SEARCH

SEARCH BY CITATION

Keywords:

  • Lemna;
  • Myriophyllum;
  • Macrophytes;
  • Algae;
  • Species Sensitivity Distribution

Abstract

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. Acknowledgements
  9. REFERENCES

Lemna spp. are the standard test species representing aquatic macrophytes in the current risk assessment schemes for herbicides and plant growth regulators in the European Union and North America. At a Society of Environmental Toxicology and Chemistry (SETAC) 2008 workshop on Aquatic Macrophyte Risk Assessment for Pesticides (AMRAP), a Species Sensitivity Distribution (SSD) working group was formed to address uncertainties about the sensitivity of Lemna spp. relative to other aquatic macrophyte species. For 11 herbicides and 3 fungicides for which relevant and reliable data were found for at least 6 macrophyte species, SSDs were fitted using lognormal regression. The positions of L. gibba (the most commonly tested Lemna species) and Myriophyllum spicatum (for which standardized test methods are under development) in each SSD were determined where data were available. The sensitivity of standard algal test species required for pesticide registration in the United States under the Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA) relative to the macrophytes in each SSD was also examined (algae were not included in the SSD). L. gibba was among the most sensitive macrophyte species for approximately 50% of the chemicals examined. M. spicatum was among the most sensitive macrophytes for approximately 25% of the chemicals. In most cases, the lowest FIFRA algal species endpoint was lower than the most sensitive macrophyte endpoint. Although no single species consistently represented the most sensitive aquatic plant species, for 12 of 14 chemicals L. gibba and the FIFRA algae included an endpoint near or below the 5th percentile of the macrophyte SSD. For the other compounds, M. spicatum was the most sensitive species of all aquatic plants considered. Integr Environ Assess Manag 2013; 9: 308–318. © 2012 SETAC


INTRODUCTION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. Acknowledgements
  9. REFERENCES

Aquatic macrophytes fulfill several critical and functional roles in aquatic ecosystems (Wetzel 2001). They sequester C, produce O2, stabilize sediments, cycle nutrients, and contribute to contaminant dissipation and degradation. By performing these roles, they supply important ecosystem services (Nienstedt et al. 2012). Consequently, aquatic macrophytes are one of the functional groups of organisms to be addressed in the risk assessment of chemicals (Maltby et al. 2010).

Lemna spp. are the standard Tier 1 test organisms representing aquatic macrophytes in the current risk assessment schemes for herbicides and plant growth regulators in the European Union (EU) (EC, 1997, 2009) and North America (USEPA 2004, 2012). Lemna spp. are small floating monocots, not rooted in the sediment, with a short doubling time. These traits differ considerably from those of sediment-rooted emergent and submerged macrophytes.

The representativeness of effects and recovery observed in Lemna spp. for other aquatic macrophytes has been raised during the development of new data requirements for registration of pesticides in the EU. This question was discussed in the Aquatic Macrophyte Risk Assessment for Pesticides (AMRAP) workshop (Maltby et al. 2010) organized by the Society of Environmental Toxicology and Chemistry (SETAC). One of the recommendations of this workshop was to collate and analyze data on macrophyte toxicity to enable an assessment of the sensitivity of Lemna spp. relative to other macrophyte species. At the workshop, a Species Sensitivity Distribution (SSD) working group was established to address these questions.

Species Sensitivity Distributions, which integrate the results of laboratory toxicity experiments with multiple species to estimate hazardous concentrations (HCx values) for a given fraction of species, were considered useful tools to address the issue of relative sensitivity (Maltby et al. 2010). In the past, HCx values for insecticides (Maltby et al. 2005), herbicides (Van den Brink et al. 2006), and fungicides (Maltby et al. 2009) have been validated by comparison with the threshold levels resulting from mesocosm studies. For the construction of macrophyte SSDs, the AMRAP guidance document (Maltby et al. 2010) recommends that a range of morphologically and taxonomically different macrophyte species should be included. Ideally, SSDs should be based on “comparable endpoints generated from tests under similar exposure scenarios and exposure durations, preferably using standardized test protocols” (Maltby et al. 2010). However, due to the diversity of aquatic plant morphologies and test designs and protocols, this ideal approach is difficult to achieve in practice. As macrophyte morphology enables the assessment of a range of endpoints that might differ in sensitivity by a factor of 10 to 1000, specific guidance for evaluation of macrophyte SSDs is needed.

In the current article, the SSD approach was applied to explore the sensitivity of Lemna spp. (especially L. gibba) relative to other macrophyte species. As Myriophyllum spp. have been suggested as new standard macrophyte test species for addressing specific issues including dicot-specific modes of action and sediment toxicity (Maltby et al. 2010), the sensitivity of Myriophyllum spp. (especially M. spicatum) relative to other aquatic macrophyte species was addressed in the data analysis as well. In addition, the 4 algal test species (Pseudokirchneriella subcapitata, Anabaena flos-aquae, Navicula pelliculosa, and Skeletonema costatum) required for pesticide registration in the United States under the Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA) were considered to evaluate the protectiveness of standard macrophytes and algal test species together.

METHODS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. Acknowledgements
  9. REFERENCES

Data compilation and evaluation

Macrophyte toxicity data were compiled from open literature and confidential test reports provided by participating companies. Algal toxicity data were obtained from the Office of Pesticide Programs (OPP) Pesticide Toxicity Database (USEPA 2011b), and, in a few cases, from company reports. A few data points (mostly for algae) were obtained from other secondary sources such as the ECOTOX database (USEPA 2011a) and the European Commission pesticide review reports.

Each primary source was examined and evaluated against criteria established at the beginning of the project. Data used in the analysis were required to meet the following criteria:

  • 1.
    Test organisms must be identified at least to genus.
  • 2.
    Test substance must be identified (active ingredient and technical-grade or formulation).
  • 3.
    Test substance must not include more than 1 active ingredient.
  • 4.
    Negative and/or solvent controls (as appropriate) must be included.
  • 5.
    Exposure medium must be reported.
  • 6.
    Exposure duration must be specified.
  • 7.
    Methods for measuring effects must be described.
  • 8.
    Test concentration units must be unambiguous (active ingredient or whole formulation, nominal or measured, initial or mean).
  • 9.
    Toxicity endpoint (e.g., median effect concentration, EC50) must be reported or calculable from data presented.

