ICES Journal of Marine Science: Journal du Conseil Advance Access originally published online on February 13, 2007
ICES Journal of Marine Science: Journal du Conseil 2007 64(4):825-833; doi:10.1093/icesjms/fsm002
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Management measures and fishers' commitment to sustainable exploitation: a case study of Atlantic salmon fisheries in the Baltic Sea
1 Faculty of Education, University of Oulu, PO Box 2000, Oulu, Finland
2 Finnish Game and Fisheries Research Institute, PO Box 2, Helsinki, Finland
3 Department of Biological and Environmental Sciences, University of Helsinki, PO Box 65, Finland
Correspondence to P. Haapasaari: tel: +358 8 553 3629; fax: +358 8 553 3600; e-mail: paivi.haapasaari{at}oulu.fi
Haapasaari, P., Michielsens, C. G. J., Karjalainen, T. P., Reinikainen, K., and Kuikka, S. 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: 825–833.Fisheries management aimed at sustainable exploitation may affect fish populations indirectly by influencing human behaviour. We propose a methodology that includes stakeholders' opinions, perceptions, and resulting behaviour, within assessment models designed to evaluate the impact of different management measures on the stocks. Based on interviews and a questionnaire, we use a Bayesian belief network to examine which factors determine fishers' commitment to sustainable fisheries goals, what impact commitment has on exploitation rate, and what measures can be taken to improve commitment. In addition to exploring alternative management measures, the analysis evaluates knowledge actions (providing information to fishers) and commitment actions (intended to increase trust, consensus, and cooperation). The method is applied in a Baltic Sea case study in which commitment is important for successful recovery of Atlantic salmon (Salmo salar) stocks. The results indicate that the more fishers rely on fishing as their source of income, the less is their commitment and the smaller is the impact of changes in commitment on subsequent catches. The results suggest that commitment can be improved by selecting management measures favoured by fishers and by combining them with commitment and knowledge actions.
Keywords: Atlantic salmon, Bayesian belief network, commitment, compliance, fisheries management, fishers' perceptions, interdisciplinary research, restoration, stakeholders
Received 30 June 2006; accepted 14 November 2006; advance access publication 13 February 2007.
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Introduction |
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 Top Introduction Approach and methodsResultsDiscussionReferences |
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Currently, European fisheries management is trying to move away from reactive ad hoc measures and towards proactive solutions that focus on long-term goals (CEC, 2002). Besides the potential benefits of sustainable exploitation, long-term objectives allow the fishing industry to plan for the future, especially if annual fluctuations in total allowable catch (TAC) can be reduced (Kell et al., 2006). Determining appropriate long-term management strategies is facilitated by simulating the underlying fishery system with all its inherent uncertainties and evaluating the potential of alternative procedures to achieve management goals (McAllister et al., 1999).
Effective management must consider fishers' reactions to management decisions. The uncertainty caused by fishers' behaviour must be acknowledged by managers when planning, because implementation of those plans is critically dependent on support from the industry. The importance of early involvement of fishers and other stakeholders in management processes, as a prerequisite for developing sustainable fisheries, is also emphasized in the revised Common Fisheries Policy (CEC, 2002). Therefore, understanding stakeholder attitudes, opinions, and perceptions is essential to the evaluation of different management strategies. If fishers accept the intended measures and comply with them, the probability of achieving the ultimate goal of sustainable exploitation will be enhanced. However, if they oppose the intended measures, the biological, social, and economic effects may become unpredictable. Although predictability may be enhanced by tight legislation and strict enforcement, we believe that changing the relationship between fishers and the management procedure may have the same effect.
We examine which factors determine fishers' commitment to sustainable fisheries goals, to what extent commitment affects exploitation rates, and which measures can improve commitment. We use Bayesian belief networks (BBNs) as a methodology for including stakeholders' viewpoints within the evaluation framework of management procedures and apply them to a case study of the Baltic Sea, where the ultimate management goal is the recovery of the Atlantic salmon (Salmo salar) stocks.
The main idea of the Bayesian approach (Gill, 2002) is to update existing (a priori) information using probabilities, as a measure of uncertainty. Unconditional probabilities are independent of other variables and conditional probabilities describe how strong variables depend on each other. BBNs are implemented by constructing a graphical model describing causal relationships between variables and by loading both the variables and the causal relations with probability values describing the uncertainty (Pearl, 2000). BBNs are now commonly applied in fisheries research (Kuikka et al., 1999; Little et al., 2003; Uusitalo et al., 2005).
