1 

## Appendix F. Estimating Aqua tic 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 Ecol ogy Division, Narragansett, RI

Sujatha Sankula USEPA, Office of Pesticide Programs, E nvironmental Fate and Effects Division, Washington, DC

Michael Lewis USEPA, Office of Research and Development, National Health & Environmental Effects Research Laboratory, Gulf Ecol ogy Division, Gulf Breeze, FL2 INTRODUCTION A difficulty with deriving hazard concentration va lues for aquatic plants using data sets of various sizes is the fact that there are essentia lly no minimum data requir ements. The availability of guidelines for the derivation of Aquatic Life Criteria (ALC) using phyto toxicity results, unlike for aquatic animals, has been characterized by hi storical uncertainty. The lack of this foundation has restricted the use of phytotoxicity data fo r criteria derivation for most chemicals. The environmental significance of this exclusion is unknown, but does show a n eed for guidance on how to address protection of aquatic plants. Ther e is a long history within ALC development of standardized use of aquatic anim al toxicity data, which includes the existence of minimum data requirements (MDRs) for both freshwater and sa ltwater. Except for the occasional call for additions to an MDR (e.g., amphibians), these calcu lated criteria are generally accepted as the norm for use in establishing thresholds of acceptabl e exposure. Aquatic plants, on the other hand, have no such history. There is no commo nly 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 th e 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 potentia lly acceptable endpoints for plants. This is especially true for both freshw ater and saltwater non-flowering macrophytes whose life histories are generally much more complex th at those of aquatic animals.
There is minimum guidance for derivation of ALC fo r 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 criteri on. Similarly the result of only one saltwater species is requ ired to establish a saltwater pl ant 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 (d ivision) should also be available”. The 1985 Guidelines further describe the calculation of the plant value as a nu mber 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 “impor tant aquatic plant” and “biologically important endpoint”; however, are not given. The lack of specificity for plant to xicity 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 mu ch improved on how to conduct pl ant toxicity te sts and how to interpret the results. Nevertheless, standardiza tion of procedures and selection of relevant endpoints are still issues. In a ddition, there still is no publis hed guidance on how many species, and from which taxonomic groups, should be sele cted in order to reasonably represent the3 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 an d Rodenticide Act (FIFRA) EPA’s Office of Pesticides Programs (OPP) has the authority to re quire data in support of the registration of a pesticide product. With respect to aquatic plan ts, data for five species are requested. These include four microalgae (representing aquati c 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 vali d for any amount of available data, the primary issue addressed is how best to ev aluate data for the pr otection of aquatic plant communities if the only data available are results fo r these five species. As menti oned 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 con centration (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 l east ten species (the methods section defines acceptable) were included. The assu mption was that this is the be st 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 5 th percentile was calculated—referred to as the HC5. The HC stands for “hazard concentration” and the 5 refers to the 5 th percentile. Data sets used were restricted to those for which toxicity results al so were included for the standard five species requested by FIFRA for pesticide registratio n—referred to throughout this document as the “FIFRA-5”. These latter data were used to es timate the parameters for a log-normal distribution and an additional HC5 was calculated for comparis on with the HC5 calculated using the full data set—often referred to in the remainde r of the paper as the “true” HC5 1 . 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. Ther e usually is not an issue of which endpoi nts to include in sensitivity distributions for aqua tic animals. Acute animal SSDs most often use survival, while mixed endpoints are frequently used for anim al 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 SSDs 2 , they can predict that some portion of species will experience populat ion reductions ranging fr om slight to severe following long-term exposures (Suter et al. 2002) . Typically, data from plant tests mostly represent sublethal measurements. These subletha l effects extend to a variety of parameters including biomass-related (e.g., germination, root el ongation, total biomass, leaf injury, pigment content, protein amino acid conc entrations), activity -related (e.g., carbon dioxide uptake, oxygen evolution, variable fluorescen ce) and biochemical parameters (e.g., ATP levels, enzyme activities). In practice, the measured endpoints mo st often used by OPP and OW are some aspect of growth or growth rate. Activity-related or bi ochemical 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 multip le 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 Eff ect Measures Distributi ons (EMDs) by Hanson and Solomon (2002, 2004) and as Intrasp ecies Endpoint Sensitivity Dist ributions (IESDs) by Brain et al. (2006). One way to think of th e approach of using many differe nt 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 sing le endpoint (e.g., growth rate) will always be the most sensit ive 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 lim itation, the current analyses sugge st an interim approach to use until formal guidance on aquatic plant minimum da ta 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 s ound, but validation of all of the data could change the absolute values of some of the data presente d 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 da ta came from EPA's ECOTOX database 3 (data inquiry run October 2010). The data were supplemented wh ere needed with data from OPP's DERs and some data from the open literature. For example, the ECOTOX inquiry fo r diuron resulted in 29 species for inclusion in the SSD, but the FIFRA-5 were not all represente d. Therefore, additional data for Anabaena flos-aquae, Navicula pelliculosa and Lemna gibba were added from an Australian government assessment report (A PVMA 2011). These data rounded out the total needed for the FIFRA-5 for diruon. In addition, there were several great er thans within the results for metsulfuron, we substituted values for A. flos-aquae from two material and safety data sheets 4

