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Electronic Supplementary Material for
Reservoirs of richness: least disturbed tropical forests are centres of
undescribed species diversity
Table of Contents Page
1. Supplementary Tables S1–S6 2
This section contains data tables as well as tables of statistical analyses
2. Supplementary Figures S1 20
This section comprises of figures that support the results presented in the main text main text
3. Appendix 1 21
A brief description of the methodology employed to identify outliers
4. Appendix 2 23
Plots of observed versus fitted number of species, and observed versus fitted
efficiency variable for amphibians
5. Appendix 3 27
Plots of observed versus fitted number of species, and observed versus fitted
efficiency variable for land mammals
1
Supplementary Table S1. List of amphibian species for which range maps are not available. Also
shown are the species authority (Authority), 2010 IUCN Red List conservation status (Status), and the
year of description of the basionym (Year of Description).
Species Authority Status Year of
Description
Adenomus kandianus (Günther, 1872) EX 1872
Albericus variegates (van Kampen, 1923) DD 1923
Alsodes laevis (Philippi, 1902) DD 1902
Ameerega labialis (Cope, 1874) DD 1874
Ameerega maculate (Peters, 1873) DD 1873
Amietophrynus chudeaui (Chabanaud, 1919) DD 1919
Atretochoana eiselti (Taylor, 1968) DD 1968
Boulengerula denhardti Nieden, 1912 DD 1912
Caecilia albiventris Daudin, 1803 DD 1803
Caecilia armata Dunn, 1942 DD 1942
Caecilia mertensi Taylor, 1973 DD 1973
Caecilia subterminalis Taylor, 1968 DD 1968
Calamita melanorabdotus Schneider, 1799 DD 1799
Calamita quadrilineatus Schneider, 1799 DD 1799
Caudacaecilia nigroflava (Taylor, 1960) DD 1960
Ceuthomantis aracamuni (Barrio-Amorós & Molina, 2006) VU 2006
Ceuthomantis cavernibardus (Myers & Donnelly, 1997) DD 1997
Chthonerpeton braestrupi Taylor, 1968 DD 1968
Chthonerpeton exile Nussbaum and Wilkinson, 1987 DD 1987
Chthonerpeton perissodus Nussbaum and Wilkinson, 1987 DD 1987
Dendropsophus grandisonae (Goin, 1966) DD 1966
Epicrionops lativittatus Taylor, 1968 DD 1968
Eurycea chamberlaini Harrison and Guttman, 2003 DD 2003
Fejervarya altilabris (Blyth, 1856) DD 1856
Fejervarya assimilis (Blyth, 1852) DD 1852
Fejervarya brama (Lesson, 1834) DD 1834
Fejervarya schlueteri (Werner, 1893) DD 1893
Gastrotheca pulchra Caramaschi and Rodrigues, 2007 DD 2007
Hyla auraria Peters, 1873 DD 1873
Hyla helenae Ruthven, 1919 DD 1919
Hyla javana Ahl, 1926 DD 1926
Hyla molitor Schmidt, 1857 DD 1857
Hyla surinamensis Daudin, 1802 DD 1802
Hylambates dorsalis Peters, 1875 DD 1875
Hyloscirtus estevesi (Rivero, 1968) DD 1968
Hyperolius acuticephalus Ahl, 1931 DD 1931
Hyperolius albofrenatus Ahl, 1931 DD 1931
Hyperolius houyi Ahl, 1931 DD 1931
Hyperolius laticeps Ahl, 1931 DD 1931
Hyperolius lucani Rochebrune, 1885 DD 1885
2
Hyperolius maestus Rochebrune, 1885 DD 1885
Hyperolius protchei Rochebrune, 1885 DD 1885
Hyperolius raveni Ahl, 1931 DD 1931
Hyperolius Ahl, 1931 DD 1931
thoracotuberculatus
Hyperolius tornieri Ahl, 1931 DD 1931
Hypsiboas albovittatus (Lichtenstein and Martens, 1856) DD 1856
Hypsiboas hypselops Cope, 1871 DD 1871
Hypsiboas palliates (Cope, 1863) DD 1863
Hypsiboas roeschmanni (DeGrys, 1938) DD 1938
Ichthyophis atricollaris Taylor, 1965 DD 1965
Ichthyophis bernisi Salvador, 1975 DD 1975
Ichthyophis humphreyi Taylor, 1973 DD 1973
Ichthyophis javanicus Taylor, 1960 DD 1960
Ichthyophis laosensis Taylor, 1969 DD 1969
Incilius intermedius (Günther, 1858) DD 1858
Kaloula macrocephala Bourret, 1942 DD 1942
Kurixalus hainanus (Zhao, Wang and Shi, 2005) DD 2005
Leptodactylus hallowelli (Cope, 1862) DD 1862
Leptodactylus pascoensis Heyer, 1994 VU 1994
Limnonectes limborgi (Sclater, 1892) DD 1892
Litoria jeudii (Werner, 1901) DD 1901
Microcaecilia Taylor, 1969 DD 1969
supernumeraria
Mimosiphonops reinhardti Wilkinson and Nussbaum, 1992 DD 1992
Nannophrys guentheri Boulenger, 1882 EX 1882
Nasikabatrachus Biju & Bossuyt, 2003 EN 2003
sahyadrensis
