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Local ecological knowledge and practical fisheries management in the tropics: A policy brief
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Marine Policy 30 (2006) 794–801 www.elsevier.com/locate/marpol
Local ecological knowledge and practical fisheries management in the tropics: A policy brief Douglas Clyde Wilson, Jesper Raakjær, Poul Degnbol Institute for Fisheries Management and Coastal Community Development (IFM), The North Sea Centre, 9850 Hirtshals, Denmark Received 12 January 2006; accepted 24 February 2006
Abstract This policy brief is a summary product of seven case studies examining the integration of local ecological knowledge in fisheries management. Each case began with a series of in-depth interviews with local fishers, after which their answers were examined using both social and biological approaches to assess the possibility of using the information as the basis of simple, valid and locally acceptable indictors for fisheries management. We found that allocation and knowledge issues are closely interlinked and must be addressed in concert, and that the negotiation of shared understandings between multiple sources of knowledge must be a continuous process within an adaptive framework rather than a question of identifying a fixed set of indicators. r 2006 Elsevier Ltd. All rights reserved. Keywords: Local ecological knowledge; Fisheries management; Indicators
1. Introduction Fisheries management cannot be effective if it is not considered legitimate by stakeholders. This is especially true when institutions are weak and implementation relies on voluntary compliance. At the same time fisheries management must address a wider range of objectives such as those identified by the World Summit on Sustainable Development, 2002. These objectives imply an extended knowledge base for management including ecosystem considerations and rebuilding of the resource base for fisheries. Meeting the need for knowledge which is both legitimate and relate to the extended considerations in fisheries management is a challenge which requires novel approaches to management institutions. As a contribution to efforts to meet this challenge, the Knowledge in Fisheries Management (KNOWFISH) project was implemented to improve our understanding of how multiple sources of knowledge, particularly local ecological knowledge (LEK) and research-based knowledge (RBK) may
Corresponding author. Tel.: +45 98 94 28 55; fax: +45 98 94 42 68.
E-mail addresses: [email protected] (D.C. Wilson), [email protected] (J. Raakjær), [email protected] (P. Degnbol). 0308-597X/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.marpol.2006.02.004
both be used to inform management. This policy brief is a summary product of this project. 1.1. The KNOWFISH project objectives This project addressed the need to develop new types of knowledge that are appropriate to the complexity of tropical aquatic ecosystems and the way management institutions in developing countries actually work. The central objective of the project was to evaluate the potential use of the LEK of fishers in developing the knowledge base for fisheries management. The strategy employed for this evaluation was a three-way discussion between fishers, biologists and social scientists around the subject of what sorts of indicators of ecosystem health would make sense in light of both the LEK of the fishers and the RBK of the biologists. The development of less complex indicators of ecosystem health and exploitation status that are both scientifically valid and widely acceptable by fisheries stakeholders is in itself an important strategy for the management of tropical aquatic ecosystems. Indicators are an important tool for managing fisheries that are compatible with the management institutions in developing countries. Indeed, their use
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implies that management be adaptive rather than being based on predictions, a trait that can be of great benefit in developing countries. Use of indicators in an adaptive framework will be more robust and less costly than traditional stock assessment approaches [1]. 1.2. Methods The project was carried out in seven case studies. Three of these case studies were of artisanal fisheries: (1) lake fisheries in Malawi; (2) freshwater lake and river fisheries in Zambia, and; (3) the fisheries in the Sedone River, Laos. Two of them were medium- and large-scale off-shore marine fisheries: (4) the shrimp fishery at the Sofala Bank, Mozambique, and; (5) the pelagic fishery in South Africa. And two of them were mixed in-shore /off-shore marine fisheries: (6) the coastal fisheries of the Mekong Delta, Dam Doi district, Viet Nam, and; (7) the coastal fisheries in Khanh Hoa Province, Viet Nam. In addition to these seven case studies two cross-cases survey methodologies were used. The first of these was consensus analysis [2], an anthropological method used to study the degree to which knowledge is shared and how such shared knowledge is distributed. The second was a standard sociological household survey that was used to evaluate various ways of structuring cooperative fisheries management that are in use in southern Africa and which have implications for how knowledge is used in decisionmaking. The same four basic steps were carried out in each case. In the first basic step, LEK was identified by interviewing stakeholders using standard methods devised for LEK interviews such as open-ended questions, map drawing and developing historical timelines about ecological changes in the fishery. Beyond a general description of the fishery in question the main products of these interviews were a set of ‘‘candidate indicators’’ which then became the main focus on the discussions from that point on. The second product was a list of statements that respondents had made about their fishery. These statements were in two sets. Throughout the remainder of the KNOWFISH project, other fishers, as well as non-fishing stakeholders, were presented with these two kinds of statements in various ways and asked to respond to them. The first set was a series of very simple factual observations that other respondents could judge as ‘‘true’’ or ‘‘false’’. Only a portion of these statements was related directly to the candidate indicators because the main point was to use them in the consensus analysis, and the focus of that analysis was on the nature of the LEK in general. However, the consensus analysis did, of course, use
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statements that were relevant to the indicators, so it provided information about the reliability of information relevant to the candidate indicators. The second set of statements was about causal relationships with relevance for fisheries management. These causal statements were still extracted from the initial interviews in as simple a form as possible while still retaining causal content. The second and third basic steps were the disciplinary evaluations of the candidate indicators. Biologists and ecologists evaluated them in terms of their agreement with the biological literature, and where possible with data on the actual fisheries under study. Sociologists and anthropologists evaluated them in terms of how they fit into the general political discussions around fisheries management. Part of this work was done by soliciting responses to the causal statements and relating these responses to management issues. The social scientists also carried out the consensus analyses, as well as other methodologies from the anthropology of knowledge such as pile sorts and Qsorts [3], which are methods of comparing perceptions of causal relationships, and the review of public discussions of fisheries knowledge issues in newspapers and elsewhere. The fourth basic step was a synthesis, which mainly took the form of the social and natural scientists working together to create a final report on their cases. These individual case reports, as well as the survey results, are available and are expected to be published in various outlets. This policy brief summarizes the main results of the KNOWFISH project with an eye towards practical implications for management. Beyond this introduction it contains five sections. Section 2 synthesizes the artisanal fishery cases, Section 3 synthesizes the cases dealing with mixed coastal fishery cases in Viet Nam, and Section 4 synthesizes the two cases dealing with capital-intensive marine fisheries. Next, Section 5 summarizes the results of the two main cross-case analyses, the consensus analysis and the household survey. Section 6 then outlines the main results of KNOWFISH project. 2. Main results from the artisanal cases [4–8] 2.1. The problematic idea of an ‘‘indicator’’ Perhaps the key finding from the work with artisanal fishers is that the idea of an ‘‘indicator’’ is something that arises from the need for management decision-making and is not an idea that fits immediately into the way the fishers think about the fishery. In all cases a body of LEK was found that it was cohesive enough to characterize, but tying that knowledge directly to management proved very difficult. The idea of an ‘‘indicator’’ did not communicate well to the fishers and several expressed disbelief that any observation made in the present could give any meaningful information about future fish catches. The temporal focus of the LEK is on a smaller scale. The only general
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exception to this was in respect to water levels. No really new information from a formal biological standpoint was found through the LEK interviews. Even if the basic idea of an indicator were a good fit with the LEK outlook, that would just be the beginning of working out a common idea of what the indicators could be. Indicators as a basis for management decisions could also prove difficult due to both the limited trust in data and disagreements regarding the significance of specific observations. With regard to the observational data the differences between researchers, managers and fishers include differences in possibilities for aggregation of data and information and data storage capacities. 2.2. LEK and specific potential indicators One general finding was that observations of size and species composition were more reliable types of information than catch rates. In Laos the consensus interviews found that statements about that fish size and diversity had the highest average of ‘‘correct’’ answers, correct being defined by the cultural consensus as discussed below in the section on consensus analysis. This highest level was still only 80%, indicating that one of every five fishers would give a different response. Observations about water quality had the lowest levels of agreement at around 50%, indicating that every other fisher would give a different answer. While for most of the other types of indicators 70% answered correctly. A similar pattern emerged in Zambia where separate analyses were done for Lake Mweru and the Luapula River. In the lake area two consensus statements were directly related to long-term changes in overall catch rates. The mean percentage of correct answers for these two statements was 61%. Four statements that appeared in the analyses for both the lake and the river were related to changes in species composition and two statements in the river area were related to changes in the size structure of fish. The mean percentage of correct answers for these two statements was also around 80%. 2.3. Potentials for ‘‘common ground’’ Beyond the basic finding that the idea of an indicator is not one that fits easily into LEK, the specific economic interests of the different groups obviously plays a role in what are seen as important, and sometimes even true, facts. In Laos a pile sort was used in which respondents, working in groups, were asked to sort a set of items that had emerged as important in the in-depth interviews into as many categories as they saw fit. What emerged were some clear differences in perspectives between the fishers and the government officers. One difference was very clear; the fishers associated the lost fish abundance primarily with changes in habitat, while the officers associate it primarily with fishing. The pile sort results demonstrate a clear difference in both the scale at which the overall fisheries system is perceived and the main drivers of its changes.
