The PREDICTS database: a global database of how local terrestrial biodiversity responds to human impacts

Zurich Open Repository and Archive University of Zurich Main Library Strickhofstrasse 39 CH-8057 Zurich www.zora.uzh.ch Year: 2014 The PREDICTS database: a global database of how local terrestrial biodiversity responds to human impacts Hudson, Lawrence N ; Newbold, Tim ; Contu, Sara ; et al ; Diekötter, Tim ; Herzog, Felix ; Kessler, Michael ; Knop, Eva ; Schüepp, Christof ; Lachat, Thibault ; Purvis, Andy Abstract: Biodiversity continues to decline in the face of increasing anthropogenic pressures such as habitat destruction, exploitation, pollution and introduction of alien species. Existing global databases of species’ threat status or population time series are dominated by charismatic species. The collation of datasets with broad taxonomic and biogeographic extents, and that support computation of a range of biodiversity indicators, is necessary to enable better understanding of historical declines and to project – and avert – future declines. We describe and assess a new database of more than 1.6 million samples from 78 countries representing over 28,000 species, collated from existing spatial comparisons of localscale biodiversity exposed to different intensities and types of anthropogenic pressures, from terrestrial sites around the world. The database contains measurements taken in 208 (of 814) ecoregions, 13 (of 14) biomes, 25 (of 35) biodiversity hotspots and 16 (of 17) megadiverse countries. The database contains more than 1% of the total number of all species described, and more than 1% of the described species within many taxonomic groups – including flowering plants, gymnosperms, birds, mammals, reptiles, amphibians, beetles, lepidopterans and hymenopterans. The dataset, which is still being added to, is therefore already considerably larger and more representative than those used by previous quantitative models of biodiversity trends and responses. The database is being assembled as part of the PREDICTS project (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems – www.predicts.org.uk). We make site-level summary data available alongside this article. The full database will be publicly available in 2015. DOI: https://doi.org/10.1002/ece3.1303 Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-102245 Journal Article Published Version The following work is licensed under a Creative Commons: Attribution 4.0 International (CC BY 4.0) License. Originally published at: Hudson, Lawrence N; Newbold, Tim; Contu, Sara; et al; Diekötter, Tim; Herzog, Felix; Kessler, Michael; Knop, Eva; Schüepp, Christof; Lachat, Thibault; Purvis, Andy (2014). The PREDICTS database: a global database of how local terrestrial biodiversity responds to human impacts. Ecology and Evolution, 4(24):4701-4735. DOI: https://doi.org/10.1002/ece3.1303 2 The PREDICTS database: a global database of how local terrestrial biodiversity responds to human impacts Lawrence N. Hudson1*, Tim Newbold2,3*, Sara Contu1, Samantha L. L. Hill1,2, Igor Lysenko4, Adriana De Palma1,4, Helen R. P. Phillips1,4, Rebecca A. Senior2, Dominic J. Bennett4, Hollie Booth2,5, Argyrios ~ o1,4, Morgan Garon4, Michelle L. Choimes1,4, David L. P. Correia1, Julie Day4, Susy Echeverr
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sz221, Teja Tscharntke30, Edgar C. Turner222, Jason M. Rebecca Tonietto219,220, Be 4,223 224 Tylianakis , Adam J. Vanbergen , Kiril Vassilev225, Hans A. F. Verboven226, Carlos H. Vergara227, Pablo M. Vergara228, Jort Verhulst229, Tony R. Walker166,230, Yanping Wang231, James I. Watling232, ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. 1 The PREDICTS Database L. N. Hudson et al. Konstans Wells233,234, Christopher D. Williams235, Michael R. Willig236,237, John C. Z. Woinarski238, Jan H. D. Wolf239, Ben A. Woodcock240, Douglas W. Yu241,242, Andrey S. Zaitsev243,244, Ben Collen245, € rn P. W. Scharlemann2,6 & Andy Purvis1,4 Rob M. Ewers4, Georgina M. Mace245, Drew W. Purves3, Jo 1 Department of Life Sciences, Natural History Museum, Cromwell Road, London, SW7 5BD, U.K. United Nations Environment Programme World Conservation Monitoring Centre, 219 Huntingdon Road, Cambridge, CB3 0DL, U.K. 3 Computational Ecology and Environmental Science, Microsoft Research, 21 Station Road, Cambridge, CB1 2FB, U.K. 4 Imperial College London, Silwood Park Campus, Buckhurst Road, Ascot, SL5 7PY, U.K. 5 Frankfurt Zoological Society, Africa Regional Office, PO Box 14935, Arusha, Tanzania 6 School of Life Sciences, University of Sussex, Brighton, BN1 9QG, U.K. 7 Center for Macroecology, Climate and Evolution, the Natural History Museum of Denmark, Universitetsparken 15, 2100 Copenhagen, Denmark 8 School of Biological and Ecological Sciences, University of Stirling, Bridge of Allan, Stirling, FK9 4LA, U.K. 9 Department of Animal and Plant Sciences, University of Sheffield, Alfred Denny Building, Western Bank, Sheffield, S10 2TN, U.K. 10 School of Biological Sciences, Queen’s University Belfast, 97 Lisburn Road, Belfast BT9 7BL, U.K. 11 Evolutionary Ecology Group, University of Antwerp, Groenenborgerlaan 171, 2020 Antwerp, Belgium 12 Nees Institute for Plant Biodiversity, University of Bonn, Meckenheimer Allee 170, 53113 Bonn, Germany 13 Department of Wildlife and Range Management, FRNR, CANR, KNUST, Kumasi, Ghana 14 SAVE THE FROGS! Ghana, Box KS 15924, Adum-Kumasi, Ghana 15 Escuela de Biolog
ıa, Universidad de Costa Rica, 2060 San Jos
e, Costa Rica 16 CONICET, Lab. INIBIOMA (Universidad Nacional del Comahue-CONICET), Pasaje Gutierrez 1125, 8400 Bariloche, Rio Negro, Argentina 17 HUTAN – Kinabatangan Orang-utan Conservation Programme, PO Box 17793, 88874 Kota Kinabalu, Sabah, Malaysia 18
noma de M
Museo de Zoolog
ıa, Facultad de Ciencias, Universidad Nacional Auto exico, M
exico D.F., Mexico 19
n de Tejidos, Instituto de Investigacio
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gicos Alexander von Humboldt, Km 17 Cali-Palmira, Valle del Cauca, Colombia Coleccio 20 Department of Biology, Universidad del Valle, Calle 13 #100-00, Cali, Colombia 21 Biodiversity Unit, Institute of Bioscience, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia 22 Faculty of Forestry, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia 23
n, Instituto de Investigaciones Biolo
gicas Clemente Estable, Montevideo, Uruguay Laboratorio de Gen
etica de la Conservacio 24 Department of Forest and Water Management, Forest & Nature Lab, Ghent University, Geraardsbergsesteenweg 267, 9090 Gontrode, Belgium 25 Terrestrial Ecology Unit,Department of Biology, Ghent University, K. L. Ledeganckstraat 35, 9000 Gent, Belgium 26
t, Hungary MTA Centre for Ecological Research, Alkotm
any u. 2-4, 2163 V
acr
ato 27 University of Washington, 1900 Commerce Street, Tacoma, Washington 98402 28 Lancaster Environment Centre, Lancaster University, Lancaster, LA1 4YQ, U.K. 29 MCT/Museu Paraense Em
ılio Goeldi, Bel
em, Par
a, Brazil 30 €ttingen, Germany Agroecology, Georg-August University, Grisebachstrasse 6, 37077 Go 31 University of Birmingham, Edgbaston, Birmingham, B15 2TT, U.K. 32 Department of Biological Sciences, University of Alberta, CW 405 – Biological Sciences Centre, Edmonton, AB T6G 2E9, Canada 33 EDP Biodiversity Chair, CIBIO/InBio, Centro de Investigacß~ao em Biodiversidade e Recursos Gen
eticos, Universidade do Porto, Campus Agr
ario de Vair~ ao, 4485-601 Vair~ao, Portugal 34 The Swedish University of Agricultural Sciences, The Swedish Biodiversity Centre, SE 750 07 Uppsala, Sweden 35 University of Edinburgh, School of GeoSciences, Crew Building, King’s Buildings, West Mains Road, Edinburgh EH9 3JN, U.K. 36 Durrell Institute of Conservation and Ecology (DICE), School of Anthropology and Conservation, University of Kent, Canterbury CT2 7NR, U.K. 37 Iwokrama International Centre for Rainforest Conservation and Development, 77 High Street, Georgetown, Guyana 38 Department of Animal Ecology, Philipps-University Marburg, Karl-von-Frisch Strasse 8, 35032 Marburg, Germany 39 €r Naturforschung, Senckenberganlage 25, 60325 Frankfurt am Biodiversity and Climate Research Centre (BiK-F), Senckenberg Gesellschaft fu Main, Germany 40 Institute for Ecology, Evolution & Diversity, Biologicum, Goethe University Frankfurt, Max von Laue St. 13, D 60439 Frankfurt am Main, Germany 41 CBS-KNAW Fungal Biodiversity Centre, Utrecht, The Netherlands 42 Environment Canada, Science & Technology Branch, Carleton University, 1125 Colonel By Drive, Raven Road, Ottawa, ON K1A 0H3, Canada 43 ^le des Maladies Animales Exotiques et Emergentes, Centre de Coop
Unit
e Mixte de Recherche Contro eration Internationale en Recherche Agronomique pour le D
eveloppement (CIRAD), 34398 Montpellier, France 44 ^le des Maladies Animales Exotiques et Emergentes, Institut national de la recherche agronomique (INRA), Unit
e Mixte de Recherche 1309 Contro 34398 Montpellier, France 45 School of Science and the Environment, Manchester Metropolitan University, Chester Street, Manchester, M1 5GD, U.K. 46 University of
Evora – ICAAMA, Apartado 94, 7002-554
Evora, Portugal 47 Southern Swedish Forest Research Centre, Swedish University of Agricultural Sciences, Box 49, 230 53 Alnarp, Sweden 48 Department of Entomology, Purdue University, 901 W. State Street, West Lafayette, 47907 Indiana 49 Centro de Ecologia Funcional, Departamento de Ci^encias da Vida, Universidade de Coimbra, Calcßada Martim de Freitas, 3000-456 Coimbra, Portugal 50
rio Central do LBA, Instituto Nacional de Pesquisa da Amazo ^nia, Av. Andr

jo, 2936, Campus II, Aleixo, CEP 69060-001, Manaus, Escrito e Arau AM, Brazil 51 Department of Botany, School of Natural Sciences, Trinity College Dublin, College Green, Dublin 2, Ireland 52 Departamento de Zoologia, Instituto de Bioci^encias, Universidade de S~ ao Paulo, S~ ao Paulo, SP 05508-090, Brazil 53 Department of Ecology and Animal Biology, Faculty of Sciences, University of Vigo, 36310 Vigo, Spain 2 2 ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. L. N. Hudson et al. The PREDICTS Database 54 Department of Entomology, University of Illinois, Urbana, Illinois 61801 Museu de Zoologia da Universidade de S~ao Paulo, Av. Nazar
e 481, 04263-000, S~ ao Paulo, SP, Brazil 56 ^nia, Av. Andr
e Arau
polis, CEP 69067-375, Manaus, AM, Brazil
jo, 2.936, Petro Instituto Nacional de Pesquisas da Amazo 57 Centre for Functional Ecology, Department of Life Sciences, University of Coimbra, Calcßada Martim de Freitas, 3000-456 Coimbra, Portugal 58 Instituto de Biotecnologia y Ecologia Aplicada (INBIOTECA), Universidad Veracruzana, Av. de las Culturas Veracruzanas, 101, Col. Emiliano Zapata, CP 91090 Xalapa, Veracruz, Mexico 59
mico Tropical de Investigacio
n y Ensen ~anza (CATIE), Tropical Agricultural Research and Higher Education Center, 7170 Cartago, Centro Agrono Turrialba, 30501 Costa Rica 60 Department of Quantitative Methods and Information Systems, Faculty of Agronomy, University of Buenos Aires, Av. San Mart
ın 4453, Ciudad
noma de Buenos Aires, Argentina C.P. 1417, Argentina Auto 61 Normandie Univ., EA 1293 ECODIV-Rouen, SFR SCALE, UFR Sciences et Techniques, 76821 Mont Saint Aignan Cedex, France 62 University of Aberdeen, Aberdeen, AB24 2TZ, U.K. 63 Department of Biology, CESAM, Universidade de Aveiro, Campus Universit
ario de Santiago, 3810-193 Aveiro, Portugal 64 Sustainability Research Institute, University of East London, 4-6 University Way, London E16 2RD, U.K. 65 Department of Biology, University of Naples “Federico II”, Naples, Italy 66
s-graduacß~ao em Ecologia, Universidade Federal de Santa Catarina, Floriano
polis, Santa Catarina, CEP 88040-900, Brazil Programa de Po 67 British Trust for Ornithology, University of Stirling, Stirling FK9 4LA, U.K. 68 €nen Institute of Biodiversity, Bundesallee 50, 38116 Braunschweig, Germany Thu 69 CNRS,
Ecologie des For^ets de Guyane (UMR-CNRS 8172), BP 316, 97379 Kourou cedex, France 70 Universit
e de Toulouse, UPS, INP, Laboratoire
