Baltic herring fisheries management: Stakeholder views to frame the problem

Ecology and Society

Volumn 17, Issue 3, 2012, Pages

  Haapasaari, Päivi ^a   Mäntyniemi, Samu ^a   Kuikka, Sakari ^a  

^a UNIVERSITY OF HELSINKI   (Finland)

Author keywords

Bayesian belief networks; Influence diagrams; Objectives; Participatory modeling; Problem framing; Stakeholders; Structural uncertainty

Indexed keywords

FISHERY MANAGEMENT; FISHERY PRODUCTION; HOLISTIC APPROACH; MANAGEMENT PRACTICE; NONGOVERNMENTAL ORGANIZATION; STAKEHOLDER; STOCK ASSESSMENT;

ATLANTIC OCEAN; BALTIC SEA;

CLUPEA HARENGUS; CLUPEIDAE;

EID: 84867136840     PISSN: None     EISSN: 17083087     Source Type: Journal    
DOI: 10.5751/ES-04907-170336     Document Type: Article

References (65)

1. Austin, D. E. 1994. Incorporating cognitive theory into environmental policymaking. Environmental Professional 16 (3):262-274.
2. Berger, P. L., and T. Luckmann. 1967. The social construction of reality: a treatise in the sociology of knowledge. Penguin, London, UK.
3. Borsuk, M. E., C. A. Stow, and K. H. Reckhow. 2004. A Bayesian network of eutrophication models for synthesis, prediction, and uncertainty analysis. Ecological Modelling 173(2-3):219-239. http://dx.doi.org/10.1016/j.ecolmodel.2003.08.020
4. Cain, J. 2001. Planning improvements in natural resources management: guidelines for using Bayesian networks to support the planning and management of development programmes in the water sector and beyond. Centre for Ecology and Hydrology, Wallingford, UK. [online] URL: http://64.40.97.24/downloads/BBN%20Guidelines%20-%20Cain.pdf.
5. Charles, A. T. 1998. Living with uncertainty in fisheries: analytical methods, management priorities and the Canadian groundfishery experience. Fisheries Research 37(1-3):37-50. http://dx.doi.org/10.1016/S0165-7836(98)00125-8
6. Charles, A. T. 2001. Sustainable fishery systems. Blackwell Science, Oxford, UK. http://dx.doi.org/10.1002/9780470698785
7. Clark, R. N., and G. H. Stankey. 2006. Integrated research in natural resources: the key role of problem framing. General technical report PNW-GTR-678. United States Department of Agriculture Forest Service, Washington, D.C., USA. [online] URL: http://www.fs.fed.us/pnw/pubs/pnw_gtr678.pdf.
8. Commission of the European Communities (CEC). 2002. Council Regulation (EC) No 2371/2002 of 20 December 2002 on the conservation and sustainable exploitation of fisheries resources under the Common Fisheries Policy. Official Journal of the European Union L358:59-80. [online] URL: http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2002:358:0059:0080:EN:PDF.
9. Commission of the European Communities (CEC). 2008. Communication from the Commission to the Council and the European Parliament: The role of the CFP in implementing an ecosystem approach to marine management [SEC (2008) 449]. COM(2008) 187 final. Commission of the European Communities, Brussels, Belgium. [online] URL: http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=COM:2008:0187:FIN:EN:PDF.
10. Commission of the European Communities (CEC). 2009. Green paper: reform of the Common Fisheries Policy. COM (2009) 163 final. Commission of the European Communities, Brussels, Belgium. [online] URL: http://eurlex.europa.eu/LexUriServ/LexUriServ.do?uri=COM:2009:0163:FIN:EN:PDF.
11. Commission of the European Communities (CEC). 2010. Commission non-paper: on the establishment of a multiannual plan for pelagic stocks in the Baltic Sea and the fisheries exploiting those stocks. Commission of the European Communities, Brussels, Belgium. [online] URL: http://www.bsrac.org/archive/Dokumenter/Pelagic%20WG/150410/Non-Paper%20pelagics%20final.pdf.
12. De Young, B., R. M. Peterman, A. R. Dobell, E. Pinkerton, Y. Breton, A. T. Charles, M. J. Fogarty, G. R. Munro, and C. T. Taggart. 1999. Canadian marine fisheries in a changing and uncertain world: a report prepared for the Canadian Global Change Program of the Royal Society of Canada. Canadian Special Publication of Fisheries and Aquatic Sciences 129. National Research Council of Canada, Ottawa, Canada.
