# The database of the Predicts (Projecting responses of ecological diversity in changing terrestrial systems) project

Lawrence N. Hudson, Tim Newbold, Sara Contu, Samantha L. L. Hill, Igor Lysenko, Adriana de Palma, Helen R.P. Phillips, Tamera I. Alhusseini, Felicity E. Bedford, Dominic J. Bennett, et al.

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## To cite this version:

Lawrence N. Hudson, Tim Newbold, Sara Contu, Samantha L. L. Hill, Igor Lysenko, et al. The database of the Predicts (Projecting responses of ecological diversity in changing terrestrial systems) project. Ecology and Evolution, 2017, 7 (1), pp.145-188. 10.1002/ece3.2579. hal-01608816

HAL Id: hal-01608816

https://hal.science/hal-01608816

Submitted on 18 Dec 2017

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<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
**To link to this article:** DOI: 10.1002/ece3.2579
URL: http://dx.doi.org/10.1002/ece3.2579

**To cite this version:** Hudson, Lawrence N. and Newbold, Tim and Contu, Sara [et al.] *The database of the Predicts (Projecting responses of ecological diversity in changing terrestrial systems) project*. (2017) Ecology and Evolution, vol. 7 (n° 1). pp. 145-188. ISSN 2045-7758
# The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project

Lawrence N. Hudson¹* ID | Tim Newbold²,³* | Sara Contu¹ | Samantha L. L. Hill¹,² |

Igor Lysenko⁴ | Adriana De Palma¹,⁴ | Helen R. P. Phillips¹,⁴ | Tamera I. Alhusseini⁵ |

Felicity E. Bedford⁶ | Dominic J. Bennett⁴ | Hollie Booth²,⁷ | Victoria J. Burton¹,⁸ |

Charlotte W. T. Chng⁴ | Argyrios Choimes¹,⁴ ID | David L. P. Correia⁹ | Julie Day⁴ |

Susy Echeverría-Londoño¹,⁴ | Susan R. Emerson¹ | Di Gao¹ | Morgan Garon⁴ |

Michelle L. K. Harrison⁴ | Daniel J. Ingram¹⁰ | Martin Jung¹⁰ ID | Victoria Kemp¹¹ |

Lucinda Kirkpatrick¹² | Callum D. Martin¹³ | Yuan Pan¹⁴ | Gwilym D. Pask-Hale¹ |

Edwin L. Pynegar¹⁵ | Alexandra N. Robinson⁵ | Katia Sanchez-Ortiz¹⁶ |

Rebecca A. Senior¹⁴ | Benno I. Simmons⁴ | Hannah J. White¹⁷ | Hanbin Zhang¹⁶ |

Job Aben¹⁸,¹⁹ | Stefan Abrahamczyk²⁰ | Gilbert B. Adum²¹,²² |

Virginia Aguilar-Barquero²³ | Marcelo A. Aizen²⁴ | Belén Albertos²⁵ | E. L. Alcala²⁶ |

Maria del Mar Alguacil²⁷ | Audrey Alignier²⁸,²⁹ | Marc Ancrenaz³⁰,³¹ |

Alan N. Andersen³² | Enrique Arbeláez-Cortés³³,³⁴ | Inge Armbrecht³⁵ |

Víctor Arroyo-Rodríguez³⁶ | Tom Aumann³⁷ | Jan C. Axmacher³⁸ | Badrul Azhar³⁹,⁴⁰ |

Adrián B. Azpiroz⁴¹ | Lander Baeten⁴²,⁴³ | Adama Bakayoko⁴⁴,⁴⁵ | András Báldi⁴⁶ |

John E. Banks⁴⁷ | Sharad K. Baral⁴⁸ | Jos Barlow⁴⁹,⁵⁰ | Barbara I. P. Barratt⁵¹ |

Lurdes Barrico⁵² | Paola Bartolommei⁵³ | Diane M. Barton⁵¹ | Yves Basset⁵⁴ |

Péter Batáry⁵⁵ | Adam J. Bates⁵⁶,⁵⁷ | Bruno Baur⁵⁸ | Erin M. Bayne⁵⁹ | Pedro Beja⁶⁰ |

Suzan Benedick⁶¹ | Åke Berg⁶² | Henry Bernard⁶³ | Nicholas J. Berry⁶⁴ |

Dinesh Bhatt⁶⁵ | Jake E. Bicknell⁶⁶,⁶⁷ | Jochen H. Bihn⁶⁸ | Robin J. Blake⁶⁹,⁷⁰ |

Kadiri S. Bobo⁷¹,⁷² | Roberto Bóçon⁷³ | Teun Boekhout⁷⁴ | Katrin Böhning-Gaese⁷⁵,⁷⁶ |

Kevin J. Bonham⁷⁷ | Paulo A. V. Borges⁷⁸ | Sérgio H. Borges⁷⁹ | Céline Boutin⁸⁰ |

Jérémy Bouyer⁸¹,⁸² | Cibele Bragagnolo⁸³ | Jodi S. Brandt⁸⁴ | Francis Q. Brearley⁸⁵ |

Isabel Brito⁸⁶ | Vicenç Bros⁸⁷,⁸⁸ | Jörg Brunet⁸⁹ | Grzegorz Buczkowski⁹⁰ |

Christopher M. Buddle⁹¹ | Rob Bugter⁹² | Erika Buscardo⁹³,⁹⁴,⁹⁵ | Jörn Buse⁹⁶ |

Jimmy Cabra-García⁹⁷,⁹⁸ | Nilton C. Cáceres⁹⁹ | Nicolette L. Cagle¹⁰⁰ |

María Calviño-Cancela¹⁰¹ | Sydney A. Cameron¹⁰²,¹⁰³ | Eliana M. Cancello¹⁰⁴ |

Rut Caparrós²⁵,¹⁰⁵ | Pedro Cardoso⁷⁸,¹⁰⁶ | Dan Carpenter¹⁰⁷,¹⁰⁸ | Tiago F. Carrijo¹⁰⁹ |

Anelena L. Carvalho⁷⁹ | Camila R. Cassano¹¹⁰ | Helena Castro⁵² |

<footnote>*These authors contributed equally to this work.</footnote>

<footnote>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.</footnote>

<footnote>© 2016 The Authors. *Ecology and Evolution* published by John Wiley & Sons Ltd.</footnote>
Alejandro A. Castro-Luna¹¹¹ | Rolando Cerda B.¹¹² | Alexis Cerezo¹¹³ |

Kim Alan Chapman¹¹⁴ | Matthieu Chauvat¹¹⁵ | Morten Christensen¹¹⁶ |

Francis M. Clarke¹¹⁷ | Daniel F.R. Cleary¹¹⁸ | Giorgio Colombo¹¹⁹ | Stuart P. Connop¹²⁰ |

Michael D. Craig¹²¹,¹²² | Leopoldo Cruz-López¹²³ | Saul A. Cunningham¹²⁴ |

Biagio D'Aniello¹²⁵ | Neil D'Cruze¹²⁶ | Pedro Giovâni da Silva¹²⁷ | Martin Dallimer¹²⁸ |

Emmanuel Danquah²¹ | Ben Darvill¹²⁹ | Jens Dauber¹³⁰ | Adrian L. V. Davis¹³¹ |

Jeff Dawson¹³² | Claudio de Sassi¹³³ | Benoit de Thoisy¹³⁴ | Olivier Deheuvels¹³⁵,¹³⁶ |

Alain Dejean¹³⁷,¹³⁸,¹³⁹ | Jean-Louis Devineau¹⁴⁰ | Tim Diekötter¹⁴¹,¹⁴²,¹⁴³ |

Jignasu V. Dolia¹⁴⁴,¹⁴⁵ | Erwin Domínguez¹⁴⁶ | Yamileth Dominguez-Haydar¹⁴⁷ |

Silvia Dorn¹⁴⁸ | Isabel Draper¹⁵⁰ | Niels Dreber¹⁴⁹,¹⁵⁰ | Bertrand Dumont¹⁵¹ |

Simon G. Dures⁴,¹⁵² | Mats Dynesius¹⁵³ | Lars Edenius¹⁵⁴ | Paul Eggleton¹ |

Felix Eigenbrod¹⁵⁵ | Zoltán Elek¹⁵⁶,¹⁵⁷ | Martin H. Entling¹⁵⁸ | Karen J. Esler¹⁵⁹,¹⁶⁰ |

Ricardo F. de Lima¹⁶¹,¹⁶² | Aisyah Faruk¹⁶³,¹⁶⁴ | Nina Farwig¹⁶⁵ | Tom M. Fayle⁴,¹⁶⁶,¹⁶⁷ |

Antonio Felicioli¹⁶⁸ | Annika M. Felton¹⁶⁹ | Roderick J. Fensham¹⁷⁰,¹⁷¹ |

Ignacio C. Fernandez¹⁷² | Catarina C. Ferreira¹⁷³ | Gentile F. Ficetola¹⁷⁴ |

Cristina Fiera¹⁷⁵ | Bruno K. C. Filgueiras¹⁷⁶ | Hüseyin K. Fırıncıoğlu¹⁷⁷ |

David Flaspohler¹⁷⁸ | Andreas Floren¹⁷⁹ | Steven J. Fonte¹⁸⁰,¹⁸¹ | Anne Fournier¹⁸² |

Robert E. Fowler¹⁰ | Markus Franzén¹⁸³ | Lauchlan H. Fraser¹⁸⁴ |

Gabriella M. Fredriksson¹⁸⁵,¹⁸⁶ | Geraldo B. Freire-Jr¹⁸⁷ | Tiago L. M. Frizzo¹⁸⁷ |

Daisuke Fukuda¹⁸⁸ | Dario Furlani¹⁹¹ | René Gaigher¹⁵⁹ | Jörg U. Ganzhorn¹⁸⁹ |

Karla P. García¹⁹⁰,¹⁹¹ | Juan C. Garcia-R¹⁹² | Jenni G. Garden¹⁹³,¹⁹⁴,¹⁹⁵ |

Ricardo Garilleti²⁵ | Bao-Ming Ge¹⁹⁶ | Benoit Gendreau-Berthiaume¹⁹⁷ |

Philippa J. Gerard¹⁹⁸ | Carla Gheler-Costa¹⁹⁹ | Benjamin Gilbert²⁰⁰ | Paolo Giordani²⁰¹ |

Simonetta Giordano¹²⁵ | Carly Golodets²⁰² | Laurens G. L. Gomes²⁰³ |

Rachelle K. Gould²⁰⁴ | Dave Goulson¹⁰ | Aaron D. Gove²⁰⁵,²⁰⁶ | Laurent Granjon²⁰⁷ |

Ingo Grass⁵⁵,¹⁶⁵ | Claudia L. Gray¹⁰,²⁰⁸ | James Grogan²⁰⁹ | Weibin Gu²¹⁰ |

Moisès Guardiola²¹¹ | Nihara R. Gunawardene²⁰⁶ | Alvaro G. Gutierrez²¹² |

Doris L. Gutiérrez-Lamus²¹³ | Daniela H. Haarmeyer²¹⁴ | Mick E. Hanley²¹⁵ |

Thor Hanson²¹⁶ | Nor R. Hashim²¹⁷ | Shombe N. Hassan²¹⁸ | Richard G. Hatfield²¹⁹ |

Joseph E. Hawes²²⁰ | Matt W. Hayward²²¹,²²²,²²³ | Christian Hébert²²⁴ |

Alvin J. Helden²²⁰ | John-André Henden²²⁵ | Philipp Henschel²²⁶ | Lionel Hernández²²⁷ |

James P. Herrera²²⁸ | Farina Herrmann⁵⁵ | Felix Herzog²²⁹ | Diego Higuera-Diaz²³⁰ |

Branko Hilje²³¹ | Hubert Höfer²³² | Anke Hoffmann²³³ | Finbarr G. Horgan²³⁴,²³⁵ |

Elisabeth Hornung²³⁶ | Roland Horváth²³⁷ | Kristoffer Hylander²³⁸ |

Paola Isaacs-Cubides²³⁹ | Hiroaki Ishida²⁴⁰ | Masahiro Ishitani²⁴¹ | Carmen T. Jacobs¹³¹ |

Víctor J. Jaramillo²⁴² | Birgit Jauker²⁴³ | F. Jiménez Hernández²⁴⁴ |

McKenzie F. Johnson¹⁰⁰ | Virat Jolli²⁴⁵,²⁴⁶ | Mats Jonsell²⁴⁷ | S. Nur Juliani²⁴⁸ |

Thomas S. Jung²⁴⁹ | Vena Kapoor²⁵⁰ | Heike Kappes²⁵¹ | Vassiliki Kati²⁵² |
Eric Katovai²⁵³,²⁵⁴ | Klaus Kellner²⁵⁵ | Michael Kessler²⁵⁶ | Kathryn R. Kirby²⁵⁷ |

Andrew M. Kittle²⁵⁸ | Mairi E. Knight²⁵⁹ | Eva Knop²⁶⁰ | Florian Kohler²⁶¹ |

Matti Koivula²⁶² | Annette Kolb²⁶³ | Mouhamadou Kone²⁶⁴,²⁶⁵ | Ádám Kőrösi¹⁵⁶,²⁶⁶ |

Jochen Krauss¹⁷⁹ | Ajith Kumar²⁶⁷ | Raman Kumar²⁶⁸ | David J. Kurz²⁶⁹ |

Alex S. Kutt²⁷⁰ | Thibault Lachat²⁷¹,²⁷² | Victoria Lantschner²⁷³ | Francisco Lara¹⁰⁵ |

Jesse R. Lasky²⁷⁴ | Steven C. Latta²⁷⁵ | William F. Laurance²⁷⁶ | Patrick Lavelle²⁷⁷,²⁷⁸ |

Violette Le Féon²⁷⁹ | Gretchen LeBuhn²⁸⁰ | Jean-Philippe Légaré²⁸¹ |

Valérie Lehouck²⁸² | María V. Lencinas²⁸³ | Pia E. Lentini²⁸⁴ | Susan G. Letcher²⁸⁵ |

Qi Li²⁸⁶ | Simon A. Litchwark²⁸⁷ | Nick A. Littlewood²⁸⁸ | Yunhui Liu²⁸⁹ |

Nancy Lo-Man-Hung²⁹⁰ | Carlos A. López-Quintero²⁹¹ | Mounir Louhaichi²⁹²,²⁹³ |

Gabor L. Lövei²⁹⁴ | Manuel Esteban Lucas-Borja²⁹⁵ | Victor H. Luja²⁹⁶ |

Matthew S. Luskin²⁶⁹ | M Cristina MacSwiney G²⁹⁷ | Kaoru Maeto²⁹⁸ | Tibor Magura²⁹⁹ |

Neil Aldrin Mallari³⁰⁰,³⁰¹ | Louise A. Malone³⁰² | Patrick K. Malonza³⁰³ |

Jagoba Malumbres-Olarte³⁰⁴ | Salvador Mandujano³⁰⁵ | Inger E. Måren³⁰⁶ |

Erika Marin-Spiotta³⁰⁷ | Charles J. Marsh³⁰⁸ | E. J. P. Marshall³⁰⁹ | Eliana Martínez³¹⁰ |

Guillermo Martínez Pastur²⁸³ | David Moreno Mateos³¹¹ | Margaret M. Mayfield³¹² |

Vicente Mazimpaka¹⁰⁵ | Jennifer L. McCarthy³¹³ | Kyle P. McCarthy³¹⁴ |

Quinn S. McFrederick³¹⁵ | Sean McNamara³¹⁶ | Nagore G. Medina¹⁰⁵,³¹⁷ |

Rafael Medina³¹⁸ | Jose L. Mena³¹⁹ | Estefania Mico³²⁰ | Grzegorz Mikusinski³²¹ |

Jeffrey C. Milder³²²,³²³ | James R. Miller³²⁴ | Daniel R. Miranda-Esquivel³²⁵ |

Melinda L. Moir²⁸⁴,³²⁶ | Carolina L. Morales³²⁷ | Mary N. Muchane³²⁸ |

Muchai Muchane³²⁹ | Sonja Mudri-Stojnic³³⁰ | A. Nur Munira³³¹ |

Antonio Muoñz-Alonso³³² | B. F. Munyekenye³³³ | Robin Naidoo³³⁴ | A. Naithani³³⁵,³³⁶ |

Michiko Nakagawa³³⁷ | Akihiro Nakamura³³⁸,³³⁹ | Yoshihiro Nakashima³⁴⁰ |

Shoji Naoe³⁴¹ | Guiomar Nates-Parra³⁴² | Dario A. Navarrete Gutierrez³⁴³ |

Luis Navarro-Iriarte³⁴⁴ | Paul K. Ndang'ang'a³⁴⁵,³⁴⁶ | Eike L. Neuschulz⁷⁵ |

Jacqueline T. Ngai³⁴⁷ | Violaine Nicolas³⁴⁸ | Sven G. Nilsson³⁴⁹ |

Norbertas Noreika³⁵⁰,³⁵¹ | Olivia Norfolk³⁵² | Jorge Ari Noriega³⁵³ |

David A. Norton³⁵⁴ | Nicole M. Nöske³⁵⁵ | A. Justin Nowakowski³⁵⁶ |

Catherine Numa³⁵⁷ | Niall O'Dea³⁵⁸ | Patrick J. O'Farrell³⁵⁹,³⁶⁰ | William Oduro²¹,³⁶¹ |

