Introduction
Gregarious animals face a constant trade-off regarding the costs and
benefits associated with group living (Krause and Ruxton 2002). This
trade-off can be heavily influenced by reproductive strategy,
competition, the distribution of food or predators in space and by
processes that facilitate collective decision-making (Janson and
Goldsmith 1995, Nowak et al. 2010, Stranberg-Peshkin et al. 2015, Gil et
al. 2017). Consequently, the interplay between movement, space use and
social behaviour within animal populations is complex and often
difficult to disentangle (Spiegel et al. 2015, 2017). Colonial animals
are defined by multiple individuals sharing and showing fidelity to the
same location, and traditionally benefits have been associated with
breeding (Evans et al. 2016). However, more recently the benefits
related to social information sharing between colony members have also
been highlighted (Evans et al. 2016).
Social information about foraging locations, whether intentionally
communicated or otherwise, can initiate spontaneous aggregation in
memory-based foragers (Lang and Farine 2017, Riotte-Lambert and
Matthiopoulos 2018). Social foraging can consist of local enhancement
(where animals in a group are within sensory range of other foraging
individuals and can simultaneously forage and observe conspecifics,
Buckley 1997), recruitment (an individual ‘recruits’ others to a patch,
with foraging success increasing with group size, i.e. the recruitment
hypothesis, Buckley 1997), and for central place foraging (CPF) animals,
public information sharing (where uninformed individuals follow informed
individuals to prey patches, Barta and Giraldeau 2001, Bijleveld et al.
2010). Public information sharing is often conceptualised using the
producer-scrounger game, where individuals either search for prey
(producers) or follow other individuals to prey patches (scroungers),
but within this framework no individual can both forage and observe
other individuals simultaneously (Vickery et al. 1991, Barta and
Giraldeau 2001). The transmission of public information has even been
proposed as being responsible for driving the evolution of colonial
behaviour (i.e. the information centre hypothesis (ICH)), with or
without the added benefits of local enhancement and recruitment (Barta
and Giraldeau 2001, Bijleveld et al. 2010).
While the ICH remains controversial (Barta and Giraldeau 2001), evidence
for social foraging with public information sharing has been found in
seabirds, non-breeding roosting vultures, and bats (Wakefield et al.
2013, Harel et al. 2017, Egert-Berg et al., 2018). Furthermore, public
information sharing can have ecological implications by contributing to
spatial separation between colonies, as seen in seabirds (Wakefield et
al. 2013). While models demonstrate the collective advantage of
information sharing amongst colony members, they have all assumed that
CPF itself, is a necessary requirement for animals returning to nests or
shelters (Buckley 1997, Barta and Giraldeau 2001). However, there are
examples of CPF animals that form social groups unrelated to breeding
(e.g. non-breeding roosting vultures, large fish, Meyer et al. 2007,
Harel et al. 2017), where there is no obvious need for them to return to
a central place.
Within a producer-scrounger game, the scrounger strategy will be
negatively frequency dependent (either as a fixed strategy or for
animals that switch between producer/scrounger roles). Consequently, an
evolutionarily stable strategy (ESS) would predict approximately equal
frequencies of each role (Giraldeau and Beauchamp 1999). In simpler
information sharing models (local enhancement), predictions include
social associations between individuals with assortment related to
foraging abilities (Giraldeau and Beauchamp 1999). Under scenarios where
social or public information plays an important role in colony formation
(or at least subsistence) then we might expect reciprocity in terms of
how often individuals behave as leaders or followers when leaving the
colony, without the requirement of reciprocal altruism (Vickery et al.
1991, Giraldeau and Beauchamp 1999, Barta and Giraldeau 2001).
Non-breeding roosting vultures for example, show no preference overall
for leadership or follower roles when searching for carcasses,
suggesting individuals adopt both roles in equal measures (Harel et al.
2017).
Many shark species are upper level predators that can form large
aggregations and maintain social associations under both laboratory and
field conditions (Schilds et al. 2019, Mourier et al. 2012, 2018). They
are also known to be able to learn socially via observation of
conspecific behaviours (Guttridge et al. 2009). Mating and reproduction
in reef sharks is seasonal, which influences patterns of movement, but
residency on the reef can occur extensively throughout the year leading
to frequent aggregation (Heupel and Simpfendorfer 2014). Tropical reef
sharks will often behave as central place foragers, with multiple
individuals sharing the same central place on small regions of the reef,
but the reasons behind this behaviour are unclear (Papastamatiou et al.