Noblella peruviana (Noble, 1921) DD 1921
Odorrana sinica (Ahl, 1927) DD 1927
Oreolalax weigoldi (Vogt, 1924) DD 1924
Oreophryne wolterstorffi (Werner, 1901) DD 1901
Oreophryne zimmeri Ahl, 1933 DD 1933
Oscaecilia equatorialis Taylor, 1973 DD 1973
Oscaecilia zweifeli Taylor, 1968 DD 1968
Pelophryne macrotis (Boulenger, 1895) DD 1895
Pelophylax demarchii (Scortecci, 1929) DD 1929
Philautus dubius (Boulenger, 1882) DD 1882
Phrynobatrachus congicus (Ahl, 1925) DD 1925
Platypelis cowanii Boulenger, 1882 DD 1882
Plethodontohyla angulifera Werner, 1903 DD 1903
Pleurodeles nebulosus (Guichenot, 1850) VU 1850
Polypedates hecticus Peters, 1863 DD 1863
Pristimantis festae (Peracca, 1904) EN 1904
Pseudophilautus extirpo (Manamendra-Arachchi & EX 2005
Pethiyagoda, 2005)
Pseudophilautus hypomelas (Günther, 1876) EX 1876
3
Pseudophilautus leucorhinus (Lichtenstein & Martens, 1856) EX 1856
Pseudophilautus (Ahl, 1927) EX 1927
malcolmsmithi
Pseudophilautus nanus (Günther, 1869) EX 1869
Pseudophilautus nasutus (Günther, 1869) EX 1869
Pseudophilautus (Günther, 1872) EX 1872
oxyrhynchus
Pseudophilautus pardus (Meegaskumbura, Manamendra- EX 2007
Arachchi, Schneider & Pethiyagoda,
2007)
Pseudophilautus rugatus (Ahl, 1927) EX 1927
Pseudophilautus temporalis (Günther, 1864) EX 1864
Pseudophilautus variabilis (Günther, 1858) EX 1858
Pseudophilautus zal (Manamendra-Arachchi & EX 2005
Pethiyagoda, 2005)
Pyxicephalus cordofanus Steindachner, 1867 DD 1867
Ranitomeya flavovittata (Schulte, 1999) DD 1999
Ranitomeya rubrocephala (Schulte, 1999) DD 1999
Raorchestes beddomii (Günther, 1876) NT 1876
Raorchestes flaviventris (Boulenger, 1882) DD 1882
Rhacophorus depressus Ahl, 1927 DD 1927
Rhacophorus edentulus Müller, 1894 DD 1894
Rhinella sima (Schmidt, 1857) DD 1857
Rhinella truebae (Lynch and Renjifo, 1990) DD 1990
Scaphiophryne obscura (Grandidier, 1872) DD 1872
Scinax strigilatus (Spix, 1824) DD 1824
Scutiger bhutanensis Delorme and Dubois, 2001 DD 2001
Siphonops leucoderus Taylor, 1968 DD 1968
Sphaenorhynchus (Werner, 1894) DD 1894
platycephalus
Zachaenus roseus Cope, 1890 DD 1890
4
Supplementary Table S2. List of land mammal species for which range maps are not available. Also
shown are the species authority (Authority), 2010 IUCN Red List conservation status (Status), and the
year of description of the basionym (Year of Description).
Species Authority Status Year of
Description
Acomys nesiotes Bate, 1903 DD 1903
Arctocephalus australis (Zimmermann, 1783) LC 1783
Arctocephalus forsteri (Lesson, 1828) LC 1828
Arctocephalus galapagoensis Heller, 1904 EN 1904
Arctocephalus gazella (Peters, 1875) LC 1875
Arctocephalus philippii (Peters, 1872) NT 1872
Arctocephalus pusillus (Schreber, 1775) LC 1775
Arctocephalus townsendi Merriam, 1897 NT 1897
Arctocephalus tropicalis Gray, 1872) LC 1872
Bettongia pusilla McNamara, 1997 EX 1997
Bison bonasus (Linnaeus, 1758) VU 1758
Boromys offella Miller, 1916 EX 1916
Boromys torrei Allen, 1917 EX 1917
Bos primigenius Bojanus, 1827 EX 1827
Brotomys voratus Miller, 1916 EX 1916
Callorhinus ursinus (Linnaeus, 1758) VU 1758
Caloprymnus campestris (Gould, 1843) EX 1843
Canis rufus Audubon & Bachman, 1851 CR 1851
Chaeropus ecaudatus (Ogilby, 1838) EX 1838
Conilurus albipes (Lichtenstein, 1829) EX 1829
Coryphomys buehleri Schaub, 1937 EX 1937
Crocidura dhofarensis Hutterer & Harrison, 1988 DD 1988
Cryptonanus ignites (Díaz, Flores & Barquez, 2002) EX 2002
Cryptoprocta spelea G. Grandidier, 1902 EX 1902
Cuscomys oblativa (Eaton, 1916) EX 1916
Cystophora cristata (Erxleben, 1777) VU 1777
Desmodus draculae Morgan, Linares & Ray, 1988 EX 1988
Dusicyon australis (Kerr, 1792) EX 1792
Elaphurus davidianus Milne-Edwards, 1866 EW 1866
Eptesicus kobayashii Mori, 1928 DD 1928
Equus ferus Boddaert, 1785 CR 1785
Erignathus barbatus (Erxleben, 1777) LC 1777
Eudorcas rufina (Thomas, 1894) DD 1894
Eumetopias jubatus (Schreber, 1776) EN 1776
Gazella Arabica (Lichtenstein, 1827) DD 1827
Gazella bilkis Groves & Lay, 1985 EX 1985
Gazella saudiya Carruthers & Schwarz, 1935 EX 1935
