The Importance of Demographic
Niches to Tree Diversity
Richard Condit,1,2* Peter Ashton,3 Sarayudh Bunyavejchewin,4 H. S. Dattaraja,5 Stuart Davies,3
Shameema Esufali,6 Corneille Ewango,7 Robin Foster,8 I. A. U. N. Gunatilleke,6
C. V. S. Gunatilleke,6 Pamela Hall,9 Kyle E. Harms,10 Terese Hart,11 Consuelo Hernandez,12
Stephen Hubbell,13 Akira Itoh,14 Somboon Kiratiprayoon,15 James LaFrankie,16
Suzanne Loo de Lao,2 Jean-Remy Makana,11 Md. Nur Supardi Noor,17 Abdul Rahman Kassim,17
Sabrina Russo,3 Raman Sukumar,5 Cristia?n Samper,18 Hebbalalu S. Suresh,5 Sylvester Tan,19
Sean Thomas,20 Renato Valencia,12 Martha Vallejo,21 Gorky Villa,12 Tommaso Zillio1,2
Most ecological hypotheses about species coexistence hinge on species differences, but quantifying
trait differences across species in diverse communities is often unfeasible. We examined the
variation of demographic traits using a global tropical forest data set covering 4500 species in 10
large-scale tree inventories. With a hierarchical Bayesian approach, we quantified the distribution
of mortality and growth rates of all tree species at each site. This allowed us to test the prediction
that demographic differences facilitate species richness, as suggested by the theory that a tradeoff
between high growth and high survival allows species to coexist. Contrary to the prediction, the
most diverse forests had the least demographic variation. Although demographic differences may
foster coexistence, they do not explain any of the 16-fold variation in tree species richness
observed across the tropics.
Comparative studies of tree demographytypically consider the entire commu-nity as a unit, ignoring species differ-
ences (1), simply because most tree inventories
include small samples of many species (2, 3).
Comparative studies show that tropical forests
typically have higher turnover than do temper-
ate forests (4) and that higher tree turnover
associates with higher tree diversity (5). These
studies cannot, however, test ecological hypothe-
ses about diversity, coexistence, and demography
(6?10).
A tradeoff between rapid growth and long
life span permits species coexistence and can
foster diversity: Species reproducing early in life
persist despite poor competitive ability by
growing rapidly on disturbed sites where re-
sources are abundant. Long-lived species coexist
by outliving the weedy invaders, persisting
where resources are scarce. This is a familiar
and widely known tradeoff in plant and animal
communities (9?11) called the successional-
niche hypothesis (7, 12). At a deterministic
equilibrium, an indefinite number of species
can coexist by this mechanism, each differing
from all others along a continuum from short
life span (with high growth) to long life span
(and low growth). With stochastic demog-
raphy, however, there is limiting similarity
and the equilibrium species richness is finite
(11, 13). This hypothesis is widely quoted as
an explanation for tropical forest diversity
(14?16). Here, we ask whether species differ-
ences along a demographic axis explain why
some tropical forests have many more species
than others.
If demographic niches are a key force con-
trolling forest diversity, then more diverse for-
ests have more demographic niches. More
niches could come about either by spreading
demographic rates over a wider range or pack-
ing more in the same range. Here, we focus on
the first prediction: Tropical forests gain di-
versity by having a wider range of demographic
niches, as reflected by the range of mortality
and growth rates across species.
We provide a direct test by quantifying
mortality and growth of 4500 tree species in 10
different forests in America, Asia, and Africa
(17). The 10 sites form a large-scale observa-
tion program, spanning a wide range of envi-
ronmental conditions, designed to provide
species-specific information for little-known
tropical trees (18). At each site, a 20- to 52-ha
tree census was set out in extensive, largely
undisturbed forest (table S1). Species richness
within the census plots differed by 16-fold,
from 73 species per 50 ha in a dry forest at
Mudumalai, India, to 1167 species per 50 ha
in a wet dipterocarp forest in Sarawak, Malay-
sia (19).
