Exploring Expert Knowledge of Forest Succession: An Assessment of Uncertainty and a Comparison with Empirical Data

Chapter

Abstract

Landscape-scale forest succession models are often used to simulate forest dynamics, and the results of these simulations are used to forecast future forest states. Such forecasts are frequently the basis for strategic decisions about forest management policy and planning. However, large gaps in empirical data stemming from insufficient sampling of the landscapes or poorly understood processes often make it difficult to design and apply the models (Kangas and Leskinen 2005). In consequence, expert knowledge is often used to supplement empirical data during the design and application of the models (e.g., Forbis et al. 2006), though its use remains mostly implicit or inadequately explained by researchers.

References

  1. Davies A, Ruddle K (2010) Constructing confidence: rational skepticism and systematic enquiry in local ecological knowledge research. Ecol App 20:880–894CrossRefGoogle Scholar
  2. Diaz-Balteiro L, Romero C (2008) Making forestry decisions with multiple criteria: a review and an assessment. For Ecol Manage 255:3222–3241CrossRefGoogle Scholar
  3. Drescher MD, Perera AH (2010a) A network approach for evaluating and communicating forest change models. J Appl Ecol 47:57–66CrossRefGoogle Scholar
  4. Drescher M, Perera AH (2010b) Comparing two sets of forest cover change knowledge used in forest landscape management planning. J Environ Planning Manage 53:591–613CrossRefGoogle Scholar
  5. Drescher M, Perera AH, Buse LJ et al (2006) Identifying uncertainty in practitioner knowledge of boreal forest succession in Ontario through a workshop approach. Ontario Ministry of Natural Resources, Ontario Forest Research Institute, Sault Ste. Marie. Forest Research Report No 165Google Scholar
  6. Drescher M, Perera AH, Buse LJ et al (2008a) Boreal forest succession in Ontario: An analysis of the knowledge space. Ontario Ministry of Natural Resources, Ontario Forest Research Institute, Sault Ste. Marie. Forest Research Report No. 171Google Scholar
  7. Drescher M, Perera AH, Buse LJ et al (2008b) Uncertainty in expert knowledge of forest succession: A case study from boreal Ontario. For Chron 84:194–209Google Scholar
  8. Failing L, Gregory R, Harstone M (2007) Integrating science and local knowledge in environmental risk management: a decision-focused approach. Ecol Econ 64:47–60CrossRefGoogle Scholar
  9. Fazey I, Fazey JA, Salisbury JG et al (2006) The nature and role of experiential knowledge for environmental conservation. Environ Conserv 33:1–10CrossRefGoogle Scholar
  10. Forbis TA, Provencher L, Frid L, Medlyn G (2006) Great Basin land management planning using ecological modeling. Environ Manage 38:62–83PubMedCrossRefGoogle Scholar
  11. Harary, F. (1969) Graph Theory. Addison-Wesley, ReadingGoogle Scholar
  12. Haenni, R. (2009) Non-additive degrees of belief. In: Huber F, Schmidt-Petri C (eds.) Degrees of belief. Springer Science+Business Media B.V., Dordrecht, pp 121–159CrossRefGoogle Scholar
  13. Kahneman D, Slovik P, Tversky A (eds) (1982) Judgment under uncertainty: heuristics and biases. Cambridge University Press, CambridgeGoogle Scholar
  14. Kangas AS, Kangas J (2004) Probability, possibility and evidence: approaches to consider risk and uncertainty in forestry decision analysis. For Pol Econ 6:169–188CrossRefGoogle Scholar
  15. Kangas J, Leskinen P (2005) Modelling ecological expertise for forest planning calculations—rationale, examples, and pitfalls. J Environ Manage 76:125–133PubMedCrossRefGoogle Scholar
  16. Mackinson S (2001) Integrating local and scientific knowledge: an example in fisheries science. Environ Manage 27:533–545PubMedCrossRefGoogle Scholar
  17. Morgan MG, Henrion M (1990) Uncertainty: a guide to dealing with uncertainty in quantitative risk and policy analysis. Cambridge University Press, New YorkGoogle Scholar
  18. O’Hagan A, Buck CE, Daneshkhah A et al (2006) Uncertain judgments: eliciting experts’ probabilities. Wiley, ChichesterCrossRefGoogle Scholar
  19. Perera AH, Ouellette MR, Cui W et al (2008) BFOLDS 1.0: A spatial simulation model for exploring large scale fire regimes and succession in boreal forest landscapes. Ontario Ministry of Natural Resources, Sault Ste. Marie. Forest Research Report No 152Google Scholar
  20. Smithson M (1999) Conflict aversion: preference for ambiguity vs conflict in sources and evidence. Org Behav Human Decis Processes 79:179–198CrossRefGoogle Scholar
  21. Sterman JD (1994) Learning in and about complex systems. System Dynam Rev 10:291–330CrossRefGoogle Scholar
  22. Theil H (1972) Statistical decomposition analysis: with applications in the social and administrative sciences. North-Holland Publishing Company, AmsterdamGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.School of PlanningUniversity of WaterlooWaterlooCanada
  2. 2.Ontario Forest Research InstituteSault Ste. MarieCanada

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