Testing a global standard for quantifying species recovery and assessing conservation impact

Abstract

Recognizing the imperative to evaluate species recovery and conservation impact, in 2012 the International Union for Conservation of Nature (IUCN) called for development of a “Green List of Species” (now the IUCN Green Status of Species). A draft Green Status framework for assessing species’ progress toward recovery, published in 2018, proposed 2 separate but interlinked components: a standardized method (i.e., measurement against benchmarks of species’ viability, functionality, and preimpact distribution) to determine current species recovery status (herein species recovery score) and application of that method to estimate past and potential future impacts of conservation based on 4 metrics (conservation legacy, conservation dependence, conservation gain, and recovery potential). We tested the framework with 181 species representing diverse taxa, life histories, biomes, and IUCN Red List categories (extinction risk). Based on the observed distribution of species’ recovery scores, we propose the following species recovery categories: fully recovered, slightly depleted, moderately depleted, largely depleted, critically depleted, extinct in the wild, and indeterminate. Fifty‐nine percent of tested species were considered largely or critically depleted. Although there was a negative relationship between extinction risk and species recovery score, variation was considerable. Some species in lower risk categories were assessed as farther from recovery than those at higher risk. This emphasizes that species recovery is conceptually different from extinction risk and reinforces the utility of the IUCN Green Status of Species to more fully understand species conservation status. Although extinction risk did not predict conservation legacy, conservation dependence, or conservation gain, it was positively correlated with recovery potential. Only 1.7% of tested species were categorized as zero across all 4 of these conservation impact metrics, indicating that conservation has, or will, play a role in improving or maintaining species status for the vast majority of these species. Based on our results, we devised an updated assessment framework that introduces the option of using a dynamic baseline to assess future impacts of conservation over the short term to avoid misleading results which were generated in a small number of cases, and redefines short term as 10 years to better align with conservation planning. These changes are reflected in the IUCN Green Status of Species Standard.

Full text

Received: 11 September 2020 Revised: 29 March 2021 Accepted: 10 April 2021
DOI: 10.1111/cobi.13756
CONTRIBUTED PAPER
Testing a global standard for quantifying species recovery and
assessing conservation impact
Molly K. Grace1,2H. Resit Akçakaya3,2Elizabeth L. Bennett4
Thomas M. Brooks5,6,7Anna Heath8Simon Hedges4,9,10 Craig Hilton-Taylor11
Michael Hoffmann2,12 Axel Hochkirch13 Richard Jenkins14 David A. Keith15,16,2
Barney Long17 David P. Mallon18,19 Erik Meijaard20 E.J. Milner-Gulland21
Jon Paul Rodriguez2,22 P.J. Stephenson23,24 Simon N. Stuart8,2Richard P. Young25
Pablo Acebes26 Joanna Alfaro-Shigueto27 Silvia Alvarez-Clare28
Raphali Rodlis Andriantsimanarilafy29 Marina Arbetman30 Claudio Azat31
Gianluigi Bacchetta32 Ruchi Badola33 Luís M.D. Barcelos34 Joao Pedro Barreiros35
Sayanti Basak33 Danielle J. Berger36 Sabuj Bhattacharyya37 Gilad Bino38
Paulo A.V. Borges39 Raoul K. Boughton40 H. Jane Brockmann41
Hannah L. Buckley42 Ian J. Burfield43 James Burton44 Teresa Camacho-Badani45
Luis Santiago Cano-Alonso46 Ruth H. Carmichael47 Christina Carrero28
John P. Carroll48 Giorgos Catsadorakis49 David G. Chapple50 Guillaume Chapron51
Gawsia Wahidunnessa Chowdhury52 Louw Claassens53 Donatella Cogoni54
Rochelle Constantine55 Christie Anne Craig56 Andrew A. Cunningham57
Nishma Dahal58 Jennifer C. Daltry59 Goura Chandra Das33 Niladri Dasgupta33
Alexandra Davey59 Katharine Davies60 Pedro Develey61 Vanitha Elangovan62
David Fairclough63 Mirko Di Febbraro64 Giuseppe Fenu54
Fernando Moreira Fernandes65 Eduardo Pinheiro Fernandez66 Brittany Finucci67
Rita Földesi68 Catherine M. Foley69 Matthew Ford70 Michael R.J. Forstner71
Néstor García72 Ricardo Garcia-Sandoval73 Penny C. Gardner74
Roberto Garibay-Orijel75 Marites Gatan-Balbas76 Irene Gauto77
Mirza Ghazanfar Ullah Ghazi33 Stephanie S. Godfrey78 Matthew Gollock79
Benito A. González80 Tandora D. Grant81 Thomas Gray82 Andrew J. Gregory83
Roy H.A. van Grunsven84 Marieka Gryzenhout85 Noelle C. Guernsey86
Garima Gupta87 Christina Hagen88 Christian A. Hagen89 Madison B. Hall90
Eric Hallerman91 Kelly Hare92 To m H ar t 93 Ruston Hartdegen94
Yvette Harvey-Brown60 Richard Hatfield95 Tahneal Hawke38 Claudia Hermes43
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.
© 2021 The Authors. Conservation Biology published by Wiley Periodicals LLC on behalf of Society for Conservation Biology
Conservation Biology. 2021;35:e13756. wileyonlinelibrary.com/journal/cobi 1833 of 1849
https://doi.org/10.1111/cobi.13756

1834 of 1849 GRACE ET AL.
Rod Hitchmough96 Pablo Melo Hoffmann97 Charlie Howarth25 Michael A. Hudson25
Syed Ainul Hussain33 Charlie Huveneers98 Hélène Jacques99 Dennis Jorgensen100
Suyash Katdare33 Lydia K.D. Katsis101 Rahul Kaul102 Boaz Kaunda-Arara103
Lucy Keith-Diagne104 Daniel T. Kraus105 Thales Moreira de Lima106
Ken Lindeman107 Jean Linsky60 Edward Louis Jr.108 Anna Loy109
Eimear Nic Lughadha110 Jeffrey C. Mangel27 Paul E. Marinari111
Gabriel M. Martin112 Gustavo Martinelli113 Philip J.K. McGowan87
Alistair McInnes114 Eduardo Teles Barbosa Mendes106 MichaelJ.Millard
115
Claire Mirande116 Daniel Money117 Joanne M. Monks118 Carolina Laura Morales30
Nazia Naoreen Mumu119 Raquel Negrao110 Anh Ha Nguyen120
Md. Nazmul Hasan Niloy52 Grant Leslie Norbury121 Cale Nordmeyer122
Darren Norris123 Mark O’Brien124 Gabriela Akemi Oda125 Simone Orsenigo126
Mark Evan Outerbridge127 Stesha Pasachnik128 Juan Carlos Pérez-Jiménez129
Charlotte Pike79 Fred Pilkington59 Glenn Plumb130 Rita de Cassia Quitete Portela131
Ana Prohaska132 Manuel G. Quintana133 Eddie Fanantenana Rakotondrasoa29
Dustin H. Ranglack134 Hassan Rankou135 Ajay Prakash Rawat33
James Thomas Reardon136 Marcelo Lopes Rheingantz137 Stephen C. Richter138
Malin C. Rivers60 Luke Rollie Rogers134 Patrícia da Rosa113 Paul Rose139
Emily Royer122 Catherine Ryan140 Yvonne J. Sadovy de Mitcheson141 Lily Salmon142
Carlos Henrique Salvador143 Michael J. Samways144 Tatiana Sanjuan145 Amanda Souza
dos Santos146 Hiroshi Sasaki147 Emmanuel Schutz148 Heather Ann Scott149
Robert Michael Scott149 Fabrizio Serena150 Surya P. Sharma33 John A. Shuey151
Carlos Julio Polo Silva152 John P. Simaika153 David R. Smith154 Julia L.Y. Spaet155
Shanjida Sultana52 Bibhab Kumar Talukdar156 Vikash Tatayah157 Philip Thomas158
Angela Tringali159 Hoang Trinh-Dinh120 Chongpi Tuboi33 Aftab Alam Usmani33
Aída M. Vasco-Palacios160,161 Jean-Christophe Vié162 Evelyn Virens62 Alan Walker163
Bryan Wallace164 Lauren J. Waller165,166 Hongfeng Wang167 Oliver R. Wearn120
Merlijn van Weerd168 Simon Weigmann169,170 Daniel Willcox171 John Woinarski172
Jean W.H. Yong173 Stuart Young44
1Department of Zoology, University of Oxford, Oxford, UK
2IUCN Species Survival Commission, Caracas, Venezuela
3Department of Ecology and Evolution, Stony Brook University, Stony Brook, New York, USA
4Wildlife Conservation Society, Bronx, New York, USA
5International Union for Conservation of Nature (IUCN), Gland, Switzerland
6World Agroforestry Center (ICRAF), University of the Philippines, Los Baños, Philippines
7Institute for Marine & Antarctic Studies, University of Tasmania, Hobart, Tasmania,Australia
8Synchronicity Earth, London, UK
9IUCN SSC Asian Elephant Specialist Group, Noida, India
10 IUCN SSC Asian Wild Cattle Specialist Group, Chester, UK

