letters to nature
15. Soref, R. A. & Lorenzo, J. P. Light-by-light modulation in silicon-on-insulator waveguides. Digest of
the OSA Integrated and Guided-Wave Optics Topical Meeting 86–89 (Optical Society of America,
Washington DC, 1989).
16. Barrios, C. A., Almeida, V. R. & Lipson, M. Low-power-consumption short-length and highmodulation-depth silicon electrooptic modulator. J. Lightwave Technol. 21, 1089–1098 (2003).
17. Soref, R. A. & Bennett, B. R. Kramers-Kronig analysis of electro-optical switching in silicon. Proc. SPIE
704, 32–37 (1987).
18. Zhao, C. Z., Li, G. Z., Liu, E. K., Gao, Y. & Liu, X. D. Silicon on insulator Mach–Zehnder waveguideinterferometers operating at 1.3 mm. Appl. Phys. Lett. 67, 2448–2449 (1995).
19. Stepanov, S. & Ruschin, S. Modulation of light by light in silicon-on-insulator waveguides. Appl. Phys.
Lett. 83, 5151–5153 (2003).
20. Liu, A. et al. A high-speed silicon optical modulator based on a metal-oxide-semiconductor capacitor.
Nature 427, 615–618 (2004).
21. Almeida, V. R., Panepucci, R. R. & Lipson, M. Nanotaper for compact mode conversion. Opt. Lett. 28,
1302–1304 (2003).
22. Verdeyen, J. T. Laser Electronics, 3rd edn 153 (Prentice Hall, Upper Saddle River, NJ, 2000).
23. Chin, A., Lee, K. Y., Lin, B. C. & Horng, S. Picosecond photoresponse of carriers in Si ion-implanted
Si. Appl. Phys. Lett. 69, 653–655 (1996).
24. Meindl, J. D. et al. Interconnect opportunities for gigascale integration. IBM Res. Dev. 46, 245–263(2002).
25. Weiss, S. M., Molinari, M. & Fauchet, P. M. Temperature stability for silicon-based photonic band-gap
structures. Appl. Phys. Lett. 83, 1980–1982 (2003).
26. Cheben, P., Xu, D.-X., Janz, S. & Delâge, A. Scaling down photonic waveguide devices on the SOI
platform. Proc. SPIE 5117, 147–156 (2003).
27. Vlasov, Y. A. & McNab, S. J. Losses in single-mode silicon-on-insulator strip waveguides and bends.
Opt. Express 12, 1622–1631 (2004).
28. Pardo, F. et al. Optical MEMS devices for telecom systems. Proc. SPIE 5116, 435–444 (2003).
29. Miller, D. A. B. Optical interconnects to silicon. IEEE J. Sel. Top. Quant. Electron. 6, 1312–1317 (2000).

Acknowledgements We acknowledge support by the Cornell Center for Nanoscale Systems,
funded by the National Science Foundation (NSF), by the Air Force Office of Scientific Research(AFOSR) and by the CS-WDM programme of the Defense Advanced Research Project Agency.
V.R.A. acknowledges sponsorship support provided by the Brazilian Defence Ministry. This work
was performed in part at the Cornell Nano-Scale Science & Technology Facility (CNF), a memberof the National Nanotechnology Infrastructure Network (NNIN) which is supported by the NSF,
its users, Cornell University and Industrial Affiliates.
Competing interests statement The authors declare that they have no competing financial
interests.
Correspondence and requests for materials should be addressed to M.L. (lipson@ece.cornell.edu).

