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Behavioral signature of intraspecific competition and density dependence

Behavioral signature of intraspecific competition and density dependence in colony-breeding marine predators Greg A. Breed1, W. Don Bowen2 & Marty L. Leonard1

1Department of Biology, Dalhousie University, 1355 Oxford Street, Halifax, Nova Scotia, B3H 4J1, Canada 2Bedford Institute of Oceanography, 1 Challenger Drive, Dartmouth, Nova Scotia, B2Y 4A2, Canada


Animal movement, compensatory population

regulation, correlated random walk, foraging

ecology, juvenile mortality, marine mammal,

seal, switching state-space model.


Greg A. Breed, Department of Biological

Sciences University of Alberta, Edmonton, AB

T6G 2E9, Canada. Tel: 780-492-7942;


Funding information

This work was supported by the Future of

Marine Animal Populations program, Fisheries

and Oceans Canada, Dalhousie University,

and NSERC grants awarded to Marty Leonard

and W. Don Bowen. This research was

conducted under the authorization of the

Canadian Ministry of Fisheries protocol nos.

04-13, 02-91, 00-051, and 98-078.

Received: 24 May 2013; Revised: 26 July

2013; Accepted: 12 August 2013

Ecology and Evolution 2013; 3(11): 3838–


doi: 10.1002/ece3.754


In populations of colony-breeding marine animals, foraging around colonies

can lead to intraspecific competition. This competition affects individual forag-

ing behavior and can cause density-dependent population growth. Where

behavioral data are available, it may be possible to infer the mechanism of

intraspecific competition. If these mechanics are understood, they can be used

to predict the population-level functional response resulting from the competi-

tion. Using satellite relocation and dive data, we studied the use of space and

foraging behavior of juvenile and adult gray seals (Halichoerus grypus) from a

large (over 200,000) and growing population breeding at Sable Island, Nova

Scotia (44.0 oN 60.0 oW). These data were first analyzed using a behaviorally

switching state-space model to infer foraging areas followed by randomization

analysis of foraging region overlap of competing age classes. Patterns of habitat

use and behavioral time budgets indicate that young-of-year juveniles (YOY)

were likely displaced from foraging areas near (<10 km) the breeding colony by adult females. This displacement was most pronounced in the summer. Addi-

tionally, our data suggest that YOY are less capable divers than adults and this

limits the habitat available to them. However, other segregating mechanisms

cannot be ruled out, and we discuss several alternate hypotheses. Mark–resight data indicate juveniles born between 1998 and 2002 have much reduced survi-

vorship compared with cohorts born in the late 1980s, while adult survivorship

has remained steady. Combined with behavioral observations, our data suggest

YOY are losing an intraspecific competition between adults and juveniles,

resulting in the currently observed decelerating logistic population growth.

Competition theory predicts that intraspecific competition resulting in a clear

losing competitor should cause compensatory population regulation. This func-

tional response produces a smooth logistic growth curve as carrying capacity is

approached, and is consistent with census data collected from this population

over the past 50 years. The competitive mechanism causing compensatory regu-

lation likely stems from the capital-breeding life-history strategy employed by

gray seals. This strategy decouples reproductive success from resources available

around breeding colonies and prevents females from competing with each other

while young are dependent.


Intraspecific competition is a primary mechanism of den-

sity-dependent population regulation (Nicholson 1954;

May et al. 1974; Furness and Birkhead 1984). The type of

intraspecific competition (e.g., scramble, contest, interfer-

ence) and the limiting resource, however, can have major

effects on the functional response of a population as it

approaches carrying capacity (K) (May et al. 1974). Com-

petition is often for food resources, but it can also be for

space, breeding sites, territories, or other vital resources.

Although intraspecific competition is the most common

cause of density dependence, conclusive demonstration of

such competition usually requires removal of individuals

3838 ª 2013 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use,

distribution and reproduction in any medium, provided the original work is properly cited.

to relax competition for suspected limiting resources.

Where such manipulations cause competing population

segments to move into a previously occupied niche, expe-

rience increased survival, show better condition, or have

increased fecundity, intraspecific competition is occurring

(Dayton 1971; Paine 1984). Such manipulations are often

impractical in free-living populations, and intraspecific

competition usually must be inferred circumstantially

(Hansen et al. 1999). Such inferences are often based on

overlap or segregation in diets or space (e.g., Tinker et al.

