Report
Extreme Inverted Trophic Pyramid of Reef Sharks
Supported by Spawning Groupers
Graphical Abstract
Authors
Johann Mourier, Jeffrey Maynard,
Valeriano Parravicini, Laurent Ballesta,
Eric Clua, Michael L. Domeier,
Serge Planes
Correspondence
johann.mourier@gmail.com
In Brief
Mourier et al. report extremely high shark
biomass in pristine Fakarava pass,
French Polynesia, producing an inverted
trophic pyramid. To escape such
constraints, predators typically forage
long range on multiple pyramids. This
study presents a new mechanism in
which subsidies directly come to
predators in the form of spawning
aggregations.
Highlights
d
Extremely high shark biomass in Fakarava pass produces an
inverted trophic pyramid
d
Prey inhabiting the pass do not satisfy shark energy
requirements
d
Subsidies are brought directly to sharks via fish spawning
aggregations
d
Concentrating local energy is a new mechanism for
maintaining inverted pyramids
Mourier et al., 2016, Current Biology 26, 1–6
August 8, 2016 ª 2016 Elsevier Ltd.
http://dx.doi.org/10.1016/j.cub.2016.05.058
Please cite this article in press as: Mourier et al., Extreme Inverted Trophic Pyramid of Reef Sharks Supported by Spawning Groupers, Current Biology
(2016), http://dx.doi.org/10.1016/j.cub.2016.05.058
Current Biology
Report
Extreme Inverted Trophic Pyramid
of Reef Sharks Supported by Spawning Groupers
Johann Mourier,1,2,* Jeffrey Maynard,1,3 Valeriano Parravicini,1 Laurent Ballesta,4 Eric Clua,1 Michael L. Domeier,5
and Serge Planes1,6
1EPHE,
PSL Research University, UPVD, CNRS, USR 3278 CRIOBE, 66360 Perpignan, France
of Science and Engineering, Department of Biological Sciences, Macquarie University, Sydney, NSW 2109, Australia
3SymbioSeas and the Marine Applied Research Center, Wilmington, NC 28411, USA
4Andromede Oceanology, Place Cassan, 34280 Carnon, France
5Marine Conservation Science Institute, 68-1825 Lina Poepoe Street, Waikoloa, HI 96738, USA
6Laboratoire d’Excellence CORAIL
*Correspondence: johann.mourier@gmail.com
http://dx.doi.org/10.1016/j.cub.2016.05.058
2Faculty
SUMMARY
RESULTS AND DISCUSSION
The extent of the global human footprint [1] limits
our understanding of what is natural in the marine
environment. Remote, near-pristine areas provide
some baseline expectations for biomass [2, 3] and
suggest that predators dominate, producing an inverted biomass pyramid. The southern pass of
Fakarava atoll—a biosphere reserve in French Polynesia—hosts an average of 600 reef sharks, two to
three times the biomass per hectare documented
for any other reef shark aggregations [4]. This huge
biomass of predators makes the trophic pyramid
inverted. Bioenergetics models indicate that the
sharks require 90 tons of fish per year, whereas
the total fish production in the pass is 17 tons per
year. Energetic theory shows that such trophic
structure is maintained through subsidies [5–9],
and empirical evidence suggests that sharks must
engage in wide-ranging foraging excursions to
meet energy needs [9, 10]. We used underwater surveys and acoustic telemetry to assess shark residency in the pass and feeding behavior and used
bioenergetics models to understand energy flow.
Contrary to previous findings, our results highlight
that sharks may overcome low local energy availability by feeding on fish spawning aggregations, which
concentrate energy from other local trophic pyramids. Fish spawning aggregations are known to
be targeted by sharks, but they were previously
believed to play a minor role representing occasional
opportunistic supplements. This research demonstrates that fish spawning aggregations can play a
significant role in the maintenance of local inverted
pyramids in pristine marine areas. Conservation of
fish spawning aggregations can help conserve shark
populations, especially if combined with shark fishing bans.
Extremely High Shark Biomass at Fakarava
We described and quantified a large aggregation of reef sharks in
the 17.5 hectares of the southern channel of Fakarava (Figure 1).
