Modelling the impact of fisheries and oceanographic variables on the main target species of the Andalusian purse seine fleet

  1. Domínguez-Bustos, Ángel Rafael 1
  2. Castro-Gutiérrez, Jairo 23
  3. Gómez-Enri, Jesús 4
  4. Cabrera-Castro, Remedios 15
  1. 1 Departamento de Biología, Facultad de Ciencias del Mar y Ciencias Ambientales, Campus de Puerto Real, Universidad de Cádiz, 11510 Puerto Real, Spain.
  2. 2 Departamento de Ciencias Agroforestales, Escuela Técnica Superior de Ingeniería, Campus El
  3. 3 Carmen, Universidad de Huelva, 21007 Huelva, SPAIN.
  4. 4 Departamento de Física Aplicada, Facultad de Ciencias del Mar y Ciencias Ambientales, Campus de Puerto Real, Universidad de Cádiz, 11510 Puerto Real, Spain.
  5. 5 Instituto Universitario de Investigación Marina (INMAR), Campus de Excelencia Internacional del Mar (CEIMAR), 11510 Puerto Real, Spain
Actas:
VIII International Symposium on Marine Sciences (ISMS 2022)

ISBN: 9788490424773

Año de publicación: 2022

Páginas: 533-534

Tipo: Aportación congreso

DOI: 10.20420/1715.2023.579 GOOGLE SCHOLAR lock_openacceda editor

Resumen

Small pelagics are one of the most important group of fishes in the world due to their ecologic significance in the trophic web (Casaucao et al., 2021). Small pelagics are the main target of world fishery industry, and European anchovy, European sardine, mackerel and horse mackerel represent 43.7% of the total landings in Andalusia (southern Spain) (Báez et al., 2021; Castro-Gutiérrez et al., 2022). Others works have studied impact of climatic oscillation in their ecology (Báez & Real, 2011; Leitão et al., 2014; Jghab et al., 2019). This work had as main objective analyze the different factors that could be affecting the abundance of these small pelagics in the both main andalusian fishing areas: Gulf of Cadiz and North Alboran Sea. For that purpose, multiple Generalized Additive Models were performed using different oceanographic variables and landing time series as explanatory variables. The explanatory variables were also included lagged up to three years. A total of 4776 partial models were performed and eight models ( the best one for each species in both areas) were extracted to analyse them. GAM models explained up to 61% of total variance. Most of the models showed a strong non-linear relationship between different fish landings, and all models showed primary production as a key environmental factor to explain variations in the abundance of these small pelagics. However, results also revealeddifferences in the fishing pressure for each specie in both fishing grounds. The use of nonlinear models as an analytic tool could be useful for improving the knowledge on small pelagics and the management of the small pelagics fishing industry.