Geocapromys columbianus (J. Fischer, 1829) EX 1829
Geocapromys thoracatus (True, 1888) EX 1888
Gerbillus agag Thomas, 1903 DD 1903
5
Gerbillus burtoni F. Cuvier, 1838 DD 1838
Halichoerus grypus (Fabricius, 1791) LC 1791
Heteropsomys insulans Anthony, 1916 EX 1916
Hexolobodon phenax Miller, 1929 EX 1929
Hippopotamus lemerlei Grandidier in Milne-Edwards, EX 1868
1868
Hippopotamus Guldberg, 1883 EX 1883
madagascariensis
Hippotragus leucophaeus (Pallas, 1766) EX 1766
Histriophoca fasciata Zimmerman, 1783 DD 1783
Homo sapiens Linnaeus, 1758 LC 1758
Hydrurga leptonyx (de Blainville, 1820) LC 1820
Isolobodon montanus (Miller, 1922) EX 1922
Isolobodon portoricensis Allen, 1916 EX 1916
Juscelinomys candango Moojen, 1965 EX 1965
Lagorchestes asomatus Finlayson, 1943 EX 1943
Lagorchestes leporides (Gould, 1841) EX 1841
Lagostomus crassus Thomas, 1910 EX 1910
Leptonychotes weddellii (Lesson, 1826) LC 1826
Lobodon carcinophaga (Hombron & Jacquinot, 1842) LC 1842
Macropus greyi Waterhouse, 1845 EX 1845
Macrotis leucura (Thomas, 1887) EX 1887
Makalata obscura (Wagner, 1840) DD 1840
Megalomys desmarestii (J. Fischer, 1829) EX 1829
Megalomys luciae (Forsyth Major, 1901) EX 1901
Megaoryzomys curioi (Niethammer, 1964) EX 1964
Mirounga angustirostris (Gill, 1866) LC 1866
Mirza zaza Kappeler & Roos, 2005 DD 2005
Monachus monachus (Hermann, 1779) CR 1779
Monachus schauinslandi Matschie, 1905 CR 1905
Monachus tropicalis (Gray, 1850) EX 1850
Mus musculus Linnaeus, 1758 LC 1758
Myotis australis (Dobson, 1878) DD 1878
Myotis insularum (Dobson, 1878) DD 1878
Neotoma anthonyi J.A. Allen, 1898 EX 1898
Neotoma bunkeri Burt, 1932 EX 1932
Neotoma martinensis Goldman, 1905 EX 1905
Neovison macrodon (Prentis, 1903) EX 1903
Nesophontes edithae Anthony, 1916 EX 1916
Nesophontes hypomicrus Miller, 1929 EX 1929
Nesophontes major Arredondo, 1970 EX 1970
Nesophontes micrus G.M. Allen, 1917 EX 1917
Nesophontes paramicrus Miller, 1929 EX 1929
Nesophontes zamicrus Miller, 1929 EX 1929
Nesoryzomys darwini Osgood, 1929 EX 1929
Nesoryzomys indefessus (Thomas, 1899) EX 1899
6
Noronhomys vespuccii Carleton & Olson, 1999 EX 1999
Notomys amplus Brazenor, 1936 EX 1936
Notomys longicaudatus (Gould, 1844) EX 1844
Notomys macrotis Thomas, 1921 EX 1921
Notomys mordax Thomas, 1922 EX 1922
Nycticeius aenobarbus Temminck, 1840 DD 1840
Odobenus rosmarus (Linnaeus, 1758) DD 1758
Oligoryzomys victus (Thomas, 1898) EX 1898
Ommatophoca rossii (Gray, 1844) LC 1844
Onychogalea lunata (Gould, 1841) EX 1841
Oryx dammah (Cretzschmar, 1826) EW 1826
Oryx leucoryx (Pallas, 1777) EN 1777
Oryzomys antillarum Thomas, 1898 EX 1898
Oryzomys curasoae McFarlane & Debrot, 2001 DD 2001
Oryzomys nelson Merriam, 1898 EX 1898
Otaria flavescens (Shaw, 1800) LC 1800
Pagophilus groenlandicus (Erxleben, 1777) LC 1777
Palaeopropithecus ingens G. Grandidier, 1899 EX 1899
Perameles eremiana Spencer, 1897 EX 1897
Peromyscus pembertoni Burt, 1932 EX 1932
Phoca largha (Pallas, 1811) DD 1811
Phoca vitulina Linnaeus, 1758 LC 1758
Phocarctos hookeri (Peters, 1866) VU 1866
Phoniscus aerosa (Tomes, 1858) DD 1858
Pipistrellus sturdeei Thomas, 1915 DD 1915
Plagiodontia ipnaeum Johnson, 1948 EX 1948
Potorous platyops (Gould, 1844) EX 1844
Prolagus sardus (Wagner, 1832) EX 1832
Pseudomys glaucus Thomas, 1910 EX 1910
Pseudomys gouldii (Waterhouse, 1839) EX 1839
Pteropus brunneus Dobson, 1878 EX 1878
Pteropus pilosus K. Andersen, 1908 EX 1908
Pteropus subniger (Kerr, 1792) EX 1792
Pteropus tokudae Tate, 1934 EX 1934
Pusa caspica (Gmelin, 1788) EN 1788
Pusa hispida (Schreber, 1775) LC 1775
Pusa sibirica (Gmelin, 1788) LC 1788
Rattus macleari (Thomas, 1887) EX 1887
Rattus nativitatis (Thomas, 1889) EX 1889
Rhinopoma macinnesi Hayman, 1937 DD 1937
Rucervus schomburgki (Blyth, 1863) EX 1863
Sciurus aureogaster F. Cuvier, 1829 LC 1829
Solenodon marcanoi (Patterson, 1962) EX 1962
Solomys salamonis (Ramsay, 1883) DD 1883
Thylacinus cynocephalus (Harris, 1808) EX 1808
Tupaia moellendorffi Matschie, 1898 DD 1898
7
Xenothrix mcgregori Williams & Koopman, 1952 EX 1952