Ecologie Fonctionnelle et Environnement (Ecolab), 118 route de Narbonne, 31062 Toulouse, France 71 Department of Landscape Ecology, Institute for Nature and Resource Conservation, Kiel University, Olshausenstrasse 75, 24098 Kiel, Germany 72 Department of Biology, Nature Conservation, University Marburg, Marburg, Germany 73 Institute of Integrative Biology, ETH Zurich, Switzerland 74 Programa de Biolog
ıa, Universidad del Atl
antico Km 7 v
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antico, Colombia 75 Biometry and Environmental System Analysis, University of Freiburg, Tennenbacher Strasse 4, 79106 Freiburg, Germany 76 INRA, UMR1213 Herbivores, 63122 Saint-Genes-Champanelle, France 77 Institute of Zoology, Zoological Society of London, Nuffield Building, Regents Park, London, NW1 4RY, U.K. 78 Department of Ecology and Environmental Science, Ume a University, 901 87 Ume a, Sweden 79 Wildlife, Fish and Environmental Studies, Swedish University of Agricultural Sciences, 901 83 Ume a, Sweden 80 €tvo € s Lo
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MTA-ELTE-MTM Ecology Research Group, Hungarian Academy of Sciences, c/o Biological Institute, Eo and University, P
azm
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any 1/C., 1117 Budapest, Hungary and Hungarian Natural History Museum, Baross u. 13., 1088 Budapest, Hungary 81 University of Koblenz-Landau, Institute for Environmental Sciences, Fortstr. 7, 76829 Landau, Germany 82 Department of Ecology – Conservation Ecology, Faculty of Biology, Philipps-Universit€ at Marburg, Karl-von-Frisch-Street 8, 35032 Marburg, Germany 83 Faculty of Science, University of South Bohemia and Institute of Entomology, Biology Centre of Academy of Sciences Czech Republic, 
e Budejovice, Czech Republic Branisovsk
a 31, 370 05 Cesk 84 Institute for Tropical Biology and Conservation, Universiti Malaysia Sabah, 88999 Kota Kinabalu, Sabah, Malaysia 85 Dipartimento di Scienze Veterinarie, Universita di Pisa, Viale delle Piagge, n2, 56124 Pisa, Italy 86 The Southern Swedish Forest Research Centre, The Swedish University of Agricultural Sciences, PO Box 49, 23453 Alnarp, Sweden 87 Laboratoire d’Ecologie Alpine (LECA), Universit
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s-Graduacß~ao em Biologia Animal, Universidade Federal de Pernambuco, Recife PE 50670-901, Brazil Programa de Po 89 Department of Plant Sciences, University of California, Davis, California 95616 90 Department of Natural Resource Sciences, Thompson Rivers University, 900 McGill Road, Kamloops, BC V2C 0C8, Canada 91 IDEA Consultants Inc, Okinawa Branch Office, Aja 2-6-19, Naha, Okinawa 900-0003, Japan 92 €nigsallee 9 – 21, 37081 Go €ttingen, Germany Carl Zeiss Microscopy GmbH, Ko 93 University of Hamburg, Biocentre Grindel, Martin-Luther-King Platz 3, 20146 Hamburg, Germany 94 Seed Consulting Services, 106 Gilles Street, Adelaide 5000 SA, Australia 95 School of Geography, Planning and Environmental Management, The University of Queensland, St Lucia 4072, Qld, Australia 96 Ecologia Aplicada/Applied Ecology, Universidade Sagrado Coracß~ ao (USC), Rua Irm~ a Arminda, 10-50, Jardim Brasil, Bauru, S~ ao Paulo, Brazil 97 DISTAV, University of Genova, Corso Dogali 1M,16136 Genova, Italy 98 Dipartimento di Biologia, Universita di Napoli Federico II, Campus Monte S. Angelo, Via Cinthia 4, 80126 Napoli, Italy 99 Universidade Federal de Pelotas (UFPel), PO Box 354, CEP 96010-900, Pelotas RS, Brazil 100 Astron Environmental Services, 129 Royal Street, East Perth WA 6004, Australia 101 Department of Environment and Agriculture, Curtin University, Kent Street, Bentley, WA 6102, Australia 102 Mount Holyoke College, Department of Biological Sciences, South Hadley, Massachusetts 01075 103 School of Biological Science, University of Plymouth, Drake’s Circus, Plymouth, PL4 8AA, U.K. 104 351 False Bay Drive, Friday Harbor, Washington 98250 105 International University of Malaya-Wales, Jalan Tun Ismail, 50480 Kuala Lumpur, Malaysia 106 Coordenacß~ ao de Bot^anica, Museu Paraense Em
ılio Goeldi, Caixa Postal 399, CEP 66040-170, Bel
em, Par
a, Brazil 107 School of Environmental Sciences, University of East Anglia, Norwich Research Park, Norwich, NR4 7TJ, U.K. 108 Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, 1055 du P.E.P.S., PO Box 10380, Qu
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ıvar, Venezuela 112 Agroscope, Reckenholzstr. 191, 8046 Zurich, Switzerland 113
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Corporacio a, Colombia 114
gico de Costa Rica, Apartado 159-7050, Cartago, Costa Rica Escuela de Ingenier
ıa Forestal, Tecnolo 115
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n y el Estudio de la Biodiversidad (ACEBIO), Casa 15, Barrio Los Abogados, Zapote, San Jos
Asociacio e, Costa Rica 116 International Rice Research Institute, DAPO Box 7777, Metro Manila, The Philippines 117 University of Debrecen, Department of Ecology, PO Box 71, 4010 Debrecen, Hungary 118 Department of Ecology, Environment and Plant Sciences, Stockholm University, 106 91 Stockholm, Sweden 119
gicos Alexander von Humboldt, Bogot
Instituto de Investigaciones y Recursos Biolo a, Colombia 120 Hiroshima University, Graduate School of Education, 1-1-1, Kagamiyama, Higashi-Hiroshima 739-8524, Japan 121 Scarab Research Group, University of Pretoria, Pretoria, South Africa 122
noma de M
Centro de Investigaciones en Ecosistemas, Universidad Nacional Auto exico, A.P. 27-3 Santa Mar
ıa de Guido, Morelia, Michoac
an, M
exico C.P. 58090, Mexico 123 Department of Animal Ecology, Justus-Liebig University, Heinrich-Buff-Ring 26-32, 35392 Giessen, Germany 124 Swedish University of Agricultural Sciences, Department of Ecology, Box 7044, 750 07 Uppsala, Sweden 125 Yukon Department of Environment, P.O. Box 2703, Whitehorse, YT Y1A 2C6, Canada 126 Nature Conservation Foundation, Mysore, India 127 Department of Environmental & Natural Resources Management, University of Patras, Seferi 2, 30100 Agrinio, Greece 128 Centre for Tropical Environmental and Sustainability Science (TESS) and School of Marine and Tropical Biology, James Cook University, Cairns, Qld, Australia 129 School of Science and Technology, Pacific Adventist University, Port Moresby, Papua New Guinea 130 Institute of Systematic Botany, University of Zurich, Zollikerstrasse 107, 8008 Zurich, Switzerland 131 Institute of Ecology and Evolution, University of Bern, Baltzerstrasse 6, 3012 Bern, Switzerland 132 Institute of Ecology, University of Bremen, FB2, Leobener Str., 28359 Bremen, Germany 133 MTA-ELTE-MTM Ecology Research Group, P
azm
any P
eter s. 1/c, Budapest 1117, Hungary 134 €rzburg, Glasshu €ttenstr. 5, 96181 Rauhenebrach, Germany Field Station Fabrikschleichach, Biocenter, University of Wu 135 €rcherstrasse 11, 8903 Birmensdorf, Switzerland Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Zu 136 Instituto Nacional de Tecnolog
ıa Agropecuaria, EEA Bariloche, 8400 Bariloche, Argentina 137 INRA, UR 406 Abeilles et Environnement, F-84914 Avignon, France 138 Department of Biology, San Francisco State University, 1600 Holloway Ave, San Francisco, California 94132 139 Laboratoire de diagnostic en phytoprotection, Ministere de l’agriculture, des p^ echeries et de l’alimentation du Qu
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noma de Nayarit, Unidad Acad
emica de Turismo, Coordinacio
n de Investigacio
n y Posgrado, Ciudad de la Cultura Amado Universidad Auto Nervo s/n, C.P. 63155 Tepic, Nayarit, Mexico 147 Graduate School of Agricultural Science, Kobe University, Kobe, 657-8501, Japan 148 Hortob
agy National Park Directorate, 4002 Debrecen, P.O.Box 216, Hungary 149 Fauna & Flora International Philippines, #8 Foggy Heights Subdivision San Jose, Tagaytay City 4120, Philippines 150 ~as, West Ave, Dasmarin ~as 4115, Philippines De La Salle University-Dasmarin 151 Department of Geography, University of Wisconsin-Madison, 550 North Park Street, Madison, Wisconsin 53706 152 Marshall Agroecology Ltd, 2 Nut Tree Cottages, Barton, Winscombe, BS25 1DU, U.K. 153 Escuela de Posgrados, Facultad de Agronom
ıa, Doctorado en Agroecolog
ıa, Universidad Nacional de Colombia, Cra 30 No. 45-03, Ciudad Universitaria, Bogot
a, Colombia 154 The University of Queensland, School of Biological Sciences, Brisbane, Qld 4120, Australia 155 € Wildlife Research Station, 730 91 Riddarhyttan, Sweden Swedish University of Agricultural Sciences, Department of Ecology, Grimso 156 Rainforest Alliance, 233 Broadway, 28th Floor, New York City, New York 10279 157 Department of Natural Resources and Environmental Sciences, N-407 Turner Hall, MC-047, 1102 South Goodwin Ave., Urbana, Illinois 61801 158 National Museums of Kenya, Botany Department, P.O. Box 40658, 00100 Nairobi, Kenya 159 Department of Zoology, National Museums of Kenya, P.O. Box 40658, 00100 Nairobi, Kenya 160 WWF, 1250 24th Street NW, Washington, District of Columbia 20037 161 Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, CAS, Menglun, Mengla, Yunnan, 666303 China 162 Forestry and Forest Products Research Institute, Matsunosato 1, Tsukuba Ibaraki 305–8687, Japan 163 Laboratorio de Investigaciones en Abejas, Departamento de Biolog
ıa, Facultad de Ciencias, Universidad Nacional de Colombia, Sede Bogot
a, Colombia Carrera 30 No. 45-03, Edificio 421, Oficina 128, Bogot
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ıa y Ecolog
ıa Acu
atica – LAZOEA, Universidad de Los Andes, Bogot
a, Colombia 168 BIO-Diverse, Ließemer Str. 32 a, 53179 Bonn, Germany 169 Oxford University Centre for the Environment, University of Oxford, South Parks Road, Oxford, OX1 3QY, U.K. 170 Department of Wildlife and Range Management, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana 171 Forestry Research Institute of Ghana, Kumasi, Ghana 172 Department of Animal & Environmental Biology, University of Benin, Benin City, Nigeria 173 The Royal Society for the Protection of Birds (RSPB), The Lodge, Sandy, Bedfordshire, SG19 2DL, U.K. 174 Laboratorio Ecotono, CONICET–INIBIOMA, Universidad Nacional del Comahue, Quintral 1250, Bariloche 8400, Argentina 175 Departamento de Biologia, Faculdade de Filosofia Ci^encias e Letras de Ribeir~ ao Preto, Universidade de S~ ao Paulo, Avenida. Bandeirantes, 3900 – CEP 14040-901 – Bairro Monte Alegre, Ribeir~ao Preto, SP, Brazil 176 Laboratorio de Investigaciones en Abejas-LABUN, Departamento de Biolog
ıa, Facultad de Ciencias, Universidad Nacional de Colombia, Carrera 45 N° 26-85, Edificio Uriel Guti
errez, Bogot
a DC, Colombia 177
rdoba, Argentina Instituto de Diversidad y Ecolog
ıa Animal (CONICET-UNC) and Centro de Zoolog
ıa Aplicada (UNC), Rondeau 798 X5000AVP Co 178 School of Environmental Sciences, University of East Anglia, Norwich, NR4 7TJ, U.K. 179 Lund University, Department of Biology/Biodiversity, Ecology Building, 223 62 Lund, Sweden 180 Laboratory of Biogeography & Ecology, Department of Geography, University of the Aegean, 81100 Mytilene, Greece 181 Department of Zoology, University of Cambridge, Cambridge, CB2 3EJ, U.K. 182 Department of Biology, Western Kentucky University, 1906 College Heights Blvd., Bowling Green, Kentucky 42101 183 Entomology, Cornell University, 4126 Comstock Hall, Ithaca, New York 14850 184 School of Natural Sciences, Trinity College Dublin, College Green, Dublin 2, Ireland 185 Center for Environmental Sciences and Engineering & Department of Ecology and Evolutionary Biology, University of Connecticut, 3107 Horsebarn Hill Road, Storrs, Connecticut 06269-4210 186 IN+, Instituto Superior T
ecnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal 187 CRA-ABP, Consiglio per la Ricerca e la sperimentazione in Agricoltura, Centro di ricerca per l’agrobiologia e la pedologia, Via Lanciola 12/A, 50125 – Cascine del Riccio, Firenze, Italy 188 The Royal Society for the Protection of Birds (RSPB), 2 Lochside View, Edinburgh Park, Edinburgh, EH12 9DH, U.K. 189 Department of Forest Ecosystems and Society, Oregon State University, Corvallis, Oregon 97331 190
v~ Universidade Federal de Sergipe, Cidade Universit
aria Prof. Jos
e Alo
ısio de Campos, Jardim Rosa Elze, S~ ao Cristo ao, Brazil 191
gicas e da Sau
