Environmental and behavioral drivers of Automatic Identification System gaps of Turkish trawlers in the Black Sea

TANER YILDIZ^1,2*Nurdan Cömert²Carmen Ferrà³Uygar Şaşmaz⁴Alessandro Galdelli⁵Anna Nora Tassetti³

• ¹Istanbul University, Istanbul, Türkiye
• ²Faculty of Aquatic Sciences, Istanbul, Türkiye
• ³National Research Council, Institute for Biological Resources and Marine Biotechnologies, Ancona, Italy
• ⁴Biruni Universitesi, Istanbul, Türkiye
• ⁵Universita Politecnica delle Marche, Ancona, Italy

This study investigates the spatial, temporal, environmental, and behavioral drivers of Automatic Identification System (AIS) signal gaps in trawl fishing vessels operating in the Black Sea. AIS deliberate or accidental signal gaps, which may cause vessels to become temporarily invisible to AIS-based surveillance systems, hinder maritime monitoring, compliance enforcement, and fisheries management -even though such vessels may still be detectable via alternative systems such as VMS. The analysis focused on two primary trawl types; bottom and pelagic trawl. Using a comprehensive dataset of AIS signals, environmental variables and vessel activity, the study integrated spatial and temporal analyses with XGBoost machine learning technique to identify key predictors of AIS gaps. The results reveal distinct seasonal and spatial patterns in AIS gap behavior, with significant variation between trawl types. For bottom trawls, AIS gaps were concentrated near the northern entrance of the Istanbul Strait, while pelagic trawls exhibited broader distributions along the Black Sea coast, particularly near Zonguldak and Samsun. Machine learning model demonstrated strong predictive performance, with an accuracy of 80.26%, AUC of 0.8855, TSS of 0.6052, MAE of 1336.74 minutes, and RMSE of 3205.54 minutes for bottom trawls. For pelagic trawls, the model achieved 61.68% accuracy, an AUC of 0.6663, TSS of 0.2336, MAE of 2011.05 minutes, and RMSE of 4400.40 minutes, indicating moderate predictive capabilities. Key predictors included environmental factors such as chlorophyll concentration and sea surface temperature, alongside spatial metrics like depth and proximity to shore and port. Partial dependence plots highlighted the non-linear effects of these variables, with chlorophyll concentration showing a critical threshold around 3.5 mg/m³ and sea surface temperature influencing gaps most significantly at approximately 15°C. This study provides the first systematic analysis of AIS gaps in Black Sea fisheries, contributing valuable insights into their drivers and implications for fisheries management. By identifying high-risk zones and temporal patterns, the findings could support improved monitoring strategies, regulatory enforcement, and sustainable resource use in this ecologically significant region.