ArticlePDF Available

Physiological stress responses to natural variation in predation risk: evidence from white sharks and seals

  • Marine Biodiversity Observation Network
  • Oceans and Coasts
  • Oceans and Coasts

Abstract and Figures

Predators can impact ecosystems through consumptive or risk effects on prey. Physiologically, risk effects can be mediated by energetic mechanisms or stress responses. The predation-stress hypothesis predicts that risk induces stress in prey, which can affect survival and reproduction. However, empirical support for this hypothesis is both mixed and limited, and the conditions that cause predation risk to induce stress responses in some cases, but not others, remain unclear. Unusually clear-cut variation in exposure of Cape fur seals (Arctocephalus pusillus pusillus) to predation risk from white sharks (Carcharodon carcharias) in the waters of Southwestern Africa provides an opportunity to test the predation-stress hypothesis in the wild. Here, we measured fecal glucocorticoid concentrations (fGCM) from Cape fur seals at six discrete islands colonies exposed to spatiotemporal variation in predation risk from white sharks over a period of three years. We found highly elevated fGCM concentrations in seals at colonies exposed to high levels of unpredictable and relatively uncontrollable risk of shark attack, but not at colonies where seals were either not exposed to shark predation or could proactively mitigate their risk through antipredatory behavior. Differences in measured fGCM levels were consistent with patterns of risk at the site and seasonal level, for both seal adults and juveniles. Seal fGCM levels were not correlated with colony population size, density, and geographic location. Investigation at a high risk site (False Bay) also revealed strong correlations between fGCM levels and temporal variation in shark attack rates, but not with shark relative abundance. Our results suggest that predation risk will induce a stress response when risk cannot be predicted and/or proactively mitigated by behavioral responses.
Content may be subject to copyright.
VOL. 98, NO. 12, 2991–3251 ECOLOGY DECEMBER 2017
Physiological stress responses to natural variation in predation
risk: evidence from white sharks and seals
Fighting an uphill battle: the recovery of
frogs in Australia’s Wet Tropics
ESA 2018
Sunday, August 5 – Friday, August 10
Ernest N. Morial Convention Center
New Orleans, Louisiana
Abstract Submission Deadline:
February 22, 2018 | 5:00 PM EST
Linking extreme events,
ecosystem resilience
and human well-being
ECY_v98_i12_OC.indd 1ECY_v98_i12_OC.indd 1 23-11-2017 19:26:5823-11-2017 19:26:58
Physiological stress responses to natural variation in predation risk:
evidence from white sharks and seals
Department of Marine Ecosystems and Society, Rosenstiel School of Marine and Atmospheric Sciences,
University of Miami, Miami, Florida 33149 USA
Leonard and Jayne Abess Center for Ecosystem Science and Policy, University of Miami, Coral Gables, Florida 33146 USA
Branch: Oceans and Coasts, Department of Environmental Affairs, Private Bag X4390, Cape Town 8000 South Africa
Apex Shark Expeditions, Shop 3 Quayside Center, Simonstown Cape Town 7975 South Africa
Department of Ecology, Montana State University, Bozeman, Montana 59717 USA
Abstract. Predators can impact ecosystems through consumptive or risk effects on prey.
Physiologically, risk effects can be mediated by energetic mechanisms or stress responses. The
predation-stress hypothesis predicts that risk induces stress in prey, which can affect survival
and reproduction. However, empirical support for this hypothesis is both mixed and limited,
and the conditions that cause predation risk to induce stress responses in some cases, but not
others, remain unclear. Unusually clear-cut variation in exposure of Cape fur seals (Arcto-
cephalus pusillus pusillus) to predation risk from white sharks (Carcharodon carcharias) in the
waters of Southwestern Africa provides an opportunity to test the predation-stress hypothesis
in the wild. Here, we measured fecal glucocorticoid concentrations (fGCM) from Cape fur
seals at six discrete islands colonies exposed to spatiotemporal variation in predation risk from
white sharks over a period of three years. We found highly elevated fGCM concentrations in
seals at colonies exposed to high levels of unpredictable and relatively uncontrollable risk of
shark attack, but not at colonies where seals were either not exposed to shark predation or
could proactively mitigate their risk through antipredatory behavior. Differences in measured
fGCM levels were consistent with patterns of risk at the site and seasonal level, for both seal
adults and juveniles. Seal fGCM levels were not correlated with colony population size, density,
and geographic location. Investigation at a high risk site (False Bay) also revealed strong corre-
lations between fGCM levels and temporal variation in shark attack rates, but not with shark
relative abundance. Our results suggest that predation risk will induce a stress response when
risk cannot be predicted and/or proactively mitigated by behavioral responses.
Key words: antipredator behavior; apex predator; ecology of fear; ecophysiology; glucocorticoid; noncon-
sumptive effects; predation risk; risk effect; seal; shark; stress.
Apex predators can strongly alter ecosystems by affect-
ing the numbers, distribution and behavior of their prey
(Werner and Peacor 2003, Peckarsky et al. 2008). More-
over, such behavioral modifications in response to preda-
tion risk can manifest in physiological changes in prey
that carry fitness costs and affect population dynamics
(Peckarsky et al. 2010, Zanette et al. 2014). Together,
these predator effects can have cascading impacts on
other species (Schmitz et al. 1997, Brashares et al. 2010).
Two general hypotheses have been proposed for the
physiological mechanism underlying predation risk effects
on prey populations (outlined in Creel et al. 2009). The
predator-sensitive food hypothesis predicts that predators
constrain foraging activity or efficiency of their prey, thus
increasing energetic or nutritional constraints on prey
reproduction or survival (Sinclair and Arcese 1995). Many
studies across a broad range of taxa have found empirical
evidence in support of this hypothesis (Werner and Peacor
2003). Growing research has revealed that food-mediated
reactions of prey to predators (e.g., foodrisk trade-offs)
are not simply an artifact of predator encounter rates and
often depend on predator hunting mode, anti-predator
behavior, and on landscape features that can influence the
probability of death given an encounter with a predator
(Schmitz 2008, Heithaus et al. 2009). The predation-stress
hypothesis predicts that exposure to risk causes increased
secretion of glucocorticoids (or other physiological stress
responses; Romero 2004). However, studies of this hypoth-
esis in natural settings have been limited and the results
mixed (Boonstra et al. 1998, Clinchy et al. 2004, Creel
et al. 2009, Sheriff et al. 2009). Moreover, most investiga-
tions of this hypothesis in the wild have generally
employed study designs for monitoring of potential stress
changes through time at a single site (Boonstra et al.
1998, Zanette et al. 2014); but few natural study systems
have been conducive to investigating the effects of risk on
Manuscript received 28 September 2017; accepted 6 October
2017. Corresponding Editor: John F. Bruno.
Ecology, 98(12), 2017, pp. 31993210
©2017 by the Ecological Society of America
stress responses in prey populations with replication
within both a low risk and high risk condition. Addition-
ally, many studies assume that risk effects are mediated by
fear of predation, which is manifested in stress; however,
anti-predatory behavioral responses to risk can arise
through mechanisms that do not involve a stress response.
For example, in the greater Yellowstone ecosystem, elk
(Cervus elaphus) forage in suboptimal habitats to reduce
their exposure to predation from wolves (Canis lupus;
Creel et al. 2005), which incurs physiological costs associ-
ated with lower nutritional intake, but does not result in a
stress response (Creel et al. 2009). Taken together, a key
question remains: under what conditions are risk effects
mediated by stress in the wild? This knowledge gap is lar-
gely driven by the inherent challenges of working with
predators and their prey in nature. Such studies are partic-
ularly important at this time given declines in predator
populations worldwide (Estes et al. 2011), together with
successful predator recovery programs in some ecosystems
(Marshall et al. 2016).
The waters off the Western Cape of South Africa pro-
vides an opportunity to study predatorprey interactions
involving white sharks Carcharodon carcharias and Cape
fur seals Arctocephalus pusillus pusillus, and an ideal sys-
tem to test the effects of predation risk on prey physiology
(Fig. 1A). Here, the population of Cape fur seals in the
region is segregated into discrete colonies that inhabit dif-
ferent inshore islands; but the seals from the different
island colonies move offshore to feed where they share
the same general foraging grounds (Rand 1959, 1967,
Oosthuizen 1991). White sharks only actively target cer-
tain seal colonies, creating a natural spatial variation in
seal colonies to threat of attack (Bonfil et al. 2005, Dud-
ley 2012, De Vos et al. 2015a, Andreotti et al. 2016). At
these locations, there also exists clear seasonal variation
in predation risk, as sharks only actively hunt Cape fur
FIG. 1. (A) Photograph of a white shark lunging toward a Cape fur seal at the edge of Seal Island (background) in False Bay,
South Africa. White sharks aggregate during the cool season at specific island colonies to hunt seals when they enter and exit the
water to and from offshore foraging. (B) A white shark patrols the border of a kelp bed that surrounds the seal colony at Geyser
Rock. The kelp is used by seals as refuge from sharks while traversing to and from the island. (C) Kelp and other high relief land-
scape features are absent from the waters around the island colony in False Bay, where attack rates on seals are 18 times higher than
at Geyser Rock. (D) A large seal bearing a fresh injury from a white shark attack hauls out on to Seal Island in False Bay where it is
investigated by a group of pups and juvenile seals. All images by C. Fallows. [Color figure can be viewed at]
3200 NEIL HAMMERSCHLAG ET AL. Ecology, Vol. 98, No. 12
seals during winter months (Hammerschlag et al. 2006,
Jewell et al. 2013, Towner et al. 2016). However, among
the different colonies targeted by sharks, predation risk to
seals can vary significantly due to landscape features that
either offer protection to seals traversing the gauntlet or
leave them exposed to unpredictable risk of attack (Wcisel
et al. 2015). For example, white shark attack rates on
seals at the seal colony in False Bay (1.97 attacks/h) are
18 times higher than at Geyser Rock (0.1 attacks/h),
because the latter is surrounded by dense kelp beds and
reefs that serve as a refuge from sharks when the seals tra-
verse from their colony (Wcisel et al. 2015; Fig. 1B). Such
landscape features are absent from False Bay (Fig. 1C),
where predation rates on seals average 6.7 attacks per day
during the cool season, with success rates as high as 0.55
kills per attack (Hammerschlag et al. 2006). Rarely do
spatially discrete natural prey populations share the same
general food supply, while being exposed to such distinct
variation in predation risk as in the current study system.