.
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 50 th percentile); however, were verified since these would have the most effect on the calculated values. In addition, any data th at appeared to be outliers were double checked with the original citation. No “outliers” were removed from the data sets if the data were de termined to be correct.

Table 1 . List of compounds meeting the minimum re quirements 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 ERGO N. DuPont Material Safe ty Data Sheet for Ally.6 Data Standardization

Toxicity data for each pesticide were deri ved using a variety of endpoints and exposure durations. After these data were downloaded the fo llowing actions were done in order to cull the data to a standard sel ection 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 concentra tions (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 50 th percentile data was chosen to repr esent 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 id entified were eliminated. Some of the tested organisms from ECOTOX were identified as “alg ae”, “phytoplankton” or some other nonspecific identifier. These were not included. Species identif ied only to the genus le vel were excluded if another representative of that genus was in the da ta 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 eliminat ed. 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 elim inated. These were tests within which a species was exposed for a short period of time and then th e 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 limite d to 3, 4 and 5 days for microalgal tests; 7 to 14 days for Lemna species, and 14 to 35 days for other vascular pl ants. Occasionally, durations of 2 days were included. This occurred when an endpoint such as seaweed spore ge rmination was available. These lower limits on duration eliminated many of the no n-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 an y 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 set s were created containing only the values for the FIFRA-5. With the exception of diquat, the microalg al 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 Datab ase of available toxicity data submitted as part of the pesticide registration process. This summary database (March 2009 c opy 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 submitte d FIFRA-5 data set would include L. gibba —which is why the analyses defaulted to that species wh en data for both were available.7 8. Three of the compounds (atrazine, diuron and irgaro l) had enough data available from the full data set to create a sub-set of data that used only so me 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 re ported as greater thans were in cluded (as absolute values), but the greater than was retained with the resultin g geometric mean. When toxic effect values for more than one endpoint were available for the sa me 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 representati on 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 th e 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 wa s 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 di stributions for which a linearized form can be derived (Lee and Wang 2003). An a dditional advantage of a linearized plot is that it is easy to observe if the data deviate from th e distribution. In general, the f our distributions using ln of the concentration performed well, especially if conf ining the observations to the most sensitive half of the SSDs. Because log-normal probability plot s consistently performed well, only those plots were used for the calculations re ported herein. While for any given pesticide used in this paper, one of the other SSD equations may have had a be tter statistical fit to the available data, lognormal distribution was only used for consistency.

The data for each data set were fit to linearize d form of the normal distribution, which was taken from Lee and Wang (2003). The cumulative dist ribution function for a log-normal plot is:

     

     c c F )

( Equation 18 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 va lues in the statistical population; and Φ = the standard normal distribution function, wh ich calculates the percen tage 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 standa rd 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 standa rd normal distribution function ( NORMSINV in Excel) to both sides of the above equation results in:
     


  
  c c F )

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

   