magnetic activity and almost all of the earlier high-activity
periods were shorter than the present episode. Although therarity of the current episode of high average sunspot numbers
may indicate that the Sun has contributed to the unusual climatechange during the twentieth century, we point out that solar
variability is unlikely to have been the dominant cause of thestrong warming during the past three decades3.
Sunspots—strong concentrations of magnetic flux at the solar
surface—are the longest-studied direct tracers of solar activity.
Regular telescopic observations are available after AD 1610. In
addition to the roughly 11-year solar cycle, the number of sunspots,
formalized in the group sunspot number1 (GSN), exhibits prominent fluctuations on longer timescales. Notable are an extended
period in the seventeenth century called the Maunder minimum,
during which practically no sunspots were present2, and the periodof high solar activity since about AD 1940 with average sunspot
numbers above 70.
A physical approach to reconstruction of the sunspot number backin time is based on archival proxies, such as the concentration of thecosmogenic isotopes 14C in tree rings4–6 or 10Be in ice cores7,8. Thisapproach has recently been strengthened by the development ofphysics-based models describing each link in the chain of processes
connecting the concentration of cosmogenic isotopes with thesunspot number9–12. This advance allowed a reconstruction of thesunspot number since AD 850 based on 10Be records from Antarctica
and Greenland13,14. The current period of high solar activity is
unique within this interval, but the covered time span is too short tojudge just how unusual the current state of solar activity is.
Here we present a reconstruction of the sunspot number covering
the Holocene epoch, the modern period of relatively warm climatethat superseded the glacial period about 11,000 years ago. The
reconstruction is based on D14C, the 14C activity in the atmosphere15 obtained from high-precision 14C analyses on decadal
samples of mid-latitude tree-ring chronologies. The data set has
been created in an international collaboration of dendrochronolo-..............................................................

Unusual activity of the Sun during
recent decades compared to theprevious 11,000 years
S. K. Solanki1, I. G. Usoskin2, B. Kromer3, M. Schüssler1 & J. Beer4
1
Max-Planck-Institut für Sonnensystemforschung (formerly the Max-PlanckInstitut für Aeronomie), 37191 Katlenburg-Lindau, Germany
2
Sodankylä Geophysical Observatory (Oulu unit), University of Oulu,
90014 Oulu, Finland
3
Heidelberger Akademie der Wissenschaften, Institut für Umweltphysik,
Neuenheimer Feld 229, 69120 Heidelberg, Germany
4
Department of Surface Waters, EAWAG, 8600 Dübendorf, Switzerland.............................................................................................................................................................................

Direct observations of sunspot numbers are available for the past
four centuries1,2, but longer time series are required, for example,
for the identification of a possible solar influence on climate andfor testing models of the solar dynamo. Here we report areconstruction of the sunspot number covering the past 11,400
years, based on dendrochronologically dated radiocarbon concentrations. We combine physics-based models for each of theprocesses connecting the radiocarbon concentration with sunspot number. According to our reconstruction, the level of solar
activity during the past 70 years is exceptional, and the previous
period of equally high activity occurred more than 8,000 years
ago. We find that during the past 11,400 years the Sun spent onlyof the order of 10% of the time at a similarly high level of1084

Figure 1 Atmospheric radiocarbon level D14C (expressed as deviation, in ‰, from the AD
1950 standard level15) derived from mostly decadal samples of absolutely dated tree-ringchronologies (INTCAL98 data set)16. The D14C measurement precision is generally
2–3‰, although in the earlier part of the time series it can reach up to 4–5‰. The
INTCAL98 data for times earlier than 11,400 BP are not directly employed for thereconstruction because of larger errors and uncertainties in the carbon cycle acting at thattime. See Supplementary Information for more information on the data set, initialconditions used for the reconstruction, and error estimates. The long-term decline(indicated by the red curve) is caused by a reduction in 14C production rate due mainly to
an increase in the geomagnetic shielding of the cosmic ray flux. The short-term
fluctuations (duration one to two centuries) reflect changes of the production rate due tosolar variability. Years BC are shown negative here and in other figures.