2018a, 2018b). We hypothesize that grey reef sharks (Carcharhinus
amblyrhinchos ) at an isolated Pacific atoll, display colonial like
behaviour by forming long term social associations, assorted by patterns
of space use. We further hypothesize that a key driver of social
associations are social information sharing between colony members and
that this could provide an explanation for the evolution of CPF colonial
behaviour in large fishes. To test these ideas we use dynamic social
networks from movements of individuals over a four year period, combined
with simulation models of social foraging.
METHODS
Study population and location. Palmyra Atoll (5°54’N 162°05’W)
is located at the northern end of the Line Island chain, in the Central
Pacific Ocean, and has been a US Federal Wildlife refuge since 2001,
with only a research station on the island. Consequently, the atoll has
large numbers of grey reef sharks (Carcharhinus amblyrhinchos),with approximately 8000 individuals distributed heterogeneously around
the forereef, with average densities of 21 sharks/km2
(Bradley et al. 2017).
Quantifying movements and colony assignment. Grey reef sharks
were caught on hook and line and had a uniquely coded V16 (69 kHz, Vemco
ltd, Nova Scotia) acoustic transmitter surgically implanted into their
body cavity. Individual animals (n = 41) were tracked across a network
of 65 VR2W acoustic receivers which were attached to submerged moorings
on the reef, and retrieved and downloaded annually. Receiver nodes that
were overlapping in their detection ranges (specifically in the SW of
the atoll) were reduced in number to avoid detections being recorded
multiple times simultaneously, a prerequisite for the mixture model
analysis (see below). This resulted in the exclusion of 18 receivers but
did not reduce the total area under acoustic surveillance. Movement
networks were constructed from the departure and arrival profile of the
acoustic data (i.e. a shark was detected at one receiver and then
subsequently detected at another, Jacoby et al. 2012). Community
detection of the resultant movement network was implemented in the R
package igraph using the Fast-Greedy algorithm, to reveal
statistically significant clusters of movement (Clauset et al. 2004,
Casardi and Nepusz 2006, Finn et al. 2014). Community modularity within
the movement network was high (Q = 0.589), suggestive of
restricted movements, and resulted in the formation of five distinct
communities. Individual sharks were then assigned to communities,
forming groups that we refer to as ‘colonies’. Colony membership was
determined based on calculating residency indices (RI) for each
individual across each location and assigning an individual to a colony
based on its most resident receiver location (i.e. the receiver with the
highest RI). RI for each individual per location can be defined as;
\begin{equation}
\text{RI}_{i}=\frac{D_{h}}{D_{\text{al}}}\times\ 100\nonumber \\
\end{equation}
where Dh is the number of hours detected at a
given location/receiver and Dal , the hours at
liberty in the array as a whole. Thus, a location where an individual
spends all of its time at liberty is assigned a 1 and none of its time
at liberty a 0.
Social stability and leadership. We produced dynamic social
networks using a ‘gambit of the group’ approach, where animals
co-occurring in time and space are assumed to represent social
associations after controlling for individual spatial preferences
(Franks et al. 2010). Shark social networks were inferred directly from
the detection data stream using the Gaussian mixture modelling approach,
GMMEvents (Psorakis et al. 2012, Jacoby et al. 2016). Clusters of
detections, produced by visits of multiple individuals to the same place
at the same time, varying temporally to reflect the variation expected
in the temporal distribution of animal aggregations, were determined
using a Variational Bayesian mixture model. From these clusters,
associations were assigned to an adjacency matrix. Randomisation of the
individual-by-location bipartite graph, a procedure built in to the
GMMEvents model, excludes non-random associations attributable to purely
spatial drivers of aggregation, leaving only significant associations to
populate the adjacency matrix. Social duration matrices and leadership
matrices were extracted from the model, reflecting the cumulative time
(in seconds) dyadic pairs spent together, and determined by the order
with which individuals within a pair arrived (leader = 1, follower = 0)
respectively, within significant social clustering events.
Networks were constructed in this way for each of the four years of
tracking data and tested for weighted assortative mixing
(rwd ) by colony membership
using the R package ‘assortnet ’ (Farine 2014). Each annual
network was then tested for significant assortment by colony against
10,000 networks in which interactions were randomised. Constraining the
number of individuals per colony and the number of associations measured
that particular year, edge weights were randomly assigned andrwd calculated for each
permutation. The observed assortativity coefficient was then compared to
the posterior distribution from the null model. We tested for social
stability between years using Mantel tests reflecting the correlation in
strength of dyadic relationships year on year when individuals were
present across two consecutive years (1&2, 2&3, 3&4) and finally for
those dyads that remained at liberty for the duration of the study
(years 1&4).
