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14 results for “eBird”
Birdwatching, eBird and citizen science in India: qualitative interviews with participants, practitioners and ecologists
<h1>Abstract</h1> <p>This study consists of qualitative interviews about birdwatching, citizen science, and the use of the birdwatching data platform <em>eBird </em>in India. Interview partners are birdwatchers, citizen science practitioners, and ecologists who have used eBird data. Some of the main topics covered include: the nature of the birdwatching community and styles of birdwatching in India; the history of the adoption of eBird in India; the value of birdwatching and citizen science; challenges involved in conducting or participating in citizen science; opportunities and limitations of using data from eBird and citizen science; processes of data collection and quality control in eBird; and ecological research, conservation priorities, and environmental activism in India. This study is part of the project A Philosophy of Open Science for Diverse Research Environments (PHIL_OS).</p> <h1>Methods</h1> <p>The data in this study was collected using semi-structured qualitative interviews.</p> <p>Interview partners were recruited by snowball sampling through their engagement with eBird India and related organisations. There were 17 interview partners, interviewed either once or several times. 19 interviews were conducted in total.</p> <p>Interview guides/questionnaires were designed for each interviewee depending on their status as birdwatchers, citizen science coordinators, and eBird data users.</p> <p>Interviews were conducted between April 2022 and June 2023. The interviews took place online using Zoom videoconferencing software. Interviews lasted 35-70 minutes. When participants provided their written consent, interviews were audio-recorded and transcribed smart verbatim using otter.ai and manual proofreading. Sensitive information was removed before publishing transcripts.</p> <p>Transcripts were analysed using semi-grounded coding. Codes were organised into parent codes using an inductive approach based on emergent categories.</p> <h1>Description of the data and file structure</h1> <p>Documentation files include interview guides, the information sheet and consent form, ethics approval, and the data narrative. Documentation files are named according to the structure: authorname_filename_DOCUMENTATION.</p> <p>Data files consist of a summary of participants, 17 of the interview transcripts, and a code list. Interview transcript files are named according to the structure: authorname_interviewnumber_date.</p> <p>A full list of files is provided in the README file.</p> <h1>Notes</h1> <p>This study was conducted as part of the project A Philosophy of Open Science for Diverse Research Environments (PHIL_OS). More information can be found at <a href="https://opensciencestudies.eu/">https://opensciencestudies.eu</a></p> <p>This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 101001145).</p>
2021_2022_E4_EBIRD_30spp_SEASONAL_BirdDistribution_WMV_hosts
<p><strong>Abstract</strong></p> <p>A series of weekly bird abundance distribution datasets is now available from EBIRD (<a href="https://science.ebird.org/en/status-and-trends" target="_blank" rel="noopener">https://science.ebird.org/en/status-and-trends</a>). ERGO has processed these data in several tranches to provide weekly species richness and weekly aggregated abundance indices at 3km resolution. Data for thirty species have now been processed. These species have been selected as being West Nile Virus hosts, using literature search, inference from mosquito WNV vector blood meals and from bird serology reports. The two tranches are a) all 30 selected species and b) the top 15 E4Warning priority species . Details are provided in the accompanying Excel Spreadsheet (e4ebird readmeJune24.xls).