   ) ( 1 c F c Equation 3

This is a linear function between Φ -1 F ( 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 paramete rs 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 fo r which that propo rtion of species are expected to be affected. On this scal e, zero corresponds to 0.50 proportional rank—half of the species are more sensitive and half are less se nsitive at this point on the graph. In practice 0.05 is often selected as the proportion of interest (the 5 th 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). Thes e analyses gave the slope and the intercept for each use of Equation 3, as well as the stan dard error for each. Oracle Crystal Ball, Fusion Edition (Release 11.1.2.0.00), an add-in for Microsof t Excel (2007 version used), was used to estimate the median HC5 and various other pe rcentiles of the HC5 values. The slopes and intercepts were assumed to be normally distribu ted 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 th e collection of 10,000 HC5 values the median was selected as the best representa tion of the final HC5 (Aldenberg and Jaworska 2000) for the full data sets (although other percenti les 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 pl ot 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 fo r the fit of the linear equation (E quation 3) to each of the full data sets are shown in Table 2 along with the me dian HC5 values resulting from the Crystal Ball simulations. The median HC5 values are essentia lly the same values which would be derived using the linear regression equati ons directly. In addition, the HC5 25th percentile values also are listed. Figure 3 is a plot showing the relationshi p between the median and the 25th percentile of the HC5 values from the ten full data sets. The slope of this relationshi p is 1.10, indicating that on average the 25th percentile is w ithin 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 sp ecies 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 sp ecies expected to be more sens itive than the most sensitive FIFRA-5 species. These proportio ns ranged from 0.04 to 0.31. The relationship between the lowest FIFRA-5 value and the median HC5 value from the corresponding fu ll data set is plotted in Figure 4 (open circles). Eight of the ten FI FRA-5 values are greater than the HC5 paired values—the points are below the one-to-one da shed line. The ratios of the lowest FIFRA-5 values to the respective HC5 were greatest for diquat and metsulfu ron (6.6 and 6.3, respectively). The average of these values would be the current best estimate for an extr apolation factor to use to adjust the lowest FIFRA-5 values to minimi ze under-protection. These new, adjusted values also are plotted in Figu re 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 HC 5 values are from the full data sets—as in Figure 3. With the exception of diuron and linur on, the points are near or above the one-to-one line. On or above this da shed line indicates that th e value on the x-axis is equal to or less than the value represented by the y-axis (i.e., FIFRA-5 values more prot ective). Even with diuron and linuron the FIFRA-5 estimated HC5 values are onl y 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 fo r any of the available standard endpoi nts (see text for explanation of which endpoints included). The x- axis is the z-score, Φ -1 is the inverse of the standard normal distribution function, and F(c) is the proportion of the cumulative sensitivity rank fo r each species. Open circles are the results from the full data sets. Solid circles are the results from the FIFRA-5 sp ecies 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 2.5Ln (EC50)

NORMSINV(p) Atrazine A

-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 2.0Ln(EC50)

NORMSINV(p) Diuron B

-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 2.5Ln(EC50)

NORMSINV(p) Irgarol C

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 2.0Ln(EC50) NORMSINV(p) Linuron D

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 2.0Ln(EC50)

Φ -1 F(c) Metribuzin E

-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 2.0Ln(EC50)

Φ -1 F(c) Prometryn F11 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 -1 is the inverse of the standard normal distribution function, and F(c) is the proportio n of the cumulative sensitivity rank for each spec ies. Open circles are the data from the full data sets. Solid circles are the data from the FIFRA-5 sp ecies 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 2.0Ln(EC50)

NORMSINV(p) Diquat A

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 2.0Ln(EC50) NORMSINV(p) Metolachlor 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 2.0Ln(EC50)

Φ -1 F(c) Metsulfuron C

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 2.0Ln(EC50)

Φ -1 F(c) Pentachlorophenol D12 Table 2 . Parameters from the linear regressions of the full data sets. For compounds listed as “lower half” only the data from the most sensitiv e half of the species we re used in the linear regressions.

Compound Portion of data N Slope a SE Slope Intercept (ln EC50) SE Intercept r 2 Median HC5 (μg/L) HC5 25 th 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 SE Slope and SE Intercept are the standard errors for the slope and interc ept, 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 25 th 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 10Median Full Data Set HC5 (ug/L)

25th Percentile of Full Data Set HC5 (ug/L)13 Table 3. Parameters from the linear re gressions of the FIFRA-5 da ta sets. For compounds listed as “lower half” only the most se nsitive half of the data were used in the linear regressions.