While we could calculate leadership/follower roles between dyadic pairs
based on when they arrived or departed spatial-temporal clusters, we
could not calculate the frequency of leader/follower roles, because we
do not know the total number of individuals from the population
departing or arriving, or group sizes. However, we hypothesise that
under a social foraging scenario (public or social information), with
social associations between colony members, relative reciprocity in
leadership between dyadic pairs within colonies, should be higher than
those between colonies (controlling for the number of times individuals
associate). From the leadership matrix, pairs of individuals were
assigned a normalised, dyadic reciprocity index reflecting the
probability of leadership behaviour being reciprocated between
associates (Wang et al. 2015). This index takes the form
\begin{equation}
R_{\text{ij}}\ =|ln\left(p_{\text{ij}}\right)-\ln\left(p_{\text{ji}}\right)|\nonumber \\
\end{equation}
with
\begin{equation}
p_{\text{ij}}=\ \frac{w_{\text{ij}}}{w_{i+}}\nonumber \\
\end{equation}
where wij is the raw, directed edge weight
between nodes i and j and wi+ is
the strength of the i th node given by Barrat et al. 2004, and
Wang et al. 2015 as
\begin{equation}
w_{i+}=\ \sum_{j\in N(i)}w_{\text{ij}}\nonumber \\
\end{equation}
in which N(i) is the set of nodes that immediately neighbouri via outgoing edges. An index of 0 represents full reciprocity
(mutual leader-follower behaviour) and increasing values from 0
represent increasingly more unbalanced ties. Crucially, this index
quantifies the normalized weighted difference (i.e. probability of
reciprocal leadership), rather than raw edge weights, accounting for the
fact that some dyads simply associate more than others (e.g. within
colonies as opposed to between colonies). A Welch’s two-samplet -test was used to compare the distributions of within colony and
between colony reciprocity and a negative binominal GLM to explore the
relationship between reciprocity and the cumulative duration dyads were
in association.
Group size. To estimate minimum group sizes, we deployed two
animal-borne camera tags on grey reef sharks caught off the SW region of
the atoll. Sharks had DVL400 video loggers (Little Leonardo, Japan)
attached to the dorsal fin which record at 640x480 pixels at 30
frames/second and recording duration of 11 h (Papastamatiou et al.
2018a). Cameras were embedded in copolymer floats along with VHF (ATS)
and SPOT satellite (Wildlife Computers) transmitters. A time release
mechanism caused tags to pop-off 48-72h later, where they would float to
the surface and could be recovered via the VHF and SPOT transmitters.
Cameras were programmed to turn on the day after capture at 7:00-8:30
AM, to avoid the period of stress associated with capture. For each
30-minute segment we produced a conservative estimate of the minimum
number of sharks in a group ensuring that individuals could not be
counted twice (i.e. shark in frame or seen sequentially while swimming
in a straight line), including the individual carrying the camera (i.e.
minimum size=1).
Individual based models: In order to investigate potential
determinants and subsequent benefits to both sociality and central-place
foraging in reef sharks, we developed two-dimensional individual-based
models (IBMs) to examine a range of scenarios that may have influenced
the evolution of these behavioural strategies. All models were
constructed in the individual-based multi-agent modelling environment
Netlogo 5.5 (Wilensky 1999), and the basic parameters of these models
were previously described (Papastamatiou et al. 2018b).
