Black Scabbardfish Species Distribution: Geostatistical Inference Under Preferential Sampling

Paula Simões, M. Lucília Carvalho, Ivone Figueiredo, Andreia Monteiro, Isabel Natário

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Black Scabbardfish (BSF) is a highly prized deep-sea species that occurs in continental waters at depths greater than 800 m. It has been recognized that improving knowledge of its biodiversity and abundance along the Portuguese coast of BSF species is a scientifically and socially relevant issue, mainly due to the fact of absence of dedicated deep-water research surveys in this area, the spatial distribution of its abundance is mainly inferred from commercial deep-water longline fishery that operates along the continental slope. Black Scabbardfish (BSF) captures are modelled using a geostatistical analysis combined with a preferential sampling technique which enables to better capture the variability of the BSF captures providing a more realistic pattern of BSF distribution. This approach allows a better knowledge os BSF spatial distribution assuming that the selection of the sampling locations depends on the values of the observed variable of interest. BSF captures are jointly modeled with their locations, using a Bayesian approach and INLA methodology, considering stochastic partial differential equations (SPDE) in the geostatistical model and in the Log-Cox point process model for the locations. Several different covariates and random effects were considered. The best two fits are presented, the first including covariate depth in the intensity of the point process besides the shared spatial effect with the response, and the second fit having covariate vessel tonnage in the response adding to the shared spatial effect and covariate depth again included in the point process intensity.
Original languageEnglish
Title of host publicationComputational Science and Its Applications – ICCSA 2023 Workshops
Subtitle of host publicationAthens, Greece, July 3–6, 2023, Proceedings, Part II
EditorsOsvaldo Gervasi, Beniamino Murgante, Francesco Scorza, Ana Maria A. C. Rocha, Chiara Garau, Yeliz Karaca, Carmelo M. Torre
Place of PublicationCham
Number of pages12
ISBN (Electronic)978-3-031-37108-0
ISBN (Print)978-3-031-37107-3
Publication statusPublished - 1 Jul 2023
Event23rd International Conference on Computational Science and Its Applications, ICCSA 2023 - Athens, Greece
Duration: 3 Jul 20236 Jul 2023

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference23rd International Conference on Computational Science and Its Applications, ICCSA 2023


  • Geostatistics
  • INLA
  • Point process
  • Preferential sampling
  • SPDE


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