Hunting to extinction: biology and regional
economy inﬂuence extinction risk and
the impact of hunting in artiodactyls
Samantha A. Price
and John L. Gittleman
Department of Biology, Gilmer Hall, University of Virginia, Charlottesville, VA 22904, USA
National Evolutionary Synthesis Center, 2024 W. Main Street, A200 Erwin Mills Building, Durham, NC 27705, USA
Half of all artiodactyls (even-toed hoofed mammals) are threatened with extinction, around double the
mammalian average. Here, using a complete species-level phylogeny, we construct a multivariate model to
assess for the ﬁrst time which intrinsic (biological) and extrinsic (anthropogenic and environmental)
factors inﬂuence variation in extinction risk in artiodactyls. Globally artiodactyls at greatest risk live in
economically less developed areas, have older weaning ages and smaller geographical ranges. Our ﬁndings
suggest that identifying predictors of threat is complicated by interactions between both biological and
anthropogenic factors, resulting in differential responses to threatening processes. Artiodactyl species that
experience unregulated hunting live in signiﬁcantly less economically developed areas than those that are
not hunted; however, hunted species are more susceptible to extinction if they have slower reproductive
rates (older weaning ages). In contrast, risk in non-hunted artiodactyls is unrelated to reproductive rate
and more closely associated with the economic development of the region in which they live.
Keywords: Artiodactyla; extinction; bushmeat; hunting;
economic development and phylogenetic comparative methods
Understanding the biological processes underlying species
extinction is one of the most important goals for
conservation biology, particularly for effective manage-
ment of populations and predicting species that require
immediate conservation measures (Mace & Balmford
2000). It has been shown in a variety of taxa, at both local
and global scales, that biological characteristics are
important determinants of variation in extinction risk
(see review by Reynolds 2002). To date, studies have
largely focused on overall correlates of extinction risk of
mammals (e.g. Purvis et al. 2000; Cardillo & Bromham
2001; Jones et al. 2003), while not assessing the complex-
ity of how different traits predispose species to extinction
via different threatening processes (Jennings et al. 1998;
Owens & Bennett 2000; Fisher & Owens 2004; Isaac &
Cowlishaw 2004; Keane et al. 2005).
Anthropogenic factors such as human population
density and economic development are expected to
correlate with extinction risk (e.g. Davies et al. 2006)
regardless of threatening process. Higher human densities
lead to higher levels of human impact on species and the
environment (e.g. McKee et al. 2004; Keane et al. 2005;
Davies et al. 2006) and have been found to be important in
explaining local and global extinctions (Brashares et al.
2001; Cardillo et al. 2004, respectively). Economic status
is also important; it affects how much governments can
spend on conservation efforts and how its people exploit
natural resources. The situation in economically less
developed countries often necessitates opportunistic and
short-term exploitation of the local ﬂora and fauna by its
citizens, increasing the risk of local extinction (Rodriguez
2000; Matos & Bovi 2002). For example, gross domestic
product (GDP) is negatively related to the number of
threatened birds globally (Davies et al. 2006).
Habitat loss (including fragmentation and degradation)
and exploitation are the two major processes threatening
mammals today; they account for 46.6 and 33.9% of all
recorded threats, respectively (Mace & Balmford 2000).
Each threat process exerts fundamentally different selec-
tive pressures: exploitation acts directly upon the species
by increasing mortality while habitat loss acts indirectly by
reducing the carrying capacity of the environment. The
Artiodactyla (even-toed hoofed mammals) provide a
unique test case for evaluating the importance of hunting
because exploitation is the primary threatening process
responsible for 40% of recorded threats, while habitat loss
accounts for 36% (www.redlist.org).
Predictions concerning how species traits increase
susceptibility to exploitation can be divided into two main
categories: those pertaining to reproductive rates and those
related to hunter behaviour (see review by Fitzgibbon
1998). As an example, the Quaternary mammalian
megafauna extinctions have often been associated with
over-hunting and the spread of modern humans (see review
by Barnosky et al.2004), and both hunter selection of large-
bodied prey (blitzkrieg hypothesis, Martin 1984)andslow
reproductive rates (Johnson 2002) have been proposed as
determinants of the loss of mammal species.
Proc. R. Soc. B (2007) 274, 1845–1851
Published online 17 May 2007
Electronic supplementary material is available at http://dx.doi.org/10.
1098/rspb.2007.0505 or via http://www.journals.royalsoc.ac.uk.
