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14 results for “dawn chorus”
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>
Vocal performance increases rapidly during the dawn chorus in Adelaide's warbler
<p><span>Many songbirds sing intensely during the early morning, resulting in a phenomenon known as the dawn chorus. We tested the hypothesis that male Adelaide's warblers (<em>Setophaga</em> <em>adelaidae</em>) warm up their voices during the dawn chorus. If warming up the voice is one of the functions of the dawn chorus, we predicted that vocal performance would increase more rapidly during the dawn chorus compared to the rest of the morning and that high song rates during the dawn chorus period contribute to the increase in vocal performance. The performance metrics <em>recovery time, voiced frequency modulation</em>, and <em>unvoiced</em> <em>frequency</em> <em>modulation</em> were low when birds first began singing, increased rapidly during the dawn chorus, and then leveled off or gradually diminished after dawn. These changes are attributable to increasing performance within song types. Reduction in the duration of the silent gap between notes is the primary driver of improved performance during the dawn chorus. Simulations indicated that singing at a high rate during the dawn chorus period increases performance in two of the three performance measures (<em>recovery</em> <em>time</em> and <em>unvoiced</em> <em>frequency</em> <em>modulation</em>) relative to singing at a low rate during this period. These findings are consistent with the hypothesis that vocal warm-up is one benefit of participation in the dawn chorus. </span></p>
Vocal performance increases rapidly during the dawn chorus in Adelaide’s warbler
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Data from: Sunrise in the city: disentangling drivers of the avian dawn chorus onset in urban greenspaces
Urban systems are known to have a number of effects on avian richness, density, and morphological and behavioral traits. However, no study to date has simultaneously examined the wide range of urban variables in relation to the avian dawn chorus, a complex behavioral phenomenon. Previous studies investigating adjustments of the dawn chorus onset in urban settings have mainly been confined to relationships with noise and light levels. In addition to noise and light levels, in this study we included other potentially related environmental characteristics describing vegetation structure, urban infrastructure, and human activity, all of which have been shown to be drivers of bird diversity in urban areas. We conducted dawn chorus surveys at 38 Los Angeles urban greenspaces and used a classification and regression tree analysis to identify specific urban scenarios that best explained timing differences in the dawn chorus onset. Our results show that light level was the most important variable related to the dawn chorus onset time, in which, counter-intuitively, bird communities in greenspaces with higher light levels had later onsets. In addition, noise was an important factor for the chorus onset in greenspaces with higher light levels. Although our results differ from those of previous studies, these findings highlight the importance of noise and light levels in explaining dawn chorus onset variation, indicating the need for further research in untangling this complex and ecologically important phenomenon.
Figure 3. Centroids with 95 in Insect noise avoidance in the dawn chorus of Neotropical birds
Figure 3. Centroids with 95% confidence intervals plotted for the first two discriminant functions from a quadratic discriminant function analysis using peak frequency, song complexity and song rate for 477 songs from 27 different bird species (see Appendix, Table A1 for species names).
Figure 5 in Insect noise avoidance in the dawn chorus of Neotropical birds
Figure 5. First call time measured as minutes from sunrise plotted against (a) peak frequency of each species and (b) the difference between background noise at first call time and over all average background noise for the 1 h period. Background noise values were calculated at each species' peak frequency band. Each point represents the mean over all days and sites (±SE) per species. Negative noise values indicate that birds started singing at times when ambient noise level at their songs' peak frequency was lower than the rest of the recording period. Singing location is displayed as either in the canopy (circles, solid line) or below (triangles, dashed line) the canopy. See Table 2 for the parameter estimates associated with these variables.
Figure 2 in Insect noise avoidance in the dawn chorus of Neotropical birds
Figure 2. (a) Avian dawn chorus as defined by the number of singing events by any species averaged (±SE) over sites and days for each 5 min time bin. Note that calling activity peaked before the end of the recording period. (b) The first call time averaged (±SE) over sites and days for each species (see Appendix, Table A1 for species names) displayed as time from sunrise.
Figure 1 in Insect noise avoidance in the dawn chorus of Neotropical birds
Figure 1. Typical spectrogram (FFT - 4096) of a recording made at dawn. Letters indicate (A) a cicada playback and songs of (B) white-flanked antwren, (C) great tinamou, Tinamus major, (D) chestnut-backed antbird, (E) western slaty antshrike and (F) cocoa woodcreeper. Bands of nonavian insect noise are visible between 4 and 8 kHz.
Figure 4 in Insect noise avoidance in the dawn chorus of Neotropical birds
Figure 4. Nonavian noise level at different frequencies up to 9 kHz. Each line displays the average background noise colour-coded for each 5 min time interval from 30 min before until 30 min after sunrise. Black triangles indicate peak frequency of songs from the 27 bird species recorded. Amplitudes of nonavian noise are given as negative values relative to the maximum input of the recording units.
Data from: Sunrise in the city: disentangling drivers of the avian dawn chorus onset in urban greenspaces
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Data from: Repeatability of signalling traits in the avian dawn chorus
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Data from: Experimental illumination of a forest: no effects of lights of different colours on the onset of the dawn chorus in songbirds
Light pollution is increasing exponentially, but its impact on animal behaviour is still poorly understood. For songbirds, the most repeatable finding is that artificial night lighting leads to an earlier daily onset of dawn singing. Most of these studies are, however, correlational and cannot entirely dissociate effects of light pollution from other effects of urbanization. In addition, there are no studies in which the effects of different light colours on singing have been tested. Here, we investigated whether the timing of dawn singing in wild songbirds is influenced by artificial light using an experimental set-up with conventional street lights. We illuminated eight previously dark forest edges with white, green, red or no light, and recorded daily onset of dawn singing during the breeding season. Based on earlier work, we predicted that onset of singing would be earlier in the lighted treatments, with the strongest effects in the early-singing species. However, we found no significant effect of the experimental night lighting (of any colour) in the 14 species for which we obtained sufficient data. Confounding effects of urbanization in previous studies may explain these results, but we also suggest that the experimental night lighting may not have been strong enough to have an effect on singing.
Data from: Experimental illumination of a forest: no effects of lights of different colours on the onset of the dawn chorus in songbirds
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Figure 6 in Insect noise avoidance in the dawn chorus of Neotropical birds
Figure 6. (a) Effect of 1 min cicada playbacks on singing behaviour of seven common birds represented as the average (±SE) number of songs counted 1 min during or after the playback (PB), expressed as a difference from the number counted 1 min before the playback. (b) Songs counted during playbacks minus songs counted before playbacks, plotted against peak frequency of songs, with the frequencies covered by the cicada playback illustrated by a gradient bar. For species names see Appendix, Table A1.
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