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65 results for “Bird flight”
Supplementary material for "In-flight reactions of nocturnally migrating birds to winds"
<p><strong>Abstract</strong></p> <p>Available knowledge on in-flight reactions of nocturnal bird migrants to winds is reviewed, with emphasis on the challenging topographical and meteorological conditions in Western Europe, and differences from the situation in North America discussed. Conclusions drawn are used for a new approach: using individual radar tracks of nocturnal migrants (mainly passerines) as well as winds measured at their flight altitudes, we defined the basic direction (BD=average flight direction of all migrants tracked under negligible wind influence) as a reference. For two altitudinal zones above a radar site near Nuremberg, we modelled the deviations of tracks and headings from BD for increasing wind from six 60° sectors. A comparison of birds’ air speeds Va with winds from four 90°-sectors confirmed that Va increased with opposing winds from ~11 to 13 (14) m/s; a similar increase occurred with side winds. An expected, slight decrease of Va with increasing following winds was only indicated for high-flying, not for low-flying birds. A predicted increase in average Va due to decreasing air density with increasing height was not observed; possible explanations (birds climbing to high altitudes in following, but not in strong opposing winds) are discussed. Over the whole autumn migration season, headings were concentrated in a sector of ±30° around 230° in both altitudinal zones. Prevailing winds from 230 to 320° (SW–NW, i.e. opposing from right) led to widely scattered tracks primarily between 190° and 270°, but additional ones in the SE sector (mainly 100°–170°). The analysis of tracks and headings relative to BD revealed the following features. (1) Overcompensation was frequently observed at low wind speeds (<3 m/s); (2) under all wind conditions, but particularly with opposing winds and at low flight levels, tracks were widely scattered, including birds deviating more than 90° from BD. (3) Under opposing and side winds from the right compensatory efforts led to partial drift compensation up to wind speeds of ~8–10 m/s. Because efforts to compensate drift dwindled with increasing wind speeds, birds were fully drifted. Many even shifted their heading to due south and, hence, overdrifted. (4) Opposing and side winds from the left induced partial compensation at low flight levels and full drift above 1500 m asl. (5) The lateral components of the rare and weak following winds led to tracks close to expected minimal drift (without important compensation needed). In general, migrants compensated less for deviations by wind force than expected. The tendency of birds to maintain headings close to BD under opposing winds was so strong that many individuals continued migration with minimal progress over ground or even with retrograde migration as an extreme. On the other hand, there was an omnipresent fraction of birds with tracks far from seasonally favourable directions, including reverse migration.</p>
Migrating birds real flight V-formation spatial configuration.
<p>Bird real flight V-formation dataset: Arbitrary (pixel) coordinates of migrating birds, probably Geese, flying in V-formation. Photo is taken in an angle so their formation data is only a cross-section in 3-D perspective but with entire pack. However, despite this limitation this V-formation configuration provides a quantitative data for the understanding for the spatial properties, i.e., V-shape characteristics. There are 95 birds in total including the lead bird. Lower V-arm is denoted with tags dXX has 51 birds and upper V-arm is denoted by tags uXX has 43 birds. Lead bird has two entries d00 and u00 for consistency. Annotated image provides boxes and labels. The data is given under bird_arbitrary_coordinates as pixel location on the plane with tags. In coordinate annotation head of the bird is taken as a refrence point.</p>
Morphological adaptations linked to flight efficiency and aerial lifestyle determine natal dispersal distance in birds