Beyond these minimum criteria, other criteria were considered in evaluating the relevance and reliability of the data. These additional criteria were applied in particular cases based on the professional judgment of the reviewer:

  • 1.
    Were the data derived using a standard, validated test method?
  • 2.
    Was the source of test organisms described?
  • 3.
    Were the plants maintained under appropriate conditions before use in the test?
  • 4.
    Were the test organisms healthy at the beginning of the exposure period?
  • 5.
    Did the study include multiple exposure concentrations? Tests with only 2 or 3 concentrations are insufficient for determination of ECx values.
  • 6.
    Were exposure concentrations confirmed by chemical analysis?
  • 7.
    Were response measurements reported for each exposure concentration, or only statistical endpoints such as EC50 values?
  • 8.
    Were response measurements for controls and treatment groups reported?
  • 9.
    Was control performance acceptable?
  • 10.
    Were methods documented sufficiently?

Specific information of interest includes organism collection methods, pre-exposure acclimation and/or culture conditions, plant condition at initiation of exposure, exposure system, number of replicates at each concentration, procedures for randomization, exposure medium composition, and exposure conditions (light, temperature, aeration, agitation, etc.).

Because some of the data used in this analysis were taken from confidential study reports, and because the objective was to examine the relative sensitivity of aquatic plants rather than to characterize the toxicity of specific chemicals, the chemicals were not named but were instead identified by codes. Each code consisted of a letter (A through F) representing a particular mode of action, and, in cases where a mode of action was represented by multiple chemicals, a number also (e.g., “D1” and “D2”).

Data selection

From the publications and reports judged to be relevant and reliable according to the criteria described above, data points were selected for SSD analysis. Because of the differing characteristics of the species tested, EC50s were derived from a variety of biological measurements. Therefore, equivalent endpoints were not available for all species for a single chemical. Restricting the database to equivalent endpoints would have reduced the amount of data available and few chemical data sets would have contained sufficient species (i.e., 6 or more) for SSD analysis.

Given the difficulties of restricting data selection for SSDs based on categories of measurement data points, the SSDs examined in this project used the lowest reported reliable EC50 for each species (after calculation of the geometric mean of identical measurement endpoints as recommended by Brock et al. [2011]), regardless of the biological measurement on which the EC50 was based. Although selection of the lowest available EC50 is standard regulatory practice (USEPA 2004; EC, 2011), it leaves open the possibility that a data point based on a nonstandard measurement parameter could unduly influence the SSD, and might influence the interpretation of relative species sensitivity. Specific cases are evaluated in the Discussion section.

SSD analysis

Methods for statistical analysis of SSDs have been thoroughly reviewed by others (Posthuma et al. 2002; Intrinsik 2009). To determine the position of Lemna spp. and Myriophyllum spp. in the SSD, and to evaluate algal sensitivity relative to macrophyte species, we considered it sufficient to apply a single, generally applicable distribution model, the lognormal, to estimate the SSDs for all chemicals. The lognormal model is often used for SSD analysis (Aldenberg and Jaworska 2000; Van Vlaardingen et al. 2004; Maltby et al. 2005; Rodney and Moore 2008 and references cited therein). The rationale and basic properties of the lognormal distribution were described by Limpert et al. (2001). The ranking of species according to sensitivity is unaffected by the SSD model used, though comparisons involving HCx values, especially in the tails of the distribution, could be model-dependent.

For each chemical analyzed, the lowest EC50 was selected from the available data for each species as described above. The species data points for that chemical were sorted (lowest to highest) and a linear regression was fitted to these data using the log of the EC50 as the independent variable and the normalized probability as the dependent variable. The slope and intercept of the regression were used to estimate the concentration at which a specified fraction of species is affected (the HCx value, where x represents the percentage of species, typically 5% or 50%), and to estimate the fraction of species affected (FA) at a specified concentration. (The meaning of “affected” depends on the data points used in the SSD. In this analysis, the SSDs were based on EC50 values, so FA indicates the expected fraction of species for which the response measurement is reduced by 50% or more.) The calculations were implemented in a Microsoft Excel 2007 spreadsheet.

Nondeterminate (greater than) values were included when sorting and ranking data points for each chemical but were not used in the lognormal regression. Definitive values greater than the lowest nondeterminate value were also excluded from the regression because their true position in the distribution was unknown.

RESULTS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. Acknowledgements
  9. REFERENCES

EC50 values for 6 or more macrophyte species were available for 11 herbicides and 3 fungicides. The modes of action of these chemicals included photosynthesis inhibition (5 chemicals), inhibition of multiple biosynthesis pathways (4 chemicals), inhibition of fungal respiration (2 chemicals), and other inhibition pathways (3 chemicals). For one of these chemicals only 3 of the EC50 values were determinate, which was insufficient for SSD analysis. Parameters of the lognormal regression (slope, intercept, r2, N) for the remaining 13 chemicals are shown in Table 1. The SSDs for each chemical are shown in Figure 1. Findings for each chemical are summarized below.

Table 1. Lognormal regression parameters for macrophyte SSDs of herbicides and fungicides
ChemicalMode of actionN (total)N (regression)r2HC5 (µg/L)HC50 (µg/L)
  1. HC5 = concentration affecting 5% of species; HC50 = concentration affecting 50% of species; N = number of data points.

AInhibits amino acid biosynthesis16140.89650.018 (0.0056–0.060)0.39 (0.14–1.1)
BAuxin simulator15130.9397.7 (3.0–20)177 (78–400)
CInhibits cell division and elongation1060.97234.8 (1.7–14)366 (156–855)
D1Inhibits fungal respiration980.843136 (7.5–174)459 (135–1557)
D2Inhibits fungal respiration1090.89922.8 (0.49–144)229 (8.7–5966)
E1Inhibits multiple biosynthesis pathways63Insufficient dataInsufficient dataInsufficient data
E2Inhibits multiple biosynthesis pathways1260.90351.4 (0.067–30)2428 (163–36119)
E3Inhibits multiple biosynthesis pathways870.85636.5 (0.65–66)244 (43–1389)
E4Inhibits multiple biosynthesis pathways880.97396.3 (3.5–11)51 (32–81)
F1Inhibits photosystem II990.84522.5 (0.24–26)95 (14–638)
F2Inhibits photosystem II880.81474.1 (1.6–11)14 (6.8–30)
F3Inhibits photosystem II660.76021.8 (0.12–28)22 (3.2–154)
F4Inhibits photosystem II10100.936610 (4.8–22)75 (39–142)
F5Inhibits photosystem II880.98322.9 (1.8–4.7)26 (18–39)
thumbnail image

Figure 1. Species Sensitivity Distributions (SSD) for macrophytes. Larger circles indicate nondeterminate points extrapolated from the SSD as described in text.

