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What Is Expert Knowledge, How Is Such Knowledge Gathered, and How Do We Use It to Address Questions in Landscape Ecology?

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Book cover Expert Knowledge and Its Application in Landscape Ecology

Abstract

Expert knowledge plays an integral role in applied ecology and conservation (Burgman 2005). Environmental systems are characterized by complex dynamics, multiple drivers, and a paucity of data (Carpenter 2002). Action is often required before uncertainties can be resolved. Where empirical data are scarce or unavailable, expert knowledge is often regarded as the best or only source of information (Sutherland 2006; Kuhnert et al. 2010). Experts may be called upon to provide input for all stages of the modeling and management process, and specifically to inform the definition and structuring of the problem (Cowling and Pressey 2003; Sutherland et al. 2008), to inform the selection of data or variables, model structures, and assumptions about functional relationships between variables (Pearce et al. 2001; Czembor and Vesk 2009), and to inform the analysis of data, estimation of parameters, interpretation of results, and the characterization of uncertainty (Alho and Kangas 1997; Martin et al. 2005).

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References

  • Al-Awadhi SA, Garthwaite PH (1998) An elicitation method for multivariate normal distributions. Commun Stat A-Theor 27:1123–1142

    Article  Google Scholar 

  • Alho JM, Kangas J (1997) Analyzing uncertainties in experts’ opinions of forest plan performance. For Sci 43:521–528

    Google Scholar 

  • Anderson EL, Hattis D (1999) A. Uncertainty and variability. Risk Anal 19:47–49

    Google Scholar 

  • Anderson JL (1998) Embracing uncertainty: the interface of Bayesian statistics and cognitive psychology. Ecol Soc 2(1), article 2. Available from http://www.consecol.org/vol2/iss1/art2/ (accessed May 2011)

    Google Scholar 

  • Armstrong JS (ed) (2001) Principles of forecasting: a handbook for researchers and practitioners. Kluwer Academic Publishers, Norwell

    Google Scholar 

  • Armstrong JS (2006) Findings from evidence-based forecasting: methods for reducing forecast error. Int J Forecasting 22:583–598

    Article  Google Scholar 

  • Arnott D (2006) Cognitive biases and decision support systems development: a design science approach. Inform Syst J 16:55–78

    Article  Google Scholar 

  • Ausden M, Sutherland WJ, James R (2001) The effects of flooding lowland wet grassland on soil macroinvertebrate prey of breeding wading birds. J Appl Ecol 38:320–338

    Article  Google Scholar 

  • Ayyub BM (2001) Elicitation of expert opinions for uncertainty and risks. CRC Press, Boca Raton

    Book  Google Scholar 

  • Baran N (2000) Effective survey methods for detecting plants. MSc Thesis. University of Melbourne, Melbourne

    Google Scholar 

  • Bates JM, Granger CWJ (1969) The combination of forecasts. Oper Res Q 20:451–468

    Article  Google Scholar 

  • Beyth-Marom R (1982) How probable is probable? A numerical translation of verbal probability expressions. J Forecasting 1:257–269

    Article  Google Scholar 

  • Booker JM, McNamara LA (2004) Solving black box computation problems using expert knowledge theory and methods. Reliab Eng Syst Safe 85:331–340

    Article  Google Scholar 

  • Bransford JD, Brown AL, Cocking RR (2000) How people learn: brain, mind, experience and school. National Academy Press, Washington

    Google Scholar 

  • Brun W, Teigen KH (1988) Verbal probabilities: ambiguous, context-dependent, or both. Organ Behav Hum Dec 41:390–404

    Article  Google Scholar 

  • Budescu, DV, Wallsten TS (1985) Consistency in interpretation of probabilistic phrases. Organ Behav Hum Dec 36:391–405

    Article  Google Scholar 

  • Burgman MA (2000) Population viability analysis for bird conservation: prediction, heuristics, monitoring and psychology. Emu 100:347–353