2008a). Although the later may represent niche partition-

ing, differential response to predation risk, or other non-

competitive segregating mechanism, population growth

accompanied with decreased performance or survival in

one of the suspected competing groups more strongly

suggests intraspecific competition.

Because of the difficulty of studying wild populations,

much of our understanding of intraspecific competition

and density-dependent population dynamics relies upon

laboratory mesocosm experiments and population

modeling (e.g., Bellows 1981). Using these approaches,

Maynard-Smith and Slatkin (1973) and Bellows (1981)

predicted that different kinds of intraspecific competition

should give rise to different density-dependent functional

responses as a population approaches K. Scramble compe-

tition, where all competitors are equally able to acquire a

limiting resource, should cause overcompensatory regula-

tion, with the potential for major population-level

mortality or reproductive failures near K. When resources

become limiting, the equal competitors all receive an

inadequate ration and theoretically all starve. The number

of starving animals can be far larger than the degree to

which a population exceeds K.

Competition that results in one competitor or compet-

ing group being better able to acquire or defend a limiting

resource (e.g., contest competition) should cause compen-

satory regulation. In this case, when resources become

limiting, better competitors receive an adequate ration,

and poorer competitors starve. The number which starve

is thus directly proportional to the degree a population

exceeds K. Compensatory regulation should result in a

gradual decrease in population-level reproductive success

and a gentle approach to K. Where enough behavior and

population data are available, it should be possible to infer

the type of intraspecific competition and predict the func-

tional form of regulation experienced by a population.

Many marine birds, mammals, and reptiles breed at colo-

nies, and intraspecific competition can be keen and develop

rapidly or unexpectedly when resources around colonies

fluctuate (Furness and Birkhead 1984; Schreiber and Schrei-

ber 1984; Trillmich and Limberger 1985; Wanless et al.

2005). At the same time, colony-breeding marine species

are often top predators and may also be of conservation

concern. Understanding the population dynamics of such

species can be extremely important for population conser-

vation as well as overall ecosystem management.

Here, we examine evidence for intraspecific competi-

tion and its behavioral signature in animal movement

patterns from a growing population of gray seals (Halic-

hoerus grypus) in the Northwest Atlantic (Plate 1). This

population breeds as a colony on Sable Island, Nova Sco-

tia, and was historically suppressed by hunting and boun-

ties at coastal haulouts and nearshore waters of eastern

Canada. In the early 1960s, gray seals were considered

rare, and the Sable Island population produced only a

few hundred offspring annually. The population grew

exponentially from 1962 until the early 2000s (Bowen

et al. 2003), but more recently appears to have entered

the deceleration phase of density-dependent logistic

growth (Bowen et al. 2007, 2011).

The dramatic growth of gray seals has been accompa-

nied by major changes in the structure and functioning of

the Scotian Shelf ecosystem (Frank et al. 2005, 2011).

There is also concern that gray seal predation on com-

mercially important or rare fishes may cause serious harm

to these stocks (e.g., Trzcinski et al. 2006; Benôıt et al.

2011). Understanding the mechanism of density depen-

dence will help predict future population trends, gray seal

predation rates on fishes, gray seal impacts on the struc-

ture and stability of the Scotian Shelf ecosystem, and the

results of potential management actions. More broadly,

such information may help predict which types of behav-

iors and intraspecific interactions should be expected

around marine animal colonies and how populations

might respond to those interactions.

Our analysis focuses on differences in distribution and

habitat usage among age and sex classes to gain insight

into the forces influencing movement decisions and for-

aging behavior. To understand the context and cause of

Plate 1. Gray seals in the surf at Sable Island, Nova Scotia.

ª 2013 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. 3839

G.A. Breed et al. Intraspecific Competition in Gray Seals

the differences, we synthesize our results within a larger

body of data that includes changes in survival of juve-

niles, diet breadth, physiological limitations of juveniles,

and prey distribution and quality.

Taken together, the evidence supports a number of

potential segregating mechanisms. We present the case for

several alternative hypotheses, including differential habi-

tat preference, niche partitioning, and differential

response to shark predation. Although our results do not

conclusively exclude these noncompetitive segregating

mechanisms, the pattern of habitat segregation is most

consistent with intraspecific competition and we contend

this competition is resulting in the currently observed

density-dependent population growth. Finally, we discuss

the potential impact of the hypothesized intraspecific

competition on this population and predict the functional

response of density-dependent regulation should be com-

pensatory and not overcompensatory and compare this

prediction to the observed population dynamic.


