[PDF 1.43MB]

[PDF 1.43MB]
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
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Mayfield154, Grzegorz Mikusinski155, Jeffrey C. Milder156, James R. Miller157, Carolina L. Morales16,
Mary N. Muchane158, Muchai Muchane159, Robin Naidoo160, Akihiro Nakamura161, Shoji Naoe162,
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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 Biologıa, 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 Zoologıa, Facultad de Ciencias, Universidad Nacional Auto
exico, M
exico D.F., Mexico
19
n de Tejidos, Instituto de Investigacio
n de Recursos Biolo
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 Genetica 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, Alkotmany 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 Emılio Goeldi, Belem, Para, 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 Developpement (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. Andre 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 Martın 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 Biologıa, Universidad del Atlantico Km 7 vıa Puerto Colombia, Atl
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
r
MTA-ELTE-MTM Ecology Research Group, Hungarian Academy of Sciences, c/o Biological Institute, Eo
and University, P
azm
any P
eter
s
et
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), Universite Grenoble Alpes, F-38000 Grenoble, France
88
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 Emılio 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
ebec, QC G1V 4C7,
Canada
109
Animal & Environmental Research Group, Department of Life Sciences, Anglia Ruskin University, East Road, Cambridge, CB1 1PT, U.K.
55
ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
3
The PREDICTS Database
L. N. Hudson et al.
110
University of Tromsø, Department of Arctic and Marine Biology, 9037 Tromsø, Norway
Universidad Nacional Experimental de Guayana, Apdo. Postal 8050, Puerto Ordaz 8015, Estado Bolıvar, Venezuela
112
Agroscope, Reckenholzstr. 191, 8046 Zurich, Switzerland
113
n Sentido Natural, Carrera 70H No. 122 – 98, Apartamento 101, Bogot
Corporacio
a, Colombia
114
gico de Costa Rica, Apartado 159-7050, Cartago, Costa Rica
Escuela de Ingenierıa Forestal, Tecnolo
115
n para la Conservacio
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 Marıa 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, Pazmany Peter 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 Tecnologıa 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
ebec, 2700 rue Einstein, QC
G1P 3W8, Canada
140
Purchase College (State University of New York), 735 Anderson Hill Road, Purchase, New York 10577
141
The James Hutton Institute, Craigiebuckler, Aberdeen, AB15 8QH, U.K.
142
Universidad de Antioquia, Calle 67 No. 53 – 108, Medellın, Colombia
143
International Center for Agricultural Research in the Dry Areas (ICARDA), P.O. Box 950764, Amman, 11195 Jordan
144
Aarhus University, Department of Agroecology, Flakkebjerg Research Centre, Forsøgsvej 1, 4200 Slagelse, Denmark
145
Castilla La Mancha University, School of Advanced Agricultural Engineering, Department of Agroforestry Technology and Science and Genetics,
Campus Universitario s/n, C.P. 02071, Albacete, Spain
146
noma de Nayarit, Unidad Academica 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 Agronomıa, Doctorado en Agroecologıa, Universidad Nacional de Colombia, Cra 30 No. 45-03, Ciudad
Universitaria, Bogota, 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 Biologıa, Facultad de Ciencias, Universidad Nacional de Colombia, Sede Bogot
a,
Colombia Carrera 30 No. 45-03, Edificio 421, Oficina 128, Bogot
a, Colombia
164
El Colegio de la Frontera Sur, Carretera Panamericana y Perif
erico Sur S/N. 29290, Chiapas, Mexico
111
4
ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
L. N. Hudson et al.
The PREDICTS Database
165
Department of Biosciences and Department of Environmental Sciences, Urban Ecology Research Group, University of Helsinki, Viikinkaari 2a,
P.O. Box 65, FI-00014 Helsinki, Finland
166
School of Biology, The University of Nottingham, University Park, Nottingham, NG7 2RD, U.K.
167
Laboratorio de Zoologıa y Ecologıa Acuatica – 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 Biologıa, Facultad de Ciencias, Universidad Nacional de Colombia, Carrera
45 N° 26-85, Edificio Uriel Gutierrez, Bogota DC, Colombia
177
rdoba, Argentina
Instituto de Diversidad y Ecologıa Animal (CONICET-UNC) and Centro de Zoologıa 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 Tecnico, 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 Universitaria Prof. Jos
e Aloısio 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 Biologıa 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, Universite du Quebec 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 Ecologıa Funcional, Instituto de Ecologıa 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 Para, 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 Ecologıa 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 Quımico-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: [email protected]
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: [email protected]
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 (Vie
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 (Vackar 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
The PREDICTS Database
L. N. Hudson et al.
social bee (Bombus pascuorum) in agricultural landscapes.
Mol. Ecol. 16:1167–1178.
Hietz, P. 2005. Conservation of vascular epiphyte diversity in
Mexican coffee plantations. Conserv. Biol. 19:391–399.
Higuera, D., and J. H. D. Wolf. 2010. Vascular epiphytes in dry
oak forests show resilience to anthropogenic disturbance,
Cordillera Oriental, Colombia. Caldasia 32:161–174.
Hilje, B., and T. M. Aide. 2012. Recovery of amphibian species
richness and composition in a chronosequence of secondary
forests, northeastern Costa Rica. Biol. Conserv. 146:170–176.
Hoffmann, A., and U. Zeller. 2005. Influence of variations in
land use intensity on species diversity and abundance of
small mammals in the Nama Karoo, Namibia. Belg. J. Zool.
135:91–96.
Hooper, D. U., E. C. Adair, B. J. Cardinale, J. E. K. Byrnes, B.
A. Hungate, K. L. Matulich, et al. 2012. A global synthesis
reveals biodiversity loss as a major driver of ecosystem
change. Nature 486:105–108.
Horgan, F. G. 2009. Invasion and retreat: shifting assemblages
of dung beetles amidst changing agricultural landscapes in
central Peru. Biodivers. Conserv. 18:3519–3541.
Hu, C., and Z. P. Cao. 2008. Nematode community structure
under compost and chemical fertilizer management practice,
in the north China plain. Exp. Agric. 44:485–496.
Hudson, L. N., T. Newbold, D. W. Purves, J. P. W.
Scharlemann, G. Mace, and A. Purvis. 2013. Projecting
responses of ecological diversity In Changing Terrestrial
Systems (PREDICTS): can you help? BES Bull. 44:36–37.
Hurtt, G. C., L. P. Chini, S. Frolking, R. A. Betts, J. Feddema,
G. Fischer, et al. 2011. Harmonization of land-use scenarios
for the period 1500-2100: 600 years of global gridded annual
land-use transitions, wood harvest, and resulting secondary
lands. Clim. Change 109:117–161.
Hylander, K., and S. Nemomissa. 2009. Complementary roles
of home gardens and exotic tree plantations as alternative
habitats for plants of the Ethiopian montane rainforest.
Conserv. Biol. 23:400–409.
Hylander, K., and H. Weibull. 2012. Do time-lagged
extinctions and colonizations change the interpretation of
buffer strip effectiveness? – a study of riparian bryophytes in
the first decade after logging. J. Appl. Ecol. 49:1316–1324.
Ims, R. A., and J. A. Henden. 2012. Collapse of an arctic bird
community resulting from ungulate-induced loss of erect
shrubs. Biol. Conserv. 149:2–5.
International Union for Conservation of Nature. 2013. The
IUCN red list of threatened species [http://
www.iucnredlist.org/].
Isaacs-Cubides, P. J., and J. N. Urbina-Cardona. 2011.
Anthropogenic disturbance and edge effects on anuran
assemblages inhabiting cloud forest fragments in Colombia.
Natureza Conservacao 9:39–46.
Isbell, F., V. Calcagno, A. Hector, J. Connolly, W. S. Harpole,
P. B. Reich, et al. 2011. High plant diversity is needed to
maintain ecosystem services. Nature 477:199–203.
Ishitani, M., D. J. Kotze, and J. Niemela. 2003. Changes in
carabid beetle assemblages across an urban-rural gradient in
Japan. Ecography 26:481–489.
Jacobs, C. T., C. H. Scholtz, F. Escobar, and A. L. V. Davis. 2010.
How might intensification of farming influence dung beetle
diversity (Coleoptera: Scarabaeidae) in Maputo Special
Reserve (Mozambique)? J. Insect Conserv. 14:389–399.
Johnson, E. A., and K. Miyanishi. 2008. Testing the
assumptions of chronosequences in succession. Ecol. Lett.
11:419–431.
Johnson, M. F., A. G
omez, and M. Pinedo-Vasquez. 2008.
Land use and mosquito diversity in the Peruvian Amazon. J.
Med. Entomol. 45:1023–1030.
Jones, K. E., J. Bielby, M. Cardillo, S. A. Fritz, J. O’Dell, D. L.
Orme, et al. 2009. PanTHERIA: a species-level database of
life history, ecology, and geography of extant and recently
extinct mammals. Ecology 90:2648.
Jones, J. P. G., B. Collen, G. Atkinson, P. W. J. Baxter, P.
Bubb, J. B. Illian, et al. 2011. The why, what, and how of
global biodiversity indicators beyond the 2010 target.
Conserv. Biol. 25:450–457.
Jonsell, M. 2012. Old park trees as habitat for saproxylic beetle
species. Biodivers. Conserv. 21:619–642.
Julier, H. E., and T. H. Roulston. 2009. Wild bee abundance
and pollination service in cultivated pumpkins: farm
management, nesting behavior and landscape effects. J.
Econ. Entomol. 102:563–573.
Jung, T. S., and T. Powell. 2011. Spatial distribution of meadow
jumping mice (Zapus hudsonius) in logged boreal forest of
northwestern Canada. Mammalian Biology 76:678–682.
Justice, C. O., E. Vermote, J. R. G. Townshend, R. Defries, D.
P. Roy, D. K. Hall, et al. 1998. The moderate resolution
imaging spectroradiometer (MODIS): Land remote sensing
for global change research. IEEE Trans. Geosci. Remote
Sens. 36:1228–1249.
Kapoor, V. 2008. Effects of rainforest fragmentation and
shade-coffee plantations on spider communities in the
Western Ghats, India. J. Insect Conserv. 12:53–68.
Kappes, H., L. Katzschner, and C. Nowak. 2012. Urban
summer heat load: meteorological data as a proxy for
metropolitan biodiversity. Meteorol. Z. 21:525–528.
Kati, V., K. Zografou, E. Tzirkalli, T. Chitos, and L. Willemse.
2012. Butterfly and grasshopper diversity patterns in humid
Mediterranean grasslands: the roles of disturbance and
environmental factors. J. Insect Conserv. 16:807–818.
Katovai, E., A. L. Burley, and M. M. Mayfield. 2012.
Understory plant species and functional diversity in the
degraded wet tropical forests of Kolombangara Island,
Solomon Islands. Biol. Conserv. 145:214–224.
Kattge, J., S. Diaz, S. Lavorel, C. Prentice, P. Leadley, G.
Bonisch, et al. 2011. TRY – a global database of plant traits.
Glob. Change Biol. 17:2905–2935.
Kessler, M., P. J. A. Kessler, S. R. Gradstein, K. Bach, M.
Schmull, and R. Pitopang. 2005. Tree diversity in primary
28
ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
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 Domınguez 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 Hernandez et al. 2012; 42 Calvin
2010; 45 Milder et al. 2010; 46 Helden and Leather 2004; 47 McNamara et al. 2012; 48 Katovai et al. 2012; 49 Berry et al. 2010; 50 Letcher and
Chazdon 2009; 51 Romero-Duque et al. 2007; 52 Marin-Spiotta et al. 2007; 53 Power and Stout 2011; 54 Norfolk et al. 2012; 55 Poveda et al.
2012; 56 Cabra-Garcıa et al. 2012; 57 Turner and Foster 2009; 58 Woodcock et al. 2007; 59 Lachat et al. 2006; 60 Rousseau et al. 2013; 61
€tter et al. 2008; Le F
Nakamura et al. 2003; 62 Basset et al. 2008; 63 Hanley 2011; 64 Billeter et al. 2008; Dieko
eon et al. 2010; 65 Sung et al.
mico Tropical de Investigacio
n y Ensen
~anza (CATIE) 2010; 68 Endo et al. 2010; 69 Alcala et al.