Scope for agreement on specific measures, however, was found. A common ground can be established by agreeing to take certain measures at particular places and times, for example by closing certain fishing grounds based on agreed understanding that stocks are overexploited, as was the case of Upper Shire river in Malawi between 1994 and 1997. One of the most striking findings in all the cases was uncovered by the Q-sort analysis in Zambia. This was a surprisingly high consensus about the understanding of the basic causal processes that underlie management. In the Qsort interviews respondents ranked statements about causal relationships in nature in terms of both agreement and relative importance. The Q-sort on statements about causal processes in nature yielded a first factor that was 6.33 times greater than the second. As is discussed in more detail in the section on consensus analysis below, this ratio would be sufficient to confirm that a cultural consensus existed about simple factual observations. To find this in a method based on the ranking for agreement and importance of causal statements suggests that there is a great deal of common ground among these stakeholders. What the analysis found was a clear consensus on the basics: ‘‘destructive’’ methods are the main problem and what makes a gear destructive is having too much impact on juvenile fish and on spawning; water level is critical; and so is fishing pressure. 3. Coastal fisheries in Viet Nam [9,10] These case studies both addressed two similar fisheries. One was a multi-gear fishery operated from small vessels operating in-shore in the Nam Dinh province and the other was a medium scale trawler fishery operating off the Mekong Delta in Dam Doi Province. 3.1. Areas of basic agreement In the Nam Dinh case, the analysis found a common agreement, at a very general level, that the resources were in decline. Overall, there was agreement among the stakeholders that the illegal gears were destructive, and that the ban on these gears should be enforced. This seemed to be a strong consensus, although individual voices, mostly young men, expressed doubts about the level of destructiveness. This is a strong contrast to the Dam Doi case. In that case the opinions of the fishers and other stakeholders were analysed separately using Q-sorts. Among the other stakeholders, which include local and provincial government officers and scientists, there is indeed a dominant discourse that blames resource decline on small boats fishing in-shore with small-meshed nets. The village level fisheries staff, however, opposes this dominant discourse. They are much less concerned with catching juvenile shrimp in-shore. Among fishers, however, the analysis found no such basic agreement about management and related beliefs about the fishery.
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3.2. LEK and specific potential indicators Both cases found a great deal of difference between the candidate indicators in terms of the reliability of the LEK as measured by the degree of consensus among the fishers. In the Dam Doi only observations of salinity and the size of nursery areas indicate a high agreement (97–100% correct).1 Most of the other indicators show quite low agreement in both fisheries, ranging from 48% to 87%. The average for all the statements is 76%. The Namh Dinh results are not divided between the in-shore and off-shore populations. But agreement in this case is on the same basic level as that in Dam Doi ð79%Þ. In the Dam Doi case the results of the consensus analysis was compared with data gathered by government enumerators. In five of the eight statements where high agreement was found among the respondents (75–100%), the enumerator data strongly agreed with the fishers’ perceptions. In Dam Doi there were substantial disagreements between the in-shore and off-shore areas in respect to management in general, and in some respects these were linked to particular indicators. The major areas of disagreement stemmed from in-shore activities, the use of small mesh nets, push nets and the development of mangrove areas, which the off-shore fishers blamed for reductions in shrimp catches. All of these results suggest that the level of agreement among the fishers, while it certainly exists on a general level, is not sufficient to be able to rely exclusively on fishers’ observations for any particular piece of data. They also suggest that there are very significant geographical differences among fishers who would need to cooperate with each other for the management of the same fish stock. The basic conclusion of the biological analysis is that interviewing local fishers can be a method of obtaining basic information about the fishery and the resource, but this information is too weak to constitute the basis of management decisions alone. The basic conclusion of the sociological analysis is that a rough consensus about conditions in the fishery does exist among the fishers but that the degree of consensus is highly variable. In general, higher consensus is found in reference to those natural conditions, which appear similar across wide areas. 4. Capital-intensive marine fisheries in transformation [11,12] The shrimp trawl fishery at the Sofala Bank in Mozambique and the purse-seine fishery for small pelagic resources off South Africa are undergoing similar transformations. In Mozambique access rights are being moved from a small group of companies to two joint venture companies, which historically have controlled almost the 1 As discussed in the section on consensus analysis, ‘‘correct’’ here means in agreement with the answer reflecting the cultural consensus. Putting aside a slight technical difference, this is the same thing as saying 97–100% agreement among the fishers.
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entire fishery. In South Africa, where the fishery in the past was entirely controlled by a handful of companies whose growth and development had been largely premised on the support of successive apartheid governments, the situation is changing to one where a number of new players are becoming involved. The established companies in South Africa had for many years been deeply involved in the management process. To a large degree, the fishery prior to 1994 was co-managed and a common language was developed between the scientific community and the fishing companies. The two large joint-venture companies in Mozambique have been closely collaborating and have been the prime data source for the research community. In both countries a set of indicators has been developed in close collaboration with the established users. In contrast, the newcomers do not have the same amount of knowledge about the fishery and their main interest has been on allocation rather than ensuring sustainable fisheries. In South Africa, along with this transformation process, the Ecosystem Approach to Fisheries (EAF) has been introduced. Industry stakeholders fear that this will extend the concept of sustainability and protection of resources, while ignoring the reality that fishing is an economic activity that many communities are dependent on for their livelihoods. Under this concept, uncertainties in science are considered purely from a resource conservation perspective. It is important, particularly in a South African context, to raise the question as to why uncertainties should not be to the benefit of the people who depend on the resources. Maintaining stability in pelagic landings is a very important socio-economic consideration. Thus two somewhat contradictory processes are happening simultaneously, with the EAF being driven by formal scientific knowledge. The EAF is likely to favour the traditional companies and to some degree undermine the transformation processes, jeopardizing the legitimacy of the management institutions. In Mozambique the economic importance of the fishery for the national economy and export earning has attracted a substantial amount of donor funding to support fisheries research institutions. At present fisheries research in Mozambique responds to two imperatives. One is to support the traditional approach of focusing on the biomass-recruitment relationship. The other emphasizes the management of the fishery as an economic asset and seeks to ensure the optimum growth of recruits, while understanding recruitment as largely being driven by environmental factors. Both of these fisheries, because of their economic importance, have attracted a political interest in the redistribution of the wealth they generate that has triggered transformation policies. From the perspective of the knowledge base, as long as the transformation process is ongoing, it is extremely difficult to address an ecosystem approach and discuss ecological knowledge, as this will simply be a part of the overall political debate. However,
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independently both cases illustrate that common ground has been reached in the past between industry and the research community. Trust and genuine facilitation is prerequisite to ensuring a return to this situation in Mozambique and South Africa, processes that are in line with the democratisation processes that the two countries are undergoing. 5. Survey methods carried out across cases 5.1. The consensus analysis [13] The consensus analysis was carried out in four of the seven KNOWFISH cases in ways that made possible the direct comparison of the results. In the other three cases researchers chose to make changes in the methodology that made direct comparisons impossible, although we are able to point to comparable results from the other approaches. The consensus analysis consisted of administering a set of simple questions about their observations to the fishers and analysing their responses using a factor analysis. The factor analysis measures linear influences on the pattern of agreement in the responses. We assume that the first influence is the observations that the fishers are making of the environment, i.e., they agree because they see the same things. If the first influence has at least three times the impact on the pattern of agreement that the next largest influence has, then we are able to assume that the data fits a ‘‘cultural consensus model’’ that we can then use to generate ‘‘correct’’ answers to each question. Correct, that is, according to this cultural consensus. This, in turn, allows us to score respondents by the percentage of correct answers they provided and to score questions by the percentage of respondents that answered them correctly. 5.1.1. Results In each case the minimum test that the consensus model fits, the data was passed, i.e., each case found the first factor to be at least three times larger than the second. The seven consensus analyses reported on here vary between the first influence being 3.5 times greater than the next, to the first influence being 8.3 times greater than the next. The average percentage of correct answers varied in these cases between 72%, in the off-shore trawl fishery in Viet Nam to 82% in the small-scale, multi gear fishery on the Luapula River in Zambia. One of the first questions we wanted to explore with the consensus data was if we could identify categories of LEK that could be used to predict the degree of consensus among the fishers. No consistent pattern emerged between the different cases in terms of the percentage of correct answers following the categories of fish abundance, fish habitat, or fish behaviour. This failure has serious implications for the use of LEK-based indicators. These three categories reflect very basic common sense within the scientific worldview in that they express the simplest ecological concepts: the individual animal; the species