©2004 Nature Publishing Group

NATURE | VOL 431 | 28 OCTOBER 2004 | www.nature.com/natureletters to nature
gists and radiocarbon laboratories16. The absolutely and precisely
dated original data set used for the sunspot number reconstruction
is represented by the black line in Fig. 1. Starting at a level 15%
higher than the reference level of AD 1950, the atmospheric 14C
shows a long-term trend (indicated by the red line), which is mainly
the result of changes in the intensity of the geomagnetic dipole
field before and during the Holocene epoch. The fluctuations on
shorter timescales predominantly result from variations of the 14C
production rate due to heliomagnetic variability, which modulates
the cosmic ray flux.
The atmospheric 14C level may also be affected by changes in thepartition of carbon between the major reservoirs, that is, deep
ocean, ocean mixed layer, biosphere and atmosphere. Variations inocean circulation17 could influence 14C via a variable uptake of CO2
into the ocean or by the exchange of 14C-depleted carbon from thedeep ocean, but, owing to the rather small 14C gradients among thereservoirs, strong changes in these processes need to be invoked. For
the Holocene, there is no evidence of considerable oceanic variability, so we can assume that the short- and mid-term fluctuations of
14
C predominantly reflect solar variability. This is supported by thestrong similarity of the fluctuations of 10Be in polar ice corescompared to 14C, despite their completely different geochemical
history18–20.

Figure 2 Comparison between directly measured sunspot number (SN) and SN
reconstructed from different cosmogenic isotopes. Plotted are SN reconstructed from
D14C (blue), the 10-year averaged group sunspot number1 (GSN, red) since 1610 and the
SN reconstruction14 from 10Be under the two extreme assumptions of local (green) andglobal (magenta, dashed) production, respectively. The slightly negative values of thereconstructed SN during the grand minima are an artefact; they are compatible with
SN ¼ 0 within the uncertainty of these reconstructions as indicated by the error bars.
D14C is connected with the 14C production rate via a carbon cycle model21. The
connection between the 14C production rate, R, and the cosmic ray flux is given byÐp Ð1
R ¼ v¼0 P c ðv;MÞ X ðP; FÞY ðP ÞdP sin vdv; where v is the colatitude relative to thegeomagnetic dipole axis, and Pc (v, M) is the local cosmic ray rigidity cutoff (which
depends on v and the virtual geomagnetic dipole moment, M )23. X(P, F) is the differential
cosmic ray rigidity spectrum near Earth, F is the modulation strength describing the
average rigidity losses of cosmic rays inside the heliosphere, Y(P ) is the differential yieldfunction24 of 14C, and P is the rigidity of the primary cosmic rays. For studies of long-termchanges of the cosmic ray flux, the parameter F alone adequately describes themodulation of the cosmic ray spectrum X(P )11,24. The two most abundant cosmic ray
species, protons and a-particles, are taken into account in the model13. The cosmic raytransport model relates R to F, which in turn depends on the Sun’s open magnetic flux12.
The open flux is linked with the magnetic flux in sunspots (and thus with the SN) via thesource term in a system of differential equations9,10. The value of R is obtained from D14C
and M is known for the whole interval of interest25,26, so that F can be obtained from theinversion of the equation given above. Error bars depict the 68% confidence interval forthe reconstructed SN, which takes into account both random and systematic uncertainties(see Supplementary Information).
NATURE | VOL 431 | 28 OCTOBER 2004 | www.nature.com/nature

We first determine the 14C production rate in the Earth’s
atmosphere following Usoskin and Kromer21. They used two distinct methods, which take into account carbon cycle effects indifferent ways. Both methods give similar results when applied tothe tree-ring D14C data set described above. For the current
reconstruction we use the average of the 14C production rate
deduced using both techniques. In accordance with the decadal

