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202 results for “Migratory birds”
Inter- and intra-annual temperature and precipitation variability (1950-2022) across the ranges of non-migratory birds and their association with generation length
While environmental variability is theorized to impact the life history characteristics of organisms, these hypotheses have not been thoroughly tested with empirical data. To fill this gap, we synthesized a global data set of environmental variability metrics and life history characteristics across the ranges of 7,477 non-migratory, non-marine avian species. These data are derived from the ERA5 climate reanalysis, AVONET, BirdTree, and BirdLife databases as well as previously published research. By extracting environmental variability values across individual species' ranges, this data set allows users to evaluate avian species' pace of life in response to environmental change.
Supplementary material for "Decomposing fecundity and evaluating demographic influence of multiple broods in a migratory bird"
<p>Data and code files used in the paper. The four data files are provided in ASCII format (marr_S.TXT, broods_S.TXT, marr_G.TXT, broods_G.TXT). The code file (script_IPM_wryneck_Tenan_etal.txt) is a space delineated text file. The code file is written for R, but models are run in NIMBLE from R. The code file also contains the description of the data files and code for data management.</p>
NBM: an Open Dataset for the Acoustic Monitoring of Nocturnal Migratory Birds in Europe
<p>This is the dataset presented in the paper "NBM: an Open Dataset for the Acoustic Monitoring of Nocturnal Migratory Birds in Europe", which can be found here: <a href="https://arxiv.org/pdf/2412.03633">https://arxiv.org/pdf/2412.03633</a>.</p> <p> </p> <p><strong>Update 2025, June 13th</strong></p> <p>- A <strong>metadata file</strong> can now be found alongside the dataset ("metadata.csv"): it contains the recording date for original NBM files whenever available, and date and location for all but two of the files originating from Xeno-Canto. All audio samples in the train_nbm_orig folder were recorded in France, but no precise location is available for this collection.</p> <p>- Irregularities due to unconstrained text input in several annotation files have been fixed; in particular all annotated parasitic and background noise are now gathered in two classes: <strong>0: Background </strong>and<strong> 0: Other biophonia</strong>. Unidentified bird vocalizations fall under the <strong>Other </strong>category.</p> <p> </p> <p><strong>File description</strong></p> <p>Files come in three directories: train_nbm_orig, train_nbm_xc and test.</p> <p>Each is composed of a list of .wav files with its .txt annotations file. All bird vocalizations are linked to a <strong>species </strong>and are annotated both in <strong>time </strong>and <strong>frequency</strong>.</p> <p>The train dataset is composed of a total of 2,077 audio files and 13,359 annotations, for a total of ~38 hours of recording. Among these, 883 files were downloaded from Xeno-canto and finely hand-annotated.</p> <p> </p> <p><strong>Citation </strong>(Bibtex)</p> <pre>@article{airale2024nbm, title={NBM: an Open Dataset for the Acoustic Monitoring of Nocturnal Migratory Birds in Europe}, author={Airale, Louis and Pajot, Adrien and Linossier, Juliette}, journal={arXiv preprint arXiv:2412.03633}, year={2024} }<br><br></pre> <p><strong>License</strong></p> <p>This dataset is distributed under a non-commercial, non-derivative license (CC BY-NC-ND 3.0).</p> <p> </p> <p><strong>Acknowledgments</strong></p> <p><span lang="EN-US">Special thanks to all contributors to the initial NBM database: Adrien Pajot, Aymeric Mousseau, Christophe Mercier, Frédéric Cazaban, Gaëtan Mineau, Ghislain Riou, Guillaume Bigayon, Hervé Renaudineau, Kévin Leveque, Lionel Manceau, Mathurin Aubry, Maxence Pajot, Nidal Issa, Willy Raitière, and equal thanks to all anonymous further contributors.