    Article  Google Scholar 

  • Burgman MA (2005) Risks and decisions for conservation and environmental management. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Burgman MA, Carr A, Godden L et al (2011) Redefining expertise and improving ecological judgement. Conserv Lett 4:81–87

    Article  Google Scholar 

  • Camerer CF, Johnson EJ (1997) The process-performance paradox in expert judgment: how can experts know so much and predict so badly? In: Goldstein WM, Hogarth RM (eds) Research on judgment and decision making: currents, connections and controversies. Cambridge University Press, Cambridge, pp 342–364

    Google Scholar 

  • Campbell LM (2002) Science and sustainable use: views of marine turtle conservation experts. Ecol Appl 12:1229–1246

    Article  Google Scholar 

  • Carpenter SR (2002) Ecological futures: building an ecology of the long now. Ecology 83:2069–2083

    Google Scholar 

  • Chase WG, Simon HA (1973) The mind’s eye in chess. In: Chase WG (ed) Visual information processing. Academic Press, New York, pp 215–281

    Google Scholar 

  • Chi MTH (2006) Two approaches to the study of experts’ characteristics. In: Ericsson KA, Charness N, Feltovich PJ, Hoffman, RR (eds) The Cambridge handbook of expertise and expert performance. Cambridge University Press, New York, pp 21–30

    Google Scholar 

  • Christen JA, Nakamura M (2000) On the analysis of accumulation curves. Biometrics 56:748–754

    Article  PubMed  CAS  Google Scholar 

  • Christensen-Szalanski JJJ, Bushyhead JB (1981) Physicians’ use of probabilistic information in a real clinical setting. J Exp Psychol Human Percept Perform 7:125–126

    Article  Google Scholar 

  • Chuenpagdee R, Morgan LE, Maxwell SM et al (2003) Shifting gears: assessing collateral impacts of fishing methods in the U.S. waters.  Front Ecol Environ 10:517–524

    Article  Google Scholar 

  • Clemen RT (1989) Combining forecasts: a review and annotated bibliography. Int J Forecasting 5:559–583

    Article  Google Scholar 

  • Clemen RT, Winkler RL (1985) Limits for the precision and value of information from dependent sources. Oper Res 33:427–442

    Article  Google Scholar 

  • Clemen RT, Winkler RL (1999) Combining probability distributions from experts in risk analysis. Risk Anal 19:187–203

    Google Scholar 

  • Cohen MJ, Carstenn S, Lane CR (2004) Floristic quality indices for biotic assessment of depressional marsh condition in Florida. Ecol Appl 14:784–794

    Article  Google Scholar 

  • Collins, HM, Evans R (2007) Rethinking expertise. University of Chicago Press, Chicago

    Google Scholar 

  • Cooke RM (1991) Experts in uncertainty: opinion and subjective probability in science. Oxford University Press, New York

    Google Scholar 

  • Cooke RM, Goossens LHJ (2000) Procedures guide for structured expert judgement in accident consequence modelling. Radiat Prot Dosim 90:303–309

    Google Scholar 

  • Cosmides L, Tooby J (1996) Are humans good intuitive statisticians after all? Rethinking some conclusions from the literature on judgment under uncertainty. Cognition 58:1–73

    Article  Google Scholar 

  • Cowling RM, Pressey RL (2003) Introduction to systematic conservation planning in the Cape Floristic Region. Biol Conserv 112:1–13

    Article  Google Scholar 

  • Crance JHBR (1987) Guidelines for using the Delphi technique to develop habitat suitability index curves. U.S. Fish Wildl Serv., Washington. Biological Report#82(10.134)

    Google Scholar 

  • Crome FHJ, Thomas MR, Moore LA (1996) A novel Bayesian approach to assessing impacts of rain forest logging. Ecol Appl 6:1104–1123