<p>Natal dispersal—the movement from birthplace to breeding location—is often considered the most significant dispersal event in an animal's lifetime. Natal dispersal distances may be shaped by a variety of intrinsic and extrinsic factors, and remain poorly quantified in most groups, highlighting the need for indices that capture variation in dispersal among species.</p> <p>In birds, it is hypothesized that dispersal distance can be predicted by flight efficiency, which can be estimated using wing morphology. However, the use of morphological indices to predict dispersal remains contentious and the mechanistic links between flight efficiency and natal dispersal are unclear.</p> <p>Here, we use phylogenetic comparative models to test whether hand-wing index (HWI, a morphological proxy for wing aspect ratio) predicts natal dispersal distance across a global sample of 114 bird species. In addition, we assess whether HWI is correlated with flight usage in foraging and daily routines.</p> <p>We find that HWI is a strong predictor of both natal dispersal distance and a more aerial lifestyle.</p> <p>Our results support the use of HWI as a valid proxy for relative natal dispersal distance, and also suggest that evolutionary adaptation to aerial lifestyles is a major factor connecting flight efficiency with patterns of natal dispersal.</p>
Nocturnal flight calls dataset: long-term acoustic monitoring of birds migrating at night
<p><strong>General Description:</strong></p> <p>This is a development set used in the experiments in the Ph.D. thesis: "Nowe metody akustycznej identyfikacji ptaków migrujących nocą" (<em>"Novel methods of acoustic identification of birds migrating at night"</em>) by Hanna Pamula. The project focuses on the detection (and - partially - classification) of passerine birds' calls from long-term audio recordings collected during bird autumn migration between 2016 and 2019. The dataset consists of >56,5 hours of recordings with annotations of nocturnal flight calls of passerine birds migrating along the Baltic Sea coast, Poland.</p> <p> </p> <p><strong>Folder Structure</strong></p> <p>Development_Set_3.1.zip</p> <p>|_Development_Set_3.1/</p> <p> |__Training_Set/</p> <p> |____*.wav</p> <p> |____*.txt</p> <p> |__Validation_Set/</p> <p> |____*.wav</p> <p> |____*.txt</p> <p> |__Testing_Set/</p> <p> |____*.wav</p> <p> |____*.txt</p> <p>Training Set: 86 recordings</p> <p>Validation Set: 8 recordings</p> <p>Testing set: 18 recordings (BUT: uploaded 20 recordings, as in the previous version of the dataset - version 3, two additional recordings were used. Then, they were deleted in the final version of development set 3.1. Two additional recordings are: 'BUK5_20161101_002104a and BUK5_20161101_002104b)</p> <p>Names of waveforms and annotations are matching.</p> <p><strong>Waveforms:</strong></p> <p>The whole dataset consists of 114 recordings. One hundred thirteen recordings are about 30 minutes long (29min56s – 29min 59s), one recording is 1min20s. All data were recorded at 44,100 Hz sampling rate, one channel, with SM2 Wildlife Acoustics recorders + SMX-NFC microphone. The recording sessions were performed at night (starting time and date denoted in a file name) on the Baltic Sea coast in Poland (Dąbkowice, near Darłowo).</p> <p><strong>Annotations:</strong></p> <p>Transcriptions were produced using Audacity 2.4.1: https://www.audacityteam.org/ by an experienced birdwatcher, Hanna Pamula. While every effort has been made to ensure the quality and accuracy of the labels, some errors may occur, taking into account the difficulty of nocturnal call recognition and transcription tasks in general.</p> <p>Transcription format:</p> <p>[Starting time (sec)] [Ending time (sec)] [Label]</p> <p><strong>Meaning of the labels:</strong></p> <p>1. Positive classes – migrating passerine birds:</p> <ul> <li>'s' – song thrush call (Turdus philomelos)</li> <li>'k' – blackbird call (Turdus merula)</li> <li>'d' – redwing call (Turdus iliacus)</li> <li>'r' – robin call (Erithacus rubecula)</li> <li>‘kwiczol’ – fieldfare call (Turdus pilaris)</li> <li>‘skowronek’ – skylark call (Alauda arvensis)</li> <li>Each of the above labels could also have a question mark '?', e.g. 'r?', 'k?' – meaning that it's not a sure label. In a bird call detection task, they are regarded as positive chunks containing bird call(s).