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Investigating remote synchronous patterns in fisheries

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The hypothesis that the population dynamics of distant fish stocks may be synchronized by climate variations is largely based on a few major pelagic fisheries or stocks (called here ‘target series’). We broadened the study of synchrony to all available fisheries in the world by making use of all available data in the FAO catch database during the last 50 years. We investigated synchronous patterns in remote fisheries using two different approaches. The first approach consisted of simulating catch time-series as random walk processes and comparing the synchronies found between these time-series to the synchronies found in FAO data. In the second approach, we applied classification algorithms to analyse synchronies in the FAO catch database. We used K-means clustering and neural networks to answer the following four questions: (1) Do the target series appear in the same cluster? (2) Do the clusters regroup series from the same region (local synchrony) or from distant ones (remote synchrony)? (3) Are there any numerically dominant patterns (clusters) in the catch series? (4) Do the species displaying the same pattern share some common bio-ecological features or ranges of abundance? Our results indicate some degree of local rather than remote synchrony. Nonetheless, it does not prove that such remote synchronies do not exist, but simply that further process studies are required before accepting them as a new paradigm.
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Keywords: K-means; catch data; neural network; pelagic fish; regime shift

Document Type: Research Article

Affiliations: ForeTrade Support, 9, Silwood Road, 7701 Rondebosch, South Africa

Publication date: 2003-09-01

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