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.
Supported by national funds through the FCT - Fundação para a Ciência e a Tecnologia, I.P., under the scope of the projects PREFERENTIAL, PTDC/MAT-STA/28243/2017, UIDP/00297/2020 (Center for Mathematics and Applications) and UIDB/00006/2020(CEAUL)x.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
André, L.M., Figueiredo, I., Carvalho, M.L., Simões, P., Natário, I.: Spatial modelling of black scabbardfish fishery off the Portuguese coast. In: Gervasi, O., et al. (eds.) ICCSA 2020. LNCS, vol. 12249, pp. 332–344. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58799-4_25
Blangiardo, M., Cameletti, M., Baio, G., Rue, H.: Spatial and spatio-temporal models with R-INLA. Spat. Spatio-Temporal Epidemiol. 4, 33–49 (2013)
Chipeta, M., Terlouw, D., Phiri, K., Diggle, P.: Adaptive geostatistical design and analysis for prevalence surveys. Spat. Stat. 15, 70–84 (2016)
Chipeta, M., et al.: Inhibitory geostatistical designs for spatial prediction taking account of uncertain covariance structure. Environmetrics 28(1), e2425 (2017)
Diggle, P., Menezes, R., Su, T.: Geostatistical inference under preferential sampling. J. Roy. Stat. Soc.: Ser. C (Appl. Stat.) 59(2), 191–232 (2010)
Fuglstad, G.A., Simpson, D., Lindgren, F., Rue, H.: Constructing priors that penalize the complexity of Gaussian random fields. J. Am. Stat. Assoc. 114(525), 445–452 (2019)
Krainski, E.T., et al.: Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA. CRC Press, Boca Raton (2018)
Lindgren, F., Lindström, J., Rue, H.: An explicit link between Gaussian fields and Gaussian Markov random fields: the SPDE approach. Centre for Mathematical Sciences, Faculty of Engineering, Lund University, Mathematical Statistics (2010)
Martínez-Minaya, J., Cameletti, M., Conesa, D., Pennino, M.G.: Species distribution modeling: a statistical review with focus in spatio-temporal issues. Stoch. Env. Res. Risk Assess. 32(11), 3227–3244 (2018). https://doi.org/10.1007/s00477-018-1548-7
Pennino, M., et al.: Accounting for preferential sampling in species distribution models. Ecol. Evol. 9(1), 653–663 (2019)
Rue, H., Martino, S., Chopin, N.: Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J. R. Stat. Soc.: Ser. b (Stat. Methodol.) 71(2), 319–392 (2009)
Simpson, D., Rue, H., Riebler, A., Martins, T.G., Sorbye, S.H.: Penalising model component complexity: a principled, practical approach to constructing priors. Stat. Sci. 32(1), 1–28 (2017)
Simões, P., Carvalho, M.L., Figueiredo, I., Monteiro, A., Natário, I.: Geostatistical sampling designs under preferential sampling for black scabbardfish. In: Bispo, R., Henriques-Rodrigues, L., Alpizar-Jara, R., de Carvalho, M. (eds.) SPE 2021. Springer Proceedings in Mathematics and Statistics, vol. 398, pp. 137–151. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-12766-3_11
Spiegelhalter, D., Best, G., Carlin, P., Van Der Linde, A.: Bayesian measures of model complexity and fit. J. R. Stat. Soc.: Ser. b (Stat. Methodol.) 64(4), 583–639 (2002)
Watson, J., Zidek, J.V., Shaddick, G.: A general theory for preferential sampling in environmental networks. Ann. Appl. Stat. 13(4), 2662–2700 (2019)
Acknowledgments
This work is funded by national funds through the FCT - Fundação para a Ciência e a Tecnologia, I.P., under the scope of the projects PREFERENTIAL, PTDC/MAT-STA/28243/2017, UIDP/00297/2020 (Center for Mathematics and Applications) and UIDB/00006/2020(CEAUL).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Simões, P., Carvalho, M.L., Figueiredo, I., Monteiro, A., Natário, I. (2023). Black Scabbardfish Species Distribution: Geostatistical Inference Under Preferential Sampling. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2023 Workshops. ICCSA 2023. Lecture Notes in Computer Science, vol 14105. Springer, Cham. https://doi.org/10.1007/978-3-031-37108-0_19
Download citation
DOI: https://doi.org/10.1007/978-3-031-37108-0_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-37107-3
Online ISBN: 978-3-031-37108-0
eBook Packages: Computer ScienceComputer Science (R0)