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126 results for “chorus”
INFORMATE Project - CHORUS Report Summaries - 20231106
<p>These data provide a summary of the All, Author Affiliation, and Dataset Reports generated by the <a href="https://dashboard.chorusaccess.org/">CHORUS Dashboard</a> for three agencies: the U.S. National Science Foundation, U.S. Geological Survey, and the U.S. Agency for International Development. The reports summarized here was collected on November 6-7, 2023 as part of the INFORMATE Project funded by NSF.</p><p>The columns are:</p><p>Column Definition</p><p>agency The funding agency [NSF, USGS, or USAID]</p><p>date. The date of data retrieval (YYYYMMDD)</p><p>report. The report [all, authors, datasets]</p><p>Property Name of the column in the input file</p><p>count Number of values (rows) of the property</p><p>unique Number of unique values of the property</p><p>top Most common value of the property</p><p>freq Number of occurrences (frequency) of the most common value</p><p>Count % The percentage of rows that include the property</p>
Audio tagging of avian dawn chorus recordings in California, Oregon, and Washington
<p><strong>General Summary</strong></p> <p>This acoustic data collection includes 1,575 5-minute soundscape recordings randomly selected from passive acoustic recordings made at 525 sites during 2022 on federally managed lands in western California, Oregon, and Washington, USA. We fully labeled 141 recordings (11.75 hrs) with 39,717 annotations for 118 sound types, including 58 avian species, two mammalian species, six aggregated biotic sounds, and eight non-biotic sound types. An additional 215 recordings were partially annotated with 1,466 annotations. The remaining unlabeled recordings have been included to facilitate novel research applications and methodological evaluations. Beyond the labeled soundscape recordings, we have included township and range identifications and 38 environmental covariates for each recording location.</p> <p><strong>Data Collection</strong></p> <p>Lesmeister et al. (2021) collected passive acoustic recordings during 2022 in support of long-term monitoring of federally threatened northern spotted owl (<em>Strix occidentalis caurina) </em>populations under the Northwest Forest Plan Effective Monitoring Program (U. S. Fish and Wildlife Service 1990, U. S. Department of Agriculture and U. S. Department of the Interior 1994). These data were collected at 643 hexagons that were randomly selected from a tessellation of 5 km2 hexagons covering the entire range of the northern spotted owl (Northern California, Oregon, Washington) under a selective constraint that hexagons contain ≥ 50 % forest-capable lands (<em>def.</em> forested lands or lands capable of developing closed-canopy forests) and be ≥ 25% federal ownership (Davis et al., 2011).</p> <p>Each hexagon was sampled by four Song Meter 4 (SM4) acoustic recording units (Wildlife Acoustics, Maynard, MA) deployed in a standardized spatial arrangement, such that recorders on a site were placed ≥ 500 m apart and were ≥ 200 m from the edge of the sampling hexagon boundary. Recorders were mounted to small trees (15 – 20 cm diameter at breast height) approximately 1.5 m above the ground and were placed on mid-to-upper slopes and ≥ 50 m from roads, trails, and streams. The SM4 devices each have two built-in omnidirectional microphones with a signal-to-noise ratio of 80 dB, typical at 1 kHz, and a recording bandwidth of 20 Hz – 48 kHz. Each device recorded ~11 hours of audio daily for six weeks from March to August at a sampling rate of 32 kHz. The daily recording schedule included a 4-hour window from two hours before sunrise to two hours after sunrise, a 4-hour window from one hour before sunset to 3 hours after sunset, and 10-minute recordings outside the two longer recording blocks at the start of every hour.</p> <p><strong>Data Sampling</strong></p> <p>The goal of this project was to develop a tagged audio dataset (hereafter project dataset) focused on the avian dawn chorus, which is an ecologically important period for the study of avian behavior (McNamara et al. 1987, Staicer et al. 1996, Zhang et al. 2015) and monitoring avian biodiversity (Bibby et al. 2000), but remains a challenging problem for acoustic classification systems (Duan et al. 2013, Stowell 2022). Passive acoustic monitoring on our sites occurs throughout the day. We filtered the full dataset to recordings collected between May and August during the hour immediately after sunrise. From the recordings meeting our filtering criteria, we randomly selected three 5-minute files from each site, which were assigned ordinal labels 'A, 'B,' or 'C.' The final project dataset comprised 131.25 hours of acoustic data.