Interactions between fishing and dolphins can be detrimental, since on one hand dolphins can be lethally entangled by nets and trawls, and on the other dolphins can predate fish caught by nets. For dolphins, this interaction can be dangerous as they can be wounded or accidentally killed; for fishers, the predation of their catch results in economic losses due to reduced quantity and/or quality of catches and damage to fishing gear. During July and November 2020, we surveyed the “dolphin–fisheries conflict” through compiling 209 fisher interviews from nine locations in Italy and Croatia. Fishers mentioned the common bottlenose dolphin (Tursiops truncatus) as the species primarily interacting with fishing, with the major issue being catch damage by predation. The interaction probability varied among gears and seasons, with some fishing activities (e.g., passive nets) more affected than others (e.g., bottom trawls), especially in terms of economic loss (1000–10,000 €/year on average). More than 70% of the fishers claimed that dolphin populations have increased over the last 10 years, in different degrees and based on different areas. Dolphin bycatch rates are generally low; however, 34.6% of respondents reported having captured at least one dolphin during their career. The fishers’ attitude towards acoustic deterrents (“pingers”) as a mitigation measure revealed that few of them were aware of these devices or were using them.

This study aimed to test the effectiveness of a “guarding net”, a device placed at the bottom of a trammel net, for reducing unwanted catches in the caramote prawn trammel net fishery of the Ligurian Sea. This specialized and profitable fishery is affected by unwanted catches that generate high discard rates and damage to the nets, with environmental impacts and costs for fishermen. The experimental study consisted in comparing the catches of a standard trammel net (STN) with those of two “experimental” trammel nets, e.g. STNs provided with a guarding net of 19 cm (TGN20) and 24 cm height (TGN25), respectively. The guarding net, a strip of gillnet placed at the bottom of the net, can be considered a by-catch reducer device (BRD). Some fishermen of the investigated fishery have been using this device for several years. The results of the 15 experimental fishing trials performed from June to July 2016 indicate that the guarding nets significantly reduce discards (e.g. crabs and other invertebrates); the biomass of the unwanted species caught was 75% lower than that produced by the STN. The catch rates of the target species obtained with TGN20 and TGN25 were also significantly lower than those of the STN, though of a lesser amount. Nonetheless, this economic loss can be compensated by the decrease in sorting time and material and labour costs that can be achieved using the guarding net.

Passive acoustics are widely used to monitor the presence of dolphins in the marine environment. This study aims to introduce a low-cost and homemade approach for assembling a complete underwater microphone (i.e., the hydrophone), employing cheap and easy to obtain components. The hydrophone was assembled with two piezo disks connected in a balanced configuration and encased in a plastic container filled with plastic foam. The hydrophone’s performance was validated by direct comparison with the commercially available AS-1 hydrophone (Aquarian Hydrophones, Anacortes, U.S.) on different underwater acoustic signals: artificial acoustic signals (ramp and multitone signals) and various dolphin vocalizations (whistle, echolocation clicks, and burst pulse signals). The sensitivity of the device’s performance to changes in the emission source position was also tested. The results of the validation procedure on both artificial signals and real dolphin vocalizations showed that the significant cost savings associated with cheap technology had a minimal effect on the recording device’s performance within the frequency range of 0–35 kHz. At this stage of experimentation, the global cost of the hydrophone could be estimated at a few euros, making it extremely price competitive when compared to more expensive commercially available models. In the future, this effective and low-cost technology would allow for continuous monitoring of the presence of free-ranging dolphins, significantly lowering the total cost of autonomous monitoring systems. This would permit broadening the monitored areas and creating a network of recorders, thus improving the acquisition of data.

Latest developments in acoustic research suggest that using surveying methods based on artificial intelligence (AI) could improve the effectiveness of underwater monitoring. Passive acoustic monitoring (PAM) has proven to be a cost-effective approach for gathering information about the acoustic behavior of dolphins and plays a crucial role in studying their vocalizations, particularly whistles. This study investigates the efficiency of a binary convolutional neural network (CNN) in detecting dolphin whistles amidst high-density vocalizations in an aquatic environment. Specifically, this analysis intends to determine whether a properly trained CNN can recognize a single whistle even in challenging condition, including situations where multiple dolphins vocalize simultaneously, resulting in overlapping whistles that may have different shapes and durations. To this aim, experimental trials were conducted at Oltremare marine park, Riccione, Italy, where underwater recordings of seven-dolphin vocalizations were collected over 22 consecutive hours. The CNN was trained on labeled whistle spectrograms. The model, comprising three convolutional layers followed by max pooling layers and ReLU activation functions, was evaluated using a 10-fold cross-validation approach. Confusion matrix and performance metrics indicate that the proposed approach achieves results comparable to those reported in the literature, despite the more challenging working conditions. The study supports the potential of AI models in enhancing passive acoustic monitoring techniques.

The current invasion of the Mediterranean Sea by the Atlantic blue crab Callinectes sapidus requires the implementation of effective strategies of control and management. In native areas, the species is highly appreciated as a hard- and soft-shell seafood, and hard-shell fisheries are developing in a number of invaded countries. Here, to verify alternative approaches for enhancing the commercial value of the species, we carried out a pilot experiment to test a flow-through system for the production of soft-shell blue crabs. Fifty crabs were collected in the Lesina lagoon (Adriatic Sea, Italy) and inspected for the coloration of the line on the distal edge of the fifth pereiopod. Accordingly, they were grouped into three groups showing no, white, and red lines, symptomatic of the progression of the pre-molt phase, and maintained for 31 days in a flow-through pond system. The overall mortality rate determined during the trial was similar across the three groups and ranged between 10 and 20%, indicating that the flow-through system was effective for rearing the crabs. In addition, the molting rate was 10%, 65%, and 85% for individuals at an early, intermediate, and advanced pre-molt stage, confirming that the line coloration can be used as an effective indicator of the advancement of the molting process. Our study provides the first contribution towards the introduction in the Mediterranean fishery sector of novel procedures and practices for the production of soft-shell blue crabs, which may represent a cost-effective strategy to enhance the capture and commercialization of this invasive species as a high-quality and valuable shellfish product.

Monitoring dolphins in the open sea is essential for understanding their behavior and the impact of human activities on the marine ecosystems. Passive Acoustic Monitoring (PAM) is a non-invasive technique for tracking dolphins, providing continuous data. This study presents a novel approach for classifying dolphin vocalizations from a PAM acoustic recording using a convolutional neural network (CNN). Four types of common bottlenose dolphin (Tursiops truncatus) vocalizations were identified from underwater recordings: whistles, echolocation clicks, burst pulse sounds, and feeding buzzes. To enhance classification performances, edge-detection filters were applied to spectrograms, with the aim of removing unwanted noise components. A dataset of nearly 10,000 spectrograms was used to train and test the CNN through a 10-fold cross-validation procedure. The results showed that the CNN achieved an average accuracy of 95.2% and an F1-score of 87.8%. The class-specific results showed a high accuracy for whistles (97.9%), followed by echolocation clicks (94.5%), feeding buzzes (94.0%), and burst pulse sounds (92.3%). The highest F1-score was obtained for whistles, exceeding 95%, while the other three vocalization typologies maintained an F1-score above 80%. This method provides a promising step toward improving the passive acoustic monitoring of dolphins, contributing to both species conservation and the mitigation of conflicts with fisheries.