2012; 66 St-Laurent et al. 2007; 67 Centro Agrono
2004; 70 Bicknell and Peres 2010; 71 Woinarski et al. 2009; 72 Garden et al. 2010; 73 Hylander and Weibull 2012; 74 Giordano et al. 2004; 75
€m et al. 2009; 76 Ro
€mbke et al. 2009; 77 Giordani 2012; 78 Hu and Cao 2008; 79 Edenius et al. 2011; 80 O’Dea and Whittaker 2007; 81 Ims
Stro
and Henden 2012; 82 Rosselli 2011; 83 Arbelaez-Cortes et al. 2011; 84 Santana et al. 2012; 85 Sheldon et al. 2010; 86 Wang et al. 2010; 87
Sodhi et al. 2010; 88 Naoe et al. 2012; 89 Cerezo et al. 2011; 90 Lantschner et al. 2008; 91 Chapman and Reich 2007; 92 B
aldi et al. 2005; 93
Farwig et al. 2008; 94 Shahabuddin and Kumar 2007; 95 Borges 2007; 96 Wunderle et al. 2006; 97 Politi et al. 2012; 98 Moreno-Mateos et al.
ßcon 2010;
2011; 99 Mallari et al. 2011; 100 Latta et al. 2011; 101 Sosa et al. 2010; 102 Miranda et al. 2010; 103 Flaspohler et al. 2010; 104 Bo
105 Azpiroz and Blake 2009; 106 Aben et al. 2008; 107 Cockle et al. 2005; 108 Vergara and Simonetti 2004; 109 Azhar et al. 2013; 110 Reid
et al. 2012; 111 Neuschulz et al. 2011; 112 Dawson et al. 2011; 113 Naidoo 2004; 114 Dures and Cumming 2010; 115 Meyer et al. 2009; 116
€epp et al. 2011; 120 Bates et al. 2011; 121 Quintero et al.
Summerville 2011; 117 Cleary et al. 2004; 118 Mudri-Stojnic et al. 2012; 119 Schu
2010; 122 Vergara and Badano 2009; 123 Kohler et al. 2008; 124 Meyer et al. 2007, 125 Hoffmann and Zeller 2005; 126 Caceres et al. 2010;
127 Lantschner et al. 2012; 128 Wells et al. 2007; 129 Bernard et al. 2009; 130 Martin et al. 2012; 131 Gheler-Costa et al. 2012; 132 Sridhar
et al. 2008; 133 Scott et al. 2006; 134 Oke 2013; 135 Oke and Chokor 2009; 136 Kappes et al. 2012; 137 Walker et al. 2006; 138 Lo-Man-Hung
et al. 2008; 139 Zaitsev et al. 2002; 140 Robles et al. 2011; 141 Brito et al. 2012; 142 Luja et al. 2008; 143 Smith-Pardo and Gonzalez 2007; 144
€epp et al. 2012; 145 Tylianakis et al. 2005; 146 Verboven et al. 2012; 147 Osgathorpe et al. 2012; 148 Tonietto et al. 2011; 149 Samneg
Schu
ard
et al. 2011; 150 Cameron et al. 2011; 151 Malone et al. 2010; 152 Marshall et al. 2006; 153 Shuler et al. 2005; 154 Quaranta et al. 2004; 155
L
egar
e et al. 2011; 156 Noreika 2009; 157 Otavo et al. 2013; 158 Numa et al. 2012; 159 Jonsell 2012; 160 Mico et al. 2013; 161 Rodrigues et al.
et al. 2007; 164 Banks et al. 2007; 165 Elek and Lovei 2007; 166 Fukuda et al. 2009; 167 Castro-Luna
2013; 162 Sugiura et al. 2009; 163 Verdu
et al. 2007; 168 Shafie et al. 2011; 169 Struebig et al. 2008; 170 Threlfall et al. 2012; 171 Presley et al. 2008; 172 Willig et al. 2007; 173
MacSwiney et al. 2007; 174 Clarke et al. 2005; 175 Sedlock et al. 2008; 176 Verdasca et al. 2012; 177 D’Aniello et al. 2011; 178 Berg et al.
2011; 179 Summerville et al. 2006; 180 Hawes et al. 2009; 181 Cleary and Mooers 2006; 182 Krauss et al. 2003; 183 Ishitani et al. 2003; 184
Safian et al. 2011; 185 Furlani et al. 2009; 186 Isaacs-Cubides and Urbina-Cardona 2011; 187 Gutierrez-Lamus 2004; 188 Adum et al. 2013; 189
Watling et al. 2009; 190 Pillsbury and Miller 2008; 191 Pineda and Halffter 2004; 192 Ofori-Boateng et al. 2013; 193 de Souza et al. 2008; 194
Faruk et al. 2013; 195 Hilje and Aide 2012; 196 Alberta Biodiversity Monitoring Institute (ABMI) 2013; 197 Zaitsev et al. 2006; 198 Arroyo et al.
2005; 199 Paradis and Work 2011; 200 Buddle and Shorthouse 2008; 201 Kapoor 2008; 202 Alcayaga et al. 2013; 203 Magura et al. 2010; 204
} ro
€si et al. 2012; 206 Oliveira et al. 2013; 207 Carrijo et al. 2009; 208 Reis and Cancello 2007; 209 Chauvat et al.
Littlewood et al. 2012; 205 Ko
2007; 210 Otto and Roloff 2012; 211 Zimmerman et al. 2011; 212 Pelegrin and Bucher 2012; 213 Savage et al. 2011; 214 Bragagnolo et al. 2007;
215 Jung and Powell 2011; 216 Bartolommei et al. 2013; 217 Dominguez-Haydar and Armbrecht 2010; 218 Armbrecht et al. 2006; 219 Hashim
et al. 2010; 220 Schmidt et al. 2012; 221 Maeto and Sato 2004; 222 Bihn et al. 2008; 223 Delabie et al. 2009; 224 Fayle et al. 2010; 225 Gove
et al. 2005; 226 Buczkowski and Richmond 2012; 227 Buczkowski 2010; 228 Noriega et al. 2012; 229 Navarro et al. 2011; 230 Noriega et al.
2007; 231 Horgan 2009; 232 Gardner et al. 2008; 233 da Silva 2011; 234 Silva et al. 2010; 235 Jacobs et al. 2010; 236 Slade et al. 2011; 237
Filgueiras et al. 2011; 238 Navarrete and Halffter 2008; 239 Davis and Philips 2005; 240 Parra-H and Nates-Parra 2007; 241 Fierro et al. 2012; 242
Nielsen et al. 2011; 243 Julier and Roulston 2009; 244 Winfree et al. 2007; 245 Hanley 2005; 246 Liu et al. 2012; 247 Gu et al. 2004; 248 Noreika
and Kotze 2012; 249 Rey-Velasco and Miranda-Esquivel 2012; 250 Vanbergen et al. 2005; 251 Koivula et al. 2004; 252 Weller and Ganzhorn
2004; 253 Carvalho et al. 2010; 254 Aguilar-Barquero and Jim
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 Feon 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 [email protected]
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, Moises Barbosa de Souza, Olivier
Deheuvels, Jimenez Hernandez 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 Vazquez, Jose Verd
u,
Rachael Winfree, Volkmar Wolters, Joseph Wunderle,
Sakato Yoshikura and Gregory Zimmerman.
Conflict of Interest
None declared.
References
Aben, J., M. Dorenbosch, S. K. Herzog, A. J. P. Smolders, and
G. Van Der Velde. 2008. Human disturbance affects a
deciduous forest bird community in the Andean foothills of
Central Bolivia. Bird Conserv. Int., 18:363–380.
Adum, G. B., M. P. Eichhorn, W. Oduro, C. Ofori-Boateng,
and M. O. Rodel. 2013. Two-stage recovery of amphibian
assemblages following selective logging of tropical forests.
Conserv. Biol. 27:354–363.
Aguilar-Barquero, V., and F. Jimenez-Hernandez. 2009.
Diversidad y distribuci
on de palmas (Arecaceae) en tres
fragmentos de bosque muy h
umedo en Costa Rica. Rev.
Biol. Trop. 57(Supplement 1):83–92.
Alberta Biodiversity Monitoring Institute (ABMI). 2013. The
raw soil arthropods dataset and the raw trees & snags
dataset from Prototype Phase (2003–2006) and Rotation 1
(2007-2012) [http://www.abmi.ca].
Alcala, E. L., A. C. Alcala, and C. N. Dolino. 2004. Amphibians
and reptiles in tropical rainforest fragments on Negros
Island, the Philippines. Environ. Conserv., 31:254–261.
Alcayaga, O. E., J. Pizarro-Araya, F. M. Alfaro, and J.
Cepeda-Pizarro. 2013. Spiders (Arachnida, Araneae)
associated to agroecosystems in the Elqui Valley (Coquimbo
Region, Chile). Rev. Colomb. Entomol., 39:150–154.
Algar, A. C., H. M. Kharouba, E. R. Young, and J. T. Kerr.
2009. Predicting the future of species diversity:
macroecological theory, climate change, and direct tests of
alternative forecasting methods. Ecography 32:22–33.
Alkemade, R., M. van Oorschot, L. Miles, C. Nellemann, M.
Bakkenes, and B. ten Brink. 2009. GLOBIO3: a framework
to investigate options for reducing global terrestrial
biodiversity loss. Ecosystems 12:374–390.
Ancrenaz, M., B. Goossens, O. Gimenez, A. Sawang, and I.
Lackman-Ancrenaz. 2004. Determination of ape distribution
and population size using ground and aerial surveys: a case
study with orang-utans in lower Kinabatangan, Sabah,
Malaysia. Anim. Conserv. 7:375–385.
Arbelaez-Cortes, E., H. A. Rodrıguez-Correa, and M.
Restrepo-Chica. 2011. Mixed bird flocks: patterns of activity
and species composition in a region of the Central Andes of
Colombia. Rev. Mex. Biodivers. 82:639–651.
Armbrecht, I., I. Perfecto, and E. Silverman. 2006. Limitation
of nesting resources for ants in Colombian forests and
coffee plantations. Ecol. Entomol. 31:403–410.
23
The PREDICTS Database
L. N. Hudson et al.
Arroyo, J., J. C. Iturrondobeitia, C. Rad, and S.
Gonzalez-Carcedo. 2005. Oribatid mite (Acari) community
structure in steppic habitats of Burgos Province, central
northern Spain. J. Nat. Hist. 39:3453–3470.
van Asselen, S., and P. H. Verburg. 2012. A Land System
representation for global assessments and land-use
modelling. Glob. Change Biol. 18:3125–3148.
Azhar, B., D. B. Lindenmayer, J. Wood, J. Fischer, A. Manning,
C. McElhinny, et al. 2013. The influence of agricultural
system, stand structural complexity and landscape context on
foraging birds in oil palm landscapes. Ibis 155:297–312.
Azpiroz, A. B., and J. G. Blake. 2009. Avian assemblages in
altered and natural grasslands in the northern Campos of
Uruguay. Condor 111:21–35.
Baeten, L., M. Hermy, S. Van Daele, and K. Verheyen. 2010a.
Unexpected understorey community development after 30
years in ancient and post-agricultural forests. J. Ecol.
98:1447–1453.
Baeten, L., D. Velghe, M. Vanhellemont, P. De Frenne, M.
Hermy, and K. Verheyen. 2010b. Early trajectories of
spontaneous vegetation recovery after intensive agricultural
land use. Restor. Ecol. 18:379–386.
Baldi, A., P. Batary, and S. Erd}
os. 2005. Effects of grazing
intensity on bird assemblages and populations of Hungarian
grasslands. Agric. Ecosyst. Environ. 108:251–263.
Banks, J. E., P. Sandvik, and L. Keesecker. 2007. Beetle
(Coleoptera) and spider (Araneae) diversity in a mosaic of
farmland, edge, and tropical forest habitats in western Costa
Rica. Pan-Pac. Entomol. 83:152–160.