population; and the community or landscape. Whether this failure to classify statements and find systematic differences in levels of knowledge is based on substance or methodology, the implication is that we do not know how to categorize LEK in a way that is useful for quickly developing management-related indicators. The second hypothesis we explored using the consensus data was that the amount of experience that fishers have, both in general and in this particular fishery, would affect their scores. We found little support for this hypothesis. It is very likely that we did not interview enough fishers who had only been working a short time, i.e. less than three years, to detect this relationship. The only case where it was found, and then only weakly (r ¼ :2, p ¼ :1) was in Mozambique. In this case experience is also correlated with type of gear because the semi-industrial fleet is both fairly new and has attracted a number of outsiders to the fishery. What did make a difference in levels of LEK was the gear being used. Fishers who fish using smaller-scale gear have higher LEK than those who use larger scale gear. This result is clearest in the two Viet Nam cases where the comparison is between large stationary gear, and large trawl-like gear and the operators of the smaller gear. In Laos, Mozambique and Zambia the same result is still clearly evident, but there are other factors to consider. In Zambia and Laos the number of different types of gear used also influences levels of LEK, and these two variables are related in that fishers that use smaller-scale gear also tend to use more types of gear. In Laos, the smallest gear is the fish trap and the trap fishers have slightly more LEK than the other fishers, but using more types of gear has an even greater influence than the use of traps. In Zambia the type of gear used and the number of different types of gear used have a fairly equal influence on levels of LEK. The type of gear is slightly more important, especially in respect to the fishers who use the ‘kutumpula’ a device for scaring fish into stationary gill nets. In Mozambique three groups were examined, small-scale fishers, semi-industrial fishers using smaller sized trawl gear, and industrial fishers on large trawlers often operated by foreign (mainly European) skippers. The small-scale fishers have the highest levels of LEK, but they are followed by the industrial fishers with the semi-industrial fishers having the lowest levels. The semi-industrial fishery is a new fishery and these skippers are less experienced than the industrial skippers. This result suggests that fisheries development may be reducing the value of LEK as a source of useful knowledge. Many approaches to gathering LEK rely on the use of keyinformants who are identified by various techniques such as asking local fishers who they think are the most knowledgeable. With the development of fisheries, these informants may be becoming less representative of the general fishing community. The conclusion from the consensus analysis is that in each case we found a body of shared knowledge that could meaningfully be called local ecological knowledge. The
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reliability of this knowledge, as measured by the degree of consensus about any particular observation, is not great enough so that we can consider simply asking fishers about their observations by itself a sufficient way to gather data for management. We do not know how to classify LEK very well to organize it for systematic data gathering.
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enforcement mechanisms. The survey results also supply a general confirmation of the idea that a more responsive management institution is also seen as a more effective one. They also show that co-management institutions that are made up mainly of fishers are seen as more effective than the ones that try to incorporate a broad range of other stakeholders.
5.2. The institutional analysis survey [14] 6. Project results overall The KNOWFISH project also contained a parallel research effort to evaluate existing fisheries co-management institutions in Southern Africa through a formal household survey. The survey was carried out with 1164 respondents in Malawi, Mozambique and Zambia between February of 2003 and December of 2004. It was carried out in villages where there were operating Village Management Committees (VMC), meaning that the VMC had met at least once in the preceding year. An analysis was carried out to assess the most important influences on success or failure of the VMCs. Specifically responses to the following four questions were used as the dependent variables: 1. Do you think that the there are more fish now because the VMC has been working? 2. Do you think the village is better off or worse off because of the VMC? 3. How has the number of people punished for violating fisheries rules changed because of the work of the VMC? 4. How has the number of people violating the fisheries rules in this village changed because of the work of the VMC? One finding of interest is a distinction between two ‘‘types’’ of dependent variables. First, the two ‘‘general effectiveness’’ variables—‘‘do you think there are more fish’’ and ‘‘do you think the village is better off’’ correlate with one another (r ¼ :29, p ¼ 0) and not with the two variables related to enforcement. Secondly they have three statistically significant predictor variables in common: (a) the percentage of fishers on VMC; (b) a measure of VMC responsiveness based on answers to questions about how well the respondent thinks the VMC listens to the various groups in the village, and; (c) the respondent’s perception of how much the Department of Fisheries supports the VMC. With the exception of the importance of headman’s support, which is significantly related to question 2, the traditional authority system (tribal kings, chiefs, sub-chiefs and in some cases village headmen) has nothing to do with this perception of general effectiveness. The enforcement variables are almost a mirror image of this. The enforcement variables are entirely linked to perceptions of the support and operational style of the traditional authorities. The bottom line is that there are major differences between local conservation efforts being seen as generally effective and making a positive contribution to village life and co-management institutions operating as rule
The main findings of the study are that LEK, with a potential to inform local management institutions, is case specific, that allocation and knowledge issues are closely interlinked and must be addressed in concert, and that the negotiation of shared understandings between multiple sources of knowledge is a continuous process within an adaptive framework rather than a question of identifying a fixed set of indicators to guide future decisions. Perhaps the clearest general finding of the project is that in all cases we examined fishers within a fishery agreed with each other to the extent that it is reasonable to say that a body of LEK does exist. However, this agreement averaged between 72 and 82% for what were, in fact, very simple statements about factual observations. From this we must conclude that LEK is not in and of itself a reliable ‘‘instrument of observation’’ for developing a scientific basis for management. The KNOWFISH project did not uncover any new indicators of aquatic ecosystem health to add to the established lists. One early hope was that an exploration of local knowledge in respect to indicators would discover some completely new ideas for potential indicators that would be useful at least in the local context from which it emerged. In two cases potential exotic indicators suggested by the initial interviews were explored. In the Sedone River case some fishers associated amounts of visible green algae and various smells with fish abundance. In the Dam Doi case some fishers claimed that a large number of white sacks appearing on the water were associated with a larger shrimp catch. In both of these cases many other fishers disagreed and these associations were not found to be useful as indicators. In all cases it was the kinds of indicators already identified in the literature that turned out to be the most relevant. When statements about causal processes were examined through Q-sorting, a good deal less agreement was found. An important exception to this was the Zambian case, where agreement among the various stakeholders about the basic natural processes, important to management, is so high it could reasonably be called a cultural consensus. This consensus does not extend to agreement on management measures but, interestingly, it is found in a case where an active fisheries co-management programme has been in place for ten years, suggesting that interactions between fishers and fisheries biologists can increase agreement about the science. The high consensus found in the South African case, where there has also been a long-term