</span></p> <p><span lang="EN-US">Acknowledgments also go to all contributors of the Xeno-Canto database whose recordings where used to build this dataset:</span></p> <p><span lang="EN-US">Joost van Bruggen, Ricardo Hevia, Paul Kelly, Franck Hollander, Frédéric Cazaban, Ireneusz Oleksik, Irish Wildlife Sounds, Stanislas Wroza, Cedric Mroczko, Will Scott, Sergi, Chris Batty, Martin Billard, Juha Saari, Julien Bottinelli, Mark Pearson, Uku Paal, Christophe Mercier, Thierry THOMAS, Samuel Levy, Paul Coiffard, Lluís Brotons, Jorge Leitão, Alan Dalton, David Tattersley, Xavier Riera, Lars Mogensen, Feliu López i Gelats, José Manuel Reyes Páez, Jon Sparshott, Sergi Carreras, Marta Celej, julien Rochefort, Chèvremont Fabian, MILLON Xavier, Ed Stubbs, Niels Van Doninck, Jarek Matusiak, Anthony ROUX, Dan Lombard, C PAL, Ray Tsu </span><span lang="EN-US">诸仁</span><span lang="EN-US">, Martijn Verdoes, Pierrick Devoucoux, Manceau Lionel, Lionel Manceau, Vandousselaere Patrick, Volker Arnold, Lars Edenius, Krzysztof Deoniziak, Albert Noorlander, Robert Ekman, Oriol Baena, Mr Mark Shorten, Beatrix Saadi-Varchmin, Simon Kiesé, Robert Manzano, Martin Fousert, Simon Gillings, Lisette, Marco Dragonetti, Cédric PEIGNOT, Adrien CHARBONNEAU, Gosse Hoekstra, Christian Kerihuel, Koen Lepla, Daniele Baroni, Peter van Vlaardingen, Albert Subirà, Alain Malengreau, Julien Piette, Calum Mckellar, Mikael Litsgård, Martin Grienenberger, Sven Kransel, Oliwier Myka, Susanne Kuijpers, Pierre Foulquier, Paolo Zucca, Maties, Geoffrey Monchaux, Jean COURTIN, Pere Josa, Stein Ø. Nilsen, florent yvert, John Sirrett, Toby Carter, Grzegorz Lorek, Tom Jordan, Miguel Tirado, Helder Cardoso, Ignaas Robbe, James P, Corentin Rivière, Romuald Mikusek, David Santamaría Urbano, Diego Fernandez Martinez, Tanguy Loïs, Camille Vacher, Frank Pierik, Testaert Dominique, Piotr Szczypinski, Rafael Costas, Patrick Franke, Ad Hilders, J. Veeken, Thijs Calu, David Pennington, Nic Hallam, Albert Lastukhin, Dominique Guillerme, W. Agster, Nittert van de Water, Marcos Prada Arias, Francesco Barberini, Enrico Hübner, Luca Forneris, Graham Clarke, Mike Douglas, Johan Willner, Sébastien Arriubergé, Rafał Szczerbik, Alessandro Pavesi, Jarred Johnson, Tomasz Wałachowski, Cédric JOUVE, Tom Gheskiere, jesus carrion, David Darrell-Lambert, Graham Sparshott, Théo Hervé, Martin Sutherland, Quentin Giraud, Kévin Giraudin, LE ROY Renaud, August Thomasson, Marcin Sołowiej, Filippo Ceccolini, BirdingAlbufera, Armin Kreusel, Robert Thorpe, Sannier, Oscar Vilches, Paweł Szymański, Jerome Fischer, Matt Slaymaker, Gil Lissens, Sidney M, Matthias Feuersenger, JC Paniagua, Anita Rakitić, david m, Maxime Pirio, brickegickel, Mark Newsome, Sophie REVERDIAU, Oriol, Michaël Bridoux, Sonothèque ADVL, Kieran Nixon, Jochem verweij, Sjouke Scholten, Michael Brunhøj Hansen, Peter Stronach, Pere Josa Anguera, Esperanza Poveda, Johannes Dag Mayer, Pablo Valverde, Friedemann Arndt, Tristan Guillebot de Nerville, Adam Cross, Richard Drew, Cindie Arlaud, Petr Večeřa, Domagoj Tomičić, Guillaume Petitjean, riou, Peter Mattsson, Michał Jezierski, SCECB NATURA-Z, Karol Łanocha, Twan Mols, Klaus Fink, Dawid Jablonski, Arnold Meijer, Paul Ehlers, José Carlos Sires, manuel Grosselet, Birding The Strait, Charlie Bodin, Juan Carlos Paniagua, Nabholz Benoit, Fergus Crystal, Agris Celmins, Albert Cama, Iván Vega, John Muddeman, Adrien Mauss, Gerard Troost, Serge Hoste, Stefano Miceli, Joan Balfagon, Ace, Frank A. Roos, Tom Wulf, Vincent Palomares, Marcel Tenhaeff, Gabriel Hasan, James Lidster, Frank van de Weijer, Herman van Oosten, Alain Verneau, Herman van der Meer, Romain SPELLER, Johan Lorentzon, Thomas ARMAND, Chris Beach, Jacob Spinks, András Schmidt, Jonathan van Erkel, James Spencer, Rowan Wakefield, LEPAGE Frédéric, Itziar Gutiérrez, Severin Racky, NEVILLE MADON, B Whyte, Bodhuin Maxime, Benoit Paepegaey, Ruysschaert, Soulier Pierrick, Frederik Fluyt, Thomas Roux, Eduardo Realinho, Simon Elliott, Oscar Vilches Mendoza, Juan Pita-Romero Caamaño, Gary Elton, Marc Hughes, Raul Pascual, Nicolas Selosse, Arnaud Hedel, Gaëtan Mineau, Peter Boesman, Steve Flynn, Gerry p oneill, Andy Hultberg, Helmut Schaffer, Luca Giussani, edouard dansette, Gavia Stellata, Robert Schouw, Sławomir Karpicki-Ignatowski, mvallespirc, Mark Plummer, Chacron, Seynaeve Adriaan, Kristian Whittaker, Andreas Pettersson, Rafał Kurowski, Delpit, Anja van Halbeek, Lars Burnus, Benjamin Schedl, Sreekumar Chirukandoth, Mathias Götz, Mikhail Velikanov, Paul Bourdin, Török Tamás, Mehmet Ali Demiral, Manuel Grosselet, Olivier Swift, Yoann Blanchon, Jacob