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26 results for “Bird calls”
Calling activity of Birds in the White Mountain National Forest: Audio Recordings (2016 and 2018)
We collected 410 10-minute sound recordings of birds in and near the Hubbard Brook Experimental Forest in New Hampshire. Recordings, which encompassed most of the bird breeding season in each of two years, included 130,776 vocalizations from 46 taxa. In the associated publication, we report species lists, rarefaction curves, and vocalization descriptions. We also provide analyses of habitat associations, phenology, and spatial patterning in vocalization activity. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Calling activity of Birds in the White Mountain National Forest: Manifest of 99,778 acoustic recordings from bird plots in the Hubbard Brook Forest: 2016 - 2023
During 2016 - 2023, during the bird breeding season, we collected 99,778 files of bioacoustic recordings in and near the Hubbard Brook Experimental Forest in New Hampshire. Here, we provide a manifest of the sound files. Most files are one-hour recordings collected at 32 kHz and saved in FLAC format (~ 25 MB per file, ~ 13 TB total). Typical recording configuration was 05:00 - 08:00 and 17:30 - 20:30 local time. The full sound files have been saved in three respositories: two copies at Dartmouth College (Ayres lab) and one copy at the Macauley Library, Cornell Laboratory of Ornithology. The full sound files are available upon request. The file attributes within the manifest include date, start time, and recorder group: e.g., Main, 10ha, Oven, VW, AshBirch, and Ridge. Each recorder group had 5 - 20 recorders at plots separated by >100 m. Coordinates of each recorder are associated with plot names within metadata. The bird species expected to occur in these recordings are those from Holmes et al. (2021). These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. Holmes, R., S. Sillett, and M. Hallworth. 2021. Bird species recorded within the Hubbard Brook Experimental Forest and vicinity (1963-2020; updated January 2021). ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/da6cbb1ed8142d52a9d72762983742d8 (Accessed 2024-10-24).
Nocturnal flight calls dataset: long-term acoustic monitoring of birds migrating at night
<p><strong>General Description:</strong></p> <p>This is a development set used in the experiments in the Ph.D. thesis: "Nowe metody akustycznej identyfikacji ptaków migrujących nocą" (<em>"Novel methods of acoustic identification of birds migrating at night"</em>) by Hanna Pamula. The project focuses on the detection (and - partially - classification) of passerine birds' calls from long-term audio recordings collected during bird autumn migration between 2016 and 2019. The dataset consists of >56,5 hours of recordings with annotations of nocturnal flight calls of passerine birds migrating along the Baltic Sea coast, Poland.</p> <p> </p> <p><strong>Folder Structure</strong></p> <p>Development_Set_3.1.zip</p> <p>|_Development_Set_3.1/</p> <p> |__Training_Set/</p> <p> |____*.wav</p> <p> |____*.txt</p> <p> |__Validation_Set/</p> <p> |____*.wav</p> <p> |____*.txt</p> <p> |__Testing_Set/</p> <p> |____*.wav</p> <p> |____*.txt</p> <p>Training Set: 86 recordings</p> <p>Validation Set: 8 recordings</p> <p>Testing set: 18 recordings (BUT: uploaded 20 recordings, as in the previous version of the dataset - version 3, two additional recordings were used. Then, they were deleted in the final version of development set 3.1. Two additional recordings are: 'BUK5_20161101_002104a and BUK5_20161101_002104b)</p> <p>Names of waveforms and annotations are matching.</p> <p><strong>Waveforms:</strong></p> <p>The whole dataset consists of 114 recordings. One hundred thirteen recordings are about 30 minutes long (29min56s – 29min 59s), one recording is 1min20s. All data were recorded at 44,100 Hz sampling rate, one channel, with SM2 Wildlife Acoustics recorders + SMX-NFC microphone. The recording sessions were performed at night (starting time and date denoted in a file name) on the Baltic Sea coast in Poland (Dąbkowice, near Darłowo).