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ENDANGERED SPECIES RESEARCH
Endang Species Res
Vol. 38: 147–151, 2019
https://doi.org/10.3354/esr00950 Published March 28
1. INTRODUCTION
Both ecological theory and empirical data demon-
strate that predators can affect prey communities, and
subsequently initiate trophic cascades, even in the
absence of prey consumption (Creel & Christianson
2008, Estes et al. 2016, Hammerschlag et al. 2019).
However, some recent discussions on the ecological
roles of sharks have been increasingly focused on
aspects of diet (e.g. Grubbs et al. 2016, Roff et al. 2016,
Bond et al. 2018). For example, when evaluating for
the potential for shark declines to initiate trophic cas-
cades, Grubbs et al. (2016) argued that 5 criteria need
to be considered, among them being that prey be a sig-
nificant part of a predator’s diet and also that the pred-
ator be the primary source of prey predation mortality.
In light of these discussions, the goal of this paper is to
evoke relevant ecological theory to demonstrate that
relying on dietary information can be misleading when
trying to quantify the strength of top-down predation
effects of sharks, and consequently, the potential for
their population declines or recoveries to initiate
trophic cascades. To aid in future investigations into
shark ecological roles, I also provide a set of predic-
tions, based on functional attributes of sharks, prey
and the environment, which would lead to increases in
the magnitude of shark predation effects on prey pop-
ulations. I also review key study approaches currently
being employed to quantify effects of sharks on prey. I
note upfront that because the ecological concepts
being cited here have already been reviewed else-
where, I will not do so again in this paper.
© The author 2019. Open Access under Creative Commons by
Attribution Licence. Use, distribution and reproduction are un -
restricted. Authors and original publication must be credited.
Publisher: Inter-Research · www.int-res.com
*Corresponding author: nhammerschlag@miami.edu
OPINION PIECE
Quantifying shark predation effects on prey:
dietary data limitations and study approaches
Neil Hammerschlag1,2,*
1Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, FL, 33149, USA
2Leonard and Jayne Abess Center for Ecosystem Science and Policy, University of Miami, Coral Gables, FL, 33146, USA
ABSTRACT: Understanding the ecological impacts of sharks on prey populations has become a
research priority given widespread shark population declines due to overfishing, combined with
significant conservation efforts. Accordingly, many studies have conducted analyses of shark
stomach contents and/or used biomarkers, such as stable isotope signatures, to assess dietary pat-
terns in order to infer ecological roles. Here, I summarize how relying on stomach contents and/or
stable isotope signatures to assess the potential for sharks to initiate trophic cascades can be mis-
leading and may significantly underestimate the strength of shark top-down predation effects on
prey. However, a study approach that measures attributes of the sharks (e.g. hunting mode),
potential prey (e.g. escape speed) and the environment (e.g. habitat rugosity) can provide greater
insights for quantifying the magnitude of top-down predation effects of sharks and the potential
for their population declines or recoveries to trigger trophic cascades. To aid future investigations,
I provide a set of predictions, based on ecological theory, which would specifically lead to in -
creases in the magnitude of shark predation effects on prey populations. I also present key study
approaches currently being employed by researchers to test such predictions.
KEY WORDS: Shark · Apex predator · Predation risk · Trophic cascade · Coral reef · Fishing
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Endang Species Res 38: 147–151, 2019
148
2. LIMITATIONS OF DIETARY INFORMATION
Besides the well known limitations of gut content
analyses (reviewed by Baker et al. 2014), there are
various reasons why assessments of trophic levels or
ecological roles of sharks, in particular, cannot rely
primarily on this method. First, shark stomachs are
usually empty as they often evert their stomach upon
capture for sampling (Brunnschweiler et al. 2011). Sec-
ond, while sharks and other predators can most obvi-
ously affect prey by killing and consuming them, pred-
ators can also initiate strong top-down effects on prey
even when prey are absent from predator stomachs
and the direct predation mortality to prey is zero (re-
viewed in Creel & Christianson 2008). This is because
antipredator responses of prey, and associated risk ef-
fects, will lower direct predation mortality and as a re-
sult, prey will be rare or even absent in the stomach of
predators (Creel & Christianson 2008; Fig. 1).
In addition to stomach content analysis, stable iso-
tope signatures of nitrogen (N) and carbon (C) from
tissues are increasingly being used to infer trophic
niches of sharks (reviewed by Hussey et al. 2012,
Shiffman et al. 2012). There are many considerations
and limitations when applying this tool to study the
trophic ecology of individuals or populations (see re-
views by Layman et al. 2012); yet, many studies con-
tinue to use isotopic ratios from tissues to estimate
trophic position of sharks relative to other species in
the community and subsequently infer ecological
roles. However, among other possible issues, stable
isotope signatures are a long-term integration of re-
sources and it is possible that 2 species can have dis-
tinct diets but very similar isotopic signatures, if one
consumer’s isotopic signature represents the average
of 2 isotopically extreme resource bases. This is par-
ticularly relevant in the case of sharks since they are
opportunistic trophic omnivores, feeding across the
food web and among different habitats (Hussey et al.