</p> <p><strong>Annotation Protocol</strong></p> <p>We randomly selected 141 sites from the project dataset and fully annotated each recording at a 2-second resolution. We applied labels to each 2-second window of the selected recordings following a predefined sound phonology library (available in the 'metadata.tsv' file), which concatenated the 2021 eBird taxonomy codes (Clements list; Clements et al. 2022) with standardized sonotype codes that incremented depending on the species repertoire (i.e., 'call_1,' 'song_1,' 'drum_1'). For example, 'herthr_song_1' is the label for Hermit Thrush, song_1. Unknown signals were labeled 'unknown,' and clips with no biotic signals (or noise classes of interest documented in metadata.tsv) were labeled 'empty.' Windows were labeled 'complete' and considered fully annotated when every signal was assigned an annotation. Files were deemed fully annotated when every 2-second window contained the 'complete' label.</p> <p><strong>Environmental Covariates</strong></p> <p>Sampling locations will not be published to afford protections for Federally Threatened or Endangered species which may occur on our sites. However, we provide the State, Township, and Range for each sampling location along with the site-specific values for 38 forest structure, topographic, and climatic environmental covariates developed by the Landscape Ecology, Modeling, Mapping, and Analysis group in the Pacific Northwest (<a href="https://lemma.forestry.oregonstate.edu/data">https://lemma.forestry.oregonstate.edu/data</a>; Ohmann and Gregory 2002). State, Township, and Range values are sufficient to explore geographic variation in species- or community-specific call and song phenology and the extracted environmental covariates may provide useful contextual information for novel machine-learning developments (Liu et al. 2018). </p> <p><strong>Description of Data Format</strong></p> <p>The fully annotated audio files can be accessed by downloading and extracting "annotated_recordings.zip." Partially annotated and non-annotated audio files can be accessed by downloading and extracting "additional_recordings_part_1.zip" or "additional_recordings_part_2.zip." Acoustic file names contain site and replicate indicators, such that file "Site_001_Rep_A.wav' was recorded on site 1 and is the A replicate random draw from the available set of dawn chorus recordings. The site and replicate numbers link to additional recording information in "files.tsv," annotations in "annotations.tsv" and "partial_annotations.tsv," as well as site and replicate specific environmental characteristics in "environmental_characteristics.tsv."</p> <p>Metadata describing sound classes and environmental characteristics can be found in "metadata.tsv," and "environmental_characteristics_metadata.tsv."</p> <p><strong>Acknowledgments</strong></p> <p>Acoustic data collection was funded and collected by the US Forest Service and the US Bureau of Land Management. Annotation work was funded by Google. We would also like to thank the many biologists that collected and processed the data compiled here. The use of trade or firm names in this publication is for reader information and does not imply endorsement by the U.S. Government of any product or service.</p>
sounding_out_chorus
<p>This repository contains the data for the paper <strong>Towards interpretable learned representations for Ecoacoustics using variational auto-encoding</strong>.<strong> </strong>This dataset contains a series of 1 min wav files recorded across UK and Ecuadorian habitats. Each sample has 26 acoustic indices calculated and a full list of avian species and abundances. This dataset is an updated version of a <a href="https://zenodo.org/record/1255218">previous release (10.5281/zenodo.1255218)</a> including KML maps containing GPS data for each sample site and updated label metadata. A data module for use in a PyTorch machine learning pipeline is <a href="https://gitlab.com/ecolistening/sounding_out_torch">available here</a>.</p> <p><strong>Abstract</strong><br> Ecoacoustics is an emerging science that seeks to understand the role of sound in ecological processes.