Past studies on the demography of indi-
vidual tree species were based on direct
measures of rate constants. These excluded
many rare species because their rate estimates
are subject to high error (20, 21). To
overcome this limitation, we did not simply
record species_ rates of mortality and growth;
instead, we quantified the distribution of
demographic rates across the entire commu-
nity. A hierarchical Bayesian approach ac-
complishes this with explicit probability
models covering both the observations of
individual trees within species and the varia-
tion among species; all species, including rare
ones, are included. For mortality, within-
species distributions were modeled with the
binomial distribution; for growth, we chose
the log-normal based on the tendency for
individual growth rates within a species to be
highly skewed to the right. By separating
within-species variation, the hierarchical mod-
el allows focus on the question of how species
differ (10, 22, 23).
At the community level, we had to de-
scribe the variation in species_ demographic
rates across species, and again, we used the
log-normal to account for the skew to the
right. Histograms of mortality rate m and
growth rate g (24) are fitted well by the log-
normal when rare species are excluded (Fig.
1). The log-normal requires two parameters, m
and s, the mean and standard deviation of the
natural logarithm of m or g, respectively. We
were able to estimate values of m and s that
best describe a community_s demography with
the use of the Gibbs sampler, simultaneously
producing for every species estimates of
mortality and growth rates that are adjusted
for abundance. That is, for abundant species,
the estimate is barely different from the
observed rate, but for rare species, it is guided
by the community-wide pattern (25).
Fitted log-normal distributions for the
Lambir forest in Malaysia are plotted with ob-
served histograms of sapling mortality and
growth (Fig. 1). The fit is close for more com-
mon species (filled bars), demonstrating that
the large number of zeroes in the mortality
1National Center for Ecological Analysis and Synthesis, 735
State Street, Santa Barbara, CA 93101, USA. 2Center for
Tropical Forest Science, Smithsonian Tropical Research
Institute, Unit 0948, APO AA 34002?0948, USA. 3Center
for Tropical Forest Science, Asia Program, Arnold Arboretum
Asia Program, Harvard University Herbaria, 22 Divinity
Avenue, Cambridge, MA 02138, USA. 4Thai National Park
Wildlife and Plant Conservation Department, Research Office,
61 Phaholyothin Road, Chatuchak, Bangkok 10900, Thailand.
5Center for Ecological Sciences, Indian Institute of Science,
Bangalore 560012, India. 6Department of Botany, Faculty of
Science, University of Peradeniya, Peradeniya 20400, Sri
Lanka. 7University of Missouri, 8001 Natural Bridge Road, St.
Louis, MO 63121?4499, USA. 8Botany Department, The Field
Museum, Roosevelt Road at Lake Short Drive, Chicago, IL
60605?2496, USA. 9Department of Biology, Florida State
University, 5051 Quail Valley Road, Tallahassee, FL 32309,
USA. 10Department of Biological Sciences, Louisiana State
University, Baton Rouge, LA 70803, USA. 11Wildlife Conser-
vation Society, 1725 Avenue Monjiba, Chanic Building, 2nd
floor, Ngalinema, Bo??te Postale 240, Kinshasa I, Democratic
Republic of Congo. 12Pontif??cia Universidad Cato?lica de
Ecuador, Post Office Box 17012184, Quito, Ecuador. 13Depart-
ment of Plant Sciences, University of Georgia, 2502 Miller
Plant Sciences Building, Athens, GA 30602, USA. 14Plant Ecol-
ogy Lab, Faculty of Science, Osaka City University, Sugimoto
3-3-138, Sumiyoshi-ku, Osaka 558, Japan. 15Faculty of Science
and Technology, Thammasat University (Rangsit), Klongluang,
Patumtani 12121, Thailand. 16Center for Tropical Forest
Science, Asia Program, National Institute of Education,
Nanyang Technological University, 1 Nanyang Walk, 637616
Singapore. 17Forest Environment Division, Forest Research
Institute Malaysia, Kepong, Kuala Lumpur 52109, Malaysia.
