Estimating Aquatic Plant Community 

Hazard Concentrations for 

Pesticide Effects

FIFRA Scientific Advisory Panel Review

January 31, 2012

Glen Thursby

ORD Atlantic Ecology Division

Narragansett, RI

Sujatha Sankula

OPP Environmental Fate & Effects Division

Washington, DC

Michael Lewis

ORD Gulf Ecology Division

Gulf Breeze, FL 1
Definitions

• FIFRA-5 data set

• Full data set

• HC5

2

• Reference HC5
Problem Definition

3

Lack of consensus of minimum data requirements 

for deriving criteria for plants
Primary Conclusion

Can derive a reasonable

estimate of plant effects 

using FIFRA-5 data

4
• “Results of at least one acceptable test with a saltwater 

[freshwater] alga or vascular plant.”

OW Plant Data for Aquatic Life Criteria

• “A plant value is the result of a 96-hr test conducted 

with an alga or a chronic test conducted with an aquatic 

vascular plant.” 

• “If plants are among the aquatic organisms most sensitive to 

the material, results of a test with a plant in another phylum 

(division) should also be available.”

5

• Acceptable test

• Important aquatic plant species

• Biologically important endpoint
Navicula pelliculosa

Anabaena flos-aquae

OPP Plant MDR for Pesticide Registration

FIFRA-5

Navicula pelliculosa

Skeletonema costatum

Lemna gibba

Pseudokirchneriella subcapitata

6
FIFRA-5 minimum aquatic 

plant requirement exceeds 

“requirement” in the OW 

1985 Guidelines for ALC

7
Problem Definition, cont’d

PEP-carboxylase

RuBisCO

8
Problem Definition, cont’d

Given that there are minimal requirements . . . 

Are useful estimates of an

aquatic plant Hazard Concentration 

possible with FIFRA-5 data as the 

minimum?

9
Methods

• Developed database for 10 pesticides

• Included pesticides with FIFRA-5 & at least 

5 other aquatic plant species

• Data from ECOTOX & OPP toxicity 

databases

10

• QA/QC of data

• Combined FW & SW

• Combined vascular & non-vascular

• Combined measured & unmeasured
Methods, cont’d

• Endpoints

• EC50 values

• Standard durations

• 3,4,5 d for microalgae

• 7-14 d for duckweeds

• 14-35 d for other vascular plants

11

• Multiple endpoints include

• Biomass, cell counts, chl a, photosynthetic rate, 

growth rate, etc. 

• Atypical endpoints excluded

• Variable fluorescence, enzyme or ATP

concentration, etc

• Most sensitive EC50 value used
Compound Class Mode-of-Action #

Species % Vascular % SW

Atrazine Herbicide Photosystem II 54 26 24

Diuron Herbicide Photosystem II 32 25 31

Irgarol Herbicide Photosystem II 38 24 55

Linuron Herbicide Photosystem II 10 40 0

Pesticides used in Plant Analyses

Metribuzin Herbicide Photosystem II 18 50 6

Prometryn Herbicide Photosystem II 13 31 15

Diquat Herbicide Membrane Disruption 13 23 8

Metolachlor Herbicide Amino Acid Synthesis 17 35 6

Metsulfuron Methyl Herbicide Cell Division 19 63 5

Pentachlorophenol Fungicide Mitochondrial ATPase 27 37 26

12
Methods, cont’d

• Species Sensitivity Distributions 

Used two approaches to compare HC5 

estimates from FIFRA-5 data sets with 

reference HC5 values

• Species Sensitivity Distributions 

• Extrapolation Factors

13
0.5

0.6

0.7

0.8

0.9

1.0

C

u

m

ulativ

e

P

r

o

p

o

rtio

n--F(c)

Methods, Species Sensitivity Distribution

Log normal distribution

0.0

0.1

0.2

0.3

0.4

-2 0 2 4 6 8 10

C

u

m

ulativ

e

P

r

o

p

o

rtio

n

Ln EC50











 

 



c 

F(c)

c = ln EC50

μ = mean of ln EC50s

σ = std dev of ln EC50s

14
3

4

5

6

7

8

9

10

Methods, SSD cont’d

    



( )

1

c F c

μ

L

n

E

C

5

0

-3

-2

-1

0

1

2

-3 -2 -1 0 1 2 3

Slope = estimate of std dev of 15 ln EC50s

Intercept = estimate of the mean of ln EC50s

Φ-1F(c)
3.0

4.0

5.0

6.0

Methods, HC5 calculation

L

n

E

C

5

0

FIFRA-5 Data

0.0

1.0

2.0

3.0

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

Ln (HC5)

