Incorporating stakeholder views is a key element in successful environmental management, particularly if the managed system delivers cultural and provisioning ecosystem services directly to the stakeholders, or if there are conflicting views about the ecosystem functioning or its optimal management. One such system is the Archipelago Sea in the Southwestern coast of Finland. It is an area with high biodiversity, offering a range of ecosystem services, from regulating services to provisioning and cultural services. Furthermore, it is subjected to a variety of human activities ranging from eutrophication and marine transport to fishing. The management of the area is also a topic of debate, including discussions of minimum landing size of fish, seal hunting quotas, and the role of cormorants in the ecosystem. Fuzzy cognitive mapping offers a method to evaluate and quantitatively compare different actors’ views on ecosystem structure. The models can be compared quantitatively and simulated to illustrate how they respond to various pressure scenarios. This may reveal differences in the perceptions about what are the important interactions in the ecosystem, and how the system would respond to management measures, potentially explaining differing opinions about the best management strategy. In this work, 30 stakeholders, including policy makers, scientists, eNGOs, fisheries, and recreational users created fuzzy cognitive maps (FCMs) of the Archipelago Sea food web. We found that despite the debate about the management of the area, the stakeholders’ views about the food web structure were not clustered based on the stakeholder group, i.e. the different stakeholder groups did not have distinct ideas about the ecosystem structure. The FCM complexity did not show a pattern based on the stakeholder group either. While the general pattern of the FCMs indicated a shared view of the food web structure across most respondents, there was one map from the recreational group that stood out. The exact setup of the models varied. Across all maps, cod, perch, fishing, zooplankton, and herring were the variables having most links with the other variables. The simulated ecosystem responses indicated that fishing was seen as a key factor affecting food web components, while increases of salinity and oxygen levels have a positive impact on multiple ecosystem components. The value of the approach is to enable a two-way discussion about the food webs and how management of pressures may impact the components.
BACC II Author Team. 2015. Second Assessment of Climate Change for the Baltic Sea Basin. Regional climate studies. 501 pp. https://doi.org/10.1007/978-3-319-16006-1
Chan, K.M.A., Satterfield, T., Goldstein, J., 2012. Rethinking ecosystem services to better address and navigate cultural values. Ecol. Econ. 74, 8–18. https://doi.org/10.1016/j.ecolecon.2011.11.011
Christen, B., Kjeldsen, C., Dalgaard, T., Martin-Ortega, J., 2015. Can fuzzy cognitive mapping help in agricultural policy design and communication? Land Use Policy 45, 64–75. https://doi.org/10.1016/j.landusepol.2015.01.001
Fairweather, J., 2010. Farmer models of socio-ecologic systems: Application of causal mapping across multiple locations. Ecol. Model. 221, 555–562. https://doi.org/10.1016/j.ecolmodel.2009.10.026
Fisher, M. & Sablan, T. 2017. Evaluating environmental conflict resolution: Practitioners, projects, and the movement. Conflict Resolution Quarterly. 2018;36:7–19.
Game, E.T., Bremer, L.L., Calvache, A., Moreno, P.H., Vargas, A., Rivera, B., Rodriguez, L.M., 2018. Fuzzy Models to Inform Social and Environmental Indicator Selection for Conservation Impact Monitoring. Conserv. Lett. 11, e12338. https://doi.org/10.1111/conl.12338
Giabbanelli, P.J., Gray, S.A., Aminpour, P., 2017. Combining fuzzy cognitive maps with agent-based modeling: Frameworks and pitfalls of a powerful hybrid modeling approach to understand human-environment interactions. Environ. Model. Softw. 95, 320–325. https://doi.org/10.1016/j.envsoft.2017.06.040
Gray, S., Chan, A., Clark, D., Jordan, R., 2012. Modeling the integration of stakeholder knowledge in social–ecological decision-making: benefits and limitations to knowledge diversity. Ecological Modelling, 229, pp.88-96.Haapasaari, P., S. Mäntyniemi, and S. Kuikka. 2012. Baltic herring fisheries management: stakeholder views to frame the problem. Ecology and Society 17(3): 36. http://dx.doi.org/10.5751/ES-04907-170336
Hansson, S., Bergström, U., Bonsdorff, E., Härkönen, T., Jepsen, N., Kautsky, L., Lundström, K., Lunneryd, S.-G., Ovegård, M., Salmi, J., Sendek, D., Vetemaa, M., 2018a. Competition for the fish – fish extraction from the Baltic Sea by humans, aquatic mammals, and birds. ICES J. Mar. Sci. 75, 999–1008. https://doi.org/10.1093/icesjms/fsx207
Hansson, S., Kautsky, L., Bergström, U., Bonsdorff, E., Jepsen, N., Lundström, K., Lunneryd, S.-G., Ovegård, M., Salmi, J., Sendek, D., Vetemaa, M., 2018b. Response to comments by Heikinheimo et al. (in press) on Hansson et al. (2018): competition for the fish—fish extraction from the Baltic Sea by humans, aquatic mammals, and birds. ICES J. Mar. Sci. 75, 1837–1839. https://doi.org/10.1093/icesjms/fsy087
Harding, K.C., Härkönen, T.J., 1999. Development in the Baltic Grey Seal (Halichoerus grypus) and Ringed Seal (Phoca hispida) Populations during the 20th Century. Ambio 28, 619–627.