In brief, in all model contexts outlined below, starting conditions
comprised 100 simulated individual ‘sharks’ that moved and foraged
within a simple environment consisting of a fixed number of prey patches
(100, 200 or 300 depending on the specific simulation set) randomly
distributed across an unbounded torus. Simulated individuals moved at a
constant speed of 0.6 m/s (Papastamatiou et al. 2018a) and initially
used a naive random search pattern. On discovering a prey patch,
individuals remained there until the prey moved, at which point the
predator commenced a more restricted search pattern based on a tighter
turn angle for 300 time-steps. Simulated shark lifespans were
constrained by an energy term, with all individuals starting with 800
energy units, losing one unit per time step while searching for prey
patches, and dying if this reached zero. Successfully locating a prey
patch resulted in an energy gain for the shark (150, 300 or 450 units
depending on the specific set of simulations). If individuals gained
sufficient energy (individual energy score > 1000 units)
then they could potentially reproduce, with the probability of
reproduction of a single offspring drawn from a log-normal distribution
(reflecting the low reproductive rates of this species). Prey patches
(i.e. shoals or individual reef fishes) are likely semi-predictable in
space as a result of diel and tidally influenced movements (e.g. Meyer
et al. 2010), as well as specific territoriality and habitat
requirements, so prey patches followed a random walk within model space
with step length drawn from a normal distribution (mean = 0, SD = 2). In
addition, to incorporate less predictable prey movements, at each time
step there was a 5% chance of prey patches relocating to another random
position in model space. Finally, following discovery and the
commencement of foraging, prey patches had an increased probability of
dispersing, mimicking predator avoidance and escape behaviour. These
movements could be either localized or larger jumps as described above,
and were made more likely through a doubling of the acceptability
threshold for movement from a random draw made at each time-step. One
hundred simulations were run for each combination of model parameters
(number of prey patches, energetic value of prey patches), with each
simulation run for 4000 time steps. A burn-in of 1000 time-steps at the
start of each simulation allowed individuals to explore their
environment so that they were not completely naive, and some degree of
prey patch knowledge was established.
The first set of models were devised to examine the importance of using
both social and private information during foraging, in this case the
ability to both find prey oneself and the ability to identify and join
foraging aggregations. This model set therefore included two types of
individual: i) ‘social’ foragers that are able to both discover prey for
themselves (i.e. private information), and to observe successful feeding
in others and aggregate around prey patches, mimicking passive social
information transfer including visual and chemical cues in the water;
and ii) ’lone’ foragers that are only able to find prey for themselves
and do not recognize and move towards other foraging individuals. In
these models, undiscovered prey patches were only detectable at short
distances (0.3 unit radius). However, following discovery and
commencement of feeding by either a ‘lone’ or ‘social’ individual, such
discovered patches became visible to other ‘social’ individuals at a
four-fold greater distance. ‘Lone’ individuals could also join
discovered patches, but would only locate them through moving to close
proximity as described above. One hundred simulations were run for each
combination of food patch density (100 or 200 patches) and energy gain
(150, 300 or 450 units per successful forage), with the proportion of
‘social’ and ‘lone’ individuals recorded through model time.
The second set of models then examined whether, if all individuals are
able to use both public and private information sources, there is any
additional benefit to being a central place forager under these
circumstances. Thus, in these simulations all individuals behaved as
‘social’ individuals described above in terms of their ability to
forage. However, a varying proportion (20, 50 or 80%) were
central-place foragers (‘central place’), returning to a fixed spatial
location at every 500th timestep, with the rest
starting in random positions and moving continuously through model space
throughout each simulation run (‘wanderer’). All simulations had three
fixed locations positioned based on draws from a random number
generator. These three fixed locations remained the same for all
simulations within a model set (i.e. central places were fixed but prey
patches and ‘wanderer’ starting locations changed with each simulation).
As in the previous model, 100 simulations were run for each combination
of food patch density (100 or 200 patches) and energy gain (150, 300 or
450 units per successful forage), with the length of model time the
‘central place’ and ‘wanderer’ individuals survived recorded for each
simulation.
Results
We tracked the movements of 41 individual grey reef sharks
(Carcharhinus amblyrhinchos ) over 13,800 accumulative tracking
days (26 Female, 13 Male, 1 unknown, Total Length: 142 ± 18 cm). Tagged
grey reef sharks were assigned to five distinct movement communities,
based on similarity of individual movement networks (network modularity,Q = 0.589). Thus, individuals were organised into groups that
predominantly only used small, sub-sections of the available reef.
Colony members had 50% utilization distributions ranging in area from
<1 to 7.53 km2 (mean ± SE = 1.26 ± 0.32
km2, Fig. 1b). Although movements of individuals
between areas were limited, there was some spatial overlap between
colonies, suggesting that observed social patterns were not simply
artefacts of animals having restricted and non-overlapping home ranges
(particularly as spatial preferences were also controlled for in our
inference models; see Methods).
