Skip to main content
NCBI home page
Scientific Data logoLink to Scientific Data
. 2020 Oct 13;7:344. doi: 10.1038/s41597-020-00690-0

GalliForm, a database of Galliformes occurrence records from the Indo-Malay and Palaearctic, 1800–2008

Elizabeth H Boakes 1,✉, Richard A Fuller 2, Georgina M Mace 1, Changqing Ding 3, Tzo Tze Ang 4, Alistair G Auffret 4,5, Natalie E Clark 4,6, Jonathon Dunn 7, Jennifer Gilbert 4, Viktor Golovnyuk 8, Garima Gupta 7, Ulrike Irlich 4,9, Emily Joachim 4, Kim O’ Connor 4, Eugene Potapov 10, Roald Potapov 11, Judith Schleicher 4,12, Sarah Stebbing 4, Terry Townshend 13, Philip J K McGowan 7
PMCID: PMC7553924  PMID: 33051443

Abstract

Historical as well as current species distribution data are needed to track changes in biodiversity. Species distribution data are found in a variety of sources, each of which has its own distinct bias toward certain taxa, time periods or places. We present GalliForm, a database that comprises 186687 galliform occurrence records linked to 118907 localities in Europe and Asia. Records were derived from museums, peer-reviewed and grey literature, unpublished field notes, diaries and correspondence, banding records, atlas records and online birding trip reports. We describe data collection processes, georeferencing methods and quality-control procedures. This database has underpinned several peer-reviewed studies, investigating spatial and temporal bias in biodiversity data, species’ geographic range changes and local extirpation patterns. In our rapidly changing world, an understanding of long-term change in species’ distributions is key to predicting future impacts of threatening processes such as land use change, over-exploitation of species and climate change. This database, its historical aspect in particular, provides a valuable source of information for further studies in macroecology and biodiversity conservation.

Subject terms: Biodiversity, Conservation biology, Macroecology, Ecological modelling

Measurement(s) geographic location • Species • Occupancy
Technology Type(s) georeferencing • digital curation
Sample Characteristic - Organism Galliformes sp.
Sample Characteristic - Location Palearctic Region • Indomalayan Region
Open in a new tab

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12886931

Background & Summary

Gathering primary biodiversity data is necessary to improve our knowledge of the ecology and conservation status of species. International commitments such as the Convention on Biological Diversity1 call for a halt to biodiversity loss and therefore require data to measure biodiversity change. Recent trends in changes in population sizes or geographical ranges can be used to track progress toward biodiversity targets but longer-term trends are needed if we are to put the status of present-day biota into a proper historical context2,3. Similarly, if we are to understand the impacts of climate and land use change on species distributions, historical data are required. Ideally, this biodiversity information must be comprehensive, covering common species as well as threatened, and areas of lower biodiversity as well as hotspots.

Our knowledge of species’ distributions is extremely coarse compared to most other environmental variables4. Analyses of species’ geographical ranges often rely on predictions of where a species might occur. Predictions might be gleaned from expert opinion (e.g. https://birdsoftheworld.org/bow/home) (and in some instances may be influenced by historical data), the extent of suitable habitat5, gridded survey data6 or point occurrences7. Prominent conservation datasets such as the Living Planet Index8 and IUCN’s species distribution maps (https://www.iucnredlist.org/resources/spatial-data-download) are regularly used to assess rates of biodiversity loss but these data sources do not extend back beyond around 1970. Longer-term trends can reveal major shifts in abundance and composition of biological communities, information that should be considered when setting conservation targets9.

While aggregated population trends or extent of occurrence maps are useful conservation tools, primary data allow us to investigate biodiversity loss in far greater detail. For example, if species’ ranges are punctuated with local extinction events we might overlook or underestimate species’ declines because we lack the precision to measure them10. Additionally, data summaries may be at coarser resolutions than the original data or missing attributes attached to the original record. Freely available primary data allow new questions to be investigated, for which data summaries might not be suitable.

The avian order Galliformes has relatively high quality historical distribution data. This is in part due to their economic and cultural value and their attraction for collectors and ornithologists11. Almost all species are non-migratory, making delimitation of their current and historical ranges more tractable. In recent times they have received much conservation attention through being one of the most threatened avian orders – over 25% of species are threatened (www.iucnredlist.org) and many local extinctions have been reported12 (http://datazone.birdlife.org/home). Galliformes are subject to a variety of threats including habitat loss, hunting, and agricultural intensification and disturbance (http://datazone.birdlife.org/home). The order exhibits a wide range of ecological characteristics and life history traits, and occurs in a diversity of habitats, meaning that the Galliformes lend themselves well to macroecological studies13.

Here we present GalliForm14, a database of 186687 occurrence records covering the 130 species of the avian order Galliformes that occur in the Palaearctic and Indo-Malay biogeographic realms (see Fig. 1 for spatial distribution of records). Records cover the period 1648 to 2008 although 95% of records date from 1877 onwards. Records increase markedly though time (Fig. 2). Records were collected from museums, peer-reviewed and grey literature, bird atlases, banding records and birding trip report websites (see15 for spatial biases within sources). Where possible, data were informally refereed by local experts who, if necessary, supplemented the data with their personal records. Each data source was found to have a distinct set of spatial, temporal and taxonomic biases15. Combining biodiversity data from a variety of primary sources helps to minimise data bias.

Fig. 1.

Fig. 1

Open in a new tab

The spatial distribution of those records in GalliForm that contain sufficient information to be georeferenced to an accuracy of 30 minutes. The records of Lagopus lagopus and Lagopus muta from North America are omitted.

Fig. 2.

Fig. 2

Open in a new tab

The cumulative number of occurrence records through time. The number of occurrence records has been converted to a natural logarithmic scale.

The GalliForm dataset14 is an extremely valuable resource for ecological and conservation studies. Occurrence data underpin species distribution modelling but geographic ranges are changing rapidly due to the diverse impacts caused by human activities. Historical occurrence data, coupled with climate and land-use data, may improve our understanding of populations’ responses to climate change, land-use change and hunting. The species occurrence data described here have been used to assess the completeness of geographic range size estimates16, to investigate patterns of range collapse with respect to distance to range edge17 and to assess species extirpations outside Protected Areas12. Nine publications10,12,15–21 have so far arisen from this database but many avenues remain to be explored.

Methods

These methods are an expanded version of those in our related work, Boakes et al.15.

The database was compiled over the period 2005–2008. Data collection equates to around 1500 person-days and data were gathered by a team of 21 people. Between them, team members were fluent in English, French, German, Mandarin, Russian, Spanish and Swedish. These languages were extremely helpful in transcribing museum specimen labels and in translating publications. However, the majority of publications were in English and we acknowledge that the database will be biased toward records published in English-language publications.

Our study focuses on the 130 galliform species that occur within the Palaearctic and Indo-Malay biogeographic realms22 (see Online-only Table 1). We have additionally included records of the Imperial Pheasant (Lophura imperialis) although it is now recognised that this is a hybrid and not a species. The geographic range of two of the species in the database, the Red Grouse (Lagopus lagopus) and the Rock Ptarmigan (Lagopus muta), extends to North America. North American data was often included in the information which museums sent us and in these instances we entered those records into the database since we thought they might be of use to researchers studying these species. However, it should be noted that we did not search exhaustively for records of these species in North America, we have merely included those that we came across.

