, 1846 (2007);
et al.Ransom A. Myers,
Sharks from a Coastal Ocean
Cascading Effects of the Loss of Apex Predatory
www.sciencemag.org (this information is current as of January 4, 2008 ):
The following resources related to this article are available online at
version of this article at:
including high-resolution figures, can be found in the online
Updated information and services,
can be found at:
Supporting Online Material
, 8 of which can be accessed for free:
cites 9 articles
12 article(s) on the ISI Web of Science.
This article has been
4 articles hosted by HighWire Press; see:
This article has been
This article appears in the following
in whole or in part can be found at:
permission to reproduce
of this article or about obtaining
Information about obtaining
registered trademark of AAAS.
is a Science 2007 by the American Association for the Advancement of Science; all rights reserved. The title
Copyright American Association for the Advancement of Science, 1200 New York Avenue NW, Washington, DC 20005.
(print ISSN 0036-8075; online ISSN 1095-9203) is published weekly, except the last week in December, by the Science
on January 4, 2008
Within each ensemble member, emergent
model ecotypes typically followed the abun-
dance ranking of their geographically identified
real-world counterparts (Fig. 2 and fig. S4):
Model ecotypes m-e1 and m-e2 ranked first and
second (compare these with eMIT9312 and
eMED4, respectively), with m-e3 consistently at
lower abundances (compare this with ecotypes
eNATL2A and eMIT9313).
There is a simultaneous correspondence
between the physiological characteristics of emer-
gent, modeled ecotypes and cultured represent-
atives of the wild population. Each cultured
strain of Prochlorococcus and the emergent
model ecotypes from all 10 ensemble members
were characterized by an optimal temperature
(Topt) and photon flux (Iopt) for growth, the tem-
perature or light intensity at which growth rates
are greatest if all other limitations are set aside
(fig. S1). Potentially viable Prochlorococcus ana-
logs were seeded in the model over wide ranges
of optimal temperature and photon fluxes (all
circles, Fig. 3), but those that maintained signif-
icant abundances along the AMT transect (solid
large circles, Fig. 3) were all characterized by
Topt> 15°C. This is consistent with the observa-
tions of Prochlorococcus in warmer waters and
with the warm Toptof cultured strains (17). Our
model indicates that the oligotrophic conditions
confined Prochlorococcus analogs to warmer wa-
ters and selected for warm Topt, an emergent “ad-
aptation” driven by other environmental factors.
In the cooler waters of the model, nutrients are
typically abundant, and so larger phytoplankton,
with higher intrinsic maximum growth rates, have
an advantage. In the highly oligotrophic (typically
warmer) regions, the Prochlorococcus analogs’
lower half-saturation (consistent with their very
small size) is advantageous.
Across the ensemble of 10 integrations, the
geographically defined model ecotypes were clus-
tered in optimal temperature and light parameter
space (Fig. 3): Model ecotype m-e1 (red circles)
generally occupied the warmest area of parameter
space over a broad, upper range of optimal pho-
ton fluxes; m-e2 (blue circles) generally had a
lower Toptbut a similar range of Iopt. This is con-
sistent with their surface-oriented habitats and
latitudinal (or temperature) separation. In con-
trast, m-e3 (green circles) occupied a wider
range of Toptbut only in the region of lowest
Iopt, consistent with its expression of subsurface
maxima. Although there were exceptions, the
clustering of geographically defined model eco-
types in physiological parameter space indicated
that robust ecological controls were operating
across the 10 integrations. The physiological char-
acteristics (Topt, Iopt) of real-world ecotypes
(colored diamonds, Fig. 3) are notably consistent
with the grouping of their model counterparts. This
correspondence was not imposed, but emerged
as a feature of the model solution.