Sabine Oertli³⁶² | Caleb Ofori-Boateng³⁶³,³⁶⁴ | Christopher Omamoke Oke³⁶⁵ |

Vicencio Oostra³⁶⁶ | Lynne M. Osgathorpe³⁶⁷ | Samuel Eduardo Otavo³⁶⁸ |

Navendu V. Page³⁶⁹ | Juan Paritsis³⁷⁰ | Alejandro Parra-H³⁷¹ | Luke Parry³⁷²,³⁷³ |

Guy Pe'er¹⁸³,³⁷⁴ | Peter B. Pearman³⁷⁵,³⁷⁶ | Nicolás Pelegrin³⁷⁷ | Raphaël Pélissier³⁷⁸,³⁷⁹ |

Carlos A. Peres³⁸⁰ | Pablo L. Peri³⁸¹,³⁸²,³⁸³ | Anna S. Persson³⁴⁹ |

Theodora Petanidou³⁸⁴ | Marcell K. Peters³⁸⁵ | Rohan S. Pethiyagoda³⁸⁶ | Ben Phalan³⁸⁷ |

T. Keith Philips³⁸⁸ | Finn C. Pillsbury³⁸⁹ | Jimmy Pincheira-Ulbrich¹⁹⁰,³⁹⁰ |

Eduardo Pineda³⁹¹ | Joan Pino²¹¹,³⁹² | Jaime Pizarro-Araya³⁹³ | A. J. Plumptre³⁹⁴ |
Santiago L. Poggio³⁹⁵ | Natalia Politi³⁹⁶ | Pere Pons³⁹⁷ | Katja Poveda³⁹⁸ |

Eileen F. Power³⁹⁹ | Steven J. Presley⁴⁰⁰ | Vânia Proença⁴⁰¹ | Marino Quaranta⁴⁰² |

Carolina Quintero³⁷⁰ | Romina Rader⁴⁰³ | B. R. Ramesh³⁷⁹ | Martha P. Ramirez-Pinilla⁴⁰⁴ |

Jai Ranganathan⁴⁰⁵ | Claus Rasmussen⁴⁰⁶ | Nicola A. Redpath-Downing⁴⁰⁷ |

J. Leighton Reid⁴⁰⁸ | Yana T. Reis⁴⁰⁹ | José M. Rey Benayas⁴¹⁰ | Juan Carlos Rey-Velasco⁴¹¹ |

Chevonne Reynolds⁴¹²,⁴¹³ | Danilo Bandini Ribeiro⁴¹⁴ | Miriam H. Richards⁴¹⁵ |

Barbara A. Richardson⁴¹⁶,⁴¹⁷ | Michael J. Richardson⁴¹⁶,⁴¹⁷ | Rodrigo Macip Ríos⁴¹⁸ |

Richard Robinson⁴¹⁹ | Carolina A. Robles⁴²⁰ | Jörg Römbke⁴²¹,⁴²² |

Luz Piedad Romero-Duque⁴²³ | Matthias Rös⁴²⁴ | Loreta Rosselli⁴²⁵ |

Stephen J. Rossiter¹¹ | Dana S. Roth⁴²⁶ | T'ai H. Roulston⁴²⁷,⁴²⁸ | Laurent Rousseau⁴²⁹ |

André V. Rubio⁴³⁰ | Jean-Claude Ruel⁹ | Jonathan P. Sadler⁴³¹ | Szabolcs Sáfián⁴³² |

Romeo A. Saldaña-Vázquez⁴³³ | Katerina Sam¹⁹⁴,⁴³⁴,⁴³⁵ | Ulrika Samnegård²³⁸,³⁴⁹ |

Joana Santana⁶⁰ | Xavier Santos⁶⁰ | Jade Savage⁴³⁶ | Nancy A. Schellhorn⁴³⁷ |

Menno Schilthuizen⁴³⁸,⁴³⁹ | Ute Schmiedel⁴⁴⁰ | Christine B. Schmitt⁴⁴¹,⁴⁴² |

Nicole L. Schon⁴⁴³ | Christof Schüepp²⁶⁰ | Katharina Schumann⁴⁴⁴ | Oliver Schweiger¹⁸³ |

Dawn M. Scott⁴⁴⁵ | Kenneth A. Scott⁴⁴⁶ | Jodi L. Sedlock⁴⁴⁷ | Steven S. Seefeldt⁴⁴⁸ |

Ghazala Shahabuddin⁴⁴⁹ | Graeme Shannon²²³,⁴⁵⁰ | Douglas Sheil⁴⁵¹ |

Frederick H. Sheldon⁴⁵²,⁴⁵³ | Eyal Shochat⁴⁵⁴,⁴⁵⁵ | Stefan J. Siebert²⁵⁵ |

Fernando A. B. Silva⁴⁵⁶ | Javier A. Simonetti⁴³⁰ | Eleanor M. Slade²⁰⁸ | Jo Smith⁴⁵⁷ |

Allan H. Smith-Pardo⁴⁵⁸,⁴⁵⁹ | Navjot S. Sodhi⁴⁶⁰ | Eduardo J. Somarriba¹¹² |

Ramón A. Sosa⁴⁶¹ | Grimaldo Soto Quiroga¹¹²,⁴⁶² | Martin-Hugues St-Laurent⁴⁶³ |

Brian M. Starzomski⁴⁶⁴ | Constanti Stefanescu²¹¹,³⁹²,⁴⁶⁵ | Ingolf Steffan-Dewenter¹⁷⁹ |

Philip C. Stouffer⁴⁶⁶,⁴⁶⁷ | Jane C. Stout³⁹⁹ | Ayron M. Strauch⁴⁶⁸ | Matthew J. Struebig⁶⁶ |

Zhimin Su⁴⁶⁹,⁴⁷⁰ | Marcela Suarez-Rubio⁴⁷¹ | Shinji Sugiura²⁹⁸ | Keith S. Summerville⁴⁷² |

Yik-Hei Sung⁴⁷³ | Hari Sutrisno⁴⁷⁴ | Jens-Christian Svenning⁴⁷⁵ | Tiit Teder⁴⁷⁶ |

Caragh G. Threlfall⁴⁷⁷ | Anu Tiitsaar⁴⁷⁶ | Jacqui H. Todd³⁰² | Rebecca K. Tonietto⁴⁷⁸ |

Ignasi Torre⁴⁶⁵ | Béla Tóthmérész⁴⁷⁹ | Teja Tscharntke⁵⁵ | Edgar C. Turner⁴⁸⁰ |

Jason M. Tylianakis⁴,⁴⁸¹ | Marcio Uehara-Prado⁴⁸² | Nicolas Urbina-Cardona⁴⁸³ |

Denis Vallan⁴⁸⁴ | Adam J. Vanbergen⁴⁸⁵ | Heraldo L. Vasconcelos⁴⁸⁶ | Kiril Vassilev⁴⁸⁷ |

Hans A. F. Verboven⁴⁸⁸ | Maria João Verdasca⁴⁸⁹ | José R. Verdú³²⁰ |

Carlos H. Vergara⁴⁹⁰ | Pablo M. Vergara⁴⁹¹ | Jort Verhulst⁴⁹² | Massimiliano Virgilio⁴⁹³ |

Lien Van Vu⁴⁹⁴ | Edward M. Waite⁴⁹⁵ | Tony R. Walker³⁵²,⁴⁹⁶ | Hua-Feng Wang⁴⁹⁷ |

Yanping Wang⁴⁹⁸ | James I. Watling⁴⁹⁹ | Britta Weller¹⁸⁹ | Konstans Wells⁵⁰⁰,⁵⁰¹ |

Catrin Westphal⁵⁵ | Edward D. Wiafe⁵⁰² | Christopher D. Williams⁵⁰³ |

Michael R. Willig⁵⁰⁴,⁵⁰⁵ | John C. Z. Woinarski⁴⁴⁶ | Jan H. D. Wolf⁵⁰⁶ |

Volkmar Wolters²⁴³ | Ben A. Woodcock⁵⁰⁷ | Jihua Wu⁵⁰⁸ | Joseph M. Wunderle, Jr⁵⁰⁹ |

Yuichi Yamaura³⁴¹ | Satoko Yoshikura⁵¹⁰ | Douglas W. Yu⁵¹¹,⁵¹² | Andrey S. Zaitsev²⁴³,⁵¹³ |

Juliane Zeidler⁵¹⁴ | Fasheng Zou⁵¹⁵ | Ben Collen³ | Rob M. Ewers⁴ |

Georgina M. Mace³ | Drew W. Purves⁵¹⁶ | Jörn P. W. Scharlemann²,¹⁰ | Andy Purvis¹,⁴ |
¹Department of Life Sciences, Natural History Museum, London, UK

²United Nations Environment Programme World Conservation Monitoring Centre, Cambridge, UK

³Department of Genetics, Evolution and Environment, Centre for Biodiversity and Environment, Research, University College London, London, UK

⁴Department of Life Sciences, Imperial College London, Ascot, UK

⁵Imperial College London, South Kensington, London, UK

⁶Department of Zoology, Cambridge University, Cambridge, UK

⁷Frankfurt Zoological Society, Africa Regional Office, Arusha, Tanzania

⁸Science and Solutions for a Changing Planet DTP and the Department of Life Sciences, Imperial College London, South Kensington, London, UK

⁹Centre d'étude de la forêt., Université Laval, Laval, QC, Canada

¹⁰School of Life Sciences, University of Sussex, Brighton, UK

¹¹School of Biological and Chemical Sciences, Queen Mary University of London, London, UK

¹²School of Biological and Ecological Sciences, University of Stirling, Stirling, UK

¹³School of Biological Sciences, Royal Holloway University of London, Egham, Surrey, UK

¹⁴Department of Animal and Plant Sciences, University of Sheffield, Western Bank, Sheffield, UK

¹⁵School of Environment, Natural Resources and Geography, Bangor University, Bangor, Gwynedd, UK

¹⁶University College London, London, UK

¹⁷School of Biological Sciences, Queen's University Belfast, Belfast, UK

¹⁸Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, UK

¹⁹Evolutionary Ecology Group, University of Antwerp, Antwerp, Belgium

²⁰Nees Institute for Plant Biodiversity, University of Bonn, Bonn, Germany

²¹Wildlife and Range Management Department, Faculty of Renewable Natural Resources (FRNR), College of Agriculture and Natural Resources (CANR), Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana

²²SAVE THE FROGS! Ghana, Adam-Kumasi, Ghana

²³Escuela de Biología, Universidad de Costa Rica, San José, Costa Rica

²⁴Laboratorio Ecotono-CRUB, Universidad Nacional del Comahue and INIBIOMA, Río Negro, Argentina

²⁵Departamento de Botánica, Facultad de Farmacia, Universidad de Valencia, Burjassot, Valencia, Spain

²⁶Marine Laboratory, Silliman University-Angelo King Center for Research and Environmental Management, Silliman University, Dumaguete City, Philippines

²⁷Department of Soil and Water Conservation, CSIC-Centro de Edafología y Biología Aplicada del Segura, Murcia, Spain

²⁸INRA, UR 0980 SAD-Paysage, Rennes Cedex, France

²⁹INRA, UMR 1201 DYNAFOR, Castanet Tolosan Cedex, France

³⁰HUTAN - Kinabatangan Orang-utan Conservation Programme, Kota Kinabalu, Malaysia

³¹Borneo Futures, Kota Kinabalu, Malaysia

³²CSIRO Land & Water Flagship, Winnellie, NT, Australia

³³Museo de Zoología, Facultad de Ciencias, Universidad Nacional Autónoma de México, México D.F., Mexico

³⁴Colección de Tejidos, Instituto de Investigación de Recursos Biológicos Alexander von Humboldt, Valle del Cauca, Colombia

³⁵Biology Department, Universidad del Valle, Cali, Colombia

³⁶Instituto de Investigaciones en Ecosistemas y Sustentabilidad, Universidad Nacional Autónoma de México, Morelia, Mexico

³⁷College of Science, Engineering & Health, RMIT University, Melbourne, Vic., Australia

³⁸UCL Department of Geography, University College London, London, UK

³⁹Biodiversity Unit, Institute of Bioscience, Universiti Putra Malaysia, Serdang, Malaysia

⁴⁰Faculty of Forestry, Universiti Putra Malaysia, Serdang, Malaysia

⁴¹Departamento de Biodiversidad y Genética, Instituto de Investigaciones Biológicas Clemente Estable, Montevideo, Uruguay

⁴²Forest & Nature Lab, Department of Forest and Water Management, Ghent University, Gontrode, Belgium

⁴³Terrestrial Ecology Unit, Department of Biology, Ghent University, Ghent, Belgium

⁴⁴UFR Science de la Nature, Université Naangui Abrogoua, Abidjan, Ivory Coast

⁴⁵Centre Suisse de Recherches Scientifiques en Côte d'Ivoire, Abidjan, Ivory Coast

⁴⁶MTA Centre for Ecological Research, Vácrátót, Hungary

⁴⁷University of Washington Tacoma, Tacoma, WA, USA

⁴⁸Northern Hardwoods Research Institute, Edmundston, NB, Canada

⁴⁹Lancaster Environment Centre, Lancaster University, Lancaster, UK

⁵⁰MCT/Museu Paraense Emílio Goeldi, Belém, Brazil
⁵¹AgResearch Limited, Invermay Agricultural Centre, Puddle Alley, Mosgiel, New Zealand

⁵²Centre for Functional Ecology, Department of Life Sciences, University of Coimbra, Coimbra, Portugal

⁵³COT (Tuscan Ornithological Society), Livorno, Italy

⁵⁴Smithsonian Tropical Research Institute, Balboa, Ancon, Panama City, Republic of Panama

⁵⁵Agroecology, Department of Crop Sciences, Georg-August University, Göttingen, Germany

⁵⁶Biosciences, School of Science & Technology, Nottingham Trent University, Clifton, Nottingham, UK

⁵⁷University of Birmingham, Edgbaston, Birmingham, UK

⁵⁸Section of Conservation Biology, Department of Environmental Sciences, University of Basel, Basel, Switzerland

⁵⁹Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada

⁶⁰CIBIO/InBio, Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, Vairão, Portugal

⁶¹Faculty of Sustainable Agriculture, Universiti Malaysia Sabah, Sandakan, Malaysia

⁶²The Swedish University of Agricultural Sciences, The Swedish Biodiversity Centre, Uppsala, Sweden

⁶³Institute for Tropical Biology and Conservation, Universiti Malaysia Sabah, Jalan UMS, Kota Kinabalu, Malaysia

⁶⁴School of Geosciences, University of Edinburgh, Edinburgh, UK

⁶⁵Department of Zoology & Environmental Science, Gurukula Kangri University, Haridwar, India

⁶⁶Durrell Institute of Conservation and Ecology (DICE), School of Anthropology and Conservation, University of Kent, Canterbury, UK

⁶⁷Iwokrama International Centre for Rainforest Conservation and Development, Georgetown, Guyana

⁶⁸Department of Ecology-Animal Ecology, Faculty of Biology, Philipps-Universität Marburg, Marburg, Germany

⁶⁹Compliance Services International, Pentlands Science Park, Penicuik, Edinburgh, UK

⁷⁰Centre for Agri-Environmental Research, School of Agriculture, Policy and Development, University of Reading, Reading, UK

⁷¹School for the Training of Wildlife Specialists Garoua, Garoua, Cameroon

⁷²Department of Forestry, Faculty of Agronomy and Agricultural Sciences, University of Dschang, Dschang, Cameroon

⁷³Mater Natura - Instituto de Estudos Ambientais, Curitiba, Brazil

⁷⁴CBS Fungal Biodiversity Centre (CBS-KNAW), Utrecht, The Netherlands

⁷⁵Senckenberg Biodiversity and Climate Research Centre (BiK-F), Frankfurt am Main, Germany

⁷⁶Institute for Ecology, Evolution & Diversity, Goethe University Frankfurt, Biologicum, Frankfurt am Main, Germany

⁷⁷School of Land and Food, University of Tasmania, Sandy Bay, Tas., Australia

⁷⁸Departamento de Ciências Agrárias, cE3c - Centre for Ecology, Evolution and Environmental Changes/Azorean Biodiversity Group and Universidade dos Açores, Angra do Heroísmo, Açores, Portugal

⁷⁹Instituto Nacional de Pesquisas da Amazônia, Manaus, Brazil

⁸⁰Environment and Climate Change Canada, Science & Technology Branch, Carleton University, Ottawa, ON, Canada

⁸¹Unité Mixte de Recherche Contrôle des Maladies Animales Exotiques et Emergentes, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Montpellier, France

⁸²Unité Mixte de Recherche 1309 Contrôle des Maladies Animales Exotiques et Emergentes, Institut national de la recherche agronomique (INRA), Montpellier, France

⁸³Departamento de Zoologia, Instituto de Biociências, Universidade de São Paulo, São Paulo, Brazil

⁸⁴Human Environment Systems Center, Boise State University, Boise, ID, USA

⁸⁵School of Science and the Environment, Manchester Metropolitan University, Manchester, UK

⁸⁶Universidade de Évora - ICAAM, Évora, Portugal

⁸⁷Natural Parks Technical Office, Diputació de Barcelona, Barcelona, Spain

⁸⁸Natural History Museum of Barcelona, Barcelona, Catalonia, Spain

⁸⁹Swedish University of Agricultural Sciences, Southern Swedish Forest Research Centre, Alnarp, Sweden

⁹⁰Department of Entomology, Purdue University, West Lafayette, IN, USA

⁹¹Department of Natural Resource Sciences, McGill University, Ste-Ann-de-Bellevue, QC, Canada

