Measuring Terrestrial Area of Habitat (AOH) and Its Utility for the IUCN Red List
Authors: Thomas M. Brooks, Stuart L. Pimm, H. Resit Akçakaya, Graeme M. Buchanan, Stuart H.M. Butchart, Wendy Foden, Craig Hilton-Taylor, Michael Hoffmann, Clinton N. Jenkins, Lucas Joppa, Binbin V. Li, Vivek Menon, Natalia Ocampo-Peñuela, Carlo Rondinini
Journal: Trends in Ecology & Evolution (2019)

The IUCN Red List of Threatened Species assesses the extinction risk of nearly 100 000 species, including documentation of a range map, habitat, and elevation data for each species.Numerous recent studies have matched these habitat and elevation data with remotely sensed land cover and elevation datasets to map AOH (also known as extent of suitable habitat) within the range of each species.AOH differs from the two spatial metrics used in the IUCN Red List criteria for extinction risk assessment: EOO (minimum convex polygon around all present native occurrences of a species); and AOO (area actually occupied by a species).AOH can be of value in locating target areas for species-specific field surveys, assessing the proportion of a species' habitat within protected areas, and monitoring habitat loss and fragmentation. The International Union for Conservation of Nature (IUCN) Red List of Threatened Species includes assessment of extinction risk for 98 512 species, plus documentation of their range, habitat, elevation, and other factors. These range, habitat and elevation data can be matched with terrestrial land cover and elevation datasets to map the species' area of habitat (AOH; also known as extent of suitable habitat; ESH). This differs from the two spatial metrics used for assessing extinction risk in the IUCN Red List criteria: extent of occurrence (EOO) and area of occupancy (AOO). AOH can guide conservation, for example, through targeting areas for field surveys, assessing proportions of species' habitat within protected areas, and monitoring habitat loss and fragmentation. We recommend that IUCN Red List assessments document AOH wherever practical. The International Union for Conservation of Nature (IUCN) Red List of Threatened Species includes assessment of extinction risk for 98 512 species, plus documentation of their range, habitat, elevation, and other factors. These range, habitat and elevation data can be matched with terrestrial land cover and elevation datasets to map the species' area of habitat (AOH; also known as extent of suitable habitat; ESH). This differs from the two spatial metrics used for assessing extinction risk in the IUCN Red List criteria: extent of occurrence (EOO) and area of occupancy (AOO). AOH can guide conservation, for example, through targeting areas for field surveys, assessing proportions of species' habitat within protected areas, and monitoring habitat loss and fragmentation. We recommend that IUCN Red List assessments document AOH wherever practical. The IUCN Red List of Threatened Species [1International Union for Conservation of Nature The IUCN Red List of Threatened Species (Version 2018-1).2018Google Scholar] aspires to assess the extinction risk of the world's species, and to serve as a 'barometer of life' of the state of nature [2Stuart S.N. et al.The barometer of life.Science. 2010; 328: 177Crossref PubMed Scopus (121) Google Scholar]. Of the approximately 2 million named species [3Scheffers B.R. et al.What we know and don't know about Earth's missing biodiversity.Trends Ecol. Evol. 2012; 27: 501-510Abstract Full Text Full Text PDF PubMed Scopus (251) Google Scholar], the IUCN Red List has assessed 98 512 species (having increased from fewer than 20 000 in 2002i). The process of assessment classifies species in different categories of extinction risk. It does so through an open, rigorously defined process [4International Union for Conservation of Nature Rules of Procedure for IUCN Red List Assessments 2017–2020 (Version 3.0).2016Google Scholar] that is objective and transparent. Petitioners can challenge decisions made by Red List Authorities. An important use of the IUCN Red List is the assessment of changes in species extinction risk to monitor changes in the status of individual species, classes and other groups of species, and species-level biodiversity overall [5Butchart S.H.M. et al.Measuring global trends in the status of biodiversity: Red List indices for birds.PLoS Biol. 2004; 2e383Crossref PubMed Scopus (334) Google Scholar, 6Rodrigues A.S.L. et al.Spatially explicit trends in the global conservation status of vertebrates.PLoS One. 2014; 9e113934Crossref PubMed Scopus (49) Google Scholar]. These are essential, for example, in reporting on the Aichi Targets [7Secretariat of the Convention on Biological Diversity Global Biodiversity Outlook 4.Convention on Biological Diversity. 