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Figure 1 from: Carbajal-Márquez RA, Díaz-Gamboa LF, Ramírez-Valverde T, García-Balderas CM, Nahuat-Cervera PE, Cedeño-Vázquez JR (2019) Description of the male of Craugastor yucatanensis (Lynch, 1965) (Anura, Craugastoridae), its advertisement call, and additional data on females. ZooKeys 900: 129-139. https://doi.org/10.3897/zookeys.900.37591
Figure 1 Male of Craugastor yucatanensis (ECO-CH-H-4542) view inside of mouth (A) in dorsal aspect (B) ventral view of hand (C) and ventral view of foot (D). Photos by Humberto Bahena Basave.
Figure 1 from: Hardy H, Knapp S, Allan EL, Berger F, Dixey K, Döme B, Gagnier P-Y, Frank J, Haston EM, Holstein J, Kiel S, Marschler M, Mergen P, Phillips S, Rabinovich R, Sanchez Chillón B, Sorensen MV, Thines M, Trekels M, Vogt R, Wilson S, Wiltschke-Schrotta K (2020) SYNTHESYS+ Virtual Access - Report on the Ideas Call (October to November 2019). Research Ideas and Outcomes 6: e50354. https://doi.org/10.3897/rio.6.e50354
Figure 1 Indicative proposal flow for SYNTHESYS+ Virtual Access proposals. Blue boxes indicated proposer(s) actions/responsibilities; violet boxes VA coordinator actions/responsibilities; green boxes indicate where communication and discussion between proposers and VA coordinators will be paramount; white (no fill) boxes indicate post-proposal actions. Double-ended arrows indicate where back-and-forth will occur between proposer(s) and VA coordinator(s).
Significant baseflow reduction in the Sao Francisco River Basin, Brazil. Is it time to call for attention on groundwater withdrawals?
<p>Hydrometeorological and irrigation demand dataset used in the manuscript "Significant baseflow reduction in the Sao Francisco River Basin, Brazil. Is it time to call for attention on groundwater withdrawals?", submitted to Hydrological Processes.</p> <p>Streamflow records was downloaded from the Brazilian Water Agency (ANA, in portuguese) gauge stations.</p> <p>Precipitation dataset is derived from Xavier et al. (2016) (https://doi.org/10.1002/joc.4518).</p> <p>Evapotranspiration and Potential Evapotranspiration dataset is derived from Global Land Evaporation Amsterdam Model (GLEAM).</p> <p>Terrestrial Water Storage is derived from Gravity Recovery and Climate Experiment (GRACE) satellite - Jet Propulsion Laboratory (JPL) Release 06 (RL06) mascons solution.</p>
Simulated read data analysed in "Removing reference bias and improving indel calling in ancient DNA data analysis by mapping to a sequence variation graph"
<p>Simulated read data analyzed in "Removing reference bias and improving indel calling in ancient DNA data analysis by mapping to a sequence variation graph".</p> <p><strong>1) Human sequence data</strong></p> <p><strong>HO_chr11_50bp_sliding_window*fq.gz:</strong><br> All possible 50 bp reads overlapping chromosome 11 SNPs in the Human Origins dataset. Files with the word "alternate" in their filename carry the alternate allele, otherwise, they carry the reference allele. Deamination has been added into these simulated reads using gargammel (Renaud 2016) based on empirically estimated post-mortem damage in a dataset of 102 ancient genomes (Allentoft et al., 2015).</p> <p><strong>2) microbial data</strong></p> <p><strong>simulation_*_s.fq.gz:</strong><br> Simulated microbial read data from a set of microbial reference genomes identified in the ancient Clovis genome (Rasmussen 2014), using gargammel.</p>
Calling song of Gryllotalpa stepposa Zhantiev, 1991 (Orthoptera: Gryllotalpidae)
<p>male calling signal of Gryllotalpa stepposa Zhantiev, 1991, field recording, kassette recorder Sony, air temperature at recodring 34oC. Recordist V.Chukanov, Checheno-Ingushetia, USSR, near Grozny, 1986</p>
Data from: Helping decisions and kin recognition in long-tailed tits: is call similarity used to direct help towards kin?