* Author and address for correspondence: National Evolutionary
Synthesis Center, 2024 W. Main Street, A200 Erwin Mills Building,
Durham, NC 27705, USA (email@example.com).
Present address: Institute of Ecology, University of Georgia, Athens,
GA 30602, USA.
Received 12 April 2007
Accepted 26 April 2007
1845 This journal is q 2007 The Royal Society
Hunters have been shown to prefer to hunt larger-
bodied species (Mittermeier 1987; Jerozolimski & Peres
2003), and the large body size has been found to correlate
with threat status in exploited birds (Owens & Bennett
2000; Keane et al. 2005) and hunted primates (Isaac &
Cowlishaw 2004). However, the harvest rate may reﬂect
the encounter rate, which is controlled by hunter
numbers, hunter behaviour and prey biology (Fitzgibbon
1998). Increasing size is also associated with slow
reproductive rates, which increase vulnerability to extinc-
tion via hunting. The principle of sustainable harvesting
(Hartig 1796; Clark 1990) suggests that a population will
remain stable even when individuals are harvested as long
as off-take does not exceed rN(1KN/K ), where N is the
population size, K is the carrying capacity and r is the
intrinsic rate of population growth. Therefore, species
with fast rates of increase (r) will have a larger surplus
available for harvesting and should be more resilient to
increased mortality (Bodmer 1995; Bodmer et al. 1997;
Jennings et al. 1998). Thus, traits that reﬂect reproductive
speed such as weaning age, age at sexual maturity and
body mass will potentially show an association with
extinction risk via exploitation (Owens & Bennett 2000;
Purvis 2001; also see reviews by Cowlishaw & Dunbar
2000; Kokko et al. 2001).
Here, we have constructed a multivariate phylogenetic
regression model to identify the biological, ecological and
anthropogenic variables that correlate with levels of threat
across the Artiodactyla as given by the IUCN Red List
(sensu Purvis et al. 2000). We then investigated whether
different traits elevate the risk of extinction depending on
whether a species is hunted or not. The majority of
artiodactyls are hunted for food, often termed bushmeat
hunting, while others are hunted as trophies (e.g. Urial
sheep, Ovis vignei; sable antelope, Hippotragus niger) or for
their soft hair (e.g. chiru, Pantholops hodgsoni ). The
analyses were repeated with the dataset partitioned into
two: the hunted species forming one partition and the
species that we could not validate as being hunted forming
the other ‘non-hunted’ partition.
2. MATERIAL AND METHODS
The IUCN (2006) Red List (IUCN 2006) was used as the
measure of current extinction risk. The Red List categories
were divided into six levels (following Purvis et al. 2000): least
concern, 0; near threatened, 1; near threatened conservation
dependent and vulnerable, 2; endangered, 3; critically
endangered, 4; extinct in wild and extinct, 5, although no
extinct species were added to the analysis. To ameliorate the
effect of autocorrelation between species traits and the criteria
for IUCN classiﬁcation, all analyses were performed on
species whose Red List classiﬁcation was based on a decline in
population density or geographical range size (criterion A),
rather than the absolute measures of those variables. After the
data deﬁcient species and those not classiﬁed under criterion
A were removed, 144 artiodactyl species categorized from
least concern to critically endangered were available for
analysis (electronic supplementary material).
Eleven predictor variables were chosen to represent the
anthropogenic, hunter behaviour and reproductive rates
hypotheses (summarized in table 1). Five additional variables
were also included to represent other commonly cited
hypotheses concerning elevated extinction risk: small popu-
lation size as indicated by small geographical range and low
population density (e.g. Gaston 1994); poor ecological
ﬂexibility (e.g. Brown 1971; Laurance 1991) as represented
by narrow habitat breadth and dietary specialization; and,
ﬁnally, large home ranges which make species especially
vulnerable to habitat loss (e.g. Woodroffe & Ginsberg 1998).