2014Google Scholar] and Sustainable Development Goals [8United Nations The Sustainable Development Goals Report 2017. United Nations, 2017Crossref Google Scholar], as well as progress in conserving species [9Hoffmann M. et al.The impact of conservation on the status of the world's vertebrates.Science. 2010; 330: 1503-1509Crossref PubMed Scopus (971) Google Scholar]. The need for rigour and consensus in assessing extinction risk can potentially bring the process into conflict with those who seek to harness rapidly expanding geographic databases and remote sensing technologies to assess species' status [10Ocampo-Peñuela N. Pimm S.L. Setting practical conservation priorities for birds in the Western Andes of Colombia.Conserv. Biol. 2014; 28: 1260-1270Crossref PubMed Scopus (28) Google Scholar, 11Ocampo-Peñuela N. et al.Incorporating explicit geospatial data shows more species at risk of extinction than the current Red List.Sci. Adv. 2016; 2e1601367Crossref PubMed Scopus (67) Google Scholar, 12Ramesh V. et al.IUCN greatly underestimates threat levels of endemic birds in the Western Ghats.Biol. Conserv. 2017; 210: 205-221Crossref Scopus (32) Google Scholar]. Numerous publications have illustrated how increasingly sophisticated and high-resolution regional and global remote sensing and spatial datasets or models can inform the existing Red List criteria [13Buchanan G.M. et al.Using remote sensing to inform conservation status assessment: estimates of recent deforestation rates on New Britain and the impacts upon endemic birds.Biol. Conserv. 2008; 141: 56-66Crossref Scopus (94) Google Scholar, 14Tracewski Ł. et al.Toward quantification of the impact of 21st-century deforestation on the extinction risk of terrestrial vertebrates.Conserv. Biol. 2016; 30: 1070-1079Crossref PubMed Scopus (54) Google Scholar]. The many-fold growth in the availability of these data over the last decade, coupled with increasing computing power to process them, has allowed the development of methods for estimation of the Area of Habitat (AOH, see Glossary and Supplemental Information) remaining for terrestrial species. It is therefore timely to review, standardise, and stabilise how AOH is measured, how it relates to the Red List criteria, and sources of error in its derivation. Specifically, we show here that AOH is equivalent to neither extent of occurrence (EOO) nor to area of occupancy (AOO). Rather, the area of the minimum convex polygon around a species' AOH can be used to estimate the upper bound of EOO. Moreover, if a species' AOH is measured at (or scaled to) a 2 × 2 km reference scale it can be used to estimate the upper bound of AOO. We conclude by highlighting the relevance of measurement of terrestrial species' area of habitat in guiding conservation, for example through targeting areas for field surveys, assessing proportions of species' habitat within protected areas, monitoring habitat loss and fragmentation, and increasing consistency between Red List assessments. Five different criteria are used to assess a species' extinction risk [15International Union for Conservation of Nature IUCN Red List Categories and Criteria (Version 3.1).2012Google Scholar]. In practice, for many terrestrial species [16Collen B. et al.Clarifying misconceptions of extinction risk assessment with the IUCN Red List.Biol. Lett. 2016; 1220150843Crossref PubMed Scopus (90) Google Scholar], the key criterion (the B criterion) is the size of its geographical distribution plus evidence of at least two of (i) severe fragmentation, (ii) continuing decline, or (iii) extreme fluctuations. Two spatial metrics of distribution are defined for application of this criterion, both of which have definitions that are both theoretical and empirical [15International Union for Conservation of Nature IUCN Red List Categories and