Text S1. Variable summary
Table S1 provides a list of variables used in modelling species extinction risk and predicting
Data Deficient status. This set of variables is derived from the dataset analyzed by Sodhi et
al. (2008), and more detailed description of these variables is found therein. We updated the
2008 dataset to reflect changes in species taxonomy and distributions, updating associated
spatial variables to reflect changed distributions.
Extinction risk status and taxonomy were derived directly from the IUCN Redlist and Extent
of occurrence (EOO) was derived from IUCN/GAA range maps. Both sources are updated
from the 2008 dataset to reflect most recent available information from the respective
sources.
Species trait data were compiled by Sodhi et al. (2008) and remain unchanged. This data was
compiled from a combination of field guides, herpetology textbooks, monographs, journal
articles, online amphibian databases and websites and expert opinion (see Sodhi et al. 2008
for references). These variables were originally chosen as they represent both available data,
and data considered to be important in determining species susceptibility to extinction risk.
Percentage range lost was calculated by Sodhi et al. (2008) using the GAA range polygons to
extract data from modified version 3 of the Global Land Cover 2000 dataset (GLC 2000),
where landuse within each species range classified as human modified was considered as
‘lost’. We updated these values, using the same GLC data, to reflect changed species
polygons.
Mean bioclimatic variables within each species distribution range were similarly recalculated
to account for changed species ranges since the 2008 dataset. These were re-estimated
following the procedure used by Sodhi et al. (2008). Bioclimatic variables were determined
using the ‘WorldClim’ database (Version 1.4; Hijmans et al., 2005).
Extent of occurrence (area) is widely found to be the most important determinant of species
extinction risk (eg. Cooper et al., 2008). Thermal and humidity/precipitation variables are
likely to be highly important for aquatic breeding ectotherms and have interactions with
disease susceptibility (Pounds et al., 2006). Latitude and longitude are included in the model
to allow for differing regional patterns in extinction threat. Habitat loss is considered the
single largest threat to species persistence globally and this is incorporated as percentage
range lost (eg. Stuart et al., 2004). Intrinsic, life-history, characteristics are likely to modify
species specific extinction risk through complex interactions with other threat processes (eg.
Murray et al., 2011). A number of life-history variables are included in the model, given preconceived hypothesise as to their importance: body-size has links to hunting pressure
(Warkentin et al., 2009); adult niche, egg deposition and larval development site are likely to
alter species susceptibility to extinction risks; fertilization mode (internal/external),
reproductive cycle (seasonal/seasonal) and reproductive mode (egg laying, live-bearing,
direct developing) are all linked to populations’ ability to recruit numbers following declines;
parental care behaviour is likely to enhance offspring survival; species primary broad habitat
preference is linked to the species ability to persist in degraded habitats.
Group
Variable
Source
Dependant
IUCN extinction risk
From (IUCN, 2011)
Area (EOO)
GAAS (IUCN, 2011)
Lat./Long. Max./Min.
From GAAS (IUCN, 2011)
Temperature
Max./Min./Mean
Precipitation
Max./Min/Mean
To GAAS from (Hijmans et al.,
2005)
To GAAS from (Hijmans et al.,
2005)
To GAAS from (Hijmans et al.,
2005)
To GAAS from (European
Commission’s Joint Research Centre
(JRC) Institute for Environment and
Sustainability (IES), 2002)
Geographic
Humidity
Extrinsic Threat
Life History
Percent range lost
Area commonly the most
important driver of extinction
risk.
Different threat processes
may occur in different spatial
regions.
Ectotherms rely on
environmental temperatures.
Most amphibians require
water bodies to breed.
Most amphibians require
humid conditions.
Habitat loss a major driver of
extinction.
Size class
Robustness and hunting
pressure related to body size.
Niche
(arboreal/terrestrial/fossorial/
riparian)
Land use change removes
niches?
Fertilization mode
(internal/external)
Reproductive success.
Reproductive cycle
(seasonal/a seasonal)
Reproductive success.
Reproductive mode (direct
development/larval)
Egg site
(stream/phytotelm/pond/
vegetation)
Taxonomic
Role in extinction risk
From published literature, original
species descriptions and expert
opinion
Reproductive success.
Site availability affected by
habitat loss?
Larval site
(stream/phytotelm/pond)
As above.
