1 

Appendix F. Estimating Aquatic Plant Community Hazard 

Concentrations for Pesticide Effects 

December 20, 2011 

Glen Thursby

USEPA, Office of Research and Development, National Health & Environmental Effects 

Research Laboratory, Atlantic Ecology Division, Narragansett, RI 

Sujatha Sankula

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

Washington, DC 

Michael Lewis 

USEPA, Office of Research and Development, National Health & Environmental Effects 

Research Laboratory, Gulf Ecology Division, Gulf Breeze, FL 

 
2 

INTRODUCTION 

A difficulty with deriving hazard concentration values for aquatic plants using data sets of 

various sizes is the fact that there are essentially no minimum data requirements. The availability 

of guidelines for the derivation of Aquatic Life Criteria (ALC) using phytotoxicity results, unlike 

for aquatic animals, has been characterized by historical uncertainty. The lack of this foundation 

has restricted the use of phytotoxicity data for criteria derivation for most chemicals. The 

environmental significance of this exclusion is unknown, but does show a need for guidance on 

how to address protection of aquatic plants. There is a long history within ALC development of 

standardized use of aquatic animal toxicity data, which includes the existence of minimum data 

requirements (MDRs) for both freshwater and saltwater. Except for the occasional call for 

additions to an MDR (e.g., amphibians), these calculated criteria are generally accepted as the 

norm for use in establishing thresholds of acceptable exposure. Aquatic plants, on the other hand, 

have no such history. There is no commonly accepted norm for minimum plant data 

requirements that sufficiently represents the taxonomic diversity for any given aquatic plant 

community. In addition, unlike for animals where the standard endpoints of survival, growth and 

reproduction are commonly accepted, there is no consensus on standard endpoints for most 

plants. Even though abundance or growth rate are the more commonly used endpoints for 

microalgal tests there are many other potentially acceptable endpoints for plants. This is 

especially true for both freshwater and saltwater non-flowering macrophytes whose life histories 

are generally much more complex that those of aquatic animals. 

There is minimum guidance for derivation of ALC for aquatic plants other than to use the lowest 

toxic effect value of the available data. Plant test values acceptable for ALC are “the result of a 

96-hr test conducted with an alga or a chronic test conducted with an aquatic vascular plant” (US 

EPA 1985—referred as “1985 Guidelines”). Although a plant effects value is not required for the 

derivation of ALC, if such a value is desired, then the minimum data required is one acceptable 

test with a freshwater alga or vascular plant for the freshwater criterion. Similarly the result of 

only one saltwater species is required to establish a saltwater plant criterion. In the event that 

plants are likely to be more sensitive than animals to a given compound (e.g., herbicides), then 

the “results with a plant in another phylum (division) should also be available”. The 1985 

Guidelines further describe the calculation of the plant value as a number that “should be 

obtained by selecting the lowest result from an 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 of “important aquatic plant” and “biologically important 

endpoint”; however, are not given. The lack of specificity for plant toxicity data is due largely to 

the fact that at the time of the writing of the 1985 Guidelines the “procedures for conducting tests 

with plants and interpreting the results of such tests [were] not as well developed”. Current 

scientific understanding is much improved on how to conduct plant toxicity tests and how to 

interpret the results. Nevertheless, standardization of procedures and selection of relevant 

endpoints are still issues. In addition, there still is no published guidance on how many species, 

and from which taxonomic groups, should be selected in order to reasonably represent the 
3 

sensitivity of the aquatic plant community. The selection of plant test species is essentially out of 

convenience and ease of culture. 

Under the Federal Insecticide, Fungicide and Rodenticide Act (FIFRA) EPA’s Office of 

Pesticides Programs (OPP) has the authority to require data in support of the registration of a 

pesticide product. With respect to aquatic plants, data for five species are requested. These 

include four microalgae (representing aquatic non-vascular plants) which usually are 

Pseudokirchneriella subcapitat (a freshwater green alga formerly known as Selenastrum 

capricornutum), Anabaena flos-aquae (a freshwater cyanobacterium), Navicula pelliculosa (a 

freshwater diatom) and Skeletonema costatum (a saltwater diatom). The fifth plant is a species of 

Lemna (freshwater; duckweed)—usually L. minor or L. gibba (although in practice most often L. 

gibba). While the approach explored below is valid for any amount of available data, the primary 

issue addressed is how best to evaluate data for the protection of aquatic plant communities if the 

only data available are results for these five species. As mentioned above, this is confounded by 

the fact that there currently is no MDR guidance for aquatic plants with which to compare 

conclusion from currently available data sets. 

An estimate of the “true” community effect concentration (or threshold) was needed for the 

above evaluation. In the absence of an MDR for aquatic plants relative to ALC phytotoxicity, 

only data sets having acceptable data for at least ten species (the methods section defines 

acceptable) were included. The assumption was that this is the best estimate for representing the 

community sensitivity range. This appendix did not test this assumption. From these data sets, a 

log-normal species sensitivity distribution was constructed, and from this distribution, the 

concentration representing the 5th percentile was calculated—referred to as the HC5. The HC 

stands for “hazard concentration” and the 5 refers to the 5th percentile. Data sets used were 

restricted to those for which toxicity results also were included for the standard five species 

requested by FIFRA for pesticide registration—referred to throughout this document as the 

“FIFRA-5”. These latter data were used to estimate the parameters for a log-normal distribution 

and an additional HC5 was calculated for comparison with the HC5 calculated using the full data 

set—often referred to in the remainder of the paper as the “true” HC51

. In addition, since past 

ALC and OPP risk assessments generally relied on the lowest plant toxicity value, comparisons 

also were made between the lowest FIFRA-5 values and the corresponding true HC5 values. 

In order to maximize the number of species for a given aquatic plant sensitivity distribution more 

than one endpoint was used. There usually is not an issue of which endpoints to include in 

sensitivity distributions for aquatic animals. Acute animal SSDs most often use survival, while 

mixed endpoints are frequently used for animal chronic SSDs. Although the interpretation of 

 1 True HC5 is just the best estimate derived using all of the available acceptable data. This value will have 

more uncertainty the fewer data points available. The optimal number of test values needed for estimating 

HC5 for animal acute data ranges from 15 to 55 (Newman et al. 2000). The estimated optimum number 

needed by Etterson (Appendix C) was 20.
4 

chronic SSDs is perhaps less definitive than results from animal acute SSDs2, they can predict 

that some portion of species will experience population reductions ranging from slight to severe 

following long-term exposures (Suter et al. 2002). Typically, data from plant tests mostly 

represent sublethal measurements. These sublethal effects extend to a variety of parameters 

including biomass-related (e.g., germination, root elongation, total biomass, leaf injury, pigment 

content, protein amino acid concentrations), activity-related (e.g., carbon dioxide uptake, oxygen 

evolution, variable fluorescence) and biochemical parameters (e.g., ATP levels, enzyme 

activities). In practice, the measured endpoints most often used by OPP and OW are some aspect 

of growth or growth rate. Activity-related or biochemical parameters are generally not used, but 

there is no reason not to include multiple endpoints in plant SSDs. In addition, although not used 

in SSDs, the evaluation of multiple plant endpoints simultaneously to estimate a species relative 

sensitivity has been proposed, although currently not widely accepted, as a new approach for 

estimating thresholds of toxicity in ecological risk assessment (Hanson and Solomon 2002). 