18National Museum of Natural History, Smithsonian Institution,
10th Street and Constitution Avenue, NW, Washington, DC
20560, USA. 19Forest Research Center, Sarawak Forest
Department, Km10 Jalun Datak Amar Kalong Ningkan,
93250 Kuching, Sarawak, Malaysia. 20University of Toronto,
33 Willcock Street, Toronto, Ontario M5S 3B3, Canada.
21Instituto Alexander von Humboldt, Carrera 13 #28-01, Piso
7, Edifico Palma Real, Bogota?, Colombia.
*To whom correspondence should be addressed: condit@
ctfs.si.edu
7 JULY 2006 VOL 313 SCIENCE www.sciencemag.org98
REPORTS
histogram are sampling artifacts in rare species.
Growth rates are also spread by rare species,
though not as conspicuously. Fitted distribu-
tions for all the forests can be compared graph-
ically (Fig. 2) or with estimated 2.5 and 97.5
percentiles (Table 1). In the supporting online
material, tables of mortality and growth rates
for all species are provided (database S1).
Most of the forests were dominated by spe-
cies with sapling mortality rates near 1% yearj1
(Fig. 2). Even the high-mortality forests, such
as Barro Colorado Island (BCI) and Hua Khae
Khaeng National Park (HKK), had modes close
to 1% yearj1 and low rates around 0.4% yearj1.
The main feature separating these high-rate
forests from the low-rate sites (such as Pasoh in
Malaysia) is the long tail reaching 20% yearj1
mortality; at Pasoh, nearly all species had
mortality rates of G3% yearj1. Thus, forests
fell broadly in two groups: BCI, HKK, and La
Planada had upper sapling mortality rates above
20%, whereas Sinharaja, Lambir, Pasoh, and
Yasuni had upper rates below 8%. The Congo
sites had exceedingly low mortality stretching
to a modest 10% at the upper end.
Distributions of growth rates were similar
to distributions of mortality, but growth was
about half as variable across species (Fig. 2).
Conspicuously, sites with less variation in
mortality also had less variation in growth
(Fig. 3). These patterns held for larger trees,
although mortality and growth rates were
lower (table S2 and fig. S1).
Examples from individual species help
illustrate. At BCI, a fast-growing understory
treelet, Palicourea guianensis, had a population
of 376 saplings in 1982, and every single one
had died by 2005 (nevertheless, the population
grew to 851). Although Palicourea_s mortality
rate is infinite by direct calculation, the Gibbs
sampler produces an estimate of 33% yearj1.
Alloplectus schultzei, a small, weedy treelet at
La Planada, also suffered 33% yearj1 mortal-
ity, losing 284 of 335 individuals over 6 years.
In contrast, of 1161 species at Lambir, none
had mortality of 30% yearj1, and only two had
rates above 20%; at Pasoh between 1987 and
2000, the very highest Gibbs-corrected mortal-
ity rate among 802 species was 14% yearj1, in
Melastoma malabathricum.
At the other end of the distribution, Cupania
sylvatica, a midsized tree of the BCI under-
story, lost only 10 of 1102 individuals between
1990 and 1995 (0.23% yearj1), and Carapa
guianensis at La Planada, a large and valuable
timber tree, lost only 11 of 894 (0.28% yearj1).
In three census intervals at Pasoh, the lowest
Fig. 1. Distribution of sapling demographic rates of all species in the
Lambir plot. (A) Annual mortality, m, for all individuals with dbh 0 10 to
99 mm. Filled bars show the histogram of observed mortality rates for the
746 species with Q50 individuals; open bars add the 415 species with
G50 individuals. The open bar at m 0 0 extends off the graph (162
species had no mortality; 6 of these had G50 individuals). The horizontal
axis is m, expressed as a percent. The solid line is the fitted log-normal,
based on all 1161 species. The dashed vertical line indicates the mean of
the logarithm of the fitted distribution (parameter m, Table 1), which is
very close to the median. (B) Annual growth, g, for individuals 10 to 49 mm
in diameter. Filled bars are the histogram for 995 species with Q10
individuals; open bars for the remaining 154 species. The solid curve and
dashed line are the fitted log-normal and the mean of the logarithm,
respectively, as in (A). Both histograms are curtailed at 8% to accentuate
details where most of the species fall. The number of species above 8% is
indicated by arrows.