2012, Shiffman et al. 2012). Accordingly, studies that
rely primarily on diet and stable isotope information
may underestimate top-down effects of sharks.
In a recent review, Roff et al. (2016) argued that
large reef sharks are trophically equivalent to sym-
patric piscivorous teleosts, such as large groupers and
snappers. They further concluded that the removal of
reef sharks by humans is therefore unlikely to initiate
strong top-down effects on large teleosts and therefore
unlikely to trigger trophic cascades. These arguments
were largely based on 2 primary lines of reasoning.
First, large piscivorous teleosts are often rare or
absent in the diet of sampled reef sharks. Second, sta-
ble isotope signatures generally overlapped between
reef sharks and these large piscivorous tele osts. How-
ever, these arguments did not take into account the
limitations of relying solely on studies of diet and sta-
ble isotopes to infer strengths of top-down predator
effects as described above. These limitations are evi-
denced by recent data which suggest that the
targeted removal of large-bodied reef sharks has con-
tributed to changes in the abundance, di-
versity, diet, condition and morphology
of coral reef fishes (Ruppert et al. 2013,
2016, Barley et al. 2017a,b, Hammer-
schlag et al. 2018).
3. APPROACHES FOR QUANTIFYING
SHARK PREDATION EFFECTS
Several papers have developed frame-
works for determining the strength of
predator effects on prey populations.
Taken together, these works have
shown that functional attributes of the
predator (e.g. hunting mode), prey (e.g.
grouping) and the environment (e.g.
habitat structure) can influence the
outcome of predator−prey interactions
(Schmitz 2008, Creel 2011, 2018,
Schmitz et al. 2017, Gaynor et al. 2019).
By synthesizing these works in combi-
nation with an understanding of preda-
Fig. 1. Conceptual diagram of how expression of antipredatory responses
by prey can increase risk effects of predators, while reducing direct preda-
tion mortality (adapted from Creel & Christianson [2008]). Consider 3 prey
species (α, β, and φ) that become exposed to increasing densities of preda-
tors. In such a scenario, (A) individuals of surviving prey α, β, and φmay
exhibit antipredatory responses (e.g. induce defensive armor or hiding
behavior) that scale with their respective level of encounter rates with
attacking predators. However, (B) if φincreases its expression of antipreda-
tor traits effectively, direct predation will decrease on φand consequently φ
will become rare or absent in predator diets, although predators still have
strong risk effects on φ. Moreover, if antipredatory responses of φcause
predators to shift their hunting efforts towards α and β, then (C) direct
effects and risk effects can even become negatively correlated
Hammerschlag: Quantifying shark predation effects
tor−prey interactions involving sharks based on my
own in situ studies, I provide a set of predictions that
would specifically lead to increases in the magnitude
of shark predation effects on prey, and distinguish
whether consumptive or risk effects would dominate
these interactions. These are listed in Table 1 and
may be useful for identifying potential situations
where sharks can affect prey through either con-
sumptive or risk effects.
There are a variety of methodological approaches
already being used for measuring relative attributes
of sharks, prey and the environment at appropriate
scales for assessment of shark predation effects. Rel-
evant shark attributes for investigation include
assessing their hunting mode (search and pursuit vs.
sit and wait), hunting periods, hunting areas, group-
ing, feeding rates, and gape size. For example, using
underwater visual surveys, Robbins & Renaud (2016)
documented differences in hunting strategies and
predation success rate among grey reef sharks,
Carharhinus amblyrhynchos, at Fakarava Atoll,
French Polynesia. During morning hours, the sharks
targeted spawning grouper, employing burst speed
to capture fish engaged in spawning. In contrast,
sharks switched prey targets to other fish species at
night, employing a slow, controlled approach which
minimized the distances between sharks and prey
fish prior to any predation attempts (Robbins &
Renaud 2016). These differences in hunting strategy
appeared to be related to the mobility of the different
species being targeted and the different environ-
mental conditions (day vs. night). When direct obser-
vation of shark hunting is not possible, as is usually
the case, multi-sensor biotelemetry and biologging
tools are particularly valuable for remotely recording
this aspect of shark behavior (Hussey et al. 2015).
This could include combining shark tracking with
accelerometers (Papastamatiou et al. 2018b), preda-
tion tags (Halfyard et al. 2017) and animal-borne
cameras (Papastamatiou et al. 2018a).