de, Universidade Federal de Mato Grosso do Sul, P.O Box 549, 79070-900 Campo Grande, Brazil Centro de Ci^ encias Biolo 192 165 Braid Road, Edinburgh, EH10 6JE, U.K. 193 Associate Scientist, Luquillo LTER, Institute for Tropical Ecosystem Studies, College of Natural Sciences, University of Puerto Rico at Rio Piedras, P.O. Box 70377, San Juan, Puerto Rico 00936-8377 194 PROPLAME-PRHIDEB-CONICET, Departamento de Biodiversidad y Biolog
ıa Experimental, Facultad de Ciencias Exactas y Naturales, Universidad
noma de Buenos Aires, Argentina de Buenos Aires, Ciudad Universitaria, PB II, 4to piso, (CP1428EHA) Ciudad Auto 195 €ttgerstr. 2-14, 65439 Flo €rsheim, Germany ECT Oekotoxikologie GmbH, Bo 196 Universidad de Ciencias Aplicadas y Ambientales U.D.C.A., Cl 222 No. 55-37 Bogot
a, Colombia 197 School of Biological and Chemical Sciences, Queen Mary University of London, Mile End Road, London, E3 5GN, U.K. 198 Department of Environmental Sciences, University of Virginia, Charlottesville, Virginia 22904-4123 199 Blandy Experimental Farm, 400 Blandy Farm Lane, Boyce, Virginia 22620 200 D
epartement des sciences biologiques, Universit
e du Qu
ebec  a Montr
eal (UQAM), Case postale 8888, Succursale Centre-ville, Montr
eal, QC H3C 3P8, Canada 201 School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, B15 2TT, U.K. 202 Institute of Silviculture and Forest Protection, University of West Hungary, Bajcsy-Zsilinszky u. 4., 9400 Sopron, Hungary 203 Red de Ecolog
ıa Funcional, Instituto de Ecolog
ıa A.C. Carretera Antigua a Coatepec, N 351 El Haya, CP 91070 Xalapa, Veracruz, Mexico 204 Stockholm University, Department of Ecology, Environment and Plant Sciences, SE106 91 Stockholm, Sweden 205 Helmholtz Centre for Environmental Research – UFZ, Theodor-Lieser-Strasse 4, 06120 Halle, Germany 206 Lawrence University, 711 E. Boldt Way, Appleton, Wisconsin 54911 207 School of Human Ecology, Dr. B.R. Ambedkar University, Lothian Road, Delhi 110006, India 208 Department of Ecology and Natural Resource Management (INA), Norwegian University of Life Sciences (NMBU), Box 5003, 1432  As, Norway 209 Center for International Forestry Research, Bogor, 16000 Indonesia 210
gicas, Rua Augusto Correa, 01, Bel
Universidade Federal do Par
a, Instituto de Ci^encias Biolo em, 66075-110 Par
a, Brazil 211 Department of Zoology, University of Oxford, South Parks Road, Oxford, OX1 3PS, U.K. 212 USDA – APHIS – PPQ, 389 Oyster Point Blvd. Suite 2, South San Francisco, California 94080 213 Universidad Nacional de Colombia, Cra. 64 X Cll. 65. Bloque 11, Oficina 207, Medellin, Colombia 214 Department of Biological Sciences, National University of Singapore, 14 Science Drive 4, Singapore City 117543, Republic of Singapore 215

n en Ecolog
ıa de Comunidades Aridas EComAS (Grupo de Investigacio y Semi
aridas), Dpto. de Recursos Naturales, Facultad de Ciencias Exactas y Naturales, Universidad Nacional de La Pampa, Santa Rosa, Argentina 216 School of Natural Sciences and Trinity Centre for Biodiversity Research, Trinity College Dublin, College Green, Dublin 2, Ireland 217 Kadoorie Conservation China, Kadoorie Farm and Botanic Garden, Lam Kam Road, Tai Po, New Territories, Hong Kong SAR, China 218 Department of Resource Management and Geography, The University of Melbourne, 500 Yarra Boulevard, Richmond, VIC 3121, Australia ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. 5 The PREDICTS Database L. N. Hudson et al. 219 Northwestern University Program in Plant Biology and Conservation, 2205 Tech Drive, O.T. Hogan Hall, Room 2-144, Evanston, Illinois 60208 Chicago Botanic Garden, 1000 Lake Cook Road, Glencoe, Illinois 60022 221 MTA-DE Biodiversity and Ecosystem Services Research Group, Egyetem ter 1, Debrecen 4032, Hungary 222 University Museum of Zoology, Downing Street, Cambridge, CB2 3EJ, U.K. 223 University of Canterbury, Private bag 4800, Christchurch 8140, New Zealand 224 NERC Centre for Ecology & Hydrology, Bush Estate, Penicuik, Edinburgh, EH26 0QB, U.K. 225 Institute of Biodiversity and Ecosystem Research, Bulgarian Academy of Science, 23 Akademik Georgi Bonchev str., Block 23, 1113 Sofia, Bulgaria 226 Department of Earth and Environmental Science, Division Forest, Nature and Landscape, KU Leuven, Celestijnenlaan 200E, 3001 Leuven, Belgium 227
gicas, Universidad de las Am
Departamento de Ciencias Qu
ımico-Biolo ericas Puebla, 72810 Cholula, Puebla, Mexico 228
n Central, Santiago, Chile Universidad de Santiago de Chile, Avenida Alameda Libertador Bernardo O’Higgins 3363, Estacio 229 Spotvogellaan 68, 2566 PN, The Hague, The Netherlands 230 Dillon Consulting Limited, 137 Chain Lake Drive, Halifax, NS B3S 1B3, Canada 231 The Key Laboratory of Conservation Biology for Endangered Wildlife of the Ministry of Education, College of Life Sciences, Zhejiang University, Hangzhou 310058, China 232 University of Florida, 3205 College Avenue, Fort Lauderdale, Florida 33314 233 The Environment Institute and School of Earth and Environmental Sciences, The University of Adelaide, SA 5005, Australia 234 Institute of Experimental Ecology, University of Ulm, Albert-Einstein-Allee 11, 89069 Ulm, Germany 235 Behavioural Ecology and Biocontrol, Department of Biology, National University of Ireland, Maynooth, Co. Kildare, Ireland 236 Center for Environmental Sciences & Engineering, University of Connecticut, 3107 Horsebarn Hill Road, Storrs, Connecticut 06269-4210 237 Department of Ecology & Evolutionary Biology, University of Connecticut, 3107 Horsebarn Hill Road, Storrs, Connecticut 06269-4210 238 Charles Darwin University, 7 Ellengowan Dr, Brinkin NT 0810, Australia 239 University of Amsterdam, Institute for Biodiversity and Ecosystem Dynamics (IBED), P.O. Box 94248, 1090 GE Amsterdam, The Netherlands 240 NERC Centre for Ecology & Hydrology, Crowmarsh Gifford, Wallingford, Oxfordshire, OX10 8BB, U.K. 241 University of East Anglia, Norwich Research Park, Norwich, Norfolk, NR4 7TJ, U.K. 242 Kunming Institute of Zoology, Kunming, Yunnan, 650023, China 243 Institute of Animal Ecology, Justus-Liebig-University, Heinrich-Buff-Ring 26, 35392 Giessen, Germany 244 A. N. Severtsov Institute of Ecology and Evolution, Leninsky Prospekt 33, 119071 Moscow, Russia 245 Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, Gower Street, London, WC1E 6BT, U.K. 220 Keywords Data sharing, global change, habitat destruction, land use. Correspondence Lawrence Hudson, Natural History Museum, Cromwell Road, London, SW7 5BD, U.K. Tel: +44 (0)20 7942 5819; Fax: +44 (0)20 7942 5175; E-mail: l.hudson@nhm.ac.uk and Tim Newbold, UNEP World Conservation Monitoring Centre, 219 Huntingdon Road, Cambridge, CB3 0DL, U.K. Tel: +44 (0)1223 277 314; Fax: +44 (0)1223 277 136; E-mail: tim.newbold@unep-wcmc.org Funding Information The PREDICTS project was supported by the U.K. Natural Environment Research Council (Grant Number NE/J011193/1) and is a contribution from the Imperial College Grand Challenges in Ecosystems and the Environment initiative. Adriana De Palma was supported by the U.K. Biotechnology and 6 Abstract Biodiversity continues to decline in the face of increasing anthropogenic pressures such as habitat destruction, exploitation, pollution and introduction of alien species. Existing global databases of species’ threat status or population time series are dominated by charismatic species. The collation of datasets with broad taxonomic and biogeographic extents, and that support computation of a range of biodiversity indicators, is necessary to enable better understanding of historical declines and to project – and avert – future declines. We describe and assess a new database of more than 1.6 million samples from 78 countries representing over 28,000 species, collated from existing spatial comparisons of local-scale biodiversity exposed to different intensities and types of anthropogenic pressures, from terrestrial sites around the world. The database contains measurements taken in 208 (of 814) ecoregions, 13 (of 14) biomes, 25 (of 35) biodiversity hotspots and 16 (of 17) megadiverse countries. The database contains more than 1% of the total number of all species described, and more than 1% of the described species within many taxonomic groups – including flowering plants, gymnosperms, birds, mammals, reptiles, amphibians, beetles, lepidopterans and hymenopterans. The dataset, which is still being added to, is therefore already considerably larger and more representative than those used by previous quantitative models of biodiversity trends and responses. The database is being assembled as part of the PREDICTS project (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems – www.predicts.org.uk). ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. L. N. Hudson et al. Biological Sciences Research Council (Grant Number BB/F017324/1). Helen Philips was supported by a Hans Rausing PhD Scholarship. The PREDICTS Database We make site-level summary data available alongside this article. The full database will be publicly available in 2015. Received: 11 August 2014; Revised and Accepted: 30 September 2014 doi: 10.1002/ece3.1303 *These authors contributed equally to this paper. Despite the commitment made by the Parties to the Convention on Biological Diversity (CBD) to reduce the rate of biodiversity loss by 2010, global biodiversity indicators show continued decline at steady or accelerating rates, while the pressures behind the decline are steady or intensifying (Butchart et al. 2010; Mace et al. 2010). Evaluations of progress toward the CBD’s 2010 target highlighted the need for datasets with broader taxonomic and geographic coverage than existing ones (Walpole et al. 2009; Jones et al. 2011). Taxonomic breadth is needed because species’ ability to tolerate human impacts – destruction, degradation and fragmentation of habitats, the reduction of individual survival and fecundity through exploitation, pollution and introduction of alien species – varies among major taxonomic groups (Vi
e et al. 2009). For instance, the proportion of species listed as threatened in the IUCN Red List is much higher in amphibians than in birds (International Union for Conservation of Nature 2013). Geographic breadth is needed because human impacts show strong spatial variation: most of Western Europe has long been dominated by human land use, for example, whereas much of the Amazon basin is still close to a natural state (Ellis et al. 2010). Thus, in the absence of broad coverage, any pattern seen in a dataset is prone to reflect the choice of taxa and region as much as true global patterns and trends. The most direct way to capture the effects of human activities on biodiversity is by analysis of time-series data from ecological communities, assemblages or populations, relating changes in biodiversity to changes in human activity (Vack
