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Structured additive distributional regression for analysing landings per unit effort in fisheries research

AutorMamouridis, Valeria ; Klein, Nadja; Kneib, T.; Cadarso-Suárez, C.; Maynou, Francesc
Palabras claveLandings per unit effort
Red shrimp fishery
North Atlantic Oscillation
North-Western Mediterranean
Generalised additive models for location
Scale and shape
Mixed models
Markov chain Monte Carlo simulation
Fecha de publicaciónene-2017
EditorElsevier
CitaciónMathematical Biosciences 283: 145-154 (2017)
ResumenWe analysed the landings per unit effort (LPUE) from the Barcelona trawl fleet targeting the red shrimp (Aristeus antennatus) using novel Bayesian structured additive distributional regression to gain a better understanding of the dynamics and determinants of variation in LPUE. The data set, covering a time span of 17 years, includes fleet-dependent variables (e.g. the number of trips performed by vessels), temporal variables (inter- and intra-annual variability) and environmental variables (the North Atlantic Oscillation index). Based on structured additive distributional regression, we evaluate (i) the gain in replacing purely linear predictors by additive predictors including nonlinear effects of continuous covariates, (ii) the inclusion of vessel-specific effects based on either fixed or random effects, (iii) different types of distributions for the response, and (iv) the potential gain in not only modelling the location but also the scale/shape parameter of these distributions. Our findings support that flexible model variants are indeed able to improve the fit considerably and that additional insights can be gained. Tools to select within several model specifications and assumptions are discussed in detail as well
Descripción10 pages, 7 figures, 3 tables, 1 appendix supplementary materials https://doi.org/10.1016/j.mbs.2016.11.016
Versión del editorhttps://doi.org/10.1016/j.mbs.2016.11.016
URIhttp://hdl.handle.net/10261/154310
DOI10.1016/j.mbs.2016.11.016
Identificadoresdoi: 10.1016/j.mbs.2016.11.016
issn: 0025-5564
e-issn: 1879-3134
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