There are numerous relevant behavioral, physio-
logical and morphological attributes for investigating
149
Focus Attribute and prediction
Shark Both RE and CE will be high when sharks hunt in packs or hunt individually, but simultaneously
Both RE and CE will be high when sharks have a sensory advantage over prey (e.g. increased visual
acuity under low light conditions)
Both RE and CE will be high when sharks have a cognitive advantage over prey (e.g. when they exhibit
social learning or can refine hunting strategy over time)
RE will be higher than CE for ambush predatory sharks which require specific forms of habitat for cover
CE will be higher than RE for active or coursing predatory sharks that do not associate with specific forms
of habitat for cover
RE will be higher than CE when sharks are abundant and predictably distributed
CE will be higher than RE when sharks are widespread or rare
Prey RE and CE will be high for prey that do not employ grouping or schooling behavior or during periods
where they exhibit reduced group or school size
RE will be higher than CE in dietary generalists that can shift habitats in response to sharks
CE will be higher than RE in dietary specialists that cannot alter feeding habits in response to sharks
RE will be higher than CE for prey that can alter or adapt morphological traits in response to sharks
CE will be higher than RE for prey that cannot alter or adapt morphological traits in response to sharks
CE will be higher than RE when prey spatially or temporally aggregate to exploit a predictable temporal
resource pulse, such as an ephemeral food source
CE will be higher than RE when prey spatially or temporally aggregate to engage in a critical life history
event that increases population fitness (e.g. spawning or mating)
RE will be higher than CE when prey have advanced cognition and sensory perception
Environment RE will be higher than CE in more heterogenous environments where prey have more options to hide or
take refuge, such as reefs with high rugosity or dense kelp beds that exclude sharks
CE will be higher than RE in more homogeneous environments were prey have limited options to hide or
take refuge, such as open sand flats
RE will be higher than CE under environmental conditions that offer prey increased ability to detect
predators, such as good water visibility and during daylight hours
CE will be higher than RE under environmental conditions in which prey have reduced ability to detect
predators (e.g. low light, high turbidity)
Table 1. Predicted attributes of sharks, prey and the environment leading to increases in the magnitude of shark top-down
predation effects on prey, and whether consumptive effects (CE) or risk effects (RE) dominate these interactions. Predictions
developed from synthesizing works of Schmitz (2008), Creel (2011, 2018), Schmitz et al. (2017), and Gaynor et al. (2019), in
combination with an understanding of predator−prey interactions involving sharks based on my own experiences
Endang Species Res 38: 147–151, 2019
150
antipredatory responses in prey. These include
schooling or grouping behavior, escape mode, space
or time devoted to vigilance or refuge use, excursion
distances and activity space (in 3 dimensions), forag-
ing rates, body mass and condition, movement rate,
stress levels, nutritional condition, and morpho -
logical structures associated with defense, detection,
or evasion. As in the case for sharks, behavioral re-
sponses of prey could be measured via observational
and/or biotelemetry and biologging tools (Wirsing et
al. 2007, Madin et al. 2010, De Vos et al. 2015). For
example, using underwater camera monitoring on
Australian coral reefs, Atwood et al. (2018) measured
grazing rates of herbivorous fishes at varying dis-
tances from refuges under threat of predation. Physi-
ological responses of prey could be assessed non-
invasively via tissue or scat sampling (Hammerschlag
et al. 2017, Oliveira et al. 2017). Morphological re-
sponses include measuring shape and size of preda-
tory defense structure (e.g. claws or spines; Miller et
al. 2015), detection structures (e.g. eyes, olfactory
bulbs; Smith & Litvaitis 1999, Møller & Erritzøe 2014),
and/or evasive locomotory structures (e.g. flipper or
fins; Hammerschlag et al. 2018). Relevant environ-
mental attributes widely found to decrease predation
risk include increased habitat complexity (Schmitz
1998), proximity to refuge (Atwood et al. 2018), and
visibility (Hammerschlag et al. 2006) as well as de-
creased depth (Rypel et al. 2007). These environmen-
tal attributes can be measured in situ or via remotely
sensed data (e.g. Madin et al. 2011).
In terms of an experimental approach, these pred-
ator−prey attributes can be investigated under natu-
ral conditions in response to temporal and/or spatial
variation of hunting sharks (e.g. Wirsing et al. 2007,
Hammerschlag et al. 2012) or with introduced model
sharks (Madin et al. 2010, Rizzari et al. 2014). An
emerging opportunity are comparisons over time or
between areas exposed to differences in targeted
shark removals or protections (e.g. Barley et al.
2017a,b, Speed et al. 2018). In addition to these field-
based approaches, mesocosom or laboratory experi-
mentation of prey reactions to predators are valuable
for understanding the often cryptic nature of interac-
tions between shark and their prey (Bedore et al.
2015, Barrios-O’Neill et al. 2017, Stump et al. 2017).
4. CONCLUSION
In summary, relying on stomach contents and/or stable
isotope signatures to assess the potential for sharks to
initiate trophic cascades can be misleading and may
significantly underestimate the strength of shark top-
down predation effects on prey. A study approach that
measures relevant functional attributes of sharks, prey
and the environment can provide greater insights for
quantifying the magnitude of top-down predation
effects (Schmitz 2008, Creel 2011, 2018, Schmitz et al.
2017, Gaynor et al. 2019) and the potential for shark
population declines or recoveries to trigger trophic cas-
cades. As outlined by Ruppert et al. (2016), there is a
need for research efforts to focus on predator−prey
relations (rather than simply the ecology of the preda-
tor) to understand the process of predation.
Acknowledgements. Thanks to Brendan Godley, who en -
couraged me to submit this work, and to the anonymous
reviewers whose comments helped significantly strengthen
this paper. I also thank the many great colleagues whose
research laid the groundwork for this paper and enabled me
to synthesize the ideas presented.
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