Enhancing fishery-dependent information in data-poor fisheries; integrating gear-in–gear-out sensors and mobile reporting technology in a mixed Irish Sea static-gear fishery

Inshore static gear fisheries such as those targeting predominately shellfish play an import socio-economic role across the northeast Atlantic. Despite this, assessment techniques are heavily reliant on fishery dependent data which is typically aggregated over large spatial scales and lacking in key environmental and biotic data. In this study, we trialled the implementation of an enhanced electronic reporting system (EERS) and gear-in–gear-out (GIGO) technology in a data-limited, mixed species, static gear fishery for brown crab Cancer pagurus and European lobster Homarus gammarus. EERS/GIGO systems were deployed on two commercial vessels for 12 months and collected data from 812 strings, equating to 29826 pots, with precise geo-located landings per unit effort (LPUE) and environmental data. Cluster analysis identified spatially distinct patterns in fishing activity, corresponding to different target species. Generalized additive modelling was used to investigate the effect of environmental variables, inter-specific interactions and geo-location on LPUE in both species. Sea bottom temperatures had a significant positive effect on LPUE in both C. pagurus and H. gammarus. In addition, GAM analysis showed the importance of inter-specific interactions; increases in capture of competing non-target commercial species (H. gammarus/C. pagurus) resulted in the decreases in target species LPUE (C. pagurus/H. gammarus).The significant effect of environmental variables and inter-specific interactions demonstrate the value of understanding these interactions in order to produce robust standardized LPUE metrics. The EERS/GIGO system successfully demonstrated its application, and value in collecting geospatially defined fishery dependent data in historically data limited fisheries. Co-development of such an approach between fisheries administrations and industry has the potential to significantly enhance data collection and management in many data poor fisheries.