European Journal of Wildlife Research

, Volume 51, Issue 1, pp 48–59 | Cite as

The role of game management in wildlife populations: uncertainty analysis of expert knowledge

  • Jani Pellikka
  • Sakari Kuikka
  • Harto Lindén
  • Olli Varis
Original Paper

Abstract

Uncertainties about future states of wildlife populations make it difficult to pre-adapt to possible threats and ensure sustainability of resources and harvesting over the long term. This uncertainty is partly due to the unknown impact and future states of many factors that explain population sizes and variation. In this paper, the effect of local game management activities on the uncertainty of future population sizes of groups of Finnish wildlife species (ungulates, forest grouse, large predators, small predators and mountain hare) was analysed using expert knowledge and the Bayesian belief networks (BBNs) modelling techniques. As a result, the current knowledge and agreement of the relationships between wildlife population sizes and the game management activities explaining their variation as well as trends are evaluated. Information given to hunters and the number of hunters were seen as the most effective factors for the management of game populations. However, there were great uncertainties in the expectations regarding future trends in the management activities, especially in feeding, and there was disagreement in the direction of the trend in the length of the hunting season. The trends in the size of forest grouse populations were viewed as the most uncertain trend among species groups. At the same time, forest grouse were seen as the most regulated species group by local game management. Among interest variables, experts were very uncertain and they disagreed about the direction of the trend in the recreational value of hunting.

