Population Models for Ecological

Risk Assessment of Chemicals: The

Past, CREAM, and the Future

CREAM Open Conference

June 2013

Leipzig, DE

R. Pastorok

DRAFT for internal use only – Do Not

Distribute

Fibonacci Sequence in Nature

Fibonacci Sequence

Year 1202:

A page of Leonardo Fibonacci's Liber

Abaci from the Biblioteca Nazionale di

Firenze showing (in box on right) the

Fibonacci sequence with the position

in the sequence labeled in Latin and

Roman numerals and the value in

Hindu-Arabic numerals

Today’s Topics

• Roots of population modeling

— Especially as related to ecological risk assessment

(ERA)

• Key milestones in the development of

population modeling for ERA

• Key issues for the future of chemical risk

assessment???

Birth of Scientific Demography: Graunt 1662

Source: Hutchinson 1978 after Guildhall Museum Library in London

Bill of Mortality:

London,11-18 February 1661

Exponential Population Growth – The

Malthusian Model, 1798

dN dt

rmaxN

• “A struggle for existence inevitably follows from the

high rate at which all organic beings tend to increase”

Darwin’s Struggle for Existence:

Geometrical Ratio of Increase, 1859

• From a single pair of elephants,

“after a period of from 740 to

750 years there would be nearly

19 million elephants alive”

There are Limits to Growth, 1683 -1838 -1925

William Petty

Maximum sustainable population size,

now known as ‘carrying capacity’ K

Logistic population growth

St. Paul Island, Alaska

Al. Lotka

The Roots of Population Modeling in ERA (1)

Year 1800

Year 1900

1859 – Darwin: geometric ratio of increase after Sir William Hale 1677

1925 – Elements of Physical Biology, Lotka

1838-1847 – Logistic population growth model, Verhulst

1662 – Birth of scientific demography, Graunt: Natural and Political

Observations… upon the Bill of Mortality

1798 – Exponential population growth model, Malthus

1202 – Fibonacci sequence for rabbit population growth, Leonardo of Pisa

1683 – Maximum sustainable population size, Petty: Another Essay in Political Arithmetic

1925 – The Biology of Population Growth, Pearl

Population Fluctuations can be Oscillatory

or “Chaotic”

Saether et al. 2002

Source: Reid et al. 2012 http://www.arctic.noaa.gov/reportcard/lemmings.html

Lemmings, far North Pied Flycatcher, Netherlands

Stochasticity or Time Delays in Population

Models Produces ‘Chaotic’ Fluctuations

http://www.simulistics.com/files/examples/popdy/pop5c.gif

Early 1970s: Discrete logistic,

Robert M. May

1948: Differential logistic

equation with time delay,

G.E. Hutchinson

1963 IUCN Red List and 1966 Endangered

Species Act Sparked the Bloom of PVA

The Roots of Population Modeling in ERA (2)

1964-1974 – International Biological Program, systems perspective

Mid 1940s – Population ecology blooms

Year 1980

Year 1940

Year 1960 1960s-70s – Community ecology blooms: theory, size-selective predation, keystone spp.

1975 – Simple-delay logistic model applied to data: chaos theory, May

1950s - von Bertalanffy general system theory; Ricker Spawner-Recruit

1945 – On the use of matrices in certain population mathematics, Leslie

Cellular automata, von Neumann

1972 – JABOWA forest model, Botkin et al.

1979 – Fish cohort growth model, DeAngelis et al.

1964 – Douglas fir forest IBM, Newnham

1963, 1966 – ICUN Red List, U.S. Endangered Species Act

1970 – Use of GIS in regional planning sets the stage for spatial models

1978 – Population viability analysis (PVA) minimum population size, Shaffer

Year 1950

Year 1970

1969 – Metapopulation model, Richard Levins

1974 – Ecological Modelling, Sven Jorgensen (ed.)

1950s – Stochastic population modeling

The Modern Era (1)

1986 – User’s Manual for Ecological Risk Assessment, Barnthouse et al.

1980’s – Development of population models for pesticides+, Grant et al. +

Year 1990

Year 1980

1982 – Ecosystem modeling O’Neill et al. 1982

1980s-90s – Population viability analysis (PVA) blooms

1984 – IBM and chemical stress, Kooijman et al. 1983 – Population model for seabird recovery after oil spill, Samnels & Ladino.

1987 – Fish population models: life history, uncertainty and exploitation, Barnthouse et al.

1990s – ATLSS, Across Trophic Level Simulation System, DeAngelis et al.

1982 – Quasi-extinction as a risk concept, Ginzburg et al.

