Bioacoustic Studies Research Articles

Voice Analysis in Dogs with Deep Learning: Development of a Fully Automatic Voice Analysis System for Bioacoustics Studies

Extracting behavioral information from animal sounds has long been a focus of research in bioacoustics, as sound-derived data are crucial for understanding animal behavior and environmental interactions. Traditional methods, which involve manual review of extensive recordings, pose significant challenges. This study proposes an automated system for detecting and classifying animal vocalizations, enhancing efficiency in behavior analysis. The system uses a preprocessing step to segment relevant sound regions from audio recordings, followed by feature extraction using Short-Time Fourier Transform (STFT), Mel-frequency cepstral coefficients (MFCCs), and linear-frequency cepstral coefficients (LFCCs). These features are input into convolutional neural network (CNN) classifiers to evaluate performance. Experimental results demonstrate the effectiveness of different CNN models and feature extraction methods, with AlexNet, DenseNet, EfficientNet, ResNet50, and ResNet152 being evaluated. The system achieves high accuracy in classifying vocal behaviors, such as barking and howling in dogs, providing a robust tool for behavioral analysis. The study highlights the importance of automated systems in bioacoustics research and suggests future improvements using deep learning-based methods for enhanced classification performance.

Improving acoustic species identification using data augmentation within a deep learning framework

Convolutional neural networks (CNNs) are effective tools for acoustic classification tasks such as species identification. Large datasets of labelled recordings are required to develop CNN classifiers which can be difficult to obtain, particularly if species are rare or vocalise infrequently. Additionally, data often requires manual labelling which can be time consuming requiring expert analysis. Artificially generating data using augmentation can address these challenges, however the impact of data augmentation on CNN performance is poorly understood and often omitted in bioacoustic studies. Here, we empirically test the impact of CNN architecture and 20 data augmentation methods on classifier performance. We use acoustic identification of 18 small mammal species as a case study of a species group that can be effectively surveyed by acoustic monitoring, but recordings for training data are scarce and difficult to collect. Networks that achieved the highest accuracy across all sample sizes was a 10-layer CNN (96.43 %) and a pre-trained ResNet50 model (96.37 %). Overall, all augmentation effects improved ResNet50 model performance and 17 effects improved Conv10 performance, increasing relative change in accuracy (RCA) by 0.021–0.641. Three augmentation effects negatively impacted Conv10 RCA by −0.042 to −0.182. We also show that adding augmented data when the number of original samples is low has the greatest positive impact on accuracy and this effect was larger with ResNet50 models. Our work demonstrates that using data augmentation where few original samples are available can considerably improve model performance and highlights the potential of augmentation in developing acoustic classifiers for species where data are limited or difficult to obtain.

The importance of acoustic background modelling in CNN-based detection of the neotropical White-lored Spinetail (Aves, Passeriformes, Furnaridae)

ABSTRACT Machine learning tools are widely used in support of bioacoustics studies, and there are numerous publications on the applicability of convolutional neural networks (CNNs) to the automated presence-absence detection of species. However, the relation between the merit of acoustic background modelling and the recognition performance needs to be better understood. In this study, we investigated the influence of acoustic background substance on the performance of the acoustic detector of the White-lored Spinetail (Synallaxis albilora). Two detector designs were evaluated: the 152-layer ResNet with transfer learning and a purposely created CNN. We experimented with acoustic background representations trained with season-specific (dry, wet, and all-season) data and without explicit modelling to evaluate its influence on the detection performance. The detector permits monitoring of the diel behaviour and breeding time of White-lored Spinetail solely based on the changes in the vocal activity patterns. We report an advantageous performance when background modelling is used, precisely when trained with all-season data. The highest classification accuracy (84.5%) was observed for the purposely created CNN model. Our findings contribute to an improved understanding of the importance of acoustic background modelling, which is essential for increasing the performance of CNN-based species detectors.