These data evaluations are referred to as Effect Measures Distributions (EMDs) by Hanson and 

Solomon (2002, 2004) and as Intraspecies Endpoint Sensitivity Distributions (IESDs) by Brain et 

al. (2006). One way to think of the approach of using many different endpoints is that what is 

wanted is to represent the true relative sensitivity of aquatic plants. Just as there is no 

consistently most sensitive species or other taxonomic grouping, no single endpoint (e.g., growth 

rate) will always be the most sensitive endpoint (Hanson and Solomon 2002). 

One of the significant limitations to the analyses presented herein is the lack of MDRs for 

aquatic plants. Because of this limitation, the current analyses suggest an interim approach to use 

until formal guidance on aquatic plant minimum data requirements can be established. With this 

caveat in mind, extrapolation of results of SSDs using data for FIFRA-5 species in order to 

calculate HC5 values shows promise for estimating thresholds, or benchmarks, for the protection 

of aquatic plant communities. In addition, because 100 percent of the data in the databases used 

were not check for accuracy against the original literature, the analyses presented below are for 

demonstration purposes only. The approach is sound, but validation of all of the data could 

change the absolute values of some of the data presented in the tables. 

 

 2 Acute SSDs not only predict that something will happen for a given exposure, they also approximate what 

will happen—some degree of mortality (Suter, et al. 2002).
5 

METHODS 

Data Selection

The toxicants used for the analyses are listed in Table 1. Only compounds covered by FIFRA 

(i.e., pesticides) were considered, and there were two criteria for selection. First there needed to 

be at least ten species in the data set and second, five of the species had to be the standard 

FIFRA-5 species. Almost all of the data came from EPA's ECOTOX database3 (data inquiry run 

October 2010). The data were supplemented where needed with data from OPP's DERs and 

some data from the open literature. For example, the ECOTOX inquiry for diuron resulted in 29 

species for inclusion in the SSD, but the FIFRA-5 were not all represented. Therefore, additional 

data for Anabaena flos-aquae, Navicula pelliculosa and Lemna gibba were added from an 

Australian government assessment report (APVMA 2011). These data rounded out the total 

needed for the FIFRA-5 for diruon. In addition, there were several greater thans within the 

results for metsulfuron, we substituted values for A. flos-aquae from two material and safety data 

sheets4

. 

All data downloaded from the ECOTOX website were not checked for accuracy against the 

original citations. Those data from the most sensitive half of the data sets (i.e., below the 50th

percentile); however, were verified since these would have the most effect on the calculated 

values. In addition, any data that appeared to be outliers were double checked with the original 

citation. No “outliers” were removed from the data sets if the data were determined to be correct. 

Table 1. List of compounds meeting the minimum requirements for inclusion in the analyses. 

Compound CAS

Number

Class Mode‐of‐Action

Atrazine 1912‐24‐9 Herbicide Photosystem II inhibition

Diquat 85‐00‐7 Herbicide Membrane Disruption

Diuron 330‐54‐1 Herbicide Photosystem II Inhibition

Irgarol 28159‐98‐0 Herbicide Photosystem II Inhibition

Linuron 330‐55‐2 Herbicide Photosystem II Inhibition

Metolachlor 51218‐45‐2 Herbicide Amino Acid Synthesis

Inhibition

Metribuzin 21087‐64‐9 Herbicide Photosystem II Inhibition

Metsulfuron

Methyl

74223‐64‐6 Herbicide Cell Division Inhibition

Pentachlorophenol 87‐86‐5 Fungicide Mitochondrial ATPase

Inhibition

Prometryn 7287‐19‐6 Herbicide Photosystem II Inhibition

 3 http://cfpub.epa.gov/ecotox/ 

4

 ROTAM Europe Limited, Safety Data Sheet for ERGON. DuPont Material Safety Data Sheet for Ally. 
6 

Data Standardization 

Toxicity data for each pesticide were derived using a variety of endpoints and exposure 

durations. After these data were downloaded the following actions were done in order to cull the 

data to a standard selection for each chemical. 

1. Toxicity data that were not expressed as EC50, IC50 or LC50 values were deleted. The original 

data base included effects values for various percentiles (e.g., EC10, EC25), as well as values for no 

observable or lowest observable effect concentrations (NOEC or LOEC). This does not mean that 

these eliminated data would not be useful in a risk assessment for the pesticides in question. It only 

means that the 50th percentile data was chosen to represent the relative sensitivity of a given 

endpoint. All results not already in μg/L units were converted to that unit. 

2. Data for which the organism tested was not identified were eliminated. Some of the tested 

organisms from ECOTOX were identified as “algae”, “phytoplankton” or some other nonspecific 

identifier. These were not included. Species identified only to the genus level were excluded if 

another representative of that genus was in the data set and identified to species. For example, 

Chlorella sp. would not be used if Chlorella vulgaris was available. However, Navicula sp. was 

included in the data set for diquat because data were not available for a specific Navicula species. 

3. Data from tests using unusual isolates were eliminated. For example, some data were for species for 

which a special variety had been selected for its resistance to an herbicide. These varieties should 

not be considered representative of that species “normal” sensitivity and as such were not used. 

4. Data from tests using pulsed exposures were eliminated. These were tests within which a species 

was exposed for a short period of time and then the pesticide treatment was replaced with control 

medium. Then the plant continued growing for an additional period of time before the endpoint was 

measured. Data from tests within which a species were exposed to treatments consisting of single 

exposures, but without renewal of media, were included. 

5. The exposure durations were limited to 3, 4 and 5 days for microalgal tests; 7 to 14 days for Lemna

species, and 14 to 35 days for other vascular plants. Occasionally, durations of 2 days were 

included. This occurred when an endpoint such as seaweed spore germination was available. These 

lower limits on duration eliminated many of the non-standard endpoints (e.g., most of the variable 

fluorescence data and most of the biochemistry data such as enzyme or ATP concentration). 