</li> <li>'ni' – non identified bird call (distant/quiet/not recognized)</li> </ul> <p>Only the supposed calls of migrating passerine birds were labeled; other sounds of species were ignored (e.g., robin's tik-calling, which can be often heard at dusk, and may be regarded as warning sounds).</p> <p>2. Negative classes – other marked sound events:</p> <ul> <li>'g' – other bird calls/songs/sounds. Sounds that could confuse the model; for example, sounds of migrating geese, cranes, plovers calls, etc.</li> <li>'gh' – human voices</li> <li>'t' – cracks, clicks, raindrops, other noise</li> <li>‘puszczyk’ – tawny owl voice (Strix aluco)</li> <li>'czapla' – grey heron voice (Ardea cinerea)</li> </ul> <p>Not all occurrences of the negative sounds were labeled – only some chosen examples to represent the possible noises/negative samples. Thus these annotations can't be used for entirely different detection / classification tasks than intended, e.g., detecting migrating cranes or human voices in long-term recordings.</p> <p>3. Labels to be excluded from analysis:</p> <ul> <li>'???', '??? mysz', '??? high freq' – unknown, not sure if the sound event is a birds' call or not. Uncertainty about belonging to a positive/negative class in the detection task.</li> </ul>
Dataset for: Methodology to identify and quantify flight path dependent bird strike scenarios over aircraft
<h1>ScenarioGenerator</h1> <h2>Description</h2> <p>This project is a Python project containing a demonstrations of the methodology developed by J. Bertholdt. <br>The code takes stl files and flight path data in order to create bird strike impact scenarios for each cell. <br>With this data it is possible to approximate the impact intensity and create heat maps over the geometry.</p> <h2>Features</h2> <p>- Data processing: The code creates scenarios (impact vector, angle, velocity and bird data) for hit areas and estimates peak pressure and total impulse. <br>- Data saving: The code saves the data in forms of csv files.<br>- Data reader: The code can read the csv files and recreate the processed data and mesh.<br>- Data visualization: The code contains examples for data filtering and plotting. </p> <h2>Installation</h2> <p>1. Download Code<br>2. Adjust directories in data_reader_demo.py and stl_processing_demo.<br>3. Create a virtual environment:</p> <h3>Required packages:</h3> <p>- numpy<br>- birdpressure<br>- matplotlib<br>- pyvista</p>
Interpolated maps of bird density and flight vector over Europe
<p>This dataset contains the interpolated values of bird density and bird flight speed (N-S and E-W) resulting from the methodology presented in [<em>reference</em>].The methodology is explained in less detail at <a href="https://rafnuss-postdoc.github.io/BMM/">rafnuss-postdoc.github.io/BMM</a>. The resulting interpolation is a probability distribution (define the probability of each value to occurs). Only the median, quantile 10 and 90 are given in this file. </p> <p>The spatio-temporal grid has a resolution of 0.2° in latitude (43°-68°) and longitude (-5°-30°) and 15 minutes in time (19 September to 10 October 2016), resulting in 127x176x2017 nodes. Over this large data cube, the estimation are only computed at the nodes located (1) over land, (2) within 200km of the nearest radar and (3) during nighttime.</p> <p>The same dataset can be visualised on a dedicated web interface: <a href="https://bmm.raphaelnussbaumer.com/">www.bmm.raphaelnussbaumer.com</a> and data can be queried on a API (<a href="https://github.com/Rafnuss-PostDoc/BMM-web#how-to-use-the-api">documentation</a>).</p> <p>The csv file is structured as a table with the following columns:</p> <ul> <li>density_estimation: Median bird density [bird/km^2] (quantile 50)</li> <li>density_quantile10: Quantile 10 of bird density [bird/km^2]</li> <li>density_quantile90: Quantile 90 of bird density [bird/km^2]</li> <li>speedu_estimation: Mean bird speed east(+)/west(-) [m/s]</li> <li>speedu_std: Standard deviation of bird speed east(+)/west(-) [m/s]</li> <li>speedv_estimation: Mean bird speed north(+)/south(-) [m/s]</li> <li>speedv_std: Standard deviation of bird speed north(+)/south(-) [m/s]</li> <li>latitude</li> <li>longitude</li> <li>time</li> </ul> <p> </p>