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co-management programme in place, also supports this conclusion. Another broad finding is that variation in LEK is systematically related to other variables. The most important factors are the type and number of different types of gear. Fishers using a greater number of smallerscale gears were found to have higher levels of LEK in four different cases involving seven independent consensus analyses. The institutional analysis of co-management institutions carried out through the household survey yielded three strong conclusions. The first is a general confirmation of what has long been an insight from qualitative research that a more responsive management institution is also seen as a more effective one. Again, where knowledge is shared and people see that they are being listened to, they also see greater effectiveness. The second is that co-management institutions that are made up mainly of fishers are seen as more effective than the ones that try to incorporate a broad range of other stakeholders. This suggests that more knowledgeable people making decisions create greater legitimacy, rather than an abstract representation of various putative categories of stakeholders. The third conclusion is that there are major differences between local conservation efforts being seen as generally effective and making a positive contribution to village life and comanagement institutions operating as rule enforcement mechanisms. A critical factor is that studies of knowledge are difficult because of the various levels of ability to articulate. Different stakeholders in different knowledge cultures hold different forms of knowledge. One important distinction is between tacit and discursive knowledge. Tacit knowledge is knowledge that is not (easily) expressed, usually based on skills and experience. A second critical distinction is between oral and written knowledge. A third is between anecdotal and systematic information. This applies to data, i.e. a set of individual observations, rather than to knowledge as such. Systematic data is gathered by specific procedures. It is a way to package information at one scale level so that processes happening at a higher level can be understood. These different forms of knowledge are important because of the close link between knowledge and power. When it comes to participating in the give and take of management, holding tacit, oral, or anecdotal knowledge rather than discursive, written, or systematic knowledge can mean real disadvantages. From our observations, the way this difference in ability to articulate is expressed in day-to-day management is most often by people simply refusing to go to the meetings because they do not know how to contribute or when they feel they are either being ignored or that their knowledge has been changed into an alien form by others. Early in the project we imagined that the indicators we were searching for would be based in some sort of ‘‘common ground’’ where LEK met RBK. We now believe that the common ground idea is an illusion, if what is
meant is that there is some sort of LEK ‘‘sitting on a shelf’’ in fishing villages that managers can go out and find, evaluate, and then use, as the basis of management decisions. Knowledge is always a community product and communities have different knowledge cultures with different ideas about what it means to ‘‘know’’ something. The idea that there are two basic knowledge cultures, i.e., the Western scientific and LEK, is something we use as a kind of shorthand. There are actually many different knowledge cultures—among scientists and local communities alike. Common ground is not found, it is negotiated. 7. Conclusion Using multiple sources of knowledge in management depends interactively on the needs and interests of the different groups producing the knowledge. LEK is directly linked to the problems users are facing, and ecological sustainability has better chances of being achieved if allocation issues are being addressed as an integral part of the process. Indeed, RBK also reflects the influence of the interests of those who produce it [15], though that has not been a focus of this research. Hence, the selection of indicators to serve management decisions must relate to local agendas if they are to be accepted by stakeholders. Such indicators need to be backed by research-based knowledge in order to address both allocation and longerterm sustainability, which will require a multidisciplinary approach to both research and implementation to support management institutions. The backbone of a good adaptive fisheries management system is a good data collecting system that enables multi-disciplinary analysis and provides the assessment to support to management institutions. LEK has a critical role to play in making management effective from the perspective of both the content and timeliness of information and increased legitimacy and cooperation. To make an effective contribution, however, such information can only be revealed as part of comprehensive studies involving ongoing interactions between fishers, scientists and other stakeholders. It is absurd to suggest that such information can be gathered through such methods as Rapid Rural Appraisal as is currently being suggested in some approaches to managing or evaluating tropical fisheries. The main, practical result of the present research is that using simple indicators for management does not substitute for ongoing interactions between scientists, fishers and managers where management goals are set and refined. Such interactions must include the identification and re-identification of indicators that tell the stakeholders when those goals are reached. Indicators are part of a larger process and cannot be understood when abstracted from that process. Acknowledgements The policy brief was prepared with the support of the EU INCO-DEV research programme (KNOWFISH,
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Contract No. ICA-4-CT-2001-10033). A great many people contributed to the success of the KNOWFISH project. Twenty-three people are listed as authors on the KNOWFISH products that we draw on for this brief. In a very real sense they are all its co-authors. The institutions involved include: CASS—Centre for Applied Social Sciences, University of Zimbabwe, Harare, Zimbabwe. CenTER—Dept. Genetics & Ecology, Aarhus University, Aarhus, Denmark. Chr. Michelsen Institute, Bergen, Norway. Department of Fisheries, Lilongwe, Malawi. Department of Fisheries, Chilanga, Zambia. Department of Fisheries and Marine Biology, University of Bergen, Bergen, Norway. DIFRES—Danish Institute for Fisheries Research, Charlottenlund, Denmark. Faculty of Geography, Hanoi National Pedagogic University, Hanoi, Viet Nam. Fish Culture and Fisheries Group, Wageningen University, Wageningen, Holland. IDPPE—Instituto de Desenvolvimento de Pesca de Pequena Escala, Maputo, Mozambique. VIFEP–Institute for Fisheries Economics and Planning, Hanoi, Viet Nam. IFM—Institute for Fisheries Management and Coastal Community Development, Hirtshals, Denmark. Institute for Marine Aquaculture, Cantho University, Cantho, Viet Nam. Instituto Nacional de Investigaca˜o Pesqueira (Fisheries Research Institute), Maputo, Mozambique. LARReC—Living Aquatic Resources Research Center, Vientiane, LAO PDR. Marine and Coastal Management, Rogge Bay, Cape Town, South Africa. NIBR—Norwegian Institute for Urban and Regional Research, Oslo, Norway. PLAAS—Programme for Land and Agrarian Studies, University of the Western Cape, Bellville, Cape Town, South Africa. RIMF—Research Institute for Marine Fisheries, Haiphong, Viet Nam.
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
References [14] [1] Degnbol P, Jarre A. Review of indicators in fisheries management—a development perspective. African Journal of Marine Science 2004;26:302–26. [2] Romney AK, Susan CW, William HB. Culture as consensus: a theory of culture and informant accuracy. American Anthropologist 1986; 88:313–38. [3] Brown SR. Q technique and method: principles and procedures. In: Berry WD, Lewis-Beck MS, editors. New tools for social scientists:
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advances and applications in research methods. Beverly Hills: Sage Publications; 1986. p. 57–76 [Chapter 3]. Wilson DC, Jepsen N, Phounsavath S. Research activities report for case study, 5: small riverine fisheries in the Sedone River, Laos. In: The final report of the Knowledge in Fisheries Management Project, Annex 2. Brussels: Research Directorate General; 2005. van Zwieten PAM, Banda M, Mwakiyongo K, Manase M, Kanyerere G, Nyasulu T. Case study: South-East Arm of Malawi, Part 1: potential biological state and pressure indicators to evaluate the fisheries of Lake Malawi SE arm and Lake Malombe: evaluative capacity of Fisheries Research Unit of Malawi. In: The final report of the Knowledge in Fisheries Management Project, Annex 4. Brussels: Research Directorate General; 2005. Donda SJ, Njaya FJ. Case study of South East Arm of Lake Malawi and Lake Malombe in Malawi: Part 2. In: The final report of the Knowledge in Fisheries Management Project, Annex 5. Brussels: Research Directorate General; 2005. Kapasa CK, Malasha I, Wilson DC. Experience based knowledge and fisheries management in the Mweru-Luapula system. In: The final report of the Knowledge in Fisheries Management Project, Annex 11. Brussels: Research Directorate General; 2005. Kapasa CK. Knowledge based on fish: a case study to evaluate biological indicators of fish resources based on the local ecological knowledge (LEK) of fishers of Mweru-Luapula fishery—Northern Zambia. In: The final report of the Knowledge in Fisheries Management Project, Annex 6 . Brussels: Research Directorate General; 2005. Duc DTM, Thinh NV, Huy NT, Thi DV, Bat NK, van Zwieten P, et al. Case study chapter: Nam Dinh Province. Perceiving changes in fish stocks and species composition in the Red River Delta estuary, Viet Nam: Scale of fishing and the potential to develop quantitative indicators based on local observations in a variable and rapidly changing environment with high species diversity. In: The final report of the Knowledge in Fisheries Management Project, Annex 7. Brussels: Research Directorate General; 2005. Wilson DC, Christensen (DIFRES) S, Thi DV, Minh TH. Research activities report for case study 8: medium size shrimp fisheries in the Mekong Delta, Dam Doi, Viet Nam. In: The final report of the knowledge in Fisheries Management Project, Annex 8. Brussels: Research Directorate General; 2005. Nielsen JR, de Sousa LP, Hele EP, Christensen S, Degnbol P, JulLarsen E. The knowledge base for management of the Sofala Bank shallow water shrimp fishery in Mozambique. In: The final report of the Knowledge in Fisheries Management Project, Annex 9. Brussels: Research Directorate General; 2005. Fairweather T, Hara M, van der Lingen C, Nielsen JR, Shannon L, Louw G, et al. The knowledge base for management of the capitalintensive fishery for small pelagic fish off South Africa. African Journal of Marine Science, 2006, forthcoming. Wilson DC. The distribution of fishers’ knowledge in three case studies. In: Paper presented at the annual science conference of the International Council for the Exploration of the Sea, Aberdeen, Scotland, 20–24 December 2005. Wilson DC, Ahmed M, Delaney A, Donda S, Kapasa CK, Malasha I, et al. Comparative research on fisheries co-management Institutions in Southern Africa. Institute for Fisheries Management Research Paper No. 132; 2006. Wilson DC. Fisheries co-management and the knowledge base for management decisions. In: Wilson DC, Nielsen JR, Degnbol P, editors. The fisheries co-management experience: accomplishments, challenges and prospects. Dordrecht, The Netherlands: Kluwer Academic Publishers; 2003 [Chapter 15].
