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39 results for “Nocturnal migration”
Supplementary material for 'Revealing patterns of nocturnal migration using the European weather radar network'
<p>This package contains data, filters and visualizations from <a href="https://doi.org/10.1111/ecog.04003">Nilsson and Dokter et al. (2019)</a>.</p> <p><strong>Files</strong></p> <p><strong>radar_metadata.csv</strong>: Metadata for the 84 European radars considered for this study. Includes radar code (<code>odim_code</code> = <code>country</code> + <code>odim_code_3char</code> and alternative radar code <code>vp_radar</code>), radar site location (<code>location</code>, <code>latitude</code>, <code>longitude</code>), radar site elevation (<code>site_altitude_asl</code> in meters above sea level) and radar altitude range used in this study (<code>min_height_cut_asl</code> and <code>max_height_cut_asl</code> in meters above sea level).</p> <p><strong>vp.zip</strong>: Vertical profiles of birds (vp) data, processed from the radar volume data following procedures described by Dokter et al. (2011), using the vol2bird algorithm in the R package bioRad. Zip file includes vp data for the 84 European radars considered for this study from September 19 to October 9, 2016 (21 days). This time period is characterized by strong passerine migration throughout Europe. Files are organized in radar (= <code>odim_code</code>), date and hour directories and follow the <a href="https://github.com/adokter/vol2bird/wiki/ODIM-bird-profile-format-specification">ODIM bird profile format specification</a>. Data can be read with the <a href="https://github.com/adokter/bioRad/">R package bioRad</a>.</p> <p><strong>vp_processing_settings.yaml</strong>: Data selection setting for this study, based on data quality criteria. File lists for each radar the altitudes to include (<code>include_heights</code>), time periods to exclude (<code>exclude_datetimes</code>) and reasons for exclusion (comments). 70 of the 84 radars were retained after filtering.</p> <p><strong>vp_processed_70_radars_20160919_20161009.csv</strong>: Processed vp data for 70 radars. Is the result of processing <code>vp.zip</code> with <code>vp_processing_settings.yaml</code> and <code>radar_metadata.csv</code> using <a href="https://doi.org/10.5281/zenodo.1173544">vp-processing</a> (Desmet & Nilsson 2018). Note: includes all timestamps: day and night & those marked for exclusion (marked in <code>exclusion_reason</code>). This data file forms the basis for analysis in the study.</p> <p>Headers are:</p> <ul> <li><code>radar_id</code>: odim_code of the radar</li> <li><code>datetime</code>: timestamp</li> <li><code>HGHT</code>: lower altitude of altitude bin (m above sea level)</li> <li><code>u</code>: bird ground speed towards east (m/s)</li> <li><code>v</code>: bird ground speed towards north (m/s)</li> <li><code>dens</code>: bird density (birds/km3)</li> <li><code>dd</code>: bird flight direction (degrees from north)</li> <li><code>ff</code>: bird ground speed (m/s)</li> <li><code>DBZH</code>: reflectivity factor (dBZ) in horizontal polarisation</li> <li><code>mtr</code>: migration traffic rate (birds/km/h)</li> <li><code>day_night</code>: timestamp occurs during <code>day</code> or <code>night</code> (based on sunrise/sunset)</li> <li><code>date_of_sunset</code>: date at sunset, with night timestamps between midnight and sunrise belonging to the previous date</li> <li><code>exclusion_reason</code>: reason timestamp is excluded in vp_processing_settings.yaml (if applicable). Excluded timestamps have <code>NA</code> values for <code>u</code>, <code>v</code>, <code>dens</code>, <code>dd</code>, <code>ff</code>, <code>DBZH</code>, and <code>mtr</code>.</li> </ul> <p><strong>vp_flowviz.csv:</strong> Input data for visualizations. Is the result of processing <code>vp_processed_70_radars_20160919_20161009.csv</code> using <code>vp-to-flowviz.Rmd</code> in <a href="https://doi.org/10.5281/zenodo.1173544">vp-processing</a> (Desmet & Nilsson 2018). Aggregates data in hourly bins for 200-2000m (<code>altitude_band</code> = 1) and above (<code>altitude_band</code> = 2). Only altitude band 1 is used in visualizations.</p> <p><strong>flowviz.mov:</strong> Screencast of <code>vp_flowviz.csv</code> visualized with <a href="https://doi.org/10.5281/zenodo.57472">Bird migration flow visualization v2</a> (Desmet et al. 2016, Shamoun-Baranes et al. 2016). The visualization extrapolates the migration over the entire sampling range (cropped in the screencast due to technical limitations and thus excluding the Bulgarian radar), not taking topography or water bodies into account, and shows the ground speed (length of arrows) and direction of migration over time. Note that density is not shown: low density movements can therefore appear as strong as high density movements when ground speeds are similar.</p> <p><strong>cartoviz.mov:</strong> Screencast of <code>vp_flowviz.csv</code> visualized as an interactive map with <a href="https://carto.com">CARTO</a>. Visualization shows migration density (size of circles) and mean direction (colour) over time. The interactive map is available at <a href="https://inbo.carto.com/u/lifewatch/builder/8685140f-8d8c-4d06-9e1e-25d051d43748/embed">https://inbo.carto.com/u/lifewatch/builder/8685140f-8d8c-4d06-9e1e-25d051d43748/embed</a>.</p>