Bosma, Alexis Bukowski, Götz Ellwanger, Guillaume Bigayon, Charlie BODIN, Olivier SWIFT, Manuel Grosselet, Tero Linjama, Ad Postma, Eetu Paljakka, Jan Hein van Steenis, Alwin van Lubeck, Jacobo Ramil MIllarengo, Andrew Cobley, guus van duin, Meinolf Ottensmann, Steve Thorpe, Antoine Salmon, Alain Beuget, Simon Busuttil, Bertrand Dallet, Bill Haines, Robbin van Dijk, Jacopo Barchiesi, Johan Jordaans, Simon BAUDOUIN, Nelson Conceição, Teet Sirotkin, Dean McDonnell, Jocce Ekstrom, Michael John O Mahony, Fred Prak, Joachim Pintens, Christophe Legrand, Daniel Beuker, G Berger, Steve Blain, Boris Delahaie, Will Langdon, David Melichar, Emmanuel Roy, Jonas Brüggeshemke, YvesDS, FRIEDRICH Richard</span></p>
Male song structure predicts offspring recruitment to the breeding population in a migratory bird
<p>Bird song is a classic example of a sexually selected trait, but much of the work relating individual song components to fitness has not accounted for song typically being composed of multiple, often-correlated components, necessitating a multivariate approach. We explored the role of sexual selection in shaping complex male song of house wrens (<em>Troglodytes aedon</em>) by simultaneously relating its multiple components to fitness using multivariate selection analysis, which is widely used in insect and anuran studies but not in birds. The analysis revealed significant variation in the form and strength of selection acting on song across different selection episodes, from nest-site defense to recruitment of offspring to the breeding population. Males that sang more song typically employed in close communication sired more offspring that were subsequently recruited to the breeding population than those that sang far-communication song. However, this relationship was not consistent across earlier selection episodes, as evidenced by non-linear selection acting on these song components in other contexts. Collectively, our results present a complex picture of multivariate selection on male song structure that would not be evident using univariate approaches and suggest possible trade-offs within and among song components at different points of the breeding season. </p>
The role of tropical rainfall in driving range dynamics for a long-distance migratory bird
<p>Predicting how the range dynamics of migratory species will respond to climate change requires a mechanistic understanding of the factors that operate across the annual cycle to control the distribution and abundance of a species. Here we use multiple lines of evidence to reveal that environmental conditions during the nonbreeding season influence range dynamics across the lifecycle of a migratory songbird, the American redstart (<em>Setophaga ruticilla</em>). Using long-term data from the nonbreeding grounds and breeding origin estimated from stable hydrogen isotopes in tail feathers, we found that the relationship between nonbreeding season survival and migration distance is mediated by precipitation, but only during dry years. A long-term drying trend throughout the Caribbean is associated with higher mortality for individuals from the northern portion of the species' breeding range, resulting in an approximate 500 km southward shift in breeding origins of this Jamaican population over the past 30 years. This shift in connectivity is mirrored by changes in the redstarts breeding distribution of abundance. These results demonstrate that the climatic effects on demographic processes originating during the tropical nonbreeding season is actively shaping range dynamics in a migratory bird.</p>
Energetic trade-offs in migration decision-making, reproductive effort, and subsequent parental care in a long-distance migratory bird