Significantly, there was simultaneous con-
sistency between the geographical habitat, rank
abundance, and physiological specialization of the
emergent Prochlorococcus model ecotypes and
their real-world counterparts. These parallels indi-
cate that the stochastic, self-organizing represen-
tation of marine ecosystems reflects real-world
processes and is suitable for application in eco-
logical and biogeochemical studies. This approach
circumvents some of the obstacles facing most
current ocean ecosystem models, such as the a
priori imposition of low diversity, the prescrip-
tion of dominant functional types, and the dif-
ficulty of specifying the physiological rate
coefficients that define them. This function-
based approach can naturally evolve to exploit
the growing body of genomic and metagenomic
data mapping the oceans in terms of genes and
their encoded physiological functionality (25, 26).
Finally, because the ecosystem structure and
function are, by design, emergent and not tightly
prescribed, this modeling approach is ideally
suited for studies of the relations between marine
ecosystems, evolution, biogeochemical cycles,
and past and future climate change.
References and Notes
1. D. Tilman, Ecology 58, 338 (1977).
2. R. Margalef, Perspectives in Ecological Theory (Univ. of
Chicago Press, Chicago, 1968).
3. C. Pedrós-Alió, Trends Microbiol. 14, 257 (2006).
4. S. L. Pimm, J. H. Lawton, Nature 268, 329 (1977).
5. A. Kleidon, H. A. Mooney, Glob. Change Biol. 6, 507 (2000).
6. J. K. Moore, S. Doney, J. Kleyplas, D. Glover, I. Fung,
Deep-Sea Res. II 49, 403 (2001).
7. W. W. Gregg, P. Ginoux, P. S. Schopf, N. W. Casey,
Deep-Sea Res. II 50, 3143 (2003).
8. E. Litchman, C. A. Klausmeier, J. R. Miller,
O. M. Schofield, P. G. Falkowski, Biogeosciences 3, 585
9. C. LeQuere et al., Glob. Change Biol. 11, 2016 (2006).
10. T. R. Anderson, J. Plankton Res. 27, 1073 (2005).
11. R. R. Hood et al., Deep-Sea Res. II 53, 459 (2006).
12. P. A. Thompson et al., Limnol. Oceanogr. 34, 1014
13. C. Wunsch, P. Heimbach, Physica D 10.1016/
14. Y. Dandonneau, Y. Montel, J. Blanchot,
J. Giraudeau, J. Neveux, Deep-Sea Res. I 53, 689
15. M. V. Zubkov, M. A. Sleigh, P. H. Burkill, R. J. G. Leakey,
Prog. Oceanogr. 45, 369 (2000).
16. H. A. Bouman et al., Science 312, 918 (2006).
17. Z. I. Johnson et al., Science 311, 1737 (2006).
18. L. R. Moore, G. Rocap, S. W. Chisholm, Nature 393, 464
19. G. Rocap et al., Nature 424, 1042 (2003).
20. L. R. Moore, S. W. Chisholm, Limnol. Oceanogr. 44, 628
21. L. R. Moore, A. F. Post, G. Rocap, S. W. Chisholm, Limnol.
Oceanogr. 47, 989 (2002).
22. G. E. Hutchinson, Am. Nat. 95, 137 (1961).
23. R. A. Armstrong, R. McGehee, Am. Nat. 115, 151
24. M. W. Lomas, F. Lipschultz, Limnol. Oceanogr. 51, 2453
25. J. C. Venter et al., Science 304, 66 (2004).
26. E. F. DeLong et al., Science 311, 496 (2006).
27. S. Bertilsson, O. Berglund, D. M. Karl, S. W. Chisholm,
Limnol. Oceanogr. 48, 1721 (2003).
28. Thanks to J. Marshall, R. Williams, P. Falkowski, J. Cullen,
and J. Bragg for inspiration and encouragement. Thanks
also to M. Coleman, R. Hood, and three anonymous
reviewers for stimulating comments on the manuscript; to
C. Hill for computing guidance; and to P. Heimbach,
C. Wunsch, and the ECCO group for ocean circulation
state estimates. We are grateful for funding from the
PARADIGM consortium of the National Ocean Partnership
Program, NSF (M.J.F., S.D.), NSF, DOE (S.W.C.), and the
Gordon and Betty Moore Foundation (S.W.C., M.J.F.).