<p>Most cooperative breeders live in discrete family groups, but in a minority, breeding populations comprise extended social networks of conspecifics that vary in relatedness. Selection for effective kin recognition may be expected for individuals in such kin neighbourhoods to maximise indirect fitness. Using a long-term social pedigree, molecular genetics, field observations and acoustic analyses, we examine how vocal similarity affects helping decisions in the long-tailed tit <i>Aegithalos caudatus</i>. Long-tailed tits are cooperative breeders in which help is typically redirected by males that have failed in their own breeding attempts towards the offspring of male relatives living within kin neighbourhoods. We identify a positive correlation between call similarity and kinship suggesting that vocal cues offer a plausible mechanism for kin discrimination. Furthermore, we show that failed breeders choose to help males with calls more similar to their own. However, although helpers fine-tune their provisioning rates according to how closely related they are to recipients, their effort was not correlated with their vocal similarity to helped breeders. We conclude that although vocalisations are an important part of the recognition system of long-tailed tits, discrimination is likely to be based on prior association and may involve a combination of vocal and non-vocal cues.</p>
First use of acoustic calls to distinguish cryptic fish species: Dascyllus aruanus complex as a case study
From a practical point of view, the determination of species in the wild is based on their phenotypes. Consequently, many species remain unknown because they are visually indistinguishable from described species. Although molecular methods and advances in bioacoustical analysis have been extensively used to uncover cryptic species, the combination of both methodologies is still rare and concerns only some terrestrial taxa such as insects, bats, frogs and birds. In this study, we aim to determine whether the sounds produced by different populations of fish can also be a tool to distinguish and identify cryptic species. The humbug damselfish complex, Dascyllus aruanus, is widely distributed across the Indo-Pacific Ocean and, since 2019, is thought to be composed of at least two species with Dascyllus aruanus in the Pacific Ocean and Dascyllus abudafur in the Indian Ocean. Recordings were made over a large geographical area with populations from Madagascar (Indian Ocean), Taiwan (Pacific Ocean) and French Polynesia (Society Islands). Two kinds of sounds were used for analysis: sounds associated with conspecific chases, and sounds produced during the "signal jump" of courtship behaviour. The sounds associated with signal jumps differ geographically. Acoustic feature differences between Taiwan and Madagascar align with the existence of genetic differences confirming specific status and supporting for the first time that sounds can help to discriminate cryptic species in Teleosts. However, differences in both acoustic features and genetic data can also be found between Taiwan and French Polynesia suggesting two clearly distinct populations. Using the same reasoning, we propose to resurrect the epithet "emamo" (Lesson 1830) for the Society Island humbug damselfish. Interestingly, sounds associated to conspecific chases are more variable than sounds related to signal jumps, suggesting that there are more constraints on sounds related to courtship since they would serve as indicators for species identity and contribute to premating isolation.
Anuran accents: Continental-scale citizen science data reveal spatial and temporal patterns of call variability
<p>Data and code associated with the 2020 publication in Ecology and Evolution (doi:10.1002/ece3.6833).</p>
Figure 2 from: Emmrich M, Vences M, Ernst R, Köhler J, Barej MF, Glaw F, Jansen M, Rödel M-O (2020) A guild classification system proposed for anuran advertisement calls. Zoosystematics and Evolution 96(2): 515-525. https://doi.org/10.3897/zse.96.38770
Figure 2 Key to anuran advertisement call guilds (compare text); each guild illustrated by schematic waveform and spectrogram.
Figure 3 from: Emmrich M, Vences M, Ernst R, Köhler J, Barej MF, Glaw F, Jansen M, Rödel M-O (2020) A guild classification system proposed for anuran advertisement calls. Zoosystematics and Evolution 96(2): 515-525. https://doi.org/10.3897/zse.96.38770
Figure 3 Examples for all different anuran advertisement call guilds (A–H), with a time scale of 0 to 1 s on x-axis and frequency scale of 0 to 8 kHz on y-axis (compare text). Guild A) non-frequency modulated, non-pulsed simple call (Bombina bombina; Bombinatoridae; dfrq/ms = 0.00 Hz/ms) (based on Schneider 2005); Guild B) frequency modulated, non-pulsed simple call (Leptodactylus fuscus; Leptodactylidae; dfrq/ms = 7.22 Hz/ms) (based on Márquez et al. 2002); Guild C) non-frequency modulated pulsed call (Hyla meridionalis; Hylidae; dfrq/ms = 0.67 Hz/ms) (based on Masó and Pijoan 2011); Guild D) frequency modulated pulsed call (Hyperolius pickersgilli; Hyperoliidae; dfrq/ms = 2.32 Hz/ms) (based on Du Preez and Carruthers 2009); Guild E) non-frequency modulated call with uniform notes (Sclerophrys mauritanica; Bufonidae; dfrq/ms = 0.31 Hz/ms) (based on Masó and Pijoan 2011); Guild F) frequency modulated call, with uniform notes (Pseudopaludicola boliviana; Leptodactylidae; dfrq/ms = 2.38 Hz/ms) (based on Márquez et al. 2002); Guild G) non-frequency modulated complex call (Smilisca sila; Hylidae; dfrq/m = 0.43 Hz/ms) (based on Ibanéz 1999); Guild H) frequency modulated complex call (Hyperolius nasutus; Hyperoliidae; dfrq/ms = 1.66 Hz/ms) (based on Du Preez and Carruthers 2009).