6. The final culling of the data was to eliminate any endpoints still in the list that were non-standard 

endpoints. These included the elimination of any remaining enzyme concentrations or nonchlorophyll a pigment concentrations. The final list of endpoints included were those associated 

primarily with biomass, counts, length, weight, area, chlorophyll a concentration, photosynthetic 

rate or growth rate. The most sensitive endpoint for each species was used in the plant SSDs. 

7. From the resulting full data sets, separate data sets were created containing only the values for the 

FIFRA-5. With the exception of diquat, the microalgal data included only data for the four standard 

microalgal species. As mentioned in paragraph #2 above, Navicula sp. was used for diquat since 

there was no results specifically attributed to Navicula pelliculosa were available. When data for 

both of Lemna minor and L. gibba were available data for L. gibba were selected. EPA’s Office of 

Pesticides Programs maintains an Ecotoxicity Database of available toxicity data submitted as part 

of the pesticide registration process. This summary database (March 2009 copy of the spreadsheet) 

includes the results of 360 tests using L. gibba, and only 16 for L. minor. The probability is 

substantially greater that a submitted FIFRA-5 data set would include L. gibba—which is why the 

analyses defaulted to that species when data for both were available. 
7 

8. Three of the compounds (atrazine, diuron and irgarol) had enough data available from the full data 

set to create a sub-set of data that used only some aspect of growth as the endpoint. Distinction was 

not made between different methods for how growth rate may have been calculated. For example, 

no distinction was made between g/day and g/g/day (specific growth rate). 

Data Analyses 

When more than one toxic effects value for the same endpoint (for the same species) was 

available within the standardize data sets described above, the geometric mean of those data was 

used for that endpoint. Results reported as greater thans were included (as absolute values), but 

the greater than was retained with the resulting geometric mean. When toxic effect values for 

more than one endpoint were available for the same species, the endpoint with the lowest value 

represented the sensitivity of that species. No distinctions were made among measured, 

unmeasured (nominal), static, renewal, etc. Although the EC50 values from tests using the 

FIFRA-5 species were generally based on measured pesticide concentrations, one of the 

limitations of plant data sets is the common lack of measured values for many tests. In order to 

maximize the taxonomic representation within the data sets unmeasured values were included. 

SSD Equations 

Six commonly used distribution equations were initially selected for evaluation: normal, lognormal, Gumbel (also called Gompertz), log-logistic, Weibull and inverse Weibull (also called 

Log Gompertz). A linearized form was used for each. A function of proportional rank was 

plotted on the x-axis and the concentration on the y-axis (concentration for normal and Gumbel; 

ln of the concentration for the other four). Proportional rank was calculated as R/(N+1), where R 

was the cumulative rank of the datum and N was the total number of values in the SSD. The 

performance of each of the SSD equations was judged graphically (visually). Graphical 

inspections of a linearized form is commonly used to fit a normal distribution (Chapman et al. 

2007), and has been used for a variety of other distributions for which a linearized form can be 

derived (Lee and Wang 2003). An additional advantage of a linearized plot is that it is easy to 

observe if the data deviate from the distribution. In general, the four distributions using ln of the 

concentration performed well, especially if confining the observations to the most sensitive half 

of the SSDs. Because log-normal probability plots consistently performed well, only those plots 

were used for the calculations reported herein. While for any given pesticide used in this paper, 

one of the other SSD equations may have had a better statistical fit to the available data, lognormal distribution was only used for consistency. 

The data for each data set were fit to linearized form of the normal distribution, which was taken 

from Lee and Wang (2003). The cumulative distribution function for a log-normal plot is: 





 



   



c  F(c)

 Equation 1

 
8 

Where: 

F(c) = proportion of values less than or equal to c, i.e., the proportional rank; 

c = in our case is the ln of a species EC50 value from the SSD; 

μ = arithmetic mean of all EC50 values in the statistical population modeled by the 

distribution; 

σ = the standard deviation of all EC50 values in the statistical population; and 

Φ = the standard normal distribution function, which calculates the percentage of values that 

are less than or equal to c. In Microsoft Excel this function is NORMSDST. 

The quantity (c-μ)/σ is also referred to as a z-score. It normalizes the difference of a value from 

the mean relative to the standard deviation. In other words, it calculates how may standard 

deviations a given value of c is from the mean of all possible c values. The theoretical range for 

these scores is -∞ to +∞, but more typically between -5 and +5. 

Applying the inverse of the standard normal distribution function (NORMSINV in Excel) to both 

sides of the above equation results in: 





 



    



c  F(c) 1

 Equation 2

Note that Equation 2 is the equation for z-scores. Rearranging and solving for c: 

      ( ) 1c F c Equation 3

This is a linear function between Φ-1F(c) on the x-axis and c on the y-axis. The slope and 

intercept of the equation fit to the data give an estimate of the parameters needed for the log 

normal distribution model of the data. Solving the equation for F(c) equal to any proportion of 

interest gives the value of c equivalent to the concentration for which that proportion of species 

are expected to be affected. On this scale, zero corresponds to 0.50 proportional rank—half of 

the species are more sensitive and half are less sensitive at this point on the graph. In practice 

0.05 is often selected as the proportion of interest (the 5th percentile). This resulting 

concentration is usually referred to as an HC5—hazard concentration affecting 5% of the species 

(TenBrook and Tjeerdema 2006). For each subset of the ten pesticide data sets (full, FIFRA-5 

and, where available, growth only subsets) Equation 3 was used to estimate an HC5. 

HC5 Calculations 

Linear regressions were performed for both full and FIFRA-5 data sets using the regression 

routine within Microsoft Excel (version 2007). These analyses gave the slope and the intercept 

for each use of Equation 3, as well as the standard error for each. Oracle Crystal Ball, Fusion 

Edition (Release 11.1.2.0.00), an add-in for Microsoft Excel (2007 version used), was used to 

estimate the median HC5 and various other percentiles of the HC5 values. The slopes and 

intercepts were assumed to be normally distributed for the purpose of re-sampling using Crystal 

Ball. A total of 10,000 re-samplings of slope and intercept were used. For each slope and 

intercept pair an HC5 was calculated. From the collection of 10,000 HC5 values the median was 

selected as the best representation of the final HC5 (Aldenberg and Jaworska 2000) for the full 

data sets (although other percentiles were estimated). For the smaller, FIFRA-5 data sets, the 

median HC5 values also were estimated, but in addition percentiles in 5% increments were 

calculated from 5 to 95%. 
9 

RESULTS 

SSD Plot 

Figures 1 and 2 show the log normal probability plots for compounds meeting the criteria for 

inclusion in the analyses. Each plot contains two graphs, one for all of the available data and the 

other for just the FIFRA-5 data. Figure 1 shows results for the six compounds with photosystem 

II inhibition as their mode of action. Figure 2 shows results for pesticides with the four additional 

modes of action. The parameters for the fit of the linear equation (Equation 3) to each of the full 

data sets are shown in Table 2 along with the median HC5 values resulting from the Crystal Ball 

simulations. The median HC5 values are essentially the same values which would be derived 

using the linear regression equations directly. In addition, the HC5 25th percentile values also are 

listed. Figure 3 is a plot showing the relationship between the median and the 25th percentile of 

the HC5 values from the ten full data sets. The slope of this relationship is 1.10, indicating that 

on average the 25th percentile is within 10% of the median value. 