⁹²Alterra, part of Wageningen University and Research, RB Wageningen, The Netherlands

⁹³Departamento de Ciências da Vida, Centro de Ecologia Funcional, Universidade de Coimbra, Coimbra, Portugal

⁹⁴Departamento de Biologia Vegetal, Instituto de Biologia, Universidade Estadual de Campinas, Campinas, Brazil

⁹⁵Department of Botany, School of Natural Sciences, Trinity College Dublin, Dublin 2, Ireland

⁹⁶Institute for Environmental Sciences, University Koblenz-Landau, Landau, Germany

⁹⁷Departamento de Zoologia, Instituto de Biociências, Universidade de São Paulo, São Paulo, Brazil

⁹⁸Departamento de Biología, Grupo de investigación en Biología, Ecología y Manejo de Hormigas, Sección de Entomología, Universidad del Valle, Cali, Colombia
⁹⁹Department of Biology, Federal University of Santa Maria, CCNE, Santa Maria, Brazil

¹⁰⁰Nicholas School of the Environment, Duke University, Durham, NC, USA

¹⁰¹Department of Ecology and Animal Biology, Faculty of Sciences, University of Vigo, Vigo, Spain

¹⁰²Department of Entomology, University of Illinois, Urbana, IL, USA

¹⁰³Program in Ecology, Evolution and Conservation Biology, University of Illinois, Urbana, IL, USA

¹⁰⁴Museu de Zoologia da Universidade de São Paulo, São Paulo, Brazil

¹⁰⁵Departamento de Biología (Botánica), Facultad de Ciencias, Universidad Autonoma de Madrid, Madrid, Spain

¹⁰⁶Finnish Museum of Natural History, University of Helsinki, Helsinki, Finland

¹⁰⁷Parks and Countryside, Bracknell Forest Council, Bracknell, UK

¹⁰⁸Soil Biodiversity Group, Life Sciences Department, Natural History Museum, London, UK

¹⁰⁹Museu de Zoologia da Universidade de São Paulo, São Paulo, Brazil

¹¹⁰Laboratório de Ecologia Aplicada à Conservação, Universidade Estadual de Santa Cruz, Ilhéus, Brazil

¹¹¹Instituto de Biotecnologia y Ecología Aplicada (INBIOTECA), Universidad Veracruzana, Xalapa, Mexico

¹¹²Centro Agronómico Tropical de Investigación y Enseñanza (CATIE), Tropical Agricultural Research and Higher Education Center, Turrialba, Costa Rica

¹¹³Department of Quantitative Methods and Information Systems, Faculty of Agronomy, University of Buenos Aires, Buenos Aires, Argentina

¹¹⁴Applied Ecological Services, Inc., Prior Lake, MN, USA

¹¹⁵Normandie Univ, EA 1293 ECODIV-Rouen, SFR SCALE, UFR Sciences et Techniques, Mont Saint Aignan Cedex, France

¹¹⁶MC-Consult, Sorø, Denmark

¹¹⁷Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, UK

¹¹⁸Department of Biology, CESAM, Universidade de Aveiro, Aveiro, Portugal

¹¹⁹Dipartimento di Biologia, Università degli Studi di Milano, Milano, Italy

¹²⁰Sustainability Research Institute, University of East London, London, UK

¹²¹Centre of Excellence for Environmental Decisions, School of Plant Biology, University of Western Australia, Nedlands, WA, Australia

¹²²School of Veterinary and Life Sciences, Murdoch University, Murdoch, WA, Australia

¹²³Grupo Ecología de Artrópodos y Manejo de Plagas, El Colegio de la Frontera Sur, Tapachula, Mexico

¹²⁴CSIRO Land and Water Flagship, Canberra, ACT, Australia

¹²⁵Dipartimento di Biologia, Università di Napoli Federico II, Napoli, Italy

¹²⁶Wildlife Conservation Research Unit, Department of Zoology, University of Oxford, Recanati-Kaplan Centre, Tubney, UK

¹²⁷Programa de Pós-Graduação em Ecologia, Universidade Federal de Santa Catarina, Florianópolis, Brazil

¹²⁸Sustainability Research Institute, School of Earth and Environment, University of Leeds, Leeds, UK

¹²⁹British Trust for Ornithology, Stirling, UK

¹³⁰Thünen Institute of Biodiversity, Braunschweig, Germany

¹³¹Scarab Research Group, Department of Zoology & Entomology, University of Pretoria, Hatfield, South Africa

¹³²Durrell Wildlife Conservation Trust, Trinity, Jersey

¹³³Center for International Forestry Research, Bogor, Indonesia

¹³⁴Kwata NGO, Cayenne, French Guiana

¹³⁵CIRAD, UMR System, Montpellier, France

¹³⁶ICRAF, Regional Office for Latin America, Lima, Peru

¹³⁷UPS, INP, Laboratoire Écologie Fonctionnelle et Environnement, Université de Toulouse, Toulouse, France

¹³⁸CNRS – UMR 5245, Ecolab, Toulouse, France

¹³⁹CNRS – UMR 8172, Écologie des Forêts de Guyane, Kourou cedex, France

¹⁴⁰CNRS – UMR 7206 (retired) CNRS/MNHN, Paris, France

¹⁴¹Department of Landscape Ecology, Institute of Natural Resource Conservation, Kiel University, Kiel, Germany

¹⁴²Department of Biology, Nature Conservation, University Marburg, Marburg, Germany

¹⁴³Institute of Integrative Biology, ETH Zürich, Zürich, Switzerland

¹⁴⁴Post Graduate Program in Wildlife Biology and Conservation, National Centre for Biological Sciences, Bangalore, India

¹⁴⁵Wildlife Conservation Society (India Program), Centre for Wildlife Studies, Bangalore, India

¹⁴⁶Instituto de Investigaciones Agropecuarias – INIA – CRI – Kampenaike, Punta Arenas, Chile

¹⁴⁷Programa de Biología, Universidad del Atlántico, Barranquilla, Colombia

¹⁴⁸Applied Entomology, ETH Zürich, Zürich, Switzerland

¹⁴⁹Unit for Environmental Sciences and Management, North-West University, Potchefstroom, South Africa
¹⁵⁰Department of Ecosystem Modelling, Büsgen-Institute, Georg-August-University of Göttingen, Göttingen, Germany

¹⁵¹INRA, UMR 1213 Herbivores, Saint-Genès Champanelle, France

¹⁵²Institute of Zoology, Zoological Society of London, Regents Park, London, UK

¹⁵³Department of Ecology and Environmental Science, Umeå University, Umeå, Sweden

¹⁵⁴Department of Wildlife, Fish and Environmental Studies, Swedish University of Agricultural Sciences, Umeå, Sweden

¹⁵⁵Centre for Biological Sciences, University of Southampton, Southampton, UK

¹⁵⁶MTA-ELTE-MTM Ecology Research Group, Hungarian Academy of Sciences, c/o Biological Institute, Eötvös Lóránd University, Budapest, Hungary

¹⁵⁷Hungarian Natural History Museum, Budapest, Hungary

¹⁵⁸Institute for Environmental Sciences, University of Koblenz-Landau, Landau, Germany

¹⁵⁹Department of Conservation Ecology and Entomology, Stellenbosch University, Matieland, South Africa

¹⁶⁰Centre for Invasion Biology, Stellenbosch University, Matieland, South Africa

¹⁶¹CE3C - Centre for Ecology, Evolution and Environmental Changes, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal

¹⁶²Associação Monte Pico, Monte Café, Mé Zóchi, São Tomé and Príncipe

¹⁶³Kew Gardens, Wakehurst, Ardingly, Haywards Heath, Sussex, UK

¹⁶⁴Wild Asia, Upper Penthouse, Wisma RKT, Kuala Lumpur, Malaysia

¹⁶⁵Conservation Ecology, Faculty of Biology, Philipps-Universität Marburg, Marburg, Germany

¹⁶⁶Institute of Entomology, Biology Centre of Academy of Sciences Czech Republic, České Budějovice, Czech Republic

¹⁶⁷Institute for Tropical Biology and Conservation, Universiti Malaysia Sabah, Kota Kinabalu, Malaysia

¹⁶⁸Dipartimento di Scienze Veterinarie, Università di Pisa, Pisa, Italy

¹⁶⁹Swedish University of Agricultural Sciences, Alnarp, Sweden

¹⁷⁰Department of Biological Sciences, University of Queensland, St Lucia, Qld, Australia

¹⁷¹Queensland Herbarium (DSITIA), Toowong, Qld, Australia

¹⁷²School of Sustainability, Arizona State University, Tempe, AZ, USA

¹⁷³Department of Biology, Trent University, Peterborough, ON, Canada

¹⁷⁴Laboratoire d'Ecologie Alpine (LECA), Université Grenoble Alpes, Grenoble, France

¹⁷⁵Institute of Biology Bucharest of Romanian Academy, Bucharest, Romania

¹⁷⁶Universidade Federal de Pernambuco - UFPE, Cidade Universitaria, Recife, Brazil

¹⁷⁷Tarla Bitkileri Merkez Araştırma Enstitüsü, Yenimahalle-Ankara, Turkey

¹⁷⁸School of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI, USA

¹⁷⁹Department of Animal Ecology and Tropical Biology, Biocenter, University of Würzburg, Würzburg, Germany

¹⁸⁰Department of Plant Sciences, University of California, Davis, CA, USA

¹⁸¹Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO, USA

¹⁸²IRD-UMR 208 PALOC IRD/MNHN, Paris, France

¹⁸³Department of Community Ecology, UFZ, Helmholtz Centre for Environmental Research, Halle, Germany

¹⁸⁴Department of Natural Resource Sciences, Thompson Rivers University, Kamloops, BC, Canada

¹⁸⁵Institute for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam, GE Amsterdam, The Netherlands

¹⁸⁶PanEco/Yayasan Ekosistem Lestari, Sumatran Orangutan Conservation Programme, Medan, Indonesia

¹⁸⁷Programa de Pós Graduação em Ecologia, Universidade de Brasília, Brasília, Distrito Federal, Brazil

¹⁸⁸IDEA Consultants Inc., Okinawa Branch Office, Naha, Japan

¹⁸⁹Biocentre Grindel, University of Hamburg, Hamburg, Germany

¹⁹⁰Departamento de Zoología, Facultad de Ciencias Naturales y Oceanográficas, Universidad de Concepción, Concepción, Chile

¹⁹¹Departamento de Planificación Territorial, Facultad de Ciencias Ambientales, Centro EULA-Chile, Universidad de Concepción, Concepción, Chile

¹⁹²Hopkirk Institute, Massey University, Palmerston North, New Zealand

¹⁹³Seed Consulting Services, Adelaide, SA, Australia

¹⁹⁴Environmental Futures Research Institute, Griffith University, Brisbane, Qld, Australia

¹⁹⁵Barbara Hardy Institute, University of South Australia, Mawson Lakes, SA, Australia

¹⁹⁶Jiangsu Key Laboratory for Bioresources of Saline Soils, Yancheng Teachers University, Yancheng, China

¹⁹⁷Département des sciences biologiques, Centre d'études de la forêt Université du Québec à Montréal Succursale Centre-ville, Montréal, QC, Canada

¹⁹⁸AgResearch, Ruakura Research Centre, Hamilton, New Zealand

¹⁹⁹Ecologia Aplicada/Applied Ecology, Universidade Sagrado Coração (USC), Bauru, Brazil

²⁰⁰Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, ON, Canada
²⁰¹DIFAR, University of Genova, Genova, Italy

²⁰²Tel Aviv University, Tel Aviv, Israel

²⁰³World Wildlife Fund, Inc. (WWF) Guianas, Paramaribo, Suriname

²⁰⁴Rubenstein School of Natural Resources, University of Vermont, Burlington, VT, USA

²⁰⁵Astron Environmental Services, East Perth, WA, Australia

²⁰⁶Department of Environment and Agriculture, Curtin University, Perth, WA, Australia

²⁰⁷Centre de Biologie pour la Gestion des Populations (CBGP), INRA, IRD, CIRAD, SUPAGRO, Montferrier-sur-Lez cedex, France

²⁰⁸Department of Zoology, University of Oxford, Oxford, UK

²⁰⁹Department of Biological Sciences, Mount Holyoke College, South Hadley, MA, USA

²¹⁰China International Engineering Consulting Corporation, Haidian District, Beijing, China

²¹¹CREAF, Cerdanyola del Vallès, Catalonia, Spain

²¹²Departamento de Ciencias Ambientales y Recursos Naturales Renovables, Facultad de Ciencias Agronómicas, Universidad de Chile, La Pintana, Chile

²¹³Grupos de Fauna, Instituto amazónico de investigaciones científicas Sinchi., Bogotá, Colombia

²¹⁴Biodiversity, Evolution and Ecology of Plants (BEE), Biocentre Klein Flottbek and Botanical Garden, University of Hamburg, Hamburg, Germany

²¹⁵School of Biological Science, University of Plymouth, Plymouth, UK

²¹⁶Friday Harbor, WA, USA

²¹⁷International University of Malaya-Wales, Jalan Tun Ismail, Kuala Lumpur, Malaysia

²¹⁸Department of Wildlife Management, Sokoine University of Agriculture, Morogoro, Tanzania

²¹⁹The Xerces Society for Invertebrate Conservation, Portland, OR, USA

²²⁰Animal & Environment Research Group, Department of Life Sciences, Anglia Ruskin University, Cambridge, UK

²²¹Walter Sisulu University, Mthatha, Transkei, South Africa

²²²Centre for African Conservation Ecology, Nelson Mandela Metropolitan University, Port Elizabeth, South Africa

²²³College of Natural Sciences, Bangor University, Bangor, Gwynedd, UK

²²⁴Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, Québec, QC, Canada

²²⁵Department of Arctic and Marine Biology, University of Tromsø, Tromsø, Norway

²²⁶Panthera, New York, NY, USA

²²⁷Universidad Nacional Experimental de Guayana, Puerto Ordaz, Venezuela

²²⁸Richard Gilder Graduate School, American Museum of Natural History, New York, NY, USA

²²⁹Agroscope, Zürich, Switzerland

²³⁰Corporación Sentido Natural, Bogotá, Colombia

²³¹Earth and Atmospheric Sciences Department, University of Alberta, Edmonton, AB, Canada

²³²State Museum of Natural History Karlsruhe (SMNK), Biosciences, Karlsruhe, Germany

²³³Museum für Naturkunde – Leibniz Institute for Evolution and Biodiversity Science, Berlin, Germany

²³⁴University of Technology Sydney, Sydney, NSW, Australia

²³⁵University of New Brunswick, Fredericton, NB, Canada

²³⁶Department of Ecology, Faculty of Veterinary Science, SZIE University, Budapest, Hungary

²³⁷Department of Ecology, University of Debrecen, Debrecen, Hungary

²³⁸Department of Ecology, Environment and Plant Sciences, Stockholm University, Stockholm, Sweden

²³⁹Instituto de Investigaciones y Recursos Biológicos Alexander von Humboldt, Bogotá, Colombia

²⁴⁰Institute of Natural and Environmental Sciences, University of Hyogo, Hyogo, Japan

²⁴¹Hiroshima UniversityLeading-program, Higashihiroshima, Kagamiyama, Japan

²⁴²Instituto de Investigaciones en Ecosistemas y Sustentabilidad, Universidad Nacional Autónoma de México, Morelia, México C.P., Mexico

²⁴³Department of Animal Ecology, Justus-Liebig-University, Giessen, Germany

²⁴⁴Escuela de Biologia, Universidad de Costa Rica, San Pedro, Costa Rica

²⁴⁵Biodiversity and Environmental Sustainability, Rohini, India

²⁴⁶Department of Environmental Studies, Shivaji College (University of Delhi), New Delhi, India

²⁴⁷Department of Ecology, Swedish University of Agricultural Sciences, Uppsala, Sweden

²⁴⁸School of Biological Sciences, Universiti Sains Malaysia, Minden, Malaysia

²⁴⁹Yukon Department of Environment, Whitehorse, YT, Canada

²⁵⁰Nature Conservation Foundation, Mysore, India

²⁵¹Cologne Biocenter, Zoological Institute, University of Cologne, Köln, Germany
²⁵²Department of Environmental & Natural Resources Management, University of Patras, Agrinio, Greece

²⁵³Centre for Tropical Environmental and Sustainability Science (TESS) & College of Marine and Environmental Sciences, James Cook University, Cairns, Qld, Australia

²⁵⁴School of Science and Technology, Pacific Adventist University, Port Moresby, Papua New Guinea

²⁵⁵Unit for Environmental Sciences and Management, North-West University, Potchefstroom, South Africa

²⁵⁶Department of Systematic and Evolutionary Botany, University of Zürich, Zürich, Switzerland

²⁵⁷Department of Ecology and Evolutionary Biology and Department of Geography and Planning, University of Toronto, Toronto, ON, Canada

²⁵⁸The Wilderness & Wildlife Conservation Trust, Colombo, Sri Lanka

²⁵⁹School of Biological Sciences, Plymouth University, Plymouth, UK

²⁶⁰Institute of Ecology and Evolution, University of Bern, Bern, Switzerland

²⁶¹Section Environnement, Développement durable et Territoire, Division Environnement et Territoire, Bundesamt für Statistik, Neuchâtel, Switzerland