Criteria (Version 3.1).2012Google Scholar]. EOO is the area contained within the shortest continuous imaginary boundary that can be drawn to encompass all the current known localities, as well as inferred occurrence and projected occurrence of a species (although it excludes vagrant localities). AOO is the area occupied by a species (Figure 1). The intent of EOO is to 'measure the degree to which risks from threatening factors are spread spatially across the taxon's geographic distribution' [17International Union for Conservation of Nature Standards and Petitions Subcommittee Guidelines for Using the IUCN Red List Categories and Criteria (Version 13).2017Google Scholar], while the primary intent of AOO is 'as a measure of the "insurance effect", whereby taxa that occur within many patches or large patches across a landscape or seascape are "insured" against risks from spatially explicit threats' [17International Union for Conservation of Nature Standards and Petitions Subcommittee Guidelines for Using the IUCN Red List Categories and Criteria (Version 13).2017Google Scholar]. For the IUCN Red List, EOO must be measured as the minimum convex polygon that includes all the identified occupied areas [17International Union for Conservation of Nature Standards and Petitions Subcommittee Guidelines for Using the IUCN Red List Categories and Criteria (Version 13).2017Google Scholar, 18Joppa L.N. et al.Impact of alternative metrics on estimates of extent of occurrence for extinction risk assessment.Conserv. Biol. 2016; 30: 362-370Crossref PubMed Scopus (47) Google Scholar]. AOO must be measured at (or scaled to) a reference scale of 2 × 2 km [17International Union for Conservation of Nature Standards and Petitions Subcommittee Guidelines for Using the IUCN Red List Categories and Criteria (Version 13).2017Google Scholar, 19Keith D.A. et al.Scaling range sizes to threats for robust predictions of risks to biodiversity.Conserv. Biol. 2018; 32: 322-332Crossref PubMed Scopus (22) Google Scholar]. The latter is more demanding of data, especially for species with large distributions, and consequently used considerably less frequently. Illustrating this, 68% of mammals, birds, amphibians, chondrichthyans, conifers, and cycads assessed as threatened under the B criterion qualify using EOO, 15% using AOO, and 17% both [1International Union for Conservation of Nature The IUCN Red List of Threatened Species (Version 2018-1).2018Google Scholar]. Thus, a species qualifies for the lowest threatened category, vulnerable, if its EOO is <20 000 km2 and there is evidence of at least two of (i) severe fragmentation (or ≤10 locations based on threats); (ii) continuing decline (in one or more of EOO, AOO, area, extent and/or quality of habitat, number of locations or subpopulations or mature individuals); or (iii) extreme fluctuations (in the same parameters except habitat). More severely threatened categories have lower thresholds for EOO, AOO, and the number of locations. The thresholds for AOO are 10% of the corresponding thresholds for EOO, for example, <2000 km2 for vulnerable. Required documentation for the IUCN Red List also includes the application of a standard Habitat Classification Schemeii, recording maximum and minimum elevation, and provision of a range map [20International Union for Conservation of Nature Documentation Standards and Consistency Checks for IUCN Red List Assessments and Species Accounts (Version 2).2013Google Scholar]. Mapped range has no theoretical definition, just an empirical one – the range map 'should aim to provide the current known distribution of the taxon within its native range. The limits of distribution are determined using known occurrences of the taxon, and knowledge of its habitat preferences, remaining suitable habitat, elevation limits, etc' [20International Union for Conservation of Nature Documentation Standards and Consistency Checks for IUCN Red List Assessments and Species Accounts (Version 2).2013Google Scholar] (Figure 1). Like EOO, the range should include inferred and projected occurrences. Coding of spatial data according to a species' presence [20International Union for Conservation of Nature Documentation Standards and Consistency Checks for IUCN Red List Assessments and Species Accounts (Version 2).2013Google