Calculating a weighted assortativity coefficient for each annual network
revealed significant social assortment
(rwd : Y1 = 0.204; Y2 = 0.129;
Y3 = 0.176; Y4 = 0.130) when tested against a null model of 10,000
random networks (Fig. 1c). Each year, social associations were
positively assorted by colony membership with no evidence for assortment
based on sex (rwd (SE): Y1:
-0.074 (0.065), Y2: 0.129 (0.015), Y3: 0.177 (0.025), Y4: -0.043
(0.042)). Mantel tests revealed that there was a strong correlation in
the dyadic association strength between pairs for years 1&2 (n=29,
Mantel r=0.74, CI=0.13-0.30, p<0.001), 2&3 (n=35, Mantel
r=0.85, CI=0.13-0.29, p<0.001), 3&4 (n=31, Mantel r=0.78,
CI=0.13-0.27, p<0.001) and finally for the duration of the
study for years 1&4 (n=22, Mantel r=0.76, CI=0.16-0.35,
p<0.001). While we were only tracking a small proportion of
the grey reef shark population, densities per area of the forereef are
relatively low (Bradley et al. 2017) and our camera deployments suggest
group sizes of approximately 20 individuals (see below). Furthermore,
sharks at geographic locations were generally all caught and tagged at
the same time and depth, hence we are confident that we likely caught
and tagged individuals within groups. Footage from camera tags deployed
on two sharks showed that group size typically varied between 2-16
individuals, with group size increasing throughout the day and peaking
in the afternoon (Supplementary material Fig. S1). Close following
behaviour, where individuals were approximately less than 1 m from a
conspecific, was commonly observed (Fig. S1, Movie 1).
From the leadership-follower matrix, the distribution ofRij differed significantly for dyads within
rather than between colonies (Welch’s two-sample t -test,t (2416) = 8.938, P < 0.001; Fig.
2a), suggesting leader-follower behaviour is more reciprocal at the
colony level than between individuals outside of colony affiliates.
Furthermore, those dyads that were relatively more reciprocal also spent
more time together with a significant negative effect ofRij on association duration across all four years
(negative binomial GLM, F = - 0.804, P < 0.001, Fig.
2b).
Our first IBMs showed that individuals using only private information to
locate resources (loners) have much lower fitness than those using
social and private information (social, Fig. 3a, b). Under all simulated
scenarios of starting ratios of prey quality (energetic reward) and
patch density, the proportion of ‘loner’ individuals rapidly declined
typically to extinction, unless energetic rewards were extremely high
(Fig. 3a, b, Fig. S2). Our second series of models assumed that all
individuals use both private and social information to find prey
patches, with some individuals exhibiting random movement within a home
range (wanderers) and others consistently returning to a central place
within the home range (central place foragers, CPFs). Again, regardless
of prey quality, patch density, or the starting ratio of wanderers to
CPFs, in all modelling scenarios, CPFs had much greater survival times
(Fig. 3c, d, Fig. S3, Table S1).
Discussion
Grey reef sharks form social communities akin to colonies formed by
other species, with social structure assorted by patterns of space use
and with associations persisting interannually. Furthermore, our dynamic
social network approach showed that dyadic associations were stable,
signifying that the same individuals were associating, in some cases for
up to four years. Spatial assortment of social communities is relatively
common in animals of high cognitive abilities, including birds, bats,
dolphins, and seals (e.g. Wolf et al. 2007, Kerth et al. 2011, Shizuka
et al. 2014, Titcomb et al. 2015). Although across-year social
associations have been recorded in some birds and mammals, they are
rarely quantified in animal communities, highlighting that sharks form
relatively complex social communities over long time periods (Kerth et
al. 2011, Shizuka et al. 2014, Ilany et al. 2015). These long-term
associations persist despite sharks exhibiting fission-fusion dynamics
where group membership will change, within the confines of colony
membership, as is also the case in bats (Kerth et al. 2011). However,
unlike bats and birds, shark social communities are not seasonal but
occur year-round. To the best of our knowledge this is the first
documentation of similar interannual social associations in fish.
Given that grey reef sharks do not need to return to a nest site or
shelter area, why would social communities and central place foraging
evolve in these predators? Here, we provide evidence that social
information sharing, potentially within a foraging context, can explain
both social associations and the broader advantage of CPF behavior. We
show that there is greater reciprocity in leadership roles between
dyadic pairs within a colony compared to dyadic associations between
colony members. Regardless of whether social foraging is the product of
simple information sharing (e.g. local enhancement) or public
information where sharks follow other individuals to patches, we would
expect some degree of reciprocity in leadership roles for stable social
groups (Barta and Giraldeu 2001, Giraldeau and Beauchamp 2001). For
example, vultures use public information to find carcasses but
individuals do not show an overall preference for leader or follower
roles (Harel et al. 2017). Such reciprocity would not require reciprocal
altruism but could instead represent an evolutionary stable strategy of
the frequency of scroungers in a producer-scrounger games, or sharks
socially assorting with individuals with similar foraging traits and
abilities (Vickery et al. 1991, Barta and Giraldeau 2001, Giraldeau and
Beauchamp 1999). In addition, the duration of social associations
between dyaydic pairs was a positive function of reciprocity; simply
put, pairs that switched leader-follower roles spent more time together.