Download figure to PowerPoint

Chemical A

Chemical A inhibits amino acid biosynthesis. Myriophyllum spicatum was the most sensitive of the 16 macrophyte species for which data were available, whereas M. sibiricum and M. aquaticum were in the upper half of the macrophyte SSD. Lemna minor and L. gibba ranked 4th and 5th in the SSD, respectively, whereas L. trisulca was in the upper tail. Two macrophyte species had nondeterminate EC50s. Of the FIFRA algal species, 3 had nondeterminate EC50s as high as greater than 92 800 µg/L (N. pelliculosa). The only definitive algal EC50 (P. subcapitata) was greater than 14 of the 16 macrophyte EC50 values (i.e., all except the nondeterminate EC50s).

All of the macrophyte data points were for standing crop. Most (11 of 16 EC50s) were for leaf area. Three data points, including the 2 most sensitive species (M. spicatum and Elodea nuttallii) as well as M. sibiricum, were for root length or weight. The position of M. spicatum and E. nuttallii at the low end of the SSD was not simply a reflection of root measurements, however: the EC50 for new shoot length was nearly as low as the lowest EC50 for M. spicatum, and the EC50 for plant dry weight was nearly as low as the lowest EC50 for E. nuttallii.

Chemical B

Chemical B is an auxin herbicide. M. sibiricum, M. spicatum, and M. aquaticum, with EC50s in a narrow range, were the most sensitive of 15 macrophyte species. Myriophyllum brasiliense was the least sensitive rooted macrophyte species, more than 40 times less sensitive than the other 3 Myriophyllum species. This difference may be due to different response measurements: the data point for M. brasiliense was 14-d plant transpiration, a functional measurement, whereas the other 3 were based on standing crop measurements (root length for M. sibiricum, bud number for M. spicatum, and carotenoids for M. aquaticum). Lemna minor, L. gibba, and L. trisulca were the 3 least sensitive macrophyte species. All algae were less sensitive than all sediment-rooted macrophytes.

Chemical C

Chemical C inhibits cells division and elongation. Lemna gibba was the most sensitive of 10 macrophyte species. Myriophyllum spicatum and M. aquaticum were relatively insensitive (nondeterminate values). Three of the 4 FIFRA algal species (excluding A. flos-aquae) were more sensitive than all macrophyte species and about twice as sensitive as L. gibba. Most of the macrophyte data points were for standing crop (weight or length) or standing crop increase. The 2 Lemna data points were for growth rate.

Chemical D1

Chemical D1 inhibits fungal respiration. The most sensitive of 9 macrophyte species was E. nuttallii. Myriophyllum spicatum was in the lower tail of the macrophyte SSD and M. aquaticum was near the middle. Lemna gibba and L. trisulca were in the upper end of the SSD. Three FIFRA algal species were more sensitive than all macrophytes and the fourth species (P. subcapitata) was more sensitive than all macrophytes except E. nuttallii. Six of the macrophyte data points were for standing crop (dry weight, shoot length, or root length). The other 3 macrophyte data points, including M. spicatum, were for growth rate.

Chemical D2

Chemical D2 inhibits fungal respiration. Macrophyte sensitivity to this chemical was highly variable, with EC50 values for 10 species spanning 3 orders of magnitude. The most sensitive macrophyte species was E. canadensis. Myriophyllum spicatum and L. gibba were near the midpoint of the macrophyte SSD, and L. minor and L. trisulca were in the upper end. S. costatum, the most sensitive of the FIFRA algal species, was 5 times less sensitive than E. canadensis, but the EC50 was in the low end of the macrophyte SSD; EC50s for all FIFRA algal species were in the lower half.

The lowest EC50s for most macrophyte species were based on standing crop data points for roots (dry weight or length). Data points based on shoot or whole plant standing crop were as much as 1000 times higher than root data points for all rooted species. No root-based EC50 values were available for Potamogeton cripsus or Callitriche platycarpa, and these species were in the upper end of the macrophyte SSD.

Chemical E1

Chemical E1 inhibits multiple biosynthesis pathways. There was a broad range of aquatic plant sensitivity to this herbicide, with EC50s for 6 species spanning 4 orders of magnitude. Three macrophyte species had nondeterminate EC50s, leaving only 3 definitive EC50 values which was insufficient for an SSD. However, the relative sensitivity of the aquatic plants was clear. Lemna gibba was the most sensitive of the 6 macrophyte species. Myriophyllum spicatum was among the 3 macrophyte species with indeterminate EC50s, at least 75 times less sensitive than L. gibba. Two algal species, P. subcapitata and S. costatum, were similar in sensitivity to L. gibba. Five of the 6 macrophyte data points were for standing crop (dry weight or frond number); one (the highest) was for growth rate.

Chemical E2

Chemical E2 inhibits multiple biosynthesis pathways. There was a broad range of sensitivity among 12 macrophyte species, with a factor of at least 2750 between the lowest and highest EC50. Lemna gibba was the most sensitive macrophyte. All other macrophyte species (including L. minor) were more than 40 times less sensitive than L. gibba. Myriophyllum spicatum was in the low end of the SSD. Many macrophyte species had nondeterminate EC50s. Pseudokirchneriella subcapitata, the only algal species for which data were available, was comparable in sensitivity to L. gibba and much more sensitive than all other macrophyte species. All of the macrophyte data points were for standing crop.

Chemical E3

Chemical E3 inhibits multiple biosynthesis pathways. Lemna gibba was the most sensitive of 8 macrophyte species, whereas L. paucicostata and L. minor were in the middle and upper portion of the SSD. Data for M. spicatum were not available. The EC50 for M. heterophyllum was nondeterminate, and greater than all other tested macrophytes. The most sensitive algal species was P. subcapitata, and the EC50 for S. costatum was also in the lower end of the macrophyte SSD. Other algal species were in the upper end of the macrophyte SSD. All of the macrophyte data points were for standing crop.

Chemical E4

Chemical E4 inhibits multiple biosynthesis pathways. EC50s for 8 macrophyte species were within a 25-fold range. The most sensitive macrophyte species was C. demersum. Data were not available for L. gibba. Lemna minor was near the middle of the macrophyte SSD, but only 3 times less sensitive than C. demersum. Myriophyllum spicatum was in the upper end of the macrophyte SSD. Pseudokirchneriella subcapitata was more sensitive than all macrophyte species. The only other algal species for which data were available, A. flos-aquae, was less sensitive than all macrophyte species. All macrophyte data points were based on standing crop; 6 of the 8 endpoints were for shoot length increase.