Scholar] separates current mapped range from areas where the species has been extirpated. The last 15 years have seen a rapid increase in the availability of regional and global scale spatial data sets that are available in geographic information systems to strengthen the quantification and repeatability of estimates of species ranges. These include detailed global maps of elevation at 30-m resolution [21Jarvis A. et al.Hole-filled SRTM for the Globe Version 4.2008Google Scholar], global land-cover maps [22Bartholomé E. Belward A. GLC2000: a new approach to global land cover mapping from Earth observation data.Int. J. Remote Sens. 2005; 26: 1959-1977Crossref Scopus (1289) Google Scholar], and global forest cover at 30-m resolution [23Hansen M.C. et al.High-resolution global maps of 21st-century forest cover change.Science. 2013; 342: 850-853Crossref PubMed Scopus (5465) Google Scholar, 24Sexton J.O. et al.Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error.Int. J. Digit. Earth. 2013; 6: 427-448Crossref Scopus (418) Google Scholar]. By converting species' habitat requirements, as documented by application of the IUCN Red List Habitat Classification Scheme, to land-cover types, these can be applied along with the range map of any given terrestrial species to derive the area of habitat falling within a species' altitudinal limits (Box 1). While some work has used the term ESH to describe this measure [13Buchanan G.M. et al.Using remote sensing to inform conservation status assessment: estimates of recent deforestation rates on New Britain and the impacts upon endemic birds.Biol. Conserv. 2008; 141: 56-66Crossref Scopus (94) Google Scholar, 14Tracewski Ł. et al.Toward quantification of the impact of 21st-century deforestation on the extinction risk of terrestrial vertebrates.Conserv. Biol. 2016; 30: 1070-1079Crossref PubMed Scopus (54) Google Scholar, 18Joppa L.N. et al.Impact of alternative metrics on estimates of extent of occurrence for extinction risk assessment.Conserv. Biol. 2016; 30: 362-370Crossref PubMed Scopus (47) Google Scholar, 25International Union for Conservation of Nature A Global Standard for the Identification of Key Biodiversity Areas (Version 1.0). International Union for Conservation of Nature, 2016Google Scholar], we establish the term AOH here because using the term 'suitable' is a tautology (habitat is, by definition, suitable for the species in question). Moreover, area is more accurate than extent: the latter implies spread, as in extent of occurrence. Conceptually, AOH is defined as the habitat available to a species, that is, habitat within its range. However, in practice, AOH is often based on mapped range, habitat preferences, and altitudinal limits (Box 1), giving the areas likely to be suitable for the species within its mapped range.Box 1Specific Approaches Used to Calculate AOHVarious approaches have been used to calculate AOH. The most widely applied approach [10Ocampo-Peñuela N. Pimm S.L. Setting practical conservation priorities for birds in the Western Andes of Colombia.Conserv. Biol. 2014; 28: 1260-1270Crossref PubMed Scopus (28) Google Scholar, 11Ocampo-Peñuela N. et al.Incorporating explicit geospatial data shows more species at risk of extinction than the current Red List.Sci. Adv. 2016; 2e1601367Crossref PubMed Scopus (67) Google Scholar, 12Ramesh V. et al.IUCN greatly underestimates threat levels of endemic birds in the Western Ghats.Biol. Conserv. 2017; 210: 205-221Crossref Scopus (32) Google Scholar, 13Buchanan G.M. et al.Using remote sensing to inform conservation status assessment: estimates of recent deforestation rates on New Britain and the impacts upon endemic birds.Biol. Conserv. 2008; 141: 56-66Crossref Scopus (94) Google Scholar, 14Tracewski Ł. et al.Toward quantification of the impact of 21st-century deforestation on the extinction risk of terrestrial vertebrates.Conserv. Biol. 2016; 30: 1070-1079Crossref PubMed Scopus (54) Google Scholar, 18Joppa L.N. et al.Impact of alternative metrics on estimates of extent of occurrence for extinction risk assessment.Conserv. Biol. 2016; 30: 362-370Crossref PubMed Scopus (47) Google Scholar, 27Beresford A. et al.Minding the protection gap: estimates of species' range sizes and holes in the protected area network.Anim. Conserv. 