# Ecological Modeling: Exploring the Effects of Pesticides at the 

# Population Level Andrew R Kanarek EPA/OPP/EFED/EISB 

### EMPM 

### 10/28/14 EMPM 10/28/142 

# Presentation Objectives
• Illustrate modeling approaches to understand the 

### effects of pesticides to populations of non -target organisms • Highlight insights and issues associated with 

### methodologies3 

### • Why populations? • Population models for risk assessment • Ecological factors • Structure and complexity 

### • Approaches • Birds • Bees • Plants 

### • Conclusions • Challenges and Uncertainties 

# Outline4 

# 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 valuation5 

# 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 connectivity6 

# 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 data7 

# Potential applications of a population
level perspective for pesticide RA
• Estimating exposures and characterizing risks of non target 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 options8 

# 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”

- NRC9 “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

Ecology10 

# Types of Population Models Level of Biological

Aggregation

Level of Spatial Aggregation s patially complex s patially uniform identical individuals

unique individuals Network and Meta population Models Demographically Structured Models (MM/IPM)

Scalar Abundance Models (PDE) (ODE/IDE/DE/BD)

Agent- Based Models (IBM/IPS/CA) MICRO MACRO11 Refinement (data & scope) [ precision & realism] 

# Iterative modeling process General (simple) Uncertainties Acceptable? Risk Decision Risk Decision no yes12 

# Case study: Birds TIM - MCnest -> POP MM 1 2 S 1 S 2 F 2 F 1 F 1 F2 S1 S2 n1 t

n2 t = F1 n1 t + F2 n2 t

S 1 n1 t + S2 n2 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 success13 

# Case study: Bees Colony Simulation14 

# Case study: Plants Individual - based, spatially- explicit herbaceous perennial dicot1516 

# 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