Fig. 2. Comparing the fitted distributions of sapling demography in four forests. (A) Annual mortality rate, m. (B) Annual growth rate, g. The lower
end of the growth distribution in saplings is limited by measurement accuracy (30).
www.sciencemag.org SCIENCE VOL 313 7 JULY 2006 99
REPORTS
mortality rate recorded was 0.34% yearj1, in
Cynometra malaccensis.
The Mudumalai forest stood out. Saplings
had greatly elevated mortality and growth, with
rates stretching much higher than any other
site. Between 1988 and 1992, every species at
Mudumalai had sapling growth of 96% yearj1,
and only BCI and HKK had many rates this
high. At Lenda and Sinharaja, no species grew
by 6% yearj1. For larger trees (Q100 mm
diameter), however, Mudumalai was in line
with other forests, having modes of mortality
and growth near 1% yearj1 (table S2). Indeed,
trees at Mudumalai had among the lowest rates
as well as the highest: Anogeissus latifolia had
116 deaths out of 2179 trees from 1988 to
2000, whereas Kydia calycina had 1272 of
1328 trees die over the same interval, many
because of elephant herbivory (26). Their rates
differ by 50-fold: 27% yearj1 in Kydia com-
pared with 0.47% yearj1 in Anogeissus.
Three of the sites with long tails of ele-
vated mortality and growth?BCI, HKK, and
Mudumalai?have intense annual dry seasons
(table S1). Mudumalai and HKK also burn in
some years (26) (other plots do not burn and
none suffer large-scale wind damage). It was
not surprising that annual drought elevated mor-
tality. Many species at these sites, however, had
exceedingly low rates of mortality and evident-
ly did not suffer much from drought. Converse-
ly, forests lacking the tail of high growth and
mortality had no or modest annual dry sea-
sons, including the three forests dominated
by Dipterocarpaceae (Sinharaja, Pasoh, and
Lambir). Seasonality, however, was not the
only factor predicting high variation in demog-
raphy; the ever-wet cloud forest at La Planada,
Colombia, had a wide spread of growth and
mortality, comparable to the seasonally dry
sites.
Mudumalai and HKK have relatively open
canopies compared with all of the other sites, a
typical feature of dry forests, and many
saplings at Mudumalai are sprouts from large
root systems. These are likely reasons for
elevated sapling growth at the two sites. In
contrast, both BCI and La Planada have dense
canopies and dark understories, so canopy
openness does not obviously account for the
high-growth species at those two sites.
Contrary to the prediction that demographic
variability begets species richness, diverse
forests had the least variation in demography
(Fig. 3 and fig. S1). If anything, the most di-
verse forests had the fewest demographic
niches. At Lambir, high species richness cou-
pled with a low diversity of demographic rates
meant that 135 tree species coexisting in close
proximity had sapling mortality rates in a nar-
row window from 0.8 to 1.0% yearj1.
We do not question that demographic var-
iability plays some role in species coexistence.
In American forests, the familiar genus Cecro-
pia is found exclusively in small forest clear-
ings (or outside the forest), where it rapidly
Table 1. Variation in sapling mortality and growth rates across species in
tropical forests. For mortality, all individuals with dbh 0 10 to 99 mm
were included; for growth, those with dbh 0 10 to 49 mm were included.
Species number refers to those with at least one 10- to 99-mm sapling
alive at the outset of a given census interval. Under mortality are
percentiles of the distribution of mortality rate parameters (m) across
species: the median plus lower and upper percentiles (2.5 and 97.5) of
the fitted log-normal. Similarly, under growth are percentiles for the
distribution of growth rates (g) across species. Rates are expressed as
percentages (100m or 100g)?that is, 5 0 5% 0 0.05. For each of the
percentiles, confidence limits are given, based on the Gibbs sampler (25).
Information about the sites is presented in table S1.