Defining commitment
Commitment implies a consistent line of activity in human behaviour (Becker, 1960). It means that parties voluntarily restrict their behavioural alternatives in response to the demands of a commonly agreed long-term orientation and are prepared to accept short-term sacrifices in the expectation of long-term benefits (Dwyer et al., 1987; Gundlach et al., 1995). Dasgupta (2000) describes commitment as a situation in which an explicitly or implicitly agreed-upon course of action is credible, i.e. the actors are trusted to act accordingly. This is because an initially adverse situation is expected to change into one that is beneficial to all. When pondering whether to commit oneself, each participant compares the costs of commitment with the anticipated gains. These costs represent the price each participant has to pay to trust others to act according to the agreement. Gundlach et al. (1995) emphasize three components in commitment: an input affirming the relationship and creating self-interest towards it; an enduring attitudinal component signifying the intention to create a stable relationship; and a temporal component encompassing a long-term orientation.
The term "commitment" is widely used in the rhetoric of sustainable development and legally non-binding international agreements (so-called "soft law"; Baker, 1997; Shelton, 2003). It has been analysed and applied in different contexts in sociology and economics (Lincoln and Kalleberg, 1990; Meyer and Allen, 1997). Although fisheries research has not focused on commitment, Nielsen (1994) and Paramor et al. (2003), for instance, highlight the importance of stakeholder support and compliance for successful management. Fishers' reactions have traditionally been studied more in terms of compliance (Hönneland, 1999; Nielsen, 2003; Hatcher and Pascoe, 2006) and, whereas compliance is related to imposed and legally binding management measures, commitment relates to a general attitude of voluntary support. Commitment is not necessarily an objective in itself, but may be seen as an aid to increase compliance and to decrease enforcement costs.
Therefore, commitment to sustainable exploitation means that fishers consistently act in ways that support the management goal. Such a promise is informal, or may even be implicit, but it leads indirectly to acceptance of management measures, if fishers can be convinced that such measures are in their own long-term interest. Consequently, commitment is the result of an evaluation process, related to the extent to which the values and goals of the individual are congruent with those of the management plan (Mowday et al., 1982) and the resultant costs and gains (Dasgupta, 2000). The costs imply self-control of one's fishing activity, possibly leading to short-term losses. The potential long-term gains relate to expectations of increased catches, after depleted stocks have recovered. However, such gains are often uncertain, because they depend on both ecological factors and the actions of fellow fishers.
Salmon management in the Baltic Sea
In response to declining stocks of wild salmon in the Baltic Sea, the International Baltic Sea Fisheries Commission (IBSFC) introduced the salmon action plan (SAP) programme in 1997, a programme aimed at strengthening the remaining stocks and at rebuilding stocks in rivers where salmon had disappeared, but where environmental conditions were considered favourable enough for successful re-introductions. In Finland (Romakkaniemi et al., 2003), the SAP programme targeted two rivers in which wild salmon stocks survived ("established salmon rivers": Tornionjoki and Simojoki) and three rivers where stocks might be rebuilt ("potential salmon rivers": Kuivajoki, Kiiminkijoki, and Pyhäjoki). The mouths of these rivers are located near the terminal fishing areas of the dammed rivers Kemijoki, Iijoki, and Oulujoki, where sea-ranched hatchery-reared salmon are harvested (Figure 1; ICES, 2006).
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Salmon within the area are fished by five heterogeneous user groups, ranging from professional fishers operating along the coast to recreational fishers operating within the rivers (Table 1). Overall, the commercial salmon fishery in the Baltic has gradually become less profitable owing to declining prices, reduced marketability because of high levels of dioxin, and increased predation by seals. Therefore, landings by professionals have been below the TAC for the past ten years. In contrast, non-commercial fisheries are continuously increasing in importance and currently account for 21% of the total salmon catches in the Baltic Sea (ICES, 2006).
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Obviously, the stock-restoration process would be enhanced were it supported by all user groups. However, little is known about fishers' attitudes and their commitment to the SAP programme and related management measures. Also, the number of different measures applied so far is limited, and predicting the effect of potential alternative measures is subject to high uncertainty.