Data collection

In late May and early June 2004, twenty-four (12 male,

12 female) young-of-year (YOY), 15 adults (7 male, 8

female), and 6 subadults (2–3 years old; 4 male, 2 female) gray seals were captured on Sable Island, Nova Scotia

(44.0°N 60.0°W). Seals were captured with handheld nets,

anaesthetized with Telazol, and Argos satellite transmitters

(Sea Mammal Research Unit model SRDL 7000) were

attached with 5-minute epoxy as described in Breed et al.

(2011a). The adults became part of a larger data set that

included a total of 81 adults tagged between 1995 and

2005 (Breed et al. 2006), allowing for comparison of

adult, subadult, and YOY at-sea movement.

Movement and spatial behavior

Argos tracking data were analyzed with a behaviorally dis-

criminating state-space model (SSM), which statistically

reduces the influence of Argos location error, estimates

locations at regular time-intervals, and infers behavioral

states. We chose a time-step of 8 h or three points per

day as this was approximately the observation frequency

of our lowest quality tracks (see Breed et al. 2009, 2011b

for more information on time-step selection and the

effect of data quality on inference and model perfor-

mance). The model explicitly estimated behavioral state

by switching between two sets of parameters describing a

correlated random walk (CRW). Displacements better fit

by parameters of high first-order autocorrelation and turn

angles near 0° were nominally termed “transiting,” while

displacements that were better fit by parameters of low

autocorrelation and turn angles near 180° were nominally

termed “foraging.” Movements inferred as “foraging”

cause animals to remain in a small area of ocean or are

part of an area-restricted search. Such movement patterns

are well documented to be associated with increased prey

capture rates (Austin et al. 2006; Dragon et al. 2012).

Transiting states were associated with rapid directional


We used a two-state model for simplicity. Fitting two

states can be performed relatively simply with fairly

course spatial data and identifying two behavioral classes

extracts considerable behaviorally relevant information.

Inferring three or more states is less straightforward and

usually requires higher quality behavioral data and often

strong Bayesian priors (Breed et al. 2012; McClintock

et al. 2012). Without such high-quality data (e.g., high

spatiotemporal resolution GPS or triaxial accelerometry

data), inferring three or more states usually encounters

significant identifiably issues. The model was fit to demo-

graphically homogenous groups of up to 15 tracks so that

short tracks could borrow power from longer tracks to

infer behavioral states (see Breed et al. 2009 for details).

The movement parameters themselves were similar across

all groups, and this approach is validated in an analysis in

the appendix of Breed et al. (2009), which shows that

tracks fit individually had similar estimates of movement

parameters and inferred behavioral states as those fit in

groups. The SSM was implemented using the freely avail-

able software packages R and WinBUGS (Spiegelhalter

et al. 2007; R Development Core Team 2012). Working

code and implementation details of this behavior-switch-

ing SSM can be found in Breed et al. (2009, 2011b).

Dive and time budget data

The 45 Argos tags deployed in 2004 collected diving data,

but Argos bandwidth limited the data available to 3-h

binned summaries of dive characteristics. Numerous dive

characteristics were available, but we chose three key vari-

ables: maximum dive depth, mean dive duration, and

haulout (i.e., time spent on land as indicated by wet/dry

switches), which are most strongly limited by physiologi-

cal immaturity. Dive data were used to investigate age-

related differences in diving ability. Haulout data were

compared to SSM foraging locations that occurred within

10, 20, and 30 km of shore as well as those beyond

30 km to produce time budgets of time spent foraging in

areas of varying accessibility relative to haulout sites.

These budgets were used to investigate displacement of

juveniles from the most accessible foraging regions to

more distant areas by adults and to validate the kernel

overlap analysis, which we describe shortly. Time budgets

3840 ª 2013 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.

Intraspecific Competition in Gray Seals G.A. Breed et al.

and binned dive summary characteristics of demographic

groups were statistically compared using mixed-effects

models with either a log or logit link in the nlme package

of R (R Development Core Team 2012).

Spatial distribution and habitat use

Clumps of inferred foraging locations from YOY, adult

males, and adult females, appear to occur in different

areas on the Scotian Shelf. To test if these apparent differ-

ences were caused by chance or were real, we employed

the kernel density overlap analysis described in Breed

et al. (2006). In this analysis, we overlay the kernel densi-

ties of different groups and measure their areal overlap.