Damage caused by singing cicadas (Hemiptera: Cicadidae) in the field protective forest belts in South Dobrudzha, Bulgaria

During the period 2020-2023, strong damage caused by singing cicadas (Hemiptera: Cicadidae) were registered on ash trees (Fraxinus spp.) in the field protective forest belts (FPFBs) in South Dobrudzha, northeastern Bulgaria. Bioacoustic studies have shown that the sounds are of Cicada orni. Many exuvia of the species were also found on the trunks and branches of ash trees. On the upper shoots and petioles, numerous oviposition holes were observed, which lead to leaf fall and drying of branch tips. In different FPFBs, tree crown damage ranges from a moderate (25-60% defoliation) to a severe (over 60% defoliation) degree. The attacks were stronger on Fraxinus excelsior and F. americana compared to F. angustifolia. The cicadas affect both old trees and young ash saplings. In young plantations, other tree species (Sophora japonica, Gleditsia triacanthos) were also affected. Imaginal activity of Cicada orni was recorded in July and August, and the peak of egg hatching occurred from early August to mid-September. The high number of Cicada orni necessitates the development of measures to control the pest in the FPFBs.

Communication among animals and bioacoustics studies on bees.

The communication of honey bees encompasses diverse forms, focusing on signals that resemble those in the learning process. Bioacoustics plays a crucial role in understanding this phenomenon, especially in the context of social insects, where communicative coding is complex and essential for survival. In this article, various aspects of bee communication are analyzed, examining both acoustics and other forms of information transmission. In the bee family, there are several communication mechanisms, such as the dance of bees, vibrations, buzzing, and singing. The dance communication of honey bees, based on dance movements, dates back as far as 40 million years, making it older than human language. This form of communication is linked to environmental needs and determines the survival of bees. Signals from queen bees are associated with reproductive needs within the colony, and other individuals exhibit diverse sound characteristics. Different species of bees utilize various aspects of the environment for communication, including gravity and the position of the sun. The precision of transmitted signals may depend on the distance to a food source or potential threat. Sounds emitted by bees serve diverse functions, such as deterring predators or recruiting other individuals for food collection. Modern technologies, especially bioacoustics, enable more precise studies of bee communication. Advanced methods of vibrational spectrum analysis even allow the prediction of swarming with high effectiveness, which can be particularly useful in beekeeping practices. Studies on the bioacoustic hive, utilizing stereo microphones and soundproofing, indicate the potential of these technologies for a better understanding of bee communication in natural conditions. However, many aspects of bee communication still require more detailed research, such as the role of honey-filled cells in transmitting vibrations, necessitating the use of precise tools and technologies. The bioacoustic hive project, based on traditional bee frames, provides new insights into bee communication, especially concerning sounds generated during bee collisions within the hive. Conclusions from bioacoustic studies have significant practical potential in apiculture. The use of stereo microphones and soundproofing improves the perception and assessment of acoustic phenomena within the hive. Prospects for utilizing sound recorders for continuous monitoring of life inside the hive, and the assessment of zoo-hygienic conditions, underscore the growing importance of bioacoustics in beekeeping practices. These discoveries open new perspectives for research into the social life of these crucial insect species and their conservation.

Software-dependent biases in the recognition of di- and tri-syllabic bird songs can create false interpretations of bird abundance and singing activity

The use of autonomous recording units (ARUs) for passive acoustic monitoring has recently gained a lot of importance. It is now widely used for scientific purposes and has been increasingly applied in the field of conservation biology. However, while ARUs may greatly increase cost-effectiveness in monitoring, they are not exempt from possible biases. In this study, we compared the performances of three different software in recognizing bi- and tri-syllabic bird songs and tested if their results varied significantly in a real setting. We focused on the interpretation of the relative abundance among habitats and the time of vocal activity, also using different numbers of training records to assess if the results of the software improved. Even with the quality measures of the recognizer being consistent with those of previously published studies, in several cases the results produced significantly different interpretations depending on the software used. While increasing the number of training records slightly improved the overlap in the estimates of activity among the different software, differences in the relative abundance of each song were present even with a fairly solid number of training records. Our results suggest that reliable biological interpretations might only be attained with a large number of training records and very high values of precision, recall, and the F-score. In fact, even with very high precision and F-scores, lower values for recall led not only to statistically significant differences, but also to different interpretations of relative abundance and time of activity between the two best-performing software. Thus, great caution must be taken in interpreting the results of bioacoustics studies that use automated recognition, and standards of minimum quality should be created for recognizers in scientific and technical studies.

