Reconstruction of spatiotemporal capture data by means of orthogonal functions: the case of skipjack tuna (&lt;i&gt;Katsuwonus pelamis&lt;/i&gt;) in the central-east Atlantic

José J Castro,Aida Alvera-Azcárate,Oihane Erdaide,Unai Ganzedo,Aaron Trujillo-Santana

doi:10.3989/scimar.03881.07a

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 fishery management advice, even when the amount of missing data is very high.

Highlights

In order to reduce the error range of their estimations, most population dynamics models (e.g. Virtual Population Analysis in Beverton and Holt 1957) require a big quantity of historical data of fishing effort and captures carried out by the different fishing fleets that target these stocks (Cushing 1983, Pitcher and Hart 1983, Hilborn and Walters 1992, Farrugio 1993, Lleonart 1993, Quinn and Deriso 1999, Cadima 2003)
Under-reports of anchovy catches given by Peru between 1951 and 1982 (Castillo and Mendo 1987), or those over-reported by China during the 1990s, as a consequence of its regional organization of fisheries statistical recording systems (Pang and Pauly 2001)
The main objective of this study is to validate the Data INterpolating Empirical Orthogonal Functions (DINEOF) method of missing data reconstruction, applied here for first time to fisheries data, as an effective method to compensate the data loss observed in the historical catch series (Alvera-Azcárate et al 2007)

Summary

Introduction

In order to reduce the error range of their estimations, most population dynamics models (e.g. Virtual Population Analysis in Beverton and Holt 1957) require a big quantity of historical data of fishing effort and captures carried out by the different fishing fleets that target these stocks (Cushing 1983, Pitcher and Hart 1983, Hilborn and Walters 1992, Farrugio 1993, Lleonart 1993, Quinn and Deriso 1999, Cadima 2003). There is not always enough historical data available to run these estimations and to effectively assess and manage the fisheries stock and its status (ICES 2005, Kelly and Codling 2006). The first Yearbook of World Captures edited by Food and Agriculture Organization (FAO) was published in 1950, as a partial solution to this problem. This and the following yearbooks of captures have many deficiencies due to the lack of data related to discards, sport fishing and unreported or unrecorded catches (Pauly 2009). Under-reports of anchovy catches given by Peru between 1951 and 1982 (Castillo and Mendo 1987), or those over-reported by China during the 1990s, as a consequence of its regional organization of fisheries statistical recording systems (Pang and Pauly 2001)

Objectives

Methods

Results

Discussion

Conclusion