Thirteen quantitative traits were obtained from the
ANTHERIA trait database ( K. E. Jones, J. Bielby, A. Purvis,
D. Orme, A. Teacher, J. L. Gittleman, R. Grenyer, et al.,
unpublished manuscript): weaning age; maximum lifespan;
gestation length; age at ﬁrst birth; inter-birth interval; age at
sexual maturity; home range; body mass; group size;
population density; geographical range; mean human popu-
lation density; and actual evapotranspiration (AET). AET
was included as a measure of primary productivity, which is
thought to be a confounding factor when using mean human
population density (Balmford et al. 2001). The database is the
product of a collaborative effort to construct a comprehensive
Table 1. Summary of hypotheses.
traits predicted to be associated with an elevated
risk of extinction explanation
anthropogenic high human population density higher human population densities lead to higher
levels of inﬂuence on species and the environment.
low gross national income lower national income necessitates opportunistic and
short-term exploitation of the local ﬂora and fauna
by its citizens, increasing the risk of local extinction.
hunter behaviour large body mass hunters prefer to hunt larger species.
large group size larger groups are more visible to hunters.
polygamy polygamous mating systems require larger groups and
are therefore more visible. However, polygamous
mating strategies may also be associated with lower
threat as not all males are required for breeding
success; this requires selective hunting of non-
slow reproductive rate as indicated by older weaning
ages, longer gestation lengths, longer inter-birth
intervals, older ages at ﬁrst birth and older ages at
species with slow rates of increase (r) have smaller
surpluses available for harvesting and are therefore
less resilient to increased mortality from hunting.
large body mass and greater maximum longevity larger body masses and greater lifespans are
associated with slower reproductive rates.
1846 S. A. Price & J. L. Gittleman Hunting to extinction
Proc. R. Soc. B (2007)
database of major biological traits for all mammal species;
information is drawn from both primary and secondary
sources. All quantitative data used in this analysis represent
central tendency measures for each species and were C1
natural log transformed.
Gross national income (GNI; US $ millions) taken from
UNEP (2006) was added as an indicator of the socio-
economic condition experienced by a species across its range.
The 2003 estimates of GNI were used unless unavailable; the
most recent estimate was then used. Using A
weighted average of GNI across the species range was
calculated from country estimates of GNI and the area of
the species range (km
) in each country (ranges taken from
Sechrest 2003). Three categorical traits were collected from
the literature: mating strategy (polygamous, monogamous);
diet (specialist (grazer or browser) or generalist (mixed
grazer/browser or omnivore)); and habitat breadth. Habitat
breadth can vary between the levels 1 and 6 and was
calculated from the dataset presented in Caro et al. (2004),
which lists a species as living in as many as six different
habitats (grassland/scrubland, dense forest, desert, rocky,
tundra and swamp). All data were converted to the taxonomy
of Grubb (1993) and are available in the electronic
The artiodactyl dataset of 144 species was used to identify
traits that correlate with extinction risk across artiodactyls.
The complete dataset was then partitioned on the basis of
threatening process and reanalysed to look for the differences
between traits that predispose hunted and non-hunted
species to extinction. Literature searches (Web of Science,
Biological Abstracts and Zoological Record) using the terms
Artiodactyl and hunting or bushmeat were used to identify
species that are known to be hunted, and we also followed up
citations in the papers found during the literature search. If
the only citations we could ﬁnd referred to regulated hunting
then that species was not added to the hunted partition, e.g.
Ovis dalli. It is important to exclude species that only
experience regulated hunting from the hunted partition as,
theoretically at least, hunting should not be a threatening
process if appropriate quotas are set and enforced. We
identiﬁed 111 artiodactyl species that are cited within the
primary and secondary literature as being hunted without
regulation; 94 of these were classiﬁed under criterion A in the
IUCN Red List (IUCN 2006). The list of hunted species is a
conservative estimate of the species hunted worldwide.
Species that have experienced over-hunting in the past, but
are now protected, are not included, and species with small
geographical ranges are likely to be under-represented in our
sample, as we are restricted to the species that live within the
areas where studies of hunting have been undertaken.
All statistical analyses were conducted in R (http://www.
r-project.org/) on phylogenetically independent contrasts
(PICs; Felsenstein 1985), as the majority of traits showed
phylogenetic patterning using Pagel’s l-statistic (results not
shown; Pagel 1999). The topology presented in Price et al.
(2005) was used to generate the PICs. Independent contrasts,
including the modiﬁed version for categorical traits, were
calculated in R (http://www.r-project.org/) using the A
package (Paradis) and code provided by Andy Purvis, David
Orme and Rich Grenyer (P
ENDEK package in development,
available upon request from firstname.lastname@example.org). The
independent contrasts were standardized using branch
lengths (taken from Bininda-Emonds et al. 2007), which
were then transformed on a trait-by-trait basis following
Garland et al. (1992). Polytomies in the phylogeny were
treated as soft; they were resolved arbitrarily and the resulting
n contrasts from each down-weighted by 1/n, with each
node thereby contributing one degree of freedom (following
Purvis & Garland 1993).
