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Integrating knowledge to predict spatial dynamics of herring shoals using an expert system

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Title: Integrating knowledge to predict spatial dynamics of herring shoals using an expert system
Author: Mackinson, Steven
Degree Doctor of Philosophy - PhD
Program Resource Management and Environmental Studies
Copyright Date: 1999
Abstract: By modifying and adapting their behaviour, herring display the remarkable plasticity required to succeed in a changing biological and physical environment. The basis of adaptation lies in decisions of individual fish, that make second to second evaluations of possible trade-offs, deciding accordingly whether to join, leave or stay with a shoal. Such actions are manifest as changes in the structure, dynamics and distribution of shoals; facets which for many of the world's pelagic stocks have considerable importance to central issues in fisheries management including stock structure, stock assessment, resilience and harvest control. Since fisheries generally operate within the meso-scale realm (lOO's m - lO's km, hour-weeks), descriptors of meso-scale spatial dynamics of fish shoals are critical diagnostics for management. The meso-scale wild studies detailed in this thesis describe spatial pattern of herring shoals using a simple quantitative index, termed the 'cluster ratio', that links scales of distribution pattern among shoals. It can be used to compare shoal clustering pattern for surveys made at different places and seasons. Despite recent spatial dynamics studies, much of our understanding of fish behaviour and distribution remains qualitative or uncertain. A model is presented in this thesis that attempts to bridge existing gaps in our basic understanding of the biological and ecological mechanisms underpinning behavioural responses of herring, and how these govern spatial dynamics of shoals. The approach combines two fundamental sources of information: (i) 'hard data' from fieldwork and published sources; (ii) 'practical knowledge' from interviews with experts and fishery professionals including fishers,fishery managers, scientists and First Nations people. The model, CLUPEX, is developed in the framework of an expert system and utilises fuzzy logic to capture and integrate scientific and local knowledge in the form of heuristic rules. Using input pertaining to biotic and abiotic environmental conditions, CLUPEX uses the rules to provide quantitative and qualitative predictions on the structure, dynamics and meso-scale distribution of shoals of migratory adult herring during different life stages of their annual life cycle. Predictions are generalised to two different herring species and may be used as input to harvest models, to examine the impacts of shoal structure and distribution on management of herring fisheries. An important feature of the model is that predictions constitute testable hypotheses on which to base future experiments and field observations. Test predictions correspond well with observed shoal patterns, although accuracy for specific circumstances may be limited by the resolution of the knowledge. However, by adding specific local knowledge and adjusting weighting parameters, CLUPEX can be adapted to provide more accurate and precise predictions. The user interface combines hypertext and an explanation facility that is fully cross-referenced to a database, to provide an intuitive and transparent feel rarely found in more traditional analytical models.
URI: http://hdl.handle.net/2429/11955
Series/Report no. UBC Retrospective Theses Digitization Project [http://www.library.ubc.ca/archives/retro_theses/]

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