    Article  Google Scholar 

  • Currie F, Elliott G (1997) Forests and birds: a guide to managing forests for rare birds. Forestry Authority, Cambridge, and Royal Society for the Protection of Birds, Sandy

    Google Scholar 

  • Czembor CA, Vesk PA (2009) Incorporating between-expert uncertainty into state-and-transition simulation models for forest restoration. For Ecol Manage 259:165–175

    Article  Google Scholar 

  • Dawes RM, Kagan J (1988) Rational choice in an uncertain world. Harcourt Brace Jovanovich, San Diego

    Google Scholar 

  • Dickinson JP (1973) Some statistical results in combination of forecasts. Oper Res Q 24:253–260

    Article  Google Scholar 

  • Dickinson JP (1975) Some comments on combination of forecasts. Oper Res Q 26:205–210

    Article  Google Scholar 

  • Drescher, MA. Perera AH, Buse LJ et al (2008) Uncertainty in expert knowledge of forest succession: a case study from boreal Ontario. For Chron 84:194–209

    Google Scholar 

  • Ericsson KA (2004) Deliberate practice and the acquisition and maintenance of expert performance in medicine and related domains. Acad Med 79:S70–S81

    Article  PubMed  Google Scholar 

  • Ericsson KA, Charness N (1994) Expert performance: its structure and acquisition. Am Psychol 49:725–747

    Article  Google Scholar 

  • Ericsson KA, Charness N, Feltovich PJ et al (eds) (2006) The Cambridge handbook of expertise and expert performance. Cambridge University Press, New York

    Google Scholar 

  • Ericsson KA, Kintsch W (1995) Long-term working memory. Psychol Rev 102:211–245

    Article  PubMed  CAS  Google Scholar 

  • Ericsson KA, Lehmann AC (1996) Expert and exceptional performance: evidence of maximal adaptation to task constraints. Annu Rev Psychol 47:273–305

    Article  PubMed  CAS  Google Scholar 

  • Ericsson KA, Towne TJ  (2010)  Expertise.  Wiley Interdisciplinary Reviews: Cognitive Science 1:404–416

    Article  Google Scholar 

  • Fazey I, Fazey JA, Fazey DMA (2005) Learning more effectively from experience. Ecol Soc 10(2), article 4. Available from http://www.ecologyandsociety.org/vol10/iss2/art4/ (accessed May 2011)

    Google Scholar 

  • Ferrell WR (1994) Discrete subjective probabilities and decision analysis: elicitation, calibration and combination. In: Wright G, Ayton P (eds) Subjective probability. Wiley, New York

    Google Scholar 

  • Ferson S, Ginzburg LR (1996) Different methods are needed to propagate ignorance and variability. Reliab Eng Syst Safe 54:133–144

    Article  Google Scholar 

  • Fischhoff B, Slovic P, Lichtenstein S (1982) Lay foibles and expert fables in judgments about risk. Am Stat 36:240–255

    Article  Google Scholar 

  • Fisher L (2009) The perfect swarm: the science of complexity in everyday life. Basic Books, New York

    Google Scholar 

  • Garthwaite PH, Kadane JB, O’Hagan A (2005) Statistical methods for eliciting probability distributions. J Am Stat Assoc 100:680–700

    Article  CAS  Google Scholar 

  • Genest C, McConway KJ (1990) Allocating the weights in the linear opinion pool. J Forecasting 9:53–73

    Article  Google Scholar 

  • Genest C, Zidek JV (1986) Combining probability distributions: a critique and an annotated bibliography. Stat Sci 1:114–148

    Article  Google Scholar 

  • Gigerenzer G (1999) Simple heuristics that make us smart. Oxford University Press, New York

    Google Scholar 

  • Gigerenzer G (2002) Calculated risks: how to know when the numbers deceive you. Simon and Schuster, New York

    Google Scholar 

  • Gigerenzer G (2008) Rationality for mortals: how people cope with uncertainty. Oxford University Press, New York