To test if this overlap is larger or smaller than might be

expected by chance, we compare the size of the overlap to

the overlap of kernels calculated from two null groups.

These null groups are produced by randomly assigning

tracks to each of two groups to be compared instead of

grouping them according to their age by sex demographic

class. If the real overlap is smaller than a large set (95%)

of randomly produced null overlaps, the lack of overlap is

significant and biologically relevant.

The method was modified to include the behavioral state

information available in switching SSM results. As our goal

was to test for differences in foraging areas, kernels were

constructed using only at-sea SSM inferred foraging loca-

tions, excluding transit locations and all locations within

2 km of shore. The size of the grid (100 by 100 nodes, with

10.75 km between nodes), the fixed kernel smoothing, and

width parameters, as well as the choice of 98.5% density

contour used to calculate the range overlaps, were the same

as those used in Breed et al. (2006).

Kernels were computed for YOY, adult males, and

adult females for each month from June to January.

There were too few data to include subadults and too few

data from YOY to compare with adults from February to

May (Table 1). Areal overlap of the 98.5% density con-

tour was calculated first for male versus female YOY to

test for evidence of sexual segregation within this age

group, then for adult males versus YOY and again for

adult females versus YOY. Adult males versus adult

females were not tested because the results of a similar

analysis can be found in Breed et al. (2006).

A randomization analysis was used to test the null

hypothesis that kernel overlap in a given monthly pair-

wise comparison was no larger than expected by chance

given the sample available. To create a null test statistic,

tracks were randomly assigned to two groups after the

real overlap had been calculated for that month. Tracks

were randomly shuffled by individual seal (as opposed to

individual point) so that the appropriate sample sizes and

individual random effect were imposed. The two groups

were composed of the same number of tracks as the real

observations (Pesarin 2001). Kernel densities were calcu-

lated for each of the two random groups, and the size of

the overlap of their respective 98.5% density contours

used as the null test statistic (Fig. 1). The random assign-

ments were permuted 1000 times without replacement. P-

values were determined by the proportion of random

overlaps that were smaller than the observed overlap, so

that if the observed real overlap was smaller than all 1000

randomly generated overlaps, then P ≤ 0.001. For most months tested, 1–3 tracks terminated due to

tag failure during the month and thus contributed an

incomplete sample of points. Similarly, because we used

only the inferred foraging locations and individual ani-

mals had differing ratios of foraging to transiting, each

animal contributed a different number of points to the

monthly kernels. We did not up- or down-weight these

samples based on the differing contributions of each ani-

mal. The randomization analysis employed is fundamen-

tally robust to such data imbalances (Pesarin 2001).

Although short tracks may skew or bias individual kernel

densities constructed with them, the variously sized tracks

were randomly assigned to each of the two possible null

kernels to create the 1000 null overlaps. If 95% of the null

overlaps are still larger than the true overlap, any skew or

bias introduced by the unevenness in track sample is unli-

kely to account for this difference, as the null overlaps

were also created using the imbalanced data. Increasingly

imbalanced data will not skew or bias results, but instead

gradually lower the power of the analysis as the sample

becomes more imbalanced. In the case of this analysis,

when data become increasingly imbalanced, the overlap

of the null kernels shrinks. When the imbalance is too

high, the true overlap size ranks above the a = 0.05 level

Table 1. Sample sizes for the kernel density randomization analysis.


Number of Tracks Number of Locations

Males Females YOY Males Females YOY

Jun 14 14 24 364 509 1088

Jul 14 15 23 594 627 1017

Aug 14 15 23 473 515 960

Sep 26 25 19 622 653 843

Oct 32 31 17 1158 1363 895

Nov 31 31 17 1367 1989 862

Dec 25 28 14 1027 1480 561

Jan 18 24 13 186 496 475

Total 43 38 24 7022 9538 7239

“Number of Tracks” is the number of animals with locations in a

given month. Animals whose tracks spanned more than one month

were counted in the sample of each month the track spanned. “Num-

ber of Locations” is the number of at-sea SSM inferred foraging loca-

tions recorded in a given month; inferred transiting, haulout, and

locations within 2 km of shore were excluded.