</p> <p><strong>Annotations:</strong></p> <p>Transcriptions were produced using Audacity 2.4.1: https://www.audacityteam.org/ by an experienced birdwatcher, Hanna Pamula. While every effort has been made to ensure the quality and accuracy of the labels, some errors may occur, taking into account the difficulty of nocturnal call recognition and transcription tasks in general.</p> <p>Transcription format:</p> <p>[Starting time (sec)] [Ending time (sec)] [Label]</p> <p><strong>Meaning of the labels:</strong></p> <p>1. Positive classes – migrating passerine birds:</p> <ul> <li>'s' – song thrush call (Turdus philomelos)</li> <li>'k' – blackbird call (Turdus merula)</li> <li>'d' – redwing call (Turdus iliacus)</li> <li>'r' – robin call (Erithacus rubecula)</li> <li>‘kwiczol’ – fieldfare call (Turdus pilaris)</li> <li>‘skowronek’ – skylark call (Alauda arvensis)</li> <li>Each of the above labels could also have a question mark '?', e.g. 'r?', 'k?' – meaning that it's not a sure label. In a bird call detection task, they are regarded as positive chunks containing bird call(s).</li> <li>'ni' – non identified bird call (distant/quiet/not recognized)</li> </ul> <p>Only the supposed calls of migrating passerine birds were labeled; other sounds of species were ignored (e.g., robin's tik-calling, which can be often heard at dusk, and may be regarded as warning sounds).</p> <p>2. Negative classes – other marked sound events:</p> <ul> <li>'g' – other bird calls/songs/sounds. Sounds that could confuse the model; for example, sounds of migrating geese, cranes, plovers calls, etc.</li> <li>'gh' – human voices</li> <li>'t' – cracks, clicks, raindrops, other noise</li> <li>‘puszczyk’ – tawny owl voice (Strix aluco)</li> <li>'czapla' – grey heron voice (Ardea cinerea)</li> </ul> <p>Not all occurrences of the negative sounds were labeled – only some chosen examples to represent the possible noises/negative samples. Thus these annotations can't be used for entirely different detection / classification tasks than intended, e.g., detecting migrating cranes or human voices in long-term recordings.</p> <p>3. Labels to be excluded from analysis:</p> <ul> <li>'???', '??? mysz', '??? high freq' – unknown, not sure if the sound event is a birds' call or not. Uncertainty about belonging to a positive/negative class in the detection task.</li> </ul>
Рис. 3. «ГуΑки» самцов (1) и птенцов (2): a — I. sinensis; b — гибриΑных птиц 2007 г.; c — гибриΑных птиц 2010 г. Fig. 3. "Beeps" of males (1) and nestlings (2): a — I. sinensis; b — hybrid birds 2007; c — hybrid birds 2010 in Call repertoire of Bitterns Ixobrychus in Russian Far East
Рис. 3. «ГуΑки» самцов (1) и птенцов (2): a — I. sinensis; b — гибриΑных птиц 2007 г.; c — гибриΑных птиц 2010 г. Fig. 3. "Beeps" of males (1) and nestlings (2): a — I. sinensis; b — hybrid birds 2007; c — hybrid birds 2010
Рис. 4. Крики беΑствия самок (1): a — I. sinensis; b — гибриΑной птицы; крики беΑствия самцов (2): a — I. minutes (Wroza 2017); b — I. minutes 2007 г.; сΛётков (3): a — 13-суточных I. sinensis; b — 29-суточных I. sinensis; c — 13-суточных гибриΑных птиц; d — 29-суточных гибриΑных птиц Fig. 4. Distress calls of females (1): a — I. sinensis; b — hybrid bird; distress calls of males (2): a — I. minutes (Wroza 2017); b — I. minutes 2007; fledglings (3): a — 13-day-old I. sinensis; b — 29-day-old I. sinensis; c — 13-day-old hybrid birds; d — 29-day-old hybrid birds in Call repertoire of Bitterns Ixobrychus in Russian Far East
Рис. 4. Крики беΑствия самок (1): a — I. sinensis; b — гибриΑной птицы; крики беΑствия самцов (2): a — I. minutes (Wroza 2017); b — I. minutes 2007 г.; сΛётков (3): a — 13-суточных I. sinensis; b — 29-суточных I. sinensis; c — 13-суточных гибриΑных птиц; d — 29-суточных гибриΑных птиц Fig. 4. Distress calls of females (1): a — I. sinensis; b — hybrid bird; distress calls of males (2): a — I. minutes (Wroza 2017); b — I. minutes 2007; fledglings (3): a — 13-day-old I. sinensis; b — 29-day-old I. sinensis; c — 13-day-old hybrid birds; d — 29-day-old hybrid birds
Рис. 6. Контактно-тревожная позывка «перекΛичка» птенцов I. sinensis (a) и гибриΑных птиц (b) Fig. 6. Contact-alarm call "roll call" of I. sinensis nestlings (a) and hybrid birds (b) in Call repertoire of Bitterns Ixobrychus in Russian Far East