    Google Scholar 

  • Gigerenzer G, Hoffrage U (1995) How to improve Bayesian reasoning without instruction: frequency formats. Psychol Rev 102:684–704

    Article  Google Scholar 

  • Gilovich T, Griffin D, Kahneman D (eds) (2002) Heuristics and biases: the psychology of intuitive judgement. Cambridge University Press, Cambridge

    Google Scholar 

  • Grier JW, Elder JB, Gramlich FJ et al (1993) The bald eagle in the northern United States. Bird Conserv 1:41–66

    Google Scholar 

  • Griffiths SP, Kuhnert PM, Venables WN, Blaber SJM (2007) Estimating abundance of pelagic fishes using gillnet catch data in data-limited fisheries: a Bayesian approach. Can J Fish Aquat Sci 64:1019–1033

    Article  Google Scholar 

  • Helander B, Marquiss M, Bowerman W (eds) (2003) Sea Eagle 2000. In: Proceedings from an International Conference at Bjökö, Sweden, 13–17 September 2000. Swedish Society for Nature Conservation, Stockholm, pp 129–132

    Google Scholar 

  • Helander B, Stjernberg,T (2003) Action plan for the conservation of white-tailed Sea Eagle (Haliaeetus albicilla). The Convention on the Conservation of European Wildlife and Natural Habitats, Strasbourg

    Google Scholar 

  • Hertwig R, Gigerenzer G (1999) The ‘conjunction fallacy’ revisited: how intelligent inferences look like reasoning errors. J Behav Dec Making 12:275–305

    Article  Google Scholar 

  • Hofer E (1996) When to separate uncertainties and when not to separate. Reliab Eng Syst Safe 54:113–118

    Article  Google Scholar 

  • Hoffman FO, Kaplan S (1999) Beyond the domain of direct observation: how to specify a probability distribution that represents the “state of knowledge” about uncertain inputs. Risk Anal 19:131–134

    Google Scholar 

  • Hoffman RR (1998) How can expertise be defined? Implications of research from cognitive psychology. In: Williams R, Faulkner W, Fleck J (eds) Exploring expertise. Macmillan, New York, pp 81–100

    Google Scholar 

  • Hogarth RM (1977) Methods for aggregating opinions. In: Jungermann H, DeZeeuw G (eds) Decision making and change in human affairs. Reidel, Dordrecht, pp 231–255

    Chapter  Google Scholar 

  • Hogarth RM (1987) Judgment and choice: the psychology of decision. Wiley, New York

    Google Scholar 

  • Hogarth RM (2001) Educating intuition. The University of Chicago Press, Chicago

    Google Scholar 

  • Hokstad P, Oien K, Reinertsen R (1998) Recommendations on the use of expert judgment in safety and reliability engineering studies: two offshore case studies. Reliab Eng Syst Safe 61:65–76

    Article  Google Scholar 

  • Hora SC (1992) Acquisition of expert judgment: examples from risk assessment. J Energy Dev 118:136–148

    Google Scholar 

  • Hora SC (2004) Probability judgments for continuous quantities: linear combinations and calibration. Manage Sci 50:597–604

    Article  Google Scholar 

  • Jacobs RA (1995) Methods for combining experts probability assessments. Neural Comput 7:867–888

    Article  PubMed  CAS  Google Scholar 

  • Johnson CJ, Gillingham MP (2004) Mapping uncertainty: sensitivity of wildlife habitat ratings to expert opinion. J Appl Ecol 41:1032–1041

    Article  Google Scholar 

  • Kadane JB, Dickey JM, Winkler RL et al (1980) Interactive elicitation of opinion for a normal linear model. J Am Stat Assoc 75:845–854

    Article  Google Scholar 

  • Kadane JB, Wolfson LJ (1998) Experiences in elicitation. J Roy Stat Soc D-Sta 47:3–19