Figure 3 Reconstructed sunspot number and its uncertainty for the whole interval of time
considered. a, 10-year averaged SN reconstructed from D14C data since 9500 BC (bluecurve) and 10-year averaged group sunspot number1 (GSN) obtained from telescopic
observations since 1610 (red curve). The horizontal dotted line marks the threshold above
which we consider the Sun to be exceptionally active. It corresponds to 1.3 standard
deviations above the mean. b, Evolution of the virtual geomagnetic dipole moment26 witherror bars that take into account the scatter between different palaeomagnetic
reconstructions. (The error bars give the s.d. in the reconstructed virtual geomagnetic
dipole moment.) The geomagnetic field data of ref. 25 are given by the dotted line.
c, Uncertainty in the reconstructed SN. It includes errors introduced at each step of thereconstruction process. The largest sources of random errors are the uncertainty in theknowledge of the geomagnetic dipole moment and in the 14C production rate. We also
consider systematic errors—for example, due to uncertainties in the 14C production rateprior to the considered period of time. A discussion of how these uncertainties are
estimated is given in Supplementary Information. Clearly, the uncertainties are sufficiently
small that they do not affect the presence or absence of grand minima or of episodes ofhigh activity, except in already marginal cases. d, A detail from the full time series ofreconstructed SN with expanded temporal scale. The chosen interval (corresponding to
the shaded part of a) exhibits three episodes of high solar activity and a grand minimum.
The error bars indicate the total uncertainty, j, in the reconstruction. (They depict the 68%
confidence interval for the reconstructed SN, which takes into account both random andsystematic uncertainties (see Supplementary Information).) The two strongest maxima lie
2.1j and 3.0j, respectively, above the high-activity threshold of 50. Hence the probability
that they are due to statistical fluctuations related to these errors is 3% and 0.2%,
respectively. The probability that a whole episode of high activity (lasting, say, 50 years) isdue to a statistical fluctuation is significantly smaller.

©2004 Nature Publishing Group

1085

letters to naturesampling of the 14C data we reconstruct the 10-year averaged
sunspot number. Because the D14C data are contaminated byextensive burning of 14C-free fossil fuel since the late nineteenth
century22 and later by atmospheric nuclear bomb tests, we use 14C
data before AD 1900 only and take the historical sunspot number
record for the most recent period.
From the 14C production rate we obtain the sunspot number inmultiple steps, each substantiated by a physics-based model. A
model describing the transport and modulation of galactic cosmic
rays within the heliosphere11 is inverted to find the cosmic ray
flux corresponding to the determined 14C production rate. The
transport of galactic cosmic rays in the heliosphere is affected by theSun’s open magnetic flux, that is, the fraction of the Sun’s totalmagnetic flux that reaches out into interplanetary space12. The open
flux is linked with the sunspot number by inverting a model
describing the evolution of the open magnetic flux for given sunspot
number9,10. All adjustable parameters entering this chain of models

Figure 4 Distribution of the duration of episodes of high solar activity and the probability
that the current episode will reach a given duration. a, Histogram of the distributionfunction of the duration of episodes of high solar activity during which the 10-year
averaged SN exceeds 50. Some bins have been enlarged in order to improve the statistics;inpﬃﬃﬃsuchﬃ cases the average number per bin is given. The vertical error bars correspond to
N ; where N is the number of events combined in one data point. The length of thecurrent period of high activity is marked by the arrow. The solid line is a least-squares
exponential fit to the plotted points. The dashed and dotted lines represent exponential fits
to the distributions obtained from extreme SN reconstructions including the influence ofrandom and systematic errors as given in Fig. 3 (see also the discussion in Supplementary
Information). b, The probability of the total duration of a state of high activity (SN levelexceeding 50). For the current episode, which started in AD 1940 (, 65 years ago), thestart of the diagram corresponds to the year AD 2015. Each curve is based upon thecorresponding fit shown in a. The probability (for the reference curve) that the high activity
continues for another 5 decades for a total duration of 115 years is only 8%.
1086

