@article {53057, title = {Species Richness (of Insects) Drives the Use of Acoustic Space in the Tropics}, journal = {Remote Sensing}, volume = {9}, year = {2017}, month = {Jan-11-2017}, pages = {1096}, abstract = {

Acoustic ecology, or ecoacoustics, is a growing field that uses sound as a tool to evaluate animal communities. In this manuscript, we evaluate recordings from eight tropical forest sites that vary in species richness, from a relatively low diversity Caribbean forest to a megadiverse Amazonian forest, with the goal of understanding the relationship between acoustic space use (ASU) and species diversity across different taxonomic groups. For each site, we determined the acoustic morphospecies richness and composition of the biophony, and we used a global biodiversity dataset to estimate the regional richness of birds. Here, we demonstrate how detailed information on activity patterns of the acoustic community (\<22 kHz) can easily be visualized and ASU determined by aggregating recordings collected over relatively short periods (4\–13 days). We show a strong positive relationship between ASU and regional and acoustic morphospecies richness. Premontane forest sites had the highest ASU and the highest species richness, while dry forest and montane sites had lower ASU and lower species richness. Furthermore, we show that insect richness was the best predictor of variation in total ASU, and that insect richness was proportionally greater at high-diversity sites. In addition, insects used a broad range of frequencies, including high frequencies (\>8000 Hz), which contributed to greater ASU. This novel approach for analyzing the presence and acoustic activity of multiple taxonomic groups contributes to our understanding of ecological community dynamics and provides a useful tool for monitoring species in the context of restoration ecology, climate change and conservation biology.

}, keywords = {amphibians, ARBIMON, Biodiversity monitoring, birds, community ecology, insects, passive acoustic monitoring, rapid assessments, Soundscape, species richness}, doi = {10.3390/rs9111096}, url = {http://www.mdpi.com/2072-4292/9/11/1096}, author = {T. Mitchell Aide and Hern{\'a}ndez-Serna, Andres and Campos-Cerqueira, Marconi and Acevedo-Charry, Orlando and Jessica L. Deichmann} } @article {52562, title = {Chronic anthropogenic noise disrupts glucocorticoid signaling and has multiple effects on fitness in an avian community}, journal = {Proceedings of the National Academy of Sciences}, year = {2018}, month = {Aug-01-2018}, pages = {201709200}, abstract = {

Anthropogenic noise is a pervasive pollutant that decreases environmental quality by disrupting a suite of behaviors vital to perception and communication. However, even within populations of noise-sensitive species, individuals still select breeding sites located within areas exposed to high noise levels, with largely unknown physiological and fitness consequences. We use a study system in the natural gas fields of northern New Mexico to test the prediction that exposure to noise causes glucocorticoid-signaling dysfunction and decreases fitness in a community of secondary cavity-nesting birds. In accordance with these predictions, and across all species, we find strong support for noise exposure decreasing baseline corticosterone in adults and nestlings and, conversely, increasing acute stressor-induced corticosterone in nestlings. We also document fitness consequences with increased noise in the form of reduced hatching success in the western bluebird (Sialia mexicana), the species most likely to nest in noisiest environments. Nestlings of all three species exhibited accelerated growth of both feathers and body size at intermediate noise amplitudes compared with lower or higher amplitudes. Our results are consistent with recent experimental laboratory studies and show that noise functions as a chronic, inescapable stressor. Anthropogenic noise likely impairs environmental risk perception by species relying on acoustic cues and ultimately leads to impacts on fitness. Our work, when taken together with recent efforts to document noise across the landscape, implies potential widespread, noise-induced chronic stress coupled with reduced fitness for many species reliant on acoustic cues.

}, keywords = {acoustic environment, anthropogenic noise, birds, perceived predation risk, stress response}, issn = {0027-8424}, doi = {10.1073/pnas.1709200115}, url = {http://www.pnas.org/lookup/doi/10.1073/pnas.1709200115https://syndication.highwire.org/content/doi/10.1073/pnas.1709200115}, author = {Kleist, Nathan J. and Guralnick, Robert P. and Cruz, Alexander and Lowry, Christopher A. and Francis, Clinton D.} } @article {51471, title = {Species Richness (of Insects) Drives the Use of Acoustic Space in the Tropics}, journal = {Remote Sensing}, volume = {9}, year = {2017}, month = {Jan-11-2017}, pages = {1096}, abstract = {

Acoustic ecology, or ecoacoustics, is a growing field that uses sound as a tool to evaluate animal communities. In this manuscript, we evaluate recordings from eight tropical forest sites that vary in species richness, from a relatively low diversity Caribbean forest to a megadiverse Amazonian forest, with the goal of understanding the relationship between acoustic space use (ASU) and species diversity across different taxonomic groups. For each site, we determined the acoustic morphospecies richness and composition of the biophony, and we used a global biodiversity dataset to estimate the regional richness of birds. Here, we demonstrate how detailed information on activity patterns of the acoustic community (\<22 kHz) can easily be visualized and ASU determined by aggregating recordings collected over relatively short periods (4\–13 days). We show a strong positive relationship between ASU and regional and acoustic morphospecies richness. Premontane forest sites had the highest ASU and the highest species richness, while dry forest and montane sites had lower ASU and lower species richness. Furthermore, we show that insect richness was the best predictor of variation in total ASU, and that insect richness was proportionally greater at high-diversity sites. In addition, insects used a broad range of frequencies, including high frequencies (\>8000 Hz), which contributed to greater ASU. This novel approach for analyzing the presence and acoustic activity of multiple taxonomic groups contributes to our understanding of ecological community dynamics and provides a useful tool for monitoring species in the context of restoration ecology, climate change and conservation biology.