13. Degnbol, P., and B. J. McCay. 2007. Unintended and perverse consequences of ignoring linkages in fisheries systems. ICES Journal of Marine Science 64(4):793-797. http://dx.doi.org/10.1093/icesjms/fsm040
14. Dennis, B. 1996. Discussion: should ecologists become Bayesians? Ecological Applications 6(4):1095-1103. http://dx.doi.org/10.2307/2269594
15. Druzdzel, M. J., and M. Henrion. 1993. Efficient reasoning in qualitative probabilistic networks. Pages 548-553 in Proceedings of the Eleventh Annual National Conference on Artificial Intelligence (AAAI-93) (Washington, D.C., 1993). AAAI Press, Palo Alto, California, USA.
16. Etienne, M., D. R. Du Toit, and S. Pollard. 2011. ARDI: a coconstruction method for participatory modeling in natural resources management. Ecology and Society 16(1): 44. [online] URL: http://www.ecologyandsociety.org/vol16/iss1/art44/.
17. Flick, U. 1998. An introduction to qualitative research. Sage Publications, London, UK.
18. Food and Agriculture Organization (FAO). 2003. Fisheries management: 2. The ecosystem approach to fisheries. FAO Technical Guidelines for Responsible Fisheries 4, Supplement 2. FAO, Rome, Italy. [online] URL: ftp://ftp.fao.org/docrep/fao/005/y4470e/y4470e00.pdf.
19. Fortuin, K. P. J., C. S. A. van Koppen, and R. Leemans. 2011. The value of conceptual models in coping with complexity and interdisciplinarity in environmental sciences education. Bioscience 61(10):802-814. http://dx.doi.org/10.1525/bio.2011.61.10.10
20. Francis, R. I. C. C., and R. Shotton. 1997. "Risk" in fisheries management: a review. Canadian Journal of Fisheries and Aquatic Sciences 54(8):1699-1715.
21. Garcia, S. M., and A. T. Charles. 2007. Fishery systems and linkages: from clockworks to soft watches. ICES Journal of Marine Science 64(4):580-587. http://dx.doi.org/10.1093/icesjms/fsm013
22. Garcia, S. M., and A. T. Charles. 2008. Fishery systems and linkages: implications for science and governance. Ocean and Coastal Management 51(7):505-527. http://dx.doi.org/10.1016/j.ocecoaman.2008.05.001
23. Gelman, A., J. B. Carlin, H. S. Stern, and D. B. Rubin. 1995. Bayesian data analysis. Chapman & Hall, London, UK.
24. Gréboval, D., editor. 2002. Report and documentation of the International Workshop on Factors Contributing to Unsustainability and Overexploitation in Fisheries. Bangkok, Thailand, 4-8 February 2002. FAO Fisheries Report 672. FAO, Rome, Italy. [online] URL: ftp://ftp.fao.org/docrep/fao/005/y3684e/y3684e00.pdf.
25. Haapasaari, P., and T. P. Karjalainen. 2010. Formalizing expert knowledge to compare alternative management plans: sociological perspective to the future management of Baltic salmon stocks. Marine Policy 34(3):477-486. http://dx.doi.org/10.1016/j.marpol.2009.10.002
26. Haapasaari, P., C. G. J. Michielsens, T. P. Karjalainen, K. Reinikainen, and S. Kuikka. 2007. Management measures and fishers' commitment to sustainable exploitation: a case study of Atlantic salmon fisheries in the Baltic Sea. ICES Journal of Marine Science 64(4):825-833. http://dx.doi.org/10.1093/icesjms/fsm002
27. Hammond, T. R., and C. M. O'Brien. 2001. An application of the Bayesian approach to stock assessment model uncertainty. ICES Journal of Marine Science 58(3):648-656. http://dx.doi.org/10.1006/jmsc.2001.1051
28. Heemskerk, M., K. Wilson, and M. Pavao-Zuckerman. 2003. Conceptual models as tools for communication across disciplines. Conservation Ecology 7(3): 8. [online] URL: http://www.consecol.org/vol7/iss3/art8/.
29. Hukkinen, J. 1993. Bayesian analysis of agricultural drainage problems in California's San Joaquin valley. Journal of Environmental Management 37(3):183-200. http://dx.doi.org/10.1006/jema.1993.1015
30. Hukkinen, J. 1999. Institutions in environmental management: constructing mental models and sustainability. Routledge, London, UK.
31. International Council for the Exploration of the Sea (ICES). 2009. 8.3.3.1 Multi-annual management of pelagic fish stocks in the Baltic. Advice 2009, Book 8. ICES, Copenhagen, Denmark. [online] URL: http://www.ices.dk/committe/acom/comwork/report/2009/Special%20Requests/EC%20Multiannual%20management%20pelagic%20stocks%20Baltic.pdf.
