slab (Fig. 4B). This complexity suggests strong
stress heterogeneity in subducted slabs (17). However, supershear rupture during the Mw 6.7 earthquake brings its stress drop down to 32 MPa and
its radiation efficiency to about 1.0 (Fig. 4B),
which are much closer to values for the Mw 8.3
Okhotsk mainshock. Therefore, strong stress heterogeneity inside subducted slabs is not required
to explain the 2013 Okhotsk mainshock and its
Mw 6.7 aftershock. However, the difference in rupture speed (subshear versus supershear) indicates
substantial spatial heterogeneity in the fracture
strength or fracture energy within the slab.
Compared with shallow supershear events,
this deep event has a relatively small rupture
dimension and higher static stress drop (by a
factor of ~10). Our estimate of high radiation
efficiency (hR ≈1.0) during the Mw 6.7 event is
also consistent with theoretical predictions of
low fracture energy during supershear ruptures
(30). This constraint of low fracture energy bears
on the question of deep earthquake faulting
mechanisms, which is still enigmatic (15, 19).
The 1994 Bolivia earthquake involved a large
amount of fracture/thermal energy and radiated
relatively little energy in seismic waves (16). In
terms of energy partitioning, the supershear Mw
6.7 earthquake represents the opposite end member from the Bolivia earthquake, with almost all
the available strain energy being radiated as
seismic waves. This contrast is consistent with
the idea of more than one rupture mechanism for
deep earthquakes in slabs with different thermal
states (18, 20, 21). The Okhotsk mainshock and
aftershock in a cold slab ruptured with the transformational faulting mechanism, whereas the
Bolivia earthquake in a warm slab was dominated
by shear melting (18).
REFERENCES AND NOTES
1. R. Burridge, Geophys. J. R. Astron. Soc. 35, 439–455 (1973).
2. D. Andrews, J. Geophys. Res. 81, 5679–5687 (1976).
3. K. Xia, A. J. Rosakis, H. Kanamori, Science 303, 1859–1861
4. R. J. Archuleta, J. Geophys. Res. 89, 4559–4585 (1984).
5. M. Bouchon et al., Geophys. Res. Lett. 28, 2723–2726
6. M. Bouchon, M. Vallée, Science 301, 824–826 (2003).
7. K. T. Walker, P. M. Shearer, J. Geophys. Res. 114 (B2), B02304
8. M. Vallée, E. M. Dunham, Geophys. Res. Lett. 39, L05311
9. E. M. Dunham, R. J. Archuleta, Bull. Seismol. Soc. Am. 94,
10. D. Wang, J. Mori, Bull. Seismol. Soc. Am. 102, 301–308 (2012).
11. H. Yue et al., J. Geophys. Res. 118, 5903–5919 (2013).
12. M. Bouchon et al., Tectonophysics 493, 244–253 (2010).
13. H. Zhang, X. Chen, Geophys. J. Int. 167, 917–932 (2006).
14. Y. Kaneko, N. Lapusta, Tectonophysics 493, 272–284 (2010).
15. H. Houston, in Treatise on Geophysics, G. Schubert, Ed.
(Elsevier, Amsterdam, 2007), pp. 321–350.
16. H. Kanamori, D. L. Anderson, T. H. Heaton, Science 279,
17. L. Ye, T. Lay, H. Kanamori, K. D. Koper, Science 341,
18. Z. Zhan, H. Kanamori, V. C. Tsai, D. V. Helmberger, S. Wei,
Earth Planet. Sci. Lett. 385, 89–96 (2014).
19. C. Frohlich, Deep Earthquakes (Cambridge Univ. Press,
20. D. A. Wiens, Phys. Earth Planet. Inter. 127, 145–163 (2001).
21. R. Tibi, G. Bock, D. A. Wiens, J. Geophys. Res. 108, 2091
22. S.-C. Park, J. Mori, J. Geophys. Res. 113, B08303 (2008).
23. M. Suzuki, Y. Yagi, Geophys. Res. Lett. 38, L05308 (2011).
24. K. Kuge, J. Geophys. Res. 99 (B2), 2671–2685 (1994).
25. Z. Zhan, D. Helmberger, D. Li, Phys. Earth Planet. Inter.
232, 30–35 (2014).