<p>Migratory species trade-off long-distance movement with survival and reproduction, but the spatiotemporal scales at which these decisions occur is relatively unknown. Technological and statistical advances allow fine-scale study of animal decision-making, improving our understanding of possible causes and therefore conservation management. We quantified effects of reproductive preparation during spring migration on subsequent breeding outcomes, breeding outcomes on autumn migration characteristics, and autumn migration characteristics on subsequent parental survival in Greenland white-fronted geese (<em>Anser albifrons flavirostris</em>). These are long-distance migratory birds with a ~50% population decline from 1999 to 2022. We deployed GPS-acceleration devices on adult females to quantify up to five years of individual decision-making throughout the annual cycle. Weather and habitat-use affected time spent feeding and overall dynamic body acceleration (i.e., energy expenditure) during spring and autumn. Geese that expended less energy and fed longer during spring were more likely to successfully reproduce. Geese with offspring expended more energy and fed for less time during autumn, potentially representing adverse fitness consequences of breeding. These behavioural comparisons among Greenland white-fronted geese improve our understanding of fitness trade-offs underlying abundance. We provide a reproducible framework for full annual cycle modelling using location and behaviour data, applicable to similarly studied migratory animals.</p>
Data from: Early-life variation in migration is subject to strong fluctuating survival selection in a partially migratory bird
<p>Population dynamic and eco-evolutionary responses to environmental variation and change fundamentally depend on combinations of within- and among-cohort variation in phenotypic expression of key life-history traits, and on corresponding variation in selection on those traits. Specifically, in partially migratory populations, spatio-seasonal dynamics depend on the degree of adaptive phenotypic expression of seasonal migration versus residence, where more individuals migrate when selection favours migration.</p> <p>Opportunity for adaptive (or, conversely, maladaptive) expression could be particularly substantial in early life, through initial development of migration versus residence. However, within- and among-cohort dynamics of early-life migration, and of associated survival selection, have not been quantified in any system, preventing any inference on adaptive early-life expression. Such analyses have been precluded because data on seasonal movements and survival of sufficient young individuals, across multiple cohorts, have not been collected.</p> <p>We undertook extensive year-round field resightings of 9,359 colour-ringed juvenile European Shags (<em>Gulosus aristotelis</em>) from 11 successive cohorts in a partially-migratory population. We fitted advanced Bayesian multi-state capture-mark-recapture models to quantify early-life variation in migration versus residence and associated survival across short temporal occasions through each cohort's first year from fledging, thereby quantifying the degree of adaptive phenotypic expression of migration within and across years.</p> <p>All cohorts were highly partially migratory, but the degree and timing of migration varied considerably within and among cohorts. Episodes of strong survival selection on migration versus residence occurred both on short timeframes within years, and cumulatively across whole years, generating instances of instantaneous and cumulative net selection that would be obscured at coarser temporal resolutions. Further, the magnitude and direction of selection varied among years, generating strong fluctuating survival selection on early-life migration across cohorts, as rarely evidenced in nature. Yet, the degree of migration did not strongly covary with the direction of selection, indicating limited early-life adaptive phenotypic expression.</p> <p>These results reveal how dynamic early-life expression and selection on a key life-history trait, seasonal migration, can emerge across seasonal, annual, and multi-year timeframes, yet be substantially decoupled. This restricts the potential for adaptive phenotypic, micro-evolutionary, and population dynamic responses to changing seasonal environments.</p>
Fig. 3 in Prevalence and genetic diversity of haematozoa in South American waterfowl and evidence for intercontinental redistribution of parasites by migratory birds
