An integrated approach to assess fish spatial pattern around offshore gas platforms: A pilot study in the Adriatic Sea

Bottlenose dolphin distribution in a Mediterranean area exposed to intensive trawling

The Adriatic Sea is one of the areas most exposed to trawling, worldwide. We used four years (2018-2021) and 19,887 km of survey data to investigate factors influencing daylight dolphin distribution in its north-western sector, where common bottlenose dolphins Tursiops truncatus routinely follow fishing trawlers. We validated Automatic Identification System information on the position, type and activity of three types of trawlers based on observations from boats, and incorporated this information in a GAM-GEE modelling framework, together with physiographic, biological and anthropogenic variables. Along with bottom depth, trawlers (particularly otter and midwater trawlers) appeared to be important drivers of dolphin distribution, with dolphins foraging and scavenging behind trawlers during 39.3% of total observation time in trawling days. The spatial dimension of dolphin adaptations to intensive trawling, including distribution shifts between days with and without trawling, sheds light on the magnitude of ecological change driven by the trawl fishery.

Semi-Automated Data Processing and Semi-Supervised Machine Learning for the Detection and Classification of Water-Column Fish Schools and Gas Seeps with a Multibeam Echosounder

Multibeam echosounders are widely used for 3D bathymetric mapping, and increasingly for water column studies. However, they rapidly collect huge volumes of data, which poses a challenge for water column data processing that is often still manual and time-consuming, or affected by low efficiency and high false detection rates if automated. This research describes a comprehensive and reproducible workflow that improves efficiency and reliability of target detection and classification, by calculating metrics for target cross-sections using a commercial software before feeding into a feature-based semi-supervised machine learning framework. The method is tested with data collected from an uncalibrated multibeam echosounder around an offshore gas platform in the Adriatic Sea. It resulted in more-efficient target detection, and, although uncertainties regarding user labelled training data need to be underlined, an accuracy of 98% in target classification was reached by using a final pre-trained stacking ensemble model.