French Polynesian atolls host healthy populations of sharks [11]
that have been protected by law from local and international
fishing since 2006. We first constructed a high-resolution bathymetry map using a multibeam sonar system to characterize
the study site (Figure 1) and georeferenced the aggregation of
gray reef sharks, Carcharhinus amblyrhynchos, using a toweddiver methodology. Video-assisted underwater visual census
surveys (n = 13), conducted as drift transects across the
entire shark school, were used to provide a precise total shark
census within the pass (June–December 2014). The total number
of gray reef sharks fluctuated between 251 and 705 individuals
(mean ± SEM = 491.7 ± 34.9) corresponding to a density of
14–40 sharks , ha1 and a biomass of 37.54–112.51 g , m2
(Tables S1 and S2). These densities represent the highest ever
documented for this species (previously reported maxima are
11 sharks , ha1 [4]). Considering all five shark species
observed in the pass, abundance fluctuated between 438
and 892 individuals, representing a total biomass of 55.54–
130.57 g , m2 (Table S2).
Shark Dietary Needs
The pass is the area over which we investigated the trophic
pyramid. We examined local energy availability to understand
how the extreme shark biomass is maintained. To assess
whether the pass provides enough food for the sharks, we first
described the trophic structure of the fish assemblage using
biomass spectra. Data were obtained from underwater visual
census (UVC) surveys [12], from which we obtained estimates
of the yearly fish biomass production using metabolic theory.
Biomass spectra revealed that biomass tends to increase as
body mass increases (slope = 0.51 ± 0.13; Figure 2A); i.e., there
is an inverted biomass pyramid where typical prey biomass
(132.33 g , m2) in the pass roughly matches the predator
biomass (130.57 g , m2) (Figure 2B). Using species-specific
parameters (Table S3) implemented within a bioenergetics
model, we determined that depending on the season, the shark
Current Biology 26, 1–6, August 8, 2016 ª 2016 Elsevier Ltd. 1
Please cite this article in press as: Mourier et al., Extreme Inverted Trophic Pyramid of Reef Sharks Supported by Spawning Groupers, Current Biology
(2016), http://dx.doi.org/10.1016/j.cub.2016.05.058
Figure 1. Gray Reef Shark Aggregation within the Fakarava Pass, in the Tuamotu Archipelago of French Polynesia
These sharks (A) form large schools of up to 700 individuals that use the strong current of this narrow channel (B) (about 100 m wide 3 30 m deep) to rest.
Photo ª G. Funfrock. See also Figure S1 and Tables S1 and S2.
community needs to consume between 147 and 350 kg fish ,
day1 (Figure 3A) to satisfy energy requirements. According to
metabolic theory, we estimate the fish biomass production at
about 17 tons per year, far lower than the 91 tons needed by
sharks. During the study period, the average daily fish production (46 kg fish , day1) never met shark needs (Figure 3A). Previous research [9] reported that the slope of biomass by mass
class cannot be steeper than 0.25 unless the food web system
is subsidized; the slope we computed from Fakarava is 0.51 (Figure 2A). This raises questions as to where the subsidies are coming from, requiring us to observe shark foraging and feeding
behavior. In particular, are resident sharks escaping the constraints of low energy availability by foraging outside the aggregation area, as documented at other pristine reefs [10]?
Foraging and Feeding Behavior
We evaluated shark residency in the pass using acoustic telemetry to determine whether sharks are regularly leaving the pass to
forage. Thirteen gray reef sharks were equipped with acoustic
transmitters (Table S4) and were monitored by an array of six
hydrophones (Figure S1). Generalized linear mixed models
(GLMMs) and generalized additive mixed models (GAMMs)
applied to telemetry data resulted in higher probabilities of
presence between May and October, with sharks spending
significantly more time per day at the aggregation between
June and October (Figure S2). Overall, sharks showed different
degree of residency (mean ± SEM = 42.21% ± 7.75% of days
present in the pass; range = 2.1%–95.9%; Table S3), with three
transient (<20% residency), six semi-resident (20%–70% residency), and four highly resident (>70% residency) sharks (Figure S2). Although semi-resident sharks leave the pass between
November and April, the remaining resident individuals account
2 Current Biology 26, 1–6, August 8, 2016
for an extremely high density (>14 sharks , ha1 in December;
Table S1). These sharks largely stay within the pass, and local
prey cannot meet energy needs. Sharks are clearly more resident during austral winter than during summer, when they
must range at larger scales. The significant increase of time
spent in the pass in winter suggests a foraging strategy within
the pass that ensures that energy needs are met.
