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O R I G I N A L A R T I C L E
Apes in a changing world – the effects of global warming on the behaviour and distribution of African apes
Julia Lehmann1,2*, Amanda H. Korstjens1,3 and Robin I. M. Dunbar1,4
1British Academy Centenary Research Project,
School of Biological Sciences, Crown Street,
University of Liverpool, Liverpool L69 7ZB,
UK, 2 Department of Life Sciences, Roehampton
University, London SW15 4JD, UK, 3 Conservation Sciences, Bournemouth
University, Poole BH12 5BB, UK, 4Institute of
Cognitive and Evolutionary Anthropology,
University of Oxford, Oxford OX2 6PE, UK
*Correspondence: Julia Lehmann, Life Science
Department, Holybourne Avenue, Roehampton
University, London SW15 4JD, UK.
E-mail: [email protected]
A B S T R A C T
Aim In this study we use a modelling approach to identify: (1) the factors
responsible for the differences in ape biogeography, (2) the effects that global
warming might have on distribution patterns of African apes, (3) the underlying
mechanisms for these effects, and (4) the implications that behavioural flexibility
might be expected to have for ape survival. All African apes are highly
endangered, and the need for efficient conservation methods is a top priority. The
expected changes in world climate are likely to further exacerbate the difficulties
they face. Our study aims to further understand the mechanisms that link climatic
conditions to the behaviour and biogeography of ape species.
Location Africa.
Method We use an existing validated time budgets model, derived from data on
20 natural populations of gorillas (Gorilla beringei and Gorilla gorilla) and
chimpanzees (Pan troglodytes and Pan paniscus), which specifies the relationship
between climate, group size, body weight and time available for various activities,
to predict ape distribution across Africa under a uniform worst-case climate
change scenario.
Results We demonstrate that a worst-case global warming scenario is likely to
alter the delicate balance between different time budget components. Our model
points to the importance of annual temperature variation, which was found to
have the strongest impact on ape biogeography. Our simulation indicates that
rising temperatures and changes in rainfall patterns are likely to have strong
effects on ape survival and distribution, particularly for gorillas. Even if they
behaved with maximum flexibility, gorillas may not be able to survive in most of
their present habitats if the climate was to undergo extreme changes. The survival
of chimpanzees was found to be strongly dependent on the minimum viable
group size required.
Main conclusions Our model allows us to explore how climatic conditions,
individual behaviour and morphological traits may interact to limit the
biogeographical distributions of these species, thereby allowing us to predict
the effects of climate change on African ape distributions under different climate
change regimes. The model suggests that climate variability (i.e. seasonality) plays
a more important role than the absolute magnitude of the change, but these data
are not normally provided by climate models.
Keywords
Africa, apes, behavioural flexibility, biogeography, climate change, global
warming, Gorilla, habitat loss, Pan, time budget model.
Journal of Biogeography (J. Biogeogr.) (2010) 37, 2217–2231
ª 2010 Blackwell Publishing Ltd www.blackwellpublishing.com/jbi 2217 doi:10.1111/j.1365-2699.2010.02373.x
I N T R O D U C T I O N
Many analyses have demonstrated that global warming will
affect species distribution patterns as well as biodiversity in
general (Parmesan & Yohe, 2003; Root et al., 2003; Thuiller,
2004). The most common models used to predict such effects
are bioclimatic envelope models (Thuiller, 2003), which aim to
determine the climate envelope that defines a species’ range by
correlating its distribution patterns with selected climate
variables. Although such models are useful for predicting
biogeographical distribution patterns, they have recently been
criticized because they fail to include a number of other
important variables (Guisan & Thuiller, 2005; Heikkinen et al.,
2006; Austin, 2007) and do not yield actual information about
the underlying mechanisms that limit a species’ distribution.
Although it is widely appreciated that a species’ distribution is
also shaped by historical patterns of the distribution of key
ecological resources (Ganzhorn, 1998; Reed & Bidner, 2004;
Graham et al., 2005; Lehman, 2006), few studies have
attempted to provide an explanation for the mechanisms that
underpin such effects.
In order to correctly predict the effects that changing
(climatic) conditions might have on species survival, it is
becoming increasingly important to understand: (1) the exact
mechanisms determining a species’ biogeographical distribu-
tion, and (2) whether, and how, species will be able to change
their behaviour and/or the kinds of habitats they can survive
in. The latter point has largely been overlooked in previous
models, but might become increasingly important for cogni-
tively more advanced species: in birds, for example, larger-
brained species cope better with seasonal changes in their
environments and are more resistant to extinction (Sol et al.,
2002; Shultz et al., 2005), and might be able to buffer
themselves more effectively against modest levels of climate
change. Understanding the interactive effects of phenotypical
and ecological constraints and behavioural flexibility on
species biogeographical distribution patterns and their reaction
to changes in global climate should thus be accorded higher
priority. In contrast to studies investigating projected change
of habitat with climate warming, this study investigates
projected change of behaviour with climate warming and its
effect on species survival.
Africa has been identified as being the most vulnerable of all
continents to the effects of climate change (Lovett et al., 2005;
IPCC, 2007), and numerous cases have been reported where
species or populations have altered their ranges or even life-
history variables in response to changes in climatic conditions
(e.g. Pounds et al., 1999; Parmesan & Yohe, 2003). Many
African primates are already highly endangered, with the most
vulnerable species being those that have highly restricted
ranges, a slow life history and a large body mass (for reviews
see Cowlishaw & Dunbar, 2000; Chapman et al., 2006). African
apes, i.e. gorillas (Gorilla spp.) and chimpanzees (Pan spp.),
show all these characteristics. Although all species of African
apes can occur in similar habitats and have somewhat
overlapping diets (Tutin & Fernandez, 1993; Stanford &
Nkurunungi, 2003; Morgan & Sanz, 2006), they differ
substantially in their biogeographical ranges. Gorilla, which
now has a disjunct distribution limited to relatively small areas
in the western and eastern parts of central Africa, is much
more limited in its distribution than Pan, which occurs
throughout central Africa as well as in West Africa. The
reasons for this remain unclear. On a finer scale, present-day
species distribution patterns are patchy and heavily influenced
by human activities, because these species often ‘compete’ for
space with humans, who are rapidly destroying ape habitats.
Because the increasing human population will need more
resources (e.g. wood and meat), apes will inevitably be driven
even closer to extinction as human populations expand
(Chapman et al., 2006). In order to implement efficient
conservation measures, it is essential not only to know about
anthropogenic effects on ape survival but also to have a much
better understanding of both the factors that naturally limit
ape distributions and the specific ecological requirements these
species have – or can adapt to. This might then allow us to
predict more effectively how a species’ range is likely to be
affected by environmental changes.