</p> <p><strong>Description</strong></p> <p>This dataset has been requested for 'the Horizon e4Warning project on mapping and modelling West Nile Virus Disease and its Hosts' then been downloaded from ebird.org and it includes weekly abundance geospatial tifs for 30 species:</p> <ol> <li>weekly presence</li> <li>weekly species richness</li> <li>week abundance sum</li> </ol> <p>Data obtained from Ebird https://science.ebird.org/en/status-and-trends/species/</p> <p><strong>Species Names: </strong></p> <table> <tbody> <tr> <td><strong>Species</strong></td> <td><strong>English Name</strong></td> <td><strong>filname code</strong></td> </tr> <tr> <td>Alcedo atthis</td> <td>Common Kingfisher</td> <td>comkin1</td> </tr> <tr> <td>Anas platyrhynchos</td> <td>Mallard</td> <td>mallar3</td> </tr> <tr> <td>Anser anser</td> <td>Graylag Gose</td> <td>gragoo</td> </tr> <tr> <td>Athene noctua</td> <td>Little Owl</td> <td>litowl1</td> </tr> <tr> <td>Bulbulcus ibis</td> <td>Cattle Egret</td> <td>categr</td> </tr> <tr> <td>Buteo buteo</td> <td>Common Buzzard</td> <td>combuz1</td> </tr> <tr> <td>Columba palumbus</td> <td>Commin Wood Piegon</td> <td>cowpig</td> </tr> <tr> <td>Corvus cornix</td> <td>Hooded Crow</td> <td>hoocro1</td> </tr> <tr> <td>Corvus corone cornix</td> <td>Carrion Crow</td> <td>carcro1</td> </tr> <tr> <td>Corvus monedula</td> <td>Eurasian Jackdaw</td> <td>eurjac</td> </tr> <tr> <td>Cyanistes caeruleus</td> <td>Blue Tit</td> <td>blutit</td> </tr> <tr> <td>Egretta garzetta</td> <td>(Little Egret)</td> <td>litegr</td> </tr> <tr> <td>Eremophila alpestris</td> <td>Horned Lark</td> <td>horlar</td> </tr> <tr> <td>Falco tinnunculus</td> <td>Eurasian Kestrel</td> <td>eurkes</td> </tr> <tr> <td>Garrulus glandarius</td> <td>Eurasian Jay</td> <td>eurjay1</td> </tr> <tr> <td>Hirundo rustica</td> <td>Barn Swallow</td> <td>barswa</td> </tr> <tr> <td>Larus argentatus</td> <td>Herring Gull</td> <td>hergul</td> </tr> <tr> <td>Lulua arborea</td> <td>Woodlark</td> <td>woolar1</td> </tr> <tr> <td>Luscinia Luscinia</td> <td>Thrush Nightinglae</td> <td>thrnig1</td> </tr> <tr> <td>Luscinia megarhynchos</td> <td>Common nightingale</td> <td>comnig1</td> </tr> <tr> <td>Passer domesticus (including Passer italiae and Passer hispaniolensis)</td> <td>House Sparrow</td> <td>houspa</td> </tr> <tr> <td>Pica pica</td> <td>Eurasian Magpie</td> <td>eurmag1</td> </tr> <tr> <td>Streptopelia decaocto</td> <td>Eurasian Collared Dove</td> <td>eucdov</td> </tr> <tr> <td>Turdus merula</td> <td>Eurasian Blackbird</td> <td>eurbla</td> </tr> <tr> <td>Ciconia ciconia</td> <td>White Stork</td> <td>whisto1</td> </tr> <tr> <td>Sturnus vulgaris</td> <td>European Starling</td> <td>eursta</td> </tr> <tr> <td>Sylvia atricapilla</td> <td>Eurasian Bl;ackcap</td> <td>blackc1</td> </tr> <tr> <td>Acrocephalus scirpaceus</td> <td>Common Reed Warbler</td> <td>eurwar1</td> </tr> <tr> <td>Fulica atra</td> <td>Eurasian Coot</td> <td>eurcoo</td> </tr> <tr> <td>Columba livia</td> <td>Rock Pigeon</td> <td>rocpig</td> </tr> <tr> <td>Gallus gallus</td> <td>Domestic chicken</td> <td> </td> </tr> </tbody> </table> <p><strong>File Names:</strong></p> <div> <div><strong>a)</strong> e4ebirdabundanceall30weeklyJune24 All weekly abundance datasets for 30 availablke spp at 3km resolution, June 24</div> <div><strong>b) </strong>e4ebirdPAall30weeklyJune24 Presence absence with missing recoded to 0 for all 30 species available in June 24. This recoding is based of ad hoc checks of weekly datasets against the birdlife species ranges, which suggest that the maximum extents of combined weekly abundance distributions match the rage boundares fairly well </div> <div> </div> <div><strong>c)</strong> e4ebirdspprichnessall23SUMMEANweeklyFeb24 Summed and mean weekly presence absence for 23 available species calc Feb24. If a species in missing a weekly dataset, missing weeks are filled with last valid presence week up to halfway through the gap in availability, then with the first available distribution after the gap</div> <div><strong>d)</strong> e4ebirdspprichnesse415SUMMEANweeklyJun24 Summed and mean weekly presence absence for e4 15 priority species calc June 24. If a species in missing a weekly dataset, missing weeks are filled with last valid presence week up to halfway through the gap in availability, then with the first available distribution after the gap</div> <div><strong>e)</strong> e4ebirdspprichnessall30SUMMEANweeklyJun24 Summed and mean weekly presence absence for 30 available species calc June 24. If a species in missing a weekly dataset, missing weeks are filled with last valid presence week up to halfway through the gap in availability, then with the first available distribution after the gap</div> <div> </div> <div><strong>f)</strong> e4ebirdabundanceall30summeanweekJun24 Summed and mean weekly median abundance for 30 available species calc June 24. If a species in missing a weekly dataset, missing weeks are filled with last valid presence week up to halfway through the gap in availability, then with the first available distribution after the gap</div> <div><strong>g)</strong> e4ebirdeabundance415summeanweekJun24 Summed and mean weekly median abundance for e4 15 priority species calc June 24. If a species in missing a weekly dataset, missing weeks are filled with last valid presence week up to halfway through the gap in availability, then with the first available distribution after the gap</div> <p> </p> </div> <p> </p> <p> </p>
