Parameterising Bayesian Networks:

A Case Study in Ecological Risk Assessment

Carmel A. PollinoWater Studies Centre

Monash University

Owen Woodberry, Ann Nicholson, Kevin Korb

Computer Science and Software EngineeringMonash University

Ecological Risk Assessment Process for improving environmental

management to protect ECOLOGICAL values/assets

Focus: Managing physical, chemical and biological processes to protect ecological endpoints

Ecological sustainability/integrity poorly dealt with in many integrative analyses (not just environmental sustainability – human focus)

Ecological Risk Assessment

Problem Formulation What are the environmental values of the

system? What is it you want to protect? Conceptual Model

Risk Analysis (Risk = Likelihood x Consequence)

Risk Characterisation Risk Management

Ecological Risk Assessment Stakeholder engagement (adoption)

technical non-technical

Modular (multiple stressors - multiple models in single framework)

Promotion of Iterative and Adaptive approaches to environmental management Inform future monitoring and targeted research

needs

Models required for multiple stressor / hazard problems (complex issues)

Models need to: incorporate information with high

uncertainties incorporate disparate information be able to prioritise risks to

endpoint be applicable in risk management

Modelling Complex Issues (ERA)

Address Uncertainty and Complexity Increasingly being used in ecological applications

Modular DSS Complex system composed of simpler parts (or multiple

models) Inputs: expert opinion, literature, data, other

models Predictions able to be tested (test robustness of

predictions) and models easily updated Models simple (pragmatic), transparent and easily

interpreted (adoption into risk management)

Bayesian Networks

Two componentsStructure (Graph Theory)Probabilities (Probability Theory)

Links in graph represent relationships between variables (as with a conceptual model)

Probabilistic relationships (strengths) between variables

Bayesian Networks

ERA Case Study

Goulburn Catchment, Victoria, Australia

Important irrigation Area Important habitat for endangered

and threatened native fish species Problem Formulation:

Reduced abundance and diversity of native fish in the Goulburn Catchment, Victoria, Australia

Adoption: Goulburn Murray Water

Goulburn Broken Catchment

Lk. Eildon to Trawoolbarrie r, tem pera ture change, flowa lte ra tions, changes to hab itat, non-native fish

Lk. Nagam bie/Goulburn Weirbarrie r, irriga tion run -o ff, flowa lte ra tions, changes to hab itat, non-native fish

Murchison to Murray Riverirriga tion run-o ff, poten tia l saline flow s, suspended sed im ents, flowa lte ra tions, changes to hab itat, non-native fish

Lk. Eildon to Trawoolbarrie r, tem pera ture change, flowa lte ra tions, changes to hab itat, non-native fish

Lk. Nagam bie/Goulburn Weirbarrie r, irriga tion run -o ff, flowa lte ra tions, changes to hab itat, non-native fish

Murchison to Murray Riverirriga tion run-o ff, poten tia l saline flow s, suspended sed im ents, flowa lte ra tions, changes to hab itat, non-native fish

Model Structure

Expert (Workshop 1) and Automated

IN-STREAM STRUCTUTAL

HABITAT

WATER QUALITY

FLOW

REDUCED NATIVE FISH

ABUNDANCE AND DIVERSITY

BARRIERSFLOODPLAIN GEOMORPHOLOGY SUBSTRATERIPARIAN VEGETATIONINDUSTRIAL FISH FARMSSEPTIC, SEWAGEIRRIGATION

DRAINS

TOXICANTS TEMPERATUREORGANIC CARBON

NUTRIENTS

NH3 / pH

DISSOLVED OXYGEN

TURBIDITY / SEDIMENT

FLOW REGIME

INCREASE

(SUMMER)

DECREASE

(WINTER)

REDUCED POOL

FORMATION

REDUCED NURSERY HABITAT

REDUCED RECRUITMENT

REDUCED FLOODING

REDUCED FORAGING

REDUCED SPAWNING

STRATIFICATION

ANOXIA

SALINITY

MIGRATION

FRAGMENTATION & ISOLATION OF

POPULATIONS

REDUCED BACKWATERSREDUCED

MACROINVERTEBRATES & ZOOBENTHOS

HABITAT STRUCTURE –

AVAILABILITY & DIVERSITY

BIOLOGICAL INTERACTIONS - ALIEN & TRANSLOCATED SPP.