CONSERVATION BIOLOGY 1835 of 1849
11 IUCN Red List Unit, Cambridge, UK
12 Conservation Programmes, Zoological Society of London, London, UK
13 Department of Biogeography, Trier University, Trier, Germany
14 IUCN Global Species Programme, Cambridge, UK
15 Centre for Ecosystem Sciences, School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, New South Wales, Australia
16 NSW Office of Environment and Heritage, Hurstville, New South Wales, Australia
17 Re:wild, Washington, DC, USA
18 Division of Biology and Conservation Ecology, Manchester Metropolitan University, Manchester, UK
19 IUCN SSC Antelope Specialist Group, Manchester, UK
20 IUCN SSC Wild Pig Specialist Group and Centre of Excellence for Environmental Decisions, University of Queensland, Brisbane, Queensland, Australia
21 Merton College, University of Oxford, Oxford, UK
22 Instituto Venezolano de Investigaciones Científicas, and Provita, Caracas, Venezuela
23 IUCN SSC Species Monitoring Specialist Group, Gingins, Switzerland
24 Laboratory for Conservation Biology, Department of Ecology & Evolution, UNIL - University of Lausanne, Lausanne, Switzerland
25 Durrell Wildlife Conservation Trust, Trinity, UK
26 Centro de Investigación en Biodiversidad y Cambio Global, Departamento de Ecología, Universidad Autónoma de Madrid, Madrid, Spain
27 Carrera de Biologia Marina, Universidad Cientifica del Sur, Lima, Peru
28 The Morton Arboretum, Lisle, Illinois, USA
29 Madagasikara Voakajy, Antananarivo, Madagasikara
30 Grupo Ecología de la Polinización, INIBIOMA, Universidad Nacional del Comahue, CONICET, Bariloche, Argentina
31 Sustainability Research Centre & PhD Programme in Conservation Medicine, Faculty of Life Sciences, Universidad Andres Bello, Santiago, Chile
32 Centre for Conservation of Biodiversity, University of Cagliari, Cagliari, Italy
33 Wildlife Institute of India, Dehradun, India
34 Azorean Biodiversity Group, Centre for Ecology, Evolution, and Environmental Changes, Faculty of Agricultural and Environmental Sciences, University of the Azores, Angra do
Heroísmo, Portugal
35 Universidade dos Açores, Faculdade de Ciências Agrárias e do Ambiente, Rua Capitão João d’Ávila, Angra do Heroísmo, Portugal
36 School of Natural Resources, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
37 Centre for Ecological Sciences, Indian Institute of Sciences, Bangalore, India
38 University of New South Wales, Centre for Ecosystem Science, School of Biological, Earth & Environmental Sciences, University of New South Wales, Randwick, New South Wales,
Australia
39 Departamento de Ciências e Engenharia do Ambiente Universidade dos Açores, Azores, Portugal
40 Range Cattle Research and Education Center, University of Florida, Gainesville, Florida, USA
41 Department of Biology, University of Florida, Gainesville, Florida, USA
42 Auckland University of Technology, Auckland, New Zealand
43 BirdLife International, Cambridge, UK
44 IUCN SSC Asian Wild Cattle Specialist Group, Cedar House, Chester, UK
45 Museo de Historia Natural Alcide d’ Orbigny, Cochabamba, Bolivia
46 Department of Zoology and Physical Anthropology, Complutense University of Madrid, Madrid, Spain
47 Dauphin Island Sea Lab, Dauphin Island, Alabama, USA
48 University of Nebraska, School of Natural Resources, Lincoln, Nebraska, USA
49 Society for the Protection of Prespa, Agios Germanos, Greece
50 School of Biological Sciences, Monash University, Clayton, Victoria, Australia
51 Department of Ecology, Swedish University of Agricultural Sciences, Riddarhyttan, Sweden
52 Department of Zoology, University of Dhaka, Dhaka, Bangladesh
53 Rhodes University, Grahamstown, South Africa
54 Dipartimento di Scienze della Vita e dell’Ambiente, Centro Conservazione Biodiversità, Università degli Studi di Cagliari, Cagliari, Italy
55 School of Biological Sciences & Institute of Marine Science, University of Auckland, Auckland, New Zealand
56 Endangered Wildlife Trust, Office 8 & 9, Centre for Biodiversity Conservation, Cape Town, South Africa
57 Institute of Zoology, Zoological Society of London, London, UK

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58 CSIR-Institute of Himalayan Bioresource Technology, Palampur, India
59 Fauna & Flora International, Cambridge, UK
60 Botanic Gardens Conservation International, Richmond, UK
61 BirdLife/SAVE Brasil, Fernão Dias, Brazil
62 University of Otago, Dunedin, New Zealand
63 Department of Primary Industries and Regional Development, Department of Fisheries, Hillarys, Western Australia, Australia
64 University of Molise, Pesche, Italy
65 Sociedade de Amigos da Fundação Zoobotânica de Belo Horizonte, (Pampulha), Belo Horizonte, Brazil
66 Brazilian National Centre for Flora Conservation, Rio de Janeiro, Brazil
67 National Institute of Water and Atmospheric Research, Wellington, New Zealand
68 Institute of Crop Science and Resource Conservation, University of Bonn, Bonn, Germany
69 Hawai’i Institute of Marine Biology, University of Hawai’i at M¯
anoa, Kaneohe, Hawai’i, USA
70 Museum für Naturkunde, Leibniz Institute for Evolution and Biodiversity Science, Berlin, Germany
71 Texas State University, San Marcos, Texas, USA
72 Pontificia Universidad Javeriana, Bogotá, Colombia
73 Facultad de Ciencias, Universidad Nacional Autónoma de México, Circuito exterior s/n, Ciudad Universitaria, Coyoacán, Mexico
74 Danau Girang Field Centre, c/o Sabah Wildlife Department, Kota Kinabalu, Malaysia
75 Instituto de Biología, Universidad Nacional Autonoma de Mexico, Tercer Circuito s/n, Ciudad Universitaria, Ciudad de México, México
76 Mabuwaya Foundation Inc., ISU Garita, Cabagan, Philippines
77 Asociación Etnobotánica Paraguaya, Lambaré, Paraguay
78 Department of Zoology, University of Otago, Dunedin, New Zealand
79 Zoological Society of London, London, UK
80 Laboratorio de Ecología de Vida Silvestre, Facultad de Ciencias Forestales y de la Conservación de la Naturaleza, Universidad de Chile, Santiago, Chile
81 San Diego Zoo Institute for Conservation Research, San Diego, California, USA
82 Wildlife Alliance, Phnom Penh, Cambodia
83 Bowling Green State University, School of Earth Environment and Society, Bowling Green, Ohio, USA
84 Dutch Butterfly Conservation, Wageningen, the Netherlands
85 Department of Genetics, University of the Free State, Bloemfontein, South Africa
86 World Wildlife Fund Inc., Northern Great Plains Program, Bozeman, Montana, USA
87 School of Natural and Environmental Sciences, Newcastle University, Newcastle, UK
88 BirdLife South Africa, Roggebaai, South Africa
89 Department of Fisheries & Wildlife, Oregon State University, Corvallis, Oregon, USA
90 Department of Biology, University of Central Florida, Orlando, Florida, USA
91 Department of Fish and Wildlife Conservation, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA
92 Urban Wildlife Trust, Wellington/Hamilton, New Zealand
93 Department of Zoology, Oxford University, Oxford, UK
94 Dallas Zoo, Dallas, Texas, USA
95 The Xerces Society for Invertebrate Conservation, Portland, Oregon, USA
96 Department of Conservation—Te Papa Atawhai, Wellington, New Zealand
97 Sociedade Chauá, Rua Julio Gorski, Paraná, Brazil
98 Southern Shark Ecology Group, Flinders University, Adelaide, South Australia, Australia
99 IUCN Otter Specialist Group, Brie et Angonnesù, France
100 World Wildlife Fund Inc., Northern Great Plains Program, Bozeman, Montana, USA
101 Wildlife Conservation Research Unit, Department of Zoology, University of Oxford, Recanati-Kaplan Centre, Abingdon, UK
102 Wildlife Trust of India, Noida, India
103 Department of Fisheries and Aquatic Sciences, University of Eldoret, Eldoret, Kenya
104 African Aquatic Conservation Fund, Thies, Senegal
105 University of Waterloo, School of Environment, Resources and Sustainability, Waterloo, Ontario, Canada