Space weather disrupts nocturnal bird migration
<p>Our paper tests for the effects of space weather-induced geomagnetic disturbances on radar-detected nocturnal bird migration. We find evidence for a ~10% decrease of migration intensity after controlling for weather variables and spatiotemporal autocorrelation, and also for a decrease in the effort birds spent flying against the wind in the fall, especially under overcast conditions. This repository provides the data and the code used to arrive at these conclusions and plot the main results. Weather radar data was processed from the NOAA NEXRAD network, weather data was accessed from the North American Regional Reanalysis, and magnetometer data was accessed from the SuperMAG inventory. </p>
Social behavior among nocturnally migrating birds revealed by automated moonwatching
<p>Migrating birds often fly in group formations during the daytime; whereas at night, it is generally presumed that they fly singly. However, it is difficult to quantify group behavior during nocturnal migration as there are few means of directly observing interactions among individuals. We employed an automated form of moonwatching to estimate percentages of birds that appear to migrate in groups during the night within the Central Flyway of North America. We compared percentages of birds in groups across the spring and fall and examined overnight temporal patterns of group behavior. We found groups were rare in both seasons, never exceeding 10% of birds observed, and were almost nonexistent during the fall. We also observed an overnight pattern of group behavior in the spring wherein groups were more commonly detected early in the night and again just before migration activity ceased. This finding may be related to changes in species composition of migrants throughout the night, or alternatively it suggests that group formation may be associated with flocking activity on the ground as groups are most prevalent when birds begin and end a night of migration.</p>
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>
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>
Social behavior among nocturnally migrating birds revealed by automated moonwatching
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Data from: A call in the dark: Nocturnal flight calls and their potential to advance the study of avian migration
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Winds at departure shape seasonal patterns of nocturnal bird migration over the North Sea
On their migratory journeys, terrestrial birds can come across large inhospitable areas with limited opportunities to rest and refuel. Flight over these areas poses a risk especially when wind conditions en route are adverse, in which case inhospitable areas can act as an ecological barrier for terrestrial migrants. Thus, within the East-Atlantic flyway, the North Sea can function as an ecological barrier. The main aim of this study was to shed light on seasonal patterns of bird migration in the southern North Sea and determine whether departure decisions on nights of intense migration were related to increased wind assistance. We measured migration characteristics with a radar that was located 18 km off the NW Dutch coast and used simulation models to infer potential departure locations of birds on nights with intense nocturnal bird migration. We calculated headings, track directions, airspeeds, groundspeeds on weak and intense migration nights in both seasons and compared speeds between seasons. Moreover, we tested if departure decisions on intense migration nights were associated with supportive winds. Our results reveal that on the intense migration nights in spring, the mean heading was towards E, and birds departed predominantly from the UK. On intense migration nights in autumn, the majority of birds departed from Denmark, Germany and north of the Netherlands with the mean heading towards SW. Prevailing winds from WSW at departure were supportive of a direct crossing of the North Sea in spring. However, in autumn winds were generally not supportive, which is why many birds exploited positive wind assistance which occurred on intense migration nights. This implies that the seasonal wind regimes over the North Sea alter its migratory dynamics which is reflected in headings, timing and intensity of migration.