²⁶²School of Forest Sciences, University of Eastern Finland, Joensuu, Finland

²⁶³Institute of Ecology, FB2, University of Bremen, Bremen, Germany

²⁶⁴Université Peleforo Gon Coulibaly, Korhogo, Ivory Coast

²⁶⁵Station d'Ecologie de Lamto, N'Douci, Ivory Coast

²⁶⁶Theoretical Evolutionary Ecology Group, Department of Animal Ecology and Tropical Biology, Biocenter, University of Würzburg, Würzburg, Germany

²⁶⁷Wildlife Conservation Society-India, National Centre for Biological Sciences, Bangalore, India

²⁶⁸Nature Science Initiative, Dehradun, India

²⁶⁹Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA, USA

²⁷⁰School of BioSciences, University of Melbourne, Melbourne, Vic., Australia

²⁷¹School of Agricultural, Forest and Food Sciences HAFL, Bern University of Applied Sciences, Zollikofen, Switzerland

²⁷²Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland

²⁷³Instituto Nacional de Tecnología Agropecuaria, EEA Bariloche, Bariloche, Argentina

²⁷⁴Department of Biology, Pennsylvania State University, University Park, PA, USA

²⁷⁵National Aviary, Allegheny Commons West, Pittsburgh, PA, USA

²⁷⁶Centre for Tropical Environmental and Sustainability Sciences, College of Marine and Environmental Science, James Cook University, Cairns, Qld, Australia

²⁷⁷Université Pierre-et-Marie-Curie, Paris, France

²⁷⁸Institute of Ecology and Environmental Sciences, Paris, France

²⁷⁹INRA, UR 406 Abeilles et Environnement, Avignon, France

²⁸⁰Department of Biology, San Francisco State University, San Francisco, CA, USA

²⁸¹Laboratoire de diagnostic en phytoprotection, Ministère de l'agriculture, des pêcheries et de l'alimentation du Québec, Ville de Québec, QC, Canada

²⁸²Research Unit Terrestrial Ecology, Ghent University, Ghent, Belgium

²⁸³Laboratorio de Recursos Agroforestales, Centro Austral de Investigaciones Científicas (CADIC), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Ushuaia, Argentina

²⁸⁴School of Biosciences, University of Melbourne, Parkville, Vic., Australia

²⁸⁵Purchase College (State University of New York), Purchase, NY, USA

²⁸⁶Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, China

²⁸⁷School of Biological Sciences, University of Canterbury, Christchurch, New Zealand

²⁸⁸The James Hutton Institute, Aberdeen, UK

²⁸⁹College of Resources and Environmental Sciences, China Agricultural University, Beijing, China

²⁹⁰Carste Ciência e Meio Ambiente, Floresta, Belo Horizonte, Brazil

²⁹¹TEHO Laboratory, Institute of Biology, University of Antioquia, Medellín, Colombia

²⁹²International Center for Agricultural Research in the Dry Areas (ICARDA), Amman Office, Amman, Jordan

²⁹³Animal and Rangeland Sciences Department, Oregon State University, Corvallis, OR, USA

²⁹⁴Department of Agroecology, Flakkebjerg Research Centre, Aarhus University, Slagelse, Denmark

²⁹⁵Department of Agroforestry Technology and Science and Genetics, School of Advanced Agricultural Engineering, Castilla La Mancha University, Albacete, Spain

²⁹⁶Unidad Académica de Turismo, Coordinación de Investigación y Posgrado, Universidad Autónoma de Nayarit, Tepic, Mexico

²⁹⁷Centro de Investigaciones Tropicales, Universidad Veracruzana, Xalapa, Mexico

²⁹⁸Graduate School of Agricultural Science, Kobe University, Kobe, Japan

²⁹⁹Department of Ecology, University of Debrecen, Debrecen, Hungary

³⁰⁰Center for Conservation Innovation, San Jose Tagaytay City, Philippines

³⁰¹Biology Department, De La Salle University, Manila, Philippines
³⁰²The New Zealand Institute for Plant & Food Research Limited, Auckland, New Zealand

³⁰³National Museums of Kenya, Nairobi, Kenya

³⁰⁴Center for Macroecology, Evolution and Climate, Natural History Museum of Denmark, University of Copenhagen, Copenhagen Ø, Denmark

³⁰⁵Red de Biología y Conservación de Vertebrados, Instituto de Ecología A.C., Xalapa, Mexico

³⁰⁶Department of Geography, University of Bergen, Bergen, Norway

³⁰⁷Department of Geography, University of Wisconsin-Madison, Madison, WI, USA

³⁰⁸School of Biology, University of Leeds, Leeds, West Yorkshire, UK

³⁰⁹Marshall Agroecology Ltd, Barton, Winscombe, UK

³¹⁰Universidad Nacional de Colombia, Ciudad Universitaria, Bogotá, Colombia

³¹¹Basque Centre for Climate Change - BC3, Bilbao, Spain

³¹²School of Biological Sciences, The University of Queensland, Brisbane, Qld, Australia

³¹³Associate of Arts Program, University of Delaware - Wilmington, Wilmington, DE, USA

³¹⁴Department of Entomology and Wildlife Ecology, University of Delaware, Newark, DE, USA

³¹⁵Department of Entomology, University of California, Riverside, CA, USA

³¹⁶Centre for Mined Land Rehabilitation, The University of Queensland, Brisbane, Qld, Australia

³¹⁷Departamento de Biogeografía y Cambio Global, Museo Nacional de Ciencias Naturales (CSIC), Madrid, Spain

³¹⁸Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT, USA

³¹⁹Museo de Historia Natural "Vera Alleman Haeghebaert", Universidad Ricardo Palma, Lima 33, Peru

³²⁰Centro Iberoamericano de la Biodiversidad (CIBIO), Universidad de Alicante, Alicante, Spain

³²¹Department of Ecology, Swedish University of Agricultural Sciences, Grimsö Wildlife Research Station, Riddarhyttan, Sweden

³²²Rainforest Alliance, New York, NY, USA

³²³Department of Natural Resources, Cornell University, Ithaca, NY, USA

³²⁴Department of Natural Resources & Environmental Sciences, University of Illinois, Urbana, IL, USA

³²⁵Universidad Industrial de Santander, Bucaramanga, Colombia

³²⁶School of Plant Biology, University of Western Australia, Crawley, WA, Australia

³²⁷Lab. Ecotono, INIBIOMA (Universidad Nacional del Comahue-CONICET), Bariloche, Argentina

³²⁸Botany Department, National Museums of Kenya, Nairobi, Kenya

³²⁹Department of Wildlife Management, University of Eldoret, Eldoret, Kenya

³³⁰Department of Biology and Ecology, Faculty of Sciences, University of Novi Sad, Novi Sad, Serbia

³³¹School of Biological Sciences, Universiti Sains Malaysia, Penang, Malaysia

³³²El Colegio de la Frontera Sur, Ecología Evolutiva y Conservación, San Cristóbal de las Casas, Mexico

³³³Nature Kenya, Nairobi, Kenya

³³⁴WWF, Washington, DC, USA

³³⁵Independent Research Scholar, New Delhi, India

³³⁶Avian Diversity and Bioacoustic Lab, Department of Zoology, Gurukula Kangri University, Haridwar, India

³³⁷Graduate School of Bioagricultural Sciences, Nagoya University, Nagoya, Japan

³³⁸Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Menglun, China

³³⁹Environmental Futures Research Institute, and Griffith School of Environment, Griffith University, Nathan, Brisbane, Qld, Australia

³⁴⁰College of Bioresource Science, Nihon University, Fujisawa, Japan

³⁴¹Forestry and Forest Products Research Institute, Tsukuba, Japan

³⁴²Laboratorio de Investigaciones en Abejas (Departamento de Biología), Universidad Nacional de Colombia, Bogotá, Colombia

³⁴³Laboratorio de Información Geográfica, El Colegio de la Frontera Sur (ECOSUR), San Cristóbal de las Casas, Mexico

³⁴⁴CMRPZ - I.E. Plaza Bonita, San Andrés de Sotavento (Córdoba), Colombia

³⁴⁵BirdLife International - Africa Partnership Secretariat, Nairobi, Kenya

³⁴⁶Ornithology Section, National Museums of Kenya, Nairobi, Kenya

³⁴⁷Department of Zoology, University of British Columbia, Vancouver, BC, Canada

³⁴⁸Institut de Systématique, Évolution, Biodiversité, ISYEB - UMR 7205 - CNRS, MNHN, UPMC, EPHE, Muséum national d'Histoire naturelle, Sorbonne Universités, Paris, France

³⁴⁹Department of Biology/Biodiversity, Lund University, Lund, Sweden

³⁵⁰Department of Biosciences, University of Helsinki, Helsinki, Finland

³⁵¹Department of Environmental Sciences, University of Helsinki, Helsinki, Finland
³⁵²School of Biology, The University of Nottingham, University Park, Nottingham, UK

³⁵³Laboratorio de Zoología y Ecología Acuática - LAZOEA, Universidad de Los Andes, Bogotá, Colombia

³⁵⁴School of Forestry, University of Canterbury, Christchurch, New Zealand

³⁵⁵BIO-Diverse, Bonn, Germany

³⁵⁶Department of Wildlife, Fish and Conservation Biology, University of California, Davis, Davis, CA, USA

³⁵⁷IUCN-Centre for Mediterranean Cooperation, Campanillas, Málaga, Spain

³⁵⁸Oxford University Centre for the Environment, University of Oxford, Oxford, UK

³⁵⁹Natural Resources and the Environment, CSIR, Stellenbosch, South Africa

³⁶⁰Plant Conservation Unit, Biological Sciences, University of Cape Town, Rondebosch, South Africa

³⁶¹International Programme Office (IPO), Vice Chancellor's Office, Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana

³⁶²Naturschutz - Planung und Beratung, Wiesendangen, Switzerland

³⁶³Department of Wildlife and Range Management, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana

³⁶⁴Forestry Research Institute of Ghana, Kumasi, Ghana

³⁶⁵Department of Animal & Environmental Biology, University of Benin, Benin City, Nigeria

³⁶⁶Department of Genetics, Evolution and Environment, University College London, London, UK

³⁶⁷The Royal Society for the Protection of Birds (RSPB), Sandy, Bedfordshire, UK

³⁶⁸Laboratorio de Ecología del Paisaje, Facultad de Ciencias Forestales, Universidad de Concepción, Concepción, Chile

³⁶⁹Indian Institute of Science, Bangalore, India

³⁷⁰Laboratorio Ecotono, CONICET-INIBIOMA, Universidad Nacional del Comahue, Bariloche, Argentina

³⁷¹Laboratorio de Investigaciones en Abejas, LABUN, Universidad Nacional de Colombia, Bogotá D.C., Colombia

³⁷²Lancaster Environment Centre, Lancaster University, Lancaster, UK

³⁷³Universidade Federal do Pará (UFPA), Núcleo de Altos Estudos Amazonicos (NAEA), Belém, Brazil

³⁷⁴German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, Leipzig, Germany

³⁷⁵Department of Plant Biology and Ecology, Faculty of Science and Technology, University of the Basque Country, Leioa, Spain

³⁷⁶IKERBASQUE. Basque Foundation for Science, Bilbao, Spain

³⁷⁷Instituto de Diversidad y Ecología Animal (IDEA, CONICET-UNC) and Centro de Zoología Aplicada, FCEFyN, Universidad Nacional de Córdoba, Córdoba, Argentina

³⁷⁸IRD, UMR AMAP, TA A51/PS2, Montpellier cedex 05, France

³⁷⁹French Institute of Pondicherry, UMIFRE 21 CNRS-MAEE, Puducherry, India

³⁸⁰School of Environmental Sciences, University of East Anglia, Norwich, UK

³⁸¹National Institute of Agricultural Technology (INTA), Río Gallegos, Argentina

³⁸²National University of Southern Patagonia (UNPA), Río Gallegos, Argentina

³⁸³National Commission of Scientist Research and Technology (CONICET), Buenos Aires, Argentina

³⁸⁴Laboratory of Biogeography & Ecology, Department of Geography, University of the Aegean, Mytilene, Greece

³⁸⁵Department of Animal Ecology and Tropical Biology, Biocenter, University of Würzburg, Würzburg, Germany

³⁸⁶University of Cambridge, Cambridge, UK

³⁸⁷Conservation Science Group, Department of Zoology, University of Cambridge, Cambridge, UK

³⁸⁸Systematics and Evolution Laboratory, Department of Biology, Western Kentucky University, Bowling Green, KY, USA

³⁸⁹Department of Natural Resource Ecology and Management, Iowa State University, Ames, IA, USA

³⁹⁰Facultad de Recursos Naturales, Escuela de Ciencias Ambientales, Laboratorio de Planificación Territorial, Universidad Católica de Temuco, Temuco, Chile

³⁹¹Biología y Conservación de Vertebrados, Instituto de Ecología A.C., El Haya, Xalapa, Mexico

³⁹²Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain

³⁹³Laboratorio de Entomología Ecológica, Departamento de Biología, Facultad de Ciencias, Universidad de La Serena, La Serena, Chile

³⁹⁴Albertine Rift Program, Wildlife Conservation Society, Kampala, Uganda

³⁹⁵IFEVA/Cátedra de Producción Vegetal, Departamento de Producción Vegetal, Facultad de Agronomía, Universidad de Buenos Aires/CONICET., Buenos Aires, Argentina

³⁹⁶Directora del Programa Conservación de Biodiversidad en Bosques Subtropicales, Cátedra de Desarrollo Sustentable y Biodiversidad, Facultad de Ciencias Agrarias, Universidad Nacional de Jujuy, CIT-Jujuy CONICET, Fundación CEBio, San Salvador de Jujuy, Argentina

³⁹⁷Departament de Ciències Ambientals, Universitat de Girona, Girona, Spain

³⁹⁸Entomology, Cornell University, Ithaca, NY, USA

³⁹⁹Botany, School of Natural Sciences, Trinity College Dublin, Dublin 2, Ireland
⁴⁰⁰Center for Environmental Sciences and Engineering & Department of Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT, USA

⁴⁰¹MARETEC, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal

⁴⁰²CREA-ABP, Consiglio per la ricerca in agricoltura e l'analisi dell'economia agraria, Centro di ricerca per l'agrobiologia e la pedologia, Firenze, Italy

⁴⁰³Ecosystem Management, School of Environment and Rural Science, University of New England, Armidale, NSW, Australia

⁴⁰⁴Escuela de Biología, Universidad Industrial de Santander, Bucaramanga, Colombia

⁴⁰⁵National Center for Ecological Analysis and Synthesis, University of California, Santa Barbara, Santa Barbara, CA, USA

⁴⁰⁶Department of Bioscience, Aarhus University, Aarhus C, Denmark

⁴⁰⁷The Royal Society for the Protection of Birds (RSPB), Edinburgh Park, Edinburgh, UK

⁴⁰⁸Center for Conservation and Sustainable Development, Missouri Botanical Garden, Saint Louis, MO, USA

⁴⁰⁹Departamento de Biologia, Universidade Federal de Sergipe, São Cristóvão/Se, Brazil

⁴¹⁰Life Sciences Department, University of Alcalá, Alcalá de Henares, Spain

⁴¹¹Entomology Collection, Systematics and Biogeography Laboratory, School of Biology, Industrial University of Santander, Bucaramanga, Colombia

⁴¹²Percy FitzPatrick Institute of African Ornithology, DST/NRF Centre of Excellence, University of Cape Town, Rondebosch, Cape Town, South Africa

⁴¹³School of Animal, Plant and Environmental Sciences, University of the Witwatersrand, Wits, South Africa

⁴¹⁴Centro de Ciências Biológicas e da Saúde, Universidade Federal de Mato Grosso do Sul, Campo Grande, Brazil

⁴¹⁵Department of Biological Sciences, Brock University, St. Catharines, ON, Canada

⁴¹⁶Edinburgh, UK

⁴¹⁷Luquillo LTER, Institute for Tropical Ecosystem Studies, College of Natural Sciences, University of Puerto Rico at Rio Piedras, San Juan, PR, USA

⁴¹⁸Escuela Nacional de Estudios Superiores, Universidad Nacional Autónoma de México, Morelia, Mexico

⁴¹⁹Science and Conservation Division, Department of Parks and Wildlife, Manjimup, WA, Australia

⁴²⁰PROPLAME-PRHIDEB-CONICET, Departamento de Biodiversidad y Biología Experimental, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, (CP1428EHA) Ciudad Autónoma de Buenos Aires, Argentina

⁴²¹ECT Oekotoxikologie GmbH, Flörsheim am Main, Germany

⁴²²LOEWE Biodiversity and Climate Research Centre BiK-F, Frankfurt/Main, Germany

⁴²³Facultad de Ciencias Ambientales, Universidad de Ciencias Aplicadas y Ambientales U.D.C.A, Bogotá, Colombia

⁴²⁴Catedras CONACYT, CIIDIR, Unidad Oaxaca, IPN, Santa Cruz Xoxocotlán, Mexico

⁴²⁵Universidad de Ciencias Aplicadas y Ambientales U.D.C.A., Bogotá, Colombia

⁴²⁶School of Natural Resources and Environment, University of Michigan, Ann Arbor, MI, USA

⁴²⁷Department of Environmental Sciences, University of Virginia, Charlottesville, VA, USA

⁴²⁸Blandy Experimental Farm, Boyce, VA, USA

⁴²⁹Département des sciences biologiques (SB), Universitédu Québec à Montréal (UQÀM), Montréal, QC, Canada

