Estimation and classification of temporal trends to support integrated ecosystem assessment. (27th September 2020)

• Record Type: Journal Article
• Authors: Solvang, Hiroko Kato; Planque, Benjamin
• Editors: Link, Jason
• Is Part Of: ICES journal of marine science. Volume 77:Number 7/8(2020)
• Page Start: 2529
• Page End: 2540
• Publication Date: 2020-09-27
• Subjects: common trend -- Kalman filter -- marine ecosystem -- non-stationary time series modelling -- polynomial regression model -- stakeholders -- state space model; Ocean -- Periodicals; Fisheries -- Periodicals; Fishes -- Periodicals; Marine biology -- Bibliography -- Periodicals; 551.4605
• DOI: 10.1093/icesjms/fsaa111
• Languages: English
• ISSNs: 1054-3139

Abstract

Abstract: We propose a trend estimation and classification (TREC) approach to estimating dominant common trends among multivariate time series observations. Our methods are based on two statistical procedures that includes trend modelling and discriminant analysis for classifying similar trend (common trend) classes. We use simulations to evaluate the proposed approach and compare it with a relevant dynamic factor analysis in the time domain, which was recently proposed to estimate common trends in fisheries time series. We apply the TREC approach to the multivariate short time series datasets investigated by the ICES integrated assessment working groups for the Norwegian Sea and the Barents Sea. The proposed approach is robust for application to short time series, and it directly identifies and classifies the dominant trends underlying observations. Based on the classified trend classes, we suggest that communication among stakeholders like marine managers, industry representatives, non-governmental organizations, and governmental agencies can be enhanced by finding the common tendency between a biological community in a marine ecosystem and the environmental factors, as well as by the icons produced by generalizing common trend patterns.