PREDATION

DISEASE

COMPETITION

SNAGS

HIGH ALGAL BIOMASS

Fish Network-5 sub-networksWater QualityFlowStructural HabitatBiological Interactions

-2 query nodesFish AbundanceFish Diversity

-23 sites 6 reaches

-2 temporal scales 1 and 5 year changes

Model Parameterisation

Expert (Workshop 2) and Automated

Parameterisation

Expert Elicitation used to parameterise aspects of model not represented by data (lack of variability in data set)

Iterative process of updating expert derived probabilities (prior probabilities) using data (automated process)

Needed this process to be supervised

KEBN

BUT For parameterisation of a BN, there are no detailed methodologies for combining: qualitative and quantitative derived

probabilities expert elicitation and automated

discovery Develop KEBN – formalised process

for parameterisation

Parameter Estimation KEBN has 3 “paths”:

(1) Elicited from experts (2) Learned from data

Routine Monitoring Data Targeted Research

(3) Generated from a combination of sources

Evaluation process essential to assess parameterisation process

Quantitative KEBN Spiral

Fish Network

Parameter Estimation

Data variables: initially given uniform distribution

Sparse or no data variables: elicited. Experts were asked to report confidence

in estimates (equivalent sample size – ESS), to be used by data learning/training method: EM (Expectation Maximisation) algorithm.

Parameter Estimation

Parameterisation after learning compared to original in “Assess Degree of Change” process.

Identify where large changes occur Changes focus on where there are

discrepancies in expert elicited probabilities and data derived probabilities.

Model Evaluation

Quantitative Sensitivity Analyses Predictive Accuracy

Qualitative Expert Real data vs. Model Prediction

Quantitative Evaluation Sensitivity Analysis

Identify if a variable is either too sensitive or insensitive to other variables in particular contexts

Identify errors in either BN structure or Conditional Probabilities

Identify knowledge gaps

Case study: Sensitivity analyses found that when water quality is low, other variables have less impact on Future Abundance. This agrees with expert’s understanding.

Quantitative EvaluationSensitivity to Findings

P (Future Abundance) = High

0.0 0.2 0.4 0.6 0.8 1.0

Var

iabl

e (R

ange

0 to

1)

Future Diversity

Water Quality Habitat

Biological Potential

Hydraulic Habitat

Barrier

Temperature Modification

Quantitative Evaluation

Predictive Accuracy Data split (80% training, 20% testing) Error Rate (Future Abundance) = 5.8%

Limited data Lack of variability in abundance of fish

communities throughout catchment (mostly low – poor condition)

0

20

40

60

80

100

120

GEi

GA

GY G

TG

NG

Mu

GS

GM

cG

UG

Ec

Rel

ativ

e ab

un

dan

ce

0

0.2

0.4

0.6

0.8

1

P(N

ativ

e F

ish

) =

Hig

h

Figure 5: Relative Abundance Data (left axis - bars) versus BN Model Predictions (right axis - line) for Sites in the Goulburn Main Channel.

Qualitative Evaluation

Test aspects of network not represented in data set Conditions required for ‘healthy’ native

fish communities

Robustness of network Fish Ecologists Environmental Managers / Natural

Resource Managers

Expert Evaluation

Risk Management framework

Prioritise risks

Identify knowledge gaps

Allocate resources for:

Further monitoring and research

Risk mitigation

GBC Bayesian Networks

Adaptive Management framework

Monitor and Update

Test assumptions in model

Adopt and learn as:

New information becomes available

New situations arise

GBC Bayesian Networks

Model specific for different

communities of fish in Murray-

Darling Basin Currently biased towards Low Flow Specialists

Model represent dynamic changes

(temporal changes)

Future Improvements

Acknowledgements

National Program for Sustainable Irrigation

Goulburn Murray Water Stakeholders