⁴³⁰Facultad de Ciencias, Universidad de Chile, Santiago, Chile

⁴³¹School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK

⁴³²Institute of Silviculture and Forest Protection, University of West Hungary, Sopron, Hungary

⁴³³Red de Ecología Funcional, Instituto de Ecología A.C. Carretera antigua a Coatepec, El Haya, Xalapa, Mexico

⁴³⁴Biology Centre CAS, Institute of Entomology, Ceske Budejovice, Czech Republic

⁴³⁵Faculty of Science, University of South Bohemia, Ceske Budejovice, Czech Republic

⁴³⁶Bishop's University, Sherbrooke, QC, Canada

⁴³⁷CSIRO, Dutton Park, Qld, Australia

⁴³⁸Naturalis Biodiversity Center, CR Leiden, The Netherlands

⁴³⁹Institute for Tropical Biology and Conservation, Universiti Malaysia Sabah, Jalan UMS, Kota Kinabalu, Malaysia

⁴⁴⁰Biocentre Klein Flottbek & Botanical Garden, University of Hamburg, Hamburg, Germany

⁴⁴¹Center for Development Research (ZEF), University of Bonn, Bonn, Germany

⁴⁴²Chair for Landscape Management, University of Freiburg, Freiburg, Germany

⁴⁴³AgResearch Limited, Lincoln Research Centre, Christchurch, New Zealand

⁴⁴⁴Institute for Ecology, Evolution and Diversity, Goethe University Frankfurt, Frankfurt am Main, Germany

⁴⁴⁵Biology and Biomedical Sciences Division, University of Brighton, Brighton, UK

⁴⁴⁶Charles Darwin University, Brinkin, NT, Australia

⁴⁴⁷Lawrence University, Appleton, WI, USA

⁴⁴⁸School of Natural Resources and Extension, University of Alaska Fairbanks, Fairbanks, AK, USA

⁴⁴⁹Center for Ecology, Development and Research, Dehradun, India
⁴⁵⁰School of Life Sciences, University of KwaZulu-Natal, Durban, South Africa

⁴⁵¹Department of Ecology and Natural Resource Management (INA), Norwegian University of Life Sciences (NMBU), Ås, Norway

⁴⁵²Museum of Natural Science and Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, USA

⁴⁵³Baton Rouge, LA, USA

⁴⁵⁴Department of Life Sciences, Ben-Gurion University of the Negev, Be'er Sheva, Israel

⁴⁵⁵The Yerucham Center of Ornithology and Ecology, Yerucham, Israel

⁴⁵⁶Instituto de Ciências Biológicas, Universidade Federal do Pará, Belém, Brazil

⁴⁵⁷Organic Research Centre, Elm Farm, Newbury, UK

⁴⁵⁸United States Department of Agriculture, South San Francisco, CA, USA

⁴⁵⁹Universidad Nacional de Colombia, Sede Medellín, Medellín, Colombia

⁴⁶⁰Department of Biological Sciences, National University of Singapore, Singapore, Singapore

⁴⁶¹Ecología de Comunidades Áridas y Semiaridas (EComAS), Departamento de Recursos, Facultad de Ciencias Exactas y Naturales, UNLPam., Santa rosa, La Pampa, Uruguay

⁴⁶²Gobierno Autónomo Departamental Santa Cruz, Santa Cruz de la Sierra, Bolivia

⁴⁶³Université du Québec à Rimouski, Centre for Northern Research, Centre for Forest Studies, Rimouski, QC, Canada

⁴⁶⁴School of Environmental Studies, University of Victoria, Victoria, BC, Canada

⁴⁶⁵Museu de Ciències Naturals de Granollers, Granollers, Barcelona, Spain

⁴⁶⁶School of Renewable Natural Resources, Louisiana State University Agricultural Center, Baton Rouge, LA, USA

⁴⁶⁷Biological Dynamics of Forest Fragments Project, Instituto Nacional de Pesquisas da Amazônia, Manaus, Brazil

⁴⁶⁸Department of Natural Resources and Environmental Management, University of Hawaii, Manoa, Honolulu, HI, USA

⁴⁶⁹Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Chaoyang District, Beijing, China

⁴⁷⁰State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Haidian District, Beijing, China

⁴⁷¹Institute of Zoology, University of Natural Resources and Life Sciences, Vienna, Austria

⁴⁷²Department of Environmental Science and Policy, Drake University, Des Moines, IA, USA

⁴⁷³Department of Biology, Hong Kong Baptist University, Kowloon Tong, Hong Kong SAR, China

⁴⁷⁴Zoological Division, Research Center For Biology, The Indonesian Institute of Sciences, Cibinong, Bogor, Indonesia

⁴⁷⁵Section for Ecoinformatics & Biodiversity, Department of Bioscience, Aarhus University, Aarhus C, Denmark

⁴⁷⁶Department of Zoology, Institute of Ecology and Earth Sciences, University of Tartu, Tartu, Estonia

⁴⁷⁷School of Ecosystem and Forest Science, Faculty of Science, The University of Melbourne, Richmond, Vic., Australia

⁴⁷⁸Department of Biology, Saint Louis University, St. Louis, MO, USA

⁴⁷⁹MTA-DE Biodiversity and Ecosystem Services Research Group, Debrecen, Hungary

⁴⁸⁰Insect Ecology Group, Department of Zoology, University of Cambridge, Cambridge, UK

⁴⁸¹Centre for Integrative Ecology, School of Biological Sciences, University of Canterbury, Christchurch, New Zealand

⁴⁸²Instituto Neotropical: Pesquisa e Conservação, Curitiba, Brazil

⁴⁸³Department of Ecology and Territory, School of Environmental and Rural Studies, Pontifícia Universidade Javeriana, Bogota, Colombia

⁴⁸⁴Naturhistorisches Museum Basel, Leiter Biowissenschaften, Basel, Switzerland

⁴⁸⁵NERC Centre for Ecology & Hydrology, Bush Estate, Penicuik, Edinburgh, UK

⁴⁸⁶Instituto de Biologia, Universidade Federal de Uberlândia (UFU), Uberlândia, Brazil

⁴⁸⁷Institute of Biodiversity and Ecosystem Research, Bulgarian Academy of Science, Sofia, Bulgaria

⁴⁸⁸Division Forest, Nature, and Landscape, Department of Earth & Environmental Sciences, KU Leuven, Leuven, Belgium

⁴⁸⁹Museu Nacional de História Natural e da Ciência, Borboletário - Depart. Zoologia, Lisboa, Portugal

⁴⁹⁰Departamento de Ciencias Químico-Biológicas, Universidad de las Américas Puebla, Cholula, Mexico

⁴⁹¹Departamento de Gestión Agraria, Universidad de Santiago de Chile, Santiago, Chile

⁴⁹²Den Haag, The Netherlands

⁴⁹³Royal Museum for Central Africa - Joint Experimental Molecular Unit, Tervuren, Belgium

⁴⁹⁴Vietnam National Museum of Nature, Vietnam Academy of Science and Technology, Cau Giay, Hanoi, Vietnam

⁴⁹⁵Botany Department, University of Otago, Dunedin, New Zealand

⁴⁹⁶School for Resource and Environmental Studies, Faculty of Management, Dalhousie University, Halifax, NS, Canada

⁴⁹⁷Key Laboratory of Protection and Development Utilization of Tropical Crop Germplasm Resource, Ministry of Education, College of Horticulture and Landscape Agriculture, Hainan University, Haikou, China
**Correspondence**

Lawrence N. Hudson, Department of Life Sciences, Natural History Museum, London, UK.

Email: l.hudson@nhm.ac.uk

**Funding information**

U.K. Natural Environment Research Council, Grant/Award Number: NE/J011193/2 and NE/L002515/1; United Nations Environment Program World Conservation Monitoring Centre; Biotechnology and Biological Sciences Research Council, Grant/Award Number: BB/F017324/1; Hans Rausing PhD Scholarship; COLCIENCIAS (Departamento Administrativo de Ciencia, Tecnología e Innovación de Colombia)

**Abstract**

The PREDICTS project—Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (www Predicts.org.uk)—has collated from published studies a large, reasonably representative database of comparable samples of biodiversity from multiple sites that differ in the nature or intensity of human impacts relating to land use. We have used this evidence base to develop global and regional statistical models of how local biodiversity responds to these measures. We describe and make freely available this 2016 release of the database, containing more than 3.2 million records sampled at over 26,000 locations and representing over 47,000 species. We outline how the database can help in answering a range of questions in ecology and conservation biology. To our knowledge, this is the largest and most geographically and taxonomically representative database of spatial comparisons of biodiversity that has been collated to date; it will be useful to researchers and international efforts wishing to model and understand the global status of biodiversity.

**KEYWORDS**

data sharing, global biodiversity modeling, global change, habitat destruction, land use

# 1 | INTRODUCTION

Many indicators are available for tracking the state of biodiversity through time, for example, in order to assess progress toward goals such as the Convention on Biological Diversity's 2010 target or the newer Aichi Biodiversity Targets (Pereira et al., 2013; Tittensor et al., 2014). Most of the available indicators are taxonomically or ecologically narrow in scope, and many are based on the global status of species (e.g., Butchart et al., 2010; Tittensor et al., 2014), because of

the finality of extinction. However, using a more representative set of taxa and considering local biodiversity offers several advantages. First, average responses of species to human impacts typically vary among higher taxa and ecological guilds (Lawton et al., 1998; McKinney, 1997; Newbold et al., 2014; WWF International, 2014), meaning that indicators need to be broadly based and as representative as possible, if they are to be used as proxies for biodiversity as a whole. Second, the taxa for which most data on trends are available (typically, charismatic groups such as birds or butterflies) are not always the most

<footnote n="498">⁴⁹⁸College of Life Sciences, Zhejiang University, Hangzhou, China</footnote>

<footnote n="499">⁴⁹⁹Department of Biology, John Carroll University, University Heights, OH, USA</footnote>

<footnote n="500">⁵⁰⁰The Environment Institute and School of Earth and Environmental Sciences, The University of Adelaide, Adelaide, SA, Australia</footnote>

<footnote n="501">⁵⁰¹Environmental Futures Research Institute, Griffith University, Brisbane, Qld, Australia</footnote>

<footnote n="502">⁵⁰²Department of Environmental and Natural Resources, Presbyterian University College, Akropong Akuapem, Ghana</footnote>

<footnote n="503">⁵⁰³School of Natural Sciences and Psychology, Liverpool John Moores University, Liverpool, UK</footnote>

<footnote n="504">⁵⁰⁴Center for Environmental Sciences & Engineering, University of Connecticut, Storrs, CT, USA</footnote>

<footnote n="505">⁵⁰⁵Department of Ecology & Evolutionary Biology, University of Connecticut, Storrs, CT, USA</footnote>

<footnote n="506">⁵⁰⁶Institute for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam, GE Amsterdam, The Netherlands</footnote>

<footnote n="507">⁵⁰⁷NERC Centre for Ecology & Hydrology, Crowmarsh Gifford, Wallingford, UK</footnote>

<footnote n="508">⁵⁰⁸Institute of Biodiversity Science, School of Life Sciences, Fudan University, Shanghai, China</footnote>

<footnote n="509">⁵⁰⁹International Institute of Tropical Forestry, USDA Forest Service, Sabana Field Research Station, Luquillo, PR, USA</footnote>

<footnote n="510">⁵¹⁰Tsukuba University, Ibaraki, Japan</footnote>

<footnote n="511">⁵¹¹School of Biological Sciences, University of East Anglia, Norwich Research Park, Norwich, UK</footnote>

<footnote n="512">⁵¹²State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China</footnote>

<footnote n="513">⁵¹³A. N. Severtsov Institute of Ecology and Evolution, Moscow, Russia</footnote>

<footnote n="514">⁵¹⁴Integrated Environmental Consultants Namibia (IECN), Windhoek, Namibia</footnote>

<footnote n="515">⁵¹⁵Guangdong Entomological Institute/South China Institute of Endangered Animals, Guangzhou, China</footnote>

<footnote n="516">⁵¹⁶Computational Ecology and Environmental Science, Microsoft Research, Cambridge, UK</footnote>
important for the continued functioning of ecosystems and delivery of ecosystem services (Norris, 2012). Third, although many of the ultimate drivers behind biodiversity loss are global, the most important pressure mechanisms usually act much more locally (Brook, Ellis, Perring, Mackay, & Blomqvist, 2013). Fourth, most ecosystem services and their underpinning processes are mediated by local rather than global biodiversity (Cardinale et al., 2012; Grime, 1998): It is local rather than global functional diversity, for example, that determines how ecosystems function in a given set of conditions (Steffen et al., 2015). Finally, presence/absence and especially abundance of species at a site respond more rapidly to disturbance than extent of geographic distribution or global/national extinction risk (Balmford, Green, & Jenkins, 2003; Collen et al., 2009; Hull, Darroch, & Erwin, 2015), so local changes are likely to be detected before large global changes or extinction.

For these reasons, there is a need to model the response of local biodiversity to human pressures and, thus, to estimate biodiversity changes at local scales, but across a wide spatial domain (ideally globally) and for a wide range of taxa. We therefore need comparable high-quality data on local biodiversity at different levels of human pressure, from many different taxa and regions. At present, spatial comparisons of how biodiversity responds to variation in pressures provide the only feasible way to collate a large, globally representative evidence base and to model responses to human impacts. Although large temporal datasets are available (e.g., Butchart et al., 2004; Collen et al., 2009; Dornelas et al., 2014; Vellend et al., 2013), they may not be sufficiently representative of anthropogenic pressures for the trends they show to be taken at face value (Gonzalez et al., 2016). Furthermore, in the absence of contemporaneous site-specific information about pressures, it is not straightforward to use these data to model how biodiversity responds to pressures or to project changes into the future (but see Visconti et al., 2015). Spatially extensive field data of suitable quality and resolution are time-consuming and expensive to collect. The most convenient and readily available source of suitable biodiversity data is the published literature: Thousands of published papers are based on datasets that would be of value to global modeling efforts. However, it has been rare for such papers to publish data in full, even as supporting information, meaning that many potentially valuable datasets are “dark data” (Hampton et al., 2013), effectively at risk of being lost to science if they have not been lost already.

Since 2012, the PREDICTS project has been collating data on local biodiversity at different levels of human pressure from published papers, where necessary contacting those papers' corresponding authors to request the underlying biodiversity data, species' identities, and precise sampling locations. We have enhanced the collated data by scoring site characteristics relating to human pressures such as the predominant land use and how intensively the land is used by humans. We also used the geographical coordinates of the sites to match them to a number of published spatially explicit datasets. The database has already been used to conduct global (e.g., Newbold et al., 2015; Newbold, Hudson, Arnell, et al., 2016), regional (De Palma et al., 2016) and national (Echeverría-Londoño et al., 2016) analyses of the responses of local biodiversity to land use and related human

pressures. The database was first described by Hudson et al. (2014) who published an interim version (March 2014) of the site-level metadata along with a detailed description of how the database has been collated and validated. Since that time, the database has nearly doubled in size. Here, we describe the status of the database and make available the full species-level data themselves (not just the site metadata previously released) to facilitate other research, especially into human impacts on ecological assemblages. We also include suggestions for how the database can be used.

## 2 | METHODS

We sought datasets describing the abundance or occurrence of species, or the diversity of ecological assemblages of species at multiple sites in different land uses or at different levels of other human pressures (e.g., differing levels of land-use intensity). Data were primarily collated through subprojects on particular regions, land uses, or taxa. We also made general requests for data at conferences and through published articles (Hudson, Newbold, et al., 2013; Hudson et al., 2014; Newbold et al., 2012). Through the course of the project, searches were increasingly targeted toward under- or unrepresented regions, biomes, or taxa, in order to mitigate biased coverage in the literature.

To be included in the database, data were required to meet the following criteria: (1) the dataset was part of a published work, or the sampling methods were published; (2) the same sampling procedure was carried out at each site within each study (sampling effort was permitted to vary so long as it was recorded for each site); and (3) we could acquire the geographical coordinates of each sampled site. Where the author of the original publication was unable to supply the geographical coordinates, sites were georeferenced from maps in the publication (Hudson et al., 2014). Sites' land use—primary vegetation, secondary vegetation (divided according to stage of recovery into mature, intermediate and young; or indeterminate where information on stage was unavailable), plantation forest, cropland, pasture and urban—and, within each land-use class, intensity—minimal, light and intense—were classified from the description given in the source publication or information subsequently provided by data contributors (see Hudson et al., 2014 for full details). These land-use categories were chosen to be as compatible as possible with those used in the harmonized land-use scenarios for 1500–2100 (Hurtt et al., 2011) in order to facilitate spatial and temporal projections of modeled land-use effects on biodiversity (e.g., Newbold et al., 2015). For some sites, land use and/or use intensity could not be established, so were given missing values.

The data were arranged in a hierarchical structure. The data from an individual published work, typically a published paper, constituted a “DataSource.” Where different sampling methods were used within aDataSource, for example, because different taxonomic groups were collected, and the data were made available separately, the data were divided into separate “Studies.” Data from a givenDataSource were also split into multiple Studies if they covered large geographic areas (e.g., several countries), to reduce the effect of biogeographic differences within Studies. Each Study contained a set of sampled “Sites”
<figure><img src="image_3.png" /><figcaption>**FIGURE 1** Sampling locations. Map colors indicate biomes, taken from the Terrestrial Ecoregions of the World dataset (The Nature Conservancy, 2009), shown in a geographic (WGS84) projection. Circle radii are proportional to $log_{10}$ of the number of samples at that Site. All circles have the same degree of partial transparency. Sites added to the database since Hudson et al. (2014) are shown in pink</figcaption></figure>

and “Taxa”; at each Site a set of “Measurements” (typically the abundance or occurrence of a set of taxa) were taken. The provided database extracts contain, for each Site, the raw measurement values, the sampling efforts and, where relevant, the effort-corrected abundance values (corrected across Sites within a Study by dividing the abundance measurement by sampling effort, assuming that sampled abundances increase linearly with sampling effort, after first rescaling effort values within each Study to a maximum value of one). The measurements were not corrected for different detectability (Hayward et al., 2015; MacKenzie et al., 2002).