Keywords

Bayes Belief networks Hunting 

References

  1. Ahlburg DA, Lutz W (1998) Introduction: the need to rethink approaches to population forecasts. Popul Devel Rev 24:1–14Google Scholar
  2. Bodmer RE, Eisenberg JF, Redford KH (1997) Hunting and the likelihood of extinction of amazonian mammals. Conserv Biol 11:460–466Google Scholar
  3. Collopy F, Armstrong JC (1992) Rule-based forecasting: development and validation of an expert systems approach for combining time series extrapolation. Manage Sci 38:1394–1414Google Scholar
  4. FACE (1995) Handbook of hunting in Europe. FACE, BelgiumGoogle Scholar
  5. Finnish Forest Research Institute (2002) Finnish Statistical Yearbook of Forestry. SVT agriculture, forestry and fishery 2002:45Google Scholar
  6. Hatter IW (1998) A Bayesian approach to moose population assessment and harvest decisions. Alces 34:47–58Google Scholar
  7. Howson C, Urbach P (1991) Bayesian reasoning in science. Nature 350:371–374Google Scholar
  8. Kuikka S (1998) Uncertainty analysis in fisheries management science—Baltic Sea applications. Doctoral thesis, Faculty of agriculture and forestry, University of HelsinkiGoogle Scholar
  9. Kuikka S, Varis O (1997) Uncertainties of climatic change impacts in Finnish watersheds: a Bayesian network analysis of expert knowledge. Boreal Env Res 2:109–128Google Scholar
  10. Lande R, Sæther B, Engen S (1997) Threshold harvesting for sustainability of fluctuating resources. Ecology 78:1341–1350Google Scholar
  11. Lehmkuhl JF, Kie JG, Berder LC, Servheen G, Nyberg H (2001) Evaluating the effect of ecosystem management alternatives on elk, mule deer, and white-tailed deer in the interior Columbia River basin, USA. For Ecol Manage 153:89–104Google Scholar
  12. Lindén H (1981) Hunting and tetraonid populations in Finland. Finnish Game Res 39:69–78Google Scholar
  13. Lindén H (1988) Latitudinal gradients in predator–prey interactions, cyclicity and synchronism in voles and small game populations in Finland. Oikos 52:34–42Google Scholar
  14. Lindén H (1991) Patterns of grouse shooting in Finland. Ornis Scand 22:241–244Google Scholar
  15. Lindén H, Helle E, Helle P, Wikman M (1996) Wildlife triangle scheme in Finland: methods and aims for monitoring wildlife populations. Finnish Game Res 49:4–11Google Scholar
  16. Ludwig D, Hilborn R, Walters C (1993) Uncertainty, resource exploitation and conservation: lessons from history. Science 260:17–36Google Scholar
  17. Luoma A (2002) Moose hunting in Finland—management of heavily harvested population. Doctoral thesis, Faculty of Science, University of HelsinkiGoogle Scholar
  18. Lutz W, Sanderson WC, Scherbov S (1998) Expert-based probabilistic population projections. Popul Devel Rev 24:139–155Google Scholar
  19. Nichols JD, Johnson FA, Williams BK (1995) Managing North American waterfowl in the face of uncertainty. Annu Rev Ecol Syst 26:177–199Google Scholar
  20. Pearl J (1986) Fusion, propagation, and structuring in belief networks. Artif Intell 29:241–288Google Scholar
  21. Pearl J (1988) Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan-Kaufmann, San MateoGoogle Scholar
  22. Pellikka J, Nummi P (2002) Evaluation of moose density in Finland using the analytic hierarchy process (AHP). Suomen Riista 48:80–89Google Scholar
  23. Redford KH (1992) The empty forest. Bioscience 42:412–422Google Scholar
  24. Regan HM, Colyvan M, Burgman MA (2002) A taxonomy and treatment of uncertainty for ecology and conservation biology. Ecol Appl 12:618–628Google Scholar
  25. Sutherland JW (1983) Normative predicates of next-generation management support systems. IEEE Trans Syst Man Cybern SMC 13:279–297Google Scholar
  26. Sutherland WJ (2001) Sustainable exploitation: a review of principles and methods. Wildl Biol 7:131–140Google Scholar
  27. United Nations (1992) Earth summit ‘92: the United Nations conference on environment and development, Rio de Janeiro 1992. The Regency Press Corporation, LondonGoogle Scholar
  28. Varis O (1998a) A belief network approach to optimisation and parameter estimation: application to resource and environmental management. Artif Intell 101:135–163Google Scholar
  29. Varis O (1998b) FC BeNe. Fully Connected Belief Networks. Users guide. Yliopistopaino, HelsinkiGoogle Scholar
  30. Vikberg P, Orava R, Svensberg M (2002a) Metsästystutkimus 2000. Nuorisokato ja ukkoutuminen paheneva ongelma metsästysseuroissa. Metsästäjä 1:36–39Google Scholar
  31. Vikberg P, Orava R, Svensberg M (2002b) Metsästystutkimus 2000. Metsästysseurat tuntevat vastuunsa riistakannoista. Metsästäjä 6:40–43Google Scholar
  32. Vikberg P, Orava R, Svensberg M (2002c) Metsästystutkimus 2000. Riistanhoito kuuluu seurojen keskeisiin tehtäviin. Metsästäjä 4:24–27Google Scholar
  33. Vikberg P, Orava R, Svensberg M (2002d) Metsästystutkimus 2000. Maanomistus tai kotipaikka toiminta-alueella turvaavat metsästysseuraan pääsyn. Metsästäjä 2:32–35Google Scholar
  34. Vikberg P, Orava R, Svensberg M (2002e) Metsästystutkimus 2000. Vierasmetsästyksestä apua metsästysmahdollisuuksia vailla oleville. Metsästäjä 3:24–27Google Scholar
  35. Williams BK (1997) Approaches to the management of waterfowl under uncertainty. Wildl Soc Bull 25:714–720Google Scholar
  36. Williams BK, Johnson FA, Wilkins K (1997) Uncertainty and the adaptive management of waterfowl harvest. J Wildl Manage 60:223–232Google Scholar

Copyright information

© Springer-Verlag 2005

Authors and Affiliations

  • Jani Pellikka
    • 1
  • Sakari Kuikka
    • 2
  • Harto Lindén
    • 3
  • Olli Varis
    • 4
  1. 1.Department of Applied BiologyUniversity of HelsinkiHelsinkiFinland
  2. 2.Department of Bio- and Environmental SciencesUniversity of HelsinkiHelsinkiFinland
  3. 3.Finnish Game and Fisheries Research InstituteHelsinkiFinland
  4. 4.Laboratory of Water ResourcesHelsinki University of TechnologyEspooFinland

Personalised recommendations