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57 results for “Vocal Repertoire”
Figure 7 in The vocal repertoire of Myrmeciza loricata (Lichtenstein, 1823) (Aves: Thamnophilidae)
Figure 7. Sonograms of other notes (call III) of Myrmeciza loricata. (A) Notes "D + E". (B) Note "F". (C) Note "I" emitted with call II. (D) Note "G". (E) Note "H". (F) Note "J" emitted with call II.
Figure 4 in The vocal repertoire of Myrmeciza loricata (Lichtenstein, 1823) (Aves: Thamnophilidae)
Figure 4. Sonograms of the call I (alarm) of Myrmeciza loricata. (A) Rattle phrases sequence. (B) Zoom showing the series of vertical tick notes.
Figure 4 in Breeding biology, diet and vocal repertoire of White-rumped Monjita Xolmis velatus
Figure 4. Seasonality in breeding records of White-rumped Monjita Xolmis velatus in Brazil based on citizen science data, the literature and this study.
Figure 3 in Breeding biology, diet and vocal repertoire of White-rumped Monjita Xolmis velatus
Figure 3. Food delivered to nestlings of White-rumped Monjita Xolmis velatus, Rio Claro, São Paulo, Brazil. A: larva, B: Oligochaeta, C: Erythemis vesiculosa, D: Zammara tympanum, E: Lepidoptera (moth), F: Blattodea, G: Myriapoda, H: Kentropyx aff. paulensis (Luiz Carlos Ramassotti)
Figure 2 in Breeding biology, diet and vocal repertoire of White-rumped Monjita Xolmis velatus
Figure 2. Food delivered to nestlings of White-rumped Monjita Xolmis velatus, Rio Claro, São Paulo, Brazil. A‒C = Scarabaeidae; D = Grylloidea; E = Ensifera; F = Tettigoniidae, Conocephalinae, Copiphorini; G‒H = Lycosidae (Luiz Carlos Ramassotti)
Figure 1 in Breeding biology, diet and vocal repertoire of White-rumped Monjita Xolmis velatus
Figure 1. Nests of White-rumped Monjita Xolmis velatus. A: nest 1 with nestlings, Analândia, São Paulo, Brazil, 15 November 2008 (Rogério Carlos Machado); B: nest 2 with eggs, Marília, São Paulo, Brazil, 23 October 2010 (Manuel Gonzales); C‒F: nest 3, Rio Claro, São Paulo, Brazil; C: adult at entrance to PVC pipe, 20 October 2021 (Luiz Ramassotti); D: nest, 29 October 2021 (Carlos Otávio Araujo Gussoni); E: nest with nestlings, 20 October 2021 (Carlos Otávio Araujo Gussoni); F: nestling, 22 October 2021 (Carlos Otávio Araujo Gussoni)
Figure 5 in Breeding biology, diet and vocal repertoire of White-rumped Monjita Xolmis velatus
Figure 5. Begging calls of a fledgling (A) and calls of an adult (B) White-rumped Monjita Xolmis velatus. Sonogram made using software Raven Pro 1.6.1 (Center for Conservation Bioacoustics 2019).
Figure 2 in Vocalizations of the Brazilian torrent frog Hylodes heyeri (Anura: Hylodidae): Repertoire and influence of air temperature on advertisement call variation
Figure 2. Territorial call of Hylodes heyeri from the Municipality of Morretes, Parana´, Brazil. Recorded on 11 January 2002, at 22.4°C. (A) Power spectrum; (B) spectrogram; (C) oscillogram.
Figure 3 in Vocalizations of the Brazilian torrent frog Hylodes heyeri (Anura: Hylodidae): Repertoire and influence of air temperature on advertisement call variation
Figure 3. Mean number of advertisement calls (bars) emitted by males of Hylodes heyeri during 5 min of monitoring each hour, and air temperature (line).