Fig. 3. Bayesian phylogenetic tree of haematozoa mitochondrial DNA cytochrome b haplotypes obtained from infected waterfowl. Trees were rooted with mammalian Plasmodium outgroups. Node tips are labeled with parasite genus (Haem = Haemoproteus, Leuc = Leucocytozoon, and Plas = Plasmodium), followed by the lineage name, GenBank accession number for each sequence, host order (passerine/waterfowl), and the country/state from which the samples were collected. All haplotypes identified in this study are highlighted in red. Numbers on branches represent posterior probabilities from the analysis. Asterisks after node tip labels indicate sequences from our study that were identical to lineages previously found in non-waterfowl hosts. All reference sequences were obtained from the National Center for Biotechnology Information website.
Fig. 2. Minimum spanning network for haematozoa mitochondrial DNA cytochrome b in Prevalence and genetic diversity of haematozoa in South American waterfowl and evidence for intercontinental redistribution of parasites by migratory birds
Fig. 2. Minimum spanning network for haematozoa mitochondrial DNA cytochrome b haplotypes detected in South American waterfowl. Shaded circles represent unsampled nodes. All circles are drawn proportional to the frequency at which haplotypes were observed. Lines separating nodes are drawn to scale based on the number of nucleotide mutations, unless otherwise indicated by hash marks. Only haplotypes with a length of 358 bp or greater were included. Haplotype name abbreviations are as follows: Haem = Haemoproteus, Leuc = Leucocytozoon, and Plas = Plasmodium.
Fig. 1 in Prevalence and genetic diversity of haematozoa in South American waterfowl and evidence for intercontinental redistribution of parasites by migratory birds
Fig. 1. Map of sampling locations in Peru and Argentina. The number of waterfowl blood samples collected at each site is provided in parentheses.
BirdVox-full-season: 6672 hours of audio from migratory birds
<p><strong>BirdVox-full-season: 6672 hours of audio from migratory birds</strong></p> <p><strong>================================================</strong></p> <p>Version 1.1, May 2022.</p> <p> </p> <p>The full-season dataset contains 6671 hours of audio from the Fall 2015 migration season in Tompkins County, NY.</p> <p> </p> <p><strong>Created By</strong></p> <p><strong>---------------</strong></p> <p>Andrew Farnsworth (1), Benjamin Mark Van Doren (1), Steve Kelling (1), Vincent Lostanlen (2), Justin Salamon (3), Aurora Cramer (4), Juan Pablo Bello (4)</p> <p>(1): Cornell Lab of Ornithology (CLO)</p> <p>(2): Laboratoire des Sciences du Numérique de Nantes (LS2N), CNRS</p> <p>(3): Adobe Research</p> <p>(4): New York University</p> <p>https://wp.nyu.edu/birdvox</p> <p> </p> <p><strong>Data acquisition</strong></p> <p><strong>--------------------</strong></p> <p>In 2015, we placed nine bioacoustic sensors - Recording and Observing Bird Identification Node (ROBIN) developed by the Cornell Lab of Ornithology - in residential areas of Tompkins County, NY, USA, primarily surrounding the town of Ithaca, NY, USA. All sensors had the same hardware configurationcomprising a Knowles EK23132 microphone element, an analog-to-digital converter, a Raspberry Pi Model B single-board computer, a solid-state memory card, and a battery. The microphone element is omnidirectional and has an approximately flat sensitivity of 53±5 dB between 2 and 10 kHz; that is, the frequency range of flight calls. The microphone element sits at the bottom of a small horn-shaped enclosure oriented upwards. In turn, this enclosure sits inside a hard plastic housing, whose purpose is to reject lateral sound sources, such as insects or car engines.</p> <p>The analog-to-digital converter encodes the monophonic signal recorded by the microphone into a linear pulse-code modulation sequence at a sample rate of 24 kHz and a sample depth of 16 bits. This sample rate corresponds to an appropriate Nyquist bound to capture the diversity of avian flight calls, which occur almost exculsively below 11 kHz in frequency. The single-board computer streams this sequence under the form of 20-second buffers, which are progressively appended to a lossless audio file in FLAC format. This acquisition procedure is repeated every night from civil twilight dawn to dusk between August 3rd, 2015 and December 8th, 2015. This temporal period corresponds to the general pattern of the timing of nocturnal bird migration, which birds usually initiate 30-45 minutes after local sunset and cease in the hours around dawn; this is not always the case for cessation.