<br> Passive acoustic monitoring is increasingly being used to collect vast quantities of whole-soundscape<br> audio recordings in order to study variations in acoustic community activity across spatial and<br> temporal scales. However, extracting relevant information from audio recordings for ecological<br> inference is non-trivial. Recent approaches to machine-learned acoustic features appear promising<br> but are limited by inductive biases, crude temporal integration methods and few means to interpret<br> downstream inference. To address these limitations we developed and trained a self-supervised<br> representation learning algorithm - a convolutional Variational Auto-Encoder (VAE) - to embed<br> latent features from acoustic survey data collected from sites representing a gradient of habitat<br> degradation in temperate and tropical ecozones and use prediction of survey site as a test case for<br> interpreting inference. We investigate approaches to interpretability by mapping discriminative<br> descriptors back to the spectro-temporal domain to observe how soundscape components change<br> as we interpolate across a linear classification boundary traversing latent feature space; we advance<br> temporal integration methods by encoding a probabilistic soundscape descriptor capable of capturing<br> multi-modal distributions of latent features over time. Our results suggest that varying combinations<br> of soundscape components (biophony, geophony and anthrophony) are used to infer sites along a<br> degradation gradient and increased sensitivity to periodic signals improves on previous research using<br> time-averaged representations for site classification. We also find the VAE is highly sensitive to<br> differences in recorder hardware’s frequency response and demonstrate a simple linear transformation<br> to mitigate the effect of hardware variance on the learned representation. Our work paves the way for<br> development of a new class of deep neural networks that afford more interpretable machine-learned<br> ecoacoustic representations to advance the fundamental and applied science and support global<br> conservation efforts.<br> <br> <strong>Sampling Methods (extract from paper)</strong><br> Surveys were designed to monitor the acoustic characteristics of sites across a gradient of degradation, ranging from primary forest, through secondary forest (or areas in the process of ecological restoration), to agricultural monocultures, providing a space-for-time substitution to investigate changes in soundscapes across a gradient of ecological status. Samples were taken for 1 minute in every 15 for 10 sequential days at each site. Full dawn and dusk recordings were also collected. In each site, 15 recorders were placed in a grid-like system spaced a minimum of 200m away from their neighbours in the UK - 300m in Ecuador - to mitigate acoustic overlap and avoid spatial pseudo-replication. Wildlife Acoustics Song Meters equipped with two channel omni-directional microphone were used. Seven SM2+ and eight SM3 devices were deployed. Gains were matched between recorders (analogue gains at +36dB on SM2+ and +12dB on SM3 which has inbuilt +12dB gain) and recordings made at resolution of 16 bits with a sampling rate of 48 kHz. To provide a cleaner validation data set, local weather recordings were used to select 3 days with lowest wind and rain from each site giving 4725 1 min recording in total. Sites are labelled by their quality in descending order i.e. UK1 (primary), UK2 (regenerating), UK3 (degraded).</p>
Cue the chorus: Canyon treefrog calling phenology on the falling limb of spring floods and warming nights
Phenology is the timing of life events tied to environmental or abiotic cues. We used autonomous recording units (ARUs) across spring-summer months in 2022 to capture breeding calls from canyon treefrog (Hyla arenicolor). ARUs were placed in perennial and intermittent stream reaches across five Wilderness Areas within the upper Verde River basin in Arizona. We monitored streams by installing stream flow gauges (water level recorders). Treefrogs call at relatively low flow after spring floods. This suggests that stream-dwelling anurans may breed in response to flooding followed by prolonged periods of base flows which could be important for tadpole metamorphosis. Implications for stream regulation suggest maintaining the magnitude and timing of flood pulse events can benefit recruitment of stream-breeding amphibians.