Data for: Considerations for fitting occupancy models to data from eBird and similar volunteer-collected data
<p>An occupancy model makes use of data that are structured as sets of repeated visits to each of many sites, in order estimate the actual probability of occupancy (i.e., proportion of occupied sites) after correcting for imperfect detection using the information contained in the sets of repeated observations. We explore the conditions under which preexisting, volunteer-collected data from the citizen science project eBird can be used for fitting occupancy models. The data archived here are used to explore two ways in which the single-visit records could be used in occupancy models. First, we use empirical data contained within this archive to assess the potential for space-for-time substitution: aggregating single-visit records from different locations within a region into pseudo-repeat visits. The archived data are used to illustrate that the locations chosen for data collection by observers were not always representative of the habitat in the surrounding area, which would lead to biased estimates of occupancy probabilities when using space-for-time substitution. Second, create a large set of simulated data (output from the simulations contained in this archive) that we used to explore the utility of including data from single-visit records to supplement sets of repeated-visit data.</p>
Data for: Considerations for fitting occupancy models to data from eBird and similar volunteer-collected data
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Republished EOD - eBird Observation Dataset
<p>This publication contains a republished eBird Darwin Core Archive "dwca-1.0.zip" as discovered via GBIF on 2019-04-08 via http://ebirddata.ornith.cornell.edu/downloads/gbiff/dwca-1.0.zip .</p> <p>Levatich T, Padilla F (2019). EOD - eBird Observation Dataset. Cornell Lab of Ornithology. Occurrence dataset hash://sha256/ec3ff57cb48d5c41b77b5d1075738b40f598a900e8be56e7645e5a24013dffc4 https://doi.org/10.15468/aomfnb accessed via GBIF.org on 2019-04-08 with provenance hash://sha256/5a39b7bbe9d1bc46ed2eb7bd76c490b5c85a09369a7cf7dc18fa04532679e9a7 .</p> <p> </p> <p> </p> <p> </p>
The island biogeography of the eBird citizen-science program
Aim: Island biotas face an array of unique challenges under global change. Monitoring and research efforts, however, have been hindered by the large number of islands, their broad distribution and geographic isolation. Global citizen-science initiatives have the potential to address these deficiencies. Here, we determine how the eBird citizen-science program is currently sampling island bird assemblages annually and how these patterns are developing over time. Location: Global. Taxa: Birds. Methods: We compiled occurrence information of non-marine bird species across the world's islands (n = 21,813) over an 18-year period (2002-2019) from eBird. We estimated annual survey completeness and species richness across islands, which we examined in relation to six geographical and four climatic features. Results: eBird contained bird occurrence information for ca. 20% of the world's islands (n = 4,205) with ca. 8% classified as well surveyed annually (n =1,644). eBird participants tended to survey larger islands that were more distant from the mainland. These islands had lower proximity to other islands and contained a broader range of elevations. Temperature, precipitation, and temperature seasonality were at intermediate levels. Precipitation seasonality was at low and intermediate levels. Islands located between 10-60° N latitude and 30-40° S latitude were overrepresented, and islands located between 60-130° W longitude were underrepresented. From 2002 to 2019, the number of islands surveyed annually increased by ca. 96.3 islands/year. During this period, island size decreased, distance from mainland did not change, proximity to other islands increased, and elevation range decreased. Main conclusions: The eBird program tends to survey larger islands containing intermediate climates that are more isolated from the mainland and other islands. These findings provide a framework to support the rigorous application of eBird data in avian island biogeography. Our findings also emphasize citizen science as a resource to support ecological research, conservation, and monitoring efforts across remote regions of the globe.
