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FIGURE 5 in An inordinate fondness for inconspicuous brown frogs: integration of phylogenomics, archival DNA analysis, morphology, and bioacoustics yields 24 new taxa in the subgenus Brygoomantis (genus Mantidactylus) from Madagascar
FIGURE 5. Maximum-Likelihood tree based on a partitioned analysis of 12,818 nuclear-encoded markers obtained via the FrogCap strategy, calculated with IQ-tree, for 58 representative individuals of species-level lineages in the subgenus Mantidactylus (Brygoomantis). Note that three species are missing from this analysis (M. bletzae sp. nov., M. marintsoai sp. nov., and M. riparius sp. nov.) and the identity of the two samples of M. katae sp. nov. in this tree is uncertain. For those lineages that previously (Perl et al. 2014; Vieites et al. 2009) had candidate species numbers assigned, these are reported in parenthesis after the name used in the classification proposed herein. All branches were fully supported by SH-like approximate likelihood ratio tests with 1000 pseudoreplicates (100% support, symbolized by black dots at nodes). The tree was rooted with Mantidactylus grandidieri (subgenus Mantidactylus) as outgroup (removed from graphical representation for better visualization of ingroup relationships), with the inclusion of M. melanopleura (subgenus Chonomantis) as hierarchical outgroup. Red arrows in the M. curtus clade indicate the two reticulation events detected by a Phylonetworks analysis performed separately for this clade (note that the topology recovered by this analysis in the Phylonetworks analysis differs in the position of M. bourgati; see original results of the Phylonetworks analysis in Zenodo repository, DOI 10.5281/zenodo.668741
FIGURE 2 in An inordinate fondness for inconspicuous brown frogs: integration of phylogenomics, archival DNA analysis, morphology, and bioacoustics yields 24 new taxa in the subgenus Brygoomantis (genus Mantidactylus) from Madagascar
FIGURE 2. Maximum Likelihood tree of 1305 sequences of a fragment of the 16S rRNA gene (alignment length 519 bp) from a RAxML analysis. Numbers at nodes are bootstrap proportions in percent (100 ML fast bootstrap replicates); not shown if <50%. A sequence of Mantidactylus melanopleura was used as the outgroup (removed from the tree after analysis for better graphical representation). Note that some of the 'Museomics' sequences (obtained by targeted capture from historical type specimens) are represented twice, after assembly with different reference sequences. Some sequences in the analysis (several 'Museomics' sequences as well as others obtained from Illumina sequencing) only partially covered the fragment analysed (226 sequences <300 bp). We emphasize that this tree is based on a single short mitochondrial marker and therefore is unlikely to represent the deep relationships among lineages correctly; for such relationships, refer to the phylogenomic tree (Fig. 5).
FIGURE 1 in An inordinate fondness for inconspicuous brown frogs: integration of phylogenomics, archival DNA analysis, morphology, and bioacoustics yields 24 new taxa in the subgenus Brygoomantis (genus Mantidactylus) from Madagascar
FIGURE 1. Graphic scheme indicating (a) regions of the body and (b) morphometrics measurements of Mantidactylus specimens of the subgenus Brygoomantis (exemplified by a specimen of M. ulcerosus), as referred to in the descriptions of morphology and tables throughout the manuscript. Labels in (a) refer to regions of the body and not necessarily to anatomical features. Femoral gland terminology as explained in the text. Measurement abbreviations in (b) are explained in the text; FORL (stretched forelimb length), HIL (stretched hindlimb length), and FOTL (foot length including tarsus) are not shown.
FIGURE 3 in An inordinate fondness for inconspicuous brown frogs: integration of phylogenomics, archival DNA analysis, morphology, and bioacoustics yields 24 new taxa in the subgenus Brygoomantis (genus Mantidactylus) from Madagascar
FIGURE 3. Distribution of uncorrected pairwise genetic distances for a fragment of the mitochondrial 16S rRNA gene, in an alignment of 976 sequences, complete or almost complete for 488 bp.