</p> <p>This duty cycle corresponds to roughly 1,500 hours of audio per sensor, and thus 13,500 hours for the entire sensor network. However, due to intermittent failures of sensing hardware, a common feature of many autonomous recording platforms, we retrieved only 6,651 hours successfully.</p> <p>We gathered FLAC files according to their location of provenance or "unit". Note that unit09 was never deployed: hence, the unit IDs are 01, 02, 03, 04, 05, 06, 07, 08, and 10.</p> <p>Despite hard plastic housing and deployment locations that attempted to physically facilitate avoidance of non-target signal capture, these sensors captured audio of nocturally migrating birds as well as additional features of this soundscape including human activities (anthrophony), meteorological phenomena (e.g. geophony) and non-targeted avian and other biological signals (biophony). These latter signals include non-human mammals (e.g. White-tailed Deer, flying squirrel, canines), diurnal vocalizations of resident and migrant birds that are not flight calls (e.g. Blue Jays, American Crow, American Goldfinch), anurans (e.g. spring peepers), and many insect species (e.g. Fork-tailed Bush-Katydid, Snowy Tree Cricket). </p> <p> </p> <p><strong>Derivative Datasets</strong></p> <p><strong>----------------------------</strong></p> <p>A representative subset of these audio recordings were selected for annotation. Ornithologist Andrew Farnsworth used the Raven software to pinpoint and label every avian flight call in time and frequency. He found 26138 sound events, of which 21546 are flight calls from Passeriformes. Of those, 13385 are identifiable in terms of family, and 8669 are identifiable in terms of both family and species. The annotation process took over 600 hours. This subset of recordings and the corresponding annotations have been released as BirdVox-296h (<a href="https://doi.org/10.5281/zenodo.4415480">https://doi.org/10.5281/zenodo.4415480</a>). The isolated flight calls and annotations have been released as BirdVox-14SD (<a href="https://doi.org/10.5281/zenodo.3667093">https://doi.org/10.5281/zenodo.3667093</a>) and its follow-up release BirdVox-25SD (<a href="https://doi.org/10.5281/zenodo.5889214">https://doi.org/10.5281/zenodo.5889214</a>).</p> <p> </p> <p><strong>Feedback</strong></p> <p><strong>-------------</strong></p> <p>Please help us improve BirdVox-full-night by sending your feedback to:</p> <p>vincent.lostanlen@ls2n.fr and af27@cornell.edu</p> <p>In case of a problem, please include as many details as possible.</p> <p> </p> <p><strong>Acknowledgement</strong></p> <p><strong>--------------------------</strong></p> <p>We thank the following people for their contributions to the development, construction, maintenance, deployment, and acquisition of the ROBIN units: Jessie Barry, Ian Davies, Tom Fredericks, Jeff Gerbracht, Sara Keen, Holger Klinck, Anne Klingensmith, Ray Mack, Peter Marchetto, Ed Moore, Matt Robbins, Ken Rosenberg, and Chris Tessaglia-Hymes, Chris Wood. Initial data collection activities were supported by NSF 1125098, Wolf Creek Foundation, and Leon Levy Foundation; analyses were supported largely by NSF 1633206 as well as Leon Levy Foundation and NSF 1661329.</p> <p>Cornell University is located on the traditional homelands of the Gayogo̱hó꞉nǫ' (Guy-yo-KO-no) (the Cayuga Nation). The Gayogo̱hó꞉nǫ' are members of the Haudenosaunee (Ho-di-no-so-ni) Confederacy, an alliance of six sovereign Nations with a historic and contemporary presence on this land. The Confederacy precedes the establishment of Cornell University, New York state, and the United States of America. We acknowledge the painful history of Gayogo̱hó꞉nǫ' dispossession, and honor the ongoing connection of Gayogo̱hó꞉nǫ' people, past and present, to these lands and waters. We acknowledge that the land on which the data was collected is the unceded territory of the Cayuga nation.</p> <p> </p>
Figure 1. A in Survey of the Non-Migratory Birds of the Rock Islands Southern Lagoon World Heritage Site in Palau
Figure 1. A. Map of Palau. B. Map of the island groups of the Rock Islands Southern Lagoon World Heritage Site study area (inset). Our survey included all 10 island groups. Scale marker = 5 km..