The purpose of this study was to test the predation-
stress hypothesis using this shark-seal system off South
Africa. First, we analyzed movement patterns of satellite
tagged white sharks to evaluate spatial and temporal pat-
terns of white shark residency and density at six seal colo-
nies that vary in risk from shark attack. Next, we
collected seal scat samples from these six seal colonies
over multiple years and seasons for measurements of fecal
glucocorticoid metabolites (fGCM) to evaluate if concen-
trations were associated with patterns of spatial and tem-
poral occurrence of white sharks. Finally, we conducted a
multi-year study at Seal Island in False Bay (a high risk
site), to test if seal fecal cortisol levels were correlated with
either relative abundance of white sharks or measured
attack rates by sharks on seals. We used these data to (1)
evaluate if seal colonies exhibit spatial and seasonal varia-
tion in fGCM concentrations consistent with patterns of
white shark predation risk, (2) test if fGCM levels in seals
correlate with shark relative abundance or measured sur-
face attack rates by sharks on seals in False Bay, (3) test if
differences in colony-specific landscape features that
influence the type and magnitude of shark predation risk
to seals are associated with differences in seal fGCM con-
centrations, and (4) determine whether fecal cortisol levels
differ in in adult seals vs. juveniles under risk of preda-
tion. Additionally, we tested the alternative hypotheses
that fGCM concentrations were associated with seal col-
ony population size, density and geographic location (and
associated variation in environmental factors).
Study system
Cape fur seals exhibit high site fidelity to specific
inshore island colonies, which therefore remain fairly
discrete units (Rand 1959); however, seals from different
colonies share the same general offshore feeding grounds
as revealed through markrecapture (Oosthuizen 1991).
Previous satellite and acoustic tracking of white sharks
within South Africa have demonstrated that sharks
aggregate at certain seal colonies during cool months of
the year to target Cape fur seals. These high shark
abundanceseal colonies include False Bay, Mossel Bay,
and Geyser Rock, where shark habitat use and predation
has been relatively well studied (Martin et al. 2005,
Johnson et al. 2009, Fallows et al. 2012, Jewell et al.
2013, Kock et al. 2013, Towner et al. 2016, Wcisel et al.
2015). During warm months, white sharks shift the focus
of their hunting away from seals at the colonies, presum-
ably to feed on teleosts and elasmobranchs, resulting in
lower shark occurrence and predation pressure to seals
at these high shark abundance colonies during the sum-
mer (Hammerschlag et al. 2006, Kock et al. 2013, De
Vos et al. 2015a). There are also low shark abundance
seal colonies within the region that are not targeted by
sharks at any time of year (Bonfil et al. 2005, Kock et al.
2013, De Vos et al. 2015a). These spatiotemporal pat-
terns of white shark abundance have also been found
through region-wide survey data of white sharks
through standardized boat-based baited surveys as part
of a project to estimate population sizes of white sharks
in South Africa (Andreotti 2015, Andreotti et al. 2016).
In this study, we focused our investigation on six seal
colonies that varied in exposure to the seasonal presence
of hunting white sharks: three high shark abundance
colonies (False Bay, Mossel Bay, and Geyser Rock) and
three low shark abundance colonies (Bird Island in Lam-
berts Bay, Jutten Island, and Robbesteen; Fig. 2) during
both the high predation season(winter, JuneSeptem-
ber) and low predation season(summer, October
May). However, to confirm this previously described
spatial and temporal variation in exposure of the six
focal seal colonies to white sharks during the study per-
iod, we analyzed white shark movement data that were
collected as part of a larger collaborative satellite tagging
project between the USA-based non-profit OCEARCH
and South African researchers.
White shark tracking
Between March and May of 2012, a total of 37 white
sharks (14 males, 23 females) were captured and tagged
at five different localities across the Western Cape of
South Africa: Algoa Bay, False Bay, Gansbaai, Mossel
Bay, and Struisbaai. Details on capture and handling
methods can be found in Wcisel et al. (2015). Briefly,
sharks were captured with baited barbless hooks and
carefully lead onto a hydraulic platform. One or two
hoses where then inserted into the sharks mouth to
pump fresh oxygenated saltwater over the gills. Sharks
received antibiotics and electrolyte injections to enhance
recovery time. Smart Position-only and Temperature
Transmitting tags (SPOT5 tags; Wildlife Computers,
Redmond, Washington, USA ) were affixed to the dorsal
fin of sharks. Depending on their size, sharks were
tagged with either a small SPOT tag (2-yr battery life for
sharks <3 m total length) or a large SPOT tag (5-yr bat-
tery life for sharks >3 m total length).
Locations were acquired whenever the dorsal-fin
mounted SPOT tag broke the water surface and transmit-
ted a signal to a passing ARGOS satellite. Geographic
locations of SPOT tagged sharks were determined by
Doppler-shift calculations made by the ARGOS Data
Collection and Location Service (Argos CLS, Toulouse,
France ). Accuracy of position data is variable depending
on number of transmissions received by ARGOS and cat-
egorized into location classes (LC) as follows:
LC 3 <250 m, 250 m <LC 2 <500 m, 500 m <LC 1 <
1,500 m. The median error for LC 0, A, and B ranges
from 1 to 3 km. Class Z indicates that the location pro-
cess failed and estimates of position are highly inaccurate
and are removed before spatial analysis.
Seal fecal sample collection
Between 2012 and 2015, a total of 502 fecal samples
were collected and analyzed for fGCM, with at least two
collection periods in the high predation season and two
collection periods in the low predation season for each
colony (N=92 samples from Geyser Rock, N=44 from
Robbesteen, N=52 from Jutten Island, N=145 from
LambertsBay,N=114 from Seal Island in False Bay,
N=55 from Seal Island in Mossel Bay). At each sam-
pling occasion, ~20 g samples from clearly distinct
defecations by single seals were collected and placed in
50-mL screw-lid vials and frozen within 12 h of collec-
tion. Where possible, fecal samples could be assigned to
seal age classes (adult vs. juvenile) based on differences in
size (though no effect of age class was detected: see
Results). The juvenile category also included young-of-
the year seals. Based on direct observation, juvenile scats
ranged in size from 6 to 10 mm diameter; whereas adult
scats ranged in size from 15 to 20 mm diameter. Adult
scats could also be further distinguished based on a rela-
tively higher frequency of fish bones in the scat, which are
fewer in juveniles. Ambiguous samples and scats between
11 and 15 mm were not assigned to an age class.
Steroid hormone metabolites were extracted from
fecal samples by drying the scat and boiling a known
mass of dry feces in ethanol using methods that have
been described in detail previously (Monfort et al. 1997,
Creel et al. 2009).
We measured glucocorticoid metabolite concentrations
in fecal extracts (fGCM) using an enzyme-linked
immunoassay with a cortisol antibody (Enzo Life
Sciences ADI-900-071, Farmingdale, New York, USA))
that has broad cross-reactivity and has been procedurally
and biologically validated for assay of fecal extracts in a
broad range of species (Monfort et al. 1997, Creel et al.
Lambert's Bay,
Bird Island (LS)
Juen Island (LS)
Robbesteen (LS)
False Bay,
Seal Island (HS)
Geyser Rock (HS)
Mossel Bay,
Seal Island (HS)
FIG. 2. Locations of six focal colonies off the Western Cape of South Africa (inset). LS, low shark abundance colony; HS, high
shark abundance colony. [Color figure can be viewed at]
3202 NEIL HAMMERSCHLAG ET AL. Ecology, Vol. 98, No. 12
2009, 2013). We expressed fGCM concentrations as mil-
ligrams of cortisol immunoreactivity per gram of dry
feces. Antibody binding was parallel for a dilution series
of cortisol standards and seal fecal extracts diluted from
1:1 to 1:256 (log-linear slopes 0.011 and 0.014, respec-
tively). Quantitative recovery of cortisol added to seal
fecal samples was highly accurate (r
b=1.00 0.03 [mean SE]) for a range from 156 to
5,000 pg of cortisol added to fecal extracts at working
concentration. Assay sensitivity was several orders of
magnitude below the concentration of fecal extracts.
Based on preliminary analysis, we assayed extracts at
1:100 dilution to maximize sensitivity, assaying all sam-
ples in duplicate with a seven-standard curve, controls
and measures of total activity, zero-steroid binding, and
non-specific binding on each plate. Intra- and inter-assay
coefficients of variation from pooled fecal extracts were
13.36 and 13.71, respectively. The composition of scats
did not vary between sites or seasons, and we detected no
association between fGCM concentrations and the com-
position of scats, as measured by the proportions of water
and indigestible matter.
Seal population data
Aerial photographs of seal colonies from fixed wing
and helicopter aircrafts were taken at the peak of the seal
pupping season and the numbers of newborn pups of
the year were counted on the photographs. This is
because pups are confined to land (during their first
month of life) and their numbers can be used to infer
total population size of adults and pups, subject to cer-
tain assumptions (Kirkman et al. 2011). Because Jutten
Island is primarily a non-breeding colony where rela-
tively few pups are currently born, both pups and adult
seals were counted.
Aerial seal counts were conducted annually at all six
seal colonies between 2011 and 2014, excluding 2012 at
Mossel Bay and Lamberts Bay and 2013 at Robbesteen,
Geyser Rock, False Bay, and Mossel Bay. Annual seal
counts were then averaged across the study period (here-
after referred to as colony population size). We also cal-
culated an estimate of seal density (seals/ha) at each
colony by dividing the number of seals counted by the
area of the island occupied by the seals (hereafter
referred to as colony density).
Shark predation and relative shark abundance
in False Bay
Between February 2014 and August 2015, standard-
ized surveys were conducted at Seal Island in False Bay
to evaluate for the potential differential effects of shark
occurrence vs. hunting behavior (i.e., components of pre-
dation risk) on seal fGCM concentrations.
Predation by white sharks on Cape fur seals was
recorded daily around the seal island colony in False
Bay for one week prior to fecal sample collection, which
occured on seven dates: 11 February, 25 April, 30 July,
and 12 September 2014 as well as on 19 March, 7 July,
and 12 August 2015. Previous laboratory studies that
have subjected sea lions (Eumetopias jubatus)toan
adrenocorticotropic hormone (ACTH) challenge found
a lag of up to 4 d between ACTH injection and peak
fGCM (Hunt et al. 2004). Accordingly, for one week
prior to collecting fecal samples on the dates specified
above, standardized observations for shark predation
events were performed from a research vessel daily
between 07:00 and 09:30, sea conditions permitting.
Monitoring of shark predations on seals were conducted
following the approach of Martin et al. (2005), and Fal-
lows et al. (2016). Here, sharks primarily attack Cape
fur seals at the surface via a vertical breach when seals
are surface porpoising to and from the Island. Attacks
are concentrated on the southern side of the Island, close
to shore (within 2 km). By positioning at the south end
of Seal Island where the majority of predatory activity
occurs, we were able to survey the waters surrounding
the island for predations up to a distance of at least
3 km, although the majority of predations occur within
400 m of southwest end of the island (Martin et al.