Simulation data of "Controlling the chirping of chorus waves via magnetic field inhomogeneity"
<p>Simulation data of "Controlling the chirping of chorus waves via magnetic field inhomogeneity", including waveform recorded at certain locations, part of 2D wave field and wave spectrogram obtained with 2D FFT. </p>
HDF5 datasets and python scripts to generate figures in "Butterfly distribution of relativistic electrons driven by parallel propagating lower band whistler chorus waves"
<p>HDF5 datasets and python scripts to generate figures in "Butterfly distribution of relativistic electrons driven by parallel propagating lower band whistler chorus waves"</p> <p>RBW simulation datasets in HDF5 format:</p> <ul> <li>300pT.h5 The particle dataset to generate the figures.</li> </ul> <p>Python scripts to generate figures in the manuscript.</p> <p>- Environment: Python 3.6.7 :: Anaconda 4.4.0 (64-bit)</p> <p>- Required modules: matplotlib, numpy, h5py</p> <ul> <li>Figure1.py Generate figure 1.</li> <li>Figure2.py Generate figure 2.</li> <li>Figure3.py Generate figure 3.</li> <li>Figure4.py Generate figure 4.</li> <li>QLDe.py Calculate bounce averaged diffusion coefficients according to Shprits et al. (2006) (doi: https://doi.org/10.1029/ 2006JA011725).</li> </ul> <p> </p>
Simulation data for "Nonlinear electron phase-space dynamics in spontaneous excitation of falling-tone chorus" submitting to Geophysical Research Letters
<p>Simulation data for "Nonlinear electron phase-space dynamics in spontaneous excitation of falling-tone chorus" submitting to Geophysical Research Letters.</p> <p>Including the simulation input parameter file and the necessary output data for analysis described in the article. The output data consists of waveform data, wave intensity profile, binned phase space distribution, etc. A detailed guide to load the output data is included in the zipped file as well. </p>
Neurogenomic divergence during speciation by reinforcement of mating behaviors in chorus frogs (Pseudacris) – De novo reference transcriptome: Assemblerd contigs and gene annotations
<p>Assembled contigs (Trinity) and gene annotations (Trinotate) of a reference transcriptome for the Upland Chorus Frog, <em>Pseudacris feriarum</em>. Data to assemble the contigs were obtained by sequencing four tissue types: Brain, eyes, testis, and somatic (liver/heart/lung/skin/muscle). Raw reads are stored in the NCBI-SRA database (BioProject PRJNA723357).</p>
Data and toolkit for: SoundScape learning: An automatic method for separating fish chorus in marine soundscapes
<p class="MsoNormal">Marine soundscapes provide the opportunity to non-invasively learn about, monitor, and conserve ecosystems. Some fishes produce sound in chorus, often in association with mating, and there is much to learn about fish choruses and the species producing them. Manually analyzing years of acoustic data is increasingly unfeasible, and is especially challenging with fish chorus, as multiple fish choruses can co-occur in time and frequency and can overlap with vessel noise and other transient sounds. SoundScape Learning (SSL) is a novel unsupervised automated method, to separate fish chorus from soundscape. SSL is an integrated technique that makes use of randomized robust principal component analysis (RRPCA), unsupervised clustering, and a neural network. SSL was applied to 14 recording locations off southern and central California and was able to detect a single fish chorus of interest in 5.3 yrs of acoustically diverse soundscapes. Through application of SSL, the chorus of interest was found to be nocturnal, increased in intensity at sunset and sunrise, and was seasonally present from late Spring to late Fall. Further application of SSL will improve understanding of fish behavior, essential habitat, species distribution, and potential human and climate change impacts, and thus allow for protection of vulnerable fish species. This repository provides example data and code for the JASA paper: <em><span>SoundScape Learning: an automatic method for separating fish chorus in marine soundscapes.</span></em></p>
Sex-related communicative functions of voice spectral energy in human chorusing
<p>Music is a human communicative art whose evolutionary origins may lie in capacities that support cooperation and/or competition. A mixed account favoring simultaneous cooperation and competition draws on analogous interactive displays produced by collectively signalling non-human animals (e.g., crickets and frogs). In these displays, rhythmically coordinated calls serve as a beacon whereby groups of males "cooperatively" attract potential female mates, while the likelihood of each male competitively attracting an actual mate depends on the precedence of his signal. Human behaviour consistent with the mixed account was previously observed in a renowned boys choir, where the basses—the oldest boys with the deepest voices—boosted their acoustic prominence by increasing energy in a high-frequency band of the vocal spectrum when girls were in an otherwise male audience. The current study tested female and male sensitivity and preferences for this subtle vocal modulation in online listening tasks. Results indicate that while female and male listeners are similarly sensitive to enhanced high-spectral energy elicited by the presence of female audience members, only female listeners exhibit a reliable preference for it. Findings suggest that human chorusing is a flexible form of social communicative behavior that allows simultaneous group cohesion and sexually motivated competition.</p>
Data from: Female chorus frogs delay mate choice under suboptimal environmental conditions
Open the record for dataset details and reuse information.