Online-only Table 1.

Summary of records of each species in GalliForm. Species taxonomy is that accepted by the IUCN and BirdLife.

Species Common name Number of records Number of localities in which recorded Year of earliest record Year of most recent record
Alectoris barbara Barbary Partridge 715 328 1821 2007
Alectoris chukar Chukar 2992 1702 1830 2007
Alectoris graeca Rock Partridge 1058 713 1820 2006
Alectoris magna Przewalski’s Partridge 112 76 1872 2007
Alectoris melanocephala Arabian Chukar 193 149 1852 2006
Alectoris philbyi Philby’s Rock Partridge 75 32 1845 1998
Alectoris rufa Red-legged Partridge 5683 4320 1817 2007
Ammoperdix griseogularis See-see Partridge 694 381 1836 2006
Ammoperdix heyi Sand Partridge 534 264 1820 2006
Arborophila ardens Hainan Hill-partridge 108 54 1891 2005
Arborophila atrogularis White-cheeked Hill-partridge 254 133 1803 2007
Arborophila brunneopectus Brown-breasted Hill-partridge 479 243 1873 2006
Arborophila cambodiana Cambodian Hill-partridge 66 36 1927 2006
Arborophila campbelli Malaysian Hill-partridge 38 22 1907 2000
Arborophila charltonii Chestnut-necklaced Hill-partridge 85 48 1896 1994
Arborophila chloropus Scaly-breasted Hill-partridge 477 253 1873 2007
Arborophila crudigularis Taiwan Hill-partridge 157 74 1864 2007
Arborophila davidi Orange-necked Hill-partridge 42 28 1925 2006
Arborophila gingica Collared Hill-partridge 116 86 1899 2004
Arborophila graydoni Sabah Hill-partridge 102 52 1833 2001
Arborophila hyperythra Bornean Hill-partridge 117 43 1887 2001
Arborophila javanica Javan Hill-partridge 234 56 1826 2002
Arborophila mandelii Chestnut-breasted Hill-partridge 104 73 1876 2007
Arborophila orientalis Grey-breasted Hill-partridge 65 14 1896 1989
Arborophila rolli Tan-breasted Hill-partridge 27 15 1898 2000
Arborophila rubrirostsris Red-billed Hill-partridge 107 33 1878 2001
Arborophila rufipectus Sichuan Hill-partridge 133 82 1921 2007
Arborophila rufogularis Rufous-throated Hill-partridge 946 433 1847 2007
Arborophila sumatrana Sumatran Hill-partridge 31 18 1826 1939
Arborophila tonkinensis Tonkin Hill-partridge 49 16 1925 2006
Arborophila torqueola Necklaced Hill-partridge 712 350 1841 2007
Argusianus argus Great Argus 706 342 1836 2004
Bambusicola fytchii Mountain Bamboo-partridge 447 215 1876 2007
Bambusicola sonorivox Taiwan Bamboo-partridge 232 85 1861 2007
Bambusicola thoracicus Chinese Bamboo-partridge 1010 867 1861 2007
Bonasa bonasia Hazel Grouse 6004 4226 1815 2007
Bonasa sewerzowi Severtzov’s Grouse 300 194 1873 2007
Caloperdix oculeus Ferruginous Partridge 201 113 1860 2004
Catreus wallichii Cheer Pheasant 913 436 1820 2007
Chrysolophus amherstiae Lady Amherst’s Pheasant 414 260 1869 2007
Chrysolophus pictus Golden Pheasant 494 357 1863 2007
Coturnix coromandelica Black-breasted Quail 494 261 1829 2006
Coturnix coturnix Common Quail 14805 9962 1810 2007
Coturnix japonica Japanese Quail 1022 531 1837 2007
Crossoptilon auritum Blue Eared-pheasant 251 139 1869 2007
Crossoptilon crossoptilon White Eared-pheasant 342 196 1890 2007
Crossoptilon harmani Tibetan Eared-pheasant 160 96 1880 2007
Crossoptilon mantchuricum Brown Eared-pheasant 274 164 1866 2005
Falcipennis falcipennis Siberian Spruce Grouse 162 116 1840 1994
Francolinus francolinus Black Francolin 1724 803 1819 2007
Francolinus gularis Swamp Partridge 443 234 1846 2007
Francolinus pictus Painted Francolin 277 161 1845 2001
Francolinus pintadeanus Chinese Francolin 802 521 1788 2007
Francolinus pondicerianus Grey Francolin 962 576 1829 2007
Galloperdix bicalcarata Sri Lanka Spurfowl 168 61 1865 2006
Galloperdix lunulata Painted Spurfowl 173 91 1832 2007
Galloperdix spadicea Red Spurfowl 389 197 1814 2007
Gallus gallus Red Junglefowl 2706 1524 1801 2007
Gallus lafayettii Sri Lanka Junglefowl 466 168 1827 2006
Gallus sonneratii Grey Junglefowl 435 234 1660 2007
Gallus varius Green Junglefowl 271 118 1820 2004
Haematortyx sanguiniceps Crimson-headed Partridge 102 52 1893 2001
Ithaginis cruentus Blood Pheasant 1456 680 1845 2007
Lagopus lagopus Red Grouse 11545 6625 1750 2007
Lagopus muta Rock Ptarmigan 5280 2366 1800 2006
Lerwa lerwa Snow Partridge 387 218 1822 2007
Lophophorus impejanus Himalayan Monal 1055 631 1648 2007
Lophophorus lhuysii Chinese Monal 210 114 1869 2006
Lophophorus sclateri Sclater’s Monal 294 171 1879 2007
Lophura bulweri Bulwer’s Pheasant 202 140 1874 2001
Lophura diardi Siamese Fireback 342 179 1819 2007
Lophura edwardsi Edward’s Pheasant 108 59 1922 2000
Lophura erythrophthalma Malay Crestless Fireback 121 68 1818 1998
Lophura ignita Bornean Crested Fireback 321 153 1836 2003
Lophura imperialis Imperial Pheasant 34 26 1923 2000
Lophura inornata Salvadori’s Pheasant 122 50 1878 2005
Lophura leucomelanos Kalij Pheasant 1861 967 1836 2007
Lophura nycthemera Silver Pheasant 1221 704 1841 2007
Lophura pyronota Bornean Crestless Fireback 119 62 1843 1999
Lophura rufa Malay Crested Fireback 251 102 1807 2004
Lophura swinhoii Swinhoe’s Pheasant 232 97 1863 2007
Lyrurus mlokosiewiczi Caucasian Grouse 340 192 1866 2006
Lyrurus tetrix Black Grouse 11915 6829 1819 2007
Megapodius cumingii Philippine Megapode 106 68 1866 2006
Megapodius nicobariensis Nicobar Megapode 116 45 1860 1998
Melanoperdix niger Black Wood Partridge 209 102 1826 1991
Ophrysia superciliosa Himalayan Quail 48 29 1865 1989
Pavo cristatus Common Peafowl 726 484 1851 2007
Pavo muticus Green Peafowl 1065 620 1828 2007
Perdicula argoondah Rock Bush Quail 228 105 1836 2007
Perdicula asiatica Jungle Bush Quail 665 298 1839 2007
Perdicula erythrorhyncha Painted Bush Quail 197 81 1840 2007
Perdicula manipurensis Manipur Bush Quail 80 38 1881 2006
Perdix dauurica Daurian Partridge 883 513 1855 2007
Perdix hodgsoniae Tibetan Partridge 578 321 1850 2007
Perdix perdix Grey Partridge 32425 29877 1727 2007
Phasianus colchicus Common Pheasant 40451 36456 1783 2007
Phasianus versicolor Green Pheasant 197 81 1845 2006
Polyplectron bicalcaratum Grey Peacock-pheasant 733 435 1838 2007
Polyplectron chalcurum Sumatran Peacock-pheasant 129 56 1848 2004
Polyplectron germaini Germain’s Peacock-pheasant 116 74 1880 2007
Polyplectron inopinatum Mountain Peacock-pheasant 73 36 1902 2008
Polyplectron katsumatae Hainan Peacock-pheasant 55 34 1905 2004
Polyplectron malacense Malayan Peacock-pheasant 132 58 1851 2003
Polyplectron napoleonis Palawan Peacock-pheasant 192 59 1831 2005
Polyplectron schleiermacheri Bornean Peacock-pheasant 78 49 1888 2003
Pucrasia macrolopha Koklass Pheasant 1145 681 1825 2007
Rheinardia ocellata Crested Argus 216 101 1886 2003
Rhizothera dulitensis Dulit Partridge 11 7 1894 1902
Rhizothera longirostris Long-billed Wood Partridge 162 100 1818 2003
Rollulus rouloul Crested Partridge 805 329 1806 2004
Synoicus chinensis King Quail 1185 546 1839 2007
Syrmaticus ellioti Elliot’s Pheasant 415 278 1871 2006
Syrmaticus humiae Mrs Hume’s Pheasant 473 244 1840 2004
Syrmaticus mikado Mikado Pheasant 106 56 1897 2007
Syrmaticus reevesii Reeves’ Pheasant 426 249 1839 2002
Syrmaticus soemmerringii Copper Pheasant 478 321 1833 2004
Tetrao urogalloides Black-billed Capercaillie 369 244 1828 2004
Tetrao urogallus Western Capercaillie 5736 3830 1816 2007
Tetraogallus altaicus Altai Snowcock 153 72 1834 2004
Tetraogallus caspius Caspian Snowcock 137 83 1869 2007
Tetraogallus caucasicus Caucasian Snowcock 166 96 1840 1994
Tetraogallus himalayensis Himalayan Snowcock 720 417 1841 2006
Tetraogallus tibetanus Tibetan Snowcock 507 323 1870 2007
Tetraophasis obscurus Verreaux’s Monal Partridge 141 97 1869 2007
Tetraophasis szechenyii Szechenyi’s Monal Partridge 220 133 1892 2007
Tragopan blythii Blyth’s Tragopan 389 187 1838 2007
Tragopan caboti Cabot’s Tragopan 305 144 1868 2004
Tragopan melanocephalus Western Tragopan 766 429 1841 2006
Tragopan satyra Satyr Tragopan 527 298 1845 2007
Tragopan temminckii Temminck’s Tragopan 577 368 1869 2007
Open in a new tab