Barlow, J., T. A. Gardner, I. S. Araujo, T. C. Avila-Pires, A. B.
Bonaldo, J. E. Costa, et al. 2007. Quantifying the biodiversity
value of tropical primary, secondary, and plantation forests.
Proc. Natl Acad. Sci. USA, 104:18555–18560.
Bartolommei, P., A. Mortelliti, F. Pezzo, and L. Puglisi. 2013.
Distribution of nocturnal birds (Strigiformes and
Caprimulgidae) in relation to land-use types, extent and
configuration in agricultural landscapes of Central Italy.
Rend. Lincei. Sci. Fis. Nat. 24:13–21.
Basset, Y., O. Missa, A. Alonso, S. E. Miller, G. Curletti, M. De
Meyer, et al. 2008. Changes in arthropod assemblages along
a wide gradient of disturbance in Gabon. Conserv. Biol.
22:1552–1563.
Bates, A. J., J. P. Sadler, A. J. Fairbrass, S. J. Falk, J. D. Hale,
and T. J. Matthews. 2011. Changing bee and hoverfly
pollinator assemblages along an urban-rural gradient. PLoS
ONE 6:e23459.
Baur, B., C. Cremene, G. Groza, L. Rakosy, A. A. Schileyko, A.
Baur, et al. 2006. Effects of abandonment of subalpine hay
meadows on plant and invertebrate diversity in
Transylvania, Romania. Biol. Conserv. 132:261–273.
Berg, A., K. Ahrne, E. Ockinger, R. Svensson, and B.
Soderstrom. 2011. Butterfly distribution and abundance is
affected by variation in the Swedish forest-farmland
landscape. Biol. Conserv. 144:2819–2831.
Bernard, H., J. Fjeldsa, and M. Mohamed. 2009. A case study
on the effects of disturbance and conversion of tropical
lowland rain forest on the non-volant small mammals in
north Borneo: management implications. Mammal Study
34:85–96.
Berry, N. J., O. L. Phillips, S. L. Lewis, J. K. Hill, D. P.
Edwards, N. B. Tawatao, et al. 2010. The high value of
logged tropical forests: lessons from northern Borneo.
Biodivers. Conserv. 19:985–997.
Bicknell, J., and C. A. Peres. 2010. Vertebrate population
responses to reduced-impact logging in a neotropical forest.
For. Ecol. Manage. 259:2267–2275.
Bihn, J. H., M. Verhaagh, M. Braendle, and R. Brandl. 2008.
Do secondary forests act as refuges for old growth forest
animals? Recovery of ant diversity in the Atlantic forest of
Brazil. Biol. Conserv. 141:733–743.
Billeter, R., J. Liira, D. Bailey, R. Bugter, P. Arens, I. Augenstein,
et al. 2008. Indicators for biodiversity in agricultural
landscapes: a pan-European study. J. Appl. Ecol. 45:141–150.
Blois, J. L., J. W. Williams, M. C. Fitzpatrick, S. T. Jackson,
and S. Ferrier. 2013. Space can substitute for time in
predicting climate-change effects on biodiversity. Proc. Natl
Acad. Sci. USA 110:9374–9379.
B
oßcon, R. 2010. Riqueza e abund^ancia de aves em tr^es estagios
sucessionais da floresta ombr
ofila densa submontana,
Antonina, Parana. PhD thesis. Universidade Federal do
Parana, Brazil.
Borges, S. H. 2007. Bird assemblages in secondary forests
developing after slash-and-burn agriculture in the Brazilian
Amazon. J. Trop. Ecol. 23:469–477.
Boutin, C., A. Baril, and P. A. Martin. 2008. Plant diversity in
crop fields and woody hedgerows of organic and
conventional farms in contrasting landscapes. Agric. Ecosyst.
Environ. 123:185–193.
Bouyer, J., Y. Sana, Y. Samandoulgou, J. Cesar, L. Guerrini, C.
Kabore-Zoungrana, et al. 2007. Identification of ecological
indicators for monitoring ecosystem health in the
trans-boundary W Regional park: a pilot study. Biol.
Conserv. 138:73–88.
Bragagnolo, C., A. A. Nogueira, R. Pinto-da-Rocha, and R.
Pardini. 2007. Harvestmen in an Atlantic forest fragmented
landscape: evaluating assemblage response to habitat quality
and quantity. Biol. Conserv. 139:389–400.
Brearley, F. Q. 2011. Below-ground secondary succession in
tropical forests of Borneo. J. Trop. Ecol. 27:413–420.
Brito, I., M. J. Goss, M. de Carvalho, O. Chatagnier, and D.
van Tuinen. 2012. Impact of tillage system on arbuscular
mycorrhiza fungal communities in the soil under
Mediterranean conditions. Soil Tillage Res. 121:63–67.
Brunet, J., K. Valtinat, M. L. Mayr, A. Felton, M. Lindbladh,
and H. H. Bruun. 2011. Understory succession in
post-agricultural oak forests: habitat fragmentation affects
forest specialists and generalists differently. For. Ecol.
Manage. 262:1863–1871.
24
ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
L. N. Hudson et al.
The PREDICTS Database
Buczkowski, G. 2010. Extreme life history plasticity and the
evolution of invasive characteristics in a native ant. Biol.
Invasions 12:3343–3349.
Buczkowski, G., and D. S. Richmond. 2012. The effect of
urbanization on ant abundance and diversity: a temporal
examination of factors affecting biodiversity. PLoS ONE 7:
e41729.
Buddle, C. M., and D. P. Shorthouse. 2008. Effects of
experimental harvesting on spider (Araneae) assemblages in
boreal deciduous forests. Can. Entomol. 140:437–452.
Buscardo, E., G. F. Smith, D. L. Kelly, H. Freitas, S.
Iremonger, F. J. G. Mitchell, et al. 2008. The early effects of
afforestation on biodiversity of grasslands in Ireland.
Biodivers. Conserv. 17:1057–1072.
Butchart, S. H. M., M. Walpole, B. Collen, A. van Strien, J. P.
W. Scharlemann, R. E. A. Almond, et al. 2010. Global
biodiversity: indicators of recent declines. Science 328:1164–
1168.
Cabra-Garcıa, J., C. Berm
udez-Rivas, A. M. Osorio, and P.
Chac
on. 2012. Cross-taxon congruence of alpha and beta
diversity among five leaf litter arthropod groups in
Colombia. Biodivers. Conserv. 21:1493–1508.
Caceres, N. C., R. P. Napoli, J. Casella, and W. Hannibal.
2010. Mammals in a fragmented savannah landscape in
south-western Brazil. J. Nat. Hist. 44:491–512.
Cagle, N. L. 2008. Snake species distributions and temperate
grasslands: a case study from the American tallgrass prairie.
Biol. Conserv. 141:744–755.
Calvi~
no-Cancela, M., M. Rubido-Bara, and E. J. B. van Etten.
2012. Do eucalypt plantations provide habitat for native
forest biodiversity? For. Ecol. Manage. 270:153–162.
Cameron, S. A., J. D. Lozier, J. P. Strange, J. B. Koch, N.
Cordes, L. F. Solter, et al. 2011. Patterns of widespread
decline in North American bumble bees. Proc. Natl Acad.
Sci. USA 108:662–667.
Cardinale, B. J., J. E. Duffy, A. Gonzalez, D. U. Hooper, C.
Perrings, P. Venail, et al. 2012. Biodiversity loss and its
impact on humanity. Nature 486:59–67.
Cardoso, P., T. L. Erwin, P. A. V. Borges, and T. R. New.
2011. The seven impediments in invertebrate conservation
and how to overcome them. Biol. Conserv. 144:2647–2655.
Carrijo, T. F., D. Brandao, D. E. de Oliveira, D. A. Costa, and
T. Santos. 2009. Effects of pasture implantation on the
termite (Isoptera) fauna in the Central Brazilian Savanna
(Cerrado). J. Insect Conserv. 13:575–581.
Carvalho, A. L.d., E. J. L. Ferreira, J. M. T. Lima, and A. L. de
Carvalho. 2010. Floristic and structural comparisons among
palm communities in primary and secondary forest
fragments of the Raimundo Irineu Serra Environmental
Protection Area – Rio Branco, Acre, Brazil. Acta Amazon.,
40:657–666.
Castro, H., V. Lehsten, S. Lavorel, and H. Freitas. 2010.
Functional response traits in relation to land use change in
the Montado. Agric. Ecosyst. Environ. 137:183–191.
Castro-Luna, A. A., V. J. Sosa, and G. Castillo-Campos. 2007.
Bat diversity and abundance associated with the degree of
secondary succession in a tropical forest mosaic in
south-eastern Mexico. Anim. Conserv. 10:219–228.
Center for International Forestry Research (CIFOR). 2013a.
Multidisciplinary Landscape Assessment – Cameroon
[http://www.cifor.org/mla].
Center for International Forestry Research (CIFOR). 2013b.
Multidisciplinary Landscape Assessment – Philippines
[http://www.cifor.org/mla].
Centro Agron
omico Tropical de Investigaci
on y Ense~
nanza
(CATIE) 2010. Unpublished data of reptilian and amphibian
diversity in six countries in Central America [http://
catie.ac.cr/].
Cerezo, A., M. Conde, and S. Poggio. 2011. Pasture area and
landscape heterogeneity are key determinants of bird
diversity in intensively managed farmland. Biodivers.
Conserv., 20:2649–2667.
Chapman, A. D. 2009. Numbers of living species in Australia
and the world. Pp. 78. Australian Biological Resources
Study, Canberra, Australia.
Chapman, K., and P. Reich. 2007. Land use and habitat
gradients determine bird community diversity and
abundance in suburban, rural and reserve landscapes of
Minnesota, USA. Biol. Conserv. 135:527–541.
Chauvat, M., V. Wolters, and J. Dauber. 2007. Response of
collembolan communities to land-use change and grassland
succession. Ecography 30:183–192.
Clarke, F. M., L. V. Rostant, and P. A. Racey. 2005. Life after
logging: post-logging recovery of a neotropical bat
community. J. Appl. Ecol. 42:409–420.
Cleary, D. F. R., and A. O. Mooers. 2006. Burning and logging
differentially affect endemic vs. widely distributed butterfly
species in Borneo. Divers. Distrib. 12:409–416.
Cleary, D. F. R., A. O. Mooers, K. A. O. Eichhorn, J. van Tol,
R. de Jong, and S. B. J. Menken. 2004. Diversity and
community composition of butterflies and odonates
in an ENSO-induced fire affected habitat mosaic: a case
study from East Kalimantan, Indonesia. Oikos 105:
426–446.
Cockle, K. L., M. L. Leonard, and A. A. Bodrati. 2005.
Presence and abundance of birds in an Atlantic forest
reserve and adjacent plantation of shade-grown yerba mate,
in Paraguay. Biodivers. Conserv. 14:3265–3288.
Connop, S., T. Hill, J. Steer, and P. Shaw. 2011. Microsatellite
analysis reveals the spatial dynamics of Bombus humilis and
Bombus sylvarum. Insect Conserv. Divers. 4:212–221.
Conservation International Foundation. 2011. The biodiversity
hotspots [http://www.conservation.org/where/priority_areas/
hotspots/Pages/hotspots_main.aspx].
D’Aniello, B., I. Stanislao, S. Bonelli, and E. Balletto. 2011.
Haying and grazing effects on the butterfly communities of
two Mediterranean-area grasslands. Biodivers. Conserv.
20:1731–1744.
ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
25
The PREDICTS Database
L. N. Hudson et al.
Darvill, B., M. E. Knight, and D. Goulson. 2004. Use of
genetic markers to quantify bumblebee foraging range and
nest density. Oikos 107:471–478.
Davis, A. L. V., and T. K. Philips. 2005. Effect of deforestation
on a southwest Ghana dung beetle assemblage (Coleoptera:
Scarabaeidae) at the periphery of Ankasa conservation area.
Environ. Entomol. 34:1081–1088.
Davis, E. S., T. E. Murray, U. Fitzpatrick, M. J. F. Brown, and
R. J. Paxton. 2010. Landscape effects on extremely
fragmented populations of a rare solitary bee, Colletes
floralis. Mol. Ecol. 19:4922–4935.