This would suggest benefits of spending time with individuals that are
unlikely to cheat in terms of leadership roles.
The benefits and evolutionary significance of group foraging are well
known and studied using both empircal and modelled data (Clarke and
Mangel 1984, Buckley 1997, Barta and Giraldeau 2001, Kraus and Ruxton
2002). Our series of IBMs supplement these studies by suggesting that
for sharks using social information (local enhancement), central place
foraging, with multiple individuals using the same central place,
provides a significant advantage over random wandering within the home
range. Hence, regardless of whether social communities act as
information centers, multiple individuals sharing a central place and
sharing social information will outperform random walkers. Although
group formation will also lead to increased competition, the negative
effect of competition on information sharing within the group may be
minimised if travel times to patches are short, resources are
distributed heterogeneously, and patch locations are unpredictable
(Sernland and Olsson 2003, Smolla et al. 2015). Models of local
enhancement by foraging seabirds suggest that group foraging is only
advantageous if prey patches are ephemeral, scarce, and of little future
value (Buckley et al. 1997). Empirical support for these predictions can
be found in bats where social foraging is only seen in species foraging
on ephemeral prey (Egert-Berg et al. 2018).
These prey conditions are likely met by our focal sharks that regularly
prey on fish schools situated offshore, whose location in time and space
are extremely unpredictable (McCauley et al. 2012, Papastamatiou et al.
2018a). Over 80% of grey reef shark diet at Palmyra is estimated to
consist of pelagic prey, and sharks have been observed foraging on fish
baitballs offshore (McCauley et al. 2012, Y. Papastamatiou pers. comm.).
In addition to meeting model criteria set above, these prey schools
would also likely support the recruitment hypothesis, as multiple sharks
are needed to succesfully forage on schooling baitballs (Thiebault et
al. 2016). In addition, foraging also takes place during the day on the
reef where roaming fish shoals are also dynamically distributed in time
and space (Papastamatiou et al. 2018a). Hence social information
sharing, underpinned by a stable social stucture, is likely to be an
advantageous strategy for grey reef sharks.
To our knowledge, this is the first evidence to suggest that
elasmobranchs are capable of colonial behaviour, spatially structuring
themselves within restricted areas of a small reef ecosystem and forming
long-term social associations between colony members, comparable to
birds and mammals. Social information transfer can be a key foraging
mechanism, and by itself, may be sufficient to select for CPF behaviour
in animals that do not require the use of a nest or shelter, including
many fishes (e.g. Meyer et al. 2006). Social information is important
for linking individual behaviour to population- and community-level
dynamics and can enable positive, density-dependent growth of
populations, as well as change the frequency and strength of species
interactions (Gil et al. 2015). Information sharing in combination with
density-dependence competition, is also thought to drive spatial
separation between seabird colonies (Wakefield et al. 2013). The grey
reef shark population around Palmyra Atoll appear to show similar
patterns with limited movement of individuals between colonies. As with
seabirds, the mechanism behind spatial separation may include
intra-specific competition and information sharing by sharks within
colonies (Papastamatiou et al. 2018b). While the importance of social
information in colonial birds and mammals is now well established (Evans
et al. 2016), we show that these concepts likely also apply to some
species of shark. Tantalisingly, our analyses may be suggestive of a
process (colonial behaviour) that may be much more widespread than
originally thought in free-ranging, marine animals.
Ethics statement: All shark tagging and handling was approved by the
University of California Santa Barbara IACUC committee Protocol no. 856,
and U.S. Fish and Wildlife Service special use permits (Permit numbers
#12533–14011, #12533–13011, #12533–12011, #12533–11007,
#12533–10011, #12533–09010, #12533–08011, and #12533–07006).
Funding: Field research at Palmyra was supported by funds from the
Marisla Foundation. DMPJ was supported by the Bertarelli Foundation and
the project contributed to the Bertarelli Programme in Marine Science.