It is important to note that the data in the database are often not exactly the same as those used in the source papers. Numbers of sites may differ because datasets provided may have been partial or included extra sites, or because we have aggregated or disaggregated data differently. Likewise, numbers of taxa may differ because of variation or because more data were provided than had been used in the source paper. Because our focus was to make these data as useful as possible for PREDICTS analyses, rather than to act as a repository for datasets from previous publications, it will often not be possible to use these data to replicate the analyses presented in the source papers.

We were limited by the rate at which we could process new data because so many datasets were contributed. This led to the development of a backlog, which we had to clear by the end of the first phase of funding for PREDICTS. During this stage of the project, in order to process all the datasets in hand within the time available, we focused our

efforts on the fields shown to be most important in our models to that point (De Palma et al., 2015; Newbold et al., 2014, 2015). As a result, DataSources processed since early 2015 often lack data for some fields, including coordinate precision and maximum linear extent; details of the potentially affected fields are listed in Supporting Information.

Team members were trained in how to score datasets received, using written definitions and descriptions of fields and terms, as well as practice datasets. All data underwent basic validation checks to ensure values entered in each field were appropriate (Hudson et al., 2014). Geographical coordinates were visually inspected on a map after entry into the database, and our software automatically detected coordinates falling outside of the expected country (e.g., because latitude and longitude values were accidentally swapped). For the calculation of biodiversity metrics such as species richness, we accepted the identifications of species provided by the authors of the source publications; these were determined at the time of the original research, and so will not reflect subsequent taxonomic changes or re-identifications. We also matched taxonomic names to the Catalogue of Life 2013 checklist (COL; Roskov et al., 2013), allowing us to validate many of the names, assess taxonomic coverage and relate measurements to species-level datasets such as those describing ecological traits. We make available both the original species classifications and those from COL (field names are given in Supporting Information). We reviewed and corrected a number of potential error cases, such as names without a matching COL record, and names for which the higher taxonomic
<figure><img src="image_4.png" /><figcaption>**FIGURE 2** Coverage of biomes. The percentage of Studies (a and b), Sites (c and d), and samples (e and f) against percentages of terrestrial NPP (Net Primary Productivity, computed as in Hudson et al., 2014; a, c, and e) and terrestrial area (b, d, and f). Biome codes and colors are as in Figure 1</figcaption></figure>

rank of the matching COL record was unexpected (e.g., a COL record for a true fly within a Study that examined birds). Many more validation checks were applied; a complete description is in Hudson et al. (2014).

# 3 | RESULTS

## 3.1 | Geographical coverage

This release of the PREDICTS database contains 3,250,404 records, from 26,114 sampled Sites (Figure 1), collated from 480 DataSources

and 666 Studies. The data represent all of the world's 14 terrestrial biomes, in approximate proportion to their contribution to global total primary productivity (Figure 2). The sampled Sites span 94 of the world's countries (including all 17 megadiverse countries; Mittermeier, Gil, & Mittermeier, 1997), 281 of the 814 terrestrial ecoregions (The Nature Conservancy 2009) and 32 of Conservation International's 35 biodiversity hotspots (Myers, Mittermeier, Mittermeier, da Fonseca, & Kent, 2000; circles on Figure 3). Although the database focuses on land use, it also includes data from regions that have so far seen relatively little land-use change, such as some
<figure><img src="image_5.png" /></figure>

**FIGURE 3** Numbers of Sites against the areas of biodiversity hotspots and of high biodiversity wilderness areas (HBWAs). Hotspots are shown by circles and HBWAs by squares; symbols are colored by the predominant biogeographic realm in which they fall. Hotspots are 1 California Floristic Province, 2 Madrean Pine-Oak Woodlands, 3 Atlantic Forest, 4 Caribbean Islands, 5 Cerrado, 6 Chilean Winter Rainfall and Valdivian Forests, 7 Mesoamerica, 8 Tropical Andes, 9 Tumbes-Choco-Magdalena, 10 Irano-Anatolian, 11 Japan, 12 Mediterranean Basin, 13 Mountains of Central Asia, 14 Mountains of Southwest China, 15 Cape Floristic Region, 16 Coastal Forests of Eastern Africa, 17 Eastern Afromontane, 18 Guinean Forests of West Africa, 19 Madagascar and the Indian Ocean Islands, 20 Maputaland-Pondoland-Albany, 21 Succulent Karoo, 22 Himalaya, 23 Indo-Burma, 24 Philippines, 25 Sundaland, 26 Western Ghats and Sri Lanka, 27 East Melanesian Islands, 28 Forests of East Australia, 29 New Zealand, 30 Southwest Australia, 31 Wallacea, 32 Polynesia-Micronesia and HBWAs are 33 Amazonia, 34 Congo Forests, 35 New Guinea, 36 North American Deserts. Unrepresented are the hotspots Caucasus, Horn of Africa, New Caledonia and the HBWA Miombo-Mopane Woodlands and Savannas

high biodiversity wilderness areas (Mittermeier et al., 2003; squares on Figure 3).

## 3.2 | Taxonomic coverage

Records in the PREDICTS database represent 47,044 species (see Hudson et al., 2014 for how species numbers are estimated in the face of imprecise taxon names), which is over 2% of the number thought to have been formally described (Chapman, 2009)—29,737 animals, 15,545 plants, 1,759 fungi, and three protists. The taxonomic distribution of taxa in the database is in rough proportion to the numbers of described species in major taxonomic groups of animals and plants (Figure 4), and the data represent more than 1% as many species as have been described in the following groups: Amphibia, Arachnida, Archaeognatha, Ascomycota, Aves, Basidiomycota, Bryophyta, Chilopoda, Coleoptera, Collembola, Dermaptera, Diptera, Embioptera, Ferns and allies, Glomeromycota, Gymnosperms, Hemiptera, Hymenoptera, Isoptera, Lepidoptera, Magnoliophyta, Mammalia, Mantodea, Mecoptera, Neuroptera, Odonata, Onychophora, Orthoptera, Reptilia, Symphyla and Zoraptera (Figure 4). Vertebrates— and especially birds—are overrepresented owing to biases in the published literature (Figure 4), but less so than in many other data compilations (e.g., over half of the records currently in the Global Biodiversity Information Facility [GBIF] are of birds; www.gbif.org, accessed in April 2016). Most Studies in the PREDICTS database sampled at least multiple families, if not multiple orders, classes, phyla,

or even kingdoms (Figure 5). However, some Studies sampled only a single family, genus, or even species (Figure 5).

## 3.3 | Temporal coverage

We focused primarily on data sampled since 2000 because most global layers describing human pressure are collected after this year and, in particular, to facilitate use of contemporaneous Moderate-resolution Imaging Spectroradiometer (MODIS) remotely sensed data (Justice et al., 1998; Tuck et al., 2014) in modeling. However, in filling certain taxonomic and geographic gaps, we also collated some data that were sampled before 2000 (Figure 6). Data are sparse after 2012 because of the natural time lags between data collection in the field, publication and then assimilation into the PREDICTS database (Figure 6).

## 3.4 | Data access and structure

This 2016 release of the database—the complete dataset and also site-level summaries—is available on the data portal of the Natural History Museum, London (doi: 10.5519/0066354) as comma-separated variable (CSV) files and as RDS files, the latter for use with the R statistical modeling language (R Core Team 2015; RDS files were generated using R 3.3.1). A complete description of the columns in the extracts, along with a visualization of the database schema, is given in Supporting Information. This paper makes all the data in this version of the database freely available to anyone wishing to use
<figure><img src="image_6.png" /><figcaption>**FIGURE 4** Taxonomic coverage. The numbers of species in our database against the numbers of described species within each of 59 higher taxa, as estimated by Chapman (2009), on logarithmic axes. 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 represented (Diplura and Zoraptera) are not shown</figcaption></figure>

them for any purpose. The terms of the license require that anyone publishing research based on these data should cite this paper and/ or the original sources of the data used, as appropriate. The dataset at doi: 10.5519/0066354 contains bibliographic information for all DataSources in both CSV and BibTeX formats.

4 | DISCUSSION

The PREDICTS database is designed to be able to address a range of questions about how land use and related pressures have influenced the occurrence and abundance of species and the diversity of ecological assemblages. The highly structured nature of the data, with comparable surveys having been carried out at each Site within a Study, was chosen to facilitate such modeling. Table 1 identifies a range of long-standing general questions for which the PREDICTS data may be useful, referencing early papers addressing questions of each type. It also outlines the steps required to tackle each kind of question, in conjunction with other information about the Sites and species where necessary, and refers to papers that have performed so.

Changes in attitudes to—and the increasing ease of—data sharing have contributed to rapid growth in open compilations of structured biodiversity data and related pressure data targeted toward particular

kinds of research question. Examples of data types featured in such compilations include population time series (e.g., Inchausti & Halley, 2001), assemblage time series (e.g., Dornelas et al., 2014), assemblage inventories (e.g., Thibault, Supp, Giffin, White, & Ernest, 2011), and species traits (e.g., Madin et al., 2016). Other projects have collated or are collating large compilations of structured biodiversity data, such as BIOFRAG (Pfeifer et al., 2014; habitat fragmentation), BIOTIME (The BioTIME Research Group, 2016; detailed time-series data, still being compiled) and GLOBIO3 (Alkemade et al., 2009; pristine versus disturbed habitats, not publicly available).

The largest open compilation of biodiversity data is the Global Biodiversity Information Facility (GBIF; www.gbif.org), which aggregates mostly unstructured species occurrence data. The unstructured nature of most GBIF data limits the range of questions to which they can easily be put, although they are increasingly used in modeling species distributions (e.g., Pineda & Lobo, 2008) and habitat suitability (e.g., Ficetola, Rondinini, Bonardi, Baisero, & Padoa-Schioppa, 2015). As of April 2016, GBIF holds over 560 million georeferenced occurrence records of around 1.5 million species, although coverage is taxonomically uneven (e.g., most records are of birds) and patchy even among the best-recorded groups (Meyer, Kreft, Guralnick, & Jetz, 2015).

Databases of species traits continue to be collated and published, and many of them are relevant to taxa in the PREDICTS
<figure><img src="image_7.png" /></figure>

**FIGURE 5** 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 4. Numbers on the right are the primary references from which data were taken: 1 Basset et al. (2008), 2 Buscardo et al. (2008), 3 Christensen and Heilmann-Clausen (2009), 4 Domínguez, Bahamonde, and Muñoz-Escobar (2012), 5 López-Quintero, Straatsma, Franco-Molano, and Boekhout (2012), 6 Nöske et al. (2008), 7 Norton, Espie, Murray, and Murray (2006), 8 Peri, Lencinas, Martínez Pastur, Wardell-Johnson, and Lasagno (2013), 9 Robinson and Williams (2011), 10 Barratt et al. (2005), 11 Bonham, Mesibov, and Bashford (2002), 12 Boutin, Martin, and Baril (2009), 13 Carpenter et al. (2012), 14 Gaigher and Samways (2010), 15 Ge et al. (2012), 16 Hayward (2009), 17 Leighton-Goodall, Brown, Hammond, and Eggleton (2012), 18 Muchane et al. (2012), 19 Ngai et al. (2008), 20 Richardson, Richardson, and Soto-Adames (2005), 21 Schon, Mackay, Minor, Yeates, and Hedley (2008), 22 Schon, Mackay, Yeates, and Minor (2010), 23 Schon, Mackay, and Minor (2011), 24 Smith (2006), 25 Smith, Potts, Woodcock, and Eggleton (2008), 26 Smith, Potts, and Eggleton (2008), 27 Todd et al. (2011), 28 Vasconcelos et al. (2009), 29 Walker, Wilson, Norbury, Monks, and Tanentzap (2014), 30 Baeten, Velghe, et al. (2010), 31 Bakayoko, Martin, Chatelain, Traore, and Gautier (2011), 32 Center for International Forestry Research (CIFOR) (2013a), 33 Center for International Forestry Research (CIFOR) (2013b), 34 Dumont et al. (2009), 35 Firincioglu, Seefeldt, Sahin, and Vural (2009), 36 Haarmeyer, Schmiedel, Dengler, and Bosing (2010), 37 Joubert, Esler, and Privett (2009), 38 Norfolk, Eichhorn, and Gilbert (2013), 39 Page, Qureshi, Rawat, and Kushalappa (2010), 40 Proença, Pereira, Guilherme, and Vicente (2010), 41 Sheil et al. (2002), 42 Wang, Lencinas, Ross Friedman, Wang, and Qiu (2011), 43 Alignier and Deconchat (2013), 44 Baeten, Hermy, Van Daele, and Verheyen (2010), 45 Barlow, Gardner, et al. (2007), 46 Barrico et al. (2012), 47 Baur et al. (2006), 48 Berry et al. (2010), 49 Boutin, Baril, and Martin (2008), 50 Bouyer et al. (2007), 51 Brearley (2011), 52 Brunet et al. (2011), 53 Calviño-Cancela, Rubido-Bará, and van Etten (2012), 54 Castro, Lehsten, Lavorel, and Freitas (2010), 55 de Lima, Dallimer, Atkinson, and Barlow (2013), 56 Devineau, Fournier, and Nignan (2009), 57 Fensham, Dwyer, Eyre, Fairfax, and Wang (2012), 58 Fernandez and Simonetti (2013), 59 Fredriksson, Danielsen, and Swenson (2007), 60 Gendreau-Berthiaume, Kneeshaw, and Harvey (2012), 61 Golodets, Kigel, and Sternberg (2010), 62 Grass, Berens, Peter, and Farwig (2013), 63 Gutierrez et al. (2009), 64 Helden and Leather (2004), 65 Hernández, Delgado, Meier, and Duran (2012), 66 Hietz (2005), 67 Higuera and Wolf (2010), 68 Hylander and Nemomissa (2009), 69 Ishida, Hattori, and Takeda (2005), 70 Kati, Zografou, Tzirkalli, Chitos, and Willemse (2012), 71 Katovai, Burley, and Mayfield (2012), 72 Kessler et al. (2005), 73 Kessler et al. (2009), 74 Kolb and Diekmann (2004), 75 Krauss, Klein, Steffan-Dewenter, and Tscharntke (2004), 76 Krauss et al. (2010), 77 Kumar and Shahabuddin (2005), 78 Letcher and Chazdon (2009), 79 Louhaichi, Salkini, and Petersen (2009), 80 Lucas-Borja et al. (2011), 81 Måren (2011), 82 Måren, Bhattarai, and Chaudhary (2013), 83 Marin-Spiotta, Ostertag, and Silver (2007), 84 Mayfield, Ackerly, and Daily (2006), 85 McNamara, Erskine, Lamb, Chantalangsy, and Boyle (2012), 86 Milder et al. (2010), 87 O'Connor (2005), 88 Paritsis and Aizen (2008), 89 Phalan, Onial, Balmford, and Green (2011), 90 Pincheira-Ulbrich, Rau, and Smith-Ramirez (2012), 91 Poggio, Chaneton, and Ghersa (2013), 92 Power and Stout (2011), 93 Power, Kelly, and Stout (2012), 94 Ramesh et al. (2010), 95 Romero-Duque, Jaramillo, and Perez-Jimenez (2007), 96 Schmitt, Senbeta, Denich, Preisinger, and Boehmer (2010), 97 Shannon et al. (2008), 98 Siebert (2011), 99 Vassilev, Pedashenko, Nikolov, Apostolova, and Dengler (2011), 100 Williams, Sheahan, and Gormaly (2009), 101 Yamaura et al. (2012), 102 Alcala, Alcala, and Dolino (2004), 103 Bicknell and Peres (2010), 104 Centro Agronómico Tropical de Investigación y Enseñanza (CATIE) (2010); Deheuvels, Avelino, Somarriba, and Malézieux (2012), Deheuvels et al. (2014); Rousseau, Deheuvels, Rodriguez Arias, and Somarriba (2012), 105 Craig et al. (2009).