M.J.F. is also grateful for the MIT Global Habitat
Longevity Award. We acknowledge the Atlantic Meridional
Transect consortium (NER/O/S/2001/00680), which
enabled the biogeographical observations first published
in (17) (AMT contribution no. 107).
Supporting Online Material
Materials and Methods
Figs. S1 to S4
References and Notes
7 December 2006; accepted 5 March 2007
Cascading Effects of the Loss of
Apex Predatory Sharks from a
Ransom A. Myers,1Julia K. Baum,1* Travis D. Shepherd,1
Sean P. Powers,2Charles H. Peterson3*
Impacts of chronic overfishing are evident in population depletions worldwide, yet indirect
ecosystem effects induced by predator removal from oceanic food webs remain unpredictable.
As abundances of all 11 great sharks that consume other elasmobranchs (rays, skates, and small
sharks) fell over the past 35 years, 12 of 14 of these prey species increased in coastal northwest
Atlantic ecosystems. Effects of this community restructuring have cascaded downward from the
cownose ray, whose enhanced predation on its bay scallop prey was sufficient to terminate a
century-long scallop fishery. Analogous top-down effects may be a predictable consequence of
eliminating entire functional groups of predators.
cological impacts of eliminating top pred-
ators can be far-reaching (1) and include
release of mesopredator prey populations
from predatory control (2) and induction of
subsequent cascades of indirect trophic interac-
tions (3–5). In the oceans, fishing has dispropor-
30 MARCH 2007 VOL 315
on January 4, 2008
tionately reduced abundances of apex preda-
tors (6, 7), eliciting concern about their con-
servation and the indirect effects that might
ensue from their removal. Despite a rich eco-
logical literature on trophic cascades, conse-
quences of removing oceanic apex predators
remain uncertain (8–11). Although some have
argued that in complex marine food webs with
many interacting species top-down effects may
attenuate (10, 11), fundamental constraints on
studying oceanic food webs and apex predators
may alternatively hinder detection of such ef-
fects. We met this challenge by using a unique
compilation of time series data and predator
exclusion experiments to investigate ecosystem
consequences of functionally eliminating apex
Exploitation of large (>2 m) sharks has in-
tensified worldwide in recent decades, driven by
an upsurge in demand for shark fins and meat
(12) and in bycatch in many fisheries. Data to
assess direct impacts of exploitation on the great
sharks are limited but consistently indicate that
they have been driven to low levels (12–14).
Whether functional elimination of great sharks
also induces indirect ecosystem effects, how-
ever, is an open question (14).
We hypothesized that weakened top-down
control by all elasmobranch-consuming sharks
could increase abundances of their elasmobranch
prey (rays, skates, and small sharks) and that the
enhanced predation by these mesopredators
might cascade to lower trophic levels. Because
mesopredatory elasmobranchs are relatively
large, even as juveniles, and are thus consumed
almost exclusively by great sharks (15), we
inferred that these prey would be the most likely
affected. Moreover, interannual variability in
elasmobranch populations is minimal because of
their low reproductive rates, such that changes
effected by predator removal should be de-
tectable in time series data. We tested these
hypotheses for 1970–2005 on the United States’
eastern seaboard between Cape Cod, Massa-
chusetts (41.5°N) and Cape Canaveral, Flori-
For each elasmobranch, we modeled popu-
lation trends in each of several data sets and also
in a meta-analysis to yield synthetic estimates
of rates of change (15). We first assembled all
available species-specific data from scientific
research surveys that began before 1990 and
used standardized methodology. Seventeen sur-
veys, which together cover the eastern U.S.