were fixed using independent data prior to the current reconstruction, so that no free parameter remains when reconstructing thesunspot number from 14C data (see Supplementary Table S1). This
reconstruction method was previously applied to 10Be data fromGreenland and Antarctica. Only the first step changes when using
D14C instead of 10Be data to reconstruct the sunspot number.
Hence, possible errors and uncertainties in the later steps are similar
to those studied in our earlier papers13,14.
Applying our reconstruction method to D14C, we first determine
the sunspot number since AD 850 in order to compare these values
with the historical record of GSNs since 1610 and with thereconstruction on the basis of 10Be data14. Figure 2 shows that thereconstructed average sunspot number from D14C is remarkably
similar to the 10-year averaged GSN series (correlation coefficient26
0:925þ0:02
20:03 with a false alarm probability ,10 ). The difference
between the reconstructed and measured sunspot number is nearlygaussian with a standard deviation of 5.8, which is smaller than
the theoretical estimate of the reconstructed sunspot number
uncertainty (about 8 for the last millennium, see Supplementary
Information), indicating the conservative nature of the latter. Two
10
Be-based sunspot number reconstructions are plotted in Fig. 2,
which correspond to extreme assumptions about the geographic
area of 10Be production relevant for its deposition in polar ice. The
local polar production model (green curve) provides an upper limit
to the sunspot number14, while the global production model(magenta dashed curve) gives a lower limit13. The sunspot number
time series obtained from D14C lies between the two 10Be-based
curves, and for the period after AD 1200 is closer to the 10Be-based
reconstruction under the assumption of global production.
Figure 3a shows the reconstruction based on the 11,400-year setof D14C data. Clearly, the level of activity has remained variable,with episodes of particularly low numbers of sunspots (grandminima) distributed over the whole record. Episodes of highactivity are also present. These are mostly concentrated in theearliest three millennia (before 6000 BC ), which also exhibit ahigh average sunspot number (35.6 compared to 25.6 after
6000 BC ). During the last eight millennia, the episode with thehighest average sunspot number is the ongoing one that started
about 60 years ago. The sunspot number averaged over the whole
period is 28.7 with a standard deviation of 16.2. The average numberof 75 since 1940 thus lies 2.85 standard deviations above this longterm average.
A major uncertainty in the reconstructed sunspot number isrelated to the evolution of the geomagnetic field, which is represented in Fig. 3b. A weaker geomagnetic field leads to an increased
cosmic ray flux impinging on the terrestrial atmosphere and thus toa higher 14C production rate, mimicking a lower value of sunspot
number if not properly taken into account. The uncertainties in thereconstructed sunspot numbers are discussed in Supplementary
Information and enter into Fig. 3c, where the sum of the random
and systematic uncertainties affecting the reconstruction is given.
For the whole time interval, the mean uncertainty of the reconstructed sunspot number is about 10.
In Fig. 3d we show a sub-interval of 700 years duration fromour reconstruction, which exhibits three prominent periods ofhigh sunspot number (average values exceeding 50) and a grandminimum. Within the 95% confidence interval, the Sun spent in
total between 780 and 1,060 years in a high-activity state (sunspot
number .50), which corresponds to 6.9–9.3% of the total durationof our reconstruction. The most probable values are 950 years and
8.3%, respectively. Although the rarity of the current episode of highaverage sunspot number may be taken as an indication that the Sun
has contributed to the unusual degree of climate change during thetwentieth century, we stress that solar variability is unlikely to be theprime cause of the strong warming during the last three decades3. In
ref. 3, reconstructions of solar total and spectral irradiance as well asof cosmic ray flux were compared with surface temperature records©2004 Nature Publishing Group