Table 3 shows similar information for the FIFRA-5 data sets as described above for the full data 

sets. Also included are the EC50 values for the most sensitive species from each set of FIFRA-5 

values. There was no pattern among the FIFRA-5 species for their relative sensitivity, and there 

was no species that was consistently the most sensitive. The SSDs for the full data sets were used 

to estimate the proportion of species expected to be more sensitive than the most sensitive 

FIFRA-5 species. These proportions ranged from 0.04 to 0.31. The relationship between the 

lowest FIFRA-5 value and the median HC5 value from the corresponding full data set is plotted 

in Figure 4 (open circles). Eight of the ten FIFRA-5 values are greater than the HC5 paired 

values—the points are below the one-to-one dashed line. The ratios of the lowest FIFRA-5 

values to the respective HC5 were greatest for diquat and metsulfuron (6.6 and 6.3, respectively). 

The average of these values would be the current best estimate for an extrapolation factor to use 

to adjust the lowest FIFRA-5 values to minimize under-protection. These new, adjusted values 

also are plotted in Figure 4 (closed circles). 

Figure 5 is similar to Figure 3 except in this plot the 25th percentile is from the HC5 values 

derived from FIFRA-5 only data. The median HC5 values are from the full data sets—as in 

Figure 3. With the exception of diuron and linuron, the points are near or above the one-to-one 

line. On or above this dashed line indicates that the value on the x-axis is equal to or less than the 

value represented by the y-axis (i.e., FIFRA-5 values more protective). Even with diuron and 

linuron the FIFRA-5 estimated HC5 values are only factors of 3.2 and 4.0 greater, respectively, 

than those estimated using the full data sets. 
10 

 

 

 Figure 1. Species sensitivity distributions for the six herbicides with a photosystem II inhibition mode of action, 

using a linearized form of the log-normal probability plot. Each point represents a single species and is the ln of the 

lowest available median effect value for any of the available standard endpoints (see text for explanation of which 

endpoints included). The x-axis is the z-score, Φ-1is the inverse of the standard normal distribution function, and 

F(c) is the proportion of the cumulative sensitivity rank for each species. Open circles are the results from the full 

data sets. Solid circles are the results from the FIFRA-5 species only evaluations. Triangles are for results expressed 

as greater thans. Solid and dashed black lines are the best fit regressions. The vertical red dashed line is the position 

of the 0.05 proportion. It crosses the regression lines at the ln of the HC5. 

0.0

2.0

4.0

6.0

8.0

10.0

12.0

-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5

Ln (EC50)

NORMSINV(p)

A Atrazine

-2.0

0.0

2.0

4.0

6.0

8.0

10.0

12.0

-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0

Ln(EC50)

NORMSINV(p)

B Diuron

-5.0

-3.0

-1.0

1.0

3.0

5.0

7.0

-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5

Ln(EC50)

NORMSINV(p)

C Irgarol

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0

Ln(EC50)

NORMSINV(p) D Linuron

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0

Ln(EC50)

Φ-1F(c)

E Metribuzin

-1.0

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0

Ln(EC50)

Φ-1F(c)

F Prometryn
11 

 

 Figure 2. Species sensitivity distributions for four compounds with modes of action other than photosystem II 

inhibition, using a linearized for of the log-normal probability plot. Each point represents a single species and is the 

ln of the lowest available median effect value for any of the available standard endpoints (see text for explanation of 

which endpoints included). The x-axis is the z-score, Φ-1is the inverse of the standard normal distribution function, 

and F(c) is the proportion of the cumulative sensitivity rank for each species. Open circles are the data from the full 

data sets. Solid circles are the data from the FIFRA-5 species only. Triangles are for results expressed as greater 

thans. Solid and dashed black lines are the best fit regressions. The vertical red dashed line is the position of the 0.05 

proportion. It crosses the regression lines at the ln of the HC5.

 

-2.0

0.0

2.0

4.0

6.0

8.0

10.0

12.0

-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0

Ln(EC50)

NORMSINV(p)

A Diquat

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

10.0

-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0

Ln(EC50)

Metolachlor NORMSINV(p) B

-5.0

-3.0

-1.0

1.0

3.0

5.0

7.0

9.0

11.0

13.0

-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0

Ln(EC50)

Φ-1F(c)

C Metsulfuron

0.0

2.0

4.0

6.0

8.0

10.0

12.0

-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0

Ln(EC50)

Φ-1F(c)

D Pentachlorophenol
12 

Table 2. Parameters from the linear regressions of the full data sets. For compounds listed as 

“lower half” only the data from the most sensitive half of the species were used in the linear 

regressions. 

Compound Portion 

of data N Slope a

SESlope 

Intercept 

(ln EC50) SEIntercept r

2 

Median 

HC5 

(μg/L) 

HC5 25th

percentile 

(μg/L) 

Atrazine lower 

half 54 0.919 0.042 4.241 0.039 0.950 15.30 14.51 

Diuron lower 

half 32 1.307 0.093 2.598 0.847 0.933 1.57 1.40 

Irgarol lower 

half 38 1.787 0.146 0.224 0.134 0.897 0.066 0.055 

Linuron all 10 1.198 0.141 3.196 0.111 0.901 3.41 2.87 

Metribuzin lower 

half 18 0.667 0.047 3.130 0.040 0.966 7.65 7.21 

Prometryn all 13 1.482 0.139 2.662 0.115 0.911 1.25 1.05 

Diquat all 13 2.824 0.271 4.134 0.223 0.908 0.60 0.43 

Metolachlor all 17 1.914 0.083 6.281 0.071 0.973 22.98 20.73 

Metsulfuron lower 

half 19 1.903 0.294 0.381 0.247 0.840 0.064 0.044 

Pentachlorophenol lower 

half 27 1.956 0.187 5.721 0.164 0.901 12.26 9.73 

a

SESlope and SEIntercept are the standard errors for the slope and intercept, respectively. These values, along with the 

slopes and intercepts were used in the Crystal Ball simulations. 