Marine Policy 30 (2006) 794–801 www.elsevier.com/locate/marpol
Local ecological knowledge and practical fisheries management in the tropics: A policy brief Douglas Clyde Wilson, Jesper Raakjær, Poul Degnbol Institute for Fisheries Management and Coastal Community Development (IFM), The North Sea Centre, 9850 Hirtshals, Denmark Received 12 January 2006; accepted 24 February 2006
Abstract This policy brief is a summary product of seven case studies examining the integration of local ecological knowledge in fisheries management. Each case began with a series of in-depth interviews with local fishers, after which their answers were examined using both social and biological approaches to assess the possibility of using the information as the basis of simple, valid and locally acceptable indictors for fisheries management. We found that allocation and knowledge issues are closely interlinked and must be addressed in concert, and that the negotiation of shared understandings between multiple sources of knowledge must be a continuous process within an adaptive framework rather than a question of identifying a fixed set of indicators. r 2006 Elsevier Ltd. All rights reserved. Keywords: Local ecological knowledge; Fisheries management; Indicators
1. Introduction Fisheries management cannot be effective if it is not considered legitimate by stakeholders. This is especially true when institutions are weak and implementation relies on voluntary compliance. At the same time fisheries management must address a wider range of objectives such as those identified by the World Summit on Sustainable Development, 2002. These objectives imply an extended knowledge base for management including ecosystem considerations and rebuilding of the resource base for fisheries. Meeting the need for knowledge which is both legitimate and relate to the extended considerations in fisheries management is a challenge which requires novel approaches to management institutions. As a contribution to efforts to meet this challenge, the Knowledge in Fisheries Management (KNOWFISH) project was implemented to improve our understanding of how multiple sources of knowledge, particularly local ecological knowledge (LEK) and research-based knowledge (RBK) may
Corresponding author. Tel.: +45 98 94 28 55; fax: +45 98 94 42 68.
E-mail addresses: [email protected] (D.C. Wilson), [email protected] (J. Raakjær), [email protected] (P. Degnbol). 0308-597X/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.marpol.2006.02.004
both be used to inform management. This policy brief is a summary product of this project. 1.1. The KNOWFISH project objectives This project addressed the need to develop new types of knowledge that are appropriate to the complexity of tropical aquatic ecosystems and the way management institutions in developing countries actually work. The central objective of the project was to evaluate the potential use of the LEK of fishers in developing the knowledge base for fisheries management. The strategy employed for this evaluation was a three-way discussion between fishers, biologists and social scientists around the subject of what sorts of indicators of ecosystem health would make sense in light of both the LEK of the fishers and the RBK of the biologists. The development of less complex indicators of ecosystem health and exploitation status that are both scientifically valid and widely acceptable by fisheries stakeholders is in itself an important strategy for the management of tropical aquatic ecosystems. Indicators are an important tool for managing fisheries that are compatible with the management institutions in developing countries. Indeed, their use
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implies that management be adaptive rather than being based on predictions, a trait that can be of great benefit in developing countries. Use of indicators in an adaptive framework will be more robust and less costly than traditional stock assessment approaches [1]. 1.2. Methods The project was carried out in seven case studies. Three of these case studies were of artisanal fisheries: (1) lake fisheries in Malawi; (2) freshwater lake and river fisheries in Zambia, and; (3) the fisheries in the Sedone River, Laos. Two of them were medium- and large-scale off-shore marine fisheries: (4) the shrimp fishery at the Sofala Bank, Mozambique, and; (5) the pelagic fishery in South Africa. And two of them were mixed in-shore /off-shore marine fisheries: (6) the coastal fisheries of the Mekong Delta, Dam Doi district, Viet Nam, and; (7) the coastal fisheries in Khanh Hoa Province, Viet Nam. In addition to these seven case studies two cross-cases survey methodologies were used. The first of these was consensus analysis [2], an anthropological method used to study the degree to which knowledge is shared and how such shared knowledge is distributed. The second was a standard sociological household survey that was used to evaluate various ways of structuring cooperative fisheries management that are in use in southern Africa and which have implications for how knowledge is used in decisionmaking. The same four basic steps were carried out in each case. In the first basic step, LEK was identified by interviewing stakeholders using standard methods devised for LEK interviews such as open-ended questions, map drawing and developing historical timelines about ecological changes in the fishery. Beyond a general description of the fishery in question the main products of these interviews were a set of ‘‘candidate indicators’’ which then became the main focus on the discussions from that point on. The second product was a list of statements that respondents had made about their fishery. These statements were in two sets. Throughout the remainder of the KNOWFISH project, other fishers, as well as non-fishing stakeholders, were presented with these two kinds of statements in various ways and asked to respond to them. The first set was a series of very simple factual observations that other respondents could judge as ‘‘true’’ or ‘‘false’’. Only a portion of these statements was related directly to the candidate indicators because the main point was to use them in the consensus analysis, and the focus of that analysis was on the nature of the LEK in general. However, the consensus analysis did, of course, use
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statements that were relevant to the indicators, so it provided information about the reliability of information relevant to the candidate indicators. The second set of statements was about causal relationships with relevance for fisheries management. These causal statements were still extracted from the initial interviews in as simple a form as possible while still retaining causal content. The second and third basic steps were the disciplinary evaluations of the candidate indicators. Biologists and ecologists evaluated them in terms of their agreement with the biological literature, and where possible with data on the actual fisheries under study. Sociologists and anthropologists evaluated them in terms of how they fit into the general political discussions around fisheries management. Part of this work was done by soliciting responses to the causal statements and relating these responses to management issues. The social scientists also carried out the consensus analyses, as well as other methodologies from the anthropology of knowledge such as pile sorts and Qsorts [3], which are methods of comparing perceptions of causal relationships, and the review of public discussions of fisheries knowledge issues in newspapers and elsewhere. The fourth basic step was a synthesis, which mainly took the form of the social and natural scientists working together to create a final report on their cases. These individual case reports, as well as the survey results, are available and are expected to be published in various outlets. This policy brief summarizes the main results of the KNOWFISH project with an eye towards practical implications for management. Beyond this introduction it contains five sections. Section 2 synthesizes the artisanal fishery cases, Section 3 synthesizes the cases dealing with mixed coastal fishery cases in Viet Nam, and Section 4 synthesizes the two cases dealing with capital-intensive marine fisheries. Next, Section 5 summarizes the results of the two main cross-case analyses, the consensus analysis and the household survey. Section 6 then outlines the main results of KNOWFISH project. 2. Main results from the artisanal cases [4–8] 2.1. The problematic idea of an ‘‘indicator’’ Perhaps the key finding from the work with artisanal fishers is that the idea of an ‘‘indicator’’ is something that arises from the need for management decision-making and is not an idea that fits immediately into the way the fishers think about the fishery. In all cases a body of LEK was found that it was cohesive enough to characterize, but tying that knowledge directly to management proved very difficult. The idea of an ‘‘indicator’’ did not communicate well to the fishers and several expressed disbelief that any observation made in the present could give any meaningful information about future fish catches. The temporal focus of the LEK is on a smaller scale. The only general
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exception to this was in respect to water levels. No really new information from a formal biological standpoint was found through the LEK interviews. Even if the basic idea of an indicator were a good fit with the LEK outlook, that would just be the beginning of working out a common idea of what the indicators could be. Indicators as a basis for management decisions could also prove difficult due to both the limited trust in data and disagreements regarding the significance of specific observations. With regard to the observational data the differences between researchers, managers and fishers include differences in possibilities for aggregation of data and information and data storage capacities. 2.2. LEK and specific potential indicators One general finding was that observations of size and species composition were more reliable types of information than catch rates. In Laos the consensus interviews found that statements about that fish size and diversity had the highest average of ‘‘correct’’ answers, correct being defined by the cultural consensus as discussed below in the section on consensus analysis. This highest level was still only 80%, indicating that one of every five fishers would give a different response. Observations about water quality had the lowest levels of agreement at around 50%, indicating that every other fisher would give a different answer. While for most of the other types of indicators 70% answered correctly. A similar pattern emerged in Zambia where separate analyses were done for Lake Mweru and the Luapula River. In the lake area two consensus statements were directly related to long-term changes in overall catch rates. The mean percentage of correct answers for these two statements was 61%. Four statements that appeared in the analyses for both the lake and the river were related to changes in species composition and two statements in the river area were related to changes in the size structure of fish. The mean percentage of correct answers for these two statements was also around 80%. 2.3. Potentials for ‘‘common ground’’ Beyond the basic finding that the idea of an indicator is not one that fits easily into LEK, the specific economic interests of the different groups obviously plays a role in what are seen as important, and sometimes even true, facts. In Laos a pile sort was used in which respondents, working in groups, were asked to sort a set of items that had emerged as important in the in-depth interviews into as many categories as they saw fit. What emerged were some clear differences in perspectives between the fishers and the government officers. One difference was very clear; the fishers associated the lost fish abundance primarily with changes in habitat, while the officers associate it primarily with fishing. The pile sort results demonstrate a clear difference in both the scale at which the overall fisheries system is perceived and the main drivers of its changes.
