Combining multiple data sets to unravel the spatiotemporal dynamics of a data-limited fish stock

Type Article
Date 2019-08
Language English
Author(s) Pinto Cecilia1, 2, Travers-Trolet MorganeORCID2, Macdonald Jed3, Rivot Etienne4, Vermard YouenORCID5
Affiliation(s) 1 : European Commission Joint Research Centre Ispra Sector, 99013, D Sustainable Resources -D.02 Water and Marine Resources, Via Enrico Fermi 2749, Ispra, Italy
2 : IFREMER, 150, quai Gambetta, BP 699, F-62321 Boulogne-sur-Mer, cedex, France
3 : University of Iceland, Faculty of Life and Environmental Sciences, Reykjavik, Iceland
4 : Agrocampus Ouest, UMR 0985 INRA / Agrocampus Ouest ESE, Agrocampus, Ecologie Halieutique, 65, rue de St Brieuc, Rennes, France
5 : IFREMER, Unité EMH, Rue de l'ile d'Yeu, Nantes, France
Source Canadian Journal Of Fisheries And Aquatic Sciences (0706-652X) (Canadian Science Publishing), 2019-08 , Vol. 76 , N. 8 , P. 1338-1349
DOI 10.1139/cjfas-2018-0149
WOS© Times Cited 15

The biological status of many commercially-exploited fishes remains unknown, mostly due to a lack of data necessary for their assessment. Investigating the spatio-temporal dynamics of such species can lead to new insights into population processes, and foster a path towards improved spatial management decisions. Here, we focused on striped red mullet (Mullus surmuletus), a widespread, yet data-limited species of high commercial importance. Aiming to quantify range dynamics in this data-poor scenario, we combined fishery-dependent and -independent datasets through a series of Bayesian mixed-effects models designed to capture monthly and seasonal occurrence patterns near the species’ northern range limit across 20 years. Combining multiple datasets allowed us to cover the entire distribution of the northern population of Mullus surmuletus, exploring dynamics at different spatio-temporal scales, and identifying key environmental drivers (i.e. sea surface temperature, salinity) that shape occurrence patterns. Our results demonstrate that even when process and/or observation uncertainty is high, or when data is sparse, by combining multiple datasets within a hierarchical modelling framework accurate and useful spatial predictions can still be made.

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