Рис. 6. Контактно-тревожная позывка «перекΛичка» птенцов I. sinensis (a) и гибриΑных птиц (b) Fig. 6. Contact-alarm call "roll call" of I. sinensis nestlings (a) and hybrid birds (b)
Рис. 5. Контактно-пищевая позывка «мяуканье» (1) и пищевое «шипение» (2) птенцов: a — I. eurythmus; b — I. sinensis; c — гибриΑных птиц Fig. 5. Contact-food "meow" call (1) and food "hissing" (2) of nestlings: a — I. eurythmus; b — I. sinensis; c — hybrid birds in Call repertoire of Bitterns Ixobrychus in Russian Far East
Рис. 5. Контактно-пищевая позывка «мяуканье» (1) и пищевое «шипение» (2) птенцов: a — I. eurythmus; b — I. sinensis; c — гибриΑных птиц Fig. 5. Contact-food "meow" call (1) and food "hissing" (2) of nestlings: a — I. eurythmus; b — I. sinensis; c — hybrid birds
Nestling birds learn socially to eavesdrop on heterospecific alarm calls through acoustic association
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Calling structural variants with confidence from short-read data in wild bird populations
<p>Comprehensive characterisation of structural variation in natural populations has only become feasible in the last decade. To investigate the population genomic nature of structural variation (SV), reproducible and high-confidence SV callsets are first required. We created a population-scale reference of the genome-wide landscape of structural variation across 33 Nordic house sparrows (<em>Passer domesticus</em>) individuals. To produce a consensus callset across all samples using short-read data, we compare heuristic-based quality filtering and visual curation (Samplot/PlotCritic and Samplot-ML) approaches. We demonstrate that curation of SVs is important for reducing putative false positives and that the time invested in this step outweighs the potential costs of analysing short-read discovered SV datasets that include many potential false positives. We find that even a lenient manual curation strategy (e.g. applied by a single curator) can reduce the proportion of putative false positives by up to 80%, thus enriching the proportion of high-confidence variants. Crucially, in applying a lenient manual curation strategy with a single curator, nearly all (>99%) variants rejected as putative false positives were also classified as such by a more stringent curation strategy using three additional curators. Furthermore, variants rejected by manual curation failed to reflect the expected population structure from SNPs, whereas variants passing curation did. Combining heuristic-based quality-filtering with rapid manual curation of structural variants in short-read data can therefore become a time- and cost-effective first step for functional and population genomic studies requiring high-confidence SV callsets.</p>
Behavioural changes in aposematic Heliconius melpomene butterflies in response to their predatory bird calls
<p>Prey-predator interactions have resulted in the evolution of many anti-predatory traits. One of them is the ability of prey to listen to predators and avoid them. Although prey anti-predatory behavioural responses to predator auditory cues are well described in a wide range of taxa, studies on whether butterflies change their behaviours in response to their predatory calls are lacking. <em>Heliconius </em>butterflies are unpalatable and form Müllerian mimicry rings as morphological defence strategies against their avian predators. Like many other butterflies in the <em>Nymphalidae </em>family, some <em>Heliconius </em>butterflies possess auditory organs, which are hypothesized to assist with predator detection. Here we test whether <em>Heliconius melpomene </em>changes their behaviour in response to their predatory bird calls by observing the behaviour of male and female <em>H. m. plessini </em>exposed to calls of <em>Heliconius</em> avian predators: rufous-tailed jacamar, migratory Eastern kingbird, and resident tropical kingbird. We also exposed them to the calls of the toco toucan, a frugivorous bird as a control bird call, and an amplified greenhouse background noise as a noise control. We found that individuals<em> </em>changed their behaviour in response to Jacamar calls only. Males increased their walking and fluttering behaviour, while females did not change their behaviour during the playback of the jacamar call. Intersexual behaviours like courtship, copulation, and abdomen lifting did not change in response to bird calls. Our findings suggest that despite having primary predatory defences like toxicity and being in a mimicry ring, <em>H. m. plessini </em>butterflies changed their behaviour in response to predator calls. Furthermore, this response was predator-specific, as <em>H. m. plesseni</em> did not respond to either the Eastern kingbird or the tropic kingbird calls. This suggests that <em>Heliconius</em> butterflies may be able to differentiate predatory calls, and potentially the birds associated with those calls.</p>