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106 Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
107 Florida Institute of Technology, Program in Sustainability Studies, Melbourne, Florida, USA
108 Omaha’s Henry Doorly Zoo and Aquarium, Omaha, Nebraska, USA
109 Department of Biosciences and Territory, University of Molise, Pesche, Italy
110 Royal Botanic Gardens, Richmond, Surrey, UK
111 Smithsonian Conservation Biology Institute, Front Royal, Virginia, USA
112 Centro de Investigación Esquel de Montaña y Estepa Patagónica, CONICET, Buenos Aires, Argentina
113 National Center for Flora Conservation (CNCFlora), Rio de Janeiro, Brazil
114 Seabird Conservation Programme, BirdLife South Africa, Foreshore, South Africa
115 U.S. Fish and Wildlife Ser vice, Lamar, Pennsylvania, USA
116 International Crane Foundation, Baraboo, Wisconsin, USA
117 Department of Zoology, University of Cambridge, Cambridge, UK
118 Department of Conservation, Dunedin, New Zealand
119 Center for Natural Resource Studies, Dhaka, Bangladesh
120 Fauna & Flora International - Vietnam Programme, Hanoi, Vietnam
121 Manaaki Whenua Landcare Research, Alexandra, New Zealand
122 Minnesota Zoo, Apple Valley, Minnesota, USA
123 School of Environmental Sciences, Federal University of Amapá, Macapá, Brazil
124 BirdLife International Pacific Regional Office, Suva, Fiji
125 Federal Rural University of Rio de Janeiro – UFRRJ, Department of Environmental Sciences, Forestry Institute, Seropédica, Rio de Janeiro, Brazil
126 Dipartimento di Scienze della Terra e dell’Ambiente, Università di Pavia; Dipartimento di Scienze della Vita e dell’Ambiente, Centro Conservazione Biodiversità, Università degli Studi di
Cagliari, Cagliari, Italy
127 Bermuda Government, Department of Environment and Natural Resources, Paget, Bermuda
128 International Iguana Foundation, Fort Worth, Texas, USA
129 El Colegio de la Frontera Sur, Lerma, Campeche, Mexico
130 US National Park Service, Livingston, Montana, USA
131 Ecology Department, Biology Institute, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
132 GeoGenetics Group, Department of Zoology, University of Cambridge, Cambridge, UK
133 Division of Invertebrates, Argentine Museum of Natural Sciences, Buenos Aires, Argentina
134 University of Nebraska Kearney, Kearney, Nebraska, USA
135 IUCN SSC Orchid Specialist Group, Royal Botanic Gardens, Richmond, Surrey, UK
136 Department of Conservation, New Zealand, Fiordland District Office, Te Anau, New Zealand
137 Universidade Federal do Rio de Janeiro, Laboratório de Ecologia e Conservação de Populações, Centro de Ciências da Saúde - Instituto de Biologia, Rio de Janeiro, RJ, Brazil
138 Division of Natural Areas and Department of Biological Sciences, Eastern Kentucky University, Richmond, Kentucky, USA
139 University of Exeter, Exeter, UK
140 Auckland University of Technology, School of Science, Auckland City, New Zealand
141 Swire Institute of Marine Science, University of Hong Kong, Hong Kong
142 Nottingham Trent University, Brackenhurst Campus, Southwell, Nottinghamshire, UK
143 Cooperative Caipora, Florianópolis, Brazil
144 Department of Conservation Ecology & Entomology, Stellenbosch University, Stellenbosch, South Africa
145 Grupo Micologos Colombia, Calle, Colmbia
146 Universidade Federal do Rio de Janeiro, Health Science Centre, Biology Institute, Plant Ecology Laboratory, Rio de Janeiro, Brazil
147 Chikushi Jogakuen University, Dazaifu, Japan
148 D’ABOVILLE Foundation and Demo Far m Inc, Makati, Philippines
149 Namibia Crane Working Group, Swakopmund, Namibia
150 Institute for Biological Resources and Marine Biotechnology, National Research Council-(CNR –IRBIM), Mazara del Vallo, Italy
151 The Nature Conservancy, Indianapolis, Indiana, USA
152 Facultad de Ciencias Naturales e Ingeniería, Universidad de Bogotá Jorge Tadeo Lozano, Bogotá, Colombia
153 Department of Water Resources and Ecosystems, IHE Delft Institute for Water EducationDelft, The Netherlands
154 U.S. Geological Survey, Kearneysville, West Virginia, USA
155 Evolutionary Ecology Group, Department of Zoology, University of Cambridge, Cambridge, UK
156 IUCN Asian Rhino Specialist Group, Guwahati, India