Chemical F1

Chemical F1 is a photosynthesis inhibitor. There were EC50 values for 9 macrophyte species. Some of these data came from secondary sources (USEPA 2011b and a peer-reviewed risk assessment) and were not evaluated for quality. Three macrophyte species with very similar EC50s (E. canadensis, C. demersum, and Najas sp.) were at the low end of the SSD. L. gibba was the next most sensitive species, with an EC50 within a factor of 2 of E. canadensis, C. demersum, and Najas sp., whereas L. minor was less sensitive. Myriophyllum spicatum was the least sensitive species, with an EC50 10-fold greater than the next most sensitive macrophyte. The most sensitive algal species, S. costatum, was as sensitive as the most sensitive macrophytes, and EC50s for other algae were scattered across the macrophyte range.

Chemical F2

Chemical F2 is a photosynthesis inhibitor. The range of EC50s among 8 macrophyte species was very narrow, with only a 5-fold difference between the most sensitive (E. nuttallii) and the least sensitive (L. minor). Myriophyllum spicatum was nearly as sensitive as E. nuttallii. Lemna gibba was near the upper end of the macrophyte SSD, but the EC50 was still within a factor of 3 of E. nuttallii. The FIFRA algal species were less sensitive than all macrophyte species except the 2 Lemna species. Nevertheless, even the least sensitive algal species, P. subcapitata, was within a factor of 10 of the most sensitive macrophyte.

Most of the macrophyte EC50s were based on a functional measurement, photosynthesis. The Lemna data points were based on standing crop (L. gibba) and growth rate (L. minor). The 2 lowest EC50s were for photosynthesis after 35 days of exposure, whereas the other photosynthesis EC50s were derived from measurements made after 1 day of exposure. Data for both exposure durations were available for E. nuttallii and the resulting EC50s were similar, implying that the difference in exposure duration among species data points was not a significant factor in this SSD.

Chemical F3

Chemical F3 is a photosynthesis inhibitor. There were EC50 values for 6 macrophyte species. The lognormal distribution did not fit the data well (r2 = 0.7602); thus the HCx values and ratios based on them were uncertain. However, the relative sensitivity of species could still be determined from the data. The most sensitive macrophyte species was L. minor and the least sensitive species was L. gibba. These EC50s spanned a 20-fold range. Data were not available for M. spicatum, whereas M. heterophyllum was near the middle of the macrophyte SSD. The most sensitive algal species, P. subcapitata, was nearly as sensitive as L. minor. EC50s for other algal species were scattered throughout the macrophyte SSD. All of the macrophyte data points were based on standing crop.

Chemical F4

Chemical F4 is a photosynthetic inhibitor. There was only a 15-fold range among EC50 values for 10 macrophyte species. The most sensitive macrophyte species was L. gibba and the least sensitive was L. trisulca. Lemna minor was in the lower part of the SSD. M. spicatum was near the middle of the SSD, but its EC50 was within a factor of 4 of the EC50 for L. gibba. Pseudokirchneriella subcapitata, the most sensitive algal species, was 5-fold more sensitive than L. gibba. Navicula pelliculosa was also more sensitive than all macrophyte species. EC50s for the other 2 FIFRA algal species were in the middle of the macrophyte SSD. All of the macrophyte data points except L. gibba were for standing crop. The L. gibba EC50 was obtained from the OPP database (USEPA 2011b), which did not specify the measurement endpoint.

Chemical F5

Chemical F5 is a photosynthesis inhibitor. The most sensitive of 8 macrophyte species was Spirodela polyrhiza. There were no data for L. gibba; L. minor was the least sensitive macrophyte species, and L. trisulca was near the middle of the SSD. M. spicatum was less sensitive than all macrophytes except L. minor. Skeletonema costatum, the only standard FIFRA species for which data were available, was more sensitive than all macrophyte species. Nearly all of the macrophyte data points were based on growth rate. EC50s for both Lemna species were based on a functional measurement, photosynthesis.

Positions of L. gibba, M. spicatum, and FIFRA algae

The positions of L. gibba, M. spicatum, and the most sensitive algal species relative to the macrophyte SSD were characterized in 3 ways:

  • 1.
    The position of the species in the macrophyte SSD. For L. gibba and M. spicatum, position was expressed as a Weibull percentile (p = n/(N + 1)). For algae, position was expressed as the fraction of macrophyte species with EC50 below the EC50 of the algal species (FA), which was estimated from the lognormal SSD regression. This indicates the fraction of macrophyte species more sensitive than the species under consideration.
  • 2.
    The ratio of the EC50 for the species to the HC5 of the macrophyte SSD. This indicates the relative difference in concentration between the EC50 and the HC5; in a steep SSD, even an EC50 corresponding to a high percentile may be relatively close to the HC5.
  • 3.
    The ratio of the EC50 for the species to the EC50 of the most sensitive macrophyte. This indicates the relative difference in sensitivity between the species under consideration and the most sensitive macrophyte species.

Lemna gibba

The position of L. gibba in the macrophyte SSD for the 12 chemicals for which data are available ranged from the 7.7th to 85.7th percentile (Table 2). For 5 of the 12 chemicals, L. gibba was below the 15th percentile. For the other chemicals, L. gibba EC50s were distributed through the middle and upper end of the SSD. The L. gibba EC50 was within a factor of 10 of the HC5 for 6 of 11 chemicals. (The HC5 could not be calculated for Chemical E1 because fewer than 6 definitive EC50s were available.) For the other 5 chemicals, the EC50/HC5 ratio ranged from 14 to 90. The L. gibba EC50 was within a factor of 10 of the lowest macrophyte EC50 for 9 of the 12 chemicals. For the other 3 chemicals, the EC50 to lowest EC50 ratio ranged from 20 to 63.

Table 2. Position of Lemna gibba in macrophyte SSDs
ChemicalEmpirical percentileEC50/HC5EC50/lowest EC50
  • HC5 = concentration affecting 5% of species; HC50 = concentration affecting 50% of species; N = number of data points.

  • a

    No SSD analysis (fewer than 6 definitive EC50 values), so HC5 is not determined.

A29.407.82.6
B81.309054
C9.102.51
D170.00145.4
D245.509063
E114.30No SSDa1
E27.701.61
E311.106.61
E4No dataNo dataNo data
F140.00151.8
F277.805.22.5
F385.708820
F49.101.51
F5No dataNo dataNo data

By all of these measures, L. gibba was among the most sensitive macrophyte species for 5 of the 12 chemicals for which data were available. For these chemicals plus 4 others, the L. gibba EC50 was within a factor of 10 of both the macrophyte HC5 and the lowest macrophyte EC50. For the other 3 chemicals (Chemicals B, D2, and F3), L. gibba was among the least sensitive macrophyte species.