DNA sequence and bioacoustic data of the Guibemantis liber complex from Madagascar (Amphibia, Mantellidae)
<p>Data from a taxonomic revision of the Guibemantis liber complex from Madagascar. The following data are included:</p> <p>- Advertisement call recordings of Guibemantis liber, G. razoky and G. razandry from different localities in wav format. See associated publication for metadata.</p> <p>- A table in Excel format with all DNA sequences used, metadata of the respective samples and specimens, and Genbank accession numbers.</p>
Hainan gibbon (Nomascus hainanus) bioacoustics dataset for machine learning
<p>Data accompanying the paper: "<strong>Empowering Deep Learning Acoustic Classifiers with Human-like Ability to Utilize Contextual Information for Wildlife Monitoring</strong>"</p> <p>We provide the audio data (.wav) used to test our neural network classifier along with the corresponding labelled text files (.svl). The audio and labelled files can easily be viewed in Sonic Visualiser. Drag and drop the audio file. Create the spectrogram layer. Drag and drop the corresponding .svl file.</p> <p>The dataset provided here is a subset of the full dataset provided here: 10.5281/zenodo.3991714. This dataset has additional files that were manually annotated, which were not manually verified in the original version (10.5281/zenodo.3991714).</p> <p><strong>Files provided</strong></p> <ul> <li><strong>Audio_x.zip</strong> -- we provide x number of .zip files containing audio files, numbered 1 to 4. These were created in batches to simplify downloads.</li> <li><strong>Annotations.zip -</strong>- .svl files which contain the manually verified labels. These files can be read in Sonic Visualiser, or as .XML files in a programming language. We took care to annotate the start and stop time of each gibbon call. The height of each bounding box is not important as the frequency range of Hainan gibbons is already known.</li> <li><strong>model_weights_tensorflow.hdf5 </strong>-- the Tensorflow model. Load the model using: model = tf.keras.models.load_model(model_filepath) note that the model expects a three channel input as explained in the research article.</li> </ul>
F0 estimation for bioacoustics: A benchmark/training dataset of non-human vocalisations with annotated frequency contours
Open the record for dataset details and reuse information.
Data from: Interactive bioacoustic playback as a tool for detecting and exploring nonhuman intelligence: "Conversing" with an Alaskan humpback whale
<p>Here we report on a rare and opportunistic acoustic turn-taking with an adult female humpback whale, known as Twain, in Southeast Alaska. Post hoc acoustic and statistical analyses of a 20-minute acoustic exchange between the broadcast of a recorded contact call, known as a 'whup/throp', with call responses by Twain revealed an intentional human-whale acoustic (and behavioral) interaction. Our results show that Twain participated both physically and acoustically in three phases of interaction (Phase 1: Engagement, Phase 2: Agitation, Phase 3: Disengagement), independently determined by blind observers reporting on surface behavior and respiratory activity of the interacting whale. A close examination of both changes to the latency between Twain's calls and the temporal matching to the latency of the exemplar across phases indicated that Twain was actively engaged in the exchange during Phase 1 (Engagement), less so during Phase 2 (Agitation), and disengaged during Phase 3 (Disengagement). These results, while preliminary, point to several for effective playback design, namely the importance of salient, dynamic and adaptive playbacks, that should be utilized in experimentation with whales and other interactive nonhuman species.</p>
