Report on the STECF Expert Working Group 17-12 Fisheries Dependent Information: ‘New-FDI’

The STECF expert working group (EWG) on Fisheries Dependant Information (FDI) took place in JRC, Ispra from 23 to 27 October 2017 to review the data transmitted by Member States under a new data call (‘New-FDI’). The new data call specification was designed with three broad aims in mind i) Compatibility between the New-FDI data and the data held in the Fleet Economic database. ii) Ability to encompass all EU registered vessels including those from the Mediterranean, Black Sea and external waters fleets. iii) Ability to assess effects of management measures. The main purpose of the EWG was to judge if the call specification was appropriate to accomplish the above aims and to consider any difficulties encountered by member states in fulfilling the data call. Two terms of reference also allowed trial analyses to be conducted of a type relevant to the third broad aim. The EWG addressed all Terms of Reference during the meeting and drew conclusions on the modifications required for the New-FDI data call going forwards. Prior to the EWG it had been agreed by STECF Bureau that the report of the meeting would not be presented to STECF for approval as an STECF report but published separately (as a JRC technical report). This report therefore presents the data, methods observations and findings of an EWG of the STECF but the findings presented in this report do not necessarily constitute the opinion of the STECF or reflect the views of the European Commission and in no way anticipate the Commission’s future policy in this area.JRC.D.2-Water and Marine Resource

Machine learning in marine ecology: an overview of techniques and applications

Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets.Machine learning in marine ecology: an overview of techniques and applicationspublishedVersio

Machine learning in marine ecology: an overview of techniques and applications

Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets

Automatic discard registration in cluttered environments using deep learning and object tracking: class imbalance, occlusion, and a comparison to human review

This paper presents and evaluates a method for detecting and counting demersal fish species in complex, cluttered, and occluded environments that can be installed on the conveyor belts of fishing vessels. Fishes on the conveyor belt were recorded using a colour camera and were detected using a deep neural network. To improve the detection, synthetic data were generated for rare fish species. The fishes were tracked over the consecutive images using a multi-object tracking algorithm, and based on multiple observations, the fish species was determined. The effect of the synthetic data, the amount of occlusion, and the observed dorsal or ventral fish side were investigated and a comparison with human electronic monitoring (EM) review was made. Using the presented method, a weighted counting error of 20% was achieved, compared to a counting error of 7% for human EM review on the same recordings

Data underlying the publication: Automatic discard registration in cluttered environments using deep learning and object tracking: class imbalance, occlusion, and a comparison to human review

Data for training and evaluation of a method for detection and counting demersal fish species in complex, cluttered and occluded environments that can be installed on the conveyor belts of fishing vessels. The data mainly exists of images of fish on a conveyer belt with the corresponding annotations. This was used to train a neural network (YOLOv3) to detect and classify fish species. Because each fish is visible in multiple images, the fishes were tracked over consecutive images and the total number of fish per specie was counted. These counts were compared to human review

Discarded fish in European waters: general patterns and contrasts

To reduce the practice of discarding commercially fished organisms, several measures such as a discard ban and extra allowances on top of landings quotas (“catch quota”) have been proposed by the European Commission. However, for their development and successful implementation, an understanding of discard patterns on a European scale is needed. In this study, we present an inter-national synthesis of discard data collected on board commercial, towed-gear equipped vessels operating under six different national flags spanning from the Baltic to the Mediterranean Seas mainly between 2003 and 2008. We considered discarded species of commercial value such as Atlantic cod (Gadus morhua), haddock (Melanogrammus aeglefinus), European hake (Merluccius merluccius), and European plaice (Pleuronectes platessa). Comparisons of discard per unit effort rates expressed as numbers per hour of fishing revealed that in the Mediterranean Sea minimum size-regulated species such as hake are generally discarded in much lower numbers than elsewhere. For most species examined, variability in discard rates across regions was greater than across fisheries, suggesting that a region-by-region approach to discard reduction would be more relevant. The high uncertainty in discard rate estimates suggests that current sampling regimes should be either expanded or complemented by other data sources, if they are to be used for setting catch quotas