We attempted to gather all species distribution data that could be accessed from five different sources; museum collections, literature records, banding (ringing) data, ornithological atlases and birdwatchers’ trip report websites. For each data source, exhaustive and systematic search strategies were adopted.

Museum collections

Using web-based searches and Roselaar23, 377 natural history collections were identified. We found contact details for 338 of these collections and requested by email or letter a list of the Galliformes in their holdings along with collection localities and dates. Non-respondents were recontacted. 135 museums were able to share data with us (see Online-only Table 2). Museum records were obtained through publicly available online databases e.g. ORNIS, electronic or paper catalogues sent to us by the museums or by visiting the museums and transcribing data directly from specimens or card catalogues. Almost half of the museums we contacted did not respond despite at least one follow-up enquiry, and there was substantial variation in the amount and format of data contributed by those that did reply. Altogether, over 50% of the records came from just six museums (Natural History Museum, London; Zoological Institute of the Russian Academy of Sciences, St Petersburg; Zoological Museum of Lomonosov Moscow State University; Field Museum of Natural History, Chicago; American Museum of Natural History, New York; National Museum of Natural History, Leiden), a single museum (the Natural History Museum, London) contributing nearly 20% of the museum records that could be georeferenced and dated15. Following databasing and/or georeferencing, records were returned to larger collections and to those who had requested the data.

Online-only Table 2.

The museums that shared data with GalliForm.