Data from: Large-scale bioacoustic monitoring to elucidate the distribution of a non-native katydid
<p>For animals that produce species-specific audible sounds, environmental recordings combined with automated acoustic monitoring software (passive acoustic monitoring, PAM) may be an effective monitoring tool because it allows audio data from many, widely distributed autonomous recording units (ARUs) to be processed in a relatively short period of time. Males of many insect species produce loud, species-specific mating songs, yet acoustic insects have received less attention from PAM relative to vertebrates.</p> <p>We evaluated the use of PAM to monitor, <em>Roeseliana roeselii</em> (Orthoptera, Tettigoniidae), an acoustic insect that has expanded its range to Alberta, Canada far outside its naturalized North American range. We analyzed environmental recordings from ARUs: 1) at two control sites known to be occupied by <em>R. roeselii</em>, and 2) across Alberta established by the Alberta Biodiversity Monitoring Institute (ABMI) to search for new populations.</p> <p>PAM successfully detected <em>R. roeselii</em> at the two control sites, but not at any of the 74 ABMI sites that we analyzed. Despite the failure to detect new locations of <em>R. roeselii</em>, our analysis of ABMI environmental recordings detected several other species of acoustic insects, including <em>Orchelimum gladiator</em>, <em>Gryllus</em> sp. and <em>Allonemobius</em> spp.</p> <p>Our results add to the growing body of work showing the feasibility of using PAM for acoustic insects. We make suggestions for how to maximize the effectiveness of this monitoring tool for the conservation and management of singing insects in North America.</p>
Bioacoustic data for evaluating wind denoising and detection methods
<p>Acoustic survey data accompanying publication "Wind-robust sound event detection and denoising for bioacoustics", by Julius Juodakis and Stephen Marsland.</p> <p>Contents (see the publication for details):</p> <ul> <li>pilotdata/: short audio files used for selecting the best-fit wind model</li> <li>surveys/LSK_additional_wavs/: remaining recordings from the little spotted kiwi survey that have not been deposited previously</li> <li>surveys/rawannots/: annotations produced by the unadjusted, OLS-adjusted and QR-adjusted detectors, for two bird surveys</li> <li>surveys/reviewed/: annotations passing human review for the unadjusted and OLS-adjusted detectors, for two bird surveys</li> <li>denoising/: examples of noise and signal which were mixed to evaluate the denoising.</li> </ul> <p>Audio files are provided in WAV PCM format. Annotations and filters are intended for use with AviaNZ software (https://www.avianz.net/), and provided in AviaNZ-compatible JSON format.</p>
DCASE 2022 Task 5: Few-shot Bioacoustic Event Detection Evaluation Set
<p><strong>General Description</strong></p> <p>The evaluation set for task 5 of DCASE 2022 "Few-shot Bioacoustic Event Detection" consists of 46 audio files acquired from different bioacoustic sources. </p> <p>The first 5 annotations are provided for each file, with events marked as positive (POS) for the class of interest. </p> <p>This dataset is to be used for evaluation purposes during the task</p> <p><strong>Folder Structure</strong></p> <p><em>Evaluation_Set.zip</em></p> <p> |___DC/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___CT/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___CHE/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___MGE/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___MS/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___QU/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> </p> <p><em>Evaluation_Set_5shots.zip</em> has the same structure but contains only the *.wav files.