Mathematical models of long term evolution of blue whale song types' frequencies

The linear decrease in the frequency of blue whale songs around the world is, to date, an unexplained phenomenon. We show it can be reproduced by a mathematical model considering two antagonistic behavioral trends: first, a bias towards conformity in the song, and second, a tendency to try and sing lower than the other whales. We check the robustness of our model by considering some more complex premises. First, different hierarchical relations between the singers are explored, adapting methods used in the flocking motion studies. Then a population-dependant simulation shows that even considering the gradual addition of new whales, the evolution is still globally linear. Finally, we show that intra-annual variations surging from different causes can be naturally incorporated into the model. We then conclude that, unlike other explanations, a cultural hypothesis seems compatible with the observed linearity of the blue whales's songs frequency shift.

Seasonal Trends and Diel Patterns of Downsweep and SEP Calls in Chilean Blue Whales

To learn more about the occurrence and behaviour of a recently discovered population of blue whales, passive acoustic data were collected between January 2012 and April 2013 in the Chiloense ecoregion of southern Chile. Automatic detectors and manual auditing were used to detect blue whale songs (SEP calls) and D calls, which were then analysed to gain insights into temporal calling patterns. We found that D call rates were extremely low during winter (June–August) but gradually increased in spring and summer, decreasing again later during fall. SEP calls were absent for most winter and spring months (July–November) but increased in summer and fall, peaking between March and April. Thus, our results support previous studies documenting the austral summer residency of blue whales in this region, while suggesting that some individuals stay longer, highlighting the importance of this area as a blue whale habitat. We also investigated the daily occurrence of each call type and found that D calls occurred more frequently during dusk and night hours compared to dawn and day periods, whereas SEP calls did not show any significant diel patterns. Overall, these findings help to understand the occurrence and behaviour of endangered Chilean blue whales, enhancing our ability to develop conservation strategies in this important Southern Hemisphere habitat.

Sounding the Call for a Global Library of Underwater Biological Sounds

Aquatic environments encompass the world’s most extensive habitats, rich with sounds produced by a diversity of animals. Passive acoustic monitoring (PAM) is an increasingly accessible remote sensing technology that uses hydrophones to listen to the underwater world and represents an unprecedented, non-invasive method to monitor underwater environments. This information can assist in the delineation of biologically important areas via detection of sound-producing species or characterization of ecosystem type and condition, inferred from the acoustic properties of the local soundscape. At a time when worldwide biodiversity is in significant decline and underwater soundscapes are being altered as a result of anthropogenic impacts, there is a need to document, quantify, and understand biotic sound sources–potentially before they disappear. A significant step toward these goals is the development of a web-based, open-access platform that provides: (1) a reference library of known and unknown biological sound sources (by integrating and expanding existing libraries around the world); (2) a data repository portal for annotated and unannotated audio recordings of single sources and of soundscapes; (3) a training platform for artificial intelligence algorithms for signal detection and classification; and (4) a citizen science-based application for public users. Although individually, these resources are often met on regional and taxa-specific scales, many are not sustained and, collectively, an enduring global database with an integrated platform has not been realized. We discuss the benefits such a program can provide, previous calls for global data-sharing and reference libraries, and the challenges that need to be overcome to bring together bio- and ecoacousticians, bioinformaticians, propagation experts, web engineers, and signal processing specialists (e.g., artificial intelligence) with the necessary support and funding to build a sustainable and scalable platform that could address the needs of all contributors and stakeholders into the future.

Evolutionary drivers of morphological differentiation among three bottlenose dolphin lineages, Tursiops spp. (Delphinidae), in the northwest Indian Ocean utilizing linear and geometric morphometric techniques

Local adaptation and adaptive radiations are typically associated with phenotypic variation suited to alternative environments. In the marine environment, the nature of relevant ecological or environmental transitions is poorly understood, especially for highly mobile species. Here we compare three genetic lineages in the genus Tursiops (bottlenose dolphins), using linear measurements and geometric morphometric techniques, in the context of environmental variation in the northwest Indian Ocean. Cranial morphology was clearly differentiated comparing Tursiops truncatus and Tursiops aduncus, while a recently discovered genetic lineage, found in the Arabian Sea, was morphologically most similar to T. aduncus from the same region, but distinct for various measures, particularly metrics associated with the lateral dimension of the skull. The extent of divergence between T. truncatus and T. aduncus compared to differences between the T. aduncus lineages is consistent with the recent phylogeny for these species. Therefore, with the corroboration of genetic and morphological inference, we propose two conservation units of T. aduncus be recognized in the region at a sub-specific level so that their conservation can be managed effectively. We consider possible evolutionary mechanisms associated with regional habitat characteristics and the exploitation of distinct prey resources.

