1. Linking Threats to Assets in Complex Ecological and Socio-Economic Systems: Qualitative Modelling for Tourism Development in North Western Australia Jeffrey Dambacher & Keith Hayes CSIOR Mathematical and Information Sciences Geoff Hosack Oregon State University

2. Risk-Based Management of Natural Resources Depends on models Risk assessment predicated on model linking threats to asset Natural systems are complex Realistic representation of causality is difficult Ecological and socioeconomic systems have feedback Experts versus stakeholder participation Stakeholders typically not involved in model development yet live with the risk Model uncertainty Difficult to address Results conditional on all parameters Typically a narrow field of models considered Threat Asset Model

3. Model Uncertainty Parametric Uncertainty Precise measurements Expert opinion Simulations with plausible parameter space Receives majority of attention and effort in modelling Model Structure Uncertainty Within a model: feedback cycles with opposing sign Between models: different interactions or variables Largely ignored

4. “Model structure uncertainty is the 800 pound gorilla in the middle of the room that no-one talks about” Scott Ferson

5. Methods Causal Graphical Models Bayesian belief network (BBN) Qualitative model (QM) Model uncertainty Qualitative Prediction weights Merging of BBN and QM

6. Bayesian Belief Networks group of nodes connected by directed arrows such that there are no cycles (loops) “child” nodes with incoming arrows are probabilistically dependent on “parents” values X1X1 X2X2 X3X3 X1X1 X2X2 X3X3

7. S IGNED D IGRAPH - α 1,2 +α 2,1 0 0 0 0 0 0 α -α 1,2 α +α 2,1 A = Qualitative Modelling C OMMUNITY M ATRIX

9. PressPerturbationsPressPerturbations

10. Effect to 2 from positive input to 1 a 2,1 a 3,3 a 3,1 a 2,3

11. Krebs et al. (1995) Experiment Hare food addition: hares increased Predator exclosures: hares increased Food and exclosures: hare increase multiplicative Fertilization: vegetation increased, hares neutral ? Hare food addition: hares increased Predator exclosures: hares increased Food and exclosures: hare increase multiplicative Fertilization: vegetation increased, hares neutral ?

12. Positive Input V EG. H ARE P RED. Positive Input V EG. H ARE P RED. ++++0+++++0+ -++-+0-++-+0 +-++-++-++-+ M ODEL A M ODEL B V EG. H ARE P RED. Response V EG. H ARE P RED. V EG. H ARE P RED. Response V EG. H ARE P RED. C RITICAL E XPERIMENT

13. Qualitative Prediction Weights Impact to Coral from Input to Algae 358 feedback cycles + 82, - 276, 194 net Prediction weight W = 194/276 = 0.54 Impact to Coral from Input to Algae 358 feedback cycles + 82, - 276, 194 net Prediction weight W = 194/276 = 0.54 Negative response in coral seems likely.... but how likely?

14. Testing prediction weights (W) through simulations

15. Proportion of simulations with correct sign given by least square fit to non-linear function Sign of each element of adj(–ºA) converted into a probability and incorporated into the CPTs of a BBN via a linear relationship Qualitative predictions to CPT

16. Snowshoe hare example Model A Model B Sym_Adj (-°A)Sym_Adj (-°B) Adj (-°A)WAdj (-°B)W

17. The null model = fully connected community matrix Sym_Adj_Null Adj_Null W_Null

18. Snowshoe hare CPT

19. Snowshoe hare BBN

20. Ningaloo Project Objectives Qualitative models of ecosystem and socio- economic system with tourism impacts in a marine park Complement quantitative modelling Integrative framework for expert and stakeholder knowledge Evaluate management strategies

21. - + Increased nutrients + + + Increased harvest - monitoring management Monitoring and management add two negative feedback cycles Management problem: trophic cascade effects of recreational fishing

22. Coral Reef Model

23. Netica Example

24. Monitoring and management add 1026 feedback cycles Coral Reef Model

25. Thank you