Identifying ecosystem patterns from time series of anchovy ( Engraulis ringens ) and sardine ( Sardinops sagax ) landings in northern Chile

Useful knowledge acquisition from known and systematized information (data) is a big challenge for researchers, users and finally, decision makers. In this sense, knowledge discovery from data (KDD) process represents a valuable tool for information analysis. Moreover, this work presents an approach...

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Bibliographic Details
Published in:Journal of statistical computation and simulation 2018-07, Vol.88 (10), p.1863-1881
Main Authors: Plaza, Francisco, Salas, Rodrigo, Yáñez, Eleuterio
Format: Article
Language:English
Subjects:
Data analysis
Data mining
El Nino
Fisheries
Fisheries management
Information management
Knowledge acquisition
Literature reviews
Multivariate analysis
Technical literature
Time series
Variations
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Summary:Useful knowledge acquisition from known and systematized information (data) is a big challenge for researchers, users and finally, decision makers. In this sense, knowledge discovery from data (KDD) process represents a valuable tool for information analysis. Moreover, this work presents an approach through KDD in time series pattern identification for anchovy and sardine fisheries and environmental data, in northern Chile. Time series, multivariate analysis and data mining techniques, along with technical literature review for results validation. The KDD approach and the data mining techniques implemented achieved an integration between these variables, identifying relevant patterns associated with fisheries abundance fluctuations and strong association with environmental changes such as El Niño and long-term cold-warm regimes between them, establishing anchovy and sardine pre-dominant time-periods, associated with environmental conditions are identified. The latter establishes groundwork for studying underlying functional relationships that could reduce gaps in the national fisheries management policies for those fisheries.
ISSN:0094-9655
1563-5163
DOI:10.1080/00949655.2017.1410150