Here we use an established time budget model for African
apes (Lehmann et al., 2008a) to investigate how climate
warming (in the absence of further anthropogenic factors)
might affect ape survival. In addition, we assess the extent to
which behavioural flexibility might influence ape survival. As
far as we are aware, only one previous quantitative evaluation
of the potential effects of climate change on a primate species
using a time budget model has been published (Dunbar, 1998);
this suggested dramatic effects of global warming on gelada
baboons (Theropithecus gelada), mainly due to the fact that an
increase in ambient temperature will lead to severe habitat
fragmentation. Time budget models of this kind are based on
individual behaviour, i.e. on an individual’s allocation of time
to feeding, resting, travelling and socializing (Dunbar, 1992a,b,
1996). A general overview of time budget models and their
background theory is given by Dunbar et al. (2009). The
essence of this approach is that because time is limited, it
creates a constraint on the size of group (and hence population
density) that a species can maintain in a given habitat, thereby
determining the species’ biogeographical distribution (Dunbar,
1992a,b, 1998; Williamson & Dunbar, 1999; Hill & Dunbar,
2002; Korstjens et al., 2006; Korstjens & Dunbar, 2007;
Lehmann et al., 2007a). While time budget models predict
species biogeographical distributions at least as well as more
conventional climate envelope models (for three different
primate genera: Korstjens & Dunbar, 2007; Willems & Hill,
2009), they have the added advantage of providing insight into
the mechanisms by which a species is prevented from using
certain habitats as well as the level of ecological stress that a
species faces in those areas where it can survive. In these
models, the amount of time an individual has to invest in
essential activities depends on the ecological conditions at
specific locations, as well as on the number of competitors (i.e.
group size). Because these models are based on simple
climatological variables (which ultimately determine the
J. Lehmann et al.
2218 Journal of Biogeography 37, 2217–2231 ª 2010 Blackwell Publishing Ltd
distribution/availability of food and other resources), they are
ideally suited to assessing the possible effects of climate
warming on species distribution patterns.
M A T E R I A L S A N D M E T H O D S
The model
We use an existing and previously validated systems model
(Lehmann et al., 2008a) to investigate the effects of future
climate change on the distribution patterns of African apes.
The model uses multivariate equations (see Table 1) for time
budget and behavioural ecology variables (including diet,
group biomass, subgroup size and minimum viable group size)
derived from data on 20 natural populations of the two species
of gorillas (Gorilla beringei and Gorilla gorilla) and two species
of chimpanzees (Pan troglodytes and Pan paniscus) for which
behavioural and climatological information was available (for
details see Lehmann et al., 2008a; data are provided in
Appendices S1 and S2 in Supporting Information). These
equations describe how climate influences (directly through
thermoregulation as well as indirectly through resource
availability and distribution) individual ape behaviour. We
use the model to find the maximum ecologically tolerable
community size (the largest number of individuals that can live
together while still balancing their time, and hence energy,
budgets) by using this set of equations to calculate an average
individual’s time budget under given climate conditions as
community size is increased algorithmically until the sum of
the time budget variables reaches 100%. We used a large
climate database for sub-Saharan Africa to model the distri-
bution of maximum ecologically tolerable community size
across Africa, and then used this to deduce the presence/
absence of ape species on a continent-wide basis (see below for
details). We have tested the validity of this model in four
separate ways: (1) by showing that it correctly predicts
presence/absence at a set of 639 independent sites in Africa,
at about half of which apes are known to live; (2) by showing
that it correctly predicts community sizes at sites where
individual ape species live; (3) by showing that it correctly
predicts the continent-wide distribution of the two ape genera,
using a matrix of 11,670 data points from the Willmott &
Matsuura (2001) African climate database; and (4) by running
sensitivity analyses to determine the susceptibility of the
equation parameter values to errors of estimation (see
Lehmann et al., 2007a, 2008a,b).
Time budget equations
Equations to calculate feeding and moving time were derived
by applying standard multivariate model-finding procedures to
observational data from 20 study sites for which such data
were available, collated from the literature (for details, see
Lehmann et al., 2008a). The analyses used 11 different climate
indices as possible independent variables (although we have
elsewhere shown that these broadly reduce to three principal
dimensions: Williamson & Dunbar, 1999); forest cover was
estimated from satellite image data (DeFries et al., 2000). The
equations so obtained are listed in Table 1. These indicate that
feeding and moving time were determined by climate (mainly
annual rainfall and rainfall- and temperature-seasonality), diet,
body mass, foraging party size and/or community size. For
social time and resting time, we used a different approach and
derived generic equations from analyses of a wide range of
primate species. In primates, social time is an important factor
for group cohesion (and hence resistance to permanent fission)
(Dunbar, 1988, 1991) and we thus need to be able to specify
the amount of social time individuals ought to spend in social
activities in order to maintain social cohesion in a group of a
given size. (What time they actually devote to socializing often
Table 1 Equations used in the model to calculate individual time budgets of African apes (Pan spp. and Gorilla spp.).
Variable Equation
Feeding (%) 33.089 + 0.005 · group biomass + 0.143 · body weight + 0.158 · %fruit ) 0.006 · Pann Moving (%) 18.74 + 13.92 · TmoSD + 0.35 · prtysz ) 4.94 · P2T + 0.32 · (P2T)
2
Grooming (%) 1.01 + 0.23 · community size Min. resting (%) )29.467 + 1.278 · Tann + 0.336 · % leaf + 5.954 · TmoSD Group biomass 4.24 · body weight + 29.83 · group size %fruit 169.429 ) 50.651 · log(body weight) ) 0.021 · altitude ) 62.023 · moimomx
+ 0.39 · forest cover %leaf 100 ) %fruit Party size (chimp) 21.489 + 0.072 · forest cover ) 0.33 · Pmo + 0.0012 · Pmo
2
Min. party (chimp) e (2.25)0.23·ln(forest cover)+0.36·ln(Tann))/3
Min. group (gorilla) e(2.25)0.23·ln(forest cover)+0.36·ln(Tann))/7
Equations used in the model for predicting ape time budget components; all equations are based on observational data (see Appendices S1 and S2);
see Lehmann et al. (2008a) for details on the derivation of these equations.