Vertical profiles and integrated time series of bird density and flight speed vector (19.09.2016-10.10.2016)
<p><strong>Description</strong></p> <p>This dataset contains the vertical profiles and integrated time series of bird density and flight speed (NS and EW) used in Nussbaumer (2019) [open access: <a href="https://www.mdpi.com/2072-4292/11/19/2233">https://www.mdpi.com/2072-4292/11/19/2233</a>]. Data are stored in a JavaScript Object Notation (JSON) file for each radar, with the following structure:</p> <pre><code>{ "name" : "bejab", //code name of the radar (http://eumetnet.eu/wp-content/themes/aeron-child/observations-programme/current-activities/opera/database/OPERA_Database/index.html) "lat" : 51.1917, //Latitude "lon" : 3.0642, //Longitude "height" : 50, //Height of the radar antenna [m] a.s.l. "maxrange" : 25, //Maximum range [km] used for profile "alt" : [100, 300,...], "time" : ["19-Sep-2016 00:00:00", "19-Sep-2016 00:05:00",...], "dens" : [[...],...], //Vertical profile of bird density [1/km3] "u" : [[...],...], //Vertical profile of bird flight speed in East(+)/West(-) [m/s] "v" : [[...],...], //Vertical profile of bird flight speed in North(+)/South(-) [m/s] "denss" : [...], //Integrated profile of bird density [1/km2] "us" : [...], //Integrated profile of bird flight speed in East(+)/West(-) [m/s] "vs" : [...], //Integrated profile of bird flight speed in North(+)/South(-) [m/s] }</code></pre> <p> </p> <p><strong>Procedure</strong></p> <p>The raw data are downloaded on the <a href="http://enram.github.io/data-repository/">ENRAM repository</a>,( see Dokter (2011) and (2019) for more details) and processed according to the procedure described below.</p> <ol> <li>Of the 84 radars contributing data during the study period, 11 radars are discarded because of their poor quality due to S-band radar type, poor processing or large gaps (temporal or altitude cut). The same radars were removed in Nilsson et al. (2019).In addition, the 4 radars from Bulgaria and Portugal were excluded because of their geographic isolation.</li> <li>The full vertical profile was discarded when rain was present at any altitude bin. A dedicated MATLAB GUI was used to visualise the data and manually set bird densities to “not-a-number” in such cases. </li> <li>Zones of high bird densities can sometimes be incorrectly eliminated in the raw data. To address this, Nilsson et al. (2019) excluded problematic time or height ranges from the data. Here, in order to keep as much data as possible, the data was manually edited to replace erroneous data either with “not-a-number”, or by cubic interpolation using the dedicated MATLAB GUI.</li> <li>Due to ground scattering,the lower altitude layers are sometimes contaminated by errors or excluded in the raw data. We vertically interpolated bird density by copying the first layer without error into to the lower ones. This approach is relatively conservative as bird migration intensity usually decreases with height in the absence of obstacles, and more so in autumn (Bruderer, 2018)</li> <li>The vertical profiles are vertically integrated from the radar altitude and up to 5000 m asl.</li> <li>The data recorded during daytime are excluded. Daytime is defined at each radar by the civil dawn and dusk (6° below horizon).</li> <li>Finally, the data of 10 radars with high temporal resolution (5-10minutes) was down-sampled to 15 minutes to preserve a balanced representation of each radar.</li> </ol> <p>The resulting cleaned vertical-integrated time series of nocturnal bird density can be viewed in vp_corrected.zip.</p> <p>More details and illustrations are available in Nussbaumer (2019) [open access: <a href="https://www.mdpi.com/2072-4292/11/19/2233">https://www.mdpi.com/2072-4292/11/19/2233</a>], </p> <p><strong>Acknowledgement</strong></p> <p>We acknowledge the <a href="http://eumetnet.eu/activities/observations-programme/current-activities/opera/">European Operational Program for Exchange of Weather Radar Information (EUMETNET/OPERA)</a> for providing access to European radar data, faciliated through a research-only license agreement between EUMETNET/OPERA members and <a href="http://enram.eu/">ENRAM</a>.