We used a three-stage process to identify correlates of
extinction risk in all three data partitions (complete
artiodactyl dataset, hunted partition and non-hunted
partition). The ﬁrst stage involved regressing IUCN threat
rating against each continuous predictor variable; although
threat is a discrete variable, a continuous distribution
underlies the categories (Purvis et al. 2000). To test the
effect of treating threat as a continuous variable, we ran
analyses with IUCN threat category converted to a binary
response variable (non-threatened, least concern and near
threatened; threatened, near threatened conservation depen-
dent through to critically endangered) with each life-history
trait in turn as the Y variable. Trait values were not available
for every species (electronic supplementary material), which
meant that sometimes the degrees of freedom were quite low
(10–20 degrees of freedom); however, no analysis was run
without at least 10 degrees of freedom. Body mass was added
as a covariate to all analyses that included weaning age, home
range, population density, age at ﬁrst birth, inter-birth
interval and gestation length, as preliminary analysis
conﬁrmed that they covaried with body mass. Collinearity,
however, should not be a problem as all correlations between
traits and body mass indicated R-squared values of under
0.40. AET, as a measure of primary productivity, was added
to all analyses including mean human population density, as
preliminary analysis conﬁrmed that it had the potential to be a
confounding factor due to its signiﬁcant association with
mean human population density. Categorical data were
analysed using the Wilcoxon signed-rank test on the
contrasts. The second stage involved repeating the regression
analyses including geographical range to allow comparisons
to previous mammalian extinction risk analyses (Purvis et al.
2000; Jones et al. 2003). During each of these stages, the
regressions were plotted, and contrasts that had undue
inﬂuence over the regression line were deleted. Finally, the
third stage involved building a multivariate regression model
for each data partition that included all signiﬁcant ( p!0.05)
and marginally signiﬁcant ( p!0.1) continuous traits from
the ﬁrst two stages. This regression model formed the starting
set for model simpliﬁcation to ﬁnd the minimum adequate
model (MAM), following the procedure outlined in Purvis
et al. (2000).
Of the 144 artiodactyls that are categorized under
criterion A in the IUCN Red List, 67 are currently
threatened with extinction. Biological, ecological and
anthropogenic correlates of extinction risk across all
artiodactyls are presented in table 2. When single traits
were regressed against threat status, small geographical
range, older weaning age and low GNI were all
signiﬁcantly associated with a higher risk of extinction.
When geographical range was added to the regression
model, weaning age lost some signiﬁcance and population
density became marginally signiﬁcant. The MAM
(table 3) revealed weaning age, population density and
GNI as the important predictors of extinction risk in
Hunting to extinction S. A. Price & J. L. Gittleman 1847
Proc. R. Soc. B (2007)
the Artiodactyla, explaining 36.2% of the variance.
Geographical range explains 6.9% but loses signiﬁcance
when placed in a multivariate model with weaning age
(d.f.Z60, tZK0.885, pZ 0.38). None of the three
categorical traits showed a signiﬁcant relationship with
When the effect of threat distribution was removed
using the dichotomous threat variable, older weaning was
conﬁrmed as the most signiﬁcant predictor of elevated
threat (VZ168, pZ0.002). GNI was conﬁrmed as an
important predictor of threat (VZ117, pZ0.051); species
that live in economically less developed countries are
more threatened. Larger body mass and longer gestation
length showed associations with higher threat (body
mass VZ299, pZ0.079; gestation VZ232, pZ0.063),
while geographical range (VZ190.5, pZ1) and popu-
lation density (VZ101, pZ0.8) showed no signiﬁcant
(b) Hunted and non-hunted species
Approximately 50% of the species in each partition are
threatened with extinction. Traits that are associated with
elevated threat levels in hunted artiodactyls are different
from those that increase vulnerability to extinction in
artiodactyls that are not known to experience uncontrolled
hunting (table 4). In the ﬁrst two stages of the analysis,
weaning age and geographical range were the only
signiﬁcant predictors of threat status in hunted species,
while age at ﬁrst birth and population density were nearly
signiﬁcant ( p!0.1). The MAM for the hunted partition
contains only weaning age, which explains 19.7% of the
variance in threat rating. By contrast, in the analysis of the
non-hunted partition, geographical range, population
density and GNI were signiﬁcantly correlated with threat
status, although population density lost signiﬁcance when
geographical range was added to the model. The MAM for
the non-hunted partition contains both geographical range
and GNI and explains 26.8% of the variance in threat
rating. None of the categorical traits were signiﬁcantly
related to threat status in either partition (results not shown);
the degrees of freedom are very low (d.f.!12).