Zalophus californianus (Lesson, 1828) LC 1828
Zalophus japonicas (Peters, 1866) EX 1866
Zalophus wollebaeki Sivertsen, 1953 EN 1953
8
Supplementary Table S3. Names of realm-biomes used in the analyses
Code Realm-biome
AA1 Australasia - Tropical and subtropical moist broadleaf forests
AA2 Australasia - Tropical and subtropical dry broadleaf forests
AA4 Australasia - Temperate broadleaf and mixed forests
AA7 Australasia - Tropical and subtropical grasslands, savannas, and shrublands
AA8 Australasia - Temperate grasslands, savannas, and shrublands
AA10 Australasia - Montane grasslands and shrublands
AA11 Australasia - Tundra
AA12 Australasia - Mediterranean forests, woodlands, and scrub or Sclerophyll
forests
AA13 Australasia - Deserts and xeric shrublands
AA14 Australasia - Mangrove
AT1 Afrotropics - Tropical and subtropical moist broadleaf forests
AT2 Afrotropics - Tropical and subtropical dry broadleaf forests
AT7 Afrotropics - Tropical and subtropical grasslands, savannas, and shrublands
AT8 Afrotropics - Temperate grasslands, savannas, and shrublands
AT9 Afrotropics - Flooded grasslands, savannas
AT10 Afrotropics - Montane grasslands and shrublands
AT12 Afrotropics - Mediterranean forests, woodlands, and scrub or Sclerophyll
forests
AT13 Afrotropics - Deserts and xeric shrublands
AT14 Afrotropics - Mangrove
IM1 Indomalaya - Tropical and subtropical moist broadleaf forests
IM2 Indomalaya - Tropical and subtropical dry broadleaf forests
IM3 Indomalaya - Tropical and subtropical coniferous forests
IM4 Indomalaya - Temperate broadleaf and mixed forests
IM5 Indomalaya - Temperate coniferous forests
IM7 Indomalaya - Tropical and subtropical grasslands, savannas, and shrublands
IM9 Indomalaya - Flooded grasslands, savannas
9
IM10 Indomalaya - Montane grasslands and shrublands
IM13 Indomalaya - Deserts and xeric shrublands
IM14 Indomalaya - Mangrove
NA2 Nearctic - Tropical and subtropical dry broadleaf forests
NA3 Nearctic - Tropical and subtropical coniferous forests
NA4 Nearctic - Temperate broadleaf and mixed forests
NA5 Nearctic - Temperate coniferous forests
NA6 Nearctic - Boreal forests
NA7 Nearctic - Tropical and subtropical grasslands, savannas, and shrublands
NA8 Nearctic - Temperate grasslands, savannas, and shrublands
NA11 Nearctic - Tundra
NA12 Nearctic - Mediterranean forests, woodlands, and scrub or Sclerophyll forests
NA13 Nearctic - Deserts and xeric shrublands
NT1 Neotropics - Tropical and subtropical moist broadleaf forests
NT2 Neotropics - Tropical and subtropical dry broadleaf forests
NT3 Neotropics - Tropical and subtropical coniferous forests
NT4 Neotropics - Temperate broadleaf and mixed forests
NT7 Neotropics - Tropical and subtropical grasslands, savannas, and shrublands
NT8 Neotropics - Temperate grasslands, savannas, and shrublands
NT9 Neotropics - Flooded grasslands, savannas
NT10 Neotropics - Montane grasslands and shrublands
NT12 Neotropics - Mediterranean forests, woodlands, and scrub or Sclerophyll
forests
NT13 Neotropics - Deserts and xeric shrublands
NT14 Neotropics - Mangrove
OC1 Oceania - Tropical and subtropical moist broadleaf forests
OC2 Oceania - Tropical and subtropical dry broadleaf forests
OC7 Oceania - Tropical and subtropical grasslands, savannas, and shrublands
PA1 Palearctic - Tropical and subtropical moist broadleaf forests
PA4 Palearctic - Temperate broadleaf and mixed forests
10
PA5 Palearctic - Temperate coniferous forests
PA6 Palearctic - Boreal forests
PA8 Palearctic - Temperate grasslands, savannas, and shrublands
PA9 Palearctic - Flooded grasslands, savannas
PA10 Palearctic - Montane grasslands and shrublands
PA11 Palearctic - Tundra
PA12 Palearctic - Mediterranean forests, woodlands, and scrub or Sclerophyll forests
PA13 Palearctic - Deserts and xeric shrublands
11
Supplementary Table S4. Candidate GLMs used to identify the predictors of the proportion of
undescribed species. Predictor terms are human footprint index (HFI) and a binary classifier where a
realm-biome is either a tropical forest or not (TropFor).