Museum Country
Australian National Wildlife Collection, CSIRO, Australia Australia
Museum Victoria, Melbourne, Australia Australia
South Australian Museum Australia
Biologie Zentrum des Oberostereichisches Landesmuseums, Linz, Austria Austria
Natural History Musuem, Vienna, Austria Austria
Institut Royal des Sciences Naturelles de Belgique, Belgium Belgium
Plodiv Natural Science Museum, Bulgaria Bulgaria
Ruse Natural History Museum, Bulgaria Bulgaria
Canadian Museum of Nature Canada
Royal Alberta Museum, Canada Canada
Beijing Institute of Zoology, China China
Nature Museum of Sichuan University, China China
Normal University of Xihuan, China China
Muzeum J A Komenskeho, Prerov, Czech Republic Czech Republic
Vlastivedne Muzeum v Olomouci, Czech Republic Czech Republic
Naturhistoriska Museum, Aarhus, Denmark Denmark
University of Copenhagen Museum of Zoology, Denmark Denmark
Chelmsford Museum, Essex, UK UK
Zooloogia Muuseum, Tartu, Estonia Estonia
Musee Guimet d’Histoire Naturelle, France France
Musee Zoologique de l’Universite Louis Pasteur et de la Ville de Strasbourg, France France
Museum d’Histoire Naturelle de Grenoble, France France
Museum d’Histoire Naturelle de Bordeaux, France France
Museum National d’Histoire Naturelle, Paris, France France
Institut fur Vogelforschung ’Vogelwarte Helgoland’, Wilhelmshaven, Germany Germany
Museum für Naturkunde Berlin, Germany Germany
Museum fur Naturkunde, Magdeburg, Germany Germany
Naturhistoriches Museum Mainz, Germany Germany
Naturkunde Museum im Ottoneum, Kassel, Germany Germany
Pfalzmuseum fur Naturkunde, Bad Duerkheim, Germany Germany
Senckenberg Museum, Forschungsinstitut Senckenberg (FIS), Germany Germany
Staatliches Museum fur Naturkunde, Karlsruhe, Germany Germany
Staatliches Museum fur Naturkunde, Stuttgart, Germany Germany
Uberseemuseum, Bremen, Germany Germany
Universitaet Halle, Germany Germany
Westfalisches Museum fur Naturkunde, Munster, Germany Germany
Zoologischen Sammlung der Universitat Rostock, Germany Germany
Zoologisches Forschungsinstitut und Museum Alexander Koenig, Germany Germany
Zoologisches Institut und Zoologisches Museum, Hamburg, Germany Germany
Zoologisches Museum der Christian-Albrechts Universitat, Germany Germany
Zoological Museum Amsterdam, Netherlands Netherlands
Regional Museum of Natural History, India India
Museum of Zoology, Bogor, Indonesia Indonesia
National Museum of Ireland Ireland
Coll. "A. Noro", City of Graglia (Biella), Italy Italy
Museo Civico de Storia Naturale ’Giacomo Doria’, Genoa, Italy Italy
Museo Civico di Storia Naturale di Carmagnola, Italy Italy
Museo di Storia Naturale del Mediterraneo, Livorno, Italy Italy
Museo di Storia Naturale di Terrasini, Italy Italy
Museo Ornitologico ’F. Foschi’, Italy Italy
Museo Regionale di Scienze Naturali, Torino, Italy Italy
Museo Zoologico de La Specola, Florence, Italy Italy
Museo Zoologico dell’ Accademia del Fisiocrtici, Italy Italy
Universita di Pavia, Italy Italy
Kaunas Zoological Museum, Lithuania Lithuania
Ulster Museum, Belfast, UK N Ireland
Fries Natuurmuseum, Leeuwarden, Netherlands Netherlands
National Museum of Natural History, Leiden, Netherlands Netherlands
Auckland Museum, New Zealand New Zealand
Museum of Natural History and Archaeology, Trondheim, Norway Norway
Universitets Museet I Tromso, Norway Norway
Zoologisk Museum, Bergen, Norway Norway
Museum of Natural History, Wroclaw University, Poland Poland
Zaklad Zoologii Systematycznej I Doswiadczalnej, Poland Poland
Museo Municipal do Funchal, Portugal Portugal
Museu Bocage, Lisbon, Portugal Portugal
Museu de Historia Natural-Zoologia, Porto, Portugal Portugal
Muzeul ’Tarii Crisurilor’, Oradea, Romania Romania
Zoological Institute RAS, St Petersburg, Russia Russia
Zoological Museum of Moscow University (ZMMU), Moscow, Russia Russia
Zoological Reference Collection, Singapore Singapore
South African Museum, Cape Town, South Africa South Africa
Estacion Biologica de Donana, Seville, Spain Spain
Museo Nacional de Ciencias Naturales, Madrid, Spain Spain
Ajtte Svensk Fjall- och Samemuseum, Sweden Sweden
Malmo Museer, Sweden Sweden
Naturhistoriska Museet, Gothenburg, Sweden Sweden
Swedish Museum of Natural History, Stockholm, Sweden Sweden
Zoologisk Museum, Lund, Sweden Sweden
Musee Zoologie, Lausanne, Switzerland Switzerland
Museum d’Histoire Naturelle de la Ville de Geneve, Switzerland Switzerland
Museum d’Histoire Naturelle de Neuchatel, Switzerland Switzerland
Naturhistorisches Museum Bern, Switzerland Switzerland
Naturhistorisches Museum, Basel, Switzerland Switzerland
Zoologisches Museum der Universitat Zurich-Irchel, Switzerland Switzerland
Booth Museum of Natural History, Brighton, UK UK
Bristol Museums and Art Gallery Service, UK UK
Dorman Museum, Middlesbrough, UK UK
Glasgow Art Gallery and Museum, UK UK
Great North Museum: Hancock, UK UK
Leicester City Museums Service, UK UK
Liverpool Museum, UK UK
Manchester Museum, University of Manchester, UK UK
National Museums and Galleries of Wales UK
Nottinghamshire Biological and Geological Records Centre, UK UK
Oxford University Museum of Natural History, UK UK
Royal Albert Memorial Museum and Art Gallery, UK UK
Saffron Walden Museum, UK UK
Shropshire County Museum Service, UK UK
The Herbert Museum, Coventry, UK UK
The Natural History Museum, London, UK (BMNH) UK
Tullie House Museum and Art Gallery, Carlisle, UK UK
British Library National Sound Archive (NSA), UK UK
University Museum of Zoology Cambridge, UK UK
Academy of Natural Sciences, Philadelphia, USA USA
American Museum of Natural History, New York, USA USA
Bernice P. Bishop Museum, Hawai’i, USA USA
Borror Laboratory of Bioacoustics, Ohio, USA USA
Burke Museum of Natural History and Culture, Seattle, USA USA
California Academy of Sciences, USA USA
Carnegie Museum of Natural History, Pittsburgh, USA USA
Cleveland Museum of Natural History, Ohio, USA USA
Colorado University Museum, USA USA
Cornell University Museum of Vertebrates, UK USA
Delaware Museum of Natural History, USA USA
Denver Museum of Nature and Science, USA USA
Donald R Dickey Bird and Mammal Collection, UCLA, USA USA
Florida Museum of Natural History, USA USA
Humboldt State University Wildlife Museum, USA USA
Los Angeles County Museum of Natural History, USA USA
Michigan State University Museum, USA USA
Museum of Comparative Zoology, Harvard, USA USA
Museum of Vertebrate Zoology, Berkeley, USA USA
Museum of Zoology, University of Michigan (UMMZ), USA USA
New York State Museum, USA USA
North Carolina State Museum of Natural Science, USA USA
Sam Noble Museum of Natural History, University of Oklahoma, USA USA
Santa Barbara Museum of Natural History, USA USA
Slater Museum of Natural History, WA, USA USA
Smithsonian National Museum of Natural History, USA USA
The Bell Museum, Minnesota, USA USA
The Field Museum, Chicago, USA USA
University of Nebraska State Museum, USA USA
Utah Museum of Natural History, University of Utah, USA USA
Yale Peabody Museum, USA USA
Open in a new tab

Literature

Data from the literature were added to those previously collected by McGowan24. Entire series of key English-language international and regional ornithological journals such as Ibis, Bird Conservation International, Journal of the Bombay Natural History Society, and Kukila were scanned for relevant information, availability allowing. We began at the library of the Zoological Society of London and followed up missing journal issues at the BirdLife International library, Cambridge UK; the British Library, London, UK; the Edward Grey Institute, University of Oxford, UK. Relevant Chinese literature was also scanned. Additionally, data were obtained from regional reports, personal diaries, letters, newsletters etc stored in the archives of BirdLife International, Cambridge, UK; the World Pheasant Association, Newcastle, UK; the Edward Grey Institute, University of Oxford, UK. Several of the species/regional experts we consulted also contributed their personal records which were recorded in the database as ‘personal communications’. As far as it were possible, records were classed as primary or secondary data within the ‘dynamicProperties’ field of GalliForm14. It is important to note that some primary records or museum specimens will be duplicated within the database in the secondary data.

Banding records

Eighty-three ornithological banding groups were identified using web-based searches and were contacted via email. Thirty of these groups replied and only seven were able to provide us with data (see Table 1). The majority of galliform species tend not to be banded due to their large body sizes and spurs. Additionally, many of the banding groups kept their records on paper and were not able to send them to us. Nevertheless, we were able to access and georeference 15,152 banding records.

Table 1.

The ringing groups that shared data with GalliForm.

