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Using Local and Scientific Knowledge to Predict Distribution and Structure of Herring Shoals

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Using Local and Scientific Knowledge to Predict Distribution and Structure of Herring Shoals NOT TO BE CITED WITHOUT ICES CM 1998/J:11 PRIOR REFERENCE TO THE Session (J): Variation in the Pattern ofFish aggregation: Measurement AUTHORS and analysis at different spatial and temporal scales and implications Using local and scientific knowledge to predict distribution and structure of herring shoals Steven Macldnson! and Nathaniel Newlands (Fisheries Centre, 22 04 Main Mall, University of British Columbia, Vancouver, Canada, V6T 1Z4,: corresponding author:email: [email protected],phone: (604) 822-2731,fax: (604) 822-8934) , Abstract Attempting to predict the distribution and structure of herring shoals is an ominous task given our incomplete understanding of ecological mechanisms. Despite recent attempts to link cross scale behaviour dynamics and distribution studies, there are still large gaps in our basic knowledge of shoal movements. Nonetheless, the knowledge of fishers and fishery managers is not incorporated in to our scientific analysis but such information is rich in observation since knowledge of fish behaviour and distribution is a pre-requisite for their profession. Combining such observations with more conventional scientific studies and theoretical interpretations provides a means by which, we may bridge some gaps in our knowledge. We present a framework in the form of a fuzzy logic expert systemwhose knO\vledge base incorporates both local and scientific knowledge in the form of heuristic rules, The knowledge base is flexible in the ,sense that it can easily be modified to add additional information or change current information. Additional 'operational rules' govern how the system utilises, its knowledge by setting priorities to certain factors under specific situations. The fuzzy system is driven by responses from an external user who provides information on biological and environmental aspects pertaining to the locality and time of year in which they wish to learn about In response, the system predicts structure and distribution patterns of herring shoals. It is context sensitive and 'intelligent', asking the user the minimum questions required to make its predictions. Hypertext screens ". and-graphical- mtert"ace providesuser-rnindl:Y·inriufaJi.oorifpiit 'toileiliei\Vifh-the-capacit)'-to-fiilly--" explain facility of how the system arrived atits conclusion. Keywords: Heuristics, fuzzy logic, model, prediction, herring, school, distribution 1 Introduction On broad geographic scales, herring populations world-wide display remarkable pattern in their spatial and temporal distribution. The annual migration paths of migratory stocks may cover 1000's of kilometeres between spawning, feeding and overwintering sites, many of which have been known for quite some time (Harden-Jones 1968, McKeown 1984). As ecologists, our challenge is to fathom how such repeatable pattern can be derived from the complex, day to day behaviour of individual herring. It is an onerous task; one that is matched in complexity by the tremendous inherent variations within and between our scales of observation. To develop spatially explicit predictive models needed for management and to allow us to respond to change, we must learn how to interface disparate scales of interest and understand how information is transferred from fine to broad scale and vice versa (Levins 1992). A key element guiding our research on the distribution of herring, focuses on understanding how selection has favoured schooling as an adaptive behaviour. Experimental behavioural studies (review by Pitcher 1993) together with detailed in situ acoustic observations (e.g. Gerlotto et al. 1994 Masse et al. 1996, N0ttestad et al. 1996, Pitcher et al. 1996, Petitgas and Levenez 1996, Maraveliasand Reid 1997, Misund et al. 1997) offer insight on which to base our ecological interpretations. The structure and distribution of herring shoals is currently believed to be governed by the strive to achieve 3 main goals; survival; growth arid reproduction (Ferno et al. 1998). Previously, most attentibnhas been directed to small scale (0.1 to 10 m, seconds to minutes), school organisation and dynamics; or, large scale (100' s km, weeks to months), stock structure and migration studies. Information on the mesoscale (0.1 to 100km, day to weeks) distribution pattern of schools and school clusters is particularly lacking. Since many herring fisheries are typically conducted at spatial scales of one to tens of kilometres and occur for periods of days to weeks, both fishers and fishery managers alike operate within themesoscale realm. By virtue of their profession it is prerequisite that they have knowledge regarding the distribution and behaviour of