Spatio-temporal variations in glass eel recruitment at the entrance pathways of a Mediterranean delta. (7th July 2022)

• Record Type: Journal Article
• Authors: Bouchard, Colin; Drouineau, Hilaire; Lambert, Patrick; Boutron, Olivier; Nicolas, Delphine
• Editors: Durif, Caroline
• Is Part Of: ICES journal of marine science. Volume 79:Number 6(2022)
• Page Start: 1874
• Page End: 1887
• Publication Date: 2022-07-07
• Subjects: Anguilla anguilla -- Dirichlet distribution -- glass eel -- Hierarchical Bayesian Model -- population dynamics -- time-series; Ocean -- Periodicals; Fisheries -- Periodicals; Fishes -- Periodicals; Marine biology -- Bibliography -- Periodicals; 551.4605
• DOI: 10.1093/icesjms/fsac122
• Languages: English
• ISSNs: 1054-3139

Abstract

Abstract: Understanding spatio-temporal dynamics of glass eel recruitment is necessary to characterize eel population status and the stock of future elvers. Despite numerous studies that have characterized recruitment across Europe, multiple systems along the Mediterranean coasts need a deeper look. We built a Bayesian State-Space Model to investigate the temporal variations in glass eel recruitment in the Rhône delta (France). The model was suited to address the spatial heterogeneity due to the complexity of this system constituted by numerous entrance pathways. Over 13 years, we found inter-annual variations without a particular trend. Here, an overall migration peak occurred in February–March for the system, but substantial spatial variations in the resulting recruitment were visible. Spatial variations in the inter-annual dynamics and migration peak pointed out the necessity to account for spatial heterogeneity in the dynamics of glass eel recruitment. The highlighted inter-annual variations are consistent with analysis of coincident time-series in other sites in Europe (e.g. WGEEL), and the migration peak's timing is similar to peaks observed in other estuaries. Spatial variations in the migration peak suggest a potential effect of local environment conditions on the recruitment. Our model provided a suitable approach to investigate temporal variations while accounting for spatial heterogeneity.