ª 2013 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. 3841

G.A. Breed et al. Intraspecific Competition in Gray Seals

when compared to the null overlaps. So, even if there is

segregation of habitat use, it is not detected because the

imbalanced data do not provide sufficient statistical


To visualize differences in foraging areas as calculated

from kernel densities, we took advantage of the normal-

ized property of kernels. After normalizing, the volume

under a 3-d kernel density surface equals one regardless

of how many points were used to construct it. Given this,

subtracting a kernel from itself will produce a perfectly

flat surface equal to zero everywhere. Subtracting different

kernels from each other will produce a surface with

positive and negative regions which will vary depending

upon how habitat usage differed between the groups.

In the case of kernel A minus kernel B, positive regions

will indicate where habitat use was high for the population

of animals used to construct kernel A and low for the

population represented by kernel B; negative regions

would indicate the opposite. Regions near zero indicate

where habitat use was similar in both A and B. The result-

ing 3-d surface can then be contoured to highlight habitats

that were used differentially between two groups of ani-

mals. We term this surface the “kernel density anomaly.”

Because journal space is limited, we did not visualize











Random overlap

63ºW 62ºW 61ºW 60ºW 59ºW 58ºW 57ºW

63ºW 62ºW 61ºW 60ºW 59ºW 58ºW 57ºW

True overlap

Figure 1. Example of one permutation of

random overlap versus true overlap of kernel

densities, in this case comparing adult females

(black) to YOY (blue) in July. Red contour

indicates the overlap of the 98.5% density

area between the two kernels. Panel (A) shows

the true overlap of the two groups, while (B)

shows the overlap when tracks are assigned to

each group randomly. Overlap of 1000

permutations of group assignment was

compared to true overlap to assess if true

overlap was smaller than would be produced

by chance given the sample available.

3842 ª 2013 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.

Intraspecific Competition in Gray Seals G.A. Breed et al.

kernel density anomalies for every month, but instead cal-

culated and plotted anomalies by season: summer (July– September), autumn (October–December), and winter (January–April); monthly kernel density anomaly plots are, however, provided in Figs. S1–S10. The kernel density overlap analysis and the kernel density anomaly figures

were produced using custom scripts in MATLAB Version

7.13 (MATLAB 2011) and visualized using the M Map

mapping toolbox (Pawlowicz 2011). Working MATLAB

code for the kernel overlap analysis, as well as sample data

from August, is included as electronic supplement.


Spatial distribution and habitat use

SSM model diagnostics (MCMC convergence, Rhat val-

ues, etc.) suggest good model performance and reasonable

behavioral inference for all tracks reported here (SSM

diagnostics are reported in the appendices of Breed et al.

2009, 2011a). Older tracks with large data gaps (multiple

gaps >2 days, and/or high spatial error) did not perform as well and were subsequently removed from the analysis

(see Breed et al. 2011b for a careful analysis of the effect

of data quality on location estimation and behavioral

inference). The proportion of at-sea behaviors inferred as

foraging, transiting, or uncertain varied dynamically

through the year and by demographic group and was also

subject to variation between individuals; these results are

explored in depth in Breed et al. (2009, 2011a). An exam-

ple SSM fit to an Argos track of a juvenile gray seal is

shown in Fig. 2.

Results of the randomization tests revealed no signifi-

cant differences between the monthly utilization distribu-

tions of male and female YOY (Table 2). Therefore, the

sexes were combined into a single sample of YOY for

comparison with adults.

Comparisons of YOY with adults indicate significant

segregation of space use (Table 2). Although their distri-

butions differed only marginally in June, areas used by

YOY and adult females overlapped significantly less than

expected during July, August, October, November, and

January. Segregation was most pronounced in the sum-

mer and fall, when YOY used areas off the tips of Sable

Island, Banqreau, and Western Bank, while adult females

occupied areas immediately adjacent to Sable Island and

the tops of the nearby Middle and Canso Banks (Figs. 3A,

4A, 5A). Spatial overlap between YOY and adult males

was less than expected during June and also from

November to January (Table 2). Adult males also occu-

pied areas adjacent to Sable in summer, but not as much

as adult females, and used areas near the continental shelf

break more than YOY (Figs. 3B, 4B, 5B).

The winter pattern of habitat use differed markedly

from that during summer and fall. YOY did not use

regions near Sable Island and instead foraged over off-

shore banks scattered across the northern half of the Sco-

tian Shelf (Fig. 5A,B). For all groups, winter foraging

locations tended to be farther from Sable Island and

became more diffuse—animals both spread out as they move away from Sable and also forage in less discrete

patches than other seasons.