</p> <p><em>Evaluation_Set_5shots_annotations_only.zip</em> has the same structure but contains only the *.csv files</p> <p>The subfolders denote different recording sources and there may or may not be overlap between classes of interest from different wav files.</p> <p><strong>Annotation structure</strong></p> <p>Each line of the annotation csv represents an event in the audio file. The column descriptions are as follows:<br> [ Audiofilename, Starttime, Endtime, Q ]</p> <p><strong>Development Set</strong></p> <p>The development set for the same task can be found at: <a href="http://doi.org/10.5281/zenodo.4543504">https://doi.org/10.5281/zenodo.6012309</a></p> <p><strong>Open Access</strong></p> <p>This dataset is available under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.<br> </p> <p><strong>Contact info</strong></p> <p>Please send any feedback or questions to:<br> Ines Nolasco: i.dealmeidanolasco@qmul.ac.uk</p>
Carsington Water bioacoustics project - wav files
<p>wav files recorded on Wildlife Acoustics SM2 recorders.</p> <p>Data from four recorders placed on land at northeast side of Carsington Water in April/May 2018.</p> <p>Locations of recorders: </p> <p><strong>latitude longitude site_name unit microphones</strong></p> <p>53.0748524 -1.6096798 Reedbed 8621 Two</p> <p>53.0711544- 1.6118927 Plantation mixed 8535 Two</p> <p>53.0668604 -1.6170512 Scrub 8607 Two</p> <p>53.0703651 -1.6140515 Woodland 8552 Two</p>
Bioacoustic monitoring reveals shifts in breeding songbird populations and singing behaviour with selective logging in tropical forests
<b>Description: </b><p>Counts of individual male songbirds, males and females, songs and duets and original WAV audio recordings used to generate them</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/131"><b>Population and behavioral responses of songbirds to logging and rain forest fragmentation</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3366104">here</a></p><p><b>Files: </b>This dataset consists of 13 files: Pillay_et_al_Songbirds_Acoustic_Counts_Vegetation_Cover.xlsx, 2013_B.zip, 2013_D.zip, 2013_E.zip, 2013_F.zip, 2013_OG1.zip, 2013_OG2.zip, 2014_B.zip, 2014_D.zip, 2014_E.zip, 2014_F.zip, 2014_OG1.zip, 2014_OG2.zip</p><p><b>Pillay_et_al_Songbirds_Acoustic_Counts_Vegetation_Cover.xlsx</b></p><p>This file contains dataset metadata and 5 data tables:</p><ol><li><p><b>CountsMale</b> (described in worksheet CountsMale)</p><p>Description: Counts of male individuals of songbird species</p><p>Number of fields: 12</p><p>Number of data rows: 5700</p><p>Fields: </p><ul><li><b>year</b>: Year of Survey (Field type: id)</li><li><b>ftype</b>: Forest Type (Field type: categorical)</li><li><b>block</b>: Unique ID of Blocks within which Sampling Plots are located (Field type: id)</li><li><b>location</b>: Unique ID of Sampling Plots (Field type: location)</li><li><b>fragment</b>: Future fragments within which sampling plots in logged forest plots were located; not applicable for unlogged forest plots (Field type: id)</li><li><b>species</b>: Species Identity (Field type: taxa)</li><li><b>day</b>: Days 1 to 2 of sampling in each plot (Field type: numeric)</li><li><b>date</b>: Date of Sampling (Field type: date)</li><li><b>jul.date</b>: Julian Date of Sampling (Field type: numeric)</li><li><b>time1-6AM</b>: Counts of male individuals for 6:00-6:05 AM; detection histories generated by collapsing counts to 1/0 data (Field type: numeric)</li><li><b>time2-7AM</b>: Counts of male individuals for 7:00-7:05 AM; detection histories generated by collapsing counts to 1/0 data (Field type: numeric)</li><li><b>time3-8AM</b>: Counts of male individuals for 8:00-8:05 