Scope for agreement on specific measures, however, was found. A common ground can be established by agreeing to take certain measures at particular places and times, for example by closing certain fishing grounds based on agreed understanding that stocks are overexploited, as was the case of Upper Shire river in Malawi between 1994 and 1997. One of the most striking findings in all the cases was uncovered by the Q-sort analysis in Zambia. This was a surprisingly high consensus about the understanding of the basic causal processes that underlie management. In the Qsort interviews respondents ranked statements about causal relationships in nature in terms of both agreement and relative importance. The Q-sort on statements about causal processes in nature yielded a first factor that was 6.33 times greater than the second. As is discussed in more detail in the section on consensus analysis below, this ratio would be sufficient to confirm that a cultural consensus existed about simple factual observations. To find this in a method based on the ranking for agreement and importance of causal statements suggests that there is a great deal of common ground among these stakeholders. What the analysis found was a clear consensus on the basics: ‘‘destructive’’ methods are the main problem and what makes a gear destructive is having too much impact on juvenile fish and on spawning; water level is critical; and so is fishing pressure. 3. Coastal fisheries in Viet Nam [9,10] These case studies both addressed two similar fisheries. One was a multi-gear fishery operated from small vessels operating in-shore in the Nam Dinh province and the other was a medium scale trawler fishery operating off the Mekong Delta in Dam Doi Province. 3.1. Areas of basic agreement In the Nam Dinh case, the analysis found a common agreement, at a very general level, that the resources were in decline. Overall, there was agreement among the stakeholders that the illegal gears were destructive, and that the ban on these gears should be enforced. This seemed to be a strong consensus, although individual voices, mostly young men, expressed doubts about the level of destructiveness. This is a strong contrast to the Dam Doi case. In that case the opinions of the fishers and other stakeholders were analysed separately using Q-sorts. Among the other stakeholders, which include local and provincial government officers and scientists, there is indeed a dominant discourse that blames resource decline on small boats fishing in-shore with small-meshed nets. The village level fisheries staff, however, opposes this dominant discourse. They are much less concerned with catching juvenile shrimp in-shore. Among fishers, however, the analysis found no such basic agreement about management and related beliefs about the fishery.
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3.2. LEK and specific potential indicators Both cases found a great deal of difference between the candidate indicators in terms of the reliability of the LEK as measured by the degree of consensus among the fishers. In the Dam Doi only observations of salinity and the size of nursery areas indicate a high agreement (97–100% correct).1 Most of the other indicators show quite low agreement in both fisheries, ranging from 48% to 87%. The average for all the statements is 76%. The Namh Dinh results are not divided between the in-shore and off-shore populations. But agreement in this case is on the same basic level as that in Dam Doi ð79%Þ. In the Dam Doi case the results of the consensus analysis was compared with data gathered by government enumerators. In five of the eight statements where high agreement was found among the respondents (75–100%), the enumerator data strongly agreed with the fishers’ perceptions. In Dam Doi there were substantial disagreements between the in-shore and off-shore areas in respect to management in general, and in some respects these were linked to particular indicators. The major areas of disagreement stemmed from in-shore activities, the use of small mesh nets, push nets and the development of mangrove areas, which the off-shore fishers blamed for reductions in shrimp catches. All of these results suggest that the level of agreement among the fishers, while it certainly exists on a general level, is not sufficient to be able to rely exclusively on fishers’ observations for any particular piece of data. They also suggest that there are very significant geographical differences among fishers who would need to cooperate with each other for the management of the same fish stock. The basic conclusion of the biological analysis is that interviewing local fishers can be a method of obtaining basic information about the fishery and the resource, but this information is too weak to constitute the basis of management decisions alone. The basic conclusion of the sociological analysis is that a rough consensus about conditions in the fishery does exist among the fishers but that the degree of consensus is highly variable. In general, higher consensus is found in reference to those natural conditions, which appear similar across wide areas. 4. Capital-intensive marine fisheries in transformation [11,12] The shrimp trawl fishery at the Sofala Bank in Mozambique and the purse-seine fishery for small pelagic resources off South Africa are undergoing similar transformations. In Mozambique access rights are being moved from a small group of companies to two joint venture companies, which historically have controlled almost the 1 As discussed in the section on consensus analysis, ‘‘correct’’ here means in agreement with the answer reflecting the cultural consensus. Putting aside a slight technical difference, this is the same thing as saying 97–100% agreement among the fishers.
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entire fishery. In South Africa, where the fishery in the past was entirely controlled by a handful of companies whose growth and development had been largely premised on the support of successive apartheid governments, the situation is changing to one where a number of new players are becoming involved. The established companies in South Africa had for many years been deeply involved in the management process. To a large degree, the fishery prior to 1994 was co-managed and a common language was developed between the scientific community and the fishing companies. The two large joint-venture companies in Mozambique have been closely collaborating and have been the prime data source for the research community. In both countries a set of indicators has been developed in close collaboration with the established users. In contrast, the newcomers do not have the same amount of knowledge about the fishery and their main interest has been on allocation rather than ensuring sustainable fisheries. In South Africa, along with this transformation process, the Ecosystem Approach to Fisheries (EAF) has been introduced. Industry stakeholders fear that this will extend the concept of sustainability and protection of resources, while ignoring the reality that fishing is an economic activity that many communities are dependent on for their livelihoods. Under this concept, uncertainties in science are considered purely from a resource conservation perspective. It is important, particularly in a South African context, to raise the question as to why uncertainties should not be to the benefit of the people who depend on the resources. Maintaining stability in pelagic landings is a very important socio-economic consideration. Thus two somewhat contradictory processes are happening simultaneously, with the EAF being driven by formal scientific knowledge. The EAF is likely to favour the traditional companies and to some degree undermine the transformation processes, jeopardizing the legitimacy of the management institutions. In Mozambique the economic importance of the fishery for the national economy and export earning has attracted a substantial amount of donor funding to support fisheries research institutions. At present fisheries research in Mozambique responds to two imperatives. One is to support the traditional approach of focusing on the biomass-recruitment relationship. The other emphasizes the management of the fishery as an economic asset and seeks to ensure the optimum growth of recruits, while understanding recruitment as largely being driven by environmental factors. Both of these fisheries, because of their economic importance, have attracted a political interest in the redistribution of the wealth they generate that has triggered transformation policies. From the perspective of the knowledge base, as long as the transformation process is ongoing, it is extremely difficult to address an ecosystem approach and discuss ecological knowledge, as this will simply be a part of the overall political debate. However,
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independently both cases illustrate that common ground has been reached in the past between industry and the research community. Trust and genuine facilitation is prerequisite to ensuring a return to this situation in Mozambique and South Africa, processes that are in line with the democratisation processes that the two countries are undergoing. 5. Survey methods carried out across cases 5.1. The consensus analysis [13] The consensus analysis was carried out in four of the seven KNOWFISH cases in ways that made possible the direct comparison of the results. In the other three cases researchers chose to make changes in the methodology that made direct comparisons impossible, although we are able to point to comparable results from the other approaches. The consensus analysis consisted of administering a set of simple questions about their observations to the fishers and analysing their responses using a factor analysis. The factor analysis measures linear influences on the pattern of agreement in the responses. We assume that the first influence is the observations that the fishers are making of the environment, i.e., they agree because they see the same things. If the first influence has at least three times the impact on the pattern of agreement that the next largest influence has, then we are able to assume that the data fits a ‘‘cultural consensus model’’ that we can then use to generate ‘‘correct’’ answers to each question. Correct, that is, according to this cultural consensus. This, in turn, allows us to score respondents by the percentage of correct answers they provided and to score questions by the percentage of respondents that answered them correctly. 5.1.1. Results In each case the minimum test that the consensus model fits, the data was passed, i.e., each case found the first factor to be at least three times larger than the second. The seven consensus analyses reported on here vary between the first influence being 3.5 times greater than the next, to the first influence being 8.3 times greater than the next. The average percentage of correct answers varied in these cases between 72%, in the off-shore trawl fishery in Viet Nam to 82% in the small-scale, multi gear fishery on the Luapula River in Zambia. One of the first questions we wanted to explore with the consensus data was if we could identify categories of LEK that could be used to predict the degree of consensus among the fishers. No consistent pattern emerged between the different cases in terms of the percentage of correct answers following the categories of fish abundance, fish habitat, or fish behaviour. This failure has serious implications for the use of LEK-based indicators. These three categories reflect very basic common sense within the scientific worldview in that they express the simplest ecological concepts: the individual animal; the species