Blue light attracts nocturnally migrating birds
<p>Light pollution is increasing and artificial light sources have great impacts on animals. For migrating birds, collisions caused by artificial light pollution are a significant source of mortality. Laboratory studies have demonstrated that birds have different visual sensitivities to different colors of light, but few field experiments have compared birds' responses to light of different wavelengths. We used three monochromatic lights (red, green, and blue) and polychromatic yellow light to study the impact of wavelength on phototaxis at two gathering sites of nocturnally migrating birds in Southwest China. For both sites, short-wavelength blue light caused the strongest phototactic response. In contrast, birds were rarely attracted to long-wavelength red light. The attractive effect of blue light was greatest during nights with fog and headwinds. As rapid urbanization and industrialization cause an increase in artificial light, we suggest that switching to longer wavelength lights is a convenient and economically effective way to reduce bird collisions.</p>
Data from: Concealed by darkness: interactions between predatory bats and nocturnally migrating songbirds illuminated by DNA sequencing
Recently, several species of aerial-hawking bats have been found to prey on migrating songbirds, but details on this behaviour and its relevance for bird migration are still unclear. We sequenced avian DNA in feather-containing scats of the bird-feeding bat Nyctalus lasiopterus from Spain collected during bird migration seasons. We found very high prey diversity, with 31 bird species from eight families of Passeriformes, almost all of which were nocturnally flying sub-Saharan migrants. Moreover, species using tree hollows or nest boxes in the study area during migration periods were not present in the bats' diet, indicating that birds are solely captured on the wing during night-time passage. Additional to a generalist feeding strategy, we found that bats selected medium-sized bird species, thereby assumingly optimizing their energetic cost-benefit balance and injury risk. Surprisingly, bats preyed upon birds half their own body mass. This shows that the 5% prey to predator body mass ratio traditionally assumed for aerial hunting bats does not apply to this hunting strategy or even underestimates these animals' behavioural and mechanical abilities. Considering the bats' generalist feeding strategy and their large prey size range, we suggest that nocturnal bat predation may have influenced the evolution of bird migration strategies and behaviour.
Data from: Forecasting nocturnal bird migration for dynamic aeroconservation: the value of short-term dataset
<p>Placing wind turbines within large migration flyways, such as the North Sea basin, can contribute to the decline of vulnerable migratory bird populations by increasing mortality through collisions. Curtailment of wind turbines limited to short periods with intense migration can minimize these negative impacts, and near-term bird migration forecasts can inform such decisions. Although near-term forecasts are usually created with long-term datasets, the pace of environmental alteration due to wind energy calls for urgent development of conservation measures that rely on existing data, even when it does not have long temporal coverage. Here, we use five years of tracking bird radar data collected off the western Dutch coast, weather, and phenological variables to develop seasonal near-term forecasts of low-altitude nocturnal bird migration over the southern North Sea. Overall, the models explained 71% of the variance and correctly predicted migration intensity above or below a threshold for intense hourly migration in more than 80% of hours in both seasons. However, the percentage of correctly predicted intense migration hours (top 5% of hours with the most intense migration) was low, likely due to the short-term dataset and their rare occurrence. We, therefore, advise careful consideration of a curtailment threshold to achieve optimal results. Synthesis and applications: Near-term forecasts of migration fluxes evaluated against measurements can be used to define curtailment thresholds for offshore wind energy. We show that to minimize collision risk for 50% of migrants, if predicted correctly, curtailments should be applied during 18 hours in spring and 26 in autumn in the focal year of model assessments, resulting in an estimated annual wind energy loss of 0.12%. Drawing from the Dutch curtailment framework, which pioneered the 'international first' offshore curtailment, we argue that using forecasts developed from limited temporal datasets alongside expert insight and data-driven policies can expedite conservation efforts in a rapidly changing world. This approach is particularly valuable in light of increasing interannual variability in weather conditions.</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>