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Approach and methods |
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TopIntroductionApproach and methodsResultsDiscussionReferences |
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Bayesian belief networks
The BBN methodology (Jensen, 2001) was used to (i) graphically present the factors influencing fishers' commitment to sustainable exploitation, (ii) quantitatively express the effect of these factors on commitment, (iii) predict changes in commitment given expected future changes in the causal factors as a result of changes in management measures, and (iv) predict the effect of changes in commitment on future catches and the exploitation of the stocks. The BBN model was constructed in three steps (Figure 2), defined below.
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Step 1. The first phase addressed the collection of data and information to formulate model structure. A preliminary structure was based on material obtained through focused, semi-structured interviews (Flick, 1998) of 35 key persons, with the main aim of exploring factors potentially affecting commitment to the restoration of salmon stocks. The informants represented different groups of fishers and fisheries administrators. The interviews were transcribed and coded with QSR NVivo software for qualitative data analysis (Gibbs, 2002). This led to a preliminary structure using Hugin software (Madsen et al., 2005). Additional data indicating the factors affecting commitment were obtained through a structured questionnaire (Babbie, 1995) sent to 1000 salmon fishers within the study area, in which each node/arrow within the preliminary BBN was covered by one or more questions. Questions related to perceptions on factors affecting stock restoration (e.g. "Do/Not"), their opinions on management and the SAP programme, and issues such as trust and social justice (e.g. "Fishing regulations have treated me justly: agree/slightly agree/slightly disagree/disagree"). The sample included 20% registered (semi-)professionals (return rate, rr = 44%), 18% household fishers registered at fishers' and landowners' associations (rr = 22%), and 62% recreational fishers representing a random sample from the 2003 lists of licence holders within the different SAP rivers (rr = 30%). No rewards were promised for responding, nor were any reminders sent. In all, 33% of the questionnaires were returned. The results were used to define the final model structure and to complete the conditional probability tables underlying the nodes and arrows directly affecting commitment.
In addition to the causal factors influencing commitment directly, the model also includes action nodes, which indirectly affect commitment through the causal factors. As many of the management, commitment, and knowledge actions evaluated had never been applied, questions about their impacts on the causal factors were inevitably of a more hypothetical nature, and appropriate answers might have been difficult to obtain through a questionnaire (e.g. "Do you think the media provide an effective way to share information about current fisheries issues? Does the information reach fishers, and Does communication through the media evoke confidence? Why?"). The additional information required on these action nodes was obtained through interviews with six key informants who were considered to be opinion leaders.
Step 2. This phase addressed how the information obtained through the questionnaires and interviews could be used to complete the conditional probability tables underlying the nodes and arrows, so populating the BBN model. We assumed that the conditional probability distributions according to the results from the questionnaire were multinomial and that the prior uncertainty followed a Dirichlet distribution, ensuring that their sum was equal to one. Therefore, the parameters of the posterior probability distribution to be input within the conditional probability tables could be calculated as the sum of the parameters of the prior distribution and the observed counts (Gelman et al., 1995). In the case of interviews, the qualitative answers of key informants were summarized in one-sentence statements, then converted applying Druzdzel's (1996) translation table between verbal and numerical probabilities. The probabilities resulting from different interviews were averaged. Therefore, the final tables were based on data obtained from the questionnaire and on expert views (Burgman, 2005; Uusitalo et al., 2005).
Step 3. Once the conditional probability tables were populated, the network was ready for analysis. The final BBN model does not describe thoughts of individual fishers, but rather synthesizes heterogenic data collected from many fishers, describing their thinking and behaviour in general terms and indicating potential changes in their commitment, on average, as a group. Although parts of the network might be described in more detail, these limitations are accounted for by greater uncertainties in some of the probabilistic dependencies.
Model structure
Each arrow within the network (Figure 2) describes probabilistic dependencies between variables. The uppermost unconditioned node, "way-of-fishing", includes the five groups of fishers distinguished (Table 1). This node, together with the node "value-of-equipment", contains information on the economic factors related to fishing. The links between these two nodes and commitment indicate the relation between economic dependence and willingness to commit. This includes an implicit assessment of the costs and possible gains, i.e. the economic effect of committing to sustainable exploitation.