(Continues)
FIGURE 5 (Continued)

106 Craig et al. (2012), 107 Craig, Grigg, Hobbs, and Hardy (2014), 108 Craig, Stokes, St. Hardy, and Hobbs (2015), 109 de Thoisy et al. (2010), 110 Endo et al. (2010), 111 Garden, McAlpine, and Possingham (2010), 112 Kurz, Nowakowski, Tingley, Donnelly, and Wilcove (2014), 113 Kutt and Woinarski (2007), 114 Kutt, Vanderduys, and O'Reagain (2012), 115 Lehouck et al. (2009), 116 Macip-Ríos and Muñoz-Alonso (2008), 117 McCarthy, McCarthy, Fuller, and McCarthy (2010), 118 Parry, Barlow, and Peres (2009), 119 Peres and Nascimento (2006), 120 St-Laurent, Ferron, Hins, and Gagnon (2007), 121 Sung, Karraker, and Hau (2012), 122 Urbina-Cardona, Olivares-Perez, and Reynoso (2006), 123 Woinarski and Ash (2002), 124 Woinarski et al. (2009), 125 Billeter et al. (2008); Le Féon et al. (2010), 126 Borges et al. (2006), 127 Cabra-García, Bermúdez-Rivas, Osorio, and Chacón (2012), 128 Hanley (2011), 129 Lachat et al. (2006), 130 Cardoso et al. (2009); Meijer, Whittaker, and Borges (2011), 131 Nakamura, Proctor, and Catterall (2003), 132 Norfolk, Abdel-Dayem, and Gilbert (2012), 133 Poveda, Martinez, Kersch-Becker, Bonilla, and Tscharntke (2012), 134 Rousseau, Fonte, Tellez, van der Hoek, and Lavelle (2013), 135 Turner and Foster (2009), 136 Uehara-Prado et al. (2009), 137 Waite (2012); Waite, Closs, Van Heezik, Berry, and Dickinson (2012), 138 Woodcock et al. (2007), 139 Albertos, Lara, Garilleti, and Mazimpaka (2005), 140 Draper, Lara, Albertos, Garilleti, and Mazimpaka (2006), 141 Giordano et al. (2004), 142 Hylander and Weibull (2012), 143 Medina et al. (2010), 144 Hu and Cao (2008), 145 Wu, Fu, Chen, and Chen (2002), 146 Zhang, Li, and Liang (2010), 147 Giordani et al. (2010), 148 Giordani (2012), 149 Aben, Dorenbosch, Herzog, Smolders, and Van Der Velde (2008), 150 Arbeláez-Cortés, Rodríguez-Correa, and Restrepo-Chica (2011), 151 Aumann (2001), 152 Azhar et al. (2013), 153 Azman et al. (2011), 154 Azpiroz and Blake (2009), 155 Báldi, Batáry, and Erdos (2005), 156 Barlow, Mestre, Gardner, and Peres (2007), 157 Bóçon (2010), 158 Borges (2007), 159 Brandt et al. (2013), 160 Cerezo, Conde, and Poggio (2011), 161 Chapman and Reich (2007), 162 Cockle, Leonard, and Bodrati (2005), 163 Dallimer, Parnell, Bicknell, and Melo (2012), 164 Dawson et al. (2011), 165 Dures and Cumming (2010), 166 Edenius, Mikusinski, and Bergh (2011), 167 Farwig, Sajita, and Boehning-Gaese (2008), 168 Flaspohler et al. (2010), 169 Gomes, Oostra, Nijman, Cleef, and Kappelle (2008); Oostra, Gomes, and Nijman (2008), 170 Hassan et al. (2013), 171 Ims and Henden (2012), 172 Lantschner, Rusch, and Peyrou (2008), 173 Lasky and Keitt (2010), 174 Latta, Tinoco, Astudillo, and Graham (2011), 175 Mallari et al. (2011), 176 Doulton, Marsh, Newman, Bird, and Bell (2007), 177 Marsh, Lewis, Said, and Ewers (2010), 178 Miranda, Politi, and Rivera (2010), 179 Moreno-Mateos et al. (2011), 180 Munyekenye, Mwangi, and Gichuki (2008), 181 Naidoo (2004), 182 Naithani and Bhatt (2012), 183 Naoe, Sakai, and Masaki (2012), 184 Ndang'ang'a, Njoroge, and Githiru (2013), 185 Neuschulz, Botzat, and Farwig (2011), 186 O'Dea and Whittaker (2007), 187 Owiunji and Plumptre (1998), 188 Pearman (2002), 189 Politi, Hunter Jr. and Rivera (2012), 190 Pons and Wendenburg (2005), 191 Ranganathan, Chan, and Daily (2007), 192 Ranganathan, Daniels, Chandran, Ehrlich, and Daily (2008), 193 Reid, Harris, and Zahawi (2012), 194 Rey-Benayas, Galvan, and Carrascal (2010), 195 Reynolds and Symes (2013), 196 Rosselli (2011), 197 Sam, Koane, Jeppy, and Novotny (2014), 198 Santana, Porto, Gordinho, Reino, and Beja (2012), 199 Shahabuddin and Kumar (2006, 2007), 200 Sheldon, Styring, and Hosner (2010), 201 Sodhi et al. (2010), 202 Soh, Sodhi, and Lim (2006), 203 Sosa, Benz, Galea, and Poggio Herrero (2010), 204 Stouffer, Johnson, Bierregaard, Richard, and Lovejoy (2011), 205 Suarez-Rubio and Thomlinson (2009), 206 Vergara and Simonetti (2004), 207 Verhulst, Báldi, and Kleijn (2004), 208 Waite, Closs, van Heezik, and Dickinson (2013), 209 Wang, Bao, Yu, Xu, and Ding (2010), 210 Wunderle, Henriques, and Willig (2006), 211 Li, Zou, Zhang, and Sheldon (2013), 212 Bates et al. (2011), 213 Blake, Westbury, Woodcock, Sutton, and Potts (2011), 214 Blanche, Ludwig, and Cunningham (2006), 215 Cleary et al. (2004), 216 Farwig et al. (2009), 217 Franzén and Nilsson (2008), 218 Kohler, Verhulst, van Klink, and Kleijn (2008), 219 Litchwark (2013), 220 Meyer, Gaebele, and Steffan-Dewenter (2007), 221 Jauker, Krauss, Jauker, and Steffan-Dewenter (2013); Meyer, Jauker, and Steffan-Dewenter (2009), 222 Mudri-Stojnic, Andric, Jozan, and Vujic (2012), 223 Quintero, Morales, and Aizen (2010), 224 Rader, Bartomeus, Tylianakis, and Laliberte (2014), 225 Schüepp, Herrmann, Herzog, and Schmidt-Entling (2011), 226 Summerville (2011), 227 Vergara and Badano (2009), 228 Bernard, Fjeldsa, and Mohamed (2009), 229 Cáceres, Nápoli, Casella, and Hannibal (2010), 230 Cassano, Barlow, and Pardini (2014), 231 Danquah, Oppong, and Nutsuakor (2012), 232 Garmendia, Arroyo-Rodríguez, Estrada, Naranjo, and Stoner (2013), 233 Gheler-Costa, Vettorazzi, Pardini, and Verdade (2012), 234 Granjon and Duplantier (2011), 235 Henschel (2008), 236 Hoffmann and Zeller (2005), 237 Kittle, Watson, Chanaka Kumara, and Nimalka Sanjeewani (2012), 238 Lantschner, Rusch, and Hayes (2012), 239 Martin, Gheler-Costa, Lopes, Rosalino, and Verdade (2012), 240 McShea et al. (2009), 241 Mena and Medellín (2010), 242 Nakagawa, Miguchi, and Nakashizuka (2006), 243 O'Farrell, Donaldson, Hoffman, and Mader (2008), 244 Scott et al. (2006), 245 Sridhar, Raman, and Mudappa (2008), 246 Wells, Kalko, Lakim, and Pfeiffer (2007), 247 Hylander, Nilsson, and Gothner (2004), 248 Kappes, Katzschner, and Nowak (2012), 249 Oke and Chokor (2009), 250 Oke (2013), 251 Schilthuizen, Liew, Bin Elahan, and Lackman-Ancrenaz (2005), 252 Ström, Hylander, and Dynesius (2009), 253 Torre, Bros, and Santos (2014), 254 Wronski et al. (2014), 255 Freire and Motta (2011), 256 Lo-Man-Hung, Gardner, Ribeiro-Júnior, Barlow, and Bonaldo (2008), 257 Shochat, Stefanov, Whitehouse, and Faeth (2004), 258 Zaitsev, Chauvat, Pug, and Wolters (2002), 259 Walker, Crittenden, Young, and Prystina (2006), 260 Malonza and Veith (2012), 261 Alguacil, Torrecillas, Hernandez, and Roldan (2012), 262 Brito, Goss, de Carvalho, Chatagnier, and van Tuinen (2012), 263 Baral and Katzensteiner (2009), 264 Robles, Carmaran, and Lopez (2011), 265 Römbke, Schmidt, and Höfer (2009), 266 Luja, Herrando-Perez, Gonzalez-Solis, and Luiselli (2008), 267 Cameron et al. (2011), 268 Cunningham, Schellhorn, Marcora, and Batley (2013), 269 Fowler (2014), 270 Gould et al. (2013), 271 Lentini, Martin, Gibbons, Fischer, and Cunningham (2012), 272 Malone et al. (2010), 273 Marshall, West, and Kleijn (2006), 274 Oertli, Muller, and Dorn (2005), 275 Osgathorpe, Park, and Goulson (2012), 276 Quaranta et al. (2004), 277 Richards et al. (2011), 278 Samnegård, Persson, and Smith (2011), 279 Schüepp, Rittiner, and Entling (2012), 280 Shuler, Roulston, and Farris (2005), 281 Smith-Pardo and Gonzalez (2007), 282 Tonietto, Fant, Ascher, Ellis, and Larkin (2011), 283 Tylianakis, Klein, and Tscharntke (2005), 284 Verboven, Brys, and Hermy (2012), 285 Barlow, Overal, Araujo, Gardner, and Peres (2007), 286 Berg, Ahrné, Öckinger, Svensson, and Söderström (2011), 287 Bobo, Waltert, Fermon, Njokagbor, and Muhlenberg (2006), 288 Cleary and Mooers (2006), 289 D'Aniello, Stanislao, Bonelli, and Balletto (2011), 290 de Sassi, Lewis, and Tylianakis (2012), 291 Dolia, Devy, Aravind, and Kumar (2008), 292 Hawes et al. (2009), 293 Ishitani, Kotze, and Niemela (2003), 294 Krauss, Steffan-Dewenter, and Tscharntke (2003), 295 Littlewood (2008), 296 Pe'er, Maanen, Turbe, Matsinos, and Kark (2011), 297 Safian, Csontos, and Winkler (2011), 298 Summerville and Crist (2002), 299 Summerville, Conoan, and Steichen (2006), 300 Sutrisno (2010), 301 Uehara-Prado, Brown, Spalding, and Lucci Freitas (2007), 302 Verdasca et al. (2012), 303 Vu (2005), 304 Vu (2009), 305 Banks, Sandvik, and Keeseger (2007), 306 Barratt et al. (2012), 307 Blanche and Cunningham (2005), 308 Buse, Levanony, Timm, Dayan, and Assmann (2008), 309 Elek and Lovei (2007), 310 Ewers, Thorpe, and Didham (2007), 311 Gaublomme, Hendrickx, Dhuyvetter, and Desender (2008), 312 Gray, Slade, Mann, and Lewis (2014), 313 Jonsell (2012), 314 Légaré, Hébert, and Ruel (2011), 315 Mico, (Continues)
FIGURE 5 (Continued)

Garcia-Lopez, Brustel, Padilla, and Galante (2013), 316 Noreika (2009), 317 Numa, Verdu, Rueda, and Galante (2012), 318 Nyeko (2009), 319 Otavo, Parrado-Rosselli, and Noriega (2013), 320 Rodrigues, Uchoa, and Ide (2013), 321 Sugiura, Tsuru, Yamaura, and Makihara (2009), 322 Verdú et al. (2007), 323 Adum, Eichhorn, Oduro, Ofori-Boateng, and Rodel (2013), 324 de Souza, de Souza, and Morato (2008), 325 Eigenbrod, Hecnar, and Fahrig (2008), 326 Faruk, Belabut, Ahmad, Knell, and Garner (2013), 327 Furlani, Ficetola, Colombo, Ugurlucan, and De Bernardi (2009), 328 Gutierrez-Lamus (2004), 329 Hilje and Aide (2012), 330 Isaacs-Cubides and Urbina-Cardona (2011), 331 Ofori-Boateng et al. (2013), 332 Pethiyagoda and Manamendra-Arachchi (2012), 333 Pillsbury and Miller (2008), 334 Pineda and Halffter (2004), 335 Vallan (2002), 336 Watling, Gerow, and Donnelly (2009), 337 Castro-Luna, Sosa, and Castillo-Campos (2007), 338 Clarke, Rostant, and Racey (2005), 339 Fukuda, Tisen, Momose, and Sakai (2009), 340 MacSwiney, Vilchis, Clarke, and Racey (2007), 341 Presley, Willig, Wunderle, Joseph, and Saldanha (2008), 342 Sedlock et al. (2008), 343 Shafie, Sah, Latip, Azman, and Khairuddin (2011), 344 Struebig, Kingston, Zubaid, Mohd-Adnan, and Rossiter (2008), 345 Threlfall, Law, and Banks (2012), 346 Willig et al. (2007), 347 Alcayaga, Pizarro-Araya, Alfaro, and Cepeda-Pizarro (2013), 348 Buddle and Shorthouse (2008), 349 Clark, Gerard, and Mellsop (2004), 350 Kapoor (2008), 351 Lo-Man-Hung et al. (2011), 352 Magura, Horvath, and Tothmeresz (2010), 353 Malumbres-Olarte et al. (2014), 354 Paradis and Work (2011), 355 Raub, Hoefer, Scheuermann, and Brandl (2014), 356 Alberta Biodiversity Monitoring Institute (ABMI) (2013), 357 Arroyo, Iturrondobeitia, Rad, and Gonzalez-Carcedo (2005), 358 Zaitsev, Wolters, Waldhardt, and Dauber (2006), 359 Körösi, Batáry, Orosz, Rédei, and Báldi (2012), 360 Littlewood, Pakeman, and Pozsgai (2012), 361 Moir, Brennan, Koch, Majer, and Fletcher (2005), 362 Carrijo, Brandao, de Oliveira, Costa, and Santos (2009), 363 Oliveira, Carrijo, and Brandão (2013), 364 Reis and Cancello (2007), 365 Zeidler, Hanrahan, and Scholes (2002), 366 D'Cruze and Kumar (2011), 367 Fabricius, Burger, and Hockey (2003), 368 Pelegrin and Bucher (2012), 369 Urbina-Cardona, Londoño-Murcia, and García-Ávila (2008), 370 Chauvat, Wolters, and Dauber (2007), 371 Fiera (2008), 372 Savage, Wheeler, Moores, and Taillefer (2011), 373 Virgilio, Backeljau, Emeleme, Juakali, and De Meyer (2011), 374 Andersen, Ludwig, Lowe, and Rentz (2001), 375 Otto and Roloff (2012), 376 Zimmerman, Bell, Woodcock, Palmer, and Paloniemi (2011), 377 Hornung, Tothmeresz, Magura, and Vilisics (2007), 378 Magrini, Freitas, and Uehara-Prado (2011), 379 Laurance and Laurance (1996), 380 Bragagnolo, Nogueira, Pinto-da-Rocha, and Pardini (2007), 381 Herrera, Wright, Lauterbur, Ratovonjanahary, and Taylor (2011), 382 Jung and Powell (2011), 383 Bartolommei, Mortelliti, Pezzo, and Puglisi (2013), 384 Andersen and Hoffmann (2011), 385 Armbrecht, Perfecto, and Silverman (2006), 386 Bihn, Verhaagh, Braendle, and Brandl (2008), 387 Buczkowski (2010), 388 Buczkowski and Richmond (2012), 389 Delabie et al. (2009), 390 Dominguez-Haydar and Armbrecht (2010), 391 Fayle et al. (2010), 392 Floren, Freking, Biehl, and Linsenmair (2001), 393 Frizzo and Vasconcelos (2013), 394 Gove, Majer, and Rico-Gray (2005), 395 Gunawardene, Majer, and Edirisinghe (2010), 396 Hashim, Akmal, Jusoh, and Nasir (2010), 397 Kone, Konate, Yeo, Kouassi, and Linsenmair (2010), 398 Maeto and Sato (2004), 399 Roth, Perfecto, and Rathcke (1994), 400 Schmidt, Fraser, Carlyle, and Bassett (2012), 401 Uehara-Prado (2005), 402 Vasconcelos (1999), 403 Vasconcelos, Vilhena, and Caliri (2000), 404 Fierro, Cruz-Lopez, Sanchez, Villanueva-Gutierrez, and Vandame (2012), 405 Hanley (2005), 406 Julier and Roulston (2009), 407 Liow, Sodhi, and Elmqvist (2001), 408 Nielsen et al. (2011), 409 Parra-H and Nates-Parra (2007), 410 Rasmussen (2009), 411 Winfree, Griswold, and Kremen (2007), 412 da Silva (2011), 413 Davis and Philips (2005), 414 Filgueiras, Iannuzzi, and Leal (2011), 415 Gardner, Hernandez, Barlow, and Peres (2008), 416 Horgan (2009), 417 Jacobs, Scholtz, Escobar, and Davis (2010), 418 Navarrete and Halffter (2008), 419 Navarro, Roman, Gomez, and Perez (2011), 420 Noriega, Realpe, and Fagua (2007), 421 Noriega, Palacio, Monroy-G, and Valencia (2012), 422 Rös, Escobar, and Halffter (2012), 423 Silva, Costa, Moura, and Farias (2010), 424 Slade, Mann, and Lewis (2011), 425 Gu, Zhen-Rong, and Dun-Xiao (2004), 426 Koivula, Hyyrylainen, and Soininen (2004), 427 Liu, Axmacher, Wang, Li, and Yu (2012), 428 Noreika and Kotze (2012), 429 Rey-Velasco and Miranda-Esquivel (2012), 430 Vanbergen, Woodcock, Watt, and Niemela (2005), 431 Weller and Ganzhorn (2004), 432 Aguilar-Barquero and Jiménez-Hernández (2009), 433 Carvalho, Ferreira, Lima, and de Carvalho (2010), 434 Svenning (1998), 435 Benedick et al. (2006), 436 Fermon, Waltert, Vane-Wright, and Muhlenberg (2005), 437 Ribeiro and Freitas (2012), 438 Breedt, Dreber, and Kellner (2013), 439 Scott, Setterfield, Douglas, and Andersen (2010), 440 Cagle (2008), 441 Johnson, Gómez, and Pinedo-Vasquez (2008), 442 Su, Zhang, and Qiu (2011), 443 Gottschalk, De Toni, Valente, and Hofmann (2007), 444 Axmacher et al. (2009), 445 García, Ortiz Zapata, Aguayo, and D'Elia (2013), 446 Jolli and Pandit (2011), 447 Saldaña-Vázquez, Sosa, Hernández-Montero, and López-Barrera (2010), 448 Nicolas, Barriere, Tapiero, and Colyn (2009), 449 Sakchoowong, Nomura, Ogata, and Chanpaisaeng (2008), 450 García-R, Cárdenas-H, and Castro-H (2007), 451 Yoshikura, Yasui, and Kamijo (2011), 452 Connop, Hill, Steer, and Shaw (2011), 453 Darvill, Knight, and Goulson (2004), 454 Diekötter, Walther-Hellwig, Conradi, Suter, and Frankl (2006), 455 Goulson, Lye, and Darvill (2008), 456 Goulson et al. (2010), 457 Hanley et al. (2011), 458 Hatfield and LeBuhn (2007), 459 McFrederick and LeBuhn (2006), 460 Redpath, Osgathorpe, Park, and Goulson (2010), 461 Schumann, Wittig, Thiombiano, Becker, and Hahn (2011), 462 Nakashima, Inoue, and Akomo-Okoue (2013), 463 Wiafe and Amfo-Otu (2012), 464 Peters, Fischer, Schaab, and Kraemer (2009), 465 Peters, Lung, Schaab, and Waegele (2011), 466 Matsumoto, Itioka, Yamane, and Momose (2009), 467 Rubio and Simonetti (2011), 468 Herrmann, Westphal, Moritz, and Steffan-Dewenter (2007), 469 Knight et al. (2009), 470 Ancrenaz, Goossens, Gimenez, Sawang, and Lackman-Ancrenaz (2004), 471 Felton, Engstrom, Felton, and Knott (2003), 472 Knop, Ward, and Wich (2004), 473 Ewers, Bartlam, and Didham (2013), 474 Davis, Murray, Fitzpatrick, Brown, and Paxton (2010), 475 Hanson, Brunsfeld, Finegan, and Waits (2008), 476 Strauch and Eby (2012), 477 Ramos-Robles, Gallina, and Mandujano (2013), 478 Ferreira and Alves (2005, 2009), 479 Luskin (2010), 480 Grogan et al. (2008)