    Article  Google Scholar 

  • Kahneman D (1991) Judgment and decision making: a personal view. Psychol Sci 2:142–145

    Article  Google Scholar 

  • Kahneman D, Tversky A (eds) (1982) Judgment under uncertainty: heuristics and biases. Cambridge University Press, Cambridge

    Google Scholar 

  • Kangas AS, Kangas J (2004) Probability, possibility and evidence: approaches to consider risk and uncertainty in forestry decision analysis. For Policy Econ 6:169–188

    Article  Google Scholar 

  • Kaplan S (1992) ‘Expert information’ versus ‘expert opinions’. Another approach to the problem of eliciting/combining/using expert knowledge in PRA. Reliab Eng Syst Safe 35:61–72

    Google Scholar 

  • Kardes FR (2006) When should consumers and managers trust their intuition? J Consum Psychol 16:20–24

    Article  Google Scholar 

  • Keeney RL, von Winterfeldt D (1991) Eliciting probabilities from experts in complex technical problems. IEEE Trans Eng Manage 38:191–201

    Article  Google Scholar 

  • Keith DW (1996) When is it appropriate to combine expert judgments? Climatic Change 33:139–143

    Article  Google Scholar 

  • Kidd A, Welbank M (1984) Knowledge acquisition. In: Fox J (ed) Infotech state of the art report on expert systems. Pergamon, London

    Google Scholar 

  • Kuhnert PM, Martin TG, Griffiths SP (2010) A guide to eliciting and using expert knowledge in Bayesian ecological models. Ecol Lett 7:900–914

    Article  Google Scholar 

  • Kunda Z (1990) The case for motivated reasoning. Psychol Bull 108:480–498

    Article  PubMed  CAS  Google Scholar 

  • Kynn M (2004) Eliciting expert knowledge for Bayesian logistic regression in species habitat modelling. Department of statistics, Queensland University of Technology, Brisbane

    Google Scholar 

  • Kynn M (2008) The ‘heuristics and biases’ bias in expert elicitation. J Roy Stat Soc A-Sta 171:239–264

    Google Scholar 

  • Larkin J, McDermott J, Simon DP, Simon, HA (1980) Expert and novice performance in solving physics problems. Science 208:1335–1342

    Article  PubMed  CAS  Google Scholar 

  • Lock A (1987) Integrating group judgments in subjective forecasts. In: Wright G, Ayton P (eds) Judgmental forecasting. Wiley, Chichester, pp 109–128

    Google Scholar 

  • Low-Choy S, O’Leary R, Mengersen K (2009) Elicitation by design in ecology: using expert opinion to inform priors for Bayesian statistical models. Ecology 90:265–277

    Article  Google Scholar 

  • Ludwig D, Mangel M, Haddad B (2001) Ecology, conservation, and public policy. Annu Rev Ecol Syst 32:481–517

    Article  Google Scholar 

  • MacMillan DC, Marshall K (2006) The Delphi process: an expert-based approach to ecological modelling in data-poor environments. Anim Conserv 9:11–19

    Article  Google Scholar 

  • MacNally, R (2007) Consensus weightings of evidence for inferring breeding success in broad-scale bird studies. Austral Ecol 32:479–484

    Article  Google Scholar 

  • Marsh H, Dennis A, Hines H et al (2007) Optimizing allocation of management resources for wildlife. Conserv Biol 21:387–399

    Article  PubMed  Google Scholar 

  • Martin TG, Kuhnert PM, Mengersen K, Possingham, HP (2005) The power of expert opinion in ecological models using Bayesian methods: impact of grazing on birds. Ecol Appl 15:266–280

    Article  Google Scholar 

  • McCoy ED, Sutton PE, Mushinsky HR (1999) The role of guesswork in conserving the threatened sand skink. Conserv Biol 13:190–194