Response variable Predictor Hypothesized relationship
variables
HFI A decrease in HFI increases the proportion of
undescribed species within biomes
TropFor A tropical forest realm-biome increases the
Response = logit (p),
proportion of undescribed species within
where biomes
Yi ~ Bin(ni, pi), HFI + TropFor A decrease in HFI and being a tropical forest
increases the proportion of undescribed
Yi is the estimated number of
species within biomes simultaneously.
undescribed species in realm-
biome i, HFI + TropFor + A decrease in HFI and being a tropical forest
HFI*TropFor increases the proportion of undescribed
ni is the total number of species
species within biomes simultaneously.
in realm-biome I,
Also an additional interaction effect where
pi is the probability of a species
being a tropical forest increases the slope of
being an undescribed species in
the human footprint term.
realm-biome i
Null The proportion of undescribed species within
biomes is stochastic and cannot be predicted
by either HFI or TropFor.
12
Supplementary Table S5. The estimated number and proportion of undescribed species globally and in each realm-biome. Realm-biome units ranked in
order of the estimated proportion ofundescribed amphibian species in each realm-biome. Ndesc = number of described species currently known to science;
Ntot = estimated total number of species; Nundesc = estimated number of undescribed species; % = proportion of undescribed species in each realm-biome,
expressed as a percentage; Outliers = the time-periods that were identified as outliers and removed from the analysis, a dash represents realm-biomes
where outliers are absent; IQR = the interquartile range for the estimated parameters; TropFor is a binary classifier where 1 represents a tropical forest
biome and 0 represents a non-tropical forest biome. HFI refers to the mean human footprint index of each realm-biome unit. DNC is used to represent
realm-biomes where we were unable to obtain estimates due to a lack of convergence.
Amphibians Land mammals
Code Realm-biomes TropFor HFI Ndesc Ntot (IQR) Nundesc % Outliers Ndesc Ntot (IQR) Nundesc % Outliers
(IQR) (IQR)
Global amphibians 6296 9347 3051 32.64
(9276- (2980-
9511) 3215)
Global land mammals 1762 5398 5561 163 (159- 2.93 1762
(5557- 173)
5571)
AA1 Australasia - Tropical and subtropical 1 15 415 949 (929- 534 (514- 56.27 NA 471 517 (516- 46 (45-47) 8.90 NA
moist broadleaf forests 1068) 653) 518)
AA10 Australasia - Montane grasslands and 0 11.4 71 133 (125- 62 (54-81) 46.62 NA 62 62 (62-62) 0 (0-0) 0.00 NA
shrublands 152)
NT1 Neotropics - Tropical and subtropical 1 15.1 2673 4454 1781 39.99 NA 1173 1253 80 (76-85) 6.38 1762,
moist broadleaf forests (4443- (1770- (1249- 1902
4646) 1973) 1258)
PA5 Palearctic - Temperate coniferous 0 19.3 141 229 (226- 88 (85-92) 38.43 1762 309 335 (334- 26 (25-27) 7.76 1762,
forests 233) 336) 1872,
1912
13
NT10 Neotropics - Montane grasslands and 0 15.8 270 394 (379- 124 (109- 31.47 NA 240 240 (240- 0 (0-0) 0.00 NA
shrublands 397) 127) 240)
AT13 Afrotropics - Deserts and xeric 0 12.9 80 115 (114- 35 (34-38) 30.43 NA 272 274 (274- 2 (2-3) 0.73 1762
shrublands 118) 275)
IM1 Indomalaya - Tropical and subtropical 1 28.5 936 1340 404 (391- 30.15 NA 888 906 (905- 18 (17-19) 1.99 1762
moist broadleaf forests (1327- 417) 907)
1353)
AT12 Afrotropics - Mediterranean forests, 0 21.4 42 59 (58-59) 17 (16-17) 28.81 NA 37 37 (37-37) 0 (0-0) 0.00 NA
woodlands, and scrub or Sclerophyll
forests
PA12 Palearctic - Mediterranean forests, 0 34.3 92 127 (125- 35 (33-36) 27.56 NA 161 183 (181- 22 (20-22) 12.02 1762
woodlands, and scrub or Sclerophyll 128) 183)
forests