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157 Mauritian Wildlife Foundation, Vacoas, Mauritius
158 Royal Botanic Garden, Edinburgh, UK
159 Archbold Biological Station, Venus, Florida, USA
160 Grupo de Microbiología Ambiental - BioMicro, Escuela de Microbiología, Universidad de Antioquia, UdeA, Medellín, Colombia
161 Fundación Biodiversa Colombia, FBC, Bogotá, Colombia
162 Fondation Franklinia, Genève, Switzerland
163 Centre for Environment, Fisheries & Aquaculture Science, Lowestoft, Suffolk, UK
164 Ecolibrium Inc, Boulder, Colorado, USA
165 Southern African Foundation for the Conservation of Coastal Birds, Cape Town, South Africa
166 Department of Biodiversity and Conservation Biology, University of the Western Cape, Belville, South Africa
167 Northeast Forestry University, Harbin City, China
168 Institute of Environmental Sciences (CML), Leiden University, Leiden, the Netherlands
169 Elasmo-Lab, Elasmobranch Research Laboratory, Hamburg, Germany
170 Center of Natural History, University of Hamburg, Hamburg, Germany
171 Save Vietnam’s Wildlife, Cuc Phuong National Park, Ninh Bình Province, Vietnam
172 Charles Darwin University, Casuarina, Northern Territory, Australia
173 Department of Biosystems and Technology, Swedish University of Agricultural Sciences, Alnarp, Sweden
Correspondence
Molly K. Grace, Department of Zoology, University
of Oxford, Oxford, OX13SZ, UK.
Email: molly.grace@zoo.ox.ac.uk
Article impact statement: IUCN’s Green Status
of Species shows the value of conservation despite
identifying many depleted species.
Abstract
Recognizing the imperative to evaluate species recovery and conservation impact, in 2012
the International Union for Conservation of Nature (IUCN) called for development of a
“Green List of Species” (now the IUCN Green Status of Species). A draft Green Status
framework for assessing species’ progress toward recovery, published in 2018, proposed
2 separate but interlinked components: a standardized method (i.e., measurement against
benchmarks of species’ viability, functionality, and preimpact distribution) to determine
current species recovery status (herein species recovery score) and application of that method
to estimate past and potential future impacts of conservation based on 4 metrics (conserva-
tion legacy, conservation dependence,conservation gain,andrecovery potential). We tested the frame-
work with 181 species representing diverse taxa, life histories, biomes, and IUCN Red
List categories (extinction risk). Based on the observed distribution of species’ recovery
scores, we propose the following species recovery categories: fully recovered, slightly depleted,
moderately depleted, largely depleted, critically depleted, extinct in the wild, and indeter-
minate. Fifty-nine percent of tested species were considered largely or critically depleted.
Although there was a negative relationship between extinction risk and species recovery
score, variation was considerable. Some species in lower risk categories were assessed as
farther from recovery than those at higher risk. This emphasizes that species recovery is
conceptually different from extinction risk and reinforces the utility of the IUCN Green
Status of Species to more fully understand species conservation status. Although extinc-
tion risk did not predict conservation legacy, conservation dependence, or conservation
gain, it was positively correlated with recovery potential. Only 1.7% of tested species were
categorized as zero across all 4 of these conservation impact metrics, indicating that conserva-
tion has, or will, play a role in improving or maintaining species status for the vast majority
of these species. Based on our results, we devised an updated assessment framework that
introduces the option of using a dynamic baseline to assess future impacts of conservation
over the short term to avoid misleading results which were generated in a small number of
cases, and redefines short term as 10 years to better align with conservation planning. These
changes are reflected in the IUCN Green Status of Species Standard.
KEYWORDS
conservation action, Green Status of species, IUCN, recovery categories, red list
Resumen: Reconociendo que era imperativo evaluar la recuperación de especies y el
impacto de la conservación, la Unión Internacional para la Conservación de la Naturaleza
(UICN) convocó en 2012 al desarrollo de una “Lista Verde de Especies” (ahora el Estatus
Verde de las Especies de la UICN). Un marco de referencia preliminar de una Lista Verde

CONSERVATION BIOLOGY 1839 of 1849
de Especies para evaluar el progreso de las especies hacia la recuperación, publicado en
2018, proponía 2 componentes separados pero interconectados: un método estandarizado
(i.e., medición en relación con puntos de referencia de la viabilidad de especies, funcionali-
dad y distribución antes del impacto) para determinar el estatus de recuperación actual (pun-
tuación de recuperación de la especie) y la aplicación de ese método para estimar impactos en el
pasado y potenciales de conservación basados en 4 medidas (legado de conservación, dependencia
de conservación, ganancia de conservación y potencial de recuperación). Probamos el marco de referen-
cia con 181 especies representantes de diversos taxa, historias de vida, biomas, y categorías
(riesgo de extinción) en la Lista Roja de la IUCN. Con base en la distribución observada
de la puntuación de recuperación de las especies, proponemos las siguientes categorías de
recuperación de la especie: totalmente recuperada, ligeramente mermada, moderadamente mer-
mada, mayormente mermada, gravemente mermada, extinta en estado silvestre, e indert-
erminada. Cincuenta y nueve por ciento de las especies se consideraron mayormente o
gravemente mermada. Aunque hubo una relación negativa entre el riesgo de extinción y la
puntuación de recuperación de la especie, la variación fue considerable. Algunas especies en
las categorías de riesgo bajas fueron evaluadas como más lejos de recuperarse que aquellas
con alto riesgo. Esto enfatiza que la recuperación de especies es diferente conceptualmente
al riesgo de extinción y refuerza la utilidad del Estado Verde de las Especies de la UICN
para comprender integralmente el estatus de conservación de especies. Aunque el riesgo
de extinción no predijo el legado de conservación, la dependencia de conservación o la
ganancia de conservación, se correlacionó positivamente con la potencial de recuperación.
Solo 1.7% de las especies probadas fue categorizado como cero en los 4 indicadores de impacto
de la conservación, lo que indica que la conservación ha jugado, o jugará, un papel en la
mejoría o mantenimiento del estatus de la especie la gran mayoría de ellas. Con base en
nuestros resultados, diseñamos una versión actualizada del marco de referencia para la
evaluación que introduce la opción de utilizar una línea de base dinámica para evaluar
los impactos futuros de la conservación en el corto plazo y redefine corto plazo como
10 años.
Palabr as Clave:
acciones de conservación, categorías de recuperación, estatus verde de especies, IUCN, lista roja
INTRODUCTION
The aims of conservation include protection and restoration of
natural systems and the recovery of species and their ecologi-
cal functions. Until recently, there has been no standardized way
of thinking about and measuring species recovery. In 2012, the
International Union for Conservation of Nature (IUCN) began
working to fill that gap by developing an IUCN Green List of
Species, based on objective, transparent, and repeatable crite-
ria for systematically assessing successful species conservation
(WCC-2012-Res-041).
The IUCN Green List of Species was envisioned as a com-
plement to the IUCN Red List of Threatened Species (IUCN
2020), which has become the global standard for assessment
of species’ extinction risk. The supporting information accom-
panying each IUCN Red List assessment is a valuable source
of data about each species’ status, trends, habitat, distribu-
tion, threats, and conservation, and the list has informed global
species conservation efforts for more than 50 years (Rodrigues
et al., 2006). The IUCN Green List of Species would add new
information about species’ recovery level, as well as the impact
of conservation actions.
Following international consultations between 2012 and
2018, the Species Conservation Success Task Force proposed a
framework for an IUCN Green List of Species (Akçakaya et al.,
2018). The name has since changed to IUCN Green Status of
Species to prevent the erroneous interpretation that species on
a green list are no longer in need of conservation action. The
goal of the IUCN Green Status of Species is to provide a stan-
dardized way of assessing a species’ level of recovery and under-
standing the past and potential future importance of conserva-
tion in improving or maintaining recovery status (Figure 1).
A new way to communicate conservation’s
impact
A species moving to a lower category of extinction risk on
the IUCN Red List due to conservation measures is a use-
ful indicator of conservation impact (Butchart et al., 2006).
However, many species may remain in a high threat cate-
gory for long periods despite successful conservation efforts.
For example, the Round Island bottle palm (Hyophorbe lageni-
caulis) has been listed as critically endangered since 1998 (Page,

1840 of 1849 GRACE ET AL.
FIGURE 1 Simplified example of a provisional Green Status assessment with the Echo Parakeet (Psittacula eques). This assessment, and others conducted for
this article, is provisional and should not be cited until its publication on the International Union for Conservation of Nature Red List website. Documentation of
uncertainty about species state within a spatial unit in steps 3 and 4 is not shown. Details of the procedures followed and definitions of terms (e.g., viable,functional)
are in Akçakaya et al. (2018) and Appendix S1 and Appendix S3 (data entry workbook used by assessors to generate the data analyzed here) of this article