[Datasets] Del Monte al Chaco: eBird revela la migración del Piojito Trinador (Serpophaga griseicapilla)
<p><strong>Material Suplementario S1</strong></p> <p>Registros utilizados para ejecutar los modelos de distribución (bosque aleatorio) del Piojito Trinador (<em>Serpophaga griseicapilla</em>) tanto para el modelo de cría como de invernada. Los registros fueron obtenidos del set de datos básicos de eBird (eBird Basic Dataset 2021).</p> <p><strong>Material Suplementario S3</strong></p> <p>Registros utilizados para correr los modelos de abundancia (GAMs) del Piojito Trinador (<em>Serpophaga griseicapilla</em>) para los cuadrantes del chaco, litoral, sierras centrales y monte austral (ver métodos: modelos de fenología). Los registros fueron obtenidos del set de datos básicos de eBird (eBird Basic Dataset 2021).</p>
eBird data for: Avian behaviour changes in response to human activity during the COVID-19 lockdown in the United Kingdom
<p>Human activities may impact animal habitat and resource use, potentially influencing contemporary evolution in animals. In the United Kingdom (UK), COVID-19 lockdown restrictions resulted in sudden, drastic alterations to human activity. We hypothesized that short-term daily and long-term seasonal changes in human mobility might result in changes in bird habitat use, depending on the mobility type (home, parks, grocery) and the extent of change. Using Google human mobility data and 872 850 bird observations, we determined that during lockdown, human mobility changes resulted in altered habitat use in 80% (20/25) of our focal bird species. When humans spent more time at home, over half of affected species had lower counts, perhaps resulting from the disturbance of birds in garden habitats. Bird counts of some species (e.g. rooks, gulls) increased over the short-term as humans spent more time parks, possibly due to human-sourced food resources (e.g. picnic refuse), while counts of other species (e.g. tits and sparrows) decreased. All affected species increased counts when humans spent less time at grocery services. Avian species rapidly adjusted to the novel environmental conditions and demonstrated behavioural plasticity, but with diverse responses, reflecting the different interactions and pressures caused by human activity.</p>
eBird data for: Avian behaviour changes in response to human activity during the COVID-19 lockdown in the United Kingdom
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The island biogeography of the eBird citizen-science program
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Data from: Urban environmental predictors of group size in cliff swallows (Petrochelidon pyrrhonota): A test using community-science eBird data
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eBird Checklists (2017-2018) and Environmental Covariates for 31 Avian Species in Southwest Oregon, USA
<p>Dataset of eBird Checklists (2017-2018) and Environmental Covariates for 31 Avian Species in Southwest Oregon, USA</p> <p><strong>checklist_data.zip</strong>: contains eBird checklists for 31 bird species over southwestern Oregon, United States.</p> <p><strong>occupancy_feature_raster.zip</strong>: occupancy feature rasters for informing spatial clustering algorithms and species distribution models, and for predicting occupancy maps.</p>
Macro-ecological analysis of bird migration routes on the North American continent from eBird data
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Examining the influence of sociodemographics, residential segregation, and historical redlining on eBird and iNaturalist data disparities in three US cities
<p>Code and data for the manuscript "Examining the influence of sociodemographics, residential segregation, and historical redlining on eBird and iNaturalist data disparities in three US cities<strong></strong><strong>"</strong></p>
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