Aggregation is commonly thought to improve animals’ security. Within aquatic ecosystems, group-living prey can learn about immediate threats using cues perceived directly from predators, or from collective behaviours, for ...

This report documents a workshop organised by the COGMAR and CRIMAC projects. The objective of the workshop was twofold. The first objective was to give an overview of ongoing work using machine learning for Acoustic Target ...

Underwater camera systems are commonly used for monitoring fish and fishing gear behaviours. More recently, camera systems have been applied to scientific trawl surveys for improved spatial resolution and less invasive ...

Oceans constitute over 70% of the earth’s surface, and the marine environment and ecosystems are central to many global challenges. Not only are the oceans an important source of food and other resources, but they also ...

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 ...

PELFOSS har utviklet en observasjonssystem-simulator for pelagisk fisk i Norskehavet. Simulatoren er en dataløype der romlig fordeling av fiskebestander fra en økosystemmodell er benyttet til å simulere ulike tokt. Disse ...

Acoustic target classification in multi-frequency echosounder data is a major interest for the marine ecosystem and fishery management since it can potentially estimate the abundance or biomass of the species. A key problem ...

Scientists across the globe conduct survey programs to monitor and characterize abundance, population structure, biodiversity and geographical distributions. To assess the state of marine fish and zooplankton, population ...