Extant aquatic mammals are a key component of aquatic ecosystems. Their morphology, ecological role and behaviour are, to a large extent, shaped by their feeding ecology. Nevertheless, the nature of this crucial aspect of their biology is often oversimplified and, consequently, misinterpreted. Here, we introduce a new framework that categorizes the feeding cycle of predatory aquatic mammals into four distinct functional stages ( prey capture, manipulation and processing, water removal and swallowing), and details the feeding behaviours that can be employed at each stage. Based on this comprehensive scheme, we propose that the feeding strategies of living aquatic mammals form an evolutionary sequence that recalls the land-to-water transition of their ancestors. Our new conception helps to explain and predict the origin of particular feeding styles, such as baleen-assisted filter feeding in whales and raptorial 'pierce' feeding in pinnipeds, and informs the structure of present and past ecosystems.

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Marx F.G., Hocking D.P., Park T., Ziegler T., Evans A.R. and Fitzgerald, E.M.G. 2016. Suction feeding preceded filtering in baleen whale evolution. Memoirs of Museum Victoria 75: 71-82.The origin of baleen, the key adaptation of modern whales (Mysticeti), marks a profound yet poorly understood transition in vertebrate evolution, triggering the rise of the largest animals on Earth. Baleen is thought to have appeared in archaic tooth-bearing mysticetes during a transitional phase that combined raptorial feeding with incipient bulk filtering. Here we show that tooth wear in a new Late Oligocene mysticete belonging to the putatively transitional family Aetiocetidae is inconsistent with the presence of baleen, and instead indicative of suction feeding. Our findings suggest that baleen arose much closer to the origin of toothless mysticete whales than previously thought. In addition, they suggest an entirely new evolutionary scenario in which the transition from raptorial to baleen-assisted filter feeding was mediated by suction, thereby avoiding the problem of functional interference between teeth and the baleen rack.

When hunting at sea, pinnipeds should adapt their foraging behaviors to suit the prey they are targeting. We performed captive feeding trials with two species of otariid seal, Australian fur seals (Arctocephalus pusillus doriferus) and subantarctic fur seals (Arctocephalus tropicalis). This allowed us to record detailed observations of how their foraging behaviors vary when presented with prey items that cover the full range of body shapes and sizes encountered in the wild. Small prey were captured using suction alone, while larger prey items were caught in the teeth using raptorial biting. Small fish and long skinny prey items could then be swallowed whole or processed by shaking, while all prey items with body depths greater than 7.5 cm were processed by shaking at the water's surface. This matched opportunistic observations of feeding in wild Australian fur seals. Use of “shake feeding” as the main prey processing tactic also matches predictions that this method would be one of the only tactics available to aquatic tetrapods that are unable to secure prey using their forelimbs.

Background: Semi-automating the analyses of accelerometry data makes it possible to synthesize large data sets. However, when constructing activity budgets from accelerometry data, there are many methods to extract, analyse and report data and results. For instance, machine learning is a robust approach to classifying data. We used a new method, super learning, that combines base learners (different machine learning methods) in an optimal manner to achieve overall improved accuracy. Other facets of super learning include the number of behavioural categories to predict, the number of epochs (sample window size) used to split data for training and testing and the parameters on which to train the models. Results:The super learner accurately classified behaviour categories with higher accuracy and lower variance than comparative models. For all models tested, using four behaviours, in comparison with six, achieved higher rates of accuracy. The number of epochs chosen also affected the accuracy with smaller epochs (7 and 13) performing better than longer epochs (25 and 75). Conclusions:Correct model selection, training and testing are imperative to creating reliable and valid classification models. To do so means model fitting must use a wide array of selection criteria. We evaluated a number of these including model, number of behaviours to classify and epoch length and then used a parameter grid search to implement the models. We found that all criteria tested contributed to the models' overall accuracies. Fewer behaviour categories and shorter epoch length improved the performance of all models tested. The super learner classified behaviours with higher accuracy and lower variance than other models tested. However, when using this model, users need to consider the additional human and computational time required for implementation. Machine learning is a powerful method for classifying the behaviour of animals from accelerometers. Care and consideration of the modelling parameters evaluated in this study are essential when using this type of statistical analysis.

