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9 results for “wading birds”
Surveys of salt marsh wading birds and shorebirds in Massachusetts at Plum Island Sound, PIE LTER and portions of Essex Bay
This data set is part of a study of how wading birds, primarily Great and Snowy Egrets are using different estuarine habitats at Plum Island Sound. It contains the results of count surveys of wading birds in the salt marshes of the Plum Island Estuary and parts of adjacent Essex Bay. Surveys were carried out from May to October in 2012 and 2013, additional sampling occurred in July 2014 and summer of 2018. Bird counts took place from 37 observation points around the bay and documented species, estimated distance from observer, habitat of each individual bird counted and its behavior (feeding, resting, etc.).
Good vibrations: Remote-tactile foraging success of wading birds is positively affected by the water content of substrates they forage in
<p>Some taxa of wading birds can locate buried prey by detecting vibratory cues in their foraging substrates while probe-foraging, using a sensory modality called "remote-touch". As more saturated substrates transmit vibrations better, we predict that these birds can detect prey in wetter substrates more easily. We used sensory assays to test whether substrate water content affects the remote-touch foraging success rate of Hadeda Ibises, <em>Bostrychia hagedash</em>. The birds were more successful at locating prey using vibratory cues than when relying on random direct contact with the beak alone. Their remote-touch foraging success rate was positively affected by increasing water contents of the soil, but water content had no effect on their direct contact foraging success (indicating this is not an artefact of ease of probing). This may partially explain the link between the range expansion of this species in southern Africa and increased soil irrigation, as it is easier for the birds to detect prey in wetter substrates. Thus, it is likely that the distribution of other remote-touch foraging birds is affected by substrate water content, and as many of these species are endangered and rely on sensitive wetland habitats, it is vital to understand their sensory requirements for foraging.</p>
Fig. 1 in Diversity and prevalence of hemoparasites of wading birds in southern Florida, USA
Fig. 1. Plasmodium (Novyella) sp. stages observed in blood smear of a Glossy Ibis from Florida, USA that matched haplotype pMYCAME02. Representative trophozoites (*), erythrocytic meronts (arrow), macrogametocytes (double arrow), and microgametocytes (arrowhead) are marked. Some erythrocytes were infected with multiple parasites, e.g., (a) a cell with 4 trophozoites, (h) a cell with three early meronts, and (t) a cell with two early meronts. Scale bar = 10 Mm.
Good vibrations: Remote-tactile foraging success of wading birds is positively affected by the water content of substrates they forage in
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Fitness consequences of anthropogenic subsidies for a partially migratory wading bird
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EverWatch airborne bird imagery dataset for detection and species classification of Everglades wading birds
<p>Training and evaluation data to support the development of wading bird species detection models in the Everglades.</p> <div> <h1>Dataset contents</h1> <ul> <li>Training data: 4438 images with 45069 annotations</li> <li>Validation data: 493 images with 5422 annotations</li> <li>Test data: 196 images with 4113 annotations</li> </ul> </div> <div> <h1>Dataset structure</h1> All data is combined in everwatch_dataset.zip. This archive contains image crops (stored as PNG files) and their associated annotations in CSV format (stored in train_annotations.csv, validation_annotations.csv, and test_annotations.csv) and COCO format (stored in train_annotations.json, validation_annotations.json, and test_annotations.json). The CSV files with the annotations contain the following columns.</div> <ul> <li>image_path - basename of the image</li> <li>label - Species label</li> <li>xmin, xmax, ymin, ymax - coordinates for the ground truth bounding box on the image</li> </ul> <p>COCO formatted data follows COCO conventions so labels are in “categories” and “category_id”, image paths are in “file_name”, and bounding boxes are stored as “bbox” in the default “ltwh” format [left, top, width, height].</p> <p>Data on the annotation type for each image is stored in annotation_types.csv, which contains the following columns:</p> <ul> <li>image_path - basename of the image</li> <li>dataset - which data split the image is part of (train, validation, or test)</li> <li>annotation type - approach used for annotating the data (crop_level or colony_level) </li> </ul> <div> <h2>Labels</h2> <ul> <li>'Great Egret'</li> <li>'Great Blue Heron'</li> <li>'White Ibis'</li> <li>'Roseate Spoonbill'</li> <li>'Wood Stork'</li> <li>'Snowy Egret'</li> <li>'Anhinga'</li> <li>'Unknown' (test only)</li> </ul> <h1>Annotation methods</h1> <p>To provide data for training and testing computer vision models we used a pragmatic combination of labels from a variety of different approaches. Seven species were identified when labeling: White Ibis (<em>Eudocimus albus</em>), Great Egret (<em>Ardea alba</em>), Great Blue Heron (<em>Ardea herodias</em>), Snowy Egret (<em>Egretta thula</em>), Wood Stork (<em>Mycteria americana</em>), Roseate Spoonbill (<em>Platalea ajaja</em>), and Anhinga (<em>Anhinga anhinga</em>). Anhinga have not traditionally been included in airborne counts because they are difficult to see from the air, but were included in our labeling as a nuisance class because they were occasionally detected by the models and classified as Great Blue Herons.</p> <h2>Crop level annotations</h2> <p>Our primary approach labeled birds in 3145 1500×1500 pixels crops from surveys conducted in 2020 and 2021. Crops were automatically extracted from the orthomosaics for each survey. Labeling was conducted using a private Zooniverse project and every bird in each crop was annotated as a point location (the center of the bird) with a species label. The point-based labels were converted into bounding boxes for model training and evaluation by centering a bounding box on each point using a 50x50 pixel box for larger species (Great Egret, Great Blue Heron, Wood Stork, Roseate Spoonbill, Anhinga) and a 36x36 pixel for smaller species (White Ibis, Snowy Egret).