Parental care (present/absent)
Increased embryo/larval
survival?
Broad habitat (primary
forest/secondary/riparian)
Habitat obligates more at
risk?
Order
From (IUCN, 2011)
Differential responses based
on taxonomy.
Table S1. Variables used in predictive models.
Geographic variables are taken from the IUCN Global Amphibian Assessment shapefiles
(GAAS) or derived from these areas using Geographic Information Systems (GIS) (IUCN,
2011). Climatic variables extracted to GAAS from WorldClim database (Hijmans et al.,
2005). Range loss derived from European Commission Joint Research Centre (2003).
Complete detail of sources of life history data is given in (Sodhi et al., 2008).
Dataset
Total
4982 (81.5)
Assessed
DD
Anura
IUCN
Total
6115
3886 (63.5)
1096 (17.6)
Caudata
615
502 (81.6)
458 (74.5)
44 (7.2)
Gymnophiona
189
167 (88.4)
58 (30.7)
109 (57.7)
Total
6919
5651 (81.8)
4402 (63.6)
1249 (18.1)
Order
Table S2. Completeness of amphibian taxonomic richness in analyzed dataset.
Overall, and by taxonomic order, amphibian richness is based on most recent values from the
IUCN, totalling 6919 species. The number of species we analyze in our dataset are given for
the total dataset and for assessed and Data Deficient subsets, with percentages of IUCN total
richness in parentheses.
Total
dataset
152577
DD
subset
31971
Assessed
subset
117132
Missing data points
(% of total)
3474
1752
1722
(2.3)
(5.2)
(1.4)
Number (%) species
with 1+ missing data
point
4024/5651
1227/1249
2797/4402
(71.2)
(98.2)
(63.5)
Data points
Table S3. Summary of missing data points in the analyzed amphibian database.
The number of data points in our database is shown for the total dataset (5651 species x 27
predictor variables) and for Data Deficient and assessed subsets. The number of missing
predictor variable values in the respective datasets is indicated as well as the number of
species with any missing data. The majority of species records contain some missing
information (>63.5%) far outweighing the actual amount of missing information (< 5.2%).
~Area + IUCN trend + Taxonomy
NA excluded
error %
(N=1605)
37.4
NA imputed
error %
(N=4402)
30.8
~Life history- Area + Taxonomy
57.0
46.7
~Geographic – Area + Taxonomy
43.6
34.4
~Area + Life history + Geographic + Taxonomy
33.4
26.3
Model
Table S4. Model inaccuracy (OOB error %) for IUCN assessed species subset with missing
data excluded and imputed. ‘IUCN trend’ is only relevant to IUCN assessed species (see
IUCN, 2011 for further details) and could not be used in predictive models for Data Deficient
species.
Predicted
IUCN assessed
LC
NT
VU
EN
CR
EW
EX
total
LC
2119
190
123
48
26
0
0
2506
NT
27
64
29
9
2
0
0
131
VU
45
59
289
96
26
0
1
516
EN
39
47
126
482
134
2
9
839
CR
10
4
33
84
253
0
9
393
EW
0
0
0
0
0
0
0
0
EX
0
0
0
2
1
0
14
17
total
2240
364
600
721
442
2
33
4402
Table S5. Model accuracy for IUCN assessed species. Comparison of IUCN risk to the model
output was assessed by chi-squared test on column/row totals and merging extinct categories.
Model mis-classification was assessed by row-wise averaging of the number of mis-classified
species by the numeric distance (classes) to their correct classification across the matrix.
Figure S1. Species richness distribution for Data Deficient amphibians. Showing most recent
available IUCN data (correct as Feb 2012) and including Data Deficient species excluded
from our analysis.
Data S1.
Text file containing a list of Data Deficient species analyzed, their predicted extinction class
and certainty of prediction (proportion of 1000 randomForest trees matching predicted status)
and complete extinction risk class probability distribution.
REFERENCES
European Commission’s Joint Research Centre (JRC) Institute for Environment and
Sustainability (IES) (2002) GLC 2000: global land cover mapping for the year 2000.
Available at: http://bioval.jrc.ec.europa.eu/products/glc2000/glc2000.php (accessed 12 July
2008).
Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G. & Jarvis, A. (2005) Very high
resolution interpolated climate surfaces for global land areas. International Journal of
Climatology, 25, 1965–1978.