ar 2012). However, long-term data suitable for such modeling have limited geographic and taxonomic coverage, and often record only the presence or absence of species (e.g., Dornelas et al. 2013). Time-series data are also seldom linked to site-level information on drivers of change, making it hard to use such data to model biodiversity responses or to project responses into the future. Ecologists have therefore more often analyzed spatial comparisons among sites that differ in the human impacts they face. Although the underlying assumption that biotic differences among sites are caused by human impacts has been criticized (e.g., Johnson and Miyanishi 2008; Pfeifer et al. 2014), it is more likely to be reasonable when the sites being compared are surveyed in the same way, when they are well matched in terms of other potentially important variables (e.g., Blois et al. 2013; Pfeifer et al. 2014), when analyses focus on community-level summaries rather than individual species (e.g., Algar et al. 2009), and when the spatial and temporal variations being considered are similar in magnitude (Blois et al. 2013). Collations of wellmatched site surveys therefore offer the possibility of analyzing how biodiversity is responding to human impacts without losing taxonomic and geographic breadth. Openness of data is a further important consideration. The reproducibility and transparency that open data can confer offer benefits to all areas of scientific research, and are particularly important to research that is potentially relevant to policy (Reichman et al. 2011). Transparency has already been highlighted as crucial to the credibility of biodiversity indicators and models (e.g., UNEP-WCMC 2009; Feld et al. 2010; Heink and Kowarik 2010) but the datasets underpinning previous policy-relevant analyses have not always been made publicly available. We present a new database that collates published, in-press and other quality-assured spatial comparisons of community composition and site-level biodiversity from terrestrial sites around the world. The underlying data are made up of abundance, presence/absence and speciesrichness measures of a wide range of taxa that face many different anthropogenic pressures. As of March 2014, the dataset contains more than 1.6 million samples from 78 countries representing over 28,000 species. The dataset, ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. 7 Introduction The PREDICTS Database which is still being added to, is being assembled as part of the PREDICTS project (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems – http://www.predicts.org.uk), the primary purpose of which is to model and project how biodiversity in terrestrial communities responds to human activity. The dataset is already considerably larger and more representative than those used in existing quantitative models of biodiversity trends such as the Living Planet Index (WWF International 2012) and GLOBIO3 (Alkemade et al. 2009). In this paper we introduce the database, describe in detail how it was collated, validated and curated, and assess its taxonomic, geographic and temporal coverage. We make available a summary dataset that contains, for each sampling location, the predominant land use, landuse intensity, type of habitat fragmentation, geographic coordinates, sampling dates, country, biogeographic realm, ecoregion, biome, biodiversity hotspot, taxonomic group studied and the number of measurements taken. The full dataset constitutes a large evidence base for the analysis of: • The responses of biodiversity to human impacts for different countries, biomes and major taxonomic groups; • The differing responses within and outside protected areas; • How traits such as body size, range size and ecological specialism mediate responses and • How human impacts alter community composition. The summary dataset permits analysis of geographic and taxonomic variation in study size and design. The complete database, which will be made freely available at the end of the current phase of the project in 2015, will be of use to all researchers interested in producing models of how biodiversity responds to human pressures. L. N. Hudson et al. • Measurements within each data source were taken using the same sampling procedure, possibly with variation in sampling effort, at each site and time; • The paper reported, or authors subsequently provided, geographical coordinates for the sites sampled. One of the modeling approaches used by PREDICTS is to relate diversity measurements to remotely sensed data, specifically those gathered by NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) instruments (Justice et al. 1998). MODIS data are available from early 2000 onwards so, after a short initial data collation stage, we additionally required that diversity sampling had been completed after the beginning of 2000. Where possible, we also obtained the following (see Site characteristics, below, for more details): • The identities of the taxa sampled, ideally resolved to species level; • The date(s) on which each measurement was taken; • The area of the habitat patch that encompassed each site; • The maximum linear extent sampled at the site; • An indication of the land use at each site, e.g. primary, secondary, cropland, pasture; • Indications of how intensively each site was used by people; • Descriptions of any transects used in sampling (start point, end point, direction, etc.); • Other information about each site that might be relevant to modeling responses of biodiversity to human activity, such as any pressures known to be acting on the site, descriptions of agriculture taking place and, for spatially blocked designs, which block each site was in. Searches We considered only data that met all of the following criteria: • Data are published, in press or were collected using a published methodology; • The paper or report presents data about the effect of one or more human activities on one or more named taxa, and where the degree of human activity differed among sampling locations and/or times; • Some measure of overall biodiversity, or of the abundance or occurrence of the named taxa, was made at two or more sampling locations and/or times; We collated data by running sub-projects that investigated different regions, taxonomic groups or overlapping anthropogenic pressures: some focused on particular taxa (e.g., bees), threatening processes (e.g., habitat fragmentation, urbanization), land-cover classes (e.g., comparing primary, secondary and plantation tropical forests), or regions (e.g., Colombia). We introduced the project and requested data at conferences and in journals (Newbold et al. 2012; Hudson et al. 2013). After the first six months of broad searching, we increasingly targeted efforts toward under-represented taxa, habitat types, biomes and regions. In addition to articles written in English, we also considered those written in Mandarin, Spanish and Portuguese – languages in which one or more of our data compilers were proficient. 8 ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. Methods Criteria for inclusion L. N. Hudson et al. Data collection To maximize consistency in how incoming data were treated, we developed customized metadata and data capture tools – a PDF form and a structured Excel file – together with detailed definitions and instructions on their usage. The PDF form was used to capture bibliographic information, corresponding author contact details and meta-data such as the country or countries in which data were collected, the number of taxa sampled, the number of sampling locations and the approximate geographical center(s) of the study area(s). The Excel file was used to capture details of each sampling site and the diversity measurements themselves. The PDF form and Excel file are available in Supplementary Information. We wrote software that comprehensively validates pairs of PDF and Excel files for consistency; details are in the “Database” section. Most papers that we considered did not publish all the information that we required; in particular, site coordinates and species names were frequently not published. We contacted authors for these data and to request permission to include their contributed data in the PREDICTS database. We used the insightly customer relationship management application (https://www.insightly.com/) to manage contact with authors. The PREDICTS Database matrices that we received contained empty cells, which we interpreted as follows: (1) where the filled-in values in the matrix were all non-zero, we interpreted blanks as zeros or (2) where some of the values in the matrix were zero, we took empty cells as an indication that the taxa concerned were not looked for at those Sites, and interpreted empty cells as missing values. Where possible, we recorded the sampling effort expended at each Site and allowed the units of sampling effort to vary among Studies. For example, if transects had been used, the (Study-level) sampling effort units might be meters or kilometers and the (Site-level) sampling efforts might be the length of the transects. If pitfall traps had been used, the (Study-level) sampling effort units might be “number of trap nights” and the (Sitelevel) sampling efforts might be the number of traps used multiplied by the number of nights that sampling took place. Where possible, we also recorded an estimate of the maximum linear extent encompassed by the sampling at each Site – the distance covered by a transect, the distance between two pitfall traps or the greatest linear extent of a more complex sampling design (see Figure S1 in Supplementary Information for details). Site characteristics We structured data into Data Sources, Studies, and Sites. The highest level of organization is the Data Source. A Data Source typically represents data from a single published paper, although in some cases the data were taken from more than one paper, from a non-governmental organization report or from a PhD or MSc thesis. A Data Source contains one or more Studies. A Study contains two or more Sites, a list of taxa that were sampled and a site-by-species matrix of observations (e.g., presence/ absence or abundance). All diversity measurements within a Study must have been collected using the same sampling method. For example, a paper might present, for the same set of Sites, data from pitfall traps and from Malaise traps. We would structure these data into a single Data Source containing two Studies – one for each trapping technique. It is therefore reasonable to directly compare observations within a Study but not, because of methodological differences, among Studies. Sometimes, the data presented in a paper were aggregates of data from multiple sampling methods. In these cases, provided that the same set of sampling methods was applied at each Site, we placed the data in a single Study. We classified the diversity observations as abundance, occurrence or species richness. Some of the site-by-species We recorded each Site’s coordinates as latitude and longitude (WGS84 datum), converting where necessary from local grid-based coordinate systems. Where precise coordinates for Sites were not available, we georeferenced them from maps or schemes available from the published sources or provided by authors. We converted each map to a semi-transparent image that was georeferenced using either ArcGIS (Environmental Systems Research Institute (ESRI) 2011) or Google Earth (http://www.google.co.uk/ intl/en_uk/earth/ ), by positioning and resizing the image on the top of ArcGIS Online World Imagery or Google Maps until we achieved the best possible match of mapped geographical features with the base map. We then obtained geographic coordinates using geographic information systems (GIS) for each Site center or point location. We also recorded authors’ descriptions of the habitat at each Site and of any transects walked. For each Site we recorded the dates during which sampling took place. Not all authors presented precise sampling dates – some gave them to the nearest month or year. We therefore recorded the earliest possible start date, the latest possible end date and the resolution of the dates that were given to us. Where dates were given to the nearest month or year, we recorded the start and end dates as the earliest and latest possible day, respectively. For example, if the authors reported that sampling took place between June and August of 2007, we recorded the ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. 