Baleen whales face the challenge of finding patchily distributed food in the open ocean. Their relatively well-developed olfactory structures suggest that they could identify the specific odours given off by planktonic prey such as krill aggregations. Like other marine predators, they may also detect dimethyl sulfide (DMS), a chemical released in areas of high marine productivity. However, dedicated behavioural studies still have to be conducted in baleen whales in order to confirm the involvement of chemoreception in their feeding ecology. We implemented 56 behavioural response experiments in humpback whales using two food-related chemical stimuli, krill extract and DMS, as well as their respective controls (orange clay and vegetable oil) in their breeding (Madagascar) and feeding grounds (Iceland and Antarctic Peninsula). The whales approached the stimulus area and stayed longer in the trial zone during krill extract trials compared to control trials, suggesting that they were attracted to the chemical source and spent time exploring its surroundings, probably in search of prey. This response was observed in Iceland, and to a lesser extend in Madagascar, but not in Antarctica. Surface behaviours indicative of sensory exploration, such as diving under the stimulus area and stopping navigation, were also observed more often during krill extract trials than during control trials. Exposure to DMS did not elicit such exploration behaviours in any of the study areas. However, acoustic analyses suggest that DMS and krill extract both modified the whales’ acoustic activity in Madagascar. Altogether, these results provide the first behavioural evidence that baleen whales actually perceive prey-derived chemical cues over distances of several hundred metres. Chemoreception, especially olfaction, could thus be used for locating prey aggregations and for navigation at sea, as it has been shown in other marine predators including seabirds.

Bioacoustic research spans a wide range of biological questions and applications, relying on identification of target species or smaller acoustic units, such as distinct call types. However, manually identifying the signal of interest is time-intensive, error-prone, and becomes unfeasible with large data volumes. Therefore, machine-driven algorithms are increasingly applied to various bioacoustic signal identification challenges. Nevertheless, biologists still have major difficulties trying to transfer existing animal- and/or scenario-related machine learning approaches to their specific animal datasets and scientific questions. This study presents an animal-independent, open-source deep learning framework, along with a detailed user guide. Three signal identification tasks, commonly encountered in bioacoustics research, were investigated: (1) target signal vs. background noise detection, (2) species classification, and (3) call type categorization. ANIMAL-SPOT successfully segmented human-annotated target signals in data volumes representing 10 distinct animal species and 1 additional genus, resulting in a mean test accuracy of 97.9%, together with an average area under the ROC curve (AUC) of 95.9%, when predicting on unseen recordings. Moreover, an average segmentation accuracy and F1-score of 95.4% was achieved on the publicly available BirdVox-Full-Night data corpus. In addition, multi-class species and call type classification resulted in 96.6% and 92.7% accuracy on unseen test data, as well as 95.2% and 88.4% regarding previous animal-specific machine-based detection excerpts. Furthermore, an Unweighted Average Recall (UAR) of 89.3% outperformed the multi-species classification baseline system of the ComParE 2021 Primate Sub-Challenge. Besides animal independence, ANIMAL-SPOT does not rely on expert knowledge or special computing resources, thereby making deep-learning-based bioacoustic signal identification accessible to a broad audience.

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Cooperative hunting involves individual predators relating in time and space to each other’s actions to more efficiently track down and catch prey. The evolution of advanced cognitive abilities and sociality in animals are strongly associated with cooperative hunting abilities, as has been shown in lions, chimpanzees and dolphins. Much less is known about cooperative hunting in seemingly unsocial animals, such as the harbour porpoise (Phocoena phocoena Linnaeus, 1758). Using drones, we were able to record 159 hunting sequences of porpoises, out of which 95 sequences involved more than one porpoise. To better understand if the harbour porpoises were individually attracted by the fish school or formed an organized hunting strategy, the behaviour of each individual porpoise in relation to the targeted fish school was analysed. The results indicate role specialization, which is considered the most sophisticated form of collaborative hunting and only rarely seen in animals. Our study challenges previous knowledge about harbour porpoises and opens up for the possibility of other seemingly non-social species employing sophisticated collaborative hunting methods.

Many aquatic birds use sounds extensively for in-air communication. Regardless of this, we know very little about their hearing abilities. The in-air audiogram of a male adult great cormorant (Phalacrocorax carbo) was determined using psychophysical methods (method of constants). Hearing thresholds were derived using pure tones of five different frequencies. The lowest threshold was at 2 kHz: 18 dB re 20 µPa rms. Thresholds derived using signal detection theory were within 2 dB of the ones derived using classical psychophysics. The great cormorant is more sensitive to in-air sounds than previously believed and its hearing abilities are comparable to several other species of birds of similar size. This knowledge is important for our understanding of the hearing abilities of other species of sea birds. It can also be used to develop cormorant deterrent devices for fisheries, as well as to assess the impact of increasing in-air anthropogenic noise levels on cormorants and other aquatic birds.