The nodes "belief in river conditions" and "belief in wild salmon" convey whether the SAP project is considered worth committing to (Morgan and Hunt, 1994), i.e. whether fishers believe in the potential for stocks to recover (are convinced that wild salmon return to their rivers, that river conditions are suitable for salmon to reach the spawning grounds and to reproduce, and that stocks can be rebuilt at all). Their belief that wild salmon make up a substantial percentage of the catch implies that they believe that their fishing harms recovery. Fishers interpret these issues through their "fishers' knowledge", bound to their diverse social and cultural contexts. Therefore, there are several parallel interpretations (Heikkilä, 2006). According to Karjalainen and Habeck (2004), fishers' knowledge can be defined as a synthesis of the interaction between perception (individual experiences and observations) and cognition, including traditional knowledge (Ingold and Kurttila, 2000) and scientific knowledge.
The variable "trust in actors" represents the individual evaluation of the trustworthiness (reliability and integrity) of the other actors (other fishers, researchers, officials, etc.) to act according to the same management goal (Morgan and Hunt, 1994). Dasgupta (2000) distinguishes between two types of conditions around which the concept of trust revolves: when an individual has to choose his own course of action without knowing how others will act, and when an individual does not fully know the disposition or motivation of the persons with whom he should transact. In this specific case, the first type refers especially to trust in other fishers and the second to trust in researchers and other authorities working behind the scenes for the SAP. Another aspect of trust is represented by "confidence in management". This node represents confidence in the ability of management agencies to make unbiased decisions and to take appropriate measures, based on reliable and valid scientific research (Luhmann, 2000).
The sense of "social justice" was so prominently present in the responses that it deserved a separate variable. Social justice refers to the way in which basic rights, duties, and advantages are distributed in society, and how the economic opportunities and social conditions are divided among societal sectors (Rawls, 1986). Here, this broad concept has been applied to a specific local context and refers to viewpoints on issues of socially just management, for instance regarding the distribution of access rights (Walzer, 1983; Elster, 1992; Hernes et al., 2005). The focus here is on the common-sense concept of fairness, i.e. requirements of equal treatment and equal contribution (Elster, 1992). Therefore, the variable "sense of justice" indicates how fishers feel treated by the administration and influences their trust in and distrust of state authorities.
Because we wanted to examine whether commitment to management goals might be improved, the model also includes action nodes, i.e. variables that could change the states of other variables. Three types of node involving action of the administration have been identified: management, commitment, and knowledge actions (Table 2). Management actions relate to alternative policies and/or regulations for the salmon fishery. By including different options, the type of policy that is most generally accepted by fishers can be examined. Commitment actions refer to different ways of increasing trust, consensus, and cooperation among actors. Knowledge actions relate to sharing the latest scientific information, its shortcomings, and its consequences with the fishers.
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From a management perspective, the most interesting point is how commitment could affect exploitation rate. By linking the variables "commitment" and "value of equipment" with "catch", we investigate if and to what extent commitment might reduce catches and also examine the impact of economic factors on both commitment and catch. The node "salmon abundance" refers to perceived changes in abundance available to the fishery. This node, in combination with the node "catch", would allow the linking of this socio-economic model to biological BBNs developed for stock assessment purposes (Hammond and O'Brien, 2001).
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Results |
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TopIntroductionApproach and methodsResultsDiscussionReferences |
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Fishers' perceptions of the potential for Baltic salmon stocks to recover appear a real stumbling block to commitment to the SAP. The less their belief, the more difficulty they have accepting that their own fishing activity could have a major impact on recovery. The probability distributions for the node "belief in wild salmon" indicate that all fishers, except those operating within or near terminal fishing areas, have a strong perception of catching notable quantities of wild salmon. Most fishers (76%) are convinced that conditions in the Tornionjoki are suitable for salmon reproduction, but belief in other SAP rivers is much less. About half of all respondents believe that conditions in the Simojoki are suitable, whereas 24%, 16%, and 15% believe they are in the Kiiminkijoki, Kuivajoki, and Pyhäjoki, respectively. The perception of fishers living near the Pyhäjoki is even worse: only 9% are convinced that conditions are suitable in that river.
The observation that fishers do not believe that salmon can successfully reproduce in the "potential salmon rivers" poses problems, especially for those located close to terminal fishing areas. Within those areas, fishers assume that they are catching predominantly hatchery-reared salmon, but actually may be intercepting passing wild salmon during their spawning migration. Because their belief in river conditions is low, they do not see their own activity as an obstacle to recovery of wild salmon stocks in those rivers. Only fishers in the Tornionjoki loudly express concern about the effects of fishing on "their" stock.