Dawson, J., C. Turner, O. Pileng, A. Farmer, C. McGary, C.
Walsh, et al. 2011. Bird communities of the lower Waria
Valley, Morobe Province, Papua New Guinea: a comparison
between habitat types. Trop. Conserv. Sci. 4:317–348.
Delabie, J. H. C., R. Cereghino, S. Groc, A. Dejean, M.
Gibernau, B. Corbara, et al. 2009. Ants as biological
indicators of Wayana Amerindian land use in French
Guiana. C.R. Biol. 332:673–684.
Dickman, C. R. 1987. Habitat fragmentation and vertebrate
species richness in an urban environment. J. Appl. Ecol.
24:337–351.
Diek€
otter, T., K. Walther-Hellwig, M. Conradi, M. Suter, and
R. Frankl. 2006. Effects of landscape elements on the
distribution of the rare bumblebee species Bombus
muscorum in an agricultural landscape. Biodivers. Conserv.
15:57–68.
Diek€
otter, T., R. Billeter, and T. O. Crist. 2008. Effects of
landscape connectivity on the spatial distribution of insect
diversity in agricultural mosaic landscapes. Basic Appl. Ecol.
9:298–307.
Domınguez, E., N. Bahamonde, and C. Mu~
noz-Escobar. 2012.
Efectos de la extracci
on de turba sobre la composici
on y
estructura de una turbera de Sphagnum explotada y
abandonada hace 20 a~
nos, Chile. Anales Instituto Patagonia
(Chile) 40:37–45.
Dominguez-Haydar, Y., and I. Armbrecht. 2010. Response of
ants and their seed removal in rehabilitation areas and
forests at El Cerrejon coal mine in Colombia. Restor. Ecol.
19:178–184.
Dornelas, M., A. E. Magurran, S. T. Buckland, A. Chao, R. L.
Chazdon, R. K. Colwell, et al. 2013. Quantifying temporal
change in biodiversity: challenges and opportunities. Proc.
Biol. Sci. 280:10.
Dumont, B., A. Farruggia, J. P. Garel, P. Bachelard, E. Boitier,
and M. Frain. 2009. How does grazing intensity influence
the diversity of plants and insects in a species-rich upland
grassland on basalt soils? Grass Forage Sci. 64:92–105.
Dures, S. G., and G. S. Cumming. 2010. The confounding
influence of homogenising invasive species in a globally
endangered and largely urban biome: does habitat quality
dominate avian biodiversity? Biol. Conserv. 143:768–777.
Edenius, L., G. Mikusinski, and J. Bergh. 2011. Can repeated
fertilizer applications to young Norway spruce enhance
avian diversity in intensively managed forests? Ambio
40:521–527.
Elek, Z., and G. L. Lovei. 2007. Patterns in ground beetle
(Coleoptera: Carabidae) assemblages along an urbanisation
gradient in Denmark. Acta Oecol. International Journal of
Ecology32:104–111.
Ellis, E. C., K. K. Goldewijk, S. Siebert, D. Lightman, and N.
Ramankutty. 2010. Anthropogenic transformation of the
biomes, 1700 to 2000. Glob. Ecol. Biogeogr. 19:589–606.
Endo, W., C. Peres, E. Salas, S. Mori, J. Sanchez-Vega, G.
Shepard, et al. 2010. Game vertebrate densities in hunted
and nonhunted forest sites in Manu National Park, Peru.
Biotropica 42:251–261.
Environmental Systems Research Institute (ESRI). 2011.
ArcGIS Desktop: Release 10. Environmental Systems
Research Institute, Redlands, CA.
Faruk, A., D. Belabut, N. Ahmad, R. J. Knell, and T. W. J.
Garner. 2013. Effects of oil-palm plantations on diversity of
tropical anurans. Conserv. Biol. 27:615–624.
Farwig, N., N. Sajita, and K. Boehning-Gaese. 2008.
Conservation value of forest plantations for bird
communities in western Kenya. For. Ecol. Manage.
255:3885–3892.
Fayle, T. M., E. C. Turner, J. L. Snaddon, V. K. Chey, A. Y. C.
Chung, P. Eggleton, et al. 2010. Oil palm expansion into
rain forest greatly reduces ant biodiversity in canopy,
epiphytes and leaf-litter. Basic Appl. Ecol. 11:337–345.
Feld, C. K., J. P. Sousa, P. M. da Silva, and T. P. Dawson.
2010. Indicators for biodiversity and ecosystem services:
towards an improved framework for ecosystems assessment.
Biodivers. Conserv. 19:2895–2919.
Felton, A. M., L. M. Engstrom, A. Felton, and C. D. Knott.
2003. Orangutan population density, forest structure and
fruit availability in hand-logged and unlogged peat swamp
forests in West Kalimantan, Indonesia. Biol. Conserv.
114:91–101.
Fensham, R., J. Dwyer, T. Eyre, R. Fairfax, and J. Wang. 2012.
The effect of clearing on plant composition in mulga (Acacia
aneura) dry forest, Australia. Austral Ecol. 37:183–192.
Fermon, H., M. Waltert, R. I. Vane-Wright, and M.
Muhlenberg. 2005. Forest use and vertical stratification in
fruit-feeding butterflies of Sulawesi, Indonesia: impacts for
conservation. Biodivers. Conserv. 14:333–350.
Ferreira, C., and P. C. Alves. 2005. Impacto da implementacß~ao
~es de
de medidas de gest~ao do habitat nas populacßo
coelho-bravo (Oryctolagus cuniculus algirus) no Parque
Natural do Sudoeste Alentejano e Costa Vicentina. Pp. 95.
Centro de Investigacß~ao em Biodiversidade e Recursos
Geneticos (CIBIO), Vair~ao, Portugal.
Fierro, M. M., L. Cruz-Lopez, D. Sanchez, R.
Villanueva-Gutierrez, and R. Vandame. 2012. Effect of biotic
factors on the spatial distribution of stingless bees
(Hymenoptera: Apidae, Meliponini) in fragmented
neotropical habitats. Neotrop. Entomol. 41:95–104.
26
ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
L. N. Hudson et al.
The PREDICTS Database
Filgueiras, B., L. Iannuzzi, and I. Leal. 2011. Habitat
fragmentation alters the structure of dung beetle
communities in the Atlantic Forest. Biol. Conserv. 144:362–
369.
Flaspohler, D. J., C. P. Giardina, G. P. Asner, P. Hart, J. Price,
C. K. Lyons, et al. 2010. Long-term effects of fragmentation
and fragment properties on bird species richness in
Hawaiian forests. Biol. Conserv. 143:280–288.
Fontaine, C., I. Dajoz, J. Meriguet, and M. Loreau. 2006.
Functional diversity of plant-pollinator interaction webs
enhances the persistence of plant communities. PLoS Biol.
4:129–135.
Fukuda, D., O. B. Tisen, K. Momose, and S. Sakai. 2009. Bat
diversity in the vegetation mosaic around a lowland
dipterocarp forest of Borneo. Raffles Bull. Zool. 57:213–221.
Furlani, D., G. F. Ficetola, G. Colombo, M. Ugurlucan, and F.
De Bernardi. 2009. Deforestation and the structure of frog
communities in the Humedale Terraba-Sierpe, Costa Rica.
Zoolog. Sci. 26:197–202.
Garden, J. G., C. A. McAlpine, and H. P. Possingham. 2010.
Multi-scaled habitat considerations for conserving urban
biodiversity: native reptiles and small mammals in Brisbane,
Australia. Landscape Ecol. 25:1013–1028.
Gardner, T. A., M. I. M. Hernandez, J. Barlow, and C. A.
Peres. 2008. Understanding the biodiversity consequences of
habitat change: the value of secondary and plantation forests
for neotropical dung beetles. J. Appl. Ecol. 45:883–893.
Gheler-Costa, C., C. A. Vettorazzi, R. Pardini, and L. M.
Verdade. 2012. The distribution and abundance of small
mammals in agroecosystems of southeastern Brazil.
Mammalia 76:185–191.
Gibson, L., T. M. Lee, L. P. Koh, B. W. Brook, T. A. Gardner,
J. Barlow, et al. 2011. Primary forests are irreplaceable for
sustaining tropical biodiversity. Nature 478:378–383.
Giordani, P. 2012. Assessing the effects of forest management
on epiphytic lichens in coppiced forests using different
indicators. Plant Biosyst. 146:628–637.
Giordano, S., S. Sorbo, P. Adamo, A. Basile, V. Spagnuolo,
and R. C. Cobianchi. 2004. Biodiversity and trace element
content of epiphytic bryophytes in urban and extraurban
sites of southern Italy. Plant Ecol. 170:1–14.
Golodets, C., J. Kigel, and M. Sternberg. 2010. Recovery of
plant species composition and ecosystem function after
cessation of grazing in a Mediterranean grassland. Plant Soil
329:365–378.
Gottschalk, M. S., D. C. De Toni, V. L. S. Valente, and P. R.
P. Hofmann. 2007. Changes in Brazilian Drosophilidae
(Diptera) assemblages across an urbanisation gradient.
Neotrop. Entomol. 36:848–862.
Goulson, D., G. C. Lye, and B. Darvill. 2008. Diet breadth,
coexistence and rarity in bumblebees. Biodivers. Conserv.
17:3269–3288.
Goulson, D., O. Lepais, S. O’Connor, J. L. Osborne, R. A.
Sanderson, J. Cussans, et al. 2010. Effects of land use at a
landscape scale on bumblebee nest density and survival. J.
Appl. Ecol. 47:1207–1215.
Gove, A. D., J. D. Majer, and V. Rico-Gray. 2005. Methods for
conservation outside of formal reserve systems: the case of
ants in the seasonally dry tropics of Veracruz, Mexico. Biol.
Conserv. 126:328–338.
Grogan, J., S. B. Jennings, R. M. Landis, M. Schulze, A. M. V.
Baima, J.D. C. A. Lopes, et al. 2008. What loggers leave
behind: impacts on big-leaf mahogany (Swietenia
macrophylla) commercial populations and potential for
post-logging recovery in the Brazilian Amazon. For. Ecol.
Manage., 255:269–281.
Gu, W.-B., Y. Zhen-Rong, and H. Dun-Xiao. 2004. Carabid
community and its fluctuation in farmland of salinity
transforming area in the North China Plain: a case study in
Quzhou County, Hebei Province. Biodivers. Sci. 12:262–268.
Gutierrez-Lamus, D. L. 2004. Composition and abundance of
Anura in two forest types (natural and planted) in the
eastern Cordillera of Colombia. Caldasia 26:245–264.
Hanley, M. E. 2005. Unpublished data of bee diversity in UK
croplands.
Hanley, M. E. 2011. Unpublished data of bee diversity in UK
croplands and urban habitats.
Hanley, M. E., M. Franco, C. E. Dean, E. L. Franklin, H. R.
Harris, A. G. Haynes, et al. 2011. Increased bumblebee
abundance along the margins of a mass flowering crop:
evidence for pollinator spill-over. Oikos 120:1618–1624.
Hanson, T. R., S. J. Brunsfeld, B. Finegan, and L. P. Waits.
2008. Pollen dispersal and genetic structure of the tropical
tree Dipteryx panamensis in a fragmented Costa Rican
landscape. Mol. Ecol. 17:2060–2073.
Hashim, N., W. Akmal, W. Jusoh, and M. Nasir. 2010. Ant
diversity in a Peninsular Malaysian mangrove forest and oil
palm plantation. Asian Myrmecol. 3:5–8.
Hatfield, R. G., and G. LeBuhn. 2007. Patch and landscape
factors shape community assemblage of bumble bees,
Bombus spp. (Hymenoptera: Apidae), in montane meadows.
Biol. Conserv. 139:150–158.
Hawes, J., C. da Silva Motta, W. L. Overal, J. Barlow, T. A.
Gardner, and C. A. Peres. 2009. Diversity and composition
of Amazonian moths in primary, secondary and plantation
forests. J. Trop. Ecol., 25:281–300.