Pann = mean annual rainfall (in mm); Tann = mean annual mean temperature (in �C); Tmosd = temperature variation between months (calculated as the standard deviation across average values for 12 months); Pmo = average rainfall per month (in mm); P2T = plant productivity index [= the
number of months in the year in which rainfall (in mm) was more than twice the average monthly temperature (Le Houérou, 1984)];
moimomx = maximum monthly moisture index (Willmott & Feddema, 1992); min. = minimum; prtysz = party size; %fruit/leaf = percentage fruit/
leaves in the diet. Minimum group/party sizes are scaled to female body weight.
Global warming and ape biogeography
Journal of Biogeography 37, 2217–2231 2219 ª 2010 Blackwell Publishing Ltd
represents a compromise in managing their time budget as a
whole; Dunbar et al., 2009.) We used the generic equation
specifying required social time as a function of social group
size that was obtained by Lehmann et al. (2007b) in an analysis
of a large sample of African primates (n = 40 species). Resting
time is less straightforward to determine than the other time
budget components because observed resting time in animals
consists of two separate components: free (or uncommitted)
time (which can be drawn on when environmental conditions
require more time for feeding, moving or socializing) and
enforced resting time (time when animals are forced to rest to
avoid heat overload or hyperthermia and/or to allow digestive
processing) (Korstjens et al., 2010). Because enforced resting
time cannot be transformed into other more urgent activities,
it represents an environmentally driven reduction in the
effective length of the animal’s active day and adds an
important constraint on a species’ capacity to maintain
communities of a minimum viable size in a given habitat
(Dunbar et al., 2009; Korstjens et al., 2010). For enforced
resting time, we used a generic equation derived from an
analysis of data from 78 species of primates (Dunbar et al.,
2009; Korstjens et al., 2010): enforced resting time is deter-
mined by diet composition (the percentage of leaves in the
diet), mean annual temperature and monthly temperature
variation. Both of these equations are given in Table 1.
Minimum party size
Although apes are generally large-bodied, predation and
infanticide (which can be viewed as within-taxon predation)
still remain serious threats (Boesch & Boesch-Achermann,
2000). This is especially relevant for chimpanzees, which spend
most of their time in very small subgroups. Thus, we assume
that, dependent on ecological conditions and predation risk,
apes need to maintain a certain minimum party size to be safe
(Dunbar, 1996; Hill & Dunbar, 1998; Shultz et al., 2004). This
will be especially important in habitats with low forest cover
and a high density of predators (Lehmann & Dunbar, 2009).
Minimum party size was estimated from forest cover (tree
cover indexes the availability of refuges) and bush cover
(increased bush cover implies increased risk of being caught
unawares by a predator) using a body mass corrected version
of the equation given by Dunbar (1996) (Table 1). For apes to
survive at a given location, the model requires that average
party size be equal to or larger than minimum party size (for
details see Dunbar et al., 2009; Lehmann & Dunbar, 2009).
Diet composition
Although gorillas and chimpanzees show some dietary overlap
(Tutin & Fernandez, 1993; Stanford & Nkurunungi, 2003;
Morgan & Sanz, 2006), gorillas consume less fruit than
chimpanzees. We therefore used the data on ape diets from the
sample of study sites to derive a general equation for the
percentage of feeding time devoted to fruit versus leaves as a
function of ecological conditions and body mass (Lehmann
et al., 2008a) (see Table 1). Although these values allow apes to
be more flexible than has been observed in the field, this
approach is justified by the fact that we do not know where the
real limits of dietary flexibility are for apes. However, for the
purposes of the model, we set limits on dietary flexibility at 10–
100% folivory.
Body mass
The two taxa, Pan and Gorilla, are represented in the model by
two distinct weight categories (40 and 120 kg), which roughly
correspond to the mean weight of male and female chimpan-
zees and gorillas, respectively (Smith & Jungers, 1997; Calde-
cott & Miles, 2005). Although ape subspecies can differ in their
body masses (Caldecott & Miles, 2005) and there is significant
sexual dimorphism in body mass in both ape taxa, we chose
this approach for the sake of simplicity, because presenting
separate models for males, females and subspecies would result
in unnecessary complexity.
Social demography
We draw a distinction between two types of groups that
characterize ape societies. These are the community (the set of
individuals who share a common range area, and whose
membership is relatively stable over time) and the foraging
subgroup or party (the set of individuals who happen to be
together at any given moment, and whose composition is
usually unstable over time, but whose membership is invari-
ably drawn from one particular community). Gorillas live in
smaller and more stable groups than chimpanzees, and the
community and the party are usually one and the same in their
case. In some equations, we use group biomass as an
alternative to including both body mass and group size
separately. The equation used to calculate group biomass
reflects species-specific changes in group composition when
overall group size increases, and was derived by Lehmann et al.
(2008a); it provides a conservative estimate of overall group
biomass rather than using a simple multiplication of group size
and mean individual body mass.
Climate and ape biogeographical distribution
We used a large (10,075 locations) climate database for Africa
(obtained from Willmott & Matsuura, 2001) to provide
continuous climate data on a grid of 0.5� latitude and 0.5� longitude across Africa. A linear program in dBase (1994,
Borland International Inc., Scotts Valley, CA, USA) used these
values and the equations given in Table 1 to calculate the
maximum ecologically tolerable community size for each
location in the dataset under given climate conditions. The
model assumes that apes are able to live at a particular site if:
(1) average party size is larger than the minimum party size,
and (2) predicted maximum ecologically tolerable community
size is larger than a set minimum, which varies as a function of
body mass. In the initial analyses, minimum community size
J. Lehmann et al.
2220 Journal of Biogeography 37, 2217–2231 ª 2010 Blackwell Publishing Ltd
for Pan was set to 10 individuals (because almost all known
chimpanzee populations live in communities larger than this:
Lehmann et al., 2007a), while for gorillas this limit was set to
five individuals (the minimum stable group size for known
gorilla groups: Anderson et al., 2002; Lehmann et al., 2008b).
In addition, we also ran the model, using a less conservative
and more ecologically driven approach to the problem of
minimum size (i.e. using information about forest cover we
estimated a habitat-dependent minimum party size that is
required to ensure sufficient protection against predation) (see
Table 1 and below for further details). Apes were then assumed
to be able to survive within a given habitat if their ecologically
tolerable community size exceeded the minimum party size.