coast (fig. S1), met these criteria (tables S2
and S3; mean time span = 28 years). Trends in
relative abundance of each elasmobranch were
estimated by fitting generalized linear models
(GLMs, table S4) to each survey in which the
species appeared in at least 3 years. Because
not all great sharks were sampled in surveys
and because the U.S. pelagic longline fishery
covers a much greater proportion of the sharks’
northwest Atlantic ranges, trends for these spe-
cies also were estimated from this fishery’s
observer and logbook data [by fitting general-
ized linear mixed models and GLMs, respec-
The eastern seaboard’s longest continuous
shark-targeted survey (UNC), conducted annu-
ally since 1972 off North Carolina, demonstrates
sufficiently large declines in great sharks to im-
ply their likely functional elimination. Declines
in seven species range from 87% for sandbar
sharks (Carcharhinus plumbeus); 93% for
blacktip sharks (C. limbatus); up to 97% for
tiger sharks (Galeocerdo cuvier); 98% for scal-
loped hammerheads (Sphyrna lewini); and 99%
or more for bull (C. leucas), dusky (C. obscurus),
and smooth hammerhead (S. zygaena) sharks
(Fig. 1 and table S5). Because this survey is
1Department of Biology, Dalhousie University, 1355 Oxford
Street, Halifax, NS B3H 4J1, Canada.2Department of Marine
Sciences, University of South Alabama, and Dauphin Island
Sea Lab, 101 Bienville Boulevard, Dauphin Island, AL 36528,
Carolina (UNC) at Chapel Hill, Morehead City, NC 28557,
*To whom correspondence should be addressed. E-mail:
email@example.com (J.K.B.); firstname.lastname@example.org (C.H.P.)
3Institute of Marine Sciences, University of North
Fig. 1. Change over time in species at each trophic level as estimated from individual
data sources: great sharks (top), elasmobranch mesopredators (middle), and bivalves
(bottom). The top and middle graphs show trends in relative abundance [overall trend (solid line)
and individual yearly estimates (■)] of great sharks (UNC survey) and elasmobranch meso-
predators (survey acronyms as in table S3) estimated from GLMs. The trend in North Carolina bay
scallops (National Marine Fisheries Service landings) is shown with a loess curve from a gen-
eralized additive model.
VOL 315 30 MARCH 2007
on January 4, 2008
situated where it intercepts sharks on their
seasonal migrations, these trends in abun-
dance may be indicative of coastwide popula-
tion changes. The UNC survey also showed
the loss of the largest individuals, with de-
clines in mean lengths of blacktip, bull, dusky,
sandbar, and tiger sharks of 17 to 47% (fig.
S3), suggesting that overexploitation has left
few mature individuals in these populations.
The remaining four elasmobranch-consuming
great sharks were caught too rarely to detect
trends from this survey. Two of those, great
white (Carcharodon carcharias) and sand tiger
(Carcharias taurus) sharks, were each caught
only once and early in the UNC survey (in
1974 and 1978, respectively). The only survey
that has caught enough sand tigers to note a
trend targets sharks in Chesapeake Bay and
suggests a decline of over 99% between 1974
and 2004 (15, 16).
Consistent with the UNC survey, all but one
of the other six significant survey trends indicate
decreasing great shark abundances (table S5).
The only significant increase is for juvenile
hammerheads from a single survey and conse-
quently may reflect recently increased survival
after losses of their only predators, larger apex
predatory sharks. Accordingly, meta-analytic
results portray a consistent pattern of declines
in great sharks (Fig. 2A).