Integrating light-level geolocation with activity tracking reveals unexpected nocturnal migration patterns of the tawny pipit
Migratory birds complete their seasonal journeys between breeding and non-breeding sites with a series of migratory flights that are separated by prolonged stopovers. While songbirds are the most common taxa among migratory birds, empirical data on flight and stopover behaviour along their entire migratory journeys are still rare. Here, we integrate activity and barometric pressure tracking with classical light-level geolocation to describe migration behaviour of tawny pipits Anthus campestris breeding in Central Europe. Surprisingly, tracked pipits used, on average, as many as 10 stopover sites during their six week, >5000 km long autumn migration. This conforms to a typical hop-type pattern of migration. In contrast to common knowledge which considers the tawny pipit as a typical diurnal migrant, our data revealed that more than 2/3 of all migratory movements were carried out at night. Nocturnal departure times were highly variable within individuals and spread across the entire night while landing most often took place within the first few hours after sunrise. Consequently, there was a negative relationship between departure timing relative to sunset and flight duration. Short flights of up to 2 h were most common and median flight duration was 4.5 h. There was a hyperbolic relationship between flight duration and maximum flight altitude and flight altitudes during night were two times higher compared to daytime. The overall ratio of flight vs stopover duration during migration was on average 1:6.5. This closely matches predictions from theoretical models. We show that multi-sensor tracking has the potential to provide unprecedented details on migratory behaviour of individual birds along their entire migratory journeys, and it also improves the precision of geographical locations derived from light-level geolocators.
Bird-building collisions increase with weather conditions that favor nocturnal migration and with inclement and changing weather
<p>Collisions with building windows are a top bird mortality source, but few studies have evaluated how bird-window collisions are influenced by the weather. By monitoring collisions daily at 21 buildings in Minneapolis, Minnesota, over 4 migration seasons, we show that weather influences the number of window collisions of nocturnal migrants in spring and fall, indicating that collisions may be forecastable based on weather conditions. Collisions increased with weather favoring migration, such as consecutive nights of south wind in spring and even short periods of north wind in fall. We also found evidence that spring and fall collisions increase with weather changes that impede migration, such as changes from fair conditions and tailwinds early in the night to headwinds near sunrise. Our study suggests complex weather effects never before considered in the context of bird collisions, including possible time lag effects of conditions two and three nights before collisions occur, effects of multi-day sequences of conditions, and interactions between conditions at different times of the night. More research is needed to determine if the accuracy of weather-based collision prediction systems improves by integrating such nuances and to clarify mechanisms through which these complex effects operate, such as influences of weather on migration intensity and collision avoidance behavior. Weather-based forecasts may allow refinement of collision mitigation approaches (e.g., reducing building lighting on certain nights or using temporary glass coverings or treatments). However, because challenges remain to communicating such temporally targeted actions and implementing them in a timely manner, other bird-friendly practices (e.g., season-long lighting reduction and permanent glass treatments) should continue to be prioritized.</p>
Data from: Concealed by darkness: interactions between predatory bats and nocturnally migrating songbirds illuminated by DNA sequencing
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Winds at departure shape seasonal patterns of nocturnal bird migration over the North Sea
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Bird-building collisions increase with weather conditions that favor nocturnal migration and with inclement and changing weather
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Data from: Forecasting nocturnal bird migration for dynamic aeroconservation: the value of short-term dataset
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Data from: Evaluation of methods to estimate nocturnal bird migration activity: A comparison of radar and nocturnal flight call monitoring in the American West
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Integrating light-level geolocation with activity tracking reveals unexpected nocturnal migration patterns of the tawny pipit
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