Ringing group
EURING
Zagreb Ringing Scheme
Hungarian Bird Ringing Centre
Finnish Museum of Natural History, Ringing Centre
Beringungszentrale Hiddensee
Coturnix ringing records, Italy
National Parks Board, Singapore (Ringing Centre)
Open in a new tab

Ornithological atlases

We digitised location data from 20 ornithological atlases (see Table 2). Data from several other atlases were not used since the range of dates for the records was wider than 20 years.

Table 2.

The atlases that were digitised to be included in GalliForm.

Atlas Year Editors
The EBCC atlas of European breeding birds: their distribution and abundance6 1997 Hagemeijer, E.J.M. & Blair, M.J.
The atlas of breeding birds in Britain and Ireland30 1976 Sharrock, J.T.R.
The new atlas of breeding birds in Britain and Ireland31 1993 Gibbons, D.W.
Atlas of breeding birds of the West Midlands32 1970 Lord, J., Munns, D.J.
Atlas of the breeding birds of Andorra33 2002 Alamany, O., Auclair, R., Bertrand, A.
Atlas des oiseaux nicheurs de Belgique34 1988 Devilliers, P., Roggeman, W., Tricot, J., Del Marmol, P., Kerwijn, C., Jacob, J-P., Anselin, A.
Atlas of breeding birds in Luxembourg35 1987 Melchior, E.
Atlas van de Nederlandse Broedvogels 1973–197736 1979 Teixeira, R.M.
Atlas van de Nederlandse Broedvogels 1978–198337 1987
Atlas das aves que nidificam em Portugal Continental38 1989 Rufino, R.
Atlante degli uccelli nidificanti e svernanti in Toscana39 1997 Florenzano, G.T., Arcamone, E., Baccetti, N., Meschini, E., Sposimo, P.
Atlas Hnizdniho Rozsireni Ptaku V CSSR40 1987 Stastny, K., Randik, A., Hudec, K.
Birds of Moscow city and the Moscow region41 2006 Kalyakin, M.V., Voltzit, O.V.
Eesti Linnuatlas42 1993 Renno, O.
Latvian breeding bird atlas43 1989 Priednieks, J., Strazds, M, Strazds, A. and Petrins, A.
Zimski ornitoloski atlas Slovenije44 1993 Sovinc, A.
Breeding bird atlas of Oman45 1998 Eriksen, J.
An interim atlas of the breeding birds of Arabia46 1995 Jennings, M.C.
Distribution atlas of Sudan’s birds with notes on habitat and status47 1987 Nikolaus, G.
Atlas of wintering birds of Japan48 2004
Open in a new tab

Trip report website data

We used the two trip report websites that were popular with birders during the data recording period (2005–2008), www.travellingbirder.com and www.birdtours.co.uk. At that time, eBird (probably the most relevant current online source today) did not cover the majority of the countries within our study region, and our intention with the deposition of this dataset is to focus on pre-eBird data that are more difficult and time consuming to access. We extracted data from all trip reports of birdwatching visits to European, Asian and North African countries. Care was taken to enter reports that featured on both websites once only.

Criteria for data inclusion

To be included in the database, records had to meet the following criteria:

  1. The record identified the species of the bird concerned.

  2. The record contained either a verbal description of the locality at which the bird concerned was observed or the co-ordinates at which the bird was observed.

Records of captive birds were excluded. Records relating to non-native occurrences were included but were flagged in the ‘establishmentMeans’ field as “introduced”.

Data entry

GalliForm14 was originally compiled in the programme Microsoft Access 2003. To maximise uniformity in data entry, all data recorders were given thorough and consistent training and each was provided with a set of database guidelines. An Access Database form was created to standardise data entry and to enable multiple members of the team to collect data simultaneously.

Each entry in GalliForm14 corresponds to a single record of a single species recorded in a specific location. The data fields of GalliForm14 are described in Online-only Table 3. The taxonomy used has been updated to be consistent with the BirdLife International 2019 taxonomy (datazone.birdlife.org). All information was entered exactly as it was described in the data source, with as much information extracted as possible. Multiple records from different sources which recorded the same information were still included in the interest of completeness. The only exception to this is the trip report data in which we did not enter identical records which occurred on both the Travelling Birder and Bird Tours websites.

Online-only Table 3.

Explanation of the Field Names in GalliForm. All records have the following fields filled: catalogNumber, locality and scientificName.

Field Name Darwin Core class Description of contents
institutionCode Record-level This name of the institution having custody of the object or information referred to in the record.
basisOfRecord Record-level The specific nature of the data record, as defined by the standard labels of the Darwin Core classes, choices being “PreservedSpecimen” or “HumanObservation”.
dynamicProperties Record-level The type of data source (coded within the field as “dataSource”) from which the record came, choices being “Literature”; “Museum”; “Atlas”; “Ringing”; “Website Trip Report”. Where known, from the literature were categorised as “primary” or “secondary” (coded as “dataType”).
catalogNumber Occurrence A unique number (within GalliForm) for each record.
recordedBy Occurrence The name of the person or expedition that collected the specimen.
individualCount Occurrence The number of individual birds that the record relates to.
organismQuantity Occurrence A qualitative status statement relating to whether the species is common, rare etc in that locality.
sex Occurrence The sex of the individual(s) represented by the Occurrence
lifeStage Occurrence The life stage of the individuals(s) represented by the Occurrence.
establishmentMeans Occurrence Coded as “introduced” if the Occurrence is outside a species’ native range.
occurrenceStatus Occurrence A statement about the presence or absence of a taxon at a location
preparations Occurrence The medium by which a museum specimen is preserved: Study Skin; Mounted Skin; Sound; Frozen Material; Tissue; Fluid-preserved Carcass; Fluid-preserved Skeleton; Egg; Nest; Skeletal Material; Wings.
associatedReferences Occurrence The reference associated with the Occurrence.
otherCatalogNumbers Occurrence The catalogue number assigned to a specimen by a museum.
occurrenceRemarks Occurrence Any information associated with the record that the data miner perceived as having potential relevance for the user, also any notes given on a museum label.
eventDate Event The date or interval when the Event was recorded. 1890–12–2 would mean some time during the day of the 2nd of December, 1890; 1910–11 would mean some time during the month of November 1910, 2002 would mean some time during the year 2002; 1930–1935 would mean some time between the 1st of January 1930 and the 31st of December 1935; /2008 would mean some time before 31st December 2008.
year Event The year in which the individual(s) was recorded.
month Event The month in which the individual(s) was recorded, numerically coded, i.e. 1 represents January.
day Event The day of the month on which the individual(s) was recorded.
habitat Event The type of habitat in which the individual was recorded, choices being bush; cultivation; desert; disturbed forest; forest; grassland; meadow; moor; road; rocky; scrubland; taiga; tundra; urban.
eventRemarks Event The way the Event was observed: Specimen; Sight Record; Heard Record; Heard and Seen; Second Hand (i.e. the observer was told of the species’ presence by another.)
higherGeography Location A list (concatenated and separated) of geographic names less specific than the information captured in the locality term.
country Location The name of the country in which the Location occurs. In a few cases, relating to older records, historical major administrative units are referred to e.g. USSR.
locality Location The specific description of the Location. The term may contain information modified from the original to correct perceived errors or standardise the description.
verbatimLocality Location The original textual description of the locality.
minimumElevationInMeters Location The lower limit of the altitude at which the individual(s) was recorded, as measured in metres.
maximumElevationInMeters Location The upper limit of the altitude at which the individual(s) was recorded, as measured in metres.
decimalLatitude Location The geographic latitude of the Location. Positive values are north of the Equator, negative values are south of it.
decimalLongitude Location The geographic longitude of the Location. Positive values are east of the Greenwich Meridian, negative values are west of it.
geodeticDatum Location The ellipsoid on which the geographic coordinates given in decimalLatitude and decimalLongitude are based.
coordinateUncertaintyInMeters Location The horizontal distance (in metres) from the given decimalLatitude and decimalLongitude describing the smallest circle containing the whole of the Location.
georeferenceProtocol Location A reference to the methods used to determine the coordiantes and uncertainties.
georeferenceSources Location A list of maps, gazetteers or other sources used to georeferenced the Location.
scientificName Taxon The full scientific name, (as given by BirdLife’s taxonomic checklist).
originalNameUsage Taxon The taxon name as given by the original data source, e.g. museum label, report.
kingdom Taxon The full scientific name of the kingdom in which the Taxon is classified.
phylum Taxon The full scientific name of the phylum in which the Taxon is classified.
class Taxon The full scientific name of the class in which the Taxon is classified.
order Taxon The full scientific name of the order in which the Taxon is classified.
family Taxon The full scientific name of the family in which the Taxon is classified.
genus Taxon The full scientific name of the genus in which the Taxon is classified.
specificEpithet Taxon The name of the species epithet of the scientific name.
vernacularName Taxon The vernacular name as given by the original data source, e.g. museum label, report.
Open in a new tab

