Non-native species change the tune of tundra soils: Novel access to soundscapes of the Arctic earthworm invasion.

Over the last decade, an increasing number of studies have used soundscapes to address diverse ecological questions. Sound represents one of the few sources of information capable of providing in situ insights into processes occurring within opaque soil matrices. To date, the use of soundscapes for soil macrofauna monitoring has been experimentally tested only in controlled laboratory environments. Here we assess the validity of laboratory predictions and explore the use of soil soundscape proxies for monitoring soil macrofauna (i.e., earthworm) activities in an outdoor context. In a common garden experiment in northern Sweden, we constructed outdoor mesocosm plots (N = 36) containing two different Arctic vegetation types (meadow and heath) and introduced earthworms to half of these plots. Earthworms substantially altered the ambient soil soundscape under both vegetation types, as measured by both traditional soundscape indices and frequency band power levels, although their acoustic impacts were expressed differently in heath versus meadow soils. While these findings support the as-of-yet untapped promise of using belowground soundscape analyses to monitor soil ecosystem health, direct acoustic emissions from earthworm activities appear to be an unlikely proxy for tracking worm activities at daily timescales. Instead, earthworms indirectly altered the soil soundscape by 're-engineering' the soil matrix: an effect that was dependent on vegetation type. Our findings suggest that long-term (i.e., seasonal) earthworm activities in natural soil settings can likely be monitored indirectly via their impacts on soundscape measures and acoustic indices. Analyzing soil soundscapes may enable larger-scale monitoring of high-latitude soils and is directly applicable to the specific case of earthworm invasions within Arctic soils, which has recently been identified as a potential threat to the resilience of high-latitude ecosystems. Soil soundscapes could also offer a novel means to monitor soils and soil-plant-faunal interactions in situ across diverse pedogenic, agronomic, and ecological systems.

A machine learning approach for classifying and quantifying acoustic diversity.

1. Assessing diversity of discretely varying behavior is a classical ethological problem. In particular, the challenge of calculating an individuals' or species' vocal repertoire size is often an important step in ecological and behavioral studies, but a reproducible and broadly applicable method for accomplishing this task is not currently available. 2. We offer a generalizable method to automate the calculation and quantification of acoustic diversity using an unsupervised random forest framework. We tested our method using natural and synthetic datasets of known repertoire sizes that exhibit standardized variation in common acoustic features as well as in recording quality. We tested two approaches to estimate acoustic diversity using the output from unsupervised random forest analyses: (i) cluster analysis to estimate the number of discrete acoustic signals (e.g., repertoire size) and (ii) an estimation of acoustic area in acoustic feature space, as a proxy for repertoire size. 3. We find that our unsupervised analyses classify acoustic structure with high accuracy. Specifically, both approaches accurately estimate element diversity when repertoire size is small to intermediate (5-20 unique elements). However, for larger datasets (20-100 unique elements), we find that calculating the size of the area occupied in acoustic space is a more reliable proxy for estimating repertoire size. 4. We conclude that our implementation of unsupervised random forest analysis offers a generalizable tool that researchers can apply to classify acoustic structure of diverse datasets. Additionally, output from these analyses can be used to compare the distribution and diversity of signals in acoustic space, creating opportunities to quantify and compare the amount of acoustic variation among individuals, populations, or species in a standardized way. We provide R code and examples to aid researchers interested in using these techniques.

Comparative bioacoustics: a roadmap for quantifying and comparing animal sounds across diverse taxa.

Animals produce a wide array of sounds with highly variable acoustic structures. It is possible to understand the causes and consequences of this variation across taxa with phylogenetic comparative analyses. Acoustic and evolutionary analyses are rapidly increasing in sophistication such that choosing appropriate acoustic and evolutionary approaches is increasingly difficult. However, the correct choice of analysis can have profound effects on output and evolutionary inferences. Here, we identify and address some of the challenges for this growing field by providing a roadmap for quantifying and comparing sound in a phylogenetic context for researchers with a broad range of scientific backgrounds. Sound, as a continuous, multidimensional trait can be particularly challenging to measure because it can be hard to identify variables that can be compared across taxa and it is also no small feat to process and analyse the resulting high-dimensional acoustic data using approaches that are appropriate for subsequent evolutionary analysis. Additionally, terminological inconsistencies and the role of learning in the development of acoustic traits need to be considered. Phylogenetic comparative analyses also have their own sets of caveats to consider. We provide a set of recommendations for delimiting acoustic signals into discrete, comparable acoustic units. We also present a three-stage workflow for extracting relevant acoustic data, including options for multivariate analyses and dimensionality reduction that is compatible with phylogenetic comparative analysis. We then summarize available phylogenetic comparative approaches and how they have been used in comparative bioacoustics, and address the limitations of comparative analyses with behavioural data. Lastly, we recommend how to apply these methods to acoustic data across a range of study systems. In this way, we provide an integrated framework to aid in quantitative analysis of cross-taxa variation in animal sounds for comparative phylogenetic analysis. In addition, we advocate the standardization of acoustic terminology across disciplines and taxa, adoption of automated methods for acoustic feature extraction, and establishment of strong data archival practices for acoustic recordings and data analyses. Combining such practices with our proposed workflow will greatly advance the reproducibility, biological interpretation, and longevity of comparative bioacoustic studies.

Social learning of acoustic anti-predator cues occurs between wild bird species.