After about 09:30 , our research vessel anchored on
the southern side of Seal Island and conducted stan-
dardized boat-based baited surveys of white sharks
using an approach modified from Hammerschlag and
Fallows (2005). Between 10:00 and 12:00, sea conditions
permitting, sharks were attracted to the boat using a
large tuna head and seal decoy. Individual white shark
can be identified based on a combination of visual mak-
ers, including unique scarring, presence/absence of clas-
pers, and individual variation in pigmentation patters on
the gill flaps, pelvic fins, and caudal fins (Domeier and
Nasby-Lucas 2007). The duration of baited surveys were
recorded, along with the number of different individual
white sharks observed during this period. Using these
data, we calculated the number of different white sharks
observed per hour of baited survey as a metric of relative
shark abundance.
White shark tracking analysis
SPOT satellite tagging data were downloaded from
ARGOS CLS. Given variable tag size (and thus battery
life) and possible issues related to biofouling with
increasing tag age, we included only positions within a
year of tagging for each individual shark. Given irregu-
lar transmissions and varying levels of position accuracy
(see Hammerschlag et al. 2011 for details), all SPOT
location data were interpolated and regularized to a con-
stant 6-h interval using a hierarchical, first-difference,
correlated, random-walk, switching (hDCRWS), state-
space model (SSM) described by Jonsen (2016). This
approach accounts for both the variability in geoposi-
tion accuracy provided through ARGOS location classes
and the irregularity of surfacing. Locations were not
interpolated for data gaps >14 d and for shark tracks
with fewer than 20 positions (per instructions by I. Jon-
sen). The SSM model also estimates a behavioral state
(bt) for each position ranging from one (transient,
migratory state) to two (resident, area-restricted state;
Jonsen 2016). Following Acu~
na-Marrero et al. (2017),
each position was classified as being either predomi-
nantly transient (bt <1.25) or predominantly resident
(bt >1.75). Given we were interested in predation risk
to seals from hunting sharks residing at the colonies,
subsequent analysis were restricted to 6-h interpolated
positions classified as predominately resident (bt >1.75)
and plotted in ArcMap 10.1 (ESRISA).
To examine relative differences in the spatial distribu-
tion of white sharks in relation to the focal seal colonies,
we applied kernel density analysis to the position data in
ArcMap 10.1. This calculated and plotted the number of
shark positions per km
using a kernel function (Silver-
man 1986). We then used ArcMap spatial analysis tools to
calculate the average kernel densities (KD) of white shark
positions within a 2 km radius of the high vs. low shark
abundance colonies. This analysis was conducted sepa-
rately for the warm (OctoberMay) and the cool season
(JuneSeptember). To evaluate patterns of shark residency
at the colonies, we calculated the mean, minimum, and
maximum number of days individual white sharks spent
within 2 km of the high vs. low shark abundance colonies
within each season. We selected a 2 km radius given the
spatial accuracy of the SPOT data and also because white
sharks patrolling the colonies concentrate hunting effort
within 2 km of shore (Johnson et al. 2009, Fallows et al.
2012, Jewell et al. 2013, Towner et al. 2016).
Statistical analysis
In addition to colony-specific and seasonal variation
in predation risk to seals, differences in colony popula-
tion size and/or density could contribute to seal stress
levels. Moreover, given that colonies were widely dis-
tributed along the coast, differences in their geographic
locations (i.e., latitude and longitude) could potentially
expose the colonies to differences in environmental fac-
tors that could influence seal stress levels. Accordingly,
generalized linear models (GLiMs) were employed to
test the effects of these explanatory variables on mea-
sured fGCM concentrations, which included seal colony
(Jutten Island, Lamberts Bay, Robbesteen, Geyser
Rock, Mossel Bay, False Bay), season (warm, cool), the
interaction between colony and season (colony 9sea-
son), colony population size, density, latitude, and longi-
tude. GLiMs were constructed with backward and
forward stepwise selection, starting with a model con-
taining all the plausible explanatory variables. Retention
or removal of variables were based on the Akaike infor-
mation criterion (AIC), with the lowest AIC suggesting
the best fitting model. Based on results of the GLiMs,
which revealed that the best explanatory model included
only the interactive effects of colony 9season, Tukeys
standardized range (HSD) tests were used for pairwise
evaluations among colonies across both seasons.
For the high shark abundance colonies (False Bay,
Mossel Bay, Geyser Rock), analysis of variance
(ANOVA) was used to test for potential differences in
fGCM between seal age classes (adult vs. juveniles),
between seasons, and the interaction of these effects.
Tukey s HSD tests were then used for pairwise evalua-
tions between age classes among colonies.
For predation data recorded in False Bay, hourly attack
rates by white sharks on seals were averaged across the
seven days prior to each fecal sampling. Pearsonscorre-
lation was used to test if weekly shark attack rates were
correlated with associated fGCM concentrations. Simi-
larly, for shark survey data, hourly relative abundance of
white sharks were averaged across the seven days prior to
each fecal sampling and Pearsons correlation was used
to test if shark relative abundance was correlated with
associated fGCM concentrations.
In addition to the GLiMs testing for effects of plausi-
ble explanatory variables on measured fGCM concen-
trations, we used Pearson correlations to separately test
for significant relationships between colony population
size and density on mean fecal cortisol concentrations.
Similarly, we used Pearson correlations to separately test
for potential significant relationships between latitude
and longitude on seal fGCM values in both the warm
and cool season.
All statistical analyses were conducted in SAS statisti-
cal software.
Thirty-four tagged white sharks provided geoposition
data (three tags failed to report) for spatial analyses of
locations regularized to 6-h intervals using SSM. Sixteen
individual sharks provided resident positions (bt >1.75)
within 2 km of a high shark abundance colony (Geyser
Rock, Mossel Bay, and False Bay) in both the warm and
cool season. In contrast, no sharks provided resident
positions within 2 km of a low shark abundance colony
(Jutten Island, Lamberts Bay, Robbesteen) in either sea-
son. In fact, no sharks tracked occurred within 2 km of
these colonies; the closest a resident individual came
within proximity of a low shark abundance colony was a
single individual 44 km off Jutten Island. Within 2 km
of the high shark abundance colonies, average density of
shark positions were 2.5 times higher in the cool season
(KD =0.35 km
) than warm season (KD =0.14 km
In terms of residency, sharks were resident 1.5 times
more days within 2 km of high shark abundance colo-
nies during the cool season than during the warm season
(Table 1). Moreover, the maximum number of days an
individual shark was resident at a high shark abundance
colony during the cool season was double that of the
warm season (Table 1).
Fecal GCM concentrations showed considerable vari-
ation, ranging from a minimum of 26.66 mg/g to a
3204 NEIL HAMMERSCHLAG ET AL. Ecology, Vol. 98, No. 12
maximum of 3,372.11 mg/g. The best fitting GLiM
included only the interactive effects of colony and season
(Colony 9Season) as an explanatory variable, which
was highly significant (P<0.0001; Appendix S1:
Table S1). Pairwise comparisons using Tukeys HSD
Tests revealed fGCM concentrations were significantly
higher in samples from the high shark abundance colo-
nies in False Bay and Mossel Bay (Fig. 3; Appendix S1:
Table S2). While fGCM concentrations for these two
colonies did not differ from one another in the high pre-
dation season, both were significantly higher than from
collection made from those same colonies in the low pre-
dation season as well as significantly greater than those
from any other colony in either season, including the
high shark abundance colony at Geyser Rock (Fig. 3).
Concentrations of fGCM measured from the island
colonies in False Bay and Mossel Bay (1,737.8 689.16
SD mg/g) were on average 2.8 times greater than the
mean of all other colonies across both seasons
(627.26 523.88 mg/g), including Geyser Rock during
the high predation season (620.01 388.19 m/g).
For the high shark abundance colonies, there was a sig-
nificant interaction between season, seal age class, and
individual colony on measured fGCM values (ANOVA,
N=261, df =7, F=10.26, P<0.0001; Appendix S1:
Table S3). At these sites, fecal glucocorticoid metabolite
concentrations did not differ between adults and juveniles
in either the low or high predation season, but values for
both adults and juveniles were significantly higher in the
high vs. low predation season (Appendix S1: Table S4).
The exception was for Geyser Rock, in which there were
no differences in fGCM values between age classes during
the high vs. low predation season (Appendix S1: Table S4).
At Seal Island in False Bay, white shark predation rates
on seals were recorded on seven occasions during the
week prior to fecal sampling. Mean weekly predation
rates ranged from 0 attacks/h (410 February 2014) to
3.49 attacks/h (511 August 2015). Pearson correlation
revealed a strong positive linear correlation between mean
fGCM concentrations and mean predation rates (attacks/
h) over the prior week (r=0.96, P=0.0007; Fig. 4A). In
contrast, fGCM concentration were not correlated with
shark relative abundance (r=0.2, P=0.66; Fig 4B).
TABLE 1. Mean kernel density (KD) estimates of white shark
interpolated position per km
and the mean, minimum (min.)
and maximum (max.) number of days individual sharks were
resident within 2 km of a low shark abundance colony (Jutten
Island, Lamberts Bay, and Robbesteen) or a high shark
abundance seal colony (Geyser Rock, Mossel Bay, and False
Bay) in the warm (low predation) and cool (high predation)
season within one year of tagging.
Colony Season
Low warm 0 0 0 0
Low cool 0 0 0 0
High warm 0.14 7.1 1 22
High cool 0.35 10.8 1 47
Jutten Island
Geyser Rock
S.I. Mossel Bay
S.I. False Bay
Jutten Island
Geyser Rock
S.I. Mossel Bay
S.I. False Bay
fGCM (mg/g)
HP season
LP season
FIG. 3. A comparison of fecal glucocorticoid metabolite concentrations (fGCM) across all six seal colonies in both the low preda-
tion season and high predation season. Fecal samples were collected from each colony at least twice per season for at least two years
between 2012 and 2015. High shark abundance colonies are solid bars and low shark abundance colonies are vertical patterned bars.
Values are means and whiskers are 95% confidence intervals. Values with different letters denote statistical differences. HS, high shark
abundance colony; LS, low shark abundance colony; HP, high predation season; LP, low predation season; S.I., Seal Island.
Average counts of seals at each colony across the study
period and associated density can be found in App-
endix S1: Table S5. Model selection did not include these
explanatory variables in the final GLiM, and seal colony
population size and density was not correlated with mean
fGCM concentrations at that colony (Fig. 5A,B). More-
over, seal fGCM concentrations were not correlated with
either colony latitude or longitude in either the warm or
cold season (Fig. 5CF).