The source of the data, i.e. literature, museum, atlas, ringing or website trip report is recorded in the ‘dynamicProperties’ field under the code “dataSource”. For literature data, (where known) the nature of the record, i.e. primary or secondary, is recorded under the code “datatype”.

Taxonomy has of course changed considerably over time. To allow for this we recorded the taxonomy as it was described in the data source in the ‘originalNameUsage’ field. The current taxonomy was then selected from a look-up table. If at the time of data entry, the data compiler was unsure which species the synonym referred to, the species was tagged as “unknown” and the species was designated at a later date following further research on the synonym.

Identical localities can also be described in multiple ways. We recorded the locality as it was given in the data source in the ‘verbatimLocality’ field. If the ‘verbatimLocality’ clearly tallied with a locality already within the database, the record was linked to that locality in order to increase georeferencing efficiency.

It was rare for a source to record absence of evidence, i.e. a survey for a species at a particular locality which failed to find that species. However, in the few cases where we did come across such records, the locality and date of the survey were recorded and “absent” was recorded in the ‘occurrenceStatus’ field.

Each record refers to an independent observation. For museum and ringing records, this means a single individual. For literature, atlas or trip report records this may refer to a group of birds observed in one particular locality, on one particular day. If given, the number of total individuals is recorded in the ‘individualCount’ field. The number of males and females is recorded in the ‘sex’ field and the number of juveniles and adults in the ‘lifeStage’ field. If the ‘lifeStage’ field is blank, it is reasonable to assume the individual(s) is an adult.

Occasionally, additional information about the observation might be included in the data source, for example the habitat the bird was observed in or whether the bird was common or rare in that locality. These data are recorded in the ‘habitat’ and ‘organismQuantity’ fields, respectively. Any additional information which did not fit within the structure of the database was recorded in the ‘occurrenceRemarks’ field, along with any notes found on museum labels.

For the purposes of data deposition, the database was converted to a tab-delimited CSV file with all fields following Darwin Core format. A full summary of these fields is given in Online-only Table 3.

Georeferencing

Locality descriptions were converted to geographic co-ordinates using a wide range of atlases and gazetteers, co-ordinates generally only being assigned if accurate to one degree (although in the majority of cases the locations were accurate to within 30 minutes, Table 3). We would initially search for a locality within the gazetteers available to us at the time. If the locality was not listed within those gazetteers we would search for the locality using atlases. Since this fieldwork was conducted, MaNIS standards have become widely used for studies of this kind, but these weren’t fully developed at the time of data collection25. Named places, e.g. towns or counties, were georeferenced using their geographic centre and georeferencing uncertainty measured from the centre to the edge of the named place. Often localities were given simply as the name of a river, mountain or Protected Area. In these instances we used the midpoint of the river between source and mouth (uncertainty measured as distance from midpoint to source/mouth), the summit of the mountain (uncertainty measured as distance from summit to approximate mountain foot) and the rough centre of the Protected Area (uncertainty measured as distance from centre to Protected Area edge). If a particular locality description matched two or more places their midpoint was taken (uncertainty measured as distance from midpoint to place). Offsets from localities (e.g. “50 km N of Kuala Lumpur”; “8 miles along the road from Sheffield to Chesterfield”) were measured using a digital atlas (uncertainty was approximated at the georeferencer’s discretion in these instances, usually between 3 and 10 arc-minutes, depending on the vagueness of the offset.) For georeferencing done ‘in house’, the gazeteer/atlas used was recorded.

Table 3.

Georeference and date completeness of the records.

Record Class No. records No. georeferenced to within 2 minutes No. georeferenced to within 10 minutes No. georeferenced to within 30 minutes No. dated to within one year No. dated to within 10 years No. georeferenced to within 30 minutes and dated to within one year
Event 186687 57173 (31%) 58773 (32%) 152930 (82%) 91973 (49%) 165312 (89%) 65913 (35%)
Locality 118907 26282 (22%) 26755 (23%) 109651 (92%) N/A N/A N/A
Open in a new tab

When possible, localities we could not georeference ourselves were sent to regional experts.

92% of our localities are georeferenced to an accuracy of 30 minutes, corresponding to 82% of occurrence records (see Table 3).

We had less success at georeferencing museum records than literature records15, due in part to difficulties in reading hand-writing on specimen labels. Older records were also harder to georeference, presumably due to changes in place names over time, and to some early ornithologists failing to document the collection locality. As might be expected, localities from countries that do not use the Roman alphabet were also harder to georeference.

Some records were excluded from the database based on their locality: records which we thought were trading localities, notably Malacca in Malaysia and Leadenhall Market in the UK; records from captive specimens, e.g. zoological gardens.

Dating

49% of records are dated to within an accuracy of one year. Where possible, we assigned date ranges to undated records. For example, if the name of the collector was given on a museum specimen and we knew when that collector was active in that region, we assigned a date range covering that period. There remain undated records which could perhaps be dated in this way. Undated literature records were designated as occurring before their publication date. We were able to date 89% of records to within 10 years.

Data Records

A relational database structure was created in Microsoft Access to organise and store the species occurrence records with their spatial dependencies and data sources and to keep track of synonyms. For the purposes of publication, this database was converted to a tab-delimited CSV file that followed the Darwin Core format.

We provide a dataset for Galliformes occurrences within the Palaearctic and Indo-Malay realms at species level. These data, obtained and curated as explained above, are available from the Global Biodiversity Information Facility (https://doi.org/10.15468/9825yw). Online-only Table 3 lists and describes the fields of GalliForm14.