    Article  Google Scholar 

  • Meyer M, Booker J (1991) Eliciting and analyzing expert judgment: a practical guide. Academic Press, New York

    Google Scholar 

  • Morgan MG, Henrion M (1990) Uncertainty: a guide to dealing with uncertainty in quantitative risk and policy analysis. Cambridge University Press, New York

    Google Scholar 

  • Morris PA (1974) Decision analysis expert use. Manage Sci 20:1233–1241

    Article  Google Scholar 

  • Morris PA (1977) Combining expert judgments: a Bayesian approach. Manage Sci 23:679–693

    Article  Google Scholar 

  • Moss R, Schneider, SH (2000) Uncertainties in the IPCC TAR: Recommendations to lead authors for more consistent assessment and reporting. In: Pachauri R, Taniguchi R, Tanaka K (eds) Guidance papers on the cross cutting issues of the third assessment report of the IPCC. World Meteorological Organisation, Geneva, pp 33–51

    Google Scholar 

  • Murphy AH, Winkler RL (1984) Probability forecasting in meteorology. J Am Stat Assoc 79:489–500

    Article  Google Scholar 

  • O’Hagan A (1998) Eliciting expert beliefs in substantial practical applications. J Roy Stat Soc D–Statistics 47:21–35

    Google Scholar 

  • O’Hagan A, Buck CE, Daneshkhah AR et al (2006). Uncertain judgments: eliciting expert probabilities. John Wiley, West Sussex

    Book  Google Scholar 

  • O’Neill SJ, Osborn TJ, Hulme M et al (2008) Using expert knowledge to assess uncertainties in future polar bear populations under climate change. J Appl Ecol 45:1649–1659

    Article  Google Scholar 

  • Otway H, von Winterfeldt D (1992) Expert judgment in risk analysis and management: process, context, and pitfalls. Risk Anal 12:83–93

    Article  PubMed  CAS  Google Scholar 

  • Pate-Cornell ME (1996) Uncertainties in risk analysis: six levels of treatment. Reliab Eng Syst Safe 54:95–111

    Article  Google Scholar 

  • Payne S (1951) The art of asking questions. Princeton University Press, Princeton

    Google Scholar 

  • Pearce JL, Cherry K, Drielsma M et al (2001) Incorporating expert opinion and fine-scale vegetation mapping into statistical models of faunal distribution. J Appl Ecol 38:412–424

    Article  Google Scholar 

  • Pellikka J, Kuikka S, Lindén H, Varis O (2005) The role of game management in wildlife populations: uncertainty analysis of expert knowledge. Eur J  Wildlife Res 51:48–59

    Article  Google Scholar 

  • Peterson CR, Beach LF (1967) Man as an intuitive statistician. Psychol Bull 68:29–46

    Article  PubMed  CAS  Google Scholar 

  • Price PC (1998) Effects of a relative-frequency elicitation question on likelihood judgment accuracy: the case of external correspondence. Organ Behav Hum Dec 76:277–297

    Article  Google Scholar 

  • Reading RP, Clark TW, Seebeck JH, Pearce J (1996) Habitat suitability index model for the eastern barred bandicoot, Perameles gunnii. Wildlife Res 23:221–235

    Article  Google Scholar 

  • Regan HM, Colyvan M, Burgman MA (2002) A taxonomy and treatment of uncertainty for ecology and conservation biology. Ecol Appl 12:618–628

    Article  Google Scholar 

  • Renooij S (2001) Probability elicitation for belief networks: issues to consider. Knowl Eng Rev 16:255–269

    Article  Google Scholar 

  • Richman HB, Gobet F, Staszewski JJ, Simon HA (1995) Simulation of expert memory using EPAM IV. Psychol Rev 102:305–333

    Article  PubMed  CAS  Google Scholar 

  • Roloff GJ, Kernohan BJ (1999) Evaluating reliability of habitat suitability index models. Wildlife Soc Bull 27:973–985