For 10 of the 11 chemicals for which data were available for L. gibba and at least one other Lemna species, L. gibba was the most sensitive Lemna species. The exception was Chemical F3, to which L. minor was the most sensitive macrophyte and L. gibba the least sensitive macrophyte, with EC50 values separated by a factor of 20.

M. spicatum

The position of M. spicatum in the macrophyte SSD for 12 chemicals ranged from the 5.9th to 90th percentile (Table 3). Myriophyllum spicatum was within the lower quartile for 3 of the 12 chemicals (Chemicals A, B, and E2). The M. spicatum EC50 was within a factor of 10 of the macrophyte HC5 for 5 of 11 chemicals. Among the other 6 chemicals, the EC50/HC5 ratio ranged from 13 to 1473. The M. spicatum EC50 was within a factor of 10 of the lowest macrophyte EC50 for 6 of the 12 chemicals. For the other 6 chemicals, the EC50/lowest EC50 ratio ranged from 16 to 176. Overall, M. spicatum was among the most sensitive macrophyte species for 25% of the chemicals, and for approximately 50% of the chemicals the M. spicatum EC50 was within a factor of 10 of both the macrophyte HC5 and the lowest macrophyte EC50. For the remaining half of the chemicals, M. spicatum was among the least sensitive macrophyte species.

Table 3. Position of Myriophyllum spicatum in macrophyte SSDs
ChemicalEmpirical percentileEC50/HC5EC50/lowest EC50
  • HC5 = concentration affecting 5% of species; HC50 = concentration affecting 50% of species; N = number of data points.

  • a

    No SSD analysis (fewer than 6 definitive EC50 values), so HC5 is not determined.

A5.9031
B12.501.81.1
C72.70>84>34
D130.005.52.1
D236.408559
E1≥57.1No SSDa>74
E223.107545
E3No dataNo dataNo data
E466.70137.9
F190.001473176
F233.302.91.4
F3No dataNo dataNo data
F436.405.33.4
F577.802616

For 5 of the 6 chemicals for which data were available for M. spicatum and at least one other Myriophyllum species, M. spicatum was the most sensitive Myriophyllum species. The exception was Chemical F1, to which both species were relatively insensitive but M. heterophyllum was 30 times more sensitive than M. spicatum.

Algae

EC50s for the most sensitive of the FIFRA algal test species for 13 chemicals corresponded to positions on the macrophyte SSD ranging from less than the 1st to 99.8th percentile (Table 4). (Algae were not used to calculate the SSDs.) For 7 of the 13 chemicals, the lowest algal EC50 was below the 10th percentile of the macrophyte SSD; 10 were in the lower quartile. The only chemicals for which algae were substantially less sensitive than the most sensitive macrophytes were Chemicals A, B, and F2. It should be noted that not all chemicals had data for all 4 FIFRA algal test species. The lowest algal EC50 was within a factor of 10 of the HC5 of the macrophyte SSD for 11 of 13 chemicals. The only exceptions were Chemicals A and B. For 10 of 14 chemicals, algal EC50 values were less than or equal to the EC50 of the most sensitive macrophyte. The 4 exceptions were Chemicals A, B, D2, and F2. By these measures, algae were at least as sensitive as the most sensitive macrophytes to nearly all of these chemicals. For Chemicals A and B, algae were much less sensitive than macrophytes.

Table 4. Sensitivity of the most sensitive of the FIFRA algal species relative to macrophyte SSDs
ChemicalFraction affected (%)EC50/HC5EC50/lowest EC50
  • HC5 = concentration affecting 5% of species; HC50 = concentration affecting 50% of species; N = number of data points.

  • a

    No SSD analysis (fewer than 6 definitive EC50 values), so HC5 is not determined.

A99.8051101702
B90.00261155
C5.301.10.4
D11.000.40.1
D218.307.35.1
E1No SSDaNo SSDa0.5
E25.401.20.7
E37.301.50.2
E40.520.30.2
F126.609.61.1
F247.903.41.7
F325.404.51
F40.430.30.2
F50.550.30.2

Combined data for L. gibba, algae, and M. spicatum

When the most sensitive species of interest (i.e., L. gibba, M. spicatum, and the FIFRA algae) were considered, the lowest of the EC50s was within the lower quartile of the macrophyte SSD for 11 chemicals and within the lower 10th percentile for 8 chemicals (Table 5). The EC50 for the most sensitive of these species was near or below the corresponding macrophyte HC5 for 6 chemicals, and within a factor of 10 of the HC5 for all chemicals. The lowest of these EC50s was near or below the EC50 of the most sensitive macrophyte for all chemicals except Chemicals D2 and F2; even for these exceptions, the difference was within a factor of 5.

Table 5. Sensitivity of the most sensitive species of Lemna gibba, FIFRA algae, or Myriophyllum spicatum relative to macrophyte SSDs
ChemicalMost sensitive of the 6 speciesa,bPercentile or fraction affectedEC50/HC5EC50/lowest EC50
  • HC5 = concentration affecting 5% of species; HC50 = concentration affecting 50% of species; N = number of data points.

  • a

    Numeric values in table are based on the most sensitive of the 6 species under consideration.

  • b

    When multiple species are similar in sensitivity (EC50 within a factor of 3), all are listed in order of sensitivity.

  • c

    No SSD analysis (fewer than 6 definitive EC50 values), so HC5 is not determined.

AM. spicatum, L. gibba5.93.01.0
BM. spicatum12.51.81.1
CS. costatum, P. subcapitata, N. pelliculosa, L. gibba5.31.10.4
D1S. costatum, N. pelliculosa1.00.40.1
D2S. costatum, A. flos-aquae, P. subcapitata18.37.35.1
E1P. subcapitata, L. gibba, S. costatum14.3No SSDc0.5
E2P. subcapitata, L. gibba5.41.20.7
E3P. subcapitata7.31.50.2
E4P. subcapitata0.520.30.2
F1S. costatum, L. gibba, P. subcapitata, N. pelliculosa26.69.61.1
F2M. spicatum, N. pelliculosa, L. gibba, S. costatum33.32.91.4
F3P. subcapitata, N. pelliculosa, A. flos-aquae25.44.51.0
F4P. subcapitata0.430.30.2
F5S. costatum0.550.30.2

DISCUSSION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. Acknowledgements
  9. REFERENCES

The use of SSDs has been widely discussed in the field of ecotoxicology (Posthuma et al. 2002) and has been recommended for regulatory assessment of pesticides (Campbell et al. 1999; Maltby et al. 2005, 2009; Van den Brink et al. 2006; Brock et al. 2011). In the analysis reported here, SSDs were used to address the issue of sensitivity of standard macrophyte and algal test species relative to other macrophyte species.

