Ecological Modeling:

Exploring the Effects of 

Pesticides at the 

Population Level

Andrew R Kanarek

EPA/OPP/EFED/EISB

EMPM

10/28/14 EMPM 10/28/14
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Presentation Objectives

• Illustrate modeling approaches to understand the 

effects of pesticides to populations of non-target 

organisms

• Highlight insights and issues associated with 

methodologies
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• Why populations?

• Population models for 

risk assessment

• Ecological factors

• Structure and 

complexity

• Approaches

• Birds

• Bees

• Plants

• Conclusions

• Challenges and 

Uncertainties

Outline
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Populations as Assessment Endpoints

• Responsive to intent of legislation and policy

• “Because the ESA is concerned with species or listed units 

within named species, the effects of pesticides must be 

expressed at the population level” 

(Committee on ERA under FIFRA and ESA, NRC, 2013)

• Ecologically relevant context

• “Given that most of the services under 

the selected specific protection goals 

are performed by populations or 

groups of populations, development 

of appropriate population models for 

use in risk assessment is needed” 

(EFSA PPR Panel, 2010)

• Societal valuation
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Utility of a population-level perspective

• Identify how risks to individuals scale up to impact higher 

levels of biological organization

• Allow for understanding of interdependent biological 

processes

• Interpret and relate sublethal measurement endpoints to 

assessment endpoints

• Inform the design of field studies

• e.g., timing, scale, replication, duration

• Assist interpretation of field studies

• Account for observed variability in endpoints (e.g., season)

• Account for other non-chemical factors (e.g., weather)

• Incorporate data from laboratory and field studies

• Integrate chemical and non-chemical stressors

• Allow for consideration of effects that are difficult to 

measure

• e.g., density dependence, metapopulation connectivity
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Features of a population model

• Account for major factors that determine population 

dynamics

• Intra-specific and inter-specific interactions

• Quantitatively link various measurement endpoints to 

assessment endpoints

• biomass, density, growth rate, extinction probability

• Readily parameterized, using existing biological and 

pesticide-specific data

• Adaptable to pesticide uses that differ across regions

• Account for geographic variation in use pattern, crops, 

climate, landscape

• Scientifically defensible, transparent, well documented, 

publically available

• Model predictions are consistent with empirical data

• Sensitivity and uncertainty analysis

• Evaluation relative to empirical data
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Potential applications of a populationlevel perspective for pesticide RA

• Estimating exposures and characterizing risks of nontarget species to pesticides when field studies are not 

available

• Evaluating results of laboratory and field studies

• Conducting a sensitivity analysis of lethal and sublethal 

measurement endpoints to determine which endpoints 

are critical to the survival of a population

• Used to inform toxicity test study designs or regulatory 

thresholds for risk assessments

• Exploring alternative pesticide application scenarios 

(e.g., lower rates, different times) that may be 

considered as risk mitigation options
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Population models require three basic 

inputs

• Changes in survival or reproduction as a function of 

pesticide concentration

• Exposure estimates of pesticide concentration over time 

and space

• Demographic and life history information

• There are a variety of population models, and the 

choice will depend on the data available

• Population structure, density dependence, landscape 

structure, spatio-temporal scales, multiple stressors and 

tox data, life-history characteristics, species interactions

“in the absence of detailed demographic information, it is reasonable to 

use simple generic models that characterize the life history of a group of 

species to estimate the effects of a pesticide on a given species”

-NRC
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“It is a common fallacy to confuse scientists’ models of reality with 

reality itself. A model is a map. A map is not the territory it describes.” 

-Richard Casement

Generality

Realism Precision

Theoretical 

Ecology

Levins 1966

Ecological 

Modeling

Mathematical 

Ecology
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Types of Population Models

Level of Biological 

Aggregation

Level of Spatial Aggregation spatially 

complex

spatially 

uniform

identical 

individuals

unique 

individuals

Network and Metapopulation Models

Demographically Structured

Models (MM/IPM)

Scalar Abundance Models

(PDE) (ODE/IDE/DE/BD)

Agent-Based Models 

(IBM/IPS/CA)

MICRO

MACRO
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Refinement

(data & scope)

[precision & realism]

Iterative modeling process

General 

(simple)

Uncertainties

Acceptable?

Risk

Decision

Risk

Decision

no

yes
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Case study: Birds

TIM-MCnest -> POP MM 1 2

S1

S2

F2

F1

F1 F2

S1 S2

n1 t

n2 t

=

F1n1 t + F2n2 t

S1n1 t + S2n2 t

=

n1 t+1

n2 t+1

n1 t+1 + n2(t+1)

n1(t) + n2(t)

=

N(t+1)

N(t)

= ΔN = λ

TIM: exposure profiles and mortality 

(time of death) for individual birds

MCnest: projects annual avian 

reproductive success
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Case study: Bees

Colony Simulation
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Case study: Plants

Individual-based, spatially-explicit 

herbaceous perennial dicot
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Conclusions

• Overall goal is to minimize false positive/false 

negative ERA-driven decision errors

• “the effects must be estimated at a range of 

concentrations that includes all values that the 

populations being assessed might plausibly 

experience” - NRC

• “population models provide a framework for 

incorporating baseline conditions and 

projected future cumulative effects into an 

effects analysis” – NRC

• Population models will help meet the goals of 

both FIFRA and the ESA