Figure 1 from: Emmrich M, Vences M, Ernst R, Köhler J, Barej MF, Glaw F, Jansen M, Rödel M-O (2020) A guild classification system proposed for anuran advertisement calls. Zoosystematics and Evolution 96(2): 515-525. https://doi.org/10.3897/zse.96.38770
Figure 1 Basic types of anuran vocalizations based on their temporal structure, shown as schematic waveforms, modified after Littlejohn (2001): (a) non-pulsed call, (b) pulsed call, (c) call with uniform pulsed notes, (d) complex call containing different note types, and (e) two complex calls in a call series. Black arrows mark inter-note intervals and red arrow marks inter-call interval.
Supplementary material 1 from: Emmrich M, Vences M, Ernst R, Köhler J, Barej MF, Glaw F, Jansen M, Rödel M-O (2020) A guild classification system proposed for anuran advertisement calls. Zoosystematics and Evolution 96(2): 515-525. https://doi.org/10.3897/zse.96.38770
Database for the definition of anuran call guilds
Automated detection of Hainan gibbon calls for passive acoustic monitoring
<p>Data accompanying the paper: "Automated detection of Hainan gibbon calls for passive acoustic monitoring"</p> <p><strong>Please cite this dataset as:</strong></p> <blockquote> <p>Dufourq, Emmanuel and Durbach, Ian and Hansford, James and Hoepfner, Amanda and Ma, Heidi and Bryant, Jessica and Stender, Christina and Li, Wenyong and Liu, Zhiwei and Chen, Qing and Zhou, Zhaoli and Turvey, Samuel. (2020). Automated detection of Hainan gibbon calls for passive acoustic monitoring. BioRxiv doi: https://doi.org/10.1101/2020.09.07.285502</p> </blockquote> <p>The Hainan gibbon is the world's rarest primate and one of the world's rarest mammals, with only a single population of about 30 individuals surviving in Bawangling National Nature Reserve (BNNR), Hainan, China. Eight Song Meter SM3 recorders (Wildlife Acoustics, Maynard, Massachusetts) were used to collect acoustic data from 1 March to 20 August 2016 within BNNR. Recorders were attached to trees at approximately 1.5 meters from the ground in tropical evergreen forest. Recorders were set to record for eight hours each day from the time of sunrise, which varied between approximately 05:00 and 06:00 during the study period. Devices did not record continuously throughout the entire survey period due to logistical and technical issues; in total, survey days per recorder varied between 79 and 129 days, and roughly 6,000 hours of recordings were collected. The majority of recordings were made with a sampling rate of 9,600Hz and bit depth of 16, with isolated recordings at 28,800Hz.</p> <p>We provide the audio data (.wav) used to train and test our neural network classifier along with the corresponding labelled text files (.data).</p> <p><strong>Files provided</strong></p> <p>Train.zip - contains the training .wav audio files</p> <p>Train_Labels.zip - contains the labels for the training data</p> <p>Test.zip - contains the testing .wav audio files</p> <p>Test_Labels.zip - contains the labels for the test data</p> <p>Extra_Labelled_Data.zip - contains extra data that was labelled and non-gibbon calls used for training</p> <p>Extra_Labels.zip - contains the labels for the extra labelled data</p> <p>Unlabelled_Data.zip - contains additional .wav audio files which have not been labelled. These are split into various files (1-15) and can be downloaded individually.</p> <p>Code.zip - contains all the software scripts and notebooks</p> <p>Manual-zip - contains the user manual</p> <p><strong>Labels</strong></p> <p>The names of the labelled files start with either "g_" or "n_", for example "g_HGSM3D_0+1_20160429_051600.data" and "n_HGSM3D_0+1_20160429_051600.data". Files starting with "g_" contain the timestamps of the gibbon calls, and files starting with "n_" contain the timestamps of non-gibbon calls (e.g. background noise and bird calls). An audio file will have both a "g_" and "n_" file. Each file has the following format: Start,End,Duration,Type,Notes, where "start" denotes the start time in seconds, "end" denotes the end time in seconds, "duration" denotes the duration (end - start) in seconds, "type" denotes the type of call/noise and "notes" are additional notes which we labelled.