In many species, individuals gather information about their environment both through direct experience and through information obtained from others. Social learning, or the acquisition of information from others, can occur both within and between species and may facilitate the rapid spread of antipredator behaviour. Within birds, acoustic signals are frequently used to alert others to the presence of predators, and individuals can quickly learn to associate novel acoustic cues with predation risk. However, few studies have addressed whether such learning occurs only though direct experience or whether it has a social component, nor whether such learning can occur between species. We investigate these questions in two sympatric species of Parids: blue tits (Cyanistes caeruleus) and great tits (Parus major). Using playbacks of unfamiliar bird vocalizations paired with a predator model in a controlled aviary setting, we find that blue tits can learn to associate a novel sound with predation risk via direct experience, and that antipredator response to the sound can be socially transmitted to heterospecific observers, despite lack of first-hand experience. Our results suggest that social learning of acoustic cues can occur between species. Such interspecific social information transmission may help to mediate the formation of mixed-species aggregations.

Automated detection of low-frequency rumbles of forest elephants: A critical tool for their conservation.

African forest elephants (Loxodonta cyclotis) occupy large ranges in dense tropical forests and often use far-reaching vocal signals to coordinate social behavior. Elephant populations in Central Africa are in crisis, having declined by more than 60% in the last decade. Methods currently used to monitor these populations are expensive and time-intensive, though acoustic monitoring technology may offer an effective alternative if signals of interest can be efficiently extracted from the sound stream. This paper proposes an automated elephant call detection algorithm that was tested on nearly 4000 h of field recordings collected from five forest clearings in Central Africa, including sites both inside protected areas and in logging concessions. Recordings were obtained in different seasons, years, and under diverse weather conditions. The detector achieved an 83.2% true positive rate when the false positive rate is 5.5% (approximately 20 false positives per hour). These results suggest that this algorithm can enable analysis of long-term recording datasets or facilitate near-real-time monitoring of elephants in a wide range of settings and conditions.

Correction: Can Nocturnal Flight Calls of the Migrating Songbird, American Redstart, Encode Sexual Dimorphism and Individual Identity?

[This corrects the article DOI: 10.1371/journal.pone.0156578.].

Can Nocturnal Flight Calls of the Migrating Songbird, American Redstart, Encode Sexual Dimorphism and Individual Identity?

Bird species often use flight calls to engage in social behavior, for instance maintain group cohesion and to signal individual identity, kin or social associations, or breeding status of the caller. Additional uses also exist, in particular among migrating songbirds for communication during nocturnal migration. However, our understanding of the information that these vocalizations convey is incomplete, especially in nocturnal scenarios. To examine whether information about signaler traits could be encoded in flight calls we quantified several acoustic characteristics from calls of a nocturnally migrating songbird, the American Redstart. We recorded calls from temporarily captured wild specimens during mist-netting at the Powdermill Avian Research Center in Rector, PA. We measured call similarity among and within individuals, genders, and age groups. Calls from the same individual were significantly more similar to one another than to the calls of other individuals, and calls were significantly more similar among individuals of the same sex than between sexes. Flight calls from hatching-year and after hatching-year individuals were not significantly different. Our results suggest that American Redstart flight calls may carry identifiers of gender and individual identity. To our knowledge, this is the first evidence of individuality or sexual dimorphism in the flight calls of a migratory songbird. Furthermore, our results suggest that flight calls may have more explicit functions beyond simple group contact and cohesion. Nocturnal migration may require coordination among numerous individuals, and the use of flight calls to transmit information among intra- and conspecifics could be advantageous. Applying approaches that account for such individual and gender information may enable more advanced research using acoustic monitoring.

Flight calls signal group and individual identity but not kinship in a cooperatively breeding bird.

In many complex societies, intricate communication and recognition systems may evolve to help support both direct and indirect benefits of group membership. In cooperatively breeding species where groups typically comprise relatives, both learned and innate vocal signals may serve as reliable cues for kin recognition. Here, we investigated vocal communication in the plural cooperatively breeding superb starling, Lamprotornis superbus, where flight calls-short, stereotyped vocalizations used when approaching conspecifics-may communicate kin relationships, group membership, and/or individual identity. We found that flight calls were most similar within individual repertoires but were also more similar within groups than within the larger population. Although starlings responded differently to playback of calls from their own versus other neighboring and distant social groups, call similarity was uncorrelated with genetic relatedness. Additionally, immigrant females showed similar patterns to birds born in the study population. Together, these results suggest that flight calls are learned signals that reflect social association but may also carry a signal of individuality. Flight calls, therefore, provide a reliable recognition mechanism for groups and may also be used to recognize individuals. In complex societies comprising related and unrelated individuals, signaling individuality and group association, rather than kinship, may be a route to cooperation.

Pitch- and spectral-based dynamic time warping methods for comparing field recordings of harmonic avian vocalizations.

Quantitative measures of acoustic similarity can reveal patterns of shared vocal behavior in social species. Many methods for computing similarity have been developed, but their performance has not been extensively characterized in noisy environments and with vocalizations characterized by complex frequency modulations. This paper describes methods of bioacoustic comparison based on dynamic time warping (DTW) of the fundamental frequency or spectrogram. Fundamental frequency is estimated using a Bayesian particle filter adaptation of harmonic template matching. The methods were tested on field recordings of flight calls from superb starlings, Lamprotornis superbus, for how well they could separate distinct categories of call elements (motifs). The fundamental-frequency-based method performed best, but the spectrogram-based method was less sensitive to noise. Both DTW methods provided better separation of categories than spectrographic cross correlation, likely due to substantial variability in the duration of superb starling flight call motifs.