We conducted behavioral surveys at night to identify predatory interactions, enabling us to determine whether sharks are
feeding, rather than only resting (Figure 1). These observations
confirmed that hundreds of sharks actively feed on a large variety
of prey (at least 14 fish species; Figures 4 and S3). In particular,
sharks feed aggressively on the large number of groupers present during spawning aggregations in June and July [13]. Shark
abundance and residency times both increase when camouflage
groupers (Epinephelus polyphekadion) arrive from the surrounding reef area to spawn. High shark densities last far longer than
the spawning (Figure 3A). Dedicated visual censuses revealed
that the spawning aggregation included up to 17,000 individual
groupers (971.4 fish , ha1), corresponding to 31 tons
(775 kg , day1) of potential prey available for sharks (Figure 3A).
Due to their high trophic level, the massive input of groupers
does not make the pyramid bottom heavy and results in an increase in the biomass spectrum (Figure 2C); i.e., the trophic pyramid becomes even more inverted. However, the spawning
aggregation drastically decreases the shark-prey biomass ratio
(Figure 2D) since the biomass of prey (311.45 g , m2) far exceeds that of predators (130.57 g , m2). Using the initial
biomass of prey taken from UVCs, we simulated prey biomass
through time as a function of fish production and shark consumption (Figure 3B). The model is based on the assumption
that prey biomass will decline if sharks consume prey at a rate
Please cite this article in press as: Mourier et al., Extreme Inverted Trophic Pyramid of Reef Sharks Supported by Spawning Groupers, Current Biology
(2016), http://dx.doi.org/10.1016/j.cub.2016.05.058
Figure 2. Trophic Structure and Predator-Prey Dynamics in the Fakarava Pass
(A) Biomass spectrum of the trophic structure typically observed in the pass is characterized by a positive slope (0.51), indicating an inverted biomass pyramid.
(B) Total shark biomass is similar to the biomass of their potential prey (fish >12.5 cm).
(C) During grouper spawning aggregation, numerous large-bodied fish enter the system, increasing the slope of the biomass spectrum (0.55).
(D) The grouper aggregation decreases the predator-prey ratio by doubling the amount of prey available for sharks.
Gray bands indicate 95% confidence intervals in (A) and (C), and SEMs are given with slope values in (A) and (C). See also Tables S1 and S2.
that exceeds the rate of prey production [14]. Without considering any input from the spawning aggregation, the simulated
biomass of prey declines rapidly after 75 days as prey are
not sufficient to satisfy shark energy requirements. However,
the biomass input from the grouper aggregation delays the
collapse of prey for more than 30 days (Figure 3B). This delay
allows sharks to survive at least until the next new or full moon,
when surgeonfish, parrotfish, and other fish species form successive spawning aggregations [15] (Figure S4).
Spawning Aggregations and Reef Shark Residency
The energetic cost of hunting outside the pass is high, and the
success rate of predation is low [13]. Sharks in the pass are using fish spawning aggregations as energetic subsidies to reduce
the need for costly foraging excursions outside the pass boundaries, enabling this energy to be directed to other physiological
functions. This is a new mechanism by which the largest size
classes can ‘‘escape the tyranny of low energy availability’’
[9]. Trebilco et al. present two mechanisms by which low energy
availability is typically escaped: the largest predators (1) feed at
sufficiently expansive spatial scales to ‘‘skim the tops of multi-
ple spatially discrete local biomass pyramids’’ (i.e., foraging
outside the pass, which we rarely observed) or (2) forage extensively at the bottom of ‘‘widely dispersed and seasonally variable pyramids,’’ which applies to whale sharks, but not reef
sharks [9].
Shark foraging and feeding behavior in Fakarava pass indicates that the bottom and mid-sections of local biomass pyramids can come to the sharks, i.e., concentrating energy from a
number of local pyramids.
Fish spawning aggregations are known to be targeted by
sharks [13, 16] but were believed to only represent occasional
opportunistic supplements. Our work demonstrates the potentially high importance of grouper spawning aggregations in the
maintenance of local inverted pyramids in pristine marine areas.
In addition, other fish species may constitute important subsidies during the year in this location (Figure S4). However, our
results also suggest that when the spawning aggregation
subsidies become scarcer during summer and metabolic
rate increases due to warmer waters, sharks shift to investing
in foraging excursions to escape low energy availability in the
pass.