Heikinheimo, O., Lehtonen, H., Lehikoinen, A., 2018. Comment to Hansson, S. et al. (2017): “Competition for the fish – fish extraction from the Baltic Sea by humans, aquatic mammals, and birds”, with special reference to cormorants, perch, and pikeperch. ICES J. Mar. Sci. 75, 1832–1836. https://doi.org/10.1093/icesjms/fsy054
Hobbs, B.F., Ludsin, S.A., Knight, R.L., Ryan, P.A., Biberhofer, J., Ciborowski, J.J.H., 2002. Fuzzy Cognitive Mapping as a Tool to Define Management Objectives for Complex Ecosystems. Ecol. Appl. 12, 1548–1565. https://doi.org/10.1890/1051-0761(2002)012[1548:FCMAAT]2.0.CO;2
Jetter, A.J., Kok, K., 2014. Fuzzy Cognitive Maps for futures studies—A methodological assessment of concepts and methods. Futures 61, 45–57. https://doi.org/10.1016/j.futures.2014.05.002
Jones, N. A., H. Ross, T. Lynam, P. Perez, Leitch, A. 2011. Mental models: an interdisciplinary synthesis of theory and methods. Ecology and Society 16(1):46. http://www.ecologyandsociety.org/vol16/iss1/art46
Kauhala, K., Korpinen, S., Lehtiniemi, M., Raitaniemi, J., 2019. Reproductive rate of a top predator, the grey seal, as an indicator of the changes in the Baltic food web. Ecol. Indic. 102, 693–703. https://doi.org/10.1016/j.ecolind.2019.03.022
Kontogianni, A.D., Papageorgiou, E.I., Tourkolias, C., 2012. How do you perceive environmental change? Fuzzy Cognitive Mapping informing stakeholder analysis for environmental policy making and non-market valuation. Appl. Soft Comput., Theoretical issues and advanced applications on Fuzzy Cognitive Maps 12, 3725–3735. https://doi.org/10.1016/j.asoc.2012.05.003
Kosko, B., 1986. Fuzzy cognitive maps. Int. J. Man-Mach. Stud. 24, 65–75. https://doi.org/10.1016/S0020-7373(86)80040-2
Kraufvelin, P., Pekcan-Hekim, Z., Bergström, U., Florin, A.-B., Lehikoinen, A., Mattila, J., Arula, T., Briekmane, L., Brown, E.J., Celmer, Z., Dainys, J., Jokinen, H., Kääriä, P., Kallasvuo, M., Lappalainen, A., Lozys, L., Möller, P., Orio, A., Rohtla, M., Saks, L., Snickars, M., Støttrup, J., Sundblad, G., Taal, I., Ustups, D., Verliin, A., Vetemaa, M., Winkler, H., Wozniczka, A., Olsson, J., 2018. Essential coastal habitats for fish in the Baltic Sea. Estuar. Coast. Shelf Sci. 204, 14–30. https://doi.org/10.1016/j.ecss.2018.02.014
LaMere K., Mäntyniemi S., Vanhatalo J., Haapasaari, P., 2020. Making the most of mental models: Advancing the methodology for mental model elicitation and documentation with expert stakeholders. Environmental Modelling and Software 124, 104589. https://doi.org/10.1016/j.envsoft.2019.104589
Leppäkoski, E., Helminen, H., Hänninen, J., Tallqvist, M., 1999. Aquatic biodiversity under anthropogenic stress: an insight from the Archipelago Sea (SW Finland). Biodivers. Conserv. 8, 55–70. https://doi.org/10.1023/A:1008805007339
Markóczy, L., Goldberg, J., 1995. A method for eliciting and comparing causal maps. J. Manag. 21, 305–333. https://doi.org/10.1016/0149-2063(95)90060-8
Meliadou, A., Santoro, F., Nader, M.R., Dagher, M.A., Al Indary, S. and Salloum, B.A., 2012. Prioritising coastal zone management issues through fuzzy cognitive mapping approach. Journal of environmental management, 97, pp.56-68.