</p> <p> </p> <p><strong>References</strong></p> <p>Bruderer, B.; Liechti, F. Variation in density and height distribution of nocturnal migration in the south of israel. <em>Israel Journal of Zoology</em> <strong>1995</strong>, <em>41</em>, 477–487. <a href="http://doi.org/10.1080/00212210.1995.10688815">doi:10.1080/00212210.1995.10688815</a>.</p> <p>Dokter A. M. , F. Liechti, H. Stark, L. Delobbe, P. Tabary, and I. Holleman, “Bird migration flight altitudes studied by a network of operational weather radars,” <em>J. R. Soc. Interface</em>, vol. 8, no. 54, pp. 30–43, Jan. <strong>2011</strong>. <a href="http://doi.org/10.1098/rsif.2010.0116">doi:10.1098/rsif.2010.0116</a></p> <p>Dokter A. M. , P. Desmet, J. H. Spaaks, S. van Hoey, L. Veen, L. Verlinden, C. Nilsson, G. Haase, H. Leijnse, A. Farnsworth, W. Bouten, and J. Shamoun‐Baranes, “bioRad: biological analysis and visualization of weather radar data,” <em>Ecography </em>(Cop.)., vol. 42, no. 5, pp. 852–860, May <strong>2019</strong>. <a href="http://doi.org/10.1111/ecog.04028">doi: 10.1111/ecog.04028</a></p> <p>Nilsson, C.; Dokter, A.M.; Verlinden, L.; Shamoun-Baranes, J.; Schmid, B.; Desmet, P.; Bauer, S.; Chapman, J.; Alves, J.A.; Stepanian, P.M.; Sapir, N.;Wainwright, C.; Boos, M.; Górska, A.; Menz, M.H.M.; Rodrigues, P.; Leijnse, H.; Zehtindjiev, P.; Brabant, R.; Haase, G.; Weisshaupt, N.; Ciach, M.; Liechti, F. Revealing patterns of nocturnal migration using the European weather radar network. <em>Ecography </em><strong>2019</strong>, <em>42</em>, 876–886. <a href="http://doi.org/10.1111/ecog.04003">doi:10.1111/ecog.04003</a>.</p> <p>Nussbaumer R., L. Benoit, G. Mariethoz, F. Liechti, S. Bauer, and B. Schmid, “A Geostatistical Approach to Estimate High Resolution Nocturnal Bird Migration Densities from a Weather Radar Network,” <em>Remote Sens</em>., vol. 11, no. 19, p. 2233, Sep. <strong>2019</strong>. <a href="https://www.mdpi.com/2072-4292/11/19/2233">doi: 10.3390/rs11192233</a></p> <p> </p> <p> </p>
Fig. 4 in Scapular orientation in theropods and basal birds, and the origin of flapping flight
Fig. 4. Angles between furcular arms in non−avian theropods and birds. Data sources listed in Table 1.
Fig. 1 in Scapular orientation in theropods and basal birds, and the origin of flapping flight
Fig. 1. Scapular position and glenoid orientation in articulated skeletons of non−avian dinosaurs, with glenoids indicated by arrows. A. Dorsal view of the ornithischian dinosaur Psittacosaurus mongoliensis, AMNH 6254, showing lateral position and wide spacing of scapulae. B. Lateral view of the ornithischian dinosaur Centrosaurus apertus, AMNH 5351, showing ventral orientation of glenoid and position of glenoid anteroventral to ribcage. C. Lateral view of the ornithomimid theropod dinosaur Struthiomimus altus, AMNH 5339, showing position of glenoid anteroventral to ribcage. D. The deinonychosaurian theropod dinosaur Velociraptor mongoliensis, IGM 100/976, in dorsal (D1), right lateral (D2), and anterior (D3) views, with the furcula outlined in white for clarity, showing that the scapulae are widely spaced, laterally positioned, and exhibit ventrally oriented glenoids, as in other dinosaurs. Broken white lines in (D1) indicate lateral extemities of vertebral column.
Fig. 3 in Scapular orientation in theropods and basal birds, and the origin of flapping flight
Fig. 3. Articulated skeletons of Mesozoic birds, showing scapular position and glenoid orientation, with glenoids indicated by arrows. A. AMNH cast of "Berlin specimen" of Archaeopteryx lithographica, showing that the glenoids are anteroventral to the ribcage. The unnatural position of the left humerus above the glenoid is an artifact of dislocation of the left shoulder. B. AMNH cast of the "Eichstätt specimen" of Archaeopteryx lithographica, showing that the glenoid is anteroventral to the ribcage. C. Confuciusornis sanctus in dorsal view, showing wide spacing and lateral position of scapulae, with lateral extremities of vertebral column (extrapolated from dimensions of disarticulated dorsal vertebrae) represented by a pair of broken lines. Modified from Chiappe et al. (1999). D. The enantiornithine bird Eoalulavis hoyasi, LH 13500a, in dorsal view, showing close spacing and dorsal position of scapulae.