Calling structural variants with confidence from short-read data in wild bird populations
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Contextual variations in calls of two non-oscine birds: the blue petrel and the Antarctic prion
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Data from: Evaluation of methods to estimate nocturnal bird migration activity: A comparison of radar and nocturnal flight call monitoring in the American West
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Behavioural changes in aposematic Heliconius melpomene butterflies in response to their predatory bird calls
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Data from: Why does noise reduce response to alarm calls? Experimental assessment of masking, distraction and greater vigilance in wild birds
1. Environmental noise from anthropogenic and other sources affects many aspects of animal ecology and behaviour, including acoustic communication. Acoustic masking is often assumed in field studies to be the cause of compromised communication in noise, but other mechanisms could have similar effects. 2. We tested experimentally how background noise disrupted the response to conspecific alarm calls in wild superb fairy-wrens, Malurus cyaneus, assessing the effects of acoustic masking, distraction and changes in vigilance. We first examined the birds' response to alarm-call playbacks accompanied by different amplitudes of background noise that overlapped the calls in acoustic frequency. We then scored and videoed their response to alarm calls in two types of background noise, that did or did not overlap call frequency, but were broadcast at a constant amplitude. 3. Birds were less likely to flee to alarm calls in higher amplitudes of overlapping noise, demonstrating that noise itself compromised communication independently of environmental correlates. Background noise affected the response only if it overlapped in frequency with the alarm calls, implying that the effect was not due to distraction. Further, birds were equally vigilant during background noise of overlapping or non-overlapping frequency, indicating that the lack of response to alarm calls in overlapping noise was not due to enhanced vigilance and awareness that there was no predator. 4. We conclude that alarm-call reception was compromised by masking, a mechanism that is often assumed but rarely tested in an ecological context. Masking compromised reception of high-frequency 'aerial' alarm calls and so could reduce survival in background noise of similar frequency. While anthropogenic noise, which is often of lower frequency, is unlikely to affect communication with these calls, it could affect reception of acoustic cues of danger, or other conspecific or heterospecific alarm calls.
Data from: A phylogenetically controlled meta-analysis of biologging device effects on birds: deleterious effects and a call for more standardized reporting of study data
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Data from: Cooperative bird discriminates between individuals based purely on their aerial alarm calls
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Data from: Why does noise reduce response to alarm calls? Experimental assessment of masking, distraction and greater vigilance in wild birds
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Data from: Flight calls signal group and individual identity but not kinship in a cooperatively breeding bird
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Data from: Alarming features: birds use specific acoustic properties to identify heterospecific alarm calls
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.