CONSERVATION BIOLOGY 1841 of 1849
1998). Despite dedicated conservation, it still meets the cri-
teria for critically endangered (V. Tatayah, personal commu-
nication). This does not mean that conservation has failed;
it is highly likely the species would have gone extinct with-
out conservation (Asmussen-Lange et al., 2011). By standard-
izing and generalizing the process of using prevented declines
to assess conservation impact (e.g., Bolam et al., 2020; Hoff-
mann et al., 2015), the IUCN Green Status of Species can help
improve understanding of what works, recognize the efforts of
conservationists, and ensure continued donor and government
support.
Furthermore, even when conservation action results in an
improvement in IUCN Red List Category, it can be compli-
cated to communicate these actions as a conservation success.
For example, the downlisting of the giant panda (Ailuropoda
melanoleuca) from endangered to vulnerable in 2016 was at first
subject to controversy. Some feared that the category change
could promote a simplistic narrative of success for a species
that remained highly conservation dependent (Swaisgood et al.,
2018). This reluctance to report conservation achievements is
a problem because conservation science fights an uphill battle
against a “culture of despair” (Swaisgood & Sheppard, 2010).
Conservation impact as communicated through
Green Status
The IUCN Green Status of Species introduces a new way
of thinking about conservation impact by defining conserva-
tion status in terms of progress toward species recovery: a
fully recovered species is viable and ecologically functional
throughout its indigenous range (Appendix S1;IUCN2021).
Full recovery is not realistic for many species; but rather, it is
used as a benchmark. An assessment reflects a species’ cur-
rent standing relative to this benchmark, as well as the past
and expected future impact of conservation actions on this
standing.
This ambitious definition of recovery should help com-
bat “shifting baseline syndrome” (Pauly 1995; Papworth et al.,
2009). Recognizing that humans have significantly altered nat-
ural systems over time, there are calls to use historical data,
specifically species’ distribution and status prior to major human
impacts, as a recovery benchmark (Sanderson 2019; Stephenson
et al., 2019). Some species with negligible extinction risk exist at
levels far below their preimpact baseline (Rodrigues et al., 2019).
Inclusion of ecological functionality is another ambitious
aspect of the IUCN Green Status of Species’ definition of
recovery. Although maintaining species’ viability and preventing
extinctions caused by human activities is the first goal of species
conservation, this should not preclude actions to maintain func-
tionality (i.e., the set of interactions that contribute to ecological
processes) and thus prevent “ecological extinctions” (Redford,
1992). The call to incorporate ecological function into conserva-
tion is not new (Redford & Feinsinger 2001; Soulé et al., 2003).
The IUCN Green Status of Species is the first global framework
to incorporate functionality in assessment of species recovery
(Akçakaya et al., 2018;2020).
Testing the Green Status of Species
We applied the Akçakaya et al. (2018) framework for a Green
Status of Species to a sample of species. Our aims were to apply
the assessment method to species across different taxonomic
groups, systems, geographies, and IUCN Red List Category of
extinction risk; identify changes necessary to make the frame-
work universally applicable; examine new insights Green Status
assessments can add to conservation and demonstrate that these
assessments represent more than simply a red list in reverse; and
propose meaningful categories of recovery status based on test
data.
METHODS
Species selection and assessors
Between 2018 and 2020, we sent invitations to all IUCN Species
Survival Commission Specialist Groups and IUCN Red List
Authorities (RLA), the groups responsible for IUCN Red List
assessments (n=135 in 2018). We also recruited species experts
with no formal affiliation with IUCN by creating a project web-
site with joining instructions. The selection of species was at the
discretion of the assessors. Other than our attempt to engage all
specialist groups and RLAs, each of which focuses on a unique
taxon or geographic region, we did not attempt a systematic or
representative sampling of global diversity. Appendix S2 lists
the assessors for each species. All assessors who wished to be
included are authors of this article. To determine the geographic
coverage of testing, species’ countries of occurrence as reported
in the IUCN Red List (IUCN 2020) were extracted using the
package rredlist (Chamberlain, 2020).
We standardized the testing process by providing uniform
materials, including a standardized assessment workbook con-
taining all instructions, data entry, and documentation fields
(Appendix S3). Participants engaged with a coordinator (M.G.)
throughout the process to reduce the potential for misinterpre-
tation of the framework.
Green Status of Species framework
Species were assessed using the Akçakaya et al. (2018) frame-
work. In Figure 1, it is applied to an example species. The basis
of an assessment is the estimation of 5 green scores, which repre-
sent species condition relative to the fully recovered state, from
0% (extinct or extinct in the wild) to 100% (fully recovered)
(Figure 1explains the green-score calculation). The green score
at the time of assessment, based on observed or inferred infor-
mation, is called the species recovery score. Green scores were also
estimated based on scenarios exploring the past and expected
future impact of conservation actions; these scenario-based
green scores were used to calculate 4 conservation impact metrics
(Figure 1): conservation legacy (impact of past conservation); con-
servation dependence (expected impact of halting all conservation
in the short term, i.e., the longer of 10 years or 3 generations

1842 of 1849 GRACE ET AL.
of the species); conservation gain (expected impact of continu-
ing conservation in the short term); and recovery potential (max-
imum possible recovery within 100 years). Full definitions of
these terms and a summary of the assessment procedure are in
Appendix S1.
Relationship between Green Status of Species
outputs and IUCN Red List category
To investigate the potential of Green Status of Species assess-
ments to provide novel conservation insights, we evaluated
whether species recovery scores were predicted by IUCN Red
List category by performing beta regression in the R package
betareg (Cribari-Neto & Zeileis, 2010) in R version 4.0.0 (R Core
Team 2020). We excluded species considered extinct in the wild
(EW) on the IUCN Red List because by definition their species
recovery score is 0. Our data set included 0s (pre-exclusion of
EW) and 1s, but we did not use zero-one inflated beta regression
because it assumes that the 0s and 1s are special cases generated
under different processes than other data points (Buis, 2010),
which was not true for our data set. To allow for regular beta
regression (where the data set cannot contain 0s or 1s), we used
the rescaling method recommended by Smithson and Verkuilen
(2006): y’=[y(N– 1) * 1/2]/ N, where Nis the total sample
size. No transformation was greater than adding or subtracting
0.003 from the original data point.
Model terms were evaluated using the function joint_tests
(package “emmeans [Lenth, 2020]) and the pseudo R2obtained
using R base package summary. Pairwise comparison of esti-
mated marginal means (a.k.a. least-squares means) was per-
formed using the function cld (package multcomp [Hothorn
et al., 2008]); estimated marginal means were compared rather
than observed means to account for unbalanced sampling.
Unlike species recovery score, the 4 conservation impact met-
rics can take negative values. Because this more closely rep-
resents a continuous distribution, Welch’s analysis of variance
(ANOVA) (which does not assume equal variance between
groups) was used to investigate the relationship between met-
ric values and IUCN Red List categories at the time of
assessment (function oneway.test, package onewaytests [Dag
et al., 2018]). Pairwise comparisons were performed using the
Games–Howell test (function oneway, package userfriendly-
science [Peters, 2018]). Though species considered EW on the
IUCN Red List can obtain nonzero values for conservation gain
and recovery potential, they were excluded from this analysis
because of small sample size (n=2).
Species recovery categories
The IUCN Red List of Threatened Species has demonstrated
the value of categories in communicating conservation infor-
mation (Betts et al., 2020). We, therefore, sought to create cate-
gories with which species recovery score percentages would be
more easily interpreted. We proposed 7 IUCN species recov-
ery categories: fully recovered (species recovery score 100%),
slightly depleted (>80%), moderately depleted (>50%), largely
depleted (>20%), critically depleted (>0%), extinct in the wild
(0%), and indeterminate. Although the species recovery score
required for inclusion in 2 of these categories is definitional
(extinct in the wild, fully recovered), the thresholds between
other categories are somewhat subjective. We examined the dis-
tribution of test data against these categories to check that the
proposed categories were both meaningful (e.g., values in the
category reflect the state suggested in the name) and useful (e.g.,
there are more than a negligible and less than an overwhelming
proportion of species in each category). This mirrors the con-
ceptual basis of IUCN Red List thresholds (Collen et al., 2016).
We included an indeterminate category for species with large
uncertainty around the species recovery score. This uncertainty
threshold was determined using visual examination of the data
and is reported in “Results.”
Conservation impact metrics categories
Like the species recovery score, the 4 conservation impact met-
rics take percentage values (Figure 1). To aid in communica-
tion of these metrics, we defined the following categories: high,
medium, low, zero, negative, and indeterminate. As with the
species recovery score categories, breaks between these cate-
gories are delimited by threshold values. If the uncertainty asso-
ciated with a metric value (maximum – minimum estimate)
exceeded 40%, it was placed in the indeterminate category.
Additionally, metrics could be assigned to the high category not
only by surpassing a fixed threshold value, but also if they were
high relative to the current value or if they represented preven-
tion or reversal of extinction in the wild. See Appendix S4 for
the rules used to assign metric categories.
Collection of feedback
In addition to the quantitative elements of assessments—
species recovery score, conservation impact metrics, and
categories––assessors provided qualitative feedback. Unstruc-
tured feedback took the form of comments made during one-
on-one communication between the assessors and members of
the task force. Structured feedback was collected using feedback
fields in the workbook; assessors were invited to comment on
the different stages of the assessment process.
RESULTS
Test assessors
Of 135 IUCN groups contacted, 52 contributed test assess-
ments (38.5%). Taxonomic focus of assessors (Appendix S2)
closely tracked the proportional representation of taxonomic
focus within IUCN Specialist Groups and Red List Authori-
ties overall (Appendix S5). Specialist Groups and RLAs were
the predominant source of test assessments (78%), although