Φ-1F(c)

L

n

F(c) = 0.05

16
6.0

8.0

10.0

12.0

Methods, SSD cont’d

Full Data Set

E

C

5

0

0.0

2.0

4.0

6.0

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

Φ-1F(c)

L

n

E

C

5

0

17
6.0

8.0

10.0

12.0

L

n (E

C

5

0)

A Atrazine

HC5 Estimation for Atrazine

Full data set

HC5 = 12.7μg/L

0.0

2.0

4.0

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

L

n (E

C

5

0)

NOΦRMSINV(p) -1F(c)

18

FIFRA-5

HC5 = 15.3μg/L
6.0

8.0

10.0

12.0

L

n(E

C

5

0)

B Diuron

(E

C

5

0) HC5 Estimation for Diuron

FIFRA-5

HC5 = 5.8 μg/L

-2.0

0.0

2.0

4.0

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

L

n(E

C

5

0)

NORMSINV(p)

Φ-1F(c)

L

n (E

C

5

0)

19

Full data set

HC5 = 1.57

HC5 = 5.8 μg/L
5.0

6.0

7.0

8.0

9.0

L

n(E

C

5

0)

E Metribuzin

HC5 Estimation for Metribuzin

Full data set

HC5 = 7.65 μg/L

0.0

1.0

2.0

3.0

4.0

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

L

n(E

C

5

0)

Φ-1F(c)

20

FIFRA-5

HC5 = 1.8 μg/L
ATZ

DIU

LIN

MTC

MBZ PCP

PMT

1

10

100

R

e

f

e

r

e

n

c

e

H

C

5 (μ

g/L)

Summary of SSD Results

FIFRA-5 more 

conservative

DQT

MSF IRG

0.01

0.1

1

0.01 0.1 1 10 100

R

e

f

e

r

e

n

c

e

H

C

5 (μ

g/L)

FIFRA-5 HC5 (μg/L) 

21

Reference more

conservative
• Also wanted to accommodate in the HC5 

estimation a provision by which the relationship 

to the reference HC5 would improved as the 

number of EC50 values in the FIFRA data set 

increases.

Summary of SSD Results, cont’d

22

• Appendix F: 

“. . .the 25th percentile of the HC5 value”
10

100

R

e

f

e

r

e

n

c

e

H

C

5 (μ

g/L)

Summary of SSD Results, cont’d

FIFRA-5 more 

conservative

0.01

0.1

1

0.01 0.1 1 10 100

R

e

f

e

r

e

n

c

e

H

C

5 (μ

g/L)

FIFRA-5 HC5 (μg/L) 

23
10

100

R

e

f

e

r

e

n

c

e

H

C

5 (μ

g/L)

Summary of SSD Results, cont’d

0.01

0.1

1

0.01 0.1 1 10 100

R

e

f

e

r

e

n

c

e

H

C

5 (μ

g/L)

FIFRA-5 HC5 (μg/L) 

24
3.0

4.0

5.0

6.0

E

C

5

0

FIFRA-5 “plus”

Summary of SSD Results, cont’d

• Estimate of μ and σ

0.0

1.0

2.0

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

Φ-1F(c)

L

n

25

• Estimate of μ and σ

improve as N increases

• Estimate of HC5 should 

improve
Summary of SSD Results, cont’d

• Advantage of using the SSD approach over 

some of the other approaches (e.g., some 

function of just the lowest value)—it uses all 

of the available data.

26

• Some preliminary work suggests that just increasing 

the number of EC50 values may reduce uncertainty 

with less bias than using the percentile approach 

suggested in Appendix F.
• Species Sensitivity Distributions 

• Extrapolation Factors

Used two approaches to generate HC5 values

27
Use of Extrapolation Factors

• Potential use of extrapolation factors still uses 

SSDs for the estimation of the reference HC5 

from the full data set.