Our analyses conﬁrm the prediction that the inﬂuence of
particular threatening processes on extinction risk
depends on species-speciﬁc biological and ecological
traits. Traits that were signiﬁcant predictors of extinction
risk across artiodactyls segregated among the hunted and
non-hunted processes; geographical range was the only
trait to remain signiﬁcant regardless of threatening
process. This result contrasts the comparative analyses
of exploited primates and carnivores, which show the same
set of extinction risk correlates across all species regardless
of threat process (Purvis 2001).
Although geographical range was the only trait
signiﬁcantly associated with extinction risk in all dataset
partitions, weaning age appears to be the key determinant
of artiodactyl threat rating. Weaning age explains the
greatest amount of variance in artiodactyl threat rating
Z11%) and, unlike geographical range, weaning age is
retained in the artiodactyl MAM and remains signiﬁcant
when threat is treated as a binary variable (threatened or
non-threatened). This ﬁnding is not consistent with other
studies in which geographical range is the single most
important predictor of global mammalian extinction risk
(carnivores and primates, Purvis et al. 2000; marsupials,
Cardillo & Bromham 2001; bats, Jones et al. 2003). The
greater importance placed on a reproductive trait in
artiodactyls may relate to the fact that artiodactyls are
primarily threatened by hunting (Mace & Balmford
2000), and weaning age is only associated with extinction
in hunted artiodactyls. The mechanism for this result may
be that hunted artiodactyls are more prone to extinction if
they wean at older ages, a conclusion which is consistent
with the prediction based on sustainable yield theory
(Hartig 1796; Clark 1990). Species with older weaning
ages have slower reproductive rates and, consequently,
Table 3. Minimum adequate models (MAMs). (
p!0.001; n.s., not signiﬁcant; - - -, trait not
added to the model as not signiﬁcant in the single predictor
Artiodactyla hunted non-hunted
d.f. 45 43 35
0.3637 0.1974 0.2684
geographical range n.s. n.s. K2.462
body mass n.s. n.s. n.s.
weaning age 4.452
population density K2.094
age at ﬁrst birth - - - n.s. - - -
n.s. - - - - - -
Table 2. Correlates of extinction risk: complete artiodactyl
adult body mass 99 0.852 93 1.506
44 1.088 42 0.352
72 K1.37 68 K1.959
86 0.276 82 0.132
age at ﬁrst birth
48 0.062 46 K0.291
55 0.689 53 0.338
70 K0.219 67 K0.519
81 K0.067 77 0.37
social group size
57 K0.177 56 K0.419
habitat breadth 96 K0.95 91 K0.727
95 K0.169 92 K0.052
Bodymass was added as a covariate.
Actual evapotranspiration (AET) was added as a covariate.
1848 S. A. Price & J. L. Gittleman Hunting to extinction
Proc. R. Soc. B (2007)
have lower sustainable off-takes, making them more prone
to extinction via unsustainable harvesting. Our conclusion
that the global extinction of hunted artiodactyls is related
to slow reproductive rate is congruent with evidence from
local extinctions of hunted mammals (Bodmer et al. 1997)
and studies of bushmeat hunting, which show that slow-
growing species are being hunted unsustainably (e.g.
Barnes 2002; Bennett et al. 2002; Fa et al. 2003).
There is no evidence that hunter behaviour is playing a
role in determining global artiodactyl extinction risk;
hunter preference for large body size and hence slow life-
history traits (e.g. Isaac & Cowlishaw 2004) cannot
explain our results as body mass is not a signiﬁcant
predictor of threat. Our conclusion that present vulner-
ability to hunting-induced extinction is related to slow
reproductive rates, not hunter preference for body size, is
consistent with recent conclusions regarding the
determinants of past exploitation-related extinctions
(Johnson 2002). Extrapolation from extant relatives of
species that went extinct during the Late Quaternary
mammalian ‘megafaunal’ extinctions has shown that
species with slow reproductive rates were more likely to
become extinct regardless of body size (Johnson 2002).