The predation-stress hypothesis predicts that risk from
predators causes activation of physiological stress reac-
tions in prey, which can directly or indirectly reduce
reproduction and survival (Romero 2004, Clinchy et al.
2013). Observational and experimental studies have
found mixed evidence for such increases in stress hor-
mones of prey in response to predators or predation cues
(Boonstra et al. 1998, Clinchy et al. 2004, Creel et al.
2009, Sheriff et al. 2009) and to date there have been too
few studies of the predation-stress hypothesis in the wild
to infer general relationships (Clinchy et al. 2013). In this
study, we found that spatiotemporal patterns of stress in
Cape fur seals, measured by fGCM concentrations, were
consistent with predation risk from white sharks at the
site and seasonal level. In contrast, fGCM levels were not
detectably related to seal colony population size, density
and geographic location. Moreover, our subsequent study
in False Bay, revealed seal fGCM concentrations were
strongly positively correlated with temporal variation in
shark predation rates, but not with shark relative abun-
dance (Fig. 4). These findings provide empirical support
for the predation-stress hypothesis in the wild from a nat-
ural experiment involving large long-lived apex predators
and their prey. It is worth considering here that seals may
also incur costs consistent with the predator-sensitive
food hypothesis, which could be assessed through the
future research using energetic or nutritional indicators
(Gallagher et al. 2017a,b). The two mechanisms are not
mutually exclusive (Creel et al. 2009).
Below we separately discuss plausible alternative expla-
nations for the patterns found; however, it is likely that
some unmeasured variables (e.g., storms) are contributing
at least partly to the variation in measured fecal cortisol
levels, such as the seasonal difference in mean fGCM that
were found for the colony in LambertsBay.However,
the data gathered suggest that these factors are not the
primary drivers of the nearly quadrupling in fGCM levels
that occurred at the False Bay and Mossel Bay high risk
seal colonies during the high predation season. In con-
trast, we believe the majority of this variation is attributa-
ble to predation stress. While our data is correlative, our
inference is strengthened by the added results from the
False Bay study where the magnitude in variations of
fGCM levels were associated with recorded predation
rates on seals, measured on multiple occasions across dif-
ferent seasons over the course of two years (Fig. 4A).
The occurrence of some white sharks at the high shark
abundance colonies during the warm season, albeit lower
than during the cool season (Table 1), may seem difficult
to reconcile with the low fGCM stress levels found there,
which were comparable to the low shark abundance colo-
nies where no tracked sharks occurred. We suggest this is
because during the warm season at the high shark abun-
dance sites, white sharks are not actively hunting Cape
fur seals (Hammerschlag et al. 2006, Wcisel et al. 2015).
Corroborating these data is behavioral information previ-
ously gathered from Cape fur seals using acoustic teleme-
try in False Bay (De Vos et al. 2015a,b). For example,
seals engaged in safer behaviors, such as swimming in lar-
ger groups and avoiding deeper water, during the high
predation season and not during the low predation sea-
son (De Vos et al. 2015a,b).
Although exposed to hunting white sharks during the
cool season, seals at Geyser Rock did not exhibit signifi-
cant seasonal increases in fGCM levels in a manner simi-
lar to False Bay and Mossel Bay. This pattern can be
explained by the previously described variation in seals
use of landscape features unique to the waters surround-
ing Geyser Rock. Specifically, these waters contain a
mosaic of structurally complex reef and dense kelp beds
that are used by seals as refugia from white sharks and
appear to effectively reduce attack rates on seals (Wcisel
et al. 2015). So while encounter rates between white
sharks and seals are high during the cool season at Gey-
ser Rock, the seals can behaviorally mitigate their expo-
sure to risk by using the reef and kelp as a relatively safe
0 0.5 1.0 1.5 2
fGCM (mg/g)
Shark relative abundance (sharks/h)
r= 0.2
P = 0.66
fGCM (mg/g)
Predation rate (attacks/h)
r= 0.96
P = 0.0007
FIG. 4. Correlation between fGCM concentrations and (A)
mean weekly predation rates (attacks/h) and (B) mean weekly
relative shark abundance (sharks/h) at Seal Island in False Bay.
Fecal sampling occurred on seven occasions between February
2014 and August 2015. Values are fGCM means SE.
3206 NEIL HAMMERSCHLAG ET AL. Ecology, Vol. 98, No. 12
underwater pathway to escape the immediate vicinity of
the colony (Wcisel et al. 2015; Fig. 1B). In comparison,
the waters in the immediate vicinity of the two other high
shark abundance colonies (Mossel Bay and False Bay)
are relatively deep and featureless, lacking landscape
characteristics that allow seals to proactively mitigate
their risk of attack (Fig. 1C). Indeed, recorded hourly
predation rates by white sharks on seals in False Bay are
more than 18 times higher than at Geyser Rock (Wcisel
et al. 2015). Moreover, the False Bay investigation
revealed strong positive correlations between seal fecal
stress levels and shark attack rates, but not with shark rel-
ative abundance. These results are consistent with grow-
ing theoretical and empirical predatorprey research
indicating that predator presence alone does not equate
to risk, as predator hunting mode, prey anti-predator
behavior, and habitat characteristics that mediate both
will influence risk (Schmitz 2008, Heithaus et al. 2009).
The finding from False Bay that seal fecal cortisol levels
were correlated with attack rates at a weekly scale suggests
seals can accurately assess and subsequently mount a
stress reaction in relation to the magnitude of actual risk
posed from shark predation. The base of information used
by seals leading to these responses might be obtained from
direct observations of daily shark attack rates around the
colony, by in-water encounter rates with attacking sharks,
or by on-island encounter rates with conspecifics bearing
fresh bite wounds (Fig 1D). It is likely that such informa-
tion could be socially transmitted at colonies or in water
among these highly social mammals.
Alternative explanations
High population density or crowding can impact stress
levels in animals and could have contributed in part to some
of the variation in glucocorticoid secretion measured among
colonies. However, it is unlikely that density differences
among colonies or between seasons was the primary driver
of fGCM patterns found, for several reasons. First, mean
fGCM values were not correlated with our measures of seal
0 20,000 40,000 60,000 80,000
Density (Seals/ha)
B) Colony density r= -0.09
P = 0.87
0 5,000 10,000 15,000
fGCM (mg/g)
Individual Seals
A) Colony population size r= 0.63
P = 0.17
31.5 32.0 32.5 33.0 33.5 34.0 34.5 35.0
fGCM (mg/g)
Latitude (ºS)
31.5 32.0 32.5 33.0 33.5 34.0 34.5 35.0
Latitude (ºS)
C) Warm season latitude r= 0.12
P = 0.82
16 18 20 22 24
fGCM (mg/g)
Longitude (ºE)
16 18 20 22 24
Longitude (ºE)
E) Warm season longitude r= -0.71
P = 0.12
D) Cool season latitude r= 0.24
P = 0.65
F) Cool season longitude r= 0.44
P = 0.39
FIG. 5. Plots showing lack of correlation between fGCM concentrations and seal colony (A) population size, (B) density, (C)
latitude, with data in the warm season, (D) latitude, with data in the cool season, (E) longitude, with data in the warm season, and
(F) longitude, with data in the cool season. Values are fGCM means SE.
colony density or population size across the study period
(Fig. 5A,B). Second, as long-lived, k-selected species, female
Cape fur seals only give birth to one offspring per year
(Kirkman et al. 2011) and established colonies in the study
region do not exhibit significant monthly or seasonal fluctu-
ations in population size or density (Huisamen et al. 2011).
The only period during which the islands exhibit relatively
large changes in seal density is during the breeding season
(December); however, we did not collect fecal samples at
this time to avoid disturbing breeding animals.
Glucocorticoids often rise in late gestation of mam-
mals due to an increase in binding globulins (see Creel
et al. 2009 for a discussion), but this phenomenon can-
not explain the observed differences among colonies or
the correlation of fGCM with exposure to risk at the
weekly time scale in False Bay. Additionally, Jutten
Island is not a breeding colony.
While annual or seasonal changes in climate could
impact seal stress levels, this is unlikely to be the primary
driver of spatiotemporal variation in stress levels mea-
sured given that all the study islands occur within the
same region. Similarly, changes in food supply could be
expected to influence stress levels in the seals, but the
colonies examined shared a common food supply (Rand
1959, 1967, Oosthuizen 1991). Although colonies vary
geographically and thus could be exposed to an associ-
ated gradient in environmental factors that may influ-
ence seal stress levels, we found no correlation between
latitude and longitude on fGCM levels at the colonies.
Hypothesized conditions leading to stress-mediated
responses to risk
It has been previously suggested that differences in the
frequency and magnitude of predation risk may affect
whether prey mount a physiological stress response (Creel
et al. 2009). Seals at the high risk colonies of False Bay and
Mossel Bay may behaviorally reduce their individual level
of risk by employing grouping when in the water (dilution
effect and increased vigilance; Fallows et al. 2012) and/or
perhaps by shifting movements to the night (presumably to
benefit from the cover of darkness; Johnson et al. 2009,
De Vos et al. 2015b, Fallows et al. 2016). However, once
in the water and traversing the gauntlet, the seals cannot
reliably predict or detect a hunting shark prior to the
launch of an ambush attack (Martin et al. 2005). For
example, shark kill rate averages 0.48 per day in False
Bay, with frequency of attacks ranging from 0 to 45 per
day (Hammerschlag et al. 2006). Thus, when traversing
the gauntlet in False Bay and Mossel Bay, seals are
exposed to unpredictable, potentially lethal, and rela-
tively uncontrollable risk of attack, precisely the condi-
tions known to produce physiological stress in controlled
biomedical experiments (Weiss 1970, Romero 2004,
Sapolsky 2005). As discussed above, despite a high abun-
dance of hunting white sharks at Geyser Rock, relative
predation risk to seals is comparatively low due to land-
scape features permitting seals to proactively mitigate
risk of shark attack (Wcisel et al. 2015). Here again, the
ability to control exposure to a stressor is known to
reduce glucocorticoid responses in biomedical experi-
ments (Weiss 1970). A similar predatorprey scenario to
the one at Geyser Rock may exist between wolves and
elk in the Yellowstone ecosystem. Specifically, the pres-
ence of wolves has not been found to cause activation of
the fGCM stress response (Creel et al. 2009), likely
because elk can proactively mitigate their risk by altering
their patterns of habitat selection, grouping and behav-
ior, and wolves can be detected proactively using a com-
bination of vision, olfaction, hearing, and perhaps social
transmission of information (Creel and Winnie 2005,
Fortin et al. 2005, Christianson and Creel 2010).