Data from: Vocal repertoire expansion in singing mice by co-opting a conserved midbrain circuit node
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A small vocal repertoire during the breeding season expresses complex behavioral motivations and individual signature in the Common Coot
<p><b>Backgroun</b><b>d:</b> Although acoustic communication plays an essential role in the social interactions of Rallidae, our knowledge of how Rallidae encode diverse types of information using simple vocalizations is limited. We recorded and examined the vocalizations of a Common Coot (<i>Fulica atra</i>) population during the breeding season to test the hypotheses that 1) different call types can be emitted under different behavioral contexts, and 2) variation in the vocal structure of a single call type may be influenced both by behavioral motivations and individual signature. We measured a total of 61 recordings of 30 adults while noting the behavioral activities in which individuals were engaged. We compared several acoustic parameters of the same call type emitted under different behavioral activities to determine how frequency and temporal parameters changed depending on behavioral motivations and individual differences.</p> <p><b>Results: </b>We found that adult Common Coots had a small vocal repertoire, including 4 types of call, composed of a single syllable that was used during 9 types of behaviors. The 4 calls significantly differed in both frequency and temporal parameters and can be clearly distinguished by discriminant function analysis. Minimum frequency of fundamental frequency (F<sub>0min</sub>) and duration of syllable (T) contributed the most to acoustic divergence between calls. Call <i>a</i> was the most commonly used (in 8 of the 9 behaviors detected), and maximum frequency of fundamental frequency (F<sub>0max</sub>) and interval of syllables (TI) contributed the most to variation in call <i>a</i>. Duration of syllable (T) in a single call <i>a</i> can vary with different behavioral motivations after individual vocal signature being controlled.</p> <p><b>Conclusions:</b> These results demonstrate that several call types of a small repertoire, and a single call with function-related changes in the temporal parameter in Common Coots could potentially indicate various behavioral motivations and individual signature. This study advances our knowledge of how Rallidae use "simple" vocal systems to express diverse motivations and provides new models for future studies on the role of vocalization in avian communication and behavior.</p>
Figure 4 in Vocal repertoire and group-specific signature in the Smooth-billed Ani, Crotophaga ani Linnaeus, 1758 (Cuculiformes, Aves)
Figure 4. Boxplots (median and quartiles) of acoustic parameters of the similar vocalizations of Charqueada and Guararema groups of Smooth-billed Ani. Vocalizations:"Ahnee","Whine", "Pre-flight", "Flight" and "Vigil". Acoustic parameters: DUR = duration; MPF = maximum peak frequency; MFF = maximum fundamental frequency; MIF = minimum frequency; MAF = maximum frequency.
Figure 3 in Vocal repertoire and group-specific signature in the Smooth-billed Ani, Crotophaga ani Linnaeus, 1758 (Cuculiformes, Aves)
Figure 3. Spectrograms of the ten types of vocalizations of the Smooth-billed Ani: "Ahnee" (A), "Whine" (B, C, D, E, F and G), "Pre-flight" (H), "Shout" (I), "Flight" (J and K), "Hoot" (L), "Grunt" (M), "Ee-oo-ee" (N), "Vigil" (O),"INR" (P and Q).
Figure 1 in Vocal repertoire and group-specific signature in the Smooth-billed Ani, Crotophaga ani Linnaeus, 1758 (Cuculiformes, Aves)
Figure 1. Location of the studied groups of Smooth-billed Ani in the municipality of Alegre, ES, Brazil.
A small vocal repertoire during the breeding season expresses complex behavioral motivations and individual signature in the Common Coot
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Fig. 4 in Acoustic communication in the Lusitanian toadfish, Halobatrachus didactylus: evidence for an unusual large vocal repertoire
Fig. 4. Long grunt trains (LGT) were significantly longer (A), were made up of more grunts (B), but had similar grunt periods (C) than grunt trains (GT). Grunts in LGT had similar duration (D) and dominant frequency (F), but had a higher number of pulses (E) than grunts in GT. Medians and quartiles are depicted. Mann–Whitney tests,, P, 0.001;, P, 0.01.
Fig. 3 in Acoustic communication in the Lusitanian toadfish, Halobatrachus didactylus: evidence for an unusual large vocal repertoire
Fig. 3. During the present study, nesting Lusitanian toadfish males emitted sounds that have not previously been described such as triple croaks (A), long grunt trains (B) and combinations of long grunt trains with other sound types (C). In (C) a long grunt train (thin line) combines with a grunt train (double line) that ends in a croak (thick line), which blends into a boatwhistle (dashed line), which is then followed by another long grunt train (thin line). Note that in (C), the LGT is hardly visible in the oscillogram due its much lower amplitude than the other sounds. Sonograms used a 30 Hz filter bandwidth.
Fig. 2 in Acoustic communication in the Lusitanian toadfish, Halobatrachus didactylus: evidence for an unusual large vocal repertoire
Fig. 2. Mean percentage of the different sound types emitted per hour by 16 nesting males during one week in the peak of the breeding season.
FIGURE 10 in Larval morphology and complex vocal repertoire of Rhacophorus helenae (Anura: Rhacophoridae), a rare flying frog from Vietnam
FIGURE 10. Proportions (%) of simple tonal calls (black) and other call types (grey) in the three parts of a natural calling session of Rhacophorus helenae containing 15 call series.
FIGURE 9 in Larval morphology and complex vocal repertoire of Rhacophorus helenae (Anura: Rhacophoridae), a rare flying frog from Vietnam
FIGURE 9. Proportions (%) of different calls types in the initial and final (A) and middle (B) parts of all series within the recorded calling session of Rhacophorus helenae.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.