GNI was a signiﬁcant predictor of threat across all
artiodactyls: species that live in areas of low economic
development are more threatened. These ﬁndings agree
with the results from a recent study (Davies et al.2006)on
the global distribution of extinction risk in birds, wherein
areas of high economic development (as measured by GDP)
are coincident with lower numbers of threatened species
worldwide. When the dataset was partitioned, GNI was only
associated with threat status in non-hunted artiodactyls,
which contradicts our prediction that a weak economy will
elevate the threat of extinction for all species regardless of
threatening process. Although the threat status of hunted
species is not associated with national economic status, risk
of being hunted is associated with GNI: artiodactyls that
experience uncontrolled hunting live in areas with signi-
ﬁcantly lower GNI than non-hunted species (Mann–
Whitney test, WZ5889, pZ3.047!10
). It is perhaps
not surprising that species that experience unregulated
hunting live in countries with low GNI because regulation
(setting and enforcing quotas, etc.) of hunting is costly.
Thus, the exclusion of artiodactyls that experience only
regulated hunting may actually prevent us from seeing a
relationship between threat status and GNI in the hunted
with higher GNI.
Species that are in the ‘non-hunted’ partition are likely to
be primarily threatened by habitat loss, as it is the second
most important threatening process in artiodactyls,
accounting for 36% of all threats (www.redlist.org). Several
hypotheses are commonly associated with extinction due to
habitat loss: those reﬂecting ecological ﬂexibility (Brown
1971; Laurance 1991; Norris & Harper 2004; but see
Vazquez & Simberloff 2002) and those associated with small
population size (Te r b or gh 1 9 74 ; Simberloff 1986; Owens &
Bennett 2000; Koh et al.2004). Low population densities
and small geographical ranges are associated with extinction
risk in non-hunted artiodactyls, which is consistent with the
small population hypothesis, but neither of the traits that
reﬂect ecological ﬂexibility, dietary specialization and
habitat breadth, is associated with threat.
We conclude that different biological traits elevate
vulnerability to extinction in artiodactyl species depending
on whether a species is hunted. Correlates of extinction
risk across all artiodactyls are a composite of the traits that
increase vulnerability to different threats. Hunted artio-
dactyls with slower reproductive rates are more at risk of
extinction, even though artiodactyls per se are less
vulnerable than primates to extinction via hunting due to
their relatively fast rates of reproduction (Bodmer et al.
1997). It is therefore important to know what type of
threat a species is facing, particularly whether it is hunted
or not, before identifying correlates of extinction risk. This
study is an initial step in understanding how artiodactyls
respond to anthropogenic extinction processes; the effects
of habitat loss and hunting are often synergistic (Peres
2001). To improve the predictive ability of our extinction
risk models, future studies must quantify how species
Table 4. Correlates of extinction risk: hunted and non-hunted partitions. (
hunted species non-hunted species
range (t-statistic) d.f.
geographical range 62 K3.365
—— 36 K0.2743
adult body mass 64 0.668 61 1.359 38 0.226 35 0.185
24 K0.478 21 K0.626 20 0.616 18 K0.091
42 K0.562 41 K1.832
58 0.832 56 0.549 31 0.469 28 0.403
age at ﬁrst birth
28 0.505 32 0.46 29 0.984
34 1.014 32 K0.103 22 K0.154 19 K0.256
23 0.296 21 0.393
sexual maturity age
45 1.596 43 0.693 28 K0.171 25 0.07
50 K0.488 48 K0.713 33 0.335 29 0.984
social group size
38 1.194 36 1.061 15 K1.541 14 K1.749
habitat breadth 60 K0.176 59 K0.899 38 K0.085 35 0.9227
mean human popu-
63 K0.225 60 0.055 35 K1.068 34 K0.383
gross national income 63 K0.716 61 K0.205 36 K2.888
Bodymass was added as a covariate.
Actual evapotranspiration (AET) was added as a covariate.
Hunting to extinction S. A. Price & J. L. Gittleman 1849
Proc. R. Soc. B (2007)
respond to multiple extinction threats (Isaac & Cowlishaw
2004) and determine the spatial and/or temporal variation
in the different threats ( Fisher & Owens 2004).
We thank Andy Purvis, Rich Grenyer and David Orme for
supplying the ‘R’ code for the phylogenetic independent
contrast analysis, Hilmar Lapp for help with ‘R’, Georgina
Mace, Rich Grenyer, Jonathan Davies and Jessica Partain for
helpful discussion, Andy Purvis and two anonymous
reviewers for comments and suggestions on earlier drafts of
the manuscript, and Kate Jones for help with P
and comments on a more recent draft of the manuscript.
Financial support was received from the National
Science Foundation (DEB/0129009) and latterly a NESCent
post-doctoral fellowship to S.A.P. (NESCent NSF
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