Review of the Growing Modeling Toolkit

1. Review of the Growing Modeling Toolkit Bruce G. Marcot USDA Forest Service Portland, Oregon USA

2. Marcot, B. G. 2006. Review of the growing modeling toolkit: special session. Presented 5 December 2006 at: Habitat and Habitat Supply Modeling Practitioner's Workshop, 5-7 December 2006. Ministry of Forests, Research Branch, British Columbia, Canada. [Invited]. Chase, B.C. Canada.

3. Models, Models, Models

4. Models, Models, Models

5. Models, Models, Models

6. Models, Models, Models

7. Models, Models, Models

8. Lots of models to choose from !

9. measurable surrogate inference ? biodiversity parameter

10. Influence diagrams

11. Influence diagrams – “Concept mapping” “Concept diagrams” “Cognitive Map” “Mental Map, Mind Map” http://intraspec.ca/cogmap.php http://www.cs.joensuu.fi/~marjomaa/Knowledge_Representation/doc/Knowledge_Representation-56.htm

12. Influence diagrams Source: Marcot, B. G., et al. 2001. Forest Ecology and Management 153(1-3):29-42.

13. Influence diagrams – • Mindjet MindManager Pro • Inspiration • Personal Brain • Netica

14. Mindjet MindManager Prohttp://www.mindjet.com

15. Inspirationhttp://www.inspiration.com/

16. Personal Brianhttp://www.thebrain.com/

17. Neticahttp://www.norsys.com

18. IHMC CmapToolshttp://cmap.ihmc.us/

19. Building influence diagrams – • empirical data • expert judgment / opinion • “knowledge engineering” • peer review • expert paneling (e.g., Delphi) • combination

20. From influence diagram … to models galore !

21. Path regression…Quality Deer Management (QDM) Source: Woods, G. R., D. C. Guynn, W. E. Hammitt, and M. E. Patterson. 1996. Determinants of participant satisfaction with quality deer management. Wildl. Soc. Bull. 24(2):318-324.

22. Source: Hudson, R. J. 1995. Paths to conservation. Pp. 318-322 in: J. A. Bissonette and P. R. Krausman, ed. Integrating people and wildlife for a sustainable future. The Wildlife Society, Bethesda, Maryland. 715 pp. Process model – STELLA http://www.iseesystems.com/

23. Process model – STELLA http://www.iseesystems.com/

24. neural network

25. Types of Models • Analytic and numerical population models • Leslie matrix life tables • Genetic models of inbreeding, genetic drift • Simulation models • GIS-based models • Spatially explicit, individual-based models • Knowledge-based (expert) models • Expert systems • Other expert-based models

26. Types of Models • Statistical empirical models • Correlation, multivariate models • Regression tree, classification tree

27. Regression tree – 3 viability risk levels

28. Types of Models • Statistical empirical models • Correlation, multivariate models • Structural equation models (SEMs) – a modeling procedure

29. Structural Equation Models (SEMs) • A way to formalize and construct relationships among variables. • Observational data • A generalization of many statistical techniques • Regression, discriminant analysis, canonical correlation, factor analysis • Differentiates among direct relationships, indirect causal relationships, spurious relationships, & association without causation

30. Structural Equation Models (SEMs) • Create the model structure as an influence diagram … including unexplained variance. • Expand the latent variables into their components … e.g., “habitat” into measurable veg. variables. • Compute regression weights for each variable. • Partial correlation analysis • Bayesian conditional probabilities • Estimate measurement errors of each component variable. • This depicts the amount of uncertainty in the habitat-species relations represented in the model.

31. Structural Equation Models (SEMs) • The final SEM model depicts: • Specific variable relations • Degree of uncertainty of those variables • The relations among the variables • SEM tests the hypothesized underlying causal relations among variables … by analyzing their covariance structure. • Goodness-of-fit tests of congruence between the variance-covariance matrix derived from observational data … to that suggested by the hypothetical causal structure of the model (the predicted moment matrix).

32. Structural Equation Models (SEMs) • Methods of estimation for the goodness-of-fit tests: • MLE (maximum likelihood estimation), for multivariate normal data & N>200 samples • WLS (weighted least squares; asymptotically distribution free) methods, for continuous but nonnormal data • Polychoric correlation analysis, for ordinal variables (computes correlation between unobserved normal variables & then uses WLS methods) • Software for doing SEM: • LISREL, EQS, AMOS, CALIS, SYSTAT

33. Statistical empirical models • Post-hoc pattern analysis • Knowledge discovery • Rule induction (problems w overfitting data) • Data mining (association analysis) • Text mining

34. Types of Models • Statistical empirical models • Post-hoc pattern analysis • Knowledge discovery • Rule induction • Data mining • Text mining

35. Text Mining • Biodiversity • biocomplexity • ecological complexity • ecological functions • disturbance regimes • ecosystem resilience • stability, resistance • ecological integrity • ecosystem services • sustainability …. etc.

36. Text Mining • Biodiversity • biocomplexity • ecological complexity • ecological functions • disturbance regimes • ecosystem resilience • stability, resistance • ecological integrity • ecosystem services • sustainability …. etc. • >13,000 references • EndNote biblio. database • “concept proximity analysis”

37. Text mining – Concept map Marcot, B. G. In revision. Biodiversity and the lexicon zoo. Forest Ecology and Management

38. http://www.kartoo.com

39. http://www.kartoo.com

40. Data mining Information mapping - topographical maps - closeness maps - interactive trees - concept clustering - (many others)

41. Decision-Support Models

42. Decision-Support Models • Many tools • Bayesian statistics, Bayesian belief networks • Data and text mining • Decision tree analysis • Expert systems • Fuzzy logic, fuzzy set theory • Genetic algorithms • Rule and network induction • Neural networks • Reliability analyses • Landscape simulators

43. Bayesian Belief Network Model

44. Influence Diagrams as Bayesian Belief Network Model

45. Influence Diagrams as Bayesian Belief Network Model

46. sensitivity analysis • identifies most influential factors • identifies degree of influence

47. Influence Diagrams as Bayesian Belief Network Model

48. Node Mutual Variance of ---- Info Beliefs Caves or mines 0.02902 0.0069284 Lg snags or trees 0.00953 0.0023053 Cliffs 0.00599 0.0014514 Forest edges 0.00599 0.0014514 Bridges, buildings 0.00063 0.0001543 Boulders 0.00002 0.0000038 Influence Diagrams as Bayesian Belief Network Model

49. Fuzzy logic model – NetWeaverPenn St. Univ. fuzzy logic – (NetWeaver, Penn St. Univ.)

50. fzcalc.exe http://www.vspdecision.uni-hannover.de/ Fuzzy arithmetic