9 Structure of data The PREDICTS Database L. N. Hudson et al. date resolution as “month,” the start of sampling as June 1, 2007 and end of sampling as August 31, 2007. This scheme meant that we could store sampling dates using regular database structures (which require that the year, month, and day are all present), while retaining information about the precision of sampling dates that were given to us. We assigned classifications of predominant land use and land-use intensity to each Site. Because of PREDICTS’ aim of making projections about the future of biodiversity under alternative scenarios, our land-use classification was based on five classes defined in the Representative Concentration Pathways harmonized landuse estimates (Hurtt et al. 2011) – primary vegetation, secondary vegetation, cropland, pasture and urban – with the addition of plantation forest to account for the likely differences in the biodiversity of natural forest and plantation forest (e.g., Gibson et al. 2011) and a “Cannot decide” category for when insufficient information was available. Previous work has suggested that both the biodiversity and community composition differ strongly between sites in secondary vegetation of different maturity (Barlow et al. 2007); therefore, we subdivided secondary vegetation by stage – young, intermediate, mature and (when information was lacking) indeterminate – by considering vegetation structure (not diversity). We used authors’ descriptions of Sites, when provided, to classify land-use intensity as minimal, light or intense, depending on the land use in question, again with “Cannot decide” as an option for when information was lacking. A detailed description of how classifications are assigned is in the Supplementary section “Notes on assigning predominant land use and use intensity” and Tables S1 and S2. Given the likely importance of these classifications as explanatory variables in modeling responses of biodiversity to human impacts, we conducted a blind repeatability study in which one person (the last author, who had not originally scored any Sites) rescored both predominant land use and use intensity for 100 Sites chosen at random. Exact matches of predominant land use were achieved for 71 Sites; 15 of the remaining 29 were “near misses” specified in advance (i.e., primary vegetation versus mature secondary; adjacent stages of secondary vegetation; indeterminate secondary versus any other secondary stage; and cannot decide versus any other class). Cohen’s kappa provides a measure of inter-rate agreement, ranging from 0 (agreement no better than random) to 1 (perfect agreement). For predominant land use, Cohen’s kappa = 0.662 (if only exact agreement gets credit) or 0.721 (if near misses are scored as 0.5); values in the range 0.6–0.8 indicate “substantial agreement” (Landis and Koch 1977), indicating that our categories, criteria and training are sufficiently clear for users to score Sites reliably. Moving to use intensity, we found exact agreement for 57 of 100 Sites, with 39 of the remaining 43 being “near misses” (adjacent intensity classes, or cannot decide versus any other class), giving Cohen’s kappa values of 0.363 (exact agreement only) or 0.385 (near misses scored as 0.5), representing “fair agreement” (Landis and Koch 1977); agreement is slightly higher among the 71 Sites for which predominant land use was matched (exact agreement in 44 of 71 Sites, kappa = 0.428, indicating “moderate agreement”: Landis and Koch 1977). Where known, we recorded the number of years since conversion to the present predominant land use. If the Site’s previous land use was primary habitat, we recorded the number of years since it was converted to the current land use. If the habitat was converted to secondary forest (clear-felled forest or abandoned agricultural land), we recorded the number of years since it was converted/ clear-felled/abandoned. Where ranges were reported, we used mid-range values; if papers reported times as “greater than N years” or “at least N years,” we recorded a value of N 9 1.25. Based on previous work (Wilcove et al. 1986; Dickman 1987), we assigned one of five habitat fragmentation classes: (1) well within unfragmented habitat, (2) within unfragmented habitat but at or near its edge, (3) within a remnant patch (perhaps at its edge) that is surrounded by other habitats, (4) representative part of a fragmented landscape and (5) part of the matrix surrounding remnant patches. These are described and illustrated in Table S3 and Figure S2. We also recorded the area of the patch of predominant habitat within which the Site was located, where this information was available. We recorded a value of 1 if the patch area was unknown but large, extending far beyond the sampled Site. 10 ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. Database Completed PDF and Excel files were uploaded to a PostgreSQL 9.1 database (PostgreSQL Global Development Group, http://www.postgresql.org/) with the PostGIS 2.0.1 spatial extension (Refractions Research Inc, http://www.postgis.net/). The database schema is shown in Figure S3. We wrote software in the Python programming language (http://www.python.org/) to perform comprehensive data validation; files were fully validated before their data were added to the database. Examples of lower level invalid data included missing values for mandatory fields, a negative time since conversion, a latitude given as 1° 61’, a date given as 32nd January, duplicated Site names and duplicated taxon names. Commonly encountered L. N. Hudson et al. higher level problems included mistakes in coordinates, such as latitude and longitude swapped, decimal latitude and longitude incorrectly assembled from DD/MM/SS components, and direction (north/south, east/west) swapped round. These mistakes typically resulted in coordinates that plotted in countries not matching those given in the metadata and/or out to sea. The former was detected automatically by validation software, which required that the GIS-matched country for each Site (see “Biogeographical coverage” below) matched the country name entered in the PDF file for the Study; where a Study spanned several countries, we set the country name to “Multiple countries.” We visually inspected all Site locations on a map and compared them to maps presented in the source article or given to us by the authors, catching coordinates that were mistakenly out to sea and providing a check of accuracy. Our database linked each Data Source to the relevant record in our Insightly contact management database. This allows us to trace each datum back to the email that granted permission for us to include it in our database. Biogeographical coverage In order to assess the data’s geographical and biogeographical coverage, we matched each Site’s coordinates to GIS datasets that were loaded into our database: • Terrestrial Ecoregions of the World (The Nature Conservancy 2009), giving the ecoregion, biome and biogeographic realm; • World Borders 0.3 (Thematic Mapping 2008), giving the country, United Nations (UN) region and UN subregion; • Biodiversity Hotspots (Conservation International Foundation 2011). Global GIS layers appear coarse at local scales and we anticipated that Sites on coasts or on islands could fall slightly outside the relevant polygons. Our software therefore matched Sites to the nearest ecoregion and nearest country polygons, and recorded the distance in meters to that polygon, with a value of zero for Sites that fell within a polygon; we reviewed Sites with non-zero distances. The software precisely matched Sites to hotspot polygons. The relative coarseness of GIS polygons might result in small errors in our assessments of coverage (i.e., at borders between biomes, ecoregions and countries, and at the edges of hotspots) – we expect that these errors should be small in number and unbiased. We also estimated the yearly value of total net primary production (TNPP) for biomes and five-degree latitudinal belts, using 2010 spatial (0.1-degree resolution) monthly datasets “NPP – Net Primary Productivity 1 month- ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. The PREDICTS Database Terra/MODIS” compiled and distributed by NASA Earth Observations (http://neo.sci.gsfc.nasa.gov/view.php? datasetId=MOD17A2_M_PSN&year=2010). We used the NPP values (average for each month assimilation measured in grams of carbon per square meter per day) to estimate monthly and annual NPP. We then derived TNPP values by multiplying NPP values by the total terrestrial area for that ecoregion/latitudinal belt. We assessed the representativeness of land use and land-use intensity combinations by comparing the proportion of Sites in each combination to a corresponding estimate of the proportion of total terrestrial area for 2005, computed using land-use data from the HYDE historical reconstruction (Hurtt et al. 2011) and intensity data from the Global Land Systems dataset (van Asselen and Verburg 2012). Taxonomic names and classification We wanted to identify taxa in our database as precisely as possible and to place them in higher level groups, which required relating the taxonomic names presented in our datasets to a stable and authoritative resource for nomenclature. We used the Catalogue of Life (http://www.cata logueoflife.org/) for three main reasons. First, it provides broad taxonomic coverage. Second, Catalogue of Life publishes Annual Checklists. Third, Catalogue of Life provides a single accepted taxonomic classification for each species that is represented. Not all databases provide this guarantee; for example, Encyclopedia of Life (http:// www.eol.org/) provides zero, one or more taxonomic classifications for each represented species. We therefore matched taxonomic names to the Catalogue of Life 2013 Annual Checklist (Roskov et al. 2013, henceforth COL). There was large variation in the form of the taxonomic names presented in the source datasets, for example: • A Latin binomial, with and without authority, year and other information; • A generic name, possibly with a number to distinguish morphospecies from congenerics in the same Study (e.g., “Bracon sp. 1”); • The name of a higher taxonomic rank such as family, order, class; • A common name (usually for birds), sometimes not in English; • A textual description, code, letter or number with no further information except an indication of some aspect of higher taxonomy. Most names were Latin binomials, generic names or morphospecies names. Few binomials were associated with an authority – even when they were, time constraints mean that it would not have been practical to make use 11 The PREDICTS Database of this information. Many names contained typographical errors. We represented each taxon by three different names: “Name entered,” “Parsed name,” and “COL query name.” “Name entered” was the name assigned to the taxon in the dataset provided to us by the investigators who collected the data. We used the Global Names Architecture’s biodiversity package (https://github.com/GlobalNames Architecture/biodiversity) to parse “Name entered” and extract a putative Latin binomial, which we assigned to both “Parsed name” and “COL query name.” For example, the result of parsing the name “Ancistrocerus trifasciatus M€ ull.” was “Ancistrocerus trifasciatus.” The parser treated all names as if they were scientific taxonomic names, so the result of parsing common names was not sensible: e.g. “Black and White Casqued Hornbill” was parsed as “Black and.” We expected that common names would be rare – where they did arise, they were detected and corrected as part of our curation process, which is described below. Other examples of the parser’s behavior are shown in Table S4. We queried COL with each “COL query name” and stored the matching COL ID, taxonomic name, rank and classification (kingdom, phylum, class, order, family, genus, species and infraspecies). We assumed that the original authors gave the most authoritative identification of species. Therefore, when a COL search returned more than one result, and the results were made up of one accepted name together with one or more synonyms and/or ambiguous synonyms and/or common names and/or misapplied names, our software recorded the accepted name. For example, COL returns three results for the salticid spider Euophrys frontalis – one accepted name and two synonyms. When a COL search returned more than one result, and the results included zero or two or more accepted L. N. Hudson et al. names, we used the lowest level of classification common to all results. For example, COL lists Notiophilus as an accepted genus in two beetle families – Carabidae and Erirhinidae. This is a violation of the rules of nomenclature, but taxonomic databases are imperfect and such violations are to be expected. In this case, the lowest rank common to both families is the order Coleoptera. Curating names We reviewed: • Taxa that had no matching COL record; • Taxa that had a result at a rank higher than species and a “Name entered” that was either a Latin binomial or a common name; • Cases where the same “Parsed name” in different Studies linked to different COL records; • Studies for which the lowest common taxonomic rank did not seem appropriate; for example, a Study of birds should have a lowest common taxonomic rank of class Aves or lower rank within Aves. Where a change was required, we altered “COL query name”, recording the reason why the change was made, and reran the COL query. Sometimes, this curation step had to be repeated multiple times. In all cases, we retained the names given to us by the authors, in the “Name entered” and “Parsed name” columns. Typographical errors were the most common cause for failed COL searches; for example, the hymenopteran Diphaglossa gayi was given as Diphaglosa gayi. Such errors were detected by visual inspection and by performing manual searches on services that perform fuzzy matching and suggest alternatives, such as Google and Encyclopedia of Life. In cases where “Parsed name” was Figure 1. Site locations. Colors indicate biomes, taken from The Nature Conservancy’s (2009) terrestrial ecoregions of the world dataset, shown in a geographic (WGS84) projection. Circle radii are proportional to log10 of the number of samples at that Site. All circles have the same degree of partial transparency. 