CONSERVATION BIOLOGY 1843 of 1849
FIGURE 2 Spatial distribution of taxa used to test the International Union for Conservation of Nature (IUCN) Green Status method (n=181): (a) number of
tested terrestrial and freshwater taxa (n=118 and n=37, respectively) by country whose ranges include that country (small islands are not visible at this scale) and
(b) number of tested marine taxa (n=26) by exclusive economic zone (EEZ) whose ranges include that EEZ (EEZs for Antarctica [200 NM], South Georgia, and
Sandwich Islands are mapped). Taxa that spend part of their time in the ocean and part on land or in freshwater were mapped as marine taxa and are not represented
in (a). In (a) and (b), taxa presence in countries or EEZs are based on geographic range reported in IUCN Red List (IUCN 2020). When a taxon’s origin code in a
country (as specified in its Red List account) was introduced, vagrant, or origin uncertain, or when its seasonality code was passage (RLTWG 2018), we did not map
that country for that taxon
independent experts made substantial contributions (22%).
More than 200 people, working in 38 countries, volunteered
as assessors to produce test assessments (Appendix S5) (mean
[SD] =2.2 assessors/species [2.7]) (Appendix S2).
Test species and biases
The framework was tested with 181 taxa (172 species, 7 sub-
species, and 2 regional groupings) (Appendix S2). These taxa
(hereafter species for simplicity) represent diverse taxonomic
groups across plants, animals, and fungi (Appendix S5), geo-
graphic regions (Figure 2), and range sizes. Test species’ extent
of occurrence (IUCN Standards and Petitions Committee 2019)
ranged from 0.04 km2to 298 million km2(Appendix S5). The
IUCN has assessed extinction risk for 97% of tested species.
Sixty-seven percent of species were in a threatened IUCN Red
List category (vulnerable [VU], endangered [EN], or critically
endangered [CR]), and all IUCN Red List categories were rep-
resented, except data deficient (DD). Terrestrial, freshwater, and
marine species were represented (65%, 21%, and 14%, respec-
tively). Because these species are not a representative sam-
ple of global biodiversity (biased toward terrestrial, threatened
species), percentages here serve only to characterize the data set
and cannot be extrapolated further. Nonetheless, the diversity
of species tested allowed for identification of taxon- and life-
history-specific challenges (see “Feedback”).
Distribution of species recovery scores
The species recovery scores (Appendix S6) covered the range
of all possible values (Figure 3a). The species were distributed
among the proposed Species Recovery categories as follows
(Figure 3a): fully recovered, 5%; slightly depleted, 7%; moder-
ately depleted, 14%; largely depleted, 46%; critically depleted
14%; and extinct in the wild, 2%. The spike in Figure 3a at
the x-axis value of 33% results from the properties of the green
score calculation (Figure 1). If a species had only 1 spatial unit
(SU), the only values the green score could take would be 0%
(species absent in SU), 33% (present), 67% (viable), or 100%
(functional), and 95% of tested species with 1 SU are listed as

1844 of 1849 GRACE ET AL.
FIGURE 3 For 181 tested taxa, (a) distribution
of species recovery scores (SRSs) and proposed
species recovery category thresholds (EW, extinct in
the wild; bins, increments of 5% exclusive of low
values and inclusive of high values, except first bin
[0%] and last bin [100%]; no shading, species for
which the best estimate of SRS was in that bin but
uncertainty around the best estimate was large
enough for it to be categorized as indeterminate;
spike at 33% due to properties of green score
calculation (Figure 1); see text and (b) distribution of
uncertainty (max – min) of reported SRSs (dashed
vertical line, cutoff for placement of species in the
indeterminate category)
threatened on the IUCN Red List (if a population in an SU is
threatened, the state in the SU usually is present, with some
exceptions [IUCN 2021]).
Based on the distribution of uncertainty around species
recovery scores (Figure 3b), we applied the indeterminate cat-
egory when uncertainty was >40%; 12% of species were cate-
gorized thus.
Relationship between species recovery score
and IUCN Red List category
The IUCN Red List category was a significant predictor of
species recovery score (Figure 4). Species at higher extinction
risk generally had a lower species recovery score (beta regres-
sion, F=69.7, df =6, p<0.0001; pseudo R2=0.45). Nonethe-
less, within a given IUCN Red List category, the range of species
recovery scores was wide; standard deviation of species recov-
ery scores within a RL category ranged from 13% (CR) to
22% (VU) (calculated using observed values, not model values).
Species recovery scores were not significantly different between
some categories (Figure 4). It was not uncommon for a species
FIGURE 4 (a) Relationship between species recovery score (SRS) and
IUCN Red List extinction risk categories (LC, least concern; NT, near
threatened; VU, vulnerable; EN, endangered; CR, critically endangered)
excluding species extinct in the wild because their SRS is by definition 0% (box
limits, first and third quartiles, respectively; horizontal lines, median; whiskers,
smallest and largest values no farther than 1.5 interquartile range); points,
values beyond interquartile range; numbers in boxes, sample size) and (b)
estimated marginal means of SRS calculated from the beta regression model
and used to compare groups with unequal samples (bars, 95% CI around
estimated marginal mean; differing letters, significantly different with
Tukey-adjusted p<0.05)