</p> <p><strong>Types</strong></p> <p>The legend for the "type" column in the labelled files is defined as follows. The types for gibbon and non-gibbon files are different and we distinguish this below.</p> <p> </p> <p>Gibbon files ("g_")</p> <p>type 1 = one pulse gibbon call</p> <p>type 2 = multiple pulse gibbon call (check "notes column" below)</p> <p>type 3 = duet gibbon call</p> <p> </p> <p><em>Notes column (only available in gibbon files)</em></p> <p>One of the following: 2 pulse call, 3 pulse call, 4 pulse call, 5 pulse call or 6 pulse call.</p> <p> </p> <p>Non-gibbon files ("n_")</p> <p>type 1 = rain</p> <p>type 2 = other species (e.g. birds)</p> <p>type 3 = rain and other species</p> <p>type 4 = rain and external noise (e.g. aircraft)</p> <p>type 5 = natural sounds and external noise</p> <p>type 6 = natural sounds and other species</p> <p><strong>Training files (containing gibbon calls):</strong></p> <p>HGSM3AC_0+1_20160309_055600<br> HGSM3AC_0+1_20160312_055400<br> HGSM3A_0+1_20160304_060000<br> HGSM3BD_0+1_20160305_060000<br> HGSM3AC_0+1_20160314_055200<br> HGSM3B_0+1_20150616_050500<br> HGSM3BD_0+1_20160402_053600<br> HGSM3D_0+1_20160429_051600<br> HGSM3B_0+1_20160305_060000<br> HGSM3C_0+1_20160501_051500<br> HGSM3SOL_0+1_20160320_054700<br> HGSM3SOL_0+1_20160405_053400<br> HGSM3BD_0+1_20160401_053700</p> <p><strong>Testing files:</strong></p> <p>HGSM3B_0+1_20160323_054500<br> HGSM3B_0+1_20160321_054700<br> HGSM3B_0+1_20160306_055900<br> HGSM3B_0+1_20160308_055700<br> HGSM3B_0+1_20160309_055600<br> HGSM3B_0+1_20160316_055100<br> HGSM3B_0+1_20160311_055500<br> HGSM3B_0+1_20160304_060000<br> HGSM3B_0+1_20160322_054600</p>
[NGI Explorers | Open Call 2] Webinars
<p>Webinar series providing information about the Open Call 2</p>
An endangered bird calls less when invasive birds are calling
<p>Novel noises can affect various animal behaviours, and changes to vocal behaviour are some of the most documented. The calls of invasive species are an important source of novel noise, yet their effects on native species are poorly understood. We examined the effects of invasive bird calls on the vocal activity of an endangered Australian finch to investigate whether: (i) native finch calling behaviour was affected by novel invasive bird calls, and (ii) the calls of the finches overlapped in frequency with those of invasive birds. We exposed a wild population of Black-throated Finch southern subspecies (<i>Poephila cincta cincta</i>) to the vocalisations of two invasive birds, Nutmeg Mannikins (<i>Lonchura punctulata</i>) and Common Mynas (<i>Acridotheres tristis</i>), a synthetic 'pink' noise, and a silent control. To determine whether the amount of Black-throated Finch calling differed in response to treatments, we recorded and quantified Black-throated Finch vocalisations, and assessed the amount of calling using a generalised linear mixed model followed by pairwise comparisons. We also measured, for both Black-throated Finches and the stimulus noises: dominant, minimum and maximum frequency, and assessed the degree of frequency overlap between Black-throated Finch calls and stimulus noises. Compared to silent controls, Black-throated Finches called less when exposed to Common Myna calls and pink noise, but not to Nutmeg Mannikin calls. We also found that pink noise overlapped most in frequency with Black-throated Finch calls. Common Myna calls also somewhat overlapped the frequency range of Black-throated Finch calls, whereas Nutmeg Mannikin calls overlapped the least. It is possible that masking interference is the mechanism behind the reduction in calling in response to Common Myna calls and pink noise, but more work is needed to resolve this. Regardless, these results indicate that the calls of invasive species can affect the behaviour of native species, and future research should aim to understand the scope and severity of this issue.</p>
Calling in the face of danger: do nestling Red-winged Blackbirds (Agelaius phoeniceus) suppress begging in response to predator playbacks?