In summary, our study had four primary results. First,
differences in measured Cape fur seal fGCM levels were
strongly associated with patterns of white shark predation
risk at the site and seasonal level, equally for both adults
and juveniles, based on multi-year sampling from six pop-
ulations. Second, seal fGCM levels were not correlated
with colony population size or density, nor with colony
geographic location (a proxy for geographic gradients in
environmental factors that might be expected to influence
stress levels). Third, within a high risk site (False Bay),
fecal cortisol concentrations across two years were strongly
correlated with temporal variation in shark attack rates on
seals at the weekly scale, but not with shark relative abun-
dance. Finally, seals from the Geyser Rock colony did not
sharks despite being exposed to high encounter rates with
sharks. However, in contrast to the other focal colonies tar-
geted by white sharks, seals at Geyser Rock can proactively
mitigate their risk behaviorally through use of subsurface
habitat refuges (Wcisel et al. 2015). Based on results from
the current sharkseal study system and in comparison to
other systems (e.g., elk exposed to wolves), we hypothesize
that predation risk will produce physiological costs, in the
form of a stress response, when risk cannot be adequately
predicted or mitigated by behavioral responses.
Glucocorticoid stress responses can carry fitness and
reproductive costs to individuals (Weiss 1970, Sapolsky
2005) and previous research has revealed that acute and
chronic physiological stress experienced by fur seals can
consequently result in mortality (Seguel et al. 2014).
Thus, future research is needed to determine the potential
differential contribution of various shark effects (e.g., pre-
dation mortality, physiological stress, foraging mediated
costs) and environmental variables (e.g., food limitation)
on seal fitness and population dynamics at the colonies.
This is especially important given recent conflicting data
on declining white shark populations in the study region
and associated concerns for the ecological consequences
(Towner et al. 2013, Andreotti et al. 2016).
Understanding the mechanisms and underlying
circumstances that can lead to differences in the occurrence,
3208 NEIL HAMMERSCHLAG ET AL. Ecology, Vol. 98, No. 12
magnitude, and type of prey response to a predator in
the wild is a challenge, but an important one given wide-
spread declines of top predators globally and associated
growing conservation recovery efforts and successes
(Myers and Worm 2003, Estes et al. 2011, Neubauer
et al. 2013, Marshall et al. 2016). It is probable that path-
ways leading to stress reactions of prey to predation risk
will vary among taxa, contexts, and systems, with differ-
ing consequences for prey population dynamics, as has
been found for food-mediated responses to risk (Schmitz
2008). The hypothesis presented here, describing the con-
ditions leading to physiological stress responses in prey,
provides a testable null model that may aid in future
empirical investigations of the physiological mechanisms
underlying predation risk effects on prey in the wild.
For providing satellite tagging data used in this study, we wish
to thank OCEARCH and collaborating researchers from the
OCEARCH South Africa Expedition, including Chris Fischer, Ali-
son Kock, Dylan Irion, Alison Towner, Enrico Gennari, Ryan
Johnson, and Malcolm Smale. For providing white shark survey
data throughout the Western Cape province of South Africa, we
are thankful to Sara Andreotti. Seal fecal samples were collected
under research permits from the South African National Parks
and the South African Department of Environmental Affairs
(DEA). For assisting with collection and organization of fecal sam-
ples, we are grateful to Yves Chesselet (Cape Nature), Deon Kotze,
Steven McCue, Laurenne Snyders, Bruce Dyer, Lieze Swart, and
several DEA interns. For assisting with predation data and field
work, thanks to Monique Fallows and Lacey Williams from Apex
Shark Expeditions. Thanks to Rachel Skubel and Emily Nelson
for helping with paper formatting. We thank John Bruno, Mark
Hebblewhite and the anonymous reviewers of our manuscript for
their comments that significantly strengthened our paper.
na-Marrero, D., A. N. H. Smith, N. Hammerschlag,
A. Hearn, M. J. Anderson, H. Calich, M. D. M. Pawley, C. Fis-
cher, and P. Salinas-de-Le
on. 2017. Residency and movement
patterns of an apex predatory shark (Galeocerdo cuvier) at the
Galapagos Marine Reserve. PLoS ONE 12:e0183669.
Andreotti, S. 2015. The conservation of South African white
sharks: population number, genetic distinctiveness and global
connections. Dissertation. Stellenbosch University, Stellen-
bosch, South Africa.
Andreotti, S., M. Rutzen, S. Van der Walt, S. Von der Heyden,
R. Henriques, M. Me
yer, H. Oosthuizen, and C. A. Matthee.
2016. An integrated mark-recapture and genetic approach to
estimate the population size of white sharks in South Africa.
Marine Ecology Progress Series 552:241253.
Bonfil, R., M. Me
yer, M. C. Scholl, R. Johnson, S. OBrien,
H. Oosthuizen, S. Swanson, D. Kotze, and M. Paterson.
2005. Transoceanic migration, spatial dynamics, and popula-
tion linkages of white sharks. Science 310:100103.
Boonstra, R., D. Hik, G. R. Singleton, and A. Tinnikov. 1998.
The impact of predator-induced stress on the snowshoe hare
cycle. Ecological Monographs 68:371394.
Brashares, J. S., L. R. Prugh, C. J. Stoner, and C. W. Epps. 2010.
Ecological and conservation implications of mesopredator
release. Pages 221240 in J. Terbough and J. A. Estes, editors.
Trophic cascades: predators, prey, and the changing dynamics
of nature. Island Press, Washington DC, USA.
Christianson, D., and S. Creel. 2010. A nutritionally mediated
risk effect of wolves on elk. Ecology 91:11841191.
Clinchy, M., M. J. Sheriff, and L. Y. Zanette. 2013. Predator-
induced stress and the ecology of fear. Functional Ecology
Clinchy, M., L. Zanette, R. Boonstra, J. C. Wingfield, and
J. N. M. Smith. 2004. Balancing food and predator pressure
induces chronic stress in songbirds. Proceedings of the Royal
Society B 271:24732479.
Creel, S., D. Christianson, and P. Schuette. 2013. Glucocorticoid
stress responses of lions in relationship to group composition,
human land use, and proximity to people. Conservation Physi-
ology 1:111.
Creel, S., J. A. Winnie, and D. Christianson. 2009. Glucocorti-
coid stress hormones and the effect of predation risk on elk
reproduction. Proceedings of the National Academy of Sciences
USA 106:1238812393.
Creel, S., J. Winnie, B. Maxwell, K. Hamlin, and M. Creel.
2005. Elk alter habitat selection as an antipredator response
to wolves. Ecology 86:33873397.
Creel, S., and J. A. Winnie. 2005. Responses of elk herd size to
fine-scale spatial and temporal variation in the risk of preda-
tion by wolves. Animal Behaviour 69:11811189.
De Vos, A., M. J. ORiain, M. A. Me
yer, P. G. H. Kotze, and
A. A. Kock. 2015a. Behavior of Cape fur seals (Arctocephalus
pusillus pusillus) in response to spatial variation in white
shark (Carcharodon carcharias) predation risk. Marine Mam-
mal Science 3:12341251.
De Vos, A., M. J. ORiain, M. A. Me
yer, P. G. H. Kotze, and
A. A. Kock. 2015b. Behavior of Cape fur seals (Arctocephalus
pusillus pusillus) in response to temporal variation in preda-
tion risk by white sharks (Carcharodon carcharias) around a
seal rookery in False Bay, South Africa. Marine Mammal
Science 31:11181131.
Domeier, M. L., and N. Nasby-Lucas. 2007. Annual re-sight-
ings of photographically identified white sharks (Carcharodon
carcharias) at an eastern Pacific aggregation site (Guadalupe
Island, Mexico). Marine Biology 150:977984.
Dudley, S. F. J. 2012. A review of research on the white shark,
Carcharodon carcharias (Linnaeus). Pages 511532 in M. L.
Domeier, editor. Global perspectives on the biology and life
history of the white shark. Taylor and Francis Group, Boca
Raton, Florida, USA.
Estes, J. A., et al. 2011. Trophic downgrading of planet Earth.
Science 333:301306.
Fallows, C., R. A. Martin, and N. Hammerschlag. 2012. Pages
105117 in M. L. Domeier, editor. Global perspectives on the
biology and life history of the white shark. CRC Press, Boca
Raton, Florida.
Fallows, C., M. Fallows, and N. Hammerschlag. 2016. Effects of
lunar phase on predator-prey interactions between white shark
(Carcharodon carcharias) and Cape fur seals (Arctocephalus
pusillus pusillus). Environmental Biology of Fishes 99:805812.
Fortin, D., H. L. Beyer, M. S. Boyce, D. W. Smith, T. Duchesne,
and J. S. Mao. 2005. Wolves influence elk movements: behav-
ior shapes a trophic cascade in Yellowstone National Park.
Ecology 86:13201330.
Gallagher, A. J., S. Creel, R. P. Wilson, and S. J. Cooke. 2017a.
Energy landscapes and the landscape of fear. Trends in Ecol-
ogy & Evolution 32:8896.
Gallagher, A. J., R. A. Skubel, H. R. Pethybridge, and N. Ham-
merschlag. 2017b. Energy metabolism in mobile, wild-
sampled sharks inferred by plasma lipids. Conservation
Physiology 5.
Hammerschlag, N., and C. Fallows. 2005. Galapagos sharks
(Carcharhinus galapagensis) at the Bassas da India atoll:
First record from the Mozambique Channel and possible
significance as a nursery area. South African Journal of Mar-
ine Science 101:375377.
Hammerschlag, N., A. J. Gallagher, and D. M. Lazarre. 2011.
A review of shark satellite tagging studies. Journal of Experi-
mental Marine Biology and Ecology 398:18.
Hammerschlag, N., R. A. Martin, and C. Fallows. 2006. Effects
of environmental conditions on predatorprey interactions
between white sharks (Carcharodon carcharias) and Cape fur
seals (Arctocephalus pusillus pusillus) at Seal Island, South
Africa. Environmental Biology of Fishes 76:341350.
Heithaus, M. R., A. J. Wirsing, D. Burkholder, J. Thomson, and
L. M. Dill. 2009. Towards a predictive framework for predator
risk effects: the interaction of landscape features and prey
escape tactics. Journal of Animal Ecology 78:556562.
Huisamen, J., S. P. Kirkman, L. H. Watson, P. A. Pistorius, and
V. G. Cockcroft. 2011. Re-colonisation of the Robberg Penin-
sula (Plettenberg Bay, South Africa) by Cape fur seal. African
Journal of Marine Science 33:453462.