Vocal repertoire of wild Andean night monkeys (Aotus lemurinus) in an Andean forest in Colombia

Abstract Vocal communication is particularly important for nocturnal species as well as those living in dense forests, where visual abilities can be somewhat constrained. The Andean night monkey (Aotus lemurinus) is a nocturnal American primate living in mountain forests in the Northern Andes with scant information on its behavior and ecology. The main goal of this study is to describe the vocal repertoire of a group of wild Andean night monkeys and compare it with earlier bioacoustics studies on the only nocturnal platyrrhines. We recorded the vocal behavior of a group of night monkeys living in the eastern Andes of Colombia between August and December 2019. Based on an auditory and a visual inspection of the vocal records, and through a quantitative analysis of the acoustic parameters of the vocalizations, we were able to identify five different calls emitted by the Andean night monkey. Four of these calls are stereotyped while the fifth vocalization (Squeak) is more variable, having different forms. Additionally, one call (Acetate) was found to be unique to this species. The result of this study contributes to the scant information on the ecology and behavior of the Andean night monkey and sets baseline information on the vocal behavior of night monkeys that may be used in future studies on communication of these and other nocturnal primates.

The impacts of fine-tuning, phylogenetic distance, and sample size on big-data bioacoustics.

Vocalizations in animals, particularly birds, are critically important behaviors that influence their reproductive fitness. While recordings of bioacoustic data have been captured and stored in collections for decades, the automated extraction of data from these recordings has only recently been facilitated by artificial intelligence methods. These have yet to be evaluated with respect to accuracy of different automation strategies and features. Here, we use a recently published machine learning framework to extract syllables from ten bird species ranging in their phylogenetic relatedness from 1 to 85 million years, to compare how phylogenetic relatedness influences accuracy. We also evaluate the utility of applying trained models to novel species. Our results indicate that model performance is best on conspecifics, with accuracy progressively decreasing as phylogenetic distance increases between taxa. However, we also find that the application of models trained on multiple distantly related species can improve the overall accuracy to levels near that of training and analyzing a model on the same species. When planning big-data bioacoustics studies, care must be taken in sample design to maximize sample size and minimize human labor without sacrificing accuracy.

A Collection of Best Practices for the Collection and Analysis of Bioacoustic Data

The field of bioacoustics is rapidly developing and characterized by diverse methodologies, approaches and aims. For instance, bioacoustics encompasses studies on the perception of pure tones in meticulously controlled laboratory settings, documentation of species’ presence and activities using recordings from the field, and analyses of circadian calling patterns in animal choruses. Newcomers to the field are confronted with a vast and fragmented literature, and a lack of accessible reference papers or textbooks. In this paper we contribute towards filling this gap. Instead of a classical list of “dos” and “don’ts”, we review some key papers which, we believe, embody best practices in several bioacoustic subfields. In the first three case studies, we discuss how bioacoustics can help identify the ‘who’, ‘where’ and ‘how many’ of animals within a given ecosystem. Specifically, we review cases in which bioacoustic methods have been applied with success to draw inferences regarding species identification, population structure, and biodiversity. In fourth and fifth case studies, we highlight how structural properties in signal evolution can emerge via ecological constraints or cultural transmission. Finally, in a sixth example, we discuss acoustic methods that have been used to infer predator–prey dynamics in cases where direct observation was not feasible. Across all these examples, we emphasize the importance of appropriate recording parameters and experimental design. We conclude by highlighting common best practices across studies as well as caveats about our own overview. We hope our efforts spur a more general effort in standardizing best practices across the subareas we’ve highlighted in order to increase compatibility among bioacoustic studies and inspire cross-pollination across the discipline.