<p>Nest predation is the most frequent cause of nest failure in birds such as the Red-winged Blackbird (<i>Agelaius phoeniceus</i>) that nest on or near the substrate. Nestlings should therefore exhibit adaptations to reduce the risk of nest predation. We tested the nestling antipredator hypothesis by examining the begging responses of Red-winged Blackbird nestlings to vocalizations of: (1) an important nest predator (American Crow, <i>Corvus brachyrhynchos</i>)<i>,</i> (2) a<i> </i>predator that rarely preys on nestlings (Cooper's Hawk, <i>Accipiter cooperii</i>), and (3) a nonpredator (Northern Flicker, <i>Colaptes auratus</i>). We performed playbacks with: (1) both parents present at the nest, (2) male at the nest, and (3) neither parent present. Following playback, we measured duration of nestling begging after the parent departed (begging persistence), bouts of otherwise normal begging when no parent was present (parent-absent begging), and calling without postural components of begging (nonpostural begging). When the male or both parents were present during playback, adults responded with alarm calls and nestlings significantly reduced parent-absent begging following American Crow and Cooper's Hawk playbacks. Nonpostural begging was significantly reduced following Cooper's Hawk playback, but there were no significant differences in the other begging variables. When neither parent was present, we found no significant differences in nonpostural begging in response to the three playback types, but parent-absent begging was significantly reduced following American Crow and Cooper's Hawk playbacks when compared to Northern Flicker playbacks. These results show that nestlings suppress their vocal begging in response to calls of predators including Cooper's Hawks even though they are not common nest predators.</p>
Treefrogs adjust their acoustic signals in response to harmonics structure of intruder calls
<p>Spectral properties of animal acoustic signals may help individuals to assess the characteristics of rivals and to adjust their competitive strategies in territorial disputes. Thus, we hypothesize that the distribution of energy across frequency bands in anuran calls determine behavioral responses in male-male competition. Using playback experiments, we investigated the relevance of the harmonic calls in the acoustic communication of the treefrog Dendropsophus minutus. We exposed territorial males to three synthetic acoustic stimuli composed of aggressive notes: (1) standard call (all harmonics and peak frequency corresponding to the second band); (2) inverted-energy call (all harmonics and peak frequency corresponding to the first band); and (3) concentrated-energy call (all energy contained in the second harmonic). Males responded aggressively to all stimuli, mainly by increasing the rate and duration of their aggressive notes. However, when exposed to stimuli with different harmonic configurations, males changed the harmonic structure of their own calls, emitting more A- and B-notes with peak power in the fundamental frequency, particularly when exposed to the concentrated-energy call. Our results suggest that male frogs may use the harmonic structure of calls to assess opponents and modulate territorial and aggressive behavior, triggering complex acoustic adjustments. This study contributes to our knowledge about the functions of acoustic traits in amphibian social interactions, and particularly of the presence of harmonics that has received less attention compared to other acoustic properties in the study of animal acoustic communication.</p>
Figure 5 from: Olimpio de Souza A, Oliveira SR, Proto Dias G, Bastos RP, Ribeiro de Morais A (2020) Annual and daily patterns of calling activity in male Scinax fuscomarginatus (Anura: Hylidae) from Central Brazil. Zoologia 37: 1-6. https://doi.org/10.3897/zoologia.37.e54148
Figure 5 Nocturnal variation in the calling activity (number of advertisement calls per minute) of the Scinax fuscomarginatus population in the municipality of Rio Verde, Goiás state, Brazil. The mean and standard error (SE) were estimated based on the recordings from all the study ponds (2013–2014).
Figures 3-4 from: Olimpio de Souza A, Oliveira SR, Proto Dias G, Bastos RP, Ribeiro de Morais A (2020) Annual and daily patterns of calling activity in male Scinax fuscomarginatus (Anura: Hylidae) from Central Brazil. Zoologia 37: 1-6. https://doi.org/10.3897/zoologia.37.e54148
Figures 3-4 (3) Monthly precipitation (gray bars), minimum air temperature (dashed line), and maximum air temperature (solid line) recorded between November 2013 and October 2014 in the municipality of Rio Verde, Goiás state, Brazil. (4) The mean number of advertisement calls (calling activity) recorded per minute (white bars) per day at all the study ponds.
Figures 6-8 from: Olimpio de Souza A, Oliveira SR, Proto Dias G, Bastos RP, Ribeiro de Morais A (2020) Annual and daily patterns of calling activity in male Scinax fuscomarginatus (Anura: Hylidae) from Central Brazil. Zoologia 37: 1-6. https://doi.org/10.3897/zoologia.37.e54148
Figures 6-8 Results of the Generalized Additive Model for the relationship between calling activity and (6) minimum and (7) maximum air temperatures and (8) precipitation. The inner tick marks on the x axes represent the raw values of the climatic variables. The shaded area indicates the 95% confidence interval. (Tmin) Minimum air temperature, (Tmax) maximum air temperature, (Prec) precipitation.
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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)
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.