DataSheet_1_Impact of environmental conditions on English Channel long-finned squid (Loligo spp.) recruitment strength and spatial location.docx

• Main Authors: Anna Marcout, Eric Foucher, Graham J. Pierce, Jean-Paul Robin
• Format: Dataset
• Language: unknown
• Published: 2024
• Subjects: Oceanography; Marine Biology; Marine Geoscience; Biological Oceanography; Chemical Oceanography; Physical Oceanography; Marine Engineering; squid; recruitment; English Channel; forecasting; environment; loligo forbesii; loligo vulgaris; Northeast Atlantic
• Online Access: https://doi.org/10.3389/fmars.2024.1433071.s001; https://figshare.com/articles/dataset/DataSheet_1_Impact_of_environmental_conditions_on_English_Channel_long-finned_squid_Loligo_spp_recruitment_strength_and_spatial_location_docx/26588686

The English Channel has the highest long-finned squid landings in the Northeast Atlantic, making squid one of the most valuable resources exploited by demersal fisheries operating in this area. This resource consists of two short-lived long-finned squid species: Loligo forbesii and L. vulgaris, which have a similar appearance (they are not distinguished by fishers) but differ in the timing of their life cycle: in L. forbesii, the recruitment peak occurs in July while in L. vulgaris recruitment peak occurs in November. The abundance and distribution of cephalopod species, such as Loligo spp., depends on favourable environmental conditions to support growth, reproduction and successful recruitment. This study investigated the role of several environmental variables (bottom temperature, salinity, current velocity, phosphate and chlorophyll concentrations) on recruitment biomass (in July for L. forbesii and November for L. vulgaris), as based on environmental data for pre-recruitment period from the Copernicus Marine Service and commercial catches of French bottom trawlers during the recruitment period over the years 2000 to 2021. To account for non-linear relationship between environmental descriptors and the biological response, General Additive Models (GAM) were fitted to the data. Separate models were obtained to forecast L. vulgaris and L. forbesii biomass indices during their respective recruitment periods. These models explain a high percentage of variation in biomass indices (65.8% for L. forbesii and 56.7% for L. vulgaris) and may be suitable to forecast the abundance (in terms of biomass) and spatial distribution of the resource. Such forecasts are desirable tools to guide fishery managers. Since these models can be fitted shortly before the start of the fishing season, their routine implementation would take place in real-time fishery management (as promoted by fishery scientists dealing with short-lived species).