CONSERVATION BIOLOGY 1845 of 1849
FIGURE 5 For 181 tested taxa, (top row) conservation impact metric values relative to the taxon’s International Union for Conservation of Nature extinction
risk category (box plot elements defined in Figure 4legend) and (bottom row) distribution of conservation impact metric categories by extinction risk category: (a)
conservation legacy, (b) conservation dependence, (c) conservation gain, (d) recovery potential (LC, least concern; NT, near threatened; VU, vulnerable; EN,
endangered; CR, critically endangered; EW, extinct in the wild)
in a nonthreatened IUCN Red List category (LC or NT) to have
the same or lower species recovery score as a species in a threat-
ened category (Figure 4).
Conservation impact metrics
There was no significant difference in the numeric values of
conservation legacy among IUCN Red List categories (Welch’s
ANOVA, F=1.42, p=0.236) (Figure 5a). The assessments
for more than half of test species showed a positive impact
of past conservation, including more than half of the threat-
ened (VU, EN, CR) species tested (Figure 5a). Twenty-eight
percent of tested species overall showed high conservation
legacy. Of tested species, the high category included 33 cur-
rently threatened species for which past conservation actions
may have prevented extinction (i.e., best estimate is that extinc-
tion was prevented). For 10 species, no uncertainty in this result
was reported (i.e., extinction prevented in lower bound, upper
bound, and best estimates). The remaining species’ conserva-
tion legacies were classified as indeterminate (17%) or zero
(31%); no species was found to have a negative conservation
legacy.
For 17/56 species where conservation legacy was categorized
as zero, this classification was because no past conservation
action had been taken. For the remaining species in this cate-
gory, where conservation actions had taken place but there was
no evidence that the current Green Score would be different if
they had not, various reasons were reported (Table 1).
There was no significant difference in numeric values of
conservation dependence among IUCN Red List categories
(Welch’s ANOVA, F=0.789, p=0.537) (Figure 5b). More
than half (61%) of tested species had positive conservation
dependence (Figure 5b). This was the conservation impact
metric for which the largest number of species fell into the
high category (67 species, 37%), indicating that continued con-
servation action is vital to prevent declines in status. For 39
of the 181 tested species, it was estimated that halting con-
servation actions could result in extinction within 3 gener-
ations; assessments of 7 species reported no uncertainty in
this result (i.e., extinction prevented in lower bound, upper
bound, and best estimates). Species recovery scores of these 39
species varied from 6% (critically depleted) to 67% (moderately
depleted).
There was also no significant difference in numeric val-
ues of conservation gain between IUCN Red List categories
(Welch’s ANOVA, F=1.14, p=0.345) (Figure 5c). Just
under half of tested species (48%) showed positive conserva-
tion gain (i.e., indicating opportunities exist to achieve better-
than-current recovery status in the next 10 years or 3 gener-
ations if planned conservation actions take place). In contrast
to conservation dependence, conservation gain was the met-
ric with the lowest number of species in the high category:
14 species (8%).
Conservation gain was the metric for which the largest num-
ber of species fell into the negative category—10 species (5.5%)
(Figure 5c). Two species were categorized as having a negative
conservation dependence (Figure 5b). The negative category

1846 of 1849 GRACE ET AL.
TAB L E 1 Reported reasons species subject to conservation action have a conservation legacy score of 0 (n=41 species)*
Reason Species (%)
Action affected only a small part of the global species population (smaller than a spatial unit) 46
Action had a positive effect, but the effect was not enough to change the species’ status in its spatial unit or units (i.e., Green
Status of Species method not sensitive enough to record relatively limited impact)
39
Action occurred ex situ only 22
Action did not address relevant issue or threat 20
Action did not address most significant problem or threat 17
Lack of evaluation of action or species monitoring 17
Poor management or enforcement of action 15
Action started but not completed 12
Action completed, but duration of action was not long enough to have an impact 12
Action started too recently to show an effect 7
Action started too late to counteract threat 2
*Often, >1 reason was applied to a species, so the percentages reflect the percentage of species for which the factor was reported.
was created to indicate that the species would be worse off if
conservation continued (negative conservation gain) or that it
would be better off if conservation stopped (negative conserva-
tion dependence). However, for the tested species, neither sit-
uation was detected. Rather, in these cases, conservation con-
ferred a benefit, but species’ status was expected to deteriorate
even with conservation (see Discussion).
The majority of tested species (70%) had positive recov-
ery potential (Figure 5d), suggesting significant opportunities
within the next 100 years for species recovery where the species
is extant, for restoration to areas where it has been extirpated,
or for expansion into expected additional range. For more than
half of species, recovery potential was categorized as medium
(40%) or high (20%), indicating that there is substantial space
for ambitious recovery planning.
Recovery potential was the only metric for which numeric
values were significantly different between IUCN Red List cat-
egories (Welch’s ANOVA, F=8.90, p<0.0001) (Figure 5d).
Although recovery potential values between 2 adjacent cate-
gories were never significantly different, significant contrasts
(p<0.05) between higher threat and lower threat IUCN Red
List categories were observed in many cases, indicating that
the higher the extinction risk, the higher the recovery potential
tended to be (see Appendix S7 for full list of contrasts).
Relatively few species had zero recovery potential (10%).
More than half of these species (10 of 18) were considered
fully recovered (i.e., species recovery score =100%). The other
species in the zero recovery potential category included species
for which zero recovery potential was reported as the most
likely outcome, with uncertainty indicating that some recovery
could be possible (5 of 18). Finally, some species had no uncer-
tainty; they had experienced degradation and loss that assessors
considered irreversible or assessors judged future degradation
within the range unstoppable or immitigable (3 of 18).
Four percent of tested species were estimated to have nega-
tive recovery potential, which means that, under the most opti-
mistic scenario within 100 years, the species is expected to have a
lower green score than it does now (e.g., Antiguan racer [Alsophis
antiguae)]).
Finally, both extinct in the wild species tested (Franklin tree
[Franklinia alatamaha]andAylacostoma chloroticum, a freshwater
snail) were considered to have a high recovery potential because
individuals exist in ex situ collections and there is a good proba-
bility that within 100 years successful reintroductions to the wild
could take place.
Feedback
Several areas for improvement of the method emerged multi-
ple times from test assessors and workshop participants (sum-
marized in Appendix S8). One major recommendation was
to change the period for conservation gain and conservation
dependence to 10 years, rather than 10 years or 3 generations.
Another was that calculating conservation gain and conserva-
tion dependence relative to the species recovery score created
the potential for false negative categorizations.
DISCUSSION
Our results showed that the IUCN Green Status of Species is
applicable to a wide range of species and provides important
and unique information about the status of biodiversity that
complements the information provided by the IUCN Red List.
It is not possible to predict Green Status outcomes based on
a species’ IUCN Red List categories alone (Figures 4and 5).
Nonetheless, there was a significant relationship between the
two. That over two-thirds of tested species were in a threatened
IUCN Red List categories likely explains why over half of tested
species were categorized as largely depleted or critically depleted
(Figure 3). However, 5 of 17 near threatened species and 4 of
33 least concern species were also considered largely depleted.
By using species’ preimpact distribution as a baseline and

