Probabilistic Risk Assessments for Listed Species Potentially Exposed to 

Pesticides

Dwayne R.J. Moore – Intrinsik Environmental Sciences Inc., New Gloucester, ME

October 6th, 2014
Common Concerns With 

Quantitative Uncertainty Analysis

• Lack of available expertise

• Hard to do

• Resource intensive

• Need lots of data

• Decisions less obvious than with risk quotients

• Goal of this presentation: Demonstrate that above 

concerns can be overcome ... and can be done so 

rather easily
General Approach

• Consider generic receptor groups 

and scenarios at the outset

– If risk negligible for all fish species in a 

standard pond, then no need for PRAs 

for most, if not all, listed fish species

– If some species at risk, move to 

species-specific analyses

• Approach QUAs for listed species 

in a tiered fashion

– Simple, conservative scenarios may be 

sufficient for decision making

– Highly refined analyses only when 

necessary

• Decision criteria required in this 

process

# Species
QUAs for General Receptor Groups

0

20

40

60

80

100

0 0.5 1 1.5

Concentration (μg/L)

Reverse Cumulative Probability (%)

Typical Application Rate

Maximum Application Rate

• Consider a simple example: 

Chemibears

• Only a few major use patterns

• Focus on worst-case use 

pattern

• Concern is for acute risk to fish 

from SLERA

• PRA conducted using 

PRZM/EXAMS standard pond 

model

• Peak EECs estimated for 30 

years
Fish SSD for Chemibears

0

20

40

60

80

100

1 10 100 1000 10000 100000

Concentration (μg/L)

P

erc

e

nt Affe

cte

d

Fitted SSD (LC/EC50s)

Confidence Limits on the Fitted SSD

LC/EC50 Data

Low Toxicity SSD
Risk Curve for Fish Exposed to 

Chemibears

• Near zero probability of 

exceeding low LCx of 

even most sensitive 

species

• De minimis risk

• Although probabilistic, 

still a conservative RA

– Max application rate

– Standard pond adjacent to 

treated field

– Exposure duration of 

acute tests = 96 hrs

• Little need to assess 

each listed fish species

Freshwater Fish, Maximum Application Rate (Scenario 1)

0

20

40

60

80

100

0 20 40 60 80 100

% SSD Species Affected

E

x

c

e

e

d

e

n

c

e

P

r

o

b

a

bility (%)

Risk Curve

De Minimis-Low Risk Boundary

Low-Intermediate Risk Boundary

Intermediate-High Risk Boundary

Fish
A Simple Species-specific Example

• FIFRA QUA for chemibears indicated 

risk to mammals

• Louisville bat forages in proximity to 

treated areas

• SLERA indicates potential risk

• Simple Monte Carlo analysis

– Distributions for insect residues, bat body 

weight and foraging behavior

– Include residue decline over time

– Calculate daily oral exposure

– Compare to reproduction threshold

• Output indicates that reproduction 

threshold never exceeded for 

>100,000 bats for even a single day
A Higher Tier, Species-specific QUA: 

California Red-legged Frog

• CRLF listed as threatened in 1996

• SLERA indicated potential risk to 

aquatic-phase CRLF

• Refined exposure estimates derived 

for three scenarios in Watsonville 

Slough watershed in CA

– Worst-case scenarios

– Ponds, flowing waters, ditch

– CRLF core areas, critical habitat, 

elemental occurrences and/or use patterns 

co-exist in this watershed
Exposure Assessment for Ponds

• Strawberries and lettuce most important crops 

• Maximum rates assumed

• 3 ponds connected, surrounded by agriculture 

(83.2% of total crop land)

• PRZM, EXAMS and VFSMOD used to 

simulate hydrology and pesticide transport in 

multiple fields surrounding the ponds

• Parameterized to reflect site-specific soil 

characteristics, topography, size of each pond

• Distributions for

– Spatial and temporal distribution of applications

– Hydrologic characteristics

– Spray drift
Exposure Output: Pond 1

0% 0

20%

40%

60%

80%

100%

0.001 0.01 0.1 1 10

C

u

m

ula

tiv

e

P

r

o

b

a

bilit

y

Concentration (μg/L)
Exposure Assessment: River

• Largest remaining freshwater riparian 

habitat for CRLF

• Historically high chemibears, >99% of 

average upstream use in CRLF core areas

• Surrounding soils have high runoff potential

• Watershed divided into 10 sub-basins, each 

sub-basin parameterized based on sitespecific characteristics

• SWAT used to estimate EECs by simulating 

the transport via moving water within the 

watershed 

• Distributions for water body characteristics 

and pesticide application in watershed
Exposure Assessment: Ditch

• Drainage ditch to Watsonville 

Slough, one of four with recorded 

CRLF occurrences 

• Ditch catchment consists of almost 

100% agriculture 

• Ditch within 6 m of agricultural land 

prone to runoff (50% clay) 

• SWAT model and Monte Carlo 

simulation used to derive EEC 

distributions 
Concentration-response Curve for 

Xenopus laevis

0

0.2

0.4

0.6

0.8

1

0 5000 10000 15000 20000

P

r

o

p

o

rtio

n

M

o

rtality

Concentration (μg/L)

Data from Palmer (2011)

Probit Model

95% Confidence Limits
Risk Characterization

• No direct risk to aquatic-phase CRLFs in ponds and the 

river and little risk in ditch (<1% probability of 1% mortality)

• Other analyses indicated low risk to prey species

• Weight-of evidence assessment

– Probabilistic modeling indicates little to no risk

– Monitoring data indicate lower levels than model predictions

– Field study: “no adverse acute or long-term effects were obvious in 

either fish or aquatic invertebrate communities”. Over 13 applications 

to cotton had been made to the adjacent field over a three-year study

– Mesocosm studies: Only transient effects to sensitive, aquatic 

invertebrates at modeled concentrations
Case Studies: Lessons Learned

• FIFRA SLERA and co-occurrence analysis may 

result in many listed species needing further 

assessment 

• Higher tier, species-specific analyses useful and 

doable, but resource-intensive

• Need intermediate options

– QUAs for generic receptor groups and conservative standard 

scenarios are useful following FIFRA SLERA

– Lower tier, species-specific QUAs may be sufficient to 

alleviate risk concerns

• Decision criteria are required to guide assessors 

through the tiered assessment
Decision Making With Quantitative 

Uncertainty Analysis

• “Bright lines” lacking for the 

outputs of QUA

• Probabilistic bright lines can 

assist communication and 

decision making

• Bright lines will likely differ 

between receptor groups

‒ Indirect vs direct effects

‒ Low vs high fecundity listed 

species

‒ Threatened vs endangered

0

20

40

60

80

100

0 20 40 60 80 100

E

xceedence Probability (%)

% Species or Individuals Affected

Low risk?

Intermediate risk?

High risk?

Mitigation required?
Those Pesky Concerns ...

• Lack of available expertise, hard to do

– Intermediate QUA options are easy and require little expertise beyond that 

required to run standard models

– Modest training and proper guidance can deal with this issue

– EPA has developed excellent guidance on QUA

• Resource intensive

– Intermediate options require little effort beyond screening-level analyses

– Higher-tier, species-specific analyses are resource intensive

– Need effective filters in endangered species assessment process

• Need lots of data

– For intermediate options, little additional data required

– Absolutely critical to assess data and use only acceptable and relevant data

• Decisions less obvious than with risk quotients

– Not if probabilistic bright lines are developed to guide assessors