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.
Highlights
The integrated ecosystem assessment (IEA) is one approach to organizing scientific information at multiple scales and across sectors to support ecosystem-based fisheries management (EBFM) (Levin et al, 2009)
There is a long history of making quantitative assessments of individual fish stocks, and with it, common vocabularies and practices that support efficient communication between natural scientists, managers, the fishing industry, and other interested stakeholders (Hilborn and Walters, 1992; ICES, 2013)
trend estimation and classification (TREC) is proposed as an approach to analysing common trends in a marine ecosystem
Summary
The integrated ecosystem assessment (IEA) is one approach to organizing scientific information at multiple scales and across sectors to support ecosystem-based fisheries management (EBFM) (Levin et al, 2009). The outcome of an IEA can take multiple forms, which generally include descriptions of the main interacting ecosystem, human components, and past changes in these components. It gives an assessment of the risks associated with possible future trajectories of the ecosystem. This information can be fed back into the design of dedicated observational efforts (monitoring plans), the definition of multi-sectorial management objectives, or the establishment of new indicators and associated reference points (Levin et al, 2009, 2014; DePiper et al, 2017). Even when the numerical methods used to reconstruct individual fish stock histories are complex, the outputs of fish stock assessments are communicated in a standardized manner that can generally
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