© 2005 Nature Publishing Group
Determinants of woody cover in African savannas
, Niall P. Hanan
, Robert J. Scholes
, Jayashree Ratnam
, David J. Augustine
, Brian S. Cade
, Steven I. Higgins
, Xavier Le Roux
, Fulco Ludwig
, Jonas Ardo
, Feetham Banyikwa
, Gabriela Bucini
, Kelly K. Caylor
, Michael B. Coughenour
, Alioune Diouf
, Christie J. Feral
, Edmund C. February
, Peter G. H. Frost
, Pierre Hiernaux
, Kristine L. Metzger
, Herbert H. T. Prins
, Susan Ringrose
, William Sea
& Nick Zambatis
Savannas are globally important ecosystems of great signiﬁcance
to human economies. In these biomes, which are characterized
by the co-dominance of trees and grasses, woody cover is a chief
determinant of ecosystem properties
. The availability of
resources (water, nutrients) and disturbance regimes (ﬁre, herbi-
vory) are thought to be important in regulating woody cover
but perceptions differ on which of these are the primary drivers of
savanna str ucture. Here we show, using data from 854 sites across
Africa, that maximum woody cover in savannas receiving a mean
annual precipitation (MAP) of less than ,650 mm is constrained
by, and increases linearly with, MAP. These arid and semi-arid
savannas may be considered ‘stable’ systems in which water
constrains woody cover and permits grasses to coexis t, while
ﬁre, herbivory and soil properties interact to reduce woody
cover below the MAP-controlled upper bound. Above a MAP of
,650 mm, savannas are ‘unstable’ systems in w hich MAP i s
sufﬁcient for woody canopy closure, and disturbances (ﬁre, herbi-
vory) are required for the coexistence of trees and grass. These
results provide insights into the nature of African savannas and
suggest that future changes in precipitation
affect their distribution and dynamics.
Savannas occupy a ﬁfth of the earth’s land surface and support a
large proportion of the world’s human population and most of its
rangeland, livestock and wild herbivore biomass
. A deﬁning feature
of savanna ecosystems is the coexistence of trees and grasses in the
. The balance between these two life forms inﬂuences both
plant and livestock production, and has profound impacts on several
aspects of ecosystem function, including carbon, nutrient and
. The mechanisms that promote tree–grass
coexistence and the factors that determine the relative proportions of
these two life forms across different savanna types remain, however,
. Because savannas are antici pated to be among the
ecosystems that are most sensitive to future changes in land use
, a thorough understanding of factors that structure
savanna communities is urgently required to guide management
Explanations for the persistence of tree–grass mixtures in savannas
are varied and invoke such different mechanisms as competition for
water and nutrients
, demographic bottlenecks to tree recruit-
, and disturbances including ﬁre
and large mammal
. Empirical studies provide support both for and
against each alternative mechanism and, consequently, perceptions
differ on the relative impor tance of resource limitation versus
disturbances in controlling savanna structure
. The lack of con-
sensus arises, in part, because most studies have been small-scale and
site-speciﬁc, and have often focused on a single determinant
savanna sy stems are diverse and occur under a wide range of
, and it is likely that the importance of
different processes in regulating woody cover may vary in different
savanna regions. Thus, a comprehensive model that explains both
coexistence and the relative productivity of tree and grass com-
ponents across diverse types of savanna is unlikely to arise from
studying individual systems in isolation: it requires a synthesis of
data from savannas across broad environmental gradients
Here we use a continental scale analysis of African savannas to
investigate h ow the relative importance of resource availability
(water, nutri ents) and disturbance regimes (ﬁre, herbivor y) in
regulating woody cover varies across broad environmental gradients.
In particular, we are interested in determining whether broad-scale
trends in savanna structure are indicative of ‘stable’ or ‘unstable’
, or whether savannas show elements of both across their
geographic range of occurrence. We use ‘stable’ in a limited sense to
mean that coexistence of trees and grasses in savannas is not
dependent on disturbances such as ﬁre and mammalian herbivory,
while recognizing that woody community biomass and cover are
dynamic, not static, properties of the system.
Speciﬁcally, we considered that if water availability is the primary
determinant of woody cover in savannas
, then precipitation
Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, Colorado 80523, USA.