Heink, U., and I. Kowarik. 2010. What criteria should be used
to select biodiversity indicators? Biodivers. Conserv.
19:3769–3797.
Helden, A. J., and S. R. Leather. 2004. Biodiversity on urban
roundabouts – Hemiptera, management and the species-area
relationship. Basic Appl. Ecol. 5:367–377.
Hernandez, L., L. Delgado, W. Meier, and C. Duran. 2012.
Empobrecimiento de bosques fragmentados en el norte de la
Gran Sabana, Venezuela. Interciencia 37:891–898.
Herrmann, F., C. Westphal, R. F. A. Moritz, and I.
Steffan-Dewenter. 2007. Genetic diversity and mass
resources promote colony size and forager densities of a
ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
27
The PREDICTS Database
L. N. Hudson et al.
social bee (Bombus pascuorum) in agricultural landscapes.
Mol. Ecol. 16:1167–1178.
Hietz, P. 2005. Conservation of vascular epiphyte diversity in
Mexican coffee plantations. Conserv. Biol. 19:391–399.
Higuera, D., and J. H. D. Wolf. 2010. Vascular epiphytes in dry
oak forests show resilience to anthropogenic disturbance,
Cordillera Oriental, Colombia. Caldasia 32:161–174.
Hilje, B., and T. M. Aide. 2012. Recovery of amphibian species
richness and composition in a chronosequence of secondary
forests, northeastern Costa Rica. Biol. Conserv. 146:170–176.
Hoffmann, A., and U. Zeller. 2005. Influence of variations in
land use intensity on species diversity and abundance of
small mammals in the Nama Karoo, Namibia. Belg. J. Zool.
135:91–96.
Hooper, D. U., E. C. Adair, B. J. Cardinale, J. E. K. Byrnes, B.
A. Hungate, K. L. Matulich, et al. 2012. A global synthesis
reveals biodiversity loss as a major driver of ecosystem
change. Nature 486:105–108.
Horgan, F. G. 2009. Invasion and retreat: shifting assemblages
of dung beetles amidst changing agricultural landscapes in
central Peru. Biodivers. Conserv. 18:3519–3541.
Hu, C., and Z. P. Cao. 2008. Nematode community structure
under compost and chemical fertilizer management practice,
in the north China plain. Exp. Agric. 44:485–496.
Hudson, L. N., T. Newbold, D. W. Purves, J. P. W.
Scharlemann, G. Mace, and A. Purvis. 2013. Projecting
responses of ecological diversity In Changing Terrestrial
Systems (PREDICTS): can you help? BES Bull. 44:36–37.
Hurtt, G. C., L. P. Chini, S. Frolking, R. A. Betts, J. Feddema,
G. Fischer, et al. 2011. Harmonization of land-use scenarios
for the period 1500-2100: 600 years of global gridded annual
land-use transitions, wood harvest, and resulting secondary
lands. Clim. Change 109:117–161.
Hylander, K., and S. Nemomissa. 2009. Complementary roles
of home gardens and exotic tree plantations as alternative
habitats for plants of the Ethiopian montane rainforest.
Conserv. Biol. 23:400–409.
Hylander, K., and H. Weibull. 2012. Do time-lagged
extinctions and colonizations change the interpretation of
buffer strip effectiveness? – a study of riparian bryophytes in
the first decade after logging. J. Appl. Ecol. 49:1316–1324.
Ims, R. A., and J. A. Henden. 2012. Collapse of an arctic bird
community resulting from ungulate-induced loss of erect
shrubs. Biol. Conserv. 149:2–5.
International Union for Conservation of Nature. 2013. The
IUCN red list of threatened species [http://
www.iucnredlist.org/].
Isaacs-Cubides, P. J., and J. N. Urbina-Cardona. 2011.
Anthropogenic disturbance and edge effects on anuran
assemblages inhabiting cloud forest fragments in Colombia.
Natureza Conservacao 9:39–46.
Isbell, F., V. Calcagno, A. Hector, J. Connolly, W. S. Harpole,
P. B. Reich, et al. 2011. High plant diversity is needed to
maintain ecosystem services. Nature 477:199–203.
Ishitani, M., D. J. Kotze, and J. Niemela. 2003. Changes in
carabid beetle assemblages across an urban-rural gradient in
Japan. Ecography 26:481–489.
Jacobs, C. T., C. H. Scholtz, F. Escobar, and A. L. V. Davis. 2010.
How might intensification of farming influence dung beetle
diversity (Coleoptera: Scarabaeidae) in Maputo Special
Reserve (Mozambique)? J. Insect Conserv. 14:389–399.
Johnson, E. A., and K. Miyanishi. 2008. Testing the
assumptions of chronosequences in succession. Ecol. Lett.
11:419–431.
Johnson, M. F., A. G
omez, and M. Pinedo-Vasquez. 2008.
Land use and mosquito diversity in the Peruvian Amazon. J.
Med. Entomol. 45:1023–1030.
Jones, K. E., J. Bielby, M. Cardillo, S. A. Fritz, J. O’Dell, D. L.
Orme, et al. 2009. PanTHERIA: a species-level database of
life history, ecology, and geography of extant and recently
extinct mammals. Ecology 90:2648.
Jones, J. P. G., B. Collen, G. Atkinson, P. W. J. Baxter, P.
Bubb, J. B. Illian, et al. 2011. The why, what, and how of
global biodiversity indicators beyond the 2010 target.
Conserv. Biol. 25:450–457.
Jonsell, M. 2012. Old park trees as habitat for saproxylic beetle
species. Biodivers. Conserv. 21:619–642.
Julier, H. E., and T. H. Roulston. 2009. Wild bee abundance
and pollination service in cultivated pumpkins: farm
management, nesting behavior and landscape effects. J.
Econ. Entomol. 102:563–573.
Jung, T. S., and T. Powell. 2011. Spatial distribution of meadow
jumping mice (Zapus hudsonius) in logged boreal forest of
northwestern Canada. Mammalian Biology 76:678–682.
Justice, C. O., E. Vermote, J. R. G. Townshend, R. Defries, D.
P. Roy, D. K. Hall, et al. 1998. The moderate resolution
imaging spectroradiometer (MODIS): Land remote sensing
for global change research. IEEE Trans. Geosci. Remote
Sens. 36:1228–1249.
Kapoor, V. 2008. Effects of rainforest fragmentation and
shade-coffee plantations on spider communities in the
Western Ghats, India. J. Insect Conserv. 12:53–68.
Kappes, H., L. Katzschner, and C. Nowak. 2012. Urban
summer heat load: meteorological data as a proxy for
metropolitan biodiversity. Meteorol. Z. 21:525–528.
Kati, V., K. Zografou, E. Tzirkalli, T. Chitos, and L. Willemse.
2012. Butterfly and grasshopper diversity patterns in humid
Mediterranean grasslands: the roles of disturbance and
environmental factors. J. Insect Conserv. 16:807–818.
Katovai, E., A. L. Burley, and M. M. Mayfield. 2012.
Understory plant species and functional diversity in the
degraded wet tropical forests of Kolombangara Island,
Solomon Islands. Biol. Conserv. 145:214–224.
Kattge, J., S. Diaz, S. Lavorel, C. Prentice, P. Leadley, G.
Bonisch, et al. 2011. TRY – a global database of plant traits.
Glob. Change Biol. 17:2905–2935.
Kessler, M., P. J. A. Kessler, S. R. Gradstein, K. Bach, M.
Schmull, and R. Pitopang. 2005. Tree diversity in primary
28
ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
L. N. Hudson et al.
The PREDICTS Database
forest and different land use systems in Central Sulawesi,
Indonesia. Biodivers. Conserv. 14:547–560.
Kessler, M., S. Abrahamczyk, M. Bos, D. Buchori, D. D. Putra,
S. R. Gradstein, et al. 2009. Alpha and beta diversity of
plants and animals along a tropical land-use gradient. Ecol.
Appl. 19:2142–2156.
Knight, M. E., J. L. Osborne, R. A. Sanderson, R. J. Hale, A. P.
Martin, and D. Goulson. 2009. Bumblebee nest density and
the scale of available forage in arable landscapes. Insect
Conserv. Divers. 2:116–124.
Knop, E., P. I. Ward, and S. A. Wich. 2004. A comparison of
orang-utan density in a logged and unlogged forest on
Sumatra. Biol. Conserv. 120:183–188.
Kohler, F., J. Verhulst, R. van Klink, and D. Kleijn. 2008. At
what spatial scale do high-quality habitats enhance the
diversity of forbs and pollinators in intensively farmed
landscapes? J. Appl. Ecol. 45:753–762.
Koivula, M., V. Hyyrylainen, and E. Soininen. 2004. Carabid
beetles (Coleoptera: Carabidae) at forest-farmland edges in
southern Finland. J. Insect Conserv. 8:297–309.
Kolb, A., and M. Diekmann. 2004. Effects of environment,
habitat configuration and forest continuity on the
distribution of forest plant species. J. Veg. Sci. 15:199–208.
P. Batary, A. Orosz, D. Redei, and A. Baldi. 2012.
K}
or€
osi, A.,
Effects of grazing, vegetation structure and landscape
complexity on grassland leafhoppers (Hemiptera:
Auchenorrhyncha) and true bugs (Hemiptera: Heteroptera)
in Hungary. Insect Conserv. Divers. 5:57–66.
Krauss, J., I. Steffan-Dewenter, and T. Tscharntke. 2003. How
does landscape context contribute to effects of habitat
fragmentation on diversity and population density of
butterflies? J. Biogeogr. 30:889–900.
Krauss, J., A. M. Klein, I. Steffan-Dewenter, and T. Tscharntke.
2004. Effects of habitat area, isolation, and landscape
diversity on plant species richness of calcareous grasslands.
Biodivers. Conserv. 13:1427–1439.
Kumar, R., and G. Shahabuddin. 2005. Effects of biomass
extraction on vegetation structure, diversity and
composition of forests in Sariska Tiger Reserve, India.
Environ. Conserv. 32:248–259.
Lachat, T., S. Attignon, J. Djego, G. Goergen, P. Nagel, B.
Sinsin, et al. 2006. Arthropod diversity in Lama forest
reserve (South Benin), a mosaic of natural, degraded and
plantation forests. Biodivers. Conserv. 15:3–23.
Landis, J. R., and G. G. Koch. 1977. The measurement of
observer agreement for categorical data. Biometrics 33:159–
174.
Lantschner, M. V., V. Rusch, and C. Peyrou. 2008. Bird
assemblages in pine plantations replacing native ecosystems
in NW Patagonia. Biodivers. Conserv. 17:969–989.
Lantschner, M. V., V. Rusch, and J. P. Hayes. 2012. Habitat
use by carnivores at different spatial scales in a plantation
forest landscape in Patagonia, Argentina. For. Ecol. Manage.
269:271–278.
Latta, S. C., B. A. Tinoco, P. X. Astudillo, and C. H. Graham.
2011. Patterns and magnitude of temporal change in avian
communities in the Ecuadorian Andes. Condor 113:24–40.
Le Feon, V., A. Schermann-Legionnet, Y. Delettre, S. Aviron,
R. Billeter, R. Bugter, et al. 2010. Intensification of
agriculture, landscape composition and wild bee
communities: a large scale study in four European countries.
Agric. Ecosyst. Environ. 137:143–150.
Legare, J.-P., C. Hebert, and J. Ruel. 2011. Alternative
silvicultural practices in irregular boreal forests: response of
beetle assemblages. Silva Fennica. 45:937–956.
Letcher, S. G., and R. L. Chazdon. 2009. Rapid recovery of
biomass, species richness, and species composition in a
forest chronosequence in northeastern Costa Rica.
Biotropica 41:608–617.
Littlewood, N. A., R. J. Pakeman, and G. Pozsgai. 2012. Grazing
impacts on Auchenorrhyncha diversity and abundance on a
Scottish upland estate. Insect Conserv. Divers. 5:67–74.
Liu, Y. H., J. C. Axmacher, C. L. Wang, L. T. Li, and Z. R. Yu.
2012. Ground beetle (Coleoptera: Carabidae) assemblages of
restored semi-natural habitats and intensively cultivated
fields in northern China. Restor. Ecol. 20:234–239.