32. International Council for the Exploration of the Sea (ICES). 2011. 8.4.4 Ecoregion stock: Baltic Sea: herring in subdivisions 25-29 and 32 (excluding Gulf of Riga herring). Advice 2011, Book 8. ICES, Copenhagen, Denmark. [online] URL: http://www.ices.dk/committe/acom/comwork/report/2011/2011/Her-2532-Ex-Go.pdf.
33. Jensen, F. V. 2001. Bayesian networks and decision graphs. Springer, New York, New York, USA.
34. Jones, N. A., H. Ross, T. Lynam, P. Perez, and A. Leitch. 2011. Mental models: an interdisciplinary synthesis of theory and methods. Ecology and Society 16(1): 46. [online] URL: http://www.ecologyandsociety.org/vol16/iss1/art46/.
35. Kearney, A. R., and S. Kaplan. 1997. Toward a methodology for the measurement of knowledge structures of ordinary people: the conceptual content cognitive map (3CM). Environment and Behavior 29(5):579-617. http://dx.doi.org/10.1177/0013916597295001
36. Kiljunen, M., M. Vanhatalo, S. Mäntyniemi, H. Peltonen, S. Kuikka, H. Kiviranta, R. Parmanne, J. T. Tuomisto, P. J. Vuorinen, A. Hallikainen, M. Verta, J. Pönni, R. I. Jones, and J. Karjalainen. 2007. Human dietary intake of organochlorines from Baltic herring: implications of individual fish variability and fisheries management. Ambio 36(2-3):257-264. http://dx.doi.org/10.1579/0044-7447(2007)36[257:HDIOOF]2.0.CO;2
37. Kraak, S. B. M., C. J. Kelly, E. A. Codling, and E. Rogan. 2010. On scientists' discomfort in fisheries advisory science: the example of simulation-based fisheries managementstrategy evaluations. Fish and Fisheries 11(2):119-132.
38. Kuikka, S., P. Haapasaari, I. Helle, S. Kulmala, and S. Mäntyniemi. 2011. Experiences in applying Bayesian integrative models in interdisciplinary modeling: the computational and human challenges. Pages 2135-2141 in F. Chan, D. Marinova, and R. S. Anderssen, editors. MODSIM2011, 19th International Congress on Modelling and Simulation (Perth, 2011). Modelling and Simulation Society of Australia and New Zealand. www.mssanz.org.au/modsim2011/E9/kuikka.pdf.
39. Kuikka, S., M. Hildén, H. Gislason, S. Hansson, H. Sparholt, and O. Varis. 1999. Modelling environmentally driven uncertainties in Baltic cod (Gadus morhua) management by Bayesian influence diagrams. Canadian Journal of Fisheries and Aquatic Sciences 56(4):629-641. http://dx.doi.org/10.1139/cjfas-56-4-629
40. Kuikka, S., and O. Varis. 1997. Uncertainties of climatic change impacts in Finnish watersheds: a Bayesian network analysis of expert knowledge. Boreal Environment Research 2(1):109-128.
41. Levontin, P., S. Kulmala, P. Haapasaari, and S. Kuikka. 2011. Integration of biological, economic, and sociological knowledge by Bayesian belief networks: the interdisciplinary evaluation of potential management plans for Baltic salmon. ICES Journal of Marine Science 68(3):632-638. http://dx.doi.org/10.1093/icesjms/fsr004
42. Lynam, T., W. de Jong, D. Sheil, T. Kusumanto, and K. Evans. 2007. A review of tools for incorporating community knowledge, preferences, and values into decision making in natural resources management. Ecology and Society 12(1): 5. [online] URL: http://www.ecologyandsociety.org/vol12/iss1/art5/.
43. Mäntyniemi, S., P. Haapasaari, and S. Kuikka. 2009a. Incorporating stakeholders' knowledge to stock assessment: How? ICES CM 2009/O:12. ICES Annual Science Conference, Berlin, Germany. [online] URL: http://www.ices.dk/products/CMdocs/CM-2009/O/O1209.pdf.
44. Mäntyniemi, S., S. Kuikka, M. Rahikainen, L. T. Kell, and V. Kaitala. 2009b. The value of information in fisheries management: North Sea herring as an example. ICES Journal of Marine Science 66(10):2278-2283. http://dx.doi.org/10.1093/icesjms/fsp206
45. O'Hagan, A., C. E. Buck, A. Daneshkhah, J. R. Eiser, P. H. Garthwaite, D. J. Jenkinson, J. E. Oakley, and T. Rakow. 2006. Uncertain judgements: eliciting experts' probabilities. Wiley, London, UK.
46. Özesmi, U., and S. L. Özesmi. 2004. Ecological models based on people's knowledge: a multi-step fuzzy cognition mapping approach. Ecological Modelling 176(1-2):43-64. http://dx.doi.org/10.1016/j.ecolmodel.2003.10.027