Division of Forest Science and Technology, CSIR, PO Box 395, Pretoria 001,
USDA Forest Service, Commanche National Grassland, PO Box 12, Springﬁeld, Colorado 81073, USA.
US Geological Survey, Fort Collins Science Center,
2150 Centre Avenue, Building C, Fort Collins, Colorado 80526-8818, USA.
Ecole Normale Superieure, Laboratoire d’ecologie, UMR 7625 CNRS, Universite
de Paris 6 ENS, 46 Rue
d’Ulm, 75230 Paris Cedex 05, France.
Umweltforschungszentrum Leipzig Halle, Sekt Okosyst Anal, Permoserstrasse 15, D-04318 Leipzig, Germany.
Laboratory, UMR 5557 CNRS, Universite
Lyon 1 USC INRA 1193, Ba
timent G. Mendel, 43 Boulevard du 11 Novembre 1918, 69622 Villeurbanne, France.
CSIRO Centre for
Environment and Life Sciences, CSIRO Plant Industry, Private Bag 5, Wembley, Western Australia 6913, Australia.
Department of Physical Geography and Ecosystems Analysis,
Lund University, So
lvegatan 12, 223 62 Lund, Sweden.
Department of Botany, University of Dar es Salaam, PO Box 35060, Dar es Salaam, Tanzania.
Agriculture and Game Management, Private Bag X6011, Port Elizabeth Technikon, Port Elizabeth 6000, South Africa.
Department of Civil & Environmental Engineering,
Princeton University, Princeton, New Jersey 08544, USA.
Centre de Suivi Ecologique, BP 15532, Dakar, Senegal.
University of Nairobi, Department of Range Management,
PO Box 29053, Nairobi, Kenya.
Environmental Sciences Department, University of Virginia, PO Box 400123, 291 McCormick Road, Charlottesville, Virginia 22904 -4123, USA.
Department of Botany, University of Cape Town, University Private Bag, Rondebosch 7700, South Africa.
Institute of Environmental Studies, University of Zimbabwe, PO Box
MP 167, Mount Pleasant, Hara re, Zimbabwe.
CESBIO, 18 Avenue E. Belin, 31401 Toulouse Cedex 9, France.
Mammal Research Institute, University of Pretoria, Pretoria 002,
Department of Zoology, University of Wisconsin, 430 Lincoln Drive, Madison, Wisconsin 53706, USA.
Resource Ecology Group, Wageningen University,
Bornsesteeg 69, 6708 PD Wageningen, The Netherlands.
Harry Oppenheimer Okavango Research Centre, University of Botswana, Private Bag 285, Maun, Botswana.
University of Potsdam, Institute of Biochemistry & Biology, Plant Ecology & Nature Conservation, Maulbeerallee 2, D-14469 Potsdam, Germany.
Scientiﬁc Services, Kruger
National Park, Private Bag X402, Skukuza 1350, South Africa.
Vol 438|8 December 2005|doi:10.1038/nature04070
© 2005 Nature Publishing Group
should limit the potential tree cover that can be supported at any
given site, and maximum realizable woody cover should gradually
inc rease with MAP
. By contrast, if disturbances such as ﬁre
and herbivory primarily maintain savannas
, then we expect an
abrupt, rather than gradual, increase in maximum realizable woody
cover with increasing MAP
: below a critical threshold of rainfall
sufﬁcient to permit tree growth outside riparian areas or depressions,
grasslands should dominate; above this threshold, the maximum
woody cover should correspond to a closed-canopy woodland state
Depending on the level of disturbance, a particular location might
have reduced woody cover, but the upper bound would not depend
We evaluated relationships between woody cover and MAP, soil
characteristics (texture, percentage nitrogen, nitrogen mineraliza-
tion, total phosphorus) and disturbance regimes (ﬁre-return inter-
vals, mammalian herbivore biomass) from 854 sites across Africa
(Supplementary Fig. S1 and Methods). Woody cover ranges from 0 to
90% across sites and tends to increase with MAP (Fig. 1). More
particularly, within a narrow range of MAP from ,100 to 650 mm,
an upper bound exists on the maximum realizable woody cover
(Fig. 1). In these arid to semi-arid sites (,650 ^ 134 mm MAP;
see Fig. 1), maximum realized woody cover increases with MAP
(Fig. 2a), but shows no relationship w ith ﬁre-return inter vals,
herbivore bio mass or soil characteristics (Fig. 2b–f), su ggesting
that the observed upper limit on woody cover in arid and semi-
arid African savannas is primarily a consequence of m oisture
limitation. The presence of an upper bound on woody cover in
these savannas that is linked primarily to MAP is not consistent w ith