2011; 14: 114-116Crossref Scopus (14) Google Scholar, 29Li B.V. et al.Remotely sensed data informs Red List evaluations and conservation priorities in Southeast Asia.PLoS One. 2016; 11e0160566PubMed Google Scholar, 30Beresford A. et al.Poor overlap between the distribution of protected areas and globally threatened birds in Africa.Anim. Conserv. 2011; 14: 99-107Crossref Scopus (67) Google Scholar, 31Bird J. et al.Incorporating projected deforestation estimates into conservation priority-setting in Amazonia.Divers. Distrib. 2011; 18: 273-281Crossref Scopus (51) Google Scholar, 32Buchanan G.M. et al.Identifying priority areas for conservation: a global assessment for forest-dependent birds.PLoS One. 2011; 6e29080Crossref PubMed Scopus (65) Google Scholar, 33Butchart S.H.M. et al.Shortfalls and solutions for meeting national and global conservation area targets.Conserv. Lett. 2015; 8: 329-337Crossref Scopus (271) Google Scholar, 34Foden W.B. et al.Identifying the world's most climate change vulnerable species: a systematic trait-based assessment of all birds, amphibians and corals.PLoS One. 2013; 8e65427Crossref PubMed Scopus (506) Google Scholar, 35Harris G. Pimm S.L. Range size and extinction risk in forest birds.Conserv. Biol. 2008; 22: 163-171Crossref PubMed Scopus (116) Google Scholar, 36Li B.V. Pimm S.L. China's endemic vertebrates sheltering under the protective umbrella of the giant panda.Conserv. Biol. 2016; 30: 329-339Crossref PubMed Scopus (81) Google Scholar, 37Rondinini C. et al.Habitat suitability models and the shortfall in conservation planning for African vertebrates.Conserv. Biol. 2005; 19: 1488-1497Crossref Scopus (105) Google Scholar, 38Schnell J.K. et al.Quantitative analysis of forest fragmentation in the Atlantic Forest reveals more threatened bird species than the current Red List.PLoS One. 2013; 8e65357Crossref PubMed Scopus (27) Google Scholar, 39Visconti P. et al.Projecting global biodiversity indicators under future development scenarios.Conserv. Lett. 2016; 9: 5-13Crossref Scopus (127) Google Scholar] (Figure I) has been to use geographic information systems to select those areas in a land-cover map that (i) fall within the mapped range of a terrestrial species; (ii) fall within the bounds of the altitudinal limits of the species' distribution; and (iii) that map to the known habitat preferences of the species. Most approaches have restricted the latter to habitats coded as suitable or of major importance by IUCN, although habitats of unknown or marginal importance have also been included for analyses requiring a less conservative approach [28Rondinini C. et al.Global habitat suitability models of terrestrial mammals.Phil. Trans. R. Soc. Lond B. 2011; 366: 2633-2641Crossref PubMed Scopus (187) Google Scholar, 34Foden W.B. et al.Identifying the world's most climate change vulnerable species: a systematic trait-based assessment of all birds, amphibians and corals.PLoS One. 2013; 8e65427Crossref PubMed Scopus (506) Google Scholar].Rondinini et al. [28Rondinini C. et al.Global habitat suitability models of terrestrial mammals.Phil. Trans. R. Soc. Lond B. 2011; 366: 2633-2641Crossref PubMed Scopus (187) Google Scholar] and Ficetola et al. [40Ficetola G.F. et al.Habitat availability for amphibians and extinction threat: a global analysis.Divers. Distrib. 2015; 21: 302-311Crossref Scopus (68) Google Scholar] used a slightly different approach, defining the habitat suitability of all land-cover classes of areas meeting (i) and (ii) above as high, medium, or low suitability depending on the match to habitat type and a separate score for level of tolerance to human impacted natural habitat types (degraded or mosaic). Suitability scores for specific land-cover classes were then modified manually in some cases if more detailed information was available. In addition, for species whose distribution is restricted to within a small distance to water bodies, all areas farther than 1 km from water bodies were classified as unsuitable.The land-cover products used all derive from remote sensing and include Global Land Cover 2000 [27Beresford A. et al.Minding the protection gap: estimates of species' range sizes and holes in the protected area network.Anim. Conserv. 