PA1 Paleartic - Tropical and subtropical 1 28.7 98 125 (125- 27 (27-29) 21.60 NA 136 136 (136- 0 (0-0) 0.00 1872
moist broadleaf forests 127) 136)
NA4 Nearctic - Temperate broadleaf and 0 31.6 155 190 (190- 35 (35-36) 18.42 1822, 111 111 (111- 0 (0-0) 0.00 NA
mixed forests 191) 1832, 111)
1867
NA3 Nearctic - Tropical and subtropical 1 20.2 57 69 (68-70) 12 (11-13) 17.39 NA 117 121 (121- 4 (4-5) 3.31 NA
coniferous forests 122)
NT7 Neotropics - Tropical and subtropical 0 19.6 430 516 (513- 86 (83-88) 16.67 NA 560 580 (580- 20 (20-21) 3.45 1762
grasslands, savannas, and shrublands 518) 581)
NT13 Neotropics - Deserts and xeric 0 25.9 197 227 (226- 30 (29-31) 13.22 NA 291 293 (293- 2 (2-2) 0.68 1762
shrublands 228) 293)
AA7 Australasia - Tropical and subtropical 0 5.57 116 133 (131- 17 (15-18) 12.78 NA 177 182 (181- 5 (4-5) 2.75 1827
grasslands, savannas, and shrublands 134) 182)
AT1 Afrotropics - Tropical and subtropical 1 21.4 751 846 (844- 95 (93- 11.23 NA 821 879 (879- 58 (58-61) 6.60 1762
moist broadleaf forests 853) 102) 882)
PA4 Palearctic - Temperate broadleaf and 0 35.2 206 231 (230- 25 (24-27) 10.82 1762, 437 454 (453- 17 (16-18) 3.74 1762
mixed forests 233) 1772 455)
AA4 Australasia - Temperate broadleaf and 0 21.3 93 102 (101- 9 (8-9) 8.82 NA 95 95 (95-95) 0 (0-0) 0.00 NA
mixed forests 102)
AT9 Afrotropics - Flooded grasslands, 0 21 40 43 (42-43) 3 (2-3) 6.98 NA 53 53 (53-53) 0 (0-0) 0.00 NA
14
savannas
PA10 Palearctic - Montane grasslands and 0 14 68 73 (72-74) 5 (4-6) 6.85 NA 268 273 (272- 5 (4-5) 1.83 NA
shrublands 273)
NT2 Neotropics - Tropical and subtropical 1 28.4 458 491 (489- 33 (31-36) 6.72 NA 522 522 (522- 0 (0-0) 0.00 1902
dry broadleaf forests 494) 522)
IM3 Indomalaya - Tropical and subtropical 1 31.7 43 46 (46-46) 3 (3-3) 6.52 NA 52 52 (52-52) 0 (0-0) 0.00 NA
coniferous forests
NA5 Nearctic - Temperate coniferous forests 0 16.3 149 158 (158- 9 (9-9) 5.70 NA 230 231 (231- 1 (1-1) 0.43 1762
158) 231)
NA13 Nearctic - Deserts and xeric shrublands 0 15.7 99 104 (104- 5 (5-5) 4.81 NA 282 290 (289- 8 (7-8) 2.76 NA
104) 290)
NT3 Neotropics - Tropical and subtropical 1 33.9 373 390 (389- 17 (16-17) 4.36 NA 232 232 (232- 0 (0-0) 0.00 1897,
coniferous forests 390) 232) 1902
AT10 Afrotropics - Montane grasslands and 0 29.7 161 168 (168- 7 (7-8) 4.17 NA 257 262 (262- 5 (5-5) 1.91 1762
shrublands 169) 262)
NT8 Neotropics - Temperate grasslands, 0 18.9 50 52 (52-53) 2 (2-3) 3.85 NA 104 104 (104- 0 (0-0) 0.00 NA
savannas, and shrublands 104)
NT9 Neotropics - Flooded grasslands, 0 16.2 70 72 (72-73) 2 (2-3) 2.78 1862 54 55 (55-55) 1 (1-1) 1.82 NA
savannas
IM4 Indomalaya - Temperate broadleaf and 0 23.7 55 56 (56-56) 1 (1-1) 1.79 NA 67 67 (67-67) 0 (0-0) 0.00 NA
mixed forests
IM2 Indomalaya - Tropical and subtropical 1 34.9 135 136 (136- 1 (1-1) 0.74 NA 257 257 (257- 0 (0-1) 0.00 NA
dry broadleaf forests 136) 258)
AT7 Afrotropics - Tropical and subtropical 0 20 432 434 (434- 2 (2-2) 0.46 NA 791 807 (807- 16 (16-17) 1.98 1762,
grasslands, savannas, and shrublands 434) 808) 1912
PA13 Palearctic - Deserts and xeric shrublands 0 10 34 34 (34-34) 0 (0-0) 0.00 NA 329 372 (370- 43 (41-44) 11.56 1762,
373) 1787,
1797,
1852,
2002
PA8 Palearctic - Temperate grasslands, 0 27.1 30 30 (30-30) 0 (0-0) 0.00 1762; 254 277 (275- 23 (21-25) 8.30 1762
savannas, and shrublands 1772 279)
IM5 Indomalaya - Temperate coniferous 0 25.7 25 25 (25-25) 0 (0-0) 0.00 NA 35 37 (37-37) 2 (2-2) 5.41 NA
15
forests
AA13 Australasia - Deserts and xeric 0 3.57 32 32 (32-32) 0 (0-0) 0.00 NA 84 87 (87-87) 3 (3-3) 3.45 NA
shrublands
AA12 Australasia - Mediterranean forests, 0 15.6 45 45 (45-45) 0 (0-0) 0.00 NA 58 58 (58-58) 0 (0-0) 0.00 NA