• A comparison is made between the lowest 

28

• A comparison is made between the lowest 

FIFRA-5 EC50 and this reference HC5.
Use of Extrapolation Factors

Lemna 31.5

Skeletonema 35.7

Navicula 60.0

Example: Atrazine

FIFRA-5 EC50s (μg/L)

29

Navicula 60.0

Pseudokirchneriella 73.2

Anabaena 224.9

Reference HC5 (μg/L) 15.3

Ratio = 2.1
Extrapolation Factors, cont’d

8.0

10.0

12.0

L

n (E

C

5

0)

Atrazine

30

0.0

2.0

4.0

6.0

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

L

n (E

C

5

0)

Φ-1F(c)

proportion less than 

this value = 0.19
Extrapolation Factors, cont’d

Compound Ratio (lowest/HC5) % of full data set lower 

than FIFRA value

Atrazine 2.1 19

Diuron 4.5 31

Irgarol 1.5 8

Linuron 4.0 31

Geomean of Ratios = 2.6 31

Linuron 4.0 31

Metribuzin 1.1 6

Prometryn 0.8 4

Diquat 6.6 17

Metolachlor 2.7 13

Metsulfuron methyl 6.4 25

Pentachlorophenol 2.9 13
ATZ

DIU

LIN

MBZ

PMT

MTC

PCP

10

100

R

efere

n

c

e

H

C

5 (μ

g/L)

Extrapolation Factor, cont’d

Lowest FIFRA Value

FIFRA-5 more 

conservative

IRG

DQT

MSF

0.01

0.1

1

0.01 0.1 1 10 100

R

efere

n

c

e

H

C

5 (μ

g/L)

Lowest FIFRA EC50 (μg/L)

32

Reference more

conservative
10

100

R

e

f

ere

n

c

e

H

C

5 (μ

g/L)

Extrapolation Factor, cont’d

EF = largest ratio

FIFRA-5 more 

conservative

0.01

0.1

1

0.01 0.1 1 10 100

R

e

f

ere

n

c

e

H

C

5 (μ

g/L)

Lowest FIFRA EC50/Largest EF (μg/L)

33

Reference more

conservative
Extrapolation Factor, cont’d

EF = geomean

1

10

100

R

e

f

ere

n

c

e

H

C

5 (μ

g/L)

34

0.01

0.1

1

0.01 0.1 1 10 100

R

e

f

ere

n

c

e

H

C

5 (μ

g/L)

Lowest FIFRA EC50/Geomean EF (μg/L)
• With only ten compounds, and most of them 

PSII inhibitors—do we have confidence in the 

range of ratios?

Issues with Extrapolation Factor for Plants

Use of Extrapolation Factor, cont’d

range of ratios?

• How could we account for N > 5?

35
Growth Rate

• One final issue that the plant analyses addressed is whether there 

is a need to standardize effects analyses on a single endpoint—

specifically on growth rate.

• Often growth rate is desired because this endpoint may be less 

influenced by the duration of exposure than effects based on yield.

• Only three compounds (atrazine, diuron & irgarol) had 

enough species with growth EC50s to make 

comparison

• All three PSII inhibitors

36

• This issue is independent of SSD vs. EF
Growth Rate vs. Other Endpoints: Atrazine

6.0

8.0

10.0

12.0

L

n(E

C

5

0)

Atrazine

37

0.0

2.0

4.0

6.0

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

L

n(E

C

5

0)

Φ-1F(c)

ECOTOX All Endpoints
Growth Rate vs. Other Endpoints: Atrazine

6.0

8.0

10.0

12.0

L

n(E

C

5

0)

Atrazine

38

0.0

2.0

4.0

6.0

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

L

n(E

C

5

0)

Φ-1F(c)

ECOTOX All Endpoints

ECOTOX Growth Rate
Growth Rate vs. Other Endpoints: Atrazine

6.0

8.0

10.0

12.0

L

n(E

C

5

0)

Atrazine

39

0.0

2.0

4.0

6.0

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

L

n(E

C

5

0)

Φ-1F(c)

Erickson Data

ECOTOX All Endpoints

ECOTOX Growth Rate
Growth Rate vs. Other Endpoints

Compound Reference 

HC5 (μg/L)

Growth Rate 

HC5 (μg/L) Ratio (Growth/Ref)

Atrazine 15.30 19.36 1.3

Diuron 1.57 3.38 2.2

40

• While there may be occasions where standardization on growth 

rate is desirable, it may not be necessary.

• Using a variety of endpoints generally will mean that more 

species will be represented in the SSD

Irgarol 0.066 0.024 0.4
• FIFRA-5 shows promise for being able 

to estimate HC5

Main Conclusions

• SSD approach has advantage over EF 

since estimate for HC5 should 

• Standardization based on growth 

rate may not be necessary

41

since estimate for HC5 should 

automatically improve with additional 

data—uses all of the available data