We then use our model to assess the likely effects that an
increase in temperature and rainfall will have on ape group
sizes and biogeographical distributions. Because (1) the exact
extent to which climate will change is not known and heavily
debated, and (2) our interests are in determining the worst-
case scenario to inform conservation planning, we decided to
use a ‘worst-case scenario’ estimate for the predicted level of
climate change. Current ‘best guess’ estimates of climate
change for Africa range between 3.5 �C and 6.5 �C (see IPCC, 2007) and a 15% increase in rainfall by 2100 (corresponding to
a conservative assumption of a 3% increase in precipitation per
degree kelvin; see, e.g. Wentz et al., 2007). We have therefore
used a value for overall increase in temperature of 5.2 �C – a value in the upper half of this range, but conservatively well
below the upper limit – and an increase in rainfall of 15%. For
reasons of simplicity we decided to use a uniform climate
change scenario; it is generally assumed that temperatures will
increase across Africa. With regard to rainfall, some models
actually predict a regional decrease in rainfall; however, as we
are interested in the overall pattern of how climate variables
will affect ape distributions, we opted to model an increase in
rainfall as a worst-case scenario, especially as this is the
predicted pattern across the current distribution range of apes
Table 2 (a) Predicted distribution of maximum ecologically tol-
erable community sizes for chimpanzees (40 kg), depending on
mean annual temperature variation (TmoSD) and annual rainfall
(Pann). Dashes indicate that these particular climatic conditions
did not occur on the African continent. Shading indicates climatic
conditions at which chimpanzees have been observed. (b) Pre-
dicted distribution of maximum ecologically tolerable group sizes
for gorillas (120 kg), depending on mean annual temperature
variation (TmoSD) and annual rainfall (Pann). Dashes indicate that
these particular climatic conditions did not occur on the African
continent. Shading indicates climatic conditions at which gorillas
have been observed.
(a)
TmoSD
(�C)
Pann (mm)
0–500 500–1000 1000–1500 1500–2000 2000–2500 2500–3000
0–0.5 – 79.0 86.0 90.0 88.0 89.0
0.50–1 19.5 49.0 61.0 72.0 61.0 59.0
1–1.5 3.0 25.0 35.0 43.0 48.5 49.0
1.5–2 0.0 3.0 10.0 11.5 19.0 7.0
2–2.5 0.0 0.0 0.0 0.0 0.0 3.5
2.5–3 0.0 0.0 0.0 0.0 0.0 –
3–3.5 0.0 0.0 0.0 0.0 – –
(b)
TmoSD
(�C)
Pann (mm)
0–500 500–1000 1000–1500 1500–2000 2000–2500 2500–3000
0–0.5 – 20.0 23.0 24.0 25.0 29.0
0.50–1 0.0 4.0 10.0 15.0 11.0 12.0
1–1.5 0.0 0.0 0.0 2.0 4.0 6.0
1.5–2 0.0 0.0 0.0 0.0 0.0 0.0
2–2.5 0.0 0.0 0.0 0.0 0.0 0.0
2.5–3 0.0 0.0 0.0 0.0 0.0 –
3–3.5 0.0 0.0 0.0 0.0 – –
Table 3 Equations used to calculate the effect of an increase in temperature and rainfall on the remaining climate variables in the model of
African ape biogeography.
Climate variables r F P
P2T )2.49 + 3.752 · log10(Pann) ) 0.004 · (Tann) 2
0.89 19,951 ***
TmoSD )7.891 ) 0.002 · Pann + 11.05 · log10(Tann) ) 0.004 · (Tann) 2 + 0.034 · lat 0.78 3791 ***
moimomx )0.027 ) 0.001 · (Tann) 2 + 0.001 · lat + 0.001 · Pann 0.86 9336 ***
Pmo 4.137 + 0.081 · Pann 0.97 18,879 *** moimoav )0.11 + 0.001 · Pann ) 0.028 · Tann ) 0.0000117 · altitude ) 0.000000139 · (Pann)
2 0.97 40,547 ***
ml100 12.644 ) 0.007 · Pann + 0.00000091 · (Pann) 2 0.95 48,501 ***
Frcover 88.046 ) 0.121 · Pmo + 22.944 · moimoav ) 5.511 · ml100 0.8 5943 ***
All equations are based on linear regression and curvilinear estimation procedures estimating the effects of temperature and rainfall on each of the
other climate variables of interest.
P2T = plant productivity index [the number of months in the year in which rainfall (in mm) was more twice the average monthly temperature (Le
Houérou, 1984)]; Pann = mean annual rainfall (in mm); Tann = mean annual mean temperature (in �C); lat = latitude; TmoSD = temperature variation between months (calculated as the standard deviation across average values for 12 months); Pmo = average rainfall per month (in mm);
ml100 = number of months per year with < 100 mm of rainfall; Frcover = forest cover; moimoav = average monthly moisture index (Willmott &
Feddema, 1992); moimomx = maximum monthly moisture index (Willmott & Feddema, 1992); r = correlation coefficient of the regression
model; F = F-value of the equation; P = level of significance.
***P < 0.001.
Global warming and ape biogeography
Journal of Biogeography 37, 2217–2231 2221 ª 2010 Blackwell Publishing Ltd
(IPCC, 2007). We additionally provide predictions for a range
of rainfall scenarios (Table 2).
We used linear regression and curvilinear estimation
procedures (with quadratic or logarithmic functions) with
the Willmott & Matsuura (2001) climate database to obtain
multivariate equations estimating the effects of temperature
and rainfall on each of the other climate variables of interest
(see Table 3). We then used this new set of climate variables to
recalculate ape group sizes under the presumed climatic
conditions of 2100.
Statistics
Because not all of the variables were normally distributed, we
used Wilcoxon matched pair statistics (WSR) to compare
predicted group sizes and time budget components across
conditions. Analyses were done using spss 14.0 for Windows
(�SPSS Inc., Chicago, IL, USA). In addition, we aimed at evaluating the strength of the effects by calculating effect sizes
(r) for non-normal distributions using the equation r = z/�n given by Rosenthal (1991). The value r was then transformed
into Cohen’s d (Rosenthal, 1991). A WSR test was only
considered to be significant and biologically meaningful if
P < 0.05 and d > 1.
R E S U L T S
Ape biogeography and climate
As reported in detail elsewhere (Lehmann et al., 2008a;
Lehmann & Dunbar, 2009), the model provides a good fit to
the current demographic and biogeographical distributions of
both genera as given by Caldecott & Miles (2005) (Figs 1a &
2a), although the model overestimates the extent of their
distributions into eastern Africa and towards the south (e.g.
into Angola). We show elsewhere (Lehmann et al., 2008a) that,
for both taxa, this is likely to be due to biogeographical barriers
that have prevented these genera from colonizing certain areas
(for gorillas, the Congo River and the Dahomey Gap; for Pan,
open savanna belts in the Rift Valley and northern Zambia).