How different farming practices influence the activity of insectivorous Neotropical bats

Recent bioacoustic studies have shown different responses of insectivorous bats to native habitat loss. We examined the activity and species/sonotypes composition of aerial insectivorous bats present in a human-modified karst landscape in Southeast Brazil, characterized by the presence of semideciduous forest, pastures and Eucalyptus globulus monocultures. Using ultrasonic detectors, we investigated activity and identified bat species and/or sonotypes in the three habitat types. We compared the activity (as a surrogate for abundance) and composition of species/sonotypes present and used Generalized Linear Models to investigated whether canopy density, understory density and food availability influence the response of bats in these habitat types. Our main results demonstrate that the variables general passes and species/sonotype richness did not differ significantly between forest and pasture, however, both variables in these two environments differed significantly from the values ​​found for eucalyptus. We conclude that, in the studied agropastoral landscape, pastures interspersed with forest areas can be used by aerial insectivorous bats during foraging. However, we also found evidence that eucalyptus monocultures, not yet mature and without an understory, have a negative impact on the species/sonotype richness and activity of Neotropical aerial insectivorous bats.

Rookognise: Acoustic detection and identification of individual rooks in field recordings using multi-task neural networks

Individual-level monitoring is essential in many behavioural and bioacoustics studies. Collecting and annotating those data is costly in terms of human effort, but necessary prior to conducting analysis. In particular, many studies on bird vocalisations also involve manipulating the animals or human presence during observations, which may bias vocal production. Autonomous recording units can be used to collect large amounts of data without human supervision, largely removing those sources of bias. Deep learning can further facilitate the annotation of large amounts of data, for instance to detect vocalisations, identify the species, or recognise the vocalisation types in recordings. Acoustic individual identification, however, has so far largely remained limited to a single vocalisation type for a given species. This has limited the use of those techniques for automated data collection on raw recordings, where many individuals can produce vocalisations of varying complexity, potentially overlapping one another, with the additional presence of unknown and varying background noise. This paper aims at bridging this gap by developing a system to identify individual animals in those difficult conditions. Our system leverages a combination of multi-scale information integration, multi-channel audio and multi-task learning. The multi-task learning paradigm is based the overall task into four sub-tasks, three of which are auxiliary tasks: the detection and segmentation of vocalisations against other noises, the classification of individuals vocalising at any point during a sample, and the sexing of detected vocalisations. The fourth task is the overall identification of individuals. To test our approach, we recorded a captive group of rooks, a Eurasian social corvid with a diverse vocal repertoire. We used a multi-microphone array and collected a large scale dataset of time-stamped and identified vocalisations recorded, and found the system to work reliably for the defined tasks. To our knowledge, the system is the first to acoustically identify individuals regardless of the vocalisation produced. Our system can readily assist data collection and individual monitoring of groups of animals in both outdoor and indoor settings, even across long periods of time, and regardless of a species’ vocal complexity. All data and code used in this article is available online.

Soundscapes show disruption across the diel cycle in human modified tropical landscapes

1. Fluctuations in the diel cycle, especially when compared across different land-use types, can reveal key changes in acoustic activity and the biological community. Yet few studies have assessed the effects of land use change on soundscapes across the diel cycle. The emergence of passive acoustic monitoring (PAM) allows us to monitor landscapes over longer and continuous periods, providing data on temporal variability across the diel cycle.2. Using AudioMoth acoustic recorders we collected data at 120 sites on the Osa Peninsula, Costa Rica, across a gradient of land use intensity. Information was extracted from recordings using a suite of nine acoustic indices. Principal component analysis reduced the indices into two axes, the first reflecting acoustic activity in the mid frequency bands, where the majority of biotic sound is present, and the second, representing acoustic activity in the upper frequency bands and the ratio of activity between the lower and mid-frequency bands.3. In disturbed land use types we found reduced acoustic activity during the characteristic dawn and dusk peaks in the diel cycle; known as the dawn and dusk chorus. Palm oil plantations showed a complete loss of these peaks, while teak plantations retained evidence of a weaker dawn and dusk chorus. Restricting the analysis to narrower temporal windows masks these differences among habitats.4. Synthesis and applications. Evaluating acoustic diversity at specific times of the day, which is common practice in bioacoustics studies, may be misleading, as pronounced changes in acoustic activity at dawn and dusk were obscured. By assessing trends across the diel cycle, we can gain a much better representation of the changes in acoustic activity. Our results show that in disturbed ecosystems there is a deviation in acoustic activity from that seen in a healthy native forest ecosystem, suggesting that there are likely changes within the biotic community in these ecosystems.