population; and the community or landscape. Whether this failure to classify statements and find systematic differences in levels of knowledge is based on substance or methodology, the implication is that we do not know how to categorize LEK in a way that is useful for quickly developing management-related indicators. The second hypothesis we explored using the consensus data was that the amount of experience that fishers have, both in general and in this particular fishery, would affect their scores. We found little support for this hypothesis. It is very likely that we did not interview enough fishers who had only been working a short time, i.e. less than three years, to detect this relationship. The only case where it was found, and then only weakly (r ¼ :2, p ¼ :1) was in Mozambique. In this case experience is also correlated with type of gear because the semi-industrial fleet is both fairly new and has attracted a number of outsiders to the fishery. What did make a difference in levels of LEK was the gear being used. Fishers who fish using smaller-scale gear have higher LEK than those who use larger scale gear. This result is clearest in the two Viet Nam cases where the comparison is between large stationary gear, and large trawl-like gear and the operators of the smaller gear. In Laos, Mozambique and Zambia the same result is still clearly evident, but there are other factors to consider. In Zambia and Laos the number of different types of gear used also influences levels of LEK, and these two variables are related in that fishers that use smaller-scale gear also tend to use more types of gear. In Laos, the smallest gear is the fish trap and the trap fishers have slightly more LEK than the other fishers, but using more types of gear has an even greater influence than the use of traps. In Zambia the type of gear used and the number of different types of gear used have a fairly equal influence on levels of LEK. The type of gear is slightly more important, especially in respect to the fishers who use the ‘kutumpula’ a device for scaring fish into stationary gill nets. In Mozambique three groups were examined, small-scale fishers, semi-industrial fishers using smaller sized trawl gear, and industrial fishers on large trawlers often operated by foreign (mainly European) skippers. The small-scale fishers have the highest levels of LEK, but they are followed by the industrial fishers with the semi-industrial fishers having the lowest levels. The semi-industrial fishery is a new fishery and these skippers are less experienced than the industrial skippers. This result suggests that fisheries development may be reducing the value of LEK as a source of useful knowledge. Many approaches to gathering LEK rely on the use of keyinformants who are identified by various techniques such as asking local fishers who they think are the most knowledgeable. With the development of fisheries, these informants may be becoming less representative of the general fishing community. The conclusion from the consensus analysis is that in each case we found a body of shared knowledge that could meaningfully be called local ecological knowledge. The
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reliability of this knowledge, as measured by the degree of consensus about any particular observation, is not great enough so that we can consider simply asking fishers about their observations by itself a sufficient way to gather data for management. We do not know how to classify LEK very well to organize it for systematic data gathering.
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enforcement mechanisms. The survey results also supply a general confirmation of the idea that a more responsive management institution is also seen as a more effective one. They also show that co-management institutions that are made up mainly of fishers are seen as more effective than the ones that try to incorporate a broad range of other stakeholders.
5.2. The institutional analysis survey [14] 6. Project results overall The KNOWFISH project also contained a parallel research effort to evaluate existing fisheries co-management institutions in Southern Africa through a formal household survey. The survey was carried out with 1164 respondents in Malawi, Mozambique and Zambia between February of 2003 and December of 2004. It was carried out in villages where there were operating Village Management Committees (VMC), meaning that the VMC had met at least once in the preceding year. An analysis was carried out to assess the most important influences on success or failure of the VMCs. Specifically responses to the following four questions were used as the dependent variables: 1. Do you think that the there are more fish now because the VMC has been working? 2. Do you think the village is better off or worse off because of the VMC? 3. How has the number of people punished for violating fisheries rules changed because of the work of the VMC? 4. How has the number of people violating the fisheries rules in this village changed because of the work of the VMC? One finding of interest is a distinction between two ‘‘types’’ of dependent variables. First, the two ‘‘general effectiveness’’ variables—‘‘do you think there are more fish’’ and ‘‘do you think the village is better off’’ correlate with one another (r ¼ :29, p ¼ 0) and not with the two variables related to enforcement. Secondly they have three statistically significant predictor variables in common: (a) the percentage of fishers on VMC; (b) a measure of VMC responsiveness based on answers to questions about how well the respondent thinks the VMC listens to the various groups in the village, and; (c) the respondent’s perception of how much the Department of Fisheries supports the VMC. With the exception of the importance of headman’s support, which is significantly related to question 2, the traditional authority system (tribal kings, chiefs, sub-chiefs and in some cases village headmen) has nothing to do with this perception of general effectiveness. The enforcement variables are almost a mirror image of this. The enforcement variables are entirely linked to perceptions of the support and operational style of the traditional authorities. The bottom line is that there are major differences between local conservation efforts being seen as generally effective and making a positive contribution to village life and co-management institutions operating as rule
The main findings of the study are that LEK, with a potential to inform local management institutions, is case specific, that allocation and knowledge issues are closely interlinked and must be addressed in concert, and that the negotiation of shared understandings between multiple sources of knowledge is a continuous process within an adaptive framework rather than a question of identifying a fixed set of indicators to guide future decisions. Perhaps the clearest general finding of the project is that in all cases we examined fishers within a fishery agreed with each other to the extent that it is reasonable to say that a body of LEK does exist. However, this agreement averaged between 72 and 82% for what were, in fact, very simple statements about factual observations. From this we must conclude that LEK is not in and of itself a reliable ‘‘instrument of observation’’ for developing a scientific basis for management. The KNOWFISH project did not uncover any new indicators of aquatic ecosystem health to add to the established lists. One early hope was that an exploration of local knowledge in respect to indicators would discover some completely new ideas for potential indicators that would be useful at least in the local context from which it emerged. In two cases potential exotic indicators suggested by the initial interviews were explored. In the Sedone River case some fishers associated amounts of visible green algae and various smells with fish abundance. In the Dam Doi case some fishers claimed that a large number of white sacks appearing on the water were associated with a larger shrimp catch. In both of these cases many other fishers disagreed and these associations were not found to be useful as indicators. In all cases it was the kinds of indicators already identified in the literature that turned out to be the most relevant. When statements about causal processes were examined through Q-sorting, a good deal less agreement was found. An important exception to this was the Zambian case, where agreement among the various stakeholders about the basic natural processes, important to management, is so high it could reasonably be called a cultural consensus. This consensus does not extend to agreement on management measures but, interestingly, it is found in a case where an active fisheries co-management programme has been in place for ten years, suggesting that interactions between fishers and fisheries biologists can increase agreement about the science. The high consensus found in the South African case, where there has also been a long-term