The following figures and tables summarise the dataset. Figure 1 shows the spatial distribution of records; Fig. 2 shows the accumulation of records through time; Fig. 3 shows the spatial distribution of the number of records, species richness and the most recent year of record; Fig. 4 shows the completeness of selected data fields. Table 1 lists the ringing groups which were able to share data with us; Table 2 lists the atlases that we digitised; Table 3 details the completeness of records which are georeferenced and/or dated to within 1 year. Online-only Table 1 details the number of records per species and the time span these records cover; Online-only Table 2 lists the museums which were able to share data with us; Online-only Table 3 describes the Field Names of GalliForm14.

Fig. 3.

Fig. 3

Open in a new tab

The spatial distribution of the records, coloured coded by (a) the natural logarithm of the number of records within each cell, (b) the number of species within each cell and (c) the most recent year of record within each cell (cells which do not contain any dated records are shaded light grey). Cells are equal area and represent approximately 23,322 km2. Cells were drawn using the dgGridR package28 in R29.

Fig. 4.

Fig. 4

Open in a new tab

Percentage of data completeness of selected fields of GalliForm. Field descriptions are given in Online-only Table 3.

Technical Validation

Georeferenced data were subject to the following checks:

  1. That each data point was in the country that its locality described.

  2. That each data point was within reasonable distance of the species’ known historical range.

  3. That each data point that identifiably came from a protected area listed in the World Database of Protected Areas (https://www.protectedplanet.net/) was indeed within that protected area.

Finally, data were sent to experts on regions/species for informal ‘refereeing’ to highlight dubious or missing data. We were able to referee approximately one third of the records in this way.

Usage Notes

The dataset described here can be used to investigate the spatial and temporal patterns of Galliformes distributions at multiple scales and resolutions. The dataset was first used to examine bias in different sources of biodiversity data15. It has also been used to investigate predictors of range change18, to examine the effects of missing data on estimates of biodiversity metrics10, to assess the completeness of geographic range estimates16, to investigate the position of local extinctions with respect to species’ range edges17, to explore the optimisation of Protected Area networks20, to examine the local extirpation of species outside Protected Areas12 and to model the potential distributions of highly threatened species19,21. There remains much scope for this database to inform further biodiversity or conservation related studies, for example, investigations of geographic range change or predictors of extinction risk.

The data presented here do need to be interpreted carefully with respect to data bias and to missing data. Biodiversity data may be biased in a variety of ways, for example geographically, towards particular ecosystems or towards more charismatic species e.g.26,27. Additionally, these data biases may change over time. Although our database is based on a systematic and thorough search of all the data available to us from all regions covered, the data are still likely to be biased because there will have been intrinsic biases in the available data sources. For example, in this database, central India is under-represented in terms of recent research locales and it is hard to disentangle whether this is due to a lower number of ecologists focussing their studies there or if it is a justified skew as a result of biodiversity loss in this area. More recent records also show a bias toward threatened species and Protected Areas15. There are very few records of species absence although of course absence may be inferred if there are many records of other species in a particular locality. For a more detailed discussion of bias and missing data see Boakes et al.15 and Boakes et al.10.

Acknowledgements

This work was funded by grant number F/07/058/AK from the Leverhulme Trust. We are extremely grateful to the museums, libraries and ringing groups which shared their collections with us as well as the many taxonomic and regional experts who reviewed our data. We thank Hajir Al-Khairullah, Kate Harris, Cecilia Orme and Helen Pine who helped collect data but with whom we have since lost touch and could not offer co-authorship and also Pavel Tomkovich who facilitated data collection at the Zoological Museum, Lomonosov Moscow State University. Sophia Ratcliffe helped us convert the database to Darwin Core format.

Online-only Tables

Author contributions

E.H.B. wrote the manuscript, managed the data compilation, assisted with data collection and georeferencing and performed validation checks. R.A.F. shared previously compiled data, assisted with data collection and georeferencing and performed validation checks. G.M.M. oversaw and commented on the data compilation process. C.D. shared previously compiled data and assisted with data collection and georeferencing. T-T. A. constructed the database, coded the data-entry processes and assisted with data collection and georeferencing. A.A. assisted with data collection and georeferencing. N.E.C. assisted with data collection and georeferencing. J.D. assisted with data collection and georeferencing. G.G. assisted with data collection and georeferencing. J.G. assisted with data collection and georeferencing. V.G. assisted with data collection and georeferencing. U.I. assisted with data collection and georeferencing. E.J. assisted with data collection. K.O. assisted with data collection and georeferencing. E.P. assisted with data collection and georeferencing. R.P. assisted with data collection and georeferencing. J.S. assisted with data collection and georeferencing. S.S. assisted with data collection and georeferencing. T.T. assisted with data collection and georeferencing. P.J.K.M. conceived the idea, shared previously compiled data and performed validation checks.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Deceased: Roald Potapov.