1990 – Density-dependence form and strength affects extinction risk, Ginzburg et al.

1989 – Structured population models: a tool for linking effects at individual and

population level, Nisbet et al.

1982 – The use of life-tables for evaluating the chronic toxicity of pollutants, Gentile et al.

The Modern Era (2)

Year 2000

Year 1990

2002 – Ecological Modeling in Risk Assessment, Pastorok et al.

2000 – Multiple model types applied to pesticide ERA, Bartelll et al.

1997 – First use of population modeling in Superfund ERA, Munns et al.

1996 – Inferring Ecological Risk from Toxicity Bioassays, Ferson et al.

1998 – Action at Distance metapopulation modeling, Spromberg et al.

1998 – EPA Guidelines for Ecological Risk Assessment

2000 – Stochastic population models with endpoint fraction of K, Iwasa et al.

1990s – Population modeling for pesticide risk assessment, Stark et al.

1996 – IBM for pesticide effects on lumbricids, Baveco and DeRoos

2002 – Compensatory response mitigates pesticide effects, Moe et al.

1991 – Use of RAMAS to estimate ecological risk, Ferson et al.

Counter-intuitive Outcomes

“A risk-analytic summary such as the risks

of population decline, rather than any

simple hazard quotient or scalar estimate

of population growth, is the appropriate

endpoint for ecological risk assessment”

Source: Ferson et al. 1996

- - - - = Control Simulation

___ = Decreased Fecundity 20%

Use of Population Modeling in EPA

Superfund Program

Source: Munns et al. 1997

Spatially Explicit Population Modeling

Expands in the 1990s and 2000s

Source: Charles et al. 2009

Source: Topping et al. 2009

The Modern Era (3)

Year 2010

Year 2000

2009-2013 – CREAM: ODD Protocol, TRACE, Grimm et al.

2012-2013 – MODELINK

2007 – LEMTOX workshop, followed by Thorbek et al. 2009, Ecological Models…

2005 – Individual-Based Modelling and Ecology, Grimm and Railsback

2008 – Risk Assessment Forum Technical Workshop on Population-level ERA, USEPA

2005 – DEBtox models with Leslie matrix, Lopes et al.

2007 – Population-level endpoints, Landis et al.

2008 – Population-level Ecological Risk Assessment, Barnthouse et al.

2012 – First ABM in Superfund ERA: mink population, Luxon et al.

2009 – Integration: AChE inhibition, feeding, growth, and population models, Baldwin 2009

2011 – Population models evaluate residual effects of Exxon Valdez, Monson et al. 2011

2002 – Herring gull extinction risk from DDT, DD matrix model, Nakamaru et al. 2002

2004 – First ABM-landscape model combination: skylark, Topping and Odderskaer 2004

2013 – Assessing Risks to Endangered and Threatened Species from Pesticides, NRC

2005 – Pattern-oriented modeling of agent-based complex systems, Grimm et al.

2008 – Demographic Toxicity, Akcakaya et al.

The Future is Here!

• Essential elements for integrating population

modeling into regulatory programs

— Harmonization / Consistency / Credibility of models

— Simplicity of risk communication

— Risk metrics development (precautionary)

— Population-relevant and cost-effective toxicity tests

— Decision framework

— Guidance documents

The Future is Here!

• Research issues

— Behavior of models relative to reality

» Pattern-oriented modeling

— Density dependence

» Form & strength for focal species

» Toxicant interactions

— Landscape effects and scale

— Comparison of different kinds of models

— Quantification of compounding conservatism

Effects of Compounding Conservatism need

to be Explicit

Source: Topping et al. 2009

Creative Exploration of Risk Metrics is

Needed to Address Today’s Issues

Source: Ilwasa et al. 2000 http://www5.ocn.ne.jp/~hakoyama/hiroshi/pdf/Popul_Ecol_2000.pdf

T = Extinction Time

Metric Type Affects Risk Perception

Source: Applied Biomathematics and Woodlot Alternatives 2003

Neq can be used as an Assessment Endpoint

Source: Hayashi et al. 2009

= Brook trout

= Fathead minnow

Note: Neq, equilibrium population size, is

normalized to Neq for 30 ug/L

Is Neq More Sensitive than λ to Toxic Chemicals?

• Uniform density-dependent model for fathead minnow

• Ricker function (e-bN) for all matrix elements

λ = population growth rate

Neq = equilibrium population size

F1, F2, etc = vital rates

Source: Hayashi et al. 2009

Density Independent Uniform Density Dependent

Are Key Metrics Related?