Civil war is associated with longer escape distances among Sri Lankan birds - flight-initiation distances of Sri Lankan birds
<p>War influences wildlife in a variety of ways but may influence their escape responses to approaching threats, including humans, because of its effect on human populations and behaviour, and landscape change. We collected 1,400 Flight-Initiation Distances (FIDs) from 157 bird species in the dry zone of Sri Lanka, where civil war raged for 26 years, ending in 2009. Accounting for factors known to influence FIDs (phylogeny, starting distance of approaches, body mass, prevailing human density, group size and location), we found birds have longer FIDs in the part of the dry zone which experienced civil war. Larger birds, often preferred by human hunters, showed greater increases in FID in the war zone, consistent with the idea that war was associated with greater hunting pressure, that larger birds experienced longer-lasting trauma, or had more plastic escape behaviour, than smaller species. While the mechanisms linking the war and avian escape responses remain ambiguous, wars evidently leave legacies which extend to behavioural responses in birds.</p>
Morphological adaptations linked to flight efficiency and aerial lifestyle determine natal dispersal distance in birds
Open the record for dataset details and reuse information.
Civil war is associated with longer escape distances among Sri Lankan birds - flight-initiation distances of Sri Lankan birds
Open the record for dataset details and reuse information.
Plasma metabolites of three migrant bird species during nocturnal endurance flight
<p>Plasma metabolites of the fat-, protein and carbohydrate metabolism were analysed in European robins, garden warblers and pied flycatchers during their nocturnal migration on a Swiss Alpine pass, Col de Bretolet. Birds were caught in high mist nets out of their nocturnal flight; hence the plasma metabolites reflect the metabolism of a flying, fasting bird.</p>
Ancient insect vision tuned for flight amongst rocks and plants underpins natural flower colour diversity - rock, mineral, stick, bark, leaf, bird- and insect-flower petal reflectance spectra
<p>Understanding the origins of flower colour signalling to pollinators is fundamental to evolutionary biology and ecology. Flower colour evolves under pressure from visual systems of pollinators, like birds and insects, to establish global signatures among flowers with similar pollinators. However, an understanding of the ancient origins of this relationship remains elusive. Here, we employ computer simulations to generate artificial flower backgrounds assembled from real material sample spectra of rocks, leaves, and dead plant materials, against which to test flowers' visibility to birds and bees. Our results indicate how flower colours differ from their backgrounds in strength, and the distributions of salient reflectance features when perceived by these key pollinators, to reveal the possible origins of their colours. Since Hymenopteran visual perception evolved before flowers, the terrestrial chromatic context for its evolution to facilitate flight and orientation consisted of rocks, leaves, sticks, and bark. Flowers exploited these pre-evolved visual capacities of their visitors, and in response evolved chromatic features to signal to bees, and differently to birds, against a backdrop of other natural materials. Consequently, it appears that today's flower colours may be an evolutionary response to the vision of diurnal pollinators navigating their world millennia prior to the first flowers.</p>
The respiratory system influences flight mechanics in soaring birds
<p>The subpectoral diverticulum (SPD) is an extension of the respiratory system in birds that dives between the primary muscles responsible for flapping the wing. Surveying the pulmonary apparatus in 68 species showed that the SPD was present in virtually all soaring taxa investigated yet absent in non-soarers. We find that this structure independently evolved with soaring flight at least seven times, indicating that the diverticulum may have a functional and adaptive relationship with this flight style. Using the soaring hawks <em>Buteo jamaicensis </em>and <em>B. swainsoni</em> as models, we show that the SPD is not integral for ventilation, that an inflated SPD can increase the moment arm of cranial parts of the pectoralis, and that pectoralis muscle fascicles are significantly shorter in soaring hawks than in non-soaring birds. This coupling of an SPD-mediated increase in pectoralis leverage with force-specialised muscle architecture produces a pneumatic system adapted for the isometric contractile conditions expected in soaring flight. The discovery of a mechanical role for the respiratory system in avian locomotion underscores the functional complexity and heterogeneity of this organ system and suggests that pulmonary diverticula likely have other undiscovered secondary functions. These data provide a mechanistic explanation for the repeated appearance of the SPD in soaring lineages, demonstrating that the respiratory system can be co-opted to provide novel biomechanical solutions to the challenges of flight and thereby influence the evolution of avian volancy.</p>