Furthermore, the model fit improves significantly if we assume
less conservative values for minimum viable group sizes
Remaining suitable in 2100 Suitable but communities below 45 individuals Unsuitable in 2100x
(a)
(b)
Community size: 11-45 individuals >45 individuals
Figure 1 Model predictions of (a) the cur-
rent biogeographical distribution and (b) the
changes in biogeographical distribution fol-
lowing a 5.2 �C increase in temperature with a 15% increase in rainfall for chimpanzees
(40 kg), using the Willmott & Matsuura
(2001) climate database. The heavy black
lines outline the present-day combined dis-
tribution of the two chimpanzee species
studied (Pan troglodytes and P. paniscus);
dashed lines indicate major rivers (possible
geographical barriers for the distribution of
the great apes). Shadings indicate differences
in predicted group sizes. The cross-hatched
areas in (b) indicate locations which the
model predicts can currently be occupied but
which will no longer be viable habitat after
the changes in climate.
J. Lehmann et al.
2222 Journal of Biogeography 37, 2217–2231 ª 2010 Blackwell Publishing Ltd
(especially in the case of chimpanzees; see Fig. 1), as discussed
in Lehmann et al. (2007a). The model also shows that the
differences in distribution patterns between gorillas and Pan
are primarily a consequence of body mass, while the differ-
ences in maximum ecologically tolerable community size are
primarily due to differences in their social systems (i.e. fission–
fusion sociality in chimpanzees: Lehmann et al., 2007a).
Of all the climate variables tested, temperature variation and
annual rainfall were found to be the most important deter-
minants of ape time budgets, and hence distributions. Table 2
shows median values across locations for maximum predicted
group sizes for each taxon as a function of these two key
climatic predictors. Both ape genera are able to cope with a
wide range of annual rainfall regimes, but both are very
sensitive to variation in temperature and survive best in
habitats whose monthly mean temperatures are relatively
constant throughout the year. Note that gorillas cope less well
than chimpanzees with arid regions, and generally have a
smaller range of tolerance as well as smaller group sizes.
Effect of climate change
Biogeography
Figures 1(b) and 2(b) show the effects on ape distribution
patterns of a 5.2 �C increase in temperature and a 15% increase in rainfall as predicted by the model and in
comparison with predictions under present climate conditions.
As can be seen, both genera will suffer a loss of habitat with
very little new habitat arising to offset this. This loss is due to
the effects of climate warming on individual time budgets (see
below), which in turn affects the number of individuals that
can live as a socially cohesive group at any one location. If this
value falls below the minimum required at a specific location,
apes will no longer be able to live there. With respect to the
actual known range of the apes (depicted in Figs 1b & 2b as a
solid black outline), climate warming will cause chimpanzees
to lose 10% of the habitat within their present distribution
area, while gorillas will lose 75% of their habitat. Even though
Remaining suitable in 2100 Newly suitable in 2100 Unsuitable in 2100x
Community size:
(a)
(b)
5-20 individuals >20 individuals
Figure 2 Model predictions of (a) the cur-
rent biogeographical distribution and (b) the
changes in biogeographical distribution fol-
lowing a 5.2 �C increase in temperature with a 15% increase in rainfall for gorillas
(120 kg), using the Willmott & Matsuura
(2001) climate database. The heavy black
lines outline the present-day combined dis-
tribution of the two gorilla species studied
(Gorilla gorilla and G. beringei); dashed lines
indicate major rivers (possible geographical
barriers for the distribution of the great
apes). Shadings indicate differences in pre-
dicted group sizes. The cross-hatched areas in
(b) indicate locations which the model pre-
dicts can currently be occupied but which
will no longer be viable habitat after the
changes in climate.
Global warming and ape biogeography
Journal of Biogeography 37, 2217–2231 2223 ª 2010 Blackwell Publishing Ltd
the model predicts that gorillas will then be able to live in
coastal Nigeria and Cameroon (outside their present distribu-
tion), this genus will only be able to survive at a few very
isolated sites in the extreme western and eastern ends of its
current distribution.
Changes in climate will most likely also cause distributions
in tree and bush cover to change (the two variables
determining predation risk in the model), which will lead
to changes in required minimum party size. If, following
Dunbar (1996) and Lehmann & Dunbar (2009), we allow
minimum party size requirements in the model to vary in
response to vegetation cover (rather than having a fixed
value), using the equations given in Table 1, chimpanzees will
fail to meet the criterion (party size equal to or larger than
minimum party size) under our 2100 climate change condi-
tions at a further 29% of locations, increasing potential
Chimpanzee Gorilla
futurepresentfuturepresent
120
100
80
60
40
20
0
P re
di ct
ed c
om m
un it
y si
ze
Figure 3 Boxplots of the effect of increasing
temperature (by 5.2 �C) and rainfall (by 15%) on ape (40 kg chimpanzee and 120 kg
gorilla) community sizes. This comparison
only includes sites at which apes were
predicted to be present under both climate
regimes.
Chimpanzees Gorillas
-100
-50
0
50
100
non-surviving populations
% c
h an
g e
fr o
m v
al u
es u
n d
er p
re se
n t
c li
m at
ic c
o n
d it
io n
s
surviving populations (a)
(b)
-100
-50
0
50
100
diet move feed rest
Figure 4 Effects of climate change on ape
diet and time budgets. The graphs depict
percentage change in time budget compo-
nents and diet compared with values under
present climatic conditions (i.e. negative
values indicate a decrease following climate
change, while positive values indicate an
increase). The upper panel (a) shows data for
sites at which apes are predicted to be present
before and after climate change, thereby
indicating how climate change will affect
surviving ape populations. The lower panel
(b) depicts data for locations at which apes
are predicted not to be able to survive
following the changes in climate, thereby
indicating which time budget variables are
responsible for their extinction. Diet is
indexed as percentage of leaves in the
diet.
J. Lehmann et al.
2224 Journal of Biogeography 37, 2217–2231 ª 2010 Blackwell Publishing Ltd
habitat loss to 39%. In addition, the effect of climate change
on biogeography is critically dependent on the minimum
viable community size that apes require for survival, because
this will ultimately determine whether they can survive at a
given location. Because no data from the field are available for
minimum viable group sizes in apes (it is not the same as
minimum observed group sizes), we used conservative values
in our model. In reality, the true values are likely to be higher,
in which case the effects will be even more extreme than those
described here (for chimpanzees, up to 50% loss of habitat if
a minimum of 45 individuals is required, as suggested by
Lehmann et al., 2007a).