Source: Hendriks et al. 2005

Maybe Not!

Toxicity Test Design Affects Uncertainty

Source: Barnthouse et al. 1990

Concentration-response functions

and uncertainty bands for the effect

of chronic trifluralin exposure on the

average annual recruitment of Gulf

menhaden Brevoortiupatronus.

(A) Life-cycle test data for the

species of interest

(B) Life-cycle test data for a

surrogate species

(C) 96-h LC50 for a surrogate

species

(D) QSARs

Using Demographic Models to Aid Design of

Toxicity Tests

Life History Traits Sensitive to Toxics Have

Low Elasticity for PGR

Source: Forbes et al. 2010

• 4 species

• 54 data sets

Population Modeling of Various Life History

Types Gives Insight to Population Regulation

X = Fish

= Algae

= Benthic macroinvertebrate

+ = Daphnid

Source: Forbes and Calow 2002

Risk Assessment on the Basis

of Simplified Life Histories Calow et al. (1997)

Criteria for Soil, Sediment or Water Quality

Source: Lin and Meng 2009

Comparison of the cumulative distributions of the concentration at λ = 1 for

various dose–response models

First Spatially-Explicit ABM for Mink used to

derive Sediment Criteria at a Superfund Site

Source: Windward Environmental 2013 (related paper by Luxon et al submitted to IEAM)

Mink Spatially-Explicit ABM (cont.)

Note: Additional detailed steps on habitat acquisition removed for brevity.

Source: Windward Environmental 2013 (related paper by Luxon et al submitted to IEAM)

Acceptable Population Risk Translated to

Sediment Remedial Goal for PCBs – Mink ABM

• <30% decrease in kit

production caused no effect

on population size

• Uncertainty in the PCBs

exposure model translated to

95% CI on the remedial goal

(RG)

Source: Windward Environmental 2013 (related paper by

Luxon et al submitted to IEAM)

Zero Effect on

Population Size

Kit Production

NOEL

Tissue Residue

NOEL

Sediment

Remedial Goal

Mean =

85 ug/kg

‘Standard’ Models and Decision Frameworks

for Modeling are Needed

• MODELINK

• Roskilde workshop on Integrating Population

Modeling in Ecological Risk Assessment

• Decision framework for pesticide risk assessment

based on population modeling

Flexibility is Essential

Source: Kapustka et al. (in prep.) for Intrinsik, Inc

Source: Pastorok 2000 SETAC meeting based on DeAngelis et al. 1998

Linking Population Models – The ATLSS

Approach by DeAngelis et al.

Various Model Types for a Pesticide (Diquat

Dibromide) Case Study

Daphnia IBM – based on Hallam et al. 1990

Source: Bartell et al. 2000 and DeAngelis et al. 1989

Bluegill Matrix Model

CASM Ecosystem Model

What Kind of Model and How Complex?

“The issue is not model complexity or population versus

ecosystem models but identifying the necessary and

sufficient model structure and processes that produce

useful and reliable estimates for specific risk assessments

and management decisions based on risk”

Source: http://www.nsf.gov/news/mmg/media/images/daphnia4_h.jpg (left)

http://www.outdooralabama.com/fishing/images/Bluegill400.jpg (middle)

Adapted from DeAngelis et al. 1989 (right)

Climate Change: All Bets are Off

Conclusions

• Population modeling will undoubtedly play a major

role in chemical risk assessments in the near future

• We’ve come a long ways, but significant issues in

risk expression and communication remain

• The effects of climate change will pose new

challenges for risk assessors

• CREAM has already contributed a great deal

• We can look forward to significant ongoing

scientific contributions by CREAM fellows

June 24,2013

Integral Consulting Inc. 1

BIBLIOGRAPHY1

Population Models for Ecological Risk Assessment of Chemicals: The Past, CREAM, and the Future

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Applied Biomathematics and Woodlot Alternatives. 2003. A stochastic population model incorporating PCB effects for wood frogs (Rana sylvatica) breeding in vernal pools associated with the Housatonic River - Pittsfield to Lenoxdale, Massachusetts. Report to U.S. Environmental Protection Agency, Region 1, Boston, MA. 43 pp.

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1 This bibliography is based on the professional experiences of R.A. Pastorok from 1972 to present and is intended to accompany a keynote presentation titled Population Models for Ecological Risk Assessment of Chemicals: The Past, CREAM, and the Future delivered on June 11, 2013 at the CREAM Open Conference in Leipzig, Germany. This bibliography is not comprehensive and is based mainly on references used for the presentation.

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