Hunt, K. E., A. W. Trites, and S. K. Wasser. 2004. Validation of
a fecal glucocorticoid assay for Steller sea lions (Eumetopias
jubatus). Physiology & Behavior 80:595601.
Jewell, O. J., R. L. Johnson, E. Gennari, and M. N. Bester.
2013. Fine scale movements and activity areas of white sharks
(Carcharodon carcharias) in Mossel Bay, South Africa. Envi-
ronmental Biology of Fishes 96:881894.
Johnson, R., M. N. Bester, S. F. Dudley, W. H. Oosthuizen,
M. Me
yer, L. Hancke, and E. Gennari. 2009. Coastal swimming
patterns of white sharks (Carcharodon carcharias) at Mossel Bay,
South Africa. Environmental Biology of Fishes 85:189200.
Jonsen, I. 2016. Joint estimation over multiple individuals
improves behavioural state inference from animal movement
data. Scientific Reports 6.
Kirkman, S. P., W. H. Oosthuizen, M. A. Meyer, S. M. Seaka-
mela, and L. G. Underhill. 2011. Prioritising range-wide sci-
entific monitoring of the Cape fur seal in southern Africa.
African Journal of Marine Science 33:495509.
Kock, A., M. J. ORiain, K. Mauff, M. Me
yer, D. Kotze, and
C. Griffiths. 2013. Residency, habitat use and sexual segrega-
tion of white sharks, Carcharodon carcharias in False Bay,
South Africa. PloS ONE 8:e55048.
Marshall, K. N., A. C. Stier, J. F. Samhour, R. P. Kelly, and E. J.
Ward. 2016. Conservation challenges of predator recovery.
Conservation Letters 9:7078.
Martin, R. A., N. Hammerschlag, R. S. Collier, and C. Fallows.
2005. Predatory behaviour of white sharks (Carcharodon car-
charias) at Seal Island, South Africa. Journal of the Marine
Biological Association of the United Kingdom 85:11211135.
Martin, R. A., D. K. Rossmo, and N. Hammerschlag. 2009.
Hunting patterns and geographic profiling of white shark
predation. Journal of Zoology 279:111118.
Monfort, S. L., K. L. Mashburn, S. K. Wasser, M. Burke, B. A.
Brewer, and S. R. Creel. 1997. Steroid metabolism and valida-
tion of noninvasive endocrine monitoring in the African wild
dog (Lycaon pictus). Zoo Biology 6:533548.
Myers, R. A., and B. Worm. 2003. Rapid worldwide depletion
of predatory fish communities. Nature 423:280283.
Neubauer, P., O. P. Jensen, J. A. Hutchings, and J. K. Baum.
2013. Resilience and recovery of overexploited marine popu-
lations. Science 340:347349.
Oosthuizen, W. H. 1991. General movements of South African
(Cape) fur seals Arctocephalus pusillus pusillus from analysis
of recoveries of tagged animals. South African Journal of
Marine Science 11:2129.
Peckarsky, B. L., C. A. Cowan, M. A. Penton, and C. Ander-
son. 2010. Sublethal consequences of stream-dwelling preda-
tory stoneflies on mayfly growth and fecundity. Ecology 74:
Peckarsky, B. L., et al. 2008. Revisiting the classics: considering
nonconsumptive effects in textbook examples of predator-
prey interactions. Ecology 89:24162425.
Rand, R. 1959. The Cape fur seal (Arctocephalus pusillus):
Distribution, abundance and feeding habits off the south
western coast of the Cape Province. Investigational Report
No. 34, South African Division of Fisheries, South Africa.
Rand, R. 1967. The Cape fur seal (Arctocephalus pusillus): Gen-
eral behaviour on land and at sea. Investigational Report No.
60, South African Division of Fisheries, South Africa.
Romero, L. M. 2004. Physiological stress in ecology: lessons
from biomedical research. Trends in Ecology & Evolution
Sapolsky, R. M. 2005. The influence of social hierarchy on pri-
mate health. Science 308:648652.
Schmitz, O. J. 2008. Effects of predator hunting mode on grass-
land ecosystem function. Science 319:952954.
Schmitz, O. J., A. P. Beckerman, and K. M. OBrien. 1997.
Behaviorally mediated trophic cascades: effects of predation
risk on food web interactions. Ecology 78:13881399.
Seguel, M., E. Paredes, H. Pav
es, and N. L. Gottdenker. 2014.
Capture-induced stress cardiomyopathy in South American
fur seal pups (Arctophoca australis gracilis). Marine Mammal
Science 30:11491157.
Sheriff, M. J., C. J. Krebss, and R. Boonstra. 2009. The sensitive
hare: sublethal effects of predator stress on reproduction in
snowshoe hares. Journal of Animal Ecology 78:2491258.
Silverman, B. W. 1986. Density estimation for statistics and data
analysis. Chapman and Hall, New York, New York, USA.
Sinclair, A. R. E., and P. Arcese. 1995. Population consequences
of predation-sensitive foraging: the Serengeti wildebeest.
Ecology 76:882891.
Towner, A. V., V. Leos-Barajas, R. Langrock, R. S. Schick,
M. J. Smale, T. Kaschke, O. J. Jewell, and Y. P. Papastama-
tiou. 2016. Sex-specific and individual preferences for hunting
strategies in white sharks. Functional Ecology 30:13971407.
Towner, A. V., M. A. Wcisel, R. R. Reisinger, D. Edwards, and
O. J. D. Jewell. 2013. Gauging the threat: the first population
estimate for white sharks in South Africa using photo identi-
fication and automated software. PLoS ONE 8:612.
Wcisel, M., M. J. ORiain, A. de Vos, and W. Chivell. 2015. The
role of refugia in reducing predation risk for Cape fur seals by
white sharks. Behavioral Ecology and Sociobiology 69:127138.
Weiss, J. M. 1970. Somatic effects of predictable and unpre-
dictable shock. Psychosomatic Medicine 32:97408.
Werner, E. E., and S. D. Peacor. 2003. A review of trait-
mediated indirect interactions in ecological communities.
Ecology 84:10831100.
Zanette, L. Y., M. Clinchy, and J. P. Suraci. 2014. Diagnosing
predation risk effects on demography: Can measuring physi-
ology provide the means? Oecologia 176:637651.
Additional supporting information may be found in the online version of this article at
3210 NEIL HAMMERSCHLAG ET AL. Ecology, Vol. 98, No. 12
... Here, seals exhibit a pronounced stress response to high levels of unpredictable and relatively uncontrollable risk of attack from white sharks as measured through elevated faecal glucocorticoid concentrations (fGCMs) ( [19], see electronic supplementary material). These physiological stress responses have been detected in both adults and juvenile seals, with a strong positive correlation between fGCM levels and weekly variation in white shark attack rate [19]. ...
... Here, seals exhibit a pronounced stress response to high levels of unpredictable and relatively uncontrollable risk of attack from white sharks as measured through elevated faecal glucocorticoid concentrations (fGCMs) ( [19], see electronic supplementary material). These physiological stress responses have been detected in both adults and juvenile seals, with a strong positive correlation between fGCM levels and weekly variation in white shark attack rate [19]. While white shark abundance at Seal Island was relatively stable for a long period, their numbers began to precipitously decline after 2015 [20]. ...
... Seal faecal samples were collected from Seal Island during 2014 and 2015 prior to the onset of shark decline (see [19]) and during the decline and eventual disappearance of white sharks from the study site in 2016, 2017 and 2019 (see electronic supplementary material). Steroid hormone metabolites were extracted from faecal samples by drying the scat and boiling a known mass of dry faeces in ethanol following [26]. ...
Full-text available
Predators can impact prey via predation or risk effects, which can initiate trophic cascades. Given widespread population declines of apex predators, understanding and predicting the associated ecological consequences is a priority. When predation risk is relatively unpredictable or uncontrollable by prey, the loss of predators is hypothesized to release prey from stress; however, there are few tests of this hypothesis in the wild. A well-studied predator-prey system between white sharks (Carcharodon carcharias) and Cape fur seals (Arctocephalus pusillus pusillus) in False Bay, South Africa, has previously demonstrated elevated faecal glucocorticoid metabolite concentrations (fGCMs) in seals exposed to high levels of predation risk from white sharks. A recent decline and disappearance of white sharks from the system has coincided with a pronounced decrease in seal fGCM concentrations. Seals have concurrently been rafting further from shore and over deeper water, a behaviour that would have previously rendered them vulnerable to attack. These results show rapid physiological and behavioural responses by seals to release from predation stress. To our knowledge, this represents the first demonstration in the wild of physiological changes in prey from predator decline, and such responses are likely to increase given the scale and pace of apex predator declines globally.
... Considerable research has explored morphological traits, such as body shape (Brönmark & Miner, 1992;Cott, 1940;Price et al., 2015;Young et al., 2004), behavioural strategies, including activity and exploration (Heinen-Kay et al., 2016;Hossie et al., 2010;Ydenberg & Dill, 1986), and life-history traits, such as offspring number and size (Hagmayer et al., 2020;Reznick et al., 1990;Riesch et al., 2013) linked to enhanced fitness under varying predation risk. By comparison, fewer studies have examined how predators may drive evolutionary shifts in underlying physiological processes (neuroendocrine and cardiovascular processes) that act to increase survival and maintain physiological homeostasis (Clinchy et al., 2013;Hammerschlag et al., 2017). ...
... Previous work has independently suggested important roles for predation risk and food availability/quality in stress reactivity, but these factors have not been simultaneously considered, and most prior research involved plastic (or potentially plastic) responses rather than evolutionary divergence in the stress response (e.g. Clinchy et al., 2013;Hammerschlag et al., 2017;Hawlena & Schmitz, 2010;Herring et al., 2011;Kitaysky et al., 1999Kitaysky et al., , 2007Romero & Wikelski, 2001). Together, our results suggest a scenario where three key attributes might together largely explain evolutionary patterns of the vertebrate stress response based on the relative costs and benefits of reacting to stressful events: (1) intensity of stressful encounters, (2) frequency of stressful encounters and ...
