Document: Article

Abstract: The assessment and mitigation of bycatch, currently identified as the most significant threat to marine mammals, represents a substantial challenge for society. This issue is particularly acute in developing countries, where data on small-scale fisheries are scarce, and knowledge gaps exist regarding the distribution and abundance of various marine mammal species. Artisanal fisheries, particularly in developing countries, have been linked to significant mortality levels of marine organisms due to bycatch. The magnitude of this phenomenon reveals alarming figures. Notably, there is a high incidence of interactions between the bottlenose dolphin (Tursiops truncatus) and nearshore gillnets, where the overlap in their coastal distribution creates high-risk zones. The imperative to assess bycatch is driven not only by conservation principles but is also essential for sustainability in developing countries due to U.S. government regulations on imports of fishery products aimed at reducing bycatches worldwide. This study proposes an innovative methodology to investigate marine mammal bycatch in the southern Gulf of Mexico. This methodology is based on the development of artificial intelligence models, the integration of stakeholder input, and the use of habitat suitability models. This approach utilizes 11 years of sighting records and 1,654 spatial-temporal fishing effort data points collected through interviews with fishers. Additionally, the study develops artificial intelligence models, specifically Random Forest algorithms in Python, to enhance the analysis and prediction of bycatch risk. This research identified monthly variations in high-risk zones for marine mammal bycatch in the southern Gulf of Mexico, highlighting regions with a higher likelihood of interaction with gillnets. This pioneering work of applying artificial intelligence to marine mammal bycatch provides a complementary analysis for areas with limited economic and data resources.

Key Words: bycatch, artificial intelligence, machine learning, Geographic Information System, Python

DOI: https://doi.org/10.1578/AM.51.3.2025.281

Page Number: 281-296