Current Biology 26, 1–6, August 8, 2016 3
Please cite this article in press as: Mourier et al., Extreme Inverted Trophic Pyramid of Reef Sharks Supported by Spawning Groupers, Current Biology
(2016), http://dx.doi.org/10.1016/j.cub.2016.05.058
and Belize). Although our study relates to a single location
with uncertain generality, long-term persistence of high shark
densities depends on spawning aggregations, at least at this
site. More generally, shark fishing bans are unlikely to prove
sufficient to guarantee healthy shark populations if not
jointly implemented with conservation plans for fish spawning
aggregations.
EXPERIMENTAL PROCEDURES
Figure 3. Temporal Dynamics of Prey Biomass as a Function of
Shark Daily Food Requirements
(A) Daily food requirement of sharks (kg , day1; orange) varies within the year
based on shark abundance and is higher than predicted prey production from
the fish community of the pass (blue). In June and July, the grouper spawning
aggregation supplies an additional 775 kg fish , day1 (green), which exceeds
shark needs.
(B) Prey biomass then rapidly collapses with a rate that depends on the proportion of grouper consumed (d). If the diet does not include groupers (blue;
d = 0), then no prey are available after 77 days. This collapse is delayed to
114 days if shark diet is made of 100% of grouper during the month of the
spawning aggregation (green; d = 1). Note that d = 0 for all scenarios at time tS
(end of the spawning aggregation).
See also Figures S2 and S4 and Tables S3 and S4.
Conclusions
This study adds to the evidence that inverted biomass pyramids
are an indicator of pristine marine ecosystems. However, these
are temporary conditions; missing energy to fulfil predator
needs is supplied by external subsidies, and fish spawning aggregations can play a critical role. Fish spawning aggregations
have historically been heavily exploited, and many have been
drastically reduced or depleted [17]. Although further research
at other locations is required, our findings suggest that the overexploitation of spawning aggregations can fundamentally alter
the natural predator-prey equilibrium, limiting foraging options
for reef sharks within aggregation sites. The consequence is
sharks being forced to undertake energetically costly widerrange foraging as the only option to meet energy requirements.
The consequence of this high cost/low reward strategy (relative
to feeding on spawning aggregations) is difficult to quantify but
likely to be far from negligible [18–21]. Our results offer a new
perspective on the known negative linear relationship between
shark density and human population density [4], highlighting
the role overfishing may play on these dynamics. Bans on shark
fishing are a current trend (e.g., French Polynesia, Maldives,
4 Current Biology 26, 1–6, August 8, 2016
Study System Mapping, Visual Censuses, and Shark Residency
The study was conducted on the 17.5 ha southern channel (16 300 S 145
270 W; Figure S1) of the atoll of Fakarava in the Tuamotu Archipelago of French
Polynesia. We used the multibeam sonar system (MBSS) GeoSwath coupled
with a georeferenced audio towed diver to create a precise bathymetric map
of our study location. We also delineated the spatial zones of the shark and
grouper aggregations.
To assess the number of reef sharks in the pass, we developed a videoassisted method adapted to our study site. In June–December 2014, SCUBA
diver observers entered the water 50 m upstream of the aggregation and
filmed the shark school while drifting in the middle of the channel. We then
counted each individual shark of any species progressively going out of the
video frame screen (n = 3 counts). Whitetip reef shark (Triaenodon obesus)
and blacktip reef shark (Carcharhinus melanopterus) minimum abundances
were assessed using photo-identification [22, 23]. Shark foraging activity
was filmed at night with a 4K Phantom Flex4K camera (500 frames/s), enabling
identification of prey species.
A network of six VR2 acoustic receivers (VEMCO) was installed along
the pass in June 2011 (Figure S1). Sharks were captured within the pass,
sexed, measured (total length, TL), and a V13-1x H (13 mm 3 36 mm,
11 g) coded acoustic transmitter (VEMCO) was surgically implanted into
their peritoneal cavity. Receivers recorded the time, date, and transmitter
number of each tagged shark passing within the 50 m radius detection
range. Data recorded between July 20, 2011 and January 30, 2013
(561 days) were analyzed. For each tagged shark, a residency index was
calculated by dividing the number of days it was present by the number
of monitoring days (i.e., 561). We used a GLMM to examine the effects
of time of day, season, and location on the presence of sharks in the
pass. A GAMM was used to evaluate the effect of time of the year on the proportion of time per day sharks spent within the detection range of the
receivers.