Compound Lowest FIFRA value a (μg/L) Prop. b Slope c SE Slope Intercept (ln EC50) SE Intercept r 2 Median HC5 (μg/L) HC5 25 th 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

Metribuzin d 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 th e position of this species within the SSD for the full data set.

c SE Slope and SE Interceept are the standard errors for the slope and interc ept, 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 25 th 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 10.00Median 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 10Median Full Data Set HC5 (ug/L)

25th Percentile of FIFRA-5 HC5 (ug/L) "Full" more sensitive "FIFRA" more sensitive15 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 gr owth endpoints. Figure 6 shows three plots comparing different full data set scenarios for atr azine. One of the plots is a repeat of the SSD using all available endp oints (same data as presented in Figure 1A). Two SSDs using only growth rate as the acceptable endpoint are also plot ted 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 Ericks on (2010) within whic h 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 e ndpoints resulted in SSDs that were slightly less sensitive than when using all of the endpoints—th e plots for growth over the sensitive lower half of the data ranges are above that using all en dpoints. 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 highe r that the full data set HC5 using all endpoints, 15.30 μg/L.

Figures 7 and 8 are similar plots comparing SSD s for all endpoints versus those using growth data only, for diuron and irgarol. As with atrazi ne, growth as an endpoint for diuron overall was slightly less sensitive than an analysis based on all ava ilable 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 irgaro l the difference between SSDs us ing growth only and using all endpoints was not as obvious. The HC5 for irga rol 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 a ll 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 2.5Ln(EC50)

Φ -1 F(c) Atrazine

Erickson Data ECOTOX All Endpoints

ECOTOX Growth Rate16 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 on ly 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 on ly 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 2.0Ln(EC50)

Φ -1 F(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 2.0Ln(EC50)

Φ -1 F(c) Irgarol17 DISCUSSION For the ideal calculation of an aquatic plant comm unity toxicity benchmark, the sensitivities of a wide variety of aquatic plant groups and resu lts using a standard se t of study durations and measurement endpoints are available. Unfortunately, this situation does not exist for the majority of pesticides. The excepti on to this is for tests conducted for regulatory pu rposes. In these cases, the results are more likely from standard test s using routine duration s 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 data base 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 co mpounds 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 on ly pesticides that had data for aquatic vascular plants other than Lemna spp. For both compounds the othe r 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 emphasi ze the need to optimize the use of the available data 5 , since data rich situations are likel y to be uncommon—especially for newer compounds.

Aquatic plants is a broad categ ory that includes species in f our kingdoms—Bacteria, Protozoa, Chromista and Plantae ( sensu Cavalier-Smith 2004) representi ng thousands of genera. In addition to a broad range of ta xonomic representatives, aquatic plants have members in every conceivable freshwater and saltwater habitat. Thes e areas include plants that are attached (rooted or other holdfast), free-floating, submerged or emer gent (only partially submerged for part or all of their life history). All of th ese habitats include both micro- and macrophytic growth forms. Any aquatic plant community will likely have seve ral 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 re sults. The goal, rather, is to ha ve 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 numb er of test results to which a data set consisting of results fo r 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 HC 5 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; ho wever, only eight species are required by OW to establish an HC5 for acute animal effects (but ther e are specific requirements for which