Fisheries data from the past 2 decades help
confirm losses of elasmobranch-consuming
great sharks (Fig. 2A). Logbook data show de-
clines between 1986 and 2000 ranging from
40% in makos [predominantly shortfin mako
(Isurus oxyrinchus)] to 89% in hammerheads
[scalloped, great (S. mokarran), and smooth]
(13) (table S5). Trend estimates from observer
data collected between 1992 and 2005 differ
from logbook data for tiger sharks (after a
decline, this species may have recently in-
creased) but are concordant for all other spe-
cies: Makos declined moderately (38%), whereas
large coastals (genus Carcharhinus, including
dusky, sandbar, blacktip, and bull) and hammer-
heads declined by 67% and 76%, respectively
Concurrent with reductions in great sharks,
their mesopredatory elasmobranch prey have in-
creased along the eastern seaboard. This group
of 14 rays, skates, and small sharks is taxonomi-
cally diverse (seven families) and includes de-
mersal and pelagic species from estuaries and
the inshore coast to the continental shelf and
slope. Individual surveys indicate that little
skate (Leucoraja erinacea), Atlantic sharp-
nose shark (Rhizoprionodon terraenovae), chain
catshark (Scyliorhinus retifer), and smooth but-
terfly ray (Gymnura altavela) populations may
have each increased by about an order of magni-
tude (Fig. 1). Overall, meta-analyses of research
survey data reveal increases over the past 16 to
35 years for 12 of the species, with estimated
mean instantaneous rates of increase ranging
from 0.012 for bullnose eagle ray (Myliobatis
freminvillii) to 0.228 for smooth butterfly ray
Most conspicuous (17) among the increasing
mesopredators is the cownose ray (Rhinoptera
bonasus). Six of seven surveys covering the
U.S. Atlantic population’s range (southeast
Florida to Raritan Bay, New Jersey, with re-
cent expansion to Long Island, New York) show
significant increases (Fig. 1 and table S5).
Together, these rates of change (mean = 0.087,
95% confidence interval 0.021 to 0.127) (Fig.
2B) indicate an order-of-magnitude increase in
cownose rays coastwide since the mid-1970s
and, when combined with earlier population
estimates from aerial surveys in Chesapeake
Bay (18), suggest that there may now be over
40 million rays in the population. When
considered with its known late maturity and
low fecundity, this high population growth rate
would make the cownose ray anomalous among
fishes in its combination of life-history traits
(15). Only if its natural mortality rate were
substantially greater than at present would the
life history conform, implying that higher
predation by great sharks prevailed in the past
and possible reduction in bycatch is insufficient
to explain the ascent of this ray.
Collectively, the hyperabundant cownose ray
population consumes a large quantity of bi-
valves, implying a high potential for trophic
cascades. Cownose rays migrate southward in
autumn from northerly estuaries to overwinter-
Fig. 2. Instantaneous rates of change in relative abundance (±95% confidence intervals) for (A)
great sharks and (B) elasmobranch mesopredators, as estimated by random-effects meta-analyses
of research survey (■) and fisheries (▲) data.
30 MARCH 2007VOL 315
on January 4, 2008
ing grounds on the Florida shelf (19), often
entering bays and sounds en route. Their diet
consists largely of bay scallops (Argopecten
irradians), soft-shell clams (Mya arenaria),
hard clams (Mercenaria mercenaria), oysters
(Crassostrea virginica), and several smaller,
noncommercial bivalves (18, 20). Annual bi-
valve demand within the Chesapeake Bay,
based on our abundance estimate, individual
daily consumption rates of ~210 g shell-free wet
weight (15), and 100-day occupancy each year,
may approach 840,000 metric tons. In com-
parison, the 2003 commercial bivalve harvest
in Virginia and Maryland totaled only 300
metric tons, substantially lower than historic
A second link in an apparent trophic cas-
cade has emerged over the past 2 decades as
the cownose ray population grew coastwide
(Figs. 1 and 3). Field sampling in 1983 and
1984 before and after ray presence during late-
summer migration showed no impacts of ray
predation on bay scallops (Fig. 3A) (21). Anal-
ogous recent sampling, confirmed by controlled
ray-exclusion experiments using stockades, dem-
onstrates that since 1996 migrating cownose
rays have caused almost complete scallop
mortality by early fall (Fig. 3A) (22) at every
site with initial adult scallop densities above a
threshold for intensive ray foraging (~2 m−2,
Fig. 3, B and C). Bay scallop abundance de-
clined much less inside cownose ray exclo-
sures than on unprotected grounds (Fig. 3A)
and, in the absence of scallop emigration,
numbersinside stockadeswouldprobably have
remained nearly constant (22). Unlike the
fishery harvest, which occurs after, ray preda-
tion occurs before spawning of this annual
species (23). By 2004, ray predation had
terminated North Carolina’s century-old bay
scallop fishery because too few scallops
survived into fall to sustain fishing and a con-
sequent Allee effect (apparently induced at
adult densities below ~1 to 2 m−2) limited re-
productive success (23). The fishery has re-
mained closed through 2007 (Fig. 1) because
of low recruitment and continued ray preda-
tion on any high-density patch of scallops.
