As animals vocalize, their vocal organ transforms motor commands into vocalizations for social communication. In birds, the physical mechanisms by which vocalizations are produced and controlled remain unresolved because of the extreme difficulty in obtaining in vivo measurements. Here, we introduce an ex vivo preparation of the avian vocal organ that allows simultaneous high-speed imaging, muscle stimulation and kinematic and acoustic analyses to reveal the mechanisms of vocal production in birds across a wide range of taxa. Remarkably, we show that all species tested employ the myoelastic-aerodynamic (MEAD) mechanism, the same mechanism used to produce human speech. Furthermore, we show substantial redundancy in the control of key vocal parameters ex vivo, suggesting that in vivo vocalizations may also not be specified by unique motor commands. We propose that such motor redundancy can aid vocal learning and is common to MEAD sound production across birds and mammals, including humans.

Passive acoustic monitoring has proven to be an indispensable tool for many aspects of baleen whale research. Manual detection of whale calls on these large data sets demands extensive manual labor. Automated whale call detectors offer a more efficient approach and have been developed for many species and call types. However, calls with a large level of variability such as fin whale (Balaenoptera physalus) 40 Hz call and blue whale (B. musculus) D call have been challenging to detect automatically and hence no practical automated detector exists for these two call types. Using a modular approach consisting of faster region-based convolutional neural network followed by a convolutional neural network, we have created automated detectors for 40 Hz calls and D calls. Both detectors were tested on recordings with high- and low density of calls and, when selecting for detections with high classification scores, they were shown to have precision ranging from 54% to 57% with recall ranging from 72% to 78% for 40 Hz and precision ranging from 62% to 64% with recall ranging from 70 to 73% for D calls. As these two call types are produced by both sexes, using them in long-term studies would remove sex-bias in estimates of temporal presence and movement patterns.

Passive acoustic monitoring is a method that is commonly used to collect long-term data on soniferous animal presence and abundance. However, these large datasets require substantial effort for manual analysis; therefore, automatic methods are a more effective way to conduct these analyses and extract points of interest. In this study, an energy detector and subsequent pre-trained neural network were used to detect and classify six fish call types from a long-term dataset collected in the northern Gulf of Mexico. The development of this two-step methodology and its performance are the focus of this paper. The energy detector by itself had a high recall rate (>84%), but very low precision; however, a subsequent neural network was used to classify detected signals and remove noise from the detections. Image augmentation and iterative training were used to optimize classification and compensate for the low number of training images for two call types. The classifier had a relatively high average overall accuracy (>87%), but classifier average recall and precision varied greatly for each fish call type (recall: 39–91%; precision: 26–94%). This coupled methodology expedites call extraction and classification and can be applied to other datasets that have multiple, highly variable calls.

Significance: Vocal communication is critical to the reproduction and survival of mammals and birds. Computational physics-based models can facilitate understanding the relation of voice physiology to sound, but there is currently an absence of thorough experimental validation. Here, we present and experimentally test a high-fidelity, continuum model for voiced sound production in birds that includes anatomically realistic geometries of the syrinx and associated vocal tract. We show that, driven by physiologically quantifiable inputs, our model accurately predicts vibration and sound parameters. These data strongly support the continuum model approach as an important component of a causal model of voiced sound production.

Length and depth of fish larvae are part of the fundamental measurements in many marine ecology studies involving early fish life history. Until now, obtaining these measurements has required intensive manual labor and the risk of inter‐ and intra‐observer variability. We developed an open‐source software solution to semi‐automate the measurement process and thereby reduce both time consumption and technical variability. Using contrast‐based edge detection, the software segments images of a fish larva into “larva” and “background.” Length and depth are extracted from the “larva” segmentation while taking curvature of the larva into consideration. The graphical user interface optimizes workflow and ease of usage, thereby reducing time consumption for both training and analysis. The software allows for visual verification of all measurements. A comparison of measurement methods on a set of larva images showed that this software reduces measurement time by 66%–78% relative to commonly used software. Using this software instead of the commonly used manual approach has the potential to save researchers from many hours of monotonous work. No adjustment was necessary for 89% of the images regarding length (70% for depth). Hence, the only workload on most images was the visual inspection. As the visual inspection and manual dimension extraction works in the same way as currently used software, we expect no loss in accuracy.

The assessment of heart rate (HR) and heart rate variability (HRV) based on electrocardiograms (ECG) is considered a good proxy for stress in a wide range of animal species. However, problems can occur e.g., when measuring ECG in ambulatory settings such as recording of unrestrained animals using non‐invasive devices. Artefacts caused by technical (i.e. bad electrode contact) or physiological (i.e. ectopic beats, non‐cardiac muscle potentials) sources are common and can disturb the ECG signal. As HRV analysis is highly sensitive to artefacts in the interbeat interval (RR‐interval) time series the process of visual inspection of the raw signal to detect and correct these is essential. Most of the commercially available software requires intensive training and extensive manual work to accomplish this task and/or is often not available to access for free. EasieRR is an open‐source, stand‐alone software optimized for analysing ECG in non‐restrained animals. The program allows a species‐specific analysis and calculation of recommended standard HRV parameters in both, the time‐ and the non‐linear domain (RMSSD, SDNN, SD1, and SD2). Visualization of data by using Poincaré plots and tachograms of RR‐intervals eases the validation of correct heart cycle interval detection and minimizes manual work for the user. Automatically detected peaks can be manually corrected via deletion, correction of spurious detections or marking of undetected peaks. The HRV analysis can be exported using common formats (TXT, MAT). Figures can be plotted and exported in various formats (PDF, SVG, PNG, JPG, TIFF, EMF, EPS). Included in EasieRR is the possibility for synchronization of ECG data with video in order to link cardiac responses to specific behavioural responses.