Figure 3. A comparison of the median HC5 values from the full data sets with the 25th percentile of the distribution 

of HC5 calculated values from those same data sets. These are based on the Crystal Ball simulations. The dashed 

line is the one-to-one line.

y = 1.1106x

R2 = 0.9947

0.01

0.1

1

10

0.01 0.1 1 10

Median Full Data Set HC5 (ug/L)

25th Percentile of Full Data Set HC5 (ug/L)
13 

Table 3. Parameters from the linear regressions of the FIFRA-5 data sets. For compounds listed 

as “lower half” only the most sensitive half of the data were used in the linear regressions. 

Compound 

Lowest 

FIFRA 

valuea

(μg/L) 

Prop.b

 Slope cSESlope 

Intercept 

(ln EC50) SEIntercept r

2 

Median 

HC5 

(μg/L) 

HC5 25th

percentile 

(μg/L) 

Atrazine 31.46 0.19 0.987 0.199 4.165 0.133 0.892 12.66 9.99 

Diuron 7.09 0.31 0.585 0.124 2.718 0.083 0.881 5.77 4.96 

Irgarol 0.1 0.08 1.822 0.181 -0.384 0.122 0.971 0.034 0.027 

Linuron 13.7 0.31 0.752 0.104 3.474 0.069 0.946 9.33 8.29 

Metribuzind 8.09 0.06 1.414 0.489 2.930 0.327 0.736 1.81 1.1 

Prometryn 1 0.04 1.680 0.365 2.135 0.244 0.876 0.53 0.34 

Diquat 4 0.17 3.086 0.617 4.049 0.413 0.893 0.359 0.172 

Metolachlor 61 0.13 1.592 0.275 5.256 0.184 0.918 14.08 10.13 

Metsulfuron 0.41 0.25 5.518 1.232 4.507 0.825 0.870 0.010 0.0024 

Pentachlorophenol 35.26 0.13 1.086 0.135 4.597 0.091 0.955 16.61 14.02 

a

Lowest FIFRA value refers to just the lowest value from the five standard species. 

b

Prop. = proportion of species that the lowest FIFRA-5 value is greater than—based on the position of this species 

within the SSD for the full data set. 

c

SESlope and SEInterceept are the standard errors for the slope and intercept, respectively. These values, along with the 

slopes and intercepts were used in the Crystal Ball simulations. 

d

Non-significant regression, slope not statistically different from zero. 

 
14 

Figure 4. A comparison of the median HC5 values from the full data sets with the lowest FIFRA-5 EC50 value 

(open circles) and with these lowest values divided by an extrapolation factor of 6.5 (solid circles). The dashed line 

is the one-to-one line. 

Figure 5. A comparison of the median HC5 values from the full data sets with the 25th percentile of the distribution 

of HC5 calculated values using only the FIFRA-5 data. These are based on the Crystal Ball simulations. The dashed 

line is the one-to-one line. 

 

0.01

0.10

1.00

10.00

0.01 0.10 1.00 10.00

Median Full Data Set HC5 (ug/L)

Lowest FIFRA EC50 (ug/L)

Lowest FIFRA

Lowest/6.5

ATZ

DQT

DIU

IRG

LIN

MTC

MBZ

MSF

PCP

PMT

0.01

0.1

1

10

0.001 0.01 0.1 1 10

Median Full Data Set HC5 (ug/L)

25th Percentile of FIFRA-5 HC5 (ug/L)

"Full" more sensitive

"FIFRA" more sensitive
15 

Growth vs. Other Endpoints for Aquatic Plant SSDs: 

Sufficient toxicity data were available for three of the ten (atrazine, diuron and irgarol) pesticides 

to create separate SSDs using only data for growth endpoints. Figure 6 shows three plots 

comparing different full data set scenarios for atrazine. One of the plots is a repeat of the SSD 

using all available endpoints (same data as presented in Figure 1A). Two SSDs using only 

growth rate as the acceptable endpoint are also plotted in Figure 6. One of these plots contains a 

subset of the ECOTOX data, but culled to use only growth rate. The data for the other plot is 

taken from a report by Erickson (2010) within which the author selected only tests using 

measured chemistry values and where sufficient data were available so that specific growth rates 

could be calculated. Using only growth related endpoints resulted in SSDs that were slightly less 

sensitive than when using all of the endpoints—the plots for growth over the sensitive lower half 

of the data ranges are above that using all endpoints. In addition, the estimated median HC5 

values for these latter two SSDs were 17.11 and 19.36 μg/L, respectively. However, these values 

are only factors of 1.1 and 1.3 higher that the full data set HC5 using all endpoints, 15.30 μg/L. 

Figures 7 and 8 are similar plots comparing SSDs for all endpoints versus those using growth 

data only, for diuron and irgarol. As with atrazine, growth as an endpoint for diuron overall was 

slightly less sensitive than an analysis based on all available endpoints. The estimated median 

HC5 value using only growth was 3.38 μg/L, compared to 1.57 μg/L using all endpoints—a 

factor of only 2.2. For irgarol the difference between SSDs using growth only and using all 

endpoints was not as obvious. The HC5 for irgarol using growth only was 0.024 μg/L; less than 

the HC5 value of 0.066 μg/L (a factor of 0.36) for the full data set using all available endpoints. 

Figure 6. Comparison of atrazine SSD using lowest of all available ECOTOX endpoints for each species—same 

data as Figure 1A—with SSDs using only growth rate data from ECOTOX, and with growth data from Erickson 

(2010). The vertical red dashed line is the position of the 0.05 proportion. 

0.0

2.0

4.0

6.0

8.0

10.0

12.0

-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5

Ln(EC50)

Φ-1F(c)

Atrazine

Erickson Data

ECOTOX All Endpoints

ECOTOX Growth Rate
16 

Figure 7. Comparison of diuron SSD using lowest of all available ECOTOX endpoints for each species (open 

circles)—same data as Figure 1B—with an SSD using only growth rate data from ECOTOX (solid circles). The 

vertical red dashed line is the position of the 0.05 proportion. 

Figure 8. Comparison of irgarol SSD using lowest of all available ECOTOX endpoints for each species (open 

circles)—same data as Figure 1C—with an SSD using only growth rate data from ECOTOX (solid circles). The 

vertical red dashed line is the position of the 0.05 proportion. 

