The Relative Sensitivity of Macrophyte and Algal Species to

77 Pages·2011·1.21 MB·English

by  Jeff Giddings

The Relative Sensitivity of Macrophyte and Algal Species to Herbicides and
Fungicides: An Analysis Using Species Sensitivity Distributions

Jeffrey M. Giddings, Ph.D.
Compliance Services International
Rochester, Massachusetts, USA

for the Species Sensitivity Distribution Working Group (SSD WG), functioning under the umbrella of the
Aquatic Macrophyte Ecotoxicology Group (AMEG)
Society of Environmental Toxicology and Chemistry (SETAC)

September 30, 2011

CSI Report No. 11702
SSD Analysis of Macrophyte Sensitivity to Herbicides and Fungicides p. 2
Acknowledgments

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 (APC); 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 (U. Copenhagen, with support from
the Danish Environmental Protection Agency), Cheminova, Dow AgroSciences, DuPont, Fraunhofer
Institute, Makhteshim-Agan, Mark Hanson (U. 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.

Disclaimer

This report is the result of an activity arising from the 2008 Society of Environmental Toxicology and
Chemistry (SETAC) workshop on Aquatic Macrophyte Risk Assessment for Pesticides (AMRAP). Projects
originating from AMRAP, 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). This report presents the views of its authors, and is not necessarily endorsed by or
representative of the policy or views of SETAC.

Compliance Services International Report No. 11702
SSD Analysis of Macrophyte Sensitivity to Herbicides and Fungicides p. 3

Table of Contents
page
Acknowledgments .............................................................................................................................2
Disclaimer ..........................................................................................................................................2
Table of Contents ...............................................................................................................................3
List of Tables ......................................................................................................................................4
List of Figures .....................................................................................................................................4
1. Introduction: Background and Objectives .....................................................................................6
2. Data Compilation and Evaluation .................................................................................................7
3. SSD Analysis Methods ..................................................................................................................9
3.1 Overview of SSD Analysis ................................................................................................................. 9
3.2 Data Selection ................................................................................................................................ 10
3.3 Lognormal Regression .................................................................................................................... 11
3.4 Presentation of Results .................................................................................................................. 12
4. Results ....................................................................................................................................... 12
4.1 Chemical A ..................................................................................................................................... 12
4.2 Chemical B...................................................................................................................................... 15
4.3 Chemical C ...................................................................................................................................... 17
4.4 Chemical D1 ................................................................................................................................... 19
4.5 Chemical D2 ................................................................................................................................... 21
4.6 Chemical E1 .................................................................................................................................... 23
4.7 Chemical E2 .................................................................................................................................... 24
4.8 Chemical E3 .................................................................................................................................... 26
4.9 Chemical E4 .................................................................................................................................... 28
4.10 Chemical F1 ............................................................................................................................. 30
4.11 Chemical F2 ............................................................................................................................. 32
4.12 Chemical F3 ............................................................................................................................. 34
4.13 Chemical F4 ............................................................................................................................. 36
4.14 Chemical F5 ............................................................................................................................. 38
5. Discussion .................................................................................................................................. 40
5.1 Lemna gibba ................................................................................................................................... 40
5.2 Algae .............................................................................................................................................. 43
5.3 Myriophyllum spicatum ................................................................................................................. 45
5.4 Combined data for Lemna gibba, algae, and Myriophyllum spicatum .......................................... 48
6. Uncertainties ............................................................................................................................. 50
7. Conclusions and Recommendations............................................................................................ 52
8. References ................................................................................................................................. 54
Appendix A. AMRAP SSD database ................................................................................................... 55