Dive depths and time budgets

On average, adults made deeper dives than YOY, but

owing to a large number of shallow dives made by all

demographic classes, the means of the entire dive-depth

distribution were not significantly different (Fig. 6,

Table 3). However, when examining only the tails of the

dive-depth distributions (dives >150 m), adults clearly make many more deep dives than YOY. When mixed-

effects models for dive bouts are adjusted to only examine

bins with dives deeper than 50 m, these differences

become statistically detectable and are strongly significant

in summer and fall (Table 3). The differences disappear

in winter either because YOY dive performance has

improved with age (Noren et al. 2005; Bennett et al.

2010), a strong ecological driver intercedes, or both.

Additionally, YOY average dive durations were half that

of adults year-round, a strongly significant difference

(Table 3). Overall, YOY made significantly shorter and

shallower dives than adults. Subadult dive-depth profiles

and mean dive durations were intermediate between YOY

and adults (Table 3, Table S1).

Haulout and nearshore time budgets also differed

among adults, subadults, and YOY. Through the summer

and early fall, adults consistently spent about 20% of their

time hauled out (Fig. 7A). Haulout increased in Decem-

ber at the start of the breeding season, peaked in January,

then decreased but remained elevated over the winter as

compared to summer. YOY and subadults displayed less

dramatic seasonal fluctuations than adults, spending 20– 30% of their time hauled out in summer and early fall,

10–15% in late fall, and 5–10% in January and February (Fig. 7A).

Comparison of at-sea time budgets provided an esti-

mate of time animals spent in shallow, coastal habitat

versus offshore habitat. Adults, especially adult females,

spent 50–80% of their time within 10 km of shore during summer (Tab. 4, Fig. 7B–D). Adults abruptly ended coastal habitat use in October, and from October to April

approached shore only to haulout. Subadults also heavily

used inshore habitats and spent more than 50% of their

time at sea within 10 km of shore from July to December,

moving away from shorelines in January. Unlike adults or

ª 2013 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. 3843

G.A. Breed et al. Intraspecific Competition in Gray Seals

subadults, YOY spent little or no time foraging near shore

at any time of year (Table 4, Fig. 7B–D), a strongly sig- nificant difference compared with adult time budgets.

Finally, during the summer, YOY spent significantly more

time in the band between 10 and 30 km of shore than

adults (Table 4). This corroborates the kernel overlap

analysis and suggests YOY either do not prefer or are

displaced from waters within 10 km of shore to habitats

between 10 and 30 km from shore.


Our data clearly demonstrate a number of key behavioral

differences between YOY and adult gray seals in diving

behavior and habitat use. We show that YOY make

shallower, shorter dives compared with adults. The

shorter dives limit the benthic habitats available to YOY.

We further show that foraging habitats are spatially segre-

gated, with YOY either preferring benthic habitats farther

from haulout sites or that YOY are excluded from the

most accessible foraging areas near haulout sites by older

animals, particularly in the summer.

The spatial segregation of foraging areas could arise

from both competitive and noncompetitive ecological

mechanisms. However, the perceived potential for the

Sable Island gray seal population to negatively impact

commercial fish stocks (Benôıt et al. 2011) has led to

the funding of ecological and demographic investiga-

tions of unusual intensity and duration resulting in

excellent data on age-specific survival, diet, reproductive

69ºW 66ºW 63ºW 60ºW





Figure 2. Example behavior-switching SSM

model fit to a two-year-old subadult gray seal

tracked by the Argos satellite network. This

animal was outfitted with an Argos PTT tag

manufactured by the Sea Mammal Research

Unit in June 2004 and was tracked until May


Table 2. Kernel density overlap randomization results of three demographic comparisons.