woodlands, and scrub or Sclerophyll
forests
AA8 Australasia - Temperate grasslands, 0 12.2 27 27 (27-27) 0 (0-0) 0.00 NA 47 47 (47-47) 0 (0-0) 0.00 NA
savannas, and shrublands
IM13 Indomalaya - Deserts and xeric 0 35.7 30 30 (30-30) 0 (0-0) 0.00 NA 83 83 (83-83) 0 (0-0) 0.00 NA
shrublands
NA12 Nearctic - Mediterranean forests, 0 29.2 34 34 (34-34) 0 (0-0) 0.00 NA 43 43 (43-43) 0 (0-0) 0.00 NA
woodlands, and scrub or Sclerophyll
forests
NA8 Nearctic - Temperate grasslands, 0 25.1 82 82 (82-82) 0 (0-0) 0.00 NA 168 168 (168- 0 (0-0) 0.00 NA
savannas, and shrublands 168)
IM10 Indomalaya - Montane grasslands and 0 26.9 40 40 (40-40) 0 (0-0) 0.00 NA 19 Low
shrublands sample
size
AA2 Australasia - Tropical and subtropical 1 24.8 11 Low 41 44 (43-44) 3 (2-3) 6.82 NA
dry broadleaf forests sample
size
AA14 Australasia - Mangrove 0 10.8 3 Low 63 63 (63-63) 0 (0-0) 0.00 NA
sample
size
AT14 Afrotropics - Mangrove 0 30 1 Low 34 34 (34-34) 0 (0-0) 0.00 NA
sample
size
IM14 Indomalaya - Mangrove 0 35.8 7 Low 111 111 (111- 0 (0-0) 0.00 NA
sample 111)
size
NA11 Nearctic - Tundra 0 2.97 1 Low 60 60 (60-60) 0 (0-0) 0.00 1762,
sample 1967,
size 1997
16
NA6 Nearctic - Boreal forests 0 3.36 14 Low 79 79 (79-79) 0 (0-0) 0.00 1762,
sample 1967,
size 1997
NT12 Neotropics - Mediterranean forests, 0 26.3 10 Low 28 28 (28-28) 0 (0-0) 0.00 1762,
woodlands, and scrub or Sclerophyll sample 1837,
forests size 1897,
1947
NT14 Neotropics - Mangrove 0 30.7 8 Low 30 30 (30-30) 0 (0-0) 0.00 1762,
sample 1792
size
NT4 Neotropics - Temperate broadleaf and 0 14 46 Decreasing 57 57 (57-57) 0 (0-0) 0.00 1782
mixed forests efficiency
(excluded)
PA11 Palearctic - Tundra 0 3.94 3 Low 47 47 (47-47) 0 (0-0) 0.00 1762,
sample 1972
size
PA6 Palearctic - Boreal forests 0 8.53 11 Low 115 115 (115- 0 (0-0) 0.00 1762,
sample 115) 1997
size
PA9 Palearctic - Flooded grasslands, 0 28.5 6 Low 22 22 (22-22) 0 (0-0) 0.00 NA
savannas sample
size
AA11 Australasia - Tundra 0 - 0 Low 11 Low
sample sample
size size
AT2 Afrotropics - Tropical and subtropical 1 19.3 39 DNC 87 DNC
dry broadleaf forests
AT8 Afrotropics - Temperate grasslands, 0 23.6 2 Low 4 Low
savannas, and shrublands sample sample
size size
IM7 Indomalaya - Tropical and subtropical 0 38 10 Low 12 Low
grasslands, savannas, and shrublands sample sample
size size
17
IM9 Indomalaya - Flooded grasslands, 0 15.9 0 Low 3 Low
savannas sample sample
size size
NA2 Nearctic - Tropical and subtropical dry 1 22.9 8 Low 19 Low
broadleaf forests sample sample
size size
NA7 Nearctic - Tropical and subtropical 0 34.4 14 Low 5 Low
grasslands, savannas, and shrublands sample sample
size size
OC1 Oceania - Tropical and subtropical moist 1 25.9 3 Low 11 Low
broadleaf forests sample sample
size size
OC2 Oceania - Tropical and subtropical dry 1 33.3 1 Low 6 Low
broadleaf forests sample sample
size size
OC7 Oceania - Tropical and subtropical 0 23.2 0 Low 0 Low
grasslands, savannas, and shrublands sample sample
size size
18
Supplementary Table S6. Generalised linear mixed models (GLMMs) investigating the relationship
between year of description and probability of endangerment for amphibians (a) and mammals (b)
after removing species listed as threatened because of a limited range and/or population size . The
models are ranked by Akaike’s Information Criterion corrected for small sample size (AICc). Predictor
terms shown are Year = year of description. Also shown are the number of parameters (k), log
likelihood (LL), the difference in AICc of each model from the highest ranked model (∆AICc), AICc
weights representing the probability of each model being the best (wAICc), and the percent deviance
explained by each model (%DE).
