Comparing context-dependent call sequences employing machine learning methods: an indication of syntactic structure of greater horseshoe bats

Vocal convergence and social proximity shape the calls of the most basal Passeriformes, New Zealand Wrens

Despite extensive research on avian vocal learning, we still lack a general understanding of how and when this ability evolved in birds. As the closest living relatives of the earliest Passeriformes, the New Zealand wrens (Acanthisitti) hold a key phylogenetic position for furthering our understanding of the evolution of vocal learning because they share a common ancestor with two vocal learners: oscines and parrots. However, the vocal learning abilities of New Zealand wrens remain unexplored. Here, we test for the presence of prerequisite behaviors for vocal learning in one of the two extant species of New Zealand wrens, the rifleman (Acanthisitta chloris). We detect the presence of unique individual vocal signatures and show how these signatures are shaped by social proximity, as demonstrated by group vocal signatures and strong acoustic similarities among distantly related individuals in close social proximity. Further, we reveal that rifleman calls share similar phenotypic variance ratios to those previously reported in the learned vocalizations of the zebra finch, Taeniopygia guttata. Together these findings provide strong evidence that riflemen vocally converge, and though the mechanism still remains to be determined, they may also suggest that this vocal convergence is the result of rudimentary vocal learning abilities.

Convolutional Neural Networks for the Identification of African Lions from Individual Vocalizations

The classification of vocal individuality for passive acoustic monitoring (PAM) and census of animals is becoming an increasingly popular area of research. Nearly all studies in this field of inquiry have relied on classic audio representations and classifiers, such as Support Vector Machines (SVMs) trained on spectrograms or Mel-Frequency Cepstral Coefficients (MFCCs). In contrast, most current bioacoustic species classification exploits the power of deep learners and more cutting-edge audio representations. A significant reason for avoiding deep learning in vocal identity classification is the tiny sample size in the collections of labeled individual vocalizations. As is well known, deep learners require large datasets to avoid overfitting. One way to handle small datasets with deep learning methods is to use transfer learning. In this work, we evaluate the performance of three pretrained CNNs (VGG16, ResNet50, and AlexNet) on a small, publicly available lion roar dataset containing approximately 150 samples taken from five male lions. Each of these networks is retrained on eight representations of the samples: MFCCs, spectrogram, and Mel spectrogram, along with several new ones, such as VGGish and stockwell, and those based on the recently proposed LM spectrogram. The performance of these networks, both individually and in ensembles, is analyzed and corroborated using the Equal Error Rate and shown to surpass previous classification attempts on this dataset; the best single network achieved over 95% accuracy and the best ensembles over 98% accuracy. The contributions this study makes to the field of individual vocal classification include demonstrating that it is valuable and possible, with caution, to use transfer learning with single pretrained CNNs on the small datasets available for this problem domain. We also make a contribution to bioacoustics generally by offering a comparison of the performance of many state-of-the-art audio representations, including for the first time the LM spectrogram and stockwell representations. All source code for this study is available on GitHub.

Navigating in a challenging semiarid environment: the use of a route-based mental map by a small-bodied neotropical primate

To increase efficiency in the search for resources, many animals rely on their spatial abilities. Specifically, primates have been reported to use mostly topological and rarely Euclidean maps when navigating in large-scale space. Here, we aimed to investigate if the navigation of wild common marmosets inhabiting a semiarid environment is consistent with a topological representation and how environmental factors affect navigation. We collected 497 h of direct behavioral and GPS information on a group of marmosets using a 2-min instantaneous focal animal sampling technique. We found that our study group reused not only long-route segments (mean of 1007 m) but entire daily routes, a pattern that is not commonly seen in primates. The most frequently reused route segments were the ones closer to feeding sites, distant to resting sites, and in areas sparse in tree vegetation. We also identified a total of 56 clustered direction change points indicating that the group modified their direction of travel. These changes in direction were influenced by their close proximity to resting and feeding sites. Despite our small sample size, the obtained results are important and consistent with the contention that common marmosets navigate using a topological map that seems to benefit these animals in response to the exploitation of clustered exudate trees. Based on our findings, we hypothesize that the Caatinga landscape imposes physical restrictions in our group's navigation such as gaps in vegetation, small trees and xerophytic plants. This study, based on preliminary evidence, raises the question of whether navigation patterns are an intrinsic characteristic of a species or are ecologically dependent and change according to the environment.