5 OPP augments data submitted by re gistrants 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 remember ing that an exhaustive search of the open literature was not conducted to confirm this assumptio n for any of these pesticides. Any formal risk assessment for these compounds should include not only a thorough revi ew 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 aqua tic plants in general. First, what is the relationship between a conclusi on made from the smaller data set (i.e., use of the lowest species toxic effect value, or calcula tion 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 touc hed 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 repres ent 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 de rived 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. Divi sion 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 cons ervative) the “true” HC5 va lues by a factor of up to 8.1. The purpose of an EF is rather to mi nimize the likelihood of under-protection of the aquatic plant community. It does not guard ag ainst 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 uncerta in. 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 woul d not be needed when at least these numbers of toxicity data values are available. However, what shou ld 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 th e HC5 based on the FIFRA-5 would be more19 conservative for seven of the ten pesticides . Of the three less conservative results, pentachlorophenol differed by a factor of onl y 1.4, and the other tw o, diuron and linuron, differed by 3.7 and 4.5. The range of FIFRA-5 effect values for th ese latter two compounds from low to high was quite narrow—less than a factor of five. Comparing the 25 th percentile (or any other percentile) of the FIFR A-5 HC5 with the full data set median HC5 only improves the relationship slightly. There is a distinct advantag e; 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 increase s 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 valu e 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 estimat e of the slope and intercept which in turn might change the median HC5 value. And to the ex tent that more data lessens the standard error of the slope and intercep t, 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 25 th 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 25 th percentile and the median values. In other words, as the number of data points increases we get a bette r 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 me thod of using the 25 th percentile of the HC5 value has the potential to minimize the probability of bot h 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 regres sion analysis shows a non- significant regression, suggesting that the slope is not statistically si gnificant from zero. This was the case for both metribuzin (Figure 1E) where one of the data po ints was substantially di fferent 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 fourvalues, 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 situa tion will result in overprotection to some degree. However, if the oppos ite situation is true—tha t 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 gi ven 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 fi t a linear regression quit well. The difference between the highe st 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 ra nge is less than a factor of five, then more data would be desirable. The flip si de 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 differe nt from zero, then the confiden ce in the conclusion drawn from only the FIFRA-5 results increases substantiall y. 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 phytotoxi city 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 associ ated with how well the results within “full” data sets represent the distributi on 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 th e representativeness of the generally used surrogates cannot be adequate ly judged. While there is no evidence to suggest that the underrepresented plant ta xonomic groups have some speci al sensitivity to pesticides, neither is there any evidence that they do not. As st ated 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 assess ed without some consideration (and testing) of taxonomic groups and species that are rarely, if ever tested. This is especially tr ue for marine macrophytes, both vascular and non-vascular (i.e.

, seaweeds), and of freshwater macroalgae. The relative sensitivities of freshwater and saltwat er plants is unknown for most pesticides. In addition, unlike animals, plants have a much wide r variety of life history patterns. Plants often have more than one free-living, “adult” phase, of ten 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 avai lable comparing the relative sensitivities of different life history patterns, or even much information on the relative sensitivity of different life stages. Unlike an imal chronic toxicity tests whic h may include full and partial life cycle tests, and early life st age tests, many aquatic plant test s are generally analogous to only testing the “adults”. This is important in the context that sexual repr oduction in seaweeds is among their most sensitive endpoints (Thursb y 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 chem icals may be attributed to the mode of action of the chemical and a plant’s physiology. Therefor e, besides consideration of underrepresented taxonomic groups, differences among physiological pathways within those groups may make a difference in sensitivity. For instance, herbic ides often target va rious aspects of the photosynthetic pathway; there could be differences in response betw een vascular plants that have C3 vs. those with C4 pathways 6 . 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 pigm ents (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 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).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 fa vor 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 incons istencies. Growth rate of microalgal species essentially always refers to popula tion 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 st andardization based on growth rate as the end point is warranted. Erickson had a specific, and reasonable, reason for consistency among the aquatic plant endpoints. He wa nted a way to summarize the total effect to the plant community by essentially integrating growth rate across i ndividual species doseresponses. His approach; however, requires that the species have a rapid recovery from the compound of interest (each day’s exposure is trea ted separately, no cumulative effects). Atrazine fits this requirement (as perh aps many photosystem II inhibitors ). Erickson’s technique also requires that all growth be treated the same, th at is, a given percent reduction in growth of a rapidly growing microalga will ha ve the same community consequences as that same percentage reduction in a much slower grow ing aquatic vascular plant. In many situations; however, the selection of just growth rate may not be justifie d, 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 plan ts are likely to be adversely affected? Confining the definition of adverse t oxicity effect to only effects on growth would ignore the fact that growth is often not the mo st sensitive endpoint. This was certainly the case with the SSDs for atrazine and diuron 7 , although the HC5 values were not that different. As mentioned above, this is also the case for th e relative sensitivity of sexual reproduction (although for aquatic plants this is currently known onl y for seaweeds). In addition, available growth results may not be the most ecologically releva nt 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 plan ts that are rooted in the sediment, this can make a substantial difference in th eir survivability. Includi ng a broader base of endpoints (beyond just growth rate) will provide a more complete representation of the range of species sensitivities within th e aquatic plant community.

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