47. Pahl-Wostl, C. 2007. The implications of complexity for integrated resources management. Environmental Modelling and Software 22(5):561-569. http://dx.doi.org/10.1016/j.envsoft.2005.12.024
48. Pavao-Zuckerman, M. A. 2000. The conceptual utility of models in human ecology. Journal of Ecological Anthropology 4:31-56.
49. Pearl, J. 1988. Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, San Mateo, California, USA.
50. Punt, A. E., and R. Hilborn. 1997. Fisheries stock assessment and decision analysis: the Bayesian approach. Reviews in Fish Biology and Fisheries 7(1):35-63. http://dx.doi.org/10.1023/A:1018419207494
51. Renooij, S., and L. C. van der Gaag. 1998. Decision making in qualitative influence diagrams. Utrecht University: Information and Computing Sciences 1998-03. [online] URL: http://igitur-archive.library.uu.nl/math/2007-0118-200212/van_der_gaag_98_decision.pdf.
52. Röckmann, C., C. Ulrich, M. Dreyer, E. Bell, E. Borodzicz, P. Haapasaari, K. H. Hauge, D. Howell, S. Mäntyniemi, D. Miller, G. Tserpes, and M. Pastoors. 2012. The added value of participatory modelling in fisheries management - What has been learnt? Marine Policy 36(5):1072-1085.
53. Schnute, J. T., and L. J. Richards. 2001. Use and abuse of fishery models. Canadian Journal of Fisheries and Aquatic Sciences 58(1):10-17. http://dx.doi.org/10.1139/cjfas-58-1-10
54. Shachter, R. D. 1986. Evaluating influence diagrams. Operations Research 34(6):871-882. http://dx.doi.org/10.1287/opre.34.6.871
55. Spiegelhalter, D. J., A. P. Dawid, S. L. Steffen, and R. G. Cowell. 1993. Bayesian analysis in expert systems. Statistical Science 8(3):219-247. http://dx.doi.org/10.1214/ss/1177010888
56. Uusitalo, L. 2007. Advantage and challenges of Bayesian networks in environmental modelling. Ecological Modelling 203(3-4):312-318. http://dx.doi.org/10.1016/j.ecolmodel.2006.11.033
57. Van der Sluijs, J. P., M. Craye, S. O. Funtowicz, P. Kloprogge, J. R. Ravetz, and J. S. Risbey. 2005a. Combining quantitative and qualitative measures of uncertainty in model-based environmental assessment: the NUSAP system. Risk Analysis 25(2):481-492. http://dx.doi.org/10.1111/j.1539-6924.2005.00604.x
58. Van der Sluijs, J. P., M. Craye, S. O. Funtowicz, P. Kloprogge, J. R. Ravetz, and J. S. Risbey. 2005b. Experiences with the NUSAP system for multidimensional uncertainty assessment. Water Science and Technology 52(6):133-144.
59. Varis, O., and S. Fraboulet-Jussila. 2002. Water resources development in the lower Senegal River basin: conflicting interests, environmental concerns and policy options. Water Resources Development 18(2):245-260. http://dx.doi.org/10.1080/07900620220135085
60. Varis, O., and S. Kuikka. 1997. BENE-EIA: a Bayesian approach to expert judgment elicitation with case studies on climatic change impacts on surface waters. Climatic Change 37(3):539-563. http://dx.doi.org/10.1023/A:1005358216361
61. Varis, O., and S. Kuikka. 1999. Learning Bayesian decision analysis by doing: lessons from environmental and natural resources management. Ecological Modelling 119(2-3):177-195. http://dx.doi.org/10.1016/S0304-3800(99)00061-7
62. Varis, O., and V. Lahtela. 2002. Integrated water resources management along the Senegal River: introducing an analytical framework. Water Resources Development 18 (4):501-521. http://dx.doi.org/10.1080/0790062022000017374
63. Verweij, M. C., and W. L. T. van Densen. 2010. Differences in causal reasoning about resource dynamics and consequences for the participatory debate on North Sea fisheries. Marine Policy 34(6):1144-1155. http://dx.doi.org/10.1016/j.marpol.2010.03.014
64. Walker, W. E., P. Harremoës, J. Rotmans, J. P. van der Sluijs, M. B. A. van Asselt, P. Janssen, and M. P. Krayer von Krauss. 2003. Defining uncertainty: a conceptual basis for uncertainty management in mode-based decision support. Integrated Assessment 4(1):5-17. http://dx.doi.org/10.1076/iaij.4.1.5.16466
65. Wellman, M. P. 1990. Fundamental concepts of qualitative probabilistic networks. Artificial Intelligence 44(3):257-303. http://dx.doi.org/10.1016/0004-3702(90)90026-V