The variable "trust in actors" reveals a deep-rooted distrust, originating from many past and present conflicts. Only 27% of professionals trust recreational fishers living along the SAP rivers, and just 36% trust tourist anglers exploiting these rivers. No more than 25% and 15% of recreational fishers trust professionals and semi-professionals, respectively. Another gap exists between fishers and the administration: 38% of all respondents express trust towards the Finland ministry of fisheries, 36% towards the EU, and 43% towards the IBSFC. Salmon researchers and local fisheries officials score higher, with 81% and 72% of the respondents putting trust in them, although the degree varies among groups: only 58% of professionals trust salmon researchers, whereas 91% of recreational fishers do so.
Of course, the node "sense of justice" affects the node "confidence in management". The worse fishers feel they are treated, the less they believe in the benefits of regulations (and research). On average, of those feeling justly treated, 75% have confidence in management, as opposed to 27% of those feeling unjustly treated. Lack of confidence in management obviously goes hand in hand with a lack of commitment (and interest in collaboration). The more fishers depend on fishing as their source of income, the more they feel unjustly treated and the less they feel committed to the SAP. As much as 73% of all professionals feel treated at least slightly unjustly, whereas the values for semi-professionals (II and III), household fishers, and recreational fishers are 51%, 65%, 48%, and 40%, respectively. However, superimposed on this are regional differences: 71% of all fishers in the Tornionjoki area and 67% of those in the Kiiminkijoki area feel unjustly treated. These results reflect serious conflicts in those areas, caused by the measures taken to protect salmon and to distribute access rights (Salmi and Salmi, 2005). In the terminal fishing areas of the dammed rivers Kemijoki and Iijoki, no restrictions or regulations apply, resulting in only 33% and 37% of the fishers feeling unjustly treated.
In general, the most important factor determining commitment is economic interest. The more fishers depend on fishing as a source of income, the less committed they are. For example, 71% of professionals who have invested <
50 000 feel at least somewhat committed, whereas the percentage is just 50% for those who have invested more. For recreational fishers, 88% of those who have invested <2000 are committed, compared with 83% of those who have invested more.
Figure 3 shows the predicted effectiveness of various management, commitment, and knowledge actions in enhancing commitment (as measured on a numerical utility scale between 0 and 100 points; Jensen, 2001). The choice of the management action appears to have the most pronounced effects, with considerable differences among different groups. The management action that treats all fishers equally ("cheese slicer"; obviously related to a sense of social justice) is preferred by all groups and would result in the greatest commitment overall (61 points). For most groups, a system by which measures concentrate on catching the obligatory released hatchery-reared salmon would also increase commitment (57–71 points). Only professionals do not favour this action (41 points), because many do not have access to the terminal fishing areas in which harvesting of reared salmon is most effective, and because they exploit mainly areas where wild and reared salmon are mixed. If a quota system were introduced, commitment would increase above current levels among both professionals and recreational fishers. These two groups appear to have more confidence in the allocation process than semi-professionals and household fishers. The introduction of an ITQ system or the catch-and-release of wild salmon (Siira et al., 2006) would reduce overall commitment below current levels (from 55 to 51 points). Fishers are not familiar with the ITQ system and might fear unfavourable consequences. Also, fishers are not inclined to release non-fin-clipped (wild) salmon they have caught, because the chance that these fish, after capture, might successfully spawn in their home rivers is considered small anyhow. Another possibility is that they may not trust each other to release wild fish. Moreover, some may doubt whether it is feasible to separate reared and wild salmon on the basis of fin clipping. Overall, professional fishers find this action easier to accept than the other groups do.
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For all groups except recreational fishers, any commitment or knowledge action would enhance commitment above its current level. The recreational fishers already seem so committed that further actions will have no effect. The model does not predict major differences between the beneficial effects of the different actions, but the impact of knowledge and commitment actions is strongest when carried out together. The results imply that allowing fishers to express their opinions and to take an active part in the management process would enhance their commitment. Combining commitment and knowledge actions with the most favoured management action (i.e. cheese slicer), an average utility of 62 points might be reached. The values obtained for the individual groups were: I, 51; II, 56; III, 61; IV, 69; and V, 71.