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List of Tables

Table 1. Chemical A: Macrophyte and algal toxicity values used in the analysis. Algal data were not
used to construct the SSD. .................................................................................................................. 14
Table 2. Chemical B: Macrophyte and algal toxicity values used in the analysis. Algal data were not
used to construct the SSD. .................................................................................................................. 16
Table 3. Chemical C: Macrophyte and algal toxicity values used in the analysis. Algal data (other
than macroalgae) were not used to construct the SSD. ..................................................................... 18
Table 4. Chemical D1: Macrophyte and algal toxicity values used in the analysis. Algal data were not
used to construct the SSD. .................................................................................................................. 20
Table 5. Chemical D2: Macrophyte and algal toxicity values used in the analysis. Algal data were not
used to construct the SSD. .................................................................................................................. 22
Table 6. Chemical E1: Macrophyte and algal toxicity values used in the analysis. Algal data were not
used to construct the SSD. .................................................................................................................. 23
Table 7. Chemical E2: Macrophyte and algal toxicity values used in the analysis. Algal data were not
used to construct the SSD. .................................................................................................................. 25
Table 8. Chemical E3: Macrophyte and algal toxicity values used in the analysis. Algal data were not
used to construct the SSD. .................................................................................................................. 27
Table 9. Chemical E4: Macrophyte and algal toxicity values used in the analysis. Algal data (other
than macroalgae) were not used to construct the SSD. ..................................................................... 29
Table 10. Chemical F1: Macrophyte and algal toxicity values used in the analysis. Algal data were
not used to construct the SSD. ............................................................................................................ 31
Table 11. Chemical F2: Macrophyte and algal toxicity values used in the analysis. Algal data (other
than macroalgae) were not used to construct the SSD. ..................................................................... 33
Table 12. Chemical F3: Macrophyte and algal toxicity values used in the analysis. Algal data were
not used to construct the SSD. ............................................................................................................ 35
Table 13. Chemical F4: Macrophyte and algal toxicity values used in the analysis. Algal data were
not used to construct the SSD. ............................................................................................................ 37
Table 14. Chemical F5: Macrophyte and algal toxicity values used in the analysis. Algal data were
not used to construct the SSD. ............................................................................................................ 39
Table 15. Position of Lemna gibba in macrophyte SSDs. ............................................................................ 41
Table 16. Rank of Lemna gibba among Lemna species in sensitivity to herbicides and fungicides. .......... 41
Table 17. Sensitivity of the most sensitive of the FIFRA algal species relative to macrophyte SSDs. ........ 43
Table 18. Position of Myriophyllum spicatum in macrophyte SSDs. .......................................................... 45
Table 19. Rank of Myriophyllum spicatum among Myriophyllum species in sensitivity to herbicides
and fungicides. .................................................................................................................................... 46
Table 20. Sensitivity of the most sensitive species of Lemna gibba, FIFRA algae, or Myriophyllum
spicatum relative to macrophyte SSDs. .............................................................................................. 48

List of Figures
page
Figure 1. Macrophyte SSD for Chemical A .................................................................................................. 13
Figure 2. Macrophyte SSD for Chemical B .................................................................................................. 15
Figure 3. Macrophyte SSD for Chemical C .................................................................................................. 17
Figure 4. Macrophyte SSD for Chemical D1 ................................................................................................ 19
Figure 5. Macrophyte SSD for Chemical D2 ................................................................................................ 21
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Figure 6. Macrophyte SSD for Chemical E2 ................................................................................................ 24
Figure 7. Macrophyte SSD for Chemical E3 ................................................................................................ 26
Figure 8. Macrophyte SSD for Chemical E4 ................................................................................................ 28
Figure 9. Macrophyte SSD for Chemical F1 ................................................................................................. 30
Figure 10. Macrophyte SSD for Chemical F2. .............................................................................................. 32
Figure 11. Macrophyte SSD for Chemical F3. .............................................................................................. 34
Figure 12. Macrophyte SSD for Chemical F4. .............................................................................................. 36
Figure 13. Macrophyte SSD for Chemical F5. .............................................................................................. 38
Figure 14. Sensitivity of Lemna gibba relative to all macrophytes. ............................................................ 42
Figure 15. Sensitivity of standard algal species relative to all macrophytes. ............................................. 44
Figure 16. Sensitivity of Myriophyllum spicatum relative to all macrophytes. ........................................... 47
Figure 17. Sensitivity of the most sensitive species of Lemna gibba, FIFRA algae, and Myriophyllum
spicatum relative to all macrophytes. ................................................................................................. 49

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1. Introduction: Background and Objectives

In January 2008, the Society of Environmental Toxicology and Chemistry (SETAC) held a workshop on
Aquatic Macrophyte Risk Assessment for Pesticides (AMRAP) in The Netherlands (Maltby et al. 2010). At
the workshop, a Species Sensitivity Distribution (SSD) working group was formed to address questions
about the sensitivity of standard surrogate aquatic plant test species relative to other aquatic
macrophyte species. For various practical and historical reasons, the macrophytes most widely used in
toxicity tests with pesticides are duckweeds of the genus Lemna. However, the sensitivity of Lemna spp.
relative to other macrophyte species is largely unknown. The primary objective of the SSD working
group was to investigate this question using available data on the toxicity of pesticides, especially
herbicides, to aquatic macrophytes. The SSD working group selected Compliance Services International
(CSI) to conduct the analysis. Seven companies whose scientists were members of the working group
generously provided funding for the project. In 2009, a new SETAC advisory group, the Aquatic
Macrophyte Ecotoxicology Group (AMEG), was formed to continue the efforts initiated at the AMRAP
workshop. AMEG assumed responsibility for the AMRAP projects, including the SSD project.