    Google Scholar 

  • Rosqvist T, Tuominen R (2004) Qualification of formal safety assessment: an exploratory study. Safety Sci 42:99–120

    Article  Google Scholar 

  • Rothlisberger JD, Lodge DM, Cooke RM, Finnoff DC (2010) Future declines of the binational Laurentian Great Lakes fisheries: the importance of environmental and cultural change. Front Ecol Environ 8:239–244

    Article  Google Scholar 

  • Saati TL (1980) The analytic hierarchy process. New York, McGraw-Hill

    Google Scholar 

  • Sanderson EW, Redford KH, Chetkiewicz CLB et al (2002) Planning to save a species: the jaguar as a model. Conserv Biol 16:58–72

    Article  Google Scholar 

  • Seaver DA (1978) Assessing probability with multiple individuals: group interaction versus mathematical aggregation. Social Science Research Institute, University of Southern California, Los Angeles. Report# SSRI-78-3

    Google Scholar 

  • Shanteau J (1992) Competence in experts: the role of task characteristics. Organ Behav Hum Dec 53:252–266

    Article  Google Scholar 

  • Shanteau J, Stewart TR (1992) Why study expert decision-making: some historical perspectives and comments. Organ Behav Hum Dec 53:95–106

    Article  Google Scholar 

  • Shephard GG, Kirkwood CW (1994) Managing the judgmental probability elicitation process: a case study of analyst/manager interaction. IEEE Trans Eng Manage 41:414–425

    Article  Google Scholar 

  • Shrader-Frechette K (1996) Value judgments in verifying and validating risk assessment models. In: Cothern CR (ed) Handbook for environmental risk decision making: values, perception and ethics. CRC Lewis Publishers, Boca Raton, pp 291–309

    Google Scholar 

  • Slottje P, van der Sluijs JP, Knol AB (2008) Expert elicitation: methodological suggestions for its use in environmental health impact assessments. RIVM, Copernicus Institute for Sustainable Development and Innovation., Bilthoven. Report 630004001/2008

    Google Scholar 

  • Slovic P (1999) Trust, emotion, sex, politics and science: surveying the risk-assessment battlefield. Risk Anal 19:689–701

    PubMed  CAS  Google Scholar 

  • Slovic P, Finucane ML, Peters E, MacGregor DG (2004) Risk as analysis and risk as feelings: some thoughts about affect, reason, risk, and rationality. Risk Anal 24:311–322

    Article  PubMed  Google Scholar 

  • Slovic P, Monahan J, MacGregor DG (2000) Violence risk assessment and risk communication: the effects of using actual cases, providing instruction, and employing probability versus frequency formats. Law Human Behav 24:271–296

    Article  CAS  Google Scholar 

  • Speirs-Bridge A, Fidler F, McBride M et al (2010) Reducing overconfidence in the interval judgments of experts. Risk Anal 30:512–523

    Article  PubMed  Google Scholar 

  • Spetzler CS, Stael Von Holstein CAS (1975) Probability encoding in decision analysis. Manage Sci 22:340–358

    Article  Google Scholar 

  • Stern PC, Fineberg HV (eds) (1996) Understanding risk: informing decisions in a democratic society. National Academies Press, Washington

    Google Scholar 

  • Sutherland WJ (2006) Predicting the ecological consequences of environmental change: a review of the methods. J Appl Ecol 43:599–616

    Article  Google Scholar 

  • Sutherland WJ, Bailey MJ, Bainbridge IP et al (2008) Future novel threats and opportunities facing UK biodiversity identified by horizon scanning. J Appl Ecol 45:821–833

    Article  Google Scholar 

  • Sutherland WJ, Pullin AS, Dolman PM, Knight TM (2004) The need for evidence-based conservation. Trends Ecol Evol 19:305–308

    Article  PubMed  Google Scholar 

  • Tallman I, Leik RK, Gray LN, Stafford MC (1993) A theory of problem-solving behavior. Soc Psychol Quart 56:157–177

