University of Oulu

Leena Pasanen, Päivi Laukkanen-Nevala, Ilkka Launonen, Sergey Prusov, Lasse Holmström, Eero Niemelä & Jaakko Erkinaro (2017) Extraction of sea temperature in the Barents Sea by a scale space multiresolution method – prospects for Atlantic salmon, Journal of Applied Statistics, 44:13, 2317-2336, DOI: 10.1080/02664763.2016.1252731

Extraction of sea temperature in the Barents Sea by a scale space multiresolution method : prospects for Atlantic salmon

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Author: Ruha (née Pasanen), Leena1; Laukkanen-Nevala, Päivi2; Launonen, Ilkka1;
Organizations: 1Department of Mathematical Sciences, University of Oulu, Pentti Kaiteran katu 1, PO Box 3000, FI -90014, University of Oulu, Finland
2Natural Resources Institute Finland, PO Box 413, FI -90014 Oulu, Finland
3Knipovich Polar Research Institute of Marine Fisheries and Oceanography, 183763, Knipovich Street, 6, Murmansk, Russia
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.6 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2019040911539
Language: English
Published: Informa, 2017
Publish Date: 2019-04-09
Description:

Abstract

Variation of marine temperature at different time scales is a central environmental factor in the life cycle of marine organisms, and may have particular importance for various life stages of anadromous species, for example, Atlantic salmon. To understand the salient features of temperature variation we employ scale space multiresolution analysis, that uses differences of smooths of a time series to decompose it as a sum of scale-dependent components. The number of resolved components can be determined either automatically or by exploring a map that visualizes the structure of the time series. The statistical credibility of the features of the components is established with Bayesian inference. The method was applied to analyze a marine temperature time series measured from the Barents Sea and its correlation with the abundance of Atlantic salmon in three Barents Sea rivers. Besides the annual seasonal variation and a linear trend, the method revealed mid time-scale (∼10 years) and long time-scale (∼30 years) variation. The 10-year quasi-cyclical component of the temperature time series appears to be connected with a similar feature in Atlantic salmon abundance. These findings can provide information about the environmental factors affecting seasonal and periodic variation in survival and migrations of Atlantic salmon and other migratory fish.

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Series: Journal of applied statistics
ISSN: 0266-4763
ISSN-E: 1360-0532
ISSN-L: 0266-4763
Volume: 44
Issue: 13
Pages: 2317 - 2336
DOI: 10.1080/02664763.2016.1252731
OADOI: https://oadoi.org/10.1080/02664763.2016.1252731
Type of Publication: A1 Journal article – refereed
Field of Science: 112 Statistics and probability
Subjects:
Funding: The work of Leena Pasanen, Ilkka Launonen and Lasse Holmström was supported by the Academy of Finland under Grant [250862]; Research Council for Natural Sciences and Engineering.
Academy of Finland Grant Number: 250862
Detailed Information: 250862 (Academy of Finland Funding decision)
Copyright information: © 2017 Informa UK Limited. This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Applied Statistics on 10.11.2016, available online: http://www.tandfonline.com/10.1080/02664763.2016.1252731