Data from: Evidence for seasonal compensation of hunting mortalities in a long-lived migratory bird
<p>Understanding whether hunting mortality is additive to or compensated by other mortality sources is at the heart of managing harvested populations. Long-lived species are expected to exhibit hunting mortality additive to other sources of mortality, making them ideal candidates for population management through sport harvest. Previous studies on these processes have focussed on density-dependent natural mortality compensating for hunting mortality, but when harvest occurs in distinct periods of the year, heterogeneity in hunting vulnerability between individuals could also lead to compensatory mortality between these periods. We explore this new idea using the case of the greater snow goose (<em>Anser caerulescens atlantica</em>), a harvested species whose population became overabundant in the late 20<sup>th</sup> century. To control this population, wildlife agencies liberalized hunting regulations with unprecedented actions such as special hunting seasons implemented in spring 1999 in Canada and in winter 2009 in the USA. To determine the relative impact of each measure on survival, we estimated survival of adult geese on a seasonal basis using 30 years of capture-mark-reencounter data in a joint live-and-dead-encounter multievent model. We also used this quasi-experimental set-up to evaluate possible compensation in hunting mortality between seasons. We found that both special hunting seasons decreased goose survival in the seasons and periods in which they were implemented. However, survival increased during the spring hunting season after the establishment of the special winter hunting season in the USA in 2009. There was a negative relationship between annual spring and winter mortalities, suggesting that the increase in hunting mortality in winter was compensated by a reduction in spring mortality after 2009.</p> <p><em>Synthesis and applications:</em> To our knowledge, we report the first documented instance of hunting mortality in one season being compensated by a reduction in hunting mortality in a subsequent season. We suggest that heterogeneity in hunting vulnerability among individuals, possibly linked to the presence of juveniles, may explain this phenomenon. A better knowledge of seasonal patterns and relationships between mortality components is needed to improve our understanding of population dynamics and management of harvested populations.</p>
Data from: Ontogeny of migration destination, route and timing in a partially migratory bird
<p><strong>Abstract</strong></p> <ol> <li>In migratory animals, the developmental period from inexperienced juveniles to breeding adults could be a key life stage in shaping population migration patterns. Nevertheless, the development of migration routines in early life remains underexplored. While age-related changes in migration routes and timing have been described in obligate migrants, most investigations into the ontogeny of partial migrants only focused on age-dependency of migration as a binary tactic (migrant or resident), and variations in routes and timing among individuals classified as ‘migrants’ is rarely considered. </li> <li>To fill this gap, we study the ontogeny of migration destination, route and timing in a partially migratory red kite (<em>Milvus milvus</em>) population. Using an extensive GPS-tracking dataset (292 fledglings and 38 adults, with 1 – 5 migrations tracked per individual), we studied how 9 different migration characteristics changed with age and breeding status in migrant individuals, many of which become resident later in life.</li> <li>Individuals departed later from and arrived earlier at the breeding areas as they aged, resulting in a gradual prolongation of stay in the breeding area by two months from the first to the fifth migration. Individuals delayed southward migration in the year prior to territory acquirement, and they further delayed it after occupying a territory. Migration routes became more direct with age. Individuals were highly faithful to their wintering site. Migration distance shortened only slightly with age and was more similar among siblings than among unrelated individuals.</li> <li>The large gradual changes in northward and southward migrations suggest a high degree of plasticity in temporal characteristics during the developmental window. However, the high wintering site fidelity points towards large benefits of site familiarity, prompting spatial migratory plasticity to be expressed through a switch to residency. </li> <li>The contrasting patterns of trajectories of age-related changes between spatial and temporal migration characteristics might reflect different mechanisms underlying the expression of plasticity. Investigating such patterns among species along the entire spectrum of migration tactics would enable further understanding of the plastic responses exhibited by migratory species to rapid environmental changes.</li> </ol>
Linked collectors and determiners for: Western Palearctic migratory birds in continental Africa.