2011; 14: 114-116Crossref Scopus (14) Google Scholar, 34Foden W.B. et al.Identifying the world's most climate change vulnerable species: a systematic trait-based assessment of all birds, amphibians and corals.PLoS One. 2013; 8e65427Crossref PubMed Scopus (506) Google Scholar, 35Harris G. Pimm S.L. Range size and extinction risk in forest birds.Conserv. Biol. 2008; 22: 163-171Crossref PubMed Scopus (116) Google Scholar] and GlobCover [28Rondinini C. et al.Global habitat suitability models of terrestrial mammals.Phil. Trans. R. Soc. Lond B. 2011; 366: 2633-2641Crossref PubMed Scopus (187) Google Scholar, 40Ficetola G.F. et al.Habitat availability for amphibians and extinction threat: a global analysis.Divers. Distrib. 2015; 21: 302-311Crossref Scopus (68) Google Scholar]. Typically, the land-cover classes of these maps are matched to preferred habitat types (or scored for suitability) usually from information in the literature supplemented by expert opinion. Studies generally publish these crosswalks to enable readers to review decisions. Validation of AOH maps following these approaches is increasingly recognised as important.We reserve AOH for the approaches described here, and so differentiate it from approaches to modelling species distributions or ecological niches [41Franklin J. Mapping Species Distributions: Spatial Inference and Prediction. Cambridge University Press, 2010Crossref Scopus (1614) Google Scholar, 42Peterson A.T. et al.Ecological Niches and Geographic Distributions. Princeton University Press, 2011Crossref Google Scholar], sometimes characterised as deductive and inductive approaches, respectively [37Rondinini C. et al.Habitat suitability models and the shortfall in conservation planning for African vertebrates.Conserv. Biol. 2005; 19: 1488-1497Crossref Scopus (105) Google Scholar]. While these may predict substantially larger areas than the recorded distribution of a species [43Pimm S.L. et al.Unfulfilled promise of data-driven approaches: response to Peterson et al.Conserv. Biol. 2017; 31: 944-947Crossref PubMed Scopus (7) Google Scholar], and so may be less useful than AOH for many conservation applications, they can be especially appropriate when projecting future expansion of a species beyond its current range; for example, in considering climate change impacts [17International Union for Conservation of Nature Standards and Petitions Subcommittee Guidelines for Using the IUCN Red List Categories and Criteria (Version 13).2017Google Scholar]. Such models allow calculation of the area above a threshold value of probability or suitability. Depending on the number of records, the threshold may be based on the lower tail of the distribution of suitability values of the occurrences, or on balancing sensitivity and specificity [17International Union for Conservation of Nature Standards and Petitions Subcommittee Guidelines for Using the IUCN Red List Categories and Criteria (Version 13).2017Google Scholar, 44Guillera-Arroita G. et al.Is my species distribution model fit for purpose? Matching data and models to applications.Glob. Ecol. Biogeogr. 2015; 24: 276-292Crossref Scopus (445) Google Scholar]. New-generation point-process approaches to species distribution modelling also take sampling biases into account [45Renner I.W. et al.Point process models for presence-only analysis.Methods Ecol. Evol. 2015; 6: 366-379Crossref Scopus (179) Google Scholar, 46Evans M.E.K. et al.Towards process-based range modeling of many species.Trends Ecol. Evol. 2016; 31: 860-871Abstract Full Text Full Text PDF PubMed Scopus (78) Google Scholar, 47Schank C.J. et al.Using a novel model approach to assess the distribution and conservation status of the endangered Baird's tapir.Divers. Distrib. 2017; 23: 1459-1471Crossref Scopus (28) Google Scholar]. Various approaches have been used to calculate AOH. The most widely applied approach [10Ocampo-Peñuela N. Pimm S.L. Setting practical conservation priorities for birds in the Western Andes of Colombia.Conserv. Biol. 2014; 28: 1260-1270Crossref PubMed Scopus (28) Google Scholar, 11Ocampo-Peñuela N. et al.Incorporating explicit geospatial data shows more species at risk of extinction than the current Red List.Sci. Adv. 2016; 2e1601367Crossref PubMed Scopus (67) Google Scholar, 12Ramesh V. et al.IUCN greatly underestimates threat levels of endemic birds in the Western Ghats.Biol. Conserv. 