YOY sexual segregation YOY versus Adult Females YOY versus Adult Males

% Overlap Random P % Overlap Random P % Overlap Random P

Jun 34.5 33.6 � 0.3 0.21 33.1 34.5 � 0.4 0.052 26.4 32.3 � 0.4 0.050 Jul 38.4 41.5 � 0.3 0.24 17.6 34.1 � 0.3 0.001 33.3 36.5 � 0.3 0.798 Aug 36.0 35.7 � 0.4 0.06 16.0 35.7 � 0.3 0.024 23.0 34.7 � 0.3 0.690 Sep 24.8 30.5 � 0.4 0.08 26.9 32.4 � 0.3 0.124 21.6 34.8 � 0.4 0.133 Oct 40.3 35.0 � 0.4 0.38 32.7 44.6 � 0.4 0.003 26.7 38.5 � 0.3 0.319 Nov 31.4 31.5 � 0.4 0.26 34.1 47.9 � 0.4 0.048 15.4 37.4 � 0.3 0.006 Dec 36.8 36.2 � 0.4 0.44 32.1 33.5 � 0.3 0.223 30.0 36.1 � 0.4 0.047 Jan 11.9 17.8 � 0.3 0.17 5.1 31.8 � 0.4 <0.001 9.7 33.0 � 0.3 <0.001

Overlaps were divided by the area of the larger home range (bounded by the 98.5% contour) to create a normalized percentage.

Bold values indicate statistically significant habitat segregation.

3844 ª 2013 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.

Intraspecific Competition in Gray Seals G.A. Breed et al.

investment, and lifetime reproductive success to comple-

ment our findings on space use and movement (e.g.,

Austin et al. 2004; Iverson et al. 2004; Bowen et al.

2007; Lang et al. 2009; den Heyer et al. In press). We

synthesize our new results within this larger body of

data. Using these data, we discuss a number of poten-

tial segregating mechanisms. Although our data cannot

conclusively demonstrate intraspecific competition as the

segregating mechanism, we argue this hypothesis is best

supported given the data available. Finally, we speculate

that this competition is resulting in, or at least consis-

tent with, compensatory density-dependent population


Case for intraspecific competition

Exclusion of YOY from areas near Sable Island

Kernel anomalies, time budgets, and the randomization

analysis indicate YOY avoid nearshore habitats to forage

at more distant foraging patches from foraging areas near

Sable Island. There are three likely reasons for this.

First, adults and YOY might feed on different prey and

these prey are located in different areas around Sable

Island. Other observations suggest that this is unlikely.

Although YOY foraged farther from Sable, they used shal-

low banks to the east, west, and north that are similar to



Figure 3. July to September kernel density

anomaly for (A) adult females versus YOY and

(B) adult males versus YOY. White points are

SSM foraging locations of YOY, while black

points are foraging locations from adults. Blue

points indicate areas used more heavily by

YOY, while yellow and red points indicate

regions used more heavily by adults. Green

areas were used equally, while white were

used by neither group. Kernel density anomaly

plots for each month (as opposed to season)

are available in Figs. S1–S10.

ª 2013 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. 3845

G.A. Breed et al. Intraspecific Competition in Gray Seals

the sandy areas around the island. All age groups foraged

in shallow areas and made shallow dives in the summer

and early fall, suggesting a large degree of overlap in the

use of shallow sandy areas and therefore prey taken.

Although we have no summer diet data for subadults or

YOY, during spring, individual YOY consume a wide

range of prey species, suggesting a generalist foraging

strategy of taking any prey item they encounter and are

able to catch (Beck et al. 2007). Beck et al. (2007) also

showed that adults are usually highly specialized on one

or a small number of prey types. As a group, adult

females consume the same broad spectrum of prey items

as YOY, but they are individually specialized, so the varia-

tion is between rather than within individuals. Such spe-

cializations have been shown to lead to more efficient

foraging and arise when intraspecific competition limits

resources (Holbrook and Schmitt 1992; Tinker et al.


The second potential cause of spatial segregation is dif-

ferential response to predation. The large number of seals

using Sable Island attract sharks, which prey on both gray

and harbor seals (Phoca vitulina) (Brodie and Beck 1983).

In winter, seals are thought to be incidentally killed by

Greenland sharks (Somniosus microcephalus), while migra-

tory sharks such as white sharks (Carcharodon carcharias)

target seals as prey in the summer (Lucas and Stobo


(B) Figure 4. October to December kernel density

anomaly for (A) adult females versus YOY (B)

and adult males versus YOY. See Fig. 3 legend

for explanation of color.

3846 ª 2013 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.

Intraspecific Competition in Gray Seals G.A. Breed et al.

2000). The small size of juveniles makes them more

vulnerable to shark predation, and they suffer greater

shark-induced mortality than adults. Vulnerable gray seal

YOY may forage farther from the colony to lessen preda-

tion risk, and travel through near-shore waters around

the colony quickly when moving to and from haulout.

