Ndiaye M. auteur aut IRD Dabo-Niang S. auteur aut IRD Ngom P. auteur aut IRD Thiam N. auteur aut IRD Brehmer Patrice auteur aut IRD El Vally Y. auteur aut IRD text bookSection eng text/pdf born digital access Fisheries science has been trying to identify the best way to analyze and predict fish biomass and its spatial distribution since several decades using, among others, kirigng model, co-kiriging model, Species Distribution Modeling and Joined Species Distribution Modeling, based on conventional statistical methods as Generalized Linear Models and Generalized Additive Models, with contested results. We consider a bio-ecological issue applying a non parametric spatial prediction based on a spatio-functional regression models, in a fixed design sampling context, as a supervised classification method when the variable of interest belongs to a predefined class set. The proposed predictor takes into account the spatial fish distribution and environmental variable such as salinity and temperature. The development of the method depends on two kernels to control both interactions between observations and locations. The results show that this nonparametric spatial functional supervised classification method is an efficient tool applied to predict spatial distribution of demersal coastal fish off Senegal. specialized SENEGAL ATLANTIQUE 126TELAPP05 126TELTRN05 Seck D. ed. edt IRD Kangni K. ed. edt IRD Salomon Sambou M. ed. edt IRD Nang P. ed. edt IRD Fall M.M. ed. edt IRD 69-100 Cham Birkhäuser 2024 The NLAGA's Biennial International Research Symposium, 3., Mbour (SEN), 2023/08/21-27 https://www.documentation.ird.fr/hor/fdi:010093033 10.1007/978-3-031-52681-7_3 978-3-031-52680-0 [F B010093033] https://www.documentation.ird.fr/hor/fdi:010093033 https://www.documentation.ird.fr/intranet/publi/2025-02/010093033.pdf Accès réservé (Intranet de l'IRD) IRD - Base Horizon / Pleins textes 2025-02-05 2025-11-06 fdi:010093033 fre