All is fish that comes to the net: metabarcoding for rapid fisheries catch assessment.

Monitoring marine resource exploitation is a key activity in fisheries science and biodiversity conservation. Since research surveys are time consuming and costly, fishery-dependent data (i.e., derived directly from fishing vessels) are increasingly credited with a key role in expanding the reach of ocean monitoring. Fishing vessels may be seen as widely ranging data-collecting platforms, which could act as a fleet of sentinels for monitoring marine life, in particular exploited stocks. Here, we investigate the possibility of assessing catch composition of single hauls carried out by trawlers by applying DNA metabarcoding to the dense water draining from fishing nets just after the end of hauling operations (hereafter "slush"). We assess the performance of this approach in portraying ß-diversity and examining the quantitative relationship between species abundances in the catch and DNA amount in the slush (read counts generated by amplicon sequencing). We demonstrate that the assemblages identified using DNA in the slush satisfactorily mirror those returned by visual inspection of net content (about 71% of species and 86% of families of fish) and detect a strong relationship between read counts and species abundances in the catch. We therefore argue that this approach could be upscaled to serve as a powerful source of information on the structure of demersal assemblages and the impact of fisheries.

The MINOUWApp: a web-based tool in support of by-catch and discards management.

Current fishing practices often do not allow adequate selection of species or sizes of fish, resulting in unwanted catches, subsequently discarded, with the consequent negative effects on both marine communities and fisheries profitability. The cross-analysis of density patches of potential unwanted catches and distribution of fishing effort can support the identification of spatial-temporal hot-spots in which the fishing pressure should be reduced to limit the amount of discards. The MinouwApp represents a technological and methodological framework to bring different, and structurally complex, sources of georeferenced data together into a simple visual interface aiming to interactively explore temporal ranges and areas of interest. The objective is to improve the understanding of fisheries dynamics, including discards, thus contributing to the implementation of discard management plans in a context of participative, ecosystem-based fisheries management strategies.

VMSbase: an R-package for VMS and logbook data management and analysis in fisheries ecology.

VMSbase is an R package devised to manage, process and visualize information about fishing vessels activity (provided by the vessel monitoring system--VMS) and catches/landings (as reported in the logbooks). VMSbase is primarily conceived to be user-friendly; to this end, a suite of state-of-the-art analyses is accessible via a graphical interface. In addition, the package uses a database platform allowing large datasets to be stored, managed and processed vey efficiently. Methodologies include data cleaning, that is removal of redundant or evidently erroneous records, and data enhancing, that is interpolation and merging with external data sources. In particular, VMSbase is able to estimate sea bottom depth for single VMS pings using an on-line connection to the National Oceanic and Atmospheric Administration (NOAA) database. It also allows VMS pings to be assigned to whatever geographic partitioning has been selected by users. Standard analyses comprise: 1) métier identification (using a modified CLARA clustering approach on Logbook data or Artificial Neural Networks on VMS data); 2) linkage between VMS and Logbook records, with the former organized into fishing trips; 3) discrimination between steaming and fishing points; 4) computation of spatial effort with respect to user-selected grids; 5) calculation of standard fishing effort indicators within Data Collection Framework; 6) a variety of mapping tools, including an interface for Google viewer; 7) estimation of trawled area. Here we report a sample workflow for the accessory sample datasets (available with the package) in order to explore the potentialities of VMSbase. In addition, the results of some performance tests on two large datasets (1×10(5) and 1×10(6) VMS signals, respectively) are reported to inform about the time required for the analyses. The results, although merely illustrative, indicate that VMSbase can represent a step forward in extracting and enhancing information from VMS/logbook data for fisheries studies.