Having depleted the more readily targeted
epibiotic bay scallops, it is reasonable to ex-
pect future expansion of cownose ray foraging
on infaunal bivalves, with associated uproot-
ing of seagrass and thus loss of nursery habitat
Increased predation by cownose rays also
may now inhibit recovery of hard clams, soft-
shell clams, and oysters (17), compounding
the effects of overexploitation, disease, habitat
destruction, and pollution, which have de-
pressed these species (7). Landings data for
these bivalves and bay scallops from within the
cownose ray’s range show them falling without
substantial recovery (fig. S2) as the rays in-
creased, despite active shellfish enhancement
and habitat restoration. In contrast, areas be-
yond the ray’s northernmost limit show exam-
ples of stable or increasing bivalve landings
Analogous elasmobranch community inver-
sions and trophic cascades are probably occur-
ring in other coastal oceans. Studies in the
northeast Atlantic Ocean have shown increasing
abundances of several mesopredatory elasmo-
branchs despite substantial exploitation (25, 26).
In Japan’s Ariake Sound in the northwest Pa-
cific Ocean, where exploitation of apex preda-
tory sharks is probably intense, wild stocks and
cultured populations of multiple shellfish spe-
cies are now decimated annually by expanding
numbers of another elasmobranch mesopred-
ator, the longheaded eagle ray (Aetobatus
flagellum) (27). Many other prey depletions may
be going unrecognized because little monitor-
ing and research exists for noncommercial ma-
Our study provides evidence for an oceanic
ecosystem transformation that is most parsimo-
niously explained by the functional elimination
of apex predators, the great sharks, instead of
assuming numerous coincidental increases in
their mesopredatory prey. Consequences of this
region-wide proliferation of rays, skates, and
smaller sharks have cascaded down the food
web through cownose rays to bay scallops and
possibly other bivalves. This cascade potential-
ly could extend to seagrass habitat, exacerbat-
ing stresses on already highly degraded coastal
Fig. 3. (A) Map of southeastern United States indicating the study location (inset) and North
Carolina bay scallop monitoring sites. Total mortality (black bars) compares August (pre–cownose
ray migration) to late September and October (postmigration) densities. Low scallop densities
before ray migration are indicated by asterisks (<1 to 2 m−2) or zeroes (0 m−2). Hatched bars
represent mortality within experimental stockades that exclude rays (performed in a subset of
years). Scallops were free to emigrate from stockades. Arrows denote direction of ray migration. (B)
Mean scallop density measured in midsummer and mortality from early summer to early fall at
Oscar Shoal for 10 years. (C) Scallop density trends at Oscar Shoal, based on 12 weekly surveys in
1998 and 8 in 2002 and 2003.
VOL 31530 MARCH 2007
on January 4, 2008
benthic systems. Thus, like the classic killer
whale–sea otter–urchin–kelp trophic cascade (5),
eliminating great sharks carries risks of broader
ecosystem degradation. Prevailing theory sug-
only in simple food webs lacking functional
redundancy (4, 10), but we propose that top-
down effects must be widely expected whenever
entire functional groups of predators are de-
pressed, as can occur with industrial fisheries.
Illuminating the operation of indirect species
interactions within marine and other environ-
ments brightens the future for development of
what is now so widely sought, ecosystem-based
management to achieve sustainability of natural
References and Notes
1. J. E. Duffy, Oikos 99, 201 (2002).
2. K. R. Crooks, M. E. Soulé, Nature 400, 563 (1999).
3. R. T. Paine, J. Anim. Ecol. 49, 667 (1980).
4. M. L. Pace, J. G. Cole, S. R. Carpenter, J. F. Kitchell,
Trends Ecol. Evol. 14, 483 (1999).