12 ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. L. N. Hudson et al. The PREDICTS Database Table 1. Coverage of hotspots. Hotspot None Nearctic California Floristic Province Madrean Pine–Oak Woodlands Neotropic Atlantic Forest Caribbean Islands Cerrado Chilean Winter Rainfall and Valdivian Forests Mesoamerica Tropical Andes Tumbes-ChocoMagdalena Palearctic Caucasus Irano-Anatolian Japan Mediterranean Basin Mountains of Central Asia Mountains of Southwest China Afrotropic Cape Floristic Region Coastal Forests of Eastern Africa Eastern Afromontane Guinean Forests of West Africa Horn of Africa Madagascar and the Indian Ocean Islands Maputaland–Pondoland– Albany Succulent Karoo Indo-Malay Himalaya Indo-Burma Philippines Sundaland Western Ghats and Sri Lanka Australasia East Melanesian Islands Forests of East Australia New Caledonia New Zealand Southwest Australia Wallacea Oceania Polynesia–Micronesia Studies (%) Sites (%) Samples (%) Terrestrial area (%) 50.72 63.63 52.33 84.01 0.96 1.30 0.12 0.20 0.24 0.01 <0.01 0.31 3.11 0.48 1.91 2.39 1.16 0.67 0.66 1.69 0.28 2.59 0.11 0.32 0.83 0.15 1.37 0.27 8.13 6.46 0.48 7.83 3.02 0.37 8.94 4.11 0.10 0.76 1.04 0.18 0.00 0.00 1.67 5.98 0.00 0.00 0.00 0.60 5.52 0.00 0.00 0.00 0.17 2.63 0.00 0.36 0.61 0.25 1.41 0.58 0.00 0.00 0.00 0.18 0.24 0.00 0.29 0.00 0.20 0.00 0.05 0.20 1.20 2.15 1.27 1.04 0.83 0.54 0.07 0.42 0.00 0.48 0.00 0.18 0.00 0.01 1.12 0.40 0.72 0.52 0.50 0.18 0.00 0.00 0.00 0.07 0.00 0.72 1.20 6.46 0.48 0.00 0.23 0.77 6.12 0.13 0.00 0.10 0.44 23.55 0.09 0.50 1.60 0.20 1.01 0.13 0.24 0.72 0.00 0.72 0.00 1.67 0.36 1.45 0.00 0.10 0.00 0.69 1.13 0.31 0.00 0.01 0.00 0.58 0.68 0.17 0.01 0.18 0.24 0.23 0.48 0.38 0.01 0.03 Hotspots are shown grouped by realm. ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. a binomial without typographical errors but that was not recognized by COL, we searched web sites such as Encyclopedia of Life and The Plant List (http://www.theplant list.org/) for synonyms and alternative spellings and queried COL with the results. Where there were no synonyms or where COL did not recognize the synonyms, we searched COL for just the genus. If the genus was not recognized by COL, we used the same web services to obtain higher level ranks, until we found a rank that COL recognized. Some names matched COL records in two different kingdoms. For example, Bellardia, Dracaena and Ficus are all genera of plants and of animals. In such cases, we instructed our software to consider only COL records from the expected kingdom. We also constrained results when a name matched COL records in two different branches within the same kingdom; for example, considering the Notiophilus example given above – if the Study was of carabid beetles, we would instruct of software to consider only results within family Carabidae. COL allows searches for common names. Where “Name entered” was a common name that was not recognized by COL, we searched web sites as described above and set “COL query name” to the appropriate Latin binomial. Some studies of birds presented additional complications. Some authors presented taxon names as four-letter codes that are contractions of common names (e.g., AMKE was used by Chapman and Reich (2007) to indicate Falco sparverius, American kestrel) or of Latin binomials (e.g., ACBA was used by Shahabuddin and Kumar (2007) to indicate Accipiter badius). Some of these codes are valid taxonomic names in their own right. For example, Shahabuddin and Kumar (2007) used the code TEPA to indicate the passerine Terpsiphone paradisi. However, Tepa is also a genus of Hemiptera. Left uncurated, COL recognized TEPA as the hemipteran genus and the Study consequently had a lowest common taxonomic rank of kingdom Animalia, not of class Aves or a lower rank within Aves, as we would expect. Some codes did not appear on published lists (e.g., http://www.birdpop.org/alphacodes.htm, http:// www.pwrc.usgs.gov/bbl/manual/speclist.cfm, http://www. carolinabirdclub.org/bandcodes.html and http://infohost. nmt.edu/~shipman/z/nom/bbs.html) or in the files provided by the authors, either because of typographical errors, omissions or incomplete coverage. Fortunately, codes are constructed by following a simple set of rules – the first two letters of the genus and species of binomials, and a slightly more complex method for common names of North American birds (http://infohost.nmt.edu/~shipman/z/nom/bbl rules.html). We cautiously reverse-engineered unrecognized codes by following the appropriate rules and then searched lists of birds of the country concerned for possible matches. 13 The PREDICTS Database L. N. Hudson et al. 70 60 50 40 30 20 10 0 −10 −20 −30 −40 −50 25 20 15 10 5 0 % studies/sites/samples 0 2 4 6 8 % total terrestrial area/NPP Figure 2. Latitudinal coverage. The percentage of Studies (circles), Sites (crosses) and samples (pluses) in five-degree bands of latitude. We computed each Study’s latitude as the median of its Sites’ latitudes. The solid and dashed lines show the percentage of total terrestrial area and percentage of total terrestrial NPP, respectively, in each five-degree band (see “Biogeographical coverage” in Methods). The dotted horizontal lines indicate the extent of the tropics. For example, we deduced from the Wikipedia list of birds of India (http://en.wikipedia.org/wiki/List_of_birds_of_India) that KEZE – used in a study of birds in Rajasthan, northwestern India (Shahabuddin and Kumar 2007) – most likely indicates Ketupa zeylonensis. Another problem is that collisions occur – the same code can apply to more than one taxon. For example, PEPT is the accepted code for Atalotriccus pilaris (pale-eyed pygmy tyrant – http://www.bird pop.org/alphacodes.htm), a species that occurs in the Neotropics. The same code was used by the Indian study of Shahabuddin and Kumar (2007) to indicate Pernis ptilorhynchus (crested honey buzzard). We therefore reverse-engineered bird codes on a case-by-case basis. Where a code could represent more than one species, we set “COL query name” as the lowest taxonomic rank common to all matching species. It was not possible to precisely count the number of species represented in our database because of ambiguity inherent in the taxon names provided with the data. We estimated the number of species as follows. Names with a COL result at either species or infraspecies level were counted once per name. Names with a COL result resolved to higher taxonomic ranks were counted once per Study. To illustrate this scheme, consider the bat genus Eonycteris, which contains three species. Suppose that Study A sampled all three species and that the investigators could distinguish individuals as belonging to three separate species but could not assign them to named species, reporting them as Eonycteris sp. 1, Eonycteris sp. 2 and Eonycteris sp. 3. Study B also sampled all three species of Eonycteris and again reported Eonycteris sp. 1, Eonycteris sp. 2 and Eonycteris sp. 3. We would erroneously consider these taxa to be six different species. We did not attempt to determine how often, if at all, such inflation occurred. In order to assess the taxonomic coverage of our data, we computed a higher taxonomic grouping for each taxon as: (1) order where class was Insecta or Entognatha; (2) class where phylum was Arthropoda (excluding Insecta), Chordata or Tracheophyta; otherwise 3) phylum. So the higher taxonomic group of a 14 ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. Counting the number of species L. N. Hudson et al. (A) A B C D E Biome F G H J K L M N P The PREDICTS Database 50 sites 1,582 samples 1,041 sites 105,989 samples 420 sites 15,057 samples 5,203 sites 453,762 samples 198 sites 18,505 samples 1,026 sites 133,766 samples 823 sites 46,431 samples 105 sites 2,821 samples 818 sites 79,319 samples 205 sites 30,931 samples 0 sites 0 samples 381 sites 27,840 samples 3,035 sites 706,189 samples 32 sites 2,493 samples 19 92 19 93 19 94 19 95 19 96 19 97 19 98 19 99 20 00 20 01 20 02 20 03 20 04 20 05 20 06 20 07 20 08 20 09 20 10 20 11 20 12 (B) 70 60 Afrotropic Australasia Indo-Malay Nearctic Neotropic Oceania Palearctic latitude 50 40 30 20 10 0 19 92 19 93 19 94 19 95 19 96 19 97 19 98 19 99 20 00 20 01 20 02 20 03 20 04 20 05 20 06 20 07 20 08 20 09 20 10 20 11 20 12 Figure 3. Spatiotemporal sampling coverage. Site sampling dates by biome (A) and absolute latitude (B). Each Site is represented by a circle and line. Circle radii are proportional to log10 of the number of samples at that Site. Circle centers are at the midpoints of Site sampling dates; lines indicate the start and end dates of sampling. Y-values in (A) have been jittered at the study level. Circles and lines have the same degree of partial transparency. Biome colors and letters in (A) are as in Fig. 1. Colors in (B) indicate biogeographic realm. bee is order Hymenoptera (following rule 1), the higher taxonomic group of a wolf is class Mammalia (rule 2), and the higher taxonomic group of a snail is phylum Gastropoda (rule 3). For each higher taxonomic group, we compared the numbers of species in our database to the estimated number of described species presented by Chapman (2009). Some of the higher taxonomic groups that we computed did not directly relate to the groups presented by Chapman (2009) so, in order to compare counts, we computed Magnoliophyta as the sum of Magnoliopsida and Liliopsida; Gymnosperms as the sum of Pinopsida and Gnetopsida; Ferns and allies as ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. 15 The PREDICTS Database L. N. Hudson et al. (A) log10 (% Studies) 1.5 N D (B) 1.5 1.0 1.0 G G JB B J M F C 0.5 E 0.0 N D M 0.5 E P 0.0 K A 0.0 F C H 0.5 1.0 (C) P K 1.5 −0.5 A 0.0 0.5 1.0 (D) D 1.5 H D 1.5 log10 (% Sites) N 1.0 0.5 0.5 K K A −0.5 P 0.5 1.0 log10 (% Samples) D 1.0 A P −0.5 N 1.5 H 1.5 (E) 0.0 N 1.5 D F B J G K −0.5 J M 0.0 C B G M E 1.0 (F) 0.5 K 0.5 1.0 F 0.0 E 0.0 H 0.5 J MC E 0.0 B F G MC 0.0 −0.5 1.0 B J F G N C E −0.5 H P −1.0 0.0 H P −1.0 A 0.5 1.0 1.5 log10 (% NPP) A −0.5 0.0 0.5 1.0 log10 (% area) Figure 4. Coverage of biomes. The percentage of Studies (A and B), Sites (C and D) and samples (E and F) against percentages of terrestrial NPP (A, C and E) and terrestrial area (B, D and F). Biome colors and letters are as in Fig. 1. the sum of Polypodiopsida, Lycopodiopsida, Psilotopsida, Equisetopsida and Marattiopsida; and Crustacea as Malacostraca. For some of our analyses, we related taxonomic names to databases of species’ traits. To do this, we synthesized, for each taxon, a “Best guess binomial”: • The COL taxon name if the COL rank was Species; • The first two words of the COL taxon if the rank was Infraspecies; • The first two words of “Parsed name” if the rank was neither Species nor Infraspecies and “Parsed name” contained two or more words; • Empty in other cases. 