2017; 210: 205-221Crossref Scopus (32) Google Scholar, 13Buchanan G.M. et al.Using remote sensing to inform conservation status assessment: estimates of recent deforestation rates on New Britain and the impacts upon endemic birds.Biol. Conserv. 2008; 141: 56-66Crossref Scopus (94) Google Scholar, 14Tracewski Ł. et al.Toward quantification of the impact of 21st-century deforestation on the extinction risk of terrestrial vertebrates.Conserv. Biol. 2016; 30: 1070-1079Crossref PubMed Scopus (54) Google Scholar, 18Joppa L.N. et al.Impact of alternative metrics on estimates of extent of occurrence for extinction risk assessment.Conserv. Biol. 2016; 30: 362-370Crossref PubMed Scopus (47) Google Scholar, 27Beresford A. et al.Minding the protection gap: estimates of species' range sizes and holes in the protected area network.Anim. Conserv. 2011; 14: 114-116Crossref Scopus (14) Google Scholar, 29Li B.V. et al.Remotely sensed data informs Red List evaluations and conservation priorities in Southeast Asia.PLoS One. 2016; 11e0160566PubMed Google Scholar, 30Beresford A. et al.Poor overlap between the distribution of protected areas and globally threatened birds in Africa.Anim. Conserv. 2011; 14: 99-107Crossref Scopus (67) Google Scholar, 31Bird J. et al.Incorporating projected deforestation estimates into conservation priority-setting in Amazonia.Divers. Distrib. 2011; 18: 273-281Crossref Scopus (51) Google Scholar, 32Buchanan G.M. et al.Identifying priority areas for conservation: a global assessment for forest-dependent birds.PLoS One. 2011; 6e29080Crossref PubMed Scopus (65) Google Scholar, 33Butchart S.H.M. et al.Shortfalls and solutions for meeting national and global conservation area targets.Conserv. Lett. 2015; 8: 329-337Crossref Scopus (271) Google Scholar, 34Foden W.B. et al.Identifying the world's most climate change vulnerable species: a systematic trait-based assessment of all birds, amphibians and corals.PLoS One. 2013; 8e65427Crossref PubMed Scopus (506) Google Scholar, 35Harris G. Pimm S.L. Range size and extinction risk in forest birds.Conserv. Biol. 2008; 22: 163-171Crossref PubMed Scopus (116) Google Scholar, 36Li B.V. Pimm S.L. China's endemic vertebrates sheltering under the protective umbrella of the giant panda.Conserv. Biol. 2016; 30: 329-339Crossref PubMed Scopus (81) Google Scholar, 37Rondinini C. et al.Habitat suitability models and the shortfall in conservation planning for African vertebrates.Conserv. Biol. 2005; 19: 1488-1497Crossref Scopus (105) Google Scholar, 38Schnell J.K. et al.Quantitative analysis of forest fragmentation in the Atlantic Forest reveals more threatened bird species than the current Red List.PLoS One. 2013; 8e65357Crossref PubMed Scopus (27) Google Scholar, 39Visconti P. et al.Projecting global biodiversity indicators under future development scenarios.Conserv. Lett. 2016; 9: 5-13Crossref Scopus (127) Google Scholar] (Figure I) has been to use geographic information systems to select those areas in a land-cover map that (i) fall within the mapped range of a terrestrial species; (ii) fall within the bounds of the altitudinal limits of the species' distribution; and (iii) that map to the known habitat preferences of the species. Most approaches have restricted the latter to habitats coded as suitable or of major importance by IUCN, although habitats of unknown or marginal importance have also been included for analyses requiring a less conservative approach [28Rondinini C. et al.Global habitat suitability models of terrestrial mammals.Phil. Trans. R. Soc. Lond B. 2011; 366: 2633-2641Crossref PubMed Scopus (187) Google Scholar, 34Foden W.B. et al.Identifying the world's most climate change vulnerable species: a systematic trait-based assessment of all birds, amphibians and corals.PLoS One. 2013; 8e65427Crossref PubMed Scopus (506) Google Scholar]. Rondinini et al. [28Rondinini C. et al.Global habitat suitability models of terrestrial mammals.Phil. Trans. R. Soc. Lond B. 2011; 366: 2633-2641Crossref PubMed Scopus (187) Google Scholar] and Ficetola et al. [40Ficetola G.F. et al.Habitat availability for amphibians and extinction threat: a global analysis.Divers. Distrib. 2015; 21: 302-311Crossref Scopus (68) Google Scholar] used a slightly different approach, defining the habitat suitability of all land-cover classes of