AM; detection histories generated by collapsing counts to 1/0 data (Field type: numeric)</li></ul></li><li><p><b>CountsMaleFemale</b> (described in worksheet CountsMaleFemale)</p><p>Description: Counts of male plus female individuals of songbird species</p><p>Number of fields: 12</p><p>Number of data rows: 1000</p><p>Fields: </p><ul><li><b>year</b>: Year of Survey (Field type: id)</li><li><b>ftype</b>: Forest Type (Field type: categorical)</li><li><b>block</b>: Unique ID of Blocks within which Sampling Plots are located (Field type: id)</li><li><b>location</b>: Unique ID of Sampling Plots (Field type: location)</li><li><b>fragment</b>: Future fragments within which sampling plots in logged forest plots were located; not applicable for unlogged forest plots (Field type: id)</li><li><b>species</b>: Species Identity (Field type: taxa)</li><li><b>day</b>: Days 1 to 2 of sampling in each plot (Field type: numeric)</li><li><b>date</b>: Date of Sampling (Field type: date)</li><li><b>jul.date</b>: Julian Date of Sampling (Field type: numeric)</li><li><b>time1-6AM</b>: Counts of male and female individuals for 6:00-6:05 AM; detection histories generated by collapsing counts to 1/0 data (Field type: numeric)</li><li><b>time2-7AM</b>: Counts of male and female individuals for 7:00-7:05 AM; detection histories generated by collapsing counts to 1/0 data (Field type: numeric)</li><li><b>time3-8AM</b>: Counts of male and female individuals for 8:00-8:05 AM; detection histories generated by collapsing counts to 1/0 data (Field type: numeric)</li></ul></li><li><p><b>CountsSong</b> (described in worksheet CountsSong)</p><p>Description: Counts of songs</p><p>Number of fields: 9</p><p>Number of data rows: 2850</p><p>Fields: </p><ul><li><b>year</b>: Year of Survey (Field type: id)</li><li><b>ftype</b>: Forest Type (Field type: categorical)</li><li><b>block</b>: Unique ID of Blocks within which Sampling Plots are located (Field type: id)</li><li><b>location</b>: Unique ID of Sampling Plots (Field type: location)</li><li><b>fragment</b>: Future fragments within which sampling plots in logged forest plots were located; not applicable for unlogged forest plots (Field type: id)</li><li><b>species</b>: Species Identity (Field type: taxa)</li><li><b>day3-6AM</b>: Counts of songs for 6:00-6:05 AM on day 3 or third consecutive day of sampling in each plot (Field type: numeric)</li><li><b>day3-7AM</b>: Counts of songs for 7:00-7:05 AM on day 3 or third consecutive day of sampling in each plot (Field type: numeric)</li><li><b>day3-8AM</b>: Counts of songs for 8:00-8:05 AM on day 3 or third consecutive day of sampling in each plot (Field type: numeric)</li></ul></li><li><p><b>CountsDuet</b> (described in worksheet CountsDuet)</p><p>Description: Counts of duets</p><p>Number of fields: 9</p><p>Number of data rows: 500</p><p>Fields: </p><ul><li><b>year</b>: Year of Survey (Field type: id)</li><li><b>ftype</b>: Forest Type (Field type: categorical)</li><li><b>block</b>: Unique ID of Blocks within which Sampling Plots are located (Field type: id)</li><li><b>location</b>: Unique ID of Sampling Plots (Field type: location)</li><li><b>fragment</b>: Future fragments within which sampling plots in logged forest plots were located; not applicable for unlogged forest plots (Field type: id)</li><li><b>species</b>: Species Identity (Field type: taxa)</li><li><b>day3-6AM</b>: Counts of duets for 6:00-6:05 AM on day 3 or third consecutive day of sampling in each plot (Field type: numeric)</li><li><b>day3-7AM</b>: Counts of duets for 7:00-7:05 AM on day 3 or third consecutive day of sampling in each plot (Field type: numeric)</li><li><b>day3-8AM</b>: Counts of duets for 8:00-8:05 AM on day 3 or third consecutive day of sampling