}, keywords = {amphibians, ARBIMON, Biodiversity monitoring, birds, community ecology, insects, passive acoustic monitoring, rapid assessments, soundscapes, species richness}, doi = {10.3390/rs9111096}, url = {http://www.mdpi.com/2072-4292/9/11/1096}, author = {T. Mitchell Aide and Hern{\'a}ndez-Serna, Andres and Campos-Cerqueira, Marconi and Acevedo-Charry, Orlando and Jessica L. Deichmann} } @article {47718, title = {Spectrogram Enhancement Using Multiple Window Savitzky-Golay (MWSG) Filter for Robust Bird Sound Detection}, journal = {IEEE/ACM Transactions on Audio, Speech, and Language Processing}, volume = {25}, year = {2017}, month = {Jan-06-2017}, pages = {1183 - 1192}, abstract = {

Bird sound detection from real-field recordings is essential for identifying bird species in bioacoustic monitoring. Variations in the recording devices, environmental conditions, and the presence of vocalizations from other animals make the bird sound detection very challenging. In order to overcome these challenges, we propose an unsupervised algorithm comprising two main stages. In the first stage, a spectrogram enhancement technique is proposed using a multiple window Savitzky\–Golay (MWSG) filter. We show that the spectrogram estimate using MWSG filter is unbiased and has lower variance compared with its single window counterpart. It is known that bird sounds are highly structured in the time\–frequency (T\–F) plane. We exploit these cues of prominence of T-F activity in specific directions from the enhanced spectrogram, in the second stage of the proposed method, for bird sound detection. In this regard, we use a set of four moving average filters that when applied to the enhanced spectrogram, yield directional spectrograms that capture the direction specific information. We propose a thresholding scheme on the time varying energy profile computed from each of these directional spectrograms to obtain frame-level binary decisions of bird sound activity. These individual decisions are then combined to obtain the final decision. Experiments are performed with three different datasets, with varying recording and noise conditions. Frame level F-score is used as the evaluation metric for bird sound detection. We find that the proposed method, on average, achieves higher F-score (10.24\% relative) compared to the best of the six baseline schemes considered in this work.

}, keywords = {bioacoustic monitoring, bird sound detection, birds, directional spectrograms, hidden Markov models, monitoring, noise measurement, robustness, Savitzky-Golay filter, spectrogram}, issn = {2329-9290}, doi = {10.1109/TASLP.2017.2690562}, url = {http://ieeexplore.ieee.org/document/7933047/}, author = {Koluguri, Nithin Rao and Meenakshi, G. Nisha and Ghosh, Prasanta Kumar} } @article {47717, title = {On-Bird Sound Recordings: Automatic Acoustic Recognition of Activities and Contexts}, journal = {IEEE/ACM Transactions on Audio, Speech, and Language Processing}, volume = {25}, year = {2017}, month = {Jan-06-2017}, pages = {1193 - 1206}, abstract = {

We introduce a novel approach to studying animal behavior and the context in which it occurs, through the use of microphone backpacks carried on the backs of individual free-flying birds. These sensors are increasingly used by animal behavior researchers to study individual vocalizations of freely behaving animals, even in the field. However, such devices may record more than an animal\&$\#$39;s vocal behavior, and have the potential to be used for investigating specific activities (movement) and context (background) within which vocalizations occur. To facilitate this approach, we investigate the automatic annotation of such recordings through two different sound scene analysis paradigms: A scene-classification method using feature learning, and an event-detection method using probabilistic latent component analysis. We analyze recordings made with Eurasian jackdaws (Corvus monedula) in both captive and field settings. Results are comparable with the state of the art in sound scene analysis; we find that the current recognition quality level enables scalable automatic annotation of audio logger data, given partial annotation, but also find that individual differences between animals and/or their backpacks limit the generalization from one individual to another. we consider the interrelation of \“scenes\” and \“events\” in this particular task, and issues of temporal resolution.

}, keywords = {acoustics, birds, context, event detection, image analysis, microphones, pattern recognition, speech, zoology}, issn = {2329-9290}, doi = {10.1109/TASLP.2017.2690565}, url = {http://ieeexplore.ieee.org/document/7933044/}, author = {Stowell, Dan and Gill, Lisa F.} }