Taken together, the results described above for the 14 chemicals show that neither L. gibba nor M. spicatum was consistently among the most sensitive macrophyte species for all herbicides and fungicides examined. These findings are in accordance with other researchers (Fairchild et al. 1997, 1998; Vervliet-Scheebaum et al. 2006; Arts et al. 2008) who concluded that no single macrophyte species was consistently the most sensitive, and that a suite of aquatic plant test species may be needed to perform accurate risk assessments for herbicides. In that light, the research described in this article shows that a combination of FIFRA algae, L. gibba and M. spicatum are protective for effects on other macrophyte species. For the majority of the chemicals, the most sensitive of the FIFRA algal species was more sensitive than the most sensitive macrophyte species. For 13 of the 14 chemicals, one or more of the EC50s for L. gibba and the FIFRA algal species was near or below the EC50 for the most sensitive macrophyte species. For the remaining chemical, although neither L. gibba nor the FIFRA algal species were within the lower portion of the macrophyte SSD, M. spicatum was among the most sensitive species. Similar findings were reported by Vervliet-Scheebaum et al. (2006).

The results of this analysis were dependent on the limitations and uncertainties of different aspects of the data available. Each particular data point–each toxicity test result–varies according to many factors, and the SSD analysis is itself conditioned by data selection and statistical methodology (Maltby et al. 2010). As data become available for additional chemicals representing a wider range of mechanisms of action, a greater variety of test species, and more consistent measurement endpoints, our confidence in the conclusions should increase. Given the data currently available, the present analysis rested on certain assumptions and restrictions:

  • In most cases, only data for technical grade active ingredient were used.

  • Information on specific mechanisms of action was not factored into the analysis. In some cases SSDs were similar for chemicals with similar mechanism of action, but there were several exceptions.

  • No distinctions were made based on macrophyte growth habit or habitat. That is, we did not examine SSDs for subsets of the available macrophyte species (rooted species, flowing water species, etc.). Individual SSDs may be dominated by different groups of macrophytes, but this was not factored into the analysis.

  • The analysis was based entirely on EC50 values. Data for other statistical endpoints (e.g., EC20s, NOECs) were much more limited.

  • No distinctions were made based on the nature of the measured biological response. For each species, the lowest reliable EC50 value was selected regardless of the measured effect or the duration of exposure. This is common regulatory practice and a worst-case approach, but it is not ideal for scientific analysis of SSDs. Instead, response endpoints used for risk assessment should be selected for their ecological relevance as well as sensitivity.

Each individual toxicity test result used in the SSD is also affected by a suite of experimental variables, such as:

  • Test organism: Source (e.g., laboratory culture, field collection, or commercial supplier); plant part (e.g., shoot, whole plant) used as inoculum; and age, size, and condition of the plants at the start of test

  • Test conditions and procedures: Source and type of aqueous medium; presence or absence and source of sediment; light, temperature, and other water quality parameters during exposure; and exposure system (e.g., static, semi-static, or flow-through)

  • Chemical exposure regime: Most of the data were based on aqueous chemical exposure, and all data points were given as aqueous concentrations (µg/L). Some studies reported results for surface spray (µg/m2) or spiked sediment (µg/kg), but these were not incorporated into the SSD analysis. Results were reported based on nominal or measured concentrations, with measured concentrations preferred. When concentrations decrease during the exposure period, data points based on mean measured concentrations may differ from data points based on initial concentrations.

Because toxicity test results are understood to be dependent on these and other methodological factors, standardized test guidelines attempt to reduce variability by specifying the methods and conditions required for an acceptable test. Standardization may enhance the comparability of results from different tests but does not eliminate the uncertainties, for at least 3 reasons: 1) test guidelines cannot specify the acceptable range for every conceivable experimental parameter, 2) the influence of many experimental conditions on toxicity is unknown, and 3) the specified test conditions cannot represent the full range of conditions encountered in the field.

Finally, the analysis was subject to several sources of statistical uncertainty. Some of these were associated with individual data points used in the SSDs. These uncertainties apply to virtually all toxicity tests:

  • Experimental design: Parameters such as the number and spacing of test concentrations, the number of replicates per concentration, and number of organisms per replicate, influence the calculation of endpoints

  • Experimental variability: The accuracy and precision of point-based estimates and the power of hypothesis tests are a function of the variability of measurements among individual organisms, between experimental replicates, and between treatment groups in a given test. Some of this variability is presumably inherent in the biology of the test organisms, whereas some is due to laboratory technique (test organism handling, maintenance of test conditions, etc.) and some reflects measurement error. These factors exist in toxicity testing with any organism, but are of particular concern in the case of newly developed test methods with macrophytes.

  • Statistical method for deriving endpoints: Estimation of EC50, NOEC, and other endpoints from a given set of experimental data entails a suite of statistical uncertainties related to model selection and model fitting. In this analysis, confidence limits around EC50 values were ignored.

The same statistical issues that apply to individual EC50 values are relevant to the SSD analysis itself, and have been addressed by others (Posthuma et al. 2002; Hart et al. 2006; Rodney and Moore 2008; Intrinsik 2009). In addition to these, an SSD analysis is subject to uncertainties arising from the criteria used to select or derive the final data set from the larger database of available test results. Particular issues are described below. Decisions made about these issues can affect the results and interpretation of the analysis.

  • Data points from multiple tests: In situations where multiple equally reliable data points are available for a species and a chemical, a geometric mean is often considered the most appropriate representation of the species in the SSD (Van Vlaardingen and Verbruggen 2007; Brock et al. 2011; EC, 2011). This approach was followed in the current analysis. Some regulatory agencies prefer to select the lowest data point as a precautionary policy. This may be appropriate for some purposes, but the objective of SSD analysis is to quantify the distribution of sensitivity among species, and that quantification is more robust when based on species means rather than on extreme test results.