Bioenergetics Modeling
Daily shark food requirement or daily ration (g , day1) was calculated using
the equation
DR = 1:37 3 ðM + GÞ=F;
1
(Equation 1)
1
where DR is the daily ration (g , day ), M (J , day ) is the total energy of
metabolism, G (J , day1) is the energy for growth and reproduction, 1.37
represents the 27% of energetic loss due to excretion and egestion [24],
and F is the energy value of food sources (J , g1 wet weight). Daily
ration was calculated for each species using species-specific parameters,
shark length distributions from capture sessions in the area [25], or
visual estimations and was then expressed as percent body weight W
(i.e., DR 3 100 / W = %BW , day1). After adjustment to the species-specific
proportion of fish in the shark’s diet (Table S4), these values were used to
estimate the total weight of fish requirement (kg) for all sharks per day in
the pass.
Fish Biomass Estimates and Community Structure
Fish biomass was estimated using data from SCUBA belt transect surveys
conducted during the Reef Life Survey (RLS; http://reeflifesurvey.com) from
June 19–21, 2012 [12]. Fish abundance and size estimates were converted
to biomass using length-mass conversion factors obtained from FishBase
[26]. We restricted our analysis to fish size representative of prey size range
for sharks (>12.5 cm TL). For community biomass spectra, individual fish
Please cite this article in press as: Mourier et al., Extreme Inverted Trophic Pyramid of Reef Sharks Supported by Spawning Groupers, Current Biology
(2016), http://dx.doi.org/10.1016/j.cub.2016.05.058
Figure 4. Photo Examples of Foraging on Fish in the Pass at Night
(A) Spawning aggregation of Epinephelus polyphekadion occurring between full moon of June and July each year.
(B and C) Gray reef sharks foraging at night on E. polyphekadion.
(D) Gray reef sharks foraging at night on Naso annulatus.
These photos represent natural predation. Lights from cameras are unlikely to have modified the hunting behavior, as the sharks were observed hunting out of
light range. Photo ª L. Ballesta. See also Figure S3 and Table S3.
were assigned to body mass classes (W) [27], and body mass was summed
for each class to determine a biomass (B) per unit area (g , m2). The
biomass spectrum was modeled as a linear regression model with the midpoints of the log2 body-mass bins (log2 (W)) as the predictor and log2 (B) as
the response.
Total camouflage grouper (Epinephelus polyphekadion) density and
biomass during spawning aggregation were estimated between June and
July using a 25 3 25 m quadrat divided into four 6.25 3 25 m blocks in the
middle of the aggregation.
Predator-Prey Interaction Model
The rate of annual fish production (P; g , ha1 , year1) was estimated by converting the body mass (W; g) of fish to rates of annual biomass production
using the metabolic theory [28] as a function of body mass and temperature
[28–30] as
P = expð25:22 E=kTÞW 0:76 ;
(Equation 2)
where E is the activation energy of metabolism (0.63 eV), k is the Boltzmann’s
constant (8.62 3 105 eV , K1), and T is the temperature in Kelvin 300.5 K,
equivalent to 27.5 C P was calculated for each fish in transect surveys,
summed, and converted into a daily production DPPrey in kg , day1 after being
extrapolated to the scale of Fakarava pass. We then ran a simple dynamic
model that investigates the evolution of prey biomass as a function of shark
consumption based on energy requirements.
See the Supplemental Experimental Procedures for more details on all
methods presented in the sections above.
SUPPLEMENTAL INFORMATION
Supplemental Information includes Supplemental Experimental Procedures,
four figures, and four tables and can be found with this article online at
http://dx.doi.org/10.1016/j.cub.2016.05.058.
AUTHOR CONTRIBUTIONS
J.M., E.C., and S.P. conceived of the study; J.M., L.B., E.C., M.L.D., and S.P.
collected the data; J.M., V.P., and S.P. developed and implemented the analyses; and J.M., J.M., and S.P. wrote the manuscript with comments from V.P.,
E.C., L.B., and M.L.D.