Site Years No. of species
Annual mortality (%) Relative growth (%)
Median Lower Upper Median Lower Upper
BCI 82?85 284 3.14 T 0.46 0.38 T 0.10 26.0 T 6.9 2.84 T 0.16 1.35 T 0.14 6.0 T 0.7
BCI 85?90 282 2.56 T 0.37 0.31 T 0.08 21.5 T 5.5 2.41 T 0.18 0.85 T 0.12 6.8 T 1.0
BCI 90?95 282 2.85 T 0.43 0.32 T 0.09 25.3 T 6.7 2.15 T 0.13 0.89 T 0.09 5.2 T 0.7
BCI 95?00 285 3.35 T 0.42 0.48 T 0.11 23.3 T 5.7 1.97 T 0.12 0.81 T 0.09 4.8 T 0.6
BCI 00?05 285 2.91 T 0.41 0.40 T 0.10 21.4 T 5.5 2.10 T 0.16 0.73 T 0.10 6.1 T 0.9
Yasuni 96?03 1077 1.55 T 0.10 0.31 T 0.04 7.7 T 0.9 1.67 T 0.04 0.83 T 0.04 3.4 T 0.2
La Planada 97?03 218 3.22 T 0.47 0.45 T 0.13 22.9 T 5.9 2.30 T 0.17 0.93 T 0.13 5.7 T 0.8
Pasoh 87?90 802 1.04 T 0.06 0.36 T 0.04 3.0 T 0.3 2.25 T 0.05 1.38 T 0.05 3.7 T 0.2
Pasoh 90?95 801 1.35 T 0.07 0.42 T 0.05 4.3 T 0.4 1.59 T 0.03 1.02 T 0.04 2.5 T 0.1
Pasoh 95?00 804 1.69 T 0.09 0.47 T 0.06 6.0 T 0.6 1.55 T 0.03 1.07 T 0.03 2.3 T 0.1
Lambir 92?97 1161 1.32 T 0.07 0.34 T 0.03 5.2 T 0.4 1.57 T 0.03 0.96 T 0.03 2.6 T 0.1
HKK 93?99 256 4.11 T 0.57 0.70 T 0.19 24.1 T 6.6 4.83 T 0.45 1.53 T 0.26 15.2 T 2.7
Mudumalai 88?92 56 13.06 T 3.48 2.65 T 1.42 64.4 T 36 7.87 T 1.26 4.65 T 1.52 13.3 T 3.8
Mudumalai 92?96 52 17.06 T 6.43 2.35 T 1.51 124 T 113 6.35 T 1.67 2.57 T 1.38 15.7 T 11
Mudumalai 96?00 39 7.96 T 2.70 1.73 T 1.13 36.6 T 25 5.71 T 1.53 2.42 T 1.69 13.4 T 9.8
Sinharaja 95?01 205 1.35 T 0.17 0.30 T 0.07 6.0 T 1.3 1.38 T 0.07 0.75 T 0.07 2.5 T 0.2
Edoro 94?00 368 1.43 T 0.20 0.21 T 0.06 9.6 T 2.5 1.41 T 0.09 0.57 T 0.07 3.5 T 0.5
Lenda 94?00 346 1.26 T 0.19 0.18 T 0.06 8.8 T 2.2 1.06 T 0.04 0.66 T 0.05 1.7 T 0.1
Fig. 3. Range of sapling
demographic rates for
tree species within a com-
munity versus the num-
ber of species at the site.
The range is the loga-
rithmof the ratio between
the 97.5 and 2.5 percent-
iles of the fitted distribu-
tions (Table 1). The range
for mortality is given by
filled circles; the range
for growth is given by
open triangles. Sites can
be identified by the num-
ber of species; for exam-
ple, Lambir is the most
diverse and farthest to
the right. Multiple cen-
suses at BCI, Pasoh, and Mudumalai are included, and in each case, fall in a tight group.
