Using geolocator tracking data and ringing archives to validate citizen-science based seasonal predictions of bird distribution in a data-poor region

Global Ecology and Conservation

卷:24

Sergei M. Smirenski¹  Ilya Panov²  Ramona J. Heim³  Sissel Sjöberg⁴  Anders P. Tøttrup⁵  Johannes Kamp⁶  Alexander Thomas⁷  Kasper Thorup⁸  Kiyoaki Ozaki⁹  Wieland Heim¹⁰  Ilka Beermann¹⁰  Oleg A. Burkovskiy¹⁰  Pavel Ktitorov¹¹  Ivan M. Tiunov¹²  Norbert Hölzel¹³  Yury Gerasimov¹⁴  Mikkel Willemoes¹⁵  Martha Maria Sander¹⁶ 

[1] Biological Station Rybachy, ZIN RAS, Russia;

[2] Birds Russia, Yuzhno-Sakhalinsk, Russia;

[3] Corresponding author.;

[4] Bird Ringing Centre of Russia, IPEE RAS, Russia;

[5] Center for Macroecology, Evolution and Climate, Globe Institute, University of Copenhagen, Denmark;

[6] Department of Biology, Lund University, Sweden;

[7] Department of Life Sciences and Systems Biology, University of Turin, Italy;

[8] Federal Scientific Center of the East Asia Terrestrial Biodiversity, Far Eastern Branch, Russian Academy of Sciences, Vladivostok, Russia;

[9] Institute of Biological Problems of the North, Far-eastern Branch of Russian Academy of Science, Magadan, Russia;

[10] Institute of Landscape Ecology, University of Münster, Germany;

[11] Kamchatka Department of Pacific Geographical Institute, Far-eastern Branch of Russian Academy of Science, Petropavlovsk-Kamchatskiy, Russia;

[12] Muraviovka Park for Sustainable Land Use, Blagoveshchensk, Russia;

[13] Natural History Museum of Denmark, University of Copenhagen, Denmark;

[14] Sakhalin Energy, Investment Company LTD, Yuzhno-Sakhalinsk, Russia;

[15] School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China;

[16] Yamashina Institute for Ornithology, Chiba, Japan;

关键词: East Asian flyway;    eBird;    MaxEnt;    Migration;    Species distribution model;    Tracking;   

DOI  :  

来源: DOAJ

【 摘 要 】

Unstructured citizen-science data are increasingly used for analysing the abundance and distribution of species. Here we test the usefulness of such data to predict the seasonal distribution of migratory songbirds, and to analyse patterns of migratory connectivity.We used bird occurrence data from eBird, one of the largest global citizen science databases, to predict the year-round distribution of eight songbird taxa (Agropsar philippensis, Calliope calliope, Cecropis daurica, Emberiza aureola, Hirundo rustica, Locustella certhiola, Oriolus chinensis, Saxicola torquatus stejnegeri) that migrate through East Asia, a region especially poor in data but globally important for the conservation of migratory land birds. Maximum entropy models were built to predict spring stopover, autumn stopover and wintering areas. Ring recovery and geolocator tracking data were then used to evaluate, how well the predicted occurrence at a given period of the annual cycle matched sites where the species were known to be present from ringing and tracking data.Predicted winter ranges were generally smaller than those on published extent-of-occurrence maps (the hitherto only available source of distribution information). There was little overlap in stopover regions. The overlap between areas predicted as suitable from the eBird data and areas that had records from geolocator tracking was high in winter, and lower for spring and autumn migration. Less than 50% of the ringing recoveries came from locations within the seasonal predicted areas, with the highest overlap in autumn. The seasonal range size of a species affected the matching of tracking/ringing data with the predictions. Strong migratory connectivity was evident in Siberian Rubythroats and Barn Swallows. We identified two migration corridors, one over the eastern mainland of China, and one along a chain of islands in the Pacific.We show that the combination of disparate data sources has great potential to gain a better understanding of the non-breeding distribution and migratory connectivity of Eastern Palearctic songbirds. Citizen-science observation data are useful even in remote areas to predict the seasonal distribution of migratory species, especially in periods when birds are sedentary and when supplemented with tracking data.

【 授权许可】

Unknown