5. J. A. Estes, M. T. Tinker, T. M. Williams, D. F. Doak,
Science 282, 473 (1998).
6. D. Pauly, V. Christensen, J. Dalsgaard, R. Froese,
F. Torres Jr., Science 279, 860 (1998).
7. J. B. C. Jackson et al., Science 293, 629 (2001).
8. J. Bascompte, C. J. Melián, E. Sala, Proc. Natl. Acad. Sci.
U.S.A. 102, 5443 (2005).
9. K. T. Frank, B. Petrie, J. S. Choi, W. C. Leggett, Science
308, 1621 (2005).
10. D. R. Strong, Ecology 73, 747 (1992).
11. S. Jennings, M. J. Kaiser, Adv. Mar. Biol. 34, 201
12. S. L. Fowler et al., Eds., Sharks, Rays and Chimaeras: The
Status of the Chondrichthyan Fishes (Shark Specialist
Group, Species Survival Commission, World Conservation
Union, Cambridge, 2005).
13. J. K. Baum et al., Science 299, 389 (2003).
14. J. D. Stevens, R. Bonfil, N. K. Dulvy, P. A. Walker, ICES J.
Mar. Sci. 57, 476 (2000).
15. Species information and detailed methods are available
on Science Online.
16. D. H. Ha, thesis, College of William and Mary, Gloucester
Point, VA (2006).
17. D. A. Fahrenthold, Washington Post, www.
2004Aug24.html (25 August 2004).
18. R. A. Blaylock, Estuaries 16, 255 (1993).
19. D. S. Grusha, thesis, College of William and Mary,
Gloucester Point, VA (2005).
20. J. W. Smith, J. V. Merriner, Estuaries 8, 305 (1985).
21. C. H. Peterson, H. C. Summerson, S. R. Fegley,
R. C. Prescott, J. Exp. Mar. Biol. Ecol. 127, 121
22. C. H. Peterson, F. J. Fodrie, H. C. Summerson, S. P. Powers,
Oecologia 129, 349 (2001).
23. C. H. Peterson, H. C. Summerson, R. A. Luettich Jr.,
Mar. Ecol. Prog. Ser. 132, 93 (1996).
24. R. J. Orth, Chesapeake Sci. 16, 205 (1975).
25. N. K. Dulvy, J. D. Metcalfe, J. Glanville, M. G. Pawson,
J. D. Reynolds, Conserv. Biol. 14, 283 (2000).
26. S. I. Rogers, J. R. Ellis, ICES J. Mar. Sci. 57, 866 (2000).
27. A. Yamaguchi, I. Kawahara, S. Ito, Environ. Biol. Fish. 74,
28. We thank J. Boylan, J. Collie, E. Durell, D. Gaskill,
J. Hoey, W. Hogarth, D. Kahn, J. Kraeuter, R. Lipcius,
M. McDuff, P. Rago, D. Ricard, R. Seitz, D. Simpson,
F. Schwartz, J. Smith, G. Ulrich, K. West, and the North
Carolina Division of Marine Fisheries (NCDMF) for sharing
data. North Carolina biological data were provided at the
authors' request by the NCDMF. Analyses of these data
and conclusions drawn there from are those of the
authors and do not necessarily represent the views of
NCDMF. We also thank D. Gaskill for assistance
conducting the field research and the UNC Institute of
Marine Sciences for providing support to conduct the
longline shark survey. V. Garcia, J. Hoenig, H. Lotze,
L. Lucifora, A. Sugden, J. Valentine, B. Worm, and the
referees provided helpful comments on the manuscript.
We acknowledge funding from Pew Institute for Ocean
Science, Sloan Census of Marine Life, Natural Sciences
and Engineering Research Council of Canada, Killam
Trusts, NC Fisheries Resource Grants Program, NC Sea
Grant, and NSF.