NATURE | VOL 431 | 28 OCTOBER 2004 | www.nature.com/natureletters to nature
covering approximately 150 years. It was shown that even under theextreme assumption that the Sun was responsible for all the global
warming prior to 1970, at the most 30% of the strong warming sincethen can be of solar origin.
There are 31 periods during which the 10-year averaged sunspot
number consistently exceeds a level of 50. The average length of such
episodes is about 30 years, the longest being 90 years (around 9000
BC ). The distribution of the durations of such episodes is given in
Fig. 4a. The number of high-activity periods decreases exponentially with increasing duration. The current level of high solar
activity has now already lasted close to 65 years and is marked by
the arrow on the figure. This implies that not only is the current
state of solar activity unusually high, but also this high level ofactivity has lasted unusually long. Assuming the previous episodesof high activity to be typical, we can estimate the probability withwhich the solar activity level will remain above a sunspot number of
50 over the next decades. The result is given in Fig. 4b, which showsthat there is only a probability of 8%þ3%
24% that the current highactivity episode will last another 50 years (and thus reach a total
duration of 115 years), while the probability that it will continue
until the end of the twenty-first century is below 1%.
A
Received 20 February; accepted 1 September 2004; doi:10.1038/nature02995.
1. Hoyt, D. V. & Schatten, K. H. Group sunspot numbers: A new solar activity reconstruction. Sol. Phys.
179, 189–219 (1998).
2. Eddy, J. A. The Maunder minimum. Science 192, 1189–1202 (1976).
3. Solanki, S. K. & Krivova, N. Can solar variability explain global warming since 1970? J. Geophys. Res.
108, doi: 10.1029/2002JA009753 (2003).
4. Stuiver, M. & Braziunas, T. F. Atmospheric 14C and century-scale solar oscillations. Nature 338,
405–408 (1989).
5. Stuiver, M. & Braziunas, T. F. Sun, ocean, climate and atmospheric 14CO2: an evaluation of causal andspectral relationships. Holocene 3, 289–305 (1993).
6. Damon, P. E. & Sonett, C. P. in The Sun in Time (eds Sonnet, C. P., Giampapa, M. S. & Matthews, M. S.)
360–388 (Univ. Arizona, Tucson, 1991).
7. Beer, J. et al. Use of 10Be in polar ice to trace the 11-year cycle of solar activity. Nature 347, 164–166(1990).
8. Beer, J. Long-term indirect indices of solar variability. Space Sci. Rev. 94, 53–66 (2000).
9. Solanki, S. K., Schüssler, M. & Fligge, M. Evolution of the Sun’s large-scale magnetic field since theMaunder minimum. Nature 408, 445–447 (2000).
10. Solanki, S. K., Schüssler, M. & Fligge, M. Secular variation of the Sun’s magnetic flux. Astron.
Astrophys. 383, 706–712 (2002).
11. Usoskin, I. G., Alanko, K., Mursula, K. & Kovaltsov, G. A. Heliospheric modulation strength during
the neutron monitor era. Sol. Phys. 207, 389–399 (2002).
12. Usoskin, I. G., Mursula, K., Solanki, S. K., Schüssler, M. & Kovaltsov, G. A. A physical reconstructionof cosmic ray intensity since 1610. J. Geophys. Res. 107, doi:10.1029/2002JA009343 (2002).
13. Usoskin, I. G., Mursula, K., Solanki, S. K., Schüssler, M. & Alanko, K. Reconstruction of solar activity
for the last millenium using 10Be data. Astron. Astrophys. 413, 745–751 (2004).
14. Usoskin, I. G., Solanki, S. K., Schüssler, M., Mursula, K. & Alanko, K. A millenium scale sunspot
number reconstruction: evidence for an unusually active Sun since the 1940s. Phys. Rev. Lett. 91,
211101 (2003).
15. Stuiver, M. & Pollach, P. Discussion: reporting of 14C data. Radiocarbon 19, 355–363 (1977).
16. Stuiver, M. et al. INTCAL98 Radiocarbon age calibration. Radiocarbon 40, 1041–1083 (1998).
17. Broecker, W. S. An unstable superconveyor. Nature 367, 414–415 (1994).
18. Bond, G. et al. Persistent solar influence on North Atlantic surface circulation during the Holocene.
Science 294, 2130–2136 (2001).
19. Muscheler, R., Beer, J. & Kromer, B. Solar Variability as an Input to the Earth’s Environment 305–316(ESA SP-535, European Space Agency, Noordwijk, 2003).
20. Bard, E., Raisbeck, G. M., Yiou, F. & Jouzel, J. Solar modulation of cosmogenic nuclide production
over the last millennium: comparison between 14C and 10Be records. Earth Planet. Sci. Lett. 150,
453–462 (1997).
21. Usoskin, I. G. & Kromer, B. Reconstruction of the 14C production rate from measured relative
abundance. Radiocarbon (in the press).
22. Suess, H. E. Radiocarbon content in modern wood. Science 122, 415–417 (1955).
23. Elsasser, W., Ney, E. P. & Winckler, J. R. Cosmic-ray intensity and geomagnetism. Nature 178,
1226–1227 (1956).
24. Castagnoli, G. & Lal, D. Solar modulation effects in terrestrial production of carbon-14.
Radiocarbon 22, 133–158 (1980).
25. Hongre, L., Hulot, G. & Khokhlov, A. An analysis of the geomagnetic field over the past 2000 years.
Phys. Earth Planet. Inter. 106, 311–335 (1998).
26. Yang, S., Odah, H. & Shaw, J. Variations in the geomagnetic dipole moment over the last 12000 years.
Geophys. J. Int. 140, 158–162 (2000).