The model also indicates the effect of commitment on fishing behaviour in terms of total catches (Figure 2). The effect becomes weaker as the role that fishing plays in the livelihood increases. Catches of completely committed professionals are on average 9% smaller than catches of their uncommitted colleagues. Commitment would reduce the catches of household and recreational fishers by as much as 38%.
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Discussion |
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TopIntroductionApproach and methodsResultsDiscussionReferences |
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Our results indicate that providing fishers with the opportunity to participate in management would enhance their commitment to sustainable exploitation of the salmon stocks. The benefits of such co-management from the point of view of rule compliance have been discussed in many studies (e.g. Jentoft, 1989; Nielsen and Vedsmand, 1999). Commitment to sustainable exploitation and to the necessary measures to achieve such a goal is a critical component for successful management, especially in the long term. Getting fishers committed reduces uncertainty in the outcome of management. Although commitment may not have to be studied in detail for every fishery, an understanding of the mechanisms and processes involved is required for proper planning and implementation of recovery plans.
We have demonstrated how qualitative measures of commitment can be quantified by condensing verbal information to a model of probabilities using a BBN, and how this model can be used to predict fishers' responses to different combinations of management, knowledge, and commitment actions. BBN methods provide a relatively simple and transparent approach to supporting discussion among stakeholders, because the consequences of different actions following different stakeholders' viewpoints can be demonstrated easily (Bacon et al., 2002). Perceptions and opinions of stakeholders are usually qualitative and rarely find their way into quantitative modelling approaches. The fact that Bayesian methods allow the use of different sources of information may help fishers to accept the results of the analysis. The integration of qualitative information within quantitative models closes the gap between social and natural sciences in this type of research. Even for such an abstract concept as commitment, a BBN provides a workable tool to integrate fishers' attitudes into a management planning framework.
The return rate for the questionnaire was not high. Supposedly, there are different reasons for non-responding, e.g. (i) inexperience, especially among (semi)professionals and household fishers, in expressing thoughts through ticking and writing; (ii) ignorance among recreational fishers on the salmon issue; and (iii) distrust among household fishers (using commercial gear types) of the aims of the research. On the whole, however, the low response rate does not indicate a significant response bias, although some groups among the household fishers may not behave the same way that the respondents do.
Applying Bayesian methods to analyse fishers' opinions and behaviour has led to exhaustive rethinking on the basis of the modelling approach. It was difficult to regard human behaviour as purely causal, to piece together the directed, acyclic links between factors, and to restrict the number of variables and links to a tractable level. However, applying the approach also allowed systematic discussions about planning of data collection and the appropriate analysis, focusing on the main factors as well as summarizing all available information in a coherent framework.
The resulting BBN model is only one representation of reality, based on the views of fishers at a particular point in time. The resulting predictions are based on these opinions and perceptions. If opinions change in future and additional factors turn out to influence commitment or catches, the model will no longer be valid. Although additional explanatory factors affecting commitment (e.g. the possibility to utilize other fish species) might have been added to the network, not all such factors can necessarily be influenced by management action, and their incorporation would merely reduce the impact of the explanatory factors that can be influenced. Such additional factors are implicitly accounted for within the current model by the uncertainties in the probabilistic dependencies.
We emphasize the importance of multidisciplinary cooperation in fisheries management. Although the need for multidisciplinarity is widely acknowledged in applied problem-solving sciences, case studies providing methodological solutions are still rare. Our experience is that this type of BBN modelling promotes the integration of social, economic, and biological analyses, giving way to a more holistic approach in fisheries research. Lane and Stephenson (1995, 1998) and Stephenson and Lane (1995) made a plea for such a conceptual change and the development of a new discipline of "Fisheries Management Science", which would require combining biological considerations with operational, social, and economic considerations. To realize this, they call for a framework in which objectives from all elements of the fishery can be defined, articulated, and evaluated in the light of others. Our BBN model appears to have achieved this type of integration, and also created a suitable framework.
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Acknowledgements |
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We thank all fishers who contributed to this study through interviews or questionnaires. The methodology used benefited greatly from the constructive feedback of Atso Romakkaniemi, Jaakko Erkinaro, Samu Mäntyniemi, Tapani Pakarinen, and Keijo Juntunen. The study was funded by the Finnish Academy through the Baltic Sea Research Programme (BIREME) and by EU project 502289: COMMIT: "Creation of Multi-annual Management Plans for Commitment".
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