 

-2.0

0.0

2.0

4.0

6.0

8.0

10.0

-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0

Ln(EC50)

Φ-1F(c)

Diuron

-5.0

-3.0

-1.0

1.0

3.0

5.0

7.0

-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0

Ln(EC50)

Φ-1F(c)

Irgarol
17 

DISCUSSION 

For the ideal calculation of an aquatic plant community toxicity benchmark, the sensitivities of a 

wide variety of aquatic plant groups and results using a standard set of study durations and 

measurement endpoints are available. Unfortunately, this situation does not exist for the majority 

of pesticides. The exception to this is for tests conducted for regulatory purposes. In these cases, 

the results are more likely from standard tests using routine durations and endpoints. This 

minimum set of data, like any minimum data set; however, represents only a small portion of the 

potentially effected taxonomic groups. The OPP database of results evaluated for registrations 

(March 2009 version) has aquatic plant data for 445 pesticides (degradation and metabolic 

products were not counted). Only 33 of these compounds had toxicity results for more than five 

species, and only three had more than nine. Only eight compounds had toxicity results for more 

than one aquatic vascular plant. Six of these were when results were available for the freshwater 

duckweeds Lemna gibba and L. minor. Two herbicides, ethametsulfuron methyl and 

thiencarbazone methyl, were the only pesticides that had data for aquatic vascular plants other 

than Lemna spp. For both compounds the other species were the freshwater Myriophyllum 

spicatum (Eurasian milfoil) and Potamogeton pectinatus (sago pondweed). We point this out not 

as a criticism of the regulatory procedures, but rather to emphasize the need to optimize the use 

of the available data5

, since data rich situations are likely to be uncommon—especially for newer 

compounds. 

Aquatic plants is a broad category that includes species in four kingdoms—Bacteria, Protozoa, 

Chromista and Plantae (sensu Cavalier-Smith 2004) representing thousands of genera. In 

addition to a broad range of taxonomic representatives, aquatic plants have members in every 

conceivable freshwater and saltwater habitat. These areas include plants that are attached (rooted 

or other holdfast), free-floating, submerged or emergent (only partially submerged for part or all 

of their life history). All of these habitats include both micro- and macrophytic growth forms. 

Any aquatic plant community will likely have several hundred species. The goal, however, is not 

necessarily to have an even representation of all major aquatic plant taxonomic or morphological 

groups in a set of toxicity test results. The goal, rather, is to have as accurate a portrayal of the 

distribution of sensitivity to a given toxicant as practical, and to accomplish this with minimally 

available data. The current minimum phytotoxicity data requirements for pesticide registration in 

the U.S. will include toxicity test results for four microalgae and one aquatic vascular plant—

what we have referred to as the FIFRA-5. The assumption is that for any regulated pesticide 

these data will always be available, therefore, five is the minimum number of test results to 

which a data set consisting of results for at least ten species were compared. 

As stated earlier, there is no consensus to what species should be included for a minimum data 

requirement (MDR) to establish a sensitivity distribution for aquatic plants. This would be a data 

set sufficiently diverse to establish an HC5 value which could be routinely accepted as 

representing the “true” HC5. In the absence of an MDR, we settled for what was available and 

selected a requirement of at least 10 species with no requirements for which taxonomic groups 

should be included other than the 10 must include results for the FIFRA-5. The selection of 10 as 

the number of species was somewhat arbitrary; however, only eight species are required by OW 

to establish an HC5 for acute animal effects (but there are specific requirements for which 

 5

 OPP augments data submitted by registrants with data from the open literature. The actual data available 

for a risk assessment may represent a broader spectrum of plants than that submitted under FIFRA. 
18 

families should be represented, that is, an MDR). With this limitation in mind, we assumed that 

an HC5 from the available “full” data set was not only the best available estimate of the “true” 

HC5, but was an acceptably accurate portrayal of the aquatic plant communities expected range 

of effects. It bears remembering that an exhaustive search of the open literature was not 

conducted to confirm this assumption for any of these pesticides. Any formal risk assessment for 

these compounds should include not only a thorough review of all of the original citations from 

which the downloaded ECOTOX data came, but also verify that ECOTOX has the most updated 

information. 

There are two primary uncertainty questions relative to evaluating the use of data sets of various 

sizes to establish aquatic plant screening values for the protection of aquatic plants in general. 

First, what is the relationship between a conclusion made from the smaller data set (i.e., use of 

the lowest species toxic effect value, or calculation of an HC5 with only five data points) and 

that for a larger data set. This is essentially a precision issue. The second uncertainty issue: how 

representative is the SSD for a full data (minimum of 10 species) relative to the true sensitivity 

distribution in the aquatic plant community? This latter issue is touched upon above and will be 

discussed in detail in a separate section below, but the main focus of this paper is the small-large 

data set comparison. 

Representativeness of FIFRA-5 Results: 

The HC5 based on results from the full data sets was selected to represent the sensitivity of 

aquatic plant communities to each pesticide. Two approaches are presented for comparison of 

results from the FIFRA-5 minimum data sets and the HC5 values from the larger data sets. One 

approach was to focus on the lowest toxic effects value available from the FIFRA-5 test species. 

OPP and OW allow use of the lowest species values to estimate risk to aquatic plants. The other 

approach was to compare estimates of the HC5 derived from the FIFRA-5 data and that derived 

from a larger data set. 

Except for prometryn, the lowest FIFRA-5 effects value was always greater than the estimated 

median HC5 value from the full data set. Division by an extrapolation factor (EF), which is 

commonly done, results in the FIFRA-5 data being conservative relative to those from the full 

data sets. The purpose of the EF would not be to estimate the true HC5 value, since clearly some 

of the adjusted values were less than (more conservative) the “true” HC5 values by a factor of up 

to 8.1. The purpose of an EF is rather to minimize the likelihood of under-protection of the 

aquatic plant community. It does not guard against over-protection. There are a couple of 

cautions with using these data to establish an EF. Since only ten pesticides were used, the 

maximum value that could be selected is uncertain. Another issue is that there is insufficient 

information on how to adjust the EF when more phytotoxicity data than those for the FIFRA-5 

are available. The analyses presented in this paper considered a full data set to contain results for 

a minimum of 10 species. Based on this, an EF would not be needed when at least these numbers 

of toxicity data values are available. However, what should be done when toxicity data are 

available for more than five species, but less than ten species are available is not clear. 