in each plot (Field type: numeric)</li></ul></li><li><p><b>VegetationCover</b> (described in worksheet VegetationCover)</p><p>Description: Vegetation cover data</p><p>Number of fields: 6</p><p>Number of data rows: 50</p><p>Fields: </p><ul><li><b>location</b>: Unique ID of Sampling Plots (Field type: location)</li><li><b>forest.type</b>: Forest Type (Field type: categorical)</li><li><b>udens</b>: Proportion understory cover (Field type: numeric)</li><li><b>cc</b>: Proportion canopy cover (Field type: numeric)</li><li><b>can.ht</b>: Average canopy height (Field type: numeric)</li><li><b>max.canopy</b>: Maximum height of standing vegetation (Field type: numeric)</li></ul></li></ol><p><b>2013_B.zip</b></p><p>Description: WAV files from 2013 for site B</p><p><b>2013_D.zip</b></p><p>Description: WAV files from 2013 for site D</p><p><b>2013_E.zip</b></p><p>Description: WAV files from 2013 for site E</p><p><b>2013_F.zip</b></p><p>Description: WAV files from 2013 for site F</p><p><b>2013_OG1.zip</b></p><p>Description: WAV files from 2013 for site OG1</p><p><b>2013_OG2.zip</b></p><p>Description: WAV files from 2013 for site OG2</p><p><b>2014_B.zip</b></p><p>Description: WAV files from 2014 for site B</p><p><b>2014_D.zip</b></p><p>Description: WAV files from 2014 for site D</p><p><b>2014_E.zip</b></p><p>Description: WAV files from 2014 for site E</p><p><b>2014_F.zip</b></p><p>Description: WAV files from 2014 for site F</p><p><b>2014_OG1.zip</b></p><p>Description: WAV files from 2014 for site OG1</p><p><b>2014_OG2.zip</b></p><p>Description: WAV files from 2014 for site OG2</p><p><b>Date range: </b>2013-04-09 to 2014-07-26</p><p><b>Latitudinal extent: </b>4.6881 to 4.7530</p><p><b>Longitudinal extent: </b>116.9477 to 117.6249</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div> -  Animalia <br> -  -  Chordata <br> -  -  -  Aves <br> -  -  -  -  Passeriformes <br> -  -  -  -  -  Timaliidae <br> -  -  -  -  -  -  <i>Stachyris</i> <br> -  -  -  -  -  -  -  <i>Stachyris maculata</i> <br> -  -  -  -  -  -  -  <i>Stachyris erythroptera</i> <br> -  -  -  -  -  -  -  <i>Stachyris poliocephala</i> <br> -  -  -  -  -  -  <i>Macronus</i> <br> -  -  -  -  -  -  -  <i>Macronus bornensis</i> <br> -  -  -  -  -  -  -  <i>Macronus ptilosus</i> (as synonym: <i>Macronous ptilosus</i>)<br> -  -  -  -  -  -  <i>Stachyridopsis</i> <br> -  -  -  -  -  -  -  <i>Stachyridopsis rufifrons</i> (as synonym: <i>Stachyris rufifrons</i>)<br> -  -  -  -  -  -  <i>Pomatorhinus</i> <br> -  -  -  -  -  -  -  <i>Pomatorhinus montanus</i> <br> -  -  -  -  -  Pellorneidae <br> -  -  -  -  -  -  <i>Trichastoma</i> <br> -  -  -  -  -  -  -  <i>Trichastoma bicolor</i> <br> -  -  -  -  -  -  <i>Alcippe</i> <br> -  -  -  -  -  -  -  <i>Alcippe brunneicauda</i> <br> -  -  -  -  -  -  <i>Pellorneum</i> <br> -  -  -  -  -  -  -  <i>Pellorneum capistratum</i> <br> -  -  -  -  -  -  <i>Malacocincla</i> <br> -  -  -  -  -  -  -  <i>Malacocincla malaccensis</i> <br> -  -  -  -  -  -  <i>Malacopteron</i> <br> -  -  -  -  -  -  -  <i>Malacopteron magnirostre</i> <br> -  -  -  -  -  -  -  <i>Malacopteron magnum</i> <br> -  -  -  -  -  -  -  <i>Malacopteron cinereum</i> <br> -  -  -  -  -  -  -  <i>Malacopteron affine</i> <br> -  -  -  -  -  Pycnonotidae <br> -  -  -  -  -  -  <i>Alophoixus</i> <br> -  -  -  -  -  -  -  <i>Alophoixus bres</i> <br> -  -  -  -  -  -  -  <i>Alophoixus phaeocephalus</i> <br> -  -  -  -  -  -  <i>Tricholestes</i> <br> -  -  -  -  -  -  -  <i>Tricholestes criniger</i> <br> -  -  -  -  -  -  <i>Iole</i> <br> -  -  -  -  -  -  -  <i>Iole olivacea</i> <br> -  -  -  -  -  -  <i>Pycnonotus</i> <br> -  -  -  -  -  -  -  <i>Pycnonotus atriceps</i> <br> -  -  -  -  -  -  -  <i>Pycnonotus simplex</i> <br> -  -  -  -  -  -  -  <i>Pycnonotus eutilotus</i> <br> -  -  -  -  -  -  -  <i>Pycnonotus brunneus</i> <br> -  -  -  -  -  -  -  <i>Pycnonotus erythropthalmos</i> <br> -  -  -  -  -  Stenostiridae <br> -  -  -  -  -  -  <i>Culicicapa</i> <br> -  -  -  -  -  -  -  <i>Culicicapa ceylonensis</i> <br> -  -  -  -  -  Muscicapidae <br> -  -  -  -  -  -  <i>Cyornis</i> <br> -  -  -  -  -  -  -  <i>Cyornis superbus</i> <br> -  -  -  -  -  -  -  <i>Cyornis unicolor</i> <br> -  -  -  -  -  -  <i>Rhinomyias</i> <br> -  -  -  -  -  -  -  <i>Rhinomyias umbratilis</i> <br> -  -  -  -  -  -  <i>Trichixos</i> <br> -  -  -  -  -  -  -  <i>Trichixos pyrropygus</i> <br> -  -  -  -  -  -  <i>Copsychus</i> <br> -  -  -  -  -  -  -  <i>Copsychus stricklandii</i> <br> -  -  -  -  -  Monarchidae <br> -  -  -  -  -  -  <i>Terpsiphone</i> <br> -  -  -  -  -  -  -  <i>Terpsiphone paradisi</i> <br> -  -  -  -  -  -  <i>Hypothymis</i> <br> -  -  -  -  -  -  -  <i>Hypothymis azurea</i> <br></div><p></p>
Figure 1 in Bioacoustic study of a sound produced with forced air by larvae of Phileurus didymus (Linnaeus) (Coleoptera: Scarabaeidae: Dynastinae: Phileurini)
Figure 1. Phileurus didymus third instar larvae.
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>
The impacts of fine-tuning, phylogenetic distance, and sample size on big-data bioacoustics
<p>Vocalizations in animals, particularly birds, are critically important behaviors that influence their reproductive fitness. While recordings of bioacoustic data have been captured and stored in collections for decades, the automated extraction of data from these recordings has only recently been facilitated by artificial intelligence methods. These have yet to be evaluated with respect to accuracy of different automation strategies and features. Here, we use a recently published machine learning framework to extract syllables from ten bird species ranging in their phylogenetic relatedness from 1 to 85 million years, to compare how phylogenetic relatedness influences accuracy. We also evaluate the utility of applying trained models to novel species. Our results indicate that model performance is best on conspecifics, with accuracy progressively decreasing as phylogenetic distance increases between taxa. However, we also find that the application of models trained on multiple distantly related species can improve the overall accuracy to levels near that of training and analyzing a model on the same species. When planning big-data bioacoustics studies, care must be taken in sample design to maximize sample size and minimize human labor without sacrificing accuracy.</p>
FIGURE 2 in An inordinate fondness for inconspicuous brown frogs: integration of phylogenomics, archival DNA analysis, morphology, and bioacoustics yields 24 new taxa in the subgenus Brygoomantis (genus Mantidactylus) from Madagascar
FIGURE 2. (Continued).
FIGURE 2 in An inordinate fondness for inconspicuous brown frogs: integration of phylogenomics, archival DNA analysis, morphology, and bioacoustics yields 24 new taxa in the subgenus Brygoomantis (genus Mantidactylus) from Madagascar
FIGURE 2. (Continued).
FIGURE 2 in An inordinate fondness for inconspicuous brown frogs: integration of phylogenomics, archival DNA analysis, morphology, and bioacoustics yields 24 new taxa in the subgenus Brygoomantis (genus Mantidactylus) from Madagascar
FIGURE 2. (Continued).
FIGURE 2 in An inordinate fondness for inconspicuous brown frogs: integration of phylogenomics, archival DNA analysis, morphology, and bioacoustics yields 24 new taxa in the subgenus Brygoomantis (genus Mantidactylus) from Madagascar
FIGURE 2. (Continued).
ScienceDex guides
Understand access before you commit
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.