Community size
In addition to presence/absence, our model allows us to
quantify the effects that climate change may have on the
demography of the surviving populations. Figure 3 shows
predicted community sizes under present climate conditions
and after climate change for the two ape genera at sites where
they are predicted to survive under both sets of climatic
conditions (518 sites). Under the climate conditions predicted
for 2100, average chimpanzee community size will be
about 30% lower than is currently the case at these sites
(WSRchimpanzees: community size of 52 ± 26 vs. 73 ± 14,
respectively; z = 12.9, n = 518, P < 0.0001). In contrast, the
few gorilla populations predicted to survive (55 sites) will be
able to increase group sizes (WSRgorilla: 21 ± 10 vs. 15 ± 7;
z = )3.3, n = 55, P < 0.001).
Time budgets
Our model enables us to identify the specific time budget
variables that will render previously suitable habitats unsuit-
able in 2100. Because time budget variables are dependent on
community size, we calculated mean feeding and moving time
for a constant community size (an arbitrarily chosen 10
individuals for communities and five for parties) so that values
can be compared across categories and species. All time budget
variables (with the exception of chimpanzee moving time)
change significantly following climate warming (Fig. 4; WSR,
all z > 4, all P < 0.001, all d > 3). However, Fig. 4 shows that
the extent of change differs between taxa, as well as between
sites where they are predicted to survive as compared to sites
where they will go extinct. In surviving chimpanzee popula-
tions (Fig. 4a), resting time almost doubles, due to the
combined effect of an increasingly leaf-based diet (reflecting
a demand for enhanced processing time) and increased
temperature. This increase in resting time plus an increase in
average party size (by on average 1.6 individuals) causes the
collapse in community size noted above. Gorillas, on the other
hand, experience smaller shifts in diet and hence resting time;
as a result, the decrease of almost 60% in moving time that
occurs under higher temperatures in those locations where
they can still survive more than offsets the increase in resting
time, thus allowing gorillas to increase their overall group sizes
(at least at these sites).
The reason why apes will not be able to survive in some
locations that were previously suitable is thus related to the
combined effect of increased resting and moving time in these
locations (Fig. 4b).
Behavioural flexibility and extreme climate change
To investigate whether apes could possibly survive the effects of
climate change by altering either their diet or their behaviour (by
switching from a coherent group to fission–fusion, or vice
versa), we examined the effect of diet and fission–fusion
intensity on maximum ecologically tolerable community sizes
in apes. Using constant climate variables (representative for
locations at which apes occur), we used the equations from
Table 1 to determine the maximum community sizes possible
for chimpanzees and gorillas under varying dietary and social
conditions. Figure 5 shows the results, expressed as percentage
change in community size from a baseline of observed dietary
and social conditions, i.e. no fission–fusion and 20% fruit for
gorillas, and for chimpanzees 60% fruit and fission into 10
parties, which corresponds to an average chimpanzee commu-
nity size of 50 individuals and average observed party size of
about five individuals (Lehmann & Boesch, 2004).
Two points may be noted. First, the effect of body mass is far
larger than the effects of either diet or social system (no
-100
-50
0
50
100
150
200
20
40
60
80
100
2 5
10 20
50
% ch
a n
g e
in co
m m
u n ity
si ze
% fru
it in
di et
Number of parties formed
-100 -50 0 50 100 150 200
max
no ff
*
% change in community size:
Figure 5 Effects of diet and social system on maximum tolerable
community size. Data are expressed as percentage change in
community size, using known ape behaviour as a baseline [60%
frugivory and an average of 10 parties for chimpanzees, and 20%
frugivory and no fission–fusion (ff) for gorillas]. The upper surface
depicts values for gorillas (120 kg ape), the lower surface depicts
the equivalent values for chimpanzees (40 kg ape). Surface shading
corresponds to z-axis values (% change in community size),
illustrating bands of 50% change in community size. Note that
party size and diet are depicted as categories. Calculations are
based on average (constant) climatic conditions. The asterisk
indicates the extent of behavioural flexibility in gorillas used to
calculate distribution patterns in Fig. 6.
Global warming and ape biogeography
Journal of Biogeography 37, 2217–2231 2225 ª 2010 Blackwell Publishing Ltd
overlap between the two surfaces). In other words, gorillas
with a chimpanzee-like social system and diet would still live in
smaller groups than chimpanzees due to their larger body mass
and its effect on feeding time. Second, the figure suggests that
chimpanzees have already exploited most of their capacity for
flexibility, and so fissioning into even smaller parties would
hardly have any effect on the size of their communities (most
changes are negative): opting for the extremes of a diet
consisting of 100% fruit and splitting into the smallest parties
possible (one individual) would only increase community size
by at most 20%. Gorillas, on the other hand, would benefit by
splitting into parties and/or adopting a more frugivorous diet.
Although the latter might be difficult in reality due to their
large body size, splitting the community into two separate
parties (which has been observed in gorillas in the wild; Doran
& McNeilage, 1998) would allow gorillas to increase commu-
nity size by a very significant 45%. An increase of this
magnitude could improve long-term survival for gorillas.
Finally, we asked how living on a diet of 80% fruit and with
extreme fission–fusion (party size of one individual) would
affect the biogeographical distribution of gorillas. [Although
these conditions seem extreme, they do occur in another ape,
the orangutan (Pongo spp.), and are thus not beyond generic
ape capabilities.] If they could do this, gorillas would gain
significant respite as habitat loss is reduced from 75% without
such behavioural flexibility to 18% with maximum flexibility
(Fig. 6).
D I S C U S S I O N
Our aim was to evaluate the impact of predicted climate
change on the behaviour and biogeography of African great
apes using a novel approach based on individual time budgets.
Even though we used a worst-case climate change scenario, our
model highlights the importance of individual behavioural
requirements for the ability of apes to survive in particular
locations. Although gorillas and chimpanzees live in very
similar habitats, gorillas are more restricted by temperature
variation (Table 2) than chimpanzees. Because of this, gorillas
might suffer more strongly from the effects of global warming.