Supporting Online Material
Materials and Methods
Figs. S1 to S3
Tables S1 to S5
11 December 2006; accepted 5 March 2007
Protein Composition of Catalytically
Active Human Telomerase from
Scott B. Cohen,1Mark E. Graham,2George O. Lovrecz,3Nicolai Bache,4
Phillip J. Robinson,2Roger R. Reddel1*
Telomerase is a ribonucleoprotein enzyme complex that adds 5′-TTAGGG-3′ repeats onto the ends of
human chromosomes, providing a telomere maintenance mechanism for ~90% of human cancers. We
have purified human telomerase ~108-fold, with the final elution dependent on the enzyme’s ability to
catalyze nucleotide addition onto a DNA oligonucleotide of telomeric sequence, thereby providing
specificity for catalytically active telomerase. Mass spectrometric sequencing of the protein components
and molecular size determination indicated an enzyme composition of two molecules each of
telomerase reverse transcriptase, telomerase RNA, and dyskerin.
division (2), providing a counting mechanism
elomeres, repetitive nucleoprotein struc-
tures at the ends of linear chromosomes
(1), shorten during each cycle of cell
to limit the number of times a cell can divide
(3). Many cancer cells escape limits on pro-
liferation by activating the ribonucleoprotein
enzyme telomerase to catalyze the synthesis
of telomeric repeats (4). The protein compo-
nent, human telomerase reverse transcriptase
(hTERT), contains conserved catalytic reverse
transcriptase motifs (5, 6), and the human
telomerase RNA component (hTR) (7) directs
the addition of deoxynucleotide triphosphates
(dNTPs) by means of an internal template com-
plementary to the telomeric repeat sequence
Telomerase has previously been purified
only from the ciliate Euplotes aediculatus as
a complex of TERT, RNA, and associated
protein p43 (8). At least 32 distinct proteins
have been proposed to associate with human
telomerase (table S1). Size measurements of
human telomerase have indicated a complex
larger than expected for a composition of
one hTERT (127 kD) and one hTR (153 kD)
(9, 10) but smaller than the sum of all pro-
posed protein associations (~2.6 MD). None-
theless, the precise composition of the active
enzyme complex within the cell has remained
We measured the size of the active human
telomerase complex in a panel of immortal cell
lines (MCF-7, A2182, HCT-116, TE-85, HT-
1080, and HEK-293, derived from cancers of
the breast, lung, colon, bone, and connective
tissue, and from embryonic kidney cells, re-
spectively). Quantification of telomerase was
performed with a direct (non–polymerase
chain reaction) primer-extension activity assay
(fig. S1). Whole-cell lysates (11) from all cell
lines exhibited a similar sedimentation profile,
with ≥60% of total activity eluting in fractions
9 and 10 (Fig. 1, A to C). Thyroglobulin (669
kD) peaked in fraction 9 (Fig. 1B), indicating
that telomerase exists as an enzyme complex
of ~650 to 670 kD.
We developed a purification scheme that
achieved ~108-fold enrichment of active telomerase
in three steps (11). The first step was immuno-
affinity purification with a sheep polyclonal
antibody generated against the peptide antigen
ARPAEEATSLEGALSGTRH (hTERT amino
acids 276 to 294). HEK-293 lysate was incu-
1Cancer Research Unit, Children’s Medical Research Institute,
214 Hawkesbury Road, Westmead NSW 2145, Australia.2Cell
Signalling Unit, Children’s Medical Research Institute, 214
Hawkesbury Road, Westmead NSW 2145, Australia.
mentation Lab, Commonwealth Scientific and Industrial
Research Organisation, Molecular and Health Technologies,
343 Royal Parade, Parkville VIC 3052, Australia.4Department
of Biochemistry and Molecular Biology, University of Southern
Denmark, Campusvej 55, 5230 Odense M, Denmark.
*To whom correspondence should be addressed. E-mail:
30 MARCH 2007VOL 315
on January 4, 2008