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Reconstruction of spatiotemporal capture data by means of orthogonal functions: the case of skipjack tuna (Katsuwonus pelamis) in the central-east Atlantic
Ganzedo, U.; Erdaide, O.; Trujillo-Santana, A.; Alvera-Azcárate, A.; Castro, J.J. (2013). Reconstruction of spatiotemporal capture data by means of orthogonal functions: the case of skipjack tuna (Katsuwonus pelamis) in the central-east Atlantic. Sci. Mar. (Barc.) 77(4): 575-584. dx.doi.org/10.3989/scimar.03881.07A
In: Scientia Marina (Barcelona). Consejo Superior de Investigaciones Científicas. Institut de Ciènces del Mar: Barcelona. ISSN 0214-8358; e-ISSN 1886-8134, meer
Peer reviewed article  

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Trefwoord
    Marien
Author keywords
    catches; missing data; spatiotemporal data reconstruction; singularvalue decomposition; DINEOF

Auteurs  Top 
  • Ganzedo, U.
  • Erdaide, O.
  • Trujillo-Santana, A.
  • Alvera-Azcárate, A., meer
  • Castro, J.J.

Abstract
    The information provided by the International Commission for the Conservation of Atlantic Tunas (ICCAT) on captures of skipjack tuna (Katsuwonus pelamis) in the central-east Atlantic has a number of limitations, such as gaps in the statistics for certain fleets and the level of spatiotemporal detail at which catches are reported. As a result, the quality of these data and their effectiveness for providing management advice is limited. In order to reconstruct missing spatiotemporal data of catches, the present study uses Data INterpolating Empirical Orthogonal Functions (DINEOF), a technique for missing data reconstruction, applied here for the first time to fisheries data. DINEOF is based on an Empirical Orthogonal Functions decomposition performed with a Lanczos method. DINEOF was tested with different amounts of missing data, intentionally removing values from 3.4% to 95.2% of data loss, and then compared with the same data set with no missing data. These validation analyses show that DINEOF is a reliable methodological approach of data reconstruction for the purposes of fish¬ery management advice, even when the amount of missing data is very high.

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