Sex-related communicative functions of voice spectral energy in human chorusing
Open the record for dataset details and reuse information.
Data and toolkit for: SoundScape learning: An automatic method for separating fish chorus in marine soundscapes
Open the record for dataset details and reuse information.
Simulation dataset of "Particle-in-cell simulations of characteristics of rising-tone chorus waves in the inner magnetosphere"
<p>Simulation dataset of "Particle-in-cell simulations of characteristics of rising-tone chorus waves in the inner magnetosphere", including magnetic fields and parallel and perpendicular temperatures of energetic electrons.</p>
The phantom chorus: birdsong boosts human well-being in protected areas
<p>Spending time in nature is known to benefit human health and well-being, but evidence is mixed as to whether biodiversity or perceptions of biodiversity contribute to these benefits. Perhaps more importantly, little is known about the sensory modalities by which humans perceive biodiversity and obtain benefits from their interactions with nature. Here, we used a "phantom bird song chorus" consisting of hidden speakers to experimentally increase audible birdsong biodiversity during "on" and "off" (i.e., ambient conditions) blocks on two trails to study the role of audition in biodiversity perception and self-reported well-being among hikers. Hikers exposed to the phantom chorus reported higher levels of restorative effects compared to those that experienced ambient conditions on both trails; however, increased restorative effects were directly linked to the phantom chorus on one trail and indirectly linked to the phantom chorus on the other trail through perceptions of avian biodiversity. Our findings add to a growing body of evidence linking mental health to nature experiences and suggest that audition is an important modality by which natural environments confer restorative effects. Finally, our results suggest that maintaining or improving natural soundscapes within protected areas may be an important component to maximizing human experiences.</p>
Neurogenomic divergence during speciation by reinforcement of mating behaviors in chorus frogs (Pseudacris) – De novo reference transcriptome raw data, contigs and gene annotations
<p>RNA-Seq raw data used in the assembly and annotation of a reference transcriptome for the Upland Chorus Frog, <em>Pseudacris feriarum</em>. Raw data were obtained by sequencing of four tissue types: Brain, eyes, testis, and somatic. Assembled contigs (Trinity) and gene annotations (Trinotate) are also provided.</p>
Data from: Multi-night territorial behavior, chorus attendance, and mating success in red-eyed treefrogs
<p>For many frog species that aggregate around ponds or streams, chorus attendance, the percentage of time or nights a given male is present and actively calling at an aggregation, is the strongest documented predictor of inter-male variation in reproductive success in the wild. Males are, thus, thought to compete via endurance rivalry, where available energetic reserves and individual physiology interact to determine chorus tenure. Frogs often exhibit territorial behavior within these aggregations, and territorial status is likely to influence a male's rate of energy expenditure. While males of several anuran species have been shown to hold territories across nights, it is not well understood whether such calling site fidelity is correlated with chorus attendance or mating success. Using subdermal RFID (PIT) tags, to minimize disturbance to chorus structure, we quantified site fidelity, chorus attendance, and mating success for all male red-eyed treefrogs (<em>Agalychnis callidryas</em>) within a breeding aggregation in Panama across 50 consecutive nights. We found that nearly half of these males held territories across nights, that this cross-night territorial behavior was highly correlated with chorus attendance, and that chorus attendance was, in turn, the strongest predictor of male mating success. Males were most faithful to calling sites containing vegetation contiguous with adjacent sites and were more likely to remain at a site if they were successful in acquiring a mate there on the previous night. To our knowledge, this is the first study linking male site fidelity to chorus attendance and mating success in anurans. While female mate choice is an established driver of lineage diversification and the evolution of sexual signals, agonistic interactions between males at breeding aggregations are well-documented from a wide range of anuran taxa. The relationship between male-male interactions and mating success deserves broader research attention among anuran species.</p>