ACKNOWLEDGMENTS
This research was supported by the Ministry for Environment, Sustainable
Development and Energy in France, Direction de l’Environnement (DIREN) of
French Polynesia, IFRECOR France, IFRECOR Polynesia, BlancPain Ocean
Commitment, and Arte. Other sponsors supported the expedition: Tahiti Tourisme, Air Tahiti Nui, Aqualung, Nikon, Seacam, and ApDiving. We thank T. Vignaud, A. Guilbert, S. Dumont, M. Lefèvre, C. Gentil, Y. Gentil, Y. Hubert,
R. Rinaldi, J.-M. Belin, S. Girardot, G. Kebaı̈li, F. Blanchard, M. Taquet, and
Y. Sadovy for assistance with the fieldwork. We thank S. and A. Richemond
from Tetamanu lodging for hosting the research team and providing technical
assistance during the field work. Many thanks to A. Guilbert for construction of
the bathymetric map of the pass, V. Truchet for the photos in Figure S4, and
Current Biology 26, 1–6, August 8, 2016 5
Please cite this article in press as: Mourier et al., Extreme Inverted Trophic Pyramid of Reef Sharks Supported by Spawning Groupers, Current Biology
(2016), http://dx.doi.org/10.1016/j.cub.2016.05.058
G. Funfrock for the photo in Figure 1. We also acknowledge N. Dulvy, R. Trebilco, and S.J. Green for helpful discussion about trophic theory. Work on
sharks was conducted under Direction de l’Environnement DIREN’s approval
(arrete no. 9524, renewed on October 30, 2015).
Received: February 19, 2016
Revised: April 29, 2016
Accepted: May 24, 2016
Published: July 28, 2016
REFERENCES
1. Halpern, B.S., Walbridge, S., Selkoe, K.A., Kappel, C.V., Micheli, F.,
D’Agrosa, C., Bruno, J.F., Casey, K.S., Ebert, C., Fox, H.E., et al. (2008).
A global map of human impact on marine ecosystems. Science 319,
948–952.
2. Stevenson, C., Katz, L.S., Micheli, F., Block, B., Heiman, K.W., Perle, C.,
Weng, K., Dunbar, R., and Witting, J. (2007). High apex predator biomass
on remote Pacific islands. Coral Reefs 26, 47–51.
3. Sandin, S.A., Smith, J.E., DeMartini, E.E., Dinsdale, E.A., Donner, S.D.,
Friedlander, A.M., Konotchick, T., Malay, M., Maragos, J.E., Obura, D.,
et al. (2008). Baselines and degradation of coral reefs in the Northern
Line Islands. PLoS ONE 3, e1548.
4. Nadon, M.O., Baum, J.K., Williams, I.D., McPherson, J.M., Zgliczynski,
B.J., Richards, B.L., Schroeder, R.E., and Brainard, R.E. (2012). Recreating missing population baselines for Pacific reef sharks. Conserv.
Biol. 26, 493–503.
5. Nakano, S., and Murakami, M. (2001). Reciprocal subsidies: dynamic
interdependence between terrestrial and aquatic food webs. Proc. Natl.
Acad. Sci. USA 98, 166–170.
6. Polis, G.A., Anderson, W.B., and Holt, R.D. (1997). Toward an integration
of landscape and food web ecology: the dynamics of spatially subsidized
food webs. Annu. Rev. Ecol. Syst. 28, 289–316.
7. Field, R.D., and Reynolds, J.D. (2011). Sea to sky: impacts of residual
salmon-derived nutrients on estuarine breeding bird communities. Proc.
Biol. Sci. 278, 3081–3088.
8. Hocking, M.D., Dulvy, N.K., Reynolds, J.D., Ring, R.A., and Reimchen,
T.E. (2013). Salmon subsidize an escape from a size spectrum. Proc.
Biol. Sci. 280, 20122433.
9. Trebilco, R., Baum, J.K., Salomon, A.K., and Dulvy, N.K. (2013).
Ecosystem ecology: size-based constraints on the pyramids of life.
Trends Ecol. Evol. 28, 423–431.
10. McCauley, D.J., Young, H.S., Dunbar, R.B., Estes, J.A., Semmens, B.X.,
and Micheli, F. (2012). Assessing the effects of large mobile predators
on ecosystem connectivity. Ecol. Appl. 22, 1711–1717.
11. Mourier, J., Planes, S., and Buray, N. (2013). Trophic interactions at the top
of the coral reef food chain. Coral Reefs 32, 285.
12. Edgar, G.J., and Stuart-Smith, R.D. (2014). Systematic global assessment
of reef fish communities by the Reef Life Survey program. Sci. Data 1,
140007.
