effects have the potential to be influential agents
of natural selection (25). Imbalances of expectation and reward may therefore have broad
effects on health and physiology in humans and
may represent a powerful evolutionary force in
References and Notes
1. J. Apfeld, C. Kenyon, Nature 402, 804–809
2. S. Libert et al., Science 315, 1133–1137 (2007).
3. N. J. Linford, T. H. Kuo, T. P. Chan, S. D. Pletcher,
Annu. Rev. Cell Dev. Biol. 27, 759–785 (2011).
4. P. C. Poon, T. H. Kuo, N. J. Linford, G. Roman,
S. D. Pletcher, PLOS Biol. 8, e1000356 (2010).
5. E. D. Smith et al., BMC Dev. Biol. 8, 49 (2008).
6. J. Alcedo, C. Kenyon, Neuron 41, 45–55 (2004).
7. S. J. Lee, C. Kenyon, Curr. Biol. 19, 715–722
8. R. Xiao et al., Cell 152, 806–817 (2013).
9. R. M. Sapolsky, Science 308, 648–652 (2005).
10. L. Partridge, N. H. Barton, Nature 362, 305–311
11. L. Partridge, D. Gems, D. J. Withers, Cell 120, 461–472
12. J. C. Billeter, J. Atallah, J. J. Krupp, J. G. Millar,
J. D. Levine, Nature 461, 987–991 (2009).
13. J. F. Ferveur, Behav. Genet. 35, 279–295 (2005).
14. J. F. Ferveur et al., Science 276, 1555–1558 (1997).
15. M. P. Fernández et al., PLOS Biol. 8, e1000541
16. M. C. Larsson et al., Neuron 43, 703–714 (2004).
17. W. Boll, M. Noll, Development 129, 5667–5681
18. R. Thistle, P. Cameron, A. Ghorayshi, L. Dennison,
K. Scott, Cell 149, 1140–1151 (2012).
19. G. Shohat-Ophir, K. R. Kaun, R. Azanchi,
H. Mohammed, U. Heberlein, Science 335,
20. S. P. Kalra, J. T. Clark, A. Sahu, M. G. Dube, P. S. Kalra,
Synapse 2, 254–257 (1988).
21. M. Heilig, Neuropeptides 38, 213–224 (2004).
22. M. Ashburner, K. Golic, R. S. Hawley, Drosophila:
A Laboratory Handbook (Cold Spring Harbor Laboratory
Press, Cold Spring Harbor, NY, ed. 2, 2004).
23. G. Landis, J. Shen, J. Tower, Aging 4, 768–789
24. T. H. Kuo et al., PLOS Genet. 8, e1002684 (2012).
25. J. W. McGlothlin, A. J. Moore, J. B. Wolf, E. D. Brodie 3rd,
Evolution 64, 2558–2574 (2010).
Acknowledgments: We thank the members of the Pletcher
laboratory for Drosophila husbandry, N. Linford for comments
on the revision, P. J. Lee for figure illustration, and members
of the Dierick and Pletcher laboratories for suggestions on
experiments and comments on the manuscript. Supported
by NIH grants R01AG030593, TR01AG043972, and
R01AG023166, the Glenn Foundation, the American
Federation for Aging Research, and the Ellison Medical
Foundation (S.D.P.); Ruth L. Kirschstein National Research
Service Award F32AG042253 from the National Institute on
Aging (B. Y.C.); NIH grant T32AG000114 (B. Y.C.); NIH grants
T32GM007863 and T32GM008322 (Z.M.H.), a Glenn/AFAR
Scholarship for Research in the Biology of Aging (Z.M.H.); NSF
grant IOS-1119473 (H.A.D.); and the Alexander von Humboldt
Foundation and Singapore National Research Foundation grant
RF001-363 (J. Y. Y.). This work made use of the Drosophila
Aging Core of the Nathan Shock Center of Excellence in the
Biology of Aging, funded by National Institute on Aging grant
P30-AG-013283. RNA-seq expression data are provided in
table S1. The funders had no role in study design, data
collection and analysis, decision to publish, or preparation
of the manuscript. The authors declare that they have no
competing interests. C.M.G., T.-H.K., Z.M.H., and S.D.P.
conceived and designed the experiments; C.M.G., T.-H.K.,
Z.M.H., B. Y.C., J. Y. Y., H.A.D., and S.D.P. performed the
experiments; C.M.G., T.-H.K., Z.M.H., B. Y.C., J. Y. Y., and
S.D.P. analyzed the data; and C.M.G., T.-H.K., J. Y. Y., H.A.D.,
and S.D.P. wrote the paper.