the view that savannas are inherently unstable systems maintained by
Within this MAP range (,650 ^ 134 mm MAP), our analysis
suggests that tre e–grass coexistence is stable to the extent that
disturbances such as ﬁre and herbivory, although capable of modify-
ing tree to grass ratios, are not necessary for coexistence. In these
“climatically determined savannas”
(,650 ^ 134 mm MAP),
restrictions on maximum woody cover as a result of water limitation
permit grasses to persist in the system. By contrast, in areas that
receive a MAP in excess of 650 ^ 134 mm, water availability seems to
be sufﬁcient to allow trees to approach canopy closure such that
grasses may be effectively excluded. These “disturbance-driven
represent unstable systems in which disturbances such
as ﬁre, grazing and browsing are required to maintain both trees
and grasses in the system by buffering against transitions to a closed-
Whereas MAP drives the upper bound on woody cover in arid and
semi-arid savannas, disturbance regimes and soil characteristics
impose signiﬁcant controls on savanna structure by inﬂu encing
woody cover below the bound. A regression tree analysis of mean
woody cover for a restricted subset of sites for which all data were
available (Fig. 3 and Methods) further highlights the importance of
MAP as a principal driver of savanna structure and suggests that
MAP also mediates the relative importance of other savanna drivers
such as ﬁre and soil characteristics.
Below a MAP of ,350 mm, woody cover is typically low (Fig. 3). In
these sites, soil properties and disturbances such as ﬁre and herbivory
rarely regulate woody cover. As MAP increases above this threshold,
ﬁre in particular becomes a common factor that reduces woody cover
Figure 1 | Change in woody cover of African savannas as a function of
Maximum tree cover is represented by using a 99th quantile piece-
wise linear reg ression. The regression analysis identiﬁes the breakpoint (the
rainfall at which maximum tree cover is attained) in the interval
650 ^ 134 mm MAP (between 516 and 784 mm; see Methods). Trees are
typically absent below 101 mm MAP. The equation for the line quantifying
the upper bound on tree cover between 101 and 650 mm MAP is
Cover(%) ¼ 0.14(MAP) 2 14.2. Data are from 854 sites across Africa.
Figure 2 | Woody cover as a function of MAP, soil properties and
disturbance regimes in arid and semi-arid savannas.
between woody cover and MAP (a; n ¼ 529), ﬁre-return intervals
(b; n ¼ 302), herbivore biomass (c; n ¼ 145), percentage of clay
(d; n ¼ 234), nitrogen mineralization potential (e; n ¼ 109) and soil total
phosphorus (f; n ¼ 118) for savannas receiving ,650 mm MAP. Unbroken
and broken lines represent the 99th and 90th linear quantiles, respectively.
Maximum woody cover increased with MAP, but showed no consistent
relationship with other vari ables. For MAP, both quantile slopes were
signiﬁcantly different from zero. For ﬁre-return intervals, herbivore
biomass, clay and nitrogen mineralization rates, neither regression line had
a signiﬁcant non-zero slope. For total phosphorus, the 90th but not the 99th
quantile slope differed from zero.
NATURE|Vol 438|8 December 2005 LETTERS
© 2005 Nature Publishing Group
below the MAP-controlled upper bound (Fig. 3). Woody cover is
higher, on average, where ﬁres are infrequent (ﬁre-return interval
.10.5 yr). In sites with more frequent ﬁres, woody cover is typically
low, except on very sandy soils (mostly concentrated on the Kalahari
sand sheets), which tend to support higher woody cover (Fig. 3). The
dependence of ﬁre frequency on MAP presumably arises because
increased grass production in mesic sites leads to greater fuel loads
that can support more frequent ﬁres
(Supplementary Fig. S2). Very
high sand content, which correlates with low nutrient availability
(Supplementary Table S1), may promote higher woody cover if the
positive effects of coarse-textured soils, such as lower wilting points
and greater water percolation to soil layers below grass rooting
, override the negative effects associated w ith lower
nutrient availability in these soils
Herbivore effects on woody cover are, however, less apparent.