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co-management programme in place, also supports this conclusion. Another broad finding is that variation in LEK is systematically related to other variables. The most important factors are the type and number of different types of gear. Fishers using a greater number of smallerscale gears were found to have higher levels of LEK in four different cases involving seven independent consensus analyses. The institutional analysis of co-management institutions carried out through the household survey yielded three strong conclusions. The first is a general confirmation of what has long been an insight from qualitative research that a more responsive management institution is also seen as a more effective one. Again, where knowledge is shared and people see that they are being listened to, they also see greater effectiveness. The second is that co-management institutions that are made up mainly of fishers are seen as more effective than the ones that try to incorporate a broad range of other stakeholders. This suggests that more knowledgeable people making decisions create greater legitimacy, rather than an abstract representation of various putative categories of stakeholders. The third conclusion is that there are major differences between local conservation efforts being seen as generally effective and making a positive contribution to village life and comanagement institutions operating as rule enforcement mechanisms. A critical factor is that studies of knowledge are difficult because of the various levels of ability to articulate. Different stakeholders in different knowledge cultures hold different forms of knowledge. One important distinction is between tacit and discursive knowledge. Tacit knowledge is knowledge that is not (easily) expressed, usually based on skills and experience. A second critical distinction is between oral and written knowledge. A third is between anecdotal and systematic information. This applies to data, i.e. a set of individual observations, rather than to knowledge as such. Systematic data is gathered by specific procedures. It is a way to package information at one scale level so that processes happening at a higher level can be understood. These different forms of knowledge are important because of the close link between knowledge and power. When it comes to participating in the give and take of management, holding tacit, oral, or anecdotal knowledge rather than discursive, written, or systematic knowledge can mean real disadvantages. From our observations, the way this difference in ability to articulate is expressed in day-to-day management is most often by people simply refusing to go to the meetings because they do not know how to contribute or when they feel they are either being ignored or that their knowledge has been changed into an alien form by others. Early in the project we imagined that the indicators we were searching for would be based in some sort of ‘‘common ground’’ where LEK met RBK. We now believe that the common ground idea is an illusion, if what is
meant is that there is some sort of LEK ‘‘sitting on a shelf’’ in fishing villages that managers can go out and find, evaluate, and then use, as the basis of management decisions. Knowledge is always a community product and communities have different knowledge cultures with different ideas about what it means to ‘‘know’’ something. The idea that there are two basic knowledge cultures, i.e., the Western scientific and LEK, is something we use as a kind of shorthand. There are actually many different knowledge cultures—among scientists and local communities alike. Common ground is not found, it is negotiated. 7. Conclusion Using multiple sources of knowledge in management depends interactively on the needs and interests of the different groups producing the knowledge. LEK is directly linked to the problems users are facing, and ecological sustainability has better chances of being achieved if allocation issues are being addressed as an integral part of the process. Indeed, RBK also reflects the influence of the interests of those who produce it [15], though that has not been a focus of this research. Hence, the selection of indicators to serve management decisions must relate to local agendas if they are to be accepted by stakeholders. Such indicators need to be backed by research-based knowledge in order to address both allocation and longerterm sustainability, which will require a multidisciplinary approach to both research and implementation to support management institutions. The backbone of a good adaptive fisheries management system is a good data collecting system that enables multi-disciplinary analysis and provides the assessment to support to management institutions. LEK has a critical role to play in making management effective from the perspective of both the content and timeliness of information and increased legitimacy and cooperation. To make an effective contribution, however, such information can only be revealed as part of comprehensive studies involving ongoing interactions between fishers, scientists and other stakeholders. It is absurd to suggest that such information can be gathered through such methods as Rapid Rural Appraisal as is currently being suggested in some approaches to managing or evaluating tropical fisheries. The main, practical result of the present research is that using simple indicators for management does not substitute for ongoing interactions between scientists, fishers and managers where management goals are set and refined. Such interactions must include the identification and re-identification of indicators that tell the stakeholders when those goals are reached. Indicators are part of a larger process and cannot be understood when abstracted from that process. Acknowledgements The policy brief was prepared with the support of the EU INCO-DEV research programme (KNOWFISH,
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Contract No. ICA-4-CT-2001-10033). A great many people contributed to the success of the KNOWFISH project. Twenty-three people are listed as authors on the KNOWFISH products that we draw on for this brief. In a very real sense they are all its co-authors. The institutions involved include: CASS—Centre for Applied Social Sciences, University of Zimbabwe, Harare, Zimbabwe. CenTER—Dept. Genetics & Ecology, Aarhus University, Aarhus, Denmark. Chr. Michelsen Institute, Bergen, Norway. Department of Fisheries, Lilongwe, Malawi. Department of Fisheries, Chilanga, Zambia. Department of Fisheries and Marine Biology, University of Bergen, Bergen, Norway. DIFRES—Danish Institute for Fisheries Research, Charlottenlund, Denmark. Faculty of Geography, Hanoi National Pedagogic University, Hanoi, Viet Nam. Fish Culture and Fisheries Group, Wageningen University, Wageningen, Holland. IDPPE—Instituto de Desenvolvimento de Pesca de Pequena Escala, Maputo, Mozambique. VIFEP–Institute for Fisheries Economics and Planning, Hanoi, Viet Nam. IFM—Institute for Fisheries Management and Coastal Community Development, Hirtshals, Denmark. Institute for Marine Aquaculture, Cantho University, Cantho, Viet Nam. Instituto Nacional de Investigaca˜o Pesqueira (Fisheries Research Institute), Maputo, Mozambique. LARReC—Living Aquatic Resources Research Center, Vientiane, LAO PDR. Marine and Coastal Management, Rogge Bay, Cape Town, South Africa. NIBR—Norwegian Institute for Urban and Regional Research, Oslo, Norway. PLAAS—Programme for Land and Agrarian Studies, University of the Western Cape, Bellville, Cape Town, South Africa. RIMF—Research Institute for Marine Fisheries, Haiphong, Viet Nam.
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advances and applications in research methods. Beverly Hills: Sage Publications; 1986. p. 57–76 [Chapter 3]. Wilson DC, Jepsen N, Phounsavath S. Research activities report for case study, 5: small riverine fisheries in the Sedone River, Laos. In: The final report of the Knowledge in Fisheries Management Project, Annex 2. Brussels: Research Directorate General; 2005. van Zwieten PAM, Banda M, Mwakiyongo K, Manase M, Kanyerere G, Nyasulu T. Case study: South-East Arm of Malawi, Part 1: potential biological state and pressure indicators to evaluate the fisheries of Lake Malawi SE arm and Lake Malombe: evaluative capacity of Fisheries Research Unit of Malawi. In: The final report of the Knowledge in Fisheries Management Project, Annex 4. Brussels: Research Directorate General; 2005. Donda SJ, Njaya FJ. Case study of South East Arm of Lake Malawi and Lake Malombe in Malawi: Part 2. In: The final report of the Knowledge in Fisheries Management Project, Annex 5. Brussels: Research Directorate General; 2005. Kapasa CK, Malasha I, Wilson DC. Experience based knowledge and fisheries management in the Mweru-Luapula system. In: The final report of the Knowledge in Fisheries Management Project, Annex 11. Brussels: Research Directorate General; 2005. Kapasa CK. Knowledge based on fish: a case study to evaluate biological indicators of fish resources based on the local ecological knowledge (LEK) of fishers of Mweru-Luapula fishery—Northern Zambia. In: The final report of the Knowledge in Fisheries Management Project, Annex 6 . Brussels: Research Directorate General; 2005. Duc DTM, Thinh NV, Huy NT, Thi DV, Bat NK, van Zwieten P, et al. Case study chapter: Nam Dinh Province. Perceiving changes in fish stocks and species composition in the Red River Delta estuary, Viet Nam: Scale of fishing and the potential to develop quantitative indicators based on local observations in a variable and rapidly changing environment with high species diversity. In: The final report of the Knowledge in Fisheries Management Project, Annex 7. Brussels: Research Directorate General; 2005. Wilson DC, Christensen (DIFRES) S, Thi DV, Minh TH. Research activities report for case study 8: medium size shrimp fisheries in the Mekong Delta, Dam Doi, Viet Nam. In: The final report of the knowledge in Fisheries Management Project, Annex 8. Brussels: Research Directorate General; 2005. Nielsen JR, de Sousa LP, Hele EP, Christensen S, Degnbol P, JulLarsen E. The knowledge base for management of the Sofala Bank shallow water shrimp fishery in Mozambique. In: The final report of the Knowledge in Fisheries Management Project, Annex 9. Brussels: Research Directorate General; 2005. Fairweather T, Hara M, van der Lingen C, Nielsen JR, Shannon L, Louw G, et al. The knowledge base for management of the capitalintensive fishery for small pelagic fish off South Africa. African Journal of Marine Science, 2006, forthcoming. Wilson DC. The distribution of fishers’ knowledge in three case studies. In: Paper presented at the annual science conference of the International Council for the Exploration of the Sea, Aberdeen, Scotland, 20–24 December 2005. Wilson DC, Ahmed M, Delaney A, Donda S, Kapasa CK, Malasha I, et al. Comparative research on fisheries co-management Institutions in Southern Africa. Institute for Fisheries Management Research Paper No. 132; 2006. Wilson DC. Fisheries co-management and the knowledge base for management decisions. In: Wilson DC, Nielsen JR, Degnbol P, editors. The fisheries co-management experience: accomplishments, challenges and prospects. Dordrecht, The Netherlands: Kluwer Academic Publishers; 2003 [Chapter 15].