FN Archimer Export Format
PT J
TI Combining scientific survey and commercial catch data to map fish distribution
BT 
AF ALGLAVE, Baptiste
   RIVOT, Etienne
   ETIENNE, Marie-Pierre
   WOILLEZ, Mathieu
   THORSON, James T
   VERMARD, Youen
AS 1:1,2;2:2;3:3;4:4;5:5;6:1;
FF 1:PDG-RBE-HALGO-EMH;2:;3:;4:PDG-RBE-HALGO-LBH;5:;6:PDG-RBE-HALGO-EMH;
C1 DECOD (Ecosystem Dynamics and Sustainability), IFREMER, Institut Agro, INRAE, Nantes 44980, France
   DECOD (Ecosystem Dynamics and Sustainability), Institut Agro, IFREMER, INRAE, Rennes 35042, France
   Mathematical Research Institute of Rennes IRMAR, Rennes University, Rennes 35042, France
   DECOD (Ecosystem Dynamics and Sustainability), IFREMER, Institut Agro, INRAE, Brest 29280, France
   Habitat and Ecological Processes Research Program, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA, Seattle, WA 98112, USA
C2 IFREMER, FRANCE
   INST AGRO RENNES-ANGERS, FRANCE
   UNIV RENNES, FRANCE
   IFREMER, FRANCE
   NOAA, USA
SI NANTES
   BREST
SE PDG-RBE-HALGO-EMH
   PDG-RBE-HALGO-LBH
UM DECOD
IN WOS Ifremer UMR
   WOS Cotutelle UMR
   copubli-france
   copubli-univ-france
   copubli-int-hors-europe
IF 3.3
TC 18
UR https://archimer.ifremer.fr/doc/00754/86604/92056.pdf
LA English
DT Article
DE ;hierarchical model;integrated modelling;species distribution model;survey data;Template Model Builder (TMB);VMS and logbook data
AB Developing Species Distribution Models (SDM) for marine exploited species is a major challenge in fisheries ecology. Classical modelling approaches typically rely on fish research survey data. They benefit from a standardized sampling design and a controlled catchability, but they usually occur once or twice a year and they may sample a relatively small number of spatial locations. Spatial monitoring of commercial data (based on logbooks crossed with Vessel Monitoring Systems) can provide an additional extensive data source to inform fish spatial distribution. We propose a spatial hierarchical framework integrating both data sources while accounting for preferential sampling (PS) of commercial data. From simulations, we demonstrate that PS should be accounted for in estimation when PS is actually strong. When commercial data far exceed scientific data, the later bring little information to spatial predictions in the areas sampled by commercial data, but bring information in areas with low fishing intensity and provide a validation dataset to assess the integrated model consistency. We applied the framework to three demersal species (hake, sole, and squids) in the Bay of Biscay that emphasize contrasted PS intensity and we demonstrate that the framework can account for several fleets with varying catchabilities and PS behaviours. 
PY 2022
PD MAY
SO Ices Journal Of Marine Science
SN 1054-3139
PU Oxford university press
VL 79
IS 4
UT 000767468800001
BP 1133
EP 1149
DI 10.1093/icesjms/fsac032
ID 86604
ER

EF