Foraging behaviours used by two female Australian fur seals (Arctocephalus pusillus doriferus) were documented during controlled feeding trials. During these trials the seals were presented with prey either free-floating in open water or concealed within a mobile ball or a static box feeding device. When targeting free-floating prey both subjects primarily used raptorial biting in combination with suction, which was used to draw prey to within range of the teeth. When targeting prey concealed within either the mobile or static feeding device, the seals were able to use suction to draw out prey items that could not be reached by biting. Suction was followed by lateral water expulsion, where water drawn into the mouth along with the prey item was purged via the sides of the mouth. Vibrissae were used to explore the surface of the feeding devices, especially when locating the openings in which the prey items had been hidden. The mobile ball device was also manipulated by pushing it with the muzzle to knock out concealed prey, which was not possible when using the static feeding device. To knock prey out of this static device one seal used targeted bubble blowing, where a focused stream of bubbles was blown out of the nose into the openings in the device. Once captured in the jaws, prey items were manipulated and re-oriented using further mouth movements or chews so that they could be swallowed head first. While most items were swallowed whole underwater, some were instead taken to the surface and held in the teeth, while being vigorously shaken to break them into smaller pieces before swallowing. The behavioural flexibility displayed by Australian fur seals likely assists in capturing and consuming the extremely wide range of prey types that are targeted in the wild, during both benthic and epipelagic foraging.

Constructing activity budgets for marine animals when they are at sea and cannot be directly observed is challenging, but recent advances in bio-logging technology offer solutions to this problem. Accelerometers can potentially identify a wide range of behaviours for animals based on unique patterns of acceleration. However, when analysing data derived from accelerometers, there are many statistical techniques available which when applied to different data sets produce different classification accuracies. We investigated a selection of supervised machine learning methods for interpreting behavioural data from captive otariids (fur seals and sea lions). We conducted controlled experiments with 12 seals, where their behaviours were filmed while they were wearing 3-axis accelerometers. From video we identified 26 behaviours that could be grouped into one of four categories (foraging, resting, travelling and grooming) representing key behaviour states for wild seals. We used data from 10 seals to train four predictive classification models: stochastic gradient boosting (GBM), random forests, support vector machine using four different kernels and a baseline model: penalised logistic regression. We then took the best parameters from each model and cross-validated the results on the two seals unseen so far. We also investigated the influence of feature statistics (describing some characteristic of the seal), testing the models both with and without these. Cross-validation accuracies were lower than training accuracy, but the SVM with a polynomial kernel was still able to classify seal behaviour with high accuracy (>70%). Adding feature statistics improved accuracies across all models tested. Most categories of behaviour -resting, grooming and feeding—were all predicted with reasonable accuracy (52–81%) by the SVM while travelling was poorly categorised (31–41%). These results show that model selection is important when classifying behaviour and that by using animal characteristics we can strengthen the overall accuracy.

Pinnipeds generally target relatively small prey that can be swallowed whole, yet often include larger prey in their diet. To eat large prey, they must first process it into pieces small enough to swallow. In this study we explored the range of prey‐processing behaviors used by Australian sea lions (Neophoca cinerea) when presented with large prey during captive feeding trials. The most common methods were chewing using the teeth, shaking prey at the surface, and tearing prey held between the teeth and forelimbs. Although pinnipeds do not masticate their food, we found that sea lions used chewing to create weak points in large prey to aid further processing and to prepare secured pieces of prey for swallowing. Shake feeding matches the processing behaviors observed in fur seals, but use of forelimbs for “hold and tear” feeding has not been previously reported for other otariids. When performing this processing method, prey was torn by being stretched between the teeth and forelimbs, where it was secured by being squeezed between the palms of their flippers. These results show that Australian sea lions use a broad repertoire of behaviors for prey processing, which matches the wide range of prey species in their diet.