database. Recent examples include mammalian generation time (Pacifici et al., 2013), a variety of mammalian traits (Jones et al., 2009), foraging attributes of birds and mammals (Wilman et al., 2014), field metabolic rates of birds and mammals (Hudson, Isaac, & Reuman, 2013) and functional traits of vascular plants (Kattge et al., 2011). Additional databases provide more abstract concepts such as species' threat status (International Union for Conservation of Nature, 2016) and estimates of the degrees of protection required

(Convention on International Trade in Endangered Species of Wild Fauna and Flora, 2016). Relating such data with measurements in the PREDICTS database makes possible investigation into how traits mediate species' responses to changes in land use and land-use intensity. Examples of published analyses have examined habitat specialization and geographical range size of birds and mammals (Newbold et al., 2014), functional traits of vascular plants (Bernhardt-Römermann et al., 2011) and a range of morphometric,
<figure><img src="image_8.png" /><figcaption>**FIGURE 6** Spatiotemporal sampling coverage. Site sampling dates are shown by biome. Each Site is represented by a circle and line. Circle radii are proportional to $log_{10}$ 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 have been jittered at the Study level. Circles and lines have the same degree of partial transparency. Biome colors and letters are as in Figure 1</figcaption></figure>

physiological, and functional traits of bees (De Palma et al., 2015); see Table 1, Q. 3.

Although our targeting of data from underrepresented biomes and taxa (Hudson et al., 2014) reduces the effects of geographic and taxonomic biases in available data, the PREDICTS database nonetheless has many limitations, of which four are particularly important to note. First, our individual datasets seldom take a whole-ecosystem perspective, being instead taxonomically or ecologically restricted; consequently, our data shed little light on how trophic webs or other interactions are affected by human pressures. Second, even within the groups sampled, our data do not provide complete inventories of the species that would be found with comprehensive sampling; thus, failure to record a species from a Site does not provide strong evidence of absence. Third, Latin binomials were not available for a sizeable fraction of the species in our DataSources, limiting the prospects for linking the observations of occurrence and abundance to other information about the species (e.g., functional traits; Kattge et al., 2011). Last, because our database was designed to test hypotheses about local-scale variation in biodiversity, it is not particularly informative about large-scale biodiversity patterns such as the latitudinal gradient in species richness or how pressures with a coarse spatial grain (e.g., atmospheric nitrogen deposition; Simkin et al., 2016) influence Site-level diversity.

When using the PREDICTS database, or indeed any database, to model biodiversity responses, it is important to be aware of potential

mismatches in scale between Site-level data and pressure data such as MODIS remotely sensed data (Justice et al., 1998) and the harmonized land-use scenarios (Hurtt et al., 2011) and also between Site-level response variables and the scales of interest. The PREDICTS database contains some structural features that help with these issues. First, we assigned the Site-level land use and land-use intensity classifications based on the authors' descriptions of the habitats so these classifications do not suffer from the problem of scale mismatch. Second, Sites are represented as precisely as possible: Sites often represent individual quadrats, traps, or other points within a broader sampling regime (such as a transect), and we recorded (as latitude and longitude) the coordinates of each Site rather than aggregating them into coarser summaries across the broader sampling regime. Third, where the relevant information was available, we also recorded the maximum extent of sampling as a linear value in meters (for 22,199 Sites, see Hudson et al. (2014) for details). Users of the database therefore have flexibility in deciding how measurements in the PREDICTS database are related to available pressure data. Possible solutions to scale mismatches between biodiversity data and pressure data would be (1) to exclude from analyses any Sites where the extent of sampling is substantially greater than the grain size of the pressure data or (2) to conduct some sort of spatial averaging of the pressure data. Novel methods have been published both for downscaling pressure data (e.g., Hoskins et al., 2016) and for upscaling local biodiversity
<table><caption>TABLE 1 Questions that could be answered using the PREDICTS database</caption><thead><tr><th>Question</th><th>Early example references</th><th>Approach</th><th>Example using PREDICTS database</th></tr></thead><tbody><tr><td colspan="4"><em>Questions about taxa</em></td></tr><tr><td>Q 1. What factors influence the occurrence and/or abundance of a particular focal species?</td><td>Austin, Nicholls, and Margules (1990)</td><td>Filter to remove species not of interest. Merge PREDICTS data with data on any additional site-level characteristics of interest. One possible analytical approach is to model effects of site characteristics on presence-absence and log (abundance when present) separately, the first with binomial errors and the second with Gaussian errors, while accounting for among-Study differences (e.g., using mixed-effects models).</td><td>-</td></tr><tr><td>Q 2. Do changes in land-use facilitate success of invasive species?</td><td>Dukes and Mooney (1999), Theoharides and Dukes (2007)</td><td>Obtain lists of invasive species for the regions of interest and model presence-absence and/or abundance of invasives as above.</td><td>-</td></tr><tr><td>Q 3. Which ecological attributes of species make them more or less sensitive to human pressures?</td><td>McKinney (1997), Davies, Margules, and Lawrence (2000), Cardillo et al. (2005)</td><td>Merge PREDICTS data with species-level data on traits of interest. Model how site and species characteristics affect presence-absence and log (abundance when present) separately as above, accounting for Study-level and taxon-level differences (e.g., using mixed-effects models).</td><td>Newbold et al. (2014), De Palma et al. (2015)</td></tr><tr><td>Q 4. Which taxa have species that are more sensitive to human pressures, and which have less sensitive species?</td><td>Lawton et al. (1998), Mace and Balmford (2000), Gibson et al. (2011)</td><td>Add taxonomic group into models above as a fixed effect interacting with other fixed effects.</td><td>-</td></tr><tr><td>Q 5. Are phylogenetically distinct species particularly sensitive?</td><td>Gaston and Blackburn (1997), Purvis, Agapow, Gittleman, and Mace (2000)</td><td>Analyze phylogenetic distinctiveness or unique evolutionary history in the same way as ecological attributes.</td><td>-</td></tr><tr><td>Q 6. What are the relationships between geographic range size or occupancy and abundance?</td><td>Brown (1984)</td><td>Merge PREDICTS data with species-level data on range sizes or occupancy. Filter to the land uses of interest (e.g., primary vegetation if the focus is on natural systems), and examine within-Study relationship between abundance and relative range size or occupancy.</td><td>-</td></tr><tr><td>Q 7. Do suitability estimates from environmental niche models predict abundance?</td><td>VanDerWal, Shoo, Johnson, and Williams (2009)</td><td>Use other data on occurrences of species to fit niche models for all species in within selected Studies and thereby estimate suitability of each Site. Various modeling options are then possible depending on the precise question: for example, fit land use interacting with suitability when modeling abundance in order to test whether any correlation depends on land use.</td><td>-</td></tr><tr><td colspan="4"><em>Questions about sites</em></td></tr><tr><td>Q 8. Which land uses and other Site-level pressures have the strongest net impact on levels of local biodiversity?</td><td>Lawton et al. (1998), Gibson et al. (2011)</td><td>Aggregate biodiversity data within a site to estimate relevant diversity metric (e.g., within-sample species richness, total abundance, rarefaction-based richness, species evenness). Merge Site-level biodiversity data with any additional data on Site-level characteristics of interest (e.g., from remotely sensed data) if required. Model Site-level diversity as a function of Site characteristics while accounting for among-Study differences (e.g., using mixed-effects models).</td><td>fig 1b,c in Newbold et al. (2015)</td></tr></tbody></table>

(Continues)
<table><caption>TABLE 1 (Continued)</caption><thead><tr><th>Question</th><th>Early example references</th><th>Approach</th><th>Example using PREDICTS database</th></tr></thead><tbody><tr><td>Q 9. How do land use and other pressures reduce compositional intactness?</td><td>Scholes and Biggs (2005)</td><td>Because net changes are affected by gains of non-native species as well as losses of those originally present, modeling compositional intactness gives a more sensitive indication of human impacts. Model Site-level abundance as a function of pressures as above, and how compositional similarity to assemblages in primary vegetation differs among land uses. Combine these models to estimate the Biodiversity Intactness Index (Scholes & Biggs, 2005)—the average abundance of a diverse set of species, relative to their abundance in an unimpacted assemblage.</td><td>Newbold, Hudson, Arnell, et al. (2016)</td></tr><tr><td>Q 10. Do land use and related pressures influence community trait values?</td><td>Garnier et al. (2007)</td><td>Combine data on species' occurrences or abundance with trait data to obtain average or community-weighted mean trait values, which can then be modeled like the Site-level response variables above.</td><td>fig 1d in Newbold et al. (2015)</td></tr><tr><td>Q 11. Does the biotic response to a given pressure vary regionally?</td><td>Gibson et al. (2011)</td><td>Add region as a fixed effect and test for interaction with other fixed effects.</td><td>-</td></tr><tr><td>Q 12. Which characteristics of Sites (e.g., duration of human impact and rate of climate change) mean that given land-use changes have particularly severe effects on biodiversity?</td><td>Balmford (1996), Travis (2003)</td><td>Merge Site-level diversity data with Site-level data on characteristics to be tested and assess the interaction of these variables with land use.</td><td>Gray et al. (2016)</td></tr><tr><td>Q 13. How accurate are global land-use data?</td><td>Giri, Zhu, and Reed (2005)</td><td>Use Site-level land-use data to calculate the receiver operating characteristic curve (i.e., sensitivity versus false-positive rate), using the area under the curve to quantify agreement. An extension of this could be to use the PREDICTS Site-level land use data as input into land use/land cover classification procedures, for example, by the remote sensing community, or at least use PREDICTS data to cross-check and validate land use and land cover maps with independent PREDICTS data.</td><td>Hoskins et al. (2016)</td></tr><tr><td colspan="4">Questions above the site level</td></tr><tr><td>Q 14. Is beta diversity lower in human-dominated than more natural land uses?</td><td>Tylianakis et al. (2005)</td><td>Estimate desired measures of similarity among Sites within studies. Model how biotic similarity among Sites depends on similarity of other attributes (including characteristics from remote sensing or Dynamic Global Ecosystem Models if required), accounting for among-Study differences (e.g., using mixed-effects models).</td><td>Newbold, Hudson, Hill, et al. (2016)</td></tr><tr><td>Q 15. Are land-sparing or land-sharing strategies optimal for local biodiversity?</td><td>Green, Cornell, Scharlemann, and Balmford (2005)</td><td>Analyze species by Sites and by Study and relate back to Q. 1. The overarching question about sparing versus sharing can be addressed by looking at the individual responses of species to land-use intensity, as measured by yield suggested by Green et al. (2005); this requires data on agricultural yields at relevant Sites in the PREDICTS database.</td><td>-</td></tr></tbody></table>

(Continues)
<table><caption>**TABLE 1** (Continued)</caption><thead><tr><th>Question</th><th>Early example references</th><th>Approach</th><th>Example using PREDICTS database</th></tr></thead><tbody><tr><td colspan="4"><em>Other questions</em></td></tr><tr><td>Q 16. How accurate are current extent of occurrence/range maps, for example, those produced by International Union for Conservation of Nature (2016)?</td><td>-</td><td>Cross-check existing extents of occurrence and ranges with PREDICTS data.</td><td>-</td></tr><tr><td>Q 17. How representative are species catalogues?</td><td>-</td><td>Query clade-level (e.g., The Plant List, World List of Mammalian Species, Platnick's Spider Catalogue) and aggregated (e.g., Encyclopedia of Life and Catalogue of Life) lists with the Latin binomials and trinomials that were provided to PREDICTS by the data collectors. Subquestions include<ul><li>How does coverage vary among taxonomic groups?</li><li>How does coverage depend on region?</li><li>Are there substantial differences among the aggregated services?</li><li>How well are synonyms and homonyms represented and resolved?</li></ul></td><td>-</td></tr></tbody></table>

measurements to estimate changes in gamma diversity over broader areas (e.g., Azaele et al., 2015); both approaches offer potential solutions to mismatches in scale.

The PREDICTS database continues to increase in size and currently contains a further 22 Studies with embargo dates that prevent their inclusion in this release. We intend to publish occasional updates to make these data freely available. We have also received a number of further offers of datasets that we hope to incorporate into the database and include in future releases. There are three priority categories of data that we are still seeking actively: bees from outside Western Europe; soil invertebrates and fungi; and geographic islands. The current database focuses entirely on spatial "control-impact" comparisons. A follow-on project that has recently begun focuses instead on temporal comparisons, collating data from "before-after" and (especially) "before-after-control-impact" studies of the effects of land-use change on terrestrial assemblages. We are therefore seeking datasets, linked to peer-reviewed publications, of comparable species-level surveys conducted at each sampling location, with temporal changes in land use and/or land-use intensity. If corresponding authors of such papers wish to offer their data, please complete our online form, available at www Predicts.org.uk/pages/contribute.html. As with PREDICTS, the new project will seek to make its data freely available.

## ACKNOWLEDGMENTS

PREDICTS has been supported by U.K. Natural Environment Research Council grants (NE/J011193/2 and NE/L002515/1), the United Nations Environment Program World Conservation Monitoring

Centre, Biotechnology and Biological Sciences Research Council grant (BB/F017324/1), a Hans Rausing PhD Scholarship and COLCIENCIAS (Departamento Administrativo de Ciencia, Tecnología e Innovación de Colombia). We thank the many researchers who generously contributed their data to the PREDICTS project; including The Nature Conservation Foundation, Ros Blanche, Zhi Ping Cao, Kristina Cockle, Emily Davis, Moisés Barbosa de Souza, Carsten F Dormann, Christo Fabricius, Colin Ferguson, Heleen Fermon, Toby Gardner, Eva Gaublomme, Marco S Gottschalk, Peter Hietz, Juan Carlos Iturrondobeitia, Daniel L Kelly, Lee Hsiang Liow, Takashi Matsumoto, William McShea, Elder F Morato, Andreas Müller, Philip Nyeko, Tim O'Connor, Clint Otto, Simon Paradis, Marino Rodrigues, Watana Sakchoowong, Hari Sridhar, Susan Walker, Rachael Winfree, Timothy T Work, Torsten Wronski, Gregory Zimmerman and all the field assistants, parataxonomists and taxonomists who collected and identified the animals, plants and fungi in the database. We thank all the many funding agencies and other organizations that have supported the original research that produced these data; these include Natural Sciences and Engineering Research Council of Canada and Tembec, the University of Miami Beyond the Book Research Scholarship, the NSF Graduate Research Fellowship and the National Science Foundation Research Experience for Undergraduates Supplemental Award. We thank Technical Solutions and Informatics staff at the Natural History Museum, London, especially Srinivas Patlola, Simon Rycroft, Ben Scott and Chris Sleep.

## CONFLICT OF INTEREST

None declared.
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