@inproceedings{f71f4671318b4532bc1aaf763fbd9e30,
title = "Black Scabbardfish Species Distribution: Geostatistical Inference Under Preferential Sampling",
abstract = "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.",
keywords = "Geostatistics, INLA, Point process, Preferential sampling, SPDE",
author = "Paula Sim{\~o}es and Carvalho, {M. Luc{\'i}lia} and Ivone Figueiredo and Andreia Monteiro and Isabel Nat{\'a}rio",
note = "Funding Information: info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00006%2F2020/PT# info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F00297%2F2020/PT# info:eu-repo/grantAgreement/FCT/Concurso para Financiamento de Projetos de Investiga{\c c}{\~a}o Cient{\'i}fica e Desenvolvimento Tecnol{\'o}gico em Todos os Dom{\'i}nios Cient{\'i}ficos - 2017/PTDC%2FMAT-STA%2F28243%2F2017/PT# Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 23rd International Conference on Computational Science and Its Applications, ICCSA 2023 ; Conference date: 03-07-2023 Through 06-07-2023",
year = "2023",
month = jul,
day = "1",
doi = "10.1007/978-3-031-37108-0_19",
language = "English",
isbn = "978-3-031-37107-3",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "303--314",
editor = "Osvaldo Gervasi and Beniamino Murgante and Francesco Scorza and Rocha, {Ana Maria A. C.} and Chiara Garau and Yeliz Karaca and Torre, {Carmelo M.}",
booktitle = "Computational Science and Its Applications – ICCSA 2023 Workshops",
address = "Netherlands",
}