Introducing the Software CASE (Cluster and Analyze Sound Events) by Comparing Different Clustering Methods and Audio Transformation Techniques Using Animal Vocalizations.

Simple SummaryUnsupervised clustering algorithms are widely used in ecology and conservation to classify animal vocalizations, but also offer various advantages in basic research, contributing to the understanding of acoustic communication. Nevertheless, there are still some challenges to overcome. For instance, the quality of the clustering result depends on the audio transformation technique previously used to adjust the audio data. Moreover, it is difficult to verify the reliability of the clustering result. To analyze bioacoustic data using a clustering algorithm, it is, therefore, essential to select a reasonable algorithm from the many existing algorithms and prepare the recorded vocalizations so that the resulting values characterize a vocalization as accurately as possible. Frequency-modulated vocalizations, whose frequencies change over time, pose a particular problem. In this paper, we present the software CASE, which includes various clustering methods and provides an overview of their strengths and weaknesses concerning the classification of bioacoustic data. This software uses a multidimensional feature-extraction method to achieve better clustering results, especially for frequency-modulated vocalizations.Unsupervised clustering algorithms are widely used in ecology and conservation to classify animal sounds, but also offer several advantages in basic bioacoustics research. Consequently, it is important to overcome the existing challenges. A common practice is extracting the acoustic features of vocalizations one-dimensionally, only extracting an average value for a given feature for the entire vocalization. With frequency-modulated vocalizations, whose acoustic features can change over time, this can lead to insufficient characterization. Whether the necessary parameters have been set correctly and the obtained clustering result reliably classifies the vocalizations subsequently often remains unclear. The presented software, CASE, is intended to overcome these challenges. Established and new unsupervised clustering methods (community detection, affinity propagation, HDBSCAN, and fuzzy clustering) are tested in combination with various classifiers (k-nearest neighbor, dynamic time-warping, and cross-correlation) using differently transformed animal vocalizations. These methods are compared with predefined clusters to determine their strengths and weaknesses. In addition, a multidimensional data transformation procedure is presented that better represents the course of multiple acoustic features. The results suggest that, especially with frequency-modulated vocalizations, clustering is more applicable with multidimensional feature extraction compared with one-dimensional feature extraction. The characterization and clustering of vocalizations in multidimensional space offer great potential for future bioacoustic studies. The software CASE includes the developed method of multidimensional feature extraction, as well as all used clustering methods. It allows quickly applying several clustering algorithms to one data set to compare their results and to verify their reliability based on their consistency. Moreover, the software CASE determines the optimal values of most of the necessary parameters automatically. To take advantage of these benefits, the software CASE is provided for free download.

Technological prospecting on bioacoustic studies in bats

The development of technologies for the study of bats allows for more in-depth research on this essential group for the ecosystem, as they act in the ecological balance, as the main dispersers, pollinators and controllers of agricultural pests in certain regions of the planet. Patent technology for bioacoustics studies of the order Chiroptera, based on data platforms: National Institute of Industrial Property (INPI), European Patent Office (EPO), World Intellectual Property Organization (WIPO) and Google Patents. The search terms were: bat echolocation, bat bioacoustics, bats echolocation and bats bioacoustics. The word “Bats” was predominant in Google Patents 47.8%, EPO 12.5%, WIPO 6.4%. The data obtained suggest that technologies aimed at bat bioacoustics are emerging, having increased in the last decade, an important factor for the applicability of new techniques for conservation and monitoring of fauna.