herring. Such a rich information source should not be unutilised. Typically however, local ecological knowledge has been considered by moretniditional analytical fisheries science as 'anecdotal', and for the most part been absent from fish stock assessment or during development of management' plans. In contrast, social science emphasises the importance of local knowledge in a contextual and historical perspective, but again for the most part, has been unsuccessful in directly incorporating this informatiori into fisheries management (see Neis et at 1996 for exception). The relt!ctance and inability to,lltilise J1911-scientificknoWledgeasdata is asignifiqmt affliction hindering the progress of fisheries science and management (Mackinson and N0ttestad, in press). There is an urgent need to maximise the usefulness ofinformation by incorporating alternative sources of data and finding new ways of using traditional ones. We present a formal framework for combining local ecological knowledge and scientific knowledge in the form of heuristic rules and demonstrate how it can be used to predict the structure and distribution of adult herring shoals for different phases during their annual life cycle. Heuristic Data Sources (developing the knowledge base) This heuristic model is developed within the framework of an expert system (Figure 1) and incorporates two fundamental data sources of information on fish distribution and behaviour; (i) 'practical' data: local knowledge of interviewed fishers, fishery managers and First Nations people; (ii) 'hard' data: scientific information from interviewed fisheries scientists, field work studies and published literature sources. 2 Briet1y, expert systems are a branch of artificial intelligence; theories .and methods for automating intelligent behaviour. They are computer programs that provide assistance in solving complex problems normally handled by experts. They use rules to· store. knowledge. When the system is asked to solve a problem, it uses this knowledge to infer solutions. The software used to develop the system presented here is Exsys®' Professional by Multilogic. Figure 1. near here Interviews A total of 30 formal interviews were conducted, half with fishery scientists (8) and fishery managers (7) who generally provided technical or hard scientific knowledge, and half with fishers (9) and First Nations people (6, all previous or currently herring fishers) who provided professional practical local knowledge. With the exception of one gillnetter who specifically undertook herring surveys, most fishers interviewed were seine fishers, with a collective experience (CE) of approximately 270 fishing years. Seiners were specifically chosen as candidates in contrast to gillnetters or roe-on-kelpfishers, since seine-fishing typically requires specific knowledge of fish distribution and movements when searching broad areas. The First nations CE amounted to approximately 290 fishing years. Selection of interviewees was deliberately non-random. An attempt was made' to interview those fishers who had the most experience fishing herring during different seasons, at different locations and who were held with respect by other fishers in the community. For this reason, progressive selection of interviewees was conducted by word of mouth, one candidate suggesting another to talk to. This method proved to be very successful. Fishery scientists and managers were selected based ontheir experience with herring. The current regional herring co-ordinator and 3 long time fishery managers (CE approx. 160 years) offered a more technical 'field based' perspective that complimented observations by fishers. Three herring scientists from the Pacific biological sta.tion, Department of Fisheries and Oceans (CE approx .. 75 years), and a further 5 from Norway (Institute of Marine Research and University of Bergen; CE approx. 80 years) provided hard scientific observations from field and experimental studies. Typical interView duration was 2 hours but some ranged from 1- 4. With two exceptions, all interviews were conducted on a separate individual basis at the preferred location of the candidate. For help with interpretation, it was necessary on one occasion to interview two fishers together. In another . instance, a meeting was held withtl1e. first Nations Sliamm()nband.elders, that included men and. women who had traditionally been involved in herring fishing prior to the demise of their localised hei:ring stock. Two basic questions were asked; what? and why? Firstly, interviews were asked to recount what theY'had observed regarding distribution and behaviour of herring and secondly to offer possible explanations to account for their obserVations. All candidates were asked the same type of questions although specific interViews were 'free range' or 'adaptive' in the sense that the format and directness in which the questions
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