Özesmi, U., Özesmi, S.L., 2004. Ecological models based on people’s knowledge: a multi-step fuzzy cognitive mapping approach. Ecol. Model. 176, 43–64. https://doi.org/10.1016/j.ecolmodel.2003.10.027
Özesmi, U., Özesmi, S.L., 2003. A Participatory Approach to Ecosystem Conservation: Fuzzy Cognitive Maps and Stakeholder Group Analysis in Uluabat Lake, Turkey. Environ. Manage. 31, 0518–0531. https://doi.org/10.1007/s00267-002-2841-1
Pacilly, F.C.A., Groot, J.C.J., Hofstede, G.J., Schaap, B.F., van Bueren, E.T.L., 2016. Analysing potato late blight control as a social-ecological system using fuzzy cognitive mapping. Agron. Sustain. Dev. 36, 35. https://doi.org/10.1007/s13593-016-0370-1
Pahl-Wostl, C., Craps, M., Dewulf, A., Mostert, E., Tabara, D., Taillieu, T., 2007. Social Learning and Water Resources Management. Ecol. Soc. 12. https://doi.org/10.5751/ES-02037-120205
R Core Team, 2018. R: A language and environment for statistical computing.
Salliou, N., Barnaud, C., Vialatte, A., Monteil, C. 2017. A participatory Bayesian Belief Network approach to explore ambiguity among stakeholders about socio-ecological systems. Environmental Modelling & Software 96: 199-209.https://doi.org/10.1016/j.envsoft.2017.06.050.
Salmi, J.A., Auvinen, H., Raitaniemi, J., Kurkilahti, M., Lilja, J., Maikola, R., 2015. Perch (Perca fluviatilis) and pikeperch (Sander lucioperca) in the diet of the great cormorant (Phalacrocorax carbo) and effects on catches in the Archipelago Sea, Southwest coast of Finland. Fish. Res. 164, 26–34. https://doi.org/10.1016/j.fishres.2014.10.011
Sluis, T. van der, Arts, B., Kok, K., Bogers, M., Busck, A.G., Sepp, K., Loupa-Ramos, I., Pavlis, V., Geamana, N., Crouzat, E., 2019. Drivers of European landscape change: stakeholders’ perspectives through Fuzzy Cognitive Mapping. Landsc. Res. 44, 458–476. https://doi.org/10.1080/01426397.2018.1446074
Solana-Gutiérrez, J., Rincón, G., Alonso, C., García-de-Jalón, D., 2017. Using fuzzy cognitive maps for predicting river management responses: A case study of the Esla River basin, Spain. Ecol. Model. 360, 260–269. https://doi.org/10.1016/j.ecolmodel.2017.07.010
Stier, A.C., Samhouri, J.F., Gray, S., Martone, R.G., Mach, M.E., Halpern, B.S., Kappel, C.V., Scarborough, C., Levin, P.S., 2017. Integrating Expert Perceptions into Food Web Conservation and Management. Conserv. Lett. 10, 67–76. https://doi.org/10.1111/conl.12245
Taber, W.R., 1991. Knowledge processing with fuzzy cognitive maps. Expert Syst. Appl. 2, 83–87.
Uusitalo, L. 2007. Advantages and challenges of Bayesian networks in environmental modelling. Ecological Modelling 203:312-318.van Vliet, M., Kok, K., Veldkamp, T., 2010. Linking stakeholders and modellers in scenario studies: The use of Fuzzy Cognitive Maps as a communication and learning tool. Futures 42, 1–14. https://doi.org/10.1016/j.futures.2009.08.005
Vasslides, J.M., Jensen, O.P., 2016. Fuzzy cognitive mapping in support of integrated ecosystem assessments: Developing a shared conceptual model among stakeholders. J. Environ. Manage. 166, 348–356. https://doi.org/10.1016/j.jenvman.2015.10.038
Venables, W.N., Ripley, B.D., 2002. Modern Applied Statistics with S, Fourth Ed. Springer, New York.
Viirret, E., Raatikainen, K.J., Fagerholm, N., Käyhkö, N., Vihervaara, P., 2019. Ecosystem Services at the Archipelago Sea Biosphere Reserve in Finland: A Visitor Perspective. Sustainability 11, 421. https://doi.org/10.3390/su11020421
Virtanen, E.A., Viitasalo, M., Lappalainen, J., Moilanen, A., 2018. Evaluation, Gap Analysis, and Potential Expansion of the Finnish Marine Protected Area Network. Front. Mar. Sci. 5. https://doi.org/10.3389/fmars.2018.00402
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.