  • Representation of nondeterminate values: Although nondeterminate toxicity values are less useful than determinate values in evaluating relative sensitivity, to ignore them completely would be to lose valuable information and would distort the SSD. However, a large number of nondeterminate values adds uncertainty to the SSD, both by reducing the number of data points available and by concentrating the analysis on the lower end of the sensitivity distribution. In the long run, the best solution to this situation is to develop determinate toxicity data and avoid generating nondeterminate results.

  • Data quality: When studies are conducted and reported according to appropriate good laboratory practices (GLP), they are usually considered reliable. Deficiencies of studies reported under GLP can almost always be clearly seen by an experienced reviewer, and questionable studies can be identified. Studies that are not conducted or reported according to GLP may be fully as robust and reliable as GLP studies, but are not always so, and deficiencies are more difficult to evaluate because fewer details are provided in published articles. When both GLP and non-GLP studies are available for a single species and chemical, the GLP study will usually, but not always, be found to be more complete and reliable and therefore preferred over the non-GLP study. However, reliable data from non-GLP studies are extremely valuable in developing SSDs, because nearly all GLP studies are conducted with a limited number of species for which standard methods exist. Open literature data are allowed to be considered in the risk assessment under the new pesticide regulation 1107/2009 in Europe (EC, 2009).

The SSD analysis presented here was subject to all of these sources of uncertainty: limitations in scope (chemicals, species, and data points), experimental variability (test organisms, measurement endpoints, test conditions and procedures including exposure regimes), statistical methods, and data selection decisions. In the light of these uncertainties, there is clearly a need for more guidance on the construction of SSDs for primary producers to be used in the risk assessment of chemicals. This guidance should focus on the selection of species and endpoints, as this selection has consequences for the HCx values derived.

For the construction of macrophyte SSDs, the AMRAP guidance document (Maltby et al. 2010) recommends that a range of morphologically and taxonomically different macrophytes should be included. The current data analysis was restricted by data availability. As a result, submerged and floating macrophytes dominate the SSDs presented, whereas emergent macrophytes are hardly represented. For some chemicals, it is probably acceptable to combine different types of macrophytes and algae in one SSD, as shown by Brock et al. (2000) for photosynthesis inhibitors, whereas for other chemicals the different groups should be separated. In the risk assessment for pesticides in Europe, the most sensitive group in the first tier drives the higher tier risk assessment (Brock et al. 2011). As a result, SSDs usually focus on the most sensitive taxonomic groups for the toxic mode of action under consideration.

The current data analysis has considered only the lowest toxicity value for each species, which is a worst case approach. Ideally, SSDs should be based on toxicity values for comparable measurement endpoints. However, such an analysis can only be performed for data-rich compounds. Extension of the data set compiled for this project will give opportunities to shed more light on questions related to the selection of species and endpoints to be included in SSDs for primary producers in general and macrophytes specifically.

CONCLUSIONS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. Acknowledgements
  9. REFERENCES
  • Neither L. gibba nor M. spicatum was consistently among the most sensitive macrophyte species for all herbicides and fungicides.

  • L. gibba was among the most sensitive macrophyte species for approximately 50% of the herbicides and fungicides examined. L. gibba was quite insensitive to approximately 25% of the chemicals. L. gibba was the most sensitive Lemna species for nearly all chemicals.

  • M. spicatum was among the most sensitive macrophyte species for approximately 25% of the herbicides and fungicides examined. M. spicatum was among the least sensitive macrophytes to several other chemicals. M. spicatum was the most sensitive Myriophyllum species for most chemicals where comparisons were possible.

  • For a majority of the chemicals examined, the most sensitive of the FIFRA algal species was more sensitive than the most sensitive macrophyte. In 2 cases, the algae were much less sensitive than most macrophytes.

  • Although no single species consistently represented the most sensitive macrophyte species, the combination of L. gibba and the 4 FIFRA algae almost always included an EC50 near or below the lowest macrophyte EC50 and the macrophyte HC5.

  • For the exceptional chemicals for which the EC50s of L. gibba and the FIFRA algae were not near or below the lowest macrophyte EC50, M. spicatum was among the most sensitive species.

  • Overall, these results support the usefulness of testing L. gibba, M. spicatum, and the FIFRA algae for assessing pesticide risk to aquatic macrophytes.

These conclusions are subject to the limitations of the available data. This analysis is based on chemicals representing 6 different modes of action, but some modes of action are represented by only one chemical. As data become available for additional chemicals, it may be possible to refine the analysis.

Acknowledgements

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. Acknowledgements
  9. REFERENCES

This project was initiated under the sponsorship of the Society of Environmental Toxicology and Chemistry (SETAC) as an activity arising from the 2008 SETAC workshop on Aquatic Macrophyte Risk Assessment for Pesticides (AMRAP). AMRAP projects, including this one led by the Species Sensitivity Distribution (SSD) Working Group, were later incorporated into the activities of the SETAC Aquatic Macrophyte Ecotoxicology Group (AMEG). The SSD Working Group consists of Stefania Loutseti, Chair (DuPont); Gertie Arts (Alterra, Wageningen University and Research Centre); Heino Christl (Tier3 Solutions); Jo Davies (Syngenta); Michael Dobbs (Bayer CropScience); Mark Hanson (U. Manitoba); Udo Hommen (Fraunhofer Institute for Molecular Biology and Applied Ecology); Joy Honegger (Monsanto); Phil Manson (Cheminova); Giovanna Meregalli (Dow AgroSciences); and Gabe Weyman (Makhteshim-Agan). Data were contributed by Alterra (through governmental funding by the Ministry of Economic Affairs, Agriculture and Innovation), Bayer CropScience, Nina Cedergreen (University of Copenhagen, with support from the Danish Environmental Protection Agency), Cheminova, Dow AgroSciences, DuPont, Fraunhofer Institute, Makhteshim-Agan, Mark Hanson (University of Manitoba), Monsanto, and Syngenta. Funding for data analysis and reporting was provided by Bayer CropScience, Cheminova, Dow AgroSciences, DuPont, Makhteshim-Agan, Monsanto, and Syngenta. Jeffrey Wirtz (Compliance Services International) contributed to the compilation and evaluation of the data. Thomas Priester (Compliance Services International) assisted with development of the SSD calculation spreadsheet.