16 ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. This scheme meant that even though COL did not recognize all of the Latin binomials that were given to us, we could maximize matches between names in our databases with names in the species’ trait databases. L. N. Hudson et al. The PREDICTS Database Minimal use Light use Intense use Difference Primary vegetation 16.16% (−15.11%) 6.91% (−0.32%) 3.29% (+2.80%) 5 Secondary vegetation 11.44% (−5.62%) 5.24% (+0.54%) 2.65% (+2.25%) 0 Plantation forest 2.82% (+2.57%) 3.43% (+3.19%) 2.26% (+2.01%) −5 Cropland 4.24% (+1.11%) 6.34% (+2.38%) 7.05% (+2.14%) −10 Pasture 4.59% (+3.74%) 3.97% (−18.83%) 1.41% (−0.62%) −15 Urban 2.26% (+1.95%) 2.34% 1.45% (+1.31%) −20 Figure 5. Representativeness of predominant land use and land-use intensity classes. Numbers are the percentage of Sites assigned to each combination of land use and intensity. Numbers in brackets and colors are the differences between these and the proportional estimated total terrestrial area of each combination of land use and land-use intensity for 2005, computed from the HYDE (Hurtt et al. 2011) and Global Land Systems datasets (van Asselen and Verburg 2012); no difference is shown for “Urban”/”Light use” because these datasets did not allow us to compute an estimate for this combination. The 12.15% of Sites that could not be assigned a classification for predominant land use and/or landuse intensity are not shown. Between March 2012 and March 2014, we collated data from 284 Data Sources, 407 Studies and 13,337 Sites in 78 countries and 208 (of 814) ecoregions (Fig. 1). The best-represented UN-defined subregions are North America (17.51% of Sites), Western Europe (14.14%) and South America (13.37%). As of March 31, 2014, the database contained 1,624,685 biodiversity samples – 1,307,947 of abundance, 316,580 of occurrence and 158 of species richness. The subregions with the most samples are Southeast Asia (24.66%), Western Europe (11.36%) and North America (10.88%). Of the world’s 35 biodiversity hotspots, 25 are represented (Table 1). Hotspots together account for just 16% of the world’s terrestrial surface, yet 47.67% of our measurements were taken in hotspots. The vast majority of measurements in hotspots were taken in the Sundaland hotspot (Southeast Asia) and the latitudinal band with the most samples is 0° to 5° N (Fig. 2); many of these data come from two studies of higher plants from Indonesia that between them contribute just 284 sites but over 320,000 samples (Sheil et al. 2002). The best-represented biomes are “Temperate Broadleaf and Mixed Forests” and “Tropical and Subtropical Moist Broadleaf Forests” (Figs 3, 4). “Flooded Grasslands and Savannas” is the only biome that is unrepresented in our database (Figs 3, 4); although this biome is responsible for only 0.7% of global terrestrial net primary productivity, it is nevertheless ecologically important and will be a priority for future collation efforts. Two biomes – “Tundra” and “Deserts and Xeric Shrublands” – are underrepresented relative to their areas. Of the world’s 17 megadiverse countries identified by Mittermeier et al. (1997), only Democratic Republic of Congo is not represented (Figure S4). The vast majority of sampling took place after the year 2000 (Fig. 3), reflecting our desire to collate diversity data that can be related to MODIS data, ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. 17 Results The PREDICTS Database L. N. Hudson et al. 4.0 Magnoliophyta Hymenoptera 3.5 Coleoptera Aves Lepidoptera log10 (N represented in database) Arachnida Diptera 3.0 Hemiptera Bryophyta Ascomycota Basidiomycota Mammalia Ferns and allies 2.5 Amphibia Reptilia Mollusca Isoptera Collembola Orthoptera 2.0 Odonata Diplopoda Nematoda Gymnosperms 1.5 Chilopoda Thysanoptera Neuroptera Crustacea Annelida Blattodea Glomeromycota 1.0 Dermaptera Archaeognatha Mantodea 0.5 Ephemeroptera Trichoptera Embioptera 2.5 3.0 3.5 4.0 4.5 5.0 5.5 log10 (N estimated described) Figure 6. Taxonomic coverage. The number of species in our database against the number of described species as estimated by Chapman (2009). Vertebrates are shown in red, arthropods in pink, other animals in gray, plants in green and fungi in blue. The dashed, solid and dotted lines indicate 10, 1 and 0.1% representation, respectively. Groups with just a single species in the database – Diplura, Mycetozoa, Onychophora, Pauropoda, Phasmida, Siphonaptera, Symphyla and Zoraptera – are not shown. Table 2. Names represented in species attribute databases. Attribute database Trait Group Best guess binomials GBIF IUCN CITES PanTHERIA TRY TRY TRY Range size Red list status CITES appendix Body mass Seed mass Vegetative height Generative height All taxa All taxa All taxa Mammalia Plantae Plantae Plantae 17,801 17,801 17,801 376 6,924 6,924 6,924 Attribute database names Species matches 20,094 3,542 26,107 2,822 9,911 14,514 3,521 467 310 2,017 772 1,633 Genus matches Total matches 62 2,820 768 2,546 14,514 3,521 467 372 4,837 1,540 4,179 GBIF (Global Biodiversity Information Facility, http://www.gbif.org/, queried 2014-03-31), IUCN (International Union for Conservation of Nature, http://www.iucn.org/, queried 2014-03-31), CITES (Convention on International Trade in Endangered Species of Wild Fauna and Flora, http:// www.cites.org/, downloaded 2014-01-27), PanTHERIA (Jones et al. 2009), TRY (Kattge et al. 2011). Best guess binomials: the number of unique “Best guess binomials” in the PREDICTS database within that taxonomic group. Attribute database names: the number of unique binomials and trinomials for that attribute in attribute database. Species matches: the number of “Best guess binomials” that exactly match a record in the attribute database. Genus matches: the number of generic names in the PREDICTS database with a matching record in the attribute database (only for binomials for which there was not a species match). Total matches: sum of species matches and genus matches. We did not match generic names for GBIF range size, IUCN category or CITES appendix because we did not expect these traits to be highly conserved within genera. 18 ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. L. N. Hudson et al. The PREDICTS Database which are available from early 2000 onwards. The database’s coverage of realms, biomes, countries, regions and subregions is shown in Supplementary Tables S5– S11. The distribution of Site-level predominant land use and use intensity is different from the distribution of the estimated total terrestrial area in each land use/landuse intensity combination for 2005 (v2 = 28,243.21, df = 16, P < 2.2 9 10 16; we excluded “Urban”/”Light use” from this test because the HYDE and Global Land Systems datasets did not allow us to compute an estimate for this combination). The main discrepancies are that the database has far fewer than expected Sites that are classified as “Primary habitat”/“Minimal use”, “Secondary vegetation”/“Light use” and “Pasture”/“Light use” (Fig. 5). We were unable to assign a classification of predominant land use to 3.34% of Sites and of use intensity to 12.09% of Sites. The most common fragmentation layout was “Representative part of a fragmented landscape” (27.95% of Sites; Table S12) – a classification that indicates either that a Site is large enough to encompass multiple habitat types or that the Site is of a particular habitat type that is inherently fragmented and dominates the landscape e.g., the site is in an agricultural field and the landscape is comprised of many fields. We were unable to assign a fragmentation layout to 15.47% of Sites. We were able to determine the maximum linear extent of sampling for 60.09% of Sites – values range from 0.2 m to 39.15 km; median 120 m (Figure S5). The precise sampling days are known for 45.44% of Sites; 42.19% are known to the nearest month and 12.37% to the nearest year. The median sampling duration was 91 days; sampling lasted for 1 day or less at 9.90% of Sites (Figure S6). The area of habitat containing the site is known for 25.49% of Sites – values are approximately log-normally distributed (median 40,000 square meters; Figure S7). We reviewed all cases of Sites falling outside the GIS polygons for countries (0.82% of Sites; Figure S8) and ecoregions (0.52% of Sites; Figure S9). These Sites were either on coasts and/or on islands too small to be included in the GIS dataset in question. The database contains measurements of approximately 28,735 species (see “Counting the number of species” in Methods) – 17,733 animals, 10,201 plants, 800 fungi and 1 protozoan. We were unable to place 97 taxa in a higher taxonomic group because they were not sufficiently well resolved. The database contains more than 1% as many species as have been described within 20 higher taxonomic groups (Fig. 6). Birds are particularly well represented, reflecting the sampling bias in favor of this charismatic group. Our database contains measurements of 2,479 species of birds – 24.81% of those described (Chapman 2009) – and 2,368 of these are resolved to either species or infraspecies levels. A total of 228,644 samples – more than 14% of the entire database – are of birds. In contrast, just 397 species of mammals are represented, but even this constitutes 7.24% of described species. Chiroptera (bats) are the best-represented mammalian order with 188 species. Of the 115,000 estimated described species of Hymenoptera, 3,556 (3.09%) are represented in the database, the best representation of an invertebrate group. The hymenopteran family with the most species in the database is Formicidae with 2,060 species. The database contains data for 4,056 species of Coleoptera – 1.07% of described beetles. Carabidae is the best-represented beetle family with 2,060 species. Some higher taxonomic groups have well below 1% representation and, as might be expected, the database has poor coverage of groups for which the majority of species are marine – nematodes, crustaceans and molluscs. Of the 28,735 species, 43.26% are matched to a COL record with a rank of species or infraspecies, 37.47% to a COL record with a rank of genus and 19.27% to a COL record with a higher taxonomic rank (Fig. 7). The species with the largest number of measurements – 1,305 – is Bombus pascuorum (the common carder bee), and bees constitute 35 of the top 100 most frequently sampled species: this results from a PREDICTS subproject that is examining pollinators. Birds make up most of the remaining top 100, with 36 species. Of the 407 Studies, 126 sampled within a single order (Fig. 8); just 12 Studies ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. 19 100 % species 80 60 40 20 0 r Inf p as ec ies Sp ec ies Ge nu s Fa mi ly r de Or Cl as s Ph ylu m Ki ng do m Figure 7. Cumulative percentage of species in the database, by the taxonomic rank at which the name was matched to COL. The PREDICTS Database examined a single species. The six most commonly examined higher taxonomic groups are Tracheophyta (12.04% of Studies), Aves (11.06%), Hymenoptera (7.86%), L. N. Hudson et al. Arthropoda (4.67%), Formicidae (4.67%) and Insecta (4.42%). The database contains 17,802 unique values of “Best guess binomial”. The overlap with species attribute Figure 8. Number of Studies by lowest common taxonomic group. Bars show the number of Studies within each lowest common taxon (so, one Study examined the species Swietenia macrophylla, three Studies examined the species Bombus pascuorum, ten Studies examined multiple species within the genus Bombus, and so on). Colors are as in Figure 6. Numbers on the right are the primary references from which data were taken: 1
pez-Quintero et al. 2012; 2 Buscardo et al. 2008; 3 Dom
ınguez et al. 2012; 4 No €ske et al. 2008; 5 Center for International Forestry Research Lo (CIFOR) 2013a; 6 Center for International Forestry Research (CIFOR) 2013b; 7 Sheil et al. 2002; 8 Dumont et al. 2009; 9 Proenca et al. 2010; 10 Baeten et al. 2010b; 11 Richardson et al. 2005; 12 Schon et al. 2011; 13 Muchane et al. 2012; 14 V
