular 5 60 a 60

Neochloris sp. Green alga SW Non

- vascular 3 growth 82 b 82

Nitzschia closterium Diatom SW Non

- vascular 3 290 a 290

Pavlova sp

. Golden/brown alga SW Non

- vascular 4 growth 147 b 147

Platymonas sp. Green alga SW Non

- vascular 3 100 b 100

Porphyridium cruentum Red alga SW Non

- vascular 3 growth 79 b 79

Potamogeton perfoliatus Redheadgrass pondweed SW Vascular 28 photosynthesis 55 b 20.49

Potamogeton perfoliatus Redheadgrass pondweed SW Vascular 35 final biomass 30 b

Pseudanabaena geleata Cyanobacterium FW Non

- vascular 4 cell number 14 b 14

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 b 14

Skeletonema costatum Diatom SW Non

- vascular 2 growth 265 b 265

Thalassiosira fluviatilis Diatom SW Non

- vascular 3 110 a 110Aquatic Plants Page 42 of 56

Zostera marina Eelgrass SW Vascular 21 survival 540 b 291.6

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

a EC 50 /IC 50 /LC 50 data from OPP database

b EC 50 /IC 50 /LC 50 data from WQC draft (June 23, 2009)



### Diquat

Species Common name Medium Species Group Duration
(d ) Endpoint EC 50 /IC 50 a
(μ g/L) SMPV

(μ g/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 57Aquatic 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

a Data from ECOTOX databaseAquatic Plants Page 44 of 56



### Diuron

Species Common Name Species Group Medium Duration
(d ) Endpoint EC 50 /IC 50 /

LC 50 a
(μ g/L) SMPV

(μ g/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 44.60Aquatic 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 26.46Aquatic 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 1500Aquatic 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

a Data from ECOTOX database



### Irgarol

Species Common name Species Group Media
Type Duration

(d) Endpoint EC 50 /IC 50 a
(μ g/L) SMPV

(μ g/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 7.71Aquatic 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 3Aquatic 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 4.102Aquatic 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

a Data from ECOTOX database



### Metolachlor

Species Common name Species

group Medium Duration
(d ) Endpoint EC 50 /IC 50 a
(μ g/L) SMPV

(μ g/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 100.61 105.27Aquatic 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 50.9Aquatic 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

a Data from ECOTOX database



### Pentachlorophenol

Species Common name Species Group Media
Type Duration

(d ) Endpoint EC 50 a (μ g/L) SMPV
(μ g/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 360 327.92Aquatic 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 170Aquatic 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 512Aquatic 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 922Aquatic 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

a Data from ECOTOX database