Full-text available
Predation risk is often invoked to explain variation in stress responses. Yet, the answers to several key questions remain elusive, including: 1) how predation risk influences the evolution of stress phenotypes, 2) the relative importance of environmental versus genetic factors in stress reactivity, and 3) sexual dimorphism in stress physiology. To address these questions, we explored variation in stress reactivity (ventilation frequency) in a post‐Pleistocene radiation of live‐bearing fish, where Bahamas mosquitofish (Gambusia hubbsi) inhabit isolated blue holes that differ in predation risk. Individuals of populations coexisting with predators exhibited similar, relatively low stress reactivity as compared to low‐predation populations. We suggest that this dampened stress reactivity has evolved to reduce energy expenditure in environments with frequent and intense stressors, such as piscivorous fish. Importantly, the magnitude of stress responses exhibited by fish from high‐predation sites in the wild changed very little after two generations of laboratory rearing in the absence of predators. By comparison, low‐predation populations exhibited greater among‐population variation and larger changes subsequent to laboratory rearing. These low‐predation populations appear to have evolved more dampened stress responses in blue holes with lower food availability. Moreover, females showed a lower ventilation frequency, and this sexual dimorphism was stronger in high‐predation populations. This may reflect a greater premium placed on energy efficiency in live‐bearing females, especially under high predation risk where females show higher fecundities. Altogether, by demonstrating parallel adaptive divergence in stress reactivity, we highlight how energetic trade‐offs may mould the evolution of the vertebrate stress response under varying predation risk and resource availability.
... Predation risk is an important selective environmental factor that can drive the evolution of animal behavior and morphological characteristics [22,23]. Prey animals are affected by predation risk in a variety of characteristics from physiology to behavior, which can lead to a range of ecological consequences [24][25][26]. When encountering predators, damselflies, Enallagma vesperum, increase levels of arginine kinase to raise their swimming speed and escape ability [27]. ...
Full-text available
Animal personality is of great ecological and evolutionary significance and has been documented in many animal taxa. Despite genetic background, personality might be prominently shaped by external environments, and it is significant to explore the environmental factors that influence the ontogeny of animal personality in early life. Here, we reared newborn mosquitofish Gambusia affinis under different treatments of risk predictability (i.e., no risks, unpredictable risks, risks at 5 min after feeding and risks at 2 h after feeding) and measured their two personality traits at sexual maturity. We measured the behavioral repeatability, correlation between behavioral characteristics, and the impact of risk predictability. We found that the fish showed repeatability in exploration in all risk treatments, as well as repeatability in shyness under predictable risks. When growing up in risk treatments, no matter predictable or unpredictable, shyness and exploration showed a negative correlation, suggesting a behavioral syndrome between the two behavioral traits. The fish reared under predictable risks were less explorative than those under unpredictable risks, while there were no differences in shyness among treatments. Besides, smaller fish were bolder and more explorative than larger ones. Our findings imply that risk predictability in early life may play an important role in shaping animal personality and modifying the average behavioral levels.
... Furthermore, α-MSH can modulate the stress response by binding to MC4R, resulting in reduced levels of circulating glucocorticoid concentrations, a process assumed to generate a greater resistance to stressors (Chaki et al., 2003;Racca et al., 2005). Since the constant presence of predator cues may cause a chronically stressful situation for prey, with glucocorticoid concentrations above baseline levels (Balm & Pottinger, 1995;Boonstra, 2013;Clinchy et al., 2013;Hammerschlag et al., 2017), an improved stress resistance from higher MC4R expression may be advantageous by reducing the deleterious effects of chronic stress (Boonstra, 2013;Romero, 2004). This may be particularly important for males in teleost fish, since earlier studies have shown that the stress response of males is significantly stronger than the stress response of females (Rambo et al., 2017;Vinterstare et al., 2021). ...
Full-text available
Inducible defences allow prey to increase survival chances when predators are present while avoiding unnecessary costs in their absence. Many studies report considerable inter-individual variation in inducible defence expression, yet what underlies this variation is poorly understood. A classic vertebrate example of a predator-induced morphological defence is the increased body depth in crucian carp (Carassius carassius), which reduces the risk of predation from gape-size limited predators. Here, we report that among-individual variation in morphological defence expression can be linked to sex. We documented sexual dimorphism in lakes in which crucian carp coexisted with predators, where females showed shallower relative body depths than males, but not in a predator-free lake. When exposing crucian carp from a population without predators to perceived predation risk in a laboratory environment (presence/absence of pike, Esox lucius), we found that males expressed significantly greater morphological defence than females, causing sexual dimorphism only in the presence of predators. We uncovered a correlative link between the sex-specific inducible phenotypic response and gene expression patterns in major stress-related genes (POMC, MC3R, and MC4R). Together, our results highlight that sex-specific responses may be an important, yet underappreciated, component underlying inter-individual differences in the expression of inducible defences, even in species without pronounced sexual dimorphism.
... In the same way, the indirect effects of the presence of predators at a breeding site govern the behaviour of the Cormo -rants, in our situation being another top-predator of the aquatic system. Other examples of animals that incur stress-related costs because they are tied to a breeding site with fixed geo-conditions and thereby have limited control of their exposure to predators include nesting songbirds (Passeri; Clinchy et al. 2004) and fur seals (Arctocephalinae) at rookeries (Hammerschlag et al. 2017). ...
Full-text available
Ground-nesting Great Cormorants were monitored in three neighbouring colonies at Lake IJsselmeer, The Netherlands. Using aerial photographs taken during peak breeding time, nest density and nearest neighbour distance were determined for four sequential years. In addition, species and number of predators were determined. In total, five mammalian and nine avian predatory species were associated with the Cormorant breeding colonies. Spatial distribution of nests mostly showed dispersed and random patterns rather than a contagious pattern. The latter distribution, with less distance between nests than expected both from a random and equal distribution pattern, was found in the colony of De Ven in 2013 during the last year of its existence. The predator Red Fox Vulpes vulpes arrived at the colony in 2010. In all three colonies, nest density was highest and nearest neighbour distance shortest in colonies with the highest number of predators. At low to moderate predatory pressure, ground-nesting Cormorants left free space between nests that was used by adult birds during take-off and landing. During the last years of its existence the shrinking colony of De Ven showed an almost circular shape, with an extreme nest density and the lowest edge-to-surface area ratio. But with Foxes present, breeding at the fringe still caused greater losses due to direct predation. Breeding success fluctuated synchronously between colonies but was lower in colonies where the number of predators was higher. The arrival of Red Foxes in De Ven caused extreme losses of young and over the years resulted in a strong decline in number of breeders, eventually leading to complete abandoning of the site in 2014. Large gulls formed another important group of predators but did not cause the Cormorants to abandon the breeding site. In the Vooroever colony, bush and tree cover supplied shelter and allowed birds to breed in greater density without causing nearest neighbour density to decrease, as was the case when no cover was available. Greater nest density and reduced nearest neighbour distances are considered to be a pro-active response by individual birds to the presence of predators. When predator numbers increased, the within-colony open spaces that normally exist under circumstances of moderate density were filled up with nests, leaving little or no room for landing and departure. This leads to reduced edge effects and a circular shape of the colony, thereby potentially limiting predation risk. As a consequence of extreme high nest densities, breeding success was lower due to interference by other Cormorants. This study is the first to show that colony structure in waterbirds is affected by forces of attraction and repulsion between founding birds that are predator driven.
... As regional endotherms, white sharks have high metabolic demands (Ezcurra et al., 2012) and high energy requirements (Carey et al., 1982;Watanabe et al., 2019). We propose that the cold, upwelled, and often oxygen-depleted waters along the west coast of South Africa (Jarre et al., 2015) may not provide suitable conditions for white sharks, especially for extended periods (see also Hammerschlag et al., 2017). Therefore, even though the west coast is highly productive with abundant prey, including several extensive Cape fur seal colonies, it might be too energetically expensive for white sharks to forage or breed in this region. ...
Full-text available
Human activities in the oceans increase the extinction risk of marine megafauna. Interventions require an understanding of movement patterns and the spatiotemporal overlap with threats. We analysed the movement patterns of 33 white sharks ( Carcharodon carcharias ) satellite-tagged in South Africa between 2012 and 2014 to investigate the influence of size, sex and season on movement patterns and the spatial and temporal overlap with longline and gillnet fisheries and marine protected areas (MPAs). We used a hidden Markov model to identify ‘resident’ and ‘transient’ movement states and investigate the effect of covariates on the transition probabilities between states. A model with sex, total length and season had the most support. Tagged sharks were more likely to be in a resident state near the coast and a transient state away from the coast, while the probability of finding a shark in the transient state increased with size. White sharks moved across vast areas of the southwest Indian Ocean, emphasising the need for a regional management plan. White sharks overlapped with longline and gillnet fisheries within 25% of South Africa’s Exclusive Economic Zone and spent 15% of their time exposed to these fisheries during the study period. The demersal shark longline fishery had the highest relative spatial and temporal overlap, followed by the pelagic longline fishery and the KwaZulu-Natal (KZN) shark nets and drumlines. However, the KZN shark nets and drumlines reported the highest white shark catches, emphasising the need to combine shark movement and fishing effort with reliable catch records to assess risks to shark populations accurately. White shark exposure to shark nets and drumlines, by movement state, sex and maturity status, corresponded with the catch composition of the fishery, providing support for a meaningful exposure risk estimate. White sharks spent significantly more time in MPAs than expected by chance, likely due to increased prey abundance or less disturbance, suggesting that MPAs can benefit large, mobile marine megafauna. Conservation of white sharks in Southern Africa can be improved by implementing non-lethal solutions to beach safety, increasing the observer coverage in fisheries, and continued monitoring of movement patterns and existing and emerging threats.
... Using parasites shed in faecal material, the prevalence of infections in social species can also be linked to factors such as density and group size (Snaith et al. 2008). Glucocorticoid hormones help to restore homeostasis following acute exposure to stressors like predator encounters (Romero 2004), and thus faecal material with relatively high glucocorticoid concentrations is indicative of populations facing elevated predation risk (Hammerschlag et al. 2017). Because it is not always possible to examine stomach contents or blood, non-invasive sampling of hair and faecal material has become a common approach for obtaining diet (Leighton et al. 2020), parasite load (Snaith et al. 2008), and stress information from wild mammals (Sheriff et al. 2011). ...
Animal diet and health influence fitness, making individual variation in these markers essential for understanding how individuals and populations respond to their environments. Faecal and hair samples provide a record of this information and can be non-invasively collected from animals in the field. However, physiology, diet, and susceptibility to parasitic infections vary within individuals, requiring repeated samples from individuals. We developed a technique using biotelemetry data for individual identification of non-invasive faecal material and hair sampled from female elk (Cervus canadensis). We non-invasively collected individually genotyped faecal and hair samples from resting sites, then compared the accuracy of supervised machine learning models to predict the individual identities of the samples. We found both the tightness of global positioning system point clusters and activity level surrounding the sample allowed us to positively identify samples belonging to specific individuals with 77% accuracy. Our approach can be applied to other populations for which biotelemetry data are available and is potentially adaptable for other species. Furthermore, application of our approach will reduce the need for individual identification of non-invasive samples using genetic analysis, which is costly and prone to low recovery success. Increased access to physiological, dietary, and health information obtainable from individual non-invasive samples will strengthen our understanding of animal responses to their environments.