Data and codes from: Flight hampers the evolution of weapons in birds
<p>Birds are a remarkable example of how sexual selection can produce diverse ornaments and behaviors. Specialized fighting structures like deer's antlers, in contrast, are mostly absent among birds. Here, we investigated if the birds' costly mode of locomotion — powered flight — helps explain the scarcity of weapons among members of this clade. Our simulations of flight energetics predicted that the cost of bony spurs — a specialized avian weapon — should increase with time spent flying. Bayesian phylogenetic comparative analyses using a global spur dataset corroborated this prediction. First, extant species with flight-efficient wings (which presumably fly more frequently) tend to have fewer or no bony spurs. Second, this association likely arose because flying more leads to more frequent evolutionary loss of spurs. Together, these findings suggest that, much like pneumatic bones, absence of weaponry may be another feature of the avian body plan that allows birds to efficiently explore the aerial habitat.</p>
Interspecific tandem flights in nocturnally migrating terrestrial birds
<p>We report some interspecific nocturnal tandem flights involving the Eurasian woodcock and other terrestrial non-passerine species such as the Japanese green pigeon in two bird observatories in northern Japan. Our observation suggests that this previously undescribed interspecific interaction may be a novel form of commensalism or mutualism in nocturnal migrants. Here we upload the supporting dataset that includes the date and time, abundance, the presence/absence of nocturnal flight calls, flight direction, flight altitude, and observation location for each observed pass-by species. </p>
Flight initiation distances of birds
<p>Habitat destruction and fragmentation increasingly brings humans into close proximity with wildlife, particularly in urban contexts. Animals respond to humans using nuanced anti-predator responses, especially escape, with responses influenced by behavioural and life history traits, the nature of the risk, and aspects of the surrounding environment. Although many studies examine associations between broad-scale habitat characteristics (i.e., habitat type) and escape response, few investigate the influence of fine-scale aspects of the local habitat within which escape occurs. We test the 'habitat connectivity hypothesis' which suggests that, given the higher cost of escape within less connected habitats (due to the lack of protective cover), woodland birds should delay escape (tolerate more risk) than when in more connected habitat. We analyse flight-initiation distances (FIDs) of five species of woodland birds in urban Melbourne, south-eastern Australia. A negative effect of habitat connectivity (the proportion of the escape route with shrubs/trees/perchable infrastructure) on distance fled was evident for all study species, suggesting a higher cost of escape associated with lower connectivity. FID did not vary with connectivity at the location at which escape was initiated (four species), apart from a positive effect of habitat connectivity on FID for Noisy Miner Manorina melanocephala. We provide some support for two predictions of the 'habitat connectivity hypothesis' in at least some taxa, and conclude it warrants further investigation across a broader range of taxa inhabiting contrasting landscapes. Increasing habitat connectivity within urban landscapes may reduce escape stress experienced by urban birds.</p>
The effect of flight efficiency on gap‐crossing ability in Amazonian forest birds
<p>We used this dataset to examine the role of flight efficiency on gap-crossing ability in Amazonian forest birds. We used data from the Biological Dynamics of Forest Fragments Projects on recaptures of banded birds in an Amazonian forest bisected by a road. For a total of 45 species, we estimated flight efficiency using the hand-wing index (a proxy for the wing's aspect ratio) and used it as a predictor of the probability of road crossing in phylogenetic binomial regression models. We found that flight efficiency was a strong predictor of road-crossing probability: species with high hand-wing indices crossed the road more frequently than those with low hand-wing indices. In contrast, other characteristics such as body mass, diet, flocking behavior, and foraging stratum did not show significant associations with road crossing probability. Our results suggest that proxies of flight efficiency such as the hand-wing index can be powerful tools for predicting the vulnerability of bird species to forest fragmentation.</p>
ScienceDex guides
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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.