In addition, due to their large body mass and small group sizes
they are less buffered than chimpanzees against the risk of
extinction, and even extreme behavioural flexibility will only
improve their situation to a limited extent. Although in the
models chimpanzee biogeography appears to be less affected by
global warming than gorilla biogeography, our analysis dem-
onstrates that chimpanzee communities will be significantly
reduced in size even at locations where they are predicted to
survive. The two critical factors that ultimately determine their
survival at these locations are minimum viable community size
and minimum party size. Depending on the values for these
two key parameters, chimpanzees can also expect to suffer
severely from changes in global climate. These findings
highlight the gap between simply simulating effects on
biogeographical distributions and understanding the mecha-
nisms driving them (see also Willems & Hill, 2009).
The effects of extreme climate change
It is important to note that this study represents a worst-case
scenario with respect to projected changes in temperature and
rainfall. Thus, rather than interpreting the actual values and
distribution maps of this model as definitive, it is more
informative to look at the mechanisms by which climate
change may affect the ability of apes to survive in a given
habitat. Indeed, the real increase in temperatures and rainfall
might be not as extreme (more in the region of 3–4 �C and only a local increase of precipitation to the predicted extent;
IPCC, 2007), but our model would still predict that apes will
suffer habitat loss, simply due to time budgeting problems.
These problems might even be reinforced by several indirect
effects, such as the effect of temperature on leaf quality (Van
Suitable without behavioural flexibility Suitable only with behavioural flexibility
Figure 6 Predicted distribution patterns for
gorillas following a 5.2 �C increase in annual temperature and a 15% increase in annual
rainfall, with and without a fission–fusion-
like social system. Gorillas were set to a diet
of 80% fruit and allowed to fission into
parties of one individual only (i.e. maximum
flexibility). The two shadings indicate sites
for which their presence was predicted irre-
spective of behaviour (dark circles) versus
sites where they would survive only if maxi-
mum behavioural flexibility was possible
(lighter circles).
J. Lehmann et al.
2226 Journal of Biogeography 37, 2217–2231 ª 2010 Blackwell Publishing Ltd
Soest, 1982) and the possible effects of climate change on
predation levels. More importantly, perhaps, we currently have
very little understanding of the effects of climate change on one
of the critical model variables, namely temperature seasonality.
Table 2 shows that apes are predicted to go extinct if
temperature variation becomes more extreme; absolute
changes in temperature or rainfall are, by comparison, almost
irrelevant. Given the apparent importance of variance (or
seasonality) for the ability of primates to survive in particular
habitats, the fact that most climate change models simply
predict changes in annual temperature and rainfall is partic-
ularly unfortunate – especially because it is unlikely that
primates are special in this respect. Indeed, it has been argued
that climatic conditions are generally becoming more extreme
(Rind et al., 1989; Hannah et al., 2002), and it could well be
that an increase in annual rainfall is actually due to extreme
rainfall in just part of the year. Our analysis has skirted around
the issue of regional variations in climate change, mainly to
avoid becoming bogged down in too much detail. These will,
of course, be important, and Table 2 provides some insights
into how local variation in climate parameters might be
expected to influence the capacities of these species to cope
with climate change.
Note that our approach differs from most conventional
conservation approaches in emphasizing community size
rather than animal density as the critical variable for extinction
risk. Although density is an appropriate variable for many
species, it may not be for intensely social species such as
primates that use socially bonded groups as their primary
defence against predators (Shultz et al., 2004; Shultz &
Dunbar, 2007). In such taxa, groups are functional units,
and the minimum size may introduce an Allee effect (Stephens
& Sutherland, 1999) at a population density well above the
minimum that might normally be considered viable (Dunbar
et al., 2009).
Our model suggests that even without more explicit
anthropogenic influences, the effects of the relatively simple
changes in climate will be quite dramatic, with apes facing
extinction at most locations within their present range. The
two genera will, however, respond quite differently to these
changes in climate. Chimpanzees will primarily suffer a
reduction in community size, due to the fact that they have
to spend an increasing amount of time moving and resting (in
part due to the direct impact of increased temperatures on
time budget components and in part to changes in diet). Thus,
chimpanzees appear to be somewhat buffered against the
initial effects of climate change on biogeography by their large
starting community sizes. With the parameters used in this
model, and ignoring the effects of predation risk by using a set
minimum group size required for survival, biogeographical
distribution does not change dramatically for chimpanzees.
However, this might be an overly optimistic interpretation of
their situation because the conclusions about biogeographical
distributions depend critically on the minimum community
size assumed to be viable. If the true value is actually higher
than the assumed 10 individuals (perhaps closer to the 45
individuals suggested by the analyses of Lehmann et al.,
2007a), then chimpanzees will find it very difficult to maintain
communities of this size in any of their present habitats, and
the genus Pan will go extinct (see Fig. 1b). In addition, the
model assumed that party sizes have to be larger than a
minimum set by the risk of predation. Changes in climate are
likely to alter both average and minimum party size and, under
climate change, party size will seldom exceed the predicted
minimum at most locations within current chimpanzee
habitat. Thus, although our model appears to indicate that
chimpanzee biogeographical distribution is not affected to the
same extent as that of the gorilla, this may be a radical
oversimplification.
Gorillas already live in small groups and are therefore at
high risk of extinction (Cowlishaw & Dunbar, 2000), so that
changes in global climate might be expected to have a stronger
effect on gorilla biogeography. Indeed, our simulation shows
that gorillas will experience a dramatic reduction in the
availability of suitable habitat. Many regions where they
currently could survive will be uninhabitable for them because
of an excessive demand for moving and resting time following
the change in climate (mediated by food abundance and
distribution). However, the few surviving populations should
be able to maintain group sizes comparable to present-day
conditions and well above observed minima, thus making
them locally stable.
Behavioural flexibility
Most models on the impact of climate change ignore the fact that
species may be able to adapt their behaviour to some extent to
changed conditions. Apes are especially well known for their
behavioural flexibility (Kortlandt, 1995), and this capacity might
enable them to survive despite changed conditions. In our model
we allowed chimpanzees to live in a fission–fusion society in
which they regularly split into smaller parties, as has been
observed in the wild (e.g. Nishida, 1968). We have previously
shown that this reduces the time that chimpanzees need to spend
travelling and therefore allows them to live in larger commu-
nities than would otherwise be possible (Lehmann et al., 2007a).
Gorillas, on the other hand, do not normally live in such a fluid
social system; instead, all group members are usually found
together. However, there are reports that western lowland gorilla
groups do occasionally split into smaller subgroups (Remis,
1994; Goldsmith, 1996; Tutin, 1996), indicating that gorillas
may also have some behavioural flexibility in their social systems
that might allow them to cope with certain environmental
constraints. If gorillas can opt for a fission–fusion social system,
especially if this entails solitary foraging as observed in orangu-
tans, then they may be able to cope rather better with the effects
of climate change.