    Article  Google Scholar 

  • Tavana M, Kennedy DT, Mohebbi B (1997) An applied study using the analytic hierarchy process to translate common verbal phrases to numerical probabilities. J Behav Dec Making 10:133–150

    Article  Google Scholar 

  • Teck SJ, Halpern BS, Kappel CV et al (2010) Using expert judgment to estimate marine ecosystem vulnerability in the California Current. Ecol Appl 20:1402–1416

    Article  PubMed  Google Scholar 

  • Tversky A, Kahneman D (1974) Judgment under uncertainty: heuristics and biases. Science 185:1124–1131

    Article  PubMed  CAS  Google Scholar 

  • Tversky A, Kahneman D (1983) Extensional versus intuitive reasoning: the conjunction fallacy in probability judgment. Psychol Rev 90:293–315

    Article  Google Scholar 

  • Tversky A, Koehler DJ (1994) Support theory: a nonextensional representation of subjective-probability. Psychol Rev 101:547–567

    Article  Google Scholar 

  • van der Gaag LC, Renooij S, Witteman CLM et al (1999) How to elicit many probabilities. In: Laskey KB, Prade H (eds) Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence, Stockholm, July–August 1999. Morgan Kaufmann, San Francisco

    Google Scholar 

  • van der Gaag LC, Renooij S, Witteman CLM et al (2002) Probabilities for a probabilistic network: a case study in oesophageal cancer. Artif Intell Med 25:123–148

    Article  PubMed  Google Scholar 

  • van Steen JFJ (1992) A perspective on structured expert judgment. J Hazard Mater 29:365–385

    Article  Google Scholar 

  • von Winterfeldt D, Edwards W (1986) Decision analysis and behavioral research. Cambridge University Press, Cambridge

    Google Scholar 

  • Walls L, Quigley J (2001) Building prior distributions to support Bayesian reliability growth modelling using expert judgement. Reliab Eng Syst Safe 74:117–128

    Article  Google Scholar 

  • Wallsten TS, Budescu DV (1995) A review of human linguistic probability processing: general principles and empirical evidence. Knowl Eng Rev 10:43–62

    Article  Google Scholar 

  • Wallsten TS, Budescu DV, Erev I, Diederich A (1997) Evaluating and combining subjective probability estimates. J Behav Dec Making 10:243–268

    Article  Google Scholar 

  • Wallsten TS, Budescu DV, Rapoport A et al (1986) Measuring the vague meanings of probability terms. J Exp Psychol Gen 115:348–365

    Article  Google Scholar 

  • Whitfield DP, Ruddock M, Bullman R (2008) Expert opinion as a tool for quantifying bird tolerance to human disturbance. Biol Conserv 141:2708–2717

    Article  Google Scholar 

  • Wilson AG (1994) Cognitive factors affecting subjective probability assessment. Duke University, Institute of Statistics and Decision Sciences, Durham. Report #94–02

    Google Scholar 

  • Windschitl PD, Wells GL (1996) Measuring psychological uncertainty: verbal versus numeric methods. J Exp Psychol-Appl, 2:343–364

    Article  Google Scholar 

  • Winkler RL, Makridakis S (1983) The combination of forecasts. J Roy Stat Soc A-Sta 146:150–157

    Article  Google Scholar 

  • Yamada K, Elith J, McCarthy M, Zerger A (2003) Eliciting and integrating expert knowledge for wildlife habitat modelling. Ecol Model 165:251–264

    Article  Google Scholar 

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McBride, M.F., Burgman, M.A. (2012). What Is Expert Knowledge, How Is Such Knowledge Gathered, and How Do We Use It to Address Questions in Landscape Ecology?. In: Perera, A., Drew, C., Johnson, C. (eds) Expert Knowledge and Its Application in Landscape Ecology. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1034-8_2

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