... We found that HPA axis activation of O. longicaudatus was higher during culpeo treatment. As we expected, rodents triggered the glucocorticoid release when facing the most threatening treatment (i.e., culpeo cues), which would allow prey to mobilize energy towards the organs and tissues where it is needed to display successful antipredator strategies (e.g., flight, finding shelter, defensive attack), and thus, survive [76,77]. Even though rodents could have felt safe inside traps, once they were caught, they were continuously exposed to fox faeces (which were next to the entrance of the trap), and this may have triggered and maintained a higher physiological antipredator response [10,11]. ...
Full-text available
Even though behavioural and physiological reactions to predation risk exhibited by prey species have received considerable attention in scientific journals, there are still many questions still unsolved. Our aim was to broaden the knowledge on one specific question: do long-tailed pygmy rice rats adapt their behavioural and physiological antipredator strategies depending on the predator species? For this question, we live-trapped in a temperate forest in Southern Chile long-tailed pygmy rice rats (Oligoryzomys longicaudatus), which were exposed to three predator odour phases (Phase 0: preliminary, no predator cues; Phase 1: one plot with culpeo fox faeces (Lycalopex culpaeus), one plot with lesser grison (Galictis cuja) faeces and one plot acting as a control with no odour; Phase 2: post treatment, no predator cues). We measured the behavioural response by the capture ratio. To assess the physiological stress response, we collected fresh faecal samples to quantify faecal corticosterone metabolites (FCM). Our results showed that O. longicaudatus increased both the capture ratio and FCM levels in the presence of culpeo cues. Culpeo foxes have higher densities in the study area than G. cuja and exhibit a higher activity pattern overlap with O. longicaudatus. Moreover, it has been also been reported in other regions that L. culpaeus consumption of O. longicaudatus is more frequent compared to G. cuja diet. The increase in capturability could be because traps can be regarded as a shelter in high-risk settings, but it can also be explained by the predator inspection behaviour. The increase in FCM concentrations during culpeo treatment can be linked to the adaptive mobilisation of energy to execute antipredator responses to increase survival chances.
.Differences in the stress experience of sessile organisms across the intertidal zone can differentially influence phenotype. For example, Balanus glandula barnacles from the low intertidal zone have higher lactate levels, greater lactate dehydrogenase (LDH) activity and reduced cirral activity compared to conspecifics from the high intertidal zone. We tested the hypothesis that enhanced anaerobic capacity in lower intertidal B. glandula results from increased predation and hypoxia-inducing shell closure. To investigate this hypothesis, we compared the density of whelk predators across the intertidal zone, and quantified the behavioral response and LDH activity levels of B. glandula exposed to predators in the lab. We consistently found more predators in the low intertidal zone, although the response of B. glandula to predators was short-term operculum closure (<1hr) which did not result in significant differences in LDH activity. Thus, increased predation is not the cause of high anaerobic capacity in lower intertidal B. glandula.
Satellite telemetry as a tool in marine ecological research continues to adapt and grow and has become increasingly popular in recent years to study shark species on a global scale. A review of satellite tag application to shark research was published in 2010, provided insight to the advancements in satellite shark tagging, as well as highlighting areas for improvement. In the years since, satellite technology has continued to advance, creating smaller, longer lasting, and more innovative tags, capable of expanding the field. Here we review satellite shark tagging studies to identify early successes and areas for rethinking moving forward. Triple the amount of shark satellite tagging studies have been conducted during the decade from 2010 to 2020 than ever before, tracking double the number of species previously tagged. Satellite telemetry has offered increased capacity to unravel ecological questions including predator and prey interactions, migration patterns, habitat use, in addition to monitoring species for global assessments. However, <17% of the total reviewed studies directly produced results with management or conservation outcomes. Telemetry studies with defined goals and objectives produced the most relevant findings for shark conservation, most often in tandem with secondary metrics such as fishing overlap or management regimes. To leverage the power of telemetry for the benefit of shark species, it remains imperative to continue improvements to tag function and maximize the outputs of tagging efforts including increasing data sharing capacity and standardization across the field, as well as spatial and species coverage. Ultimately, this review offers a status report of shark satellite tagging and the ways in which the field can continue to progress.
Full-text available
The potential effectiveness of marine protected areas (MPAs) as a conservation tool for large sharks has been questioned due to the limited spatial extent of most MPAs in contrast to the complex life history and high mobility of many sharks. Here we evaluated the movement dynamics of a highly migratory apex predatory shark (tiger shark Galeocerdo cuvier) at the Galapagos Marine Reserve (GMR). Using data from satellite tracking passive acoustic telemetry, and stereo baited remote underwater video, we estimated residency, activity spaces, site fidelity, distributional abundances and migration patterns from the GMR and in relation to nesting beaches of green sea turtles (Chelonia mydas), a seasonally abundant and predictable prey source for large tiger sharks. Tiger sharks exhibited a high degree of philopatry, with 93% of the total satellite-tracked time across all individuals occurring within the GMR. Large sharks (> 200 cm TL) concentrated their movements in front of the two most important green sea turtle-nesting beaches in the GMR, visiting them on a daily basis during nocturnal hours. In contrast, small sharks (< 200 cm TL) rarely visited turtle-nesting areas and displayed diurnal presence at a third location where only immature sharks were found. Small and some large individuals remained in the three study areas even outside of the turtle-nesting season. Only two sharks were satellite-tracked outside of the GMR, and following long-distance migrations, both individuals returned to turtle-nesting beaches at the subsequent turtle-nesting season. The spatial patterns of residency and site fidelity of tiger sharks suggest that the presence of a predictable source of prey and suitable habitats might reduce the spatial extent of this large shark that is highly migratory in other parts of its range. This highly philopatric behaviour enhances the potential effectiveness of the GMR for their protection.
Full-text available
Evaluating how predators metabolize energy is increasingly useful for conservation physiology, as it can provide information on their current nutritional condition. However, obtaining metabolic information from mobile marine predators is inherently challenging owing to their relative rarity, cryptic nature and often wide-ranging underwater movements. Here, we investigate aspects of energy metabolism in four free-ranging shark species (n = 281; blacktip, bull, nurse, and tiger) by measuring three metabolic parameters [plasma triglycerides (TAG), free fatty acids (FFA) and cholesterol (CHOL)] via non-lethal biopsy sampling. Plasma TAG, FFA and total CHOL concentrations (in millimoles per litre) varied inter-specifically and with season, year, and shark length varied within a species. The TAG were highest in the plasma of less active species (nurse and tiger sharks), whereas FFA were highest among species with relatively high energetic demands (blacktip and bull sharks), and CHOL concentrations were highest in bull sharks. Although temporal patterns in all metabolites were varied among species, there appeared to be peaks in the spring and summer, with ratios of TAG/CHOL (a proxy for condition) in all species displaying a notable peak in summer. These results provide baseline information of energy metabolism in large sharks and are an important step in understanding how the metabolic parameters can be assessed through non-lethal sampling in the future. In particular, this study emphasizes the importance of accounting for intra-specific and temporal variability in sampling designs seeking to monitor the nutritional condition and metabolic responses of shark populations. Cite as: Gallagher AJ, Skubel RA, Pethybridge HR, Hammerschlag N (2017) Energy metabolism in mobile, wild-sampled sharks inferred by plasma lipids. Conserv Physiol 5(1): cox002; doi:10.1093/conphys/cox002.
Full-text available
Predator-prey relationships can be influenced by environmental conditions, including changes in moon phase and associated lunar illumination. Two primary hypotheses have been proposed underlying the effects of moonlight on predator-prey interactions: the predation risk hypothesis and visual acuity hypothesis. However, few studies have tested these hypotheses during twilight hours or involved large mobile aquatic species. In the present study, we evaluated these hypotheses using data collected over 16 years on predator-prey interactions between white shark (Carcharodon carcharias) and Cape fur seals (Arctocephalus pusillus pusillus) at sunrise. Data from 1476 natural predation events demonstrated shark attack frequency and seal capture success was significantly higher at sunrise during periods of low (0–10 %) versus high (90–100 %) lunar illumination, which is consistent with the visual acuity hypothesis. We propose that during full moon periods, white sharks at night are at a visual and tactical advantage over seals which are silhouetted at the surface in the moonlight and thus easier to isolate in darkness, while sharks remain camouflaged hunting from below through deep water. However, at sunrise, we hypothesize this advantage shifts to seals as the added lunar illumination, combined with emerging sunlight, may decrease shark stealth and increase the ability of seals to detect and avoid sharks. These finding suggest that lunar effects on predator-prey dynamics can be context specific, likely moderated by visual acuity of predators and prey which may change according to the photoperiod.
Full-text available
The loss of apex marine predators has been reported to have a cascade of detrimental effects on marine ecosystems; however, the general lack of empirical data can severely limit our understanding of the ecological interactions among marine species. In this study we propose an integrated approach using mark-recapture and genetic techniques to assess population estimates of white sharks Carcharodon carcharias. Between 2009 and 2011, 4389 dorsal fin photographic identifications were collected in Gansbaai, South Africa, from 426 white sharks and used in markrecapture analyses. Saturation of new sightings occurred once 400 individuals were catalogued and the open population model POPAN suggested ranges between 353 and 522 individuals (95% confidence) and a point estimate of N = 438. Between 2010 and 2013, 302 biopsy samples were collected from 233 white sharks and used for a comparative genetic population estimate. Analyses of 14 microsatellite markers revealed a contemporary effective population size (CNe) of 333 individuals (95% CI = 247-487, pcrit = 0.02). These values were at least 52% less than those estimated in previous mark-recapture studies. Using this combination of techniques, we propose a Ne:N ratio of 0.76 for white sharks, which advances our ability to accurately make inferences on elasmobranch population numbers in general. Given the low population numbers of white sharks along the South African coastline, we predict a negative effect on the ecological stability of the marine environment in this region.
Animals are not distributed randomly in space and time because their movement ecology is influenced by a variety of factors. Energy landscapes and the landscape of fear have recently emerged as largely independent paradigms, both reshaping our perspectives and thinking relating to the spatial ecology of animals across heterogeneous landscapes. We argue that these paradigms are not distinct but rather complementary, collectively providing a better mechanistic basis for understanding the spatial ecology and decision-making of wild animals. We discuss the theoretical underpinnings of each paradigm and illuminate their complementary nature through case studies, then integrate these concepts quantitatively by constructing quantitative pathways of movement modulated by energy and fear to elucidate the mechanisms underlying the spatial ecology of wild animals.