FP236 Risk based Bayesian networks as a tool for determining environmental conditions for disaster restoration W.G. Landis, K. Kolb Ayre, Institute of Environmental Toxicology, Huxley College of the Environment, Western Washington University, 516 High St., ES518, MS9180, Bellingham, WA 98225-9180. http://www.wwu.edu/toxicology/

After a natural or human derived disaster restoration quickly becomes a priority. In many instances there is a desire to instigate restoration activities without a quantitative framework from which to make decisions. Often the ecological services provided by the impacted region are critical to the economic and cultural systems. Constructing such a framework can be problematic. Often there is a lack of data on conditions prior to the event, including interactions, dynamics, and spatial interactions. Qualitative information, expert opinion, and local anecdotal information may be the best data available. Restoration involves both deterministic and stochastic processes, so any model framework should innately incorporate each aspect. There will also be clear ecological services that will be targets for the restoration.

In our research Bayesian networks based upon the relative risk model for regional risk assessment have demonstrated promise. Bayesian networks (BNs) can easily incorporate a variety of kinds of knowledge, reflect cause-effect relationships while incorporating stochastic processes. We have constructed Bayesian networks for two systems, the Interior Northwest Landscape Analysis System (INLAS) forest of eastern Oregon and the infection of Colorado River cutthroat trout by whirling disease in the southwestern United States. In the INLAS system, fires, disease and pests are threats. In both systems the utility of the risk-derived Bayesian approach in identifying impacts and restoration targets characteristics for restoration is presented. Of particular importance is the flexibility of the BNs in incorporating information, the ability to examine “what if” scenarios, and the presentation of output as a probability.

Relative Risk Method-Conceptual Model for Disasters

Summary

We have found that Bayesian networks are an excellent framework for calculating relative risks . Bayesian networks (BNs) are well understood mathematically, have been applied to a number of applications and software is available for straightforward calculation and sensitivity analysis. BNs have several characteristics that are useful in the context of regional risk assessment.

1.BNs can combine different types of data including model predictions and expert judgment.

2.The structure of a BN reflects the causal pathways inherent in the model.

3.Uncertainty is inherently reflected in the probability distributions that are used to construct the BN.

4. Sensitivity analysis of the model is straightforward and provides information on the variables that are most influential to the calculation.

5. BNs are easily updated when new information or knowledge becomes available.

IntroductionAt this year's SETAC meeting our goal is to demonstrate the utility of ecological risk assessment as a general tool for environmental management. This is one of three presentations demonstrating the various uses. This poster discusses the use of risk assessment as one part of an approach for examining risks in case of a disaster and then plotting remediation-restoration activities. Sources of

StressorsHabitats

Likelihood of Impacts

Exposures Effects

Location of Multiple Sources

FireFloodVolcanic eruptionTornadoToxic chemical spillsOil spillsDiseaseExplosionSpecies invasions

Location of Multiple Affected Entities

UrbanLow income housingHigh density housingLow density housingCommercial areasIndustrialResidentialAgriculturalMarineFreshwaterForestEstuariesTransportation corridorsMedical facilities

Location of Multiple Responses

Human welfareEpidemicsEconomic stoppageHousingWater quantityWater qualityAir qualityFisheriesForest productsAgricultural productivityManaged species (T and E, Redbook etc.)Landscape alteration

Relative Risk Method-Bayesian Networks

It is possible to construct a conceptual model for disaster scenarios using the basic format of the relative risk model for multiple stressors within a landscape.

In 1999 the Olympic pipeline leaked and caused a major explosion in Bellingham,WA. Three people were killed and damage was done to a variety of ecological services.

Disease

Forestry and Fire—An Example

Upper Grande Ronde

Watershed

Major Streams

Cold Forest

Moist Forest

Warm Dry Forest

Riparian

Unknown

Grassland

Habitats

INLAS Study AreaWallowa-Whitman National Forest

The first scenario is the INLAS area of the Wallowa-Whitman National Forest in eastern Oregon. This area has an extensive database and has been the subject of prior risk assessments.

The area is managed for multiple uses, from recreation to salmon protection. A conceptual model was developed by Anderson (2007) and this was used as the basis for the BN for just the Upper Grande Ronde (UGR) Watershed. The BN software is Netica produced by Norsys Software Corp (http://www.norsys.com/)

Sources

Habitats

Impacts

Original RRM Model UGR Bayesian Network

The UGR BN is shown is for current conditions where there is already a moderate to high probability of fire (top left box). The probabilities of impacts are presented in the bottom row. Note that Salmon are the endpoint at greatest risk.

© Central Oregon Intergovernmental Council

Now let us add a fire to the scenario. One that is within the bounds of the model but that reduces the uncertainty of a fire at the highest level.

Conclusions

It is possible to construct models and scenarios to allow the exploration of potential effects to an ecological system. Data on the ecological system and a list of critical environmental services that should be managed, defined by managers and stakeholders, must be available.It is possible to manage landscapes for a variety of inputs. The whirling disease model covers parts of five states in the US Southwest.

The BN approach allows a variety of scenarios to be demonstrated very quickly and provides a visual indication of how the probability for the endpoints change with the different stressor frequencies.

Original Condition Fire

The addition of the fire scenario allows the rapid visualization of which endpoints are now at risk within the UGR watershed. Mitigation efforts can now be targeted to those habitats and endpoints that are considered the highest priority. One of the critical features of BNs is that they are easily updated as data become available. As monitoring information becomes available it will be possible to change the model and recalculate the expected outcomes rapidly.

The new scenario demonstrates an increase in risk to salmon, Historic range of variability for fire and other endpoints. Note that some habitats and endpoints change very little from the original scenario.

We have also constructed a risk assessment BN model for whirling disease in the Southwestern United States. The model can be used in a similar fashion as barriers are removed or the incidence of infection increases.

Study Area

BN Model for Whirling Disease to Cutthroat Trout

This research was supported by grant 1-56232 from Montana State University-Bozeman and grant 1-54086 from the U.S. Forest Service