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17(6)

A multivariate approach

for metier definition:

A case study of

Basque Country trawlersAne Iriondo

Raúl Prellezo

Marina Santurtún

Dorleta García

Iñaki Quincoces

Estanis Mugerza

139 | Revista de Investigación Marina, 2010, 17(6)

Iriondo, A., Prellezo, R., Santurtún, M., García, D., Quincoces, I. Mugerza , E., 2010. A multivariate approach for 'Revista de Investigación Marina

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Distribución gratuita en formato PDF a través de la web: www.azti.es/RIM Revista de Investigación Marina, 2010, 17(6) | 140 study of Basque Country trawlers

Ane Iriondo

a,b

Raúl Prellezo

a , Marina Santurtún a , Dorleta García a

Iñaki Quincoces

a and Estanis Mugerza a

Abstract

Key words

Introduction

use of fisheries data sector in support of the scientific advice st data. This stock based sampling programmes provided the across species and the use of different technologies depending the assessment and management. Moreover, the reform of the approach to management. a

Address: AZTI-TECNALIA, Marine Research Division,

Txatxarramendi Ugartea z/g, 48395 Sukarrieta, Spain.

Fax: +34 94 6574000 Tel: +34 94 6572555

b

E-mail: airiondo@azti.es

141
| Revista de Investigación Marina, 2010, 17(6) similar (assemblage of) species, using similar gear, during the same fleet segment different metiers could be identified. targeted, the area visited, and the gear used, at the start of et al ., 2001; Marchal et al et al appropriate segmentation useful for the application of data definitions obtained using the approach investigated are demands or economic reasons.

Material and methods

Description of the fleet analyzed

Divisions VIIIa,b,d & Subareas VI & VII (Figure 1). The landed at French ports (Lorient, La Rochelle) and transported

Data used

presented in Table 1 and Table 2.

Figure 1.

Revista de Investigación Marina, 2010, 17(6) | 142

Statistical approach

groups of trips and to characterize them taking into account To aggregate the information to a metier group, multivariate approaches are able to provide reduced descriptions of large et alet al.,

Principal Component Analysis

correlated variables into a smaller number of uncorrelated variables called principal components. The first principal as possible, and each succeeding component accounts for as obtain a reduced description of the large species landing data set and to merge all those species in principal components.

Table 1

Number of vesselsVIIIVIIVITotal

2003
20761
172

Total 2003 37927141941162

2004
12270
6

Total 200434827128701025

2005
16 27

Total 20052980012264986

2006

1662621

7

Total 2006318086963940

2007

207060

1111

Total 2007316608160832

2008
1660

Total 2008216661360760

Table 2

. Variables from the selling sheets used in the multivariate approach.

Variables Description

VesselVessel name

Harbour

Month AreaIces Subareas VI, VII and Divisions VIIIa,b,d, Gear

Species landings All species landed in kg.

143
| Revista de Investigación Marina, 2010, 17(6)

Figure 2.

Revista de Investigación Marina, 2010, 17(6) | 144 can be valuable to characterize each cluster and the procedure informs about the variance of the data series.

Cluster Analysis

principal components, all the principal components obtained (nine principal components) are retained, and a hierarchical descending classification procedure (clustering) has been into subsets (called clusters) so that observations in the same cluster are similar in some sense. Hierarchical algorithms find

Results

large. The number of factors complicates the interpretation of the

Figure 3.

Table 3

MetierNantes proposalGearSubareaSpeciesSeason200320042005200620072008 1

VIIIhakeall

2 VIII catshark, pouting...)all VIII

VIIIhorse mackerel

VIIImackerel

6

VIIall

7 VI megrim all

100%100%100%100%100%100%

145
| Revista de Investigación Marina, 2010, 17(6) representation. In each plot the position of each observation (trip) and the average of each variable (species) are represented in order to describe the meaning of each component. the highest accumulation of variance. The representation of the can be said that component 1 is separating trips targeting hake size of the cluster (in number of trips), target species (one if metiers depending on the fishing area and target species. More VIIIa,b,d. It is interesting to point out that in 2007 landings

Merlangius merlangus

) is species (hake, megrim, monkfish, catshark, pouting,...) in

Figure 4.

Revista de Investigación Marina, 2010, 17(6) | 146

Figure 5.

147
| Revista de Investigación Marina, 2010, 17(6)

Discussion

the seven metiers described in this paper if needed, could be This metier based information of catches, effort and more to multispecies and multifleet data collection. This is a step operating in Division VIIIa,b,d and Subareas VI and VII, and of the percentage of metier nº 2 could be also due to the fact that some trips, as the species composition is diverse, are assigned time series and the appearance of them made the other metiers considered consistent along the time series disappear from the changing; the catches of this fleet are targeting more demersal another cluster. Revista de Investigación Marina, 2010, 17(6) | 148 precise.

Acknowledgements

Environment, Land Use Planning, Agriculture and Fisheries of from AZTI's Marine Research Division.

References

et al in Space. Philosophical Magazine 2 Estimation of technical interactions due to the competition for resource

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