Combining scientific survey and commercial catch data to map fish distribution

Type Article
Date 2022-05
Language English
Author(s) Alglave Baptiste1, 2, Rivot Etienne2, Etienne Marie-Pierre3, Woillez MathieuORCID4, Thorson James T5, Vermard YouenORCID1
Affiliation(s) 1 : DECOD (Ecosystem Dynamics and Sustainability), IFREMER, Institut Agro, INRAE, Nantes 44980, France
2 : DECOD (Ecosystem Dynamics and Sustainability), Institut Agro, IFREMER, INRAE, Rennes 35042, France
3 : Mathematical Research Institute of Rennes IRMAR, Rennes University, Rennes 35042, France
4 : DECOD (Ecosystem Dynamics and Sustainability), IFREMER, Institut Agro, INRAE, Brest 29280, France
5 : Habitat and Ecological Processes Research Program, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA, Seattle, WA 98112, USA
Source Ices Journal Of Marine Science (1054-3139) (Oxford university press), 2022-05 , Vol. 79 , N. 4 , P. 1133-1149
DOI 10.1093/icesjms/fsac032
WOS© Times Cited 16
Keyword(s) hierarchical model, integrated modelling, species distribution model, survey data, Template Model Builder (TMB), VMS and logbook data

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.

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