13. Robbins, W.D., and Renaud, P. (2015). Foraging mode of the grey reef
shark, Carcharhinus amblyrhynchos, under two different scenarios.
Coral Reefs 35, 253–260.
6 Current Biology 26, 1–6, August 8, 2016
14. Green, S.J., Dulvy, N.K., Brooks, A.M., Akins, J.L., Cooper, A.B., Miller, S.,
and Côté, I.M. (2014). Linking removal targets to the ecological effects of
invaders: a predictive model and field test. Ecol. Appl. 24, 1311–1322.
15. Craig, P.C. (1998). Temporal spawning patterns of several surgeonfishes
and wrasses in American Samoa. Pac. Sci. 52, 35–39.
16. Weideli, O.C., Mourier, J., and Planes, S. (2008). A massive surgeonfish
aggregation creates a unique opportunity for reef sharks. Coral Reefs
34, 835.
17. Sadovy, Y., and Domeier, M. (2005). Are aggregation-fisheries sustainable? Reef fish fisheries as a case study. Coral Reefs 24, 254–262.
18. Heithaus, M.R., Frid, A., Wirsing, A.J., and Worm, B. (2008). Predicting
ecological consequences of marine top predator declines. Trends Ecol.
Evol. 23, 202–210.
19. Ferretti, F., Worm, B., Britten, G.L., Heithaus, M.R., and Lotze, H.K. (2010).
Patterns and ecosystem consequences of shark declines in the ocean.
Ecol. Lett. 13, 1055–1071.
20. Ruppert, J.L., Travers, M.J., Smith, L.L., Fortin, M.J., and Meekan, M.G.
(2013). Caught in the middle: combined impacts of shark removal and
coral loss on the fish communities of coral reefs. PloS ONE 8, e74648.9.
21. Roff, G., Doropoulos, C., Rogers, A., Bozec, Y.M., Krueck, N.C., Aurellado,
E., Priest, M., Birrell, C., and Mumby, P.J. (2016). The ecological role of
sharks on coral reefs. Trends Ecol. Evol. 31, 395–407.
22. Mourier, J., Vercelloni, J., and Planes, S. (2012). Evidence of social communities in a spatially structured network of a free-ranging shark species.
Anim. Behav. 83, 389–401.
23. Whitney, N.M., Pyle, R.L., Holland, K.N., and Barcz, J.T. (2012).
Movements, reproductive seasonality, and fisheries interactions in the
whitetip reef shark (Triaenodon obesus) from community-contributed photographs. Environ. Biol. Fishes 93, 121–136.
24. Wetherbee, B.M., and Gruber, S.H. (1993). Absorption efficiency of the
lemon shark Negaprion brevirostris at varying rates of energy intake.
Copeia 1993, 416–425.
25. Vignaud, T.M., Mourier, J., Maynard, J.A., Leblois, R., Spaet, J., Clua, E.,
Neglia, V., and Planes, S. (2014). Blacktip reef sharks, Carcharhinus melanopterus, have high genetic structure and varying demographic histories
in their Indo-Pacific range. Mol. Ecol. 23, 5193–5207.
26. Froese, R., and Pauly, D. (2011). FishBase (http://www.fishbase.org).
27. Trebilco, R., Dulvy, N.K., Stewart, H., and Salomon, A.K. (2015). The role of
habitat complexity in shaping the size structure of a temperate reef fish
community. Mar. Ecol. Prog. Ser. 532, 197–211.
28. Brown, J.H., Gillooly, J.F., Allen, A.P., Savage, V.M., and West, G.B.
(2004). Toward a metabolic theory of ecology. Ecology 85, 1771–1789.
29. Ernest, S.K., Enquist, B.J., Brown, J.H., Charnov, E.L., Gillooly, J.F.,
Savage, V.M., White, E.P., Smith, F.A., Hadly, E.A., Haskell, J.P., et al.
(2003). Thermodynamic and metabolic effects on the scaling of production
and population energy use. Ecol. Lett. 6, 990–995.
30. Jennings, S., Mélin, F., Blanchard, J.L., Forster, R.M., Dulvy, N.K., and
Wilson, R.W. (2008). Global-scale predictions of community and
ecosystem properties from simple ecological theory. Proc. Biol. Sci.
275, 1375–1383.