7 JULY 2006 VOL 313 SCIENCE www.sciencemag.org100
REPORTS
colonizes and rapidly dies. The upper end of
sapling mortality and growth distributions in
America is set by gap specialists: C. obtusifolia
at BCI (12% yearj1 mortality, 14% yearj1
growth), C. sciadophylla at Yasuni (5.0%
yearj1 mortality, 6.3% yearj1 growth), and C.
monostachya at La Planada (8.8% yearj1
mortality, 8.2% yearj1 growth). Diverse South-
east Asian forests lacked species with such high
rates (27).
The most diverse tropical forests are the
least diverse demographically. It remains plau-
sible that demographic niches are packed more
tightly in some forests than others, but this
seems unlikely, because packing should depend
only on population size and turnover, which do
not vary much. Moreover, the successional-
niche hypothesis is not favored by the strong
peak in demographic rates near 1 to 2% yearj1;
if demographic niches were crucial, then rates
ought to be spread evenly over the entire range
(28). Instead, the similarity in demography of
many species suggests trait convergence (29).
We believe that broad diversity differences are
due to the source pool of different biogeo-
graphic regions, and that demographic differ-
ences play a minor role in species coexistence.
References and Notes
1. M. D. Swaine, D. Lieberman, F. E. Putz, J. Trop. Ecol. 3,
359 (1987).
2. Most forest censuses cover a single hectare or less and
include only larger trees. Except in Europe and North
America, such small samples capture less than half the
local species, with many represented by a single individual.
3. N. Pitman, J. Terborgh, M. R. Silman, P. Nu?n?ez V., Ecology
80, 2651 (1999).
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(2005).
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R. Va?squez, Proc. Natl. Acad. Sci. U.S.A. 91, 2805 (1994).
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Structure of Plant Communities (Princeton Univ. Press,
Princeton, NJ, 1988).
7. S. W. Pacala, M. Rees, Am. Nat. 152, 729 (1998).
8. B. M. Bolker, S. W. Pacala, C. Neuhauser, Am. Nat. 162,
135 (2003).
9. M. Rees, R. Condit, M. Crawley, S. Pacala, D. Tilman,
Science 293, 650 (2001).
10. J. S. Clark, J. Mohan, M. Dietze, I. Iban?ez, Ecology 84, 17
(2003).
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111 (2004).
12. J. S. Clark, S. LaDeau, I. Iban?ez, Ecol. Monogr. 74, 415
(2004).
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(2004).
14. P. J. Grubb, Biol. Rev. Camb. Philos. Soc. 52, 107
(1977).
15. S. J. Wright, Oecologia 130, 1 (2002).
16. J. Silvertown, Trends Ecol. Evol. 19, 605 (2004).
17. Table 1 includes 4956 species, but some occur at more than
one site, and taxonomy has not been fully aligned across
sites. We estimate 4500 different species at the 10 sites.
18. R. Condit, Trends Ecol. Evol. 10, 18 (1995).
19. The Sarawak plot covers 52 ha. For comparison, the
number 1167 was taken from a 50-ha subset.
20. J. K. Vanclay, J. Trop. For. Sci. 4, 15 (1991).
21. R. Condit, S. P. Hubbell, R. B. Foster, Ecol. Monogr. 65,
419 (1995).
22. A. Gelman, J. B. Carlin, H. S. Stern, D. B. Rubin, Bayesian
Data Analysis (Chapman and Hall, Boca Raton, FL, 1995).
23. M. Liermann, R. Hilborn, Can. J. Fish. Aquat. Sci. 54,
1976 (1997).
24. The rate constant m is the derivative of population size
N with respect to time, dNdt , due to mortality alone.
It is approximated by lnN2 j lnN1t2 j t1 . Relative growth
rate is g 0 lndbh2 j lndbh1t2 j t1 , where dbh is the stem
diameter at breast height.
25. Materials and methods are available as supporting
material on Science Online.
26. R. Sukumar, H. S. Suresh, H. S. Dattaraja, N. V. Joshi, in
Forest Biodiversity: Research and Modeling, F. Dallamier,
J. Comiskey, Eds. (Smithsonian, Washington, DC, 1998),
pp. 495?506.
27. R. Condit et al., Phil. Trans. R. Soc. London Ser. B 354,
1739 (1999).