Another important parameter for survival is the extent to
which apes can exhibit dietary flexibility (Isbell & Young,
1996). Both African ape genera are known to vary in their diet
between sites and seasons (Tutin & Fernandez, 1993; Remis,
1994; Kuroda et al., 1996; Yamagiwa et al., 1996; Doran, 1997;
Global warming and ape biogeography
Journal of Biogeography 37, 2217–2231 2227 ª 2010 Blackwell Publishing Ltd
Doran & McNeilage, 1998; Doran et al., 2002; Morgan & Sanz,
2006), but little is known about the extent to which they can
switch between frugivory and folivory or how well they can
sustain an extreme diet over the long term. In our simulation,
diet is determined by ecological conditions, and we assumed
that both genera could, in principle, range from a diet of 100%
leaves to one of 90% fruit. However, body mass and diet are
strongly linked, and heavier apes such as gorillas rely to a
larger extent on leaves (Stanford & Nkurunungi, 2003;
Yamagiwa & Basabose, 2006), which in turn forces them to
live in smaller groups because of the way diet influences time
budgets (Lehmann et al., 2008a). Although we do not yet
know how flexible apes are in their dietary requirements, the
model allows us to quantify the effects that dietary and social
flexibility might have on ape group sizes. The results suggest
that a shift from a folivorous towards a more frugivorous diet
would allow a modest increase in community size (assuming
that sufficient fruit is available), while splitting the community
into two or more parties would have an even stronger effect
(see Fig. 5). Our analysis suggests that chimpanzees have
already exploited most of their leverage in this respect, so that
splitting into even smaller parties than they currently do will
not prevent community sizes from collapsing under climate
change. Gorillas, on the other hand, have some scope for
splitting into parties (as is observed in western lowland
gorillas) and/or changing to a more fruit-based diet, and this
should provide them with some buffering against the effects of
climate change. In both cases, however, everything depends on
the extent to which predation risk limits minimum foraging
party sizes.
The orangutan, the only Asian great ape, offers evidence that
such extreme behavioural flexibility is not beyond the capac-
ities of apes: this species relies mostly on fruit, despite its size,
and is largely solitary (though it can live socially) with a
dispersed community size of 10–15 individuals (Delgado & van
Schaik, 2000). We know that, within Holocene times,
orangutans lived much further north on the Indo-Chinese
peninsula mainland (Mackinnon, 1974), and now live in what
is effectively a retreat habitat on the equator (following
Holocene climate change as well as extreme human-caused
habitat loss). Thus, their current socio-ecology may represent
precisely such an attempt to cope with past climate warming in
habitats that are nutritionally poorer than those in Africa (only
50% of Southeast Asian swamp forest and 65% of dry zone
dipterocarp forest may be useable habitat for orangutans;
Caldecott & Miles, 2005). Because they are already at the limits
of flexibility (solitary foraging and 100% frugivory), it may
come as no surprise that they are on the verge of extinction
(van Schaik et al., 2001), such that even modest anthropogenic
influences, i.e. persecution and habitat loss/fragmentation, are
enough to push them over the edge. Our model suggests that,
if gorillas are flexible enough to adopt an orangutan-like
lifestyle, they would be able to live in significantly larger
communities. This would presumably give them a modest
degree of buffering against climate change in some locations;
however, even such extreme behavioural flexibility would have
only a limited effect on their biogeographical distribution
(Fig. 6).
Another substantive difference between orangutans and
African apes may also be that orangutans (so long as they
remain in trees) live in habitats that are effectively free of
predators large enough to threaten them, thus allowing
individual animals to forage alone (i.e. foraging party
size = 1). In contrast, mainland African apes live in environ-
ments with significant numbers of large-bodied predators (as
was probably the case for Holocene orangutans on the Indo-
Chinese mainland) and this may prevent them from fissioning
into parties of minimum size.
C O N C L U S I O N S
Our aim has been to evaluate the impact of climate change on
the biogeography and extinction risk of African great apes.
While several anthropogenic factors (notably deforestation and
hunting) are known to be the principal threats to chimpanzees
and gorillas, our focus is on whether background ecological
processes place these taxa at risk irrespective of these current
threats. Our results show that solving the direct local
anthropogenic threats may not be sufficient to prevent the
extinction of these flagship species. Ensuring safe havens in
optimal habitat must be a critical component of any conser-
vation strategy, lest all current conservation efforts (which
focus on the direct anthropogenic threats) prove to be in vain
(see also Cowlishaw & Dunbar, 2000). The survival of both
genera is critically dependent on climatic factors and on social
factors such as minimum viable community size and mini-
mum party size. Our findings also highlight the gap between
simply simulating effects on biogeographical distributions and
understanding the mechanisms driving them.
A C K N O W L E D G E M E N T S
For this project, J.L. was funded by the British Academy
Centenary Research Project, and A.K. by a grant from the
Leverhulme Trust. R.D. was supported by a British Academy
Research Professorship.
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S U P P O R T I N G I N F O R M A T I O N
Additional Supporting Information may be found in the
online version of this article:
Appendix S1 Summary of non-climate data used for deriv-
ing model equations.
Appendix S2 Summary of climatic and location data used
for deriving model equations.
As a service to our authors and readers, this journal
provides supporting information supplied by the authors.
Such materials are peer-reviewed and may be re-organized
for online delivery, but are not copy-edited or typeset.
Technical support issues arising from supporting informa-
tion (other than missing files) should be addressed to the
authors.
B I O S K E T C H E S
Julia Lehmann is a senior lecturer in Biological Sciences at
Roehampton University. Her main research interest is con-
cerned with the evolution of mammalian sociality, with a
special focus on social complexity in species living in dispersed
groups.
Amanda H. Korstjens is a senior lecturer at the School of
Conservation Sciences at Bournemouth University. Her main
interest is the complexity of behavioural strategies and how
these are determined by the social and ecological environment
that an individual inhabits, especially in primates.
Robin Dunbar is Professor of Evolutionary Anthropology at
the University of Oxford, a Fellow of the British Academy and
co-Director of the British Academy’s Centenary Research
Project. His principal research interests focus on social
evolution in mammals (with particular reference to ungulates,
primates and humans).
Editor: Melodie McGeoch
Global warming and ape biogeography
Journal of Biogeography 37, 2217–2231 2231 ª 2010 Blackwell Publishing Ltd
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