Natural history specimen data linked to collectors and determiners held within, "Western Palearctic migratory birds in continental Africa". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/84c58b60-f762-11e1-a439-00145eb45e9a">https://bionomia.net/dataset/84c58b60-f762-11e1-a439-00145eb45e9a</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/84c58b60-f762-11e1-a439-00145eb45e9a">https://gbif.org/dataset/84c58b60-f762-11e1-a439-00145eb45e9a</a>. Formatted as a Frictionless Data package.
Data from: Spatial consistency in drivers of population dynamics of a declining migratory bird
<p>1. Many migratory species are in decline across their geographical ranges. Single-population studies can provide important insights into drivers at a local scale, but effective conservation requires multi-population perspectives. This is challenging because relevant data are often hard to consolidate, and state-of-the-art analytical tools are typically tailored to specific datasets.</p> <p>2. We capitalized on a recent data harmonization initiative (SPI-Birds) and linked it to a generalized modeling framework to identify the demographic and environmental drivers of large-scale population decline in migratory pied flycatchers (<em>Ficedula</em> <em>hypoleuca</em>) breeding across Britain.</p> <p>3. We implemented a generalized integrated population model (IPM) to estimate age-specific vital rates, including their dependency on environmental conditions, and total and breeding population size of pied flycatchers using long-term (34–64 years) monitoring data from seven locations representative of the British breeding range. We then quantified the relative contributions of different vital rates and population structures to changes in short- and long-term population growth rates using transient life table response experiments (LTREs).</p> <p>4. Substantial covariation in population sizes across breeding locations suggested that change was the result of large-scale drivers. This was supported by LTRE analyses, which attributed past changes in short-term population growth rates and long-term population trends primarily to variation in annual survival and dispersal dynamics, which largely act during migration and/or non-breeding season. Contributions of variation in local reproductive parameters were small in comparison, despite sensitivity to local temperature and rainfall within the breeding period.</p> <p>5. We show that both short- and longer-term population changes of British-breeding pied flycatchers are likely linked to factors acting during migration and in non-breeding areas, where future research should be prioritized. We illustrate the potential of multi-population analyses for informing management at (inter)national scales and highlight the importance of data standardization, generalized and accessible analytical tools, and reproducible workflows to achieve them.</p>
Figure 3. A European Robin, Erithacus rubecula. A in Capturing migratory birds and examining for ticks (Acari: Ixodida)
Figure 3. A European Robin, Erithacus rubecula. A. Examination of tick presence, B. Collection of immature ticks by a tweezer.
Figure 2 in Capturing migratory birds and examining for ticks (Acari: Ixodida)
Figure 2. Willow Warbler, Phylloscopus trochilus, parasitized by a fully engorged nymph (Hyalomma sp.). A. Dorsal view, B. Lateral view.
Figure 1. A in Capturing migratory birds and examining for ticks (Acari: Ixodida)
Figure 1. A. Erected mist net in Cernek bird ringing station, B. Caught birds in mist net, C. A Garden Warbler, Sylvia borin, captured by mist net.
Energetic trade-offs in migration decision-making, reproductive effort, and subsequent parental care in a long-distance migratory bird
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