Although we found a tendency for grazers to enhance woody cover
and browsers and mixed feeders to depress it, such effects were weak
and could not be generalized beyond our data set (see Methods;
measures of herbivore biomass were retained in the complete, but not
pruned, regression tree). The lack of consistent herbivore effects
across sites most probably reﬂects differences in herbivore guilds,
seasonality of herbivory, and variation in herbivore body-size distri-
butions across sites, features for which data were not available.
Larger, more detailed data sets will undoubtedly provide greater
resolution of how different driver variables interact to inﬂuence
mean woody cover.
These results have the power to inform savanna management
strategies because they bear directly on our ability to predict savanna
responses to changing environmental drivers. In particular, our data
indicate that woody encroachment, a phenomenon in which many
savannas across the world show a directional trend of increasing
, may be a bounded process in savannas receiving a
MAP of ,650 ^ 134 mm, ultimately limited by water availability.
For sites close to or at the MAP-controlled bound (Fig. 1), changes in
precipitation regimes that lead to increased water availability
fore may be a cause for concern with respect to woody encroachment.
However, the enormous variation in woody cover, with most sites far
from the climatic bound (Fig. 1), suggests that processes other than
MAP regulate actual tree cover in many savannas of Africa. In
particular, our results suggest that if disturbances by ﬁre, browsers
and humans were absent, then large sections of the African continent
would switch to a wooded state (hatched regions in Fig. 4).
The patterns described here for African savannas suggest that the
dominant ecological theories for tree–grass coexistence in these
systems need to be combined: it is clear that most savannas are
strongly affected by disturbances that maintain woody cover well
below the resource-limited upper bound. Disturbance-based models
do not consider and are unable to explain, however, the upper bound
to tree cover. The result s emerging from this continental scale
analysis strongly indicate that water limits the maximum cover of
woody species in many African savanna systems, but that disturbance
dynamics control savanna structure below the maximum. These
results have important implications both for our understanding of
the fundamental nature of African savanna systems and for our
ability to predict their responses to changing environmental drivers.
It remains to be established whether the patterns obser ved here for
African savannas also hold in other tropical savanna regions or in
temperate savannas where the effects of winter precipitation and
temperature on moisture distribution through the soil proﬁle can
markedly alter water par titioning between woody and herbaceous
plants, and thus can inﬂuence maximum woody cover.
Data collection. Data on projected woody cover (the percentage of ground
surface covered when crowns are projected vertically), MAP, soil characteristics
(texture, total nitrogen and phosphorus, and nitrogen mineralization), ﬁre and
herbivory regimes were gathered from several sources for a range of sites across
Africa. We included only sites for which vegetation was sampled over sufﬁciently
large spatial scales (.0.25 ha for plot measurements and .100 m for transect
sampling). Sites located in riparian or seasonally ﬂooded areas, or in net water
run-on areas such as depressions, and sites in which trees were known to access
ground water resources (that is, sources of water not dependent on rainfall in the
immediate vicinity or in recent years) were excluded from the analysis because
MAP is not a relevant descriptor of water availability in these sites. We also
excluded sites that had been cultivated or harvested by humans ,10 yr before
sampling from the analysis.
Rainfall data included estimates from ﬁeld measurements and regional
rainfall maps (n ¼ 469) and from ﬁtted climatic g rids (0.058 resolution,
Figure 4 | The distributions of MAP-determined (‘stable’) and disturbance-
determined (‘unstable’) savannas in Africa.
Grey areas represent the
existing distribution of savannas in Africa according to ref. 30. Vertically
hatched areas show the unstable savannas (.784 mm MAP); cross-hatched
areas show the transition between stable and unstable savannas (516–
784 mm MAP); grey areas that are not hatched show the stable savannas
(,516 mm MAP).