Developing a novel bioacoustic monitoring for Garden Dormice using passive sound recorders and machine learning

One of the challenges of small mammal conservation is to be able to find the target species in the field. This is especially true for small nocturnal hibernators like dormice. Passive bioacoustic monitoring, as a non-invasive method, can be a useful tool to more efficiently find vocalizing animals in the field. However, bioacoustic methods produce a large amount of data, of which the manual analysis is highly time consuming. Therefore, there is need for an automatized process for identifying animal vocalization in acoustic data. Two types of recorders, audiomoths and BAR-LT recorders, were installed at a total of 10 locations of known Garden Dormouse (Eliomys quercinus) activity in Germany and were left recording in the field from June to September, producing a total of 3.54 TB of data. Based on our own and volunteers’ observations, Garden Dormouse vocalizations were manually identified in a subset of the sound files produced. These vocalizations, as well as ambient sound samples, were labelled and extracted to train a TensorFlow model, which was then tested on new subsets of the complete dataset. Comparing sound quality and acquisition costs of the two recorder types shows the potential for large-scale monitoring applications using the less expensive and open source audiomoth. Next steps include a time analysis of Garden Dormouse calls to find out when they are vocally more active during the study period. Such knowledge can help narrow the temporal scale of future bioacoustic studies on this species.

The Use of Machine Learning Algorithms in the Classification of Sound

This study is a systematic review of literature on the classification of sounds in three domains - Bioacoustics, Biomedical acoustics, and Ecoacoustics. Specifically, 68 conferences and journal articles published between 2010 and 2019 were reviewed. The findings indicated that Support Vector Machines, Convolutional Neural Networks, Artificial Neural Networks, and statistical models were predominantly used in sound classification across the three domains. Also, the majority of studies that investigated medical acoustics focused on respiratory sounds analysis. Thus, it is suggested that studies in Biomedical acoustics should pay attention to the classification of other internal body organs to enhance diagnosis of a variety of medical conditions. With regard to Ecoacoustics, studies on extreme events such as tornadoes and earthquakes for early detection and warning systems were lacking. The review also revealed that marine and animal sound classification was dominant in Bioacoustics studies.

Comprehending Information in Acoustic Signals: AReview of Bioacoustic Studies

Development in bioacoustic studies have provided new opportunities to understand the information related with different types of vocalization produced by humans and other living organisms in different conditions. Bioacoustics not only help humans to understand the different types of hidden information present in the acoustic signals of living organisms but also assist in understanding how acoustic signals can be used in improving their health and diagnosing severe diseases easily. Bioacoustics benefit both humans and animals by decoding the relation of the acoustic signals with the behaviour of the animal and environment in which they live. Bioacoustic studies also aid in understanding the effects of anthropogenic activities on acoustic signals of other animals. Monitoring through acoustic tools is most convenient and a large number of precision tools are available in the recent times. This paper reviews the findings of bioacoustic studies published between 2000 to 2020 worldwide. Significant researches in the field of sound communication of different animal species have been cited in this review. Varied animal behaviours can be accounted for based on the sound produced by animals under different physiological state, environmental influences and human activities. Study of animal sounds may be of remarkable value from welfare and conservation perspective.