Lo-Man-Hung, N. F., T. A. Gardner, M. A. Ribeiro-J
unior, J.
Barlow, and A. B. Bonaldo. 2008. The value of primary,
secondary, and plantation forests for Neotropical epigeic
arachnids. J. Arachnol. 36:394–401.
L
opez-Quintero, C. A., G. Straatsma, A. E. Franco-Molano,
and T. Boekhout. 2012. Macrofungal diversity in Colombian
Amazon forests varies with regions and regimes of
disturbance. Biodivers. Conserv. 21:2221–2243.
Louhaichi, M., A. K. Salkini, and S. L. Petersen. 2009. Effect of
small ruminant grazing on the plant community
characteristics of semiarid Mediterranean ecosystems. Int. J.
Agric. Biol. 11:681–689.
Lucas-Borja, M. E., F. Bastida, J. L. Moreno, C. Nicolas, M.
Andres, F. R. Lopez, et al. 2011. The effects of human
trampling on the microbiological properties of soil and
vegetation in Mediterranean mountain areas. Land Degrad.
Dev. 22:383–394.
Luja, V., S. Herrando-Perez, D. Gonzalez-Solis, and L. Luiselli.
2008. Secondary rain forests are not havens for reptile
species in tropical Mexico. Biotropica 40:747–757.
Luskin, M. S. 2010. Flying foxes prefer to forage in farmland
in a tropical dry forest landscape mosaic in Fiji. Biotropica
42:246–250.
Mace, G. M., W. Cramer, S. Diaz, D. P. Faith, A. Larigauderie,
P. Le Prestre, et al. 2010. Biodiversity targets after 2010.
Curr. Opin. Environ. Sustain. 2:3–8.
MacSwiney, M. C. G., P. L. Vilchis, F. M. Clarke, and P. A. Racey.
2007. The importance of cenotes in conserving bat assemblages
in the Yucatan, Mexico. Biol. Conserv. 136:499–509.
Maeto, K., and S. Sato. 2004. Impacts of forestry on ant
species richness and composition in warm-temperate forests
of Japan. For. Ecol. Manage. 187:213–223.
ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
29
The PREDICTS Database
L. N. Hudson et al.
Magura, T., R. Horvath, and B. Tothmeresz. 2010. Effects of
urbanization on ground-dwelling spiders in forest patches,
in Hungary. Landscape Ecol. 25:621–629.
Mallari, N. A. D., N. J. Collar, D. C. Lee, P. J. K. McGowan,
R. Wilkinson, and S. J. Marsden. 2011. Population densities
of understorey birds across a habitat gradient in Palawan,
Philippines: implications for conservation. Oryx 45:234–242.
Malone, L., J. Aulsford, B. Howlett, C. Scott-Dupree, N.
Bardol, and B. Donovan. 2010. Observations on bee species
visiting white clover in New Zealand pastures. J. Apic. Res.
49:284–286.
Marin-Spiotta, E., R. Ostertag, and W. L. Silver. 2007.
Long-term patterns in tropical reforestation: plant
community composition and aboveground biomass
accumulation. Ecol. Appl. 17:828–839.
Marshall, E. J. P., T. M. West, and D. Kleijn. 2006. Impacts of
an agri-environment field margin prescription on the flora
and fauna of arable farmland in different landscapes. Agric.
Ecosyst. Environ. 113:36–44.
Martin, P. S., C. Gheler-Costa, P. C. Lopes, L. M. Rosalino,
and L. M. Verdade. 2012. Terrestrial non-volant small
mammals in agro-silvicultural landscapes of Southeastern
Brazil. For. Ecol. Manage. 282:185–195.
Matsumoto, T., T. Itioka, S. Yamane, and K. Momose. 2009.
Traditional land use associated with swidden agriculture
changes encounter rates of the top predator, the army ant,
in Southeast Asian tropical rain forests. Biodivers. Conserv.
18:3139–3151.
Mayfield, M. M., D. Ackerly, and G. C. Daily. 2006. The
diversity and conservation of plant reproductive and
dispersal functional traits in human-dominated tropical
landscapes. J. Ecol. 94:522–536.
McFrederick, Q. S., and G. LeBuhn. 2006. Are urban parks
refuges for bumble bees Bombus spp. (Hymenoptera:
Apidae)? Biol. Conserv. 129:372–382.
McNamara, S., P. D. Erskine, D. Lamb, L. Chantalangsy, and
S. Boyle. 2012. Primary tree species diversity in secondary
fallow forests of Laos. For. Ecol. Manage. 281:93–99.
Meyer, B., V. Gaebele, and I. D. Steffan-Dewenter. 2007. Patch
size and landscape effects on pollinators and seed set of the
horseshoe vetch, Hippocrepis comosa, in an agricultural
landscape of central Europe. Entomologia Generalis 30:173–
185.
Meyer, B., F. Jauker, and I. Steffan-Dewenter. 2009.
Contrasting resource-dependent responses of hoverfly
richness and density to landscape structure. Basic Appl.
Ecol. 10:178–186.
Mico, E., A. Garcia-Lopez, H. Brustel, A. Padilla, and E. Galante.
2013. Explaining the saproxylic beetle diversity of a protected
Mediterranean area. Biodivers. Conserv. 22:889–904.
Milder, J. C., F. A. J. DeClerck, A. Sanfiorenzo, D. M. Sanchez,
D. E. Tobar, and B. Zuckerberg 2010. Effects of farm and
landscape management on bird and butterfly conservation
in western Honduras. Ecosphere, 1:art2.
Miranda, M. V., N. Politi, and L. O. Rivera. 2010. Unexpected
changes in the bird assemblage in areas under selective
logging in piedmont forest in northwestern Argentina.
Ornitol. Neotrop., 21:323–337.
Mittermeier, R. A., P. R. Gil, and C. G. Mittermeier. 1997.
Megadiversity: earth’s biologically wealthiest nations. Pp.
501. CEMEX/Agrupaci
on Sierra Madre, Mexico City,
Mexico.
Moreno-Mateos, D., J. M. Rey Benayas, L. Perez-Camacho, E.
de la Montana, S. Rebollo, and L. Cayuela. 2011. Effects of
land use on nocturnal birds in a Mediterranean agricultural
landscape. Acta Ornithologica. 46:173–182.
Muchane, M. N., D. Karanja, G. M. Wambugu, J. M. Mutahi,
C. W. Masiga, C. Mugoya, et al. 2012. Land use practices
and their implications on soil macro-fauna in Maasai Mara
ecosystem. Int. J. Biodivers. Conserv. 4:500–514.
Mudri-Stojnic, S., A. Andric, Z. Jozan, and A. Vujic. 2012.
Pollinator diversity (Hymenoptera and Diptera) in
semi-natural habitats in Serbia during summer. Arch. Biol.
Sci. 64:777–786.
Naidoo, R. 2004. Species richness and community composition
of songbirds in a tropical forest-agricultural landscape.
Anim. Conserv. 7:93–105.
Nakamura, A., H. Proctor, and C. P. Catterall. 2003. Using
soil and litter arthropods to assess the state of rainforest
restoration. Ecol. Manag. Restor. 4(Supplement):S20–S28.
Naoe, S., S. Sakai, and T. Masaki. 2012. Effect of forest shape
on habitat selection of birds in a plantation-dominant
landscape across seasons: comparison between continuous
and strip forests. J. For. Res. 17:219–223.
Navarrete, D., and G. Halffter. 2008. Dung beetle (Coleoptera:
Scarabaeidae: Scarabaeinae) diversity in continuous forest,
forest fragments and cattle pastures in a landscape of
Chiapas, Mexico: the effects of anthropogenic changes.
Biodivers. Conserv. 17:2869–2898.
Navarro, I. L., A. K. Roman, F. H. Gomez, and H. A. Perez.
2011. Seasonal variation in dung beetles (Coleoptera:
Scarabaeidae: Scarabaeinae) from Serrania de Coraza, Sucre
(Colombia). Revista Colombiana de Ciencia Animal 3:102–
110.
Neuschulz, E. L., A. Botzat, and N. Farwig. 2011. Effects of
forest modification on bird community composition and
seed removal in a heterogeneous landscape in South Africa.
Oikos 120:1371–1379.
Newbold, T., L. N. Hudson, D. W. Purves, J. P. W.
Scharlemann, G. Mace, and A. Purvis. 2012. PREDICTS:
projecting Responses of Ecological Diversity in Changing
Terrestrial Systems. Front. Biogeogr. 4:155–156.
Newbold, T., J. P. W. Scharlemann, S. H. M. Butchart, C. H.
Sekercioglu, R. Alkemade, H. Booth, et al. 2013. Ecological
traits affect the response of tropical forest bird species to
land-use intensity. Proc. Biol. Sci. 280:8.
Newbold, T., J. P. W. Scharlemann, S. H. M. Butchart, C
ß . H.
Sßekercioglu, L. Joppa, R. Alkemade, et al. 2014a. Functional
30
ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
L. N. Hudson et al.
The PREDICTS Database
traits, land-use change and the structure of present and
future bird communities in tropical forests. Glob. Ecol.
Biogeogr. 23:1073–1084.
Newbold, T., L. N. Hudson, H. R. P. Phillips, S. L. L. Hill, S.
Contu, I. Lysenko, et al. 2014b. A global model of the
response of tropical and sub-tropical forest biodiversity to
anthropogenic pressures. Proc. Biol. Sci. 281:10.
Nicolas, V., P. Barriere, A. Tapiero, and M. Colyn. 2009.
Shrew species diversity and abundance in Ziama Biosphere
Reserve, Guinea: comparison among primary forest,
degraded forest and restoration plots. Biodivers. Conserv.
18:2043–2061.
Nielsen, A., I. Steffan-Dewenter, C. Westphal, O. Messinger, S.
G. Potts, S. P. M. Roberts, et al. 2011. Assessing bee species
richness in two Mediterranean communities: importance
of habitat type and sampling techniques. Ecol. Res. 26:
969–983.
Noreika, N. 2009. New records of rare species of Coleoptera
found in Ukmerg_e district in 2004–2005. New and Rare for
Lithuania Insect Species 21:68–71.
Noreika, N., and D. J. Kotze. 2012. Forest edge contrasts have
a predictable effect on the spatial distribution of carabid
beetles in urban forests. J. Insect Conserv. 16:867–881.
Norfolk, O., M. Abdel-Dayem, and F. Gilbert. 2012. Rainwater
harvesting and arthropod biodiversity within an arid
agro-ecosystem. Agric. Ecosyst. Environ. 162:8–14.
Noriega, J. A., E. Realpe, and G. Fagua. 2007. Diversidad de
escarabajos coprofagos (Coleoptera: Scarabaeidae) en un
bosque de galeria con tres estadios de alteracion. Univ. Sci.
12:51–63.
Noriega, J. A., J. M. Palacio, J. D. Monroy-G, and E. Valencia.
2012. Estructura de un ensamblaje de escarabajos coprofagos
(Coleoptera: Scarabaeinae) en tres sitios con diferente uso
del suelo en Antioquia, Colombia. Actualidades Biol
ogicas
34:43–54.
Norris, K. 2012. Biodiversity in the context of ecosystem
services: the applied need for systems approaches. Philos.
Trans. R. Soc. Lond. B Biol. Sci. 367:191–199.
N€
oske, N. M., N. Hilt, F. A. Werner, G. Brehm, K. Fiedler, H.
J. M. Sipman, et al. 2008. Disturbance effects on diversity of
epiphytes and moths in a montane forest in Ecuador. Basic
Appl. Ecol. 9:4–12.
Numa, C., J. R. Verdu, C. Rueda, and E. Galante. 2012.
Comparing dung beetle species assemblages between
protected areas and adjacent pasturelands in a
Mediterranean savanna landscape. Rangeland Ecol. Manage.
65:137–143.
O’Connor, T. G. 2005. Influence of land use on plant
community composition and diversity in Highland Sourveld
grassland in the southern Drakensberg, South Africa. J.
Appl. Ecol. 42:975–988.
