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

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 (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.

Full text

VOL. 98, NO. 12, 2991–3251 ECOLOGY DECEMBER 2017
ARTICLES
Physiological stress responses to natural variation in predation
risk: evidence from white sharks and seals
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Physiological stress responses to natural variation in predation risk:
evidence from white sharks and seals
NEIL HAMMERSCHLAG,
1,2,6
MICHAEL ME
€
YER,
3
SIMON MDUDUZI SEAKAMELA,
3
STEVE KIRKMAN,
3
CHRIS FALLOWS,
4
AND SCOTT CREEL
5
1
Department of Marine Ecosystems and Society, Rosenstiel School of Marine and Atmospheric Sciences,
University of Miami, Miami, Florida 33149 USA
2
Leonard and Jayne Abess Center for Ecosystem Science and Policy, University of Miami, Coral Gables, Florida 33146 USA
3
Branch: Oceans and Coasts, Department of Environmental Affairs, Private Bag X4390, Cape Town 8000 South Africa
4
Apex Shark Expeditions, Shop 3 Quayside Center, Simonstown Cape Town 7975 South Africa
5
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.
INTRODUCTION
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., food–risk 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.
6
E-mail: nhammerschlag@miami.edu
3199
Ecology, 98(12), 2017, pp. 3199–3210
©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 predator–prey 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
A
D
B
C
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 wileyonlinelibrary.com]
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).
MATERIALS AND METHODS
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 mark–recapture (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
abundance”seal 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-
bert’s Bay, Jutten Island, and Robbesteen; Fig. 2) during
both the “high predation season”(winter, June–Septem-
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 shark’s 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
December 2017 SHARK PREDATION AND STRESS IN SEALS 3201

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
Lambert’sBay,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 1–2 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.
Immunoassay
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)
Africa
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 wileyonlinelibrary.com]
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
2
=0.997,
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 Lambert’s 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.
2009).
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
December 2017 SHARK PREDATION AND STRESS IN SEALS 3203

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
2
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 (October–May) and the cool season
(June–September). 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, Lambert’s 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, Tukey’s
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. Pearson’scorre-
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 Pearson’s 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.
RESULTS
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, Lambert’s 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
2
) than warm season (KD =0.14 km
2
).
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 Tukey’s 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 (4–10 February 2014) to
3.49 attacks/h (5–11 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
2
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, Lambert’s 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
Mean
KD
Mean
days
Min.
days
Max.
days
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
0
500
1,000
1,500
2,000
2,500
Jutten Island
Lambert's
Robbesteen
Geyser Rock
S.I. Mossel Bay
S.I. False Bay
Jutten Island
Lambert's
Robbesteen
Geyser Rock
S.I. Mossel Bay
S.I. False Bay
fGCM (mg/g)
a
a
a,b
aa
a
b
a,b
a,b
a,b
cc
LS
colony
HS
colony
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.
December 2017 SHARK PREDATION AND STRESS IN SEALS 3205

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. 5C–F).
DISCUSSION
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 Lambert’sBay.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
400
800
1200
1600
2000
2400
0 0.5 1.0 1.5 2
fGCM (mg/g)
Shark relative abundance (sharks/h)
r= 0.2
P = 0.66
B
0
400
800
1200
1600
2000
2400
01234
fGCM (mg/g)
Predation rate (attacks/h)
r= 0.96
P = 0.0007
A
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 predator–prey 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
400
800
1,200
1,600
0 20,000 40,000 60,000 80,000
Density (Seals/ha)
B) Colony density r= -0.09
P = 0.87
0
400
800
1,200
1,600
0 5,000 10,000 15,000
fGCM (mg/g)
Individual Seals
A) Colony population size r= 0.63
P = 0.17
0
400
800
1,200
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
0
400
800
1,200
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
0
800
1,600
2,400
D) Cool season latitude r= 0.24
P = 0.65
0
800
1,600
2,400
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.
December 2017 SHARK PREDATION AND STRESS IN SEALS 3207

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 predator–prey 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).
CONCLUSIONS
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
showapronouncedriseinfGCMlevelsinresponseto
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 shark–seal 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.
ACKNOWLEDGMENTS
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.
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