The second approach for comparison of conclusions from FIFRA-5 only and full data sets offers 

an alternative that will allow adjustments of the value used to estimate an aquatic plant screening 

value as more data become available. A direct comparison of the median HC5 values for both the 

FIFRA-5 and full data sets shows that the HC5 based on the FIFRA-5 would be more 
19 

conservative for seven of the ten pesticides. Of the three less conservative results, 

pentachlorophenol differed by a factor of only 1.4, and the other two, diuron and linuron, 

differed by 3.7 and 4.5. The range of FIFRA-5 effect values for these latter two compounds from 

low to high was quite narrow—less than a factor of five. Comparing the 25th percentile (or any 

other percentile) of the FIFRA-5 HC5 with the full data set median HC5 only improves the 

relationship slightly. There is a distinct advantage; however, of using a percentile other than the 

50th (the median value) for making comparisons with the “true” HC5. By definition, as the 

number of data points increases the difference between any percentile and the median gets 

narrower (i.e., the confidence limits for the median get smaller). All else being equal, the median 

value would not change. Using the median value for the FIFRA-5 HC5 would not automatically 

improve the relationship to the true median by just increasing the number of values. Although, 

adding more data points would improve the estimate of the slope and intercept which in turn 

might change the median HC5 value. And to the extent that more data lessens the standard error 

of the slope and intercept, also would contribute to narrowing the difference between the median 

value and any other percentile. Therefore, as the number of data points increases there would be 

a corresponding improvement in the estimate of the true percentile of interest. The 25th percentile 

was selected for the purpose of these analyses. The data from the full data sets show that there is 

a fairly narrow difference between the 25th percentile and the median values. In other words, as 

the number of data points increases we get a better estimate of the true median HC5 value, which 

is what we want. Unlike the EF method with the lowest species value which protects only against 

under-protection, the method of using the 25th percentile of the HC5 value has the potential to 

minimize the probability of both under- and over-protection. 

There were at least two situations when the results from the FIFRA-5 HC5 estimations were 

problematic. The first is when the linear regression analysis shows a non-significant regression, 

suggesting that the slope is not statistically significant from zero. This was the case for both 

metribuzin (Figure 1E) where one of the data points was substantially different from the other 

four, which themselves were fairly similar to one another. The “outlier” was the least sensitive, 

about a factor of 15 greater than the other four values, resulting in an estimate that was overprotective. The best option would be to get at least one or two more EC50 values in order to 

improve the regression. If this is not possible, then this type of data situation will result in overprotection to some degree. However, if the opposite situation is true—that is, the outlier being 

substantially less sensitive than the other effects values—then there result from the FIFRA-5 

estimate will be under-protective. If this latter situation occurs, and more data is not possible, 

then consideration could be given to an EF approach. 

The other situation when the results may need additional consideration is when the range of 

values is narrow. For both diuron and linuron the FIFRA-5 data fit a linear regression quit well. 

The difference between the highest EC50 and the lowest EC50 was only a factor of 3.3 for 

diuron and 4.9 for linuron. These narrow ranges are likely to be rare when considering much 

larger data sets (broader range of test species). If the range is less than a factor of five, then more 

data would be desirable. The flip side of all this is that if the data range is greater than a factor of 

10, and certainly if greater than a factor of 100, and the linear regression results in the slope 

being highly significantly different from zero, then the confidence in the conclusion drawn from 

only the FIFRA-5 results increases substantially. Although these conclusions are based only on a 

few data sets, the validity could be checked using computer simulations. 
20 

Representativeness of Currently Available Data: 

Understanding the relationship of conclusions drawn from phytotoxicity data sets of various 

sizes is the primary objective of the data analyses presented in this paper. The accuracy of this 

comparison; however, has some uncertainty associated with how well the results within “full” 

data sets represent the distribution of aquatic plant sensitivities in the “real world”. While this 

source of uncertainty also is true for aquatic animal data sets, at least with the animal data there 

exist currently accepted MDRs assumed to be a reasonable surrogate for representing that 

community at large. For aquatic plants, such an MDR does not exist so the representativeness of 

the generally used surrogates cannot be adequately judged. While there is no evidence to suggest 

that the underrepresented plant taxonomic groups have some special sensitivity to pesticides, 

neither is there any evidence that they do not. As stated above, the true goal of the compilation of 

aquatic plant toxicity results is to determine a relevant distribution of sensitivity. The validity of 

this distribution; however, cannot be fully assessed without some consideration (and testing) of 

taxonomic groups and species that are rarely, if ever tested. This is especially true for marine 

macrophytes, both vascular and non-vascular (i.e., seaweeds), and of freshwater macroalgae. The 

relative sensitivities of freshwater and saltwater plants is unknown for most pesticides. In 

addition, unlike animals, plants have a much wider variety of life history patterns. Plants often 

have more than one free-living, “adult” phase, often with separate male and female plants and 

yet another separate sporophyte. In some species the gametophytic phase dominates (relative to 

size), in others the sporophyte. Few, if any, studies are available comparing the relative 

sensitivities of different life history patterns, or even much information on the relative sensitivity 

of different life stages. Unlike animal chronic toxicity tests which may include full and partial 

life cycle tests, and early life stage tests, many aquatic plant tests are generally analogous to only 

testing the “adults”. This is important in the context that sexual reproduction in seaweeds is 

among their most sensitive endpoints (Thursby et al. 1985, Eklund 1998) and that sexual 

reproduction has not been evaluated in freshwater algae or aquatic vascular plants in general. 

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

the chemical and a plant’s physiology. Therefore, besides consideration of underrepresented 

taxonomic groups, differences among physiological pathways within those groups may make a 

difference in sensitivity. For instance, 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 pathways6

. For example, Lemna species are C3 plants (Longstreth 1989), 

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

plants (e.g., species of Salicornia and Scirpus) are C3 (Drake 1989). In some species of 

amphibious freshwater plants (e.g., the sedges Elocharis vivipara and E. baldwinii), the 

emergent phase exhibits C4 photosynthesis while the submerged phase is C3 (Cronk and 

Fennessy 2001). In addition, there is a wide variety of photosynthetic pigments among the 

various algal groups, so type of accessory pigments (and thus different pigment synthesis 

pathways) also might make a difference in sensitivity. 

 

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

phosphoglyceric acid (a 3 carbon compound) and in C4 plants it is oxaloacetic acid (a 4 carbon compound).
21 

Use of Growth Rate as a Standard Endpoint: 

A common dilemma that arises when constructing SSDs for aquatic plants is which endpoints to 

include in the data set. There are those who favor only using growth or relative growth rate 

(commonly used endpoints) so that a common link can be made among all of the test values. 