O’Dea, N., and R. J. Whittaker. 2007. How resilient are
Andean montane forest bird communities to habitat
degradation? Biodivers. Conserv. 16:1131–1159.
Ofori-Boateng, C., W. Oduro, A. Hillers, K. Norris, S. K.
Oppong, G. B. Adum, et al. 2013. Differences in the effects
of selective logging on amphibian assemblages in three west
African forest types. Biotropica 45:94–101.
Oke, C. 2013. Land snail diversity in post extraction secondary
forest reserves in Edo State, Nigeria. Afr. J. Ecol. 51:244–254.
Oke, O. C., and J. U. Chokor. 2009. The effect of land use on
snail species richness and diversity in the tropical rainforest
of south-western Nigeria. African Sci. 10:95–108.
Oliveira, D. E., T. F. Carrijo, and D. Brand~ao. 2013. Species
composition of termites (Isoptera) in different Cerrado
vegetation physiognomies. Sociobiology 60:190–197.
Osgathorpe, L. M., K. Park, and D. Goulson. 2012. The use of
off-farm habitats by foraging bumblebees in agricultural
landscapes: implications for conservation management.
Apidologie 43:113–127.
Otavo, S. E., A. Parrado-Rosselli, and J. A. Noriega. 2013.
Scarabaeoidea superfamily (Insecta: Coleoptera) as a
bioindicator element of anthropogenic disturbance in an
amazon national park. Rev. Biol. Trop. 61:735–752.
Otto, C. R. V., and G. J. Roloff. 2012. Songbird response to
green-tree retention prescriptions in clearcut forests. For.
Ecol. Manage. 284:241–250.
Paradis, S., and T. T. Work. 2011. Partial cutting does not
maintain spider assemblages within the observed range of
natural variability in eastern Canadian black spruce forests.
For. Ecol. Manage. 262:2079–2093.
Paritsis, J., and M. A. Aizen. 2008. Effects of exotic conifer
plantations on the biodiversity of understory plants, epigeal
beetles and birds in Nothofagus dombeyi forests. For. Ecol.
Manage. 255:1575–1583.
Parra-H, A., and G. Nates-Parra. 2007. Variation of the orchid
bees community (Hymenoptera: Apidae) in three altered
habitats of the Colombian “llano” piedmont. Rev. Biol.
Trop. 55:931–941.
Pelegrin, N., and E. H. Bucher. 2012. Effects of habitat
degradation on the lizard assemblage in the Arid Chaco,
central Argentina. J. Arid Environ. 79:13–19.
Pfeifer, M., V. Lefebvre, T. A. Gardner, V. Arroyo-Rodriguez,
L. Baeten, C. Banks-Leite, et al. 2014. BIOFRAG – a new
database for analyzing BIOdiversity responses to forest
FRAGmentation. Ecol. Evol., 4:1524–1537.
Phalan, B., M. Onial, A. Balmford, and R. Green. 2011.
Reconciling food production and biodiversity conservation:
land sharing and land sparing compared. Science 333:1289–
1291.
Pillsbury, F. C., and J. R. Miller. 2008. Habitat and landscape
characteristics underlying anuran community structure
along an urban-rural gradient. Ecol. Appl. 18:1107–1118.
Pineda, E., and G. Halffter. 2004. Species diversity and habitat
fragmentation: frogs in a tropical montane landscape in
Mexico. Biol. Conserv. 117:499–508.
Politi, N., M. Jr Hunter, and L. Rivera. 2012. Assessing the
effects of selective logging on birds in Neotropical piedmont
ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
31
The PREDICTS Database
L. N. Hudson et al.
and cloud montane forests. Biodivers. Conserv. 21:3131–
3155.
Poveda, K., E. Martinez, M. Kersch-Becker, M. Bonilla, and T.
Tscharntke. 2012. Landscape simplification and altitude
affect biodiversity, herbivory and Andean potato yield. J.
Appl. Ecol. 49:513–522.
Power, E. F., and J. C. Stout. 2011. Organic dairy farming:
impacts on insect-flower interaction networks and
pollination. J. Appl. Ecol. 48:561–569.
Power, E. F., D. L. Kelly, and J. C. Stout. 2012. Organic
farming and landscape structure: effects on insect-pollinated
plant diversity in intensively managed grasslands. PLoS ONE
7:e38073.
Presley, S. J., M. R. Willig, J. M. Jr Wunderle, and L. N.
Saldanha. 2008. Effects of reduced-impact logging and forest
physiognomy on bat populations of lowland Amazonian
forest. J. Appl. Ecol. 45:14–25.
Proenca, V. M., H. M. Pereira, J. Guilherme, and L. Vicente.
2010. Plant and bird diversity in natural forests and in
native and exotic plantations in NW Portugal. Acta Oecol.
International Journal of Ecology 36:219–226.
Quaranta, M., S. Ambroselli, P. Barro, S. Bella, A. Carini, G.
Celli, et al. 2004. Wild bees in agroecosystems and
semi-natural landscapes. 1997-2000 collection period in
Italy. Bull. Insectology, 57:11–62.
Quintero, C., C. L. Morales, and M. A. Aizen. 2010. Effects of
anthropogenic habitat disturbance on local pollinator
diversity and species turnover across a precipitation
gradient. Biodivers. Conserv. 19:257–274.
Redpath, N., L. M. Osgathorpe, K. Park, and D. Goulson.
2010. Crofting and bumblebee conservation: the impact of
land management practices on bumblebee populations in
northwest Scotland. Biol. Conserv. 143:492–500.
Reichman, O. J., M. B. Jones, and M. P. Schildhauer. 2011.
Challenges and opportunities of open data in ecology.
Science 331:703–705.
Reid, J. L., J. B. C. Harris, and R. A. Zahawi. 2012. Avian
habitat preference in tropical forest restoration in southern
Costa Rica. Biotropica 44:350–359.
Reis, Y. T., and E. M. Cancello. 2007. Termite (Insecta,
Isoptera) richness in primary and secondary Atlantic Forest
in southeastern Bahia. Iheringia Ser. Zool. 97:229–234.
Rey-Velasco, J. C., and D. R. Miranda-Esquivel. 2012.
Unpublished data of the response of ground beetles
(Coleoptera: Carabidae) in the northeastern Colombian
Andes to habitat modification.
Ribeiro, D. B., and A. V. L. Freitas. 2012. The effect of
reduced-impact logging on fruit-feeding butterflies in
Central Amazon, Brazil. J. Insect Conserv. 16:733–744.
Richardson, B. A., M. J. Richardson, and F. N.
Soto-Adames. 2005. Separating the effects of forest type
and elevation on the diversity of litter invertebrate
communities in a humid tropical forest in Puerto Rico. J.
Anim. Ecol. 74:926–936.
Robles, C. A., C. C. Carmaran, and S. E. Lopez. 2011.
Screening of xylophagous fungi associated with
Platanus acerifolia in urban landscapes: biodiversity and
potential biodeterioration. Landsc. Urban Plan. 100:129–135.
Rodrigues, M. M., M. A. Uchoa, and S. Ide. 2013. Dung
beetles (Coleoptera: Scarabaeoidea) in three landscapes in
Mato Grosso do Sul, Brazil. Braz. J. Biol. 73:211–220.
R€
ombke, J., P. Schmidt, and H. H€
ofer. 2009. The earthworm
fauna of regenerating forests and anthropogenic habitats in
the coastal region of Parana. Pesqui. Agropecu. Bras.
44:1040–1049.
Romero-Duque, L. P., V. J. Jaramillo, and A. Perez-Jimenez.
2007. Structure and diversity of secondary tropical dry
forests in Mexico, differing in their prior land-use history.
For. Ecol. Manage. 253:38–47.
Roskov, Y., T. Kunz, L. Paglinawan, T. Orrell, D. Nicolson, A.
Culham, et al. 2013. Species 2000 & ITIS Catalogue of Life,
2013 Annual Checklist [http://catalogueoflife.org/
annual-checklist/2013/].
Rosselli, L. 2011. Factores ambientales relacionados con la
presencia y abundancia de las aves de los humedales de la
Sabana de Bogota. PhD thesis. Universidad Nacional de
Colombia, Colombia.
Rousseau, L., S. J. Fonte, O. Tellez, R. van der Hoek, and P.
Lavelle. 2013. Soil macrofauna as indicators of soil quality
and land use impacts in smallholder agroecosystems of
western Nicaragua. Ecol. Ind. 27:71–82.
Safian, S., G. Csontos, and D. Winkler. 2011. Butterfly
community recovery in degraded rainforest habitats in the
Upper Guinean forest zone (Kakum forest, Ghana). J. Insect
Conserv. 15:351–359.
Sakchoowong, W., S. Nomura, K. Ogata, and J. Chanpaisaeng.
2008. Diversity of pselaphine beetles (Coleoptera:
Staphylinidae: Pselaphinae) in eastern Thailand. Entomol.
Sci. 11:301–313.
Saldana-Vazquez, R. A., V. J. Sosa, J. R. Hernandez-Montero,
and F. Lopez-Barrera. 2010. Abundance responses of
frugivorous bats (Stenodermatinae) to coffee cultivation and
selective logging practices in mountainous central Veracruz,
Mexico. Biodivers. Conserv. 19:2111–2124.
Samneg
ard, U., A. S. Persson, and H. G. Smith. 2011. Gardens
benefit bees and enhance pollination in intensively managed
farmland. Biol. Conserv. 144:2602–2606.
Santana, J., M. Porto, L. Gordinho, L. Reino, and P. Beja.
2012. Long-term responses of Mediterranean birds to forest
fuel management. J. Appl. Ecol. 49:632–643.
Savage, J., T. A. Wheeler, A. M. A. Moores, and A. G.
Taillefer. 2011. Effects of habitat size, vegetation cover, and
surrounding land use on diptera diversity in temperate
nearctic bogs. Wetlands 31:125–134.
Schmidt, A. C., L. H. Fraser, C. N. Carlyle, and E. R. L.
Bassett. 2012. Does cattle grazing affect ant abundance and
diversity in temperate grasslands? Rangeland Ecol. Manage.
65:292–298.
32
ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
L. N. Hudson et al.
Schon, N. L., A. D. Mackay, and M. A. Minor. 2011. Soil
fauna in sheep-grazed hill pastures under organic and
conventional livestock management and in an adjacent
ungrazed pasture. Pedobiologia 54:161–168.
Sch€
uepp, C., J. D. Herrmann, F. Herzog, and M. H.
Schmidt-Entling. 2011. Differential effects of habitat
isolation and landscape composition on wasps, bees, and
their enemies. Oecologia 165:713–721.
Sch€
uepp, C., S. Rittiner, and M. H. Entling. 2012. High
bee and wasp diversity in a heterogeneous tropical farming
system compared to protected forest. PLoS ONE 7:e52109.
Scott, D. M., D. Brown, S. Mahood, B. Denton, A. Silburn,
and F. Rakotondraparany. 2006. The impacts of forest
clearance on lizard, small mammal and bird communities in
the arid spiny forest, southern Madagascar. Biol. Conserv.
127:72–87.
Sedlock, J. L., S. E. Weyandt, L. Cororan, M. Damerow, S.
Hwa, and B. Pauli. 2008. Bat diversity in tropical forest and
agro-pastoral habitats within a protected area in the
Philippines. Acta Chiropt. 10:349–358.
Shafie, N. J., S. A. M. Sah, N. S. A. Latip, N. M. Azman, and
N. L. Khairuddin. 2011. Diversity pattern of bats at two
contrasting habitat types along Kerian River, Perak,
Malaysia. Trop. Life Sci. Res. 22:13–22.
Shahabuddin, G., and R. Kumar. 2007. Effects of extractive
disturbance on bird assemblages, vegetation structure and
floristics in tropical scrub forest, Sariska Tiger Reserve,
India. For. Ecol. Manage. 246:175–185.
Sheil, D., R. K. Puri, I. Basuki, M. van Heist, M. Wan, N.
Liswanti, et al. 2002. Exploring biological diversity,
environment and local people’s perspectives in forest
landscapes: methods for a multidisciplinary landscape
assessment. Research, Center for International Forestry
Research (CIFOR), Jakarta [http://www.cifor.org/mla].