(a) Amphibians (n = 3075, 312 threatened)
Rank Model k LL AICc ∆AICc wAICc %DE
1 ~Year 3 –978.73 1963.47 0.00 ≈1 3.05
2 Null 2 –1009.48 2022.97 59.50 ≈0
(b) Mammals (n = 3905, 487 threatened)
Rank Model k LL AICc ∆AICc wAICc %DE
1 ~Year 4 –1124.55 2257.12 0.00 ≈1 2.07
2 Null 2 –1148.34 2300.68 43.56 ≈0
19
Supplementary Figure S1. The relationship between extinction proneness and year of description
after removing range and population restricted species for amphibians (a) and mammals (b) (see
table S6 for multimodel inference statistics). Black lines represent population level fitted values. The
fitted relationship between year of description and proportion of species threatened within each
phylogenetic (order) group are represented by colored, dotted lines. Mammal order abbreviations
— Afr: Afroscoricida, Car: Carnivora, Cet: Cetartiodactyla, Chr: Chiroptera, Cin: Cingulata, Das:
Dasyuromorphia, Der: Dermoptera, Did: Didelphimorphia, Dip: Diprotodontia, Eul: Eulipotyphla, Hyr:
Hyracoidea, Lag: Lagomorpha, Mac: Macroscelidea, Mic: Microbiotheria, Mon: Monotremata, Pau:
Paucituberculata, Pera: Peramelemorphia, Per: Perissodactyla, Pho: Pholidota, Pil: Pilosa, Pri:
Primates, Pro: Proboscidea, Rod: Rodentia, Sca: Scandentia, Tub: Tubulidentata.
20
Appendix 1.
Identifying outlying time periods
In our analysis, outliers were identified by comparing the fitted versus observed values of
the number of species observed in each time period ( and , respectively) as well as the
efficiency variable ( and , respectively). We adapted an approach proposed by Motulsky
and Brown [1] to identify and remove outliers in our data. We briefly summarise the
method here. For more detailed information, readers can refer to the original paper by
Motulsky and Brown [1].
Motulsky and Brown [1] suggested a three-step method in identifying and removing
outliers in nonlinear regression problems where the goal is to minimize the sum of squares
of residuals. First, the model is to be fitted to the data using robust nonlinear regression.
Robust nonlinear regression fit is used as a ‘baseline’ from which outliers are detected. The
robust method is useful here because it gives progressively less weight to data points that
are far away from the fitted curve, therefore, ‘real’ outliers will be farther from the fitted
line and more likely to be identified as outliers in the second step of the method.
Second, after the curve is fitted, we need to decide if each point is far enough from
the fitted curve to be declared an outlier. Moltulsky and Brown [1] adapted the concept of
the False Discovery Rate (FDR) [2,3] to identify outliers in the data. First, we calculate the
ratio of each residual to the robust standard deviation of the residuals (RSDR). The RSDR is
quantified by calculating the 68.27th percentile of the absolute values of the residuals with a
small-sample size correction (equation 1),
(1)
where n = sample size, and k = number of parameters in the model. The ratio of each
residual to RSDR approximately follows a t distribution. We calculate a t-statistic and its
corresponding two-tailed P-value for each data point with n-k degrees of freedom.
Following that, we need to decide on a value of Q, which represents the maximum number
of statistically-significant data points that are false-positives. For example, if Q is set to 1%,
we can expect less than 1% of the statistically significant findings to be false positives, while
more than 99% are real outliers. Motulsky and Brown suggested Q to be set at 1% after
testing the method with simulated data. They found that setting Q to 10% removes outliers
too liberally, and conversely, setting Q to 0.1% makes outlier removal too conservative.
Following the recommendations of Motulsky and Brown, we calculate an alpha for each
data-point which accounts for multiple-comparisions (equation 2),
. (2)
We then test if the P-value is lower than the corresponding α for each data point. If it is
lower, than this data point , together with all data points with a higher absolute residual, are
21
considered as outliers. Finally, after identifying the outlying data points, we delete them and
run least-squares nonlinear regression on the remaining points.
In our analysis of outliers, the robust nonlinear regression models (implemented in R v.
2.12 [4], nlrob function in robustbase package) for many of the realm-biomes failed to
converge. Therefore, we used the regular least-squares fit instead of the robust fit to
establish the baseline fit. Taking into account that the regular least-squares fit reduces the
size of the residuals for potential outlying points, we set Q to 5% so that we are able to
capture outliers more efficiently. We feel that this is the fairest way to identify outliers as
the same model fit is applied to both amphibians and mammals in all realm-biomes. Using
this method across all realm-biomes also ensures that the results are comparable across
regions and taxa.
References
1. Motulsky H. J., Brown R. E. 2006 Detecting outliers when fitting data with nonlinear
regression – a new method based on robust nonlinear regression and the false
discovery rate. BMC Bioinformatics 7, 1–20.
2. Benjamini Y., Hochberg Y. 1995 Controlling the false discovery rate: A practical and
powerful approach to multiple testing. J. Roy. Stat. Soc. B. 57, 290–300
3. Benjamini Y., Yekutieli D. 2001 The control of the false discovery rate in multiple
testing under dependency. Ann. Stat. 29,1165–1188
4. R Development Core Team. 2009 R: A language and environment for statistical
computing. Vienna: R Foundation for Statistical Computing.
22
Appendix 2. Plots of observed versus fitted number of amphibian species (left panel) and fitted versus
observed value of efficiency (right panel) for each realm-biome and the global dataset. Left panel: Observed
number of species described in each five year period (dots), number of taxonomists (dashed line), expected
number of species as fitted by the model (bold line). Right panel: Observed value for efficiency E (=
(dots), fitted value of E ( ) (line).
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25
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Appendix 3. Plots of observed versus fitted number of land mammal species (left panel) and fitted versus
observed value of efficiency (right panel) for each realm-biome and the global dataset. Figure legends follow
Appendix 2.
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