FN Archimer Export Format
PT J
TI Combining multiple data sets to unravel the spatiotemporal dynamics of a data-limited fish stock
BT 
AF Pinto, Cecilia
   Travers-Trolet, Morgane
   Macdonald, Jed
   Rivot, Etienne
   Vermard, Youen
AS 1:1,2;2:2;3:3;4:4;5:5;
FF 1:PDG-RBE-HMMN-LRHBL;2:PDG-RBE-EMH;3:;4:;5:PDG-RBE-EMH;
C1 European Commission Joint Research Centre Ispra Sector, 99013, D Sustainable Resources -D.02 Water and Marine Resources, Via Enrico Fermi 2749, Ispra, Italy
   IFREMER, 150, quai Gambetta, BP 699, F-62321 Boulogne-sur-Mer, cedex, France
   University of Iceland, Faculty of Life and Environmental Sciences, Reykjavik, Iceland
   Agrocampus Ouest, UMR 0985 INRA / Agrocampus Ouest ESE, Agrocampus, Ecologie Halieutique, 65, rue de St Brieuc, Rennes, France
   IFREMER, Unité EMH, Rue de l'ile d'Yeu, Nantes, France
C2 JRC, ITALY
   IFREMER, FRANCE
   UNIV ICELAND, ICELAND
   AGROCAMPUS OUEST, FRANCE
   IFREMER, FRANCE
SI BOULOGNE
   NANTES
SE PDG-RBE-HMMN-LRHBL
   PDG-RBE-EMH
IN WOS Ifremer UPR
   copubli-france
   copubli-europe
IF 2.849
TC 15
UR https://archimer.ifremer.fr/doc/00460/57187/59150.pdf
   https://archimer.ifremer.fr/doc/00460/57187/66082.pdf
   https://archimer.ifremer.fr/doc/00460/57187/66083.pdf
LA English
DT Article
CR CGFS : CHANNEL GROUND FISH SURVEY
AB 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. 
PY 2019
PD AUG
SO Canadian Journal Of Fisheries And Aquatic Sciences
SN 0706-652X
PU Canadian Science Publishing
VL 76
IS 8
UT 000477599400009
BP 1338
EP 1349
DI 10.1139/cjfas-2018-0149
ID 57187
ER

EF