CONSERVATION BIOLOGY 1847 of 1849
incorporating ecological functionality, the IUCN Green Status
of Species provides a definition of recovery that can be con-
sidered linked to, but distinct from, extinction risk (Mace et al.,
2008). To maximize synergy and benefits, IUCN has linked the
2 approaches, requiring an IUCN Red List assessment to exist
(or to be conducted simultaneously) for species undergoing an
IUCN Green Status of Species assessment (IUCN 2021).
The conservation impact metrics—conservation legacy,
conservation dependence, conservation gain, and recovery
potential—allowed for a nuanced examination of the effective-
ness of past species conservation efforts and the potential for
future conservation. These metrics put the species recovery
score and IUCN Red List categories in context—the knowledge
that a species is largely depleted (IUCN Green Status) and vul-
nerable (IUCN Red List) reads negatively, but combined with
a high conservation legacy that prevented extinction, the story
becomes one of success. The IUCN Green Status of Species’
use of short-term and long-term milestones creates a vision
of potential futures that can be incorporated in conservation
planning to inform strategies to minimize losses and maximize
potential gains. These metrics could help in the evaluation of
effectiveness of conservation actions. For example, if a species’
conservation legacy is zero, despite active efforts, it would be
useful to determine why (Table 1).
By introducing a formal measure of conservation depen-
dence, the IUCN Green Status of Species may provide a resolu-
tion to the controversies that sometimes accompany a species’
downlisting to lower IUCN Red List categories. The IUCN Red
List guidelines currently allow species that would otherwise be
considered least concern to be placed in the near threatened
category if they are thought to be “conservation dependent”
(IUCN Standards and Petitions Committee 2019). The IUCN
Green Status of Species creates a formal mechanism for quan-
tifying conservation dependence and recognizes that species in
any IUCN Red List category can be conservation dependent
(Figure 5b).
Conservation gain highlights opportunities for recovery in
the short-term and could play an important role in incentivizing
future conservation action. Achieving a high value was less com-
mon for conservation gain than the other conservation impact
metrics (Figure 5c). Although loss and degradation often hap-
pen relatively quickly, recovery can be a comparatively slow pro-
cess (Novacek & Cleland, 2001), which may explain this result.
Of the 14 species with high conservation gain, 10 were cate-
gorized as such not because conservation gain was intrinsically
high, but because it was relatively high compared with the cur-
rent species recovery score (Appendix S6). For example, the
pale-headed brushfinch (Atlapetes pallidiceps) was placed in the
high category despite an expected conservation gain of only
17%. This was because the species recovery score was only 8%,
and the expected conservation gain of 17% therefore repre-
sented a substantial move toward recovery.
The ability of assessments to highlight near-term opportu-
nities through conservation gain counters the necessarily long
process of recognizing reduced extinction risk. For long-lived
species, ceasing to meet the criteria for a given IUCN Red List
category may take decades, which is far too long for policy mak-
ers or donors wanting to assess the impact of funding and poli-
cies. For this reason, it makes sense to change the definition of
short term in the IUCN Green Status of Species assessment from
10 years or 3 generations to simply 10 years (IUCN 2021).
Finally, the recovery potential metric allows conservation
planners to envision the maximum recovery that could be
achieved if all opportunities for conservation action and inno-
vation over the next 100 years were taken. Two things should
be noted. First, it will not be realistic for most species to have
a green score of 100% even after recovery potential is fulfilled,
and this does not indicate conservation failure. Humans have
converted large areas of the world, and climate change threat-
ens the persistence or precludes the return of many species
in parts of their indigenous range. Recovery potential merely
seeks to estimate how much recovery is possible in the con-
text of the modern world. The observation that species that are
more highly threatened tended to have higher recovery potential
(Figure 5d) provides encouragement that ambitious conserva-
tion actions could greatly improve their status. Second, achiev-
ing the recovery potential estimated in the assessment is not
necessarily a conservation goal; rather, it can help guide conser-
vation planning by indicating opportunities available for ambi-
tious species recovery action.
Future directions
Although our tests covered a diversity of species, geographies,
and biomes, we did not sample in a systematic or representative
way (which is why we do not, e.g., report statistics by taxon).
Our data set was biased toward threatened species (67%), so
our figures of extinctions prevented by past conservation or
likely to be prevented by future conservation cannot be gen-
eralized. Comprehensive evaluation of counterfactual status for
all species within a taxon yielded lower rates of extinctions pre-
vented (Butchart et al., 2006; Hoffmann et al., 2015). Although
Green Status of Species assessments could eventually improve
understanding of the global impact of conservation, the values
reported here are not representative. Understanding the recov-
ery status and trajectories of a systematic sample of the world’s
major species groups would provide valuable information for
conservation planning. With reassessments over time, changes
in species recovery scores could be used to track changes in
recovery status.
The current status of 12% of tested species presented
enough uncertainty that these species were placed in the species
recovery category indeterminate (Figure 3). Uncertainty was
even higher within the conservation impact metrics (Figure 5).
Although categorization was possible for the majority of tested
species, indeterminate values highlight knowledge gaps—in the
case of the conservation impact metrics, gaps in understanding
of the impacts of conservation actions. This uncertainty can be
reduced in the short term by engaging larger groups of experts
in the assessment process for a species and employing struc-
tured elicitation methods (e.g., Hemming et al., 2018)and,in
the long term, by rigorously designing conservation interven-
tions so that their impact can be evaluated (Baylis et al. 2016).

1848 of 1849 GRACE ET AL.
Our testing highlighted several potential areas for improve-
ment of the method (Appendix S8), which have been incorpo-
rated in the IUCN Green Status of Species Standard (IUCN
2021). The challenges of identifying indigenous range and eco-
logical functionality of a species have been discussed elsewhere
(Akçakaya et al., 2020; Grace et al., 2019). However, one area
highlighted for improvement is most relevant to the interpre-
tation of the results presented here. For the tested species,
conservation gain and conservation dependence were the dif-
ference between the current green score (i.e., species recov-
ery score) and the green scores generated in the future-
with-conservation and future-without-conservation scenarios,
respectively (Figure 1). Using the species recovery score to cal-
culate these metrics represents the use of a “static baseline”
(Ferraro, 2009), where it is assumed that continued conserva-
tion action would result in a future green score greater or equal
to the species recovery score and discontinued conservation
would result in a future green score less than or equal to the
species recovery score. However, this is not necessarily the case,
even with continued conservation, because the species’ status
may deteriorate in the future (if threats to a species multiply or
amplify independently [Maron et al., 2015]). This explains the
negative values for conservation gain and conservation depen-
dence observed in testing (Figure 5b and c). To avoid giving the
false impression that conservation action is predicted to have
negative impacts on species’ status, in the future assessors will
have the option of using a “dynamic baseline” estimated based
on a species’ predicted trajectory (Ferraro, 2009).
Our results suggest that the IUCN Green Status of Species
method is a practical and operational way to assess species
recovery in a manner that usefully complements assessment of
extinction risk. The IUCN Green Status of Species will continue
to undergo development and refinement in the years to come,
following a process similar to the IUCN Red List of Threat-
ened Species, which has evolved over the decades as improve-
ments were identified (Hilton-Taylor, 2014). This iterative pro-
cess of improvements will ensure that the IUCN Green Status
of Species develops as a robust and useful measure of species
recovery and conservation success.
ACKNOWLEDGMENTS
This work would not have been possible without the countless
hours volunteered by species experts around the globe, includ-
ing many assessors who did not choose to be listed as authors.
We thank the IUCN Species Survival Commission and Red List
Unit for their help coordinating this input and to the National
Geographic Society for supporting a suite of test assessments.
Workshops were funded by the Prince Albert of Monaco Foun-
dation (administered via the Cambridge Conservation Initiative
Collaborative Fund) and by Fondation Franklinia. We thank
Global Wildlife Conservation for their help coordinating the
workshops. Special thanks are due to A. Rodrigues for her active
involvement in every stage of developing the IUCN Green Sta-
tus of Species. We thank T. Kuiper for help with data analyses
and M. Clark for commenting on an early draft. M.G. was sup-
ported by a NERC Knowledge Exchange Fellowship and the
IUCN SSC and the World Wildlife Fund. H.R.A. was supported
by the Stony Brook University OVPR Seed Grant Program.
This project received funding from the European Union’s Hori-
zon 2020 Research and Innovation Programme under a Marie
Skłodowska-Curie grant agreement 766417 to M.F.
ORCID
Molly K. Grace https://orcid.org/0000-0002-1978-615X
H. Resit Akçakaya https://orcid.org/0000-0002-8679-5929
Craig Hilton-Taylor https://orcid.org/0000-0003-1163-1425
Axel Hochkirch https://orcid.org/0000-0002-4475-0394
David A. Keith https://orcid.org/0000-0002-7627-4150
Jon Paul Rodriguez https://orcid.org/0000-0001-5019-2870
P.J. Ste phe nson https://orcid.org/0000-0002-0087-466X
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SUPPORTING INFORMATION
Additional supporting information may be found online in the
Supporting Information section at the end of the article.
How to cite this article: Grace MK, Akçakaya HR,
Bennett EL et al., Testing a global standard for
quantifying species recovery and assessing conservation
impact. Conservation Biology, 2021;35:1833–1849.
https://doi.org/10.1111/cobi.13756