Supplementary Information accompanies the paper on www.nature.com/nature.
Competing interests statement The authors declare that they have no competing financial
interests.
Correspondence and requests for materials should be addressed to S.K.S. (solanki@mps.mpg.de).
NATURE | VOL 431 | 28 OCTOBER 2004 | www.nature.com/nature..............................................................

Archaeology and age of a newhominin from Flores in eastern
Indonesia
M. J. Morwood1, R. P. Soejono2, R. G. Roberts3, T. Sutikna2,
C. S. M. Turney3, K. E. Westaway3, W. J. Rink4, J.- x. Zhao5,
G. D. van den Bergh6, Rokus Awe Due2, D. R. Hobbs1, M. W. Moore1,
M. I. Bird7 & L. K. Fifield8
1Archaeology and Palaeoanthropology, School of Human and Environmental
Studies, University of New England, Armidale, New South Wales 2351, Australia
2Indonesian Centre for Archaeology, Jl. Raya Condet Pejaten No. 4, Jakarta 12001,
Indonesia
3
GeoQuEST Research Centre, School of Earth and Environmental Sciences,
University of Wollongong, Wollongong, New South Wales 2522, Australia
4
School of Geography and Geology, McMaster University, Hamilton, Ontario
L8S 4K1, Canada
5
Advanced Centre for Queensland University Isotope Research Excellence(ACQUIRE), University of Queensland, Brisbane, Queensland 4072, Australia
6
Royal Netherlands Institute for Sea Research, 1790 AB Den Burg, Texel,
The Netherlands
7
School of Geography and Geosciences, University of St Andrews, St Andrews,
Fife KY16 9AL, UK
8
Research School of Physical Sciences and Engineering, Australian National
University, Canberra, ACT 0200, Australia.............................................................................................................................................................................

Excavations at Liang Bua, a large limestone cave on the island of
Flores in eastern Indonesia, have yielded evidence for a population of tiny hominins, sufficiently distinct anatomically to be
assigned to a new species, Homo floresiensis1. The finds comprisethe cranial and some post-cranial remains of one individual, as
well as a premolar from another individual in older deposits.
Here we describe their context, implications and the remainingarchaeological uncertainties. Dating by radiocarbon (14C), luminescence, uranium-series and electron spin resonance (ESR)
methods indicates that H. floresiensis existed from before 38,000
years ago (kyr) until at least 18 kyr. Associated deposits containstone artefacts and animal remains, including Komodo dragon
and an endemic, dwarfed species of Stegodon. H. floresiensis
originated from an early dispersal of Homo erectus (including
specimens referred to as Homo ergaster and Homo georgicus)1
that reached Flores, and then survived on this island refuge until
relatively recently. It overlapped significantly in time with Homosapiens in the region2,3, but we do not know if or how the two
species interacted.
Liang Bua is a cave formed in Miocene limestone on Flores, anisland in eastern Indonesia located midway between the Asian andAustralian continents (Fig. 1). The cave is situated 14 km north of
Ruteng and 25 km from the north coast, overlooking the Wae
Racang river valley at an altitude of 500 m above sea level (088 31 0
50.4 00 S, 1208 26 0 36.9 00 E). It is 30 m wide and 25 m high at theentrance, and up to 40 m deep (Fig. 2). Formed as an undergroundcavern by karst dissolution, the northern end was then exposed byinvasion of the Wae Racang. This river now lies 200 m distant fromand 30 m below Liang Bua, but five river terraces at different
elevations in the valley indicate a complex process of incision overa substantial period.
Our research at Liang Bua aims to recover evidence for thehistory of hominin evolution, dispersal and cultural and environmental change on Flores—an island with evidence of Early
Pleistocene hominin occupation by 840 kyr4,5. Work involved
removing backfill from four previously excavated Sectors (I, III,IV and VII) and then continuing the excavations. We havereached a maximum depth of 11 m without encountering
bedrock.

©2004 Nature Publishing Group1087