26. S. E. Persh, H. Houston, J. Geophys. Res. 109, B04311
27. A. Tocheport, L. Rivera, S. Chevrot, J. Geophys. Res. 112,
28. Materials and methods are available in the supplementary
29. C. J. Ammon et al., Science 308, 1133–1139 (2005).
30. R. Madariaga, K. B. Olsen, Pure Appl. Geophys. 157, 1981–2001
31. G. P. Hayes, D. J. Wald, R. L. Johnson, J. Geophys. Res. 117,
We thank two anonymous reviewers for their helpful comments.
The Incorporated Research Institutions for Seismology (IRIS)
provided the seismic data. This work was supported by NSF
(grants EAR-1142020 and EAR-1111111). All data used are available
from the IRIS data center at www.iris.edu.
Materials and Methods
Figs. S1 to S9
27 February 2014; accepted 2 June 2014
Multispecies diel transcriptional
oscillations in open ocean
heterotrophic bacterial assemblages
Elizabeth A. Ottesen,1,2,3 Curtis R. Young,1,2 Scott M. Gifford,1,2
John M. Eppley,1,2 Roman Marin III,4 Stephan C. Schuster,5
Christopher A. Scholin,4 Edward F. DeLong1,2,6*
Oscillating diurnal rhythms of gene transcription, metabolic activity, and behavior are
found in all three domains of life. However, diel cycles in naturally occurring heterotrophic
bacteria and archaea have rarely been observed. Here, we report time-resolved
whole-genome transcriptome profiles of multiple, naturally occurring oceanic bacterial
populations sampled in situ over 3 days. As anticipated, the cyanobacterial
transcriptome exhibited pronounced diel periodicity. Unexpectedly, several different
heterotrophic bacterioplankton groups also displayed diel cycling in many of their gene
transcripts. Furthermore, diel oscillations in different heterotrophic bacterial groups
suggested population-specific timing of peak transcript expression in a variety of
metabolic gene suites. These staggered multispecies waves of diel gene transcription
may influence both the tempo and the mode of matter and energy transformation
in the sea.
The coordination of biological activities into daily periodic cycles is a common feature of eukaryotes and is widespread among plants, fungi, and animals, including man (1). Among single celled noneukaryotic
microbes, diel cycles have been well documented
in cyanobacterial isolates (2–4), one halophilic
archaeon (5), and bacterial symbionts of fish and
squid (6, 7). Some evidence for diel cycling in
microbial plankton has also been suggested on
the basis of bulk community amino acid incor-
poration, viral production, or metabolite consump-
tion (8–10). However, the existence of regular diel
oscillations in free-living heterotrophic bacterial
species has rarely been assessed.
Microbial community RNA sequencing techniques now allow simultaneous determination
of whole-genome transcriptome profiles among
multiple cooccurring species (11, 12), enabling
high-frequency, time-resolved analyses of microbial community dynamics (12, 13). To better
understand temporal transcriptional dynamics
in oligotrophic bacterioplankton communities,
we conducted a high-resolution multiday time
series of bacterioplankton sampled from the North
Pacific Subtropical Gyre (14).
To facilitate repeated sampling of the same
planktonic microbial populations through time,
automated Lagrangian sampling of bacterioplankton was performed every 2 hours over 3 days
by using a free-drifting robotic Environmental
Sample Processor (ESP) (13, 15) (fig. S1). After
instrument recovery, planktonic microbial RNA
was extracted, purified, converted to cDNA, and
sequenced to assess whole-genome transcriptome
dynamics of predominant planktonic microbial
populations (tables S1 and S2). The recovered
cDNAs were dominated by transcripts from
SCIENCE sciencemag.org 11 JULY 2014 • VOL 345 ISSUE 6193 207
1Department of Civil and Environmental Engineering,
Massachusetts Institute of Technology, Cambridge, MA
02139, USA. 2Center for Microbial Oceanography: Research
and Education (C-MORE), University of Hawaii, Honolulu, HI
96822, USA. 3Department of Microbiology, University of
Georgia, Athens, GA 30602, USA. 4Monterey Bay Aquarium
Research Institute, Moss Landing, CA 95039, USA.
5Singapore Centre on Environmental Life Sciences
Engineering, Nanyang Technological University, 637551
Singapore. 6Department of Biological Engineering,
Massachusetts Institute of Technology, Cambridge, MA
*Corresponding author. E-mail: firstname.lastname@example.org
RESEARCH | REPORTS