CSI collected macrophyte and algal toxicity data for nearly 60 herbicides and fungicides from the open
literature and confidential company reports. CSI reviewed each data source according to predefined
criteria, and only data from studies determined to meet the quality criteria were included in the
analysis. (In a few cases data were taken from reliable secondary sources and data quality was not
independently confirmed.) For 11 herbicides and 3 fungicides, useful toxicity data were found for at
least 6 macrophyte species, which was considered the minimum needed for SSD analysis. Macrophyte
SSDs for 13 of these chemicals were fitted using lognormal regression as described below; for one
chemical, too many “greater-than” values prevented calculation of an SSD. The position of Lemna gibba
in each SSD, as well as the sensitivity of the 4 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, were determined. The position of a rooted macrophyte species, Myriophyllum
spicatum, was also determined where data were available, because a standardized Myriophyllum test is
currently under development through another AMRAP working group (Maltby et al. 2010; Dohmen
2010) and through a UBA ring-test (Maleztki and Kussatz 2011; Maleztki et al. 2011) and is
recommended under the recent SANCO 11802-2010 draft regulation.

To maintain the confidentiality of data provided by pesticide registrants, the identity of the herbicides
and fungicides was revealed only to CSI and was not shared with the SSD workgroup. In this report, the
chemicals are identified by codes that indicate the mode of action (MoA) of each chemical but not its
specific identity. The MoAs included inhibition of amino acid synthesis (Chemical A), auxin simulation
(Chemical B), inhibition of cell division or elongation (Chemical C), inhibition of fungal respiration
(Chemicals D1 and D2), inhibition of multiple biosynthesis pathways (Chemicals E1, E2, E3, and E4), and
inhibition of photosynthesis (Chemicals F1, F2, F3, F4, and F5). Because the six MoAs were not equally
represented in the database and three were represented by only a single chemical, conclusions about
the relationship between MoA and species sensitivity must be made with caution.

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2. Data Compilation and Evaluation

Macrophyte toxicity data were compiled from open literature and from confidential test reports
provided by participating companies. Algal toxicity data were obtained from the OPP Pesticide Toxicity
Database (EPA 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 EPA ECOTOX database (EPA 2011a) and the
European Commission pesticide review report.

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

Test organisms must be identified at least to genus.
Test substance must be identified.
o Active ingredient (a.i.).
o Form (technical-grade or specified formulation, including % a.i.).
Test substance must not include more than one active ingredient.
Negative and/or solvent controls (as appropriate) must be included.
Exposure medium must be reported.
Exposure duration must be specified.
Methods for measuring effects must be described.
Test concentration units must be unambiguous.
o Active ingredient or whole formulation.
o Nominal or measured.
o Initial or mean.
Toxicity endpoint (e.g., EC50, NOEC) 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:

Were the data derived using a standard, validated test method?
Was the source of test organisms described?
Were the plants maintained under appropriate conditions before use in the test?
Were the test organisms healthy at the beginning of the exposure period?
Did the study include multiple exposure concentrations?
o Tests with only two or three concentrations are insufficient for determination of
ECx values.
o No Observed Effect Concentrations (NOECs) are useful only if at least three closely-
spaced concentrations are tested.
o Exception: exposure to a single concentration at the water solubility limit is
sufficient to generate a useful NOEC or a “greater than” ECx value.
Were exposure concentrations confirmed by chemical analysis?
o Measured concentrations are preferred for endpoint calculation.
o Measured concentrations should be at least 50% of nominal concentrations.
o Nominal concentrations may be acceptable for relatively stable substances well
below their solubility limits.
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Are response measurements reported for each exposure concentration, or only statistical
endpoints such as EC50 or NOEC values?
o If only the endpoint is reported, the statistical method must be specified.
Are response measurements for controls and treatment groups reported?
Is control response acceptable?
Are methods documented sufficiently?
o Organism collection methods
o Pre-exposure acclimation/culture conditions
o Size, age, condition, life stage at initiation of exposure
o Exposure system
o Number of replicates at each concentration
o Procedures for randomization
o Exposure medium composition
o Exposure conditions (light, temperature, aeration, agitation, etc.)

From publications and reports judged to be sound according to the criteria described above, data points
usable for SSD analysis were identified. The data points were compiled in a database (Microsoft Access
2007). For each data point, the following supporting information was recorded:

Study ID (a unique ID for each test).
Reference ID (linked to document information, kept confidential to protect chemical identity).

Active Ingredient Code (linked to confidential information such as chemical name, CAS number,
chemical class, etc.).
Test Substance Code (linked to confidential information such as product name, percent a.i.,
formulation type, etc.).

Species scientific name.
Species common name.
Source of test organisms (field, culture).
Part/size/age at test initiation.

Experimental unit.
Test container.
Medium.
Sediment.
Light (intensity, photoperiod).
Temperature.
Other conditions.