Túngara frog call-timing decisions arise as internal rhythms interact with fluctuating chorus noise
<p>For chorusing males, optimally timing their calls relative to nearby rivals' calls and fluctuations in background chorus noise is crucial for reproductive success. A caller's acoustic environment will vary by chorus density and the properties of his chorus-mates' calls and will fluctuate unpredictably due to chorusing dynamics emerging among his chorus-mates. Thus, callers must continuously monitor moment-to-moment fluctuations in the acoustic scene they perceive at the chorus for advantageous times to call. In live experimental choruses, we investigated the factors influencing túngara frog call-timing responses to chorus-mates' calls on an interaction-by-interaction basis, revealing that intrinsic and extrinsic factors influenced call-timing decisions. Callers were more likely to overlap calls from smaller chorus-mates and chorus-mates at intermediate distances, as well as calls containing lower frequencies and exhibiting lower final amplitude minima. Consequently, variation among males in call properties led to variation in levels of call-interference received when calling in the same social environment. Additionally, callers were more likely to overlap chorus-mates' calls after experiencing extended periods of inhibition and were less likely to overlap synchronized chorus-mates' calls relative to single calls. In chorusing species, female choice is influenced by inter-caller dynamics, selecting for male call-timing strategies which, in turn, constitute the selective environment further refining these same strategies. Thus, understanding the specific factors driving call-timing decisions is essential for understanding how sexual selection operates in chorusing taxa.</p>
Counting the chorus: A bioacoustic indicator of population density
<p>Passive acoustic monitoring has grown in utility for tracking wildlife populations, though challenges remain when using acoustic detections to monitor population size and density. Distance sampling is considered the 'gold standard' for estimating animal densities but has several important limitations. Here, we have compiled data and code from a case study that demonstrates a fast bioacoustic analysis method leveraging a simple metric call density. Using three years of synchronously collected bioacoustic and point-transect distance sampling data for eight forest bird species native to Hawai‘i, including four endangered species, we found strong correlations between call density and distance sampling-based animal density estimates. These findings indicate that call density is a reliable indicator of animal density that can be used independently or combined with traditional monitoring methods. This approach could enhance passive acoustic monitoring by providing more sensitive population health indicators than commonly used detection/nondetection methods, facilitating prompt conservation and management decisions.</p>
Data for the detection of the boreal chorus frog (Pseudacris maculata) using environmental DNA and call surveys at 180 ponds sampled in 2017-2018 in southeastern Québec, Canada
<p>The boreal chorus frog (<em>Pseudacris maculata</em>) is at risk of extinction in parts of its range in Canada. Our objectives were to quantify the influence of local and landscape characteristics on the occurrence of the species in wetlands in southern Québec. We hypothesized that site occupancy depends on local characteristics and landscape characteristics contributing to site connectivity. We developed an environmental DNA (eDNA) method to detect the species and compared the detection probability of this method to traditional call surveys. We collected water samples at a total of 180 sites (90 in 2017, 110 in 2018), whereas we surveyed a subset of 63 sites using both eDNA and call surveys in 2018. Site occupancy varied across years, but was higher in sites where the species had been previously detected during the last 12 years by other studies. Site occupancy did not vary with other local and landscape characteristics, in part due to an apparent decrease in the number of sites occupied by the species since the last 12 years. Detection probability via eDNA (0.81; 95% CI: [0.31; 0.98]) did not differ from that of call surveys (0.62; 95% CI: [0.25; 0.89]). To identify the optimal sampling period for the boreal chorus frog, future studies should estimate the detection probability of eDNA during the breeding season and the larval development period of the species.</p>
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