</p> <p>We combined these point-based data with explicit bounding box-based labels created using a combination of QGIS and LabelStudio. We selected imagery for this labeling to help address small sample sizes for some species in the initial training datasets. Imagery was selected using a combination of expert field knowledge of where less common species were nesting and automated detection of likely rare species. For automated detection we ran a preliminary model based on the crop data over large amounts of survey imagery and identified crops that the model thought included less common species. These crops were then provided to annotators for labeling. This type of “active learning”, where an existing model is used to identify images for labeling is increasingly recognized as a valuable approach for rapidly producing improved machine learning models (Kellenberger et al. 2019, 2021, Norouzzadeh et al. 2021).</p> <p>In combination these crop-based annotations included 2948 of 1500x1500m crops, including 41173 birds, that were used for model training. The remaining 197 crops, including 4113 birds, were reserved for model evaluation. These annotations are indicated by 8 digit numeric file names.</p> <h2>Colony level annotations</h2> <p>We combined the crop-based labels with full colony counts from 2022 conducted as part of reporting maximum counts. This labeling was conducted using Photoshop to label every bird in an entire colony as a point location with a species label, but due to the scale of labeling the point locations are not placed carefully on the center of the bird. To convert these point locations into bounding boxes needed for model training and evaluation, we used the bird detector module in the DeepForest Python package (Weinstein et al. 2022) to predict the location of birds within the image. We then associated the species label from the photoshop points with the overlapping bounding box prediction. For the minority of boxes that did not overlap a predicted bird, we drew a 1m buffer around the point. We used data from 13 full colony counts, totaling 9318 birds, which were cropped into 1983 1500x1500 pixel crops. Full colony counts did not overlap with existing training and testing data from the 1500×1500 pixels crops because they were from different years. These annotations are indicated by variable length file names that start with a site name, followed by a date, followed by a number.</p> <h1>Difficult to distinguish species</h1> <p>Model training for most object detection algorithms requires that all birds in an image are labeled. Therefore, for all labeling approaches, the annotator’s best estimate of the species label was included even in cases where the species identity could not be conclusively determined from the imagery. The most common difficulty for annotators in distinguishing between species is White Ibis and Snowy Egrets. These birds both have white feathers, are of similar size, and appear similar in airborne imagery. As a result, annotators often use information beyond what is visibly apparent from individual birds to distinguish between these species, including what is known from the field about which species are present in the associated colonies and patterns in how the species aggregate in space.</p> <h1>Cleaning of evalutation dataset</h1> <p>Test data for evaluating the performance of the model was based exclusively on the crop-based labels. To ensure that the test data was as accurate as possible, all labels in the test set were checked and adjusted if necessary by the lead of the labeling effort (Lindsey Garner). This included both checking the species identification, adjusting any bounding boxes that did not properly contain the associated bird, and adding any labels that were missing entirely. To allow for understanding when it was not possible for a human to label a bird confidently to species, the labels for birds in the test set that could not be identified by the labeling lead were changed to indicate that the species was unknown (n = 94 birds out of 4113 test labels).</p> </div>
Urban specialization reduces habitat connectivity by a highly mobile wading bird
<p>Background</p> <p>Mobile animals transport nutrients and propagules across habitats, and are crucial for the functioning of food webs and for ecosystem services. Human activities such as urbanization can alter animal movement behavior, including site fidelity and resource use. Because many urban areas are adjacent to natural sites, mobile animals might connect natural and urban habitats. More generally, understanding animal movement patterns in urban areas can help predict how urban expansion will affect the roles of highly mobile animals in ecological processes.</p> <p>Methods</p> <p>Here, we examined movements by a seasonally nomadic wading bird, the American white ibis (Eudocimus albus), in South Florida, USA. White ibis are colonial wading birds that forage on aquatic prey; in recent years, some ibis have shifted their behavior to forage in urban parks, where they are fed by people. We used a spatial network approach to investigate how individual movement patterns influence connectivity between urban and non-urban sites. We built a network of habitat connectivity using GPS tracking data from ibis during their non-breeding season and compared this network to simulated networks that assumed individuals moved indiscriminately with respect to habitat type.</p> <p>Results</p> <p>We found that the observed network was less connected than the simulated networks, that urban-urban and natural-natural connections were strong, and that individuals using urban sites had the least-variable habitat use. Importantly, the few ibis that used both urban and natural habitats contributed the most to connectivity.</p> <p>Conclusions</p> <p>Habitat specialization in urban-acclimated wildlife could reduce the exchange of propagules and nutrients between urban and natural areas, which has consequences both for beneficial effects of connectivity such as gene flow and for detrimental effects such as the spread of contaminants or pathogens.</p>
Urban specialization reduces habitat connectivity by a highly mobile wading bird
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Figure 1 in Prey consumed by wading birds in mangrove swamps of the Caribbean coast of Colombia
Figure 1. Map of the study area, the Sinú River deltaic estuary, black dots show sites where individual wading birds were captured.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.