Geographically distinct blue whale song variants in the Northeast Pacific

The Northeast Pacific (NEP) population of blue whales Balaenoptera musculus musculus is currently managed as a single stock. We investigated the fine-scale frequency characteristics of 1 NEP blue whale song unit, the B call. We analyzed B calls from passive acoustic data collected between 2010 and 2013 at 2 low-latitude sites, Palmyra Atoll and the Hawaiian Islands, and 3 higher-latitude sites, off southern California, off Washington state and in the Gulf of Alaska. Frequency measurements were extracted along the contour of the third harmonic from each call, and data from each region were compared. Calls from the Gulf of Alaska and Hawai‘i presented a downshift in frequency, beginning just past the midway point of the contour, which was not present in calls recorded from southern California or Palmyra Atoll. Calls from Washington displayed intermediate characteristics between those from the other 2 high-latitude sites. Cluster analysis resulted in consistent grouping of call contours from Washington and southern California, in what we termed the NEP B1 variant, while contours from Hawai‘i and the Gulf of Alaska were grouped together, as a NEP B2 variant. Frequency differences were also observed among the variants; the Gulf of Alaska displayed the highest frequency on average, followed by Washington, then southern California. Consistent with other studies, a yearly decline in the frequency of B calls was also observed. This discovery of at least 2 geographically distinct variants provides the first evidence of vocally distinct subpopulations within the NEP, indicating the possibility of a need for finer-scale population segmentation.

Detecting, classifying, and counting blue whale calls with Siamese neural networks

The goal of this project is to use acoustic signatures to detect, classify, and count the calls of four acoustic populations of blue whales so that, ultimately, the conservation status of each population can be better assessed. We used manual annotations from 350 h of audio recordings from the underwater hydrophones in the Indian Ocean to build a deep learning model to detect, classify, and count the calls from four acoustic song types. The method we used was Siamese neural networks (SNN), a class of neural network architectures that are used to find the similarity of the inputs by comparing their feature vectors, finding that they outperformed the more widely used convolutional neural networks (CNN). Specifically, the SNN outperform a CNN with 2% accuracy improvement in population classification and 1.7%-6.4% accuracy improvement in call count estimation for each blue whale population. In addition, even though we treat the call count estimation problem as a classification task and encode the number of calls in each spectrogram as a categorical variable, SNN surprisingly learned the ordinal relationship among them. SNN are robust and are shown here to be an effective way to automatically mine large acoustic datasets for blue whale calls.

Multiple pygmy blue whale acoustic populations in the Indian Ocean: whale song identifies a possible new population

Blue whales were brought to the edge of extinction by commercial whaling in the twentieth century and their recovery rate in the Southern Hemisphere has been slow; they remain endangered. Blue whales, although the largest animals on Earth, are difficult to study in the Southern Hemisphere, thus their population structure, distribution and migration remain poorly known. Fortunately, blue whales produce powerful and stereotyped songs, which prove an effective clue for monitoring their different ‘acoustic populations.’ The DGD-Chagos song has been previously reported in the central Indian Ocean. A comparison of this song with the pygmy blue and Omura’s whale songs shows that the Chagos song are likely produced by a distinct previously unknown pygmy blue whale population. These songs are a large part of the underwater soundscape in the tropical Indian Ocean and have been so for nearly two decades. Seasonal differences in song detections among our six recording sites suggest that the Chagos whales migrate from the eastern to western central Indian Ocean, around the Chagos Archipelago, then further east, up to the north of Western Australia, and possibly further north, as far as Sri Lanka. The Indian Ocean holds a greater diversity of blue whale populations than thought previously.