However, even with this endpoint there are inconsistencies. Growth rate of microalgal species 

essentially always refers to population growth rate, while growth of aquatic vascular plants (with 

the possible exception of Lemna spp.) almost always refers to growth of individual plants. There 

are situations, such as the atrazine work by Erickson (2010), when standardization based on 

growth rate as the endpoint is warranted. Erickson had a specific, and reasonable, reason for 

consistency among the aquatic plant endpoints. He wanted a way to summarize the total effect to 

the plant community by essentially integrating growth rate across individual species doseresponses. His approach; however, requires that the species have a rapid recovery from the 

compound of interest (each day’s exposure is treated separately, no cumulative effects). Atrazine 

fits this requirement (as perhaps many photosystem II inhibitors). Erickson’s technique also 

requires that all growth be treated the same, that is, a given percent reduction in growth of a 

rapidly growing microalga will have the same community consequences as that same percentage 

reduction in a much slower growing aquatic vascular plant. In many situations; however, the 

selection of just growth rate may not be justified, and may even be counter to the need of best 

representing the true sensitivity of species. Ultimately the question being asked is, for a given 

exposure scenario what proportion of the species of aquatic plants are likely to be adversely 

affected? Confining the definition of adverse toxicity effect to only effects on growth would 

ignore the fact that growth is often not the most sensitive endpoint. This was certainly the case 

with the SSDs for atrazine and diuron7

, although the HC5 values were not that different. As 

mentioned above, this is also the case for the relative sensitivity of sexual reproduction (although 

for aquatic plants this is currently known only for seaweeds). In addition, available growth 

results may not be the most ecologically relevant growth endpoint. Growth results of aquatic 

vascular plants most often are available only for shoots. In many instances, when growth has 

been measured for both shoots and roots, growth of roots is substantially more sensitive than that 

of shoots (Lin et al. 2002, Arts et al. 2008). For aquatic plants that are rooted in the sediment, 

this can make a substantial difference in their survivability. Including a broader base of 

endpoints (beyond just growth rate) will provide a more complete representation of the range of 

species sensitivities within the aquatic plant community. 

 

 7 Note that the three pesticides for which a growth SSD was possible were all photosystem II inhibitors. 

Pesticides with other modes of actions may not necessarily yield the same conclusion.
22 

REFERENCES 

Aldenberg T and JS Jaworska. 2000. Uncertainty of the hazardous concentration and fraction 

affected for normal species sensitivity distributions. Ecotoxicology and Environmental 

Safety 46:1-18. 

APVMA. 2011. Diuron Environmental Assessment. Australian Pesticides and Veterinary 

Medicines Authority. July 2011. URL. 

www.apvma.gov.au/products/review/docs/diuron_environment.doc 

Arts, GHP, JDM Delgers, CH Hoekzema and JTNM Thissen. 2008. Sensitivity of submersed 

freshwater macrophytes and endpoints in laboratory toxicity tests. Environmental 

Pollution 153:199-206. 

Brain, RA, H Sanderson, PK Sibley and K Solomon. 2006. Probabilistic ecological hazard 

assessment: Evaluating pharmaceutical effects on aquatic higher plants as an example. 

Ecotoxicology and Environmental Safety 54:128-135. 

Cavalier-Smith, T. 2004. Only six kingdoms of life. Proceedings of the Royal Society of London 

B. 271:1251-1262. 

Chapman, PF, M Reed, A Hart, W Roelofs, T Aldenbert, K Solomon, J Tarazona, M Liess, P 

Byrne, W Powley, J Green, S Ferson and H Galicia. 2007. Methods of Uncertainty 

Analyses: Work Package 4 in EUFRAM: Concerted action to develop a European 

Framework for probabilistic risk assessment of the environmental impacts of pesticides. 

Versin 3, March 2007. URL. www.eufram.com 

Cronk, JK and MS Fennessy. 2001. Wetland Plants: Biology and Ecology. CRC Press. 463 pp. 

Drake, BG. 1989. Photosynthesis of salt marsh species. Aquatic Botany 34:167-180. 

Eklund, B. 1998. Reproductive performance and growth response of the red alga Ceramium 

strictum under the impact of phenol. Marine Ecology Progress Series 167:119-126. 

Erickson, R. 2010. Proposed methodology for specifying atrazine levels of concern for 

protection of plant communities in freshwater ecosystems. Draft Report to Environmental 

Fate and Effects Division, Office of Pesticide Programs, USEPA. September 15, 2010. 

Hanson, ML and KR Solomon. 2002. New technique for estimating thresholds of toxicity in 

ecological risk assessment. Environmental Science and Technology 36:3257-3264. 

Hanson, ML and KR Solomon. 2004. Haloacetic acids in the aquatic environment. Part I: 

macrophyte toxicity. Environmental Pollution 130:371-383. 

Lee, ET and JW Wang. 2003. Statistical Methods for Survival Data Analysis. 3rd Ed. Wiley & 

Sons. 513 pp, 

Lin, Q, IA Mendelssohn, MT Suidan, K Lee and AD Venosa. 2002. The dose-response 

relationship between No. 2 fuel oil and the growth of the salt marsh grass, Spartina 

alterniflora. Marine Pollution Bulletion. 44:897-902. 

Longstreth, DJ. 1989. Photosynthesis and photorespiration in freshwater emergent and floating 

plants. Aquatic Botany 34:287-299. 

Newman, MC, DR Ownby, LCA Mézin,DC Powell, TRL Christensen, SB Lerberg and B-A 

Anderson. 2000. Applying species-sensitivity distributions in ecological risk assessment: 
23 

Assumptions of distribution type and sufficient numbers of species. Environmental 

Toxicology and Chemistry 19(2):508-515. 

Suter II, GW, TP Traas and L Poshuma. 2002. Issues and practices in the derivation and use of 

species sensitivity distributions. pp 437-474 (in) L Posthuma, GW Suter II and TP Traas 

(eds) Species Sensitivity Distributions in Ecotoxicology. Lewis Publishers 587 pp. 

TenBrook, PL and RS Tjeerdema. 2006. Methodology for derivation of pesticide water quality 

criteria for the protecton of aquatic life in Sacramento and San Joaquin River basins. 

Phase I: review of existing methodologies. Report to the Central Valley Regional Water 

Quality Board. April 2006. 

Thursby, GB, RL Steele and ME Kane. 1985. Effects of organic chemicals on growth and 

reproduction in the marine red alga Champia parvula. Environmental Toxicology and 

Chemistry. 4:797-805. 

US EPA. 1985. Guidelines for deriving numerical national water quality criteria for the 

protection of aquatic organisms and their uses. EPA Report 822/R-85-100 (NTIS Report 

PB85-227049.