REFERENCES

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. METHODS
  5. RESULTS
  6. DISCUSSION
  7. CONCLUSIONS
  8. Acknowledgements
  9. REFERENCES
  • Aldenberg T, Jaworska JS. 2000. Uncertainty of the hazardous concentration and fraction affected for normal species sensitivity distributions. Ecotoxicol Environ Saf 46:118.
  • Arts GHP, Belgers JDM, Hoekzema CH, Thissen JTNM. 2008. Sensitivity of submersed freshwater macrophytes and endpoints in laboratory toxicity tests. Environ Pollut 153:199206.
  • Brock TCM, Arts GHP, ten Hulscher TEM, de Jong FMW, Luttik R, Roex EWM, Smit CE, van Vliet PJM. 2011. Aquatic effect assessment for plant protection products. Dutch proposal that addresses the requirements of the Plant Protection Regulation and Water Framework Directive. Alterra-report 2235. The Netherlands: Wageningen. 140 p.
  • Brock TCM, Lahr J, Van den Brink PJ. 2000. Ecological risks of pesticides in freshwater ecosystems. Part 1: Herbicides. Alterra-Report 088. The Netherlands: Wageningen. 124 p.
  • Campbell PJ, Arnold DJS, Brock TCM, Grandy NJ, Heger W, Heimbach F, Maund SJ, Streloke M. 1999. Guidance document on Higher-tier Aquatic Risk Assessment for Pesticides (HARAP). Brussels, Belgium: SETAC.
  • [EC] European Commission. 1997. Council Directive 97/57/EC of 22 September 1997 establishing Annex VI to Directive 91/414/EEC concerning the placing of plant protection products on the market. Official Journal of the European Union C 240: 1–23. [cited 2013 January 30]. Available from: http://ec.europa.eu/food/plant/protection/evaluation/legal_en.htm
  • [EC] European Commission. 2009. Regulation (EC) No 1107/2009 of the European parliament and the council of 21 October 2009. concerning the placing of plant protection products on the market and repealing Council Directives 79/117/EEC and 91/414/EEC. OJEU 309: 1–50.
  • [EC] European Commission. 2011. Technical guidance document for deriving environmental quality standards. Common implementation strategy for the Water Framework Directive (2000/60/EC). Guidance Document No. 27. 204 p. [cited 2013 January 30]. Available from https://circabc.europa.eu/sd/d/0cc3581b-5f65-4b6f-91c6-433a1e947838/TGD-EQS%20CIS-WFD%2027%20EC%202011.pdf
  • Fairchild JF, Ruessler DS, Haverland PS, Carlson AR. 1997. Comparative sensitivity of Selenastrum capricornutum and Lemna minor to sixteen herbicides. Arch Environ Contam Toxicol 32:353357.
  • Fairchild JF, Ruessler DS, Carlson AR. 1998. Comparative sensitivity of five species of macrophytes and six species of algae to Atrazine, metribuzin, alachlor, and metolachlor. Environ Toxicol Chem 17:18301834.
  • Hart A. 2006. EUFRAM-Concerted action to develop a European Framework for probabilistic risk assessment of the environmental impacts of pesticides. Final report. Vol 1–4. [cited 2011 August 2]. Available from: http://www.eufram.com
  • Intrinsik. 2009. Framework and derivation of benchmarks for the protection of aquatic life from pesticides. Report prepared by Intrinsik Environmental Sciences for National Guidelines and Standards Office. Ottawa, Ontario: Environment Canada.
  • Limpert E, Stahel WA, Abbt M. 2001. Log-normal distributions across the sciences: keys and clues. Bioscience 51:341352.
  • Maltby L, Arnold D, Arts G, Davies J, Heimbach F, Pickl C, Poulsen V. 2010. Aquatic macrophyte risk assessment for pesticides. Pensacola (FL): SETAC.
  • Maltby L, Blake N, Brock TCM, van den Brink PJ. 2005. Insecticide species sensitivity distributions: Importance of test species selection and relevance to aquatic ecosystems. Environ Toxicol Chem 24:379388.
  • Maltby L, Brock TCM, van den Brink PJ. 2009. Fungicide risk assessment for aquatic ecosystems: Importance of interspecific variation, toxic mode of action, and exposure regime. Environ Sci Technol 43:75567563.
  • Nienstedt KM, Brock TCM, van Wensem J, Montforts M, Hart A, Aagaard A, Alix A, Boesten J, Bopp SK, Brown C., et al. 2012. Development of a framework based on an ecosystem services approach for deriving specific protection goals for environmental risk assessment of pesticides. Sci Total Environ 415:3138.
  • Posthuma L, Traas TP, Suter GW. 2002. Species sensitivity distributions in risk assessment. Boca Raton (FL): CRC Press. 616 p.
  • Rodney SI, Moore DRJ. 2008. Development of an Excel-based tool for fitting and evaluating species sensitivity distributions. Report prepared for National Guidelines and Standards Office. Ottawa, Ontario: Environment Canada.
  • [USEPA] US Environmental Protection Agency. 2004. Overview of the ecological risk assessment process in the Office of Pesticide Programs. Washington, DC: US Environmental Protection Agency. 92 p.
  • [USEPA] US Environmental Protection Agency. 2011a. ECOTOX database. [cited 2011 January]. Available from: http://cfpub.epa/ecotox/
  • [USEPA] US Environmental Protection Agency. 2011b. Office of Pesticide Programs (OPP) pesticide toxicity database. [cited 2011 January]. Available from: http://www.ipmcenters.org/Ecotox/index.cfm
  • [USEPA] US Environmental Protection Agency. 2012. Data requirements for pesticide registration. [cited 2012 February]. Available from: http://www.epa.gov/pesticides/regulating/data_requirements.htm
  • Van den Brink PJ, Blake N, Brock TCM, Maltby L. 2006. Predictive value of species sensitivity distributions for effects of herbicides in freshwater ecosystems. Human Ecol Risk Assess 12:645674.
  • Van Vlaardingen P, Traas TP, Wintersen AM, Aldenberg T. 2004. A program to calculate hazardous concentrations and fraction affected, based on normally distributed toxicity data. Report No. 601501028/2004. National Institute for Public Health and Environment (RIVM). the Netherlands: Bilthoven.
  • Van Vlaardingen PLA, Verbruggen EMJ. 2007. Guidance for the derivation of environmental risk limits within the framework of “International and national environmental quality standards for substances in the Netherlands” (INS). National Institute for Public Health and the Environment (RIVM). RIVM Report 601782001/2007. The Netherlands: Bilthoven.
  • Vervliet-Scheebaum M, Knauer K, Maund SJ, Grade R, Wagner E. 2006. Evaluating the necessity of additional aquatic plant testing by comparing the sensitivities of different species. Hydrobiologia 570:231236.
  • Wetzel RG. 2001. Limnology: Lake and river ecosystems, 3rd ed. San Diego (CA): Academic Press; Elsevier. 1006 pp.