Materials and Methods
Figs. S1 to S14
16 July 2013; accepted 31 October 2013
Published online 28 November 2013;
Relationships Differ Among Continents
Caroline E. R. Lehmann,1,2 T. Michael Anderson,3 Mahesh Sankaran,4,5
Steven I. Higgins,6,7 Sally Archibald,8,9 William A. Hoffmann,10 Niall P. Hanan,11
Richard J. Williams,12 Roderick J. Fensham,13 Jeanine Felfili,14 Lindsay B. Hutley,15
Jayashree Ratnam,4 Jose San Jose,16 Ruben Montes,17 Don Franklin,15
Jeremy Russell-Smith,15 Casey M. Ryan,2 Giselda Durigan,18 Pierre Hiernaux,19
Ricardo Haidar,14 David M. J. S. Bowman,20 William J. Bond21
Ecologists have long sought to understand the factors controlling the structure of savanna
vegetation. Using data from 2154 sites in savannas across Africa, Australia, and South America,
we found that increasing moisture availability drives increases in fire and tree basal area, whereas
fire reduces tree basal area. However, among continents, the magnitude of these effects varied
substantially, so that a single model cannot adequately represent savanna woody biomass
across these regions. Historical and environmental differences drive the regional variation in
the functional relationships between woody vegetation, fire, and climate. These same differences
will determine the regional responses of vegetation to future climates, with implications for
global carbon stocks.
Savannas cover 20% of the global land sur- face and account for 30% of terrestrial net primary production (NPP) and the vast ma-
jority of annual global burned area (1–3). Savanna
ecosystem services sustain an estimated one-fifth
of humans, and savannas are also home to most
of the remaining megafauna (1). Tropical savanna
is characterized by the codominance of C3 trees
and C4 grasses that have distinct life forms and
photosynthetic mechanisms that respond differ-
ently to environmental controls (4). Examples
include the differing responses of these func-
tional types to temperature and atmospheric CO2
concentrations, predisposing savannas to altera-
tions in structure and extent in the coming cen-
Tropical savannas are defined by a contin-
uous C4 herbaceous layer, with a discontinuous
stratum of disturbance-tolerant woody species
(7). Although savanna tree cover varies greatly in
space and time (8, 9), the similarities in structure
among the major savanna regions of Africa,
Australia, and South America have led to an
assumption that the processes regulating veg-
etation structure within the biome are equiva-
lent (10, 11). Current vegetation models treat
savannas as a homogenous entity (12, 13). Recent
studies, however, have highlighted differences
in savanna extent across continents (14, 15), and
it remains unknown how environmental drivers
interact to determine the vegetation dynamics
and limits of the biome (10, 14, 15).
We sought universal relationships between
savanna tree basal area (TBA, m2 ha−1), a key
metric of woody biomass within an ecosystem,
and the constraints imposed by resource availability (moisture and nutrients), growing conditions (temperature), and disturbances (fire).
Ecologists have devoted considerable effort to
the identification of universal relationships to describe the structure and function of biomes (16).
However, it has not been clear whether such relationships exist. Any such relationships may
be confounded by the unique evolutionary and
environmental histories of each ecological setting (11).
Across Africa and Australia, TBA scales similarly with rainfall, but the intercepts and the 95th
quantile differ substantially (Fig. 1, A to C). On
average, at a given level of moisture availability,
TBA is higher in Africa and lower in Australia.
However, in South America there is almost no
relationship between rainfall and TBA, which is
probably in part attributable to the narrow range
of rainfall that savanna occupies on this continent
(Fig. 2). Further, across the observed range of
rainfall, the upper limits of TBA increase linearly
with effective rainfall for Australian savannas
(Fig. 1B) but show a saturating response in
African and South American savannas (Fig. 1, A
and C). When TBA is used to estimate aboveground woody biomass (AWB) (17), the large
differences in intercepts between Africa and
Australia are reduced but substantial differences
in the limits remain (fig. S1, A to C). By con-