Sheldon, F., A. Styring, and P. Hosner. 2010. Bird species
richness in a Bornean exotic tree plantation: a long-term
perspective. Biol. Conserv. 143:399–407.
Shuler, R. E., T. H. Roulston, and G. E. Farris. 2005. Farming
practices influence wild pollinator populations on squash
and pumpkin. J. Econ. Entomol. 98:790–795.
da Silva, P. G. 2011. Especies de Scarabaeinae (Coleoptera:
Scarabaeidae) de fragmentos florestais com diferentes nıveis
de alteracß~ao em Santa Maria, Rio Grande do Sul. MSc
thesis. Universidade Federal de Santa Maria, Brazil.
Silva, F. A. B., C. M. Q. Costa, R. C. Moura, and A. I. Farias.
2010. Study of the dung beetle (Coleoptera: Scarabaeidae)
community at two sites: atlantic forest and clear-cut,
Pernambuco, Brazil. Environ. Entomol. 39:359–367.
Slade, E. M., D. J. Mann, and O. T. Lewis. 2011. Biodiversity
and ecosystem function of tropical forest dung beetles under
contrasting logging regimes. Biol. Conserv. 144:166–174.
Smith-Pardo, A., and V. H. Gonzalez. 2007. Bee diversity
(Hymenoptera: Apoidea) in a tropical rainforest succession.
Acta Biol
o. Colomb. 12:43–55.
ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
The PREDICTS Database
Sodhi, N. S., D. S. Wilcove, T. M. Lee, C. H. Sekercioglu, R.
Subaraj, H. Bernard, et al. 2010. Deforestation and avian
extinction on tropical landbridge islands. Conserv. Biol.
24:1290–1298.
Sosa, R. A., V. A. Benz, J. M. Galea, and I. V. Poggio Herrero.
2010. Efecto del grado de disturbio sobre el ensamble de
aves en la reserva provincial Parque Luro, La Pampa,
Argentina. Revista de la Asociaci
on Argentina de Ecologıa
de Paisajes, 1:101–110.
de Souza, V. M., B. de Souza, and E. F. Morato. 2008. Effect
of the forest succession on the anurans (Amphibia: Anura)
of the Reserve Catuaba and its periphery, Acre,
southwestern Amazonia. Rev. Bras. Zool. 25:49–57.
Sridhar, H., T. R. S. Raman, and D. Mudappa. 2008.
Mammal persistence and abundance in tropical rainforest
remnants in the southern Western Ghats, India. Curr. Sci.
94:748–757.
St-Laurent, M. H., J. Ferron, C. Hins, and R. Gagnon. 2007.
Effects of stand structure and landscape characteristics on
habitat use by birds and small mammals in managed boreal
forest of eastern Canada. Canadian Journal of Forest
Research-Revue Canadienne de Recherche Forestiere
37:1298–1309.
Str€
om, L., K. Hylander, and M. Dynesius. 2009. Different
long-term and short-term responses of land snails to
clear-cutting of boreal stream-side forests. Biol. Conserv.
142:1580–1587.
Struebig, M. J., T. Kingston, A. Zubaid, A. Mohd-Adnan, and
S. J. Rossiter. 2008. Conservation value of forest fragments
to Palaeotropical bats. Biol. Conserv. 141:2112–2126.
Su, Z. M., R. Z. Zhang, and J. X. Qiu. 2011. Decline in the
diversity of willow trunk-dwelling weevils (Coleoptera:
Curculionoidea) as a result of urban expansion in Beijing,
China. J. Insect Conserv. 15:367–377.
Sugiura, S., T. Tsuru, Y. Yamaura, and H. Makihara. 2009.
Small off-shore islands can serve as important refuges for
endemic beetle conservation. J. Insect Conserv. 13:377–385.
Summerville, K. S. 2011. Managing the forest for more than
the trees: effects of experimental timber harvest on forest
Lepidoptera. Ecol. Appl. 21:806–816.
Summerville, K. S., C. J. Conoan, and R. M. Steichen. 2006.
Species traits as predictors of lepidopteran composition in
restored and remnant tallgrass prairies. Ecol. Appl. 16:891–900.
Sung, Y. H., N. E. Karraker, and B. C. H. Hau. 2012.
Terrestrial herpetofaunal assemblages in secondary forests
and exotic Lophostemon confertus plantations in South
China. For. Ecol. Manage. 270:71–77.
The Nature Conservancy. 2009. Terrestrial ecoregions of the
world [http://maps.tnc.org/gis_data.html].
Thematic Mapping. 2008. World borders [http://
thematicmapping.org/downloads/world_borders.php].
Threlfall, C. G., B. Law, and P. B. Banks. 2012. Sensitivity of
insectivorous bats to urbanization: implications for
suburban conservation planning. Biol. Conserv. 146:41–52.
33
The PREDICTS Database
L. N. Hudson et al.
Tonietto, R., J. Fant, J. Ascher, K. Ellis, and D. Larkin. 2011. A
comparison of bee communities of Chicago green roofs,
parks and prairies. Landsc. Urban Plan. 103:102–108.
Turner, E. C., and W. A. Foster. 2009. The impact of forest
conversion to oil palm on arthropod abundance and
biomass in Sabah, Malaysia. J. Trop. Ecol. 25:23–30.
Tylianakis, J. M., A. M. Klein, and T. Tscharntke. 2005.
Spatiotemporal variation in the diversity of
hymenoptera across a tropical habitat gradient. Ecology
86:3296–3302.
UNEP-WCMC. 2009. International expert workshop on the
2010 biodiversity indicators and post-2010 indicator
development. Pp. 65. UNEP-WCMC, Cambridge, U.K.
Vackar, D. 2012. Ecological footprint, environmental
performance and biodiversity: a cross-national comparison.
Ecol. Ind. 16:40–46.
Vanbergen, A. J., B. A. Woodcock, A. D. Watt, and J. Niemela.
2005. Effect of land-use heterogeneity on carabid
communities at the landscape scale. Ecography 28:3–16.
Vassilev, K., H. Pedashenko, S. C. Nikolov, I. Apostolova, and
J. Dengler. 2011. Effect of land abandonment on the
vegetation of upland semi-natural grasslands in the Western
Balkan Mts., Bulgaria. Plant Biosyst. 145:654–665.
Vazquez, D. P., and D. Simberloff. 2002. Ecological
specialization and susceptibility to disturbance: conjectures
and refutations. Am. Nat. 159:606–623.
Verboven, H. A. F., R. Brys, and M. Hermy. 2012. Sex in the
city: reproductive success of Digitalis purpurea in a gradient
from urban to rural sites. Landsc. Urban Plan. 106:
158–164.
Verdasca, M. J., A. S. Leitao, J. Santana, M. Porto, S. Dias, and
P. Beja. 2012. Forest fuel management as a conservation tool
for early successional species under agricultural
abandonment: the case of Mediterranean butterflies. Biol.
Conserv. 146:14–23.
Verd
u, J. R., C. E. Moreno, G. Sanchez-Rojas, C. Numa, E.
Galante, and G. Halffter. 2007. Grazing promotes dung
beetle diversity in the xeric landscape of a Mexican
Biosphere Reserve. Biol. Conserv. 140:308–317.
Vergara, C. H., and E. I. Badano. 2009. Pollinator diversity
increases fruit production in Mexican coffee plantations: the
importance of rustic management systems. Agric. Ecosyst.
Environ. 129:117–123.
Vergara, P. M., and J. A. Simonetti. 2004. Avian responses to
fragmentation of the Maulino Forest in central Chile. Oryx
38:383–388.
Vie, J., C. Hilton-Taylor, and S. N. Stuart. 2009. Wildlife in a
Changing World: an analysis of the 2008 IUCN Red List of
Threatened Species. Pp. 180. International Union for
Conservation of Nature, Gland, Switzerland.
Walker, T. R., P. D. Crittenden, S. D. Young, and T. Prystina.
2006. An assessment of pollution impacts due to the oil and
gas industries in the Pechora basin, north-eastern European
Russia. Ecol. Ind. 6:369–387.
Walpole, M., R. E. A. Almond, C. Besancon, S. H. M.
Butchart, D. Campbell-Lendrum, G. M. Carr, et al. 2009.
Tracking Progress Toward the 2010 Biodiversity Target and
Beyond. Science 325:1503–1504.
Wang, Y., Y. Bao, M. Yu, G. Xu, and P. Ding. 2010.
Nestedness for different reasons: the distributions of birds,
lizards and small mammals on islands of an inundated lake.
Divers. Distrib. 16:862–873.
Watling, J. I., K. Gerow, and M. A. Donnelly. 2009. Nested
species subsets of amphibians and reptiles on Neotropical
forest islands. Anim. Conserv. 12:467–476.
Weller, B., and J. U. Ganzhorn. 2004. Carabid beetle
community composition, body size, and fluctuating
asymmetry along an urban-rural gradient. Basic Appl. Ecol.
5:193–201.
Wells, K., E. K. V. Kalko, M. B. Lakim, and M. Pfeiffer. 2007.
Effects of rain forest logging on species richness and
assemblage composition of small mammals in Southeast
Asia. J. Biogeogr. 34:1087–1099.
Wilcove, D. S., C. H. McLellan, and A. P. Dobson. 1986.
Habitat fragmentation in the temperate zone. Pp. 237–256.
in M. E. Soule, ed. Conservation biology. The science of
scarcity and diversity, Sinauer Associates, Inc., Sunderland,
MA.
Williams, C. D., J. Sheahan, and M. J. Gormally. 2009.
Hydrology and management of turloughs (temporary lakes)
affect marsh fly (Sciomyzidae: Diptera) communities. Insect
Conserv. Divers. 2:270–283.
Willig, M. R., S. J. Presley, C. P. Bloch, C. L. Hice, S. P.
Yanoviak, M. M. Diaz, et al. 2007. Phyllostomid bats of
lowland Amazonia: effects of habitat alteration on
abundance. Biotropica 39:737–746.
Winfree, R., T. Griswold, and C. Kremen. 2007. Effect of
human disturbance on bee communities in a forested
ecosystem. Conserv. Biol. 21:213–223.
Woinarski, J. C. Z., B. Rankmore, B. Hill, A. D. Griffiths, A.
Stewart, and B. Grace. 2009. Fauna assemblages in regrowth
vegetation in tropical open forests of the Northern
Territory, Australia. Wildlife Res. 36:675–690.
Woodcock, B. A., S. G. Potts, E. Pilgrim, A. J. Ramsay, T.
Tscheulin, A. Parkinson, et al. 2007. The potential of grass
field margin management for enhancing beetle diversity in
intensive livestock farms. J. Appl. Ecol. 44:60–69.
Wunderle, J. M., L. M. P. Henriques, and M. R. Willig. 2006.
Short-term responses of birds to forest gaps and understory:
an assessment of reduced-impact logging in a lowland
Amazon forest. Biotropica 38:235–255.
WWF International. 2012. Living Planet Report 2012. Pp. 160.
WWF International, Gland, Switzerland.
Yoshikura, S., S. Yasui, and T. Kamijo. 2011. Comparative
study of forest-dwelling bats’ abundances and species
richness between old-growth forests and conifer plantations
in Nikko National Park, central Japan. Mammal Study
36:189–198.
34
ª 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
L. N. Hudson et al.
Zaitsev, A. S., M. Chauvat, A. Pflug, and V. Wolters. 2002.
Oribatid mite diversity and community dynamics in a
spruce chronosequence. Soil Biol. Biochem. 34:1919–1927.
Zaitsev, A. S., V. Wolters, R. Waldhardt, and J. Dauber. 2006.
Long-term succession of oribatid mites after conversion of
croplands to grasslands. Appl. Soil Ecol. 34:230–239.
Zimmerman, G., F. W. Bell, J. Woodcock, A. Palmer, and J.
Paloniemi. 2011. Response of breeding songbirds to
vegetation management in conifer plantations established in
boreal mixedwoods. Forestry Chron. 87:217–224.
Supporting Information
Additional Supporting Information may be found in the
online version of this article:
Figure S1. 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
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