References

  • 1.Convention on Biological Diversity. COP 10 Decision X/2 - Strategic Plan for Biodiversity 2011–2020. (Montreal, Canada, 2010).
  • 2.Sheppard C. The shifting baseline syndrome. Mar. Poll. Bull. 1995;30:766–767. doi: 10.1016/0025-326X(95)90079-Q. [DOI] [Google Scholar]
  • 3.Willis KJ, et al. How can a knowledge of the past help to conserve the future? Biodiversity conservation and the relevance of long-term ecological studies. Philos. T. R. Soc. B. 2007;362:175–186. doi: 10.1098/rstb.2006.1977. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Jetz W, McPherson JM, Guralnick RP. Integrating biodiversity distribution knowledge: toward a global map of life. Trends Ecol. Evol. 2012;27:151–159. doi: 10.1016/j.tree.2011.09.007. [DOI] [PubMed] [Google Scholar]
  • 5.Rondinini C, et al. Global habitat suitability models of terrestrial mammals. Philos. T. R. Soc. B. 2011;366:2633–2641. doi: 10.1098/rstb.2011.0113. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Hagemeijer, W. & Blair, M. The EBCC Atlas of European Breeding Birds. (T & AD Poyser, England, 1997).
  • 7.Sullivan BL, et al. eBird: A citizen-based bird observation network in the biological sciences. Biol. Conserv. 2009;142:2282–2292. doi: 10.1016/j.biocon.2009.05.006. [DOI] [Google Scholar]
  • 8.Collen B, et al. Monitoring change in vertebrate abundance: the Living Planet Index. Conserv. Biol. 2009;23:317–327. doi: 10.1111/j.1523-1739.2008.01117.x. [DOI] [PubMed] [Google Scholar]
  • 9.Balmford A. (Less and less) great expectations. Oryx. 1999;33:87–88. doi: 10.1046/j.1365-3008.1999.00054.x. [DOI] [Google Scholar]
  • 10.Boakes EH, Fuller RA, McGowan PJK, Mace GM. Uncertainty in identifying local extinctions: the distribution of missing data and its effects on biodiversity measures. Biol. Lett. 2016;12:20150824. doi: 10.1098/rsbl.2015.0824. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.McGowan PJK, Garson PJ. The Galliformes are highly threatened: Should we care? Oryx. 2002;36:311–312. doi: 10.1017/S0030605302000601. [DOI] [Google Scholar]
  • 12.Boakes EH, Fuller RA, McGowan PJK. The extirpation of species outside protected areas. Conserv. Lett. 2019;12:e12608. doi: 10.1111/conl.12608. [DOI] [Google Scholar]
  • 13.Madge, S. & McGowan, P. Pheasants, partridges and grouse: A guide to the pheasants, partridges, quails, grouse, guineafowl, buttonquails and sandgrouse of the world. (Christopher Helm Publishers Ltd, 2002).
  • 14.Boakes EH. 2020. GalliForm: Galliformes occurrence records from the Indo-Malay and Palaearctic, 1800–2008. The Global Biodiversity Information Facility. [DOI] [PMC free article] [PubMed]
  • 15.Boakes EH, et al. Distorted views of biodiversity: spatial and temporal bias in species occurrence data. PLoS Biol. 2010;8:e1000385. doi: 10.1371/journal.pbio.1000385. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Gupta G, Dunn J, Sanderson R, Fuller R, McGowan PJK. A simple method for assessing the completeness of a geographic range size estimate. Glob. Ecol. Conserv. 2020;21:e00788. doi: 10.1016/j.gecco.2019.e00788. [DOI] [Google Scholar]
  • 17.Boakes EH, Isaac NJB, Fuller RA, Mace GM, McGowan PJK. Examining the relationship between local extinction risk and position in range. Conserv. Biol. 2018;32:229–239. doi: 10.1111/cobi.12979. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Mace GM, Collen B, Fuller RA, Boakes EH. Population and geographic range dynamics: implications for conservation planning. Philos. T. R. Soc. B. 2010;365:3743–3371. doi: 10.1098/rstb.2010.0264. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Dunn JC, Buchanan GM, Cuthbert RJ, Whittingham MJ, McGowan PJK. Mapping the potential distribution of the Critically Endangered Himalayan Quail Ophrysia superciliosa using proxy species and species distribution modelling. Bird Conserv. Int. 2015;25:466–478. doi: 10.1017/S095927091400046X. [DOI] [Google Scholar]
  • 20.Dunn JC, Buchanan GM, Stein RW, Whittingham MJ, McGowan PJK. Optimising different types of biodiversity coverage of protected areas with a case study using Himalayan Galliformes. Biol. Conserv. 2016;196:22–30. doi: 10.1016/j.biocon.2016.01.015. [DOI] [Google Scholar]
  • 21.Grainger MJ, Ngoprasert D, McGowan PJK, Savini T. Informing decisions on an extremely data poor species facing imminent extinction. Oryx. 2019;53:484–490. doi: 10.1017/S0030605317000813. [DOI] [Google Scholar]
  • 22.Olson DM, et al. Terrestrial ecoregions of the world: A new map of life on earth. Bioscience. 2001;51:933–938. doi: 10.1641/0006-3568(2001)051[0933:TEOTWA]2.0.CO;2. [DOI] [Google Scholar]
  • 23.Roselaar CS. An inventory of major European bird collections. Bull. Br. Ornithol. Club. 2003;123A:253–337. [Google Scholar]
  • 24.McGowan PJK. Mapping the distribution of Asian partridges and pheasants as a requirement for identifying conservation priorities. Acta Zool. Sinica. 1996;42S:11–19. [Google Scholar]
  • 25.Wieczorek J, Guo Q, Hijmans R. The point-radius method for georeferencing locality descriptions and calculating associated uncertainty. Int. J. Geogr. Inf. Sci. 2004;18:745–767. doi: 10.1080/13658810412331280211. [DOI] [Google Scholar]
  • 26.Isaac NJB, Pocock MJO. Bias and information in biological records. Biol. J. Linn. Soc. 2015;115:522–531. doi: 10.1111/bij.12532. [DOI] [Google Scholar]
  • 27.Collen B, Ram M, Zamin T, McRae L. The tropical biodiversity data gap: addressing disparity in global monitoring. Trop. Conserv. Sci. 2008;1:75–88. doi: 10.1177/194008290800100202. [DOI] [Google Scholar]
  • 28.Barnes, R. dggridR: Discrete Global Grids for R. https://cran.r-project.org/web/packages/dggridR/ (2016).
  • 29.R Core Team. R: Version 3.5.1. A Language and Environment for Statistical Computing. http://www.R-project.org (2018).
  • 30.Sharrock, J.T.R. The Atlas of Breeding Birds in Britain and Ireland (Poyser, 1977).
  • 31.Gibbons, D.W. The New Atlas of Breeding Birds in Britain and Ireland (Poyser, 1993).
  • 32.Lord, J. & Munns, D.J. Atlas of Breeding Birds of the West Midlands (Collins, 1970).
  • 33.Alamany, O., Auclair, R. & Bertrand, A. Atlas of the Breeding Birds of Andorra (Associasió per a la Defensa de la Natura, Andorra, 2002).
  • 34.Devilliers, P. et al. Atlas des Oiseaux Nicheurs de Belgique (Institut Royale des Sciences Naturelles, 1988).
  • 35.Melchior, E. Atlas of Breeding Birds in Luxembourg (Lëtzebuerger Natur- a Vulleschutzliga, 1987).
  • 36.Teixeira, R.M. Atlas van de Nederlandse Broedvogels 1973-1977 (Natuurmonumenten 1979).
  • 37.Atlas van de Nederlandse Broedvogels 1978-1983 (Sovon, 1987).
  • 38.Rufino, R. Atlas das Aves que Nidificam em Portugal Continental (ICNB/Assírio & Alvin 1989).
  • 39.Florenzano, G.T., Arcamone, E., Baccetti, N., Meschini, E. & Sposimo, P. Atlante Degli Uccelli Nidificanti e Svernanti in Toscana (CentrOrnitologicoToscano, 1997).
  • 40.Stastny, K., Randik, A. & Hudec, K. Atlas Hnizdniho Rozsireni Ptaku V CSSR (Academia, 1987).
  • 41.Kalyakin, M.V. & Voltzit, O.V. Birds of Moscow City and the Moscow Region (Pensoft, 2006).
  • 42.Renno, O. Eesti Linnuatlas (Light, 1993).
  • 43.Priednieks, J., Strazds, M, Strazds, A. & Petrins, A. Latvian Breeding Bird Atlas (Riga Zinatne, 1989).
  • 44.Sovinc, A. Zimski Ornitoloski Atlas Slovenije (Tehniška založba Slovenije, 1994).
  • 45.Eriksen, J. Breeding bird atlas of Oman (Oman Bird Records Committee, 1998).
  • 46.Jennings, M.C. An Interim Atlas of the Breeding Birds of Arabia (National Commission for Wildlife Conseravtion, 1995).
  • 47.Nikolaus, G. Distribution Atlas of Sudan’s Birds with Notes on Habitat and Status (Zoologisches Forschungsmuseum Alexander Koenig, 1987).
  • 48.Atlas of Wintering Birds of Japan (Ministry of Environment, 2004).

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Citations

  1. Boakes EH. 2020. GalliForm: Galliformes occurrence records from the Indo-Malay and Palaearctic, 1800–2008. The Global Biodiversity Information Facility. [DOI] [PMC free article] [PubMed]

Articles from Scientific Data are provided here courtesy of Nature Publishing Group

RESOURCES