Many species balance predation risk against potential

foraging success or efficiency (e.g., Mittelbach 1981; Lima

and Valone 1986; Sih et al. 1998), and YOY may

undertake longer trips with more transit time to avoid

sharks around the colony. Thus, differential response to

predation risk by YOY is another possible explanation of

the observed patterns given available observations.

Finally, and we believe most likely, intraspecific com-

petition with adults, females in particular, may exclude

YOY from foraging near Sable Island as well as other

key foraging areas during summer and early fall. The

population of gray seals breeding on Sable Island grew

exponentially from 1962 into the early 2000s, with an

estimated 2004 population of 159,000 animals (Trzcinski

et al. 2006). The rate of increase in pup production has

recently declined and is now following a logistic growth

curve (Bowen et al. 2007, 2011). Most of this popula-

tion feeds on the Scotian Shelf, with considerable forag-

ing effort focused near Sable Island (Trzcinski et al.

2006; Breed et al. 2011a; this study). This large increase


(B) Figure 5. January to April kernel density

anomaly for (A) adult females versus YOY and

(B) adult males versus YOY. See Fig. 3 legend

for explanation of color.

ª 2013 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. 3847

G.A. Breed et al. Intraspecific Competition in Gray Seals

in population size has likely increased competition for

prey. Mark–resight data from uniquely marked females indicate that animals born between 1998 and 2002

recruited into the reproductive population at half the

rate of animals born in the late 1980s (Fig. 8). Over

the same period, adult survival has remained high (den

Heyer et al In press). These observations, combined

with at-sea distribution of YOY compared with adults,

suggest that YOY are being outcompeted by adults and

particularly by adult females. Spatial segregation of for-

aging areas has been implicated as evidence of intraspe-

cific competitions around marine breeding colonies in

other species (e.g., Gr�emillet et al. 2004; Field et al.

2005), until this study, however, survival data have

never been available to confirm if or how the apparent

competition affects the population.

0 50 100 200 300

A du

lt m

al es

0 50 100 200 300 0 50 100 200 300

0 50 100 200 300

A du

lt fe

m al


0 50 100 200 300 0 50 100 200 300

0 50 100 200 300

S ub −a

du lts

0 50 100 200 300 0 50 100 200 300

0 50 100 200 300

Max dive depths

Y ou

ng −o

f− ye


0 50 100 200 300 Depth (m)

0 50 100 200 300

Summer Fall Winter

Figure 6. Probability density histograms and kernel densities (black line) of maximum depths reached during each 3-h bin from the onboard TDR

of tags deployed in 2004 (8 adult females, 7 adult males, 24 YOY, and 6 subadults).

3848 ª 2013 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.

Intraspecific Competition in Gray Seals G.A. Breed et al.

To understand how intraspecific competition may be

responsible for the observed segregation patterns, we first

need to understand seasonal fish migration patterns on the

Scotian Shelf and around Sable Island. Fish migrations are

caused by seasonal changes in water temperature, oceanog-

raphy, and primary productivity (Perry and Smith 1994;

Swain et al. 1998; Comeau et al. 2002). These migrations

have been best documented in ground fish species, includ-

ing Atlantic cod (Gadus morhua), haddock (Melanogram-

mus aeglefinus), silver hake (Merluccius bilinearis), and

American plaice (Hippoglossoides platessoides) (Perry and

Smith 1994; Swain et al. 1998). None of these species are

especially prominent in gray seal diets (Beck et al. 2007),

but the seasonal pattern of fish migration is pervasive and

present in a large fraction of fish species on the Scotian Shelf

(Horsman and Shackell 2009).

The general migratory pattern is for fish to move into

warm, shallow water in the summer to forage, while win-

tering in deep basins where waters remain above zero

(Perry and Smith 1994) to avoid freezing.

In summer, waters become warm and productive,

which cause fish to move out of wintering areas and into

shallow areas such as the broad, shallow, sandy platform

surrounding Sable Island to forage. This migration brings

prey within close proximity of the main seal colony. As

they forage through the summer, fishes develop increased

lipid reserves, which peak in August or September

(Comeau et al. 2002). The net result of warm productive

Table 3. Mixed-effects analysis of three key dive parameters by

demographic groups.

Females Males Subadults YOY

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