HydroMoth: Testing a prototype low‐cost acoustic recorder for aquatic environments

AbstractPassive acoustic monitoring (PAM) involves recording the sounds of animals and environments for research and conservation. PAM is used in a range of contexts across terrestrial, marine and freshwater environments. However, financial constraints limit applications within aquatic environments; these costs include the high cost of submersible acoustic recorders. We quantify this financial constraint using a systematic literature review of all ecoacoustic studies published in 2020, demonstrating that commercially available autonomous underwater recording units are, on average, five times more expensive than their terrestrial equivalents. This pattern is more extreme at the low end of the price range; the cheapest available aquatic autonomous units are over 40 times more expensive than their terrestrial counterparts. Following this, we test a prototype low‐cost, low‐specification aquatic recorder called the ‘HydroMoth’: this device is a modified version of a widely used terrestrial recorder (AudioMoth), altered to include a waterproof case and customisable gain settings suitable for a range of aquatic applications. We test the performance of the HydroMoth in both aquaria and field conditions, recording artificial and natural sounds, and comparing outputs with identical recordings taken with commercially available hydrophones. Although the signal‐to‐noise ratio and the recording quality of HydroMoths are lower than commercially available hydrophones, the recordings with HydroMoths still allow for the identification of different fish and marine mammal species, as well as the calculation of ecoacoustic indices for ecosystem monitoring. Finally, we outline the potential applications of low‐cost, low‐specification underwater sound recorders for bioacoustic studies, discuss their likely limitations, and present important considerations of which users should be aware. Several performance limitations and a lack of professional technical support mean that low‐cost devices cannot meet the requirements of all PAM applications. Despite these limitations, however, HydroMoth facilitates underwater recording at a fraction of the price of existing hydrophones, creating exciting potential for diverse involvement in aquatic bioacoustics worldwide.

Species-Specific Responses of Bird Song Output in the Presence of Drones

Drones are now widely used to study wildlife, but their application in the study of bioacoustics is limited. Drones can be used to collect data on bird vocalizations, but an ongoing concern is that noise from drones could change bird vocalization behavior. To test for behavioral impact, we conducted an experiment using 30 sound localization arrays to track the song output of 7 songbird species before, during, and after a 3 min flight of a small quadcopter drone hovering 48 m above ground level. We analyzed 8303 song bouts, of which 2285, from 184 individual birds were within 50 m of the array centers. We used linear mixed effect models to assess whether patterns in bird song output could be attributed to the drone’s presence. We found no evidence of any effect of the drone on five species: American Robin Turdus migratorius, Common Yellowthroat Geothlypis trichas, Field Sparrow Spizella pusilla, Song Sparrow Melospiza melodia, and Indigo Bunting Passerina cyanea. However, we found a substantial decrease in Yellow Warbler Setophaga petechia song detections during the 3 min drone hover; there was an 81% drop in detections in the third minute (Wald test, p < 0.001) compared with before the drone’s introduction. By contrast, the number of singing Northern Cardinal Cardinalis cardinalis increased when the drone was overhead and remained almost five-fold higher for 4 min after the drone departed (p < 0.001). Further, we found an increase in cardinal contact/alarm calls when the drone was overhead, with the elevated calling rate lasting for 2 min after the drone departed (p < 0.001). Our study suggests that the responses of songbirds to drones may be species-specific, an important consideration when proposing the use of drones in avian studies. We note that recent advances in drone technology have resulted in much quieter drones, which makes us hopeful that the impact that we detected could be greatly reduced.

Acoustic propagation in heterogeneous environments and its implications on the active space of rock ptarmigan

Acoustic communication networks among birds are widely studied in homogeneous environment like tropical forest or open field. There is no study in heterogeneous environment to our knowledge. However, the propagation of an acoustic signal is strongly influenced by topography, meteorological and ground effects, especially at long distance. For bird species living in high mountains, these effects can have a prominent impact on how their vocalization spreads. Here we try to redefine the active space notion, using the rock ptarmigan as a model. We develop a sound propagation code dedicated to bioacoustic studies based on the parabolic equation method and taking into account the above mentioned effects. Propagation measurements were carried out at a mountain site to provide information on typical conditions encountered and to test the validity of our model in this context. Using this model, we describe how the mountainous conditions affect active spaces of communication compared to typical homogeneous situations. We further test how singing during display flights can be a good strategy for a transmitter to expand its active space in such a context. A fine modeling of the propagation of acoustic signals is likely to provide important cues for understanding communication networks of mountainous species.