28. S. J. Wright, H. C. Muller-Landau, R. Condit, S. P. Hubbell,
Ecology 84, 3174 (2003).
29. S. P. Hubbell, Ecology, in press.
30. Errors in dbh measurements are 1 to 2 mm in saplings,
and many saplings show slight negative growth (25). We,
thus, cannot measure relative growth rates below about
0.5% yearj1. Where close to 0.5% yearj1, the low end of
the sapling growth distributions are probably artifacts. In
larger trees, errors are smaller relative to dbh, eliminating
the problem.
31. Analyses were supported by NSF grant DEB-9806828
of the Research Coordination Network Program to the
Center for Tropical Forest Science. Data collection was
funded by many organizations, principally the NSF,
Andrew W. Mellon Foundation, Peninsula Community
Foundation, Smithsonian Tropical Research Institute,
Arnold Arboretum (Harvard), Indian Institute of Science,
Forest Research Institute of Malaysia, Royal Thai
Forest Department, National Institute of Environmental
Studies ( Japan), and John D. and Catherine T. MacArthur
Foundation. We thank the hundreds of field workers
who have measured and mapped trees.
Supporting Online Material
www.sciencemag.org/cgi/content/full/1124712/DC1
Materials and Methods
Fig. S1
Tables S1 to S4
References
Database S1
Computer Codes
9 January 2006; accepted 31 May 2006
Published online 8 June 2006;
10.1126/science.1124712
Include this information when citing this paper.
A Single Amino Acid Mutation
Contributes to Adaptive Beach
Mouse Color Pattern
Hopi E. Hoekstra,1* Rachel J. Hirschmann,1 Richard A. Bundey,2
Paul A. Insel,2 Janet P. Crossland3
Natural populations of beach mice exhibit a characteristic color pattern, relative to their mainland
conspecifics, driven by natural selection for crypsis. We identified a derived, charge-changing
amino acid mutation in the melanocortin-1 receptor (Mc1r) in beach mice, which decreases
receptor function. In genetic crosses, allelic variation at Mc1r explains 9.8% to 36.4% of the
variation in seven pigmentation traits determining color pattern. The derived Mc1r allele is present
in Florida?s Gulf Coast beach mice but not in Atlantic coast mice with similar light coloration,
suggesting that different molecular mechanisms are responsible for convergent phenotypic
evolution. Here, we link a single mutation in the coding region of a pigmentation gene to adaptive
quantitative variation in the wild.
The identification of the specific molecu-lar changes underlying adaptive variationin quantitative traits in wild populations
is of prime interest (1, 2). Pigmentation pheno-
types are particularly amenable to genetic
dissection because of their high heritability and
our knowledge of the underlying developmental
pathway (3). In a series of classic natural history
studies (4, 5), Sumner documented pigment
variation in Peromyscus polionotus, including
eight extremely light-colored Bbeach mouse[
subspecies, which inhabit the primary dunes
and barrier islands of Florida_s Gulf and
Atlantic coasts (6). This light color pattern is
driven by selection for camouflage (7, 8) as
major predators of P. polionotus include visual
hunters (9). Because the barrier islands on the
Gulf Coast are G6000 years old (10), this
adaptive color variation may have evolved
rapidly.
We examined the contribution of the
melanocortin-1 receptor gene (Mc1r) to this
adaptive color patterning. MC1R, a G protein?
coupled receptor, plays a key role in melano-
genesis by switching between the production of
dark eumelanin and light pheomelanin (11).
Mutations in Mc1r have been statistically
associated with Mendelian color polymor-
phisms in several mammalian species (e.g.,
12?14) and in natural variants of avian plumage
(15, 16).
1Division of Biological Sciences and 2Department of
Pharmacology, University of California, San Diego, La
Jolla, CA 92093, USA. 3Peromyscus Genetic Stock Center,
Department of Biological Sciences, University of South
Carolina, Columbia, SC 29208, USA.
*To whom correspondence should be addressed. E-mail:
hoekstra@ucsd.edu
www.sciencemag.org SCIENCE VOL 313 7 JULY 2006 101
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