azquez and Simberloff 2002; 15 Bouyer et al. 2007; 16 O’Connor 2005; 17 Higuera and Wolf 2010; 18 Kati et al. 2012; 19 Lucas-Borja et al. 2011; 20 Louhaichi et al. 2009; 21 Power et al. 2012; 22 Brearley 2011; 23 Baeten et al. 2010a; 24 Williams et al. 2009; 25 Mayfield et al. 2006; 26 Kolb and Diekmann 2004; 27 Phalan et al. 2011; 28 Vassilev et al. 2011; 29 Paritsis and Aizen 2008; 30 Boutin et al. 2008; 31 Baur et al. 2006; 32 Fensham et al. 2012; 33 Brunet et al. 2011; 34 Kessler et al. 2009; 35 Hylander and Nemomissa 2009; 36 Barlow et al. 2007; 37 Kumar and Shahabuddin 2005; 38 Kessler et al. ~o-Cancela et al. 2012; 43 Golodets et al. 2010; 44 Castro et al. 2005; 39 Hietz 2005; 40 Krauss et al. 2004; 41 Hern
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enez-Hern
andez 2009; 255 Fermon et al. 2005; 256 Ribeiro and Freitas 2012; 257 Gottschalk et al. 2007; 258 Cagle 2008; 259 Johnson et al. 2008; 260 Su et al. 2011; 261 Saldana-Vazquez et al. 2010; 262 Nicolas et al. 2009; 263 Sakchoowong et al. 2008; 264 Yoshikura et al. 2011; 265 Hanley et al. 2011; 266 Connop et al. 2011; 267 Redpath et al. 2010; 268 Goulson €tter et al. 2006; 273 Darvill et al. 2010; 269 Goulson et al. 2008; 270 Hatfield and LeBuhn 2007; 271 McFrederick and LeBuhn 2006; 272 Dieko et al. 2004; 274 Matsumoto et al. 2009; 275 Knight et al. 2009; 276 Herrmann et al. 2007; 277 Ancrenaz et al. 2004; 278 Felton et al. 2003; 279 Knop et al. 2004; 280 Davis et al. 2010; 281 Hanson et al. 2008; 282 Ferreira and Alves 2005; 283 Luskin 2010; 284 Grogan et al. 2008. 20 ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. L. N. Hudson et al. The PREDICTS Database databases is often much higher than would be expected by chance (Table 2), greatly facilitating analyses that integrate PREDICTS data with species attributes (Newbold et al. 2013, 2014a,b). Of the 284 Data Sources, 271 were taken from articles published in scientific peer-reviewed journals; the rest came from unpublished data (5), internet databases (3), PhD theses (2), agency reports (1) and other sources (2). The vast majority – 273 (96.13%) – of Data Sources are taken from English articles; the remainder are in Mandarin (0.35%), Portuguese (1.06%) or Spanish (2.46%). 29.15% of Data Sources come from just four journals (Fig. 9): Biological Conservation (11.07%), Biodiversity Kingdom (10) Phylum (90) Class (87) Order (126) Family (67) Genus (11) Species (12) and Conservation (8.86%), Forest Ecology & Management (5.17%) and Journal of Applied Ecology (4.06%). The Journal of Applied Ecology contributed many more Studies, Sites and samples than expected from the number of Data Sources (Fig. 9) because of a single Data Source that contributed 21 pan-European Studies and over 140,000 samples (data taken from Billeter et al. 2008; Diek€ otter et al. 2008 and Le F
eon et al. 2010). Discussion The coverage of the PREDICTS dataset illustrates the large number of published articles that are based on Multiple kingdoms Plantae Animalia Tracheophyta Arthropoda Chordata Bryophyta Mollusca Annelida Ascomycota Nematoda Aves Insecta Mammalia Gastropoda Lecanoromycetes Arachnida Agaricomycetes Glomeromycetes Magnoliopsida Reptilia Hymenoptera Coleoptera Chiroptera Lepidoptera Anura Sarcoptiformes Araneae Hemiptera Isoptera Collembola Orthoptera Passeriformes Squamata Diptera Opiliones Rodentia Strigiformes Formicidae Scarabaeidae Apidae Carabidae Arecaceae Nymphalidae Drosophilidae Colubridae Culicidae Curculionidae Phyllostomidae Sciomyzidae Soricidae Staphylinidae Vespertilionidae Bombus Aenictus Bombus pascuorum Pongo pygmaeus Clethrionomys gapperi Colletes floralis Dipteryx oleifera Oryctolagus cuniculus Pteropus tonganus Swietenia macrophylla 1−4 5−10 11−14 1,5−7,14−53 54−64 65−72 35,73−74 75 76 77 78 9,27,29,34,45,66,71,79−114 15,53,64,115−124 86,125−133 31,134−136 137 138−139 140 141 20 142 34,64,143−154 29,34,155−165 36,166−175 18,31,45,176−184 185−195 12,196−198 164,199−203 46,204−205 206−208 196,209 8,18,36 210−211 86,133,212 36,213 214 215 216 71,217−227 228−239 36,240−245 183,246−252 253−254 34,255−256 36,257 258 259 260 261 24 262 263 264 265−273 274 275−276 277−279 66 280 281 282 283 284 0 10 ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. 20 30 40 21 The PREDICTS Database L. N. Hudson et al. Biological conservation Biodiversity and conservation Forest ecology and management Journal of applied ecology Journal of insect conservation Biotropica Conservation biology Agriculture, ecosystems and environment Insect conservation and diversity Ecological applications PLOS ONE Oikos Journal of tropical ecology Basic and applied ecology Animal conservation Restoration ecology Molecular ecology Landscape and urban planning Ecography 0 2 4 6 Percentage 8 10 Figure 9. Data contributions by journal. The percentage of Data Sources (bars), Studies (circles), Sites (crosses) and samples (pluses) taken from each journal. Only journals from 12 which more than one Data Source was taken are shown. local-scale empirical data of the responses of diversity either to a difference in land-use type or along a gradient of land-use intensity or other human pressure. Such data can be used to model spatial responses of local communities to anthropogenic pressures and thus changes over time. This is essential for understanding the impact of biodiversity loss on ecosystem function and ecosystem services, which operate at the local level (Fontaine et al. 2006; Isbell et al. 2011; Cardinale et al. 2012; Hooper et al. 2012). Regardless of scale, no single Study is or could ever be representative, but the sheer number and diversity of Studies means that a collation of these data can provide relatively representative coverage of biodiversity. The majority of Data Sources (271 of 284) come from peerreviewed publications and all data have used peer-reviewed sampling procedures. There are doubtless very many more published data than we have so far acquired and been given permission to use. For the majority of Data Sources (225), it was necessary to contact the author(s) in order to get more information such as the Site coordinates or the names of the taxa studied: even now that supplementary data are commonplace and often extensive, we usually had to request more detail than had been published. The database currently lacks Sites in ten biodiversity hotspots and one megadiverse country (Democratic Republic of the Congo). It also has no data from many large tropical or partially tropical countries such as Angola, Tanzania and Zambia. Many countries are underrepresented given their area and/or the distinctiveness of their biota e.g., Australia, China, Madagascar, New Zealand, Russia and South Africa. We have few data from islands and just 57 Sites from the biogeographic realm of Oceania (Fig. 3 and Table S8): we have not yet directly targeted Oceania or island biota more generally. The database contains no studies of microbial diversity and few of parasites – major shortcomings that also apply to other large biodiversity databases such as the Living Planet Index (WWF International 2012), the IUCN Red List (International Union for Conservation of Nature 2013) and BIOFRAG (Pfeifer et al. 2014). Fewer than 50% of the taxa in our database are matched to a Catalogue of Life record with a rank of species or infraspecies (Fig. 6). The quality and coverage of taxonomic databases continues to improve and we hope to improve our database’s coverage by making use of new Catalogue of Life checklists as they become available. Improved software would permit the use of fuzzy searches to reduce the current manual work required to curate taxonomic names. Intersecting our data with datasets of species attributes (Table 2) indicates much greater overlap among largescale data resources than might be expected simply based on overall numbers of species. This suggests that the same species are being studied for different purposes, because of either ubiquity, abundance, interest or location. In one 22 ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. L. N. Hudson et al. sense this is useful, allowing a thorough treatment of certain groups of species, for example by incorporating trait data in analyses. On the other hand, it highlights the fact that many species are poorly studied in terms of distribution, traits and responses to environmental change. Indeed, many taxonomic groups that matter greatly for ecosystem functions (e.g., earthworms, fungi) are routinely underrepresented in data compilations (Cardoso et al. 2011; Norris 2012), including – despite our efforts toward representativeness – ours. The PREDICTS database is a work in progress, but already represents the most comprehensive database of its kind of which we are aware. Associated with this article is a site-level extract of the data: columns are described in Table S13. The complete database will be made publicly available in 2015, before which we will attempt to improve all aspects of its coverage by targeting underrepresented hotspots, realms, biomes, countries and taxonomic groups. In addition to taking data from published articles, we will integrate measurements from existing large published datasets, where possible. We welcome and greatly value all contributions of suitable data; please contact us at enquiries@predicts.org.uk. Acknowledgments We thank the following who either contributed or collated data: The Nature Conservation Foundation, E.L. Alcala, Paola Bartolommei, Yves Basset, Bruno Baur, Felicity Bedford, Roberto B
oßcon, Sergio Borges, Cibele Bragagnolo, Christopher Buddle, Rob Bugter, Nilton Caceres, Nicolette Cagle, Zhi Ping Cao, Kim Chapman, Kristina Cockle, Giorgio Colombo, Adrian Davis, Emily Davis, Jeff Dawson, Mois
es Barbosa de Souza, Olivier Deheuvels, Jim
enez Hern
andez Fabiola, Aisyah Faruk, Roderick Fensham, Heleen Fermon, Catarina Ferreira, Toby Gardner, Carly Golodets, Wei Bin Gu, Doris Gutierrez-Lamus, Mike Harfoot, Farina Herrmann, Peter Hietz, Anke Hoffmann, Tamera Husseini, Juan Carlos Iturrondobeitia, Debbie Jewitt, M.F. Johnson, Heike Kappes, Daniel L. Kelly, Mairi Knight, Matti Koivula, Jochen Krauss, Patrick Lavelle, Yunhui Liu, Nancy Lo-Man-Hung, Matthew S. Luskin, Cristina MacSwiney, Takashi Matsumoto, Quinn S. McFrederick, Sean McNamara, Estefania Mico, Daniel Rafael Miranda-Esquivel, Elder F. Morato, David Moreno-Mateos, Luis Navarro, Violaine Nicolas, Catherine Numa, Samuel Eduardo Otavo, Clint Otto, Simon Paradis, Finn Pillsbury, Eduardo Pineda, Jaime Pizarro-Araya, Martha P. Ramirez-Pinilla, Juan Carlos Rey-Velasco, Alex Robinson, Marino Rodrigues, Jean-Claude Ruel, Watana Sakchoowong, Jade Savage, Nicole Schon, Dawn Scott, Nur Juliani Shafie, Frederick Sheldon, Hari Sridhar, Martin-Hugues St-Laurent, Ingolf ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. The PREDICTS Database Steffan-Dewenter, Zhimin Su, Shinji Sugiura, Keith Summerville, Henry Tiandun, Diego V
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Maximum linear extents of sampling. Figure S2. Graphical representations of fragmentation layouts. Figure S3. Database schema. Figure S4. Countries represented by area. Figure S5. Histogram of Site maximum linear-extents of sampling. Figure S6. Histogram of Site sampling durations. Figure S7. Histogram of the area of habitat surrounding each Site. Figure S8. Histogram of the distance from each Site to ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. The PREDICTS Database the nearest country GIS polygon. Figure S9. Histogram of the distance from each Site to the nearest ecoregion GIS polygon. Table S1. Classification of land-use intensity for primary and secondary vegetation based on combinations of impact level and spatial extent of impact. Table S2. Combinations of predominant land use and use intensity. Table S3. Habitat fragmentation classifications. Table S4. Examples of parsing different styles of taxonomic name with the Global Names Architecture’s biodiversity package (https://github.com/GlobalNames Architecture/biodiversity). Table S5. Coverage of countries. Table S6. Coverage of regions. Table S7. Coverage of subregions. Table S8. Coverage of realms. Table S9. Coverage of biomes. Table S10. Distribution of samples by biome and kingdom. Table S11. Distribution of samples by subregion and kingdom. Table S12. Coverage of fragmentation layouts. Table S13. Data extract columns. Data S1. Data extract. 35