Exposure type (static, semi-static renewal, flow-through).
Exposure duration.
Exposure concentrations.
Analytical confirmation (none, stock solution, initial exposure concentration, mean measured,
etc.).
Measured concentration as percent of nominal concentration.
Controls.
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Number of replicates.

Response: plant part (shoot, root, whole plant, etc.).
Response: measurement (number, length, biomass, etc.).
Response: interval (final, increase/decrease during exposure, specific growth rate during
exposure, etc.).
Endpoint (EC50, NOEC, etc.).
Concentration.
Measured or nominal (applies to endpoint concentration).
Control performance.

Comments.

The database, without the confidential information about chemical identity and without details about
test methods, is provided in Appendix A of this report.
3. SSD Analysis Methods

Methods for statistical analysis of SSDs have been thoroughly reviewed by others (e.g., Posthuma et al.
2002; Intrinsik 2009), and advancing the methodology was outside the scope of this project. Given the
uncertainties related to data selection and other factors (Section 6), SSD results should be considered
approximations regardless of the rigor of the statistical method. To address the question of Lemna gibba
and Myriophyllum spicatum position in the SSD, and algal sensitivity relative to all macrophytes, we
believe it is sufficient, at least for an initial analysis, to apply a single, generally applicable distribution
model, the lognormal, to estimate the SSDs for all chemicals. In specific cases other models may fit the
data better than the lognormal. We acknowledge that applying a single statistical model to all datasets
adds another source of uncertainty to the results. The analysis reported here could be refined through
exploration of alternative distribution models. The ranking of species according to sensitivity is
unaffected by the SSD model used.

3.1 Overview of SSD Analysis

The SSD analysis method used in this investigation is based on the assumption that the sensitivity of
aquatic plant species (represented by data points from toxicity tests) follows a lognormal distribution.
For each herbicide or fungicide to be analyzed, one data point is selected from the available data for
each species (see Section 3.2). The species data points for that herbicide or fungicide are sorted (lowest
to highest), the rank of each data point is transformed based on a normal distribution, and a linear
regression is fitted to the data using the log of the data point concentration as the independent variable
and the normalized rank as the dependent variable. The slope and intercept of the regression can be
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
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species affected (FA)1 at a specified concentration. The calculations are implemented in a Microsoft
Excel 2007 spreadsheet.

3.2 Data Selection

The database contained a variety of statistical endpoints, but only median effect concentrations (EC50s)
were available for a sufficient number of species to support SSD analysis. The EC50s were based on a
wide variety of biological measurements, and these had to be pooled for SSD analysis. Basing SSDs on a
variety of measurements was necessary for two reasons. First, differences in biology of the test species
necessitate differences in measured responses (e.g. frond number, root length, plant dry weight); to
construct an SSD that includes macrophytes with different morphology and growth characteristics
requires the use of differently-derived EC50s for different species. Second, as a practical matter,
subdividing the database by categories of measured data points severely reduces the number of SSDs
that can be evaluated, because equivalent data are often unavailable for 6 or more species.
Comparisons across chemicals are also limited if they are based on subsets of the measurement data
points.

Toxicity data points for aquatic plants are typically derived from single or repeated measurements of
plant standing crop. Standing crop can be quantified as biomass (wet or dry weight), shoot length,
chlorophyll, or a similar measurement. The effect of the chemical on the plant can be assessed based on
(a) the standing crop at a particular point in time, (b) the absolute increase in standing crop over a span
of time, or (c) the relative rate of increase (specific growth rate). Even for a single standing crop
measurement (e.g., whole plant dry weight), toxicity data points based on (a), (b), and (c) from a single
test will differ.

Bergtold and Dohmen (2011) present reasons why data points based on specific growth rate are more
informative and better suited to effects characterization than data points based on standing crop or
standing crop increase (the “yield” response). Growth rate data points are preferred (e.g., OECD
Guideline 201) because they are independent of the absolute level of the control growth rate, the slope
of the concentration-effect curve, and the test duration; in contrast, all of these factors affect the
numerical value of a yield-based data point. For mathematical reasons, an EC50 calculated for growth
rate is usually greater than an EC50 calculated for yield from the same experimental data.

A small percentage of the data points in the AMRAP database were based on functional measurements,
mainly photosynthesis. There were not enough of these data points to support any firm conclusions
about their sensitivity relative to yield-based or growth rate data points. Methods for functional
measurements are even less standardized than other aspects of aquatic macrophyte ecotoxicology.

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 EC50 for each species, regardless of
the biological measurement upon which the EC50 was based. While selection of the lowest available
EC50 is standard regulatory practice (e.g., US EPA 2004), it leaves open the possibility that a data point
based on a non-standard measurement parameter could unduly influence the SSD. If all of the original

1 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 (e.g. biomass) is
reduced by 50% or more.
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