Figure 3 | Regression tree showing generalized relationships between
woody cover and MAP, ﬁre-return interval and percentage of sand.
tree is pruned to four terminal nodes and is based on 161 sites for which all
data were available. No consistent herbivore effects were detected. Branches
are labelled with criteria used to segregate data. Values in terminal nodes
represent mean woody cover of sites grouped within the cluster. The pruned
tree explained ,45.2% of the variance in woody cover, which is signiﬁcantly
more than a random tree (P , 0.001). Of this, 31% was accounted for by the
ﬁrst split; the second split explained an additional 10% of the variance in
LETTERS NATURE|Vol 438|8 December 2005
© 2005 Nature Publishing Group
n ¼ 383) of monthly mean rainfall for Africa from the ANU-CRES (ref. 20;
http://cres.anu.edu.au/outputs/africa.php). Fire-return periods were obtained
from ﬁeld records (n ¼ 182) and from burnt-area maps of Africa at 5-km
resolution (n ¼ 670) derived from AVHRR (advanced very high resolution
radiometer) images based on 8 yr of data (1981–1983 and 1985–1991; ref. 21).
Herbivore density estimates were available for 180 sites. Soils were obtained from
166 sites and analysed under standardized laboratory conditions for texture,
total nitrogen and phosphorus, and nitrogen mineralization potential (s ee
Supplementary Information). Our data set included sites encompassing a
wide range of rainfall (132–1, 185 mm MAP), ﬁre-return intervals (1 to
.50 yr), herbivore biomass (0–8,000 kg km
), soil texture (sand, 6.7–98%;
clay, 0.6–62.8%), soil percentage nitrogen (0.013–0.31%), soil total phosphorus
) and potential nitrogen mineralization rates (222.8 to
per week; see Supplementary Fig. S3).
Data analyses. To characterize the effects of MAP on the upper limit to woody
cover across sites, we analysed data using a bent-cable form of a piece-wise linear
estimated with nonlinear quantile regression
, as implemented in the
‘quantreg’ librar y in the statistical package R (http://www.r-project.org/). We
used 0.90 to 0.99 conditional quantiles to obtain estimates near the upper
boundary of the percentage of woody cover as it changes with MAP, which better
reﬂects the process of MAP limiting maximum woody cover than does mean
(see Supplementary Information for details of this and additional
analyses). We conducted additional analyses on the subset of sites that received
,650 mm rainfall annually to investigate further how ﬁre regimes, herbivory
and soil properties inﬂuenced the upper bound on woody cover that was evident
in these sites. We analysed these data by linear quantile regression
implemented in the ‘quantreg’ library, which permits computation of conﬁdence
intervals for estimated parameters
and enabled us to test whether the regression
slopes were different from zero.
In addition to analysing patterns in maximum woody cover, we use d
regression tree analysis
, as implemented in the ‘rpart’ library in R, to determine
how resource availability and disturbance regimes inﬂuenced mean realized
woody cover in sites (see Supplementary Information). After tree construction,
cross-validation procedures were used to prune trees to a size that best
represented relationships that could be generalized outside the sample to the
rest of the continent
. Woody cover values were log-transformed to stabilize
. To avoid problems arising from collinearity among soil variables,
only sand content was retained for the analysis as it was the variable that was
most strongly correlated to other soil variables (Supplementary Table S1). The
results of the analysis were unchanged if grazer biomass and mixed feeder plus
browser biomass were retained as two separate variables, or if total herbivore
biomass was used as the predictor variable. The analysis was based on 161 sites
for which data on MAP, ﬁre-return intervals, herbivore biomass density and soil
sand content were available. To determine whether the pruned tree explained
more variance than a random tree of equal complexity, the square of the
correlation coefﬁcient (r
) of the pruned tree was compared with r
similar sized trees generated from 2,000 random associations between predictor
variables and woody cover
. Further details on the methodology and results
from additional analyses are provided in the Supplementary Information.
Received 26 April; accepted 22 July 2005.
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Acknowledgements This paper arose from a workshop on savanna complexity
funded by the NSF. We thank R. Boone, I. McHugh, R. Grant, H. Biggs,
W. T. Starmer, P. M. Barbosa, D. Ruess, J. Rettenmayer, C. Williams, J. Klein,
M. T. Anderson, W. J. Parton, J. C. Neff, N. Govender and the Kruger Park
Scientiﬁc Services for comments, help with data collection and analysis, and for
providing access to otherwise unpublished data.
Author Contributions All authors contributed data or intellectual input to the
Author Information Reprints and permissions information is available at
npg.nature.com/reprintsandpermissions. The authors declare no competing
ﬁnancial interests. Correspondence and requests for materials should be
addressed to M.S. (email@example.com).
NATURE|Vol 438|8 December 2005 LETTERS