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4 results for “Varecia variegata”
Passive acoustic monitoring applied to black-and-white ruffed lemurs (Varecia variegata) in Ranomafana National Park, Madagascar
<p>Data accompanying the paper: <strong>"An integrated passive acoustic monitoring and deep learning pipeline applied to black-and-white ruffed lemurs (\textit{Varecia variegata}) in Ranomafana National Park, Madagascar"</strong></p> <p>Fieldwork was conducted at Mangevo (21.3833S, 47.4667E), an isolated and undisturbed forest location within Ranomafana National Park (RNP), located in southeastern Madagascar, during the period of May to July 2019. To facilitate passive acoustic monitoring, we deployed a total of two SongMeter SM4 devices (manufactured by Wildlife Acoustics) and two Swift units (provided by the Cornell Yang Center for Conservation Bioacoustics). The placement of these recorders was strategically chosen within the central regions of known subgroups, ensuring a minimum distance of 300 meters between each device. The SongMeter devices operated at a sampling rate of 48 kHz, while the Swift units operated at 32 kHz, respectively, enabling comprehensive audio data collection throughout the study period.</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> <ul> <li><strong>Test_Audio.zip </strong>-- contains (.wav) testing audio files</li> <li><strong>Test_Annotations.zip </strong>-- contains (.svl) manually annotated testing files which can be read in using Sonic Visualiser or by parsing the XML file in Python or another programming language. Load in the audio file into Sonic Visualiser and then drag-and-drop the corresponding .svl file.</li> <li><strong>Training_Audio_batch_x.zip -</strong>- several .zip files were created to simplify downloading. There are 10 batches, each is roughly 4GB. Each batch contains (.wav) training audio files</li> <li><strong>Training_Annotations.zip</strong> -- contains (.svl) manually annotated training files which can be read in using Sonic Visualiser or by parsing the XML file in Python or another programming language. Load in the audio file into Sonic Visualiser and then drag-and-drop the corresponding .svl file.</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>
Data from: A description of nesting behaviors, including factors impacting nest site selection, in black-and-white ruffed lemurs (Varecia variegata)
Nest site selection is at once fundamental to reproduction and a poorly understood component of many organisms' reproductive investment. This study investigates the nesting behaviors of black-and-white ruffed lemurs, Varecia variegata, a litter-bearing primate from the southeastern rainforests of Madagascar. Using a combination of behavioral, geospatial, and demographic data, I test the hypotheses that environmental and social cues influence nest site selection, and that these decisions ultimately impact maternal reproductive success. Gestating females built multiple large nests throughout their territories. Of these, females used only a fraction of the originally constructed nests, as well as several parking locations as infants aged. Nest construction was best predicted by environmental cues, including the size of the nesting tree and density of feeding trees within a 75 m radius of the nest, whereas nest use depended largely on the size and average distance to feeding trees within that same area. Microhabitat characteristics were unrelated to whether females built or used nests. Although unrelated to nest site selection, social cues, specifically the average distance to conspecifics' nest and park sites, were related to maternal reproductive success; mothers whose litters were parked in closer proximity to others' nests experienced higher infant survival than those whose nests were more isolated. This is likely because nesting proximity facilitated communal crèche use by neighboring females. Together, these results suggest a complex pattern of nesting behaviors that involves females strategically building nests in areas with high potential resource abundance, using nests in areas according to their realized productivity, and communally rearing infants within a network of nests distributed throughout the larger communal territory.
Black-and-white ruffed lemur (Varecia variegata) calls for passive acoustic monitoring
<p>Data accompanying the paper: "Passive Acoustic Monitoring and Transfer Learning"</p> <p><strong>Please cite this dataset as:</strong></p> <blockquote> <p>Dufourq, Emmanuel and Batist, Carly and Foquet, Ruben and Durbach, Ian. (2022). Passive Acoustic Monitoring and Transfer Learning. BioRxiv doi: </p> </blockquote> <p>This dataset contains approximately 60 hours of audio that contained calls of the critically endangered Black-and-white ruffed lemur (Varecia variegata). The audio data was collected in a sub-humid rainforest site (Mangevo) in the southeast of Ranomafana National Park in Madagascar using 2 Swift recorders (Cornell Center for Conservation Bioacoustics). The sampling rate was set to 48,000Hz and the recordings were collected intermittently between May 2019 and November 2020. A larger dataset exists and further recordings will be released.</p> <p>The annotations files are in (.svl) format which is compatible with SonicVisualiser (https://www.sonicvisualiser.org/). Each audio file has a corresponding .svl file. Each .svl has segments of audio that were manually annotated as either ''thyolo-alethe" (presence class) or "noise" (absence class) -- this dataset can be used to train a binary classification model.</p> <p>The audio files are provided in "Audio.zip" and the manually verified annotation in "Annotations.zip".</p>
Data from: A description of nesting behaviors, including factors impacting nest site selection, in black-and-white ruffed lemurs (Varecia variegata)
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